rafmacalaba/datause-encoder-data huggingface dataset:
- Gulyani, Sumila and Debarata Talukdar. 2008. “Slum Real Estate: The Low-Quality High Price Puzzle in Nairobi’s Slum Rental Market and its Implication for Theory and Practice.” _World Development_ 36(10): 1916-1937.

- Hardoy, Jorge E., Diane Mitlin, and David Satterthwaite. 2001. _Environmental Problems in an Urbanizing World_ . London: Earthscan Publications.

- Jimenez, Emmanuel. 1982. “The Value of Squatter Dwellings in Developing Countries.” _Economic Development and Cultural Change_ 30(4): 739-752.

- Kain, John F. and John M. Quigley. 1970. “Measuring the Value of Housing Quality.” _Journal of the American Statistical Association_ 65(330): 532-548.

- Kissick, Duane; David Leibson; Morey Kogul; John Bachmann; Jerome Anderson, and Joseph Eckert. 2006. “Housing for All: Essential to Economic, Social, and Civic Development,” prepared for the World Urban Forum III, Vancouver.

- Konadu-Agyemang, Kwadwo. 2001. “A Survey of Housing Conditions and Characteristics in Accra, an African City.” _Habitat International_ 25(1): 15-34.

- Malpezzi, Stephen, and Stephen K. Mayo. 1987. “The Demand for Housing in Developing Countries: Empirical Estimates from Household Data.” _Economic Development and Cultural Change_ 35(4) (July 1): 687-721.

- Mayo, Stephen K., Stephen Malpezzi, and David J. Gross. 1986. “Shelter Strategies for the Urban Poor in Developing Countries.” _World Bank Research Observer_ 1(2): 183-203.

- Megbolugbe, Isaac F. 1986. “Econometric Analysis of Housing Trait Prices in a Third World City.” _Journal of Regional Science_ 26(3).

- Mulford, John. 1979. “Income Elasticity of Housing Demand.” Housing Assistance Supply Experiment. RAND, Santa Monica, CA.

- Regmi, Anita, M.S. Deepak, James L. Seale Jr., and Jason, Bernstein. 2001. “Cross-Country Analysis of Food Consumption Patterns” in _Changing Structure of Global Food Consumption and Trade_ , ed. Economic Research Service, USDA, 14-22. Washington, D.C.: United States Department of Agriculture.

- Rojas, Eduardo and Nadine Medellin. 2011. ”Housing Policy Matters for the Poor: Housing Conditions in Latin America and the Caribbean, 1995-2006.” IDB Working Paper Series No. IDB-WP-289. Washington, D.C.: Inter-American Development Bank.

- Rosen, Sherwin. 1974. “Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition.” _Journal of Political Economy_ 82(1): 34-55.

- Struyk, Raymond J., Michael L. Hoffman, and Harold M. Kastura. 1990. The Market for Shelter in Indonesian Cities. Urban Institute Press: Washington D.C.

- Tipple, A. Graham. “The Need for New Urban Housing in Sub-Saharan Africa: Problem or Opportunity.” _African Affairs_ 93 (373): 587-608.

26
# **5. A Randomized Experiment**

In order to measure the causal impact of blogging, we would ideally randomly assign some people to read a blog, and others not to. This is difficult to do for existing well-known blogs, since most potential readers would have heard of the blog, and potentially sampled it to see whether they are interested or not. We therefore took advantage of the launch of a new blog, _Development Impact_<sup>_15_</sup> , which was launched at the start of April 2011, and conduct a randomized encouragement experiment with this blog.

