rafmacalaba/datause-encoder-data huggingface dataset:
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- Fajgelbaum, P. D., P. K. Goldberg, P. J. Kennedy, and A. K. Khandelwal (2020). The return to protectionism. _The Quarterly Journal of Economics 135_ (1), 1–55.

- Fajgelbaum, P. D. and A. K. Khandelwal (2016). Measuring the unequal gains from trade. _The Quarterly Journal of Economics 131_ (3), 1113–1180.

- Farrokhi, F. and A. Soderbery (2020). Trade elasticities in general equilibrium.

- Feenstra, R. C. and D. E. Weinstein (2017). Globalization, markups, and us welfare. _Journal of Political Economy 125_ (4), 1040–1074.

- Flaaen, A., A. Hortaçsu, and F. Tintelnot (2020). The production relocation and price effects of us trade policy: the case of washing machines. _American Economic Review 110_ (7), 2103–27.

- Flaaen, A. and J. R. Pierce (2019). Disentangling the effects of the 2018-2019 tariffs on a globally connected us manufacturing sector.

- Galle, S., A. Rodríguez-Clare, and M. Yi (2023). Slicing the pie: Quantifying the aggregate and distributional effects of trade. _The Review of Economic Studies 90_ (1), 331–375.

- Kee, H. L., A. Nicita, and M. Olarreaga (2008). Import demand elasticities and trade distortions. _The Review of Economics and Statistics 90_ (4), 666–682.

- Krugman, P. (1980). Scale economies, product differentiation, and the pattern of trade. _The American Economic Review 70_ (5), 950–959.

- Lashkaripour, A. and V. Lugovskyy (2022). Profits, scale economies, and the gains from trade and industrial policy. Technical report, Center for Applied Economics and Policy Research, Department of Economics.

- Lind, N. and N. Ramondo (2018). Trade with correlation. Technical report, National Bureau of Economic Research.

- Mattoo, A., N. Rocha, and M. Ruta (2020). Handbook of deep trade agreements. _World Bank_ .

- Mau, K. (2017). Us policy spillover (?)–china accession to the wto and rising exports to the eu. _European Economic Review 98_ , 169–188.

- Morales, E., G. Sheu, and A. Zahler (2019). Extended gravity. _The Review of Economic Studies 86_ (6), 2668–2712.

- Novy, D. (2012). International trade without ces: Estimating translog gravity. _Journal of International Economics 89_ (2), 271–282.

- Reyes-Heroles, R., S. Traiberman, and E. Van Leemput (2020). Emerging markets and the new geography of trade: The effects of rising trade barriers. _IMF Economic Review 68_ , 456–508.

- Waugh, M. E. (2019). The consumption response to trade shocks: Evidence from the us-china trade war. Technical report, National Bureau of Economic Research.

13
<mark>Bhalotra, Sonia, and Samantha B. Rawlings. 2011. “Intergenerational Persistence in Health in Developing Countries: The Penalty of Gender Inequality?”</mark> _<mark>Journal of Public Economics</mark>_ <mark>95 (3–4): 286– 99.</mark> Brooks-Gunn, Jeanne, Wen-Jui Han, and Jane Waldfogel. 2002. “The Effects of Early Maternal Employment on Child Cognitive Development.” _Demography_ 39(2):369–392.

