rafmacalaba/data-use-mentions-extended huggingface dataset:
Overall wage work is negatively associated with both enrollment and attendance disproportionately more than household chores, regardless of gender or work intensity. Intensity seems to play a smaller role, but still matters for girls participating in household chores. Moreover, it is important to remember that even though working longer hours might not directly be correlated with enrollment and attendance, it could affect learning outcomes as it reduces the available time to study and do homework as shown in other studies (Emerson, Ponczek and Souza 2014;, Heady 2003;, Patrinos and Psacharopoulous 1997). The analysis highlighted some other important factors that contribute to the probability of being enrolled in school. The education level of the household and who holds the highest level of education, the level of household welfare, income and whether they receive remittances, and having an alive parent positively affect schooling, while the child’s age, the number of siblings, disabilities and having a female head of household negatively affect our outcomes of interest. However, as Myanmar quickly developed after 2015 before being hit by multiple crises in the early 2020s, the findings might not reflect this changing socioeconomic environment and the most recent challenges faced by Myanmar.

# Bibliography

Akabayashi, Hideo, and George Psacharopoulos. 1999. "The trade‐off between child labour and human capital formation: A Tanzanian case study." _Journal of Development Studies_ 35: 120-140.

- Allais, Federico Blanco. 2009. _Assessing the gender gap: Evidence from SIMPOC surveys._ International Labour Organization. International Programme on the Elimination of Child Labour (IPEC), Statistical Information and Monitoring Programme on Child Labour (SIMPOC). Geneva, Switzerland: ILO.

- Basu, Kaushik, and Pham Hoang Van. 1998. "The economics of child labor." _American Economic Review_ 88 (3): 412-427.

Beegle, Kathleen, Rajeev Dehejia, and Roberto Gatti. 2009. "Why should we care about child labor? The education, labor market, and health consequences of child labor." _Journal of
_Source:_ Authors’ analysis based on Economic Census and data from Encuesta Nacional de Ocupación y Empleo and Encuesta Nacional de Empleo.

_Note:_ * Sum of legal and formal and mixed. ** Sum of legal and illegal; see table S1.1 in appendix S1.

Employment reflects these changes, and grows more in smaller than in larger ones (that is, in firms that are less intensive in more highly educated workers). These differences are magnified if we focus on firm types: employment in informal firms increases by 114.5 percent vs. 5.7 percent in formal ones. These trends are noteworthy given that, during this period, there were large improvements in the schooling composition of the labor force; so one cannot argue that the absence of a growing supply of educated workers is responsible for them.

# **Absent Increasing Misallocation, There Would Be Increasing Excess Demand for Skilled Workers**

The trends in the size and type distribution of firms can be contrasted with the trends in the supply of workers by schooling levels as shown in table 5. While the periods do not match exactly, the overlap is substantial. The key point is that, over practically the same period, the educational composition of the working-age population (potential supply) and the economically active population (observed supply) changed importantly as the number of educated persons and workers increased more rapidly than the average of all persons and workers (and, indeed, the number of those with the least education fell in absolute terms). In parallel, however, the degree of the misallocation of resources increased, and always in the direction of firms that are less intensive in educated workers. So, the broad picture that emerges is that of an economy within which, on one hand, the supply of educated workers is increasing relatively quickly but, on the other, given that misallocation is also increasing, firm and employment growth is faster in the informal sector.

To illustrate the effects of these trends, equation (3
in effect the percent change in the non-agricultural

> 25 This is in addition to being robust against different measurement issues, including different metrics for poverty (poverty gaps, percentage point changes) and metropoles (750,000 inhabitants instead of 1 million as cut-off), as well as the decomposition of growth in its sources (agriculture and non-agriculture) and non-linearity in the population shares (Christiaensen and Todo, 2013).

