Dr Zhongmin Wu

Mark Baimbridge

Yu Zhu

Multiple Job Holding in the United Kingdom: Evidence from the British Household Panel Survey

Keywords: J22 -Time Allocation and Labor Supply < J2 -Time Allocation, Work Behavior, Moonlighting, Labour supply, Tobit model, Job satisfaction, BHPS JEL classification: J22, J23 Moonlighting, Labour supply, Tobit model, Job satisfaction, the BHPS

This paper examines the determinants of multiple job holding in the United Kingdom. We address these issues using data from the first eleven waves of the British Household Panel Survey, which covered the period from 1991 to 2001.

Evidence from the BHPS does not support the hypotheses of main job hours constrained and main job insecurity. We argue that the incentive for moonlighting in the United Kingdom is due to financial pressures and the desire for heterogeneous jobs. The empirical work is carried out separately for men and women.

Introduction

Evidence from the eleventh wave of the British Household Panel Survey (BHPS) indicates that in 2001 approximately 10.5% of British workers held second jobs, while 56.6% of British people held main jobs (Table 1). Given that there were 27.5 million workers in the UK in 2001 (ONS 2003), it is estimated that approximately 2.888 million workers were engaged in multiple job holding in the UK in 2001. This far exceeds official figure of 1.158 million workers with second jobs (ONS, 2003), which is based on the Quarterly Labour Force Survey (QLFS). The large disparity between is mostly likely driven by the differences in the reference period across the two datasets: whereas the BHPS asks people about their second jobs in the last calendar month, the second jobs in the QLFS refer to jobs in the reference week (week ending last Sunday). However, limited attention has been focused on examining the determinants of multiple job holding, for instance, UK research over the last 20 years is confined to the studies of [START_REF] Bell | Multiple Job-holding as a "Hedge" against Unemployment[END_REF], [START_REF] Boheim | The dynamics of second job holding in the UK Manuscript[END_REF] and [START_REF] Heineck | Fly Me to the Moon: The Determinants of Secondary Jobholding in Germany and the UK IZA Discussion Paper[END_REF]. Similarly, there is a paucity of contemporary research work on multiple job holding in other countries, most notably the United States [START_REF] Shishko | The Economics of Multiple Job Holding[END_REF][START_REF] Hunt | The Effect of Taxation on Labour Supply: The Case of Moonlighting[END_REF][START_REF] Paxson | The Dynamics of Dual Job Holding and Job Mobility[END_REF][START_REF] Averett | Moonlighting: Multiple Motives and Gender Differences[END_REF][START_REF] Partridge | Moonlighting in a High Growth Economy: Evidence from U.S. State-Level Data[END_REF], Canada [START_REF] Weersink | Multiple Job Holdings among Dairy Farm Families in New York and Ontario[END_REF], Poland [START_REF] Bedi | Sector Choice, Multiple Job Holding and Wage Differentials: Evidence from Poland[END_REF], Sweden [START_REF] Lundborg | Job amenity and the incidence of double work[END_REF], Italy [START_REF] Masi | Moonlighting Under the Italian Sun: A Series on Multiple-Job Holding[END_REF], Yugoslavia [START_REF] Reilly | Employees and Secondary Job-holding in the Federal Republic of Yugoslavia: An Empirical Analysis[END_REF], Russia [START_REF] Foley | Multiple Job Holding in Russia during Economic Transition[END_REF] and France [START_REF] Menger | Artistic labour markets and careers[END_REF].

Hence, given both the contradiction between reality and official statistics, together with relatively little academic attention, this study seeks to shed light on the issue of who moonlights and why do some people choose to do so.

To answer this question there are four main hypotheses. Firstly, the main job hours constrained model is where an individual's willingness to take a second job depends on whether they can work enough hours at their prevailing primary wage rate to satisfy their income goals [START_REF] Shishko | The Economics of Multiple Job Holding[END_REF]. Therefore, individuals take a second job in addition to their main job because their employers do not, for various reasons, offer enough hours on the main job. In relation to this, [START_REF] Friesen | Overtime Pay Regulation and Weekly Hours of Work in Canada[END_REF] shows that the constraints created by overtime pay regulation appear to induce a considerable number of workers to take up a second job. Thus, hours constraint models assume that 3 the main job wage exceeds the second job wage, creating a convex kink in the budget constraint due to the hours constraint on the main job [START_REF] Foley | Multiple Job Holding in Russia during Economic Transition[END_REF].

Secondly, the heterogeneous jobs model hypothesis suggests that labour supplied to different jobs may not be perfect substitutes or, put differently, the wage paid and utility lost from the foregone leisure may not completely reflect the benefits and costs of working [START_REF] Conway | Male Labour Supply Estimates and the Decision to Moonlight[END_REF]. Therefore, the incentive for moonlighting is that having two jobs enables individuals to engage in activities of particular interest to them.

Thirdly, the main job insecurity model hypothesis is where an individual may hold a second job if they believe that their main job has a high risk of termination. Thus a second job may cushion the financial impact of losing their main source of earnings, especially if there is scope for increasing the number of hours worked in the second job. Therefore, multiple job holding may be a response to perceived job insecurity and may be a "hedge against unemployment" [START_REF] Bell | Multiple Job-holding as a "Hedge" against Unemployment[END_REF]. Alternatively, multiple job holding may be used as a way of smoothing uncertain incomes 1 .

Finally, workers in jobs associated with non-pecuniary benefits are more prone to moonlighting whereby if earnings fall for such jobs, it is rational to have two jobs [START_REF] Lundborg | Job amenity and the incidence of double work[END_REF]. In contrast, for jobs with no non-pecuniary benefits a fall in earnings causes a rational worker to leave that employment altogether. Consequently, we have the so-called target income model, in which the individual determines their allocation of work on different jobs to reach a certain income level.

The purpose of this paper is to examine the determinants of UK multiple job holding. Firstly, we investigate how many moonlighters there are in the United Kingdom and what activities they engage in for their second jobs. Secondly, we compare moonlighter to main job holder in terms of income, main job satisfaction, education, location, and main job occupation. Thirdly, we examine what characteristics of their main jobs favour or encourage moonlighting and what personal or family characteristics of the individuals increase the propensity for second job holding. We address these issues using data from the initial eleven waves (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001) of the BHPS, with empirical work carried out separately for both men and women.

The remainder of this paper is organised as follows. Section two presents stylised facts about moonlighting in the UK. Section three develops a theoretical model to guide the empirical work. Section four discusses the data and the empirical results.

Section five offers a brief conclusion.

Stylised facts of UK moonlighting

According to the BHPS, from 1991 to 2001, on average, main job holders (single and multiple job holders combined) worked 33.78 hours per week for their main jobs, which included both full-time and part-time work. In the same period, moonlighters worked 25.42 hours per month for their second jobs. The average number of hours worked for the main job (per week) was almost constant over the eleven waves, but the average number of hours worked for the second job (per month) varied over the eleven waves (Figure 1). In terms of remuneration, real pay per week for the main job, on average, increased from £220.43 in 1991 to £243.81 in 2001, a rise of 10.61 percent. In contrast, real earnings per month from the second job rose, on average, from £181.38 in 1991 to £185.78 in 2001 (a weekly equivalent of £41.89 and £42.91 respectively), an increase of only 2.43 percent (Figure 2). Thus not only are real earnings from second jobs less than those from the main job by a magnitude of some 80 percent, but there rate of increase over the period examined was also substantially smaller at approximately one-fifth of the rate for the main job. A reversal in the above trends occurs, however, when examining part-time employment. Of the total workforce, some 12.6% of main job holders were selfemployed on their main job, whereas 45.3% of moonlighters were self-employed on their second job (Figure 3). 6 moonlighters mainly work as employees rather than self-employed for their main job, while half of them work as self-employed on their second job.

Analysis by gender indicates that whilst 53.5% of total observations were female, they only account for 47.4% of main jobholders, reflecting the fact that women's overall participation rate is still much lower than that of men. Of the total of main job holder employees, 51% were female, but only 27.4% of self-employed people were female, suggesting that either women prefer to be employed rather than self-employed compared with men, or possess little other choice. However, more women take second jobs than men do, since 54.7% of the moonlighters were female. Again, while 62.1% of employed moonlighters were female, they only make up 46.2% of selfemployed moonlighters reinforcing the notion that women prefer not to be selfemployed, whether for the main job or for the second job. Finally, over the initial eleven waves of the BHPS, some 9,888 second job holders reported their second job occupations, and only 1,136 moonlighters reported their occupations as being the same as their main job occupations. Medical practitioners, nurses, motor mechanics, auto engineers, carpenters and joiners, cleaners and domestics, together with authors, writers and journalists are more likely to take two similar jobs. Whilst the BHPS indicates that more than 88.5% of moonlighters have second jobs that are different from their main jobs which significantly exceeds that reported in the US [START_REF] Paxson | The Dynamics of Dual Job Holding and Job Mobility[END_REF]. Consequently, these facts would appear to lend more credibility to the existence of the heterogeneous jobs hypothesis in the UK.

