rafmacalaba/datause-encoder-data huggingface dataset:
**European Union (EU):** After an agreement between the European Commission (EC) and the European Automobile Manufacturers Association (ACEA) in 1998 and similar agreements with the Japanese and Korean manufacturers (JAMA and KAMA) in 1999, the EU automobile industry committed to a target by 2008/2009. The major provisions of the ACEA Agreement, signed in March 1998, include a CO2 emission target of 140 g CO2/km, representing a 25 percent reduction from the 1995 level of 186 g CO2/km, to be reached by 2008 with the possibility of an extension of the agreement to 120 g CO2/km by 2012 (Dieselnet, 2005). The difference between the agreements signed by the European Commission (EC) with the European Automobile Manufacturers Association (ACEA) in 1998 and with the Japanese and Korean manufacturers in 1999 is that the target of 140 g CO2/km is delayed by one year, to 2009, for, JAMA and KAMA (Dieselnet, 2005).

# **3.3 Impacts of Fuel Economy Standards on Fuel Consumption and Emissions**

The impacts of fuel economy standards on fuel consumption (Geller et al., 1992; Goldberg, 1998; Greene, 1998) and emission reduction (Decicco, 1995) are helpful in assessing the performance of these standards and their suitability for replication in developing countries. Parry et al. (2004) used the Arizona I/M program data collected in 1995 and 2002 on car and truck emissions of volatile organic compounds (VOC), nitrogen oxides (NO _x_ ), and carbon monoxide (CO) to study the effects of fuel economy standards on emission rates in the United States. They found emission rates were significantly affected by fuel economy standards in 1995 but not so in 2002. This is mainly because the projected CO, hydrocarbon (HC) and NO _x_ emissions per mile for cars and trucks with certified fuel economy of 20 and 30 mpg are virtually indistinguishable over vehicle lifetimes. Based on their findings, they proposed that lifetime emission rates are equivalent for different cars and for different light trucks. Using a vehicle stock turnover model, Decicco (1995) estimated the effect of enhanced fuel economy standards on gasoline consumption, GHG emissions, and hydrocarbon emissions for light duty vehicles in the United States. The author found that an improvement of 6 percent per year in fuel economy would result in savings of 2.9 million barrel of gasoline per day and 147 10
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# About AICD and its country reports

This study is a product of the Africa Infrastructure Country Diagnostic (AICD), a project designed to expand the world’s knowledge of physical infrastructure in Africa. The AICD provides a 2006 baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from donor support. It also offers a solid empirical foundation for prioritizing investments and designing policy reforms in Africa’s infrastructure sectors.

The AICD is based on an unprecedented effort to collect detailed economic and technical data on African infrastructure. The project has produced a series of original reports on public expenditure, spending needs, and sector performance in each of the main infrastructure sectors, including energy, information and communication technologies, irrigation, transport, and water and sanitation. _Africa’s Infrastructure— A Time for Transformation_ , published by the World Bank and the Agence Française de Développement (AFD) in November 2009, synthesized the most significant findings of those reports.

The focus of the AICD country reports is on benchmarking sector performance and quantifying the main financing and efficiency gaps at the country level. These reports are particularly relevant to national policy makers and development partners working on specific countries.

The AICD was commissioned by the Infrastructure Consortium for Africa following the 2005 G8 (Group of Eight) summit at Gleneagles, Scotland, which flagged the importance of scaling up donor finance for infrastructure in support of Africa’s development.

The first phase of the AICD focused on 24 countries that together account for 85 percent of the gross domestic product, population, and infrastructure aid flows of Sub-Saharan Africa. The countries are: Benin, Burkina Faso, Cape Verde, Cameroon, Chad, Côte d’Ivoire, the Democratic Republic of Congo, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, South Africa, Sudan, Tanzania, Uganda, and Zambia. Under a second phase of the project, coverage was expanded to include as many of the remaining African countries as possible. The data collected under the first phase of the project covered the period 2001 to 2006, while that collected under the second phase covered the period 2001 to 2008.

