rafmacalaba/datause-encoder-data huggingface dataset:
# **References**

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- Aid?”. _American Economic Review_ 92(4), 1126 1137.

- [2] Alstadsæter, Annette, Niels Johannesen, and Gabriel Zucman. 2017. “Tax Evasion and Inequality”. _American Economic Review_ 109(6), p. 2073-2103.

- [3] Alworth, Julian and Andresen, S., 1992. “The determinants of cross-border non-bank deposits and the competitiveness of financial market centers ”. _Money Affairs_ July-December, p. 105-133.

- [4] Andersen, Jørgen J., Niels Johannesen, David D. Lassen, and Elena Paltseva. 2017. “Petro Rents, Political Institutions, and Hidden Wealth: Evidence from Offshore Bank Accounts”. –

- _Journal of the European Economic Association_ 15(4), 818 860.

- [5] Boone, Peter. 1996. “Politics and the Effectiveness of Foreign Aid”. _European Economic_ –

- _Review_ 40, 289 329.

- [6] Burnside, Craig and David Dollar. 2000. “Aid, Policies, and Growth”. _American Economic_ –

- _Review_ 90(4), 847 868.

- [7] Bjørnskov, Christian. 2010. “Do elites benefit from democracy and foreign aid in developing countries?” _Journal of Development Economics_ 92(2), 115-124

- [8] Chinn, Menzie D. and Hiro Ito (2006). “What Matters for Financial Development? Capital Controls, Institutions, and Interactions”. _Journal of Development Economics_ 81(1), 163192.

- [9] Collier, Paul and David Dollar. 2002. “Aid Allocation and Poverty Reduction”. _European_ –

- _Economic Review_ 46, 1475 1500.

- [10] Dalgaard, Carl-Johan, Henrik Hansen and Finn Tarp, 2004. “On The Empirics of Foreign Aid and Growth”. _Economic Journal_ 114, 191-216.

- [11] Djankov, S., Montalvo, J.G. and Reynal-Querol, M., 2008. “The curse of aid.”. _Journal of economic growth_ 13(3), p. 169-194.

- [12] Djankov, Simeon, Rafael La Porta, Florencio Lopez-de-Silanes and Andrei Shleifer, 2010. “Disclosure by Politicians”. _American Economic Journal: Applied Economics_ 2(2), 179209.

- [13] Easterly, William. 2006. “The Big Push D ́ej`a Vu: A Review of Jeffrey Sachs’s The End of Poverty: Economic Possibilities for Our Time”. _Journal of Economic Literature_ XLIV, 96 – 105.

- [14] FATF. 2011. “Laundering the Proceeds of Corruption”. Financial Action Task Force, OECD, Paris.

20
running the 2SLS regression for the SME and large firm samples. This entire exercise is conducted separately for the level and growth rate of labor productivity as dependent variables.

The first stage IV regression results are provided in Panel B. These show that a longer duration of human settlement is positively correlated with ethnic fractionalization in the current time. The relationship is significant at the 1 percent level. The F-statistic for the first stage IV estimation is above the recommended level of 10, implying that our instrument does reasonably well in predicting ethnic fractionalization.

The second stage IV results are provided in Panel A in Table 9. These results confirm our baseline results above. That is, higher ethnic fractionalization is associated with a significantly lower level and growth rate of labor productivity (columns 1 and 5). Further, the interaction term between (instrumented values of) ethnic fractionalization and the SME dummy is negative and significant (columns 2 and 6). Thus, larger firm size significantly mitigates the negative impact of higher ethnic fractionalization on the level and growth rate of labor productivity. The split sample results reveal a similar picture. Higher ethnic fractionalization is associated with a significantly lower level and growth rate of labor productivity in the SME firm sample (columns 3 and 6). For the large firm sample, the relationship is also negative but much smaller in magnitude. It is statistically insignificant when the dependent variable is the level of labor productivity (column 4) and significant when it is the growth rate of labor productivity (column 8).

