rafmacalaba/data-use-mentions-extended huggingface dataset:
are more likely to work in service sectors that may be more conducive to remote working arrangements such as finance, information, administration, and other professional activities, they are still significantly engaged in economic sectors for which remote work arrangements is more difficult such as retail, tourism, and manufacturing. Relatively limited scope for remote work means that the pandemic and continuing containment measures in low and middle-income countries may affect workers across the distribution similarly. At the same time, households may differ in their ability to cope and adapt to similar shocks depending on their welfare status at the onset of the crisis. Some coping mechanisms that poorer households in low and middle-income countries may resort to – namely, selling productive assets, increasing indebtedness levels, reducing food intake at critical ages, removing children from school – may have deleterious impacts on human capital and long-term individual and household prospects, which could, in turn, have serious implications for future inequality. Therefore, the question of whether the pandemic has had an equalizing effect or has exacerbated inequalities in lowand middle-income economies merits empirical exploration.

Recent studies have employed household survey data from low and middle-income countries around the world and have found that the economic impacts of the pandemic and the recovery have been uneven, with female, younger, less educated, and urban workers being more likely to stop working (Agrawal, et al., 2021; Kugler, et al., 2021). This paper adds to this literature by using high-frequency household survey data from East Asia and Pacific (EAP) countries to examine the socioeconomic and distributional impacts of the pandemic. Specifically, in this paper, we look at the pandemic’s distributional impacts in the context of three questions. First, _have poorer workers been more vulnerable to employment and earning losses than wealthier workers, or has the likelihood of experiencing losses been widespread irrespective of welfare status?_ Second, _how have strategies employed by households to cope with these shocks differed across welfare groups?_ Third _, what are the potential longer-term effects that the
Policy Research Working Paper 7572

# **Abstract**

This paper examines how the dismantling of coercive institutions associated with the end of apartheid in South Africa in 1994 affected the distribution of rents from natural resource exports. It identifies the interplay between coercive institutions and natural resource rents as an important driver of local development. Using data from the 1996 census, the paper documents large income gaps between communities located just-inside and just-outside the former self-governing territories set aside for black inhabitants. Examining relative changes between 1996 and 2011, the paper finds that spatial income convergence was considerably stronger among marginalized communities with higher initial exposure to resource rents. These results accord with standard bargaining theory in which the dismantling of coercive institutions improves the negotiating position of unionized workers in the mining industry.

This paper is a product of the Trade and International Integration Team, Development Research Group. 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 authors may be contacted at pbastos@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
the population census (2010). For a detailed descriptions of the variables used in this table, see Appendices A.1–A.2. The dependent variables are standardized with respect to their means and the standard deviations in the control group. All _p_ -values account for clustering at the school level. Asymptotic _p_ -values reported in brackets are clustered at the school level.

As reported by the mentors, parents follow up with their children on homework and other pedagogical material whenever the mentor is busy attending tasks outside of the community: “ _Parents used to provide support with homework whenever mentors are visiting other communities ensuring pedagogical support, so that upon the return of the mentors they are able to make progress in the schooling activities without setbacks._ ” We next empirically investigate the second prediction of the model, as depicted in Figure 2 and discussed above. We take advantage of information from the 2010 locality-level census on the degree of social hostility in the community (see Appendix A.1), which is based on hostile event related to land property, religion, elections, crime, or drug addiction. This measure proxies the degree of collaboration in the community. In the 224 communities that form part of the second experiment, we construct an indicator variable for the presence of a conflict in the community if at least one of these hostile events is reported. We then interact this variable with the experimental API _Plus_ assignment and regress the various survey-based measures of parental engagement collected during the second experiment on these interaction terms.

