rafmacalaba/data-use-mentions-extended huggingface dataset:
A second variable we added was an estimate of the average baseline 2014 school readiness score for students in each school. This was calculated as the average baseline school readiness score for the students in the school that were linked to the 2014 TECHI. This variable was used to establish balance in baseline TEHCI school readiness between treatment and non-treatment students using our mapping approach. An analogous variable was defined using the TEHCI home activity index.

We defined three sub-samples of interest for estimating the effect of the school readiness intervention on TEGRA achievement. The first sub-sample consisted of 1<sup>st</sup> grade students sampled at the beginning and end of the 2016 school year, and the second sub-sample consisted of 1<sup>st</sup> grade students sampled at the beginning of the 2016 school year and end of the 2017 school year. Students starting first grade in 2016 would have been exposed to the school readiness intervention in the year prior to starting primary school. This cohort would have been mostly born in 2010. These two sub-samples are used to estimate the effect of one year of exposure to the school readiness intervention on 1 and 2 years of schooling, respectively. The third subsample consisted of students in 1<sup>st</sup> grade during the 2017 school year. They would have been exposed to the school readiness intervention in the two years prior to starting primary school and been born mostly in 2011. This sub-sample was used to estimate the effect of two years of the school readiness intervention and one year of schooling. It should be noted that 2015 was the implementation year of the school readiness intervention, and playgroups did not become established until approximately halfway through the year.

# _Empirical approach_

Let _i_ and _j_ index the _i_<sup>th</sup> student in the _j_<sup>th</sup> school, and let T��,�� denote whether the student came from a school readiness treatment community or not. We defined the following model for
62 Figure 8: Price Effects: A Summary <!-- Start of picture text --> 0.3<br>0.2<br>a,)<br>o 0.1,<br>0<br>-02<br>0.3<br>-0.2- _ ^ ~5<br>Percentiles<br>r".'-alphas and betas - alphas beta<br>Source: "Pesquisa Nacional por Amostra de Domicilios" (PNAD), 1976 and 1996.<br><!-- End of picture text -->
# **Figure 5 Fiscal injections to SOEs as percent of GDP, by sector**

**Panel a. Median injections Panel b. Injections with outliers**

<!-- Start of picture text --> 0.8 2.5<br>2.0<br>0.6<br>1.5<br>0.4<br>1.0<br>0.2<br>0.5<br>0.0<br>0.0<br>Power Airlines and airports Roads Railways Power Airlines and airports Roads Railways<br>Fiscal injections as percent of GDP Fiscal injections as percent of GDP<br><!-- End of picture text --> _Source_ : Authors, based on data from the World Bank Infrastructure SOE Database. _Note_ : Data are for 2009–18. Boxes show interquartile variation (25–75 percent) of fiscal injections to GDP. Blue lines across the boxes depict the median per year. Whiskers show the maximum and minimum of this variable per year, capped at 1.5 times the interquartile range. Panel a excludes outliers. Panel b depicts outliers as dots. Distributions are over positive fiscal injection events.

**Figure 6 Average fiscal injections to SOEs as percent of GDP, by country and type of support**

<!-- Start of picture text --> 0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>Operations subsidies Government equity injections<br>Deferred tax liabilities Government loans<br>SOE loans<br>AlbaniaArgentinaBhutanBrazilBulgariaBurundiCroatiaEthiopiaGeorgiaGhanaIndonesiaKenyaKosovoPeruSolomon IslandsRomaniaSouth AfricaUkraineUruguay<br>Fiscal injections as percent of GDP<br><!-- End of picture text --> _Source_ : Authors, based on data from the World Bank Infrastructure SOE Database. Note: Data are for 2009–18.

18
absence of migration. For example, Basok (2000) conducted a snowball sample of Mexican workers in Canada’s seasonal worker program in one area of Canada and in one village in Mexico and asked them what they had spent their money earned on, finding that many said they had built houses and paid for schooling. Macours and Vakis (2010) look at the impact of seasonal migration on early childhood development, using a cross-sectional survey of Nicaraguan households near the border with Honduras, where half of the seasonal workers migrate to Honduras or neighboring Central American countries. They attempt to identify the impact of seasonal migration by using wage, price, and adult illness shocks experienced by the households as instruments, and find seasonal migration by mothers to reduce stunting and improve cognitive development in young children. However, these instruments seem likely to fail the exclusion restriction: as they acknowledge, the shocks could directly affect early childhood outcomes through other channels such as nutrition.

