rafmacalaba/data-use-mentions-extended huggingface dataset:
Y is a coping behavior variable, D is the female indicator; E is the displacement status indicator, X is a flexible vector of control variables (including local fixed effects); and ɛ is the error term. In our main specifications, we cluster our standard errors at the ward level (53 clusters).

In conditional analyses, we focus on parsimonious specifications to avoid issues arising from the inclusion of “bad controls” (Angrist and Pischke, 2009, 2014). That is, our baseline models do not include variables that may themselves be outcomes of the gender or displacement variables, which would complicate the interpretation of estimated coefficients of interest due to endogeneity and spurious correlations. Similarly, we specify and test an additional set of models that estimate the main and interactive impacts of local violence shocks, where we distinguish rural and urban areas. As the number of clusters is below 50 for both the rural and urban analyses, we use HC2 standard errors for these regressions.

# **4 Data and descriptive statistics**

**Household survey data** . We analyze household survey data collected by the World Bank and collaborating partners between June and August 2018 (Pape and Kwenin, 2018). The sample is representative of the IDP population in North-east Nigeria and includes internally displaced persons and comparable host communities in the six north-eastern states of Nigeria. The survey

8
<!-- Start of picture text --> Primary Enrollment Rate Learning in Primary School<br>100.0 600<br>95.0<br>500<br>90.0<br>400<br>85.0<br>North America<br>300 East Asia and Pacific<br>80.0 Europe & Central Asia<br>LAC<br>MENA<br>75.0 200 SSA<br>2000 2002 2004 2006 2008 2010 2000 2005 2010 2015<br>Year Year<br>FIGURE III<br>Enrollment versus Learning (2000-2010), by Region<br><!-- End of picture text --> _Notes:_ Primary enrollment and learning estimates are averaged within regions. LAC refers to Latin American and the Caribbean; MENA refers to the Middle East and North Africa; and SSA refers to Sub-Saharan Africa. We have a total of 73 countries which have both learning and enrollment data over this period and at least two data points for each.

_Source:_ Primary enrollment rates are from Lee and Lee (2016) and are available until 2010. Learning estimates are taken from our database.

<!-- Start of picture text --> Learning Conditional on Enrollment<br>600<br>500<br>400<br>300<br>200<br>2000 2002 2004 2006 2008 2010<br>year<br>Learning, Conditional on Enrollment<br>Learning, Conditional on Enrollment and Country-Fixed Effects<br>Learning<br><!-- End of picture text --> FIGURE IV Learning Conditional on Enrollment (2000-2010) _Notes:_ Primary enrollment and learning estimates are averaged.

_Source:_ Primary enrollment rates are from Lee and Lee (2016). Learning estimates are taken from our database.

9
geographical areas are likely to have a similar set of characteristics. Our sample of households from Mogadishu belongs to four PSUs in Wave 1 and ten in Wave 2. The small size of our exposed group of households introduces an important caveat to our estimates and findings. Nonetheless, we try to ease such concerns by considering different identification strategies and robustness checks.

# 3.3 Identification strategy

Using the four groups of households identified (exposed and control households before and after the incidents), we first employ a difference-in-difference (DiD) approach with repeated cross-sections to compare outcomes of households exposed to the terrorist attack against households who were not exposed to the incidents (Imbens and Wooldridge 2007). The identification strategy exploits spatial and time variation of the data. For this, we estimate the following equation: i W E W ∗E E i i (1) ii ii ii ii aa YY = αα+ λλW + μμE EEWWEE + ββ(W EEWWEE ) + ηηLL + φφXX + uu where i refers to the outcome of interest for household i in period t, W W to the period t of data collection (Wave 1 in 2016 or Wave 2 in 2017-18). E E corresponds to the status YY of household i according to our definition of treatment, to location fixed effects for EEWWEE geography a –which refers regions or districts– and i to a vector of covariates capturing aa LL characteristics of the household and household head, dwelling characteristics, exposure XX to drought, as well as humanitarian aid received.<sup>18</sup> Moreover, the probability of being exposed to an incident is likely to be associated to the location of households and the composition of regions or districts. Hence, we focus on regional comparisons (i.e., within-location variation) through the inclusion of location fixed effects.<sup>19</sup> For the DiD analysis we use the sample of households from Mogadishu as we can include fixed effects at the district level –which is the lowest
a total of 1,245 households. There were two types of ranking questions: RMG priority ranking and ranking based on specific criteria:

