rafmacalaba/data-use-mentions-extended huggingface dataset:
**Appendix B. List of survey waves in the WBES’ standardized data set for the period 2006-2022**

**Table B.1. List of survey waves.**

|**Country**|**Freq.**|**Percent**|**Cu**|**m.**|**Country**<br>**Fr**|**eq.**<br>**Per**|**cent**|**Cum.**|
|---|---|---|---|---|---|---|---|---|
|Angola 2006|212|1.63||1.63|Ethiopia 2011|258|1.98|19.29|
|Angola 2010|71|0.55||2.17|Ethiopia 2015|335|2.57|21.86|
|Benin 2009|71|0.55||2.72|Gambia, The 2006|32|0.25|22.1|
|Benin 2016|63|0.48||3.2|Gambia, The 2018|72|0.55|22.66|
|Botswana 2006|114|0.88||4.08|Ghana 2007|284|2.18|24.84|
|Botswana 2010|85|0.65||4.73|Ghana 2013|371|2.85|27.69|
|Burkina Faso 2009|82|0.63||5.36|Guinea 2006|130|1|28.68|
|Burundi 2006|98|0.75||6.11|Guinea 2016|24|0.18|28.87|
|Burundi 2014|59|0.45
for households with no access to clean water.<sup>8</sup> CHWs are recruited locally and their training follows the C-IMCI (Rosales and Weinhauer (2003)), a set of guidelines for community healthworkers that incorporates the WHO’s and UNICEF’s “Integrated Management of Childhood Illness” recommendations for when to refer children to formal health care (WHO (2014); WHO Department of Child and Adolescent Health and Development (2005); see also below).

Note that the role of the CHW may be more expansive in locations where formal care is less easily accessible. In rural areas, the CHW may for example provide basic medications and administer rapid tests. This is not the case in the CHW program we consider here. While CHWs programs were initially conceived as a way to reach patients in remote locations, many CHW work in practice in urban or peri-urban areas. Comprehensive data is sparse, but even in national CHW programs that target rural populations, a significant share of healthworkers operate in urban areas (e.g. 20% of CHW in Pakistan, Brook (2009)). In Mali, 6% of CHW work in Bamako ((Pascal Saint-Firmin et al., 2021)).

The research design took advantage of the second planned roll-out wave of AfH. Mali Health conducted a census in their expansion area in mid-2012 to enumerate all families with children under five years of age (or a pregnant mother) who satisfied a proxy-means test designed to select approximately the poorest third of households. After the baseline survey in 2012, data were collected in two surveys in 2013 and 2014 during the period of highest malaria and diarrhea incidence (September-November). All households identified in the census that were found at baseline were included in the random assignment to the different treatment groups and revisited in 2013.

In this study we use demographic, location, and household asset data collected in the baseline survey, and daily health diary data on children collected during the 2013 follow-up. The
a spatial area with subsequent female entry. We hope that further research continues to refine our understanding of this interface between incumbent conditions and gender ratios of new entrants.

The plan of this paper is as follows. Section 2 discusses our entrepreneurship data and spatial differences in femalebusiness ownershipacross India. Section 3 reviews the spatial determinants of entrepreneurshipand our metrics.Section 4 quantifies the spatial determinants of female entrepreneurship in manufacturing and services. Section 5 presents our instrumental variable specifications that use lagged incumbent conditions from 1994. The final section concludes.

# **Female Entrepreneurship Rates in India**

We employ cross-sectional establishment-level surveys of manufacturing and services enterprises carried out by the Government of India. Our manufacturing data are taken from

> 1 For example, Lall et al. (2004), Lall and Mengistae (2005),Nataraj (2009), Kathuria et al. (2010), Hasan and Jandoc (2010), Dehejia and Panagariya (2010), Fernandes and Pakes (2010), Ghani (2010b), Fernandes and Sharma (2011), and Mukim and Nunnenkamp (2011).

