rafmacalaba/data-use-mentions-extended huggingface dataset:
(0.004)|(0.004)|(0.005)|
|Controls|_yes_|_yes_|_yes_|_yes_|_yes_|
|Number of observations|388,602|69,101|106,410|100,728|112,363|

_Notes:_ Models estimated using entropy-weighted individual-level data from the 2014-17 Gallup World Poll. Standard errors are in parentheses. References categories are single or never married, elementary education, employed fulltime, poorest quintile, rural area or farm, and the year 2014. Each model includes country and years fixed effects. Low-income countries are defined as those with a gross national income (GNI) per capita of $995 or less, lowermiddle-income countries are those with a GNI per capita of more than $996 but less than $3,895, and upper-middleincome countries are those with a GNI per capita of more than $3,895 but less than $12,055, and high-income countries are those with a GNI per capita of more than $12,055. * Significance at the 0.10 level. ** Significance at the 0.05 level. *** Significance at the 0.01 level.

Column 1 of Table 4 presents the results from the total sample after matching, while Columns 2 through 5 represent the matched sample decomposed into economic development rankings. Overall, the results are very similar when compared to the non-matched sample (Table 3): food insecurity in each income category is associated with a decrease in both dimensions of trust across 15
proceeds as follows. Section 2 describes the economic constraints facing ultrapoor households in our sample. Section 3 describes the TUP program. Section 4 lays out the Design and Methods. Section 5 presents the results. Section 6 presents a cost-benefit analysis of the program, and Section 7 describes the study limitations. Section 8 concludes.

# 2. SOCIOECONOMIC CONDITIONS OF THE ULTRA POOR

We study ultra-poor households in four districts of Afghanistan’s Balkh province. To identify these households, the poorest villages in the province were initially chosen through a qualitative assessment by the program implementer, a government-owned entity. After this selection, a Participatory Rural Appraisal (PRA) was conducted, including a community poverty wealth ranking and physical verification of the program’s eligibility criteria, resulting in 1,219 ultra-poor households in 80 study villages (details of this process are reported in Section 4). In addition to the main TUP sample, we selected approximately 20 households from each study village, randomly drawn from the PRA population census list (excluding TUPeligible households), to provide a representative benchmark for the TUP sample. Baseline and follow-up data were collected from 1,680 households using this approach, which we 6
_Figure 14: Self‐reported well‐being changes of the households (share of households, %)_ <!-- Start of picture text --> 40<br>35<br>30<br>25<br>20<br>15<br>10<br>5<br>0<br>2014 2015 2016 2017<br>Well-being worsened Well-being improved<br><!-- End of picture text -->

# Source: Authors’ estimates based on HSS data.

The main factor determining the reduction of the well‐being of the population is inflation, which exceeded the growth rate of nominal income and thus led to a reduction in real incomes. However, in 2015–2016 significant contributors to the deterioration of well‐being were loss of employment and falling nominal wages, the latter registering a particularly sharp reduction. Post‐recession, in 2017, these trends were less pronounced and the inflation‐related reduction in real incomes became again the dominant reason of well‐ being loss for the population. Note, in particular, that this is against the background of relatively low inflation rates in 2017, but wages did not start registering increases until the second half of 2017.

Individuals vulnerable to the risk of poverty appear to be more affected by nominal cuts in wages than the population on average – a tendency that was exacerbated during the crisis, especially in 2016. They also faced a higher risk of losing their job compared to other population groups, although by 2017 these differences leveled off (Figure 15). In other words, the crisis resulted in the increased share of those who lost jobs or suffered a deterioration of terms of employment among vulnerable groups of the population. The effect of the crisis on the poor (based on the official definition) through loss of labor income is even more pronounced. Those below the poverty threshold had a substantially higher than average risk of losing their jobs and of falling nominal wages (see Annex,
in the literature on the best indicator to measure droughts (Trenberth et al., 2014) and weather shocks, in general.<sup>7</sup> Our paper also contributes to this literature by showing that temperature and NDVI are likely more appropriate drought measures in arid and semi-arid climatic conditions over rainfall-based measures, such as in Afghanistan.<sup>8</sup>

