rafmacalaba/data-use-mentions-extended huggingface dataset:
# **In Someone Else’s Shoes: Reducing Prejudice Through Perspective Taking**<sup>*</sup>

Marisol Rodr ́ıguez Chatruc<sup>†</sup> Sandra V. Rozo<sup>‡</sup> Originally published in the Policy Research Working Paper Series on _November 2021._ This version is updated on _May 2024._ To obtain the originally published version, please email prwp@worldbank.org.

## **JEL Classification:** C91, D91, J15 **Keywords:** migration, prejudice, perspective taking, altruism, trust, prosocial behaviors

> *We thank the editor Andrew Foster and the two anonymous referees for insightful suggestions and comments. We are also extremely grateful to Mar ́ıa Jos ́e Urbina, Tatiana Hiller, Camila Cort ́es, and Lucina Rodr ́ıguez for providing excellent research assistance. We benefited from comments from Pablo Bachelet, Mariana Blanco, Guillermo Cruces, David McKenzie, Berk Ozler, Salma Mousa, Margaret Peters, Yannay Spitzer, Mart ́ın Rossi, Subha Mani, and audiences at the 2021 Symposium on Economic Experiments in Developing Countries, LACEA BRAIN 2021, the 14th International Conference on Migration and Development, Universitat de Barcelona, Universidad de San Andr ́es, IZA 2021 Migration Webinar, and LACEALAMES 2021. We also thank Mariana Blanco and Andr ́es Zambrano from the Experimental and Behavioral Economics Lab at Universidad del Rosario (REBEL) for implementing the experiment, Gast ́on Cleiman for developing the interactive applications, and Carlos Macher, who produced “Walking for Freedom,” for adapting the documentary for our purposes. Finally, we thank Adela Barrio, Marcela Colmenares, Alejandra Holguin, and Carolina Aclan for their administrative support. USC IRB approval UP-20-00522 and Universidad del Rosario IRB Approval CS239. This study is registered in the AEA RCT Registry with unique identifying number AEARCTR-0006190. The contents of this study are the responsibility of the authors and do not necessarily reflect the views of their employers
united in their common struggle to bargain for lower production quotas.<sup>8</sup> Consequently workers made no claims against property rights distinct from managers. Together, workers and managers constituted a pressure group, which dedicated itself towards ensuring that enterprise insiders received the numerous privileges that ultimately, made their way into the Russian MPP. As Gaidar himself later conceded, "Beginning in May and June [of 1992] it was impossible to stand up to the pressure of the industrial lobby."<sup>9</sup> Thus it is that the designers of the Russian MPP confronted the following political reality: pre-existing institutional arrangements that distort free markets cannot be wiped clean by governmental fiat.

The scope and results of the MPP are well known and require little summary. A central difference between the MPP in Russia and similar programs in the Czech and Slovak Federal Republic and in Poland was in the tradability of Russian vouchers. Under the three variants of the Russian program, insiders could purchase up to _51%_ of enterprise shares. The tradability of vouchers, on the other hand, allowed insiders to purchase additional shares-65% on average.'<sup>0</sup> Tradable vouchers, then, may have turned out to be an instrument of insider control, rather than the intended means of consolidating outsider ownership.

Despite conflicting evidence on managerial turnover in thle past year, there is general agreement that managers and salaried employees continue to hold approximately 50-60% of shares in privatized enterprises." Estimates of ownership structure have varied across different enterprise surveys, which are difficult to compare over time. We present estimate ranges for stock ownership in privatized Russian enterprises below. It is interesting to note that the greatest variation tends to be for empLoyees and managers as owners, perhaps reflecting the empirical (as well as theoretical) difficulties in separating the two.

Nevertheless, one of the critical shortcomings of the Russian MPP is that it did not enable an institutional concentration of ownership through fiLnancial intermediaries such as investment
, agricultural marketing choices, and non-farm enterprise sales. These outcomes explore possible mechanisms that might explain why previous research finds that exposure to conflict can lead to diminished food security (George _et al._ , 2020). Our analysis in this section requires that we restrict our data to the 2018-2019 round of data, where the variables measuring harvest use, agricultural marketing, and enterprise sales are consistently and accurately recorded and available. The use of these cross-sectional data requires a similar but slightly more restrictive identifying assumption compared to the use of our panel data in previous sections. In particular, our analysis in this section requires we assume that exposure to herder-related violence is exogenous to our outcome variables of interest within the 2018-2019 cross-section of our data, and does not allow us to account for time-invariant household-level characteristics.

