rafmacalaba/datause-encoder-data huggingface dataset:
Policy Research Working Paper 8459

# **Abstract**

This paper constructs two longitudinal datasets that record students’ test scores in a national standardized exam in Mexico and track students from the end of primary (Grade 6) to the end of lower (Grade 9) and upper (Grade 12) secondary school, then to university and labor market participation up to two years after graduation from upper secondary. The results show that test scores are a strong predictor of future education and labor market outcomes. Using a large sample of twins in the data, the paper shows that the relationship between Grade 6 test scores and future education outcomes goes beyond family background. Finally, the paper exploits the within-individual correlation between subject test scores and finds evidence that this standardized assessment captures in a meaningful way the specific skills that it is designed to measure. These results show that, despite their limitations, large-scale standardized tests can capture skills that are important for future individual wellbeing.

This paper is a product of the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at rdehoyos@ worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
favorable) interest rates, are denominated in local currency and transparently disclosed. A debt composition that is less vulnerable to market disruptions reduces the likelihood that a decline in market sentiment, sharp depreciations, or interest rate spikes erode debt sustainability. This is particularly important in EMDEs, which tend to suffer sharp capital flow stops or reversals during times of stress in financial markets.

Low-cost debt can be just the medicine EMDEs need to mend some of their development gaps. But, as the old adage goes, the dose makes the poison: excessive debt accumulation has adverse side effects. In this context, EMDEs should not allow low interest rates to lull them into complacency. Even if the cost of debt is currently low, the historical record suggests that it could increase sharply during periods of financial stress, as some EMDEs have painfully learned once again in recent years. When an EMDE has an elevated level of debt, it often takes time to reduce it (Figure 7.A and 7.B).<sup>21</sup> Excessive debt burdens may make governments more vulnerable to crises, limit the size and effectiveness of fiscal stimulus during future cyclical downturns, and weigh on investment and longer-term growth. As the long history of financial crises in EMDEs has repeatedly shown, debt cannot be counted as free lunch.

> 21 IMF (2012) also concludes that reducing public debt takes time based on public debt accumulation episodes in advanced economies.

18
First, all spreadsheet monitoring information was converted into a spatial panel database. This was an unbalanced panel, with many time‐series observations for some monitoring locations and very sparse observations for others.

The second step was to estimate a fixed‐effects regression, expressed as follows: where _S_ equals salinity (ppt) at monitoring location _i_ for period _t_ , _DS_ is the monitor dummy variable (1 for monitoring location _j_ and 0 otherwise), _DM_ equals the month dummy variable, and _y_ is the year (2012, 2013, 2014, and 2015). The testing of various yearly trends in nine spatial clusters of monitors found no significant differences from the overall trend.

The third step was to generate a projection database for the four years (2012–15) with full dummy variables for monitoring locations and months. Fourth, the projection database was used to predict salinity for all monitoring stations in all months and years. Fifth, predicted salinity was extracted for all monitoring stations in the month of highest salinity (i.e., May 2015). Sixth, the observations were mapped in ArcGIS. Finally, a high‐resolution point file was created via spatial interpolation among the observations.

# _<u>Step 3. Estimate Salinity Tolerance of Various Mangrove Species</u>_

Salinity tolerances of various relevant mangrove types (species and mixed species) were computed using geographic overlays of base mangrove distributions derived in Step 1 and the high‐resolution spatial aquatic salinity profile developed in Step 2.

# _<u>Step 4. Estimate Future (2050) Aquatic Salinity for the Indian Sundarbans</u>_

To estimate future aquatic salinity for the Indian Sundarbans, a projection model using high‐resolution point data was first developed for the Bangladesh Sundarbans. First, the _SP_ ratio (salinity in 2012 divided by salinity in 2050) was computed for each point. Next, the _SM_ ratio (salinity in 2012 divided by the maximum salinity in 2012 for all points) was computed for each point. The _SP_ is distributed (0, 1). Finally, fractional logit was used to estimate _SP = β0 + β1SM_ for all points. This functional form preserves the (0, 1) bound, while specifying the growth rate of salinity from 2012 to 2050 at a point as a function of the gap between its current salinity and the maximum salinity in the point set (i.e., effectively ocean salinity). The function fits the data extremely well.

To estimate salinity in 2050 for the Indian Sundarbans, the _SM_ ratio (salinity in 2012 divided by the maximum salinity in 2012 for all points) was computed for each point. Next, the regression coefficients from the Bangladesh computation above were used to estimate the _SP_ ratio (salinity in 2012 divided by salinity in 2050). Finally, _SP_ was used to compute salinity in 2050 and salinity in 2012 for each point.

