rafmacalaba/data-use-mentions-extended huggingface dataset:
with higher moving costs are hurt more in the metal sector, as they are less likely to leave their sector and suffer from the lower wages. Intriguingly, the workers with higher moving costs benefit more in the manufacturing sector. Because workers with higher moving costs are less likely to leave manufacturing, and more likely enjoy higher wages in the long run compared to others. The manufacturing workers with lower moving costs are more likely to end up in the metal sector; this decreases the change in their expected welfare relative to the workers with higher moving costs. This result can be generalized for age as well: As workers get older their moving costs increase and the impact of trade shocks increases. However, at the same time workers’ time horizon gets shorter; this decreases the impact simultaneously. The second effect starts to dominate after workers reach age 50. Therefore, in general, large moving costs magnify the impact of trade shocks for both winners and losers (with an exception of workers who are close to retirement).

# 7 Conclusion

We estimate a dynamic model of labor mobility using US data, CPS and NLSY. We find that moving costs increase with age and experience, but are unchanged with workers’ education level. Then, we simulate a counterfactual trade liberalization in the metal manufacturing sector using the estimated labor mobility model and calibrated production function parameters in a general equilibrium setting. We consider two alternatives with different levels of heterogeneity and find that aggregating worker types does not change the welfare implications of the model.

We show that trade shocks affect workers with higher mobility costs more, for both winners and losers of the policy shocks. In the metal sector, workers with higher moving costs are hurt more, while in the manufacturing they benefit more. But the effects are non-monotonic and they taper off over a worker’s lifetime, especially when they are close to retirement. We also find that a worker’s sector is the main determinant of how he is affected; this shows that moving costs are large enough
largest 4 countries in our sample.<sup>13</sup> Regression results for the reduced sample are provided in Table 8. For brevity, only some of the specifications are shown. Table 8 clearly shows that the qualitative nature of the relationship between corruption (overall corruption, incidence and depth of petty corruption) and _Population_ remains intact.

# 3.6 _Additional controls_

Starting with our final baseline specification (column 4, Table 4), we experimented by adding some more controls for extra robustness. The controls include dummy variables for the region (region fixed effects)<sup>14</sup> ; religious affiliation of countries captured by the proportion of population that is Catholic, Muslim and Protestant (omitted category is all other religions)<sup>15</sup> ; a measure of the independence of the judiciary taken from Freedom House’s Economic Freedom of the World database (Judicial independence); and a measure of the functioning of the government based on whether elected officials determine the policies of the government, steps taken by the government to combat corruption and whether the media and public can freely express their views regarding corruption, and if the government is answerable to the electorate between elections and if it works with transparency, taken from Freedom House’s Economic Freedom of the World database (Functioning of the government).<sup>16</sup> There is no qualitative change in our main results due to these controls (Table A3 in the Appendix provides the full results).

> 13 The largest 4 counties excluded have a population of more than 190 million and include Brazil, China, India, and Indonesia. We experimented with alternative samples such as excluding the largest 5 percent and the smallest 5 percent of the countries, but this did not change the qualitative nature of the results discussed in this section.

> 14 The regions are Sub-Saharan Africa, East Asia and the Pacific, Eastern Europe and Central Asia, Latin America and the Caribbean, South Asia,
sition and individual characteristics. More importantly, it collects various sources of income-related information including income from wages, self-employment, transfers, pensions and imputed rent.<sup>4</sup> In addition, it allows identifying informal labor income, i.e., coming from an unregistered labor relation that does not pay taxes, and is not subject to any labor regulation.

High levels of informality in the Honduran labor market limit the ability of administrative systems to document individual incomes. As of 2019, approximately 80 percent of the Honduran labor force was informal relative to an average of 54 percent in Latin America (Gasparini & Tornarolli, 2009, World Bank, 2020). This highlights the importance of relying on household survey information when measuring income outside the top of the distribution.

