Carlos Pestana Barros

José Bernd Frick

Carlos ; Barros

Keywords: Unemployment: Models, Duration, Incidence, and Job Search < J6 -Mobility, Unemployment, Duration Models, Head Coach Dismissal, Soccer, Bundesliga, Duration Models, Head Coach Dismissal, Soccer, Bundesliga, Germany L83, M12, M51

come

Introduction

The empirical study of head coach career duration in professional football can benefit from the application of event history analysis, a technique that focuses on the effects of factors that determine the length of time until the occurrence of some event, such as the death of a patient or the dismissal of an employee [START_REF] Yamaguchi | Event History Analysis[END_REF], [START_REF] Allison | Event History Analysis[END_REF], [START_REF] Cleves | An Introduction to Survival Analysis Using STATA[END_REF], [START_REF] Cox | Analysis of Survival Data[END_REF]. This technique has been previously used in sports by Okhusa (1999Okhusa ( , 2001) ) and [START_REF] Frick | Career Duration in a Competitive Environment: The Market for Soccer Players[END_REF] and is currently being adopted elsewhere in fields such as labour economics [START_REF] Carrasco | Transitions to and from Self-Employment in Spain: An Empirical Analysis[END_REF][START_REF] Tribó | An Analysis of Length of labour and Financial Contracts: a study for Spain[END_REF][START_REF] Cueto | An Analysis of Self-Employment Subsidies with Duration Models[END_REF][START_REF] Collier | Unemployment duration and individual heterogeneity: a regional study[END_REF][START_REF] Gonzalo | A New Look at the UI Effect on Transition from Unemployment into Wage Employment in Spain: The Limited Duration of the UI Benefits Entitlement[END_REF][START_REF] Gonzalo | A New Look at the UI Effect on Transition from Unemployment into Wage Employment in Spain: The Limited Duration of the UI Benefits Entitlement[END_REF], in international relations [START_REF] Box-Steffensmeier | Nonproportional hazard and event history in international relations[END_REF][START_REF] Barros | The Timing of ETA Attacks[END_REF], corporate finance (Holtz-Eakin et al. 1994, Orbe, Ferreira and[START_REF] Orbe | Length of Time Spent in Chapter 11 bankruptcy: a Censored partial regression model[END_REF][START_REF] Leung | Entry of foreign banks in the people's republic of China: A survival analysis[END_REF] and industry [START_REF] Requena-Silvente | Competition and Product Survival in the UK car Market[END_REF].

In this paper we analyze the determinants of head coach career length using a hitherto unavailable data set from the "Bundesliga", the first division in German football.

The motivation for this research is the following: First, coach dismissals have always been and still are used occasionally when (professional) teams perform poorly on the pitch. However, dismissing the old and hiring a new head coach not necessarily leads to positive results. It is, therefore, important to ascertain the covariates which explain the decision to dismiss a coach. Second, a dismissal is a decision in time which in an individual coach's career happens once in a while -possibly depending on the duration of his career in the league. This time event characteristic of career length and dismissal allow their analysis using an event history model. Finally, it is important for policy purposes to investigate the covariates of head coach dismissals. If one knew the characteristics leading to these events, then one could better allocate resources used in countering such events.

The paper contributes to the theme's literature in three ways. First, by adopting a panel data framework, it uses a hazard model, previously applied by Okhusa (1999Okhusa ( , 2001)). A Repeated events duration model is adopted. Second, it specifically analyzes head coach dismissals, an issue that so far has not inspired much research in Europe, despite its increasing importance, allowing for salary information. Finally, it analyzes data from one of the major European football leagues which, in turn, allow some broader generalizations. This paper is organised as follows. In section 2 we summarize the literature that is of relevance in our context. Section 3 describes the contextual setting, i.e. the labour market for head coaches in German soccer. In section 4 we present the theoretical 3 framework that underlies the empirical analysis, in section 5 the data and in section 6. the results. Section 7 discusses the limitations and some possible extensions of our research and, finally, section 8 concludes.

Literature Survey

The number of papers adopting event history analysis in (professional) sports has been increasing considerably over the past few years as more and more data has been made available. [START_REF] Spurr | The Effect of Performance on a Worker's Career: Evidence from Minor League Baseball[END_REF] analyze the promotion, demotion and turnover of pitchers in minor league Baseball in the US in the years 1975-1988 using the Weibull model, the Gomperz model and the quadratic model, identifying the variables that were more important in predicting promotion, demotion and turnover. [START_REF] Hoang | The NBA, Exit Discrimination and Career Earnings[END_REF] study exit discrimination in the NBA during the 1980s concluding that white players had a lower risk of being cut than black players. [START_REF] Atkinson | Flexible Modelling of Time to Failure in Risky Careers[END_REF] identify individual player determinants of career duration in the NFL (1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979)(1980) with Cox, Weibull, Exponential and Burr-type model, using data on injuries, individual and team performance, education, and player position. [START_REF] Ohkusa | Additional Evidence for the Career Concern Hypothesis with Uncertainty of the Quit Period: The Case of Professional Baseball Players in Japan[END_REF][START_REF] Ohkusa | An Empirical Examination of the Quit Behavior of Professional Baseball Players in Japan[END_REF] analyzes the quit decision among baseball players in Japan with Cox's proportional hazard model. Del Corral, Barros and Prieto-Rodriguez (2007) analyse the pattern of player substitutions during a soccer match, using data from the Spanish First Division football League in the 2004-2005 season.

More in line with the present research, a number of studies look at the impact of firing the head coach on team performance in various European soccer leagues: [START_REF] Tena | Within-season dismissal of football coaches: Statistical analysis of causes and consequences[END_REF] examine the consequences of within-season dismissals of head coaches in the "Primera Division", the top division of Spanish football, in the seasons 2002/03-2004/05 with an order probit model. [START_REF] Koning | An econometric evaluation of the effect of firing a coach on team performance[END_REF] uses data from the first Dutch division, covering the period 1993/94-1997/98 with a Poisson model. The study conducted by [START_REF] Bruinshoofd | Manager to go? Performance dips reconsidered with evidence from Dutch football[END_REF] also uses data from the first Dutch division, but covers an even longer period of time (12 seasons, 1988/89-1999/00) using differences -in-differences estimator. [START_REF] Poulsen | Should He Stay or Should He Go? Estimating the Effect of Firing the Manager in Soccer[END_REF] uses data from the first two divisions in England, covering the seasons 1993/94-1997/98. Audas, Dobson andGoddard (1997, 2002) cover 21 and 28 consecutive seasons (1972/73-1992/93 and 1972/73-1999/00 respectively) and look at all four division of British professional football with an ordered tobit model. [START_REF] Breuer | Trainerwechsel im Laufe der Spielsaison und ihr Einfluss auf den Mannschaftserfolg[END_REF] use data from 32 consecutive seasons from the German first division (1963/64-1994/95); the study by [START_REF] Salomo | The Relationship of Performance and managerial Succession in the German Premier Soccer League[END_REF] also uses data from the Bundesliga, but covers a slightly different time period (1978/79-1997/98). Finally, [START_REF] Braendle | Replacement of Executives and Corporate Culture[END_REF] uses data from ten consecutive seasons from the first division in Austria (1994/95-2003/04).

