rafmacalaba/datause-encoder-data huggingface dataset:
- Cunningham, W., and P. Villaseñor. 2016. “Employer Voices, Employer Demands, and Implications for Public Skills Development Policy Connecting the Labor and Education Sectors.” _World Bank Research Observer_ 31 (1): 102–34.

- DE4A (Digital Economy for Africa). 2021. _Digital Skills: The Why, the What, and the How - Methodological Guidebook for Preparing Digital Skills Country Action Plans for Higher Education and TVET (V2.0)_ . Washington, DC: World Bank.

- Deming, D. J. 2017. “The Growing Importance of Social Skills in the Labor Market.” _Quarterly Journal of Economics_ 132(4): 1593–1640.

- Deming, D. J., and L. B. Kahn. 2018. “Skill Requirements across Firms and Labor Markets: Evidence from Job Postings for Professionals.” _Journal of Labor Economics_ 36(1): 337–69.

- Deming, D. J., and K. Noray. 2020. “Earnings Dynamics, Changing Job Skills, and STEM Careers.” _Quarterly Journal of Economics_ 135(4): 1965–2005.

- DeStefano and Timmis _forthcoming._ “Firm Digital Adoption during COVID-19.” _Manuscript_ . Discussed in World Bank (2021, pp. 28–29).

- Friedman, J. 2012. “Whether to probit or to probe it: in defense of the Linear Probability Model.” _Development Impact Blog_ . Washington, DC: World Bank.

- Goos, M., Manning, A., Salomons, A. 2014. “Explaining Job Polarization: Routine-Biased Technological Change and Offshoring.” _American Economic Review: Papers & Proceedings_ 99(2): 58–63.

- Grundke, R., L. Marcolin, T. L. B. Nguyen, and M. Squicciarini. 2018. “Which skills for the digital era?: Returns to skills analysis.” _OECD Science, Technology and Industry Working Papers_ 2018/09. Paris: OECD.

- Guerra, N., K. Modecki, and W. Cunningham. 2014. “Social-Emotional Skills Development across the Life Span: PRACTICE.” _World Bank Policy Research Working Paper_ 7123. Washington, DC: World Bank.

- Hardy W., R. Keister R., and P. Lewandowski. 2018. “Educational upgrading, structural change and the task content of jobs in Europe.” _Economics of Transition_ 26(2): 201–231.

- IFC (International Finance Corporation). 2019. _Digital skills in sub-Saharan Africa: Spotlight on Ghana._ Washington, DC: IFC.

- ILO (International Labour Organization). 2012. _International Standard Classification of Occupations 2008. Volume 1: Structure, group definitions and correspondence tables_ . Geneva: ILO.

- ITU (International Telecommunication Union). 2020. _Digital Skills Assessment Guidebook_ . Geneva: ITU.

- Lewandowski, P., A. Park, W. Hardy, and Y. Du. 2021. “Technology, Skills, and Globalization: Explaining International Differences in Routine and Nonroutine Work Using Survey Data. _World Bank Economic Review (forthcoming)_ .

- Lo Bello, S., M. L. Sánchez-Puerta, and H. Winkler. 2019. “From Ghana to America: The Skill Content of Jobs and Economic Development.” _World Bank Policy Research Working Paper_ 8758, Washington, DC: World Bank.

38
# **C. DOMESTIC OR OVERSEAS MIGRANT IN HOUSEHOLD**

|_Reference Category: No Mi_<br>|_grant_<br>||||
|---|---|---|---|---|
|Has domestic migrant|0.03**|-0.00|-0.00|-0.00|
||(0.01)|(0.01)|(0.01)|(0.00)|
|Has foreign migrant|0.05***|-0.02|-0.01|-0.00|
||(0.01)|(0.01)|(0.01)|(0.00)|
|Constant|0.59***|0.87***|0.06***|0.01***|
||(0.01)|(0.00)|(0.00)|(0.00)|
|**D. HOUSEHOLD DEMO**<br>**CHARACTERISTICS**<br>Household size|**GRAPHIC**<br>-0.01**|-0.00|0.00|0.00**|
||(0.01)|(0.00)|(0.00)|(0.00)|
|No. of children (0-6)|-0.01|0.01|-0.01|-0.00|
||(0.01)|(0.01)|(0.00)|(0.00)|
|No. of elderly (65+)|-0.03***|-0.01|0.00|-0.00|
||(0.01)|(0.01)|(0.01)|(0.00)|
|No. of adults (15-65)|0.02***|0.01**|-0.00|-0.00|
||(0.01)|(0.00)|(0.00)|(0.00)|

Notes: Standard errors in parentheses. * Significant at ten per cent; ** significant at five per cent; *** significant at one per cent. _Source_ : World Bank Staff estimates based on data from Perceptions of Poverty, Prosperity and Economic Mobility in Nepal, 2014.

