rafmacalaba/datause-encoder-data huggingface dataset:
Gibson, John, David McKenzie, Halahingano Rohorua and Steven Stillman (2018) “The long-term impacts of international migration: Evidence from a lottery”, _World Bank Economic Review_ 32(1): 127-147.

Gibson, John, David McKenzie, Halahingano Rohorua and Steven Stillman (2019) “The long-term impact of international migration on economic decision-making: Evidence from a migration lottery and lab-in-the-field experiments”, _Journal of Development Economics_ 138: 99-115.

Gollin, Douglas, Martina Kirchberger and David Lagakos (2021) “Do urban wage premia reflect lower amenities? Evidence from Africa”, _Journal of Urban Economics_ 121: article 103301.

Gollin, Douglas, David Lagakos and Michael Waugh (2014) “The Agricultural Productivity Gap”, _Quarterly Journal of Economics_ 129(2): 939-993.

Hamory, Joan, Marieke Kleemans, Nicholas Y Li, and Edward Miguel (2021) “Reevaluating agricultural productivity gaps with longitudinal microdata”, _Journal of the European Economics Association_ 19(3): 1522-55.

Harris, John and Michael Todaro (1970) “Migration, Unemployment and Development: A Two Sector Analysis”, _American Economic Review_ 60 (1): 126–42.

Huber, Peter and Klaus Nowotny (2020) “Risk aversion and the willingness to migrate in 30 transition countries”, _Journal of Population Economics_ 33: 1463-1498.

Imbert, Clément and John Papp (2020) “Costs and benefits of rural-urban migration: Evidence from India”, _Journal of Development Economics_ 146: article 102473.

Jasso, Guillermina and Mark Rosenzweig (2008) “Selection Criteria and the Skill Composition of Immigrants: A Comparative Analysis of Australian and U.S. Employment Immigration”, _IZA Discussion Paper no. 3564._ John, Anett and Kate Orkin (2021) “Can simple psychological interventions increase preventative health investment?”, Mimeo.

Kahanec, Martin (2012) “Labor mobility in an enlarged European Union”, _IZA Discussion Paper_ , No. 6485 Lagakos, David (2020) "Urban-Rural Gaps in the Developing World: Does Internal Migration Offer Opportunities?" _Journal of Economic Perspectives_ , 34 (3): 174-92.

Lagakos, David, Samuel Marshall, A. Mushfiq Mobarak, Corey Vernot and Michael Waugh (2020) “Migration Costs and Observational Returns to Migration in the Developing World”, _Journal of Monetary Economics_ 113: 138-154.

McKenzie, David (2007) “Paper Walls Are Easier to Tear Down: Passport Costs and Legal Barriers to Emigration”, _World Development_ 35(11): 2026-2039.

McKenzie, David (2008) “A profile of the world’s young developing country international migrants”, _Population and Development Review_ 34(1): 115-35.

24
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Table 10. Diffusion through supply chain linkages within and outside firm boundaries

||Dep variable|: Normalized|share of tech|nology-related|job adverts|
|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|
|Pioneer|4.630***|4.448***|4.533***|4.453***|4.362***|
||(0.398)|(0.394)|(0.391)|(0.394)|(0.390)|
|Pioneer_×_years since emergence|-0.202***|-0.190***|-0.196***|-0.193***|-0.187***|
||(0.036)|(0.036)|(0.035)|(0.036)|(0.035)|
|**Linkages of pioneer frms**||||||
|Buyer within org.||3.201|||0.158|
|||(3.630)|||(3.841)|
|Buyer outside org.||1.868***|||1.470***|
|||(0.299)|||(0.319)|
|Supplier within org.|||6.733||4.383|
||||(4.736)||(6.414)|
|Supplier outside org.|||0.686***||0.076|
||||(0.171)||(0.170)|
|Innovation partner within org.||||4.241|2.141|
|||||(2.874)|(3.455)|
|Innovation partner outside org.||||1.617***|1.023***|
|||||(0.274)|(0.292)|
|**Linkages of pioneer frms over time**||||||
|Buyer within org. _×_years since emergence||0.146|||0.217|
|||(0.266)|||(0.266)|
|Buyer outside org._×_years since emergence||-0.054*|||-0.054*|
|||(0.029)|||(0.030)|
|Supplier within org. _×_years since emergence|||-0.376||-0.404|
||||(0.307)||(0.488)|
|Supplier outside org. _×_years since emergence|||-0.003||0.025|
||||(0.018)||(0.018)|
|Innovation partner within org. _×_years since emergence||||-0.019<br>(0.190)|0.089<br>(0.299)|
|Innovation partner outside org. _×_years since emergence||||-0.034<br>(0.026)|-0.015<br>(0.027)|
|Technology FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Firms FE|Yes|Yes|Yes|Yes|Yes|
|Adjusted R-squared|0.041|0.041|0.041|0.041|0.041|
|Observations|10,072,709|10,072,709|10,072,709|10,072,709|10,072,709|

