rafmacalaba/data-use-mentions-extended huggingface dataset:
employment and returns to wealth affecting the income distribution within a population. Hence, the need to combine macro and micro approaches to policy analysis is obvious.

**Several approaches have been proposed to integrate macro-micro frameworks that allow for the simultaneous study of the distributional and macro impact of policy reforms** (Bourguignon and Bussolo 2013). The usual problem of data availability and quality is, in the case of a macro-micro model, compounded because data from national accounts and from household surveys – the two most common sources of macro and micro data – need to be reconciled, let alone the economic treatment of their respective sources. In this context, the modeling framework used in this paper represents a departure from the static simple top-down approach. It incorporates a macro-micro framework operating in a dynamic setting.

**This paper discusses the CGE-GIDD macro-micro methodology that can be used for the ex-ante assessment of trade policy and shocks.** This paper uses the Global Income Distribution Dynamics (GIDD) microsimulation framework in combination with the global computable general equilibrium (CGE) model called LINKAGE/ENVISAGE. CGE-GIDD is a “top-down” macro-micro simulation framework that exploits heterogeneity observed in household surveys to distribute macroeconomic shocks. CGE-GIDD was developed by the World Bank (Bussolo, De Hoyos, and Medvedev 2010), inspired by previous efforts involving simulation exercises (Bourguignon, Ferreira, and Leite 2008; Bourguignon, Bussolo, and Pereira da Silva 2008; Davies 2009). The full technical documentation of the LINKAGE/ENVISAGE model is provided by van der Mensbrugghe (2013; 2020); the following sub-section briefly describes the main structure of the models.

3
., 2002), describing the inability of developing countries to adequately borrow domestically or internationally when issuing debt in their own currency. Instead, countries issue debt denominated in USD or EUR, which insulates the creditor from domestic inflation, liquidity or solvency concerns of the issuing country. However, Hausmann et al. (2003) do not find evidence that these concerns

> 7The data used in this paper was obtained from the World Bank’s wealth team and is subject to change prior to the release of the new wealth data described in Lange et al. (2021).

8
. Included among these factors are income distribution, economic structure, governance structures (including domestic political institutions and international organizations), ideology, and political organization.

Armed with the rich political-economic formulations that have emerged over the last few decades, it is possible to explain several of the stylized facts presented in Section 2.<sup>12</sup> They include not only the general tendency of countries in the course of their economic development to gradually move from taxing to subsidizing agriculture but also to counter international price –, to adopt policies with an anti-trade bias, to adopt new types of instruments, and, in some high-income countries, to reduce agricultural production assistance to farmers.

11 The historical origins of the political-economic lens can be traced back to the original architects of the economics discipline, namely Adam Smith, Mill, Wicksell, and Marshall, none of whom was a stranger to political-economic analysis. Modern political-economic analysis was initiated by Anthony Downs with his seminal 1957 book, _An Economic Theory of Democracy._ Many publications have surveyed the politicaleconomic literature during the last few years. General reviews of the literature have been presented by Mueller (2003) and Weingast and Whitman (2006). For reviews from leading economic journals focusing on gametheory formulations since the year 2000, the surveys of Dewan and Shepsle (2008a; 2008b) are invaluable. More specific reviews are available for trade policy (Grossman and Helpman 2001; 2002; Rodrik 1995); fiscal and monetary policy (Persson and Tabellini 2000); the relationship between governance structures and fiscal and growth-promoting policies (Persson and Tabellini 2003); and agricultural policies (de Gorter and Swinnen 2002; Rausser and Goodhue 2002).

> 12 For a review and assessment of six alternative schools of political-economic thought, see Rausser, Swinnen, and Zusman 2011, Chap. 1.
any savings in both groups is almost non-existent (2%), while UP households are more likely to be indebted (68%) than non-UP households (52%). Most of the debt for UP households is for consumption smoothing and health shocks (89%), rather than investment (4%), and comes from informal sources such as family and friends or grocery stores (88%). Supply-side constraints may partially explain the low coverage of formal institutions, with only 5% of villages reporting the presence of a microfinance institution, and none reporting the presence of a bank (Table 2).

UP households also report consistently lower levels of psychological well-being than non-UP households. UP primary women report lower life satisfaction than women in non-UP households (5.0 vs. 6.7 points in a 1-10 scale, where 1 indicates very unsatisfied and 10 indicates very satisfied). Using standard cutoffs for depression on the Center for Epidemiological Studies Depression (CES-D) scale, 69% of women in UP households and 52% of women in non-UP households report suffering major depression. For reference, UP women would rank last on life satisfaction in the list of 60 countries for which data are available in the 2016 World Values Survey. Non-UP women would rank 39th in the same list.<sup>8</sup> UP primary men also report high levels of depression, but much lower than women in the same

> 6 In this section, all figures for Afghanistan come from the ALCS 2016 and global indicators come from the World Bank Open Data Indicators, unless otherwise stated.