Development Impact is a blog initiated by the authors and two other World Bank researchers (Jed Friedman and Markus Goldstein), and is hosted by the World Bank. It covers a range of issues related to impact evaluations and evaluative research, including discussions of new research papers, reviews of new books about impact evaluations, methodological issues, and experiences from evaluations around the world. In May and June 2011 it received a total of just under 50,000 page views, or just over 800 page views per day. Whilst small relative to the most-read mainstream economics blogs like Freakonomics and Marginal Revolution, this traffic level would place it in the top 50 of economics and business blogs according to one ranking<sup>16</sup> . Moreover, this makes it the most read World Bank blog during this period, and perhaps a reasonable representative of blogs in economic development: it has about 40 percent of the traffic of Chris Blattman‟s blog, one of the most popular development blogs, exceeds that of Innovation for Poverty Action‟s blog, and has a similar number of Google Reader subscribers as the Center for Global Development‟s Views from the Center

> 15 http://blogs.worldbank.org/impactevaluations/

> 16 <u>http://www.gongol.com/lists/bizeconsites/ [accessed March 2011 rankings on July 28, 2011].</u> Marginal Revolution and Freakonomics traffic data from SiteMeter and www.websitevalue.us.

18
29

- Larrain, Mauricio. 1989. "How the 1981-83 Chilean Banking Crisis was Handled." World Bank Policy Working Paper, No. 300. Washington, D.C.

- La Porta, Rafael, Florencio Lopez-de-Silanes, and Andrei Shleifer. 1998. "Law and Finance." _Journal of Political Economy_ 106: 1113-55.

. 1999. "Corporate Ownership Around the World." _Journal of Finance,_ 54(2): 471-518.

- Luders, Rolf 1989. "El Saneamiento del Sistema Financiera en Chile." In Juan<sup>CarlosCasas,</sup> ed., _Saneamiento de Bancos, Estudios de Crisis Financieras en la Argentina, Colombia, Chile, Espana, E.E. U. U., Italia y Uruguay._ Ediciones El. Cronista Comercial.<sup>Buenos</sup> Aires.

- Marshall, Jorge and Schmidt-Hebbel, Klaus. 1994. "Chile: Fiscal Adjustment and Successful Performance" In William Easterly, C. A. Rodriguez and K. Schmidt-Hebbel, eds., _Public Sector Deficits and Macroeconomic Performance._ Oxford University Press.

- Mizala, Alejandra. 1991. _Financial Market Liberalization in Chile._ Garland Publishing, New York and London.

- Montes-Negret, Fernando and Luca Papi. 1997. "The Polish Experience with Bank and Enterprise Restructuring." World Bank Policy Research Working Paper, No. 1705. Washington, D.C.

- Norway's Royal Ministry of Finance and Customs. 1992. _Report by the Commission on the Banking Crisis._

- Pohl, Gerhard, Simeon Djankov and Robert E. Anderson. 1996. "Restructuring Large Industrial Firms in Central and Eastem Europe: An Empirical Analysis." World Bank Technical Paper, No. 332. Washington, D.C.

- Pohl, Gerhard, Robert E. Anderson, Stijn Claessens, and Simeon Djankov. 1997. "Privatization and Restructuring in Central and Eastern Europe: Evidence and Policy Options." World Bank Technical Paper, No. 368. Washington, D.C.

- Sanhueza, Gonzalo I. 1999. "La Crisis Financiera de los Anos 80 en Chile: Analisis de sus Soluciones y su Costo." _Economia Chilena,_ 2 No 1. April, pp. 43-68.

- _ 2000a. "Chilean Banking Crisis of the Eighties: Solutions and Estimation of the Costs."

- Mimeo. January.

