Do Horizontal and Vertical Mismatches Affect Workers' Incomes in Cameroon? Results From the Instrumental Variable Models Using the Function-control Approach

Do Horizontal and Vertical Mismatches Affect Workers' Incomes in Cameroon? Results From the Instrumental Variable Models Using the Function-control Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Do Horizontal and Vertical Mismatches Affect Workers' Incomes in Cameroon? Results From the Instrumental Variable Models Using the Function-control Approach Joseph Dabo Doubla, Valère Amassangka Haman Hamen, Constant Tchaltouang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5913807/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Human capital theory postulates that education improves an individual's productivity and earning potential. However, when there is a mismatch between an individual's education and employment, the return on investment in human capital decreases, resulting in reduced earnings. This paper uses employment data from Cameroon's National Institute of Statistics to examine the wage effects of horizontal and vertical mismatches. To this end, we estimate the augmented Mincer model using the double ordinary least squares method via the control function approach to deal with endogeneity bias and the quantile regression method. The results indicate that the mismatch between education and employment, whether horizontal or vertical, is linked to a wage penalty. In particular, the wage penalty associated with horizontal mismatch is greater than that associated with vertical mismatch. Furthermore, the results reveal that private sector workers suffer greater penalties due to horizontal mismatch, while those in the public sector are more affected by vertical mismatch. Additionally, the results reveal a significant trend towards a greater wage penalty for men than for women. JEL Classification : I21 ; J24 ; J31 ; J41 Microeconomics Vertical mismatch Horizontal mismatch income Cameroon 1. Introduction Like other developing countries, Cameroon has made significant progress in school enrolment and completion over the past two decades. Indeed, between 2000 and 2023, the gross enrolment rate rose significantly, from 86.59–112.64% in primary education, from 27.97–44.39% in secondary education, and from 4.68–15.79% in higher education (World Bank, 2024 ). Nevertheless, despite the progress observed, marked by an exponential increase in the number of graduates, the results on the job market are disappointing. Indeed, in most cases, the training supply does not match the needs of the labor market (Carranza and McKenzie, 2023 ; Njifen, 2024 ). This situation reduces the productivity of investments in human capital, resulting in lower incomes for workers. In addition, analysis of the national economic fabric reveals that of the 203,000 businesses listed in the second General Business Census (RGE2), the overwhelming majority − 98.5% - belong to the category of Very Small Enterprises (VSE) and Small Enterprises (SE). Medium-sized Enterprises (ME) and Large Enterprises (GE) account for just 1.3% and 0.2% of the total respectively (Zamo, 2021 ). This situation tends to reinforce competition in the labor market and maintain a high unemployment rate among graduates. This dynamic tends to intensify competition in the labor market and maintain underemployment and underutilization of the workforce (Fokam et al., 2023 ). More specifically, the mismatch between the training acquired through education and the needs of the labor market has been identified as a form of human capital waste. Indeed, it is difficult for individuals and society to recover educational investments. The knowledge and skills acquired by individuals after graduation cannot be optimally exploited (Yokoyama et al., 2024 ; Neffke et al., 2024 ; McGuinness, 2006). Two forms of educational mismatch are well documented among the working population: vertical mismatch and horizontal mismatch. Vertical mismatch occurs when the level of education held by an individual does not match the level of education required for the job. Horizontal mismatch, on the other hand, occurs when an individual's field of study does not match the specific requirements of the job performed (Park, 2018). Thus, a worker whose level of education is below that required for the position held is in a situation of “under-education”. Conversely, a worker whose level of education is higher than the requirements of the position he or she occupies is in a situation of “overeducation”. The mismatch between education and employment can have consequences in the labor market, including job frustration, deterioration of abilities (Montt, 2017 ), wage penalties, and job dissatisfaction (Coraggio et al., 2025 ; Njifen, 2024 ; Allen and Van der Velden, 2001 . Abdulla, 2024 ; Francisco Castro et al., 2024 , Draissi et al., 2023 ; Morsy and Mukasa, 2019 ; Freeman, 1976 ; Battu and Bender, 2020 ; Putri and Febriani, 2020 ; Njifen and Smith, 2023 ). An in-depth analysis of the effects of the mismatch between education and employment on workers' incomes is necessary, as higher wages represent one of the main mechanisms by which economic growth can generate “shared prosperity” (Acemoglu and Johnson, 2023 ). Indeed, the well-being of workers and their families is closely linked to the level of wages they receive in the labor market (Wilson et al ., 2024). Moreover, low-income workers often find it difficult to meet their food requirements, due to the need for a combination of wages and social benefits, which harms their quality of life and health status (Beck et al ., 2019). In Africa, educational mismatch is a major concern, which has been the subject of more or less in-depth scientific research. The results of research carried out on the subject reveal marked disparities. By way of illustration, the results of the study conducted by Jacobs et al. (2019) highlight a significant wage penalty suffered by workers born in Africa, attributable to the mismatch between their training and the requirements of the labor market. This penalty is influenced by demographic factors and company characteristics. Furthermore, the Ragoobur (2022) study highlights that overqualified Mauritians face a wage penalty of around 11%, while undereducated workers face a penalty of around 9.6% compared to their well-trained counterparts. A more in-depth analysis of ten African countries reveals that 8.3% of youth are overeducated, resulting in wage penalties, while 56.9% are undereducated, which is surprisingly correlated with a wage premium (Morsy and Mukasa, 2019 ; Morsy and Mukasa, 2020 ). Some researchers have focused on analyzing labor market behavior in Cameroon. For example, the study by Yogo (2011) examined the impact of social capital on wages. His results reveal that individuals using social networks receive a wage premium of 1.53% over the average wage. In addition, he observes that the social network helps explain wage disparities according to gender and institutional sectors (formal vs. informal). Meanwhile, a study by Baye et al. ( 2016 ) focused on gender wage disparities in Cameroon. They highlight that the impact of education on wages increases with percentiles and that a substantial part of the wage gap originates in disparities between men and women in terms of characteristics. This analysis validates earlier studies, notably that of Ndamsa et al . (2015), which suggested that the majority of the observed gap was attributable to potential discrimination in the labor market rather than to wage differences between men and women in terms of average values of productive and job-related characteristics. The results of the analysis conducted by Atangana Ondoa ( 2019 ) on the effects of education on wage inequality in the informal sector reveal that the level of education is positively correlated with wage growth and wage inequality in this sector. Njifen and Smith ( 2023 ) analyzed the effect of education-employment mismatch on the return to education in Cameroon, using employment data provided by the National Institute of Statistics. Based on the estimation of the Verdugo and Verdugo model, their results show that overeducation is associated with a wage penalty, while undereducation leads to a wage premium. The main objective of this study is to assess the impact of the mismatch between education and employment, in both its vertical and horizontal forms, on workers' earnings. This is an area that remains largely unexplored in Cameroon. Indeed, research carried out to date in Cameroon has focused exclusively on vertical mismatch, i.e. over- and under-education. However, recent research suggests that employees working in occupations that do not correspond to their field of study (horizontal mismatch) could see their wages affected. (Li et al ., 2018; Takeuchi, 2023 ). In addition, we eliminate the problem of the potential endogeneity of job mismatch when examining its effect on compensation. Indeed, some studies did not take this problem into account, which could have biased the results obtained. On the other hand, some studies have used the instrumental variable (IV) regression method to overcome this difficulty. However, this approach has its limitations in terms of instrument suitability. Indeed, an inadequate selection of instrumental variables can not only aggravate the endogeneity problem but also compromise the reliability of the results (Giles and Murtazashvili, 2012 ; Guevara and Hess, 2019 ; Cameron and Trivedi, 2022 ). To overcome this limitation, our study relies on the instrumental variable regression method using the control function approach. This approach circumvents the constraints associated with instrument relevance (Giles and Murtazashvili, 2012 ; Guevara and Hess, 2019 ; Cameron and Trivedi, 2022 ). The second major contribution of our study lies in the analysis of the potential role that employment contracts might play in the relationship between educational mismatches and pay. Understanding the mechanisms by which different forms of contract can generate variable wage responses could contribute to the optimization of job market policies and employment practices. The main results of our study reveal that educational mismatch exerts a negative and significant influence on pay. This analysis highlights a significantly more detrimental effect in the presence of horizontal mismatch than vertical mismatch. Furthermore, the results of the study reveal that the mismatch between qualifications and employment has a more substantial pay impact on workers with fixed-term contracts than on those with open-ended contracts. Following this introductory section, the remainder of this article is structured as follows: Section 2 presents the literature review, while Section 3 details the methodology employed and the data collected. Section 4 highlights the results obtained, and Section 5 is devoted to the conclusion and recommendations for economic policy. 2. Literature Review The theoretical underpinnings of the relationship between educational mismatch and labor market remuneration can be traced back to the 1960s when the modern human capital theory was developed by Schultz ( 1961 ) and Becker (1957). This theory posits a correlation between investment in human capital and remuneration, emphasizing the significance of human capital in economic growth and individual income formation. The notion that human capital can contribute to economic development and individual income generation suggests that investment in education or training can be a catalyst for enhancing these outcomes. This notion is further reinforced by the seminal economic growth theory propounded by Romer ( 1986 ), Barro ( 1996 ), and Lucas ( 1988 ), who conceptualize human capital as an endogenous factor contributing to economic growth. Leveraging this theoretical framework, the nexus between educational mismatch and labor market remuneration has garnered implicit attention, largely attributable to the empirical validation of human capital theory. Arseneau (2014) employed a general equilibrium model to analyze employment dynamics in situations of skill mismatch, highlighting that vertical mismatch enables a faster transition from unemployment to employment but generally leads to lower wage compensation. Budría and Moro-Egido (2014) conducted a comparative analysis of 12 European countries, showing that the negative impact of horizontal mismatch on wages is more pronounced than that of vertical mismatch. This finding is corroborated by subsequent studies, which consistently demonstrate that the incongruity between job seekers' qualifications and the demands of the labor market negatively impacts their remuneration (Njifen and Smith, 2023 ; Jacobs et al., 2021 ; Liu et al., 2020 ; Putri and Febriani, 2020 ; Hartato, 2020 ; Sellami et al ., 2020). This phenomenon can be attributed to the fact that such a mismatch hinders the optimal utilization of skills, leading to diminished productivity in the workplace and, consequently, reduced remuneration. Moreover, it functions as a dysfunctional element within the labor market, exerting adverse consequences for individuals, organizations, and the national economy in its entirety (Shahidan et al. , 2019; Voces & Cainzos, 2021). In their contribution to the debate, Huertas and Raymond ( 2024 ) emphasize that both vertical and horizontal mismatches have a detrimental effect on self-rated health. This dynamic can potentially result in lower productivity and, consequently, lower remuneration in the labor market. Furthermore, their results suggest that vertical mismatch would harm women's self-rated health, while horizontal mismatch would affect men's health. Furthermore, the repercussions of the incongruity between educational qualifications and labor market imperatives on remuneration may be subject to variation according to gender and sector of activity. In this regard, Rim and Kim ( 2023 ) emphasize that the discrepancy between education and labor market requirements exerts a more substantial influence on women's wages in the service sector compared to the industrial sector. In their study, Lasso-Dela-Vega et al. (2023) examined the impact of educational inadequacy on fertility intentions, offering insights into the broader socio-economic ramifications of inadequacy that extend beyond immediate wage effects. Their research suggests that undereducation negatively impacts fertility intentions among women, indicating that the opportunity costs associated with educational inadequacy may encompass personal life decisions. This interaction between educational mismatch and life decisions may indirectly influence wage trajectories as individuals orient themselves in their careers and personal aspirations. According to Zhang et al . (2023), individuals in situations of educational mismatch are more likely to engage in entrepreneurship, due to the lower opportunity costs associated with job dissatisfaction and the acquisition of diverse skills. This transition to entrepreneurship may influence wages in a differentiated way, as self-employment may generate distinct incomes compared to a traditional career, further complicating the analysis of the effects of educational mismatch on wages. Furthermore, the findings of Stenard and Sauermann ( 2016 ) highlight that job dissatisfaction linked to mismatch is correlated with an increased likelihood of transition to entrepreneurship, which may offer different remuneration opportunities compared to conventional career paths. In their study, Chemli et al. ( 2024 ) looked at the articulation between digital skills and educational mismatch among higher education graduates, focusing on the tourism sector. Their longitudinal analyses reveal that vertical skills mismatch positively impacts employment rates, while a lack of digital skills hurts employment outcomes. In sum, this literature review highlights the complex and multifactorial effect of the mismatch between education and employment on workers' earnings. This phenomenon is influenced by a variety of factors, including gender, sector of activity, degree of job satisfaction, and personal life choices. The results of this literature suggest that skills mismatch can lead to significant wage penalties, particularly for specific demographic groups and in certain sectors of activity. As the scientific community continues to investigate this complex phenomenon, it seems vital to consider the broader implications of educational mismatch on economic and social outcomes, to deepen our understanding of labor market dynamics. 3. Methodology 3.1. Specification of the model and estimation technique This study follows the standard model of Mincer ( 1974 ), which laid the foundations of applied labor economics. It has been extended in various forms (Duncan & Hoffman, 1981; Verdugo & Verdugo, 1989 ; Allen and Van der Velden, 2001 ; Tran et al., 2023 ). Based on this model, we can specify the following payoff function: $$\:{\varvec{Y}}_{\varvec{i}}={\varvec{a}}_{0}+{\varvec{a}}_{1}{\varvec{E}}_{\varvec{i}}+\sum\:_{\varvec{k}=2}^{3}{\varvec{a}}_{\varvec{k}}{\varvec{E}\varvec{M}}_{\varvec{i}\varvec{k}}+\sum\:_{\varvec{k}=4}^{\varvec{m}}{\varvec{a}}_{\varvec{k}}{\varvec{X}}_{\varvec{i}\varvec{k}}+{\varvec{\epsilon\:}}_{\varvec{i}}$$ 1 In this equation, \(\:{\varvec{Y}}_{\varvec{i}}\) represents the value of the logarithm of the monthly wage of individual i ; E represents the number of years of education; EM is a dichotomous variable that takes the value 1 in the case of a mismatch between education and employment of the respondent, and zero otherwise; and X is a set of control variables that are assumed to affect compensation in the labor market. The above model is traditionally estimated using ordinary least squares (OLS). However, the variables of interest, vertical mismatch, and horizontal mismatch are suspected to be endogenous. According to the literature, there are two likely sources of endogeneity: simultaneity endogeneity and variable omission endogeneity. In the specific context of this study, the emphasis is on omitted endogeneity. This can be attributed to the fact that the variables included in the database used as indicators of educational mismatch could potentially be correlated with certain unobservable factors that also influence labor market earnings. Indeed, educational mismatch can be influenced by socio-economic and demographic factors such as place of residence, educational level of the head of household, or age of the individual. The same factors may also affect labor market compensation, suggesting the endogenous nature of both vertical and horizontal forms of educational mismatch. A relevant methodological approach to deal with endogeneity problems arising from the use of such data is the use of the instrumental variable double least squares (IV-2SLS) method (Morsy and Mukasa, 2019 ; Njifen and Smith, 2023 ). However, as pointed out by Haider et al . (2017), the choice of a valid instrument proves to be complex, as it must be relevant and exogenous. Indeed, it must be closely related to the educational mismatch while remaining independent of any influence of compensation outcomes in the labor market. In this sense, we use the instrumental "control function" approach, which eliminates the question of the relevance of the chosen instruments. This approach consists of two steps. In the first step, the model is estimated using ordinary least squares, and the residuals are extracted. The second step involves integrating the residuals obtained among the independent variables to control for the endogenous variable that is also included in the regression (Cameron and Trivedi, 2022 ). The educational level of the head of household is considered a relevant instrument in this study. 