# **8. Conclusion**

We investigated the impact of ethnic fractionalization on the level and growth rate of labor productivity in private firms in 84 developing countries. The focus of the paper was on how the impact is attenuated by firm size. The motivation for the exercise came from the existing studies 35
best execution, to carry out a portion of their trades through a network of participating brokers, who refund some of their commission income to the NPRF.

Close monitoring of the performance of external managers is ensured by the development of a sophisticated system of reporting and measurement of risks and returns that complements the work of the custodian. Formal review meetings of external asset managers are held by the NTMA and the latter's opinion is then considered by the Commission. During 2003, one mandate was terminated.

The Commission made considerable progress in 2004 and 2005 implementing the revised investment strategy that emphasized increased allocations in small caps, private equity, real estate and commodities. New mandates were awarded following the same competitive process as with the original appointments.

Much of the portfolio, covering half of the large cap allocation, is passively managed. But active management, especially for less liquid assets, is growing. This has resulted in the payment of increasing management fees to external managers.

# **4.6 Investment Performance**

In 2001 all assets were invested in cash. Implementation of the strategic asset allocation was slowed down in 2002 because of adverse market conditions. Thus, at the end of 2002 57 percent was invested in real assets and 43 percent in monetary assets compared with the long-term target of 80/20 in real and monetary assets. Considerable progress was made in 2003 in aligning the portfolio composition to the long-term strategic objectives.

Real assets now account for just over 80 percent of assets (Table 4.3). Alternative assets amount to nearly 5 percent of assets compared to the target of 20 percent by 2009. Rather surprisingly, bank deposits continue to represent over 5 percent of assets. The equity portfolio is equally divided between Eurozone and global equities. Investments in Irish equities are less than 1 percent of total assets. Investments in Irish government bonds are not allowed; all investments in government bonds are in bonds of other Eurozone countries.

|Table<br>|4.3:Ass<br>|etAllocat<br>|ion,2001<br>|-2006<br>|||
|---|---|---|---|---|---|---|
|end December<br>percent|2001|2002|2003|2004|2005|2006|
|Equities||56.8|70.8|76.1|77.9|75.7|
|Commodities|||||1.3|1.1|
|Property|||||0.6|3.2|
|Private Equity||||||0.5|
|Government Bonds||17.6|14.6|12.8|11.7|12.7|
|Corporate Bonds||||||0.5|
|Foreign Currency Funds||||||0.5|
|Bank Deposits|100.0|25.7|14.6|11.1|8.4|5.8|
|Total|100.0|100.0|100.0|100.0|100.0|100.0|

Source: NPRF 52
_Panel B. Number of countries that took at least one measure (up to September 1_<sup>_st_</sup> _, 2020)_

|**Category**<br>**Focus**|All<br>countries|High<br>Inc.|EAP|ECA|LAC|MEN<br>A|SAR|SSA|
|---|---|---|---|---|---|---|---|---|
|**_Total # of countries_**|**_154_**|**_37_**|**_14_**|**_19_**|**_25_**|**_11_**|**_8_**|**_40_**|
|**Banking Sector**|**150**|**37**|**14**|**19**|**25**|**11**|**8**|**36**|
|Prudential|117|32|11|18|20|4|7|25|
|Support borrowers|141|37|14|19|24|11|8|28|
|Integrity|48|24|3|7|7|0|4|3|
|Operational continuity|41|18|6|4|5|1|3|4|
|Crisis management|11|3|3|3|1|0|0|1|
|**Financial Markets and NBFIs**|**_85_**|**_28_**|**_7_**|**_12_**|**_9_**|**_1_**|**_4_**|**_24_**|
|Market functioning|65|23|7|10|8|1|4|12|
|Non-bank financial institutions|50|26|4|6|7|0|2|5|
|Public debt management|28|6|3|4|4|0|2|9|
|**Liquidity and funding**|**_143_**|**_36_**|**_13_**|**_18_**|**_22_**|**_7_**|**_8_**|**_39_**|
|Liquidity support|125|33|12|13|20|5|8|34|
|Easing of policy rates|106|25|12|15|18|5|5|26|
|Assetpurchases|30|18|1|1|3|0|2|5|
|**Payments Systems**|**_90_**|**_21_**|**_6_**|**_10_**|**_12_**|**_7_**|**_6_**|**_28_**|
|Digital payments|82|17|6|9|11|7|6|26|
|Relaxation of compliance requirements|17|3|0|2|1|0|2|9|
|Cash / Cheque usage restrictions|14|2|0|2|1|2|2|5|
|Cash acceptance|15|4|2|3|2|2|1|1|

