Abstract
Purpose
The objectives of this study are threefold: firstly, to measure the impact of educational inequality on income inequality, and per capita income; secondly, to measure the impact of gender inequality in education on income inequality, per capita income and educational inequality; and lastly, to test the Kuznets inverted U-shape hypothesis between inequality in education and average year of schooling.
Design/methodology/approach
The study has adopted the Marin and Psacharopoulos (1976) model of human capital in which income earned by an individual can be estimated as a function of number of year spent in schooling or education. Gini coefficient is used as a measure of income inequality, while inequality in education is measured by Gini index of educational inequality. Gender inequality in education is measured by the difference between male and female enrolment ratios as a proportion of male enrolment. The study utilizes the data of six South Asian countries, i.e. Bangladesh, India, Maldives, Nepal, Pakistan and Sri Lanka from 1980 to 2010 at five-year average and employs fixed effect model (FEM) and random effect model (REM) for estimation.
Findings
Result suggests that educational inequality and average year of schooling have positive and significant impact on income inequality. Primary (basic) education and tertiary (higher) education reduce income inequality, while secondary education widens income inequality. Negative relationship exists between educational inequality and per capita income. Unequal distribution of education among boys and girls at primary level increases income inequality, while reduces income inequality at tertiary level. Gender inequality in secondary and tertiary level of education reduces per capita income, while unequal distribution of education among boys and girls further increases the educational inequality. Kuznets inverted U-shape hypothesis does not hold between education expansion and educational inequality, while weak U-shape relationship exists in South Asian countries.
Practical implications
Government has to provide free education in poor regions and makes employment programs to reduce the income and educational inequality respectively, while to remove gender inequality in education it is necessary to build more schools especially for girls. Government has to launch different online education programs for expansion in education at all levels.
Originality/value
This study adds to the literature by analyzing whether the inequality in income increases (decreases) due to increase (decrease) in educational and gender inequality in South Asian countries. This study contributes in the existing literature by developing a measure of educational and gender inequality in education in South Asian countries.
Peer review
The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2020-0226.
Keywords
Citation
Munir, K. and Kanwal, A. (2020), "Impact of educational and gender inequality on income and income inequality in South Asian countries", International Journal of Social Economics, Vol. 47 No. 8, pp. 1043-1062. https://doi.org/10.1108/IJSE-04-2020-0226
Publisher
:Emerald Publishing Limited
Copyright © 2020, Emerald Publishing Limited
1. Introduction
Income inequality has been the most interesting and debating topic among economists and policy makers from the last few decades. An increase in income inequality affects the development process of a country. According to Kuznets (1955) inequality in income increases in the early stages of development and reaches at maximum, while in the later stages of development inequality generally declines. This inverted-U relationship between income inequality and economic development is known as Kuznets curve. Inequality in income leads to increase the inequality in education among different income classes and creates income inequality for the next generation (Abdelbaki, 2012). Human capital is the most important determinant to increase the income level and economic growth of a country (Connolly, 2004). If inequality in education is high in a country then income inequality is expected to be high in that country. Higher and equal access to education plays a major role in reducing income inequality (Gregorio and Lee, 2002). Gender inequality in education also affects the income level of a country badly (Klasen, 2002). In developed countries higher level of education helps to reduce the distribution of income among people or different income classes (O'Neill, 1995). Increase in average year of schooling (AYS) helps to reduce inequality in income and educational inequality (Lin, 2007).
An African proverb says, “If we educate a boy, we educate one person. If we educate a girl, we educate a family and a whole nation”. To remove the gender disparity at the primary and secondary level of education by 2015 at all level of education was one of the Millennium Development Goals (MDG) set by United Nations (UN, 2000). Gender inequality in education causes reduction in literacy rate (Summers, 1994; Hill and King, 1995). Investment in education can help to resolve the problem of poverty and income inequality which support the development process of a country. Previous research highlighted that the expansion of education is a major factor that increases the income level of people and helps to reduce income inequality (Abdelbaki, 2012; Baye and Epo, 2015; Connolly, 2004; Ismail and Yussof, 2010). However, other studies showed that more education and even distribution of education among people plays a major role in reducing income inequality of a country (Gregorio and Lee, 2002; Lin, 2007). There is a growing literature on the negative association between gender inequality in education and income or growth level of a country (Klasen, 2002; Baliamoune-Lutz and McGillivray, 2009; Klasen and Lamanna, 2009; Baliamoune-Lutz and McGillivray, 2015).
In developing countries, female education plays a major role in reducing infant mortality and helps in improving the health of family and children as well as the quality and quantity of education of children. Female education is important to increase the labor productivity (Knowles et al., 2002). Gender inequalities in education or employment decrease the growth level of a country significantly (Klasen and Lamanna, 2009). According to Rama et al. (2014) income inequality in South Asia is higher in richest countries and increases as countries become more developed. A country that has high per capita income is characterized by high income inequality (Rama et al., 2014). Enrolment ratio at secondary and tertiary level of education is very low and the regional literacy rate is approximately 62.4% in South Asia (ADB, 2015). The students who belong to lower income class are less likely to complete their higher education in South Asian countries and it is a major problem especially in Nepal, Bangladesh and Sri Lanka. Access to higher education for female is improving from 1990 to 2009 and it is approximately 57% in Sri Lanka and Bangladesh; however, in Pakistan the women participation rate in higher education is about one-third, while in India there is less progress in that period. The second worst country in term of gender equality is Pakistan (WEF, 2013).
