This study analyses on the basis of a multivariate analysis ex post the effects on the jobs market of a soccer World Cup, in this case the 2006 World Cup held in Germany. In addition to three methods already used for other analyses in studies of sporting events, an extended "Difference-in-Difference" estimate is used in order to compare the development of the numbers of unemployed in the 12 World Cup venues with the development of the numbers of unemployed in 63 other German cities. The results demonstrate that in none of the respective match venues did the effect of the sporting event on unemployment differ significantly from zero.

Introduction 1

Before the 2006 World Cup in Germany a series of analyses was published, according to which the investments of around €6 billion in connection with the World Cup competition and the expenditure of the expected 1-2 million foreign visitors would markedly affect income and employment. The estimates fluctuated between a €2 billion and a €10 billion increase in income growth, or up to 10,000 additional jobs [START_REF] Ahlert | The economic effects of the Soccer World Cup 2006 in Germany with regard to different financing[END_REF][START_REF] Capital | Exklusiv-Analyse: Der ökonomische Effekt der Fußballweltmeisterschaft[END_REF], Deutsche Industrie und Handelskammer 2006, Deutsche Postbank AG 2005a[START_REF] Heyne | Experience goods and biases in CVM -The case of Soccer WC[END_REF][START_REF] Kurscheidt | Erfassung und Bewertung der wirtschaftlichen Effekte der Fussball-WM 2006, Unabhängiges wissenschaftliches Gutachten für wegweiser GmbH[END_REF].

Even in retrospect the soccer World Cup competition was universally felt to be an outstanding and positive event for Germany. However, these perceptions derive from only a few 1 We are thankful for the anonymous referees´ valuable comments. Multivariate studies are clearly more restrained in their assessment of the effects of major sporting events and also specifically of the soccer World Cup. [START_REF] Baade | The quest for the cup: Assessing the economic impact of the World Cup[END_REF] investigated in a multiple analysis ex post the effect on the income of people in the match venues of the soccer World Cup of 1994 in the USA. They concluded that income developed in an equally weak fashion in 9 of the 13 regions of the contest. Overall, the soccer World Cup had a negative effect on the income of the match venue of more than US$9 billion. [START_REF] Szymanski | The economic Impact of the World Cup[END_REF] collected data on the twenty largest economies in terms of current GDP over the past thirty years, many of which have hosted the Olympic Games or the soccer World Cup at least once during that period. Using a simple regression model, he came to the conclusion that the growth of these countries was significantly lower in soccer World Cup years. 2 The results of these two studies of soccer World Cups are in agreement with other econometric studies of various large sporting events or sports venues. The majority of these studies suggest that the sporting events or sports stadia have little or no significant effect on regional wages, income and/or employment (e.g. Baade, 1987; Baade and Dye, 1990; Baade, 1994; Baade and Sanderson, 1997; Baade and Matheson, 2000, 2001, 2003; Carlino and Coulson 2004 3 ). A number of works, particularly those of [START_REF] Coates | The growth effects of sport franchises, stadia, and arenas[END_REF], 2000aand b, 2002, 2003a and b) or [START_REF] Teigland | Mega-events and impacts on tourism: The predictions and realities of the Lillehammer Olympics[END_REF], have even arrived at significant negative effects. To our knowledge, only very few studies have found significant positive effects of sports facilities and sports events ex post. [START_REF] Baim | The Sports Stadium as a Municipal Investment[END_REF] found positive employment effects for Major League baseball and football for 15 cities in the USA. [START_REF] Hotchkiss | Impact of the 1996 Summer Olympic Games on employment and wages in Georgia[END_REF] found significant positive effects on employment in regions of Georgia (USA) affiliated or close to activities of the Atlanta Olympic Games in 1996, but they did not find significant effects on wages. the USA. This is particularly interesting set against the background of the contrasting modes of functioning of the labour markets in the USA and Europe. In addition, it also tests for method sensitivity by running the dataset in parallel with the three methods usually applied in the studies of [START_REF] Baade | An assessment of the economic impact of the American Football Championship, the Super Bowl, on host communities[END_REF][START_REF] Baade | Home run or wild pitch? Assessing the economic impact of Major League Baseball's All-Star Game[END_REF], 2003[START_REF] Kurscheidt | Erfassung und Bewertung der wirtschaftlichen Effekte der Fussball-WM 2006, Unabhängiges wissenschaftliches Gutachten für wegweiser GmbH[END_REF], [START_REF] Coates | The growth effects of sport franchises, stadia, and arenas[END_REF], 2000aand b, 2002, 2003a and b) and [START_REF] Hotchkiss | Impact of the 1996 Summer Olympic Games on employment and wages in Georgia[END_REF] as well as with a fourth method that attempts to overcome some potential shortcomings associated with the three other methods. Section 2 elaborates on the methods, data and results. Section 3 concludes.

Methods, Data and Results

The period of observation in our study comprised 111 months from January 1998 to March 2007. 4 Hence, the period of observation had already begun more than two years before Germany was selected on 6 July 2000 as the venue for the World Cup and it ends with the latest period for which data are available. In order to clarify the extent to which the differences in the development of unemployment figures in the two comparative groups -after controlling for the customary explanatory variables of joblessness -is significantly correlated with the occurrence of the World Cup, we first use the three methods commonly employed in studies in the USA in investigating the 5 The shares contributed to the gross value production in the year 1999 -the year preceding the selection of Germany to host the World Cup -were used, since data in the period are not available for the whole period under consideration but only on a yearly basis.

The excluded industry category is the finance, leasing and venture service. 5 economic effects of major sporting events: those of [START_REF] Baade | An assessment of the economic impact of the American Football Championship, the Super Bowl, on host communities[END_REF][START_REF] Baade | Home run or wild pitch? Assessing the economic impact of Major League Baseball's All-Star Game[END_REF], 2003[START_REF] Kurscheidt | Erfassung und Bewertung der wirtschaftlichen Effekte der Fussball-WM 2006, Unabhängiges wissenschaftliches Gutachten für wegweiser GmbH[END_REF], [START_REF] Coates | The growth effects of sport franchises, stadia, and arenas[END_REF], 2000aand b, 2002, 2003aand b), and Hotchkiss et al. (2003).

Hence, according to the method of [START_REF] Baade | An assessment of the economic impact of the American Football Championship, the Super Bowl, on host communities[END_REF][START_REF] Baade | Home run or wild pitch? Assessing the economic impact of Major League Baseball's All-Star Game[END_REF], 2003[START_REF] Kurscheidt | Erfassung und Bewertung der wirtschaftlichen Effekte der Fussball-WM 2006, Unabhängiges wissenschaftliches Gutachten für wegweiser GmbH[END_REF]) the following equation is derived:

(1)

+ + + + + + + + + + = = t i t i i t i t i t i n i t t i t i WC DumSeas Trend East Pop Unemp Unemp Unemp n Unemp Unemp , 9 8 7 6 5 3 , 4 2 , 3 1 , 1 2 , 1 0 , 2006 1999 ln
The notation of equation ( 1) is explained in the appendix. Table 1 shows in column (1) the results of this estimation. 6 The variable The other estimation models used in this paper are special cases of model ( 2):

(2) The notation of equation ( 2) is explained in the appendix.

+ + + + + + + + + + + + + + + = t i t i t i t i t i i i i i i t i
The model according to [START_REF] Coates | The growth effects of sport franchises, stadia, and arenas[END_REF], 2000aand b, 2002, 2003a and b) [START_REF] Hotchkiss | Impact of the 1996 Summer Olympic Games on employment and wages in Georgia[END_REF] use a standard "Difference-in-Difference" estimate in order to be able to detect changes in a) the intercept, i.e. in the levels of the employment and wages, and b) the slope, i.e. in the growth of the two variables. The "Difference-in-Difference" estimate compares the variable of interest before and after the incidence of a given event in a region with the change in the same variable in another region that was not affected by that event. 8 For this it is assumed that the development in the affected region would have matched the development in the unaffected region if the event had not occurred. The difference between the model of [START_REF] Hotchkiss | Impact of the 1996 Summer Olympic Games on employment and wages in Georgia[END_REF] and the models of [START_REF] Baade | An assessment of the economic impact of the American Football Championship, the Super Bowl, on host communities[END_REF][START_REF] Baade | Home run or wild pitch? Assessing the economic impact of Major League Baseball's All-Star Game[END_REF], 2003[START_REF] Kurscheidt | Erfassung und Bewertung der wirtschaftlichen Effekte der Fussball-WM 2006, Unabhängiges wissenschaftliches Gutachten für wegweiser GmbH[END_REF]) and of [START_REF] Coates | The growth effects of sport franchises, stadia, and arenas[END_REF], 2000aand b, 2002, 2003a and b) is that these last two test solely the effects during the course of the actual event, whereas with the model of [START_REF] Hotchkiss | Impact of the 1996 Summer Olympic Games on employment and wages in Georgia[END_REF] the medium-term effects can also be determined. The model according to [START_REF] Hotchkiss | Impact of the 1996 Summer Olympic Games on employment and wages in Georgia[END_REF] estimates the log unemployment by the shares of gross value added of selected economic sectors, a dummy for match venues of the World Cup 2006, a dummy for period after the World Cup 2006 (1 for period after, 0 for period before the World Cup), and a dummy for match venues and period after the World Cup 2006.

