rafmacalaba/datause-encoder-data huggingface dataset:
18 James Meernik (1996) investigated whether US interventions tended to promote democratization. Meernik collected data on 27 cases of US intervention involving the use of ground troops in the period 1948–90. Using Polity III as his measure of democracy, he measured political change for up to three years after the intervention. In most of the cases the outcome was ‘no change’, but when he compared the intervention cases with other international crises in which the US did not intervene with ground troops, he found ‘qualified evidence that US military intervention exercises a significant and positive effect on democratization’ (Meernik, 1996: 397). He also found that when the US was truly committed to promoting democracy (as evidenced by presidential statements) it was generally quite successful (op.cit.: 400). Prior anti-US violence in the target country and prior US opposition to the regime were not significant in his study.

Margaret Hermann & Charles Kegley in a series of articles have examined democracies’ use of military intervention. They find that democracies make frequent use of military intervention. Somewhat more surprisingly, they also find that other democracies are targeted quite frequently. In one of their first studies (Kegley & Hermann, 1995b) they found 15 cases between 1974 and 1988 where free states (using the Freedom House classification) intervened in other free states. In this and in later publications, they characterize this as a possible ‘danger zone in the democratic peace’. Using data for military intervention during the period 1974–91 from Tillema (1991), Kegley & Hermann (1996: 314) found that roughly one fifth to one third of all interventions were initiated by democracies (depending on whether democracy was measured by Polity or Freedom House) and that a substantial share of them (19% when using Polity) was initiated by semi-democracies. This finding is somewhat confusing in relation to the idea of democratic interventionism. How can democratic states spread democracy by intervening in other democracies? Tures (2001) has largely cleared up this puzzle. He shows that the Kegley & Hermann studies used a Polity measure with annual dating of the polity changes. This may lead to erroneous conclusions with regard to the regime type of the target state at the time of the intervention, a problem which has plagued other studies of the democratic peace as well (Mitchell et al., 1998). Kegley & Hermann have also included interventions in democratic states where the intervener was asked by the local government to help it suppress a rebel movement, as well as interventions into occupied states. Thus, the US and allied interventions to free Kuwait from Iraqi occupation is coded as a democratic intervention targeting a ‘partly
# **2.1 Selection of Countries to Study**

The Docquier and Marfouk (2005) database reveals that the Pacific Islands have the highest brain drain rate of any region in the world. If general brain drain is to be a concern anywhere, this should be the region where this concern is greatest. We therefore focus on a selection of small island countries within the Pacific. By focusing on one region, with similar migrant destinations, we can take advantage of economies of scale in surveying emigrants, and compare neighboring countries with differing emigration options.

Within the Pacific we selected countries which provide a broad range of development levels and migration experiences, and where we had the necessary contacts to make the surveying feasible. The countries chosen, along with their population, and 2000 brain drain rates for those who entered their destination country after age 18 are<sup>4</sup> :

- **Tonga** :112,000 population, 65.1% brain drain

- **Papua New Guinea** : 6.3 million population, 36.9% brain drain

- **New Zealand** : 4.1 million population, 15.8% brain drain

These brain drain rates are the share of all tertiary-educated adults from the country who are living in an (another in the case of New Zealand) OECD country and who migrated there after age 18. Both the numerator and denominator are measured with substantial error. The denominator relies on the Barro-Lee estimates of tertiary education in the country, which for Tonga are imputed from attainment in other Pacific Islands. The numerator in many countries is based on the 5 percent or 10 percent subsamples of the census that microdata are available for, which can involve considerable sampling error when it comes to looking at say tertiary-educated Papua New Guineans living in the United States. Moreover, they do not distinguish between tertiary education earned abroad and that earned at home.

