rafmacalaba/datause-encoder-data huggingface dataset:
Elaborating on this point, Kremer and Clemens (2016: 71) note “[i]f one believes that the primary impact of the [World] Bank comes from its specific investments, then one might reasonably evaluate the Bank by assessing what proportion of its investments yield, for example, a 7 percent annual rate of return. If one believes, as we and many other observers do, that the biggest effects of the World Bank arise through its role in influencing developing country policy, then one’s assessment of the overall impact of the [World] Bank will hinge primarily on one’s beliefs about the effects of these types of policies. A regular bank hopes to obtain a positive return on the vast majority of its investments. In contrast, the [World] Bank could potentially achieve a high social rate of return through a few big wins [. . . ]. Clearly, if one believes that the [World] Bank has reduced poverty primarily by using its financial resources as part of a bargaining process to promote a few important policy reforms, measuring the Bank’s impact becomes difficult. Running a regression with country-level outcomes as the dependent variable and World Bank lending as a key explanatory variable will miss the point if a substantial share of the Bank’s impact came from the spread of conditional cash transfers, or reforms inspired by the Doing Business reports, or the impact of former Bank officials on Indian economic reforms in the 1990s.” To help close this evidence gap, we designed and implemented the _2014 Reform Efforts Survey_ in the summer of 2014 in partnership with the National Opinion Research Center (NORC) at the University of Chicago. It provides a unique source of micro-level survey data on the policy influence of nearly 150 other aid agencies and development banks, as observed and experienced by the local counterparts of these institutions in 126 low-income and middle-income countries.<sup>11</sup> Unlike traditional elite surveys that are based on convenience sampling (where a population of interest is not identified and sample representativeness cannot be evaluated), we first identified our populations of interest in 126 low-income and middle-income countries and carefully constructed sampling frames for each of these countries in a consistent and

> 11The questionnaire is available in the online appendix of Parks et al. (2015).

11
nutrition outcomes (Baird et al. 2007; Ferreira and Schady 2008; Paxon and Schady 2005).

There are, however, a few studies that analyze the link between income shocks and short-term health-related coping strategies. For example, Conceição et al. (2009) acknowledge that an income shock reduces the household‘s ability to pay for maintaining or improving health generally decreases and private spending on food, medicine and health care also decreases. Cutler et al. (2002) find that during the economic crisis in Mexico, out-of-pocket health expenditures on hospitalization, doctors visit and dental care declined (3.9 to 3.1 percent of GDP) between 1994 and 1995. The decline was more evident among families with elderly members and that led to higher mortality rates in post crisis Mexico. Frankenberg et al. (1999) report a significant decline in the proportion of household incomes spent on health in Indonesia. They show that public healthcare use declined sharply and the percentage of children under five visiting community primary health care centers fell by half.

# 3 Data

This paper analyzes data from five Crisis Response Surveys (CRS) conducted in Armenia, Bulgaria, Montenegro, Romania, and Turkey. All the surveys were conducted between mid-2009 and early-2010. These CRSs are shorter versions of regular household surveys (such as Living Standards Measurement Surveys) but special modules focus on income shocks and coping strategies. Although the survey contains information on whether incomes rose, fell or stayed the same, a key drawback of the surveys is that they have no measure of the intensity of the effect on any of the income components.<sup>6</sup> CRS for four countries were launched in rural and urban locations, but the Turkey CRS only surveyed urban households.

Households affected by income shocks are those households that report that any one of the following applies within the recall period (6-12 months): (i) wage rates of any working age individual in the household falls; (ii) working hours of any working age individual in the household

> 6 Income modules are often difficult to implement and data from those modules are often suspect because households are reluctant to provide accurate information.

6
deposit-insurance adoption, design and performance (see for instance, Demirgüç-Kunt and Kane 2002). We show that good supervision can affect the state-varying benefits and costs of deposit insurance. Strong supervision and regulation can enhance the stabilization effects during crisis periods while dampening the negative effects associated with moral hazard during normal times.

While there is widespread agreement in the academic literature that deposit insurance affects bank risk through two channels, stabilization and moral hazard, our paper is the first to document that the net impact of deposit insurance varies with the relative importance of these two effects. Our results indicate that deposit insurance indeed enhanced depositor confidence and had positive stabilization effects during the recent global financial crisis. Our findings also emphasize the role of regulation and supervision in the effectiveness of deposit insurance schemes by maximizing the benefits of deposit insurance while minimizing costs associated with incentives and moral hazard.

The rest of the paper is organized as follows. Section 2 describes the construction of the sample and variables. Section 3 presents the empirical results and discusses the implications. Section 4 concludes.