Using data from two of the four Major Leagues in the US (Baseball, 1901[START_REF] Wei | Regression analysis of multivariate incomplete failure time data by modelling marginal distributions[END_REF]and Basketball, 1949/50-1989/90, Scully (1992a, 1992b, 1994) finds that firing the manager is rational in the sense that clubs improve their rank order finish the season after the head coach had been fired. [START_REF] Borland | Matching and Mobility in the Market for Australian Rules Football Coaches[END_REF] analyze coach separations in Australian rules football over a period of more than sixty years . [START_REF] Mixon | How Race Affects Dismissals of College Football Coaches[END_REF] and [START_REF] Kahn | Race, Performance, Pay and Retention among National Basketball Association Head Coaches[END_REF] study the impact of race on the career duration of head coaches in college football and in the NBA, respectively. The influence of either managerial quality or managerial efficiency on team performance is studied by [START_REF] Porter | Measuring Managerial Efficiency: The Case of Baseball[END_REF] using data from MLB (1961MLB ( -1980)), [START_REF] Kahn | Managerial Quality, Team Success, and Individual Player Performance in Major League Baseball[END_REF] also using major league baseball data (1969)(1970)(1971)(1972)(1973)(1974)(1975)(1976)(1977)(1978)(1979)(1980)(1981)(1982)(1983)(1984)(1985)(1986)(1987), [START_REF] Fizel | Managerial Efficiency, Managerial Succession and Organizational Performance[END_REF] using data from 147 US-college basketball teams over the period [START_REF] Allison | Event History Analysis[END_REF][START_REF] Yamaguchi | Event History Analysis[END_REF][START_REF] Horowitz | On the Manager as Principal Clerk[END_REF] again using MLB data for the period 1903-1992[START_REF] Jacobs | Leadership and Organizational Performance: Isolating Links between managers and Collective Success[END_REF] as well as [START_REF] Singell | Managers, Specific Human Capital, and Firm Productivity in Major League Baseball[END_REF] analyze data from Major League Baseball (1945Baseball ( -1965)). Idson andKahane (2000, 2004) analyse the salary determination in USA national Hockey League. Finally, [START_REF] Frick | The Impact of Managerial Quality on Organizational Performance: Evidence from German Soccer[END_REF] study data from the German Bundesliga (for a description of the data set see below) and [START_REF] Mccormick | Intrafirm Profit Opportunities and Managerial Slack. Evidence from Professional Basketball[END_REF] rely on data from the NBA (1980/81-1986/87). Focusing on survival models, it is verified that they are common in several fields of research, but coach survival is a rare issue, [START_REF] Audas | Organizational Performance and managerial Turnover[END_REF], [START_REF] Audas | Modelling Employment Duration of NHL Head Coaches: Turnover and Post-Succession Performance[END_REF]. Moreover, while these paper adopt matching results and coach age, they o not include coach salary and team salary, which may be an important predictor of survival.

The Labour Market for Head Coaches in German Football

In all but one of the 22 seasons for which we have been able to compile the data used in this paper (see below) 18 teams were playing in the German first division ("Bundesliga"). At the end of each season the three weakest teams are relegated and replaced by the three best-performing teams from the second division 1 .

In the history of the Bundesliga more than 300 head coach dismissals have been recorded. The annual number of dismissals varies between 4 and 14. While some 1 In 1991/92, when the two best teams from the first division of the former German Democratic Republic ("Oberliga") were admitted to the Bundesliga, the number of teams was temporarily increased to 20. After that season, four teams were relegated to division 2 while only two were promoted to division 1, resulting in the well-known size of the league again. As coaches are one of the main important managers in football the analysis of their dismissal in a sport league is an important issue for sport labour market policy.

F

2 Otto Rehhagel, currently head coach of the Greek national team, was formerly employed by Kickers Offenbach, Werder Bremen, Borussia Dortmund, Arminia Bielefeld, Fortuna Duesseldorf, Bayern Muenchen and 1. FC Kaiserslautern. Moreover, a number of head coaches had up to six different employers in the first division (Guyla Lorant, Joerg Berger, Rudi Gutendorf, Kuno Kloetzer, Manfred Krafft and Branco Zebec). Some of these coaches have not only been working in the first, but also in the second division. Moreover, some coaches have been working for a team not only once, but have been hired again after a while. The hypotheses to be tested in the empirical part of our paper are as follows:

H1: The relative salary of a head coach determines his career length. Coaches with relatively high salaries are more likely to remain in their position even in case of poor performance, because the opportunity costs of replacing them are quite high.

This hypothesis will be tested with the variable Rsal-relative salary of head coach Previous research has already tested this assumption (see [START_REF] Dawson | Stochastic Frontiers and the Temporal Structure of Managerial Efficiency in English Soccer[END_REF]Gerrard 2000, Porter and[START_REF] Porter | Measuring Managerial Efficiency: The Case of Baseball[END_REF]. We are, however, the first to use detailed salary information over a long period of time for the Bundesliga.

H 2: The career length of the coaches is also determined by the relative wage bills of their teams. We assume that coaches working with expensive teams, i.e. those with relatively high wage bills, are more likely to be fired when performance lags behind expectations. This hypothesis will be tested with the variable RWBrelative wage bill of the team.

It is one of the few "stylized facts" in the sports economics literature that expensive teams are performing better than clubs with moderate or even low wage bills (see Szymanski 2003, Forrest and Simmons 2004).

H3: There is a positive trend for the career durations of head coaches, i.e. the probability of being dismissed has decreased over time. This hypothesis will be tested with the Bosman effect variable.

Although competitive pressures have increased for the players following the "Bosman Germany. The number of foreign coaches is low and dismissed coaches can expect to be re-hired soon by another team.

H4: Career duration of head coaches in football is positively affected by their win percentage, i.e. their career length increases with the number of relative points won by their teams. This hypothesis will be tested with the variable CWP -win percentage of the head coach and RP-relative point won.

Coaches are constantly monitored not only by the management of their clubs but also my millions of football fans. Since the coaches are responsible for the teams' performance on the pitch, more successful coaches are likely to survive longer.

H5: Coaching experience increases the probability of surviving in the current job. The longer a coach has been working in professional football, the more human capital he has accumulated which, in turn, reduces the probability of getting fired. This hypothesis will be tested with the variable CEXP-experience of head coach in the Bundesliga.

In one of his seminal publications [START_REF] Mincer | Schooling, Experience and Earnings[END_REF] analyzed the relationship between experience and earnings showing that career length is a good predictor of current income (this relationship, however, is not a linear one).

Research Design

In the study of the career durations of head coaches in German professional football the event we want to explain is the dismissal, i.e. the premature end to an employment relationship. A dismissal can either mean that the coach has to end his career or that he is later on re-employed by another team. Many of the coaches whose careers we study have held a number of different jobs in the Bundesliga. Since such repeated events are unlikely to be independent the Cox proportional hazard model for single event data is inadequate for estimation, [START_REF] Kalbfleisch | The Statistical Analysis of Failure Time Data[END_REF]. Ignoring this dependence might lead to erroneous variance estimates and possibly biased estimates. One possible solution to this problem is considering only the time until the first occurrence of an event. This specification, however, makes the strong assumption that the time to the first event is similar to the time to all events. Moreover this specification implies throwing away some data. Some semi-parametric proportional hazard-type models have been proposed in the literature to be used in case of repeated events, such as the independent increments model of [START_REF] Anderson | Cox's regression model for counting process: a large sample study[END_REF], the conditional risk-set model in either elapsed or gap time of Prentice, Williams and Peterson (1981), and the marginal risk-set model of [START_REF] Wei | Regression analysis of multivariate incomplete failure time data by modelling marginal distributions[END_REF]. All these models are variance-correction models for repeated events and differ in the way they define the risk set and the event time, e.g., Box-Steffensmeier and Zorn (2002).