# **2.2. Selection of a Sampling Frame**

The very name “brain drain” refers to migration of a nation’s most highly skilled individuals. As Docquier and Rapoport (2006) note in their New Palgrave Dictionary

> 4 Brain drain rates are from the new estimates controlling for age of entry, from Beine, Docquier and Rapoport (2007). Additional surveys are also planned for the Solomon Islands and the Federated States of Micronesia.

- 5 -
# **Table 4: Regressions of the average rank of maize yield across seasons on skill constructs**

||||M|**SKILLS CO**<br>M|**NSTUCTS US**<br>M|**ED AS REG**|**RESSORS:**|M|M|M|
|---|---|---|---|---|---|---|---|---|---|---|
|VARIABLES|Naïve<br>Score|Improved<br>Index|ean<br>Naïve<br>Score|ean<br>improved<br>Index|ean<br>improved<br>Index|Naïve<br>Score|Improved<br>Index|ean<br>Naïve<br>Score|ean<br>improved<br>Index|ean<br>improved<br>Index|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|(10)|
|Cognitive skills|2.87***|2.27***|2.27***|1.61**|3.74***|2.69**|2.62**|3.29***|3.33***|4.19***|
||(0.753)|(0.736)|(0.809)|(0.802)|(0.769)|(1.159)|(1.142)|(1.101)|(1.138)|(1.102)|
|Noncognitive skills|4.03***|3.90***|4.83***|4.40***|5.40***|3.84***|3.97***|4.28***|4.22***|4.67***|
||(0.723)|(0.701)|(0.863)|(0.847)|(0.895)|(0.754)|(0.729)|(0.946)|(0.908)|(0.922)|
|Technical skills|3.37***|4.40***|5.47***|6.41***||0.57|1.39|2.37**|3.03***||
||(0.828)|(0.829)|(0.989)|(0.951)||(0.924)|(0.867)|(1.055)|(1.045)||
|Observations|903|893|903|893|893|903|893|903|893|893|
|R‐squared|0.121|0.145|0.146|0.169|0.122|0.431|0.438|0.441|0.449|0.442|
|Controls|No|No|No|No|No|Yes|Yes|Yes|Yes|Yes|
|F Test Diff.|0.614|0.239|0.102|0.00735|0.257|0.0215|0.104|0.407|0.706|0.782|

Note: Dependent variable is the average rank of maize yields calculated over the 4 seasons (short rain 14 to long rain 16). Controls include education, literacy, gender, age and age squared of the farmer, land and cattle ownership, size and quality of the house, household size, whether the farmer is the household head, household head’s gender, village fixed effects and enumerator‐assignment fixed effects. Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 45
- 14 -

# Tr 8: **ChUang CoumpoItlon** of Nontam ActMity, hd. 1961 to 1981

||**AWfI**|Tota|Mwhn|HouSe<br>Hold<br>MfV.|Non-A<br>Houser<br>Hdod<br>M1r.|griculture<br>Conw"|Conrnm|_Trans_|oaer<br>Sess|TOTAL|
|---|---|---|---|---|---|---|---|---|---|---|
||||(Full-Tim|e Employ|ment per|1,000 Po|pulation)||||
|Rural Employment|||||||||||
|1981|290|57|1|11|12|4|10|4|17|346|
|1971|287|51|1|11|8|2|8|3|18|338|
|1961 al|369|81|-|28|7|3|9|2|32|450|
|Urban Employment|||||||||||
|1981|38|253|2|14|72|12|58|27|as|289|
|1971<br>|37<br>|256|3|15|67|10|59|29|;3|293|
|1961al|42|293|-|26|70|12|55|27|102|335|

a/ Labor fnrce definion in t981 not comparabl with those in 1971 and 1961.