The objectives of this study are threefold: firstly, to measure the impact of educational inequality on income inequality, and per capita income; secondly, to measure the impact of gender inequality in education on income inequality, per capita income and educational inequality; and lastly, to test the Kuznets inverted-U hypothesis between inequality in education and average year of schooling. However, there is scarce work on the impact of educational inequality on income inequality, and per capita income, especially, in the context of gender inequality in education on income inequality, per capita income and educational inequality in South Asian countries. This study adds to the literature by analyzing whether the inequality in income increases (decreases) due to increase (decrease) in educational and gender inequality in South Asian countries. Various policy implications are provided by this study to explore the importance of equal distribution of education to reduce income inequality and increase per capita income in South Asian countries.
The remainder of the paper is organized in the following manner. Previous literature is discussed in Section 2. Model, methodology and data are described in section 3. Results are analyzed in section 4. Section 5 contains concluding remarks and policy recommendations.
2. Literature review
2.1 Literature on educational inequality on income and income inequality
Marin and Psacharopoulos (1976) analyzed the relationship between schooling and income inequality in the United States. They found that distribution in education and income inequality are directly proportional to each other. They concluded that increase in average year of schooling reduces income inequality. O'Neill (1995) examined the pattern of human capital convergence on income inequality in developed and developing countries from 1967 to 1985. Results of the study showed that income inequality decreases in developed countries due to increase in the education level. Thomas et al. (2001) investigated the link between educational inequality, average year of schooling (AYS), standard deviation of schooling and gender inequality for 85 countries from 1960 to 1990. They employed direct and indirect method to calculate the education Gini index to investigate the educational inequality. They found that educational inequality is negatively related to average year of schooling and per capita GDP.
Castello and Domenech (2002) provided a new measure of human capital inequality based on educational inequality. They found that inequality in human capital (educational inequality) is negatively related with economic growth of a country. Gregorio and Lee (2002) analyzed the relationship between educational inequality and income inequality across countries from 1960 to 1990. They found that equal distribution and higher level of education play significant role in reducing income inequality of a country. Connolly (2004) examined the importance of human capital in convergence across the states from 1850 to 1950. The study found that human capital is essential for technology diffusion which increases the level of income and growth rate of a state. The study concluded that South’s low human capital level and resistance to education reduce its speed of convergence with the rest of the nation.
Jamal and Khan (2005) explored the relationship of inequality in education on economic development at district level in Pakistan. They developed a district education index (DEI) to measure the performance of district in terms of education. They found that educational inequality is very high especially in rural areas among females. Zhang and Zhang (2005) analyzed the impact of longevity on saving, education, fertility and economic growth for 75 countries from 1960 to 1989. They found that life expectancy has negative and significant effect on fertility, while positive and significant effect on saving rate, education and economic growth. Mesa (2007) examined the degree of educational inequality in Philippine for 16 regions and 78 provinces from 1960 to 2000. The study found that educational inequality is high in poor provinces as compared to non-poor provinces, while educational inequality is negatively related with average year of schooling and gross domestic regional product and positively related with income inequality and poverty.
Lin (2007) analyzed the impact of education expansion and educational inequality on income inequality in Taiwan from 1976 to 2003. The results of the study showed that an increase in average year of schooling has decreased the educational inequality and income inequality in Taiwan. Rodriguez-Pose and Tselios (2009) examined the impact of microeconomic changes in the human capital on income inequality from 1995 to 2000 for 102 regions of EU. They found that income inequality is negatively related with urbanization, agriculture, industry, female participation in labor force and population aging, while positively related with income per capita, educational inequality, financial sector and unemployment. Ismail and Yussof (2010) analyzed the effect of human capital on income distribution in Malaysia by using 4,003 household data collected in 2007–2008. They found that all human capital variables play significant role in determining the household income which reflects income inequality.
Abdelbaki (2012) examined the relationship between education inequality and income inequality in Bahrain from 1980 to 2006. The results of the study showed that educational inequality positively affects income inequality which is suffered by future generations. Ibourk and Amaghouss (2013) analyzed the relationship between educational inequality and economic growth in MENA region from 1970 to 2010 across 15 countries. They found that educational inequality negatively affects economic growth in all the countries, while educational inequality is high in middle-income countries compared to high-income countries. Sauer and Zagler (2014) examined the relationship between level of education and educational inequality on economic growth of 134 countries from 1950 to 2005 at five-year interval. They found that average year of schooling positively impacts economic growth, while educational inequality negatively affects economic growth.
Baye and Epo (2015) examined the impact of human capital endowment on income inequality in Cameroon. They found that human capital endowment is necessary to reduce income inequality, while human capital endowment is positively correlated with household economic wellbeing. Munir and Sultan (2017) explored the macroeconomic determinants of income inequality in India and Pakistan from 1973 to 2015. They found that per capita GDP, fertility rate, urban population, globalization, government consumption expenditure, value addition by agricultural sector and per capita arable land are the major macroeconomic determinants of income inequality in India and Pakistan. Arshed et al. (2018) examines the nonlinear impact of education on income inequality in SAARC countries from 1990 to 2015. They found that inverted u-shape relationship exists among primary enrollment and income inequality, as well as among secondary enrollment and income inequality, while u-shape relationship exists among tertiary enrollment and income inequality.