This paper seeks to provide credible evidence on the development impact of seasonal migration by means of a prospective multi-year evaluation of New Zealand’s Recognised Seasonal Employer (RSE) scheme. The RSE began in 2007 and aims to ease labor shortages in New Zealand’s horticulture and viticulture industries and at the same time aid economic development in the Pacific Islands. The policy was developed taking account of lessons from previous seasonal worker programs elsewhere and is viewed as a possible model for other countries. For example, the ILO good practices database states “The comprehensive approach of the RSE scheme towards filling labor shortages in the horticulture and viticulture industries in New Zealand and the system of checks to ensure that the migration process is orderly, fair, and circular could service as a model for other destination countries.”<sup>3</sup> Our evaluation was designed prospectively, alongside the launch of the program. Thanks to the World Bank’s strong support for this policy and close collaboration with the governments of New Zealand, Tonga, and Vanuatu, we were able to
the appendix. There are three types of votes: popular initiative (a proposal to change the constitution, which can be initiated by a group of citizens with 100’000 signatures), mandatory referendum (the parliament proposes to change a constitutional article or an international treaty, which has to be approved by popular vote) and optional referendum (a law voted by the parliament is challenged by a group of citizens with 50’000 signatures). All these different types of votes have in common that the outcome of the popular vote is binding, since they result in a change in the constitution or the legal framework.

> 18. We use 64 as the upper age bound given that in Switzerland the ordinary retirement age for women is 64 years. 19. Data for natives are only available after 2010. When we carry out comparisons between natives and refugees (and natives and migrants) in section 5.1, we limit the sample to the period 2010–2018.

11
020)|
|ALRI prevalence 7 days|0.051|-0.047***<br>(0.018)<br>[-89%]|0.051|-0.016<br>(0.022)|0.056|-0.021<br>(0.023)|
|Weight-for-age z-score|-0.69|0.106<br>(0.082)|-0.69|0.000<br>(0.085)|-0.82|0.107<br>(0.087)|
|Length/Height-for-age z-score|-1.453|0.037<br>(0.080)|-1.453|-0.056<br>(0.079)|-1.619|0.083<br>(0.088)|
|Weight-for-length/height z-score|0.203|0.14*|0.203|0.076|0.165|0.106|
|||(0.076)||(0.078)||(0.083)|
|Anemia (Hb < 110 g/L)|0.283|-0.046|0.283|-0.026|0.278|-0.022|
|||(0.033)||(0.033)||(0.029)|
|**Parasites in child stools**|||||||
|Prevalence of parasites|-|-|-|-|0.227|-0.0508<br>(0.046)|
|Parasite count|-|-|-|-|0.303|-0.0851|
|||||||(0.062)|
|Clustered standard errors in
.gov.np/sectoral_statistics/population/population_monograph_nepal_2003_all.

- Central Bureau of Statistics. 1996. “Nepal Living Standards Survey Report 1996 Volue I: Main Findings.” Available at: http://cbs.gov.np/nada/index.php/catalog/7.

- Datt, G., and M. Ravallion. 1992. “Growth and redistribution components of changes in poverty measures: A decomposition with applications to Brazil and India in the 1980s.” _Journal of Development Economics_ 38(2):275–295. Available at: http://linkinghub.elsevier.com/retrieve/pii/030438789290001P.

- Inchauste, G., and S. Olivieri. 2014. “Understanding Poverty Reduction in Bangladesh: A MicroDecomposition Approach.” _Bangladesh Development Studies_ XXXVII(June):21–56.

- Mitra, S. 2014. “Synergies Among Monetary, Multidimensional and Subjective Poverty: Evidence from Nepal.” _Social Indicators Research_ . Available at: http://link.springer.com/10.1007/s11205-0140831-3.

- Muzzini, E., and G. Aparicio. 2013. _Urban Growth and Spatial Transition in Nepal: An Initial Assessment_ . Available at: http://elibrary.worldbank.org/content/book/9780821396599.

- Oxford Poverty and Human Development Initiative (OPHI). 2013. “Country Briefing : Nepal Multidimensional Poverty Index (MPI) At a Glance.” Available at: http://www.ophi.org.uk/wpcontent/uploads/Nepal-2013.pdf.