Table 2 reports estimates of changes in household consumption patterns in the 2019 post-harvest season associated with exposure to herder-related violence in the previous planting season. The first two columns use a continuous measure of total household consumption, in terms of (log) naira. We find no statistically significant relationship between exposure to herder-related violence in the post-planting season and overall consumption. The exception is that when we differentiate the effect by previous violence exposure, those households that both were exposed to an HRV event in the post-planting season and with 19
|-0.02|
|Ratio of agricultural area to total||-0.03|-0.02|
|Agricultural employment share|0.22|0.22|0.22|
|GDP per inhabitant|-0.01**|-0.01**|-0.01**|
|Share of inhabitants with secondary education|0.28+|0.28+|0.30+|
|Share of inhabitants with tertiary education|0.16|0.16|0.17|
|Year fixed effects|Y|Y|Y|
|R-Squared:||||
|Within|0.42|0.42|0.43|
|Between|0.14|0.14|0.13|
|Overall|0.12|0.12|0.11|
|Observations|367|367|367|

Notes: (1) Panel Fixed Effects; All regressions include year fixed effects; (2) Anchored Relative Poverty line (60% of median at 2011); (3) Data Source: Farm Structure Survey, Eurostat; EU‐SILC, Eurostat ; (4) Symbols: ** P<0.01, * P<0.05, +P<0.1

# **5. Poverty, agriculture, and the CAP: A comprehensive analysis**

So far, we have documented how CAP funds can contribute towards inclusive growth by reducing poverty and inequality. With this message in mind, the specific areas for improvement will depend on a country’s level of structural transformation and on its current allocation of the CAP based on high poverty areas. The goal of this section is to integrate the parts of the past analyses; on one side we have the relationship between agriculture and poverty, which provides the state of a country 19
**Figure 11. Service Export Sophistication & Goods Export Sophistication**

<!-- Start of picture text --> LUX<br>IRL<br>IND GBRCHE<br>MLI CIV BEN GHA GABPNG TZACMRJAMGUYBWABLZMNGECUARMHNDLSOGMBCOGPERPRYCHLGTMLKAALBSENSLEMUSMARKGZSLVPAN ISLTUN COLJORMOZ ARGBGRAUSEGYZAFROMGRCLVARUSBRAURYTURVENLTUBOLIDNHRV CRICYPESTNORPRTPOLITATHACHNESPMEXSVNNLDAUTFRAPHLUSAMYSHUNSWESAUKORFINJPNSGP<br>SDN NAMKEN<br>NIC<br>NPL<br>PAK<br>RWA<br>BGD<br>BDI<br>45°<br>8 8.5 9 9.5 10<br>ln Goods EXPY 2005<br>10<br>9.5<br>9<br>8.5<br>8<br>7.5<br><!-- End of picture text --> _Source_ : Authors‘ calculations.

# **5. Empirical Methodology and Results**

We now turn to the analysis of the relationship between the ―dynamic commercial service EXPY‖, referred to as _Service EXPY_ ( _sEXPY_ ) and constructed as described in the previous section, and economic growth. We first present the results of the cross sectional analysis followed by various dynamic panel estimation techniques to capture the time series dimensions in the evolution of service export sophistication and its impact on growth. Since we are interested in investigating the empirical validity of a phenomenon that started in the mid nineties and its nature is changing rapidly, we choose three year panel for our analysis. The data includes up to 103 countries over the time span of 1990-2007. Choice of the time period is guided by two considerations- first, the consistent reporting of service exports in the Balance of Payments only started in the late 1980‘s and second, we are interested in capturing a phenomenon which gained momentum only in the 90s. We control for the standard determinants of growth in our reduced form regressions and include initial level of income per capita to control for the convergence effects.

The choice of explanatory variables in these growth regressions is never straightforward. A survey of empirical methodology by Durlauf et
(2001), Chapter 6, for more details. According to census data, in 1962, there were about 50,000 Portuguese nationals in France (i.e., 2.3% of foreign population); in 1968, there were more than 296,000, that is 11.3% of foreign population; and in 1975, they represented 22% of foreign French population with almost 759,000 residents (Weil, 1995, Annex VI).