In this paper we use the conditional risk-set model in gap time developed by Prentice, Williams and Peterson (1981). In this model, an individual is not at risk for a later event until all prior events have occurred and event time is defined as time elapsed since the previous event. To estimate this model we cluster on coach identification and stratify by event number. The hazard is then specified as

) exp( ) ( ) | ( 1 0 ik k k ik ik X t t h X t h =
where k denotes event number, (.) 0k h is the baseline hazard and varies by event number, X is a vector of covariates which can be time dependent and is a vector of parameters.

The parameters are estimated using the partial likelihood which is given by ( )

( ) = = = = = n i K k n i K k ik ik ik i ik i X Y X L 1 1 1 1 exp exp ) (
where is a censoring indicator equal to one if observed and zero if censored and Y is a risk indicator which is equal to one if the individual is at risk for the current event and zero otherwise.

We also consider two parametric specifications: the exponential and the Weibull model.

In the exponential model the baseline hazard is stratified by event number and is constant at each event k, with hazard rate, In the Weibull model the baseline is defined by

) exp( ) ( ) | ( 1 ik k k k ik ik X t t I X t h =
1 1 1 0 ) ( ) ( = k k k k k t t t t h
where the time dependent parameter, k , is estimated separately for each event. Both models are estimated through maximum likelihood.

Data and Findings

The data used to study the determinants of head coach career duration cover the seasons 1981/82-2002/2003. The data come from a Sunday newspaper (Die Welt) that publishes team wage bills and head coach salaries immediately before the start of a season. Supplementary data on team playing records were obtained from Kicker soccer magazine. These data give us an unbalanced panel of 398 team-season observations featuring 39 teams. Six of these (Bayern Muenchen, Werder Bremen, Borussia Dortmund, Hamburger Sportverein, Bayer Leverkusen and VfB Stuttgart) have appeared in Bundesliga 1 over the entire sample period; five clubs (Blau-Weiss Berlin, Darmstadt 98, VfB Leipzig, Kickers Offenbach and SSV Ulm) were relegated after just one season. During the period under consideration we observe different 114 coaches. To ensure the validity and reliability of the data, several steps were taken. First, the source data is a reliable German newspaper recognised by its rigour and objectivity. Second, the data was examined by professionals working in the field, namely labour union members, in order to ascertain the reliability of the values. Third, based in this investigation we can discard any falsity or contradictions in the data set concluding that it is consistent. Fourth, the reliability of the data was examined, analyzing it extensively with alternative methods and reaching the same conclusions, Frick, [START_REF] Frick | Analysing Coach Dismissal in Bundesliga with a Mixed Logit Model[END_REF]. Table 1 presents the characteristics of the data used in the analysis. There are 114 coaches in the sample wh0 are responsible for 179 events and 10 censored observations (models M2 -M4). The frequency of events is shown in Table 3. In all four models the results are quite similar in the main effects. The relative wage bill (RWB) has a positive effect in the hazard implying that coaches of more expensive teams tend to be fired earlier, i.e. expectations of management and fans are apparently higher than in teams that spend less money for their players. The sporting performance of the team (RP) and the regime shift (Bosman effect, change in labour market regulation of the players) have a negative impact on the hazard rate. This means that coaches playing working under the "Bosman effect" tend to survive longer.

F

Finally, in model 4 the coaches' career win percentage is negative and statistically significant. This implies that better (in the sense of more successful) coaches tend to survive longer, too. Moreover, the time dependent parameter of the baseline hazard, k , is greater than one for all the occurrences of an event and its value increases with event number, implying that the risk of being fired increases with repeated events. This conclusion, however, should be taken with care because there are only a few observations for recurrent events greater than 3 (recall from Table 3 that only 7 out of 114 coaches experience more than 3 events). Thus, further research using a much larger dataset is required.

Discussion

In our paper we analyze the determinants of head coach dismissals in the top tier of the German football league using a number of different survival models. We first have to reject H1 because the coefficient of RSAL has the expected sign, but is not statistically significant. We find support for our H2 because the respective coefficient is statistically significant, i.e. the coaches of more expensive teams (those with higher values of RWB) are more likely to be fired. Moreover, we find that coaches working under the "the Bosman effect" tend to survive longer, leading us to accept H3. We also find support for H4 which suggests that coaches producing more wins (i.e. whose teams have a better What are the implications of these results? They are, of course, intuitive: Success on the pitch is the major determinant of survival. Head coaches who are able to produce more wins are likely to survive longer. Working with an expensive team makes the coach more vulnerable: Since the team's team wage bill and the management's as well as the fans expectations are highly correlated, a poor performance very often leads to an immediate dismissal (sometimes after a few weeks of the season already). Surprisingly, however, clubs seem not to blame coaches for a poor performance as quickly as they did in the past. In more recent years, coaches tend to survive longer. This outcome may be due to a number of reasons: First, coaching talent may be an even more scarce resource than it used to be (the teams in Germany rarely hire foreign coaches, i.e. the globalization of football has not contributed to an increased inflow of talent as opposed to the market for players). Second, since the salaries of head coaches went up considerably over the last years, it may simply be too expensive to dismiss a head coach with a valid contract. Third, replacing a head coach in the middle of the season may be a difficult task: If the teams at the beginning of the season hire the most able coaches it is clear that those still looking for a job in the middle of the season are, on average, of lower quality. This, in turn, may very often be a reason to keep the incumbent job -the available alternatives are even worse. What are the policy implications of our findings? The most important result here is that organizational changes (Bosman Effect) have an impact on the survival of head coaches in the German Bundesliga. Thus, organizational changes should be taken into account when studying head coach departures over time. Head coaches themselves should take into account that although their salary reflects talent, it has no statistical influence on the probability of surviving in the present position. What as an impact, however, is the money spent on player salaries (head coach salaries and team wage bills are only moderately correlated with r=+0.45).

How does our research compare with previous papers that have been published on this topic? Comparing the present results with [START_REF] Audas | Organizational Performance and managerial Turnover[END_REF] who analysed voluntary and involuntary job termination for English football coaches from 1972-19997 andAudas, Goddard and[START_REF] Audas | Modelling Employment Duration of NHL Head Coaches: Turnover and Post-Succession Performance[END_REF] Therefore we validate previous research in this field. The results of [START_REF] Audas | Modelling Employment Duration of NHL Head Coaches: Turnover and Post-Succession Performance[END_REF] are not directly comparable with the present paper. Therefore, while validating previous research we also point out that the salary of the coach and the wage bill of the team are also predictors of coach departure.

Summary and Conclusions

In our paper we analyze the survival of head coaches in the first division in German professional soccer over the period 1981/82-2002/03. Using a unique and hitherto unavailable data set with information on team wage bills and head coach salaries we compare the results of different estimations. We estimate, first, a Cox proportional hazard model, second, a conditional risk-set in gap time, third a parametric exponential model and, fourth, a Weibull model. We find that while the salary of the head coach is statistically insignificant in explaining survival, the opposite holds for the team wage bill. Head coach experience as well as the career win percentage are statistically insignificant. Finally, organizational changes matter in the sense that the adoption of the Bosman effect" had a positive influence on the survival probabilities of head coaches.

  The focus of this paper is on football coaches in the German Bundesliga in the years 1981/82-2002/2003. The length of a football coach's career depends on three critical factors: The individual's football specific skills and the characteristics of the local labour market (preferences for a certain type of football are likely to differ between the clubs in a league) are certainly important. What is of prime importance, however, is the head coach's performance, i.e. his win percentage in a particular job.

  function for durations in event k and k the estimated baseline at event number k.