Munir and Arshad (2018) analyzed the relationship between physical capital and human capital on economic growth of Pakistan from 1973 to 2014. They found that accumulation of human capital increases per capita income, employment level, labor productivity and economic growth. Arshed et al. (2019) explores the quadratic relationship between education and income inequality in Asian developing countries from 1960 to 2015. They found that inverted u-shape relationship exists among primary enrollment and income inequality, secondary enrollment and income inequality as well as among tertiary enrollment and income inequality. Munir and Bukhari (2020) analyze the impact of globalization on income inequality in Asian developing economies from 1980 to 2014. They found that technological globalization and enrollment in primary education play positive role in reducing income inequality and reduces the gap between poor and rich.
2.2 Literature on gender inequality in education on income and income inequality
Dollar and Gatti (1999) examined the effect of gender inequality in education on income per capita and economic growth of 127 countries from 1975 to 1990. They found that female education plays significant role to increase economic growth, while inverse association exists between gender inequality in education and per capita income. Klasen (2002) examined the effect of gender inequality in education on long-term economic growth from 1960 to 1992 across countries. The results of the study showed that gender inequality in education lowers the average level of human capital and affects economic growth directly. Knowles et al. (2002) analyzed the effect of male and female education on economic growth from 1960 to 1990 across countries. They found that female education plays a major role in increasing the labor productivity and economic growth.
Klasen and Lamanna (2009) examined the effect of gender inequality in education and employment on economics growth for a panel of countries from 1960 to 2000. They found that gender inequality in education and employment significantly reduces economic growth, while East Asia and MENA region reduces gender inequality in education rapidly than the South Asian region. Baliamoune-Lutz and McGillivray (2009) analyzed the impact of gender inequality in education on economic growth of 31 sub-Saharan African and 10 Arab countries from 1974 to 2004. They found that gender inequality negatively effects economic growth, while the effect is stronger in Arab countries. Baliamoune-Lutz and McGillivray (2015) examined the effect of gender inequality in education at different levels on per capita income of sub-Saharan African, North African and Middle Eastern countries. They found that gender inequality in primary and secondary level of education negatively effects per capita income, while the effect is stronger in North African and Middle Eastern countries
Existing literature shows that education plays significant role in increasing the income level of people. Due to increase in income inequality, educational inequality also increases among different income classes that result in creating the income inequality for the next generation. Due to inequality in education and income, the uneducated people face problems like poverty and unemployment and involved in illegal or harmful activities. Moreover, female education helps to improve the labor productivity of a country and the gender inequality in education and employment considerably reduces the economic growth or income level of a country. There is hardly any study in the literature which explores the impact of educational inequality and gender inequality in education on income inequality and per capita income in South Asian countries.
3. Model, methodology and data
3.1 The model
According to human capital theory in labor market the income earned by an individual can be estimated as a function of number of year spent in schooling or education. Inequality in income or earning level of a person can be higher even though they get the same level of education due to other factor like quality of education and specialization, especially when it comes to higher level of education. Following Marin and Psacharopoulos (1976) the income level of a person with S year of schooling can be expressed as:
where
In order to apply this equation to an individual with S year of schooling, one considers the following two directions: firstly, year of schooling for all individuals and average rate of return is supposed to be constant; and secondly, for each individual the rate of return to education is considered as random variable but assumed to be independent of S.
By using
Taking variance on both sides of equation (2) and assuming that r and S are not correlated with
When the rate of return and year of schooling are treated as random variables, it enables to forecast the impact of changes in the rate of return to the education and year of schooling. The right-hand side of equation (3) depends on whether two variables (rate of return to the education and year of schooling) are independent or not. If the rate of return to education and year of schooling are explanatory random variables then the function can be approximated as:
If the rate of return to the education and year of schooling are dependent then the function is approximated as:
Since, all the terms are positive on the right-hand side of equation (4), one gets the prediction that if the rate of return and year of schooling are independent then increments in the level of education lead to increase in the inequality in the income level of individuals. Moreover, the expansion in the education can decrease the inequality in income if the covariance is negative between the rate of return to the education and year of schooling.
3.2 Methodology
The study measures the impact of educational inequality on income inequality by the following regression equation:
The study measures the impact of different level of education (complete) on income inequality by the following regression equation:
The study measures the impact of educational inequality on per capita income by the following regression equation:
The study measures the effect of gender inequality at different levels of education (complete) on income inequality by the following regression equation:
The study measures the impact of gender inequality at different levels of education (complete) on per capita income by the following regression equation:
The study measures the impact of gender inequality at different levels of education (complete) on educational inequality by the following regression equation:
Following Ibourk and Amaghouss (2013), this study measures the impact of average year of schooling and its square on educational inequality to examine the existence of Kuznets inverted-U curve by the following regression equation:
where GInc is the Gini index of income inequality, GEdu is the Gini index of educational inequality, AYS is average year of schooling, GDPPC is income per capita, GGend is the Gini index of gender inequality at different levels of education and u is error term.
Panel data are usually preferred over time series or cross-sectional data because the panel estimates take heterogeneity into account and gives cross-section specific effects. Another advantage of using panel data is that it gives more information about the data, shows greater variation, more efficiency and more degree of freedom. The panel data are more suited for dynamic studies and minimizes the bias. For all these advantages, this study employs panel data for estimation of the models (Gujarati, 2005; Hsiao, 2014; Wooldridge, 2010).
