rafmacalaba/datause-encoder-data huggingface dataset:
|**_Table7dcont_**|**_inued: 70%_**|**_replace_**|**_ment ra_**<br>|**_te_**<br>|||**__ __**<br>|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|llIlldIYY|=||=%||d|Y =1|%|d|Y = 1|5%|dY|2%/<br>||
|Contribution|ge|18|21|25|18|21|25|18|21|25|18|21|125|
|Real i = 4%|||||||||||||I|
|Retire at 55|80|18|20|23|21|23|27|23|25|29|26|28|31|
||81|18|20|23|22|24|27|24|26|29|26|28|32|
||83|19|21|24|23|25|28|25|27|30|27|29|33|
||85|20|21|25|24|26|29|26|28|32|28|30|34|
|Retire at 60|80|12|13|15|15|16|18|17|18|20|19|20|22|
||81|13|14|16|16|17|19|17|18|21|19|20|23|
||83|13|**14**|17|16|18|20|18|19|22|20|21|24|
||85|14|15|18|17|18|21|19|20|23|21|**I**22|25|
|Retireat65|80|8|9|10|10|11|13|12|13|14|13||14|15|
||81|8|9|11|II|12|13|12|13|14|14|15|16|
||83|9|10|11|12|13|14|13|14|16|15|16|17|
||85|10|11|12|12|13|15|14|15|17|16|1 7|18|
|Retire at 70|80|5|5|6|7|7|8|8|8|9|9|9|10|
||81|5|6|7|7|8|8 1|<br>8|9|10|9|10|11|
||83|6|6|7|8|8|9|9|10|11|10|11|12|
||85|7|7|8|9|9 I|<br>10|10|11|12|11|1 12|13|

Note: * indicates a contribution rate of over 45% 39
|**Mean**<br>**TABLE 1**<br>**Sample Summary Statistics**|**P**|**ercentiles**||
|---|---|---|---|
||**10th**|**50th**|**90th**|
|**I. Household-level Data (Panel households;****_N_ = 3,424)**||||
|**Per Capita Income (Constant 1986 Yuan):**||||
|1987-88<br>526|229|448|896|
|1990-91<br>518|234|425|870|
|1995-96<br>709|316|593|1,192|
|1997-98<br>742|330|630|1,223|
|1999-00<br>749|310|614|1,312|
|2001-02<br>826|334|678|1,443|
|**Annualized Growth Rates from 1987-88 to:**||||
|1990-91<br>-0.009|-0.178|-0.009|0.157|
|1995-96<br>0.036|-0.045|0.039|0.116|
|1997-98<br>0.034|-0.034|0.034|0.102|
|1999-00<br>0.027|-0.035|0.029|0.089|
|2001-02<br>0.030|-0.026|0.031|0.085|
|**Composition of Income (Shares):**||||
|Agriculture, 1987-88<br>0.55|0.17|0.56|0.89|
|Wages, 1987-88<br>0.22|0.00|0.13|0.61|
|Business, 1987-88<br>0.14|0.00|0.04|0.45|
|Agriculture, 2001-2002<br>0.36|0.03|0.31|0.79|
|Wages, 2001-2002<br>0.34|0.00|0.31|0.79|
|Business, 2001-2002<br>0.16|0.00|0.00|0.59|
|**Other Household Characetristics in 1987-88:**||||
|Household Education<br>5.56|2.88|6.00|9.00|
|Household Size<br>4.77|3.00|5.00|7.00|
|Dependency Ratio<br>0.43|0.20|0.50|0.60|
|Cultivated Land (Mu)<br>1.46|0.50|1.17|2.81|
|Head Age <= 30<br>0.09||||
|Head Age between 31 and 40<br>0.33||||
|Head Age between 41 and 50<br>0.35||||
|Head Age between 51 and 60<br>0.16||||
|Head Age 61 and over<br>0.06||||
|**II. Village-level Characteristics in 1987-88 (****_N_= 82)**||||
|**Inequality of Per Capita Household Income:**||||
|Gini Coefficient<br>0.20|0.14|0.19|0.28|
|Mean Log Deviation<br>0.07|0.03|0.06|0.12|
|90-10 Ratio<br>2.56|1.91|2.46|3.39|
|**Other Characteristics:**||||
|Household Education<br>5.53|4.00|5.66|7.13|
|Share of Income from Agriculture<br>0.54|0.25|0.59|0.74|
|Share of Income from Wages<br>0.20|0.07|0.16|0.37|
|Share of Income from Family Enterprises<br>0.15|0.05|0.13|0.30|
|Village Total Tax Revenue Per Capita<br>0.46|0.06|0.31|0.92|
|<br>Village Total Public Expenditure Per Capita<br>0.46|0.06|0.30|0.90|
|<br>Mountainous Terrain<br>0.26||||
|Hilly Terrain<br>0.39||||
|Near a City<br>0.06<br>_Notes:_<br>1) Household-level statistics are calculated over the 3,424 panel house<br>statistics are calculated over all available households in the 82 villages|holds, while<br>in 1987-88|the village-<br>(4,847)|level|
_Third_ , the extremely rich _Gojek_ data allows us to provide much more fine-grained, and, therefore, in-depth, evidence on the complementarity versus substitution hypothesis. Notably, **1)** we show the effect(s) of the opening of the MRT on a _daily_ , as opposed to a monthly, basis, allowing us to show how they evolve throughout each phase of Jakarta MRT’s staggered opening; **2)** we document how the MRT opening affects both rush- and non-rush hour ride-hailing activity, as well as regular weekday, and weekend and public holiday ride-hailing activity; **3)** we show that results differ for station locations at which there was no previous alternative BRT stop; and **4)** the data on daily tap-ins and tap-outs into each of the newly opened MRT stations allow us to show that daily _Gojek_ ride volume and the actual use of the MRT system are positively associated, thereby alleviating concerns that the increased ride-hailing volume might be associated with, for example, the opening of new urban amenities around the MRT stations rather than the use of the MRT system itself.