The theoretical model

The theoretical framework for identifying the determinants of multiple job holding is well known [START_REF] Shishko | The Economics of Multiple Job Holding[END_REF][START_REF] Krishnan | The Economics of Moonlighting: A Double Self-Selection Model[END_REF][START_REF] Ballou | Causes and Consequences of Teacher Moonlighting[END_REF][START_REF] Bell | Multiple Job-holding as a "Hedge" against Unemployment[END_REF][START_REF] Foley | Multiple Job Holding in Russia during Economic Transition[END_REF][START_REF] Conway | Male Labour Supply Estimates and the Decision to Moonlight[END_REF].

Consider a representative individual with a well-behaved utility function

) , ( L C U Utility = (1)
where C is a composite consumption good and L is leisure. Suppose each person holds a main job and supplies 1 h hours of work at wage rate 1 w , which the main job holder is powerless to affect. The number of hours worked at a second job 2 h depends on the wage rate 2 w , which the second job holder is also powerless to affect.

The worker faces a budget constraint restricting the level of consumption of C to the sum of all labour and non-labour income.

The utility function written in Eq. ( 1) is maximized subject to both a budget and a time constraint, or

Y h w h w C + + = 2 2 1 1 (2) and L h h T + + = 2 1 (3)
Where Y is non-wage income and T is the total amount of time available. Substituting Eqs. ( 2) and (3) into (1) gives the utility maximisation problem that the individual faces:

) , ( ) , ( :

2 1 2 2 1 1 , 2 1 h h T Y h w h w U L C U Max h h + + = (4)
Where 1 h and 2 h are two choice variables. The first order condition gives us the two necessary conditions:

F o r P e e r R e v i e w 8 0 0 2 2 2 2 1 1 1 1 = = + = = = + = L U C U w h L L U h C C U h U L U C U w h L L U h C C U h U (5)
On the conditions of both a budget and a time constraint from Eq. ( 2) and Eq. ( 3), we

have C U L U w = 2 . If C U L U w > 2
, then the number of hours worked in the second job will increase.

If C U L U w < 2
, then the number of hours worked in the second job will decrease. The same analysis applies for the main job. Eq. ( 5

Data and empirical results

The sample used in this paper includes all adults in the first eleven waves of the These provide us with a suitable data set for assessing UK multiple job holding.

Table 2 defines the dependent and independent variables, whilst summary statistics of the sample are detailed in Table 3 for men and women. The first column describes the sample of moonlighters, the second column describes the sample of main job holders and the third column describes the full sample. [START_REF] Averett | Moonlighting: Multiple Motives and Gender Differences[END_REF] points out that when studying moonlighting behaviour one must be careful about the tendency of moonlighters to fail to report their income. However, over the eleven waves of the BHPS, 4,663 workers (10,084 observations) reported that they had a second job and 4,301 workers (8,889 observations) reported their second job pay. Thus although 11.85% of moonlighters did not report their income, this did not just apply to second job holders as 17,702 workers (73,556 observations) reported that they have a main job, but 15,304 workers (61,733 observations) reported their main job salary. Hence, some 16.07% of main job holders failed to report their income from their main job. It therefore appears that the proportion of people failing A further consideration is that second jobs, on average, yield a much higher wage rate than main jobs [START_REF] Foley | Multiple Job Holding in Russia during Economic Transition[END_REF]. This begs the question why do workers who earn a higher wage in their second job not take it as their main job. One possible explanation is that so many of the second-jobs are in self-employment 2 . Moreover, an exclusive focus on the mean wage ratio might be misleading if the distribution is highly skewed.

In the US, the mean ratio of second job wage to main job wage is 1.839 for men and 1.715 for women with the median wage ratios being 1.05 and 1.00 respectively [START_REF] Paxson | The Dynamics of Dual Job Holding and Job Mobility[END_REF]. Our own calculation based on the BHPS reveals that for the UK the mean ratio of second job wage to main job wage is 2.02 for men and 1.52 for women whilst the median ratios were 0.81 and 0.76 respectively. Hence, on average, the second job wage is much higher than the main job wage. However, if we exclude the wages of the top 5% of moonlighting earners, the mean second job wage is lower than the mean main job wage indicating that the main body of moonlighters are poorer rather than richer. In the case of an hours constrained moonlighter, the wage rate on the main job will be higher than the wage rate on the second job.

However, in the case of an individual moonlighter for any reason other than hours constraint, the wage rate on the second job will not necessarily be lower than the wage rate on the main job [START_REF] Averett | Moonlighting: Multiple Motives and Gender Differences[END_REF]. Indeed, the BHPS indicates that over 70% of male and some 65% of female moonlighters earn more on their second jobs than their main jobs, suggesting a lack of support for the first hypothesis, namely that British moonlighters are motivated by having their main job hours constrained.

In relation to the second group of explanatory variables, the job satisfaction variables are derived from the BHPS with workers reporting in scales 5, 6 and 7 grouped as satisfied, while others are grouped as non-satisfied. However, it is important to note the presence of a participation effect whereby for cultural reasons, women who are dissatisfied at work may find it easier to leave the labour force than their male equivalents. Thus, satisfied women workers may be a statistical construct, as more of the women who would be dissatisfied at work are not working [START_REF] Clark | Job Satisfaction in Britain[END_REF]. Additionally, [START_REF] Booth | Temporary Jobs: Stepping Stones or Dead Ends?[END_REF] find that, in general terms that seasonalcasual men and women are significantly less likely to be satisfied with their jobs than permanent workers. However, no difference in overall job satisfaction emerges between workers in permanent jobs and those on fixed-term contracts. When that temporary workers are less satisfied than permanent workers, who have promotion prospects and job security. Hence, the image of the worker with a second job is not that of someone who is necessarily satisfied with the world of work, consequently, they potentially possess an exaggerated tendency to report lower levels of job satisfaction, but this is less significant than their dissatisfaction with pay [START_REF] Clark | Job Satisfaction in Britain[END_REF].

We estimate the moonlighting labour supply equation via a Tobit Model separately for men and women using the maximum likelihood estimation [START_REF] Greene | Econometric Analysis, Fourth Edition[END_REF]. Tables 4 and5 report the results of a Tobit work hours equation for male and female moonlighters with and without wave dummy variables. As [START_REF] Conway | Male Labour Supply Estimates and the Decision to Moonlight[END_REF] point out, given the large proportion of non-moonlighters, one would expect the participation decision to dominate the hours supplied decision in a Tobit model. To explore the robustness of the results to alternative techniques, we also estimate Probit Model and Logit Model 3 in light of previous studies such as [START_REF] Averett | Moonlighting: Multiple Motives and Gender Differences[END_REF] which estimates a bivariate probit model and finds that the determinants of dual-job holding are fairly similar both for men and women.

In terms of what affects moonlighters' decisions to devote time and effort to their second jobs? Tables 4 and5 indicate that income is a consistently significant criterion.

The decision to moonlight involves a trade-off between income and leisure, whereby higher second job wages (J2WAGE) increase the labour supply of second jobs both for men and women, so that the substitution effect dominates the income effect. The labour supply curve of second jobs, however, has not reached the point of backward bending, although the average second job wage is much higher than the average main job wage (see Table 3). Higher main job wages reduce the incentive for moonlighting, by decreasing the labour supply for second jobs. Furthermore, higher levels of wealth and non-labour income (WEALTH) for all workers appear to increase the reservation wage, thereby lowering the propensity to take second jobs. to take second jobs. However, these do not suggest that British moonlighting is due to an hours constraint of main jobs. Male workers who are unhappy with the total pay of their main job (SATPAY) appear very keen to moonlight. The incentive for them to be multiple jobholders would appear to be due to financial pressures and the desire to raise standards of living, whilst female workers who are satisfied with the actual work of the main job (SATWORK) are more likely to undertake two jobs. Workers being satisfied or not with the job security of their main job (SATSECURITY) do not have a significant effect on moonlighting. These results provide no support for the hypothesis that job insecurity increases the probability of a worker holding a second job.

F

The more children a man has, the greater the probability of moonlighting, while for women having more children negatively affects taking a second job. This potentially reflects men taking more responsibility for financially supporting their children by taking a second job, while women have been discouraged from taking a second job because of the need to take care of their children either directly or indirectly through the prohibitive cost of childcare or its unavailability. Moreover, married women are less likely to moonlight, while marriage does not affected male moonlighting. For women a further apparent deterrent to moonlight is if they spend significant time travelling to work for their main job, which again could reflect their wider responsibilities. Finally, age has a significant negative effect on second job labour supply, with young workers being more likely to take second jobs, potentially through either a combination of financial necessity (repayment of student debt/loans, seeking to establish themselves in the housing market etc), or by simply possessing the necessary energy.

We also find that for workers education is associated with a higher probability of holding a second job in all categories relative to possessing no formal qualifications.

