Hiccups of ‘Bangladesh Development Surprise’

20181031_091336Bangladesh’s economic growth and development performance over the past two decades have been impressive. With the poor quality of institutions, such a performance has often been termed as a ‘development surprise’ or ‘Bangladesh paradox’. But is it at all a ‘surprise’ or a ‘paradox’ – since anything beyond any reasonable explanation can appear as a paradox? Is Bangladesh’s development performance beyond any ‘reasonable’ explanation?

If we look at the quality of institutions in Bangladesh, the performance has been very poor. According to the World Governance Indicators (WGI), in 2016, out of 156 countries, Bangladesh ranked 114 in terms of ‘Voice and Accountability’, 101 for ‘Political Stability’, 138 for ‘Government Effectiveness’, 114 for ‘Regulatory Quality, 101 for ‘Rule of Law’, and 117 for ‘Control of Corruption’. Other indicators of institutional quality also portray similar pictures. For example, in the case of the World Bank’s Doing Business indicator of 2019, out of 190 countries, Bangladesh’s ranking was 176. With respect to Transparency International’s Corruption Perceptions Index of 2018, Bangladesh’s ranking was 149 out of 180 countries. In the case of the Global Competitiveness Index (GCI) of 2017-2018, Bangladesh’s ranking was 99 out of 137 countries.

Against the aforementioned poor quality of institutions, the average GDP growth rate in Bangladesh increased from 3.7 percent in the 1970s to 6.6 percent in the 2010s. Bangladesh has been able to increase the average GDP growth rate by one percentage point for each decade since the 1990s. The country cut down the poverty rate from as high as 71 percent in the 1970s to 24 percent in 2016, became the second largest exporter of readymade garments in the world, and registered some notable progress in social sectors.

How do we reconcile the above mentioned two contrasting scenarios? Difficulty in such reconciliation perhaps has led to the emergence of the ideas of ‘surprise’ or ‘paradox’. However, we can try offering some reasonable explanations to this so-called ‘surprise’ or ‘paradox’. We also argue that, without significant improvements in the quality of institutions, such ‘surprise’ will continue to lead to periodic ‘hiccups’ like the accidents in the RMG sector (several fire incidents, Rana Plaza incident of factory collapse); frequent road accidents; frequent fire incidents in the residential and commercial areas; repeated scams in the financial sector; serious environmental degradation in cities, rivers and forest areas; periodic labour unrest; uncontrolled scams in public examinations; social disintegration among youth in the forms of extremism and drugs; etc.

Now, coming back to some reasonable explanations of ‘surprise’ or ‘paradox’, if we look at the well-known institutional indicators (WGI, Doing Business, Transparency International, and GCI), all refer to the quality of formal institutions. However, in countries like Bangladesh, placed at the lower level of the development spectrum, what governs is a host of informal institutions, and the development of formal institutions is weak and fragile. There are some interesting political economy frameworks to understand the importance of informal institutions in developing countries. For example, Mushtaq Khan’s framework of ‘growth-enhancing institutions’ in contrast to ‘market-enhancing institutions’ elaborates how the role of informal institutions can be critical in developing countries. Some developing countries, especially East and Southeast Asian countries, have been successful in steering the unconventional institutions to drive growth. Another framework, proposed by Lant Pritchett, Kunal Sen and Eric Wrecker, relates to the idea of ‘deals space’. Deals (informal), in contrast to rules (formal), among the political and economic elites, are prevalent in the developing countries. Deals can be open (access is open to all) or closed (access is restricted), and also they can be ordered (deals are respected) or disordered (deals are not respected). According to this view, countries are likely to exhibit high growth when deals are open and ordered.

Informal institutions can have two distinct roles with respect to the stages of development. At the early stage of development, if countries can steer the informal institutions to the extent they are ‘growth-enhancing’ as well as the ‘deals space’ is more ordered (either open or closed), countries can manage a regime of strong growth rate and can also achieve some improvements in the social sector. However, for the transition from a lower stage of development to a higher stage, whether the country can maintain the high growth rate and achieve larger development goals, is depended on the dynamics of how the informal institutions evolve and formal institutions become stronger and functional. Not many developing countries have been able to do this. Certainly, the East Asian and most of the Southeast Asian countries are the success stories in using the informal institutions efficiently at the early stage of development as well as making some notable successes in the transition towards functional formal institutions.