_____. 2000b. E-mail to the author. February 16.

- Sheng, Andrew. 1996a. "Banking Fragility in the 1980s." In Sheng, Andrew, ed. _Bank Restructuring. Lessonsfrom the 1980s._ World Bank. Washington, D.C.
|TOGO CommunityDevelopment and SafetyNets Project|Gbetoho Joachim Boko|
|---|---|
|TOGO CommunityDevelopment and SafetyNets Project|Gbetoho Joachim Boko|
|TOGO CommunityDevelopment and SafetyNets Project|Gbetoho Joachim Boko|
|TOGO CommunityDevelopment and SafetyNets Project|Gbetoho Joachim Boko|
|TOGO CommunityDevelopment and SafetyNets Project|Gbetoho Joachim Boko|
|TOGO CommunityDevelopment and SafetyNets Project|Gbetoho Joachim Boko|
|TOGO CommunityDevelopment and SafetyNets Project|Gbetoho Joachim Boko|
|TOGO CommunityDevelopment and SafetyNets Project|Gbetoho Joachim Boko|
|TOGO CommunityDevelopment and SafetyNets Project|Gbetoho Joachim Boko|
|TOGO CommunityDevelopment and SafetyNets Project|Gbetoho Joachim Boko|
|Vietnam Climate Change Development Policy2|Christophe Crepin|
|RebuildingEnergyInfrastructure and Access|Koffi Ekouevi|
|RebuildingEnergyInfrastructure and Access|Koffi Ekouevi|
|RebuildingEnergyInfrastructure and Access|Koffi Ekouevi|
|RebuildingEnergyInfrastructure and Access|Koffi Ekouevi|
|RebuildingEnergyInfrastructure and Access|Koffi Ekouevi|
|RebuildingEnergyInfrastructure and Access|Koffi Ekouevi|
|RebuildingEnergyInfrastructure and Access|Koffi Ekouevi|
|RebuildingEnergyInfrastructure and Access|Koffi Ekouevi|
|RebuildingEnergyInfrastructure and Access|Koffi Ekouevi|
|RebuildingEnergyInfrastructure and Access|Koffi Ekouevi|
|RebuildingEnergyInfrastructure and Access|Koffi Ekouevi|
|RebuildingEnergyInfrastructure and Access|Koffi Ekouevi|
|RebuildingEnergyInfrastructure and Access|Koffi Ekouevi|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Niger - Competitiveness & Growth Support|Magueye Dia|
|Economic Reconstruction and Growth Development PolicyCredit|Evans Jadotte|
|Economic Reconstruction and Growth Development PolicyCredit|Evans Jadotte|
|Economic Reconstruction and Growth Development PolicyCredit|Evans Jadotte|
|Private Sector Rehabilitation & Agribusiness Development|Francisco Moraes Leitao Campos|
|Private Sector Rehabilitation & Agribusiness Development|Francisco Moraes Leitao Campos|
|Private Sector Rehabilitation & Agribusiness Development|Francisco Moraes Leitao Campos|
|Private Sector Rehabilitation & Agribusiness Development|Francisco Moraes Leitao Campos|
providing empirical support for the NK model’s third prediction (the MPS increases more with persistence in countries with floating rather than fixed ER regimes).

To visualize how the empirical estimates compare with the NK model’s predictions, Panel A of Figure 2 plots the 2SLS fitted values of the MPS for EMDEs (column (3) of Table 3) against the half-life of the commodity price index for floating (blue dotted line) and fixed ERs regimes (red dotted line). The blue and red shaded areas represent the 95% confidence interval around the fitted MPS for floating and fixed ERs, respectively. The MPS implied by the NK model (Section III) is shown for a fixed ER (solid red line) and a floating ER (solid blue line).

At 6 years of half-life, which is one of the lowest persistence in the sample, the empirical MPS is 0.1 regardless of the ER regime. For floating ERs, the MPS increases with half-life, reaching 0.4 and 0.8 when the half-life is 10 and 20 years, respectively. For fixed ERs, the MPS is almost flat, reaching only 0.2 when the half-life is 20 years.<sup>53</sup> Comparing the dotted and solid lines, one can see that the empirical estimates of the MPS are broadly consistent with those produced in the NK model, as expected from the results in Table 3. However, the empirical estimates of the MPS are less procyclical for floating ER and less countercyclical for fixed ERs. These differences might be explained by other frictions (like budget rigidities) not included in the NK model. However, one can see that countries with floating ERs that face highly persistent shocks tend to have procyclical fiscal spending, as in the NK model.

