rafmacalaba/datause-encoder-data huggingface dataset:
graduates continue to come from urban centers and nuclear families. Most of the graduates have been able to escape from poverty in the past although recently this trend has been changing.

The rate of return to schooling increases with higher education and job experience. It is observable that in the wage sector primary education without skills has not been very rewarding. The return to an additional year of schooling has been varying between 4-6 percent in Ghana which is quite high for a Sub-Saharan African country. However, it is very important to note that the sectoral wage differences and public sector does reward formal sector better on average. Also, there has been the concern that the quality of education is declining. This is an issue which needs further investigation and might be useful in understanding the relevance of present education system for emerging new employment opportunities in Ghana. The returns to education analysis based on wage workers show that private and social returns to education are higher for primary than secondary or post-secondary. However, it is worth noting that higher education carries the larger amount of public subsidization compared to primary and might be useful for designing educational reform and policies in Ghana. This again is an issue which needs further investigation. Also, the rate of returns to education from selfemployment might have a different story to tell and one needs to explore this before making broad inferences about education-employment mismatch (Vijverberg, 1995).

The present overview paper has elaborated some interesting hypothesis and broad relationships which can be further analyzed and used for policy design and sector investment. The current state of knowledge although useful does not give robust results to make causal links. Also, the GLSS data itself does not lend itself to meaningful analysis and one needs to see other sources of information and methodologies. Female education, female employment and self-employment sector are currently very weak in terms of causal links and until one has some firm findings it is not easy to be useful for policy design or even poverty alleviation in this country. A country with more than one third of its population below poverty level and almost 70 percent in agriculture and selfemployment, any useful analysis has to give due weight to the various sectoral compositions of employment in the economy. This has to be a high priority in the future analytical work on labor markets and poverty in Ghana.

48
**Table 2: Impact of Parents' Average Years of Schooling on Children's Years of Schooling**

|**Adult Children**|**Parents' edu**|**Parents'edu**<br>**+ coastal**<br>**dummy**|**Parents' edu**<br>**+province**<br>**fixed effects**|**Parents' edu +**<br>**county fixed**<br>**effects**|**Parents' edu +**<br>**county fixed**<br>**effect + HH**<br>**wealth**|**Parents'**<br>**edu + HH**<br>**wealth**|**Parents' edu +**<br>**county fixed**<br>**effect + HH**<br>**wealth + other**<br>**controls**|
|---|---|---|---|---|---|---|---|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|**(7)**|
|_1988‐‐daughters_||||||||
|Marginal Effect|**0.37**|**0.37**|**0.33**|**0.28**|**0.25**|**0.34**|**0.24**|
|T statistics|18.35|18.32|14.02|10.49|7.64|13.49|7.37|
|_1988‐‐sons_||||||||
|Marginal Effect|**0.23**|**0.22**|**0.21**|**0.17**|**0.14**|**0.18**|**0.14**|
|T statistics|11.92|11.71|9.61|6.97|4.85|6.42|4.81|
|_2002‐‐daughters_||||||||
|Marginal Effect|**0.32**|**0.32**|**0.28**|**0.23**|**0.20**|**0.30**|**0.20**|
|T statistics|16.09|15.83|13.44|11.01|8.83|13.89|8.85|
|_2002‐‐sons_||||||||
|Marginal Effect|**0.33**|**0.32**|**0.28**|**0.27**|**0.22**|**0.25**|**0.22**|
|T statistics|20.86|20.38|17.41|15.64|11.43|14.21|11.37|

NOTES

1. Adult children >= 1967; the samples are: 2057 (1988, daughters), 1884 (1988, sons), 2264 (2002, daughters), 3616 (2002, sons);

2. Parents' average years of schooling is the average of mother's and father's years of schooling; 3. "HH wealth" stands for indicators of household wealth: includes mother's age, father's age, parents' occupation dummy

- (non‐farm=1), communist party member dummy, ethnic minority dummy, number of grandparents alive, female household head dummy, irrigation dummy,  land, house value and electricity dummy;