The period from June 2006 is selected as the post-event period ( Post =1), corresponding to the beginning of the World Cup on 9 June 2006. Column (3) in Table 1 represents the results from the estimation according to [START_REF] Hotchkiss | Impact of the 1996 Summer Olympic Games on employment and wages in Georgia[END_REF] for this follow-up period. The relevant variable, t i PostWC , , is not significant. Therefore the levels of the unemployed in the 12 match 7 The results of the evaluation are available from the authors on request. 8 Frequently, this concerns a political event, such as the introduction of a new law. The classic use of the "Difference-in-Difference" estimate originated with [START_REF] Card | Minimum Wages and Employment: A Case Study of the Fast Food Industry in New Jersey and Pennsylvania[END_REF], who used it to investigate the consequences of minimum wages in two States of the USA. To test for an effect on the growth of the numbers of unemployed through the soccer World Cup, we also included, closely following the procedure of [START_REF] Hotchkiss | Impact of the 1996 Summer Olympic Games on employment and wages in Georgia[END_REF] a time trend.

Again the relevant variable t i

TrPostWC , , does not differ significantly from zero (column (4) in table 1). For the period after the World Cup, the match venues show in comparison with the non-venues no trend significantly different from zero in the development of unemployment.

Finally, we extend the standard "Difference-in-Difference" estimates of [START_REF] Hotchkiss | Impact of the 1996 Summer Olympic Games on employment and wages in Georgia[END_REF], in that in our model we simultaneously take into account changes as much in the levels as also in the trends of the dependent variable. In this way we avoid distorted results, for example if an unemployment level in a city lower than before the World Cup is exclusively attributable to an already existing negative trend.9

One shortcoming of the estimation models used by [START_REF] Baade | An assessment of the economic impact of the American Football Championship, the Super Bowl, on host communities[END_REF][START_REF] Baade | Home run or wild pitch? Assessing the economic impact of Major League Baseball's All-Star Game[END_REF], 2003[START_REF] Kurscheidt | Erfassung und Bewertung der wirtschaftlichen Effekte der Fussball-WM 2006, Unabhängiges wissenschaftliches Gutachten für wegweiser GmbH[END_REF], [START_REF] Coates | The growth effects of sport franchises, stadia, and arenas[END_REF], 2000aand b, 2002, 2003aand b), and Hotchkiss et al. (2003) which have been discussed so far is that they do not attempt to overcome the problem of serial correlation, which often exists in data with time series dimensions. Since, as shown by [START_REF] Bertrand | How much should we trust in differencesin-differences estimates?[END_REF], "Difference-in-Difference" models are frequently subject to serial correlations and also tend to overestimate the significance of the results, in the following we use White coefficient covariance estimators, which are robust with regard to serial correlation. [START_REF] Bertrand | How much should we trust in differencesin-differences estimates?[END_REF] recommend this procedure particularly for "Difference-in-Difference" models with a sample in which N > 50. These two variables have proved not to differ significantly from zero. Hence, neither the levels nor the trends of the unemployment figures in the period after the soccer World Cup relative to the period before the competition have developed significantly differently in the match venues from those of the unemployment figures in the non-venues. Therefore, an effect of the World Cup on employment in the 12 match venues can not be demonstrated.

Column (5) in table 1 shows the results of our model on the basis of

Conclusion, and economic and political implications

Our study has demonstrated that the 2006 World Cup could not influence unemployment in the 12 match venues to an extent that was significantly different from its pattern in the nonvenues.

Our results not only correspond with those of [START_REF] Baade | The quest for the cup: Assessing the economic impact of the World Cup[END_REF], which were unable to prove any income effects significantly different from zero in the host cities of the 1994 Football World Cup in the USA; they also correspond with almost all ex post multivariate income and employment analyses of major sporting events and venues which, with the exception of [START_REF] Hotchkiss | Impact of the 1996 Summer Olympic Games on employment and wages in Georgia[END_REF] for the 1996 Olympic Summer Games in Atlanta and Jasmand/ Maennig (forthcoming) for the 1972 Olympic Summer Games in Munich show no income and/or employment effects that are significantly positively different from zero. We nevertheless hesitate to share the concern expressed both implicitly and explicitly in many of the comparable sports economy studies that the positive effects of the sporting events claimed by many sports protagonists are not true and that (bids to host) major sporting events are inefficient from an economic point of view, for three reasons. Firstly, other effects such as the feelgood benefit for the population and/or difficult to quantify image effects may be sufficiently important to justify major sporting events and/or subsidies for them via public funds. In both of the above-mentioned fields of possible effects, sporting economic empiricism is still in its infancy. 10 Secondly, the treatment group in the selected form of municipality areas might be too large and too highly aggregated to statistically prove significant effects. Studies on the effects of major sports venues on property values in surrounding areas indicate a maximum affect area of around 3,000 metres [START_REF] Tu | How does a new sport stadium affect housing values? The case of FedEx Field[END_REF], Ahlfeldt and Maennig forthcoming).

Thirdly, the employment effects claimed by the sports protagonists, which are usually based on corresponding ex-ante impact studies, cannot strictly speaking be rejected by testing for significant differences from zero. Their rejection would be possible if the postulated values were tested directly. However, this would not be regularly successful in the relevant studies because the effects claimed are so close to zero (Baade and Matheson 2006). 11 To illustrate this: the value of 0.001967 for PostWC in column (5) of Table 1, with a standard deviation of 0.029605 is usually interpreted to mean that there are no effects on unemployment. Sports protagonists can argue that with the existing estimates a reduction of unemployment of up to around (0.001967 -2 * 0.029605=) -0.057243 cannot be refuted. This would nevertheless 10 For the measurement of the experiential benefit of the Olympic Games in London 2012 cf. [START_REF] Atkinson | Quantifying the "un-quantifiable": Valuing the intangible impacts of hosting the Summer Olympic Games[END_REF], for the measurement of the willingness to pay for the Soccer World Cup 2006 (before and after the event cf. [START_REF] Heyne | Experience goods and biases in CVM -The case of Soccer WC[END_REF]. 11 [START_REF] Baade | Padding Required: Assessing the Economic Impact of the Super Bowl[END_REF] test hypotheses against both a zero impact and against the impact claimed by sports boosters. They are able to reject any boosters' claims of economic impact from the game of greater than $300 million at a 5% significance level. J a n 9 9 J u l 9 9 J a n 0 0 J u l 0 0 J a n 0 1 J u l 0 1 J a n 0 2 J u l 0 2 J a n 0 3 J u l 0 3 J a n 0 4 J u l 0 4 J a n 0 5 J u l 0 5 J a n 0 6 J u l 0 6 J a n 0 7

Average unemployed in match venues (basis Jan.

  post, that are moreover exclusively descriptive in nature (cf., in particular, Bundesministerium des Innern 2006, Brenke and Wagner 2007).

  supplements previous publications in a number of respects. It is the first work that examines the effects of World Cup 2006 in Germany on an ex post basis. It is the first multivariate study to examine the employment effects of a major sporting event outside

