rafmacalaba/datause-encoder-data huggingface dataset:
a rich country. The context is the migration of Tongans to New Zealand through a migrant lottery program. This involves at least three major changes in life circumstances. The first is a massive, and permanent increase in income for those who migrate. In earlier work (McKenzie et al, 2010), we show this migration results in a 263 percent gain in income within the first year, while in recent work (Gibson et al, 2015), we show this income gain appears approximately stable over time, with migrants continuing to earn almost 300 percent more than non‐migrants 10 years later, for an approximate life‐time net present value income gain of US$250,000. Second, this gain is accompanied by a major change in the institutions, values, and culture individuals are exposed to, with migrants experiencing a more individualistic and market‐oriented economy. Third, it involves a prolonged separation between extended family members, with migrants moving with their spouse and children, but leaving their parents and siblings behind in Tonga. Such large, comprehensive, and permanent changes in life circumstances provide a stringent test of the malleability of economic preferences and beliefs.

We use a long‐term follow‐up survey of individuals who applied to the migration lottery ten years earlier, with comparison of the winners and losers in this lottery enabling us to obtain causal estimates of the migration impact. We measure risk aversion using both survey questions and incentivized experimental games, and time preferences through survey questions. We examine the extent to which individuals have changed their economic beliefs using the pro‐market beliefs questions of di Tella et al. (2007). Each applicant is then linked to a partner household in Tonga that contains either their parent or sibling. Migrants are therefore in a different country from this partner, while the control group of non‐migrants are typically living in the same village, and in some cases, even the same dwelling. Principal applicants and their partners then play three variants of an incentivized trust game in order to measure the efficiency of extended‐household decision‐making.

Despite the large changes in life circumstances that winning the migrant lottery entails, we find that international migration has no significant impact on any of our measures of preferences and beliefs. This includes a lack of significant effect on any of five different measures of risk preferences, any of three different measures of time preferences, any of four different measures of pro‐market beliefs, and any of five different measures of household efficiency. We follow Young (2016) in conducting a randomization‐ inference based omnibus test of overall significance of all these outcomes and fail to reject the null that there is no treatment effect whatsoever (p=0.738). We would reject such a test if there had been even relatively small average impacts of migration on preferences and beliefs. This stability in the face of such a large and life‐changing event lends credence to economic models of migration that treat these 3
poverty headcount rate under the international line of $1.25 PPP 2005 a day. Under this scenario, the fiscal system in 2016/17 increased poverty by approximately 2.3 percentage points; the analogous increase in poverty due to 2012/13-era Ugandan fiscal policy was approximately 0.1 percentage points. The impact of fiscal policy (excluding the government-cost value of publicly-provided in-kind health and education services) on poverty in Uganda is less poverty inducing than the impact of fiscal policy in Kenya and Tanzania, but far more poverty inducing than the impact of fiscal policy in South Africa.

_Figure 7. Poverty Before and After Fiscal Policy a. Poverty Headcount Rate (under the national poverty line)_ <!-- Start of picture text --> 24.07%<br>23.70%<br>21.49% 21.39% 21.44% 21.42%<br>21.13%<br>Market Income Market Income Net Market Disposable Disposable Disposable Consumable<br>(PGT) plus Pensions Income Income Income + All Income - All Income<br>(PDI) indirect indirect Taxes<br>Subsidies<br>Percentage of the population<br><!-- End of picture text -->

- _b. Poverty Reduction Effect, Sub-Saharan African countries_

<!-- Start of picture text --> 15.80<br>-0.12<br>-1.33 -0.75<br>-2.31<br>-4.40<br>-7.78<br>Tanzania Kenya (2015) Uganda (2016) Ethiopia (2010) Ghana (2012) Uganda (2012) South Africa<br>(2011) (2010)<br>Percentage points<br><!-- End of picture text --> _Source: Authors’ calculation based on UNHS 2016/17; CEQ Institute database; and World Bank (2018a). Note for b): The year for which the analysis was conducted is indicated in parenthesis. The graph is ranked from the smallest to the largest poverty reduction effect, which is the change in the Poverty Headcount from market income plus pensions to consumable income. Except for Kenya, where the effect is the change from market income_ 23
19 The econometric estimates strongly suggest that the Korean footwear industry would have contracted during the period of the OMA. This prediction is borne out by inspection of time series on output, employment, and wages of the Korean footwear sector (see table Al in the appendix). The data displayed in figure 3 report footwear output and employment relative to the corresponding series for the entire manufacturing sector. This normalization is necessary to control for the Korean recession of 1980. Even when it is made, the time patterns show clearly that the footwear sector experiences a notable slump during the period when the OHA with the U.S. was in effect. 7/