4. "Other controls " include topography and coastal location dummy; the number of adult male and female children in the household, old revolutionary base dummy, ethnic minority region, impoverished region dummy, and suburb of big city and adult child's age and age 51
## CONTENTS

# Page

|1.|Introd|uction<br>.........................................................|1|
|---|---|---|---|
|2.|Labor|market activty, income formation and welfare .....<br>...................<br>.|4|
||2.1|Measurement and decompositlon of inequaRty ..........................<br>.|4|
||2.2|Inequality In distributions of hours of work for||
|||females and males<br>.................................|7|
||2.3|Inequality In distributions of consumption for households ........<br>..|..........<br>10|
||2.4|Inequality In distributions of per-capita household consumption ......<br>..|........<br>14|
|3.|The e|conometric framework<br>..............................................|16|
||3.1|Theoretical model<br>.......................<br>........................|16|
||3.2|Model specification .................................<br>....|.....<br>19|
|4.|Summ|ary statistics and parameter estimates ...................................|22|
|5.|Policy|simulation results for Uma ..........................................|28|
||5.1|Wage effects<br>.........................<br>.........................|29|
||5.2|Education effects<br>.......................<br>........................|32|
|6.|Conc|lusion<br>.........................................|................<br>35|
|Refer|ences|.........................<br>I..............I........37||
|Appe|ndix 1.|Estimates of Inequality based on the Gini-coefficient.|38|
|Appe|ndix 2.|Deflnhtions of main variables<br>..|39|
|Appe|ndix3.|Figures relating to observed and simulated distributions of key||
|||variables<br>..|41|
**Table 5:  Avoided Costs of Acute Respiratory Illness per Year, per Family by Technology**

||Low|Mid|High|
|---|---|---|---|
|_Technology_|_Avoided_<br>_relative t_|_illness cost per_<br>_o traditional wood_|_family per year_<br>_stove ($/yr)_|
|Traditional wood|NA|NA|NA|
|Improved wood|0.08|15|252|
|Traditional charcoal|0|7.5|144|
|Improved charcoal|0.08|15|252|
|Kerosene|0.36|22.5|270|
|Propane|0.36|22.5|270|
|Electric|0.36|22.5|270|

Calculations based on Jeuland and Pattanayak (2012).

# **6. Concluding Remarks**

This paper discusses some of the critical elements associated with assessing preferences for and social opportunity costs of alternative cooking energy and technology choices, and some of the key challenges in making those assessments. A significant number of socioeconomic, demographic, behavioral, social, and cultural factors influence preferences. If not effectively addressed, some of these preference determinants become barriers to cleaner cooking adoption. For example, while the cost of modern cookstoves or energy sources can be a barrier for lowincome households, the uptake of less expensive ICS can be low even when heavily subsidized. In these cases, stove designs that are unreliable or inconvenient to use, or do not provide customary food tastes, are more significant considerations.

The opportunity cost of cooking energy use likewise depends on a considerable amount of location- and context-specific data on fuel and stove costs and quality, and the opportunity cost of household time, as well as adjustments for a range of market and policy failures affecting cooking energy incentives and choices. In particular, to assess the value of avoided health damages, information is needed on baseline disease incidence, cost of illness and the value of reduced mortality risk, as well as individual attitudes toward such health risks, while information on ecosystem damages and their economic consequences is needed to assess environmental impacts.

What are some priorities among these topics for attention in further empirical work? In a sensitivity analysis examining the principal influences on costs across fuels and stove technologies, Jeuland and Pattanayak (2012) find that for improved stoves burning wood, the key factors influencing cost are the frequency with which improved stoves are used and the cooking time required for each cooking event. If a stove is used infrequently and as a consequence is not used very efficiently, the opportunity cost will be greater than a traditional wood stove. For improved charcoal stoves, time efficiency also matters, but the main drivers of overall cost are the cost of charcoal and improvement in energy efficiency relative to traditional stoves. One implication of these observations is that better understanding stove adoption and use decisions in the field is important for reducing uncertainty about social costs.

