rafmacalaba/datause-encoder-data huggingface dataset:
B data, more thai a quarter of Hung, ry'u total imp.rts and a third of **its** manufactured imports originated in the EC by lS88.6 Table 6. Geographical Distribution of Hungary's Imporle, 1980, 1985, 1988.

||Sm|1980|1<br>$Sm|984<br>%|1<br>Sm|98',|
|---|---|---|---|---|---|---|
|World|9,212|lC0.0|8,084|100.0|9,855|100.0|
|Austria|497|5.4|408|5.0|626|6.4|
|EC-12|2,135|23.2|1,651|20.4|2,426|24.6|
|Germany, F.R.|640|6.9|515|6.4|628|6.4|
|USA|252|2.7|199|2.5|251|2.5|
|USSR|2,556|27.7|2,354|29.1|2,805|28.5|

# Source: COMTRADE

The basic structure of Hungary's imports has not been changir&g signif cantly over time. HungLry imports manufactured products from the EC, more than 90%, and half its imports from the USSR are minerals and fuels **(see Annex** Tables Al and A2).

The EC is by far t:;Fr principal euppli :. of manufactu:ed products, and incorporation of the GDR into the EC will only reinforce this, particularly for machinery. On the other hand, most Hungarian fuels imports originate in the USSR, which ia also the prime foreign supplier of agricultural products. A further cra1e liberalization in Hungary and a reform or withering away of the CMEA would probably increase the share of non-CMEA suppliers, including that of the EC. There is sonte movement in this direction. The USSR has indicated that wishes to reduce ite fuel deliveries to Hurgary in the short run (see later).

Table 7: Geographical Distribution of Hurgary's .mports by Main Category of Products, 1987 (percent)

||EC-12|USSR|GDR|AUSTRIA|USA|Rest of the<br>world|
|---|---|---|---|---|---|---|
|Total trade|24.6|<br>28.5|6.4|6.4|2.5|31.7|
|Agriculture|15.5|<br>25.1|1.5|3.7|3.0|51.3|
|Mineral fuels|1.7|85.0|1 7|0.2|0.4|11.1|
|Chemicals|4.2|<br>13.9|5.2|V.3|4.5|24.9|
|Machines,tra.,s.equip.|<br>27.2|<br>17.5|12.7|8.3|3.3|31.1|
|Other manuf.products|33.8|<br>11.9|5.8|10.1|1.5|37.0|

# Source: COMTRADE

6 According to the Yearbook of the Hungarian Statistical office the share of the EC12 increased moderately in 1988 to 25.3%.
|-<br>Manufacturing||||||
|---|---|---|---|---|---|
|<br>-<br>Trade/Services||||||
|2006||||||
|-<br>Agriculture/Mining|||0.384|||
|<br>-<br>Manufacturing|||0.377|||
|<br>-<br>Trade/Services|||0.547|||
|2007||||||
|-<br>Agriculture/Mining|1.213|0.303||-|0.868|
|<br>-<br>Manufacturing|0.903|0.328||1.045*|0.560|
|<br>-<br>Trade/Services|0.787|0.809||0.843|0.742|

* Not statistically significant.

Table 14 and the charts that follow (Figures 8 to 14) show the evolution of return to skill over time by major sector in relation to the evolution of the supply of skilled workers by major sector. Overall, there is indeed some evidence that the evolution of skill premiums has been rather sector specific in Indonesia, Philippines and Thailand (with generally decreasing returns in agriculture, mixed performance in manufacturing, and increasing in services (Indonesia and the Philippines)), while more evident across the board (with an edge for the service sector) in the other countries. The dynamism of the demand in the service sector in most countries points to the importance of ensuring a better tailoring of curriculum design and pedagogical approaches to its needs (all the more in countries such as Indonesia and the Philippines where demand in other sectors has been sluggish).

In Indonesia there was a steady increase in the proportion of skilled workers, especially in trade/services, which leveled of after 2003 in agriculture/mining and manufacturing. Return to skill was declining in agriculture/mining and for those with secondary or higher education in manufacturing until 2003, rebounding thereafter. The return to skill in trade/services remained steady or even slightly increased over the entire period despite the steady increase in the supply of skilled workers in this sector.

