rafmacalaba/data-use-mentions-extended huggingface dataset:
implications, noting that from an economic perspective, intervention programs are warranted only if the impacts of tenure insecurity are larger than the cost of implementing a program to reduce tenure insecurity.<sup>10</sup>

# **3. Data and descriptive evidence**

Descriptive data highlight three results. First, tenure insecurity is high with 22% of cultivators concerned about land loss and 15% having experienced land disputes in the last decade. Second, village data highlight increased land scarcity and transactions. Finally, comparing the productivity of estates with that of smallholders points towards marked differences in terms of intensity and efficiency of land use, including a pronounced negative relationship between farm size and productivity over the entire farm-size spectrum.

# **3.1 Sampling and characterization of smallholder farmers**

Our data are from the 2006/07 NACAL implemented by Malawi’s National Statistical Office (NSO) with the Ministry of Agriculture. Beyond household-, individual-, and village-level data collection instruments administered to a sample of smallholders, the survey also includes estates. While estates were drawn from a nation-wide list,<sup>11</sup> smallholder farms to be sampled were selected in two stages: In a first stage, enumeration areas (EAs) were randomly selected by district with stratification by agro- ecological zone and a listing conducted in those that had been selected. In the second stage, farm households in each of the selected EAs were randomly drawn with a target of 10 small (< 2 acres) and 5 medium sized (≥ 2 acres) farms each.<sup>12</sup> For a sample of 931 estates and 23,896 smallholder farmers, the survey collected information on household composition, welfare, food security, assets (incl. livestock), marketing, and parcel-level data on land tenure and investment, in the 12-month period starting October 2006. Data gaps reduced the sample for our analysis to 17,672 smallholders and 868 estates with complete information. Also
6 26th level corresponding to a female life expectancy of 82.5 was added. The appropriate level for each time period was chosen given life expectancy, with interpolation if necessary between adjacent levels. Choice among life table families was based on region and mortality level, as follows: Region and mortality level Life table family Sub-Saharan Africa Below 12 South 12-17 North 18 and above West North Africa, the Middle East, Latin America, and the Caribbean Below 18 South 18 and above West Northern America, Europe, Asia, and Oceania All levels West Over time, countries in particular regions may therefore switch from one life table family to another. To avoid discontinuities, the switch is made gradually, using life tables linearly interpolated between the two families involved for five intervening periods.

# Fertility level and trend

Base-period total fertility rates (TFRs) are derived from the same mix of sources as mortality rates, including the U.N. 1988 revision; official sources, as cited in Population and Vital Statistics Reports or in government publications; the U.N. 1984 assessment; the U.S. Bureau of the Census (1985); Eurostat (1987); and Celade (1987). We relied on two sources more frequently for information on fertility than mortality: various surveys, especially Demographic and Health Surveys and World Fertility Surveys; and the Institut National d'ttudes Demographiques (Monnier 1987). Often, all these sources give fertility for a different period or in terms of the crude birth rate, and corresponding TFRs had to be estimated using available data on age-sex composition and age-specific fertility, or obtained from a projection using an estimated TFR for an earlier period. Where no reliable source was available, an unattributed Bank estimate may be indicated in the Appendix.

Future fertility trends are specified by selecting a year when the net reproduction rate (NRR) reaches unity, i.e., when the average woman's fertility results in exactly replacing herself. (This is referred to here as the replacement year
# 1. Introduction

Globally, the negative socioeconomic impacts of COVID-19 are expected to affect women more than men. For example, on average, the share of women is larger in the informal sector (ILO 2020), and given that the pandemic affected the informal sector more, the female working population is more likely to be economically affected. Beyond economic impacts, COVID-19 can disproportionately affect women’s health, education, and voice and agency through different channels, including increased unpaid care work, reallocation of health care resources, and increased gender-based violence during lockdown (UN Women 2020). On the other hand, worldwide male COVID-19 patients are more likely to be directly affected by the virus, needing more intensive treatment or succumbing to mortality (Peckham et al. 2020).