# _“RMG Priority Ranking”:_

Please rank who you would prioritize to join the RMG if it were solely up to you and not determined by eligibility and a lottery. Start with the highest priority to the least priority. For example, if I were in the list and you really thought I should receive the RMG job, you would rank me number 1. If you think I am the last person in the list you would give the job to, rank me (total number in the list).<sup>12</sup>

# _Criteria Rankings:_

- Rank who owns the least to most square meters of land, agricultural and residential land combined.

- Rank the likelihood that, in the last 12 months, the person or someone in her household was hungry but did not eat because there was not enough rice or food at home, and not enough money or other tradeable goods to purchase rice or food. Start with the person or household most likely to have had been hungry but did not eat.

- Rank who needs the most to least help right now.

The variety of data collected, including actual data on selection, respondent self-reports, and data collected on hypothetical ranking by village heads, combined with an intention sampling design that includes both selected and non-selected among eligible and a random sample of villagers, enable us to explore both the actual targeting by village heads, the proximate inputs into the targeting choices, and the potential information constraints that may limit the ability of village heads to target the poorest households in their villages.

4. Do village heads effectively select poorer households for participation in RMGs? Given the extensive baseline surveys administered to the RMG-eligible and the RS, we can compare them on a wide range of dimensions, including income, assets, land holding, etc. Notably, the baseline questionnaires did not include a comprehensive
across communities. In this paper, community is all households within the province level, which is a large area, so it is implicitly assumed that covariate shocks are not correlated across provinces. Finally, it is important to consider the benefits and costs of merging climate and natural hazards data with household survey data. The main costs involved are time, effort, limitations, and the need to make adhoc decisions to match the climate and natural hazards variables with households, as detailed in Skoufias and Baez (2023). Moreover, if the vulnerability related to idiosyncratic shocks is significantly higher than the vulnerability related to covariate climate shocks, the benefit of including climate and natural shocks in the multilevel approach might be attenuated. This paper aims to capture the part of the expected variance related to N so that the proposed model could provide insights from a conceptual perspective. However, from a cost perspective, there might be better options. If the costs of including climate and natural hazards data are very high, running a simple model with only Z as covariate variables in the equations may offer a more suitable alternative. Nonetheless, the interpretation of the volatility in each case would be different.

The analysis is conducted individually for each country, using country-specific variables to estimate the household’s expected mean income, and the expected variances. In practice, **t** he following steps are carried out to measure vulnerability to poverty due to climate risk (CR) in each country:<sup>8</sup>

- 1- Estimate the natural logarithm per capita income equation (1) assuming constant coefficients at the household level, including only household-specific variables ( i ): XX

- ln I I i 0 1 i 2 i i = ββ + ββ XX + ββ tt + ee

To perform this estimation, we first estimate the equation considering 19 pre-determined household variables with LASSO and choose the final sub-set of variables following LASSO results. 2- Once the household-specific variables ( i ) are chosen from LASSO, we estimate
and other features of the economy (not modeled) that affect job flows, for instance, will affect the estimate of the labor mobility costs. In consequence, these costs are in some sense “reduced-form” labor mobility costs. I will explore some of their microfoundations in later sections.

A key feature of this theory is that the time-varying idiosyncratic costs allow for a gradual reallocation of workers to a trade shock. This creates a rich dynamic response to trade policy that provides a very detailed illustration of how an economy moves during the transition. In principle, while the authors use the simulation exercises to assess trade policy, I believe it is possible to set up exercises of green growth policy too. I elaborate on this idea in section 4.3 below.