> 7 For reference, 17.3% of adult women and 10.2% of adult men worldwide are illiterate. Afghanistan and world figures are estimated for adults 15 years and over for 2016.

> 8 Comparison is done with the life satisfaction rating for all women in the sample of 60 countries.

7
9

# **Authorship**

On multi-author papers, we give each author equal credit for the paper insofar as EconLit allows us to. This is in contrast to Rubin and Chang (2003) who gave authors of papers with two authors only half a paper‘s credit each, and authors of papers with three authors only one third a paper‘s credit. Unfortunately, EconLit lists only the first author when the original list includes more than three or four authors.<sup>13</sup> In such cases, authors other than the first get no credit in our study as in Rubin and Chang‘s; the EconLit user has no alternative in such cases.

The author data required a good deal of cleaning. Many authors appear in different forms, sometimes with different spellings. The top 250 authors in terms of publication counts were all checked manually for multiple spellings and forms, and the scattered records aggregated; the same manual cleaning was applied to other authors who ranked highly on one or more of the indicators. The trickiest person proved to be Matthias von der Schulenburg, whose name appeared in no less than ten different forms.<sup>14</sup> The funniest was Adam Wagstaff, who after years of writing with Eddy van Doorslaer became ―Adam van Wagstaff‖ in one paper.

# **Affiliation**

The EconLit author-address field gives the author‘s address (affiliation) at the time of publication of the article (actually at the time of proof-correction). The field is empty until the early 1980s, so the results below are for the period 1981-2010. The information in the authoraddress field needed some manual cleaning, and some explanation of what was done is in order. Some clarification is also in order on the issue of joint authorship.

Each unit record (one per publication) generated multiple author-address records depending on the number of authors and whether or not they shared an affiliation. The maximum number of lines in the author-address field is equal to the
increases in the budgetary allowance for the program. Green Card expenditures rose significantly in real terms between 2004 and 2008.

The stated aim of the program was to improve access and quality of essential health services for the uninsured, mostly poor. Apart from raising both the level and quality of care on aggregate, the very concept of insurance, with a defined bundle of inpatient and outpatient health services as well as prescription drugs, was to provide households with uninterrupted access to vital health services, including in times of household level income shocks. The Green Card program, which had started out as a stand-alone program with a separate budget and was the main flagship social protection program of the Turkish government in the past decade, was—as of January 2012—fully integrated into the Universal Health Insurance Scheme covering all Turkish citizens (under Law 3816).

This paper assesses the Green Card program along several dimensions. First, we look at who was reached by the massive coverage expansion between 2003 and 2008. Using repeated cross-section household survey date, we report both average and marginal incidence changes. Second, we assess whether the program had a significant impact on improving the financial protection of households by lowering out-of-pocket expenditures which would have been both impoverishing as well as catastrophic in nature. Third, using a special household survey which was collected during the economic crisis in Turkey in 2008 and 2009, we evaluate whether the Green Card non-contributory health insurance program protected health care utilization during the economic crisis period during which households faced income shocks.

The structure of our paper is as follows: We start in Section 2 by providing information on the institutional set-up and targeting mechanism of the program. Section 3 describes the data used in this paper. Section 4 <mark>presents the results as to who benefited from the massive expansion of the program that occurred between 2003 and 2008. Section 5 reports our results on the financial protection impact of the program while S</mark> ection 6 includes an assessment of the program with respect to protecting the health
# **1. Introduction**

Triggered by the so-called commodity super cycle, Latin America experienced vigorous growth during the 2000s coupled with falling household income inequality (Alvaredo and Gasparini 2015; Lakner and Milanovic 2013). More than redistributive policies, the main force behind this inequality reduction was falling wage dispersion (L ́opez-Calva and Lustig 2010; Azevedo, Inchauste, and Sanfelice 2011; Rodr ́ıguez-Castel ́an et al. 2016). This is in stark contrast with increasing wage inequality in developed (Acemoglu and Autor 2011; Atkinson 2008) and other developing countries, including China (Ge and Yang 2014), India (Lee and Wie 2017), and Indonesia (Lee and Wie 2015). These differences raise two important questions. First, what are the main patterns behind the reduction in wage inequality in Latin America? Second, what are the forces behind these patterns? This paper breaks new ground on these questions using household surveys and matched employer-employee data.

