rafmacalaba/datause-encoder-data huggingface dataset:
Table 9. Impacts of EGPP Participation on Food Consumption

|Log (monthly<br>|T|otal|Per|capita|Num of<br>|
|---|---|---|---|---|---|
|expenditure)|IV|Heckman|IV|Heckman|obs|
|Cereal|0.040|0.032|0.014|0.013|6,718|
||(0.028)|(0.027)|(0.026)|(0.025)||
|Meat and dairy|0.234***|0.204***|0.217***|0.196***|6,083|
||(0.079)|(0.074)|(0.079)|(0.074)||
|Fish|0.241*|0.156|0.234*|0.161|4,364|
||(0.127)|(0.119)|(0.128)|(0.119)||
|Fruit|-0.195|-0.206*|-0.223*|-0.226*|4,918|
||(0.125)|(0.118)|(0.126)|(0.118)||
|Vegetables|-0.040|-0.069*|-0.066*|-0.087**|6,718|
||(0.038)|(0.036)|(0.038)|(0.036)||
|Food prepared outside<br>home|-0.099**|-0.125***|-0.125**|-0.143***|6,720|
||(0.050)|(0.047)|(0.050)|(0.047)||
|Covariates,fixed effects|Y|es|Y|es||

Notes: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The table presents estimated coefficients of EGPP participation on various outcomes using two different approaches with a full specification based on column (4) in Table 7.

Table 10 indicates that EGPP participating households have significantly increased their expenditures on health care and other items including leisure, festivals, and gifts. The large and significant increases in the expenditure for health care is notable for both genders. The average expenditure on health care among the sample households is about BDT 527 per month, which is equivalent to 6 percent of total household expenditure as noted earlier, an increase by almost 35 percent that amounts to BDT 184. Increased spending on leisure, festivals, and gifts — mostly luxury rather than necessity goods — indicates a strong income effect of EGPP participation and extra income. Considering that the average expenditure on these items is about BDT 512 per month, the 80 percent increase in expenditures is about BDT 410, a very significant resource allocation. In contrast to increased spending on health care and leisure, mostly for adults, investment in education and purchase for clothes and shoes shows insignificant differences or slight reduction for EGPP participating households. This situation may be attributable in part to the fact that childless households comprise close to one-quarter (23 percent) of the sample, and households with children may be already receiving educational subsidies. Alternatively, with labor required for public works, intra-household resource allocation may be made favorably to adults, especially in their health, than children.

27
A Duration Analysis of CONALEP Kaplan-Meier is a strictly empirical approach (non-parametric), but it does not consider the influence of covariates. The estimators are given by: where:<sup>_n_</sup> _k_<sup>is the number of individuals whose observed duration is at least</sup><sup>_T_</sup> _k_<sup>, and</sup> _d k_ is the number of observed drop-outs at time<sup>_T_</sup> _k_<sup>.</sup> The Cox model allows exploration of the relationship between the survival experience of an individual and a set of explanatory variables or covariates. In the present analysis, the hazard rate is the risk to find a job after being unemployed. The model specifies that the hazard is given by: where: λ0 (<sup>_t_</sup> ) is the baseline hazard, or the hazard for an individual with **X** = **0** .

**X** _i_ is the vector of explanatory variables for individual _i_ .

**X** _i_ includes variables such age, gender, schooling, region, gross domestic product (GDP).

The parameter estimates, β<sup>ˆ</sup> ; are obtained maximizing the partial likelihood.

The estimated survival function for the individual _i_ is: where: _S_ ˆ0 ( _t_ ) is the estimated baseline survival function (for an individual with **X** = **0** ).

The Cox regression model is semi-parametric because no particular probability distribution is assumed for the survival times, although the model is based on the assumption of proportional hazards. The adequacy of the fitted model was also tested throughout the residuals. Residuals were calculated for each individual in the sample. Their behavior is approximately known when the fitted model is satisfactory. A number of residuals have been proposed, among them the martingale residuals, which take values between - ∞ and 1; they have properties similar 11
# 2. **Government Deficit.** As noted earlier, economic crises, which make it more difficult

<!-- Start of picture text --> 0.18<br>0.16 /*j<br>0.14 -<br>0.12-<br>0.1<br>0.08<br>0.06 /<br>0.04 -<br>0.02<br>1992 1993 1994 1995 1996<br>Year<br><!-- End of picture text -->

**Figure** 1: **Median Deficit as %** of Revenues.

for the government to subsidize lossmaking state-owned enterprises, might increase the likelihood of privatization. Table 1 shows that cross-sectional evidence does not strongly support this hypothesis. There is little difference between the 1992 fiscal deficits of provinces that started the privatization process prior to December 1994, those that started the process after December 1994, and thlose that did not privatize at all.'16 However, in the econometric analysis, after controlling for the political and bank performance variables, we do find that high fiscal deficits (as a percentage of revenues) increased the probability of privatization. This result may be primarily caused by time series variation in deficits. Figure 1 shows that the median provincial deficits varied greatly over this period (from 4.5% of revenues in 1996 to 17% in 1995).

