rafmacalaba/data-use-mentions-extended huggingface dataset:
**Panel B.** In-kind benefits

<!-- Start of picture text --> 100%<br>90%<br>80%<br>70%<br>60%<br>50%<br>40%<br>30%<br>20%<br>10%<br>0%<br>Contributions Health Education Child Care Preschool and Secondary Tertiary<br>to Health Primary<br>Quintile 1 Quintile 2 Quintile 3 Quintile 4 Quintile 5<br><!-- End of picture text --> Source: Authors’ estimates based on ECH (2017), INE and Fiscal Accounts and CEQ Methodology Notes: Quintiles are defined by per capita Pre-fiscal Income It is also relevant to consider groupings other than income to analyze public programs. This allows for a complementary scope on public policies, especially when it comes to identifying the focused support provided to the most vulnerable sectors of the population in a relatively egalitarian country. In this case, we consider both geographic regions and types of households. Since the groups comprise varying shares of the Uruguayan population, it is important to normalize the results. To keep things simple, we calculate the difference between the share (paid or received) of the observed policy and the population share of the group, divided by the population share. Thus, this population-adjusted incidence will be positive whenever a group pays (or receives) more than what a uniform distribution would imply.

When considering geographic regions, it is clear that direct taxes are being paid by the wealthier zones (Montevideo) and direct transfers are being focused at the poorer. In seen in Figure 4, Panel A, direct taxes paid in Montevideo are 65 percent higher than what they would be if they were equally distributed among the population. As for transfers, AFAM-PE and non-contributive pensions in Artigas, Rivera and Cerro Largo are 70 and 85 percent higher than their share respectively, while food and other direct transfers are
relevant test scores for grade three in the rst follow-up (rst column) and second follow-up (second column), on the treatment indicator and including the usual controls, clustered by school. The eects of the grant are generally spread over most of the distribution as shown by the index in the fourth row, although the results are less precise due to smaller sample sizes, particularly in the second follow-up. In the second follow-up, for mathematics the eects are larger at the lower end of the distribution, while for French, oral, and PPVT scores the eects are somewhat larger in the mid- to the upper end of the distribution.

In the remaining part of the paper we look in greater detail at these results and consider

> to a lack of power. If the point estimates for grade 5 were as large as those for grade 3 it is likely that we would be able to reject that they were statistically equal to zero. When designing our study we anticipated that with our sample we would be able to detect program impacts of between 0.2 and 0.3 standard deviations, which is in line with what we nd.

> 14Ideally we would want to do this using 2 _nd_ and, say, 4 _th_ grade students, but we do not have the follow-up data for these teachers, although we have baseline data for them.

15
# **3 Predictive Power of Satellite Imagery in the CrossSection**

This section presents the methodology to correlate satellite data with economic activity data and report the empirical results in the cross-section.

## **3.1 Econometric Setup**

This study starts by exploring the predictive power of satellite imagery in the crosssection. It estimates simple linear ordinary least squares (OLS) regressions to predict economic variables using the remotely sensed satellite data for each sample year separately:<sup>7</sup> where _yi_ denotes the economic outcome of commune _i_ and vector _satellitedata_ contains commune-specific statistics from the remotely sensed satellite products. The parameters of interested are _β_ and the R-squared measure as an indicator of the predictive power of remotely sensed data for economic outcomes.

Later specifications control for the area of the communes which turns out to be an important predictor for economic outcomes. Commune area is not random and accounting for the endogenous choice of commune boundaries is important. depicts the strong negative relationship between the number of enterprises and employees, expenditure, and commune size (See binned scatterplot in the appendix). On average, small communes have higher economic activity with estimated elasticities of -.52 (enterprises) and -.65 (employment)

> 7The appendix explores the sensitivity of the results to alternative estimation specifications. Poisson models for count data yield measures of predictive power very similar to OLS.

10
Institute (IFPRI); The World Bank; and Centro Internacional de Agricultura Tropical (CIAT). 2004b. "Global Rural‐Urban Mapping Project (GRUMP), Alpha Version: Urban Extents". Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University. Available at http://sedac.ciesin.columbia.edu/gpw.  Downloaded December 22, 2005.