  M1) is the Cox proportional hazard model considering only the first occurrence of an event. Model 2 is the conditional risk-set model in gap time ofPrentice, Williams and Peterson (1981). Model 3 is the parametric exponential model with the baseline hazard stratified by event. Finally, Model 4 is the Weibull model where the baseline hazard parameter, k , k=2,…6, is estimated separately at each occurrence of an event.

  tend to survive longer. Finally, H5 has to be rejected again, because prior coaching experience has no influence on the probability of surviving in a particular job.

  coaches have been working for one team only, others have been employed by as many as seven different clubs 2 .

	Figure 1
	Number of Head Coaches Dismissed per Season (1963/64-2005/06)
	14
	12
	10
	6 8	r o
	4	P
		e
	2	r e
	0
	1963/64	1973/74	v e R 1983/84	1993/94	2003/04
			i e
			w

Source: Kicker, special issue "40 years of

Bundesliga",

; Kicker Finale 05/06 Our analysis, however, concentrates on the period 1981/82-2002/03 since we have information on head coach salaries for these 22 consecutive seasons only. Coaching in the Bundesliga is similar to other European football leagues, but the relative importance of this league at European level make it a representative case study. Dismissal is the termination of a contract of employment. It can be by mutual agreement or with litigation.

Table 1 :

 1 Characteristics of the Variables

	o r
	P
	e
	e r
	R e v
	i e
	w

Table 3

 3 presents the results of our estimations. We present a number of different duration models for comparative purposes. The dependent variable is always tenure with the current team, measured in years. The estimated coefficients are always in the proportional-hazard metric.

Table 3 :

 3 Estimation Results

		M1		M2		M3	M4
		Coef.	s.e.	Coef	s.e.(2)	Coef	s.e.(2)	Coef	s.e.(2)
	RSAL	0.106	0.293	0.011	0.237	0.009	0.144 -0.334 0.244
	RWB	0.815	0.218	0.683	0.183	0.448	0.154	0.623	0.215
	Bosman	-0.656	0.217	-0.772	0.156 -0.559 0.109 -0.907 0.193
	CWP	0.327	0.531	0.436	0.480	0.310	0.320 -1.001 0.503
	RP	-8.493	0.993	-8.415	0.726 -5.801 0.534 -5.725 0.679
	CEXP	0.001	0.038	0.030	0.018	0.017	0.016 -0.039 0.026
	2					0.023	0.122
	3					0.118	0.139
	4					0.075	0.195
	5					-0.035 0.409
	6					0.723	0.162
	Constant					1.356	0.176	1.519	0.238
	Ln( 2 )							0.160	0.086
	Ln( 3 )							0.269	0.126
	Ln( 4 )							0.373	0.082
	Ln( 5 )							0.497	0.152
	Ln( 6 )							0.813	0.075
	Ln(Const)							0.587	0.089
	LL	-369.3		-544.4		-205.3		-159.3
	(1) -All models were estimated in Stata 9
	(2) -Robust standard errors
	k -baseline parameter for event, k=2,…,6,
	k -parameter of the Weibull base line hazard for event k=2,…,6
	LL -Log Likelihood

Page 11 of 17 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

	Submitted Manuscript
	F o r
	P
	e
	e r
	R e v
	i e
	w
	12

Table 4 :

 4 Frequencies

	No. of Events	1	2	3	4	5	6
	Frequencies	114	48	20	4	2	1

  who analysed coach departure in the USA National Hockey League from 1967-2002, the sole papers directly comparable with the present one. Common results with Audas, Dobson and Goddard are that the hazard is sensitive to coach age, match results and winning ratio in both papers.

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


Claudia M Buch

Serkan Yener
email: serkan.yener@stat.uni-muenchen.de

F36 -Financial Aspects of Economic Integration < F3 -International Finance < F -International Economics, F41 -Open Economy Macroeconomics < F00 -General < F0 -General < F -International Economics

Keywords: consumption volatility, financial integration, G7 countries consumption volatility, financial integration, G7 countries JEL classification: F36, F41

Economic theory predicts that the integration of financial markets lowers the volatility of consumption. In this paper, we study long-term trends in the consumption volatility of the G7 countries. Using different measures of financial openness, we find evidence that greater financial openness has been associated with lower consumption volatility. However, volatility of consumption relative to output has not declined.

In this paper, we use time series evidence to test whether the integration into international financial markets has helped consumers in developed countries to reduce the volatility of domestic consumption. In contrast to earlier work focusing on cross-country or panel evidence, we use long-run time series data for the G7 countries. We cover the post-war period for two reasons. First, we want to capture a time period during which the capital account regime of the countries under study has changed significantly. The end of the Bretton Woods system of fixed exchange rates in the early 1970s has been associated with a gradual phasing out of capital controls in many countries. Hence, the past 40 years provide us with a sufficiently long time frame to cover both the pre-and the post-capital-controls period.

Second, although we could have gone back even further in history, combining data for the pre-and the post-war period would imply that we would have to deal with significant structural breaks in the data.

In contrast to [START_REF] Bekaert | Growth Volatility and Financial Liberalization[END_REF], we control for macroeconomic shocks, we focus on a narrower set of G7 countries, and we use a larger time window covering the past 40 years.

The reason for this is that we want to capture the time-series dimension of the liberalization periodes. Hence, our identification of a possible liberalization effect comes from the timeseries (pre-versus post-liberalization) dimension only.

We find that capital account liberalization has lowered consumption volatility in Canada, Italy, Japan, and the United Kingdom. These results are robust against modifications of the model such as including proxies for macroeconomic shocks and adding of interaction terms between openness and macroeconomic shocks. However, volatility of consumption relative to output has not declined.

Methodologically, there are two main questions to be addressed when studying the link between consumption volatility and financial openness. The first is the measure of volatility.

We use the volatility of consumption growth, computed as the standard deviation of a rolling The second issue is the measurement of financial openness. We use the regulatory measure developed by Quinn (1997), which combines information on the imposition of capital controls with qualitative information on the intensity of controls. The second measure we use comes from [START_REF] Kaminsky | Short-Run Pain, Long-Run Gain: The Effects of Financial Liberalization[END_REF] which additionally includes information on the development and integration of equity markets.

In Part Two, we briefly present the theoretical background on the link between consumption volatility and financial openness. In Part Three, we present and discuss our measures of financial openness and consumption volatility. In Part Four, we present our empirical results for the link between consumption volatility and financial openness. We also analyze whether the link between consumption volatility and financial openness depends on the type of shock that hits an economy. Part Five concludes.