The estimation often delivers extremely high average moving costs and very high variance of moving costs. Using U.S. data, for instance, Artuc, Chaudhuri and McLaren (2010) report moving costs that range from 4 to 13 times the annual wage income. Using data from Turkey, Artuc and McLaren (2010) also report moving costs equivalent to roughly 5-13 times the annual wage.<sup>7</sup> For Argentina, Bet, Brambilla and Porto (2011) report costs of around 9 times the annual wage. In all these instances, the moving costs show a very high variance indicating that, at each period, workers can be subject to very large positive or negative idiosyncratic shocks.

While the estimated costs appear too large, they typically produce a very realistic dynamic aggregate behavior in the economy. For the ease of exposition and to exemplify the results, I reproduce in Figure 3 the dynamics of labor allocations, real wages, and the present value for workers obtained in the simulations of Artuc, Chaudhuri and McLaren (2010). The adjustment of the labor market to a trade shock is sluggish: it takes several years to approach the new steady state. The import-competing sector

> 7 Note that Artuc, Chadhuri and McLaren (
We can try to follow where the 6.5 percent of respondents who no longer claim religious reasons for refusal went<sup>21</sup> . On average, 3% actually decided to apply for the loan, an additional 0.8% said they did not trust the offer, 1.4% continued to refuse the loan but on economic grounds instead of religious grounds, and the remaining 1.3% changed their response to a generic reason for refusal.

We can further use these shifts to compare the experimental measures of the importance of religious considerations to the survey-based measures that are more widely used. Of those in the control group that claimed religious reasons for not applying for the loan, about one third of them shifted into applying for the loan, about one third continued to claim religious reasons for not applying, and the final third changed their answer to something else, close to evenly across economic reasons and to generic reasons. Together this implies that nearly two thirds of those that claim religious reasons for not taking out loans seem to act consistent with religious objections being the main reason for not applying for a loan, while the remaining third shift to a different excuse when the religious angle is addressed. This suggests that survey data on financial exclusion due to religious objections may be overestimating the importance of this characteristic by a third.

# _Alternative Interpretations_

This study has several limitations. The label of “sharia-compliant” may do more than merely represent adherence to religious considerations. We identify six potential alternatives.

First, if an individual does not trust financial institutions, the sharia-compliant label may signal trustworthiness and thus increase the take-up rate by consumers. This issue was the motivation for the independent auxiliary experiment on the branding of the conventional loan. In this auxiliary experiment, we learn that the known brand name (Jordan Microfinance Company) generates 2.2 percentage point (se=1.1) higher take-up rates than the unknown brand name (Tathmeer) for a conventional loan, all else constant
the sample. Note that the latter is about 78 percent of the former. The non-linear effect of population on tax administration is also confirmed using the ordered logit specification.

Instrumental variables regression results for the non-linear model are provided in Table 8. The estimation method involves taking the predicted values of population from Table 5 (first stage IV regression results discussed above) and using these predicted values and their square in place of population and the square of population respectively. Note that the specification or the set of controls used in the first stage (Table 5) and second stage (Table 8) is same; that is, the various controls are treated as included instruments.

Regression results in Table 8 confirm the non-linear relationship between country size and the dependent variable, although the relationship is somewhat weak (significant at the 10 percent level) when we include the various firm-level variables in the specification (columns 7 and 8, Table 8). To be on the conservative side, we suggest that our results for the non-linear specification be treated with due caution, requiring more work in the future to ascertain or reject the broader issue of non-linearity in the effect of country size on socio-economic variables.