Consistent with the genesis of the project, the main focus is on the 48 countries south of the Sahara that face the most severe infrastructure challenges. Some components of the study also cover North African countries so as to provide a broader point of reference. Unless otherwise stated, therefore, the term _Africa_ is used throughout this report as a shorthand for _Sub-Saharan Africa._ 56
This method involves a 13-question module designed to measure social desirability (Crowne & Marlowe, 1960; Reynolds, 1982). Given each statement, subjects answered whether they completely agree, partially agree, neither agree nor disagree, partially disagree, or completely disagree. To avoid priming the respondents or the enumerators into choosing the first response options, we randomly varied the order with which we displayed the response options (Appendix C). We code the response to each statement as 1 if the subject gives a socially desirable answer. For example, if a subject completely disagrees or partially disagrees with the statement “I sometimes feel resentful when I don’t get my way”, we code their response as 1 (and code the response as 0 if they neither disagree nor agree, partially agree, or completely agree). We sum the coded responses over statements, so that subjects have a social desirability score of between 0 and 13. We standardize the score by subtracting the mean score across all subjects, and then dividing by the standard deviation of the score across all subjects.

To investigate social desirability bias’s influence on our wild meat consumption results, we replace the eight individual characteristics controls in Equation 1 with two variables: the standardized social desirability score ( _StdSoci_ ), and its interaction with the treatment indicator ( _Ti × StdSoci_ ). The dependent variable is an indicator of whether a subject ordered wild meat: In this equation _σ_ 2 is the coefficient of interest, indicating the potential interaction between treatment and social desirability. A negative _σ_ 2 would imply that subjects in the treatment group less likely to order wild meat also exhibited higher social desirability.

14
Sundarban _upazila_ s using 2010 poverty incidence estimates provided by the World Bank (2014a) and 2011 population estimates from the Bangladesh Bureau of Statistics. Following World Bank (2014a), we use two standards to determine poverty incidence: the upper poverty line, for households whose food expenditures are at or below the food poverty line established by the Bangladesh Bureau of Statistics;<sup>15</sup> and the lower poverty line, for extremely poor households whose total expenditures are at or below the food poverty line.

Table 4 and Figure 6 provide summary statistics for the five _upazila_ s. Although all five are poor by national standards, their national percentiles vary from 71.5 to 92.3 for lower-line poverty and 77.0 to 91.0 for upper-line poverty. Koyra and Shyamnagar are the poorest (by both standards) and most populous. Koyra’s lower-line and upper-line poverty incidences -- 29.1% and 49.1% -- are in the national 86th and 89th percentiles, respectively. Its lower- and upper-line poverty populations are 56,400 and 95,200. Shyamnagar’s lower- and upper-line poverty incidences are in the national 92nd and 91st percentiles, with poverty populations of 107,600 and 159,800.

**Table 4:  Poverty status of Sundarbans** **_upazila_ s**

|Upazila|Lo<br>%Poor|wer Poverty <br>National<br>Percentile|Line<br>Population<br>(‘000)|U<br>%Poor|pper Poverty <br>National<br>Percentile|Line<br>Population<br>(‘000)|
|---|---|---|---|---|---|---|
|Mongla|22.7|71.5|31.0|41.9|77.0|57.2|
|Dacope|24.9|77.6|37.9|44.5|82.2|67.8|
|Sarankhola|28.2|83.1|33.6|48.0|88.1|57.2|
|Koyra|29.1|86.0|56.4|49.1|89.2|95.2|
|Shyamnagar|33.8|92.3|107.6|50.2|91.0|159.8|

> 15 See Report of the Household Income and Expenditure Survey/HIES 2010.  Bangladesh Bureau of Statistics, Government of Bangladesh.

21
The Price Is Not Always Right: On the Impacts of (Commodity) Prices on Households (and Countries)<sup>_∗_</sup> Daniel Lederman<sup>_†_</sup> Guido Porto<sup>_‡_</sup> LCRCE Dept. of Economics The World Bank Universidad de La Plata JEL Classification: O1, F1, D1, D6 Keywords: Commodity prices, household welfare, price transmission

> _∗_ Laura Chioda, Louise Cord, Irene Brambilla, and Emily Sinnott provided insightful comments during the early stages of this research project. We are grateful to Emmanuel Jiminez and three anonymous referees for detailed comments on a previous version of this article. We are especially grateful to Cecilia Peluffo and German Bet for excellent research assistance. All remaining errors are our responsibility.