28
for introducing an even very modest wealth tax. But more needs to be done to exploit those opportunities. The World Bank (and other international organizations), for example, often provide loans and technical assistance to support tax reforms in developing countries. It would be relatively easy to require governments to release (suitably anonymized) distributional statistics to their citizens.

Armed with better data, a number of important research questions can be addressed, including some very basic ones: What is the ‘true’ level and trend of inequality? Can the trends in the micro data and the national accounts be reconciled after accounting for capital incomes and the top of the distribution? What do corporate tax data tell us about the true extent of economic concentration and should it be a concern for competition authorities? How do capital income dynamics compare with the more frequently analyzed dynamics of labor incomes? How does the current system of taxes and transfers affect the distribution of income? Is that fiscal system optimal? How do households in different parts of the income and skill distribution interact with the global economy – as consumers, sellers of their labor, owners of capital – and how is this different in poorer and richer countries?

This essay has provided a global perspective on inequality in an attempt to complement Piketty’s analysis in C21. In contrast to the sharp increases in top incomes in Anglo-Saxon countries observed by Piketty and co-authors, inequality viewed from a global perspective appears to have stabilized or even fallen slightly, driven by convergence in average incomes across countries. After rising strongly in the 1980s and 1990s, inequality within the average developing country has fallen in the 2000s. These results are based on household surveys, which are known to underestimate top incomes, especially capital incomes. Furthermore, these surveys often use consumption expenditures, which might not capture increases in living standards at the top. Hence, although much more evidence has become available in poor countries, these results are subject to considerable uncertainty. Therefore, better data in developing countries remains a top priority to move forward the research agenda on inequality in these countries.

18
Source: author calculations using Financial Times fDI Markets Database The manufacturing sector has traditionally attracted the largest share of greenfield investments in total, but the average size of a project is the largest in primary industries—including oil and gas extraction and mining—at $750 million per project (Table 1). The average size of a project in the manufacturing sector was $80 million, while for services it was $32 million. Hence to obtain a full picture of investment activity, it is important to also track the extensive margin.

**Table 1: Average greenfield deal size ($ millions)**

|Period|Manufacturing|Services|Primarysector|
|---|---|---|---|
|2019|79.8|31.8|748.8|
|2018|127.7|38|480.8|
|2017|77.7|28.1|287.7|

Notes: Financial Times fDi Markets Database.

Greenfield investments do not necessarily follow the same trend as FDI inflows measured by BoP. Our main data source for foreign investment, _fDi Markets_ data, traces firm level investment activity (which may not be recorded in BoP), and hence identifies the ultimate investment ownership. Thus, this firm level investment activity is not mis-measured by the phenomena of transshipping or roundtripping, where FDI from a given country may be routed to the destination through intermediate countries. In 2018 for example, the nominal value of greenfield investments and M&As (data which similarly traces firm level investment activity) spiked more sharply than the value of FDI recorded by BoP, suggesting that many countries in the region may have acted as a conduits for FDI ultimately directed outside Asia. Therefore, in addition to the fact that greenfield investment data is disaggregated at the sector level, it is also a more accurate measure of origin and destination of FDI flows. With this in mind, we turn to how this financing may impact productivity.

# 3.2 Conceptual framework

To guide the empirical analysis, this section introduces the conceptual framework. The first subject of interest is establishing a _direct_ impact of FDI on the labor productivity of the recipient sector. FDI could generate spillovers since workers from multinational firms are mobile and can bring
Table 4. The Incidence of OECD Tariffs on<sup>Sub-Saharan Countries' Non-Oil Exports (all figures m percent).</sup>

