rafmacalaba/datause-encoder-data huggingface dataset:
coyote business as well as because many migrants travel to border towns on their own and then contract coyotes there (Spener, 2009). Indeed, migrants who connect with coyotes in this way, at borders, are “in effect, giving themselves over to fate” (Spener, 2009, p. 179). Our qualitative work also shows that it is unusual for the same coyote to take people from Guatemala all the way into the US border. Usually, there are changes in the people guiding; sometimes, these new guides are the ones who extort the migrants. For example, one respondent emphasizes how extremely dangerous points of the trip are at bus stations in Mexico, where there are many kidnappers. Coyotes are known to recruit migrants at Mexican bus stations (Spener, 2009). Even as interviewees recommend that new migrants know the coyotes with whom they leave Guatemala, the advice they are able to provide beyond that is limited. For example, one interviewee indicates that the best way to protect oneself from abuse is to “be obedient so that they don’t mistreat you,” thus suggesting that migrants are frequently unable to know in advance which coyotes will be the least likely to victimize them.

Table 1 below provides support for the randomness of extortion by showing that the “extortion” and “non-extortion” samples of respondents are well-balanced upon reentry to Guatemala. Among migrants who used a coyote in our broader sample, there is not a statistically significant difference between individuals who were extorted and individuals who were not in terms of their age, ethnicity, years of education in Guatemala (prior to migration), having children (in Guatemala and in the United States), deportation from the U.S. border or not, the number of years spent in the United States, or whether they have visible tattoos. However, it appears that women were slightly more likely to be extorted during the migration journey. This gender difference between the non-extortion and extortion sample is small, though, and only significant at the 90% confidence level. This suggests that people may be targeted for extortion not because they have the most money but rather as a result of more generalized prejudice against women. It is important to take this difference regarding gender with a grain of salt, however, given that very few of the deportees in our sample were women (8%). Additionally, the extortion group has a slightly higher mean number of past migration trips (1.77) compared to the non-extortion sample (1.62).

Similarly, since much of our later analysis relies on follow-up surveys with migrants, we consider the possibility that non-random survey attrition could lead to imbalance in extortion experiences. Table A2 in the appendix shows that most observable variables remain balanced when we consider the restricted follow-up sample only. However, the sample experiencing extortion (64 respondents) does have slightly higher proportions of women and a higher number of children in the US, when compared to the non-extortion sample (275 respondents), and those experiencing extortion were less likely to be detained at the border. To help 15
economies of scale associated with the liberalization and restructuring of China’s automobile sector.

How do these simulated gains compare to the real effects of WTO accession? The difference between the average annual export growth rates before and after this historical event implies a phenomenally large WTO accession impact. It appears, as shown on Figure 3, that between the two 4-year periods before and after 2001 China tripled its annual real export growth rate. But, how much of this increase can be purely attributed to China’s WTO accession? It is difficult to answer this question because China’s export growth was influenced by many other factors unrelated to accession reforms. In addition, average growth rates vary substantially across different periods and across periods of different lengths, making it difficult to infer the magnitude of accession-related gains from historical export data. Figure 3 shows that export growth in the period immediately preceding WTO accession was half of what it was during the years leading to the Asian financial crisis. It averaged 13.1% in the period 1987-93 (Yang, 1996) and slightly more than that (13.5%) in the period 1993-1997 but then it dropped down to just 7.1% in the period 1998-2001.

**Figure 3 China’s export growth and East Asian GDP growth (%)**

<!-- Start of picture text --> 30 5<br>25<br>4<br>20<br>3<br>15<br>2<br>10<br>1<br>5<br>0 0<br>1992‐1997 1997‐2001 2001‐2005<br>Annual export growth Annual export growth to the US<br>Processing exports East Asia GDP Growth<br><!-- End of picture text --> Source: Exports growth rates, shown on the left-hand side, are computed based on export values obtained from China Customs Statistics and deflated using 1992 US Consumer Price Index, as discussed in Amiti and Freud (2007). Data on GDP growth, shown on the right-hand side, come from the World Development Indicators, World Bank.

15
- Chong, A., I. Cohen, E. Field, E. Nakasone, and M. Torero (2016, October). Iron deficiency and schooling attainment in peru. _American Economic Journal: Applied Economics 8_ (4), 222–55.

- Cunha, J. M. (2014). Testing paternalism: Cash versus in-kind transfers. _American Economic Journal: Applied Economics 6_ (2), 195–230.