In the Philippines, the evidence shows clear patterns of declining skill premiums in agriculture and manufacturing and increasing in services. Together with increasing proportion of skilled workers, this evidence suggests increased demand for skills in the service sector.

43
Table 11b: Impacts of PROSPERA on Type of Secondary School 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008 School Fixed Effects

||Telesec|General|Technical|
|---|---|---|---|
|Variables|(1)|(2)|(3)|
|PROSPERA beneficiary 2008|0.075***|0|-0.072***|
||(0.011)|(0.006)|(0.012)|
|Female beneficiary|0.009|-0.016*|0.005|
||(0.015)|(0.008)|(0.017)|
|Female|-0.007|0.012|-0.004|
||(0.014)|(0.007)|(0.015)|
|Constant|0.380***|0.072**|0.529***|
||(0.06)|(0.032)|(0.064)|
|Observations|11,200|11,200|11,200|
|R-squared|0.556|0.454|0.529|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Control variables include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, and calculator.

Table 11c: Impacts of PROSPERA on Type of Secondary School: 6<sup>th</sup> Graders Enrolled in Indigenous Primary Schools in 2008 Nearest Neighbor Matching Models

|||Enrollment<br>||
|---|---|---|---|
|Variables|7thgrade Telesecondary|7thgrade General school|7thgrade Technical school|
||Propensity score matching|with exact matching on age|and state|
|Girls|0.097***|-0.011|-0.083***|
||(0.017)|(0.0098)|(0.019)|
|Observations|4,931|3,931|3,931|
|Boys|0.105***|-0.018*|-0.087***|
||(0.017)|(0.009)|(0.018)|
|Observations|4,751|4,751|4,751|

Note: Estimates significance level *** p<0.01, ** p<0.05, * p<0.1. We report standard errors in parenthesis. Variables which enter the propensity score include: age, number of siblings, household size, lives with mother, lives with father, health problems, whether currently working, household ownership of DVD, television, MP3 player, cellular, phone, computer, calculator, student teacher ratio, whether the school has a computer, whether the school has rooms with more than one grade, average teacher education levels and marginality index components. Community level variables include the margination index, proportion illiterate, proportion of households without piped water, proportion without electricity and proportion without drainage.

As was the case for the sample of children in medium to very high areas of poverty, the impact of the program is to increase the probability of enrollment in tele-secondary versus other schools.

30
The Morocco survey took place in four regions of Morocco between November 2011 and February 2012. Four areas in Morocco were selected for their representation of key migration characteristics. These were: the Tadla, the Tingitane Peninsula, the Atlantic Axis consisting of the agglomeration of Casablanca, Rabat and Kénitra, and the Rif Central. Smaller geographic units within these areas from which to sample households were then chosen through purposive sampling, and within these areas, the interviewed households were selected through a “random walk”. The departure point - usually a well-known reference point like a school or a mosque - of the random walk was chosen with the help of a community representative. Interviewers followed a predetermined route. Depending on the density of buildings, either each 5<sup>th</sup> household or, in less populated areas, each 3<sup>rd</sup> household was interviewed. The resulting sample consists of 1485 households given the list randomization question. Survey enumerators alternated to include the fourth list question on every second survey.

The Mexican survey took place in Mexico City. A convenience sample was first obtained between April and June 2011 by intercepting individuals in five branches of a major Mexican bank, as well as on the streets in public plazas and busy avenues in seven parts of Mexico City. These individuals were then used in an experiment to test the impact of a financial literacy workshop run by this Mexican bank (Bruhn et al, 2013). The follow-up survey for this intervention was conducted between February and June 2012. Randomization was done conditional on agreeing to participate in the follow-up survey, as was done via a computer draw which determined which version of the question an interviewer should ask. In total 1,044 individuals were given the list randomization module in this paper.<sup>2</sup> The Philippines survey took place between March and August 2010 in Sorsogon Province. Sorsogon is a relatively isolated and rural province, located approximately 12 hours by bus from Manila. International migration rates are lower than the Philippines average as a result of this isolation, and so this location was chosen for an experiment designed to test several interventions aimed at lowering barriers to international migration (Beam et al, 2013). The sample was obtained by first selecting municipalities within this province, then randomly selecting baranguays (local administrative areas) and conducting a door-to-door listing of households in

> 2 An additional group of respondents were randomized into a Group C, which had the same first three questions as groups A and B, and then a fourth question asking about debt stress. We do not use this group in this paper.

