rafmacalaba/datause-encoder-data huggingface dataset:
and Maksimovic **(1999)** empirically study these issues **by** working with **10** developing countries, mostly during the 1980s. Our data set, which includes the 1990s, enables us to expand this literature **by** analyzing the effect of financial liberalization and crises on financial structure. Also, the data on access to global capital markets enable us to study the effects of financial integration at the firm level.

This paper also sheds light on the finance literature, which studies the efficiency of different financial systems in the intermediation between saving and investment. In particular, the literature discusses the pros and cons of bank-based vis-4-vis market-based models of organization.' The evaluation of managers and firms performance on the part of lenders may be an expensive activity. Therefore, there is a trade-off between liquidity of financial instruments and control of debtors.3 While market-based systems are better suited to offer liquid financial instruments to investors, bank-based systems promote long-term relationships between intermediaries and borrowers and facilitate corporate control. This implies that the different systems may be better at providing funds for different firms. Banks may be well prepared to fund start-up firms, while public markets can be better prepared to finance established firms, typically with more tangible assets.' Our data set enables us to study whether firms' financing choices are different in countries with bankbased and market-based systems. Also, we can study whether access to more developed financial markets affects bank-based and market-based systems differently.

structure" is also used to denote differences in the composition of financial systems-for example, bankbased vs. market-based financial systems.

See DemirgiAq-Kunt and Levine **(1997).**

> Financial. securities, which are quickly and cheaply exchanged in the market, can save resources allocated to exercise some control over corporations. See Bhide **(1993).**

> The need to monitor firms might lead banks to provide stage financing. This type of financing enables banks to monitor firms at different stages of the investment projects. So, the characteristics of long-term

**3**
Table 2 show poverty trends when we restrict the samples to the common areas covered in both surveys during the two years. As discussed before, the common survey period includes only the first six months for both rounds. These samples are the ones that are used for the HIES to LFS imputation analysis. Meanwhile, the fifth and sixth columns show poverty rates when we include all 12 month data but focus on common geographic areas of both rounds of HIES. These are the samples are used for the HIES to HIES imputation analysis, which will be discussed in detail later.

The changes in the survey period and geographic coverage have a modest effect on estimated poverty rates. For both rounds and all areas, poverty rates are in general higher when focusing on the common survey duration and geographic coverage of LFS and HIES, which are reported under columns named “Common districts: 6 months”.<sup>5</sup> It is clear that in general, poverty declined in all areas and all combinations of samples and duration.

**Table 2: Poverty Trends in Sri Lanka between 2006/07 and 2009/10**

|Poverty<br>headcount(%)|Full sa<br>2006/7|mple<br>2009/10|Common districts: 6<br>months*<br>2006<br>2009|Common dist<br>months<br>2006/7|ricts: 12<br>**<br>2009/10|
|---|---|---|---|---|---|
|National|13.3|8.9|15.3<br>8.6|13.5|8.2|
|Urban|6.0|5.2|7.4<br>6.1|6.2|4.4|
|Rural|13.7|9.4|15.4<br>8.9|13.8|8.6|
|Estate|27.7|11.4|33.6<br>10.4|27.7|11.4|

Source: Authors’ estimations using HIES 2009/10 data, using revised poverty lines based on CCPI (2002=100) are used for computations.

Note: *6 months and common districts between two years and HIES and LFS; ** 12 months and common districts between two years and HIES and LFS.

# **III. Imputation model**

The imputation process follows the methodology developed in ELL (2003). The methodology has two stages: in the first stage, a model of log per capita consumption expenditure of household _h_ is estimated in one round of HIES data. The model is defined as where _ych_ is log per capita expenditure, α is the intercept, Xch′ is the vector of explanatory variables for household _h_ and location _c_ , β is the vector of regression coefficients, _Z c_ ′ is the vector of location specific variables for location _c_ , γ is the vector of coefficients for these variables, and _uch_ is a stochastic error term. This error term is composed of two independent components: _uch_ =η _c_ + ε _ch_ where η _c_ is a clusterspecific effect and ε _ch_ is a household-specific effect. As is standard, both η _c_ and ε _ch_ are assumed to be orthogonal to X and Z and independent of each other and have a mean of zero. This error structure allows for both a location effect—common to all households in the same area—and heteroskedasticity in the household-specific errors. Location-specific variables Z can be defined at any level, such as province,

