rafmacalaba/datause-encoder-data huggingface dataset:
- 55 -

Table 4.2: Griffin's Estimates of Crogs-Price Elasticities in the ElectrLcity Sector

||Gas/Fuel Oil|Coal/Fuel Oil|Coal/Gas|
|---|---|---|---|
|Canada<br>|0.66<br>|0.53<br>|0.13|
|U.S.A.<br>|0.53<br>|0.50<br>|0.16|
|Japan<br>|3.67<br>|0.62<br>|-0.07|
|Austria<br>|0.50<br>|0.57|0.21|
|Belgium<br>|0.54|0.56<br>|0.19|
|Denmark<br>|-<br>|0.61|-|
|Finland<br>|0.73<br>|0.74<br>|0.12|
|France<br>|0.49<br>|0.52<br>|0.21|
|Went Germany<br>|0.77|0.30<br>|0.08|
|Greece<br>|-|1.11<br>|-|
|Ireland<br>||1.41|-|
|Italy<br>|1.25<br>|1.05|-0.04|
|Netherlands<br>|1.91|0.51<br>|-0.03|
|Norway<br>|-<br>|5.52|-|
|Portugal<br>|1.69<br>|0.96<br>|-0.07|
|Spain<br>|2.93<br>|0.49<br>|-0.05|
|Sweden<br>|8.22|3.14|-0.79|
|Switzerland<br>|-<br>||-|
|Turkey<br>|1.18<br>|0.68|0.01|
|U.K.<br>|2.18<br>|0.38|-0.03|
|O.E.C.D.|0.66|0.47|0.11|

Although this table also shows substantial variations between countries it does suggest that the degree of substitutability between coal and gas was very low over the period. Griffin attributes this in part to the tendency to use coal for base load and gas for peak load in many countries, and to the empirical fact that few countries have indigenous supplies of both coal and gas (which are much leoss traded than oil). The Hudson/Jorgenson estimatea for the U.S. power sector were for gas/oil at 0.20, coal/oil at 0.43 and coal/gas at -0.20. Again the longer run nature of Griffin's data supports a view of a higher degree of substitutability.

There have been relatively few attempts to measure econometrically the potential for fuel switching in power generation using more recent data. The series of studies published just before and just after Griffin's cross country analysis concentrated largely on problems of disentangling long and short run elasticities. Donelly [19871 estimated values for Australia using slightly later data, but there appears to be no wide rangLng study using data covering a
|P110602|2009|2015|5/1/2015|11 Closed|Montenegro|
|---|---|---|---|---|---|
|P110602|2009|2016|10/31/2015|12 Closed|Montenegro|
|P110602|2009|2016|4/12/2016|13 Closed|Montenegro|
|P110602|2009|2017|10/26/2016|14 Closed|Montenegro|
|P110602|2009|2017|5/26/2017|15 Closed|Montenegro|
|P110602|2009|2018|12/6/2017|16 Closed|Montenegro|
|P110602|2009|2018|6/22/2018|17 Closed|Montenegro|
|P110602|2009|2019|12/29/2018|18 Closed|Montenegro|
|P110602|2009|2019|6/28/2019|19 Closed|Montenegro|
|P110602|2009|2020|9/11/2019|20 Closed|Montenegro|
|P110603|2010|2012|11/21/2011|11 Closed|Yemen,Republic of|
|P110603|2010|2012|6/19/2012|12 Closed|Yemen,Republic of|
|P110603|2010|2013|12/26/2012|13 Closed|Yemen,Republic of|
|P110603|2010|2014|9/21/2013|14 Closed|Yemen,Republic of|
|P110603|2010|2014|4/28/2014|15 Closed|Yemen,Republic of|
|P110603|2010|2015|12/12/2014|16 Closed|Yemen,Republic of|
|P110603|2010|2015|6/19/2015|17 Closed|Yemen,Republic of|
|P110614|2009|2009|10/27/2008|1 Closed|Brazil|
|P110614|2009|2009|5/15/2009|2 Closed|Brazil|
|P110614|2009|2009|6/30/2009|3 Closed|Brazil|
|P110614|2009|2010|12/10/2009|4 Closed|Brazil|
|P110614|2009|2010|5/8/2010|5 Closed|Brazil|
|P110614|2009|2011|12/3/2010|6 Closed|Brazil|
|P110614|2009|2011|6/29/2011|7 Closed|Brazil|
|P110614|2009|2012|12/31/2011|8 Closed|Brazil|
|P110614|2009|2012|6/28/2012|9 Closed|Brazil|
|P110616|2009|2009|12/5/2008|1 Closed|Africa|
|P110616|2009|2009|4/6/2009|2 Closed|Africa|
|P110616|2009|2010|11/6/2009|3 Closed|Africa|
|P110616|2009|2010|6/9/2010|4 Closed|Africa|
|P110616|2009|2011|12/9/2010|5 Closed|Africa|
|P110616|2009|2012|12/13/2011|6 Closed|Africa|
|P110616|2009|2012|4/8/2012|7 Closed|Africa|
|P110616|2009|2013|9/26/2012|8 Closed|Africa|
|P110616|2009|2013|1/29/2013|9 Closed|Africa|
|P110618|2008|2008|6/30/2008|1 Closed|Togo|
|P110618|2008|2009|4/13/2009|2 Closed|Togo|
|P110618|2008|2009|6/29/2009|3 Closed|Togo|
|P110632|2011|2011||0 Closed|China|
|P110632|2011|2012|10/5/2011|1 Closed|China|
|P110632|2011|2013|11/12/2012|2 Closed|China|
|P110632|2011|2013|6/26/2013|3 Closed|China|
|P110632|2011|2014|12/22/2013|4 Closed|China|
|P110632|2011|2014|6/24/2014|5 Closed|China|
|P110632|2011|2015|11/26/2014|6 Closed|China|
|P110632|2011|2015|6/21/2015|7 Closed|China|
|P110632|2011|2016|12/15/2015|8 Closed|China|
Figure 1: Reported Days in Customs - Trading Across Borders <!-- Start of picture text --> 0 5 10 15 0 5 10 15<br>Days in Import Customs (Doing Business data) Days in Export Customs (Doing Business data)<br>50 50<br>40 40<br>Frequency 30 Frequency 30<br>20 20<br>10 10<br>0 0<br><!-- End of picture text --> Note: Distributions of days in import customs and days in export customs, respectively. Data from _Doing Business - Trading Across Borders 2012_ for 182 countries.

