rafmacalaba/datause-encoder-data huggingface dataset:
Haddad, Lawrence, Harold Alderman, Simon Appleton, Lina Song, and Yisehac Yohannes. 2003.

“Reducing Child Malnutrition: How Far Does Income Growth Take Us?” _World Bank Economic Review_ 17(1): 107-131.

Hoddinott, John, John Maluccio, Jere Behrman, Rafael Flores, and Reynaldo Martorell, 2008, “Effect of a Nutrition Intervention During Early Childhood on Economic Productivity in Guatemalan Adults,” _Lancet_ 371:411–6.

Hong, Rathavuth and Rathmony Hong. 2007. “Economic Inequality and Undernutrition in Women: Multilevel Analysis of Individual, Household, and Community Levels in Cambodia.” _Food and Nutrition Bulletin_ 28(1): 59-66.

Howe, Laura, James Hargreaves, Sabine Gabrysch, and Sharon Huttly. 2009. “Is the Wealth Index a Proxy for Consumption Expenditure? A Systematic Review.” _Journal of Epidemiology and Community Health_ 63(11): 871-877.

Kakwani, Nanak, Adam Wagstaff and Eddy van Doorslaer. 1997. “Socioeconomic Inequalities in Health: Measurement, Computation and Statistical Inference,” _Journal of Econometrics_ 77(1): 87-103.

Kidd, Stephen and Emily Wylde. 2011. “Targeting the Poorest: An Assessment of the Proxy Means Test Methodology,” AusAid Research Paper, AusAid, Canberra.

Lambert, Sylvie, Martin Ravallion and Dominique van de Walle. 2014. “Intergenerational Mobility and Interpersonal Inequality in an African Economy,” _Journal of Development Economics_ 110: 327344.

Madise, Nyovani, Zoe Matthews, and Barrie Margetts. 1999. “Heterogeneity of Child Nutritional Status between Households: A Comparison of Six Sub-Saharan African Countries.” _Population Studies_ 53(3): 331-43.

Manley, James, Seth Gitter, and Vanya Slavchevska. 2013. "How Effective are Cash Transfers at Improving Nutritional Status?" _World Development_ 48: 133-155.

Ngure, Francis, Brianna Reid, Jean Humphrey, Mduduzi Mbuya, Gretel Pelto and Rebecca Stoltzfus. 2014. “Water, Sanitation and Hygiene (WASH), Environmental Enteropathy, Nutrition, and Early Child Development: Making the Links,”" _Annals of the New York Academy of Sciences_ 1308(1): 118-128.

O’Donnell, Owen, Eddy van Doorslaer, Adam Wagstaff and Magnus Lindelow. 2008. Analyzing Health Equity Using Household Survey Data, World Bank.