Source: World Bank COVID-19 Financial Sector Policy Response Database.

Note: World Bank regional classification. High Inc. = High income; EAP = East Asia Pacific; ECA = Europe & Central Asia; LAC = Latin America & Caribbean; SAR = South Asia; MENA = Middle East & North Africa; and SSA = Sub-Saharan Africa. WBG regions exclude high income countries.

# _2.3 Financial Policy Response Activity Index_

This section introduces the _Financial Policy Response Activity Index_ (FPRAI), a simple, transparent proxy that can be used to compare financial sector policy activity across countries _._ FPRAI is computed by summing up all the policy measures implemented up to time ( _t_ ) in country ( _c_ ) across the four categories: _Banking Sector_ , _Financial Markets and NBFIs_ , _Liquidity and Funding_ , and _Payments Systems_ .<sup>9</sup> As such, it is important to keep in mind that the FPRAI is silent on the _scale_ and _effectiveness_ of the financial policy response. Figure 2 displays the FPRAI and shows that most financial sector policy makers around the world have been active, although to a lesser extent in Sub-Saharan Africa (see Table A3 in Appendix A for the descriptive statistics of FPRAI by country groupings).

> 9 Alternative, less transparent approaches, such as min-max transformations and averaging across categories produce similar results.

13
Temporary work has been increasing relative to permanent employment in agriculture and especially fruit production. For example, Gómez, S. and J. Echeñique (1988) estimated that the total permanent agricultural labor force fell from 208,000 to 120,000 between 1964 and 1987, while the total temporary agricultural labor force increased from 147,000 to 300,000 in the same period.<sup>1</sup> Fruit production is concentrated in specific geographical areas. Workers are generally unwilling and unable to migrate from one area to another for short periods. Accordingly, in many areas the fruit sector has demanded more labor than the traditional labor force has been able to supply during the summer months.<sup>2</sup> The shortage of labor during the peak season encouraged employers within the fruit sector (mainly in the packing sheds) to develop mechanisms to attract, motivate and compensate workers for seasonal work. Piece rate pay emerged to facilitate the incorporation of heterogeneous workers into the labor force, while also providing direct motivation to increase effort over a longer workday. A growing number of women were attracted to work in the fruit sector. By the early 1990s, we estimate that female employment in the fruit sector amounted to roughly 30% of total fruit sector employment. Notwithstanding, women

> 1  Official data on many aspects of agricultural employment are lacking or of poor quality.  Definitions change from period to period, and from study to study.  Comparisons are thus difficult to make.  Assertions should often be treated as working hypotheses.

> 2 For example, Yaksic (1986) estimated that the table grape sub-sector in the Provinces of San Felipe and Los Andes required labor equal to only about 3 percent of the locally available labor in the months of April and May, 1983, but needed absolutely more workers than were locally available during the month of February. However, Newman and Jarvis encountered few truly migratory workers among the packing shed workers in the three areas surveyed during January-February, 1992.  It may be that Yaksic’s estimates were in error, or that the local labor force grew significantly between 1983 and 1992.  Although the temporary labor force in northern valleys, such as Copiapo, has long depended mainly on migratory workers, in other areas the labor force is composed overwhelmingly of workers from local rural and urban areas who travel daily from home to work and back (Venegas).