As [START_REF] Abdukadir | Liquidity Constraints as a Cause of Moonlighting[END_REF] points out, workers are more likely to moonlight if they have low current incomes relative to their educational attainment. Furthermore, having permanent and public-sector jobs as main jobs encourages workers to moonlight, as their main jobs may help them to find second jobs. These estimates provide no support for the hypothesis that multiple job holding may be a response to perceived job insecurity and may be a safeguard against unemployment. However, possessing a England are more likely to be multiple job holders, perhaps reflecting the high cost of living, not least in terms of house prices, in those regions, or the greater opportunity of additional employment. Finally, main job occupations in agriculture, education, health, community and household encourage moonlighting across both men and women. Although employment in the transport sector affects moonlighting behaviour differently, with female workers more likely to moonlight than their male counterparts.

Conclusion

This paper examines the determinants of UK multiple job holding with data obtained from the initial eleven waves of the BHPS. Our analysis does not support the view that British moonlighting is due to main job hours constraint since workers who are satisfied with their main job work hours are more likely to take second jobs. BHPS data indicates that over 70% of male moonlighters earn more on second jobs than on main jobs, while about 65% of female moonlighters earn more on second jobs than on main jobs. Satisfaction or otherwise with job security of the main job does not have a significant effect on moonlighting per se unless they are permanent and in the publicsector. However, these estimates provide no support for the hypothesis that multiple job holding may be a response to perceived job insecurity and may be a 'hedge against unemployment'.

Over the period of our study (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)) more than 88.5% of moonlighters have second jobs that are different from their main job, which supports the heterogeneous jobs hypothesis. Male workers who are unhappy with the total pay of their main job are very keen to moonlight whilst higher second job wages increase the labour supply for second jobs both for men and women. The incentive for them to moonlight therefore appears to stem from financial pressures and the desire to raise, or secure, standards of living with multiple job holding as one dimension to achieving such goals.

Figure 1

 1 Figure1

  Figure 2

Figure 3 P

 3 Figure3

  income were consistent between the main and second job holders. Thus there appears no special tendency for UK moonlighters to fail to report their income.

  aspects of job satisfaction separately,[START_REF] Booth | Temporary Jobs: Stepping Stones or Dead Ends?[END_REF] find

  their main job work hours (SATHOUR) are more likely

  se job discourages a worker from moonlighting, which is consistent with the time constraint condition of the theoretical model.The geographical variables indicate that relative to the omitted regions (Wales, Scotland and Northern Ireland) people from Southeast, Southwest and Eastern

Table 1

 1

	reports the numbers of main job holders and moonlighters from the initial
	eleven BHPS waves, whereby 7.8% of people held a second job whilst 56.5% of
	people held a main job. In terms of the nature of employment, almost half of
	moonlighters are self-employed on their second job. However, available data indicates
	that nearly 90% of moonlighters undertake their main job as an employee, while only
	just over 10% of moonlighters do their main job as self-employed. Hence,

Table 1 . Number of Main Job Holders and Moonlighters

 1

		Total	Total	Main job	Main job	Second job	Second job
		observations	observations	holders	holders	holders	holders
			(Female)		(Female)		(Female)
	Wave1	10264	5431	5794	2671	805	452
	Wave2	9845	5215	5480	2550	760	434
	Wave3	9600	5124	5390	2558	759	433
	Wave4	9481	5041	5371	2530	806	450
	Wave5	9249	4929	5295	2503	822	448
	Wave6	9438	5003	5465	2586	877	476
	Wave7	11193	5981	6353	2993	1033	569
	Wave8	10906	5851	6288	2997	923	503
	Wave9	15625	8436	8749	4215	1113	581
	Wave10	15605	8427	8793	4229	1075	592
	Wave11	18869	10238	10576	5170	1110	580
	Total	130077	69678	73556	34941	10084	5519
	Source: Codebook, Wave 1 to 11, the British Household Panel Survey

Table 4 Random-effects Tobit Regression for Men

 4 Sigma_u and sigma_e denote the panel-level variance component and the overall variance respectively. Rho is the percent contribution to the total variance of the panel-level variance component. When rho equals zero, the panel estimator is no different from the pooled estimator.

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

Table 5 Random-effects Tobit Regression for Women

 5 For definitions of Sigma_u, sigma_e and rho, see note of Table4.

			With wave dummy	Without wave dummy
	J2hours	Coef.	Std. Err.	Coef.	Std. Err.
	J2wage	0.137***	0.005	0.137***	0.005
	Jbwage	-0.021	0.013	-0.022*	0.013
	Wealth	-0.00005***	0.00001	-0.0001***	0.00001
	Satpay	-0.093	0.110	-0.117	0.109
	Satsecurity	0.038	0.119	0.039	0.119
	Satwork	0.571***	0.148	0.594***	0.148
	Sathour	0.339***	0.123	0.340***	0.123
	Satall	-0.148	0.160	-0.149	0.160
	Travel	-0.006**	0.003	-0.006**	0.003
	Children	-0.188***	0.048	-0.194***	0.047
	Married	-0.391***	0.091	-0.392***	0.091
	Ownhealth	0.065	0.083	0.114	0.071
	Age	-0.035***	0.003	-0.036***	0.003
	Degree	0.413**	0.166	0.386**	0.165
	GCSE	0.387***	0.125	0.394***	0.125
	Alevel	0.762***	0.159	0.766***	0.158
	Otherhi	0.834***	0.148	0.793***	0.147
	Permanent	0.272**	0.129	0.249*	0.129
	Public	0.847***	0.165	0.858***	0.164
	Fulltime	-1.622***	0.113	-1.634***	0.113
	Union	-0.242*	0.137	-0.186	0.135
	London	0.008	0.187	0.207	0.183
	Southeast	1.114***	0.149	1.310***	0.145
	Southwest	0.421**	0.194	0.608***	0.191
	Eastern	0.466***	0.172	0.661***	0.169
	Westmidlands	-0.088	0.196	0.109	0.193
	Northwest	0.179	0.183	0.374**	0.179
	North	-0.188	0.160	0.005	0.157
	Agriculture	2.820***	0.565	2.829***	0.565
	Manufacturing	0.188	0.183	0.208	0.183
	Hotels	0.298	0.193	0.313	0.193
	Transport	0.774**	0.301	0.769**	0.301
	Finance	-0.229	0.284	-0.212	0.284
	Business	0.275	0.202	0.287	0.202
	Publicadmin	-0.183	0.269	-0.208	0.269
	Education	0.856***	0.217	0.842***	0.216
	Health	0.537**	0.218	0.517**	0.217
	Community	1.029***	0.179	1.029***	0.179
	Households	0.919***	0.302	0.931***	0.302
	Sigma_u	3.746***	0.049	3.744***	0.049
	Sigma_e	7.961***	0.023	7.966***	0.023
	Rho	0.181	0.004	0.181	0.004
	Log likelihood		-248322.84	-248359.24
	Wald chi2		1825.66***	1752.43***
	Prob > chi2		0.0000	0.0000
	Number of obs		69678
	Number of groups		14470
	Obs per group: min		1
	Obs per group: avg		4.8
	Obs per group: max		11
	Note: * p<0.10; ** p<0.05; *** p<0.01 .

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

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

APPENDIX

We undertake robustness checks with respect to the exclusion of the self-employed and the exclusion of the job satisfaction variables in the following tables.

Table A1 and A2 present Random-effects Tobit estimates with and without job satisfaction variables for men and women respectively, using a sample without selfemployed people in either the main or the second job. We do not report results with wave dummies as they are very similar. Comparing the first two columns to the corresponding columns in Tables 4 and5 (i.e. specification without wave dummies),

we can see that the estimates all have the same signs and if anything, become more significant statistically. For instance, the effect of the wage in the main job remains negative but turns from being statistically insignificant for men and marginally significant for women to significant at the 1% level for both gender. On the other hand, the effect of the second-job wage remains highly significant statistically but increases in size. Moreover, removing the job satisfaction variables appear to make virtually no difference to the estimates, with perhaps the exception of the main job wage for men, where it increases slightly in size.

Table A3 and A4 focus on the robustness of our main findings in Tables 4 and5 with respect to the exclusion of the self-reported job satisfaction variables. It turns out that the two sets of estimates are remarkably similar. The only difference worth mentioning is the effect of the wage for the main job, which not only increases in size, but also become much more significant statistically, after we exclude the job satisfaction scores. This is not surprising, given that the job satisfaction variables are likely to partially capture the impact of various domains of the main job, including the satisfaction with pay.

Taken together, our sensitivity analyses suggest that our main findings are reasonably robust with respect to the exclusion of self-employed and the exclusion of the selfreported job satisfaction scores. 4.

Robustness checks with respect to the inclusion of job satisfaction variables

Notes

1 We thank an anonymous referee for pointing this out. 2 There is a very small literature on the determinants of self-employment, see e.g. [START_REF] Ajayi-Obe | The Changing Nature of Work among the Self_emplyed in the 1990s: Evidence from Britain[END_REF], [START_REF] Blau | A time-series analysis of self-employment in the United States[END_REF][START_REF] De Wit | Models of self-employment in a competitive market[END_REF] and Clark et al. (1993), [START_REF] Cueto | An analysis of Self-employment Subsidies with Duration Models[END_REF]. However, to the best of our knowledge, no study has ever attempted to model self-employment and multiple-job-holding jointly, due to problems with measurement and identification. We argue that a full-treatment of selfemployment is beyond the scope of this paper, but will show evidence in the Appendix that our main findings remain robust with respect to the exclusion of all self-employed (in either the main or the second-job). 3 Results of these estimations are available from the first author upon request.


