rafmacalaba/datause-encoder-data huggingface dataset:
HEs ownership is not common among youth in low-income SSA (Figure 10). Youth under 25 are the least likely to be HE owners. This observation is fairly consistent across countries **.** On average less than five percent of those between 15 and 19 and only 12 percent of those below 25 own a HE, across the sample of countries, despite the young age of the labor force in low-income SSA, where about half of the population of working age is below 25 years of age. Individuals between the ages of 35 and 50 are most likely to own an HE. This may be influenced by the difficulty of obtaining the necessary capital to start a business. On average there are more young HE owners among rural youth, but the pattern is not observed in all countries and the magnitude is small in most countries. When youth work in the sector, it is likely to be as contributing family workers (not owners), or as apprentices.

**Figure 10 Age and HE owners**

<!-- Start of picture text --> 35<br>30<br>25<br>20<br>Distribution of HE owners<br>15<br>10<br>Share of age group being in<br>5 HE owners<br>0<br>%<br><!-- End of picture text --> _Source: Annex table 3 and 8_ HEs are commonly perceived as the job creation strategy of recent migrants to urban areas, but the role of migration in the development of this sector may be overstated. In Republic of Congo and Mozambique, HEs are more common among recent migrants to urban areas for economic reasons, but this is not the case for Ghana, Uganda and Rwanda where urban nonmigrants are more likely to be HE owners (Table 2).<sup>8</sup> In Tanzania HEs are a strategy employed by both migrants and non-migrants equally. Hence, on the surface it does not look like the HE sector is primarily a product of migration. Migration could be a contributing cause if the inflow of students and highly educated individuals to urban areas strengthened competition for available wage jobs, crowding out long-time residents. This could lead to more HE start-ups.

**Table 2 Share of employed migrants and non-migrants being HE owners, urban areas**

||R. Congo|Ghana|Mozambique|Rwanda|Tanzania|Uganda|Weighted<br>average|
|---|---|---|---|---|---|---|---|
|Non<br>migrant|29.8|41.7|27.8|17.4|37.4|38.4|35.2|
|Migrant|36.5|32.7|37.6|14.5|36.2|32.9|32.9|

Source: Annex table 9. Notes: table shows share of employed migrants working in the HE sector and share of employed non-migrant working in the HE sector.

> 8 This table does not include migrants outside of the labor force e.g. common migrants as students in secondary schools.

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**-** **<u>Table 6.4 IV estimated impact on enrollment and child development outcomes (Above average parenting score at baseline)</u>**<sup>**a**</sup>

