rafmacalaba/datause-encoder-data huggingface dataset:
as shown in Figure C1(c) and the share of BTF in total transport expenditure also fell as seen in Figure C1(d).

In Table A2, we present the average treatment effects of the scheme on household expenditures, as estimated using equation (7). Across columns 1 to 5, it is evident that households in and Tamil Nadu less on com- Punjab spend considerably transportation pared to their counterparts in neighboring states during the post-treatment periods relative to the pre-period. Specifically, following the scheme implementation, expenditure on BTF as a proportion of total transport expenses for treated households decreased by an average of 6.6 percentage points compared to the control group (column 5).

A concern might be that differences in COVID regulation stringency measures across treatment and control groups could drive changes in household expenditures. Although we have controlled for COVID-19 cases in our main specification, as a robustness check, we control for the share of the state had either recom- additionally days government mended or required closing public transport in a month and the share of days the state government had either recommended or required individuals not to leave the house in a month. We present the results in Appendix Table D6. Estimated coefficients remain robust to adding these controls with magnitudes and standard errors similar to those in the baseline Table A2. Finally, the significance levels of the baseline estimates remain robust when the standard errors are clustered at the district level (see Appendix Table D7).

# **G.2 Evidence from the Delhi Primary Survey**

We next utilize the sample from the Delhi Primary survey data to examine changes in monthly transportation expenditure, including bus expenses, at the _individual_ level.<sup>31</sup> Among non-users, 64% spent |1 _∼_ 1,000 per month on transportation and the remaining spent more than |1,000. Of users, both new and continuous, 55% did not spend any money on transportation, including buses, per month in the period after the scheme’s launch. The remaining 45% spent between |1 and |1,000 on transportation. Specifically, among new bus users (see Figure C2), 82% spent more than |1,000 per month on transportation before the scheme, but this percentage decreased dramatically to 1% after the scheme was implemented. This 81% difference is also statistically significant at a 1% level. Furthermore, while none of the new bus users reported any spending on transportation before the scheme (the |0 category in Figure C2), the proportion increased to

> 31The survey inquires about the monthly travel expenditures of all respondents after the launch of the _Pink Slip Program_ in Delhi. However, it only asks new bus users about their monthly travel expenditures before the program’s launch, that is, only for the new bus users the survey records expenditures before and after the scheme.

10
Source: Markandya and Wilkinson (2007) Compared to air pollution caused mortality, accidental and occupational mortality is negligible. Despite the general perception that nuclear power has the highest mortality rate, in fact its coal and lignite power generation that have higher mortality rate as compared to nuclear power generation. Like in the case of mortality, coal and lignite based power generation causes most morbidity rate, no matter whether it is minor illness or major illness.

Markandya and Wilkinson (2007) is not the only one reporting the scale of health impacts of coal based power generation, a number of other studies do so. For example, Greenpeace Indonesia (2015) reports that two air pollutants (PM2.5 and Ozone) from existing coal power plants cause an estimated 7,100 premature deaths each year in Indonesia, of which almost 40% from strokes, 36% from ischemic heart disease and remaining 24% from chronic obstructive pulmonary disease, lung cancer and other cardiovascular and respiratory diseases. In a similar study for Thailand, Greenpeace, Southeast Asia (2015) also reports that these two air pollutants emitted from coal fired plants in Thailand caused 1,550 premature deaths in year 2011.

# **7. Policy Implications and Conclusions**

This review leads to several policy implications. First, air pollution, both ambient and household, is the major source of human health impacts from the energy supply chain (mining, production, transformation and utilization). More than six million people are estimated to die each year due to ambient and household air pollution emitted from the energy supply chain, making air pollution one of the main risk factors to human health. Therefore, control of local air pollution, specifically particulate matter, should be one of the main policy focuses in developing countries. At present, such environmental issues are often overshadowed by climate change agendas, whereas developing countries suffer more from local air pollution problems. The estimated deaths of more than 6 million people per year is clear evidence of how developing countries suffer more from local air pollution problems.

