TOPIC :- INEQUALITY IN EDUCATION
AUTHOR :- PRACHI KADAM
Heading :- IS ENTREPRENEURSHIP EDUCATION REDUCING POVERTY AND INCOME INEQUALITY IN LESS DEVELOPED COUNTRIES? EVIDENCE FROM NIGERIA.
ADEBAYO, N. A. (2022) states that, Entrepreneurship policies are commonly used in low-income countries like Nigeria to address poverty and income inequality, but a study in South Western Nigeria found weak relationships between entrepreneurship education and poverty reduction and income inequality among graduates who started their own businesses. He reported that less developed countries, particularly in sub-Saharan Africa, face significant challenges related to poverty and income inequality. Entrepreneurship education aims to address these issues by reducing unemployment and empowering individuals to start their own businesses. The study focuses on the impact of Entrepreneurship Education on poverty reduction and income inequality in Nigeria. The research uses Pearson Correlation Coefficient to analyze the relationship between Entrepreneurship Education and variables such as entrepreneurial intention, skills, and self-efficacy, as well as poverty and income inequality. The study shows that Entrepreneurship Education (EED) positively influences individuals to start their own businesses, with a desire to own a personal business being a key outcome. Challenges faced by businesses in Nigeria include small market size and poor customer patronage. The study participants are graduates of Nigerian tertiary institutions who have undergone mandatory Entrepreneurship Education.
Less developed countries (LDCs), especially in sub-Saharan Africa (SSA), are the most seriously affected in terms of poverty and income inequality. The taxonomy drives home the ultimate expected outcomes of EED by stating that it is “the teaching of skill, knowledge and attitude to go out and create their own returns and solve their problems.” The problems expected to be solved by EED include unemployment and its consequential effects of poverty and income inequality, which are the central focus of this study. The program took off in 2007/2008, its seed was sown earlier in 2004, when the Federal Government launched the National Economic Empowerment and Development Strategy (NEEDS), which was “to address the issue of poverty reduction, employment generation, wealth creation and value re-orientation through EED in tertiary institutions” (Agboola, 2010).
Variable Entrepreneurship Education Entrepreneurial Intention Entrepreneurial Skills Entrepreneurial Self-Efficacy Poverty Reduction Income Inequality. The main instrument of data collection was a structured questionnaire that elicited information regarding the socio-demographic status and business history of the respondents as well as on the variables of interest in the study—EED, entrepreneurial intention, entrepreneurial skills, entrepreneurial self-efficacy, and income earning capacity. The main statistical tool was Pearson Correlation Coefficient, which established the relationship between EED as the independent variable and the mediating variables of Entrepreneurial Intention (EIN), Entrepreneurial Skills (ESK), and Entrepreneurial Self – Efficacy (ESE) on the one hand and the outcome variables Poverty Reduction (POR) and Income Inequality (INQ) on the other. A very significant percentage of the respondents across the three study locations claimed EED positively influenced them to own their personal businesses. It was only in Oyo state that more than 50 percent of the respondents claimed EED positively influenced the management of their business.
Desire to own a personal business ranked first across the three study locations in terms of specific areas of influence of EED. Small market size or poor customer patronage was the number three business challenge in Lagos and Oyo States but number five in Ondo State. A very significant percentage of the study sample viewed this as a challenge; 92.1, 85.9 and 75 percent for Lagos, Oyo and Ondo States, respectively. Respondents of this article are graduates of Nigeria’s tertiary institutions who had undertaken mandatory EED.
Heading :- EDUCATIONAL INEQUALITY AND THE EXPANSION OF UK HIGHER EDUCATION.
BLANDEN, J. and MACHIN, S. (2013) states that, the UK Higher Education (HE) System has expanded massively in recent decades, with student numbers rising from 400,000 in the 1960s to 2,000,000 at the turn of the new century (Greenaway and Haynes, 2003). They concentrate on a number of aspects of the inequality of educational expansion using three data sources: the National Child Development Study; the British Cohort Study (BCS); and the British Household Panel Survey (BHPS). They finds significant effects of income on enrolment, with students in the lowest family income quartile being 12 percentage points less likely to be enrolled in college 2 years after 12th grade than those in the top income quartile, even controlling for test scores in 8th grade and parental education level. In case of missing income measures at age 16 we allow family income to be observed at 15 or 17, and allow the graduation outcome to be observed at 22 if the individual is not retained through the sample until 23. One advantage of the BHPS is that the annual data enables us to be confident about the intermediate educational outcomes. For example, they can find out about the A level achievement by looking at information from the wave after individuals turn 18.