In January 2020, the Government of Honduras updated its official income measurement methodology for the period 2014-2019, with the aim of creating a more accurate poverty profile for the country. The new official income aggregate includes improvements in data cleaning, annualized labor income for salaried workers receiving 13<sup>_th_</sup> and 14<sup>_th_</sup> salaries (commonly known as _aguinaldo_ and _catorceavo_ in Honduras) and imputed rent for home owners. On average, these changes increased official household income by 16 percent, driven primarily by imputed rent (INE 2021). These changes have inevitably resulted in a break in comparability with previous years. In order to extend comparability for our analysis, we apply the new income measurement methodology for 2003-2013 (see Table A.1).

# **3.2 Administrative tax microdata**

We use a range of datasets that record income sources of individuals and corporations. These are administrative records from SAR (from the Spanish acronym for _Servicio de Administraci ́on de Rentas_ or Revenue Administration Service), the tax authority in Honduras.

## **3.2.1 Personal Income Tax (PIT) data**

Honduras has a dual personal income tax system
Table 3: Robustness to sample selection

||(1)|(2)|(3)|(4)|(5)|
|---|---|---|---|---|---|
|Dep. Var.:Growth inper capita i|ncome(1996-2|011)||||
|Homeland (=1)|1.256**|0.660|0.841**|1.433***||
||[0.508]|[0.454]|[0.423]|[0.454]||
|Share Employed mining1996|-0.120|-0.447*|-0.497***|-0.356**||
||[0.243]|[0.258]|[0.190]|[0.154]||
|Homeland*Share Employed|0.604**|0.840***|0.952***|0.766***|0.363***|
|mining1996|[0.267]|[0.284]|[0.219]|[0.188]|[0.111]|
|Observations|7,338|9,583|12,003|15,854|10,340|
|R-squared|0.646|0.634|0.636|0.644|0.614|
|1996 Controls|Yes|Yes|Yes|Yes|Yes|
|Municipality FE|Yes|Yes|Yes|Yes|Yes|
|Sample|<15 km|<30 km|<60 km|All SA|onlyHL|

Notes: Data from the 1996 and 2011 population census at the community (sub-place) level. Each column corresponds to a separate OLS
96. In addition, GHSL‐ Landsat data are available for multiple years, whereas GUF‐TerraSAR data are available only for 2011. This is a constraint, because we want to estimate not only the urban extent, but also the speed of urbanization over time. We use GHSL‐Landsat data for 2001 and 2013/14 for the census years 2001 and 2011, respectively. But we check that the results remain the same when using GUF‐TerraSAR data for the year 2011.

For the measurement of the lit‐up area we rely on the Global Radiance Calibrated Nighttime Lights product. This product is derived from images taken by the Operational Linescan System (OLS) sensors aboard satellites from the U.S. Air Force Defense Meteorological Satellite Program (DMSP). Data are available from the National Geophysical Data Center at the National Oceanic and Atmospheric Administration (NOAA).

This is not the only product available for nighttime light images, but it has a higher quality than its competitors because it captures stable lights, addresses the saturation problem, and is intercalibrated to reduce inconsistencies across satellites (Elvidge and others 2009; Hsu and others 2015; NOAA 2014). The intensity of nighttime light is reported in Digital Numbers. We use data for 2001 and 2011, thereby matching the census years. Because the data are not generated in the same way in both years, we apply the intracalibration formula proposed by Hsu and others (2015) to ensure consistency over time.

A key challenge when using data on nighttime light is that even areas without human activity may appear to be lit up (Henderson and others 2003; Li and Zhou 2017; Zhou and others 2014). This may be due to blooming, or to the reflection of light from surrounding water and other land covers, or to the sensitivity of the sensors, or to the cumulative effects of geo‐referencing errors made in the capture and composting of satellite imagery. To avoid overestimation of the lit‐up share, a threshold is generally set up
would be to calibrate values for θ based on a parametric expression for a Bernoulli utility function, as illustrated in the Appendices of Baland et al. (2023) and applied for a large set of countries in Decerf et al. (2024). This approach could be further refined by calibrating the parametric expression for the Bernoulli utility from the Value of Statistical Life literature (Kniesner and Viscusi, 2019), as for instance done in Becker et al. (2005); Jones and Klenow (2016); Boarini et al. (2016).

