Does employment status matter for the wellbeing of rural households in Bangladesh?

Selim Raihan and Fatima Tuz Zohora

In rural Bangladesh, a great challenge is to tackle the low pay, poor-quality jobs that are unrecognized and unprotected by law, widespread underemployment, the absence of rights at work, inadequate social protection, and the lack of representative voice. There remains a big question whether poverty in rural Bangladesh is concentrated in certain employment categories.

Our paper uses the data from the Bangladesh Integrated Household Survey (BIHS) of IFPRI. This data are nationally representative data of rural Bangladesh for the year 2011-2012 where the sample size is 6,500 households in 325 primary sampling units (PSUs). The reason for using the BIHS database for this study is that this is the latest available survey data on rural Bangladesh. Our paper has attempted systematic analysis in understanding the association between employment status and wellbeing of rural households in Bangladesh.

From the BIHS data, our study has used consumption expenditures as the principal indicator of household economic status or wellbeing, and has used per capita consumption expenditure as the proxy for income. The total consumption expenditure is measured as the sum of total food consumption and total non-food expenses excluding lumpy expenditures. Income (expenditure) deciles have been created by dividing the households into ten groups from the lowest to the highest in terms of households’ total income. Employment statuses have been constructed for those household heads who are able and eligible to participate in the labor market. By definition, the labor force consists of everyone above the age of 15 who is employed (including individuals working without pay) or unemployed but actively seeking employment. Household head, not counted in the labor force, includes students, retired people, disabled people, and discouraged workers who are not seeking work.

The distribution of the different employment categories in the labor force is shown in Figure 1. In the x-axis, 10 deciles are organized in ascending order on the basis of monthly consumption expenditure of the rural households. Therefore, first decile is the poorest one and the 10th decile is the richest one. The figure summarizes that, while wage employment is mostly concentrated in the poorer deciles, self-employment is concentrated mostly in the richer deciles. Salaried employed maintains smaller shares among poorer deciles.

Fig1

Fig2

Fig3

Figures 2 and 3 show the educational status of the male and female workers by employment categories in the rural areas. Males with no education seem to be highly concentrated in the wage employment in both farm and nonfarm sector. They are also densely present in the self-employment activities. In the salaried employment category, the dominant share is of the males with less than secondary level but higher than primary education. However, males with HSC and beyond HSC account for around 25% of the salaried employment. Females with no education also seem to be highly concentrated in wage employment (Figure 3). Females with less than primary education has a dominant share in the case of unemployed (55.56 %). In the case of the unpaid family job for the female adults, around 28% of them are with less than secondary but higher than primary education.

In order to investigate the factors affecting wellbeing of rural household in Bangladesh we have used the cross section multinomial logistic regression models. The income status of the household is considered as the dependent variable, where per capita consumption expenditure is used as a proxy for households’ income status. For the explanatory variables, we have used different categories of employment of household head e.g. wage labor in the farm and nonfarm sector, self-employed in the farm and nonfarm sector, salaried worker and unpaid worker. All of these variables are dummy variables, where ‘unemployed’ has been considered as the base employment status. Other explanatory variables are age of household head, years of education of the head, number of dependent members per household, per capita landholding and a dummy variable on whether the household receives international remittance or not.

The major findings from multinomial logistic regressions can be summarized as follows. First, wage employment in the farm sector has statistically significant association with all income declies between 6 and 10. However, such employment status doesn’t have any statistically significant association with income deciles between 2 and 5. For a wage worker in the farm sector, relative probabilities to be in deciles 6, 7, 8, 9 and 10 are respectively 39 percent, 44 percent, 75 percent, 85 percent and 90 percent lower than to be in decile 1. The result depicts the fact that wage employment in the farm sector are more concentrated among the poorer households and doesn’t play any pivotal role in shifting up the status of a household. The result is quite analogous for the wage-employed in the nonfarm sector too: if the household head is employed in nonfarm activities, the relative probability to be in the deciles 9 and 10 are 62 percent and 78 percent lower (respectively) than to be in decile 1.

Second, in case of self-employment, if the household head is engaged in the farm sector, the relative probability of that household to be in decile 10 is 44 percent lower than to be in the base decile 1. This association is insignificant for all other deciles meaning that, self-employment in the farm sector does not necessarily improve the income status. On the contrary, if the household head is self-employed in the nonfarm sector, the relative probabilities to be in deciles 3, 4, 5, 6, 7, and 8 compared to the base category are higher by 90 percent, 86 percent, 124 percent, 84 percent and 72 percent respectively. It shows that, self-employment in nonfarm sector has a strong transitory power to improve household wellbeing.

