rafmacalaba/data-use-mentions-extended huggingface dataset:
CRPs in each of these districts, primarily due to idiosyncrasies in schedules and other obligations on the day of the survey. We interviewed 40 CRPs from SERP out of a total of 120 who were actively working in Rajasthan at the time of the survey.<sup>21</sup> We supplement this qualitative and quantitative data with two other sources of secondary data that are intended to provide perspectives on the operational aspects of the program as well as its impact.

<u>A survey of RRLP districts: A baseline survey of RRLP districts was conducted in early 2012</u> with the intention of evaluating the impact of the program. For the purposes of this paper we focus on the “treatment” areas where RRLP was scheduled to work in order to check if Intensive and Resource blocks had statistically similar characteristics prior to the entry of NRLM facilitators. This sub-sample consists of 3,852 households with 6 villages sampled per block in each of the 32 RRLP treatment blocks, scattered across 17 districts.<sup>22</sup> <u>Administrative data:</u> Aggregate block-level outcomes are obtained from the Rajasthan government’s online progress report. We examine block-level outcomes of SHG performance as well as outcomes of individual SHG members to compare the performance of Intensive and Resource blocks. A comparison of individual SHG members in Intensive and Resource blocks is done using a survey of 302 women, split across the two types of blocks in October 2014.

# Analysis

We should first note that the two types of blocks were largely well matched prior to the start of the project. Using the baseline household survey data, we can compare pre-program differences between the Resource Blocks and Intensive Blocks. These results are presented in Table 1. The Resource Blocks appear to have fewer SC/ST (Scheduled Caste/ Scheduled Tribe) members and slightly higher rates of home ownership, but most differences between the two groups are small and not statistically significant, particularly when it comes to existing levels of SHG membership
indicators come from the same data source. We start by describing this data and then discuss the other data sources used.

## The Global Monitoring Database and the Global Subnational Atlas of Poverty

Our measure of income is based on the per capita income or consumption measure collected in national household surveys that forms the basis for poverty measurement. These survey-year income (or consumption) estimates are collated and harmonized across countries and over time to maximize comparability as part of the World Bank’s Poverty and Inequality Platform (PIP). These estimates also go into the Global Monitoring Database (GMD) which contains not only the income or consumption aggregate but other data on household characteristics such as access to improved water, access to electricity, and education of adult household members, all of which we use here.<sup>16</sup> This income or consumption data is further extrapolated or interpolated to a common reference year, in this case 2019, and the share of the population falling beneath the international poverty lines in this year—or any other line—can be determined.

To overlay with the geographic data on exposure, we use subnational data on income (or consumption) and other measures when possible. We obtain this using the latest edition (October 2023 vintage) of the World Bank’s Global Subnational Atlas of Poverty (GSAP) for the reference year 2019 for 1,733 subnational or national areas across 168 economies, accounting for 97 percent of the global population.<sup>17</sup> The subnational areas are typically administrative boundary level 1 areas, corresponding to the highest subnational unit level such as provinces or states, but they can also be a group of regions defined by the specific sampling strategy and representativeness of the household survey. There are 47 economies (24 percent of the global population) for which poverty estimates are only available at the national level due

> 16Refer to World Bank (2023b) for more information on the methodology to calculate poverty rates.

> 17 More information about
The survey also includes a self-reported measure of income shocks.<sup>8</sup> Individuals are asked, ― _Did you (your family) experience an unexpected significant reduction of your income over the past 12 months,_ ‖ and we define a dummy ―Income shocks‖ for those who answer yes to this question. The summary statistics in Table 1 show that 35.9% of the sample reported the experience of a negative income shock during 2009, showing that the crisis and associated decline in income affected a large share of the Russian population.

# **[Insert Table 2 about here]**

# **4.2** **_Use of Formal and Informal Credit_**

Our next set of variables measures financial inclusion, which includes variables related to respondent affiliation with financial institutions and borrowing behavior.<sup>9</sup> Our first variable is ― _Bank Account,_ ‖ which indicates whether an individual uses a bank account (which includes the use of debit cards). In Russia it is common practice for an employer to provide employees with an account and associated debit card, so-called salary or ―plastic‖ cards, at a bank chosen by the employer, and salaries are paid to these accounts only. However, the employee can use this account only to withdraw salary, and cannot make deposits to the account; thus, this may overestimate the actual voluntary

> < US$833; 2<sup>nd</sup> quartile: $833 ≤ monthly income < $1,433; 3<sup>rd</sup> quartile: $1,433 ≤ monthly income < $2,084; 4<sup>th</sup> quartile: monthly income ≥ $ 2,084.

