rafmacalaba/datause-encoder-data huggingface dataset:
experimental groups. Because the program was randomly assigned across villages, treatment status should be orthogonal to household and child characteristics that might be correlated with the outcomes. Insofar as this holds, it will be sufficient to compare outcomes across the treatment and control groups to evaluate the impacts of the program.

To assess comparability, we estimate the differences in mean household and child characteristics between program and control villages, at baseline and follow-up. In table 2, columns (1) and (3) report mean characteristics in control villages, at baseline and follow-up, respectively. Columns (2) and (4) report the differences in mean characteristics between program and control villages, at baseline and follow-up, respectively.

Differences in means in virtually all household and child characteristics between program and control villages were small and statistically insignificant. As an exception, the percentage of girls in program villages was slightly higher (4.2 percentage points and 3.0 percentage points at baseline and follow-up, respectively).

Analogously structured to table 2, appendix table A1 reports the differences in mean household and child characteristics between villages under the gender-uniform and -differentiated subsidy treatments. Differences in mean characteristics between the two subsidy treatments were small, and always statistically insignificant.

# **6 Program impacts**

## **6.1 Enrollment**

School enrollment information was collected in two ways. First, the household survey respondent was asked whether the child was enrolled during the just-concluded school term. Second, the enrollment of the child aged 5–10 in a given school was verified using a student attendance list compiled through a headcount conducted during the school survey.<sup>11</sup> We discuss results based on both reported and verified enrollment measures.

Table 3 reports the pooled-treatment impacts on school enrollment and grade attainment. Panels A and B report results for young children (aged 5–10) and older children (aged 11–17), respectively. Columns (1) through (4) report impacts on reported enrollment, with different sets of controls. Columns (5) and (6) report impacts on verified enrollment and grade attained, respectively, with the full set of controls. Based on the model with the full set of controls, the program increased reported enrollment among young children by 31.5 percentage points, and verified enrollment by 29.4 percentage points. In addition, the program increased attainment by 0.4 grades.

> 11The school surveys were conducted first, so that the school-attendance decision would not be influenced by the presence of survey interviewers in the communities. Using the attendance sheets collected during the school survey, the survey interviewers verified the child’s enrollment status reported by the household-survey respondent.

10
# _Matching Transfers_

Conditional matching grants, or cost-sharing programs, require that funds be spent for specific purposes and that the recipient match the funds to some degree. Figure 3 shows the effect on a local government budget of a 25 percent subsidy program for transportation. AB indicates the no subsidy line—the combination of transportation and other public goods and services a city can acquire with a budget of OA = OB. A federal subsidy of 25 percent of transportation expenditures (that is, a grant of $1 for every $3 of local funds for spent on transportation) shifts the budget line of attainable combinations to AC. At any level of other goods and services, the community can obtain one-third more transportation services. If the community chooses combination M before the grant, it will likely select a combination such as N afterward. At N more transportation is acquired.

**Figure 3 Effect of Open-Ended Matching Grant**

<!-- Start of picture text --> Spending<br>on other<br>public<br>goods<br>A<br>N<br>M<br>66.6% subsidy<br> No subsidy 25%<br>subsidy<br>O B C Spending on assisted public<br>goods<br><!-- End of picture text --> _Source_ : Shah (1994) and McMillan, Shah and Gillen (1980) The subsidy has two effects, an income effect and a substitution effect. The subsidy gives the community more resources, some of which go to acquiring more transportation services (the income effect). Since the subsidy reduces the relative price of transportation services, the community acquires more transportation services from a given budget (the substitution effect). Both effects stimulate higher spending on transportation.

Although the grant is for transportation, more other public goods and services may also be acquired, even though they become relatively more expensive, as a result of the substitution effect. If the income effect is sufficiently large, it will dominate and the grant
_5_

# 11. COLLECTING DATA ON HOUSEHJOL 4<sup>D ENERGY USE</sup>

# Comtmon Methodologies

'Typically, information relating to residential energy sector consumption has been dealt with through country energy balaices, national budget surveys and micro-studies. Each of these has certain advantages which, however, are outweighed by their drawbacks. Their limitations render them inadequate tools for crafting a usable household energy database. Instead, it wil be suggested that a dedicated, nationwide household energy survey is the most effective means of generating relevant information for planners, policymakers and evaluators.