# **2. Data**

# **2.1. Sample and Bank Level Variables**

Our sample consists of all publicly traded banks covered by the Bankscope database. We use stock market information from Compustat for international banks and stock market information from CRSP for U.S. banks. The Bankscope database reports detailed balance sheet and income statement information for both public and private banks and covers over 90% of the total banking assets in a given country. The Compustat database provides daily stock price information for both active and delisted companies, accounting for 98% of the global stock market capitalization. CRSP is the standard source for stock price information of U.S. companies. Our final sample consists of 4,109 banks in 96 countries over the time period 20042009.

Bank level variables are constructed from the Bankscope database. We follow Laeven and Levine (2009) and compute a traditional measure of bank risk, which is calculated as average bank return on assets (net income divided by total assets) plus bank equity to assets ratio, scaled by the standard deviation of return on assets over a five-year rolling window. Higher 5
_Figure 4: Drought effect along the consumption expenditure distribution, rural areas._ <!-- Start of picture text --> 10%<br>0%<br>‐10%<br>‐20%<br>‐30%<br>‐40%<br>‐50%<br>‐60%<br>0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1<br>Income percentile<br>Drought effect Smoothed Drought Effect 95% Confidence Interval<br>Drought impact on consumption<br><!-- End of picture text --> Source: Authors’ calculations.

Varying levels of drought exposure along the consumption distribution do not explain these differences, as the median drought intensity among the poorest 10 percent of households is similar to the overall average drought exposure. With an average poverty gap of 72 percent, this group is very poor. These households may have relied on humanitarian assistance already before the onset of the drought, thus being isolated from the impact of the drought on consumption.

More drought exposed households were also more likely to experience hunger. As levels of hunger rose across all Somali regions, rural households in highly drought‐exposed areas were most severely affected. Higher drought exposure led to an 11 percent decrease in food consumption, accompanied by a 19 percent increase in the probability of experiencing hunger in December 2017. The effect is much less pronounced among urban households (Table A.3; Table A.4).

_Figure 5: Drought effect on hunger and food consumption._ <!-- Start of picture text --> 40%<br>30%<br>20%<br>10%<br>0%<br>‐10%<br>‐20%<br>‐30%<br>Urban Rural<br>Hunger Food consumption<br>Percentage change<br><!-- End of picture text --> Source: Authors’ calculations.

7
value 1 in period _t_ after the event and 0 otherwise. Since the time units differ between the CP and PoI data, we set _t_ as 1 month for the household consumption regression and four months (equivalent to one wave) for the individual-level time usage regression. For example, in the household-level data (CP data), _Time_ = 0 corresponds to 1 month before the event; in the individual-level data (PoI data), _Time_ = 0 corresponds to 1 wave (equivalent to four months) before the event. The omitted base period (or _Time_ = 0) is the wave (or month) of the survey before the start of the program. We then pool the preand post-treatment periods together to estimate In both equations (6) and (7), _αi_ are individual (or household) fixed effects that control for all time-invariant characteristics of an individual (or a household). _αt_ represents the time-fixed effects, encompassing month and year-fixed effects in the regression that examines changes in household consumption. In the analysis of PoI data, _αt_ comprises wave and year-fixed effects. Appendix Table D2 provides a detailed definition for _Timet_ and _Postt_ . To account for the impact of the pandemic on people’s behavior, we also control for average daily confirmed COVID-19 cases as a share of the population. Because the treatment is at the state level, we cluster standard errors at the state level for statistical inference. Our parameters of interest are _βt_ and _β_ .

# **G Treatment Effect on Expenditure**

We use both the CMIE CPHS data and the Delhi survey to isolate the effect of treatment on transportation expenditure.

## **G.1 Evidence from The CPHS Sample**

To examine the of the _Pink_ on _household_ we impact _Slip Program_ monthly expenses, plot the estimated coefficient _β_<sup>ˆ</sup> _t_ from the event-study specification (6) in Figure C1 and detect no pre-trends. However, after implementation, we see a substantial decrease in household expenditure on transportation in the treated states. Treated households report 10.637.2 log points lower expenses on all kinds of transport relative to control households after the program was implemented (Figure C1(a)). We also find that the share of transport expense in total expenditure decreases by 0.3-0.6 percentage points (Figure C1(b)). Consistently, we find a reduction in the expenditure on bus/train/ferries category (BTF) 9
38 <!-- Start of picture text --> Price Band: Tia Juana Light and WTI<br>CUSS PER BARREL, week Iy)<br>40 -<br>36<br>34-<br>32-<br>30<br>28<br>2 26<br>22-<br>18<br>16<br>14<br>12<br>86 67 88 69 90 91<br>Year<br>0 Tia Juana WTI<br><!-- End of picture text -->

