rafmacalaba/data-use-mentions-extended huggingface dataset:
## **3.3 Other Data**

### **3.3.1 Night Lights Data**

We employ night light luminosity (NTL) data constructed by Elvidge, Zhizhin, Ghosh, Hsu, and Taneja (2021) as a proxy for electrification. More specifically, we use the average VIIRS (Visible Infrared Imaging Radiometer Suite) annual cloud-free composite VNL version 2 for the year 2015. The resolution of these data is 15 arc seconds (approximately 0.0327 km around the Equator). Thus, after extracting it for SSA, we aggregate the NTL measure at the level of each GAEZ grid.

### **3.3.2 Water-Usage Data**

Large variations exist in water-usage patterns within countries. We employ annual countrylevel freshwater-withdrawals data for agriculture, industry, and municipal (i.e., domestic) use.<sup>5</sup> Annual freshwater withdrawals refer to total water withdrawals, not including water loss due to evaporation from storage basins. Withdrawals also include water from desalination plants in countries where they are a significant source. Using this data, we calibrate country-level thresholds needed for crop irrigation purposes. By subtracting the freshwater needed for sanitation and industrial production purposes from the total freshwater consumption of the country, the thresholds represent the share of freshwater that is available for irrigation purposes. The agricultural water withdrawals is collected at the country level. As a result, we are unable to account for within-country variation in access to water for sanitation.

### **3.3.3 TerraClimate Data**

To incorporate climatic factors into our yield estimations, we utilize the dataset curated by Abatzoglou, Dobrowski, Parks, and Hegewisch (2018). This dataset provides monthly climatic information at a spatial resolution of approximately 4 km (1/24th degree). For each of the GAEZ grids, we extract data on mean monthly temperature, total monthly precipitation, and monthly Palmer Drought Severity Index (PDSI) for 2010.
>c</sup>|-|-|4|-|4|-|2|-|2|
|Sudurpachim<sup>c</sup>|95|61|37|5|198|16|39|39|94|
|Total|1731|876|372|60|3039|110|364|492|966|

Source: (ILO et al., 2020) Notes: a Grade A is well baked, shiny, standard size, uniformly yellow or red, of regular texture, and uniformly shaped. Such bricks are free from pebbles, gravel, or organic materials. Grade B bricks are slightly over-cooked and may have irregular shapes and textures in comparison to Grade A bricks. Grade C bricks are crumbling, small pieces, overlooked, and irregular in shape. Others include tiles and other materials.

b Small kilns have employees of less than 50, medium kilns have employees between 50 and 100, and large kilns have employees of more than 100.

c Brick production data for Karnali is not available. We assume the type of technology of two operational kilns in Karnali is CK and they produce Grade C bricks. Note that out of 41 million Grade C bricks produced in Karnali and Sudurpachim provinces combined, we assume 4.2 million [i.e., (202 ⁄ 96) × 2)] Grade C bricks are produced in Karnali, and the remaining 36.8 million Grade C bricks are produced in Sudurpachim. The value, 202, is the total number of bricks produced (Karnali + Sudurpachim), the value, 96, is the total number of kilns (Karnali + Sudurpachim), and the value, 2, is the total number of kilns in Karnali. _The percentage weight by brick quality type_ [ ~~w~~<sup>]</sup> jj,gg **Table A2.2** : Estimated percentage weight by brick quality type ~~(w~
The baseline survey was conducted between February and May 2014, thus ending just as the first cases of Ebola were being reported. This covered 5 _,_ 775 young women, corresponding to 27% of all eligibles, and recorded information on their time use, education and skills, labor market activities, risky behaviors, marriage/pregnancies, empowerment and aspirations, expenditure/finances, and social networks.

Time use data plays an important role in our analysis. To elicit reliable information, rather than just asking about hours, we asked respondents to use a physical representation of time (beans) to show how they divided their time over the past week. We did so for broad activities (education, income generation, household chores, leisure), and also specifically for time devoted to leisure (sexual relationships with men, with friends, social activities, alone).<sup>22</sup> The survey took place during the school year so respondents could feasibly have been attending school on survey dates. The credibility of the time use data is underpinned by the fact that: (i) the number of beans recorded across categories summed up to 25 for 90% (99%) of respondents at baseline (endline); (ii) 87% of respondents report sleeping 5 to 10 hours per night; (iii) the average number of hours spent per week at ELA clubs is three, which is realistic. We convert time use measures into hours per week.

