rafmacalaba/datause-encoder-data huggingface dataset:
**Table 6. Conditional Correlations of Being Active or in School with Skills, across Subsamples**

|Outcome||Being|active or in s|chool (versu|s nonstudent inactiv|e)|
|---|---|---|---|---|---|---|
|Method||||Logit|||
||||||Less educated<br>|More educated<br>|
|Subsample|Men|Women|Younger<br>(15–34)|Older<br>(35–64)|(maximum,<br>incomplete<br>secondary)|(minimum,<br>complete<br>secondary)|
||(1)|(2)|(3)|(4)|(5)|(6)|
|Reading proficiency|-0.001|**0.039****|**0.030****|0.012|**0.035****|0.015|
||(0.01)|(0.02)|(0.01)|(0.02)|(0.02)|(0.02)|
|Extraversion|-0.002|0.017|**0.023*****|-0.016|**0.029***|0.003|
||(0.01)|(0.02)|(0.01)|(0.02)|(0.02)|(0.01)|
|Conscientiousness|0.003|**0.043*****|**0.020****|**0.027***|**0.046*****|0.004|
||(0.01)|(0.02)|(0.01)|(0.02)|(0.02)|(0.01)|
|Openness to experience|0.004|**0.029***|**0.022****|**0.027***|0.010|0.016|
||(0.01)|(0.02)|(0.01)|(0.02)|(0.02)|(0.01)|
|Emotional stability|-0.008|0.021|0.007|0.017|0.007|0.011|
||(0.01)|(0.02)|(0.01)|(0.02)|(0.02)|(0.01)|
|Agreeableness|-0.011|**-0.027***|-0.010|**-0.029***|**-0.045*****|-0.002|
||(0.01)|(0.02)|(0.01)|(0.02)|(0.02)|(0.01)|
|Grit|0.006|0.003|-0.003|0.012|0.004|0.006|
||(0.01)|(0.02)|(0.01)|(0.02)|(0.02)|(0.01)|
|Hostile attribution bias|-0.009|-0.012|-0.006|-0.009|0.018|**-0.018****|
||(0.01)|(0.01)|(0.01)|(0.02)|(0.02)|(0.01)|
|Decision making|-0.013|-0.000|-0.005|0.002|-0.000|-0.007|
||(0.01)|(0.02)|(0.01)|(0.02)|(0.02)|(0.01)|
|Observations|933|1,369|1,233|1,123|864|1,492|

_Source:_ Authors’ calculations based on Colombia STEP Household Survey (2012).

_Note:_ Standard errors in parentheses. Regressions control for being a woman (dummy), age, age-squared, mother’s education (dummies; primary education is the reference category), cities of living and their metropolitan areas (dummies; Bogota-Barranquilla-Villavicencio is the reference category), and a self-reported categorical variable on parental involvement in one’s education at the age of 12 (three levels). Average marginal effects are reported and reflect the changes in the probability of being observed in a labor or school participation situation with respect to the variables evaluated at the mean. Measures of reading proficiency and socioemotional skills are standardized. Regression coefficients and standard errors of reading proficiency are the average of the 10 estimations using plausible values.

> * _p_ < 0.1  ** _p_ < 0.05  *** _p_ < 0.01

20
Andr ́ee <!-- Start of picture text --> Residuals vs Leverage<br>Boekel G<br>1<br>G<br>0.5<br>G Bernheze<br>G<br>G<br>G G Rozendaal G<br>G G<br>G GGGGGGGGGGGGG G GGGGGGGGGGGGGGGGGG G GGG G GGG G GGGGGGG G G G G GGGGGG GG GGGGGGGGGGGGGG G GG G GG G G G GGGGGGGGGGGGGGGGGGGGGGGGGG G GGGGGGGGGGG G GGGGGGG G G G GGGG G GG G GG GGGGGGGGGGGGGGGGGG GG GGGGGG GGG GGGGG G GGGGGGGG G GGGGGG G G G GGGGG G G G GG G GGGG GG GGGGGGGG G GGG G GGGG G GGGGGGGG G GGGGGGG G GG G GGGGGGGGGGGGGGGGGG G GGG G GGGGGGGG GGGGGG G GGGGG G GGG GGGGG GG GG G GG G G GGGG G G G G G G G G GG G GG G G G<br>Cook's distance<br>0.00 0.05 0.10 0.15<br>Leverage<br>glm(corona_density ~ population_density + from_25_to_44 + above_65 + unmarr ...<br>10<br>8<br>6<br>4<br>Std. Pearson resid.<br>2<br>0<br>−2<br><!-- End of picture text --> Figure 8: Residuals versus Leverage plot using Cook’s distance.