> 5This could be expected due to temporal monthly price changes not adjusted. Average CPI for first half of the survey 2006/07 is 145.0 compared to 156.0 in the second half. The difference between two half periods is high in 2006 /07compared to 2009/10 as the comparable CPI for 2009/10 are 209.9 and 215.7

7
**Table 10: Percentage of 12 to 18 year olds doing other activities than schooling**

||Migrant<br>Household<br>Males|Non-migrant <br>Household<br>12 to 15|Migrant<br>Household<br>Males|Non-migran<br>Household<br>16 to 18|t Migrant<br>Household <br>Female|Non-migran<br> Household<br>s 12 to 15|t Migrant<br>Household<br>Female|Non-migrant<br>Household<br>s 16 to 18|
|---|---|---|---|---|---|---|---|---|
|Percent attending school|70.7|74.8|26.9|32.4|67.0|66.9|24.4|26.8|
|Percent who have migrated|3.4|0.1|8.2|0.2|2.4|0.1|3.1|0.3|
|Percent working|39.3|36.8|70.9|69.0|16.5|17.5|33.2|35.4|
|Percent working and not in school|20.6|19.2|61.3|58.7|9.6|11.2|29.0|30.9|
|Percent housework as main activity|1.8|1.7|1.2|1.1|23.2|21.8|45.2|40.7|
|Percent unpaid familyworkers|25.4|21.7|26.3|21.3|9.0|9.0|8.8|10.2|

**Table 11: Marginal Effect of being in a Migrant Household on Other Activities of 12 to 18 year olds**

||Coefficient<br>Males 1|Z-statistic<br>2 to 15|Coefficient<br>Males 1|Z-statistic<br>6 to 18|Coefficient<br>Females|Z-statistic<br>12 to 15|Coefficient<br>Female|Z-statistic<br>s 16 to 18|
|---|---|---|---|---|---|---|---|---|
|Has Migrated themselves|0.022|6.50**|0.073|11.53**|0.018|6.95**|0.023|7.54**|
|Works|0.040|0.22|-0.087|0.47|-0.095|0.87|-0.203|1.59|
|Works and is not in school|0.059|0.82|0.002|0.03|-0.058|0.94|-0.149|1.20|
|Does housework as main activity|0.011|0.62|-0.004|0.31|0.093|2.23*|0.346|3.74**|
|Is an unpaid familyworker|0.010|0.06|0.015|0.08|-0.090|1.11|-0.076|0.85|

Coefficients are marginal effects from probit (Migration) and iv-probit (other activities) Probits contain all controls in Tables 3 through 6 * significant at 5%; ** significant at 1% 42
look for correlates associated with acceleration and deceleration episodes and examine the probability that an economy will undergo a growth acceleration or deceleration.

This paper is organized as follows. Section 2 briefly discusses the Africa’s growth experience. Section 3 presents the methodology for identifying growth accelerations and decelerations. Section 4 presents the main results. Section 5 examines whether growth accelerations and decelerations matter for economic and social outcomes and looks at correlates of accelerations and decelerations. Section 6 concludes.

# **2.  Africa’s growth 1975-2005**

Data on GDP per capita (PPP at 2000 international prices) and its growth rate are taken from the World Development Indicators, unless otherwise specified. Our sample includes all Sub-Saharan countries, except Liberia and Somalia, for which there are no GDP per capita PPP data.<sup>1</sup> Because we are primarily interested in examining the representative country, we use unweighted country data in the aggregate analysis unless otherwise stated.

Our time series spans from 1975 to 2005.<sup>2</sup> We thus have an unbalanced panel of data with T=31 and N=45. This period follows the first oil-shock and includes the commodities prices plunge, when many African economies collapsed and several conflicts erupted, the introduction of structural reforms, which brought significant changes in many economies, and the recently observed growth recovery.

Figure 1 shows that mean GDP per capita in Africa had a slow, positive long term trend, consisting of about 20 years of virtual stagnation with a point of inflexion upwards in mid-1990s. Since then, actual income has remained above the trend most of the time and the variance appears to have declined.<sup>3</sup> Figure 2 shows Africa’s growth path over the same period. Trend growth declined until the late 1980s, and increased thereafter, although there is evidence of a slowdown in the last years. Variance has declined since the mid-1990s and actual growth has tended to be above and closer to the trend.