# **5. Evidence using macro-level data**

We now extend our sample to 180 countries using macro or country-level data on a measure of the quality of tax administration. The measure equals the time (hours per year) it takes to prepare, file and pay three major types of taxes and contributions: the corporate income tax, value added or sales tax and labor taxes, including payroll taxes and social contributions ( _Tax Time_ ). We use average values of the variable over all years for which data are available (20052010). A more detailed definition of the variable is provided in Table 1. Much like the microlevel measure used above, the macro-level measure, _Tax Time,_ is also an outcome based 17
Table 13a: Impacts of PROSPERA on ENLACE Tests 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008 State Fixed Effects

|Variables|7th-6th score<br>9th-6th score|12th-6th level|
|---|---|---|
||_Panel A: Spanish_||
|PROSPERA beneficiary 2008|9.321*<br>12.644**|-0.023|
||(5.211)<br>(6.142)|(0.037)|
|Female beneficiary|-3.54<br>-0.779|0.056|
||(7.085)<br>(8.346)|(0.049)|
|Female|10.664*<br>-0.023|-0.052|
||(6.477)<br>(7.631)|(0.045)|
|Constant|-101.052***<br>-9.61|0.661***|
||(26.207)<br>(30.968)|(0.193)|
|Observations|4,439<br>4,616|2,402|
|R-squared|0.078<br>0.15|0.053|
||_Panel B: Mathematics_||
|PROSPERA beneficiary 2008|9.314<br>10.673|0.009|
||(5.756)<br>(7.819)|(0.038)|
|Female beneficiary|-5.422<br>-2.401|-0.004|
||(7.826)<br>(10.626)|(0.05)|
|Female|7.077<br>7.663|-0.108*
contains data on all the labor market contracts in the formal sector in Brazil. However, in Brazil, 20 percent of laborers work in the informal sector.<sup>13</sup> In this section, the nationally representative household survey, PNAD, is used to compare the gender wage ratios in the formal and informal sectors and to discuss how the differences might affect the main results.

The 2011–15 editions of PNAD are used with a few sample restrictions. First, the focus is on individuals ages 15–49, which is the age-group of interest in the main analysis. Second, the following categories of workers are excluded from the validation exercise: the

> 13 The rate depends on the definition of informality. Here, the self-employed are not counted as informal workers. If they were so counted, the rate of informality would nearly double, to around 40 percent. 19
and rope.

Table 3 presents descriptive statistics on the GPS and compass and rope area measurements completed as part of the methodological studies. Mean plot size is small in all countries, ranging from 0.27 acres in Tanzania to 1.31 acres in Nigeria. The mean difference between compass and rope and GPS measurement is very small. The sample mean bias in all three countries is plus or minus 0.01 acre, which translates in a 1 to 3 percent difference when expressed in relative terms (note that the values are not expressed in absolute value and as such negative and positive figures are averaged). Mean GPS and CR measurements are significantly different at the 1% level in Ethiopia, Tanzania, and the pooled data. In Nigeria, GPS and CR area measurements are significantly different at 10% level. Furthermore, in Nigeria the GPS and

> 7 Data on measurement duration are not available for Nigeria.

17
insufficient to meet existing needs. For example, preschool starts relatively late, typically around age 6, only lasts for one year, and covers less than half of the population. In addition, the supply of secondary education is also insufficient and skewed towards urban and more prosperous areas of the country. In the departments with the highest poverty rates, there is only one secondary school for every 400-500 secondary-school age youth, while in lower poverty departments, this ratio is closer to one school for every 100 youth.

Yet there is a pressing need for targeted and scalable action in the short term among the large pool of youth already at high risk of dropping out. Evidence from across Latin America shows that greater shares of youth who are out of school and out of work contribute to a range of negative social and economic outcomes, including higher rates of crime, long-term reductions in labor productivity and growth, and increases in inequality (De Hoyos, Rogers, and Székely 2016).

# 3. Intervention and study design

The ENTRE program In 2017, motivated by the challenges and constraints described above, the Government of Guatemala, with support from the World Bank, designed the ENTRE program as a scalable and low-cost early warning system. The program is aimed at reducing dropout in the transition from primary to lower secondary school, where almost a third of students stop their formal education.