There are three possible ways to estimate a model that possess panel data characteristics. The first method of estimation is pooled OLS model, in which cross-section and characteristic of time series is ignored and the model is estimated as one grand model. The drawback of using this method of estimation is that it will not give cross-section and period-specific effects. The second method for estimation of panel data is fixed effect least square dummy variable model for estimation. In this type of model all observations are pooled together but each cross-sectional unit has its own intercept. This means that cross-section specific characteristics of a unit can be discriminated and studied while using this model for estimation. The last model that can be used for estimation while using a panel data is the random effects model. In this model the cross-section specific characteristics are assumed to be the part of random term (Gujarati, 2005; Hsiao, 2014; Wooldridge, 2010). This study utilizes the fixed effect model (FEM) and random effect model (REM) for estimation.
3.2.1 Measures of income, education and gender inequality
Gini coefficient is used as a measure of income inequality. The value of Gini coefficient lies from 0 to 1, where 0 means perfect equality in the income level and 1 means perfect inequality in the income level of a country. Previous studies also utilize the Gini coefficient to measure the inequality in education. Thomas et al. (2001) and Lin (2007) used the concept of Gini index to measure the educational inequality by using the data of educational attainment level and year of schooling at seven level of education. Castello and Domenech (2002) and Sauer and Zagler (2014) calculate the education Gini with the help of Barro and Lee (2001, 2013) dataset.
This study also adopts the concept of Gini index to measure the educational inequality by using the data of educational attainment level and year of schooling. The attainment level of education is divided into seven groups, i.e. no schooling, partial primary, complete primary, partial secondary, complete secondary, partial tertiary and complete tertiary. The average year of schooling for seven different level of education is calculated by the following formula:
where
The study calculates the standard deviation of schooling by using the following formula:
where
To calculate the education inequality for each level of education in term of women, men and total educational inequality Thomas et al. (2001) formula is used as:
where GEdu is Gini index of education inequality, AYS is average year of schooling,
The study uses the following definition to calculate the year of schooling at seven level of education:
No schooling:
Partial-Primary level of schooling:
Complete-Primary level of schooling:
Partial-Secondary level of schooling:
Complete-Secondary level of schooling:
Partial-Tertiary level of schooling:
Complete-Tertiary level of schooling:
where
Following Baliamoune-Lutz and McGillivray (2015), Dollar and Gatti (1999) and Lantican et al. (1996), this study measures the gender inequality in education by taking difference between male and female enrolment ratios as a proportion of male enrolment as:
3.3 Data
The study uses data for six South Asian countries, i.e. Bangladesh, India, Maldives, Nepal, Pakistan and Sri Lanka from 1980 to 2010 at five year average, however, Afghanistan is dropped from the analysis due to unavailability of data. The data of the proportion of male, female and total population aged from 15 and over for each attainment level of education are obtained from Barro and Lee (2013) dataset. Data of income inequality are collected from World Development Indicators and World Income Inequality Databases. Data on cycle of schooling at primary, secondary and tertiary level are collected from Psacharopoulos and Arriagada (1986). The data on per capita GDP in current US dollars are collected from World Development Indicators.
4. Results
4.1 Impact of educational inequality on income inequality and per capita income
Figure 1 shows the trend of educational inequality in South Asian countries from 1980 to 2010. Figure shows that in 1980 the inequality in education is very high in all countries of South Asia except in Sri Lanka. After 1980, inequality in education is declining for all the South Asian countries from 1980 to 2010. The decreasing trend of inequality in education shows that there is expansion in education over time. Inequality in education decreases from 0.8 to 0.6 in Bangladesh, 0.77 to 0.56 in India, 0.86 to 0.66 in Pakistan, 0.40 to 0.23 in Sri Lanka, 0.64 to 0.48 in Maldives and 0.90 to 0.63 in Nepal. Due to decrease in education inequality the poor people will get the education which increases skills of the labor and economic growth would become possible in the long run.
Figure 2 represents the trend of average year of schooling in South Asian countries from 1980 to 2010. Average year of schooling increased in all countries over time. The increasing trend of average year of schooling shows that on average, the people of these countries received more education over time. In Maldives, the average year of schooling first increases then decreases and after 2000 it shows increasing trend. An increase in average year of schooling is associated with higher learning and accumulation of human capital which increases labor productivity. Increase in average year of schooling also increases the innovative capacity, products and processes which promotes economic growth.
Figure 3 shows the trend of income inequality in South Asian countries from 1980 to 2010. The study measures the inequality in income by Gini coefficient. Following Jamal (2006) the study interpolates the missing observation of Gini coefficient. Income inequality was very high in Maldives and Nepal in 1980. From 1980 to 2010 the inequality in income in India decreases from 0.35 to 0.33, in Pakistan it declines from 0.33 to 0.30, in Maldives it declines from 0.60 to 0.37 and in Nepal it drops from 0.49 to 0.35. However, income inequality increases from 0.31 to 0.32 in Bangladesh and 0.33 to 0.38 in Sri Lanka. An increase in the level of income inequality leads to lower economic growth and per capita income.
Table 1 reports the result of educational inequality and income inequality by using FEM and REM. The dependent variable is income inequality and the explanatory variables are educational inequality and average year of schooling. Coefficient of educational inequality and average year of schooling shows positive and significant impact on income inequality. These results are consistent with the finding of Marin and Psacharopoulos (1976), Rodriguez-Pose and Tselios (2009), Gregorio and Lee (2002) and Park (1996). These results implies that equal distribution of education is important to increase the income level of a country because if education is not equally distributed among the people than a big part of profit retained by well-educated people which will further widen the income gap.
