rafmacalaba/datause-encoder-data huggingface dataset:
18

# **Table 10: Growth in World Commercial Service Exports and Imports by Income Group and Region**

||A|nnual Expor|t Growth(in|%)|An|nual Import|Growth(in|%)|
|---|---|---|---|---|---|---|---|---|
|CountryGroup|1980-90|1990-00|2000-10|1980-10|1980-90|1990-00|2000-10|1980-10|
|**BY INCOME LEVEL**|||||||||
|Developed economies|8.7|5.9|8.4|7.7|9.2|5.3|7.5|7.3|
|Developing economies|8.4|7.9|12.7|9.7|4.4|8.0|12.0|8.1|
|High income developing economies|10.7|6.4|11.8|9.6|8.0|6.1|11.3|8.4|
|Upper middle income economies|6.6|10.1|12.0|9.5|2.5|9.8|12.1|8.0|
|Lower middle income economies|7.9|7.2|15.9|10.3|3.2|8.5|13.0|8.2|
|Low income economies|4.8|5.8|11.0|7.2|2.6|3.9|10.7|5.6|
|Of which: Least developed countries|3.6|6.5|11.4|7.1|3.2|4.3|13.7|7.0|
|**Memo:**|||||||||
|Developing econ, excl. China & India|8.6|7.0|10.7|8.7|4.2|6.9|10.3|7.1|
|Upper middle income, excl. China|6.3|8.5|8.7|7.8|2.2|7.6|9.4|6.4|
|Lower middle income,excl. India|8.6|5.5|11.5|8.5|2.6|7.6|9.8|6.6|
|**BY REGION**|||||||||
|World|7.9|6.3|9.4|7.9|7.3|5.9|8.8|7.3|
|Developing economies|8.4|7.9|12.7|9.7|4.4|8.0|12.0|8.1|
|Asia|13.0|8.4|14.2|11.9|11.4|10.1|12.9|11.5|
|Latin America & Caribbean|5.4|7.1|7.6|6.7|2.0|7.5|8.5|5.9|
|Europe, Middle East & North Africa|2.2|8.2|11.3|7.2|-0.6|4.6|11.9|5.2|
|Sub-Saharan Africa|2.5|4.7|9.7|5.6|0.2|3.0|13.5|5.4|
|**Memo:**|||||||||
|BRICS 5 countries|10.5|10.4|18.6|13.1|11.1|9.9|17.4|12.8|
|OPEC 12 members|-2.4|6.7|7.3|3.8|-3.1|3.3|13.6|4.4|

Source: Based on World Bank World Development Indicator database.
This paper has five sections. Following this introduction, the empirical context and setting for evaluating the IR is presented in section 2. The methodology and data used for the empirical analysis are described in section 3, followed by the results and discussion in section 4. The paper ends with the conclusions and policy recommendations emerging from this study in section 5.

# **2. The empirical setting in the context of the IR debate**

Bangladesh has made commendable progress over the past 40 years, overcoming dire predictions in the early 1970s of widespread starvation to attain its goal of self-sufficiency in rice, its main staple (Hossain and Bayes 2009). Underlying this achievement has been impressive agricultural growth, particularly since the mid-1990s, despite a persistent macro-policy bias against agriculture and high vulnerability to exogenous weather shocks that afflict Bangladesh with regularity – primarily floods and hurricanes _<mark>Figure 1: Agricultural Growth Rate and Volatility 1990-</mark>_ (Figure 1).<sup>3</sup> A combination of policy 6 reforms, technological progress, Agriculture GDP Ag. GDP Volatility 5 investments in infrastructure and human capital, and the enterprise of 4 rural Bangladeshi households have 3 been credited with driving the trend 2 growth rate (i.e., growth rate over 1 rolling 10 year periods) steadily higher 0 for the past two decades, reaching about 5% in recent years (Gautam and _Source_ : Gautam and Faruqee (2016) Faruqee 2016).<sup>4</sup>

> 3 Macro-policy bias is estimated as the Relative Rate of Assistance to agriculture from the global database on agricultural incentive distortions (see Anderson and Nelgen, 2012).