In contrast to many other comparable countries of Asia and Africa at the similar stage of development, least developed countries, in particular, Bangladesh has been successful in creating some efficient pockets of ‘growth-enhancing’ informal institutions against an overall distressing picture of formal institutions. This is how the ‘Bangladesh Surprise’ story unfolds. The examples of ‘pockets of efficient informal institutions’ in Bangladesh include the well-functioning privileges and special arrangements for the RMG sector, promotion of labour exports, agricultural research and development related to food security, and microfinance.

However, the next question is how could Bangladesh create such ‘pockets of efficient informal institutions’ and make the ‘best’ use of them? The explanations include both historical and political economy perspectives. Two historical events strongly influenced the mindset of the political and economic elites in Bangladesh. First, the 1971 liberation war led to the emergence of an independent Bangladesh state which gave unprecedented, enormous and first time independent power to the burgeoning political and economic elites of the Bengali nation of this part of the world. Also, the citizens, in general, enjoyed some benefits of such power. Largely, the entrepreneurship nature of the people of this country is deeply rooted in this feeling of power. The reflection of successful entrepreneurship is seen in the cases of the RMG sector, labour migration, and microfinance. As Bangladesh is not rich in natural resources, elites found the RMG sector as a basis of generation of substantial rents. The sources of rents in the RMG sector include the Multifibre Arrangements (MFA) quota (which no longer exists) and the Generalised Systems of Preference (GSPs), different forms of subsidies, tax exemptions, a suppressed labour regime, and weak compliance. Through large scale employment generation in the RMG sector and its induced effects of poverty alleviation and female empowerment, the elites were also able to draw support from the non-elites for this sector. The second event relates to the experience of the 1974 famine, which led the elites to realize that a country like Bangladesh, with a huge population in a small piece of land, cannot afford anything like this in the future. Therefore, subsequent governments focused on the development of the agricultural sector to ensure food security. All these also helped achieve some notable progress in the social fronts.

Despite the aforementioned achievements, the fundamental question is whether Bangladesh can continue its success and achieve larger development goals with the business as usual processes. There are concerns that the weak institutional capacity of the country may work as a binding constraint as the country eyes to meet the stiff targets of the Sustainable Development Goals (SDGs) by 2030, aspires to become an upper middle-income country by 2031, and visions to become a developed country by 2041. Dividends from the existing ‘pockets of efficient informal institutions’ are on a decline, and the elites have not been able to create any new such ‘pockets’ apart from the ones mentioned above.

How is Bangladesh performing in terms of the transition from some ‘pockets of efficient informal institutions’ to well-functioning formal institutions? This can be answered by looking at how well the formal institutions are taking shape. The trends in the quality of formal institutions between 1996 and 2016, as are manifested by the movements of the World Governance Indicators, suggest that, with some fluctuations, there are deteriorations in the cases of ‘Voice and Accountability’, ‘Political Stability’ and ‘Government effectiveness’, and some trivial improvements in the cases of ‘Regulatory Quality’, ‘Rule of Law’ and ‘Control of Corruption’. As the country is plunged with a number of challenges related to slow progress in structural transformation, lack of economic diversification, high degree of informality in the labour market, slow pace of job creation, poor status of social and physical infrastructure, slow reduction in poverty, and rising inequality, such poor improvements in formal institutions will make hiccups of ‘Bangladesh development surprise’ a rule rather than an exception.

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Governing emerging development challenges: A South Asian perspective

IMG_46412Despite the divergence in economic and political trajectories, South Asian countries share commonalities with respect to the urge for governing emerging development challenges in the wake of the new world and regional dynamics. As far as future economic and social developments are concerned, for most of the South Asian countries, there are four major confronting areas, which are related to inclusive development, global and regional trade integration, financing development programmes, and politics of development.