||IVMode|l:Baseline|IVMod|el:Midline|IVMod|el:Endline|
|---|---|---|---|---|---|---|
||Actual|Batch 1|Actual|Batch 1|Actua|l Batch 1|
||Coeff|Robust SE|Coeff|Robust SE|Coeff|Robust SE|
|**Enrollment in ECED Programs**|||||||
|Was child enrolled in project playgroups in survey year (1=Yes)|0.150***|(0.047)|0.199***|(0.060)|0.221***|(0.065)|
|Was child enrolled in non-project playgroups in survey year (1=Yes)|0.015|(0.035)|0.037|(0.044)|0.027|(0.046)|
|Was child enrolled in Islamic Kindergarten in survey year (1=Yes)|-0.005|(0.013)|-0.040|(0.028)|-0.047|(0.044)|
|Was child enrolled in Kindergarten in survey year (1=Yes)|-0.014|(0.023)|-0.070|(0.060)|-0.055|(0.072)|
|Was child enrolled in any ECED programs in survey year (1=Yes)|0.147***|(0.056)|0.036|(0.051)|-0.004|(0.032)|
|Was child enrolled in Primary school in survey year (1=Yes)|NA|NA|0.023|(0.015)|0.009|(0.006)|
|Cumulated number of months of project playgroup|1.537***|(0.457)|2.822***|(0.826)|3.721***|(1.116)|
|Cumulated number of months of non-project playgroup|0.151|(0.353)|0.364|(0.551)|0.115|(0.622)|
|Cumulated number of months of Islamic Kindergarten|-0.046|(0.129)|-0.473|(0.375)|-0.866|(0.701)|
|Cumulated number of months of Kindergarten|-0.109|(0.231)|-0.823|(0.696)|-1.326|(1.232)|
|Cumulated number of months of any ECED programs|1.533***|(0.555)|1.891**|(0.916)|1.644|(1.122)|
|Cumulatednumberof months of Primary school|NA|NA|0.231|(0.150)|0.222|(0.669)|
|**Standardized Child Development Outcomes**|||||||
|EDI: Physical Health & Well-Being|0.300**|(0.127)|0.096|(0.103)|0.001|(0.101)|
|EDI: Social Competence|-0.010|(0.091)|-0.056|(0.090)|-0.080|(0.099)|
|EDI: Emotional Maturity|0.048|(0.109)|-0.026|(0.103)|0.060|(0.088)|
|EDI: Language & Cognitive Development|0.047|(0.060)|0.076|(0.089)|-0.034|(0.037)|
|EDI: Communication & General Knowledge|-0.071|(0.046)|-0.119**|(0.053)|-0.035|(0.125)|
|SDQ: Emotional Symptoms|-0.141|(0.115)|-0.027|(0.098)|-0.010|(0.106)|
|SDQ: Conduct Problems|-0.064|(0.111)|-0.042|(0.090)|-0.049|(0.100)|
|SDQ: Hyperactivity & Inattention|0.040|(0.110)|0.028|(0.098)|0.021|(0.114)|
|SDQ: Peer Problems|0.121|(0.111)|0.095|(0.108)|-0.069|(0.108)|
|SDQ: Pro-social Behavior|0.131|(0.103)|0.018|(0.120)|-0.143|(0.096)|
|SDQ:Total Difficulties|-0.042|(0.119)|0.009|(0.095)|-0.041|(0.112)|

Notes: *** p<0.01, ** p<0.05, * p<0.1;<sup>a</sup> IVs are two dummy variables indicating original batches 2 or 3 with original batch 1 as a reference group. Each row is the result of a separate regression. Models are estimated separately for baseline, midline and endline. Controls include: child’s age, gender, household size, household wealth and mother’s education level. Robust standard errors are clustered at the village level. Sample size for each year is 641.

37
Since households were randomized within EAs, one concern is the occurrence of intracluster contamination, whereby the response of a participant assigned to one treatment arm could affect the response of another respondent assigned to a different treatment arm within the same cluster. To mitigate this issue, we collected all surveys simultaneously within the same EA. In addition, intra-cluster contamination occurs more often when multiple rounds of data are collected. In our case, the EAs were large, and the experiment consisted of only one survey.

# **4 Empirical Strategy**

The random allocation of households to LOA, ESR, and C in the survey experiment provides for exogenous variation in the treatment status that allows us to estimate the LOA module’s and enforced self-reporting’s effects on the reporting of labor market outcomes. We report treatment effect estimates based on the following specification: 0 1L 2E (1) where is the outcome of interest (employment or work) for individual iihss hss hss iihss ss iihss _I_ , in household _h,_ in YY = αα + ββ LL + ββ EE + ƟXX + bb + εε enumeration area iihss _s_ . L is a dummy indicating that household _h_ in stratum _s_ was randomly YY chosen to respond the LOA module before providing labor information. hss E consists of a LL dummy indicating that the members of household _h_ were required to complete the labor module hss EE themselves rather than by proxy. is a vector of control variables. To select the variables included in this vector, we control only for variables that are thought to influence the outcomes iihss XX of interest (Bruhn & McKenzie, 2009). Since we collected several such variables, we use a DoubleLeast Absolute Shrinkage and Selection Operator (LASSO) regression approach to select the variables that are more relevant for each outcome (Urminsky et al., 2016). We include all of the individual-level characteristics presented in Table 2 in the LASSO specification.<sup>15</sup> To test for the robustness of the estimated coefficients, we also estimate Equation (1) by excluding the vector of control variables, controlling for those variables for which we have differences in means across the three treatment groups, and controlling for the variables for which there are differences in means between the original and substitute households. captures stratum fixed effects for the EA, which consist of the interaction between the department (San Salvador or Usulután) and the ss bb area of residence (rural or peri-urban).

> 15 Table A3 presents the list of control variables that LASSO selected for each outcome and model.

