rafmacalaba/datause-encoder-data huggingface dataset:
institutions in four countries—the United States (330 individuals), the United Kingdom (90), Germany (69) and France (33)—have been awarded over 80 percent of these Nobel Prizes.<sup>3</sup> The US domination of these awards is fueled in large part by emigration of prominent scientists. In the first third of the time period since 1901, 9 percent of these Nobel Prize winners were born in the United States and 13 percent were affiliated with US institutions at the time of their winning. In the most recent third of the time period, however, academics associated with American institutions have won over 65 percent of these Nobel Prizes, yet only 46 percent of this group was born in the United States. Of all Nobel Prizes across the four subject areas, 31 percent (203 of 661) have been awarded to immigrants, of whom 53 percent (107 of 203) were affiliated with American institutions. The asymmetry of the flows is remarkable. Only four Americans were affiliated with non‐American institutions when they received the award, out of 230 total Nobel Prizes being awarded to Americans. Even among advanced economies, the most elite researchers are often clustered in very select locations.<sup>4</sup> Figure 1 shows that these observations are indicative of wider trends. OECD countries continue to attract larger numbers of high‐skilled migrants, particularly from non‐OECD countries. While high‐skilled migration within OECD countries rose 68 percent to 10.2 million between 1990 and 2010, the total number of high‐skilled migrants from non‐OECD countries increased 185 percent, from 6.2 million to 17.6 million. This pattern is also evident in the extreme cases of sending countries. For the two decades prior to 2010, the United Kingdom was the largest origin country in terms of numbers of outbound skilled migrants. It was supplanted in 2010 by India (2.1 million) and the Philippines (1.5 million), while China (1.4 million) also had high absolute numbers of high‐ skilled emigrants. In terms of the greatest increases over time (and considering only those large countries that sent at least 25,000 highly skilled workers abroad), Algeria (a rise of 954 percent), Russia (910 percent), Bangladesh (459 percent), Romania (428 percent), Venezuela (423

> 3 Data for Nobel prizes is derived from <u>https://www.aggdata.com/awards/nobel_prize_winners, supplemented with</u> data from the Nobel Prize website for the years 2011‐2015.

> 4 As a second example from our profession, the John Bates Clark Medal is awarded annually to a top “American” economist under the age of forty. Four of its nine winners since 2005 were born outside of the United States.

6
# Table 18

Composition of New Supply of Industrial Equipment Fund by Major Branch of Manufacturing Industry (billion yen and percentages)

|T|otal|Stocks|Bonds|Loans|PFIS|6FI8|Special<br>Account|
|---|---|---|---|---|---|---|---|
|Textile||||||||
|1962/64|435|68<br>|23<br>|344<br>|285<br>|59<br>|--<br>|
|||15.6|5.3|79.1|65.5|13.6|0.0|
|1965/67|489|7<br>|34<br>|447<br>|365<br>|82<br>|--<br>|
|||1.4|7.0|91.4|74.6|16.8|0.0|
|Chemicals||||||||
|1962/64|976|210|24<br>|742<br>|667<br>|75<br>|--<br>|
|||21.5|2.5|76.0|68.3|7.7|0.0|
|1965/67|1267|116<br>|68<br>|1083<br>|1004<br>|79<br>|--<br>|
|||9.2|5.4|85.4|79.2|6.2|0.0|
|Machinery||||||||
|1962/64|1149|257<br>|63<br>|829<br>|727<br>|102<br>|--<br>|
|||22.4|5.5|72.1|63.3|8.9|0.0|
|1965/67|1280|62<br>|166<br>|1051<br>|919<br>|118<br>|14<br>|
|||4.8|13.0|82.1|71.8|9.2|1.1|
|Iron<br>& Stee|l|||||||
|1962/64|732|220|47<br>|466<br>|441<br>|25<br>|--<br>|
|||30.1|6.4|63.9|60.2|3.4|0.0|
|1965/67|780|8<br>1.0|101<br>12.9|670<br>85.9|643<br>82.4|27<br>3.5|--<br>0.0|

Source: Bank of Japan, Economic Statistics Annual, 1964 (pp. 35-36) and 1967 (pp. 53-54) 49
ASIA DEPARTMENT I

