rafmacalaba/datause-encoder-data huggingface dataset:
The advantages of this type of operation are: flexibility in the timing of training--students do not have to enrol at the beginning of a school year and do not have to commit themselves to completing the training in one session; efficiency in the utilization of resources--as one student drops out or terminates, he is replaced by another; and a distributed flow of graduates throughout the year rather than a batch at a single date.

# Economies of scale

School-based training has a potential for exploiting economies of scale similar to that of nonformal training. In practice the fact that the trainees' time is only partly devoted to training, as opposed to general education, makes the realization of the economies of scale considerably more difficult. The fact that students spend a much smaller proportion of their time in workshops in school-based training than in nonformal training means that, unless school enrollments are much greater than nonformal training centre enrollments, the shops, and their instructors, can be expected to have a relatively low utilization rate.

# Selection of trainees

Failure to select trainees for motivation and aptitude can have a significant negative impact on both the internal and external efficiency of a training mode, contributing to high drop-out rates and a failure of graduates to seek training-related occupations. This is most likely to be a problem with secondary vocational/technical education in that it is commonly entered by students who would have preferred to have remained in academic education but were denied access. The problem is particularly acute in those developing countries where students enrol in vocational schools only because there is a shortage of places in general secondary education. Even where students enrol in vocational education by choice, their youth and immaturity have an adverse effect on the likelihood that they can predict their future careers with sufficient accuracy to be able to make appropriate choices for themselves--and school administrators may be no better placed to make decisions on their behalf, even if they had the power and motivation to do so.

# Quality of training inputs

# Instructors

Schools typically have the utmost difficulty in recruiting competent instructors. A UNESCO survey of the experience of 23 developing countries (UNESCO, 1978, p. 105) states that inadequate teaching staff is a major problem common to virtually all the countries under study. Bureaucratic constraints often result in instructors having inappropriate qualifications. The Asian experience, summarized by Chandrakant (1980) is representative: "an over-emphasis on academic qualifications, such as degrees, has brought into the teaching profession a large number of persons with little or no industrial experience. As a result, much of the learning in technician institutions is theoretical, out of text-books, and lacks adequate practical content and application."

- 75 -
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- Barker, N., Bryan, G. T., Karlan, D., Ofori-Atta, A., and Udry, C. R. (2021). Mental health therapy as a core strategy for increasing human capital: Evidence from ghana. Technical report, National Bureau of Economic Research.

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37
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**Figure 4: Organizational**<sup>**Chart of Revenue Administration in Orissa**</sup>

19
With our battery of market-level measures added to the panel using country and month-year identifiers, we focus on measuring the concentration of PM2 _._ 5 around combustion power plants. Specifically, we use information on monthly mean surface PM2 _._ 5 concentrations measured in micrograms per cubic meter (μg/m<sup>3</sup> ) from the V5.GL.03 dataset detailed in Van Donkelaar et al. (2021).<sup>5</sup> This geophysicalhybrid dataset provides estimates of PM2 _._ 5 concentrations by combining data from satellites, chemical transport models, and ground-based monitors. It provides information at 0.01<sup>_◦_</sup> x0.01<sup>_◦_</sup> resolution at a monthly frequency between 1998 and 2021. Because previous literature has shown that there are considerable health effects within 50 km of a power plant (Qiu et al., 2023; Fraenkel et al., 2024; Clay et al., 2016), we compute the average PM2 _._ 5 concentrations for every plant and month at this distance and at 10 km. Figure 2 shows the distribution of the combustion plants in our sample and provides a visual example of how the PM2 _._ 5 concentrations are calculated. To run placebo exercises, we also compute these measures of PM2 _._ 5 concentrations within 50 km of non-combustion power plants (i.e., solar, wind, nuclear, geothermal).

Next, we include in the dataset variables that can help us account for factors that confound the relationship between PM2 _._ 5 concentrations and our market-level measures of drought. Wildfires, which are more prevalent during droughts and lead to higher PM2 _._ 5 concentrations (McClure and Jaffe, 2018), are a particular source of concern. To limit the influence of surface PM2 _._ 5 related to wildfires, we follow Qiu et al. (2023) and omit observations plausibly affected by wildfires. Specifically, we use data from the Global Fire Emissions Database version 4 (GFED4s)<sup>6</sup> described in Van Der Werf et al. (2017). This dataset provides monthly information on carbon emissions from fires at a spatial resolution of 0.25<sup>_◦_</sup> x0.25<sup>_◦_</sup> and at a monthly frequency between 1997 and 2022. Using this information, we identify and exclude from the sample plant-month-year observations where fire emissions are observed within 50 km of a power plant.