While the gender-differentiated transmission channels and impacts of COVID-19 are widely recognized, and there is new, sprawling evidence on the gendered impact of COVID-19, few studies quantitatively assess the possible or actual economic impacts on women through labor distribution channels and less so for women in fragile and conflict-prone countries. This paper aims to assess the possible and actual economic impacts of COVID-19 on women and female-headed households in Chad using Computable General Equilibrium (CGE) model-based simulations and evidence from a household phone survey conducted during the pandemic.

Chad has a large informal economy and ranks 147 of 153 countries in the global gender gap index. The first cases of COVID-19 in Chad were reported in March 2020, and since then, as of March 2021, fewer than 5,000 cases of COVID-19 have been reported. There was also a compound health risk with the outbreak of malaria and chikungunya in 2020. The risks of the virus spreading remain high with the advent of new variants. Even though COVID-19 cases may not seem to be high in landlocked Chad (and neighboring countries), the measures necessitated to mitigate the spread of the disease, including the closure of land borders and the main airport, within-country movement restrictions, individual’s behavioral changes in fear of contracting the disease
in rural areas). Estimates based on census data and the number of connections reported by utilities suggest that access to grid electricity was only about 74 percent in 2016 (90 percent in urban areas and 63 percent in rural areas in 2016) (IEA 2017). The household survey sponsored by IFC suggests that 35 percent of Pakistan’s population lacked access to electricity in 2014 and that the access rate in parts of the country was still alarmingly low (almost half of the population in Sindh lived off‐grid, for example).

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during structured observations of study participants by our research team. However, participants in the intervention group were 9 pp more likely to report washing with soap or ash the previous day. This underscores the limitations of self-reported data, particularly when the socially desirable outcome is likely to be known and salient.

Sustainability is a widely-recognized challenge for WASH interventions. At five months postintervention, Healthy Villages and Schools increased access to improved water sources and improved sanitation facilities[26]. Notably, these improvements largely persisted to 3.6 years postintervention, as did the improvements in WASH institutions. Yet these improvements did not result in any measurable effect on diarrhea or growth faltering. This highlights that it is crucial to measure health outcomes directly and not assume that better inputs are sufficient to yield improvements.

This study has several limitations. First, the trial had incomplete adherence: 86% of villages in the intervention group reported that they had implemented the community action plan to address WASH challenges (e.g. by building new infrastructure) by the 3.6-year follow-up. However, this is a realistic level of adherence for a government-implemented program. Indeed, given that many study villages were conflict-affected, the take-up rate was substantial. Second, we have no baseline measures. Although the randomized design means that such measures are not required for unbiased estimates of treatment effects, there are theoretical challenges; for example, if permanent migration out of study villages was affected by the intervention, then our estimates may be biased. However, at 3.6 years post-intervention we were able to re-interview 88% of the
Unfortunately, this is the best data available in terms of coverage, which could be driven by structural bias affecting the availability of survey data during crisis times – Surveys are not necessarily known to be a priority during macroeconomic crises.

If we then take a more strictly distributional view and ask how the income of households at different levels of the income curve is affected during output collapses associated to a default, our cases point to a regressive relationship. The cumulative drop in the level of national income going to the bottom 10 percent<sup>31</sup> of the distribution is a whopping 9 percent when measured from peak to trough during a t-1 to t+2 window around default.<sup>32</sup> For the bottom half of the distribution (including bottom 10 percent), it drops roughly 6.4 percent. But, on the flip side, households in the top 10 percent gained roughly 8.7 percent – all despite an average cumulative GDP p.c. collapse of roughly 8.5 percent.

These summary statistics are based on 8 cases for which we have data and which present GDP downturns along with the default, since we are trying to better ascertain how regressive an output collapse happening concurrently with a default is.<sup>33</sup> Some heterogeneity remains. In two cases, Russia 1998 and Argentina 2014, the share of income going to the bottom 10 percent increased. For all others, it decreased. In cases like Greece 2011 and Argentina 2001, the share of income received by the least fortunate plunged by far more than overall GDP. In Greece’s case, the bottom 10 percent’s share of income fell by 44% while the output collapse amounted to nearly 18%; for Argentina the bottom 10 percent lost roughly 86% of their share of income while the output collapse amounted to 16.7%.