||OECD A|verage|European C|onmmunity|Japan||United St|ates|
|---|---|---|---|---|---|---|---|---|
|Exporting Country|African<br>Tariff|Preference<br>Margin'|African<br>Tariff|Preference<br>Margin'|African<br>Tariff|Preference<br>Margin'|African<br>Tariff|Preference<br>Margin|
|Angola|0.2|-1.5|0.3|-3.2|1.8|0.0<br>|0.1<br>|-0.4<br>|
|Botswana|0.3|-2.8|0.1|-2.9|0.0|-2.1|3.5|-1.1|
|Cameroon|0.4|-2.5|0.1|-2.8|0.0|0.0|2.1|-1.1<br>|
|Central African Republic|0.2|-2.2|0.2|-2.3|0.0|0.0|0.0|-1.1|
|<br>Chad|0.4|-2.7|0.2|-2.9|2.5|0.0<br>|1.6<br>|0.0<br>|
|Congo|0.1|-1.4|0.0|-2.2|0.0|0.0|0.3|-0.6<br>|
|<br>Cote d'lvoire|0.7|-3.1|0.3|-3.3|1.2<br>|-0.5<br>|3.3<br>|-2.0<br>|
|Ethiopia|0.7|-1.3|0.1|-1.9|1.5|-1.3|2.0|0.4<br>|
|Gabon|0.6|-2.0|0.0|-2.7|0.0|0.0|2.9|0.7|
|Ghana|1.0|-2.2|0.1|-3.1|2.3|0.0|2.6<br>|-0.9<br>|
|Guinea|0.6|-2.3|0.0|-2.9|1.8|-1.9|1.9|-1.0|
|Kenya|0.5|-3.3|0.2|-3.5|2.4|-1.1<br>|3.I<br>|-2.3<br>|
|Liberia|0.6|-1.7|0.3|-1.9|0.0|-0.3|2.5|-1.1|
|Madagascar|0.5|-2.0|0.4|-2.7|0.8|-0.2<br>|0.8<br>|-1.0<br>|
|Malawi|1.1|-2.4|0.1|-3.5|0.0|-0.1|5.4<br>|-0.6<br>|
|Mali|0.4|-3.4|0.2|-3.5|0.0|-1.6|3.1|-2.2|
|Mauritania|1.7|-2.3|0.2|-3.9|3.6|-0.4<br>|1.2<br>|-1.6<br>|
|Mauritius|1.3|-3.1|0.2|-3.4|4.8|-1.1|6.4|-1.8|
|Niger|0.1|-3.0|0.0<br>|-3.0<br>|0.0<br>|0.0<br>|3.3<br>|-1.6<br>|
|Nigeria|2.7|-0.9|0.1|-2.6|3.7|-0.8|5.2<br>|0.7<br>|
|Senegal|0.5|-3.3|0.3|-3.5|3.6|0.1|4.9|-1.2|
|<br>Sierra Leone|0.5|-3.1|0.0|-4.0|2.6|-0.7|2.3<br>|-0.2<br>|
|Sudan|0.1|-1.5|0.1|-1.9|0.0|0.0|0.7|-1.0|
|Swaziland|0.8|-4.4|0.5|-4.9|6.7<br>|-3.0<br>|3.5<br>|-1.9<br>|
|Togo|0.3|-2.8|0.2|-2.8|9.8|-0.8|0.2|-2.8<br>|
|<br>Uganda|0.9|-2.4|0.6|-3.0|0.0<br>|0.0<br>|2.1<br>|-0.3<br>|
|United Republic Tanzania|0.1|-2.3|0.0|-2.5|1.4|-1.0|0.0|-2.4|
|<br>Zaire|0.3|-2.1|0.1|-2.4|0.0|-0.5<br>|1.3<br>|-1.1<br>|
|Zambia|0.3|-1.7|0.5|-2.9|0.0|-0.6|1.4|-1.4|
|Zimbabwe|0.9|-2.5|0.2|-3.3|1.2|-1.0|4.0|-1.0|
|MEMO ITEM|||||||||
|Taiwan, China|6.1|0.9|7.5|4.0|2.5|-2.2|6.8|0.7<br>|
|Republic of Korea|6.0|0.6|7.8|4.2|2.7|-2.2|7.1|0.7|

Positive values indicate 'Negative values show the average preferential tariff margins<sup>(in points) that the African exporter has over all other exporters of the same goods.</sup> that the exporter faces a higher than average tariff due<sup>to preferences other countries receive. All tariffs shown above are the simple average (unweighted) of duties paid on</sup> the country's exports.
estimation method.

