Does institution matter for human capital development?

graph_human capital

A fundamental proposition of new growth theories is that human capital is a key driver of economic growth. Development of human capital for the people of a country encompasses not only the diffusion and assimilation of available knowledge, but also the generation of new knowledge – the source of innovation and technological change – which boosts economic growth.

It is rather a challenging task to measure a country’s stock of human capital. Popular indicators, used to measure human capital, include adult literacy rate, school enrolment rates, average years of schooling, quality of schooling etc. The Penn World Table version 8.1 provides a dataset on an index of human capital (HCI) for 134 countries over a period of 6 decades. HCI is an index of human capital per person which is related to the average years of schooling and the return to education. In 2010, United States had the highest HCI value (3.62) and Mozambique had the lowest one (1.27). In that year, among the 134 countries, 33 countries had HCI values higher than 3; 48 countries had values between 2.5 and 2.99; 28 countries had values between 2 and 2.49; and 25 countries had values less than 2. In South Asia, in 2010, the HCI values for Bangladesh, India, Nepal, Pakistan and Sri Lanka were 2.07, 1.93, 1.71, 1.99 and 3.16 respectively.

Why do some countries have higher level of human capital than others? Empirical literature have looked at different factors such as spending (both public and private) on education and health, and differences in income levels; but hardly there has been any emphasis on differences in institutional capabilities among the countries. However, quality of institution, as it affects economic growth process, can also have a bearing on the quality of human capital. Therefore, a valid question can be asked: does institution matter for human capital development? Of course there could be a bi-directional causality between human capital and quality of institution, where quality of institution could also be influenced by the level of human capital. Nevertheless, leaving aside the causality, here we are more interested to know about the association between these two.

The scatter-plot, as presented in the graph, has been generated using the data of index of human capital and index of institution for 93 countries over a period of 1984-2010 with over 2500 observations. We have constructed the index of institution using the data of six major ICRG (www.prsgroup.com) variables, namely bureaucracy quality, control of corruption, investment profile, democratic accountability, government stability, and law and order. As values of these six ICRG variables have different scales, we have rescaled them between 0 and 10. The aggregate institution index is the average of these six indicators with the range between 0 and 10, where 0 and 10 respectively indicate the lowest and highest levels of quality of institution.

The scatter-plot suggests a very strong positive association between quality of institution and level of human capital, which signifies the importance of better institution for higher level of human capital. Interestingly, if we compare Bangladesh with Malaysia, levels of both institution and human capital of Bangladesh in 1990 (1.62 and 1.52 respectively) were much lower than those of Malaysia in 1990 (6.05 and 2.31 respectively). Despite the fact that during 1990 and 2010, Bangladesh made some notable progresses in both fronts, by 2010, the levels of these two indices of Bangladesh (5.52 and 2.07 respectively) were below than what Malaysia had in 1990!

Results from a more sophisticated cross-country panel econometric regression reinforces this association. In this regression, the index of human capital has been considered as the dependent variable. We have also created two institutional indices: economic institution and political institution. The economic institution index is comprised of three ICRG indicators – bureaucracy quality, control of corruption and investment profile; whereas the political institution index consists of other three ICRG indicators – democratic accountability, government stability and law and order. Other explanatory variables include initial GDP per capita, public expenditure on education as a percentage of GDP, and under-five mortality rate. The regression results indicates that after controlling for initial GDP per capita (which has a positive significant association with human capital index), public expenditure on education has a statistically significant positive association and under-five mortality rate has a statistically significant negative association with the human capital index. The highly significant and positive coefficients of both economic and political institution indices suggest strong positive associations between these institutional variables and human capital index. The z-score regression analysis, however, refers to larger importance of political institution over economic institution in human capital development.

The aforementioned analysis points to the fact that better economic and political institutions matter for human capital development. While countries need to make critical spending for human capital development, improvement in institutional environment is unequivocally essential.

Published at the Thinking Aloud on 1 July 2016

Published at The Financial Express on 18 July 2016

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How pervasive is regional disparity in primary education in Bangladesh?

Selim Raihan and Mansur Ahmed

CEDI14_1

Sound knowledge on educational performances of different regions across the country can be helpful in the decision making process for better resource allocation and policy formulation. A multidimensional composite measure of educational development, that captures many dimensions such as access, inputs, quality, gender-parity, and outcome, would enable policy makers to target and to channel scarce resources in lagging regions more efficiently.

This paper develops a multidimensional composite index for the primary education development for 483 upazilas (sub-districts) in Bangladesh and identifies the lagging regions for potential policy intervention. More specifically, this paper constructs the Education Development Index (EDI) for the primary education sector of Bangladesh. This index facilitates cross-sectional analysis of the levels of attainment in education among different regions of Bangladesh. Furthermore, it draws policy attention to crucial parameters for achieving equity in access and attainment in educational development.

Bangladesh has one of the largest primary education systems in the world with an estimated 16.4 million primary school aged children (6 to 10 years). This study uses the data from a census which was carried out in 2011, which covered all 11 types of primary schools with a total number of more than 80,000 schools. Education Management of Information System (EMIS) division of Directorate of Primary Education (DPE) under Ministry of Primary and Mass Education (MoPME) undertook the census.

Five broad parameters and 19 sub-parameters (individual indicators) are used in the construction of EDI. The broad parameters are (i) Access, (ii) Infrastructure, (iii) Quality, (iv) Gender Equity, and (v) Outcome. This study has applied the Principal Component Analysis (PCA) method for each broad parameter and calculated weights for each of the indicators within the broad parameter. (For detail methodology of EDI construction and list of indicators, see Raihan and Ahmed, 2016). The objective of PCA is to reduce the dimensionality (number of indicators) of the data set but retain most of the original variability in the data. The overall EDI constructed for this analysis is again a weighted summation of five broad EDIs – access EDI, infrastructure EDI, quality EDI, gender equity EDI and outcome EDI, with weights derived from the PCA on these five EDIs. The index value of 1 indicates the highest educational development with 0 as the lowest development.