The starting point is a probit model relating the probability of having a degree by age 23, the 0–1 variable D, for person i in cohort c to their parent’s log income, Y, and a set of control variables Z: Dic 1⁄4 ac þ bcfðYicÞ þ ccZic þ eic where f(Á) denotes the functional form for parental income, which is the independent variable of interest, and e is an error term. The pattern of rising degree-income relations is not damaged by the inclusion of test scores, as the rise in educational inequality Dwc0c is estimated as a strongly significant 0.12, or 12 percentage points.
The relationship of interest is between the probability of obtaining a degree and log family income: For example, under the assumption that the income data in the BHPS and BCS were completely accurate the measurement error in the NCDS would need to be very high at 38% and 36%, respectively, to close the gap to the margins of statistical significance. Despite the fact that many more children from higher income backgrounds participated in HE before the recent expansion of the system, the expansion acted to widen participation gaps between rich and poor children.
Heading :- DO EDUCATION AND INCOME REALLY EXPLAIN INEQUALITIES IN HEALTH? APPLYING A TWIN DESIGN.
GERDTHAM, U. ‐G. et al. (2016) states that, a twin design is applied to examine the relationship between health and education and income. The estimated associations between health and education and income, controlling for unobserved endowments, at the twin‐pair level, are lower than estimates obtained via ordinary least‐squares (OLS) on the same sample. Thus, OLS‐based effects of education and income are biased, exaggerating the contribution of education and income to health inequality. The main part of health inequality is explained by within‐twin‐pair fixed effects, incorporating family background and genetic inheritance. It appears that education and income policies have less to offer for reducing health inequality than is usually assumed.
Numerous studies report a strong socioeconomic gradient in health and longevity, regardless of the population studied and regardless of how socioeconomic status and health are measured (e.g., Ettner, 1996; Smith, 1999; Bloom and Canning, 2000; Gerdtham and Johannesson, 2000, 2002, 2004; Benzeval and Judge, 2001; Deaton, 2003; van Doorslaer and Koolman, 2004; Baum and Ruhm, 2009). This is in contrast to the fixed effects panel data approach, where identification requires that the variable of interest varies over time; this might be true for certain factors such as income, but not for others, such as education; see, for example, Wildman (2003) and Islam et al (2010). Report results based both on a health production function estimated by ordinary least squares (OLS), with the health of twins treated as independent observations, and by within-twin-pair (WTP) estimations, which removes the influence of factors shared by twins.
In line with most prior cross-sectional studies, the OLS-based analysis indicates that factors such as education and income are strongly associated with health. WTP estimates might still be biased if there are important unobserved differences between the twins that relate to health as well as education and/or income. As noted by Bound and Solon (1999), even MZ twins can differ in factors such as birth weight, for instance, which has been linked to both adult earnings and education (e.g., Behrman and Rosenzweig, 2004; Black et al, 2007; Royer, 2009). There could exist ability differences between MZ twins, so that the twin with greater ability has better health, irrespective of education or income. Such results were obtained by Sandewall et al (2014), who showed that the difference between MZ twins in cognitive test scores at age 18 was a significant predictor of their later schooling. If cognitive ability has positive effects on both later income and health, our twin-based estimates still risk being biased upward, as do not have data on cognitive test scores.
The measurement error problems expected in the WTP estimation to be relatively small, because register information is used for the key factors of education and income, use self-reported health as dependent variable, and gender, age, year of interview, and education or/and income, as independent variables. OLS estimations based on all complete same-sex twin pairs are reported in Columns 1–3 of Table 3 for education (Model A) or income (Model B) and combined (Model C), respectively, and the corresponding WTP estimations are reported in Columns 4–6.
