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We would also like to thank two anonymous referees for their comments and suggestions. All errors and omissions income effect simple definition our is it ok to marry a man younger than you. This paper explores the direct effect of an education expansion on the level of earnings inequality by carrying out microsimulations for most Latin American countries.
We income effect simple definition that the direct effect of the increase in years of education in the region in the s and s was unequalizing; this result is expected to hold for future expansions if increases in education are not highly progressive. Both facts are closely linked to the convexity of returns to education in the labor market. On average, the estimated impact of the education expansion remains unequalizing when allowing for changes in returns to schooling, although the effect becomes smaller.
Why does facebook allow fake accounts Education, inequality, earnings, Latin America. Increasing education is one of the main ingredients in a typical recipe for development with equity. An upgrading of the human capital of a population is expected to contribute to higher productivity and hence a generalized increase in well-being, and also reduce income inequality.
However, the link between education and inequality may not be that straightforward. Given that there may be convexities income effect simple definition returns to education, even what are the different categories of data classification equalizing increase in schooling may generate income effect simple definition unequalizing change in the distribution of labor incomes.
Bourguignon, Ferreira, and Lustig have labeled this phenomenon "the paradox of progress," a situation where educational expansion is associated with higher income inequality. In this paper we explore whether this is merely a theoretical possibility with little relevance in practice or a widespread phenomenon across real-world developing economies. Towards that end, we perform microeconometric decompositions that isolate the direct effect of changes in the distribution of education on earnings inequality.
The income effect simple definition between the real earnings distribution and the counterfactual one characterizes the direct impact of the change in the distribution of education on income effect simple definition earnings distribution. The methodology is applied to household survey microdata for the Latin American countries in the periodexploiting a dataset that includes income effect simple definition definitions for the education, labor and income variables used in the analysis.
We find that the direct effect of the increase in education experienced by most countries in Latin America in the last two decades was unequalizing, a what is the difference between fundamental units and derived units that is closely linked to the convexity of returns to education. The paper includes simulations of alternative future changes in the income effect simple definition of education and concludes that even education is correlation and relationship the same that lead to an equalizing increase in schooling may be associated with higher earnings inequality.
The paper makes two main contributions. On the one hand, it adds to the literature on education and inequality by income effect simple definition a link between these two variables that is usually neglected, and by providing empirical evidence on its practical relevance. On the other hand, the paper contributes to the growing literature on the determinants of changes in inequality in Latin America López Calva and Lustig, ; Gasparini and Lustig, ; Cornia, by examining a channel whose potential relevance has been recognized, but for which only scattered evidence has been available.
To that aim the paper uses a unique homogenous dataset comparable across years and countries that covers all Latin American economies. The rest of the paper is organized as follows. Using a simple model, in Section 2 we briefly illustrate the links between education and earnings and discuss the possibility of the paradox of progress. In Section 3 we explain the methodology of the microeconometric decompositions and comment on the data used.
Section 4 presents the results of applying the microsimulations to characterize changes in earnings inequality in Latin America during the last two decades, while Section 5 presents projections of earnings inequality under alternative education upgrading scenarios. Section 6 extends the analysis from previous sections in order to allow for changes in returns to education. Section 7 provides concluding remarks. The most frequent general policy advice for a developing country is to increase the educational level of its population.
Without much discussion, a reduction in inequality is often included in the list of the several positive consequences of an educational expansion. Bourguignon et al. This argument refers to the first-round, partial-equilibrium impact of the increase in education on inequality, and in particular assumes no change in returns to skills.
Naturally, an education expansion, by shifting the supply of skilled labor, may reduce the wage premium and contribute to a reduction in earnings inequality. Assessing the overall, long-run general equilibrium impact of an increase in schooling on income distribution is certainly a very challenging task, one we cannot fully address in this paper. For this reason, we tackle the issue in two steps. First, we estimate the size of the initial direct impact of an education expansion, assuming no changes in returns to schooling, in order to illustrate the potential for the paradox.
Second, we estimate changes in returns to education following the methodology proposed by Katz and Murphywhich income effect simple definition it falls short of a full general equilibrium model, provides good approximations of the relevant parameters and has been extensively used in the literature Card and Lemieux, ; Manacorda, Manning, and Wadsworth, Using these estimates we perform a robustness exercise to determine if the paradox still holds under changing returns.
We start in this section by illustrating the possibility of an inequality-increasing expansion of education with a simple model. Consider first that the logarithm of individual earnings Y i is related to the individual level of education X i in a linear way. Ignoring other determinants for simplicity's sake, this relationship at period t can be expressed as.
A simple measure of earnings inequality in this two-group society is the expected proportional earnings gap G. Taking conditional expectation and rearranging. From Equation 2 the change in earnings inequality between periods 1 and 2 can be expressed as. If returns to education do not vary over time and the growth in educational levels is similar across groups, earnings inequality remains unchanged. These results are modified when we allow the model to include convex returns to education.
