rafmacalaba/datause-encoder-data huggingface dataset:
42

|**Civil**<br>Country|**Society Ac**|**tivity Across**<br>Percenta|**Table 1:**<br>**Countries: Af**<br>ge of Respon<br>|**robarome**<br>dents Answ<br>Often|**ter Round 4, 2**<br>ering “Yes” to:|**008**<br>|
|---|---|---|---|---|---|---|
||Voted in<br>last<br>elections|Active<br>member of<br>a group|Often<br>attended<br>group<br>meetings|joined<br>others<br>to raise<br>|Willing to<br>demonstrate<br>or protest|Punishment<br>likely if<br>people<br>complain|
|_Polity IV Score_<br>|_Between 10_<br>|_and 6(“full de_|<br>_mocracies”)_|an issue|||
|South Africa|65|16|29|20|45|46|
|Botswana|59|17|55|39|52|19|
|Lesotho|61|28|67|50|52|74|
|Ghana|81|35|40|38|30|32|
|Senegal|79|28|60|55|62|60|
|Mali|77|29|57|55|59|46|
|Kenya|79|43|44|35|40|37|
|Benin|91|23|53|50|65|25|
|Zambia|59|24|45|33|45|47|
|Liberia|78|45|50|42|24|40|
|Malawi|75|22|60|54|50|27|
|**_Avg: _**|**_73.09_**|**_28.18_**|**_50.91_**|**_42.82_**|**_47.64_**|**_41.18_**|
|_Polity IV Score_|_Between 6 a_|_nd 2:_|||||
|Mozambique|60|18|49|45|57|47|
|Nigeria|60|27|30|26|43|57|
|**_Avg:_**|**_60_**|**_22.5_**|**_39.5_**|**_35.5_**|**_50_**|**_52_**|
|_Polity IV Score_|_Between 2 a_|_nd -2:_|||||
|Zimbabwe|61|16|51|38|42|44|
|Madagascar|70|4|75|29|20|61|
|Burkina|72|18|59|52|63|53|
|Uganda|70|31|44|33|35|41|
|Tanzania|82|35|64|51|65|57|
|**_Avg: _**|**_71_**|**_20.80_**|**_58.6_**|**_40.6_**|**_45_**|**_51.2_**|
data to show that remittance fees are negatively correlated with total remittance flows at the country level.

Additional research has found that alleviating information asymmetries within transnational households (between migrants and families back home) could also raise remittances sent by migrants. Improved information on the conditions of families back home could lead migrants to send more. In addition, if families know more about the conditions of migrants overseas, and in particular if migrants have incentives to understate their economic situation so as to dampen requests for assistance, improving families’ information on migrants’ situations could lead to higher remittances.

Ambler (2013) implements a lab-in-the-field experiment aimed at revealing the consequences of asymmetric information within transnational households. Study participants are migrants from El Salvador who are living and working in the US (in Washington DC), and a close relative in El Salvador. A key question in the study is whether migrants send more remittances home out of their earnings when potential remittance recipients in the home country have better information about migrant earnings. Specifically, migrants are asked to specify how much of a $600 windfall they would like to share with their El Salvador relative, and are randomly assigned to a treatment where the relative is told that the migrant got this windfall and had to decide how much to share (otherwise, the relative is told nothing). Strikingly, migrants do share significantly more with relatives when relatives are informed about this windfall. This result suggests (extrapolating outside the experimental context) that migrants may share more with their families back home when families have better information on migrants’ income realizations.

A complementary set of findings comes from Batista and Narciso (2013), who implemented a randomized study among 1,500 immigrants in Ireland to estimate the impact of facilitating communication between migrants and their contacts abroad on the extent and value of remittance flows. In the experiment, migrants were randomly assigned international telephone calling credit to their home country for a given time period. The authors find a sizable, positive impact of the intervention on the value of migrant remittances sent to individuals who have been receiving remittances from the migrant already, but no impact on other individuals.