<mark>Bharadwaj, Prashant, and Rinchan Ali Mirza. 2019.</mark> **<mark>“</mark>** <mark>Displacement and development: Long term impacts of population transfer in India.”</mark> _<mark>Explorations in Economic History</mark>_ <mark>73: 14-4983</mark> <mark>Caruso, Germán Daniel. 2017. “The legacy of natural disasters: The intergenerational impact of 100 years of disasters in Latin America.”</mark> _<mark>Journal of Development Economics</mark>_ <mark>127: 209-233.</mark> <mark>Caruso, Germán Daniel, and Sebastian Miller. 2015. “Long run effects and intergenerational transmission of natural disasters: a case study on the 1970 Ancash Earthquake.”</mark> _<mark>Journal of Development Economics</mark>_ <mark>117:134-150.</mark> <mark>Caruso, Germán Daniel. 2015. “Intergenerational Transmission of Shocks in Early Life: Evidence from the Tanzania Great Flood of 1993.” Unpublished</mark> <u>http://www.Germáncaruso.com/IntergenerationalPaper11_11_14.pdf</u> <mark>Case, Anne, and Christina Paxson. 2008. “Stature and Status: Height, Ability, and Labor Market Outcomes.” Journal of Political Economy 116 (3): 499–532.</mark> <mark>Case, Anne, and Christina Paxson. 2010. “Causes and Consequences of Early-Life Health.” Demography 47: S65–85.</mark> <mark>Ceritoglu, Evren, Burcu Gurcihan Yüncüle, Huzeyfe Torun, and Semih Tumen. 2017. “The Impact of Syrian refugees on natives' labor market outcomes in Turkey: Evidence from a quasi-experimental design.”</mark> _<mark>IZA Journal of Labor Policy</mark>_ <mark>6(5): 1-28.</mark> <mark>Cook, C. Justin, Jason M. Fletcher, and Angela Forgues. 2018. "Multigenerational Effects of Early Life Health Shocks."</mark> _<mark>NBER Working Papers 25377</mark>_ <mark>.</mark> <mark>Currie, Janet. 2</mark> 009. "Healthy, Wealthy, and Wise: Socioeconomic Status, Poor Health in Childhood, and Human Capital Development." _Journal of Economic Literature_ , 47 (1): 87-122.

<mark>Cunha, Flavio, James J. Heckman, and Lance Lochner. 2006. “Interpreting the evidence on life cycle skill formation.” In</mark> _<mark>Handbook of the Economics of Education</mark>_ <mark>1 (12): 697-812.</mark> <mark>Cunha, Flavio, and James J. Heckman. 2007. “The technology of skill formation.”</mark> _<mark>American Economic Review</mark>_ <mark>97 (2): 31-47.</mark> <mark>Cunha, Flavio, James J. Heckman, and Suzanne M. Schennach.2010. “Estimating the technology of cognitive and noncognitive skill formation.”</mark> _<mark>Econometrica</mark>_ <mark>78 (3): 883-931.</mark> <mark>Del Carpio, Ximena V., and Mathis Wagner. 2015. “The Impact of Syrian Refugees on the Turkish Labor Market.”</mark> _<mark>Policy Research Working Paper WPS No. 7402</mark>_ <mark>.</mark> 24
Figure 2: Robusta prices on international reference markets and producer selling prices during the three surveys <!-- Start of picture text --> 280<br>240<br>200<br>160<br>120<br>80<br>40<br>0<br>US cents per kg<br>10|91 12|91 02|92 04|92 06|92 08|92 10|92 12|92 02|93 06|95 08|95 10|95 12|95 02|96 03|99 05|99 07|99 09|99 11|99 01|00<br><!-- End of picture text -->

Sources: International Coffee Organization (www.ico.org), September 2005, IHS 1992/93, UNHS 95/96, UNHS 99/00 Note: The upper line is the international price in US cents per kg for Robusta coffee provided by International Coffee Organisation (ICO). The lower line is the median price of bulk Kiboko per kilogram converted to US cents. Kiboko is the green coffee bean specific to Uganda and the bulk price of Kiboko a good proxy for the prices farmers receive. Monthly average exchange rates from the International Financial Statistics are used to convert the prices into US cents. We compute the Kiboko prices from the crop and community sections of the household surveys.

.

10
worked almost exclusively as temporary laborers, usually in tasks paid on a piece rate basis, and often at a time rate during the slack season.<sup>3</sup>

# **3. Data Overview**

The study utilizes primary data collected from a random sample of workers in 56 table grape packing sheds during the harvest season (mid-January to mid-March) of 1992.<sup>4</sup> The sample consists of 690 individuals. Data were collected regarding productivity and pay in the task they were performing at the time interviewed, as well as other information regarding age, education, work experience, household demographics, and household income, plus opinions and preferences regarding many aspects of work experience. We also obtained data regarding each worker’s labor force participation, employment, and earnings, daily throughout 1991.