Last, but certainly not least, our study complements several more qualitative studies that provide surveybased evidence on either ride-hailing customers’ (Irawan _et al._ , 2020), or public transport users’ (Saffan and Rizki, 2018; <mark>Sunitiyoso</mark> _<mark>et al</mark>_ <mark>, 2022) commuting behavior to better understand the role of ride-hailing services in public transport trips in Jakarta. Also, we</mark> contribute to a literature that is specifically focused on evaluating the impacts of various transportation policies for Jakarta. Gaduh, Gračner, and Rothenberg (2022) evaluate the impacts of Jakarta’s BRT system, the _TransJakarta_ , on vehicle ownership, travel times and commuting flows. Kreindler _et al._ (2023) add to this by showing how the recent expansions of the BRT network affected bus ridership and overall commuting flows. Meanwhile, Hanna, Kreindler, and Olken (2017) identify the effect of Jakarta’s so-called “3-in-1” high occupancy vehicle policy, aimed to reduce congestion, on travel speeds in the city.

The remainder of the paper is structured as follows. Section 2 provides an overview of Jakarta’s MRT system. Section 3 describes our event study design, which leverages the highly spatially and temporally granular data that we describe in Section 4. Section 5 presents our results. Finally, Section 6 concludes.

# **2. Overview of Jakarta’s MRT System**

Jakarta is Indonesia’s largest city. In 2016 it had a population of 10 million in the “city proper”, and of 31 million in the wider metropolitan area, also known as Jabodetabek.<sup>5</sup> It is also consistently rated one of the 10 most congested cities in the world (Roberts, Gil Sander, and Tiwari, 2019). Around 3.2 million (or 11 percent) of Jabodetabek’s inhabitants are commuters (BPS, 2020). Most of them (63.3 percent) use their own motorbike for travel, followed by “public” transport (26.9 percent) (BPS, 2020). The latter

> 5 Jakarta’s “city proper” corresponds to the district of DKI ( _Daerah Khusus Ibukota_ ) Jakarta.

