rafmacalaba/datause-encoder-data huggingface dataset:
The survey data confirm that the pandemic had a profound impact on people’s well-being, particularly in the beginning of the pandemic in early 2020. Subsequent recovery is observed for many well-being indicators from mid-2020 onwards. While no single variable performs well in predicting changes in well-being, various combinations of indicators from Google search terms, mobility, and satellite data have considerable explanatory power. These predictive models explain the largest share of the variation when measuring the prevalence of income declines and work stoppage since the onset of the crisis in the initial wave of the survey. This is partly due to the large variation across different regions and countries in economic disruption caused by the initial impact of COVID-19. However, the real-time indicators are also correlated with predetermined regional characteristics, like the overall level of development, that were themselves correlated with the intensity of the shock. When predicting variation within regions across time, however, real-time indicators maintain substantial explanatory power. For example, the indicators can explain about a quarter of the variation in the prevalence of income declines across regions, and about 15 percent of the variation in current employment, income gain, food security, and the share of households with children that stopped school. The real-time indicators considered here are significantly stronger predictors of the prevalence of reported income declines across urban regions, where the indicators explain 55 percent of the prevalence of income declines across regions. The models suggest that Google search terms are more powerful at distinguishing differences in the prevalence of reported income declines across urban regions than rural regions. This is consistent with the fact that most Google search queries are originating from urban areas, due to greater connectivity.

Of the real-time indicators, Google mobility data and search data for food, jobs, assistance, exercise, and religion were generally most predictive, though relative importance varies from indicator to indicator. Google search variables accounted for all of the explained variation of the prevalence of total income decrease, both in the first wave and over time, and at least 40 percent of the variation of all indicators related to income and employment. Measures of vegetation (EVI) and pollution (NO2) were also predictive in some cases. However, most models contain a variety of different indicators, highlighting the benefits of training a model that can combine information from disparate sources.

23
_Learning_ Learning is measured using tests conducted in school in 2012 and 2013, conditional on the student being enrolled and present on the day of the exam. In each year, two exams were administered: a pre-program test conducted just prior to, and a post-program test conducted just after, the learning component of the program was implemented.<sup>10</sup> The tests were based on ASER exams, which are standardized exams validated across India testing Hindi, English, and Math.<sup>11</sup> The exams are short, taking 5-10 minutes per student, testing the same skills in both years. For Hindi and English, students are tested on letters, words, a short paragraph, and a longer story, while math tests knowledge of single- and double- digit number recognition, two-digit subtraction with borrowing, and three-digit by one-digit division. Enumerators assess the highest level a student can comfortably perform. Following the ASER criteria, the tests are scored categorically from A (highest score) to E (lowest).

We construct two measures of learning from the exam scores. First, we normalized test scores to the control after assigning each letter grade a numeric value from one (E, lowest) to five (A, highest). Second, we create an indicator for whether the student’s test score improved from pre-treatment to post-treatment. These two measures are motivated by the observation that, with the raw scores on a categorical scale, the difference between two adjacent scores may not be constant in terms of measuring learning outcomes. The indicator for a student’s score increasing represents a way of looking at non-linear effects.

Tests were administered conditional on the student being present in school on the day of the exam. We address the possible bias due to differential test-taking in our analysis.

> 10 In 2012, pre-program tests were administered in Nov-Dec 2012, while post-program tests were administered in March 2013. In 2013, pre-program tests were administered in Sept-Oct 2013, while post-program tests were administered in March 2014.

> 11 See www.asercentre.org for more information about the ASER tool.