Figure 8 highlights that Boekel and Bernheze are relatively influential, but not critically. This is reassuring, nevertheless it is important due to the small sample nature of the applications to evaluate whether the identified mild violations have a drastic impact on the estimation results. Two regressions are performed to analyze this. First, Model 5c replaces the dependent values of the 8 observations that have visibly the largest residuals in the Q-Q plot with predicted values from Model 5 and re-estimates the specification. This allows comparing directly how the parameter estimates change when these outlier observations are replaced with values that lie closer to the normal range of the data. It is important to note that if these observations are not outliers in an additive sense, but simply reflect the nature of the data-generating process, then these new estimates have in fact an increased bias resulting from further mis-specification. To evaluate whether outliers can be addressed through model-specification, Model 5d first normalizes the data using a power transformation (Johnson) by finding the transformation that minimizes the _p_ -value of a Shapiro test for Normality, and then re-estimates Model 5’s specification on the more Gaussian data. These parameter values cannot be directly compared to the parameter values of Model 5 because the new relationship is nonlinear (logarithmic-type). Nevertheless, Model 5d informs whether the significance of relationships remains intact when the Normality violations are neutralized.

18
<!-- Start of picture text --> Mathematics test scores<br>by age, treatment, wave and school enrollment<br>Baseline Endline<br>= x<br>iB<br>£ &<br>Lo =o<br>==<br>£— f&<br>Og a ——= = @ oO —<br>9 _ aoc 8 _ sor<br>° ao — a _ we —<br>D ao > rae<br>oN wo gt oo<br>@ a o oT<br>Z = Aan<br>¢ Y<br>[o) N Za<br>5 6 7 8 9 10 11 12 13 14 15 5 6 7 8 9 10 11 12 13 14 15<br>Age Age<br>——— Treated, schooled ——-—-- Treated, not schooled<br>———\ Control, schooled —-—--- Control, not schooled<br>1 point for correct response, 0 for wrong.<br>Child has a vaccination card<br>ELEP 2012 Guinee<br>Po<br>gN<br>c<br>&<br>s<br>gk<br>><br>oO<br>£<br>Se<br>ch<br>oO<br>is}<br>=<br>[S)<br>ek<br>S<br>c<br>fo}<br>Q<br>fo}<br>an<br>0 6 12 18 24 30 36 42 48 54 60<br>Child age in months<br><!-- End of picture text -->
show that girls significantly outperform boys on reading tests—by 0.162 SD in kindergarten—and that this female advantage persists into the fifth grade (Cornwell et al. 2013).

Recent evidence from developing countries also shows gender gaps in educational achievement. Dickerson et al. (2015) find that on average, boys have a 0.1 SD advantage over girls in mathematics in fifth grade in 19 African counties but the magnitude of the gap varies widely from a non-significant 0.06 SD in Mauritius to as much as a 0.34 SD in Tanzania. Similarly, boys in Chile score 0.08 SD higher in mathematics than girls in fourth grade, and this gap increases to 0.2 SD by grade eight (Bhardwaj et al. 2015). In Indonesia, the gender gap in maths scores in primary school is in the opposite direction, with girls scoring 0.08 SD higher than boys at age 11 (Suryadarama 2015). Moreover, analysis using international assessment data suggests significant gender gaps in secondary school among low- and middle-income countries, with boys scoring 0.25 SD higher than girls in mathematics at age 15 (Bhardwaj 2015). A question that emerges from the existing literature from developing counties is whether gender gaps in cognitive skills emerge as early as those observed in high income countries (i.e., the first year of schooling).<sup>6</sup> Researchers have also paid increasing attention to gender gaps in non-cognitive skills given the growing evidence suggesting that non-cognitive skills observed in early childhood affect academic performance and labor market outcomes in later years (Cunha and Heckman 2008, Cunha et al. 2010). In the United States, girls consistently score higher than boys from kindergarten to grade 5 on a teacher-reported measure of children’s socio-emotional development (Cornwell et al. 2013, DiPrete and Jennings 2012). Similarly, results from Australia and Canada show that in kindergarten, girls outperform boys on the Early Development Instrument (EDI)—a holistic measure of child development that includes measures of non-cognitive skills. Gender gaps in the EDI are particularly pronounced in the social competence domain (i.e., children’s ability to cooperate with others and follow rules) and the emotional maturity domain (i.e., children’s ability to deal with feelings at the age-appropriate level) (Australian Government 2013, Janus and Duku 2007). To our knowledge, research from developing countries has yet to examine the early emergence of gender gaps in non-cognitive skills.