- 6 -
movements have a small effect on employment and that job destruction is not substantially affected. Goldberg and Tracy (2001) offer an explanation for the finding that industry wages are significantly more responsive than industry employment to exchange rate changes. They find that the main mechanism for exchange rate effects on wages occurs through job turnover and the strong consequences this has for the wages of workers undergoing such job transitions. Workers who remain with the same employer experience little if any wage impacts from exchange rate shocks. In addition, they find that the least educated workers -who also have the most frequent job changes - shoulder the largest adjustments to exchange rates.

Insofar as appreciations affect the probability of job losses, whereas depreciation does not, differential effects may depend on whether industries (firms) are exporters or import-competing. Losses from appreciation are more likely to be concentrated in import-competing sectors. Revenga (1992) finds that in the US import competing industries reduce employment overall during currency appreciations. All these results suggest asymmetrical effects in the USA between appreciations and depreciations. This probably reflects a persistent pressure towards job reductions in tradables (due, perhaps, to technology or competition), with the exchange rate acting as a trigger for inevitable adjustments.

Using French firm-level data, Gourinchas (1999b) also finds that exchange rate appreciations reduce net employment growth, because of lower job creation and increased job destruction. Bentivogli and Pagano (1999) find for a number of European countries rather limited, but diverging effects of exchange rates changes on job flows. The latter may reflect differences in labor market institutions. Thus, Burgess and Knetter (1998) find in that in countries with the most rigid labor institutions, such as Germany and Japan, employment is not sensitive to exchange rates, while in other countries appreciations are associated with reductions in employment.

Work on developing countries has tended to be much more explicitly motivated by trade reforms. An early discussion of trade and employment was Krueger (1983) who argued that developing-country trade liberalization should boost labor–intensive output and increase employment. Her case studies showed that developing countries’ manufactured exports were, indeed, labor–intensive, but that the employment effects of liberal trade policies were generally 14
# **Acknowledgements:**

We are grateful for the comments and suggestions received from many colleagues over the years at seminars at the World Bank, ECB, OECD, EU Directorates General for Trade and for Economic and Financial Affairs, Harvard, MERIT Maastricht, University of Groningen, Trinity College, Korea Productivity Centre, Dutch Ministry of Economic Affairs, Upjohn Institute, and at various WIOD meetings, the final conference of INDICSER in Budapest, the WIOD launch meeting in Brussels and the EP panel meeting in Dublin. Particular thanks to Richard Baldwin for encouragement and advice. Reitze Gouma, Abdul Azeez Erumban and Neil Foster provided excellent research assistance. Comments from the Panel discussants Josep Pijoan-Mas and Alexander Michaelides, four anonymous referees and an editor are also gratefully acknowledged. This paper is part of the World Input-Output Database (WIOD) project that was funded during the period from 2009 to 2012 by the European Commission, Research Directorate General as part of the 7th Framework Programme, Theme 8: Socio-Economic Sciences and Humanities, grant Agreement no: 225 281. More information on the WIOD-project can be found at www.wiod.org.

**_NOTE:_** This paper is part of a series of selected papers from a recent conference on Global Value Chains— _National Competitiveness, Scalability of International Value Chains and Location of Production_ —which took place in April 2013. The International Trade Unit within the World Bank's Economic Policy, Debt and Trade Department co-hosted the conference and oversaw the publishing of this paper in the Policy Research Working Paper series.  An extended version of this paper was prepared for the 57th Panel Meeting of Economic Policy, April 2013, and was published in _Economic Policy_ , October 2013.

2
# **C Analyzing Cerutti, Classens and Rose: frequency, aggregation, estimation**

As noted in the main text, our results regarding the variance contribution of the estimated factors stand in contrast with those of Cerutti et al. (2017c), who report much smaller figures. From this they conclude that the ’global financial cycle’ (or, more broadly, common shocks) is a relatively minor force behind capital flows.

