Dynamics of economic growth in Bangladesh

Selim Raihan and Wahid Ferdous Ibon

Rapid and sustained economic growth is very critical for Bangladesh economy in its way towards a middle income country. In this article, we have investigated the major determinants of economic growth in Bangladesh using time series data for 44 years (1972-2015). We start with a production function approach, which incorporates the features of neo classical and new-growth theories. Subsequently, we have investigated the impacts of trade policies, fiscal policies, FDI, interest rate, inflation, infant mortality rate, enrolment in secondary education, infrastructure and institution on growth in Bangladesh’s real GDP (gross domestic product). A new database (World Economy Database, version 9.1) has been used, which is complemented by data from the Peen World Table (PWT8.1) and World Bank’s World Development Indicators (WDI). Most of the variables under consideration are found to be non-stationary (integrated of order one). Two non-stationary time series may lead to a spurious relationship between them if they are not co-integrated. Therefore, we checked for the possibility of co-integrating relationship, using the Johansen co-integration test, and found at least one co-integrating relationship in all the regressions, which confirms that the long run estimates show causal relationships. We ignore bi-directional causality in the regression model, as this is not what we want to explore in this analysis.

The basic production function

With the aim of identifying the determinants of economic growth in Bangladesh, we start with a Cobb-Douglas production function. Along with employment and physical capital stock, we have incorporated human capital into the production function. We multiply the data on human capital with employment data to create the human capital adjusted employment variable. The regression results suggests that, in the long run, on average, one percent increase in the human capital adjusted employment leads to 0.25% increase in the real GDP. Furthermore, one percent increase in the physical capital stock leads to 0.12% increase in the real GDP. As the variables of the production function are co-integrated, there must be an Error Correction representation which shows the short run adjustments of the variables under consideration if there is any deviation from the long run equilibrium relationship. Error Correction term is -0.0197 which is statistically significant, negative and less than unity, as expected. About 1.97% error is thus being corrected each year following any deviation from the long run equilibrium.

Secondary school enrolment helps

There are both theoretical and empirical literature which provide evidence that the educational level and its quality are important causal determinant of income, both at the individual and national levels. A highly educated labor is more productive relative to his/her less educated counterpart, and this increased labor productivity helps a nation grow faster. Education is a key component of human capital. In terms of the net secondary school enrolment, though Bangladesh made a progress during 1972 and 2015 from around 16% to 52%, still there is a need for substantial further improvement. Here, we have investigated the effect of the net enrolment in secondary school on real GDP and have found positive effect, as expected. One percentage point rise in the net secondary school enrolment ratio leads to, on average, 0.013% increase in the real GDP.

Reduction in the infant mortality rate helps

Bangladesh has shown its capacity to reduce infant mortality rate rapidly over the past four decades. Among 1000 live births, the rate came down from 148 in 1972 to 30.7 in 2015. In the regression, the infant mortality rate appears with a negative and significant coefficient. On average, one point reduction in the infant mortality rate contributes to the rise in real GDP by 0.01%.

Greater trade-orientation promotes growth

Theoretically, trade liberalization results in productivity gains through increased competition, efficiency, innovation and acquisition of new technology. Trade policy works by inducing substitution effects in the production and consumption of goods and services through changes in prices. These effects, in turn, change the level and composition of exports and imports. 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 the effects of knowledge spill over.

Since its independence, Bangladesh underwent a variety of trade policy reforms, which resulted in the rise in trade-GDP ratio, import-GDP ratio and export-GDP ratio from 10.6%, 6.5% and 4.1% respectively in 1972 to 41.7%, 23.3% and 18.4% respectively in 2015. To identify the growth effects of these three trade-orientation variables, we incorporated them into the production function through three separate regressions. The regression results indicate that, these variables are statistically significant with positive signs. One percentage point increase in trade-GDP ratio, import-GDP ratio, and export-GDP ratio account for, on average, 0.014%, 0.023% and 0.029% increase in the real GDP respectively.

Larger FDI-orientation propels growth

Foreign direct investment (FDI) is another driver of economic growth, particularly for a least developed country (LDC) like Bangladesh. FDI contributes to transfer the technical knowhow from advanced countries to the less developed countries. In 2015, the FDI inflow in Bangladesh was only US$ 2.2 billion which was about 1% the GDP, whereas government, as stated in the 7th five year plan, aims to achieve a level of FDI inflow of US$ 9.6 billion by 2020. In the regression, the coefficient of the FDI-GDP ratio is found to be statistically significant and positive, as expected. One percentage point increase in the FDI-GDP ratio leads to the rise in real GDP, on average, by 0.12%. In order to attract more FDI, there is a need to maintain political stability, improvement in infrastructure and reduction in the cost of doing business. The planned 100 special economic zones, if they are implemented successfully, can be helpful in attracting FDI.

