rafmacalaba/datause-encoder-data huggingface dataset:
In statistical terms, a child is stunted if his/her height-for-age Z-score (HAZ) is more than 2 standard deviations (SD) below the median height of a healthy reference population (that is, HAZ <-2).<sup>1</sup> The measure of HAZ and thus stunting used in this study is based on the new WHO child growth reference population. The incidence of stunting among children ages 36-59 months is a consequence of the growth faltering in first 1,000 days from conception. Growth faltering is the rapid decline in height- and weight-for-age of children in the first two years of life and is common in many developing countries. Growth faltering among children was first documented in a study by Shrimpton et al. (2001) and to a large extent, it is in response to these finding that several global health policy and information campaigns with emphasis on the first 1,000 days window have been initiated (Prentice et al., 2013).

Figure 1 presents the cross-sectional age profile of growth faltering (HAZ scores) for children between 0 and 59 months of age in the two countries analyzed in this paper. As is easily apparent, growth faltering in the first 1,000 days is prevalent among children in both the Republic of Congo and São Tomé and Príncipe, with growth faltering (or HAZ decreasing) accelerating rapidly with age in the first 21-24 months of life, somewhat recovering at a slower rate afterwards with minor fluctuations.<sup>2</sup>

**Figure 1: Growth faltering in the Republic of Congo and in São Tomé & Príncipe**

<!-- Start of picture text --> Congo: Average HAZ Sao Tome & Principe: Average HAZ<br>Children under 60 months Children under 60 months<br>0 20 40 60 0 20 40 60<br>Age in months Age in months<br>Source: Rep. of Congo MICS 2014-15 and São Tomé and Príncipe MICS 2014-15<br>-.2 0<br>-.4<br>-.5<br>-.6<br>Average HAZ -.8 Average HAZ -1<br>-1<br>-1.5<br><!-- End of picture text --> Much of the empirical literature on the role of a mother’s education focuses on children less than 60 months without making any distinction about the age of the child (exceptions are Skoufias et al., 2019;

> 1 A child is classified as severely stunted if HAZ<-3.

> 2 For a recent cross-country study on the determinants of growth faltering, see Rieger and Trommlevora (2016). The growth faltering curves are derived using a local polynomial smooth of HAZ with the Stata command lpoly.

5
Table B1: ANOVA Decomposition of FDI Concentration at the CountryIndustry-Year Level

||Conce|ntration|Measure,||
|---|---|---|---|---|
|||% Varian|ce||
||CR5<br>(1)|HHI<br>(3)|CR5<br>(3)|HHI<br>(4)|
|Destination-Year:|||||
|Year|1.77|1.98|2.33|2.44|
|Country|79.56|70.12|75.24|66.02|
|Region-Year|10.93|11.70|13.79|14.21|
|Country|66.91|56.99|60.37|51.13|
|Observations|2,349|2,349|2,047|2,047|
|Destination-Industry-Year:|||||
|Sector-Year|32.94|20.07|31.45|15.49|
|Country-Year|36.55|35.40|42.66|43.57|
|Sector-Year|8.93|3.01|7.78|2.72|
|Country-Year|29.80|33.14|37.45|43.27|
|Country-Sector|29.32|12.05|23.67|8.39|
|Observations|9,464|9,464|5,422|5,422|
|Minimum # of|||||
|Foreign Investors||5|1|0|

_Note_ : The unit of observation is at the destination-industry-year level. Industries are grouped into 17 ISIC codes (1-digit). “CR5” stands for the concentration ratio of the top 5 investing firms; “HHI” stands for the HerfindahlHirschman concentration index (computed using the “CR” sample).

27
THE DEMOCRATIC REPUBLIC OF CONGO’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# The continental perspective

The Africa Infrastructure Country Diagnostic (AICD) has gathered and analyzed extensive data on infrastructure in around 40 Sub-Saharan countries, including the DRC. The results have been presented in reports covering different areas of infrastructure—ICT, irrigation, power, transport, water and sanitation—and different policy areas, including investment needs, fiscal costs, and sector performance.

This report presents the key AICD findings for the DRC, allowing the country’s infrastructure situation to be benchmarked against that of its African peers. Given that the DRC is a fragile state trying to catch up with other low-income countries (LICs) in the region, both fragile-state and LIC African benchmarks will be used to evaluate the DRC’s situation. Detailed comparisons will also be made with immediate regional neighbors in Central Africa.

