rafmacalaba/data-use-mentions-extended huggingface dataset:
of covariates by attrition status is not significant.

At the time of the endline survey, approximately half of the WEDP-X loans had fully matured and the other half had just a few months of repayment left. Wasasa also shared administrative data on loan characteristics and monthly loan repayment for the entire period of the study from June 2018-March 2022 (i.e. covering the months after the endline when some loans were still active) for all 62 women in the treatment group.

# **5 Empirical Strategy**

Our main impact estimations use the following ANCOVA specification: where _Yib,t_ =1 is the outcome variable measured at endline. _Ti_ is a dummy variable taking the value of one if the individual was in the treatment group. _β_ 1 gives the intent-to-treat effect of being assigned to the treatment group (i.e. randomly assigned to be offered an uncollateralized loan), compared to the control group who passed the cut-off score, documentation check and business appraisal but were not assigned to be offered a loan. _Yi,t_ =0 is the baseline value of the outcome variable, set to zero for missing values. If a similar but non-identical question was asked at baseline (e.g. the same outcome but for a different recall period), we control for the similar variable as measured at baseline instead. _Mi,t_ =0 is a dummy variable indicating whether the baseline value is missing. _testi,t_ =0 is an indicator for whether the individual’s psychometric score at baseline was above the median within the RCT sample. _Xi,t_<sup>_′_</sup> =0<sup>isavectorofanyunbalancedbaselinecontrolvariablesfromTable1.10</sup>

> 10We pre-specified a version of Equation 1 indexed by branch _b_ and including a branch fixed effect. However, in the final sample only eight observations are from one of the two branches; so we omit these fixed effects due to power concerns. Branch fixed effects are not needed for inference, as randomization was not stratified by
002***|
|primary education or less|(0.001)|(0.001)|(0.001)|(0.000)|(0.001)|(0.001)|
|Natural log of GRP per capita|0.069***|0.058***|0.068***|0.045***|0.039***|0.037***|
|(constant 2010 IDR)|(0.015)|(0.009)|(0.013)|(0.008)|(0.007)|(0.007)|
|Growth in GRP per capita|0.003*|0.001|-0.000|0.000|0.001|0.001|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Number of observations|272|272|272|272|272|272|
|R-squared|0.672|0.746|0.464|0.805|0.755|0.675|
|Year dummies|Yes|Yes|Yes|Yes|Yes|Yes|

Source: Author’s calculations using Sakernas data Notes: Robust standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1.

24
All medical expenses|4,276|5,750|
|(c)|Heterogenous price increase<br>(average 47percent)*|Out-of-pocket only|5,600|6,924|

_Source:_ Author estimations with results from section VIII, based on data from the IHS 2012-2016, HIES 2017, GBD (2016), Goodchild et al. (2018) and WDI. _Note:_ *Estimation using a price shock of 100% for deciles 1 to 2, 36% for deciles 3 to 8, and 25% for deciles 9 and 10.

# X. Discussion

<mark>The negative health and economic consequences of smoking are a major policy concern in Georgia. In a context of demographic pressures, high public and private expenses in of the health care system, and efforts to maintain fiscal balance (World Bank 2018a), tobacco consumption generates high social and economic burdens. By reducing the consumption of tobacco, individuals observe health benefits, that translate—among many other benefits—to reductions in medical expenses and in the years of productive life that would be lost due to smoking-related premature deaths. This paper uses empirical estimations of the price elasticities of demand by income decile and applies the extended-cost benefit analysis model to account for those—often disregarded—economic benefits of tobacco taxation in the case of Georgia. Different price-shocks and elasticity scenarios are simulated, to grasp on the short-, medium- and long-</mark> term effects of a tobacco tax reform.