8
ECOWAS‘S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Power demand within WAPP is expected to triple over the next decade. Taking into account the need to fully satisfy existing demand for power, compounded by the anticipated expansion in market demands driven by economic growth in commerce and industry and the need to provide additional power to support the planned expansion of electrification from 45 percent to 66 percent of households for the region, it is estimated that by 2015 power demand could reach 94.3 terawatt hours, requiring the development of 18,000 MW of new generation capacity—180 percent of existing capacity (table 3.2). These projections are based on economic growth forecasts made before the onset of the global financial crisis of 2008. If the economic crisis halves anticipated economic growth rates in the region, 80 percent of the demand reduction would occur in Nigeria (14.6 TWh) and 14 percent in Ghana.

**Table 3.2  Demand and suppressed demand for power in WAPP**

All figures are in TWh unless noted otherwise

|Country|Total net demand in<br>2005|Percentage of<br>effective demand<br>metin 2005|Market demand<br>2015|Social demand with<br>nationaltargets2015|Total net demand<br>2015|
|---|---|---|---|---|---|
|Benin|0.6|94|0.9|0.8|1.7|
|BurkinaFaso|0.5|98|0.6|0.9|1.5|
|Côte d‘Ivoire|2.9|88|4|1.4|5.4|
|Gambia|0.1|78|0.2|0.2|0.4|
|Ghana|5.9|85|10.8|2|12.8|
|Guinea|0.7|76|1.3|0.8|2.2|
|Guinea-Bissau|0.1|88|0.1|0.1|0.2|
|Liberia|0.3|71|0.6|0.7|1.3|
|Mali|0.4|95|0.6|1.2|1.8|
|Mauritania|0.2|98|0.5|0.3|0.8|
|Niger|0.4|98|0.6|0.7|1.2|
|Nigeria|16.9|61|45.6|13.6|59.2|
|Senegal|1.5|85|2.5|1|3.5|
|SierraLeone|0.2|51|0.5|0.5|1|
|Togo|0.6|89|0.8|0.7|1.5|
|**WAPP**|**31.3**|**70**|**69.6**|**24.8**|**94.3**|
|SAPP|258.8|99|383|14|396.9|
|EAPP/NB|100.6|99|144.8|24.2|169|
|CAPP|10.7|92|17.1|3.1|20.2|
|Island States|1.1|5|1.6|1.5|3|

Source: Rosnes and Vennemo 2009. Note: The East Africa Power Pool is expanded to include key Nile Basin trading partners Egypt, Ethiopia, and Sudan.

Future power demand can be met either by expanding national production or by expanding crossborder power trade within WAPP. Two alternative scenarios will be considered here. The _trade stagnation_ scenario assumes that no additional cross-border interconnectors will be built, so that trade is constrained at the levels observed today and countries will be obliged to meet incremental power demands solely through the development of their domestic power sectors. For many WAPP countries that lack significant energy resources of their own, this entails increased reliance on thermal generation fueled 46
in debt markets. In Morocco, public pensions funds play a more active role in public debt markets, particularly Caisse de Dépôt et de Gestion (CDG) holding 9 percent of public debt. It is the most important participant in the primary market and the second largest on the secondary market for government securities. This has raised concerns about its potential to influence public debt market prices.

**The contribution of the insurance sector to public debt markets is very limited with the exception of Morocco, due to the sector’s very small size across the region.** Assets are mainly technical reserves for the property and casualty insurance business which are likely to be invested in very liquid instruments. Life insurance is underdeveloped in the region, and life insurance companies are not expected to make a major contribution to fixed income market development in the short run. Morocco’s insurance industry is the largest with assets amounting in 2008 to 17.3 percent of GDP, and holdings of 12 percent of public debt.  However, there is a high degree of concentration with the three largest companies holding about 50 percent of market share in terms of premiums collected.

# **<u>Foreign investors</u>**

**Foreign investors have been key agents to develop local currency government bond markets in many emerging market countries.** Country cases illustrating their importance can be found in Central and Eastern Europe (Poland, Hungary and Czech Republic), Latin America (Brazil and Mexico), and South East Asia (Malaysia and Indonesia). Statistics measuring foreign investors ́ exposure to public debt are not always available or accurate, and may differ depending on the source (e.g. Central Banks ́ official registers, industry data). Comparisons across countries cannot always be straightforward given that in some countries investments may be indirect through structured off-shore derivatives<sup>47</sup> or that domestic investors may be registered as foreign holders.