3.2. Data source and variables For this study, we used data from the third survey on employment dynamics and the informal sector, conducted in 2021 by the National Institute of Statistics of Cameroon, in collaboration with the Ministry of Economy, Planning and Regional Development of Cameroon. The survey involved a sample of 27,378 individuals, and its main objective was to assess and monitor trends in employment and the informal sector in Cameroon. The methodology used involved the distribution of three separate questionnaires in two phases. The first phase, focusing on employment, aims to gather information on the evolution of the labor market, particularly in terms of household labor supply, underemployment, and unemployment. This phase also allows us to identify several indicators that can be used to measure the level and main characteristics of pluriactivity and its impact on household income, changes in working conditions and the determinants of job mobility, the different sources of income and changes in their distribution and differentiation (National Institute of Statistics, 2022 ). The second phase focuses on informal production units. These are identified in the first phase by analyzing the data collected and, more specifically, by identifying the promoters of production units among individuals who exercise a professional activity. This phase aims to calculate the number of informal production units to finally assess the share of the informal sector in the national economy. It also aims to identify the most dynamic sectors of activity; to understand the needs, constraints, and opportunities of units in this sector; to identify the actors involved, such as operators and partners in the sector; to estimate the rate of utilization of production capacities; to assess knowledge of the social security system in place; and to analyze employer-employee relations within informal production units. Based on the data collected, a classification system was developed for each worker. This system identifies vertical and horizontal mismatches. This approach is based on a taxonomy of 43 occupational classifications, which allows these different forms of mismatch to be accurately identified. In addition, the review of major degrees is organized around nine categories: General Studies; Social Sciences, Business and Law; Education; Humanities and Arts; Engineering, Manufacturing and Production; Agriculture and Veterinary Sciences; Health and Welfare; Services; and Other. A deviation method is used to create the vertical mismatch variable. According to this approach, individuals whose standard deviation is one standard deviation below or above the mean number of years of education for a given occupation are considered to be vertically mismatched (Takeuchi, 2023 ). In addition, we use the number of workers and the distribution within an occupation to create the horizontal mismatch variable. The method is to consider the majors with the highest number of workers in each occupation to be in a horizontal match, while the others are classified as in a horizontal mismatch. This approach is supported by previous research (Mahuteau et al., 2015 ). To overcome the biases inherent in measuring the number of employees by occupation, the distributional method is advocated. This method, which normalizes the number of graduates to 100, makes it possible to correct for the biases associated with the distribution of employees by specialty. The analysis of the data collected has led to the identification of horizontal matches, defined as high percentages in the same profession, and horizontal mismatches, characterized by low or zero percentages in the same profession. This methodological approach resolves the potential problem of arbitrary primary classification (Takeuchi, 2023 ). The other control variables are the level of education obtained by the employee, the professional experience calculated in terms of months of seniority, the way the job was obtained, the sector of activity, the age and sex of the employee, and the type of remuneration. All these variables are presented in the table below: Table 1 Definition and description of variables Variables Definition/Measurement Income Monthly income in logarithm Horizontal mismatch 1 in case of horizontal mismatch and 0 otherwise Vertical mismatch 1 in case of vertical mismatch and 0 otherwise Sex 1 if male, 0 otherwise Age Individual's age in years Level of education attained Secondary 1st Cycle 1 if the level of education attained is secondary 1st Cycle, and 0 otherwise secondary 2nd Cycle 1 if the level of education attained is secondary 2nd Cycle, and 0 otherwise Higher education 1 if the level of education attained is higher education, and 0 otherwise Professional experience Work experience years Form of pay Fixed salary 1 if the form of pay is a fixed salary, and 0 otherwise Per day or hour of work 1 if the form of pay is per day or hour of work, and 0 otherwise At the task 1 if the form of pay is at the task, and 0 otherwise Activity sector Private sector 1 if the individual is employed in the private sector and 0 otherwise Public Sector 1 if the individual is employed in the public sector and 0 otherwise How to get a job Personal relations 1 if the individual got his job through a personal network and 0 otherwise Directly with the employer 1 if the person received the job directly from the employee, otherwise 0 Small announcements 1 if the person got his job through small announcements and 0 otherwise. Competitive exams 1 if the person got the job through competitive exams, otherwise 0 Source: Authors 4. Estimation results and discussions 4.1. Analysis of stylized facts The statistics presented in Table 2 show a significant disparity in the monthly compensation levels of matched and mismatched workers. The average monthly compensation of the former is higher than that of the latter. In terms of horizontal mismatch, this table shows that the log of monthly compensation of horizontally matched employees is 10.42 on average, while that of horizontally mismatched employees is 10.08, a difference of 0.34 points. Applying the Chi-square test, we conclude that this difference in compensation between horizontally matched and mismatched employees is positive and significant at the 1% level in favor of the former. Furthermore, the statistics presented in the table show that the logarithm of compensation for vertically aligned employees is on average 10.59, while that of vertically misaligned employees is 10.08. This indicates a significant difference of 0.5035 in favor of the former. Table 2 Monthly Income by Type of Educational Mismatch Variables Horizontal mismatch Difference-in-means test (1)- (2) Yes(1) No(2) Average monthly income 10.4219 10.0827 0.3392*** (0.0187) (0.0086) (0.0192) Vertical mismatch Difference-in-means test (1)- (2) Yes(1) No(2) Average monthly income 10.5926 10.0890 0.5035*** (0.02486) (0.0081) (0.0232) Source: Authors. Note: Standard errors are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1 4.2. Basic model results Table 3 shows the results of our basic model. This model assesses the impact of vertical and horizontal mismatches on workers' earnings. The results are based on two separate specifications, each using a rigorous estimation technique to overcome potential biases. The first two columns assess the impact of vertical and horizontal mismatches on earnings, respectively. This analysis was conducted using OLS. The third and fourth columns, on the other hand, use the instrumental variables method to address the issue of endogeneity. The results show a significant negative correlation between educational mismatch and labor market earnings. To illustrate, the results of the OLS estimator show that, on average, the vertically unmatched group earns a monthly wage income approximately 2.69% lower than the matched group. In addition, the horizontally unmatched group earns a monthly wage income about 11.6% lower than the matched group. Overall, our results are consistent with previous studies that have found that workers in mismatched jobs are subject to a wage penalty. These results are consistent with those of Takeuchi ( 2023 ), who showed that vertical mismatches negatively affect the earnings of high-skilled workers in Japan, leading to wasted human capital and wage penalties. However, the results of our study show that the wage penalty is significantly higher for IV-2SLS estimators. The wage penalty is almost five times higher in the IV-2SLS estimator compared to the OLS estimator. This difference could be due to omitted variable bias in the OLS estimator, as Becker and Heywood (2016) and Glewwe and Todd (2022) pointed out. For example, self-selection information is not observable in our study, which could explain this difference. Given that self-selection is closely related to both mismatch and earnings, excluding it from the OLS regression means that the effect of self-selection on earnings is then imputed to the inadequacy variable, which may lead to an underestimation of the direct effect of mismatch on earnings (Tran et al., 2023 ). In addition, it is important to note that the coefficients of the residual of the endogenous variable "educational mismatch" are all significantly different from zero. Consequently, the estimation of our instrumental variable double least squares (IV-2SLS) model using the "control function" approach is free of endogeneity bias. Concerning the control variables, the results obtained appear to support the extant literature. In this context, regression analyses conducted on the entire population demonstrate that enhancements in human capital indicators are associated with an increase in labor market remuneration. Indeed, these analyses indicate that the impact of education on remuneration is predominantly positive and increases significantly with the level of education. These findings appear to provide validation for the majority of research conducted on human capital (Mincer, 1974 ), as well as studies utilizing Cameroonian data (Abessolo, 1997; Njifen and Smith, 2023 ). However, these results contrast with those of Tafah-Edokat (1998) on Cameroon and, more generally, with those of Psacharopoulos (1994) in the context of a global analysis. The positive impact of human capital accumulation can also be seen in the coefficient on the work experience variable, which shows a positive and statistically significant trend. This observation can be interpreted in terms of experiential learning, which provides individuals with the necessary skills to increase their productivity. In addition, the coefficient associated with gender is positive and statistically significant, confirming the conclusions of previous analyses. This result suggests the existence of positive wage discrimination in favor of men in the Cameroonian labor market. This trend, which runs counter to previous discrimination, has been highlighted by Lachaud (1994) in Côte d'Ivoire and by Tsafack (2000) in Cameroon. According to Neumark (1999), this difference can be attributed to statistical bias, indicating purely statistical discrimination. In addition, access to employment via a social network appears to be detrimental to employees' monthly wages. This correlation suggests a possible causal relationship between the use of a social network to obtain a job and a reduction in salary bargaining power. Employees who feel indebted to the social network would be more willing to accept insecure jobs or a downgraded career, which are often associated with lower pay. This tendency could be attributed to the similarity between the social network and the professional context, which would reduce the individual's bargaining power. Table 3 Impact of Vertical and Horizontal Mismatches on Income VARIABLES OLS IV-2SLS (1) (2) (3) (4) Income Income Income Income Vertical mismatch -0.0269** - -1.393** - (0.0750) - (7.708) - Horizontal mismatch - -0.116** - -5.24*** - (0.0939) - (10.18) Estimated residuals - - 1.394*** 5.22*** - - (6.228) (10.18) Age 0.0153* 0.0150* 1.102*** 0.108*** (0.00888) (0.00886) (1.805) (0.0236) Professional experience -0.107 -0.112 4,915*** 0.123*** (0.106) (0.106) (13,075) (0.105) Level of education attained Secondary 1st Cycle -2.151 -2.086 4.122 2.36 (1.769) (1.764) (8.345) (4.875) secondary 2nd Cycle -1.771 -1.722 2.105** 2.662*** (1.762) (1.754) (2.036) (2.063) Higher education -1.447 -1.329 9.511** 3.846* (1.737) (1.727) (2.683) (2.088) Company size Medium-sized company 0.297*** 0.307*** 9.662*** 1.438*** (0.105) (0.105) (5.547) (0.424) Large company 0.594*** 0.602*** 9.941*** 3.407*** (0.117) (0.118) (2.971) (0.919) Table 3 Continued How to get a job Personal relations -0.353** -0.359** -5.945 -0.687*** (0.160) (0.159) (2.370) (0.176) Directly with the employer 0.130 0.127 -3.350 0.842*** (0.190) (0.190) (1.321) (0.250) Small announcements -0.0334 -0.0476 -1.624*** -1.721*** (0.175) (0.173) (2,963) (0.414) Competitive exams 0.334* 0.303 2.194*** 2.07*** (0.223) (0.222) (6.468) (2.796) Form of pay Fixed salary 0.943*** 0.946*** 1.525** 3.347*** (0.124) (0.124) (5.758) (0.542) Per day or hour of work -1.355*** -1.358*** -2.157** -2.900*** (0.236) (0.237) (4.966) (0.411) At the task -0.755*** -0.758*** -1.412 -3.354*** (0.209) (0.210) (3.717) (0.614) Commission -0.707 -2.124*** -4.200*** - (0.458) (0.564) (9.749) - Constant 1.468*** 1.449*** -4,055 -4.058*** (1.770) (1.763) (8.075) (6.808) Observations 14,622 14,622 14,622 14,622 R-squared 0.058 0.057 0.071 0.070 Source: Authors. Note: Standard errors are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1 Following on from these results, Table 4 shows the impact of educational mismatch on earnings by type of contract. As in the previous results, the data in this table show a negative and significant correlation between educational mismatch and labor market earnings, regardless of the type of contract. The coefficient associated with the education-employment mismatch variable is systematically negative and significant at the 1% threshold for all forms of employment. Specifically, these results indicate that educational mismatch has a more significant impact on the earnings of employees with fixed-term contracts (3.257) than on those with permanent contracts (0.34). This observation also holds for employees without a contract, who suffer a wage penalty of -1.163, which is less significant than for those with fixed-term contracts but higher than for those with permanent contracts. The difference between employees with fixed-term contracts and those with permanent contracts is due to differences in bargaining power. According to the insider/outsider theory, employees with fixed-term contracts have lower redundancy costs and less loyalty to their employer. The consequence of this vulnerability is to reduce the bargaining power of outsiders (employees with fixed-term contracts) and to concentrate wage bargaining power in the hands of insiders (employees with permanent contracts). Thus, when these two conditions are met, insiders can demand high salaries and those who are promoted and given permanent contracts can also demand the same salaries. Table 4 Effect of educational mismatches by type of employment contract Contract for employment No contract Written fixed-term contract Written indefinite contract Variables (1) (2) (3) (4) (5) (6) Income Income Income Income Income Income Vertical mismatch -1.875*** - -12.46*** - -0.992 - (4.608) - (2.902) - (5.942) - Horizontal mismatch - -3.206*** - -8.751*** - 0.335 - (78.54) - (2.039) - (1.945) Estimated residuals 1,875*** 3.219*** 12.09*** 8.965*** 0.997 -0.149 (460.8) (78.55) (2.918) (2.038) (5.943) (1.945) Age 8.883*** -0.313*** -0.0505 0.0971*** 0.0126 0.00679 (2.184) (0.0769) (0.0315) (0.0253) (0.0357) (0.0157) Professional experience 6.118*** -9.094*** 0.503 -0.164 -0.244 -0.173 (28.74) (2.218) (0.315) (0.260) (0.324) (0.249) Level of education attained Secondary 1st Cycle -1,226*** 1.389*** - - - - (3.008) (3.466) - - - - secondary 2nd Cycle -7.933*** 6.721*** -1.722 7.986*** 0.0483 -0.609 (194.5) (17.05) (1.487) (1.387) (2.785) (1.721) Higher education 1,747*** 2.001*** 2.410*** 7.552*** 0.749 0.509 (4.290) (5.909) (3.276) (1.462) (0.934) (1.725) Company size Réf. Réf. Réf. Réf. Réf. Réf. Medium-sized company 3.315*** 3.168*** -0.0858 0.156 -0.131 0.0513 (13.13) (3.296) (0.301) (0.268) (1.012) (0.207) Large company 4.268*** 13.80*** 0.353 -0.445 0.148 0.293 (60.72) (3.333) (0.286) (0.419) (0.673) (0.282) How to get a job Personal relations 1.118*** 2.774*** 0.492* -0.409 -0.253 -0.316 (2.751) (0.762) (0.280) (0.293) (0.435) (0.223) Directly with the employer 2.081*** 3.416*** 0.903** 0.331 -0.0458 -0.0292 (51.01) (8.220) (0.407) (0.338) (0.274) (0.250) Small announcements -2.654*** -2.463*** -1.591*** -1.603*** -0.101 -0.197 (6.516) (5.968) (0.455) (0.490) (0.549) (0.251) Competitive exams 6.257*** 7.517*** 1.293* 2.067*** 0.275 0.186 (13.57) (18.28) (0.664) (0.656) (1.132) (0.619) Form of pay Fixed salary 1.009*** 1.764*** -3.326*** -1.536*** -0.489 -0.513* (2.519) (4.702) (0.888) (0.497) (0.373) (0.279) Per day or hour of work 1.045*** 1.206*** -4.218*** -0.660 -0.889 -0.615 (2.606) (3.330) (1.256) (0.729) (1.615) (0.901) At the task -6.002*** -1.679*** 1.177 -3.010*** -0.895 -0.465 (14.97) (4.327) (0.806) (1.122) (2.628) (0.864) Constant 3.180*** -7.212*** 4.030*** 1.063*** 1.378*** 1.298*** (7.781) (5.697) (7.434) (0.958) (4.862) (1.056) Observations 7,115 7,115 2,604 2,604 4,903 4,903 R-squared 0.120 0.123 0.109 0.096 0.022 0.024 Source: Authors. Note: Standard errors are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1 4.3. Effect of educational mismatch on earnings: additional analysis The repercussions of educational mismatch on job compensation are a multifaceted issue that varies across industries and demographic groups. To furnish comprehensive information on the impact of educational mismatch on earnings, we undertake several supplementary analyses in this subsection. Initially, we assess the effect of educational mismatch by gender. Subsequently, we assess this effect by institutional sector. Firstly, we estimate our model by distinguishing between men and women. The results presented in Table 5 demonstrate that men encounter a more pronounced penalty compared to women. Specifically, vertical mismatch results in a reduction of men's pay by 2.818, while for women, it is 1.756. Horizontal mismatch leads to a decrease in men's pay by 3.101, and for women, it is 2.068. The observed outcomes could be attributed to the hypothesis that women possess a lower level of education compared to men, thereby validating the findings of Tan et al. (2023), who demonstrated that the impact of this mismatch on remuneration is nearly double for men compared to women. However, these results are at odds with the conclusions of Lasso-Dela-Vega et al. (2023), who reported that women encounter a more pronounced penalty due to educational mismatch in comparison to men, suggesting that men accrue benefits from attaining higher levels of education than women. Secondly, the estimate is conducted based on the sector of activity. The results presented in Table 6 demonstrate that vertical mismatch has a greater impact on the remuneration of public sector employees than private sector employees. Conversely, horizontal mismatch exerts a more significant influence on private-sector employees. Table 5 Impact of mismatch on workers' income by gender Hommes Femmes Variables (1) (2) (3) (4) Income Income Income Income Vertical mismatch -2.818 - -1.756*** - (8.303) - (18.36) - Horizontal mismatch - -3.101*** - -2.068*** - (6.429) - (3.482) Estimated residuals 3.818* 1.100*** 2.410*** 3.702*** (7.839) (3.649) (8.307) (3.487) Age -1.368 0.328*** 0.509*** 0.0141 (4.062) (0.113) (0.128) (0.0155) Professional experience 1.551 -1.088*** -2.502*** -0.210 (5.327) (0.380) (0.604) (0.189) Level of education attained Secondary 1st Cycle -1.727 -6.634 -4.973 -5.113 (5.024) (23.82) (1.101) (0.645) secondary 2nd Cycle -7.381 0.569*** -4.669*** 2.184 (12.012) (1.853) (10.19) (2.259) Higher education -2.874 -2.174*** -7.762*** 4.815* (6.277) (8.138) (18.18) (2.710) Company size Medium-sized company -1.939 -4.654*** -4.803*** 0.139 (6.662) (1.765) (1.371) (0.197) Large company -1.724 -1.084*** -9.525*** -0.229 (4.459) (4.155) (2.565) (0.277) Table 5 Continued How to get a job Personal relations 2.208 2.305** 2.797*** -1.345*** (4.901) (0.960) (0.858) (0.369) Directly with the employer 1.409 7.185*** -1.090*** -0.773** (2.908) (2.534) (0.418) (0.372) Small announcements 2.001 2.758*** 6.772*** -2.194*** (7.948) (0.972) (1.811) (0.577) Competitive exams 5.356 -4.197*** 9.766*** -6.125*** (7.295) (5.518) (2.429) (1.673) Form of pay Fixed salary 1.032 -3.447*** 0.120 -2.507*** (3.402) (0.988) (0.383) (0.366) Per day or hour of work 7.413 3.230** -2.334*** -4.031*** (6.465) (1.525) (0.309) (0.594) At the task 4.603 -4.229*** 3.105*** -2.770*** (1.494) (1.362) (1.182) (0.672) Commission 2.079 - - - (7.349) - - - Constant 4.069 3.108*** 5.147*** 2.118 (1.335) (33.11) (31.95) (0.801) Observations 8,660 8,660 5,962 5,962 R-squared 0.065 0.065 0.102 0.100 Source: Authors. Note: Standard errors are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1 Table 6 Effect of mismatch on income by sector of activity Public sector Private sector (1) (2) (3) (4) VARIABLES Income Income Income Income Vertical mismatch -5.51** - -3.7*** - (4.300) - (5.701) - Horizontal mismatch - -6.89* - -8.79*** - (5.002) - (11.56) Estimated residuals 5.50*** 11.94*** 2.9*** 8.77*** (44.30) (9.020) (5.071) (11.56) Age 0.421 0.0588 -0.616*** 0.151*** (0.311) (0.0423) (0.149) (0.0346) Professional experience 2.173* 0.402* 8.141*** 0.0855 (1.473) (0.292) (1.911) (0.115) Level of education attained Secondary 1st Cycle - - -1.361*** -2.573*** - - (3.283) (5.872) Secondary 2nd Cycle 3.438 -0.982 1.009*** -6.303*** (2.193) (1.622) (2.443) (2.059) Higher education 2.140*** 2.222*** 2.180*** 5.066** (17.35) (0.638) (50.61) (2.306) Company size Medium-sized company -3.696 2.785* 1.825*** 1.785*** (3.290) (1.670) (4.259) (0.507) Large company -8.017 1.371 2.161*** 2.706*** (6.169) (1.002) (4.961) (0.821) Table 6 Continued How to get a job Personal relations 2.540 -1.938 -1.855*** 0.0941 (2.044) (1.420) (4.322) (0.206) Directly with the employer -0.682 0.0233 -6.454*** 0.976*** (0.797) (0.448) (1.574) (0.293) Small announcements 6.024 -0.459 -3.377*** -1.984*** (4.326) (0.718) (7.994) (0.489) Competitive exams 7.684 -3.449 -5.747*** -4.016*** (5.800) (2.663) (13.77) (1.139) Form of pay Fixed salary 0.373 1.350** 2.284*** 3.070*** (0.924) (0.558) (0.329) (0.505) Per day or hour of work 0.0352 3.379 -4.706*** -3.586*** (0.421) (2.448) (0.818) (0.568) At the task 2.074 -2.874** -1.864*** -3.393*** (2.695) (1.355) (0.339) (0.660) Commission - - - - - - - - Constant 2.845 1.714* -3.334** 3.494* (5.039) (4.060) (9.208) (7.160) Observations 1,966 1,966 12,656 12,656 R-squared 0.073 0.071 0.072 0.070 Source: Authors. Note: Standard errors are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1 4.4. Robustness checks Robustness is tested at two levels. Firstly, the model is estimated using the quantile method. Secondly, the pay gap between matched and mismatched employees is analyzed using the Blinder-Oaxaca decomposition (Blinder 1973; Oaxaca 1973 ). The results of the robustness tests demonstrate that educational mismatch harms pay, both vertically and horizontally. 