  We use data regarding the 75 largest urban districts (kreisfreie Städte) in Germany including the 12 match venues of the 2006 soccer World Cup. The selection of the 75 largest urban districts was made according to the criterion of the population in 1999. Match venues of the 2006 soccer World Cup in Germany were the twelve cities Berlin, Dortmund, Frankfurt on the Main, Gelsenkirchen, Hamburg, Hanover, Kaiserslautern, Cologne, Leipzig, Munich, Nuremberg and Stuttgart, whose location in Germany is shown in Figure 1. Berlin, Hamburg, , Cologne and Frankfurt on the Main are among Germany's largest cities. In contrast, Kaiserslautern is ranked at only No. 74 in the table of the most populous urban districts. The number of inhabitants of the urban districts in 1999 -the year before Germany was selected to host the World Cup competition -were taken from the comprehensive economic records of the regions (Arbeitskreis Volkswirtschaftliche Gesamtrechnung der Länder 2005). The shares contributed to the gross value added by the various economic sectors in 1999 were obtained from the comprehensive economic records of the regions (Arbeitskreis Volkswirtschaftliche Gesamtrechnung der Länder 2005). 5 Dependent variables are the monthly numbers of the unemployed for the urban districts obtained from the Federal Labour Agency (Bundesagentur für Arbeit 2006, 2007). The development in unemployment in the group of the 12 match venues and the group of the 63 non-venues is compared in Figure 2; the development in unemployment in the match venues and non-venues at first progressed generally in parallel (Figure 2). From about January 2001, unemployment in the match venues rose more strongly than in the non-venues. At the beginning of 2005 the two groups of comparative data again approached each other; however, in July 2005 the jobless figures in the non-venues again fell in comparison with the match venues. In the World Cup year 2006 and the beginning of 2007, the development of unemployment in the match venues and non-venues ran largely parallel, with unemployment in the non-venues falling somewhat more steeply than in the match venues from July 2006.

  measures effects on unemployment in the match venue during the course of the World Cup in the months of June and July 2006, does not differ significantly from zero.

  period after the World Cup have not developed significantly differently from those in the other cities in the survey.

  Bertrand et al.. The values of the independent variables used have the expected sign and turn out to be almost without exception significant. The value of the variable t Post , differing significantly and positively indicates that in the whole sample in the period after the World Cup there is a significantly higher level of unemployment than in the period before the competition. The significantly negative value of the variable t TrPost shows for the whole sample a significant negative trend in the numbers of the unemployed in the period after the World Cup, in comparison with the period before the competition. Relevant for possible employment effects of the World Cup in the match venues are the two variables

  Figure 2: Comparison of the jobless figures in the match venues and non-venues, monthly averages; (1998 = 100)

  uses a "Fixed Effects" model, regressing the log unemployment on log population in city i in the year 1999, city-specific time trends, time-specific dummy variables and a dummy variable for the World Cup 2006 in the months of June and July 2006. Column (2) in Table 1 presents the results of this model. The estimated values of the city-specific time trends and of the time-

			6
	specific dummy variables are not reported here, although they were in most cases
	significant. 7 In this model too, the variable	WC	t 2006 proves to be not significantly different i ,
	from zero.
	F o
	r
	P
	e e r
	R e v i e w

6

The results of the seasonal dummies are not reported. They are available from the authors on request.

Table 1 : Results of estimations

 1 Eastdummy for urban districts in the region of the former East Germany, TrWC trend variable for match venues of the World Cup 2006 (1 if match venue and 1st phase of the period under consideration, 2 if match venue and 2nd phase of the period, etc., otherwise 0), TrPostWC , trend variable for match venues and period after the World Cup 2006 (1 if match venue and 1st phase after the World Cup, 2 if match venue and 2nd phase after the World Cup, etc., otherwise 0), and disturbance variable.

	1998 = 100)

i East dummy for urban districts in the region of the former East Germany, Trend time trend, t DumSeas dummies for the month of February to December , i od1999 Pr share of gross value added of the manufactoring industry sector in city i in the year 1999, Page 18 of 18 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK i i WC dummy for match venues of the World Cup 2006 (1 for match venue, 0 if not a match venue), t Post dummy for period after the World Cup 2006 (1 for period after, 0 for period before the World Cup), and i Page 19 of 18 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

No significant effects at all are registered for the Olympic Games.

Although[START_REF] Carlino | Compensating differentials and the social benefits of the NFL[END_REF] reach the conclusion that having a NFL team allows the cities to "enjoy" rents that are 8 percent higher.

For the period before January 1998, data for the numbers of unemployed at the district level were published only quarterly.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

[START_REF] Galster | Supportive housing and neighborhood property value externalities[END_REF] use a similar extended "Difference-in-Difference" estimate in order to investigate the effects on housing prices of accommodation for the disabled.

Services < L -Industrial Organization, R53 -Public Facility Location Analysis; Public Investment; Capital Federal labour agency (Bundesagentur für Arbeit 2006, 2007a).


Niels Hermes

Vu Thi

Hong Nhung

The Impact of Financial Liberalization on Bank Efficiency: Evidence from Latin America and Asia

Keywords: financial liberalization, bank efficiency, Asia, Latin America

This paper investigates the impact of financial liberalization on bank efficiency, using data for a sample of over 4,000 bank-year observations from ten emerging economies for the period 1991-2000. We use Data Envelop Analysis (DEA) to calculate bank efficiency at the individual bank level. Bank efficiency measures are then aggregated at the country level to investigate the relationship between financial liberalization and bank efficiency, using a panel least square fixed-effects model. Overall, we find strong support for the positive impact of financial liberalization programmes on bank efficiency.

INTRODUCTION

Over the last two decades, many emerging economies have implemented financial liberalization policies. These policies aim at enhancing competition, improving resource allocation, and acquiring more efficient financial institutions, by making them less state-directed and by exposing them to increased market competition [START_REF] Barajas | The impact of liberalization and foreign investment in Colombia's financial sector[END_REF]. The question, however, is whether these policies have indeed been successful in achieving these results.

Only a few studies have looked into the efficiency effects of financial liberalization policies in emerging economies. The availabe evidence provides mixed results on the relationship between bank efficiency and financial liberalization. These mixed results may be due to various reasons, of which we mention two here. First, most available studies focus on just one country, leaving open the possibility that in one country bank efficiency improves after liberalization, while in another country the opposite is found. Country-specific studies, therefore, may make it more difficult to come up with general conclusions regarding the impact of financial liberalization on bank efficiency. Second, most studies focus on just one or a few dimensions of financial liberalization.

This study aims at improving on the previous empirical literature by using a multi-country sample. Moreover, we use a unique dataset, provided by [START_REF] Laeven | Does financial liberalization reduce financial constraints?[END_REF], which includes different dimensions of financial liberalization policies. Based on this dataset we are able to measure the depth of financial liberalization at the country level and link changes in the depth of financial liberalization to changes in bank efficiency over time.

Our dataset consist of more than 4,000 bank-year observations for ten emerging economies in Latin America and Asia, i.e. Argentina, Brazil, Peru, Mexico, India, Until the late 1980s, financial repression policies were responsible for the poor operations of banks in most developing and emerging economies. In many of these economies the banking industry was heavily controlled by the government. In particular, banks -many of which were directly owned by the state -were obliged to allocate part of their total loan portfolio to specific sectors. Moreover, the government determined the interest rates on deposits and loans. It also regulated the licensing of market entry of new domestic and foreign banks, and controlled the establishment of new bank branches. Finally, it put restrictions on foreign financial transactions (Kumbhakar andSarkar, 2003, Isik and[START_REF] Isik | Financial deregulation and total factor productivity change: An empirical study of Turkish commercial banks[END_REF].

In such an environment, banks had little motivation to improve their performance by reducing operating costs, increasing the mobilization of deposits and improving the efficient allocation of loans. From the late 1980s, policy makers in developing economies became aware of the importance of the financial system and the process of financial intermediation for economic growth (World Bank, 1989;[START_REF] King | Finance and growth: Schumpeter might be right[END_REF]. To improve the process of financial intermediation these governments therefore implemented financial liberalization policies aimed at improving the efficiency and productivity of the banking system.

