rafmacalaba/datause-encoder-data huggingface dataset:
Table 3: Early Life Exposure to Coffee Mills and Test Scores: Effects by Subjects

|||All Subjects||||ST|EM|||
|---|---|---|---|---|---|---|---|---|---|
|||Aggregate|||Maths|||Science||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|CatchmentArea X Post|0.0905***|0.1015***|0.0882***|0.0700***|0.0566**|0.0472**|0.0724***|0.0904***|0.0789***|
||(0.0269)|(0.0224)|(0.0214)|(0.0231)|(0.0225)|(0.0221)|(0.0268)|(0.0222)|(0.0213)|
||[ 0.002 ]|[ 0.001 ]|[ 0.001 ]|[ 0.004 ]|[ 0.015 ]|[ 0.016 ]|[ 0.009 ]|[ 0.001 ]|[ 0.001 ]|
|R-squared|0.3866|0.4260|0.4261|0.3459|0.3764|0.3765|0.3783|0.4191|0.4191|
|Observations|1487355|1487036|1487036|1487359|1487040|1487040|1487359|1487040|1487040|
||||||Non STEM|||||
|||Social|||English||K|inyarwand|a|
|CatchmentArea X Post|0.0728**|0.0848***|0.0760***|0.0935***|0.1054***|0.0914***|0.0934***|0.1115***|0.1006***|
||(0.0287)|(0.0230)|(0.0223)|(0.0260)|(0.0206)|(0.0195)|(0.0254)|(0.0232)|(0.0225)|
||[ 0.014 ]|[ 0.001 ]|[ 0.001 ]|[ 0.001 ]|[ 0.001 ]|[ 0.001 ]|[ 0.001 ]|[ 0.001 ]|[ 0.001 ]|
|R-squared|0.3842|0.4227|0.4227|0.4057|0.4469|0.4470|0.2317|0.2767|0.2768|
|Observations|1487359|1487040|1487040|1487359|1487040|1487040|1487359|1487040|1487040|
|Controls|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Exam Year FE|Yes|No|No|Yes|No|No|Yes|No|No|
|YOB FE|Yes|No|No|Yes|No|No|Yes|No|No|
|Municipality X Exam Year FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|Municipality X YOB FE|No|Yes|Yes|No|Yes|Yes|No|Yes|Yes|
|CatchmentArea X Exam Year FE|No|No|Yes|No|No|Yes|No|No|Yes|

Notes: Dependent variable is the standardized test scores in the respective subject groups in the Rwanda Primary School National exam. STEM subjects include Math and Science while Non STEM courses include English, Social Studies, and Kinyarwanda. CatchmentArea X Post is an indicator variable equal to 1 if a student was born within the vicinity ( _≤_ 2.5km) of an operational coffee mill and 0 if otherwise. Controls include gender of the student, average annual temperature and precipitation in the exam year, as well as baseline controls such as coffee suitability and road density interacted with time trend. Robust standard errors clustered at school level in parenthesis. False discovery rate (FDR) adjusted p-values for CatchmentArea X Post are reported in square brackets (Anderson 2008). The outcome variables accounted for in the p-values adjustments include the standardized test scores of: All subjects (aggregate), STEM, and Non-STEM subjects, as well as the subject-specific test scores i.e., Maths, Science, Social, English, and Kinyarwanda.

> _∗_ Significant at 10 percent level _∗∗_ Significant at 5 percent level _∗∗∗_ Significant at 1 percent level

33
congress led to the impeachment of its president, a strong supporter of the government’s economic policy. In this context, crucial reforms with fiscal impact, namely the public servants social security and tax reforms, were left pending. Other reforms, such as the extension of the financial transactions tax, or CPMF, stalled. With the political campaign heating up in the first quarter, it was practically impossible to get support for any adjustment. Additionally, any change would have been interpreted as transitory given that a new government would take office in the near future.

18. Uncertainty regarding the future government’s commitment to fiscal adjustment (irrespective of who won the election) generated concerns about the future value or liquidity of public debt. Given the concentration of public debt holdings in mutual funds (to be discussed in the next section), a significant resource outflow affected them in the period April-October. In its peak, the run represented more than 6 percent of the intermediaries’ net worth (Figure 3).

