Productivity, real wages, and gender. A study in Colombian manufacturing

Productivity, real wages, and gender. 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A study in Colombian manufacturing", "datePublished": "2025-03-17T17:34:03", "dateModified": "2026-04-15T04:57:32", "author": [ { "@type": "Person", "name": "Andrés Mauricio Gómez-Sánchez" }, { "@type": "Person", "name": "Zoraida Ramírez-Gutiérrez" }, { "@type": "Person", "name": "Isabel Cristina Rivera-Lozada" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Orthodox microeconomic theory establishes a positive link between employee wages and productivity in competitive markets. However, this perspective often overlooks gender and treats the workforce as a homogeneous category labelled as labor, potentially obscuring issues of gender discrimination. This article addresses the gender wage gap by analyzing, for the first time, at least in an emerging economy such as Colombia, the impact of manufacturing firm’s productivity on wages explicitly including gender. Method First, we use the GMM two-equation system proposed by Wooldridge (2009) to obtain consistent and unbiased estimates of output elasticities and TFP, respectively. Secondly, we explore wages-productivity linkage by gender, implementing a dynamic random effect generalized least squares model (GLS) with panel data to deal with endogeneity issues. Results Our main findings reveal, among others, that firms with a higher proportion of female workers (female firms) generally have higher productivity than those with a higher proportion of male workers (male firms). Conclusions The effect of female firm’s productivity on wages is lower than that of male firm’s productivity, which could indicate gender wage discrimination " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-306/v1", "name": "Productivity, real wages, and gender. A study in Colombian manufacturing" } } ] } Home Browse Productivity, real wages, and gender. A study in Colombian manufacturing ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Gómez-Sánchez AM, Ramírez-Gutiérrez Z and Rivera-Lozada IC. Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :306 ( https://doi.org/10.12688/f1000research.161343.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved] Andrés Mauricio Gómez-Sánchez https://orcid.org/0000-0002-6582-4129 1 , Zoraida Ramírez-Gutiérrez https://orcid.org/0000-0001-7772-7302 2 , Isabel Cristina Rivera-Lozada 3 Andrés Mauricio Gómez-Sánchez https://orcid.org/0000-0002-6582-4129 1 , Zoraida Ramírez-Gutiérrez https://orcid.org/0000-0001-7772-7302 2 , Isabel Cristina Rivera-Lozada 3 PUBLISHED 17 Mar 2025 Author details Author details 1 Departament of Economics, Universidad del Cauca, Popayan, Cauca, Colombia 2 Departament of Accounting, Universidad del Cauca, Popayan, Cauca, Colombia 3 Departament of Economics, Universidad del Cauca, Popayan, Cauca, Colombia Andrés Mauricio Gómez-Sánchez Roles: Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Zoraida Ramírez-Gutiérrez Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing Isabel Cristina Rivera-Lozada Roles: Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS Abstract Background Orthodox microeconomic theory establishes a positive link between employee wages and productivity in competitive markets. However, this perspective often overlooks gender and treats the workforce as a homogeneous category labelled as labor, potentially obscuring issues of gender discrimination. This article addresses the gender wage gap by analyzing, for the first time, at least in an emerging economy such as Colombia, the impact of manufacturing firm’s productivity on wages explicitly including gender. Method First, we use the GMM two-equation system proposed by Wooldridge (2009) to obtain consistent and unbiased estimates of output elasticities and TFP, respectively. Secondly, we explore wages-productivity linkage by gender, implementing a dynamic random effect generalized least squares model (GLS) with panel data to deal with endogeneity issues. Results Our main findings reveal, among others, that firms with a higher proportion of female workers (female firms) generally have higher productivity than those with a higher proportion of male workers (male firms). Conclusions The effect of female firm’s productivity on wages is lower than that of male firm’s productivity, which could indicate gender wage discrimination READ ALL READ LESS Keywords Wages, Gender, Firms productivity, Manufacturing, Emerging Economy Corresponding Author(s) Zoraida Ramírez-Gutiérrez ( [email protected] ) Close Corresponding author: Zoraida Ramírez-Gutiérrez Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2025 Gómez-Sánchez AM et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Gómez-Sánchez AM, Ramírez-Gutiérrez Z and Rivera-Lozada IC. Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :306 ( https://doi.org/10.12688/f1000research.161343.1 ) First published: 17 Mar 2025, 14 :306 ( https://doi.org/10.12688/f1000research.161343.1 ) Latest published: 15 Apr 2026, 14 :306 ( https://doi.org/10.12688/f1000research.161343.2 )  There is a newer version of this article available. Suppress this message for one day. Introduction The economic literature, particularly within the industrial organization, suggests that firm wages depend on several factors, including firm size ( Carlsson, et al. 2016 ), employee education and skills ( Mincer, 1974 ; Schultz, 1961 ), economic subsector ( Suhányi, et al. 2023 ), geographic location ( Méjean & Patureau, 2010 ), and the presence or absence of unions ( Card, et al. 2017 ). Nevertheless, productivity is the most critical factor influencing wages ( Feldstein, 2008 ). Based on the neoclassical microeconomic theory, increases in labor productivity positively affect real wage growth in the long-run ( Mankiw, 2015 ). In line with this, industrial organization literature acknowledges that firm productivity significantly influences employees’ wages. Although empirical evidence supporting this hypothesis varies in strength depending on the country and industries analyzed, productivity is widely recognized as a key determinant of wages, whether at the individual or firm level ( Meager & Speckesser, 2011 ). Despite this, previous literature has left two issues aside. First, the method used to measure productivity and second, the role of gender in these topics. Regarding productivity measurement, several studies, particularly in macroeconomics, have used the average product of labor as a proxy. This approach is limited as it measures labor productivity rather than firm productivity and only applies in the short-run ( Sargent & Rodriguez, 2000 ). While other studies, particularly in microeconomics, have correctly used total factor productivity (TFP) at the firm level, many studies generate flawed estimates due to the use of statistical methods that generate biased and inconsistent parameters, leading to inaccurate conclusions and recommendations ( Gómez Sánchez, 2020 ). The second issue is the exclusion of gender from productivity analysis. Microeconomic and Industrial Organisation theory often treats employees under the label “labor.”, which can lead to biases, especially in emerging economies such as Colombia, where industrial subsectors such as Beverages, Garments, Textiles, Pharmaceuticals, or Chemicals, a significant proportion of employees are female. This is an important consideration, as productivity may respond differently to the presence of women than men, and this difference should influence employees' wages. In this order of ideas, this paper aims to assess the relationship between wages and productivity in firms with a high proportion of female employees or female firms hereafter, compared to firms with a higher proportion of male employees, referred to as male firms hereafter ( Tsou & Yang, 2019 ; Gomez Sanchez, et al., 2025 ). To do so, we first estimate unbiased total factor productivity using the two-stage method proposed by Wooldridge (2009) , and for the first time, we disaggregate the workforce into male and female to obtain gender- a gender-specific productivity estimate rather than an aggregate measure of TFP as the tradition. In this regard, we implement the methodology in two steps. First, we use the two-equation system proposed by Wooldridge (2009) to obtain consistent estimates of input elasticities and unbiased TFP estimates. This system is jointly estimated under the Generalised Method of Moments (GMM) framework. As a novelty, we separate employees by gender to estimate TFP. Second, we implement a dynamic random effect generalized least squares model (GLS) with panel data to address endogeneity. Moreover, we also include industrial organization covariates, such as exports or innovations, along with wage persistence; that is, firms with higher (or lower) wages in the past tend to continue those wage levels in the present. In addition, the model also deals with potential initial condition problems ( Blundell & Bond, 1998 ). The data comes from The Annual Manufacturing Survey (EAM) and The Technological Development and Innovation Survey (EDIT), both published by the Statistics Department of Colombia (DANE) for the period 2013-2020. After merging eight waves of EAM and EDIT, we obtain an unbalanced panel with 59,355 observations. Our main findings are summarised as follows: i) Female firms exhibit higher productivity than male firms. ii) The impact of female firms’ productivity on wages is lower than female firms’ productivity, suggesting wage discrimination in the Colombian manufacturing industry. iii) Female labor output elasticity is higher than male elasticity, indicating that women contribute more to firm output than men iv) TFP displays lower input elasticities than gender-specific TFP estimates, suggesting that TFP underestimates firm productivity when gender is considered. The remainder of this paper is organized as follows. Section 2 examines theoretical and empirical literature. Section 3 describes the data. Section 4 displays TFP and stochastic model estimates. Section 5 discusses the results, and Section 6 offers conclusions. Methods Literature review The existing literature on gender and productivity pay disparities primarily focuses on led-women or led-male firms. These definitions differ slightly from the concept of male and female firms explored in this article. Due to data limitations in EAM survey, we classify firms as female or male based on the proportion of female or male workers employed, respectively, following the approach of Tsou and Yang (2019) . Consequently, much of the literature is less concerned with our adopted firm’s definition, which focuses on led-women or led-male firms. The orthodox microeconomic theory postulates that a worker's productivity positively correlates with wages, as outlined in the efficiency wage theory ( Akerlof & Yellen, 1986 ; Shapiro & Stiglitz, 1984 ). Nevertheless, this paper demonstrates that wages do not necessarily align with a firm’s productivity. It highlights how female-led firms often exhibit higher productivity than male-led ones, yet wage disparities do not reflect these productivity differences. This phenomenon can be attributed to gender wage gaps masked by the low levels of gender parity worldwide ( World Economic Forum, 2024 ) and employers’ perceptions of female worker's productivity being negatively influenced by traditional caregiving roles and motherhood responsibilities ( Todaro et al., 2002 ). As Goldin (2014) argues, women have adapted their participation in the labor market over time. Initially, they balanced household and work life, often leaving their employments during motherhood. Subsequently, they increased their educational attainment, equalling or even surpassing that of men to remain competitive in the workforce. Despite these efforts, wage compensation has not reflected women’s labor market resilience. Gender wage gaps persist globally ( World Bank, 2024 ), and wage disparities widen with age. In Colombia, since the early days of industrialization, women have played a significant role in the workforce, particularly in sectors like Food and Textiles. This participation was largely driven by the need to supplement family income ( Santos, 2017 ; Arango, 1991 ). Nonetheless, their contributions were often associated with low wages, influenced by religiously rooted work ethics and disciplinary norms ( Arango, 1991 ). More recently, gender wage gaps have persisted despite evidence showing that female-led firms perform better. For instance, women-led firms, identified through the Unified Business and Social Registry (RUES, by its Spanish acronym), account for 59% of Colombia’s 1.2 million registered firms and 1.8 million microenterprises owned by women, according to Emicron Survey (2022) published by National Statistics Department. These firms report higher productivity (34.3% compared to 33.25% for men) and more significant efficiency improvements [25.8% for women versus 17.9% for men] ( WCP, 2024 ). Santos (2017) , considers that despite their higher productivity and significant contributions to the manufacturing sector, wage firms differentials in this country have consistently exceeded productivity gaps. This is theoretically grounded on three factors. Firstly, following Todaro et al., (2002) , perceived costs of female labor as hired women are often considered more expensive due to factors related to motherhood, which are believed to affect productivity, despite firms lacking systems for measuring labor costs disaggregated by sex. Secondly, Santos (2017) suggests that wages diverge from efficiency theory: women-led firms take advantage of relatively lower wages to increase profits, contrary to efficiency wage theory. Thirdly, Goldin, (2014) pointed out the sectoral occupational segregation, that is, led-women firms tend to concentrate on specific sectors, perpetuating occupational segregation. The scarce international literature on these topics shows mixed results. Tsou and Yang (2019) find that Chinese firms with a higher proportion of female workers generally have lower productivity than those with a higher proportion of male workers. However, female workers with a high education level significantly improve the firm’s productivity, especially in small private and foreign firms, but not for medium or large public ones. These contradictory results also depend on the statistical method used. Pfeifer and Wagner (2014) find lower productivity in female firms than in male firms under the OLS method, whilst they obtain opposite results if they use a GMM framework. TFP estimates by gender TFP broken down by gender follows Wooldridge’s (2009) method. This framework takes advantage of the GMM estimation to shorten the standard error calculation and avoid using bootstrapping techniques. Expressly, we assume that the production process follows a linearised Cobb-Douglas production function: (1) y it = β 0 + β lw l it w + β lm l it m + β k k it + β m m it + β e ene it + ω it + ε it y it denotes firm’s i production in period t ; l it w is the women labor;; l it m is the men labor; k it is the firm’s capital stock; and, m it is the materials consumption, and ene it is the electric energy consumption. Besides, ω it is the firm’s productivity, which is only observable or predictable by the firm. As well, ε it represents an error term unobserved or unpredictable by firms. Olley & Pakes (1992) assume that capital stock evolves according to the perpetual inventory method, and it is determined in the previous period (state variable). Therefore, current productivity shocks do not affect capital stock ( Eslava et al., 2013 ). We also assume that labor by gender and firm’s energy consumption are chosen in the same period, as they are consumed (freely variable factors). However, Ackerberg, et al., (2015) demonstrate that these choices are correlated with ω it , so the equation (1) cannot be estimated by ordinary least squares (OLS), fixed effects (FE), or instrumental variables (IV). In this sense, Levinsohn and Petrin (2003) suggests using the materials demand function as a proxy for unobserved productivity. The demand for materials is as follows: (2) m it = m t ( k it , ω it , ene it ) According to Levinsohn and Petrin (2003) , this demand is monotonically increasing in productivity, so we can invert this function to express firms’ productivity in terms of observables: (3) ω it = m t − 1 ( k it , ω it , ene it ) = h t ( k it , ω it , ene it ) h t is an unknown function of k it ; m it ; k it ; ω it ; and ene it . Replacing (3) in (1): (4) y it = β 0 + β lw l it w + β lm l it m + β k k it + β m m it + β e ene it + h t ( k it , ω it , ene it ) + ε it h t ( ∙ ) is unknown, so it is proxied by third-degree polynomials in the respective arguments. Nevertheless, β m and β k are collinear with h t ( ∙ ) , so we cannot to identify these parameters. Following Olley & Pakes (1992) , and Levinsohn and Petrin (2003) , it is necessary to introduce the law of motion of productivity as a Markov process: (5) ω it = E [ ω it | ω it − 1 ] + ξ it = f ( ω it − 1 ) + ξ it where f ( ∙ ) is an unknown function that join productivity in t and in t − 1 plus an innovation term, ξ it , which is uncorrelated to k it . By replacing (5) in (1), we get: (6) y it = β 0 + β l l it + β k k it + β m m it + β e ene it + β w wat it + f ( ω it − 1 ) + ξ it + ε it Furthermore, by lagging and replacing equation (3) into (6), we get: (7) y it = β 0 + β lw l it w + β lm l it m + β k k it + β m m it + β e ene it + h ( k it − 1 , m it − 1 , ene it − 1 ) + v it where v it is a composed error term ( ξ it + ε it ) . Because h ( ∙ ) is an unknown function, it is proxied by third degree polynomials in the arguments. Equations (4) and (7) are the two-equation systems proposed by Wooldridge (2009) ; they are jointly estimated by using the GMM method with suitable instruments and moment conditions showed for instance by Gómez Sánchez (2020) . Once we estimate equation (1) , we get the TFP as a residual by using output elasticities: (8) y it − ( β ̂ 0 + β ̂ lw l it w + β ̂ lm l it m + β ̂ k k it + β ̂ m m it + β ̂ e ene it ) = ω ̂ it where ω ̂ it is the TFP estimated in logs for firm i at time t . Data analysis 1 This section describes some key variables used in this study that provide insight into the empirical model. We specifically examine the output elasticity of labor without considering gender and with gender taken into account, as well as the linkage with firms’ salaries, analyzed for the entire sample and by manufacturing sectors. Table 1 presents the estimates of labor product elasticities using three different methods: Ordinary Least Squares (OLS), Generalized Least Squares (GLS), and Wooldridge’s (2009) two-step method. These estimates are analyzed under two scenarios: one considering the firm's total labor force (Labor) and the other distinguishing employees by gender (women and men’s labor). Table 1. Estimates of labor output elasticities. Total labor and labor by gender. OLS GLS Wooldridge OLS GLS Wooldridge Labor 0.298*** 0.252*** 0.286*** (0.002) (0.003) (0.002) Women labor 0.141*** 0.100*** 0.136*** (0.001) (0.002) (0.001) Men labor 0.136*** 0.137*** 0.132*** (0.002) (0.003) (0.002) Capital 0.042*** 0.023*** 0.089*** 0.045*** 0.022*** 0.093*** (0.001) (0.002) (0.006) (0.001) (0.002) (0.006) Materials 0.711*** 0.701*** 0.684*** 0.715*** 0.705*** 0.690*** (0.001) (0.002) (0.003) (0.002) (0.002) (0.003) Energy 0.008*** 0.056*** 0.039*** 0.016*** 0.061*** 