rafmacalaba/datause-encoder-data huggingface dataset:
9. **Water and sanitation** is measured at the household-level and includes three outcomes: an index that tracks households’ access to potable water and toilet facilities; an index that measures public water and toilet facilities in each community; and an index of questions related to households’ satisfaction (measured on the four-point Likert scale) with water, sanitation, and cleanliness in their community.

10. **Economic outcomes** is measured at the household-level and comprises four outcomes: indices of physical assets, agricultural assets (e.g., livestock, farm tools), and dwelling materials; as well as an index capturing total consumption expenditure over the last month.

Families 1–4 (general utilization, maternal utilization, health outcomes, and satisfaction) convey the main effects and mechanisms for our medium-run results. Families 5–8 (clinic quality, health service delivery, contributions to clinic, and CDPE) represent other plausible but unsupported mechanisms. Finally, families 9 and 10 (water and sanitation and economic outcomes) are additional outcomes presented in the appendix.

# **3.2.2 Ebola Case Data**

We rely on the Epi Info Viral Hemorrhagic Fever (VHF) database, which was the primary data management system used for case and contact tracing during the outbreak. The Ministry of Health and Sanitation, with support from the CDC implemented and maintained the VHF database through the end of the epidemic, and McNamara et al. (2016, 39) describe it as “the most comprehensive epidemiologic and laboratory data on Ebola cases available in Sierra Leone.” As noted above, the VHF cannot be used to measure the underlying incidence of Ebola; rather, it reflects reported cases — a particularly important outcome for stopping contagion and containing the epidemic (Enserink 2014).

We use a de-identified version of the VHF database (where patient names and characteristics have been redacted).<sup>16</sup> While the data were compiled at the National Ebola Response Center in Freetown, data entry occurred at the district-level. Surveillance officers employed (even prior to the crisis) by the Ministry of Health and Sanitation (MoHS) oversaw teams of case investigators charged with following up on suspected cases. Investigators learned about cases from communities, phone calls, active surveillance (e.g., contact tracing), and through walk-ins at health centers (Owada et al. 2016, 2). For each suspected case, they completed a case investigation form (CIF), which included demographic (including district, chiefdom, and village) and health information. A copy of the CIF accompanied blood samples or swabs from corpses, so that lab results could be linked back to cases to confirm or rule out an infection (McNamara et al. 2016, 37). Completed CIFs were brought back to District Ebola Response Centers and entered by data managers in the

> 16 The Njala University Ebola Museum and Archive facilitated access to this database.

14
# **I. Introduction**

Although most workers in developing counties are self-employed, relatively little is known at a broader level about their characteristics and prospects, and how types of employment evolve as economic development occurs. This paper uses a comprehensive set of household surveys to document the heterogeneity of the self-employed, by which we mean both employers and own-account workers. In developing countries, self-employed workers are often classified according to their perceived prospects for growth. A small minority of self-employed are innovative, successful entrepreneurs with further growth potential and ambition (de Soto, 1989; Bennet and Estrin, 2007). On the other hand, the majority of the self-employed work for themselves and earn little, either because they are rationed out of wage jobs (Fields, 1975; Tokman, 2007, de Mel, et al, 2010) or because they prefer the autonomy and flexibility of selfemployment (Maloney, 2004).

These less successful self-employed workers, whether self-employed by choice or not, are also heterogeneous. For example, Grimm, Knorringa and Lay (2011) distinguish between two types of unsuccessful entrepreneurs in several West African cities. The first type has the profile, in terms of age, education, and sector of work, of more successful entrepreneurs, but has yet to acquire significant capital. Although it is impossible to know exactly why these entrepreneurs’ enterprises have failed to grow, the authors assume that their lack of success is partly attributable to personal and environmental constraints, such as inadequate skills and experience, access to capital, or physical infrastructure. The second group of unsuccessful selfemployed, on the other hand, does not share the same characteristics as successful entrepreneurs, and are therefore assumed to be more likely to be constrained by their age, education, and sector of work than unobserved features of their skill set or external environment.

In this paper, using data from nationally representative micro-level household surveys from almost 100 countries, we examine the characteristics of the self-employed throughout much of the developing world. Building on our profile of the self-employed, we use two admittedly coarse but nonetheless meaningful measures to classify the self-employed as successful: whether a self-employed worker is an employer as opposed to an own-account worker, and whether the self-employed worker lives in a non-poor household. Given data limitations, the analysis is unable to isolate which characteristics or factors cause some self-employed to be successful along these measures. Nonetheless, we can characterize the extent to which the currently unsuccessful self-employed possess basic traits that are correlated with success, which may lead them to have greater potential to become successful.

