rafmacalaba/datause-encoder-data huggingface dataset:
third of the urban population in the area is employed in the informal sector. 31 Accurate estimates of the wage-employment elasticity in the formal sector of the economy are not easy to obtain. The most recent evidence appears to indicate values in the range between 0.2 and 0.5 (Bourguignon [1988] and Solimano (1987]).32 Based on these parameters, the PTTE would be positive (a reduction in the minimum wage worsens the informal sector's terms of trade) for values of the income- elasticity of the demand for the informal sector good (eyf) ranging between 0.65 and 1.63. Lustig [1979] has used Mexican data to estimate income-elasticities for traditional manufacturing goods (clothing, furniture, etc.) and services by low income families that range between 1.06 and 2.19. Tokman (1978] suggests that the income-elasticity for goods produced in the informal sector in Chile and Mexico is close to 2.0. Lluch et al (1977], have estimated income-elasticities of demand by low income households in a number of Latin American cities. The elasticities corresponding to clothing (the other items

> 31 According to PREALC (1987] 30.7% of the urban population, in a sample of nine Latin American countries, worked in the informal sector in 1985.

> 32 Solimano (1987] estimates the wage-employment elasticities for the manufacturing sector in Brazil and Chile under three macroeconomic regimes: Keynesian, Forniz;n Exchange Constraint, and Classical. The estimates corresponding to the first two regimes are either positive or close to zero. Under the classical regime the elasticity takes values within the above mentioned range.

35
# **APPENDIX D: BURKINA FASO**<sup>**18**</sup>

Cotton was introduced in Burkina Faso towards the end of the colonial period, and the country is now SSA’s largest cotton producer; its seed cotton output av‐ eraged half a million tons during the past three seasons (about one percent of world output) and exceeded 700,000 tons in 2006/07. Cotton is the country’s most important export item, accounting for more than half of its total merchandise trade and about 5 percent of GDP.

Development of the sector was originally the responsibility of the French state‐owned company CFDT, which remained in charge until 1975 when it was replaced first by a joint venture between the government and CFDT and in 1979 by a new cotton parastatal, SOFITEX ( _Sociètè Burkinabé des Fibres et Textiles_ ).

Reforms of the cotton sector were first considered in 1991 when the man‐ agement responsibilities of the sector were transferred to growers and SOFITEX. In 1998, the government further reduced its stake in the cotton company by transferring 30 percent of its shares to a producer organization and 30 percent to CFDT’s successor DAGRIS ( _Société du Développement Agricole du Sud_ ).<sup>**19**</sup> In 1999 a committee was formed to coordinate the functions of the cotton company and the producers’ organization for activities such as the determination of farmgate pric‐ es and the management of the research program. Finally, the sector was opened to (limited) competition when two private cotton companies received permission to enter the market. These companies—SOCOMA ( _Société Cotonniére du Gurma_ ) and _Faso Coton_ —were given an eight‐year exclusive right to buy cotton from spe‐ cific zones which produce about 15 percent of the country’s cotton output. In 2006, an umbrella organization, the Burkina Cotton Interprofessional Association (AICB, _Association Interprofessionnelle du Coton du Burkina_ ), which represents cot‐ ton farmers and ginners, was created to coordinate actions of all three cotton companies.

In response to the CFAf devaluation by 100 percent in 1994, the policy re‐ forms, and the high prices received by cotton growers induced considerable supply response, with seed cotton output reaching a record high of 713,000 tons in the 2004/05 season, a two‐fold increase from a decade earlier.

Subsequently, however, the unsustainably high prices paid to cotton growers due to the deterioration of the world cotton market imposed fiscal pres‐ sure on the cotton companies (and the government), in turn causing production

> **18** Most of the information in this Appendix is based on Zanfongnon (2009).

> **19** In January 2008 DAGRIS was purchased by the _Advens Group_ (51 percent) and CMA‐CGM (49 percent) and was renamed to _Geocoton_ .

