rafmacalaba/data-use-mentions-extended huggingface dataset:
women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The 2012 Global Findex database provides such indicators, measuring how people in 148 economies save, borrow, make payments, and manage risk (Demirguc-Kunt and Klapper, 2012).<sup>3</sup> These new indicators are constructed with survey data from interviews with more than 150,000 nationally representative and randomly selected adults age 15 and above. The survey was carried out over the 2011 calendar year by Gallup, Inc. as part of its Gallup World Poll Survey and includes more than 40,000 interviews across 41 economies in Africa.

# **_3.1 Account Penetration_**

Overall, 23% of adults in the Africa region have an account. Within Africa, there is a large variation in account ownership: 24% of adults in Sub-Saharan Africa report having an account at a formal financial institution, though this ranges ranging from 51% in Southern Africa to 11% in Central Africa (Figure 1).<sup>4</sup> In the Democratic Republic of Congo and Central African Republic, more than 95% of adults are ―unbanked‖ (i.e. do not have an account at a formal financial institution). In North Africa 20% of adults have an account at a formal financial institution ranging from 39% in Morocco to 10% in Egypt.

> 3 The complete database and related reports are available at: www.worldbank.org/globalfindex.

> 4 Subregional classifications are based on those of the United Nations Statistical Division and the World Bank. In the analysis, Djibouti is excluded, and Sudan is considered part of Central Africa. The regional and subregional aggregates omit economies for which Gallup excludes more than 20% of the population in the sampling either because of security risks or inaccessibility. In Sub-Saharan Africa, these excluded economies are the Central African Republic, Madagascar, and Somalia. In North
# **Annex 3: Installed capacity data**

## **Table A3.1: Installed capacity and other parameters in MW in 2014**

|Country|Hydro-<br>power|Natural<br>gas|Diesel and<br>HFO|Coal|Nuclear|Other<br>renewable<br>energy|Total<br>installed<br>capacity|
|---|---|---|---|---|---|---|---|
|Angola|1,126|326|925|0|0|0|**2,377**|
|Benin|0|100|249|0|0|0|**349**|
|Botswana|0|0|160|732|0|1|**893**|
|Burkina Faso|32|0|253|0|0|0|**285**|
|Burundi|25|0|21|0|0|0|**45**|
|Cameroon|732|216|301|0|0|0|**1,249**|
|Cape Verde|0|0|107|0|0|31|**138**|
|Central African Republic|19|0|7|0|0|0|**26**|
|Chad|0|0|125|0|0|0|**125**|
|Comoros|0|0|27|0|0|0|**27**|
|Congo, Dem. Rep.|2,488|28|0|0|0|0|**2,516**|
|Congo, Rep.|234|350|44|0|0|0|**628**
- 17 -

2.16 There is a clear parallel between the income level of the country and the average size of the institutions: low income countries had the smallest institutions, followed by lower-middle income countries, and upper-middle income countries (Table 6).

Table 6: Average Size of Institutions Supworter By Mode and Income Level

|Income<br>Level|Secondary|M<br>Post-<br>Seconary|ode<br>Non-<br>Formal|Teacher<br>Traininrg|Overall<br>ISector<br>:Averages|
|---|---|---|---|---|---|
|Low|334|N6o|228|273|1<br>341|
|Lower-Mid|dle778|OD2|305|405|**I**<br>379|
|LUwer-Ilddle|<br>881|990|383|333|**1**<br>683|
|Overall<br>Averages|843|we|319|34S|I<br>506|

2.17 This suggests that stronger economies with larger modern sectors have higher perceived demand for VET graduates.

# Planned Costs Per Place

2.18 Given data on places supported and disaggregated cost data for the components in the sample, it is possible to derive crude indicators of the average total investment cost per place.