**12 |** P a g e
® total quantity of TRI chemicals released or transferred; ® total quantity of metals released or transferred.

A separate figure was calculated for metals and their compounds because of the specific risks associated with their accumulation in the environment and concentration as they are passed up the food-chain. The TRI metals are listed in the Annex and follow the same definition as those in "Toxics in the Community" (1989), published by the US EPA.

With each facility assigned a 4-digit ISIC code and six TRI release and transfer parameters, sectoral totals for each parameter were calculated by summing across all facilities falling within the same ISIC category.

# 4.2. Scaling by Shipment Value to Give Pollution Intensity

The final element in the creation of risk-weighted measures of pollutant intensity was the scaling of all six TRI parameters by shipment (output)value. This was achieved by summing facility shipment values within the 4-digit ISIC sectors in the matched TRI-LRD dataset, and dividing the result into the TRI totals. This produced the Upper Bound (UB) estimates discussed in the previous section. Of the six pollutant intensity estimates for each sector, four are dimensioned as risk-weighted pounds of TRI chemicals released and transferred per $1000 of gross output, and two are unweighted pounds of TRI chemicals per $1000 of output. It should be noted that this set of six sectoral pollutant intensity indices is probably unique. Not only is the TRI 21
thirds of a standard deviation), and recalled 1.3 more digits (.85 of a standard deviation).<sup>9</sup> On the Raven and financial literacy tests, the gaps were smaller, 0.4 questions or onefifth of a standard deviation for Raven and 0.14 or one-third of a standard error for financial literacy. The table also shows the first principal component of the Raven, digitspan and CRT tests.

We find no difference between wage workers and own account workers in the Raven, CRT or financial literacy tests. Wage workers are able to recall more digits, a difference which is statistically significant at the .10 level but which represents only about a third of a standard deviation. They also have 1.1 more years of schooling on average. For ability measures, then, score one for Tokman’s view. Own account workers appear to be closer to wage workers than to SME owners. These findings are also consistent with the Lucas framework with the more able self employed owning larger enterprises.

We next examine differences in parental background. (See Table 2.) For parent’s schooling, the pattern is the same as for ability. The parents of SME owners have higher schooling than the parents of wage workers, whose parents have insignificantly higher schooling than the parents of own account workers. The gap between SME owners and own account workers is 1 year for either parent, about one-third of a standard deviation. However, the parents of both SME owners and own account workers were much more likely to have been self employed when the respondent was 12 years of age than were the parents of wage workers.

That mothers of the own account workers were more likely and the fathers were less likely to be self employed than for SME owners may suggest differences in income levels during childhood between these two groups. Table 3 contains other indications of childhood income, from a series of questions we asked each of the three groups about their life when they were 12 years of age. We first asked what percentage of the respondents neighbors “did not have enough to eat or sometimes got by with difficulty.” We also asked what percentage of the respondent’s childhood cohort sat for the O-level

> 9 Even SME owners do not compare favorably with other groups on CRT scores. Frederick reports averages for students at nine universities in the United States ranging from 2.18 (MIT) to 0.59 (University of Toledo). A sample of first and fourth year undergraduates at the University of Peradeniya averaged 0.94 correct responses, with engineering students doing significantly better than students in the arts and humanities (1.49 correct vs. 0.38 correct on average).
Policy Research Working Paper 8849

# **Abstract**

In many low-income countries, teachers do not master the subject they are teaching, and children learn little while - attending school. Using unique data from nationally rep resentative surveys of schools in seven Sub-Saharan African countries, this paper proposes a methodology to assess the effect of teacher subject content knowledge on student learning when panel data on students are not available. The paper shows that data on test scores of the student’s current and the previous year’s teachers, and knowledge of the correlation structure of teacher knowledge across time and grades, allow estimating two structural parameters of interest: the contemporaneous effect of teacher content knowledge, and the extent of fade out of teacher impacts in earlier grades. The paper uses these structural estimates to understand the magnitude of teacher effects and simulate the impacts of various policy reforms. Shortfalls in teachers’ content knowledge account for 30 percent of the shortfall in learning relative to the curriculum, and about 20 percent of the cross-country difference in learning in the sample. Assigning more students to better teachers would potentially lead to substantial cost-savings, even if there are negative class-size effects. Ensuring that all incoming teachers have the officially mandated effective years of education, along with increasing the time spent on teaching to the officially mandated schedule, could almost double student learning within the next 30 years.