Third, when considering salaried employment, the study finds no significant influence of salaried employment over shifting the well-being status from income decile 1 to higher income deciles. On the other hand, if the household head is employed as an unpaid worker the relative probability to be in deciles 8, 9 or 10 is more than 80 percent lower than to be in the decile 1.

Among other variables, household characteristics like age of the head, dependent member per household, per capita land holding and remittance status hold significant impact on the nature of economic status of the household. If the age of the household head increases by one additional year, the relative probability to be in the top four deciles compared to the decile 1 increases by around 1.2 percentage points. It is also seen that, with the rise in number of dependents in a family the relative probability of the household to be in a higher decile compared to decile 1 becomes lower. The regression results also suggest that, education and international remittances play a role of pull factor in case of shifting household status from the lowest decile to upper deciles. An increase in the years of education of the household head by one additional year increases the relative probability to be in decile 2 compared to decile 1 by 10 percentage points; whereas, for the same increment, the relative probability to be in decile 10 compared to decile 1 increases by 35 percentage points. In case of remittances, households that receive remittance have more than 3 fold relative probability to be in decile 4 or above. For the remittance receiving households, the relative probability to be in decile 10 compared to the decile 1 is more than 25 times higher than a household that does not receive remittances.  Along with these, per capita land holding is appeared as an important household characteristics that can help a household to be on the higher deciles.

The findings of this paper provide a significant indication that rural nonfarm sector has a crucial role in reducing poverty and increasing the wellbeing of the rural household in Bangladesh. The study also specifies the importance of addressing the concern in the national policy making that poverty in rural Bangladesh is highly linked with certain employment categories.

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Cross-country differences in income inequality: Where do South Asian countries stand?

inequality_graph

In recent years, there has been a growing interest among general people, researchers and policy makers in income inequality, its causes, and its effects. The most popular index of income inequality is the ‘Gini index’ which measures the inequality among levels of income of the people of any country. A Gini coefficient of zero means perfect equality, where everyone has the same income, and a Gini coefficient of 1 (or 100%) expresses maximum inequality.

For meaningful comparisons among different countries with respect to their levels and trends in income inequality we need comparable data. National surveys on households’ incomes and expenditures in different countries provide data on the Gini index of these countries for some years. However, we are not in a position to use these data for cross-country comparisons due to various reasons. In those surveys there are differences in the population covered, differences in coverage on geography, age and employment status, differences in the definition on welfare (whether to use market income or consumption data), differences in the use of equivalence scale (whether to use household per capita or household adult equivalence), and differences in the treatment of various other items, such as non-monetary income and imputed rents. The Standardized World Income Inequality Database (SWIID), introduced in 2008, provides a dataset on income inequality that facilitates comparability for the largest possible sample of countries and years. A custom missing-data algorithm is used to standardize data on cross-country income inequality using the data from national surveys (Solt, 2016). Using the SWIID database, the World Economy Database (WED) version 9.1 has generated a time series database on the “Gini index” for 207 countries over the period between 1970 and 2015 by filling missing observations with the help of different estimation methods.

Using the WED 9.1, we have produced a scatter plot diagram with data on Gini indices for 207 countries in 1980 in the horizontal axis and data on Gini indices of the same countries in 2015 in the vertical axis. In the scatter plot, dots around the 45 degree line are the countries with ‘no or very small’ changes in Gini indices during 1980-2015; dots above the 45 degree line are the countries which experienced an increase in the Gini index; and finally, dots below the 45 degree line are the countries which experienced a decline in the Gini index. Out of those 207 countries, 18 experienced ‘no or very small’ changes in Gini indices, 109 experienced increases and 80 experienced declines. Among the 8 south Asian countries, 5 countries (Afghanistan, Bangladesh, India, Pakistan and Sri Lanka) observed rises while the rest 3 countries (Bhutan, Maldives and Nepal) experienced declines. We also brought China and South Korea into the picture, and it appears that the Gini index in China increased quite astonishingly, whereas that of South Korea declined.