31
107|257|357||1,119|<br>361|1,480|
|**Rual**<br>200|<br>202|175|239||816|<br>208|1,024|
|Nafive<br>197|<br>114|122|259||692|<br>205|897|
|Conru*<br>19|<br>11|29|59||118|<br>Z7|145|
|**TOaW**<br>814|<br>434|583|914||2,745|<br>801|3,546|
|Source: PARE's databa<br>**T**<br>**Native &**<br>**Rural.**<br>**With**|se<br>**able 4. St**<br>**Native &**<br>**Rural. With**<br>**comp. 1358**|**udents inclu**<br>**Sub-total**<br> <br>**included in**<br>|**ded i**<br>**Nati**<br>**Rural.**<br>**so**|**n the**<br>**ve &**<br>**With**<br>**me**|<br>**analysis**<br>**Urban**<br>**Commu**|<br> <br>**, 1992.**<br>**&**<br>Sub-total<br>excluded<br>**nity**<br>**
at least one eligible woman, i.e., a married woman aged 18-30 who (i) had at least one living child, (ii) was neither sterilized nor had undergone a hysterectomy, and (iii) was neither currently pregnant nor within six months postpartum. These inclusion criteria were selected to identify a sample of young married women of reproductive age with a potential unmet need of FP and for whom an FP intervention, such as ours, would likely be effective.<sup>15</sup> We enrolled no more than one woman per household. If multiple women from the same household were eligible based on the inclusion criteria mentioned above, the youngest eligible woman from the household was chosen to participate. From 698 women who were invited to participate in the study, 671 consented and were recruited. Figure A.3 presents the map of our intervention site and study sample.

# **2.2 Experimental Design**

Figure 1 presents our experimental design. The experiment consisted of a baseline survey, followed by randomization of women into one of three intervention arms, a 10-month long intervention, and an endline survey. All communication with the study sample occurred in Hindi, the local language. The baseline survey was administered by female enumerators during July and August 2018 to 671 women. To address information gaps on FP among our sample women, a brochure on modern FP methods and the benefits of healthy timing and spacing of births was given to all sample women at the conclusion of the baseline survey (Figure A.4).

Soon after completion of the baseline survey, we conducted individual-level randomization to assign 350 women to the control group, 156 women to the Own voucher group, and 165 women to the BAF voucher group. Following Bruhn and McKenzie (2009), we implemented a stratified randomization protocol by balancing treatment assignment according to the following baseline characteristics of a woman: village of residence, current use of FP, years of schooling, desire for another child, and the number
Several significant contributions have already been made in economic literature. Atkin & Donaldson (2015) show that the effect of log distance on trade costs within Ethiopia or Nigeria is 4-5 times larger than in the US, and _≤_ 30% of this effect is explained by the availability and quality of roads. Using 10 years of disaggregated monthly price data, Porteous (2019) estimates a dynamic model of agricultural storage and trade comprising 6 grains traded across 230 regional markets in 42 SSA countries, connected in a network with 413 links to 30 international ports. He finds median trade costs 5x higher than elsewhere in the world. Reducing trade costs to the world average would yield a 46% reduction in grain prices and a welfare gain equivalent to 2.17% of GDP. He further shows that 88% of the welfare gain can be achieved by lowering trade costs through ports and links representing just 18% of the trade network, supporting corridor-based approaches.

Graff (2024) studies spatial inefficiency in African national road networks using a regular 0.5<sup>_◦_</sup> grid-network parameterized with Open Street Map (OSM) routes between adjacent cells. Employing data on population, nightlights (productivity) and ruggedness (Nunn & Puga, 2012), and the quantitative framework of Fajgelbaum & Schaal (2020), he generates ideal national networks, and estimates that Africa would gain 1.3% of total welfare from better organizing its national road systems (with national welfare gains ranging between 6.6% for Somalia and 0.5% for South Africa), and 0.8% from a 10% optimal expansion. He also documents that colonial infrastructure projects skewed trade networks towards a sub-optimal equilibrium and that regional favoritism and inefficient aid provision hinder optimal network investments (Graff, 2024). The latter is consistent with findings by Jedwab & Moradi (2016); Jedwab et al. (2017), Burgess et al. (2015), and Bonfatti et al. (
68 _Social Capital, Household Welfare and Poverty in Indonesia_

