Impact of corruption and unemployment shocks on income inequality in Sub-Saharan Africa: A quantile-on-quantile approach

Impact of corruption and unemployment shocks on income inequality in Sub-Saharan Africa: A quantile-on-quantile approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of corruption and unemployment shocks on income inequality in Sub-Saharan Africa: A quantile-on-quantile approach Obadiah Jonathan Gimba, Mehdi Seraj, Huseyin Ozdeser, Muhammad Mar'I This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4178118/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This paper examines the impact of corruption and unemployment on income inequality in Sub-Saharan Africa (SSA). We employ the quantile-on-quantile technique which allows examining the impact of quantiles of the independent variable on quantiles of the dependent variable of the distribution. The outcome shows that countries at the upper range of the GDP per capita such as Botswana, Seychelles, and Mauritius had fewer negative supply shocks from corruption and unemployment which is associated with higher income equality. For countries in the middle range such as Nigeria, Kenya and Angola, corruption supplies extreme negative shocks to inequality in Nigeria and Angola. However, Kenya experienced a mixed shock but the negative is more evident. Unemployment provided a negative shock to unequal income distribution for almost all quantiles in the middle range income countries. Considering countries at the lower range such as Burundi, Central African Republic (CAR) and Niger. Corruption supplies a mixed shock on income inequality for almost all the quantiles in Niger and Burundi but supplies a positive shock on inequality in CAR. The shocks supply from unemployment to inequality is positive in Burundi and Niger except for CAR which exhibits a mixed shock. Income Inequality Corruption Unemployment Quantile-on-Quantile Sub-Saharan Africa Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The tenth goal of the United Nations Sustainable Development Goals (SDGs) 2030 is to “reduce inequality within and among countries”. However, in Sub-Saharan Africa (SSA) the attainment of this goal is being threatened by the “twin evil” of corruption and unemployment. Before the SDGs, the Millennium Development Goals could not be achieved in many SSA countries partly due to corruption (UN-MDGs, 2013). Corruption is a constrain to socioeconomic development, poverty reduction and good governance (Omoteso & Mobolaji, 2013 ). According to the World Institute for Development Economics Research (2020), SSA has remained the most unequal region in the world even more unequal to the rest of the world developing countries. Within the continent, the inequality gap is also massive. For example, the mean income of the top 20 percent earners is 10 times greater than the 20 percent at the bottom. Furthermore, most of the inequality comes from seven countries, namely Angola, Central African Republic, Botswana, Zambia, Namibia, Comoros and South Africa. The majority of the SSA are characterized by resource dependence with a high level of inequality. The extractive industries employ relatively few workers from within the citizens who are not skilful. The highly skilled jobs are usually given to foreigners thereby increasing inequality (Bhorat et al., 2017 ). The International Labour Organization ( 2013 ) ascribes the problem of unemployment in SSA to skills mismatch whereby skills sought by employers varies considerably from the skills owned by individuals. Unemployment has been fostered due to the high population growth occasioned by the high fertility rate (Gomes-Salvador & Leiner-Killinger, 2008). There are approximately 133 million youths who are excluded from social and productive activities because of the lack of education and little or no skills (African Economic Outlook, 2013 ). It is a fact that educated persons have better chances than uneducated in acquiring new jobs and wage increases, therefore education lessens the danger of being unemployed (Kabaklarli, et al., 2011 ). The challenges associated with escalating unemployment rate include poverty, income inequality, slow growth and political instability. The main aim of this paper is to study the relationship between income inequality, corruption and unemployment. The contribution of this study is as follows: first countries within the SSA are classified (upper, middle and lower) according to a range of a GDP per capita as evident from the World Development Indicators (2020) of the World Bank. Income inequality in these countries is examined based on shocks associated with corruption and unemployment. The estimation approach used is unique. The quantile-on-quantile is a nonlinear technique that is understood to be an integration of quantile regression and nonparametric estimation. It allows the modelling of quantiles of the independent variable and quantiles of the dependent variable of a given distribution. This avails the opportunity to make objective conclusions between variables across time. According to Rose-Ackerman ( 1999 ), the major determinants of corruption are motivation and opportunities. Countries with substantial availability of natural resources are particularly more corrupt perhaps because of the windfall which provides higher opportunity and motivation for corruption (Gylfason, 2001 ). The economies of Sub-Saharan African countries are characterized by the large deposit of natural resources with rent-seeking activities. This has contributed to fostering a culture of corruption among its elite. Corruption can influence growing income inequality in diverse ways. Bribes are usually collected by government or private officials who are at advantage positions causing their income to rise. Government agents may use their influence for private benefits in designing and executing government policies through a skewed tax system that favours wealthy individuals, educational and health policies which makes accessibility and affordability of these services difficult to the low-income earners. Consequently, corruption produces a biased allocation of government resources benefiting the rich at the detriment of the poor. It impedes the equitable distribution of income. To worsen the situation, proceeds of corruption are transferred from low-income countries to high-income countries, thereby aggravating the between-country inequality. This is substantiated by the World Bank ( 2021 ) report which asserts that the most extravagant forms of corruption are achieved through institutions of developed countries. In the Transparency International ( 2020 ) report, in 2019, Sub-Saharan African was the least ranking region on the corruption perception index (i.e. the highest corrupt region), with a regional score of 32 out of 100. The world average score is 43, this shows how the public sector in the region is adjudged to be corrupt. Even within Africa, the incidence of corruption differs among countries. For example, Seychelles, Rwanda, Botswana, Cape Verde and Namibia were the least corrupt countries. While Somalia, South Sudan, Equatorial Guinea, the Democratic Republic of Congo and Guinea Bissau were the highest corrupt countries. The impact of unemployment among others include loss of income, loss of skills and in a long-term situation, it makes the chances of getting employed more difficult. The loss of income to the household creates more social division and enhances inequality. It is general knowledge that employment is a function of skills. The main feature of the labour market in the SSA is the low level of skills. It has been identified that the actual number of educated in Africa is less than any other region of the world. The resultant effect of low skills is the rise in unemployment (Teal, 2000 ). This article is structured as follows. Section 2 is the literature review. This provides an overview of empirical works of the effect of corruption and unemployment on inequality. Section 3 is the methodology. This includes countries selection, data and the model specification. Section 4 is the presentation of results and Section 5 is the discussion and conclusion. 2. Literature Review Gupta et al. ( 2002 ) find that the growing incidence of corruption raises income inequality and poverty. Specifically, they discover that a one standard deviation increase in corruption is related to one standard deviation rise in uneven income distribution. Similarly, a one standard deviation rise in the growth rate of corruption lessens the growth rate of income of the poor by 7.8 percentage points. Aidt ( 2010 ) examine the relationship between corruption and the growth of wealth per capita for a sample of 110 countries. The result shows that the indices of corruption used such as the corruption perception index of Transparency International and the control of corruption index of the World Bank lessens the growth of wealth per capita. Ullah and Ahmad ( 2016 ) analyzed the impact of corruption on income inequality, among other explanatory variables, for a panel of 71 countries using the GMM estimation technique. Their findings revealed that corruption is significantly linked to a higher level of income inequality. Dusha ( 2019 ), models the relationship between persistent inequality, corruption and factor productivity. The study concludes that a higher degree of corruption will result in escalating differences in wealth which affect total factor productivity. Gyimah-Brempong ( 2002 ) explores the effect of corruption on economic growth and income distribution for 21 African countries using the GMM estimation method. The outcome of the study shows that corruption hurts the GDP growth rate and per capita income. Corruption was found to associate positively with income inequality. Saenz-Castro and Garcia-Gonzalez (2019) used Pedroni cointegration to estimate the relationship between corruption and income inequality for 24 departments in Colombia. The corruption index was estimated as the number of persons convicted for criminal activities related to corruption. The results suggest a positive relationship between corruption and inequality. Irrespective of the level of development and growth experience, Gupta et al. (1998) find that escalating rate of corruption tends towards increasing inequality and poverty which ultimately reduces economic growth. Li et al. ( 2000 ) adopted the model of Murphy et al. (1991) to investigate the effect of corruption on income distribution and growth. Their findings revealed that corruption is responsible for the large Gini disparity between developed and developing countries. However, in countries where asset distribution is uneven, corruption is associated with a negligible rise in inequality. Wong ( 2016 ) asserts that corruption can be beneficial to distribution most especially if the corruption involves buying of votes. This can lead to a reduction of income inequality through redistribution. Gimba et al. ( 2021 ) examined the drivers of income inequality in Sub-Saharan Africa and its sub-regions using the bootstrap cointegration and the autoregressive distributed lag estimation. The result shows that a decrease in corruption in the short-run and long-run is associated with falling inequality. Fakir et al. (2017) argued that inequality as a result of corruption can fundamentally be examined as the weakness of institutions to work accordingly. They contended that the level of corruption is a major determinant of inequality in any society. Contrasting Marx, Ricardo and Piketty’s views which saw inequality as fundamentally inevitable because of the deep-rooted contradictions of capitalist society. Thus, they asserted that inequality thrives when the redistribution policies and agents of corruption are compromised. The credibility of this argument was tested using data for developed and developing countries to find out if corruption drives inequality or if inequality is the resultant feature of capitalism. Their findings show that a 1 point rise in corruption perception index is associated with 0.397 increases in inequality and that corruption drives higher inequality than the dynamics of capitalism. In their model, Alesina and Angeletos ( 2005 ) consider individuals sources of income to include income from legitimate activities and income from rent-seeking and non-market activities. They assume that inequality resulting from legitimate productive activities is