## 1. zstimates and Projections

|Tow|PupAltts|**I".**<br>"etie|Paled|**el-ON ,4**<br>rate||WI|TUE|ftm|**F**<br>**tmti**|IM|xi=|
|---|---|---|---|---|---|---|---|---|---|---|---|
|195<br>1o9<br>1¶99<br>2000<br>**200**<br>2010<br>2015<br>2020<br>2025<br>2050<br>2075<br>2100<br>21n<br>2150|136396<br> <br>150626<br>1¶6971<br>¶M34<br>200765<br>21756<br>233927<br>248581<br>26Is81<br>323011<br>355763<br>374662<br>387518<br>394210|89.2<br>83.1<br>73.4<br>67.4<br>62.4<br>58.4<br>54.5<br>50.3<br>47.5<br>50.3<br>59.1<br>65.0<br>69.4<br>71.8|1965-89<br>1990-96<br>1995-99<br>2000-04<br>2005-09<br>2010-14<br>2014-19<br>2020-24<br>2025-49<br>2050-74<br>2075-99<br>2100-24<br>2125-49|2.26<br>2.06<br>1.39<br>1.79<br>1.63<br>1.42<br>1.22<br>1.11<br>0.62<br>0.39<br> 0.21<br>0.13<br>0.07|36.v<br>33.4<br>30.6<br>26.6<br>26.2<br>23.4<br>20.7<br>19.4<br>16.6<br>13.9<br>12.9<br>42.3<br>12.0|13.6<br>12.5<br>11.3<br>10.4<br>9.7<br>9.0<br>8.5<br>6.2<br>8.4<br>10.1<br>10.6<br>10.9<br>11.3|4.9<br>4.37<br>3.82<br>3.44<br>3.09<br>2.76<br>2.48<br>2.35<br>2.19<br>2.10<br>2.06<br>2.06<br>2.06|53.1<br>54.3<br>55.7<br>57.4<br>59.0<br>60.6<br>62.3<br>64.0<br>68.4<br>73.8<br>_77.4_<br>60.3<br>81.3|52.9<br>54.4<br>56.1<br>S8.1<br>59.9<br>61.7<br>63.7<br>65.7<br>71.1<br>77.5<br>82.0<br>05.3<br>87.4|114<br>106<br>95<br>4<br>76<br>67<br>59<br>51<br>32<br>12<br>7<br>3<br>2|167<br>154<br>137<br>116<br>106<br>94<br>61<br>69<br>41<br>1s<br>s<br>4<br>4|

# 2. **Age** structure (percent): 1990 and 2025

<!-- Start of picture text --> 10 6 4 2 0 2 4 6 8 10<br><!-- End of picture text --> <!-- Start of picture text --> 75 +<br>70-74<br>65-69<br>60-64<br>55-59<br>50-54<br>45.49<br>40--4<br>35.39<br>30-34<br>25-29<br>20-24<br>15-19<br>10-14<br>569<br>0-4<br>4 2 6 S 0<br><!-- End of picture text -->

## 3. Distribution of population (percent): 1990 and 2025

<!-- Start of picture text --> Lii Cs - .s A,<br>*A5 C7 90 1 As C 7 MO D<br>AN C34 go AM~~~~~~~~~~~~£5 (24 U)<br>2 SCAv1't < AU.u<br>< //(43t < /(J~~~2<br><!-- End of picture text -->
Figure 2: FDI flows are larger in oil-exporting countries in Sub-Saharan Africa* <!-- Start of picture text --> FDI (US$ bil.)<br>10.0<br>Oil-exporters<br>8.0<br>6.0<br>4.0<br>2.0<br>Non oil-exporters*<br>0.0<br>1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005<br><!-- End of picture text -->

* Excluding South Africa

Source: Authors’ calculations, _Global Development Finance_ database, September 2007.

However, FDI flows to Sub-Saharan Africa appear to be concentrated in enclave sectors such as oil and natural resources (McDonald, Treichel, and Weisfeld 2006; World Bank 2007a). FDI flows to oil-exporting and commodities-exporting countries were larger than in other countries in the region from 1990 onwards (figure 2). Oil exporters received nearly 70 percent of FDI to going to Sub-Saharan African countries other than South Africa in 2005. Net FDI inflows to four major oil-producing countries in Sub-Saharan Africa—Angola, Equatorial Guinea, Nigeria, and Sudan—alone were estimated at $10 billion in 2006, half of all FDI to low-income countries in 2006 (World Bank 2007a). Non-resource-intensive countries other than South Africa recorded rising but substantially lower inflows.

# **_Private debt flows to Sub-Saharan Africa are small and short-term_**

Debt flows to Sub-Saharan African countries are small compared with other developing regions. Countries other than South Africa received an estimated $-0.6 billion annually during 2005-06 in private medium and long-term debt flows, and $ 2.8 billion in short-term debt flows (usually in the form of trade credits) during 2005-06, almost half of all short-term debt flows to the region. The high share of short-term debt in private debt flows reflects the high risk of lending on unsecured terms and at longer maturities to Sub-Saharan African firms. These shortterm flows were relatively volatile and carry the risk of rapid reversal (see box 1).