# **3.2 Quantitative measures of border policy**

The primary goal of this paper is to relate border management policies and procedures to border outcomes. Heretofore this has been a difficult issue to address empirically because quantitative representations of border policies/procedures have not been available.<sup>15</sup> In anticipation of the Bali negotiations the OECD set out to correct this problem, identifying 12 categories of trade facilitation policies and procedures. After documenting national policies and procedures, the OECD scored countries on their proximity to the world’s best practice in each of the 12 policy categories.

The raw material for the OECD’s 12 trade facilitation indicators was a detailed catalogue of the policies and procedures used in border management agencies around the world. The OECD’s initial catalogue, based in 2010, attempted to document 95 different indicators of border management procedures in OECD countries. A subsequent catalogue, representing the situation in 2012, attempted to track 78 procedures in non-OECD countries.<sup>16</sup> The OECD dropped 7 questions be15Measures such as the World Bank’s Logistics Performance Index, or information from TAB on the number of documents required to trade provide some high-level information. But these do not represent the breadth of the policy reforms considered in the TFA, nor do they provide sufficient policy detail for our purposes. 16Our empirical specifications pool the two data series, but includes an OECD dummy variable. Among other things, this should control for any systematic differences between the situations in 2010 and 2012. The working hypothesis is that procedural changes are slow, so pooling is not overly dangerous in and of itself.

8
## **Online Appendix to: Gender Bias and Intergenerational Educational Mobility: Theory and Evidence from China and India**

## **M. Shahe Emran**

IPD, Columbia University

## **Hanchen Jiang**

Johns Hopkins University

**Forhad Shilpi** World Bank

# **NOT FOR PUBLICATION**

# **Omitted Proofs for the Results on Urban India in Section (2.3)**

(1) Using equation (11) in the text, we have the following: where _χ_<sup>_j_</sup> 0<sup>=</sup><sup>_α_2+</sup><sup>_δ_</sup> 2<sup>_jσjRcj_andthelastinequalityabovefollowsfromtheobservation</sup> that 2 _α_ 2 _Y_ 0<sup>_p_+ 2 ̃</sup><sup>_δ_</sup> 1<sup>_jσjRcj−α_1</sup> _>_ 0 when _θ_ 0<sup>_j>_0whichisinfactthecaseforboth</sup><sup>_j_=</sup><sup>_s, d_</sup> ( ) according to the empirical evidence reported in the text. Thus, a higher estimate of ability by parents would result in a higher slope of the curve at each point.