fair while that rent-seeking is unfair. The only role of government is redistribution. Their model predicts that the effect of corruption means more inequality and unfairness. Contrary to popular opinion and empirical shreds of evidence, results from 19 Latin American countries shows that a lower level of corruption heightens uneven income distribution. However, this suggestion of erroneous institutional reform policies (Andres & Ramlogan-Dobson, 2011). Similarly, You and Sanjeev ( 2005 ) argued that inequality heightens the level of corruption. This is possible via the material and normative mechanism. With a rise in income inequality, wealthy individuals tend to have more to lose in the unbiased political, judicial and administrative process. The wealthy also has more resources at their disposal to influence the outcome of political, social and economic events. As the income gap widens, the majority of the population are left poorer and may seek redistribution through progressive tax. The demand for redistribution will coerce the rich to use corrupt political influence to reduce taxes and other redistributive policies. Dobson and Ramlogen-Dobson ( 2010 ) examined the trade-off between income inequality and corruption in Latin America. The outcome also revealed a divergent situation where lower corruption is attributed to higher inequality. This relationship is attributed to the large informal sector in Latin America. Tregenna ( 2011 ) examined the ways through which unemployment and how those employed in the formal and informal sectors affect wage inequality. Using South Africa as a case study, empirical findings suggest that unemployment is responsible for the general rise in wage inequality. Earning differentials between individuals employed in formal and informal jobs also contribute to rising inequality but a lesser degree. Chechi et al. (2008) explore the relationship between labour market institutions and household income inequality. The outcome of the study implies that robust institutions are associated with decreasing inequality. However, these institutions in some instances trigger rising unemployment as a result of higher unemployment benefits which tend to reduce inequality. Gustafsson and Johnson (1999) investigate the factors that influence income distribution using panel data for 16 high-income countries. Their findings show that high trade union participation and large public sector lessen income inequality. A low industrial sector was found to enhance inequality. However, there was no association between income inequality and unemployment. Sheng ( 2011 ) explore the nexus between persistent high unemployment and income inequality in the US. It discovers that a strong tradeoff exists between the rate of unemployment and wage share. This infers the presence of a positive correlation between unemployment and the uneven distribution of income. Helpman et al. ( 2010 ) explore the relationship between inequality and unemployment in the global economy. They created a model for analyzing the determinants of wage distribution that centres on labour market frictions, variations in workforce composition across firms and within industry allocation. The outcome of the model suggests that more productive firms pay higher wages. Trade openness promotes wage inequality. Similarly, wage inequality increases when the economy is at trade equilibrium than in autarky. And trade liberalization has the capacity of increasing and decreasing inequality. Deyshappriya ( 2017 ) study the determinants of income distribution for 33 Asian countries using the GMM estimation technique. The study observed that higher inflation, unemployment, political risk and terms of trade significantly heighten unequal income while education, labour force participation and official development assistance lessen inequality. In the US, it has been discovered that wage inequality intensifies as a result of an increase in unemployment. In Europe however, wage inequality declined substantially with a fall in unemployment. This outcome can be attributed to the demand for high skilled workers due to globalization and technological change. The surge in the demand for a skilled workforce in the US-led to higher inequality but in Europe, higher institutions have hindered wage adjustment enhancing unemployment for the low skilled (Dumont, 2013 ). The model from Acemoglu ( 1999 ) also substantiates this outcome where a small number of skilled workers and the difference between skilled-unskilled productivity is small will lead a firm to hire all workers. However, a rise in the number of skilled workers will eventually lead to wage inequality and unemployment. Gonzalez and Menendez ( 2000 ) used a micro-simulation method to examine the effect of unemployment on labour earnings inequality for Argentina. The findings reveal that unemployment is responsible for a large proportion of earnings inequality. Storer and Van-Audenrode ( 1998 ) examined survey data of individuals displaced from work in Canada and the US to determine wages after displacement. The study observed that unemployment insurance in Canada contributes to better wage outcomes especially for those unemployed for long. An investigation into rising income inequality in the situation of employment and labour market policies and how labour market policies can lead to a decline in inequality was done by van der Hoeven ( 2010 ). It was observed that the present rising inequality which is influenced by labour market institutions is controlled by globalization. There is a need to prioritize consistency between economic policy and labour market policies to abate growing inequality. Employment and income inequality can be viewed as ideals of economic strategy. For instance, Angeles-Castro ( 2006 ) find that a rise in the level of employment lowers inequality, particularly in the industrial sector. A policy of employment for decreasing inequality was advocated. Similarly, Freeman ( 2007 ) argued that the responsibility of labour market institutions in high and low-income countries is minimizing inequality but this has a fair and unpredictable time changing effect on employment. 3. Methodology Countries selection The rationale for the selection and inclusion of countries for this study is based according to the GDP per capita. According to the World Development Indicators (2020) of the World Bank, the annual Sub-Saharan African countries GDP per capita range from $ 274 to $ 11425. At the lower point of this range lie countries like the Central African Republic, Chad, Niger, Somalia and Democratic Republic of Congo among others. The middle point of the range has countries such as Angola, Kenya, Nigeria, Eswatini and Sao Tome and Principe among others. The upper point in the range include countries such as Botswana, Seychelles, Mauritius, South Africa, and Gabon among others. Three countries are selected from the lower, middle, and upper points of this range based on the availability of data. The lower point GDP per capita countries included are Burundi, Niger and the Central African Republic. The middle point countries included are Angola, Nigeria, and Kenya. The upper point countries by GDP per capita are Botswana, Seychelles and Mauritius. Data The dataset for this study is made up of three variables: Gini coefficient this is the dependent variable that measures income inequality. It is the ratio of the area between the diagonal and the Lorenz curve and the area of a triangle below the diagonal. The Gini coefficient lies between 0 and 1. The closer to zero the Gini is, the more equally distributed the income and the farther away from zero the Gini is, the more unequal the distribution. The Gini coefficient is derived from the Standardized World Income Inequality Database, Solt (2020). Corruption perception index (CPI) this is an indicator of the prevalence of corruption across the countries. It is measured by an index of 0 to 100, with 0 signifying highly corrupt and 100 not corrupt. A rise in the CPI means a decline in corruption and a fall in CPI implies a rise in corruption. This data is derived from the Transparency International ( 2020 ) database. Unemployment This is measured as the total percentage of the labour force that is not engaged in any work. It includes persons without work but who are able and willing to work. The data is obtained from the World Development Indicators (2020) of the World Bank. All the data for each country are presented as annual time series from 2000 to 2020. Table 1 presents the descriptive statistics for the Gini coefficient, corruption perception index and unemployment rate for each of the countries. Within this period of study, Burundi has the lowest mean Gini coefficient, meaning that it has the most equally distributed income even though a low GDP per capita country. Ironically, it has one of the highest corruption perception indexes, with a low rate of unemployment. However, within the same group as Burundi, the mean Gini coefficient for CAR and Niger is higher than Burundi also with a high perception of corruption and low unemployment. The standard deviation for Nigeria’s Gini coefficient is the highest, meaning that income inequality in Nigeria is highly volatile. The countries at the middle of the GDP per capita range all have inequality higher than the countries at the lower range except for Nigeria. The perception of corruption between countries at the lower point of the range and countries at the middle is not substantially different. Unemployment is higher in the middle GDP per capita countries. The perception towards corruption in Seychelles is extremely volatile as indicated by the standard deviation Countries at the highest point of the range have the lowest inequality except for Botswana. The corruption perception for these countries is very low compared with the lower and middle groups. In contrast, unemployment was higher than the other groups. Unemployment is highly volatile in Angola, Nigeria and Botswana as shown by their standard deviations. Table 1 Descriptive statistics for Gini coefficient, CPI and unemployment Countries Mean Minimum Maximum Standard Dev. Burundi CAR Niger Angola Kenya Nigeria Botswana 0.388 0.544 0.391 0.551 0.606 0.533 0.586 Gini Coefficient 0.383 0.542 0.379 0.525 0.600 0.475 0.585 0.395 0.546 0.398 0.592 0.619 0.589 0.589 0.003 0.001 0.007 0.026 0.004 0.496 0.001 Mauritius Seychelles Burundi CAR Niger Angola Kenya Nigeria Botswana Mauritius Seychelles Burundi CAR Niger Angola Kenya Nigeria Botswana Mauritius Seychelles 0.381 0.392 19.619 20.857 26.238 19.905 23.523 22.333 59.809 50.190 50.762 1.164 4.317 1.280 5.711 2.794 4.849 18.211 7.758 3.296 0.377 0.381 CPI 16 13 12 15 19 10 54 41 36 Unemployment 0.800 4.040 0.320 3.612 2.599 3.539 15.880 6.360 1.720 0.386 0.402 25 26 35 27 31 28 65 57 66 1.890 4.490 3.100 9.430 2.980 9.010 23.800 9.520 5.140 0.003 0.008 2.376 4.292 7.930 2.931 3.473 5.544 3.076 4.643 8.596 0.242 0.133 0.849 1.949 0.104 1.935 1.917 0.934 0.849 Source: Authors computation Model The quantile-on-quantile (QQ) approach is used to study the relationship between income inequality and corruption, and income inequality and unemployment. The quantile regression first used by Koenker and Bassett ( 1978 ) is designed for modelling time-changing variables with regards to the structure of dependence. It is a blend of non-parametric estimation and quantile regression. The merits of this technique are numerous. Quantile regression is employed to show the relationship between an explanatory variable and the diverse quantiles of the explained variable. The classical linear regression permits a specific quantile of an explanatory variable to predict an explained variable. For this reason, combining these two techniques enables the modelling of quantiles of the explanatory variable and quantiles of the explained variable for both the top and bottom quantiles of a given distribution, thereby providing a robust outcome across time. The QQ provides better information than other estimation techniques such as the OLS and quantile regression. Let us start with the following nonparametric quantile regression equations. $${Gini}_{t}={\delta }^{\theta }\left({\varphi }_{t}\right)+{u}_{t}^{\theta } \cdots \cdots \cdots \cdots \cdots \cdots \cdots \left(1\right)$$ Where \({Gini}_{t}\) represents the income inequality of a country at time \(t\) . \({\varphi }_{t}\) is the explanatory variable