11
highly correlated with telecom usage. Second, we simulate the increase in the probability of using telecom services by assuming that all _nonusers_ would (potentially) benefit from the average welfare gains just described based on the quintile of consumption that they currently belong . Table 6 illustrates that there are limited potential gains in the extensive margin (that is, take up of telecom services). Using the low elasticity as benchmark, households in the fifth quintile would be 0.0014 percentage points more likely to use telecom services (assuming all other covariates constant). The probability of take up is correlated with households’ consumption, but differences are not significant. This result is likely due to the small gains in the short run. However, higher effects could be expected in the long run as competition leads to larger drops in prices. Besides the further drop in prices due to more competition, other indirect effects such as having neighbours who take up telecom services could also have an impact on individual take up.

**Table 6. Estimated increase in probability of using telecom services due to welfare gains**

||**Elasticity = -1.5**|**Elasticity = -2.5**|
|---|---|---|
|**Quintile 1**|n.a|n.a|
|**Quintile 2**|0.0003|0.0001|
|**Quintile 3**|0.0006|0.0002|
|**Quintile 4**|0.0008|0.0003|
|**Quintile 5**|0.0014|0.0005|

Source: own calculations using EDAM 4.

Note: Results shown in percentage points. Quintiles based on the distribution of consumption per capita. Results for the bottom quintile are not shown as it is considered that there is too little information (i.e sample size) to draw reliable estimates.

# Conclusion

In this paper, we bring some insights as to the hypothetical gains to the population that an opening of the telecom sector could bring about in Djibouti. The ICT sector is currently believed to be below its potential, thus limiting the access to such services and, somewhat indirectly, preventing more spaces for the private sector in Djibouti to become bigger players in the economy. From a relatively narrow angle of welfare gains to households, the simulations presented here hint at important gains in welfare due to the expected price decreases following increased competition in the sector.

More work is needed to fully understand the implications of changes to the telecom sector in Djibouti. For instance, a yet to be published 2018 survey on telecoms usage conducted by DISED can provide additional insights on the sector. The huge potential the sector holds for contributing to the development of the country should be enough incentive to pursue this important agenda.

14
# **Table 2: Fiscal Policy under Coalition Governments:**

||**Measured by GO**<br>|**VFRAC**<sup>_a_</sup><br>||
|---|---|---|---|
||(1)|(2)|(3)|
||Total<br>Expenditure (excl.<br>interest)/ GDP|Total<br>Tax Revenues/<br>GDP|Deficit/ GDP|
|GOVFRAC<sup>_a_</sup>|-0.090<br>(0.042)**|-0.057<br>(0.029)**|-0.017<br>(0.014)|
|Majoritarian System|0.001|-0.018|-0.002|
|(=1, 0 if Proportional)|(0.018)|(0.012)|(0.006)|
|Presidential System|-0.051|-0.037|-0.010|
|(=1, 0 if Parliamentary)|(0.019)***|(0.013)***|(0.009)|
|Regular|-0.026|-0.003|-0.015|
|Elections<br>(=1, 0 otherwise)|(0.017)|(0.010)|(0.009)*|
|Observations|207|206|239|
|R-squared|0.36|0.48|0.21|

_a_ . The variable GOVFRAC measures the probability that two legislators, drawn randomly from among all the parties that belong to the executive government, belong to two different political parties. It therefore lies between 0 and 1.

Robust standard errors in parentheses, clustered by country.

* significant at 10%; ** significant at 5%; *** significant at 1%. Controls as in Table 1. Countries included in the sample are listed in Appendix 5.

28
(6) yy<sup>II</sup> = yy<sup>AA</sup> −αα<sup>FF</sup> yy<sup>FF</sup> −αα<sup>OO</sup> yy<sup>OO</sup> where lowercase variables indicate relative change, is the estimated share of the formal sector in aggregate economic activity, and is the corresponding share for the illicit economy. αα<sup>FF</sup> In Afghanistan the informal sector has been estimated to account for 72 percent of total economic activity, αα<sup>OO</sup> with another 16 percent of it originating from illicit businesses (World Bank, 2005; SIGAR, 2018). These estimates are used in what follows for and respectively. αα<sup>FF</sup> αα<sup>OO</sup>

# **Data**

The model is estimated using non-traditional data from a variety of sources, most of which are extraneous to the Afghan official statistical system. This approach is consistent with a broader trend to rely on big data to generate finely granular high-frequency estimates of otherwise conventional economic variables.

