rafmacalaba/data-use-mentions-extended huggingface dataset:
impacts household livelihoods in developing countries and the coping mechanisms employed, regardless of individuals’ economic status.

Finally, to the best of our knowledge, this paper is the first in empirically analyzing the Covid19 lockdown measures on micro-level individual and household welfare, poverty status and labor market outcomes for the Comoros. It will inform on the thin micro literature on pandemic shocks on household welfare in a developing country context and specifically for small island developing states. The analysis will provide an understanding of the effect of the pandemic on the Comoros, which falls in the “forgotten countries” category in terms of economic research (Porteous, 2020). This paper will go beyond a descriptive assessment of Covid-19 on the socio-economic status of households. The research aims to causally estimate the lockdown impact using a detailed doorto-door household survey conducted in two phases before and after the lockdown implementation in the Comoros. An understanding of the mechanisms through which the lockdown can affect the welfare coping strategies of households is important. As such, this paper examines the impact of the pandemic on the expenditure, poverty status, asset and livestock ownership and labor market outcomes of individuals and households in the Comoros.

# **3. Contextualization and Data Description**

The Covid-19 virus was reported in the Comoros in May 2020 as the world battled with the outbreak, which was declared a pandemic by the World Health Organization on March 11, 2020. The Comoros was still recovering from the devastating cyclone Kenneth that had hit the country in April 2019 when the first Covid-19 case was recorded in May 2020. The Comoros is a densely populated country with approximately 465 inhabitants per km<sup>2</sup> (World Bank, 2020) and is susceptible to higher contagion given the nature of the virus. The measures enacted by the government encompassed sensitization from the president on March 16, closure of schools 9
water supply lasting less than 24 h per day in 44 of the 102 countries included in the database (Danilenko et al., 2014). In 2000, the World Health Organization (WHO) estimated that 60% of the population served by piped water in Latin America and the Caribbean were served by Intermittent Water Supply (IWS) and that at least one in three urban water supplies in Africa and one in two in Asia operated intermittently (WHO, 2000). Overall, 1 billion people suffer from IWS according to WHO/UNICEF Joint Monitoring Program for Water Supply, Sanitation and Hygiene (JMP).

The impact of intermittent water supply is significant, particularly on poor households. Health impacts caused by water disruptions tend to be more severe for low-income households and affect households that consume tap water (Ercumen et al., 2015; Jeandron et al., 2015; Nygård et al., 2007). In India, Ercumen et al. (2015) look at the relationship between water supply interruptions and waterborne illnesses using panel dataset with matched cohorts of households. The impact of water supply interruptions on child diarrheal illnesses depends on household income. Children from low-income households with continuous water supply have 37% lower prevalence of infectious diarrhea than lowincome households with intermittent supply. In higher-income households, there is no significant evidence of the impact of continuous water supply on child diarrheal illnesses, compared to intermittent supply. Results also show that households with continuous water supply report 42% fewer cases of typhoid fever than households with intermittent water supply, regardless of their level of income.

<mark>Studies using official information on water outages and clinic admission data identify a strong and consistent relationship between outages and health impacts.</mark> In the Democratic Republic of Congo, Jeandron et al. (2015) link water interruptions to increased cholera incidence rate using time series data combining admission information from a Cholera Treatment Center and daily variations of water supplied by the water treatment plant. They find that the suspected cholera incidence rate
showed more variability in their choice of _B_ , with five firms choosing not to shield any part of past revenues, and four firms choosing to shield between 30 and 90 percent of prior average revenue levels. The resulting share of revenue to be paid, _m_ , ranged from 0.29 to 5.85 percent. This was equivalent to a mean (median) of 8.2 percent (5.1 percent) of average monthly profits according to the profit data firms reported at the time of application. The entrepreneur’s contract choices meant that they retained a relatively large share of the incremental revenue stream, which has the advantage of lowering moral hazard concerns, but also perhaps suggests that our growth projections were conservative in their view.<sup>15</sup>

# **3.3 Contract performance**

The nine investments totaled Rs. 4,310,000 ($34,206). Table 3 summarizes the repayments for each investment. Firms reported less revenue than they had predicted, so that the total repayment due from revenue-sharing was approximately Rs. 1.3 million less than the interest that would be paid on an 18% declining-balance loan over this period, with every single investment yielding less than the 30.1 percent nominal three-year return of such a loan. However, the larger problem was enforcement of repayment, conditional on the revenues that firms reported. Only two of the firms (the vehicle repair firm A, and the motor coil rewinding firm C) made all payments on time and yielded positive returns. A third firm (firm G) experienced some repayment issues, but was able to complete the payments required with some delays.

