rafmacalaba/datause-encoder-data huggingface dataset:
**Table 1: Student Reading Achievement Trends and Estimated Decline in Scores due to School Closures, EU Countries Only**

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Departure from linear trend in 2021|-9.11***|-14.86***||-0.40|-2.25|
|_Principal report on how many weeks sc_<br>>8 weeks|_hool was affected_||-18.75***|||
|0-2 weeks(difference from_>8 weeks_)|||11.59***|||
|2-8 weeks(difference from >_8 weeks_)|||4.25|||
|_UNESCO data on school closures_<br>weeks with full closures||||-1.14***||
|weeks full or partial closures|||||-0.41***|
|_Additional controls_||||||
|country fixed effects|Yes|Yes|Yes|Yes|Yes|
|country*year|Yes|Yes|Yes|Yes|Yes|
|student grade|No|Yes|Yes|Yes|Yes|
|student age|No|Yes|Yes|Yes|Yes|
|country average student age|No|Yes|Yes|Yes|Yes|
|N of students|459039|458869|449734|458869|458869|

* p<0.05; ** p<0.01; *** p<0.001

Another way of estimating the impact of school closures is to compare the departure from the learning achievement trend between countries with shorter and longer school closures, according to UNESCO country-level data. These results are presented in columns (4) and (5) using the number of weeks of full school closures and the sum of weeks of partial and full school closures. In both cases, for the EU, countries with no closures achieved 2021 results as predicted based on their previous achievement (with an insignificant overall decline by -0.48 points and -2.25 points, depending on the specification). However, the results decline by 1.14 points every week of full closures in EU countries. Thus, ten weeks of closure resulted in 11.9 points decline (or 16% of within-countries SD), while 25 weeks of closure resulted in 29 points decline or 39% of within-countries SD (the most prolonged full closures were in Poland and lasted for 26 weeks). When considering the total number of full or partial closures weeks, the score decline is 0.41 points per week, but in this case, the closures are longer, ranging from 12 weeks (France) to 49 weeks (Latvia). Taking all ECA+ countries, estimates are not significant for the weeks of full closures with the significant average decline of 15.7 points. For the total number of full and partial closures the results show a decline in achievement by 0.32 for every week with an overall decline by 7.9 points. Thus, for 25 weeks of full or partial closures, the estimated decline is 15.9 points, and 23.9 points for 50 weeks.

7
the coefficient on the age group dummy 40-49 between return migrants and non migrants, suggesting that the age effect is concentrated among individuals that are close to retirement age. When interpreting our results, it is important to keep in mind that our sample of return migrants only consists of return migrants who came back home in the last 5 years. As a result, inference from our analysis can only be drawn for the population of recent returnees. In particular, older returnees as captured by the survey data are also individuals who returned home at an older age.

# **5.2 Occupational choice**

Conditional on being employed, the occupational choices of return migrants may also systematically differ from those of nonmigrants. We first examine the sectoral choice of return migrants relative to nonmirants, which has not been much examined by prior literature. As migrants from South Asia acquire sector-specific experience overseas, primarily in the construction sector (Ahmed and Bossavie 2021), they may be more likely to operate in those sectors after returning home to take advantage of the wage returns to experience. We first look at the unconditional distribution of return migrants across sectors of activity, compared to nonmigrants. As shown in Table 2, return migrants to South Asia are more likely to be employed in the construction sector back home compared to nonmigrants in Nepal and Pakistan, but not in Bangladesh. The difference in sectoral distribution between nonmigrants and return migrants is statistically significant in Nepal and Pakistan, while it is insignificant in Bangladesh. In Table 6, we estimate the likelihood of being employed in four main sectors of activity in a multinomial regression setting, controlling for workers’ observable characteristics. In all three countries, being a return migrant increases the likelihood of being employed in the construction sector. Return migrants thus disproportionately select into occupations they were employed in at destination when they return home.

