rafmacalaba/datause-encoder-data huggingface dataset:
**Table 8.  Values of Adjusted R**<sup>**2**</sup> **for Regressions With and Without Public Data**

||Surveyregression|Full regression|Public data regression|
|---|---|---|---|
|Overall|.262|.287|.077|
|Poor households (bottom 2/5)|.155|.188|.048|
|Very poor households (bottom quintile)|.100|.128|.030|
|Net crop sellers|.187|.216|.075|
|Urban households|.252|.274|.036|
|Household size:||||
|One person|.222|.243|.050|
|Two|.315|.343|.078|
|Three|.315|.346|.100|
|Four|.349|.390|.136|
|Five|.333|.374|.144|
|Six or more|.351|.392|.145|

Variables included:

**Survey regression:** As in Table 1: size of household; household runs a business; most income from wages; most income from profits; no. of males; no. aged under 5, 5-9, 10-14, 15-19, 20-29, 30-59, 60-64, 65-74, 75 and over; head is a widow; head is separated; no. with just primary education, just secondary education, just upper vocational education, university education, post-graduate education.

**Public data regression:** Dummies for region (N, C, NE, S, Bangkok is reference), and for urban residence; monthly series on: unemployment rate; consumer price index for south, northeast, north, center, Bangkok; U.S. dollar value of exports of agricultural products, manufacturing, overall; index of baht value of export price, export volume, import price, import volume; terms of trade; business sentiment index; expected business sentiment index; leading economic index; no. of foreign tourists; inverse of price of oil; monthly wage in private sector; monthly wage in government sector; price of 5% broken rice (in baht); price of tapioca flour (in baht); price of tin concentrate (in baht). **Full regression:** All variables in Survey and Public Data regressions. Number of observations: 176,141 households for full sample.

# **Conclusions**

In this paper we ask a straightforward question: Who was hurt in Thailand by the global recession that spread from the world’s rich countries to Thailand in late 2008 and persisted for much of 2009?

Somewhat to our surprise, we find that the recession itself was not associated with a reduction in real spending for most groups in Thai society. Neither women, nor the poor, nor rural residents, nor children, were particularly hurt by the recession. The main exception is that young adults, especially those residing in Bangkok, and with a vocational education, were negatively affected in 2009. This is consistent with the observation that export-led manufacturing, which is concentrated around Bangkok, contracted sharply for several months.

There are a number of possible explanations for the benign, and for some people, even beneficial, effects of the recession of 2008-09. The first is that the prices of food – including the Losers and Winners in Recession: Thailand 2008-09 26
- Kunwar, Surendra B. (2014), Complementarity of Wind, Solar and Hydro Resources for Combating Seasonal Power Shortage in Nepal, World Sustainability Forum 2014 – Conference Proceedings Paper.

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- Royal Government of Bhutan (RGB), 2010. Power Sector Development Plan, RGB, Thimpu.

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36
come primarily from expanding exports of wine and beer to both developing and industrial country markets. They also increased their exports of processed foods.

Despite these changes, whatever the causes might be, analysis of agricultural trade for developing countries now needs to focus on the non-traditional commodities. Currently vegetables and cut flowers constitute almost 20 percent of the exports of developing countries. Temperate zone products constitute another 30 percent. Exports of beverages and other processed foods now are almost equal to total exports of coffee, cocoa, and tea. Exports of fruits and vegetables alone are more than total exports of tropical products. While the traditional products that have received most of the attention in the literature, they now constitute only 16 percent of the exports of developing countries. Attention also has to be placed on the expanding trade within developing countries in temperate zone products such as milk, grains, and meats. These developments show that many developing countries can compete in the product categories historically dominated by industrial countries and that trade reforms in industrial countries could lead to a large expansion of exports from these developing countries.