We sampled only workers who were employed in one of four tasks in which workers were normally paid on a piece rate basis.<sup>5</sup> Three tasks were carried out primarily by females: classifying grapes by size and color ( _selección_ ), cleaning grapes prior to packing ( _limpieza_ ), and packing grapes in boxes for export ( _embalaje_ ). All workers in the fourth task, nailing lids on boxes of packed grapes and binding these boxes on pallets ( _tapado_ ), were male. Although packing plant tasks tended to be gender specialized, they were not rigidly divided by gender.<sup>6</sup> Plant level data suggest that shed managers developed strategies regarding the type of worker that they wished to hire, presumably

> 3 Venegas estimated that women accounted for only 5 percent of permanent fruit workers, but 52 percent of temporary workers in 1990. Her data show clear gender segregation between permanent and temporary work, with women concentrated into less secure, short-term employment.

> 4 These sheds were located in three regions: Santa Maria, Buin/Paine, and Lontue.

> 5 Roughly two thirds of all jobs in a packing shed are paid on a piece rate basis, with the rest paid a wage. About half of the workers interviewed in 1992 reported having been employed in a “wage” job during the 1991 harvest season. Thus, a significant number of workers in our sample that were not piece rate workers in the 1991 harvest became piece rate workers in the 1992 harvest.

- 7 -
||Travel tim<br>Populated|e to:<br>area|**Table 27:**<br>Municipal|**IV Estimatio **<br>capital|**n(2)**<br>Hospital||School||
|---|---|---|---|---|---|---|---|---|
|D|0.274|(0.417)|-0.146|(0.242)|-0.438|(0.281)|1.007|(0.297) ***|
|lnHH size|0.058|(0.106)|0.096|(0.076)|0.075|(0.089)|-0.044|(0.103)|
|Adult share|-0.045|(0.229)|-0.051|(0.159)|-0.084|(0.185)|0.029|(0.216)|
|HH head sex: Male|0.041|(0.141)|-0.062|(0.096)|-0.098|(0.110)|-0.015|(0.132)|
|HH head education: Elementary|0.146|(0.090) *|0.080|(0.065)|-0.002|(0.076)|-0.105|(0.088)|
|HH head education: Secondary|-0.113|(0.162)|-0.045|(0.106)|-0.100|(0.123)|-0.354|(0.143) **|
|HH head education: University|-0.373|(0.331)|0.070|(0.214)|-0.265|(0.248)|-0.834|(0.287) ***|
|Appliance ownership: Refrigerator|-0.255|(0.149) *|-0.138|(0.101)|-0.019|(0.114)|-0.146|(0.137)|
|Appliance ownership: Color TV|-0.335|(0.159) **|-0.291|(0.100) ***|-0.408|(0.114) ***|-0.287|(0.137) **|
|Appliance ownership: Radio|0.173|(0.086) **|-0.172|(0.061) ***|-0.086|(0.070)|0.030|(0.082)|
|Appliance ownership: Gas stove|0.044|(0.151)|-0.011|(0.105)|-0.101|(0.119)|0.028|(0.144)|
|Appliance ownership: Washing|||||||||
|machine|0.018|(0.109)|-0.144|(0.072) **|-0.096|(0.084)|-0.051|(0.097)|
|Gas use for cooking|-0.177|(0.111) *|-0.096|(0.069)|-0.031|(0.082)|0.041|(0.093)|
|Power use for lighting|-0.294|(0.171) *|-0.251|(0.108) **|-0.299|(0.121) **|-0.155|(0.142)|
|Tap water for cooking|0.072|(0.091)|-0.140|(0.065) **|-0.170|(0.076) **|-0.323|(0.087) ***|
|Constant|3.543|(0.607)***|4.644|(0.367)***|4.940|(0.428)***|2.623|(0.478)***|
|Obs.|551||841||833||816||
|R-squared|0.169||0.096||||0.118||
|Wald-stat|5.88||8.07||5.07||9.77||
|Sargan overidentifying restriction test|0.56||0.72||1.65||2.98 *||
|Hausmanexogeneity test|12.76*|**|15.60|***|16.95*|**|56.32 *|**|

Note: Standard errors are shown in parentheses. *, **, *** indicate the statistical significance at 10%, 5% and 1%, respectively.

