rafmacalaba/data-use-mentions-extended huggingface dataset:
experimental evidence and results on the influence of transparency in benefit delivery on the overall support for targeted social assistance and on program design preferences.

The paper shows that support for redistributive programs is sensitive to the level of trust in the system’s ability to deliver benefits to intended beneficiaries. In fact, although transparency does not appear to significantly affect the overall rates of giving (controlling for program design), it does have a significant positive effect on the giving by youth and by those people who exhibit low trust in the existing delivery of safety nets, especially in the case of unconditional cash transfers. Moreover, among those low-trust individuals, enhanced transparency makes cash-based transfers more attractive than in-kind transfers. Because the latter are generally less effi cient but may be perceived as less prone to elite capture, transparency could thus also enhance program effi ciency by allowing policy makers to switch from in-kind to cash transfers without losing the support of their middle-class citizens.

# Annex 1: Sample design and selection protocols

The experiment’s sampling strategy adopted the definition developed by the Government of Jordan’s study of the middle class (ESC 2008), which defined the middle class as those households that have per capita incomes between twice and four times the Jordan’s national poverty line. This definition corresponded to the population between the 4th and the 8th income decile according to the 2004 Jordanian census, the latest available at the time. For this study, middle-class primary sampling units (PSUs) in the census were selected by a three-step process: 1) constructing a proxy means test regression using Jordan’s 2010 Household Expenditure and Income Survey;<sup>30</sup> 2) applying coeffi cients from that regression to the 2004 census data; and 3) choosing PSUs with resulting average scores between 4th and 8th income deciles. Within the population of middle-class PSUs, 21 sampling units were selected for the experiment via random selection with probability proportionate to size.

Within each sampling unit, the following protocol was used to recruit the
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underprivileged? Are all these constitutions and legal actions, to the extent they are effective at all, covert vehicles for preserving privilege, or should we revise our understanding of the conventional wisdom?

Here we begin to answer these questions. Our curiosity was triggered by a book that carried out detailed studies of SE rights enforcement in Brazil, India, Indonesia, Nigeria, and South Africa (Gauri and Brinks 2008). That book offered the first systematic cross-national survey of SE rights litigation, including descriptions of the cases filed in each country, estimates of the numbers of people benefiting from constitutional health and education rights cases in Brazil, India, Indonesia, Nigeria, and South Africa, and preliminary analyses of the extent to which giving courts a more prominent role in economic and social policy-making on the basis of social and economic rights in fact benefited disadvantaged individuals and groups.

In this paper, we conduct new analyses of the distributive effects of the social and economic rights cases that came out of that five-country survey. The five countries in the sample included common law countries with records of aggressive (India and South Africa) and limited SE rights litigation (Nigeria), and civil law countries with aggressive (Brazil) and incipient (Indonesia) litigation. They included countries with (by global standards) recent and old constitutions, and countries with varying years of democratic experience. Judicial review is abstract and centralized in Indonesia; concrete and diffuse in India, Nigeria, and South Africa; and a blend in Brazil. The countries also vary in levels of national income and state capacity. The survey sought to unearth a complete or representative sample of cases in each country involving the right to health and health care, and the right to education.<sup>1</sup> These two issue areas provide

> 1 In the cases of India and South Africa, this included all cases at the High Court level and above (India 1950-2006, resulting in 382 cases, and South Africa 1994-2006, resulting in 32 cases);
food processing industry) and that often refer to sector-specific production processes.

For each general and sector-specific business functions, we measure the technologies used ( _extensive margin_ ) and the most frequently used ( _intensive margin_ ). The distinction between these margins provides a very granular measure of technology adoption to inform policy decisions. For example, when asked about the adoption of particularly technology such as digital payment methods, many firms may report that they are already using it ( _extensive margin_ ), but very few of them may be using this technology intensively ( _intensive margin_ ). By disentangling the differences between the extensive and intensive margins we can have more clarity to inform policy, such as if lack of adoption this is a problem of infrastructure, or if this is more related to lack of capabilities of firms. To our knowledge, this is the first study providing a comprehensive analysis of technology adoption at the firm-level using a standard methodology across different sectors and based on nationally representative data covering formal and informal firms in Senegal.<sup>3</sup> To advance some of the key results, most firms in Senegal are performing general business functions with pre-digital technologies. On a scale from 1 to 5, where 1 represents the most basic technology to perform a general business function, 2 is the usual threshold to use computers (e.g. using a computer with standard software for production planning) and 5, the most sophisticated technology, usually advanced digital technologies, firms in Senegal score on average 1.92 on the extensive margin and 1.29 on the intensive margin.<sup>4</sup> These results suggest that on average firms are not yet adopting basic digital technologies to perform general business functions.