4
BURKINA FASO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 29. Burkina Faso’s consumption of infrastructure services is highly differentiated by budget**

a. Mode of water supply, by income quintile <!-- Start of picture text --> 90<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Q1 Q2 Q3 Q4 Q5<br>Piped water Stand posts<br>Wells/boreholes Surface water<br>b. Mode of sanitation, by income quintile<br>120<br>100<br>80<br>60<br>40<br>20<br>0<br>Q1 Q2 Q3 Q4 Q5<br>Flush Toilet Traditional Pit Latrine<br>VIP/San Plat/Chemical No facility<br>c. Prevalence of connection to power grid among Burkina Faso population, by income quintile<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>Q1 Q2 Q3 Q4 Q5<br>Power<br>% population<br>% population<br>% population<br><!-- End of picture text -->

- b. Mode of sanitation, by income quintile

- c. Prevalence of connection to power grid among Burkina Faso population, by income quintile

_Source:_ Banerjee and others 2009. _Note:_ Q1—first budget quintile, Q2—second budget quintile, and so on.

40
# **1 Introduction**

The global coronavirus pandemic (COVID-19) dramatically slowed economic activity as governments implemented lockdown measures, individuals reacted by reducing both their mobility and economic activity, and firms’ production processes were disrupted. These broader shifts in the economy affected both firms’ demand for labor and workers’ ability and willingness to work. In developed countries where data are readily available, labor market impacts varied considerably across countries, depending on initial economic and labor market conditions and variations in policy responses. Unfortunately, however, most of the countries with post-crisis data are high-income countries, and there is little systematic knowledge about the labor market impacts of the crisis in developing countries. Understanding how the pandemic affected labor markets in the developing world is crucial as governments and other actors continue to develop responses.

This paper has three main objectives. The first is to provide evidence from high-frequency phone surveys (HFPS) on the magnitude of the initial labor market consequences of the crisis. The second is to provide a detailed description of the nature of the HFPS data. Finally, the paper aims to evaluate the consistency of the HFPS data with other sources of data. Our analysis complements other methods and estimates, and regular revisions will be important to track labor market developments over time. The measures derived from the HFPS data differ from macroeconomic projections, particularly in Sub-Saharan Africa, and therefore provide important additional insights into the initial impacts of COVID-19 in developing countries.

Our paper is the first to report cross-country results on labor market outcomes from the HFPS data. We use data on 39 of the 52 countries contained in the December 1st vintage of the harmonized data. The data contain 6 countries in Europe & Central Asia, 7 in East Asia & Pacific, 12 in Latin America & the Caribbean, 2 in Middle East & North Africa, and 12 in Sub-Saharan Africa. Surveys were started in April 2020 and have been carried out in several waves since then. Survey timelines and methodologies including questionnaires were 2
using 22 years of data, the income and the assets of the poor who were affected by natural disasters were significantly lower over the longer term than those of the poor who were not affected by them (Pleninger 2022). In Ethiopia and Honduras, natural disasters pushed poor households into poverty traps in which recovery from natural disasters took longer than it did among other households (Carter et al. 2007).

At the aggregate, Dell, Jones, and Olken (2012) find that higher temperatures reduce economic growth in poor countries <mark>. The channels include lower</mark> agricultu <mark>ral and industrial output, and more political instability. Additionally, they find that the adverse effects of temperature are concentrated in poor countries. These results raise the concern of a reduced ability to grow among poor countries, which can have medium- and long-term consequences. Indeed, evidence from typhoons suggests that climate shocks reduce per-capita income, and this effect persists up to 20 years</mark> (Hsiang and Jina 2014 <mark>). These persistent income losses following climate shocks pose the risk of poverty traps.</mark>

# _3.4.2 Mortality and human capital losses_

Climate shocks generally increase mortality and reduce the human capital of survivors (Carleton and Hsiang 2016; Baez 2010). Indeed, about 80 percent of studies that focus on human capital find that the poor are more adversely affected than the non-poor (Table 5). <mark>Deschenes (2009) estimates an immediate increase in mortality rates in the United States following extreme weather events.</mark> Poorer countries suffer more deaths from climate shocks, while higher economic development and countries with higher quality institutions suffer less death (Kahn 2005). <mark>Among those who survive, climate shocks can still negatively affect education and health. Abiona (2022) analyzes the impact of droughts on birth outcomes in rural Sierra Leone, and finds the adverse effects concentrated among poorer households, with some evidence for
against those of the dummy variable model. If the _F_ test rejects both models, the dummy variables estimates are retained. This helps for example selecting the better (more flexible) model for location‐ crop pairs with two or more harvests.

