rafmacalaba/datause-encoder-data huggingface dataset:
|**Country**|**Date r**|**ange**|**T&D lo**|**sses, %**|**Bill colle**|**ction rate,**<br>**%**|**QFD as % o**|**f revenue**|**Comment**|
|---|---|---|---|---|---|---|---|---|---|
||**AICD**|**Present**<br>**study**|**AICD**|**Present**<br>**study**|**AICD**|**Present**<br>**study**|**AICD**|**Present**<br>**study**||
|Senegal|2003–07|2012–13|22|21|98.8|87<br>93|90 → 52|8255|T&D losses steady although bill collection worsened. QFD<br>increased as tariffs did not keep pace with cost increases.|
|Tanzania|2006-10|2011-14|2615.<br>4|2518|94.0|9592|17587|8617|Bill collection rate steady. T&D losses fluctuating 15 to 26<br>percent. Improvement in QFD reductions continues, driven<br>bytariff increases.|
|Zambia|n/a|2011–14|12|17<br>13|96.5|96|93|3947|T&D losses increased and came back down. Bill collection<br>steady. QFD marginallyreduced.|

_Source_ : AICD country reports and World Bank staff calculations. _Note:_ If one number is provided, it indicates only one data point was available within the stated date range. In addition to the changes in methodology, which make it difficult to compare results directly with AICD, it is unclear that T&D losses are comparable. In some cases, T&D data in AICD are referred to as “distribution losses,” which are assumed here to include transmission in addition to distribution losses.

100
the decades in most developing countries) and demand-side reasons. One example might be that falling demand for people with low skills, due to changes in the skill composition of goods that are demanded and produced in the economy, leads to reductions in returns to schooling at lower levels.

|Table 1: New E<br>_Country_|vidence on Returns to Primary vs. Higher<br>_Study_|Levels of Education<br>_Evidence_|
|---|---|---|
|Cote d’Ivoire, Ethiopia,<br>Uganda|Appleton, Hoddinott and Krishnan<br>(1999)|Higher returns at higher<br>levels of schools|
|Cote d’Ivoire, Burkina<br>Faso, Ghana, Kenya,<br>Nigeria, South Africa|Schultz (2004)|Returns higher for<br>postsecondary education|
|Rwanda|Lassibille and Tan (2005)|Primary education returns<br>lower than secondary|
|Tanzania, Kenya|Söderbom, Teal, Wambugu and<br>Kahyarara (2006)|Average return for those<br>with post-secondary<br>education exceed 27%|
|South Africa|Moll (1996)|Effect of secondary<br>schooling substantially<br>stronger for 1960-90|
|India|Kingdon (1998); Kingdon and Unni<br>(2001); Duraisamy (2002); Vasudeva-<br>Dutta (2004)|Low returns to primary<br>relative to secondary &<br>higher|
|Argentina, Brazil,<br>Chile, Guatemala,<br>Indonesia, Mexico,<br>Mongolia, Philippines,<br>Singapore, Thailand,<br>Venezuela, Vietnam|Patrinos, Ridao-Cano and Sakellariou<br>(2009)|Except for Cambodia,<br>China, Colombia (males)<br>& Bolivia, highest returns<br>for university|
|Nigeria|Aromolaran (2006)|Returns to secondary<br>education are double of<br>primary|

7
If women and men have the same work-leisure preferences, and leisure is a normal good, then it seems reasonable to interpret these results as evidence of gender inequality – overall women have lower full incomes and so they consume less of all normal goods including leisure. There are a variety of factors that may contribute to the leisure differentials shown in the tables. The results are derived from individual records drawn randomly from households with different structures, different distributions of earning capacities, etc. Thus the overall differences in leisure means may in part be due, for example, to the gender composition of larger and poorer households (there is clear evidence of this in Table 3 below). It is not possible to isolate the contribution of intrahousehold differences even under the assumption of identical preferences.