34
and for a limited sample of countries. The advantage of the Grubel-Lloyd index (GLI) is that it can be precisely measured using bilateral trade data. In the specification below, GLI is calculated at the 4-digit level (1,237 x sectors). A value of one for a given country and China in a sector implies that total bilateral trade (exports + imports) is balanced (exports = imports), while a value of zero implies that the country either only exports to or only imports from China. One interpretation of the GLI, especially using more disaggregate data, is that it captures trade in differentiated goods because of consumer demand for variety. However, at the industry or sector level, it also captures supply chain linkages because it reflects the amount of back-and-forth trade among parts, components, and final goods and hence vertical integration, a point first made by 13
SUDAN’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

# Other

Financial Standards Foundation. 2010. ―Country Brief: Sudan.‖ E-standards Forum, New York.

- Foster. Vivien, William Butterfield, Chuan Chen and Nataliya Pushak 2009. Building Bridges China’s Growing Role as Infrastructure Financier for Sub-Saharan Africa. Public-Private Infrastructure Advisory Facility, World Bank. Washington DC

- Louis Berger Group and Doshi Borgan & Partners. 2010. ―Sudan National Transport Master Plan: Executive Summary.‖ Prepared for the Ministry of Finance and National Economy and Ministry of Transport, Roads and Bridges, Sudan.

- Southern Sudan Center for Census, Statistics and Evaluation. 2010. _Statistical Yearbook for Southern Sudan 2010_ . Juba.

- World Bank. 2008. ―Revitalizing Sudan’s Non-Oil Exports a Diagnostic Trade Integration Study (DTIS).‖ Prepared for the Integrated Framework Program, World Bank, Washington, DC.

- ———. 2009a. ―Sudan: The Road toward Sustainable and Broad-based Growth.‖ Poverty Reduction and Economic Management Unit, Africa Region, Washington, DC.

- ———. 2009b. ―Sudan Investment Climate.‖ Finance and Private Sector Development Unit, Africa Region, Washington, DC.

- ———. 2010b. ―Sudan Poverty Assessment. Part 1: Poverty Profile of the Northern States.‖ Draft version, Poverty Reduction and Economic Management Unit, Africa Region, Washington, DC.

- ———. 2010c. ―Sudan Poverty Assessment. Part 1: Poverty Profile of the Southern States.‖ Draft version, Poverty Reduction and Economic Management Unit, Africa Region, Washington, DC.

- Yoshino, Yuataka. 2010. ―Presentation of Key Findings from the Trade Facilitation Assessment Field Work: Case of Southern Sudan.‖ Draft presentation, Juba, Sudan.

> <sup>5</sup>
Table 2: Summary Statistics

||Mean|Std. Dev|Min|Max|
|---|---|---|---|---|
|ln(GDP/capita)|8.56|0.53|6.80|9.60|
|Inc-Ind (2005 ppp)|502|915|0|393,155|
|Inc-Fam (2005 ppp)|331|645|0|691,599|
|Members|4.47|2.15|1.00|43.00|
|Age|31.5|17.9|0.0|117.0|
|Male|0.57|0.49|0.00|1.00|
|Educ - High School|0.30|0.46|0.00|1.00|
|Educ - College|0.08|0.27|0.00|1.00|
|Migrant|0.29|0.45|0.00|1.00|
|Public Firm|0.09|0.28|0.00|1.00|
|Large Firm|0.23|0.42|0.00|1.00|
|Formal|0.35|0.48|0.00|1.00|
|Union|0.05|0.22|0.00|1.00|
|Urban|0.75|0.43|0.00|1.00|
|Self-Employed|0.20|0.40|0.00|1.00|
|Employer|0.03|0.18|0.00|1.00|
|Number of Obs.|8,475,013||||

_Notes_ : Authors’ calculations using SEDLAC data. Summary statistics include all observations, covering different years for
**7 Tables and Figures**