**Figure 3 Net Resource Flow to Mutual Funds (as a fraction of  net worth)**

<!-- Start of picture text --> .06<br>.04<br>.02<br>.00<br>-.02<br>-.04<br>-.06<br>-.08<br>1998 1999 2000 2001 2002 2003<br><!-- End of picture text --> 19. The sell-off of government securities caused a fall in their price (rising spreads), which in turn pressured the exchange rate to depreciate (Figure 4). The rising spreads and the exchange rate depreciation were also associated with capital outflows from Brazil. As Figure 5 shows, in September and October, capital outflows reached a peak of almost 20 percent of international reserves of the central bank.<sup>5</sup> During these months the exchange rate also reached a peak of 4 reais per dollar. Consequently, the debt level rose due to its indexing to the exchange rate. This fact aggravated concerns on debt

> 5 Capital flows exclude foreign direct investment and IMF resources.

9
readily-available monthly data on such series as international trade, production, or agricultural output? This would make it easier for other countries to build models – based on occasionallyavailable household survey data – that could then be used, along with easy-to-find data series, to track the effects of external shocks on major groups in society, such as the poor, or women, or older individuals. We note, however, that past efforts to derive welfare effects from a small number of readily-available, or easily-collectable, variables have not been successful (Ravallion 1996; Haughton and Khandker 2009). Certainly, the strong seasonal rhythm of agricultural production (see Figure 9) makes it difficult to use monthly agricultural data to help track the patterns of household consumption.

<!-- Start of picture text --> 250 14<br>12<br>200<br>10<br>150 8<br>6<br>100<br>4<br>50<br>2<br>0 0<br>2005 2006 2007 2008 2009 2010 2011 2012 2005 2006 2007 2008 2009 2010 2011 2012<br>Paddy rice production, m tonnes<br>Agricultural production index (2005 = 100)<br><!-- End of picture text -->

**Figure 9.  Index of Agricultural Production (left) and Volume of Rice Output (right), Thailand, 2005-2012**

Source: Reported by Bank of Thailand, based on data from Office of Agricultural Economics, Ministry of Agriculture and Cooperatives.

The approach we take here is to regress real consumption per capita (for each household), from the socio-economic survey data for 2007 through 2010, on a variety of household level variables (the “survey regression” variables listed in Table 8) and on a number of data series that are published monthly and are readily available (“public data regression” variables in Table 8). If there is a close fit between the public data and per capita income, then the equation would be viable for forecasting purposes. However, the final column of Table 8 shows that such equations never fit well, with values of adjusted R<sup>2</sup> always below 0.15. The fit is substantially better when one uses the survey regression variables (first column of numbers in Table 8), but these are not available in the absence of survey data. When the public data are added to the survey regression variables the improvement in fit is always very small.

One could look more systematically for potential predictors of real per capita income for major groups, but our basic explorations are not promising. There do not appear to be cheaper or easier ways to determine reliably the effect of shocks on households, and so there is a continued need for regular surveys of household living standards.

Losers and Winners in Recession: Thailand 2008-09 25
# **Strategic Interactions and Portfolio Choice in Money Management: Evidence from Colombian Pension Funds**

Alvaro Pedraza Morales<sup>_∗_</sup>

## **JEL Classification:** G11, G23, C73, C61

**Keywords:** Portfolio Choice, Strategic Interactions, Relative Performance, Institutional Investors, Pension Funds.

> _∗_ Email address: apedrazamorales@worldbank.org. I am indebted to Pete Kyle and John Shea for their invaluable guidance. This study has benefited from the comments of Anton Korinek, John Rust, Giorgo Sertsios, Paula Tkac, Russ Wermers and two anonymous referees. I also thank Shu Lin Wee, Pablo Cuba and seminar participants at the Midwest Macro, Federal Reserve Bank of Atlanta, Inter-American Development Bank and University of Maryland for comments and suggestions. All errors are my own.
Javorcik, Beata S., and Gaia Narciso, 2007, “Differentiated Products and Evasion of Import Tariffs”, Policy Research Working Paper No. 4123, The World Bank.

Kee, Hiau Looi, Nicita, Alessandro, and Olarreaga, Marcelo, 2006 “Estimating Trade Restrictiveness Indices”, Policy Research Working Paper No. 3840, The World Bank.

