rafmacalaba/datause-encoder-data huggingface dataset:
31

**Figure 6: Maturity of the Public Debt Stock and Government Securities in the Riskier Portfolio**

<!-- Start of picture text --> 3,000 Introduction of regulatory VaR . Equity and Foreign Assets allowed.<br>(Nov 29, 02) (Jan 17, 05) 2,932 days<br>2,800<br>2,600 VaR limit increases to1%<br>for riskier portfolio<br>2,400<br>(June 13, 06)<br>2,200<br>2,000<br>1,466 days<br>1,800 1,688 day s<br>1,600<br>1,400<br>Maximum WAM 900 days<br>1,200<br>(Dec 05, 01)<br>1,000<br>800 1,098 days<br>554 days<br>600 776 days Public debt (Federal Govt)<br>400 Riskier Portfolio<br>200<br>Jan 00 Jul 00 Jan 01 Jul 01 Jan 02 Jul 02 Jan 03 Jul 03 Jan 04 Jul 04 Jan 05 Jul 05 Jan 06 Jul 06 Jan 07<br>PPP<br><!-- End of picture text -->

**Figure 7: Basic and Aggressive Portfolios: VaR Limits and Actual VaRs, 2005-2006**

<!-- Start of picture text -->
1.2%<br>1.0%<br>0.8%<br>0.6%<br>0.4%<br>0.2%<br>0.0%<br>Jan-05 May-05 Sep-05 Jan-06 May-06 Sep-06 Jan-07<br>Actual VaR Basic Portfolio Actual VaR Aggressive Portfolio<br>VaR Limit Basic Portfolio VaR Limit Aggressive Portfolio<br><!-- End of picture text -->

# **VI. CONCLUDING OBSERVATIONS**

Review of the four early adopters of risk-based supervision for pension systems illustrates the potential for the application of these principles and methods across the full range of pension system designs. The nearly exclusively DB system operated by not-forprofit institutions in the Netherlands, the traditional DC employer sponsored funds in Australia, commercial pension institutions in Mexico and a hybrid system with
The above assumes that cross-country spillovers are equally strong between countries in different geographic regions or at different levels of development. To the extent that such spillovers are localized because they are mediated by trade, this seems unlikely. Whereas advanced industrialized countries and the emerging economies of East Asia have high levels of intra-regional trade<sup>2</sup> , trade between SSA countries is negligible: about one-half of observed bilateral manufacturing trade flows between SSA countries are equal to zero (Bosker and Garretsen, 2008, p 12). Formal barriers to trade in the form of tariffs and quotas are a major reason, as are inefficient customs procedures and the inadequate state of transportation infrastructure within the region (Buys _et al_ ., forthcoming). This implies that before they can even begin to contemplate the potential leveraging of spillover effects, policymakers in the region need to cultivate such effects through policies designed to encourage regional integration. This will be particularly important for resource poor landlocked countries within the region whose only credible development strategy is integration with coastal and resource rich countries.<sup>3</sup> The hope is that some of the countries with a more fortunate geographic location or with better endowments will eventually take-off, dragging the resource poor landlocked neighbors along with them (Collier and O’Connell, 2007). Switzerland provides an example. Despite being landlocked, it suffers no disadvantage because it is tightly integrated with

> 2 Intra-regional trade in East Asia today approximates that within the European Union (EU) (World Bank, 2008, p 195).

> 3 SSA is distinguished from the rest of the developing world by the disproportionate percentage of its population which lives in resource poor landlocked countries. According to Collier and O'Connell (2007, p 7), 35 % of the region's population lives in such countries compared to a mere 1 % in developing countries outside of SSA. These figures are based on a comparison of 43 SSA countries with a sample of 56 other developing countries and relate to the period 1990-2000.