A growing body of literature also shows that return migrants are likely to become selfemployed or entrepreneurs in various contexts. When options for formal wage employment at home are limited, temporary migration can be a way for aspiring entrepreneurs to ease credit constraints at home by accumulating financial capital abroad and start a business after return (Dustmann and Kirchkamp 2002; Bossavie, Gorlach, et al. 2021). The higher propensity to become self-employed among return migrants has been evidenced by Dustmann and Kirchkamp (2002) in Turkey, Mesnard (2004) in Tunisia, Piracha and Vadean (2010) in Albania, and Wahba and Zenou (2012) and Wahba (2015) in the Arab Republic of Egypt. In the context of South Asia, earlier work by Arif (1998) and Ilahi (1999) in Pakistan also reports that return migrants are more likely to be self-employed compared to nonmigrants.

14
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Table 6: Market Access and Sales at the market: Robustness Check

||Prin. Component<br>Market Facilities<br>|Tomato<br><br>Sub-s|Mango<br><br>amples|
|---|---|---|---|
||(1)|(2)|(3)|
|Market Access Index|0.02169|0.02038|0.02777|
||(4.67)***|(4.23)***|(1.83)*|
|Land owned(log)|0.00900|0.12073|-0.02615|
||(0.67)|(3.77)***|(0.20)|
|Quantity sold (log)|0.01205|0.03892|0.16726|
||(3.71)***|(3.22)***|(2.34)**|
|Market Access*Land|-0.00237|-0.00819|-0.01237|
||(3.77)***|(6.05)***|(1.85)*|
|Pseudo R<sup>2</sup>|0.5603|0.5186|0.4802|
|LogLikelihood|-96.788715|-25.90149|-92.17213|
# **Table 2: Growth in surveys vs national accounts – bias and errors**