With respect to inclusive development, the debate over quantity vs. quality of economic growth is prominent in most of the South Asian countries. While South Asia is now the fastest growing region in the world, with India and Bangladesh registering high and stable growth rates followed by Sri Lanka and Pakistan recording modest growth rates and other countries experiencing unstable growth rates, the panacea over the ‘number’ of growth rate overshadows the importance of the ‘quality’ of economic growth. Despite high economic growth rates, the region hosts more than one-fourth of the world’s extreme poor and inequality within the countries is on the rise. Furthermore, there are genuine concerns of ‘jobless growth’ as the pace of employment generation, in most of the South Asian countries, lags behind the pace of economic growth. Moreover, staggeringly high informal employment ratio, low degree of ‘decent job’, poor working conditions, and low female participation characterize the labour market of this region. The growth, employment and poverty challenges of the South Asian countries are primarily aggravated by the nature of development strategies these countries have been following over the past decades. These countries have not been successful in rapid industrialization, and few manufacturing and services sectors have been the major drivers of growth with narrow implications for employment generation, poverty alleviation and inequality reduction. Most of these countries face the challenge of ‘premature deindustrialization’. Also, the lack of preparedness in the context of the 4th industrial revolution can lead to a large-scale job loss. Given the aforementioned longstanding development challenges, the 2030 Development Agenda has created additional pressure on the development task-lists of these countries. However, it can be argued that this 2030 Development Agenda has also created new opportunities for the South Asian countries to get their development trajectories ‘right’.

The challenges related to integration with global and regional trade remain critical for the South Asian countries. As far as integration with the global trade and value chain is concerned, there are now emerging pressures, in the wake of growing scepticism in the globalization and trade integration process, as reflected by Britain’s BREXIT, escalated protectionism in the United States, and trade war between the United States and China. Furthermore, as China is going through a major economic rebalancing, the impact of this rebalancing goes beyond China’s national borders due to China’s integration with other Asian countries through manufacturing, trade and investment links. There are enhanced opportunities for Asian developing countries to take advantage from China’s economic transformation, as changes in China’s supply and demand will have spillover effects on other economies in the region and industries might shift concentration to other countries in the region. However, there are concerns whether South Asian countries have sufficient skills and capacity to take advantage of transferring or emerging industries or develop new businesses to meet the growing demand. While South Asian countries encounter the uphill tasks of diversifying their export baskets and moving into high value-added product space, these countries also have been less successful in extracting the benefits of regional integration and regional value chains. One of the major factors behind the weak regional integration in South Asia is the hostile political relation between India and Pakistan, for which many regional integration initiatives remain hostages.

Financing development goals has been a critical challenge for most of the South Asian countries. Given the changing global scenario, for financing development goals, South Asian countries will have to rely more on domestic sources, and this is, no doubt, an uphill task. The tax-GDP ratio remains low for most of these countries with heavy reliance on indirect taxes and import duties. The patterns of public expenditures on social sectors in this region suggest that, the averages of the shares of public expenditure on education, health and social protection in GDP in South Asia are only around 2.5%, little over 1%, and less than 2% respectively which should be increased to more than 5%, 4% and 10% respectively to meet a large number of development goals. In addition to the social expenditure, the countries need to spend substantially on developing their physical infrastructure, which most of these countries are seriously lagging behind. It is obvious that with the low tax-GDP ratio it is difficult to finance the aforementioned large development goals. However, the question is how to mobilize the required amount of resources domestically when these countries suffer from weak institutions and inadequate tax-infrastructure. It is also important to note that a mere generation of resources would not ensure implementation of the development goals if institutional and governance-related aspects are not addressed properly. Finally, there remains a big challenge in getting the priorities in spending ‘right’. One example of the wrong priority is the high spending on military affairs in some of these countries, especially in India and Pakistan, while these countries incur a very low level of spending on social sectors.

In order to govern the new challenges, the South Asian countries require the ‘correct’ politics of development. The past development trajectories of these countries are largely characterized by ‘crony capitalism’ with a high degree of rent-seeking activities, suppressing the elements of ‘developmental states’. Weak functioning of economic and political institutions and the dominance of informal institutions are prevalent in these countries. In the coming days, to implement the development goals, efforts need to be something extraordinary, and strong political commitments are needed to make a significant departure from past practices.

[Based on the presentation made by the author at the 4th SANEM Annual Economists’ Conference on “Governing New Challenges: Inclusive Development, Trade, and Finance’, held in Dhaka on 16-17 February 2019]

Creating new opportunities for employment in Bangladesh

image_1353_334851Bangladesh’s economic growth and development experience over the past four and half decades, since the Independence in 1971, have generated a lot of interests among the academics and development practitioners both from home and abroad. From an war-torn economy in 1972 until now, Bangladesh has been able to increase its per capita GDP by more than 17 times (from as low as around US$ 100 in 1972 to US$ 1751 in 2018), cut down the poverty rate from as high as 71 percent in the 1970s to 24 percent in 2016, became the second largest exporter of readymade garments in the world, and registered some notable progress in social sectors. In 2015, Bangladesh graduated from the World Bank’s classification of the low-income country to lower-middle income country category. Bangladesh has successfully met all three criteria for LDC graduation in the first review in March 2018. It is expected that Bangladesh will be able to meet the graduation criteria in the second review in 2021 and will finally graduate from the LDC status in 2024.