For sector-specific technologies, firms in Senegal are also far from the frontier. On a scale from 1 to 5, the sector-specific technology index is 1.59 for the extensive margin, and 1.27 for the intensive margin. Senegalese firms are less likely to adopt advanced sector-specific technologies, compared
26 comprehensive political economy analysis that does not limit its focus just to border policy measures.<sup>9</sup>

# **7. POLICY IMPLICATIONS**

The above empirical findings have a number of policy implications. First and foremost, the generally attractive results in terms of poverty and inequality alleviating effects from trade policy reforms, whether unilateral or multilateral, provide yet another reason as to why it is in the interests of countries to seek further liberalization of national and world markets.

Second, a recurring theme in the national case studies is that the benefits in terms of poverty and inequality alleviation, in addition to the standard aggregate real income gains associated with trade liberalization, are generally much greater from global reform than from just own-country reform. In the Indonesia study, for example, unilateral trade liberalization is expected to reduce poverty only very slightly, but liberalization by the rest of the world is expected to lower poverty very substantially. In the Philippines, domestic reform alone from current levels of protection may marginally increase poverty rates, whereas rest-of-world liberalization would almost fully offset that (and more than offset it in the case of just agricultural reform).

Third, the results of this set of studies show that the winners from trade reform would be found among the poorer countries and the poorest individuals within countries. However, it is also clear that even among the extreme poor, some could lose out. Hence the merit of compensatory policies, ideally ones that focus not on private goods but rather on public goods that reduce under-investments in pro-growth factors such as rural human capital.

> 9 A beginning has been made to political econometric analysis of the World Bank’s agricultural distortions database in Anderson (2010).
main results with the PPML estimator. Estimates obtained with the OLS estimator are also consistent with our main findings.<sup>17</sup> Several findings stand out from the estimates for the four main sectors in our sample, which appear in Table 1. First, overall, we note that the estimates of the effects of the standard gravity

> 17The corresponding OLS results can be found in Tables 3 and 4 of the Supplementary Appendix, where, as mentioned earlier, we also offer estimates that are obtained after replacing the missing values in the ITPD-E with zeroes (see Tables 5 and 6) and estimates that are based on panel data (see Tables 7 and 8).

15
Policy Research Working Paper 8857

# **Abstract**

This paper builds on the existing literature assessing retrospectively the quantitative effects of natural disasters on different dimensions of household welfare, to make progress toward the ex ante identification of households that are vulnerable to poverty due to natural disasters, especially typhoons. A wind field model for the Philippines is employed to estimate local wind speeds at any locality where a tropical typhoon directly passes over or nearby. The estimated wind speeds are merged with the household Family Income and Expenditure Surveys at the barangay level, and consumption expenditures are then regressed against wind speed (or a related damage index) and household socioeconomic characteristics. The estimated coefficients from the regression model are then used to estimate ex ante household vulnerability to poverty (the likelihood that household consumption falls below the poverty line) in the event of future natural disasters of different intensities.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at eskoufias@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 the Research Support Team
beneficiaries from the legacy database would be available for referral under the medium and low (70 and 50 percent) referral confidence.

This will result in the smallest number of referrals – allowing time for MoLSA budgets and systems to absorb new beneficiaries over time – but also those households receiving humanitarian assistance who would be most likely to be eligible under MoLSA eligibility criteria. As the neediest cases are absorbed by the national system, the targeting threshold can be relaxed to 25, 30, or 35 percent of the poorest population. The percentage of cases from the humanitarian database that will be referred to MoLSA for screening under the relaxed targeting thresholds are shown in Annex Table A 1. In general, higher the targeting threshold and lower the referral confidence, the more cases will be referred.