> 1 Our sample accounts for more than 98% of population and 99% of the regional GDP in 2005.

> 2 The WDI’s GDP per capita PPP series starts in 1975.

> 3 We employ the Hodrick-Prescott filter in Figures 1 and 2 to smooth the estimate of the long term trend component of the GDP series.

3
of electrification on income are problematic because it is well documented that people with lower income either put off adopting electricity once it becomes available, or may not have the money to pay for the connection fees and other costs of electricity. That is, it is not clear what causes what. In general, the common problem with an impact assessment study is the failure to address causality issues, such as endogeneity of program placement and household participation in an electrification development project. This study uses econometric techniques to determine the net effects of rural electrification, after controlling for endogeneity. More specifically, this study estimates benefits of rural electrification of the REB project on various household and individual welfare outcomes using a 2005 survey of rural households.

This paper is organized as follows. An overview of rural electrification in Bangladesh is given in Section 2. This section examines the institutional development and role of the Rural Electrification Board (REB) in providing electricity to rural customers. Since late 1970s the REB has been promoting electricity in rural areas throughout the country. In section 3, the survey methodology and data used for of this study are described. This section also provides some basic information on electricity use patterns in rural areas. In Section 4, we explore the influence of both household and village characteristics on household demand for electricity. The causality issues in estimation of electrification impacts are addressed in Section 5 using two techniques, which are Propensity Score Matching (PSM) and instrumental variable (IV) regression. Both methods are commonly used to address the complicated causality issues that are inherent in impact assessment exercises. Section 5 also reports findings based on these techniques. The impact of having electricity is taken one step further in Section 6 by looking at the benefits of long-term exposure to electricity, and examining whether the benefits tend to decline over time. Section 7 explores if the distribution of the benefits of electricity vary among rich and poor households. Finally, in the last section we conclude the paper with the policy implications of the study.

# **2. Rural electrification in Bangladesh: The contribution of REB**

After the independence of Bangladesh in 1971, the first major initiative to extend grid electricity in rural areas was taken in 1975 under a scheme called ‘Total Electrification Programme’. This program looked beyond grid connectivity towards development of the 3
# **Executive Summary**

Herders in Mongolia have suffered tremendous losses in recent dzud with mortality values of over half of the animals in a number of sum. This study examines three alternatives to insure livestock deaths in Mongolia: 1) traditional livestock insurance that pays individual herders based on their specific losses; 2) weather insurance that would pay when weather events that are likely to create serious losses occur; and 3) index insurance that would pay when livestock mortality rates exceed certain thresholds by sum.

No approach will be easy and none offers the perfect answer. However, after careful analysis, mortality index insurance appears to offer the best choice. This recommendation is made with several key performance criteria in mind: 1) the insurance should not reward poor managers; 2) the insurance must be affordable by a large number of herders and others at risk when major livestock losses occur; 3) the insurance must be sustainable and profitable for emerging private insurance companies; 4) the first products should focus on the most significant covariant risk; 5) a proper role for government should be to foster development of risk sharing markets without imposing large social cost; and 6) the insurance should work in harmony with other initiatives, including<sup>the</sup> vast array of emergency assistance that is provided.

The mortality index insurance concept meets most of the performance criteria. In particular, since this insurance would pay all herders in the same sum or bag at the same rate, the incentives for management to mitigate livestock losses remain strong. Noone would reduce their effort to collect on insurance. Those who increase their efforts during a major event (dzud) would likely be compensated for this effort even though they<sup>do not</sup> lose livestock. In some cases, they could reasonably expect to receive payments that would compensate for the added effort or the added cost of trying to save their livestock.

The mortality index insurance would pay anytime the mortality rate (adult livestock deaths divided by the total census number of livestock in the area at the beginning of the year) exceeds a well specified threshold. The payment would be a function of the mortality rate times the amount of protection (or liability) purchased by the herder. This insurance is 1) simple; 2) largely free of the common problems of adverse selection and moral hazard; 3) easy to administer with low administrative cost; and 4) largely effective for getting ready cash to herders in a region during a major event.