2.19 Interpreting cost comparisons across countries and training modes for projects spanning two decades, on the other hand, presents nearly insurmountable methodological problems. Input prices vary significantly over time, snd among countries. Some projects construct new facilities; others rehabilitate existing ones; many do both. The instructional program of an institution of a given mode in one country can and does vary significantly from that of an institution in the same mode in another country, with Important cost differences. Currency exchange rates differ. Political and natural disturbances cause significant delays and increase (or
19.7|7.8|8.8|2.9|1.8|
|Labor regulations|3.5|2.9|5.3|4.5|2.4|4.8|1|
|Political instability|11.9|15.3|9|11.1|19.5|20.7|10.8|
|Practices of the informal sector|11.5|14.4|11.6|17.4|6.8|4.6|10.6|
|Tax administration|3.9|2.4|4.5|4.4|2.7|2.2|4.3|
|Tax rates|13.2|10.6|18.6|10.9|11.7|6|9.7|
|Transportation|3.8|5|4.8|2.2|4.3|3.7|2.6|

Source: Constructed by the authors using the WBES. Note: EAP stands for East Asia and Pacific, ECA Europe and Central Asia, LAC Latin America and the Caribbean, MENA Middle East and North Africa, SAR South Asia and SSA Sub-Saharan Africa.

When the gender dimension is considered, some obstacles turn to be more impeding than others. As such barriers are primarily faced by owners and managers, we do not include in this part female employees. Thus, Figure 5 shows that access to finance, tax rates, practices from informal competitors, political instability, inadequately educated labor force and corruption are the highest obstacles for firms that either owned or managed by women. In addition, electricity turns to be impeding for firms that are owned by women. This is why, we are going to focus only on these barriers in the empirical part, as it will be shown later.<sup>4</sup> Figure 5
_0.20_|_0.28_|_0.41_|_0.46_|_0.46_|_0.45_|_0.51_|_0.49_|_0.48_|_0.30_|
|_Internet_|_0.24_|_0.07_|_0.10_|_0.13_|_0.17_|_0.22_|_0.23_|_0.25_|_0.29_|_0.31_|_0.23_|
|_Long distances (private line)_|_0.04_|_0.04_|_0.04_|_0.04_|_0.05_|_0.06_|_0.07_|_0.06_|_0.04_|_0.04_|_0.03_|
|_Public internet, public phone_|_0.04_|_0.21_|_0.13_|_0.11_|_0.11_|_0.07_|_0.08_|_0.05_|_0.03_|_0.02_|_0.01_|
|_Install. of private line phone_|_0.01_|_0.03_|_0.00_|_0.03_|_0.01_|_0.01_|_0.01_|_0.01_|_0.01_|_0.02_|_0.01_|
|_Publicphone_|_0.01_|_0.05_|_0.03_|_0.03_|_0.01_|_0.02_|_0.01_|_0.01_|_0.03_|_0.01_|_0.00_|

_Source:_ World Bank estimates using ENIGH 2014. _Note_ : Deciles based on household expenditure per capita.

# **4.2. The market of corn products**

Competition in the food and agriculture markets is a key component in determining food security in developing economies. Market concentration in the food production chain has been growing, driven by retail chains, raising concerns of higher commodity prices for consumers (McCorriston 2016). Mexico is no exception to this trend, with high market concentration
outcomes were targeted for compensatory programs such as pre-primaries expansions), our estimates would also be biased.<sup>7</sup> We can tackle these potential threats to identification by extending the baseline specification to include department dummies interacted with a linear time trend term. These interactions account for changes in outcomes in departments over time that may bias the estimates obtained from the original specification. In this case, we estimate the following equation: where _t_ corresponds to years elapsed after 1995, _γd_ represents department (or municipality) indicator variables and, thus, the interaction would capture department-specific trends over time.

We additionally exploit data from the 1994 and 2002 Population Census on the prevalence of households with running water, concrete floor and whose heads have no education and are indigenous. Using these data we generate community-year covariates by linear extrapolation. We add these covariates to the baseline specification to control for observable changes at the community-level over time. The estimated model is: where _Xct_ is a vector of covariates defined at the community-year level.

In all specifications we apply trimming and propensity score re-weighting techniques in order to increase the similarity in observable dimensions of communities in the treatment and comparison groups. We implement this method by first predicting the probability, using a logit regression, that a pre-primary is opened in a community as a function of community characteristics. Then, we drop communities with probability of treatment above the 85th percentile or below the 15th percentile. Finally, we re-weight observations _<u>PScore</u>_ in the comparison group applying a factor of 1 _−PScore_<sup>where</sup><sup>_PScore_refers to the estimated</sup> probability of treatment, and estimate the basic specification in the trimmed and reweighted sample.<sup>8</sup> The specifications described above make it possible to estimate ITT parameters of the impact of opening pre-primaries in rural communities on primary school progression. To estimate the effect of attending a pre-primary – the treatment-on-the-treated (ToT) parameter –
there are both firms entering and exiting the sample each year.