17 In Kaminski, Christiaensen and Gilbert (2016) we apply these methods to deflated Tanzanian maize and rice data using slightly longer samples. The results are comparable to those reported here. 18 We dropped a number of series from the analysis on the basis of insufficient data. We require (a) at least 24 observations, with (b) at least one observation for each month (otherwise the dummy variables estimator ceases to be identified) and (c) a maximum of 50% missing intermediate (gap) observations. We also dropped both the wholesale and retail rice price series for Niger – these only changed intermittently, suggesting that they might be administered or official prices.

18
|||
|Guinea-Bissau|2019 (MICS)|Nigeria|2018 (DHS)|||
||2009 (DHS),<br>2014 (MICS),||2011 (MICS),|||
|Guyana<br>_Notes:_DHS means tha<br>(https://dhsprogram.co|<br>2020 (MICS)<br>t the survey was<br>m/), whereas MI|North Macedonia<br>sourced from the D<br>CS means that the|<br>2019 (MICS)<br>emographic an<br>survey was sou|d Health Surveys Program<br>rced from the Multiple Indi|<br>cator|

_Notes:_ DHS means that the survey was sourced from the Demographic and Health Surveys Program (https://dhsprogram.com/), whereas MICS means that the survey was sourced from the Multiple Indicator Cluster Surveys by UNICEF (https://mics.unicef.org/).

37
- Bloom, Nicholas, John Van Reenen, and Heidi Williams. “A toolkit of policies to promote innovation”. _Journal of economic perspectives_ , vol. 33, no. 3, 2019, pp. 163–84.

- Blundell, Richard, Rachel Griffith, and John Van Reenen. “Market share, market value and innovation in a panel of British manufacturing firms”. _The review of economic studies_ , vol. 66, no. 3, 1999, pp. 529–54.

- Boeing, Philipp, and Elisabeth Mueller. “Measuring patent quality in cross-country comparison”. _Economics Letters_ , vol. 149, 2016, pp. 145–47.

- Branstetter, Lee, Guangwei Li, and Francisco Veloso. “The rise of international co-invention”. _The changing frontier: Rethinking science and innovation policy_ , 2015, pp. 135–68.

- Branstetter, Lee G, Guangwei Li, and Mengjia Ren. “Picking winners? Government subsidies and firm productivity in China”. _Journal of Comparative Economics_ , vol. 51, no. 4, 2023, pp. 1186–99.

- Callaway, Brantly, and Pedro HC Sant’Anna. “Difference-in-differences with multiple time periods”. _Journal of econometrics_ , vol. 225, no. 2, 2021, pp. 200–30.

- Cerdeiro, Diego A, Johannes Eugster, Rui C Mano, Dirk Muira, and S Jay Peirisa. “Sizing up the effects of technological decoupling1”. _Geoeconomic Fragmentation The Economic Risks from a Fractured World Economy_ , 2023.

- Chen, Jiafeng, and Jonathan Roth. “Logs with zeros? Some problems and solutions”. _The Quarterly Journal of Economics_ , vol. 139, no. 2, 2024, pp. 891–936.

- Coe, David T, and Elhanan Helpman. “International r&d spillovers”. _European economic review_ , vol. 39, no. 5, 1995, pp. 859–87.