26
**Table A8:** Summary Statistics: Province-Level Estimates of Educational IGM

||||||upward||||||d|ownwa|rd|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|country|provinces|mean|median|stdev|min|max|Nmin|Nmean|mean|median|stdev|min|max|Nmin|Nmean|
|Trinidad and Tobago|4|.62|.619|.121|.494|.746|835|7021|.1|.102|.015|.082|.117|327|2941|
|Peru|25|.534|.508|.157|.288|.787|441|7603|.114|.1|.049|.043|.249|291|4850|
|Cuba|14|.504|.501|.026|.472|.574|443|3264|.2|.199|.021|.171|.247|611|4823|
|Jamaica|14|.394|.398|.055|.322|.505|741|1957|.173|.168|.038|.117|.248|123|459|
|Bolivia|9|.364|.366|.081|.256|.485|707|12967|.201|.183|.061|.137|.306|154|3841|
|Saint Lucia|4|.325|.312|.065|.268|.406|262|373|.181|.181||.181|.181|62|62|
|Chile|44|.321|.314|.079|.154|.477|234|4438|.283|.28|.059|.175|.42|68|1627|
|Argentina|24|.317|.314|.064|.222|.56|1136|29270|.242|.241|.057|.136|.377|504|10278|
|Costa Rica|7|.297|.296|.061|.222|.391|4477|9661|.263|.227|.073|.193|.371|864|3186|
|Dominican Republic|23|.281|.29|.057|.136|.394|733|2938|.264|.234|.077|.176|.518|54|864|
|Mexico|32|.279|.274|.049|.193|.394|5016|58421|.217|.214|.025|.155|.263|1130|8763|
|Panama|7|.277|.297|.101|.087|.409|673|7697|.193|.19|.018|.173|.226|300|2912|
|Suriname|7|.273|.305|.124|.04|.392|62|344|.282|.282|.019|.269|.296|170|316|
|Ecuador|14|.266|.28|.052|.182|.342|1339|16814|.219|.191|.08|.139|.427|188|4181|
|Colombia|22|.257|.236|.075|.148|.435|645|28660|.228|.216|.067|.122|.42|132|4020|
|Venezuela|22|.253|.251|.049|.161|.36|894|15945|.291|.281|.07|.183|.487|153|3279|
|El Salvador|14|.208|.216|.066|.124|.385|1430|3963|.308|.298|.067|.207|.497|69|742|
|Uruguay|19|.189|.192|.029|.142|.272|647|3783|.492|.505|.053|.357|.574|109|1013|
|Brazil|25|.185|.168|.057|.11|.305|6098|280107|.264|.271|.049|.184|.352|1744|38217|
|Nicaragua|12|.164|.174|.075|.05|.286|918|5457|.298|.3|.048|.219|.391|70|782|
|Paraguay|14|.148|.13|.082|.08|.373|1844|6227|.275|.273|.05|.151|.345|60|723|
|Guatemala|22|.083|.076|.04|.03|.214|1967|6973|.305|.284|.064|.214|.426|51|486|
|Honduras|18|.074|.064|.04|.02|.176|297|3726|.482|.486|.079|.302|.573|65|440|
|Haiti|4|.059|.051|.021|.044|.09|4577|18354|.698|.707|.112|.553|.827|115|1426|
|total|400|.274|.267|.14|.02|.787|62|30464|.268|.247|.112|.043|.827|51|5629|

Notes: This table shows summary statistics for province-level estimates of upward and downward IGM. Upward reflects the likelihood that children, aged 19-25, whose parents have not completed secondary schooling will manage to complete at least secondary education. Downward reflects the likelihood that children, aged 19-25, whose parents have completed secondary schooling or higher will not manage to complete secondary education. “Total” shows the unweighted summary statistics across all provinces. The columns “Nmin” and “Nmean” report respectively the smallest and average sample size across provinces. Provinces with less than 50 observations are omitted.