> _†_ World Bank, email: dlederman@worldbank.org

> _‡_ Universidad Nacional de La Plata, Calle 6 e 47 y 48, Oficina 521, La Plata 1900, Argentina; email: guido.porto@depeco.econo.unlp.edu.ar
Ivanic, M., and W. Martin. 2014. “Short- and long-run impacts of food price changes on poverty.” Policy Research Working Paper 7011. Washington DC: World Bank:.

——— . 2008. “Implications of Higher Global Food Prices for Poverty in Low-Income Countries.” _Agricultural Economics_ 39 (s1), 405–16.

Ivanic, M., W. Martin, and H. Zaman. 2011. “Estimating the Short-Run Poverty Impacts of the 2010–11 Surge in Food Prices.” Policy Research Working Paper 5633, World Bank.

Jacoby, H.G. 2016. “Food prices, wages and welfare in rural India.” _Economic Inquiry_ 54: 159– 76.

Lasco, C., R.J. Myers, and R.H. Bersten. 2008. “Dynamics of rice prices and agricultural wages in the Philippines.” _Agricultural Economics_ 38: 339–48.

Loayza, N.V., and C. Raddatz. 2010. “The composition of growth matters for poverty alleviation.” _Journal of Development Economics_ 93: 137–51.

Nerlove, M. 1958. _The dynamics of supply: estimation of farmers’ response to price_ . Baltimore, MD: Johns Hopkins University Press.

Palmer‐Jones, R.W. 1993. “Agricultural wages in Bangladesh: What the figures really show?” _The Journal of Development Studies_ 29: 277–300.

Rashid, S. 2002. Dynamics of agricultural wage and rice price in Bangladesh. MSSD Discussion Paper No. 44. International Food Policy Research Institute (IFPRI), Washington DC. <http://ideas.repec.org/p/fpr/mssddp/44.html> Ravallion, M. 1990. “Rural welfare effects of food price changes under induced wage responses: theory and evidence for Bangladesh.” _Oxford Economics Papers_ 42: 574–85.

Ravallion, M., S. Chen, and P. Sangraula. 2007. “New Evidence on the Urbanization of Global Poverty.” World Bank Working Paper No. WPS4199. Washington DC: World Bank. Ravallion, M., and G. Datt. 2002. “Why has economic growth been more pro-poor in some states of India than others?” _Journal of Development Economics_ 68: 381–400.

Sah, R.K., and J.E. Stiglitz. 1984. “The Economics of Price Scissors.” Working Paper No. 1156. Cambridge, MA. <http://ideas.repec.org/p/nbr/nberwo/1156.html> Swinnen, J. 2011. “The Right Price of Food.” _Development Policy Review_ 29: 667–88. The Economist. 2012. “Global poverty: A fall to cheer.” The Economist.

Van Campenhout, B., K. Pauw, and N. Minot. 2013. “The impact of food prices shocks in Uganda: First-order versus long-run effects.” IFPRI Discussion Paper 1284, The International Food Policy Research Institute (IFPRI), Washington DC.

Verpoorten, M., M. Arora, and J. Swinnon. 2011. “Self-Reported Food Security in Africa During the Food Crisis.” LICOS Working Paper, LICOS Centre for Institutions and Economic Performance and Department of Economics, University of Leuven (KUL).

World Bank, 2013a. _PovCalNet_ . Washington DC: World Bank.

World Bank, 2013b. _World Development Indicators Online_ . Washington DC: World Bank. Zhang, X., S. Rashid, K. Ahmad, V. Mueller, H.L. Lee, S. Lemma, S. Belal, and A.U. Ahmed.

2013. _Rising wages in Bangladesh_ . International Food Policy Research Institute (IFPRI), Washington DC.

21
# BIBLIOGRAPHYs

- Easterly, W., P. Kongsamut and J<sup>Zizek (1990):"The Macroeconomic</sup> Requirements of Policy Reforms in Colombia:<sup>A Model-Based</sup> Approach", WPS, The World Bank.

- Holsen, J. (1989a): "A Simple<sup>Sources and Uses Framework for</sup> Historical and Projected Country<sup>Economic Data", Manuscript,</sup> The World Bank, Washington, D.C., April.