> 26 Taiwan, China, and South Korea experienced for example a similar per capita GDP growth of 7.1 percent between 1965 and 1990.  Both countries also started at similar levels of inequality (a Gini of about 0.32). Yet throughout the subsequent decades inequality has been lower in Taiwan, China, and higher in South Korea. Taiwan’s economic development has been based on the development of more labor intensive small and medium enterprises located in rural and suburban areas, while South Korea’s development has been led by more capital intensive urban based, large enterprises.

> 27 Similar to Datt and Ravallion’s (1996) analysis of Indian data, they also relate the effect of urban service growth on poverty to the more labor-intensive, low capital, lower skilled part of the urban service sector.

20
# **_C2.Wage premiums and relative supplies: evolution and scatter plots by country_**

## **Figure C1**

**Skilled-unskilled wage skill premium and relative supply over time Deviations from each country’s mean**

<!-- Start of picture text --> ARG BOL BRA CHL<br>COL CRI ECU HND<br>MEX NIC PAN PER<br>PRY SLV URY VEN<br>1990 1995 2000 2005 2010 1990 1995 2000 2005 2010 1990 1995 2000 2005 2010 1990 1995 2000 2005 2010<br>Wage Premium Relative Supply<br>.4 .4 .4 .2<br>.2 .2 .2 .1<br>0 0<br>-.2 0 0 -.1<br>-.4 -.2 -.2 -.2<br>.4 .2 .4<br>.2<br>.2 .1 .2<br>0<br>0 0 0<br>-.2 -.2 -.1 -.2<br>-.4 -.4 -.2 -.4<br>.2 .4 .2 .2<br>.2 .1<br>0 0 0 0<br>Skilled-Unskilled -.2 -.2 -.1<br>-.2<br>-.4 -.4 -.2<br>.4 .2 .1 .4<br>.2 0 0 .2<br>0 -.1 0<br>-.2<br>-.2 -.2 -.2<br>-.4 -.4 -.3 -.4<br><!-- End of picture text --> Source: own computations based on SEDLAC (CEDLAS and World Bank).

46
on the occurrence of financial crises are from Laeven and Valencia (2013). We also identify recessionary periods based on Braun and Larrain (2005) as in Abiad et al. (2011). Specifically, we identify recessions using real annual GDP data from the World Bank World Development Indicators (WDI) for the 1995-2015 period. First, we extract the trend in the logarithm of real GDP using a Hodrick-Prescott filter with parameter 6.25. Second, we identify recessions whenever the detrended series exceeds one country-specific standard deviation below zero. Third, we identify the start of a recession as the year following the peak until the trough.

# **3.4 Descriptive statistics**

Table 1 reports descriptive statistics for the main variables used in the empirical analysis, at the country-year level for financial variables and at the country-industry-year level for export variables (see Supplementary Table S1 for summary statistics for the individual countries); in Figure 1, we display the relationship among key variables. There is substantial variation in financial structures across countries. Looking at the relative importance of banks versus stock markets, the ratio of bank credit over the sum of stock market capitalization and bank credit equals 43% at the 25th percentile and 75% at the 75th percentile. To grasp the magnitude of these figures it is useful to observe that for Germany, an advanced country in our sample with a developed stock market but with a traditionally bank-oriented financial system, the ratio equals about 71%. Croatia, a Balkan country that features a relatively well-developed stock market, has a ratio of about 56%, and, further down in the scale, Mexico has a ratio of approximately 38%. Outside our sample, in 2000, the United States, a country often taken as an example of highly market-oriented financial system, the ratio approximately equaled 24%. Corporate bond markets tend to be smaller than stock markets, and in fact in our sample at the 25th percentile the ratio of bank credit to the sum of bond market capitalization and bank
.|23|
|Table4|Per capita risk to assets and wellbeing for the poorest quintile .|24|
|Table5|Total household expenditures, by district, as reported by the<br>2016HIES.<br>. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .|42|
|Table6|Asset vulnerability categories . . . . . . . . . . . . . . . . . . . .|46|