Heading :- NATIONAL VS LOCAL FUNDING FOR EDUCATION: EFFECTS ON GROWTH AND INEQUALITY.
GIANNINI, M. (2009) states that, in this article he investigated the effect of decentralizing public education. In general, the literature shows that decentralization reduces the economic performance as it reduces the tax base and public support. He shown that such a result is not robust in the presence of a centralized social security system linking local communities to each other. Nevertheless, decentralization involves remarkable differences in inequality and growth when communities differ in the deep parameters, notably in the return to education.
As expected, the communities with higher returns show higher growth and inequality. In order to rule out the gap, local authorities can drain resources from social services to education by varying the γ parameter. It is worth noting that such ‘fiscal substitution’ is being exploited in the USA. As pointed out in Beicker (2001), local authorities have reduced social services for increasing public education and vice versa. He shown that such fiscal policy is able to rule out the growth gap across communities, allowing, at the same time, a higher growth rate with respect to the central system. It is needless to say that had he assumed differences in a larger set of parameters and not only in the returns to education, it would be more difficult to offset disparities by calibrating γ. This warns us about the real effectiveness of the reform in the real world. The positive effects of decentralization via a proper fiscal policy aiming at levelling inequality are conditional on how local communities are similar in their economic and social structure.
Heading :- HEALTHY MINDS IN HEALTHY BODIES: AN INTERNATIONAL COMPARISON OF EDUCATION – RELATED INEQUALITY IN PHYSICAL HEALTH AMONG OLDER ADULTS.
Jürges, H. (2009) states that, this paper combine three comparable data sets on older populations in the United States, the United Kingdom, and 10 continental European countries (HRS, ELSA, and SHARE) to measure and decompose education-related inequality in health across these countries. Although the restriction to the older population is a disadvantage compared with similar studies, the data sets we use have several important advantages. Perhaps the main advantage is that health information is very detailed in all three surveys. This allows us to use a fairly comprehensive and comparable measure of physical health as our dependent variable. While others have mainly used self-rated health or more or less plausible external cardinalisations of self-rated health, we have constructed a continuous health index based on respondent’s information on ever diagnosed chronic conditions, as well as functional, ADL and IADL limitations. Since we derive disability weights from regressions of self-rated health on indicator variables of conditions and limitations, our health index combines the advantage of detailed, quasi-objective information on health states with subjective judgements about the severity of these health states.
Our study contains a number of important findings: First, better education is strongly correlated with better health both across and within countries. A positive gradient can be found in all countries in our dataset. The health concentration index as our measure of education-related inequality in health is significantly different from zero in all countries in our data. This holds whether inequality is age-sex standardized or not. Second, education-related inequality in health is significantly larger in Mediterranean and Anglo-Saxon countries than in Nordic or Western European countries. The differences are generally weaker when the concentration index is age-sex standardized but remain fairly sizeable. The concentration index in the country with the largest education-related health inequality (UK) is about double the size of the concentration index of the country with the smallest health inequality (Switzerland). Third, we find no trade-off between health levels and equity. Countries with higher average levels of health usually have lower levels of inequality in health.
Fourth, turning to the (statistical) sources of education-related inequality in health, we find that income and wealth are only moderately important. In most countries, they account for at most 25% of the age-sex standardized concentration index. An exception in this respect are the United States, where income and wealth jointly account for 40% of the education-related health inequality. Another effect of education on health identified in the theoretical literature is via health behaviors. At least among the cohorts studied in our analysis, we find that behavioral risks (here: smoking) contribute only little to the explanation of why inequalities in health favor the better educated.
Finally, we compute counterfactual health inequalities, which assume – for all countries – the same distribution of covariates or the same health effects of covariates, respectively, as in the country with the lowest measured health inequality (Switzerland). These counterfactuals suggest that most countries with relatively large health inequalities could reduce them to or even below Swiss r 2009 The Author Journal compilation r 2009 Scottish Economic Society levels if they changed the health effects of the covariates rather than their distribution across education groups. In these countries, policies that aim at reducing inequality in health are probably more successful if they target the health care system itself rather than changing the distribution of health determinants across education levels.