# **3 Data**

## **3.1 Household Data**

We use the 2019-2020 Integrated Expenditure and Labor Force Survey (IE-LFS) collected by the National Statistics Information Authority (NSIA) of Afghanistan, with the assistance of international organizations. This nationally representative household survey is the fifth in the series, a repeated cross-section collected as a follow-up to the Afghanistan Livelihoods Conditions Survey (ALCS) conducted in 2007-08 2011-12, 2013-14, and 201617, supported by the World Bank and other international organizations.<sup>9</sup> There are various advantages of using this survey. First, this survey contains geo-tagged locations of surveyed households, which we leverage to combine with the satellite-based remote sensing data on weather and vegetation for the given region. Second, this survey collects detailed information on (i) household demographics, (ii) household’s asset holdings and access to basic facilities, (iii) labor market participation of every individual in the household, and (iv) consumption expenditure. This survey is nationally and provincially representative and is used to compute welfare measures, including poverty estimates. The results are intended to inform the government’s and development partners’ policy making.

Unit non-response in IE-LFS arises from security concerns, and road access led to undersampling in some provinces. The final sample was below planned coverage (<70 percent) in three provinces (Faryab, Badghis, and Kapisa). This under-sampling can be a potential source of bias. Various checks, including comparisons with the 2016-17 survey data and sensitivity analysis to see if poverty trends are dramatically different in under-sampled and other
Lee et al. (2021) provide further evidence on the importance of recall unemployment in 2020.

The second important assumption is the institutional setup in which firms can costlessly lay off workers during the shutdown, unemployed workers collect unemployment insurance benefits with a 100% replacement rate (Ganong et al., 2020), and the unemployment insurance is financed with long-term debt. This institutional setup captures the policy adopted by the US government through the Coronavirus Aid, Relief, and Economic Security (CARES) Act. Other countries, for example the UK, supported firms and workers in non-essential sectors during the pandemic through transfers to firms to cover wage bills during the shutdown.<sup>5</sup> In our model, supporting firms’ balance sheet by covering their labor costs and providing income insurance to workers are equivalent. They only differ in the label of the workers who are not working and whose wage is paid for by taxes.

> employment extracted from an online firm survey.

> 4Without these frictions, a pure reallocation shock would have no aggregate effect.

> 5The Paycheck Protection Program (PPP) in the US CARES Act did the same, but only for small businesses.

2
that reform-minded executives in developing countries use IMF support to help overcome opposition to potentially unpopular policies. The idea is that after the executive reaches an agreement with the IMF, failure to achieve reform is more costly because rejection of those policies is also seen as a rejection of the IMF, and all domestics politicians and interest groups might find it costly to reject the IMF.<sup>12</sup> Vreeland hypothesizes that an executive’s likelihood of using IMF agreements in this way depends on the checks and balances she faces. Those facing no veto players ( _i.e._ , dictators) would have no need for IMF support. Those facing too many are unlikely to overcome opposition, despite IMF support. Thus the IMF would be unwilling to enter into agreements with them. In some intermediate range, IMF agreements should be more prevalent because they are more likely to achieve the desired objective of overcoming the opposition of veto players. We assume that World Bank adjustment loans could serve a similar purpose, and the parallel to potentially ‘strong’ and ‘weak’ reformers discussed above should be clear.

To operationalize these concepts, we use data on the number of checks and balances stipulated in country constitutions as reported in Beck _et al._ (2001). We include the number of checks and the squared number of checks in the selection equations that follow to test whether Vreeland’s hypotheses are valid for our dataset. We further hypothesize that a country’s borrowing needs might affect the likelihood of receiving World Bank adjustment loans non-linearly. It is common for developing countries to apply proceeds from new World Bank loans to repay old ones. For countries with a relatively large stock of World Bank debt, this sort of evergreening would be more likely. Beyond some point, however, debt accumulation might become problematic, making future agreements less likely. For countries with relatively little past borrowing, predictions are more difficult. This could reflect a preference for self-reliance, and thus we would expect little future borrowing.
**Figure 3: Relationship between the extent of firm innovation and adoption, and the sources of funding of fixed assets**