Since at _R_<sup>_cj_</sup> = 0, the value of _θ_ 0<sup>_j_does not depend on the ability of a child,the curve for</sup> a higher ability estimate, ceteris paribus, lies above the curve for a lower ability estimate at all positive values of returns to schooling for children.

(2) For a higher school quality, we have the following (again, using equation (11) in the main text): (3) The claim that the effects of pure son preference on the slope is not unambiguous follows from the result below: The above expression cannot be signed because we do not know the sign of ( _α_ 2 _− δ_<sup> ̃</sup> 2<sup>_jσjRcj_)</sup> .
Third, the note is based on the implicit assumption of perfect forecasts in climate change matters. But, available forecasts on more frequent and severe droughts in the coming 20 to 50 years diverge widely depending on the model used. An efficient way to address the problems of increased scarcity of water, and of its unequal distribution in the world is more trade between water-rich and/or water-efficient countries and the other countries—meaning the opening of their agricultural sectors (which use 70 to 80 percent of the water resources) by the developed countries. In short, freer trade emerges as the cheapest source of insurance against unexpected shocks in climate change.

# **References**

Antholis, W. 2009. Five “Gs”: Lessons from world trade for governing global climate change. In Bairnard, L. and I. Sorkin, editors. Climate change, trade and competitiveness: Is a collision inevitable. Brookings Trade Forum 2008/2009. The Brookings Institution.

Banks, Gary, 2010. Gaining from trade liberalization : reflections on Australia’s experience, in An <u>economy-wide view : Speeches on structural reform. Productivity Commission. Australian</u> Government.

Barrett, S. 2007. <u>Barrett proposal: A multitrack climate treaty system. Policy brief. September 5.</u> The Harvard Project on International Climate Agreements, Harvard Kennedy School.

Brenton, P., G. Edwards-Jones and M.F. Jensen, 2009. <u>Carbon labelling and low income country exports: A review of the development issues. Development Policy review. Overseas Development</u> Institute, Vol. 27(3), pp. 243-267.

Broder, J. 2010. Death of climate change idea. International Herald Tribune. March 25, 2010.

Cadot, O., C. Carrère, J. de Melo and A. Portugal-Pérez. 2005. Market access and welfare under free <u>trade agreements textiles under NAFTA. World Bank Economic Review. Vol.19, #3, pp. 379-405.</u> Carasco, A. 2010. L’idée d’une taxe écologique aux frontières ne séduit guère. Le Monde. 25 Mars 2010.

Charnovitz, S., 2003. <u>Trade and climate: potential conflicts and synergies. Working draft. Pew</u> Center on Global Climate Change. Washington, DC.

CE Delft, 2008. Impact on competitiveness from EU Emissions Trading Scheme: An analysis of the <u>Dutch industry. Delft, June.</u> Cooper, R. 2008. <u>The case for charges on greenhouse gas emissions. October, Discussion Paper</u> 2008-10, The Harvard Project on International Climate Agreements, Harvard Kennedy School.

Delgado, J. 2007. Why Europe is not carbon competitive. Policy brief 2007/05. Bruegel. Brussels.

Ellerman, A.D., F.J. Convery and C. de Perthuis, 2010. <u>Pricing carbon, The European Emissions Trading Scheme. Cambridge University Press. Cambridge, UK.</u> 29
||Standard<br>error|(2.237)|(1.506)|(1.022)|(893.0)|(207.3)|(92.46)|
|---|---|---|---|---|---|---|---|
||N|100|100|100|47|57|89|
|Low-skilled|Coefficient<br>|13.23***|11.21***|8.925***|1,838**|676.4***|467.6***|
||Standard<br>error|(1.656)|(1.156)|(0.733)|(783.9)|(104.4)|(100.4)|
||N|426|426|426|386|352|399|
|High-skilled|Coefficient<br>|13.32***|9.804***|8.117***|10,653*|516.4|-591.4|
||Standard<br>error|(1.807)|(1.231)|(0.606)|(6,212)|(936.3)|(1,275)|
||N|150|150|150|93|86|130|
|Young|Coefficient<br>|13.18***|11.38***|9.035***|2,326*|473.5***|255.8**|
||Standard<br>error|(1.727)|(1.187)|(0.781)|(1,345)|(158.8)|(116.2)|
||N|355|355|355|304|264|327|
|Old|Coefficient<br>|12.55***|9.767***|7.986***|4,794**|960.7|908.8|
||Standard<br>error|(1.662)|(1.078)|(0.533)|(2,242)|(721.9)|(618.6)|
||N|365|365|365|323|247|344|
|Rural|Coefficient<br>|10.44***|10.45***|7.657***|2,284|1,671***|1,111**|
||Standard<br>error|(0.813)|(0.389)|(0.462)|(1,583)|(385.1)|(442.1)|
||N|389|389|389|353|319|355|
|Urban|Coefficient<br>|13.56***|11.00***|8.911***|2,550*|93.32|-190.0|
||Standard<br>error|(1.779)|(1.278)|(0.777)|(1,407)|(279.8)|(275.7)|
||N|191|191|191|116|135|143|