7
Further, addressing Markusen and Zahniser’s concern, it does not seem to be the case that the internal migrant population is so significantly distinct from that migrating to the US. Table 3 tabulates the educational attainment, age and gender of non-movers in Mexico and within Mexico migrants tabulated from the Mexican census, and that of recent migrants to the US as tabulated by the US census.<sup>8</sup> Men have similar age profiles although women tend to be somewhat older when they migrate to the US, perhaps reflecting a delayed migration after their spouses. For both genders, however, what emerges is that, though not radically different, the domestic migrants are somewhat less educated than those internationally. Markusen and Zahniser’s concern that FDI and trade would not prove a disincentive to migration because it did not affect the relevant migratory population seems unfounded.

# _Simulations of US impact_

Ideally, we could use the estimates above to offer some guess at the magnitude of the impact on US migration. Clearly, for such an exercise to be relevant, it must be the case that the decision to migrate to the US, or alternatively, the types of people who decide to migrate to the US do not differ too much between the two destinations. As noted above, table 3 suggests that the demographics of the two groups are similar, and the literature is mixed although not necessarily inconsistent with this assumption.<sup>9</sup> It does appear, however, that relative to men, women are more likely to migrate within Mexico, than to the US. This may reflect the strategy of households that migrate to the border to send the husband across to engage in the riskier and more intensive work while the wife

> 8 These findings are very similar to those of Chiquiar and Hanson (2003) using the US census.

> 9 Rivera-Batiz (1986) in an often cited article implicitly suggests that migrants’ indifference between destinations:  “The impact of the maquiladoras on migration to the United States is dependent on whether they are able to raise employment by more than they increase the labor supply in the border areas through induced migration from southern regions.  If an excess supply of labor is generated at the border with surplus workers becoming either openly unemployed or underemployed, it is likely that there will be a spillover into illegal migration to the United States” (p. 265).  The field work on “staged migration” can be read both ways.  On the one hand, workers in the first stage, for instance, moving from Oaxaca to take jobs in Baja California have less information, contacts and coyote capital than when they decide to make a second stage crossing to the US. On the other hand, the underlying objective function of that individual is arguably the same and both migration costs (embodied in information or networks) and credit constraints are standard in the migration specifications. . See Durand and Massey (1992) for a review of the field literature on migration and Cornelius and Martin (1993) and Zabin and Hughes (1995)

13
**Table A8.1. Results on Science learning according to student ability**

||Sequence vs.<br>Control|Coaches vs.<br>Control|Wald Test|
|---|---|---|---|
|_Dependent variable_|(1)|(2)|(3)|
||**_Panel A: Low ability students_**|||
|Science score|0.155**|0.119*|0.31|
||(0.066)|(0.069)|[0.575]|
||598|586||
|Percent of correct answers|0.013|0.001|0.64|
||(0.012)|(0.014)|[0.425]|
||598|586||
|Percent of incorrect answers|-0.011|0.023|2.31|
||(0.019)|(0.016)|[0.128]|
||598|586||
|Percent of omitted answers|-0.035|-0.059**|0.91|
||(0.024)|(0.024)|[0.339]|
||598|586||
||**_Panel B: High ability students_**|||
|Science score|0.184**|0.237***|0.58|
||(0.078)|(0.073)|[0.448]|
||507|524||
|Percent of correct answers|0.047***|0.037**|0.47|
||(0.016)|(0.017)|[0.493]|
||507|524||
|Percent of incorrect answers|-0.033***|-0.026***|0.48|
||(0.009)|(0.009)|[0.490]|
||507|524||
|Percent of omitted answers|0.004|-0.017**|6.27|
||(0.011)|(0.007)|[0.012]|

24
8

**_Access to finance_ :** The share of working capital that is financed by sources other than retained earnings.  These might include banks, non-bank financial institutions such as microfinance institutions, credit cooperatives, credit unions or finance companies, purchases on credit from suppliers and advances from customers, and other sources such as moneylenders, friends, etc.