The choice of methodology and the scope of the data-gathering endcavor will be significantly influenced by a variety of factors. These have been summarized in the upcoming Commonwealth Scicnce Council's Biomass Handbook and include the following:

- geographical area to be covered and ease of communication

- number of regions investigated

- number of communities/eco-systems investigated per region

- relative reliance upon secondary & primary data sources

- precision and breadth of coverage

- number of persons to be questioned per socio-economic or other category

- number of topics explored per person

- level of detail & accuracy required for each topic

- number & timing of interviews per person.

Planners should compare these trade-offs with the proposed uses of data and with their available resources in order to select the optimal methodology.

Regardless of the methodology chosen, household energy data collection can not be a one-shot affair. The institution responsible for information gathering and analysis should be involved in the creation, management and updating of a database on household energy. If external assistance will be used for household energy data gathering, then staff training in survey work and data analysis should be part of the package. Once created, the database should be accessible and easy to update, e.g. on a microcomputer with user-friendly software.

# National=nerzv Balances

An energy balance presents quantities of each major fuel (by weight or by energy value) consumed by specific sectors (industrial, commercial, government, household) per year. A representative energy balance for Ethiopia is presented in Table 2.1 below.
**Figure 3. Export Shares in China and India, 2001**

<!-- Start of picture text --> 25%<br>20%<br>15%<br>10%<br>5%<br>0%<br>China B- India P- India<br>Agriculture Proc Food Energy Textiles Apparel Leather Wood Minerals Chemicals Metals Vehicles Machinery Electronics Other Manuf Trade & Tpt Comm Services OthServices<br><!-- End of picture text --> Source: Authors’ calculations.

# **Who gains and who loses from growth in China and India?**

We next explore the implications of strong growth prospects in China and India in the context of world economic expansion over 2005-2020 (Table 3).<sup>7</sup> This process provides a baseline from which we can assess the impact of an additional 2.1 percentage point annual growth in China, and 1.9 percentage point annual growth in India, in the period 2005-2020. Using the methodology for assessing potential growth impacts of reform presented in Ianchovichina and Kacker (2005), we concluded that these were potentially feasible increases relative to the baseline.<sup>8,9</sup> We implement these growth dividends using favorable, sector-neutral, annual shocks to total factor productivity (TFP) of the same size. These assessments of upside potential are perhaps conservative in that they do not explicitly take into account the potential benefits from reforms of labor market policies in India that are widely believed to have enormous potential for productivity growth and fuller participation in global production chains (Mitra and Ural 2006). Nor do they fully account for the potential benefits of reforms in services trade (Nikomborirak 2006), which Markusen, Rutherford and Tarr (2005) find to be potentially very large.

> 7 The forecasts of growth rates for real GDP, skilled and unskilled labor inputs, investment and capital accumulation, and population were based on the ‘central projections’ for 2005-2015 in the World Bank’s Global Economic Prospects database at the time the analysis was undertaken. The methodology for constructing the macroeconomic projections to 2020 (known as the “GTAP baseline”) is documented in Walmsley, Dimaranan, and McDougall (2002). The growth rates to 2020 are very close to the World Bank’s ‘central projections’ to 2020 used in Winters and Yusuf (2006).

> 8 Ianchovichina and Kacker (2005) present growth scenarios for all developing countries using a cross-country growth model. 9 The increases in TFP growth can also be interpreted as gains due to increasing returns to scale.

13
<!-- Start of picture text --> 2 | !<br>c |First round of subsidies] Second round of subsidies<br>ON | |<br>ror |<br>Eo | |<br>a | |<br>cw 4 | |<br>8() | |<br>> 4 | |<br>= |<br>()<br>OTT, | /<br>Qofi\ | /<br>ON | N | /<br>& | \ | /<br>O-oD - | \ \ | /<br>£e —<br>oO | | aor ~ _-e /<br>“oi | ~~e- 7 =~<a<br>‘eon<br>0 1 2 3 4 5 6 7 8<br>Months relative to start of Covid Lockdown<br>———__ Lifeline Customers (100% Subsidy)<br>—-—-—-_ Non-Lifeline Customers (50% Subsidy)<br><!-- End of picture text -->

~~-~~ do
a more comprehensive assessment. Through a case study of Latin America, they illustrate how aggregated approaches may lead to inflated efficiency scores.

