rafmacalaba/data-use-mentions-extended huggingface dataset:
and A2); three global circulation models (IPSL-CM4, MIROC3.2, ECHO-G)<sup>10</sup> ; and three annual subsidence rates for land in the lower Ganges Delta (2, 5 and 9 mm/year).<sup>11</sup> Figure 4 illustrates the scale of potential change by combining our 2012 salinity map with the maximum change result for 2050 (AR4 A2 scenario, IPSL global circulation model, 9 mm/year subsidence in the lower Ganges Delta). Two patterns are particularly striking in Figure 4. First, higher salinity spreads northward and eastward as the sea level rises, riverine flows change and subsidence continues in the lower Ganges Delta. Second, the transition from 2012 to 2050 is most pronounced in Koyra and northern Shyamnagar.

Figure 4 illustrates the scale of potential change by combining our 2012 salinity map with the maximum change result for 2050 (AR4 A2 scenario, IPSL global circulation model, 9 mm/year subsidence in the lower Ganges Delta).

10 Model implementing institutions are as follows: IPSL-CM4: Institut Pierre Simon Laplace, France; MIROC3.2: Center for Climate System Research, University of Tokyo,

- National Institute for Environmental Studies, Japan,

- Frontier Research Center for Global Change, Japan;

- ECHO-G: Meteorological Institute of the University of Bonn, Germany,

- Model and Data Group, Max Planck Institute for Meteorology, Hamburg, Germany, Korea Meteorological Administration.

In their salinity modeling exercise, Dasgupta et al. (2015a) started with the temperature and rainfall projections for the territory of Bangladesh from 9 GCMs. Models were then ranked from low to high based on their median projections. Finally, three GCMs (IPS-CM4L, MIROC3.2, and ECHO-G) with the highest, intermediate and lowest ranks and the corresponding projections were used to capture the uncertain extent of climate change.

11 The Ganges Delta in Bangladesh is still in an active, dynamic state. Therefore, it is critical to include projection of land subsidence
3 (1988), Ahmed et al. (1991), Khundker et al. (1994), Rahman et al. (1995), Ravallion and Sen (1996), Ravallion and Wodon (1999), and Wodon (1997). Among these, Ravallion and Wodon (1999) and Wodon (1997) are the only studies using household level data, but only up to the survey year 1991-92. For inequality, published results are much fewer. Osmani (1982) presented estimations based on surveys conducted between 1963-64 and 1973-74. The contributions of Khan (1986) and Rahman (1988) were based on surveys up to the early 1980's. Rahman and Huda (1992) considered inequality between occupational groups using the 1983-84 HES. The BBS (1998) does report estimates of inequality for the urban and rural sectors as a whole, but these measures are over-estimated because the BBS does not adequately take into account price differentials between areas. Using group data for 1991-92, Ravallion and Sen (1996) also estimated urban and rural Ginis, but they could not investigate in any further detail the impact of household characteristics such as education, land ownership, location, and occupation on inequality (or on poverty) because they did not have access to the household level data.

Section two sketches the big picture as to what happened to poverty and inequality in Bangladesh between 1983 and 1996. Section three analyzes the micro determinants (and changes thereof) of consumption, poverty, growth, and inequality, while introducing the method to compute conditional indices of between group inequality. Section four extends the work on inequality. A conclusion follows.

# I **The big picture: poverty, inequality, and decompositions**

# **_II. 1._** _Methods_

Five rounds of the Household Expenditure Surveys of the BBS are used, corresponding to the years 1983/84, 1985/86, 1988/89, 1991/92, and 1995/96. The five rounds cover from
U WI|13%|157|23|592|21|4475|21|35|2.015|21|583|22|
|25|U MD|15%|178|20|588|22|4118|24|33|1.862|24|319|50|
|25|Urban Institute|15%|155|24|534|26|3155|33|33|1.437|32|247|67|
|27|Boston U|23%|127|31|537|25|4759|19|32|2.145|19|389|38|
|28|Northwestern U|9%|116|34|503|28|3925|29|31|1.772|28|510|27|
|29|U Aberdeen|7%|114|35|456|33|2996|36|30|1.363|36|213|83|
|29|U CA, San Francisco|15%|108|38|398|40|2350|43|30|1.072|42|150|128|
|31|LSHTM|23%|121|33|430|35|2612|40|29|1.189|40|326|47|
|31|Rutgers U|13%|134|28|473|31|2776|39|29|1.265|39|207|87|
|33|<br>International Food Policy Research Institute|12%|97|46|417|36|3074|35|28|1.396|34|228|76|
|33|<br>U IL, Chicago|9%|123|32|478|30|3814|30|28|1.710|29|639|17|
|33|U Toronto|16%|135|
on family planning across different areas to estimate the impact of the show on family planning adoption. They find that access to the soap-opera increased the listeners’ belief that they could determine their family size by using contraception, and it also increased the probability that listeners would discuss family planning with their partners and peers.