Table 1: **Mean School Characteristics by Treatment Group**

||(1)<br>Full<br>Sample|(2)<br>_Clubs_|(3)<br>_Virtue_or<br>_Mindful_|(4)<br>_Virtue_|(5)<br>_Mindful_|
|---|---|---|---|---|---|
|_School characteristics_||||||
|School is located in a highly violent community|0.52|0.43|0.57|0.43|0.71|
|School is in an urban area|0.71|0.57_†_|0.79+|0.57|1.00|
|Total school enrollment (1st to 6th courses)|306.62|269.86|325.00|302.86|347.14|
|Total school enrollment (7th to 9th courses)|104.29|81.43|115.71|145.00|86.43|
|School has its own building|0.81|0.71|0.86|0.86|0.86|
|School is connected to a water supply|0.86|0.86|0.86|0.86|0.86|
|School has computers|0.76|0.86|0.71|0.71|0.71|
|School has a health program for students|0.43|0.29|0.50|0.43|0.57|
|School has a foodprogram for students|0.81|0.71|0.86|0.71|1.00|
|Observations|21|7|14|7|7|

_Notes:_ This table
are likely to be quite different in urban compared to rural levels. However, for energy-poor households that are only meeting their basic energy needs, the relationship between energy uses and income should be weak. This is the central premise for defining an energy poverty line. This method is applied to both urban and rural India using a nationally representative household survey of energy use in both rural and urban areas, containing a rich data set on energy consumption, income, and other factors necessary for assessing an energy poverty line.

# **Data Description**

The India Human Development Survey (IHDS) is a multi-sector survey with a nationally representative sample of 41,554 urban and rural households. It covers all major states and union territories of India, with the exception of Andaman/Nicobar and Lakshadweep. In all, these households were sampled from 33 states and union territories, 383 districts, 1,503 villages, and 971 urban blocks.

Different designs were used to select the urban and rural samples. The urban sample was drawn from all urban areas in a state. These areas were listed in order of their size with the number of blocks drawn from each area based on probability proportional to the block’s population. Once the number of blocks for each urban area had been determined, the enumeration blocks were selected randomly with help from the Registrar General of India. From these Census Enumeration Blocks of about 150 households, a complete household listing was conducted and a sample of 15 households was selected per block. For sampling purposes, some smaller states were combined with nearby larger ones. The rural sample took advantage of an earlier survey to draw a random sample 10
One of the reasons why RCT is a preferred design for such experiments is that randomization does away with the need for complex econometric techniques to control for selection in nonexperimental settings. However, RCT is no silver bullet in small sample environments, as it relies on the law of large numbers to ensure that expected untreated outcomes are equal in treatment and control groups. In low-income countries, interventions sometimes target very small numbers of firms (McKenzie, 2011b).<sup>27</sup> For instance, the Pesticides Initiative Program (PIP), an E.U. technical-assistance program designed to help fruit and vegetable producers cope with E.U. standards, covers less than a few dozen firms in some African countries (Jaud and Cadot, 2011). Randomization is not an option in such environments. Quasi-experimental methods may not do very well either; but if a cross-country sample is available with enough observations, econometrics may offer some scope to control for cross-country heterogeneity.<sup>28</sup> We will return to the small sample issue in the context of non-targeted interventions in section 4.2 when referring to the Cameroon customs project described in Cantens, Raballand, Bilangna, and Djeuwo (2011).

An intrinsic limitation of RCTs in the trade and other economic areas is that the study subjects are active economic agents who consciously choose their responses as opposed to the medical sciences where passive entities (e.g., cancer cells) respond endogenously following the laws of nature (Barrett and Carter, 2010). Unobservable perceptions about the benefits of a new traderelated intervention will vary among potential beneficiaries in ways that are likely to be correlated with other attributes and with the actual effects of the treatment. In section 6, we will discuss how randomization may fail to produce unbiased treatment effects in the presence of ―essential‖ heterogeneity or in the presence of spillovers.

# **4.1.2 Quasi-experimental methods**

When evaluation is not built into program design, RCT is not an
the endogenous variable, and the average bribery incidence for the whole sample as the instrumental variable.

In these regressions, we include a dummy variable ( _Applied_ ) to control for whether a firm conducted a business transaction. This dummy captures the direct effect of a business transaction on sales growth. Firms who apply for business transactions may do so because they have plans to expand; those who applied for a business transaction grew about nine percent more than firms who did not apply for a business transaction. Comparing these results to those obtained in Table 7, we observe a stronger negative effect for bribery on sales growth, firms who were bribed have growth rates that are 34.7 percentage points lower, or lower by two standard deviations. However, in column (1), we cannot reject the instrument is weak. In column (2), the total effect of corruption for firms with median sales is 35 percent, almost identical to estimates in column (1) without an interaction term. Comparison of the total impact of bribe payments shows that among firms who were bribed, firms with sales in the 25<sup>th</sup> percentile saw growth rates that were 57 percentage points lower than the rate for firms that are in the 75<sup>th</sup> percentile which is more severe than the result obtained from Table 7.

