rafmacalaba/datause-encoder-data huggingface dataset:
Information on citizenship is used for the other countries (Germany, Italy, Greece, Japan, and Korea). The educational structure can be obtained in 24 countries and can be estimated in 3 additional countries on the basis of the European Labor Force Survey (Belgium, Greece, and Portugal). As will appear in Table 2, data built on the Labor Force Survey only represent 2 percent of the OECD migration stock in 2000 (0.7 percent in 1990). In the 3 remaining countries, the educational structure is extrapolated on the basis of the Scandinavian countries (for Iceland) or the rest of the OECD (for Japan and Korea). In 1990, European Council data must be used for Hungary and Italy. These data are based on the concept of citizenship. Compared to 2000, educational attainment is not available in Italy, the Czech Republic and Hungary. The Italian educational structure is based on the rest of the EU15. For the other two countries, we use proportions computed from the rest of Europe. On the contrary, the Belgian 1991 Census is available and provides complete data by country of birth and educational attainment.

# **2.2. Emigration Rates**

In the spirit of Carrington and Detragiache (1998) and Adams (2003), our second step consists in comparing the emigration stocks to the total number of people born in the source country and belonging to the same educational category. Calculating the brain drain as a proportion of the total educated labor force is a better strategy to evaluate the pressure imposed on the local labor market. It is indeed obvious that the pressure exerted by 1,037,000 Indian skilled emigrants (4.3% of the educated total labor force) is less important than the pressure exerted by 16,000 skilled emigrants from Grenada (85% of the educated labor force).

Denote _N tj_ , _s_ as the stock of individuals aged 25+, of skill _s_ , living in country _j_ , at time _t_ , we define the emigration rates by 13
economics over the past two decades is therefore not that reflected in the content of classes taught in developing countries.

One potential critique is to query whether we should think of the teaching of top-U.S. schools as best practice, versus instead seeing developing country instructors as tailoring their courses to be more relevant to the key questions facing their countries and to the learning styles of their students. We have three responses to this. First, while we agree that it is far from clear that the topic coverage in top U.S. courses is ideal, with some key topics under-covered, the topic coverage of top U.S. courses does reflect better the ideas, lessons and methods of recent development research. Second, for the large part it does not seem that developing country instructors are carefully tailoring their content to the most pressing policy questions affecting the countries in which they teach, but rather just following a selection of chapters of Todaro and Smith (2014). Third, and most importantly, even if one can argue about topic choice and the level at which the courses can be taught in developing countries versus at the top U.S. economics departments, based on the key learning objectives of an undergraduate education, we believe that the lack of attention to data and empirical findings in developing country courses is a deficiency.

# **5. Why do we see these differences?**

We consider several possible explanations for the differences in the way undergraduate development economics is conceptualized and taught in developing countries compared to the top20 U.S. schools. A first potential explanation is that less-developed countries are further behind the technological frontier, and so are less likely to be covering topics of current research. Figure 1 shows the correlation between the average Jaccard similarity score of the topics covered by courses with the log of per-capita GNI (in PPP terms).<sup>6</sup> We see a positive and statistically significant correlation, with richer developing countries having more similar topic choices to the top-20 U.S. programs than poorer developing countries. We then turn to seeing whether this correlation persists after controlling for other potential explanations.

A second potential explanation for a difference in topic emphasis lies in the differing approach to the role of the state in development in different countries. Courses in countries in which the government plays a more active role in the economy may be more likely to emphasize topics of

> 6 Data from the World Development Indicators.

18
Heitger, Bernhard. 1986. "Import Protection and Export Performance: Their impact on Economic Growth." Weltwirtschaftliches Archiv. 260(July):1-19.

Helliwell, John F. and Alan Chung. 1990. "Macroeconomic Convergence: International Transmission of Growth and Technical Progress." Hsiao, M.W. 1987. Tests of casuality and exogeneity between exports and economic growth: the case of the Asian NICs, Journal of Economic Development, 12(2):143-159.

Jung, W. and P. Marshall. 1985. "Exports, Growth and Casuality in Developing Countries." journal of Development Economics. 14:241-250.

Kaufman, Daniel. 1991. "Determinants of the Productivity of Projects in Developing Countries: Evidence from 1,200 Projects". Background paper for World Development Report 1991.

Krueger, Anne 0. and Baran Tuncer. "An Empirical Test of the Infant Industry Argument." The American Economic Review, 72(5):1142-1152.