We first examine the characteristics associated with agricultural workers, and of nonagricultural employers, own account workers, non-paid employees and wage and salary
# **Table 19: Stepwise Targeting Regressions (All Observations) Former Soviet Union**

||_Estonia_<br>_Kyrgyz Republic_|_Russia_|
|---|---|---|
||_Best Five Predictors_||
||Wage income<br>Wage income|Wage income|
||Car<br>Car|Transfer income|
||Color TV<br>Washing machine<br>Higher education<br>Color TV|Color TV<br>Refiigerator|
||Transfer income<br>Land ownership|Household enterprise|
||_% Correct Predictions_||
|Poor|53.3<br>57.4|56.4|
|Non-poor|75.7<br>67.0|76.2|
|All|70.3<br>63.6|68.9|
||_Second Best Five Predictors_||
||Stereo<br>Number of children|Inactive head|
||Household enterprise<br>Renter|Car|
||Number of unemployed<br>Household enterprise<br>Inactive head<br>Location: rural|Location: other urban<br>Location: rural|
||Number of children<br>Location: other urban|Sewing machine|
||**_%_**_Correct Predictions_||
|Poor|58.3<br>56.7|57.1|
|Non-poor|76.8<br>68.1|77.0|
|All|72.5<br>63.7|69.5|
||_All Variables -_<br>_%Correct Predictions_||
|Poor|61.9<br>57.1|56.9|
|Non-poor|77.1<br>68.6|75.5|
|All|74.5<br>64.0|68.9|

_Note:_ Dependent variable is the log of per equivalent adult expenditure. The regressors are the same as in the welfare and poverty regressions with the addition of wage and transfer income and consumer durables.

94
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Consistent with the results in Table 2, having higher educated non-farmer parents yields the most advantages. The estimated effects are larger in magnitude for sons compared to the daughters. The estimates show that having parents with more than middle school education and nonfarm job increases the schooling attainment by more than two years for a son.

The Rosenbaum sensitivity analysis in Table A2 shows that, similar to results for the primary school cut-off for parental education, the positive effects of being born into the better educated nonfarming parents cannot be explained away by even very strong positive selection on genetic endowments of ability and preference. The evidence also confirms the finding that the children of better educated farmer parents have significantly higher schooling attainment when compared to the children of low educated nonfarmer parents, and this cannot be accounted for by genetic correlations. These conclusions are strengthened by the causal estimates from MB-NIPW and Lewbel heteroskedasticity based idetification results in Table A.3.

50
# **Figure 6**

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45
India-Africa height gap can be explained by heterogeneity in village-level open defecation rates, using individual-level data on child heights and decomposition analysis in the spirit of Oaxaca-Blinder. Reweighting Indian data to match the sanitation of an African sample counterfactually increases the height of Indian children by more than the India-Africa gap. All three approaches find a similar and quantitatively important association between height and sanitation. Finally, a concluding section 5 considers whether estimates of of the association between height and sanitation in this paper and from the literature are sufficient to account for the India-Africa gap.

# **1.1 Open defecation causes stunting**

A growing literature in economics documents that physical height has its origins in early life health ( _e.g._ Case and Paxson, 2008), especially in poor countries where environmental threats to health are more important than they are in rich countries, relative to genetics (Martorell et al., 1977; Spears, 2012b). Two existing literatures indicate that early-life exposure to fecal germs in the environment reduces children’s subsequent height. First, medical and epidemiological literatures have documented the mechanisms linking open defecation to poor health and early life human capital accumulation.<sup>1</sup> Humphrey (2009) documents that – chronic but subclinical “environmental enteropathy” a disorder caused by repeated fecal contamination which increases the small intestine’s permeability to pathogens while reducing nutrient absorption – could cause malnutrition, stunting, and cognitive deficits, even without necessarily manifesting as diarrhea ( _see also_ Petri et al., 2008; Mondal et al., 2011). Relatedly, Checkley et al. (2008) use detailed longitudinal data to study an association between childhood diarrhea and subsequent height.