# 4. Illustrative Welfare Cilculations

The results above confirm that a VER leads to output contraction and has adverse efficiency effects if the factors employed in the industry are not available to it in perfectly elastic supply. However, as discussed in section 2, a VER also results in a sales revenue effect which may either reinforce or counteract the efficiency effects. This section provides rough orders of magnitude of the potential welfare effects of a VER, using the US OMA on Korean exports of leather footwear as a reference.

For the illustrative calculations, we retain our estimate of the elasticity of transformation of section 3 as an estimate of the ease with which exporters may divert sales from restricted to unrestricted markets and complement it with guesstimates of factor supply elasticities and price elasticities of export demand. 8/ The calibrated counterfactual simulations are for the model presented in section 2 with: constant foreign price elasticities of demand; a CET function describing sales allocation; and a constant
# **APPENDIX C: BENIN**<sup>**13**</sup>

Introduced in the mid‐1950s under the auspices of the French state‐owned com‐ pany CFDT ( _Compagnie Française pour le Développement des Fibres Textiles_ ), cotton is an important cash crop in Benin. Seed cotton production has averaged 270,000 tons during the past three seasons (about 0.5 percent of global production), after reaching 400,000 tons in the early 2000s. Like Burkina Faso, Benin has underta‐ ken many policy reforms in the cotton sector during the past decade, including privatization of ginning and marketing.

In 1984 all aspects of the sector were transferred to a new parastatal, SO‐ NAPRA ( _Société Nationale pour la Production Agricole_ ). Changes in the structure of the sector were first contemplated in the early 1990s, mainly in response to an earlier crisis when a combination of a larger than expected crop, low world pric‐ es, high producer price, appreciation of the CFAf, and limited ginning capacity caused SONAPRA huge financial losses.<sup>**14**</sup> Most input supply activities were pri‐ vatized and three new ginning operations were added in 1995, followed by sev‐ eral more in 1998. Yet the new structure caused numerous conflicts resulting in frequent political interference. In response, the government created entities that assumed responsibilities for various aspects of the cotton industry.

Reforms continued during the early 2000s, with the stated objective of “developing a private but nationally integrated cotton supply chain” whose management would be transferred from the government to an inter‐professional body. The actual outcome was a highly regulated system without many competi‐ tive elements. On the other hand, following the weakening of the global cotton market, producer prices fell and cotton output was halved within just four years (from 400,000 tons of seed cotton in 2001/02 to less than 200,000 tons in 2005/06). Though cotton production has recovered somewhat during the last few years, it remains far below the levels of the early 2000s.

> **13** Most of the information in this Appendix is based on Zanfongnon (2009). Also see Gergely (2009) for an in‐depth discussion of Benin’s cotton sector.

> **14** The CFA ( _Communauté Financière Africaine_ ) franc or CFAf is a currency used in 14 countries of West and Central Africa (12 former French colonies, Guinea‐Bissau, and Equatorial Guinea). The CFAf was pegged to the French franc (FF) until 1999 and to the euro since then (1 € = 655.957 CFAf). Since its creation in 1945 it has been adjusted only twice: in October 1948 (from 1 FF = 1.70 CFAf to 2.00 CFAf) and in January 1994 (from 1 FF = 100 CFAf to 200 CFAf). Although the CFAf has been praised for offering macroeconomic stability to its members, during episodes of over‐ valuation it places the export sectors of the respective countries at a competitive disadvantage (Hinkle and Montiel 1999). It is believed that this has been the case during the early‐ and mid‐ 2000s.