20
Table 1: Summary statistics

||Mean<br>(1)|SD<br>(2)|
|---|---|---|
|Age|21.69|4.37|
|Ever married|0.18|0.38|
|Lives in rural area|0.60|0.49|
|Scheduled caste or tribe|0.40|0.49|
|Other Backward Class|0.32|0.47|
|Completed class 12 before ITI|0.83|0.38|
|Mother is illiterate|0.31|0.46|
|Ever worked before ITI|0.17|0.38|
|Aspiration gap*|2,734.91|6,374.44|
|Reservation wage*|19,740.57|7,844.89|
|Expected starting salary*|17,005.66|6,819.30|
|Unrealistic high aspiration|0.52|0.50|
|Realistic aspiration|0.26|0.44|
|Unrealistic low aspiration|0.23|0.42|
|Willing to migrate for work$|0.48|0.50|
|Household afected by COVID|0.47|0.50|
|ITI education afected by COVID|0.48|0.50|
|Observations|3,180||

_Notes:_ This table presents summary statistics for the estimation sample. Variable definitions are in the online Appendix. *INR per month. $ reflects willingness to migrate if salary increases by INR 5,000; the number of observations for this variable is 3,143.

shift to online classes), and 47 percent reported that their households were economically affected by COVID-19 through loss of jobs, business, or income, or a household member’s illness.

# **3 Empirical Strategy**

To estimate the effect of the COVID-19 pandemic, following Bau et al. (2022), we create a “leave-one-out” exposure measure for each respondent by calculating the share of other respondents from her ITI who reported being negatively affected by COVID-19.<sup>4</sup> One concern with this shock measure is that it relies upon self-reported data and might be biased due to measurement error. Thus, we leverage administrative data to construct an additional districtlevel “aggregate COVID-19 shock” measure defined as the population-adjusted cumulative number of COVID-19 deaths between January 30, 2020 and October 31, 2021. We obtained

> 4More details on variable construction are presented at the end of the paper.

4
# **I Introduction**

An influential body of research in finance has established the importance of life-cycle explanations for many fundamental corporate finance policies in the US.<sup>1</sup> Much less is known about lifecycle issues in developing countries. We do know that in the U.S., new businesses start small and, if they survive, grow fast as they age.<sup>2</sup> It is an open question as to whether firms in developing countries face a similar size-age profile as in the US given that they face entirely different business environments and operating conditions.

Furthermore, the role of institutions in determining how firm size and growth evolve over the lifecycle in developing countries is still largely a black-box. For instance, a large literature has shown the importance of financial development for firm growth (see Levine (2005) for a review). While most studies have focused on cross-country data (e.g. King and Levine, 1993; Levine and Zervos, 1998), or industry-level analyses (e.g. Rajan and Zingales, 1998), or firmlevel studies (e.g. Demirguc-kunt and Maksimovic, 1998), there have been several country case studies showing that financial development across states within a country matter. Jayaratne and Strahan (1996) and Dehejia and Lleras-Muney (2007) show that differences in state-level bank regulation in the United States affects financial development which has an impact on economic growth; Guiso, Sapienza, and Zingales (2004) examine differences in financial development across regions in Italy, particularly on small firms and entrepreneurship and find that local financial development matters for economic success. Other studies such as Bertrand, Schoar, and Thesmar (2007) examine the impact of deregulation in France on the functioning of credit

> 1Research has shown the importance of life-cycle explanations for dividends (Fama and French (2001), Grullon et al. (2002), and DeAngelo, DeAngelo, and Stulz (2006), Denis and Osobov (2008)), financing (Berger and Udell, 1998), stock valuations (Pastor and Veronesi (2003)) and acquisitions (Maksimovic and Philips (2008), Arikan and Stulz (2013)).

