Economia da Educação - Universidade da Madeira
Educação e Desenvolvimento Económico Pedro Telhado Pereira A evidência micro – económica mostra que o salário dum indivíduo aumenta quando a sua educação aumenta. Podemos perguntar: Será que a nível macro-económico, ou seja, na Economia como um todo tal se verifica? Teriamos assim uma uma equação salários “Macro-Mincer” agregada (Heckman and Kleenow (1997)), ln Y = + b S + e, onde Y é a média geométrica dos salários e S é o nível médio de educação. Heckman and Kleenow (1997) Comparando o coeficiente da educação nas equações do PIB em amostras cross-country com o coeficiente da educação dos modelos micro Mincer verificam que as estimativas macro e micro são muito semelhantes o que parece levar a concluir que não existem externalidades do capital humano. Krueger and Lindahl (1998 and 2000) Questionam os resultados da literatura macro que sugeriam que não havia ligação entre os aumentos de educação e o crescimento económico. Depois de corrigirem para os erros de medida concluíram que os efeitos das variações da educação no crescimento económico são,pelo menos, da ordem de magnitude das estimativas microeconómicas da rendibilidade da educação. Também verificaram que a taxa de crescimento não depende do nível inicial de educação. De la Fuente and Domenech (2000) Mostram que o resultado contra-intuitivo da educação não ter influência no crescimento tinha a ver com deficiências dos dados sobre o capital humano utilizado nos outros estudos. Depois de removeram as deficiências dos dados da OCDE mostram que o capital humano é um factor de produção crucial. Cohen and Soto (2001) Apresentam uma nova base de dados sobre o capital humano. Esta base de dados tenta incorporar toda a informação disponível de um grande número de fontes: base de dados da OCDE sobre educação, Census nacionais, surveys publicados pela UNESCO’s Statistical Yearbook e Census acessíveis nas páginas dos institutos nacionais de estatística. Encontraram um valor estimado para a rendibilidade da educação de 8,4% o qual está … “fairly much in line with the average return obtained from micro data”. Para testarem a robustez do resultado fizeram a regressão da taxa de crescimento do rendimento per capita no aumento dos anos de educação. O resultado obtido de 8% é muito semelhante ao acima. Quando o nível educacional foi colocado como variável explicativa na equação de crescimento, aparece como não significativamente diferente de zero. Isto leva-los a concluir: “this settles, at least for these data [the data used in this authors’ paper], the long standing opposition between the effects of levels and the effects of the increase of human capital on growth. We find quite simply that levels are correlated to levels and growth rates to growth rates”. Again in this paper we see that human capital seems to have social returns that are identical to the private ones. Education and Economic Growth: A Meta-Regression Analysis Nikos Benos and Stefania Zotou University of Ioannina March 2013 Online at https://mpra.ub.uni-muenchen.de/46143/ MPRA Paper No. 46143, posted 15 April 2013 08:48 UTC measures of education and economic growth used in the empirical literature vary. Education is a broad term and as a result, empirical studies face difficulties with its measurement. The literature uses several proxies. Most proxies concern measures of formal education and include literacy rates, enrollment rates and years of schooling. Literacy rates are typically defined as the proportion of the population aged 15 and older who are able to read and write a simple statement on his/her everyday life (UNESCO, 1993). However, literacy rates are not objectively and consistently defined across countries and omit important components of human capital (Le et al., 2005). Enrollment rates measure the number of students enrolled at a given level of education relative to the population that, according to legislation, should be attending school at that level. Enrollment rates measure the current investment in human capital that will be reflected in the future stock of human capital. Nevertheless, they are poor proxies for the present stock of human capital for many reasons. For instance, enrollment rates can be at best satisfactory proxies for human capital only in some countries. Judson (2002) argues that secondary enrollment rates will only be good indicators for human capital accumulation in countries where secondary education is expanding rapidly. The deficiencies of literacy and enrollment rates as measures of human capital have motivated researchers to look for a more powerful human capital proxy, namely years of schooling of the workforce. Schooling years quantify the accumulated educational investment in 11 the current workforce and assume that human capital embodied in workers is proportional to the years of schooling they have attained. With respect to literacy and enrollment rates, schooling years take into account the total amount of formal education acquired by the workforce, that is, schooling years proxy more accurately the existing stock of human capital in a country (Bassetti, 2007). In this context, some studies use the percentage of the working age population with primary, secondary and tertiary education. All these measures reflect the quantity of human capital. So, the above proxies do not give an indication of the skill level of the workforce. Here comes the issue of human capital quality. The lack of human capital quality data in many studies considering the relationship between education and growth may be the biggest challenge in this area of research. The quantity of education is an inadequate measure of human capital differences, since school systems vary across countries in terms of resources, organization and duration. One solution in order to account for qualitative differences across education systems, is to focus on human capital quality measures, such as educational expenditure, student/teacher ratios and test scores. These indicators can be measured at different levels of education. However, using such quality measures as proxies of human capital, it is very difficult to get a measure that can be reliably extrapolated for the entire workforce. As a result, any possible measure of education has advantages and disadvantages, and they must be taken into account when the effect of education on economic growth is estimated. Moreover, the output measure used varies across studies, being Gross Domestic Product (GDP), GDP per-capita or GDP per worker in real terms. 