3
Table A22: Impact on secondary outcomes—with double-LASSO selected controls

||(1)<br>Marriage and<br>pregnancy|(2)<br>Violence<br>(15 yo+)|(3)<br>Decision-<br>making<br>(individual)|(4)<br>Decision-<br>making<br>(couples)|(5)<br>Finance|(6)<br>Nutrition|(7)<br>Mental<br>health|
|---|---|---|---|---|---|---|---|
|T1 (safe spaces)|-0.044|0.057|0.045|0.094|0.007|-0.449***|0.241***|
||(0.059)|(0.092)|(0.118)|(0.144)|(0.119)|(0.158)|(0.090)|
|T1 + Leaders|-0.011|-0.040|0.247**|0.378***|0.041|-0.226|-0.006|
||(0.069)|(0.112)|(0.117)|(0.123)|(0.103)|(0.144)|(0.115)|
|T2 (safe spaces + IGA)|-0.091*|-0.014|0.122|0.131|0.067|-0.389**|0.243**|
||(0.055)|(0.083)|(0.122)|(0.142)|(0.087)|(0.154)|(0.109)|
|T2 + Leaders|-0.005|0.082|0.005|0.160|0.161*|-0.171|-0.032|
||(0.068)|(0.074)|(0.117)|(0.145)|(0.090)|(0.151)|(0.114)|
|T3 (safe spaces + husbands’ clubs)|-0.007|0.129|0.207**|0.278**|-0.019|-0.679***|0.077|
||(0.053)|(0.090)|(0.102)|(0.130)|(0.115)|(0.202)|(0.133)|
|T3 + Leaders|0.043|0.028|0.321***|0.276|0.133|-0.094|0.244**|
||(0.060)|(0.092)|(0.123)|(0.171)|(0.122)|(0.125)|(0.108)|
|T4 (control spillover)|0.014|0.061|0.094|0.198|-0.108|-0.218*|0.082|
||(0.056)|(0.090)|(0.110)|(0.126)|(0.076)|(0.117)|(0.104)|
|T1 = T1 + L|.688|.414|.105|.0399|.803|.22|.032|
|T2 = T2 + L|.281|.22|.418|.868|.339|.23|.0343|
|T3 = T3 + L|.481|.326|.393|.992|.308|.00454|.25|
|T1 = T2|.524|.458|.592|.831|.63|.752|.988|
|T2 = T3|.212|.131|.528|.365|.481|.2|.254|
|T1 = T3|.606|.476|.2|.256|.859|.303|.213|
|T1 + L = T2 + L|.946|.245|.077|.163|.289|.756|.846|
|T2 + L = T3 + L|.559|.544|.0274|.548|.83|.631|.0319|
|T1 + L = T3 + L|.52|.575|.595|.544|.515|.389|.0533|
|N|2,995|2,931|2,919|1,734|3,003|3,003|3,003|
|Control mean|0|0|0|0|0|0|0|

_Note:_ *** p _<_ 0.01, ** p _<_ 0.05, * p _<_ 0.1, standard errors in parentheses clustered at the locality level. IGA = income-generating activity; yo = years old. The table presents estimated impacts of our 6 interventions’ combinations and the control spillover relative to the pure control group. The p-values of the difference tests between the treatments are in the second part of the table. All regressions control for stratification fixed effects and the double-LASSO-selected variables within a set of baseline values that include the outcome variable at baseline, the adolescent individual characteristics and her household’s. When the baseline value is missing, it is imputed and if it is selected by the double-LASSO procedure a dummy indicating whether the variable is imputed or not is added as control.

54
**Figure 2: The Classification Model**

|**Business**|**Construction**<br>**Obligations**|**Private**<br>**Funding**|**Service**<br>**Delivery**|**Source of**<br>**Revenues**|
|---|---|---|---|---|
|New|Build||Bulk|Fee|
|||Finance|||
|Existing|Refurbish||User|Tariffs|

The classification model addresses five key parameters that may or may not be relevant to any given PPP project. These parameters identify the most fundamental characteristics of a PPP project. These issues are discussed in more detail in sections 2-6 below.

# **New or existing business**

Where PPP involves the project company taking over an existing business (“ _Existing_ ”), a number of additional risks arise (e.g. existing liabilities) as well as a number of benefits (e.g. existing revenue stream). Existing business means employees, contracts, undertakings, commitments and other issues requiring due diligence by prospective investors, and more complexity in the preparations required from the Government. A new business (“ _New_ ”) will be easier for project finance lenders to ring-fence with security rights and other control mechanisms. But an existing business, while hard to ring fence, offers an existing revenue stream, tested cost data, historical demand data and other useful evidence of 17
The earthquake index is constructed from computer generated contour maps by the US Geological Survey (USGS) of earthquake intensity data, commonly used as potential damage proxy (De Groeve _et al._ , 2008; GeoHazards International and United Nations Centre for Regional Development, 2001; Federal Emergency Management Agency, 2006). Utilizing the contour maps as a base for damage infliction, we combine them with the nightlight and building type data from the USGS building inventory for earthquake assessment to create fragility curves by building type; see Jaiswal & Wald (2008) and GeoHazards International and United Nations Centre for Regional Development (2001). Finally, the data are aggregated up to a district level set.

