rafmacalaba/datause-encoder-data huggingface dataset:
# **Supporting Information**

### BUILDING RESILIENT HEALTH SYSTEMS:

EXPERIMENTAL EVIDENCE FROM SIERRA LEONE AND THE 2014 EBOLA OUTBREAK Following text to be published online.

## **Contents**

|**A **|**Con**|**text**|**A3**|
|---|---|---|---|
||A.1|Administrative Boundaries . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A3|
||A.2|Cross-National Health Indicators . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A4|
|**B**|**Vari**|**able descriptions**|**A5**|
||B.1|Pre-specifed outcome variables and deviations<br>. . . . . . . . . . . . . . . .|. . . . . . . . A5|
||B.2|Descriptive statistics<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A11|
|**C **|**Inte**|**grity of the experiment**|**A14**|
|**D **|**End**|**line results**|**A16**|
||D.1|Outcome Family tables (raw, not z-scored) . . . . . . . . . . . . . . . . . . .|. . . . . . . . A16|
||D.2|Outcome family tables (z-scored) . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A22|
||D.3|Additional outcome tables<br>. . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A28|
|**E**|**Ebol**|**a**|**A31**|
||E.1|Specialized Ebola Facilities and Training . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A31|
||E.2|Effect on Reported Cases by Month<br>. . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A33|
||E.3|Time-series of Confrmed Cases<br>. . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A34|
||E.4|Effect on Patient Deaths<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A35|
||E.5|Effect on Probable and Suspected Cases . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A36|
||E.6|Dropping Triplets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A38|
||E.7|Dropping Weeks<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A39|
||E.8|Alternative Functional Forms for Reported Cases . . . . . . . . . . . . . . .|. . . . . . . . A40|
||E.9|Extending Panel to August 2014 . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A41|
||E.10|Cross-sectional Results for Reported Cases<br>. . . . . . . . . . . . . . . . . .|. . . . . . . . A42|
||E.11|Dose-response Models<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . .|. . . . . . . . A44|
||E.12|Placebo Test with Nearest Neighboring Out-of-sample Sections . . . . . . . .|. . . . . . . . A45|

A1
census, poverty estimates were not produced for each of the villages, but only for the 976 village units ( _illakas_ ). I use the _illaka_ level estimates of poverty and per-capita consumption for analyzing the impact of migration on poverty.

## **Nepal Living Standards Survey 2010 (NLSS-III)**

The second data-source I use for the analysis is the 2010 Nepal Living Standards Survey (NLSSIII), also conducted by the CBS. This cross-sectional survey collects information from about 6,000 households designed to be representative at the national, as well as sub-national level.<sup>8</sup> The survey is the premier poverty monitoring tool for the Government of Nepal and has a rich collection of outcomes including consumption, expenditure, education, labor supply, as well as on absent members of the households (migrants) and remittance receipts.

# **3.2 Empirical strategy**

The correlation between outcomes (say, consumption of, or labor supply) and migration (household level, or village level) cannot be interpreted as a causal impact of migration. One of the key problems is that the households that choose to migrate are very different from households that do not choose to migrate, and these differences are not fully accounted by the observable characteristics of the household. The same problem arises when analyzing the impact of the aggregate level of migration in the village. The villages with higher prevalence of migration are typically different from those where migration is a relatively rare phenomenon. I use two strategies, described in detail below, to partially ameliorate these concerns.

## **Instrumental variable estimates**

One of the strategies that I use is to instrument the household level migration with the migration rates of the same villages observed in 2001. The key insight behind this instrument is that households in villages that had prior migration networks are more likely to migrate subsequently. The role of migration networks in influencing future migration has been well established in the literature (see Munshi, 2003, for a seminal example). Historic migration rates have been widely used as an instrument for household migration propensity in other contexts (for instance, McKenzie and Rapoport, 2007) as well as in the context of Nepal (Phadera, 2016; Lokshin and Glinskaya, 2009). I use this strategy in the NLSS-III data to study the impact of migration or remittance receipt on, primarily, household consumption, expenditures, poverty, and wages.

Specifically, in this context, I estimate the following system of equations using a 2-SLS estima8The survey is designed to be representative at the 14 strata, which is a combination of 3 urban strata (Kathmandu, other hills, terai), and 11 rural strata. The rural strata are a combination of development regions and ecological belts.