# **V.** **<mark>Conclusion</mark>**

<
oriented enterprises. Flexibility and adjustment within the labor market are also evident, as nonexporting firms actually continued to increase their employment of migrants even over the period June to December 2008, when China experienced the sharpest negative shock to aggregate demand.

As these data capture only the behavior of surviving firms and firms of sufficient scale to have formal transactions with financial institutions, they miss the effects of firm closure even as they pick up the differences in employment dynamics across exporters and non-exporters. Just as the surveys using firm data reveal the strong effect of the crisis on migrant workers, the use of survey data on rural households with information on migrant families provides a way to capture the employment effects on rural registered residents, who were most affected by crisis-related shocks. Evidence from rural household surveys is discussed below.

# **3B. The View from the Rural Household**

Employers usually have much greater flexibility in ending employment relationships with migrant workers than with local workers. Migrants in China’s cities and booming coastal areas typically do not have formal labor contracts and lack legal recourse to employment protection; and, when they lose a job, they frequently lack severance pay or unemployment benefits. The 2005 China Urban Labor Survey, for example, noted that 85.2 percent of migrants working in five large urban cities were in the informal sector. Even after excluding the self-employed among the informal sector, 54.7 percent of wage-earning employees lacked contracts. The Labor Contract Law and a tightening of the labor market helped lower these informality rates somewhat, so that by February 2010 only 40 percent of migrants were wage-earners working without contracts. However, empirical analysis using the CNFS data suggests that manufacturing firms in cities that implemented labor regulations more strictly tended to experience slower employment growth (Park, Giles, and Du 2011). Informal employment rates for local residents, by contrast, were under 25 percent.<sup>5</sup> 9
<!-- Start of picture text --> 0.3600<br>0.3571<br>‘\ N<br>0.3500 ’*% 7 a N<br>\ Zc N .<br>» \ 0.3459 7 ‘ 43475<br>‘ 4 XN 4<br>“ 7 ¢ ‘\ 4 ,<br>‘“‘ - = --- wer \ \ 7 - -<br>a 0.3459 “SL ON 4 Oe9<br>0.3448+ a se? ae<br>0.3400<br>0.3375<br>0.3300<br>2011 (nss) 2015 2016 2017 2018 2019<br>—Approach 2 (2011) — — Approach 2(2017) === Approach 1<br>30<br>a6<br>45<br>39.87<br>40<br>35<br>32.82<br>0<br>25 22.49<br>2 wee, 18.9<br>ve 18.2<br>15 a<br>145 waa {6 1a<br>bs0 10.6‘e we TM, 10.22<br>98 we<br>; 2<br>°<br>1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2005 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019<br>O—NSS Surveys ++ @++ Projections basedon NAS fl Estimtes based on trans formed CPHS<br><!-- End of picture text -->
To check for biases in the matched sample, we compared the average consumption for both the matched and unmatched sample using a simple t‐test, and found no significant difference between the two samples.<sup>11</sup>

# **_Tenant households_**

It is common in Pakistan for the name on the electricity bill to be that of the owner of the house, even if the property is rented by another family. If this is the case, it is likely that the better‐off landlord’s PMT score is matched against the poorer tenant’s actual electricity consumption, perhaps contributing to the low recorded consumption among wealthy households.

To determine whether this affects the results of the study, we look at the extent of rentership in the study area. The PSLM 2013‐14 data indicate that in rural areas, 93 percent of families own their house, while in urban households, home ownership was around 72 percent. However, this cannot explain a large part of the findings since the service areas of the three DISCOs are predominantly semi‐urban and rural. A district‐wise representation of the billing data sample can also be found in the Appendix.