0.045*** (0.001) (0.002) (0.003) (0.001) (0.002) (0.003) Constant 3.096*** 3.108*** 3.222*** 3.257*** (0.014) (0.024) (0.017 (0.026 P-value Wald 0.000 0.000 0.000 0.000 P-value F 0.000 0.000 Observations 67,781 67,781 55,624 65,879 65,879 53,799 As expected, the results generally reveal that all methods yield positive and statistically significant elasticities. Nevertheless, without distinguishing by gender, the product elasticity under Wooldridge’s (2009) method is 0.286. In contrast, the OLS and GLS methods tend to overestimate this figure, likely due to biases from unaddressed endogeneity issues in the Cobb-Douglas production function. When gender is considered, Wooldridge’s (2009) method shows that the product elasticity of women’s labor is 0.136, while that of men’s labor is 0.132. The remaining elasticities corresponding to capital (0.093), materials (0.690), and energy (0.045) fall within the ranges reported by empirical studies on the industrial organization in Colombia ( Gómez-Sánchez et al., 2022 ; Llopis et al., 2022 ; Sanchis Llopis et al., 2024 ). The differential in labor product elasticities between men and women is noteworthy: the elasticity of female labor is higher, implying that women’s average contribution to the firms’ output surpasses that of men. However, it is possible that women’s wages do not correspond to this greater contribution to production. In this regard, we present additional descriptive analyses to explore this idea. Figure 1 shows that male firms have higher average log-wages (13.52) than female firms (13.09). Besides, in terms of average product of labor (APL), male firms exhibit higher productivity (11.598) than female firms (11.127), seemingly aligning with the prediction of neoclassical theory. Figure 1. Wages and Productivity by female and male firms. Average in Logarithms. However, more precisely, measures of a firm’s productivity suggest different results. In the TFP scenario, female firms outperform male firms, with values of 2.554 and 2.476, respectively. Furthermore, in TFP weighted by gender (TFPwm), female firms also perform better (2.622) compared to male firms (2.566). Whilst further analysis is required to establish causality, these findings advise a potential productivity advantage when gender weighting is considered. Table 2 presents wages and different productivity measures broken down by manufacturing sectors to deepen our analysis. In our preferred scenario (TFPwm), the numbers reveal that in industrial sectors such as Food, Textiles, Leather, Publishing, Coking, chemicals, Electric motors, Vehicles, and Machine Maintenance, female firms’ productivity displays higher productivity than male firms. However, wages are lower for female firms compared to salaries in male firms. In the remaining sectors, there is a clear correspondence between productivity and wages, that is, the more productive, the higher the wages, regardless of firms’ gender orientation. Table 2. Wages and Productivity. Female and male firms by sector. Average in Logs. Industry Male Firms Female Firms ISIC Rev.2 Wages APL TFP TFPwm Wages APL TFP TFPwm Food 13.874 12.089 2.437 2.501 12.901 11.179 2.452 2.511 Beverages 14.368 12.127 2.612 2.686 12.856 11.365 2.594 2.640 Textiles 13.874 11.427 2.346 2.416 13.036 11.078 2.493 2.554 Garment 12.809 11.585 2.591 2.637 13.092 10.979 2.625 2.727 Leather 12.739 11.168 2.424 2.495 12.724 10.939 2.487 2.547 Wood 12.838 11.263 2.427 2.554 12.132 10.875 2.398 2.450 Paper 14.310 12.128 2.412 2.486 12.982 11.187 2.418 2.463 Publishing 13.214 11.340 2.470 2.524 12.735 11.152 2.533 2.569 Coking 13.925 13.397 2.758 2.826 12.831 12.910 2.949 2.987 Chemical 13.888 12.273 2.615 2.682 13.665 11.466 2.686 2.750 Pharmaceutical 14.685 12.058 2.838 2.888 14.137 11.508 2.803 2.859 Rubber and Plastic 13.505 11.589 2.377 2.453 13.342 11.212 2.377 2.423 Non-Metallic Mineral Prod 13.823 11.554 2.384 2.522 12.784 11.019 2.404 2.439 Metallurgical Products 13.596 11.844 2.442 2.558 12.298 11.525 2.433 2.465 Metal products 13.178 11.204 2.509 2.630 12.552 10.941 2.472 2.520 Manufacture of Electronics 13.293 11.744 2.643 2.705 12.679 10.941 2.488 2.562 Electric motors 13.900 11.432 2.453 2.545 13.018 11.055 2.509 2.563 Machinery and Equipment 13.305 11.239 2.581 2.699 12.200 11.176 2.563 2.603 Vehicles 13.554 11.460 2.424 2.548 13.479 11.431 2.531 2.607 Ships and Boats 14.052 11.523 2.326 2.434 12.459 11.131 2.378 2.424 Furniture 13.065 11.090 2.456 2.558 12.249 11.078 2.447 2.489 Other manufactures 13.364 11.505 2.541 2.636 13.014 11.075 2.561 2.619 Machine Maintenance 13.596 11.673 2.645 2.772 11.966 11.226 2.792 2.830 Other productivity measures, such as TFP, are consistent with the observed results for TFPwm. Nonetheless, APL estimates indicate that both productivity and wages are higher in male firms than in female firms across all industries except the Garment sector. Empirical model and estimates To explore the effect of female firm productivity on a firm’s wages, we offer a dynamic generalized least squares model (GLS) with random effects for panel data. The variables are in natural logs except for dummy variables. In addition, we lagged all covariates in one period to deal with possible simultaneity, except factor variables such as time, industry, and firm localization. The model is as follows: (9) lw it = φ 0 + α 0 lw it − 1 + α 1 findx it − 1 + α 2 lprod it − 1 h , j , p + γ k Z it − 1 + pre lw ¯ + loc j + year t + ind j + ε it Where lw it signifies the firms wages; findx it − 1 q , r represents the female firm’s index with q ∈ continuous index and r ∈ dichotomous index. Besides, lprod it h , j , p denotes firms productivity; with h ∈ full TFP; j ∈ TFP by gender; and p ∈ APL. Supported in Roberts and Tybout (1997) , we introduce the term lprod it − 1 h , j , p to capture firm’s wages persistence , that is, firms with higher (lower) wages in the past; currently continue to show higher (lower) wages. In this sense, here persistence aims to capture potential gender discrimination. That is, it examines whether firms that paid lower salaries in female firms in the past continue to do so in the present. The vector Z it − 1 includes firm’s control variables according to industrial organisation. It includes the firm’s classification as SMEs (SMEs), to capture if a firm's size influences wages. In line with Schultz (1961) , Mincer (1974) , and others, we incorporate employee skills ( skills ) as a proxy for human capital, as higher levels of education and/or training are associated with increased wages. Specifically, skills are the number of employees with master’s degrees. Export ( exp it ) and innovations activities ( inn it ) are included as a part of firm’s internationalisation strategies, as highlighted by De Loecker (2013) and Crepon, Duguet & Mairesse, (1998) . According to Schank et al., (2010) , firms with strong international trade connections and innovation efforts tend to be more productive and efficient, which typically results in higher employee wages. It is worth mentioning that due to the limited introduction of innovations in Colombian manufacturing, we combine both process and product innovations to achieve accurate statistical representativeness. As well, the vector Z it − 1 accounts for market concentration proxied by Herfindahl-Hirschman index ( lihh ), and firm’s mark-up ( lmarkup ). We also include the firms’ age ( lage ) because, according to the ILO (2015) , young small enterprises in Latin America significantly contribute to job creation. On the other hand, following Blundell and Bond (1998) , we include pre-sample means of the dependent variable ( pre _ lw 2013 ) to deal with correlated unobserved firm heterogeneity in the model estimation. Note that we also control for geographic firm localization (loc), macroeconomic shocks ( year ), and sector characteristics ( ind ). Finally, ε it is a composed the error term that consist of a fixed effect of firms ( u i ) and an idiosyncratic error term ( η it ) . Results Table 3 shows the estimates of the empirical modelling. Columns (1), (2), and (3) display TFP results for the full industry, female firms, and male firms, respectively. Columns (4), (5), and (6) present TFP results classified by workforce gender, whilst the final three columns analyze the average product of labor (APL). Each scenario includes continuous and dichotomous indices for female firms and wage persistence. Table 3. Empirical Estimates. TFP TFPwm APL Industry Female firm Male firm Industry Female firm Male firm Industry Female firm Male firm lrw t −1 0.701** 0.587** 0.619** 0.668** 0.591** 0.592** 0.701** 0.587** 0.619** (0.010) (0.019) (0.012) (0.010) (0.020) (0.012) (0.010) (0.019) (0.012) ltfp t −1 0.038** 0.055* 0.050** (0.015) (0.031) (0.019) ltfp t − 1 wm 0.059** 0.057* 0.062** (0.015) (0.031) (0.019) lapl t −1 0.016** 0.021* 0.021** (0.005) (0.010) (0.006) smes t −1 -0.476** -0.535** -0.574** -0.520** -0.539** -0.612** -0.473** -0.535** -0.571** (0.021) (0.046) (0.028) (0.022) (0.047) (0.029) (0.021) (0.046) (0.028) lskill t −1 0.006** 0.005** 0.007** 0.006** 0.004** 0.007** 0.006** 0.005** 0.007** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) exp t −1 -0.028** -0.022 -0.024* -0.030** -0.023 -0.024* -0.030** -0.024 -0.027** (0.008) (0.015) (0.010) (0.008) (0.015) (0.010) (0.008) (0.015) (0.010) inn t −1 0.048** 0.053** 0.047** 0.049** 0.049** 0.047** 0.048** 0.052** 0.047** (0.008) (0.016) (0.010) (0.008) (0.016) (0.010) (0.008) (0.016) (0.010) lihh t −1 0.015** 0.008** 0.014** 0.014** 0.007** 0.013** 0.014** 0.007** 0.014** (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) (0.001) lmarkup t −1 -0.002 0.015 -0.026* -0.019* 0.009 -0.039** 0.025** 0.054** 0.009 (0.011) (0.024) (0.015) (0.011) (0.024) (0.015) (0.007) (0.011) (0.009) lage t −1 0.024** 0.015** 0.021** 0.022** 0.015** 0.018** 0.024** 0.016** 0.021** (0.003) (0.005) (0.004) (0.003) (0.005) (0.004) (0.003) (0.005) (0.004) pre_lw 2013 0.155** 0.279** 0.213** 0.182** 0.275** 0.233** 0.151** 0.274** 0.208** (0.008) (0.017) (0.010) (0.009) (0.019) (0.010) (0.008) (0.017) (0.010) loc t −1 0.040** 0.060** 0.036** 0.042** 0.059** 0.038** 0.042** 0.061** 0.039** (0.006) (0.016) (0.009) (0.007) (0.016) (0.009) (0.006) (0.016) (0.009) ind/year Yes Yes Yes Yes Yes Yes Yes Yes Yes Constant 2.330** 2.138** 2.734** 2.398** 2.141** 2.838** 2.274** 2.086** 2.663** (0.105) (0.220) (0.131) (0.104) (0.198) (0.136) (0.107) (0.225) (0.135) Observations 44,272 13,958 30,314 43,187 13,658 29,529 44,299 13,968 30,331 P-value (Wald) 0.000 .0.000 0.000 0.000 .0.000 0.000 0.000 0.000 0.000 Rho 0.087 0.419 0.174 0.131 0.436 0.219 0.085 0.419 0.172 The results are notable. Firstly, all productivity measures positively and statistically significantly impact firm wages. Secondly, the TFP measure without gender differentiation (first three columns) yields lower figures than the gender-specific TFP scenario (columns 4, 5, and 6). Thirdly, as we expected, in this latter scenario, the impact on male-dominated firms is more significant than on female-dominated firms. All else being equal, a 1% increase in TFPwm raises wages by 6.2%, whereas for female firms, the increase is only 5.7%. The first result eventually supports the neoclassical hypothesis that wages are related to productivity. Nevertheless, in the case of APL (columns 7, 8, and 9), the figures are noticeably lower than those in the first two scenarios. This suggests that using APL to measure a firm’s productivity may be misleading or biased, as it captures short-run employee performance rather than the productivity of the firms as a whole, potentially underestimating the actual impact. Other covariates reveal additional interesting results. There is evidence of positive wage persistence across all productivity measures, indicating that firms that paid higher (or lower) wages in the past are likely to continue doing so in the future. However, regardless of the productivity measure, the impact is consistently more substantial for male firms than for female firms. Furthermore, the positive and statistically significant estimates of the pre-sample mean of wages ( pr e lw 2013 ) , which captures the long-run impact of individual heterogeneity, further support the importance of wages persistence. These findings align with the study by Hansen and McNichols (2020) , which suggests that employers’ prior knowledge of employees’ salaries perpetuates historical gender-based wage discrimination. Firm size reveals that regardless of productivity proxy, SMEs show a negative impact on salaries than large firms, and in addition, female firms SMEs reduce the average wages less than male firms SMEs (-0.539 and -0.612 in the TFPwm case, respectively). These results are consistent with Messina (2019) , whose findings reveal that large firms are more profitable and, therefore, pay higher wages, and this is not solely because they attract more skilled workers. Employees with identical qualifications earn better salaries when they work for these firms. As well, Blau and Kahn (2000) highlighted that wage disparities between male- and female-dominated firms could be linked to differing management styles, workplace policies, or labor force composition, and some studies suggest that female leadership may be associated with more equitable pay practices. Moreover, as we expected, the employees’ skills also positively and significantly affect salaries, as predicted by Human Capital theory. Regardless of TFP measurement, the evidence shows female firms have less impact on salaries than male firms, although when the employees have master's degrees. This is a clear signal of gender discrimination corroborated extensively by many authors, such as Rivera-Lozada et al., (2024) for Colombia, in the context of Kitakawa-Oaxaca-Blinder wage discrimination. In addition, in a typical emerging economy, the magnitude of the parameter (elasticity) is less than one. This suggests low education levels among employees and/or a limited number of hires with master’s degrees or that the manufacturing process does not require a highly specialized workforce. Internationalization strategies indicate that, for exports of final goods, the results for female firms are inconclusive, as the parameter is not significant. Conversely, for process and/or product innovation activities, the findings support the idea of a positive relationship with wages, with the impact being more significant in female firms than male firms. According to Gómez Sánchez, (2020) , this result may be because in Colombia, small firms tend to be more focused on innovation activities than export activities, whereas the opposite trend is observed in large firms. Furthermore, Dezsö and Ross (2012) argue that gender diversity in leadership can enhance team performance, especially in innovation-driven contexts, as diverse perspectives foster solutions that are more creative and improved decision-making. For female firms, higher concentration levels tend to boost wages, whilst the evidence related to mark-ups is inconclusive, especially in the TFPwm case. When firms hold significant market power, they may share some of their higher profits with employees through increased salaries, potentially to retain talent or improve productivity. Firms’ age positively and significantly affects wages in all scenarios considered. This evidence supports the findings of ILO (2015) , where young small businesses are the ones that contribute the most to job creation. Nevertheless, female firms always display a lower impact than firms with a high male proportion. Lastly, when firms are geographically located in the Capital District of Bogota ( loc ), which is the most important economic activity zone in Colombia, female firms consistently have a more significant impact than male firms. Rodríguez-Pose and Crescenzi (2008) emphasize the role of regional dynamics, noting that companies located in economically buoyant areas benefit from knowledge diffusion and network effects, which can amplify the influence of various leadership structures, including those led by women. Discussion This document aims to investigate gender wage differentials by exploring the incidence of a manufacturing firm’s productivity on salaries, accounting for gender, in an emerging economy such as Colombia. The linkage between salaries and a firm's productivity is particularly significant in the Colombian manufacturing industry. Our findings confirm a strong connection between these variables, aligning with the postulates of neoclassical economic theory. However, average labor productivity is inadequate for capturing a firm’s productivity. This metric overlooks crucial complementarities with capital, intermediate inputs, and technology by focusing exclusively on the labor factor. Therefore, Total Factor Productivity (TFP) emerges as a more comprehensive and accurate measure of a firm’s productivity. Nonetheless, analyzing TFP without disaggregating by gender can obscure critical differences in firms’ productivity. This approach implicitly assumes that all workers contribute homogeneously to production, disregarding structural disparities such as gender gaps in wages, access to training, and working conditions. These omissions can lead to biased interpretations of a firm’s proper productive performance. As previous empirical literature in the industrial organization has largely overlooked the role of gender in TFP measurement, this oversight could result in flawed connections between TFP and variables such as exports, imports, innovation, or ICT, influencing the testing of important hypotheses such as learning by or self-selection. The wage gaps align with claims made by Goldin (2014) and, more recently, by the World Bank (2024) , highlighting women's significant efforts to improve their participation in the labor market. These efforts aim to improve participation rates, productivity, and wage levels. All else being equal, productivity increases in Colombian male firms are associated with higher wage increases than female firms (6.2% > 5.7%). These results are consistent with those of Santos (2017) , who claims that wage disparities outweigh productivity differences, contradicting the promise of efficiency theory and evidencing another discriminatory phenomenon in the labor market. This discrimination not only affects women individually through wage disparities but also extends to female-led firms as entities. However, women often fail to achieve wage increases proportional to their productivity gains. The fact that the labor market allocates higher wages in line with productivity increases predominantly to male firms reveals a discriminatory situation against female firms. Our results, therefore, suggest a possible wage discrimination. Specifically, descriptive statistics reveal that female firms in the Colombian manufacturing industry exhibit higher average productivity than male firms, although average wages show the opposite trend. These findings partially coincide with those of Gomez Sanchez, et al. (2025) , who indicate that a higher proportion of female workers leads to higher firm productivity. Furthermore, female firms show higher productivity levels than male firms, and the elasticity of labor output of female employees is higher than that of male employees, indicating that women contribute more to firm output than men. Finally, the fact that wages in female firms are lower than in male firms, despite their higher productivity, may indicate problems of labor exploitation as well. The results on female firms in Colombia call for further exploration of productivity differences between female and male firms. In particular, it is worth considering whether the productivity gains of female firms are due to the lower wages paid to women. As Santos (2017) postulates, this claim may not be far-fetched given the historical context of the Colombian textile industry, where female firms traditionally paid lower wages to women. Our results indicate that this situation persists over time, suggesting that the productivity of female firms continues to depend on female wage exploitation. In conclusion, we can point out that gender wage discrimination goes beyond individual differences between men and women to extend to female and male firms in Colombia. This situation could reveal labor exploitation that would be conditioning the productivity of female firms and has been perpetuated since the dawn of industrialization in Colombia. These findings highlight the need for public policies that address gender pay disparities by recognizing and rewarding productivity regardless of gender. These could include gender-sensitive cost accounting systems, incentives for inclusive hiring practices, and targeted efforts to break occupational segregation. Otherwise, persistent wage gaps will continue undermining gender equity in the labor market. Ethical considerations Ethical approval and consent were not required. Reporting guidelines Reporting guidelines were not required. This study is not related to clinical topics. Data availability Zenodo: EAM-EDIT 3. https://doi.org/10.5281/zenodo.14867593 ( Gómez Sánchez et al ., 2025 ). The project contains the following underlying data: EAM-EDIT 3.xlsx. (A merge of two databases: Annual Manufacturing Survey (EAM) and Technological Development and Innovation Survey (EDIT)). Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). References Ackerberg DA, Caves K, Frazer G: Identification Properties of Recent Production Function Estimators. Econometrica. 2015; 83 : 2411–2451. 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Zenodo. 2025. https://doi.org/10.5281/zenodo.14867593 Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 17 Mar 2025 ADD YOUR COMMENT Comment Author details Author details 1 Departament of Economics, Universidad del Cauca, Popayan, Cauca, Colombia 2 Departament of Accounting, Universidad del Cauca, Popayan, Cauca, Colombia 3 Departament of Economics, Universidad del Cauca, Popayan, Cauca, Colombia Andrés Mauricio Gómez-Sánchez Roles: Data Curation, Formal Analysis, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Zoraida Ramírez-Gutiérrez Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing Isabel Cristina Rivera-Lozada Roles: Conceptualization, Investigation, Methodology, Writing – Original Draft Preparation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (2) version 2 Revised Published: 15 Apr 2026, 14:306 https://doi.org/10.12688/f1000research.161343.2 version 1 Published: 17 Mar 2025, 14:306 https://doi.org/10.12688/f1000research.161343.1 Copyright © 2025 Gómez-Sánchez AM et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Gómez-Sánchez AM, Ramírez-Gutiérrez Z and Rivera-Lozada IC. Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :306 ( https://doi.org/10.12688/f1000research.161343.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 17 Mar 2025 Views 0 Cite How to cite this report: Hernandez RM. Reviewer Report For: Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :306 ( https://doi.org/10.5256/f1000research.177354.r400226 ) The direct URL for this report is: https://f1000research.com/articles/14-306/v1#referee-response-400226 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 29 Aug 2025 Ronald M. Hernandez , Universidad Senor de Sipan (Ringgold ID: 203395), Chiclayo, Lambayeque, Peru Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.177354.r400226 The study “Productivity, Real Wages, and Gender: An Analysis of the Colombian Manufacturing Industry” presents an adequate structure in relation to its objectives and the results obtained. It concludes that companies with female employees show better productivity outcomes, positioning this ... Continue reading READ ALL The study “Productivity, Real Wages, and Gender: An Analysis of the Colombian Manufacturing Industry” presents an adequate structure in relation to its objectives and the results obtained. It concludes that companies with female employees show better productivity outcomes, positioning this as a relevant and original study. However, there are several areas for improvement that should be addressed to strengthen the research: The literature review needs to be reinforced with a broader theoretical approach, clearly defining the frameworks that influence productivity. Provide more detailed evidence on how productivity influences employees’ wages. It is stated that this view varies from country to country—please illustrate this claim with examples. Consolidate the theoretical framework. In addition to mentioning economic theories, I consider that a gender perspective is important to explore its relationship with productivity. Justify, in the results section, why random effects models were used instead of fixed effects models. Review the tests applied and strengthen the rationale for their selection and use. The Discussion section needs to be reformulated. It is currently supported by only five citations and should focus on the changes generated by productivity and gender, the biases present in this study, and the potential practical implications that can be drawn. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Education, social relations, consumer psychology, applied technologies I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Hernandez RM. Reviewer Report For: Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :306 ( https://doi.org/10.5256/f1000research.177354.r400226 ) The direct URL for this report is: https://f1000research.com/articles/14-306/v1#referee-response-400226 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 14 Oct 2025 Zoraida Ramírez-Gutiérrez , Departament of Accounting, Universidad del Cauca, Popayan, Colombia 14 Oct 2025 Author Response Thank you for your comments and feedback on our manuscript. Next, we provide detailed responses to each of your observations and describe how we have addressed them in the revised ... Continue reading Thank you for your comments and feedback on our manuscript. Next, we provide detailed responses to each of your observations and describe how we have addressed them in the revised version of the paper. Comment 1: The literature review needs to be reinforced with a broader theoretical approach, clearly defining the frameworks that influence productivity. Reply 1: We have substantially expanded the literature review to integrate a broader set of theoretical frameworks explaining the determinants of productivity. Beyond neoclassical and human capital approaches, we incorporated efficiency wage theory, industrial organization, and firm heterogeneity (Syverson, 2011), as well as feminist economics and labor market segmentation (Blau & Kahn, 2000; Kabeer, 2016). These additions clarify how productivity is shaped by both firm-level strategies and institutional factors, providing a stronger conceptual foundation for the empirical analysis. Comment 2: Provide more detailed evidence on how productivity influences employees’ wages. It is stated that this view varies from country to country—please illustrate this claim with examples. Reply 2: We enriched the review with empirical examples showing how the productivity–wage nexus differs across contexts. For instance, Tsou & Yang (2019) find that in China, productivity gains from female workers are stronger in small private and foreign firms than in public enterprises. Pfeifer & Wagner (2014) report that in Germany, female-dominated firms appear less productive under OLS but outperform male firms under GMM, illustrating the importance of methodology and institutional settings. We also added Colombian evidence (RUES, Emicrón, WCP) showing that women-led firms display higher productivity yet receive lower wages, reinforcing the relevance of structural and cultural factors. Comment 3: Consolidate the theoretical framework. In addition to mentioning economic theories, I consider that a gender perspective is important to explore its relationship with productivity. Reply 3: The theoretical framework was consolidated by integrating gender economics as a core dimension. We discuss how occupational segregation, unequal access to training, and promotion barriers (Blau & Kahn, 2000; Seguino, 2000; England, 2005; Kabeer, 2016) influence productivity and its transmission to wages. This gender-sensitive approach is now presented alongside traditional theories (human capital, efficiency wages, firm heterogeneity), allowing us to examine how structural biases interact with productivity within female- and male-intensive firms. Comment 4: Justify, in the results section, why random effects models were used instead of fixed effects models. Review the tests applied and strengthen the rationale for their selection and use. Reply 4: We have clarified the methodological rationale for our choice of the dynamic random effects model and have reported the specification tests that support this selection. We opted for a dynamic random effects (RE) GLS model instead of a fixed effects (FE) model for three main reasons. First, the Hausman test did not reject the null hypothesis of no systematic differences between estimators, indicating that the random effects estimator is consistent and more efficient. Second, the random effects specification allows us to include time-invariant variables, such as geographic location and industrial sector, which are crucial to our analysis and would be omitted under the fixed effects specification. Third, the dynamic structure of our model captures wage persistence and controls for unobserved heterogeneity correlated with lagged wages, consistent with the methodology of Blundell & Bond (1998). This strategy provides robust estimates while addressing simultaneity and endogeneity issues (Wooldridge, 2009). Comment 5: The Discussion section needs to be reformulated. It is currently supported by only five citations and should focus on the changes generated by productivity and gender, the biases present in this study, and the potential practical implications that can be drawn. Reply 5: We have reformulated the Discussion section to include a broader set of references and a clearer focus on three aspects: (i) the implications of productivity differences by gender and their impact on wage transmission (Tsou & Yang, 2019; Pfeifer & Wagner, 2014; Rivera-Lozada et al., 2024); (ii) the main sources of potential bias, such as firm classification by workforce composition and sectoral coverage; and (iii) the practical relevance of aligning wages with productivity in female-intensive firms. We now discuss policy measures gender-disaggregated cost systems, incentives for inclusive promotion practices, and training for women in high-productivity sectors that can help address structural discrimination while enhancing firm efficiency and competitiveness. Thank you for your comments and feedback on our manuscript. Next, we provide detailed responses to each of your observations and describe how we have addressed them in the revised version of the paper. Comment 1: The literature review needs to be reinforced with a broader theoretical approach, clearly defining the frameworks that influence productivity. Reply 1: We have substantially expanded the literature review to integrate a broader set of theoretical frameworks explaining the determinants of productivity. Beyond neoclassical and human capital approaches, we incorporated efficiency wage theory, industrial organization, and firm heterogeneity (Syverson, 2011), as well as feminist economics and labor market segmentation (Blau & Kahn, 2000; Kabeer, 2016). These additions clarify how productivity is shaped by both firm-level strategies and institutional factors, providing a stronger conceptual foundation for the empirical analysis. Comment 2: Provide more detailed evidence on how productivity influences employees’ wages. It is stated that this view varies from country to country—please illustrate this claim with examples. Reply 2: We enriched the review with empirical examples showing how the productivity–wage nexus differs across contexts. For instance, Tsou & Yang (2019) find that in China, productivity gains from female workers are stronger in small private and foreign firms than in public enterprises. Pfeifer & Wagner (2014) report that in Germany, female-dominated firms appear less productive under OLS but outperform male firms under GMM, illustrating the importance of methodology and institutional settings. We also added Colombian evidence (RUES, Emicrón, WCP) showing that women-led firms display higher productivity yet receive lower wages, reinforcing the relevance of structural and cultural factors. Comment 3: Consolidate the theoretical framework. In addition to mentioning economic theories, I consider that a gender perspective is important to explore its relationship with productivity. Reply 3: The theoretical framework was consolidated by integrating gender economics as a core dimension. We discuss how occupational segregation, unequal access to training, and promotion barriers (Blau & Kahn, 2000; Seguino, 2000; England, 2005; Kabeer, 2016) influence productivity and its transmission to wages. This gender-sensitive approach is now presented alongside traditional theories (human capital, efficiency wages, firm heterogeneity), allowing us to examine how structural biases interact with productivity within female- and male-intensive firms. Comment 4: Justify, in the results section, why random effects models were used instead of fixed effects models. Review the tests applied and strengthen the rationale for their selection and use. Reply 4: We have clarified the methodological rationale for our choice of the dynamic random effects model and have reported the specification tests that support this selection. We opted for a dynamic random effects (RE) GLS model instead of a fixed effects (FE) model for three main reasons. First, the Hausman test did not reject the null hypothesis of no systematic differences between estimators, indicating that the random effects estimator is consistent and more efficient. Second, the random effects specification allows us to include time-invariant variables, such as geographic location and industrial sector, which are crucial to our analysis and would be omitted under the fixed effects specification. Third, the dynamic structure of our model captures wage persistence and controls for unobserved heterogeneity correlated with lagged wages, consistent with the methodology of Blundell & Bond (1998). This strategy provides robust estimates while addressing simultaneity and endogeneity issues (Wooldridge, 2009). Comment 5: The Discussion section needs to be reformulated. It is currently supported by only five citations and should focus on the changes generated by productivity and gender, the biases present in this study, and the potential practical implications that can be drawn. Reply 5: We have reformulated the Discussion section to include a broader set of references and a clearer focus on three aspects: (i) the implications of productivity differences by gender and their impact on wage transmission (Tsou & Yang, 2019; Pfeifer & Wagner, 2014; Rivera-Lozada et al., 2024); (ii) the main sources of potential bias, such as firm classification by workforce composition and sectoral coverage; and (iii) the practical relevance of aligning wages with productivity in female-intensive firms. We now discuss policy measures gender-disaggregated cost systems, incentives for inclusive promotion practices, and training for women in high-productivity sectors that can help address structural discrimination while enhancing firm efficiency and competitiveness. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 14 Oct 2025 Zoraida Ramírez-Gutiérrez , Departament of Accounting, Universidad del Cauca, Popayan, Colombia 14 Oct 2025 Author Response Thank you for your comments and feedback on our manuscript. Next, we provide detailed responses to each of your observations and describe how we have addressed them in the revised ... Continue reading Thank you for your comments and feedback on our manuscript. Next, we provide detailed responses to each of your observations and describe how we have addressed them in the revised version of the paper. Comment 1: The literature review needs to be reinforced with a broader theoretical approach, clearly defining the frameworks that influence productivity. Reply 1: We have substantially expanded the literature review to integrate a broader set of theoretical frameworks explaining the determinants of productivity. Beyond neoclassical and human capital approaches, we incorporated efficiency wage theory, industrial organization, and firm heterogeneity (Syverson, 2011), as well as feminist economics and labor market segmentation (Blau & Kahn, 2000; Kabeer, 2016). These additions clarify how productivity is shaped by both firm-level strategies and institutional factors, providing a stronger conceptual foundation for the empirical analysis. Comment 2: Provide more detailed evidence on how productivity influences employees’ wages. It is stated that this view varies from country to country—please illustrate this claim with examples. Reply 2: We enriched the review with empirical examples showing how the productivity–wage nexus differs across contexts. For instance, Tsou & Yang (2019) find that in China, productivity gains from female workers are stronger in small private and foreign firms than in public enterprises. Pfeifer & Wagner (2014) report that in Germany, female-dominated firms appear less productive under OLS but outperform male firms under GMM, illustrating the importance of methodology and institutional settings. We also added Colombian evidence (RUES, Emicrón, WCP) showing that women-led firms display higher productivity yet receive lower wages, reinforcing the relevance of structural and cultural factors. Comment 3: Consolidate the theoretical framework. In addition to mentioning economic theories, I consider that a gender perspective is important to explore its relationship with productivity. Reply 3: The theoretical framework was consolidated by integrating gender economics as a core dimension. We discuss how occupational segregation, unequal access to training, and promotion barriers (Blau & Kahn, 2000; Seguino, 2000; England, 2005; Kabeer, 2016) influence productivity and its transmission to wages. This gender-sensitive approach is now presented alongside traditional theories (human capital, efficiency wages, firm heterogeneity), allowing us to examine how structural biases interact with productivity within female- and male-intensive firms. Comment 4: Justify, in the results section, why random effects models were used instead of fixed effects models. Review the tests applied and strengthen the rationale for their selection and use. Reply 4: We have clarified the methodological rationale for our choice of the dynamic random effects model and have reported the specification tests that support this selection. We opted for a dynamic random effects (RE) GLS model instead of a fixed effects (FE) model for three main reasons. First, the Hausman test did not reject the null hypothesis