rafmacalaba/data-use-mentions-extended huggingface dataset:
one of the most regulated labor markets in the world (e.g., Botero, Djankov, La Porta, Lopez-de-Silanes and Shleifer, 2003).<sup>2</sup> Furthermore, enforcement of the law is apparently weak since approximately 40% of total employment is

> 1We do not explicitly examine the damage illegal behavior in fl icts on institutions and social protection. However, on the latter point the available evidence is not clear. Even though labor market institutions are usually used to explain differences in inequality across developed countries (e.g., Blau and Khan, 1996), it has been repeatedly shown that regulation can actually be detrimental to poverty and inequality in developing countries (e.g., Besley and Burgess, 2003, and the essays in Heckman and Pages, 2003).

> 2According to the Doing Business data set collected by the World Bank, Brazil has the third strictest labor regulation in the world. Labor market regulations changed signi fi cantly in 1988 with the new constitution, increasing the degree of worker’s protection. A brief description of the current state of labor market regulation in Brazil is presented in table A1.

2
Many countries which are net physical exporters in aggregate are net importers at the neighbor level; the correlation in net imbalance between the two measures is only 40 percent. At one end, Rwanda, Kenya, and Senegal are net importers from the world, but net exporters to their neighbors. On the other end, Brazil, Estonia, and Guyana are net exporters to the world, but net importers from their neighbors. In the case of landlocked countries such as Rwanda and Senegal, this discrepancy points toward international imports that must travel through neighboring countries. Since these flows also contribute to imbalances between landlocked countries and their neighbors, this suggests that analysis using country-country level remains inaccurate.

**Corridor-Level Imbalance** At an even finer level, I turn to evidence for geographic imbalances along specific routes or corridors, especially for landlocked countries. Using ports data, Teravaninthorn and Raballand (2009) find that Chad exports are only 30 percent of imports. In a frictionless world where all fronthaul trips are loaded, this suggests a lower bound of 35 percent of trips must be empty. In a more extreme example, Uganda has an imbalance of 10 percent, and the authors suggest this generalizes to other landlocked countries especially in the Sahel. Focusing on the corridor level, Annequin et al. (2010) find that exports from Burkina Faso to the port of Tema, Ghana, vary between 25 percent and 40 percent of imports. This imbalance is reflected in prices, where the average cost of shipping a 20’ container from Tema to Ouagadougou was $4,800, over 2.5 times as expensive as the cost of shipping in the reverse direction.

Looking across multiple corridors into a single country, Nathan Associates Inc. (2013) also finds strong export-import imbalances for countries in West Africa. For example, landlocked Burkina Faso trades through four main foreign ports: Abidjian, Tema, Lomé, and Cotonou. In Panel A of Figure 8, I plot the physical trade imbalances by port for the top four ports
- it had a record of bankruptcy or liens files on its property due to payment delinquencies.

As shown in Table 10, by these criteria a substantially higher percentage of lower-income households have bad credit records that would likely exclude them from prime loans than do higher-income households. In addition, substantially higher percentages of African-American and Hispanic respondents had bad credit records than did white respondents. **Table 10 Incidences of Impaired Credit Records**

|**Family characteristic**|**Percentage**<br>**with "bad"**<br>**credit**<br>**Record**|**Percentage**<br>**with "good" credit**<br>**record**|**Percentage**<br>**with**<br>**insufficient informa**<br>**tion**<br>**to classify**|
|---|---|---|---|
|All families|30|57|13|
|_Income (1998 dollars)_||||
|Less than 25,000|36|No data reported|No data reported|
|25,000-49,999|33|No data reported|No data reported|
|50,000-64,999|25|No data reported|No data reported|
|65,000-75,000|22|No data reported|No data reported|
|_Race or ethnicity of respondent_||||
|African-American|48|36|16|
|Hispanic|34|51|15|
|White|27|61|12|

Source: Freddie Mac Corporation (1999) In a national household survey conducted in 2001, the Federal Reserve found a similar pattern linking household incomes to indicators of credit risk. As shown in Table 11, lower-income households were much more likely to have such indicators than were higher
Fuel prices|Monthly fuel prices by major energy hub (Coal,<br>Australia; Coal, South Africa; LNG, Asia; Natural<br>Gas, EU; Natural Gas, US; Henry Hub Gas; Dubai<br>Crude; Brent Crude; WTI Crude)|<br> <br> <br>IMF Primary Commodity Prices|

As shown in Appendix Figure A. 1, fossil fuels account for around 61% and renewables (wind, solar, hydro, and others) for around 17% of electricity generation in the United States. As

> 4 All except for Malta due to data availability issues.