Several methodological issues should be borne in mind. First, because of the cross-country nature of data collection, a time lag is inevitable. The period covered by the AICD runs from 2001 to 2006. Most technical data presented are for 2006 (or the most recent year available), while financial data are typically averaged over the available period to smooth out the effect of short-term fluctuations. Second, in order to make comparisons across countries, indicators had to be standardized to place the analysis on a consistent basis. This means that some of the indicators presented here may be slightly different from those that are routinely reported and discussed at the country level.

# Why infrastructure matters

During the period from 2001 to 2005, per capita economic growth in DRC was on average 2.1 percent higher than during the period from 1991 to 1995. Despite this improvement, growth levels, which oscillated between 4 and 8 percent in the early 2000s, still fell short of the sustained 7 percent per year needed to meet the Millennium Development Goals (MDGs). Improved telecommunications infrastructure has been the main driver of this change, contributing 1.1 percentage points to the country’s per capita growth rate. Deficiencies in power infrastructure, on the other hand, held back per capita growth by 0.25 percentage point over this period. Simulations suggest that if Central Africa’s infrastructure platform could be improved to the level of the African leader, Mauritius, per capita growth rates could increase by as much as 5 percent per year. Almost half of this impact would come from improvements in the power sector alone (figures 1 and 2).

2
workers).

Second, within-occupation hourly wages are much lower for Venezuelan workers in all occupations, with the exception of government employees and outsourced workers. However, these occupations employ very few Venezuelan migrants (merely 2.5%). In the occupations employing the highest numbers of Venezuelans, Venezuelan migrants earn between 25% and 50% less than Ecuadorans. We also note that informality rates are often much higher among Venezuelan migrants than for native workers in the same occupation. For private-sector employees and self-employed, the excess informality is around 20 percentage points. In contrast, the informality rate for Venezuelan day laborers is 14% lower than for natives. Last, the last column of the table shows that the share of college-educated Venezuelan migrants in all occupations is substantially higher than the corresponding share for natives, strongly suggesting a serious problem of over-qualification for Venezuelan migrants.

Summing up, within a given occupation, Venezuelan migrants tend to earn much lower wages than native workers and have higher informality rates, despite their higher education levels. These findings suggest that educated Venezuelan migrants face large entry barriers into the more qualified occupations and that their skills may be inefficiently allocated.

# **5.2 Migration and Occupational Downgrading**

Language barriers, institutional constraints and localized knowledge imply that skill transferability across borders is imperfect. As a result, migration may entail negative effects on the productivity and wages of migrants, often reflected in terms of occupational downgrading (Jasso et al. (2000)). For instance, our survey indicates that 72% of the Venezuelans who migrated to Ecuador report that their skills were used more productively in their jobs back in Venezuela.

On the other hand, international migration typically entails moving from low to high productivity countries, which can lead to increases in wages and productivity (Clemens (2011)). Migration driven by natural or man-made disasters, such as the Venezuelan exodus, is much more likely to be of the South-South type and, as a result, this type of productivity gain may be less relevant.

An important feature of the EPEC survey is that it contains retrospective information on the last job held by migrants prior to leaving Venezuela. The goal in the remainder of this section is to compare the changes in occupation experienced by Venezuelan migrants 14
**Figure 4a: IFLS Expenditure Data – Sample Size 30**

<!-- Start of picture text -->
-2 -1 0 1 2<br>Difference in average ln hh expenditure p cap (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>2.5<br>2<br>1.5<br>Baseline 1<br>.5<br>0<br>1.5<br>1<br>3 years later.5<br>0<br><!-- End of picture text -->

# **Figure 4b: IFLS Expenditure Data – Sample Size 100**

<!-- Start of picture text -->
-1 -.5 0 .5 1<br>Difference in average ln hh expenditure p cap (weighted by std dev)<br>1 Draw 8 Strata Matched Big Stick Minmax<br>5<br>4<br>Baseline 3<br>2<br>1<br>0<br>2.5<br>2<br>1.5<br>1<br>3 years later<br>.5<br>0<br><!-- End of picture text -->
**Do Different Types of Assets Have Differential Effects on Child Education?**

# **Evidence from Tanzania**<sup>*****</sup>

Kashi Kafle<sup>†a</sup> , Dean Jolliffe<sup>b</sup> , and Alex Winter-Nelson<sup>c</sup>

