Education Gini Research Articles

ANALISIS KETIMPANGAN PENDIDIKAN DI PROVINSI SUMATERA BARAT

The purpose of this study was to analyze: (1) education imbalance, (2) the effect of education expansion on education imbalance, and (3) the correlate of education imbalance on economic growth in West Sumatera Province. This research is descriptive and asosiative, it’s describe the variable and found whether there is influence between independent variable with dependent variable. This study used Survey Sosial Ekonomi Nasional years 2005, 209 and 2013. The data examied totaled 57 observations. The analyses technique are descripyive and inductive. Inductive analyses include panel data analyses, determinant coefficient, hypothesis test and coreelation test.The result showed that: education imbalance in West Sumatera Province came to low imbalance, with the lowest imbalance on Padang City with education gini index as 0,2149. The result also showed that education imbalance on city has lower gini index than regency, (2) education expansion has a negative significantly impact on education imbalance in West Sumatera Province with regression coeffecient as 0,02557, (3) education imbalance has a negative correlation with economic growth with correlation coefficient as 0,001712. Keywords : educatio imbalance, education expansion, economic growth

(In)equality in Education and Economic Development

This paper investigates the relationship between the level and the distribution of education and economic development. We contribute to the literature by introducing an interaction term between the education Gini coefficient and average years of schooling. In a dynamic panel over 55 years and 134 countries we provide, on the one hand, strong evidence that more schooling is good for growth, but the coefficient is variable and substantially declining in the degree of inequality. The aggregate benefit to education thus depends on a country's position in the education distribution. On the other hand, we find a slight transitional increase in education inequality to be beneficial at a very low average level of schooling, but detrimental for growth at a relatively high average level. Allowing for the macroeconomic return to education to be heterogeneous with respect to the degree of inequality is therefore paramount in understanding the relationship between education and development.

The inequality–conflict nexus re-examined

The impact of inequality on the outbreak of intrastate armed conflicts or civil wars has recently attracted considerable interest in conflict research. In contrast to previous studies that have focused on inequality in the total population (vertical inequality), recent studies have analysed inequality between certain groups of people (horizontal inequality), and found that inequality significantly increases the likelihood of conflict onset. However, most of the recent studies on the inequality–conflict nexus have focused on conflicts fought between ethnic groups. The relation between inequality and other (non-ethnic) categories of conflicts has attracted less attention. The present study aims to address this gap: it implements a theoretical and empirical analysis of the relation between inequality and popular rebellions, a subset of conflicts where mobilization transcends ethnic boundaries and hostilities involve popular participation. Based on a sample of 77 popular rebellions and new global data on vertical inequality in income and education, this study shows that inequality significantly increases the likelihood of popular rebellion onset. In addition, the study reveals that inequality proxies (income and education Gini indices) outperform proxies of the absolute level of income (GDP per capita) in the model of popular rebellion onset, suggesting that it is relative, not absolute, well-being that ultimately motivates people to rise up in arms.

Assessing Inequalities in Thai Education

Using data from Thailand’s Household Socioeconomic Survey, this paper measures the inequalities of Thai education distribution in 2011. We utilize the Gini coefficients to estimate Thai educational inequalities from cumulative years of educational attainment which are between zero (no schooling) to 21 (doctoral level) years. The education Gini coefficient of the whole country is 0.349. At the provincial level, the Gini coefficients are in a range between 0.272 (Nonthaburi) and 0.521 (Mae hong son). The provinces located near the Bangkok metropolis have greater equality in education, except for Samut Sakhon, while the provinces in the northern part of Thailand have severe inequality in education, especially the border provinces. As for the effect of schooling on educational inequality, we found that at the regional level, average years of schooling was significantly and negatively associated with the educational inequality, except in the northern part of Thailand. The magnitudes of coefficients of average years of schooling in the northern and southern parts are twice that of the central part of Thailand. The policy implication of this paper is that the Thai government should pay attention to two points in adjusting the scope of distribution: reduce the number of people without schooling and extend the educational attainment of people with primary education to secondary education. At the regional level, the policy of education expansion for reducing educational inequality is workable only in central Thailand, the north, and the south. Governments should utilize different policies in each region. In addition, the Thai government should pay more attention to solving the social problems which contribute to the issue of educational inequality.