**In general, in the most advanced EMEs foreign investors have supported the lengthening of the yield curve and financial innovations that are necessary to enhance financial sector depth.** Since 2005, foreign investors have become a more stable source of funding in the medium and long-term domestic debt and they have been active secondary market traders.48 They have also boosted competition, enhanced the quality of investment analysis, and were natural agents for market knowledge transfer between mature and emerging markets. In addition, they have been instrumental in the

> 47 For further details and debates on the impact of both direct and off-shore investments in domestic Government bond markets, see BIS CGFS Papers, No 28, “Financial stability and local currency bond markets”, June 2007 and Shanaka J. Peiris “Foreign Participation in Emerging Markets’ Local Currency Bond Markets” IMF April 2010.

> 48 IMF, Chapter III of the April 2006 _Global Financial Stability Report_ and Presentation by Mexico Debt Office at OECD/WB/IMF Tenth Bond Market Forum, April 29-30, 2008.

36
27 Sahasakul, C. (1986). “The U.S. Evidence on Optimal Taxation over Time.” Journal of Monetary <u>Economics. Vol. 18, pp. 251-275.</u> Schick, Allen (1983). “The Distributive Congress.” in Schick, Allen (ed.) Making Economic Policy <u>in Congress. Washington D.C. American Enterprise Institute, pp. 257-74.</u> Vaughn, Karen I. and Richard E. Wagner (1992). Public Debt Controversies : An Essay in Reconciliation." Kyklos. Vol. 45, Fasc. 1, pp 37-49.

# BOOKS

Chen, Derek Hung Chiat (1995). <u>Ageing and Intergenerational Transfers : The Issues.</u> (Unpublished) Master's dissertation. National University of Singapore, Republic of Singapore. Chen, Derek Hung Chiat (2001). <u>Intertemporal Excess Burdens, Bequest Motives, Informal Support and the Budget Deficit. (Unpublished) Doctoral dissertation. Univeristy of California at</u> Davis.

OECD (1988). Ageing Populations : The Social Policy Implications. OECD, Paris, 1988.

O’Sullivan, Arther and Steven M. Sheffrin (1998). Macroeconomics : Principles and Tools. Prentice-Hall, Inc., New Jersey.

Rosen, Harvey S. (1999). Public Finance. (5<sup>th</sup> Edition) Irwin / McGraw-Hill, U.S.A.

Taylor, John B. (1998). Principles of Macroeconomics. (2nd Edition) Houghton Mifflin Company, U.S.A.

Todaro, Michael P. (1997) Economic Development. (6th Edition) Addison-Wesley Publishing Company, Inc., U.S.A.

# DATA SOURCES

Deininger, Klaus and Lyn Squire (1996). A New Data Set Measuring Income Inequality. World Bank Economic Review. Vol. 10, pp. 565-591.

Easterly, William (1997). “Life During Growth.” Journal of Economic Growth. Vol. 4, Issue 3, September 1999.

Gastil, R.D. (1973-1989). “The Comparative Survey of Freedom.” <u>Freedom at Issue. Various</u> Issues.

Heston, Alan and Robert Summers (1991). “The Penn World Table (Mark 5) : An Expanded Set of International Comparisons, 1950-1988.” Quarterly Journal Economics. May, pp. 327-368.

International Monetary Fund. International Financial Statistics. Various Issues.

International Monetary Fund (1998). International Financial Statistics. CD-Rom International Monetary Fund (1999). International Financial Statistics. CD-Rom Taylor, Charles Lewis (1985). World Handbook of Political and Social Indicators III, 1948-1982 (ICPSR file 7761).
# LIST OF **TABLES**