that represents either the corruption perception index or the unemployment rate. \(\theta\) denotes the \(\theta th\) quantile of the conditional distribution of Gini coefficient as predicted by \({\varphi }_{t}\) . \({u}_{t}^{\theta }\) signifies the quantile residual term. \({\delta }^{\theta }\) is an undefined factor lacking in any previous information. The QQ model enables is to examine the changing effect of corruption and unemployment across quantiles of inequality. We use a first-order Taylor expansion to approximate the unknown term \({\delta }^{\theta }\) . $${\delta }^{\theta }\left({\varphi }_{t}\right)\approx {\delta }^{\theta }\left({\varphi }^{\tau }\right)+{\delta }^{\theta }\left({\varphi }^{\tau }\right)\left({\varphi }^{\tau }-{\varphi }_{t}\right) \left(2\right)$$ Where \({\delta }^{\theta }\left({\varphi }^{\tau }\right)\) denotes the partial derivative of \({\delta }^{\theta }\left({\varphi }_{t}\right)\) with respect to each of the explanatory variables. Equation \(\left(2\right)\) can be presented as: $${\delta }^{\theta }\left({\varphi }_{t}\right)\approx {\delta }_{0}\left(\theta , \tau \right)+{\delta }_{1}\left(\theta , \tau \right)\left({\varphi }^{\tau }-{\varphi }_{t}\right) \left(3\right)$$ Substituting equation \(\left(3\right)\) into \(\left(1\right)\) gives $${Gini}_{t}={\delta }_{0}\left(\theta , \tau \right)+{\delta }_{1}\left(\theta , \tau \right)\left({\varphi }^{\tau }-{\varphi }_{t}\right)+{u}_{t} \left(4\right)$$ We can have the estimate for the linear regression as $${min}_{{\delta }_{0},{\delta }_{1}}{\sum }_{i}^{n}{\rho }_{\theta }\left[{Gini}_{t}-{\delta }_{0}-{\delta }_{1}\left({\widehat{\varphi }}_{t}-{\widehat{\varphi }}^{\tau }\right)K\left(\frac{{F}_{n}\left({\widehat{\varphi }}_{t}-\tau \right)}{h}\right)\right] \left(5\right)$$ Where \({\rho }_{\theta }\left(u\right)\) represent the quantile loss function which is expected as \({\rho }_{\theta }\left(u\right)=u\left(\theta -I\left(u<0\right)\right)\) . \(I\) is the indicator function and \(K\) is the kernel function. The quantile on quantile technique presents a detail and reliable interpretation. A three dimensional graph shows the plot of corruption shock on income inequality and unemployment shock on income inequality. The Z-axis shows the value of the estimate of the slope coefficient, \({\delta }_{1}\left(\theta , \tau \right)\) which evaluates the response of each of the \(\tau\) th quantile of corruption and unemployment shocks movement (x-axes) on the quantile of inequality \(\theta\) th in the y-axes. 4. Results Figure 1 presents CPI supply shocks to Gini in upper per capita countries. CPI supply mixed shocks to Gini, for example, the CPI of Botswana supply negative shock to Gini, the negative shocks run from 0.95 quantiles of CPI to 0.65–0.95 quantiles of Gini, with a value of -0.04, while the positive shocks run from 0.95 quantiles of CPI to 0.1–0.55 quantiles of Gini, the value of this positive shocks is 0.019. For Seychelles, we can see that CPI supply negative shocks to Gini, in extreme cases reach to -0.28, these extreme shocks run from 0.95 quantiles of CPI to 0.65 quantiles of Gini. Regarding Mauritius, the shocks that run from CPI to Gini is mixed (negative and positive), it ranges between − 0.056 and 0.129, the negative shocks run from 0.05–0.95 quantiles of CPI to 0.05–0.2 quantiles of Gini, while the positive shocks that run from CPI quantiles locate at 0.45 quantile of Gini. Figure 2 shows unemployment supply shocks to Gini in upper per capita countries. For Botswana, we can notice that unemployment supplies positive shocks to Gini, except for one negative shock that runs from 0.15 quantile of unemployment to 0.25 quantile of Gini, its value about − 0.053. Regarding Seychelles, it is clear that all quantiles of unemployment supply negative shocks at 0.3 quantiles of Gini, these negative shocks have a value of -0.6, while the positive shocks run from 0.4–0.9 quantiles of unemployment to 0.50 and 0.55 quantiles of Gini. Its slope is ranging between 0.89 and 0.99. Considering Mauritius, we can notice that unemployment supplies negative shocks to Gini at all quantiles, except the quantile 0.35 to 0.5 of unemployment, supply a positive shock to 0.95 quantiles of Gini, the value of this positive shock is about 0.13. Figure 3 presents CPI supply shocks to Gini in the middle per capita countries. These countries include Nigeria, Angola, and Kenya. Figure 3 , left-hand-side, shows that CPI supply extreme negative shocks to Gini in Nigeria, these negative shocks run from 0,05–0.65 quantiles of CPI to 0.75 quantiles of GINI, its value is about − 2, while the value of the positive shock that runs from 0.15 quantile of CPI to 0.95 quantiles of Gini is 0.49. Angola also exhibits the same pattern as Nigeria, with two mainshocks, one negative and run from 0.5 to 0.8 quantiles of CPI to 0.7 quantiles of Gini, the value of its slope is about − 2.25. In contrast, the positive shocks run 0.6 quantiles of CPI to 0.45 quantiles of Gini, the value of the positive shock is 1.7. For Kenya, we can see two positive shocks and one negative shock, and the positive shocks are located at 0.25 and 0.90–0.95 quantiles of Gini; the value of the positive shocks is 0.17. The negative shocks run from 0.05–0.2, the value of negative shock − 0.029 Regarding unemployment, supply shocks to Gini for middle per capita countries. Nigeria, have on negative shock supplied by unemployment to Gini, the negative shocks run from 0.05–0.3 quantiles of unemployment to 0.05 quantile of Gini. The rest of the shocks are positive, the interesting positive shocks run from 0.25–0.9 quantile of unemployment to 0.85 quantiles of Gini, the positive shock has the value of 0.69. For Angola, we can also notice two main shocks, negative and positive, the negative shocks run from 0.05 quantile of unemployment to 0.05 quantile of Gini, with a value of -2.3, the positive shocks run from 0.05–0.15 quantile of unemployment to 0.1–35 quantiles of Gini with a value of 3.5. For Kenya, it is clear that all shocks from unemployment to Gini are negative, except one positive shock run from 0.85-quantile of unemployment to 0.9 and 0.95 quantiles of Gini. The lower per capita countries which includes are Burundi, Niger, and the Central African Republic are represented in Fig. 5 . We can notice that CPI supply positive shocks almost in all quantiles in Burundi except the 0.05–0.2 quantile of CPI which supply negative shocks to 0.1 quantiles of Gini with a value of -0.42. The same thing for Niger, CPI supply positive shocks to Gini quantiles, except the 0.8 quantiles of Gini which supplied shock is negative with a value of -0.47. Regarding the Central African Republic, we can notice that CPI quantile supply two positive shocks to 0.5 and 0.95 quantiles of GINI. Figure 6 shows the unemployment supply shocks for lower per capita countries. We can notice that in general, all supplied shocks by unemployment are positive in Burundi and Niger, except the Central African Republic, in which the supplied shocks are mixed and ranging between − 0.02 and 0.06. The negative shocks supplied by 0.05 and 0.95 quantiles of unemployment to all quantiles of Gini, while the positive quantile supplied by the 0.95 quantiles of unemployment to 0.05–0.15 quantiles of GINI. 5. Discussion and conclusion This paper investigates the relationship between income inequality and corruption and income inequality and unemployment using a non-linear, quantile-on-quantile model for the years 2000 to 2020 in the Sub-Saharan African countries. Countries were selected based on their GDP per capita within the sub-region. Our findings are as follows: For countries at the upper range of the GDP per capita, the incidences of corruption supply a mixed shock (negative and positive) to income inequality in Botswana and Mauritius. However, corruption provided a negative shock to inequality in Seychelles. Considering the rate of unemployment, a supply of mixed shock (negative and positive) of unemployment to inequality was observed in all the countries in the upper range of the GDP per capita. Considering the countries at the middle range of the GDP per capita, mixed supply shocks (negative and positive) of corruption to inequality was found for all the countries however, the negative shock was extreme. A supply of mixed unemployment shock to inequality was also discovered for all the countries. For the countries at the lower range of the GDP per capita, corruption provides a mixed shock to inequality in Burundi and Niger. However, for the Central African Republic, mixed shock (negative and positive) is observed from corruption to inequality. Unemployment supplies a positive shock to income inequality in Burundi and Niger but a mixed supply of unemployment shock was noticed in the Central African Republic. From the observations, we can infer that a mixed supply of shocks means a trade-off between inequality and corruption and inequality and unemployment. A situation where higher quantiles of corruption (lower corruption) are associated with higher inequality was observed. This agrees with the work of Andres and Ramlogan-Dabson (2011). The rationale for this relationship can likely be attributed to the presence of a large informal sector in the SSA. Institutional reforms in the informal sector such as tax collection and other regulatory policies can lead to a decline of corruption however, the income of workers remain the same because they lack the skills to opt-out of the informal sector. It was also established that in some countries higher levels of corruption led to the abatement of inequality. This is synonymous with some of the East Asian countries (China, Indonesia, Thailand and South Korea) where they experience growth in GDP per capita and a brighter economic outlook despite a high level of corruption. This according to Wedeman ( 2002 ) tagged the “East Asian Paradox”. A probable argument for this relationship could be found in the extent of financial openness between these countries and the rest of the world where there are no barriers to financial transactions across borders which aid to launder money offshores (Neeman, et al. 2008 ). But could this explain for Africa? This provides a link through which this study can be extended. The following recommendations are given: to lower inequality associated with unemployment, individuals should acquire high skills that are relevant and in high demand for employability. Government should create a conducive environment through the provision of infrastructure and the right institutions for the private sector to thrive and create jobs. Institutional mechanisms of government should be effective and efficient in tackling corruption. Administrative measures should be created that will make stealing and other forms of corruption difficult. Declarations Author Contribution Obadiah Jonathan Gimba participated in writing the whole paper. Mehdi Seraj supervised, had the final edition, and brought crucial ideas for this paper. Huseyin Ozdeser supervised the research. Muhammad Mar'I modeled the data. Data Availability Statement : The dataset for this study is made up of three variables: Gini coefficient: this is the dependent variable that measures income inequality. It is the ratio of the area between the diagonal and the Lorenz curve and the area of a triangle below the diagonal. The Gini coefficient lies between 0 and 1. The closer to zero the Gini is, the more equally distributed the income and the farther away from zero the Gini is, the more unequal the distribution. The Gini coefficient is derived from the Standardized World Income Inequality Database, Solt (2020). Corruption perception index (CPI): this is an indicator of the prevalence of corruption across the countries. It is measured by an index of 0 to 100, with 0 signifying highly corrupt and 100 not corrupt. A rise in the CPI means a decline in corruption and a fall in CPI implies a rise in corruption. This data is derived from the Transparency International (2020) database. Unemployment: This is measured as the total percentage of the labour force that is not engaged in any work. It includes persons without work but who are able and willing to work. The data is obtained from the World Development Indicators (2020) of the World Bank. All the data for each country are presented as annual time series from 2000 to 2020. Funding (There is no funding for this research) Acknowledgement: (None) Conflicts of interest/Competing interests ( All authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.) References Acemoglu, D. (1999). Changes in unemployment and wage inequality: An alternative theory and some evidence. The American Economic Review, 89 (5), 1259 – 1278. Adres, A. R., & Ramlogan-Dobson, C. (2011). Is corruption really bad for inequality? Evidence from Latin America. Journal of Development Studies, 47 (7), 959 – 976. African Economic Outlook (2013). Promoting youth employment in Africa. Why an African economic outlook on youth employment? Aidt, T. S. (2010). Corruption and sustainable development. CWPE 1061. Alesina, A., & Angeletos, G. M. (2005). Corruption, inequality and fairness. Journal of Monetary Economics , 1227 – 1244. Angeles-Castro, G. (2006). The effects of economic liberalization on income distribution: A panel data analysis. In Hein, E., Heise, A., & Truger, A. (Eds). Wages, employment , distribution and growth. Palgrave Macmillan, Basingstoke, 151 – 180. Bhorat, H., Naidoo, K., Odusola, A. & Cornia, G. A. (2017). Income inequality trends in Sub-Saharan Africa: Divergence, determinants and consequences. African Economics Seminar Series: The World Bank. Checchi, D., Garcia-Penalosa, C., Petrongolo, B., & Zweimullar, J. (2008). Labour market institutions and income inequality. Economic Policy, 23 (56), 601 – 649. Deyshappriya, N. P. R. (2017). Impact of macroeconomic factors on income inequality and income distribution in Asian countries. ADBI Working Paper 696. Tokyo: Asian Development Bank Institute. Dobson, S., & Ramlogan-Dobson, C. (2010). Is there a trade-off between income inequality and corruption? Evidence from Latin America. Economics Letters, 107 (2), 102 – 104. Dumont, M. (2013). Is there a trade-off between wage inequality and unemployment? In: Hellier, J. & Chusseau, N. (eds). Growing income inequalities. Palgrave Macmillan, London. Dusha, E. (2019). Persistent inequality, corruption, and factor productivity. The B. E. Journal of Macroeconomics , 20180021. Fikir, A. M. S., Ahmad, A. U., Husain, M. K. M., Hossain, M. R., & Gani, R. S. (2017). The comparative effect of corruption and Piketty’s second fundamental law of capitalism on inequality. Economic Analysis and Policy , 55, 90 – 105. Freeman, R. B. (2007). Labour market institutions around the world. Paper 13242, National Bureau of Economic Research, Cambridge, M. A. Gimba, O. J., Seraj, M., & Ozdeser, H. (2021). What drives income inequality in Sub-Saharan African and its sub-regions? An examination of long-run and short-run effects. African Development Review , 1 – 13. Gomez-Salvador, R. S., & Leiner-Killinger, N. (2008). An analysis of youth unemployment in the Euro Area. European Central Bank Occasional Paper Series, No. 89. Gonzalez, M., & Menendez, A. (2000). The effect of unemployment on labour earnings inequality: Argentina in the nineties. Princeton University Press, Princeton. Gupta, S., Davoodi, H., & Alonso-Terme, R. (2002). Does corruption affect income inequality and poverty? IMF Working Paper WP/98/76. Gustafsson, B., & Johansson, M. (1999). In search of smoking guns: What makes income inequality vary over time in different countries? American Sociological Review, 64 (4), 585 – 605. Gyimah-Brempong, K. (2002). Corruption, economic growth and income inequality in Africa. Economics of Governance , 3, 183 – 209. Gylfason, T. (2001). Nature, power and growth. Scottish Journal of Political Economy , 48, 558 – 588. Helpman, E., Itskhoki, O., & Redding, S. (2010). Inequality and unemployment in a global economy. Economtrica, 78 (4), 1239 – 1283. International Labour Organization (2013). Global employment trends for youth: A generation at risk. International Labour Organization, Geneva. Kabaklarli, E., Hazel, E.P., & Bulus, A. (2011). Economic determinants of Turkish youth unemployment problem: Cointegration analysis. Presented at International Conference on Applied Economics, Held in Dubai, 16–18 May. Koenker, R. & Bassett, G. (1978). Regression quantiles. Econometrica , 46(1), 33 – 50. Li, H., Xu, L. C., & Zou, H. (2000). Corruption, income distribution and growth. CEMA Working Paper No. 472 Neeman, Z., Paserman, M. D., Simhon, A., (2008). Corruption and openness. The B. E. Journal of Economic Analysis & Policy, 8 (1) Omoteso, K. & Mobolaji, H. I. (2013). Corruption, good governance and economic growth in Sub-Saharan Africa: A need for the prioritization of reform policies. Social Responsibility Journal, 10 (2), 316 – 330. Rose-Ackerman, S. (1999). Corruption and government: Causes, consequences and reform . Cambridge: Cambridge University Press. Saenz-Castro, J. E., & Garcia-Gonzalez (2019). The relationship between corruption and inequality in Colombia: Empirical evidence using panel data for the period 2008 – 2017. Journal of Development Studies, 8 (2), 28 – 43. Sheng, Y. (2011). Unemployment and income inequality: A puzzling finding from the US in 1914 – 2010. Available at SSRN: http://dx.doi.org/10.2139/ssrn.2020744 Storer, P. A., & Van-Audenrode, M. A. (1998). Exploring the links between wage inequality and unemployment: A comparison of Canada and the US. CERF/CSLR Conference on the Canada-US unemployment gap. Transparency International (2020). Corruption Perception Index 2019: Sub-Saharan Africa. Teal, F. (2000). Employment and unemployment in Sub-Saharan Africa: An overview. Available at http://www.csae.ox.ac.uk/conferences/2000-OiA/pdfpapers/teal.PDF Tregenna, F. (2011). Earnings inequality and unemployment in South Africa. International Review of Applied Economics, 25 (5), 585 – 598. Ullah, M. A., & Ahmad, E. (2016). Inequality and corruption: Evidence from panel data. Forman Journal of Economic Studies , 12, 1 – 20. UN-MDG (2013). The millennium development goals report, Washington DC. Available at http://www.un.org/millenniumgoals/pdf/report-2013/mdg-report-2013-english.pdf. You, J. S. & Sanjeev, K. (2005). A comparative study of inequality and corruption. American Journal of Sociological Review, 70 (1), 136 – 157. van der Hoeven, R. (2010). Income inequality and employment revisited: Can one make sense of economic policy? Journal of Human Development and Capabilities, 11 (1), 67 – 84. Wedeman, A. (2002). Development and corruption: The East Asian paradox . In: Gomez, E. T. (Ed). Political Business in East Asia. London: Routledge, 34 – 61. Wong, M. Y. (2016). Public spending, corruption and income inequality: A comparative analysis of Asia and Latin America. International Political Science Review , 1 – 8. World Bank (2021). Symposium on data analytics for anticorruption in public administration. Available at https://www.worldbank.org/en/topic/governance/brief/anti-corruption Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4178118","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285709436,"identity":"adebcf68-611d-4606-8b89-4fe5a8aaeeee","order_by":0,"name":"Obadiah Jonathan Gimba","email":"","orcid":"","institution":"Federal University Lafia","correspondingAuthor":false,"prefix":"","firstName":"Obadiah","middleName":"Jonathan","lastName":"Gimba","suffix":""},{"id":285709437,"identity":"ac707ccf-bd9b-455b-9139-0d2d3845f6ef","order_by":1,"name":"Mehdi 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CPI\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4178118/v1/51883efb9569ca2209ab2bd1.png"},{"id":54043115,"identity":"c462991f-261a-4746-ab4f-3d8352ece2c9","added_by":"auto","created_at":"2024-04-03 18:28:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":368308,"visible":true,"origin":"","legend":"\u003cp\u003eUpper per capita countries are Botswana, Seychelles and Mauritius- UNE\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4178118/v1/12fcfe17c21c141238b4958a.png"},{"id":54043118,"identity":"044ddad6-c261-4ab2-b94d-9416e9b4bd62","added_by":"auto","created_at":"2024-04-03 18:28:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":568186,"visible":true,"origin":"","legend":"\u003cp\u003eMiddle per capita countries are Nigeria, Angola and Kenya-CPI\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4178118/v1/ae86497c2f228eb45fcafa96.png"},{"id":54043117,"identity":"02ea1d13-cafc-4cfb-ac94-f1ae002c6609","added_by":"auto","created_at":"2024-04-03 18:28:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":348112,"visible":true,"origin":"","legend":"\u003cp\u003eMiddle per capita are Nigeria, Angola and Kenya-UNE\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4178118/v1/5f9acad5293a32c2e7abb315.png"},{"id":54043116,"identity":"0d1caeff-87c6-4da9-ac45-6204536e0970","added_by":"auto","created_at":"2024-04-03 18:28:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":321108,"visible":true,"origin":"","legend":"\u003cp\u003eLower per capita are Burundi, Niger and Central African Republic-CPI\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4178118/v1/5efe6a1c36d808174fb79b41.png"},{"id":54043119,"identity":"415a27c3-c2da-4812-a3f3-6d8e132700af","added_by":"auto","created_at":"2024-04-03 18:28:01","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":360601,"visible":true,"origin":"","legend":"\u003cp\u003eLower per capita are Burundi, Niger and Central African Republic-UNE\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4178118/v1/4b36dd6f650309ed6cef31e4.png"},{"id":54310564,"identity":"ae44e70a-d3de-4c43-aaf7-6dcc3aeb38dc","added_by":"auto","created_at":"2024-04-08 16:50:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1934205,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4178118/v1/bf846bbf-1c41-42bb-a93b-9dda8d9849a1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of corruption and unemployment shocks on income inequality in Sub-Saharan Africa: A quantile-on-quantile approach","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe tenth goal of the United Nations Sustainable Development Goals (SDGs) 2030 is to \u0026ldquo;reduce inequality within and among countries\u0026rdquo;. However, in Sub-Saharan Africa (SSA) the attainment of this goal is being threatened by the \u0026ldquo;twin evil\u0026rdquo; of corruption and unemployment. Before the SDGs, the Millennium Development Goals could not be achieved in many SSA countries partly due to corruption (UN-MDGs, 2013). Corruption is a constrain to socioeconomic development, poverty reduction and good governance (Omoteso \u0026amp; Mobolaji, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). According to the World Institute for Development Economics Research (2020), SSA has remained the most unequal region in the world even more unequal to the rest of the world developing countries. Within the continent, the inequality gap is also massive. For example, the mean income of the top 20 percent earners is 10 times greater than the 20 percent at the bottom. Furthermore, most of the inequality comes from seven countries, namely Angola, Central African Republic, Botswana, Zambia, Namibia, Comoros and South Africa. The majority of the SSA are characterized by resource dependence with a high level of inequality. The extractive industries employ relatively few workers from within the citizens who are not skilful. The highly skilled jobs are usually given to foreigners thereby increasing inequality (Bhorat et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe International Labour Organization (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) ascribes the problem of unemployment in SSA to skills mismatch whereby skills sought by employers varies considerably from the skills owned by individuals. Unemployment has been fostered due to the high population growth occasioned by the high fertility rate (Gomes-Salvador \u0026amp; Leiner-Killinger, 2008). There are approximately 133\u0026nbsp;million youths who are excluded from social and productive activities because of the lack of education and little or no skills (African Economic Outlook, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). It is a fact that educated persons have better chances than uneducated in acquiring new jobs and wage increases, therefore education lessens the danger of being unemployed (Kabaklarli, et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The challenges associated with escalating unemployment rate include poverty, income inequality, slow growth and political instability.