Kunst, Robert M. and Dalia Marin. 1989. "On Exports and Productivity: A Casual Analysis." The Review of Economics and Statistics. 699-703. Institute of Advanced Studies.

Leamer, Edward E. 1988. "Measures of Openness," in Robert E. Baldwin,editor, Trade Policy Issues and Empirical Analysis.

Levine, Ross and David Renelt. 1990. "A Sensitivity Analysis of Cross-Country Growth Regressions." World Bank.

Lopez, Ramon. 1990. "Economic Growth, Capital Accumulation and Trade Policy in LDCs". (July) Trade Policy Division, Country Economics Department, World Bank, Washington, D.C. Nishimizu, Mieko and John M. Page, Jr. 1990. "Trade Policy, Market Orientation, and Productivity Change in Industry", in J. de Melo and A. Sapir, eds., Trade Theory and Economic Reform North. South. and East.

Nishimizu, Mieko and Sherman Robinson. 1984. "Trade Policies and Productivity Change in Semi-Industrialized Countries." Journal of Development Economics, 16:177-206.

Papageorgiou, Demetrios, Michael Michaely and Armeane M. Choksi, 1991, eds. in Liberalizina Foreign Trade: Lessons of Experience in the Developina World. 7:1-440. Basil Blackwell, Cambridge, Massachusetts.

24
Zealanders either because of cultural proximity or self‐selection on these characteristics. Unfortunately, we do not survey New Zealanders and, in general, it is difficult to find representative surveys on preferences and beliefs. However, we have been able to find some evidence that suggest that Tongan PAC migrants are quite different in terms of preferences and beliefs than New Zealanders. For example, the representative 2011 World Values Survey (WVS) asks the same question that we include on general trust (Trusting Others in Table 3). While 70 percent of Tongan migrants in our survey report that most people can be trusted, only 57 percent of non‐migrant New Zealanders in the WVS feel the same way. Rieger et al. (2014) and Wang et al. (2016) survey students in 53 countries, including 91 in New Zealand. They ask both time preference and lottery questions similar to the ones we present in Table 2. In their most similar lottery (lottery #2 in their paper), 24 percent of New Zealand students are risk seekers and 5 percent have severe risk aversion versus only 6 percent of Tongan migrants being risk seekers and 13 percent having severe risk aversion. Similarly, while 75 percent of Tongan migrants are willing to wait one year to receive 1500 NZD instead of 1000 NZD at the current moment, only 45 percent of New Zealand students are willing to wait one month to receive 3800 USD instead of 3400 USD.

# **6.3 External Validity**

How much can we expect the results here to generalize to other migration settings? Our setting here considers a common form of international migration – a nuclear household moving from a poor to rich country permanently and leaving extended family behind. To the extent that it differs from other common migration types, it differs in ways which we might think would make it more likely that we would find changes in preferences or beliefs. That is, we would expect any effects to be smaller if migration was more temporary or if it was between countries with more similar income levels, institutions, and levels of economic freedom (as would be the case for migration from one developed economy to another). We would therefore also expect to see no impact of migration on preferences and beliefs in these other migration settings.

# **7. Conclusions**

Tongans migrating to New Zealand experience major changes in life circumstances, earning much higher incomes, living in a country with a different culture and more individualistic and market‐oriented institutions, and having less interaction with their parents and siblings who remain behind in Tonga. Despite these changes, we find no significant impacts of international migration on the risk and time preferences and pro‐market beliefs of these migrants, nor on the efficiency of decision‐making with their 19
not with productivity. In contrast, the Global Competitiveness indicator of Corporate Ethics<sup>16</sup> is significantly and negatively associated with firm growth—and thus in this case firm corruption is associated with higher firm growth. The contradictory findings on corruption between local indicators and national indicators suggest that corruption does not have a robust association with firm growth. Given that the Enterprise Survey indicator of corruption captures local variations better, and previous studies have found negative association/effects of corruption on firm growth (Fisman and Svensson 2007; Xu 2011), we place slightly more weight on the findings based on the Enterprise Survey data—that is, at least at the local level, corruption is negatively related to firm growth.

