rafmacalaba/datause-encoder-data huggingface dataset:
**Table 3: Summary of reference year for analysis**

|**Reference year**|**Number of countries**|
|---|---|
|2010|1|
|2011|0|
|2012|6|
|2013|9|
|2014|22|
|2015|1|
|**Total**|**39**|

_Source_ : World Bank staff.

**Conversion to 2014 U.S. dollars.** The majority of reference documents report finances in local currency units. To minimize distortions caused by major exchange rate fluctuations between the reference year and calendar year 2014, this study reports many results in current U.S. dollars rather than in constant U.S. dollars. To compute the regional average for QFDs as the percentage of regional GDP, percentages in the 39 study countries in the reference years are weighted by their GDP weights in 2014. For crosscountry comparison of costs and revenues expressed on a per-kWh basis, the study converts reported values (with the exception of capex) to 2014 local currency units using local consumer price indices, and then to 2014 U.S. dollars by applying the official exchange rate in 2014. An analysis based on nominal values with specific inflation assumptions for each cost component is beyond the scope of this analysis. Capex is not adjusted, because it is based on generic plant cost per kW in U.S. dollars obtained from different sources (see Table A1.2 in annex 1), the applicable years of which are not precisely defined.

**Fuel costs** . Diesel and heavy fuel oil (HFO) costs comprise a significant portion of opex in many countries. Where input fuel costs are subsidized, the full costs are not captured on the utility financial statements. Unfortunately, only ten countries—Cameroon, The Gambia, Kenya, Liberia, Mauritania, Mauritius, Niger, Rwanda, the Seychelles, Sierra Leone—reported fuel costs separately. At least three SSA countries are known to subsidize the cost of fuel directly:

- **Angola** . The price of diesel for electricity generation, which accounts for about one quarter of total diesel fuel consumption, is zero (IMF 2015a). Angola, however, is not among the study countries.

- **Côte d’Ivoire** . The government provides subsidized fuel for electricity generation through two channels. One is free gas based on the government’s 15-percent equity share in domestic gas fields. The other is subsidized heavy vacuum oil provided by the oil refinery to the utility. Gas and oil subsidies for electricity generation in 2013 were estimated to be US$131 million, or US$0.03 per kWh billed (World Bank staff estimates). In 2014, the subsidy for heavy vacuum oil exceeded the initially budgeted amount by two-thirds and totaled US$100 million. The government is reportedly focusing on investing US$9 billion to revamp and expand the power infrastructure, which will eliminate the need for heavy vacuum oil (IMF 2015b).

- **Madagascar, Niger, and São Tomé and Príncipe** provide sales tax exemption or exemption from fuel import duties.

In other countries, natural gas prices are capped at artificially low prices by the government, combined with a domestic supply obligation, as in Nigeria. While this may not entail budgetary transfers to gas producers, these low prices have discouraged gas development and production, leading to gas 18
-16_

Table 4: Shares of **Trade** of Latin American **Countries** in World Trade

# [MPORTS-1991

|Country|Size of<br>Imports (Sb.)|Percentage of<br>World Imports|
|---|---|---|
|ARGENTINA|8.28|0.25%|
|BOLIVIA|0.99|0.03%|
|BRAZIL|22.98|0.70%|
|CHILE|7.45|0.23%|
|COLOMBIA|4.97|_0.15%_|
|COSTA RICA|2.23|0.07%|
|ECUADOR|2.33|0.07%|
|EL SALVADOR|0.88|0.03%|
|GUATEMALA|1.85|0.06%|
|HONDURAS|0.96|0.03%|
|JAMAICA|1.70|0.05%|
|MEXICO|38.07|_1.15%_|
|NICARAGUA|0.67|0.02%|
|PANAMA|1.69|0.05%|
|PARAGUAY|1.46|0.04%|
|PERU|2.81|0.09YO|
|URUGUAY|_1.55_|_0.05%_|
|VENEZUELA<br>........................................|10.04<br>. ..........<br>...............<br>.|0.30%<br> ......<br>...<br>......<br>. _.__... . ....|
|LAC TOTAL|110.93|3.36%|
|USA|508.94|15.43%|
|WORLD|3,297.90|100.00%/o|