The remaining six firms all paid back substantially less than the capital invested. Four of the six paid their contractual revenue-sharing and principle payments for at least seven or eight months before falling into arrears. The other two firms fell into arrears before even making their first principle payment, and ended up paying only two months of principle repayments
to end the distribution of the public registry data to banks in 2002.

# **_2. Banking supervision_**

A second main objective for establishing a public credit registry is to strengthen banking supervision. In a prior survey of PCRs from 2000-2001, approximately half indicated that banking supervision was the most important reason for operating the public registry. In that same survey, 65% stated that the PCR had been “very important” in strengthening supervision of

> 16 PCR data is shared with private credit reporting firms or bureaus in Bolivia, Chile, the Dominican Republic, Peru and Slovenia. In Argentina, negative data from the PCR is publicly available and included in private credit reports.

16
investment efficiency (measured by the quantities of goods and services needed per unit of new capital), as well as efficiency of labor and capital use but without assuming efficiency gains in intermediate inputs (such as medicines). In the absence of a gain in efficiency, the grant increase in an average year is around US$22 per capita (at 2009 prices; from the scenario _mdg-eq+fg_ ). The need for additional grant aid would be eliminated if efficiency in the health sector reached close to 5 percent per year. While such rapid gains may be infeasible, additional growth of at least 1-2 percent per year may be within the realm of the possible.<sup>14</sup>

> 13 An additional simulation showed that the domestic resources that could be mobilized via increased domestic government borrowing were insufficient to finance the health policy of the last two scenarios ( _mdg-eq+fg_ and _mdg-eq+tx_ ).

> 14 For example, on the basis of surveys in 6 low- and middle-income countries, Chaudhury _et al_ . (2006) find that, on average, primary health workers were absent 35 percent of the time. Other things being equal, a gradual reduction in their absenteeism to 17.5 percent of their time by 2030 would correspond to an annual increase in their productivity by around 1.5 percent per year. In the simulations, similar improvements are assumed for capital use and investments.

16
**Figure 5: List of studies focused on the health impact of water provision interruption and dysfunction (continued)**

|**Country,**<br>**year**|**Population**|**Exposure/Control**|**Outcome**|**Measure of**<br>**effect**|**Source**|
|---|---|---|---|---|---|
|Taiwan,<br>China, 2004-<br>2006|Insurance claims<br>(n=1,000,000<br>insured persons)|Number of insurance claims<br>during outages**/Number<br>of insurance claims during a<br>normal period|(1)Gastroenteritis<br>(2)Skin diseases<br>(3) Eye diseases|(1)RR =1.31<br>(1.26 – 1.37)<br>(2)RR =1.36<br>(1.30 – 1.42)<br>(3)RR =1.34<br>(1.26 –1.44)|(Huang et<br>al., 2011)|
|Zambia, 2017|District month<br>(n=1230)|Impact of the number of<br>outstanding water supply<br>complaints per month on<br>the number of health clinic<br>admissions|(1)Diarrhea<br>(2)Typhoid fever<br>(3)Respiratory<br>infection<br>(4)Measles|(1)1.00<br>(0.42)***<br>(2)0.002 (0.001)<br>(3)2
Policy Research Working Paper 5701

# **Abstract**

Country export quality (measured by unit values) is correlated with income level suggesting that studying quality dynamics potentially offers insights into the development process. This paper uses highly disaggregated trade data to explore the export quality (unit value) dynamics of goods exported to the United States over the 1990–2000 period. In addition to finding considerable heterogeneity in the relative quality of exports across countries and across goods within countries, the authors find that the rate of quality growth varies substantially across countries, as well. Specifically, the fastest growth is seen in exports from the richer (OECD) countries, implying an evolving divergence in product quality across regions. This divergence obtains despite evidence of conditional convergence in quality over time- goods with lower initial relative quality levels experience faster growth in quality. The data suggest that part of this divergence is driven by the product mix itself—OECD exported products experience intrinsically higher growth rates. This is consistent with the argument of Hausmann, Hwang and Rodrik (2007) that what countries export does matter for growth. However, it is partly driven by a higher growth rate of quality in the richer countries independent of convergence effects, suggesting that other country-specific factors impeding overall convergence are at work. Finally, there is very limited technological “leap-frogging” by countries across product lines as the relative quality of new exports, on average, is roughly the same as incumbent exports, both in richer countries and elsewhere.