Heading :- GENDER INEQUALITY AND ECONOMIC DEVELOPMENT: FERTILITY, EDUCATION AND NORMS.
KLEVEN, H. and LANDAIS, C. (2023) states that, this paper have documented the evolution of gender inequality in labour market outcomes over the long run of development, and we have discussed some of the factors— primarily fertility, education and norms—that may be driving the observed patterns. This paper complements many excellent overviews written on gender gaps in the labour market as well as on economic growth and fertility.
A key contribution of this paper lies in the data gathering effort that underlies the analysis: they have created a micro database covering 53 countries over the period 1967–2014 by assembling 248 different surveys from a variety of sources. The dataset covers a wide range of development levels, and the fact that they observe countries more than once allows us to absorb country fixed effects when studying gender convergence over the development path. This reduces the noise introduced by the differential selection of countries across GDP per capita levels, and allows them to better capture the within-country effect of moving up the development ladder.
They have shown that there is large gender convergence in total earnings across levels of development. This is driven by female labour force participation and wage rates, but not hours worked conditional on working. They have argued that the most important factor behind this convergence is the demographic transition that takes place across development levels. Lifetime fertility rates decline from more than 6 children per woman to less than 2 children per woman across the range of GDP per capita that they consider.
Given the large effects of children on gender gaps at both low and high levels of development, such large fertility declines have drastic implications for gender inequality. They also argue that education convergence plays a significant role for earnings convergence—though not as large as fertility—and highlight that it is empirically difficult to separate the implications of fertility and education (in a true causal sense) as they feed into each other over the development path, as implied by growth models with endogenous fertility. Finally, they have documented a set of striking changes in the views on gender roles, and especially those related to working women with children, that take place over the development path. We discussed these patterns in the light of recent work suggesting that norms and culture could be important propagation mechanisms for gender convergence.
Heading :- CONTINUING EDUCATION, JOB TRAINING, AND THE GROWTH OF EARNINGS INEQUALITY.
MARCOTTE, D. E. (2000) states that, The dimensions of the increase in earnings inequality in the United States are well known. Beginning at least in the early 1970s, inequality began increasing within groups of workers with similar levels of education, experience, and other important characteristics. First, by examining the basic effects of continuing learning on the overall distribution of earnings; second, by examining whether differential patterns and premia of continuing learning explain some of the rapid growth in the value of postsecondary education; and, by considering whether continuing learning can help us understand what remains one of the most puzzling aspects of growing inequality: the increase in inequality within groups of workers with similar levels of education and experience. He assess trends in the amount of training and continuing education workers engaged in and the effect of such learning on earnings by comparing the experiences of these two cohorts. He did this by decomposing changes in the distribution of earnings within groups into separate effects of changes in the participation in continuing learning, returns to different types of such education, and other factors.
To understand the changing patterns and value of continuing learning and whether it has played a role in Increasing wage inequality, I make use of data from the National Longitudinal Surveys from 1966 to 1981 and the comparable National Longitudinal Survey of the Labor Market Experiences of Youth from 1979 to 1994 (NLS, collectively). As an attempt to control for the possibility that more skilled or more trainable workers receive the most training, He estimate the earnings effect of continuing learning in a model that includes AFQT scores for the NLSY sample. In the absence of changes in the distribution and relative returns to continuing learning, the college/high school earnings differential would have been $13,324 for the NLSY cohort.
Clearly, these results suggest that changes favoring more educated workers in the relative distribution and returns to continuing learning played an important role in shaping the increase in between-group earnings inequality for younger workers during the course of the 1980s and early 1990s. He identified the contributions to the growth of earnings differences due separately to changes in the earnings premium and distributions of continuing learning within the groups of high school- and college-educated workers. The observed increase in the inequality of earnings among young high school-educated men would have been smaller than observed if there had been no change in the returns to continuing learning between cohorts. Among this group, the observed increase in the premium due to continuing learning resulted in a mild increase in the growth of earnings inequality between cohorts, while changes in the distribution of those who received training within the group of college-educated workers mitigated this growth.