EXPORTS-1991

||Size of|Percentage of|
|---|---|---|
|Country|Exports|World Exports|
|ARGENTINA|11.97|0.39%|
|BOLIVIA|0.90|0.03%|
|BRAZIL|31.62|1.03%|
|CHILE|8.96|0.29%|
|COLOMBIA|7.27|0.24%|
|COSTA RICA|1.63|0.05%|
|ECUADOR|2.85|0.09Ye|
|EL SALVADOR|0.37|0.01%|
|GUATEMALA|1.20|0.04%|
|HONDURAS|0.62|0.02%|
|JAMAICA|1.05|0.03%|
|MEXICO|26.96|0.88%|
|NICARAGUA|0.27|0.01%|
|PANAMA|0.34|0.01%|
|PARAGUAY|0.74|0.020/e|
|PERU|3.09|0.10Ye|
|URUGUAY|1.57|_0.05%_|
|VENEZUELA<br>..............................<br>...........|15.13<br>........................|0.490/O<br>....<br>.......<br>..|
|LAC TOTAL|116.53|3.53%|
|USA|400.98|13.07%|
|WORLD|3,068.46|100.00%/o|

Source: Comtrade, U.N. Statistical Office and Michaely, M. (1994)
Note that there are some differences across countries in the legal requirements on employment contracts for dependent workers (see Table A1 in the Annex). In Eastern European countries (except Hungary and Poland), in Nordic countries (except Finland), as well as in Switzerland, Italy and Greece, a _written_ employment contract is always required. In most of these countries the contract must be signed in advance or immediately after starting work; in Russia and Ukraine – within 3 days; and in Greece – within 2 months.

By contrast, in most of the Western Europe, as well as in Hungary, Poland, and Portugal, having a written contract is considered good practice but is required either only for ―atypical‖ (apprenticeship; fixed-term; seasonal; part-time; replacement, etc.) employment, as in Austria, Belgium, France and Portugal, or, the other way around, only for contracts of indefinite duration (Hungary and Cyprus), or is not generally required (Poland, Finland, Germany, the Netherlands, Ireland and the UK)<sup>10</sup> . In all these cases a contract as such is required but it might be oral (in Finland – also electronic); moreover, the employee must be given _written_ terms of employment ( _ToE_ ) signed by the employer (the mandatory content of ToE is specified in the law).

From employee perspective, this latter document is as good as a contract – and it is fair to assume that an employee with an oral contract and a written ToE will not choose the answer ―No contract‖ in the questionnaire (it is important to emphasize that ESS questionnaire asks about a contract as such rather than about a written contract). On the other hand, workers with oral contracts who were not given written ToE, might well respond as if they work without a contract, but their situation is in fact closer to informal than to formal employment. In other words, there are reasons to believe that, most of the time, the ESS contract question indeed identifies informal employees even in the countries where a written contract can be replaced by written ToE. Like with the written contracts, some countries request that ToE are issued in advance or immediately after starting work, whilst others allow for this some time: Finland, the Netherlands and Cyprus - 1 month; the UK and Ireland – 2 months. In fact, during this period the employee might be considered employed informally, and the possibility to postpone signing of ToE as such is likely to increase informality (in case of inspection, the employer might say that the employee in question started to work less than a month or two ago). Whether or not this likelihood will materialize depends on other factors – institutions and social norms (including tax morale). As we will see later, four of six countries where signing the contract or ToE can be postponed substantially (Greece, Cyprus,

> 10 In Spain, a written contract  is required if either party requests it (even during the course of employment relationship), as well as for ―atypical‖ employment.