<!-- Start of picture text --> 0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>-0.1<br>Innovation Adoption Innovation Adoption Innovation Adoption<br>score score score<br>% Fixed assets funded by % Fixed assets funded by % Fixed assets funded by<br>banks NBFI other<br><!-- End of picture text --> Note: This figure reports the coefficients (denoted as diamonds) and the confidence intervals at the 90 percent level (denoted by the rectangular area) resulting from the estimation of Equation (2). The dichotomous dependent variables are the innovation score and adoption, as described in Table 1. We report the coefficients and the relative confidence intervals for the percentage of fixed assets funded by banks, or non-bank financial institutions (NBFI), or funded by others. All additional controls are as per Equation (2). We also include country and year fixed effects, as done in column (4) Table 4.

The literature stresses the importance of technological opportunity to understand the relationship between a firm characteristics and firm-level innovation. Enterprises in information technology or in other high-tech industries are more likely to innovate, according to the World Bank Enterprise Survey data for East Asia (Cirera and Mason, 2021).<sup>5</sup> We, therefore, take a closer look at the innovation performance of the manufacturing firms based on their technology intensiveness. We use the classification from the United Nations Industrial Development Organization (UNIDO),<sup>6</sup> which is based on the R&D expenditure incurred in the production of manufactured goods. Firms are categorized in two buckets: low-technology and medium-high-technology. In line with Bircan and De Haas (2020
Table 9: Names of mineral commodities and their SITC codes by countries of production

|**SITC code**|**Minerals names**|**Price data**|**List of production countries**|
|---|---|---|---|
|120|Helium|Yes|Algeria|
|264|Garnet (industrial)|Yes|Kenya|
|273|Stones (crushed, dimension), limestone,<br>gypsum, marble, and silicon|Yes|Algeria, Angola, Ethiopia, Madagascar, Malawi, Mauritania,<br>Mozambique, Rwanda, South Africa, Sudan, Tunisia, Uganda, and<br>Zambia|
|274|Sulfur|Yes|Ethiopia, Kenya, Malawi, Namibia, South Africa, Zambia, and<br>Zimbabwe|
|277|Diamond|Yes|Angola, Botswana, Cameroon, Liberia, Namibia, Sierra Leone, South<br>Africa, Tanzania, and Zimbabwe|
|278|Barite, bentonite, clay, dolomite, fluorspar,<br>graphite, salt, vermiculite, fluorine, and<br>graphite|Yes|Algeria, Botswana, Chad, Djibouti, Ethiopia, Kenya, Madagascar,<br>Morocco, Mozambique, Namibia, South Africa, Tanzania, Tunisia,<br>Uganda, and Zimbabwe|
|281|Iron ore|Yes|Sierra Leone, South Africa, Tunisia, Zambia, and Zimbabwe|
|283|Copper|Yes|Algeria, Democratic Republic of Congo, Mauritania, Morocco, Namibia,<br>Nigeria, Republic of Congo, South Africa, Zambia, and Zimbabwe|
|287|Cobalt, chromium, niobium (columbium),<br>tantalum, titanium, zirconium, and tungsten|Yes|Botswana, Burundi, Ethiopia, Gambia, Madagascar, Mozambique,<br>Nigeria, Rwanda, Sierra Leone, South Africa, Uganda, and Zimbabwe|
|
to do in at least one domain;

- (ii) No difficulty or ‘some’ difficulty for all domains

Third, for data sets with graded scales, we partition individuals into three categories:

- (a) No difficulty for all domains;

- (b) Some difficulty in at least one domain but no “a lot of difficulty” or “unable to do”.

- (c) A lot of difficulty or unable to do in at least one domain.

This categorization is more granular and may be able to identify a possible gradient in socioeconomic disadvantage. The second and third categorizations above are both conducted for the subset of 19 data sets that do not have a yes/no answer scale. We use these three categorizations in our analysis and are presented in the tables as panels 1, 2, and 3 respectively. The analysis conducted at the household level categorizes households depending on the functional difficulty status of its members age 15 and older along the three ways of partitioning the population described above.