Karima ; Saci

Gianluigi Giorgioni

Kenneth Holden
email: k.holden@ljmu.ac.uk

C23 -Models with Panel Data < C2 -Econometric Methods: Single Equation Models < C -Mathematical and Quantitative Methods, O16 -Financial Markets|Saving and Capital Investment < O1 -Economic Development < O -Economic Development, Technological Change, and Growth

Keywords: Financial development, panel data, economic growth

come

Introduction

Since the pioneering work of [START_REF] Mckinnon | Money and Capital in Economic Development[END_REF] and [START_REF] Shaw | Financial Deepening in Economic Development[END_REF] suggesting a link between growth and financial development many attempts (e.g. King & Levine 1993a[START_REF] Levine | Stock markets, banks, and economic growth[END_REF], Beck et al., 2000, Levine et al., 2000[START_REF] Tang | The effect of financial development on economic growth: evidence from the APEC countries[END_REF][START_REF] Zang | Does financial development precede growth? Robinson and Lucas might be right[END_REF] have been made at providing evidence for this link. However, Driffill (2003, p. 363) after reviewing the empirical evidence argues that the question of "whether finance plays a causal role or merely follows economic development remains an open one". Indeed, recent contributions to the literature show that results either in support or rejecting the role of finance are highly dependent on the model specification, the level of development (financial and/or economic) of a country, the choice of financial variables and the econometric technique used. 2 [START_REF] Favara | An empirical reassessment of the relationship between finance and growth IMF Working Papers[END_REF] concludes that the importance of private credit and liquid liabilities upon economic growth is highly dependent on the choice of estimation method. While Bhattacharya and Sivasubramaniam (2003) provide evidence of unidirectional effect of financial development upon economic growth in India, [START_REF] Dawson | Financial development and growth in economies in transition[END_REF] shows that financial development, approximated by the variable liquid liabilities, does not affect economic growth in a sample of transition economies in Central and Easter Europe. [START_REF] Rioja | Does one size fit all?: a reexamination of the finance and growth relationship[END_REF], for a sample of 74 countries at different stages of development , use generalised method of moments (GMM) estimation to conclude that evidence of an influence of financial development upon economic growth is highly dependent on the level of development of the financial sector of a particular country or group of countries: at low levels of financial development, the effect on growth is mixed, whereas at an intermediate level of financial development the effect is positive and strong . This positive effect weakens for countries at a very high level of financial development, although it does remain positive. [START_REF] Shan | Does financial development 'lead' economic growth? A vector auto-regression appraisal[END_REF] for a sample of 10 OECD countries and China finds, at best, weak support for the hypothesis that financial development (proxied by total credit) influences economic growth. Finally, [START_REF] Chang | Financial development and economic growth: the case of[END_REF] provide mixed support to the hypothesis that financial development leads economic growth for Taiwan.

In this paper, we use annual panel data for 30 developing countries and utilise recently developed methods-of-moments techniques for dynamic models, which attempt to deal with the problems of biased and inconsistent estimates resulting from endogeneity and autocorrelation. To measure financial development we use two variables relating to the stock market -the traded value and market turnover -and three variables relating to the development of the banking system -domestic credit to the private sector as a percentage of GDP, the ratio of liquid liabilities to GDP (or M3/GDP), and the ratio of commercial bank assets to all (commercial plus central) bank assets. Following [START_REF] Rousseau | Equity markets and growth: cross-country evidence on timing and outcomes, 1980-1995[END_REF], the usual control variables are not included in the equations. However, they are used as instruments to correct problems of simultaneity and endogeneity of the explanatory variables. The measurement of financial development is discussed in section 2, where the focus is on the roles of financial intermediaries and of the stock market. In section 3 the estimation method is set out. The empirical evidence is reported in section 4 and the conclusions are presented in section 5.

Measuring Financial Development

To align this paper with the extant literature and allow comparability with results from previous empirical work, we have selected the following variables, widely used in the literature, to capture the level of financial development: domestic credit to the private sector as a % of GDP, the ratio of liquid liabilities to GDP (or M3/GDP), the ratio of commercial bank assets to all (commercial plus central) bank assets for the banking sector and the turnover ratio and the ratio of value of shares traded to GDP for financial markets .

(a) Measures of Financial Intermediaries Development

The level of financial services is commonly measured by domestic credit to the private sector as a % of GDP (CPS) (e.g. King & Levine 1993a[START_REF] Levine | Stock markets, banks, and economic growth[END_REF], Beck et al., 2000and Levine et al., 2000). This distinguishes between the credit issued to the private sector and that to government and public enterprises. This variable should capture the ability of intermediaries (both privately owned and state-owned) to evaluate information and identify profitable investment projects. Higher levels of this ratio could be therefore interpreted as indicating lower transaction costs and higher levels of financial services and therefore greater financial intermediary development. However, it could be argued that given some of the characteristics of the financial systems in developing countries such as repeated and substantial interventions by the government leading to moral hazard problems, lack of a strong regulatory system, lax supervision, lack of skills in the banking personnel [START_REF] Gregorio | Financial Development and Economic Growth[END_REF][START_REF] Gregorio | Financial Development and Economic Growth[END_REF][START_REF] Brownbridge | Financial Regulation in Developing Countries[END_REF][START_REF] Brownbridge | Financial Regulation in Developing Countries[END_REF], a high value of credit to the private sector, in cases of over-lending or careless lending, could actually lead to a reduction in economic growth, due to its association with high, but less efficient, investment [START_REF] Gregorio | Financial Development and Economic Growth[END_REF]. To measure the overall size of the financial intermediary sector, the ratio of liquid liabilities to GDP (LL) (or M3/GDP) is used (e.g. [START_REF] Goldsmith | Financial Structure and Development[END_REF], King & Levine 1993a[START_REF] Rousseau | Equity markets and growth: cross-country evidence on timing and outcomes, 1980-1995[END_REF][START_REF] Rioja | Does one size fit all?: a reexamination of the finance and growth relationship[END_REF], and Levine et al., 2000). LL is the ratio of broad money to GDP. Broad money consists of currency held outside the bank system plus interest-bearing total deposit liabilities of banks and other financial institutions. However, the interpretation of this variable could be difficult because a low ratio could be the result either of underdevelopment of the banking sector or equally, the result of a highly sophisticated financial sector that allows economic agents to reduce money balances held with the banking system and invest them in other products. Therefore care should be taken when interpreting this variable (see King & Levine, 1993b, Morisson, 2000[START_REF] Gregorio | Financial Development and Economic Growth[END_REF]. Also, this variable does not necessarily reflect the allocation of savings and so may not be an accurate indicator of the activities of financial intermediaries.

Finally, the ratio of commercial bank assets to all (commercial plus central) bank assets (BA) is used to measure the degree to which commercial banks versus the central bank allocate society's savings (e.g. Demirguc-Kunt & Levine 1996[START_REF] Andres | The Role of the Financial System in the Growth -Inflation Link: The OECD Experience Banco de Espana Documento de Trabajo[END_REF], Levine et al., 2000[START_REF] Rioja | Does one size fit all?: a reexamination of the finance and growth relationship[END_REF]. The intuition underlying this is that commercial banks are more likely to identify profitable investments, monitor managers' decisions, facilitate risk management and mobilise savings than central banks. Commercial banks are thought to be more effective than central banks in allocating savings to productive investment projects.

Hence, an increase in this ratio indicates an expansion of the financial sector [START_REF] Levine | Financial intermediation and growth: causality and causes[END_REF]. Levine &[START_REF] Levine | Stock markets, banks, and economic growth[END_REF][START_REF] Rousseau | Equity markets and growth: cross-country evidence on timing and outcomes, 1980-1995[END_REF][START_REF] Rousseau | Equity markets and growth: cross-country evidence on timing and outcomes, 1980-1995[END_REF]. The turnover ratio (TR) is the trading volume of the stock market relative to the average market capitalisation and it measures stock market liquidity, showing the importance and the credibility of available information. In emerging markets, an increase in liquidity is a good indication of financial development. Also, it indicates low transactions costs, which facilitate fund transfers and increase the number of firms and traded shares. Hence, it promotes growth [START_REF] Rousseau | Equity markets and growth: cross-country evidence on timing and outcomes, 1980-1995[END_REF].

The ratio of value of shares traded to GDP (TV) is used to measure stock market activity. It measures trading volume relative to the size of the economy. Being the product of market price and the number of shares traded, it includes elements of both liquidity and size [START_REF] Beck | Stock Markets, Banks and Growth: Panel Evidence[END_REF].