- Costanza, R., and J., Farley, 2007, Ecological Economics of Coastal Disasters: Introduction to the Special Issue, _Ecological Economics_ , 63(3): 249‐253.

- Dartmouth Flood Observatory, 2008, Dartmouth Flood Dataset, Downloaded from ~

- <u>http://www.dartmouth.edu/ floods/Archives/index.html.</u>

- Delavande, A., X., Gine, and D., McKenzie, 2010, Measuring Subjective Expectations in Developing Countries: A Critical Review and New Evidence, _Journal of Development Economics_ , forthcoming

- De Smith, Michael J., Michael F. Goodchild, and Paul A. Longley.  2007. _Geospatial Analysis: A Comprehensive Guide to Principles, Techniques and Software Tools_ , 2d ed.  Leicester, UK: Matador.  Web version available at http://www.spatialanalysisonline.com/.
technically competent bureaucrats. The second is the quality of public services (evaluated by the _ variables rule of law, enforceability of contracts/repudiation of contracts by government, and expropriation/nationalization risk), which determine an environment where contractual obligations are respected both by the state and private agents, where legal disputes are effectively resolved in the - judicial system, and where legal rules for investment, employment, and taxation are not arbitrarily modified by the state.

.

The BERI Index (see Chart 17, left panel) shows that Argentina, Chile, Ecuador, and Peru made some progress in the area of governance from 1990 to 1995. For the latter two countries, this meant a recovery toward their 1985 level after a fall in the late 1980s. By contrast, governance in 27
and Peter Van Oudheusden. 2015. “The Global Findex Database 2014: Measuring Financial Inclusion around the World.” Policy Research Working Paper 7255, World Bank, Washington, DC.

Johnston, Don Jr., and Jonathan Morduch. 2008. “The Unbanked: Evidence from Indonesia.” World Bank Economic Review 22 (3): 517-537. - <u>http://www.nyudri.org/wp content/uploads/2011/10/unbanked.pdf</u> McKenzie, David, and Christopher Woodruff . 2008. “Experimental Evidence on Returns to Capital and Access to Finance in Mexico” World Bank Economic Review Volume 22 (3): 457 – 482. <u>http://elibrary.worldbank.org/doi/pdf/10.1093/wber/lhn017</u> Molinas Vega, José R.; Paes de Barros, Ricardo; Saavedra Chanduvi, Jaime; Giugale, Marcelo; Cord, Louise J.; Pessino, Carola; Hasan, Amer. 2012. Do Our Children Have a Chance? A Human Opportunity Report for Latin America and the Caribbean. World Bank. © World Bank. https://openknowledge.worldbank.org/handle/10986/2374 License: CC BY 3.0 IGO.” Paes de Barros, Ricardo; Ferreira, Francisco H. G.; Molinas Vega, Jose R.; Saavedra Chanduvi, Jaime. 2009. Measuring Inequality of Opportunities in Latin America and the Caribbean. Washington, DC: World Bank; New York: Palgrave Macmillan. © World Bank. https://openknowledge.worldbank.org/handle/10986/2580 License: CC BY 3.0 IGO.

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SOUTH SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE South Sudan has not attracted as much private finance into infrastructure as other African peers. Over the early 2000s, private flows into ICT and a 2006 seaport concession near Juba were equivalent to slightly less than 1 percent of GDP. Many other African countries have done significantly better in this area, suggesting there is room for capturing additional resources vis-à-vis the size of the economy (figure 25). Countries such as the Democratic Republic of Congo, Liberia, Nigeria, Uganda, Kenya, and Senegal have all captured between 1.8 and 2.5 percent of GDP, while the most successful country in this regard— Guinea-Bissau—has captured in excess of 3 percent of GDP.

**Figure 25. Numerous African countries capture more private investment than South Sudan**

<!-- Start of picture text --> 3.5<br>3.0<br>2.5<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>Power ICT Transport Water<br>% GDP<br><!-- End of picture text --> _Source:_ PPI Project Database. (http://ppi.worldbank.org), in current $ millions.

China and India might be sources of additional funding given the track record of these investors in neighboring countries, particularly Sudan in the power sector. In fact one project financed by China ($200 million) has already been identified—though not confirmed—for developing power distribution for villages and rural areas in the White Nile State. The role of donors is also expected to increase, which is critical in making the funding gap manageable.