5
|Min. score in HS GPA or national entry test|0.66|0.47|0|1|1|2,103|
|---|---|---|---|---|---|---|
|Percent of governing body that belongs to:|||||||
|Private sector|18.90|21.20|0|12|100|2,103|
|Government|11.90|16.40|0|1|100|2,103|
|Faculty*|39.00|27.80|0|33|100|2,103|
|Current students or other sectors|25.60|22.30|0|21.5|100|2,103|
|**Panel B. Student body, program, and institution characteristics**|||||||
|_Student body characteristics_|||||||
|Academic deficiencies|||||||
|Mathematics is lacking in incoming students|0.82|0.39|0|1|1|2,103|
|Oral expression is lacking in incoming students|0.57|0.49|0|1|1|2,103|
|Reading is lacking in incoming students|0.70|0.46|0|1|1|2,103|
|Writing is lacking in incoming student|0.68|0.47|0|1|1|2,103|
|Other competencies are lacking in incoming students|0.15|0.36|0|0|1|2,103|
|Percent of students that are|||||||
|25+ years old|28.94|29.30|0|20|100|2,103|
|full-time|43.89|39.00|0|33|100|2,103|
|Women|38.19|29.20|0|40|100|2,103|
|Student body characteristics (PCA score)|-0.02|1.40|-2.78|-0.31|4.88|2,103|
|_Program characteristics_|||||||
|Program duration (semesters)|5.20|0.97|2|6|8|2,101|
|Number of cities where the HEI offers the program|3.55|8.32|1|1|151|2,083|
|Program has high quality accreditation|0.19|0.39|0|0|1|2,103|
|Total number of students in the program last year|221.60|332.80|1|125|4,321|2,030|
|Program age (years)|11.50|9.46|0|10|70|2,103|
|Program characteristics (PCA score)|0.02|1.25|-7.59|0.20|2.43|2,082|
|_Institution characteristics_|||||||
|HEI is public|0.30|0.46|0|0|1|2,103|
|HEI is a university|0.21|0.41|0|0|1|2,103|
|HEI is for profit|0.20|0.40|0|0|1|2,103|
|HEI age|37.84|30.80|1|32|481|2,094|
|Number of programs in the HEI|21.59|36.30|1|10|268|2,103|
|<br>HEI characteristics (PCA score)|0.08|1.16|-1.84|-0.21|9.57|2,094|
|**Panel C. Noise controls**|||||||
|Survey conducted during COVID|0.48|0.50|0|0|1|2,103|
|Number of attempts to complete the survey|8.36|3.16|1|9|17|2,103|
|Survey completed by phone|0.33|0.47|0|0|1|2,103|

_Sources_ : Own calculations using WBSCPS and administrative data.

_Notes_ : This table shows the descriptive statistics of the main variables used in the analysis (Table 1 presents an abridged version). An observation corresponds to a program. Dummy variables included in the list are those with means between 0.1 and 0.9. Statistics are weighted by WBSCPS sampling weights. Panel A refers to quality determinants, presented by category. Panel B refers to characteristics of the student body, program, and higher education institution (HEI); Panel C refers to survey noise controls. Total number of surveys completed is 2,103. Values in the “Obs.” column vary depending on the number of valid responses. * indicates the omitted category in the analysis. + indicates that the variable is conditional on another. For example, “Mandatory internships during the program” is conditional on “Internships outside institution are mandatory” = 1; therefore, its coefficient is relative to not having mandatory internships outside the institution or having them but not during the program. Tuition is 69
We now explore whether the impact of land titling depends on the linkage to the government based on firm ownership. SOEs have a natural connection to the government, while non-SOEs owned by private individuals and other non-state entities likely do not have such connections.<sup>26</sup> We estimate the land titling effects either separately for SOEs and non-SOEs (columns (1) and (2) of Table 8) or applying the interaction of the SOE dummy and _Titling Firm_ in the pooled sample (Column (3)). Both methods yield similar results.

The land titling effect is statistically significant and pronounced only for SOEs, but not for non-SOEs. The magnitude of the titling effect is several times larger for SOEs than nonSOEs, and the effect is positive and significant only for SOEs. The results suggest that the benefits of urban land titling apply only to politically connected SOEs but not to non-SOEs, which are still vulnerable to government expropriation (of land) even after land titling. The results support the connection-rights complementarity hypothesis.