\u003c/p\u003e \u003cp\u003eThe main aim of this paper is to study the relationship between income inequality, corruption and unemployment. The contribution of this study is as follows: first countries within the SSA are classified (upper, middle and lower) according to a range of a GDP per capita as evident from the World Development Indicators (2020) of the World Bank. Income inequality in these countries is examined based on shocks associated with corruption and unemployment. The estimation approach used is unique. The quantile-on-quantile is a nonlinear technique that is understood to be an integration of quantile regression and nonparametric estimation. It allows the modelling of quantiles of the independent variable and quantiles of the dependent variable of a given distribution. This avails the opportunity to make objective conclusions between variables across time.\u003c/p\u003e \u003cp\u003eAccording to Rose-Ackerman (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), the major determinants of corruption are motivation and opportunities. Countries with substantial availability of natural resources are particularly more corrupt perhaps because of the windfall which provides higher opportunity and motivation for corruption (Gylfason, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The economies of Sub-Saharan African countries are characterized by the large deposit of natural resources with rent-seeking activities. This has contributed to fostering a culture of corruption among its elite.\u003c/p\u003e \u003cp\u003eCorruption can influence growing income inequality in diverse ways. Bribes are usually collected by government or private officials who are at advantage positions causing their income to rise. Government agents may use their influence for private benefits in designing and executing government policies through a skewed tax system that favours wealthy individuals, educational and health policies which makes accessibility and affordability of these services difficult to the low-income earners. Consequently, corruption produces a biased allocation of government resources benefiting the rich at the detriment of the poor. It impedes the equitable distribution of income. To worsen the situation, proceeds of corruption are transferred from low-income countries to high-income countries, thereby aggravating the between-country inequality. This is substantiated by the World Bank (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) report which asserts that the most extravagant forms of corruption are achieved through institutions of developed countries.\u003c/p\u003e \u003cp\u003eIn the Transparency International (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) report, in 2019, Sub-Saharan African was the least ranking region on the corruption perception index (i.e. the highest corrupt region), with a regional score of 32 out of 100. The world average score is 43, this shows how the public sector in the region is adjudged to be corrupt. Even within Africa, the incidence of corruption differs among countries. For example, Seychelles, Rwanda, Botswana, Cape Verde and Namibia were the least corrupt countries. While Somalia, South Sudan, Equatorial Guinea, the Democratic Republic of Congo and Guinea Bissau were the highest corrupt countries.\u003c/p\u003e \u003cp\u003eThe impact of unemployment among others include loss of income, loss of skills and in a long-term situation, it makes the chances of getting employed more difficult. The loss of income to the household creates more social division and enhances inequality. It is general knowledge that employment is a function of skills. The main feature of the labour market in the SSA is the low level of skills. It has been identified that the actual number of educated in Africa is less than any other region of the world. The resultant effect of low skills is the rise in unemployment (Teal, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis article is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e is the literature review. This provides an overview of empirical works of the effect of corruption and unemployment on inequality. Section 3 is the methodology. This includes countries selection, data and the model specification. Section 4 is the presentation of results and Section 5 is the discussion and conclusion.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eGupta et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) find that the growing incidence of corruption raises income inequality and poverty. Specifically, they discover that a one standard deviation increase in corruption is related to one standard deviation rise in uneven income distribution. Similarly, a one standard deviation rise in the growth rate of corruption lessens the growth rate of income of the poor by 7.8 percentage points. Aidt (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) examine the relationship between corruption and the growth of wealth per capita for a sample of 110 countries. The result shows that the indices of corruption used such as the corruption perception index of Transparency International and the control of corruption index of the World Bank lessens the growth of wealth per capita. Ullah and Ahmad (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) analyzed the impact of corruption on income inequality, among other explanatory variables, for a panel of 71 countries using the GMM estimation technique. Their findings revealed that corruption is significantly linked to a higher level of income inequality. Dusha (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), models the relationship between persistent inequality, corruption and factor productivity. The study concludes that a higher degree of corruption will result in escalating differences in wealth which affect total factor productivity. Gyimah-Brempong (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) explores the effect of corruption on economic growth and income distribution for 21 African countries using the GMM estimation method. The outcome of the study shows that corruption hurts the GDP growth rate and per capita income. Corruption was found to associate positively with income inequality. Saenz-Castro and Garcia-Gonzalez (2019) used Pedroni cointegration to estimate the relationship between corruption and income inequality for 24 departments in Colombia. The corruption index was estimated as the number of persons convicted for criminal activities related to corruption. The results suggest a positive relationship between corruption and inequality. Irrespective of the level of development and growth experience, Gupta et al. (1998) find that escalating rate of corruption tends towards increasing inequality and poverty which ultimately reduces economic growth. Li et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) adopted the model of Murphy et al. (1991) to investigate the effect of corruption on income distribution and growth. Their findings revealed that corruption is responsible for the large Gini disparity between developed and developing countries. However, in countries where asset distribution is uneven, corruption is associated with a negligible rise in inequality. Wong (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) asserts that corruption can be beneficial to distribution most especially if the corruption involves buying of votes. This can lead to a reduction of income inequality through redistribution. Gimba et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined the drivers of income inequality in Sub-Saharan Africa and its sub-regions using the bootstrap cointegration and the autoregressive distributed lag estimation. The result shows that a decrease in corruption in the short-run and long-run is associated with falling inequality.\u003c/p\u003e \u003cp\u003eFakir et al. (2017) argued that inequality as a result of corruption can fundamentally be examined as the weakness of institutions to work accordingly. They contended that the level of corruption is a major determinant of inequality in any society. Contrasting Marx, Ricardo and Piketty\u0026rsquo;s views which saw inequality as fundamentally inevitable because of the deep-rooted contradictions of capitalist society. Thus, they asserted that inequality thrives when the redistribution policies and agents of corruption are compromised. The credibility of this argument was tested using data for developed and developing countries to find out if corruption drives inequality or if inequality is the resultant feature of capitalism. Their findings show that a 1 point rise in corruption perception index is associated with 0.397 increases in inequality and that corruption drives higher inequality than the dynamics of capitalism. In their model, Alesina and Angeletos (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) consider individuals sources of income to include income from legitimate activities and income from rent-seeking and non-market activities. They assume that inequality resulting from legitimate productive activities is fair while that rent-seeking is unfair. The only role of government is redistribution. Their model predicts that the effect of corruption means more inequality and unfairness.\u003c/p\u003e \u003cp\u003eContrary to popular opinion and empirical shreds of evidence, results from 19 Latin American countries shows that a lower level of corruption heightens uneven income distribution. However, this suggestion of erroneous institutional reform policies (Andres \u0026amp; Ramlogan-Dobson, 2011). Similarly, You and Sanjeev (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) argued that inequality heightens the level of corruption. This is possible via the material and normative mechanism. With a rise in income inequality, wealthy individuals tend to have more to lose in the unbiased political, judicial and administrative process. The wealthy also has more resources at their disposal to influence the outcome of political, social and economic events. As the income gap widens, the majority of the population are left poorer and may seek redistribution through progressive tax. The demand for redistribution will coerce the rich to use corrupt political influence to reduce taxes and other redistributive policies. Dobson and Ramlogen-Dobson (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) examined the trade-off between income inequality and corruption in Latin America. The outcome also revealed a divergent situation where lower corruption is attributed to higher inequality. This relationship is attributed to the large informal sector in Latin America.\u003c/p\u003e \u003cp\u003eTregenna (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) examined the ways through which unemployment and how those employed in the formal and informal sectors affect wage inequality. Using South Africa as a case study, empirical findings suggest that unemployment is responsible for the general rise in wage inequality. Earning differentials between individuals employed in formal and informal jobs also contribute to rising inequality but a lesser degree. Chechi et al. (2008) explore the relationship between labour market institutions and household income inequality. The outcome of the study implies that robust institutions are associated with decreasing inequality. However, these institutions in some instances trigger rising unemployment as a result of higher unemployment benefits which tend to reduce inequality. Gustafsson and Johnson (1999) investigate the factors that influence income distribution using panel data for 16 high-income countries. Their findings show that high trade union participation and large public sector lessen income inequality. A low industrial sector was found to enhance inequality. However, there was no association between income inequality and unemployment. Sheng (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) explore the nexus between persistent high unemployment and income inequality in the US. It discovers that a strong tradeoff exists between the rate of unemployment and wage share. This infers the presence of a positive correlation between unemployment and the uneven distribution of income. Helpman et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) explore the relationship between inequality and unemployment in the global economy. They created a model for analyzing the determinants of wage distribution that centres on labour market frictions, variations in workforce composition across firms and within industry allocation. The outcome of the model suggests that more productive firms pay higher wages. Trade openness promotes wage inequality. Similarly, wage inequality increases when the economy is at trade equilibrium than in autarky. And trade liberalization has the capacity of increasing and decreasing inequality. Deyshappriya (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) study the determinants of income distribution for 33 Asian countries using the GMM estimation technique. The study observed that higher inflation, unemployment, political risk and terms of trade significantly heighten unequal income while education, labour force participation and official development assistance lessen inequality.