**Table 2:  EMNV98 data means: time allocations by age and gender** (std. dev. in parenthesis)

**–** **<u>Panel A: Individuals aged 18 59 years</u>**

|Minutes per day<br>Female|Std.dev|Male|Std.dev|
|---|---|---|---|
|SNA work<br>182|(266)|406|(274)|
|Domestic work + care<br>339|(232)|85|(124)|
|Totalwork<br>520|(245)|491|(267)|
|Sample size<br>2518||2326||
|**Panel B: Individuals aged 10 - 17 years**||||
|Minutes per day<br>Female|Std.dev|Male|Std.dev|
|SNA work<br>33|(110)|124|(211)|
|Domestic work + care<br>205|(198)|92|(131)|
|Totalwork<br>237|(219)|216|(240)|
|Sample size<br>1185||1230||

While it may be reasonable to interpret a female/male leisure gap as an indicator of gender inequality, in general the idea that leisure and welfare are positively correlated needs to be viewed cautiously. Importantly, leisure may be negatively correlated with household living standards if rates of unemployment and underemployment are high among those at the bottom of the wage distribution. While households in poverty include those working long hours for very low wages, they may also comprise the unemployed working relatively few hours in the market place and at home. An explanation for the 48
<!-- Start of picture text --> Education by Survey Week<br>©<br>VAX<br>S,<br>5 ‘A<br>o \<br>\<br>O<br>a\<br>N| \<br>\AaS<br>0 400 800 1200 1600 2000 2400<br>Time of Day<br>—— March 30-April4 ———— April 7- April 11<br>April 14 - April 18 ——-— April 21 - April 25<br><!-- End of picture text -->
# **Figure 5 Quarterly Ratio of Vacancies to Job Seekers in China, by Gender, 2001–09**

<!-- Start of picture text --> Quarterly Ratio of Vacancy to Job Seeker<br>1.10<br>1.00<br>0.90<br>0.80<br>0.70<br>male female<br>0.60<br>0.50<br>2001.12001.32002.12002.32003.12003.32004.12004.32005.12005.32006.12006.32007.12007.32008.12008.32009.12009.3<br><!-- End of picture text --> _Source_ : China Labor Market Information Center (2010).

23
Skill deficits reflect a mix of demand and supply factors. To operationalize the measurement of skill deficits, TESS elicited both qualitative and quantitative information on employers’ perceptions and assessments of skill deficits, and potential responses to mitigate these skill gaps, as described below.

# <u>Skill gap indicators</u>

- _Assessments of current and needed skills_ .  Employers assessed the severity of educational deficiencies as an obstacle to the firm’s operations and rated how well current competencies of their workforce matched the skills needed by the enterprise, in technical and job-specific skills, in soft skills such as problem solving, communication, language, as well as the work ethic of employees.

- _Operational problems resulting from inadequate skills._ Employers rated the extent to which skill deficits posed problems for several specific operational areas, including maintaining production levels, quality control, use of new technology, innovation, and sales and marketing.

# <u>Potential employer responses</u>

- _Company-sponsored training_ . Firms reported on in-service training to their current employees, whether from programs run in-house or by external providers. In-service training programs are designed to remedy deficits in pre-employment skills, upgrade existing skills, or develop new skills as requirements change.

- _Hiring new workers._ Employers reported on whether they had any job vacancies over the past two years and, if so, how many new workers were hired for each occupation. New hires represent one potential employer response to specific occupational skill shortages.

- _Use of foreign workers by occupation_ . As a measure of domestic availability of specific occupational skills, employers were asked about their use of expatriate workers (and the share of foreign workers) in different occupations.

- _Outsourcing of specialized services_ . Firms often outsource accounting, engineering, legal and architectural services. Such skills may not be available in-house, and there is interest in knowing whether they are sourced domestically or abroad.

A unique feature of the survey is the questions on the firm’s skills supply chain. Employers were asked about the education and training institutions that provide them with most of their employees and the institutions/providers supplying most of the continuing education and training to current employees. Employers were asked to name three secondary schools, universities and technical institutes from which they hire the most, along with their location and public/private status, and to assess the quality of their graduates. They were also asked to do the same for three training institutions that provided external training for employees. This information provides insights into where employers source their skills, and whether the location of skill providers poses a constraint to employers’ access to needed skills.