# abstract

Traditional risk assessments use asset losses as the main metric to measure the severity of a disaster. This paper proposes an expanded risk assessment based on a framework that adds socioeconomic resilience and uses losses as the main mea- wellbeing sure of disaster severity. Using an agent-based model that represents explicitly the recovery and reconstruction process at the household level, this risk assessment provides new insights into disaster risks in Sri Lanka. The analysis indicates that regular
# 1. Introduction

Child work is a cause and a consequence of poverty and lack of opportunity. Children<sup>1</sup> in poor households in low- and middle-income countries may contribute to their families’ welfare by doing household chores or paid work outside the home. Despite the long-term returns to education, the short-term high marginal returns to labor provide a rationale for child work to exist and illustrate the fundamental difficulty in reducing it (Beegle, Dehejia and Gatti 2009). Moreover, although child work can negatively affect education and schooling outcomes, the reverse is true as well: child work can help children be more productive, raising their prospective earnings and providing incentive to join the labor force (Phoumin and Fukui 2006).

There is an important difference between child labor and child work. According to the International Labour Organization (ILO), children’s participation in work that does not affect their health and personal development or interfere with their schooling is generally considered as being something positive: it equips them with skills and experience and helps prepare them to be productive members of society in adulthood. By contrast, child labor is usually defined as work that deprives children of their childhood, their potential and their dignity. This refers to work that (1) severely interferes with their ability to attend school, which ultimately harms income-earning potential in adulthood, and (2) is mentally and physically dangerous for children. Child labor depends on the child’s age, the type and hours of work performed, the conditions under which it is performed, and the objectives pursued by individual countries.<sup>2</sup> Child labor has long been a feature of Myanmar’s economy, and has translated into a large and unprotected child labor market countrywide. Data from ILO’s 2015 Labor Force Survey (LFS) indicates that 9.3 percent of Myanmar’s total child population (12.14 million children aged 5-17 years) were engaged in child labor at the time of
be more frequent and cause greater damage (Hopkins 2002). Household crime studies are also unable to account for the financial costs incurred by the victim due to the criminal activity, as most of the surveys are unable to account for the value of property loss. Furthermore, even though household-level crime measures are more accurate than police reports, there is still the difficulty of drawing general policy inferences based on household data, as they tend to be country- or cityspecific. To elucidate this point, the issue with household surveys is that they tend to be undertaken using different sampling methodologies across different countries, and thus the derived crime rates are not comparable between countries. Thus, crime statistics from household crime surveys in different countries cannot just simply be pooled together. Finally, these household surveys typically collect data for one period of time and thus studies based on these data face the issue of time invariant omitted variables.

Our approach in this paper is to exploit cross-country heterogeneity in levels of police size to explain differences in the losses due to crime experienced by firms. Our framework uses firm-level characteristics and several macro-economic factors as explanatory variables for crime experienced by firms. The empirical strategy is based on combining firm-level survey data that are collected using a similar sampling methodology with country-specific police data from the United Nations Survey on Crime Trends and the Operations of Criminal Justice Systems. Concerns about simultaneity bias are alleviated in our use of aggregate police variables because crime experienced at the firm level should not affect country-level variables such as our measure of police size. However, endogeneity may arise because the mean country-level crime experienced by firms may affect our police measure, and thus we lag our police variable. We find that an increase in police per 100,000 of population by 1 percent is associated with a 0.43 percent reduction in the losses due to crime. The results are strengthened for estimations using firm-level fixed effects for a subset 5
|
|ITT Leader|0.02|-0.01|0.04|0.00|
||(0.10)|(0.09)|(0.09)|(0.11)|
|Observations|577|577|577|577|