**Empirical MPS in SSA** . Panel B of Table 3 reports estimates of the MPS for the SSA sample. Column (4) shows that the overall procyclicality of a typical commodity exporter in SSA is higher than in EMDEs. In SSA, a 1 dollar increase in commodity revenue leads to a 31 cent increase in government expenditure in the first year (versus 25 cents in EMDEs, shown in column (1)). Allowing the MPS to be conditional on the ER regime, we find that while the typical country with a

> 53 For a half-life equal or above 10 years, the confidence intervals of the MPS for floating and fixed ERs do not overlap, providing further evidence that the MPS for floating ERs is higher than fixed ERs at high persistence – although not necessarily at lower persistence levels.

40
Table 1 – Descriptive Statistics for Main Variables in Manufacturing Panel Data

||**N (Obs.)**|**Mean**|**s.d.**|
|---|---|---|---|
|**Avg # of Firms in Woreda**|1,916|10|40|
|**Avg Capitalper worker in Woreda**|1,916|860,107|3,698,632|
|**Avg Sales in Woreda**|1,916|252,053,444|968,001,530|
|**Avg Value of Production in Woreda**|1,916|249,461,183|954,237,179|
|**Avg Employment at in Woreda**|1,916|877|3,746|
|**Avg Book Value of Fixed Assets in Woreda**|1,916|94,437,524|331,522,005|
|**Avg Materials in Woreda**|1,916|127,692,617|542,969,363|
|**Avg Export Sales Value in Woreda**|1,916|15,636,992|105,203,671|
|**Avg Transport Cost in Woreda**|1,916|4,091,011|15,846,122|
|**Avg # of Exporter Firms in Woreda**|1,916|0.5|2.0|
|**Avg Age of Firms in Woreda**|1,912|19.8|10.7|
|**Avg Labor Productivity in Woreda**|1,916|228,403|335,585|
|**Avg Markup Materials ACF Cobb-Douglas**|1,916|2.0|1.0|
|**Avg TFP ACF Cobb-Douglas w/o constant using phi**|1,916|-0.9|1.0|
|**Avg Wage of Firms in Woreda**|1,858|1,463,913|4,999,070|

Table 1 shows the descriptive statistics for the main variables from the manufacturing panel data, including the overall number of observations (woreda-years), and for each variable we list the mean as well as the standard deviation.

# **4. Estimation Strategy and Regression Results**

## **4.1 Difference-in-Differences and Triple Differences**

As explained earlier, Ethiopia adopted the 3G broadband network in 2008, thereby substantially improving mobile communication access. Taking advantage of the geographic variation of such shock, this study employs the difference-in-differences (DID) methodology in estimating the impact of enhanced local mobile access on firm performance. Specifically, the DID models compare firm performance across treated and untreated woredas before and after the adoption of 3G networks in 2008.

14
# TABLE OF CONTENTS

# I. INTRODUCTQN

||4|
|---|---|
|H. COLLECTING DATA ON HOUSEHOLD ENGY<br>|_5_<br>|
|A. Common Methodologies|_5_|
|1. National energy balances<br>|_5_|
|2. National budget surveys<br>|6|
|3. Micro-surveys|7|
|<br>4. Additional techniques for gathering specific||
|household energy information|7|
|. HOUSEHOLD ENERGY SURVEYING<br>|8|
|A. Overall Survey Design<br>|8|
|B. Sample Design<br>|9|
|1. Sampling procedures depend on goals & resources<br>|9|
|2. Sample stratification should be considered<br>|9|
|3. Sample size should calculated<br>|10|
|C. Questionnaire Design<br>|11|
|1. Important elements in questionnaire design<br>|11|
|2. Some common mistakes|12|
|D. Survey Fieldwork and Logistics<br>|13|
|1. Logistics|14|
|2. Selection and training of interviewers|14|
|E. Data Processing and Analysis|15|
|1. Cleaning the data|15|
|2. Computer processing|15|
|3. Reporting the results|16|
|F. Costs and Timing|16|