54
34

|Country|Typeof<br>distress<br>(publicor<br>privatedebt)|Scope <br>(Peaklevelof<br>offinancial<br>Financial Sector|ofdistress<br> NPLsaspercent<br> system assets)<br>CorporateSector|Provisionof|Closureof|Governmentre<br>Pbliili|solutionstrategy<br>|||
|---|---|---|---|---|---|---|---|---|---|
|Hungary<br>|*<br>Debts owed<br>|(levelofNPIs<br>atpeakas%of<br>bankingsystem)<br>26%ofloans<br>|<br>Gross enterprise|<br>liquidity<br>support<br>Yes|<br>financial<br>institutions<br>Yes.|ucrecaptazat<br>Publicfundsfor<br>recapitalization(conditions<br>andtypeofprogram)<br>Yes|onprogran<br>Takeup<br>14bk|Privateinjectionof<br>capital<br>|Publicdebt relief<br>program<br>|
|(1991-95)|bystate-<br>owned<br>enterprises,<br>mainly,to<br>state-owned<br>banks.|(1994)|<br>losses increased from<br>lessthan1%ofGDP<br>in1988to14%in<br>1992. In1993,onthe<br>basis ofAMOF<br>survey,_50%_of<br>70,000enterprises<br>(mainlyinindustry)<br>wereloss makers.<br>Thelargestloss-<br>makingfirms(about<br>500)accounted for<br>60% oftotal<br>enterpriselossesand<br>50%ofoverduebank<br>debt.I||<br>Butnotuntil<br>1986.<br>Onebank was<br>liquidatedand<br>onespecialized<br>banksurrendered<br>banklicense.|.<br>Three recapitalization<br>programsofoneperyear<br>during1991-93without<br>subjectingthebankstoany<br>seriousconditions,Inthe<br>1992-1993programs,the<br>governmentcarvedout<br>90°hofthefaceva!ueof<br>the NPLportfolioin<br>exchangeforgovernment<br>bonds,whilethebanks<br>assumed_lO!Yo_<br>oftheface<br>valueoflosses.The1993<br>schemerequiredbanksto<br>signa consolidation<br>agreementwiththe<br>governmenttoensuretheir<br>operationaland financial<br>restructuring;inpractice.<br>theseagreernentdidnot<br>specifyquantitative<br>performancecriteriaand<br>targeTsnorspecify<br> <br>remedialrnasures."<br>|ans (including<br>the5largcststate-<br>ownedconunercial<br>banks)and60savings<br>cooperatives<br>pasticipatedinthe<br>1992scheme.Butin<br>1993,8banks,<br>including3ofthe<br>largestbanks,and<br>manycooperatives<br>werefoundtobe<br>significantly capital-<br>deficient.The1993<br>schemeentailedan<br>initialrecapitalization<br>oftheinsolventbanks<br>toK0CAR**and**two<br>subsequent<br>recapitalizations to<br>bringthebanksto4%<br>and8%CAR.<br>Cunulative<br>recapitalizationwas<br>US$3.5billion or8%<br>of1994GDP.|Yes.<br>Govemment<br>adopted new<br>strategyafterthe<br>1991-93<br>recapitalizations<br>thatfocusedsolely<br>onbank<br>privatizationfrom<br>1994onward.|No|

**'G** Private Gross enterprise banks consisted losses of rose small cooperative banks catering from 1% of GDP in 1988 to 14% to the financing needs in 1992 (World Bank of pivate 1997a). fannerm and rural households. Borish, Ding and Noel 1996a.

- **2** Also, thcre were no sanctions for nonr-conpliance.
**_Table 1: Variable Overview_**

|**Variable**|**Description**|**Units**|**Source**|
|---|---|---|---|
|pov_hcr|Poverty Headcount Rate ($ 1.90 PPP)|%|WB|
|gdp|Gross Regional Product|USD|Gennaioli et al., 2015|
|npp|Net Primary Productivity|gC/m2|NASA|
|ndvi|Normalized Difference Vegetation Index|Index (‐1 to 1)|NASA|
|soil|Soil carbon content of the topsoil|tons per hectare|European Comission (JRC)|
|soil_type|Soil classification|1 ‐14 categories|NRDC|
|cropshare|Share of area cropland|%|CCI|
|forshare|Share of area forest|%|CCI|
|grassshare|Share of area grassland|%|CCI|
|urbanshare|Share of are urban|%|CCI|
|rugged|Ruggedness Index|Index (0 to 1,000,000)|Nunn & Puga, 2012|
|roaddensity|Road Density|mean of lengths in km per provicen|PBL GeoNetwork|
|precipitation|Mean precipitation|millimeters per month|CRU|
|population|Estimate of number ofpeople|persons|GPW|

# **b. Research design**

Previous attempts to estimate the effect of environmental quality on poverty suffer from both omitted variables bias and simultaneity (as discussed above). Changes in environmental quality such as degradation of soil or degradation of vegetation vigor (through e.g. application of fertilizers) are endogenous explanatory variables, meaning that OLS would produce biased and inconsistent effect estimates and the coefficients would be correlated with the error term. To overcome these methodological challenges, we use simultaneous equation models with instrumental variables (see e.g. Greene, 2003 or Wooldridge, 2010).