> 6 A cross-country study from Ethiopia, India, Peru and Vietnam suggests that there were no gender gaps in general cognitive skills at age 4 to 6 (Cueto et al. 2009) but gender difference grew significant during middle childhood (age 12), with male advantage in cognitive achievement in Ethiopia, India and Peru, and female advantage in Vietnam (Dercon and Singh 2011).

4
up survey took place between November 2014 and March 2015, on average 28 months after the interventions. The last follow-up survey took place between June and October 2015, on average 35 months after the interventions.

Attrition was 5.7 percent in the first follow-up, 9.4 percent in the second follow-up, 10.9 percent in the third follow-up, and 10.5 percent in the fourth follow-up. Attrition was uncorrelated with treatment status (see Appendix Table 2).<sup>18</sup> Although attrition rates were low, a minimum of nine percent (in the first follow-up) and a maximum of sixteen percent (in the second follow-up) of the firms interviewed in the follow-up surveys had closed their businesses and not started a new one. This reduces the number of people in our samples that currently operate firms, but there are no differences between groups in closure rates (Appendix Table 2).

# **4. Results**

Table 3 provides take-up results based on the BRC and TPIN certificates delivered with our assistance. Overall take-up of business registration was 75 percent for those offered just the BRC. The take-up of the BRC was 85 percent among those also invited to bank sessions on separating household and business money, and 69 percent among those offered BRC plus the TPIN (since they could opt for the BRC while declining the TPIN). These differences in take-up rates of BRC are statistically significant across the treatment groups. In contrast, only 4 percent of those offered assistance with tax registration received a TPIN with our assistance.

Regardless of arm, the BRC take-up rates are extremely high compared to the formalization rates in other studies that have offered assistance with formalization (De Mel et al, 2013; Alcázar et al, 2010; Jaramillo, 2009; Andrade et al, 2016; de Giorgi and Rahman, 2013; Benhassine et al, 2018). With the exception of de Giorgi and Rahman (2013), all the existing studies have focused on tax or municipal registration, which has involved ongoing cost obligations to the firm in the form of taxes. De Giorgi and Rahman (2013) provide information to aid in business registration, but not the costless assistance that we used here. However, we see that even with costless assistance, takeup rates for the TPIN are extremely low, suggesting that it is the combination of a business

> 18 The BRC treatment group attrition was statistically different from the control group in the first and third followups.

15
**<u>Table 3: Corn consumption in Mali for different household groups, 2006</u>**

||Percentag|e of households co<br>Auto|nsuming|Average c|onsumption for all<br>Auto|households|Average|consumption for househo<br>consumption<br>Auto|lds with positive|
|---|---|---|---|---|---|---|---|---|---|
||Purchase|consumption|Total|Purchase|consumption|Total|Purchase|consumption|Total|
|**All**|21.20%|33.00%|48.10%|8,322.46|21,979.48|30,301.94|39,273.56|66,654.80|63,018.39|
|**Residence area**||||||||||
|Urban|34.10%|13.20%|42.00%|14,183.10|9,014.41|23,197.52|41,650.15|68,407.95|55,256.50|
|Rural|13.50%|44.80%|51.70%|4,824.49|29,717.77|34,542.27|35,699.30|66,346.95|66,778.31|
|**Region**||||||||||
|Kayes|25.10%|54.60%|70.60%|9,259.89|33,016.04|42,275.93|36,878.54|60,483.50|59,917.70|
|Koulikoro|13.30%|50.30%|58.00%|3,552.65|27,992.64|31,545.29|26,787.73|55,597.68|54,364.83|
|Sikasso|37.10%|71.20%|90.70%|22,855.75|81,110.92|103,966.67|61,663.67|113,899.93|114,569.77|
|Ségou|11.90%|23.80%|33.70%|3,497.97|4,767.49|8,265.46|29,499.56|19,989.54|24,507.50|
|Mopti|13.30%|14.50%|24.40%|3,439.66|3,786.25|7,225.91|25,925.79|26,149.35|29,658.65|
|Tombouctou|8.90%|9.60%|16.30%|1,238.85|1,362.21|2,601.06|13,929.48|14,198.70|15,941.03|
|Gao|20.50%|1.90%|22.10%|5,158.79|885.74|6,044.52|25,165.03|46,733.75|27,298.29|
|Kidal|9.40%|0.70%|9.40%|2,459.44|50.33|2,509.76|26,233.99|7,040.00|26,770.81|
|Bamako|40.50%|7.40%|43.50%|15,125.78|2,334.42|17,460.21|37,356.38|31,674.81|40,096.08|
|**Quintile**||||||||||
|Q1|9.30%|49.10%|53.50%|2,706.66|24,508.26|27,214.92|29,084.81|49,951.54|50,837.66|
|Q2|14.80%|42.60%|51.20%|5,217.82|29,494.45|34,712.27|35,325.74|69,160.13|67,851.52|
|Q3|17.90%|40.90%|51.40%|8,161.21|29,720.05|37,881.26|45,611.14|72,713.82|73,751.90|
|Q4|22.30%|29.30%|45.60%|9,197.36|23,089.27|32,286.63|41,214.13|78,669.09|70,804.25|
|Q5|32.70%|16.10%|43.10%|12,575.49|9,666.79|22,242.28|38,415.70|60,090.62|51,596.29|