Educational inequality in rural and urban India

This paper presents estimates of educational inequality for the major Indian states. We compute the education Gini index separately for the rural and urban sectors and examine the changes in inequality during past two decades. The estimates show the high extent of educational inequality; though the inequality declined between 1993 and 2009 the Gini index is above 50% in 2009. Using the analysis of Gini (ANOGI), a technique to decompose overall inequality into the inequality within- and between-sectors, we find that a large part of overall educational inequality is accounted for by intra-sector inequality. Further, intra-sector inequality has increased and inter-sector inequality has narrowed during the above period.

Educational attainment in educationally backward states of India: some implications for the Right to Education Act

This paper presents estimates of educational attainment and educational inequality for educationally backward states in India. Using multiple rounds of nationally representative household surveys carried out by the National Sample Survey Organisation of India, we estimate average years of schooling and the education Gini index. The estimates show that educational attainment is very low and the extent of inequality is high in all the states. The study adds to the evidence on large gender inequalities in educational attainment in the states of Bihar and Rajasthan. The results are very distressed for disadvantaged social groups of the society. There is a need to give attention on education of females belonging to these social groups, particularly in rural areas, to make the Right to Education Act successful.

KETIMPANGAN PENDIDIKAN ANTAR KABUPATEN/KOTA DAN IMPLIKASINYA DI PROVINSI JAWA TENGAH

This study aims to analyze the educational disparities among districts / cities and find out what factors affect educational inequality, the influence of educational inequality with GDP growth per capita in the province of Central Java. Data analysis method used in this study is education Gini index, with panel data regression analysis techniques Pooled EGLS. Based on the analysis of educational inequality between districts / cities and its implications in the province of Central Java is known that the calculation of Gini index of education in the province of Central Java is included in the category of low educational inequality (0.309), with contributors educational inequality is the inequality of education in districts and cities. In the first regression model it is known that the independent variable of government spending on education, and life expectancy negative effect on educational inequality significantly. While the gender gap variable is significantly positive effect on the dependent variable educational inequality. Variables that did not significantly influence educational inequality is the variable of income inequality. In the second regression model in mind that educational inequality has a significant negative effect on the growth of GDP per capita in the province of Central Java.

Education inequality in Ghana: gender and spatial dimensions

PurposeThe purpose of the paper is to examine the nature and extent of gender and spatial inequalities in educational attainment in Ghana.Design/methodology/approachThe paper uses the education Gini coefficient, computed on the basis of years of schooling of individuals, to assess education inequality in Ghana.FindingsThe paper finds evidence of gender and spatial inequality in education in Ghana. In particular, the three northern regions have lower education attainment as well as higher education Gini coefficients compared to the rest of the country. The paper finds evidence of intra‐gender and intra‐spatial inequalities in education attainment in Ghana, with females contributing proportionately more to the within‐inequality component of the education Gini. The paper also finds a positive correlation between poverty incidence and education inequality.Research limitations/implicationsThe research finds a positive correlation between poverty incidence and education inequality but requires an econometric analysis to make inferences regarding causality.Practical implicationsThe findings call for the design and implementation of policies not only to address between‐gender and spatial inequities in education in Ghana, but also to tackle within‐gender and within‐spatial inequalities. The positive correlation between poverty incidence and education inequality implies the need to create greater equity in educational opportunities across the country.Social implicationsThe need for changes in attitudes, values and cultural practices that put girls at a disadvantage when it comes to education.Originality/valueOne important and new finding of the paper is the existence of intra‐gender and intra‐spatial inequalities in education attainment in Ghana, with females contributing proportionately more to the within‐inequality component of the education Gini.