However, besides the selection of its parameter’s value, the PALE index is ready-touse. The same holds for other indices based on a premature mortality threshold.<sup>40</sup> The mortality data required are often available at country level and sometimes at subnational level. Accounting for longevity could very well be the lowest hanging fruit for practitioners

> 36Decerf and Fonton (2023) also find that mortality is sizable compared to multidimensional poverty when contrasting well-being across Nigerian states in 2019.

> 37Baland et al. (2023) study the theoretical conditions and document the empirical frequency with which poverty comparisons are reversed for all values of θ ∈ [0, 1].

> 38The value for θ depends on the poverty standard considered. A more austere poverty line implies a larger weight to poverty and thus a larger value for θ.

> 39For instance, one could ask to non-poor individuals how many years of their remaining life they would be willing to spend in poverty to extend their longevity by one year.

> 40It could still be interesting to investigate how to extend these indices for contexts where some information is available on the joint distribution between mortality and income, e.g., information on selective mortality, i.e., the differential mortality risks faced by the poor and the non-poor. Another question is how to extend these indices for gap-sensitive poverty indices.

23
Policy Research Working Paper 11060

# **Abstract**

This paper investigates gender disparities in residential property ownership and tax compliance in a large Argentine municipality using detailed tax administrative data. While ownership is evenly distributed between women, men, and co-owned properties up to the 40th percentile of the value distribution, higher-value properties exhibit significant gender disparities, with women’s share dropping to less than 20\% in the top 1\%. Tax compliance increases with property value, with an average evasion rate of 46\%, and men and women are equally likely to meet their tax obligations across the distribution. However, women face slightly higher effective tax rates due to owning lower-value properties, which are disproportionately affected by a mildly regressive tax schedule. Gender responses to enforcement measures are also comparable. A soft randomized communication campaign significantly increased timely payments equally for both men and women, with men responding more quickly. Similarly, the findings show no gender-based differences in responses to macroeconomic shocks such as COVID-19. The study underscores the role of property tax in promoting equitable revenue mobilization and highlights the importance of gender-disaggregated data for informing tax policy and enforcement strategies.

This paper is a product of the Development Research Group, Development Economic. 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 dtortarolo@worldbank.org. A verified reproducibility package for this paper is available at http:// reproducibility.worldbank.org, click **here** for direct access.

<!-- Start of picture text --> TRANSPARENT<br>ANALYSIS<br>G<br>R E<br>N<br>S<br>I P<br>Y E<br>K A<br>C A<br>R
security contributions (OECD, 2019)<sup>7</sup> . Second, despite substantial progress in recent years to reduce informality, employing workers “off the books” without paying any social security contribution remains common, particularly among small firms.<sup>8</sup> This is the case not only in Turkey, but also in the vast majority of developing economies. The effects of employment subsidies on employment in a context where registered employment is common may thus differ from a high-income context where there is hardly any informality. In addition to job creation, an increase in registered employment may thus also result from a formalization of already employed workers enabled by the subsidy.

The paper proceeds as follows. Section 2 discusses related literature. Section 3 provides some institutional background on employer social contributions in Turkey, and on the additional 6- point subsidy studied in this paper. Section 4 describes our administrative dataset. Section 5 presents our methodology and identification strategy. Section 6 reports and discusses our main results. Section 7 presents the results of the cost-benefit analysis of the subsidy, Section 8 concludes.