Theoretical Background and Earlier Evidence

To set the stage for our empirical analysis and to show how consumption volatility and financial openness are linked, we use a standard complete markets model. 1 The representative household has a known income Y 1 in period t = 1 but faces uncertainty over future income, Y 2 . Consumption plans ( ) 2 1 ,C C are conditional on aggregated uncertainty over output in period t = 2. Utility is given by:

(1)

( ) ( ) ( ) ( ) [ ] ( ) ( ) [ ] { } 2 2 1 1 , 2 2 1 2 1 1 1 C u C u C u C C U U + + = = 1
For a more detailed presentation see Obstfeld and Rogoff (1996, Chapter 5). (2)

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) r p Y p Y Y r p C p C C + + + = + + + 1 2 2 1 1 1 2 2 1 1 2 2 1 2 2 1
where ( ) ( )

r s p + 1
is the world-market price for Arrow-Debreu-securities

( ) ( ) 2 , 1 2 2 B B in
terms of current consumption. Optimizing with respect to C gives the standard Euler equation for consumption:

(3)

( ) ( ) ( ) ( ) ( ) 2 , 1 ' 1 1 ' 1 2 = + = s C u r p s C u s .
A similar condition applies to the foreign country. Foreign variables are denoted by an asterix. In a two-country model, market clearing requires:

(4)

( ) ( ) ( ) ( ) 2 , 1 ), ( * * * * 2 2 2 2 2 1 1 1 1 1 = = + = + = + = + s s Y s Y s Y s C s C Y Y Y C C W W
Resources will be optimally allocated across time and across countries if all marginal rates of substitution are equal. With CRRA utility, we have

( ) = 1 1 C C u and ( ) 1 ' C C C u = = .
The last condition, together with state-contingent prices, implies that second period consumption is given by (5)

( ) ( ) 1 1 2 2 C Y s Y s C w w = .
In financially integrated markets, the change of consumption over time is thus determined by the change in world output:

F o r P e e r R e v i e w 6 ( ) ( ) w w w I Y Y s Y C C s C C 1 1 2 1 1 2 = =
Under autarky, the change in consumption is determined by domestic output. The change in consumption volatility moving from autarky to financial integration is then given by ( ) ( )

w w w I A Y Y s Y Y Y s Y C C 1 1 2 1 1 2 =
Hence, consumption becomes less volatile if domestic and foreign output are imperfectly correlated. A similar consumption smoothing pattern would be predicted by models assuming that bonds are the only financial asset that can be traded internationally [START_REF] Baxter | Business Cycles and the Asset Structure of Foreign Trade[END_REF]. Using more richly specified models, which allow for the possibility of different types of shocks (monetary, fiscal, or productivity shocks) hitting an economy, one can show that the impact of financial openness on the volatility of consumption does not depend on the type of shock considered (see, e.g., [START_REF] Sutherland | Financial Market Integration and Macroeconomic Volatility[END_REF].

However, empirical literature testing the predictions of standard macroeconomic models of open economies finds that consumption correlations across countries are relatively small and that they are smaller than output correlations (see [START_REF] Lewis | Explaining Home Bias in Equities and Consumption[END_REF] for a survey of the literature). Moreover, domestic consumption tends to be more closely correlated with domestic output than with foreign consumption.

At least one explanation for the consumption correlation puzzle might also help to explain why consumption volatility does not respond to financial openness. One reason for the consumption correlation puzzle could be that the welfare gains from a reduction in consumption volatility might be small. Providing estimates for the US, [START_REF] Lucas | Macroeconomic Priorities[END_REF] has argued that a relatively small level of consumption volatility may not justify taking measures 2 This holds, in particular, if international financial transactions are costly. In fact, [START_REF] Prasad | Effects of Financial Globalization on Developing Countries: Some Empirical Evidence[END_REF] find the welfare gains from a reduction of volatility to be relatively modest for developed market economies such as the one we study here. Potential gains are larger for developing countries.

Yet, developing countries do not seem to benefit from increased financial integration through lower volatility of consumption [START_REF] Prasad | Effects of Financial Globalization on Developing Countries: Some Empirical Evidence[END_REF]). One explanation could be that developing countries have integrated into international capital flows more recently and less rapidly than the developed market economies. In addition, these countries have weaker institutional structures and, in particular, less developed domestic financial systems. This might have prevented them from reaping the benefits of financial integration -or, perhaps, the potential benefits need more time to materialize. Although we cannot test this hypothesis directly studying the G7 countries, our data do cover a sufficiently long time span to analyze whether the benefits of financial integration with regard to changes in the volatility of consumption appear gradually over time.

Data and Empirical Methods

The above theoretical framework has shown that consumption volatility should decline as financial markets become more integrated. This hypothesis will form the basis for our empirical tests below. Before going into the details of these tests, we discuss the measurement problems that arise with regard to volatility and financial openness. Because one goal of our analysis is to study the link between openness and volatility conditional on the shocks hitting an economy, we also describe the methodology that we use to identify shocks.

2 [START_REF] Reis | The Time-Series Properties of Aggregate Consumption: Implications for the Costs of Fluctuations[END_REF] surveys the literature and argues instead that the welfare effects of consumption volatility cannot be assessed without taking the persistence of consumption into account.

Measures of Volatility

The aim of this paper is to trace the impact of financial openness on consumption volatility for a time period that spans different capital account regimes. Because consumption data are not available for a sufficiently long time period and at a sufficiently high frequency for the full set of OECD countries, we restrict our analysis to the G7 countries, i.e., Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States. For these countries, we have quarterly consumption data starting in 1957 (Canada, Japan, United Kingdom, United States), 1960 (Germany), 1965 (France), and1970 (Italy). Our data set ends in 2000. Hence, for the majority of the countries, we can cover a time period of over 40 years.

We follow [START_REF] Bekaert | Growth Volatility and Financial Liberalization[END_REF] and compute the volatility of consumption growth over a rolling window of five years (i.e. over 20 quarterly observations). 3 This rolling-window measure of volatility generates serial correlation in the resulting time series. Using serially correlated time series as the dependent variables in OLS estimations will feed the serial correlation patterns into the residuals. This invalidates inferences based on conventional standard errors. Fortunately, OLS estimates are still consistent, and we use the residuals to correct standard errors according to the method of [START_REF] Hansen | Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis[END_REF].

Another problem implied by the rolling window approach is that shocks and noise, which appear as outliers in the original consumption data, have level effects in the time-series for volatility. Once an outlier of the original series enters the rolling window, it affects the volatility estimate for 19 consecutive time periods. We account for this effect by including dummy variables in the regression equation and by using the median absolute deviation instead of the standard deviation as an alternative volatility measure.

3

We also experimented with other approaches such as GARCH measures or the methods proposed by [START_REF] Schwert | Why Does Stock Market Volatility Change over Time?[END_REF] and [START_REF] Baxter | Measuring Business Cycles: Approximate Band-Pass Filters for Economic Time Series[END_REF], but these failed to deliver reasonable volatility estimates. Table 1 shows summary statistics for consumption and output volatility starting from the 1960s to the end of the 1990s. We also report the ratio of consumption to output volatility and consumption volatility relative to the mean of consumption. The latter gives us an idea about the possible welfare gains from a reduction in consumption volatility. We report these measures for all four decades under study separately.

If increased financial integration opens up possibilities for consumption smoothing, we would expect that the volatility of consumption declines relative to the volatility of output.

However, we fail to find this pattern in the data. In all countries except France, relative consumption volatility was higher in the 1990s than at the start of the sample. In France, relative consumption volatility followed a -shaped pattern but did not return to its level of the 1970s. Moreover, a similar -shaped pattern can be detected for the United Kingdom, whereas Italy, Japan, and the United States witnessed a reversed -shaped pattern of relative consumption volatility.

Behind these changes in the ratios are quite heterogeneous changes in consumption and output volatility. This holds in particular for consumption volatility. For France and Germany, the data resemble a -shaped pattern, while Italy, Japan, the United Kingdom, and the United States show an inverse -shaped pattern. For output volatility, the data indicate a -shaped pattern for France, Germany, and Italy, and an inverse -shaped pattern for the United Kingdom and the United States. For Japan, we find a monotonic decline in output volatility.