46
<mark>is still limited to few and mostly short-term outcomes. A recent review of this literature finds that the probability of finding a negative and significant effect on household well-being among the host population is less than 20% and less than 30% if we consider employment or wage outcomes (Verme and Schuettler, 2019). The review also finds some evidence that negative effects tend to disappear over the long-term and are mainly associated with larger crises. However, the evidence on household wellbeing is still scarce and most authors have focused on labor market outcomes in the short and mediumterm, largely ignoring outcomes in other domains and the intergenerational effects generated by forced displacement shocks. Studies measuring the impacts of refugees on households in host communities are relatively few, but rather consistent in finding growth in consumption over the short and medium-term (Alix-Garcia and Saah, 2010; Ruiz and Vargas-Silva, 2015, 2016; Del Carpio and Wagner,2015; Rozo and Sviastchiy, 2018). Similar research focusing on a narrower set of outcomes, such as employment and wages, finds different results depending on the country context and the population studied. The literature investigating the short and long-term effects of refugee influx concord that negative outcomes tend to disappear in the long-run (Del Carpio and Wagner, 2015; Ceritoglu et al., 2017; Peri and Yasenov, 2019). Overall, the review highlights the importance of extending research beyond the consumer and labor market outcomes and beyond the short and medium-term.</mark> <mark>Few studies have empirically investigated the short or long-run socio-economic impacts of refugee shocks on children’s health, providing some initial but inconsistent evidence. On the one hand, one might believe that the positive impacts induced by the sudden arrival of refugees on host communities (increased demand for economic goods, humanitarian assistance, improved infrastructure, increased agricultural production</mark><sup>4</sup> <mark>) might translate into better health care services and consequently positive children’s health outcomes. On the other hand, most studies have reported negative changes in welfare among host communities following refugees’ arrival. Disease outbreaks (K</mark> ibrea <mark>b, 1985), competition over natural resources and paid jobs, environmental damage, criminality and pressure on basic service delivery in schools and hospitals are among the most cited negative impacts of refugees on hosts. These could trigger poorer health, education and labor market outcomes of the first but also the second generation of children born years after the crisis.</mark> <mark>Baez (2011) uses secondary data from Tanzania and finds that, as compared to children living in areas not affected by refugees, children living in host communities have a higher incidence of infectious diseases, are more likely to die before the age of 5 and have a reduced stature (height) with an acute effect found for younger cohorts. However, building on Baez’s study, Mabiso et al. (2014) find no</mark>

> 4 <mark>Bharadwaj and Mirza (2019).</mark>

3
<mark>Total 100 47 40 13 100</mark>

# Source: Authors’ calculations based on ECAPOV data.

Notes: transition matrix is based on synthetic panel for 2003-2009. Threshold for poor is 162 somoni and for middle class 294 somoni per month. Explanatory variables include year of birth cohort, number of children, education of the head of household, rural/urban and regional dummies, and different interactions between these variables and means are the regional level. Sample: heads of household 25-55 years of age. Welfare aggregate is as used to calculate national poverty rates.

14. **Consistent welfare improvement is observed across the whole population in spite of the churning in the bottom 40 percent**<sup>**6**</sup> **.** From 8 to 49 percent of the population (depending on lower or upper bound) in the bottom 40 shifted upwards to the top 60 in 2009.<sup>7</sup> From 5 to 33 percent of the top 60 shifted down to bottom 40 in 2009 (tables 3.4ab). In spite of a churning, overall positive mobility prevails as can be clearly seen from figure 3.1. Average consumption of graduates from the top 60 percent of the population in 2003 was still higher than the average consumption of entrants to the top 60 in 2009. Coupled with a very high growth of consumption per capita in households staying in the bottom 40 percent in two years, overall mobility was strongly upward moving.

|**Table 3.4: Mobility matrix for the bottom 40 and top 60 percent of the population**|
|---|

|a) Uppe|r bound|||b) Lowe|r bound|||
|---|---|---|---|---|---|---|---|
|||2009||||2009||
|2003|Top60|Bottom 40|Total|2003|Top60|Bottom 40|Total|
|Top60|41|20|60|Top60|57|3|60|
|Bottom 40|19|20|40|Bottom 40|3|37|40|
|Total|60|40|100|Total|60|40|100|

Source: Authors’ calculations based on ECAPOV data.

Notes: transition matrix is based on synthetic panel for 2003-2009. Explanatory variables include year of birth cohort, number of children, education of the head of household, rural/urban and regional dummies, and different interactions between these variables and means are the regional level. Sample: heads of household 25-55 years of age. Welfare aggregate is as used to calculate national poverty rates.

> 6 Detailed explanation of churning in the bottom 40 percent is provided in box 1 in annex.