Note: Robust Standard errors are in parentheses. *,**,*** significance level at 10%, 5% and 1%.

Source: Author's estimates using data from Bangladesh LFS and UN Comtrade. This table shows the first and second stage of the 2SLS regression. In the first stage, we estimate the contribution of US import demand to the increase in Bangladesh exports, and in the second stage we estimate the effect of an increase in exports on real wages. This relationship is estimated for different worker types (male, female, rural, skilled, unskilled, young, and old).

**Table A2. Change in the informality rate after a US$100 increase in exports per worker (Real Bangladeshi Takas of 2017 and percentage) using Two-Stage Least Squares (2SLS) Regression Analysis**

||||1st Stage||2nd|Stage (Informa|lity)|
|---|---|---|---|---|---|---|---|
|Type of worker||2005-2010|2005-2013|2005-2016|2005-2010|2005-2013|2005-2016|
|||(1)|(2)|(3)|(1)|(2)|(3)|
|All|Coefficient|14.14***|10.65***|8.665***|-0.00703*|-0.00369|0.000168|
||Standard<br>error|(1.558)|(1.223)|(0.700)|(0.00361)|(0.00246)|(0.00139)|
||N|429|452|448|429|452|448|

36
Regarding _-y,_ we should only. consider the median estimator of the replicat.ions due to the character of the procedure, which restricts the search of _-y_ between zero .and one. Consequently, the mean will necessary be biased when the true _-y_ deviates from 0.5. For example if the true _-y_ is distributed around one the mean will be strictly less than one (because we search between zero and one only). Looking at the median of _-y,_ we can see a small bias downward for lower values of-y. For Chile, the median is equal to the true value for all _-y_ except 0.4. For Mexico, the median is lower than the true value of _-y_ for 0.4 and 0.6. From these Monte Carlo simulations we can conclude that, assuming our model is well specified, there is no small sample biases.

The second question we want to address is to assess any possible bias when the process assumed for· the errors in the -regressions are diff.erent to those assumed by the estimation procedure. This seems particularly important in the case of Mexico, where according to Figure 4, financial innovation appears to have a downward drift. We do tpe·next Monte Carlo simulations for Mexican data only. We depart from our· assumptions in.three ways.-First we assume that _'It_ has ' : a negative drift, so there is permanent financial innovation. Second we explore the case where _'It_ is a more complic_ated. integrated process, an ARIMA(l, 1, 0), instead of the random walk (ARIMA(0,1,0)). Finally we assume that the stationary shock of equation (7) is AR(l) instead. of a white noise. In the last two cases we use.a"coefficient ·of .autocorrelation of 0.8.

The results are presented in Table 6 for. true _-y_ equal to 0. 4, . 0. 7 and 1. 0. It is clear from this table that .when the . true _-y_ is different from one the estimator of _-y_ is biased upwards. Looking at the median estimate, we see that for a true _-y_ equal to 0.7, themedian of the· estimates of _-y_ is one in two out of the three cases , and O·. 9 5 in- the· third. When the true _-y_ is 0. 4, the median is be tween 23
- Skees, Jerry R. "A Role for Capital Markets in Natural Disasters: A Piece of the Food Security Puzzle." _Food Policy_ 25 (2000): 365-378.

- Skees, Jerry R. "Opportunities for Improved Efficiency in Risk-Sharing Using Capital Markets." _American Journal of Agricultural Economics._ 81(1999): 1228-1223.