# _Panel A. Growth gap between survey mean and HFCE mean_

|||**All surveys**<br>|<br>||**C**<br>|**onsumption s**<br>|**urveys**<br>|||**Income sur**<br>|**veys**<br>||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Group**|**Obs.**|**Bia**<br>**s**|**S.E.**|**Error**|**Obs.**|**Bias**|**S.E.**|**Error**|**Obs.**|**Bias**|**S.E.**|**Error**|
|All spells|15062 (133)|-0.25|0.18|2.25|5068 (108)|-0.53 ***|0.18|2.35|9994 (61)|0.04|0.17|1.79|
|All 1 year spells|9588 (128)|-0.13|0.18|1.89|3064 (102)|-0.31 *|0.18|1.96|6524 (59)|0.10|0.14|1.43|
|All 1 to 3 year spells|3310 (97)|-0.28|0.26|3.39|1103 (54)|-0.84 ***|0.26|4.17|2207 (60)|0.07|0.34|2.91|
|All 4 to 6 year spells|3066 (119)|-0.25|0.20|2.26|1133 (94)|-0.53 ***|0.20|2.43|1933 (60)|0.10|0.18|1.62|
|Comparable spells only|7577 (122)|-0.29|0.19|2.44|2322 (80)|-0.89 ***|0.19|2.98|5255 (59)|0.11|0.17|1.95|
|Comparable, bal, 1-3 year spells|2639 (88)|-0.01|0.29|3.24|820 (44)|-0.99 ***|0.29|4.15|1819 (58)|0.39|0.29|2.78|
|Comparable, bal, 4-6 year spells|2017 (88)|-0.44 **|0.18|1.86|599 (44)|-1.28 ***|0.18|2.43|1418 (58)|0.03|0.18|1.54|
|1980s|74 (20)|1.12|0.90|3.08|22 (6)|-0.59|0.90|1.50|52 (14)|1.85|1.23|3.76|
|1990s|413 (71)|-1.57 ***|0.40|3.67|153 (50)|-2.06 ***|0.40|3.57|260 (39)|-0.95 **|<br>0.44|3.27|
|2000s|2010 (102)|-0.18|0.23|2.82|772 (62)|-0.65 ***|0.23|3.19|1238 (53)|0.59 *|0.33|2.60|
|2010s|2418 (94)|-0.41 *|0.21|2.10|722 (68)|-0.94 ***|0.21|2.11|1696 (56)|0.24|0.19|1.65|
|Low income|372 (39)|-0.41|0.64|3.59|361 (38)|-0.18|0.64|3.44|11 (2)|-4.77|4.92|5.24|
|Lower Middle Income|2253 (65)|-1.10 ***|0.39|3.21|1202 (53)|-1.66 ***|0.39|3.27|1051 (18)|-0.36|0.80|3.69|
|Upper Middle Income|3085 (45)|-0.32|0.23|2.41|964 (32)|-0.70 ***|0.23|2.58|2121 (26)|0.27|0.42|2.48|
|High Income|5872 (40)|0.12|0.19|1.61|811 (29)|-0.59 ***|0.19|1.79|5061 (38)|0.33 *|0.17|1.50|
|East Asia & Pacific|1029 (12)|-0.39|0.63|2.37|856 (8)|-0.73|0.63|2.48|173 (5)|-0.11|0.79|2.10|
|Europe & Central Asia|8109 (47)|-0.20|0.17|1.93|2973 (45)|-0.69 ***|0.17|2.11|5136 (33)|0.34 **|* 0.13|1.39|
|Latin America & Caribbean|4131 (19)|-0.30|0.42|2.33|112 (3)|-3.85 ***|0.42|4.15|4019 (19)|-0.21|0.43|2.30|
|Middle East & North Africa|244 (9)|-0.79 **|0.34|2.14|196 (7)|-0.56|0.34|1.63|48 (2)|-1.57|1.35|3.93|
|North America|1114 (2)|-0.87 **|0.37|0.94|496 (1)|-1.53 ***|0.37|1.57|618 (2)|-0.71 **|* 0.21|0.80|
|South Asia|170 (6)|-0.29|0.58|1.94|170 (6)|-0.29|0.58|1.94|||||
|Sub-Saharan Africa|265 (38)|-0.06|0.50|2.70|265 (38)|-0.06|0.50|2.70|||||
Party workers conducted a “People’s Campaign” to train citizens in deliberative planning processes, which resulted in remarkably effective local government (Heller, Harilal and Chaudhuri 2007). This campaign built on years of progressive left-leaning rule and effective land reform, sharp reductions in social inequality, high levels of civic participation, and extremely effective human development investments. In Kerala, moreover, where it is mandated that 40 percent of the state’s budget be allocated via GPs, these local bodies have substantial resources. Yet in Kerala, as elsewhere, the GS’s primary function as a part of the nationwide governance system is to select beneficiaries and allocate public goods.

In terms of their location in the structure of governance, GPs and GSs are the lowest level in a hierarchy that reaches upwards from the village to the county (Block), the district (Zila), the state, and the central government in Delhi. This entire system is staffed by elected representatives and works within the framework of the Indian constitution, which adopts affirmative action policies in order to address social and economic inequalities. The state, therefore, plays an active redistributive role. Wealth is redistributed to the poorest citizens via a technocratic process using lists of “scheduled castes” and “below poverty line” families assessed via surveys. Quotas for elected positions within the government, including seats for GP presidencies and ward representatives, are reserved for SCs in proportion to their population in the village, and a third of the seats are also reserved for women. Quotas are provided for SC enrollment in educational institutions and for government jobs. In addition, a slew of anti-poverty programs implemented through GPs are meant to allocate resources (such as concrete 14
Using Probabilistic Models to Appraise and Decide. Ley-Borrás and Fox

- Keeney, R.L. (2008) Using Decision Sciences Concepts to Think Clearly About Policy Decisions, Conference and Book Writing Honoring the Work of Howard Kunreuther Dec. 4‐5, 2008. The Wharton School, U. of Pennsylvania.