**JEL codes** : I25, J22, D13, O12

**Keywords** : LSMS-ISA, Tanzania, asset ownership, child education, highest grade completed, school performance

> * **Acknowledgements:** <mark>This paper is based on analysis of three waves of the Tanzania National Panel Survey conducted by the Tanzania National Bureau of Statistics in collaboration with the World Bank Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) Project. Funding for collecting and disseminating the data has been very generously provided by the European Union and the Bill and Melinda Gates Foundation. The authors are thankful to Anne Grant, Kathy Baylis, Ben Crost, Craig Gundersen, Hope Michelson, Lokendra Phadera, and Mani Rouhi Rad for helpful comments. The authors are also thankful to the participants in the 2015 Agricultural and Applied Economics Association (AAEA) annual meeting, San Francisco, USA; the 2016 Association of African Agricultural Economists (AAAE) conference, Addis Ababa, Ethiopia; and the 2017 Center for Studies of African Economies (CSAE) Conference, Oxford, UK. The findings, interpretations, and conclusions are those of the authors and should not be attributed to their institutions and affiliations.</mark>

> † <mark>Corresponding author (k.kafle@ifad.org)</mark>

> **a** <mark>Research ad Impact Assessment Division, International fund for Agricultural Development (IFAD), Rome, Italy</mark>

> **b** <mark>Development Research Group, World Bank, Washington, DC, USA</mark>

> c <mark>Department of Agricultural and Consumer Economics, University of Illinois, Urbana, IL, USA</mark>
model’s estimate of entry barriers against the World Bank’s DBI for the cost of starting a firm. This indicator, which is based on legislation and regulations, has been used widely in the literature to quantify the aggregate effects of barriers to firm entry. While the DBI comprises various elements, we focus on those that more directly affect the costs of starting a formal manufacturing firm: the actual time cost of getting permits and licenses to operate a new business plus the costs of getting access to electricity. The DBI translates these costs into fractions of GDP per capita, which we then turn into labor units to make them comparable with _fe ∗ τ_<sup>_E_</sup> in the model.<sup>31</sup> Figure 5: Model-Based Estimate of Entry Barriers and the World Bank’s Doing Business Indicators

**Model's Barrier vs Doing Business Indicator**

<!-- Start of picture text --> Ghana<br>Ethiopia<br>Kenya<br>Peru ColombiaSalvador India<br>Spain LatviaItaly F inlandrance BulgariaHungaryPortugalBelgiumRomaniaChile<br>−0.5 0.0 0.5 1.0 1.5 2.0<br>log(1+TauE Model)<br>Note: According to the model, the total cost of entry in units of labor is EC = fe (1 + τ e ). To get at<br>a model equivalent entry cost from the Doing Business Indicator (DBI), we proceed as follows. We start<br>adding the total cost of starting a firm and acquiring electricity as a proportion of income per capita from the<br>Doing Business Database, DB = ( start + ( Y/L electricit ) y ) . Multiplying the DBI by the inverse of the labor share,<br>which we take from the Penn World Table version 9.0, we get the level of the cost of entry in units of labor:<br>DB L = ( start +( Y/Lelectricit ) y ) ∗ WY ∗L = ( start + electricitW y ) . Expressed in this fashion, the Doing Business’s<br>cost of entry is comparable to feτ E in the model. Thus, to isolate the Doing Business counterpart of<br>τ E , we divide DB L by the calibrated value of the technological component of the cost of entry, fe (i.e.,<br>τDB E = DB fe L ). The vertical axis of the figure plots log �1 + τDB E �, the de jure proxy of the entry barrier,<br>against log �1 + τ E � in the horizontal axis, the model-based entry.<br>2.0<br>1.5<br>1.0<br>0.5<br>log(1+Doing Business)<br>0.0<br>−0.5<br><!-- End of picture text --> Figure 5 provides information on the degree of correlation and the relative

> 31See the note to Figure 5 for a description of this conversion.

37
# **Can Business Owners Form Accurate Counterfactuals?**

**Eliciting Treatment and Control Beliefs about Their Outcomes in the Alternative Treatment Status**<sup>**#**</sup>

David McKenzie, _World Bank_ _Keyword_ s: counterfactual elicitation, subjective expectations, randomized experiment, business growth. _JEL codes_ : O12, D22, C80, C31, C93

# I thank DFiD, the World Bank, the WLSME trust fund, and the John Templeton Foundation for funding the surveys used for this study, the SRP for funding work on this paper, and Martin Ravallion for helpful comments.
_j_ In particular, _mt_ , _h_ provides information about the intensity of the brain drain in the source country _j_ . It measures the fraction of skilled agents born in country _j_ and living in (other) 17 OECD countries .