Most of the mortality and morbidity from energy supply chains occur among the poor. It mainly occurs in low- or middle-income countries and within those countries it mainly falls on low-income households, because they are more vulnerable and have lower affordability to cure the 25
18 From an agronomic point of view, it is especially the interaction between heat and humidity that is most challenging to preserve food, as is borne out by the PHL increasing effect of the average temperature during the wettest quarter. The effect is substantial, with a 2.3 degree increase, which corresponds to one standard deviation of the average temperatures during the wettest quarter observed in the sample, increasing the likelihood of PHL by 23 % points and expected PHL by 1.35% points. In contrast, higher temperatures by themselves, which reduce humidity and accelerate drying, tend to reduce PHL. In addition, the year and season specific rainfall conditions also matter, with the amount of rainfall during the wettest quarter increasing PHL, even though overall rainfall levels during the post-harvest season were surprisingly found to have a mitigating effect. When combined however, their effect was negative. Pre-harvest rainfalls were not found to affect PHL.

In summary, both economic incentives (most notably the seasonal price gap, and also distance to the market place) and climatic factors (particularly lower temperatures and rainfalls during the wettest periods) lower PHL substantially, with general post primary education also associated with a substantial decline in PHL. When households are female headed, their PHL also tends to be substantially lower, an important insights when targeting interventions. Poverty was not found to be associated with the degree of PHL and after controlling for regional characteristics, no additional effect was found from living in rural or urban areas.

# **4 Concluding remarks**

Following the 2008 food crisis, post-harvest food loss and waste are back high on the policy agenda, with FAO estimates suggesting that as much as 32 percent of food produced is lost and wasted (running up to 37 percent in SSA). These estimates are weight (as opposed to calorie) based, cover all crops (including more perishable roots/tubers and fruits and vegetables with a higher water content and lower caloric contribution), and comprise all losses during each step of the food supply chain (harvesting, on-farm handling/storage, processing, marketing, consumption). PHL estimates for cereals in SSA are typically lower (23 percent) with the bulk of it happening during harvesting, on-
the Industrial Training Act of 1964. Until that date the training could be, and often was, narrow in scope with no provision for day-release for more general instruction.

Official reactions to the revelation of the scale of indigenous apprenticeship systems has varied widely. In many countries, particularly in the early seventies when government finances were less strained and etatist attitudes more prevalent, it was viewed as an inferior mode which would eventually be supplanted by government training schemes pitched at a<sup>higher</sup> level of technical sophistication. It was either tolerated with benign neglect or actively discouraged.

In some countries this attitude persists until this day. In others, two factors have led to a different view. First, caught between a rapidly rising demand for education and training and declining real resources,<sup>and</sup> noting that training is more expensive than basic education, some governments previously indifferent to apprenticeship schemes have come to appreciate their ability to mob'lize training resources. In most cases this new receptiveness is limited to the modern, regulated schemes found in public utilities<sup>and</sup> large enterprises, but there are also isolated signs of a willingness to explore to possibility of harnessing the contribution of indigenous arrangements, as recommended, for example, by the ILO study of employment in Kenya, a report which in this respect was a little ahead of its time (ILO, 1972, Chapter 14).

The second factor was the concern for the promotion of selfemployment. There is now ample evidence that skill-training is but one facilitating factor, and often a minor one. Business know-how is at least important: inputs have to be purchased, credit taken and sometimes given, workers recruited and managed, equipment purchased or hired, and markets secured. The provision of such expertise as a complement to institutional training, whether school-based or nonformal, has often proved to be expensive and ineffective. By contrast, know-how is imparted along with manual skills in apprenticeship, particularly in small enterprises, where the apprentice is expected to turn; his hand to everything and run errands as needs dictate.

# 2.6 Technician training

The previous discussion has focussed on the development of different approaches to craft-level training. Technician training in developing countries has been much more homogeneous, for the most part being incorporated in formal education, and organized on the same principles as the industrialized countries. The main issues have been whether or not to introduce it at the secondary level (and if so, how occupation-specific the curriculum should be), and whether to assign responsibility for the school or institute to the Ministry of Education or to a body more closely associated with future employers of the graduates--for example, another ministry, a professional association or an employers' association.

While the formal training of technicians is the norm, other modes do exist. In Singapore, for example, there are technician apprenticeship schemes

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Policy Research Working Paper 7615