Table 2 shows the impact of different level of education on income inequality by using FEM and REM. Coefficient of primary education and tertiary education shows negative and significant impact on income inequality, while secondary education shows positive and significant effect on income inequality. The results support the negative relationship between basic education (primary) and higher (tertiary) education on income inequality. Theoretically, education plays a major role to increase the income level of a country through knowledge and helps to reduce income inequality. A country with more educated and skilled people have less income inequality.
Table 3 shows the result of educational inequality on per capita income by using FEM and REM. Result shows the negative relationships between educational inequality and per capita income. The negative coefficient of educational inequality explains that if inequality in education increases in a country it will badly affect income level of the country because less educated people do not have the ability to increase their living standard. On the other hand, well educated people have more skills and ability to increase the productivity and income level of a country.
Figure 4 shows the relationship between educational inequality and per capita income from 1980 to 2010 for each country. The horizontal axis measures the per capita income and vertical axis measures the educational inequality. Figure for each country shows that per capita income of a country increases with a decrease in educational inequality. Because more educated people have more skills and abilities due to which per capita income of the country increases.
4.2 Impact of gender inequality in education on income inequality, per capita income and educational inequality
Figure 5 shows the trend of gender inequality in complete primary, secondary and tertiary level of education from 1980 to 2010 in each country. In 1980, the gender inequality at all levels of education was high in each country except in Maldives. After 1980, gender inequality in primary, secondary and tertiary level of education declined in all the countries. However, in Maldives the gender inequality in education at tertiary level has increased but at primary and secondary level of education decreased, while in Sri Lanka gender inequality in primary and secondary level of education shows slight decline, while rapid decline in tertiary level.
The impact of gender inequality in education (complete) is analyzed on income inequality by FEM and REM and reported in Table 4. Income inequality is used as dependent variable, while the independent variable is Gini index of gender inequality in education at complete primary, complete secondary and complete tertiary level of education. Table 4 indicates that impact of gender inequality in complete primary is positive and significant, and complete tertiary is negative and significant on income inequality. The coefficient of gender inequality in complete secondary is insignificant. Result of gender inequality in complete primary shows that unequal distribution of education among boys and girls increases income inequality. Gender inequality in education creates the education gap in the society due to which employment inequality originated and deteriorates economic growth.
The impact of gender inequality in education (complete) is analyzed on income per capita by FEM and REM and reported in Table 5. The study uses per capita income as the dependent variable and gender inequality in education at complete primary, complete secondary and complete tertiary level of education as independent variable. The coefficient of gender inequality in complete primary is insignificant. Result indicates that gender inequality in complete secondary and tertiary level of education has negative and significant impact on per capita income in South Asian countries. It implies that increase in gender inequality at complete secondary and tertiary level of education decreases per capita income. These results are consistent with the finding of Baliamoune-Lutz and McGillivray (2015).
The impact of gender inequality in education on educational inequality is analyzed by using FEM and REM and reported in Table 6. The dependent variable is educational inequality and independent variables are gender inequality in complete primary, complete secondary and complete tertiary level of education. Result of FEM and REM shows that positive and significant relationship exists between gender inequality in complete primary, complete secondary and complete tertiary level of education on educational inequality. These results imply that unequal distribution of education among boys and girls further increases the educational inequality. These results are consistent with the finding of Thomas et al. (2001).
4.3 Kuznets curve between educational inequality and average year of schooling
This study uses the standard deviation of schooling to measure the dispersion of schooling. Higher the value of standard deviation of schooling shows more spread of educational attainment, while lower value of standard deviation of schooling shows lesser spread of educational attainment. Figure 6 shows the trend of standard deviation of schooling from 1980 to 2010 for South Asian countries. The standard deviation of schooling shows increasing trend for Bangladesh, Pakistan and Sri Lanka, which implies that educational attainment has expanded in these countries. The standard deviation of schooling in India and Nepal shows sluggish increase, while Maldives shows U-shaped curve.
Table 7 reports the result of average year of schooling on educational inequality to test the Kuznets inverted-U hypothesis. The dependent variable is educational inequality and independent variables are average year of schooling and its square term. Result of FEM and REM shows that negative and significant relationship exists between average year of schooling on educational inequality, while square term of average year of schooling has positive and weakly significant coefficient. It implies that Kuznets inverted-U hypothesis does not hold between education expansion and educational inequality, while weak U-shape relationship exists in South Asian countries.
Figure 7 shows the relationship between average year of schooling and educational inequality. Horizontal axis measures the average year of schooling and vertical axis measures the educational inequality. The relationship shows negative and linear relation between the variable in all the countries except in Maldives. Figure of each country proves that Kuznets inverted-U hypothesis does not hold in South Asian countries.
Figure 8 shows the relationship between average year of schooling and standard deviation of schooling. Horizontal axis measures the average year of schooling and vertical axis measures the standard deviation of schooling. The relationship shows positive and linear relation between the variable in all the countries except in Maldives. It implies that an increase in average year of schooling increases the spread of educational attainment in each country of South Asia.