We use basic proportions to calculate the prevalence of functional difficulties in each country based on the first categorization above for all data sets and based on the second and third categorization for countries with an answer scale. Given that the primary objective of this paper is to make comparisons of development indicators across disability status within a country and not across countries, we do not present age and sex standardized disability prevalence for each 8
2 Merging**

We merge the firm-level responses from all the 31 countries into a combined dataset. For categorical variables such as region that are country-specific and thus inconsistent across surveys, we tagged the possible responses by country throughout the merge to ensure that the responses remained distinct in the final dataset. Additionally, for each country, we converted all financial variables into US dollars using the average market exchange rate in 2018 from World Bank (2020b). After merging, our sample included 20,156 observations.

## **A1.3 Management Measures**

We develop a measure of structured management practices that takes 11 survey questions that quantify practices. These management practices can be divided into operations (1 practice), monitoring (2 practice), targeting (4 practices), and incentives (4 practices). The table below lists these management practices and the scoring scale applied for each. To better align with the methodology from (Bloom et al., 2010a), we map these 11 questions to the 8 questions used by the U.S. MOPS survey (U.S. Census Bureau, 2015). Thus, we combine: 34
may explain the difference between the imputed 2018/19 GHS estimate and the 2018/19 NLSS estimate. Yet, if the GHS systematically overestimate poverty rates – as also seems to be the case when comparing estimates in other waves to the backcasted series – this would suggest an even lower “true” poverty rate in 2010/11, even lower poverty reduction in the decade to 2019, and an even larger difference with the 2009/10 HNLSS poverty estimate.

# _<u>7.3. Sense-checking with other surveys</u>_

The evidence of a slowdown in improvements to household welfare emanating from the backcasts and the survey-to-survey imputations is in line with other survey data collected in Nigeria in the decade prior to the COVID-19 crisis. For example, using data from the Demographic Health Survey (DHS) data set, we look at trends in non-monetary indicators that are highly correlated with monetary welfare over the period 2003-2018. Specifically, Figure _8_ in the Appendix shows the trends in access to electricity, improved 23
Methods

Our survey experiment was embedded in the Listening to Central Asia series of surveys in Tajikistan, Uzbekistan, and Kazakhstan, surveys that are designed to continuously monitor wellbeing and views on topical policy issues.<sup>9</sup> The surveys are nationally representative and based on fieldwork conducted in two phases. In the first phase, a national in-person household survey was conducted in each country using regionally stratified two-stage cluster sampling procedures. In the second stage, a randomly selected subsample from the first stage in each country was recruited in a monthly panel survey using a phone-based method conducted by private survey companies under the direction of World Bank staff. The sub-samples were each nationally representative and for the present study include a total of 6,783 individual responses (2,714 in Uzbekistan, 2,267 in Tajikistan, and 1,802 in Kazakhstan). Data collection for the survey experiment was conducted May-June 2021. Descriptive statistics on vaccination and concerns related to COVID-19 were collected continuously in all three countries from early 2021 to August 2022.

For the core experiment, we randomly assigned respondents to one of five groups within each country survey. Each respondent was asked if they would agree to be vaccinated, and a subset of respondents

> 8 - A list of prioritized groups by country is provided at the following website: https://graphics.reuters.com/world <u>coronavirus-tracker-and-maps/vaccination-rollout-and-access/</u>

> 9 See https://www.worldbank.org/en/country/uzbekistan/brief/l2cu; and <u>https://www.worldbank.org/en/country/tajikistan/brief/listening2tajikistan</u>

8
methods to estimate models with large sets of fixed effects with an application to matched employer-employee data from germany. FDZ Methodenreport 201201, Institute for Employment Research, Nuremberg, Germany.

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Pop-Eleches, C. and Urquiola, M. (2013). Going to a better school: Effects and behavioral responses. _The American Economic Review_ , 103(4):1289–1324.

- Pritchett, L. and Viarengo, M. (2015). Does public sector control reduce variance in school quality? _Education Economics_ , 23(5):557–576.