> 7 48 percent were obtained by dividing 19 by 40 in table 3.4a, 8 percent were obtained by dividing 3 by 40 in table 3.4b.

12
# **Tables**

**Table 1: Transition and performance indicators.**

|**Transition indicators**|**1995**|**1997**|**2001**|
|---|---|---|---|
|Privatization|2.33|2.78|3.22|
|Finance|2.00|2.34|2.50|
|Infrastructure|1.33|2.00|2.67|
|Market reforms|3.34|4.00|4.33|
|Trade|4.00|4.00|4.33|
|Domestic prices|2.67|4.00|4.33|
|Competition|2.00|2.33|2.33|
|**Performance indicators**||||
|Phone lines (per thousand)|307.60|330.97|560.94|
|Inflation rate|62.05|1,058.37|7.36|

Source: EBRD (2007); World Bank (2008).

**Table 2: Average price distances by region and year**

|**Region**|**1995**|**1997**|**2001**|
|---|---|---|---|
|Bourgass|0.289|0.293|0.259|
|Haskovo|0.221|0.397|0.187|
|Lovetch|0.215|0.311|0.196|
|Montana|0.353|0.310|0.166|
|Plovdiv|0.308|0.307|0.265|
|Rousse|0.389|0.306|0.207|
|Sofia city|0.443|0.345|0.294|
|Sofia region|0.262|0.286|0.213|
|Varna|0.318|0.337|0.262|
|Average|0.321|0.323|0.233|
|Observations|654|705|629|

Source: Bulgaria Integrated Household Surveys and authors’ calculations.

**Table 3: Analysis of variance for price distances**

||**Partial sum-of-squares**|**Share**|
|---|---|---|
|Between effects|<br>80.73<sup>a</sup>|0.69|
|Time,_B(t)_|2.29<sup>a</sup>|0.02|
|Markets,_B(s)_|77.33<sup>b</sup>|0.67|
|Within time-markets,_W(st)_|35.43|0.31|
|<br>Total|116.16|1.00|

a and b denotes significance at the 1 and 5 percent levels respectively.

21
# **_References:_**

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24
depend on the level of activism and how the manager allocates the active part of the portfolio. However, the effects described above illustrate how their country flows respond to different shocks to the extent that they follow benchmark indexes.

Several of these benchmark effects can be observed in our flows data. The direct benchmark effect helps explain the counterintuitive outflows when Israel was upgraded from the MSCI Emerging Markets Index to the MSCI World Index. To show the effect of the exogenous change in benchmark weights we compare the explicit indexing funds tracking these two indexes (Figure 5). The direct benchmark effect captures almost all the variation in country flows for both types of funds, which occur due to all the reallocations right at the time of the switch. To understand the total effect on country flows, it is important to consider both the size of the two groups of funds and the size of Israel in each index. At that time, Israel’s weight in the Emerging Markets Index was 3.17 percent and that in the World Index was 0.37 percent, and the assets in the funds following these two indexes were not very different. Emerging market funds withdrew 2 billion U.S. dollars from Israel while developed market funds injected 160 million. This outflow due to the direct benchmark effect was significant given that, according to its balance of payments, Israel witnessed a 1.4 billion U.S. dollar portfolio equity outflow during the second quarter of 2010 and average quarterly equity inflows of about 700 million between the first quarter of 2005 and the first quarter of 2010. They might also be behind the negative returns in Israel’s stock market during the period of the upgrade.

The sensitivity effect shows that countries with higher weights in a benchmark are prone to more inflows (outflows) when the funds receive injections (redemptions). This can explain why large countries might be subject to large changes in capital flows despite their fundamentals. Figure 6 illustrates this effect by showing the flows to Brazil and India from explicit indexing funds tracking the MSCI Emerging Markets Index against the flows into each of these equity funds. In each case, the relation of country and fund flows is depicted by two points in time, when each country had different benchmark weights. The relation becomes steeper as each country’s benchmark weight increases, as shown in Equation (12).

For a more systematic analysis, we regress country flows against benchmark weights multiplied by fund flows (Table 10). There is a positive and significant relationship between the two variables, which monotonically decreases with the degree of activism. For example, on average across all equity funds, an injection of one dollar to a fund is associated with country flows of 0.74 dollars times the benchmark weight. Every 25