3. **Bank Quality Variables.** Several measures of bank performance are included in the model. A poorly performing public provincial bank could impose high fiscal costs upon a province -- those provinces with the worst performing banks might have been unwilling, or unable, to subsidize the banks for political reasons. Table 2 shows that banks that were privatized earlier (banks who started the privatization process before the Tequila Crisis of December 1994) tended to be worse performers in 1992 (prior to 16 We used 1992 fiscal data because we want to compare deficits prior to any privatizations. This is important because the act of privatization might affect the provinces' deficits (for example by ending the need to finance recapitalization of the provincial banks).

10
# **2. Data and measurement**

This study uses data for over 2,000 companies from nine Asian economies-Hong Kong, Indonesia, South Korea, Japan, Malaysia, Philippines, Singapore, Taiwan and Thailand-for the year 1996 as assembled in Claessens et al. (1998a).<sup>6</sup> We restrict the sample to firms with sufficient segment and financial data to construct empirical measures, particularly the diversification measure. Of the 2,980 firms with complete ownership data, 2,187 firms have one or several years of the necessary segment and financial data. Assuming that the ownership structures of the firms do not change substantially over time, we end up with a sample of 9,559 firm-years spanning from 1991 through 1996.7 For each of the companies in the database, we identify who the ultimate owners are, what their share of cash-flow and control rights is, and whether the company is affiliated with a business group. The corporate group information is supplied by the data sources detailed in Claessens et al. (1998a). The procedure of identifying ultimate owners is similar to the one used in La Porta et al. (1999b). An ultimate owner is defined as the shareholder who has at least 5 percent of the control rights of the company and who is not controlled by anybody else. If a company does not have an ultimate owner, we classify it as widely-held. We further classify ultimate owners into four types: widely-held corporations, widely-held 1998; Klein and Saidenberg, 1998).

> 6Worldscope contains fmancial, segment, and block ownership information on companies from 49 countries. The database has been used in several other international studies (for example, Fauver et al. (1998), La Porta et al. (1998a, b), Lins and Servaes (1998) and Claessens et al. (1998a, b)). As our starting point for the data collection, we use the Worldscope database, which generally provides the names and holdings of large owners. Worldscope has over 8,000 publicly-traded firms in the nine East Asian countries, but only 2,300 companies provide detailed ownership information. We supplement the data with information from the Asian Company Handbook 1999, the Japan Company Handbook 1999, the 1997 Annual Reports of the Hong Kong, Jakarta, Seoul, Kuala Lumpur, and Manila Stock Exchanges, as well as with ownership data from the Korean Fair Trade Commission, the Securities Exchange of Thailand Companies Handbook (1998), and the Singapore Investment Guide (1998). We exclude 852 companies across the nine countries, which have proxy ownership that cannot be traced to a specific owner. In all cases, we collect the ownership structure as of December 1996 or the end of the 1996 accounting year. We end up with 2,980 companies for which have complete ownership information and where we can trace the ultimate owners.

4
# **Table 1  Governance Environment, Macroeconomic, and Reserve Adequacy Variables**

|Variable|Obs.|Mean|Std. Dev.|Min|Max|
|---|---|---|---|---|---|
|**Governance**|**environme**|**nt variable**|**s**|||
|Government Effectiveness|204|0.0|1.0|-2.4|2.2|
|Central bank independence|144|2.8|1.2|0|4|
|Corruption Perceptions Index|179|43.3|18.9|9|87|
|Governance Pillar Score|143|5.7|2.1|1.1|9.2|
|**Macroe**|**conomic va**|**riables**||||
|Country risk score|121|46.6|16.3|13|89|
|Number of currency crisis (1971–2017)|164|1.5|1.5|0|7|
|Current account balance (% of GDP)|212|-3.0|9.0|-34|34.8|
|<br>**Reserve**|**adequacy v**|**ariables**||||
|Reserves to GDP (%)|162|0.2|0.2|0.6|2.0|
|Reserves to M2 (%)|126|0.4|0.3|0.0|1.9|