Educational Inequailty in the United States: Methodology and Historical Estimates of Education Gini Coefficients

This paper estimates historical measures of equality in the distribution of education in the United States by age group and sex. Using educational attainment data for the population, the EduGini measure indicates that educational inequality in the U.S. declined significantly between 1950 and 2009. Reductions in educational inequality were more profound during the first three decades of the study than the last three, with the degree of inequality remaining relatively constant since the early 1990s. Educational inequality has historically been higher among males than females, but recently the gap has narrowed substantially. Older age groups have experienced greater declines in educational inequality compared to younger age groups. Research extensions of the data presented in this paper are discussed.

Analysis on inter‐provincial disparities of China's rural education and convergence rate

PurposeThis article aims to analyze inter‐provincial disparities of rural education and the convergence rate, and to discuss the effects of compulsory education reform after 2001.Design/methodology/approachThe article estimates the rural average education years and education Gini coefficients of China's 31 provinces (municipalities) beside Hong Kong, Macao and Taiwan from 2001 to 2008, and applies the method of “economic convergence”.FindingsThe results show that after the reform of rural compulsory education, inter‐provincial rural education disparities and educational equality have been significantly improved and trend to convergence; nevertheless the convergence rate on inter‐provincial disparities of education equality declines. The defects of the education input system – “county as principle” – has been a factor that restricts the coordinated development of rural education.Practical limitationsThis paper suggests that it is necessary for China's provincial and central government to afford the expense of compulsory education. China's present investment system would also worsen inter‐provincial inequities of education.Social implicationsEducation equality is one of the basic social priorities. In China education equality has been improved; however it could be better if China's provincial and central government afforded the expenses of compulsory education.Originality/valueThis paper applies the method of “economic convergence” to analyze China's rural education disparities among its regions.

Educational Inequality in Argentina: The Best and Worst Performers

What do we know about inequality in educational attainment across Argentina's cities? To answer this question, we present the education Gini coefficient for the period 2002-2007. Using microdata from the national household survey, we document the following results. First, educational inequality has declined in almost all metropolitan areas whereas i t has increased in Posadas, Mar del Plata, Rosario and Formosa. Second, although there are no important differences in the average years of schooling across cities, great disparities exist with respect to the education Gini. Buenos Aires City is in a leading position, especially in relation to the northeast region of the country and, particularly, Posadas city.

Measuring regional inequality of education in Turkey: an evaluation by Gini index

This study aims to determine the relationship between inequality in education in Turkey and the average years of schooling. The source of the data was the 2000 General Census of Population: Social and Economic Characteristics of Population. To determine the inequalities in education, the average years of schooling of the population at the age of 25 and over and the education Gini index were calculated. The average years of schooling in Turkey and in all the regions during the period of 1975–2000 increased, and inequality in education decreased. A negative relationship was found between average years of schooling and educational Gini index. A positive relationship was found between the rates of increase in average years of schooling and decrease in the education Gini index.

School Quality, Educational Inequality and Economic Growth

Realizing the importance of education in developing a country, many governments had begun to pay more attention in improving the education quality in their country. However whether the desired level of education quality is equally distributed is still debated on. On top of that, current literature on which level of education, either basic or tertiary education, brings greater return to the society is still inconclusive. It is not the objective of this paper to answer or add on the debate. On the other hand this paper would like to explore the relationship between school quality, namely at primary education and secondary education, and economic growth. Educational inequality at primary and secondary education would be measured with using the concept of education Gini. Using GDP as the dependent variable and regressing it with Gini coefficient of primary education and secondary education, would be able to show which level of education inequality is significant in explaining the economic growth of a country. Using Malaysian data, for the last 20 years, the relationship between education inequality of different level of education and the economic growth would be postulated.

Assessing Brazilian educational inequalities

This paper provides an evaluation of schooling inequality in Brazil using different indicators such as the Education Gini coefficient, the Education Standard Deviation and the Average number of Years of Schooling. We draw up a statistical description of Brazilian human capital dispersion in time over the last half century, across regions and states. Our analysis suggests several conclusions: 1) Strong reduction of educational inequalities measured by Education Gini index. 2) A three parts picture of Brazil seems to emerge, reflecting initial conditions. 3) High increase of the Average number of Years of Schooling. 4) A significant link between Education Gini and the average education length. 5) Education Standard Deviation leads to inverted results compared to Education Gini. 6) Brazilian data are consistent with an Education Kuznets curve if we consider Education Standard Deviation.