Industrial country export structures have also changed very little.<sup>15</sup> And product groups that have changes are very similar to those of developing countries. As expected, they are the main exporters of temperate food products, which constitute 37 percent of their exports. Temperate products plus beverages and processes foods now constitute more than half their exports.

# **_Export Shares by Product and Region_**

Next question is what has happened to the shares of exports of different regions, and to which markets they have exported these product groups. Table 11 shows the direction of exports from developing countries by product categories and by regions. The geographical structure of developing country total exports has shown some small changes in the past two decades. Almost every region has increased its share of world exports. The decline in the share of industrial country exports have been shared by most of the developing country regions. This is especially pronounced for Europe and Central Asia (ECA) which was primarily a closed region before 1991.<sup>16</sup> The increases are very limited for Middle East and South Asia. Sub-Saharan Africa is the only region that has a decrease in its export share and the loss of market shares from 1980s are still continuing. Despite preferential access, Africa’s export share in industrial country markets has decreased and the increase in its exports to developing countries has not made up for this decline.

> 15 This data includes re exports. That is why there is a large tropical product exports form industrial countries.

> 16 Intra Warsaw Pact trade might be underestimated previous to 1990.

17
resources. These results are consistent with Faria _et al._ (2007) where greater equity share in total liabilities is attained by larger countries with better institutional quality and greater reliance on natural resources.

# **4. Empirical Evidence**

This section describes the main results of our empirical analysis of the determinants of RER volatility for our sample of 82 countries, using 5-year non-overlapping observations, over the period 1975-2005. First, we describe the main statistics and present a basic correlation analysis. Then, we carry out the panel data regression analysis.

# **4.1 Basic Statistics and Correlation Analysis**

Table 1 presents the basic statistics on RER volatility, openness and the volatility of the RER fundamentals. We report the averages of these variables for the full sample of countries and for sub-samples classified according to the level of development of the economy and the income level.

We first find that RER fluctuations are more volatile in developing countries than in industrial economies. On average, RER volatility in developing countries is almost twice as volatile as that of industrial economies. This reflects the higher volatility in the RER fundamentals in developing countries: productivity shocks and monetary policy shocks in developing countries are, on average, more than twice as volatile as those in advanced economies while fiscal policy shocks are almost five times as volatile in developing countries as those shocks in industrial countries.

Second, although industrial countries have a larger history of free trade regimes according to the Wacziarg-Welch policy indicator of trade openness, data on real exports and imports as percentage of GDP shows that, on average, developing countries are more integrated to the world markets of goods than industrial economies —particularly, in trade of non-manufacturing goods (that is, trade in commodities). On the other hand, industrial economies are more integrated to international financial markets than developing countries —either using policy or outcome measures of financial openness.

Third, low-income countries display higher RER volatility as well as higher fundamental volatility (i.e. productivity shocks, monetary and fiscal policy). They are not only less integrated to the world markets of goods but also have more restrictions on crossborder transactions and lower degree of international financial integration. Moreover low16
holds self-selecting, where only those in very dire circumstances might be claiming food assistance. However, it is further important to note that the increase in assistance was persistent and continued for at least the entire year following displacement. Thus, the large amount of food assistance that is reaching displaced households is likely contributing to the quick post-displacement recovery of the worst-off households. Importantly, as food prices continue to rise and as repeated currency crises continue to translate into rapidly rising food prices, this assistance is significantly increasing in value over time.

However, the increase in assistance only reached approximately half the displaced households in the sample. Since better-off households that did not receive additional food assistance also experienced a recovery, there are likely other factors contributing to their surprisingly quick recovery. For example, it is possible that some of the better-off households had more resources (e.g., networks) to rely on after settling in the migration district or that they were generally better able to assimilate. Regardless, even with these potential advantages over relatively worse-off households, other settings have inferred much more persistent adverse outcomes following displacement using different estimation strategies, such as comparing post-displacement outcomes of displaced to host populations (e.g., IPC 2017; etc.) or analyzing the well-being of displaced households long after the conflict has ended (e.g., Ibanez and Velez 2008).