2 The respective output growth measures used as dependent variables are real GDP growth, real GDP per capita growth or real GDP per worker growth. From the previous discussion, we can argue that the coefficients estimating the relationship between education and economic growth may differ between studies partly due to differences in the type of the education and output variables used. Main conclusion Thus, it seems safe to conclude that the educationeconomic growth empirical research, exhibits substantial publication selection toward positive growth effects of education, while the economic growth impact of education after taking into account publication bias depends critically on the specific features of the study. These findings do not necessarily imply that the positive impact of education on growth postulated by theory does not exist. It may well be the case that the problems characterizing empirical research on this question are so severe that they make it impossible to uncover this effect. In any case, our paper provides important information for future empirical studies evaluating the role of education in the process of economic growth. Education and Growth: Where All the Education Went Theodore R. Breton* and Andrew Siegel Breton Universidad EAFIT February 1, 2016 VI. Conclusions For over 20 years researchers have tried without success to find an effect on GDP from increases in schooling over five-year periods. After performing one of these analyses and finding only negative correlations, Pritchett  famously asked, “Where has all the education gone?” In this paper we provide an answer to this question. The existing analyses that fail to find any effect assume that the entire effect of schooling is immediate. We examine whether the effect of schooling on GDP may be substantially delayed to determine whether this may explain the failure to find any effect. We first present data showing that increases in schooling affect workers’ earnings differently depending on their level of schooling. We also show that workers’ earnings in middle income countries only increase with experience if they have prior schooling. We conclude that increases in worker productivity and in earnings on the job are a delayed effect of their prior schooling. We then examine whether a pattern with a delayed effect similar to the one observed for workers´ earnings may characterize the relationship between increased schooling and GDP. We find that this pattern can explain changes in GDP. But we find that a pattern in which the initial effect of schooling on GDP is slightly lower than in the earnings studies (30% of its eventual effect) provides results that are more statistically significant. The clear implication is that the increase in GDP during a five-year period is due to the increases in schooling during the prior 40 years. We find that an additional year of schooling in the population age 25-64 raises GDP by 7% on average over a 40-year period, but the effect associated with this additional year in the initial five-year period is only 3%. Since average schooling typically increases by less than a year over a five-year period, the initial effect of increased adult schooling on GDP is very small. So this is where the education went. It had a small initial positive effect on GDP and then contributed steadily to workers’ productivity as they gained experience over their working lives. Even though their improvement in productivity occurred on the job, it was not independent of their prior schooling. As a consequence, it is appropriate to consider the productivity improvements on the job as a delayed effect of the workers’ earlier schooling. The results in this article highlight the reality that increasing a country’s productivity through education is a very long-term process. It begins by investing in children’s pre-schooling and continues through primary, secondary, and post-secondary schooling. If there is any effect on GDP during the schooling period, it is negative since students forego work to attend school. Even if the students entering the work force have more schooling than the existing workers, the positive effect of this additional schooling materializes slowly. In this study the initial effect did not occur until after age 25 and then it continued to increase until age 65. But the positive effects of additional adult schooling are not limited to this 40-year period. There is considerable evidence that student achievement in school is positively affected by their parents’ level of education [Fuchs and Woessmann, 2007]. In addition, students with educated parents stay in school longer and eventually are more productive on the job [Hanushek and Woessmann, 2008 ]. This continuing positive effect of additional schooling beyond an adult’s working life may explain why the effect of schooling on GDP in crosssectional analyses is greater than the effect found in this study.