rafmacalaba/datause-encoder-data huggingface dataset:
# **Table 2:  Distribution of Data Points by Type of Data in 2007 WGI**

||**Commercial**<br>**Business**<br>**Information**<br>**Providers**|**Surveys of**<br>**Firms or**<br>**Households**|**Non-**<br>**Governmental**<br>**Organizations**|**Public Sector**<br>**Organizations**|**_Total_**|
|---|---|---|---|---|---|
|**_Number of Data Points_**<br>||||||
|Voice and Accountability|492|374|751|399|2016|
|Political Stability/Absence of Violence|1024|186|0|315|1525|
|Government Effectiveness|840|388|321|393|1942|
|Regulatory Quality<br>|790|213|282|422|1707|
|Rule of Law<br>|955<br>|409<br>|439<br>|737<br>|2540<br>|
|Control of Corruption|954|493|282|393|2122|
|**_Total_**|5055|2063|2075|2659|11852|
|**_Shares of Total for Each Indicator_**||||||
|Voice and Accountability|0.24|0.19|0.37|0.20|1.00|
|Political Stability/Absence of Violence|0.67|0.12|0.00|0.21|1.00|
|Government Effectiveness|0.43|0.20|0.17|0.20|1.00|
|Regulatory Quality|0.46|0.12|0.17|0.25|1.00|
|Rule of Law|0.38|0.16|0.17|0.29|1.00|
|Control of Corruption|0.45|0.23|0.13|0.19|1.00|
|**_Total_**|0.43|0.17|0.18|0.22|1.00|
|**_Weighted Shares of Total for Each Ind_**<br>|**_icator_**<br>|||||
|Voice and Accountability|0.24|0.02|0.62|0.12|1.00|
|Political Stability/Absence of Violence|0.84|0.03|0.00|0.13|1.00|
|Government Effectiveness|0.65|0.11|0.06|0.18|1.00|
|Regulatory Quality|0.63|0.07|0.14|0.16|1.00|
|Rule of Law|0.67|0.10|0.12|0.10|1.00|
|Control of Corruption|0.65|0.13|0.11|0.11|1.00|
|**_Total_**|0.59|0.08|0.20|0.13|1.00|

30
Figure 4. Share of Sub-sectors in Services, %.

<!-- Start of picture text --> 50 45.4<br>36.6<br>40<br>30<br>20.6 19.3<br>20<br>9.6 8.7 11.1 12.5 8.7 8.8<br>10 2.8 3.4<br>0<br>Trade Communication Financial services Insurance Business services Recreation and<br>nec nec other services<br>Non-High Income Countries High Income Countries<br><!-- End of picture text --> Source: Authors’ computations using Version 9 of the Global Trade Analysis Project (GTAP) data in 2011.

# **5. Conclusion**

This paper estimates the impact of electricity consumption on the value added in three sectors: agriculture, manufacturing, and services. It uses panel data with annual observations for 126 countries for the period of 1996-2014 that is compiled using data from the International Energy Agency and the World Development Indicators database. The selection of these sectors and the timeframe for the analysis is driven by data availability for both the value added and electricity consumption, as well as for such control variables as capital and labor. Estimating the electricity-value added relationship on the sectoral level helps in the consideration of sector-specific patterns, as these sectors vary considerably in terms of their energy intensity and production technologies. For example, manufacturing substantially relies on powerintensive technologies and uses some level of automation, even in developing countries. By contrast, the electricity consumption in agriculture is small compared to other sectors across all countries, despite the use of electricity-intensive technologies in advanced economies. Also, the sectoral dimension can be useful for analyzing the growth implications of power in different countries, as the composition of their GDPs varies by sector. Finally, this paper estimates regressions for two samples of countries (all countries and non-high-income countries) and finds that the impact of electricity consumption varies by income groups.

This paper applies various econometric approaches, including fixed effects, two-step difference and system panel GMM methods. The OLS and fixed effects estimators are prone to endogeneity and reverse causality bias. The endogeneity bias is due to omitted variables that correlate with both independent and 18
Estache, Antonio and Martin Rossi (2004), “Have Consumers Benefited from the reforms in the Electricity Distribution Sector in Latin America?” Unpublished manuscript, The World Bank.

