rafmacalaba/datause-encoder-data huggingface dataset:
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rate of high skill migrants and the growth rate of low skill migrants in each source-destination corridor.

A clear positive relationship is evident both for the cross section and for the growth rates: countries that sent relatively many high skilled migrants to one country also sent relatively many low-skilled migrants to the same country. Similarly, when a country increases the number of high skill migrants it sends to a recipient country, the number of low skilled migrants to the same country also increases. To investigate further, we look for Granger causality in these datasets from low skill migrants to high skill migrants and vice versa. Granger causality measures causality in a statistical sense: variable _x_ is said to Granger cause y if the history of _x_ predicts the current value of _y,_ conditional on the history of _y_ . We regress the level of high skill migrants on the lag of high skill migrants and the lag of low skill migrants, all measured in logarithms, along with year and destination country fixed effects. In the OECD data, there is strong evidence of Granger causality from high skilled to low skilled migration (coefficient of 0.118), and strong evidence of Granger causality from low to high skilled (coefficient of 0.129), both significant at the 1 percent level. For the U.S. data, it is also the case that the number of high-skilled migrants in one period helps predict the number of low-skilled migrants 10 years later (coefficient of 0.128), with significance at the 5 percent level.

Therefore it does not appear to be the case that the rise in skilled migration is coming at the expense of less-skilled migrants – instead countries with a high number of skilled migrants also have a high number of less-skilled migrants in both the cross-section and over time.

# **Question 5: What Makes Brain Drain More Likely?**

High-skill individuals often stand to multiply their income by emigrating out of lowincome countries. Yet in most developing countries, the majority of skilled individuals do not emigrate. Recent empirical research at both the macro and micro levels offers evidence on what factors make migration of high-skill individuals more likely.

At the macroeconomic level, the rate of brain drain varies widely across countries. The average developing country has 7.3 percent of its tertiary-educated population stock in higherincome countries, with this proportion varying from 5.4 percent or below in developing countries with populations of 40 million or more to 13 percent in sub-Saharan Africa and 45 percent in 10
Table 12 compares the estimated percentage of Nikkei households with migration experience, and which receive remittances by survey. There is general support for the fourth hypothesis: both intercept surveys significantly overstate the percentage of households with a member who has returned from working or studying abroad. The snowball survey also gives a higher estimated percentage, although the smaller sample size leads this difference to not be statistically significant. From the stratified survey, we estimate that in both São Paulo and Parana, 19-21 percent of households have a member currently in Japan, and 37-41 percent have a member who has returned from working or studying in Japan. In contrast, the intercept and snowball surveys in São Paulo estimate that one in three Nikkei families have a migrant currently in Japan, compared to the one in five estimate from the stratified sample.

**Table 12: Comparison of Migration and Remittances of Nikkei Across Different Sampling Methods**

||Stratified<br>Survey|Unweighted<br>Intercept<br>Sao|Weighted<br>Intercept<br>Paulo|Snowball|Stratified<br>Survey|Unweighted<br>Intercept<br>Parana|Weighted<br>Intercept|
|---|---|---|---|---|---|---|---|
|_% of households which:_<br>**Migration**||||||||
|Have a Member currently in Japan|19|35***|33**|30|21|18|16|
|Have a Member who has returned||||||||
|from work/study in Japan|41|65***|59***|51|37|51***|49**|
|**Remittances**||||||||
|Receive remittances from Japan|14|10|10|10|16|4***|3***|
|Refuse to say if they receive remittances|3|2|3|7|5|0***|0***|
|Refuse to say how much they receive if receiving|31|73***|82***|50<sup>+</sup>|61|20***|29*|
|**Amount (Reales)**||||||||
|Annual Amount Received Conditional on Receiving||||||||
|Remittances and Reporting Amount<br>Mean|5404|1483*|1429*|9400|4143|10850**|11528**|
|Median|2500|1792|1792|2400|3000|9000|10000|
|Sample Size|204|516|516|100|199|392|392|

*, **, and *** denotes that the mean or proportion is significantly different from that in the stratified sample at the 10%, 5% and 1% significance levels respectively.

+, ++, and +++ denotes that the snowball mean or proportion is significantly different from the weighted intercept at the 10%, 5% and 1% significance levels respectively.

Despite these large differences in migration rates, the proportion of households receiving remittances from Japan is similar across the different survey methods in São Paulo. However, the Parana intercept survey substantially underestimates the percentage of households receiving remittances, compared to the stratified sample. Finally, we see that 32

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**Figure 2: Integrated vs. Less Integrated Region Gap in Log Real Per Capita Expenditures**

<!-- Start of picture text --> 0.25<br>0.2<br>0.15<br>0.1<br>0.05<br>0<br>0.05 0.15 0.25 0.35 0.45 0.55 0.65 0.75 0.85 0.95<br>Percentile<br>2000 2005<br> Figure 3: Urban-Rural Gaps in Log Real Per Capita Expenditures<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95<br>2000 2005<br><!-- End of picture text -->
reducing consumption, especially in the case of smoking (Chaloupka and others 2000; Chaloupka, Grosmman and Saffer 2002; Booth and others 2008). But other behaviors, such as risky sex or illicit drug use are often difficult to observe or monitor, either because they are illegal or because they are socially stigmatized, and are therefore difficult to tax and regulate.

