rafmacalaba/data-use-mentions-extended huggingface dataset:
visit per month from the counselor for four months. Beyond these monthly visits, counseling services were primarily demand-driven; more frequent counseling meetings were available at the respondent’s request.

# **_2.4. Summary Statistics_**

The data in this study come from two household surveys: first, a baseline survey before the start of the interventions, and second, an endline survey almost ten months after the final session of the video-based education program. To accommodate the large number of respondents, the sample was split into separate waves. Respondents were drawn from various neighborhoods (“chalis”) that were mutually exclusive across waves, and all treatments were stratified based on the respondent’s gender, neighborhood, and whether he or she is currently a microfinance client. Attrition between baseline and endline is quite low at only 6% and is uncorrelated with treatment.

Table 1 outlines our study sample (N = 959) as well as the proportion of respondents assigned to each treatment combination.<sup>6</sup> As can be seen in the table, 18% were allocated to receive only

> 6 In comparison to Carpena et al. (2017), the sample we use for this study contains fewer observations because we focus on the set of respondents for which we have endline measures of financial numeracy, awareness, and attitudes.

7
# **References**

- Acosta, P., Fajnzylber, P., and Lopez, H., (2007). The impact of remittances on poverty and human capital: Evidence from Latin American household surveys. International remittances and the household: Analysis and review of global evidence. World Bank Policy Research Working Paper 4247.

- Adams, R., (1992). The impact of migration and remittances on inequality in rural Pakistan. _Pakistan Development Review_ 31, 1189-203.

- Adams, R., (2004). Remittances and poverty in Guatemala. World Bank Policy Research Paper 3418.

- Adams, R., (2006). Remittances and poverty in Ghana. World Bank Policy Research Paper 3838.

- Adams, R. and Page, J., (2005). Do international migration and remittances reduce poverty in developing countries? _World Development_ 32, 1645-1669.

- Aggarwal, R., Demirgüç-Kunt, A., and Martínez Pería, M.S., (2011). Do remittances promote financial development? _Journal of Development Economics_ 96(2), 255-264.

- Aggarwal, R. and Horowitz, A., (2002). Are international remittances altruism or insurance? Evidence from Guyana using multiple-migrant households. _World Development_ 30(11), 2033-2044.

- Amuedo-Dorantes, C., Sainz, T. and Pozo, S., (2007). Remittances and healthcare expenditure patterns of population in origin communities: Evidence from Mexico. INTAL-ITD Working paper 25.

- Aportela, F., (1999). Effects of financial access on savings by low-income people. MIT Department of Economics Dissertation Chapter 1.

- Ashraf N., Aycinena, C., Martinez, A. and Yang, D., (2010). Remittances and the problem of control: A field experiment among migrants from El Salvador. Mimeo.

- Ashraf, N., Karlan, D. and Yin, W., (2010). Female empowerment:
By this definition, the span of control of senior bureaucrats over non-seniors is around 4 _._ 52, but again there is considerable variation across Ministries.<sup>4</sup> Around 45% of bureaucrats are women, 70% have a university education, and 31% have a postgraduate degree (seniors are more likely to be men, and have a postgraduate degree). As in other state organizations, civil service bureaucrats enjoy stable employment once in service: the average bureaucrat has 14 years in service, with their average tenure in the current organization being just under 9 years. This reflects some of the rigidities in the labor market for bureaucrats: appointments are made centrally by OHCS, bureaucrats enjoy secure tenure and transitions between bureaucracies are infrequent.

Our analysis is based on two data sources. First, we hand-coded quarterly and annual progress reports from Ministries and Departments, covering projects ongoing between January and December 2015. As detailed below, these reports enable us to code the individual projects under the remit of each organization, and the extent to which they are initiated or successfully completed. Second, we surveyed 2971 bureaucrats from all 45 civil service organizations over the period August to October 2015. Our civil servant survey covers 75% of _all_ professional bureaucrats in service, with 20% of interviewees being seniors. As detailed below, civil servants were questioned on topics including their background characteristics and work history in service, job characteristics and responsibilities, engagement with stakeholders outside the civil service, perceptions of corruption in the service, and their views on multiple dimensions of management practices. It is this last survey module from which we derive measures of management practice for each organization.