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Sandefur, J. (2018). Internationally comparable mathematics scores for fourteen African 28
,260 women of working age (25 to 49 years old) living in households were they or their partner is the household head. Our final sample covers a total of 333 municipalities (out of 340 total) and 22 departments (out of 22). FLFP increased from 26% to 32% between 2002 and 2018. Women in the 2018 sample are, on average, middle-aged, married, and low educated; most of married women have an employed husband, while women having children younger than 13 have more than one child on average. Access and use of technology is low. When looking at subnational variables, we find an average of 6 reported cases of intrafamily violence against women per 1,000 women in the municipality and an average of 9 <mark><percent of?></mark> men agreeing with having gender parity in Congress or in the Judicial system per 10 <mark><percent of?></mark> people agreeing in the department population. The average number of preprimary centers is 1.5 per 100 children ages 4 to 6. Section II in the supplementary materials present and discuss descriptive statistics in detail for all the variables we use in our analysis.

In Figure 3 we illustrate simple municipal-level correlations across FLFP, and several of FLFP drivers. The strong and positive association with the per capita GDP (Panel A) suggests that Guatemalan municipalities are in the upward “U-shaped” curve with development. There is also a

> 9 The gender wage ratio would be a better measure of the level of discrimination in local labor markets, but the 2018 National Census does not provide information on income variables.

8
# **1.  Introduction**

Banking sector outreach varies significantly across countries. In Ethiopia there is less than one branch per 100,000 people, while in Spain there are 96. In Albania, there are four loans per 1,000 people and the average loan size is 15 times GDP per capita, while in Poland there are 774 loans per 1,000 people and the average size of loans is only one-third of GDP per capita. This paper introduces a consistent set of cross-country indicators of banking sector outreach, shows how these can be used to predict household and firm use of banking services, explores their empirical association with other country characteristics, and relates them to firms’ financing obstacles as reported by entrepreneurs. These indicators were collected through a survey of bank regulatory agencies conducted in 2003-4 and complemented with publicly available data. While these are rough indicators of access to and use of banking services, this is the first compilation and analysis of consistent and comparable cross-country data on the outreach or penetration of banking systems.

Although a large literature has established a positive association between financial sector depth and economic growth at the country, industry and firm level,<sup>1</sup> little is known about the breadth of financial systems across countries, the extent to which enterprises and households use financial services, and their relationship to desirable outcomes.<sup>2</sup> This lack of knowledge stems mostly from a dearth of adequate data (see discussion of data issues in Honohan 2004b). While the literature has developed several standard indicators of financial development, with consistent and comparable data available for the vast majority of countries over the past 40 years, to our

> 1 See Levine (2005) for a review of this literature.  Specifically, Beck et al. (2000), Rajan and Zingales (1998), and Demirguc-Kunt and Maksimovic (1998) provide evidence at the cross-country, industry and firm level.  Also see Wurgler (2000) and Love (2003).

>
Table A.20: Comparison of technology sophistication between original and replacement sample

|VARIABLES|Brazil|Brazil|Vietnam|Vietnam|Senegal|Senegal|
|---|---|---|---|---|---|---|
|Original sample|-0.014|-0.037|0.030|0.043|0.021|0.028|
||(0.048)|(0.047)|(0.050)|(0.048)|(0.018)|(0.018)|
|Observations|638|637|1,484|1,484|1,753|1,666|
|R-squared|0.299|0.335|0.262|0.320|0.377|0.437|
|Controls:|||||||
|Sector|Y|Y|Y|Y|Y|Y|
|Size group|Y|Y|Y|Y|Y|Y|
|Region|Y|Y|Y|Y|Y|Y|
|Age||Y||Y||Y|
|Exporter||Y||Y||Y|
|Foreign owned||Y||Y||Y|

Note: *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1. Data are from the Brazil, Vietnam, and Senegal FAT surveys. For each country, technology sophistication ( _MOSTj_ ) is regressed on a dummy for the original sampling list with controls for the stratification (sectors, size groups, and regions) and/or firm characteristics (age groups, exporter, and foreign owned). Robust standard errors in parenthesis.

abnormal interviews or outliers by running regressions of firm-level sophistication on enumerator dummies and firm