rafmacalaba/data-use-mentions-extended huggingface dataset:
Next, we turn to two characteristics of the destination cells which are displayed in light blue in Figure 4. We examine the cell-level share of urban land in 1985 to capture whether urban centers that were already above the developing world mean in 1985 are those most affected We that cities located in more by drought.<sup>14</sup> find historically urbanized cells are particularly likely to grow due to drought. This is consistent with Glaeser’s (2014) observation that much of urban growth in the developing world has been concentrated in precipitously expanding mega-cities. On the other hand, cells that are characterized by relatively brighter night lights (again above the developing world mean) are more prone to see a lesser effect of drought.<sup>15</sup> The divergence between these two metrics is surprising because they should both capture the locations of pre-existing urban centers. One possible explanation is that urban growth in much of the developing world is taking place in informal settlements which are part and parcel of quickly growing mega-cities but which do not necessarily enjoy the necessary infrastructure that would reflect the population density via remotely sensed night lights. While we lack the required data to assess how drought impacts urban _form_ on a global scale, we provide some city-specific statistics on this question in the next section.

# **7 Beyond Grid Cells: Climate Change and Growing Cities in the Sahel**

To get a sense of how urban areas have been expanding in a region typically associated with an unfavorable climate risk profile, we combined information from World Settlement Footprint with data we collected for a related project to assess urbanization in the Sahel. A team of research assistants focused on four Sahelian capital cities (Ouagadougou in Burkina Faso, Bamako in Mali, N’Djamena in Chad, and Niamey in Niger) in an attempt to classify the relative proportions of informal and formal buildings. In each city, the team hand-coded several neighborhoods in order to train an algorithm that

> 14We again use mean rather than median because the
Policy Research Working Paper 5616

# **Abstract**

Progress in educational development in the world since 1900 has been slow and uneven between countries. Providing basic education for all children in developing countries has been and remains an unmet challenge of governments and international organizations alike. This is in sharp contrast to recent findings in the economics literature on the catalytic role of human capital for economic growth and social development in general. Using a newly constructed matched data set on education and national accounts in the 1950 to 2010 period, this paper estimates the loss of income and equity associated with not having a faster rate of human capital accumulation, using alternative methodologies and specific country examples. Such loss is projected backward (1900–1950) and forward (2010–2050) using plausible assumptions regarding what countries could have done in the past or may do in the future to accelerate human capital formation. The findings suggest that the welfare loss in terms of per capita income conservatively ranges from about 7 to 10 percent. Improved educational attainment is also shown to have an effect in reducing income inequality.

This paper is a product of the the Education Team, Human Development Network. It is part of a larger effort by the World Bank 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://econ.worldbank.org. The author may be contacted at hpatrinos@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,
# **1 . I n t r o d u c t i o n**

Livestock is the most important non-petroleum sector of the Chadian economy and the major sub-set and income source in the agriculture sector. According to the 2015 General Livestock Census, the number of livestock animals increased at an annual rate of 6 percent over the past decade, with livestock production estimated at more than 120 million animals in 2019. Goats account for the largest part of the production, with 32 percent. Sheep and cattle account, respectively, for 29 and 26 percent of the total. The other species (camels, donkeys, horses, and pigs) share the remaining 13 percent.

Although the growth rate seems sizable, the breeding sector's production level remains below potential compared to the rising market demand, particularly in Nigeria and the Central Africa region. The sector is characterized by low productivity that constrains it from creating quality jobs and generating enough income for the stakeholders. Weak infrastructure, technological capacities, and customs bottlenecks have long prevented the country from producing and exporting livestock and breeding products at its potential. The exports continue to fall short of the growing demand from its current market (Central African countries and Nigeria). It also falls short of its potential in new markets such as in Eastern and North Africa.

Global and regional trade disruptions by COVID-19 have had considerable adverse effects on perishable and agricultural products. Although it is too early to take stock of the pandemic’s impacts, the crisis is expected to cause policy reversals by plunging many into poverty, especially the large vulnerable segment of the population that depends on agriculture for income. The pandemic's economic effects suggest that ensuring the post-crisis economic recovery and resilience while scaling up non-petroleum exports in Chad would require appropriate and rapid responses to eliminate the lingering constraints and effectively unlock the potential of the livestock sector. Globally and regionally, there have been efforts to mitigate the effect of such a severe shock.