- Estache, Antonio and Martin Rossi (2007) “Regulatory Agencies: Impact on Firm Performance and Consumer Welfare,” Unpublished manuscript, The World Bank.

- Frydman, Roman, Cheryl W. Gray, Marek Hessel, and Andrzej Rapaczynski (1999), “When Does Privatization Work? The Impact of Private Ownership on Corporate Performance in Transition Economies,” Quarterly Journal of Economics, 114:4, pp. 1153-1191.

- Galal, Ahmed, Leroy Jones, Pankaj Tandon and Ingo Vogelsang (1994), “Welfare Consequences of Selling Public Enterprises,” Oxford: Oxford University Press.

- Gilardi, Fabrizio (2002), “Policy credibility and delegation to independent regulatory agencies: a comparative empirical analysis.” Journal of European Public Policy.

- Gilardi, Fabrizio (2005), “Evaluating Independent Regulators,” in “Designing Independent and Accountable Regulatory Authorities for High Quality Regulation.” Organization for Economic Cooperation and Development, Paris.

- Guach, Jose Luis and P. Spiller (1999), “Managing the Regulatory Process: Design, Concepts, Issues and the Latin America and Caribbean Story.” World Bank and Caribbean Studies, Washington DC.

- Gutierrez, Luis H. (2003), “Regulatory Governance in the Latin American Telecommunications Sector.” Utilities Policies. Obtained at www.sciencedirect.com.

- Halvorsen, R and R. Palmquist (1980), “The interpretation of dummy variables in semilogarithmic equations,” American Economic Review, 70, 474-475.

- Heckman, James and Richard Robb (1985), “Alternative Methods of Evaluating the Impact of Interventions.” In James Heckman and Burton Singer, eds., Longitudinal Analysis of Labmy Market Data. New York: Cambridge University Press. 156-245.

- Johannsen, Katja Sander (2003), “Regulatory Independence in Theory and Practice –a Survey of Independent Energy Regulators in Eight European Countries.” AKF, Denmark.

- Joskow, Paul (2003) “Electricity Sector Restructuring and Competition: Lessons Learned.” Cuadernos de Economía, Año 40, N° 121, pp. 548-558 (December).

- La Porta, Rafael and Florencio López-de-Silanes (1999), “Benefits of Privatization-Evidence From Mexico,” Q. J. Econ., 114:4, pp. 1193-1242.

- Laurin, Claude and Yves Bozec (2000), “Privatization and Productivity Improvement: The Case of Canadian National (CN),” working paper, Ecoles de HEC, Montreal.

- Maggetti, Martino (2005), “Towards a Framework to Assess the Informal Independence of Regulatory Agencies.” University of Lausanne, Switzerland.

- 27 -
Both specifications use product-origin country-year fixed effects in place of the product-origin country fixed effects used in the cross section. The coefficient estimates in column 5 are quite similar to the 2016 cross-section results in column 3. In column 6 we add importing firm-year fixed effects, and find similar coefficients to those reported in column 4, which used the 2016 cross-section alone.

Overall, we find that the relationship between the inspection rate and lagged shipment frequency is robustly negative, and sizably so when we allow variation across importing firms to drive the estimates. Our estimates do not allow us to rule out an alternative explanation for this conditional correlation: the Serbian customs agency may inspect infrequentlyobserved shipment types more often because shipments that are rarely seen are less likely to comply with import regulations. While it is quite plausible that compliance is related to frequency, we think it unlikely that this explains entirely the large negative effects we estimate in specifications without an importing-firm dummy. In our view, the sizable reduction in the estimated effect of frequency on inspection rates that occurs when importing-firm fixed effects are included in the specification also points to a role for ambiguity - the agency is not likely to have fully formed priors about the expected compliance of firms that import infrequently. Sweeping out cross-firm variation will tend to sweep out variation in the ambiguity attached to the agency’s expectation of each firm’s compliance probability. Ultimately, our focus on ambiguity depends not only on our empirical results, but also on discussions with operational experts, experts who view the inspection of new or rarely observed import shipment types as informational.

# **4 Model**

In this section we present a stylized model. Our approach to the theory is meant to illustrate the flexibility of the framework for application to different settings. In the numerical simulation in section 5, we choose a setting, functional forms and parameters that illustrate the operation of the model in a portion of the parameter space that is relevant to reform.