# **Abstract**

This paper develops a novel methodology to measure the quantity of jobs and value of wages embodied in exports for a large number of countries and sectors for intermittent years between 1995 and 2011. The resulting Labor Content of Exports database allows the examination of the direct contribution of labor to exports as well as the indirect contribution via other sectors of the economy for skilled and unskilled labor. The analysis of the new data sets documents several new findings. First, the global share of labor value added in exports has been declining globally since 1995, but it has increased in low-income countries. Second, in line with the standard Hecksher-Ohlin trade model, the composition of labor directly contained in exports is skewed toward skilled labor in high-income countries relative to developing countries. However, that is not the case for the indirect labor content of exports. Third, manufacturing exports are a key source of labor demand in other sectors, especially in middle- and low-income countries. And the majority of the indirect demand for labor spurred by exports is in services sectors, whose workers are the largest beneficiaries of exporting activities globally. Fourth, differences in the labor value added in exports share across developing countries appears to be driven more by differences in the composition of exports rather than in sector labor intensities. Finally, average wages typically increase rapidly enough with the process of economic development to more than compensate the loss in jobs per unit of exports. The paper also includes the necessary information to build the Labor Content of Exports database from the original raw data, including stata do-files and matlab files, as well as descriptions of the variables in the data set.

This paper is a product of the Trade and Competitiveness Global Practice Group. 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://econ.worldbank.org. The authors may be contacted at mcali@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
Agreement‖ under negotiation between Armenia and the European Union. We showed that it was only the deep aspects of the agreement that would benefit Armenia, especially the trade facilitation and services liberalization aspects of the agreement.

**Heterogeneous firms** . Balistreri, Hillberry and Rutherford (2011) have succeeded in incorporating heterogeneous firms into a multi-region trade model. It would be valuable to extend that approach to include services with FDI and endogenous productivity effects. Heterogeneous firms add another element of gains from trade liberalization; it comes from more efficient use of resources, by shifting resources from less efficient firms to more efficient firms.

But is the welfare gain from the improved resource allocation of incorporating heterogeneous firms of the same small order of magnitude as the welfare gains from improved resource allocation in perfect competition models? Moreover, it remains to be investigated whether there are significant quantitative differences between the heterogeneous firms model on the one hand and a model with homogeneous monopolistically competitive firms, Dixit-Stiglitz productivity effects, and sector-specific factors that give rise to increasing industry marginal costs on the other hand.

# **References**

- Adelman, Irma and Sherman Robinson (1978), _Income Distribution Policies in Developing Countries_ , Stanford: Stanford University Press.

- Alessina, A., S. Ardagna, G. Nicoletti and F. Schiantarelli (2005), ―Regulation and Investment,‖ _Journal of the European Economic Association,_ Vol. 3, 791-825.

- Annabi, N., F. Cisse, John Cockburn and Bernard Decaluwe (2005), ―Trade Liberalization, Growth and Poverty in Senegal: a Dynamic Microsimulation CGE Model Analysis,‖ CEPII Research Paper.

- Arnold, Jens M., Beata S. Javorcik, Mary Lipscomb and Aaditya Matoo (2012), ―Services Reform and Manufacturing Performance: Evidence from India,‖ World Bank Policy Research Working Paper 5948, January.

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# **IV. Is RBS for Pensions Cost Effective?**

As has been recognized in the banking and insurance sectors, introducing risk-based supervision is not without costs. In the absence of explicit targets related to the expected value of pensions at retirement age and assigning the supervisor with the objective of assessing pension fund performance against those targets, the introduction of RBS, when compared with compliance-based supervision, does not address the main issue, which is to ensure that the expected value of pensions at retirement age will be adequate, other than in relation to the contributions already made. In other words, if RBS is to be meaningful and to outweigh the cost/ benefit of compliance-based supervision, it needs to supervise also pension risk.

Migrating from compliance to RBS models, without supervising pension risk will still provide some benefits, but these may not outweigh the costs. Justification might come from the perspective of efficiency of the state, better use of the public resources, and clearer objectives for the supervisory agency.

The small number of pension fund management companies in most of the emerging economies with funded pension schemes makes the argument for introducing RBS less forceful. While the justification for better focusing scarce supervisory resources may be pertinent in countries with hundreds of pension funds, such as Australia and the United Kingdom, the argument is less relevant in countries with open pension systems which have a small number of PFMCs. As shown in Table 3, in most of the emerging countries with open funded schemes, the number of pension funds is typically between two (El Salvador, Macedonia) and 14 (Poland – before recent changes). While some of these supervisory agencies may need to upgrade the skills of the personnel, the potential availability of supervisory resources for supervising a small number of pension funds might not be considered as the main driver for introducing RBS.