9
Table 2: Determinants of technical/engineering access for age group 18-23

||Attending<br>|or complete<br>conditi<br>|d Technical<br>onal on<br>|education<br>|Attending o<br>|r completed<br>o<br>|Engineering<br>n<br>|conditional<br>|
|---|---|---|---|---|---|---|---|---|
||complet<br>seco|ed higher<br>ndary|atten<br>complete|ing or<br>d tertiary|complet<br>seco|ed higher<br>ndary|atten<br>complete|ding or<br>d tertiary|
||**2004**|**1993**|**2004**|**1993**|**2004**|**1993**|**2004**|**1993**|
|sc*|0.037|0.161*|0.015|0.218*|0.044|0.102|0.023|0.138|
|st*|0.116|0.169|0.013|0.211|‐0.490***|0.038|‐0.591***|0.044|
|muslim*|0.013|0.087|0.028|0.159|‐0.041|0.101|‐0.027|0.173|
|female|‐0.295***|‐0.385***|‐0.285***|‐0.437***|‐0.482***|‐0.411***|‐0.477***|‐0.462***|
|Rural|‐0.458***|‐0.371***|‐0.317***|‐0.264***|‐0.689***|‐0.360***|‐0.579***|‐0.261***|
|female*rural|‐0.066|0.071|‐0.067|0.183|‐0.127|0.079|‐0.152|0.192|
|quintile 2*|‐0.051|0.114|‐0.138|0.156|‐0.109|0.013|‐0.194|0.037|
|quintile 3*|0.18|0.099|0.056|0.044|0.023|0.077|‐0.103|0.023|
|quintile 4*|0.158|0.157|‐0.035|0.093|0.267|0.085|0.093|0.018|
|quintile 5*|0.561***|0.388***|0.293*|0.248*|0.794***|0.279**|0.583**|0.133|
|Number of|||||||||
|Observations|14,953|11,699|10,274|8,018|14,785|11,699|10,177|8,018|

Notes: (1) * p<0.05; ** p<0.01; *** p<0.001 (2) The coefficients are marginal effects.

(3) * denote dummy variable and marginal impact is for discrete change of dummy variable from 0 to 1.

(4) Non-SC/ST is the social group of reference.

(5) The model also includes state dummies (full results in Annex III).

# **5. Conclusion**

This paper has reviewed participation in tertiary education in India from 1983 to 2004 by computing the attainment, enrollment, and transition rates from secondary education by population groups. It is motivated by a need to establish a consensus on the basic trends in participation and contribute to more fact-driven and targeted policy-making for tertiary education.

**The findings show that Indian tertiary education progressed significantly between 1983 and 2004.** Attainment of tertiary education in the age group 25-34 doubled from 4.4 percent in 1983 to 8.8 percent in 2004. Enrollment increased by 5 percentage points, from 7.6 to 12.6 percent (a 60 percent increase).

**Nevertheless, there exist large gaps in enrollment** between:

- <u>Rich and poor households. In 2004, a young person from the top expenditure quintile was 14.5</u> times more likely to enroll in tertiary education than a young person from the bottom quintile. This is a considerable increase from 8 times in 1983.

27
size and it eclipses the hidden costs in all other countries when aggregated. In South Africa, underpricing represents 81 percent of hidden costs. There are no hidden costs from T&D losses because Eskom already operates within the reference value of 10 percent. Bill collection inefficiencies are also modest at 4 percent, while the cost of overstaffing represents 15 percent of hidden costs.

**Figure 13: Breakdown of total hidden costs in SSA**

## **a. All countries except South Africa**

<!-- Start of picture text --> Collection losses<br>T&D Losses<br><!-- End of picture text --> <!-- Start of picture text --> Under pricing<br><!-- End of picture text --> <!-- Start of picture text --> 4% 0%<br>15%<br>81%<br><!-- End of picture text --> _Source_ : World Bank staff calculations based on utility financial statements, annual reports, and other documents. _Note_ : Analysis excludes only negative costs, that is, where the utility is operating at or above benchmark performance by overpricing, having lower T&D losses than benchmark, and so on.

# 6.6 Other hidden costs

As explained in section 2.3, hidden costs are measured relative to the efficient tariff at benchmark performance. It should be noted that there are several other hidden costs not captured in the analysis presented above that are difficult to quantify but could affect the QFD. Lack of financial viability could make it challenging for countries to attract IPPs without financial incentives. In a higher-risk environment, investors will tend to expect a higher return, further pushing up costs and increasing the QFD. Another example is spending on maintenance. As noted in section 2, underspending on O&M is chronic in SSA, resulting in a dilapidated and underperforming capital base, which in turn feeds higher T&D losses. Efficient levels of O&M spending would tend to increase absolute operating costs in the short term, but could result in a better-performing network, lowering T&D losses and hidden costs in the long run.