**_Access to Land_ :** Index of access to land provided by the World Bank’s _Investing Across Borders_ project.  This is an un-weighted average of four sub-indices, which measure access to land.  These sub-indices are described on the project’s website as follows:

_Strength of lease rights index_ (0-100): Compares economies on the security of legal rights they offer to investors interested in leasing industrial land—whether or not foreign and domestic companies are treated differently and whether the land can be subleased, subdivided, mortgaged, or used as collateral.

_Strength of ownership rights index_ (0-100): Compares economies on the security of legal rights they offer to investors interested in purchasing industrial land. _Access to land information index_ (0-100): Compares economies on the ease of access to land-related information through the countries’ land administration systems including land registries, cadastres and land information systems. _Availability of land information index_ (0-100): Compares economies on the availability of key information to interested private parties through land administration institutions.

**_Skill ratio of the workforce_ :** Ratio of skilled to unskilled workers in the firm as measured by the workers’ level of education in the Enterprise Surveys.  In addition, the capitallabor ratio of the firm is included as a control variable so we can measure the effect of the skill ratio accurately.

**_Legal rights:_** The strength of legal rights index measures the degree to which collateral and bankruptcy laws protect the rights of borrowers and lenders and thus facilitate lending in the private sector.  This variable is taken from the World Bank’s Doing Business database and is measured on a scale of 0 to 10.
indicators documented in Table 1.<sup>3</sup> As of 2007, Turkey’s trade-weighted applied tariff on manufacturing products was only 1.0 percent, and its simple average applied MFN tariff was only 4.8 percent. More comprehensive and economically meaningful indicators such as the trade tariff restrictiveness index (TTRI) or the overall trade restrictiveness index (OTRI) were also quite low for Turkey during this period.<sup>4</sup> Nevertheless, there are two key indicators for Turkey from Table 1 that point to a slightly more nuanced story. The first is that Turkey’s simple average tariff binding – or the rate beyond which Turkey is _legally_ committed not to raise its MFN tariff at the WTO – for its manufacturing products was 16.9 percent; this was much higher than its MFN applied rate of 4.8 percent. The implication is that, for the products over which Turkey had made WTO binding commitments, there remained substantial “overhang” or “water” in the tariff bindings – i.e., Turkey could legally raise its average applied MFN tariff rate by more than 12 percentage points. The second is that Turkey had legally bound at the WTO only a very small share – i.e., 42.8 percent – of even its manufacturing products’ tariff lines. The implication is that Turkey could legally raise its applied MFN tariffs by any amount without WTO legal obligation for more than half of its import product lines.

On the other hand, what the relatively poor indicators for Turkey’s tariff binding overhang and low tariff binding product coverage do not capture is that Turkey has made substantial trade policy commitments outside of the WTO system through its customs union with the European Union.<sup>5</sup> First, two-way trade between Turkey and the European Union is effectively duty free. Second, Turkey has sequentially adopted many of the other free trade agreements that the EU has negotiated with third countries, thus also extending preferential tariff access to these trading partners. Combined, nearly 60 percent of Turkey’s overall exports are sent to countries with which it either has an FTA or customs union, here referred to jointly as PTAs. This implies that the trade policy indicators that take into account Turkey’s tariff preferences and that trade weight these tariffs will reveal Turkey as being even more open than the indicators of its MFN policies in isolation, given that so much of its trade is with PTA partners.

> 3 The World Bank (2010) leads with “Turkey has one of the most liberal trade regimes, based on its 1.5 percent MFN Tariff Trade Restrictiveness Index (TTRI). It ranks as the 5<sup>th</sup> least restrictive tariff regime out of a 125 country sample.” Togan (2010) provides an assessment of the WTO’s 2007 Trade Policy Review of Turkey.

> 4 These measures not only take into account elasticities, but the OTRI also considers some non-tariff measures in addition to tariffs.  For a methodological presentation of the construction of these measures, see Kee, Nicita and Olarreaga (2009).

> 5 Turkey has been in accession negotiations with the European Union since 2005.