Notes: Standard errors clustered at the firm level in parentheses. The normalized share is capped at the 99<sup>_th_</sup> percentile of non-zero observations. *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1

# **4.2.5 Considering multiple linkages**

Our analysis so far has focused on firms with a single type of linkage, whether as buyers, suppliers, innovation partners, or affiliates of a pioneer firm. Next, we expand the scope to examine firms with multiple linkages - i.e. firms that are both a supplier and a buyer, or an innovation partner and a supplier, or an innovation partner and a supplier of a pioneer firm. Based on our findings that results are driven primarily by external linkages, this section focuses on firms with external linkages. The estimates in columns (1)-(5) of Table 11 indicate that all linkages combinations considered are positively associated with the normalized share of tech-related job postings. We further observe that the combinations of buyers-suppliers, supplier and innovation partner, and innovation partners-buyers display positive and significant coefficients when controlling for other linkage combinations.

27
issues; although accurate ratings benefit investors and issuers alike, issuers are asked to pay the freight because, once it is announced, a security’s credit rating becomes public knowledge. This asymmetric arrangement poses an obvious conflict of interest for CRO managers. Borrowers have an incentive to play different CROs against one another and to hold out for higher-than-appropriate ratings. For issuers and securitizers, the counterincentive to seeking a corrupt rating is that they also need to employ a CRO that has a well-established reputation for honest and accurate work.

Until the 1970s, ratings were generally in low demand (Sylla, 2001). CROs’ value added was uncertain and as recently as the 1960s these firms employed only a halfdozen analysts, with their then-meager revenues coming mostly from sales of research (Partnoy, 2001). Ratings were long criticized as lagging the business cycle – issuing upgrades late in cyclical upswings and downgrades during slowdowns in ways that did not help investors to anticipate or protect themselves against a rise in defaults. This is amply demonstrated by the avalanche of ratings downgrades in the 1930s.<sup>8</sup> However, in 1975 the Securities and Exchange Commission created the designation of a “Nationally Recognized Statistical Ratings Organization (NRSRO),” and over time ratings-based government rules restricted the decisions of a variety of actors (pension funds, insurance companies, banks, municipalities, etc.). Government reliance on ratings encouraged private organizations to incorporate ratings into their own governance procedures or to advertise that only investments above a certain rating would be held. With financial intermediaries either strictly required to hold only highly rated instruments or allowed to hold less capital against highly rated securities, ratings understandably grew in demand, as did the pressure for “rating inflation.” By the late 1990s when the structured finance business expanded sharply, the CRO industry expanded apace. By 2006, Moody’s, which won a large share of this business, was generating over $6 million per employee and employed over 20,000 persons worldwide.

> 8 Prior to World War I, corporate default rates were significant and ratings agencies did seem to supply some modest function, though even the most authoritative study (Hickman, cited in Sylla, 2001) fail to lay to rest the critique that ratings might at most have been modestly anticipating declines in performance and did not do demonstrably better than market indicators.  In the post-WWII period through the early 1970s, the default rate on corporate bonds declined to only about 1%, so ratings were not critical to investors’ decisions.

15
show improvements on participant social skills. In such assessments, treated students report greater tolerance of others, a reduction in interactions with bad peers, and an improvement in the overall classroom environment. In addition, some students report the program reinforces their academic experience in a more enjoyable way (Glasswing International, 2012b).

# **2.2 Recruitment and Enrollment Process**

During 2016, the NGO offered and implemented this version of the ASP in five public schools in El Salvador. Using data from the country’s 2015 Educational Census, we found that these schools were similar to the underlying population of public educational centers in El Salvador. Tests for differences between participant and nonparticipant schools are shown in Table A1 in the Appendix section.<sup>15</sup> As part of the recruitment process, the NGO visited the schools to advertise the program and provided informational brochures and videos. Additionally, NGO staff invited participants from other schools to join them for these visits and share their experiences with prospective attendees. During these visits, the team gave a parental authorization form to children interested in participating in both the intervention and study, asking them to return it with a signature from a parent or tutor. They also gave teachers extra forms for students who were absent during the visit but might want to participate.