\u003c/p\u003e \u003cp\u003eIn the US, it has been discovered that wage inequality intensifies as a result of an increase in unemployment. In Europe however, wage inequality declined substantially with a fall in unemployment. This outcome can be attributed to the demand for high skilled workers due to globalization and technological change. The surge in the demand for a skilled workforce in the US-led to higher inequality but in Europe, higher institutions have hindered wage adjustment enhancing unemployment for the low skilled (Dumont, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The model from Acemoglu (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) also substantiates this outcome where a small number of skilled workers and the difference between skilled-unskilled productivity is small will lead a firm to hire all workers. However, a rise in the number of skilled workers will eventually lead to wage inequality and unemployment.\u003c/p\u003e \u003cp\u003eGonzalez and Menendez (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) used a micro-simulation method to examine the effect of unemployment on labour earnings inequality for Argentina. The findings reveal that unemployment is responsible for a large proportion of earnings inequality. Storer and Van-Audenrode (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) examined survey data of individuals displaced from work in Canada and the US to determine wages after displacement. The study observed that unemployment insurance in Canada contributes to better wage outcomes especially for those unemployed for long. An investigation into rising income inequality in the situation of employment and labour market policies and how labour market policies can lead to a decline in inequality was done by van der Hoeven (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It was observed that the present rising inequality which is influenced by labour market institutions is controlled by globalization. There is a need to prioritize consistency between economic policy and labour market policies to abate growing inequality.\u003c/p\u003e \u003cp\u003eEmployment and income inequality can be viewed as ideals of economic strategy. For instance, Angeles-Castro (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) find that a rise in the level of employment lowers inequality, particularly in the industrial sector. A policy of employment for decreasing inequality was advocated. Similarly, Freeman (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) argued that the responsibility of labour market institutions in high and low-income countries is minimizing inequality but this has a fair and unpredictable time changing effect on employment.\u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cp\u003e\u003cstrong\u003eCountries selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe rationale for the selection and inclusion of countries for this study is based according to the GDP per capita. According to the World Development Indicators (2020) of the World Bank, the annual Sub-Saharan African countries GDP per capita range from \u003cspan\u003e$\u003c/span\u003e274 to \u003cspan\u003e$\u003c/span\u003e11425. At the lower point of this range lie countries like the Central African Republic, Chad, Niger, Somalia and Democratic Republic of Congo among others. The middle point of the range has countries such as Angola, Kenya, Nigeria, Eswatini and Sao Tome and Principe among others. The upper point in the range include countries such as Botswana, Seychelles, Mauritius, South Africa, and Gabon among others. Three countries are selected from the lower, middle, and upper points of this range based on the availability of data. The lower point GDP per capita countries included are Burundi, Niger and the Central African Republic. The middle point countries included are Angola, Nigeria, and Kenya. The upper point countries by GDP per capita are Botswana, Seychelles and Mauritius.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset for this study is made up of three variables:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGini coefficient\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ethis is the dependent variable that measures income inequality. It is the ratio of the area between the diagonal and the Lorenz curve and the area of a triangle below the diagonal. The Gini coefficient lies between 0 and 1. The closer to zero the Gini is, the more equally distributed the income and the farther away from zero the Gini is, the more unequal the distribution. The Gini coefficient is derived from the Standardized World Income Inequality Database, Solt (2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorruption perception index (CPI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ethis is an indicator of the prevalence of corruption across the countries. It is measured by an index of 0 to 100, with 0 signifying highly corrupt and 100 not corrupt. A rise in the CPI means a decline in corruption and a fall in CPI implies a rise in corruption. This data is derived from the Transparency International (\u003cspan\u003e2020\u003c/span\u003e) database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUnemployment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is measured as the total percentage of the labour force that is not engaged in any work. It includes persons without work but who are able and willing to work. The data is obtained from the World Development Indicators (2020) of the World Bank.\u003c/p\u003e\n\u003cp\u003eAll the data for each country are presented as annual time series from 2000 to 2020.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;\u003cspan\u003e1\u003c/span\u003e presents the descriptive statistics for the Gini coefficient, corruption perception index and unemployment rate for each of the countries.\u003c/p\u003e\n\u003cp\u003eWithin this period of study, Burundi has the lowest mean Gini coefficient, meaning that it has the most equally distributed income even though a low GDP per capita country. Ironically, it has one of the highest corruption perception indexes, with a low rate of unemployment. However, within the same group as Burundi, the mean Gini coefficient for CAR and Niger is higher than Burundi also with a high perception of corruption and low unemployment. The standard deviation for Nigeria\u0026rsquo;s Gini coefficient is the highest, meaning that income inequality in Nigeria is highly volatile.\u003c/p\u003e\n\u003cp\u003eThe countries at the middle of the GDP per capita range all have inequality higher than the countries at the lower range except for Nigeria. The perception of corruption between countries at the lower point of the range and countries at the middle is not substantially different. Unemployment is higher in the middle GDP per capita countries. The perception towards corruption in Seychelles is extremely volatile as indicated by the standard deviation\u003c/p\u003e\n\u003cp\u003eCountries at the highest point of the range have the lowest inequality except for Botswana. The corruption perception for these countries is very low compared with the lower and middle groups. In contrast, unemployment was higher than the other groups. Unemployment is highly volatile in Angola, Nigeria and Botswana as shown by their standard deviations.\u003c/p\u003e\n\u003cdiv\u003e\n \u003cdiv align=\"left\"\u003eTable 1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Descriptive statistics for Gini coefficient, CPI and unemployment\u003c/div\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCountries\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Dev.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBurundi\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCAR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNiger\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAngola\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKenya\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNigeria\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBotswana\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.388\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.544\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.391\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.551\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.606\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.533\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.586\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGini Coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.383\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.542\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.379\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.525\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.600\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.475\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.585\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.395\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.546\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.398\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.592\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.619\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.589\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.589\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.007\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.026\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.496\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMauritius\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSeychelles\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBurundi\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCAR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNiger\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAngola\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKenya\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNigeria\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBotswana\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMauritius\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSeychelles\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBurundi\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCAR\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNiger\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAngola\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKenya\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNigeria\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBotswana\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMauritius\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSeychelles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.381\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.392\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e19.619\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e20.857\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e26.238\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e19.905\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e23.523\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e22.333\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e59.809\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e50.190\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e50.762\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.164\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4.317\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.280\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e5.711\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.794\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4.849\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e18.211