Like most early warning systems, the key goals of ENTRE are to provide targeted support to students at-risk and to take timely, preventive actions. At a minimum, this requires that school principals and teachers have a basic knowledge of effective dropout prevention strategies, are able to identify students at risk, and implement these strategies in a timely manner. However, quantitative and qualitative data—including focus groups with teachers and school principals, as well as interviews with public officials conducted to inform the design of ENTRE — showed important gaps in all these dimensions.

First, school actors have limited knowledge of, or support on, dropout prevention strategies, and school
connected firms has a real impact on real economic activity.

Table 9 presents the results of the impact of political lending on firm-level economic activity. Though the merge with Orbis data set leaves fewer observations, we still find that the preferential treatment does translate into real effects on firms. The excess credit supply due to political connection has a large effect on employment outcomes, which is statistically significant at the 1 percent level. The economic magnitude of the estimate is also meaningful. Having at least one senator serving as the chairman of any committee results in an additional increase in employment of 2.1 percentage points in the ordinary least squares specification. This estimate is comparable to that of Chodorow-Reich (2014), examining the impact of a reduction in credit supply due to the financial crisis on firm-level employment.

With higher credit availability, firms become more capable of taking full advantage of profitable opportunities; thus, they can increase investment that leads to better performance. In columns 3 to 8 we also examine the impact of excess credit supply on firm’s financial information. More specifically, political lending increases total assets, liabilities and revenue of firms by 1.2, 1.0 and 0.8 percent respectively. However, the last two estimates are no longer statistically significant. The “small versus large” literature has documented that due to greater asymmetric information or smaller buffer savings, smaller borrowers exhibit greater sensitivity to changes in credit supply (Gertler and Gilchrist, 1994; Chodorow-Reich, 2014). Therefore, we conduct a subsample analysis by only looking at the sample of small firms (firms with less than 50 employees), and document a much stronger effect. For example, political connection leads to 2.5 percent increase in employment and 2.7(2.4) percent rise in total assets (liabilities). This greater sensitivity for small borrowers provides supporting evidence of asymmetric information in the lending markets.

# **VI. Extension and Robustness Tests**

## **_VI_ .A.
enforced fairly and consistently, firms seem more likely to declare tax obligations honestly based on the promise of avoiding informality, corruption and uncertainty. By contrast, where taxes are enforced inconsistently, firms have little incentive to declare honestly, as such honest behavior is not a guarantee of avoiding informality, corruption and uncertainty – and may disadvantage them relative to their competitors. Reflecting this logic, a recent OECD study goes so far as to use measures of “tax certainty” as a direct proxy for business tax morale, owing to the current absence of alternative measures and survey data.<sup>111</sup> Inappropriate tax policies and problems with revenue forecasting sometimes lie at the root of unfair tax administration. In these cases, successful reform may need to happen “above” the level of administration. In some cases, unfairness is exacerbated by overly complex tax policies. This may be true where tax policies offer significant subjectivity in the assessment of tax liabilities – as with, for example, “market value” systems of property tax assessment, or presumptive tax systems based on administrative estimation of turnover. It may equally be true where tax policies are so

> 107 Fjeldstad, O-H., & Therkildsen, O. (2008). Mass taxation and state-society relations in East Africa. in Brautigam, D., Fjeldstad, O-H., & Moore, M. (Eds.) _Taxation and state building in developing countries: capacity and consent._ Cambridge: Cambridge University Press; Fjeldstad, O-H., & Semboja, J. (2001). Why people pay taxes: The case of the development levy in Tanzania." _World Development_ , 29 (12):2059-2074.

> 108 This can be an issue even in relatively high capacity contexts. According to anecdotal evidence South Africa, for example, has seen increasing complaints from taxpayers about what they view as aggressive application of penalties – and sometimes extra-legal behavior by tax collectors.

> 109 Campos, J. E., Lien, D.