<mark>The results contribute to the international evidence that increasing taxes on tobacco can bring significant welfare and equity benefits. In contrast to a naïve (complete pass-through) estimation that neglects the behavioral response of smokers to price changes—and consistent with extensive evidence for other countries—the results confirm that lower-income households in Georgia have higher price elasticities of</mark> 23
km<sup>2</sup> (20%) where 21 million (15%) people are currently at combined low risk of arsenic, salinity and groundwater storage depletion.

In the groundwater risk map under the model 2 (Figure 4b), the mapped area within the zone of extremely high risk is substantial (18,297 km<sup>2</sup> ; 13% of total land area) and it covers most parts of southeastern Bangladesh where nearly 13 million (9% of total population) people are exposed (Tables 1 and 2). The high-risk zone covers an area of 15,147 km<sup>2</sup> (11%) and an estimated 12 million (8%) people are currently exposed. The medium-risk zone covers an area 13,160 km<sup>2</sup> (9%) and an estimated 9 million (6%) people are currently exposed. Finally, an estimated land area of 18,797 km<sup>2</sup> (13%) and 13 million (9%) people are located in an area where there is low groundwater risk due to relatively less contamination of arsenic and salinity. Considering arsenic and salinity individually, we find that approximately 33 million (23% of total population) people are currently at variable risk of elevated (>50 μg/L) arsenic; and nearly 26 million (18%) are exposed to very high (>1500 μS/cm) salinity in shallow groundwater.

# **3.3 Groundwater risks and access to drinking and irrigation water supplies**

In addition to demographics of the population, we estimate exposure of poor people and household access to water supplies within the mapped groundwater risk zones under both models. Results are summarized in Tables 1 and 2. Groundwater risk mapping reveals that 3 million to 6 million households with an access to tubewells are currently at risk of various degrees under model 1 and 2,
ITS = LQR&Sij x a + LQPATij x b where, BERD ij <!-- Start of picture text --> PAT ij<br><!-- End of picture text --> Sj BERDij SjPAT1 j LQR&Dij = -------------LQPATij Si BERD ij Si PATij SiSj BERD ij SiSjPATij a=(stdLOPAT) b= (stdLOR&D) (stdLQPAT + stdLQR&D) (stdLQPAT + stdLQR&D) i: country; Si refers to the six major countries aggregate; j: sector; Sj refers to the manufacturing sector; BERD business enterprise Research and Development expenditures (BERD); PAT : n° of patents granted in the US Patent and Trademark Office; stdLQPAT, stdLQR&D: standard deviations of LQR&D and LQPAT, the two specialization indicators; we omit the index t, which refers to years from 1973 to 1990.

The use of standard deviations as weights is necessary to compare two variables showing different variability (note that the specialization indicators, LQR&D and LQPAT have the same dimension).

The ITS index varies between zero and -. It will be greater than one if country i in sector j is technologically more specialized then other countries. It will be less then one if the country is despecialized. To measure commercial and productive specialization we use the traditional Balassa index, computed with export and production data, respectively: 35
# **1. Introduction**

Conflict and fragile settings are becoming increasingly important contexts in which education assistance is needed. In addition to an increase in conflict in the past decade, the world’s worst conflicts are also beginning to last longer (e.g., World Bank 2017a). Given the priority of humanitarian assistance in many conflict contexts (e.g., OCHA 2019a), there is a risk that poor education during these longer-lasting conflicts will have permanent impacts on later life outcomes of school-aged children and will also have large impacts on the characteristics of the society that emerges from conflict (e.g., UNICEF 2018).

There is a large literature motivating the need to support education during conflict. One set of findings demonstrates that the onset of violence or living in close proximity to violence often has a negative impact on the educational attainment of at least a subset of children, oftentimes drawing findings from household surveys conducted long after conflict has ended.1 However, there is a wide range of conflict-related shocks aside from living in close proximity to violence that can impact access to education (e.g., Chavez et al. 2018), and a more limited but growing set of analyses investigate other types of conflict-related shocks (e.g., Di Maio and Nandi 2013; etc.). More evidence on the effect these other types of conflict shocks have on access to education, and how these effects compare to the effect of living close to violence, would help inform the design and targeting of needed interventions.