7
Using aniual data, he estimated three-equation (production, consumption and stock demand) mini-models for seven primary commodities (maize, cocoa, coffee, copper, cotton, soybeans and sugar). He specified stock demand as an error correction on production or consumption of the commodity and the expected commodity price (not the expected capital gain) with this expectation given as the forecast from an ARIMA model fitted to the price process. There are three difficulties with this approach: i) Solution of the production, consumption and stock demnand equations into the market clearing identity entails a reduced form for the commodity price and this will differ from the ARIMA model used to generate the price expectations. This is not obviously consistent with the REH and at most, therefore, the ARIMA model can be said to give an approximation to rational expectations.

ii) A particular implication of this inconsistency is that if there is a structural change in either the production or consumption equations, this will be reflected in the reduced form but not in the price ARIMA. The Lord procedure is therefore vulnerable to the Lucas (1976) critique.

iii) The use of regressors constructed from previous regressions (e.g. the predictions from an ARIMA model) in a least squares regression gives rise to biased coefficient standard errors and t statistics (Pagan, 1984, 1986). (This is the same point as that made in paragraph 16 in connection with estimated production and consumption equations). One way of looking at this in the current context is to note that the Lord approach may be rationalized as a Two Stage Least Squares (TSLS) estimator (see paragraph 34); however, Lord does not make the second stage corrections to the coefficient standard errors.

34. The REH implies that the expected price pet+11t will differ from the realized price by 21
# **Table 7**

# **Cooperativeness and the long-route to accountability (self-reported)**

||Dummy e<br>|**Depend**<br>qual to 1 if pa<br>national elec<br>|**ent variable:**<br>rent participate<br>tions,0 otherw<br>|d in the 2009<br>ise<br>|
|---|---|---|---|---|
||Probit<br>(1)|Probit<br>(2)|Probit<br>(3)|Probit<br>(4)|
|**Invested in group account in the game**|0.05***|0.042***|0.042***|0.042***|
||(0.001)|(0.001)|(0.000)|(0.000)|
|Age||0.004***|0.004***|0.004***|
|||(0.000)|(0.000)|(0.000)|
|Female||0.009|0.012|0.012|
|||(0.434)|(0.259)|(0.261)|
|Wealth index||0.017**|0.015**|0.015**|
|||(0.024)|(0.027)|(0.029)|
|Years of schooling||0.003|0.002|0.002|
|||(0.135)|(0.226)|(0.308)|
|Rural location||0.014|0.015|0.015|
|||(0.283)|(0.264)|(0.229)|
|Distance from school (Km)||-0.004|-0.003|-0.003|
|||(0.275)|(0.312)|(0.302)|
|Belongs to an organization|||--|--|
|Participated in community activities|||0.018|0.017|
||||(0.244)|(0.269)|
|Thinks village members would work together|||0.013***|0.012**|
||||(0.010)|(0.015)|
|Generalized trust|||-0.027*|-0.027*|
||||(0.091)|(0.077)|
|% of relatives and friends among other parents|||0.005|0.005|
||||(0.167)|(0.158)|
|Help pupil with homework||||0.018|
|||||(0.139)|
|Number of meetings with head teacher||||0.002|
|||||(0.158)|
|District fixed effects|YES|NO|NO|NO|
|_Joint significance of district f.e. (p-value)_|_0.000_|-|-|-|
|Geographical areas dummies|YES|YES|YES|YES|
|_Joint significance of geographical areas dummies (p-value)_|_0.001_|_0.434_|_0.510_|_0.488_|
|Region fixed effects|NO|YES|YES|YES|
|_Joint significance of county f.e. (p-value)_|_-_|_0.000_|_0.000_|_0.000_|
|Observations|1,480|1,800|1,800|1,800|

Note: Robust standard errors have been clustered at the district level. P-values in parentheses: *** p<0.01, ** p<0.05, * p<0.1. We report marginal effects of continuous variables and the effect of a change from 0 to 1 for dichotomous variables. In all regressions we control for three geographical areas dummy variables: coastal, center and mountain areas; Tirana is the excluded geographical area.