Estimation methodology

In the literature many studies of the growth -financial development relationship (see, for example, Beck &[START_REF] Beck | Stock Markets, Banks and Growth: Panel Evidence[END_REF][START_REF] Levine | Financial intermediation and growth: causality and causes[END_REF] use averaged data, usually for nonoverlapping five-year periods, in an effort to reduce the impact of the business cycle. However, [START_REF] Madsen | The causality between investment and economic growth[END_REF] demonstrates that averaging over 3, 5 or 8 years can produce contradictory results in Granger-Sims non-causality tests, so that any interpretation of causality for averaged data is likely to be flawed (see also Huh, 2005 for a discussion of these tests). Furthermore, there is a belief in the business cycle literature that the function of the business cycle is central to growth, so its impact should not be minimised. Instead of using averaged data we use annual data for a panel of developing countries. This increases the sample size and allows dynamic effects to be included. Also, previous research has not adequately dealt with the problems of simultaneity and endogeneity of the explanatory variables. Here, to deal with these problems, the method of estimation uses instrumental variables and, as well as the lagged values of the explanatory F o r P e e r R e v i e w 6 variables, the instruments include the standard conditioning variables (see, King & Levine (1993aand 1993b[START_REF] Zang | Does financial development precede growth? Robinson and Lucas might be right[END_REF]. These are the size of government consumption, the initial real GDP per capita, the inflation rate, the level of secondary school enrolment and the degree of openness of the economy.

Following [START_REF] Levine | Financial intermediation and growth: causality and causes[END_REF], [START_REF] Beck | Stock Markets, Banks and Growth: Panel Evidence[END_REF], [START_REF] Rousseau | Equity markets and growth: cross-country evidence on timing and outcomes, 1980-1995[END_REF] and [START_REF] Yao | On economic growth[END_REF] recently developed dynamic panel generalized-method-of-moments (GMM) techniques are used to assess the relationship between stock market development, intermediaries development and economic growth. Since these GMM techniques are well-known we only provide a summary of them. These techniques control for unobserved country-specific effects, first-difference non-stationary variables, overcome the endogeneity of the explanatory variables by using instruments and test for the presence of autocorrelation. The traditional cross-country growth regression can be written as:

t i i t i t i t t i X y y y , , 1 , 1 , ` + + + + = (1)
where y is the logarithm of real per capita GDP, X represents the set of explanatory variables, other than lagged per capita GDP and including our indicators of stock market and bank development, is an unobserved country-specific effect, is the error term, and the subscripts i and t represent country and time period, respectively. The dependent variable in equation ( 1) is the period's growth rate. The OLS estimator of (1) is biased and inconsistent since y it-1 is correlated with it . Subtracting the mean from each variable and estimating this equation by OLS gives the within-groups (WG) estimator. However, the WG estimator is consistent only if all the explanatory variables are strictly exogenous [START_REF] Arellano | Dynamic panel data estimation using DPD98 for GAUSS: a guide for users Mimeo[END_REF].

First-differencing the equation and using the GMM estimator with instrumental variables deals with this endogeneity problem. The validity of the instruments can be checked using Sargan's test. A consequence of the first-differencing is to introduce first-order autocorrelation, so this is expected on estimation. However, it is well-known that this first-differenced GMM method performs poorly in small samples [START_REF] Levine | Financial intermediation and growth: causality and causes[END_REF]. Instead, [START_REF] Arellano | Another look at the instrumental-variable estimation of error-components models[END_REF] and Blundell & Bond (1998) propose a system (SYS-GMM) method which uses more instruments and links the regressions in differences with regressions in levels.

To detect whether there are serious finite sample biases in the SYS-GMM estimations, [START_REF] Doornik | Panel Data estimation using DPD for Ox[END_REF] and [START_REF] Bond | GMM Estimation of Empirical Growth Models[END_REF] suggest comparing them to the within-groups estimator. Unlike [START_REF] Levine | Financial intermediation and growth: causality and causes[END_REF], [START_REF] Rousseau | Equity markets and growth: cross-country evidence on timing and outcomes, 1980-1995[END_REF] and [START_REF] Beck | Stock Markets, Banks and Growth: Panel Evidence[END_REF], who used the GMM estimation only, here the within-groups estimation is also performed for comparative purposes. The main difference is in the coefficient on the lagged dependent variable. A finding that the within-group estimate of the coefficient lies above the corresponding GMM system parameter estimate suggests that the GMM system estimates are seriously biased [START_REF] Bond | GMM Estimation of Empirical Growth Models[END_REF].

In summary, our approach is to use both the within-groups and SYS-GMM estimation and to use the Sargan test and serial correlation tests to check the validity of the assumptions.

The Empirical Results

The data are primarily from the World Bank's Global Development and 1998,2001,2002, which track market capitalization, the number of listed companies and total value traded (among other variables) for up to fifty three countries. Selecting those countries with at least ten years data gives our sample of thirty countries: Argentina, Bangladesh, Brazil, Chile, Colombia, Cote d`lvoire, Egypt, India, Indonesia, Jamaica, Jordan, Israel, Kenya, Korean Republic, Malaysia, Mauritius, Mexico, Morocco, Nigeria, Pakistan, Peru, Philippines, South Africa, Sri Lanka, Thailand, Trinidad and Tobago, Tunisia, Turkey, Venezuela and Zimbabwe for 14 years (1988 -2001). The commercial-central bank assets ratio (BA) is from the database of Levine et al. (1999). Prior to 1997, the levels of secondary school enrolment (SE) are from the UNICEF statistics database. 2. For 420 observations all the correlations are greater than 0.08 and so are significantly positive, with the highest being 0.737 for CPS with LL. This suggests that multicollinearity may be a problem if all the financial development variables are included in an equation.

F

TABLE 2 NEAR HERE

In tables 3, 4 and 5 the results for the systems (SYS-GMM) and within-groups (WG) estimations are presented, as estimated using PcGive 10. In all the estimated equations dummy variables for the year are included but are not reported. As stated in section 3, should the estimated coefficient on GY(-1) from WG estimation be greater than that from the SYS-GMM estimation, it would suggest that the SYS-GMM estimates are seriously biased. When the estimated equations included GY(-1), there was no evidence of this bias. However, since its coefficient was never significant, the reported results omit this variable. The diagnostic tests check for the overall fit of the regressions (the Wald test), the validity of the instrumental variables (the Sargan test), and the presence of first-order and second-order serial correlation. Since first-order serial correlation is introduced automatically when the basic equation is differenced, any evidence of this can be ignored, and instead the results of the tests for second-order serial correlation are considered. For all the reported results the diagnostic tests are satisfactory.

TABLE 3 NEAR HERE

In table 3, taking the SYS-GMM results, while credit to the private sector (CPS) has a significantly negative coefficient in column (1), when the stock market variable is the ratio of the value of shares traded to GDP, TV, which has a significantly positive coefficient, the effect of CPS becomes insignificant when the turnover ratio, TR, replaces TV. However, in each case the effect of the stock market variable on growth is positive and significant. This finding is consistent with the results reported by [START_REF] Beck | Stock Markets, Banks and Growth: Panel Evidence[END_REF] and [START_REF] Rousseau | Equity markets and growth: cross-country evidence on timing and outcomes, 1980-1995[END_REF]. However, unlike [START_REF] Levine | Financial intermediation and growth: causality and causes[END_REF] and [START_REF] Beck | Stock Markets, Banks and Growth: Panel Evidence[END_REF] findings, our result is that credit allocation (CPS) negatively and significantly affects growth when stock market activity (TV) is taken into account. It is worth noting that the samples of [START_REF] Levine | Financial intermediation and growth: causality and causes[END_REF] F o r P e e r R e v i e w [START_REF] Beck | Stock Markets, Banks and Growth: Panel Evidence[END_REF] include both developed and developing countries with 5-year averaged data over 1960-1995, and 1976-1998 respectively, whereas our sample includes developing countries only with annual data over 1988-2001. In fact, when Beck & Levine (2002, page 18) use annual data, instead of average data, only the stock market variable is significantly positive, while the relationship between bank credit and economic growth becomes insignificant. Also, our analysis uses the one-step system GMM whereas Levine et al., (2000) relies on the two-step system GMM which is known to be inconsistent and unreliable (see section 3). The results in table 3, however, confirm the findings of Ben Naceur & Ghazouani (2003) that credit allocation negatively affects growth in developing countries over 1979-1999.

TABLE 4 NEAR HERE

The results for the effects of the stock market and the size of financial intermediaries on economic growth are given in table 4. The SYS-GMM results reported in column (1) show a significant negative link between the liquid liabilities of the financial system (LL) and economic growth at the 5 per cent level when using value of shares traded over GDP (TV) for stock market activity. As in table 3, TV remains significant, now at the 1 per cent level, and positively related to growth. When focusing on the stock market liquidity by including the turnover ratio (TR) in table 4 column (2), the liquid liabilities ratio (LL) remains negatively but insignificantly related to growth while TR has a positive and a significant impact on growth at the 1 per cent level of significance. As with the credit to the private sector regressions in table 3, in all the specifications the size of the intermediaries (LL) negatively affects growth (at varying levels of significance) but the stock market variables always have significantly positive coefficients.