Studies also illustrate how access to the television, films, and radio media platforms shift genderspecific attitudes and behavior. For example, Jensen and Oster (2009) use a three-year individual panel data set for India to see if gender norms and behavior changed between surveys and across villages depending on if and when they got cable TV. The key assumptions to identify a causal impact hold in their study: there were no pre-existing differences in women’s status across villages that had and did not have cable TV, the timing of changes in the outcomes of interest was closely tied to the introduction of cable TV, and the outcomes of interest were not associated with future cable TV access. They find that with the introduction of cable TV, women were more likely to make autonomous decisions, report domestic violence as unacceptable, and report son preference as unacceptable.

In a complementary study, Ting et al. (2014) use NFHS-3 data to find that TV exposure in India is linked with a range of measures of women empowerment, and more specifically, a lower 37
highest levels of gender parity according to this index, followed by the richest country in our sample Indonesia, which is predominantly Muslim. Ethiopia, the poorest country in our sample, where Orthodox Christianity is the most common religion, ranks third, while Bangladesh has the lowest levels of gender parity. UNDP’s Gender Inequality Index, which is not available for Ethiopia, exhibits a similar pattern (see Table B1 in the appendix).

Yet, these aggregate indices hide substantial heterogeneity across different dimension of gender parity. For example, in terms of gender equality in educational outcomes, Bangladesh outperforms Ethiopia, where gender education and literacy gaps are very large. In addition, consistent with a U-shaped relationship between female labor market participation and development (Mammen and Paxson, 2000) gender gaps in labor participation are lowest in the two poorest countries, Ethiopia and Bangladesh, which nonetheless score lower on the aggregate gender parity indexes. A potential explanation for these participation patterns is that in poor countries women cannot afford not to work.

Differences in economic prosperity, geography and culture are also reflected in the nature of rural non-farm entrepreneurship. In Ethiopia, where the rural economy is highly fragmented and labor markets are very thin, self-employment in rural non-farm enterprises is predominantly a means to supplement farm earnings, as well as an important source of income for those lacking alternative options (Loening, Rijkers and Soderbom, 2008). By contrast, in Indonesia, where rural labor markets are well-developed and population density is high, movements out of poverty are strongly correlated with non-farm entrepreneurship (McCulloch, Weisbrod and Timmer, 2007, Priebe et al., 2009). Sri Lanka and Bangladesh’s rural non-farm sectors fall in between these two starkly dissimilar cases (Headey, Bezemer and Hazell, 2010).

7
Policy Research Working Paper 10989

# **Abstract**

Economic growth is often associated with welfare gains through job creation. However, the number and quality of new job opportunities created in a growing economy vary across countries and sectors, due in great part to changes in labor productivity. This paper provides estimates of country and sector-specific GDP-employment elasticities based on data from the past two decades, including an evaluation of the predictive power among alternative methodological approaches. The results show that employment elasticities of growth vary significantly across countries and sectors, but are in most cases below 1.0, implying that employment grows less than GDP due to increasing productivity. Across sectors, agriculture has mostly lower elasticity values, becoming negative for more than one-third of developing countries. In addition, increases in labor productivity are associated with reductions in informal employment. These empirical results are in line with the implications of a theoretical model about the relationship between GDP growth, job creation, and labor productivity in economies with varying levels of productivity and informality.

This paper is a product of the International Finance Corporation. It is part of a larger effort by the World Bank Group to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at SHovhannisyan@ifc.org and CMondragonvelez@ifc.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
):285–318.

- Coppedge, Michael, John Gerring, Carl Henrik Knutsen, Staffan I Lindberg, Jan Teorell, Nazifa Alizada, David Altman, Michael Bernhard, Agnes Cornell, M Steven Fish et al. 2021. “VDem Dataset v11.1.”.

- Crea, Thomas M, Roc ́ıo Calvo & Maryanne Loughry. 2015. “Refugee health and wellbeing: differences between urban and camp-based environments in sub-Saharan Africa.” _Journal of Refugee Studies_ 28(3):319–330.

- De Berry, Joanna Peace & Andrew James Roberts. 2018. Social cohesion and forced displacement: A desk review to inform programming and project design. Technical report The World Bank.

- De Juan, Alexander & Jan Henryk Pierskalla. 2016. “Civil War Violence and Political Trust: Microlevel Evidence from Nepal.” _Conflict Management and Peace Science_ 33(1):67–88.

- De Luca, Giacomo & Marijke Verpoorten. 2015. “Civil war and political participation: Evidence from Uganda.” _Economic Development and Cultural Change_ 64(1):113–141.