\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e7.758\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.296\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.377\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.381\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCPI\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e16\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e13\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e12\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e15\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e19\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e54\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e41\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e36\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eUnemployment\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.800\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4.040\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.320\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.612\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.599\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.539\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e15.880\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e6.360\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.720\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.386\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.402\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e25\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e26\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e35\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e27\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e31\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e28\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e65\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e57\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e66\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.890\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4.490\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.100\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e9.430\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.980\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e9.010\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e23.800\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e9.520\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e5.140\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.003\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.376\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4.292\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e7.930\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.931\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.473\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e5.544\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.076\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e4.643\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e8.596\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.242\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.133\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.849\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.949\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.104\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.935\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1.917\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.934\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0.849\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cdiv align=\"left\"\u003e\u003cstrong\u003eSource: Authors computation\u003c/strong\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe quantile-on-quantile (QQ) approach is used to study the relationship between income inequality and corruption, and income inequality and unemployment. The quantile regression first used by Koenker and Bassett (\u003cspan\u003e1978\u003c/span\u003e) is designed for modelling time-changing variables with regards to the structure of dependence. It is a blend of non-parametric estimation and quantile regression. The merits of this technique are numerous. Quantile regression is employed to show the relationship between an explanatory variable and the diverse quantiles of the explained variable. The classical linear regression permits a specific quantile of an explanatory variable to predict an explained variable. For this reason, combining these two techniques enables the modelling of quantiles of the explanatory variable and quantiles of the explained variable for both the top and bottom quantiles of a given distribution, thereby providing a robust outcome across time. The QQ provides better information than other estimation techniques such as the OLS and quantile regression. Let us start with the following nonparametric quantile regression equations.\u003c/p\u003e\n\u003cdiv id=\"Equa\"\u003e\n \u003cdiv id=\"FileID_Equa\" name=\"EquationSource\"\u003e$${Gini}_{t}={\\delta }^{\\theta }\\left({\\varphi }_{t}\\right)+{u}_{t}^{\\theta } \\cdots \\cdots \\cdots \\cdots \\cdots \\cdots \\cdots \\left(1\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere \u003cspan\u003e\u003cspan\u003e\\({Gini}_{t}\\)\u003c/span\u003e\u003c/span\u003e represents the income inequality of a country at time \u003cspan\u003e\u003cspan\u003e\\(t\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan\u003e\u003cspan\u003e\\({\\varphi }_{t}\\)\u003c/span\u003e\u003c/span\u003eis the explanatory variable that represents either the corruption perception index or the unemployment rate. \u003cspan\u003e\u003cspan\u003e\\(\\theta\\)\u003c/span\u003e\u003c/span\u003e denotes the \u003cspan\u003e\u003cspan\u003e\\(\\theta th\\)\u003c/span\u003e\u003c/span\u003equantile of the conditional distribution of Gini coefficient as predicted by \u003cspan\u003e\u003cspan\u003e\\({\\varphi }_{t}\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan\u003e\u003cspan\u003e\\({u}_{t}^{\\theta }\\)\u003c/span\u003e\u003c/span\u003e signifies the quantile residual term. \u003cspan\u003e\u003cspan\u003e\\({\\delta }^{\\theta }\\)\u003c/span\u003e\u003c/span\u003e is an undefined factor lacking in any previous information. The QQ model enables is to examine the changing effect of corruption and unemployment across quantiles of inequality.\u003c/p\u003e\n\u003cp\u003eWe use a first-order Taylor expansion to approximate the unknown term \u003cspan\u003e\u003cspan\u003e\\({\\delta }^{\\theta }\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv id=\"Equb\"\u003e\n \u003cdiv id=\"FileID_Equb\" name=\"EquationSource\"\u003e$${\\delta }^{\\theta }\\left({\\varphi }_{t}\\right)\\approx {\\delta }^{\\theta }\\left({\\varphi }^{\\tau }\\right)+{\\delta }^{\\theta }\\left({\\varphi }^{\\tau }\\right)\\left({\\varphi }^{\\tau }-{\\varphi }_{t}\\right) \\left(2\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere \u003cspan\u003e\u003cspan\u003e\\({\\delta }^{\\theta }\\left({\\varphi }^{\\tau }\\right)\\)\u003c/span\u003e\u003c/span\u003e denotes the partial derivative of \u003cspan\u003e\u003cspan\u003e\\({\\delta }^{\\theta }\\left({\\varphi }_{t}\\right)\\)\u003c/span\u003e\u003c/span\u003e with respect to each of the explanatory variables. Equation \u003cspan\u003e\u003cspan\u003e\\(\\left(2\\right)\\)\u003c/span\u003e\u003c/span\u003e can be presented as:\u003c/p\u003e\n\u003cdiv id=\"Equc\"\u003e\n \u003cdiv id=\"FileID_Equc\" name=\"EquationSource\"\u003e$${\\delta }^{\\theta }\\left({\\varphi }_{t}\\right)\\approx {\\delta }_{0}\\left(\\theta , \\tau \\right)+{\\delta }_{1}\\left(\\theta , \\tau \\right)\\left({\\varphi }^{\\tau }-{\\varphi }_{t}\\right) \\left(3\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eSubstituting equation \u003cspan\u003e\u003cspan\u003e\\(\\left(3\\right)\\)\u003c/span\u003e\u003c/span\u003e into \u003cspan\u003e\u003cspan\u003e\\(\\left(1\\right)\\)\u003c/span\u003e\u003c/span\u003e gives\u003c/p\u003e\n\u003cdiv id=\"Equd\"\u003e\n \u003cdiv id=\"FileID_Equd\" name=\"EquationSource\"\u003e$${Gini}_{t}={\\delta }_{0}\\left(\\theta , \\tau \\right)+{\\delta }_{1}\\left(\\theta , \\tau \\right)\\left({\\varphi }^{\\tau }-{\\varphi }_{t}\\right)+{u}_{t} \\left(4\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWe can have the estimate for the linear regression as\u003c/p\u003e\n\u003cdiv id=\"Eque\"\u003e\n \u003cdiv id=\"FileID_Eque\" name=\"EquationSource\"\u003e$${min}_{{\\delta }_{0},{\\delta }_{1}}{\\sum }_{i}^{n}{\\rho }_{\\theta }\\left[{Gini}_{t}-{\\delta }_{0}-{\\delta }_{1}\\left({\\widehat{\\varphi }}_{t}-{\\widehat{\\varphi }}^{\\tau }\\right)K\\left(\\frac{{F}_{n}\\left({\\widehat{\\varphi }}_{t}-\\tau \\right)}{h}\\right)\\right] \\left(5\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere \u003cspan\u003e\u003cspan\u003e\\({\\rho }_{\\theta }\\left(u\\right)\\)\u003c/span\u003e\u003c/span\u003e represent the quantile loss function which is expected as \u003cspan\u003e\u003cspan\u003e\\({\\rho }_{\\theta }\\left(u\\right)=u\\left(\\theta -I\\left(u\u0026lt;0\\right)\\right)\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan\u003e\u003cspan\u003e\\(I\\)\u003c/span\u003e\u003c/span\u003e is the indicator function and \u003cspan\u003e\u003cspan\u003e\\(K\\)\u003c/span\u003e\u003c/span\u003e is the kernel function. The quantile on quantile technique presents a detail and reliable interpretation. A three dimensional graph shows the plot of corruption shock on income inequality and unemployment shock on income inequality. The Z-axis shows the value of the estimate of the slope coefficient, \u003cspan\u003e\u003cspan\u003e\\({\\delta }_{1}\\left(\\theta , \\tau \\right)\\)\u003c/span\u003e\u003c/span\u003e which evaluates the response of each of the \u003cspan\u003e\u003cspan\u003e\\(\\tau\\)\u003c/span\u003e\u003c/span\u003eth quantile of corruption and unemployment shocks movement (x-axes) on the quantile of inequality \u003cspan\u003e\u003cspan\u003e\\(\\theta\\)\u003c/span\u003e\u003c/span\u003eth in the y-axes.\u003c/p\u003e"},{"header":"4. Results","content":"\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents CPI supply shocks to Gini in upper per capita countries. CPI supply mixed shocks to Gini, for example, the CPI of Botswana supply negative shock to Gini, the negative shocks run from 0.95 quantiles of CPI to 0.65\u0026ndash;0.95 quantiles of Gini, with a value of -0.04, while the positive shocks run from 0.95 quantiles of CPI to 0.1\u0026ndash;0.55 quantiles of Gini, the value of this positive shocks is 0.019. For Seychelles, we can see that CPI supply negative shocks to Gini, in extreme cases reach to -0.28, these extreme shocks run from 0.95 quantiles of CPI to 0.65 quantiles of Gini. Regarding Mauritius, the shocks that run from CPI to Gini is mixed (negative and positive), it ranges between \u0026minus;\u0026thinsp;0.056 and 0.129, the negative shocks run from 0.05\u0026ndash;0.95 quantiles of CPI to 0.05\u0026ndash;0.2 quantiles of Gini, while the positive shocks that run from CPI quantiles locate at 0.45 quantile of Gini.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows unemployment supply shocks to Gini in upper per capita countries. For Botswana, we can notice that unemployment supplies positive shocks to Gini, except for one negative shock that runs from 0.15 quantile of unemployment to 0.25 quantile of Gini, its value about \u0026minus;\u0026thinsp;0.053. Regarding Seychelles, it is clear that all quantiles of unemployment supply negative shocks at 0.3 quantiles of Gini, these negative shocks have a value of -0.6, while the positive shocks run from 0.4\u0026ndash;0.9 quantiles of unemployment to 0.50 and 0.55 quantiles of Gini. Its slope is ranging between 0.89 and 0.99. Considering Mauritius, we can notice that unemployment supplies negative shocks to Gini at all quantiles, except the quantile 0.35 to 0.5 of unemployment, supply a positive shock to 0.95 quantiles of Gini, the value of this positive shock is about 0.13.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents CPI supply shocks to Gini in the middle per capita countries. These countries include Nigeria, Angola, and Kenya. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, left-hand-side, shows that CPI supply extreme negative shocks to Gini in Nigeria, these negative shocks run from 0,05\u0026ndash;0.65 quantiles of CPI to 0.75 quantiles of GINI, its value is about \u0026minus;\u0026thinsp;2, while the value of the positive shock that runs from 0.15 quantile of CPI to 0.95 quantiles of Gini is 0.49. Angola also exhibits the same pattern as Nigeria, with two mainshocks, one negative and run from 0.5 to 0.8 quantiles of CPI to 0.7 quantiles of Gini, the value of its slope is about \u0026minus;\u0026thinsp;2.25. In contrast, the positive shocks run 0.6 quantiles of CPI to 0.45 quantiles of Gini, the value of the positive shock is 1.7. For Kenya, we can see two positive shocks and one negative shock, and the positive shocks are located at 0.25 and 0.90\u0026ndash;0.95 quantiles of Gini; the value of the positive shocks is 0.17. The negative shocks run from 0.05\u0026ndash;0.2, the value of negative shock \u0026minus;\u0026thinsp;0.029\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eRegarding unemployment, supply shocks to Gini for middle per capita countries. Nigeria, have on negative shock supplied by unemployment to Gini, the negative shocks run from 0.05\u0026ndash;0.3 quantiles of unemployment to 0.05 quantile of Gini. The rest of the shocks are positive, the interesting positive shocks run from 0.25\u0026ndash;0.9 quantile of unemployment to 0.85 quantiles of Gini, the positive shock has the value of 0.69. For Angola, we can also notice two main shocks, negative and positive, the negative shocks run from 0.05 quantile of unemployment to 0.05 quantile of Gini, with a value of -2.3, the positive shocks run from 0.05\u0026ndash;0.15 quantile of unemployment to 0.1\u0026ndash;35 quantiles of Gini with a value of 3.5. For Kenya, it is clear that all shocks from unemployment to Gini are negative, except one positive shock run from 0.85-quantile of unemployment to 0.9 and 0.95 quantiles of Gini.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe lower per capita countries which includes are Burundi, Niger, and the Central African Republic are represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. We can notice that CPI supply positive shocks almost in all quantiles in Burundi except the 0.05\u0026ndash;0.2 quantile of CPI which supply negative shocks to 0.1 quantiles of Gini with a value of -0.42. The same thing for Niger, CPI supply positive shocks to Gini quantiles, except the 0.8 quantiles of Gini which supplied shock is negative with a value of -0.47. Regarding the Central African Republic, we can notice that CPI quantile supply two positive shocks to 0.5 and 0.95 quantiles of GINI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the unemployment supply shocks for lower per capita countries. We can notice that in general, all supplied shocks by unemployment are positive in Burundi and Niger, except the Central African Republic, in which the supplied shocks are mixed and ranging between \u0026minus;\u0026thinsp;0.02 and 0.06. The negative shocks supplied by 0.05 and 0.95 quantiles of unemployment to all quantiles of Gini, while the positive quantile supplied by the 0.95 quantiles of unemployment to 0.05\u0026ndash;0.15 quantiles of GINI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Discussion and conclusion","content":"\u003cp\u003eThis paper investigates the relationship between income inequality and corruption and income inequality and unemployment using a non-linear, quantile-on-quantile model for the years 2000 to 2020 in the Sub-Saharan African countries. Countries were selected based on their GDP per capita within the sub-region. Our findings are as follows:\u003c/p\u003e \u003cp\u003eFor countries at the upper range of the GDP per capita, the incidences of corruption supply a mixed shock (negative and positive) to income inequality in Botswana and Mauritius. However, corruption provided a negative shock to inequality in Seychelles. Considering the rate of unemployment, a supply of mixed shock (negative and positive) of unemployment to inequality was observed in all the countries in the upper range of the GDP per capita.\u003c/p\u003e \u003cp\u003eConsidering the countries at the middle range of the GDP per capita, mixed supply shocks (negative and positive) of corruption to inequality was found for all the countries however, the negative shock was extreme. A supply of mixed unemployment shock to inequality was also discovered for all the countries.\u003c/p\u003e \u003cp\u003eFor the countries at the lower range of the GDP per capita, corruption provides a mixed shock to inequality in Burundi and Niger. However, for the Central African Republic, mixed shock (negative and positive) is observed from corruption to inequality. Unemployment supplies a positive shock to income inequality in Burundi and Niger but a mixed supply of unemployment shock was noticed in the Central African Republic.\u003c/p\u003e \u003cp\u003eFrom the observations, we can infer that a mixed supply of shocks means a trade-off between inequality and corruption and inequality and unemployment. A situation where higher quantiles of corruption (lower corruption) are associated with higher inequality was observed. This agrees with the work of Andres and Ramlogan-Dabson (2011). The rationale for this relationship can likely be attributed to the presence of a large informal sector in the SSA. Institutional reforms in the informal sector such as tax collection and other regulatory policies can lead to a decline of corruption however, the income of workers remain the same because they lack the skills to opt-out of the informal sector. It was also established that in some countries higher levels of corruption led to the abatement of inequality. This is synonymous with some of the East Asian countries (China, Indonesia, Thailand and South Korea) where they experience growth in GDP per capita and a brighter economic outlook despite a high level of corruption. This according to Wedeman (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) tagged the \u0026ldquo;East Asian Paradox\u0026rdquo;. A probable argument for this relationship could be found in the extent of financial openness between these countries and the rest of the world where there are no barriers to financial transactions across borders which aid to launder money offshores (Neeman, et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). But could this explain for Africa? This provides a link through which this study can be extended.\u003c/p\u003e \u003cp\u003eThe following recommendations are given: to lower inequality associated with unemployment, individuals should acquire high skills that are relevant and in high demand for employability. Government should create a conducive environment through the provision of infrastructure and the right institutions for the private sector to thrive and create jobs. Institutional mechanisms of government should be effective and efficient in tackling corruption. Administrative measures should be created that will make stealing and other forms of corruption difficult.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eObadiah Jonathan Gimba participated in writing the whole paper. Mehdi Seraj supervised, had the final edition, and brought crucial ideas for this paper. Huseyin Ozdeser supervised the research. Muhammad Mar'I modeled the data.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe dataset for this study is made up of three variables:\u003c/p\u003e\n\u003cp\u003eGini coefficient: this is the dependent variable that measures income inequality. It is the ratio of the area between the diagonal and the Lorenz curve and the area of a triangle below the diagonal. The Gini coefficient lies between 0 and 1. The closer to zero the Gini is, the more equally distributed the income and the farther away from zero the Gini is, the more unequal the distribution. The Gini coefficient is derived from the Standardized World Income Inequality Database, Solt (2020).\u003c/p\u003e\n\u003cp\u003eCorruption perception index (CPI): this is an indicator of the prevalence of corruption across the countries. It is measured by an index of 0 to 100, with 0 signifying highly corrupt and 100 not corrupt. A rise in the CPI means a decline in corruption and a fall in CPI implies a rise in corruption. This data is derived from the Transparency International (2020) database.\u003c/p\u003e\n\u003cp\u003eUnemployment: This is measured as the total percentage of the labour force that is not engaged in any work. It includes persons without work but who are able and willing to work. The data is obtained from the World Development Indicators (2020) of the World Bank.\u003c/p\u003e\n\u003cp\u003eAll the data for each country are presented as annual time series from 2000 to 2020.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding (There is no funding for this research)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement: (None)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests (\u003c/strong\u003eAll authors have participated in (a) conception and design, or analysis and interpretation of the data; (b) drafting the article or revising it critically for important intellectual content; and (c) approval of the final version. \u0026nbsp; This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. The authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript.)\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAcemoglu, D. (1999). Changes in unemployment and wage inequality: An alternative theory and some evidence. \u003cem\u003eThe American Economic Review, 89\u003c/em\u003e(5), 1259 \u0026ndash; 1278.\u003c/li\u003e\n\u003cli\u003eAdres, A. R., \u0026amp; Ramlogan-Dobson, C. (2011). Is corruption really bad for inequality? Evidence from Latin America. \u003cem\u003eJournal of Development Studies, 47\u003c/em\u003e(7), 959 \u0026ndash; 976.\u003c/li\u003e\n\u003cli\u003eAfrican Economic Outlook (2013). Promoting youth employment in Africa. Why an African economic outlook on youth employment?\u003c/li\u003e\n\u003cli\u003eAidt, T. S. (2010). Corruption and sustainable development. CWPE 1061.\u003c/li\u003e\n\u003cli\u003eAlesina, A., \u0026amp; Angeletos, G. M. 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Available at https://www.worldbank.org/en/topic/governance/brief/anti-corruption\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Income Inequality, Corruption, Unemployment, Quantile-on-Quantile, Sub-Saharan Africa","lastPublishedDoi":"10.21203/rs.3.rs-4178118/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4178118/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis paper examines the impact of corruption and unemployment on income inequality in Sub-Saharan Africa (SSA). We employ the quantile-on-quantile technique which allows examining the impact of quantiles of the independent variable on quantiles of the dependent variable of the distribution. The outcome shows that countries at the upper range of the GDP per capita such as Botswana, Seychelles, and Mauritius had fewer negative supply shocks from corruption and unemployment which is associated with higher income equality. For countries in the middle range such as Nigeria, Kenya and Angola, corruption supplies extreme negative shocks to inequality in Nigeria and Angola. However, Kenya experienced a mixed shock but the negative is more evident. Unemployment provided a negative shock to unequal income distribution for almost all quantiles in the middle range income countries. Considering countries at the lower range such as Burundi, Central African Republic (CAR) and Niger. Corruption supplies a mixed shock on income inequality for almost all the quantiles in Niger and Burundi but supplies a positive shock on inequality in CAR. The shocks supply from unemployment to inequality is positive in Burundi and Niger except for CAR which exhibits a mixed shock.\u003c/p\u003e","manuscriptTitle":"Impact of corruption and unemployment shocks on income inequality in Sub-Saharan Africa: A quantile-on-quantile approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-03 18:27:55","doi":"10.21203/rs.3.rs-4178118/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b20875a-2830-495b-b37a-9ca3b93abf19","owner":[],"postedDate":"April 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-08T16:42:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-03 18:27:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4178118","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4178118","identity":"rs-4178118","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}