**Figure** 5: Price Band

using Black's model, the futures prices on that date, and the estimate of the volatility of oil prices implied by the option premiums on contracts which are quoLed on that day). We use the spot WTI-price obtained from DRI on a weekly basis.<sup>3</sup> ' For the period 1/1/87 - 7/31/90 the results are as follows: buying daily over the period 1/1/87 - 3/1/90 the five-month-out $20 put option costs on average $2.07/barrel. Only a limited amount of $24 calls were available, which cost on average $0.26/barrel. Over the period 1/1/87 - 8/1/90 the put options were exercised 3"As shown earlier, the WTI price correlates closely with the Venezuelan spot price in recent periods. However, before 1989 the Tia Juana price did not follow WTI as closely (see Figure _5)._
Results by Brockerhoff (1990, 1994) point in a similar direction, but also suggest a role for other socio-economic factors that may condition the use of health services. Brockerhoff (1990) focuses on the impact of rural-to-urban migration on child survival in Senegal. Using data from the DHS survey carried out in 1998, he estimates both infant (0-15 months) and child mortality (16-59 months) proportional-hazard models including migration variables. His results indicate that children's survival chances improve when migrating from rural to urban areas, but that children of rural-to-urban migrants continue to experience a much higher under-five mortality risk than urban non-migrants. Brockerhoff (1994) extends this analysis to four regions (Sub-Saharan Africa, North Africa, Latin America and Southeast Asia), using pooled regional samples of DHS surveys for 17 countries over the 1986-1990 period. Migrating from rural to urban areas appears to increase the risk of dying within the time of moving, but significantly reduces the risk afterwards in the late 1970s and 1980s. In Mali and Senegal the use of modern health services clearly increased after migration.

A second set of empirical studies focuses and differences in child mortality between socio-economic and ethnic groups. The reviewed literature reaches different conclusions as regards the magnitude of such differences. Mturi and Curtis (1995), for example, investigate the determinants of infant and child mortality in Tanzania using the 1991/92 Tanzania Demographic and Health Survey. They find a remarkable lack of infant and child mortality differentials by socioeconomic subgroups of the population, which they posit may reflect post-independence health policy and development strategies. This results stands in stark contrast to findings from other countries in SSA.

Brockerhoff and Hewett (2000) examine inequality of child mortality among ethnic groups in 11 Sub-Saharan Africa countries (Central African Republic, Côte d’Ivoire, Ghana, Kenya, Mali, Namibia, Niger Rwanda, Senegal, Uganda, Zambia) using DHS data. Their multivariate analysis takes into consideration the effects of ethnic affiliation as well as additional variables, to determine whether infant mortality differentials among ethnic group members are related to a combination of differences in household economic conditions, women’s status, demographic behavior, and preventive health care practices, as well as residence in the largest city of the country. Their results indicate that there exist large disparities in early child survival chances among ethnic groups in a wide range of African countries. They also suggest that these differentials are closely linked with ethnic inequalities in household economic status, education of women, access to and use of health services, and degree of concentration in the largest city.

In South Africa, Burgard and Treiman (2004) examine racial differences in child 12
KENYA’S INFRASTRUCTURE: A CONTINENTAL PERSPECTIVE

**Figure 15. Underpricing of power and water in Kenya relatively low but still material**

Financial burden of underpricing in 2006, as percentage of GDP <!-- Start of picture text -->
1.0<br>0.9<br>0.8<br>0.7<br>0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0.0<br>power water<br>Kenya low-income countries<br>% of GDP<br><!-- End of picture text -->

Source: Derived from Briceño-Garmendia and others 2009.

**Figure 16. Kenyans’ consumption of infrastructure services is highly differentiated by budget**

(a) Mode of water supply, by income quintile <!-- Start of picture text --> 100<br>80<br>60<br>40<br>20<br>0<br>Q1 Q2 Q3 Q4 Q5<br>income quintile<br>piped water standposts<br>wells/boreholes surface water<br>% of population<br><!-- End of picture text --> 27
**Table 12b.  Shares of Business Services Sectors in the Regions of Russia Captured by Multinational Firms** (ad-valorem in %) -- by sector

|||||||Other||||
|---|---|---|---|---|---|---|---|---|---|
||Maritime|Rail|Truck|Pipeline|Air|Transp.|Telecom|Science|Financial|
|Moscow|0.47|0.02|0.03|0.06|0.11|0.02|0.08|0.07|0.13|
|St. Petersburg|0.70|0.06|0.10|0.06|0.50|0.08|0.30|0.20|0.18|
|Tumen|0.29|0.04|0.05|0.01|0.46|0.04|0.20|0.12|0.08|
|Northwest|0.70|0.06|0.10|0.06|0.50|0.08|0.30|0.20|0.20|
|North|0.70|0.06|0.10|0.06|0.50|0.08|0.30|0.20|0.20|
|Central|0.41|0.03|0.07|0.05|0.35|0.05|0.20|0.09|0.09|
|South|0.46|0.04|0.06|0.05|0.30|0.05|0.19|0.10|0.09|
|Urals|0.15|0.02|0.03|0.02|0.16|0.03|0.09|0.07|0.04|
|Siberia|0.28|0.02|0.05|0.03|0.26|0.04|0.15|0.09|0.07|
|Far East|0.70|0.06|0.10|0.06|0.50|0.08|0.30|0.20|0.20|
|National average|0.35|0.03|0.05|0.03|0.25|0.04|0.15|0.10|0.10|