- Ley‐Borrás, R. (2009) _Integral Decision Analysis_ ( _Análisis de Decisiones Integral_ ), Consultoría en Decisiones (decidir.org), Mexico.

- Ley‐Borrás, R. (2014) Deciding on the Decision Situation to Analyze: The Critical First Step of a Decision Analysis, _Decision Analysis_ , Forthcoming.

- Mechler, R. (2005) Cost‐benefit analysis of natural disaster risk management in developing countries. Eschborn:Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ), quoted in (Michel‐Kerjan et al. 2012).

- Michel‐Kerjan, E., S. Hochrainer‐Stigler, H. Kunreuther, J, Linnerooth‐Bayer, R. Mechler, R. Muir‐Wood, N. Ranger, P. Vaziri, M. Young (2012) Catastrophe Risk Models for Evaluating Disaster Risk Reduction Investments in Developing Countries, Working Paper # 2012‐07, Risk Management and Decision Processes Center, The Wharton School, University of Pennsylvania.

- Rose, A. (2007) “Macroeconomic Modeling of Catastrophic Events,” in J. Quigley and L. Rosenthal (eds.) _Real Estate, Catastrophic Risk, and Public Policy_ , Berkeley, CA: Berkeley Press.

- World Bank (2013) _World Development Report 2014: Risk and Opportunity – Managing Risk for Development_ . Washington, DC: World Bank. doi: 10.1596/978‐0‐8213‐9903–3.

- World Bank and G20 (2013) _Improving the Assessment of Disaster Risks to Strengthen Financial Resilience._ A Special Joint G20 Publication by the Government of Mexico and the World Bank, June 2013.

**19**
**Table. Cost of internal and external risk-financing solutions (in% of average yields)**

||Average op<br>|erating profi<br>|t margin (in<br>|% of averag<br>|e yields)<br>|
|---|---|---|---|---|---|
||5%|10%|15%|20%|25%|
|Cocoa||||||
|Cat insurance cost|0.72%|0.72%|0.72%|0.72%|0.72%|
|Non-cat insurance cost|5.49%|4.47%|3.71%|3.11%|2.57%|
|Reserve load|7.08%|6.83%|6.51%|5.36%|4.62%|
|Hedging costs|6.21%|5.19%|4.43%|3.83%|3.29%|
|Banana||||||
|Cat insurance cost|0.29%|0.29%|0.29%|0.29%|0.29%|
|Non-cat insurance cost|3.07%|2.01%|0.54%|0.36%|0.28%|
|Reserve load|2.25%|1.47%|0.51%|0.31%|0.14%|
|Hedging costs|2.54%|2.76%|0.80%|0.60%|0.43%|
|Source: Authors.||||||

# **_3.3. Toward a New Typology of Crop Business Activities_**

The crop business viability is related to the ability of the crop business to survive in the long run the exogenous shocks that occasionally generate negative returns. It determines how financially strong a farmer should be to absorb from time to time adverse production shocks without losing the overall long-term profitability of the business.

The above discussion allows us to draw a typology of crop business activities based on farmers viability in the long run, which is illustrated in Figure 3. In Quadrant I, the farmer’s asset base is large and the crop business is highly profitable. As a result, this crop business activity is viable and external risk financing solutions may enhance the crop business viability. Quadrant II includes farmers who are productive but asset poor. Market-based solutions could help them to restore the viability of the crop business by providing external capital to finance infrequent adverse production shocks. The farmer would not be able to absorb such shocks without external capital, thus jeopardizing the survivability of the crop business in the long run despite its profitability, which highlights the real value added of market-based risk financing solutions. On the contrary, market-based instruments are not a solution to farmers with a small asset base and a low profitable business, depicted in Quadrant III. These farmers have neither the financial capacity to absorb exogenous shocks because they are asset poor, nor the possibility to transfer part of these production shocks to private markets because their profit margin is too low to cover the hedging costs. Therefore, these crop business activities are not viable in the long run without public support which may or may not be socially desirable. Finally, crop businesses located in Quadrant IV rely on the farmer’s large asset base to offset the low profitability of their crop business. However, these activities are not sustainable in the long run as the assets will be eventually depleted because of frequent occurrences of adverse events.