_Note:_ The dependent variable in the first column is the first principal component of 3 sub-scales of the early childhood behavior questionnaire: effortful control (column 2), negative affectivity (column 3) and socialibility (or extraversion, column 4). Controls in panel A and B as in Table 3, controls in panel C as in Table 4. Standard errors (in parentheses).<sup>_∗_</sup> p<0.1,<sup>_∗∗_</sup> p<0.05,<sup>_∗∗∗_</sup> p<0.01 57
|8,054|
|Parental controls|Yes|Yes|Yes|Yes|
|Decades of migration dummies|Yes|Yes|Yes|No|
|Labor market controls in Egypt|No|No|No|Yes|
|Predicted log wage: Non-mig.=1.|515; Illegal mig|.=1.355; Legal|mig.=1.777||

Robust standard errors in parentheses. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1 _Notes_ : Equation 1 is the probability of temporary migration. Equation 2 is the probability of undocumented migration. Equation 3 is the probability of return migration. Equation 4 models the logarithm of hourly wages upon return. Equation (4) controls for FDI, remittances, and the exports and imports of goods and services (all as % of GDP). For identification of equation (1), we use the inflation adjusted historical oil prices (in US dollars). For identification of equation (2), we use diplomatic exchange between Egypt and the countries of destination of Egyptian migrants at time of migration. It is a dummy variable equal zero if there is no evidence of diplomatic exchange neither in Egypt, nor in the country of destination and is equal one if there is evidence of diplomatic exchange either in Egypt or in the country of destination at the level of charg ́e d’affaires, minister, ambassador or other. For identification of equation (3), we use the number of active armed conflicts that is both country and year specific from the Uppsala Conflict Data Project (UCDP). Equation (4) estimates the effect of illegality on the logarithm of hourly wages, conditional on return migration. The predicted logarithm of hourly wages for non-migrants, illegal migrants and legal migrants are reported in the last row. 60
to adjust improvements in access for the level of learning (i.e., the learning adjustment rate _L_<sup>_h_</sup> _i_<sup>)usingHarmonizedLearningOutcomes(HLO),whichareglobal</sup> measures of learning introduced by Angrist et al. (2021) and used in the World Bank Human Capital Index. The paper generates comparable learning measures across 164 countries by linking psychometrically-designed international assessments to their regional counterparts to construct globally comparable learning outcomes at a national level.

We choose HLO data over alternative test score data for various reasons. First, these data enable us to use the same learning scale for interventions from 164 countries across the world, a wide range of countries from which we also draw impact evaluation education estimates. Second, these data are used in the World Bank Human Capital Index (HCI), which enables us to produce micro-LAYS that map directly to the macro-LAYS in the HCI. The international tests of student learning that are included in the HLO data are often scaled to a mean of 500 and standard deviation of 100. For micro-LAYS, we also derive a learning scale whose lower limit plausibly represents zero learning. We use data from early grade reading assessments (EGRA), where underlying test items have a plausible zero: no reading comprehension. In Appendix Figure B2 we show that the HLO score that corresponds to a floor of zero reading comprehension is 300. In accordance with this, in our analysis we scale the HLO data with a linear transformation of 300. In Section 5, we further

> 10LAYS is a unit of measurement and therefore any result, including descriptive and observational results, can technically be expressed in terms of LAYS. Thus, this approach has uses beyond comparison of effects of impact evaluations.

15
MSERD|
|Observations left|956|610|371|358|456|
|Observations right|2110|1897|1818|571|958|
|Days on each side|74.81|54.90|42.86|42.26|61.80|
|Conventional_p_-value|0.00169|0.423|0.00620|0.000152|0.929|
|Order Loc. Poly. (p)|1|1|1|1|1|
|Order bias (q)|2|2|2|2|2|
|Covariates|Yes|Yes|Yes|Yes|Yes|

_Note_ : Robust standard errors are in parentheses. *** _p_ < 0.01, ** _p_ < 0.05, * _p_ <0.1.

We find no significant effect of change in cash assistance on the likelihood of having an employed household member approximately 16 months after receiving the grant. The impact on the likelihood of enrolling children in school also becomes insignificant after controlling for confounding factors in the neighborhood of a 62-day bandwidth.