Conversely, optimizing the power mix through a least-cost power development plan could reduce costs (substantially) further. For example, as the result of poor planning, many countries rely heavily on diesel as a source of generation. In some cases diesel plants even provide baseload power when they are more suited to meet peak demand or emergency backup needs. At least five countries— Burkina Faso, Comoros, Liberia, Rwanda, and São Tomé and Príncipe—rely on diesel for more than 50 percent of electricity generated. This can represent a significant cost, especially when liquid fuel needs to be transported thousands of kilometers overland as in Rwanda and Burundi. In almost all cases, it is more cost-effective to use HFO plants which, although still expensive, have lower operating costs—on an energy basis, HFO can be 25–45 percent cheaper than diesel—and are better suited to providing base load capacity. They also have similar benefits to diesel plants, such as short installation time and the ability to switch on quickly. The current over-reliance on diesel therefore presents a short- to mediumterm opportunity to reduce costs through switching from diesel to HFO.

51
where _i_ stands for individual i, ��is individual i’s outcome, and �is the overall mean of the outcome variable.<sup>10</sup> The reasoning behind estimation of IOO this way assumes that the i) sample distribution of outcome variable resembles population distribution, and ii) all relevant circumstances beyond an individual’s control (and used to create the types that partition the population) impact his/her chances of economic development. If this is the case, the grouping strategy creates groups of individuals facing identical circumstances. Thus, differences in outcome across these homogeneous groups reflect differences attributable to differences in circumstances.

Our strategy to explore potential sources of downward bias in IOO estimates is based on a series of Monte Carlo simulations seeking to quantify the consequences of i) not observing all relevant circumstances (i.e. circumstances that affect one’s income); ii) not observing individuals in the top of the income distribution (those most favored, the top incomes); and iii) interaction from not observing neither. We describe this approach in detail next.

# Studying Inequality of Opportunity in a Simulated Environment

i. Pseudo‐population The experimental design is implemented on a simulated population of 200,000 pseudoindividuals. In this population, there are five characteristics that define the individuals. We label these characteristics in the same way we would find relevant variables in a household or individual survey: gender, urban/rural setting, region of birth, father’s education, and mother’s education. Individuals can be either male or female, live in an urban or rural community, and have been born in one of three regions. Each individual’s father’s and mother’s education fall into one of three possible categories: illiterate, literate, or completed primary or above. Taken together, these five circumstances create (2x2x3x3x3=) 108 mutually exclusive groups or _types_ to which each pseudo-individual belongs. To keep things simple, we assign approximately the same number of individuals to each group.<sup>11</sup> To define the characteristics of each individual in the pseudo-population, we apply the following rules. A pseudo-individual is female with probability 0.52. To assign individuals into other circumstances we considered an “ordering” of groups in which group 1 contains the most disadvantaged individuals and the group 108 contains the most favored individuals. Thus for example, an individual is born from an illiterate father with probability 0.0092 ∗�109 � ������, where ������1,2, ... ,108. This pseudo-individual is assigned with probability �1 �0.0092 ∗�109 ��������∗�2/3� to a father who reads and writes, and with probability �1 �0.0092 ∗�109 ��������∗�1/3) to a father with elementary school or above. A similar

> 10 � Note that in the smoothed distribution, yi will be replaced by �� .

> 11 Each group has approximately 0.92 % of the population.

7
countries have found that additional income targeted to low-income segments of the population can lead to sizable reductions in criminal activity, the benefits being heavily concentrated among male youth (Chioda 2017). Zhang (1997) uses a set of cross-sectional U.S. state data for 1987 and shows that cash or in-kind welfare programs reduce the time allocated to illegal activities and have a negative and often significant effect on property crime. Jacob et al. (2015) analyze a housing voucher program in Chicago that transferred the equivalent of 50 percent of household income to beneficiaries, reporting a decline of roughly 20 percent in both violent and overall arrests. Kling et al. (2005) studied the assignment of housing vouchers via random lottery to public housing residents in five cities in the United States and concluded that it reduces arrests among female youth for violent and property crimes.

The literature on cash transfers, conditional or not, is even more conclusive. Camacho and Mejia (2013) have found that Colombia’s _Familias en Accion_ conditional cash transfer (CCT) program reduces urban crime in Bogota, while Pena, Urrego, and Villa (2015) conclude that the program also has positive effects on the demobilization of combatants. Crost et al. (2016) suggest that the Philippines national CCT program is responsible for a decrease in conflict-related incidents in the country. Chioda, Mello, and Soares (2015) find a robust and significant negative impact of Brazil’s _Bolsa Família_ CCT on crime. Finally, Blattman et al. (2017) found that cash transfers along with behavioral therapy in Liberia can dramatically reduce crime and violence, even over the medium term after intervention.