**_Page_ 39**
impact of smoking was not uniform and widespread, the effect of education on smoking cessation is always the highest during these two decades (see the highlighted box in table 2), even though during that period each birth cohort was in different age ranges. It seems therefore that, there is a specific period effect attached to the decades of the gradual diffusion of the information on the dangers of smoking. This is consistent with the hypothesis that a differential in the access to and in the ability to process information (Grossman 1972) might explain the positive correlation between health and education. This hypothesis will be further tested in section 4 that proposes an instrumental variable approach.

# **4. The Vietnam Draft as Instrument for College Education**

The evidence presented in sections 2 and 3 documents the appearance, after 1950, of a negative gradient between educational achievement and smoking habits. This gradient is present at both margins: smoking initiation and smoking cessation. As such, this evidence is not entirely conclusive about the mechanisms through which education affect health decisions and does distinguish between three theories explaining the correlation between health and education: (1) education is causal in reducing smoking by entering as a factor in the health production function; (2) education increases the stream of future income and consumption and ultimately the value of life, so that it provides incentives for individuals to protect this stream by reducing mortality risks; or (3) the correlation between education and health is due to unobservables like the discount factor or ability that causes the same individuals to both study longer and take care of their health.

This section presents an instrumental variable approach using the Vietnam draft as an instrument for college education in order to test whether the negative relation between education and smoking is causal.

# **_4.1 Ordinary Least Square Results_**

Table 3 reports results from ordinary least square regressions where the dependent variable is either an indicator for whether the individual is currently smoking (odd columns), or whether, among those who ever smoked, he did quit smoking (even columns). The data are taken from nine National Health Interview Surveys.<sup>19</sup> In order to allow the comparability with the results of the instrumental variable approach presented later, only individuals born between 1937 and 1956 are considered. The regressions use a simple linear probability model.

19 The nine national surveys are from 1983, 1985, 1987, 1988, 1990, 1991, 1992, 1994 and 1995. The 1978, 1979 and 1980 surveys would have included individuals under age 25 for whom the educational level might not have been definitive. The public file of the 1997, 1998, 1999 and 2000 surveys did not contain the veteran status of the individuals, a variable that is crucial for the implementation of the instrumental variable approach. Notice that the “survivor bias” described in section 2 is only marginally a concern with this subsample of the data since individuals born between 1937 and 1956 would be aged between 39 and 58 in 1995.

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# DEMAND COLLAPSE OR CREDIT CRUNCH TO FIRMS?

EVIDENCE FROM THE WORLD BANK’S FINANCIAL CRISIS SURVEY IN EASTERN EUROPE Ha Nguyen and Rong Qian<sup>1</sup> JEL: E2, E3 Keywords: financial crisis, spillover, liquidity constraint, demand contraction Sector Board: Economic Policy (EPOL)

> 1 Ha Nguyen is at the Development Research Group (DECRG). Rong Qian is at Latin America’s Poverty Reduction and Economic Management (LCSPR). We thank Roberto Fattal, Aart Kray, Luis Serven, Hui Tong, Trang Tran, Choon Liang Wang for detailed comments and feedback. We are grateful to Veselin Kuntchev and Murat Seker for sharing with us the sampling methodology of the Financial Crisis Survey, and to Hui Tong and Varvara Isyuk for sharing with us the U.S. stock index data. We also thank Hedy Sladovich for editorial help.  This paper reflects the authors' views and not necessarily those of the World Bank, its Executive Directors or the countries they represent. All errors are our own. Contact address: Ha Nguyen, Development Research Group, The World Bank, 1818 H Street, NW, Washington D.C. 20433; Fax 202-522-3518; Email: hanguyen@worldbank.org.
and the average bank’s sectoral composition (as in Equation 2, but replacing the estimated factors with reported shares). The more similar the exposures, the lower the value of the measure and the higher the exposure to common shocks.

The summary statistics of these measures in Panel B of Table 2 indicate that there is considerable heterogeneity across banks. Specifically, the standard deviation of sectoral exposures, _Dispersion (accounting)_ , varies largely with a value of 0:068 at the 5<sup>th</sup> percentile and a value of 0:184 at the 95<sup>th</sup> percentile (with a mean of 0:113). The cumulative exposure of the largest three sectors varies from 53% (5th percentile) to 96% (95th percentile), with a mean of 70%. The HHI concentration ratio has an average of 0:227 and a standard deviation of 0:088. Finally, _Differentiation_ (accounting) also exhibits substantial cross-sectional variation. The Euclidean distance between a bank’s exposure and the country’s average exposure ranges from 0:09 to 0:55 (5<sup>th</sup> and 95<sup>th</sup> percentile), with a mean of 0:26.