# **2.1 Coding Projects and Completion**

Worldwide, civil service bureaucracies di↵er greatly in whether and how they collect data on their performance. Unlike data related to the macroeconomy, households, firms, schools or labor markets, central statistical agencies are typically not involved in measuring government e↵ectiveness, and few
approximately 4 percent of informal workers transit to formal sector jobs. Given that persistence is very high (93 percent of informal sector workers stay informal over the course of a year), the estimated hazard out of the informal sector is 0 _._ 07, lower than in the formal sector.

Table 6 shows the estimated hazards.

This empirical evidence supports the assumption in the model of direct transitions from formal to informal, without intervening unemployment. Transitions from informal to formal are less important.

It is worth noticing that these probabilities are not instantaneous transition probabilities, since

> 68In longitudinal data I observe _ni_ , but that is not the case in cross-section data since I have a different sample of individuals.

> 69Time aggregation bias may arise because the yearly measurement may combine multiple transitions into a single aggregate transition, so transitions that occur at a higher frequency are not captured here.

25
computed using questions that capture the degree of stringency of banking regulation in engaging in securities, insurance, real estate, and nonfinancial business activities. The index ranges between 3 and 12, with higher values indicating stricter regulation.

# **Global Data Regulation Dataset**

Produced as part of World Bank (2021), the Global Data Regulation Diagnostic summarizes the laws and regulations concerning key aspects of a data economy for 80 countries. Following Chen (2021), the empirical analysis below relies on an index of enablers that facilitate use/reuse of data and another index of safeguards that protect the rights of market players in the data economy. The enablers index covers three dimensions: e-commerce, data produced for public purposes (e.g., public policy), and data collected by the private sector for business purposes. The safeguards index covers four dimensions: personal data, nonpersonal data, cybersecurity, and cross-border data. The indexes range from 1 to 17 with higher values indicating that a country has more of the regulations that enable data use/reuse,<sup>11</sup> or safeguard against its misuse. Index scores are aggregated across survey responses. For example, countries received one point if they had a law or regulation that explicitly governs e-commerce/e-transactions. The full list of the survey questions used to score countries is found in Annex 2 of Chen (2021).

# **Human Development Index**

Collected as part of UNDP (2022), the Human Development Index (HDI) is a summary measure of average achievement on three key dimensions of human development: a long and healthy life, being knowledgeable, and having a decent standard of living.<sup>12</sup> The health dimension is measured using life expectancy at birth; knowledge using expected years of schooling and mean

> 11 However, the highest possible value on the enablers index is 16.

> 12  UNDP (2022). Data available at: https://hdr.undp.
|
|Amount of interest earned.|44|39|46|
|Fees imposed.|42|42|42|
|Account balance.|49|39|53|
|**F.**<br>**By Law or Regulation, the Following is Part of the Disclosure**<br>**Requirements for Periodic Statements for Credit Products:**|**% Yes**|**% Yes**|**% Yes**|
|All transactions concerningthe account for theperiod covered bythe statement.|51|53|50|
|Annualpercentage rate(applied duringtheperiod).|48|44|49|
|Interest charged for theperiod.|55|50|57|
|Fees charged for theperiod.|54|47|57|
|Minimum amount due.|44|42|45|
|Date due.|48|50|48|
|Outstandingbalance.|48|50|48|
|**G. Specific Provisions in the Existing Laws and Regulations Restrict:**|**% Yes**|**% Yes**|**% Yes**|
|Deceptive advertising.|66|78|62|
|Unfair or high-pressure selling practices.|51|68|44|
|Abusive collectionpractices.|42|50|38|
|Unauthorized use of client data or breach of client confidentiality.|70|78|67|

Source: Calculations based on the 2011–12 Bank Regulation and Supervision Survey. Notes: ADV: advanced economies, EMDEs: emerging and developing economies (Table 1 provides the complete list of countries). Values in parentheses refer to the 2007 Bank Regulation and Supervision Survey.