Overall, the results in table 4 confirm the earlier findings that stock market development has a significant positive impact on growth. However, unlike most other empirical studies findings (e.g. [START_REF] Levine | Financial intermediation and growth: causality and causes[END_REF][START_REF] Beck | Stock Markets, Banks and Growth: Panel Evidence[END_REF][START_REF] Rousseau | Equity markets and growth: cross-country evidence on timing and outcomes, 1980-1995[END_REF], the liquid liabilities of the financial sector (LL), measuring financial intermediary size, negatively and significantly relates to economic growth. Our findings agree with the results of Hsu & Liu

TABLE 5 NEAR HERE

The results for the size of commercial bank assets (BA) are reported in table 5, where this variable has a positive effect on growth, but with varying levels of significance. The significance level is higher when stock market liquidity, TR, is included, than when turnover value, TV, is included.

The results in tables 3, 4 and 5 demonstrate the importance of stock market development for growth. This suggests that a trade-off between bank development and stock market development may not exist and that they are both important for economic growth [START_REF] Beck | Stock Markets, Banks and Growth: Panel Evidence[END_REF].

Conclusions

In this paper the joint contribution of stock markets and banks development to economic growth has been examined by using annual panel data for 1988 -2001 for thirty developing countries, and utilising general method-of-moments estimation (GMM). The main conclusion is that while the alternative measures of stock market development are positively and significantly linked to economic growth, their presence results in the standard measures of development of the banking sector, credit to the private sector and liquid liabilities, having a negative impact on growth. This result confirms earlier findings by De [START_REF] Gregorio | Financial Development and Economic Growth[END_REF], and [START_REF] Beck | Stock Markets, Banks and Growth: Panel Evidence[END_REF] when they used the same technique and frequency of data for a different sample of countries. The regressions also include dummy variables for the different time periods (not reported).

Instruments include lags of GY, the control variables, and the considered measure of bank and stock market development. P-values are reported in parentheses. SYS-GMM is one-step GMM estimates and WG is within group estimates. 1 The null hypothesis is that none of the variables are worth including and the alternative is that some variables are needed. 2 The null hypothesis is that the instruments used are valid and not correlated with the residuals. 3 The null hypothesis is that the errors in the first-difference regression exhibit no first-order serial correlation 4 The null hypothesis is that the errors in the first-difference regression exhibit no second-order serial correlation (listed as m2 in [START_REF] Arellano | Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations[END_REF].

  The most commonly used complementary measures of stock market size are the market turnover ratio (e.g. Demirguc-Kunt & Levine1996,Levine & Zervos 1996, Rousseau & and Beck &[START_REF] Beck | Stock Markets, Banks and Growth: Panel Evidence[END_REF]) and total value traded (e.g.[START_REF] Atje | Stock market and development[END_REF]

  Finance & World Development Indicators and the Standard & Poor's (S&P) Emerging Stock Markets Factbooks. The countries and time periods are based on the availability of data on stock market development in the annual issues of the S&P's Emerging Stock Markets Factbooks,

  that the size of financial intermediaries negatively affects growth in three developing countries over 1981 to 2001.

TABLE 1 NEAR

 1 HEREThe list of variables is summarised in table 1 and the simple correlations of the financial development variables are presented in table

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Table 2 Correlations of the variables 1988 -2001 (panel of 420 observations)

 2

		CPS	LL	BA	TR	TV
	LL	0.737*	1.000
	BA	0.436*	0.327*	1.000
	TR	0.171*	0.171*	0.149*	1.000
	TV	0.474*	0.395*	0.248*	0.583*	1.000
	GY	0.092*	0.039	0.131*	0.134*	0.195*
	* Significantly positive at the 5% level (r c = 0.0802)

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Table 3 : Growth, Stock Market Development and Credit Allocation

 3

					SYS-	WG	SYS-	WG
	Regressors		GMM		GMM
					(1)	(1)	(2)	(2)
	Constant		8.092	-	2.581	-
					(0.000)		(0.219)
	CPS		-1.736	-1.800	-0.771	-1.256
					(0.006)	(0.116)	(0.246)	(0.213)
	TV			0.917	0.983	-	-
					(0.001)	(0.006)
	TR			-	-	0.921	0.760
							(0.004)	(0.040)
	No. observations	299	299	299	299
	Wald test for joint significance 1	15.01 (0.001)	7.851 (0.020)	8.54 (0.014)	4.772 (0.092)
	Sargan test 2		432.5	-	439.5	-
					(0.993)		(0.987)
	First order serial	-2.219	-0.068	-2.205	0.082
	correlation test	3	(0.026)	(0.946)	(0.027)	(0.934)
	Second order		0.477	0.732	0.3479	0.434
	serial correlation	(0.633)	(0.464)	(0.7281)	(0.664)
	test	4
	R	2			-	0.147	-	0.127

Table 4 : Growth, Stock Market Development and the Size of the Financial Intermediaries

 4

					SYS-	WG	SYS-	WG
	Regressors	GMM		GMM
					(1)	(1)	(2)	(2)
	Constant		10.235	-	3.835	-
					(0.002)		(0.201)
	LL			-2.211	-5.020	-1.071	-4.440
					(0.012)	(0.012)	(0.205)	(0.023)
	TV			0.929	0.960	-	-
					(0.000)	(0.002)
	TR					0.933	0.693
					-	-	(0.003)	(0.039)
	No.			299	299	299	299
	observations
	Wald test for	18.63	15.35	9.23	6.779
	joint		(0.000)	(0.000)	(0.010)	(0.034)
	significance 1
	Sargan test	2	452.2	-	477.9	-
					(0.964)		(0.827)
	First order
	serial		-2.204	-0.173	-2.197	0.046
	correlation		(0.028)	(0.862)	(0.028)	(0.963)
	test	3
	Second order	0.448	-0.165	0.339	0.031
	serial		(0.654)	(0.869)	(0.734)	(0.975)
	correlation test 4
					-	0.180	-	0.156
	R	2
	See notes to table 3.

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Table 5 : Growth, Stock Market Development and the Size of Bank Assets

 5

				SYS-	WG	SYS-	WG
	Regressors	GMM		GMM
				(1)	(1)	(2)	(2)
	Constant	-10.209	-	-17.099	-
				(0.255)		(0.055)
	BA		2.878	6.071	4.133	6.776
				(0.178)	(0.042)	(0.055)	(0.017)
	TV		0.533	0.792	-	-
				(0.019)	(0.030
	TR				0.661	0.730
				-	-	(0.050)	(0.081)
	No.		299	299	299	299
	observations
	Wald test for	13.59	14.34	12.95	13.34
	joint	(0.001)	(0.001)	(0.002)	(0.001)
	significance 1
	Sargan test 2	504.5	-	486.1	-
				(0.536)		(0.751)
	First order
	serial	-2.246	-0.086	-2.231	-0.014
	correlation	(0.025)	(0.931)	(0.026)	(0.989)
	test	3
	Second order	0.4238	-0.692	0.368	0.441
	serial	(0.672)	(0.489)	(0.713)	(0.659)
	correlation
	test	4
				-	0.159	-	0.153
	R	2
	See notes to table 3.

Page 20 of 19 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

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


Daniel A Tirado
email: datirado@ub.edu

Elisenda Paluzie

Jordi Pons

market potential equation in Spain

Keywords: New Economic Geography, Spatial wage structure, Spain

A test of the market potential equation in Spain

INTRODUCTION

The economic liberalisation and stabilisation measures introduced at the end of the 1950s favoured the transition of the Spanish economy towards a new phase in its long-term economic development. This period was characterised, among other elements, by high aggregate growth rates of production, by the lead taken by the industrial sector and by the wholesale reorganization of the country's economic activity. Linked to this, Spanish economic growth in the sixties was also typified by the advances made in the construction industry and the services sector, stimulated by the growing mobility of the work force that was becoming increasingly concentrated in the big cities.

The crisis of the seventies, which in the case of Spain stretched well into the eighties, put paid to these upward trends, and similar rates of aggregate growth were not recorded until the final years of that decade. This new phase in Spanish economic growth was no longer linked to the leadership of industrial production, but rather to that of the services sector. In parallel to this shift in references, major changes were also seen in the spatial dynamics associated with the advances in production. In this case, the tendency was no longer that of the growing spatial concentration of industrial activity, but rather towards its more equal distribution. Directly related to this, the migratory flows within the country also underwent a change during these years.

Various studies have sought to account for the trends described above. Specifically, a number have analysed the factors determining the spatial location of industrial activity and the changes this underwent in parallel with the growing integration of the Spanish economy in the wider international economy at specific times between 1955 and 1995. The most recent of these include [START_REF] Viladecans | Agglomeration economies and industrial location: city level evidence[END_REF], [START_REF] Alonso | Agglomeration economies in manufacturing industries: the case of Spain[END_REF] and [START_REF] Paluzie | Regional integration and specialization patterns in Spain[END_REF], 2004). In all these studies, the importance of agglomeration economies is stressed. Furthermore, the relationship between the process of economic integration and the geographical concentration of industrial production seems to have followed an inverted U-shape path in the long term, i.e a first period of agglomeration was followed by a period of dispersion.1 This result would be in line with the stylized facts observed in other economies such as the U.S, as shown by [START_REF] Kim | Expansion of markets and the geographic distribution of economic activities: the trends in U.S regional manufacturing structure, 1860-1987[END_REF].