**_Page 45_**
# **Table 6B: Bank Ownership, Costs, Employment, Wages and Provisions (Unweighted)**

Regressions are estimated via OLS at bank level for the year 2001 to 2008. Robust t statistics in brackets and bank level clustered t statistics in parenthesis. All regressions control for time and country dummies. ***, ** and * represent significance at 1, 5 and 10% level respectively.

||8|9|10|11|12|13|
|---|---|---|---|---|---|---|
||**Overheads**<br>**to Assets**|**Personal Expenses**<br>**to Assets**|**Employment**<br>**to Assets**|**Wage (Personal**<br>**Expense)**|**Wage**<br>**(Overheads)**|**Loan Loss**<br>**Provisions to**<br>**Gross Loans**|
|**Dummy Public Ownership**|**0.364**|**0.24**|**0.035**|**-9.161**|**-18.476**|**1.26**|
||[3.61]***|[3.29]***|[7.17]***|[5.84]***|[5.79]***|[2.84]***|
||(2.17)**|(1.88)*|(4.38)***|(3.58)***|(4.40)***|(2.12)**|
|**Dummy Foreign Ownership**|**0.268**|**0.194**|**0.008**|**-1.527**|**-2.8**|**-0.057**|
||[3.51]***|[3.43]***|[2.01]**|[1.45]|[1.29]|[0.27]|
||(2.05)**|(1.80)*|(0.98)|(0.80)|(0.73)|(0.19)|
|**Dummy Listed**|**0.25**|**0.036**|**0.004**|**1.729**|**7.038**|**-0.32**|
||[3.05]***|[0.61]|[1.15]|[1.11]|[2.56]**|[1.54]|
||(1.95)*|(0.42)|(0.72)|(0.70)|(1.89)*|(1.25)|
|**Lag Total Assets (Log)**|**-0.186**|**-0.117**|**-0.01**|**4.336**|**8.737**|**-0.031**|
||[5.80]***|[5.39]***|[10.09]***|[9.45]***|[10.02]***|[0.39]|
||(3.75)***|(3.29)***|(5.90)***|(5.57)***|(7.09)***|(0.36)|
|**Non-Interest Income to Assets**|**0.275**|**0.118**|**0.007**|**2.189**|**1.76**|**0.58**|
||[6.02]***|[3.47]***|[2.54]**|[2.81]***|[1.18]|[3.25]***|
||(5.00)***|(2.46)**|(1.96)*|(2.99)***|(1.16)|(2.79)***|
|**Deposits to Assets**|**-0.012**|**-0.0004**|**0.0002**|**-0.217**|**-0.69**|**0.044**|
||[1.82]*|[0.08]|[1.15]|[3.97]***|[4.85]***|[3.45]***|
||(1.53)|(0.07)|(0.73)|(2.30)**|(3.37)***|(2.98)***|
|**Loans to Assets**|**0.006**|**0.004**|**0.0002**|**0.086**|**0.02**|**-0.017**|
||[2.07]**|[1.81]*|[1.82]*|[2.56]**|[0.28]|[2.03]**|
||(1.45)|(1.23)|(1.02)|(1.44)|(0.20)|(1.63)|
|**Observations**|575|384|387|270|384|489|
|**R-squared**|0.4|0.5|0.55|0.71|0.66|0.38|
|**Number of Countries**|9|9|9|9|9|9|
|**Number of Banks**|120|92|102|77|104|115|

41
70 exhaust. One of the unique advantages of catalysts is their ability to selectively eliminate some of the more harmful compounds in vehicle exhaust such as aldehydes, reactive hydrocarbons, and polynuclear hydrocarbons.

- Three-way Catalysts: So called because of their ability to lower HC, CO and NOx levels simultaneously, they were first introduced in the United States in 1977 by Volvo and subsequently became widely used when the U.S. NOx standard was made more stringent (1.0 grams per mile) in 1981. For three-way catalysts to work effectively, it is necessary to control air/fuel mixtures much more precisely than is needed for oxidation catalyst systems. As a result, three-way systems have indirectly fostered improved air/fuel management systems such as advanced carburetors, throttle body fuel injection, and electronic controls. Three-way catalyst systems also are sensitive to the use of leaded gasoline. An occasional tankful of leaded gasoline will have a small but lasting effect on the level of emitted pollutants.

- Thermal Reactors: They are well insulated vessels with internal baffling to allow several passes of the exhaust gas to maintain high temperature and extend the residence time. They thus promote oxidation of CO and HC emitted from the engine. To maintain high temperatures, they are often used in conjunction with exhaust port liners which reduce heat losses. In spite of this, a major problem with these systems is the difficulty in maintaining exhaust temperatures sufficiently high to promote combustion. Measures to increase exhaust temperatures such as retarded ignition, richer air/fuel ratios or valve timing delays result in increased fuel consumption. Because of these problems, systems of this type have gradually faded from use.