We have also categorized the values of Gini index as follows: a Gini index value lower than 30 is considered low; an index value between 30 and less than 40 is considered medium; an index value between 40 and less than 50 is considered high; and an index value above 50 is considered very high. Depending on these classifications, we can observe some interesting movements of the South Asian countries during 1980 and 2015. Afghanistan moved from a status of low inequality to medium inequality; Bangladesh moved from medium inequality to high inequality; though Nepal, Pakistan and Sri Lanka remained within the medium inequality range, Sri Lanka was at the border of high inequality; India moved from high inequality to very high inequality; and both Bhutan and Maldives moved from very high inequality to medium inequality. In comparison, China moved from low inequality to very high inequality, whereas South Korea moved from medium inequality to very close to low inequality.

We also explored the factors affecting inequality in the cross-country and over time contexts. Results from a fixed effect panel regression suggest that while rise in the real GDP per capita tends to have a small negative association with the Gini index, an increase in both life expectancy at birth and net secondary school enrollment are strongly associated with the decline in the Gini index. These suggest that, an increase in per capita real GDP is not a guarantee for the reduction in income inequality, whereas investment in social infrastructure with the aim of raising the life expectancy at birth and a rise in secondary school enrollment can be very instrumental in reducing income inequality.

Reference: Solt, F. (2016). “The Standardized World Income Inequality Database”. Social Science Quarterly.

First published at the Thinking Aloud on 1 September 2016

Published at The Daily Star on 1 September 2016

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

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

Why do some countries trade more than others?

Theoretically, trade liberalization results in productivity gains through increased competition, efficiency, innovation and acquisition of new technology. In particular, the changing relative prices induced by trade liberalization cause a re-allocation of resources from less efficient to more efficient uses. Trade liberalization is also thought to expand the set of economic opportunities by enlarging the market size and increasing knowledge spillover effects. Empirical research on international trade also shows that, in general, larger trade-orientation and freer trade, with supporting policies and institutions, can lead to higher welfare for a country than otherwise.

However, a major question remains some way unclear – why do some countries trade more than others? More specifically, does country size matter? How does differences in per capita income affect trade-orientation among countries? Does human capital make any difference? How does tariff liberalization promote trade-orientation? Moreover, does foreign direct investment (FDI) affect trade performance? Furthermore, does geographical location have a bearing, i.e., being an island country or a landlocked country? Also, does membership of the GATT/WTO raise trade-orientation? Finally, does institution matter in trade-orientation?

In order to answer these questions, fixed effect panel regressions using a database covering the period between 1981 and 2014 for 128 countries were conducted. We have defined country’s trade to GDP ratio as the country’s trade-orientation. We want to explain why some countries have higher trade-GDP ratio than others. The explanatory variables are the size of population (to represent country size), per capita real GDP, an index of human capital, domestic average applied tariff rate, and FDI to GDP ratio. Data for all these variables, except human capital, are taken from the World Bank’s WDI, and the data of the human capital is taken from the PWT-8.1. All variables are expressed in natural logarithm. The regression results show that all explanatory variables are statistically significant.

The negative coefficient estimate of the size of population reveals that larger countries tend to be less trade-oriented than their counterparts, as 1% rise in the size of the population is associated with 0.2% fall in the trade-GDP ratio. The reason is that countries with a large population find a ready domestic market and can substitute imports by producing for the internal market. The positive coefficient of the per capita GDP shows that a rise in the real GDP per capita by 10% is associated with a rise in the trade-GDP ratio by 2.2%. The reason behind such an association could be related to domestic producers, with the rise in per capita GDP, becoming more efficient in competing and integrating with their foreign counterparts in the world market. As expected, domestic tariff liberalization is positively associated with higher trade-GDP ratio, as a cut in tariff rate by 10% is associated with a rise in trade-GDP ratio by 0.7%.

The positive coefficient of the FDI-GDP ratio suggests that greater FDI orientation is positively associated with greater trade orientation, and a rise in the FDI-GDP ratio by 10% is positively associated with a rise in the trade-GDP ratio by 0.3%. FDI is assumed to have a positive impact on the export-orientation of any economy, as much of FDI is directed towards the export-oriented sectors. The success stories of East and South East Asian countries have suggested that FDI is a powerful tool of export promotion because multinational companies, through which most FDI is undertaken, have established-contacts and up-to-date information about foreign markets. FDI may also lead to increasing imports in the recipient country as foreign owners tend to have a higher propensity to obtain their inputs from abroad than do their domestically owned counterparts.