- Skees, Jerry R. "Agricultural Risk Management or Income Enhancement?" _Regulation: The CATO Review of Business and Government._ 22 (1999): 3543.

- Skees, Jerry R. and Barry J. Barnett. "Conceptual and Practical Considerations for Sharing Catastrophic/Systemic Risks." _Review of Agricultural Economics._ 21 (1999): 424-441.

- Skees, Jerry R. and Barry J. Bamett. "Area Yield and Weather Based Crop Insurance." _Forthcoming Book Chapter, AgriRisk, Australia._

- Skees, Jerry R. and Kimberly Zeuli. "Using Capital Markets to Increase Water Market Efficiency." Presented at the International Symposium on Society and Resource Management. Brisbane, Australia. July 8, 1999.

- Skees, Jerry R., "More Crop Insurance Reform: A Good Idea Gone Awry." _Regulation: The CATO Review of Business and Government._ 1st Quarter, 2001, 24: 16-21.

- Skees, Jerry R., Barry J. Bamett and J. Roy Black. "Designing and Rating an Area Yield Crop Insurance Contract." _American Journal of Agricultural Economics._ 79 (1997): 430438.

- Skees, Jerry R., Peter Hazell, and Mario Miranda. "New Approaches to Public / Private Crop Insurance." Forthcoming chapter in book to be published by the World Bank, Washington. D.C.

- Skees, Jerry, Panos Varangis, Donald Larson, and Paul Siegel. "Can Financial Markets be Tapped to Help Poor People Cope with Weather Risks?" World Bank Working Paper number 2812. March, 2002.

- Skees, Jerry, Stephanie Gober, Panos Varangis, Rodney Lester, and Vijay Kalavakonda. "Developing Rainfall-Based Index Insurance in Morocco." World Bank Working Paper number 2577. April, 2001.

Stipp, D. September 8, 1997. "A New Way to Bet on Disasters." _Fortune._

- U.S. General Accounting Office. "Disaster Assistance: Crop Insurance Can Provide Assistance More Effectively than Other Programs." RCED-89-21 1. Washington, DC: U.S. Government Printing Office, 1989.

- U.S. General Accounting Office. "Federal Disaster Assistance: What Should the Policy Be?" PAD-80-39. Washington, DC: U.S. Government Printing Office, 1980.

- U.S. General Accounting Office. "Federal Disaster Insurance: Goals are Good, But Insurance Programs Would Expose the Federal Government to Large Potential Losses." T-GGD94-153. Washington, DC: U.S. Government Printing Office, 1994.

- U.S. General Accounting Office. "Natural Disaster Insurance: Federal Government's Interests Insufficiently Protected Given Its Potential Financial Exposure." T-GGD-96-41. Washington, DC: U.S. Government Printing Office, 1995.

Vaughan, E. _Fundamentals of Risk and Insurance._ New York, NY: John Wiley & Sons, 1989.

- Walter, A. S. "Risk Management in Agriculture." Testimony before the U.S. Senate Committee on Agriculture, Nutrition, and Forestry.
<!-- Start of picture text --> 30<br>30 25<br>20<br>25<br>15<br>20 10<br>5<br>15<br>0<br>10 -5<br>-10<br>5<br>-15<br>0<br>Domestic Credit Growth (India) Exports Growth (India)<br>Domestic Credit Growth (Median of EM7) Exports Growth (Median of EM7)<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text --> Note: Central Statistics Office, and World Development Indicators. Data in the figures are for the respective calendar year. Credit growth is nominal.

# **3.2 Interstate Patterns of Growth during and after the GFC**

To further identify the characteristics of the slowdown after the global financial crisis, we analyze how economic growth across Indian states was impacted by the GFC.<sup>26</sup> Unsurprisingly, we see exactly the kind of economic cycle in economic growth, credit growth, investment, and manufacturing sector at the state level as is evident at the national level.<sup>27</sup> The average (mean as well as median) growth rates of all of these variables increased prior to the crisis, during 2004–08, followed by a correction that started with the global economic slowdown in 2007–08; and precipitated when the GFC took hold, with the collapse of Lehman Brothers in September 2008. While GSDP growth recovered in the post-crisis years, credit growth, investment, and manufacturing growth remained subdued.