In the context of the aforementioned development path, there are six major labour market and employment challenges in Bangladesh. These are the creation of jobs (the quantity), ensuring decent jobs (the quality), acceleration of economic growth and economic diversification, increasing female labour force participation, enhancing youth employment, and raising the productivity of labour.

In terms of the number of new jobs, there has been slower growth in job creation in recent years in Bangladesh. Between 2013 and 2016-17, while the average annual GDP growth was 6.6%, the average annual growth of jobs was only 0.9%. The number of manufacturing jobs declined by 0.77 million, and more importantly, female manufacturing jobs declined by 0.92 million. Also, manufacturing’s employment share declined in recent years: from 16.4% in 2013 to 14.4% in 2016-17. The slow growth in job creation is also reflected in the declining employment elasticity over the last decade. The overall employment elasticity with respect to GDP growth declined from 0.54 during 1995-2000 to 0.25 in 2010-2018. While the SDG 9.2 highlights the target of doubling industry’s (primarily manufacturing) share of GDP in the LDCs by 2030, with the changing nature of manufacturing, leaning towards automation, increasing the number of new jobs, especially in this sector, will remain a big challenge.

In the case of ensuring decent jobs, there are concerns about a high degree of informal employment in Bangladesh. The share of informal employment in total employment in Bangladesh remains well above 85%. A study by SANEM, using the Labour Force Survey data and a recent household survey conducted by SANEM, classified jobs into three different categories: ‘good enough’ jobs, ‘good jobs’ and ‘decent jobs’. The analysis of this study shows that the share of decent jobs in total jobs in Bangladesh increased from 10% in 2010 to only 12% in 2018. Therefore, there is an immense challenge to register a significant headway from such slow progress in ensuring decent jobs. In this case, both the government and the private sector have important roles to play.

Further acceleration of economic growth, enhancing the quality of economic growth, sustaining economic growth and economic diversification all have important implications for the labour market and employment challenges in Bangladesh. Though Bangladesh has been able to maintain an annual average real GDP growth rate of over 6% during the past decade, there are concerns with respect to the quality of growth. One of the major aspects of job creation and ensuring decent jobs is the need for economic diversification. However, economic growth, so far, has not been associated with significant economic diversification. Despite some progress in raising the manufacturing shares in GDP and employment during 1990 and 2018, Bangladesh has not been successful in moving to the next phase of industrialization. The manufacturing sector in Bangladesh is highly concentrated around low value-added readymade garments, and the country has not been yet able to move successfully to the next generation of manufacturing, especially to high value-added manufacturing. Though the private sector has the dominant role to play, the private investment-GDP ratio has remained stagnant over the past decade. Therefore, energising private sector investment for achieving the aforenoted objectives remains a critical challenge for Bangladesh. For this, the effective remedy of both the policy-induced and supply-side constraints will be imperative. A number of supply-side constraints in the form of weak infrastructure and the high cost of doing business need to be addressed within a short time span. Bangladesh has not even been able to attract much foreign direct investment (FDI) even by the LDC standard. In 2016, the FDI share in GDP in Bangladesh was only 0.9% against the LDC average of 3.3%. Weak infrastructure and poor business environment are critical problems for Bangladesh to attract both domestic private investment and FDI. According to the 2019 Doing Business index of the World Bank, Bangladesh ranks 176th among 190 countries. In terms of sub-components of the Doing Business index, Bangladesh’s worst performances are observed in the areas of ‘enforcing contracts’, ‘getting electricity’ and ‘registering property’. There is a need for rapid improvement in these areas. The initiatives taken by the Bangladesh government in setting up 100 special economic zones (SEZ) as well as the development of big infrastructural projects seem to address these issues. However, there is a need for faster and quality implementation of these projects, as delay in implementation, cost overrun, and sub-standard quality of projects are long-standing problems in Bangladesh which discourage private investment.

