Carmelo Reverte
email: carmelo.reverte@upct.es

Isidoro Guzman

Isidoro Guzmán

PRODUCTIVITY AND EFFICIENCY CHANGE AND SHAREHOLDER VALUE: EVIDENCE FROM THE SPANISH BANKING SECTOR

Keywords: Productivity change, Malmquist index, DEA, Banking, Shareholder value

d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

I. INTRODUCTION

Given the importance of the banking sector for the whole economy in general, and for the financial system in particular, a number of studies have analysed the efficiency of this sector since the early 1990s in order to assess the impact of the structural changes to which it has been subjected such as deregulation, liberalisation, introduction of new technologies and adaptation to the European Community's directives. The main literature has focused on analysing cost efficiency, using both parametric and non-parametric methodologies (see Berger and[START_REF] Berger | Efficiency of financial institutions: international survey and directions for future research[END_REF][START_REF] Goddard | European Banking: Efficiency, Technology and Growth[END_REF], for extensive surveys). However, a criticism of these efficiency studies relates to the fact that the empirical results may not account for both technological and efficiency change through time, that is, the analyst may end up without learning whether efficiency improves or deteriorates over time if efficiency is only measured with respect to a year-specific period [START_REF] Hunter | Technological change in large US commercial banks[END_REF].

To overcome this problem, a number of recent studies have focused on investigating productivity change, mainly by employing non-parametric methodologies such as the Malmquist Total Factor Productivity Index approach (e.g., [START_REF] Alam | A non-parametric approach for assessing productivity dynamics of large US banks[END_REF][START_REF] Chaffai | Technological and environmental differences in the European banking industries[END_REF], which is calculated from efficiency scores based on Data Envelopment Analysis (DEA) linear programming approach. This Malmquist index technique enables separation of the 'catching up' or 'efficiency change' effect, i.e, changes over time in the technical efficiency of each decision making unit with respect to best practice frontier, from 'technological change', i.e., the shift of best practice frontier over time due to technological progress.

On the other hand, given the current importance of shareholder value maximisation as a key objective for financial institutions, another strand of recent research (e.g., [START_REF] Fiordelisi | Shareholder value in banking[END_REF] has analysed shareholder value in European banking and its relationship with several performance indicators (such as return on assets, economic value added, etc.). 3 However, to the best of our knowledge, so far there is no study that links the aforementioned two lines of research focused on productivity and efficiency change and shareholder value. In this regard, the main contribution of our study is to verify whether those banks characterised by higher efficiency and productivity changes have a higher shareholder value, proxied by total shareholder return [START_REF] Rappaport | Creating shareholder value: The new standard for business performance[END_REF]. This measure captures the two sources of value creation for a typical shareholder, i.e., the increase in share price during the year and the dividends received in that year.

We focus our analysis on the Spanish banking industry which, according to the Spanish Banking Association (2002), plays a prominent role in European banking based on the value of the most relevant performance ratios. Over the last two decades, the Spanish banking industry, likewise many other Western European banking systems, has experienced fascinating changes, such as deregulation, liberalisation, and technological advances, which have significantly reshaped the industry. The period under study is [2000][2001][2002][2003][2004], which follows the wave of mergers and acquisitions that took place in Spain in the 1990s.

The rest of the paper is structured as follows. Section 2 describes the methodology used for measuring productivity -the Malmquist Total Factor Productivity (TFP) Index approach-. Section 3 presents the research design including the model for measuring the performance of the banks, the selection of outputs and inputs, and the data used for the empirical application. Section 4 comprises the results of the empirical study. Finally, section 5 concludes the paper.

II. MALMQUIST TOTAL FACTOR PRODUCTIVITY INDEX

Total factor productivity (TFP) measures changes in total output relative to inputs and the concept derives from the ideas of [START_REF] Malmquist | Index numbers and indifference curves[END_REF]. The non-parametric Malmquist total factor productivity index, developed from [START_REF] Caves | The economic theory of index numbers and the measurement of input, output and productivity[END_REF], has been the most commonly used measure of productivity change (see [START_REF] Färe | Malmquist Productivity Indexes: A Survey of Theory and Practice[END_REF] for a survey) 1 . The Malmquist index is based on the output distance function which, following Shepard (1970) and [START_REF] Färe | Productivity growth, technical progress and efficiency changes in industrialised countries[END_REF], is defined as:

1 Other studies measure productivity change using a parametric approach (Berger andMester, 1999, 2001). However, [START_REF] Casu | Productivity change in European banking: A comparison of parametric and non-parametric approaches[END_REF] (1) where x t and y t denote a vector of inputs and outputs respectively, and S t represents the production technology for each reference period. The distance function is the reciprocal of [START_REF] Farrell | The measurement of productive efficiency[END_REF] measure of output technical efficiency, which calculates how far an observation is from the frontier of technology.