With the onset of the crisis and all fieldwork suspended, we implemented two village-level surveys. The first was a monitoring survey to ELA club mentors in treatment villages conducted in June/July 2015. It provides information on club functioning, activities and participation. The second was a village leaders survey administered between June and October 2015. It provides information on village characteristics, public facilities, impacts of Ebola (in terms of the number of households quarantined, Ebola related cases and deaths), and policy responses (such as the functioning
positive interactions (0.24 s.d.) with their local community (Table 2, column 3). Table 6 (row 5) reveals that participants that served in Hausa states report a statistically significant lower quality of stay compared to other non-Yoruba regions.

# **6.2 Internal Migration and Knowledge of Other States**

A key aspect of national integration is the free movement of people across the country. From inception, one of the stated objectives of NYSC has been “to encourage members of the service corps to seek, at the end of their corps service, career employment all over the country thus promoting free movement of labour” (NYSC 2015b). I examine the extent to which participants who served in a different ethnic region become more likely to make their homes outside their ethnic homeland.

I find that interethnic exposure during NYSC results in a fivefold increase in the likelihood that an individual lives outside their ethnic region seven years later. The interethnic migration rate for control group subjects is 3.3 percent. This is similar to the overall rate among the Yoruba: only about 5 percent of Yorubas live outside their ethnic homeland (estimate calculated from NDHS 2008).<sup>21</sup> Table 3 (column 1) shows that, using the IV regression framework, the treatment effect is 14.4 percentage points.

Living outside one’s ethnic region after being exposed is not simply due to individuals remaining in (or returning to) the NYSC location, for example, by obtaining a permanent position with the NYSC employer or finding a job through a network of contacts. Only 20 percent of the 15.4 percent of treated participants living outside their ethnic region are living in their NYSC service state. The remaining 80 percent are living in non-coethnic states other than their NYSC state.

> 21There is significant variation in interethnic migration rates across ethnic groups; for example, over 20 percent of Igbos live outside their ethnic homeland. The rate for Hausas is similar to
**|4.30|
|No. of adult labor per ha of operated land|5.11|11.16|***|4.34|***|2.34|
|No. of male adult labor per ha of operated land|2.26|4.27|***|2.13|***|1.23|
|Number of oxen per ha of operated land|1.22|1.09||1.32|*|1.20|
|No. of observations|2,188|514||770||904|

_Source:_ Own computation from AU/WB/UG land tenure survey. **Note:** Asterisks indicate the difference in significance between means for landlord and autarky groups as compared to tenants with *, **, and *** denoting significance at 10%; 5%; and 1%. The top panel excludes 53 landless tenants while 299 pure landlords had to be dropped in the bottom panel.

18
# **1. Introduction**

The informal sector is a source of livelihood to millions of people around the world, by some estimates accounting for about a quarter of the GDP (Schneider, Buehn, and Montenegro, 2010) and around 70% of employment in the developing world (Loayza, 2018). Despite the size and importance of this sector, cross-regional comparable business-level data covering a wide range of topics around their operations are lacking. Since informal sector operations are generally not captured by official records, collecting data on the informal sector businesses, especially from representative samples, is challenging (ILO, 2013). The data on informal economic activity can be collected through two primary actors—the worker and the place of employment (i.e., business or firm). The former is often accomplished through household surveys, commonly the Labor Force Surveys (LFS); this is particularly suited to understand the 'intensive margin' of informality—i.e., unreported employment in otherwise formal sector businesses. The latter is often preferred for a detailed understanding of the workings of informal businesses, their production processes and experiences, the ‘extensive margin’ of informality.

To collect data directly from informal businesses, various survey methods have been implemented, with the corresponding data enabling insightful research into how informal businesses operate and generating development policies around them (see, e.g., Benjamin and Mbaye, 2012; LaPorta and Shleifer, 2014; Amin and Islam, 2015; Islam, 2019).<sup>2</sup> Aga et al. (2022), which this paper accompanies, examines the innovative application of the Adaptive Cluster Sampling (ACS) method (Thompson, 1990) to collecting data from informal businesses, rendering samples that are representative of the underlying population of informal businesses at the geographical area being covered.