_Table 7: Percentage of existing humanitarian database likely to be eligible for MoLSA program at different degrees of confidence and smaller program size_

|Scenario|Program Size|Referral Confidence|Percentage of Households|
|---|---|---|---|
|High confidence,<br>medium-smallprogram|Poorest 18 percent<br>nationally|90 percent|24.3 percent|
|Medium confidence,<br>medium-smallprogram|Poorest 18 percent<br>nationally|70 percent|44.2 percent|
|Low confidence, medium-<br>smallprogram|Poorest 18 percent<br>nationally|50 percent|54.1 percent|
|_Data Source_: CLCI Legacy D|atabase.|||

These results are important for two reasons. First, they suggest a large proportion of the humanitarian database could be potentially referred for MoLSA social protection programs. Second, a clear sequencing of smaller numbers with a high degree of likelihood to qualify could be referred first, with larger numbers being referred later when budgets expand.

# 4.2 Who Are the Likely Eligible? A Profile

This sub-section profiles households from the CLCI legacy database that would be deemed eligible by the PPMT. The profile focuses
However, more efforts need to be made to protect children from poverty and its lifelong deleterious consequences.

# Mobilizing Resources

More resources are needed for some types of interventions and will require even higher taxation and some reallocation across programs. Both options present difficulties. Households are taxed-explicitly or otherwise-in a large number of ways in Vietnam.<sup>3</sup> ' In rural areas, they are expected to provide up to 10 days of labor per person to build and maintain local infrastructure. Additional "contributions" are constantly being mobilized for one cause or the other. Very little is known about the distribution of the burden of these taxes and how this varies among provinces. The existing system may also impose high marginal tax rates on poor people. Households are removed from "lists of the poor" if they do too well. There will be powerful incentives to remain on the registry of poor households. How are such "poverty traps" avoided? Are there costs to participation such as stigma that limit the problem? Do the local authorities monitor work effort and how well? This too will become more difficult with increased mobility.

Freeing up resources by cutting and reallocating funds faces political hurdles. Some current expensive programs have strong political support. For example, questions could be raised about public spending for war veterans and invalids. First, more needs to be known about the living standards of the beneficiaries. Not all of these households are likely to be poor or unable to support themselves. The present system provides pecuniary benefits and bestows honorary benefits. The latter may be politically necessary but relatively cheap. It 33
7,721|0.55|0.50|
|Any primary health center|7,721|0.23|0.42|
|Any block primary health center|7,722|0.06|0.24|
|Any community health center|7,718|0.07|0.25|
|Any government hospital|7,722|0.06|0.23|
|Any government dispensary|7,713|0.11|0.32|
|Connected to a sub-center|7,726|0.86|0.34|
|Connected to a primary health center|7,726|0.81|0.39|
|Connected to a block primary health center|7,702|0.55|0.50|
|Connected to a community health center|7,725|0.72|0.45|
|Connected to a government hospital|7,725|0.75|0.43|
|Any accessible auxiliary nurse midwife|7,723|0.65|0.48|
|Any accessible female doctor|7,722|0.22|0.41|
|Janani Suraksha Yojana implemented|7,726|0.92|0.27|
|Janani Shishu Suraksha implemented|7,726|0.63|0.48|
|Kishori Shakti Yojana implemented|7,725|0.50|0.50|
|Balika Samriddhi Yojana implemented|7,725|0.42|0.49|

_Notes_ : Data comes from the 2012-13 District-level Household and Facility Survey (DLHS) of India. The table presents the number of observations, the means, and standard deviations (S.D.) for district-level characteristics of the election data and Census data matched to DLHS data in Panels A and B, and village-level public health infrastructure data from DLHS data in Panel C.

30
15|0.35|
|Disabled|0.28|0.45|0.25|0.43|
|Pensioner|0.28|0.45|0.24|0.43|
|Social aid|0.62|0.48|0.60|0.49|
|Observations|7,008||9,617||

_Notes: We define energy poverty using the multidimensional energy poverty index. "SD" is the standard deviation._ _Source: Own estimates based on EU-SILC (2020)._

> 43 Unfortunately, we do not have sources of heating and cooking in the data to control for these characteristics.