14
Policy Research Working Paper 6646

# **Abstract**

The paper examines the capital structure of regulated infrastructure firms. The authors develop a model showing that leverage, the ratio of liabilities to assets, is lower under high-powered regulation and that firms operating under high-powered regulation make proportionally larger reductions in leverage when the cost of debt increases. They test the predictions of the model using an original panel dataset of 124 transport concessions in Brazil, Chile, Colombia and Peru over 1992–2011, finding broad support for their predictions.

This paper is a product of the Research Support Unit, Development Economics Vice Presidency. 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 a.moore@lse.ac.uk.

_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
# **I. Introduction-Globalization through Trade Integration**

Globalization, defined as the integration of production, distribution, and use of goods and services among the economies of the world, has been evolving since the end of World War II. The signs of globalization are manifested at a factor level in the increasing flows of capital and labor, and at the product level in a resounding growth in world trade-above and beyond the growth of world output (see Chart 1).

Chart 1. Growth in World Trade and GDP, 1960-1994 (trade in goods and services) <!-- Start of picture text --> (Index 1960=100)<br>800<br>600 -<br>World Trade (volume)<br>400<br>200-<br>~ Wodd Output (volume)<br>60 65 70 75 80 85 90<br>Source: International Economics Dsputment, World Bank.<br><!-- End of picture text --> A ratio of trade (exports and imports) to output/expenditures is one overall expost measure of world integration. Speed of integration, defined as the difference between the growth rates of trade and of GDP, is the first order approximation of the rate of change in the trade/output ratio, and is commonly used to measure the pace of world integration. World aggregate trade/output ratio more than doubled in the past 35 years, from 21 percent in 1960 to 46 percent in 1994. During the same period, the ratio for low and middle income countries (LMIC)' increased from 31 percent in 1960 to 47 percent in 1994. The world speed of integration has not been constant during these years; there have been periods of rapid integration and stagnation (see Chart 2). However, except for periods of macro instability, the world has kept a positive pace of integration since 1950; that is, international trade has grown faster than output. In this context, recent waves of The author is an economist with the World Bank's International Economics Department. The findings, interpretations, and conclusions are the author's own and should not be attributed to the World Bank, its Executive Board of Directors, or any of its member countries. For income and regional groupings of economi.es, see Appendix A.

I
Table 31 Extended earnings function by levels of education, in Ghana.

|Variable|1987|1991|
|---|---|---|
|below Primary|-0.07|-0.07|
||_(-0.58)_|(-0.57)|
|Primary|-0.2|-0.02|
||(-1.1)|(-0.1)|
|Middle|0.09|0.42|
||(0.8)|(5.2)|
|Secondary|0.27|0.77|
||(3.8)|(8.6)|
|Higher|0.87|1.18|
||(7.4)|(10.8)|
|Age|0.11|0.09|
||(7.4)|(5.3)|
|Age-squared|-0.001|-0.008|
||(-5.7)|(-3.7)|
|Log hours worked|-0.29|-0.31|
||(-10.4)|(-7.25)|
|Other urban|0.13|-0.27|
||(1.99)|(-4.3)|
|Rural coastal|0.15|-0.08|
||_(1.5)_|(-0.9)|
|Rural forest|0.05|-0.19|
||(0.58)|(-2.5)|
|Rural Savannah|0.08|-0.18|
||(0.8)|(-1.7)|
|Constant|3.23|4.73|
||(10.3)|(10.9)|
|R-squared|0.24|0.27|
|Sample size|919|1140|

Table 32: Marginal returns to additional years of schooling in Ghana.

|**Variable**|**1987**|**1991**|
|---|---|---|
|schooling|0.13|0.063|
|Schooling-squared|-0.0021|-0.0005|
|experience|0.02|0.03|
|urban|-0.68|0.07|
|hours|-0.34|-0.46|
|constant|4.94|7.66|
|R-square|0.29|0.28|
|Sample size|512|797|