This paper seeks to investigate the impact of a series of conflict-related shocks on school attendance in the Republic of Yemen using a novel high-frequency mobile phone survey collected during active conflict. The setting is interesting for a number of reasons. First, the country has been enduring a protracted conflict for over five years that has resulted in a large increase in the share of children not attending school (e.g., OCHA 2020). Second, in
first instance of the estimated lights coefficient changing notably, and R-squared also increases. Column 5 repeats this specification, but includes satellite fixed effects. These are advisable since variability across satellite measurements even within the OLS instrument may be substantial, owing to differences in sensor settings across satellites; this approach was followed by Chen and Nordhaus (2011). The sample size increases as a result. Both the estimated coefficient and R-squared decrease against the increases from the larger sample. Column 6 starts the sample in 2005, where our NO2 data begins.

Table S2 Column 1 estimates the same relationship, but using the stray light-corrected VIIRS data set which begins in 2014. VIIRS addresses many of the shortcomings of the DMSPOLS database (Gibson et al., 2020) and has also has been used towards estimating GDP (Chen and Nordhaus, 2019). With this corrected, more accurate data, we observe a comparatively smaller estimated lights coefficient and weaker explanatory power than DMSP-OLS. There are no satellite fixed effects because VIIRS is on board a single satellite. Column 2 compares results from only the brightest lights in city cores. Column 3 compares results with NOAA’s preferable stable (i.e. non-ephemeral) lights product, which increases explanatory power and significance, and we use for the remaining specifications. Column 4 joins the previous DMSPOLS specification from Table S1 Column 5 with Column 3. Column 5 joins the previous

> 2 Stray lights are light which reaches the spacecraft as it observes parts of the Earth that are currently dark. This occurs due to the Earth’s curvature, and typically at high latitudes. Such measurements must be corrected before concluding that a retrieved luminosity is indeed man-made (Elvidge et al., 2017).

> 3Inter-calibration is a statistical method necessary to process the data before it may be used. It is necessary because the digital numbers reported in the OLS data set are, for technical reasons, not directly proportional to actual luminosity. We use the methodology described by Elvidge et al. (2014).
# **3.5 Data**

Baseline, midline, and endline surveys for women consist of five modules. The first covers household characteristics and socio-economic background. The second asks about household consumption decisions, including information on the overall contributions of household members to household income as well as expenditures in various consumption categories. The third measures business outcomes: established measures of sales and profits, business practices, investment decisions, and expectations about future business growth. Fourth, we collect detailed data on psychometric indicators including locus of control, self-efficacy, grit, and various measures of expectations and aspirations for the future.<sup>15</sup> Finally, we obtain detailed network data among the women in our sample using the photobooks produced at baseline in each location. Photobooks had 14–16 pages depending on the sample size in each location. Each of those pages displayed pictures of 16 women’s faces without any further identifying information.<sup>16</sup> For each location we produced distinct photo books with randomly ordered photographs. We then asked women to look at each page and indicate which women they knew. Identifying a woman triggered a set of questions confirming the identity of the other woman and eliciting information about the type and intensity of interactions. We construct the woman’s social network within our sample using the unilateral links that she defines when looking through the photobook. The network data allow us to test for spillovers to women in the control group, as we can observe the number of treated women each woman in the control group is connected to at baseline. We use this variation in baseline connections as a continuous measure of exposure to treatment for women in the control group.

Using photobooks allows us to collect detailed network data among the women in our sample at a relatively low cost, as the survey module on so-

> 15Appendix A contains detailed descriptions of the variables and indices we use in our empirical analysis.

> 16We ensured that all pages had 16 pictures (to effectively
medium enterprises (SMEs). The qualifications framework needs to be updated, with standards and competencies that reflect changing labor market demand. Examples of national training authorities can be found in Australia, Brazil, New Zealand, the Philippines, South Africa, and the United Kingdom. The fragmented governance has made the TVET system less efficient to manage.