- Dahl, G. B. and L. Lochner (2012, May). The impact of family income on child achievement: Evidence from the earned income tax credit. _American Economic Review 102_ (5), 1927–56.

- Dammert, A. C., J. de Hoop, E. Mvukiyehe, and F. C. Rosati (2018). Effects of public policy on child labor: Current knowledge, gaps, and implications for program design. _World Development 110_ (C), 104–123.

- De Giorgi, G. and M. Pellizzari (2014). Understanding social interactions: Evidence from the classroom. _The Economic Journal 124_ (579), 917–953.

- de Hoop, J. and F. C. Rosati (2014). Cash Transfers and Child Labor. _World Bank Research Observer 29_ (2), 202–234.

- De Hoop, J. J., J. Friedman, E. Kandpal, and F. C. Rosati (2017, September). Child schooling and child work in the presence of a partial education subsidy. Policy Research Working Paper Series 8182, The World Bank.

- De Hoyos, R. E., R. Estrada, and M. J. Vargas (2018, May). Predicting individual wellbeing through test scores : evidence from a national assessment in Mexico. Policy Research Working Paper Series 8459, The World Bank.

- Duflo, E., P. Dupas, and M. Kremer (2011a, August). Peer effects, teacher incentives, and the impact of tracking: Evidence from a randomized evaluation in kenya. _American Economic Review 101_ (5), 1739–74.

- Duflo, E., P. Dupas, and M. Kremer (2011b). Peer effects, teacher incentives, and the impact of tracking: Evidence from a randomized evaluation in kenya. _American Economic Review 101_ (5), 1739–74.

- Edmonds, E. V. and C. B. Theoharides (2019, August). The short term impact of a productive asset transfer in families with child labor: Experimental evidence from the philippines. Working Paper 26190, National Bureau of Economic Research.

27
**Table 6: Mining and Assessment of Public Services: the role of decentralization**

||(1-LPM)|(2-LPM)|(3-LPM)|(4-LPM)|(5-LPM)|(6-OLS)|(7-LPM)|
|---|---|---|---|---|---|---|---|
|_Dependent Variables:_|Livingstandards|Water & Sanitation|Jobs|Health|Education|Public Services|Optimism|
|Active 50 km|-0.038***|-0.038***|-0.047***|0.001|0.005|-0.036***|-0.064***|
||(0.009)|(0.009)|(0.008)|(0.010)|(0.010)|(0.009)|(0.011)|
|Inactive 50 km|-0.013**|-0.043***|-0.011**|-0.023***|-0.028***|-0.037***|-0.032***|
||(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.005)|(0.006)|
|Decentralization|-0.057***|-0.056***|-0.230***|-0.267***|-0.297***|-0.223***|-0.019|
||(0.016)|(0.017)|(0.015)|(0.018)|(0.017)|(0.015)|(0.018)|
|_Active 50 km * Decentralization_|0.267***|0.205***|0.205***|0.081|0.192***|0.306***|0.082|
||(0.069)|(0.064)|(0.056)|(0.067)|(0.068)|(0.066)|(0.069)|
|_Inactive 50 km * Decentralization_|0.298***|0.366***4|0.073**|0.382***|0.516***|0.492***|-0.033|
||(0.037)|(0.036)|(0.032)|(0.036)|(0.036)|(0.032)|(0.037)|
|Natural Resources Rents (ln)|-0.005**|-0.030***|0.008***|-0.036***|-0.053***|-0.026***|0.068***|
||(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|(0.002)|
|Control of Corruption|0.078***|0.067***|0.039***|0.024***|0.015***|0.035***|0.092***|
||(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|(0.003)|
|Constant|1.479***|1.354***|1.402***|1.497***|1.280***|1.710***|1.540***|
||(0.124)|(0.120)|(0.112)|(0.121)|(0.120)|(0.107)|(0.131)|
|R-squared|0.035|0.064|0.022|0.038|0.042|0.057|0.057|
|Observations|101,349|118,081|117,455|118,786|118,392|97,765|68,527|
|Country FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Round FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|

# **_Notes:_**

1: Significance level: * p<0.10, ** p<0.05, ***p<0.01. Robust standard errors are in parenthesis.