This paper is a product of the Macroeconomics and Growth 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 author may be contacted at wmaloney@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues
Source: Authors’ estimates based on HSS data.

Among the relatively well‐off population groups, another line of expenditures that underwent adjustments during the crisis was communication services, as well as culture, sports and recreation services, as in these households the consumption of these services is higher (Figure 26). Those who are employed, those with high or secondary specialized education, and residents of Minsk recorded the largest reductions in these expenditure categories. Households with children, retirees as well as non‐employed spend much less on these services, and adjusted them during the crisis years to a much smaller extent compared to the population on average, despite significant price increases.

22
<u>Abstract: This paper examines the ways in which lower-income households obtain basic finan-</u> cial services in urban communities in the United States and in Mexico. In addition, the paper discusses the efforts that private sector and government organizations are making to lower the cost or improve the quality of those services. It summarizes available information on these issues and assesses the rationale and challenges facing the strategies that both countries are employing to improve the financial services available to lower-income households, giving particular attention to “unbanked” households, meaning households that do not have deposit accounts with any regulated deposit-taking institution, and also to lower-income households in large urban areas.

In comparing the experiences of the two countries the paper reviews the extent to which lower-income households are unbanked, their use of non-bank financial services, and strategies for improving financial services to the unbanked. The underlying differences between the countries’ typical household incomes - national income per capita in Mexico in 2002 was U.S. $8,540, compared with $35,060 in the United States (World Bank, 2003) – may also influence the difference in percentage of unbanked - 9.1 percent of families in the U.S. compared to 76.4 found in a recent study in Mexico City.

The paper surveys data on the urban unbanked in the U.S. It asks five questions. Who are the unbanked? Where do the unbanked get basic financial services? Why don’t the unbanked use banks? What is the problem with being unbanked? And, finally, what private-sector and government initiatives are underway to improve financial services for the unbanked? It then addresses the same questions for Mexico. The answers to these questions are offered in a summary comparative form in the introduction. In both countries unbanked households are similarly characterized by low income and education levels although in Mexico the unbanked include persons well above the median income. The unbanked in both countries tend to rely on cash transactions and on services provided by commercial
and Wager, 2016; Athey et al., 2019). We include in the causal forest all the individual and household characteristics available at baseline and other school and neighborhood characteristics we collected.

We also follow Athey and Wager (2019) and Carlana et al. (2022) in using the predictions of the expected treatment effect for each student, given the set of covariates, to categorize participants into two groups, the _"Strong"_ and _"Weak"_ groups. This classification is achieved by categorizing the CATEs into three tiers: "High," "Medium," and "Low." The _“Strong"_ group refers to the subgroup whose CATEs fall within the “High" category (that is, above the 66th percentile) of all CATEs when switching from _Clubs_ to the _Mindful_ or _Virtue_ treatment. In contrast, the _"Weak"_ group consists of participants with "Medium" and "Low" CATEs (that is, positioned at or below the 66th percentile). Then, to understand what types of students are more likely to see an increase in the main outcomes due to the interventions, we characterize the groups using a balance test.

# **5 Results**

## **5.1 Main Results**

Table 3 presents the main results of our paper. We show the net impact of the SEL component—beyond the regular protection component—estimated using equation (1) on the primary student outcomes measured using administrative data: an indicator for whether the behavior at school report was above the school median (column (1)), an indicator for whether the students abandoned school during the 2019 academic year (column (2)), and an indicator for whether the math-score report was above the school median (column (3)).

The impact estimates show positive and significant effects on students’ behavior at school. Compared to students in _Clubs_ , participants in the psychology-based interventions ( _Virtue_ and _Mindful_ ) are 23
Policy Research Working Paper 10171

# **Abstract**

This paper assesses the reliability of poverty maps derived from remote-sensing data. Employing data for Malawi, it first obtains small area estimates of poverty by combining the Malawi household expenditure survey from 2010/11 with unit record population census data from 2008. It then ignores the population census data and obtains a second poverty map for Malawi by combining the survey data with predictors of poverty derived from remote sensing data. This allows for a clean comparison between the two poverty maps. The findings are encouraging—although that assessment depends somewhat on the evaluation criteria employed. The two approaches reveal the same patterns in the geography of poverty. However, there are instances where the two approaches obtain markedly different estimates of poverty. Poverty maps obtained using remote sensing data may do well when the decision maker is interested in comparisons of poverty between assemblies of areas, yet may be less reliable when the focus is on estimates for specific small areas.