> 8 This is a categorical variable: the first category is individuals who report that they do not have enough money, even for food (7%); the next category is individuals who report they can buy
available in the regression where _ri_ is the return on asset _i_ ; _r_ 0 is average return on all assets; _Xc_<sup>_′_</sup> ( _i_ )<sup>isavectorwhose</sup> elements are various characteristics of country _c_ where the investment is located; _εi_ is an error term. The coefficient _β_ is not interpreted as a causal effect, but rather as an estimate of the average differences in return observed across groups of countries defined by _Xc_<sup>_′_</sup> ( _i_ )<sup>.Observeddifferencesin</sup> returns confirm that capital markets are imperfectly integrated because the law of one price does not hold. If _β_ = 0, markets are said to be segmented because the return differs across capital markets. Conversely, if all countries participate in a perfectly integrated capital market, _β_ = 0, and country characteristics do not affect investments’ average financial performance because all investors receive the same return for their capital.

A key requirement to estimate Equation (1) is a long time series of returns. Even if international capital markets are perfectly integrated, idiosyncratic risk _εi_ could, over a short time horizon, lead to an estimate of _β_ = 0. A key strength of the IFC data is thus the long length (58 years) of the time series. The existing literature on international capital market integration tests for differences in returns across much shorter time periods, studying, for instance, the covariance of international public equity indices (Campbell and Hamao 1992, Harvey 1995). Caselli and Feyrer (2007) calculate the marginal product of capital (which is equal to its price in equilibrium) using data from a crosssection of national accounts. They find that it does not vary substantially across countries (i.e., that _β_ = 0), and conclude that “there is no prima facie support for the view that international credit frictions play a major role in preventing capital flows from rich to poor countries.”
to the measurement of global and national poverty. Those who are forcibly displaced and in need of international protection tend to be persons who have lost their assets, financial resources, and social networks. They are typically very poor with no obvious path out of poverty. For refugees, their number vanishes from poverty statistics of their own country because they are no longer counted in the place of origin. Both IDPs and refugees are also not properly accounted for in the country in which they reside. Their numbers – even though high in absolute terms – are often low relative to the non-displaced population (with some exceptions like Lebanon and South Sudan). Hence, they do not explicitly show up in official statistics. Even if – as in some but not all countries – their locations are appropriately included in the sampling frame, they are unlikely to be sampled due to their small proportion relative to the population and high clustering in specific locations.

> 3 https://www.unhcr.org/refugee-statistics/ 4 Note that the IASC definition of IDPs explicitly includes those fleeing from disasters.

2
fixed? In order to address this question, we carry out an analysis that integrates a novel firm-level dataset with a macroeconomic model of firm dynamics. Our dataset merges administrative data on public procurement, credit allocation at the bank-firm level, and firm outcomes for the Spanish economy over the 2000-2013 period. Our model builds on the canonical framework of firm dynamics with financial frictions (e.g., Midrigan and Xu, 2014) and incorporates two novel elements. First, there is a government that purchases goods and services from private sector firms. Firms that are willing to sell to the government must make a risky investment in advance, which reflects the costs of preparing a good proposal and increasing the chances to win the contract. Second, we allow for earnings-based borrowing constraints where the pleadgeability of firms’ earnings from procurement may differ from that from selling to the private sector.<sup>4</sup> And third, we allow for the pleadgeability of firms’ earnings from procurement to differ

> 1See EU Commission’s web and OECD for details.

> 2See Report from Congressional Research Service for details. Policies promoting small firms’ participation in public procurement are widely spread worldwide. Some other examples are Australia and Brazil.

> 3See the Public Sector Directive 2014/24/EU for details. There has been strong support from the European Parliament for explicit regulation that discriminates in favor of small firms: “From this House, we must insist that...administrative bodies incorporate terms into their tender specifications that facilitate positive discrimination in favor of SMEs and remove contractual provisions that hinder their participation.” See EU Parliament Debate.

> 4Other recent papers that embed earning-based borrowing constraints into macro models are Drechsel (2023), Brooks and Dovis (2020), and Li (2022). The importance of earning-based borrowing constraint is also supported by direct evidence, see Lian and Ma (2020).

1
and significant household wealth effect (measured in terms of household assets) for rural households living close to refugee camps and negative wealth effects for households in urban areas.

Thanks to the World Bank Kagera Health and Development panel Survey (KHDS) a number of studies were able to assess the impact of these refugees on host communities focusing mainly on household wellbeing and labor market outcomes. Maystadt and Duranton (2018) find that all types of local workers gained from the refugee presence, although the positive effects are weaker for agricultural workers and the selfemployed in non-agricultural activities. They find a positive and significant effect on household consumption in 9 of the 16 models’ specifications they propose with the rest of the results being all positive and non-significant. Maystadt and Verwimp (2014) use six different specifications of a similar model and find four of these specifications with a positive and significant effect on consumption with the remaining two specifications being positive and non-significant. They also show a differentiated impact between agricultural laborers and self-employed with the former suffering from high prices and competition on jobs and the latter benefitting from higher prices and cheap labor. Overall, doubling the refugee presence is found to increase per adult equivalent consumption of host households by 6-8 percent. The effect is also positive but lower (2-3 percent) for agricultural workers and self-employed in non-agricultural activities.