Heading :- College Education, Earning Inequality, and Market Power.
SHY, O. (2021) states that, this article suggests an additional explanation for the puzzle why earning inequality in the U.S kept rising despite the increase in the percentage of college and post-college educated workers over the past 80 years. Earlier explanations focused on skill-biased technology change, demand and supply imbalances in the market of degree workers, and globalization. The focus of this paper is on the effects of reduced competition and increased market power in sectors employing combinations of degree and nondegree workers. Reduced competition and increased market power in these sectors are caused by a variety of reasons that are discussed “Rising Market Power in U.S Product Markets” section.
The model is also consistent with the explanation that the rise in demand in sectors employing a combination of degree and nondegree workers has contributed to the widening of the earning gap. As noted in Autor (2014), several other factors have also contributed to the widening of the earning gap: (i) decades-long decline in the U.S real minimum wages, (ii) drop in employment opportunities for nondegree workers, (iii) globalization that enabled cheaper imports of goods produced by sectors employing less-educated workers, (iv) decline in union membership and bargaining power, (v) reductions in top federal marginal tax rates.
Whereas the data and the model presented in this paper help explain how increased market concentration in product markets contributes to the rising wage gap between degree and nondegree workers, the analysis abstracts from the observed increasing variations within groups, such as the rise in earning differences among workers who have a college degree. For example, Bartik and Hershbein (2016) find that the percentage boost of career earnings from a college education is much lower for individuals who grew up in lower-income families, compared with their peers who grew up in higher-income families. Presenting these data and writing such a model would require writing a different paper.
Similar to the findings in this paper, Akerman (2021) also identifies a relationship between increased product market concentration and the wage gap between skilled and unskilled workers in Sweden. The author builds a theoretical model and, using employer-employee data for Sweden over 2007–2016, and finds a strong correlation between a firm’s size and the share of college-educated workers. Therefore, an increase in market concentration increases the relative demand for college-educated workers and hence the wage gap between degree and nondegree workers. Contrary to Akerman (2021), in this paper there is no increase in the relative employment of degree workers in less competitive markets, so the demand mechanism plays no role in the current model.
Finally, analyzing the effects of earning and income inequalities on growth and welfare is beyond the scope of this paper because the sole purpose of this article is to analyze the puzzle of how the wage gap between degree and nondegree workers kept rising together with the increase in the percentage of persons who obtain college education. The effects of inequality on growth are surveyed in Barro (1999) and the effect on political instability in Alesina and Perotti (1996). Cairoand Sim (2018) show that income inequality can generate low aggregate demand, deflation pressure, excessive credit growth, and financial instability. Another possible distortion is analyzed in Cai and Heathcote (2018) who evaluate the role of rising income inequality in explaining the observed fast growth in college tuition. Finally, Frankfurt (2015) presents a different view and argues that reducing poverty should be a more socially-desirable goal than reducing inequality.
Heading :- EDUCATION, GROWTH AND INCOME INEQUALITY.
TEULINGS, C. and VAN RENS, T. (2008) states that, the evolution of the social and the private rate of return to education by a simple model of imperfect substitution between workers with various levels of education and endogenous skill-biased technological progress. Human capital enters as a factor of production in a simple constant returns to scale Cobb-Douglas economy. In the short run, the Walrasian equality between the private and the social return to education applies. In the long run, an increase in the average education level of the workforce also induces investment in new knowledge, driving up the long run social rate of return to education above the long-run private rate. They derived easy-to-interpret relationships between educational attainment, GDP, and the social rate of return, and between educational attainment, income inequality, and the private rate of return. Their empirical results provide strong support for a negative relationship between the supply of human capital and its private and social return. The estimates imply that a one-year increase in the stock of human capital reduces its return by about 2 percentage points. The estimate for the private return is in line with conventional estimates of the elasticity of substitution between low- and high-skilled workers (Katz & Murphy, 1992; Ciccone & Peri, 2005).