**Table 2** <u>Variables and designation</u> Variable Designation <u>Private capital in energy sector</u> _privinvt_ Private investment in energy projects <u>Economic development</u> _growth_ Growth rate of GDP per capita <u>Financial development</u> _findev_ Overall financial development variable _bsdev_ Banking sector development variable _cmdev_ Capital markets development variable

<u>Institutional quality and risk</u> _corruption_ Corruption index _govtstability_ Government stability variable _lawandorder_ Law and order variable _regquality_ Index of regulatory governance quality _countryrisk_ Country risk index _exchraterisk_ Exchange rate risk index <u>Energy sector regulation</u> _indepreg_ Separated regulatory authority variable

**Table 3**

<u>Summary statistics</u>

|Variable|Obs.|Mean|Std.Dev.|Min.|Max.|Median|
|---|---|---|---|---|---|---|
|_privinvt_|497|574.62|1430.47|0|13910|0|
|_growth_|666|1.84|4.88|-30.86|16.15|2.49|
|_findev_|472|-3.25E-09|1.78|-3.72|3.61|-0.54|
|_bsdev_|571|-3.21E-09|1.39|-2.70|2.60|-0.02|
|_cmdev_|536|-3.71E-09|1.53|-2.99|2.88|0.09|
|_CBA_|575|0.09|0.11|0.00|0.71|0.06|
|_DMBA_|584|0.41|0.28|0.07|1.73|0.35|
|_CBPC_|586|0.35|0.30|0.04|1.66|0.25|
|_SMC_|545|0.34|0.44|0.00|2.82|0.19|
|_SMT_|546|0.28|0.42|0.00|3.44|0.12|
|_TVT_|549|0.12|0.26|0.00|2.37|0.03|
|_corruption_|650|3|1|0|5|3|

11
# **I. Introduction**

The purpose of this paper is to serve as a guide for policy makers who are interested in upgrading the investment regulatory framework in defined contribution (DC) open pension fund systems. The paper emphasizes the need for implementing an investment regulatory framework based on the concept of Strategic Asset Allocation (SAA).<sup>2</sup> While the main tool for implementing SAA is through portfolio benchmarks, this document emphasizes the importance of having a clear pension objective, an adequate governance structure for the construction of the benchmark, and an adequate methodology for constructing and maintaining the benchmark.<sup>3</sup> The need to move to investment regulations based on SAA arises because market based regulations lead inevitably to inefficient portfolios. Rudolph and de la Torre (2016) argue that due to agency problems, the competitive pension system is unable to align the interests of the pension fund management companies (PFMCs) with the long-term objectives of the contributors to the pension fund system. They propose the need to use pension fund portfolio benchmarks as the most effective way of solving the agency problem. From a different perspective, Rudolph and others (2010) have also proposed focusing the investment regulatory framework of open pension funds on the provision of common portfolio benchmarks for the pension fund industry.

Section II starts by explaining the concept of strategic asset allocation and how lifecycle investment strategies are the optimal way of addressing long-term pension objectives. The asset portfolio of lifecycle strategies start from portfolios highly invested in equities and gradually move to more longterm fixed income assets as individuals get closer to retirement age. The section also explains that lifecycle strategies are not as simple as a combination of lifestyle pension funds and examines how far the regulatory framework in some countries is from having in place a lifecycle strategy. Finally, it proposes a methodology for managing pension risk in a competitive pension system that could be operationalized in the context of an open pension system.

Section III analyzes the three elements that need to be taken into consideration at the time of implementing a lifecycle strategy in an open pension system, guided through a portfolio benchmark: (a) the pension objective; (b) governance of the portfolio benchmark; and (c) the methodology for developing the benchmark. Section IV proposes the use of the approximation method as a starting point for building portfolio benchmarks, and illustrates step by step the method for building them. The last section concludes.

# **II. Strategic asset allocation and benchmark portfolios**

Modern portfolio theory for long- term portfolios is based on the concept of Strategic Asset Allocation (SAA). Investors decide, ex-ante, on the future asset allocation, based on long-term risk

> 2 See Campbell and Viceira (2002). 3 All codes for programing in Matlab are available upon request.

2
growth, all but China were G7‘s industrial countries. By 2009, all except the United States were emerging economies—with China as the top contributor (Figure 1.2).

The replacement of G7 by the G20 as the prime economic forum in the world since the eruption of the global crisis in 2008 has underscored the emergence of a multipolar world. The sheer size of the large emerging economies combined with dynamic and sustained growth has elevated Brazil, Russia, India, and China (BRIC) to the largest contributors to economic growth in the world (Figure 1.2). In 2006-10, almost 45 percent of global GDP was generated in the BRIC. The era of a multi-polar world is firmly established.