_Aggregate economic activity_ A stable relationship has been shown to exist between aggregate economic activity and the sum of nighttime light per area. Global level estimates suggest that a 1 percent increase in nighttime light is YY<sup>AA</sup> associated with 0.267 percent increase in economic activity (Henderson et al. 2012). For South Asian countries the increase in economic activity has been estimated at 0.248 percent, quite close to the global figure (World Bank 2017). Building on these results, it is assumed in what follows that the elasticity of aggregate economic activity to nighttime light is 0.25. This elasticity implies that the relative change in economic activity , is only a quarter of the relative change in nighttime light. The raw nighttime light data are derived from the VIIRS-DNB Cloud Free Monthly Composites, version 1, yy<sup>AA</sup> The raw nighttime light data are derived from the VIIRS-DNB Cloud Free Monthly Composites, version 1, made publicly available by the Earth Observation Group at the National Geophysical Data Center of the National Oceanic and Atmospheric Administration (NOAA).

The released data are already adjusted to correct for saturation, blooming effects, and comparability, all issues that had been detected in a previous nighttime lights product known as DMSP-OLS. The data are further cleaned for this paper, taking advantage of the 2015 annual composite of stable VIIRS nighttime lights to define a background noise mask (Elvidge et al. 2017). Only cells lying outside this background noise mask are treated as stable lights, while those inside the mask are recoded with value zero, corresponding to no light. The VIIRS-DNB data are available from 2013 to 2016 on a monthly basis, with a 11
# Table of Contents

|I.<br>Introduction|1|
|---|---|
|II. Literature Review|1|
|m.A Bird's Eye View of Repetition in Latin America|3|
|IV. An Empirical Investigation|_5_|
|Descriptive Statistics<br>Multivariate Analysis|9<br>13|
|V. Conclusion|18|
|References|19|
|Annex 1 Mean Sample Characteristics, Bolivia 1989|22|
|Annex 2 Zero-Order Correlation Matrix|24|
|Annex 3 Logistic Regression Results: The Determinants of<br>Repetition in Bolivia|25|

> * The authors wish to thank Haeduck Lee and Hongyu Yang for providing assistance during the various stages of preparing this paper.
<!-- Start of picture text --> 100<br>90<br>Def: Legal/Social<br>80<br>70<br>60<br>50<br>40<br>30<br>20<br>10<br>0<br>-<br>10,000 20,000 30,000 40,000<br>2005 GDP per capita PPP adjusted<br>Latin America Advanced Countries Rest of the world<br>Lack of Pensions (% Labor Force) 1/<br><!-- End of picture text -->

**Informality vs. income per capita**

_source:_ <u>www.worldbank.org/wmaloney</u> <!-- Start of picture text --> 60<br>50<br>Def: Productive<br>40<br>30<br>20<br>10<br>0<br>-<br>10,000 20,000 30,000 40,000<br>2005 GDP per capita PPP adjusted<br>Latin America Advanced Countries Rest of the world<br>Self Employment (% of Labor Force) 2/<br><!-- End of picture text --> 5
well-defined limiting distributions which are used for testing.<sup>6</sup>

# _Computing Factor Scores and Standard Errors_

In order to estimate factor scores from the ML method, consider a single observation on the factor model: where the lower case letters denote the vector elements of their matrix counterparts in (1). We proceed as described in Anderson (1971, p. 575). The transposed vectors are column vectors. The data vector **x**<sup>_′_</sup> and the factor score vector **a**<sup>_′_</sup> have a joint normal distribution distribution with mean ( **0**<sup>_′_</sup> _,_ **0**<sup>_′_</sup> )<sup>_′_</sup> and covariance: . The factor scores are computed by the regression of **f**<sup>_′_</sup> on **x**<sup>_′_</sup> . In terms of the population parameters, this is: Using the conditional variance formula, the covariance of the regression is Replacing the parameters by their ML estimates yields an estimate of the (conditional) covariance. They may be used to test hypotheses about scores (for an observation) on different

> 6In Stata, the “factor” command is used together with the “ml” option in order to estimate the parameters of the factor model.