# Table A2: Heterogeneity Index of Local Associations, by Region and Household Characteristics and by Type of Association

|l<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_<br>_||**Government/**<br>National<br>Groups|**Religious**<br>Groups|**Production**<br>Groups|**Social Groups**|
|---|---|---|---|---|
|**_Province_**|||||
|Jambi|43.3|35.0|34.5|46.7|
|Jawa Tengah|57.0|65.2|54.5|62.5|
|<br>NTT|62.6|66.0|57.8|56.9|
|**_Head_**_of_**_Household_**|||||
|Male|56.2|55.0|48.9|53.4|
|Female|_55.4_|48.4|51.0|47.5|
|**_Religion_**|||||
|Muslim|52.2|45.9|46.7|49.4|
|Catholic|60.1|_63.5_|57.3|_53.6_|
|Protestant|63.7|68.6|57.4|61.7|
|**_Education_**|||||
|None|54.1|54.8|47.5|57.5|
|Primary School-<br>Incomplete|54.3|54.2|48.8|50.6|
|
investment receivables (often representing shadow credits of nonbank affiliates) reached 16 percent of the total assets of smaller banks, compared to 1.6 percent for the Big 4 (Figure 23). Adding to financial vulnerabilities, many wealth management products (WMPs) were held by banks off-balance sheet. WMPs equaled 18.5 percent of the assets of smaller (joint stock and city commercial) banks in 2016, as opposed to 8.7 percent for the Big 4.

**Figure 22 Growth of total assets, 2015-16 Figure 23 Selected commercial bank average indicators, 2016**

<!-- Start of picture text --> (annual average growth in total assets, percent)<br>25<br>20<br>15<br>10<br>5<br>0<br>Big 4 Joint stock and city<br>commercial banks<br><!-- End of picture text --> Source: PBC; WIND Info; authors’ calculations.

<!-- Start of picture text --> (as a share of balance sheet assets, percent)<br>20<br>15<br>10<br>5<br>0<br>Big 4 Small and Big 4 Joiny stock<br>medium-sized and city<br>banks commercial<br>banks<br>"Other investment" Off-balance sheet WMPs<br><!-- End of picture text --> Source: CEIC; PBC; Wind Info; authors’ calculations.

The recent rapid expansion in credit to the nonfinancial sector is likely to have hindered productivity growth from two important perspectives. First, the allocation of credit toward infrastructure and housing has reduced the resources available to higher-productivity sectors and firms. For example, LGFV borrowing through loans and bonds crowded out investment by domestic private firms (Huang et al., 2017). Second, the growing burden of nonfinancial sector debt, at 250 percent of GDP in
or allowing tax payment receipts to mature into more substantive rights over time. Linking efforts to increase property taxation to reductions in registration or transaction fees could remove a major obstacle to establishment and maintenance of urban land registries, improve welfare, and yield political benefits if tax payment opens a path to acquiring real rights while at the same time creating enforcement capacity the lack of which has thus far bedeviled most efforts to increase property taxation in Africa.

# **4.2 Supporting compact and dense urban development**

Global data suggests that African cities are land-intensive, i.e. they consume lot of land and achieve density via crowding in slums rather than investment in height (Esch _et al._ 2023). This makes for sprawling cities with limited mobility (Peralta Quiros _et al._ 2019) where poor individuals can access only a limited number of jobs (Nakamura & Avner 2021). Land-intensive urban growth increases cities’ net <mark>carbon footprint,</mark><sup>19</sup> <mark>pollution exposure (</mark> Yao _et al._ 2021), and the cost of providing public services <mark>(Ashraf</mark> _<mark>et al.</mark>_ <mark>2021) including</mark> transport (Gendron-Carrier _et al._ 2022). Intra-city mobility increased knowledge spillovers and innovation in the US (Roche 2020) and is linked to higher economic growth in African cities (Brandily & Rauch 2020), suggesting that sprawling expansion may negatively affect cities’ growth potential.

