rafmacalaba/data-use-mentions-extended huggingface dataset:
# **1. Introduction**

Increasing the schooling attainment of girls is a challenge in much of the developing world. In a large number of countries, especially in the Middle East and parts of Asia and Africa, overall education for girls is low, and lags significantly behind that of boys. Nonetheless, over 180 governments have adopted universal primary education and gender parity in schooling as Millennium Development Goals. These commitments notwithstanding, there is surprisingly little evidence on policies and programs that effectively raise school attainment, including for girls (see the reviews by Glewwe 2002; Case 2004).

In this paper we evaluate the impact of a program designed to increase the enrollment of girls in secondary school in Cambodia. Cambodia is recovering from many years of internal and external strife. The Khmer Rouge regime of the 1970s and Vietnamese occupation in the 1980s had severe repercussions for all aspects of the economy and society, including the education sector (see De Walque 2004). Most Cambodian children attend some schooling, but a large share complete only very few grades. According to the 2000 Demographic and Health Survey (DHS), 85 percent of 15 to 19 year olds had completed grade 1 while only 27 percent had completed grade 7, the first year of lower secondary. These percentages are lower for rural areas, 83 and 21 percent, respectively, and lower yet for rural girls, 78 and 17 percent, respectively (Filmer 2005). To address these problems, the Cambodian government has initiated a series of reforms in the education sector, including scholarship programs for students from disadvantaged backgrounds.

The program we evaluate is the Japan Fund for Poverty Reduction (JFPR) scholarship program. This program selected 93 lower secondary schools and, within each of these schools, approximately 45 girls who were beginning 7<sup>th</sup> grade were awarded scholarships of $45 each. The value of the scholarship is large—in 2002, mean per capita GDP in Cambodia was approximately $300 (World Bank 2005).
IMSS|.318|.451|0.00|1|1748|
|Business registered with guild or association|.225|.418|0.00|1|9036|
|Amount of business organization dues paid|7.04|43.8|0.00|1500|9019|
|Any business organization dues paid|.156|.363|0.00|1|9036|
|Enumerated in the 1989 Census|.331|.471|0.00|1|5220|
|Fraction of all institutions|.279|.301|0.00|1|9036|
|Fraction of all institutions (excluding guild, dues)|.325|.371|0.00|1|9036|
|Types of work site:||||||
|Transportation: people or merchandise|.064|.245|0.00|1|9036|
|Makeshift stand in a public road|.041|.199|0.00|1|9036|
|Fixed stand in a public road|.017|.130|0.00|1|9036|
|Makeshift stand inside a marketplace|.023|.149|0.00|1|9036|
|Fixed stand forming part of a marketplace|.019|.138|0.00|1|9036|
|Door-to-door or street vendor|.045|.207|0.00|1|9036|
|Services offered via vehicle or cart|.034|.181|0.00|1|9036|
|Own home: without specialized equipment|.123|.328|0.00|1|9036|
|Own home: with specialized equipment|.051|.220|0.00|1|9036|
|Other non-permanent work site|.008|.088|0.00|1|9036|
|Fixed work site in a public
(budget and employment) across different income groups. Section 4 focuses on differences in organization and objectives of TPOs. Section 5 examines how TPOs adapted their activities and strategies during the COVID pandemic, and section 6 concludes.

# **2 Survey sample**

A total of 135 national TPOs were contacted with a 16-question survey (see the appendix). The questions in the survey were drawn from previous surveys of TPOs conducted by the World Bank in 2005 and 2010, and were adapted to better understand how the COVID pandemic affected TPOs and their strategies. The list of national TPOs was drawn from the International Trade Center’s directory of TPOs. By Spring 2022, 57 TPOs answered the survey (42 percent response rate). Table 1 lists the countries that responded in each income group based on the World Bank’s income classification.

# **3 TPOs’ size**

Figure 1 provides the distribution of budgets across all TPOs in 2021, as well as its distribution by income group in the form of box plots.

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of developed and developing countries and sectors for intermittent years between 1995 and 2011.<sup>3</sup> The resulting Labor Content of Exports (LACEX) database, consisting of two data sets on the labor value added and the jobs content of exports, decomposes the contribution of jobs and wages to exports separately through their direct and indirect components (including both forward and backward linkages), and by skilled and unskilled labor. Doing so illustrates the broader, economy-wide relationship between trade and labor market outcomes. With the LACEX database in hand, the paper then presents stylized facts of how the labor and jobs content of exports have changed over time, and potential drivers of this change by comparing across sectors and countries based on income level and region.