An empirical study on education equity in China, 1978–2004

By using the education Gini coefficient (EGC), as the indicator of education equity, this paper will calculate the degree of education equity in China during 1978–2004. Results show that although education equity as a whole has improved significantly since 1978, it is rather at a low level compared with the international average. Due to the great inequality among regions and the rural-urban disparity in education, the positive influence of education on social development and economic growth has been limited.

GALEA AND AHERN RESPOND

Moore et al. raise a concern about collinearity in our study, a concern that we shared when conducting this analysis. There are 2 distinct issues that merit discussion in relation to collinearity. First, when independent variables are strongly correlated, standard errors may be inflated and one of the independent variables may wash out the effect of the other. To examine whether these were problems in our analysis we calculated the bootstrap confidence intervals for the final models and found only minimal inflation of the confidence intervals and no change in inference for any of the models. Both mean education and the education Gini coefficient remained significantly negatively associated with the population health indicators we reported in our research. In addition, the variance inflation factor was 3.5, suggesting that the standard errors were inflated by a factor of 1.9 (). Typically the variance inflation factor is considered a problem when it is 10 or greater. Although there is clearly some collinearity between our independent variables, as we reported in our research, variance inflation and instability of estimates are not plausible explanations for our findings. As a second concern, strong collinearity also raises the issue of residual confounding. With only 59 data points, we could not adjust for mean education nonparametrically and thus we cannot be certain that we have completely adjusted for the influence of mean education in our examination of the education Gini coefficient. Allowing nonlinearity in the effect of mean education with squared terms and restricting the analysis to the range of mean education, which removed all potential outlying values of education Gini coefficients, did not change the results for any of the final models. However, further research on this topic, ideally with more observations, will have to explore the potential role of residual confounding as an explanation for the associations we have documented. As a final note, this study was one study, in one unique city, with one conceptualization of neighborhood. It was our goal to engender further inquiry into the potential role of neighborhood education distribution in different contexts. The work reported by Moore et al. in their letter is a good step toward that goal. We look forward to further work in this area, whether it supports or contradicts the findings we reported. Our results suggest a nuanced role of absolute levels and distributions of the fundamental determinants of population health. We hope that our work stimulates research that improves our understanding of the complex (and not necessarily intuitive or anticipated) role that different contextual factors play in shaping the health of populations.

IS EDUCATIONAL INEQUALITY PROTECTIVE?

Galea and Ahern1 examined the ecological association between educational distribution and a range of population health outcomes. In one of their findings, Galea and Ahern reported that higher levels of educational inequality within New York City neighborhoods were associated with lower percentages of low birthweight. The finding contradicts the more frequently found association between higher levels of income-related inequality and unfavorable population health outcomes. Is neighborhood educational inequality protective of population health? Given the provocative nature of this question, we sought to examine the ecological association of educational inequality and percentage of low-birthweight infants in Montreal. We measured area-level education and calculated the educational Gini coefficient. We found a high degree of collinearity between measures of mean education and educational Gini coefficient (−0.89; P <.001). This collinearity yielded models susceptible to unstable coefficient estimation. Galea and Ahern reported a similarly high degree of correlation between mean education and education Gini measures (−0.84; P <.01) in their analyses. Areas with higher average education tend to have lower levels of educational inequality. Considerable collinearity between mean education and educational Gini coefficient may account for the change in the direction of the β coefficient for the education Gini coefficient, as reported by Galea and Ahern when they introduced their mean education variable to the previous bivariate regression of the percentage of low-birthweight infants on the education Gini coefficient. Variance inflation factor values increased from 1.0 to 5.2 when we added mean education to our bivariate model; we observed a more modest increase from 1.0 to 1.5 when we used alternative measures of education (percentage of adults with at least a college degree) and educational distribution (standard deviation in schooling). Correlated factors that may act as confounders or effect modifiers should potentially be omitted from analysis because biased estimates may result, particularly in ecological regression.2,3 An ostensibly positive relation between educational inequality and favorable health outcomes may constitute no more than a statistical artifact. The potential political consequences of accepting the conclusion that a form of social inequality might be beneficial for health requires attention to assumptions underpinning statistical conclusion validity. A number of differences between our investigation and that of Galea and Ahern should be mentioned. First, Canadian census data do not allow the same level of discrimination in educational attainment that Galea and Ahern achieved. Second, we examined the ecological association at a smaller area of analysis (census tract) than Galea and Ahern (district level). Whether these differences have corresponding implications on the hypothesized association between educational inequality and population health remains a topic for further research. As few such studies have been published, we suggest that the considerable collinearity found between mean education and educational Gini coefficient precludes general acceptance of the conclusion that neighborhood-level educational inequality is protective of population health.