# **Section 6. Conclusion**

Using rare food security and migration data from the ongoing conflict in the Republic of Yemen, we find a sharp, immediate decline in food access after displacement and a return to pre-displacement levels within months, on average. A portion of this rapid improvement is driven by increased food assistance, but there was also a surprising improvement among a subset of the sample that did not receive additional food assistance.

Combined, these results illustrate the consequences of forced displacement during active conflict. However, there are several caveats to this analysis. First, given data constraints, this analysis focuses on food access and not other welfare metrics that could be particularly important to displaced households. Although food security is one of the 21
# **3 Impact Evaluation**

## **3.1 The Data**

Our dataset is constructed using three primary databases which together form a rich and unique combination. First, we have export data at the transaction level (aggregated to the firm level) from raw customs files over 2000–2008. Each record includes the firm’s tax ID, the product code, the country of destination, and the export value (in US dollars) and quantity (in tons) for over 500 HS8 products to 90 countries. Exports flows are reported annually. Second, the PIP’s administration in Brussels provided us with a list of the Senegalese firms that got assistance from the program in each year of the sample period.<sup>14</sup> Finally, we obtained data on employment and sales from the CNI, Senegal’s National Statistics Direction.<sup>15</sup> As the CNI also identifies firms by their tax ID, we could merge the three datasets. Among the reporting firms, almost 3% appear only once in the dataset. That is, they export only one product to one destination one year. As these observations are likely to be mis-reports or individuals, we drop them from our sample. We also drop international organizations and embassies, as well as trading and transport companies (the latter group represents about a quarter of all observations). This leaves us with a sample of almost 2’000 observations.

Let _i_ an exporting firm, _k_ be a product, _j_ a destination country, and _t_ a year. As the PIP targets _products_ (FFV), some of a firm’s products may be covered and some others not. In addition, technical assitance provided under the PIP helps make FFV marketable on EU markets, but does not necessarily help on other markets with different (or no) food-safety requirements. In view of this, we take the ( _i, k, j, t_ ) vector as our Primary Sample Unit (PSU). Our dependent variable is the annual flow of exports of product _k_ to destination _j_ by firm _i_ in year _t_ , _yikjt_ . That is, we take the intensive margin as our baseline measure of performance. Table 1 gives descriptive statistics of firm-level covariates and performance indicators for treated and non-treated flows. Covariates include firm _i_ ’s annual (overall) turnover, _salesit_ , and employment, _Nemployeeit_ , the number of products it exports. We also include _Nprodit_ , the number of products it exports to destination _j Nprodijt_ , the number of destinations to which it exports product _k_ , _Ndestikt_ and a binary variable equal to one if firm _i_ has more than one year of experience in exporting product _k_ to destination _j_ , _expikj_ are included. Performance indicators at the intensive margin include firm _i_ ’s exports of product _k_ to

> 14Variation in the intensity of the treatment across firms would induce a bias in the estimation of the effect. Unfortunately, we do not have data on the intensity of the treatment.

> 15The data is collected by the _Centre National d’Identification_ (CNI), part of the National Statistics Direction.

12
**Table 5. Index of Export Similarity: Southeast Asia vs. China**

||**Compo**<br>|**nent**<br>|**Finish**<br>|**ed**<br>|
|---|---|---|---|---|
||93-94|03-04|93-94|03-04|
|Power generating eqp|58.3|72.8|76.3|73.8|
|Industrial mchn & eqpm|55.3|60.5|40.7|63.9|
|Office, data & telecom|77.0|79.0|65.8|80.1|
|Electrical machinery|39.6|53.6|67.7|74.2|
|Road transportations|95.3|87.8|48.7|19.3|
|Other transp. Eqpm|92.9|80.0|8.7|31.3|