5. Conclusion
This study examines the effect of educational inequality on income inequality, and per capita income as well as the effect of gender inequality in education on income inequality, per capita income and educational inequality. The study also tests the Kuznets inverted-U hypothesis between inequality in education and average year of schooling. The study has adopted the Marin and Psacharopoulos (1976) model of human capital in which income earned by an individual can be estimated as a function of number of year spent in schooling or education. Gini coefficient is used as a measure of income inequality, while inequality in education is measured by Gini index of educational inequality. Gender inequality in education is measured by the difference between male and female enrolment ratios as a proportion of male enrolment. The study has used the panel data framework because it reduces collinearity among the explanatory variables and gives more efficient estimate. The study utilizes the fixed effect model (FEM) and random effect model (REM) for estimation. The study uses data for six South Asian countries, i.e. Bangladesh, India, Maldives, Nepal, Pakistan and Sri Lanka from 1980 to 2010 at five year average, however, Afghanistan is dropped from the analysis due to unavailability of data.
The decreasing trend of educational inequality shows that there is expansion in education in South Asian countries over time. The trend of average year of schooling shows that on average, the people of South Asian countries received more education over time. The trend of income inequality suggests that income inequality decreases in India, Pakistan, Maldives and Nepal, while increases in Bangladesh and Sri Lanka over time. Relationship between educational inequality and per capita income shows that per capita income of a country increases with a decrease in educational inequality. Result of FEM and REM suggests that educational inequality and average year of schooling has positive and significant impact on income inequality. Equal distribution of education is important to reduce the income gap. Primary (basic) education and tertiary (higher) education reduces income inequality, while secondary education widens income inequality. A country with more educated and skilled people have less income inequality. Negative relationship exists between educational inequality and per capita income. It implies that if inequality in education increases in a country it badly affects the income level of the country because less-educated people do not have the ability to increase their living standard.
Trend of gender inequality in primary, secondary and tertiary level of education shows that gender inequality in education at all levels of education declines in South Asian countries over time. Result of FEM and REM shows that unequal distribution of education among boys and girls at primary level increases income inequality, while reduces income inequality at tertiary level. Gender inequality in secondary and tertiary level of education reduces per capita income in South Asian countries. Result of gender inequality in education on educational inequality shows that unequal distribution of education among boys and girls further increases the educational inequality.
The trend of standard deviation of schooling shows that educational attainment has expanded in Bangladesh, Pakistan and Sri Lanka, while India and Nepal show sluggish increase and Maldives shows U-shaped curve. The relationship between average year of schooling and educational inequality shows that negative and linear relation exists in all the countries except in Maldives. The relationship between average year of schooling and standard deviation of schooling shows that an increase in average year of schooling increases the spread of educational attainment in each country of South Asia. Result of FEM and REM shows that Kuznets inverted-U hypothesis does not hold between education expansion and educational inequality, while weak U-shape relationship exists in South Asian countries.
In order to remove educational inequality, income inequality and gender inequality in education equal access of education should be the major objective in the government policies. It is necessary to make the conditions of educational institutes better and remove the disparities because disparities in educational institutes cause the disparities in the skills of people, income level and their professional opportunities. Promoting more education for girls helps to decrease the fertility rate as well as child mortality rate.
It is recommended on the basis of the results that South Asian countries have to invest more in educational system because the people who live in poor area have lesser chances to get quality education due to scarce resources. Government should provide free education in poor regions and also make employment programs to reduce the income and educational inequality respectively. To remove gender inequality in education it is necessary to build more schools especially for girls because education for women is not considered a good thing especially in rural areas. Due to unequal distribution of education, women have lesser opportunities of employment. Government should launch different online education programs for expansion in education at all levels.
Figures
Figure 1
Trend of educational inequality in South Asian countries
Figure 2
Trend of average year of schooling in South Asian countries
Figure 3
Trend of income inequality in South Asian countries
Figure 4
Relationship between educational inequality and per capita income
Figure 5
Trend of gender inequality in primary, secondary and tertiary level of education
Figure 6
Trend of standard deviation of schooling in South Asian countries
Figure 7
Relationship between average year of schooling and educational inequality