In theory, financial liberalization is expected to improve bank efficiency [START_REF] Berger | Efficiency of financial institutions: International survey and directions for future research[END_REF]. The elimination of government control and intervention aims at restoring and strengthening the price mechanism, as well as improving the conditions for market competition [START_REF] Hermes | Does financial liberalization influence saving, investment and economic growth? Evidence from 25 emerging market economies, 1973-1996[END_REF]. This, it is argued, will F o r P e e r R e v i e w 5 lead to more efficient allocation of scarce financial resources. 2 Competitive pressure stimulates banks to become more efficient by reducing overhead costs, improving on overall bank management, improving risk management, and offering new financial instruments and services [START_REF] Denizer | Financial liberalization and banking efficiency: Evidence from Turkey[END_REF]. Moreover, if domestic financial markets are opened up to foreign competition, this will further increase pressures to reduce costs, whereas at the same time, new banking and risk management techniques, as well as of new financial instruments and services may be imported [START_REF] Claessens | How does foreign entry affect domestic banking markets?[END_REF].

Although theory predicts improvements of the efficiency of banks in terms of their financial intermediation activities resulting from financial liberalization policies, there is a lack of empirical studies on this issue in the context of developing and emerging economies. Moreover, the few empirical studies investigating this relationship provide mixed results. Below, we review the empirical research focussing on a number of emerging economies. [START_REF] Gilbert | Effects of deregulation on the productivity of Korean banks[END_REF] Denizer et al. (2007), who also examine Turkish banking efficiency, using data before and after the liberalization in the period 1970-1994, find that bank efficiency has actually declined after the liberalization programs was carried out. [START_REF] Kumbhakar | The effects of deregulation on the performance of financial institutions: The case of Spanish banks[END_REF] analyze the impact of deregulation on the performance of Spanish savings banks. Using data for the period 1986-1995, they conclude that regulatory reforms lead to slightly better banking performance. In particular, they find evidence that despite declining technical efficiency, the productivity growth rate increases in the post-liberalization period. Using data for 1985[START_REF] Grifell-Tatje | Deregulation and productivity decline: The case of Spanish savings banks[END_REF], Maudos et al. (2002) ) conclude that cost efficiency of Spanish banks improved due to a more competitive environment. In contrast, [START_REF] Grifell-Tatje | Deregulation and productivity decline: The case of Spanish savings banks[END_REF] examine productive efficiency of Spanish savings banks during the period 1986-1991. Their research suggests that the deregulation programs were followed by a decline in productivity of banks. [START_REF] Bhattacharyya | The impact of liberalization on the productive efficiency of Indian commercial banks[END_REF] investigate the performance of Indian banks during the early period financial liberalization (1986)(1987)(1988)(1989)(1990)(1991). During this period the Indian government gradually introduced economic deregulation measures. Their results indicate that throughout the whole period state-owned banks operate the most efficiently, whereas private banks are the least efficient. Interestingly, foreign-owned banks do not perform well at the beginning of the period but later on their performance improves, reaching levels close to those of the state-owned banks. [START_REF] Ataullah | Financial liberalization and bank efficiency: A comparative analysis of India and Pakistan[END_REF] find evidence that financial deregulation has a positive impact on bank efficiency in both India and Pakistan. Using data for the period 1988-1998, they show that overall technical efficiency of the banking sector increases following financial liberalization, especially after 1995-1996. [START_REF] Ataullah | Economic reforms and bank efficiency in developing countries: the case of the Indian banking industry[END_REF] focus on India and that financial liberalization leads to increased bank profits in the first round of financial reform during 1991-1992. However, in subsequent years reforms do not have a positive impact on bank performance. Actually, their study shows that profitability declines after 1997. According to the authors, this is mainly due to deteriorating business conditions. [START_REF] Williams | Financial liberalization, crisis, and restructuring: A comparative study of bank performance and bank governance in South East Asia[END_REF] is one of the few studies that analyse the relationship between financial liberalization and bank efficiency in a multi-country setting. This paper considers the impact of financial liberalization on bank performance in Indonesia, Korea, Malaysia, the Philippines and Thailand during the period 1990-2003. In particular, they investigate the empirical relationship between profit efficiency, technical change, productivity and commercial bank ownership. Their findings suggest that privatization policies encourage improving bank efficiency and productivity.

The above discussion of existing empirical studies suggests that the impact of financial liberalization on bank efficiency remains unclear: the impact may be either positive or negative. This means that the relationship between the two remains an empirical issue. In this paper, we add to the empirical literature on this issue in the In reviewing existing empirical evidence, we first of all observe that most studies look at country cases. We suggest that country-specific studies may make it more difficult to come up with general conclusions regarding the impact of financial liberalization on bank efficiency, which is why in this paper we intend to take a multicountry approach. Secondly, the review shows that in most, if not all previous papers financial liberalization as such is not really quantified. Several of these papers focus on a specific period during which bank reform policies have taken place and analyze whether during this period measures of bank efficiency change. Moreover, several papers just look at one or a few dimensions of financial liberalization, such as interest rate liberalization or privatization of state banks.

Yet, in theory, at least, the extent to which financial markets are liberalized may be linked to the impact of these liberalizations on bank efficiency. In particular, the more the government retreats from influencing the allocation of scarce financial resources, the more the price mechanism will be restored and the more the conditions for market competition will be improved, which is expected to result in more efficient banking activities.

In our analysis, we explicitly take into account what we call the depth of financial liberalization policies and their effect on bank efficiency. By this we mean on how many different dimensions the government of a country has substantially liberalized markets. Based on a unique dataset provided by [START_REF] Laeven | Does financial liberalization reduce financial constraints?[END_REF], we are able to measure the depth of financial liberalization and link changes in this measure to changes in bank efficiency over time. The exact nature of the liberalization data and how we have used them in the analysis will be discussed in more detail in section 4.

MEASURING BANK EFFICIENCY

In the literature on bank efficiency various approaches have been used to measure efficiency. Basically, these approaches come down to estimating a specific form of the so-called best practice frontier such as the (maximum) production frontier, the (minimum) cost frontier, or the (maximum) profit frontier. The efficiency level of an individual bank is then defined as the distance of this individual bank's production, costs, or profits to the frontier. Discussions on the measurement of efficiency have been inspired by the work of [START_REF] Debreu | The coefficient of resource utilization[END_REF], [START_REF] Koopmans | Efficient allocation of resources[END_REF] and [START_REF] Farrell | The measurement of productive efficiency[END_REF]. [START_REF] Coelli | An introduction to efficiency and productivity analysis[END_REF] provide a comprehensive overview of the measurement of efficiency and productivity.

In this paper, we focus on measuring so-called technical efficiency. A bank is considered to be technically efficient if it produces optimal quantities of output given the amount of inputs, or alternatively, if it produces given amounts of output with minimum quantities of inputs. This also means that when measuring efficiency, we focus on production, instead of costs or profits. This choice is driven by data availability: data on input prices and/or profits of banking services are more difficult to obtain as compared to data on production of these services.

Technically efficient banks operate on the best practice production frontier, whereas technically inefficient banks perform below this frontier. Put differently, technical efficiency is measured as the difference between the observed output-to-input ratio of a bank and the same ratio achieved by those banks operating on the production frontier.

There are several methods to estimate the efficiency frontier. In general, these techniques can be divided into parametric and non-parametric approaches [START_REF] Kumbhakar | Stochastic frontier analysis[END_REF][START_REF] Berger | Efficiency of financial institutions: International survey and directions for future research[END_REF]. The nonparametric and parametric 10 methods differ in several ways such as with respect to their behavioural assumptions and whether or not they recognize random errors in the data (noise). Parametric methods require specifying a particular functional form which shapes the form of the frontier. The measure of efficiency may be biased due to specification errors if the functional form is misspecified. In contrast, nonparametric methods do not require specifying the functional form for the frontier. They allow for the possibility that if random errors exist, these errors may influence the shape and position of the frontier.