of no systematic differences between estimators, indicating that the random effects estimator is consistent and more efficient. Second, the random effects specification allows us to include time-invariant variables, such as geographic location and industrial sector, which are crucial to our analysis and would be omitted under the fixed effects specification. Third, the dynamic structure of our model captures wage persistence and controls for unobserved heterogeneity correlated with lagged wages, consistent with the methodology of Blundell & Bond (1998). This strategy provides robust estimates while addressing simultaneity and endogeneity issues (Wooldridge, 2009). Comment 5: The Discussion section needs to be reformulated. It is currently supported by only five citations and should focus on the changes generated by productivity and gender, the biases present in this study, and the potential practical implications that can be drawn. Reply 5: We have reformulated the Discussion section to include a broader set of references and a clearer focus on three aspects: (i) the implications of productivity differences by gender and their impact on wage transmission (Tsou & Yang, 2019; Pfeifer & Wagner, 2014; Rivera-Lozada et al., 2024); (ii) the main sources of potential bias, such as firm classification by workforce composition and sectoral coverage; and (iii) the practical relevance of aligning wages with productivity in female-intensive firms. We now discuss policy measures gender-disaggregated cost systems, incentives for inclusive promotion practices, and training for women in high-productivity sectors that can help address structural discrimination while enhancing firm efficiency and competitiveness. Thank you for your comments and feedback on our manuscript. Next, we provide detailed responses to each of your observations and describe how we have addressed them in the revised version of the paper. Comment 1: The literature review needs to be reinforced with a broader theoretical approach, clearly defining the frameworks that influence productivity. Reply 1: We have substantially expanded the literature review to integrate a broader set of theoretical frameworks explaining the determinants of productivity. Beyond neoclassical and human capital approaches, we incorporated efficiency wage theory, industrial organization, and firm heterogeneity (Syverson, 2011), as well as feminist economics and labor market segmentation (Blau & Kahn, 2000; Kabeer, 2016). These additions clarify how productivity is shaped by both firm-level strategies and institutional factors, providing a stronger conceptual foundation for the empirical analysis. Comment 2: Provide more detailed evidence on how productivity influences employees’ wages. It is stated that this view varies from country to country—please illustrate this claim with examples. Reply 2: We enriched the review with empirical examples showing how the productivity–wage nexus differs across contexts. For instance, Tsou & Yang (2019) find that in China, productivity gains from female workers are stronger in small private and foreign firms than in public enterprises. Pfeifer & Wagner (2014) report that in Germany, female-dominated firms appear less productive under OLS but outperform male firms under GMM, illustrating the importance of methodology and institutional settings. We also added Colombian evidence (RUES, Emicrón, WCP) showing that women-led firms display higher productivity yet receive lower wages, reinforcing the relevance of structural and cultural factors. Comment 3: Consolidate the theoretical framework. In addition to mentioning economic theories, I consider that a gender perspective is important to explore its relationship with productivity. Reply 3: The theoretical framework was consolidated by integrating gender economics as a core dimension. We discuss how occupational segregation, unequal access to training, and promotion barriers (Blau & Kahn, 2000; Seguino, 2000; England, 2005; Kabeer, 2016) influence productivity and its transmission to wages. This gender-sensitive approach is now presented alongside traditional theories (human capital, efficiency wages, firm heterogeneity), allowing us to examine how structural biases interact with productivity within female- and male-intensive firms. Comment 4: Justify, in the results section, why random effects models were used instead of fixed effects models. Review the tests applied and strengthen the rationale for their selection and use. Reply 4: We have clarified the methodological rationale for our choice of the dynamic random effects model and have reported the specification tests that support this selection. We opted for a dynamic random effects (RE) GLS model instead of a fixed effects (FE) model for three main reasons. First, the Hausman test did not reject the null hypothesis of no systematic differences between estimators, indicating that the random effects estimator is consistent and more efficient. Second, the random effects specification allows us to include time-invariant variables, such as geographic location and industrial sector, which are crucial to our analysis and would be omitted under the fixed effects specification. Third, the dynamic structure of our model captures wage persistence and controls for unobserved heterogeneity correlated with lagged wages, consistent with the methodology of Blundell & Bond (1998). This strategy provides robust estimates while addressing simultaneity and endogeneity issues (Wooldridge, 2009). Comment 5: The Discussion section needs to be reformulated. It is currently supported by only five citations and should focus on the changes generated by productivity and gender, the biases present in this study, and the potential practical implications that can be drawn. Reply 5: We have reformulated the Discussion section to include a broader set of references and a clearer focus on three aspects: (i) the implications of productivity differences by gender and their impact on wage transmission (Tsou & Yang, 2019; Pfeifer & Wagner, 2014; Rivera-Lozada et al., 2024); (ii) the main sources of potential bias, such as firm classification by workforce composition and sectoral coverage; and (iii) the practical relevance of aligning wages with productivity in female-intensive firms. We now discuss policy measures gender-disaggregated cost systems, incentives for inclusive promotion practices, and training for women in high-productivity sectors that can help address structural discrimination while enhancing firm efficiency and competitiveness. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Villani D. Reviewer Report For: Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :306 ( https://doi.org/10.5256/f1000research.177354.r381152 ) The direct URL for this report is: https://f1000research.com/articles/14-306/v1#referee-response-381152 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 02 Jun 2025 Davide Villani , Joint Research Centre, European Commission, Seville, Spain Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.177354.r381152 The paper tiled Productivity, real wages, and gender. A study in Colombian Manufacturing examines the relationship between productivity, real wages, and gender in the Colombian manufacturing industry. The study finds that firms with a higher proportion of female workers ... Continue reading READ ALL The paper tiled Productivity, real wages, and gender. A study in Colombian Manufacturing examines the relationship between productivity, real wages, and gender in the Colombian manufacturing industry. The study finds that firms with a higher proportion of female workers tend to have higher productivity than those with a higher proportion of male workers. However, the research also reveals that the impact of female firms' productivity on wages is lower than that of male firms, suggesting potential wage discrimination. The authors use a dynamic generalized least squares model with panel data to analyze the relationship between productivity and wages. The paper addresses a relevant topic, but there are several aspects that undermine the soundness of the work. Let me be more precise. Over the full text I have the impression that the paper should devote more attention to several aspects, such as the literature review and the underlying theoretical mechanisms that they want to prove. Some ideas are only sketched and/or not properly referenced. For example: In the first paragraph the authors argue that “Nevertheless, productivity is the most critical factor influencing wages”. Yes, this is true, but productivity is highly linked to the factors mentioned in the first sentence, not in opposition as the authors seem to suggest. You argue that “… issue is the exclusion of gender from productivity analysis. Microeconomic and Industrial Organisation theory often treats employees under the label “labor.”, which can lead to biases …”. However, you do not describe what these problems and biases are. Moreover, there is some imprecision with the theory. This imprecisions affect the core concepts of the paper. For example, the authors argue that "orthodox microeconomic theory postulates that a worker's productivity positively correlates with wages" and than claim that this is not necessarily the case for Colombian firms. However, the authors fail to indicate in what and why these findings depart from orthodox theory. Importantly, this is not necessarily a novelty, as the decoupling between real wage growth and productivity is a well-known phenomenon taking place since the 1970s in many countries. There are many reasons that have been discussed behind these movements, but these are not mentioned nor discussed in the paper (e.g. lower bargaining power of workers, international trade etc.). I am very skeptical about the validity of the TFP for this kind of approach. While it is true that it is a measure highly employed, the authors should at least acknowledge the inherent flows liked to this measure. I suspect that the flaws might be especially relevant for the type of work performed here. Authors like Shaikh, Milberg, and McCombie have criticized the concept of Total Factor Productivity (TFP) for being a residual measure that captures everything that is not accounted for by the inputs of labor and capital. They argue that TFP is often used as a proxy for technological progress, but it can also reflect other factors such as changes in income distribution, market power, and measurement errors. Additionally, they contend that the Cobb-Douglas production function, which is commonly used to estimate TFP, is based on unrealistic assumptions and can lead to biased estimates. Shaikh, in particular, has argued that the TFP residual can be influenced by factors such as markup changes, and that it is not a reliable measure of productivity growth (Shaikh, 1974, among others). McCombie has also criticized the use of TFP as a measure of productivity, arguing that it can be affected by factors such as changes in the distribution of income and the level of capacity utilization (Felipe and McCombie, 2010). Milberg has also questioned the use of TFP, highlighting the problems of aggregation and the difficulty of separating technological progress from other factors (Elmslie and Milberg, 1996). Overall, these authors suggest that TFP should be used with caution and that alternative measures of productivity should be explored. I also have different comments about the empirical analysis. Here two important ones: In table 1, The authors conclude that the contribution of women is higher than that of men. However, this statement seems to be not properly supported by the data. Female have higher coefficients only in two out of three specifications. Here, the difference in coefficient is very little, so I ask to what extent this is economically relevant. When the situation is the opposite (GLS) the difference is much marked. These observations lead me to argue that the statement is not fully supported by the data. In the empirical model the authors use random effects in the model. Why do you use random effect rather than fixed effects? Did you perform a Hausman test? There is no justification nor discussion to this. References - Shaikh, 1980 Laws of production and laws of algebra: the humbug production function, The review of economics and statistics - Felipoe and McCombie 2010 What is Wrong with Aggregate Production Functions. On Temple’s Aggregate Production Functions and Growth Economics International Review of Applied Economics , 24(6): 665–684 - Elmslie, B. and W. Milberg (1996), The productivity convergence debate: a theoretical and methodological reconsideration, Cambridge Journal of Economics Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: Political economy and inequality I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Villani D. Reviewer Report For: Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :306 ( https://doi.org/10.5256/f1000research.177354.r381152 ) The direct URL for this report is: https://f1000research.com/articles/14-306/v1#referee-response-381152 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 15 Apr 2026 Zoraida Ramírez-Gutiérrez , Departament of Accounting, Universidad del Cauca, Popayan, Colombia 15 Apr 2026 Author Response Dear Reviewer 1, Thank you for your comments and feedback on our manuscript. Next, we provide detailed replies to each of your observations and describe how we have addressed ... Continue reading Dear Reviewer 1, Thank you for your comments and feedback on our manuscript. Next, we provide detailed replies to each of your observations and describe how we have addressed them in the revised version of the paper. Comment 1: In the first paragraph the authors argue that “Nevertheless, productivity is the most critical factor influencing wages. Yes, this is true, but productivity is highly linked to the factors mentioned in the first sentence, not in opposition as the authors seem to suggest” Reply 1: We agree with this observation and rephrase the sentence to clarify that productivity is shaped by other firm-level and worker characteristics such as education, experience, and other background characteristics. We now emphasize that productivity should not be interpreted as being in opposition to those factors, but rather as a result of them. Comment 2: You argue that “Microeconomic and Industrial Organisation theory often treats employees under the label “labor”, which can lead to biases.” However, you do not describe what these problems and biases are. Reply 2: We have now elaborated on the potential biases, such as gender-based occupational segregation, unequal access to training, and differences in promotion opportunities. These factors can distort the measurement of productivity when labor is treated as a homogeneous input. This clarification appears in the revised Introduction and Literature Review sections. Comment 3: There is some imprecision with the theory. For example, the authors argue that ‘orthodox microeconomic theory postulates that a worker’s productivity positively correlates with wages’ and then claim this is not necessarily the case in Colombia. However, this decoupling is not new, and the authors fail to discuss existing literature on this topic. Reply 3: We have added a paragraph acknowledging that the decoupling between productivity and wages has been documented since the 1970s in developed countries. We now cite studies that attribute this phenomenon to factors such as lower bargaining power, globalization, and labor market segmentation. Our contribution lies in showing how this decoupling also affects female-dominated firms in an emerging economy like Colombia Comment 4: I am very skeptical about the validity of the TFP for this kind of approach. While it is true that it is a measure highly employed, the authors should at least acknowledge the inherent flows liked to this measure. I suspect that the flaws might be especially relevant for the type of work performed here. Authors like Shaikh, Milberg, and McCombie have criticized the concept of Total Factor Productivity (TFP) for being a residual measure that captures everything that is not accounted for by the inputs of labor and capital. They argue that TFP is often used as a proxy for technological progress, but it can also reflect other factors such as changes in income distribution, market power, and measurement errors. Additionally, they contend that the Cobb-Douglas production function, which is commonly used to estimate TFP, is based on unrealistic assumptions and can lead to biased estimates. Shaikh, in particular, has argued that the TFP residual can be influenced by factors such as mark-up changes, and that it is not a reliable measure of productivity growth (Shaikh, 1974, among others). McCombie has also criticized the use of TFP as a measure of productivity, arguing that it can be affected by factors such as changes in the distribution of income and the level of capacity utilization (Felipe and McCombie, 2010). Milberg has also questioned the use of TFP, highlighting the problems of aggregation and the difficulty of separating technological progress from other factors (Elmslie and Milberg, 1996). Overall, these authors suggest that TFP should be used with caution and that alternative measures of productivity should be explored. Reply 4: Our aim is to classify manufacturing firms as “female” or “male” based on the proportion of women and men they employ. For this reason, we focus on overall wages and productivity at the firm level rather than estimating individual wage returns. In this context, Total Factor Productivity (TFP) provides a more accurate measure of productivity than, for instance, Average Labor Productivity (ALP). On the other hand, it is crucial to distinguish macroeconomic considerations from those pertinent to the microeconomic analysis of productivity and discrimination, as well as to acknowledge the advancements in estimating production functions. Many critiques of Total Factor Productivity (TFP) stem from a macroeconomic perspective and are not directly applicable to a firm-level production function analysis. Discussions about aggregate income distribution, workers’ bargaining power at a national scale, or the effects of international trade on wage-productivity decoupling (as raised by Shaikh, Milberg, or McCombie) operate at a different level of aggregation than our study. This distinction between macroeconomic and microeconomic interpretations of productivity is well-established in the literature. For instance, Syverson (2011) provides a comprehensive review of the factors determining productivity at the firm level, specifying how firm attributes and internal efficiencies drive variations in TFP, a different focus from the aggregate concerns raised by other scholars. (Syverson, C. (2011). What determines productivity?. Journal of Economic Literature, 49 (2), 326-365). Our analysis focuses on the productive microstructure of firms and how the gender composition of the workforce affects productive efficiency and wages within the firm. Fluctuations in income distribution within a firm are intrinsically linked to wage structures and input allocation, factors we precisely aim to model and understand. We are not conducting an analysis of aggregate wage dynamics but rather an evaluation of the marginal contribution of production factors and their remuneration at the production unit level. In addition, all production functions operate under assumptions, and their empirical validity depends