# Lean Burn

At one point, it was believed that combustion advances, especially lean burn, might ultimately allow the catalyst to be eliminated. Recent experience, however, indicates that low HC and NOx levels are not attainable across the range of normal driving conditions through the use of advanced combustion technology alone. At least an oxidation catalyst is needed to control HC emissions. Also, under higher speeds and higher load driving modes, such as those reflected in the recently agreed upon European extra urban driving cycle, supplemental NOx control may also be needed. Recent European studies under high speed driving conditions have demonstrated that three-way catalysts are necessary to minimize NOx emissions. In addition, as concern with toxic pollution increases, it appears that lean burn engines would not be as effective as conventional catalyst-equipped engines in lowering polynuclear organics and other noxious compounds from motor vehicle exhausts unless they are also equipped with catalytic converters.
38 clear what caused the productivity of educated workers to increase initially, pushing up the private returns to schooling. Until such a forcing variable is isolated and measured, it is hard to confirm how it might have shifted the aggrogate demnd for educated labor **and thereby** affected fertility. But recent surveys provide a sounder basis for masuring contemporary private returns to human capital for girls and **boys. These measures** of returns for females and males may help to explain the patterns of human capital investmnts of parents in their children, and to forecast hov the pivotal differences in wage rates between women and men will evolve in the future in response to changes in human capital lnvestments.

**Wage** dlfferentials between groups with different amounts of schooling are used to approximate private returns to sche*ling. Schooling may operate as a screen to ldentify innately **more** productive individuals, or it may, as assumd in the himan capital literature, inpart disciplined work habits and productive skills. The private returns associated with schooling, plus any consumer benefits, signal the rewards that individuals and families can expe4t to receive by investing in schooling, regardless of whether the wage differentials are due to screening or acquired sklls (Schultz, 1988.).

# 4. Investments in Humn Capital and **Rates** of Return

Labor productivity can be approximted by **the wage** rate people are paid. According to this measure, women **are** on **average** less productive than mn in many labor mrkets (e.g. Schultz, 1986). **However,** comparisons of **wonmn's** dad sen's wages may not be representativo of the productive value of the time of all men and women for several reasons: 1. Many men and two-thirds of women do not report **a** wage because they do
# **<u>Tables</u>**

# **Table 1.** **<u>Summary Statistics by Product Innovation Status</u>**

||||**Prod**|**uct Innov**|**ation Stat**<br>|**us**|||
|---|---|---|---|---|---|---|---|---|
|||**Yes**|||**No**||**Both**|**Yes/Both**<br>**(%)**|
||Obs.|Mean|Std. Dev.|Obs.|Mean|Std. Dev.|Total Obs.||
|**Variable:**|||||||||
|Dummy=1 if firm has invested in R&D|12257|29.44|45.58|13549|13.55|34.23|25806|47.5|
|Dummy=1 if firm uses licensed technology|12257|10.28|30.37|13549|5.15|22.11|25806|47.5|
|logof total number of lastyear's employees|12257|4.126|1.577|13549|3.710|1.548|25806|47.5|
|Dummy=1 if foreign owned(>=10 of assets)|12257|14.89|35.60|13549|11.27|31.62|25806|47.5|
|Average capacityutilization over the lastyear|12257|74.74|20.48|13549|72.97|22.11|25806|47.5|
|Dummy=1 if exporter(exports over sales >=10)|12257|38.51|48.66|13549|28.69|45.23|25806|47.5|
|log average years of education of enterprise's labor<br>force(imputed)|12257|2.305|0.564|13549|2.232|0.714|25806|47.5|
|log average years of education of enterprise's labor<br>force|9150|2.303|0.652|9437|2.229|0.855|18587|49.2|
|Incidence of core NTBs by industry ( of tariff lines<br>subject to NTBs;imputed)|12257|7.28|14.10|13549|6.45|14.47|25806|47.5|
|Incidence of core NTBs by industry ( of tariff lines<br>subject to NTBs)|8940|9.68|13.77|9813|8.88|14.03|18753|47.7|
|Index of applied tariffs and their dispersion<br>(imputed)|12257|-0.127|0.506|13549|-0.022|0.721|25806|47.5|
|Index of applied tariffs and their dispersion|9920|-0.153|0.534|10841|-0.027|0.787|20761|47.8|
|Note: All differences in the means across the two gro<br>Obs. = number of observations<br>Source: ICSand BEEPs surveys,www.enterprisesurv|ups are sta<br>eys.org<br>.|tistically s|ignificant.||||||