- Coelli, Federica, Andreas
# **Table 10: Regulatory regimes in ECA**

|**ECA Countries**|**Pledged Kyoto commitment for**<br>**2008-12 emissions**<br>**(percentage of baseyear orperiod)**|**2008-2012 EU ETSs Cap**<br>**(annual million EUAs)**|
|---|---|---|
|**Joint Implementation - EU ETS countries**|||
|Bulgaria|-8|42.30|
|Czech Republic|-8|86.80|
|Estonia|-8|12.72|
|Hungary|-6|26.90|
|Latvia|-8|3.43|
|Lithuania|-8|8.80|
|Poland|-6|208.50|
|Romania|-8|75.90|
|Slovak Republic|-8|30.90|
|<br>Slovenia|-8|8.30|
|**Other Joint Implementation countries**|||
|Croatia|-5||
|Kazakhstan|0||
|Russian Federation|0||
|Ukraine|0||
|**CDM countries**|||
|Albania|||
|Armenia|||
|Azerbaijan|||
|Belarus|||
|Bosnia & Herzegovina|||
|FYR Macedonia|||
|Georgia|||
|Kosovo|||
|Kyrgyz Republic<br>Moldova|||
|Montenegro|||
|Serbia|||
|Tajikistan|||
|<br>Turkmenistan|||
|Uzbekistan|||
|**Annex I without Annex B commitments**|||
|Turkey|||

Source: UNFCCC and Europa (2007). Note: Kazakhstan declared its commitment to limit emissions in September 2009 (UNFCCC, FCCC/SBI/2010/6)
Correlational evidence suggest that women entrepreneurs in female-concentrated sectors earn lower profits than their counterparts in male-dominated sectors (Goldstein et al. 2019).<sup>29</sup> Interventions that encourage women to enter male-dominated sectors show promise. Additionally, women-led businesses are less likely to engage in exporting, have limited access to corporate value chains, and receive a small share of public procurement (IFC 2021b). On the demand side, constraints are linked to gender gaps in networks, skills, and finance. On the supply side, many large corporations report a lack of sexdisaggregated data on procurement and distribution channels, which hinders their ability to take positive action. Furthermore, women entrepreneurs generally have less access than male entrepreneurs to digital skills and digital technologies and platforms (Aranda and Qasim 2023). This disparity may be related to gender gaps in smart phone ownership and internet access, which can be attributed to affordability, gender norms, or security concerns, and which limit the expansion of women-led firms in e-commerce. There is still little causal evidence on the impact of interventions addressing these constraints on the performance of women-led businesses.

# 5.1 Encouraging women to enter male-dominated sectors

A report with data from ten countries provides non-causal evidence that women in male-concentrated sectors earn higher profits than women in female-concentrated sectors, even after controlling for a series of observable characteristics (World Bank 2022). Interventions encouraging women entrepreneurs to cross over to male-dominated sectors show promise. Factors correlated with entering male-dominated sectors include higher access to male mentorship (including spousal support), role models, and information (Goldstein et al. 2019, LACGIL 2021). A recent field experiment in the Republic of Congo showed that providing young women with information on trade-specific earnings can shift their preferences toward male-dominated trades (Gassier et al. 2022). Interventions that foster the participation of husbands, role models, or networking could have similar effects. Addressing men’s misperceptions and engaging adolescents in discussions about gender attitudes could encourage crossover, as these efforts have increased
Policy Research Working Paper 8627

# **Abstract**

This paper estimates the profitability of fertilizer and hybrid seeds in Uganda, using agronomic evidence on the yield returns to inputs from experimental fields, as well as output price data from local markets between 2000 and 2012. The results suggest that the returns to fertilizer are positive across the entire price range for beans, maize, and matooke and positive for the top 75 percent of prices for coffee. Commonly available improved seed varieties for maize and beans increase gains by 32 percent on average. However, accounting for the quality of the inputs available to farmers in the market, the sizable positive returns become negative for most of the price distribution, possibly explaining the low adoption of inputs in Uganda. The paper also examines the impact of other factors that could affect input adoption, by using a relatively long panel data set spanning 12 years. The analysis finds evidence that enhanced access to economic markets and past weather conditions have small effects on input use, and positive correlations between the use of extension services and knowledge and input use.

This paper is a product of the Poverty and Equity Global Practice. 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/research. The authors may be contacted at cmejiamantilla@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
. (2004) and Nordas and Piermartini (2004) use a standard gravity equation to estimate how trade facilitation costs and infrastructure affect trade flows using a gravity model.  They use data on port efficiency from the Global Competitiveness Report.  These data are based on perception surveys and may not be comparable across countries.

> 2 In a related paper, Hummels and Skiba (2004) provide evidence that trade volumes affect the timing of adopting containerized shipping and reduce shipping costs.