**_Governance Variables._** We use various indices to measure the broader governance environment in which a central bank operates. First, we use the Government Effectiveness index, as compiled by the World Bank. This index <mark>measures the "perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the</mark> government's commitment to such policies.”<sup>8</sup> The scale runs from -2.5 (lowest) to 2.5 (highest) and covers 204 countries. The index is developed by <mark>compiling and summarizing information from over 30 existing data sources that report the views and experiences of citizens, entrepreneurs, and experts in the public, private, and nongovernmental sectors. T</mark> he rationale for using this variable is that countries with strong governance and effective public sectors usually have well-implemented laws for their central banks. Relatedly, we also use data on central bank independence from the French Treasury. This variable measures the degree of central bank independence in each country. Central banks with no independence are assigned a value of zero, while those with substantial autonomy are assigned a value of 4.<sup>9</sup> The Directorate General of the Treasury of France surveys this and other institutional characteristics for 144 countries. The survey is based on perception data. The study's results are captured in the Institutional Profiles Database and were last updated in 2016. In addition, clear accountability frameworks and low corruption increase public

> 8. See https://www.msci.com/documents/10199/e092c439-34e1-4055-8491-86fb0799c38f <u>ators/h580f9aa5?country=BRA&indicator=388&viz=line_chart&years=1996,2018 and https://info.worldbank.org/governance/wgi/Home/Documents.</u> 9. See http://www.cepii.fr/institutions/EN/ipd.asp.

8
Table 2: Overview of included studies

|Study|Intervention|Country|Baseline|Number of<br>follow-ups|Time since<br>program end|Original<br>sample size|
|---|---|---|---|---|---|---|
|Blattman et al.(2014);<br>Blattman et al.(2019)|Cash grants<br>of $400|Uganda|Feb-Mar 2008|3|2 years; 4 years;<br>9-years|2,677|
|Blattman et al.(2016)|Business training<br>+ grants of $150|Uganda|Apr-Jun 2009|1|16 months|1,800|
|Brudevold-Newman<br>et al.(2017)<br>|Microfranchising<br>program or<br>grants of $239|Kenya|Oct 2013|2|7-10 months;<br>14-22 months|905|
|DeMel et al.(2008);<br>DeMel et al.(2012)<br>32|Cash or in-kind<br>grants of<br>$100 or $200|Sri Lanka|Apr 2005|9|1 to 4 quarters;<br>5 to 8 quarters;<br>5 years|408|
|DeMel et al.(2014)|Business training<br>+ grants of $130|Sri Lanka|Jan 2009|5|3-4 months; 7-8 months;<br>15-16 months; 24-25 months;<br>6 years|628|
|Fafchamps<br>et<br>al.<br>(2014)|Cash and in-kind<br>grants of $120|Ghana|Oct-Nov 2008|5|1 to 4 quarters; 3 years|793|
|McKenzie(2017b)|Grants of $50,000|Nigeria|Nov 2012-<br>May 2013|3|2 years; 3 years<br>5 years (a)|1,841|

(a) Refers to time since program application. See McKenzie (2017b), section B for a timeline of the follow-up surveys in comparison to the time of tranche payments.
The finding that female-owned livestock, fishing, and forestry activities could expand should be taken with the caveat that the model assumes that women can expand their farms as long as market conditions allow them to do so. Considering the agency of women in Chad, this might be challenging for most women. During the 2014-16 West Africa Ebola outbreak, which restricted movement, women’s agricultural productivity was affected, resulting in women defaulting to less negotiable village loans, which further impacted their long-term economic prospects (Korkoyah and Wreh, 2015).

Table 5: Percentage change in production from BaU level in female and male-owned agricultural farms due to COVID-19 impacts

|Production|2020|2021|2022|2023|2024|2025|
|---|---|---|---|---|---|---|
|Agriculture|1.6|1.0|0.5|0.2|0.1|0.0|
|Male Owned|0.6|0.3|0.0|-0.1|-0.1|0.0|
|Female Owned|3.6|2.3|1.4|0.7|0.3|0.0|
|Food Crops|0.5|0.4|0.2|0.1|0.1|0.0|
|Male Owned|2.3|0.7|0.4|0.2|0.1|0.1|
|Female Owned|-3.6|-0.3|-0.2|-0.1|0.0|0.0|
|Cash Crops|-25.4|-4.5|-2.9|-1.8|-1.1|-0.6|
|Male Owned|-23.0|-4.5|-2.9|-1.9|-1.2|-0.8|
|Female Owned|-30.4|-4.5|-2.8|-1.7|-0.9|-0.2|
|Livestock, Fish, Forestry|5.1|2.2|1.1|0.4|0.1|0.0|
|Male Owned|0.3|0.3|-0.3|-0.3|-0.3|0.0|
|Female Owned|13.6|5.6|3.4|1.7|0.8|0.1|