Source: India (1961a-d, 1971a-b, 1981c).
# **1. Introduction**

Since 2001, the number of low-income countries (LICs)—where annual per capita incomes are at or below $1,025—has more than halved, to 31 in 2020 from 64 in 2001 (Figure 1).<sup>1</sup> During this period, their number rose to a peak of 66 in 2003 before falling to 31 in 2016. Since then, however, the overall decline has stalled, with three countries—the Syrian Arab Republic, Tajikistan, and the Republic of Yemen—relapsing into LIC status amid armed conflict and terms of trade shocks, while another three countries (Cambodia, the Comoros, Zimbabwe) reached middle-income status.<sup>2</sup> The countries classified as LICs in 2001 that have converged to middle-income (MIC) status experienced average growth of 5.5 percent a year during 2001-2018—about one-half faster than non-LIC EMDEs over the same period and one-fifth faster than those 2001 LICs that have remained in the group (4.7 percent), although with wide heterogeneity.

# **Figure 1. LIC growth since 2001**

<!-- Start of picture text --> A. Number of LICs per region B. Average annual GDP growth, 2001-18<br>Percent<br>70<br>SSA SAR ECA<br>6<br>60<br>EAP LAC MNA<br>50<br>4<br>40<br>30 2<br>20<br>0<br>10<br>Other 2001 LICs 2020<br>0 EMDEs LICs turned LICs<br>2001 LICs 2020 LICs MICs<br><!-- End of picture text --> Source: World Bank.

A. LICs = low-income countries. LICs in 2001 had per capita GNI (US$, current) at $755 or below, while LICs in 2020 have per capita GNI at or below $1025 in 2018. B. Other EMDEs exclude the 2001 LICs.

Several factors have contributed to the rapid economic growth of the 2001 LICs. Twelve 2001 LICs were transition economies, of which nine rebounded sharply during the 2000s from their deep recessions in the 1990s. Among non-transition 2001 LICs, rapid investment growth boosted overall economic growth, on average contributing more than one-third to output growth during 2001-18. In six of these countries (Cameroon, the Democratic Republic of Congo, Guinea, Indonesia, Mauritania, Mozambique), investment booms followed new resource discoveries. Debt relief in the early 2000s, and the fiscal space created by it, supported government spending on non-

> 1 LICs in 2020 reflect the country classification of the 2019/20 World Bank fiscal year and are defined as countries with GNI per capita (World Bank Atlas method) of $1,025 or less in 2018; 2001 LICs reflect the country classification of the 2000/01 fiscal year and had GNI per capita of $755 or less in 1999. New thresholds are determined at the start of each World Bank fiscal year in July. Of the 64 2001 LICs, 35 moved to middle-income country status while 29 remained classified as LICs in 2019. Two countries are classified as LICs today that were not part of the 2001 LICs (the Syrian Arab Republic, South Sudan).

> 2 A fourth country, Senegal, temporarily relapsed into LIC status during this period before returning to MIC status in the 2019/20 country classification.

2
_Data processing_ How survey organizations process the data obtained from respondents is also an important issue for measuring the top tail of the income distribution. The main concern is top-coding, where the survey organization does not provide the actual income for individuals close to the top of the distribution but provides the value of some percentile for all respondents above that value- usually as a way of mitigating privacy concerns. The extent of top coding can be large- Burkhauser et al. (2012) document that 4.6% of individuals in the US CPS live in households in which some source of income was top coded.

In LMICs where statistical capacity may not be as good as in rich countries there are other data processing concerns. McLennan et al. (2021) show that in the Tanzanian Household Budget Survey (HBS) public data there are very large labor income values generated from individuals reporting high hourly rates of earnings, which are then multiplied by 8 (hours in a day) and then by 22 days in a month. These errors are so large that the implied tax revenue is substantially higher than actual tax revenue collected. McLennan et al. (2021) highlight that the public release documentation for the Tanzanian HBS contains no information about how such cases are treated. How these were processed and whether enough data is released publicly to allow alternative assumptions will matter when measuring the top tail of the distribution.

Some of the very large hourly earners in Tanzania might be contaminated incorrect outliers but it is possible they could be genuine high earners. The solution used by McLennan et al. (2021) is to cap large values at the 99<sup>th</sup> percentile of the labor income distribution, which is not suitable when trying to measure the top tail of the distribution.