This paper is a product of the Development Research Group, Development Economics and the Education Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank. org/prwp. The authors may be contacted at dfilmer@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
communities on rivers, open defecation by any household member is 6 percentage points lower in treatment communities than in control communities. The declines in open defecation are driven by declines in open defecation by people who have access to a toilet. Twenty-four percent of households that have access to a toilet reported sometimes defecating in the open; this figure is 4 percentage points lower in treatment communities than in control communities (26 versus 22 percent).

# _8.3 Handwashing_

Improvements in good hygiene practices, especially handwashing with soap at critical times (before handling food and after defecating) can also help prevent childhood diarrhea (Ejemot et al., 2008) and other illnesses such as acute lower respiratory infection (Aiello et al., 2008). Although TSSM’s message focuses on improvements in sanitation as the main vehicle for improving child health, the benefits of handwashing with soap were also discussed. The endline survey collected information on households’ handwashing facilities and behavior.

Table 8a shows that almost 100 percent of respondents reported that they wash their hands after defecating. Of these, 67 percent reported having a handwashing station where they usually wash their hands. Observation by the survey enumerators of the handwashing station revealed that water was available at about 96 percent of the stations, and soap and water were available at about 58 percent of stations. These figures had increased from the baseline but there was no significant difference between treatment and control communities.

# _8.4 Drinking Water_

Drinking water quality is another important determinant of child diarrhea and parasitic infection. The WHO defines improved drinking water sources as tap water inside the house and outside the house, protected dug well, boreholes (includes tube wells), protected springs, and rainwater collection (WHO, 2012). Table 8a shows that a large majority (more than 87 percent) of households had access to improved sources of water in both the baseline and the endline. There is no difference between treatment and control communities with respect to access to improved sources of drinking water. Note that we do not expect differences in drinking water outcomes given that this was not part of the treatment; however, it is interesting to note that differences in drinking water cannot be driving the health impacts we observe.

# _8.5 Attitudinal Change_

TSSM seeks to change sanitation-related behavior via attitudinal change. The surveys collected information on people’s attitudes about sanitation and the extent of their knowledge of the causes of diarrhea. Table 11 compares attitudes to open defecation in treatment and control communities. These questions were asked only in the endline survey. The vast majority of households (96.1 percent) agreed or strongly agreed with the statement that having a toilet benefits the community 18
# I. INTRODUCTION

# **A** System of Conversion Factors and Weights

**1.** This paper discusses issues and procedures to develop a time series of alternative conversion factors with corresponding weights for use in converting and presenting macrolevel country statistics in a common unit of account, e.g. the **U.S.** dollar. The potentially significant operational implications underlying the Bank's estimates of per capita **GNP** for the Operational Guidelines represent important considerations in the search for an appropriate conversion factor, notably for use in the Atlas. The discussion is set within the context of IEC's present procedures for choosing exchange rates for use in the Atlas and the Operational Guidelines.' Departures from the use of an exchange rate that is not closely related to the rate at which international transactions actually t **'** place are tolerated over a wide range. However, when the deviation between the official rate and the rates effectively applied to foreign transactions are egregiously large, any conversion of principal macroeconomic indicators based on the official exchange rate are rendered meaningless.