Source: CGE model simulation results The drop in labor force participation level (i.e., the level difference in FLFP from baseline) is highest among farmworkers as most women are employed in this category (Figure 3). A disproportionate reduction in FLFP (in percentage from baseline) occurs among salaried and informal non-farm (i.e., services and manufacturing) female labor (Figure 4). Inevitably, more women will lose their jobs compared to men, notably in service sectors, where most women are employed in urban areas. Preliminary evidence from a World Bank Enterprise Survey (ES) in Chad demonstrates that the proportion of women as a fraction of all permanent full-time workers decreased by 6.6 percent, driven by a steep change in medium-sized enterprises. The follow-up COVID-19 Enterprise Survey was conducted during June and July 2020 to interview respondents of 377 firms which were initially surveyed between January and May 2019 as part of the standard ES.

10
|**Mean**<br>**Std. Dev.**<br>0.23<br>0.15<br>19.26<br>7.49<br>0.22<br>0.27<br>0<br>1.02<br>0.82<br>0.34<br>0.82<br>0.34<br>0.76<br>0.36<br>0.17<br>0.28<br>0.22<br>0.35<br>0<br>1.74<br>0.3<br>0.06<br>2.62<br>1.32<br>0.09<br>0.06<br>0.83<br>0.68<br>9.61<br>5.32<br>6.45<br>3.03<br>0.64<br>0.48<br>0.22<br>0.12<br>0.39<br>0.03<br>0.32<br>1.92<br>0.04<br>0.21<br>0.06<br>0.3<br>0.06<br>0.24<br>0.17<br>0.38<br>0.59<br>0.23<br>0.55<br>0.09<br>0.38<br>0.12<br> available (N=59,424). One<br>d only for municipalities with<br> and TSE.|
|---|
|**Variable**<br>Fraction of 0-6 population enrolled in a municipal pre-primary school<br>Municipal pre-primary students per pre-primary teacher<br>Fraction of municipal pre-primary teachers with post-secondary education<br>Pre-primary teacher quality index, frst PC from 2-factor PCA<br>Fraction of municipal pre-primary institutions with own school building<br>Fraction of municipal pre-primary institutions with electricity<br>Fraction of municipal pre-primary institutions with an indoor bathroom<br>Fraction of municipal pre-primary institutions with a library<br>Fraction of municipal pre-primary institutions with a computer<br>Pre-primary school infrastructure quality index, frst PC from 5-factor PCA<br>Education spending as fraction of revenue<br>Education spending per capita, 100s 2005 Reais<br>Infrastructure and urban development spending as fraction of revenue<br>Infrastructure and urban development spending per capita, 100s 2005 Reais<br>Revenue per capita, 100s 2005 Reais<br>Simulated revenue per capita, 100s, 2005 Reais<br>Average income per capita, 10,000s of 2005 Reais<br>Median income per capita in 2000, 10,000s of 2005 Reais<br>Gini coefcient in 2000<br>Population, 100,000s<br>Population aged 0-6, 100,000s<br>Population aged 7-15, 100,000s<br>Municipality had participatory budgeting at some point during 1995-2008<br>Municipality has a socialist party mayor<br>Fraction of population that lived in urban areas in 2000<br>Labor force participtaion rate (share of population economically active)<br>Racial fractionalization index in 2000<br>_Notes:_ Data are summarized over 1995-2008 and over all municipalities for which data are <br>exception is the dummy for participatory budgeting during 1995-2008, which is summarize<br>populations of 50,000 or greater (N=5,704).<br>_Sources:_ Author’s calculations based on data from IBGE, INEP, Tesouro Nacional, IPEA,|