11
<!-- Start of picture text --> Intensive margin: higher<br>volumes of existing products<br>to existing destinations<br>| Quality margin:<br>bewenn nen nncerntanna nd higher qualityin existing<br>products<br>2 New products<br>Export growth Extensive margin: new trade<br>New destinations<br>Sustainability margin: entry<br>and survivalof new products<br>and destinations<br>Source: Authors, derived from Cadot et al (2011)<br><!-- End of picture text -->
Table 5: Conditional Average Treatment Effect: Skills Index

||(1)<br>Low Predicted CATE|(2)<br>High Predicted CATE|(3)<br>Diff.|(4)<br>_p_-value|
|---|---|---|---|---|
|Variable|||(1)-(2)|(1)-(2)|
|Conditional Average Treatment Effect(CATE)|0.085|0.248|-0.163|0.000|
|Female (%)|0.508|0.478|0.030|0.065|
|Age (years)|6.429|6.488|-0.060|0.000|
|Height-for-age z-score|-2.065|-2.176|0.111|0.003|
|ASQ Communication|-0.185|0.191|-0.376|0.000|
|ASQ Gross Motor Coordination|-0.109|0.115|-0.224|0.000|
|ASQ Fine Motor Coordination|-0.253|0.251|-0.503|0.000|
|ASQ Problem Solving|-0.363|0.297|-0.660|0.000|
|ASQ Socio-personal|-0.042|0.035|-0.077|0.022|
|Caregiver: Speaks Portuguese (%)|0.153|0.151|0.003|0.829|
|Caregiver: No schooling (%)|0.392|0.491|-0.099|0.000|
|Caregiver: Years of schooling|2.628|1.908|0.720|0.000|
|Caregiver: Is illiterate (%)|0.795|0.832|-0.037|0.004|
|Stimulation Index (Activities with children)|7.080|7.482|-0.402|0.001|
|Household Size|4.926|5.369|-0.444|0.000|
|Number of children aged 0-18 in Household|2.968|3.315|-0.346|0.000|
|<br>Number of children aged 3-5 in Household|1.077|1.071|0.006|0.502|
|Household receives remittances|0.102|0.097|0.004|0.668|
|Wealth index of household|-0.358|0.446|-0.804|0.000|
|Observations||3680|||

_Notes_ : The table reports the descriptive statistics of target children in the bottom 50% (column 1) and top 50% (column 2) of the predicted Conditional Average Treatment Effect (CATE) on the skills index. Column 3 reports the difference between column 1 and 2. Column 4 shows the _p_ -value of the t-test adjusted for multiple hypothesis testing.

sured using the ASQ). For preschool attendance and primary school enrollment, target children with lower ASQ scores at baseline benefited more from the program. However, the opposite is true for the skills index: here, target children with higher ASQ scores at baseline benefited more. The difference in results here may be due, in part, to dynamic complementarities in skills formation, where skills produced at earlier ages increase the productivity of investment at later ages (Heckman et al., 2010; Conti et al., 2016; Garcıa and Heckman, 2014). As highlighted in our discussion of mechanisms, parental practices matter: predicted impacts are higher across all three outcomes for children whose parents scored higher on the parental stimulation index at baseline. Across the three outcomes, we only observe statistically significant gender heterogeneity for the skills index, where girls were 3.5 percentage points more likely to be represented among students with a lower CATE (p-value of 0.03).