Therefore, parametric approaches take into account random errors when specifying the frontier. Parametric approaches are likely to be more appropriate when the data are heavily influenced by random errors. However, when random errors are considered to be less, a firm's output is multi-dimensional, and/or prices are difficult to obtain, nonparametric methods may be the optimal choice. Therefore, the selection of the appropriate method should be made case-by-case [START_REF] Coelli | An introduction to efficiency and productivity analysis[END_REF]. Following a number of other studies in the bank efficiency literature, e.g., among others, [START_REF] Aly | Technical, scale, and allocative efficiencies in U.S. banking: an empirical investigation[END_REF], [START_REF] Ferrier | Measuring cost efficiency in banking: Econometric and linear programming evidence[END_REF], [START_REF] Berg | Banking efficiency in the Nordic countries[END_REF], [START_REF] Wheelock | Evaluating the efficiency of commercial banks: Does our view of what banks do matter?[END_REF], [START_REF] Bhattacharyya | The impact of liberalization on the productive efficiency of Indian commercial banks[END_REF], [START_REF] Isik | Financial deregulation and total factor productivity change: An empirical study of Turkish commercial banks[END_REF] and Denizer et al. (2007) only appropriate when all banks are operating at the optimal scale. Yet, if this is the case, the size of banks is not the appropriate measure to scale in order to analyse relative efficiency among different banks, since it is assumed that all banks, small and large, are able to produce with the same input-output ratios, i.e. there are no (dis)economies of scale. To account for scale effects, we use a second version of the DEA model as proposed by [START_REF] Banker | Some models for estimating technical and scale inefficiencies in data envelopment analysis[END_REF], which explicitly allows for variable returns to scale. Calculation of efficiency based upon this method leads to a decomposition of OTE into scale (SE) and pure technical efficiency (PTE) components. Scale efficiency can be interpreted as the proportional reduction of input use to be obtained if the bank operates at the optimal scale (constant returns to scale).

PTE refers to the bank's managerial and marketing skills in using its inputs in order to maximize outputs. This relates to skills such as controlling operating expenses, effective screening and monitoring of borrowers, marketing activities focussing on attracting depositors, efficient risk management techniques, etc. To conclude, OTE is determined by economies of scale due to the size of the bank (SE) and managerial efficiency (PTE).

In order to be able to calculate efficiency, we need to select input and output measures of bank activities. In the literature five common approaches are used: the production approach, the intermediation approach, the asset approach, the user-cost approach and the value added approach. Of these five approaches the production approach and the intermediation approach are the most widely used (e.g., by [START_REF] Ferrier | Measuring cost efficiency in banking: Econometric and linear programming evidence[END_REF][START_REF] Aly | Technical, scale, and allocative efficiencies in U.S. banking: an empirical investigation[END_REF]Berger and Humphrey, 1991;[START_REF] Hunter | Core deposits and physical capital: A reexamination of bank-scale economic and efficiency with quasi-fixed inputs[END_REF].

According to the production approach, banks produce services to depositors and borrowers. The approach uses traditional production factors such as land, capital and 12 labour as inputs to produce outputs specified by the number of accounts serviced and/or transactions processed. According to the intermediation approach, banks are intermediaries, transforming and transferring financial resources they borrow from depositors into the credit lent to borrowers. This approach uses deposits collected and funds borrowed from financial markets (i.e. bank liabilities) as inputs, whereas loans and other assets are considered to be the bank's outputs. One limitation of this approach is that it may not consider all activities provided by banks, e.g. intermediation of corporate and government bonds, investment banking activities, underwriting activities, etc. [START_REF] Favero | Technical efficiency and scale efficiency in the Italian banking sector: A non-parametric approach[END_REF]. This paper uses a combination of the production and intermediation approach.

Due to data availability, this paper only uses measures of labour, physical capital and loanable funds as inputs. Labour is measured by personnel expenses. Physical capital is measured by the total book value of fixed assets, other earning assets and non-earning assets. Loanable funds include time and savings deposits, commercial deposits, bank deposits and certificates of deposits. Moreover, we use two output measures, i.e. total demand deposits and total net loans. Net loans are defined as total loans net of loan loss reserves.

DATA AND RESEARCH METHODOLOGY

First of all, we explain how we have constructed our measure of financial liberalization. For this we use a financial liberalization index that has been developed by [START_REF] Laeven | Does financial liberalization reduce financial constraints?[END_REF]. This index shows the extent to which a country has implemented financial liberalization policies in six different areas in a particular year. These six areas are interest rates, entry barriers, reserve requirements, credit controls, privatization and prudential regulation. For each year and for each policy area [START_REF] Laeven | Does financial liberalization reduce financial constraints?[END_REF] has evaluated whether there has been significant progress in taking liberalization measures. For each of the six policy areas a dummy is created, which is 0 when no significant progress has been in made in a particular year; it is 1 when there has been significant progress. The financial liberalization index for a particular year is the sum of the six dummy variables in that year. Thus, the index ranges from 0 to 6.

The index can be seen as a measure of the depth of financial liberalization implemented by the government of a country: the higher the index, the higher the number of policy areas for which the government has carried out siginficant liberalizations. Table 1 provides an overview of the financial liberalization index for the ten countries over the period 1991-1998, the period for which the data on the index are available.

<Insert Table 1 here> For our analysis we transform the data for the financial liberalization index into a dummy variable (LIBER) that takes a value of 0 if the index is 0, 1, 2, 3 or 4 and is 1 if the index is 5 or 6. We interprete a 0 as a situation of low or medium-level liberalization; a 1 is interpreted as a situation of high-level or full liberalization. Note 14 that our data on bank efficiency run until 2000, so for 1999-2000 we have no data on the financial liberalization index. However, as Table 1 shows, in 1998 for all ten countries in our sample the index was 5 or 6, meaning that LIBER gets a 1 in all cases.

We assume that for 1999-2000 financial liberalization efforts in the ten countries are not reversed from high or full to lower levels of financial liberalization, i.e. we assume that LIBER remains to be 1 during these two years. 4 The data with respect to bank activities to measure bank efficiency are taken from financial statements of banks provided by BankScope (CD-ROM version 1999 and[START_REF] Brooks | Introductory econometrics for finance[END_REF]. The data base used in the analysis covers information from ten emerging economies in Asia and Latin America, i.e. Argentina, Brazil, India, Indonesia, Korea, Mexico, Pakistan, Peru, the Philippines and Thailand, for the period 1991 to 2000. 5 Data availability does not allow us to go back further. Tables 2 and3 provide information on the data coverage for each of the ten countries in terms of the number of banks in the data set and their share in the total assets of the domestic banking system for each year. Table 3 shows that the coverage in terms of the share of total assets is substantial for most countries in most years. The only exception is Indonesia, for which the share fluctuates between 23 and 80 per cent. We have an unbalanced panel data set, since for several banks data are missing or not available for the entire 1991-2000 period. In order to be taken into account in our analysis, a bank must have at least three years observations. As discussed in section 3, we have three different measures of bank efficiency: OTE, PTE and SE.

<Insert Table 2 here> 4 The data in Table 1 show that the financial liberalization index does not reverse for any of the countries in any of the years. 3 here> We calculate bank efficiency scores at the individual bank level, using the three different efficiency measures, and then aggregate annual average efficiency scores of all banks at the country level. We use separate annual frontiers for each country (which in the literature is known as the national frontier) to calculate efficiency scores, rather than one common frontier for all countries. Both the common frontier and the national frontier have been used in the literature. The common frontier approach assumes the same technology among countries; it does not capture cross-country differences. The national frontier approach does not follow this assumption [START_REF] Coelli | An introduction to efficiency and productivity analysis[END_REF][START_REF] Berger | Efficiency of financial institutions: International survey and directions for future research[END_REF].

Tables 4A (reporting on the Latin American countries) and 4B (referring to Asian countries) provide information on the average OTE, PTE and SE per country for ten consecutive years, from 1991 through 2000. The data in the Tables show that the patterns of the three measures of efficiency are similar when looking at the individual country level. Moreover, the Tables also show that for all countries in the sample the measures fluctuate across countries during 1991-2000. In general, however, for most countries the efficiency measures are higher at the beginning of the period as compared to the value of these measures at the end of the period. This finding may be partly due to the fact that the number of banks per country for which data are available at the beginning of the period is relatively low. Thus, the figures for the efficiency measures may suffer from a sample bias in the early 1990s. When looking at trends for individual countries Tables 4A and4B indicate that for Argentina, Brazil, Indonesia, 5 Laeven (2003) provides financial liberalization data for 13 countries. In the analysis we have left out Chile, Malaysia and Taiwan, because Bankscope provides data for only a few banks from these three Recall that overall technical efficiency (OTE) is affected by both managerial practices, i.e. pure technical efficiency (PTE), as well as by the size of banks, i.e. scale efficiency (SE). When looking at the Table it is clear that for all countries in the sample OTE is lower than PTE during the period 1991-2000. 6 This result implies that the overall technical efficiency is mainly caused by the pure technical efficiency of banks in the countries in our sample. When grouping the data for countries into two regions, i.e. Latin America and Asia, it appears that Asian banks have lower overall technical efficiency than banks in Latin America. The average OTE of Latin American banks over the period 1991-2000 is 0.75; for Asian banks this is 0.71. However, these averages are not significantly different from each other. 7 The averages for PTE are almost the same. 8 <Insert Tables 4A and4B here> The empirical analysis in this paper focuses on relating our measures of bank efficiency to our measure of financial liberalization (LIBER). A positive relationship between LIBER and OTE, PTE and/or SE would indicate that financial liberalization policies carried out in emerging economies during 1991-2000 have a positive impact on bank efficiency, as is hypothesized in section 2. The econometric framework uses a countries during the period 1991-1996. 6 Except for Pakistan in 2000; in this case OTE and PTE are equal. 7 T-value is 1.37. 8 The average PTE of Latin American banks over the period 1991-2000 is 0.829; for Asian banks this is 0.813. economies. Thus, our total sample consists of 100 observations. We apply fixed effects estimations. The econometric specification of the model is as follows:

Y eit = i + LIBER it + X it + it
(1).