The paper is related to some recent efforts to quantify the labor and jobs content of exports across countries. Timmer et al. (2013) provide an estimation of the number of jobs directly and indirectly related to manufacturing output (rather than exports) using the World Input-Output Database (WIOD). Their focus is almost exclusively on high income countries, and the EU27 countries in particular. Using similar methods and the same data, Jiang and Milberg (2013) estimate the (direct and indirect) labor content of exports for a panel of mainly high income countries at the aggregate level. Stone et al. (2011) compute the factor content of trade, including also skilled and unskilled labor for OECD countries as well as for some emerging countries. Their factor content does not distinguish between domestic and foreign.

Other studies investigate the impact of exports on the number of jobs within countries. The US Department of Commerce (2010) has begun to quantify the number of jobs that exports support in the American economy both directly and indirectly. Kucera et al. (2012) investigate the impact of the recent crisis through exports on employment in India and South Africa by computing the employment content of sectoral exports, including also employment via household expenditure due to export-related earnings.

>
This data is gathered through surveys conducted with individuals from 27 states ( ’departments’), covering aspects such as education, employment, income, and housing characteristics, among others.<sup>10</sup> We primarily utilize this dataset to calculate the baseline poverty rate for each state, defined as the proportion of the population earning an income below half of the minimum wage.

# **3.2 Convergence test**

Using the RAIS dataset, we compute labor productivity for each sector and subsector in each state as follows: where _i_ represents a state, _j_ a sector or an industry, and _t_ a time period. We use the following four sectors: agriculture, extractives, manufacturing, and services. Each industry in the CNAE classifications (2-digit) is grouped into one of these sectors.<sup>11</sup> We define labor productivity using the wage bill because data value-added are available only at the sector level. However, this will not be an issue given that the wage bill is a good proxy for labor productivity. Then, we test for the _β_ -convergence across states using the estimating equation: where _τ >_ 0 and _εi,j,t_ is an error term. We consider 8-year and 16-year windows. If data rejects the null hypothesis _β_ = 0 against the alternative _β <_ 0, it shows evidence of unconditional regional convergence. Alternatively, we test for conditional convergence by controlling the state ( _si_ ) and industry ( _λj_ ) fixed effects, and for states’ initial period poverty rate, indicators of human capital, per capita tax collection, and infrastructure access: We also run equations 3 and 4 regression at the aggregate levels; that is, all the sectors combined.

> 10 See the list of states in Appendix A.

> 11 See the list of sectors and industries within the CNAE classification in Appendix A.

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in productivity may be due to better sorting across sectors, where home production is one of the sectors. Second, we explore the possibility that preschool availability may lower worker turnover among female employees, which also may result in productivity gains. Both channels are consistent with an increase in wages of the plants’ workers. Hence as a further implication of both channels, we check also whether the increase in preschool availability leads to an increase in workers’ wages, consistent with Pissarides (2000) and Beerli (2021).

# 5.1. Improvement in sorting across sectors

Hsieh et al. (2019) look at how improved allocation of talent increases aggregate productivity. Their paper introduces forces such as group-specific social norms to the standard Roy (1951) model which act as distortions to the allocation across occupations. If individuals sort not only based on their ability or preferences, as is the case in the standard Roy (1951) model, but also on the basis of other factors, such as discrimination or social norms about who should take care of the children, such sorting reduces the average quality of workers in an occupation, and hence productivity. Hsieh et al. (2019) demonstrate that between 20 percent and 40 percent of US growth from 1960 to 2010 was driven by improved allocation of talent across occupations, including women leaving the home sector for market work.

Lack of childcare may be one such barrier that lowers the likelihood that women sort into market work. Removing this barrier is likely to bring qualified women into the labor market, increasing productivity. We use two data sets to test how preschool expansion impacted female employment. First, we rely on the manufacturing survey to estimate the impact of preschool expansion on employment of women in the plants in our sample. Specifically, we estimate equation (1) with the number and share of female workers in a plant as a dependent variable. We use the sample of all manufacturing plants, and then limit it to the sample for which TFPR and TFPQ measures are
would affect African inequality through the third component of the decomposition. In other words, if relative price levels remained the same, i.e. all incomes are multiplied by the same factor, African inequality would not change for a scale-invariant measure such as GE(0). Of course, the overall effect of a change in relative prices will depend also on the country’s population size.