> 5 Norway and Switzerland are not part of the European Union but belong to the European Free Trade Association (along with Iceland and Liechtenstein). Iceland and Liechtenstein are not included in our sample due to lack of data.

5
a checkup every six months. Older children have about .1 visits. We examine curative care conditional on the child’s experiencing a symptom, including fever, diarrhea, digestive track problems, respiratory infection, malaria, and skin rash. About one in four children who experienced illness symptoms sought treatment.

# **e. Child Health Outcomes**

We measure child health outcomes for two age groups: children 0-11 months and 24-47 months. Children in the 0-11 months range in the treatment group in the 2008 survey would have been exposed to P4P during the full prenatal period and during the full time after birth. Children between 24 and 47 months in the treatment group in the 2008 survey would have been exposed to P4P for 18-23 months during the early stages of life, but not during the prenatal period.

We consider two measures of health: (i) height-for-age z-score and (ii) weight-for-age z- score. A child’s height results from her genetic potential, adjusted for insufficient nutrient intake and inability to absorb nutrition because of illness. Therefore, height is a summary measure of health and nutrition since conception. By contrast, weight is an indicator of current nutrition and illness status and does not represent factors that accumulate over the lifetime. Better prenatal care, which includes nutritional advice to mothers and the diagnosis and treatment of maternal illness, could in principle increase both infant height and weight. Better child preventive care, which includes vaccination and growth monitoring, and child curative care, which limits the duration and severity of illness, also have the potential to affect height and weight.

We measured child height and weight using standard international procedures and portable scales and stadiometers, which were recalibrated on a twice-weekly basis in the field. As part of the quality control procedures, all children were measured twice during the visit. We first report height and weight in centimeters and kilograms respectively. Then, we standardize these measurements into height-for-age and weight-for-age z-scores in accordance with World Health Organization guidelines. The z-scores measure the number
# **Box 2: Lorenz Curve Approach: SimSIP Poverty Predicting the Effect of Aggregate Growth on Poverty in Paraguay**

Datt and others (2003) have applied SimSIP Poverty to data for Paraguay in order to study the impact of growth patterns on poverty for a period of five years (from 1997 to 2001). Six cases are considered: (1) each sector (agriculture, industry, services) of the economy grows at 3 percent, (2) a 2 percent growth rate in each sector, (3) a 1 percent growth rate per sector, (4) a 2 per percent growth rate in agriculture and a 3 percent rate elsewhere, (5) a 1 percent growth in agriculture and a 3 percent rate elsewhere, and, finally, (6) a 3 percent growth in agriculture and a 1 percent rate in other sectors. The underlying data include (1) population shares by sectors (rural/urban) and three economic activities (agriculture, industry, and services) and the total national-level population and (2) the mean income per capita corresponding to the population shares.

The impact of different sectoral growth patterns on the poverty headcount is reported below. Holding inequality constant, a 3 percent annual growth in per capita income in every sector for five years would reduce poverty by 3 percentage points, to 28.95 percent. Using the table below, one can compare the contribution of different growth patterns to poverty reduction. Moreover, given that poverty rates are higher in rural areas and in agriculture, any migration out of those sectors is likely to decrease poverty.

The reported results for each scenario vary slightly depending on whether aggregate poverty is computed from the rural/urban perspective or as a weighted average of outcomes in each sector of employment. The exercise illustrates the fact that the analyst can use SimSIP Poverty to assess different patterns of growth.