For vegetation vigor, we specify a panel regression as we can exploit time series of NPP, NDVI, and rainfall. This allows us to create an unbalanced panel<sup>8</sup> as described in specification (1) below. In the case of below ground environmental quality, our measure of soil stored carbon is unfortunately time‐invariant. Therefore, we specify a cross‐sectional regression. Similarly, we have variation over time regarding the poverty headcount ratio, but we can only make use of our proxy of regional economic activity (GDP) measured at one point in time. To account for omitted variables that may drive the relationship between soil and poverty, we include a battery of control variables.

Starting with the panel specification, the reduced‐form empirical models estimated are:

> 8 It is unbalanced because of the poverty measure, where some countries have more years of subnational poverty observations than others. Moreover, the years for which subnational poverty measures are available vary from country to country. For details, see table 6 in the Appendix. Environmental variables are available for all years, with complete coverage.

7
provide consistent estimates by essentially removing the time-invariant individual effect that poses the correlation problem, but FE has the disadvantage of dropping all other time-invariant variables from the regression as well. We therefore present OLS estimates of the determinants of BMI on a single wave of the longitudinal data (the second observation for each individual), which permits consistent estimation of the coefficients of time-invariant variables if there is no correlation between the error term and the regressors, but on a much smaller sample than the complete dataset. The standard errors are not subject to serial correlation problems and have been corrected for heteroskedasticity.

In estimating the determinants of the change in BMI in adjacent periods, we take advantage of the panel nature of the dataset by estimating a 'first differenced' (FD) model in which the short-term (6-month) change in BMI is tregressed on the change in all other time varying variables: (4) Hit - Hi, I= i ( xit - xt-, ) + **(sit** - **sit-** ) The time-invariant unobserved component of the error term is eliminated, yielding consistent estimates even if the individual effect and the regressors are correlated (Wooldridge 2000). However, like FE it also sweeps away all time invariant unobservable variables at the individual, household, and community level and greatly reduces the variation in the explanatory variables (compared to the regression on BMI levels) resulting a loss of efficiency. FD can also greatly reduce the variation in the data. We use three first differences--(wave 2 - wave 1), (wave 3 - wave 2) and (wave 4 - wave 3)-and estimate the model using OLS, correcting the standard errors for serial correlation and heteroskedasticity. We include dummy variables for the second and third differences (with the first difference left out) to control for secular trends (Wooldridge 2000).

A final issue is the possibility that adult mortality in the respondent's household is not exogenous to the health of the elderly - that is, that there is a correlation between the death and some unobservable in the regression, calling into question the line of causation and the consistency of the estimates. This is a legitimate concern. First, there's substantial evidence that HIV infection and adult AIDS mortality are not random events. In many studies in Sub-Saharan Africa the risk of HIV infection is positively correlated with socioeconomic status and in the KHDS sample, adults who died from AIDS were younger and more educated than those who died from other causes (Ainsworth and Semali 1998). Second, about a third of all of the primeaged adult deaths recorded in the households during the survey were persons who moved back into the household between waves and died before they could be recorded on the household roster (Ainsworth and Semali 1995). Most were children of the head or of the head's spouse, but not all children of the head or spouse who died returned. Thus, there's the real possibility that _14_
MULTILINGUAL CHILD ASSESSMENT nonoverlapping words of varying difficulty, with 27 Luo items, 32 Swahili items, and 34 English items. Administration ended when a child failed six of a set of eight items.

# **Expressive vocabulary**

We developed our own measure of expressive vocabulary after reviewing various expressive vocabulary tests and concluding that the stimulus words and/or pictures were not appropriate to the context (see details in **Appendix B)** . The assessment was a picture-naming task, in which children were presented with flash cards bearing a single illustrated stimulus item or object (noun) per card and were asked in the child’s preferred language, “What is this?” for each item. Children were not instructed as to which language to respond in, but responses in any language were accepted. We did not provide further instruction because code-switching during conversation is common in this area, and very young children may not be aware which language they are actually speaking for a given word. Thus, a child could respond to each item in the 20item test in English, Luo, or Swahili to score a pass for expressing the word verbally. Administration ended with three consecutive fails.