> 4 Agriculture has been identified as the main driver of poverty reduction since 2000 (World Bank 2013).  Agricultural growth has also unleashed broader and unprecedented changes in the rural economy through its linkages with the rural non-fam economy; every 10 takas of agricultural income is estimated to stimulate an additional 6 takas in rural nonfarm incomes (Khandker and Samad 2016).

3
# **Table 3: Heterogeneous Effects by Household‐level Socioeconomic Status**

|Dependent variable|1(Work)|Days<br>worked|Hours<br>worked<br>|1(Work)|Days<br>worked|Hours<br>worked|
|---|---|---|---|---|---|---|
|Other worker ill|0.075**|0.294|3.354<br>|0.226**|1.696**|12.939*|
||[0.032]|[0.259]|[2.216]|[0.112]|[0.804]|[6.748]|
|Other worker ill X Female|‐0.131***|‐0.380|‐4.357|‐0.172|‐1.203|‐4.761|
||[0.047]|[0.327]|[3.101]|[0.179]|[1.177]|[8.893]|
|HH Assets at Baseline X Other worker ill|‐0.030|‐0.310|‐2.862||||
||[0.033]|[0.225]|[1.974]||||
|HH Assets at Baseline X Other worker ill|0.004|0.303|2.876||||
|X Female|[0.049]|[0.260]|[2.528]||||
|HH Average Education X Other worker ill||||‐0.014|‐0.134*|‐0.842|
|||||[0.011]|[0.077]|[0.625]|
|HH Average Education X Other worker ill||||0.003|0.064|‐0.260|
|X Female||||[0.020]|[0.129]|[0.982]|
|Net effect on Male 1 sd below mean|0.108|0.63|6.452|0.121|0.715|6.769|
|P‐value|0.029|0.029|0.014|0.006|0.034|0.032|
|Net effect on Male 1 sd above mean|0.045|‐0.013|0.519|0.045|0.006|2.312|
|P‐value|0.299|0.972|0.885|0.281|0.986|0.445|
|Net effect on Female 1 sd below mean|‐0.028|‐0.078|‐1.019|‐0.031|‐0.022|0.100|
|P‐value|0.506|0.738|0.648|0.434|0.926|0.964|
|Net effect on Female 1 sd above mean|‐0.081|‐0.093|‐0.990|‐0.092|‐0.394|‐5.735|
|P‐value|0.126|0.761|0.718|0.279|0.412|0.258|
|Mean dependent variable|0.807|4.279|37.660|0.807|4.279|37.660|
|Observations|3,769|3,766|3,766|3,951|3,948|3,948|
|R‐squared|0.512|||0.507|||

_Notes: Other worker ill = 1 if another household member reported missing work for an entire week in which s/he was planning to work and the reason for missing work was injury/illness. Sample includes respondents employed at baseline. Assets are measured by the first principal component from an index of household assets including bed, wall clock, watch, player, radio, tv, sewing machine, fan, air conditioning, fridge, freezer, gas stove, shovel, landline telephone, cell, bike, motorbike, car, computer, livestock, farm implements, generator, land; then normalized to have mean zero and standard deviation one. Column 1 is estimated by OLS and columns 2 and 3 are estimated using least squares with trimming to account for censoring of the dependent variable (Honore, 1992). Regressions include week‐of‐interview dummies interacted with gender, week‐of interview dummies interacted with city, and controls for the number of household members planning to work in that round interacted with gender. Standard errors in brackets, clustered at the household level: *** p<0.01, ** p<0.05, * p<0.1._
Goldberg, P. and M. Knetter (1997). “Goods Prices and Exchange Rates: What Have We Learned?”. Journal of Economic Literature, vol. XXXV, pp. 1243-1272.

Goldberg, P. and N. Pavcnick (2007). “Distributional Effects of Globalization in Developing Countries,” Journal of Economic Literature, vol. 45(1), pp. 39–82.