**Simulations of the Impact of Growth Patterns on Poverty in Paraguay: Some Examples**

|**N**
Panel A: FA3 vs. YRD, and Guerrilla vs. No Guerrilla

|County|Famine Control|Death Rate|County|Famine Control|Death Rate|
|---|---|---|---|---|---|
|FA3|0.688|19.7|No Guerrilla|0.754|14.57|
||(0.158)|(2.6)||(0.153)|(5.36)|
|Obs.|11|10||25|25|
|YRD|0.798|12.1|Yes Guerrilla|0.807|11.92|
||(0.132)|(0.6)||(0.125)|(6.08)|
|Obs.|48|48||34|33|
|YRD-FA3|0.11**|-7.6***|Yes-No|0.054|-2.65*|
||[0.046]|[1.75]||[0.038]|[1.51]|

Panel B: Interactions of FA3, YRD with Guerrilla Presence

||Famine Co|ntrol|||Death Rates||
|---|---|---|---|---|---|---|
|County|Guerrilla P|resence||Guerrilla|Presence||
||Yes|No|Yes-No|Yes|No|Yes-No|
|FA3|0.56|0.73|-0.17|24.3|17.8|6.5|
||(0.09)|(0.16)|[0.097]|(9.5)|(7.5)|[5.55]|
|
uses services. That is, Knobel calculates the index of service input liberalization for industry i in year t as: where ai,k is the input-output coefficient for industry i and service sector k, and INDEXk,t is the index of liberalization for service sector k in year t. As discussed above, Shepotylo and Vakhitov develop a similar index, but for each firm, not each industry.

His principal result from this model is that liberalization of the services sectors in Russia has had a positive impact on productivity in the manufacturing and mining sectors: full services sector liberalization could raise labor productivity an average of 20%, and partial liberalization — halfway to full liberalization — would raise labor productivity by 10%.

# **Liberalization of Trade in Services in Kazakhstan and Uzbekistan: Analysis of Formal and Informal Barriers, by Roman Vakulchuk, Farrukh Irnzarov and Alexander Libman**

In this paper, Roman Vakulchuk, Farrukh Irnazarov and Alexander Libman present the results of their survey research intended to provide information about trade in services in Central Asia. They focus on barriers, including informal barriers, which impede companies in Kazakhstan and Uzbekistan from successful expansion of services abroad. The authors maintain that little is known about barriers to trade in services in Central Asia, especially in Uzbekistan.<sup>13</sup> To help to fill the void in our knowledge about trade in services in Central Asia, the authors constructed and conducted a survey of 73 firms in Kazakhstan and Uzbekistan. In addition, they led three round-table discussions in the two countries. They focus on three important sectors: financial services, telecommunications and business consulting.

> 13 The Business Environment and Enterprise Survey (BEEPS) conducted jointly by the World Bank and the European Bank for Reconstruction and Development focuses on the internal business environment.

21
WTP surveys can certainly be used to generate a measure of the average households’ economic value for the proposed improvements in WSS and to examine the factors that affect these values. However, we show that beyond a general measure of economic benefits, WTP surveys often produce two other important pieces of operational information. First, they can be used to obtain a demand curve that maps the relationship between charge and the number of households who will take up the service at that particular charge. Second, they can make productive use of often collected information on various characteristics of households and their communities– e.g., income, assets, education, attitudes, location, and amenities. We show how regression modeling can be an important first step for incorporating this additional information into demand analysis.

Field implementation in and around three towns of Southwestern Sri Lanka shows that urban households value improved water services. The median WTP is at least twice the amount that the typical household is currently paying and three times higher than the implied monthly water charge. There are important cautions and conclusions regarding use of WTP data. First, in the design of tariffs, WTP data should be complemented by supply‐side information on costs and service area and by projected use levels (volumetric consumption and numbers of consumers). Second, they can be complemented by similar methods, such as conjoint analysis, to examine important dimensions of consumer preferences for service improvements other than price such as quantity, safety, and regularity (see Yang et al., 2005). Third, CV data are perhaps best viewed as a rough index of “genuine need” that can facilitate the design of subsidy policy. Fourth, the WTP data must be combined with a variety of socio‐economic characteristics to develop a defensible and complete picture of need and affordability.