In the model shipments of traded goods arrive to the country’s border, and there are 18
# **<u>Table 3 - Summary Statistics for Gender-Based Violence, exposure in the last 12 months</u>**

|T f Ii P il||Con|trol<br>|||Treat|ment<br>||Differ<br>Non-re|ence in<br>sponse|
|---|---|---|---|---|---|---|---|---|---|---|
|ype o ntmate artner Voence|Yes<br>(%)|No<br>(%)|No<br>Resp.<br>(%)|N<br>(Obs.)|Yes<br>(%)|No<br>(%)|No<br>Resp.<br>(%)|N<br>(Obs.)|Diff.|p-val.|
|**EXPOSURE TO CONTROLLING BEHAVIOR**|49.29|46.05|4.66|708|54.64|39.52|5.84|291|-1.181|0.437|
|Ever experienced jealousy|18.22|49.01|32.77|708|20.62|47.42|31.96|291|0.810|0.804|
|Ever accused of unfaithfulness|14.12|51.41|34.46|708|16.49|50.17|33.33|291|1.130|0.732|
|Ever socialisation restricted|19.63|48.59|31.78|708|19.93|43.30|36.77|291|-4.990|0.128|
|Ever family contact restricted|14.69|42.94|42.37|708|14.43|39.52|46.05|291|-3.675|0.287|
|Ever husband must know location|23.16|38.98|37.85|708|26.46|31.27|42.27|291|-4.415|0.194|
|Ever household spending restricted|14.69|42.51|42.80|708|13.40|41.92|44.67|291|-1.877|0.587|
|Ever not trusted with money|20.06|50.71|29.24|708|21.65|44.67|33.68|291|-4.440|0.167|
|**EXPOSURE TO EMOTIONAL VIOLENCE**|32.06|60.73|7.20|708|31.62|60.82|7.56|291|-0.357|0.844|
|Ever publicly embarrassed|17.66|62.85|19.49|708|20.27|57.73|21.99|291|-2.502|0.372|
|<br>Ever threatened|17.80|60.45|21.75|708|19.59|58.08|22.34|291|-0.585|0.839|
|Ever insulted/degraded|21.75|59.32|18.93|708|19.93|58.08|21.99|291|-3.067|0.270|
|**EXPOSURE TO PHYSICAL VIOLENCE**|31.78|65.11|3.11|708|33.33|62.20|4.47|291|-1.360|0.289|
|Ever pushed/shaken|17.23|63.70|19.07|708|20.27|62.89|16.84|291|2.229|0.409|
|Ever slapped|14.41|63.84|21.75|708|17.18|60.48|22.34|291|-0.585|0.839|
|Ever arm twisted/hair pulled|12.71|65.68|21.61|708|13.75|70.10|16.15|291|5.459|0.050|
|Ever punched/hit|14.41|68.22|17.37|708|15.46|63.23|21.31|291|-3.933|0.146|
|Ever kicked/beaten|13.14|69.07|17.80|708|16.15|64.60|19.24|291|-1.447|0.591|
|Ever choked/burned|11.86|71.33|16.81|708|12.37|71.13|16.49|291|0.313|0.904|
|Ever threatened with weapon|11.44|71.89|16.67|708|13.40|70.45|16.15|291|0.515|0.842|
|**EXPOSURE TO SEXUAL VIOLENCE**|19.35|72.60|8.05|708|23.37|70.45|6.19|291|1.865|0.310|
|Ever forced sex|12.15|67.66|20.20|708|12.71|71.13|16.15|291|4.047|0.139|
|Ever forced non-sex sexual acts|10.88|68.36|20.76|708|13.40|64.95|21.65|291|-0.887|0.755|
|Ever threatened into sex|10.45|70.20|19.35|708|16.49|67.01|16.49|291|2.855|0.292|