Finally, in the case of human capital variable, a rise in the index of human capital by 10% is associated with a rise in the trade-GDP ratio by 9%. This is not surprising! A higher level of human capital is likely to have a positive impact on the perception of the people, as well as on the policy making of the government, in integrating their economy with the world market.

The findings of the LSDV models show that landlocked countries and island countries are 194% and 284% respectively more trade oriented than their counterparts. Both for island and landlocked countries, international trade plays a crucial role in their economic lives as most of these countries are dependent, to an unusual degree, on imported goods and services, including foodstuffs, fuel, equipment and industrial material as well as a wide range of manufactured products. However, interestingly, being a member of the GATT/WTO doesn’t make any difference in terms of trade-orientation.

We have also explored the association between trade-orientation and different institutional variables. The data of these institutional variables are derived from the ICRG database. The fixed effect regression results suggest that countries with better bureaucracy quality, larger democratic accountability, and sounder investment profile are associated with higher trade-orientation. These results are also consistent with findings from studies on the determinants of trade flows which argue distortions or costs placed on firms under inefficient institutions and poor governance can negatively affect trade flows.

Bangladesh’s trade-GDP ratio was only 19.2% in 1981, which increased to 44.5% by 2014. Despite the fact that Bangladesh has been able to raise its trade-GDP ratio by more than two-fold during this period, in 2014, out of the 166 countries, Bangladesh ranked 150th in terms of higher trade-GDP ratio. This suggests, greater trade-orientation in Bangladesh would require further cut in tariff rates, larger FDI-orientation, investment in human capital and improvement in institutional quality.

Published at the Thinking Aloud on 1 June 2016

Published at The Daily Star on 13 June 2016

Why do countries differ in total factor productivity?

Theoretical and empirical literatures on sources of economic growth emphasized on factor accumulation and factor productivity as two major sources of growth. Though factor accumulation can explain a significant part of economic growth, it can’t explain the sustained long run economic growth, as sustained long run economic growth is attributable to growth in productivity. Productivity is the cornerstone of economic growth. Increases in productivity allow firms to produce greater output for the same level of input, and thus result in higher Gross Domestic Product (GDP).

We should make clear the difference between labor productivity, which is output per worker, and Total Factor Productivity (TFP), which is the ‘ability’ with which all factors are combined to produce outputs. TFP is the part of output which is not explained by the amount of inputs used in production. Essentially, its level is determined by how efficiently and intensely the inputs are used in the production process. TFP growth is usually measured by the Solow residual. TFP plays a key role in economic fluctuations, economic growth and cross-country differences in per capita income.

The scatter-plot using the data for 110 countries in 2011 shows a very interesting association between TFP and log of per capita GDP. The TFP data are derived from the Penn World Table version 8.1 (PWT 8.1) with some required adjustments and extensions. Here the TFP level of USA is considered as 1 and other countries’ TFP levels are indexed against USA’s TFP level. For example, among the South Asian countries, the TFP levels of Bangladesh, India, Nepal, Pakistan and Sri Lanka in 2011 were 0.15, 0.48, 0.11, 0.28 and 0.42 respectively. Similarly, TFP levels of Malaysia and Thailand were 0.47 and 0.65 respectively. Singapore’s TFP level (1.1) was higher than that of USA. The trend line shows a very strong positive association between TFP and log of per capita GDP (the correlation coefficient is 0.9). Nepal and Bangladesh, though on the trend line, are at the lower end of the association. A straightforward policy suggestion for these countries is that a rise in the TFP level is required to raise their per capita GDPs.

Fig1_prod

Why do countries differ in TFP? How to improve the level of TFP? In order to answer these questions, we have run fixed effect cross-country panel regressions for 110 countries for the period 1995-2011 considering log of TFP as the dependent variable. The explanatory variables include log of human capital (an index of human capital per person which is linked to the average years of schooling and the return to education), log of public expenditure on health as % of GDP, and log of trade-GDP ratio. The data source of human capital is the PWT 8.1, and the data of health expenditure and trade-GDP ratio are taken from the World Bank WDI. The logic behind the formulation of this model is that we want to explore how cross-country differences in statuses of education, health and openness affect the cross-country differences in the TFP. The regression results suggest that all three explanatory variables are statistically significant with expected signs. 1% rise in the human capital index is associated with 0.39% rise in the TFP. Also, 1% rise in the ratio of public health expenditure to GDP is associated with 0.03% rise in the TFP. Finally, 1% rise in the trade-GDP ratio is associated with 0.03% rise in the TFP.