Our results partly confirm findings of earlier studies, but partly they differ. Differences in the underlying samples and in the computation of volatility could be responsible for this. For example, [START_REF] Basu | Business Cycles in International Historical Perspective[END_REF] consider a sample of 15 countries, which also includes our set of countries. However, their methodology differs from ours in two respects. First, they aggregate the data over different cross-sections before computing the volatilities. Second, they look at the changing pattern of macroeconomic volatility over different historical periods for their pooled data. For the sake of comparability we construct a similar measure of volatility by computing average volatility over the period 1970s-90s for each country. A direct comparison of our Bretton Woods and Post-Bretton Woods data confirms a declining output volatility which is in line with the results obtained by Basu and Taylor. In contrast to this unambiguous decline of output volatilities, our result is mixed when looking at changes of consumption volatilities during the Bretton Woods and the Post-Bretton Woods phases. For Canada and France, we note a decrease, whereas for Germany, Japan, the United Kingdom, and the United States, there was an increase in the volatility of consumption. Hence, aggregation over different sub-groups of countries clouds differences in the patterns of consumption volatility.

Another related study is the one by [START_REF] Kose | Financial Integration and Macroeconomic Volatility[END_REF] who find an increase in consumption and output volatility moving from the 1960s to the 1970s and a decline in subsequent periods. Their sample covers 21 industrialized countries, and thus a much larger set of countries. Our more mixed results for individual G7 countries show that there is a significant amount of heterogeneity behind these aggregated figures.

In sum, our data show a somewhat mixed picture with regard to changes in consumption volatility over time. In the following sections, we will explore whether changes in consumption volatility across time have been linked to the degree of financial openness of countries.

Measures of Financial Openness

The theoretical model used in Part 2 has been based on the assumption that trade in a full set of contingent claims is possible. Such a complete markets setting does, of course, not exist in reality. Countries rather differ with regard to the degree of openness to foreign capital, the Literature has used different ways to measure the degree of restrictions on capital account transactions. [START_REF] Edison | Capital Account Liberalization and Economic Performance: Survey and Synthesis[END_REF] provide a useful survey. They classify capital account restrictions into qualitative, rule-based restrictions and measurements of the intensity with which controls are being imposed. We follow a similar approach in this paper.

Most qualitative, rule-based measures of capital account openness are based on the IMF's Annual Report on Exchange Arrangements and Exchange Restrictions (AREAER). We use a measure which has been compiled by Quinn (1997). His measure is based on the AREAER, and he additionally uses information on the intensity of capital controls from the narrative parts of this report. One further advantage of his measure is that it is available already since the 1950s. Other measures of regulations have been used in the literature that capture, for instance, the degree of regulation of stock markets, are typically not available for a sufficiently long time period (see [START_REF] Edison | Capital Account Liberalization and Economic Performance: Survey and Synthesis[END_REF], Table 1 for an Overview). [START_REF] Kaminsky | Short-Run Pain, Long-Run Gain: The Effects of Financial Liberalization[END_REF] (Figure 2). Rather than looking at the openness of financial markets for capital flows in general, we use an average over three sub-indices capturing the degree of domestic financial sector's liberalization, capital account liberalization, and stock market liberalization. The index runs from 1 to 3, and a lower index implies that countries have more liberalized markets. By the year 1991, all countries in the sample had fully liberalized their markets. Yet, the timing of liberalization differed across countries. While the United States, the United Kingdom, Canada, and Germany liberalized their capital markets until the early 1980s, Japan, France, and Italy followed only in the early 1990s.

[Insert Figure 2 do not bind. However, the disadvantage of using quantitative measures of capital account openness in our context would be that these measures are highly endogeneous. Endogeneity is less of a concern for our rule-based measures of capital account openness since the deregulation of markets has often been initiated in the context of international agreement under OECD or EU membership. We therefore use rule-based measures in the following.

Measures of Structural Shocks

Estimating the relationship between consumption volatility and financial integration requires controlling for structural shocks. To extract structural shocks, we estimate the two-country open-economy model proposed by [START_REF] Clarida | Sources of Real Exchange-Rate Fluctuations: How Important Are Nominal Shocks?[END_REF]. All variables are normalized with respect to a benchmark economy. Following Clarida and Gali, we take the US as a 13 benchmark, and we set the lag length of all SVAR specifications equal to four. The variables comprise the first difference of the real output differential, the first difference of the bilateral real exchange rate, and the CPI differential, which corresponds to the bilateral inflation differential. This trivariate model essentially features a flexible price equilibrium in the longrun and sticky-prices in the short run due to the imposed restrictions adopted from [START_REF] Blanchard | The Dynamic Effects of Aggregate Demand and Supply Disturbances[END_REF]. According to this specification, it is possible to identify three (relative) structural shocks: a supply shock, a demand shock, and a nominal shock.

Regression Results

Based on the descriptive statistics presented above, it is hard to argue that there has been a clear link between the openness of countries for financial capital and the volatility of consumption. While, generally, the G7 countries have become more open for financial capital in legal terms over the past decades and while capital flows have increased rapidly, there has been no consistent pattern for consumption volatility to increase or decrease.

In this section, we study the link between consumption volatility and the openness of countries for capital in more detail. In a first set of regressions, we regress our consumption volatility on different measures of financial openness. Next, a series of robustness checks will be conducted. We use the median absolute deviation to construct an alternative measure of volatility, we include shocks and interaction terms of shocks and openness, we include nonlinear terms to capture delayed liberalization effects, and we analyze the impact of financial integration on relative consumption volatility.

Baseline Regression Results

Using our measure of consumption volatility based on the rolling-window approach, we first check whether consumption volatility and financial openness are significantly related. For our baseline case, we regress the volatility of consumption growth, t , on a constant, and on a measure of financial openness t FINOP : [START_REF] Backus | International Real Business Cycles[END_REF] . Equation ( 6) is estimated separately for each country in our sample, i.e., Canada, France, Germany, Italy, Japan, the United Kingdom, and the United States. We also include dummy variables, t j DUMMY , which capture country-specific structural breaks and the effects of outliers in the original consumption data. Results using the Quinn measure of financial openness are reported in Panel (a) of Table 2.

[Insert Table 2 about here.] For Canada, Italy, Japan, and the United Kingdom, we find a significant decline in consumption volatility as response to financial market integration. Although the estimated coefficients look small at first sight, the impact of financial openness is quite important. Betacoefficients 4 show that about 30-70% of the variation in consumption volatility can be explained by the degree of capital account openness for these countries. For the rest of the countries in our sample, the coefficients on the Quinn measure are insignificant or have the wrong sign. In general, there is no clear-cut answer to the question of whether capital account liberalization has helped consumers to smoothen shocks to domestic income.

4

The beta-coefficients have been computed as the coefficient estimates times the standard deviation of the explanatory variable divided by the standard deviation of the dependent variable. 3.

[Insert Table 3 about here.] Except for France, we obtain highly significant coefficients on the Kaminsky-Schmukler measure for all countries in our sample, implying that greater development of financial markets has been associated with a decline in volatility. (Note that the Kaminsky-Schmukler measure is constructed such that a higher value indicates a less developed financial market.) Differences in results reported in Tables 2 and3 could be due to differences in the sample size or differences in the measure for financial openness. To test whether changes in sample size affect our results, we re-run regressions using the Quinn measure of capital account openness also for the smaller sample for which we have information on the degree of capital market liberalization. Results (not reported) show that all results are robust and that they carry over to a shortened estimation period.

Alternative Measure of Volatility

The standard deviation may be a poor measure of scale (or volatility) in data sets with small sample sizes. In these cases, a more robust measure of scale is the median absolute deviation [START_REF] Huber | Robust Statistics[END_REF][START_REF] Sachs | Applied Statistics: A Handbook of Techniques[END_REF]. As the sample size of our rolling window is 20, this seems to be a reasonable alternative. Comparing the time series of the standard-deviation-based measure of consumption volatility to the median-absolute-deviation-based measure of consumption volatility shows that the level effects of outliers are scaled down. We use this robust measure of consumption volatility as our new dependent variable in [START_REF] Backus | International Real Business Cycles[END_REF] and run this regression for the Quinn measure (Panel (b) of Table 2) and for the Kaminsky-Schmukler measure (Panel (b) of Table3).