There is a need to ensure that international trade continues
Policy Research Working Paper 9666

# **Abstract**

Between 2014 and 2016 unprecedented and consecutive climatic shocks ravaged Malawi, one of the poorest countries in the world. The largest ever emergency relief operation in the country’s history ensued. The pathways and extent to which the humanitarian response protected livelihoods remain under researched. This paper uses a unique data set that combines longitudinal household survey data with GIS-based measures of weather shocks and climate conditions and longitudinal administrative data on the World Food Programme’s aid distribution. The paper aims to understand the drivers of humanitarian aid and evaluate the impact of aid and weather shocks on outcomes related to household production and consumption in Malawi. The analysis shows that droughts and floods had consistent negative impacts on a range of welfare outcomes, particularly for households that were subject to sequential shocks. Aid receipt is demonstrated to attenuate such impacts, again particularly for households that experienced the shocks consecutively. Households living in areas subject to a weather shock and with higher World Food Programme aid distribution were more likely to receive food aid, partially explaining the success of aid in mitigating the impacts of shocks. However, there is significant scope for improving the criteria for targeting humanitarian aid beneficiaries.

This paper is a product of the Development Data Group, Development Economics. It is part of a larger effort by the World Bank 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 tkilic@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
estimates are the source for that part of the GTAP protection database. To that 3 A side issue is how the OECD’s PSE compares with the Aggregate Measure of Support (AMS) that members notify to the WTO as part of their commitments under the Uruguay Round Agreement on Agriculture. The AMS refers only to the domestic support pillar, and it excludes measures that are not subject to reduction commitments (so-called blue box and green box measures). For a comparison of the PSE and AMS methodologies, see Diakosavvas (2002). In 1999 (the most recent year for which there has been full notification to the WTO) the AMS was $88 billion for high-income countries and $2 billion for developing countries. The $88 billion comprises $52 billion from market price support and $36 billion from direct domestic subsidies. By contrast, the PSE for just OECD countries was $273 billion in that year. Of that latter amount, $182 billion was from market price support measures and only the residual ($91 billion) was direct domestic subsidies. That residual is further reduced, to $43 billion, when measures that are somewhat decoupled from production are excluded. Since those decoupled measures are not counted as part of the AMS, that $43 billion is comparable with the $36 billion notified to the WTO as that component of the AMS.

4 For the purpose of this calculation, we assume in this section that domestic and imported farm products are homogeneous, so that a tariff equal to 20 percent of the value of imports will raise the price of domestic production by 20 percent.
channels could be at least partly blocked by financial institutions that provide for better disclosure of information (about borrowers), stronger creditor rights, widespread credit reporting, etc. For instance, Blackburn and Sarmah (2006) construct a theoretical model and derive several predictions about the impact of corruption and red tape on firm’s access to finance. One of their main predictions is that ceteris paribus, higher corruption increases the size of loan or financing needed by firms to continue operations. Therefore, chance of bankruptcy among borrowers increases and with it the expected verification cost of financial intermediaries. The authors conclude that for these reasons, corruption serves to compound the inefficiencies of capital market imperfections or that greater the extent of capital market imperfections, the stronger the effects of corruption. The authors are quick to point out that bribery is different from other obstacles such as red tape. Both bribery and red tape increase the cost of business activity, but they do so for different reasons: in the case of red tape agents must spend time and effort on acquiring licenses to undertake the advanced project; in the case of bribery agents must spend physical and financial resources to obtain these licenses.

Based on the discussion in the previous paragraph, we obtain two testable hypotheses. First, increase in the likelihood of a firm being financially constrained due to higher corruption is less in countries with more developed financial institutions to address asymmetric information problem and other sources of credit market imperfections. The first proxy measure of the quality of financial institutions we use is the “Getting Credit” sub-index compiled by Doing Business, World Bank. The sub-index is a composite index of depth of credit information index and strength of legal rights index. <mark>The depth of credit information index measures practices and rules that affect both the accessibility and scope of credit information available through credit registries or bureaus.</mark> The legal rights index measures <mark>the degree to which both bankruptcy laws and collateral agreements protect the</mark> 18
not robust to including region specific linear time trends (columns 2, 4, and 6). The magnitude of the effect of rainfall two years ago becomes much lower with the linear time trends. For nonIndia migration, the effects seem to be small and negative once I control for linear time trends. The results suggest that rainfall facilitates migration to India but not to other destinations and that, more recent rainfall matters more.

To further ensure that the trends in rainfall are not driving the results, I use a falsification test arising naturally in this setting. Though past rainfall may affect current migration rates, future rainfall should not affect it, as households cannot anticipate future rainfall shocks. A failure of this test would suggest that village specific trends, and not the increase in farm incomes are driving the migration results. Table 7 shows the results of this check. As expected, the coefficients are statistically insignificant with point estimates close to zero. This result is robust to including region specific linear time trends (columns 2, 4, and 6).