Vocational education needs to acquire greater market relevance through closer private sector involvement. The development of a National Qualifications Framework for schools should be considered. Public-private partnerships to foster enterprise leadership in curricular design, delivery, and practical training opportunities have been used in other countries, such as the Meister Schools in the Republic of Korea and the German apprentice system. China has a quality assurance and accreditation system set up, but it needs to be updated to ensure relevant and reliable standards for quality improvement. Occupations and the associated competencies have been changing rapidly and curriculum and standards require inputs from the industries to stay relevant. In addition to outdated standards, the following challenges are identified as impeding factors for the training to stay relevant: teachers typically lack industry connection and therefore are not equipped with most up-to-date technical knowledge; pedagogy tends to be teacher oriented rather than student centered, making students less likely to actively engage during training process; and facilities tend to be outdated, constraining students’ acquisition of hands-on experience.

Successful systems require a high degree of coordination and partnership between government agencies and the private sector, as well as giving the demand side—businesses and individuals— a strong voice in determining training policy. The government should provide the oversight by monitoring data on program quality, encouraging autonomy and accountability, and ensuring efficiency and results orientation in government financing. Building demand-side buy-in—from employers—is a key challenge. The United Kingdom and some European countries provide useful insights for setting up sector employer councils, while East Asian countries have established independent apex training authorities, such as Singapore’s Institute for Technical Education, with strong partnerships with employers and other stakeholders.

Vocational education needs to gear
# 3 Data

## 3.1 Survey data

The data come from surveys of households and leaders implemented in 12 Mexican states in 2016. In total, we surveyed 862 communities and 8,413 households. The data were collected in order to evaluate social and environmental impacts of the PSA program for cohorts enrolled between 2011 and 2014 (Alix-Garcia, Sims, Orozco-Olvera & et al. 2018). To determine the sample, we divided the country into zones (North, Center, South and Yucatan Peninsula) and selected the states with the highest number of applicants. The sample was stratified by “medium” (2011-2012) and “shortterm” (2013-2014) cohorts, although for the purposes of this paper we combine all cohorts, as the program rules and deforestation pressures were substantially similar in all these years. Because the design of the impact evaluation exploits a threshold in the enrollment criterion for the program – based upon the point score assigned by CONAFOR – sampling of accepted and rejected applicants occurred around the acceptance threshold for the point score. The basic sampling strategy was to select treatment and control communities with scores adjacent to the cut-off for each cohort year and state, and continue selecting farther away from the cut-off until arriving at a sample size that represented the proportion of program applicants for that state and cohort. Figure A3 shows the spatial distribution of all applicants to the program between 2011 and 2014, and highlights the states in which interviews took place. The survey instrument was field-tested by project PIs and then interviews were conducted by a team of enumerators from Chapingo University in Mexico. While CONAFOR facilitated access to all communities, no CONAFOR employees were present for interviews and enumerators clearly identified themselves as coming from an independent third party.

An individual was defined as a leader if they were currently in a leadership position within the ejido – i.e., they were in one of the main elected positions of president, secretary, treasurer, or oversight committee. Surveyors asked first to speak with the president, but if the
5. Time Use and Child Labor: The fifth module examined how children spend their leisure time, their involvement in child labor, and interactions with peers.

6. Pro-social Preferences and Migration Outlook: The sixth module concentrated on adolescents’ pro-social behaviors, such as altruism and trust, and explored their expectations and intentions regarding migration.

7. Socio-emotional and Mental Health: The final module involved the administration of various scales to assess socio-emotional well-being and mental health, including trauma, behavioral problems, anxiety, and depression. The scales include the Trauma Symptom Checklist for Young Children (TSCYC), Strengths and Difficulties Questionnaire (SDQ), General Anxiety Disorder Scale (GAD-7), and Patient Health Questionnaire (PHQ-9). All these scales and the corresponding outcomes that we evaluated are described in the next subsection.