4.4.1. Estimation using the quantile method To ascertain the robustness of the impact of vertical and horizontal mismatches on job market earnings, quantile estimation was employed by Aytun and Meçik (2023). However, rather than utilizing conventional regression quantiles, we adapted the modified version based on the moment conditions that define conditional means, as proposed by Machado and Santos Silva (2019). This approach not only ensures a more streamlined handling of parameter-related issues but also enhances the overall efficiency of the analysis. The results (Table 7) demonstrate a high degree of similarity to those of the baseline model, indicating that educational mismatch, in both vertical and horizontal forms, is associated with a reduction in job market earnings in Cameroon. Tableau 7 . Effet de l’inadéquation éducative sur le revenu : régression par quantile Variables (1) (2) (3) (1) (2) (3) q10 q25 q50 q10 q25 q50 Horizontal mismatch -0.0737 -0.141** -0.0573 - - - (0.0541) (0.0555) (0.0758) - - - Vertical mismatch - - - -0.117* -0.127* -0.172** - - - (0.0618) (0.0718) (0.0671) Age 0.0288*** 0.0309*** 0.0266*** 0.0309*** 0.0303*** 0.0254** (0.00699) (0.00696) (0.0103) (0.00519) (0.00702) (0.0123) Professional experience -0.0629 0.0111 0.0418 -0.105 0.0418 0.0753 (0.0828) (0.0974) (0.0958) (0.121) (0.191) (0.0679) Company size Medium-sized company 0.164** 0.223*** 0.239** 0.185*** 0.229*** 0.175** (0.0647) (0.0790) (0.103) (0.0641) (0.0747) (0.0737) Large company 0.313*** 0.549*** 0.571*** 0.329*** 0.544*** 0.600*** (0.0752) (0.0866) (0.0953) (0.0820) (0.0962) (0.138) Level of education attained Secondary 1st Cycle -0.706 -0.352 -0.621 -0.838 -0.698 -0.589 (1.961) (2.398) (2.394) (1.444) (2.428) (3.333) Secondary 2nd Cycle -1.027 -0.254 -0.400 -1.096 -0.515 -0.419 (1.991) (2.503) (2.537) (1.369) (2.415) (3.278) Higher education -0.557 -0.109 0.145 -0.728 -0.270 0.0122 (1.982) (2.404) (2.373) (1.460) (2.383) (3.299) How to get a job Personal relations -0.320** -0.401*** -0.388*** -0.357*** -0.364*** -0.303** (0.129) (0.0893) (0.121) (0.133) (0.0973) (0.130) Directly with the employer 0.272** 0.0330 -0.0733 0.288** 0.00450 0.0692 (0.138) (0.115) (0.152) (0.131) (0.126) (0.188) Small announcements -0.147 -0.249*** -0.172 -0.137 -0.182 -0.0807 (0.133) (0.0902) (0.132) (0.125) (0.127) (0.147) Competitive exams 0.549** 0.194* -0.0592 0.498** 0.178 0.0164 (0.250) (0.117) (0.140) (0.230) (0.221) (0.179) Form of pay Fixed salary 0.601* 3.169*** 2.907*** -0.638 3.119*** 2.959*** (0.342) (0.105) (0.0646) (0.428) (0.721) (0.0767) Per day or hour of work -1.262*** -3.367*** -3.181*** -1.245** -3.277*** -3.204*** (0.312) (0.153) (0.151) (0.617) (0.803) (0.239) At the task -0.986*** -3.439*** -2.738*** -0.961** -3.431*** -2.838*** (0.317) (0.180) (0.335) (0.447) (0.761) (0.286) Commission -0.862* -3.231*** -2.637*** -0.840 -3.171*** -2.757*** (0.444) (0.236) (0.272) (0.624) (0.762) (0.424) Benefits -0.718* -3.161*** -2.103*** -0.707 -3.086*** -2.124*** (0.429) (0.150) (0.445) (0.458) (0.758) (0.564) Constant 11.20*** 13.72*** 14.04*** 11.52*** 14.04*** 14.42*** (2.030) (2.424) (2.392) (1.415) (2.523) (3.375) Observations 14,622 14,622 14,622 14,622 14,622 14,622 Source: Authors. Note: Standard errors are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1 4.4.2. The decomposition of Blinder (1973) and Oaxaca ( 1973 ) The second robustness test examines the wage gap between matched and unmatched employees in the Cameroon job market. A frequently used methodology for analyzing group job market outcomes is to decompose mean log wage differences using linear regression models in a counterfactual manner. This approach is commonly referred to as the 'Blinder-Oaxaca decomposition' (Blinder 1973; Oaxaca 1973 ). This methodological approach is based on dividing the wage differential between two groups into two components. The first component corresponds to the part 'explained' by group differences in terms of productivity characteristics, such as level of education or work experience, while the second component represents the residual part, which cannot be explained by these differences in wage determinants. This latter part, referred to as 'unexplained,' is commonly used as a measure of discrimination, although it also includes the effects of group differences in unobserved predictors. Furthermore, the following table illustrates the pay gap between individuals working in jobs that match their profile and those for which there is a mismatch, for the entire working population. This disparity is analyzed by controlling for characteristics relating to supply, demand, and the context of the labour market. The logarithmic transformation is used to decompose this difference in terms of differences in log earnings between the two groups. This approach underscores the predicted average logarithm of monthly earnings for matched and mismatched workers, respectively. The third row of the figure demonstrates that the average differential is -1.618622. Of this difference, -1.321234 is attributable to differences in the ‘endowments’, which refer to the average differences in the regressors of the two groups, and − 0.3556278 is attributable to differences in the regression coefficients. Finally, the difference of 0.0582399 is attributed to an interaction effect resulting from combining the two previous effects. Table 8 Income breakdown vertical match/mismatch Variables Predicted mean Predicted average monthly income (vertical match) 3.209 (14.82)** Predicted average monthly income (vertical mismatch) 3.184 (32.57)** Difference 0.025 (0.11) Differential due to “endowment” difference 2.210 (5.61)** Differential due to coefficient difference 0.118 (0.10) Interaction -2.303 (1.97)* Source: Authors. Note: Standard errors are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1 As illustrated in Table 9 , the data analysis discloses a discrepancy in the mean monthly wage between horizontally and meshed employees. The former group exhibits a mean monthly income of 3.308, while the latter group's mean monthly income is 2.731. This indicates a statistically significant discrepancy of -0.577, favoring the former group. A more detailed analysis of this differential reveals that 0.351 is attributable to disparities in the 'endowments', which reflect the average differences between the regressors of the two groups, and that a further difference of -1.117 is attributable to differences in the regression coefficients. Table 9 Income breakdown horizontal match/mismatch Variables Moyenne prédite Predicted average monthly income (horizontal mismatch) 2.731 (18.03)** Predicted average monthly income (horizontal match) 3.308 (31.21)** Difference -0.577 (3.12)** Differential due to “endowment” difference 0.351 (2.21)* Differential due to coefficient difference -1.117 (3.13)** Interaction 0.189 (0.54) Source: Authors. Note: Standard errors are in parentheses. *** p < 0.01, ** p < 0.05, * 5. Conclusion This article employs data on employment and the informal sector to examine the effects of vertical and horizontal mismatch on workers' incomes in Cameroon. Utilizing statistical analyses, namely the comparison of the means test, the study compares average incomes according to employee status. The findings reveal that the average monthly earnings of matched workers exceed those of unmatched employees. The econometric results, obtained using ordinary least squares and ordered instrumental variable methods with the control function approach, confirm these descriptive statistics and suggest that education-employment mismatch has a detrimental effect on workers' income in Cameroon. Furthermore, the results of our analysis reveal that horizontal mismatch is more conducive to a significant reduction in monthly income for both men and women, compared with vertical mismatch. A further analysis, distinguishing between different types of employment contracts, indicates that education-employment mismatch has a greater adverse effect on earnings for workers with fixed-term contracts than for those with open-ended contracts. These results are corroborated by wage decomposition using the Oaxaca-Blinder method (1973), which demonstrates that the mismatch between education and employment contributes to income inequality in the Cameroonian job market. Given the findings, educational establishments must offer vocational training that is more consistent with the requirements of the job market, intending to remedy the educational mismatch that is accompanying the exponential increase in the number of graduates in Cameroon. Furthermore, it is crucial to recognize that the impact of horizontal mismatch on professional integration is significantly higher than that of vertical mismatch. Consequently, it is imperative to put in place an effective career guidance system aligned with the real needs of the job market and business sectors. Consequently, further research is required to ascertain the precise causes of elevated wage penalties in instances of horizontal mismatch by conducting a thorough examination of the disciplines that serve to amplify this effect. This would empower the Government to formulate efficacious policies to regulate the least valued disciplines in the Cameroonian job market. References Abdulla, K. (2024). Quantifying the productivity effects of education-job mismatch: a cross-country analysis. Education Economics , 1‑18. Acemoglu, D. (2024). Capital and wages. International Economic Review , 00 (00), 1‑22. Acemoglu, D., and Johnson, S. (2023). Power and Progress: Our Thousand-Year Struggle over Technology and Prosperity. Albert, C., Davia, M. A., and Legazpe, N. (2021). Educational mismatch in recent university graduates. The role of labour mobility. Journal of Youth Studies , p. 13. Allen, J., and Van Der Velden, R. (2001). Educational mismatches versus skill mismatches: effects on wages, job satisfaction, and on‐the‐job search. Oxford Economic Papers , 53 (3), 434‑452. Arrow, K. J. (1973). Higher education as a filter. Journal of Public Economics , 2 (3), 193‑216. Artz, B., and Welsch, D. M. (2021). Overeducation and wages revisited: A two-cohort comparison and random coefficients approach. Southern Economic Journal , 87 (3), 909‑936. Atangana Ondoa, H. (2019). L’effet de l’éducation sur les disparités salariales dans le secteur informel : l’exemple du Cameroun. Revue internationale du travail , 158 (3), 615‑635. Barro, R. (1996). Healt hand Economic Growth. World Health Organization. Battu, H., and Bender, K. A. (2020). Educational mismatch in developing countries: A review of the existing evidence. The Economics of Education , 56 (3), 269‑289. Baye, F., Epo Ngah, B., and Ndenzako, J. (2016). Wage differentials in Cameroon: a gendrered analysis. African Development Review , 28 (1), 75‑91. Becker, G. (1962). Investment in Human Capital: A Theoretical Analysis. The Journal of Political Economy , 70 (5), 39‑49. Becker, G. (1964). Human capital : a theoretical and empirical analysis, with special reference to education (Vol. 80). New York : Columbia University Press,. Bender, K. A., and Heywood, J. S. (2016). Educational mismatch and retirement. Education Economics , 25 , 347‑365. Bezooijen, E., Van Berge, W., and Salomon, A. (2024). The Young Bunch: Youth Minimum Wages and Labor Market Outcomes. ILR Review , 77 (3), 428‑460. Cameron, C., and Trivedi, P. (2022). Microeconometrics Using Stata: Nonlinear Models and Causal Inference Methods (Second Edition, Vol. 2). College Station, Texas : A Stata Press Publication. Carranza, E., and McKenzie, D. (2023). Job Training and Job Search Assistance Policies in Developing Countries. Policy Research Working Papers, 10576. Chemli, S., Toanoglou, M., and Roman, J. L. D. V. (2024). Skills mismatch and digital skill shortages in tourism : Impact on graduate employment and implications for higher education. International Journal of Technology Enhanced Learning , 16 (4), 447‑465. Chemli, S., Toanoglou, M., and Roman, J. L. D. V. (2024). Skills mismatch and digital skill shortages in tourism : Impact on graduate employment and implications for higher education. International Journal of Technology Enhanced Learning , 16 (4), 447‑465. Cohn, E., and Khan, P. S. (1995). The wage effects of overschooling revisited. Labour Economics , 2 (1), 67‑76. Coraggio, L., Pagano, M., Scognamiglio, A., and Tåg, J. (2025). JAQ of all trades : Job mismatch, firm productivity and managerial quality. Journal of Financial Economics , 164 , 103992. Di Pietro, G., and Urwin, P. (2006). Education and skills mismatch in the Italian graduate labour market. Applied Economics , 38 (1), 79‑93. Draissi, Z., Rong, Y., and Ebrahim Suliman, M. A. (2023). Employability and Education Mismatch in the Moroccan Urban Workplace. SAGE Open , 13 (4), 21582440231217890. Ferran, E. (2024). Labor Costs and Worker Segmentation. Universitat de Girona. Fokam, D. N. D. T., Kamga, B. F., and Nchofoung. (2023). Information and communication technologies and employment in developing countries: Effects and transmission channels. Telecommunications Policy , 47 (8), 102597. Francisco Castro, J., Ortega, L., Yamada, G., and Mata, D. (2024). Overeducation and overskilling in Latin America: evidence from PIAAC. Comparative Education , 60 (2), 296‑314. Freeman, R. B. (1976). The Overeducated American . Academic Press. Giles, J., and Murtazashvili, I. (2012). A Control Function Approach to Estimating Dynamic Probit Models with Endogenous Regressors, with an Application to the Study of Poverty Persistence in China. IZA DP No. 6887. Grasso, G., and Tatsiramos, K. (2023). The Impact of Restricting Fixed-Term Contracts on Labor and Skill Demand. CESifo Working Paper No. 10693. Guevara, C., and Hess, S. (2019). A control-function approach to correct for endogeneity in discrete choice models estimated on SP-off-RP data and contrasts with an earlier FIML approach by Train & Wilson. Transportation Research Part B: Methodologica , pp. 224‑239. Hartato, H. (2020). The Analysis of Vertical Mismatch and Manufacturing Labor Wage by Using Cramer’s V Method. Journal public policy , 6 (1), 1‑6. Huertas, I. P. M., and Raymond, J. L. (2024). Education, educational mismatch and occupational status : An analysis using PIAAC data. Economia Politica , 41 (3), 717‑738. Huertas, I. P. M., and Raymond, J. L. (2024). Education, educational mismatch and occupational status : An analysis using PIAAC data. Economia Politica , 41 (3), 717‑738. Jacobs, V., Rycx, F., and Volral, M. (2021). Wage Effects of Educational Mismatch According to Workers’ Origin: The Role of Demographics and Firm Characteristics. IZA DP No. 14813 . Jacobs, V., Rycx, F., and Volral, M. (2022). Wage Effects of Educational Mismatch According to Workers’ Origin: The Role of Demographics and Firm Characteristics. De Economist , 170 (4), 459‑501. Lasso‐Dela‐Vega, E., Sánchez Ollero, J. L., and García‐Pozo, A. (2023). Effects of educational mismatch on wages across industry and occupations: sectoral comparison. International Journal of Manpower , 44 (9), 237‑255. Li, I. W., Harris, M., and Sloane, P. J. (2022). Vertical, horizontal and residual skills mismatch in the Australian graduate labour market. Economic Record , 94 (306), 301‑315. Liu, K., Salvanes, K. G., and Sørensen, E. Ø. (2020). Good skills in bad times: Cyclical skill mismatch and the long-term effects of graduating in a recession. European Economic Review , 84 , 3‑17. Lucas, R. (1988). On the mechanics of economic development. Journal of Monetary Economics , 22 (1), 3‑42. Ma, C., and Weiss, A. (1993). A signaling theory of unemployment. European Economic Review , 37 (1), 135‑157. Mahuteau, S., Mavromaras, K., Sloane, P., and Wei, Z. (2015). Horizontal and vertical educational mismatch and wages. Mankiw, N. G., Romer, D., and Weil, D. N. (1992). A contribution to the empirics of economic growth. The quarterly journal of economics , 107 (2), 407‑437. Mincer, J. (1974). Schooling, experience and earnings . New York : Columbia University Press. Montt, G. (2017). Field-of-study mismatch and overqualification: Labour market correlates and their wage penalty. IZA Journal of Labor Economics , 6 (2), 1‑20. Morsy, H. (2020). Inadéquation de la formation des jeunes Africains, quelle solution ? Project Syndicate . Morsy, H., and Mukasa, A. (2019). Youth Jobs, Skill and Educational Mismatches in Africa. [MPRA Paper]. Morsy, H., and Mukasa, A. N. (2020). Mind the mismatch? Incidence, drivers, and persistence of African youths’ skill and educational mismatches. African Development Review , 32 , 5‑19. Muhammedrisaevna, T. M., and Saidovich, D. S. (2021). Formation of wages in the labor market. Bukhara State University , 2 (6), 146‑151. National Institute Of Statistics. (2022). Third survey on employment and the informal sector in Cameroon (Rapport No. Aug. 2022). Neffke, F., Nedelkoska, L., and Wiederhold, S. (2024). Skill mismatch and the costs of job displacement. Research Policy , 53 (2), 104933. Njifen, I. (2024). Education-Job mismatch, earnings and worker’s satisfaction in African labor market : Evidence from Cameroon. Economia Politica , 41 (2), 931‑962. Njifen, I., and Smith, P. (2023). Education-Job Mismatch and Heterogeneity in the Return to Schooling: Evidence from Cameroon. Journal of the Knowledge Economy , 23 (1), 1‑28. Oaxaca, R. (1973). Male-female wage differential in urban labor market. International Economic Review , 14 (3), 693‑709. Putri, A. K., and Febriani, R. E. (2020). Analisa mismatch tenaga kerja di asia tenggara. The journal of economic development , 2 (2), 133‑149. Rim, H., and Kim, J. (2023). Status Inconsistency, Gender, and Health in Korean Young Adults : Separating Educational and Skill Mismatch. Social Indicators Research , 170 (3), 1141‑1153. Romer, P. M. (1986). Increasing Returns and Long-run Growth. Journal of Political Economy , 94 (5), 1002‑1037. Rosen, S. (1986). The Theory of equalizing differences . Ansterdam : North Holland. Rudakov, V., Figueiredo, H., Teixeira, P. N., and Roshchin, S. (2019). The Impact of Horizontal Job-Education Mismatches on the Earnings of Recent University Graduates in Russia”. IZA Discussion Papers. No 12407. Sam, V. (2018). Unemployment duration and educational mismatches: An empirical investigation among graduates in Cambodia. Economics Bulletin , 38 (3), 1554‑1565. Sánchez-Sánchez, N., and McGuinness, S. (2015). Decomposing the Impacts of Overeducation and Overskilling on Earnings and Job Satisfaction: An Analysis Using REFLEX data. Working Paper No. 393 . Sattinger, M. (1993). Capital and the distribution of income . Amsterdam : North Holland. Schultz, T. W. (1961). Investment in Human Capital. American Economic Review , 51 (1), 1‑17. Sellami, S., Verhaest, D., Nonneman, W., and Van Trier, W. (2017). The Impact of Educational Mismatches on Wages: The Influence of Measurement Error and Unobserved Heterogeneity. The B.E. Journal of Economic Analysis & Policy , 17 (1), 1‑20. Sicherman, N. (1991). Overeducation” in the labor market. Journal of Labor Economics , 9 (2), 101‑122. Sloane, P. J. (2014). Overeducation, skill mismatches, and labor market outcomes for college graduates. Overeducation in Europe , 11‑45. Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations . Chicago : University of Chicago Press. Spence, M. (1973). Job Market Signaling. The Quarterly Journal of Economics , 87 (3), 355‑374. Stenard, B. S., and Sauermann, H. (2016). Educational Mismatch, Work Outcomes, and Entry Into Entrepreneurship. Organization Science , 27 (4), 801‑824. Stenard, B. S., and Sauermann, H. (2016). Educational Mismatch, Work Outcomes, and Entry Into Entrepreneurship. Organization Science , 27 (4), 801‑824. Stiglitz, J. E. (1975). The Theory of « Screening, » Education, and the Distribution of Income. The American Economic Review , 65 (3), 283‑300. Takeuchi, M. (2023). Impact of vertical and horizontal mismatches on earnings among highly educated employees in Japan. International journal of educational research open . Tran, T. Q., Thi Vu, N. B., and Van Vu, H. (2023). Does job mismatch affect wage earnings among business and management graduates in Vietnam? Research in International Business and Finance , 65 (101982). Verdugo, R. R., and Verdugo, N. T. (1989). The Impact of Surplus Schooling on Earnings. Journal of Human Resources , 24 , 629‑643. World Bank. (2024). World Development Indicators. https://doi.org/Retrieved from http://data.worldbank.org/indicator/SE.TER.ENRR/countries/VN-4E-XN?display=default Xu, Q., and Chen, Y. (2024). Educational mismatch and mental health: Evidence from China. Social Science & Medicine , 356 (117140). Yanikkaya, H., and Tat, P. (2023). The effect of educational mismatch on the Turkish manufacturing industry. International Journal of Education Economics and Development (IJEED) , 14 (4). Yokoyama, I., Obara, T., Kiyomoto, A. S., Kusada, K., Edamura, K., and Inui, T. (2024). Endogenous decisions on acceptable worker-job mismatch level and the impact on workers’ performance. Japan and the World Economy , 72 , 101283. Zamo, A. C. (2021). L’avenir du Travail au Cameroun : Analyse contextuelle. International Labour Organization. Zhu, R. (2014). The impact of major-job mismatch on college graduates’ early career earnings: Evidence from China. Education Economics , 22 , 511‑528. Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5913807","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":407797729,"identity":"e51accbe-fe8c-4c42-bec3-1ec150f77fb0","order_by":0,"name":"Joseph Dabo Doubla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYBACxgYIwdggwcD4gIHhAIj7gGgtzAYQLSCaCKtAWtgkiNLC3N7+8HHhDgbZ/tnNx6p5au7I8TMws33Aa0HPGWPjmWcYjGfcOZZ2m+fYM2PJBmbmGXi1zMhhk+ZtY0hsuJFjdpuH7XDihgP8h/F7Y0b6898gLfOBWop5/oG0MDMT0JJgxgzSsgGoBcggRgvQL9Iz2ySMN945liw5t++wsWQzAS2GwBD7XNhmIzvvdvPBD2++HZbjZ28moKUBGNAMDBJgDhMPiMSvgYFBHlkN4w8CqkfBKBgFo2BkAgA1NUyI9EldrAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-7556-9646","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Joseph","middleName":"Dabo","lastName":"Doubla","suffix":""},{"id":407798250,"identity":"274a7556-a298-4c9d-b279-35f0f5584b60","order_by":1,"name":"Valère Amassangka Haman Hamen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Valère","middleName":"Amassangka Haman","lastName":"Hamen","suffix":""},{"id":407798518,"identity":"9fca68a7-747e-4c22-b50a-4a672286d240","order_by":2,"name":"Constant Tchaltouang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Constant","middleName":"","lastName":"Tchaltouang","suffix":""}],"badges":[],"createdAt":"2025-01-27 16:37:03","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5913807/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5913807/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74989665,"identity":"e27043f5-85d1-4a90-8ea3-07f8c621aa8d","added_by":"auto","created_at":"2025-01-29 07:02:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1972839,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5913807/v1/49120390-ad94-4349-b08e-2f6b48d9b6d7.