20
# **5 Figure**

Figure 1: Trade dynamics: with linear prediction line <!-- Start of picture text --> TUR TUR<br>LVA<br>SRB POL SRB CZE<br>BLR CZE<br>KGZ<br>ARM POL HUN KGZ UKRKAZ HRV LVA<br>BLR<br>ALB GEOAZE KAZ UKRBGR ARM GEOAZE HUN<br>BGR ROM<br>ROM MDA<br>HRV<br>MDA ALB<br>MKD<br>MKD<br>-10 0 10 20 30 -5 0 5 10 15<br>Changes 2013-2009 Changes 2013-2009<br>(a) Share of exporters (b) Share of export<br>10 5<br>0 0<br>-10 -5<br>Changes 2009-2002 Changes 2009-2002<br>-20 -10<br>-30 -15<br><!-- End of picture text --> Figure 2: Foreign banks in ECA <!-- Start of picture text --> % foreign banks<br>South Asia % foreign bank assets<br>Middle East and North Africa<br>East Asia and Pacific<br>Other High Income<br>OECD<br>Latin America and the Caribbean<br>Europe and Central Asia<br>Sub-Saharan Africa<br>0 20 40 60<br><!-- End of picture text --> 12
year observations. Each of the sample firms contain at least one year of pre-privatization data, while 150 of the 181 firms have information for at least the previous 3 years.

We matched our previous data set with a novel dataset built by the World Bank that describes the characteristics of nearly 1,000 infrastructure projects awarded in Latin American and Caribbean countries from 1989 to 2002, in the sectors of telecommunications, energy, transportation and water. (See Guasch, 2004). This dataset contains information with respect to the privatization process we know how many bidders participated, the contract process<sup>5</sup> , the award criterion<sup>6</sup> , and the type of concession<sup>7</sup> . With respect to the regulatory framework, we know how the establishment of the legal framework<sup>8</sup> , the regulation of tariffs<sup>9</sup> , if there were a possibility of contractual renegotiation, and (if this was the case) who would initiate it<sup>10</sup> .

The data also contain additional contractual clauses, such as, if it considered a termination clause, about the arbitration process, claim solving institution, obligation to provide universal service, duration of the contract, contract renewal, government’s guaranties, if the government granted subsidies, frequency of the tariff review, and how the exchange and commercial risk were born. If the contract was renegotiated, we know when it was, the reason given for it, and its outcome.

Some characteristics of the regulator include: an index of its autonomy, its budget source, the duration of the regulatory board member mandate, as well as the year of the regulatory board’s inceptions. Among these variables we selected those with enough variation across firms that allow us to better identify the effect of the differences in each outcome. Hence, Table 7 indicates the variables that we were able to use in this analysis, while Table 8 shows the summary statistics of the characteristics across the sectors.

29
# **VI.2. What are the remaining investment needs in the SAR region?**

South Asia is the second most densely populated region in the world, behind East Asia and Pacific, with large and pressing investment needs for infrastructure improvement (Bloom and Rosenberg 2011; Figure 21). Metrics of human capital provision (e.g., expenditure on education and healthcare, teacher-pupil ratios, doctor-patient ratios, availability of improved water and sanitation in rural areas), are below the EMDEs average (World Bank 2016j). This suggests that sizable additional outlays on human capital could effectively alleviate poverty (Romer 2016; Estache and Garsous 2012). Rapid urbanization and the maintenance of growth momentum, call for improvement of energy and transport infrastructure (Ellis and Roberts 2016; Inderst 2016; Battacharya 2012; ADB 2009, 2012; Andres, Biller, and Dappe 2014).

South Asia is one of the least economically integrated regions in the world (World Bank 2016a). This has been attributed to inadequacies in transport and power infrastructure (ADB 2009). Coverage differs within countries and across the region, with _India_ and _Pakistan_ somewhat better positioned than other countries.

Energy shortages (electricity, diesel) remain a critical constraint to activity in the region. Underdeveloped within-country and cross-border electricity grid network connectivity and, in some cases, geopolitical tensions have contributed to significant energy shortfalls, compounding regular electricity outages. In _India_ , dependence on imported fuels for power generation, and low electricity tariffs have hampered power generation capacity, which now requires significant expansion to meet energy shortfalls (McKinsey 2011).

_Bangladesh’s_ infrastructure quality lags behind other countries in the region: power shortages and poor transport infrastructure have affected investment and productivity (World Bank 2015r). The 7<sup>th</sup> Five Year Plan estimates that about $410 billion in financing—twice the size of 2015 GDP— is needed for developing Bangladesh’s infrastructure. Investment is also needed in public health care, where expenditure has declined from 1.1 percent of GDP in 2010 to 0.7 percent of GDP in 2014 (World Bank 2015e, 2016h).