# **Other child-level assessments**

The Malawi Developmental Assessment Tool (MDAT) was created and validated for use in rural Malawi with children 1–84 months of age (Gladstone et al., 2010). It includes four 34item subscales (fine motor/perception, language/hearing, gross motor, social-personal), with many items adapted from existing Western tests (see details of our adaptation in **Appendix C** ). The MDAT is currently being used in various countries, including Mali, Sierra Leone, Rwanda, Burkina Faso, and Zimbabwe (M. Gladstone, personal communication, June 24, 2016). The western Kenya adaptation was initiated by the first, second, and fifth authors (HK, PK, and LCHF) for the Kenya Life Panel Survey (e.g., Baird, Hicks, Kremer, & Miguel, 2016), a 9
in employment, based on data from the 2015 Turkish Household Labor Force Survey.<sup>10</sup> We also collected vacancy information from newspapers and online job portals for a period of 3 months, and we filter out occupations that tend to recruit explicitly one gender (e.g., administrative assistants).

Second, we select occupations that have a large pool of vacancies in the online job portal: the four occupational clusters that we select represent around 65 percent of the total vacancy ads in the online job portal for the selected locations.

Finally, we aim to select clusters that use different socioemotional skills in their daily tasks, based on the classifications in O*NET and the organizational psychology literature. More information on this aspect is given in Section 2.2.

# _Job vacancies_

We collect the vacancy ads and make our applications using the largest online job portal in Turkey, which has around 75,000 registered companies and 24 million CVs.

We use two criteria for selecting vacancies. The first criterion is that the minimum required work experience did not exceed 3 years. We focus on early-career candidates because socioemotional skills, especially in the form of extracurricular activities, are arguably more salient in CVs for early-career candidates. On the other hand, for more mature candidates, aspects of job experience (such as tenure, progression, and gaps in work history) may be strong signs of socioemotional skills that make other signals less salient but that are harder to manipulate in an audit study experiment.

The second criterion in vacancy selection is minimum required level of education. Here, we mostly focus on higher-skilled jobs that require a university degree. However, for vacancies in the sales cluster, we also apply for jobs that would consider candidates with high school degrees.

In addition, we took into account whether we have recently applied to a job with the same employer. If this is the case, in order to avoid detection, we wait at least 4 weeks until we apply to another job with this employer. In addition, we do not apply to more than 5 vacancies with the same employer across the entire study period. Finally, we use occupation classifications of the online platform as filters when we select the vacancies to apply for each occupational cluster. Included occupations are provided in the final column of Table 2.

> 10 Extreme values were defined as more than one standard deviation away from the mean female employment share in an occupation.

6
distribution at the lower end may partly be resulted from the high incidence of the lowpaid jobs.

**Graph 2: Distribution of nominal monthly earnings of the individuals who are employed**

**(2002-2004)**

<!-- Start of picture text --> 0 2000 4000 6000 8000 10000<br>Monthly earning<br>2002 2003<br>2004<br>.0003<br>.0002<br>Kernel density<br>.0001<br>0<br><!-- End of picture text --> Note: For practical purposes, we exclude the observations with extreme values of monthly earnings (monthly earnings > 15000 kuna), which represent less than 0.2% of the total sample.

# **1.2 Large differences in labor market opportunities across individual groups**

In order to increase the number of observations, we pool the six rounds of the LFS data 2002-2004<sup>10</sup> together, given significant changes of labor market performance in Croatia were absent in 2002-2004. We use the pooled data as if they were a single sample from a larger survey, fielded over a longer period, given each consecutive round’s sample in LFS can be considered as a random, equal-probability sub-sample of a larger sample taken in a single instance for the period as a whole for all practical purposes.<sup>11</sup> We find that, in 2002-2004, the key indicators, such as employment rate / unemployment rate and earnings level, differ largely across age groups, education groups, gender, and regions.