Adopting lower-cost technologies could substantially reduce the cost of meeting the posited infrastructure targets, and help make the funding gap manageable. Meeting the MDGs for water supply and sanitation with lower-cost technologies than previously used (such as
social capital might affect child nutritional status, as hypothesized here. In this paper, the reverse causality issue is overcome using longitudinal data which help in disentangling the temporal order. Specifically, maternal participation is measured before child’s nutritional indicators.

Third, the decision to participate might be endogenous given that becoming a member of any organization is a voluntary decision. More generally, a mother’s unobservable characteristics might drive both the decision to participate and the child nutritional status. I test for potential unobserved heterogeneity using fixed effects at community level.

In the present analysis, social participation is shown to be an effective instrument against children’s undernourishment in the first year of life for those children born to uneducated mothers. However, less clear, are the mechanisms beyond the potential positive effect of maternal social participation. I explore the hypothesis that social participation facilitates access to additional resources by increasing the likelihood of receiving support. Even if receiving support has been found to be positively associated with nutritional status at 1 year, it is not a channel through which maternal participation affects child nutritional status. Unfortunately, due to data limitations, it is not possible to test the other two main pathways suggested in the social capital literature, i.e. the “information pathway” and the “empowerment pathway”. The first one is the provision to mothers of essential information about child care and health which they did not acquire by formal education (Durlauf and Fafchamps, 2004). The second pathway suggests that social participation might increase maternal aspirations and their ability to plan the future by learning from the positive experiences of others (Macours and Vakis, 2008).

# **2.  Social Capital in the Literature**

In previous studies social capital has been defined in different ways. The more exhaustive definition of social capital includes two main components: _structural social capital_ and _cognitive social capital_ (Putnam et al., 1993). Structural social capital reflects the connectedness of individuals within a given community ( _bonding social capital_ ) in the
# **Table 3: Correlations between informal competition (cell averages)** **<u>and the baseline controls</u>**

||Informal Competition<br>(Country level cell average)|
|---|---|
|Informal Competition (Country level cell<br>average)|1|
|Initial Employment (logs)|-0.109|
|GDP per capita (lagged, logs)|-0.137|
|Age of Firm (logs)|0.050|
|Manager Experience (logs)|0.074|
|Exports (proportion of sales)|-0.128|
|Female Top Manger Y:1 N:0|-0.007|
|Multi-establishment firm Y:1 N:0|-0.064|
|Crime losses Y:1 N:0|0.092|
|Courts obstacle Y:1 N:0|0.143|
|Power outages (hours)|0.014|
|Finance obstacle (0-4 scale)|0.178|
|Overdraft Y:1 N:0|-0.201|
|Inflation (CPI, annual %)|-0.016|
|PrimaryEnrollment(Gross rate)|-0.025|

Source: Author's own calculations based on data from Enterprise Surveys and World Development Indicators (WDI, World Bank). Sample size: 20.295 firms used in the baseline regressions.

45
*|0.9|
||||||_2000_|||||
|Education HOIs|0.5|**_1.6_**|0.1|0.0|**_2.2_**|0.5|0.7|0.5|0.8|
|Basic housingservices HOIs|0.0|**_2.6_**|0.3|0.0|**_2.6_**|**_2.5_**|**_3.3_**|**_2.3_**|1.7|
|Earlychildhood development HOIs|0.3|**_1.1_**|0.1|0.4|**_1.4_**|**_3.2_**|0.9|**_1.2_**|1.1|
|Nutrition HOIs|0.5|**_1.9_**|0.3|0.2|**_0.9_**|**_1.9_**|0.1|0.6|0.8|

_Note_ : * Each cell reports the mean of {IOCs} by sector for each circumstance. See Tables A.7 a, b, c and d in the Appendix. _Source_ : HIECS 2000 and 2009 surveys, EDHS 2000 and 2008, and authors’ calculations.