# **_Representativeness of the billing data_**

The PSLM data examined in Part I are nationally representative. The billing data sample is fully random, but comes from only three non‐randomly selected DISCOs. How might the analysis differ if the other DISCOs were included? First, we note that the three DISCOs covered in this analysis comprise 38% of all households. Being better‐off districts, we would expect to see higher electricity consumption among this sample overall. This would imply that nationwide there would be proportionately less households above the 300 kWh/month level, reinforcing the above analysis. There is no reason to expect the consumption patterns across quintiles to differ significantly given the large sample.

# **_Multiple connections_**

The final potential concern is that households may have multiple connections, spreading their consumption across meters
sense, there are explicit prices for substitutes for SP within the gamut of public spending on human development. For 2017, the price data (described later) indicate that the price of health services relative to food and non-alcoholic beverages has a correlation coefficient of 0.61 with (log) GDP per capita; for education services the correlation coefficient is 0.62 (The Addendum provides the scatter plots, Figure A1). Given that health and educational services are more costly in richer countries we expect to see substitution in favor of SP within public budgets. This leads to:

**_Prices: The relative prices hypothesis._** _Higher relative prices of competing human development services in richer countries lead their governments to switch to SP transfers._ Our next set of hypotheses relates to governance and political economy, starting with:

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Leather products) and industry 361 (Pottery, china, earthenware).

Most Ricardian models suggest using labor productivity to measure comparative advantage. This is fortunate in that manufacturing output and employment data are among the most available metrics for the broad grouping of countries under study. Labor is typically the only factor of production in Ricardian models, so a natural extension might be total factor productivity that also allows for capital accumulation as well. Unfortunately, capital data at the country-industry level for this sample is too sparse to be of benefit in a panel study. An earlier version of this paper presents results using output to measure industrial specialization.

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of private preschools generally remains, it is worth noting that effect sizes do get smaller in smaller districts. The interaction coefficient of public preschools is no longer statistically significant in districts with a population of less than 0.5 million with the DDD strategy and only statistically significant at the 10 percent level with the DDD-FE strategy. This potentially suggests that preschools may be more beneficial to mothers living in highly populated districts with better economic opportunities.

# **_6.7 Sorting to better preschool access_**

To check if mothers with preschool-aged children sort into districts with high preschool access, we aggregate our constructed panel of individuals over the years to a panel of district-year.<sup>25</sup> We

> 24 1996 is the earliest round of SUSENAS, with complete coverage of the 290 districts.

> 25 By construction, one PODES year is matched to several years in the panel of mothers. For instance, PODES 1993 is assigned to year 1991-1993 in the panel of mothers. If the time unit is defined annually, we obtain, mechanically,

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**Figure 7: Dirty versus Clean Manufacturing Industries Output (in BVD) and Exports (in**

## **10,000 VD),  1997-2002**

<!-- Start of picture text --> 20000 70000<br>18000<br>60000<br>16000<br>14000 50000<br>12000<br>40000<br>10000<br>30000<br>8000<br>6000 20000<br>4000<br>10000<br>2000<br>0 0<br>1997 1998 1999 2000 2001 2002<br>Output_dirty Output_Clean<br>Exports_Dirty Exports_Clean<br>Output (in BVD) Exports (10K VD)<br><!-- End of picture text -->

# **_b. Regression Approach_**

Previous empirical studies on the relationship between trade and the environment have found varying results. Dean (2002) uses provincial level data on water pollution from China and found support for the idea that trade liberalization has both a direct and an indirect effect on emission growth and these could be opposite in sign. In contrast, Grossman and Krueger (1993) examined the environmental impacts of NAFTA and found no evidence that a comparative advantage is being created by lax environmental regulations in Mexico. Using data across different countries from 1960-1995, Mani and Wheeler (1999) found that ‘pollution haven effects’ are insignificant in developing countries because production is mainly for domestic consumption, not for export.