Since rainfall measure is essentially exogenous and affects migration only through farm income, I can interpret the rainfall shock as an instrument that shifts household income. Since I do not have income measures for the census data and the NLFS for years 2001 and 2008, I cannot use an instrumental variable estimate directly. However, I do have income measures for three cross-sections of NLSS rounds conducted in 1995/96, 2003/04 and 2010. As described in detail in Appendix B, I find that one standard deviation increase in rainfall increases farming income by Rs. 2,400 and, as seen from results in this section, increases migration to India by 0.008. Scaling the impact of rainfall on migration by the impact of rainfall on income, increase in farm income of Rs. 7,500 (USD 100) increases migration to India by 2.5 percentage points, a large 54 percent increase from its 2010 level. In terms of elasticities, the implied
.1|

Source: Microdata provided by regional focal points, members of the Global Poverty Working Group. Authors’ calculations. Here and thereafter, the POVCAL<sup>11</sup> command was used to get grouped data estimates. Notes: Grouped data are based on ventiles.

Shared prosperity estimates calculated from micro and grouped data are shown in figure 5. Overall, the differences are not substantial in absolute terms (third column in the table of figure 5). The largest differences in grouped and microdata growth rates are observed for Peru followed by Brazil. However,

> 11 POVCAL is developed by Qinghua Zhao who kindly shared it with the team.

9
# **_Households’ Access to Food and Food Markets_**

Besides the ultimate food insecurity experience, the HFPS for Ethiopia elicited responses related to households’ access to food and food markets. In particular, households were asked whether they were able to buy enough staple foods (e.g., _teff_ / _injera_ , wheat/bread, maize and cooking oil) in the previous week. These questions are important to capture households’ physical access to and affordability of food, and allow us to investigate the ways through which the conflict may have impacted food systems and livelihoods. On the one hand, the outbreak of the war has directly affected access to food by impeding the functioning of markets, especially in conflict hotspot areas. On the other hand, the war curtailed livelihood activities, leading to reductions in income and hence rendering food unaffordable. Thus, the outbreak of the war is expected to directly impact the accessibility as well as affordability of food. Table A5 shows pooled summary statistics of these measures and proxies for accessibility and affordability of food and food markets.

# **_Labor Market Participation_**

Both the 2019 face-to-face survey and the 2020–2021 phone surveys collected detailed householdlevel data on labor market participation in income-generating activities, including farming, nonfarm family businesses, and wage employment. The labor market outcomes for the 2019 LSMSISA survey were mostly collected in August and hence we refer to the baseline data as August 2019 round. The data on households’ participation in various livelihood activities are available for most rounds, but with slight differences across livelihood activities and rounds. First, the data on farming and non-farm activities are available for all rounds, but wage-related activities were dropped in some rounds (August 2020, February 2021, and May 2021) of the phone surveys. These wage-related data are available for two rounds after the outbreak of the war (December 2020 and January 2021), allowing us to assess labor market participation effects of the war in the immediate aftermath of the outbreak of the war. Second
**Figure 2 Revenue and Grants Collection**

_Source_ : Eastern Caribbean Central Bank Statistics.<sup>3</sup>

**Figure 3 Tax Revenue by Tax Type**

_Source_ : Eastern Caribbean Central Bank Statistics.<sup>4</sup> Turning to government expenditures, on average, from 2017 to 2021, 32 percent was allocated annually to social programs, including education, health, and public assistance transfers (figure 15, Appendix A2).<sup>5</sup> The most important direct transfer to poor and vulnerable households is done through SEED, which is administered through the Ministry of Social Development. Also important is the School Feeding Programme, which focuses on providing meals to needy students.

Expenditures in education increased over the period 2017–21 and accounted for roughly 10 percent of annual government expenditures (figure 16, Appendix A3). Government recurrent expenditure in public education generally is provided in four stages: pre-primary, primary, secondary, and tertiary (including vocational training). Several institutions also provide special education to serve students with disabilities and special needs. The government largely subsidizes education up to the secondary level (including special education). The level of subsidization decreases at the tertiary level, with higher fees required to access training leading

> 3 Available in https://www.eccb-centralbank.org/statistics.

> 4 Available in https://www.eccb-centralbank.org/statistics.

> 5 This is based on data retrieved from the Annual National Budget and excludes debt repayments (both principal and interest). In 2022, the Grenada government instituted a cap on gasoline prices, which constituted an energy subsidy. This measure will be analyzed in a future iteration of the tool.