_The effects of public investment quality_ Much of the theoretical and empirical literature supports the idea that public investment raises output through both demand and supply effects (see Aschauer 1989; Straub 2007; and Chakraborty and Dabla-Norris 2009). The impact of improvements in the efficiency of public investment on growth has received less attention. Theoretical work by Chakraborty and Dabla-Norris (2009) and Shen et al. (2018) suggests that public investment efficiency amplifies the positive growth effects of public investment. Gupta et al. (2014), IMF (2014, 2015), and Morozumi and Veiga (2018), provide empirical support for this hypothesis. Grigoli (2014) and Grigoli and Kapsoli (2018) show that public investment efficiency has also a positive effect on the accumulation of human capital (health and education). One striking result of Grigoli and Kapsoli (2018) is that, for any given level of expanding, low-income economies in sub-Saharan Africa <mark>could increase life expectancy by about five years by improving the quality of public investment in human capital.</mark> A contrarian view is provided by Berg et al. (2019) who note that countries with low levels of efficiency are likely to have particularly scarce public capital and, therefore, a higher marginal productivity of public capital than high-efficiency countries. As a result, the higher marginal productivity may offset any losses from lower levels of efficiency, such that the growth impact of higher investment spending is likely to be roughly invariant to the level of efficiency. However, these theoretical results do not mean that efficiency is unimportant as they emphasize that increases in efficiency can have an important impact on growth. They are thus consistent with the idea that improving efficiency within any given country has a positive impact on growth. The estimates of IMF (2015) indicate that in the countries with the highest level of public investment efficiency, public investment has twice the growth impact compared with least efficient countries.

45
TABLE 9 Regression results of labor force participation rates, robustness check: linear-quadratic trend (continued)

||**15-19 y**|**ears old**|**20-24 y**|**ears old**|**25-49 ye**|**ars old**|**50-64 ye**|**ars old**|**65+ ye**|**ars old**|
|---|---|---|---|---|---|---|---|---|---|---|
||**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|**Female**|**Male**|
|Cycle * EMDE|-16.77***|-25.50***||-12.11***|-3.91**|||||-16.58|
||(0.000)|(0.000)||(0.000)|(0.001)|||||(0.504)|
|Cycle * life expectancy *<br>EMDE|<br>|<br>|<br>|<br>|<br>|<br>|<br>|<br>|<br>|0.073<br>(0.829)|
|Coefficient of fertility in<br>EMDEs|0.067***<br>(0.000)||-0.008<br>(0.285)||||||||
|Coefficient of secondary<br>enrollment in EMDEs|-0.138***<br>(0.000)|<br>|<br>|<br>|<br>|<br>|<br>||||
|Coefficient of secondary<br>education in EMDEs|||0.011<br>(0.556)|-0.164***<br>(0.000)|||||||
|Coefficient of tertiary<br>education in EMDEs|<br>|<br>|0.032<br>(0.472)|-0.083<br>(0.253)|<br>|<br>|<br>||||
|Coefficient of cycle in<br>EMDE|-1.66**<br>(0.007)|-1.28<br>(0.103)||0.35<br>(0.740)|-0.667*<br>(0.063)||||||
|Country fixed effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Cohort fixed effects|No|No|No|No|No|No|Yes|Yes|Yes|Yes|
|County-cohort<br>fixed<br>effects|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Age fixed effects|No|No|No|No|Yes|Yes|Yes|Yes|Yes|Yes|
|Number of observations|4428|4480|3741|3789|21382|21654|12239|12261|5107|5107|
|Number of countries|163|165|151|154|158|160|145|145|168|168|
|Adjusted R-square|0.997|0.997|0.999|0.999|0.997|0.999|0.986|0.993|0.998|0.999|

Source : Barro and Lee 2013; Key Indicators of the Labor Market (KILM), International Labour Organization; Labour Force Statistics, Organisation for Economic Co-operation and Development (OECD); UN Population Prospects; World Development Indicators, World Bank; and World Bank staff estimations.