> 18 In a randomized experiment in Zambia, SMS reminders increased payment significantly, especially for those on a network that allowed mobile payment. Effects were particularly large for payers outside the capital (Ali & Deininger 2021a).

> 19 <mark>Engineering studies that account for cities’</mark> total life-cycle GHG emissions suggest 7 to 27 floor buildings are optimal (Resch _et al._ 2016),
To estimate our instrumental variables strategy described in Section 3.2, we need rainfall data for each of the 75 survey villages for each year from 1992 to 2003, which corresponds with the in utero and early childhood years for the primary school-aged children in our survey. Since none

> 10 Since the school day starts at 7:30am, ends at 5:00pm, and includes a lunch break of 2.5 hours during which children return home and another 30 minute break at school, we assign the following number of hours to correspond to each time period: 2 (before school opened), 6 (during school), 2 (during school lunch break), 2 (after school closed), 10 (all day), 0.5 (occasionally). To check the robustness of the correspondence between number of hours and time periods during the day, we also estimate several alternative variations, and results are consistent.

9
|1,684|1,063|854|200|3,801|
||**Total**|**1,938**|**1,207**|**993**|**213**|**4,351**|

**Table 2: Non-performing loan ratios across regions and periods**

This table disaggregates the average NPL ratios (non-performing loans net of provisions to capital) over 5-year intervals and regions. The numbers are computed using the IMF’s Financial Soundness Indicators data base.

|Region|1995-<br>2000|2000-<br>2005|2005-<br>2010|2010-<br>2015|2015-<br>2020|Full period|
|---|---|---|---|---|---|---|
|East Asia & Pacifc (EAP)|45.56|26.94|13.36|5.79|4.39|13.33|
|Europe & Central Asia (ECA)|32.47|26.73|26.64|34.21|26.03|27.69|
|Latin America & Caribbean (LAC)|-|-1.72|-1.78|2.97|9.59|1.82|
|Middle East & North Africa (MENA)|-|32.67|21.05|9.33|6.48|13.14|
|North America (NA)|-|3.63|8.43|10.12|5.44|7.80|
|South Asia (SA)|-|17.40|12.23|14.96|15.77|14.13|
|Sub-Saharan Africa (SSA)|-|11.12|
For empirical analysis involving NTB inventories several indices have been used. One such measure is a frequency index (P ) showing the percentage of transactions (i.e. imports of a tariff line product from a given country) covered by some preselected group of nontariff measures, where Ni is transaction i, Di is a dummy variable that takes a value of ur.Lty if one or more NTB is applied to the transaction (or zero otherwise), and Nt is the total number of transactions in the product group. The first summation in equation (1) is done over all tariff line items while the second is over all countries exporting to j. 8/ Given that matched trade data are available, in which individual countries of origin for shipments are identified, a second index showing the share of total imports subject to NTMs can be computed. This trade coverage measure (C-) is defined as, (2) Cj 2 ((Di. tm x Vi,t-N)/.Vi,t-N where Vi,t-N represents the value of imports of item i in year (t-N) and Di.t-