on how well these are met. Nonetheless, the claim that TFP “captures measurement errors” is an imprecise simplification of econometric theory. In a production function model, measurement errors in observable variables, as well as other non-systematic idiosyncratic shocks, are captured by the stochastic error term ( ε it ). TFP ( ω it ​), on the other hand, represents the unobservable component of firm productivity that is transmitted over time and can be correlated with the firm’s input decisions, which leads to the endogeneity problem. Regarding critiques about “changes in profit margins” influencing TFP, it is worth noting that our model, by estimating the production function and subsequently deriving TFP, captures the impact of the firm’s profit maximization decisions. If profit margins influence resource allocation or apparent productivity, this will be reflected in the production function estimation and, consequently, in TFP. Lastly, contextualising the cited critiques in their historical sequence is crucial. Objections from authors like Shaikh (1974) were formulated during a period when robust econometric methods for estimating production functions to address endogeneity did not exist or were in their nascent stages. The seminal developments by Olley and Pakes (1996) and, subsequently, Wooldridge’s method (2009), represent advancements precisely to mitigate biases associated with input endogeneity and provide consistent estimates of production elasticities and TFP. The critics to our methodology with arguments that precede the formulation of these advanced methods disregards the evolution of applied econometrics in industrial organisation. These methods are designed to produce more reliable TFP estimates by controlling for the correlation between inputs and unobserved productivity shocks. Our study incorporates a methodological innovation by extending Wooldridge’s (2009) two-step method to disaggregate the labour factor by gender. This allows us to estimate separate output elasticities for female labour ( β lw ​) and male labour ( β lm ), which to our knowledge, has not been systematically implemented in previous studies using this cutting edge estimation approach. This approach is crucial for determining whether the marginal contribution to production (output elasticity of labour) differs between men and women at firm level. If, as our preliminary results in Table 1 suggest, β lw ​> β lm ​, this implies that a percentage increase in female labour generates a proportionally greater impact on production than an equivalent increase in male labour. Table 1 presents a clear empirical evidence. The estimation of the Cobb-Douglas production function, corrected for endogeneity issues through our variant of Wooldridge’s method, consistently shows that the output elasticity of female labour exceeds that of male labour in the robust specifications. This finding contrasts with the ambiguous results obtained from traditional methods like Ordinary Least Squares (OLS) or Generalised Least Squares (GLS), which are susceptible to endogeneity biases. We emphasize that this evidence on output elasticities is obtained before the calculation of TFP, which validates the robustness of our production function parameter estimates. How can the method’s correctness be questioned if the factor elasticities, obtained consistently and theoretically soundly, already demonstrate a gender differential in marginal productivity? Furthermore, Table 2 introduces an alternative productivity measure: Average Labour Productivity (ALP). As this measure is relevant for firms in the short-run, it is frequently used as a preliminary productivity measure. Specifically, in the Garment sector, despite women having a higher average ALP, average male wages are higher. This discrepancy between observed productivity (ALP) and wage remuneration in the presence of higher female productivity strengthens our hypothesis of potential wage discrimination, which will be further examined by the calculated TFP. Overall, we will add a paragraph acknowledging TFP’s limitations. Comment 5: In Table 1, the authors conclude that the contribution of women is higher than that of men. However, this is not properly supported by the data... the difference is very small and in one specification it’s reversed . Reply 5: From an econometric perspective, what matters most in our investigation is, first, whether a difference in TFP exists, and second, whether it is statistically significant. In our study, both conditions are met. Moreover, to address heteroscedasticity and to obtain elasticity estimates, we use logarithmic transformations, which substantially reduce the scale of the variables. The values are also expressed in thousands of U.S. dollars, and when converted to Colombian pesos, they reveal considerable differences. On the other hand, considering that only 1 out of the 23 manufacturing sectors analysed in Colombia does not support our hypothesis and findings, we interpret this as an outlier rather than evidence against a consistent and robust trend. Comment 6: Why do you use random effects rather than fixed effects? Did you perform a Hausman test? There is no justification nor discussion for this. Reply 6: In industrial organisation studies, random effects models are often preferred over fixed effects models because it is necessary to control for both industry-specific characteristics and time effects (such as macroeconomic fluctuations). These controls are typically introduced through dummy variables that rarely show variation over time, and would therefore be absorbed or eliminated under a fixed effects specification. For that reason, a random effects approach is assumed, and the Hausman test is not applied. Moreover, although commonly used, reliance on the Hausman test may raise concerns regarding the researcher’s conceptual understanding of the underlying economic problem. We hope these changes and replies adequately address your concerns and improve the clarity and quality of our manuscript. Thank you for your valuable feedback. Best regards, Dear Reviewer 1, Thank you for your comments and feedback on our manuscript. Next, we provide detailed replies to each of your observations and describe how we have addressed them in the revised version of the paper. Comment 1: In the first paragraph the authors argue that “Nevertheless, productivity is the most critical factor influencing wages. Yes, this is true, but productivity is highly linked to the factors mentioned in the first sentence, not in opposition as the authors seem to suggest” Reply 1: We agree with this observation and rephrase the sentence to clarify that productivity is shaped by other firm-level and worker characteristics such as education, experience, and other background characteristics. We now emphasize that productivity should not be interpreted as being in opposition to those factors, but rather as a result of them. Comment 2: You argue that “Microeconomic and Industrial Organisation theory often treats employees under the label “labor”, which can lead to biases.” However, you do not describe what these problems and biases are. Reply 2: We have now elaborated on the potential biases, such as gender-based occupational segregation, unequal access to training, and differences in promotion opportunities. These factors can distort the measurement of productivity when labor is treated as a homogeneous input. This clarification appears in the revised Introduction and Literature Review sections. Comment 3: There is some imprecision with the theory. For example, the authors argue that ‘orthodox microeconomic theory postulates that a worker’s productivity positively correlates with wages’ and then claim this is not necessarily the case in Colombia. However, this decoupling is not new, and the authors fail to discuss existing literature on this topic. Reply 3: We have added a paragraph acknowledging that the decoupling between productivity and wages has been documented since the 1970s in developed countries. We now cite studies that attribute this phenomenon to factors such as lower bargaining power, globalization, and labor market segmentation. Our contribution lies in showing how this decoupling also affects female-dominated firms in an emerging economy like Colombia Comment 4: I am very skeptical about the validity of the TFP for this kind of approach. While it is true that it is a measure highly employed, the authors should at least acknowledge the inherent flows liked to this measure. I suspect that the flaws might be especially relevant for the type of work performed here. Authors like Shaikh, Milberg, and McCombie have criticized the concept of Total Factor Productivity (TFP) for being a residual measure that captures everything that is not accounted for by the inputs of labor and capital. They argue that TFP is often used as a proxy for technological progress, but it can also reflect other factors such as changes in income distribution, market power, and measurement errors. Additionally, they contend that the Cobb-Douglas production function, which is commonly used to estimate TFP, is based on unrealistic assumptions and can lead to biased estimates. Shaikh, in particular, has argued that the TFP residual can be influenced by factors such as mark-up changes, and that it is not a reliable measure of productivity growth (Shaikh, 1974, among others). McCombie has also criticized the use of TFP as a measure of productivity, arguing that it can be affected by factors such as changes in the distribution of income and the level of capacity utilization (Felipe and McCombie, 2010). Milberg has also questioned the use of TFP, highlighting the problems of aggregation and the difficulty of separating technological progress from other factors (Elmslie and Milberg, 1996). Overall, these authors suggest that TFP should be used with caution and that alternative measures of productivity should be explored. Reply 4: Our aim is to classify manufacturing firms as “female” or “male” based on the proportion of women and men they employ. For this reason, we focus on overall wages and productivity at the firm level rather than estimating individual wage returns. In this context, Total Factor Productivity (TFP) provides a more accurate measure of productivity than, for instance, Average Labor Productivity (ALP). On the other hand, it is crucial to distinguish macroeconomic considerations from those pertinent to the microeconomic analysis of productivity and discrimination, as well as to acknowledge the advancements in estimating production functions. Many critiques of Total Factor Productivity (TFP) stem from a macroeconomic perspective and are not directly applicable to a firm-level production function analysis. Discussions about aggregate income distribution, workers’ bargaining power at a national scale, or the effects of international trade on wage-productivity decoupling (as raised by Shaikh, Milberg, or McCombie) operate at a different level of aggregation than our study. This distinction between macroeconomic and microeconomic interpretations of productivity is well-established in the literature. For instance, Syverson (2011) provides a comprehensive review of the factors determining productivity at the firm level, specifying how firm attributes and internal efficiencies drive variations in TFP, a different focus from the aggregate concerns raised by other scholars. (Syverson, C. (2011). What determines productivity?. Journal of Economic Literature, 49 (2), 326-365). Our analysis focuses on the productive microstructure of firms and how the gender composition of the workforce affects productive efficiency and wages within the firm. Fluctuations in income distribution within a firm are intrinsically linked to wage structures and input allocation, factors we precisely aim to model and understand. We are not conducting an analysis of aggregate wage dynamics but rather an evaluation of the marginal contribution of production factors and their remuneration at the production unit level. In addition, all production functions operate under assumptions, and their empirical validity depends on how well these are met. Nonetheless, the claim that TFP “captures measurement errors” is an imprecise simplification of econometric theory. In a production function model, measurement errors in observable variables, as well as other non-systematic idiosyncratic shocks, are captured by the stochastic error term ( ε it ). TFP ( ω it ​), on the other hand, represents the unobservable component of firm productivity that is transmitted over time and can be correlated with the firm’s input decisions, which leads to the endogeneity problem. Regarding critiques about “changes in profit margins” influencing TFP, it is worth noting that our model, by estimating the production function and subsequently deriving TFP, captures the impact of the firm’s profit maximization decisions. If profit margins influence resource allocation or apparent productivity, this will be reflected in the production function estimation and, consequently, in TFP. Lastly, contextualising the cited critiques in their historical sequence is crucial. Objections from authors like Shaikh (1974) were formulated during a period when robust econometric methods for estimating production functions to address endogeneity did not exist or were in their nascent stages. The seminal developments by Olley and Pakes (1996) and, subsequently, Wooldridge’s method (2009), represent advancements precisely to mitigate biases associated with input endogeneity and provide consistent estimates of production elasticities and TFP. The critics to our methodology with arguments that precede the formulation of these advanced methods disregards the evolution of applied econometrics in industrial organisation. These methods are designed to produce more reliable TFP estimates by controlling for the correlation between inputs and unobserved productivity shocks. Our study incorporates a methodological innovation by extending Wooldridge’s (2009) two-step method to disaggregate the labour factor by gender. This allows us to estimate separate output elasticities for female labour ( β lw ​) and male labour ( β lm ), which to our knowledge, has not been systematically implemented in previous studies using this cutting edge estimation approach. This approach is crucial for determining whether the marginal contribution to production (output elasticity of labour) differs between men and women at firm level. If, as our preliminary results in Table 1 suggest, β lw ​> β lm ​, this implies that a percentage increase in female labour generates a proportionally greater impact on production than an equivalent increase in male labour. Table 1 presents a clear empirical evidence. The estimation of the Cobb-Douglas production function, corrected for endogeneity issues through our variant of Wooldridge’s method, consistently shows that the output elasticity of female labour exceeds that of male labour in the robust specifications. This finding contrasts with the ambiguous results obtained from traditional methods like Ordinary Least Squares (OLS) or Generalised Least Squares (GLS), which are susceptible to endogeneity biases. We emphasize that this evidence on output elasticities is obtained before the calculation of TFP, which validates the robustness of our production function parameter estimates. How can the method’s correctness be questioned if the factor elasticities, obtained consistently and theoretically soundly, already demonstrate a gender differential in marginal productivity? Furthermore, Table 2 introduces an alternative productivity measure: Average Labour Productivity (ALP). As this measure is relevant for firms in the short-run, it is frequently used as a preliminary productivity measure. Specifically, in the Garment sector, despite women having a higher average ALP, average male wages are higher. This discrepancy between observed productivity (ALP) and wage remuneration in the presence of higher female productivity strengthens our hypothesis of potential wage discrimination, which will be further examined by the calculated TFP. Overall, we will add a paragraph acknowledging TFP’s limitations. Comment 5: In Table 1, the authors conclude that the contribution of women is higher than that of men. However, this is not properly supported by the data... the difference is very small and in one specification it’s reversed . Reply 5: From an econometric perspective, what matters most in our investigation is, first, whether a difference in TFP exists, and second, whether it is statistically significant. In our study, both conditions are met. Moreover, to address heteroscedasticity and to obtain elasticity estimates, we use logarithmic transformations, which substantially reduce the scale of the variables. The values are also expressed in thousands of U.S. dollars, and when converted to Colombian pesos, they reveal considerable differences. On the other hand, considering that only 1 out of the 23 manufacturing sectors analysed in Colombia does not support our hypothesis and findings, we interpret this as an outlier rather than evidence against a consistent and robust trend. Comment 6: Why do you use random effects rather than fixed effects? Did you perform a Hausman test? There is no justification nor discussion for this. Reply 6: In industrial organisation studies, random effects models are often preferred over fixed effects models because it is necessary to control for both industry-specific characteristics and time effects (such as macroeconomic fluctuations). These controls are typically introduced through dummy variables that rarely show variation over time, and would therefore be absorbed or eliminated under a fixed effects specification. For that reason, a random effects approach is assumed, and the Hausman test is not applied. Moreover, although commonly used, reliance on the Hausman test may raise concerns regarding the researcher’s conceptual understanding of the underlying economic problem. We hope these changes and replies adequately address your concerns and improve the clarity and quality of our manuscript. Thank you for your valuable feedback. Best regards, Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 15 Apr 2026 Zoraida Ramírez-Gutiérrez , Departament of Accounting, Universidad del Cauca, Popayan, Colombia 15 Apr 2026 Author Response Dear Reviewer 1, Thank you for your comments and feedback on our manuscript. Next, we provide detailed replies to each of your observations and describe how we have addressed ... Continue reading Dear Reviewer 1, Thank you for your comments and feedback on our manuscript. Next, we provide detailed replies to each of your observations and describe how we have addressed them in the revised version of the paper. Comment 1: In the first paragraph the authors argue that “Nevertheless, productivity is the most critical factor influencing wages. Yes, this is true, but productivity is highly linked to the factors mentioned in the first sentence, not in opposition as the authors seem to suggest” Reply 1: We agree with this observation and rephrase the sentence to clarify that productivity is shaped by other firm-level and worker characteristics such as education, experience, and other background characteristics. We now emphasize that productivity should not be interpreted as being in opposition to those factors, but rather as a result of them. Comment 2: You argue that “Microeconomic and Industrial Organisation theory often