Note : Business cycles defined as deviation of real GDP from linear-quadratic trend. Sample includes unbalanced panel of 35 advanced economies and 133 EMDEs for 1987-2020. p-statistics are shown in parentheses.

56
Policy Research Working Paper 10273

# **Abstract**

Government intervention to bail out troubled banks can produce a sizable fiscal contingent liability. Drawing on a rich history of various forms of staggered bailouts, this paper studies the link between bank bailouts and fiscal contingent liabilities using bank-level data for Kazakhstan—an upper-middle-income country in Central Asia. The paper first estimates the probability that a bank in distress is bailed out, conditioning on bank characteristics and financial soundness. Second, it estimates the magnitude of bailout costs depending on the size of banks, their ownership type, financial soundness, and the type of bailout instrument used by the government. The latter aims to contrast the fiscal costs when the government uses bailout instruments without recourse on bank future profits—such as government purchases of bad loans above market value—versus instruments that require repayment of the public support with adequate compensation for the government’s alternative costs—such as properly governed equity injections. Third, the paper illustrates how the estimations could be used for projecting the expected contingent liabilities from bank bailouts.

This paper is a product of the Finance, Competitiveness and Innovation 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 dmare@worldbank.org, mmelecky@worldbank.org, and hannamurina@hotmail.com.

_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
40% of the current population – whereas they are only 22% of the population of the EU15. Over the next 15 years, the UN is projecting a sharp decline in the fertility rates in the Maghreb countries. This will result in a decline in the share of the 0-20 age group to 34% of the population. In comparison, the sharpest decline in the EU will occur in the 20-40 age group, which will decline from 28% to 24% of the population. The sharpest increase in the three Maghreb countries will take place in the 40-60 group – from 18% to 24%. The 20-40 age group – the prime working age cohort, and the one in greatest need of employment – will be the largest one in the Maghreb countries in 2020. This is the group that can take advantage of migration policies that are the subject of this paper.

# **Figure2:**

<!-- Start of picture text --> Total Job Opennings in the EU due to Replacement and Expansion Demand<br>10<br>Expansion<br>9 Replacement<br>8<br>7<br>6<br>millions 5<br>4<br>3<br>2<br>1<br>0<br>Gremany UK Italy France Spain Poland Holland Denmark Portugal Czeck Greece Belgium Hugary Sweden<br>Republic<br><!-- End of picture text --> _Source_ : Ozden, Sewadeh and Wahba (2009) Occupations with the greatest demand for workers in the EU are reported in Figure 2. This shows the number of projected job openings in the EU generated by both replacement demand (arising from exit of workers because of retirement and other reasons) and expansion demand. Between 2005 and 2016, 61 million jobs are expected to become available in the EU25. The largest number of job openings will arise in the EU’s major economies: UK, Germany, Italy and France. But smaller economies and EU members with lower per capita incomes will also generate an increasing number of jobs 5
<!-- Start of picture text --> rom oes ° ee<br>sasconsnsstseeneeeeoe 83 .. ee °<br>weannsonseneenee ° Pi ve e e<br>Aland ee oeee e °e *. Pd eee ° e o<br>2 .. wo eae 88 oreo * e .<br>[|<br><!-- End of picture text -->

<!-- Start of picture text --> F<br>|<br>1999 Poverty rate (%) Se<br>0-10<br>10- 20<br>Ml 20 - 30<br>lm 30 - 40<br>I 40 - 50<br>Wl 50 - 60<br>lm 60 - 70<br>i 70 - 80<br>MM 80 - 90<br>WH 90-100Nodata 4-4°*,-; ‘<br><!-- End of picture text -->

<!-- Start of picture text -->
*<br>4<br>ONG? ae °<br>wes<br>Noes<br>Ro<br>“ig<br>“a<br>>,<br>he<br>q<br>, a<br>‘<br>“et<br>en ie<br>dus Ce<br>ae<br>93M <<<br><!-- End of picture text -->
formally increased in all regions. In high-income OECD economies this share grew by 7 percentage points to 52 percent, while in developing economies it rose by 4 percentage points to 22 percent. The increase in formal saving is in line with the increase in account ownership over the same period, though somewhat smaller.