In extended models, we have found that the ratio of FDI to GDP is positively and significantly associated with the TFP. 1% increase in the FDI-GDP ratio is associated with 0.01% rise in the TFP. Furthermore, institutional variables like bureaucracy quality and investment profile (from the PRS database) are positively associated with the TFP with statistical significance. Improvements in the bureaucracy quality and investment profile by one unit are associated with the rise in TFP by 0.03% and 0.01% respectively.

The aforementioned analyses point to some very obvious policy suggestions. Countries like Bangladesh need to attach decisive emphasis on improving their currently low levels of human capital. This can happen by enhancing investing on education and health quite a lot in order to increase the efficiency in using inputs in the production process thus raising the level of TFP. Also, larger trade and FDI orientations and improvement in the quality of institutions are indispensably important.

Published at the Thinking Aloud on 1 May 2016

Published at The Financial Express on 10 May, 2016

Is Bangladesh poised for leaping towards a middle income country?

Bangladesh has recently been upgraded from low income country (LIC) to lower-middle income country (LMIC) as per the World Bank’s classification. There is an aspiration for graduating from the LDC status to that of a middle income country by 2021 as per the United Nations’ classification. In this context, it is very important to assess the capacity of the country’s social infrastructure in achieving the desired level of economic growth rate and subsequently the targeted per capita income level.

As far as the social infrastructure, in terms of education and health, is concerned, there is a valid question to ask, whether Bangladesh is poised for leaping towards a middle income country. In this paper, we focus on two indicators, public expenditure on education as % of GDP and public expenditure on health as % of GDP. It is well established in economic literature that strong bases of education and health are needed for a country to accelerate and sustain economic growth.

Fig1-education

Fig2-health

In Figure 1 and Figure 2, we have presented two scatter-plots involving public expenditure on education as % of GDP and public expenditure on health as % of GDP respectively against the log of GDP per capita for the lower-middle income countries and upper middle income countries as per the World Bank’s classification. The data are the averages for the years between 2010 and 2014 and they are derived from World Bank’s WDI. In both scatter-plots, the horizontal line at the level of 8 of log GDP per capita separates lower-middle income countries from upper-middle income countries. Bangladesh’s position is shown by the red circle.

Even these simple scatter-plots present some very interesting insights. In Figure 1, the trend line suggests that the association between public expenditure on education as % of GDP and log of GDP per capita for 80 countries (involving both lower-middle income and upper-middle income countries) is positive. This evokes that countries with a higher public expenditure on education as % of GDP tend to be associated with a higher GDP per capita. It is interesting to see that all upper-middle income countries are well above the trend line; whereas, most of the lower-middle income countries are well below the trend line while some of them are just on the trend line. Bangladesh’s position is among the worst performing countries, as its public expenditure on education as % of GDP (around 2%) is much lower than those of all upper-middle income countries, and is even much lower than most of the lower-middle income countries. Similar pattern is observed, as depicted in Figure 2, in case of the association between public expenditure on health as % of GDP and the log of GDP per capita. Once again, Bangladesh appears to be among the worst performing countries with only 1.2% share of public expenditure on health in GDP. Interestingly, the positive association between health expenditure and GDP per capita (Figure 2) seems to be stronger than that of between education expenditure and GDP per capita (Figure 1), which is supported by empirical literature that health expenditure has a relatively more immediate positive effect than education expenditure on economic growth.

The aforementioned exercises suggest that Bangladesh has to improve social infrastructure significantly in her way towards a middle income country. There are two critical lessons for Bangladesh. First, the current allocations for education and health expenditure as % of GDP need to be almost doubled from their current meagre levels (China and Malaysia, depicted in Figures 1 and 2, are good examples). Second, the efficiency of the public expenditure on education and health in Bangladesh needs to be improved. There are some countries (both in categories of lower-middle income and upper-middle income) who have been able to achieve much higher levels of GDP per capita than that of Bangladesh even with similar proportions of education and health expenditures that Bangladesh has.

Published at the Thinking Aloud on 1 March 2016

Published at The Daily Star on 6 March 2016