43
Table 2 presents summary statistics for all the socioeconomic indicators measured in the baseline survey and characteristics of the eligible girls and selfreported monitoring technology relevant to our research framework, by treatment arm, as well as whether p-values of t-tests of differences between each treatment arm relative to the control group indicate that those differences are statistically significant.<sup>21</sup> Table 2 suggests that the randomization of experimental arms worked well in practice. For each pair of experimental arms, an F-test cannot reject that the baseline characteristics listed in Table 2 are jointly orthogonal to treatment status.<sup>22</sup> Some individual differences are, however, statistically significant, and thus we provide robustness checks controlling for these baseline characteristics. Note that other than for the many language and religion categories, for which there are some differences across experimental groups, the only other variables with some significant baseline differences between experimental arm pairs are self-reported absences and, to a marginal extent, self-reported quality of child attendance monitoring by the parents. Given the direction of the differences (parents in conditional transfers arms reporting fewer child absences), this may well be due to the fact that, in about one fifth of the schools, the baseline survey could not be concluded before the treatments were announced, so that parents in conditional transfers arms may have been tempted to underreport child absences.

> 21 These p-values are those associated with a t-test of �� �0, �� �0 and �� �

0 respectively, obtained from estimating Equation (4) with each baseline characteristic, in turn, on the left-hand side.

22 More specifically, the p-value associated with an F-test that the set of characteristics listed in Table 2 does not explain the experimental arm classification is between 0.17 (Information v. Parents) and 0.52 (Control v. Girls) for all 6 experimental arm pairs, and thus the null of joint orthogonality cannot be rejected.

23
Policy ReseaRch WoRking PaPeR 4697

# **Abstract**

The authors introduce a simple empirical model that assumes a positive stigma (or norm) toward child labor that is common in some developing countries. They illustrate the positive stigma model using data from Guatemala. Controlling for several child and householdlevel characteristics, the analysis uses two instruments for measuring stigma: a child’s indigenous background and the household head’s childhood work experience.

This paper—a product of the Education Team, Human Development Network—is part of a larger effort in the department to analyze educational outcome. Policy Research Working Papers are also posted on the Web at http://econ.worldbank. org. The author may be contacted at hpatrinos@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Table 5: Results with regional labor market conditions indicators as control variables (ctd)

|Dependent Variable|Employed with<br>social<br>security<br>coverage|Employed<br>full time|Employed<br>with a contract|Wage Worker|Employed in<br>Agricultural<br>sector|
|---|---|---|---|---|---|
|0b.pot<br>~~e~~xp#c.indepvar|0.0769|0.322*|0.0220|0.490**|0.438***|
|1.pot<br>~~e~~xp#c.indepvar|0.0522|0.298**|-0.113|0.340***|0.189*|
|2.pot<br>~~e~~xp#c.indepvar|-0.0514|0.143|-0.0486|0.112|0.175*|
|3.pot<br>~~e~~xp#c.indepvar|-0.0904**|0.162|-0.0961|0.0212|0.0962|
|4.pot<br>~~e~~xp#c.indepvar|-0.154*|-0.0550|-0.150**|-0.175**|0.0228|
|5.pot<br>~~e~~xp#c.indepvar|-0.217***|-0.130|-0.207***|-0.233***|0.0174|
|6.pot<br>~~e~~xp#c.indepvar|-0.239***|-0.221**|-0.196**|-0.288***|-0.0842|
|7.pot<br>~~e~~xp#c.indepvar|-0.225***|-0.311***|-0.187**|-0.456***|-0.166*|
|8.pot<br>~~e~~xp#c.indepvar|-0.302***|-0.473**|-0.191***|-0.537***|-0.107|
|9.pot<br>~~e~~xp#c.indepvar|-0.175**|-0.427**|-0.0305|-0.467***|-0.132*|
|10.pot<br>~~e~~xp#c.indepvar|0.00421|-0.267*|0.126|0.161|-0.187|
|# obs|8,644|8,730|8,078|8,986|8,728|
|R-squared|0.776|0.474|0.758|0.647|0.687|
|Independent Variable|employment|employment|employment|employment|employment|

Notes: (i) Dependent variables on the individual level in each column are regressed on employment rate for individuals in the sample collapsed by governorate of residence and graduation cohort, interacted with potential experience from year 0 to 10. The potential experience is captured by the difference between the individuals age at the year of the survey and their age at the year of graduation.

(ii) Year, cohort , governorate and potential experience fixed effects are included in each regression. Controls for eudcational attainment are also included.

(iii) Sample of males who graduated between 1990-2019. Data are constructed from LFS 2006-2019.

(iv) Cluster-robust standard errors in parentheses (clustering on cohort and governorate of residence).

(v) * , ** and ***refer to significance at the 10, 5 and 1% levels respectively.

14