In _Sri Lanka_ , fiscal consolidation, coupled with priority spending to rebuild infrastructure after a 25-year civil war, has crowded out expenditure for human capital-building purposes. Government spending on education fell from 2.7 to 1.8 percent of GDP during 2006-13, while spending on health declined from 2.0 to 1.4 percent of GDP over the same period (World Bank 2016k).

41
GSMA (2018). The Mobile Gender Gap Report 2018.

- Hasbi, M. and A. Dubus, 2019. "Determinants of Mobile Broadband Use in Developing Economies: Evidence from Sub-Saharan Africa," Working Papers hal-02264651, HAL.

- Hjort, J. and J. Poulsen. 2019. "The Arrival of Fast Internet and Employment in Africa _." American Economic Review_ , 109 (3): 1032-79.

- Kaila, H. and F. Tarp. 2019. “Can the Internet Improve Agricultural Production? Evidence from Viet Nam”. _Agricultural Economics,_ 675-91.

- Klonner, S. and P. Nolen. 2010. “Does ICT Benefit the Poor? Evidence from South Africa.”, Proceedings of the German Development Economics Conference, Hannover.

- Marandino, J. and P. Wunnava. 2014. “The Effect of Access to Information and Communication Technology on Household Labor Income: Evidence from One Laptop per Child in Uruguay.” IZA Discussion Paper 8415, Institute for the Study of Labor, Bonn.

- Menon, N. 2011. "Got Technology? The Impact of Computers and Cell Phones on Productivity in a Difficult Business Climate: Evidence from Firms with Female Owners in Kenya," IZA Discussion Papers 5419, Institute of Labor Economics (IZA).

- Nsabimana, A. and P. Funjika, 2019. “Mobile phone use, productivity and labour market in Tanzania”, WIDER Working Paper, No. 2019/71, ISBN978-92-9256-705-7, The United Nations University World Institute for Development Economics Research (UNU-WIDER), Helsinki,

- Paunov, C. and V. Rollo, 2015. Overcoming Obstacles: The Internet's Contribution to Firm Development, _The World Bank Economic Review_ , Volume 29, Issue suppl_1, Pages S192–S204.

- Ritter, P. and M. Guerrero. 2014. “The Effect of the Internet and Cell Phones on Employment and Agricultural Production in Rural Villages in Peru.” Working paper, University of Piura, Piura, Peru.

- Salas Garcia, Vania B. and Q. Fan, 2015. "Information Access and Smallholder Farmers’ Selling Decisions in Peru," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205380, Agricultural and Applied Economics Association.

- Viollaz, M. and H. Winkler. 2020. “Does the Internet Reduce Gender Gaps?: The Case of Jordan." The Case of Jordan (March 13, 2020). World Bank Policy Research Working Paper 9183.

- World Bank. 2015. _More, and More Productive, Jobs for Nigeria: A Profile of Work and Workers_ . Washington, DC.

World Bank. 2016. _World Development Report: Digital Dividends_ . Washington, DC.

- Yanagizawa-Drott, D. 2014. “Propaganda and Conflict: Evidence from the Rwandan Genocide.” The Quarterly Journal of Economics (August 21). doi:10.1093/qje/qju020.