50
Several barriers are responsible for the rise in the informal trade in health services between the two countries.<sup>31</sup> A 2010 survey of patients‘ perceptions about barriers to accessing Indian health services showed a high proportion of similar complaints, from visa issues to medical insurance limitations and high air fare costs (Table 7). To reduce the barriers: (i) the Bangladesh government could allow Indian medical institutions to set up hospitals under mode 3 (India‘s Apollo Group has set up a hospital in a joint venture at Dhaka, but it is too small to meet local demand<sup>32</sup> ); (ii) professionals in health services could be allowed to move freely (under mode 4) between the two countries; and (iii) genuine patients from Bangladesh coming to India for treatment could be granted visas on arrival at Indian land ports or airports. Although both governments have agreed to facilitate the granting of long-term, multiple-entry visas to research scholars and students, and people visiting on medical grounds, the progress on the ground has been slower than what has been targeted.

|**Table 7: Perceptions of Bangladeshi Patients* abo**<br>**Particulars**|**ut Indian Health Services**<br>**Percentage of**<br>**Patients (%)**|
|---|---|
|Complicated visa|77.90|
|Fraud of informal foreign exchange traders|40.00|
|Lack of pan-South Asia medical insurance|80.00|
|High airfare|64.70|

*Sample size: 190 patients. _Source_ : Banik _et al_ . (2010).

# **3.4. Scenario Analysis with the Global Computable General Equilibrium (CGE) Model**

The global CGE modeling framework of the GTAP uses aggregated data on regions and commodities for its objectives.<sup>33</sup> Version 7
19 This finding is not news for many infrastructure practitioners.<sup>23</sup> For water and energy there are minimum consumption levels defined by international organizations and most countries (for example, 20 liters of water per person per day and 10 kilowatt-hours per household per day).<sup>24</sup> Cross-subsidies are intended to help finance these needs through redistribution. Every “field person” has great success stories on the effectiveness of these cross-subsidies. But there are also any documented experiences where well-intended targeting mechanisms were regressive.<sup>25</sup> — there is indeed evidence that under many common circumstance, even when connection subsidies are typically more important for the poor than consumption subsidies, lifelines are the main instrument and is often set too high.

Developments in evaluation techniques also allow for more systematic assessments of the distributional implications of reforms. A reform can help poor people but be regressive, but it can also be regressive without helping the poor. These issues can now be reliably handled quantitatively. New techniques allow identification and monitoring of the most vulnerable groups with respect to reforms, as well as good evaluations of the relative and nominal impacts of reforms. But this literature provides much more than ex post assessments of policy reforms. It makes the case for systematic monitoring of the effects of new reforms and projects to ensure that they internalize lessons of the past. This can be done at the project level (see Baker 2003 and Duflo 2003 for surveys) or at the macroeconomic level. This can be important when micro household data are weak or interactions with other sectors need to be assessed.

A macroeconomic assessment of infrastructure’s importance for poor people is needed because in most cases water, energy, telecom, and transport reforms have an impact on other markets (such as the labor market and investment savings market) that matter to the poor. These feedback effects are potentially significant for poverty alleviation, and an economywide analysis is needed. This usually calls for a multiagent,
investment, including in human capital (Adams and Cuecuecha 2010)—make Guatemala an important setting for examining such effects. Using household panel data collected in 2008 prior to the crisis and again in 2012, we treat the Great Recession as a natural experiment to assess the effect of a substantial economic shock in a migrant-receiving country on long-term indicators of child well-being in a migrant-sending country.

After quantitatively and qualitatively documenting significant decreases in migration and remittances in the study region consistent with the nature of the crisis that disproportionately affected the industries in which migrants from Guatemala typically worked (Kochhar 2009), we focus on child height-for-age Z-scores (HAZ), an important long-term indicator of human development particularly vulnerable to shocks in early life (Almond and Currie 2010). Based on the intuition that migrant households were more directly exposed to the shock emanating from the United States, we estimate difference-in-difference (DD) models comparing relative changes for children under three years old in migrant versus non-migrant households over time. This is done separately for both same age cross-sectional cohorts and panel cohorts over the four years. Identification rests on the exogenous nature of the economic crisis from the perspective of Guatemalan households and the common trends assumptions for each type of comparison. In addition, for the panel cohort analysis we incorporate an additional difference, estimating a triple difference model using trends in HAZ for older cohorts who—based on the nutritional literature regarding child development—were less likely to have suffered from the shock and therefore plausibly capture prior differential trends in development between migrant and non-migrant children. As in many non-randomized DD analyses, we do not have prior data and are unable to conclusively demonstrate common trends. The likely violations of common trends, however, are conceptually ambiguous in their implications for the sign of bias in the DD estimates and we
**Policy Research Working Paper Series**