The youth group has the lowest employment rate/highest unemployment rate among other working-age population – the employment rate of the individuals who are 15-25 years old is less than 25%, which represents only a third of the employment rate of the

> 10 The LFS were implemented in two six-month rounds each calendar year in 2002-2004.

> 11 See Munoz (2006) for the rational and techniques of data pooling.

5
# **Contents**

||**Page No.**|
|---|---|
|1. Introduction|.1|
|2. Political systems and labor policies ..........................................................|2|
|3. An analytical framework ..........................................................|6|
|4. Estimating equations and data .........................................................|14|
|5. Empirical results .........................................................|22|
|6. Conclusions .........................................................|27|
|Bibliography .........................................................|29|
|**Figures**||
|Figure 1 Measures of openess (1990) .........................................................|17|
|Figure 2 Measures of labor market distortions (1990)<br>....................................|...<br>19|
|Figure 3 Measures of political and civil liberties (1990)<br>..................................|....<br>21|
|Figure I Measures of openess (1990) ..........................................................|17|
|Figure 2 Measures of labor market distortions (1990)<br>....................................|...<br>19|
|Figure 3 Measures of political and civil liberties (1990)<br>...................................|...<br>21|
|**Tables**||
|Table 1 Correlation coefficients between different openness measures<br>.|.<br>17|
|Table 2 Regression Results: dependent variable is dollar index of openness<br>.|.<br>23|
|Table 3 Sensitivity to choice of openness measure ....................................................|.....<br>24|
|Table 4 Regression results: dependent variable is index of wage distortion<br>.|.<br>25|
|Table 5 Regression result: dependent variable is proportion of labor force in wage||
|employment (results in last row were obtained using instrumental variables) ...|....... ....... 26|
<!-- Start of picture text --> 3<br>2.5<br>Aw<br>cS 2<br>2)<br>Sp<br>§ 1.5<br>=<br>8<br>t 1<br>a<br>a.<br>0.5<br>0<br>2001 2002 2003 2004 2005 2006 2007 2008 2009<br>Mlosses Uunderpricing Ucollection inefficiences<br><!-- End of picture text -->
period, we also observe a divergence between developed and emerging economies with the emerging economies clearly outpacing developed economies. The euro zone, which includes some of the major trade partners of Morocco, was the worst performer among the group of countries considered in terms of average growth.

**Figure 1 - World GDP growth 2000-2010**

<!-- Start of picture text --> 10.0<br>8.0<br>6.0<br>4.0<br>2.0<br>0.0<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br>-2.0<br>-4.0<br>-6.0<br>-8.0<br>-10.0<br>World Developed countries<br>Emerging countries Euro zone<br><!-- End of picture text --> Source: Morocco Tableau de Bord (2000-2010) During the same period, Morocco experienced significant annual changes in GDP growth (Figure 2) but remained in line with world growth overall, with a growth rate hovering around 4-5% a year. The significant annual fluctuations in the output of the Moroccan economy are explained by the volatile performance of the agricultural sector. Despite accounting for only about 15% of GDP, the agricultural sector accounts for a much larger share of the annual variations in GDP. For example, growth in agricultural production accounted for 27% of total growth in 2003 and 35% in 2009.<sup>5</sup> Interestingly, Morocco has been able to counter balance the effects of the 2001 and 2007-2010 global crises thanks to exceptional agricultural years that happened in conjunction with (or following) the two global crises; as shown in Figure 2, the agricultural sector exhibits exceptional growth rates in 2001, 2008 and 2009.

> 5 Our estimates based on official statistics (Tableau de Bord, High Commission for the Plan, 2012, mimeo).

5