To model volcanic eruption intensity, we utilized a two-fold process. First, volcanic ash advisory data from Volcanic Ash Advisory Centers (VAAC) are used to detect eruptions. The advisories are produced for the airline industry to warn airplanes about impending or ongoing eruptions through color coded messages. We use only the highest warning level of ongoing eruption as a threshold of when to include an eruption in the data set or not. Second, images containing sulphur dioxide data from the OMI/AURA satellite are used to model the intensity of the eruptions. These images provide SO2 density data and have been utilized by Carn _et al._ (2009) and Ferguson _et al._ (2010) to model eruption intensity. The data are then combined with the nightlight data and aggregated up to district level.

Finally, the 2004 Christmas tsunami has been modeled following a method where Heger (2016) uses inundation maps to construct a district level damage index assuming uniform 6
Figure 4 Proposed Structure of WARPO <!-- Start of picture text --> National Water Council<br>RR BWDB<br>| SWMC | 4 ~~~~~~......................<br>MOA MOF MOE MOI PHE<br>WARPO -*. MOR LGED WASA IWTA:<br>4BR2<- NGO COC MU BFIDC<br>i.>>,mR C, ~......... txW...... .. . *--. .<br>Technical Planning Socio-Economic Data, Monitoring & t<br>Division Planning Division Evaluation Division Regulation Division<br>| SuoceWaterPlanni | EconomiicP lanning | | Suce er W aterPolicy<br>| Grounwatn Planning | | SociPlannning Gd ound Water Land-Water Managemet<br>| o AC lann 9oP Environment WaterQuality<br>MOW = Ministry of Water Fish PH For Na tAg<br>MOA = Ministry ofAgriculture<br>MOF = Ministry of Fisheries<br>MOR = Ministry of Environment<br>MOF = Ministry of Foresty Social Data<br>MOI = Ministry of Industries<br>MOR = Ministry of Roads<br>LGED = Local Govemment<br>Engineenng Deparrnrent<br>PHE = Public Hestth Engineering<br>IWTA = Inland Water<br>Transport Authority<br>FIDC = Forestry Industry Dev<br>Corporation<br>MU = Municiplaities<br>COC = Chamber of Commerce<br>NGO = Non Government Organzatbons<br>GIS = Geographical Information System<br>RRI = River Research Institute<br>SWMC = Surface Water Monitoring cell<br>BWDB = Bangladesh Water Development<br>Board<br><!-- End of picture text -->

29
Policy Research Working Paper 6099

# **Abstract**

his paper uses a novel loan-level dataset covering lending by official creditors to developing country governments to construct an instrument for public spending that can be used to estimate government spending multipliers. Loans from official creditors (primarily multilateral development banks and bilateral aid agencies) are a major source of financing for government spending in developing countries. These loans typically finance public spending projects that take several years to implement, with multiple disbursements linked to the stages of project implementation. The long disbursement periods for these loans imply that the bulk of government spending financed by official creditors in a given year reflects loan approval decisions made in many previous years, before current-year macroeconomic shocks are known. Loan-level commitment and disbursement transactions from the World Bank's Debtor Reporting System database are used to isolate a predetermined component of government spending associated with past loan approvals. This can be used as an instrument to estimate spending multipliers for a large sample of 102 developing countries. The one-year government spending multiplier is reasonably-precisely estimated to be around 0.4, and there is some suggestive evidence that multipliers are larger in recessions, in countries less exposed to international trade, and in countries with flexible exchange rate regimes.

This paper is a product of the Macroeconomics and Growth Team, Development Research Group. 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://econ.worldbank.org. The author may be contacted at akraa@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
more employees. The sampling methodology used is stratified random sampling, with stratification done by firm size (small, medium, and large, defined below), industry, and location within a country.<sup>6</sup> Our baseline sample includes all small and medium enterprises (SMEs) in the manufacturing sector in LAC. Following the definition used by WBES for stratification purposes, SMEs are all firms with fewer than 100 full-time permanent workers. The WBES also surveys large firms, defined as those with 100 or more workers. These large firms are excluded from the baseline sample because they operate in very different markets and sell different products than informal firms. Thus, informal competition is unlikely to have a significant effect on these firms.<sup>7</sup> Our sample includes 4,853 manufacturing SMEs in 16 countries in LAC, for which data are available. Sampling weights are provided by WBES and used throughout. The distribution of the sample by country is provided in Table A1 in the Appendix.