The ranking of countries by their mean consumption changed quite substantially as a result of the new PPPs in the middle group of countries, while the tails remained largely similar. Figure A1 plots the rank of a country in the distribution of 2011 PPP means against its rank according to the 2005 PPP means (all for benchmark year 2008). Relative to other African countries, Nigeria and Zambia improved their ranking as a result of the 2011 PPPs. This means that its price level declined between the 2005 and 2011 ICPs, leading to an improvement in consumption expenditure in real terms. While this re-ranking analysis is useful as a first step, it misses a number of elements which are important for assessing the effect on African inequality, such as differences in population sizes and the absolute size of the movement.<sup>54</sup> Figure A2 offers a more direct look at the effect of the PPP change on between-group inequality. We plot the contribution of differences between countries to overall inequality for all countries for which we have data in the 2008 benchmark year. The chart is very similar to Figure 3 in the main text. The two bars show the contribution under 2005 and 2011 PPPs respectively. As the previous chart showed, the new PPP data led to a downward revision of the price level in Nigeria and Zambia relative to countries with similar means. Because the between-group inequality component includes population weights, the contribution to overall GE(0) inequality has been much smaller for Zambia than Nigeria. Among the largest changes, DRC, Mozambique and Tanzania increased their contribution to total inequality, while Madagascar, Nigeria and Zambia
It can be interpreted as a measure of the strength of the economic recovery. We use the difference in the pre-pandemic (January 2020 forecast) and post-pandemic (2020, actual) poverty headcount rates in a country to evaluate the impact of social policies on poverty.<sup>4</sup> We use differences in the employment and inactivity rates between 2019 and 2020 to assess the impact of the various allocations of social policies on labor market outcomes.

> 2 Values for the 2021 GDP are based on the estimates by the World Bank Global Economic Prospects of January 2022 (World Bank 2022). For countries missing in that publication, the estimates of the IMF World Economic Outlook of October 2021 (IMF 2021) are used.

> 3 The pre-pandemic forecast corresponds to the values published by the World Bank Global Economic Prospects of January 2020 (World Bank 2020a). For countries missing in that publication, the values of IMF World Economic Outlook of October 2019 (IMF 2019) are used.

> 4 We use the poverty headcount rates corresponding to the $5.5 (2017 PPP) per day international poverty line as projected by Lakner et al. (2020).

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credit is however limited by creditworthiness considerations. In such a world, the payment of compensations to fired workers would in the short-term crowd out private investment. These constraints directly reduce the optimal speed of layoff in a manner that makes the cash flow smoother (i.e, fire and pay compensation at the speed at which the efficiency gains from previous retrenchment are realized). But they also put pressure on the government to optimally reduce the scope of compensations in order to allow sufficient credit to flow to the private sector, thereby improving labor demand, and reducing the need for high levels of compensation.

Using our estimates of marginal productivity of labor and its cost, the 1996 data shows that SOEs incurring annual losses from keeping unproductive worker is about 3053 Yuan per worker per year. As the result of labor transfers, the total gain from 43 percent of SOE industrial workers migrating to the non-state sector will save SOE the equivalent of 0.8 percent of 1996 GDP. But how much would it cost in compensation?

Table Al in the Appendix summarizes major labor redeployment programs, which have been run in various cities and provinces, and the attached average compensation cost per worker. These costs include unemployment insurance, early retirement, a basic living allowance subsidy, and a one-time lump sum payment. Some programs have also offered active labor redeployment components such as training and retraining, subsidy to individual business, tax break and subsidy to employer of hiring redundant workers. On average, compensation cost ranges from 2,000 to 10,000 Yuan per worker per year and varies from city to city<sup>17</sup> . Using a central figure of 3600 " Most of workers redundant or laid-off do not receive severance payment from Chinese firms, instead, minimum living allowance has be provided by government on the monthly basis.

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government stimulus programs as well as monthly average income and its volatility plus consumption are all influenced by unobserved community heterogeneity.

At the same time, when we relate monthly consumption to monthly income and its volatility plus some non-income factors, we invariably introduce a common month effect, which affects a household‘s monthly income, consumption and participation. That is, consumption may be completely independent of monthly variations in income and government policies, and still co-vary with monthly income, simply because of a common month effect (Khandker, 2012). For example, there is a common monthly but unobserved factor, such as preference of households for certain foods in a specific month – the New Year, for instance – in which case we would get an effect simply because of this common month factor, even though it has nothing to do with monthly variations in income or prices, or participation in government program.

Note that introducing a monthly dummy in the above equations (2)-(4) does not resolve the problem of a common-month effect, because this monthly dummy does not control for the unobserved, location-specific common time effect (  ). Correcting this sort of bias requires monthly household panel data (i.e., repeated observations across months for each household unit), which we do not have. But we have monthly data across households over years across communities. For example, we have community panel data at the sub-Changwat or sub-province level (which as mentioned earlier is the lowest common sampling unit that can help create the panel across months). Using the community-level panel, we could apply a panel fixed-effects regression, and to control for monthly unobserved bias, we can include month*community interaction variables along with a time trend to control for both  _j_ and  _m_ .