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34
contractual and especially informational frameworks can host economies and societies reap maximum benefits. The experience of the past decade has also shown that countries should choose their foreign banks carefully. Countries such as Mozambique, Tanzania, and Uganda had to renationalize their just-privatized banks as the foreign purchasers turned out to have insufficient resources or management skills. It was not until the second round that privatization was successful.<sup>26</sup> In spite of the overall stabilizing role of foreign bank participation, one should not downplay the risk of a foreign subsidiary dominating the financial system if the parent company is in trouble—a scenario that has become again more likely in the current global financial turmoil.

Although beneficial for efficiency and outreach, the rising South-South financial integration also poses challenges for supervisory authorities. There is an increasingly pressing need to improve reporting, accounting, and disclosure practices, and also to develop and respect memoranda of understanding among supervisory bodies regarding exchange of information on the soundness of financial institutions. Although there are no signs of the withdrawal of foreign banks, the risk remains and supervisory authorities in Africa need to be aware of any such risks to be able to prevent or, if not prevent, to prepare for orderly exits, should they occur.

Finally, the lack of regional integration has reduced the benefit that Sub-Saharan African countries can gain from foreign bank entry. By harmonizing bank regulatory frameworks, authorities could reduce regulatory costs for banks active across several countries of the respective region. Allowing banks to establish branches in other countries rather than having to establish subsidiaries could reduce the costs of market entry—by reducing costs of, among other aspects, multiple corporate structures—and foster competition. It is important to note, however,

> 26 World Bank 2001.

27
and support for general environmental goals” and some measure of awareness and concern regarding global warming, they also find a widespread poor understanding of climate change in the public around the world. Similarly, Dunlap (1998) reports results from a 1992 Gallup survey conducted in Canada, the US, Mexico, Brazil, Portugal, and Russia on public perceptions of global warming. Most respondents did not understand climate science well. In particular the study found, as have others, confusion between climate change and ozone depletion. Dunlap finds the public to be “poorly informed about global warming” (1998, 498).

More recently, work by Nisbet and Myers (2007) and Brechin (2003) provide updated coverage of the extent of public knowledge of climate science. Brechin (2003) compiles public opinion surveys on public understanding of climate change from 15 nations between 1991 and 2001. He finds that even as scientific consensus on climate science increases, knowledge regarding causes of climate change by the public is minimal. Brechin does find increased public understanding regarding the causes of climate change over the decade from 1991 to 2001, however citizens in all nations studied remain largely uniformed. In the 2001 survey, citizens of Mexico knew the most about the causes of climate change, but even here only one-quarter of respondents correctly identified burning fossil fuels as the primary cause of global warming. Americans, who were tied with the citizens of Brazil at 15%, were in the middle of the pack. Brechin laments the low level of awareness in the U.S.: “In the most recent international study on knowledge about global warming, the citizens of Mexico led all fifteen countries surveyed in 2001 with just twenty-six percent of the survey respondents correctly identifying that burning fossil fuels was the primary cause of global warming. The citizens of the US, among the most educated in the world, were somewhere in the middle of the pack, tied with the citizens of Brazil at fifteen percent. Even the Cubans, at seventeen percent, were slightly more informed than the American public (2003, 125)” In 2007 Nisbet and Myers published a systematic review of trends in US public opinion regarding global warming over the past 20 years. They summarize public opinion across several key dimensions including (a) public awareness of the issue of global warming; (b) public understanding of the causes of global warming and the specifics of the policy debate; (c) public perceptions of the certainty of the science and the level of agreement among experts; (d) public 7
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<!-- Start of picture text --> IBRD 39224<br>TAJIKISTAN<br>CHINA<br>PAKISTAN<br>FIGURE 1<br>JAMMU<br>STUDY DISTRICTS<br>KHYBER and<br>PROVINCE CAPITALS PAKHTUNKHWA<br>NATIONAL CAPITAL<br>PROVINCE BOUNDARIES Approximate<br>INTERNATIONAL BOUNDARIES ISLAMABAD Line of Control<br>Peshawar<br>KASHMIR<br>ATTOCK<br>0 100 200 300 KILOMETERS<br>0 50 100 150 200 MILES<br>Lahore<br>P U N J A B<br>A F G H A N I S T A N<br>Quetta<br>BAHAWALPUR<br>B A L O C H I S T A N<br>I N D I A<br>ISLAMIC REP.<br>OF IRAN<br>S I N D H<br>HYDERABAD<br>PAKISTAN<br>Karachi<br>This map was produced by the<br>Map Design Unit of The World Bank.<br>The boundaries, colors, denominations and any<br>other information shown on this map do not<br>imply, on the part of The World Bank Group, A r a b i a n<br>any judgment on the legal status of any<br>territory, or any endorsement or acceptance of S e a<br>such boundaries.<br><!-- End of picture text --> <!-- Start of picture text --> APRIL 2012<br><!-- End of picture text -->
**Table 5.2: Program targeting: MGNREGA**