|1.1:|Housing Infrastructure Indicators, Selected Countries . . . .<br>.|. .<br>3|
|---|---|---|
|2.1:|Rent Control Measures, 1963 to 1987 . . . . . . . . . . . . . .|. .<br>27|
|2.2.|Prescribed Rents for Certain Premises, 1973 . . . . . . . . . .|. .<br>28|
|2.3:|Controlled rents July 1979 (AFRCD 5, 1979) . . . . . . . . . .|. .<br>30|
|2.4:|Rental levels un6-r PNDC Law 5, 1982. . . . . . . . . . . . . .|. .<br>31|
|2.5:|Rental levels under L.I. 1318, January, 1986. . . . . . . . . .|. .<br>31|
|2.6:|Prices of Selected S.H.C. House Types . . . . . . . . . . . . .|. .<br>39|
|3.1:|Household Environmental Indicators by Tenure . . . . . . . . .|. .<br>46|
|3.2:|Household Size by Tenure . . . . . . . . . . . . . . . . . . .|. .<br>52|
|3.3:|Mean Persons per Room by Tenure, 1980 and 1986 . . . . . . . .|. .<br>53|
|3.4:|Rents by Consumption Quartile . . . . . . . . . . . . . . . . .|. .<br>54|
|3.5:|Summary Descriptive Statistics . . . . . . . . . . . . . . . .|. .<br>55|
|5.1:|Controlled and Uncontrolled Samples Compared . . . . . . . . .|. .<br>70|
|5.2:|Gross and Net Rents Compared . . . . . . . . . . . . . . . . .|. .<br>74|
|5.3:|Controlled and "Uncontrolled" Rents Paid by Sector . . . . . .|. .<br>75|
|5.4:|Malpezzi and Mayo Demand Results . . . . . . . . . . . . . . .|I.<br>77|
|5.5:|Demand Equations . . . . . . . . . . . . . . . . . . . . . . .|. .<br>83|
|5.6:|Hedonic Index . . . . . . . . . . . . . . . . . . . . . . . . .|. .<br>85|
|5.7:|Summary Cost-Benefit Measures From Cross Country Model . . . .|. .<br>86|
|5.8:|Cost-Benefit Measures From Survey Demand and Hedonic Models . .|.<br>.<br>88|
|5.9:|Summary Coat-Benefit Measures By Consumption . . . . . . . . .|. .<br>91|
|5.10:|Summary Cost-3enefit Measures By Length of Tenure . . . . . . .|. .<br>92|
|5.11:|Landlord and Renter Incomes . . . . . . . . . . . . . . . . . .|. .<br>93|
|6.1:|Cash Flow Model of Rental Investment . . . . . . . . . . . . .|. .<br>101|
|6.2:|Key Model Inputs . . . . . . . . . . . . . . . . . . . . . . .|. .<br>102|

xi
<!-- Start of picture text --> Figure 5: Earmarked and BNDES funds as share of firm credit<br>55<br>50<br>45<br>40<br>35<br>30<br>25 Earmarked/total BNDES/total<br>20<br>Percent<br>Mar‐07 Oct‐07 May‐08 Dec‐08 Jul‐09 Feb‐10 Sep‐10 Apr‐11 Nov‐11 Jun‐12 Jan‐13 Aug‐13 Mar‐14 Oct‐14 May‐15 Dec‐15 Jul‐16 Feb‐17 Sep‐17 Apr‐18<br><!-- End of picture text --> _Source: Banco Central do Brasil_

# **3. Economic additionality of BNDES financing**

Despite decades of liberalization and privatization, partially reversed by widespread bailouts following the global financial crisis, government bank ownership remains pervasive around the world (Cull et al., 2017). There are two sets of complementary theories on government’s participation in financial markets. On the positive side, the “development view” argues that government-owned banks can be a useful tool for promoting industrialization and more generally economic development by helping to overcome market failures (Gerschenkron, 1962). The related “social view” posits that state-owned banks can promote socially desirable welfare-enhancing investments by supporting projects with high externalities beyond pure profitability (Atkinson and Stiglitz, 1980). Finally, government-owned banks can play an active countercyclical role, helping stabilize credit flows during crisis times, according to the “macroeconomic view” (see Brei and Schlarek, 2015; and Bonomo et al., 2015).

On the other hand, government bank ownership can have important negative consequences. According to the “agency view”, state-owned bank managers are poorly selected and lack incentives to pursue efficiency and profitability, leading to resource misallocation and inefficiencies (Banerjee, 1997; Hart et al., 1997). In the same vein, the “political view” posits that governments (and their political coalitions) will use state-owned banks to benefit cronies and politically connected borrowers or channel funds to firms singled out as “national champions”, again resulting in misallocation and economic inefficiencies (Shleifer and Vishny, 1998).

A large literature has examined the role and impact of state-owned banks across economies, with results that are generally consistent with the “political view” and the “agency view”: state-owned banks may misallocate credit by targeting firms based on criteria other than the merit of target firms’ projects. State-owned banks increase their lending during an election year and in emerging markets they finance the government to a greater degree than do private banks (Dinç, 2005). Moreover, state-owned banks have lower profitability and higher costs than commercial banks and that gap widens during election years (Micco et al., 2007). Government ownership of banks has distorting effects on the allocation of financial 6
differences in the average hourly earnings of wage workers compared to the self-employed; the latter group earn approximately US$0.50 to US$2.00 less than wage workers in six of the eight countries. The relationship is different for Armenia and Ukraine, where the self-employed earn about US$0.20 more than wage workers; this may be due to the different nature of selfemployment in these two countries or to small-sample measurement error (the proportion of selfemployed in these countries constitutes less than 1 percent of the sample).