This paper introduces the data collected across twenty-four cities in seven countries through the survey method examined in the companion paper (Aga et al., 2022)
female to male human development index, UN Gender Inequality Index (GII), and the OECD Social Institutions and Gender Index (SIGI). The findings are provided in table 1.

The directions of the correlations are as expected – greater legal gender disparities are correlated with greater gender inequality. All the measures, apart from the SIGI and GII, higher values imply greater gender equality, and thus they are negatively correlated with the WBL measure of legal gender disparities. Accordingly SIGI and GII are positively correlated with the WBL measure of legal gender disparities as higher values of SIGI and GII imply greater gender inequality. All correlations are statistically significant at the 1 percent level. The SIGI index has the highest correlation with the WBL measure of legal gender disparities (0.70), which is not surprising given that the SIGI index covers social institutions that encompass certain legal institutions.

The correlation between the WBL measure of legal gender disparities and other measures of gender inequality does provide some support of the view that the law in the books may influence realities in the ground, addressing the first critique of the measure. Furthermore, none of the gender-related indices are perfectly correlated with the WBL measure of legal gender disparities. This implies that the measure developed captures a different and more specific nature of legal institutions unlike the other gender-related measures. Thus the WBL measure of legal gender disparities provides additional empirical information to the existing plethora of gender-related indices, addressing the second critique of being redundant.

# **5. Relationship with Gender-Specific Outcomes**

14
sectors by country such that the higher the score, the more restrictive is the country (including in a particular sector or for a particular measure). Illustratively, for the measure “foreign providers have to completely re-do the university degree, practice and exam in the domestic country”, Switzerland had an average score of 0.33 across sectors in the year 2017 compared to an average score of 0.5 in the case of Estonia and 0 in the case of Australia. This suggests that while this particular measure was not a requirement in Australia in the year 2017, it was applicable to more sectors in Estonia than in Switzerland.

The average restrictiveness towards MNP in the year 2017 for the 45 countries covered by the OECD STRI data is presented in Figure 1. The average scores range from 0.10 in the case of Colombia (at the bottom end of the distribution) to 0.72 for Russia (at the top end). The average score for non-OECD countries (0.34) is found to be lower than that for OECD (0.40) as Colombia, Latvia and Costa Rica are among the least restrictive countries in this sample in terms of policies towards MNP while nine of the top ten most restrictive MNP countries (with the exception of Russia at the top) belong to the OECD.

Annex Table 1 reports the average restrictiveness of policies towards MNP by sector in the year 2017 for the 45 countries covered by the OECD STRI data. Our analysis shows that sectors with the most restrictive MNP policies include: Audiovisual services – Broadcasting (16); Insurance services (15); Insurance services – Actuaries (16); Insurance services – Broking and Agency (33); Legal services – Domestic Law (18); Rail freight transport services (19); and Road freight transport services (21). The numbers in parentheses indicate the number of countries for which the average restrictiveness in the concerned sector is at least 0.5. In contrast, sectors with the least
allow us to investigate if migrant networks constitute an important source of market and ...rm-speci...c heterogeneity in entry costs and demand.

Portugal further provides an interesting setting for re-examining the e¤ect of common language/colonial ties on export participation and intensity. Portuguese-speaking nations belong to three di¤erent continents and di¤er markedly in terms of size. They are also at di¤erent stages of economic development. Brazil was the ...rst country to become independent in 1822. The other nations became independent in the wake of the Carnation Revolution of 1974. Table 1 reports the independence dates, o¢ cial languages, and number of Portuguese migrants in each of these countries. Table A.2 in the Appendix displays the distribution of Portuguese migrants across the full set of export destinations.