Over the past three decades, labour force participation (LFP) rate of females has increased. Nevertheless, the LFP rate of female remained stagnant between 33% and 36% during 2010 and 2016-17. We explored both the supply and demand side factors affecting female labour force participation in Bangladesh. Our analysis suggests that issues e.g. child marriage, early pregnancy, coupled with reproductive and domestic responsibilities have not changed much with the economic progress of the country, and these factors constrict female LFP. To explore the demand side factors, especially the role of innovation and technology, affecting firms’ demand for female labour, we used firm-level data from the World Bank’s Enterprise Survey of 2007 and 2013. Female employment intensity, defined as the ratio of the number of female labour to male labour, declined in major manufacturing and services sectors during 2007 and 2013. The overall female employment intensity declined from an average of 20.35% in 2007 to 17.67% in 2013. The econometric estimation suggests a negative impact of innovation and technological upgradation on firms’ female employment intensity. In these contexts, there is a need to provide incentives and remove barriers to the creation of new and higher productivity jobs in the sectors which can generate large-scale employment for females.

Youth employment is a major challenge in Bangladesh. The country is passing through the phase of the demographic dividend, and estimates by SANEM suggest that the country will continue to enjoy this dividend until 2030. However, two critical areas of concerns are there with respect to youth employment. The share of youth not in education, economic activities and training (NEET) increased from 25.4% in 2013 to 29.8% in 2016-17, and 87% of the youth NEET are female. Also, the youth unemployment rate increased from 8.1% to 10.6% during this period. In order to address these challenges, there should be targeted programs for the specific disadvantaged segments of the youth population through skill-development and appropriate labour-market policies.

In the case of raising the productivity of labour, it is important to note that the productivity of labour critically depends on both quality health and education services. However, Bangladesh lags behind significantly in ensuring quality health and education for all. The public expenditures on both health and education as percentages of GDP in Bangladesh are among the lowest in the world. The country, therefore, needs to attach vital emphasis on improving the existing low level of human capital by enhancing investment on education, skill development, and health facilities, and by making such spending more efficient.

How do public education and health spending reduce poverty?

Selim Raihan and Mehzabeen Ahmad

In recent decades, the developing world has made important progress in reducing extreme poverty. The data from the World Bank shows that the number of people living below the international poverty line of US$ 1.9 a day dwindled down from 1.85 billion people in 1990 to 768.5 million in 2016. However, the global share of the extreme poor population stands at over 10%, and there is uneven progress across different regions in the world. Therefore, eradicating poverty in all its forms and dimensions, including extreme poverty, remains the greatest global challenge and the most significant hurdle in the path of attaining sustainable development goals (SDGs) worldwide.

A majority of the global decline in poverty is explained by the reduction of poverty rates in East Asia and Pacific and even South Asia to an extent, due to the thriving economic growth experienced by these regions. However, a large population continues to suffer from poverty and a major portion of the rest remains vulnerable and at risk of falling back below the poverty line. A glaring spatial disparity can be perceived, accompanied by low levels of human development. If the qualities of health, education, employment and overall standard of living continually fail to cope with income growth, it may ultimately further hinder the capability of the masses; reinforce poverty and impede the process of growth. A similar picture can be admonished for Sub-Saharan Africa, which currently hosts the largest number of poor compared to other regions. This region’s multidimensional aspect of poverty is reflected in economic, human and social deprivation, explained by the very slow progress in Human Development Index (HDI) from the 1990s and the elevated rate of income poverty. Inequality also remains a significant crisis in the Latin American countries, in the form of chronic and transitory poverty, despite the recent upsurge of economic development in this region.

As the gap between the rich and poor widens, across and within nations, it becomes imperative to ensure a sustained resilience and global initiative against all dimensions of poverty. With that aim, the first SDG is assigned to “end poverty in all its forms everywhere” and its seven associated targets focus on various approaches to universal eradication of poverty and inequality, with a special attention to implementing necessary social protection programs, ensuring equal access to basic utilities, mobilizing global resources to extend cooperation towards the developing countries and constructing national and international policy and strategy frameworks.

In order to understand the current state of the cross-country differences in the poverty rates, we compared poverty rates across 72 developing countries (for which data is available from the World Bank’s World Development Indicators database) for the time period of 2010-15. Table 1 and Table 2 present the top 10 and bottom 10 performing countries with respect to poverty rates based on US$ 1.9 and US$ 3.2 poverty lines respectively.