However, a panel fixed-effects model may not suffice to account for time-varying unobserved factors that influence how households respond to stimulus and its associated impacts in the local economy. To account for time-varying unobserved heterogeneity, our approach in this paper is to
used in this study is averaged at the center-level, which is a less precise measure of teacher quality than those derived from matched teacher-student datasets. Future studies of early childhood quality in developing countries could benefit from matched teacher-student data to improve the estimates of teacher quality and better understand what kinds of qualification of ECD teachers are particularly successful in producing high-level student outcomes.

Another explanation for the mixed results may be due to collinearity between measures of teacher qualifications and items on the ECERS-R that focus on teacher behavior in the classroom (e.g., Item 16 - Encouraging children to communicate). Although none of the ECERS-R items directly measure teacher’s education, experience, and training, it is likely that those with higher levels of education, more experience, and better training tend to provide higher quality care as measured by the items on the ECERS-R, making it difficult to tease apart classroom quality and teacher qualifications. It is also plausible that some selection bias is taking place, whereby teachers with more education and/or experience choose to work in higher quality settings.

Finally, our analysis suggests that the quantity of exposure to early childhood education also matters. On average, children enrolled in more hours of early childhood programs in rural Indonesia scored higher on the EDI, when controlling for various child, teacher, and structural characteristics. Although previous studies examining the relationship between duration of preschool and child development outcomes have been mixed (Vandell, 2004), we find a positive association despite the fact that children are exposed to a low dose of early childhood education in rural Indonesia. On average, children in our sample were between the ages of 4 to 5, attending kindergartens for 2 to 3 hours a day for 6 days a week or in playgroups for 2 to 3 hours a day for 4 to 5 days per week. The maximum number of hours in a week was 24 hours, which was only found in 3 kindergartens in the sample. In contrast, studies that have found negative effects of 16
To target enrollment, the NGO identifies out-of-school girls before each school year, using information from community members and government records. The program volunteers hold village meetings to prepare for a house-to-house enrollment drive, targeting girls who have never enrolled or who have dropped out of school. These efforts seek both to encourage parents to support their daughters’ education and to motivate girls themselves to come to school.<sup>8</sup> To target student learning, the NGO organizes in-school lessons led by the volunteer in grades three through five. The curriculum and instructional model was designed with Pratham Rajasthan and emphasizes activity-based and playful learning through games that teach English, Hindi, and Math. The methodology emphasizes group work and student involvement in the teaching and learning process. These lessons are held during school hours for approximately two hours per day, several days per week, over four to five months. This component of the program does not focus explicitly on girls, but rather aims to increase learning levels for both girls and boys.<sup>9</sup> In tandem with the peer group learning method, students are placed in three groups according to ability, measured by diagnostic pre-program tests similar to the Annual Survey of Education Report (ASER); the tests are designed to be quick to administer so that they can be conducted individually for each student.

The program was implemented and evaluated in the academic years of 2012 and 2013. Each year, in selected villages, village volunteers conducted the door-to-door enrollment drive,

> 8 The NGO identifies out-of-school girls (aged 6 to 14) using a two-step approach. Using data from the state government child tracking system (CTS) and school records, the program develops an initial list of girls to target. To prepare for the door-to-door enrollment drive, the program then engages community members to verify records, build awareness, and increase enrollment. Volunteers organize meetings of 20 to 40 individuals, to engage village leaders and community members as champions for increasing girls’ school enrollment, and
of IPV can produce biased treatment estimates, for example, finding programs caused a reduction in IPV if measured face-to-face, but an increase if measured with the list experiment (Bulte and Lensink, 2019). Given the growing number of studies measuring program impacts on IPV, our results further highlight the risks of collecting measurement error-prone data for generating unbiased correlations and treatment effect estimates. Given that most IPV-related theory and prevention interventions are informed by face-to-face survey data, misreporting bias could have significant implications for our understanding of the prevalence of IPV, what causes it, and how to prevent it.

Our three-method comparison in Rwanda provides additional insights into potential reasons for misreporting. There were strikingly few differences between ACASI-measured and face-to-face-measured prevalence and IPV-predictor relationships. This suggests that women in Rwanda were no more comfortable reporting IPV with ACASI’s added privacy, yet the list method’s anonymity made many more women willing to report. This may suggest that on questions about illegal behaviors where respondents fear a risk of legal sanctions or repercussions from their answer, for example, in places where physical and sexual IPV are criminalized, then only anonymous survey methods may induce some respondents to report.<sup>32</sup>

> 32The similar face-to-face and ACASI reporting patterns may also be affected by the strict private interview setting in Rwanda. In a busier, more typical survey setting where privacy from curious onlookers and other household members is harder to enforce, we may have found that women would be even less willing to report IPV face-to-face, and ACASI may

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