28
# **4.5 How do the different methods compare in terms of power for detecting a given treatment effect?**

To compare the power of the different methods we simulate a treatment effect by adding a constant to the follow-up outcome variable for the treatment group. We simulate constant treatments which add 1000 Rupees (25 percent of average baseline profits) to the Sri Lankan microenterprise profits; add 920 pesos (20 percent of average baseline income) to the Mexican labor income; add 0.4 (0.5 standard deviations) to log expenditure in Indonesia, and add 0.25 standard deviations to the Pakistan math test scores and child height z-scores. For the schooling treatment, we randomly set one in three schooling drop-outs to stay in school. These treatments are all relatively small in magnitude for the sample sizes used, so that we can see differences in power across methods, rather than have all methods give power close to one.

Table 6 then summarizes the power of a hypothesis test for detecting the treatment effect, taking as the t-test on the treatment coefficient in a linear regression of the outcome variable on a constant and a dummy variable for treatment status. We report the proportion of replications where this test would reject the null hypothesis of no effect at the 10 percent level. Panels A and C report results when the regression model does not include controls for the method of randomization, while Panels B and D report the power when stratum or pair dummies, or the variables used in checking balance for rerandomization methods are included. The results for the pure random sample in panels B and D include these same set of seven baseline controls, to enable comparison of ex-post controls for baseline characteristics to ex-ante balancing.

Table 6 shows that if we do not adjust for the method of randomization, the different methods often perform similarly in terms of power, and in cases where they differ, it is because the methods which pursue balance have less power than pure randomization. For example, with a sample size of 30, the power for both the height and math test-scores is approximately 0.17 under a single random draw, but can be as low as 0.018 for the math test score under pair-wise matching, and as low as 0.052 for the height z-score with the minmax method. Adding the stratum and pair dummies or baseline variables used for re-randomizing increases power in almost all cases. Some of the increases in power can be sizeable – the power increases from 0.018 to 0.304 for the math

- 24 -
(those who enrolled and finished the bootcamp) with individuals in the control group who took an alternative course. Figure 5 shows the distributions of such scores. When we compare the scores, the treated group shows a score that is about one point higher (18 percent, 0.3 standard deviations) relative to the control group ( _p_ = 0 _._ 011). This can be taken as a piece of evidence that may indicate that the bootcamp evaluated in this study is of higher quality than the alternatives taken by some individuals in the control group.

# **4.4 Employment in Technology**

Having shown that the program has strong positive effects on skills, the next step is to investigate whether beneficiaries actually use the program as a mean to help them transition into the technology sector. In order to understand this, we analyze the program effects on three outcomes of interest. First, a dummy variable that indicates whether, in February 2020 (5 months after the intervention ended in Argentina, 2 months in Colombia) the individual is working on a job that requires coding (we call this variable "In _tech_ working"). Second, a dummy that indicates whether at follow-up the individual is acquiring formal tertiary or university education on a computer science related field (we call this variable "In _tech_ studying"). Last, we create a dummy that subsumes the previous two by taking a value of 1 if the individual is either working or studying in tech, 0 otherwise (we call this variable "In _tech_ (working or studying)").<sup>29</sup> Data Appendix A.1 explains in detail how these variables were measured and constructed. As described in section 3.1, the employment outcomes that we analyze in this section and in section 4.5 use retrospective data collected in May 2020 but referring to February 2020. This is to identify the bootcamp impacts on the main employment outcomes before the COVID-19 outbreak affected labor markets in Argentina and Colombia.<sup>30</sup> Panel B of Table 5 presents the estimates of the impacts of the training on these outcomes. We observe that the program increases the probability of working at a technology job by 9.2 percentage points, which represents a 38 percent (0.2 standard deviations) increase relative to the control group. While we do not observe an impact of the program on the acquisition of formal tertiary or university computer science education, we do find

> 29Being working or studying in _tech_ are not mutually exclusive, and this why, as observed in Table 5, the means of the first two dummies add up to a fraction slightly larger than the mean for the third one. We did not pre-specify this third outcome.

> 30Both countries experienced the outbreak of COVID-19 cases long after the Northern Hemisphere. The first measures of lockdowns in Buenos Aires occurred on March 20, 2020, while in Bogotá it was on March 25, 2020. These lockdowns happened still when both cities had very few cases.

19