# **2. Related literature**

Employment subsidies aim to reduce the cost of labor to employers. They can be applied to all employees or only to new hires (marginal subsidies). They can also be general, in the sense of applying to all workers and establishments, or specific groups, if only certain types of workers (for example, low-wage, youth, long-term unemployed, women, or disabled workers) or certain sectors or geographic locations qualify. While wage subsidies can be directed to either employees or employers, in line with the policy analyzed in our study this literature overview focuses on hiring subsidies going to employers.

Prior evidence on the employment effects of employment subsidies primarily comes from high-income countries where labor markets are characterized by very little informal employment. In addition, most studies for high-income countries examine the effect of programs targeted to the unemployed or disadvantaged workers and in many cases,
participate in these illicit activities with self-serving officials, move to another jurisdiction, exit the market, or operate in the informal sector.<sup>4</sup> The supply side of corruption has been relatively less explored. In this perspective, rather than being passive respondents to requests, harassment and threats by officials, some firms may actively seek favors, offering bribes and other informal gifts as a strategy to obtain privileges or circumvent regulations (Tanzi, 1998; Myint, 2000; Delavallade, 2012; Ufere et al., 2012; Dixit, 2016).<sup>5</sup> We use firm-level data from the WBES covering 18,005 firms in 75 developing countries. We exploit the presence of information on bribe requests from public officials in various interactions between firms and government,<sup>6</sup> as well as reported bribe payments by firms, to identify the demand and supply sides of corruption. In addition to these distinct bribe-paying behaviors, we categorize non-bribe paying firms into two groups: "incorruptible" are firms non-complying to requests for bribes by officials, and "nonpayers" are firms which neither experienced a request for a bribe nor paid a bribe. We show that firms’ bribe behavior categories are associated with specific firm characteristics. Demand and supply-side bribe payers tend to be firms facing greater informal competition than non-bribe payers. In particular, demand-side bribe payers (passive bribery) tend to be firms with larger capital investments and higher profits and are less likely to have government ownership. Supply-side bribe payers (active bribery) are more likely to be exporting firms with a larger share of foreign ownership as well as larger investments and capital stock value.

With regard to the association between bribing behavior and benefits of illicit transactions, we find that corruption in tax administration tends to be mainly a demand-side phenomenon. Paying a bribe requested by a public official (passive bribery) is associated with a 16 % increase in the share of sales not reported for tax purposes. However,
Bruhn, Miriam, and Inessa Love. 2014. “The Real Impact of Improved Access to Finance: Evidence from Mexico.” _The Journal of Finance_ , 69(3): 1347-1376.

Brune, Lasse, Xavier Giné, Jessica Goldberg, and Dean Yang. 2016. “Facilitating Savings for Agriculture: Field Experimental Evidence from Malawi.” _Economic Development and Cultural Change_ , 64(2): 187-220.

Burgess, Robin, and Rohini Pande. 2005. “Do Rural Banks Matter? Evidence from the Indian Social Banking Experiment.” _American Economic Review_ , 95(3): 780-795.

Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, and Saniya Ansar. 2022. _The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19_ . Washington, DC: World Bank.

Dupas, Pascaline, Sarah Green, Anthony Keats, and Jonathan Robinson. 2016. “Challenges in Banking the Rural Poor: Evidence from Kenya's Western Province.” In _African Successes Vol. III: Modernization and Development_ (NBER Conference Report), edited by Sebastian Edwards, Simon Johnson, and David N. Weil. University of Chicago Press.

Dupas, Pascaline, Dean Karlan, Jonathan Robinson and Diego Ubfal. 2018. “Banking the Unbanked? Evidence from three countries.” _American Economic Journal: Applied Economics_ , 10 (2): 257-297.

Dupas, Pascaline, and Jonathan Robinson. 2013. “Savings Constraints and Microenterprise Development: Evidence from a Field Experiment in Kenya.” _American Economic Journal: Applied Economics_ , 5(1): 163–192.