Using the same data, Ruiz and Vargas-Silva (2016) find that the forced migration shock led to an increase in the likelihood of Tanzanians working outside the household as caretakers and a lower likelihood of working outside the household as employees. This is particularly true for agricultural employees suggesting a certain substitution effect between locals and refugees. Ruiz and Vargas-Silva (2015) complement these results by finding that the general impact on employees is negative and significant but the impact on professionals and government employees is positive and significant. Looking closer at the relative differential impacts by gender and skill-level, Ruiz and Vargas-Silva (2017) find that, on average, the refugee influx led to women being less likely to engage in employment outside the
For both women and men in the poorest decile, in-kind education and PSNP transfers account for a large share of market income (Figures 2a and 2b). The first and the second top 10 percent (top two deciles) pay more direct taxes relative to their market income. This holds for both genders, although women in the top income group lost a larger percentage of their market income than men (Figures 2a, 2b, and 2c). The net impact of the fiscal system is progressive for both groups: all but the top two deciles received more benefits relative to their market incomes than they paid out (see figure 2d). For the bottom 10 percent the impact appears to be stronger for women than men.

**Figure 2c: Direct Transfers Net of Direct Figure 2d: Net Benefits by Income Decile and Taxes by Income Decile and Gender Gender**

<!-- Start of picture text --> 10 50<br>7.7<br>42<br>6.7 40<br>30<br>40.3<br>0 20<br>-2.4<br>-4.5 10<br>-3.5<br>-2.6<br>-4.8 0<br>-4.3<br>-10 -10<br>1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10<br>Market income decile Market income decile<br>Women Men Women Men<br>Source : Authors’ calculations; ESS 2018/19 data. Source : Authors’ calculations; ESS 2018/19 data.<br>Share of market income, percent Share of market income, percent<br><!-- End of picture text -->

# **4.2. Progressivity of Taxes and Transfers**

We assess the progressivity and pro-poorness of interventions by comparing the cumulative
It is important to keep in mind that, in the case of multiple priced goods such as utilities, households may report expenditure on more than one period or they may not report or report only in part expenditure if they are late with payments (arrears). This will affect the total expenditure on the subsidized utility and, by consequence, the estimate of the quantities consumed and the estimate of the welfare impact. It is important therefore to collect information from the utilities companies on payments and arrears to have a sense of the scale of the problem. By comparing the utility company revenues by tariff block with the revenues by block calculated from the household surveys one can see how good the survey is in capturing expenditure on utilities. Also, by using the company information on arrears by tariff block, one can use this information to impute the missing expenditure into the survey data in an effort to correct the bias caused by arrears.

# **Household expenditure**

In figure 2, we show the equilibrium of a consumer when we assume the case of an increasing marginal price schedule with . The budget constraint is the inflected line. We assume also that the consumer prefers to consume a quantity of electricity which is higher than . In equilibrium:

# **Figure 2: Consumer equilibrium**

<!-- Start of picture text --> Good 2<br>Slope of the budget constraint within block 1=<br> Budget constraint line<br> Indifference utility curve<br>Good 1<br><!-- End of picture text --> 25
# **Acknowledgments**

This paper draws on contributions from sector specialists from the Africa Infrastructure Country Diagnostic team—notably, Dick Bullock on railways, Mike Mundy on ports, Heinrich Bofinger on air transport, Rupa Ranganathan on power, Carolina Dominguez on water and sanitation, Michael Minges and Rebecca Meyer on information and communication technologies, Alberto Nogales on roads, Nataliya Pushak on public expenditure, and Alvaro Federico Barra on spatial analysis.

The paper is based on data collected by local consultants and benefited greatly from feedback provided by colleagues in World Bank country teams—notably Luiz Claudio Tavares (acting country manager, water and sanitation), Boris Utria (sector leader and power), Luiz Claudio Tavares (water and sanitation), Jose Chembeze (roads and railways), Isabel Neto (ICT), Antonio Nucifora (macro), Rafael Saute (media and communications), and local consultants Manuel Ruas, Afua Sarkodie, and Naimo Jala. Arlete Comissario provided invaluable administrative and logistic support.