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDo Horizontal and Vertical Mismatches Affect Workers' Incomes in Cameroon? Results From the Instrumental Variable Models Using the Function-control Approach\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLike other developing countries, Cameroon has made significant progress in school enrolment and completion over the past two decades. Indeed, between 2000 and 2023, the gross enrolment rate rose significantly, from 86.59\u0026ndash;112.64% in primary education, from 27.97\u0026ndash;44.39% in secondary education, and from 4.68\u0026ndash;15.79% in higher education (World Bank, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, despite the progress observed, marked by an exponential increase in the number of graduates, the results on the job market are disappointing. Indeed, in most cases, the training supply does not match the needs of the labor market (Carranza and McKenzie, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Njifen, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). This situation reduces the productivity of investments in human capital, resulting in lower incomes for workers. In addition, analysis of the national economic fabric reveals that of the 203,000 businesses listed in the second General Business Census (RGE2), the overwhelming majority \u0026minus;\u0026thinsp;98.5% - belong to the category of Very Small Enterprises (VSE) and Small Enterprises (SE). Medium-sized Enterprises (ME) and Large Enterprises (GE) account for just 1.3% and 0.2% of the total respectively (Zamo, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This situation tends to reinforce competition in the labor market and maintain a high unemployment rate among graduates. This dynamic tends to intensify competition in the labor market and maintain underemployment and underutilization of the workforce (Fokam et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMore specifically, the mismatch between the training acquired through education and the needs of the labor market has been identified as a form of human capital waste. Indeed, it is difficult for individuals and society to recover educational investments. The knowledge and skills acquired by individuals after graduation cannot be optimally exploited (Yokoyama et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Neffke et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; McGuinness, 2006). Two forms of educational mismatch are well documented among the working population: vertical mismatch and horizontal mismatch. Vertical mismatch occurs when the level of education held by an individual does not match the level of education required for the job. Horizontal mismatch, on the other hand, occurs when an individual's field of study does not match the specific requirements of the job performed (Park, 2018). Thus, a worker whose level of education is below that required for the position held is in a situation of \u0026ldquo;under-education\u0026rdquo;. Conversely, a worker whose level of education is higher than the requirements of the position he or she occupies is in a situation of \u0026ldquo;overeducation\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThe mismatch between education and employment can have consequences in the labor market, including job frustration, deterioration of abilities (Montt, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), wage penalties, and job dissatisfaction (Coraggio et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Njifen, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Allen and Van der Velden, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2001\u003c/span\u003e. Abdulla, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Francisco Castro et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e, Draissi et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Morsy and Mukasa, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Freeman, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Battu and Bender, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Putri and Febriani, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Njifen and Smith, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). An in-depth analysis of the effects of the mismatch between education and employment on workers' incomes is necessary, as higher wages represent one of the main mechanisms by which economic growth can generate \u0026ldquo;shared prosperity\u0026rdquo; (Acemoglu and Johnson, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Indeed, the well-being of workers and their families is closely linked to the level of wages they receive in the labor market (Wilson et \u003cem\u003eal\u003c/em\u003e., 2024). Moreover, low-income workers often find it difficult to meet their food requirements, due to the need for a combination of wages and social benefits, which harms their quality of life and health status (Beck et \u003cem\u003eal\u003c/em\u003e., 2019).\u003c/p\u003e \u003cp\u003eIn Africa, educational mismatch is a major concern, which has been the subject of more or less in-depth scientific research. The results of research carried out on the subject reveal marked disparities. By way of illustration, the results of the study conducted by Jacobs et al. (2019) highlight a significant wage penalty suffered by workers born in Africa, attributable to the mismatch between their training and the requirements of the labor market. This penalty is influenced by demographic factors and company characteristics. Furthermore, the Ragoobur (2022) study highlights that overqualified Mauritians face a wage penalty of around 11%, while undereducated workers face a penalty of around 9.6% compared to their well-trained counterparts. A more in-depth analysis of ten African countries reveals that 8.3% of youth are overeducated, resulting in wage penalties, while 56.9% are undereducated, which is surprisingly correlated with a wage premium (Morsy and Mukasa, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Morsy and Mukasa, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSome researchers have focused on analyzing labor market behavior in Cameroon. For example, the study by Yogo (2011) examined the impact of social capital on wages. His results reveal that individuals using social networks receive a wage premium of 1.53% over the average wage. In addition, he observes that the social network helps explain wage disparities according to gender and institutional sectors (formal vs. informal). Meanwhile, a study by Baye et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) focused on gender wage disparities in Cameroon. They highlight that the impact of education on wages increases with percentiles and that a substantial part of the wage gap originates in disparities between men and women in terms of characteristics. This analysis validates earlier studies, notably that of Ndamsa et \u003cem\u003eal\u003c/em\u003e. (2015), which suggested that the majority of the observed gap was attributable to potential discrimination in the labor market rather than to wage differences between men and women in terms of average values of productive and job-related characteristics. The results of the analysis conducted by Atangana Ondoa (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) on the effects of education on wage inequality in the informal sector reveal that the level of education is positively correlated with wage growth and wage inequality in this sector. Njifen and Smith (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) analyzed the effect of education-employment mismatch on the return to education in Cameroon, using employment data provided by the National Institute of Statistics. Based on the estimation of the Verdugo and Verdugo model, their results show that overeducation is associated with a wage penalty, while undereducation leads to a wage premium.\u003c/p\u003e \u003cp\u003eThe main objective of this study is to assess the impact of the mismatch between education and employment, in both its vertical and horizontal forms, on workers' earnings. This is an area that remains largely unexplored in Cameroon. Indeed, research carried out to date in Cameroon has focused exclusively on vertical mismatch, i.e. over- and under-education. However, recent research suggests that employees working in occupations that do not correspond to their field of study (horizontal mismatch) could see their wages affected. (Li et \u003cem\u003eal\u003c/em\u003e., 2018; Takeuchi, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, we eliminate the problem of the potential endogeneity of job mismatch when examining its effect on compensation. Indeed, some studies did not take this problem into account, which could have biased the results obtained. On the other hand, some studies have used the instrumental variable (IV) regression method to overcome this difficulty. However, this approach has its limitations in terms of instrument suitability. Indeed, an inadequate selection of instrumental variables can not only aggravate the endogeneity problem but also compromise the reliability of the results (Giles and Murtazashvili, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Guevara and Hess, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cameron and Trivedi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To overcome this limitation, our study relies on the instrumental variable regression method using the control function approach. This approach circumvents the constraints associated with instrument relevance (Giles and Murtazashvili, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Guevara and Hess, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Cameron and Trivedi, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The second major contribution of our study lies in the analysis of the potential role that employment contracts might play in the relationship between educational mismatches and pay. Understanding the mechanisms by which different forms of contract can generate variable wage responses could contribute to the optimization of job market policies and employment practices.\u003c/p\u003e \u003cp\u003eThe main results of our study reveal that educational mismatch exerts a negative and significant influence on pay. This analysis highlights a significantly more detrimental effect in the presence of horizontal mismatch than vertical mismatch. Furthermore, the results of the study reveal that the mismatch between qualifications and employment has a more substantial pay impact on workers with fixed-term contracts than on those with open-ended contracts.\u003c/p\u003e \u003cp\u003eFollowing this introductory section, the remainder of this article is structured as follows: Section 2 presents the literature review, while Section 3 details the methodology employed and the data collected. Section 4 highlights the results obtained, and Section 5 is devoted to the conclusion and recommendations for economic policy.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eThe theoretical underpinnings of the relationship between educational mismatch and labor market remuneration can be traced back to the 1960s when the modern human capital theory was developed by Schultz (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1961\u003c/span\u003e) and Becker (1957). This theory posits a correlation between investment in human capital and remuneration, emphasizing the significance of human capital in economic growth and individual income formation. The notion that human capital can contribute to economic development and individual income generation suggests that investment in education or training can be a catalyst for enhancing these outcomes. This notion is further reinforced by the seminal economic growth theory propounded by Romer (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), Barro (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), and Lucas (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), who conceptualize human capital as an endogenous factor contributing to economic growth. Leveraging this theoretical framework, the nexus between educational mismatch and labor market remuneration has garnered implicit attention, largely attributable to the empirical validation of human capital theory.\u003c/p\u003e \u003cp\u003eArseneau (2014) employed a general equilibrium model to analyze employment dynamics in situations of skill mismatch, highlighting that vertical mismatch enables a faster transition from unemployment to employment but generally leads to lower wage compensation. Budr\u0026iacute;a and Moro-Egido (2014) conducted a comparative analysis of 12 European countries, showing that the negative impact of horizontal mismatch on wages is more pronounced than that of vertical mismatch. This finding is corroborated by subsequent studies, which consistently demonstrate that the incongruity between job seekers' qualifications and the demands of the labor market negatively impacts their remuneration (Njifen and Smith, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Jacobs et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Putri and Febriani, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hartato, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sellami et \u003cem\u003eal\u003c/em\u003e., 2020). This phenomenon can be attributed to the fact that such a mismatch hinders the optimal utilization of skills, leading to diminished productivity in the workplace and, consequently, reduced remuneration. Moreover, it functions as a dysfunctional element within the labor market, exerting adverse consequences for individuals, organizations, and the national economy in its entirety (Shahidan et \u003cem\u003eal.\u003c/em\u003e, 2019; Voces \u0026amp; Cainzos, 2021). In their contribution to the debate, Huertas and Raymond (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) emphasize that both vertical and horizontal mismatches have a detrimental effect on self-rated health. This dynamic can potentially result in lower productivity and, consequently, lower remuneration in the labor market. Furthermore, their results suggest that vertical mismatch would harm women's self-rated health, while horizontal mismatch would affect men's health.\u003c/p\u003e \u003cp\u003eFurthermore, the repercussions of the incongruity between educational qualifications and labor market imperatives on remuneration may be subject to variation according to gender and sector of activity. In this regard, Rim and Kim (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasize that the discrepancy between education and labor market requirements exerts a more substantial influence on women's wages in the service sector compared to the industrial sector. In their study, Lasso-Dela-Vega et \u003cem\u003eal.\u003c/em\u003e (2023) examined the impact of educational inadequacy on fertility intentions, offering insights into the broader socio-economic ramifications of inadequacy that extend beyond immediate wage effects. Their research suggests that undereducation negatively impacts fertility intentions among women, indicating that the opportunity costs associated with educational inadequacy may encompass personal life decisions. This interaction between educational mismatch and life decisions may indirectly influence wage trajectories as individuals orient themselves in their careers and personal aspirations.\u003c/p\u003e \u003cp\u003eAccording to Zhang et \u003cem\u003eal\u003c/em\u003e. (2023), individuals in situations of educational mismatch are more likely to engage in entrepreneurship, due to the lower opportunity costs associated with job dissatisfaction and the acquisition of diverse skills. This transition to entrepreneurship may influence wages in a differentiated way, as self-employment may generate distinct incomes compared to a traditional career, further complicating the analysis of the effects of educational mismatch on wages. Furthermore, the findings of Stenard and Sauermann (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) highlight that job dissatisfaction linked to mismatch is correlated with an increased likelihood of transition to entrepreneurship, which may offer different remuneration opportunities compared to conventional career paths. In their study, Chemli et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) looked at the articulation between digital skills and educational mismatch among higher education graduates, focusing on the tourism sector. Their longitudinal analyses reveal that vertical skills mismatch positively impacts employment rates, while a lack of digital skills hurts employment outcomes.\u003c/p\u003e \u003cp\u003eIn sum, this literature review highlights the complex and multifactorial effect of the mismatch between education and employment on workers' earnings. This phenomenon is influenced by a variety of factors, including gender, sector of activity, degree of job satisfaction, and personal life choices. The results of this literature suggest that skills mismatch can lead to significant wage penalties, particularly for specific demographic groups and in certain sectors of activity. As the scientific community continues to investigate this complex phenomenon, it seems vital to consider the broader implications of educational mismatch on economic and social outcomes, to deepen our understanding of labor market dynamics.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Specification of the model and estimation technique\u003c/h2\u003e\n \u003cp\u003eThis study follows the standard model of Mincer (\u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e), which laid the foundations of applied labor economics. It has been extended in various forms (Duncan \u0026amp; Hoffman, 1981; Verdugo \u0026amp; Verdugo, \u003cspan class=\"CitationRef\"\u003e1989\u003c/span\u003e; Allen and Van der Velden, \u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e; Tran et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Based on this model, we can specify the following payoff function:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{\\varvec{Y}}_{\\varvec{i}}={\\varvec{a}}_{0}+{\\varvec{a}}_{1}{\\varvec{E}}_{\\varvec{i}}+\\sum\\:_{\\varvec{k}=2}^{3}{\\varvec{a}}_{\\varvec{k}}{\\varvec{E}\\varvec{M}}_{\\varvec{i}\\varvec{k}}+\\sum\\:_{\\varvec{k}=4}^{\\varvec{m}}{\\varvec{a}}_{\\varvec{k}}{\\varvec{X}}_{\\varvec{i}\\varvec{k}}+{\\varvec{\\epsilon\\:}}_{\\varvec{i}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eIn this equation, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Y}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e represents the value of the logarithm of the monthly wage of individual \u003cem\u003ei\u003c/em\u003e; E represents the number of years of education; EM is a dichotomous variable that takes the value 1 in the case of a mismatch between education and employment of the respondent, and zero otherwise; and X is a set of control variables that are assumed to affect compensation in the labor market.\u003c/p\u003e\n \u003cp\u003eThe above model is traditionally estimated using ordinary least squares (OLS). However, the variables of interest, vertical mismatch, and horizontal mismatch are suspected to be endogenous. According to the literature, there are two likely sources of endogeneity: simultaneity endogeneity and variable omission endogeneity. In the specific context of this study, the emphasis is on omitted endogeneity. This can be attributed to the fact that the variables included in the database used as indicators of educational mismatch could potentially be correlated with certain unobservable factors that also influence labor market earnings. Indeed, educational mismatch can be influenced by socio-economic and demographic factors such as place of residence, educational level of the head of household, or age of the individual. The same factors may also affect labor market compensation, suggesting the endogenous nature of both vertical and horizontal forms of educational mismatch.\u003c/p\u003e\n \u003cp\u003eA relevant methodological approach to deal with endogeneity problems arising from the use of such data is the use of the instrumental variable double least squares (IV-2SLS) method (Morsy and Mukasa, \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Njifen and Smith, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, as pointed out by Haider et \u003cem\u003eal\u003c/em\u003e. (2017), the choice of a valid instrument proves to be complex, as it must be relevant and exogenous. Indeed, it must be closely related to the educational mismatch while remaining independent of any influence of compensation outcomes in the labor market. In this sense, we use the instrumental \u0026quot;control function\u0026quot; approach, which eliminates the question of the relevance of the chosen instruments. This approach consists of two steps. In the first step, the model is estimated using ordinary least squares, and the residuals are extracted. The second step involves integrating the residuals obtained among the independent variables to control for the endogenous variable that is also included in the regression (Cameron and Trivedi, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The educational level of the head of household is considered a relevant instrument in this study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Data source and variables\u003c/h2\u003e\n \u003cp\u003eFor this study, we used data from the third survey on employment dynamics and the informal sector, conducted in 2021 by the National Institute of Statistics of Cameroon, in collaboration with the Ministry of Economy, Planning and Regional Development of Cameroon. The survey involved a sample of 27,378 individuals, and its main objective was to assess and monitor trends in employment and the informal sector in Cameroon. The methodology used involved the distribution of three separate questionnaires in two phases. The first phase, focusing on employment, aims to gather information on the evolution of the labor market, particularly in terms of household labor supply, underemployment, and unemployment. This phase also allows us to identify several indicators that can be used to measure the level and main characteristics of pluriactivity and its impact on household income, changes in working conditions and the determinants of job mobility, the different sources of income and changes in their distribution and differentiation (National Institute of Statistics, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). The second phase focuses on informal production units. These are identified in the first phase by analyzing the data collected and, more specifically, by identifying the promoters of production units among individuals who exercise a professional activity. This phase aims to calculate the number of informal production units to finally assess the share of the informal sector in the national economy. It also aims to identify the most dynamic sectors of activity; to understand the needs, constraints, and opportunities of units in this sector; to identify the actors involved, such as operators and partners in the sector; to estimate the rate of utilization of production capacities; to assess knowledge of the social security system in place; and to analyze employer-employee relations within informal production units.