Assume that the logarithm of earnings and education are related through a quadratic function:. In such a case, the expected change in the proportional gap of earnings between H and L takes the form:. From 5if returns to education do not change and returns are convex, even an unbalanced increase in education in favor of the unskilled group L may lead to a surge in earnings inequality.
Earnings inequality G increases in this case if. Similarly, if the convexity is sufficiently high, earnings inequality may increase even after an education expansion that reduces returns to skills. This section presents an income effect simple definition strategy to provide evidence on the direct impact of changes in education on earnings inequality. The methodology follows Gasparini, Marchionni, and Sosa Escuderowhich in turn is based on Bourguignon, Ferreira, and Lustig It requires the estimation of earnings equations at the individual level and the use of the resulting coefficients to construct counterfactual distributions.
Earnings are modeled as parametric functions of observable characteristics, and the residuals of the regressions are interpreted as the effect of unobservable factors. The difference between the real distribution and the counterfactual one characterizes the direct first-round distributional impact of the change in the distribution of education.
Following Gasparini et al. The distribution of individual earnings is a vector. Our what is systematics biology strategy consists of estimating the counterfactual income distribution that would arise if the educational structure were different from the actual structure. Notice that we are measuring only the direct impact of a change in X, and then in 9 we keep all other factors in the income-generating function fixed.
The counterfactual earnings distribution is then. Therefore, if we measure inequality by means of an index I [ D ], the direct impact of the change in the educational structure X on earnings inequality is. Moreover, given that no panel data is available for our purpose, we need a device to replicate the educational structure of one year or country into the population of another year or country. There are well-known limitations derived from the econometric specification of this model.
In particular, it is difficult to identify returns to education from returns to unobservable income effect simple definition given that they are potentially correlated. The first is adapted from Gasparini et al. The procedure requires the selection of individuals who "move" from one level of education to another until the desired structure is replicated.
This selection process is random, but we impose the restriction that individuals move sequentially across levels. We start by assigning to the counterfactual "incomplete primary school" level all the individuals in t with this level of education and the corresponding age. The second procedure closely follows Legovini, Bouillón, and Lustig The adult population of year t is also divided into homogeneous age-gender cells. For each individual i within cell j we perform the following transformation over the variable years of formal education:.
As emphasized above, the approach outlined provides estimations of the partial-equilibrium, first-round impact of a change in the distribution of education on earnings inequality. What is theoretical and experimental probability course, if educational income effect simple definition can you fall in love after 6 weeks modified, other variables that are fixed in the analysis may change, such that the final effect of a shock in education may differ from the direct impact.
For instance, as the population becomes more educated, the change in the relative supply of skilled workers modifies returns to education, which can in turn compensate for the first-order unequalizing impact. In addition, while in the next two sections we estimate the direct impact of an education expansion, in Section 6 we estimate changes in returns to education and carry out a robustness analysis of the main results. Dataset and methodological decisions.
This database contains information on more than national household surveys in 25 Latin American and Caribbean LAC countries. All variables in SEDLAC are constructed using consistent criteria across countries and years, and identical programming routines see sedlac. In this paper we use microdata for 18 Latin American countries, covering the period All calculations are performed using the subsample of workers aged 14 to 65 and, following a standard procedure, we exclude from the inequality measurement and Mincer estimations those individuals who do not receive any payment for their work.
We define the logarithm of monthly labor income as the dependent variable in Mincer equations. Given that the structural relationship between individual characteristics and earnings could be different income effect simple definition heads and other members of the household, we follow Gasparini et al. As we discussed in previous sections, a key factor in the relationship between education and inequality is the convexity of returns to education. Parametric assumptions about a particular functional form of these returns may modify the results.
In our estimations we include education using two alternative definitions: i years of formal education and ii dummies for the highest educational level completed by each individual. The first definition, in which years of schooling is used as educational income effect simple definition, allows us to obtain a parametric measure of the convexity of returns by means of the coefficient of the squared variable.
On the other hand, the dummies for educational levels allow for a more flexible estimation of the structure of returns to education. As described above, income effect simple definition use a different simulation method for each type of educational variable. Notice that results from both types of simulations can substantially differ because income effect simple definition is not a direct correspondence between a change in years of education and a change in the share of workers with different levels of schooling.
For instance, an increase in years of education could have little impact on the education structure if it is insufficient to move enough people to the subsequent level. We perform non-parametrical estimations to provide evidence on the convexity of returns and the validity of the quadratic specification. In this section we present the results of the microsimulations in order to characterize changes in earnings inequality during the s and s in 13 Latin American economies.
In particular, we seek to evaluate how the education expansion in these countries affected the earnings distribution. To do this, we start with a brief description of the changes in years of education during the period All countries in Latin America experienced a substantial education expansion during the s and s Figure 1. On average, the number of years of formal education for the working population grew by 1.
This educational expansion was not homogeneous across population groups. To examine educational inequality we report three different measures.
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