**Table 3:** Number of pension fund management companies,<sup>a</sup> 2012

|Country|# o<br>PF|f<br>MCs|Country|# of<br>PFMCs|
|---|---|---|---|---|
|E|CA||LA|C|
|Poland||14|Bolivia|2|
|Latvia||8|Chile|6|
|Estonia||6|Colombia|6|
|Lithuania||10|El Salvador|2|
|Slovakia||6|Mexico|12|
|Bulgaria||9|Peru|4|
|Romania||9|Dominican<br>R.|5|
|Croatia||4|Uruguay|4|
|Macedonia||2|Costa Rica|7|

Source: country sources a. countries with mandatory funded system 21
# **School-Based Management and Learning Outcomes: Experimental Evidence from Colima, Mexico**<sup>1</sup>

**Vicente Garcia-Moreno** XABER

## **Paul Gertler**

University of California at Berkeley

**Harry Anthony Patrinos** World Bank

JEL Codes: I20, I21, I28 Keywords: School-based management, impact evaluation, Mexico

> 1 The authors gratefully acknowledge funding from the World Bank’s Research Support Budget and the comments of participants at seminars. Thanks to Oscar Hernández and staff at the Secretariat of Education of Colima, Mexico, for all the support provided during the evaluation. Views expressed are those of the authors and should not be attributed to the World Bank Group or to their respective organizations. Comments from Marta Rubio are gratefully acknowledged.  We also appreciate the implementation support from Stefan Metzger.
measure business opportunities in the host-country. Data for both of these variables comes from the _World Development Indicators_ produced by the World Bank.

_Host-Country Risk_ refers to four different types of host-country risk indexes reported by the International Country Risk Guide (ICRG in its _country risk index._<sup>_14_</sup> ). These variables are _Economic Risk_ , _Political Risk_ , _Investment Profile Risk_ , and _Country Risk_ (composite index). The _Economic Risk_ rating assesses a country’s economic strength or weakness as a function of variables such as the country’s GDP per capita, real GDP growth, annual inflation rate, budget balance, and current account balance.<sup>15</sup> The _Political Risk_ rating provides a summary of the degree of political stability in each host country. _Investment Profile Risk_ , a subcomponent of the _Political Risk_ index, takes into account factors such as contract viability, profits repatriation, payments delays measurements, etc. that affect those who invest in the host country. Finally, the _Country Risk_ is a composite index that includes both economic and political risk variables and other variables such as exchange rate stability and total foreign debt as percent of GDP. We change the sign on these variables from how ICRG reports them, so that higher values indicate greater potential risk.

The type of organizational form explained by equation (1) can only be observed for a given host-country once the parent bank decides to establish operations in that country. Thus, in estimating equation (1) there might be a selection bias, unless we also consider how parent banks make decisions on where to establish foreign operations. We take this into account by estimating a Heckman probit model where the first stage or selection equation is modeled following

> 14 This index is defined by ICRG so that higher values mean less risk, but in our estimations we reverse the sign on this variable to give it the more intuitive interpretation that higher values mean more risk.

> 15 Although the ICRG Economic Risk index includes the host-country GDP per capita as a subcomponent, the correlation coefficient between the ICRG Economic Risk index and host-country GDP per capita variables is very low in our sample.

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# **Annex 1 Debt decomposition**

Changes in the public debt-to-GDP ratio (denoted by _d_ in equation 1.1) can be decomposed into several explanatory terms. Specifically, changes in the ratio can be decomposed into contributions from GDP growth, primary fiscal balances—which reflect the difference between revenues and non-interest expenditures—interest costs, inflation, and other factors.

Following World Bank (2023c), one equation for decomposing changes in the debt-to-GDP ratio is as follows: ) In equation 1.1 is the effective nominal interest rate; inflation (measured as the GDP deflator); and is real GDP growth. Debt, , and the primary fiscal balance, p , are scaled by nominal tt tt ii ππ GDP. The term “other factors” includes factors such as exchange rate depreciation, privatization tt tt tt proceeds, the materialization of contingent liabilities, and other ad-hoc adjustments to debt stocks. gg dd Equation 1.1 can be reorganized to identify the contributions to changes in the debt-to-GDP ratio from key components in additive form as follows: Equation 1.2 is used as the basis for decomposing the change in the debt-to-GDP ratio into the attributable components of: (1) the primary fiscal balance; (2) interest costs; (3) inflation; and (4) real GDP growth. Due to data limitations for the countries of interest, notably on ad-hoc debt stock adjustments and the currency composition of debt, the contributions from other factors are calculated as the difference between changes in the debt-to-GDP ratio and the sum of components (1) to (4). The decomposition includes all small states with data available for all terms in equation 1.2, for the period 2011 to 2026, as detailed in table 4.

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