A lack of documentation on this and other edits and decisions taken when processing data from household surveys is likely to be true in other LMICs. Kerr and Wittenberg (2021) note that earnings are imputed in South Africa’s Quarterly Labour Force Surveys, that these imputations are of poor quality but that they are undertaken and how they are undertaken is not mentioned in any public documentation. The obvious recommendation is that survey organizations and statistical offices should release as much documentation and data as possible without compromising respondent anonymity.

# _Lack of income data in household surveys in some LMICs_

A key challenge in measuring the top tail of the income distribution in LMICs is that many household surveys do not collect data on incomes. This is mainly because reported incomes are seen as less 9
# **5. Bibliography**

Al-Marhubi, F. (2000). “Export Diversification And Growth: An Empirical Investigation.” _Applied Economics Letters_ 7: 559–62.

Banco Central de Reserva del Perú (2008), Potencial y Limitantes de las Exportaciones No Tradicionales. Nota de Estudios del BCRP No. 15.

Bailey and Boyle (1971) _The optimal measure of concentration_ ; _Journal of The American Statistical Association_ , December, vol. 66, no. 336.

Besedes, Tibor and Thomas Prusa (2007). _The role of extensive and intensive margins and export growth_ ; NBER working paper 13628.

Bleaney, M., and D. Greenaway (2001). “The Impact of Terms of Trade and Real Exchange Rate Volatility on Investment and Growth in Sub-Saharan Africa.” _Journal of Development Economics_ 65 (2): 491–500.

Bonaglia F. and K. Fukasaku K. (2003) “Export Diversification in Low-Income Countries: An International Challenge After Doha” OECD Working Papers No. 209.

Brenton, Paul, Richard Newfarmer and Peter Walkenhorst (2007). “Export Diversification: A Policy Portfolio Approach.” Paper presented to the Growth Commission, The World Bank, Washington DC.

Carrére, Celine; Vanessa Strauss-Kahn and Olivier Cadot (2007). _Export Diversification: What’s behind the hump?_ Inter American Development Bank and Switzerland‟s Fonds National pour la Recherche Scientifique.

Chenery, Hollis (1979). _Structural Change and Development Policy._ New York: Oxford University Press.

de Ferranti, David, Guillermo Perry, Daniel Lederman, and William, Maloney (2002). _From natural resources to the knowledge economy: Trade and Job Quality_ . World Bank, Washington D.C.

Ghosh, A. R., and J. Ostry. 1994. “Export Instability and the External Balance in Developing Countries.” IMF Staff Papers 41: 214–35.

Gutiérrez de Piñeres A. Sheila and Michael Ferrantino (2000). _Export Dynamics and Economic Growth in Latin America: A Comparative Perspective_ , Ashgate Publishing Company, Burlington Vermont.

Hausmann, R., and D. Rodrik. 2003. “Economic Development as Self-Discovery.” Journal of Development Economics 72: 603–33.

Hausmann, R., J. Hwang, and D. Rodrik. 2006. “What You Export Matters.” Working Paper. Center for International Development, Harvard University.

Hausmann, Ricardo and Bailey Klinger (2007). “The Structure of the Product Space and the Evolution of Comparative Advantage.” Harvard University Center for International Development Working Paper #146.

Hausmann, Ricardo and Bailey Klinger (2008) _Growth diagnostic in Peru_ . CID Working Paper No. 181. Harvard University.

21
# **Table 4 Institutional drivers of fiscal procyclicality**

|**Dependent va**|**riable: Corr**<br>|**elation betw**<br>|**een real go**<br>|**vernment**<br>|**spending a**<br>|**nd real GDP**<br>|<br>|
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|Government stability|-0.09**|||||-0.01|-0.07|
|Law and order||-0.13***||||-0.12***|-0.06|
|Fiscal rules|||-0.61***||-2.11***|-0.31**|-1.26**|
|Sovereign wealth<br>funds||||-0.53**||-0.37**|-0.21|
|_F_-test||||||***|***|
|Observations|137|137|106|194|83|83|83|
|_R_-squared|0.05|0.24|0.17|0.13|n/r|0.47|0.15|

_Source_ : World Bank.