Figure 8
Relationship between average year of schooling and standard deviation of schooling
Impact of educational inequality on income inequality
| Variables | FEM | REM |
|---|---|---|
| C | −0.4464*** (0.1539) | −0.3625** (0.1545) |
| Educational inequality | 0.8976*** (0.1698) | 0.8021*** (0.1645) |
| Average year of schooling | 0.0372*** (0.0076) | 0.0338*** (0.0074) |
| R square | 0.86 | 0.36 |
Note(s): ***, and ** shows 1, and 5% level of significance respectively. Standard errors are in parenthesis
Impact of different level of education on income inequality
| Variables | FEM | REM |
|---|---|---|
| C | 0.3865*** (0.0341) | 0.4092*** (0.0375) |
| Complete primary | −0.3516** (0.1706) | −0.4304** (0.1625) |
| Complete secondary | 0.4439*** (0.1459) | 0.4102*** (0.1199) |
| Complete tertiary | −1.3003** (0.6157) | −1.5837*** (0.5761) |
| R square | 0.82 | 0.31 |
Note(s): *** and ** shows 1, and 5% level of significance respectively. Standard errors are in parenthesis
Impact of educational inequality on per capita income
| Variables | FEM | REM |
|---|---|---|
| C | 4579.029*** (1025.799) | 3184.174*** (802.2645) |
| Education inequality | −6044.206*** (1590.408) | −3869.110*** (1173.842) |
| R square | 0.55 | 0.20 |
Note(s): *** shows 1% level of significance. Standard errors are in parenthesis
Impact of gender inequality in education on income inequality
| Variables | FEM | REM |
|---|---|---|
| C | 0.3567*** (0.0034) | 0.3656*** (0.0302) |
| Complete primary | 0.0805*** (0.0148) | 0.0627* (0.03) |
| Complete secondary | −0.0041 (0.0056) | 0.0008 (0.0496) |
| Complete tertiary | −0.0135** (0.0055) | −0.0245* (0.0104) |
| R square | 0.98 | 0.66 |
Note(s): ***, ** and * shows 1, 5 and 10% level of significance respectively. Standard errors are in parenthesis
Impact of gender inequality in education on per capita income
| Variables | FEM | REM |
|---|---|---|
| C | 1150.089*** (136.9067) | 1172.941** (551.7686) |
| Complete primary | 507.1566 (413.8681) | −223.9872 (911.0025) |
| Complete secondary | −797.0476** (302.7279) | −1307.660*** (273.3153) |
| Complete tertiary | −791.2588** (315.3149) | −77.5794* (34.4323) |
| R square | 0.63 | 0.46 |
Note(s): ***, ** and * shows 1, 5 and 10% level of significance respectively. Standard errors are in parenthesis
Impact of gender inequality in education on educational inequality
| Variables | FEM | REM |
|---|---|---|
| C | 0.4967*** (0.0213) | 0.4810*** (0.0311) |
| Complete primary | 0.1891** (0.0746) | 0.2039*** (0.0670) |
| Complete secondary | 0.1578*** (0.0453) | 0.1478*** (0.0411) |
| Complete tertiary | 0.0974** (0.0392) | 0.1281*** (0.0375) |
| R square | 0.96 | 0.73 |
Note(s): *** and ** shows 1, and 5% level of significance respectively. Standard errors are in parenthesis
Impact of average year of schooling on educational inequality
| Variables | FEM | REM |
|---|---|---|
| C | 0.9365*** (0.0319) | 0.9398*** (0.0443) |
| Average year of schooling | −0.0540*** (0.0081) | −0.0537*** (0.0080) |
| Average year of schooling square | 0.0010** (0.0004) | 0.0009** (0.0004) |
| R square | 0.96 | 0.78 |
Note(s): *** and ** shows 1, and 5% level of significance respectively. Standard errors are in parenthesis
References
Abdelbaki, H.H. (2012), “An analysis of income inequality and education inequality in Bahrain”, Modern Economy, Vol. 3 No. 5, pp. 675-685.
ADB (2015), Key Indicators for Asia and the Pacific 2015, Asian Development Bank, Manila.
Arshed, N., Anwar, A., Kousar, N. and Bukhari, S. (2018), “Education enrollment level and income inequality: a case of SAARC economies”, Social Indicators Research, Vol. 140 No. 3, pp. 1211-1224.
Arshed, N., Anwar, A., Hassan, M.S. and Bukhari, S. (2019), “Education stock and its implication for income inequality: the case of Asian economies”, Review of Development Economics, Vol. 23 No. 2, pp. 1050-1066.
Baliamoune-Lutz, M. and McGillivray, M. (2009), “Does gender inequality reduce growth in Sub-Saharan Africa and Arab countries?”, African Development Review, Vol. 21 No. 2, pp. 224-242.
Baliamoune-Lutz, M. and McGillivray, M. (2015), “The impact of gender inequality in education on income in Africa and the Middle East”, Economic Modeling, Vol. 47, pp. 1-11.
Barro, R.J. and Lee, J.W. (2001), “International data on educational attainment: updates and implications”, Oxford Economic Papers, Vol. 53 No. 3, pp. 541-563.
Barro, R.J. and Lee, J.W. (2013), “A new dataset of educational attainment in the world, 1950–2010”, Journal of Development Economics, Vol. 104, pp. 184-198.
Baye, F.M. and Epo, B.N. (2015), “Impact of human capital endowments on inequality of outcomes in Cameroon”, Review of Income and Wealth, Vol. 61 No. 1, pp. 93-118.
Castello, A. and Domenech, R. (2002), “Human capital inequality and economic growth: some new evidence”, The Economic Journal, Vol. 112 No. 478, pp. C187-C200.
Connolly, M. (2004), “Human capital and growth in the Postbellum South: a separate but unequal story”, The Journal of Economic History, Vol. 64 No. 2, pp. 363-399.
Dollar, D. and Gatti, R. (1999), Gender Inequality, Income, and Growth: Are Good Times Good for Women?, Policy Research Report on Gender and Development Working Paper No. 1, World Bank, Washington, DC.
Gregorio, J.D. and Lee, J.W. (2002), “Education and income inequality: new evidence from cross-country data”, Review of Income and Wealth, Vol. 48 No. 3, pp. 395-416.
Gujarati, D.N. (2005), Basic Econometrics, 4th ed., McGraw-Hill, New York.
Hill, M.A. and King, E. (1995), “Women's education and economic well-being”, Feminist Economics, Vol. 1 No. 2, pp. 21-46.
Hsiao, C. (2014), Analysis of Panel Data, Cambridge University Press, California.
Ibourk, A. and Amaghouss, J. (2013), “Inequality in education and economic growth: empirical investigation and foundation – evidence from Mena region”, International Journal of Economics and Finance, Vol. 5 No. 2, pp. 111-124.