Our experimental measure of cooperativeness is highly statistically significant and robust to changes in the empirical specification. The estimates indicate that parents who invested in the group account in the game are about 4% more likely to have voted in the most recent parliamentary elections. Older and wealthier parents are also more likely to have voted. Expectations of general cooperative tendencies 22
numbers decreasing sharply afterwards (7.9% at 15 and 2.85% at 16).<sup>2</sup> Therefore, an analysis of the effect of conflict on primary schooling attainment should consider children aged 14 or more at the time of the survey, and control cohorts should have been at least 15 at the beginning of the conflict, and preferably slightly older. I define the treated cohort as comprising individuals aged 5 to 9 years old at the start of the conflict in 1996, so that all treated cohorts are exposed to the conflict during most of their potential primary schooling career and the youngest exposed cohort is observed at age 14 in the 2006 DHS. The control cohorts include individuals aged 16 to 19 at the start of the conflict. I exclude cohorts aged 10 to 15 in 1996, as the treatment status of these cohorts is less clear-cut: many of these individuals could have been enrolled in primary schooling during the conflict, but they were not exposed to conflict during their whole primary schooling careers.

More specifically I estimate the following equation using the fixed effects estimator, and using data from the 2006 DHS: Where _Y ijrt_ is a dummy equal to one if the individual has completed primary education, and zero otherwise; _districtj_ is a set of district fixed effects, and _regionr*yeart_ are a set of interaction terms between binary indicators for each of Nepal’s five development regions and a set of birth year dummies _yeart_ (excluding the first year). _cohortt*casualtiesj_ is the interaction between a dummy equal to one when the individual belongs to the treatment cohort and the number of conflictrelated casualties (per 1000 inhabitants) in district _j_ during 1996-2006.

Under the assumption that there is no correlation between the number of district casualties and unobserved district-time-varying factors (after controlling for region-specific year fixed effects), _β3_ identifies the average treatment effect of exposure to conflict on those exposed. I also estimate variants of Equation (1) in which I add a regressor capturing specific aspects of the conflict which are likely to have affected schooling outcomes. I include, in turn, the total number of school destructions (per 1000 inhabitants) during the conflict, and the number of abductions by Maoist forces (per 1000 inhabitants) during the conflict. In addition, I consider whether Maoist control over the district had an effect on primary attainment by estimating variants of Equation (1) in which I replace the casualties measure by an indicator variable switching on when the district was under Maoist control.

> 2 In the 2001 DHS, 44.4% of 5-year olds, 24.6% of 14 year olds, and 13.6% of 15 year olds are enrolled in primary schooling.

15
WPS3834

# **Access to Financial Services in Colombia**

## The “Unbanked” in Bogotá

_Tova Maria Solo and Astrid Manroth_<sup>_*_</sup>

### World Bank Policy Research Working Paper 3834, February 2006

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

**.**

> * Tova Maria Solo is a Senior Urban Specialist in the Urban and Water Group of the Latin America and the Caribbean Region and.  Astrid Manroth was a Young Professional in the Finance Group of the Latin America and the Caribbean region of the World Bank at the time this report was written. Questions and comments on this report should be addressed to Tsolo@worldbank.org
Figure 6. Country correlations for CE with high capital account restrictions, versus CE with low capital account restrictions, 1980-2020 <!-- Start of picture text --> Low Capital Controls High Capital Controls<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>CE with Low Capital Controls = 0.33***<br>-0.2<br>CE with High Capital Controls = 0.51***<br>-0.4<br>-0.6<br>-0.8<br>Azerbaijan Yemen Côte d'IvoireUkraine<br>NicaraguaEcuador Uruguay GuyanaLiberia Argentina<br>Rwanda Oman Congo, Rep.Trinidad and Tobago<br>Brazil Namibia Equatorial GuineaNigerBolivia KenyaZimbabwe FijiAngola Qatar<br>Sudan SenegalMali Burkina Faso<br>Ghana Burundi Saudi Arabia Benin Turkmenistan MozambiqueChad Comoros The Gambia Colombia Tanzania LibyaCentral African RepublicGabon Iceland EthiopiaMongolia<br>AlgeriaUzbekistan MyanmarSouth Africa ParaguayTajikistan<br>Iran, Islamic Rep.Madagascar<br>Kyrgyz RepublicRussian Federation Papua New Guinea Lao P.D.R. Cabo Verde Uganda Cameroon Indonesia Honduras Suriname TogoSeychelles<br>Bahrain Belize<br>Solomon Islands Iraq<br>Nigeria Peru<br>Armenia<br>Sierra Leone Kazakhstan Guinea-Bissau Mauritania Costa Rica Botswana<br>Guinea<br>Chile Zambia<br>Kuwait<br>New Zealand<br>NorwayMalawi<br>Australia Guatemala<br>United Arab Emirates<br>Correlation between cyclical components of real government spending and real GDP 1980-2020<br>Canada<br>São Tomé and Príncipe<br><!-- End of picture text --> Notes: Each bar represents the correlation between the (HP) cyclical components of real GDP and real government spending for countries with high and low capital account restrictions. Based on the Chinn-Ito index of financial openness (where a higher number represents more financial openness), a country is classified as having high (low) capital account restrictions if its Chinn-Ito index is below (above) the median. The difference between the two averages is significant at the 5 percent level. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively.