2. The Bank's practice in choosing conversion factors involves IEC staff systematically reviewing and occasionally estimating alternative conversion factors, often involving dialogue with country operational staff. When "conditions are egregiously different from those

**1A** review of the methodological issues, including choosing appropriate conversion factors, relevant to the calculation of per capita **GNP** for operational purposes is found in "Per Capita Income **-** Estimating Internationally Comparable Numbers," (International Economics Department, World Bank, January **13, 1989).** See, in particular, Annex 4 which discusses the search for better conversion factors.
# **Table 3:  Family Management and Management Practices by Region**

|**Model**|||**OLS with Country**|**FE (Cross-sectio**|**n)**||
|---|---|---|---|---|---|---|
|**Outcome Variable**|||**Overall Man**|**agement Score**|||
|||**Eastern**|||||
||**Developing**<br>**MENA**|**Europe and**<br>**Central**<br>**Asia**|**Western Europe**<br>**(Italy, Portugal,**<br>**Greece, Cyprus)**|**Developing**<br>**MENA**|**Eastern**<br>**Europe and**<br>**Central Asia**|**Western Europe**<br>**(Italy, Portugal,**<br>**Greece, Cyprus)**|
||**(1)**|**(2)**|**(3)**|**(4)**|**(5)**|**(6)**|
|Share of family management (0 to 1)|-0.124**|-0.048**|-0.055||||
||(0.054)|(0.020)|(0.054)||||
|All management is family Y/N||||-0.126***|-0.036**|-0.075**|
|||||(0.037)|(0.015)|(0.033)|
|Share of same family ownership (0 to 1)|-0.007|0.039*|0.059|-0.021|0.028|0.074|
||(0.043)|(0.020)|(0.057)|(0.031)|(0.017)|(0.047)|
|Owner, Manager or Board Political<br>Connection Y/N|-0.019|0.019|-0.261***|-0.021|0.021|-0.257***|
||(0.041)|(0.019)|(0.082)|(0.040)|(0.019)|(0.085)|
|Female top manager Y/N|0.060|0.006|-0.050|0.055|0.008|-0.044|
||(0.048)|(0.012)|(0.037)|(0.046)|(0.012)|(0.037)|
|Firm Formally Registered when Started<br>Operations|0.012|0.053*|0.125**|0.013|0.053*|0.131***|
||(0.043)|(0.030)|(0.050)|(0.042)|(0.030)|(0.049)|
|Top manager experience in sector (years)|-0.0005|-0.0005|0.0003|-0.001|-0.001|0.0002|
||(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|(0.001)|
|Log of age of firm|-0.019|-0.014*|-0.016|-0.015|-0.014|-0.012|
||(0.020)|(0.008)|(0.026)|(0.020)|(0.008)|(0.026)|
|Log of size|0.005|0.038***|0.048**|0.006|0.039***|0.049**|
||(0.013)|(0.005)|(0.020)|(0.014)|(0.005)|(0.019)|
|Direct exports 10% or more of sales Y/N|0.026|0.023**|0.111***|0.026|0.022*|0.108***|
||(0.022)|(0.011)|(0.036)|(0.021)|(0.011)|(0.035)|
|Foreign ownership Y/N|0.032|0.029**|-0.013|0.036|0.030**|-0.010|
||(0.028)|(0.012)|(0.041)|(0.028)|(0.012)|(0.040)|
|Establishment has checking or savings<br>account Y/N|0.073**|-0.004|-0.148***|0.071**|-0.001|-0.155***|
||(0.031)|(0.026)|(0.057)|(0.030)|(0.026)|(0.059)|
|Establishment has a line of credit or loan<br>Y/N|0.005|0.028***|0.014|-0.001|0.028**|0.010|
||(0.023)|(0.011)|(0.044)|(0.022)|(0.011)|(0.044)|
|ISO Certification Ownership Y/N|0.048|0.058***|0.002|0.053|0.058***|-0.004|
||(0.039)|(0.011)|(0.047)|(0.039)|(0.011)|(0.047)|
|Firm identifying labor regulations as a<br>major or severe constraint Y/N|-0.089**|-0.003|0.044|-0.089**|-0.003|0.057|
||(0.037)|(0.017)|(0.051)|(0.035)|(0.017)|(0.051)|

18