_Education_ . The first row in Table 8 shows the marginal effects of circumstances on education opportunities in 2009. The figures presented in the education row indicate that in this sector _all eight_ circumstances are significantly unequalizing, with _parents’ education_ being the most important (nearly twice the inequality penalty of other circumstances), followed by _number of children_ in the household and income per capita. These results suggest that, for education opportunities in 2009, all differences in circumstances matter for inequality of opportunities. It must be noted
consumption quintiles (Bottom 40 percent)<br>Middle & High consumption quintiles (Top 60 percent) Middle & High consumption quintiles (Top 60 percent)<br>Total Total<br>c) By consumption quintiles (2023)<br>65.963.1<br>59.5<br>47.343.5 45.1<br>26.524.9 25.6 2423.1 2...<br>10.812.111.5<br>Evacuation Shelter Trained Committee Emergency<br>routes community plan<br>Low consumption quintiles (Bottom 40 percent)<br>Middle & High consumption quintiles (Top 60 percent)<br>Total<br><!-- End of picture text --> Note a): Results from the sub-sample of 1,487 households that were panel and for which consumption quintiles could be estimated. Note b): Results for 2022 are from the 1,529 observations for which income quintiles could be assigned. Note c): Results for 2023 are from entire sample of wave 4.

10
relationship between regulatory quality and financial inclusion outcomes. The remainder of the paper is organized as follows. The next section describes the regulatory index constructed, with explanations on the rationale of selection, scoring methodology and general trends. Section III presents the econometric models used and the results of the estimations. Section IV contains some concluding remarks.

# **II. Data description**

To construct our regulatory measures, we employ data from the World Bank’s Enabling the Business of Agriculture (EBA) project. EBA finance data are collected through standard questionnaires, which are completed by three main types of respondents: financial sector supervisory authorities, financial lawyers, and legal officers of financial institutions. All data are supported by official regulations and cover 62 economies.<sup>2</sup> The EBA data set features finance indicators benchmarking regulations in areas that are key to promote access to financial services by unserved or underserved customers (table 1). Six indicators are developed from three different aspects that affect financial inclusion: 1) local providers of financial services, including MFIs and financial cooperatives; 2) delivery channels of financial services, with focus on the booming branchless banking activities; and 3) movable collateral facilitating access to credit.

**Table 1.** EBA Finance indicators

- <mark>NON-BANK LENDING INSTITUTIONS</mark> _1) Operation and prudential regulation of MFIs_  Prudential rules (capital adequacy ratio, minimum capital, loan loss provisioning)

- Loan size limits  Consumer protection (effective interest rate disclosure, deposit insurance)

- _2) Operation and governance of financial cooperatives_  Prudential rules (minimum capital, solvency/liquidity ratio)

- Consumer protection (effective interest rate disclosure, deposit insurance)

- Ability to merge

- <mark>BRANCHLESS BANKING</mark> _3) Agent Banking_  Minimum standards to operate as an agent and services offered by agents

- Exclusivity of agent contracts

- Financial institution liability for agent actions

- _4) E-money_  Allowing e-money activities (issued by either financial
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17
to grow the most in nonOECD countries, for which consistent cross-country data or transportation household surveys are difficult to find. In addition, recent works have shown the benefits of using household-level data. Gertler et al. (2016) use household surveys to document motivating evidence of a nonlinear relationship between income and both asset ownership and energy use at the individual and municipality levels. Household survey data can also be used to document the distribution of transport expenditure shares within countries, which is key for the political economy of climate policies such as carbon pricing.

We identify several extensive and intensive margins of interest to quantify the impacts of rising incomes on transport consumption that are relevant for low- and middle-income countries. In low-income countries, the share of households not consuming transport goods and services remains high. The lack of access to and affordability of such goods and services limits mobility and access to economic opportunities. As their income increases, households spend more on transport and can then choose between public and/or private transport options. While private transport might be more efficient, it is also more carbon-intensive than public transport. In 2
J Epidemiol Community Health 1998; 52; 377-384.

8. World Health Organization. 1999. “Sexually Transmitted Infections Prevalence Study Methodology. Guidelines for the implementation of STI prevalence surveys”. <u>http://www.wpro.who.int/internet/resources.ashx/HSI/docs/Guidelines_for_the_Impl ementation_of_STI_Prevalence_Surveys.pdf</u> 9. Aschengrau, A. 2003. “Essentials of Epidemiology in Public Health”. Sudbury Mass.: Jones and Barlett.