Sources: Authors’ calculations based on Chinn-Ito index and WEO (IMF) data. Commodity classification from World Bank.
Policy Research Working Paper 5761

# **Abstract**

Recent trade theory emphasizes the role of marketshare reallocations across firms (“stealing”) in driving productivity growth, while the older literature focused on average productivity improvements (“learning”). The authors use comprehensive, firm-level data from India’s organized manufacturing sector to show that market-share reallocations did play an important role in aggregate productivity gains immediately following the start of India’s trade reforms in 1991. However, aggregate productivity gains during the overall period from 1985 to 2004 were driven largely by improvements in average productivity, which can be attributed to India's trade liberalization and FDI reforms.

This paper is a product of the Development Economics Vice Presidency. 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 authors may be contacted at mhaddad@worldbank.org, ann.harrison@berkeley.edu.

_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
Policy Research Working Paper 5716

# **Abstract**

The authors combine data from 84 Demographic and Health Surveys from 46 countries to analyze trends and socioeconomic differences in adult mortality, calculating mortality based on the sibling mortality reports collected from female respondents aged 15–49. The analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women—especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban/rural residence or by school attainment.

This paper is a product of the Human Development and Public Services Team, Development Research Group. 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 authors may be contacted at ddewalque@worldbank.org and dfilmer@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
of food of animal origin exhibit lower sampling rates as well as non-compliance rates than import shipments of animal feeds during the sample period.

Table 4 shows the product groups whose import shipments are most sampled and the product groups whose sampled import shipments are most non-compliant. The two lists of product groups differ. Also, a key feature that Table 4 shows is that there are product group-country of origin-semester-year cells whose sampling rate is larger than 1, which should be theoretically impossible to observe. However, this anomaly is due to our need to use different data sets to compute the number of sampled import shipments (FVA lab samples) and the total number of shipments (EXIM) given that there is no centralized system collecting all the information in FYR Macedonia. Our conjecture is that there is an imperfect concordance in the timing of the recording of the import shipments being sampled in the FVA lab samples data and the shipments being imported in the EXIM data.

**Table 4. Most sampled and most non-compliant product groups**

|Product group|Sampling<br>rate|Non-<br>compliance<br>rate|
|---|---|---|
|_Most sampled_|||
|1 Compound feed|382.00%|10.00%|
|2 Honey|43.00%|0.00%|
|3 Eggs and egg products|37.00%|0.00%|
|4 Meat of other animals, fresh or frozen|17.00%|0.00%|
|5 Fish, fresh, frozen, and canned|15.00%|0.00%|
|_Most non-compliant_|||
|1 Prepared meat|0.00%|22.00%|
|2 Offal|0.00%|10.00%|
|3 Compound feed|382.00%|10.00%|
|4 Molluscs and crustaceans|3.00%|6.00%|
|5 Poultry meat, fresh or frozen|3.00%|5.00%|

Note: the table shows information calculated based on the FVA lab sample data set (both columns) and the EXIM data set (2<sup>nd</sup> column). The data sets are combined based on the broad product group.

Table 5 documents the specific hazards identified in non-compliant import shipments of food of animal origin in Panel A and of animal feeds in Panel B in years 2013 and 2014. The information is aggregated across FVA laboratory samples with common characteristics. Shipments of milk and milk products had the most irregular samples, most of them linked to the aflatoxin outbreak in 2013.

29