We complement the WBES with other data sources, including the World Development Indicators (WDI) and Worldwide Governance Indicators (WGI), both from the World Bank.

# 4.2 _Estimation model_

Our baseline results are based on the following logistic equation:

> 6 The firm-level WBES data that we use are publicly available at

> <u>https://www.enterprisesurveys.org/en/enterprisesurveys. All the other data sources that we use are also in the public</u> domain.

> 7 Results for the sample of large firms obtained from WBES are provided separately.

ijkt = 1) 1 pp(YY -(a+/31/nformal Competitoinkt+CFEk+YFEt+/FEj+Firm Controlsijkt+Country Controlskt+uijkt) = 1 + ee 17
Panizza, U. and Qiang, Z. (2005), “Public-private wage differentials and gender gap in Latin America: Spoiled bureaucrats and exploited women?”, _The Journal of Socio-Economics_ , 34(6): 810–833.

Rees, H. and Shah, A. (1995), “Public-Private Sector Wage Differential in the UK”, _The Manchester School_ , 63(1): 52—68.

Reimers, C. (1983), “Labor Market Discrimination Against Hispanic and Black Men”, _The Review of Economics and Statistics_ , 65(4): 570—579.

SERVIR (2010), “Estudio de brechas salariales y análisis de sensibilidad de los ingresos de las personas que prestan servicios al Estado”, Published by SERVIR in November 2010.

Van der Gaag, J., Stelcner, M., and Vijverberg, W.P.M. (1989), “A Switching Regression Model of Public-Private Sector Wage Differentials in Peru: 1985-86”, _The Journal of Human Resources_ , 24(3): 545–559.

Van der Gaag, J., Stelcner, M., and Vijverberg, W.P.M. (1989), “Wage Differentials and Moonlighting by Civil Servants: Evidence from Côte d'Ivoire and Peru”, _The World Bank Economic Review_ , 3(1): 67–95.

World Bank (2007), “Informality – Exit and Inclusion”, _World Bank Latin American and Caribbean Studies_ .

18
# **Wealth Gradients in Early Childhood Cognitive Development in Five Latin American Countries**<sup>**1**</sup>

Norbert Schady,<sup>1</sup> Jere Behrman,<sup>2</sup> Maria Caridad Araujo,<sup>3</sup> Rodrigo Azuero,<sup>4</sup> Raquel Bernal,<sup>5</sup> David Bravo,<sup>6</sup> Florencia Lopez-Boo,<sup>7</sup> Karen Macours,<sup>8</sup> Daniela Marshall,<sup>9</sup> Christina Paxson,<sup>10</sup> and Renos Vakis<sup>11</sup> <u>Keywords: early childhood education, child development, social status, testing, Latin America</u> <u>JEL codes: I24, I25 Sector Board: POV, EDU</u> 1 Inter-American Development Bank, Washington, DC, US, norberts@iadb.org

> 2 University of Pennsylvania, Philadelphia, PA, US, jbehrman@econ.upenn.edu

> 3 Inter-American Development Bank, Washington, DC, US, mcaraujo@iadb.org

> 4 University of Pennsylvania, Philadelphia, PA, US, rodazuero@gmail.com

> 5 Universidad de Los Andes, Bogotá, Colombia, rbernal@uniandes.edu.co

6 Universidad de Chile, Santiago, Chile, microdatos@gmail.com 7 Inter-American Development Bank, Washington, DC, US, florencial@iadb.org 8 Paris School of Economics, Paris, France, karen.macours@parisschoolofeconomics.eu 9 University of Pennsylvania, Philadelphia, PA, US, daniela.marshall@gmail.com 10 Brown University, Providence, RI, US, christina.paxson@gmail.com 11 World Bank Washington, DC, US, rvakis@worldbank.org

> 1 Acknowledgments:  This research was supported by the Inter-American Development Bank, the World Bank, the Eunice Shriver Kennedy National Institute of Child Health and Development (Grant R01-HD065436) and Grand Challenges Canada (Grant 10036540). The paper also benefited from two anonymous referees.
**A15 Uzbekistan**