18
[iOLICY RESEARCH WORKING PAPER 2829

# Summary findings

Minot and Baulch combine household survey and census data to construct a provincial poverty map of Vietnam and evaluate the accuracy of geographically targeted antipoverty programs. First, they estimate per capita expenditure as a function of selected household and geographic characteristics using the 1998 Vietnam Living Standards Survey. Next, they combine the results with data on the same household characteristics from the 1-999 census to estimate the incidence of poverty in each province. The results show that rural poverty is concentrated in 10 provinces in the Northern Uplands, 2 provinces in the Central Highlands, and 2 provinces in the Central Coast.

The authors use Receiver Operating Characteristics curves to evaluate the effectiveness of geographic targeting. The results show that the existing poor communes system excludes large numbers of poor people, but there is potential for sharpening poverty targeting using a snmall number of easy-to-measure household characteristics.

This paper is a joint product of Macroeconomics and Growth, Development Research Group, and the International Food Policy Research Institute. Copies of the paper are available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Rina Bonfield, room MC3-354, telephone 202-473-1248, fax 202-522-3518, email address I il:.u ,1 l Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at n.minot@cgiar.org or b.baulch@lds.ac.uk. April 2002. (43 pages) _._

> _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 counitries they represent._

Produced by the Policy Research Dissemination Center
# **Table 1:  Determinants of Taxable Capacity**

_<u>Panel A (population growth used as proxy for demographic characteristic)</u>_

|**Tax/GDP**||**1994-2003**|||**1994-1998**|||**1999-2003**||
|---|---|---|---|---|---|---|---|---|---|
||(Eq. 1)|(Eq. 2)|(Eq. 3)|(Eq. 1)|(Eq. 2)|(Eq. 3)|(Eq. 1)|(Eq. 2)|(Eq. 3)|
|GDP per capita1|2.273|2.171|2.051|0.094|0.146|-0.233|3.493|2.730|2.647|
||(8.06)**|(5.46)**|(5.15)**|(1.26)|(0.16)|(0.25)|(8.08)**|(4.79)**|(5.00)**|
|Population Growth|-2.157|-2.662|-2.560|-2.525|-2.647|-2.753|-2.37|-2.903|-2.767|
||(8.60)**|(10.64)**|(10.63)**|(7.28)**|(5.50)**|(5.75)**|(6.40)**|(7.29)**|(7.50)**|
|Trade Openness|0.059|0.046|0.046|0.062|0.040|0.038|0.048|0.039|0.040|
||(9.11)**|(7.78)**|(7.70)**|(5.23)**|(3.74)**|(3.64)**|(5.43)**|(4.63)**|(4.58)**|
|Agriculture Value<br>Added|-0.185|-0.129|-0.118|-0.196|-0.155|-0.154|-0.212|-0.141|-0.120|
||(9.18)**|(5.79)**|(5.28)**|(6.75)**|(4.30)**|(4.34)**|(6.42)**|(3.93)**|(3.37)**|
|Corruption||-0.612|||-0.693|||-0.694||
|||(4.04)**|||(2.61)**|||(2.81)**||
|Bureaucracy|||-0.560|||-0.638|||-0.666|
||||(4.59)**|||(2.87)**|||(3.60)**|
|Constant|19.559|17.321|17.093|21.053|18.185|19.015|20.341|17.913|16.976|
||(22.65)**|(15.84)**|(15.71)**|(12.96)**|(7.72)**|(9.25)**|(16.82)**|(11.38)**|(10.58)**|
|Observations|884|724|724|329|252|252|380|333|333|
|R-squared|0.49|0.52|0.52|0.42|0.39|0.40|0.56|0.58|0.58|

_<u>Panel B (Age dependency ratio used as proxy for demographic characteristic)</u>_