2
# **8 International comparison**

## **8.1 Inequality**

**This section compares fiscal policy effects on inequality across 63 countries assessed in CEQ standard indicators.** We measure this by calculating the reduction in the Gini coefficient from Market Income (prefiscal income) to Final Income (Market Income plus all taxes, transfers, and in-kind benefits from health and education). We refer to the Gini reduction as the “total redistributive impact”. If a country has been assessed multiple times, we include only the most recent datapoint.

**Globally, the total redistributive impact of the combination of taxes and transfers modelled ranges from 0.2 to 20.4 Gini points reduction).** The West Bank and Gaza achieves 6.5 Gini points of redistribution—a moderate result relative to other countries.

### **Figure 8-1 Total inequality reduction in the West Bank and Gaza is moderate relative to countries in other CEQ assessments**

Inequality reduction, Gini coefficient (change in Gini from Market to Final Income) <!-- Start of picture text --> 0<br>-5<br>West Bank and Gaza:<br>-10<br>6.5 Gini point reduction in inequality<br>-15<br>-20<br>-25<br>High incomeHigh income Upper middle incomeUpper middle income Lower middle incomeLower middle income Low inLow in c omeome<br>Source: World Bank estimations for the West Bank and Gaza based on PECS 2016-17. CEQ Data Center for the rest of the<br>countries.<br>Spain Uruguay Panama United States Croatia Mauritius Romania South Africa Argentina Brazil Mexico Namibia Georgia Venezuela Costa Rica Botswana Dominican Republic Colombia China Ecuador Thailand Peru Iraq Iran Turkey West Bank and Gaza West Bank and Gaza Belarus Malaysia Jordan Russia Albania Guatemala Paraguay Indonesia eSwatini Lesotho Zambia Tunisia Kenya Ukraine Honduras El Salvador Mongolia Bolivia India Nicaragua Moldova Egypt Tanzania Ghana Sri Lanka Comoros Cambodia Ivory
It may still be that untreated districts in our sample are not representative of statewide trends and that women in these districts may be more or less empowered than average, implying that program placement may be targeted. However, the nationally-representative NFHS-3 (International Institute for Population Studies and Macro International, 2007) and DLHS-3 (Ministry of Health and Family Welfare and International Institute for Population Studies, 2010) show that the women in untreated districts in our sample do not differ significantly from the rest of the state. For instance, the average age at at marriage for Uttarkhandi women is 20.6, while in our untreated sample, it is 19.8; 43 percent of all Uttarkhandi women work while 45 percent of the untreated women in our sample do. The total fertility rate in the state is 2.6, which corresponds closely to the average family size of one boy and one girl in our untreated sample. Finally, while 84 percent of the state has access to electricity, 90 percent of our untreated sample does. This lack of significant differences suggests that the program is not targeted at districts by levels of female empowerment.

The next concern with identifying the effect of the program is self-selection. Table 5 indicates the presence of self-selection into _Mahila Samakhya_ . The average participant is three percentage points closer in age to her husband than the average non-participant in treated districts, which suggests that women with greater initial bargaining power may self-select into the program. Further, participants tend to have older and more sons than non-participants, although the differences are not significantly different from zero. Participants are significantly more likely to be Brahmin than non-participants. Participants are less likely to live with their husbands; the difference of 19 percent is highly significant. However, in our pre-tests, we found that even women who do not live with their husbands live with other male relatives, including fathers, fathers- or brothers-in law, uncles, and sons. In all our fieldwork, we only encountered seventeen women who lived alone or without any older male
2011). Finally, LASSO model selection is implemented to verify that all key variables are accounted for. The same procedure is applied to obtain the heteroskedasticity model describing the variance of the errors. The resulting regression models are included in Appendix 3.

Among the different categories of explanatory variables, dwelling characteristics and household asset ownership rank as the strongest predictors of household consumption expenditure per capita, followed by household head education and employment variables. An interesting observation is that local area night-time-lights, agricultural land quality, and climate data (derived from public remote sensing data that serve as predictors in the SEM approach) are found to be significant when included on their own – but once we control for variables available in the population census, these variables lose their significance and are subsequently dropped from the model.

Once the models have been selected, the model parameters are estimated by going 21