11
countries. They also find that these misperceptions lead to a negative bias in preferences for redistribution, which does not disappear with the provision of objective information. Hangartner et al. (2019) find that refugee immigration to Greece increased natives’ hostility toward refugees and support for restrictive immigration policies.

There is a small literature on the effects of the Venezuelan exodus on popular opinion in Latin America. For Chile, Ajzenman et al. (2021) find negative effects on natives’ security perceptions, despite null effects on the objective crime rate.<sup>4</sup> They identify media to be an important determinant in this context due to disproportionate coverage of immigrantperpetrated homicides that trigger crime misperceptions. For Colombia, Chatruc and Rozo (2021) find that economic concerns, despite a lack of objective evidence on negative labor market effects, are another important driver of anti-immigrant sentiment. Additionally, Rozo and Vargas (2021) identify strategic electoral misinformation in Colombia as an additional channel. Exploiting rich opinion polls, we contribute to this literature by estimating the causal impact of migration on both attitudes and perceptions of natives and immigrants in the same context.

There are also a number of studies conducted within the DFID-WB-UNHCR initiative _"Preventing social conflict and promoting social cohesion in forced displacement contexts"_ that relate to our analysis. Relying on data from the Gallup World Poll, Aksoy and Ginn (2021) analyze the effect of refugee inflows on natives’ attitudes towards refugees in a global sample of low- and middle income countries. Interestingly, they do not find evidence of a negative effect on attitudes. They also investigate potential heterogeneity in those effects across camp and non-camp refugee settings as well as across progressive and restrictive right-to-work environments and find little evidence for any differential effects.

For the case of Colombia, Allen et al. (2021) conduct a survey experiment investigating the preferred policy preferences of native Colombians in response Venezuelan mass immigration. On the one hand, their results suggest that natives favor the use of numerical immigration limits and limiting the length of the residency permit. On the other hand, natives support granting conditional labor market access to Venezuelan immigrants (i.e., only in certain occupations) as well as unrestricted location choice and complete access to public services. In terms of effect heterogeneity, natives who have less contact with Venezuelans, who put more emphasis on economic matters, and who perceive the situation in Venezuela as mainly an economic problem, have more restrictive policy preferences.

Additionally, Albarosa and Elsner (2021) study the example of refugee immigration into Ger-

> 4On the effects of immigration on crime, see also: Bianchi et al. (2012) and Bell et al. (2013).

7
_Figure A4: Decomposition of changes in disposable income by income groups_ <!-- Start of picture text --> AT BE BG CY CZ<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>DE DK EE EL ES<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>FI FR HR HU IE<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>IT LT LU LV MT<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>NL PL PT RO SE<br>1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5<br>SI SK<br>1 2 3 4 5 1 2 3 4 5<br>Market incomes Automatic stabilisers New work compensation schemes - earnings part New work compensation schemes - compensation part Other COVID policies<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br><!-- End of picture text --> Note: Income groups defined based Scenario D. Source: Own calculations based on EUROMOD I4.0+.

25
government transfers are kept unchanged, which to leads to such changes in relative prices that the retired as the sole household category (with the highest share of income coming from government transfers) would marginally benefit from it. However, this would not hold if the government would decide to cut the transfers instead of raising taxes.