Table1

Table2

Table3

According to Table 1, all the 10 countries with highest rates of poverty, in terms of US$ 1.9 poverty line, are from the Sub-Saharan African region, with Madagascar displaying the staggeringly highest rate of 77.8%. In contrast, the list of top countries with lowest poverty rates is dominated by the European countries, with countries such as Belarus, Poland, and Romania displaying almost no population below the US$ 1.9 poverty line. Few countries from Asia also make it to the top with minimal levels of poverty.

Table 2 provides a similar scenario for poverty rates calculated at a poverty line of US$ 3.2. Most countries on the list of bottom 10 or highest poverty rates remained unchanged. Madagascar and Burundi have almost 90% of the population below poverty line. Among the countries which possess the lowest rates of poverty at US$ 3.2, Belarus again tops the list, while Malaysia and Hungary make an entry in the top 10 rankings.

Table 3 illustrates the situation of all South Asian countries (except Afghanistan, due to unavailability of data), in terms of poverty. The countries have been ranked from the lowest to the highest rate of poverty for US$ 1.9 and US$ 3.2 poverty lines. Sri Lanka and Bhutan top both the lists, while India and Bangladesh stand at the bottom of the list with the highest share of the population living below the poverty line.

It has long been argued in the economic literature that public spending on education and health can be a powerful policy tool in the developing countries to reduce poverty, as these expenditures not only address the symptoms of poverty but also the causes of poverty. Public spending on education and health is argued to contribute to economic growth of a country by strengthening the human capabilities of the poor people. However, empirical literature to support this view has been limited due to the unavailability of time-series data on poverty. In this article, we use a cross-country panel data of poverty, constructed by Raihan (2017), to explore how public spending on education and health can affect poverty. This dataset has been constructed by considering periodic poverty rates (of US$ 1.9 poverty line) and average values of other variables for those corresponding periods. The constructed data has seven periods between 1981 and 2015. These are 1981-1985, 1986-1990, 1991-1995, 1996-2000, 2001-2005, 2006-2010 and 2011-2015. The missing values of the poverty rates have been filled-in using extrapolation and interpolation methods. This constructed data has 72 countries and the source of the data is the World Development Indicators of the World Bank.

The fixed effect panel regression results suggest that the coefficient of the per capita GDP is negative and significant suggesting that increase in the per capita GDP is strongly associated with a reduction in the poverty rate. Also, the ratio of remittance to GDP appears to have a positive and statistically significant association with the reduction in the poverty rate. After controlling for differences in per capita GDP and remittance-GDP ratios, one percentage point rise in the share of public spending on education in GDP is associated with 1.33 percentage points fall in the head-count poverty rate, and one percentage point rise in the share of public spending on health in GDP is associated with 2.4 percentage points fall in the head-count poverty rate. Both the fixed effect coefficients of public education and health spending are highly statistically significant.

Results from the aforementioned empirical exercises have important policy implications. A large number of developing countries, with the incidence of high poverty rates, are seriously lagging behind in terms of ensuring the critical levels of public spending on education and health in proportion to their GDPs. The business-as-usual scenarios of public education and health spending will not help these countries achieve the first SDG of ‘no poverty’ by 2030. There is thus a need for some extraordinary efforts in bringing large positive changes in the business-as-usual scenarios.

Raihan, S. (2017). “A cross-country panel dataset on poverty”, mimeo. SANEM

Dr. Selim Raihan, Professor, Department of Economics, University of Dhaka & Executive Director, SANEM: selim.raihan@gmail.com

Mehzabeen Ahmad, Research Associate, SANEM: mehzabeenahmad@gmail.com

First published in the Thinking Aloud on 1 December 2017

The challenging arithmetic of poverty in Bangladesh

Selim Raihan

Bangladesh has made important progress in reducing poverty over the past one and half decades. According to the national estimates, the overall head-count poverty fell from as high as 48.9% in 2000 to 24.3% in 2016. Also, the extreme poverty fell from 34.3% to 12.9% during the same period.

Despite its progress in reducing poverty, there are some major concerns regarding whether Bangladesh will be able to achieve the targets set by Goal 1 of the Sustainable Development Goals (SDGs) by 2030 with the business-as-usual scenarios. Goal 1 of SDGs sets the targets of eradicating extreme poverty and reducing at least by half the proportion of people living in poverty according to national definitions.