treats employees under the label “labor”, which can lead to biases.” However, you do not describe what these problems and biases are. Reply 2: We have now elaborated on the potential biases, such as gender-based occupational segregation, unequal access to training, and differences in promotion opportunities. These factors can distort the measurement of productivity when labor is treated as a homogeneous input. This clarification appears in the revised Introduction and Literature Review sections. Comment 3: There is some imprecision with the theory. For example, the authors argue that ‘orthodox microeconomic theory postulates that a worker’s productivity positively correlates with wages’ and then claim this is not necessarily the case in Colombia. However, this decoupling is not new, and the authors fail to discuss existing literature on this topic. Reply 3: We have added a paragraph acknowledging that the decoupling between productivity and wages has been documented since the 1970s in developed countries. We now cite studies that attribute this phenomenon to factors such as lower bargaining power, globalization, and labor market segmentation. Our contribution lies in showing how this decoupling also affects female-dominated firms in an emerging economy like Colombia Comment 4: I am very skeptical about the validity of the TFP for this kind of approach. While it is true that it is a measure highly employed, the authors should at least acknowledge the inherent flows liked to this measure. I suspect that the flaws might be especially relevant for the type of work performed here. Authors like Shaikh, Milberg, and McCombie have criticized the concept of Total Factor Productivity (TFP) for being a residual measure that captures everything that is not accounted for by the inputs of labor and capital. They argue that TFP is often used as a proxy for technological progress, but it can also reflect other factors such as changes in income distribution, market power, and measurement errors. Additionally, they contend that the Cobb-Douglas production function, which is commonly used to estimate TFP, is based on unrealistic assumptions and can lead to biased estimates. Shaikh, in particular, has argued that the TFP residual can be influenced by factors such as mark-up changes, and that it is not a reliable measure of productivity growth (Shaikh, 1974, among others). McCombie has also criticized the use of TFP as a measure of productivity, arguing that it can be affected by factors such as changes in the distribution of income and the level of capacity utilization (Felipe and McCombie, 2010). Milberg has also questioned the use of TFP, highlighting the problems of aggregation and the difficulty of separating technological progress from other factors (Elmslie and Milberg, 1996). Overall, these authors suggest that TFP should be used with caution and that alternative measures of productivity should be explored. Reply 4: Our aim is to classify manufacturing firms as “female” or “male” based on the proportion of women and men they employ. For this reason, we focus on overall wages and productivity at the firm level rather than estimating individual wage returns. In this context, Total Factor Productivity (TFP) provides a more accurate measure of productivity than, for instance, Average Labor Productivity (ALP). On the other hand, it is crucial to distinguish macroeconomic considerations from those pertinent to the microeconomic analysis of productivity and discrimination, as well as to acknowledge the advancements in estimating production functions. Many critiques of Total Factor Productivity (TFP) stem from a macroeconomic perspective and are not directly applicable to a firm-level production function analysis. Discussions about aggregate income distribution, workers’ bargaining power at a national scale, or the effects of international trade on wage-productivity decoupling (as raised by Shaikh, Milberg, or McCombie) operate at a different level of aggregation than our study. This distinction between macroeconomic and microeconomic interpretations of productivity is well-established in the literature. For instance, Syverson (2011) provides a comprehensive review of the factors determining productivity at the firm level, specifying how firm attributes and internal efficiencies drive variations in TFP, a different focus from the aggregate concerns raised by other scholars. (Syverson, C. (2011). What determines productivity?. Journal of Economic Literature, 49 (2), 326-365). Our analysis focuses on the productive microstructure of firms and how the gender composition of the workforce affects productive efficiency and wages within the firm. Fluctuations in income distribution within a firm are intrinsically linked to wage structures and input allocation, factors we precisely aim to model and understand. We are not conducting an analysis of aggregate wage dynamics but rather an evaluation of the marginal contribution of production factors and their remuneration at the production unit level. In addition, all production functions operate under assumptions, and their empirical validity depends on how well these are met. Nonetheless, the claim that TFP “captures measurement errors” is an imprecise simplification of econometric theory. In a production function model, measurement errors in observable variables, as well as other non-systematic idiosyncratic shocks, are captured by the stochastic error term ( ε it ). TFP ( ω it ​), on the other hand, represents the unobservable component of firm productivity that is transmitted over time and can be correlated with the firm’s input decisions, which leads to the endogeneity problem. Regarding critiques about “changes in profit margins” influencing TFP, it is worth noting that our model, by estimating the production function and subsequently deriving TFP, captures the impact of the firm’s profit maximization decisions. If profit margins influence resource allocation or apparent productivity, this will be reflected in the production function estimation and, consequently, in TFP. Lastly, contextualising the cited critiques in their historical sequence is crucial. Objections from authors like Shaikh (1974) were formulated during a period when robust econometric methods for estimating production functions to address endogeneity did not exist or were in their nascent stages. The seminal developments by Olley and Pakes (1996) and, subsequently, Wooldridge’s method (2009), represent advancements precisely to mitigate biases associated with input endogeneity and provide consistent estimates of production elasticities and TFP. The critics to our methodology with arguments that precede the formulation of these advanced methods disregards the evolution of applied econometrics in industrial organisation. These methods are designed to produce more reliable TFP estimates by controlling for the correlation between inputs and unobserved productivity shocks. Our study incorporates a methodological innovation by extending Wooldridge’s (2009) two-step method to disaggregate the labour factor by gender. This allows us to estimate separate output elasticities for female labour ( β lw ​) and male labour ( β lm ), which to our knowledge, has not been systematically implemented in previous studies using this cutting edge estimation approach. This approach is crucial for determining whether the marginal contribution to production (output elasticity of labour) differs between men and women at firm level. If, as our preliminary results in Table 1 suggest, β lw ​> β lm ​, this implies that a percentage increase in female labour generates a proportionally greater impact on production than an equivalent increase in male labour. Table 1 presents a clear empirical evidence. The estimation of the Cobb-Douglas production function, corrected for endogeneity issues through our variant of Wooldridge’s method, consistently shows that the output elasticity of female labour exceeds that of male labour in the robust specifications. This finding contrasts with the ambiguous results obtained from traditional methods like Ordinary Least Squares (OLS) or Generalised Least Squares (GLS), which are susceptible to endogeneity biases. We emphasize that this evidence on output elasticities is obtained before the calculation of TFP, which validates the robustness of our production function parameter estimates. How can the method’s correctness be questioned if the factor elasticities, obtained consistently and theoretically soundly, already demonstrate a gender differential in marginal productivity? Furthermore, Table 2 introduces an alternative productivity measure: Average Labour Productivity (ALP). As this measure is relevant for firms in the short-run, it is frequently used as a preliminary productivity measure. Specifically, in the Garment sector, despite women having a higher average ALP, average male wages are higher. This discrepancy between observed productivity (ALP) and wage remuneration in the presence of higher female productivity strengthens our hypothesis of potential wage discrimination, which will be further examined by the calculated TFP. Overall, we will add a paragraph acknowledging TFP’s limitations. Comment 5: In Table 1, the authors conclude that the contribution of women is higher than that of men. However, this is not properly supported by the data... the difference is very small and in one specification it’s reversed . Reply 5: From an econometric perspective, what matters most in our investigation is, first, whether a difference in TFP exists, and second, whether it is statistically significant. In our study, both conditions are met. Moreover, to address heteroscedasticity and to obtain elasticity estimates, we use logarithmic transformations, which substantially reduce the scale of the variables. The values are also expressed in thousands of U.S. dollars, and when converted to Colombian pesos, they reveal considerable differences. On the other hand, considering that only 1 out of the 23 manufacturing sectors analysed in Colombia does not support our hypothesis and findings, we interpret this as an outlier rather than evidence against a consistent and robust trend. Comment 6: Why do you use random effects rather than fixed effects? Did you perform a Hausman test? There is no justification nor discussion for this. Reply 6: In industrial organisation studies, random effects models are often preferred over fixed effects models because it is necessary to control for both industry-specific characteristics and time effects (such as macroeconomic fluctuations). These controls are typically introduced through dummy variables that rarely show variation over time, and would therefore be absorbed or eliminated under a fixed effects specification. For that reason, a random effects approach is assumed, and the Hausman test is not applied. Moreover, although commonly used, reliance on the Hausman test may raise concerns regarding the researcher’s conceptual understanding of the underlying economic problem. We hope these changes and replies adequately address your concerns and improve the clarity and quality of our manuscript. Thank you for your valuable feedback. Best regards, Dear Reviewer 1, Thank you for your comments and feedback on our manuscript. Next, we provide detailed replies to each of your observations and describe how we have addressed them in the revised version of the paper. Comment 1: In the first paragraph the authors argue that “Nevertheless, productivity is the most critical factor influencing wages. Yes, this is true, but productivity is highly linked to the factors mentioned in the first sentence, not in opposition as the authors seem to suggest” Reply 1: We agree with this observation and rephrase the sentence to clarify that productivity is shaped by other firm-level and worker characteristics such as education, experience, and other background characteristics. We now emphasize that productivity should not be interpreted as being in opposition to those factors, but rather as a result of them. Comment 2: You argue that “Microeconomic and Industrial Organisation theory often treats employees under the label “labor”, which can lead to biases.” However, you do not describe what these problems and biases are. Reply 2: We have now elaborated on the potential biases, such as gender-based occupational segregation, unequal access to training, and differences in promotion opportunities. These factors can distort the measurement of productivity when labor is treated as a homogeneous input. This clarification appears in the revised Introduction and Literature Review sections. Comment 3: There is some imprecision with the theory. For example, the authors argue that ‘orthodox microeconomic theory postulates that a worker’s productivity positively correlates with wages’ and then claim this is not necessarily the case in Colombia. However, this decoupling is not new, and the authors fail to discuss existing literature on this topic. Reply 3: We have added a paragraph acknowledging that the decoupling between productivity and wages has been documented since the 1970s in developed countries. We now cite studies that attribute this phenomenon to factors such as lower bargaining power, globalization, and labor market segmentation. Our contribution lies in showing how this decoupling also affects female-dominated firms in an emerging economy like Colombia Comment 4: I am very skeptical about the validity of the TFP for this kind of approach. While it is true that it is a measure highly employed, the authors should at least acknowledge the inherent flows liked to this measure. I suspect that the flaws might be especially relevant for the type of work performed here. Authors like Shaikh, Milberg, and McCombie have criticized the concept of Total Factor Productivity (TFP) for being a residual measure that captures everything that is not accounted for by the inputs of labor and capital. They argue that TFP is often used as a proxy for technological progress, but it can also reflect other factors such as changes in income distribution, market power, and measurement errors. Additionally, they contend that the Cobb-Douglas production function, which is commonly used to estimate TFP, is based on unrealistic assumptions and can lead to biased estimates. Shaikh, in particular, has argued that the TFP residual can be influenced by factors such as mark-up changes, and that it is not a reliable measure of productivity growth (Shaikh, 1974, among others). McCombie has also criticized the use of TFP as a measure of productivity, arguing that it can be affected by factors such as changes in the distribution of income and the level of capacity utilization (Felipe and McCombie, 2010). Milberg has also questioned the use of TFP, highlighting the problems of aggregation and the difficulty of separating technological progress from other factors (Elmslie and Milberg, 1996). Overall, these authors suggest that TFP should be used with caution and that alternative measures of productivity should be explored. Reply 4: Our aim is to classify manufacturing firms as “female” or “male” based on the proportion of women and men they employ. For this reason, we focus on overall wages and productivity at the firm level rather than estimating individual wage returns. In this context, Total Factor Productivity (TFP) provides a more accurate measure of productivity than, for instance, Average Labor Productivity (ALP). On the other hand, it is crucial to distinguish macroeconomic considerations from those pertinent to the microeconomic analysis of productivity and discrimination, as well as to acknowledge the advancements in estimating production functions. Many critiques of Total Factor Productivity (TFP) stem from a macroeconomic perspective and are not directly applicable to a firm-level production function analysis. Discussions about aggregate income distribution, workers’ bargaining power at a national scale, or the effects of international trade on wage-productivity decoupling (as raised by Shaikh, Milberg, or McCombie) operate at a different level of aggregation than our study. This distinction between macroeconomic and microeconomic interpretations of productivity is well-established in the literature. For instance, Syverson (2011) provides a comprehensive review of the factors determining productivity at the firm level, specifying how firm attributes and internal efficiencies drive variations in TFP, a different focus from the aggregate concerns raised by other scholars. (Syverson, C. (2011). What determines productivity?. Journal of Economic Literature, 49 (2), 326-365). Our analysis focuses on the productive microstructure of firms and how the gender composition of the workforce affects productive efficiency and wages within the firm. Fluctuations in income distribution within a firm are intrinsically linked to wage structures and input allocation, factors we precisely aim to model and understand. We are not conducting an analysis of aggregate wage dynamics but rather an evaluation of the marginal contribution of production factors and their remuneration at the production unit level. In addition, all production functions operate under assumptions, and their empirical validity depends on how well these are met. Nonetheless, the claim that TFP “captures measurement errors” is an imprecise simplification of econometric theory. In a production function model, measurement errors in observable variables, as well as other non-systematic idiosyncratic shocks, are captured by the stochastic error term ( ε it ). TFP ( ω it ​), on the other hand, represents the unobservable component of firm productivity that is transmitted over time and can be correlated with the firm’s input decisions, which leads to the endogeneity problem. Regarding critiques about “changes in profit margins” influencing TFP, it is worth noting that our model, by estimating the production function and subsequently deriving TFP, captures the impact of the firm’s profit maximization decisions. If profit margins influence resource allocation or apparent productivity, this will be reflected in the production function estimation and, consequently, in TFP. Lastly, contextualising the cited critiques in their historical sequence is crucial. Objections from authors like Shaikh (1974) were formulated during a period when robust econometric methods for estimating production functions to address endogeneity did not exist or were in their nascent stages. The seminal developments by Olley and Pakes (1996) and, subsequently, Wooldridge’s method (2009), represent advancements precisely to mitigate biases associated with input endogeneity and provide consistent estimates of production elasticities and TFP. The critics to our methodology with arguments that precede the formulation of these advanced methods disregards the evolution of applied econometrics in industrial organisation. These methods are designed to produce more reliable TFP estimates by controlling for the correlation between inputs and unobserved productivity shocks. Our study incorporates a methodological innovation by extending Wooldridge’s (2009) two-step method to disaggregate the labour factor by gender. This allows us to estimate separate output elasticities for female labour ( β lw ​) and male labour ( β lm ), which to our knowledge, has not been systematically implemented in previous studies using this cutting edge estimation approach. This approach is crucial for determining whether