A tax hike directly increases the price of the harmful product or behavior, but other incentives can work in the other direction and financially encourage the corresponding safe behavior, indirectly raising the opportunity cost of the risky behavior.

Many of these financial incentives have been proposed and tested in high income countries (Sutherland, Christianson and Leatherman 2008), except for incentives for safe sex and HIV prevention which have been mainly evaluated in Africa where HIV/AIDS prevalence is highest. Given the rise of smoking and obesity rates in many middle- and low-income countries, it is useful to consider how financial incentives for preventing behaviors which are risky to one’s health could be further applied and tested in developing countries.

This paper will first develop in section 2 the theoretical foundations for the use of financial incentives to prevent undesirable behaviors. Section 3 will review several examples of incentives which have been used and evaluated to prevent undesirable behaviors, with a focus on unsafe sex and HIV prevention, smoking, alcohol and drug abuse, unhealthy diet and obesity and finally behaviors which are not directly related to a health risk, such as early marriage. This review will cover evidence from low- and middle-income countries when available, but also from high income countries. Section 4 will discuss issues which are common to these types of incentives focusing on their sustainability and how different designs can influence costeffectiveness. Section 5 concludes.

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# FIGURE 5. CUMULATIVE MORTALITY RATES BY MEASLES VACCINATION STATUS AND BY HOUSEHOLD AREA: MATLAB INTERVENTION AREA, 1982-85

<!-- Start of picture text --> 100<br>WUNVACCINATED VACCINATED<br>D 80 - 77.8<br>e<br>a<br>t 636<br>h 60-<br>e<br>r 40<br>0~~~~~~~~~~~~~~~~~~~~3.<br>0<br>0 20<br>0<br>'200 SQ.FT. 201-400 SQ.FT. 401 SQ.FT.<br>HOUSEHOLD AREA<br>CUMULATIVE MORTALITY RATE 42 MONTHS AFTER VACCINATION<br><!-- End of picture text -->
# **3.5 Impacts on Credit**

Finally we examine whether the credit portion of the financial literacy training led to individuals being more or less likely to have certain forms of credit in our final survey round. We restrict our analysis here to the Pacific Island sample, since the other two groups did not have credit covered in their financial literacy training, and because these other two groups had such high attrition in the final round survey.

Table 7 shows the credit impacts of the financial literacy training. We do not see any significant impacts on individuals having cheque accounts, savings accounts or ATM cards. Despite the emphasis in training on the Westpac prepaid card being the cheapest method to send, and credit cards being a cheaper source of financing, we do not see significant increases in usage of either. Despite (or perhaps as a result of) media coverage of the high cost of payday loans (e.g. Waikato Times, 2012) we find no one in our sample who said they had applied for a payday loan. This may be genuine, or reflect reluctance to admit use of this form of credit (Karlan and Zinman, 2008). What we do see are positive and significant impacts on obtaining hire purchase loans, and on other loans. Qualitative discussions reveal that the hire purchase loans were the result of special promotions before Christmas, in which special “zero interest rate” deals were coupled with free Christmas hams, possibly making these loans a good deal. The main other loans were extended family ROSCAs, which respondents said they had set up to avoid the high costs of payday loans. Taken together this suggests some potential positive impact of the training, although since we do not measure any reduction in use of other more expensive forms of credit, this use of cheaper forms of credit may lead to an increase in credit usage – which is of uncertain overall welfare effect.

# **3.6 What Do Participants Say the Main Benefits Have Been?**

Individuals who attended training were asked in the one month follow-up whether it had been useful, whether they would recommend it to friends and family, what the most useful topic was, and whether they had changed behavior as a result. Among the Pacific Island group, all participants said it was very useful and that they would recommend it to others. The topic rated most useful was on the different costs and methods of sending money, with a number also mentioning information on credit cards as useful. Sixty percent of respondents said they had 18
# **Table 4. Largest Increases in Russian Exports to Belarus and Kazakhstan by Product at the HS 2-digit Level, 2008–12**