Positive effect of government transfer payments

The regression result confirms a positive significant impact of government transfer (social security payments, safety net programs, pension payments etc.) on the rise in real GDP in Bangladesh economy. On average, one percentage point rise in the ratio of government transfer to GDP leads to a rise in real GDP by 0.05%.

Reduction in lending interest rate helps

Interest rate is the price of fund that private investors lend from the banks. Therefore, more private investment takes place following a reduction in lending rate, which in turn promotes economic growth. This is evident from our regression analysis that one percentage point reduction in the lending rate, on average, increases real GDP by 0.03%.

Inflation hurts growth

Rise in the general price level hurts Bangladesh’s growth. An increase in the price level decreases the real wage earned by the laborers. This lower real wage is followed by a lower aggregate private consumption demand, which in turn affects national income badly. Our regression analysis suggests, one point increase in consumer price index accounts for, on average, 0.001% reduction in real GDP.

Infrastructure promotes growth

Infrastructure is a key ingredient for high and sustained economic growth. Better infrastructure helps total factor productivity to rise by lowering transaction cost and a more efficient use of inputs of production. Due to the lack of time-series data on different dimensions of infrastructure, here we consider total number of mobile and fixed line telephone subscriptions as a proxy for infrastructure. In the regression analysis, we find that one percent increase in total telephone subscription results in, on average, 0.12% rise in real GDP.

Quality of institution matters

We have considered an index of institution in the regression. 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. In 1980, the index value was 2.15, which increased to 5.5 by 2015. The regression suggests a positive significant role of institution on real GDP in Bangladesh. On average, one point rise in the institution index leads to the rise in real GDP by 0.05%.

What do we learn?

The analysis in this article suggests that, for further economic growth acceleration in Bangladesh, there is a need for reforms in economic policies and institutions, investment in infrastructure, and making most of the demographic dividend through investment in public health, education, and human capital development. All these will require increased domestic private investment and FDI targeting broader economic and export diversification. Reform of economic and political institutions for efficiency gains is critically important.

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Transitions between growth episodes: Do institutions matter and do some institutions matter more?

Selim Raihan, Sabyasachi Kar and Kunal Sen

A large literature has examined the role of institutions in explaining economic growth. While the earlier literature has examined the role of institutions in determining long-run per capita income, a new literature examines the determinants of growth accelerations and deceleration episodes – which are large discrete changes in medium term growth rates that are common in developing countries. Some of these studies examine the onset of growth accelerations while others examine the onset of growth decelerations. However, these studies look at only the timing of the shift in the growth rate (either as an acceleration or a deceleration), and the econometric methodology they use are probit models (where the year of the break is taken as one, with other years as zero) to study the likelihood a growth break occurring in a given year, for a set of correlates. An important limitation of these studies is that they do not differentiate between the different growth episodes that a country is transitioning from or to. For example, when a country moves from a growth collapse to rapid growth, it is a different growth transition qualitatively than when it moves to an episode with slightly positive but slow growth rates.

In this paper, we investigate the role of economic and political institutions in determining the likelihood of a country transitioning from one growth episode to another. In contrast to the previous literature, in this paper, we provide a richer characterisation of the growth process where a country may move between six different types of growth episodes, ranging from growth collapses to rapid growth episodes. By doing so, we are better able to capture the episodic nature of growth and that many countries tend to switch frequently between growth collapses to slow growth episodes to rapid growth episodes.

We differentiate between six types of growth episodes – from growth collapses (where the episode specific per capita real GDP growth rate, g, is -2 per year), to negative growth (g between -2 and 0), stagnation (g between 0 and +2), stable growth (g between +2 and +4), moderate growth (g between +4 and +6), and rapid growth (g over +6). Using multinomial logit models, in the context of a panel dataset of 125 countries from 1984 to 2010, we examine the likelihood of switching from one growth episode to another growth episode. We examine the role of contract viability (as a measure of the quality of economic institutions) and the role of democracy and bureaucratic quality (as measures of political institutions) in explaining the switches that countries experience between different types of growth episodes. The data on contract viability, democracy and bureaucratic quality are derived from the ICRG database (www.prsgroup.com).