Education inequality and use of cigarettes, alcohol, and marijuana

Education inequality at the neighborhood-level may influence population health and health behavior. We assessed the relations between education inequality and substance use in 59 New York City (NYC) neighborhoods. We used Gini coefficients of education to describe neighborhood education inequality and data from a random-digit-dial phone survey of adult residents of NYC to assess use of substances. Among 1355 respondents (female = 56.2%; white = 35.7%; mean age = 40.4), 23.9% (95% confidence interval [CI] = 20.3–27.5) reported smoking, 39.4% (95% CI = 35.3–43.4) drinking, and 5.4% (95% CI = 3.6–7.3) using marijuana in the previous 30 days. In multilevel models controlling for neighborhood education, neighborhood income inequality, and individual covariates, living in a neighborhood with high education inequality was associated with a greater prevalence of drinking ( p = 0.02) and of smoking marijuana ( p = 0.004) but among current drinkers it was associated ( p = 0.03) with having fewer drinks. The odds of alcohol use (OR = 1.70) and marijuana use (OR = 3.49) were greater in neighborhoods in the 75th percentile of education Gini compared to neighborhoods in the 25th percentile of education Gini. Statisical interactions suggest that there may be a stronger relation between education inequality and marijuana use in neighborhoods with low mean education than in neighborhoods with higher mean levels of education. These findings, taken together, suggest a complex relation between education inequality and substance use; likelihood of the use of alcohol and marijuana was higher in areas with higher education inequality suggesting potential roles for substance use norms and availability, whereas quantity used among drinkers was higher in areas with low education inequality, suggesting potential roles for both disadvantage and norms.

Education Expansion, Educational Inequality, and Income Inequality: Evidence from Taiwan, 1976–2003

The expansion of higher education in Taiwan starting from the late 1980s has successfully raised the average level of education. Using the concept of the education Gini, we find that the educational inequality declined as average schooling rose during the period of 1976–2003. The impacts of a rising average schooling and a declining educational inequality are also tested empirically in this paper. The evidence supports that a higher level of average schooling will generate a lower income inequality. On the other hand, a lower educational inequality, as measured by education Gini coefficient, will also cause a lower income inequality. Skill-biased technological change that shifts the labor demand from unskilled workers toward skilled workers is the most likely cause for the rising income inequality in Taiwan. However, the trend of rising income inequality could be reversed due to possible future over-education and unemployment in the labor market.

Distribution of Education and Population Health: An Ecological Analysis of New York City Neighborhoods

We assessed the relationship between distribution of education and health indicators in a large urban area to determine if distribution of education may be a determinant of population health. We studied the association between distribution of education, measured with the education Gini coefficient, and rates of 8 health indicators in 59 neighborhoods in New York City. In separate adjusted ecological models, neighborhoods with more poorly distributed education had better population health indicators that might plausibly be associated with short-term changes in the social environment (e.g., homicide and infant mortality rate); there was no association between education distribution and health indicators more likely to be associated with long-term accumulation of social and behavioral stressors (e.g., cardiovascular disease and chronic lung disease mortality rates). These findings were robust to measures of income and to adjustment for several potential confounders (e.g., gender and race/ethnicity). The presence in a neighborhood of highly educated people may be salutary for all residents, independent of the potentially deleterious consequences of income maldistribution.