Laborde, David, Houssein Boumelassa, and Maria Cristina Mitaritonna, Forthcoming, “A consistent picture of the protection across the world in 2004: MacMapHS6 v2”, Mimeo, CEPII.

> Levchenko, Andrei, Forthcoming, “Institutional Quality and International Trade”, _Review of Economic Studies_ .

Mayer, Thierry and Soledad Zignago, 2006, “Notes on CEPII’s Distance Measures”, Mimeo.

Ranjan, Priya, and Jae Young Lee, 2007, “Contract Enforcement and International Trade”, _Economics and Politics_ , 19(2), 191-218.

Rauch, James, 1999, “Networks Versus Markets in International Trade,” _Journal of International Economics,_ 48, 7-35.

> Santos Silva, J.M.C. and Silvana Tenreyro, 2006, “The Log of Gravity”, _Review of Economics and Statistics_ , 88(4), 641-658.

Wilson, John S., Catherine L. Mann and Tsunehiro Otsuki, 2005, “Assessing the Benefits of Trade Facilitation: A Global Perspective”, _The World Economy_ , 28(6), 841-871.

Wolfe, Robert, 2003, “Regulatory Transparency, Developing Countries, and the WTO”, _World Trade Review_ , 2(2), 157-182.

Wooldridge, Jeffrey, 2001, _Econometric Analysis of Cross Section and Panel Data_ , MIT Press.

> World Bank, 2005, _World Development Report 2005: A Better Investment Climate for Everyone_ , Washington, D.C.: The World Bank.

World Bank Independent Evaluation Group, 2006, _Assessing World Bank Support for Trade, 1987-2004_ , World Bank, Washington, D.C.

# **Tables**

|**Table 1: Summary of Variables Measuring Pred**<br>**Predictability**|**ictability and Simplification**<br>**Simplification**|
|---|---|
|• Percentage of bound tariff lines (MM)<br>• Standard deviation of applied tariffs (MM)|• Number of documents required for import/export<br>(DB)|
|• Absence of “hidden” trade barriers (GCR)<br>• Standard deviation of unofficial payments in<br>imports/exports (GCR)<br>• Spread of import/export delays (LPI)<br>• Favoritism in administrative decisions (GCR)<br>• E-government readiness (UN)|• Number of days required for import/export (DB)<br>• Number of border agencies (LPI)<br>• Unofficial payments in imports and exports<br>(GCR)|

Abbreviations: GCR: Global Competitiveness Report, DB: Doing Business, LPI: Logistics Perception Index, MM: MacMap, <u>UN: United Nations E-government Readiness.</u> 33
# IV. Relationship between government spending and the Human Capital Index

There is a large body of analytical work studying the relationship between spending and human capital outcomes adopting a sector-specific view. For instance, the 2018 World Development Report (WDR) finds that the relationship between changes in public education spending and student learning is often weak. In health, flagship work in the United States using Medicare data has highlighted that higher spending is often associated with poorer outcomes (“Dartmouth Atlas Project” 2019). Given the multidimensional nature of human capital and the possible interactions between different types of spending, in this note we will try to account for the multisectoral nature of both outcomes and spending. In terms of outcomes, our preferred measure is the newly designed HCI. For spending, we create a measure of spending that includes health, education and social protection as defined above. Since the definition of spending varies across the three sectors, the sum of the three might be plagued by substantial measurement error issues.

Using a cross-section of the most recent data, we observe a weak correlation between HCI scores and social spending in Figure 3 (Pearson correlation coefficient ρ = 0.38). Measurement error might partly explain it. Moreover, countries differ along many different dimensions that can be potentially correlated with both spending and human capital outcomes. For example, higher-income countries might have better institutions that could make social spending more effective. In order to partly control for some of these differences, we plot the relationship between spending and HCI for different country income levels (Figure 4). When stratified by income levels, none of the correlation coefficients capturing the relationship between HCI and spending is statistically significantly different from zero. While the correlation between HCI and spending is clearly positive for HICs, there is no evidence that the correlation monotonically declines when we consider less affluent countries. A closer look at both Figures 3 and 4 suggests the presence of extreme outliers that make the interpretation of any correlation particularly problematic. Teasing out the potential role of GDP in this relationship, we examine the association between the variation in HCI unexplained by GDP and the variation in spending unexplained by GDP and find a similarly weak relationship (ρ = 0.37; Figure A.2).