The major insight of this exercise is that, except for the United Kingdom and the United States in the regressions involving the Quinn measure, all results carry over qualitatively.

Thus, the results from our baseline regressions seem to be quite robust with respect to the measure of consumption volatility, and we continue to use the standard deviation as a measure of consumption volatility in what follows.

Controlling for the Underlying Shocks

The stylized model that we have introduced above to show the link between financial openness and consumption volatility did not take into account the various shocks that can hit an economy. Consumption volatility in this model is the result of stochastic fluctuations in output. In reality, we need to control for other potential sources of output (and thus consumption) volatility such as monetary and fiscal shocks. In order to control for these shocks, we estimate the structural vector auto-regression (SVAR) as described in Section 3.3, and we include the shocks in equation ( 6). Hence, our consumption volatility regression now reads:

(7) , , 4 1 3 2 1 0 t j t j j t t t t e DUMMY NOMINAL DEMAND SUPPLY FINOP + + + + + + =
where SUPPLY , DEMAND , and NOMINAL denote the supply, demand, and nominal shock, respectively. We again control for structural breaks by including dummy variables.

The regression results for the Quinn measure of capital account openness are summarized in 2, results for the Kaminsky-Schmukler measure are given in Panel (c) of Table 3.

The first thing to note is that the impact of our macroeconomic shocks is insignificant for most of the countries. Most results for the link between capital account openness and consumption volatility remain unchanged. The estimated coefficients on the measure of capital account openness are similar to those obtained from the baseline regression.

As an additional test for the robustness of our results, we interact our measures of financial openness with our macroeconomic shocks. The reason for including these interaction terms is that [START_REF] Sutherland | Financial Market Integration and Macroeconomic Volatility[END_REF] shows in his dynamic general equilibrium model that the impact of financial liberalization on consumption volatility depends on the nature of shocks. In this model, financial openness alone does not affect consumption volatility. Rather, consumption volatility is lower in more open financial systems following macroeconomic shocks. In order to test this hypothesis, we extend our regressions to include interaction terms: (8) The effects of financial openness on consumption volatility might occur only gradually over time and might thus be non-linear. To test whether our data favor a non-linear specification over the linear specifications estimated so far, we include our proxies for financial openness and an additional quadratic term. This quadratic term captures potential threshold effects of financial openness.

Results are shown in Panels (e) of Tables 2 and3. For the Quinn measure of capital account openness, we now find an insignificant effect for France. The results for Canada, Italy, and the UK are overturned. The coefficient on the Quinn measure becomes significant for Germany. All countries, with the exception of the USA, feature non-linear effects of financial openness. In France and Japan, increasing openness has been associated with more volatility, increasing openness in the cases of Canada, Germany, Italy, and the UK has led to less volatility. For the Kaminsky-Schmukler measure, we confirm that increased financial openness has lower consumption volatility in all countries.

Generally, we also find evidence for non-linear effects of financial openness on consumption volatility. However, there is no consistent pattern of volatility to increase or decrease in a non-linear way. Generally, we hesitate to interpret these results further because the introduction of a nonlinear term increases the degree of multicollinearity dramatically. This is shown by the conditioning number of the design matrix with and without the nonlinear term. Moreover, this is also the reason why we were forced to exclude the constant intercept in most regressions.

Financial Openness and Relative Consumption Volatility

Results reported so far inform us about the impact of financial openness on the volatility of consumption but not about the success of consumers to smoothen shocks to domestic output.

Hence, we additionally run our baseline regression ( 6) using the ratio of consumption to output volatility as the dependent variable. Using this ratio can be thought of as an alternative way of controlling for macroeconomic shocks. We estimate the same regression as before, using the ratio of consumption to output volatility ( RATIO t ) as the dependent variable.

The results for the Quinn measure and the Kaminsky-Schmukler indicator are summarized in Table 4. Recall that we expect a negative coefficient for the Quinn measure and a positive coefficient for the Kaminsky-Schmukler measure if financial liberalization would be associated with lower relative volatility of consumption. Our results give a surprisingly clear picture that is consistent with the descriptive results on relative consumption volatility (Table 1). Both the Quinn measure and the Kaminsky-Schmukler measure convey the message that relative consumption volatility increased during the process of financial liberalization. The only exception to this finding is France where, for the Kaminsky-Schmukler measure of openness, increased financial openness lowered the relative volatility of consumption.

Summary

This paper has analyzed the link between financial openness and consumption volatility.

Consumers should benefit from financial integration by being able to smooth consumption and by shielding themselves against shocks to national income. Using long-run time series for the G7 countries, we have tested whether more open financial markets have been associated with lower consumption volatility. Our findings provide some support to the notion that greater financial openness lowers the volatility of consumption. This effect seems to be driven mostly by the liberalization of capital markets rather than liberalization of cross-border capital flows as such. Including proxies for macroeconomic shocks leaves the main result unaffected. Measured in relation to output volatility, consumption volatility has not declined, however.

Our results partly differ from those of earlier studies such as [START_REF] Bekaert | Growth Volatility and Financial Liberalization[END_REF]. While we confirm that greater financial market integration has been associated with lower consumption volatility, this does not hold necessarily for greater capital market development or for relative consumption volatility. One reason for this could be that we focus on the time series dimension. In [START_REF] Bekaert | Growth Volatility and Financial Liberalization[END_REF] the main liberalization effect seems to come from the difference between emerging markets and developed market economies. In our study, differences between countries and thus cross-sectional variation in the data are not considered. [START_REF] Kaminsky | Output Nominal gross domestic product. Seasonally adjusted. Quarterly data starting in 1957[END_REF](Germany), 1965(France), and 1970 (Italy). Our dataset ends in 2000. C is the standard deviation of real consumption growth (in %) within the corresponding time period, Y is the standard deviation of real output growth (in %), and C is the mean of real consumption growth (in %).

____________________

Table 2: Consumption Volatility and Capital Account Liberalization

The dependent variable is the volatility of consumption, computed using the rolling-window approach. The Quinn measure is an index ranging from 0 to 100, which assigns a large value to more open capital account regimes. In panel (b), the dependent variable is the volatility based on the median absolute deviation of consumption in conjunction with the rolling-window approach. The structural shocks in panel (c) have been obtained from a SVAR model as in [START_REF] Clarida | Sources of Real Exchange-Rate Fluctuations: How Important Are Nominal Shocks?[END_REF]. In panel (d), these shocks have been interacted with the measure for financial openness. In panel (e), a non-linear term is included. Standard errors are corrected using the method suggested by [START_REF] Hansen | Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis[END_REF]. Regressions for France and Germany include one dummy variable. Regressions for Canada, Italy, Japan, the UK, and the USA include two dummy variables. *** (**, *) = significant at the 1% (5%, 10%) level.

(a) Baseline regressions

3: Consumption Volatility and Financial Market Liberalization

The dependent variable is the volatility of consumption, computed using the rolling-window approach. The Kaminsky-Schmukler (K-S) measure is an index ranging from 1 to 3, which assigns a lower value to more open capital account regimes. In panel (b), the dependent variable is the volatility based on the median absolute deviation of consumption in conjunction with the rolling-window approach. The structural shocks in panel (c) have been obtained from a SVAR model as in [START_REF] Clarida | Sources of Real Exchange-Rate Fluctuations: How Important Are Nominal Shocks?[END_REF]. In panel (d), these shocks have been interacted with the measure for financial openness. In panel (e), a non-linear term is included. Standard errors are corrected using the method suggested by [START_REF] Hansen | Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis[END_REF]. Regressions for France, Germany, and Japan include one dummy variable. Regressions for Canada, Italy, the UK, and the USA include two dummy variables. *** (**, *) = significant at the 1% (5%, 10%) level.