> 6  Population numbers are for the end of December (IMES, 2004a, Table S1A.1850R; OFE, Table 182_220_1264R). It is worth noting that the number of seasonal permits is at the lowest in December since seasonal industries tend to have much higher employment in the summer.

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market-determined but the real rates were<br>sometimes slightly negative (less than 5%) or the differential<br>between the deposit and lending rates was large (8% or<br>more), countries received a rating of 8. When the real<br>deposit or lending rate was persistently negative by a<br>single-digit amount or the differential between them was<br>regulated by the government, countries were rated at 6.<br>When the deposit and lending rates were fixed by the<br>government and the real rates were often negative by<br>single-digit amounts, countries were assigned a rating of 4.<br>When the real deposit or lending rate was persistently<br>negative by a double-digit amount, countries received a<br>rating of 2. A zero rating was assigned when the deposit<br>and lending rates were fixed by the government and real<br>rates were persistently negative by double-digit amounts or<br>hyperinflation had virtually eliminated the credit market.|1970-<br>2000<br>(5-year)<br>and 2001|**Economic Freedom of the World, The Fraser Institute**<br>From Section:<br>5: Regulation of Credit, Labor, and Business<br>A. Credit Market Regulations<br>Based on: International Monetary Fund, International<br>Financial Statistics Yearbook (various issues, as well as<br>the monthly supplements)|
|v. Interest rate<br>controls<br>(efw_av)||Reflects whether interest rate controls on bank deposits<br>and/or loans are freely determined by the market.||Based on data provided by the World Economic Forum<br>Global Competitiveness Report.|
|Banking and finance<br>(ief_banking)|1<br>Very low|Government involvement in the financial sector negligible;<br>very few restrictions on
workforce is available for 30 years from 1980 – 2009 with data missing for only one intermediate year. However, the series is constructed using different surveys for different years. For example, the data for 1980 – 1988 comes from official estimates and for 2004 – 2009 comes from Household/Labor Force Survey. But more importantly the data for 1989 – 1997 comes from surveys that were geographically constrained in main cities or urban areas.

The raw series from the WDI for Agriculture (red solid circles) and Services (blue solid squares) are shown in figure A.1. The share of employment in Agriculture during 1980 – 1988 hovers around 47% while the share of employment in Services rises gradually to just under 40% by 1988. However, both series observe a massive change in 1989 as the source used to construct the series changes. The massive jump in sectoral share of Services is driven by the fact the survey in 1989 from which the share is calculated was restricted in the main cities of the country. This led to enormous over representation of workforce in Services and an equally colossal under representation of workforce in Agriculture. The shares again return to trend levels beginning 1999 with a change in source survey used. The selection process outlined above helps in filtering out the spurious data 45
# **Figure A.2 Employment and Labor Force Participation in China: Evidence from the China Urban Labor Survey**

<!-- Start of picture text --> A. Employed Share<br>Urban Male Urban Female<br>25 30 35 40 45 50 55 60 25 30 35 40 45 50 55 60<br>Age Age<br>2001 2005 2010 D10 2001 2005 2010 D10<br>Migrant Male Migrant Female<br>25 30 35 40 45 50 55 60 25 30 35 40 45 50 55 60<br>Age Age<br>2001 2005 2010 D10 2001 2005 2010 D10<br>(D10: sample distribution in 2010)<br>B. Labor Force Participation Rate<br>Urban Male Urban Female<br>25 30 35 40 45 50 55 60 25 30 35 40 45 50 55 60<br>Age Age<br>2001 2005 2010 D10 2001 2005 2010 D10<br>Migrant Male Migrant Female<br>25 30 35 40 45 50 55 60 25 30 35 40 45 50 55 60<br>Age Age<br>2001 2005 2010 D10 2001 2005 2010 D10<br>1 .1 1 .1<br>.8 .08 .8 .08<br>.6 .06 .6 .06<br>.4 .04 .4 Fraction .04<br>.2 .02 .2 .02<br>0 0 0 0<br>1 .1 1 .1<br>.8 .08 .8 .08<br>.6 .06 .6 ion .06<br>.4 .04 .4 Fract .04<br>.2 .02 .2 .02<br>0 0 0 0<br>1 .1 1 .1<br>.8 .08 .8 .08<br>.6 .06 .6 .06<br>
This paper investigates whether the negative association between secondary school availability and teenage childbearing is based on a causal relationship. We do this by looking at the effect of a large secondary school expansion across 4,884 Brazilian municipalities on teenage childbearing. Conditioning on municipal fixed effects and other determinants of school introductions, this expansion provides a plausibly exogenous source of variation in school availability. Our main results show that, on average, one additional secondary school per 100 females in the age cohort decreases teenage childbearing by between 0.250 and 0.563 births per 100 females. While being smaller than the effects that we measure using casual observation in Figures 1 and 2, our main estimates are statistically significant and economically relevant.