The short-run social return to education approximately equals the private return. The long-run social return, although imprecisely estimated, is clearly much higher than the private return. Their estimates of the GDP equation represent a substantial improvement over the existing growth literature, and we explain why previous studies did not find an effect of increases in education on GDP growth. Partly, this is because of measurement error, as argued by Krueger and Lindahl (2001). But allowing for dynamics is at least equally important. Krueger and Lindahl find that the estimated social return increases with the time intervals used. Although they attribute this effect to measurement error, it could also be driven by the fact that the long-run social return is substantially higher than the short-run return.
They find some evidence for exogenous skill-biased technological change. Mainly, however, the very high estimates for the long-run social return to education suggest there was enormous endogenous technological progress. This endogenous technological progress appears to have been largely skill-neutral and cannot have been responsible for the increased inequality in the 1980s in the United States, as suggested by Acemoglu (2002). Acemoglu argues that an increase in the average level of education may induce so much skill-biased technological progress that the initial negative effect on the private return to education gets reversed. Theoretically, their model allows for this kind of overshooting of the return to human capital in response to a shock to its supply. Empirically, however, they do not find support for this implication: a larger supply of human capital reduces the private return to education unambiguously.
Heading :- THE IMPACT OF EDUCATION EXPANSION ON WAGE INEQUALITY.
YANG, J. and GAO, M. (2018) states that, this article analyzed the price effect and structure effect of education expansion on income inequality in urban areas by FFL decomposition and constructed a counter-factor model using CHIP data for 1995, 2002, 2007, and 2013. They found that the structure effect of education expansion increases the overall income, while the price effect of education expansion displays the Matthew Effect, that is, wealthy people become wealthier and poor people become poorer after education expansion. The reason is the increasing rates of return to education for college graduates are faster than that for senior high school graduates, though the overall income increased during the past two decades.
The decomposition of income gaps between 2002 and 2013 shows that income inequality augmented in 2013, and that income inequality in high-income groups is significantly high. The structure effect of education expansion on income inequality is negative. In other words, income gaps will be reduced by expanding education. However, the process is not linear; the expansion may increase the inequality in the beginning, and decrease it subsequently. However, the price effect of education expansion is much larger than the structure effect of education expansion, and therefore, the overall effect will be positive. They further discuss the impact of education expansion on income inequality in different qualification levels. The finding shows that the structure effect of senior high school expansion increases income inequality, the structure effect of higher education expansion reduces income inequality, and the price effect of both senior high school and higher education expansion broadens the income gaps. In addition, the robustness check by young workers and data in different years all support the main conclusions (refer to Appendix A and B for details).
Therefore, the current education expansion did not ease the income inequality. With continuous higher education expansion, the supply of high-skilled workers will satisfy the demand for industry update, the structure effect of education expansion will exceed the price effect, and then the income inequality will decline. Future research should consider the equality of education opportunity and the increased higher education opportunity by disadvantaged families. In order to achieve the equality of higher education opportunity, they also need to consider the education quality and equality in basic education.
CONCLUSION :-
The articles discuss the relationship between education, income inequality, and other factors like health and economic growth.
The article 1, discusses a study in Nigeria that found weak relationships between entrepreneurship education and poverty/income inequality reduction. It reported challenges small businesses face in Nigeria like small markets. The article 2, looks at educational inequality in the UK higher education expansion. It found students from lower income families were less likely to enroll in college, even controlling for other factors. The article 3, uses twin data to examine relationships between health and education/income. It found associations were lower when controlling for shared factors, suggesting OLS estimates are biased and other factors explain more health inequality. The article 4, discusses decentralizing public education funding. It showed decentralization can reduce disparities through fiscal policy like varying spending on education vs social services. The article 5, measured education-related health inequality across countries using comparable surveys. It found inequalities were larger in some countries and no tradeoff between health levels and equity. The article 6, documented gender inequality trends over development. It attributed patterns largely to fertility decline and education increases driven by demographic transition. The article 7, explored continuing education’s role in rising earnings inequality. It found changes in returns and distributions of continuing learning contributed to higher between-group inequality. The article 8 proposed market power increases in sectors employing degree/nondegree workers help explain the persisting wage gap rise despite higher education levels. The article 9, reviewed education’s effects on growth and income inequality. It discussed how education expansion can initially increase inequality but later decrease it through structural changes. The article 10, analyzed education expansion’s price and structure effects on income inequality in China. It found the structure effect increased incomes but the price effect widened gaps, maintaining overall inequality.