**Table 13. External shock and transfer scenarios: Government receipts and spending (% of GDP)**

|||**F**|**inalyear**||||
|---|---|---|---|---|---|---|
|**Indicator**||**2012**|**base**|**fuelpwm**|**remitdecr **|**trnsfrpoor**|
|Receipts|Direct taxes|10.2|11.0|11.5|11.3|12.2|
||Import tariffs|1.7|1.7|1.8|1.6|1.6|
||Other indirect taxes|14.7|16.0|17.4|16.3|17.9|
||Private transfers|1.4|1.4|1.4|1.4|1.3|
||Foreign transfers|3.7|2.2|2.3|2.3|2.2|
||Factor income|1.6|1.8|1.8|1.8|1.8|
||Domestic borrowing|2.1|2.1|2.4|2.2|2.2|
||Foreign borrowing|1.2|1.2|1.3|1.2|1.2|
||Total|36.6|37.4|39.8|38.2|40.5|
|Spending|Consumption|18.7|18.3|18.7|18.6|18.1|
||Fixed investment|2.8|2.7|2.7|2.6|2.7|
||Private transfers|14.1|15.3|17.2|15.8|18.5|
||Foreign transfers|0.4|0.4|0.4|0.4|0.4|
||Domestic interest payments|0.1|0.2|0.2|0.2|0.2|
||Foreign interest payments|0.5|0.6|0.6|0.6|0.6|
||Total|36.6|37.4|39.8|38.2|40.5|

Note: Columns other than 2012 show results for 2020.

**Figure 19. External shock and transfer scenarios: Annual consumption growth by household type (%-age point deviation from base)**

<!-- Start of picture text --> 2.0<br>1.5<br>1.0<br>0.5<br>fuelpwm<br>0.0<br>remitdecr<br>-0.5 trnsfrpoor<br>-1.0<br>-1.5<br><!-- End of picture text --> Note: For notation, see Table 3.

31
struction, are elaborated in Appendix A, Table A2. Additionally, Table A3 in the appendix provides a gender-based breakdown of the descriptive characteristics of the respondents.

# **2.3.2 Gender Attitudes and Norms**

The survey collected data on individual personal beliefs and perceived community norms towards gender equality in opportunities, and the stereotypical roles of men and women. Each norm theme is asked as a set of personal beliefs (using a Likert scale) and perceived community norms (out of 10 of your neighbors how many do you think agree...). Below we define how the beliefs and norms are constructed in more detail:

1. **Broad Norm on Gender Equality** The survey elicited individual-level personal beliefs on gender equality by asking how much the respondent agrees with the following statement: “men and women should have equal opportunities” using a 5-point Likert scale from strongly disagree to strongly agree. The personal belief variable is coded as a dummy variable equal to 1 if the respondent agrees or strongly agrees that men and women should have equal opportunities; and 0 otherwise. Second, the survey measures perceptions of community norms, where respondents are asked to indicate out of ten neighbors in their community, how many they think would agree that men and women should have equal opportunities.<sup>15</sup> These norm constructs give us a measure of individuals’ perceptions of what others around them think, i.e. the share of the community the respondent believes would agree with equal opportunities between the sexes. In the regression analysis the perceived community norm variable is rescaled to be a value between 0 and 1 in increments of 0.1 where 0 indicates the most conservative views towards gender equality in their community and 1 indicates the most liberal views towards gender equality perceived in their community.

2. **Male Role as a Breadwinner** The individual personal belief for the male breadwinner norm asks the respondent how much they agree with the following statement: “household expenses are the responsibility of the man, even if his wife can help him”, again using the 5-point Likert scale. The personal belief male breadwinner variable is coded as a dummy variable equal to 1 if the respondent agrees or strongly agrees that household expenses are the responsibility of the man, even if his wife can help him; and 0 otherwise. For the perceived community male breadwinner norm, respondents are asked to indicate out of ten neighbors in their community, how many they think would agree that household expenses are the responsibility of the man, even if his wife can help

- 15To simplify the question for respondents, the survey asked them to report a number out of 10. We then

- convert this number to a proportion i.e. out of 100%.

8