Source: Authors’ estimation using ELIM 2006.
education, less likely to use a community coop and to have shared water source (Table 3). Our results do not qualitatively change if we include these variables in baseline controls.

# 7.2. Attrition

Of the originally selected 886 households, 450 were replaced either because the household barangay officials were not able to identify the household among current barangay residents, or because the field team could not confirm that a child of eligible age and gender was a member of approached household. We do not find difference in the likelihood to be lost to follow-up by treatment status (estimated coefficient is -0.016, with standard error of 0.044).

We also check whether attrition is affected by baseline characteristics in the control group, as well as for evidence of differential attrition by observable characteristics between treatment status. Specifically, we first estimate the probability of receiving the 4P transfer based on the wide range of 2008 characteristics and construct a propensity score. We then check for differential attrition in the following regression: 0 1T T 2 3T T (Eq.2) ii,bb bb ii bb ii ii,bb where is a binary variable equal to 1 if individual AA = ξξ + ξξ TT + ξξ PP _i_ was not tracked, + ξξ TT PP + ςςT T is a barangay level indicator of treatment and ii,bb is the value of propensity score from a regression of treatment status on 2008 bb AA TT household characteristics – we include all variables reported in Table 3. Coefficient ii 2 captures whether PP attrition differs depending on the baseline characteristics which capture the likelihood of participation in ξξ the 4P program among the control group. Coefficient 3 captures whether attrition differs by observable characteristics between our treatment and control group. Table 4 shows that none of the coefficients is ξξ significantly different from 0.

Even though we do not find evidence of differential attrition, given that the fraction of households that we were unable to track is high, we report Lee bounds (Lee, 2009) and Kling-Liebman bounds (Kling and Liebman, 2004) for all the outcomes.

# 7.3. Main effects

## _7.3.1. Impacts on primary outcomes_

As the core objective of CCT programs is to reduce inter-generational transmission of poverty through investment in human capital, we first focus on impacts on education and labor market outcomes. We find no evidence on long-term impacts of the 4P program on completion of compulsory education, years of 16
penditure and hospital visit numbers. In the U.S., each 1 _μ_ g/m<sup>3</sup> increase in PM2 _._ 5 is estimated to increase three-day emergency room (ER) visits by 2.7 per million and inpatient ER spending by $16,000 per million (Deryugina et al., 2019). Using weather variations for identification, Barwick et al. (2018) find that a 10 _μ_ g/m<sup>3</sup> decrease in PM2 _._ 5 reduces annual healthcare spending by over $9.2 billion in China, which is equivalent to 1.5 percent of average annual healthcare expenditure. Deschenes et al. (2017) estimate that NO _x_ and O3 pollution levels dropped by 35 percent and 6 percent respectively after the NO _x_ Budget Trading Program started in participating states. As a result, drug expenditures decreased by 1.9 percent in participating states.

Schlenker and Walker (2015) use network delays to instrument air pollution at California airports. They find a one standard deviation increase in daily airplane taxi time increases CO pollution by 23 percent, and a one standard deviation increase in pollution explains one third of daily asthma admissions. Relying on a driving restriction policy, Zhong et al. (2017) find that a 22 percent increase in NO2 due to traffic congestion increases ambulance calls by 12 percent in Beijing. Apart from direct medical expenditure, some studies also document increases in protective investments. For example, Ito and Zhang (2020) find that household spending on air purifiers increases by $1.34 for each 1 _μ_ g/m<sup>3</sup> increase in PM10 due to the Huai River heating policy. A 100-point increase in AQI increases spending on all masks by 54.5 percent and antiPM2 _._ 5 masks by 70.6 percent (Zhang and Mu, 2018).