This step requires using data on the size and the skill structure of the working-aged population in the countries of origin. Population data by age are provided by the United Nations<sup>18</sup> . We focus on the population aged 25 and more. Data are missing for a couple of countries but can be estimated using the CIA World Factbook<sup>19</sup> . Population data are split across educational groups using international human capital indicators. Several sources based on attainment and/or enrollment variables can be found in the literature. These data sets suffer from two important limitations. First, data sets published in the 1990s reveal a number of suspicious features and inconsistencies<sup>20</sup> . Second, given the variety of educational systems around the world, they are subject to serious comparability problems.

Three major competing data sets are available: Barro and Lee (2000), Cohen and Soto (2001) and De La Fuente and Domenech (2002). The first two sets depict the educational structure in both developed and developing countries. The latter only focuses on 21 OECD countries. Statistical comparisons between these sets reveal that the highest signal/noise ratio is obtained in De La Fuente and Domenech. These tests are conducted in OECD countries. Regarding developing countries, Cohen and Soto's set outperforms Barro and Lee's set in growth regressions. However, Cohen and Soto's data for Africa clearly underestimate official statistics. According to the South African 1996 census, the share of educated individuals amounts to 7.2 percent. Cohen and Soto report 3 percent (Barro and Lee report 6.9 percent). The Kenyan 1999 census gives 2 percent while Cohen and Soto report 0.9 percent (1.2 for Barro and Lee).

Generally speaking, the Cohen and Soto data set predicts extremely low levels of human capital for African countries<sup>21</sup> (the share of tertiary educated is lower than 1 percent in a large number of African countries) and a couple of other non-OECD countries<sup>22</sup> . The Barro and

> 17 For some countries, immigrants often travel back and forth between their new and old countries (e.g. Mexico). They are likely to be counted as still being residents in their home country. For that reason, Carrington and Detragiache (1998) provide an upper bound (m=M/N) and a lower bound (m=M/(N+M)). Since the upper bound is not interpretable for a large number of countries (higher than one), we only report the lower bound.

> 18 See <u>http://esa.un.org/unpp.</u>

> 19 See <u>http://www.cia.gov/cia/publications/factbook.</u>

> 20 This partly explains why human capital did not prove to be significant or distort the " good sign" in growth regressions.

> 21 For this reason, Cohen and Soto (2001) exclude African countries from their growth regressions.

> 22 In Cyprus, the 2001 census gives 22%, compared with 4.6% in Cohen and Soto (17.1% in Barro and Lee).

14
optimistic assessment of the precision of its small area estimates. The ELL method is based on regression models of income or expenditure with random effects at the level of survey clusters. Tarozzi and Deaton (2007) argue that this methodology relies on crucial assumptions that, they claim, are likely to fail in most real settings. First, in their view, a model of income or expenditure estimated using household survey data at the level of a region, R, is unlikely to be good enough to predict welfare at the level of a small area, _A_ , unless the region _R_ happens to be quite homogenous. In the presence of differences in tastes and prices such an assumption of homogeneity could be contentious. Second, Tarozzi and Deaton (2007) claim that an assumption of homoskedastic and independent and identically distributed cluster random effects is very strong, because within a region, sub-regional areas are likely to be integrated. This could result in spatial correlation of residual cluster effects if regressors do not sufficiently capture such integration.

An examination of the ELL method presented in Demombynes et al (2006) provides evidence that, in contrast to the claims above, the ELL method can produce reliable welfare estimates. Demombynes et al (2006) employ data from the PROGRESA program in rural Mexico in which a population census was administered in 500 villages. This census questionnaire included a measure of household consumption amongst the variables collected. These data permit the authors to implement the ELL methodology and compare predicted welfare outcomes at the local level against actual observed values of those outcomes in the same communities. Demombynes et al (2006) demonstrate that performance of the ELL approach depends crucially on the ability to incorporate into the basic consumption or income model, locality-level explanatory variables inserted into the household survey data from outside datasets such as the census and/or other ancillary databases. The study also notes that the method does not strictly depend on an assumption of homoskedastic cluster random effects, pointing out that the simulation stage of the approach allows for a variety of assumptions as to the nature and degree of spatial correlation between clusters. Although their evidence goes some way towards discounting critics’ concerns, data 3
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