However, in [START_REF] Krugman | Increasing returns and economic geography[END_REF] seminal paper on New Economic Geography, the core-periphery model, this relationship is monotonous, and economic integration leads to catastrophic agglomeration. In more complex models, the relationship between integration and agglomeration is no longer a simple, monotonous one and the bell-shaped relationship arises. Thus, in models in which barriers to labour mobility exist, such as that developed by [START_REF] Krugman | Globalization and the inequality of nations[END_REF], economic integration can enter a final phase in which, thanks to the existence of wage differentials, the regions that were initially disadvantaged are able to reindustrialise. In fact, this bell-shaped relationship can be justified by many different theoretical principles. The most recent theoretical papers that predict it use an alternative modelling strategy: a linear monopolistic competition model that differs in the specification of preferences and transport costs from the Dixit-Stiglitz framework. Using this setting, [START_REF] Picard | Agricultural sector and industrial agglomeration[END_REF] obtain the bell-shaped prediction introducing transport costs in the agricultural good, Tabuchi & Thisse (2002) do so by introducing hetorogeneity in migration behaviour and finally, [START_REF] Ottaviano | Agglomeration and trade revisited[END_REF] obtain it by introducing urban costs associated with the formation of an agglomeration. We believe that the Spanish economy in the period 1955-1995 would be more consistent with the basic hypothesis of the model by [START_REF] Ottaviano | Agglomeration and trade revisited[END_REF].

In this model, there are two factors in the economy, a factor evenly distributed across regions and spatially immobile and a factor, manufacturing labour, mobile between the regions. The hypothesis of manufacturing labour mobility is more adequate in a regional context such as the Spanish economy in this period rather than the [START_REF] Krugman | Globalization and the inequality of nations[END_REF] hypothesis of absence of labor mobility, more appropiate in an international context.

Moreover, the introduction of urban costs is also reasonable because industrial agglomerations often take the form of an urban area, and Spain was not an exception.

The forces at work in the [START_REF] Ottaviano | Agglomeration and trade revisited[END_REF] model are similar to those found in the core-periphery model. First, the immobility of the farmers is a centrifugal force. The centripetal forces are a backward and a forward linkage generated by the preference for variety. If a larger number of firms are located in a region, less varieties are imported and the equilibrium prices of all varieties sold in this region are lower. This, in turn, induces some consumers to migrate towards this region. The increase in the number of consumers creates a larger demand for the industrial good in the corresponding region, which increases operating profits and wages and leads more firms to move there. Hence, equilibrium wages exhibit a demand linkage, which forecasts that firms will be prepared to pay higher salaries in those regions that are closest to the main markets of consumption and production. Thus, the existence of a spatial wage structure, in which the nominal wage of a region increases with its market potential, would constitute evidence of the existence of agglomerative forces operating within the country. by [START_REF] Hanson | Market potential, increasing returns, and geographic concentration[END_REF] for the US counties between 1970 and 1990, by [START_REF] Mion | Spatial externalities and empirical analysis: the case of Italy[END_REF] for the Italian provinces in the period 1991-1998, by [START_REF] Brakman | The final frontier? Border Effects and German Regional Wages[END_REF], 2003) and by [START_REF] Roos | Wages and market potential in Germany[END_REF] for the German urban districts. In each of these studies the existence of a spatial wage structure is demonstrated. 3 In this article, we analyse the existence of this relationship, as well as that of any possible variations over time, during the process of integration undergone by the Spanish economy in the second half of the XX century. 4 The case of Spain between the years 1955 and 1995 is of particular interest in that we are dealing with a period in which the Spanish economy passed from being virtually autarkic to become a European economy that was fully integrated within the international markets. This article is organised as follows. First, we undertake a brief review of the evidence describing the location of industrial firms in Spain during the period 1955-1995.

Based on this evidence and drawing on the predictions of the Ottaviano, Tabuchi & Thisse (2002) model, we establish the main hypotheses that are studied in section three. Specifically, we undertake an empirical analysis regarding the existence of a relationship that relates the nominal wages paid by the Spanish regions with their market potential, as well as of its evolution over time. Finally, in section four we present our main conclusions and discuss lines for future research.

good. So if income is high in some region, we expect the real wage to be high both because the nominal wage is high and because the price index is low. (See Fujita et al., 1999). However, in this paper we focus only on the demand linkage and thus, exclusively in the relationship between nominal wages and market potential. 3 Fingleton (2005) or López-Rodriguez and [START_REF] López-Rodríguez | Does distance matter for determining regional income in the European Union? An approach through the market potential function[END_REF] have analysed other related predictions of this kind of models such as the existence of spatial structures for regional incomes or prices. 4 In [START_REF] Paluzie | Potencial de Mercado y estructura especial de los salaries en las regions españolas, 1955-1995[END_REF], this relationship is estimated as a test of one of the predictions of the [START_REF] Krugman | Increasing returns and economic geography[END_REF]

DESCRIPTIVE EVIDENCE: ECONOMIC INTEGRATION AND THE

GEOGRAPHICAL CONCENTRATION OF SPANISH INDUSTRY

In this section we seek to present evidence regarding the integration of the Spanish market, as well as evidence regarding the evolution in the location and geographical concentration of industrial production in Spain over the last fifty years.

Table 1 describes the progress towards integration made by the internal market between 1955 and 1995 based on a monitoring of the evolution in the relative endowment of infrastructure. The association between these two variables derives from the fact that the commercial integration of the Spanish market is directly linked to a reduction in the transport and transactional costs generated by the expansion and improvement of the infrastructure stock. 7 construction of highways and motorways and telecommunication networks were the most dynamic sectors. In the last decade, however, the construction of high speed railway lines was once more an important element in the growth of Spain's infrastructure stock.

Yet, the point that we wish to stress is that the dynamics of integration of the Spanish market, measured by the evolution in the offer as regards infrastructure, clearly increased in the last forty years of the period.

Given these conditions, we now need to analyse whether the evidence concerning the evolution in the geographical concentration of industry in Spain has followed a longterm profile that is in line with the changes experienced by the process of market integration. Here, it should be recalled, that in broad terms, the increasing integration of the Spanish internal market should favour both the initial appearance of a process of geographical agglomeration of industrial production and the subsequent opening up of a process of dispersion typical of a highly developed economy. In other words, the spatial concentration of industry should describe an inverted U-shaped curve during the process of integration of the Spanish market. As can be seen, there was a growing trend in productive agglomeration during the first three cut-off points to be analysed, that is the years between 1955 and 1975. Since that date, a period of gradual reduction in the geographical concentration of industry in Spain In short, the descriptive evidence is consistent with the existence of forces favouring agglomeration in the productive processes, which in the context of a reduction of transaction costs favoured an initial increase in the geographical concentration of the activity. Furthermore, in this context, the initiation of a process of dispersion, in the midseventies, could have been caused by the appearance of urban congestion costs that ended

EMPIRICAL ANALYSIS

In this empirical analysis, we shall study the impact of market potential on nominal industrial wages during the period 1955-1995. In conducting the study we shall use wage cost figures per wage earner for the 47 mainland Spanish provinces (not including those of the islands, nor Ceuta and Melilla). This information is available for the mean of each manufacturing sector, as well as for the ten branches of manufacturing production into which the total industrial production is broken down. The original data are those provided by the Fundación BBVA for 1955, 1975 and 1995.

First, we shall analyse the descriptive evidence provided by these data. Thus, Figures 2, 3 and 4 show wages (unit labour costs) in the provinces at three key dates in the period: 1955, 1975 and 1995 respectively. Provincial wage is obtained as a weighted average of the wages of the different sectors considered. From an examination of the maps, the first common characteristic that we can deduce is that the regional distribution of wages is not by any means random. The highest wages are to be found in the provinces with the highest income and, furthermore, it can be seen that the provinces with the highest wages are, on the whole, surrounded by provinces with similar wage levels. The exception to this pattern is Madrid. We perform the Shapiro-Wilk test on normality of log (W) and on lognormality of W (years 1955, 1975 and 1995). Both normality and lognormality are clearly rejected at the 1% level for every year. We conclude that the geographic distribution of nominal wages in Spain is unlikely to be the result of a random process. (2) 0 , ዊp and ዊp are the coefficients that have to be estimated. This equation can be considered a reduced form of the equilibrium wage equation that is central to main of the NEG models, such as [START_REF] Krugman | Increasing returns and economic geography[END_REF] core-periphery model, [START_REF] Helpman | The size of regions[END_REF] and Ottaviano et al. (2002). 6 The market potential equation relates the nominal wage in region r with the income in other regions, weighted by distance, and therefore, analyses whether regional wages fall with increasing distance from the regions with high income levels. In other words, we are seeking to verify whether a spatial wage structure existed in Spain in the second half of the XX century. In addition, in the case of Spain, unlike that of the USA, the job market can be considered as having been fairly rigid during the period of analysis. Were we to detect a spatial wage structure, despite this institutional framework, this would provide important evidence in support of the existence of strong agglomerative forces.

The data corresponding to the provincial GDP figures are drawn from the estimates provided by the Fundación BBVA. The distances used are the number of kilometres by road between the provincial capitals.