|(a) Government caste cate|gories|(b) SC/ST jatis||(c) Non SC/ST jatis||
|---|---|---|---|---|---|
|ST|-0.099|SC: Chamar|0.239***|OBC: Dhanuk|-0.194***|
||(0.058)||(0.016)||(0.047)|
|OBC|-0.223***|SC: Dobha/Dobh|0.063|OBC: Koeri|-0.235***|
||(0.015)||(0.033)||(0.045)|
|EBC|-0.187***|SC: Dom|-0.065|OBC: Kurmi|-0.180***|
||(0.024)||(0.055)||(0.043)|
|Muslim|-0.315***|SC: Dushad|0.244***|OBC: Shershabadia|-0.660***|
||(0.027)||(0.017)||(0.098)|
|FC|-0.327***|SC: Musahar|0.294***|OBC: Yadav|-0.228***|
||(0.024)||(0.015)||(0.022)|
|||SC: Pasi|0.022|Other OBCs|-0.212***|
||||(0.049)||(0.022)|
|||SC: Sardar|0.166**|EBC: Keuta|-0.292***|
||||(0.052)||(0.068)|
|||Other SCs|0.069|EBC: Mallah|-0.135*|
||||(0.065)||(0.060)|
|||ST: Adivasi|0.125|EBC: Nat|-0.060|
||||(0.064)||(0.050)|
|||Other STs|0.219|Other EBCs|-0.229***|
||||(0.124)||(0.032)|
|||||FC: Brahmin|-0.405***<br>(0.032)|
|||||FC: Rajput|-0.330***<br>(0.034)|
|||||Other FCs|-0.166**<br>(0.053)|
|||||Muslims: Ansari|-0.304***<br>(0.076)|
|||||Other Muslims|-0.320***<br>(0.028)|
|Some Schooling|-0.187***|Some Schooling|-0.183***|Some Schooling|-0.187***|
||(0.029)||(0.029)||(0.029)|
|Female<br>headed<br>household|-0.051***<br>(0.015)|Female<br>headed<br>household|-0.050**<br>(0.015)|Female<br>headed<br>household|-0.053***<br>(0.015)|
|Per capita expenditure|-0.134*|Per capita expenditure|-0.116|Per capita expenditure|-0.134*|
||(0.067)||(0.067)||(0.067)|
|Per capita expenditure|0.032|Per capita expenditure|0.027|Per capita expenditure|0.032|
|squared|(0.034)|squared|(0.035)|squared|(0.035)|
|Land|-0.021***|Land|-0.021***|Land|-0.022***|
||(0.003)||(0.003)||(0.003)|
|Observations|8637|Observations|8637|Observations|8637|
|Adjusted R-squared|0.180|Adjusted R-squared|0.188|Adjusted R-squared|0.183|

Notes: (1) SC is the omitted caste group in column (a), Non-SC/ST jatis are the omitted group in column (b) and SC/ST jatis are the omitted group in column (c). (2) Each column represents a separate regression wherein household's participation in NREGA is regressed on caste identity variables and a set of controls: age, age squared, marital status, age at marriage, a dummy variable for any schooling; a dummy variable for household female-headship, per-capital expenditure, per-capita expenditure squared, landholdings, education of the household head, number of members in the household and panchayat level fixed-effects; (3) Robust standard errors are in brackets. (4) We report the level of significance: * p value < .05 , ** p value < .01 and *** p value < .001.