**Table 1: Number of factory assessments per factory**

|**Number of factory assessments**|**Number of firms**|
|---|---|
|1|124|
|2|84|
|3|76|
|4|63|
|5|29|
|6|28|
|7|33|
|8|24|

Source: Author’s calculations using data from ILO‐IFC Better Work Vietnam.

**Table 2: Number of assessed factories per year**

|**Year**|**Number of firms**|
|---|---|
|2010|65|
|2011|116|
|2012|136|
|2013|139|
|2014|177|
|2015|219|
|2016|253|
|2017|286|

12
to severe based on the score. Moreover, depression is screened with the Patient Health Questionnaire, a 9-item tool administered directly to adolescents.

These instruments collectively gauge a broad spectrum of psychological and emotional states, including post-traumatic stress, emotional disturbances, behavioral issues, inattention, peer relationships, prosocial behavior, anxiety, and depression, offering a comprehensive view of the mental health and socioemotional development of the minors in our study.

Figures B.3 and B.4 depict the distribution of the raw scores for Venezuelan and Colombian minors for each of the four scales. Surprinsingly, we do not observe any stinking differences on the distribution of any of these scores across groups. We are also not able to distinguish statistical differences between Colombian and Venezuelan children in any of the scales, when we estimate the specification highlighted in equation (1) as illustrated in Table 7. This is an unexpected result considering that typically, forcibly displaced populations have a high prevalence of socioemotional and mental health issues, but might be related to the fact that Venezuelan migrants have not faced war (as many forced migrants have in other contexts) directly and as such, these issues are less prevalent. Another explanation of these results could be the vulnerability of the Colombian population who in many cases has also a long history of internal forced displacement and violence.

# **V.D Social Cohesion**

We also delve into the differences in secondary outcomes among Colombian and Venezuelan adolescents concerning social cohesion. We focus on assessing altruism, trust, identity towards specific domains, networks, and experiences of discrimination. To measure altruism and trust, we employ the questions from the Global Preference Survey, a tool developed by Falk et al. (2022) to elicit risk, time, and social preferences. Specifically, to measure altruism we ask the adolescents how much of a fictional endowment would they be willing to donate to a good cause. To measure trust, we include the 7-itme ques38
in terms of occupational mobility, Afghan refugees have lower upward mobility and higher downward mobility. In this regard, we define two dummy variables for employed individuals in two consecutive years: **upward mobility** is a dummy for unskilled workers that becomes one if they obtain a skilled job next year. **Downward mobility** is a dummy for skilled workers and is one if they have an unskilled job next year.

One important issue regarding the transition dummies is that due to refreshing the LFS rotating panel sample in 2013 and 2018 by SCI, we cannot observe the transition variables from 2012 to 2013, from 2017 to 2018, and from 2019 to 2020 (the data is not available yet). This is rather unfortunate for our study since, according to Figure 3, these years were important times in terms of macroeconomic shocks. Missing them reduces the variation of the independent variable in our estimations.

# **4.2.2. HEIS variables**

Rows (11) to (18) of Table 2 show variables computed from HEIS surveys. All of these variables are measured as the logarithm of values at constant 2011 prices. For measuring household per capita expenditure, we use the modified OECD adult equivalent scale, which gives value 1.0 to the first adult, 0.5 to each member aged 14 and over, and 0.3 to each child aged under 14. Since the HEIS recall period for the majority of expenditure items is past month, we measure per capita **total expenditure** and **food expenditure** on a monthly basis. Figure 6 shows the distribution of total expenditure per capita by nationality of the household which has a log-normal shape. We observe that Afghan households spend less on average, and their expenditure distribution has a shorter tail. The recall period of income tables is past year, and thus they have larger numbers. According to Table 2, Afghan refugees spend less overall and on food items and have lower **total
of conflict on fertility, we exploit the fact that the conflict variables are available for all data waves, including the 1992 RDHS collected before the genocide. We pool the three cross-sectional RDHS waves pair-wise, combining the 1992 and 2000 waves and the 1992 and 2005 waves. Pooling surveys allows us to compare postgenocide fertility trends relative to a pre-genocide baseline period. For instance, inferences can be made about fertility of a population subgroup (e.g. widows) after the genocide in comparison to a similar population subgroup before the genocide. A similar approach to pooling of cross-sectional survey waves is applied by Akresh and de Walque (2008).