# 4 Data

Our empirical analysis draws on a rich ...rm-destination data set that combines information from several sources: Export ‡ows. Our main data source is the Foreign Trade Statistics (FTS) of Portugal for 2005. This is the country’s o¢ cial information source on international trade statistics, gathering the shipments of virtually all exporting ...rms to each destination market. The FTS data are collected in two di¤erent ways. Data on trade with countries outside the EU (external trade) are collected via the customs clearance system, which covers the universe of external trade transactions. Data on the transactions with other EU member states (internal trade) are obtained via the Intrastat system, where the information providers are companies engaged in internal trade and registered in the VAT system whose value of annual shipments exceeds a legally binding threshold (85,000 Euros in 2005). Export values in these data are "free on board", thus excluding any duties or shipping charges. The 2005 FTS data set comprises information on 16,541 exporting ...rms and 220 destination markets. Despite the above-mentioned constraint, the export transactions included in these data aggregate to
Credit Bureau Coverage (% adults)<br>ent|-0.015*||-0.018**||-0.015*||
|nm|(-1.809)||(-2.289)||(-1.803)||
|Time to resolve insolvency (years)<br>viro|0.473***||0.509***||0.463**||
|s en|(2.623)||(3.018)||(2.564)||
|Rule of Law (Kaufmann et al.,2010)<br>ines||-1.327||-2.112**||-1.997*|
|Bus||(-1.272)||(-2.087)||(-1.859)|
|Constant|6.438***|8.995***|6.139***|7.306***|6.370***|7.914***|
||(5.320)|(8.685)|(4.944)|(6.021)|(5.239)|(7.395)|
|Observations|445|491|455|463|445|473|
|Number of country_code_|50|51|51|52|50|51|

_Note:_ Annual information between 2005 and 2017 for 51 EMDE countries. _Public debt_ includes debt data for general government. In case this data is not available, debt data for the central government is used. _St.Deviation Inflation_ refers to standard deviation of monthly inflation rate y- o-y, using a one-year rolling window. _5-year St.Deviation Inflation_ refers to standard deviation of monthly inflation rate y-o-y, using a 5-year rolling window
map affect the expansion of the RNU? While the implementation of the first RNU relied on commune poverty rates, the expansion of the RNU relies on commune vulnerability rates to determine the number of households in each commune that may be registered. Commune vulnerability includes the poverty rate as in the previous RNU, plus an estimate of the share of the nonpoor at risk of becoming poor. The new quotas will therefore shift depending on the distribution of risk-induced vulnerability rates across communes. This implies that, under the new system, a commune with a low poverty rate, but high overall vulnerability will benefit similarly to a commune with a high poverty rate. In the past, the former was not considered in RNU registrations. Accounting for the poor, but also for

> 19 The RNU includes 66 percent of households as rural though only 55 percent of the population is rural. Similarly, Dakar represents 31 percent of all households, but only 14 percent of the RNU. Kolda and Ziguinchor regions are overrepresented. They account for 18 percent of the RNU, but only 9 percent of the population.

18
# **TABLE 3: CHANGES IN REGULATION AND SUPERVISION ACROSS COUNTRIES DURING 2007-2011**

This table shows responses to questions for which we found statistically significant differences between the 2007 and 2011 surveys. Following Laeven and Valencia (2010), the “crisis” countries between 2007 and 2009 include United States, United Kingdom, Iceland, Ireland, Latvia, Belgium, Luxembourg, Netherlands, Austria, Denmark, Germany, Kazakhstan, and Ukraine (cases of systemic banking crises); France, Greece, Hungary, Portugal, Russia, Slovenia, Spain, Switzerland (borderline cases). Student’s t-test are used to test for the equality of the means between responses to the 2007 and 2011 surveys.

|**Section**|**Question**||**All**|**Crisis**|**Non-Crisis**|
|---|---|---|---|---|---|
|**Capital regime**<br>**and actual capital**<br>**levels**|What was the actual risk based capital ratio of<br>the banking system? (Average %)|2007<br>2011<br>p-value|15.45<br>17.45<br>0.003|12<br>14<br>0.126|16<br>18<br>0.012|
||What was the actual Tier 1 capital ratio of the<br>banking system?<br>(Average %)|2007<br>2011<br>|13.38<br>15.93<br>|10<br>12<br>|15<br>17<br>|
|||p-value|0.013|0.101|0.037|
|**Governance**|Is there a regulatory limit on
mutations following land transfers), Collector's office, and the Revenue Inspector _(finalpublication andpatta distribution)._ After the final publication of the RORs, the field maps are prepared at the Settlement Office to indicate the exact shape of individual plots within the village.