# _5B. Comparing SOEs and non-SOEs_

The more pronounced effects of titling on SOEs relative to non-SOEs could reflect SOEs’ advantages over non-SOEs in many aspects rather than favorable property-rights enforcement. We could exclude certain channels by showing that titling SOEs and titling non-SOEs do not differ significantly in the characteristics related to those channels. Toward this end, we compare the size of the land, the cost of the capital of equity, the extent of the financial constraints, and the quality of corporate governance. For all these aspects, as shown below, the differences between the titling SOEs and their non-SOE counterparts are not statistically significant.

Since a firm’s land size could affect profitability and investment, we first investigate whether the scale of the land held by titling SOEs and titling non-SOEs differ. We first look at the total area of the land, available from the Urban Planning, Land and Resource Commission of Shenzhen. The first row of Table 9 shows that the size of the land held by titling SOEs is

> 26 Privatized non-SOEs could have natural connections with the government. Our test of titling effects by board composition likely would capture this possibility of political connections by non-SOEs to the government.

29
# **Appendix C. Validation of Final Database**

We validate the representatives of the final database by calculating the ratio of the sum of employment and gross output in the database to their corresponding aggregates, in the same manner as Gopinath et al. (2017). Aggregates for employment and gross output are obtained from Eurostat’s Structural Business Statistics Database (SBS). Tables C.1 and C.2 show the coverage of our sample by country, separately for non-manufacturing and manufacturing sectors.

Table C.1: Coverage of Final Database Relative to Eurostat (SBS) - Non-Manufacturing

|**Country**|||**E**|**mployme**|**nt**||||||**Turnove**|**r**|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|**2010**|**2011**|**2012**|**2013**|**2014**|**2015**|**2016**|
|Austria|19%|21%|28%|32%|26%|31%|26%|16%|18%|34%|42%|36%|39%|36%|
|Belgium|44%|41%|43%|45%|44%|44%|43%|55%|56%|55%|55%|55%|53%|52%|
|Bosnia and Herzegovina||72%|34%|66%|65%|53%|60%||73%|40%|70%|63%|59%|62%|
|Bulgaria|47%|58%|73%|70%|72%|76%|71%|53%|58%|61%|62%|62%|63%|62%|
|Croatia|53%|57%|59%|60%|60%|61%|63%|66%|67%|74%|76%|72%|72%|75%|
|Czechia|64%|70%|68%|69%|66%|68%|63%|48%|48%|49%|51%|52%|54%|51%|
|Estonia|41%|42%|42%|43%|46%|45%|47%|38%|39%|41%|39%|43%|42%|44%|
|Finland|42%|45%|45%|44%|44%|41%|39%|52%|60%|56%|61%|60%|60%|57%|
|France|25%|25%|24%|28%|32%|32%|29%|30%|31%|26%|34%|39%|37%|35%|
|Germany|26%|28%|29%|29%|30%|28%|27%|47%|51%|49%|50%|49%|45%|45%|
|Hungary|38%|39%|41%|43%|44%|39%|36%|73%|77%|82%|84%|85%|79%|72%|
|Italy|39%|54%|55%|54%|56%|59%|56%|48%|58%|55%|54%|55%|55%|56%|
|Luxembourg|39%|41%|41%|45%|125%|147%|128%|57%|64%|70%|69%|71%|89%|83%|
|Montenegro|||||||||||||||
|North Macedonia|||58%|63%|66%|67%|71%|||77%|83%|87%|90%|84%|
|Norway|10%|10%|9%|7%|7%|69%|69%|9%|9%|8%|7%|11%|59%|65%|
|Poland|19%|18%|13%|7%|6%|4%|12%|21%|20%|14%|7%|7%|6%|15%|
|Portugal|42%|46%|44%|44%|46%|46%|43%|50%|56%|55%|53%|57%|56%|51%|
|Romania|43%|47%|49%|51%|54%|58%|58%|56%|63%|69%|71%|72%|81%|82%|
|Serbia|||||||59%|||||||102%|
|Slovak Republic|52%|60%|59%|65%|65%|59%|58%|54%|71%|68%|68%|66%|64%|61%|
|Slovenia|64%|65%|69%|69%|68%|66%|67%|70%|76%|78%|79%|78%|76%|74%|
|Spain|42%|44%|43%|44%|45%|45%|42%|50%|52%|52%|53%|53%|54%|49%|