**Table 2. Labor market indicators, population ages 25 to 64, sample countries**

|Country|Labor force<br>participation|Employment<br>rate|Unemployment<br>rate|Observations|
|---|---|---|---|---|
|Armenia|49.5%|27.8%|43.7%|2,076|
|Bolivia|79.5|72.6|8.6|951|
|Colombia|73.7|61.9|15.9|1,391|
|Georgia|51.6|25.0|51.6|2,080|
|Ghana|84.3|76.3|9.5|1,346|
|Kenya|83.2|66.4|20.2|1,877|
|Ukraine|58.4|50.1|14.2|1,741|
|Vietnam|72.6|71.1|2.0|2,075|

_Source_ : STEP Surveys (2014).

**Table 3. Average hourly earnings, workers ages 25 to 64, sample countries** (in 2011 PPP-adjusted U.S. dollars)

||Armenia|Bolivia|Colombia|Georgia|Ghana|Kenya|Ukraine|Vietnam|
|---|---|---|---|---|---|---|---|---|
|All workers|2.66|3.57|3.45|3.39|2.15|2.55|3.52|3.21|
||(1.79)|(3.82)|(4.07)|(3.05)|(3.81)|(3.00)|(1.92)|(3.51)|
|_N_|_530_|_653_|_830_|_481_|_560_|_1159_|_731_|_1384_|
|Wage workers|2.65|3.99|3.69|3.59|2.60|3.06|3.51|3.42|
||(1.71)|(3.98)|(4.52)|(3.14)|(4.34)|(3.35)|(1.87)|(3.18)|
|Self-employed|2.89|2.82|2.90|1.66|1.81|1.64|3.67|2.80|
||(2.62)|(3.37)|(2.69)|(1.19)|(3.33)|(1.96)|(2.44)|(4.03)|

_Note_ : Standard deviations in parentheses. _Source_ : STEP Surveys (2014).

There are large differences in educational attainment across the countries, as Figure 1 illustrates. The average completed years of education for those currently employed ranges from 8.63 years in Ghana to 15.47 years in Georgia. These differences are more pronounced when one compares the proportion of the sample completing various _levels_ of education. More than half the sampled workers in Georgia, Armenia, and Ukraine have a tertiary education degree, while in Ghana and Kenya only about 14 percent of the sample do.

11
# **1. Introduction**

The past decades have witnessed remarkable progresses for women on most fronts. According to the World Development Indicators for 187 countries (World Bank, 2019), while the labor participation rate for males has declined from 76.2 percent in 1990 to 72.3 percent in 2018, that for females has increased from 48.5 to 51.8 percent. In 2016, the average share of females (over the age of 25) with a bachelor’s degree or above was 19.7 percent, surpassing that for males (i.e., 18.3 percent). Similarly, based on harmonized learning outcome (HLO), the latest cross-country indicator of comparable human capital from the World Bank, girls out-perform boys on average in the world (Angrist et al., 2019). The advancement of women, however, is not uniform. In leadership jobs, women continue to lag significantly behind (Goldin et al., 2006; Blau and Kahn, 2017; Fairlie et al. 2017; OECD 2019). In this paper, we focus on the determinants of female leadership in businesses, and in particular, the role played by foreign direct investment (FDI), around the world.

Understanding female entrepreneurship is important. Entrepreneurship, by turning ideas into business ventures and new products, is a key driver of economic growth (Schumpeter, 2017). The body of research on entrepreneurship, however, does not say much about what determines female entrepreneurship. With increasing participation of women in economic life, and with their relatively faster growth in human capital, commonly viewed by economists as the driver of growth (Lucas, 1988; Romer, 1990), understanding female entrepreneurship around the world gains urgency, and could be a key area to facilitate development, especially among countries where women traditionally do not play important roles in working in and/or running formal firms.

FDI could be an important determinant of female entrepreneurship. In less than two decades, FDI has dramatically risen in importance: the relative stock of FDI increased three-fold from 8 percent of world GDP in 1990 to 72 percent in 2017 (OECD, 2008, 2018). Increasing international trade and capital flow have fundamentally changed the world, by facilitating economic growth (Alfaro, 2016), raising the income level of poorer countries and possibly spreading the norms of richer investing countries around the world (Dollar and Kraay, 2004). The existing literature on the effects of FDI has largely focused on economic outcomes such as the spillover effects on domestic firms’ performance and the 2