To account for other confounding factors, we also include in our dataset several meteorological variables derived from the Weather for Energy Tracker database (IEA and CMCC, 2022).<sup>7</sup> These variables provide monthly frequency information and correspond to the mean value observed within 50 km of each power plant. The meteorological variables are: temperature (<sup>_◦_</sup> C at 2m), total precipita-

> 5https://sites.wustl.edu/acag/datasets/surface-pm2-5/

> 6https://www.geo.vu.nl/ gwerf/GFED/GFED4/

> 7https://www.iea.org/data-and-statistics/data-tools/weather-for-energy-tracker.

9
v

# **EXECUTIVE SUMMARY**

To fulfill the millennium development goals and attain universal primary school completion by 2015, the donor community has made intense efforts to increase the education budgets of lowincome countries so that they can pay for buildings, salaries and materials to provide quality education. The attainment of the Education for All goal depends on the ability of countries to translate financing into time and opportunities for students to learn.

However, the delivery of basic educational services to the poor often falls far short of expectations. Curricular objectives must be covered within specific numbers of hours or days. But in low-income areas worldwide, from Brazil to Niger, only a fraction of the intended instructional time is used for learning tasks. Schools may close informally before or after holidays, start late in the day or end early. When teachers are present, they may be engaged in activities other than teaching. One outcome of limited instruction is an inability to read fluently (if at all) until the later primary grades. Lacking parental support, poorer students tend to fall behind early and repeat grades or drop out illiterate.

To assess the magnitude of the problem and find methods to measure it, a team of World Bank staff obtained grant funds from the government of the Netherlands. First the existing research was reviewed, that highlighted the following issues:

- Worldwide, governments define the number of days or hours that schools should teach specific material, usually 850-1,000 instructional hours or 180-220 days per school year, aside from breaks and extracurricular activities. (In poor countries, however, overcrowded classes may be split into two, with resulting 40% loss in instructional time and reduced learning outcomes.) Instructional time surveys are few in lower-income countries and may have measured just one aspect of time loss. For example, schools were found to be open 70% of the official time in Mali and 114 days of the official 200 days in Honduras, while a Dominican study reported an overall time use of 65%;

- Instructional time wastage is often linked to impoverished environments.  Studies in the US found that schools serving the poor often have lower time-on-task and spend this time less effectively.  For example, socioeconomic differences were found in the amount of time students spent learning to read, with classroom interruptions and disruptions more frequent in low-income areas.  Poorer students were found to spend 5% less time per day engaged in academic tasks than schools in better-off areas and need to attend school 1.5 months during the summer break in order to attain an equivalent amount of engaged learning time; time wastage accumulated over the years places poorer students at risk;

- Teacher absenteeism has figured prominently as the culprit in the wastage of education investments.  Surveys have shown figures ranging from 11% in Peru, 21% in Indonesia, 27% in Uganda, 30% in Kenya.  Overall, 20-25% of school staff may be absent on any given school day, with higher rates in rural schools.
Consistent with this, Berry and West (2005), using US data, find a negative effect on future earnings of both a higher school size and a higher pupil-teacher ratio.

The abovementioned studies focus on the estimation of educational production functions where educational performance is the variable to be explained. Other studies estimate **educational cost functions** , in which educational performance is used as an explanatory variable. Studies that use this approach tend to find that _existing schools are smaller than optimal_ (Kenny 1982, Smet 2001, Stiefel _et al._ 2009). But also among these studies there is no clear consensus on the optimal school size. For example, a review of US-based studies by Andrews _et al._ (2002) suggests that 500-600 students may be an optimal size, although there is no consistent evidence of either increasing or decreasing returns to scale. As far as **developing countries** are concerned, Hanusek (1995) summarizes the literature on the effect of class size in developing countries as follows: “The evidence provides no support for policies to reduce class size. Of the thirty studies investigating teacher-pupil ratios, only eight find statistically significant results supporting smaller classes; an equal number are significant but have the opposite sign; and almost half are statistically insignificant. These findings qualitatively duplicate those in the U.S. studies, but are particularly interesting here. Class sizes in the developing-country studies are considerably more varied than those in the U.S. studies and thus pertain to a wider set of environments, providing even stronger evidence that the enthusiasm for policies to reduce class size is misplaced (p.231)”. More recent studies confirm the mixed evidence: Urquiola (2006) finds a negative effect of class size on student performance in Bolivia, while Niaz Asadullah (2005) finds the opposite for Bangladesh. And in a recent review, Hanusek and Woessman (2007, p.66) conclude that “The lack of substantial resource effects in general, and class-size effects in particular, has been found across the developing world, including Africa (...), Latin America (...) and East Asia (...)”. There are only few studies that focus on school size in developing countries. Duflo et al (2009) included school size as one of the explanatory variables into an educational production function for Kenyan schools, and find in some specifications a small negative effect. Liu et al (2009) estimate the effect of a primary school merger policy conducted in rural China. Within this policy about 25,000 rural primary schools a year were closed between 2001 and 2005. They conclude that mergers were beneficial for the school performance of older students (4<sup>th</sup> grade) and worsened the results of younger (1<sup>st</sup> grade) students.