Preliminary results and emerging messages of this paper were discussed in a workshop in Maputo in November 2009. The workshop was hosted by Minister of Energy H.E. Salvador Namburete, Minister of Transport and Communications H.E. Paulo Zucula, and Minister of Public Works H.E. Felício Zacarias. The team is grateful for the insightful comments and recommendations of the workshop participants.
2 The Data Revolution in Justice 21

# **2.B Measurement, Diagnostics and Experimentation**

In this section, we present an initial framework to make better use of judicial data. Justice systems should start by measuring indicators of performance, quality, integrity and accessibility of justice. These measurements can also be used to improve the deployment of resources, and therefore enhance efficiency and reduce the backlog of cases. Courts should then provide diagnostics on those areas that are most valuable to their service. For example, databases can be used to identify the main causes for adjournments, understand what the barriers to justice are, or evaluate which cases would benefit the most from mediation. Finally, researchers and policy makers should take advantage of data systems to rigorously evaluate the impact of justice reforms. The random assignment of cases to judges behaves as an exogeneous shock that allows to evaluate the impact of judicial performance on the well-being of citizens and firms. By keeping track of innovations in the judiciary and experimenting with potential solutions, data systems generate the opportunity to understand the impact of reforms on people’s lives and the prosperity of nations.

## **Measurement and Diagnostics**

Despite the growing availability of case-level data, most courts around the world are underutilizing the opportunities created by these databases. Many courts are not even producing performance or management reports that summarize key variables, such as the number of incoming cases, time to disposition, and case clearance rate. Correspondingly, most courts allocate resources without taking into account important efficiency measures that may be gleaned from stronger empirical data, for example by evaluating the backlog or productivity of specific courts. As data systems become available and more courts decide to catalogue data systematically, greater opportunities to evaluate a court’s efficiency will arise.

The very basic usage of case management systems consists in measuring what happens in courts. To begin with, judiciaries could create indicators that evaluate the efficiency, access, quality and integrity of each court. A full list of indicators is available at
# 3.2. The Model

The data are a balanced panel spanning 28 years for the 6 countries in Central America (Guatemala, Honduras, Costa Rica, Panama, Nicaragua, and El Salvador). As discussed in the previous section, wind speed data was aggregated by year. When more than one event occurred in a calendar year in the same country, only the strongest event was taken into account. To evaluate the existence of other relationships between multiple hurricanes in a year and per capita GDP growth, we included controls for the number of hurricanes per year with no substantial change in the results.<sup>17</sup> During the 30 years of the study, only four hurricanes reached SS scale equal to or greater than 3,<sup>18</sup> while 19 events reached SS scale equal to or greater than 1. It is important to highlight that in this period, Panama and Costa Rica did not suffer any major events (i.e., events with SS ≥ 1). Nicaragua accounted for nearly half of the events (9 events in total) followed by Honduras (5 events in total). Storms under category SS 1 represented 63 percent of events (12 in total), while category SS 2 and SS 3 accounted equally for nearly 33 percent of total events.

Our empirical strategy exploit the natural random variation in the formation, path and intensity of hurricanes and tropical storms as a source of exogenous within‐country variation in disaster exposure to identify the causal effect of hurricanes in per capita GDP. It follows an ordinary least squares (OLS) models with fixed effects by year and country. Hence our identification strategy exploits the variation over time within countries following equation 4.

��� � ������ ����� ������ ��� ����� �4� Where:

- ��� is per capita GDP growth for country _i_ in year _t;_
repayment behavior of microcredit borrowers varies little geographically throughout the country. Rajshahi has exhibited the worst loan recovery rates as evidenced by various microcredit program reports; thus, the study survey oversampled observations from this division to permit enough variation in data, particularly on loan repayment behavior, to conduct a sound analysis. But to ensure representativeness of the findings, all of the analyses conducted were appropriately weighted using the actual group distribution among branches.

As mentioned above, most of the groups in the survey sample were from Rajshahi since this division provided more variation in loan repayment data, as well as continuity of records over the 7-year period since group inception (Table 1). Out of the 297 groups, 271 were women only (women represented more than nine-tenths of all members), while the other 26 groups were men only (these were found only in Rajshahi and Khulna in only 4 out of the 9 branches covered).

# {INSERT TABLE 1 HERE}

Although the Grameen Bank’s practice is to hold 5-member groups jointly responsible for loan recovery, group size is not always limited to 5. The average size is larger for women-only groups (8.1 individuals) than for men (6.0 individuals) (Table 2). Over the study period (1994–2002), each member, whether male or female, borrowed up to 6 times on average; however, the average loan size was higher for men, with the highest loan amounts found in Khulna.

# {INSERT TABLE 2 HERE}

As Table 2 shows, the repayment rates of women’s and men’s groups differ considerably. Rajshahi exhibits the highest default rates and thus the smallest average loan size, reflecting the fact that loan defaults influence the amount of individual borrowing. The 15