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exchange whose shares traded at least four times in a given year and report the financial information required. Of the sample firms, 340 did not establish joint ventures or attract FDI; 91 concluded joint ventures with foreign companies and 82 attracted majority foreign equity investment. Thus, 34 percent of the sample (173 firms) had a foreign link-either a joint venture or FDI, with relatively uniform distribution across sectors (Table 1).

**Table 1: Descriptive Statistics of the Sample**

|Sector|Total Sample<br>N|o Foreign Partn|er Foreign Partner<br>(FDI or J.V.)|
|---|---|---|---|
|Mining|11|8|3|
|Construction|82|55|27|
|Food and beverage|54|36|18|
|Textiles and Apparel|39|28|11|
|Furniture and Other Wood Products|11|5|6|
|Pulp and paper|14|10|4|
|Printing and Publishing|13|6|7|
|Chemicals|30|18|12|
|Shoes and Leather Products|6|5|1|
|Non-Metallic Mineral Products|21|16|5|
|Basic Metals|13|9|4|
|Fabricated Metal Products|24|12|12|
|Electric and Electronics|82|54|28|
|Transport Equipment|12|5|7|
|Other Manufacturing|10|6|4|
|Retail Services|15|11|4|
|Financial services|76|56|20|
|Number of Observations|513|340|173|
|Share in Total|100.0%|66.3%|33.7%|

The criterion used in our sample to determine the existence of a foreign partnership or ownership relationship is that at least 20 percent of the equity is owned by a single foreign entity or that the firn has established one or more joint ventures with a foreign partner. Because minority shareholders have little protection under Czech law, equity investors have an incentive to take a majority stake. Most firms with foreign equity ownership in the sample are majority foreign owned. While the share of firms with foreign linkages appears to be high, it is representative of Czech industry more generally. Aggregate statistics using a 5 percent or more 9
<!-- Start of picture text --> Percent of U.S. imports from country with largest import share<br>” Panel A: Medical goods 3 Panel B; Other critical goods<br>8V(;}a<br>|<br>|| Ii Ay | | , Np onan<br>bs | }h Mn\ i ‘ A nu?, eee ’ /SApor \ semiconductorchips<br>Lnnan a ©6~€©€6383G— mcr rnaCM<br><!-- End of picture text -->
services sectors account for about 30 percent and 70 percent of regional GDP (20 percent and 80 percent of employment), respectively. In Luzon, Visayas, and Mindanao, one-sixth to one-third of regional GDP and one-third to half of employment came from agriculture, while less than half of regional GDP and employment came from services.<sup>4</sup>

**<u>Table 5: Regional GDP and Source of Employment by Industry Group</u>**

||**GDP per capita**<br>**(2000 PHP)**|**Share of**<br>**GDP**<br>**Agri**|**Share of**<br>**Employment**<br>**culture**|**Share of**<br>**GDP**<br>**In**|**Share of**<br>**Employment**<br>**dustry**|**Share of**<br>**GDP**<br>**S**|**Share of**<br>**Employment**<br>**ervice**|
|---|---|---|---|---|---|---|---|
|NCR|109,326|0.0%|0.9%|31.4%|21.4%|68.6%|78.1%|
|Luzon|27,979|21.8%|35.1%|34.8%|17.6%|43.4%|47.3%|
|Visayas|31,048|16.4%|40.7%|30.3%|13.8%|53.3%|45.4%|
|Mindanao|26,680|31.3%|50.7%|28.5%|9.3%|40.2%|40.0%|
|**Philippines**|**38,861**|**14.5%**|**35.8%**|**31.8%**|**19.5%**|**53.7%**|**49.0%**|

_Source:_ Philippine National Statistical Coordination Board and Bureau of Labor and Employment Statistics. Employment data are for 2006 and GDP data are for 2007.

The unique labor market environment in NCR is characterized by high active population rate, low participation rate, low employment rate, high unemployment rate, and high daily wage (Table 6).<sup>5</sup> The scenarios in Mindanao and Visayas are at the other end of the spectrum, characterized by low active population rate, high participation rate, high employment rate, low unemployment rate, and low daily wage.