However, some studies have found that welfare payments can also be associated, in certain circumstances, with higher levels of crime. An analysis of daily reported incidents of major crimes in 12 U.S. cities reveals an increase in crime over the course of monthly welfare payment cycles (Fritz Foley 2008). Ellen et al. (2011) also find that additional voucher holders lead to elevated rates of crime, controlling for census tract fixed effects. And a study by Kling et al. (2005) on housing vouchers also finds that they can increase problem behavior and property crime arrests among recipient males. In such cases, it seems that additional criminal activity is associated with episodes that have a direct financial motivation, like burglary, larceny, motor vehicle theft, and robbery, as opposed to other kinds of crime like arson, assault, homicide, and rape (Burek 2005). The main hypothesis is that welfare beneficiaries tend to consume grant income quickly and then attempt to supplement it with criminal income.

4
Table A7. Bangladesh. Farm Earnings

||**2000**|**2010**|
|---|---|---|
|Primary & Lower secondary|0.132**|0.0331|
||(0.0633)|(0.0489)|
|Higher secondary & Terciary|0.295**|-0.00290|
||(0.147)|(0.119)|
|Age|0.142***|0.0795***|
||(0.0214)|(0.0172)|
|Age squared|-0.00175***|-0.000843***|
||(0.000245)|(0.000190)|
|Female|-2.243***|-0.926***|
||(0.123)|(0.0605)|
|Urban|0.197|-0.161**|
||(0.123)|(0.0816)|
|Barisal|-0.285**|0.270***|
||(0.123)|(0.0920)|
|Chittagong|-0.240**|0.164**|
||(0.0977)|(0.0680)|
|Khulna|0.405***|0.204***|
||(0.0955)|(0.0751)|
|Rajsahi|0.0125|0.303***|
||(0.0763)|(0.0629)|
|Sylhet|0.0104|0.253**|
||(0.126)|(0.108)|
|Low land|-0.107|0.743***|
||(0.0986)|(0.0666)|
|High land|0.457***|1.333***|
||(0.111)|(0.0786)|
|Irrigation|0.458***|0.408***|
||(0.0941)|(0.0707)|
|Number of members|0.525***|0.429***|
||(0.0507)|(0.0525)|
|Constant|4.007***|4.780***|
||(0.462)|(0.381)|
|Observations|1678|2956|
|R-squared|0.340|0.323|
|AdjR-squared|0.334|0.320|

Notes: Sample: Self-employed in Agriculture Illiterate and Incomplete primary education is the base for educa No-land is the base category Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 57
WPS4129

# **Enhancing the Efficiency of Securities Markets in East Asia**<sup>**†**</sup>

_Swati Ghosh and Ernesto Revilla_<sup>*</sup> East Asia and Pacific Region The World Bank

## **Abstract**

We explore the relative efficiency of stock markets across countries using newly available data on transaction costs and the quality of the informational environment of stock markets. These new measures are constructed from firm-level stock returns in a panel of 60 countries in the period 2000–2004. We then develop a framework to understand the linkages between efficiency, liquidity and their determinants. To give empirical content to the framework, we study the determinants of transactions costs and the quality of the informational environment. We find that some institutional arrangements—such as the availability of stock lending and short selling—and the openness of markets are associated with lower transactions costs. We also find that, although disclosure rules for directors and officers of listed firms are essential, the ability of shareholders to seek redress is more conducive to a better informational environment in stock markets. This in turn serves as the basis for the policy framework and recommendations for the East Asian region. In particular, the region needs to continue to strengthen the implementation and enforcement of corporate governance, to further enhance the market and institutional infrastructure, and focus on policy measures to foster a larger and more diversified investor base, to continue to see gains in the efficiency of stock markets.

## JEL Classification Numbers: G14, G15, G18, G38

World Bank Policy Research Working Paper 4129, February 2007 _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 countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org._

> † We thank Thorsten Beck, Stijn Claessens and Erik Feijen for their helpful comments.

> *  Emails: <u>sghosh@worldbank.org</u> and <u>erevilla@worldbank.org.</u>