The also the test to evaluate the devel- survey employed Peabody vocabulary cognitive opment of all participating children and adolescents. A summary of the survey modules A.1. is depicted in Table

# **III.C Sample comparability**

While Medell ́ın ranks as the third city with the highest migration in Colombia, it is crucial to recognize the degree to which migrants arriving in the city differ from those migrating to other regions in Colombia. Ideally, we would conduct a comparative analysis of the characteristics of migrants residing in Medellin versus those in other parts of the country to discern the extent of these differences. However, the lack of comprehensive data regarding the living conditions of this population makes such analysis unfeasible.

we To explore how this population compares with other migrant groups in the country, turn to the only two available data sources on migrants. First, we use the Venezuelan Refugees Panel Survey (VenRePS), conducted by Ib ́a ̃nez et al. (2022), which captures a 19
). To elaborate, the employment rates of women aged 55 to 64 and 65 and older have increased by 4.2 and 9.3 percentage points respectively in the period. On the other hand, the employment rates of men in the same age groups have increased by 2.8 and 6.2 percentage points respectively. That said, the employment rate for women remains lower than that of men despite the increase, given its relatively low starting point.

**Figure 2: Male employment as a share of the population by age group, 2016-2023 (%)**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>2016 2017 2018 2019 2020 2021 2022 2023<br>15-24 25-34 35-54 55-64 65+<br>Employment rate (%)<br><!-- End of picture text --> Source: Author’s calculations using Sakernas data

**Figure 3: Female employment as a share of the population by age group, 2016-2023 (%)**

<!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>2016 2017 2018 2019 2020 2021 2022 2023<br>15-24 25-34 35-54 55-64 65+<br>Employment rate (%)<br><!-- End of picture text --> Source: Author’s calculations using Sakernas data

**The increase in the employment rate has occurred against the backdrop of rising educational attainment.** Figure 4 shows that an increasing share of Indonesians have secondary and tertiary education. The share of the population with a secondary education increased from 49.3 percent in 2016 to 53.5 percent in 2023. In the same period, the share of the population with a tertiary education had increased from 9.4 percent to 10
18.2% of women an<br>10% of women and|d 4.5% of men<br>13.5% of men|reported gend<br>reported confl|er as a barrier<br>ict as a barrier|

# Appendix B – Variable description

## **HRM policies**

**HRM policies:** gender equality policy, maternity and/or paternity leave, subsidized childcare, paid annual and/or sick leave, and housing subsidy are all binary variables that take value 1 if the firm provides each or the combination of the policies, as in the case of maternity and/or paternity leave.

**Gender equality policies index:** this index is out of 4 points, it was created with the summative approach (as all indices in this paper) and it is composed by gender equality, maternity and paternity leave, and subsidized childcare. **Social policies index:** this index is out of 6 points and it is composed by all variables comprising the gender equality index, plus whether the firm provides sick and/or annual leave.

**Subsidy policy index:** this index is out of 3 points and it is composed by the variables asking whether the firm provides housing, food and transport subsidy.

## **Access to finance**

**Firm has bank account and documents to request loan:** binary variables taking value 1 if the firm has a bank account and if they have the necessary documents in place to request a loan.

39
%) Estimated difference<br>C7.3. SOSD: Social Sustainability Index C7.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.9*** 1***<br>H<br>Kendall tau-b 0.89*** 0.72*** 1***<br>Spearman 1*** 0.82*** 0.83***<br>A<br>Kendall tau-b 1*** 0.72*** 0.67**<br>Spearman 0.98*** 0.93*** 1***<br>M0<br>Kendall tau-b 0.94*** 0.83*** 1***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .3<br>.8 .2<br>.6<br>.1<br>CCDF<br>.4 Difference<br>.2 0<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C8. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario