rafmacalaba/data-use-mentions-extended huggingface dataset:
In a recent review of this literature, Card, Cardoso, Heining, and Kline (2018) conclude that more research is needed applying (quasi)-experimental research designs that convincingly tease out the mechanisms through which firm shocks are transmitted to workers. A few recent papers have contributed to fill this gap. Exploiting an unexpected devaluation of the Mexican currency, Fr ́ıas, Kaplan, Verhoogen, and Alfaro-Serrano (2024) carefully estimate how differential export shocks across firms impact wage premia and worker composition.<sup>3</sup> The results reveal that exports have a significant positive effect on wage premia, and that the effect on wage premia accounts for essentially all of the medium-term effect of exporting on plant-average wages.<sup>4</sup> Kline, Petkova, Williams, and Zidar (2019) estimate the effects of innovation on rent-sharing using employer-employee data and rich patent information for the United States. Under the identifying assumption that the U.S. Patent Office’s initial decision on a patent application is as good as random (conditional on observable attributes of the application and the firm), the authors find that each patent-induced additional dollar of operating surplus yields a 29-cent rise in a firm’s wage bill. Also focusing on patents, but using Finish data and a matching estimator, Aghion, Akcigit, Hyytinen, and Toivanen (2018) find that patents are positively associated with earnings of the co-workers of inventors.

Our paper makes several contributions to this literature. First, the employer-employee data we use make it possible to examine effects of unexpected demand shocks on the various components of worker compensation (bases wages, overtime and other components of pay). Recent work by Grigsby, Hurst, and Yildirmaz (2021) convincingly argues that this distinction is important for examining and understanding the extent of wage rigidity over the business cycle.<sup>5</sup> Second, we exploit the role of top managers’ skills—which we show are correlated with management practices observed
. Brighton: International Centre for Tax and Development.; Ligomeka, W. (2019). Expensive to be a female trader: The reality of taxation of flea market traders in Zimbabwe. _ICTD Research in Brief 35_ . Brighton: International Centre for Tax and Development. 125 Taxation exclusively of land is relatively uncommon, but exists, for example, in Kenya. In turn, in many other countries, valuations of improvements on land fail to capture their actual value, distorting tax liabilities.

126 Paler, L., Prichard, W., Sanchez de la Sierra, R., & Samii, C. (2017). Survey on total tax burden in the DRC, Final Report. Kinshasa: Department for International Development.

> 127 Goodfellow, T. (2017). Taxing property in the neo-developmental state: The politics of urban land value capture in Rwanda and Ethiopia. _African Affairs,_ 116(475): 549–572 _._

> 128 Kangave, J., Nakato, S., Waiswa, R., & Lumala Zzimbe, P. (2016). Boosting revenue collection through taxing high net worth individuals: The case of Uganda. _ICTD Working Paper 45_ . Brighton: International Centre for Tax and Development.; Prichard, W. (2009). _Taxation and development in Ghana: Finance, equity and accountability_ . London: Tax Justice Network; Fairfield, T., & De Luis Jorrat, M. (2016). Top income shares, business profits, and effective tax rates in contemporary Chile. _The Review of Income and Wealth_ , 62(S1):S120-S144; Bird, R., & Zolt, E. (2005). Redistribution via taxation: The limited role of the personal income tax in developing countries. _International Tax Program Paper 0508_ . Toronto: Joseph L. Rotman School of Management.

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of the sample countries from the GDP per capita PPP of each country.

**Trust in healthcare workers.** We explore whether trust in healthcare worders (e.g., doctors and nurses) before the onset of the pandemic moderates impacts of message framings. We use data from the Wellcome Global Monitor (Wellcome Trust & Gallup, 2018), which assessed trust in healthcare workers with an item rating “ _How much do you trust each of the following?_ ” for “ _Doctors and nurses in this country_ ” on a 1-4 scale, with response options of “A lot”, “Some,” “Not much,” “Not at all”. Respondents could also respond with “Don’t know,” and “Refused”—such responses were dropped from our analysis. For ease of interpretation, we reverse-coded the responses, so higher values indicate stronger trust such that 1 = _Not at all_ and 4 = _A lot_ . We aggregate trust to the country level by calculating the mean response within each country. We then center country-level trust measures by subtracting the median country-level trust measure from each country-level means.

**Perceived vaccine safety.** We included perceived vaccine safety before the COVID-19 pandemic. We used data from the Wellcome Global Monitor (Wellcome Trust & Gallup, 2018). Perceived vaccine safety was measured by asking “Do you agree, disagree, neither agree nor disagree with the following statement? Vaccines are safe.” on a 1-5 scale, with response options of “Strongly agree,” “Somewhat agree,” “Neither agree nor disagree,” “Somewhat disagree,” “Strongly disagree,” “Don’t know,” and “Refused.” We reverse-coded the responses, so higher values indicate stronger agreement, such that 1 = _Strongly disagree_ and 5 = _Strongly agree_ . We aggregate perceived vaccine safety to the country level by calculating the mean response within each country. We then center country-level vaccine safety perceptions by subtracting the median country-level trust
(Vuri 2016). And, thus, the existing studies could underestimate the aggregate response of the female population to childcare expansion policy reform. To illustrate, in Canada, the expansion of childcare facilities not only increased maternal employment, but also grandmothers’ labor supply (Karademir, P. Laliberté, and Staubli 2023). Utilizing household survey data from Uzbekistan allows us to observe not only mothers, but also informal care providers including grandmothers within the household, and observe their responses to this policy expansion reform.

> 2Average household size is 7 where usually 2 working age women co-reside. According to a 2018 survey in Uzbekistan, 43% of unemployed women are not seeking a job because they must take care of their household responsibilities, while only 7% of unemployed men reported the same reason (Strategy.uz, 2022).

4
internal structures of urban areas. This includes the extensive

> 8 The degree of urbanization uses a variety of such thresholds to distinguish different types of urban area. In “level 1” of the approach, a distinction is drawn between urban areas (“towns and suburbs”) that satisfy an overall population threshold of 5,000 and a population density threshold of 300 people per km<sup>2</sup> , and urban centers (“cities”) that satisfy thresholds of 50,000 people and 1,500 people per km<sup>2</sup> for overall population and population density respectively (Dijkstra et al. 2021).

> 9 As mentioned above, the Africapolis data utilized by OECD/SWAC (2020) uses a population threshold of 10,000 to identify urban areas.

> 10 The dartboard algorithm also excludes water bodies and both areas at extreme elevations and which have extreme slopes from the definition of potentially inhabitable area.

> 11 Henderson et al. (2022) also impose thresholds that are constant over time, fixing them to those of the last year of observation, 2014, in their data. Given that built-up area has generally expanded overtime, this mechanically increases the number of settlements that are identified over time. The full dartboard approach would have allowed for an assessment relative to each year’s overall development.

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that yearly turnover might exceed the exemption threshold, even if ex-post it does not in fact exceed it.<sup>14</sup> Our results suggest that this is unlikely. In Figure 6 we show that even when conditioning on taxpayers filing 12 months (balanced sample), 25%-30% of taxes due are from firms that fall under the exemption threshold. Alternatively, if we only consider partial filers and annualize their revenues, we still observe that 81% would have been under the threshold. Both of these facts are also consistent with what we observe in the full distribution of turnover (Figure 7). Most taxpayers that are under the exemption threshold are not simply slightly under that level – in 2022, for example, 48% had annual turnover below Ksh. 500,000, less than half the exemption threshold. It is unlikely that partial filing or uncertainty over yearly turnover would encourage these taxpayers to file and pay.

An alternative explanation is lack of knowledge: taxpayers might be unaware of the exemption threshold, for example, believing that businesses with any amount of income are liable for the TOT. This explanation is supported by the results of both surveys. The in-person survey showed that only 4.1 percent of registered businesses could correctly identify the minimum threshold for TOT. The SMS survey showed that only 13.7 percent of TOT payers correctly identified the minimum threshold, and almost half stated there was no minimum threshold.

Another possible mechanism to explain the fact that so many taxpayers are paying TOT despite being below the exemption threshold relates to the costs of proving their exemption status. Taxpayers might be aware of the exemption rules and know (or strongly believe) their revenue is low enough to be exempt, but since they often do not have bookkeeping, they may perceive that it will be hard to convince the KRA of this. If not filing/paying TOT led to hassle costs, such as the need to justify their behavior to tax officers or appeal fines, taxpayers might
developing and industrial countries.  Transport and professional services remain a bastion of protectionism.  In maritime transport, even though international shipping is today quite open, entry into cabotage and auxiliary services such as cargo handling is in many countries restricted. In air transport, most countries across all income groups restrict investment in the supply of international and domestic air passenger services.  Within professional services, accounting and the practice of foreign law tend to be more open than auditing and the practice

> 18 The STRI database contains information from a total of 102 countries, of which 78 are developing countries and 24 OECD countries; the country coverage represents all world regions and income levels.

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39 most of the cultural groups examined, children of mothers who had a large number of pregnancies for their age had lower mean heights, weights and haemoglobin levels than those of mothers with fewer pregnancies for the same age" (Omran and Standley (1981) pp 25), and similar results have been observed in India (Gopalan and Naidu (1972)) and Co'lombia (Wray and Aguirre (1969)).

The main infuences of maternal age on child survival factors are pregnancy-related, i.e. prematurity and birth weight. Studies suggest that there is a relatively "safe age band" in the 208 and early 30s, on either side of which prematurity and low birth weight (LBW) (as well as pregnancy loss) increase, but more especially for younger mothers (Omran and Standley (1981) pg 33, Selvin et al (1972), Chakraborty et al (1975)).

The other factor that qualifies these relationships is the birth interval; higher parity and family size for given maternal age imply closer spacing, and it may be this aspect of demographic structare which is most important. Thus, in a recent study of WFS data from 39 countries, it was found that, apart from first births and births to teenage mothers, there are no strong effects of parity or maternal age on infant and child mortality outcomes independent of birth spacing (Hobcraft et al (1985))16.

The variation in evidence suggests that relationships between family size, parity and maternal age and child well-being are not direct, but rather involve further relationships between family 16 Although see Pebley and Stupp (1987) pg 58
With respect to this literature, our paper is mostly linked with the contributions in the strands of literature described in ( _ii_ ) and ( _iii_ ), as follows. First, our main focus is on the consequences of IFFs in terms of tax revenues; in particular, this will require mobilizing information from the studies presented in (i), since our impact assessment analysis contrasts Non-Cooperative countries (i.e. that do not cope with international standards on combating IFFs) with Cooperative countries that present comparable characteristics including in terms of IFFs determinants.<sup>**§§**</sup> Second, a sensitivity analysis explores the possible heterogeneity of the effect of IFFs on tax revenues in various environments; as such, we are interested in policies that may help mitigate the potentially-detrimental effect of IFFs on tax revenues.

# **III. Data**

Based on IFFs data availability our study covers 58 developing and emerging countries during the period 2004-2013. Data on tax revenues comes from the International Centre for Tax and Development’s (ICTD) Government Revenue Dataset (GRD) and the IMF’s tax revenue dataset, and data on the treatment variable comes from the Financial Action Task Force (FATF). The remaining variables come from various sources including the World Bank Group (World Development Indicators and Worldwide Governance Indicators), the IMF World Economic Outlook (WEO), the Global Financial Integrity (GFI), ICRG, Kose et al. (2017) database, and Chinn & Ito (2006) index of capital openness. Our sample consists of 17 Non-Cooperative and 41 Cooperative countries (see Table A1 in the Online Appendix).

Simple descriptive statistics that compare countries before and after their inclusion in the FATF list reveal the following.<sup>*******</sup> First, Figure 1a shows that the inclusion of countries in the FATF (i.e. Non-Cooperative countries) signals a change of the IFFs trend: on average, IFFs steeply
The document is organized as follows. Section 2 presents the background and summarizes some of the significant work in this area, and begins by describing the evolution of individual earnings inequality using information from the National Urban Employment Survey (ENEU). Although educational attainment has an impact not only on income but also on other outcomes that are important for an individual’s well-being but are not necessarily measured in monetary terms, this paper does not consider the nonmonetary impacts of education. Sub-section 2.2 analyzes the evolution of educational attainment, while Sub-section 2.3 relates changes in the distribution of education to changes in earnings inequality. Section 3 presents the paper’s methodology and Section 4 examines the evolution and structure of the rates of returns to education by means of ordinary least squares and quantile regressions. The last section offers concluding remarks.

# 2. BACKGROUND AND LITERATURE REVIEW

Earnings contribute to most of the overall inequality, being responsible for almost half of inequality at the national level. These figures clearly may be affected by the underreporting of capital gains, but understanding the mechanisms that produce earnings inequality represents a large step toward understanding the behavior of total inequality. As long as labor is the main, if not the only, asset of the poor, a better knowledge of earnings inequality is a valuable input for the assessment of poverty and welfare issues. The ENEU household survey is used to examine the behavior of earnings inequality because it is extremely rich in household characteristics (see Annex 1). The population under analysis is urban individuals between 16 and 65 years old working 20 hours a week or more (seasonal workers are excluded). The two highest observations were dropped from the sample given the clear evidence of outliers in some years. Table 2.1 shows that the Gini coefficient rose from 0.395 in 1988 to 0.464 in 1996, ending with a slight overall increase to 0.396 in 2002. Another index, the R10/20, which is the ratio of the income share accruing to the
# Economic Performance under NAFTA: A Firm-Level Analysis of the Trade-Productivity Linkages<sup>∗</sup>

Rafael E. De Hoyos<sup>†</sup> Leonardo Iacovone<sup>‡</sup> Keywords: Firm-level productivity, Trade Reforms, Mexico

> ∗The authors are grateful to Alejandro Cano, Abigail Dur ́an, Gerardo Leyva and Gabriel Romero for granting access and showing how to use the industrial data at the offices of INEGI in Aguascalientes. We also thank Alan Winters, Gustavo Crespi, Sherman Robinson, Beata Javorcik, Valeria Arza, Nick Von Tunzelmann, Jorge Mattar, and seminar participants at the University of Sussex, SPRU, IADB, INEGI, ECLAC, and Anahuac University for their valuable comments. Leonardo Iacovone gratefully acknowledges the ESRC and LENTISCO financial support.

> †Independent Consultant, email: redeho@gmail.com

> ‡Development Research Group, The World Bank, 1818 H Street NW Washington DC 20USA, email: liacovone@worldbank.org

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points to a key disadvantage in relying on informal credit networks: the risk of being vulnerable to fraud and the consequent losses.

The current study has implications for anti-fraud policy. Current policies on combating fraud victimization emphasize anti-fraud campaigns and increasing financial literacy, or more generally in the case of containing crimes, strengthening law enforcement and improving the legal environment were thought to be effective to defeat crimes (Ehrlich, 1996; Levitt, 1997; Di Tella and Schargrodsky, 2004). We provide complementary perspective and evidence that policies focusing on the provision of financial services and credit to households may be as important. When encountering credit-related fraud schemes, sufficient access to credit provided to households would greatly reduce the risk of exposure to fraud schemes and allow these households to exhibit more patience—or be less subject to the temptation of quick payoffs.

# **References**

- Alves, L. M., & Wilson, S. R. (2008). The effects of loneliness on telemarketing fraud vulnerability among older adults. Journal of elder abuse & neglect, 20(1), 63-85.

- Anderson, K. B. (2007). Consumer fraud in the United States: The second FTC survey. Washington, DC: Federal Trade Commission.

- Anderson, K. B. (2013). Consumer fraud in the United States, 2011: The third FTC survey. Washington, DC: Federal Trade Commission.

- Aneja, A. P., & Avenancio-León, C. F. (2019). No Credit For Time Served? Incarceration and Credit-Driven Crime Cycles, working paper.Angrist, Jushua, and Jorn-Steffen Pischke. 2009. _Mostly Harmless Econometrics_ . Princeton: Princeton University Press.

- Ashraf, N., Karlan, D., & Yin, W. (2006). Tying Odysseus to the mast: Evidence from a commitment savings product in the Philippines. Quarterly Journal of Economics, 121(2), 635-672.

- Beshears, J., Choi, J.
and health, the subjects of SDG 3 and SDG 4) and develop strategies to reduce those barriers.

- _Tools and modeling work._ The WBG has developed an SDG benchmarking and projection framework that has been applied to well over 16 countries (Gable, Lofgren, and OsorioRodarte 2015). This framework has been updated to benchmark countries in the areas of data, finance, and attainment of the SDGs (Amin-Salem et al. 2018) and has recently been complemented by a framework that allows for additional prioritization using network analysis (El-Maghrabi et al. 2018).<sup>17</sup> For particular SDGs, different tools are continuing to be developed to assist country programs. For instance, Climate and Disaster Risks Screening Tools and Climate Metrics have been developed to assist the operational work of the WBG. Global models were also developed to assess the impact of education demographics on global inequality and inequality within countries (Ahmed et al. 2017; World Bank 2018f).

## **D. Operational alignment**

To encourage compatibility of WBG operations with the SDGs, the WBG has also mapped corporate and IDA indicators to the SDG agenda. The WBG’s Operations Policy and Country Services (OPCS) has assessed the mapping of the SDGs to the indicators in the Corporate Scorecard and the IDA18 Results Measurement System (RMS) and, broadly, found strong consistency with the SDGs. The exercise shows that 16 of the 17 SDGs are covered by at least one corporate indicator, and that nine of the indicators in RMS are taken directly from the SDGs. For some projects, the WBG uses SDG indicators as project indicators.

## **E. Advocacy**

The WBG staff actively engage stakeholders towards achieving the 2030 Agenda for Sustainable Development. Advocacy activities, coordinated through offices in Washington, DC, New York, and Geneva, include building interagency and intergovernmental partnerships, presenting SDG progress in international fora, and representing the WBG in decision-making processes related to the SDGs.

# **IV
is therefore the most appropriate dataset to best capture the second great wave of international migration (of the modern era).

Trade data are taken from Feenstra et al (2005), which provides data calculated from the UN _Comtrade_ database<sup>16</sup> for the period 1962-2000. The strength of these data lie in imports being preferred over exports - since they are frequently considered to be more accurate - the extent to which the authors clean the dataset by comparing the import and export data of each bilateral trade flow and the clear documentation the authors provide as to the adjustments made, which facilitates an accurate matching of the migration to the trade data. Since the earliest year to which the trade data refer is 1962, the migration data for 1960 is assumed to be comparable for this decade, under the assumption that these trade flows would have been similar to those two years hence. In cases where countries need to be aggregated to equate them to a trading entity, migrations between these countries are removed from the dataset. Once matched, the dataset comprises 178 countries in total.

The trade data are divided into two distinct periods, 1962-1983, for which data are complete (i.e. 178*178 countries) and 1984-2000. For this latter period bilateral trade flows values at less than $100,000 are omitted i.e. missing. Moreover, complete data are only available for 72 countries, which are reduced to 68 once the necessary aggregations have been made.<sup>17</sup> For this more recent period, the trade data are available for 68*178 countries therefore. The sample selected for estimation comprises those trade flows for those 68 countries for which data are available in each period (i.e. 68*68), termed sample 1. Additional samples are also estimated to check for robustness. Sample 2, relates to the largest possible sample (68*178), sample 3 comprises origins with fewer than 30 missing observations and sample 4
228.

- Carrell, S. E., B. I. Sacerdote, and J. E. West (2013). From natural variation to optimal policy? The importance of endogenous peer group formation. _Econometrica 81_ (3), 855–882.

- Cassel, R. N., P. Chow, D. F. Demoulin, and R. C. Reiger (2000). Extracurricular involvement in high school produces honesty and fair play needed to prevent delinquency and crime. _Education 121_ (2), 247.

- Chandler, D., S. D. Levitt, and J. A. List (2011). Predicting and preventing shootings among at-risk youth. _The American Economic Review 101_ (3), 288–292.

- Chetty, R., N. Hendren, and L. F. Katz (2016). The effects of exposure to better neighborhoods on children: New evidence from the moving to opportunity experiment. _The American Economic Review 106_ (4), 855–902.

- Cook, P. J., K. Dodge, G. Farkas, R. G. Fryer Jr, J. Guryan, J. Ludwig, S. Mayer, H. Pollack, and L. Steinberg (2015). Not Too Late: Improving Academic Outcomes for Disadvantaged Youth. _Institute for Policy Research Northwestern University._ .

- Cook, P. J., D. C. Gottfredson, and C. Na (2010). School crime control and prevention. _Crime and Justice 39_ (1), 313–440.

- Cook, P. J., J. Ludwig, and J. McCrary (Eds.) (2011). _Controlling Crime_ . University of Chicago Press.

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