|**Product**|**Product description**|**Change in Value**<br>**of Russian**<br>**Exports**<br>**(US$, millions)**|**Change in**<br>**Value of**<br>**Russian**<br>**Exports**<br>**(Percent)**|
|---|---|---|---|
|87|Vehicles other than railwayor tramwayrollingstock, andparts and accessories thereof|56|6.7|
|85|Electrical machinery and equipment and parts thereof; sound recorders and reproducers,<br>television image and sound recorders and reproducers, andparts and accessories of such articles|29|2.8|
|84|Nuclear reactors, boilers, machineryand mechanical appliances;parts thereof|21|0.9|
|73|Articles of iron or steel|19|1.3|
|39|Plastics and articles thereof|8|1.1|
|94|Furniture; bedding, mattresses, mattress supports, cushions and similar stuffed furnishings;<br>lamps and lighting fittings, not elsewhere specified or included; illuminated signs, illuminated<br>nameplates and the like;prefabricated buildings|7|3.7|
|30|Pharmaceuticalproducts|6|6.3|
|44|Wood and articles of wood; wood charcoal|6|1.5|
|33|Essential oils and resinoids;perfumery, cosmetic or toiletpreparations|5|4.5|
|86|Railway or tramway locomotives, rolling stock and parts thereof; railway or tramway track<br>fixtures and fittings and parts thereof; mechanical (including electromechanical) traffic<br>signalingequipment of all kinds|4|1.0|

_Source:_ World Bank.

> RUSSIA’S TRADE POLICY CHALLANGES – THE WORLD BANK GROUP 31
proposed PPML approach in the gravity model literature. Subsequent papers build upon the PPML approach and establish its appropriateness for gravity model estimation (Santos Silva and Tenreyro 2010, 2011, 2015).

In this paper, we apply a panel-data PPML estimator that includes the multilateral resistance terms, pair-specific fixed effects, and time-varying country effects. As a result, many of the traditional gravity variables drop out of the estimation. Standard errors are clustered on trading pairs.

# _Construction of the Main Data Set_

The dataset used in the estimation combines two separate datasets. The first dataset is the Handbook of Deep Trade Agreements (HDTA) database. The HDTA database includes a list of 279 agreements along with the year of entry and the agreement type (e.g. Free Trade Agreement, Customs Union, Economic Integration Agreement and PSA). Each chapter of the HDTA includes the relevant data tied to the name of the agreement. The HDTA also includes many, but not all, of the relevant country pairs. To complete the data, I identified the year of entry and countries included in the agreement and merged them into the HDTA database so that they could be included in the subsequent merges.

The second dataset is the 2020 version of the CEPII gravity model database (Head and Mayer 2010).<sup>6</sup> These data include common language, same country, common religion, distance between countries, and contiguity. These data include several measures of trade flows that come from either the International Monetary Fund (IMF), the United Nations COMTRADE database, and the BACI (Base pour L’Analyse du Commerce International) revisions to the COMTRADE data for both total and manufacturing trade (Gaulier and Zignago 2010). The advantage of the BACI data over the raw COMTRADE data is that the applied revisions reconcile the discrepancies found between the exporter-reported and importer-reported trade volumes and adjust for data reliability. The data also include information about whether missing values are the result of no trade (“true zeros”) and unreported positive flows. The empirical work that follows employs the first of two possible adjustment for the zero/missing data. The CEPII data also include Gross Domestic Product (GDP), distance (measured as the population-weighted

> 6 Data are available at http://www.cepii.fr/cepii/en/bdd_modele/bdd.asp.

pg. 8
not take up their initial job in New Zealand, the employment expectations of the ballot losers were lower by 19.6 percentage points.

The standard approach to evaluating the impact of a policy is to compare outcomes for those subject to the policy to outcomes for a comparable group not subject to that policy. However, as Miguel and Kremer (2004) point out, this can give misleading estimates of the effect of a policy when there are externalities. They investigate the impact of a deworming treatment in schools in Kenya which was randomized across schools. Using GPS distances at the level of the school, they control for the number of primary school pupils within a certain distance of the school, and then use the number of treated pupils within this distance to measure health spillovers. They find that naïve estimates which fail to take externalities into account would underestimate the program treatment effects, leading to the mistaken conclusion that deworming is not cost-effective.

Miguel and Kremer (2004) use GPS distances at the level of a school, and note that these are subject to some measurement error due to U.S. government using Selective Availability to downgrade GPS accuracy at the time of their survey. Now that more accurate measurement is available, a refinement to their research would be to use GPS locations of the residences of each individual child, which could then be used to construct a child-specific measure of exposure to treated and non-treated children. This would provide more variation in the extent of spillover, which could be used to examine the heterogeneity in treatment effects.

# **_3. Information on the spatial distribution of population and services is essential to understanding access to services_**

One of the most common uses of GPS information to date in developing countries has been to measure access to infrastructure and social services, particularly health care. For example, Perry and Gessler (2000) use GPS to measure access from communities to primary health care facilities in Andean Bolivia and use this to propose an alternative model of health distribution in the study area.

In addition to providing purely descriptive measures of access, GPS data on distance and travel times can be used to understand barriers to the use of particular services. Entwisle et al. (1997) examine the importance of accessibility to family planning on choice of contraceptive device, and in doing so, demonstrate two advantages of GPS over survey-based measures of

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