This paper provides systematic evidence of the evolution of wage inequality in Latin America between 1995 and 2015, emphasizing the main stylized facts with which any potential story about inequality reduction in the region should be consistent.<sup>1</sup> It first documents the main wage inequality trends, highlighting differences across countries. To this end, it uses harmonized household surveys for 16 countries during 1995–2015 (covering the formal and informal workforce). Using these data, this paper disentangles the evolution of wages at the bottom and top of the wage distribution in each country and analyzes the changes in relative wages across skill groups. It then decomposes the evolution of wage inequality into forces operating between demographic and skill groups and within them. In this vein, it examines whether changes in wage inequality occurred within sectors-occupations or were associated with compositional changes, and assesses the potential contribution of labor formalization to changes in inequality. In line with recent studies, it emphasizes the role of firms (Card, Heining, and Kline 2013; Alvarez et al
with the sector accounting for more than 80 percent of the country's exports as of 2015 and providing jobs to over 4 million low- and semi-skilled workers (Farole et al., 2017). We exploit a unique firm-level survey collected by the World Bank covering representative samples of firms in the apparel and textiles sectors and including information on each firm’s suppliers and customers, which allows the construction of novel measures of supply chain linkages to FDI firms in the country. We follow and extend the approach proposed by Kee (2015) by relating FDI firms with domestic firms that share their local input suppliers. She designates as FDI siblings those domestic firms that share local input suppliers with an FDI firm in the same sector. Using a natural experiment generated by the Everything-But-Arms Initiative of the European Union that increased market access for Bangladeshi exporters, she shows that one-quarter of the product scope expansion and one-third of the productivity gains of domestic firms in Bangladesh that were FDI siblings can be attributed to the presence of the related FDI firms.

In this paper, we first examine whether there are significant differences in terms of gender labor practices of FDI firms relative to domestic firms in Bangladesh. We show that FDI firms hire significantly more female administrative workers and production workers than domestic firms. This is true even after controlling for firm size and location and industry fixed effects. We then exploit the unique firm-to-firm relationships present in our data set. We study whether domestic firms that are related to FDI firms as their suppliers or customers are different from other domestic firms in terms of gender labor market practices. The estimates show that these suppliers or customers of FDI firms exhibit significantly more female administrative workers. Finally, we explore the gender labor practices of domestic firms that share local suppliers or customers with FDI firms. We show that these related firms employ a significantly higher number of female administrative workers. Overall, these results suggest that domestic firms that are associated with 3
/mark>_ <mark>(2), 943-1005.</mark> <mark>De Vries, G., Timmer, M., & De Vries, K. (2015). Structural transformation in Africa: Static gains, dynamic losses.</mark> _<mark>The Journal of Development Studies</mark>_ <mark>, 51(6), 674-688.</mark> <mark>Easterly, W., & Levine, R. (2001). What have we learned from a decade of empirical research on growth? It's Not Factor Accumulation: Stylized Facts and Growth Models.</mark> _<mark>the world bank economic review</mark>_ <mark>,</mark> _<mark>15</mark>_ <mark>(2), 177-219.</mark> <mark>Edmond, C., Midrigan, V., & Xu, D. Y. (2015). Competition, markups, and the gains from international trade.</mark> _<mark>American Economic Review</mark>_ <mark>,</mark> _<mark>105</mark>_ <mark>(10), 3183-3221.</mark> <mark>Faggio, G., Salvanes, K. G., & Van Reenen, J. (2010). The evolution of inequality in productivity and wages: panel data evidence.</mark> _<mark>Industrial and Corporate Change</mark>_ <mark>,</mark> _<mark>19</mark>_ <mark>(6), 1919-1951.</mark> <mark>Foster--McGregor, N., & Verspagen, B. (2016). The Role of Structural Change in the Economic Development of Asian Economies.</mark> _<
additional specifications are presented in Table 10 (Model 6). Our basic results that use our index of financial liberalization are confirmed. Small firms became less financially constrained during the period 1989-98, a period that has been characterized by substantial progress in financial liberalization in our sample of countries.

# **8 Conclusions**

We have estimated a dynamic investment model using panel data on 394 firms in 13 developing countries for the years 1988-98. Using different specifications of the 28
**Figure 7: Numbers of wage earners and pensioners (millions), the dependency ratio in the state pension system; 1990-2010**

<!-- Start of picture text --> 9<br>8<br>7<br>6<br>Average number of wage<br>5 earners (millions) (*)<br>4 Average number of state<br>pensioners (millions)<br>3 Dependency ratio in state<br>pension system<br>2<br>1<br>0<br>1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010<br><!-- End of picture text --> _Source: National House of Public Pensions of Romania (May 2011), “Statistical Indicators Pillar I”, Bucharest. -_ _<u>http://www.cnpas.org/portal/media type/html/language/ro/country/null/user/</u>_ Further reforms to the pension system were implemented during the 2000s. These included an increase in the retirement age by 5 years, reform to the benefit formula and the introduction of a multi-pillar pension system in 2008. The multi-pillar system added a mandatory, privately managed, defined contribution, second pillar and a voluntary, privately managed, third pillar to the existing public, defined benefit, first pillar of the pension system.<sup>9</sup>

> 9 Multi pillar pension systems have been introduced in most of the new member states of the EU.