Basic physical safety as captured by the Enterprise Survey indicator of a lower share of a firm’s sales spent on security expenses (Security Costs) is positively associated with a higher productivity level. Lowering it by one standard deviation is associated with increasing labor productivity by 8 log points and TFP by 4 log points. In contrast, the cross-country counterpart of the Global Competitiveness Index, Police System, does not show a significant association with firm performance. Again, with its ability to ascertain local variations, the Enterprise Survey indicator is better at capturing the effects of basic physical safety than the corresponding Global Competitiveness indicator.

_Infrastructure_ . Electricity provision is associated with a higher productivity level and firm growth. A higher Power Outage measure (from the Enterprise Survey) is associated with lower firm growth; a higher Getting Electricity measure (from _Doing Business_ ) is associated with a higher productivity level. Reducing Enterprise Survey Power Outage indicators by one standard deviation is associated with a 0.4 percentage point increase in employment growth, while increasing nationwide electricity quality (captured by the _Doing Business_ Getting Electricity indicator) by one standard deviation is associated with a 9-percentage point increase in labor productivity. The Global Competitiveness indicator of Electricity Quality, which is perhaps a narrower measure of the quality of the power supply system, is not significantly related to firm performance. Overall, the complementary findings from the Enterprise Survey and _Doing Business_ bolster the case for the importance of the power supply system for firm performance, for both productivity and growth.

Modern infrastructure, as proxied by the adoption of internet technology, is robustly associated with both productivity and firm growth. The Enterprise Survey’s Web indicator, capturing the local prevalence of internet usage for firm businesses, is positively and

> 16 A higher value of the measure indicates a lower level of corruption.

15
Tabte A1.6 Baning System Accmmts (TL Bittion)

|A|STOCK|S 1987<br>L|A|STOCK|1988|L|
|---|---|---|---|---|---|---|
|OUd|769.4|OOp<br>6,400.8|Cld|1,092.8|DOp|7,873.8|
|Ddc|1,889.5|CRd<br>-4a2.9|Ddc|4,581.6|CRd|-847.9|
|Bd|8,503.5|Ee.Fd<br>4,212.8|Bd|10,882.1|Ee-Fd|6,589.0|
|Bdb|855.0|Kbd<br>76.5|Bd)|2,256.5|Kbd|245.2|
|Bod|1,721.8|WOLd<br>1,065.6|Sod|3,265.5|lOLd|3,420.4|
|spd|5,926.7|Nd<br>-1,866.0|Bpd|5,360.1|Mud|3,420.4|
|Kd|110.4|RVd<br>2,732.2|Kd|724.0|RVTa|0.0|
|||,A<br>UCAPITAL<br>ACCII|<br>1988|aL|||
|||dCtd<br>323.4|dCOp|1,473.0|||
|||dDdc<br>2,692.1|dCRd|-365.0|||
|||d3d<br>2,378.6|d(Ee-Fd)|2,376.2|||
|||dBbd<br>1,401.5|dAFd|-356.0|||
|||dBod<br>1,543.3|CCRd|-346.4|||
|||dcpd<br>-566.6|cdRVd|3,078.6|||
|||KTdp<br>613.6|KTbd|168.7|||
||||diOLd|2,354.8|||
||||dW|S,087.0|||
||||dRVTd|-2,732.2|||
|||R<br>ORRMET|ACCoEIT 88|E&S|||
|||531.0<br>|iCW.DOp-1|1,794.9|||
|||'C <sup>4</sup><br>=<br>'<br>1,069.2|<br>iRCad-1|-245.6|||
|||icBd-t<br>3,680.5|iF-E-FXPd&1|378.6|||
|||<br>YF*E-FXdc-1<br>274.3|iF-EFGd-i|338.6|||
||||P&Ld|-1,798.4|||
||||dr|5,087.0|||

Source: See expLaietion in text.

# A1.3 Fiscal ACCounts

The public sector consist of two different subsectors: the SEEs (non-financial state economic enterprises), called "other" sector, and the budget. As explained before, public banks are classified under the domestic banking system and all fiscal accounts were adjusted accordingly. In the case of Turkey, the budget includes the consolidated Central Government, the extrabudgetary funds, local administrations, revolving funds and the social security administration.