|**Tax/GDP**||**1994-2003**|||**1994-1998**|||**1999-2003**||
|---|---|---|---|---|---|---|---|---|---|
|GDP per capita1|(Eq. 1)<br>3.037|(Eq. 2)<br>2.731|(Eq. 3)<br>2.614|(Eq. 1)<br>1.621|(Eq. 2)<br>1.343|(Eq. 3)<br>1.402|(Eq. 1)<br>3.535|(Eq. 2)<br>2.680|(Eq. 3)<br>2.620|
||(8.39)**|(6.46)**|(6.14)**|(1.97)*|(1.37)|(1.50)|(7.57)**|(4.40)**|(4.33)**|
|Age Dependency Ratio|-5.337|-8.593|-7.315|-6.859|-6.114|-5.815|-6.301|-12.680|-10.175|
||(2.75)**|(4.34)**|(3.80)**|(2.11)*|(1.84)|(1.71)|(2.05)*|(3.84)**|(3.24)**|
|Trade Openness|0.058|0.044|0.045|0.059|0.028|0.028|0.049|0.042|0.044|
||(7.80)**|(6.21)**|(6.22)**|(4.17)**|(2.30)*|(2.23)*|(4.84)**|(4.15)**|(4.12)**|
|Agriculture Value<br>Added|-0.184|-0.134|-0.133|-0.163|-0.127|-0.134|-0.236|-0.142|-0.139|
||(7.60)**|(4.90)**|(4.91)**|(4.85)**|(3.16)**|(3.20)**|(5.79)**|(3.19)**|(3.23)**|
|Corruption||-0.451|||-0.647|||-0.693||
|||(2.76)**|||(2.18)*|||(2.64)**||
|Bureaucracy|||-0.445|||-0.429|||-0.661|
||||(3.37)**|||(1.93)|||(2.95)**|
|Constant|20.055|19.967|18.963|21.310|18.109|19.341|21.808|22.291|20.158|
||(13.15)**|(12.84)**|(11.35)**|(7.24)**|(5.51)**|(6.13)**|(10.14)**|(9.22)**|(7.49)**|
|Observations|868|724|724|323|252|252|374|333|333|
|R-squared|0.41|0.44|0.44|0.30|0.29|0.28|0.50|0.52|0.52|

Robust t statistics in parentheses 1GDP per capita*10,000 Note: The dependent variable is Taxable capacity. Estimation technique is OLS.

- 11 -
Table 2: Self-interviewing (SI) Screening Questions

||(1)<br>Mean (=|(2)<br>1 if yes)|
|---|---|---|
||Malawi|Liberia|
|_Questions for which answer should be yes:_|||
|1. Are you a woman?|0.95|0.98|
|2. Do you live in [the county/district where the survey is being conducted]?|0.91|0.93|
|3. In the past week, did you sleep, during day or night?|0.78|0.86|
|4. In the past year, did it rain in your village one time or more?|0.83|0.85|
|5. Have you heard about Coronavirus?|0.93|0.94|
|_Summary measures for “passing” screening_|||
|=1 if YES to all fve questions|0.62|0.70|
|Observations|1,737|1,261|

Note: These five questions were asked in ACASI to everyone included in ACASI measurement experiment.

20
# 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. AICD provides a 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 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 AICD’s first phase 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. 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.‖ The World Bank has implemented the AICD with the guidance of a steering committee that represents the African Union, the New Partnership for Africa’s Development (NEPAD), Africa’s regional economic
symptoms of malaria, a disease that kills 63,000-96,000 Tanzanians each year. A public sector doctor in India asks one (and only one) question in the average interaction—“What’s wrong with you?” In Paraguay, the amount of time a doctor spends with a patient has nothing to do with the severity of the patient’s illness. We present systematic evidence in the paper to show that these isolated facts represent common patterns. We interpret the vignettes as a measure of what health care providers know, while direct observation shows what they actually do.

By looking at what doctors know and what they do, we are able to show four things. First, the quality of care in low-income countries as measured by what doctors know is very low. Second, the problem of low competence is compounded due to low effort—doctors provide lower standards of care for their patients than they know how to provide. Third, the poor are particularly disadvantaged both because they have access only to doctors with low level levels of competence and because less competent doctors put in less effort. Fourth, oft-tried measures such as training doctors are unlikely to work—because doctors exert very little effort, three years of medical school in Tanzania results in only a 1 percentage point increase in the probability of a correct diagnosis.

Our evidence on health care quality in low-income countries is drawn primarily from studies in four countries: Tanzania, India, Indonesia and Paraguay. Table 1 summarizes the methods used and the samples in these four countries. These results for these four countries must obviously be viewed as preliminary, and in the conclusion of the paper, we will outline some of the important theoretical and empirical questions yet to be investigated. However, the available evidence strongly suggests that if the enormous burden that poor health imposes on most of he world’s poor is to be reduced through medical care, it will be critical to look beyond the presence (or absence) of physical components of health care like proximity to facilities and to examine the 5