This paper is a product of the Development Research 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 rvanderweide@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
8 For earnings, the key outcome variable in the NSSO data is an individual measure of daily casual earnings. The NSSO surveys distinguish whether the waged work is “salaried”, which is long-term and often involves a formal contract, or “casual” that is temporary and informal. The survey records total earnings from casual labor over the past seven days for individuals working in casual labor. I compute casual earnings for each individual as the average earnings per day worked in casual labor. I then use a monthly, state-level price index for agricultural laborers from the Indian Labour Bureau to construct real earnings. For employment outcomes, I compute for each person the percentage of days in the past seven days spent in mutually exclusive activities: private sector work and public works. Private sector work includes waged work, self-employment, and domestic work. _Child labor and human capital outcomes_ : Consistent with Shah and Steinberg (2021), I use the NSS Rounds 61 (2004-2005), 64 (2007-2008), and 66 (2009-2010) to examine the impact of NREGS on schooling outcomes and child labor. Since this data set has information about the “primary activity” of each household member, this allows the construction of employment and schooling status at the individual level. An individual whose primary activity is going to school is defined as “attends school”. “Child labor” equals one if the child’s primary activity is domestic work, work at home, or work outside the home. For more details about the data and construction of child labor and human capital outcomes, see Shah and Steinberg (2021).<sup>9</sup> _Controlling for SGRY in NSS data:_ To identify the impact of NREGA on an outcome variable, studies using NSS data have relied on an indicator variable for the phase-wise implementation of NREGA. However, controlling for SGRY in the form of an indicator is not possible after acknowledging (in section 2 above) that expenditure under SGRY and NREGA was perfectly negatively correlated with each
scenarios of climate change on agricultural productivity (low, medium, or high productivity)<sup>4</sup> to evaluate the changes by 2030 on global commodity prices and the incidence on the poverty headcount rate (defined as the portion of the country’s population living on less than $1 a day).

Ahmed et al. (2009) also analyze the climate-agriculture-poverty link. Their model is practically identical to that in Hertel et al. (2010) above, and is applied to 16 countries including Mexico. The main difference is the origin of the shocks to agriculture, which in this paper is derived from three different sources: (i) the percent of annual total precipitation due to events exceeding the 1961 to 1990 95th percentile; (ii) the maximum number of consecutive dry days; and (iii) the heat wave duration index. They analyze these effects for 30-year periods from 1970 to 2000 and 2071 to 2100 through simulations under the IPCC’s A2 scenario. When they simulate the poverty impacts from extreme dry events for the period 2070 to 2100, an additional 1.8% of the population in Mexico becomes impoverished by extreme climate in the future.

Andersen and Verner (2010, 2010a) employ municipal data in Mexico (and other Latin American countries) to estimate the cross-sectional relationship between climate and income in 2000. The estimated relationships are then used to simulate the impact of the climate changes for the next 50 years. They find that average temperatures and precipitation are not significantly related to income. They assume that a negative relationship exists between per capita income and poverty, and so their conclusion is that Mexico appears to be less vulnerable to climate change than most other Latin American countries. However, the analysis is very crude. They do not estimate the poverty-output elasticity, and do not consider income and population projections which are accounted for in this paper.

To assess the effects of climate change on poverty we propose a two-stage least squares regression (2SLS) where we first
were replaced with businesses in the same strata. Permanently closed businesses in the data are businesses that were indeed contacted but during the interview reported that they had permanently shut down at the time of the interview. These businesses were not asked the questions on the variables that are the focus of our analysis, and we therefore drop them from our analysis. The fraction of these permanently closed firms that were contacted but not applied the full questionnaire does not differ between men- and women-led firms (the average for both is 2.5%). Hyland et al. (2020) also find similar rates of permanent closure for female and male-owned firms.

> 8The hospitality sector includes accommodation and food preparation services. Other services includes services other than the hospitality industry and retail and wholesale, such as transportation and storage services, information and communication services, and financial services. Others consists of agriculture and mining, and construction and utilities.

> 9Details on the sampling frames and the representativity of the BPS data are summarized in Table B3 in the appendix.

7