The level of daily wage varies widely across regions: wage level is the highest in NCR and the lowest in Visayas (Figure 1). In Luzon, Visayas, and Mindanao the level of daily wage of the employed clusters at two separate low levels; while in NCR it concentrates at a higher level. Within region, there is sharp difference in wage distribution. With daily wage one-third higher and unemployment rate almost doubling the national average, individuals in NCR face a tougher competition in getting a job but are likely to have better remuneration if employed.

> classified into four island region groups – National Capital Region (NCR), Luzon, Visayas and Mindanao – based on their geographic location. See Map 1 for details.

> 4 See Manasan and Chatterjee (2003) and Balisacan and Fuwa (2004).

> 5 See Annex 2 for illustrations.

4
8 Finally, we also apply text mining to distinguish those datasets sampling from IDPs from those that only sample from refugees or Venezuelan refugees and migrants. We evaluate whether some keywords indicating the inclusion of IDPs in the sampling frame are mentioned in the sampling section of the metadata. These keywords included “internally displaced”, “internal displacement”, “IDP”, or “IDPs.” We then perform a manual verification to make sure that IDPs are indeed included in the sampling frame to generate representative data for this population.

# 4. Results

Through the methodology described above, we find 375 publicly available micro-level datasets that have a representative sample of refugees and/or IDPs.<sup>23</sup> Figure 2 plots the number of datasets over time between 1995 and 2022.<sup>24</sup> There has been a rapid increase in the number of FDP micro-level datasets collected and published in the UNHCR/WB MDLs especially since 2015. It is worth noting that part of this upward trend may also be explained by the fact that older micro-level datasets are not stored or published in the UNHCR/WB MDLs. For instance, the UNHCR MDL was launched in 2019 and despite efforts to retrospectively cover and publish older micro-level datasets collected before the inception of the UNHCR MDL, the extent to which older datasets are still missing from the database is unknown.<sup>25</sup>

> 23 The results and codes for the analysis are all available from <u>https://github.com/takaakimasaki/DisplacementDataGap.</u>

> 24 The oldest dataset available in WB/UNHCR MDL was collected in 1995 at the time of this study.

> 25 See more recent developments in UNHCR MDL: <u>https://www.unhcr.org/blogs/responsible-and-timely-sharingdata-on-unhcrs-microdata-library-in-2022/.</u>
becomes innovative.

# **3.5 Descriptive Statistics**

Table 3 reports summary statistics of key variables comparing innovative - those with at least one PoTec worker - with non-innovative firms in 2017 for all sectors of the economy and excluding public sector institutions. Innovative firms represent only 3% of total firms in Brazil but 43% of private employment in 2017. Innovative firms are significantly larger than non-innovative. The median innovative firms have more than eight times the size of the median non-innovative firm; which is mainly a micro firm. Innovative firms have younger and more educated workers, have a larger share of male workers and also employ a larger share of high-skill occupations.

Table 3: Summary statistics of private sector firms by innovative status, 2017

||**Innovative**|**Non-Innovative**|
|---|---|---|
|Log of hourly wage|2,4|1,9|
|Employment (mean)|187,4|7,3|
|Employment (median)|25,0|3,0|
|Age|35,4|35,9|
|Share of female workers|34,1%|45,4%|
|Share of high-skill workers|67,4%|40,2%|
|Share of college graduate|33,2%|7,3%|
|Average worker tenure|45,1|42,4|

Innovative firm is defined as those with at least one PoTec workers in 2017. Employment is the number of workers by innovative status. Tenure is measured in months, and age shows the average wage of workers.

These basic descriptive statistics highlight the fact that innovative firms tend to be much larger than non-innovative firms and employ a more educated labor force. Thus, given the stylized facts established by the LFWP summarized above and the extensive Mincerian literature on the returns to education, one should expect that innovative firms also pay higher wages. This is confirmed in Figure 1, which plots the average log of wage per hour on workers’ age for innovative firms (green light) and non-innovative firms (red dotted line). The top left panel includes all sectors, also the public sector, and shows higher wages for all age groups for innovative firms, and a wage difference of around 50 log points for workers above 35 years old. Similar results are shown in the panels below for manufacturing and services firms, with also larger wages of workers in innovative firms and a similar wage gap across experience groups. In the case of agriculture (top right panel), the wage gap is also positive but much narrower.