Our estimates rely on the assumption that the secondary school expansion is orthogonal to levels and trends in municipal teenage childbearing. The paper provides an analysis of the expansion and find that, controlling for variables reflecting school supply and demand, where and when a school is introduced is independent of variation in teenage childbearing. We also find no evidence of pre-trends in childbearing prior to school introductions. Our main estimates are robust to a number of different specifications and robustness checks, including estimates identified only from variation in the timing of school introductions across municipalities of similar size.

Brazil is particularly well suited for studying our research question. The school expansion that we examine constitutes one of the largest expansions of secondary schools on record. We use information from 13 waves of the annual Brazilian school census, containing detailed information on the universe of Brazilian schools, to create a new dataset reflecting the availability of secondary schools in every Brazilian municipality between 1997 and 2009. We combine this information with vital statistics data from Brazil capturing the universe of live births including information on the age of mothers at date of conception over the same period, creating a rich and unique dataset.

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20 to assess how the contribution of vertical specialization to productivity growth has changed between the 1990s and the 2000s.

In Constantinescu, Mattoo and Ruta (2016b), we estimate the impact of (forward and backward) vertical specialization on productivity growth at the country-sector level. Specifically, we use the following panel estimation with multiple sets of fixed effects: country/industry (μci), country/year (νct) and industry/year (τit):  ln LPcit =  + β  ln LKcit +   ln BVScit-1 +   ln FVScit-1 + μci + νct + τit + εcit, where c denotes country, i is industry, and t year. The left-hand side variable LP is the change in labor productivity, measured as value-added divided by employment. The key explanatory variables are the change in backward vertical specialization (BVS), which is the foreign content in own exports, and the change in forward vertical specialization (FVS), which is own value added re-exported by the direct importer. As standard, the analysis also controls for the capital stock per employee (LK), which is capital divided by employment, where capital is derived by cumulating gross fixed capital formation.<sup>7</sup> The key insight of this regression analysis is that backward linkages have a positive impact on productivity growth, although the effect is not large, while forward linkages do not appear to have a statistically significant impact.

Specifically a one standard deviation increase in backward specialization increases productivity growth of the average sector by 0.06 percent.

> 7 The sample in Constantinescu, Mattoo and Ruta (2016b) consists of 1995-2009 annual data (as 2010 and 2011 are excluded due to lack of capital data) for 14 manufacuring sectors and  39 countries (Russian Federation is excluded due to prevalence of commodity exports).  Data come from the World Input-Output Database (Timmer et al, 2015), obtained by interacting Census data (interpolated for years between Censuses) with yearly trade data. Vertical specialization
87 where _pik_ and _Qik_ are baseline producer prices and quantities of crop _k_ in district _i_ , _Nk_ is the number of Sri Lankan districts with nonzero production of crop _k_ , _K_ is the set of crops for which data on price increases is available, and _p_<sup>_cf_</sup> _ik_<sup>(</sup><sup>_sK_)isthecounterfactualproducerpriceofcrop</sup><sup>_k_in</sup> district _i_ assuming _σK_ = _sK_ . The crops for which price increase data is available are: rice, maize, potatoes, cloves, onions and groundnuts.