(a) Baseline regressions Data for these graphs have kindly been provided by Dennis Quinn. The measure used in Quinn (1997) is defined as follows: 0 = approval for capital transfer required, 0.5 = approval required and sometimes granted, 1.0 = no restriction but official approval required plus transaction is taxed, 1.5 = no official approval needed but transaction may be taxed, 2.0 = free capital account regime. The measure used in [START_REF] Kaminsky | Short-Run Pain, Long-Run Gain: The Effects of Financial Liberalization[END_REF] runs from "less liberalization" (3) to "more liberalization" (1).

  years of quarterly data. To check the robustness of our results, we also use the volatility of consumption growth computed as the median absolute deviation of the same rolling window.

  1 and 2 denote either the time period (the lower case indices) or the state of nature (the numbers in brackets), is the subjective discount factor, and ( ) ( ) reaching the two states of nature. The intertemporal budget constraint is given by

  reduction in volatility.

  post-war period only. They find a decline in consumption and output volatility

  of their financial systems, and the types of financial assets that are traded. Therefore, we choose different proxies to measure the degree of financial openness.

Figure 1

 1 Figure 1 plots the openness measures developed by Quinn (1997). There are two countries which have been fairly open for financial capital during most of the period under study: Germany and the US. With the exception of two short periods during the late 1960s and early 1970s, these countries have been essentially open for foreign capital at least since the 1960.

  rule-based measure of financial openness has been constructed by

  about here.] As an alternative to rule-based, qualitative measures of capital mobility and capital account openness, measures of actual capital flows could be used. The advantage of such quantitative measures would be that they classify countries as financially open if de facto capital flows are large. Rule-based measures might come to different results if capital controls

  measure of financial openness provides more evidence for a significant link between consumption volatility and openness. The results for the baseline regression are reported in Panel (a) of Table

  terms capture potential indirect effects of financial openness on volatility, conditional on the nature of shocks. The estimation results for the Quinn measure and the Kaminsky-Schmukler indicator are reported in Panel (d) of Tables2 and 3, respectively. There are only minor changes in the coefficients or standard errors. Hence, estimations including proxies for macroeconomic shocks are not very different from those excluding shocks.

  (2004), authors' calculations.

Figure 1 :

 1 Figure 1: Regulatory Measures of Capital Account Openness: The Quinn Measure

  Figure 2: Regulatory Measures of Financial Development: The Kaminsky-Schmukler Measure

Table 1 :

 1 Descriptive StatisticsData in this Table are based on quarterly real consumption data starting in 1957 (Canada, Japan, United Kingdom, United States),

Table

Table 5 : Data Definitions and Sources

 5 expenditure, including nonprofit institutions serving households. Seasonally adjusted. Quarterly data starting in 1957 (Canada, Japan, United Kingdom, United States), 1960 (Germany), 1965 (France), and 1970 (Italy). Our dataset ends in 2000.

		Canada	France	Germany	Italy		Japan	UK	USA
	Constant	0.3636***	0.4759***	0.8530***	-0.2560***	0.9926***	0.9604***	-0.2406**
		(2.82)	(3.65)	(7.81)		(-3.65)		(38.09)	(14.31)	(-1.97)
	K-S	0.5942***	0.1097	0.1962**	0.6222***	0.4255***	0.3517***	0.9117***
	measure	(4.62)	(1.40)	(2.06)		(14.38)		(33.46)	(8.09)	(8.45)
	T-coeff.	0.2334	0.1052	0.0236		0.5494		0.6493	0.3234	0.5711
	Period	73:01-00:04 75:02-00:04 73:01-00:04 75:02-00:04 73:01-00:04 73:01-00:04 73:01-00:04
	N	112	103	112		103		112	112	112
	R²	0.9268	0.9571	0.9958		0.9790		0.9801	0.9330	0.9300
	(b) Median-absolute-deviation-based volatility
		Canada	France	Germany	Italy		Japan	UK	USA
	Constant	-0.0355	0.7174***	0.4448***	-0.2360***		0.6168***	0.7623***	-0.2338**
		(-0.24)	(10.34)	(3.95)		(-3.22)		(16.07)	(18.16)	(-2.12)
	K-S	0.7745***	0.0467	0.3481***	0.5058***		0.2687***	0.2888***	0.7628***
	measure	(5.28)	(1.58)		(3.68)		(11.19)		(13.50)	(10.75)	(8.11)
	T-coeff.	0.3766	0.0861	0.1237		0.7885		0.8309	0.3618	0.6109
	Period	73:01-00:04 75:02-00:04 73:01-00:04 75:02-00:04 73:01-00:04 73:01-00:04 73:01-00:04
	N	112	103		112		103		112	112	112
	R²	0.8624	0.9536	0.9639		0.9405		0.8856	0.9319	0.8715
	(c) Including structural shocks
		Canada	France	Germany		Italy	Japan	UK
	Constant	0.3765***	0.4710***	0.8556***	-0.2510***	0.9912***	0.9636***
		(2.92)	(3.58)	(7.87)		(-3.73)	(37.85)	(15.38)
	K-S	0.5819***	0.1132	0.1951**		0.6181***	0.4263***	0.3471***
	measure	(4.51)	(1.43)	(2.06)		(14.98)	(33.31)	(8.50)
	Supply	0.3857 (0.40)	0.7977 (0.71)	-0.2102 (-0.28)		0.4101 (0.54)	-0.0831 (-0.25)	3.2419** (2.44)
	Demand	0.4707 (1.48)	-0.0938 (-0.40)	0.2531 (1.16)		0.3478** (2.06)	0.1082 (0.78)	-0.0203 (-0.09)
	Nominal	0.5522 (0.29)	-0.4369 (-0.16)	4.9074 (0.57)		2.4185 (0.66)	0.7328 (0.73)	4.1227** (2.35)
	Period	73:01-00:04	75:02-00:04	73:01-00:04	75:02-00:04	73:01-00:04	73:01-00:04
	N	112			103		112		103	112	112
	R²	0.9286	0.9573	0.9959		0.9800	0.9804	0.9385

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

Acknowledgements:

This paper has been written in the context of the research project 'Volatility in the Global Economy: The Role of Financial Markets'. Financial support from the Fritz Thyssen Foundation is gratefully acknowledged. We would like to thank Christian Pierdzioch for helpful comments on an earlier draft as well as Dennis Quinn for sharing his data on capital controls.

Table 4: Consumption Volatility Relative to Output Volatility and Financial Openness

The dependent variable is the volatility of consumption relative to the volatility of output. Volatilities are computed using the rolling-window approach. The Quinn measure of capital account liberalization is an index ranging from 0 to 100, which assigns a large value to more open capital account regimes. The Kaminsky-Schmukler (K-S) measure of financial market liberalization is an index ranging from 1 to 3, which assigns a lower value to more liberalized markets. OLS estimates with standard errors corrected using the method suggested by [START_REF] Hansen | Forward Exchange Rates as Optimal Predictors of Future Spot Rates: An Econometric Analysis[END_REF]. *** (**, *) = significant at the 1% (5%, 10%) level.

(a) Capital account liberalization