In a more closely related study, Gamper-Rabindran and Jha (2004) empirically analyzed the relationship between trade liberalization and the environment in the Indian context. Their findings indicate that exports and FDI grew in the more polluting sectors relative to the less polluting sectors between the pre and post liberalization periods. This evidence provides some support for concerns raised about the environmental impact of trade liberalization. Specifically, they find that trade liberalization has resulted in an increase in exports from industries that are more water and air pollution-intensive relative
regulations will allow foreign banks to open 100% capitalized wholly-owned subsidiaries in India. Foreign banks have typically focused their operations in the top 25 cities in the country, likely due in part to restrictions on branch expansion.<sup>15</sup> The foreign banks generally use more modern equipment, pay higher salaries, and attract better-trained employees (IndiaMart 2004).

Private banks are primarily de novo entrants that were granted banking licenses during the financial liberalization in the early 1990s. A total of 25 de novo private banks began operations between 1994 and 2000. There are also a small number of incumbent private banks that existed before 1990 and some state-owned institutions that have been successfully privatized. An example of the latter is ICICI, which was formed in 1955 as a state-owned institution at the initiative of the Government of India and the World Bank to create a development financial institution for providing medium- and long-term project financing to Indian businesses. During the 1990s, ICICI was privatized and evolved into a private, fullservice bank and is now India’s second largest bank offering a wide range of services to retail and corporate customers.

As shown in Table 1, the foreign banks are most numerous, but they have relatively few branches and accounts and also have fewer deposits and assets than the other types. Nationalized banks are the largest type as measured by number of branches, accounts, deposits, and assets. The state-owned banks combined – SBI plus the nationalized banks – dominate the banking sector with about 80% of deposits and assets.

# **4. Data and Summary Statistics**

We match data from nonfinancial firms, the banks with which they have relationships, and information on their local market. The firm data are from _Prowess_ , an electronic database produced by the Centre for Monitoring Indian Economy (CMIE). This database includes firms that are required to file annual accounting reports and have submitted reports for at least three years. This comprises: (i) firms that are listed on a national exchange (National
male workers in municipality and year , the raw ww ww wage ratio is simply g � g . mm yy Brazil’s population-weighted average real gender wage ratio across all ff<sup>gg</sup> ww ww municipalities over all years of the 2011–16 panel is 0.86, indicating that women are still not being paid equally to men. The comparable statistic from the United States is around 0.80. Brazil’s corresponding population-weighted average of the leave-out wage ratio is 1.02, precisely at parity. The fact that the leave-out measure is at parity is perhaps surprising. However, it is consistent with the data of Aizer (2010), which also provide an average leave-out wage ratio of 0.97 across California counties. That the leave-out wage ratio is above the raw gender ratio reflects the fact that Brazilian wages rose more quickly in those industries in which women were relatively more highly concentrated in 2011, but into which men disproportionately moved in subsequent years. Figure 2 illustrates the similarities in the dispersion of the raw and leave-out wage ratios for the analytical sample,

> 9 See CNAE (National Classification of Economic Activities) (dashboard), Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística), Rio de Janeiro, https://www.ibge.gov.br/en/statistics/technical-documents/statistical-lists-and-classifications/17245national-classification-of-economic-activities.html?edicao=17248&t=sobre.

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01|
|Afar|0.85|0.15|
|Amhara|0.99|0.01|
|Oromia|0.95|0.05|
|Somali|0.89|0.11|
|B.G.|0.99|0.01|
|S.N.N.P.R|0.81|0.19|
|Gambella|0.75|0.25|
|Harari|0.97|0.03|
|Addis Ababa|n/a|n/a|
|Dire Dawa|0.98|0.02|
|**Total**|**0.95**|**0.05**|

Source: Central Statistics Agency documentation, see annex for details.

The key questions to be answered in terms of the household regression model have been agreed as:

- To what extent can impacts statistically associated with a drought hazard measure be causally attributed to the drought (internal validity/robustness)?

> 12 See Box 1 for data inputs, which include additional non‐stationary, but low‐variability climate data

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