23
31

# <u>Figure 5: Impoverishment due to out-of-pocket health expenditures</u>

Percentage of Households Impoverished by Out−of−Pocket Health Expenditures, PL=$1.90−a−day Percentage of Households Impoverished by Out−of−Pocket Health Expenditures, PL=$3.20−a−day <!-- Start of picture text --> 2.5–4.5 2.5–6.7<br>2.0–2.5 2.0–2.5<br>1.5–2.0 1.5–2.0<br>1.0–1.5 1.0–1.5<br>0.5–1.0 0.5–1.0<br>0.0–0.5 0.0–0.5<br>no data no data<br><!-- End of picture text --> Percentage of Households Impoverished by Out−of−Pocket Health Expenditures, PL=$5.50−a−day Percentage of Households Impoverished by Out−of−Pocket Health Expenditures, PL=$10.00−a−day <!-- Start of picture text --> 2.5–6.1 2.5–5.2<br>2.0–2.5 2.0–2.5<br>1.5–2.0 1.5–2.0<br>1.0–1.5 1.0–1.5<br>0.5–1.0 0.5–1.0<br>0.0–0.5 0.0–0.5<br>no data no data<br><!-- End of picture text --> <!-- Start of picture text --> IBRD 44310 | APRIL 2019<br><!-- End of picture text -->
|Lithuania|1993|X|X|X|X|X|X|
|---|---|---|---|---|---|---|---|
|Macedonia,FYR|1994|X||X|X|X|X|
|Madagascar|1996|X|X||X|X|X|
|Malaysia|1963||X|||||
|Mali|1988||X||X|X|X|
|Mauritania|1995|X|X||X|X|X|
|Mauritius|1968|||||||
|Mexico|1986|X|X|X|X|X|X|
|Moldova|1994|X|X||X|X|X|
|Morocco|1984|X|X||X|X|X|
|Mozambique|1995||X||X|X|X|
|Nepal|1991|X|X||X|X|X|
|New Zealand|1986|X|X|X|X|X|X|
|Nicaragua|1991|X|X||X|X|X|
|Niger|1994|X|X||X|X|X|
|Pakistan|2001|X|X||X|X|X|
|Panama|1996|X|X||X|X|X|
|Paraguay|1989|X|X||X|X|X|
|Peru|1991|X|X||X|X|X|
|Philippines|1988|X|X||X|X|X|
|Poland|1990|X|X|X|X|X|X|
|Romania|1992|X|X|X|X|X|X|
|Sierra Leone|2001||X||X|X|X|
|Singapore|1965|X|X|||||
|Slovak Republic|1991|X|X|X|X|X|X|
|Slovenia|1991|X|X|X|X|X|X|
|South Africa|1991|X|X||X|X|X|
|Sri Lanka|1991|X|X|X|X|X|X|
|Sweden|1960|X|X|X||||
|Taiwan,China|1963||X|||||
|Tajikistan|1996||X||X|X|X|
|Tanzania|1995|X|X||X|X|X|
|Trinidad and Tobago|1992|X|X|X|X|X|X|
|Tunisia|1989|X|X||X|X|X|
|Turkey|1989|X|X||X|X|X|
|Uganda|1988||X||X|X|X|
|Uruguay|1990|X|X||X|X|X|
|Venezuela,RB|1996|X|X||X|X|X|
|Yugoslavia, FR||||||||
|(Serbia/Montenegro)|2001|X|X|||||
|Zambia|1993|X|X||X|X|X|

Note: This table lists the countries included in the dataset and the year of liberalization from Wacziarg and Welch (2008 **).** In principle, the liberalization date is the date after which all of the Sachs-Warner openness criteria are continuously met (however data limitations often imposed reliance on country case studies of trade policy). Countries that according to Wacziarg and Welch (2008) had liberalized their policies prior to 1960 or that had not liberalized by 2001 are excluded from our analysis.

38
of GDP was only 22 percent in 2010, and physical and human infrastructures are still comparatively low.

**Typology placement:** India, Nigeria, and Zambia have not had the full benefit of quadrant (2), and India in particular has hovered not too far from quadrant (1). For all of them, investing in the fundamentals is now critical.

# **No Structural Change in Recent Years: Botswana and Brazil**

Unlike the rest of the countries featured in this book, Botswana and Brazil have been middle-income countries for some time. Structural change played a significant role in catapulting these countries into middle-income status, but its role has been more muted in the past two decades. Their stories, though, are quite different.