We test several possible candidate values _sK_ , ultimately choosing the value _s_<sup>_∗_</sup> _K_<sup>=2</sup><sup>_._377that</sup> generates a counterfactual average price increase of _p_ ̄<sup>ˆ</sup><sup>_cf_</sup> ( _s_<sup>_∗_</sup> _K_<sup>) = 1</sup><sup>_._2265, which matches the 22.65%</sup> increase observed in the data between January 2021 and January 2022 (shown in Appendix Figure A11). Specifically, our average crop price increase calculation is done in three steps: (i) computing the nominal price increase of each crop in _K_ between January 2021 and January 2022; (ii) deflating these nominal increases using the period’s cumulative inflation (17.61%) to obtain real price increases; and (iii) computing the average crop price increase as a weighted average of crop-level real price increases, with each crop’s weight given by its revenue share at baseline (as in equation 46). We set _σ_ ˆ _K_ = 2 _._ 377 as our estimate for the elasticity of substitution across origins of the same crop.

# **D.B Trade Costs**

In Section VI.A
# **1 Introduction**

The Sustainable Development Goals (SDGs) lay out an ambitious agenda including that of achieving gender equality by 2030. This agenda is paired with a set of goals and targets measured by concrete indicators and is adopted by nearly all countries. SDG 5 focuses on gender equality and sets 9 measurable targets (with 14 indicators) on issues that especially affect women and girls (United Nations, 2022). But gender cuts across a far wider range of the SDGs than just the indicators under Goal 5. For example, SDG 3 on ensuring good health and well-being includes a target on reducing maternal mortality (target 3.1). The SDG agenda also calls for sex disaggregated data across several goals where monitoring of gender disparities is essential for effective policy. For example, SDG 8 on promoting decent work and economic growth sets a target of achieving full employment and equal pay for all women and men (target 8.5). These gender data are promoted as key to understanding if and how patterns of progress differ between women and men or girls and boys (UN Women, 2022). Countries are, however, falling short on reporting on gender-related indicators of the SDGs. This paper analyzes the patterns underlying these data gaps.

The objective of this paper is to look at this globally agreed-upon set of gender data indicators and identify key country patterns related to the existence, or lack of, such data. We focus on the availability of data reporting on the SDGs since they represent an internationally agreedupon set of goals to meet and for countries to report on (UNSD, 2022).

Missing gender data is not a new concern. There are different approaches to diagnosing the causes of the lack of gender data. One approach put forth by Bonfert et al (2022) emphasizes four obstacles to more gender data (Figure 1): (i) lack of data sources such as core and/or specialized surveys, censuses or relevant administrative data (that is, the data simply are
is a trade competitiveness project where the achievement of the overall goal was measured in terms of the higher average annual growth rate of exports during the project duration and increases in exports' share of GDP compared with the initial year of the project.

Figure 3 Evaluation of World Bank trade-related projects 1995–2005

> 19 We thank Vivian Agbhega for compiling the data for this review. The selection of trade-related projects followed the criteria used by Steven Gunawan in an unpublished study of "Monitoring and Evaluation Lessons of Trade Projects" that served as background work for the 2011 World Bank Trade Strategy. The projects were filtered from the World Bank’s Operations portal website according to the theme ―Trade and Integration," and falling within the following criteria: i) approved only after 1995 due to obsolescence; ii) IBRD/IDA-funded; and iii) closed. A total of 321 projects were filtered, out of which 144 were development policy loans and 177 were investment loans, and 30 investment lending projects had to be dropped since they lacked ICRs. A final set of 85 investment lending projects was obtained after excluding projects that did not have any trade components. The main documents used to extract information on the projects were PADs and ICRs. For each project we collected information on the types of intervention, the types of outputs and outcomes achieved, the evaluation methods employed, and the evidence or proof of causation of the impact of the project.

> 20 A beneficiary survey consists of a formal survey of the entities that received assistance from the project, whereas a stakeholder workshop is a more informal way to collect information on the various entities affected by the project.

> 21 Our analysis of project ICRs did reveal, however, that the use of these methods is often handicapped by difficulty in quantifying some of the costs and benefits of the project. Some project ICRs explicitly say that certain benefits are not incorporated in net present value calculations due to their complexity.

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Jimlime, Koni Djodjo, Mbeni, Mirontsi, Moroni, Moya, Mremani, Mutsamudu, Ngadzale, Ongodjou, Ouani, Ounkazi, Sima and Tsembehou.

> 4 It is estimated by the population multiplied by the share of non-agricultural households, which is calculated by subregion, using the same household survey.