5
00456|0.0207***|0.000773|0.00101|0.00170|0.00570***|-0.00651**|0.0128***|
||(0.00467)|(0.00386)|(0.00184)|(0.00194)|(0.00246)|(0.00153)|(0.00289)|(0.00294)|
|Trade Openness|0.0404|-0.0247|-0.0150|-0.0260*|0.0216|-0.0203*|0.0344|0.0107|
|_(% of GDP, logs)_|(0.0739)|(0.0311)|(0.0291)|(0.0154)|(0.0389)|(0.0121)|(0.0457)|(0.0237)|
|Financial Openness|0.0234***|0.0347***|0.00129|0.00617|0.00770*|0.0114***|0.0120**|0.0157**|
|_(Chinn‐Ito Index)_|(0.00800)|(0.00813)|(0.00314)|(0.00399)|(0.00419)|(0.00313)|(0.00495)|(0.00616)|
|_External Factors_|||||||||
|Foreign Growth|-0.00252|0.00369*|0.000677|0.000529|-0.00382|0.000442|0.00159|0.00320**|
||(0.00916)|(0.00192)|(0.00362)|(0.000956)|(0.00483
<0.001)|
|Fixed effects|Region|Region|Region|Region|Region|
|Characteristics of household<br>& head|No|Yes|Yes|Yes|Yes|
|Dwelling characteristics|No|No|Yes|Yes|Yes|
|Drought affected status|No|No|No|Yes|Yes|
|F-statistic|34.9|34.6|37.7|49.8|10.7|
|Standard errors|Sampling<br>weights|Sampling<br>weights|Sampling<br>weights|Sampling<br>weights|Clustered<br>by PSU|
|Observations|2,272|2,249|2,241|2,241|2,241|

Source: Authors’ calculations based on data from the SHFS and ACLED. Note: Estimated coefficients from the 1st stage of the IV regression. The dependent variable corresponds to exposure to incidents in Wave 2. Characteristics of household refer to size and receiving remittances, while those from household head refer to age, sex and literacy. Dwelling characteristics include tenure, roof and floor material, water source and sanitation type. Humanitarian assistance is not included due to collinearity with the region fixed effects. Standard errors in parenthesis; *** p<0.01, ** p<0.05, * p<0.1.

In terms of statistical inference, the estimates from both the difference-in-difference and instrumental variables approaches are obtained with standard errors that consider the sampling weights of the surveys in all specifications, mainly due to the different sampling frame and design between Wave 1 and 2. One of the key differences is that the sampling strategy of Wave 2 included an oversampling of households in Mogadishu (Pape and Wollburg 2019b). Hence, using sampling weights to derive the standard errors is

> 23 Staiger and Stock (1994) proposed a rule of thumb cutoff of 10 for the first-stage F-statistic of an IV regression, below which
# **1 Introduction**

It is challenging to accurately measure sensitive attitudes, behaviors, and experiences such as political preferences, prejudice, risky sex, and intimate partner violence (IPV). Often, researchers and policy makers must rely on self-reported survey data, yet such data may not be accurate. For sensitive topics in particular, there are many reasons why survey respondents may be reluctant to tell the truth, including a fear of being judged, endangered, or legally penalized (Blair, Coppock and Moor, 2018). If misreporting bias is significant and systematic, researchers and policy makers risk over or under-estimating the prevalence and welfare costs of sensitive issues, and drawing inaccurate conclusions about their causes and potential solutions.

survey methods. We assess whether survey methods that increase respondent privacy and anonymity affect IPV reporting compared to the status quo of direct face-to-face interviews.<sup>1</sup> In Rwanda and Nigeria, we randomly assigned women to answer questions about their experience of emotional, physical and sexual violence using one of three methods: an indirect method (list experiment) and two direct survey methods: face-to-face questions asked by an enumerator, or audio computer-assisted self-interview (ACASI) on an electronic tablet. Compared to the direct face-to-face method, the list experiment method provides additional confidentiality as respondents do not directly disclose their experience of violence. Instead, they give a response that allows researchers to calculate average prevalence across the whole sample, essentially making respondents’ answers anonymous (Blair and Imai, 2012). The ACASI method affords more privacy than face-to-face methods as respondents answer questions themselves by listening to pre-recorded questions in a headset and then selecting their answer on a tablet.

Results suggest that estimated IPV prevalence varies depending on the survey method used to measure it. The most widely used method, face-to-face interview, resulted in the lowest IPV rates, followed by the more private ACASI method, and finally, the anonymous list method with the highest. In Rwanda, IPV rates were double, and in Nigeria
9.6|
|Pre-berth waiting time, bulk dry (hours)|1.5|1||48|||||6|
|Pre-berth waiting time, container<br>(hours)|1.6|1|12|24||1|96|38.4|12.4|
|Handling charge, bulk dry ($ per tonne)|6|5||5||5|5|2.75|3|
|Handling charge, cargo container ($ per TEU)|220|260|155|180||220|320|140|168|
|Handling charge, general cargo ($ per<br>tonne)|6.5|13.5|8|8.5|8|9|8.5|5.5|10|

_Source_ : AICD ports database downloadable from http://www.infrastructureafrica.org/aicd/tools/data. _Note_ : Data are as of 2006. —= data not available.

TEU = 20-foot equivalent units.

17