Some weeks later, the NGO and research team returned to schools to register and enroll children. Out of a total of 2,420 students from the five schools, we recruited and enrolled 1,056 students between 10 and 16 years of age. Children were allowed to self-enroll as long as they had an authorization signed by an appropriate adult.

During the registration process, students completed a form with personal and family information and their application to participate in a club. They were next assigned to a group, taking into consideration their preferences and the aggregated demand for the club category. Originally, club enrollments varied according to the number of participants interested in each category. However, for methodological reasons, our club sizes are between 13 and 15 participants on average.

# **2.3 Sample and Randomization**

> As we mentioned before, this paper provides experimental evidence for two gaps in economic literature. While it measures the direct impact an ASP has on behavioral, academic, and violence-related

> 15Both groups of schools are similar on characteristics such as location area, violence level, number of students, and additional revenues. Similarly, in terms of programs, facilities, and equipment, participant and nonparticipant schools are similar on most of these dimensions, except in the share of schools with a breakfast program or Internet access; treated schools are more likely to have both benefits.

10
Table 2: Between-sample differences in survey measurements<sup>a</sup>

||**Baseline**|**Treatment **|**Diference**|
|---|---|---|---|
||Diary|Diary-Diary|Recall-Diary|
||(1)|(2)|(3)|
||**Panel A.**|**Cumulative D**<br>**and Locatio**|**istribution**<br>**n**|
|5th percentile|2.5291|0.0059<br>_(0.0045)_|-0.0431***<br>_(0.0042)_|
|25th percentile|2.0622|0.0121|-0.1482***|
|||_(0.0085)_|_(0.0086)_|
|50th percentile|2.5291|0.0097|-0.1731***|
|||_(0.0099)_|_(0.0101)_|
|75th percentile|2.9788|0.0100<br>_(0.0085)_|-0.0944***<br>_(0.0093)_|
|95th percentile|3.6601|0.0032|-0.0032|
|||_(0.0037)_|_(0.0039)_|
|Mean|2.5184|-0.0158|0.2706***|
|||_(0.0099)_|_(0.0105)_|
||**P**|**anel B. Disper**|**sion**|
|Std. deviation|0.7560|0.0182|-0.0650***|
|||_(0.0157)_|_(0.0244)_|
|Range 10%-90%|1.8207|0.0044<br>_(0.0387)_|-0.3227***<br>_(0.0320)_|
|Range 25%-75%|0.9166|-0.0044<br>_(0.0236)_|-0.1286***<br>_(0.0207)_|
|Number of districts|119|119|119|
|Number of EAs|2,828|2,828|2,828|
|Number of households|16,530|24,750|24,750|

> a Household is the unit of analysis. Column (1) reports selected statistics of the log( _Y_<sup>_d_</sup> ) distribution in the _baseline sample_ . Panel A: Columns (2) and (3) report coefficients on the treatment dummy in regressions that control for a full set of strata effects (enumeration areas, EAs) pooling data for treatment and control samples. Standard errors (in brackets) are clustered on EAs. For cumulative distributions, the outcome in column (2) is an indicator for having log( _Y_<sup>_d_</sup> ) below the value in column (1); the outcome in column (3) is an indicator for having log( _Y_<sup>_d_</sup> ) below the value in column (1) for households in the baseline sample, and an indicator for having log( _Y_<sup>_r_</sup> ) below the value in column (1) for households in the treatment sample. For the mean, the outcome in column (2) is log( _Y_<sup>_d_</sup> ). The outcome in column (3) is log( _Y_<sup>_d_</sup> ) for households in the baseline sample, and log( _Y_<sup>_r_</sup> ) for households in the treatment sample. Panel B: The value in column (2) is the difference of statistics computed using log( _Y_<sup>_d_</sup> ); the value in column (3) is the difference of statistics computed using log( _Y_<sup>_d_</sup> ) for households in the baseline sample, and log( _Y_<sup>_r_</sup> ) for households in the treatment sample. Standard errors for these differences are computed via bootstrap using 1,000 replications. * _p <_ 0 _._ 10, ** _p <_ 0 _._ 05, *** _p <_ 0 _._ 01.