16
_Table 12: Dependent variable – The firm invests_

||(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|
|---|---|---|---|---|---|---|---|---|
|SOB|0.1571**|0.1719**|0.188|0.152|0.166|0.128|0.281*|0.248|
||(0.0700)|(0.0723)|(0.176)|(0.176)|(0.177)|(0.176)|(0.170)|(0.168)|
|SOBx(1-IQ)|||-0.073|0.048|-0.021|0.108|-0.332|-0.206|
||||(0.323)|(0.333)|(0.342)|(0.349)|(0.322)|(0.320)|
|ROA||0.0270||0.028||0.028||0.025|
|||(0.0189)||(0.019)||(0.019)||(0.019)|
|LTA||-0.0362||-0.037||-0.038||-0.035|
|||(0.0285)||(0.030)||(0.029)||(0.029)|
|ln(SIZE)|-0.1225***|-0.1252***|-0.123***|-0.125***|-0.123***|-0.125***|-0.123***|-0.125***|
||(0.0230)|(0.0228)|(0.023)|(0.023)|(0.023)|(0.023)|(0.023)|(0.023)|
|ln(AGE)|0.0348|0.0368|0.035|0.037|0.035|0.037|0.036|0.037|
||(0.0402)|(0.0402)|(0.040)|(0.040)|(0.040)|(0.040)|(0.040)|(0.040)|
|SOE|-0.0197|0.0030|-0.013|-0.001|-0.017|-0.009|0.010|0.020|
||(0.1032)|(0.1034)|(0.100)|(0.101)|(0.102)|(0.102)|(0.101)|(0.102)|
|FEM|-0.1173*|-0.1134|-0.116*|-0.114|-0.117*|-0.115|-0.115*|-0.112|
||(0.0693)|(0.0692)|(0.070)|(0.070)|(0.070)|(0.070)|(0.069)|(0.069)|
|Const|1.9873***|2.2671***|1.988***|2.275***|1.987***|2.282***|1.988***|2.255***|
||(0.1199)|(0.2538)|(0.120)|(0.265)|(0.120)|(0.263)|(0.120)|(0.256)|
|N. Obs|14,656|14,656|14,656|14,656|14,656|14,656|14,656|14,656|
|R2|0.0642|0.0666|0.064|0.067|0.064|0.067|0.065|0.067|
|CY FE|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|IQis|||Control of|Corruption|Rule o|f Law|Government|Effectiveness|

*** p<0.01; ** p<0.05; * p<0.1

29
**Table A.4.1: Total Spending in Basic Education by Year and Student Cohort**

|Aggregate|Expected Years of Schooling|Annual Spending on<br>Basic Education<br>(2017 PPP$)|Total Spending on Basic<br>Education by Cohort<br>(2017 PPP$)|
|---|---|---|---|
|EAP|11.9|1.03 T|13.5 T|
|ECA|13.0|1.56 T|21.0 T|
|LAC|11.9|0.49 T|6.0 T|
|MNA|11.5|0.34 T|4.0 T|
|NAC|13.5|1.09 T|14.6 T|
|SAR|10.5|0.40 T|4.0 T|
|SSF|8.1|0.16 T|1.4 T|
|Grand Total|11.2|5.07 T|64.6 T|
|HIC|13.3|2.84 T|38.3 T|
|LIC|11.7|1.44 T|18.0 T|
|LMC|10.1|0.74 T|7.9 T|
|UMC|7.8|0.05 T|0.4 T|
|Grand Total|11.2|5.07 T|64.6 T|
|Part I|13.4|2.75 T|37.1 T|
|IBRD|11.6|2.11 T|25.4 T|
|IDA/Blend|9.0|0.21 T|2.1 T|
|Grand Total|11.2|5.07 T|64.6 T|

Source: Authors’ calculation using the World Bank API. Results expressed in 2017 PPP dollars.

58
Policy Research Working Paper 5128

# **Abstract**

It is assumed that added time to export adds cost to and lowers the volume of trade. Time delays may also affect the composition of trade and can disproportionately reduce trade in time-sensitive goods. This paper investigates the validity of these propositions using the World Bank _Doing Business_ database and _Enterprise Surveys_ for 64 developing countries. The authors find that in countries where there is longer time needed to export firms in time-sensitive industries are less likely to become exporters. Moreover, firms that do export have lower export intensities. Their findings imply that time to export is a significant determinant of comparative advantage. For example, consider two industries that have the same export probability and intensity—but differ in time-sensitivity by one standard deviation. Action taken to cut time to export by 50 percent for one industry opens a 6 percentage point difference between the export probabilities of the two industries. In addition, steps to cut time delays increase export intensities by 1.9 percentage points. This impact applies to industries with different productivity levels—and those in developing countries with different income levels.

This paper—a product of the Trade and Integration Team, Development Research Group—is part of a larger effort in the department to explore the linkages between trade costs, facilitation, and economic development. The website for this work is accessible at http://econ.worldbank.org/projects/trade_costs. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at yli7@worldbank.org and jswilson@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