In this model, Y is a vector of efficiency measures for country i at time t; i captures the country-specific effects; LIBER is the measure of financial liberalization for country i at time t; and X is vector of control variables, including bank specific features and the macroeconomic environment for country i at time t. We have selected control variables that have been found relevant in other studies on bank efficiency. We have included a measure of the density of demand, GDP growth, and inflation rate as our macroeconomic variables.

The density of demand (DD) is defined as the ratio of total value of deposits per square kilometre. In the literature it is hypothesized that banks operating in a market with lower density of demand suffer from higher expenses in making loans and gathering deposits through their branches. Therefore, bank efficiency and demand density are positively correlated [START_REF] Dietsch | How the environment determines banking efficiency: A comparison between French and Spanish banks[END_REF][START_REF] Lozano-Vivas | European bank performance beyond country borders: What really matters?[END_REF][START_REF] Brooks | Introductory econometrics for finance[END_REF]. The annual growth rate of GDP (YG) serves as a proxy for measuring the overall level of development, which may influence the quality and the skill levels of institutions [START_REF] Claessens | How does foreign entry affect domestic banking markets?[END_REF][START_REF] Lensink | The short-term effects of foreign bank entry on domestic bank behaviour: Does economic development matter?[END_REF]. Therefore, bank efficiency is assumed to be positively correlated with overall economic development.

The annual inflation rate (INF) is included to capture potential inefficiencies due to price (high interest margin) and non-price (excessive braches) behaviour of banks [START_REF] Grigorian | Determinants of commercial bank performance in transition: An application of Data Envelopment Analysis[END_REF]. Thus, annual inflation and bank efficiency are expected The bank-specific control variables in our model are the capital (equity) to asset ratio (EQ), the return on equity ratio (ROE) and the total loans to deposits ratio (LTD).

The relationship between EQ and bank efficiency can be positive or negative. [START_REF] Berger | Problem loans and cost efficiency in commercial banks[END_REF] suggest that a higher capital to asset ratio indicates lower bad loan problems, which reduces the additional costs to recover these bad loans. [START_REF] Dietsch | How the environment determines banking efficiency: A comparison between French and Spanish banks[END_REF] and [START_REF] Lozano-Vivas | European bank performance beyond country borders: What really matters?[END_REF] argue that a lower capital to asset ratio is associated with lower bank efficiency, since it involves higher risk taking. Moreover higher leverage ratios are also more costly to the bank. Higher levels of EQ are therefore associated with higher bank efficiency. In contrast, low capital ratios may encourage banks to undertake risky business by investing in highly profitable projects.

This may help banks obtain higher efficiency at least in the short term [START_REF] Lozano-Vivas | An efficiency comparison of European banking systems operating under different environmental conditions[END_REF]. The return on equity ratio (ROE) is used as a proxy of competitiveness in the banking industry. Assuming a competitive market environment, this ratio is expected to have a positive impact on efficiency (see, e.g., [START_REF] Lozano-Vivas | European bank performance beyond country borders: What really matters?[END_REF][START_REF] Brooks | Introductory econometrics for finance[END_REF]. Finally, the loan to deposit ratio (LTD) is a measure of the efficiency of banks in terms of the extent to which they are able to transform deposits into loans.

The higher this ratio, the more efficient the process of financial intermediation provided by the bank [START_REF] Dietsch | How the environment determines banking efficiency: A comparison between French and Spanish banks[END_REF][START_REF] Fries | Cost efficiency of banks in transition: Evidence from 289 banks in 15 post-communist countries[END_REF]. Thus, higher levels of LTD are associated with higher levels of bank efficiency.

Table 5 shows the correlation matrix of the independent variables in the model.

The matrix shows that in general correlation between the exogenous variables is low (except perhaps for the correlation between LIBER and EQ), which means that multicollinearity problems are not severe or non-existent.

EMPIRICAL RESULTS

The empirical model specified in equation is estimated using the panel least square fixed effects methodology. We use the fixed effects model, since we focus on a limited of number of countries, for which we want to assess country-specific differences with respect to the relationship between financial liberalization and bank efficiency [START_REF] Hsiao | Analysis of panel data[END_REF][START_REF] Baltagi | Econometric analysis of panel data[END_REF]. The empirical model is tested for each of the three measures of efficiency, i.e. OTE, PTE and SE. The research strategy follows the specific-to-general approach [START_REF] Brooks | Introductory econometrics for finance[END_REF]. We start by investigating the relationship between the financial liberalization variable (LIBER) and efficiency. Next, we include the control variables one by one to test the stability of the main independent variable LIBER. We adjust for cross-section heteroskedasticity to make robust estimates of standard errors by using White cross-section tests since the cross-sectional units (countries in this case) may have different sizes and characteristics, which may lead different variations in regression disturbances [START_REF] Baltagi | Econometric analysis of panel data[END_REF]. The results of the estimations are presented in Tables 789.

Column [1] in Table 7 shows the results of the relationship between OTE (overall technical efficiency) and LIBER. The result shows that the coefficient of <Insert Table 7 here> Two of the three bank-specific variables are statistically significant. The capital to asset ratio (EQ) has a negative coefficient that is significant at the 1 per cent level, supporting the idea that low capital ratios encourage banks to undertake risky business by investing in highly profitable projects. The return on equity ratio (ROE) is positive and statistically significant at the 1 per cent level, which confirms our hypothesis concerning the relationship between market competition and bank efficiency. The loan to deposit ratio (LTD) is not statistically significant.

Two of the three country-specific variables are statistically significant. The coefficient of the density of demand (DD) variable is negative and statistically significant at the 1 per cent level. This is opposite to what we expected based on the theory that banks operating in markets with higher density of demand incur lower costs of mobilizing deposits and granting loans, resulting in higher bank efficiency. We have 21 no clear explanation for this unexpected result. 9 The inflation rate (INF), used as a proxy for macroeconomic instability, is not statistically significant. Finally, the GDP growth rate (YG), a proxy for the overall level of economic development of a country is positive and statistically significant at the 1 per cent, which is in line with what we expected. This result indicates that banks operating in countries with higher GDP growth are more efficient due to the corresponding quality and skills of financial institutions.

Next, we use the pure technical efficiency (PTE) as the dependent variable. The results of the empirical analysis remain to be very similar to the ones we get when we use overall technical efficiency (OTE) as the dependent variable. The results for PTE are presented in Table 8. Again, we find that the coefficient of LIBER is positive and significant at the 1 per cent level, which supports the hypothesis that financial liberalization leads to improvements of bank efficiency. Adding control variables does not change the results for LIBER: the coefficient remains to be significant and increases only slightly from 0.047 in Column [1] to 0.054 in Column [7]. Again, the majority of the control variables are statistically significant and their coefficients do not change much in the different specifications of the model. The only major difference with the results in table 7 is that when we use PTE as dependent variable YG is no longer statistically significant.

<Insert Table 8 here> Finally, we use scale efficiency (SE) as the dependent variable. The results, presented in Table 9, show that the coefficient of LIBER is positive and significant at 9 Fries and Taci ( 2005) also find a negative coefficient for DD. In their study the coefficient is not statistically significant, however. 22 the 1 per cent level. Again, therefore, we find support for our hypothesis that financial liberalization leads to improvements of bank efficiency. This result is not changed when adding control variables does not change the results for LIBER: the coefficient remains to be significant and increases only slightly from 0.046 in Column [1] to 0.053

in Column [7]. This time, however, only two control variables (ROE and YG) are found to be statistically significant with the right sign.