Table 2 shows the results of the estimates of equation ( 2 sectors considered individually (SECTOR). The main conclusion is that, in the case of Spain, we find evidence of the existence of a spatial structure of nominal wages which is related to the market potential of the regions.

The coefficients 1 and ዊp are both positive and significantly different from 0. It should be remembered that the coefficient 1 is the effect of the market potential on the wages of a given province and that ዊp represents the effect of distance to the various markets on the wages in a given province. In these conditions, the results obtained show how a high market potential has a positive influence on nominal wages. Similarly, and in line with the theoretical hypothesis, greater distances to the markets have a negative influence on the nominal wage offered in a region.

As for the magnitude of the estimated coefficients, we find that the estimated Q 1 coefficient in 1995 is very similar to that found by [START_REF] Roos | Wages and market potential in Germany[END_REF] for German districts in 1996. The 2 coefficient is higher than that obtained by [START_REF] Roos | Wages and market potential in Germany[END_REF] ( 2 =0,028) but lower than the obtained by [START_REF] Hanson | Market potential, increasing returns, and geographic concentration[END_REF] for U.S. counties ( 2 estimates are comprised between 5.4 and 16.3). 6 The estimation of the structural wage equation is not possible as we do not have all the necessary data. However, [START_REF] Hanson | Market potential, increasing returns, and geographic concentration[END_REF] and [START_REF] Brakman | The final frontier? Border Effects and German Regional Wages[END_REF] show that the results of the reduced estimation do not differ greatly from those obtained using the structural estimation. However, interesting changes were recorded in the evolution of the values of the estimated coefficients. Specifically, a marked reduction was seen in the estimated value of the coefficient that relates wage levels with the market potential of a region in the second part of the period . These results are the opposite of those reported by Hanson (1998) Although, a plausible explanation for this result might also lie in the changes to the institutional framework of the job market after 1975, which have tended to impede the emergence of wage differentials between the regions through the application of mechanisms such as national wage fixing agreements at the sector level.

By contrast, the coefficient value that links the wage level with the distance from the region in question to the various markets was found to increase over the period of analysis. Here our findings do coincide with those reported by [START_REF] Hanson | Market potential, increasing returns, and geographic concentration[END_REF] for the US economy, but the result is still surprising given that the great improvements in infrastructure made during the period led us to expect a reduction in the economic costs of distance. [START_REF] Hanson | Market potential, increasing returns, and geographic concentration[END_REF] argues that the estimated increase in trade costs could reflect the ongoing secular shift in economic activity from low-trade-cost manufacturing to high-tradecost services.

CONCLUSIONS

In this article we have conducted an empirical analysis to determine whether one of the predictions derived from New Economic Geography holds true in the case of Spain: namely, the existence of a direct relationship between the market potential of various locations and the wage levels that the firms located there are prepared to pay their workers.

The study has enabled us to demonstrate the existence of a spatial wage structure in Spain in line with the relationship predicted in the theoretical literature, and to show that this relationship remained stable throughout the period 1955-1995. Hence, there is strong evidence on the existence of demand linkages between regions. One of the agglomeration forces present in the NEG models has been verified: a backward linkage that implies that the nominal wage of a region increases with its market potential. In the case of Spain, the establishment of institutional mechanisms such as the wage setting was not sufficient to compensate for the agglomerative forces of the market.

Moreover, the evidence presented in the paper also accords to the theoretical prediction of a bell-shape relationship between economic integration and industrial agglomeration. On the one hand, the descriptive evidence shows that since the middle of the seventies, the dominant tendency was towards that of the dispersal of the location of industrial production. On the other, in the empirical analysis, we have also shown that the effect of market potential decreased over time. A weakening of the demand linkages would be characteristic of the second fragment of a bell-shape curve linking integration and agglomeration, as predicted by [START_REF] Ottaviano | Agglomeration and trade revisited[END_REF], which in the case of Spain would have been attained from 1975 on. This process has been analysed here from the perspective of the location of firms and the wages that the latter are prepared to pay their workers. Thus, a complementary path of analysis would be to observe whether there have been any changes in the factors that account for the workers' location decisions. In other words, to study whether the change in the origins and destinations of the movements of the population responded to a variation in the factors determining the decision to migrate, or whether, in agreement with the situation described here regarding the location of firms, the shift can be accounted for by the existence of changes in the market potential of the locations following the crisis of the seventies. A study of this nature would constitute a possible future line of research.

  2

  between nominal wages and market potential has been estimated

Figure 1

 1 Figure1presents an indicator whose purpose is to describe the evolution in the geographical concentration of industry in Spain during the period of study. The indicator has been constructed from data prepared by the Fundación BBVA from the provinces and autonomous communities concerning their gross added value at factor cost. More details concerning the construction of these indicators can be found in[START_REF] Paluzie | The geographical concentration of industry across Spanish Regions, 1856-1995[END_REF].

  other words, the evidence indicates, in line with the forecasts of the[START_REF] Ottaviano | Agglomeration and trade revisited[END_REF] model that industrial agglomeration followed an inverted U-shaped path in parallel with the progress experienced by the process of integration of the Spanish market.

Figure

  Figure 1. Coefficient of industrial location (Gini)

  initiation of a process of dispersion in the advanced phase of the process of internal economic integration.

  evolution in the distribution of regional wages during the period, four elements stand out: the persistence of a cluster of very high wages in the Cantabria cornice throughout this period, the gradual strengthening of a high wage cluster along the Ebro valley from Barcelona, passing through Tarragona and Zaragoza and extending up to Navarra and the País Vasco, the presence of a wage cluster in the Mediterranean axis characterised by higher wages but with certain discontinuities caused by the differential behaviour of the provinces of Girona and Castellón, and finally a cluster of relatively higher wages in the easternmost sector of Andalucía (Sevilla-Jaén-Cádiz axis) which gradually weakened over the years.

Figure 2 .Figure 4 .

 24 Figure 2. Nominal wages in 1955 (in millions of pesetas)

  ) for threecut-off dates: 1955, 1975 and 1995, using the non-linear least squares method of estimation. Estimations the sum of the sectors (TOTAL) and for the individual data of the 10

  the evolution of the estimated models, it can be seen that the estimate for 1995 does not represent a significant change compared to the results obtained for the years 1955 and 1975. The model has considerable explanatory power throughout the whole period, which would support the maintenance of the relationship between the market potential of the Spanish provinces and the location of industrial activity in them throughout the period under review.

  the level of significance at which the null hypothesis of individual significance of the estimated parameter is rejected.(*) Includes dummy variables for the ten sectors.

  for the US economy, where the effect of market potential on wages grew during the period 1970-1990. This result points towards a weakening in the demand linkages present in the Spanish economy from 1975 on. This could constitute indirect evidence on the costs that could compensate the agglomeration forces at work.

Table 1 . Infrastructure stock as a percentage of GDP in Spain, 1955-1995 1955 1965 1975 1985 1995 Infrastructure stock /GDP (%) 33.13 29.37 32.53 41.19 56.17

 1

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

Source -

Mas et al. (1995Mas et al. ( /1998) )

A number of interesting points can be drawn from the table

.

First, what is most noticeable is that the ratio under analysis presented a long-term tendency to increase during the process of Spanish economic development. In fact, only between the cut-off points of 1955 and 1965 was a small contraction seen in the values of this indicator, associated with the rapid rate of growth in Spanish GDP. Second, in the last twenty years of this period an acceleration was recorded in the growth trend of this variable. In general, in these years, the

Table 2 . Estimation of the market potential equation (Non linear least squares)

 2

		1955	1955	1975	1975	1995	1995
		TOTAL SECTOR (*)	TOTAL SECTOR (*)	TOTAL	SECTOR (*)
	Q 0	-3.642 (0.000)		-2.458 (0.000)		0.222 (0.126)
	Q 1	0.112 (0.000)	0.120 (0.004)	0.133 (0.000)	0.139 (0.000)	0.083 (0.000)	0.097 (0.000)
	Q 2	0.077 (0.014)	0.079 (0.007)	0.087 (0.005)	0.091 (0.011)	0.102 (0.007)	0.113 (0.002)
	N R 2 adj. 0.382 47	436 0.804	47 0.251	455 0.815	47 0.161	467 0.837
	Figures in brackets show

[START_REF] Pons | Integration of markets and industrial concentration: evidence from Spain, 1856-1907[END_REF] present evidence about the origins of this long-term process during the second half of the XIXth century.

In fact, this relationship is amplified to real salaries through the forward linkage or price index effect. An increase in the number of consumers is associated with a decrease in the price index of the differentiated

Other indicators such as the Hirschmann-Herfindahl index show the same tendency (See[START_REF] Paluzie | The geographical concentration of industry across Spanish Regions, 1856-1995[END_REF]).

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

ACKNOWLEDGMENTS

The authors would like to thank the participants at the VIII Encuentro de Economía Aplicada, Murcia, June 2005, for their comments, the Spanish Ministry of Science and Technology for the financial support granted through research projects BEC2002-00423, SEC2002-03212, SEJ2005-02498/ECON and SEJ2005-03196/ECON and the Catalan government (Generalitat de Catalunya) for the grants 2005SGR-00460 and 2005 SGR 00854.

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

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