6
may have a differential impact on the household’s accumulation of assets, compared with households without an absent member.<sup>16</sup> For these reasons, it is more appropriate to examine the broader impact of migration on outcomes for households rather than the impact of remittances alone. The analysis in this paper follows this reasoning, analyzing the impact of migration, instead of remittances, on the assets accumulated by households that have sent family members to work outside Kerala.

# **4. Data and Empirical Strategy**

# _A. Data_

In 1998, the first Kerala Migration Survey was completed in Kerala, India. A statewide, representative, demographic survey of 9,995 households across 200 communities ( _panchayat_ ), it was administered between March and November of that year. The KMS was the first in a series of periodic household surveys undertaken by the Center for Development Studies, headquartered in Trivandrum, Kerala. Five years after the first KMS survey, in 2003, the Center for Development Studies conducted a second wave of the KMS covering 10,012 households. A unique feature of the 2003 KMS survey was the creation of panel data: 5,000 households from the 1998 survey were selected for revisits, and the survey team successfully completed 4,795 follow-up interviews, achieving a high re-contact rate of 95.9 percent.<sup>17</sup> Both the 1998 and 2003 KMS survey waves asked whether any household member had been abroad or in another Indian state at any time prior to or during the survey. Survey teams recorded each of these household members’ current and pre-migration occupational status, together with the month and year of their departure from and return to Kerala. These questions from the 2003 KMS

> 16 In Kerala, people frequently refer to “Gulf Wives Syndrome.” This is a way of indicating the plight of a woman whose husband has left to work in the Arabian Gulf, leaving her with a stream of money at the emotional cost of separation and the responsibility of managing the family’s affairs in his absence (Zachariah et al. 2003).

> 17 The decision to conduct a panel survey was made only after the 1998 KMS was completed. The survey team selected the 2003 panel of households from 125 of the original 200 communities surveyed in 1998. In each community, they interviewed 40 households. Attrition in the panel stemmed from being unable to locate certain households due to a combination of incorrect addresses, and entire households having moved during the inter-survey period. The survey team did not identify which households had moved.

9
Table 3: Magnitude of the Difference between Natives and NonNatives (percentages)

||(1)<br>Difference<br>/ SD Total|(2)<br>Difference<br>/[Decil 9 - Decil 1]|
|---|---|---|
|Precarious housing|34.28|45.00|
|Ownership land & house|8.77|22.22|
|Years of education [24+)|16.89|19.89|
|Use computer [14+)|5.56|8.11|
|Legally married [18+)|15.95|36.36|
|Economically active [18-65]|3.36|4.76|
|Formal employment [18-65]|8.97|14.29|
|Unemployment [18-65]|4.99|20.00|
|Health insurance|24.15|33.33|
|Number of disabilities|7.53|29.41|
|Teen pregnancy [14-19]|5.29|25.00|
|Average|12.34|23.49|

_Source_ : REDATAM INDEC Censo Argentina 2010. _Notes_ : Difference corresponds to the columns without FE in Table 2. In Column (1), the difference is divided by the standard deviation of each outcome. In Column (2), the difference is divided by the difference between the top and bottom decile of each outcome (calculated at the department level).

four outcomes.

Table A3 in the Appendix allows us to compare the magnitudes of these disadvantages in terms of standard deviations and relative to the difference between counties in the first and ninth deciles. In terms of standard deviations, the disadvantage faced by Natives is greater in the US than in Argentina in all outcomes but precarious housing. And relative to the difference across counties, the magnitude of the disadvantage of Natives is greater in the US than in Argentina for all outcomes in which Natives face a disadvantage.

While a comparison of the disadvantages faced by Natives across Latin American countries falls beyond the scope of this paper, we can compare the disadvantages we document in Argentina with the disadvantages regarding labor force participation, unemployment and use of computers documented for other countries by Freire et al. (2015). Freire et al. (2015) document small unemployment gaps between Natives and non-Natives based on census data from urban areas in seven Latin American countries. In some of these countries, Natives have a lower unemployment rate, while in others they have a higher unemployment rate (the differences are 2 percentage points or less). As shown in Table 3, Natives experience 1.2 percentage points higher unemployment in Argentina (without distinguishing between rural and urban areas). For urban areas this gap is 1.3 9