7
**The Structure and Determinants of Inequality and Poverty Reduction in Ghana, 1988-92**

# **_I._ Introduction**

Ghana's economic recovery prograrn, initiated in 1983, has been judged to have been a remarkable success story -- at least until the further shocks of public sector wage increases in 1992 and subsequent events. The GDP growth rate has maintained at a very reasonable level of around 5 per cent per annum over the decade. This was accompanied by a perceptible decline in poverty. From 1988 to 1992, the period for which statistical measures exist, poverty incidence in Ghana has decreased and the inequality, measured by Gini coefficient, also reduced slightly. The poverty reduction is accompanied by significant improvement in social indicators. Infant mortality decreased from 77 to 66 per 1000 live births, child mortality decreased from 84 to 57 per 1000, malnutrition rate decreased from 31 to 26 percent and total fertility rate decreased from 6.4 to 5.5 (World Bank, 1995b).

Despite the progress that Ghana has made, poverty remains a serious and extensive problem. For over 30 percent of the population, or about 5 million people, expenditure per capita in 1992 was less than US$25 a month. A further poverty reduction can be assured only if there is a continued economic growth with a reasonable distribution of its benefits. It is pointed out in Country Economic Memorandum (CEM, World Bank, 1995a) that suitable macroeconomic policies to promote private sector growth, sustainable agriculture sector policies and human capital investments are required if growth is to be sustained and poverty is to be reduced.

Poverty monitoring and profile have been conducted extensively in CEM and Poverty Assessment (World Bank, 1995b), mainly by calculating poverty and inequality indices, such as poverty incidence, poverty gap and Gini coefficient. This study extends the methodologies to first order stochastic dominance analysis, decomposition of poverty changes and regression analysis, to search for factors that may affect income and inequality. In addition, this study extends the measures of inequality beyond Gini coefficient to the generalized entropy class of measures. These are more comprehensive methodologies that will allow us to investigate how the post-reform growth affected inequality and poverty in different sectors of the economy among different localities. Our emphasis is to provide policy recommendations for improving development strategies to assure a more effective poverty reduction.

The rest of the paper is organized as following. Section II explains the data used in this paper. Section III uses stochastic dominance analysis and entropy class measures to investigate the inequality and social welfare changes. These changes are analyzed for Ghana as a whole, for each locality and for different socio-economic group among different localities, respectively. Section IV decomposes the change in poverty incidence into three components: mean expenditure change, population shift and distribution 1
# Table A5. **Regional Emissions by type of the Pollutant in 2006**

|||**Particulate**<br>**matter**|**Sulphur**<br>**dioxide**|**Carbon**<br>**monoxide**|**Nitrogen**<br>**oxide**|**Hydrocarbons**|**Volatile**<br>**organic**<br>**components**|
|---|---|---|---|---|---|---|---|
|**Federal Districts**|**of the Russian Federation**|**PM**|**SO2**|**CO**|**NOx**|**CnHm**|**VOC**|
|FD1-Central|Central Federal District|232,7|214,5|583|267,9|125,6|108,9|
|FD2-Northwest|North-West Federal District|290,4|590,2|685|170,5|409,3|134,4|
|FD3-South|South Federal District|90,4|140,8|265,5|122,1|173,2|81,9|
|FD4-Volga|Volga Federal District|229,5|508,4|885,3|289,2|589,4|518,5|
|FD5-Urals|Urals Federal District|878,5|579,3|2653,9|413,8|868,7|873,6|
|FD6-Siberian|Siberian Federal District|811,7|2536,3|1051,4|339,8|651,4|134,2|
|FD7-FarEast|Far Eastern Federal District|309,8|195,3|214,2|99,8|9|11,7|

_Source:_ SUST-RUS. 2012(a), 2012 (b).