We ask whether there were any specific state-level characteristics that correlated with the impact of the GFC on the states. We define the states’ dependence on agriculture, the relative importance of manufacturing in economic activity, and the credit-to-GSDP ratio as an indicator of the states’ dependence on credit, and the rate of credit growth prior to the GFC (between 2004 and 2008) as an indicator of the prevalence of a credit boom in states in years prior to the GFC. While in our main specifications we compare states above and below median for these characteristics, in

> 26 Data on Gross State Domestic Product are from the CMIE’s states of India database. While India has a total of 36 states and union territories, we restrict our analysis to the 20 large states, including Andhra Pradesh, Assam, Bihar, Chhattisgarh, Gujarat, Haryana, Himachal Pradesh, Jammu and Kashmir, Jharkhand, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Tamil Nadu, Uttar Pradesh, Uttarakhand, and West Bengal. Our sample covers the years 1990 to 2015 for all states except the younger states Jharkhand, Chhattisgarh, and Uttarakhand, for which credit data are only available from 2001 onward.

> 27 Due to the unavailability of data for exports for each state, we cannot confirm the patterns in exports.

20
- <u>Table 10: Impacts of hosting refugees on ownership of non farm enterprise (NFE) and plans to start a new NFE</u>

||Ow|ns an NFE|(=1)|#|of NFEs o<br>Househol|wned<br>ds|Plans to|start a new|NFE (=1)|
|---|---|---|---|---|---|---|---|---|---|
||All|Rural|Urban|All|Rural|Urban|All|Rural|Urban|
|Refugee Intensity|0.035|0.058|-0.086|0.060|0.088|-0.058|-0.093|-0.102|0.006|
||(0.059)|(0.072)|(0.071)|(0.072)|(0.085)|(0.122)|(0.064)|(0.073)|(0.102)|
|Constant|-0.000|-0.150|0.805**|0.207|-0.124|1.046|0.707*|0.751|0.221|
||(0.334)|(0.403)|(0.390)|(0.427)|(0.469)|(0.805)|(0.409)|(0.466)|(0.646)|
|Round FEs|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|Fixed Effects (FEs)|HH|HH|HH|HH|HH|HH|HH|HH|HH|
|Controls|YES|YES|YES|YES|YES|YES|YES|YES|YES|
|R-squared|0.037|0.041|0.046|0.055|0.025|0.265|0.018|0.023|0.021|
|# of EAs|433|290|143|433|290|143|433|290|143|
|N|13519|9731|3788|13519|9731|3788|13436|9664|3772|

_Notes:_ This table provides estimates of the impacts of logarithm of refugee intensity on a binary measure of ownership of an NFE (the first three columns), number of NFEs owned (the middle three columns), and a binary measure of whether a household plans to open a new NFE in the next 12 months (the last three columns). Household sample weights from ESS2 applied. Columns labeled “All,” “Rural,” and “Urban” report results for all, rural, and urban households, respectively. All columns control for household (HH) FEs, round FEs, and 12 time-varying characteristics. The coefficient estimates for these 12 control variables are not reported here, but can be available on request. Robust standard errors clustered at EA level are given in parenthesis. Asterisks: *, **, and *** indicate statistical significance at 10%, 5% and 1% levels, respectively.

48
The fifth principle is to price deposit-insurance services appropriately. Laeven (2006) shows that countries have typically underpriced deposit insurance. He describes several methods for pricing deposit insurance accurately.

The sixth and final principle is that deposit insurers must actively involve themselves in decisions about when and how to resolve individual-bank insolvencies. Because deposit insurers are responsible for paying off insured depositors, they have a strong interest in assuring the prompt and speedy resolution of insolvent banks. Beck and Laeven (2006) argue that deposit insurers are more efficient than courts because banking supervisors better understand bank risk-taking incentives and how to remedy them. Using data for over large number of banks in over 50 countries, they show that banks are more stable and less likely to become insolvent in countries where the deposit insurer has the responsibility of intervening failed banks and the power to revoke membership in the deposit insurance scheme.

Deposit insurance is strong medicine. Whether it benefits or harms a country depends on how well it is designed and administered. It can be a useful part of a country's overall system of bank regulation and financial markets, but cross-country research stresses the importance of promptly identifying and eliminating individual-bank insolvencies, fostering informative accounting standards, and establishing reliable procedures for contract enforcement before adopting explicit deposit insurance. Research also underscores the need to build in a capacity to adapt to financial changes. Managers must be empowered and incentivized to upgrade their loss controls to disable unforseeable loopholes that regulatorinduced financial innovation will inevitably open in their system.

22