Source: UN-COMTRADE

**Table 6. Bilateral Index of Export Similarity**

||**China**|**and**|||**Indon**|**esia and**||**Malay**<br>**and**|**sia**|**Philippines**<br>**and**|
|---|---|---|---|---|---|---|---|---|---|---|
||Ind|Mys|Phl|Tha|Mys|Phl|Tha|Phl|Tha|Tha|
|Component|||||||||||
|93-94|58.2|45.3|26.9|55.3|38.2|25.3|45.9|63.1|67.8|45.9|
|03-04|74.7|53.9|42.6|70.1|49.5|41.6|66.5|77.8|72.8|60.3|
|Finished|||||||||||
|93-94|47.3|49.2|35.8|48.4|55.7|29.9|41.0|34.3|51.3|24.3|
|03-04|50.9|70.4|46.9|44.5|42.5|30.7|41.6|45.5|44.8|37.0|

Source: UN-COMTRADE

**Table 7. Revealed Comparative Advantage Index**

||Expor<br>RCA i|t<br>ndex|||Percen|t with R|CA >1||Impor<br>RCA I|t<br>ndex|Percen<br>RCA|t with<br>>1|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Comp|onents|Finish|ed|Comp|onents|Finish|ed|Comp|onents|Comp|onents|
||93-94|03-04|93-94|03-04|93-94|03-04|93-94|03-04|93-94|03-04|93-94|03-04|
|Indonesia|0.2|0.5|0.2|0.3|2.8|13.8|8.1|9.3|1.2|0.7|51.4|50.5|
|Malaysia|1.8|1.9|1.0|0.9|14.7|18.3|12.7|18.7|2.3|2.7|45.9|44.0|
|Philippines|0.8|2.4|0.3|0.6|6.4|13.9|6.2|8.1|1.1|3.0|28.4|23.1|
|Thailand|1.0|1.3|0.6|0.8|19.3|25.0|13.4|20.3|1.6|1.6|56.0|47.2|
|China|0.4|1.0|0.5|1.2|9.2|27.5|16.7|32.0|0.9|1.7|45.0|43.1|
|DevEA|0.8|1.2|0.5|1.0|16.5|24.8|16.7|28.0|1.4|1.9|56.9|48.6|

Source: UN-COMTRADE

> <sup>24</sup>
# **Appendix Table 1: Descriptive Statistics of the Variables Used in Regression Analysis**

|Variable|No. of<br>Observations|Mean|Standard<br>Deviation|Minimum|Maximum|
|---|---|---|---|---|---|
|SME Loans/Total Loans (%)|271|10.1|12.3|0.0|55.0|
|Investment Loans/SME Loans (%)|270|24.1|22.8|0.0|80.0|
|Log Total loans|339|20.8|2.4|13.8|24.7|
|Log Number of Branches|396|3.3|1.4|0.0|6.7|
|Maximum Coverage Registry or Bureau|381|8.0|9.8|0.0|36.0|
|Legal Rights Index|399|3.1|1.2|0.0|4.0|
|Time to Register Property|399|29.4|34.1|2.0|193.0|
|Time to Enforce a Contract|399|662.8|142.8|520.0|1010.0|
|Recovery rate for Closing a Business|348|31.0|15.1|10.0|63.0|
|Time to Close a Business|348|3.6|0.9|1.0|5.0|
|Partial Credit Guarantee % of GDP|399|0.1|0.2|0.0|0.9|
|Lag of Partial Credit Guarantee % of GDP|266|0.1|0.2|0.0|0.9|
|Median Coverage of PCG Scheme|210|68.1|7.5|60.0|82.5|
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# **1. Introduction**

Brain drain has long been one of the most common concerns developing countries have about migration. This concern has been amplified in recent years by the rapid increase in skilled emigration, driven in large part by developed countries shifting to more skill-intensive immigration systems. There is a long, mostly theoretical, literature on the _consequences_ of brain drain for developing countries, with recent literature also pointing to the possibility of “brain gain” from highly skilled emigration through impacts such as an increase in the incentives to acquire human capital, remittances sent home, and return migration<sup>1</sup> . There is much less research on the _determinants_ of the brain drain. Portes (1976) asked the central research question which has so far not been addressed. He wrote (p. 490) “given the...attractions of emigration, the real question is often not why some professionals migrate but why so few in fact leave”.