> 2 See, for example, Evans, 1987; Dunne, Roberts, and Samuelson, 1989; Davis, Haltiwanger, and Schuh, 1996; Caves, 1998; Hsieh and Klenow, 2012.

2
solid foundation of general skills has a significant and long-term impact on the wages of high school graduates (Levy and Murnane 2004). Research also suggests that job stability for individuals (as well as economic stability for countries) requires lifelong learning, which is contingent on a foundation in general skills (Kezdi 2006). For these reasons, almost all countries require vocational high schools to teach general skills (Kuczera et al. 2008).

Surprisingly, there is little evidence from developing countries as to whether vocational high school helps students acquire specific and general skills, especially in comparison to academic high school. Cross-national studies based on international tests such as the PISA show that students in vocational high school have lower levels of general skills than students in academic high school (by almost half a standard deviation, see Altinok 2011). However, since the PISA data do not contain detailed information on student background characteristics (such as prior test scores) that are necessary to adjust for selection bias, the PISA data are not suitable for measuring the causal impacts of attending vocational versus academic high school. Furthermore, because the PISA data are cross-sectional and not longitudinal, they cannot show how much vocational high school contributes to gains in student learning.

One exception uses longitudinal data from Indonesia in the 1990s to show that attending vocational school has little impact on students’ general skills (Chen 2009). Unfortunately, the Chen study relies on a sample of students smaller than 1,000.

Because this sample does not have sufficient vocational and academic high school students that share a common set of characteristics, the OLS regressions used in the
online appendix Table A13: Non-mask textile trade predicts mask trade

|||_Dependen_<br>Mask pu<br>�<br>_o_ <sup>Mask </sup>|_t variable:_<br>rchases_od_<br><sup>purchases</sup>_od_||
|---|---|---|---|---|
||(1)|(2)|(3)|(4)|
|Non-mask textile purchases_od_<br>�<br>_o_ <sup>Non-mask textile purchases</sup>_od_|0.107<sup>_∗∗_</sup>|0.099<sup>_∗∗_</sup>|0.103<sup>_∗∗_</sup>|0.100<sup>_∗∗_</sup>|
||(0.046)|(0.046)|(0.046)|(0.046)|
|log distance_od_||_−_0.010<sup>_∗∗_</sup><br>(0.004)||_−_0.012<sup>_∗∗∗_</sup><br>(0.004)|
|Intra-provincial trade_od_||0.002<br>(0.005)|||
|Destination Sector FE|X|X|X|X|
|Origin Sector FE|X|X|X|X|
|Origin province _×_ Destination province|||X|X|
|# clusters (sub-Districts)|128|128|128|128|
|# of observations|5,588|5,588|5,588|5,588|

Second, in Table A14 we present estimates of Equation A9. We find that higher origin sub-District _o_ mask manufacturing intensity leaving out destination sub-District _d_ predicts origin sub-District _o_ destination sub-District _d_ -specific mask manufacturing intensity. This finding is robust to the inclusion of origin Province fixed effects.

Table A14: leave-out mask manufacturing intensity predicts destination sub-Districtspecific mask manufacturing intensity

||_Dependen_<br>_M_<br>_o_<br>_M_<sup>mnf</sup><br>_od_ <br>|_t variable:_<br><sup>mnf</sup><br>_d_<br><sup>+</sup><sup>_M_oth</sup><br>_od_<br>|
|---|---|---|
||(1)|(2)|
|�<br>_d_‘=_d _<sup>_M_mnf</sup><br>_od_‘<br>�<br>_d_‘=_d _<sup>_M_mnf</sup><br>_od_‘ <sup>+</sup><sup>_M_oth</sup><br>_od_‘|0.857<sup>_∗∗∗_</sup>|0.854<sup>_∗∗∗_</sup>|
||(0.049)|(0.050)|
|Destination Sector FE|X|X|
|Origin Province FE||X|
|# clusters (sub-Districts)|181|181|
|# of observations|677|677|

A40
Finally, Table 2 shows individuals' effective use of parental leave up to nine months after the intervention. Parental leave take-up is higher among women for all treatment arms compared to men. There are strong differences by experiment, and the percentage of both men and women that made use of the parental leave program is much higher in Experiment 2. That can be explained by the fact that this sub-sample is composed of individuals that are already parents. Moreover, the demand for parental leave is slightly higher in the treated group in Experiment 1 for both men and women, and in Experiment 2 among men.