13
A potential limitation of our approach is that it is based on _de jure_ differences in entry regulation, but what matters from an entrepreneur’s point of view is how these laws are implemented; it is perfectly possible for entry to be very easy in practice even when written regulation is arduous or for entry to be difficult even when written regulation does not appear excessive. As a validation exercise we hired a leading law firm specialized in corporate law, investment, mergers and acquisitions, and international arbitration, to provide a (inevitably subjective) rating of which sectors were most difficult to obtain permission to enter, providing us with a _de facto_ proxy for the intensity of entry regulation based on the expertise of practitioners which we use for robustness checks. Since the supreme court lawyers we worked with are internationally recognized experts with more than more than 30 years of experience across a wide variety of sectors, they are well positioned to assess de facto differences in regulation. Appendix B3 provides more details on the construction of this indicator.

One issue we faced was matching the activities listed in the Investment Code to specific 5- digit sectors, which do not perfectly overlap. In some cases, the Investment Code provides a more detailed description of activities, whereas in others, the code is more general than the Tunisian NAT 96 classification that we use. With the help of officials at the Tunisian INS and the law firm described above, we create a correspondence between activities and sectors, but in some cases multiple activities were mapped to the same sector and vice versa.<sup>10</sup> As a consequence of the resulting aggregation, it is possible that some activities in a given sector are subject to entry restrictions whereas others are not; in these cases we relied on the expert judgment of lawyers to decide whether to codify the entire sectors as regulated or not. These issues are discussed in more detail in Appendix B, which describes how the regulation data were constructed. For the purpose of this analysis, we also exclude sectors that are not populated by firms covered by the Repertoire National des Entreprises (e.g. selected agricultural activities), or exclusively populated by public firms (e.g. postal services).

# **3 Descriptive Statistics**

Ben Ali firms are important from an aggregate economic point of view, as is demonstrated by descriptive statistics on the prevalence and aggregate contributions of Ben Ali firms presented in

> 10 A detailed mapping from activities to sector codes was constructed in collaboration with the Tunisian Institut National de la Statistique and is available from the authors upon request. The correspondence we developed was not fully exhaustive; a handful of activity descriptions, such as “exporting activities” were too generic to match to particular subsectors.

10
but still non-trivial. The probability of distress for a country at the 25 percentile of growth is 20 percent, as opposed to 8 percent for a country at the 75<sup>th</sup> percentile.

Since our ultimate interest is in predicting debt distress episodes based on a parsimonious set of variables, it is useful to also examine the out-of-sample predictive power of each of these specifications. We first re-estimate each regression using data through 1989. We then use the use the estimated coefficients, together with the observed right-hand-side variables to predict the outcome of each of our observations in the 1990s. In particular, we predict that a debt distress episode will occur if the predicted probability conditional on the observed data included in each regression is greater than the unconditional probability of distress in the pre-1990 sample, which is 0.23 in the sample of 163 observations in first seven columns, and slightly lower at 0.20 in the last column. The first row reports the fraction of all episodes that are correctly predicted by each model, which ranges from 0.43 (for the model with growth only) to 0.84 (for the model with debt service, CPIA, and growth). One striking feature of the univariate regressions is that the out-of-sample predictive power of the CPIA is just as good as the predictive power of debt service, and considerably better than the predictive power of the present value measure. This suggests that the quality of policy is as good or better a predictor of debt distress as traditional debt burden indicators.

The remaining two rows disaggregate the correct predictions. The second row shows the fraction of the 10 distress episodes during the 1990s that are correctly predicted, while the third row reports the fraction of normal time episodes during the 1990s incorrectly predicted as distress events, i.e. false alarms. In most specifications, 70 percent or more of distress episodes are correctly predicted (with the exception of the regression with growth only, for which only 60 percent are correctly predicted. There is a lot of variation across specification in the likelihood of false alarms. In our best specification from a predictive point of few (column (6)), only 13 percent of normal times episodes are incorrectly signalled as distress. However, predictions based only on the present value of debt or growth have quite high probabilities of false alarms, at 55 percent and 63 percent of normal times episodes, respectively.

17