49
# **Introduction**

It is usually recognized by analysts that Chile and Colombia were spared from the financial turmoil that originated in Mexico by the end of 1994; nevertheless, it is also argued that those two countries suffered difficulties following the Asian and Russian debacle at the end of that decade. Contrary to a large number of countries in the region, Chile and Colombia adopted price-based capital account regulations in order to manage their integration to international financial markets and avoid some of the deleterious side effects of liberalization such as excessive real exchange rate appreciations. The effectiveness of these regulations has been subject to heated debate (see David, 2005 for a recent survey). In this paper, we will attempt to analyze whether the controls on capital inflows adopted by the two countries were successful in reducing the vulnerability of those economies to external shocks.

This paper is divided into three sections. Firstly, we will review the channels through which the reserve requirements on capital inflows could possibly affect the transmission of international shocks to the domestic economy. Subsequently, we will present results from vector autoregressive models in order to assess the transmission of global financial shocks to the Chilean and Colombian economies. Finally, in the third section, we will use the ARDL approach to co-integration in order to attempt to isolate the effects of the capital controls on the pass-through of external disturbances to domestic interest rates.

2
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4 corporate governance. I rely heavily on Shleifer and Vishny’s (1997) comprehensive review. The next section discusses how the special features of banks complicate the governance of banks.

# **_A. Diffuse shareholder_**

In textbook models of a firm, diffuse shareholders exert corporate governance by directly voting on crucial issues, such as mergers, liquidation, and fundamental changes in business strategy, and indirectly by electing the boards of directors to represent the interests of the owners and oversee the myriad of managerial decisions. Incentive contracts are a common mechanism for aligning the interests of managers with those of shareholders. The board of directors may negotiate managerial compensation contracts that link compensation with achieving particular results.<sup>3</sup> Thus, diffuse shareholders may exert corporate governance directly through their voting rights and indirectly through the board of directors.

A variety of factors, however, keep diffuse shareholders from effectively exerting corporate control. There are large informational asymmetries between managers and small shareholders and managers have enormous discretion over the flow of information. Small shareholders frequently lack the expertise to monitor managers. Furthermore, the large costs associated with monitoring managers accompanied by each small investors small stake in the firm may induce a “free-rider” problem: each investor relies on others to undertake the costly process of monitoring managers, so there is too little monitoring. Given the difficulties in acquiring information, the voting rights mechanism may not work effectively. Also, the board of directors will not represent the interests of the minority shareholders, if – as occurs to often – management captures the board. Finally, in many countries, legal codes do not adequately protect the rights of minority shareholders and legal systems frequently do not enforce the legal codes that are actually on the books concerning minority shareholder rights. These forces work to provide managers with significant discretion over the control of corporate assets.

# **_B. Concentrated shareholders_**

One corporate governance mechanism for preventing managers from deviating too far from the interests of owners is concentrated ownership. Large investors have the incentives to acquire
choice (Friedman 1955), which while not absent in the Côte d’Ivoire context, is less central. The arrangement is more like contracting out education services – or some form of mandate from the state (Murphy and others 1996; Savas 2000).

Subsidies vary according to fee level (the higher the fee charged by the school, the lower the subsidy) and region (schools in Abidjan receive less than those outside Abidjan) (World Bank 1999). For example, in 1995/96 the government paid the equivalent of $66 per student per year to religious schools outside Abidjan whose fees are less than $50, while it paid $41 per student per year in Abidjan whose fees are less than the equivalent of $83. Schools with fees above those levels do not receive any subsidy (World Bank 1999). The state pays private lowersecondary schools $200 and private upper-secondary schools $233 per student per year for students it sponsors to attend private schools.

# **Private Education Sector**

It is estimated that in 1997/98 there were 432,277 private school students in Côte d’Ivoire. The share of private students in the education market in Côte d’Ivoire was 18 percent (12 percent at the primary level and 36 percent at the secondary level). The private sector share of enrollments varies across the different levels (Table 1). In 1997/98, 49 percent of students in private institutions were at the primary level, while 44 percent were at the secondary level and about 6 percent were at the tertiary professional and technical levels; none were at the academic level (Table 1). Between 1991/92 and 1997/98, the proportion of students in private institutions grew at annual rates of 9 percent for primary, 13 percent for secondary, and 490 percent for tertiary institutions (albeit from a very small base for the latter).

6