We make three contributions to the literature. First, studies on air pollution and its impacts primarily use wind directions to evaluate unequal exposure or instrument pollution. However, actual pollution disperses as a mass with changing directions along its pathways. We combine the HYSPLIT model and Gaussian dispersion to better simulate trajectories. Second, existing papers focus on one region or conduct program evaluations. Our paper analyzes all operating coal power plants worldwide and projects pollution on income maps. This practice provides a global picture of pollution and its coexistence with poverty. Third, while earlier evidence shows that pollution worsens labor productivity, there is no causal evidence that pollution affects aggregate economic outputs. We adopt econometric methods to test changes in GDP per capita, GDP, and HDI due to plant operations.

# **3 Data and methods**

## **3.1 Data**

In this study, we analyze air pollution originating from coal-fired power plants. Basic characteristics of coal plants are derived from the Global Coal Plant Tracker developed by CoalSwarm 5
Table 4 Korea's Policy Experiments to Reduce Underreporting in Retail Sectors

||Seller (pushing)|Buyer (pulling)|
|---|---|---|
|Deterrent|<S‐D cell><br>Monitoring and auditing (UR)<br>Penalties for no receipts (UR)<br>Mandatory use of CR (UR)|<B‐D cell><br>NA|
|Encouraging|<S‐E cell>|<B‐E cell>|
||Tax incentives for CR receipts (UR)|Cash compensation for receipts<br>(UR)|
||Tax credit for credit card sales (UR)|TIETP (UR)|
|Normative|<S‐N cell>|<B‐N cell>|
||Campaign and education on receipt<br>exchange (UR)|Campaign<br>and<br>education<br>on<br>receipt exchange (UR)|

_Source:_ Authors’ depiction of policy shift.

Korea’s experience prior to introducing TIETP was that deterrent measures were ineffective and had been complete failures in terms of enforcement. Normative approaches were also not effective in Korea, a strongly government‐led country in which public education and awareness campaigns, intrusive government guides, and recommendations were rampant. In addition, pushing policies targeting business sellers, including tax incentives for CR receipts, was ineffective, because tax benefits given to sellers could not exceed the illegal gains obtainable from underreporting, especially in a society with low tax morale. Therefore, the question became whether a pulling policy encouraging end‐consumers to provide third‐party information would be effective in reducing underreporting. Under the invoice credit VAT system, business buyers report business sellers’ sales by claiming their input VAT credit in B2B transactions. In B2C transactions, however, end‐consumers do not provide tax authorities with any information on business sellers’ sales. Therefore, pulling policies encouraging end‐consumers to play an active role in providing such information could be a very powerful tool for unveiling hidden transactions in cash businesses. If end‐consumers, encouraged by tax incentives, used electronically traceable payment means for purchases, tax authorities could access secure and reliable sales data on cash‐ intensive businesses. In the period after Korea revoked what hindsight shows to have been a poorly managed and failed receipt compensation policy, greatly different economic and social 22
**Table 3: Number of Households in the** **_Shombhob_ panel sample**

||**Jaldhaka (**<br>**Treatment (T)**|**rural)**<br>**Control (C)**|**Naraya**<br>**Treatment**|**nganj**<br>**(T)**|**(urban)**<br>**Control (C)**|
|---|---|---|---|---|---|
|Nutrition only|224|440|41||101|
|Education only|195|501|15||123|
|Nutrition + Education|281|302|34||107|
|None|-|344|-||10|
|Total hhs|700|1,587|90||341|
|Attrition|114|||173||

These same households participated in a follow-up end line survey fielded in May/June 2013, 13 months after receipt of the first _Shombhob_ payment. The follow-up survey used the original baseline questionnaire and respondent lists. To ensure there were enough households in each component of the treatment and control groups, a sample of 3,005 households was interviewed. Based on these three sets of data (from the census, baseline and endline surveys), a panel of 2,718 households remained to conduct our analysis.<sup>14</sup> Table 3 presents the different samples in the data set. In Jaldhaka, 700 households received at least one _Shombhob_ transfer, 1,587 did not receive any, and 114 households interviewed during the baseline were not found when the enumerators came back for the endline survey. In Narayanganj, 90 households received at least one _Shombhob_ transfer, 341 did not receive any, and 173 households could not be located at the time of the endline survey. The high attrition rate in Narayanganj while not unusual given the high frequency of in and out migration among the residents of urban slums, is clearly a challenge for any social program targeted towards the urban slum population. Due to this high attrition rate in Narayanganj, the impact evaluation was restricted to the Jaldhaka sample.

# **3.2 Methodology**

To determine the impact of _Shombhob_ we use the Difference-in-Differences (DiD) or double difference methodology which compares the outcomes of beneficiary households and their

> 14 2,718 corressponds to 3,005 panel households minus 287 households that moved out of the pilot location.

13