|**Title**|**Author**|**Date**|**Contact**<br>**for paper**|
|---|---|---|---|
|WPS2214 Trade Policy and Market Access<br>Issues for Developing Countries:<br>Implications for the Millennium<br>Round|Constantine Michalopou|los October 1999|L. Tabada<br>36896|
|WPS2215 Implementation<br>of Uruguay Round<br>Commitments: The Development<br>Challenge|J. Michael Finger<br>Philip Schuler|October 1999|L. Tabada<br>36896|
|WPS2216 Corruption and Trade Tariffs, or<br>a Case for Uniform Tariffs|Roberta Gatti|October 1999|R. Gatti<br>38735|
|WPS2217 Border, Border, Wide and Far,<br>How We Wonder What You Are|David C. Parsley<br>Shang-Jin Wei|November 1999|H. Sladovich<br>37698|
|WPS2218 Who Avoids and Who Escapes<br>Poverty during the Transition:<br>Evidence from Polish Panel Data,<br>1993-96|Wlodzimierz Okrasa|November 1999|S. Fallon<br>38009|
|WPS2219 The Effect of the United States'<br>Granting Most Favored Nation<br>Status to Vietnam|Emiko Fukase<br>Will Martin|November 1999|L. Tabada<br>36896|
|WPS2220 A Quantitative Evaluation of<br>Vietnam's Accession to the ASEAN<br>Free Trade Area|Emiko Fukase<br>Will Martin|November 1999|L.Tabada<br>36896|
|WPS2221 The Dynamics of Poverty and the<br>Effectiveness of Poland's Safety<br>Net (1 993-96)|Wlodzimierz Okrasa|November 1999|S. Fallon<br>38009|
|
# **Acronyms**

ANA National Water Agency ( _Agência Nacional de Águas_ ) ASSEMAE National Association of Municipal Water and Sanitation Services ( _Associação Nacional dos Serviços Municipais de Saneamento_ ) BOD Biological Oxygen Demand CETESB São Paulo State Environment Agency ( _Companhia de Tecnologia de Saneamento Ambiental_ ) COD Chemical Oxygen Demand COPPE/UFRJ Alberto Luiz Coimbra Institute – Graduate School and Research in Engineering /Federal University of Rio de Janeiro _(Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia/ Universidade Federal do Rio de Janeiro)_ CORHI São Paulo Coordinating Committee for the State Water Resources Plan ( _Comitê Coordenador do Plano Estadual de Recursos Hídricos_ ) CPMF Brazilian national tax on financial transactions ( _Contribuição Provisória sobre Movimentação ou Transmissão de Valores e de Créditos e Direitos de Natureza Financeira_ ) CRH São Paulo State Water Resources Council _(Conselho Estadual de Recursos Hídricos)_ DAEE State Water Resources Management Agency of São Paulo ( _Departamento de Águas e Energia Elétrica_ ) EMAE São Paulo Metropolitan Water and Energy Company ( _Empresa Metropolitana de Água e Energia S.A.)_ EMPLASA São Paulo State Agency for Metropolitan Development and Planning ( _Empresa Paulista de Desenvolvimento Metropolitano)_ FEHIDRO São Paulo State Water Resources Fund ( _Fundo Estadual de Recursos Hídricos_ ) FAESP São Paulo State Agricultors Federation _(Federação dos Agricultores do Estado de São Paulo)_ FIESP São Paulo State Industries Federation ( _Federação das Indústrias do Estado de São Paulo)_ FUSP University of São Paulo Foundation _(Fundação Universidade de São Paulo)_ IBGE Brazilian National Statistical Agency ( _Instituto Brasileiro de Geografia e Estatística_ ) RMSP Metropolitan Region of São Paulo ( _Região Metropolitana de São Paulo_ ).

SABESP State Water and Sanitation Company of São Paulo ( _Companhia de Água e Esgoto do estado de São Paulo_ )

- 45 -
significantly the profile of beneficiaries, and hence how to target should be at the center of the discussion when discussing a program’s objectives.

# **Alternative age groups**

All simulations assume that the targeted beneficiaries are children 0 to 5 years old, and elderly who are 65 years old or more. This section investigates whether slightly changing the eligibility age profiles can affect the poverty impact of the program. It is to be expected that the poverty impact will be affected only if the poor are concentrated in specific age or profile groups.

Figure 10 explores alternative age group definitions for children (it considers children 0 to 2 and 2 to 5 separately), to check if the poverty impact changes significantly by narrowing the age groups. With the exception of the Dominican Republic, the age group under consideration for a categorical transfer does not seem to alter significantly the results. This suggests that, overall, the distribution of income across age groups of children is such that providing higher transfers to a narrower group delivers a similar poverty impact than delivering lower transfers to a wider group. In all cases, the poverty impact under categorical targeting remains lower than under the targeted modality.

**Figure 10: Poverty impact targeting different age groups (children only)**

<!-- Start of picture text --> 80%<br>0-5 Targeted<br>0-5 Categorical<br>60%<br>0-2 Categorical<br>2-5 Categorical<br>40%<br>20%<br>0%<br><!-- End of picture text --> Source: Own calculations based on SEDLAC databases 18
priority is an empirical matter, sector-specific poverty data on access for various income groups are needed—but are not available for most developing countries. There are reasonably good databases on the number of poor (although the measurement of poverty continues to be a source of conflict), but no database tells policymakers exactly what the poor are poor in. And even for countries with some data, problems exist.

From a sectoral viewpoint, Living Standard Measurement Surveys (LSMS), Demographic and Health Surveys (DHS), and household consumption surveys raise three types of problems.<sup>16</sup> First, the fact that they do not generate data for every country trying to get access to international aid aimed at reducing poverty limits the ability of the international community to monitor progress across countries. Second, the questionnaires in the available surveys are seldom comparable, and hence cross-country comparisons and performance benchmarking are quite approximate for many of the variables of interest to policymakers, particularly for infrastructure services. Third, there are significant differences in the quality of the data available for urban and rural areas. The World Bank has started working on standardized questionnaires, but there is still a long way to go.

Ignoring for a moment the data issues, there are some fundamental questions that have not been addressed by researchers. How consistent are the ideal strategies implicit in these differentiated needs assessments for the rural and urban poor with the strategies to maximize the

> 14 Many in the water community would argue against this , at least form the viewpoint of their sector. According to WHO statistics, for developing regions, rural areas have 5.3 times more unserved people for water supply and 3.6 times more unserved for sanitation compared with urban areas (WHO (2004)). This service gap is not really well correlated with the direction of the lending program of many donors. For instance,  in the World Bank’s lending portfolio between 1900 and 2001, **urban areas received nearly six times more in loan funds*
.7|10.6|6.3|40.4|40.3|100|
|**_Memorandum:_**|||||||||||
|European Union (15)|2004|4.2|3.0|7.6|4.5|3.7|2.3|3.9|3.0|100|
|Turkey|2003|5.2|4.0|20.7|12.3|3.7|2.3|28.6|19.8|50|

Note: Agricultural goods are based on WTO classification of HS 01-24 and industrial goods are HS 25-96. * indicates non-WTO member as of August 2003. Sources: Based on UNCTAD TRAINS database and WTO bound tariff rates from WTO IDB database.

Second, since Pan-European partners have duty-free access for all their industrial products, the fact that the value of the weighted average is above that of a simple average indicates that higher tariffs on industrial products serve no well-defined protectionist purpose. They seem to be redundant. We shall return to this issue in the next section.

Despite benefits that a small economy like Bulgaria can gain from opening up, its multilateral liberalization within the WTO framework has neither led to low tariffs nor extended commitments to remove barriers to entry to services sectors. MFN applied tariff rates remain relatively high by regional standards. While Bulgaria has signed WTO agreements extending either duty free treatment to selected products (e.g., Information Technology Agreement) or countries (Generalized System of Preferences for least developed countries), it has kept relatively high MFN tariff rates especially on agricultural products. One should add that Bulgaria’s approach to multilateral trade liberalization does not differ from that of several other Central European countries including Hungary, Poland and Romania. Despite low levels of non-preferential trade, they have declined to lower MFN tariff rates (Kaminski 2001).

# **3.3.