- (1989b): "KENYA.MOD: A Simplified Example<sup>of RMSM-X",</sup>

- Manuscript, The World Bank, Washington,<sup>D.C., May.</sup>

- Khadr, A., L. McKay, K. Schmidt-Hebbel<sup>and J. Ventura (1989):</sup> "A RMSM-X Model Illustration for Zimbabwe,,,<sup>mimeo, The World</sup> Bank.

- King, B.B. (1985): "What is a SAM?",<sup>in G. Pyatt and Jeffrey I.</sup> Round eds. Social Accounting Matrices:<sup>A Basis for Planning.</sup> Washington D.C., The World Bank.

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- Serven, Luis (1990): "A RMSM-X Model<sup>for Chile", mimeo, The World</sup> Bank.

- Serven, L., and J. Ventura (1989a): "The<sup>Structure and Closure of</sup> a Macroeconomic Model", mimeo, The World<sup>Bank.</sup>

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59
1

# CONTENTS

# **Introduction**

|**1. Section 1.**|Housing Privatization in the Forner Soviet Union:||
|---|---|---|
||Factors and Social Effects|6|
|**2. Section 2.**|The Development of the Housing Market in the Context of||
||<br>Public Priorities|18|
|**Conclusions a**|**nd Policy Issues**|37|
|**Annex**|Social Survey Background|38|
|**References**||**40**|

# **List of Tables in Text**

|1.1. Lithuania, housing stock by form of ownwership (% of total)|6|
|---|---|
|1.2. Estonia, housing stock by form of ownwership (% of total)|6|
|1.3. Ukraine, housing stock by form of ownership (% of total)|7|
|1.4. Russia, housing stock by form of ownership (% of total)|7|

- 1.4. Russia, housing stock by form of ownership (% of total)

- **_1.5._** Privatization of housing in Russia and separately in Moscow, 1990-96 (cumulative)

|7|
|---|

- 1.6. Average price of housing unit quality and location, Moscow, April, 1995 (thousand dollars) 11

- 1.7. Correlation between the category and location of housing and its privatization rate in Moscow **(%/o** of households that privatized before 1995) 11

- 1.8. Housing privatization by demographic categories (% of Moscow households that privatized before 1995) 12

- 1.9. Moscow households who privatized their dwellings by type of category of housing (% of total in each group) 14

- 1.10. Moscow households who privatized their dwellings by location of housing (% of total in each group) 14

- 1.11. Percentage of Moscow households who privatized their dwellings by demographic categories (% of total in each group) 14

- 1.12. Vladimir and Gorodets, housing stock by type of ownership, 1996 (%/0) 16 1.13. Vladimir and Gorodets, sha;e of private housing by type of building, 1996 1.14. Private ownership of housing by demographic categories in Vladimir and Gorodets, 1996 (% of owners) 17

- 2.1. Markets for new housing and secondary markets in Russia (price of one square meter of total floor space, fourth quarter, 1996) 20

- 2.2. Households' intended meams of improving housing conditions, % 22 2.3. Distribution of households by demographic categories, 1996 (% total number of households in each city) 26

- 2.4. Moving plans, by demographic categories, 1996 (% of each group) 27 2.5. Sources of financing for future improvement of housing conditions as identified by households (1996) (% of all households) 27

- 2.6. Household plans to move by owner/renter status, % 31

# **List of Figures in Text**

- 1.1. Types of households living in privately owned (i) and non-privatized (ii) housing. Moscow, 1994

- 1.2. Moscow households who privatized their dwellings, by deciles based on total household income