First, Bangladesh still remains a country with a very high incidence of poverty. In 2016, there were about 40 million poor people as per the national poverty line income. The number of extreme poor is also staggering with about 21 million people living below extreme poverty line in 2016. If we consider World Bank’s Lower Middle Income Class Poverty Line, which has a value of US$3.2 (PPP, in 2010), in 2010, 59.2% people in Bangladesh were under the poverty line income in contrast to 31.5% poor people as per the national poverty line income. This suggests that small adjustments in the poverty line income can change the poverty statistics quite significantly.

graph_poverty

table-poverty

Second, the annual average reduction in poverty rates has declined gradually over the past one and half decades. During 2000-2005, the annual reduction in overall poverty rate was 1.8 percentage points, which declined to 1.7 percentage points during 2005-2010, and further declined to 1.2 percentage points during 2010-2016. The most alarming trend is that while during 2000-2005, the annual reduction in extreme poverty rate was 1.8 percentage points, the rate declined to 1.5 percentage points during 2005-2010 and to 0.8 percentage points during 2010-2016. This suggests that the scope and success in reducing overall and extreme poverty rates in Bangladesh have become limited in recent years.

Third, the poverty elasticity of economic growth declined over the past one and half decades, indicating a declining effectiveness of economic growth in reducing poverty. The poverty elasticity of economic growth shows the percentage point change in poverty rate due to a percent change in real GDP (gross domestic product). In case of overall poverty, such elasticity declined from 0.32 in 2000-2005 to 0.16 in 2010-2016. For extreme poverty, the elasticity had a larger fall as it declined from 0.33 to 0.1 during the same period.

Fourth, despite that during 2010-2016, the country witnessed the highest average annual growth rate in GDP, both the annual reduction in poverty rates and poverty elasticity of economic growth had the lowest values. This suggests that economic growth alone cannot take care of reduction in poverty. As per the calculated elasticity values of 2010-2016, and with the business-as-usual growth rate of GDP, Bangladesh will have an overall and extreme poverty rates of around 10% and 4% respectively by 2030. Even with an accelerated average growth rate of GDP of 8%, overall and extreme poverty rates, by 2030, will be around 6.5% and 2% respectively. This means that, though there will be some progress in reducing overall poverty, neither the business-as-usual nor the accelerated growth scenarios will be able to eliminate extreme poverty by 2030. Under the business-as-usual growth scenario, there will still be around 8 million extreme poor, and under the accelerated growth scenario, there will still be around 4 million extreme poor by 2030.

Despite accelerated economic growth in recent years, why has there been much slower progress in poverty reduction? Three critical factors can be attributed to this. First, the annual average number of generation of employment declined from 1.7 million in 2000-2005 to 1.3 million in 2005-2010 and further to 0.9 million in 2010-2016. This means the accelerated economic growth during 2010-2016 was not ‘employment-friendly’. Second, the annual average share of public expenditure on education in GDP remained frustratingly unchanged at around 2% throughout 2000-2016. Bangladesh is among the bottom list of countries in the world with the lowest ratio of public expenditure on education to the GDP. In contrast, such ratio is around 5% for most of the Southeast Asian countries. Third, the annual average share of public expenditure on health in GDP declined from around 1% in 2000-2005 to 0.9% in 2010-2016. The public health expenditure as the percentage of GDP in Bangladesh is one of the lowest in the world, whereas, such ratio is around 2.5% for most of the Southeast Asian countries. All these three factors contributed to a rising inequality too in Bangladesh over this period. While in 2000, the ‘gini’ coefficient, a measure of income inequality, was around 0.45, it increased to as high as 0.48 by 2016. There are now strong global evidence that the effectiveness of economic growth in lowering poverty falls with the rise in income inequality.

What needs to be done? In order to increase the effectiveness of economic growth in reducing poverty, the ‘jobless’ growth phenomenon needs to be avoided. For this, the economic growth momentum needs to be tuned for ‘meaningful’ structural transformations of the economy where promotion of labor-intensive and high-productivity sectors would be fundamental. Also, poverty reduction is not simply about raising household income, but also about expanding human capabilities. In this context, Bangladesh has to increase the shares of public expenditure on health and education in GDP quite substantially in the coming years.

Dr. Selim Raihan. Executive Director, SANEM. Email: selim.raihan@gmail.com

First published in the Thinking Aloud on 1 December 2017

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.

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