Multivariate regression modeling shows that a complex reality of factors drive household demand for network connections as expressed in their demand for service improvements. While poverty and costs are undoubtedly key determinants of demand, we find that
may be a desirable effect even if some of the microenterprises created due to the program are informal. Maloney (2004) reports that, according to the Brazilian National Household Sample Survey (PNAD), most informally self-employed in Brazil prefer their jobs over being a formal sector employee.

This paper proceeds as follows. Section 2 summarizes the short-term impact evaluation design and its results. Section 3 discusses the rationale for a long-term impact evaluation, as well as the longterm sample, data sources, and timeline. Section 4 presents the long-term impact evaluation results. Section 5 concludes.

# **2. The Short-Term Impact Evaluation (2010-2011)**

# **_2.a. Context and Research Design_**

The original study of the impact of financial education in Brazil focused on youth in the 11<sup>th</sup> and 12<sup>th</sup> grades of high school. This focus on youth was attractive and relevant for several reasons. First, good financial habits formed at an early age are likely to benefit schooling, employment, and standards of living throughout adulthood. Second, the focus on youth leverages their learning capacity, as students are primed to absorb, recall, and apply learning on a regular basis, in contrast to adults, who are typically less engaged in this learning routine. Finally, well-informed students can modify not only their own financial choices, but also act as agents of change in their households’ financial decisions.

In Bruhn et al. (2016), we tested short-term answers to these questions through a randomized controlled trial of a comprehensive financial education program for public high school students in Brazil. The program was developed and implemented as part of the National Strategy for Financial Education (ENEF). It spanned a 17-month period across the 2010 and 2011 academic years, and was integrated into classroom curricula of Mathematics, Science, History, and Portuguese. The instruction used new textbooks with interactive and repeated classroom exercises on financial education themes, take-home exercises such
the variation in the variable of interest is coming from a combination of the month and the location receiving imported cases. This shows how the identication comes from the unique combination of detailed data on travelers and the incidence in the origin. The matrix _Zit_ includes zero, one and two lags of rainfall, which capture both the agricultural seasons that could inuence movement and changes in malaria incidence due to

> 19I use the detailed knowledge of the timing from the mobile phone data to factor in how many of the 15 days were in month t and how many in month _t −_ 1 and use the incidence both in month _xjt_ and _xjt−_ 1 to determine the probability the person is infected.

16
accurate than raw payments. For example, the OLS subnational investment multiplier for the whole of Europe is 0.63 for modeled payments in Table 2, but only about half as large (0.38) when calculated using raw payments (Online Appendix Table A3.2). The corresponding IV multipliers are 0.92 using modeled payments, but 0.72 using raw payments. Multipliers are also smaller at the country level using the raw payment data in the all-Europe sample; they are almost half as large using OLS (0.97 from Table 2, 0.6 from Online Appendix Table A3.2), and IV estimates lose significance using the raw payments.<sup>49</sup> Second, Online Appendix Table A3.3 reports investment multipliers winsorizing investment data at the 90% level for NUTS2-level data and at the 98% for country-level data (analogous to approach for GDP). Results are broadly similar to those in Table 2; investment usually responds robustly to EU spending. For subnational regions (Columns 1-3), investment multipliers with winsorized data are about 0.1-0.2 _smaller_ than those in Table 2 (about one standard error) though are still significant and at least 0.5 for most specifications. Countrylevel investment multipliers with winsorized data are typically 0.05-0.2 larger than those in Table 2.

Finally, Online Appendix Table A3.4 (only at the country level) shows that a 1€ increase in modeled EU fund spending increases public investment by about 0.6€ for all countries, driven by effects in CEE countries. In contrast, effects on private investment are always insignificant. For non-CEE countries both public and private investment multipliers are insignificant and usually close to zero. These estimates reinforce the findings that EU funds are actually spent, usually on public investment, but there is not much evidence of crowding-in of private investment (neither there is of crowding-out private investment, as shown earlier).