Medium and long-term flows were mostly bank lending to middle-income Sub-Saharan African countries. One middle-income oil-exporting country, Angola, appears to account for virtually all of medium and long-term bank lending to Sub-Saharan African countries other than South Africa in 2003–05. More recent data on syndicated loans from the Loanware database 9
The unemployment rate is defined as the number of unemployed persons (years 15+) as a percentage of the total number of persons in the labor force (adults ages 15+). More specifically, the International Labor Organization (ILO) defines the unemployed as all persons of working age who are: 1) without work (neither paid employment nor selfemployment) during a reference period; 2) available for work (either paid or selfemployment); and 3) are seeking work by taking specific job-search actions in a specified period of time in pursuit of paid employment or self-employment.<sup>2</sup> This definition is useful for the ongoing because of its consideration of self-employment, which is commonly associated with “informal” employment in developing countries. The latter refers to employment without formal contracts and/or without social security (or retirement benefits provided by the state) coverage. Combined with low household savings and non-existent publicly provided unemployment benefits, which in turn imply that workers in many developing countries cannot survive without income flows, this feature of unemployment implies a negative correlation between informal employment and unemployment across developing economies.

The unemployment data were downloaded directly from the ILO’s statistical database, ILOSTAT, which contains both nationally reported and imputed data.<sup>3</sup> This data set is also reported by the World Development Indicators of the World Bank, it is not always consistent with other sources of labor-market data, including official national estimates. To prevent contamination of the estimation sample by the ILO’s imputation model, which could

> 2 This definition comes from the ILO web site as of January 27, 2020. Please see https://ilostat.ilo.org/resources/methods/description-unemployment-rate/.

> 3 The ILO utilizes a panel-data model that predicts unemployment with various national indicators of economic activity, such as GDP growth rates.

8
4

# **1. Introduction**

The prices of internationally traded fuels and food surged until mid-2008, fell sharply, and then began rising again, reversing much of the price fall by 2011. By one measure, the prices on the world market have nearly doubled since 2005 (Figure 1). The fiscal breakeven price of oil needed to balance the budget in major oil exporters has risen sharply in recent years, making the prospect of prolonged periods of low prices unlikely in the future. Equally important, the marginal cost of oil production in 2011 was US$92 a barrel for the 50 largest listed oil companies and could rise further if it continues to follow the long-term trend ( _Wall Street Journal_ 2012). Similarly, long-term growth in global demand for food and continuing U.S. ethanol demand for maize and EU biodiesel demand for vegetable oils are expected to keep prices of maize, oilseeds, and many other crops at historically high levels (USDA 2012b).

Figure 1: Monthly indices for fuel and food prices since 2005 in low- and middle-income countries <!-- Start of picture text --> 300<br>Fuel<br>250<br>Food<br>200<br>150<br>100<br>50<br>0<br>Jan-05 Jan-06 Jan-07 Jan-08 Jan-09 Jan-10 Jan-11 Jan-12 Jan-13<br>Price index, 2005 = 100<br><!-- End of picture text --> _Sources:_ World Bank 2013.

_Note_ : All prices are world prices. Fuel consists of 84.6 percent crude oil, 10.8 percent natural gas, and 4.7 percent coal. Food includes cereals, vegetable oils, meat, sugar, and fruits. Weights are based on developing countries’ export values in 2002–04. Indices are based on nominal prices in U.S. dollars.

Many governments in developing countries control petroleum product prices. In the face of mounting subsidies, a number of governments seriously explored options for pricing reform in the period leading up to mid-2008. The budgetary pressure to press on with reform subsided briefly following the price collapse in late 2008, but those governments that had done little were caught by rising prices again soon thereafter. To the extent that price increases on the world market have been transmitted to the domestic market, soaring prices have led to calls on governments across the world to take action, ranging from providing greater safety nets to the poor and increasing the minimum wage to releasing oil from strategic reserves, reducing taxes, and granting outright price subsidies.

Given the high share of household expenditure on food—in some low-income countries, the food share can be as high as half or more, and even in middle-income countries it is typically 20–30 percent (World Bank 2012b)—rising food prices have made fuel price reform, politically difficult under any circumstances, all the more challenging. Conversely, rising fuel prices have compounded the political difficulties of reducing food price subsidies, as households face rapidly increasing expenditures on other basic goods. As a result, some countries have seen people taking to the streets to protest both high food and energy prices.
-8