2: Specifications (1-5) refer to the respondents' view on how the government is handling the improvement of living standards of the poor, water and sanitation services, job creation, health, and education. The dependent variables in specification (6) is a composite indicator constructed through polychoric correlation and which combines all the respondents' view on various types of public services and policies, including those in specifications (1-5). The dependent variable in specification (7) refers to the expected living standards of the respondent in the near future (12 months after the survey collection). Decentralization is measured by the discretionary power of sub-central governments to decide over the fiscal space. Individual-level control variables are added in all specifications: age (ln), the square of the logarithm of age, gender, education, residential area (urban), employment status, interest in public affairs and poverty.

22
8 allows for transitive cross-sectional and time-series comparisons. In (1), Skand ln Pk are the hypothetical cost and price shares (derived by taking a logarithmic mean over all commodities in the sample) against which the bilateral comparisons are made. Furthermore, with the above definition of quality, one can show that the change in unit value between any two observations is the sum of quality change and the Tornqvist price index, i.e.

(2) ln Pi - ln PJ - ln Ai - ln AJ + ln P; As recognized by Aw and Roberts (1988), multilateral comparisonb can be severely affected by missing observations since the construaction of P* requires observat %ns on the price of exports for each footwear category, to each country and for each year. If the mean share for a footwear category is large, an imputed price for a missing commodity-country flow may receive substantial weight in the overall index.

Hultilateral comparisons are therefore likely to require a relatively higher level of aggregati.n to minimize the number of missing observations. For the detection of quality change, aggregation is a drawback since it limits the scope for detecting quality upgrading. (The scope for upgrading is reflected in the ratio of the highest to the lowest commodity unit value and the shares of trade in these commodities). On the other hand, time-series comparisons derived from bilateral indices are less affected by missing observations for two reasons: first, prices need not be inputed if a commodity is not **expL-** :ed in adjoining years; second, commodity-country combinations for which certain observations are zero usually have a small share in total export value and hence receive little
Table 2 Benchmark regression results (Continued)

||**OLS**|**WLS**|
|---|---|---|
|Housing|||
|Energy for cooking (omitted = Coke and co<br>|al)<br>||
|Firewood and chips|0.019*|-0.013|
||(0.011)|(0.016)|
|LPG|0.284***<br>|0.219***|
||(0.011)|(0.016)|
|Gobar gas|0.244***<br>|0.139***<br>|
||(0.038)|(0.041)|
|Dung cake|0.059***|0.016|
||(0.013)|(0.018)|
|Charcoal|0.143***|0.143**|
||(0.034)|(0.057)|
|Kerosene|-0.009|-0.037*|
||(0.014)|(0.022)|
|Electricity|0.201***|0.261***|
||(0.031)|(0.063)|
|Others|0.037**|0.004|
||(0.015)|(0.022)|
|No|0.246***|0.298***|
||(0.019)|(0.033)|
|Energy for lighting (omitted = Kerosene)|||
|Other oil|0.111**|0.087|
||(0.054)|(0.076)|
|Gas|0.246***|0.185***|
||(0.038)|(0.062)|
|Candle|0.105***|0.051|
||(0.027)|(0.042)|
|Electricity|0.150***|0.126***|
||(0.004)|(0.007)|
|Others|-0.079*|-0.056|
||(0.046)|(0.069)|
|No|0.055**|0.082|
||(0.025)|(0.059)|
|Social and religious backgrounds|||
|Scheduled Tribes|-0.130***|-0.192***|
||(0.006)|(0.010)|
|Scheduled Castes|-0.122***|-0.139***|
||(0.004)|(0.007)|
|Other Backward Castes|-0.062***|-0.078***|
||(0.004)|(0.006)|
|Hindu|-0.069***|-0.044***|
||(0.007)|(0.011)|
|Muslim|-0.054***|-0.062***|
||(0.008)|(0.012)|
|Observations|96227|96227|
|R2|0.622|0.683|
|R2 Adjusted|0.616|0.678|