To ensure that the dependent variables are comparable from before and after the genocide, the periods are now defined in relative terms. In the pooled 1992-2000 data, the period of interest
first quarter of 2009 (Bank of Thailand 2011), with exports falling about 45% in the last half of 2008. Domestic demand could not pick up the export shortfall because of rising food and fuel prices that suppressed consumption. The Thai government responded by boosting its spending and making credit cheaper. In particular, it increased spending to boost wage and salary payments to the lowestpaid public sector employees, raised payments to pensioners, subsidized utilities for the poor, capped some food prices, lowered interest rates, provided credit guarantees, and spent on temporary job creation. The question of interest to us is whether the government measures were effective in helping households weather the storm of the crisis.

There is a large literature on how households cope in times of economic shocks and crisis. Within the developing-country literature, many studies have examined the varying impacts of aggregate shocks (that affect entire communities) compared to idiosyncratic shocks (such as illness or loss of employment that affects a specific household) (Paxson, 1993; Townsend, 1995; Storesletten et. al. 2001). Households can cope with sudden risks through a number of mechanisms, including drawing on savings, reducing consumption, relying on transfers/remittances, and seeking alternative sources of credit and employment through help from the government and other sources (Besley, 1995; Dercon and Krishnan, 2000; Mukherjee and Nayyar, 2011). However, coping strategies also depend on the nature of the shock - Alderman (1996), for example, uses a household panel data set from rural Pakistan that contains detailed income and credit data, and finds that households have more difficulty in smoothing consumption after successive shocks than with a single shock, thereby capturing the limitations of self-insurance.

A number of recent papers (focused mostly on industrialized-country contexts) have examined the role of government spending in mitigating shocks, particularly in the context of the 2008 financial crisis. One confounding issue is that the public anticipates that the government will step in to solve a crisis, and this expectation affects how
characteristics. From the post-intervention survey, we use data from a 24-hour food recall for children and a time allocation module for both children and adults. The food recall was completed by the survey respondent, usually the female household head, for all children aged 2 to 6 at the time of the follow-up survey and allows us to calculate the quantity of macro- and micro-nutrients consumed. For all children 12 and older, both pre-intervention and follow-up survey ask both primary and secondary

> 9To ensure respondents would not wrongly conclude that responses could affect their eligibility for aid, surveys were administered by Mexico’s National Institute of Public Health, a different agency than the one that administered PAL.

7
apy. The idea is that, by providing relief from the day-to-day worries of budget constraints and some insurance against negative shocks, cash transfers may create the mental space needed to internalize the lessons and practice the behavioral skills obtained during therapy, thus increasing impacts. Our trial aimed to test this hypothesis, for which the evidence is mixed (Blattman et al., 2023; McKelway et al., 2023; Bossuroy et al., 2022; Haushofer et al., 2023b). Third, and finally, our trial aims to test the hypothesis that reducing depression during adolescence might translate into improved human capital accumulation. In the shortrun our primary and secondary outcomes focus on changes in aspirations, preferences, and expectations as mechanisms for investments in human capital, such as desired fertility, time preferences, educational aspirations, and ideal age of first pregnancy and marriage. In the medium-term, we examine actual changes in human capital including school enrollment and skill-based competencies, incidences of pregnancy and marriage, risky sexual behavior, and self-efficacy.