<Insert Table 9 here>

SUMMARY AND CONCLUSIONS

Using data for a sample of over 4,000 bank-year observations from ten emerging economies for the period 1991-2000, we have investigated the impact of financial liberalization on bank efficiency. We have used the DEA approach to calculate three different bank efficiency measures at the individual bank level. Next, the individual bank efficiency data have been aggregated at the country level, providing three different bank efficiency measures per country per year. These measures have been used to investigate the relationship between financial liberalization and bank efficiency, using a panel least square fixed-effects model.

Our paper contributes to the ongoing debate on the relationship between financial liberalization and bank efficiency in the sense that we present one of the very few multi-country panel data regression analyses. Moreover, contrary to previous studies we explicitly take into account the depth of financial liberalization policies and their effect on bank efficiency. This is possible since we use a unique dataset provided by [START_REF] Laeven | Does financial liberalization reduce financial constraints?[END_REF] 1991 1992 1993 1994 1995 1996 1997 1998 1999 2. NOTE: The financial liberalization index shows the extent to which a country has implemented financial liberalization policies in six different areas in a particular year. These six areas are interest rates, entry barriers, reserve requirements, credit controls, privatization and prudential regulation. For each year and for each policy area [START_REF] Laeven | Does financial liberalization reduce financial constraints?[END_REF] has evaluated whether there has been significant progress in taking liberalization measures. For each of the six policy areas a dummy is created, which is 0 when no significant progress has been in made in a particular year; it is 1 when there has been significant progress. The financial liberalization index for a particular year is the sum of the six dummy variables in that year. Thus the index ranges from 0 to 6. 1991-2000 1991 1992 1993 1994 1995 1996 1997 1998 1999 1991-2000 1991 1992 1993 1994 1995 1996 1997 1998 1999 NOTE: SE = scale efficiency (for a detailed discussion of this measure, see main text); LIBER = measure of financial liberalization (for a detailed discussion of this measure, see main text); DD = density of demand, defined as the ratio of total value of deposits per square kilometre; YG = annual growth rate of GDP; INF = annual inflation rate; EQ = capital (equity) to asset ratio; ROE = return on equity ratio; LTD = total loans to deposits ratio. ***, **, *, Indicates significance at the 1, 5 and 10 percent level, respectively. White cross-section standard errors are presented in parentheses.

  analyse changes in technical efficiency and changes in technology of Korean banks during 1980-1994. They find that the bank reforms the Korean government established in 1991 improve productivity and potential output of Korean banks. Yet, Hao et al. (2001), using data for the period 1985-1995, conclude that there is little or no positive relationship between the reforms and efficiency of Korean banks. Isik and Hassan (2003) analyze changes in total factor productivity of Turkish banks due to financial market deregulation in the period 1981-1990. Their results indicate that Turkish banks improve their performance considerably after the

  a broader set of reforms (i.e. financial reforms, fiscal reforms and private investment liberalization) and find that efficiency of banks increased due to increased competition.[START_REF] Hardy | Bank reform and bank efficiency in Pakistan[END_REF] investigate the effects of financial reforms on profitability, cost and revenue efficiency of the banking sector in Pakistan banks during 1981-1998. They show that financial liberalization has a positive impact on banking sector performance. In particular, cost and revenue efficiency of banks increases, following financial liberalization policies. Patti et al. (2005) examine cost and profit efficiency of financial liberalization in Pakistan, using data for 1981-2002. They find

  , we use the Data Envelopment Analysis (DEA) to calculate technical efficiency of banks. DEA uses linear programming methods to construct a nonparametric piece-wise surface (or frontier) over the selected sample of banks based upon measures of bank output. Efficiency of a bank is measured as the distance from each individual bank's output to this surface. We use two versions of the DEA model. The first model assumes constant returns to scale and is focused on minimizing inputs for a given level of output (i.e. the input-orientated version of DEA). The efficiency measure derived from the model reflects the overall technical efficiency (OTE). Assuming constant returns to scale is

  3

  efficiency seems to go down during most of the period, whereas for India and Korea efficiency goes up from the mid-1990s, after an initial decline during the first years of the decade. For the Philippines and Peru no clear trend is observed.

  set based upon ten years of observations for ten emerging

  negatively. The macroeconomic data were obtained from World Development Indicators provided by the World Bank.

  and significant at the 1 per cent level. This result supports the hypothesis that financial liberalization leads to improvements of overall technical efficiency of banks in the countries in our dataset. Next, we add the bank-specific and country-specific control variables (columns [2]-[7]). Adding these control variables does not change the results for LIBER: the coefficient remains to be significant and increases only slightly from 0.077 in Column [1] to 0.088 in Column [7]. The majority of the control variables are statistically significant and their coefficients do not change much in the different specifications of the model. Moreover, the explanatory power of the different models is rather high with values of the adjusted R-squared ranging from 64 to 67 per cent.

Table 6

 6 endogenous and exogenous variables used in the empirical investigation. The Table shows that some of the variables are not normally distributed, especially INF, LTD and ROE.

	provides descriptive

23 of 38 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  which explicitly measures to what extent governments have

		Submitted Manuscript
	Overall, the results from the empirical analysis strongly support the positive
	impact of financial liberalization programmes on bank efficiency. This result holds
	across all three measures of bank efficiency and all specifications we have used in
	testing the relationship.
	Future research on this issue should focus on extending the data set in terms of
	countries and years. Of course, this requires information on financial liberalization
	programmes in different countries. Such information is currently not available. In
	F o r addition, further research may apply different methods to analyze the impact of financial liberalization on bank efficiency, such as for example stochastic frontier analysis, to see whether and to what extent the results are sensitive to the methodology F o r F o r F o r F o r F o r F o r
	used.	P P P P P P P
		e e e e e e e
		e r e r e r e r e r e r e r
		R e v R e v R e v R e v R e v R e v R e v
		i e i e i e i e i e i e i e
		w w w w w w w
	liberalized markets.
		23 29

Page

Table 1 : The Financial Liberalization Index

 1

Table 2 : Number of banks per country in the sample,

 2

Table 4B : Average bank efficiency per country, Asia

 4B

											2000 Total
	Argentina	18	27	18	18	12	17	19	67	76	69	341
	Brazil	13	23	16	98 105 145	68 144 118 135	865
	India	36	53	54	65	67	74	72	71	71	62	625
	Indonesia	16	45	64	78	82	81	52	55	60	50	583
	Korea	6	17	12	29	33	35	36	27	25	23	243
	Mexico	8	10	6	22	34	35	26	43	43	37	264
	Pakistan	5	15	9	23	24	29	25	29	28	25	212
	Peru	5	16	9	23	26	27	26	29	22	21	204
	Philippines	14	20	16	31	36	42	46	43	43	36	327
	Thailand	14	27	23	41	45	46	32	37	36	37	338
	Total	135 253 227 428 464 531 402 545 522 495 4,002
		F o r F o r F o r
				P P P
				e e e
					e r e r e r
						R e v R e v R e v
								i e i e i e
								w w w

Table 5 : Correlation matrix for the exogenous variables in the model

 5 LIBER = measure of financial liberalization (for a detailed discussion of this measure, see main text); DD = density of demand, defined as the ratio of total value of deposits per square kilometre; YG = annual growth rate of GDP; INF = annual inflation rate; EQ = capital (equity) to asset ratio; ROE = return on equity ratio; LTD = total loans to deposits ratio.

	2000

NOTE:

Table 6 : Summary statistics

 6 OTE = overall technical efficiency (for a detailed discussion of this measure, see main text); PTE = pure technical efficiency (for a detailed discussion of this measure, see main text); SE = scale efficiency (for a detailed discussion of this measure, see main text); LIBER = measure of financial liberalization (for a detailed discussion of this measure, see main text); DD = density of demand, defined as the ratio of total value of deposits per square kilometre; YG = annual growth rate of GDP; INF = annual inflation rate; EQ = capital (equity) to asset ratio; ROE = return on equity ratio; LTD = total loans to deposits ratio.