18
export value and export growth, but not for export quality or the number of export destinations reached. These results suggest that the imported technology channel is an important determinant of export performance in Argentina, consistent with what has been previously found for France by Bas and Strauss-Kahn (2014).

Finally, we unpack how these relationships vary across economic sectors. We group the 2-digit HS codes into 15 sectors in table 4. The sectors that show the greatest importance of imports are animal and animal products; foodstuffs; vegetable products; and transportation. The bulk of exports from Argentina are concentrated in these sectors. The coefficients of interest are not significant in other sectors or even yield nonintuitive signs. Interestingly, the only exception to this is transportation, which includes all kinds of vehicles and motor cars. These products represent more than 6 percent of Argentinian exports and are the core of bilateral trade between Brazil and Argentina.

# **5 Concluding Remarks**

This paper examines the performance of globally engaged firms in Argentina in the past decade. We assembled a wide array of firm-level export indicators for Argentina to match those in the World Bank Exporter Dynamics Database. Employing this information, we document the progressive retreat of Argentine firms from global markets.

Benchmarking the characteristics of these exporters with similar countries, Argentine exporters are found to be disproportionally fewer and individually larger, with export value highly concentrated in few firms. Firm churning rates are disproportionally low and survival rates of entrants are high. These findings reflect exceptionally high entry costs of export, which are likely the result of anti-export bias and import substitution policies. However, we show that exporters that import intermediate inputs have better export outcomes than those that source their inputs exclusively from Argentina.

12
The coefficient of GDPpc should have a positive impact, but selectivity effects may trigger a different outcome. OPEN is the ratio of exports plus imports to GDP, and aims at capturing the degree of openness of the economy in the origin country. This variable is also available in the Penn World Tables. The coefficient of OPEN is expected to be positive provided that a high degree of an economy’s openness implies more information is available to prospective migrants. The English dummy in the model is based on CIA – The World Factbook (2002) and takes value 1 if English is the main spoken language in a country. This variable should have a qualitative effect on performance; therefore the sign of the coefficient is expected to be positive. DIST is a selectivity indicator measured as the log of distance in miles to the US<sup>32</sup> . It affects migrants’ ability distribution by its influence on migration costs. A country in the proximity of the US has a higher propensity to send unskilled migrants compared to one that is further away. Therefore distance should have a positive impact on the entry level of occupational placement. The presence of military CONFLICT has a negative selection effect. In estimations for the 1975 arrival, military conflict<sup>33</sup> is evaluated for the 1970s decades, whereas estimations for the 1985 arrival use an index based on data for the 1980s. Variable COM<sup>34</sup> indicates whether a country has a communist regime in 1970, and respectively 1985.

The next step is to determine whether country attributes impact the change in occupational placement over a decade. The model in the equation is estimated six times for the 1975 arrival, and three times for the 1985 arrival. Thus, for each pair of Census years t=1980, 1990, 2000:  _predicted_  5'' * _CONFLICT_ ln _OPc c_  '' 6  '' *  _COMc_ * _predicted_   _c_ '', _c_
one’s ability to see the world from other points of view and to increase perspective taking (Stewart et al., 2003).<sup>4</sup> On the other hand, the video exposes participants to personal narratives that can emotionally “transport” the audience to a fictional world and spur changes in their beliefs (Green and Brock, 2000).

Immediately after the interventions, we applied a survey instrument that elicited attitudes and beliefs about migrants and assessed two prosocial behaviors: altruism and self-reported trust towards Venezuelans. We measured altruism through participants’ donation behavior in a dictator game. We told participants they would be given an amount of money and then we asked how they would allocate it among themselves, an organization that helps Venezuelan migrants, and a well-known Colombian nonprofit group that builds houses for vulnerable populations. A control group did not receive any treatment and only took part in the survey.

The participants in our intervention recently experienced a drastic change in the demographic composition of their communities due to a massive influx of Venezuelan forced migrants. In just six years, more than five million people have fled the humanitarian crisis in Venezuela and relocated in other countries, making Venezuelans the second-largest internationally displaced population after Syrians. Colombia is the main destination country for this contingent, hosting 2.89 million Venezuelans, and Bogot ́a is the main recipient city, accounting for almost 20 percent of Colombia’s Venezuelan population. Attitudes towards Venezuelans have deteriorated markedly in Colombia, as reported in the media and evidenced by the decline in Gallup’s Migrant Acceptance Index, which in Colombia had the third-largest drop in the world between 2016 and 2019.<sup>5</sup>

> 4In order to ameliorate demand effects, we (unlike many studies in social psychology) do not provide explicit instructions to intervention participants when they perform the activities.

> 5Gallup compared 140 countries between 2016 and 2019 (see footnote 1); the three largest declines in migrant acceptance took place in the main destination countries