Table A15: Regional disparity trends in Uzbekistan

|**Year**|Max/Min<br>(MMR)|Simple<br>coefficient<br>of variation<br>(CVu)|Weighted<br>coefficient<br>of variation<br>(CVw|Relative<br>mean<br>Deviation<br>(Rw)|Unweighte<br>gini index<br>(Gu)|d Weighted<br>gini index<br>(Gw)|Theil<br>index (T)|
|---|---|---|---|---|---|---|---|
|1996|2.840|0.336|0.341|0.234|0.149|0.165|0.051|
|1997|3.047|0.353|0.355|0.238|0.155|0.170|0.054|
|1998|2.991|0.321|0.320|0.218|0.147|0.159|0.046|
|1999|2.779|0.304|0.301|0.206|0.142|0.152|0.041|

**Figure A15: Regional disparity trends in Uzbekistan**

<!-- Start of picture text --> 0.400<br>0.350-<br>0.300- , si :;<br>0.250<br>1996 1997 1998 1999<br>Year<br>Simple coefficient of variation (CVu) ~ Weighted coefficient of variation (CVw) Relative mean Deviation (Rw)<br>+Unweighted gini index (Gu) +Weighted gini index (Gw) -Theil index (T)<br><!-- End of picture text --> _50_
> - 28 -

structure of domestic debt of NIB, public enterprises will have to pay an additional LE 390 million to deposit banks and LE 2.7 billions to NIB, for a total of LE 3.1 billion, or between 3 and 4 percent of GDP .

To see what this means to public enterprises, consider their balance sheet for FY1988, the latest year for which data are available. In that year, gross profit (before taxes and earnings distribution) amounted to LE 2.5 billion, while total turnover is estimated at LE 32.3 billion. Thus, an increase in interest rates by 10 percentage points not only wipes out profit of the entire public enterprise sector, it also eliminates the dividends and tax contribution of this sector to the budget. The increase in interest payments represents about 10 percent of total turnover of the public enterprises.

Those public enterprises that belong to the Ministry of Industry appear to have a relatively better firancial position than the restIV. It is estimated that as of end FY88, the total debt of public enterprises under the Ministry of Industry amounted to LE 7.5 billion, and gross and net profit were estimated at LE 830 million and LE 667 million respectively. The impact of a ten point increase in the interest rates therefore on them is milder than the rest, even though it still amounts to more than their total net profit. Concerning the effect on the budget of the central government, a 10 percent increase in interest rates would cost about LE700 mWllion, or about 5 percent of the budget deficit.1L W There are currently about 393 PE's in Egypt, of which 116 are under the Ministry of Industry.

**Wi** The effect on the budget of the Central Government, depends on how much of the stock of Government debt is held by the Central Bank. If one assumes that the profits made by the Central Bank will be transferred to the budget, the interest rate charged on this stock of debt needs not be changed. In that case, the only effect of higher interest rates would be on the stock of government debt held by the comercial banks, currently about LE 7 billion.
(CIWMB 2005). There were limited data available to adequately assess the impacts of dioxins, furans, and other hazardous air pollutants, which are released by municipal wastes. However, since these are theoretical estimates and there are no conversion technology facilities in commercial operation in the United States, there is a high level of uncertainty towards their actual environmental performance.

• **LCA of agriculture:** Tilman, Hill, and Lehman (2006), through a decade-long experiment in which they compare different combinations of perennial herbaceous grassland species that can be used for producing biomass, conclude that high-diversity grasslands had increasingly higher bioenergy yield, higher carbon sequestration, and lower agrichemical pollution than monoculture crops in the long run. Moreover, since their experiment was conducted on degraded and abandoned nitrogen-poor sandy soils, this lends credence to claims that biofuels can be produced without displacing food production or causing habitat destruction. Kim and Dale (2005) perform a comparison of four different crop rotation systems: (1) corn-soybean rotation with no winter crop, (2) continuous corn with no winter crop, (3) continuous corn with no winter crop and 50% removal of stover, and (4) continuous corn with 70% stover removal and winter wheat rotation, for assessing impacts like nonrenewable energy consumption, global warming impact, acidification, and eutrophication. They found that all the cropping systems studied offered environmental benefits in terms of nonrenewable energy consumption and global warming impact, but planting cover crops was necessary to prevent acidification and eutrophication, primarily because large nitrogen (and phosphorus)-related environmental burdens are released from the soil during cultivation. Planting winter cover crops can also compensate for loss in soil organic carbon levels and soil erosion due to removal of corn stover. Cover crops also permit more corn stover to be harvested. Thus, utilization of corn stover and winter cover crops can improve the eco-efficiency of the cropping systems. But when water is scarce, the main environmental impact may be depletion of water resources. Mattson, Cederberg, and Blix (2000) outline a 31