\u003c/p\u003e\n \u003cp\u003eBased on the data collected, a classification system was developed for each worker. This system identifies vertical and horizontal mismatches. This approach is based on a taxonomy of 43 occupational classifications, which allows these different forms of mismatch to be accurately identified. In addition, the review of major degrees is organized around nine categories: General Studies; Social Sciences, Business and Law; Education; Humanities and Arts; Engineering, Manufacturing and Production; Agriculture and Veterinary Sciences; Health and Welfare; Services; and Other. A deviation method is used to create the vertical mismatch variable. According to this approach, individuals whose standard deviation is one standard deviation below or above the mean number of years of education for a given occupation are considered to be vertically mismatched (Takeuchi, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn addition, we use the number of workers and the distribution within an occupation to create the horizontal mismatch variable. The method is to consider the majors with the highest number of workers in each occupation to be in a horizontal match, while the others are classified as in a horizontal mismatch. This approach is supported by previous research (Mahuteau et al., \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). To overcome the biases inherent in measuring the number of employees by occupation, the distributional method is advocated. This method, which normalizes the number of graduates to 100, makes it possible to correct for the biases associated with the distribution of employees by specialty. The analysis of the data collected has led to the identification of horizontal matches, defined as high percentages in the same profession, and horizontal mismatches, characterized by low or zero percentages in the same profession. This methodological approach resolves the potential problem of arbitrary primary classification (Takeuchi, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe other control variables are the level of education obtained by the employee, the professional experience calculated in terms of months of seniority, the way the job was obtained, the sector of activity, the age and sex of the employee, and the type of remuneration. All these variables are presented in the table below:\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDefinition and description of variables\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDefinition/Measurement\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonthly income in logarithm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHorizontal mismatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 in case of horizontal mismatch and 0 otherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVertical mismatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 in case of vertical mismatch and 0 otherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 if male, 0 otherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndividual\u0026apos;s age in years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of education attained\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary 1st Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 if the level of education attained is secondary 1st Cycle, and 0 otherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esecondary 2nd Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 if the level of education attained is secondary 2nd Cycle, and 0 otherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 if the level of education attained is higher education, and 0 otherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWork experience years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eForm of pay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed salary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 if the form of pay is a fixed salary, and 0 otherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer day or hour of work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 if the form of pay is per day or hour of work, and 0 otherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt the task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 if the form of pay is at the task, and 0 otherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eActivity sector\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrivate sector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 if the individual is employed in the private sector and 0 otherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePublic Sector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 if the individual is employed in the public sector and 0 otherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHow to get a job\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal relations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 if the individual got his job through a personal network and 0 otherwise\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirectly with the employer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 if the person received the job directly from the employee, otherwise 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall announcements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 if the person got his job through small announcements and 0 otherwise.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompetitive exams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 if the person got the job through competitive exams, otherwise 0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003eSource: Authors\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Estimation results and discussions","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1. Analysis of stylized facts\u003c/h2\u003e\n \u003cp\u003eThe statistics presented in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eshow a significant disparity in the monthly compensation levels of matched and mismatched workers. The average monthly compensation of the former is higher than that of the latter. In terms of horizontal mismatch, this table shows that the log of monthly compensation of horizontally matched employees is 10.42 on average, while that of horizontally mismatched employees is 10.08, a difference of 0.34 points. Applying the Chi-square test, we conclude that this difference in compensation between horizontally matched and mismatched employees is positive and significant at the 1% level in favor of the former. Furthermore, the statistics presented in the table show that the logarithm of compensation for vertically aligned employees is on average 10.59, while that of vertically misaligned employees is 10.08. This indicates a significant difference of 0.5035 in favor of the former.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMonthly Income by Type of Educational Mismatch\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHorizontal mismatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDifference-in-means test (1)- (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAverage monthly income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.4219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3392***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0187)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0086)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0192)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVertical mismatch\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifference-in-means test (1)- (2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes(1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo(2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAverage monthly income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.5926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.0890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5035***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.02486)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0081)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0232)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSource:\u0026nbsp;\u003c/strong\u003eAuthors.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eStandard errors are in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2. Basic model results\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the results of our basic model. This model assesses the impact of vertical and horizontal mismatches on workers\u0026apos; earnings. The results are based on two separate specifications, each using a rigorous estimation technique to overcome potential biases. The first two columns assess the impact of vertical and horizontal mismatches on earnings, respectively. This analysis was conducted using OLS. The third and fourth columns, on the other hand, use the instrumental variables method to address the issue of endogeneity.\u003c/p\u003e\n \u003cp\u003eThe results show a significant negative correlation between educational mismatch and labor market earnings. To illustrate, the results of the OLS estimator show that, on average, the vertically unmatched group earns a monthly wage income approximately 2.69% lower than the matched group. In addition, the horizontally unmatched group earns a monthly wage income about 11.6% lower than the matched group. Overall, our results are consistent with previous studies that have found that workers in mismatched jobs are subject to a wage penalty. These results are consistent with those of Takeuchi (\u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e), who showed that vertical mismatches negatively affect the earnings of high-skilled workers in Japan, leading to wasted human capital and wage penalties. However, the results of our study show that the wage penalty is significantly higher for IV-2SLS estimators. The wage penalty is almost five times higher in the IV-2SLS estimator compared to the OLS estimator.\u003c/p\u003e\n \u003cp\u003eThis difference could be due to omitted variable bias in the OLS estimator, as Becker and Heywood (2016) and Glewwe and Todd (2022) pointed out. For example, self-selection information is not observable in our study, which could explain this difference. Given that self-selection is closely related to both mismatch and earnings, excluding it from the OLS regression means that the effect of self-selection on earnings is then imputed to the inadequacy variable, which may lead to an underestimation of the direct effect of mismatch on earnings (Tran et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, it is important to note that the coefficients of the residual of the endogenous variable \u0026quot;educational mismatch\u0026quot; are all significantly different from zero. Consequently, the estimation of our instrumental variable double least squares (IV-2SLS) model using the \u0026quot;control function\u0026quot; approach is free of endogeneity bias.\u003c/p\u003e\n \u003cp\u003eConcerning the control variables, the results obtained appear to support the extant literature. In this context, regression analyses conducted on the entire population demonstrate that enhancements in human capital indicators are associated with an increase in labor market remuneration. Indeed, these analyses indicate that the impact of education on remuneration is predominantly positive and increases significantly with the level of education. These findings appear to provide validation for the majority of research conducted on human capital (Mincer, \u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e), as well as studies utilizing Cameroonian data (Abessolo, 1997; Njifen and Smith, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, these results contrast with those of Tafah-Edokat (1998) on Cameroon and, more generally, with those of Psacharopoulos (1994) in the context of a global analysis. The positive impact of human capital accumulation can also be seen in the coefficient on the work experience variable, which shows a positive and statistically significant trend. This observation can be interpreted in terms of experiential learning, which provides individuals with the necessary skills to increase their productivity. In addition, the coefficient associated with gender is positive and statistically significant, confirming the conclusions of previous analyses. This result suggests the existence of positive wage discrimination in favor of men in the Cameroonian labor market. This trend, which runs counter to previous discrimination, has been highlighted by Lachaud (1994) in C\u0026ocirc;te d\u0026apos;Ivoire and by Tsafack (2000) in Cameroon. According to Neumark (1999), this difference can be attributed to statistical bias, indicating purely statistical discrimination.\u003c/p\u003e\n \u003cp\u003eIn addition, access to employment via a social network appears to be detrimental to employees\u0026apos; monthly wages. This correlation suggests a possible causal relationship between the use of a social network to obtain a job and a reduction in salary bargaining power. Employees who feel indebted to the social network would be more willing to accept insecure jobs or a downgraded career, which are often associated with lower pay. This tendency could be attributed to the similarity between the social network and the professional context, which would reduce the individual\u0026apos;s bargaining power.\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eImpact of Vertical and Horizontal Mismatches on Income\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eVARIABLES\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eOLS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eIV-2SLS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVertical mismatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0269**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.393**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0750)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.708)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHorizontal mismatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.116**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.24***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0939)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(10.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstimated residuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.394***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.22***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.228)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(10.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0153*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0150*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.102***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.108***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00886)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.805)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0236)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,915***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.123***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.106)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(13,075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of education attained\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary 1st Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.769)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.764)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(8.345)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.875)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esecondary 2nd Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.771\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.105**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.662***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.762)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.754)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.036)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.511**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.846*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.737)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.727)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.683)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.088)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompany size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium-sized company\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.297***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.307***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.662***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.438***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.547)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.424)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge company\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.594***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.602***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.941***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.407***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.971)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.919)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eContinued\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHow to get a job\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal relations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.353**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.359**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.687***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.159)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.370)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.176)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirectly with the employer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.842***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.321)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.250)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall announcements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.624***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.721***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.175)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2,963)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.414)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompetitive exams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.334*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.194***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.223)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.222)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.468)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.796)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eForm of pay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed salary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.943***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.946***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.525**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.347***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.124)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.542)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer day or hour of work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.355***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.358***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.157**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.900***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.237)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.966)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.411)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt the task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.755***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.758***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.412\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.354***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.209)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.210)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.717)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.614)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.124***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.200***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.564)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(9.749)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.468***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.449***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4,055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.058***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.770)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.763)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(8.075)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.808)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;14,622\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eSource:\u0026nbsp;\u003c/strong\u003eAuthors.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eStandard errors are in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n \u003cp\u003eFollowing on from these results, Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the impact of educational mismatch on earnings by type of contract. As in the previous results, the data in this table show a negative and significant correlation between educational mismatch and labor market earnings, regardless of the type of contract. The coefficient associated with the education-employment mismatch variable is systematically negative and significant at the 1% threshold for all forms of employment. Specifically, these results indicate that educational mismatch has a more significant impact on the earnings of employees with fixed-term contracts (3.257) than on those with permanent contracts (0.34). This observation also holds for employees without a contract, who suffer a wage penalty of -1.163, which is less significant than for those with fixed-term contracts but higher than for those with permanent contracts. The difference between employees with fixed-term contracts and those with permanent contracts is due to differences in bargaining power. According to the insider/outsider theory, employees with fixed-term contracts have lower redundancy costs and less loyalty to their employer. The consequence of this vulnerability is to reduce the bargaining power of outsiders (employees with fixed-term contracts) and to concentrate wage bargaining power in the hands of insiders (employees with permanent contracts). Thus, when these two conditions are met, insiders can demand high salaries and those who are promoted and given permanent contracts can also demand the same salaries.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffect of educational mismatches by type of employment contract\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eContract for employment\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eNo contract\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWritten fixed-term contract\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eWritten indefinite contract\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVertical mismatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.875***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12.46***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.608)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.902)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.942)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHorizontal mismatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.206***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.751***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(78.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.945)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstimated residuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,875***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.219***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12.09***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.965***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(460.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(78.