- F. Costs and Timing

# IV. HOUSEHOLD ENERGY SURVEY CHECKLIST

18

### ANNEXES

1. Energy Module for National Expenditure Survey 19 2. Information to be Gathered by Household Energy Surveys 21

# SELECTED BIBLIOGRAPHY

## BOXES

26

- **.**

||Sample Design in Morocco<br>Banoladesh Village Energy Survey|10<br>13|
|---|---|---|
|TABLES|||
||2.1: Ethiopia National Energy Balance (1982)|6|
||3.1: Analytical & Statistical Issues for the Policymaker|11|
WPS3374

## **COMMENTS WELCOME**

# **Is Debt Replacing Equity in Regulated Privatized Infrastructure in Developing Countries?**

**Luis Correia da Silva**

(OXERA)

**Antonio Estache**

(World Bank and ECARES, Universite Libré de Bruxelles)

## **Sakari Järvelä**

(OXERA)

### **Abstract**

The main purpose of this paper is to describe the evolution of the financing structure of regulated privatized utilities and transport companies. To do so, we rely on a sample of 121 utilities distributed over 16 countries, and 23 transport infrastructure operators and 23 transport services operators distributed over 23 countries. The paper shows that leverage rates vary significantly across sectors, with the highest rates observed in transport and the lowest in water. Moreover, the paper also shows that the 1997 Asia crisis led operators to adjust their financial structure differently in different regions. Overall, the evidence presented here shows that debt is replacing equity in financing the investment needs of utilities and transport services in developing countries. These results raise some questions as to whether the regulator’s mandate should be expanded to monitor the financial structure of companies and as to whether the international community should make a stronger commitment to more transparent regulatory accounting systems.

### World Bank Policy Research Working Paper 3374, August 2004

_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 view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._ The paper has benefited from comments and/or discussions with Ian Alexander, Phil Burns, Jose Carbajo, JoseLuis Guasch, Jean-Jacques Laffont, Anwar Ravat, Martin Rodriguez-Pardina, Richard Schlirf, German Sember, Sophie Sirtaine, Lee Travers, Lourdes Trujillo and Christian von Hirschausen.
- Heckman, J., and V. Joseph Hotz, "Choosing Among Alternative Nonexperimental Methods for Estimating the Impact of Social Programs: the Case of Manpower Training," _Journal of The American Statistical Association,_ 84 (1989), 862-80

- Holmstrom, Bengt, and Jean Tirole, "The Theory of The Firm," in _Handbook of Industrial Organization,_ vol. 1, ed. by R. Schmalensee and R.D. Willig, Elsevier Science Publishers B. V., 1989.

- Lin, Justin Yifu, "Rural Reforms and Agricultural Growth in China," _American Economic Review,_ 82(1), 34-51, 1992.

- McMillan, John, John Whalley & Lijing Zhu, "The Impact of China's Economic Reforms on Agricultural Productivity Growth," _Journal of Political Economy,_ 97 (1989), 781-807.

- Jensen, Michael C. and William Meckling, " Rights and Production Functions: an Application to Labormanaged Firms and Codetermination," _Journal ofBusiness,_ 52 (1979), 469-506.

- Jefferson, Gary H., and Thomas G. Rawski, "Enterprise Reform in Chinese Industry," _Economic Perspective,_ 8 (1994), 47-70.

- Johnson, D. Gale, "The People's Republic of China: 1978-1990," San Francisco, Calif.: ICF Press, 1990.

- Lucas, Robert E., Jr., "On the Mechanics of Economic Development," _Journal of Monetary Economics,_ January 1988, 22, 3-32.

- Moore, John H., "Agency Costs, Technological Change, and Soviet Central Planning," _Journal of Law andEconomics,_ 24 (October 1981), 189-214.

- Perkins, Dwight, "Completing China's Moves to the Market," _Economic Perspectives,_ 8 (1994), 23-46.

- Research Group for the Chinese Firm System Reform, System Reform Committee, _Management Responsibility System in Practice,_ Beijing, China: The Economic Management Press, 1988.

- Romer, Paul M., "Increasing Returns and Long-Run Growth," _Journal of Political Economy,_ October 1986, 94, 1002-37.