		Mean	Median	Maximum	Minimum St Dev Skewness	Kurtosis	Obs.
	OTE	0.726	0.749	0.99	0.398	0.139	-0.378	2.40	100
	PTE	0.819	0.827	1	0.503	0.111	-0.633	3.10	100
	SE	0.880	0.891	1	0.670	0.071	-0.762	3.13	100
	LIBER	0.61	1	1	0	0.490	-0.451	1.20	100
	EQ	0.068	0.067	0.191	-0.035	0.043	0.397	2.82	100
	DD	729.3	55.4	9079.2	3.0	2096.2	3.21	11.80	100
	INF	70.0	8.2	2075.9	-1.2	299.0	5.78	36.67	100
	YG	4.3	4.8	12.8	-13.1	4.3	-1.28	6.12	100
	LTD	1.64	0.57	87.8	0.04	8.78	9.61	94.74	100
	ROE	0.119	0.030	10.97	-1.51	1.14	8.81	84.91	100
	NOTE:

Table 7 : Panel least square estimations with OTE as dependent variable

 7 OTE = overall technical efficiency (for a detailed discussion of this measure, see main text); LIBER = measure of financial liberalization (for a detailed discussion of this measure, see main text); DD = density of demand, defined as the ratio of total value of deposits per square kilometre; YG = annual growth rate of GDP; INF = annual inflation rate; EQ = capital (equity) to asset ratio; ROE = return on equity ratio; LTD = total loans to deposits ratio. ***, **, *, Indicates significance at the 1, 5 and 10 percent level, respectively. White cross-section standard errors are presented in parentheses.

	Variables	[1]	[2]	[3]	[4]	[5]	[6]	[7]
	Constant	0.679***	0.731***	0.731***	0.731***	0.757***	0.752***	0.749***
		(0.012)	(0.018)	(0.018)	(0.019)	(0.026)	(0.025)	(0.020)
	LIBER	0.077***	0.080***	0.080***	0.083***	0.084***	0.084***	0.088***
		(0.010)	(0.020)	(0.020)	(0.022)	(0.022)	(0.021)	(0.020)
	EQ		-0.798***	-0.800***	-0.842***	-1.007***	-0.960***	-1.130***
			(0.233)	(0.233)	(0.265)	(0.338)	(0.318)	(0.274)
	LTD			0.0001	6.25E-05	4.57E-05	2.69E-05	-0.0003
				(0.0004)	(0.0004)	(0.0004)	(0.0004)	(0.0005)
	ROE				0.012***	0.012***	0.012***	0.012***
					(0.004)	(0.005)	(0.005)	(0.005)
	DD					-2.1E-05***	-2.1E-05***	-2.3E-05***
						(6.6E-06)	(6.66E-06)	(6.81E-06)
	INF						1.89E-05	1.54E-05
							(3.87E-05)	(3.80E-05)
	YG							0.003***
								(0.001)
	R 2 Adj. R 2	0.706 0.636	0.729 0.660	0.729 0.655	0.736 0.661	0.750 0.674	0.751 0.671	0.756 0.674
	Obs.	100	100	100	100	100	100	100
	F-statistic	10.14	10.60	9.97	9.78	9.89	9.40	9.19

NOTE:

Table 8 : Panel least square estimations with PTE as dependent variable

 8 PTE = pure technical efficiency (for a detailed discussion of this measure, see main text); LIBER = measure of financial liberalization (for a detailed discussion of this measure, see main text); DD = density of demand, defined as the ratio of total value of deposits per square kilometre; YG = annual growth rate of GDP; INF = annual inflation rate; EQ = capital (equity) to asset ratio; ROE = return on equity ratio; LTD = total loans to deposits ratio. ***, **, *, Indicates significance at the 1, 5 and 10 percent level, respectively. White cross-section standard errors are presented in parentheses.

	Variables	[1]	[2]	[3]	[4]	[5]	[6]	[7]
	Constant	0.791***	0.842***	0.842***	0.841***	0.867***	0.865***	0.864***
		(0.010)	(0.015)	(0.015)	(0.015)	(0.016)	(0.016)	(0.014)
	LIBER	0.047***	0.050***	0.049***	0.051***	0.052***	0.0524***	0.0540***
		(0.016)	(0.016)	(0.016)	(0.018)	(0.016)	(0.016)	(0.016)
	EQ		-0.768***	-0.772***	-0.799***	-0.961***	-0.944***	-1.010***
			(0.161)	(0.161)	(0.173)	(0.223)	(0.197)	(0.177)
	LTD			0.0002	0.0002	0.0002	0.0002	0.0001
				(0.0004)	(0.0003)	(0.0003)	(0.0003)	(0.0003)
	ROE				0.008***	0.008**	0.008**	0.008**
					(0.003)	(0.004)	(0.004)	(0.004)
	DD					-2.0E-05***	-2.1E-05***	-2.1E-05***
						(6.1E-06)	(6.2E-06)	(6.06E-06)
	INF						6.83E-06	5.48E-06
							(3.57E-05)	(3.58E-05)
	YG							0.001
								(0.001)
	R 2 Adj. R 2	0.681 0.605	0.713 0.640	0.713 0.636	0.719 0.638	0.738 0.659	0.736 0.655	0.740 0.652
	Obs.	100	100	100	100	100	100	100
	F-statistic	8.98	9.80	9.24	8.94	9.33	8.83	8.42

NOTE:

Table 9 : Panel least square estimations with SE as dependent variable

 9

	Variables	[1]	[2]	[3]	[4]	[5]	[6]	[7]
	Constant	0.851***	0.863***	0.863***	0.863***	0.865***	0.858***	0.856***
		(0.013)	(0.020)	(0.020)	(0.020)	(0.022)	(0.022)	(0.020)
	LIBER	0.046**	0.047**	0.047**	0.0490**	0.049**	0.050**	0.053**
		(0.021)	(0.021)	(0.021)	(0.022)	(0.022)	(0.021)	(0.021)
	EQ		-0.179	-0.175	-0.198	-0.211	-0.142	-0.273
			(0.224)	(0.225)	(0.238)	(0.253)	(0.253)	(0.242)
	LTD			-0.0003	-0.0003	-0.0003	-0.0003	-0.0005
				(0.0003)	(0.0003)	(0.0003)	(0.0003)	(0.0003)
	ROE				0.006*	0.007*	0.006*	0.006*
					(0.003)	(0.003)	(0.003)	(0.003)
	DD					-1.74E-06	-1.85E-06	-3.19E-06
						(3.09E-06)	(3.08E-06)	(3.44E-06)
	INF						2.80E-05	2.53E-05
							(1.69E-05)	(1.59E-05)
	YG							0.003***
								(0.001)
	R 2 Adj. R 2	0.558 0.453	0.562 0.451	0.563 0.445	0.572 0.450	0.572 0.443	0.580 0.446	0.594 0.457
	Obs.	100	100	100	100	100	100	100
	F-statistic	5.31	5.07	4.79	4.68	4.42	4.32	4.33

Note that financial liberalization may also have quantity effects, i.e. it increases the amount of resources that are intermediated between savers and investors. By introducing market principles and competition in financial markets interest rates on deposits be raised, leading to higher saving and investment rates. This is not the focus of this paper, however.

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

One of our referees pointed out that there may be a quality dimension of loans that is unmeasured in this study, and more generally in the literature on bank efficiency and financial liberalization. The quality dimension refers to the fact that before liberalization, many loans may require low screening and monitoring due to government involvement and may thus be low-quality loans, whereas after liberalization loan decisions are taken by banks that put in screening and monitoring efforts, potentially leading to higher quality loans. Thus, a simple ratio of quality-unadjusted loans to inputs may be a poor measure of efficiency. Although we agree with the referee on this point, we would also like to stress that it is very difficult to come up with better measures of efficiency, which take into account such quality adjustments. Based on the available data, we were not able to produce better measures, which is why we have used a crude measure like loans to inputs, a measure that has been used also in several other studies in this literature.

G21 -Banks|Other Depository Institutions|Mortgages < G2 -Financial Institutions and Services < G -Financial Economics, G28 -Government Policy and Regulation < G2 -Financial Institutions and Services < G -Financial Economics, O16 -Financial Markets|Saving and Capital Investment < O1 -Economic Development < O -Economic Development, Technological Change, and Growth, N25 -

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