44
# SUMMARY

Female education increases the value of women's time in economic activities by raising labor productivity and wages (with a consequential rise in household incomes and a reduction in poverty). Female education also produces social gains, by improving health (the woman's own health and the health of her children), increasing child schooling, and reducing fertility. This paper is concerned with the estimation of these social gains from female education. The paper examines the role of female educatior. (measured by gross enrollment rates at the secondary level) relative to, and or/in combination with, some health and family planning services that influence fertility and infant mortality. It uses reduced-form estimation of the total fertility rate (CMR) and infant mortality rate (IMR). The paper presents crosscountry regressions based on data drawn from 72 developing countries. The results from these crosscountry regressions should be interpreted cautiously as they may not be representative of a particular country's experience and reflect the usual problems of relying on national averages. Moreover, the aggregate measures of education, health, and family planning available to us provide little information on the specific kinds or quality of services. However, the results are broadly consistent with evidence using household data sets (see e.g. an overview by Schultz 1989, an overview by Cochrane et al 1980, and studies by Gertler and Molineaux 1972; Rosenzweig and Schultz 1987; and Schultz 1988, 1990).

ii. The analysis in this paper generally shows that female secondary education, family planning, and health programs all affect fertility and mortality, and the effect of female secondary education appears to be very strong. Moreover, our results suggest that family planning will reduce fertility more when combined with female education, especially in countries that now have low female secondary enrollment levels. The elasticities of fertility and infant mortality with respect to female education substantially exceed those with respect to family planning and health programs when the elasticities are estimated with appropriate controls. Simulations in the paper were based on our equation using gross enrollment rates for secondary education from 1975, which we consider a more plausible
**Table 1: Descriptive Statistics Based on Administrative data**

||**Total**|**1981–99**|**2000-–09**|**2010–14**|**2015–19**|
|---|---|---|---|---|---|
|No. of leases issued|58,582|6,641|4,845|39,189|7,907|
|per year|1,502|350|485|7,838|1,581|
|_of which_owned solely by males<sup>a</sup>|0.263|0.766|0.632|0.149|0.208|
|Sporadic registrations per year|619|350|485|1,222|1,313|
|Systematic registrations per year|883|||6,616|269|
|Share of residential property|0.892|0.853|0.924|0.889|0.921|
|No. of transfers per year|210|51|93|390|871|
|No. of mortgages per year<br>|168|117|87|289|400|
|Share of area built<sup>b</sup>|0.475|N/A|0.460|0.484|0.498|
|Share of registered area built<sup>b</sup>|0.128|N/A|0.066|0.155|0.223|

_Source:_ LAA land administration database from 2011 and digitization of paper records, 1981–2011. a Gender relates to the 94.4 percent of properties registered in the name of natural persons rather than legal entities. b Share of built-up area is at the block level, and data for the period 1981–99 is not available.

20
Indeed, with the primary fiscal balance having shrunk and concerns about the composition of public spending, the only factor which could explain the big fall in interest rates was a decline in country or political risk.<sup>7</sup> Informal interviews with businessmen supported this idea. The encouraging refrain was: “We no longer need political connections to do business in Kenya.” In addition, it was noted that investments and business would continue regardless of the outcome of the then-upcoming Presidential elections. In order to obtain systematic evidence on perceptions of politics and business, questions were added to the Investment Climate Assessment (ICA) conducted in June 2007. We report these results in section 5.

- 1.2  Main Findings and Organization of the Paper

Serious data deficiencies—in particular, the availability of a consistent GDP series only after 1995/96—are an impediment to an analysis requiring long time series. Our strategy therefore is to persuade the reader by “connecting the dots” among the extent of past reform; a review of the evolution of government indebtedness (measured by the debt-to-GDP ratio) and its underlying determinants; a decomposition of interest rates to gauge devaluation and default risk; and the survey results from the ICA. We conclude that the positive trends after 2003 were being driven by three factors:

- lagged benefits of price, trade, exchange rate and interest rate liberalization forced by reduced aid after the Goldenberg scandal uncovered in 1992;

- solid foundation for solvency based on significant revenue collection as a payoff to the reform of tax policy and administration which started in the mid-1990s; and critically,

- declining political risk after the successful 2002 elections fueling an improvement in sovereign creditworthiness and the private investment climate.

> 7 To paraphrase Sherlock Holmes: When you have eliminated the impossible, whatever remains, however improbable, must be the truth.