- Rosen, Sherwin, "Contracts and The Market for Executives," in Lars Werin and Hans Wijkander, eds., _Contract Economics,_ Oxford, U.K.: Basil Blackwell, 1992.

- Shleifer, Andrei, and Robert Vishny. "Politicians and Firms." _The Quarterly Journal of Economics,_ 109(4), 995-1025, November 1994.

- Tidrick, Gene, and Chen Ji Yuan, "China's Industrial Reforms", New York: Oxford university press, 1987.

- World Bank, _Bureaucrats in Business,_ New York: Oxford University Press, 1995.

- Xu, Lixin Colin, "The Determinants of the Repartitioning of Property Rights of State Enterprises," memeo., University of Chicago, 1995.

32
an increasing pattern of earnings up to the 1970-1974 cohort, and then earnings decline for the latter cohorts. The age coefficients imply a regular inverted U-shape for the age-earning profile. The negative coefficient of the interaction between the cohort and age variables is important. This implies that the earnings of younger cohorts start to decline at a younger age over time.

**Table 4: Cohort regressions. Log Hourly wage. Restricted to workers with college.**

||Males|Females|
|---|---|---|
|Cohort|0.1624|0.3269|
||[0.083]|[0.063]|
|Cohort<sup>2</sup>|-0.0224|-0.0331|
||[0.006]|[0.005]|
|Age|0.0625|0.0943|
||[0.016]|[0.012]|
|Age<sup>2</sup>|-0.0014|-0.0018|
||[0.000]|[0.000]|
|Cohort*Age|-0.0082|-0.0125|
||[0.002]|[0.002]|
|Constant|3.5632|2.8814|
||[0.264]|[0.199]|
|Observations|117|117|
|Adj. R-squared|0.931|0.955|

Notes: Authors’ calculations using quarterly Labor Force Surveys. Cohort starts in 0 and ends in 7, where 0 is the oldest cohort and 7 is the youngest cohort. Age starts at 0 (Age-23). The constant can be interpreted as the average log hourly wage for the oldest cohort with an average age of 23.

In order to disentangle the roles of labor demand across age groups, we analyze the pattern of employment by occupation level. We use the two-digit occupation category in the labor force surveys until 2012.<sup>9</sup> Figures 6 and 7 illustrate that the group that has been hurt the most is the category of _Director._ Their share in total employment declined (especially among older workers) and their real wages fell the most. This result suggests that labor demand for highly-paid occupations has declined substantially, however this has affected both young and older workers.

> 9 It is not possible to go up to 2014 because the occupation category changed in 2012. Although the National Statistics and Geography Institute (INEGI) includes a dictionary to match occupation categories between time periods, there are still large discontinuities for some of the occupations in 2012.

10
**Table 4** **_–_ Correlation Coefficient (GPS & CR)**

|Level (CR)|Ethiopia|Tanzania|Nigeria|Pooled|
|---|---|---|---|---|
|1 (< 0.05 acres)|0.95|0.81|‐|0.95|
|2 (< 0.15 acres)|0.91|0.92|0.91|0.92|
|3 (< 0.35 acres)|0.90|0.95|0.90|0.93|
|4 (< 0.75 acres)|0.91|0.96|0.87|0.92|
|5 (< 1.25 acres)|0.93|0.95|0.91|0.92|
|6 (>=1.25 acres)|0.98|0.96|1.00|1.00|
|Total|0.996|0.993|0.997|0.997|

**Table 5 – Bias and slope (measured with a clinometer)**

|Plot Slope<br>(degrees)|N|GPS|CR<br>Ethiop|Bias<br>ia|/<br>Mean CR|Difference in<br>means|
|---|---|---|---|---|---|---|
|0 ‐ 5|1076|0.36|0.35|0.00|1.3%|***|
|6 ‐ 15|562|0.44|0.43|0.01|2.2%|***|
|> 15|127|0.37|0.36|0.01|2.4%|**|
|Total|1765|0.38|0.38|0.01|1.7%|***|

*p<.1; ** p<.05; *** p<.01

35