18
Policy ReseaRch WoRking PaPeR 4523

# **Abstract**

This paper relies on the recently developed Maquette for Millennium Development Goals Simulations (MAMS) model to assess the consistency of alternative scalingup and policy packages for growth and achievement of the Millennium Development Goals in Ghana. In the baseline scenario, Ghana’s strong near and medium-term growth outlook puts it in a good position to achieve the poverty Millennium Development Goal ahead of schedule, but other goals are likely to remain elusive before 2015. In the accelerated growth scenario—which addresses the major gaps in water and sanitation and other infrastructure—even more rapid growth and poverty reduction are possible, but important targets in the areas of education, health, and environment remain unattainable. Although growth is complementary to achievement of the Millennium Development Goals, the authors also find important growth-human development trade-offs in the near term. The estimates show that the resource requirements for achieving the key Millennium Development Goals by 2015 are large, reaching US$82 per capita in an illustrative foreign-grant financed scenario. Increased intake and retention of students contribute to rising scarcity of unskilled labor, buttressing unskilled wages, while high demand for skills from the sectors related to the Millennium Development Goals raises the returns to human capital. These developments lead to improvements in the welfare of the poorest members of Ghanaian society and contribute to a small reduction in overall inequality.

This paper—a product of the Africa Poverty Reduction and Economic Management and the Development Economics Prospects Group—is part of a larger effort in the department to collect data and develop analytical tools for monitoring progress towards the achievement of the Millennium Development Goals.Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at zbogetic@worldbank.org, mbussolo@worldbank. org, and dmedvedev@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
that transfers are made as a repayment for the cost of the migrant’s education and transportation.

In a similar context to the contractual arrangement, Stark (1991) suggests a model incorporating risk-sharing motives. In this model, remittances allow risk-averse households to diversify their income sources and thus minimize the adverse effects of income shocks (Stark, 1991; Gubert, 2002). Amuedo-Dorantes and Pozo (2006) also argue that migrants are likely to behave as risk-averse economic agents and purchase insurance in the face of economic uncertainty. In this way, remittances can be considered as a payment to insure against risky income outcomes in the destination region or country. Based on data for Mexican migrants in the United States, Amuedo-Dorantes and Pozo (2006) find that income risk proxies (e.g., being an undocumented immigrant or not having social networks within the United States) are associated with a higher propensity to remit and with a higher level of remittances.

Quinn (2005), on the other hand, suggests another model of remittance behavior whereby remittances are treated as both a consumption transfer to households and as an alternative saving mechanism for migrants. The model predicts that the migrant’s remittance/saving behavior is affected by the relative rate of return on their savings and on the savings of the remittance-receiving household. Using data on Mexican workers in the United States, the author finds that migrants remit more and save less when the remittance-receiving household’s rate of return on savings increases (or the migrant’s return falls). His findings imply that an improved access to savings and investment mechanisms for recipient households in the home country may increase remittance inflows from migrants.

8
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2 Remittances refer to the money and goods that are transmitted to households by migrant workers working outside of their origin communities, either in urban areas or abroad. At the start of the 21<sup>st</sup> century these resource transfers represent one of the key issues in economic development. While the total level of internal remittances in the developing world is unknown, in 2007 international remittances to the developing world amounted to US $239 billion (World Bank, 2008b). In that year the level of international remittances was about 50 percent larger than the level of official development aid to the developing world.

From the standpoint of economic development, two key questions surround these large remittance flows: (a) What is the impact of international remittances on poverty and inequality in the developing world? and (b) How are these remittances spent or used by households in origin countries? Answers to these two key questions seem central to any attempt to evaluate the overall effect of migration and remittances on the developing countries of Latin America, Asia and Sub-Saharan Africa.

In the past, a number of studies have found that international remittances tend to reduce poverty in developing countries. For example, using data from household surveys in 71 developing countries, Adams and Page (2005) find that, on average, a 10 percent increase in international remittances in a developing country will lead to a 3.5 percent decline in the share of people living in poverty. In a similar study using household survey data from 10 Latin American countries, Acosta et al (2006) find that international remittances reduce poverty by 0.4 percent for each percentage point increase in the remittances to GDP ratio. Finally, at the country level,