10. Gordis, L. 2004. “Epidemiology”. Third Edition, Elsevier Saunders.

11. Girden, E. 2001. “Evaluating Research Articles from Start to Finish”. Second Edition, Sage Publications.

12. Hernandez-Avila, M. y col. 2000. “Diseño de estudios epidemiológicos”. Salud pública de México / vol.42, no.2, marzo-abril de 2000.

13. Szklo M. 2006. “Quality of scientific articles”. Rev Saúde Pública 2006; 40 (N Esp):30-5.

14. Velasco, V. y col. 2003. “Muestreo y Tamaño de Muestra. Una guía práctica para personal de salud que realiza investigación”. e-libro.net. Buenos Aires 15. Salganik M, Heckathorn D. 2004. “Sampling and Estimation in Hidden Populations Using Respondent-Driven Sampling”. Sociological Methodology, Vol. 34. (2004), pp. 193-239 16. Muhib F, Lin L, et al. 2001. “A venue-based method for sampling hard-to-reach populations”. Public Health Rep. 2001; 116(Suppl 1): 216–222.

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follows closely the oil price. A second period is characterized by a steep surge in oil prices between 2003 and 2008 when price volatility increases and the price of gasoline follows less closely the oil price. The third period starting in 2009 follows the global financial crisis and is characterized by high and volatile oil prices where the U.S. and European prices and the price of gasoline increasingly diverge. The fourth period shows the most recent decline in oil and gasoline prices and also continued price volatility. The period considered by this paper (20102014) is unique in terms of both the level and volatility of oil prices.

Figure 1: U.S. and European Oil Prices, May 1987–April 2016 <!-- Start of picture text --> 160 3.5<br>140 3<br>120<br>2.5<br>100<br>2<br>80<br>1.5<br>60<br>1<br>40<br>20 0.5<br>0 0<br>Cushing, OK WTI Spot Price FOB (Dollars per Barrel)<br>Europe Brent Spot Price FOB (Dollars per Barrel)<br>New York Harbor Conventional Gasoline Regular Spot Price FOB (Dollars per Gallon)<br>May‐1987 May‐1988 May‐1989 May‐1990 May‐1991 May‐1992 May‐1993 May‐1994 May‐1995 May‐1996 May‐1997 May‐1998 May‐1999 May‐2000 May‐2001 May‐2002 May‐2003 May‐2004 May‐2005 May‐2006 May‐2007 May‐2008 May‐2009 May‐2010 May‐2011 May‐2012 May‐2013 May‐2014 May‐2015<br><!-- End of picture text --> _Source:_ Elaborated from data available at EIA 2015.

The picture is similar if we consider the commodities indexes for fuel and food (figure 2)
Halac and Schmukler’s work points to the value of using survey information on financial service usage in assessing the effects of financial crises. A growing number of household surveys around the world contain information on access to financial services, though not enough to provide a range of “before and after” cases for econometric work. In particular, LSMS surveys, some of which contain quite detailed information on household access to certain financial services, are conducted rather infrequently, with the result that changes between before and after crises cannot readily be read off the findings.<sup>8</sup> However they can provide a benchmark for the before- _or_ after-crisis distribution of financial access. Table 1 provides a list of recent LSMS surveys containing financial sector information<sup>9</sup> together with the dates of the most recent banking crises reported for these countries. (This information is not pursued further in the present draft of this paper, but may be useful for future work.)

# _Tracking other channels_

There is no comparably clearcut way of isolating the likely distributional effects of the remaining channels. This is especially true of the last two, inasmuch as they could be experienced as a result of non-financial shocks. How is the analyst to distinguish between job losses that are due to a disruption of the payments process and those arising from a general downturn in the rest of the economy?

One approach involves identifying characteristic patterns of relative price change following a banking crisis. A temporary surge in inflation is a common occurrence following banking crises that have been associated with macro downturns (though this is not necessarily the case, and is not always observed). Inasmuch as inflation is typically inequality-increasing, this can be regarded as inequality increasing. Real wages have often fallen in such circumstances, and Diwan (2001) has shown a tendency for the share of labor in national income to decline during financial crises (counting currency crises as well as banking crises); however, sharp macroeconomic downturns have often been associated with a