We find that though bureaucracy quality has a positive effect while switching from negative growth episode to positive growth episodes, it doesn’t matter in most of the cases while switching from lower order growth episodes to higher order growth episodes. Both contract viability and democratization can explain the switching from negative growth episode to positive growth episodes. Contract viability and democracy can also explain the movements from lower positive growth episodes to higher positive growth episodes. However, while contract viability is important for moving from stable growth episode to rapid growth episodes, democracy is not important in explaining this switch. This suggests that while better economic and political institutions matter in taking a country from growth collapses to stable growth, economic institutions matter more than the political institutions for the switching from stable growth to rapid growth.

Our results suggest that, democratic episodes do not necessarily witness transitions to rapid growth episodes from moderately positive growth episodes. However, democratic episodes do witness a transition from negative to positive growth episodes, indicating that democratization does prevent the worst type of growth episode that a country can experience. We also find that improving state capacity in the form of the quality of the bureaucracy can help in taking a country out of negative growth episodes but that higher state capacity does not increase the likelihood of rapid growth episodes. This finding suggests that previous research that has found a positive role of bureaucratic quality in fostering economic growth need to differentiate between phases of growth, and that the relationship between bureaucratic quality and economic growth may not be monotonic.

We find that the most important institutional determinant of switching to higher order growth episodes from lower ones, and in particular, to rapid growth episodes, is the nature of property rights institutions – that is, the extent to which investors trust the viability of contracts. In contrast to the previous literature on the determinants of growth accelerations, we find that not only does institutional quality matter in bringing about a growth acceleration, it is the case that the greater the quality of property rights institutions, the higher is the likelihood of a transition to a rapid growth phase.

Our findings have clear policy implications. For a country in a growth decline or collapse, it is important to stress improvements in both political and economic institutions, such as bureaucratic quality, viability of contracts and democratization to move into an episode of positive growth. However, once the country is in a stable or moderate positive growth episode, further movements into rapid growth episodes need larger emphasis on improving the quality of property rights institutions than enhanced democratization or state capacity. Economic institutions trump political institutions in bringing about rapid growth episodes, though they both matter in reversing growth collapses.

Dr. Selim Raihan (Professor, Department of Economics, Dhaka University, Bangladesh; Email: selim.raihan@gmail.com), Dr. Sabyasachi Kar (Research Fellow, Institute of Economic Growth, Delhi, India), Dr. Kunal Sen (Professor, IDPM, University of Manchester, UK)

First published at the Thinking Aloud on 1 August 2016

Published at The Financial Express on 2 August 2016

Published at ESID blog on 1 August 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

Unearthing Bangladesh’s Comparative Advantages

Selim Raihan and Md. Jillur Rahman

The analysis of comparative advantage is important from the policy perspective. Trade policies of a country should be tuned to promote export items where the country has comparative advantage.  The Revealed Comparative Advantage (RCA) analysis, suggested by Bela Balassa in 1965, is an ex post analysis of comparative advantage and has been used in many studies. RCA index is used to calculate the relative advantage, disadvantage and trade potential of a certain product in a country.

The RCA index is measured as the ratio of a product’s share in the country’s total export relative to its share in the world’s total export. The formula for the RCA is equal to (Xij/Xit)/(Xwj/Xwt) where, Xij and Xwj are country i’s export and world export of product j respectively, while Xit and Xwt are country i’s total export and world total export respectively. If RCA is greater than unity, the country is said to have comparative advantage in that product; and if RCA is less than unity, the country has comparative disadvantage in that product. The RCA index is popular because of its simplicity, availability of data and for cross-country comparisons. The index is consistent with country’s factor endowment and productivity.

In this article, we are interested to know in which products Bangladesh has comparative advantage, and the dynamic changes of its comparative advantage. We have calculated RCA at 6-digit level of the harmonized system (HS) of classification for the periods between 2001 and 2013. RCA indices for Bangladesh are calculated using the data of export volumes of Bangladesh and the world from the Trade Map database.

Before going into the RCA analysis, let’s first explore how many products Bangladesh exports. At the 6-digit HS code level, there are approximately 5300 products. Figure 1 shows that in 2001, Bangladesh exported 896 products, which, by 2013, increased to a number of 2038. In 2012, Bangladesh exported 2126 products which was the highest among the years under consideration. This suggests that, not only in terms of volume but also in terms of number of products, Bangladesh’s export capacity increased by more than double during 2001 and 2013. On a year-to-year basis, some new products were added to the export basket and some were ceased to be exported. However, there were 375 common products which Bangladesh exported all the years under consideration.

