rafmacalaba/data-use-mentions-extended huggingface dataset:
# **III THE VENREPS-KIDS STUDY**

In this section, we offer a detailed overview of the VenRePs-kids Study, covering its design, implementation, the questionnaire utilized, and the primary outcomes that will be employed to evaluate the human development disparities between forcibly displaced children and adolescents and their peers in host communities.

# **III.A Design**

_Location._ Our study is conducted in Medell ́ın, Colombia’s second-largest city, following Bogot ́a. Medell ́ın was chosen for this study because it hosts the third-largest Venezuelan migrant population in the country, trailing only Bogot ́a and C ́ucuta, as indicated by the 2018 population census data. Additionally, previous research has demonstrated that survey response rates among migrants in Medell ́ın are notably high. For instance, a nationally representative survey of Venezuelan migrants conducted in 2018—which was representative across Colombia—revealed that Medell ́ın had the highest response rates among migrants, whereas Bogot ́a recorded the lowest (Ib ́a ̃nez et al. 2022). This finding supports the decision to focus our study exclusively on Medell ́ın, also considering the challenges and high costs associated with tracking a highly mobile population longitudinally in previous research efforts. A map highlighting Medell ́ın’s geographic position and the locations of the households interviewed for this study is provided in Figure 2, offering visual context to our research setting.

_Representativeness and stratification._ VenRepS-Kids is designed to be representative of two groups of youth. The first group consists of Colombian children and adolescents, aged 5 to 17, born to Colombian parents. The second group encompasses Venezuelan migrant children and adolescents of the same age range, born to Venezuelan parents, who migrated to Colombia between 2016 and 2020. The sample was further stratified by gender and socioeconomic levels, using Colombia’s neighborhood income-based classification system that ranges from 1 to 6, where six indicates the wealthiest neighborhoods. Our 13
data coverage and content are the main reasons why many empirical studies use data imputation based on surveys, or other alternative sources of data and information on sample populations.

Lastly, another pitfall is access and confidentiality. Depending on the regulatory premises of each country, access to administrative data may be limited to confidentiality-protected aggregate data or to disaggregated individual data. Open governments can be a great source of information, but in many cases data access is limited due to unwillingness of governmental administrations to fully share program beneficiary databases or other information that could hold administrators accountable.

Confidentiality can also prevent governments from providing full access to its records. Since some statistical techniques require individual identifiers to manipulate intermediate data, there could be public privacy concerns related to how databanks could be used against respondents.

# **Social networks**

# <u>Method</u>

Communications within an individual ́s social network can reveal a lot of information about individual interactions, mobility, and habits that correlate with socioeconomic levels. Bluemenstock, Cadamuro and On (2015) showed that an individual’s mobile phone history, for example, can infer socioeconomic status. Furthermore, they showed that it is possible to derive national and subnational wealth estimates from patterns in cellular phone trace data.

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# Table 8. Capacity utilizaticn rates in manufacturing, 1973-87

# percentages

||1973<br>peak<br>----------------------------|1979-80<br>peak<br>-------------|1982-83<br>trough|early<br>1987|
|---|---|---|---|---|
|United States|87.7|87.2|69.0|80.1|
|Japana|100.0|96.6|85.6|88.2|
|France|87.8|85.3|77.0|83.1|
|Germany|88.1|86.0|74.3|84.0|
|Italy|78.8|77.3|69.1|76.9|
|United Kingdom|90.6|87.6|73.0|87.4|
|Totalb|89.6|88.0|73.4|82.5|

a. Indices, 1973=100.

b. Using 1982 GDP weights.

Sources: EEC, European Economy, Supplement B; OECD, Monthly Economic Indicators; Statistics Bureau, Management and Co-ordination Agency, Monthly Statistics of Japan; UNCTAD, Trade and Development Report, 1987.
during the same period.<sup>34</sup> As a result, since 2005, the total amount of credit card transactions relative to the GDP has exceeded 30 percent, the highest among the 23 member countries of the Committee on Payment and Settlement Systems, Bank for International Settlements.<sup>35</sup> Song and Sung (2012) estimated that the introduction of tax credit for electronically traceable payment methods increased tax revenues by KRW880 billion in 2009: an increase of KRW2.3 trillion in broader tax base but a decrease of KRW1.4 trillion in tax revenue forgone for tax credits. Because of the success in encouraging electronic payments in B2C, the electronic tax invoice became an obvious candidate for B2B, an effective

> 31 Taxpayers now can file 12 tax returns and pay taxes including VAT and individual and corporate income taxes, obtain 23 different types of certificates, and file more than 100 tax-related applications through Home Tax Service (http://www.hometax.go.kr).