Hernandez, Emilio. 2019. _Agent Networks at the Last Mile_ . Technical Guide. Washington, D.C.: CGAP Jack, William, Adam Ray, and Tavneet Suri. 2013. “Money Management by Households and Firms in Kenya.” _American Economic Review: Papers and Proceedings_ , 103(3): 1-8.

Jack, William and Tavneet Suri. 2014. “Risk Sharing and
**Different instrument specification** Next, we re-estimate our main specification by using a different shift-share instrument. Instead of using industries to calculate the instrument, we use 2,527 different occupations based on RAIS to calculate the national growth rates (shifters) and the local occupational shares. As in our main specification of the instrument, we exclude occupations related to the public sector as well as public firms. Results shown in Panel D of Table A.4 remain positive and significant and even have slightly higher point estimates. Nevertheless, in our main estimation strategy we prefer to follow Bartik (1991) to calculate the instrument and to use industries instead of occupations.

**Validity shift-share instrument** The recent literature has addressed the topic of the validity of shift-share instrument (see Goldsmith-Pinkham _et al._ (2020) and Borusyak _et al._ (2022)). As mentioned in Section 4 we follow the approach by Borusyak _et al._ (2022) to test our shift-share instrument for shock exogeneity.<sup>30</sup>

The authors suggest aggregating the data at the industry level to examine the conditional quasi-random shock assignment and the lack of correlation among shock residuals. Summary statistics for the shocks are presented in Table A.5. With a substantial number of shocks (8,579) and industries (660 at the 4-digit level and 278 at the 3-digit level), the statistics indicate that no single industry is significantly driving our observed effects. These findings demonstrate that we can mitigate concerns regarding the influence of any particular industry.

As a second step, we redo our analysis at the industry level as proposed by Borusyak _et al._ (2022). The authors show that the shift-share IV estimator equals the “second-stage coefficient from a _sn_ -weighted shock-level IV regression that uses the shocks _gn_ as the instrument”:<sup>31</sup> where _y_ ̄ _λ_<sup>_⊥_istheoriginal(residualized)outcome(exitrate),and</
# and adolescent forced migrants.

We contribute to the body of work examining the impacts of migration on children.<sup>3</sup> The vast majority of this literature has been centered on examining the impacts of migration on left-behind children (Ohinata and Van Ours 2013, Mu and De Brauw 2015, Edwards and Ureta 2003, Tumen 2021), native children (Cascio and Lewis 2012, Geay, McNally and Telhaj 2013, Hunt 2017, Figlio et al. 2021 C ̧ akır, Erbay and Kırdar 2023), and on how voluntary (as opposed to forced) migration impacts immigrant children (Macours and Vakis 2010, Felfe, Rainer and Saurer 2020).<sup>4</sup> We contribute to these studies by collecting the first longitudinal study that collects comprehensive data on forcibly displaced children and adolescents residing in host communities, rather than in refugee camps. This analysis has been limited by the data availability as it is difficult to find these migrants. To overcome these obstacles, we developed an innovative approach for constructing sampling frames for forcibly displaced migrants living outside of refugee camps, where traditional census data or official records are unavailable. Our methodology involves an extensive listing exercise that integrates information from various sources, including population censuses, satellite imagery of new migrant settlements provided by humanitarian organizations, contributions from migrant groups and donors, field verification, and referrals from within the migrant community itself. This approach mirrors similar efforts documented in studies on the impact of regularization programs (Ib ́a ̃nez et al. 2022), but on them satellite data on new settlements and on a expands by incorporating focusing demographic group that has been less studied in this context—children and adolescents.

We also contribute to the literature by collecting rich and comprehensive information on children’s and adolescent’s such as development covering aspects physical, cognitive,

> 3See Cortina, Jimenez and Rozo (2024) or a detailed literature review of findings from 113 studies produced between 1990 and 2023 focusing on the social
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**<mark>Table A4 continued</mark>**

42