In developing economies a common alternative to saving at a financial institution is to save semiformally, by using an informal savings club or a person outside the family. About 9 percent of adults—or 17 percent of savers—in developing economies reported having saved in this way in the past 12 months.

The 2014 Global Findex survey asked about three specific reasons for saving—for old age, for education expenses, and to start, operate, or expand a business. Worldwide, almost 25 percent of adults reported having saved in the past year for old age, a similar share for education expenses, and 14 percent for a business.

# How and why people borrow

Globally, 42 percent of adults reported having borrowed money in the past 12 months. The overall share of adults with a new loan—formal or informal—was fairly consistent across regions and economies, with Latin America and the Caribbean at the low end with 33 percent and Sub-Saharan Africa at the high end with 54 percent. But the sources of new loans varied widely across regions.

In high-income OECD economies a financial institution was the most frequently reported source of new loans, with 18 percent of adults reporting that they had borrowed from one in the past 12 months. In all other regions family and friends were the most common source of new loans. Overall in developing economies, 29 percent of adults reported borrowing from family or friends, while only 9 percent reported borrowing from a financial institution. In several regions more people reported borrowing from a store (using installment credit or buying on credit) than reported borrowing from a financial institution. Less than 5 percent of adults around the world reported borrowing from a private informal lender. Between 2011 and 2014 the share of adults with a new loan from a financial institution remained relatively steady around the world.

One common reason for borrowing is to buy land or a home, the largest financial investment that many people make in their life. In high-income OECD economies 27 percent of adults reported having outstanding formal housing financing from a financial institution. In developing economies less than 10 percent did.

The 2014 Global Findex survey also asked about three other specific reasons for borrowing—for health or medical purposes, for education or school fees, or to start, operate, or expand a business. In developing economies 14 percent reported having borrowed in the past 12 months for health or medical purposes, 8 percent for education, and 8 percent for a business. In high-income OECD economies about 5 percent or fewer adults reported having borrowed for each of these three reasons.

> THE GLOBAL FINDEX DATABASE 7
EAST AFRICA’S INFRASTRUCTURE: A REGIONAL PERSPECTIVE Among road corridors serving landlocked countries, there are significant cost differences. Based on the three main intraregional arteries in East Africa, the cost of importing goods from a landlocked country lies in the range of $170 to $370 per tonnekilometer. Overall, travel to landlocked Burundi, Rwanda, and Uganda is cheaper via the northern corridor than the central corridor. For Burundi, the northern corridor has a competitive edge over the central corridor. This is striking because Bujumbura is closer to Dar es Salaam than to Mombasa.

# Even though

**Figure 2.3 . Time required to import goods by road through alternative gateways**

## a. In hours

<!-- Start of picture text --> 1000<br>900<br>800<br>700<br>600<br>500<br>400<br>300<br>200<br>100<br>0<br>Djibouti-Addis Dar es Salaam- Dar es Salaam-Kigali Dar es Salaam-<br>Bujumbura Kampala<br>Customs Border Transport Ports<br>Total time required to import goods<br><!-- End of picture text -->

## b. By step (% of total time)

<!-- Start of picture text --> 100%<br>80%<br>60%<br>40%<br>20%<br>0%<br>Djibouti-Addis Dar es Salaam- Dar es Salaam-Kigali Dar es Salaam-<br>Bujumbura Kampala<br>Customs Border Transport Ports<br>Source: Source: Data collected from ―Trading Across Borders‖; Nathan Associates 2010; and AICD ports<br>database.<br>Share of time required to import goods<br><!-- End of picture text --> both Dar es Salaam and Mombasa have significantly exceeded their handling thresholds, Mombasa is slightly cheaper and more efficient than Dar es Salaam, making it the preferred port of entry for goods destined for landlocked Burundi. Costs incurred at ports and high transport costs make up more than 90 percent of the total cost of importing goods (figure 2.4).

16