> 32 When the incentive was introduced in 1999, 10 percent of the credit or check card purchases in value term (not exceeding KRW3 million) above 10 percent of the total wage income were eligible for income deduction. As of 2013, 15 percent of the credit card purchases exceeding 25 percent of the total wage income were eligible for income deduction.

> 33 The Bank of Korea (2013) Statistics on payment, clearing and settlement in Korea (in Korean); 34 Ibid.

35 Card payments (except e-money) relative to the GDP came to 30.5% in 2005 and 38.5% in 2008 when the mandatory ETI was introduced in Korea. It reached 41.8% in 2013; data from the Committee on Payment and Settlement Systems, BIS (each year _), Statistics on payment, clearing and settlement systems in the CPSS countries._ 17
&<br>terms of premises use|high.<br>owned or leased<br>with high<br>contractual<br>rights|medium.<br>mobile enterprises;<br>however garages may be<br>owned or leased with<br>weak contractual<br>rightsa|low.<br>kiosks rent<br>land from the<br>city, no<br>contractual<br>rights.a|none.<br>daily<br>occupation|none.<br>daily<br>occupation|none.<br>daily<br>occupation|
|Percent keeping written records*|- _100%_|27%|72%|7%|46%|8%|
|Relative ease of estimating<br>aggregate<br>value added|high|medium|medium|low|low|low|

_Source:_ Informal sector surveys conducted by the author.

* excludes moonlighters; t per full-time equivalent; $ applies to ownership of the vehicle - as many taxicab drivers use their employer's vehicles without their knowledge, the separation of labor and capital is overstated; **d** the level of "contractual rights" ascribed to taxicabs and kiosks is based on the experience in the spring of 1997 when many garages and kiosks were summarily ordered to move; items in italics are reasonable _a priori_ assumptions.
However, the tests reported so far are concerned with unconditional heterogeneity, and it might be possible to gain power by testing for more specific forms of parameter heterogeneity. For example, it could be argued that, owing to network effects, the elasticity of output with respect to infrastructure should be higher in countries with larger infrastructure endowments than the rest.<sup>28</sup> Alternatively, the elasticity could vary with the level of development – as captured for example by GDP per worker – reflecting the fact that poorer countries are less able to use infrastructure effectively. As another hypothesis, the output elasticity of infrastructure could depend negatively on the size of the overall population, owing to congestion effects.

To verify this, we re-estimate the model in column 1 of Table 3 without imposing homogeneity across countries of the long-run parameter of the infrastructure synthetic index, and then look for patterns of heterogeneity in the individual-country estimates of that parameter along the three dimensions just mentioned – GDP, infrastructure endowment (both in per worker terms), and population size .

Figure 1 plots the resulting country-specific estimates of the infrastructure long-run coefficient against each of the three variables just mentioned. While there are some obvious outliers, the conclusion from all three graphs is clear: there is no relationship between the country-specific coefficient estimates and the three variables considered. This points to the absence of cross-country heterogeneity of the output elasticity of infrastructure along any of these dimensions.

Table 5 presents the results of more formal tests of parameter heterogeneity along these dimensions, using the country-specific estimates of the output elasticity of infrastructure obtained above. The first two columns of the table test if the output contribution of infrastructure varies across countries with their respective level of income per worker. We divide the countryspecific estimates into two groups, one consisting of countries with high income and the other of countries with low income. In column 1, the groups are drawn using the World Bank‘s list of ̳high income‘ countries; the low-income group is made up by all other countries
term impact of the 2011 earthquake in Japan on trade patterns. We focus on the automobile and electronics industries, for which Japan is a key supplier, especially of parts and components. For instance, as the shock severely affected production of Japanese automotive equipment, it had cascading effects on global auto producers such as Honda, Opel, Nissan, and General Motors which froze production lines in several factories worldwide (Automotive News, 2011). In electronics, the problems were similar, as many specialized inputs such as connectors, microphones, and sensors were produced in Japan and had few or no substitutes (World Bank, 2020).