_Note:_ Cross-sectional regressions for the full sample. The estimates in columns (1)-(4) and (6) are based on OLS regressions. The estimates shown in columns (5) and (7) are based on instrumental variables (two-stage least squares) regressions. ‘n/r’ means not reported by STATA. For regressions (6) and (7), the F-test evaluates the joint significance of the three institutional variables. The last row in columns (7) shows the centered R-squared. Overidentification tests do not apply to regressions (5) and (7) since equations are perfectly identified. In these regressions, the first-stage F-test of excluded instruments implies rejection of the null hypothesis of weak identification (p-value = 0.00) in both cases. *, **, and *** denote significance at the 10, 5, and 1 percent level, respectively.

48
**Table S7.4.** Exclusion-Restriction Test: Baseline Output Growth Regressions including Instrument

|**VARIABLES**|**K=0**|**K=1**|**K=2**|**K=3**|**K=4**|**K=5**|
|---|---|---|---|---|---|---|
|Instrument(t)|0.22|0.00|-0.33|-0.32|-0.53|-0.52|
||(0.002)|(0.002)|(0.004)|(0.004)|(0.005)|(0.005)|
|Observations|3,716|3,622|3,502|3,387|3,265|3,145|

# _Source:_ Authors’ calculations.

_Note:_ Driscoll-Kraay standard errors in bracket. *** p<0.01, ** p<0.05, * p<0.1.

**Figure S7.1: The Impact of Tariffs When Instrument Included in Baseline**

<!-- Start of picture text --> Panel A: Output (%) Panel B: Productivity (%)<br><!-- End of picture text --> <!-- Start of picture text --> 0.4 0.5<br>0.2<br>0<br>0.0<br>-0.2<br>-0.5<br>-0.4<br>-0.6<br>-1<br>-0.8<br>-1.0 -1.5<br>-1 0 1 2 3 4 5 -1 0 1 2 3 4 5<br><!-- End of picture text --> _Source:_ Authors’ estimations. _Note:_ The solid black line indicates the response of output/productivity to a one standard deviation increase in tariff, estimated based on baseline plus the instrument. The red dotted line represents the baseline results, estimated based on equation (1). The blue dotted lines correspond to 90% confidence bands of the baseline. The x-axis denotes time. t=0 is the year of the tariff change.

24
|**Table 18. Why the unbanked haven't attempted to**<br>**open a bank account?**|
|---|
|**Doesn't have enough money**<br>49.3%|
|**Very high Minimum balance**<br>11.0%|
|**Didn't trust in the bank**<br>8.7%|
|**Low interests rates**<br>7.5%|
|**Doesn't need it**<br>6.9%|
|**Didn't know how to open**<br>4.7%|
|**The bank is so far**<br>3.2%|
|**High fees**<br>2.6%|
|**Bad service**<br>2.0%|
|**Bank hours inconvenient**<br>1.8%|
|**Other**<br>1.8%|
|**They don't have the basic documentation**<br>0.2%|
|**Source: ENDESFI**|

A small percentage of the unbanked (2.7%) reported that they tried to open a deposit account but were not able to do so. As shown in table 19, two-thirds of them could not do so because they did not have the required minimum initial deposit. Another 22% did not have the required documents or personal references. It is likely that many of these individuals work in the informal sector where written documents are rare and where formalities are often avoided to prevent governmental authorities from tracking business operations.