13
**<u>Table 8: Impact of reservation and women’s participation in households’ day to day decision making</u>**

||<br>**Individual**<br>|<br>**Wants to**<br>|<br>**Particip**<br>|<br>**ation in hou**<br>|<br>**sehold decisi**<br>|<br>**ons on....**<br>|
|---|---|---|---|---|---|---|
||**Income**|**work more**|**Food**|**Nonfood**|**Health**|**Education**|
|Res. now (α1)|-0.071|0.013|0.022|-0.002|0.009|0.050*|
||(0.217)|(0.044)|(0.040)|(0.039)|(0.035)|(0.026)|
|Res. before (α2)|-0.195|0.006|-0.044|0.145**|0.081|0.006|
||(0.221)|(0.047)|(0.047)|(0.056)|(0.050)|(0.035)|
|Res now × fem (β1)|0.076|0.025|-0.104|-0.012|-0.028|-0.078***|
||(0.427)|(0.060)|(0.067)|(0.042)|(0.024)|(0.027)|
|Res. before × fem (β2)|0.982**|0.163**|0.213***|-0.001|0.064***|0.066**|
||(0.390)|(0.071)|(0.049)|(0.039)|(0.020)|(0.032)|
|Observations|66,362|66,362|22,571|22,571|22,571|22,571|
|R-squared|0.286|0.252|0.260|0.206|0.216|0.188|
|Dep. Var Mean|9.118|0.296|0.754|0.801|0.838|0.802|
|**Test:**|||||||
|F test (α1+α2=0; p val)|0.299|0.741|0.590|0.000212|0.00177|0.0414|
|F test (β1+β2=0; p val)|0.00859|0.0460|0.105|0.781|0.175|0.648|
|F test (α1+ β1=0; p val)|0.984|0.318|0.0746|0.749|0.614|0.308|
|F test (α2+ β2=0; p val)|0.00264|0.000463|0.000425|0.0133|0.00464|0.0923|
|Ftest (α1+β1+α2+β2=0; p val)|0.00404|0.000176|0.0458|0.00165|0.000766|0.283|

_Note:_ ‘Reserved now’ and ‘reserved before’ are indicator variables of whether village panchayats are reserved in the current or the previous panchayat periods. Regressions for desire to work and individual income include the entire sample whereas those for intra-household bargaining is limited to the states of Gujarat, Uttar Pradesh, Maharashtra, Orissa, and West Bengal where a supplemental questionnaire on intra-household bargaining was administered. Control variables the coefficients of which are not reported include household size, composition, land ownership, and the head’s marital status, gender, age, and education; individuals’ gender, marital status, age, education and their squared terms; village-level access to road, distance to town and district HQ, population, share of SCs, STs, and key religions; years since the last village election; _pradhan_ characteristics (education, caste, religion, previous tenure and candidacy for office). Standard errors are clustered at village panchayat and robust standard errors reported in parentheses and multiple hypotheses tests are adjusted using the Bonferroni method. *** p<0.01, ** p<0.05, * p<0.10.

23
Table 5.A. Subgroup Analysis: Willingness to Interact with Outgroups (by income)

|VARIABLES<br>Index Interact (z-score)|(1)<br>Income above<br>median|(2)<br>Income below<br>median|(3)<br>F-test (p-value)<br>(1)-(2)|
|---|---|---|---|
|[T1] Urban Design|0.23***<br>(0.087)|0.00<br>(0.082)|0.05|
|[T2] Diversity|0.20**<br>(0.087)|-0.03<br>(0.093)|0.07|
|[T3] Urban Design + Diversity|0.06<br>(0.085)|0.05<br>(0.086)|0.93|
|Observations|908|1,016||
|Control Mean|0.00|0.00||
|Selected Controls|X|X||

Notes: Robust standard errors are shown in parentheses. ***p<0.01 **p<0.05 *p<0.1.

A potential explanation for the mixed impact of the VR interventions on social cohesion outcomes could be that social diversity, as experienced in the VR experiment, has failed to trigger the intended effect on the respondents (especially those who belong to vulnerable groups). In particular, exposure to social diversity may have made some participants feel more inward-looking and connected to their own identity, as opposed to making them feel more open and embracing of other ethnic groups as intended by the experiment. This, in turn, may have partly resulted in the mixed social cohesion results discussed above.