To motivate the analysis, we identify certain patterns in the data. We examine imports of auto and electronics in the 15 largest auto producing countries as of 2010 and in the 15 largest exporters of final electronics products to gauge the longer term effect of the earthquake on suppliers.<sup>4</sup> The left panel of Figure 1 shows the average shares of products imported from Japan for importers where Japan is not a prominent supplier (less than 15 percent of total imports of that product by the country). The right panel focuses on importers largely dependent on Japanese suppliers (more than 15 percent of total imports in a product line). The figure shows that reliance on Japan dropped sharply following the 2011 earthquake for the countries most dependent on Japan. The drop was more than 10 percentage points for the auto industry, while in the case of electronics the earthquake appeared to accelerate pre-existing declining trends. This is prima facie evidence that large shocks do lead to a partial reconfiguration of supply chains: while less exposed importers return to near pre-crisis operations after the shock, it is the more dependent producers that tend to change production structures.

> 4 See Table A1 in the Appendix for the list of countries. While we have data for automobile production by countries, we do not have similar data for electronics and therefore rely on export data.

3
Figure 1: COVID-19 Government Restriction Stringency Score Over Time <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Ethiopia Malawi Nigeria<br>Apr20May20Jun20Jul20Aug20Sep20Oct20Nov20Dec20Jan21Feb21Mar21Apr21May21<br>Stringency Score<br><!-- End of picture text --> _Note_ : The figure presents government stringency scores for each of the three countries over time. The scores are provided by _Our World in Data_ and measure the severity of COVID-19-related government restrictions on a daily basis, with higher scores indicating stricter regulatory regimes (Mathieu et al., 2020). We average these daily scores to match with our monthly data. In general, government restrictions were most stringent in early 2020 and relaxed in late 2020. In some cases, new restrictions were imposed in early 2021 as cases surged.

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a woman or man is relatively small (around 9% on average; see Table B1). Moreover, when we drop observations that contain no information on whether the firm is led by a woman or man, the distribution of observations in our final sample across geographical regions, size categories, sectors,

> 4Having a female owner does not rule out additional male owners.

> 5Unfortunately, we cannot distinguish in our BPS data between women as top managers and women as owners - though other studies suggest that gender gaps may be somewhat more pronounced if the comparison is based on management (e.g. Aterido and Hallward-Driemeier, 2011; Islam et al., 2020; Martínez-Zarzoso, 2017).

6
br>0.05 0.05<br>0.00<br>-0.02<br>-0.20<br>-0.3 -0.7 -0.9<br>long-run elasticity for skilled labor; add -0.2 for unskilled<br>Private Public<br><!-- End of picture text --> _Source: Own estimates using the 2016 Kosovo LFS. Note: the elasticity value refers to the elasticity for skilled workers. We add -0.2 for unskilled workers._ 28
which will become competitive over time. While recognizing the limitations of incorporating all these technologies within the CGE framework, they have been modeled through broad alterations in investments and emission coefficients. The idea is that the latest vintage, added to aggregate capital stock, embodies innovation and technological improvement with no additional cost to the producer.<sup>19</sup> 18. The CGE model used in this analysis was limited in terms of formulating different policy scenarios because the current dataset included only five types of energy sources-- coal, crude oil, refined oil and coal products, natural gas and electricity. Based on data availability, the model and study can be expanded in the future to include other energy sources, such as renewable energy and carbon sequestration measures.

# **_Calibrating the Model for the BAU GDP Growth Scenario_**

19. Estimates of growth in population and labor force were based on projections made by national / international sources (e.g. the National Council for Applied Economic Research (NCAER), UN and World Bank). Medium projections were used for measuring population growth in 2007-2030 using UN demographic data. The annual

> 18 Environmental taxes are corrective measures for dealing with the environmental "externality" first studied by Pigou (1932). A Pigouvian approach sets taxes equal to the marginal damage caused to the environment by the production process thereby "internalizing" the full social marginal costs.