Table 2. Medium-term effective use of parental leave by treatment arm and sex

|**Experiment**|**Intervention**||**Men**|**Wo**|**men**|
|---|---|---|---|---|---|
|1|Control|0|0,0%|4|1%|
||Information|1|0,4%|8|2,4%|
||Information|1|1,1%|177|57,1%|
|2|Testimony|3|2,8%|186|58,9%|
||Planning|7|6,7%|175|56,1%|

Notes: The table shows individuals' effective use of parental leave between 1st September 2021 and 7th June 2022 for each treatment arm and sex. The estimation sample consists of 1,221 individuals in experiment 1 (554 men and 667 women), and 1,242 in experiment 2 (304 men and 938 women). Data includes all surveyed individuals. Source: Postintervention survey.

# _3.6. Empirical strategy_

To investigate the effects of providing information through a text message campaign, we estimate the following equation separately for men and women: where yig is the outcome of interest for individual _i_ of gender _g_ . Tig is a set of treatment dummies that varies among experiments. For experiment 1 (formal workers of reproductive age), βt when _t=1_ identifies the effects of receiving the message with information on the parental leave program. The omitted group is in this case the control group. For Experiment 2 (recent parents), we have no pure control and will have three relevant coefficients βt for _t_ =1, 2, 3, identifying the effects of (1) having received the role model text message compared to the information one, (2) the planning message compared to the information one, (3) and the role model message compared to the planning one. Xig is a vector of individual control variables, including age, nationality, geographic region, cohabiting status, maximum educational level, previous childbirths, and pregnancy status (these two last variables only for experiment 1’s sample). ∂s are sample g strata fixed effects and ε robust standard errors. i 13
|included||||||
|---|---|---|---|---|---|
|Regular GDP|Fullsample|0.67|0.64|0.61|0.64|
||Asia only|0.66|0.64|0.54|0.52|
||Europe only|0.65|0.76|0.67|0.78|
|PPP adjusted|Full sample|0.92|0.87|0.83|0.87|
|GDP|Asiaonly|0.82|0.80|0.71|0.66|
||Europe only|1.42|1.53|1.43|1.56|

The “raw-data” elasticity estimates suffer from two problems. First, the estimates for the European and Asian subsamples are based on small sets of countries, 21 and 12, respectively. This is a problem because the GDP estimates within a given country are the same across experts from that country, which causes the standard errors for the regression coefficients to be biased downward, thus exaggerating the precision of the coefficient estimates (Moulton 1986, 1990). Clustering the standard errors by country would be the usual response to this problem, but clustering does not yield consistent estimates when the number of clusters (here, countries) is small. Clustering can be expected to work better in the full sample, which includes 37 countries, a number close to the minimum of 42 recommended by Angrist and Pischke (2009). The second problem affects the “raw-data” estimates even for the full sample: the estimates do not account for the high variation in the number of experts by country, from a low of one in Bangladesh, Belgium, Cambodia, Hungary, Portugal, and Romania to a high of 70 in the United States. Countries are the implicit units of observation when estimating the income elasticities, but countries with more experts have greater influence on the estimation results than countries with fewer experts. Treating countries equally requires estimating weighted models, with the inverse of the number of experts from each country used as weights.<sup>20</sup> This problem can alternatively be addressed by estimating a model based on mean values across the experts from each country. We addressed the problem both ways, as reported below.