We find that 18%-28% of individuals in the control group (depending on the scale used to measure psychological distress) had recovered to have minimal depression approximately 12 months after the IPT-G intervention. Therapy significantly improved this primary outcome by more than 5 percentage points (pp) 12 months post therapy. However, these effects dissipated and were not statistically significant at the 24-month follow-up. We found no effects on secondary outcomes of psychological well-being, such as self-esteem, resilience, and locus of control at any of the follow-ups. It is important to note that the 14-week therapy intervention was conducted in late 2019, immediately prior to the emergence of the COVID-19 pandemic, meaning that the 12-month follow-up surveys were conducted at the height of the pandemic. While our results imply that IPT-G had some protective effect, it is plausible that the impacts of therapy may have been muted by the difficult conditions caused by the pandemic, including extensive school closures – Uganda had the longest school closures in the world at
controls are very similar. In addition, Appendix Tables B10 and B11 indicate that results are analogous when using covariates without the lasso procedure. We estimate heterogeneous treatment effects with respect to predicted malaria risk using the following specification: where _highict_ and _lowict_ are dummy variables identifying patients with above/below median predicted malaria risk, **xict** is the vector of clinic and individual-level controls (again selected using double lasso), and _λ_ **t** are date fixed effects. Since _highict_ and _lowict_ are generated regressors, we bootstrap standard errors, clustering at the clinic level.<sup>28</sup> We now turn to the data to assess our theoretical predictions empirically.

# **4.2 Impacts on Antimalarial Prescriptions and Purchases**

**Overall Impacts.** Table 3 shows treatment effects on voucher use, antimalarial prescriptions, and antimalarial purchases. We report the coefficients on the treatment dummies _βP_ and _βD_ and the p-value of a two-sided test of _H_ 0 : _βP_ = _βD_ . We also indicate the theoretical mechanism being tested (existence of gatekeeping costs, doctor-driven, or patient-driven overprescription – GC, DD, or PD) and whether we find significant evidence of that mechanism or not.

The first column looks at _voucher redemptions_ . We find strong, significant evidence in favor of gatekeeping costs: voucher redemption is 9 percentage dummies, illness duration (top-coded at the 99th percentile), patient age, an under 5 dummy, patient gender, a dummy to identify pregnant women, a dummy to identify patients who were also survey respondents, survey respondent gender, respondent ethnicity, education, literacy and knowledge of French, and a dummy variable identifying patients interviewed in the home follow-up survey. Missing values are dummied out and recoded to the sample mean. We also include squared terms for patient age and illness duration, as well as pairwise interactions between the aforementioned individual-level controls. We partial out date fixed effects, so they are included in all regressions.

> 28First we use double
Repaid ECDF<br>Not Repaid ECDF<br>Repaid ECDF<br>Not Repaid ECDF<br>0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0<br>Score Quantiles Score Quantiles<br>1.0 1.0<br>0.8 0.8<br>0.6 0.6<br>F(x) F(x)<br>0.4 0.4<br>0.2 0.2<br>0.0 0.0<br><!-- End of picture text --> _<mark>Note:</mark>_ <mark>C</mark> DR represents Call Detail Record, and BIC Bayesian Information Criterion <mark>. Cells computed based on out of sample predictions resulting from 5 fold cross validation. X axis represents quantile in the distribution of predicted repayment scores, and Y axis represents the cumulative distribution function. Source: authors’ analysis from telecom data.</mark> 6
Policy Research Working Paper 9667

# **Abstract**

Aggregated data from mobile network operators can provide snapshots of population mobility patterns in real time, generating valuable insights when other more traditional data sources are unavailable or out of date. The COVID-19 pandemic has highlighted the value of remotely collected, high-frequency, localized data in inferring the economic impact of shocks to inform decision making. However, proper protocols must be put in place to ensure end-to-end user confidentiality and compliance with international best practice. This paper demonstrates how to build such a data pipeline, for the case of The Gambia, channeling data from mobile network operators through the national regulator to the analytical users, who in turn produce policy relevant insights. The aggregated indicators analyzed offer a detailed snapshot of the decrease in mobility and increased out-migration from urban to rural areas during the COVID-19 lockdown. Recommendations based on lessons learned from this process can inform engagements with other regulators in creating data pipelines to inform policy making.

This paper is a product of the Poverty and Equity Global Practice. 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://www.worldbank.org/prwp. The authors may be contacted at mmeyer3@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