# **3.3 Cumulative Multipliers
periment, guidelines included washing hands, wearing a mask, maintaining a distance from others when outside, and praying at home instead of at a mosque. In the second half, the government removed the recommendation to avoid congregational prayers, emphasizing social distancing in general instead. The overview reflected whatever guidelines were in place at the time of the survey. The script is shown in section A.1 in the Appendix.

Respondents were likely aware of at least some of these guidelines through other channels prior to the survey experiment. We collected data on all Twitter posts made by two major media groups, ARY and GEO, in the 5 weeks prior to and throughout the duration of our experiment.<sup>8</sup> This data shows that 31% of tweeted articles focused on the coronavirus pandemic. Of these articles, 23% mentioned at least one of the guidelines. The overview therefore reinforced already available information.

Respondents assigned to a treatment group received an _additional_ message designed to increase support for these guidelines and improve assessments of the state’s capacity to manage the pandemic. Each treatment is described in detail below.<sup>9</sup>

# **2.2.1 Past state success**

Reminding respondents of the state’s success in managing a crisis in the past may change their views about the state’s ability to manage a crisis today. Respondents may not otherwise remember past successes or even if they do remember, may not link them to similar events in the present (Acemoglu et al., 2019). In this treatment, we remind respondents of the Punjab government’s response to a dengue outbreak in 2011. Post hoc, this response was considered by several experts to have been instrumental in containing the outbreak (Bhatti et al., 2015). A World Bank case study, for example, argued government measures such as closing public places for fumigation and testing slowed the spread of dengue and prevented a similar epidemic from happening the following year
Fitch Connect data provide information on income statements and balance sheets for up to 34,400 banks since 1995. As this dataset does not report ownership information, we augment the dataset with hand-coded ownership information assembled using the procedure developed in Panizza (2021).

<mark>Next, we merge the bank-level dataset with firm-level characteristics from the World Bank’s Enterprise Survey (WBES). WBES consists of a series of firm-level surveys covering a broad range of business environment topics including</mark> _<mark>access to finance</mark>_ <mark>,</mark> _<mark>corruption</mark>_ <mark>,</mark> _<mark>infrastructure,</mark>_ <mark>and</mark> _<mark>competition</mark>_ <mark>. It includes</mark> more than 180,000 firms in more than 150 countries over a 16-year period.<sup>4</sup> Most of the countries covered by WBES have been surveyed 2 or 3 times over 2006-21. For example, the Arab Republic of Egypt was surveyed in 2013, 2016, and 2020, while Tunisia was surveyed in 2013 and 2020.

A key component of the survey is the _Access to Finance_ module. This module includes questions that focus on the various dimension of firms’ financing. Among other things, there is a question on whether the enterprise has a line of credit or if it has contracted a loan in the last 24 months. If the answer is yes, there is a follow-up question that asks the name of the bank that provided the most recent line of credit or loan.<sup>5</sup> While the answer to the second question is not available in the published version of the survey, we were able to obtain bank names on a confidential basis. We then used the answer to this question to connect firms in WBES with bank-level data from Fitch Connect
fixed effect<br>In the case of Brazil, census data is used for<br>the case of Colombia, we use official aggre<br>n municipalities. For the rest of the countri|The resu<br>timated<br>in mode<br>d locatio<br>l, marita<br>s. In the<br>the perio<br>gates of<br>es, the a|lts are estimated based o<br>location premiums with<br>l (2.2).γ0<br>abrefers to the<br>n premiums controlling f<br>l status, household de<br>case of Mexico, the analy<br>d 2000-2010, while hous<br>value-added data and o<br>nalysis is based on SED|n model (2.3).γ0<br>brefers to the<br>out controlling for household<br>initial location premium after<br>or sorting based on household<br>mographics, and employment<br>sis is based on census data for<br>ehold data is used in all other<br>nly include one category for<br>LAC harmonized household|

Convergence occurred at different speeds across different types of localities (Table 7). In Mexico, convergence was fastest across metropolitan municipalities and slowest across large rural municipalities. In Brazil, convergence in labor productivity premia (i.e. location premia _before_ sorting) was fastest across the large urban and metropolitan municipalities, while convergence in place 26