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.918)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.943)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.945)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.883***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.313***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0971***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00679\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.184)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0769)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0253)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0357)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0157)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.118***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.094***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(28.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.218)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.315)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.260)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.324)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.249)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of education attained\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary 1st Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1,226***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.389***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.466)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esecondary 2nd Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.933***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.721***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.986***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(194.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(17.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.487)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.387)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.785)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.721)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,747***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.410***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.552***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.290)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.909)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.276)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.462)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.934)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.725)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompany size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u0026eacute;f.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u0026eacute;f.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u0026eacute;f.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u0026eacute;f.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u0026eacute;f.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u0026eacute;f.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium-sized company\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.315***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.168***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(13.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.296)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.301)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge company\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.268***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.80***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(60.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.333)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.286)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.419)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.673)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.282)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHow to get a job\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal relations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.118***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.774***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.492*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.751)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.762)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.280)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.293)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.435)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.223)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirectly with the employer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.081***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.416***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.903**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0292\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(51.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(8.220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.407)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.338)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.274)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.250)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall announcements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.654***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.463***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.591***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.603***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.516)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.968)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.455)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.490)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.549)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.251)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompetitive exams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.257***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.517***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.293*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.067***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(13.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(18.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.664)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.656)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.619)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eForm of pay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed salary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.009***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.764***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.326***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.536***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.489\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.513*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.519)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.702)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.497)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.373)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.279)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer day or hour of work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.045***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.206***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.218***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.615\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.606)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.330)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.729)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.615)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.901)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt the task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.002***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.679***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.010***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.465\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(14.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.327)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.806)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.628)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.864)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.180***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.212***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.030***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.063***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.378***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.298***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.781)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.697)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.434)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.958)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.862)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.056)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7,115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2,604\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4,903\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eSource:\u0026nbsp;\u003c/strong\u003eAuthors.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eStandard errors are in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3. Effect of educational mismatch on earnings: additional analysis\u003c/h2\u003e\n \u003cp\u003eThe repercussions of educational mismatch on job compensation are a multifaceted issue that varies across industries and demographic groups. To furnish comprehensive information on the impact of educational mismatch on earnings, we undertake several supplementary analyses in this subsection. Initially, we assess the effect of educational mismatch by gender. Subsequently, we assess this effect by institutional sector. Firstly, we estimate our model by distinguishing between men and women. The results presented in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e demonstrate that men encounter a more pronounced penalty compared to women. Specifically, vertical mismatch results in a reduction of men\u0026apos;s pay by 2.818, while for women, it is 1.756. Horizontal mismatch leads to a decrease in men\u0026apos;s pay by 3.101, and for women, it is 2.068. The observed outcomes could be attributed to the hypothesis that women possess a lower level of education compared to men, thereby validating the findings of Tan et al. (2023), who demonstrated that the impact of this mismatch on remuneration is nearly double for men compared to women. However, these results are at odds with the conclusions of Lasso-Dela-Vega et al. (2023), who reported that women encounter a more pronounced penalty due to educational mismatch in comparison to men, suggesting that men accrue benefits from attaining higher levels of education than women. Secondly, the estimate is conducted based on the sector of activity. The results presented in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e demonstrate that vertical mismatch has a greater impact on the remuneration of public sector employees than private sector employees. Conversely, horizontal mismatch exerts a more significant influence on private-sector employees.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eImpact of mismatch on workers\u0026apos; income by gender\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHommes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eFemmes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cem\u003eVariables\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncome\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVertical mismatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.756***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(8.303)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(18.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHorizontal mismatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.101***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.068***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.429)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.482)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstimated residuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.818*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.100***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.410***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.702***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.839)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.649)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(8.307)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.487)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.328***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.509***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.062)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0155)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.088***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.502***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.327)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.380)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.604)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of education attained\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary 1st Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.113\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(23.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.645)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esecondary 2nd Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.569***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.669***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(12.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.853)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(10.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.174***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.762***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.815*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.277)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(8.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(18.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.710)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompany size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium-sized company\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.654***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.803***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.662)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.765)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.197)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge company\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.084***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.525***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.459)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.155)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.565)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.277)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eContinued\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHow to get a job\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal relations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.305**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.797***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.345***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.901)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.858)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.369)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirectly with the employer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.185***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.090***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.773**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.908)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.534)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.418)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.372)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall announcements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.758***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.772***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.194***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.948)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.972)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.811)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.577)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompetitive exams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.197***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.766***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.125***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.295)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.518)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.429)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.673)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eForm of pay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed salary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.447***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.507***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.402)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.988)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.383)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.366)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer day or hour of work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.230**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.334***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.031***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.465)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.594)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt the task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.603\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.229***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.105***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.770***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.494)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.362)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.182)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.672)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.349)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.108***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.147***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(33.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(31.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.801)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8,660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eSource:\u0026nbsp;\u003c/strong\u003eAuthors.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eStandard errors are in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eEffect of mismatch on income by sector of activity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePublic sector\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003ePrivate sector\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(4)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVARIABLES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVertical mismatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.51**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.7***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.300)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.701)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHorizontal mismatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.89*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.79***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(11.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEstimated residuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.50***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.94***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.77***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(44.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(9.020)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.071)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(11.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.616***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.151***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.311)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0423)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.149)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0346)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.173*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.402*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.141***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.473)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.292)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.911)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.115)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of education attained\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary 1st Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.361***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.573***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.283)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.872)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary 2nd Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.009***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.303***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.622)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.443)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.059)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.140***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.222***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.180***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.066**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(17.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.638)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(50.