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There are several problems with this methodology if the objective is to present an accurate picture of privately-managed pension investment performance. First, to the extent that the pension fund sector underperformed or outperformed the indices of each type of investment, the figures will differ from the true performance of the sector. This problem takes on added importance if differences between the pension regulations or other institutional conditions in the different countries would have systematically led to underperformance in some cases. Another problem stems from the lack of data on administrative costs of the system which reduce the net returns experienced by the pension funds. The crude assumption in Figure 3.7, in which occupational returns are reduced by one percentage point in order to account for administrative costs, is considered conservative based on available information. For example, the average defined benefit, occupational pension fund (weighted by assets) in the United States in 1989 was found to have administrative costs of around 7 to 8 basis points (.7-.8% of assets) in the Form 5500 Annual Report published in 1993 by the U.S. Labor Department. Only general statements about rates of return can be made on the basis of these data and without taking into account significant differences which may exist between pension funds with larger or smaller asset bases, of multi-employer versus single employer funds and other important features. For example, if there are economies of scale in the pension industry and the average pension fund is smaller in one country than in another, these crude assumptions may lead to an underestimate of the relative net returns in the country with the larger schemes.

Rates of return in Chile are shown in several graphs in the report and are discussed in the text. In Figure 3.7 on page 95, the rate of return between 1981 and 1990 after commissions and fees is shown for a worker with an income of UF 10 or about 210 US dollars in 1990. This figure is taken directly from Table 29, page 91 of the extensive description of the Chilean system found in Chile: Experiencia con un Regimen de Capitalizacion 1981-1991, by Acuna and Iglesias published by CEPAL in 1992. On page 224, a slightly different version of the Chilean rates of return during this period are presented in Table 6.4. This table should have included Acuna and Iglesias as a source. The range of 7.5 - 10.5 percent given in the table refer to calculations by Acuna and Iglesias which show that lower income workers received lower rates of return during this period than did higher income workers due to flat rate commissions. This analysis can be found on 101-102 of Acuna and Iglesias (1992). The authors also show that the disparity between returns to higher and lower income workers tended to decline and 48
17 data set, which we compare to the GDP values to construct the value added share of each sector in GDP. We also need information on factor shares in national income for the estimation. A United Nations national account data set is obtained which has information on labor share in GDP.<sup>7</sup> One minus the labor share gives us the sum of capital share and land share, which we are not be able to separately identify. To overcome this shortcoming, we chose to estimate the land share in the productivity equation as follows. It can be shown from (18) that the log of GDP is the sum of the log of overall prices (denoted Pt for brevity), productivity (At), and the weighted log of endowments: ln(GDPt ) = ln A t + ln Pt + s Lt ln L t + s Kt ln K t + 1( − s Lt − s Kt ) ln Tt . Then we can estimate the share of land (Tt) by moving those terms for which we have data to the left-hand side: Thus, when we use the labor share and one minus the labor share to weight ln l t<sup>≡</sup> ln(L t / Tt ) and ln(K t / Tt ), respectively, we should also include capital per unit of land ln k t<sup>≡</sup> ln(K t / Tt ) on the right-hand side of the equation, as shown in (19). The estimated coefficient associated with capital per unit of land is interpreted as the negative value of the average share of land in GDP.

We proceed with a system of eight equations, consists of the seven sectoral share (16) equations and the country productivity equation. According to (18) and (19), let the dependent variable of the country productivity equation be TFP _adjusted_ for capital-land ratio and prices of nontraded goods: 7 We thank Ann Harrison for providing this data.
governments wanted modern technology, and often sought foreign partners to get it. But even with the best technology a domestic plant seldom had the scale or experience to compete internationally, and thus could survive only behind the protection of import quotas, tariffs or other trade barriers. The solution was often a joint venture between a multinational firm and the government with the government receiving a minority share of the equity. The government’s stake was intended in part to motivate it to maintain the import protection the firm needed. Many governments expropriated or bought out the private equity in these joint ventures during the 1970s and 1980s.