The “social identity theory” (Tajfel and Turner, 1979) posits that people strive to achieve positive self-concept through their belonging to certain group memberships, including multiple and overlapping identities (for example, gender, occupation, religion or ethnicity). When group membership is salient, there might be a heightened sense of identity through the “minority spotlight effect” as described by Crosby et al. (2014), in which people of color can feel uncomfortable about being singled out. The literature on “stereotype threat”, similarly, provides empirical evidence on how apparently simple questions on gender, ethnicity or age can induce emotions related to identity (for example, see O’Brien and Crandall, 2003 on gender; Steele and Aronson, 1995 on ethnicity; Lamont, Swift, & Abrams, 2015 on age). These factors may have played a mediating role in the participants’ responses on trust toward outgroups, perceptions toward outgroups, and willingness to interact with outgroups In anticipation of this possibility, the survey included a psychologically reliable measure called the “oneness scale” (Gächter et al., 2015) to examine the effect of exposure to diversity on the subjective closeness of the participants’ social relationships. One of the survey questions asked respondents to rate their “connectedness” on a scale from 1 to 5 to a variety of groups, including men and women, as well as 20
-8-

these substances become concentrated in the food chain and cannot be removed from water by conventional water treatment processes. Monitoring in the Tangerang Industrial Zone, for example, found serious pollution at all ten monitoring stations, and excess levels of chromium, cadmium, mercury and selenium in different locations (World Bank, 1990a).

26. The environmental consequences if energy production, consumption a..id distribution depend critically on the mix of energy resources employed. The large share of oil in total energy has led to the pollution of delicate estuarine areas (in Riau and South Sumatra) and mangrove swamps (Cilacap in Central Java) through spillage, blowouts, disposal of brines, venting of hydrogen sulphide, and the disposal of mercury removed from natural gas. In addition, the transportation of oil in tankers is a major source of pollution of the marine environment, especially as a result of the heavy traffic in the Straits of Malacca and the associated need for deballasting, as well as uncontrolled cleaning of tarkers, spillage during loading and unloading, and shipping accidents. Further, the use of gasoline ano diesel in transportation cauE;es significant .ir pollution. particularly in Jakarta, Surabaya and Bandung, through emissions of sulphur and nitrogen oxides, carbon monoxide, hydrocarbons and other organic compounds, other metals and sulphates, and emission of odor and noise.

27. While domestic coal consumption is still at very low levels, it is projected that consumption could rise thirty-fold by the year 2000. Experience with mining projects in Indonesia suggest that the environmental impact of exploiting coal reserves are potentially large--loss of usable land, erosion resulting in turbidity and sedimentation, and solid waste generation. Another major source of environmental management problem in the energy sector arises from hydropower projects, and revolves principally around the need tccompensate and resettle the population affected by the reservoir construction.

28. Among the factors that have contributed to the pollution problem include: inadequate institutional arrangements for implementing industrial pollution regulations; lack of an incentive framework for limiting pollution; inadequate sanitation facilities; lack of environmental standards and policies for major energy projects; and lack of appropriate project siting and industrial location policies.
2 causality.' Financial development may simply be a leading indicator, rather than an underlying cause _of_ economic growth. This paper uses new data and new econometric procedures to shed considerably more light on the issue of causality while also illuminating a close empirical association between key legal and accounting characteristics and financial intermediary development. In conducting this analysis, we evaluate the predictions that emerge from an array of theoretical models.7 Methodologically, the paper uses two econometric techniques to study causality. First, we use a pure cross-sectional estimator, where data for **71** countries are averaged over the period **1960-1995,** with one observation per country. As in much of the cross-country growth literature, the dependent variable is the growth rate of the real per capita Gross Domestic Product **(GDP).** The regressors include a variable of particular interest, in our case financial intermediary development, along with a set of conditioning information. Unlike much of the literature, we use instrumental variables to extract the exogenous component of financial intermediary development. Specifically, LaPorta, Lopez-de-Silanes, Shleifer, and Vishny **(1997, 1998;** henceforth LLSV) note that most countries can be divided into countries with predominantly English, French, German, or Scandinavian legal origins and that countries typically obtained their legal systems through occupation or colonization. Thus, we view legal origin as an exogenous "endowment." After extending the LLSV sample from 49 to **71** countries, we use the legal origin indicators as instrumental variables to extract the exogenous component of financial intermediary development.

**' King and Levine (1993a,b) and Levine and Zervos (1998)** show that measures of financial intermediary development predict economic growth over the next 18 to **30** years in a broad-cross section of countries. Further, Neusser and Kugler **(1998)** and Rousseau and Wachtel **(1998)** find that financial development Granger-causes economic performance in timeseries studies of growth and financial development.

2 See, for instance, Diamond (1984), Boyd and Prescott **(1986),** Greenwood and Jovanovic **(1990),** Bencivenga and Smith **(1991),** and King and Levine **(1993b).**