Analysis of the aforementioned census data suggests that, despite indicators related to accessibility of schools showed good scenarios, still about 20% schools were not easily accessible to the neighboring residents. Astonishingly, only 20% of schools enjoyed electricity access. Class rooms at the primary schools in Bangladesh were quite crowded as the student-room ratio was 38. Student-teacher ratio was also very high, implying crowded class rooms with low degree of interaction between students and teachers. Still a significant proportion of teachers in primary schools were without bachelor degree. In terms of gender parity at the primary school enrolment, not all upazilas achieved the gender parity. Though Ministry of education set a target that the ratio of female to male teachers should be above 60%, the observed female to male teachers ratio in the census data was about 53%, which suggests need for renewed efforts to reach at that goal. Another important indicator related to gender equity is the percent of schools with girls’ separate toilet. The census data shows that only 40% of schools had separate toilets for girls. Despite Bangladesh achieved remarkable success in primary school enrollment, average pass rate at grade V and school attendance rates were 87% and 85% respectively with wider variations among the upazilas. On average, 1 out of 10 students needed to repeat the same class and 1 out of 20 students dropped out from school.

In terms of access EDI (constructed using two sub-parameters – schools per thousand populations and accessibility of schools), most upazilas performed in the mid-range (0.4 – 0.6), suggesting a significant scope of improvement in terms of accessibility of schools. However, the upazilas around the ‘haor’ (large water bodies) regions in Sylhet division and in Mymensingh division and the upazilas from Chittagong Hill Tracts (CHT) lagged behind other upazilas badly in terms of accessibility. Some other upazilas along the Jamuna River and the Padma River (the ‘char’ lands) also performed poorly. While improvement of accessibility of schools is necessary for most upazilas, these lagging upazilas warrant special attention for their geographical locations. Upazilas located in the metropolitan areas, in contrast, performed well in terms of accessibility.

The patterns of infrastructure EDI (constructed using five sub-parameters – school with safe water, school with electricity, school with toilet per 100 students, average room condition of the school, and student-room ratio) were similar to those of the access EDI. However, the performance of upazilas in terms of infrastructure EDI was worse than that of access EDI. A large number of upazilas were in the lower mid (0.2 – 0.4) of infrastructure EDI, while most of them belonging to the Chittagong Hill Tracts and Mymensingh division. Upazilas in the south west coastal region and along the upper Jamuna River in the Rangpur division also performed poorly.

In terms of quality EDI (constructed using three sub-parameters – students-teacher ratio, qualification of teachers, and availability of teaching-learning materials), though some number upazilas performed in the upper middle range (0.6 – 0.8), only few upazilas were in the top quintile. In fact, quite a few upazilas were in the lower middle range (0.2 – 0.4). Most of the top ten performing upazilas were from metropolitan areas.

In terms of equity EDI (constructed using four sub-parameters – ratio of girls among total students, ratio of female among teachers, schools having separate toilet for girls, and gender equity in dropout rate), most upazilas in Bangladesh performed in the lower middle of the ladder (0.4 – 0.6). Some upazilas performed even poorly. Therefore, despite the ‘satisfactory’ level of gender equity in primary education at the national level, gender parity in primary education is a serious issue in a large number upazilas. Like the access EDI, upazilas from ‘haor’ regions performed poorly in gender equity. Quite understandably, upazilas from urban areas were among the top performing upazilas in terms of equity EDI.

In terms of outcome EDI (constructed using five sub-parameters – gross enrolment ratio, pass rate at grade five, attendance rate, dropout rate and repetition rate), most upazilas performed in the mid-range , implying a room for improvement for all upazilas. Upazilas from the ‘haor’ region, Chittagong Hill Tracts, and poverty-stricken North-Bengal were the worst performers.

The map presented depicts the spatial distribution of composite EDI (constructed using five EDIs – access EDI, infrastructure EDI, quality EDI, gender equity EDI, and outcome EDI). The map shows that very few upazilas were in the highest range (0.8 – 1.0) of EDI. In fact, not many upazilas were in the range of 0.6-0.8 of EDI score. Most upazilas were concentrated in the range of 0.4-0.6. Most of the top ten upazilas were from large metropolitan areas such as Dhaka, Chittagong or Khulna. Lowhajong of Munshiganj and Shibpur of Norsingdi were only the exceptions and these upazilas were also located in close proximity to the capital city Dhaka.  Upazilas in the ‘haor’ region of Sylhet division and Mymensing division and upazilas from the CHT were fatally lagging behind all other upazilas in terms of primary education development. All the bottom ten upazilas were either from the ‘haor’ region or from the CHT. Though the population density in the CHT is low, the upazilas in the ‘haor’ region are home of a sizeable portion of population of the country. Thus, these lagging regions warrant special attention to improve the overall development of the primary education in Bangladesh.

The aforementioned analysis suggests that despite many achievements during the past decades, major improvements are still needed in Bangladesh in order for all children to receive the benefit of quality primary education. Opportunities for good quality primary education in Bangladesh are limited by inequalities associated with wealth, location, ethnicity, gender, and other factors. The major challenges thus include addressing poor quality of education, high dropout rates, promotion of equity and accessing education, and targeted programs for lagging regions.

 Reference:

Raihan, S. and M. Ahmed. (2016). Spatial divergence of primary education development in Bangladesh through the lens of Education Development Index (EDI). MPRA paper 71177.

First published at the Thinking Aloud on 1 July 2016

Published at The Financial Express on 4 July 2016