4
|Dependent variable: log hourly wage<br>Table A4: Returns to technical upgrading within exporting f|(1)<br>irms by firm|(2)<br>size catego|(3)<br>ries, OLS e|(4)<br>stimates w|(5)<br>ith restricte|(6)<br>d sample|
|---|---|---|---|---|---|---|
|A. Micro and small firms (employment < 100 workers)|||||||
|Received technical upgrading (=1 if yes)|0.072***|0.048***|0.056***|0.056***|0.056***|0.055***|
||(0.008)|(0.007)|(0.011)|(0.011)|(0.011)|(0.011)|
|Observations|140,172|140,172|140,172|140,172|140,172|140,172|
|B. Medium size firms (employment between 100 and 500 workers)|||||||
|<br>Received technical upgrading (=1 if yes)|0.062***|0.051***|0.047***|0.046***|0.046***|0.045***|
||(0.005)|(0.004)|(0.006)|(0.006)|(0.006)|(0.006)|
|Observations|883,859|883,859|883,859|883,859|883,859|883,859|
|C. Large firms (employment > 500 workers)|||||||
|<br>Received technical upgrading (=1 if yes)|0.054***|0.038***|0.031***|0.030***|0.029***|0.028***|
||(0.004)|(0.005)|(0.005)|(0.005)|(0.006)|(0.006)|
|Observations|10,934,953|10,934,953|10,934,953|10,934,953|10,934,953|10,934,953|
|Worker controls|No|Yes|No|Yes|No|Yes|
|Worker effects|No|No|Yes|Yes|Yes|Yes|
|Firm controls|No|Yes|No|Yes|No|Yes|
|Firm effects|No|No|No|No|Yes|Yes|
|State effects<br>Notes: Robust standard errors clustered by firm in parentheses. *<br>controls are: age, race, gender, schooling level, and skill level b<br>workers; average tenure; male to female workers ratio; share of w<br>high school completed; share of workers with more than high scho<br>each of skill levels 1 to 4; and 2-digit industry dummies. Sample res|No<br>** p<0.01, ** <br>ased on occ<br>orkers with le<br>ol completed<br>tricted to firm|Yes<br> p<0.05, * p<br>upation. Firm<br>ss than high<br>; average log<br>s that export|No<br><0.1. All regr<br>s controls a<br> school com<br>wage; share<br>ed in all year|Yes<br>essions incl<br>re: log emp<br>pleted; share<br>of white wor<br>s during 200|No<br>ude year effe<br>loyment; ave<br> of workers <br>kers; share<br>9-2012.|Yes<br>cts. Worker<br>rage age of<br> with exactly<br>of workers in|
Some other factors have similar effects in both country groupings. A stronger effect of the recent financial crisis is associated with lower future mobility expectations. The belief that need in society is the product of injustice (the reference category being “inevitable part of modern life”) also is associated with lower expectations vis-a-vis future upward mobility.

The estimates on other variables (not reported to conserve space) suggest that in both groups of countries expectations of future upward mobility diminish with age, are higher in urban areas and for those who are not married. In the EU they are also higher at higher levels of education, whereas outside the EU they are higher for those currently employed (whereas there are no differences across education levels). Men and women have similar aspirations of upward mobility once other characteristics and beliefs are held constant.

The fact that connections do not help when they are both deemed to be important and available in the EU is an interesting finding that deserves further investigation. One possible reason for this could be due to the fact that it may be less common - and possibly less socially acceptable - to rely on informal connections in the European Union. One would certainly expect reliance on informal institutions to be more prevalent when formal institutions are weaker. It is possible to test this conjecture with LiTS data. The respondents who claimed to have connections were asked how likely they would be to rely on these connections. In the EU sample 18 percent of respondents reported that it was “not at all likely” that they would resort to personal connections if they had them (29 percent in Western European countries), whereas in the non-EU Transition Economies only 10 percent of respondents reported that they would not resort to connections if they had them (8 percent in CIS countries). The distribution of responses for each country is provided in Figure 4. To investigate this further, I split the group who think that connections are vital and report having – them) into two categories those who say that they will very likely or definitely use their connections, and the rest, whose appeal to connections, even if available, is less certain. Otherwise, the model is the same as in table 4. More formally: 18