> Second, recent econometric studies find an effect of a government sanitation program in

> 1Perhaps the recent paper most complementary to this one is Fink et al.’s (2011) regression of an indicator for child stunting on variables including sanitation in 172 pooled DHS surveys. However, one key difference is that they focus on within-country height-sanitation correlations: all regressions include DHS survey fixed effects for country-years.

4
a relationship is particularly strong or rather weak. In order to address this issue, this paper uses data from an internationally comparable student achievement test which provides data for Argentina, Colombia and selected comparison countries, thereby allowing for a benchmarking of estimated parameters relative to other countries.

Specifically, we use data from the most recent international achievement study, the Progress in International Reading Literacy Study (PIRLS), which tested the reading performance of fourth-grade students in 2001, with data released in 2003. A uniform data generation process for representative samples of students in all participating countries ensures comparability across countries. Latin America’s poor educational performance is well documented in PIRLS: Argentina and Colombia ranked 31<sup>st</sup> and 30<sup>th</sup> , respectively, out of a total of 35 participating countries,<sup>2</sup> while at the same time featuring relatively large inequality among their students.<sup>3</sup> The PIRLS study not only provides comparable information on the reading performance of students, but also extensive information on student and school background to an extent not generally available in student achievement studies. This allows for an analysis of the determinants of learning both in the realm of the family and of the school. The available information stems from background questionnaires answered, respectively, by students, teachers, school principals, and – as a particular advantage over other recent international achievement tests – a home background questionnaire completed by the parents of tested students. The latter questionnaire provides particularly relevant information which primaryschool children themselves could not have provided. The available student background features include information on the students’ pre-school reading performance, their kindergarten attendance, school starting age, language spoken at home, immigration status, parental education, occupation and income and rural versus urban location, among several others. The school features include class size, instruction time, proxies for the material endowment of the classroom, teacher characteristics including gender, education, recent training participation and experience, testing policies, curriculum determination, class formation rules and length of students’ staying with the same teacher, among others.

> 2 The only countries that performed lower were Iran, Kuwait, Morocco and Belize.

> 3 At the secondary level, Colombia participated in the 1995 TIMSS study, where it ranked 40<sup>th</sup> of 41 participating countries both in math and in science, with only South Africa performing lower. Similarly, Argentina participated in the 2002 PISA study, ranking 35<sup>th</sup> of 41 in reading, 34<sup>th</sup> in math and 37<sup>th</sup> in science, with in each case at least half of the few lower-performing countries also being Latin American.

2
Table 5: Key variables and summary statistics

|**Variables**|**Observations**|**Mean**|**S**<br>**Overall**|**tandard Devi**<br>**Between**|**ation**<br>**Within**|
|---|---|---|---|---|---|
|Total employment|12,564|6,812.303|90,112.890|85,792.800|27,619.900|
|Manufacturing employment|12,564|1,998.672|23,597.920|23,277.810|3,901.393|
|Commerce employment|12,564|2,149.258|26,322.070|25,274.430|7,369.452|
|Service employment|12,564|2,229.871|37,222.180|33,232.070|16,779.740|
|Mining employment|12,564|62.447|1,149.786|642.266|953.764|
|Market access|12,564|22.496|399.887|396.782|50.361|
|Market potential|10,470|365,276.400|573,321.300|430,809.700|378,379.700|
|Employmentksi|5,286|0.237|0.445|0.359|0.266|
|Outputksi|5,286|0.305|0.495|0.397|0.303|
|Education (literacy rate)|12,408|0.046|0.049|0.034|0.035|
|Population|12,564|47,149.370|445,092.100|442,553.500|48,283.380|
|Effciency roads within200km|11394|12.440|0.419|0.411|0.081|
|Minimum travel time to port|12,544|6.885|0.551|0.544|0.090|
|Minimum travel time to border|12,544|6.300|0.543|0.531|0.113|
|Minimum travel cost to port|12,544|0.795|0.136|0.134|0.024|
|Minimum travel cost to border|12,544|0.648|0.121|0.119|0.020|

_Note_ : the above descriptive statistics are calculated on a sample of 12,564 observations covering 2,094 localities over 6 different dates. The standard deviation is decomposed into between and within components, which measure the cross sectional and the time series variation, respectively.

and a recursion problem inherent with regressions of local employment or income variables on market access or market potential measures. We discuss below how we address these issues.