12
**Table 3. The structure of fixed capital assets at the disposal of Russian enterprises. 1/**

||In|bin rbl||as|**%**of tota|l|
|---|---|---|---|---|---|---|
||**1991**|**1992**|**1993**|**1991**|**1992**|**1993**|
|**1.Stock**of fixed capital assets, 2/<br>**o/W:**|7780|28539|37409|100|100|100|
|-main (industrial) activities|7521|25418|33830|96.67|89.06|90.43|
|- non-productive activities|157|1051|1875|2.02|3.68|5.01|
|o/w: housing|56|333|1038|0.72|1.17|2.77|
|utilities|n.a.|n.a.|250|n.a.|n.a.|0.67|
|health|n.a.|n.a.|273|n.a.|n.a.|0.73|
|education|n.a.|n.a.|141|n.a.|n.a.|0.38|
|other|n.a.|n.a.|173|n.a.|n.a.|0.46|
|2. Flow of new assets: new construction<br>o/w:||375|8541||100|100|
|- main (industrial) activities||343|7131||91.47|83.49|
|- non-productive activities||26|950||6.93|11.12|
|ojw: housing||16|707||4.27|8.28|
|**3.**Divestiture of social assets|||||||
|- all non-productive activities||10|253||||
||o/w: housing||5|126||||

# **Source: Goskomstat, own estimates**

**Notes: 1/ witnout** agriculture

2/ tby tne ena of the year
asset returns, but to relative changes in asset prices<sup>1</sup> . Their data indicates that between 1994-2006, the U.S.’s cumulative valuation effects are significant and positive. From my calculation using their data, total valuation effects from stocks and bonds during 1994-2006 period were $1295 billion, offsetting about 22.8% of the size of the U.S.’s total current account deficits. My calculation is consistent with the finding of Curcuru, Dvorak, and Warnock (2008), because the U.S.’s external assets had a lower average return than its liabilities before 2002, but higher after 2002 (Figure 4<sup>2</sup> , Appendix A). Since after 2002, the total holdings of foreign assets on which the U.S. experienced positive valuation effects are larger, the aggregate U.S.’s valuation effect in 1994-2006 is positive and stabilizing (Figure 5, Appendix A).

On the theoretical front, Devereux and Sutherland (2009b) investigate valuation effects in a two-country dynamic stochastic general equilibrium model. They relate valuation effects to return differentials. My paper however associates valuation effects with changes in asset prices. Ghironi, Lee, and Rebucci (2007) also explicitly consider asset prices but they do not consider the impact of trend productivity shocks. Coeurdacier, Kollmann, and Martin (2009) also briefly discuss valuation effects with transitory shocks, although they focus on explaining equity home bias. One key contribution of my paper is that it identifies the contrasting role of valuation effects as stabilizing after transitory shocks, and amplifying after trend shocks. Unlike the conventional wisdom that valuation effects are generally stabilizing, as showcased in empirical findings of Gourinchas and Rey (2007), and as implied by the theoretical results of Ghironi, Lee, and Rebucci (2007), Coeurdacier, Kollmann, and Martin (2009), and Devereux and Sutherland (2009b), this paper shows that valuation effects can be amplifying too. This situation is illustrated by the evolution of NFA position between the U.S. and other G.7 countries during the 1990s, which I discuss in section 1.2.

My paper follows Aguiar and Gopinath (2007) in its approach to introduce trend shocks, and Tille and van Wincoop (2007) and Devereux and Sutherland (2007) in their method of solving for portfolio choice. They develop an approximation method to characterize time-varying equilibrium portfolios in a two-country dynamic general equilibrium model, in which financial markets are incomplete<sup>3</sup> . In my paper, market incom-

> 1Bertaut and Tryon (2007) calculated valuation effects from stocks and bonds between the U.S. and every other country from 1994 to 2007.

> 2For sources of data used in all the figures and tables, see Appendix A.

> 3For different solution methods, see Evans and Hnatkovska (2007); Heathcote and Perri (2007); Pavlova and Rigobon (2009).

4
We test this view of business asssociations using data on the characteristics and performance of firms in five Russian cities. This data set was constructed by a team from the World Bank<sup>9</sup> , and includes statistics on the membership of finns in differen **t** ypes of business associations. The data were collected for 1994, a year witnessing active privatization and a dramatic decline in the volume of government subsidies to firms. These conditions suggest that 1994 is precisely the time period during which we would expect firms to actively search for survival strategies.