> 19 A more formal representation of this can be found in Conrad and Henseler-Under (1986).

11
While this seems attractive, a significant problem with this approach is that a reliable GNI deflator is needed for all countries. Another is that the choice of base year may affect the country classification in undesirable ways.

In our view, the results from this simple examination tend to point to a return to the US GDP (or GNI) deflator as the preferred adjustment factor for the thresholds. Over the period reviewed, it has been relatively stable, which was one of the key issues for making the change to the SDR basket. It also avoids the difficulty of interpretation: the use of the SDR deflator means, effectively, that the thresholds are maintained in SDR units rather than US dollars but then converted to US dollars for communication purposes. It would also eliminate the need for the use of the SDR deflator in the Atlas method, which is another source of confusion; instead, exchange rates would be adjusted using the ratio of local inflation rates to US inflation. In any case, since this change would impact the calculation of thresholds and GNI per capita estimates used for World Bank Group operational purposes, this proposal will need to be further discussed.

# **7. Redefining the thresholds, and other approaches**

If the main purpose of an analytic classification is to provide a mechanism for grouping and aggregating countries for comparison purposes, then a scheme based purely on the ranking of countries is a simple and attractive option. An obvious candidate is to divide countries into four quartiles, based on their relative GNI per capita estimates. This has many advantages: it is simple to understand; it can be constructed easily from the GNI per capita estimates used for the Bank’s operational decision-making; the number of each countries in each group is stable, by design; it does not require the selection of thresholds or procedures to update them each year; and it is more difficult to make use of the classification for “non-analytic” purposes, such as aid allocation.

One major disadvantage is that it requires estimates for
should provide important policy lessons to developing countries that are looking to apply ICT to their tax administration in order to improve tax services and compliance.

# **2. Literature Review**

Tax compliance has been a contentious subject in economic studies for the past four decades since Allingham and Sandmo (1972) analyzed tax evasion as utilitymaximizing behavior by individual taxpayers. Since then, many researchers have delved into what influences tax compliance. Bărbuţă-Mişu (2011) classified factors that contribute to tax compliance into economic factors including tax rates, tax audit, income level, and potential penalties for noncompliance, and noneconomic factors including attitude toward taxes, the personal, social, and national norms, and the perceived fairness of the tax system.

> 3 According to an IMF survey, 33 out of 36 countries that were surveyed responded that they have a statutory deadline, generally within30 days, for payment of VAT refunds (Harrison & Krelove, 2005).

2
Figure 5: Language requirements per occupation and share of at-risk jobs Panel a. % of jobs with income risks Panel b. % of jobs with health risks <!-- Start of picture text --> 910911 961 833 222<br>513 141 532 511 54 32101 226 220 221<br>325<br>9129418157527537219327517581814816523 812 821 0 813722818720512514723 510 832 322<br>910713911834712710521931930731524754515933520711313 741312522732740 742310540 140265 516 234<br>432 311422 331 3 2<br>962 611 541321<br>921 961830613832610612831531833532441421430343516351315511133431352142340325411342 2152523323143413313222502513301124103 3 130412216345111214241143121226210211134120240413220234243242322221 2 2602132263235221 261233230 264231 531 232 235 230<br>912 9 81713427 1 753 5 2834721932 750 81962718148165 8 1 2 721 8135 93 3524 121 73100 78 72659352 516 18367 12 415 10 35 3 020 1 2113723 4 83325 1 73510 4 34573241 12 740212430343351 52742 31 1431 33 50142411342401413114 2 2522153314 3 140 332 433011226502 13 41013 5 2 3 130421345 1 111 2 62411432 1 421134 1 413240200211243242122260213263 261233 264231<br>30 40 50 60 70 80 30 40 50 60 70 80<br>Language intensity per occupation (0-100) Language intensity per occupation (0-100)<br>1 1<br>.8 .8<br>.6 .6<br>.4 .4<br>Share of income-risk jobs Share of health-risk jobs<br>.2<br>.2<br>0<br>0<br><!-- End of picture text --> Source: Own elaboration based on the EU-LFS 2018 and the ad-hoc migration module of 2014. Note: The size of the circles represents the share of each occupation in the total employment in the EU.