- 8/ Walter utilized a different concept in his applications of equation (1). A four-digit SITC product (commodity) was considered to be "affected" by NTBs if one or more of the component tariff lines encounters a nontariff restriction. The reasoning here is that exporters of closely related products in the four-digit group arr normally aware of the NTB and modify their behavior to prevent the sp.A,d of the measure. Dinopoulos and Kreinin (1988) and Messerlin (1988) measure empirically the trade contraction that occurs on the part of exporters of similar products to those facing new restrictions and show that trade in these goods experienced major contractions. However, a shortcoming of the "affected" concept is that the magnitude of the trade response may vary considerably from product to product. While recent applications of equation (1) are normally made directly with tariff line data
follow the distribution of the sample by province and urban/rural location. The weights were calibrated to the DHS distribution. This addressed issues with oversampling related to the number of calls but did not adjust for differences in the distribution of the wealth index or for differences in demographic variables. To adjust for differences in the distribution of the wealth index, the DHS and mobile phone survey data were appended, and a logit model was run with the mobile phone survey equal to one and the DHS equal to zero. The dependent variables were the wealth index, square of the wealth index, and the cube of the wealth index. The inverse of the prediction was then taken and collapsed into deciles, and these deciles were multiplied with the calibrated weights. These weights were then winsorized<sup>4</sup> at the top 0.5 percentile to address any outliers, adjusted to account for households with multiple mobile phones (and therefore higher probabilities of selection), and calibrated again using the region and urban/rural location. This further adjusted the weights for differences in the distributions between the phone survey and the DHS, but only adjusted for differences in the demographics to the extent that they were correlated with wealth. As a final step, the weights were raked to match the mean values from the DHS on the following characteristics: gender of the respondent, age group of the respondent, education of the respondent, household size, province, and urban/rural location. The resulting weights were based on the household level as the DHS is a representative household survey. It is important to recognize that the low rates of phone ownership means that reweighting of responses is unlikely to be able to fully take into account unobserved characteristics of the population without access to a phone.

> 4 Winsorizing involves the transformation of statistics by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers.

10
|Pakistan|Manufacturi<br>ng firms|Power outages<br>and<br>load<br>shedding|Calculations<br>based<br>on<br>firm<br>survey|Firms<br>experience<br>annual<br>output losses of US$ 8.4<br>billion or 3.1 percent of GDP<br>due to power outages and<br>load shedding|(Zhang, 2019)|
|---|---|---|---|---|---|
|Cameroon|Industrial<br>firms|Power outages|Descriptive<br>statistics<br>from<br>firm survey|Firms on average experience<br>total output losses of at least<br>about 31 percent due to<br>power outages|(Diboma<br>and<br>Tamo<br>Tatietse,<br>2013),<br>own<br>calculation<br>of<br>average|

# **c. Elasticity of firm productivity with respect to power supply quality**

|**Country**|**Type**<br>**of**<br>**firms**|**Power**<br>**supply**<br>**quality variable**|**Method**|**Effect**<br>**of**<br>**low**<br>**electricity**<br>**supply quality on firms**|**Source**|
|---|---|---|---|---|---|
|26<br>African<br>countries|World Bank<br>Enterprise<br>survey|Number<br>
# **Introduction**

Food insecurity poses a lingering threat to national development across low- and middleincome countries (LMICs), and the recent inflationary trend has severely undercut food affordability for millions of households (World Bank, 2023; Headey & Ruel, 2023). The recent Shared Prosperity Report (World Bank, 2022) reveals that globally, 658 million individuals live below the international poverty line of $2.15 per day. Furthermore, estimates suggest that between 690 million and 783 million people worldwide experienced hunger in 2022 (FAO, IFAD, UNICEF, WFP and WHO, 2023; FSIN and Global Network Against Food Crises, 2023). Among these, over 258 million are in a food crisis, and forced to meet minimum dietary needs by employing irreversible coping strategies, including the liquidation of livelihood assets (FSIN and Global Network Against Food Crises, 2023). The international community's response to the global food crisis has encompassed significant humanitarian aid to directly protect livelihoods, as well as increased investment in resilience and prevention. Transitioning from reactive to proactive aid necessitates rigorous monitoring to gather information for early detection and to inform responses to emerging food security risks (Lentz & Maxwell, 2022).

Food and consumer price inflation significantly impact household food affordability, and both are leading indicators of food insecurity (Andrée, 2021b; Easterly & Fischer, 2021; Waterlander, et al., 2019). Generally, food price data reflect market pulse and sentiment, and capture reactions to shocks and threats. Monitoring of commodity prices and food price inflation is an important tool in early warning and rapid assessment of risks to markets and food security (Baquedano, 2015; Andrée, 2021a). However, regional or national system support for frequent tracking of changes in food prices at various market segments is rare in LMICs (Adewopo, et al., 2021; Zeug, et al., 2017; Galtier, et al., 2014).

For ex-ante and ex-post assessment of food security and market functioning