Note: Blanks correspond to country-year pairs for which the Eurostat’s SBS Database does not report information. Montenegro and Ukraine are excluded from this table as the SBS Database does not include information for any of the years in our sample.
Table S.29: Checking on spillover effects <u>(OLS)</u>

|Dependent variable:||One if|pass||
|---|---|---|---|---|
||Case A|Case B|Case C|Case D|
||(a)|(b)|(c)|(d)|
|**December 2012**|||||
|Share of treated students and their|-1.771*|0.826|-1.242|-0.854|
|siblings in each school-grade|(1.044)|(1.025)|(2.320)|(1.797)|
|Share of students in each school-grade that|0.976|-0.497|1.084|0.375|
|had no access to electricity at baseline|(0.965)|(0.545)|(1.195)|(0.860)|
|All products|0.001|-0.003|0.000|-0.000|
||(0.030)|(0.030)|(0.030)|(0.030)|
|One product|-0.008|-0.014|-0.010|-0.012|
||(0.040)|(0.040)|(0.040)|(0.040)|
|R-squared|0.222|0.215|0.216|0.213|
|No. of obs.|849|849|849|849|
||(e)|(f)|(g)|(h)|
|**December 2013**|||||
|Share of treated students and their|0.698|-0.162|0.667|0.486|
|siblings in each school-grade|(0.887)|(0.622)|(1.399)|(0.982)|
|Share of students in each school-grade that|-0.034|0.505|-0.283|0.042|
|had no access to electricity at baseline|(0.703)|(0.461)|(0.905)|(0.656)|
|All products|0.022|0.023|0.022|0.022|
||(0.031)|(0.031)|(0.031)|(0.031)|
|One product|-0.005|-0.003|-0.004|-0.003|
||(0.029)|(0.029)|(0.029)|(0.029)|
|R-squared|0.329|0.327|0.325|0.326|
|No. of obs.|849|849|849|849|
||(i)|(j)|(k)|(l)|
|**December 2014**|||||
|Share of treated students and their|0.764|-0.333|0.341|0.287|
|siblings in each school-grade|(0.679)|(0.397)|(1.151)|(0.799)|
|Share of students in each school-grade that|0.134|0.764***|-0.057|0.285|
|had no access to electricity at baseline|(0.531)|(0.280)|(0.744)|(0.457)|
|All products|-0.058|-0.057|-0.058|-0.057|
||(0.042)|(0.042)|(0.042)|(0.042)|
|One product|-0.031|-0.028|-0.028|-0.028|
||(0.039)|(0.039)|(0.038)|(0.038)|
|R-squared|0.154|0.153|0.148|0.150|
|No. of obs.|844|844|844|844|
|Baseline control|Yes|Yes|Yes|Yes|
|School FE|Yes|Yes|Yes|Yes|
|Village FE|Yes|Yes|Yes|Yes|

Notes: (1) Figures ( ) are standard errors. *** denotes significance at 1%, ** at 5%, and * at 10%. (2) Standard errors are robust to heteroskedasticity and clustered residuals within each school-grade. (3) The baseline controls include age (years); education (years); gender (dummy); household size; no. of males; head age (years); head education (years); head gender (dummy); and land size (natural number).

74
Policy Research Working Paper 8290

# **Abstract**

This study analyzed the impact of a community-based preschool program on child development and schooling outcomes in high-poverty areas of rural Mozambique. Preschools were randomly assigned to 30 of 76 eligible communities. Using a panel survey of 2,000 households with preschool aged children, the study found that children who attended preschool experienced gains in cognitive development, communication, fine motor skills, and socio-emotional skills, scoring 0.33 standard deviations higher on a child development screening test. Preschoolers were 21 percentage points more likely to be enrolled in primary school, 14.9 percentage points more likely to enroll at the appropriate age, and had higher cognitive and communication scores in first grade. Treatment effects were generally larger for children from vulnerable households, those with higher initial development levels, and those with longer exposure to treatment. The preschool intervention also generated positive spillovers by increasing the school enrollment of older siblings and labor supply of adult caregivers. At a cost of approximately $3 per child per month, community-led preschools have the potential to be a cost-effective policy option for helping children meet their development potential even in the most resource deprived parts of the world.