**Non-random road placement and the construction of the ‘doughnut’ IV.** The expansion of the road network surrounding a locality can be endogenous and this may create a correlation between increases in local market access or local market potential and our left-hand side variable, for instance employment. This may occur if road construction occurs in localities that would otherwise have experienced relative increases or decreases in average employment (our review of road investments in Mexico suggests that roads may have been built to increase accessibility in lagging regions during the studied period). To deal with that problem, we adopt the so-called ’doughnut’ instrumentation strategy as proposed by Jedwab and Storeygard (2017). The basic idea behind this approach is to instrument the market access or market potential with similar measures excluding surrounding areas within a fixed radius (in addition to ‘own locality’ exclusion). Variations in this instrument are more likely to be exogenous. Following the identification strategy of Jedwab and Storeygard (2016) also implemented by Blankespoor et al. (2017), we construct the counterfactual measures of market access and market potential

18
Policy Research Working Paper 6917

# **Abstract**

Although several studies have found a negative relationship between government spending and entrepreneurship, much debate remains regarding the components of government spending responsible for this association. This paper contributes to the literature by specifically exploring the relationship between government private subsidies and entrepreneurship. By combining macroeconomic government spending data with individual level entrepreneurship data, the paper finds a negative association between the share of private subsidies and entrepreneurship. However, findings are less straightforward when the analysis delves deeper into the components of private subsidies and their association with different kinds of entrepreneurship.

This paper is a product of the Global Indicators Group, Development Economics. 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://econ.worldbank.org. The author may be contacted at aislam@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
R&D and low NBER) and Germany<sup>7</sup> (high expenditure on R&D and high NBER).

Table 3. Average of variables of production frontier model 2002-2005 by technological level

|**Sample**|**GDP**|**GFKF**|**LF**|**R&D**||**Entrepre**<br>**capi**|**neurship**<br>**tal**|
|---|---|---|---|---|---|---|---|
|**2002-2005**<br>All|**Total**<br>475990|**Total**<br>96681|**Total**<br>21188|**Total**<br> <br>10703.65|**% GDP**<br>1.49|**NBR**<br>54698|**NBER**<br>8.83|
|High-Medium Tech<br>countries<sup>1</sup>|996684|199474|19562|24907.70|2.82|91304|9.91|
|Medium-Low-Tech<br>countries<sup>2</sup>|114397|25298|22317|839.73|0.565|29277|8.07|
|Notes:||||||||

1 High-Medium Tech countries: R&D (% GDP) > 1 (year 2002) 2 Medium-Low-Tech countries : R&D (% GDP) < 1 (year 2002) Source: Authors’ calculation from the World Bank’s World Development Indicators (WDI).

With a medium level on R&D and NBER, Italy has an robust small and medium enterprise sector (77.8% of total firms), but it has not been as successful in establishing multinational corporations<sup>8</sup> .

# 4.1. Efficiency and Productivity Analysis

When comparing efficiency levels, we can see large differences between countries (see Appendix Tables I and II). The above differences are greater when comparing High-Medium and Medium-Low-Tech countries (see Table 4).

Using a TransLog production function to estimate Technical Efficiency, we observe important differences across countries (see Appendix Tables I and II). We can see that the U.S. has a high level of technical efficiency while Bolivia has a very low level of technical efficiency, and can/should significantly improve its performance, making better use of its resources. In between we have the cases of Japan,<sup>9</sup> United Kingdom<sup>10</sup> and Canada<sup>11</sup> (high efficiency), Georgia,<sup>12</sup> ,Mexico<sup>13</sup>

> of outsourcing activities in the areas of technical support and customer services for some of the major economies of the world).

> 7 In Germany’s economy, the average annual growth rate of GDP has been on a decline since the 1980s. Germany’s economy is currently recovering, ending a phase in stagnation on the back of its traditionally strong, competitive and innovative export-oriented manufacturing sector.

> 8 Many of these companies do not have a high level of technology sophistication. Italian services today make up 69% of the economy, industry 29%, and agriculture 2%.

> 9 Japan's economy is the second largest economy in the world and the largest in Asia, based on real GDP, market exchange rates, and nominal GDP. Japan uses planned development of science and technology, and has a strong work culture. However, in the 1990s Japan experienced a “Lost Decade”, a period when the Japanese economy was stagnant.

10 The main economic activity of United Kingdom is the service sector (76.2% of GDP in 2008). Industry and manufacturing (22.8%) and agriculture (0.9%) are other important industrial sectors.

12