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.306)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompany size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium-sized company\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.696\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.785*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.825***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.785***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.290)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.670)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.507)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge company\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.161***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.706***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(6.169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.961)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.821)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eContinued\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHow to get a job\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal relations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.855***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.420)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.322)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.206)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirectly with the employer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.454***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.976***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.797)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.448)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.574)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.293)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall announcements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.377***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.984***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.326)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.718)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.994)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.489)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompetitive exams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.747***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.016***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.800)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.663)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(13.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.139)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eForm of pay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed salary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.350**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.284***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.070***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.924)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.558)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.329)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.505)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer day or hour of work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.706***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.586***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.421)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.448)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.818)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.568)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt the task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.874**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.864***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.393***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.695)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.339)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.660)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.714*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.334**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.494*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(5.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(4.060)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(9.208)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(7.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12,656\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eSource:\u0026nbsp;\u003c/strong\u003eAuthors.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eStandard errors are in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4. Robustness checks\u003c/h2\u003e\n \u003cp\u003eRobustness is tested at two levels. Firstly, the model is estimated using the quantile method. Secondly, the pay gap between matched and mismatched employees is analyzed using the Blinder-Oaxaca decomposition (Blinder 1973; Oaxaca \u003cspan class=\"CitationRef\"\u003e1973\u003c/span\u003e). The results of the robustness tests demonstrate that educational mismatch harms pay, both vertically and horizontally.\u003c/p\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e4.4.1. Estimation using the quantile method\u003c/h2\u003e\n \u003cp\u003eTo ascertain the robustness of the impact of vertical and horizontal mismatches on job market earnings, quantile estimation was employed by Aytun and Me\u0026ccedil;ik (2023). However, rather than utilizing conventional regression quantiles, we adapted the modified version based on the moment conditions that define conditional means, as proposed by Machado and Santos Silva (2019). This approach not only ensures a more streamlined handling of parameter-related issues but also enhances the overall efficiency of the analysis. The results (Table\u0026nbsp;7) demonstrate a high degree of similarity to those of the baseline model, indicating that educational mismatch, in both vertical and horizontal forms, is associated with a reduction in job market earnings in Cameroon.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTableau 7\u003c/strong\u003e. Effet de l\u0026rsquo;inad\u0026eacute;quation \u0026eacute;ducative sur le revenu : r\u0026eacute;gression par quantile\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eq10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eq25\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eq50\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eq10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eq25\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eq50\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHorizontal mismatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0737\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.141**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0541)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0555)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVertical mismatch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.117*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.127*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.172**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0618)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0718)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0671)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0288***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0309***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0266***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0309***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0303***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0254**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00699)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00696)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00519)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.00702)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0123)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProfessional experience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0828)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0974)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0958)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.191)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0679)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompany size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedium-sized company\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.164**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.223***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.239**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.185***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.229***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.175**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0647)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0790)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0641)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0747)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0737)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLarge company\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.313***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.549***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.571***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.329***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.544***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.600***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0752)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0866)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0953)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0820)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0962)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLevel of education attained\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary 1st Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.961)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.398)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.394)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.444)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.428)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.333)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSecondary 2nd Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.991)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.503)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.537)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.369)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.415)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.278)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigher education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.404)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.373)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.460)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.383)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.299)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHow to get a job\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePersonal relations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.320**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.401***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.388***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.357***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.364***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.303**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0893)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0973)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.130)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirectly with the employer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.272**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.288**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0692\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.115)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.152)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSmall announcements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.249***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0807\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.133)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0902)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.147)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCompetitive exams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.549**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.194*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.498**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0164\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.117)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.230)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.221)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.179)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eForm of pay\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFixed salary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.601*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.169***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.907***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.119***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.959***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.342)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.105)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0646)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.428)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.721)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0767)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePer day or hour of work\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.262***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.367***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.181***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.245**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.277***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.204***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.312)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.153)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.151)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.239)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt the task\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.986***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.439***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.738***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.961**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.431***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.838***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.447)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.761)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.286)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.862*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.231***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.637***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.171***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.757***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.444)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.272)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.624)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.762)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.424)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBenefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.718*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.161***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.103***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.086***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.124***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.429)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.445)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.458)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.758)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.564)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.20***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.72***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.52***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.04***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.42***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.424)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.392)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.415)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(2.523)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(3.375)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14,622\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eSource:\u0026nbsp;\u003c/strong\u003eAuthors.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eStandard errors are in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e4.4.2. The decomposition of Blinder (1973) and Oaxaca (\u003cspan class=\"CitationRef\"\u003e1973\u003c/span\u003e)\u003c/h2\u003e\n \u003cp\u003eThe second robustness test examines the wage gap between matched and unmatched employees in the Cameroon job market. A frequently used methodology for analyzing group job market outcomes is to decompose mean log wage differences using linear regression models in a counterfactual manner. This approach is commonly referred to as the \u0026apos;Blinder-Oaxaca decomposition\u0026apos; (Blinder 1973; Oaxaca \u003cspan class=\"CitationRef\"\u003e1973\u003c/span\u003e). This methodological approach is based on dividing the wage differential between two groups into two components. The first component corresponds to the part \u0026apos;explained\u0026apos; by group differences in terms of productivity characteristics, such as level of education or work experience, while the second component represents the residual part, which cannot be explained by these differences in wage determinants. This latter part, referred to as \u0026apos;unexplained,\u0026apos; is commonly used as a measure of discrimination, although it also includes the effects of group differences in unobserved predictors.\u003c/p\u003e\n \u003cp\u003eFurthermore, the following table illustrates the pay gap between individuals working in jobs that match their profile and those for which there is a mismatch, for the entire working population. This disparity is analyzed by controlling for characteristics relating to supply, demand, and the context of the labour market. The logarithmic transformation is used to decompose this difference in terms of differences in log earnings between the two groups. This approach underscores the predicted average logarithm of monthly earnings for matched and mismatched workers, respectively. The third row of the figure demonstrates that the average differential is -1.618622. Of this difference, -1.321234 is attributable to differences in the \u0026lsquo;endowments\u0026rsquo;, which refer to the average differences in the regressors of the two groups, and \u0026minus;\u0026thinsp;0.3556278 is attributable to differences in the regression coefficients. Finally, the difference of 0.0582399 is attributed to an interaction effect resulting from combining the two previous effects.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIncome breakdown vertical match/mismatch\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePredicted mean\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePredicted average monthly income (vertical match)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.209\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(14.82)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePredicted average monthly income (vertical mismatch)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.184\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(32.57)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifferential due to \u0026ldquo;endowment\u0026rdquo; difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.210\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(5.61)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifferential due to coefficient difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInteraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.303\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(1.97)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eSource:\u0026nbsp;\u003c/strong\u003eAuthors.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eStandard errors are in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/p\u003e\n \u003cp\u003eAs illustrated in Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, the data analysis discloses a discrepancy in the mean monthly wage between horizontally and meshed employees. The former group exhibits a mean monthly income of 3.308, while the latter group\u0026apos;s mean monthly income is 2.731. This indicates a statistically significant discrepancy of -0.577, favoring the former group. A more detailed analysis of this differential reveals that 0.351 is attributable to disparities in the \u0026apos;endowments\u0026apos;, which reflect the average differences between the regressors of the two groups, and that a further difference of -1.117 is attributable to differences in the regression coefficients.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eIncome breakdown horizontal match/mismatch\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMoyenne pr\u0026eacute;dite\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePredicted average monthly income (horizontal mismatch)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.731\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(18.03)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePredicted average monthly income (horizontal match)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.308\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(31.21)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(3.12)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifferential due to \u0026ldquo;endowment\u0026rdquo; difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(2.21)*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifferential due to coefficient difference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(3.13)**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInteraction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e(0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eSource:\u0026nbsp;\u003c/strong\u003eAuthors.