There are five main differences between our analysis and that of Cerutti et al. (2017c): the frequency of the data (yearly in this paper versus quarterly in theirs), the level of aggregation of capital flows (total flows versus inflows disaggregated into FDI, portfolio debt, portfolio equity and bank credit), the estimation method (principal components versus Bayesian), the normalization of the flows (by trend GDP<sup>35</sup> versus current GDP), and the estimation sample, both regarding country coverage as well as time frame (1979-2015 versus 1990Q1-2015Q4). In Tables C.1 and C.2 we show that the first two differences account for the bulk of the discrepancy. Using the data of Cerutti et al. (2017c)<sup>36</sup> we reestimate the two factors they consider (one derived from advanced non-central countries, the other from major emerging markets)<sup>37</sup> using the standard principal components approach, which estimates the factors as the eigenvalues of the sample covariance matrix. Since the data are not a balanced panel, we deal with missing values<sup>38</sup> by estimating the sample covariance matrix as:

> 35Trend GDP is calculated using an HP filter with parameter 100 (with data at yearly frequency).

36Downloaded from http://faculty.haas.berkeley.edu/arose/RecRes.htm#Reverse

> 37As detailed in table A1 of Cerutti et al. (2017c), the advanced non-central countries are Australia, Canada, Iceland, New Zealand, Norway, Sweden (large and safe-haven economies are excluded); the emerging countries are Brazil, Chile, China, Indonesia, Korea, Mexico, Philippines, Poland, the Russian Federation, South Africa, Thailand and Turkey (MSCI members with weight larger than 1%). We note that estimating a factor with data from only 6 or 12 countries can be imprecise, potentially leading to factors with smaller explanatory power.

38In the data file supplied by Cerutti et al. (2017c) there are, aside from missing values, a number of flows taking the value of exactly zero; we treat those as missing values.

66
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
|---|---|---|---|---|---|---|---|
|VARIABLES|||Heig|ht-for-age-z-sc|ores|||
|Asset index 5 (Wealthiest)|0.530***|0.555***|0.544***|0.501***|0.503***|0.500***|0.518***|
||(0.118)|(0.119)|(0.119)|(0.121)|(0.123)|(0.123)|(0.122)|
|Rural|-0.325***|-0.328***|-0.323***|-0.295***|-0.297***|-0.292***|-0.296***|
||(0.096)|(0.097)|(0.097)|(0.098)|(0.097)|(0.098)|(0.099)|
|Eastern|0.242***|0.258***|0.261***|0.271***|0.261***|0.267***|0.274***|
||(0.087)|(0.087)|(0.087)|(0.087)|(0.087)|(0.087)|(0.087)|
|Central|0.127|0.139*|0.137*|0.161**|0.152*|0.136*|0.136*|
||(0.079)|(0.079)|(0.079)|(0.079)|(0.079)|(0.079)|(0.080)|
|Mid-Western|0.010|0.016|0.020|0.048|0.033|0.021|0.014|
||(0.093)|(0.095)|(0.094)|(0.094)|(0.094)|(0.094)|(0.094)|
|Far Western|0.033|0.040|0.048|0.049|0.052|0.059|0.038|
||(0.112)|(0.115)|(0.114)|(0.112)|(0.112)|(0.112)|(0.113)|
|Constant|-0.385|-0.393|-0.388|-0.387|-0.385|-0.434*|-0.394|
||(0.259)|(0.260)|(0.259)|(0.259)|(0.258)|(0.259)|(0.260)|
|Observations|2,462|2,462|2,462|2,474|2,474|2,474|2,474|
|R-squared|0.226|0.224|0.224|0.226|0.226|0.225|0.224|

Source: Authors’ calculations. Note: Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1 Per capita kcal is the per capita caloric availability, 1-SSR is the share of calories derived from non-starchy staple food, 1-SSEXR is the expenditure share of non-starchy staple food in the total food expenditure, FCS is the food consumption score and DDS is the household level dietary diversity score, PC consumption is the per capita consumption, and Food share is the share of food expenditure on total expenditure.