27
</u> An SIF could also seek to attract impact investors or commercially oriented financial institutions that identify with the policy objectives of the fund and often require a lower financial return than traditional investors (box 8). For example, AREF received financing from Sustainable Energy for Africa, a fund administered by the African Development Bank, at a rate of return capped at 4 percent.

# **Box 8. Public Finance and Impact Investment**

Emerging and frontier economies hold significant promise for private investors and corporations that seek to diversify their portfolios and enter new high-growth markets. Yet, the high level of risk (real or perceived) is often a barrier to investment. Public and philanthropic funders can use their resources to shift the risk-return profile of investee projects or companies to create favorable conditions for private sector engagement. Such blending of public, philanthropic, and private resources can in turn significantly scale up investment in areas that are critical for sustainable development, including infrastructure, climate change solutions, agriculture, health care, and financial services.

Impact investors have diverse financial return expectations. Some intentionally invest for concessional returns in order to maximize impact or to catalyze additional investment capital by accepting a riskier position in a deal. Others pursue market-competitive and market-beating returns, which are often linked to their fiduciary responsibility toward investors. According to a survey of impact investors carried out in 2015 by the Global Impact Investing Network—a nonprofit organization dedicated to increasing the scale and effectiveness of impact investing—25 percent of respondents were pursuing below-market returns at close to the market rate, and 16 percent below-market returns close to capital preservation.

_Sources:_ Authors, based on OECD (2015) and a 2015 Global Impact Investing Network survey, results of which can be <u>perused at https://thegiin.org/impact-investing/need-to-know/#s5.</u> 17
provide lower bound estimates of the true impacts of school fees, this offers evidence that the negative effects of the economic crisis dominate the positive effects coming from a reduction in school fees. Furthermore, even though our IV is severely weakened for the crisis year (as discussed in section 4.2) and thus could only offer statistically significant estimates in two specifications (columns 1 and 4), the IV estimates have the expected negative sign and are two to three times larger than those of the non-IV estimates. These results concur with those for Table 2.<sup>43</sup>

# **6. Further Analysis of Related Outcomes**

# **6.1. Health Outcomes for Migrant Children**

Our theoretical model predicts that children brought along by migrant households may have better health outcomes (Corollary 1.1). This result is strongly supported by most, if not all, of the recent studies on China as discussed earlier. We re-examine this result with the RUMiC data, and investigate whether moving with parents impacts children's height, their body mass index (BMI) and the underweight/overweight status.<sup>44</sup> We regress these child health outcomes on a dummy variable indicating whether the child is living in the city with the migrant household, controlling for children’s and the household heads’ characteristics. The endogenous variable here is the child migration dummy variable and the IV is the public education spending shocks (the first-stage regression in this case is the reduced form regression). Unlike the previous regressions that were run at the household level, we run these regressions at the individual level for all the school-age children in the sample. Estimation results (Table 8)

> 43 Since we do not have data on migrants’ province of original residence for 2009, we do not control for these dummy variables. An alternative modeling option is to pool the 2008 and 2009 rounds of the RUMiC for analysis. While this option allows us to employ
# **Figure 5.2**

# **Change in employment of skilled workers, and between and within sector decomposition Annual percentage change, 1990s and 2000s**

<!-- Start of picture text --> 1990s<br>1.6<br>1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>Between-Sector Effect Within-Sector Effect<br>2000s<br>1.6<br>1.4<br>1.2<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.2<br>-0.4<br>Between-Sector Effect Within-Sector Effect<br>Annual % Change<br>Argentina Brazil Chile Costa Rica El Salvador Mexico Nicaragua Panama Venezuela<br>Annual % Change<br>Argentina Brazil Chile Costa Rica El Salvador Mexico Nicaragua Panama Venezuela<br><!-- End of picture text --> Source: own computations based on SEDLAC (CEDLAS and World Bank).

29
market access _mi,_ we divide it into 10 deciles. We then define a dummy for better market access ( _d_<sup>_σ_</sup> _m_<sup>)as:</sup> Incorporating the market access heterogeneity, the estimating equation for employment shares can be written as: The focus is on the estimate of _θ_ 3<sup>_σ_forvariousvaluesof</sup><sup>_σ._Animportantinsightfrom</sup> proposition (1) is that the main effect of trade shock would be felt in the areas that are integrated with the world market. We estimate the regressions for progressively higher values of _σ_ and check the statistical significance of the relevant coefficient to identify the areas that remain isolated both before and after the trade shock due to the CIV crisis.