In Brazil, structural change was rapid from the 1950s through the 1970s (especially in the 1950s and early 1960s), accounting for 40 percent of total labor productivity growth during this period (Figure O.7). As agricultural employment shrank, manufacturing jobs expanded slightly, and modern service activities—the most productive sector—absorbed the bulk of the labor. By the late 1970s, industries as a whole accounted for close to 40 percent of total labor productivity growth. This period of high-growth, rapid structural change was one

**FIGURE O.7** Within-sector changes in Brazil swamping structural changes

<!-- Start of picture text --> Decomposition of productivity growth by period and database<br>GGDC 1950–2005<br>GGDC 1995–2005<br>GGDC 1980–1994<br>GGDC 1965–1979<br>GGDC 1950–1964<br>GGDC 1990–2005 Within-sector<br>Structural<br>PNAD 1993/95–2007/08<br>–20 0 20 40 60 80 100 120 140 160 180<br>Percent<br><!-- End of picture text --> Source: Firpo and Pieri, Chapter 7 in this book. Note: The bars correspond to growth rates for the whole period indicated. For example, for the period 1950–2005, using Groningen (GGDC) data, labor productivity resulting from the within-sector and structural change effects grew by 132 percent and 24 percent, respectively. GGDC = Groningen Growth Development Centre; PNAD = Pesquisa Nacional por Amostra de Domicílios.

25
Finally, _IL_<sup>_progC_</sup> is the ratio between the poverty line and the affluence line: in Mali and Ethiopia the endogenous affluence line is roughly two times the poverty line. In South Africa, the affluence line is twenty times the poverty line. This result suggest that in South Africa the distribution above the poverty line is very unequal. A small minority (the 6.7% of individuals above the poverty line, as indicated by _IL_<sup>_progB_</sup> ) could transfer all income above the affluence line to eradicate poverty and they would still have an income level twenty times higher than the poverty line. Note that the same small minority is needed in Cote d’Ivoire (where _Il_<sup>_progB_</sup> is still 0 _._ 67), but they would end up with an income level just ten times higher than the poverty line.

Table 4: Poverty and Income Lever indexes and ranks (1-higher to 8-lower) for a selection of African countries

|Country (year)|_P_ <sup>0</sup>||_P_ <sup>1</sup>||_IL_<sup>_pro_</sup>|<sup>_p_</sup>|_IL_<sup>_fla_</sup>|<sup>_t_</sup>|_IL_<sup>_prog_</sup>|<sup>_A_</sup>|_IL_<sup>_prog_</sup>|<sup>_B_</sup>|_IL_<sup>_progC_</sup>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||_index_|_r_|_index_|_r_|_index_|_r_|_index_|_r_|_index_|_r_|_index_|_r_|_index_<br>_r_|
|Benin (2003)|0.45|3|0.152|3|0.315|2|23.13|3|2.319|3|0.273|2|0.451<br>3|
|Ivory Coast (2002)|0.25|6|0.067|7|0.045|6|3.39|7|0.119|7|0.067|6|0.099<br>7|
|Cameroon (2001)|0.35|5|0.095|5|0.089|5|6.00|5|0.267|5|0.077|5|0.131<br>6|
|Ethiopia (2000)|0.40|4|0.098|4|0.235|4|8.31|4|4.599|1|0.167|4|0.484<br>2|
|Mali (2001)|0.50|1|0.187|1|0.376|1|28.42|2|3.070|2|0.400|1|0.496<br>1|
|Mauritania (2000)|0.20|8|0.059|8|0.043|7|2.97|8|0.150|6|0.063|8|0.137<br>5|
|Uganda (2002)|0.50|1|0.179|2|0.295|3|34.01|1|1.094|4|0.200|3|0.318<br>4|
|South-Africa (2000)|0.25|6|0.090|6|0.030|8|4.88|6|0.046|8|0.067|7|0.047<br>8|

_Source: our elaboration on Global Income Distribution Database (GIDD). ‘r’ indicates rank._ As a final exercise, one may want to ask the question of whether the IL indexes proposed proxy any of the criteria currently used to allocate aid. In Table 5, we show the average net official development assistance (ODA) in percentage of the gross national income received by the same African countries considered before. The net ODA comprises grants or loans to developing countries and territories on the OECD/DAC list of aid recipients that are undertaken by the official sector with promotion of economic development and welfare as the main objective and at concessional financial terms. Among the selected countries, Mauritania received the most generous amount of aid, followed by Ethiopia. Note that Mauritania is the least poor country (see estimates in Table 4) and Ethiopia is the fourth poorest country out of the eight considered). More generally none of the criteria summarized in Table 4 could justify the distribution of aid listed in Table 5.

25