97
and distortions are caused by the same set of factors, we cannot remove bribery and keep all the distortions as they are determined at the same time. In fact, corrupt officials may create problems to exploit bribery opportunities (Myrdal (1968)). Therefore, my conclusion would differ if the level of corruption and distortion can be endogenized in a model where government officials create more distortions to exploit bribery and the agents sort themselves into three different careers: worker, manager and government official. This model environment may result in different conclusions because of two reasons: some of the high ability agents may choose to be government officials to enjoy bribery and corrupt officials may increase the distortion levels, as managers pay bribes.

# **7 Conclusion and Future Work**

In this study, I show that big plants in Türkiye spend a smaller fraction of their output on bribery compared to small plants. I focus on Turkish data as an example because of data availability. It was not selected because the problems of bribery and size dependent distortions are presumed to be more prevalent in Turkey than in other comparable countries. I build a span-of-control model where managers choose to bribe officials to decrease the tax rate, which is imposed based on their output. After I calibrate this model to match the Turkish plant size distribution and bribery payments of different size groups, I quantify the anti-bribery and anti-distortion policies on the aggregate output, mean size, the employment share of big enterprises, the average effective tax, and the average bribe rate response. Given that bribery opportunities exist, the effect of size dependent distortions is reduced, as managers can decrease them with bribery. In addition, changes in the distortion level do not have large effects on the aggregate economy, as they can be ruled out by bribery. By removing the distortions, which removes bribery incentives as well, the output level and the mean size may increase by more than 60% and 80% respectively.

Despite the fact that bribery can lower distortions for bribe payers, it can be costly for those who are not involved in it. For example, politically connected firms, rather than the most able firms, can acquire public resources through bribery (Khwaja & Mian (2005); Fisman (2001)). The case of a manager that must pay a bribe to acquire an operating license can have two social consequences: it can limit some managers from starting a business because of high entry costs and since government officials can delay the administrative process to attract more bribes (Svensson (2005); Myrdal (1968)). In addition, the effects of changes on the government policies in the economy can be different from what is aimed by the govern18
<u>Component 1: Survival. This component of the index reflects the unfortunate reality that not all</u> children born today will survive until the age when the process of human capital accumulation through formal education begins. It is measured using under‐5 mortality rates taken from the UN Child Mortality Estimates ( **Figure 1** ), with survival to age 5 as the complement of the under‐5 mortality rate. Data on under‐5 mortality are available for 198 countries, and much of the variation across countries in child mortality rates reflects differences in mortality in the first year of life.

‐ <u>Component 2: Expected Learning Adjusted Years of School. This component of the index combines</u> information on the quantity and quality of education. The quantity of education is measured as the number of years of school a child can expect to obtain by age 18 given the prevailing pattern of enrolment rates. It is calculated as the sum of age‐specific enrollment rates between ages 4 and 17. Age‐specific enrollment rates are approximated using school enrollment rates at different levels: pre‐ primary enrollment rates approximate the age‐specific enrollment rates for 4 and 5 year‐olds; the primary rate approximates for 6‐11 year‐olds; the lower‐secondary rate approximates for 12‐14 year‐ olds; and the upper‐secondary rate approximates for 15‐17 year‐olds. Data to construct this measure is available for 194 countries ( **Figure 2** ). The quality of education reflects new work at the World Bank to harmonize test scores from major international student achievement testing programs ( **Figure 2** ). The database covers over 160 countries. These are combined into a measure of expected learning‐adjusted years of school, using the conversion metric proposed in the 2018 World Development Report ( **Figure 3** ).

<u>Component 3: Health There is no single broadly‐accepted, directly‐measured, and widely‐available</u> metric of health that is analogous to years of school as a standard metric of educational attainment. In the absence of such a measure, two proxies for the overall health environment are used to populate this component of the index: (i) adult survival rates, defined as the fraction of 15 year‐olds that survive until age 60, and (ii) the rate of stunting for children under age 5 ( **Figure 4** ). Adult survival rates are calculated by the UN Population Division for 197 countries. In the context of the HCI they are used as a proxy for the range of non‐fatal health outcomes that a child born today would experience as an adult if current conditions prevail into the future. Stunting serves as an indicator for the pre‐natal, infant and early childhood health environment, summarizing the risks to good health that children born today are likely to experience in their early years – with important consequences for health and well‐being in adulthood. Data on the prevalence of stunting is reported in the UNICEF‐WHO‐World Bank Joint Malnutrition Estimates. This dataset contains 132 countries with at least one estimate of stunting in the 3