# **2.3 Measures of bank performance and stability**

Using stock return-based measures, we gauge several aspect of bank performance.<sup>7</sup> In particular, we will look at bank valuation, bank risk as well as exposure to systemic risk. More specifically, we will employ the following dependent variables in our analysis. First, bank performance is gauged by the annualized average daily _return_ over a calendar year, thus measuring profitability for shareholders. Second, _volatility_ , measured as the annualized standard deviation of a bank’s daily stock returns over the span of a calendar year, captures

> 7We prefer capital market data to accounting data because equity prices are forward-looking and hence better identifiers of prospective performance and risks associated with different strategic choices. In addition, accounting profits reflect short-run performance, rather than capturing long-run equilibrium behavior. Furthermore, accounting-based profit (such as return on assets or return on equity) adn risk measures may be noisy measures of firm performance as a result of differences in tax treatment and (discretion over) accounting practices across countries, or different provisioning and depreciation practices. Noise and biases in the

(1990)).

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<!-- Start of picture text --> 2<br>co<br>oO<br>fo<br>x=<br>ES<br>2<br>ooD +<br>a<br>O)<br>N<br>Oo<br>1980 1990 2000 2010 2020<br>Year<br>——— PreTreated ——— PostTreated<br><!-- End of picture text -->
Table A29: Robustness of impact on marriage and pregnancy index to differential attrition

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
||Main|Trimmin|g treated|Im|puting cont|rol|IPW|
||spec.|Bottom<br>UB|Top<br>LB|95th pc.|75th pc.|50th pc.||
|T1 (Safe Spaces)|-0.01|0.00|-0.04|-0.02|-0.01|0.00|-0.01|
||(0.08)|(0.08)|(0.09)|(0.08)|(0.08)|(0.08)|(0.08)|
|T1+Leaders|-0.01|0.07|-0.19***|-0.08|-0.03|0.02|-0.01|
||(0.08)|(0.08)|(0.06)|(0.07)|(0.08)|(0.08)|(0.08)|
|T2 (Safe Spaces + IGA)|0.00|0.04|-0.12**|-0.05|0.02|0.02|0.00|
||(0.06)|(0.06)|(0.05)|(0.06)|(0.06)|(0.06)|(0.06)|
|T2+Leaders|-0.05|0.03|-0.28***|-0.13**|-0.07|-0.01|-0.06|
||(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|
|T3 (Safe Spaces + Husbands Clubs)|-0.03|0.02|-0.23***|-0.14**|-0.06|0.01|-0.04|
||(0.07)|(0.07)|(0.06)|(0.07)|(0.07)|(0.06)|(0.07)|
|T3+Leaders|0.06|0.08|0.01|0.04|0.05|0.07|0.06|
||(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|
|T4 (control spillover)|0.01|0.01|0.01|0.00|0.01|0.01|-0.00|
||(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|(0.06)|(0.07)|
|N|2995|2994|2994|2996|2996|2996|2995|
|Control Mean|0|0|0|.003|.001|-.001|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. Column (1) replicates the main results presented in tables 3, 4 and 5. Columns (2)-(3) present results equivalent to Lee bounds: we re-run the estimations after dropping _K_ observations from the top/bottom of the distribution in the treatment group to calculate the lower/upper bound, where _K_ is the difference between the number of attriters in the treatment groups and the control group. Columns (4)-(6) present similar results, but instead of dropping _K_ observations in the treatment group, we impute the outcomes of _K_ attriters in the control group using the 5th, 25th and 50th percentiles of the observed distribution of the treatment group. Column (7) presents the Inverse Probability Weighting (IPW) estimates.