Ismail, R. and Yussof, I. (2010), “Human capital and income distribution in Malaysia: a case study”, Journal of Economics Cooperation and Development, Vol. 31 No. 2, pp. 25-46.
Jamal, H. and Khan, A.J. (2005), “The knowledge divide: education inequality in Pakistan”, The Lahore Journal of Economics, Vol. 10 No. 1, pp. 83-104.
Jamal, H. (2006), “Does inequality matter for poverty reduction? Evidence from Pakistan's poverty trends”, Pakistan Development Review, Vol. 45 No. 3, pp. 439-459.
Klasen, S. and Lamanna, F. (2009), “The impact of gender inequality in education and employment on economic growth: new evidence for a panel of countries”, Feminist Economics, Vol. 15 No. 3, pp. 91-132.
Klasen, S. (2002), “Low schooling for girls, slower growth for all? Cross-country evidence on the effect of gender inequality in education on economic development”, The World Bank Economic Review, Vol. 16 No. 3, pp. 345-373.
Knowles, S., Lorgelly, P.K. and Dorian, P.D. (2002), “Are educational gender gaps a brake on economic development? Some cross-country empirical evidence”, Oxford Economic Papers, Vol. 54 No. 1, pp. 118-149.
Kuznets, S. (1955), “Economic growth and income inequality”, The American Economic Review, Vol. 45 No. 1, pp. 1-28.
Lantican, C.P., Gladwin, C.H. and Seale, J.L. (1996), “Income and gender inequalities in Asia: testing alternative theories of development”, Economic Development and Cultural Change, Vol. 44 No. 2, pp. 235-263.
Lin, C.A. (2007), “Education expansion, educational inequality, and income inequality: evidence from Taiwan, 1976-2003”, Social Indicators Research, Vol. 80 No. 3, pp. 601-615.
Marin, A. and Psacharopoulos, G. (1976), “Schooling and income distribution”, The Review of Economics and Statistics, Vol. 58 No. 3, pp. 332-338.
Mesa, E.P. (2007), “Measuring education inequality in the Philippines”, The Philippines Review of Economics, Vol. 44 No. 2, pp. 33-70.
Munir, K. and Arshad, S. (2018), “Factor accumulation and economic growth in Pakistan: incorporating human capital”, International Journal of Social Economics, Vol. 45 No. 3, pp. 480-491.
Munir, K. and Bukhari, M. (2020), “Impact of globalization on income inequality in Asian emerging economies”, International Journal of Sociology and Social Policy, Vol. 40 Nos 1/2, pp. 44-57.
Munir, K. and Sultan, M. (2017), “Macroeconomic determinants of income inequality in India and Pakistan”, Theoretical and Applied Economics, Vol. 24 No. 4, pp. 109-120.
O'Neill, D. (1995), “Education and income growth: implications for cross-country inequality”, Journal of Political Economy, Vol. 103 No. 6, pp. 1289-1301.
Park, K.H. (1996), “Educational expansion and educational inequality on income distribution”, Economics of Education Review, Vol. 15 No. 1, pp. 51-58.
Psacharopoulos, G. and Arriagada, A.M. (1986), “The educational composition of the labour force: an international comparison”, International Labour Review, Vol. 125 No. 5, pp. 561-574.
Rama, M., Beteille, T., Li, Y., Mitra, P.K. and Newman, J.L. (2014), Addressing Inequality in South Asia, World Bank, Washington, DC.
Rodriguez‐Pose, A. and Tselios, V. (2009), “Education and income inequality in the region of the European union”, Journal of Regional Science, Vol. 49 No. 3, pp. 411-437.
Sauer, P. and Zagler, M. (2014), “Inequality in education and economic development”, Review of Income and Wealth, Vol. 60 No. 2, pp. 353-379.
Summers, L.H. (1994), Investing in All the People: Educating Women in Developing Countries, EDI Seminar Paper No. 45, World Bank, Washington, DC.
Thomas, V., Wang, Y. and Fan, X. (2001), Measuring Education Inequality: Gini Coefficients of Education, Policy Research Working Paper No. 2525, World Bank, Washington, DC.
UN (2000), United Nations Millennium Declaration, United Nations, New York, NY.
WEF (2013), The Global Gender Gap Report 2013, World Economic Forum, Geneva.
Wooldridge, J.M. (2010), Econometric Analysis of Cross Section and Panel Data, The MIT Press, Cambridge.
Zhang, J. and Zhang, J. (2005), “The effect of life expectancy on fertility, saving, schooling and economic growth: theory and evidence”, The Scandinavian Journal of Economics, Vol. 107 No. 1, pp. 45-66.
Corresponding author
About the authors
Kashif Munir is an associate professor in the Department of Economics at the University of Central Punjab, Pakistan. He has researched and published in the areas of monetary and fiscal policy, macroeconomic stability, social and economic inequality, and public policy. His research has been published in leading academic journals such as Empirical Economics, Journal of the Asia Pacific Economy, Review of Public Economics, Applied Economics, International Journal of Social Economics, Competitiveness Review, Applied Economics Quarterly, Environmental Science and Pollution Research and Pakistan Development Review among others.
Ayesha Kanwal is a research assistant in the Department of Economics at the University of Central Punjab, Pakistan. She has researched and published in the areas of social and economic inequality.