Several recent papers have looked at the macroeconomic determinants of why the level of brain drain varies across countries (Docquier, Lohest and Marfouk, 2007; Belot and Hatton, 2008; Beine, Docquier and Schiff, 2008). They find country size to be an important determinant, with much higher emigration levels from small states, and also consider country-level determinants such as income level, distance to major destinations, colonial origin, language, and political environment. Such analysis provides a first step towards understanding why some countries have higher brain levels than others, but does not allow us to answer the key question posed by Portes, which is at the individual level, why do some highly skilled individuals within a country leave, while others stay. Moreover, amongst those who go, why do some return?

Answering this question requires data on highly skilled individuals who stay and on the emigrants. Existing surveys consist of one group but not others, preventing this comparison being made. This paper presents evidence from new surveys designed by the authors to study the individual level microeconomic determinants of the brain drain, and the determinants of return migration among the highly skilled. Brain drain is of particular concern to small states (Beine et al. 2008). We focus our study on three small states with varying levels of development, population size, and brain drain. The three

> 1 Recent literature reviews are also found in Kapur and McHale (2005), Commander, Kangasniemi and Winters (2004) and World Bank (2006).

- 2 -
|Purchased fixed asset Y/N|16,798|0.47|0.50|0|1|
|---|---|---|---|---|---|
|Establishment has a line of credit or loan|16,700|0.40|0.49|0|1|
|Bribery depth|18,394|15.23|33.12|0|100|
|Bribery incidence|18,394|0.20|0.40|0|1|
|Informal payment/gift requested for water connection Y/N|1,029|0.21|0.41|0|1|
|Proportion of public transactions (excluding water) where<br>a gift or informal payment required|938|0.24|0.38|0|1|
|Firm experienced at least one public transaction (excl.<br>water) where a gift or informal payment was required Y/N|938|0.32|0.47|0|1|
|Wait for water connection (days)|1,029|40.55|83.28|1|1095|
|Establishment has checking or savings account Y/N|1,029|0.84|0.36|0|1|
|Female manager Y/N|1,029|0.1269|0.333|0|1|
|Use technology licensed from foreign companies Y/N|1,029|0.21|0.41|0|1|
|Severe positive precipitation shock severe (SPI > 1.5)|24,747|0.06|0.23|0|1|
|Severe negative precipitation shock (SPI< -1.5) - Drought|24,747|0.12|0.33|0|1|
|Average historical temperature 1900-2014|24,747|20.11|6.95|-8.88|31.04|

# **<u>Table 2: Determinants of Water Shortages</u>**

## **Y = No of water shortages per day in a typical month**

||**All eco**<br>**(1)**|**nomies**<br>**(2)**|**High and Upper**<br>**Middle Income**<br>**(3)**|**Low and Lower Middle**<br>**Income**<br> **(4)**|
|---|---|---|---|---|
|Positive precipitation shock severe (SPI)||0.001|-0.007**|0.003|
|||(0.009)|(0.003)|(0.013)|
|Negative precipitation shock very severe -<br>Drought (SPI)||0.054***|-0.001|0.143***|
|||(0.005)|(0.003)|(0.012)|
|No of electrical outages per day in a<br>typical month||0.057***|0.014***|0.062***|
|||(0.017)|(0.004)|(0.020)|
|Firm owns or shares a generator Y/N|0.007|0.011|0.009|0.014|
||(0.008)|(0.009)|(0.007)|(0.015)|
|Firm size: Log of total number of<br>employees|-0.003|-0.002|0.001|-0.005|

22