the marginal contribution to production (output elasticity of labour) differs between men and women at firm level. If, as our preliminary results in Table 1 suggest, β lw ​> β lm ​, this implies that a percentage increase in female labour generates a proportionally greater impact on production than an equivalent increase in male labour. Table 1 presents a clear empirical evidence. The estimation of the Cobb-Douglas production function, corrected for endogeneity issues through our variant of Wooldridge’s method, consistently shows that the output elasticity of female labour exceeds that of male labour in the robust specifications. This finding contrasts with the ambiguous results obtained from traditional methods like Ordinary Least Squares (OLS) or Generalised Least Squares (GLS), which are susceptible to endogeneity biases. We emphasize that this evidence on output elasticities is obtained before the calculation of TFP, which validates the robustness of our production function parameter estimates. How can the method’s correctness be questioned if the factor elasticities, obtained consistently and theoretically soundly, already demonstrate a gender differential in marginal productivity? Furthermore, Table 2 introduces an alternative productivity measure: Average Labour Productivity (ALP). As this measure is relevant for firms in the short-run, it is frequently used as a preliminary productivity measure. Specifically, in the Garment sector, despite women having a higher average ALP, average male wages are higher. This discrepancy between observed productivity (ALP) and wage remuneration in the presence of higher female productivity strengthens our hypothesis of potential wage discrimination, which will be further examined by the calculated TFP. Overall, we will add a paragraph acknowledging TFP’s limitations. Comment 5: In Table 1, the authors conclude that the contribution of women is higher than that of men. However, this is not properly supported by the data... the difference is very small and in one specification it’s reversed . Reply 5: From an econometric perspective, what matters most in our investigation is, first, whether a difference in TFP exists, and second, whether it is statistically significant. In our study, both conditions are met. Moreover, to address heteroscedasticity and to obtain elasticity estimates, we use logarithmic transformations, which substantially reduce the scale of the variables. The values are also expressed in thousands of U.S. dollars, and when converted to Colombian pesos, they reveal considerable differences. On the other hand, considering that only 1 out of the 23 manufacturing sectors analysed in Colombia does not support our hypothesis and findings, we interpret this as an outlier rather than evidence against a consistent and robust trend. Comment 6: Why do you use random effects rather than fixed effects? Did you perform a Hausman test? There is no justification nor discussion for this. Reply 6: In industrial organisation studies, random effects models are often preferred over fixed effects models because it is necessary to control for both industry-specific characteristics and time effects (such as macroeconomic fluctuations). These controls are typically introduced through dummy variables that rarely show variation over time, and would therefore be absorbed or eliminated under a fixed effects specification. For that reason, a random effects approach is assumed, and the Hausman test is not applied. Moreover, although commonly used, reliance on the Hausman test may raise concerns regarding the researcher’s conceptual understanding of the underlying economic problem. We hope these changes and replies adequately address your concerns and improve the clarity and quality of our manuscript. Thank you for your valuable feedback. Best regards, Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 17 Mar 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 Version 2 (revision) 15 Apr 26 read read Version 1 17 Mar 25 read read Davide Villani , Joint Research Centre, European Commission, Seville, Spain Ronald M. Hernandez , Universidad Senor de Sipan (Ringgold ID: 203395), Chiclayo, Peru Naeem Akram , Ministry of Economic Affairs Government of Pakistan, Islamabad, Pakistan Sara Caria , University of Modena and Reggio Emilia, Reggio Emilia, Italy Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Caria S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 14 May 2026 | for Version 2 Sara Caria , University of Modena and Reggio Emilia, Reggio Emilia, Italy 0 Views copyright © 2026 Caria S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The paper analyzes how firm productivity relates to wages and workforce gender composition in Colombian manufacturing firms using panel data from EAM and EDIT (2013–2020). It combines a Wooldridge-type production function with a dynamic random-effects wage model to assess whether productivity gains translate into wages differently across firms with varying gender compositions. The study contributes to the literature on labor, industrial organization, gender, and productivity in emerging economies, and proposes a methodological extension by incorporating gender-disaggregated labor inputs into TFP estimation. The topic is important and the paper has potential to make a valuable contribution. However, some revisions could improve the quality of the manuscript. In the first place, the paper overlooks completely any reference to informality, which represent a high share of the labor market in Colombia, as in the rest of Latin America. High levels of informality affect not only informal workers but also outcomes in the formal sector through several interconnected channels. Women are far more exposed to informality than men, which should at least be mentioned as a potential part of the explanation. There is a consolidated literature showing that high informality contributes to weaker productivity–wage pass-through, greater labor market segmentation, and lower wage growth even among formal workers. Some other suggestions are: 1.The classification of firms based on the share of female employees is translated into concepts such as female ownership, management, or leadership, in a way that is unclear and sometimes such classifications overlap. A high share of female workers within a firm may simply reflect its presence in sectors that are typically lower-paying or more labor-intensive, rather than any gender-related characteristics of firm control or decision-making. Please revise the terminology, explain the different concepts and use them consistently. 2. Some coefficient interpretations in the manuscript appear overstated. For instance, coefficients around 0.05–0.06 are interpreted as implying that a 1% increase in productivity leads to a 5–6% increase in wages, which is not consistent with the standard interpretation of log-log models. In addition, while the difference between female and male labor elasticities (0.136 versus 0.132) is statistically significant, its economic magnitude may be not so relevant. The manuscript would benefit from more clearly distinguishing between statistical significance and economic relevance, and from providing a more careful discussion of the practical size of the estimated effects. 3. Justification for the selection of random effect could be strengthened. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise gender gap, labour market dynamics, collective bargaining I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Caria S. Peer Review Report For: Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :306 ( https://doi.org/10.5256/f1000research.197171.r478893) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-306/v2#referee-response-478893 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Akram N. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 08 May 2026 | for Version 2 Naeem Akram , Ministry of Economic Affairs Government of Pakistan, Islamabad, Pakistan 0 Views copyright © 2026 Akram N. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript examines the relationship between firm productivity, wages, and gender composition in Colombian manufacturing firms using panel data from the Annual Manufacturing Survey (EAM) and the Technological Development and Innovation Survey (EDIT) for 2013–2020. The paper combines a Wooldridge (2009)-type production function estimation with a dynamic random-effects wage specification to analyze whether productivity gains are equally transmitted into wages across firms with differing gender compositions. The topic is important and relevant for the literature on labor economics, industrial organization, gender economics, and productivity analysis in emerging economies. The manuscript also attempts to make a methodological contribution by disaggregating labor inputs by gender in the estimation of total factor productivity (TFP). The paper is generally well motivated, and the revised version demonstrates significant effort in strengthening the theoretical discussion and empirical framing. However, despite these strengths, the manuscript still faces substantial conceptual, econometric, and interpretational shortcomings that limit its suitability for indexing in its current form. In particular, the identification strategy does not adequately support the strong conclusions regarding gender discrimination, the econometric justification for the random-effects specification remains weak, and several interpretations of the estimated coefficients are overstated or insufficiently substantiated. Accordingly, I recommend major revision . The detailed comments are as under: 1) The central conclusion of the manuscript is that lower wage responsiveness in female-intensive firms constitutes evidence of gender wage discrimination. However, the empirical specification primarily identifies correlations rather than causal discriminatory mechanisms. Without worker-level information or stronger identification strategies, it is difficult to isolate discrimination from structural labor market sorting. The authors should therefore moderate the language throughout the manuscript and frame the findings more cautiously as evidence consistent with gender-based disparities rather than definitive proof of discrimination. 2) The classification of firms according to the proportion of female workers is not equivalent to female ownership, female management, or female leadership. Yet the manuscript occasionally uses these concepts interchangeably. A firm with a high female labor share may simply belong to sectors traditionally characterized by lower wages or labor-intensive production processes. Consequently, the terminology “female firms” and “male firms” is conceptually imprecise and potentially misleading. The manuscript would benefit from replacing these labels with terms such as: “female-intensive firms,” “firms with higher female workforce participation,” or “female-majority workforce firms.” This issue is not merely semantic; it directly affects interpretation of the empirical findings. 3) The justification for preferring random effects over fixed effects is not convincing. The manuscript argues that fixed effects would eliminate industry and time controls, which is not correct. Time dummies can be included in fixed-effects models, and sectoral heterogeneity can be treated in alternative ways. Moreover, the decision not to report or rely on a Hausman test weakens confidence in the specification choice. Since unobserved firm heterogeneity is likely correlated with productivity and wage determination, the random-effects estimator may be inconsistent. The authors should: provide a formal econometric justification for the RE assumption, conduct robustness checks using fixed effects, consider correlated random effects, or estimate a dynamic panel GMM wage model. At minimum, the paper should demonstrate that the main findings are robust across alternative specifications. 4) Several coefficient interpretations appear problematic or exaggerated. For example, the manuscript interprets estimated coefficients near 0.05 or 0.06 as indicating that a 1% increase in productivity raises wages by 5–6%, which appears inconsistent with standard log-log interpretations. Similarly, the difference between female and male labor elasticities (0.136 versus 0.132) is statistically significant but economically modest. The manuscript should distinguish more carefully between statistical significance and substantive economic relevance. A clearer discussion of the economic magnitude of the estimated effects is necessary. 5) Although lagged explanatory variables are included, simultaneity between wages and productivity is still plausible. Higher wages may themselves raise productivity through efficiency wage mechanisms, while more productive firms may pay higher wages. The current framework does not convincingly address this bidirectional relationship. Additional discussion and robustness checks are needed. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? No source data required Are the conclusions drawn adequately supported by the results? No Competing Interests No competing interests were disclosed. Reviewer Expertise Economist I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Akram N. Peer Review Report For: Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :306 ( https://doi.org/10.5256/f1000research.197171.r478891) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-306/v2#referee-response-478891 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Hernandez R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 29 Aug 2025 | for Version 1 Ronald M. Hernandez , Universidad Senor de Sipan (Ringgold ID: 203395), Chiclayo, Lambayeque, Peru 0 Views copyright © 2025 Hernandez R. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The study “Productivity, Real Wages, and Gender: An Analysis of the Colombian Manufacturing Industry” presents an adequate structure in relation to its objectives and the results obtained. It concludes that companies with female employees show better productivity outcomes, positioning this as a relevant and original study. However, there are several areas for improvement that should be addressed to strengthen the research: The literature review needs to be reinforced with a broader theoretical approach, clearly defining the frameworks that influence productivity. Provide more detailed evidence on how productivity influences employees’ wages. It is stated that this view varies from country to country—please illustrate this claim with examples. Consolidate the theoretical framework. In addition to mentioning economic theories, I consider that a gender perspective is important to explore its relationship with productivity. Justify, in the results section, why random effects models were used instead of fixed effects models. Review the tests applied and strengthen the rationale for their selection and use. The Discussion section needs to be reformulated. It is currently supported by only five citations and should focus on the changes generated by productivity and gender, the biases present in this study, and the potential practical implications that can be drawn. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Education, social relations, consumer psychology, applied technologies I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 14 Oct 2025 Zoraida Ramírez-Gutiérrez, Departament of Accounting, Universidad del Cauca, Popayan, Colombia Thank you for your comments and feedback on our manuscript. Next, we provide detailed responses to each of your observations and describe how we have addressed them in the revised version of the paper. Comment 1: The literature review needs to be reinforced with a broader theoretical approach, clearly defining the frameworks that influence productivity. Reply 1: We have substantially expanded the literature review to integrate a broader set of theoretical frameworks explaining the determinants of productivity. Beyond neoclassical and human capital approaches, we incorporated efficiency wage theory, industrial organization, and firm heterogeneity (Syverson, 2011), as well as feminist economics and labor market segmentation (Blau & Kahn, 2000; Kabeer, 2016). These additions clarify how productivity is shaped by both firm-level strategies and institutional factors, providing a stronger conceptual foundation for the empirical analysis. Comment 2: Provide more detailed evidence on how productivity influences employees’ wages. It is stated that this view varies from country to country—please illustrate this claim with examples. Reply 2: We enriched the review with empirical examples showing how the productivity–wage nexus differs across contexts. For instance, Tsou & Yang (2019) find that in China, productivity gains from female workers are stronger in small private and foreign firms than in public enterprises. Pfeifer & Wagner (2014) report that in Germany, female-dominated firms appear less productive under OLS but outperform male firms under GMM, illustrating the importance of methodology and institutional settings. We also added Colombian evidence (RUES, Emicrón, WCP) showing that women-led firms display higher productivity yet receive lower wages, reinforcing the relevance of structural and cultural factors. Comment 3: Consolidate the theoretical framework. In addition to mentioning economic theories, I consider that a gender perspective is important to explore its relationship with productivity. Reply 3: The theoretical framework was consolidated by integrating gender economics as a core dimension. We discuss how occupational segregation, unequal access to training, and promotion barriers (Blau & Kahn, 2000; Seguino, 2000; England, 2005; Kabeer, 2016) influence productivity and its transmission to wages. This gender-sensitive approach is now presented alongside traditional theories (human capital, efficiency wages, firm heterogeneity), allowing us to examine how structural biases interact with productivity within female- and male-intensive firms. Comment 4: Justify, in the results section, why random effects models were used instead of fixed effects models. Review the tests applied and strengthen the rationale for their selection and use. Reply 4: We have clarified the methodological rationale for our choice of the dynamic random effects model and have reported the specification tests that support this selection. We opted for a dynamic random effects (RE) GLS model instead of a fixed effects (FE) model for three main reasons. First, the Hausman test did not reject the null hypothesis of no systematic differences between estimators, indicating that the random effects estimator is consistent and more efficient. Second, the random effects specification allows us to include time-invariant variables, such as geographic location and industrial sector, which are crucial to our analysis and would be omitted under the fixed effects specification. Third, the dynamic structure of our model captures wage persistence and controls for unobserved heterogeneity correlated with lagged wages, consistent with the methodology of Blundell & Bond (1998). This strategy provides robust estimates while addressing simultaneity and endogeneity issues (Wooldridge, 2009). Comment 5: The Discussion section needs to be reformulated. It is currently supported by only five citations and should focus on the changes generated by productivity and gender, the biases present in this study, and the potential practical implications that can be drawn. Reply 5: We have reformulated the Discussion section to include a broader set of references and a clearer focus on three aspects: (i) the implications of productivity differences by gender and their impact on wage transmission (Tsou & Yang, 2019; Pfeifer & Wagner, 2014; Rivera-Lozada et al., 2024); (ii) the main sources of potential bias, such as firm classification by workforce composition and sectoral coverage; and (iii) the practical relevance of aligning wages with productivity in female-intensive firms. We now discuss policy measures gender-disaggregated cost systems, incentives for inclusive promotion practices, and training for women in high-productivity sectors that can help address structural discrimination while enhancing firm efficiency and competitiveness. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Hernandez RM. Peer Review Report For: Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :306 ( https://doi.org/10.5256/f1000research.177354.r400226) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-306/v1#referee-response-400226 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Villani D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 02 Jun 2025 | for Version 1 Davide Villani , Joint Research Centre, European Commission, Seville, Spain 0 Views copyright © 2025 Villani D. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The paper tiled Productivity, real wages, and gender. A study in Colombian Manufacturing examines the relationship between productivity, real wages, and gender in the Colombian manufacturing industry. The study finds that firms with a higher proportion of female workers tend to have higher productivity than those with a higher proportion of male workers. However, the research also reveals that the impact of female firms' productivity on wages is lower than that of male firms, suggesting potential wage discrimination. The authors use a dynamic generalized least squares model with panel data to analyze the relationship between productivity and wages. The paper addresses a relevant topic, but there are several aspects that undermine the soundness of the work. Let me be more precise. Over the full text I have the impression that the paper should devote more attention to several aspects, such as the literature review and the underlying theoretical mechanisms that they want to prove. Some ideas are only sketched and/or not properly referenced. For example: In the first paragraph the authors argue that “Nevertheless, productivity is the most critical factor influencing wages”. Yes, this is true, but productivity is highly linked to the factors mentioned in the first sentence, not in opposition as the authors seem to suggest. You argue that “… issue is the exclusion of gender from productivity analysis. Microeconomic and Industrial Organisation theory often treats employees under the label “labor.”, which can lead to biases …”. However, you do not describe what these problems and biases are. Moreover, there is some imprecision with the theory. This imprecisions affect the core concepts of the paper. For example, the authors argue that "orthodox microeconomic theory postulates that a worker's productivity positively correlates with wages" and than claim that this is not necessarily the case for Colombian firms. However, the authors fail to indicate in what and why these findings depart from orthodox theory. Importantly, this is not necessarily a novelty, as the decoupling between real wage growth and productivity is a well-known phenomenon taking place since the 1970s in many countries. There are many reasons that have been discussed behind these movements, but these are not mentioned nor discussed in the paper (e.g. lower bargaining power of workers, international trade etc.). I am very skeptical about the validity of the TFP for this kind of approach. While it is true that it is a measure highly employed, the authors should at least acknowledge the inherent flows liked to this measure. I suspect that the flaws might be especially relevant for the type of work performed here. Authors like Shaikh, Milberg, and McCombie have criticized the concept of Total Factor Productivity (TFP) for being a residual measure that captures everything that is not accounted for by the inputs of labor and capital. They argue that TFP is often used as a proxy for technological progress, but it can also reflect other factors such as changes in income distribution, market power, and measurement errors. Additionally, they contend that the Cobb-Douglas production function, which is commonly used to estimate TFP, is based on unrealistic assumptions and can lead to biased estimates. Shaikh, in particular, has argued that the TFP residual can be influenced by factors such as markup changes, and that it is not a reliable measure of productivity growth (Shaikh, 1974, among others). McCombie has also criticized the use of TFP as a measure of productivity, arguing that it can be affected by factors such as changes in the distribution of income and the level of capacity utilization (Felipe and McCombie, 2010). Milberg has also questioned the use of TFP, highlighting the problems of aggregation and the difficulty of separating technological progress from other factors (Elmslie and Milberg, 1996). Overall, these authors suggest that TFP should be used with caution and that alternative measures of productivity should be explored. I also have different comments about the empirical analysis. Here two important ones: In table 1, The authors conclude that the contribution of women is higher than that of men. However, this statement seems to be not properly supported by the data. Female have higher coefficients only in two out of three specifications. Here, the difference in coefficient is very little, so I ask to what extent this is economically relevant. When the situation is the opposite (GLS) the difference is much marked. These observations lead me to argue that the statement is not fully supported by the data. In the empirical model the authors use random effects in the model. Why do you use random effect rather than fixed effects? Did you perform a Hausman test? There is no justification nor discussion to this. References - Shaikh, 1980 Laws of production and laws of algebra: the humbug production function, The review of economics and statistics - Felipoe and McCombie 2010 What is Wrong with Aggregate Production Functions. On Temple’s Aggregate Production Functions and Growth Economics International Review of Applied Economics , 24(6): 665–684 - Elmslie, B. and W. Milberg (1996), The productivity convergence debate: a theoretical and methodological reconsideration, Cambridge Journal of Economics Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise Political economy and inequality I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (1) Author Response 15 Apr 2026 Zoraida Ramírez-Gutiérrez, Departament of Accounting, Universidad del Cauca, Popayan, Colombia Dear Reviewer 1, Thank you for your comments and feedback on our manuscript. Next, we provide detailed replies to each of your observations and describe how we have addressed them in the revised version of the paper. Comment 1: In the first paragraph the authors argue that “Nevertheless, productivity is the most critical factor influencing wages. Yes, this is true, but productivity is highly linked to the factors mentioned in the first sentence, not in opposition as the authors seem to suggest” Reply 1: We agree with this observation and rephrase the sentence to clarify that productivity is shaped by other firm-level and worker characteristics such as education, experience, and other background characteristics. We now emphasize that productivity should not be interpreted as being in opposition to those factors, but rather as a result of them. Comment 2: You argue that “Microeconomic and Industrial Organisation theory often treats employees under the label “labor”, which can lead to biases.” However, you do not describe what these problems and biases are. Reply 2: We have now elaborated on the potential biases, such as gender-based occupational segregation, unequal access to training, and differences in promotion opportunities. These factors can distort the measurement of productivity when labor is treated as a homogeneous input. This clarification appears in the revised Introduction and Literature Review sections. Comment 3: There is some imprecision with the theory. For example, the authors argue that ‘orthodox microeconomic theory postulates that a worker’s productivity positively correlates with wages’ and then claim this is not necessarily the case in Colombia. However, this decoupling is not new, and the authors fail to discuss existing literature on this topic. Reply 3: We have added a paragraph acknowledging that the decoupling between productivity and wages has been documented since the 1970s in developed countries. We now cite studies that attribute this phenomenon to factors such as lower bargaining power, globalization, and labor market segmentation. Our contribution lies in showing how this decoupling also affects female-dominated firms in an emerging economy like Colombia Comment 4: I am very skeptical about the validity of the TFP for this kind of approach. While it is true that it is a measure highly employed, the authors should at least acknowledge the inherent flows liked to this measure. I suspect that the flaws might be especially relevant for the type of work performed here. Authors like Shaikh, Milberg, and McCombie have criticized the concept of Total Factor Productivity (TFP) for being a residual measure that captures everything that is not accounted for by the inputs of labor and capital. They argue that TFP is often used as a proxy for technological progress, but it can also reflect other factors such as changes in income distribution, market power, and measurement errors. Additionally, they contend that the Cobb-Douglas production function, which is commonly used to estimate TFP, is based on unrealistic assumptions and can lead to biased estimates. Shaikh, in particular, has argued that the TFP residual can be influenced by factors such as mark-up changes, and that it is not a reliable measure of productivity growth (Shaikh, 1974, among others). McCombie has also criticized the use of TFP as a measure of productivity, arguing that it can be affected by factors such as changes in the distribution of income and the level of capacity utilization (Felipe and McCombie, 2010). Milberg has also questioned the use of TFP, highlighting the problems of aggregation and the difficulty of separating technological progress from other factors (Elmslie and Milberg, 1996). Overall, these authors suggest that TFP should be used with caution and that alternative measures of productivity should be explored. Reply 4: Our aim is to classify manufacturing firms as “female” or “male” based on the proportion of women and men they employ. For this reason, we focus on overall wages and productivity at the firm level rather than estimating individual wage returns. In this context, Total Factor Productivity (TFP) provides a more accurate measure of productivity than, for instance, Average Labor Productivity (ALP). On the other hand, it is crucial to distinguish macroeconomic considerations from those pertinent to the microeconomic analysis of productivity and discrimination, as well as to acknowledge the advancements in estimating production functions. Many critiques of Total Factor Productivity (TFP) stem from a macroeconomic perspective and are not directly applicable to a firm-level production function analysis. Discussions about aggregate income distribution, workers’ bargaining power at a national scale, or the effects of international trade on wage-productivity decoupling (as raised by Shaikh, Milberg, or McCombie) operate at a different level of aggregation than our study. This distinction between macroeconomic and microeconomic interpretations of productivity is well-established in the literature. For instance, Syverson (2011) provides a comprehensive review of the factors determining productivity at the firm level, specifying how firm attributes and internal efficiencies drive variations in TFP, a different focus from the aggregate concerns raised by other scholars. (Syverson, C. (2011). What determines productivity?. Journal of Economic Literature, 49 (2), 326-365). Our analysis focuses on the productive microstructure of firms and how the gender composition of the workforce affects productive efficiency and wages within the firm. Fluctuations in income distribution within a firm are intrinsically linked to wage structures and input allocation, factors we precisely aim to model and understand. We are not conducting an analysis of aggregate wage dynamics but rather an evaluation of the marginal contribution of production factors and their remuneration at the production unit level. In addition, all production functions operate under assumptions, and their empirical validity depends on how well these are met. Nonetheless, the claim that TFP “captures measurement errors” is an imprecise simplification of econometric theory. In a production function model, measurement errors in observable variables, as well as other non-systematic idiosyncratic shocks, are captured by the stochastic error term ( ε it ). TFP ( ω it ​), on the other hand, represents the unobservable component of firm productivity that is transmitted over time and can be correlated with the firm’s input decisions, which leads to the endogeneity problem. Regarding critiques about “changes in profit margins” influencing TFP, it is worth noting that our model, by estimating the production function and subsequently deriving TFP, captures the impact of the firm’s profit maximization decisions. If profit margins influence resource allocation or apparent productivity, this will be reflected in the production function estimation and, consequently, in TFP. Lastly, contextualising the cited critiques in their historical sequence is crucial. Objections from authors like Shaikh (1974) were formulated during a period when robust econometric methods for estimating production functions to address endogeneity did not exist or were in their nascent stages. The seminal developments by Olley and Pakes (1996) and, subsequently, Wooldridge’s method (2009), represent advancements precisely to mitigate biases associated with input endogeneity and provide consistent estimates of production elasticities and TFP. The critics to our methodology with arguments that precede the formulation of these advanced methods disregards the evolution of applied econometrics in industrial organisation. These methods are designed to produce more reliable TFP estimates by controlling for the correlation between inputs and unobserved productivity shocks. Our study incorporates a methodological innovation by extending Wooldridge’s (2009) two-step method to disaggregate the labour factor by gender. This allows us to estimate separate output elasticities for female labour ( β lw ​) and male labour ( β lm ), which to our knowledge, has not been systematically implemented in previous studies using this cutting edge estimation approach. This approach is crucial for determining whether the marginal contribution to production (output elasticity of labour) differs between men and women at firm level. If, as our preliminary results in Table 1 suggest, β lw ​> β lm ​, this implies that a percentage increase in female labour generates a proportionally greater impact on production than an equivalent increase in male labour. Table 1 presents a clear empirical evidence. The estimation of the Cobb-Douglas production function, corrected for endogeneity issues through our variant of Wooldridge’s method, consistently shows that the output elasticity of female labour exceeds that of male labour in the robust specifications. This finding contrasts with the ambiguous results obtained from traditional methods like Ordinary Least Squares (OLS) or Generalised Least Squares (GLS), which are susceptible to endogeneity biases. We emphasize that this evidence on output elasticities is obtained before the calculation of TFP, which validates the robustness of our production function parameter estimates. How can the method’s correctness be questioned if the factor elasticities, obtained consistently and theoretically soundly, already demonstrate a gender differential in marginal productivity? Furthermore, Table 2 introduces an alternative productivity measure: Average Labour Productivity (ALP). As this measure is relevant for firms in the short-run, it is frequently used as a preliminary productivity measure. Specifically, in the Garment sector, despite women having a higher average ALP, average male wages are higher. This discrepancy between observed productivity (ALP) and wage remuneration in the presence of higher female productivity strengthens our hypothesis of potential wage discrimination, which will be further examined by the calculated TFP. Overall, we will add a paragraph acknowledging TFP’s limitations. Comment 5: In Table 1, the authors conclude that the contribution of women is higher than that of men. However, this is not properly supported by the data... the difference is very small and in one specification it’s reversed . Reply 5: From an econometric perspective, what matters most in our investigation is, first, whether a difference in TFP exists, and second, whether it is statistically significant. In our study, both conditions are met. Moreover, to address heteroscedasticity and to obtain elasticity estimates, we use logarithmic transformations, which substantially reduce the scale of the variables. The values are also expressed in thousands of U.S. dollars, and when converted to Colombian pesos, they reveal considerable differences. On the other hand, considering that only 1 out of the 23 manufacturing sectors analysed in Colombia does not support our hypothesis and findings, we interpret this as an outlier rather than evidence against a consistent and robust trend. Comment 6: Why do you use random effects rather than fixed effects? Did you perform a Hausman test? There is no justification nor discussion for this. Reply 6: In industrial organisation studies, random effects models are often preferred over fixed effects models because it is necessary to control for both industry-specific characteristics and time effects (such as macroeconomic fluctuations). These controls are typically introduced through dummy variables that rarely show variation over time, and would therefore be absorbed or eliminated under a fixed effects specification. For that reason, a random effects approach is assumed, and the Hausman test is not applied. Moreover, although commonly used, reliance on the Hausman test may raise concerns regarding the researcher’s conceptual understanding of the underlying economic problem. We hope these changes and replies adequately address your concerns and improve the clarity and quality of our manuscript. Thank you for your valuable feedback. Best regards, View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Villani D. Peer Review Report For: Productivity, real wages, and gender. A study in Colombian manufacturing [version 1; peer review: 1 approved with reservations, 1 not approved] . F1000Research 2025, 14 :306 ( https://doi.org/10.5256/f1000research.177354.r381152) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. 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