## **4.2 Robustness Tests**

We perform a variety of sensitivity checks. First we repeat the estimation by different country groupings, and for only SMEs (Table 9). Second, we use an alternative definition of the Bribery Indicator by coding refusals to bribery questions as non-responses (Table 10). Thirdly, we create clusters based on finer categories using industries, and larger categories by location only (Table 12 and Table 13). Lastly, we estimate the effects of bribery using a panel of firms surveyed in both 2006 and 2010.

# <u>A. Firm & Country Groupings</u>

First we
many places, which often leaves women struggling to find decent employment and having to work informally, without any protection and safety (Young et al., 2005; George et al., 2019). Thus, identifying and addressing gender-specific issues is considered an integral part of understanding and improving livelihoods, and ultimately of reaching durable solutions.

# **1.3 Gender inequality in Sudan**

Gender inequality in Sudan is extremely high, even in comparison with most other low-income countries. Sudan consistently ranks among the bottom 10 or 20 countries in common statistics, such as the Women, Peace and Security Index (GIWPS and PRIO, 2019) or the Gender Inequality Index.<sup>9</sup> Sudan’s laws are based on Sharia and common law, and although women's advancement and awareness of gender issues are stated as formal objectives, only little progress has been made.<sup>10</sup> For instance, forced (child-) marriage and rape are not (clearly) illegal (UNICEF, 2017). The burden of proof is on the victims, and seeking legal support raises concerns of stigma and risk of punishment (Tønnessen, 2019). Gender-based violence more generally remains a key concern and 88% of the female population are reported to have undergone genital mutilation/cutting, putting their health in extreme danger.<sup>11</sup> Furthermore, men continue to have a strong say in women’s decisions. For instance, a man is entitled to denying his wife to work outside the home and to limiting the use of contraceptives (UNDP, 2018). All these risks often affect IDPs in particular. For example, it has been documented that displaced women are at increased risk when they are traveling to agricultural plots and collecting water or firewood (UN OCHA, 2021).

Women’s empowerment and economic opportunities also remain curtailed, including issues such as low access to formal employment, poor education and legal discrimination.
|1.66|3.95|2.67|
|Number of adult men|1.0|1.3|0.4|1.2|1.1|1.0|1.0|n.a.|n.a.|1.7|0.8|
|Number of adult women|0.9|0.8|1.1|1.1|1.3|1.0|1.3|n.a.|n.a.|1.5|1.7|
|Number of children <18|1.9|2.0|1.7|3.0|2.9|n.a.|n.a.|1.4|1.7|0.7|0.1|
|Number of children <5|0.8|0.9|0.6|1.3|1.1|n.a.|n.a.|0.1|0.3|0.3|0.0|
|**Panel B: Child marriage**||||||||||||
|Number of child brides (total)|788|623|165|364|17|220|5|4|112|13|1|
|Percentage of child brides|1.4|1.7|0.9|1.7|1.6|7.0|2.0|1.3|42.3|1.6|0.1|
|_Source_: Own calculations based on ProGres and JD‐H|V database.|||||||||||

_<mark>Note</mark>_ <mark>: PA = principal applicant. PA is determined by UNHCR at the time of refugee registration.
this outcome. First, actual or perceived positive change in income/consumption for a household is more likely to reduce potential constraints of sending a child to school. Second, community mobilization and social-networking, as part of the PAF community organization (CO), may produce spillover effects such as motivating households towards child schooling.

<u>Child Underweight:</u> Close to 60% of children from sampled villages are underweight (annex table 1)<sup>9</sup> . We find that there is no statistically significant impact on child underweight rates. However, one can take this as a step in the right direction as shown by the negative sign of the coefficient, a pointer to possible effects in the coming years. Child malnutrition is a serious problem in the country, particularly among children from the poorest communities.

> 8 Separate estimate for households belonging to Dalit, Janajatis and other caste/ethnic groups (other than Brahmin, Chhetris and Newars) shows a higher decline in food insecurity (24 percentage points). 9 Data from Nepal Demographic and Health Survey (DHS) indicates similar incidence of underweight in Midwestern development region.

10