# **Robustness Test: Parametric Regression Discontinuity Design Model**

To ensure the robustness of results, we estimate the effects using a parametric strategy in which all returnee household-level observations are used. While the local linear regression approach is more likely to be unbiased in terms of estimating average treatment effects, the parametric estimation strategy, by using all available observations, provides a more precise estimate of treatment effects. The robustness check uses the following parametric model: (3) where yyii is the reintegration outcome of interest (education, house ownership, etc.); = αα+ ββDDii + ff(xxii ) + εεii indicates whether the household received a reduction in assistance ( = 1 is the reduction of assistance from $350 to $150); ii ii and yy ) is a flexible function of the running variable, the week of return (centered on zero at the cutoff)
certain number of slots for new beneficiaries. If the number of eligible petitioners exceeds the number of available slots, the prioritization strategy is intended to determine who receives the benefit.

# **4. Program Targeting**

According to the country’s Ministry of Labor, the Colombia Mayor Program is targeted at the protection of persons in conditions of extreme poverty (Colombia, Ministerio de Trabajo 2013). It is of interest to study not only the program’s impact on beneficiaries, but also the extent to which these correspond to the group at which the benefit is targeted. We start by comparing our aggregate data with the data published by the government. According to the Ministry of Labor, the number of beneficiaries rose from 718,376 in December 2012 to 1,259,333 one year later (Colombia, Ministerio de Trabajo 2014). These numbers almost perfectly coincide with those given to us by the Consorcio 8
Figure 9. Gender gap by geographic area and type of activity (men minus women). Hours per day Panel A. Market work <!-- Start of picture text --> 6.0<br>5.0<br>4.0<br>3.0<br>2.0<br>1.0<br>0.0<br>-1.0<br>Urban Rural<br>Panel B. Unpaid domestic work<br>0.0<br>-1.0<br>-2.0<br>-3.0<br>-4.0<br>-5.0<br>-6.0<br>-7.0<br>Urban Rural<br>Albania Bangladesh Ecuador Ghana Guatemala Iraq Mongolia Pakistan Palestine Peru South Africa Turkey<br>Albania Bangladesh Ecuador Ghana Guatemala Iraq Mongolia Pakistan Palestine Peru South Africa<br><!-- End of picture text --> Source: Authors’ elaboration based on survey data.

46
is not very clear just from observation on the figures, and there are many reasons other than the release of ChatGPT that affect the popularity of the keywords on Google. To investigate the correlation of ChatGPT usage and the Google Trend of each keyword more rigorously, we collect the Google Trends of each keyword searched in each economy and run panel data regression. We first take the monthly average of the Google Trend index to match Semrush monthly traffic of ChatGPT. For each keyword, we run the following regression: In the equation, the dependent variable GoogleTrend<sup>(keyword</sup> _it_<sup>_k_)</sup> is the Google Trend index of keyword _k_ of economy _i_ in month _t_ . The independent variable of interest is the intensity of ChatGPT usage. Specifically, ln(ChatGPT _it−_ 1) stands for the log of ChatGPT’s monthly visits per internet user, of economy _i_ in the previous month _t −_ 1. We use the one-month lag of ChatGPT usage to avoid the simultaneity problem due to the reverse impacts from Google Trend to ChatGPT. Control variables _Xit_ include monthly traffic of Google of economy _i_ in month _t_ , as well as an economyspecific trend. Google traffic controls for the overall Google search activity, and the economyspecific trend controls for the underlying trend of a certain keyword happening in the country, which may not be impacted by the usage of ChatGPT. We also control for month fixed effects _δt_ and economy fixed effects _θi_ . It is worth noting that as the dependent variable is normalized within an economy and country fixed effects are added in the regressions, the coefficient of interest, _α_ , can be interpreted as the within-economy change of popularity of a keyword searched on Google.

Figure 20 presents the regression results, showing point estimates of the effect of ChatGPT usage intensity on Google Trends for various keyword. For most keywords, the correlations are insignificant, which is expected given the early stage of generative AI adoption. However, three keywords stand out with