# Box 6: Nature of objections at a settlement camp

At the camp in Badavema village, Khurda district, more than 70 percent of objections related to non-recording of mutations for transferx deeds (sales and partition). About 20 percent arose out of errors during demarcation. Inheritance (3 percent), change of land title (3 percent) and land classification (1 percent) made up the rest of the objections. About 15 percent of objections relating to inheritance were brought by married women who had been denied a share in their deceased father's property by their brothers.

Nearly one-third of the total number of objections were disallowed. This is a special category which relates to encroachment on government land. The encroaching parties have no _patta_ (documentary evidence of land rights, i.e. sale deed or title) and the objection is thrown out. An Encroachment Register is prepared by the settlement staff which records encroachment on cultivable or non-cultivable government land (grazing land, burial grounds, etc.). It is handed over to the tehsildar to pursue under OPLE.

The disallowed category also includes cases in which, after filing the initial objection, the concerned party fails to pursue their claim. Such cases arise most often over jointly-held property where the opposing claimants are brothers who later resolve the dispute by agreement. However, once an objection is filed, it cannot be withdrawn. It has to be investigated by the _amin_ by interviewing witnesses near the plot. Women are rarely interviewed as witnesses.

Settlement officials claim that objections usually arise out of oversight on about 10-11 percent of the cases handled by the settlement camp. Focus-group discussions with villagers in Badavema village reveals an altogether different picture. Their perceptions are summarised in Box 7.

At any given
--|---|---|---|---|---|
|**_# of new firms registered_**|24|374|70|2364|85|109|191|253|609|
|**_# of new firms entering_**|24|336|72|2243|72|93|185|224|571|
|**_Mean employment_**|24|11.6|1.33|91.7|1.49|2.62|4.27|12.7|23.7|
|**_Distrust_**|24|0.61|0.43|0.78|0.47|0.54|0.64|0.66|0.68|

Sources: Data from Serbia’s Financial Accounts Registry and 2006 Life in Transition Survey 10
2016b), we apply a lasso regression for feature selection.<sup>22</sup> The lasso regression allows us to follow a data-driven approach when selecting the most relevant variables from the potential set of overlapping variables. We apply a 10-fold cross-validation (CV) procedure. The lasso estimation results in 7 out of 22 possible matching variables, which are households with pensioners, households with elderly, households with unemployed, a female-headed household, urban, its income category, and the number of household members with secondary education. The resulting out-of-sample R-squared is 0.151, and the CV mean prediction error is 0.014.

**In addition to the variables identified via feature selection, we include the variables that describe the underlying sample design in the set of matching variables.** This is the variable that identifies the primary sampling unit (census enumeration units in the case of the HBS). We denote this variable as psu1 in the rest of the paper. In an alternative model specification, we include the sample weights by running weighted regressions. Additionally, we explore empirical specifications, in which we abstract from weighting regressions by the given survey weights but include survey weights as an additional matching variable.

# **III. Results**

This section presents the results of the different imputation models applied in the paper and analyzes the quality of the resulting imputation.<sup>23</sup> For all regressions, we apply 20 imputations and set the seed to 12345 to ensure the reproducibility of our results.

22 The least absolute shrinkage and selection operator (lasso) forms part of penalized least square regressions. It was originally developed by Robert (1996) and performs both automatic variable selection and continuous shrinkage. For details on the lasso regression see Meinshausen and Bühlmann (2006).

23 Before applying the different imputation models we prepare our data for the usage of the mi commands in Stata. We use the data in marginal long style (mlong) as it is more memory-efficient. We
Hindu castes, but in urban areas (covering the period 2004/05–2011/12 only) they stand out as most likely to experience downward mobility. Azam (2019) goes on to show that mobility patterns across these population groups are attenuated, but do not disappear

> 6 The IHDS also collects consumption data. But compared with the NSS surveys, the IHDS has less than half the sample size and collects a much-reduced version of household consumption data (i.e. 47 consumption items in the latter, versus more than 400 items in the former).

9