# **Figure 2: Distributions of mean WTP answers under Plans A and B, Round 2, with countries ranked by increasing PPP-adjusted per capita incomes.**

> 20 Note that the reason for weighting is not to address heteroskedasticity; we use robust standard errors to address that problem. Additional weighting could arguably be done to account for differences in population across the countries, but weighting by population makes more sense when individuals are implicitly the units of observation (e.g., in studies on the effects of economic growth on poverty alleviation), not countries as in our study.

15
|**Skill**|**Zero GTI**|**Low GTI**|**High GTI**|**relative**<br>**importance**|**Skills group**|
|---|---|---|---|---|---|
|**Communication Skills**|2|4|1|1,03|Social|
|**English**|1|1|2|0,90|Foreign language|
|**Quality Assurance and**<br>**Control**|40|7|3|4,26|Project management and<br>business|
|**Fiber Optics**|1965|702|4|47,46|Field of study and industry<br>knowledge|
|**Research**|14|5|5|2,01|Cognitive (analytical)|
|**Planning**|6|6|6|1,35|Project management and<br>business|
|**People Development**|621|1156|7|13,85|Project management and<br>business|
|**Teamwork / Collaboration**|5|11|8|0,93|Social|
|**Microsoft Excel**|7|15|9|1,12|Computer (basic)|
|**Problem Solving**|13|12|10|1,24|Cognitive (analytical)|
|**Microsoft Office**|10|8|11|0,95|Computer (basic)|
|**Project Management**|39|33|12|2,10|Project management and<br>business|
|**Occupational Health and**<br>**Safety**|498|227|13|8,03|People management|
|**Environmental**<br>**Management**|963|369|14|12,06|Project management and<br>business|
|**Writing**|15|18|15|1,07|Writing|
|**Engineering**|33|22|16|1,52|Cognitive (analytical)|
|**Budgeting**|18|37|17|1,06|Financial|
|**Fundraising**|935|478|18|9,77|Customer|
|**Policy Research**|3684||19|51,22|Cognitive (analytical)|
|**Compliance Management**|1244|267|20|11,26|Project management and<br>business|

_Source_ : authors using OV data.

# Application #3: Gender and education differences in employment

Access to green jobs is not even across groups of workers, with women being less likely to hold a green job independently of the definition applied. Females hold 9 percent of green jobs (Figure 4.4 panel a). This is significantly lower when compared to zero GTI (non-green) jobs, for which female represent 41 percent of workers. Furthermore, green jobs employ more educated workers (although not necessarily at the university level) (Figure 4.4 panel b). Two-thirds of green workers have at least a high-school or vocational degree as compared to half zero GTI workers. When using the narrow definition, our analysis also suggests that workers at green jobs are younger (43 percent of non-green workers are under the age of 35 while 54 percent of green workers are), which may be related to new jobs emerging thanks to the greening of the economy. Peters (2013) found similar results in the U.S.

31
Policy Research Working Paper 9383

# **Abstract**

This paper uses data from Sri Lanka and Tanzania to evaluate the benefits of combining household surveys with geographically comprehensive geospatial indicators to generate small area estimates of non-monetary poverty. The preferred estimates are generated by utilizing subarea-level geospatial indicators in a household-level empirical best predictor mixed model with a normalized welfare measure. Mean squared errors are estimated using a parametric bootstrap procedure. The resulting estimates are highly correlated with non-monetary poverty calculated from the full census in both countries, and the gain in precision is comparable to increasing the size of the sample by a factor of three in Sri Lanka and five in Tanzania. The empirical best predictor model moderately underestimates uncertainty, but coverage rates are similar to standard survey-based estimates that assume independent outcomes across clusters. A variety of checks, including adding noise to the welfare measure and model-based and design-based simulations, confirm that the main results are robust. The results demonstrate that combining household survey data with subarea-level geospatial indicators can greatly increase the precision of survey estimates of non-monetary poverty at comparatively low cost.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at dnewhouse@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
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18