**<u>Figure 1.2: The 21st. Century is marked with the emergence of a multipolar world</u>**

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# **_Catch-up made the world less poor but not in the poorest countries_**

The narrowing of the huge income gap with the developed countries conferred on China dividends in the form of absolute poverty reduction (Figure 1.3). In the early 1980s, many African countries were more prosperous than China and India. In 1981 for instance, over 835 million Chinese were below the poverty line of less than $1 a day. In comparison, there were just over 212 million poor in Sub-Saharan Africa. Twenty-five years later, China had turned the income-poverty equation on its head. According to current economic projections, the world remains on track to reduce by half the number of people living in extreme poverty. The number living on less than $1.25 a day is projected to be 883 million in 2015, compared with 1.4 billion in 2005 and 1.8 billion in 1990, but most of this progress reflects rapid catch-up by China (Figure 1.3). Fortunately, as China‘s expansion continues and its industries create a large space for other developing countries, they too will be able to benefit from industrialization and reduce poverty.

5
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Table 7, we estimate a series of triple differenced regressions using three distinct measures of war intensity to argue that the decline in schooling is directly caused by the genocide. If we are able to show that the negative impact on schooling outcomes is larger in provinces that suffered the most from the genocide, then concluding that those negative impacts were due to the 1994 genocide rather than to other events that took place between 1992 and 2000 is more plausible.

In constructing the three distinct war intensity measures, we exploit the variation in the intensity of the genocide across provinces in Rwanda. The first measure, Measure A, is defined as the proportion of days during the genocide during which killings occurred in a given province and is based on the GenoDynamics database collected by Davenport and Stam (2007). The authors of the database attempted to collect all available information from local human rights organizations, Rwandan government ministries, and international organizations on the timing and geographic extent of all killings that took place during the one hundred days of genocide. The second measure, Measure B, is an indicator variable for the three provinces (Butare, Rural Kigali, and Kibongo) that had the most number of genocide victims based on the GenoDynmaics database (Davenport and Stam, 2007). Finally, the third measure, Measure B, is defined as the number of mass graves sites and memorials in a particular province, as recorded on an administrative map of Rwanda that was edited as part of the Rwandan Genocide Project at Yale University (Rwandan Genocide Project, 2007). Using both sources of data (discoveries of mass grave sites and memorials as well as information gathering tabulated in the GenoDynamics database about the genocide victims) increases the reliability of these measures and strengthens the robustness of our analysis.

Table 7 presents results of the triple differenced regressions in which the key variable of interest is the interaction of the difference-in-difference variable used in the previous tables with 15
# **<u>Table 4: Impacts on Formalization</u>**

||Z score|BRC|TPIN|City<br>|
|---|---|---|---|---|
|||||council|
|||Datapooled for all foll|ow upsurveys||
|**Panel A: Full sample**|||||
|Treatment 1: BRC|0.676***|0.515***|0.012*|0.017|
||(0.031)|(0.016)|(0.007)|(0.018)|
||0.000|0.000|0.089|0.358|
||0.000|0.000|0.134|0.479|
|Treatment 2: BRC + TPIN|0.665***|0.532***|0.000|-0.007|
||(0.041)|(0.024)|(0.009)|(0.024)|
||0.000|0.000|0.965|0.766|
||0.000|0.000|0.965|0.766|
|Treatment 3: BRC + IS + BBA|0.824***|0.636***|0.008|0.015|
||(0.025)|(0.013)|(0.006)|(0.016)|
||0.000|0.000|0.194|0.359|
||0.000|0.000|0.25|0.479|
|Control group mean|0.000|0.081|0.056|0.641|
|Sample size|10,900|10,900|10,900|10,900|
|p-value: Treatment 1 = Treatment 2|0.811|0.519|0.179|0.314|
|p-value: Treatment 1 = Treatment 3|0.000|0.000|0.561|0.909|
|p-value: Treatment 2 = Treatment 3|0.000|0.000|0.323|0.322|
|p-value test of equality|0.000|0.000|0.273|0.603|
|p-value test of equality of treatment effects over time|||||
|Treatment 1|0.000|0.002|0.554|0.237|
|Treatment 2|0.000|0.098|0.369|0.809|
|Treatment 3|0.000|0.001|0.044|0.304|

Notes: Data pooled for all four follow-up surveys. Specifications include strata dummies, a variable representing the initial outcome at baseline, a variable indicating missing data at baseline, and dummies for survey round. The Z score index is constructed following Kling, Liebman, and Katz (2007). Adjustments to control false discovery rate (FDR) computed following Benjamin and Hochberg (1995). p- values and q-values are reported below standard errors ( _q-values_ in italics). Clustered standard errors by firms in parentheses. *, ** and *** denote significant at the 10%, 5%, and 1% levels, respectively.

33