# II. Tariff exemptions in Argentina

In Argentina, tariff exemptions are granted for a variety of reasons and constitute part of a more comprehensive package of fiscal incentives. Fiscal incentives are geared towards the achievement of the following objectives (for a more comprehensive discussion, see Bank Report t6990-AR, Argentina: Industrial Sector Study, Nov. 1987): - Regional Promotion: Regional promotion schemes date back to the mid-1960s and were adopted to boost investment in the economically depressed areas of the country. Industries that are located in the promoted regions enjoy various types of tax exemptions and/or deferrals, which include tariff exemption. In 1986, Argentina employed three types of regional promotion schemes. The first was the Tierra del Fuego Regime that granted import duty exemption to both capital and intermediate goods. The second scheme applied tc most of the northern provinces, and granted tariff exemption to the imports of noncompeting capital goods. In the rest of the country, all imports were in theory subject to the nominal duty rates. - Industrial Promotion: Industrial promotion schemes also grant a variety of tax exemptions and/or deferrals to targeted industries. Duty exemption or a substantial reduction in the rate of duty on the imports of noncompeting capital goods is an integral part of these schemes. The main beneficiary industries have been iron and steel, cellulose and paper, petrochemicals, and most
also been provided, so that happiness economics can be regarded as both a field and a method of research (Stutzer and Frey 2002, 2010; Konow and Earley, 2008). One of the most robust findings in happiness economics is that marriage and social life play a key role in individuals’ life satisfaction, and that it is not less important than household income (Layard 2005; Nickerson et al. 2003; Wilson and Oswald 2005).

More specific studies on the role of social capital in individuals’ life satisfaction show a number of results:

- First, social capital, as captured by the three main indicators of trust, networks, and norms (Putnam, 1993:167), contributes greatly and significantly to explaining the cross-country variance of individual life satisfaction. Besides trust in people, typical proxies used for social capital in Putnam’s sense are ‘membership of social organizations’, ‘having someone to rely on’, ‘donating money or time to an organization’, contacts with family, friends and neighbors, ‘justifying cheating on taxes’, ‘importance of religion’, ‘religious practice’, and other proxies for governmental quality (Helliwell 2008, 2006). Helliwell et al. (2009) even conclude that _in all [world] regions [...] social support is tightly linked to life satisfaction, with a global coefficient that exceeds that on log income_ .<sup>2</sup>

- Second, time devoted to social relationships causally impacts on life satisfaction in a positive and relevant way (Meier and Stutzer 2008; Becchetti et al. 2008; Bruni and Stanca, 2008). Proxies for the time devoted to relations are ‘frequency in performing volunteer work’, ‘attending social gatherings’, ‘cultural and religious events’.

- Third, the deterioration of social capital in the US over the most recent decades, as claimed by Putnam (2000) and confirmed by others (Costa and Kahn 2003; Robinson and Jackson 2001), is associated with the decline of individuals’ happiness in the same country, as reported by Layard et al. (2009), Easterlin and Angelescu (2009), and Stevenson and Wolfers (2008). This linkage seems to be significant and relevant, even after controlling for individuals’ income and comparison income (Bartolini et al. 2008; see also Bjørnskov 2008).

> 2Similar conclusions are found by Winkelman (2009), who has used panel data for Germany, and by Leung et al. (2011), who have used non-panel data for Canada, while Ram (2010) has found more fragile results by using country data instead of individual data.

4
U.S. Commodity Flow Survey and covers the year 2002. A detailed description of this data set follows in the next section.

To conduct empirical tests of the other results, our main sample covers detailed trade data of for EU 15 for the year 2005.<sup>23</sup> The total number of product in the Combined Nomenclature is more than 10,000 and the total number of observation is close to 700,000. Restricting our sample to EU trade allows us to analyze the role of quality for trade between countries that have similar factor endowments, technology as well as consumption preferences. It is true that there is some variation in these variables between the 15 countries in the sample. However, the variance does not exceed to a large extent the one that exists within a given EU country. Furthermore, the variance between EU countries is far smaller than within other country groups, like between Asian or South American countries. Finally, the main economies within this group, namely Germany, France, Italy, Spain, and the UK, which produce about 87 % of EU’s GDP, share very similar factor endowments, technologies and consumption preferences. The main difference between the economies under consideration is the market size. We consider the GDP as an appropriate measure of market size (and thus the population size as a proxy). The GDPs vary from 29 billion euros in the case of Luxemburg to 224 billion in the case of Germany for the year 2005. As a proxy for market size, we also use the population size of EU countries.

For our estimations we necessarily need information about the trade costs between trading partners. As commonly done in the empirical trade literature, we assume that distance is an appropriate proxy of trade costs. For our estimation, two types of distance measures are applied: First, distance between trading partners is measured using the great circle formula. However, the great circle formula may suffer from the fact that it only takes into account the geographical location of the capital of a country. Neglecting the spatial distribution of economic activity becomes problematic, especially for shorter distances. In order to have a more appropriate measurement of distance, we also use a weighted distance measure. This measure takes into account the economic geography of a country/region by calculating the bilateral distances between the three cities with the largest population _k_ in country _i_ and _l_ in country _j_ in the trade pair and weighting them by the population share. Using this distance measure, we hope to capture in a more accurate way the economic geography of EU countries. Similar to Head and Mayer (2002) the following formula of distance between _i_ and _j_ is used:

> 23 The countries are: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Spain, Sweden, and United Kingdom.

21
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