13 15
||**_England an_**|**_d Wales, 1801-3_**|||
|---|---|---|---|---|
|Social group|Number of<br>people|Percentage of<br>people|Per capita<br>income (in £)|Income in terms<br>of per capita<br>|
|||||mean|
|Temporal peers|7175|0.08|320.0|14.59|
|Spiritual peers|390|0.004|266.7|12.16|
|Eminent merchants, bankers|20000|0.22|260.0|11.86|
|Baronets|8100|0.09|200.0|9.12|
|Knights|3500|0.04|150.0|6.84|
|Esquires|60000|0.66|150.0|6.84|
|Educators in universities|2000|0.02|150.0|6.84|
|Warehousemen, wholesale|3000|0.03|133.3|6.08|
|Manufacturers|150000|1.66|133.3|6.08|
|Building & repairing ships|1800|0.02|116.7|5.32|
|Higher civil offices|14000|0.15|114.3|5.21|
|Lesser merchants, by sea|91000|1.01|114.3|5.21|
|Shipowners, freight|25000|0.28|100.0|4.56|
|Gents|160000|1.77|87.5|3.99|
|Eminent clergymen|6000|0.07|83.3|3.80|
|<br>Law, judges to clerks|55000|0.61|70.0|3.19|
|<br>Liberal arts and sciences|81500|0.90|52.0|2.37|
|Keeping houses for lunatics|400|0.004|50.0|2.28|
|<br>Theatrical pursuits|4000|0.04|50.0|2.28|
|<br>Lesser offices|52500|0.58|40.0|1.82|
|Engineers, surveyors, etc.|25000|0.28|40.0|1.82|
|Merchant service|49393|0.55|40.0|1.82|
|Marines and seamen|52906|0.58|38.0|1.73|
|Freeholders, greater|220000|2.43|36.4|1.66|
|<br>Shopkeepers and tradesmen|372500|4.11|30.0|1.37|
|<br>Tailors, milliners, etc.|125000|1.38|30.0|1.37|
|Confined lunatics|2500|0.03|30.0|1.37|
|Naval officers|35000|0.39|29.8|1.36|
|Common soldiers|121985|1.35|29.0|1.32|
|Military officers|65320|0.72|27.8|1.27|
|Education of Youth|120000|1.33|25.0|1.14|
|Lesser clergymen|50000|0.55|24.0|1.09|
|Dissenting clergy, itinerants|12500|0.14|24.0|1.09|
|Farmers|960000|10.60|20.0|0.91|
|Innkeepers and publicans|250000|2.76|20.0|0.91|
|Freeholders, lesser|600000|6.63|18.0|0.82|
|Clerks and shopmen|300000|3.31|15.0|0.68|
|Artisans, mechanics, laborers|2005767|22.16|12.2|0.56|
|Vagrants|175218|1.94|10.0|0.46|
|Laborers in mines, canals|180000|1.99|8.9|0.41|
|Hawkers, pedlars, duffers|4000|0.04|8.0|0.36|
|Laborers in husbandry|1530000|16.90|6.9|0.31|
|Persons imprisoned for debt|10000|0.11|6.0|0.27|
|Paupers|1040716|11.50|2.5|0.11|
|_Total_|_9053170_|_100_|_21.93_|_1_|

52
# **1.** **<mark>Introduction</mark>**

<mark>This paper builds upon two literatures of high policy relevance:</mark> _<mark>teacher quality</mark>_ <mark>and</mark> _<mark>value added of higher education.</mark>_ <mark>It does so by providing evidence for Colombia on the difference in basic academic competencies between students in education majors and students in other majors. To do so, we compare test score-rankings between these two groups of students using a n</mark> ovel panel data set that combines two standardized tests, one before and one near the end of college studies. <mark>We measure test score-rankings using the z-scores of subjects assessed in both tests and compare changes in the relative position of students in the distributions of academic skills. We tackle the problem of endogeneity in the decision of becoming a teacher using instrumental variables and tackle the problem of differences in the distribution of observable characteristics between education majors and their peers using non-parametric matching.</mark> <mark>A growing body of literature establishes that teachers are essential for a high-quality educational system (Goldhaber and Brewer, 1997; Hanushek, 2002; Eide et al., 2004; Rockoff, 2004; Rivkin et al., 2005; Hanushek and Rivkin, 2006; Clotfelter et. al., 2007; Kukla-Acevedo, 2009; De Paola, 2009). More recently, Chetty et al. (2013a and 2013b) find that, after controlling for sociodemographic characteristics, those students who had high-quality teachers during their school years are more likely to attend higher education, to go to a better university and to obtain higher labor earnings. Nonetheless, although the literature has extensively addressed the role of the quality of teachers on social and academic outcomes, we know little about the role of higher education on the production of those teachers.</mark> <mark>Evidence from successful educational systems (e.g., Finland, Singapore, the Republic of Korea and China) highlights the importance of teachers and their formation. In Finland, for instance, only the best and brightest manage to become teachers after demanding undergrad and master’s programs (OECD, 2011; Sahlberg, 2011). This has been claimed as an important stepping-stone for their impressive improvement between the 1960s and the early 21st century. From having educational</mark> 2