Fig1-RCA

Figure 2 presents the numbers of products at 6-digit HS code where Bangladesh had comparative advantage during 2001 and 2013. In 2001, the number of products with RCA>1 was 316, which, with some year-to-year fluctuations, increased to 382 by 2013. The highest number of RCA>1 was observed in 2007 consisting 483 products. Figure 2 also suggests that the percentage share of RCA>1 products in total number of products declined over time: from 35% in 2001 to 19% in 2013. However, as a percentage of total exports, throughout those years, Bangladesh enjoyed comparative advantage in more than 97% of its total export. Furthermore, over those years, comparative advantage had been consistent for 130 products at the 6-digit level among which 115 products were from readymade garment industries. All these suggest that although Bangladesh was able to expand its export basket during 2001 and 2013, the number of products it had comparative advantage didn’t increase proportionately, which indicates escalated concentration of RCA in certain products.

Fig2-RCA

The escalated concentration of RCA in certain products during the period under consideration is manifested by the fact that Bangladesh’s RCAs had been concentrated around the products in the HS codes 03 (fish and shrimp), 41 (raw hides and skins and leather), 52 (cotton yarn), 53 (raw jute), 61 (knitted readymade garments), 62 (woven readymade garments) and 63 (home textile and jute hessian bags). However, a close look at Figure 3 suggests that Bangladesh’s comparative advantage has been highly concentrated around the readymade garments sector. In 2013, number of products with RCA>1 under the HS codes 61, 62 and 63 accounted for 57% of the total number of products with RCA>1. In 2007, such number was 43%. It should also be mentioned here that, readymade garments account for more than 80% of total export earnings of Bangladesh in recent years.

Fig3-RCA

Although RCA had been concentrated around the readymade garments sector, the average value of RCA declined. The maximum value of RCA in the readymade garments was 495 in 2001, which declined to 184 by 2013. Bangladesh had also been losing the very high comparative advantage it had in garments exports. Figure 4 suggests that, in 2001, Bangladesh enjoyed very high RCA (RCA>100) in 18 garments products, which declined to only 3 in 2013. In contrast, the number of products with RCA less than or equal to 30 increased over time: from 142 in 2001 to 181 in 2013.

Fig4-RCA

Similar analysis, with respect to the leather and leather goods, suggests that there had not been much variations in the number of products having RCA in this sector. And, as in readymade garments sector, Bangladesh had been losing very high comparative advantage it had in this sector. In contrast, Bangladesh had been enjoying consistently very high comparative advantage in jute and jute products, where, in all of 6 products, RCA ranged between 53 and 1068.

The aforementioned analysis shows that during the period under consideration, Bangladesh’s comparative advantage had been concentrated around low-skilled labor intensive readymade garments exports. However, in recent years, compared to early 2000s, there had been some products where Bangladesh gained comparative advantage. These include edible fruits, animal and vegetable fats and oil, preparations of cereals, flour, starch or milk and pastry cooks’ products, preparation of vegetable, fruits, nuts, residues from food industries, rubber and rubber products, copper and copper products, and furniture. However, Bangladesh lost comparative advantage in fertilizers, printing industry’s products, articles of iron and steel, and miscellaneous manufactured articles.

Finally, we are interested to know how tariff rates, both at home and partner country, affect Bangladesh’s revealed comparative advantage at the sectoral level. For this exercise, we have constructed a panel data at 6-digit HS code level for the period between 2001 and 2013. The dependent variable is the RCA which is a binary variable, where it takes a value of 1 if RCA is greater than unity and zero otherwise. The first explanatory variable is the domestic tariff rate at 6-digit HS code level, which is the effectively applied tariff rate and its data is taken from the WITS database. The second explanatory variable is the partner country’s tariff rate, which is calculated as the weighted average of simple tariff rates imposed by top export destination partners of Bangladesh namely USA, EU, Canada and India. Data of partner countries’ tariff rates are taken from the WITS and OECD-WTO database. The fixed effect panel logit regression results suggest that domestic tariff rate is negatively associated with RCA and the coefficient is statistically significant. This suggests that a cut in domestic tariff raises the likelihood of RCA greater than unity among the sectors. In contrast, the coefficient of the partner countries’ weighted tariff rate is not statistically significant. The reason behind the non-association between the RCA and partner countries’ tariff rate could be because of the fact that the large part of Bangladesh’s export to its major partner countries are under different preferences schemes; for example, Bangladesh’s exports enjoy the duty free and quota free market access in the EU market.

Published at the Thinking Aloud on 1 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