# **(7) Empirical Results**

# **(7.1) Characterizing the Pre-Crisis Equilibrium**

We use data from the pre-crisis period to portray the pattern of population and built-up densities and the employment structure in our study countries. We check for differences in the employment structure between the Abidjan and non-Abidjan areas during the prewar period using census data. Panel A of Table 1 provides the estimates for sectoral employment shares for the pre-war year available in the data set (1996 for Burkina Faso and 1998 for Mali). The estimated coefficients are not different from zero numerically or statistically. The pre-war period is thus characterized by a spatial equilibrium where the non-Abidjan and Abidjan areas display no significant differences in either employment structure. Evidence is similar for population (in levels); please see Panel B of Table 1. With richer time series data in the pre-war period, the evidence on built-up density and night22
. For GDP, I use data from the World Development Indicators Database. For spreads, I use the Emerging Market Bond Index Plus (EMBI+) for the 1994-2017 period. For inflation rates, I use data from the International Financial Statistics.

> 11I focus on this region because, in the early 1990s, most countries in the region approved central bank independence reforms at the same time. The full sample consider for this study includes Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Mexico, Paraguay, Peru, Uruguay, and Venezuela.

7
The qualitative evidence provides valuable insights into the heterogeneity of the effects at the household level and the potential obstacles that need to be overcome in order to reap the benefits of the poverty-based income transfer in a sustainable manner.

# **6. Conclusion**

This study assessed the effect of the Vision 2020 Umurenge Programme on productive asset accumulation as a measure of household well-being. The study combines quantitative and qualitative data to draw a more nuanced picture of the differences in household endowments and needs and how these shape welfare trajectories and thus also influence the role and success of social protection programs. A first conclusion points to the importance of timely, adequate benefits to be able to allow households to accumulate and maintain assets over time and provide households with risk management support. This is balanced with a recognition that for households with high care responsibilities and limited labor supply, income transfers may be a medium-term necessity during which graduation is unlikely. The main conclusion to be drawn in this study is that household context plays a strong role in the households’ and their members’ ability to access and benefit from social protection programs.

From a policy perspective, our results suggest that social protection programs and systems need to provide sufficient flexibility and complementary programming to avoid the systematic exclusion of vulnerable groups or segments. Considering households as uniform entities, as is currently done in many social protection programs that target the household level and provide largely uniform benefits to households, can lead to unintended exclusion and undermine program goals. Poverty-based income transfers based on wage work might systematically discriminate against smaller, often female headed households with lower labor capacity, a higher incidence of disability and heavier reproductive and care duties. In the context 30
# **3. Data**

This section describes the data used in our analysis. We combine the official test and school databases and the investment data that we collected in the (TEEP) divisions. For test scores and school conditions at the start of the project, we use the National Achievement Test (NAT) score data and the Basic Education Information System (BEIS) data, respectively.

The NAT data provide average test scores for grade 4 students in school year (SY) 2002/03, grade 5 in SY 2003/04, and grade 6 in SY 2004/05 for each school. We note that grade 4 in SY 2002/03, grade 5 in SY 2003/04, and grade 6 in SY 2004/05 constitute panel data that tracked the same cohort in each school.<sup>10</sup> Double differences (DD) based on the cohort panel from grade 4 (SY 2002/03) and grade 6 (SY 2004/05) is used to eliminate cohortspecific fixed effects.<sup>11</sup> Table 3.1 shows the mean and standard deviation of mathematics and overall scores of the cohort in SY 2002/03 and SY 2004/05 for TEEP and non-TEEP areas, separately. TEEP schools have significantly higher average scores than non-TEEP schools in both years. The BEIS data provide detailed information on student enrollment and achievements and teachers since SY 2002/03. The data normally disaggregate the information by grade, age, and gender.<sup>12</sup> Since BEIS was established as part of TEEP, we do not have systematically

> 10 National achievement test is self-administered at schools, which potentially creates bias in raw test scores. For grade-6 students, tests were implemented in each year.  In our analysis, however, we use an experimental introduction of the test for the same cohort: Grade 4 in SY 2002/03 and Grade 6 in SY 2004/05.

> 11 Due to delayed
values above one denote a progressive subsidy. CAR = Central African Republic; DRC = Democratic Republic of the Congo.

How expensive would utility bills become if cost-reflective tariffs were applied? With a power costrecovery tariff of $0.29 per kWh and a monthly subsistence consumption of 50 kWh, the associated utility bill would come to $14.5 per month. Based on the distribution of household budgets in Senegal, monthly utility bills at these levels would be affordable by close to 100 percent of the population (figure 26). Indeed, the share of the population that could afford the service is much higher than the share of the population that already has the service, suggesting that Senegal has scope to increase coverage before affordability could become a serious impediment. A more limited level of subsistence consumption of 25 kWh/month for power and 4 m<sup>3</sup> /month for water—which is capable of meeting the most basic needs— would cost $7.3 and $4.5 per month, respectively, and would be affordable to most of the population.

40