|**Total**<br>**Table 19. Why they couldn't open a bank account?**|
|---|
|**Didn't have basic documentation**<br>**15.6%**|
|**Didn't have money for the minimum deposit**<br>**66.7%**|
|**Didn't have personal references**<br>**6.7%**|
|**Didn't have bank references**<br>**2.2%**|
|**Other**<br>**8.9%**|
|**Source: ENDESFI**|

In the survey, 13.5 % of the respondents reported that they had applied for a loan from a bank. As shown in Table 20, of this group 74 % received the credit, 20% were rejected and 6% were still in the approval process at the time of the survey.

|**Total**<br><br><br>**Table 20. Did you apply for any credit in the last 3 years?**|
|---|
|**Accepted**<br>**10.1**<br>**Yes**|
|**In approvalprocess**<br>**0.8**<br>|
|**2.7**<br>**86.5**<br>**Yes, but it was rejected**<br>**No**|
|**Total**<br>**100.0**|
|**Source: ENDESFI**|

44
Metrics for both production and consumption objectives would help show whether a local economy is becoming more inclusive. In one recent example of such measurement, PolicyLink and the University of Southern California’s Program for Environmental and Regional Equity (PERE) have developed a series of regional equity profiles for various U.S. metropolitan areas.<sup>3</sup> The profiles offer 69 indicators of demographics, economic vitality, readiness, and connectedness in a mix of both regional “starting points” (including some of those we have discussed in this paper) and social and economic outcomes. Not every measure has a clear relationship with a desired outcome and is possible to influence with local actions. But all the categories offer at least some indicators that could be used to tell stakeholders that their economy is becoming more productive, inclusive, and innovative. Even the demographic indicators—whose trajectories are mostly affected by forces beyond local control—include measures of diversity that could associate with local efforts to attract and retain young people, especially international immigrants. Among the other areas, here are examples of some metrics mainly from the regional equity profiles that associate with more inclusive outcomes that could be monitored for action:

#  **Economic vitality**

- **Job growth** indicates the extent to which jobs are available. Job openings (i.e., positions that come open for new applicants) are another indicator of the availability of jobs.

- **Unemployment and labor‐force participation** , tracked by race, sex, age, and educational attainment, shows the extent to which adults are in the workforce and can find a job and where efforts to increase labor‐force engagement and job development might be targeted.

- **Real income and poverty rates** can expose the total purchasing power of the population as well as the extent of material want in the community. However, both of these measures need to be calibrated to local conditions because prices—especially housing costs—vary dramatically across the country. Furthermore, multiple data sources are needed for an accurate view of income and poverty considering the imprecision of self‐reports on national surveys.

3 See, for example, An Equity Profile of the Kansas City Region, <u>http://www.policylink.org/site/apps/nlnet/content2.aspx?c=lkIXLbMNJrE&b=5136581&c t=13381851, and An Equity Profile of the Houston‐Galveston Region, http://www.ourregion.org/OurRegion2040Supporting_Documents/Regional_Equity_Profil e.pdf.</u> 33
- Walker, Eduardo, “Gobierno Corporativo, Protección a los Accionistas Minoritarios y Tomas de Control,” _Elementos Conceptuales de la Ley de OPAS_ , Serie Documentos de Discusión No1, SVS, 2001

- Warner, JB, Watts, RL, & Wruck, KH (1988) Stock prices and top management changes. _Journal of Financial Economics_ , 20, 461-492.

- Weisbach, M., 1988, Outside directors and CEO turnover, Journal of Financial Economics 20, 431-60.

- Yurtoglu, B. Burcin, _Corporate Governance and Implications for Minority Shareholders in Turkey_ , (2001 data).

- Zingales, L., 1994. The value of the voting right: a study of the Milan stock exchange experience. The Review of Financial Studies 7, 125-148.

- Zingales, L., 1995. What determines the value of corporate votes. The Quarterly Journal of Economics 110, 1047-1073.

- Zingales, Luigi , "The Costs and Benefits of Financial Market Regulation" (April 2004). ECGI - Law Working Paper No. 21/2004.

28