64
Table 2 - Influence of Doing Business Rankings on FDI Inflows into Developed and Developing Countries Dependent Variable: Value of FDI Inflows (100s of million USD) GMM based on the Arellano-Bond methodology

||Column A|Column B|Column C|Column D|Column E|Column F|Column G|Column H|Column I|Column J|
|---|---|---|---|---|---|---|---|---|---|---|
|DBR|**-2.50***|**-2.64***|**-2.97****|-2.29|-2.57|**-3.19***|**-3.94****|**-4.22****|**-2.53****||
||_1.48_|_1.48_|_1.49_|_1.62_|_2.02_|_1.66_|_1.42_|_1.57_|_0.85_||
|GDPGR|**9.77****|**7.81****|**9.21****|4.36|0.92|**9.65****|**6.61****|**9.79****|**6.34****|1.97|
||_3.86_|_3.52_|_3.72_|_4.26_|_5.39_|_4.23_|_3.25_|_4.19_|_2.65_|_2.05_|
|GDPCAP|**0.01***|**0.01***|**0.01***|0.01|**0.02****|||||**0.01****|
||_0_|_0_|_0.00_|_0.00_|_0.01_|||||_0.01_|
|REER|3.01|3.22|2.73|4.01|5.36|**4.41***|0.07|3.08|0.43|0.03|
||_2.53_|_2.5_|_2.61_|_3.05_|_3.96_|_2.38_|_0.41_|_2.41_|_1.96_|_1.89_|
|INF|-1.89|-0.56|-2.44|-1.20|-1.39|-2.68||-4.83|-3.37|2.97|
||_4.43_|_4.59_|_4.26_|_5.26_|_6.52_|_3.99_||_3.69_|_2.82_|_2.53_|
|OPEN|-6.18|-6.03|-6.15|-4.49|-7.38|-6.26|-5.88|-6.28||-2.52|
||_4.74_|_4.23_|_4.67_|_3.04_|_4.62_|_4.67_|_4.64_|_4.90_||_3.38_|
|TAXPROFITS||||2.92|||||||
|||||_3.60_|||||||
|TAXINT|||||5.47||||||
||||||_5.57_||||||
|VA||-6.55*||-7.02|**-13.32****|-5.30||||-4.08|
|||_3.96_||_5.31_|_6.53_|_4.05_||||_4.38_|
|PV||0.92||1.19|**6.60***|1.07||||-1.03|
|||_2.54_||_3.27_|_3.99_|_2.90_||||_2.86_|
|CC||3.78||5.48|3.39|**5.98***||||-0.97|
|||_2.91_||_3.88_|_3.74_|_3.22_||||_1.91_|
|GOV|||-1.72||||||||
||||_2.51_||||||||
|GE||||||||||0.52|
|||||||||||_2.84_|
|RQ||||||||||3.69|
|||||||||||_3.09_|
|RL||||||||||0.96|
|||||||||||_2.14_|
|Hansen J test|0.19|0.37|0.25|0.52|0.63|0.17|0.20|0.14|0.03|0.19|
|No. of observations|323|323|323|216|182|323|331|323|340|511|
|No. of groups|84|84|84|61|53|84|85|84|86|89|
|No. of instruments|43|46|44|47|47|38|35|35|27|58|

Standard errors corrected for robustness in italics. *** Significance at the 1 percent level, ** Significance at the 5 percent level, * Significance at the 10 percent level.

A negative coefficient for the DBR variable suggests on average, an increase in the Doing Business Ranking will increase FDI Inflows all else constant (i.e. higher rankings are a lower numerical value). For the remaining variables, the opposite is true; an increase in that variable suggests, on average, an increase in FDI Inflows all else constant.

10