30
# **Annex 1. Respondents to the WB-ASBA Survey**

|Country|Head of<br>Supervision|Head of Research<br>Central Bank|
|---|---|---|
|Argentina|||
|Bolivia|X||
|Brazil|X||
|Colombia|X|X|
|Costa Rica|X|X|
|Chile|X|X|
|Ecuador|||
|El Salvador|X|X|
|Guatemala|X|X|
|Honduras|||
|Mexico|X|X|
|Nicaragua|||
|Panama|X||
|Paraguay|X|X|
|Peru|X|X|
|Dominican Republic|X||
|Uruguay|X||
|Venezuela|||
|Aruba|X|X|
|Bahamas|||
|Barbados|X||
|Belize|||
|Cayman Islands|X||
|Haiti|X||
|Netherland Antilles|||
|Suriname|||
|Trinidad and Tobago|X||
|British Virgin Islands|||
|Guyana|||
|Jamaica|||
|Eastern Caribbean Islands|X||

32
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a closer look at the history of the political economy of the Ivoirian war reveals a somewhat different picture. To some extent education catalyzed the war due to the politically masterminded unequal provision of educational facilities between the North and the South. The education system in the North has been a victim of Ivoirian politics since the early 1990s and the NorthSouth divide following the civil war only exacerbated that ongoing crisis. This makes the task of finding a causal inference of the war on education particularly challenging. In this paper we estimate the causal effect of civil war on years of education for individuals who were exposed to conflict between 2002 and 2006 in their school-going age. We use the Households Living Standards Survey (HLSS) data collected in 2008 (ENV-2008) and the data on local incidences of conflict is taken from the Armed Conflict Location and Event Database (ACLED).

We employed several strategies to identify the true impact of war and quantify its causal effect on education for the school-going age cohort. We use the year of birth and the department of birth to determine an individual‟s exposure to war. The difference-in-difference outcomes indicate that the average years of education for individuals aged 10 to 22 is .94 years fewer compared to the individuals aged 23 to 32 in war-affected regions. The validity of the finding is tested by a number of factors such as internal displacement due to war, heterogeneous selection into victimization both across and within a region and varying intensity of conflict across regions. These are potential sources of bias in the estimated causal effect and furthermore, conflict affects education in several ways. The direct impacts include destruction of infrastructure, displacement and most tragically deaths of students and teachers, problems in harmonization of school calendars across the war-affected regions and closure of schools for an indefinite period. Other effects such as loss of jobs and farm, decrease in income and experiencing violence could also affect the education of children in the same household. To realize the full potential impact of war, as a second strategy we used a set of victimization indicators
effect on women is around 50 percentage points.<sup>35</sup> Second, we explore heterogeneity by respondent’s availability of digital resources. Evidence on the gap in ownership of mobile phones by gender or income is limited, and this heterogeneity analysis addresses efforts to link phone ownership among women and poor people to obtain information from them (LeFevre et al., 2020). To proxy for respondent’s availability of digital resources, we use indicators of whether the respondent shares a phone with someone else and if she has Tigo Money, and we test for heterogeneity based on these two variables. As we show in columns (2) and (3) in Table 4, there are no differences relative to those with different availability of digital resources—that is, relative to those with phone exclusivity or without Tigo Money. Yet, the sign and the magnitude of the interaction between having Tigo Money and being assigned to the phone-based survey are consistent with the idea that respondents who are more prone to use digital resources are less responsive to a phone-based survey than to a survey delivered via WhatsApp.

We also examine treatment effect heterogeneity by age. As we emphasized in the introduction, phone usage and survey completion can differ substantially by age (Gigliotti and Dietsch, 2014). Thus we can expect that the effect of receiving a phone call on answering the survey increases with age. We test that hypothesis by estimating specification (2)—for example, by interacting our treatment indicator with an “old group” indicator for whether the respondent is older than a certain age _i_ , where _i ∈{_ 22 _,_ 23 _, ...,_ 43 _}_ . Figure 2 summarizes the age heterogeneity by modifying the age threshold for the “old group” indicator we include in each separate regression. We can see that coefficients are increasing with the age threshold that defines the old-group indicator variable, suggesting that older respondents are more responsive to the treatment. For example,