Data for a limited number of sum in nearly every aimag were available from 1969-2000. These data afforded the opportunity to perform an assessment of the risk associated with offering a mortality index insurance program across Mongolia. While anyone who knows the recent history of losses understands that a very high level of covariate risk is present, these data show that serious losses occur in livestock in about 1 in 5 years. This is the frequency of loss ratios (indemnity divided by pure premium) in excess of 200% in the simulated mortality index insurance program that would be spread
28 M. FAY, D. MARTIMORT, S. STRAUB different narratives reviewed above. In fact, we uncover an inverse U-shaped pattern, peaking up for countries in the upper-middle income range.

Countries with infrastructure deficit should therefore not consider the expansion of private finance as the sole way to increase investment. In fact, policy reforms meant to improve the business environment are likely to have competing effects. Some will make infrastructure ventures more attractive for private financiers, while others will improve public sectors’ ability to raise taxes and spend efficiently. Our analysis suggest that both paths should be pursued.

# REFERENCES

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alive in 2004. This effectively meant that the original household panel survey turned into a panel of individuals. A full household questionnaire was administered in a household where a panel respondent was found residing. Due to household dynamics, the sample size increased to more than 2,700 households.<sup>11</sup> The second KHDS follow-up was administered in 2010 with this time more than 3,300 households interviewed.<sup>12</sup> Although KHDS is a panel of individuals and the definition of a household loses meaning after 10-19 years, it is common in panel surveys to consider re-contact rates in terms of households. Excluding households for which all previous members were deceased (17 households and 27 respondents), the KHDS 2004 field team managed to re-contact 93 percent of the baseline households. In 2010, 92 percent of the initial households were re-contacted. Taking into account the long, 10 or 16 year periods between surveys, the attrition rates in KHDS-2004 and KHDS-2010 are extremely low by the standards for such panels (Alderman et al. 2001).

This paper exploits the fact that the survey includes all tracked split-offs from the original household and contains particularly rich information on the current links between them. The 2010 sample contains 3,314 households, originating from 816 initial households. The average baseline household spawned 4.1 households by 2010, out of which 1.8 were non-migrant and 2.3 were migrant households. Approximately 3 percent of the initial households (99 households) did not have any split-offs. In what follows we will refer to these networks as extended family networks.

Figure 1 provides an overview of migration patterns. By 2010 nearly 45 percent of the households were still residing in their original baseline community. We define migrants as households that in 2010 are not located in the original village but are found from a nearby village, elsewhere within Kagera or outside Kagera.<sup>13</sup> _[Figure 1 here]_

> 11 See Beegle, De Weerdt and Dercon (2006).

> 12 See De Weerdt et al (2012).

> 13 Our results are robust to alternative migrant definitions, such as also defining households that moved to a nearby village as non-migrant households.

10
WPS3511

# **Bank Privatization and Performance**

# **Empirical Evidence from Nigeria**

## **Thorsten Beck**<sup>**1**</sup> **, Robert Cull**<sup>**1**</sup> ***, and Afeikhena Jerome**<sup>**2**</sup>

1 The World Bank 2 University of Ibadan

**Abstract:** We assess the effect of privatization on performance in a panel of Nigerian banks for the period 1990-2001. We find evidence of performance improvement in nine banks that were privatized, which is remarkable given the inhospitable environment for true financial intermediation.  Our results also suggest negative effects of the continuing minority government ownership on the performance of many Nigerian banks.  Finally, our results complement aggregate indications of decreasing financial intermediation over the 1990s; banks that focused on investment in government bonds and non-lending activities enjoyed a relatively higher performance.

Keywords: Bank privatization; Nigeria; Bank performance JEL Codes: G21, G28, G34, O55

### World Bank Policy Research Working Paper 3511, February 2005

_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._

*Corresponding author: World Bank, Mail Stop MC 3-300, 1818 H Street NW, Washington, DC 20433, USA, Ph: 202-473-6365, Fax: 202-522-1155, E-mail: Rcull@worldbank.org.

We would like to thank Miguel Crivelli for outstanding research assistance, and an anonymous referee and conference participants at the World Bank conference on Bank Privatization for comments and suggestions, especially Alan Gelb and our discussants Gerard Byam and Lemma Senbet. Michael Fuchs and Abayomi Alawode also provided valuable comments.

1