We estimate a bivariate model to jointly explain the firm's probability to experience a decline in output and to join a business association. We define the probability of decline in a firm's output as a function of changes in the demand for its products and other characteristics of the firm, including the potential for disorganization in its relationships and its membership in a business association. The decision to join an association, in turn, is a function of its potential for disorganization as well as local incentives for cooperation among firms. The empirical analysis supports our intuition: being a member of a business association improves a firm's performance significantly. Moreover, firms are more likely to join an association in regions where cooperation among firms is most likely. Hence, these results characterize the use of associations by firms as a mean to coordinate activities by sharing information and reducing transaction costs.

The novelty of this work is to suggest a simple, yet promising, framework of analysis and provide empirical evidence of the link between institutional disorganization, output decline, and the endogenous emergence of new coordinating institutions. In doing so, this paper offers new evidence on the role of spontaneous institutions on firm's performance. The approach employed

> 8 See also Kranton (1998) for a detailed discussion of the coexistence of different modes of exchange.

> 9 Led by Barry Ickes Che Pennsylvania State University) and Randi Ryterman (he World Banr), with the assistance of Annette Brown. The National Council for Soviet and East European Research also provided funding.

4
such procedures as an instrument of commercial policy were soon copied by other countries. And where there is a way, there will soon be a political will.

_* The influence of trusts._ Finally, the trusts themselves may have had some influence over how they were regulated. They themselves would surely prefer antidumping regulation re..ative to the alternatives of breaking them up or removing their import protection.

The evolution of the U.S. antidumping law of 1921 In the early twentieth century the tariff was, for most countries, the major instrument for regulating imports. And in the United States, as in other countries, the evils of foreign trusts first came into trade politics as an argument for higher tariffs. But antidumping has a different history in the United States than in Canada. In Canada, poli;ing the evils of monopoly power (trusts) was never more than the rhetoric of the matter. In the United States at the early stages of antidumping regulation, there was a better match between the rhetoric and the mechanics of the matter -- early U.S. antidumping regulations were, in substance, extensions of antitrust law. But these laws did not provide what the motivating politics demanded -- restrictions of imports -- and there was continuing pressure for change. The various steps in the evolution of the U.S. antidumping law provide a graphic illustration of how different antidumping regulation is from antitrust law -- once it has become a useful instrument for regulating imports.

E___en _et-, the _h_ift £ra.t.4trt to ar.-m_..OJ , t_ vl.aw:u ab **a** change in the mechanics of regulation, with little attention to how the shift of mechanics would change the nature of what was beinR enforced -- of just what it was that the method singled out for punishment. The analysis presented here will suggest that that view is incorrect.

_ 11 -
employers in the case of wage earners. The magnitude of this tax was relatively minor: only 0.2 percent of GDP in 2017.

**Table 1.** Taxes and contributions. Uruguay, 2017

|Concept|Percentage of<br>total revenues|Percentage of GDP|Included in<br>analysis|
|---|---|---|---|
|**Taxes**|**60.22**|**17.94**||
|Indirect taxes|37.89|11.28|No|
|Direct taxes on personal income|12.27|3.65|Yes|
|IRPF|10.42|3.10|Yes|
|IASS|1.66|0.49|Yes|
|FRL|0.19|0.06|Yes|
|Other taxes|10.06|3.00|No|
|**Contributions to FONASA**|**12.32**|**3.67**||
|Employees|5.93|1.76|Yes|
|Employers|4.78|1.42|Yes|
|Retirees|1.62|0.48|Yes|
|**Contributions to Social Security**<br>**System**|**20.99**|**6.25**||
|Employees|9.51|2.83|No|
|Employers|11.48|3.42|No|
|**Non-tax revenues**|**6.47**|**1.93**|No|
|**Total**|**100.00**|**29.78**||

Sources: Banco de Previsión Social, Ministerio de Economía y Finanzas, Instituto Nacional de Empleo y Formación Profesional.

Contributions to the health system fund FONASA<sup>7</sup> and are applied to both labor income and pensions. The maximum rate of 8% is applied to contributors with a spouse and children, while the minimum rate applies to contributors with a very low income, and who are living alone. In the case of wage earners, employers must pay an additional 5%. An important feature of these contributions is that the amount paid by workers must be related to the benefit because of legal dispositions. To instrument this norm, at the end of the year the government estimates the maximum amount (equal for all contributors) to be paid by dependent workers; contributors who paid above the maximum receive the surplus back the following year. There is no maximum for contributions paid by employers.

About 10 percent of the total tax revenues in Uruguay come from taxes on business revenues, taxes on property of individuals and legal entities. Given data limitations, as well as the focus on the household as a unit of analysis, these taxes are not part of the current analysis.

> 7 Its benefits are explained in the next section.

5