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote:\u0026nbsp;\u003c/strong\u003eStandard errors are in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis article employs data on employment and the informal sector to examine the effects of vertical and horizontal mismatch on workers' incomes in Cameroon. Utilizing statistical analyses, namely the comparison of the means test, the study compares average incomes according to employee status. The findings reveal that the average monthly earnings of matched workers exceed those of unmatched employees. The econometric results, obtained using ordinary least squares and ordered instrumental variable methods with the control function approach, confirm these descriptive statistics and suggest that education-employment mismatch has a detrimental effect on workers' income in Cameroon. Furthermore, the results of our analysis reveal that horizontal mismatch is more conducive to a significant reduction in monthly income for both men and women, compared with vertical mismatch. A further analysis, distinguishing between different types of employment contracts, indicates that education-employment mismatch has a greater adverse effect on earnings for workers with fixed-term contracts than for those with open-ended contracts. These results are corroborated by wage decomposition using the Oaxaca-Blinder method (1973), which demonstrates that the mismatch between education and employment contributes to income inequality in the Cameroonian job market.\u003c/p\u003e \u003cp\u003eGiven the findings, educational establishments must offer vocational training that is more consistent with the requirements of the job market, intending to remedy the educational mismatch that is accompanying the exponential increase in the number of graduates in Cameroon. Furthermore, it is crucial to recognize that the impact of horizontal mismatch on professional integration is significantly higher than that of vertical mismatch. Consequently, it is imperative to put in place an effective career guidance system aligned with the real needs of the job market and business sectors. Consequently, further research is required to ascertain the precise causes of elevated wage penalties in instances of horizontal mismatch by conducting a thorough examination of the disciplines that serve to amplify this effect. This would empower the Government to formulate efficacious policies to regulate the least valued disciplines in the Cameroonian job market.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbdulla, K. (2024). Quantifying the productivity effects of education-job mismatch: a cross-country analysis. \u003cem\u003eEducation Economics\u003c/em\u003e, 1‑18.\u003c/li\u003e\n \u003cli\u003eAcemoglu, D. (2024). Capital and wages. \u003cem\u003eInternational Economic Review\u003c/em\u003e, \u003cem\u003e00\u003c/em\u003e(00), 1‑22.\u003c/li\u003e\n \u003cli\u003eAcemoglu, D., and Johnson, S. (2023). Power and Progress: Our Thousand-Year Struggle over Technology and Prosperity.\u003c/li\u003e\n \u003cli\u003eAlbert, C., Davia, M. A., and Legazpe, N. (2021). Educational mismatch in recent university graduates. The role of labour mobility. \u003cem\u003eJournal of Youth Studies\u003c/em\u003e, p. 13.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eAllen, J., and Van Der Velden, R. (2001). Educational mismatches versus skill mismatches: effects on wages, job satisfaction, and on‐the‐job search. \u003cem\u003eOxford Economic Papers\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(3), 434‑452.\u003c/li\u003e\n \u003cli\u003eArrow, K. J. (1973). Higher education as a filter. \u003cem\u003eJournal of Public Economics\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(3), 193‑216.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eArtz, B., and Welsch, D. M. (2021). Overeducation and wages revisited: A two-cohort comparison and random coefficients approach.\u0026nbsp;\u003cem\u003eSouthern Economic Journal\u003c/em\u003e, \u003cem\u003e87\u003c/em\u003e(3), 909‑936.\u003c/li\u003e\n \u003cli\u003eAtangana Ondoa, H. (2019). L\u0026rsquo;effet de l\u0026rsquo;\u0026eacute;ducation sur les disparit\u0026eacute;s salariales dans le secteur informel : l\u0026rsquo;exemple du Cameroun.\u0026nbsp;\u003cem\u003eRevue internationale du travail\u003c/em\u003e, \u003cem\u003e158\u003c/em\u003e(3), 615‑635.\u003c/li\u003e\n \u003cli\u003eBarro, R. (1996). Healt hand Economic Growth. World Health Organization.\u003c/li\u003e\n \u003cli\u003eBattu, H., and Bender, K. A. (2020). Educational mismatch in developing countries: A review of the existing evidence. \u003cem\u003eThe Economics of Education\u003c/em\u003e, \u003cem\u003e56\u003c/em\u003e(3), 269‑289.\u003c/li\u003e\n \u003cli\u003eBaye, F., Epo Ngah, B., and Ndenzako, J. (2016). Wage differentials in Cameroon: a gendrered analysis. \u003cem\u003eAfrican Development Review\u003c/em\u003e, \u003cem\u003e28\u003c/em\u003e(1), 75‑91.\u003c/li\u003e\n \u003cli\u003eBecker, G. (1962). Investment in Human Capital: A Theoretical Analysis. \u003cem\u003eThe Journal of Political Economy\u003c/em\u003e, \u003cem\u003e70\u003c/em\u003e(5), 39‑49.\u003c/li\u003e\n \u003cli\u003eBecker, G. (1964). \u003cem\u003eHuman capital : a theoretical and empirical analysis, with special reference to education\u003c/em\u003e (Vol. 80). New York : Columbia University Press,.\u003c/li\u003e\n \u003cli\u003eBender, K. A., and Heywood, J. S. (2016). Educational mismatch and retirement. \u003cem\u003eEducation Economics\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e, 347‑365.\u003c/li\u003e\n \u003cli\u003eBezooijen, E., Van Berge, W., and Salomon, A. (2024). The Young Bunch: Youth Minimum Wages and Labor Market Outcomes. \u003cem\u003eILR Review\u003c/em\u003e, \u003cem\u003e77\u003c/em\u003e(3), 428‑460.\u003c/li\u003e\n \u003cli\u003eCameron, C., and Trivedi, P. (2022). \u003cem\u003eMicroeconometrics Using Stata: Nonlinear Models and Causal Inference Methods\u003c/em\u003e (Second Edition, Vol. 2). College Station, Texas : A Stata Press Publication.\u003c/li\u003e\n \u003cli\u003eCarranza, E., and McKenzie, D. (2023). Job Training and Job Search Assistance Policies in Developing Countries. Policy Research Working Papers, 10576.\u003c/li\u003e\n \u003cli\u003eChemli, S., Toanoglou, M., and Roman, J. L. D. V. (2024). Skills mismatch and digital skill shortages in tourism : Impact on graduate employment and implications for higher education. \u003cem\u003eInternational Journal of Technology Enhanced Learning\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(4), 447‑465.\u003c/li\u003e\n \u003cli\u003eChemli, S., Toanoglou, M., and Roman, J. L. D. V. (2024). Skills mismatch and digital skill shortages in tourism : Impact on graduate employment and implications for higher education. \u003cem\u003eInternational Journal of Technology Enhanced Learning\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(4), 447‑465.\u003c/li\u003e\n \u003cli\u003eCohn, E., and Khan, P. S. (1995). The wage effects of overschooling revisited. \u003cem\u003eLabour Economics\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 67‑76.\u003c/li\u003e\n \u003cli\u003eCoraggio, L., Pagano, M., Scognamiglio, A., and T\u0026aring;g, J. (2025). JAQ of all trades : Job mismatch, firm productivity and managerial quality. \u003cem\u003eJournal of Financial Economics\u003c/em\u003e, \u003cem\u003e164\u003c/em\u003e, 103992.\u003c/li\u003e\n \u003cli\u003eDi Pietro, G., and Urwin, P. (2006). Education and skills mismatch in the Italian graduate labour market. \u003cem\u003eApplied Economics\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(1), 79‑93.\u003c/li\u003e\n \u003cli\u003eDraissi, Z., Rong, Y., and Ebrahim Suliman, M. A. (2023). Employability and Education Mismatch in the Moroccan Urban Workplace. \u003cem\u003eSAGE Open\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(4), 21582440231217890.\u003c/li\u003e\n \u003cli\u003eFerran, E. (2024). Labor Costs and Worker Segmentation. Universitat de Girona.\u003c/li\u003e\n \u003cli\u003eFokam, D. N. D. T., Kamga, B. F., and Nchofoung. (2023). Information and communication technologies and employment in developing countries: Effects and transmission channels. \u003cem\u003eTelecommunications Policy\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(8), 102597.\u003c/li\u003e\n \u003cli\u003eFrancisco Castro, J., Ortega, L., Yamada, G., and Mata, D. (2024). Overeducation and overskilling in Latin America: evidence from PIAAC. \u003cem\u003eComparative Education\u003c/em\u003e, \u003cem\u003e60\u003c/em\u003e(2), 296‑314.\u003c/li\u003e\n \u003cli\u003eFreeman, R. B. (1976). \u003cem\u003eThe Overeducated American\u003c/em\u003e. Academic Press.\u003c/li\u003e\n \u003cli\u003eGiles, J., and Murtazashvili, I. (2012). A Control Function Approach to Estimating Dynamic Probit Models with Endogenous Regressors, with an Application to the Study of Poverty Persistence in China. IZA DP No. 6887.\u003c/li\u003e\n \u003cli\u003eGrasso, G., and Tatsiramos, K. (2023). The Impact of Restricting Fixed-Term Contracts on Labor and Skill Demand. CESifo Working Paper No. 10693.\u003c/li\u003e\n \u003cli\u003eGuevara, C., and Hess, S. (2019). A control-function approach to correct for endogeneity in discrete choice models estimated on SP-off-RP data and contrasts with an earlier FIML approach by Train \u0026amp; Wilson. \u003cem\u003eTransportation Research Part B: Methodologica\u003c/em\u003e, pp. 224‑239.\u003c/li\u003e\n \u003cli\u003eHartato, H. (2020). The Analysis of Vertical Mismatch and Manufacturing Labor Wage by Using Cramer\u0026rsquo;s V Method. \u003cem\u003eJournal public policy\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(1), 1‑6.\u003c/li\u003e\n \u003cli\u003eHuertas, I. P. M., and Raymond, J. L. (2024). Education, educational mismatch and occupational status : An analysis using PIAAC data. \u003cem\u003eEconomia Politica\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(3), 717‑738.\u003c/li\u003e\n \u003cli\u003eHuertas, I. P. M., and Raymond, J. L. (2024). Education, educational mismatch and occupational status : An analysis using PIAAC data. \u003cem\u003eEconomia Politica\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(3), 717‑738.\u003c/li\u003e\n \u003cli\u003eJacobs, V., Rycx, F., and Volral, M. (2021). Wage Effects of Educational Mismatch According to Workers\u0026rsquo; Origin: The Role of Demographics and Firm Characteristics. \u003cem\u003eIZA DP No. 14813\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eJacobs, V., Rycx, F., and Volral, M. (2022). Wage Effects of Educational Mismatch According to Workers\u0026rsquo; Origin: The Role of Demographics and Firm Characteristics. \u003cem\u003eDe Economist\u003c/em\u003e, \u003cem\u003e170\u003c/em\u003e(4), 459‑501.\u003c/li\u003e\n \u003cli\u003eLasso‐Dela‐Vega, E., S\u0026aacute;nchez Ollero, J. L., and Garc\u0026iacute;a‐Pozo, A. (2023). Effects of educational mismatch on wages across industry and occupations: sectoral comparison. \u003cem\u003eInternational Journal of Manpower\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(9), 237‑255.\u003c/li\u003e\n \u003cli\u003eLi, I. W., Harris, M., and Sloane, P. J. (2022). Vertical, horizontal and residual skills mismatch in the Australian graduate labour market. \u003cem\u003eEconomic Record\u003c/em\u003e, \u003cem\u003e94\u003c/em\u003e(306), 301‑315.\u003c/li\u003e\n \u003cli\u003eLiu, K., Salvanes, K. G., and S\u0026oslash;rensen, E. \u0026Oslash;. (2020). Good skills in bad times: Cyclical skill mismatch and the long-term effects of graduating in a recession. \u003cem\u003eEuropean Economic Review\u003c/em\u003e, \u003cem\u003e84\u003c/em\u003e, 3‑17.\u003c/li\u003e\n \u003cli\u003eLucas, R. (1988). On the mechanics of economic development. \u003cem\u003eJournal of Monetary Economics\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 3‑42.\u003c/li\u003e\n \u003cli\u003eMa, C., and Weiss, A. (1993). A signaling theory of unemployment. \u003cem\u003eEuropean Economic Review\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e(1), 135‑157.\u003c/li\u003e\n \u003cli\u003eMahuteau, S., Mavromaras, K., Sloane, P., and Wei, Z. (2015). Horizontal and vertical \u0026nbsp;educational mismatch and wages.\u003c/li\u003e\n \u003cli\u003eMankiw, N. G., Romer, D., and Weil, D. N. (1992). A contribution to the empirics of economic growth. \u003cem\u003eThe quarterly journal of economics\u003c/em\u003e, \u003cem\u003e107\u003c/em\u003e(2), 407‑437.\u003c/li\u003e\n \u003cli\u003eMincer, J. (1974). \u003cem\u003eSchooling, experience and earnings\u003c/em\u003e. New York\u0026nbsp;: Columbia University Press.\u003c/li\u003e\n \u003cli\u003eMontt, G. (2017). Field-of-study mismatch and overqualification: Labour market correlates and their wage penalty.\u0026nbsp;\u003cem\u003eIZA Journal of Labor Economics\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(2), 1‑20.\u003c/li\u003e\n \u003cli\u003eMorsy, H. (2020). Inad\u0026eacute;quation de la formation des jeunes Africains, quelle solution ?\u0026nbsp;\u003cem\u003eProject Syndicate\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eMorsy, H., and Mukasa, A. (2019). Youth Jobs, Skill and Educational Mismatches in Africa. [MPRA Paper].\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eMorsy, H., and Mukasa, A. N. (2020). Mind the mismatch? Incidence, drivers, and persistence of African youths\u0026rsquo; skill and educational mismatches. \u003cem\u003eAfrican Development Review\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e, 5‑19.\u003c/li\u003e\n \u003cli\u003eMuhammedrisaevna, T. M., and Saidovich, D. S. (2021). Formation of wages in the labor market. \u003cem\u003eBukhara State University\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(6), 146‑151.\u003c/li\u003e\n \u003cli\u003eNational Institute Of Statistics. (2022). \u003cem\u003eThird survey on employment and the informal sector in Cameroon\u003c/em\u003e (Rapport No. Aug. 2022).\u003c/li\u003e\n \u003cli\u003eNeffke, F., Nedelkoska, L., and Wiederhold, S. (2024). Skill mismatch and the costs of job displacement. \u003cem\u003eResearch Policy\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e(2), 104933.\u003c/li\u003e\n \u003cli\u003eNjifen, I. (2024). Education-Job mismatch, earnings and worker\u0026rsquo;s satisfaction in African labor market : Evidence from Cameroon. \u003cem\u003eEconomia Politica\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(2), 931‑962.\u003c/li\u003e\n \u003cli\u003eNjifen, I., and Smith, P. (2023). Education-Job Mismatch and Heterogeneity in the Return to Schooling: Evidence from Cameroon. \u003cem\u003eJournal of the Knowledge Economy\u003c/em\u003e, \u003cem\u003e23\u003c/em\u003e(1), 1‑28.\u003c/li\u003e\n \u003cli\u003eOaxaca, R. (1973). Male-female wage differential in urban labor market. \u003cem\u003eInternational Economic Review\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(3), 693‑709.\u003c/li\u003e\n \u003cli\u003ePutri, A. K., and Febriani, R. E. (2020). Analisa mismatch tenaga kerja di asia tenggara. \u003cem\u003eThe journal of economic development\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(2), 133‑149.\u003c/li\u003e\n \u003cli\u003eRim, H., and Kim, J. (2023). Status Inconsistency, Gender, and Health in Korean Young Adults : Separating Educational and Skill Mismatch. \u003cem\u003eSocial Indicators Research\u003c/em\u003e, \u003cem\u003e170\u003c/em\u003e(3), 1141‑1153.\u003c/li\u003e\n \u003cli\u003eRomer, P. M. (1986). Increasing Returns and Long-run Growth. \u003cem\u003eJournal of Political Economy\u003c/em\u003e, \u003cem\u003e94\u003c/em\u003e(5), 1002‑1037.\u003c/li\u003e\n \u003cli\u003eRosen, S. (1986). \u003cem\u003eThe Theory of equalizing differences\u003c/em\u003e. Ansterdam\u0026nbsp;: North Holland.\u003c/li\u003e\n \u003cli\u003eRudakov, V., Figueiredo, H., Teixeira, P. N., and Roshchin, S. (2019). The Impact of Horizontal Job-Education Mismatches on the Earnings of Recent University Graduates in Russia\u0026rdquo;. \u003cem\u003eIZA Discussion Papers. No 12407.\u003c/em\u003e\u003c/li\u003e\n \u003cli\u003eSam, V. (2018). Unemployment duration and educational mismatches: An empirical investigation among graduates in Cambodia. \u003cem\u003eEconomics Bulletin\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(3), 1554‑1565.\u003c/li\u003e\n \u003cli\u003eS\u0026aacute;nchez-S\u0026aacute;nchez, N., and McGuinness, S. (2015). Decomposing the Impacts of Overeducation and Overskilling on Earnings and Job Satisfaction: An Analysis Using REFLEX data. \u003cem\u003eWorking Paper No. 393\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eSattinger, M. (1993). \u003cem\u003eCapital and the distribution of income\u003c/em\u003e. Amsterdam\u0026nbsp;: North Holland.\u003c/li\u003e\n \u003cli\u003eSchultz, T. W. (1961). Investment in Human Capital. \u003cem\u003eAmerican Economic Review\u003c/em\u003e, \u003cem\u003e51\u003c/em\u003e(1), 1‑17.\u003c/li\u003e\n \u003cli\u003eSellami, S., Verhaest, D., Nonneman, W., and Van Trier, W. (2017). The Impact of Educational Mismatches on Wages: The Influence of Measurement Error and Unobserved Heterogeneity. \u003cem\u003eThe B.E. Journal of Economic Analysis \u0026amp; Policy\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(1), 1‑20.\u003c/li\u003e\n \u003cli\u003eSicherman, N. (1991). Overeducation\u0026rdquo; in the labor market. \u003cem\u003eJournal of Labor Economics\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(2), 101‑122.\u003c/li\u003e\n \u003cli\u003eSloane, P. J. (2014). Overeducation, skill mismatches, and labor market outcomes for college graduates. \u003cem\u003eOvereducation in Europe\u003c/em\u003e, 11‑45.\u003c/li\u003e\n \u003cli\u003eSmith, A. (1776). \u003cem\u003eAn inquiry into the nature and causes of the wealth of nations\u003c/em\u003e. Chicago\u0026nbsp;: University of Chicago Press.\u003c/li\u003e\n \u003cli\u003eSpence, M. (1973). Job Market Signaling. \u003cem\u003eThe Quarterly Journal of Economics\u003c/em\u003e, \u003cem\u003e87\u003c/em\u003e(3), 355‑374.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eStenard, B. S., and Sauermann, H. (2016). Educational Mismatch, Work Outcomes, and Entry Into Entrepreneurship. \u003cem\u003eOrganization Science\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(4), 801‑824.\u003c/li\u003e\n \u003cli\u003eStenard, B. S., and Sauermann, H. (2016). Educational Mismatch, Work Outcomes, and Entry Into Entrepreneurship. \u003cem\u003eOrganization Science\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(4), 801‑824.\u003c/li\u003e\n \u003cli\u003eStiglitz, J. E. (1975). The Theory of \u0026laquo; Screening, \u0026raquo; Education, and the Distribution of Income. \u003cem\u003eThe American Economic Review\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e(3), 283‑300.\u003c/li\u003e\n \u003cli\u003eTakeuchi, M. (2023). Impact of vertical and horizontal mismatches on earnings among highly educated employees in Japan. \u003cem\u003eInternational journal of educational research open\u003c/em\u003e.\u003c/li\u003e\n \u003cli\u003eTran, T. Q., Thi Vu, N. B., and Van Vu, H. (2023). Does job mismatch affect wage earnings among business and management graduates in Vietnam? \u003cem\u003eResearch in International Business and Finance\u003c/em\u003e, \u003cem\u003e65\u003c/em\u003e(101982).\u003c/li\u003e\n \u003cli\u003eVerdugo, R. R., and Verdugo, N. T. (1989). The Impact of Surplus Schooling on Earnings. \u003cem\u003eJournal of Human Resources\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e, 629‑643.\u003c/li\u003e\n \u003cli\u003eWorld Bank. (2024). World Development Indicators. https://doi.org/Retrieved from http://data.worldbank.org/indicator/SE.TER.ENRR/countries/VN-4E-XN?display=default\u003c/li\u003e\n \u003cli\u003eXu, Q., and Chen, Y. (2024). Educational mismatch and mental health: Evidence from China. \u003cem\u003eSocial Science \u0026amp; Medicine\u003c/em\u003e, \u003cem\u003e356\u003c/em\u003e(117140).\u003c/li\u003e\n \u003cli\u003eYanikkaya, H., and Tat, P. (2023). The effect of educational mismatch on the Turkish manufacturing industry. \u003cem\u003eInternational Journal of Education Economics and Development (IJEED)\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(4).\u003c/li\u003e\n \u003cli\u003eYokoyama, I., Obara, T., Kiyomoto, A. S., Kusada, K., Edamura, K., and Inui, T. (2024). Endogenous decisions on acceptable worker-job mismatch level and the impact on workers\u0026rsquo; performance. \u003cem\u003eJapan and the World Economy\u003c/em\u003e, \u003cem\u003e72\u003c/em\u003e, 101283.\u003c/li\u003e\n \u003cli\u003eZamo, A. C. (2021). L\u0026rsquo;avenir du Travail au Cameroun : Analyse contextuelle. International Labour Organization.\u003c/li\u003e\n \u003cli\u003eZhu, R. (2014). The impact of major-job mismatch on college graduates\u0026rsquo; early career earnings: Evidence from China. \u003cem\u003eEducation Economics\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e, 511‑528.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Vertical mismatch, Horizontal mismatch, income, Cameroon","lastPublishedDoi":"10.21203/rs.3.rs-5913807/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5913807/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHuman capital theory postulates that education improves an individual's productivity and earning potential. However, when there is a mismatch between an individual's education and employment, the return on investment in human capital decreases, resulting in reduced earnings. This paper uses employment data from Cameroon's National Institute of Statistics to examine the wage effects of horizontal and vertical mismatches. To this end, we estimate the augmented Mincer model using the double ordinary least squares method via the control function approach to deal with endogeneity bias and the quantile regression method. The results indicate that the mismatch between education and employment, whether horizontal or vertical, is linked to a wage penalty. In particular, the wage penalty associated with horizontal mismatch is greater than that associated with vertical mismatch. Furthermore, the results reveal that private sector workers suffer greater penalties due to horizontal mismatch, while those in the public sector are more affected by vertical mismatch. Additionally, the results reveal a significant trend towards a greater wage penalty for men than for women.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification\u003c/strong\u003e: I21 ; J24 ; J31 ; J41\u003c/p\u003e","manuscriptTitle":"Do Horizontal and Vertical Mismatches Affect Workers' Incomes in Cameroon? Results From the Instrumental Variable Models Using the Function-control Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-29 06:54:46","doi":"10.21203/rs.3.rs-5913807/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"39442913-c1f2-4dab-9dce-92d351626cf3","owner":[],"postedDate":"January 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43554559,"name":"Microeconomics"}],"tags":[],"updatedAt":"2025-01-29T06:54:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-29 06:54:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5913807","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5913807","identity":"rs-5913807","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}