In conclusion, the articles covered topics like entrepreneurship education, educational inequality, health inequality, gender inequality, earnings inequality, and the complex relationships between education, growth, and inequality over time and development levels.
REFERENCES :-
ADEBAYO, N. A. Is Entrepreneurship Education Reducing Poverty and Income Inequality in Less Developed Countries? Evidence from Nigeria. Journal of Developmental Entrepreneurship, [s. l.], v. 27, n. 4, p. 1–26, 2022. DOI 10.1142/S1084946722500248. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=757c2031-5fab-3523-8dae-d03ae54e70a5. Acesso em: 20 fev. 2024.
BLANDEN, J.; MACHIN, S. Educational Inequality and The Expansion of UK Higher Education. Scottish Journal of Political Economy, [s. l.], v. 60, n. 5, p. 578–596, 2013. DOI 10.1111/sjpe.12024. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=7dfa2405-bb82-31dc-ace8-4a94bac2c87e. Acesso em: 20 fev. 2024.
GERDTHAM, U. ‐G. et al. Do Education and Income Really Explain Inequalities in Health? Applying a Twin Design. Scandinavian Journal of Economics, [s. l.], v. 118, n. 1, p. 25–48, 2016. DOI 10.1111/sjoe.12130. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=cd8e815b-f92d-3711-88df-bf39ccd6f703. Acesso em: 20 fev. 2024.
GIANNINI, M. National vs local funding for education: effects on growth and inequality. International Review of Applied Economics, [s. l.], v. 23, n. 3, p. 367–385, 2009. DOI 10.1080/02692170902811785. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=d699ce0e-24d8-3ccc-9087-a4c97c0359fe. Acesso em: 20 fev. 2024.
JÜRGES, H. Healthy Minds in Healthy Bodies: An International Comparison of Education-Related Inequality in Physical Health among Older Adults. Scottish Journal of Political Economy, [s. l.], v. 56, n. 3, p. 296–320, 2009. DOI 10.1111/j.1467-9485.2009.00485.x. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=7cd36116-0951-3744-8ad5-a55eef1d450e. Acesso em: 20 fev. 2024.
KLEVEN, H.; LANDAIS, C. Gender Inequality and Economic Development: Fertility, Education and Norms. Economica, [s. l.], v. 84, n. 334, p. 180–209, 2017. DOI 10.1111/ecca.12230. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=f39e019b-06c5-30d0-8d30-42650d733787. Acesso em: 20 fev. 2024.
MARCOTTE, D. E. Continuing Education, Job Training, and the Growth of Earnings Inequality. ILR Review, [s. l.], v. 53, n. 4, p. 602–623, 2000. DOI 10.1177/001979390005300403. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=3c748dd4-fb87-3042-a01e-e44e611aeba6. Acesso em: 20 fev. 2024.
SHY, O. College Education, Earning Inequality, and Market Power. Journal of Labor Research, [s. l.], v. 42, n. 3/4, p. 334–357, 2021. DOI 10.1007/s12122-021-09324-9. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=9ea82588-073c-39b8-b0fe-7ca04a55b183. Acesso em: 20 fev. 2024.
qaTEULINGS, C.; VAN RENS, T. Education, Growth, and Income Inequality. Review of Economics & Statistics, [s. l.], v. 90, n. 1, p. 89–104, 2008. DOI 10.1162/rest.90.1.89. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=af1b5e0f-c629-3eba-8663-cdd987676c65. Acesso em: 20 fev. 2024.
YANG, J.; GAO, M. The impact of education expansion on wage inequality. Applied Economics, [s. l.], v. 50, n. 12, p. 1309–1323, 2018. DOI 10.1080/00036846.2017.1361008. Disponível em: https://research.ebsco.com/linkprocessor/plink?id=5e3d2a0c-fcd8-35cd-b0dc-cc837cf74fc9. Acesso em: 20 fev. 2024.