A new generation of mixed enterprise companies appeared in the 1990s, most as a byproduct of privatization. Many firms were privatized by selling shares but with the government retaining control, at least initially. A study of 630 share issue privatizations in 59 developing and industrialized countries between 1977 and 1997 found that only 11.5 percent of the initial offers were for all the shares (Jones _et al._ , 1999, pp. 230-232). The average initial offer was for 43.9 percent of the shares, and these were frequently sold to domestic and small investors as a sign that the government would not interfere. In many cases the government eventually sold its share holdings, but in some countries, such as Singapore, it did not.<sup>18</sup> The effects of selling SOE debt to private investors have been much less studied than the effects of selling equity, but there are good reasons to believe that selling debt is less effective in encouraging commercial behavior. Investors in debt have a weaker incentive to monitor the firm’s performance since any interest and principal payments due on debt must be paid before dividends are paid to shareholders. As a result, debt holders are interested primarily in insuring that the SOE is profitable enough to pay its debts, and have little concern about the returns to equity. Moreover, debt holders may have stronger reasons to believe that the government is implicitly guaranteeing their investment. If shareholders don’t receive dividends the share price drops, which is embarrassing enough. But if debt holders are not paid then the firm can be thrown into bankruptcy with the creditors taking over, which is even more embarrassing. The fact

> 18 Singapore’s motive was not so much to improve efficiency, since its SOEs were thought to be fairly efficient already, but rather to further develop the domestic stock market.  Singapore retained a controlling share of the largest and most important SOEs, however, presumably to insure an inside look and the opportunity, in an emergency, to intervene (Gómez-Ibáñez, 2005c).

39
|**90-10 ratio**|4.3|4.3|
|---|---|---|
|Rural|4.1|4.0|
|Urban|4.5|4.4|
|**Poverty- National poverty line**|||
|**Headcount ratio, FGT(0)**|39.0|19.2|
|Rural|42.8|21.9|
|Urban|29.7|12.6|

Note: The National poverty line is 446 rupees, expressed in 2004/05 All India rural rupees, spatially deflated. Source: Calculations based on IHDS, 2005 and 2012.

While the Gini index and the ratio of the 90<sup>th</sup> and 10<sup>th</sup> percentiles of the distribution remained almost unchanged between 2005 and 2012, the poverty headcount rate fell from 39 percent in 2005 to 19.2 percent in 2012, which made for an annual rate of decline of 2.8 percent, a trend that is broadly consistent with official NSS-based estimates. Rural areas saw a higher reduction in poverty, with the 2012 poverty rate almost half of what it was in 2005. High reduction in poverty with almost no change in inequality suggests that poverty in both rural and urban areas has declined mainly as a result of growth as opposed to redistribution, which is confirmed by standard growth-inequality decompositions (not shown here).<sup>11</sup>

# _3.2. Transitions in and out of poverty_

Panel data allow us to look into some of the dynamics of poverty transitions, as we are able to identify those households that stayed in poverty in both periods (12 percent), that fell out of poverty between 2005 and 2012 (27 percent), that fell into poverty between 2005 and 2012 (7 percent) and that stayed out of poverty in both periods (54 percent). The fact that 81 percent of the population stayed out of poverty in both years or climbed out of poverty suggests strong upward mobility over this period. It is important to note that these observations are not dependent upon disproportionate contribution of any single state where measurement errors in data collection may result in higher or lower poverty estimates.

In spite of general all-round improvement, some population groups remain more vulnerable than others. For instance, households where the most educated member had less than secondary education (in 2005) are more likely to be poor in both periods or fall into poverty in 2012 compared to households with less education (Figure 2, panel a). Households that derived most of their income (70 percent or higher share of total income) in 2005 from non-agricultural labor and self-employment are more likely to be non-poor in both periods; while those who depended on agriculture income or nonlabor income are more likely to be chronically poor. Disaggregating by religion/caste (Figure 2, panel b), forward castes and other minority religions (Christian, Sikh and Jain) are much more likely to be non-poor in both periods than other groups (other backward castes or OBC, Muslims, Dalits and Adivasis); while Adivasis are much more likely to be chronically poor than other groups.

> 11 We confirm this by means of Datt and Ravallion (1992) decomposition of changes in a welfare indicator into growth and distribution, following the Shapley and nonparametric methodology suggested by Shorrocks (2013) and Shorrocks and Kolenikov (2003). Results for these decompositions are available upon request.

8