61
# **D. E Impacts of Migration Flows are Different**

Table D.5. Effects of Venezuelan Migration on Mayoral and Presidential Elections

||Election|Shar|e of Votes|for|
|---|---|---|---|---|
||Turnout<br>(1)|Left<br>(2)|Center<br>(3)|Right<br>(4)|
|_Panel A. Mayoral Elections_|||||
|Predicted Venezuelan Infows|0.022*|-0.013*|0.003|0.017*|
||(0.010)|(0.005)|(0.014)|(0.011)|
|q-values|[0.064]|[0.064]|[0.263]|[0.087]|
|R-squared|0.797|0.437|0.441|0.484|
|Observations|4,693|4,693|4,693|4,693|
|_Panel B. Presidential Elections_|||||
|Predicted Venezuelan Infows|0.002|-0.011***|0.003*|0.008***|
||(0.002)|(0.002)|(0.002)|(0.003)|
|q-values|[0.122]|[0.001]|[0.058]|[0.004]|
|R-squared|0.823|0.852|0.942|0.917|
|Observations|6,768|6,768|6,768|6,768|
|Municipality FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|
|Department FE_×_Year|Yes|Yes|Yes|Yes|
|Municipal controls_×_Year|Yes|Yes|Yes|Yes|

_Notes:_ This figure replicates the estimate by Rozo and Vargas (2021), adding the last elections for mayors in 2019 and president in 2022. Panel A shows the results for the mayoral elections between 1997 and 2019, and panel B for the presidential elections between 1994 and 2022. Predicted Venezuelan Migration is the interaction between the shift share of early settlements of Venezuelans in 1993 and the cumulative number of individuals arriving in Colombia from Venezuela each year, over the total Colombian population each year. The estimates include municipality, electoral year, and department electoral-year fixed effects, and they control for the interaction between electoral year dummies and a set of predetermined municipal characteristics, measured before the beginning of our period of analysis. Clustered standard errors at the municipal level are reported in parentheses and False Discovery Rate (FDR) q-value in brackets *** significant at the 1%, ** significant at the 5%, and * significant at the 10%.

77
Round-tripping became an important practice for Russia’s economy as well. According to Kheyfets (2010, 15), between 70% and 90% of Russian companies formally belong to companies registered in offshore zones. A high correlation of inward and outward investment flows between Russia and such financial hubs as Cyprus and the British Virgin Islands became a distinctive feature of FDI in Russia (UNCTAD 2013, 65). A surprising emergence of the island of Cyprus as the major source of FDI into Russia as well as the central destination for Russia’s outward FDIs is driven by the phenomenon of round-tripping. Cyprus ranked first among the biggest sources of FDI coming to Russia before 2013, and by 2016, the trends returned to pre-crisis levels (i.e., prior to the 2014 sanction regime). As of August 2019, Cyprus is also the most popular jurisdiction to register businesses of Russian origin. According to Russian economic consulting firm _Credinform_ , over 53% of Russian companies with offshores are controlled through Cyprus.<sup>9</sup> Other offshores such as the Seychelles and British Virgin Islands account for 14% and 11%, respectively.<sup>10</sup> Cyprus is an important offshore not only for Russia. Rabab’ah et al. (2016) conducted a social network analysis relying on the data from the Panama papers and focusing on the Middle East region network. This analysis revealed the United Arab Emirates and Cyprus as the central nodes with the highest degree of connections to other nodes in the network, implying a significant role played by these jurisdictions in financial flows going through the Middle East region.

Other resource-endowed post-Soviet countries such as Kazakhstan show similar patterns of roundtripping investment flows. A study of inward FDI by Dyker (2015, 121) reveals that since 2005 onethird of Kazakhstan’s inward FDI originates in the Netherlands. The author suggests that many Kazakh oil and gas companies, both private and state-owned, round-trip their investments through the Netherlands. A study of round-tripping by the National Bank of Ukraine has estimated that during 2010-2017 over 20% of direct investment flows (around $35.5 billion) were round-tripped (Syvak 2018). As in Russia, Cyprus has emerged as one of the leading originators of FDI investments in Ukraine, specifically, for example, in metallurgy (Saha et al. 2018, 26). Cyprus is also a major destination for outward investments from Ukraine, with Donetsk oblast emerging as a particularly prominent region in Ukraine in terms of these outward flows (Ibid., 35).

> 9 “ Top-10 of the Russian companies controlled by offshore shareholders,” _Credinform_ , August 2018 - <u>https://credinform.ru/ru RU/Publications/Article/297e5c6aef7c</u> 10 Ibid.

12