Figure 6: Correlation between occupational choices of migrants in the EU in 2005 and 2018 <!-- Start of picture text --> 0 .1 .2 .3 .4 .5<br>% of migrants in each occupation in 2005<br>.3<br>.2<br>.1<br
> equation (21). Moreover, we predict β 2<sup>></sup> 0 when log( _wH_<sup>∗</sup> / _wH_ ) is the dependent variable.<sup>_x_</sup> _ij_ , _t_ − 1 is a vector of other (lagged) controls that potentially affect log income differences between _i_ and _j_ , such as relative school enrollment rates, relative investment rates, relative urban population shares, and fixed effects for the source country to capture institutional differences to OECD destination countries. With respect to the dependent outcome measures, we focus on the year 2000 and usually measure controls other than skilled migration in lagged form (for 1990) to reduce endogeneity bias. _u_ is an error term. _ij_

To construct a measure of log( _wH_<sup>∗</sup> / _wH_ ) , we would like to use (log) wages differences for high-skilled individuals. However, because wage income by education category is not available, we construct several empirical proxy measures. Freeman and Oostendorp (2000) analyze information on earnings by occupation and industry from the ILO October Inquiry Survey from 1983 to 1998 for a number of countries.<sup>10</sup> For each country, we use Freeman and Oostendorp’s earnings measures to calculate the 80th and the 90th percentiles as two measures for wages of high-skilled workers. For most countries, data are available for only a few years. Therefore, for each country, we take the mean across the period from 1995 to 2003 to obtain wage data for the year 2000.<sup>11</sup> The two constructed (log) relative wage variables for the 80th and the 90th percentiles are denoted by _RelWage80ij,t_ and _RelWage90ij,t_ .

One may argue that migrating skilled workers do not receive wage income in the same percentile as they do at home. In particular, high-skilled workers from developing countries may not be considered highly skilled in the destination country. Therefore
# **Paid Maternity Leave and Female Employment: Evidence Using Firm-Level Survey Data for Developing Countries**

By: Mohammad Amin* and Asif Islam** Enterprise Analysis Unit, DECEA, World Bank _JEL Codes_ : J13, J16, J18, J22 _Keywords_ : Gender, Female employment, Maternity leave, Developing countries, Firms We would like to thank Jorge Luis Rodriguez Meza for helpful comments. All remaining errors are our own.

* Senior Economist, Enterprise Analysis Unit, DECEA, World Bank Malaysia Country Office and Washington, DC. Email: mamin@worldbank.org

** Economist, Enterprise Analysis Unit, DECEA, Washington DC. Email: <u>aislam@worldbank.org</u>
The last step consists of estimating market income using the individual net income that was calculated above, YijD̃ + αijπj . Specifically, net income is a function of market income, direct taxes and social insurance contributions and is estimated as follows for each type of worker: _For public sector employees:_ where YijM is the labor income component of market income, s is the social security contribution rate (NPPF in the case of public workers), GIS is the payment to the Group Savings and Insurance Scheme, and TDS(. ) is a function of tax deducted at source<sup>19</sup> which depends on gross income, social security contributions, GIS payments, and the tax rate.

_For formal private sector employees:_ where s is the social security contribution rate (provident fund in the private workers’ case), GSLI is the Group Savings Link Insurance rate, and TDS(. ) is the tax deducted at source.

_For formal self-employed workers in the farming sector:_ where RI is the payment to the rural insurance scheme.

_For formal self-employed workers in the non-farming sector:_ _For all informal workers (wage workers or self-employed workers):_ Self-employed workers are assumed to pay income taxes but not social security. We assume that informal workers do not pay any income tax or contributions as they are not covered by labor regulations. D̃ Importantly, equations (7)-(11) are defined in terms of the observed variable (Yij + αijπj), the policy

> 19 According to the guidelines described in the Rules of Income Tax 2017 and the Tax Deduction at Source, all income from salary income, rental income, dividend income, interest income and income from other sources is liable to pay the tax deduction at source (TDS) which is a provisional tax to the annual personal income tax. The taxable base is the prefiscal income after deducting social security contributions, and the provisional tax amount is calculated using the TDS contribution table. The last section shows