This paper is a product of the Education Global Practice Group. 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 snaudeau@ worldbank.org.

_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
**Dropout rates.** Several determinants are associated with reductions in dropout rates of about 1.52 pp, relative to a sample average of 14% (Figure 3 panel a; Table A11 column 1).<sup>21</sup> The largest reductions are associated with one T&C determinant—having a fixed curriculum—and two LPS determinants—providing job market information and obtaining equipment from industry for student training. The fixed curriculum finding is consistent with the US community college evidence in Bailey et al (2015) that structured pathways promote graduation. Industry connections and job search assistance, in turn, might show students their labor market prospects and motivate them to graduate. Some determinants are associated with higher dropout rates (e.g., receiving outside funding, which might come with conditions that limit the program’s margin of action).

**Estimated Time to Degree (ETG).** The determinants associated with the greatest ETG reductions (3 to 7 pp, relative to an average ETG of 18.6%) come from multiple categories (Figure 3 panel b; Table A11 column 2). Unsurprisingly, programs that teach students to work under hardship or pressure have lower ETGs, presumably by teaching them to persevere and work efficiently, as do programs that evaluate faculty more than once per year (promoting frequent teaching adjustments), engage with industry for student evaluation or curriculum design (perhaps yielding a more efficient and engaging curriculum), or have higher tuition (as students would seek to graduate fast to avoid paying it). Programs with a higher ETG (by about 3 to 5 pp) are those whose curriculum updates rely heavily on the HEI’s perception of the labor market (which might make the program unnecessarily long or involved) or that require a thesis or research project for graduation. Anecdotal evidence indicates that such projects become a hindrance to students who do not have

> 21 Figures 3-6 presents the estimated association between student outcomes and quality determinants, focusing on significant coefficients (at the 1, 5 or 10 percent level). For binary determinants (e.g., whether the program provides job market information), the figure presents the coefficient estimate. For non-binary determinants (e.g., percent of female faculty), it presents the coefficient estimate multiplied by the determinant’s standard deviation. The full regressions of the corresponding outcome on the variables selected by LASSO (including those whose coefficients are not statistically significant) are in tables A11-A14.

25
- 53 -

is based on detailed case studies of firm decision-making. Its application to the Egyptian experience generated a number of different determinants of investment that might be considered in other country case studies.

The nicrofoundations revealed the complex channels through which government policy can affect private activity. While it was not possible to test all of the transmission mechanisms empirically, the model provides a general framework for analyzing aspects that are relevant to particular economies. The econometric approach taken to test the model addressed many of the methodological weaknesses in the existing literature on investment. Thus, for example, the common problem of "spurious correlations" in investment functions was addressed by testing the time series for stationarity and insuring that the residuals from the econometric estimation were "white noise." By using two different estimation techniques along with the microfoundations, the validity of the model hypothesized was conf.rmed further. The use of an error correction approach and the application of unrestricted dynamic modelling allowed for some of the effects of expectations and uncertainty on aggregate investment. A comparison of the actual and estimated values for private investment over the 1960-86 period revealed how well the model fit the data despite the considerable shocks to the economy over the period.

The limits of econometric testing of whether the government "crowds in" or "crowds out" private investment and the impossibility of constructing the counterfactual were also discussed. While it is not possible to conclude whether crowding out or in occurred at the macroeconomic level, i.e. to accept the alternative hypothesis, it is possible to draw conclusions about what did
# **Banking Risks Around the World**

The Implicit Safety Net Subsidy Approach Luc Laeven<sup>1</sup>

**JEL Classification:** G13, G21

Keywords: Banking, Risk taking, Ownership,<sup>Deposit Insurance, Option Pricing</sup> 'The World Bank, Llaeven@worldbank.org,<sup>Tel. 202-4582939. The author would like to thank Thorsten</sup> Beck, Jerry Caprio, Stijn Claessens,<sup>Asli DemirgUc,-Kunt, Simeon Djankov, Patrick Honohan, George</sup> Pennacchi, Sweder van Wijnbergen,<sup>and semninar participants at the World Bank Financial Economics</sup> Seminar for valuable comments,<sup>and Ying Lin for excellent research assistance.</sup>