To define the Malmquist productivity index, it is necessary to define the above distance function with respect to two different time periods. Following [START_REF] Färe | Productivity growth, technical progress and efficiency changes in industrialised countries[END_REF], assuming constant returns to scale (CRS) and an output orientation, the Malmquist TFP index between period s (the base period) and period t is given by: greater than one will indicate a growth in productivity from period s to period t, while a value less than one will indicate a decline in productivity.

The Malmquist index formulation of productivity growth can be further broken down into the following two components:

2 / 1 0 0 0 0 0 0 0 ) , ( ) , ( ) , ( ) , ( ) , ( ) , ( ) , , , ( × × = s s t s s s t t t t t s s s s t t t t t s s y x D y x D y x D y x D y x D y x D x y x y M
(3)

during the period 1994-2000, conclude that both parametric and non-parametric methodologies do not yield markedly different results in terms of identifying the main components of productivity change. Following [START_REF] Färe | Productivity growth, technical progress and efficiency changes in industrialised countries[END_REF], we can calculate the required distance measures involved in equation ( 3) for the Malmquist TFP index by using Data Envelopment Analysis (DEA)-like linear programming techniques 3 , which is the method that will be followed in our empirical analysis. Under the assumption of CRS 4 , the DEA model can be expressed mathematically as follows:

max ) , ( , 1 0 = t t t y x D s.t. 0 0 0 + t it t it X x Y y (4)
2 A further decomposition of the 'technical efficiency change' component to take into account variable returns to scale (VRS) technology has been proposed, which distinguishes between 'scale efficiency' and 'pure technical efficiency change' [START_REF] Färe | Productivity growth, technical progress and efficiency changes in industrialised countries[END_REF]. However, this further decomposition has been subjected to a number of criticisms (see, e.g., [START_REF] Ray | Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries: Comment[END_REF]. In this regard, there seems to be consensus that the Malmquist index is correctly measured by the ratio of the CRS distance function even when the technology exhibits VRS [START_REF] Casu | Productivity change in European banking: A comparison of parametric and non-parametric approaches[END_REF]. 3 DEA is a technique originally introduced by [START_REF] Charnes | Measuring the efficiency of decision making units[END_REF] as a reformulation of [START_REF] Farrell | The measurement of productive efficiency[END_REF] efficiency measure to the multiple-output, multiple-input case. This technique has been usually applied to evaluate efficiency in different economic sectors, specially for financial institutions (see Berger and Humprey, 1997 for a survey). 4 The hypothesis of constant returns to scale was subsequently modified to allow for variable returns to scale (VRS) [START_REF] Banker | Some models for estimating technical and scale inefficiencies in Data Envelopment Analysis[END_REF], which is the most commonly used specification in the 1990s, because the CRS assumption is only appropiate when all DMUs are operating at an optimal scale. The VRS approach provides technical efficiency scores that are greater than or equal to those obtained using the CRS model. where Y t and X t represent the vector of outputs and inputs, respectively and defines the weight of each unit within the reference or 'peer' group to which is compared any particular observation in order to determine the distance to the efficient frontier5 .

III. DATA AND VARIABLES

Sample and data sources

As our dependent variable -total shareholder return-is computed from market share prices, we consider Spanish banks listed in the Madrid Stock Exchange on the fiveyear period 2000-2004, which follows the wave of mergers and acquisitions that took place in Spain in the 1990s. The list of the 14 quoted banks is reported in Table 1.

Inputs and outputs necessary to compute efficiency and productivity measures are based on data from balance sheets and income statements collected from the website of the Spanish Securities Commission (www.cnmv.es). Regarding information on share prices and dividends, data come from Global Vantage Compustat.

Table 1. Sample Banks

Input and output definition

The output and input selection becomes a crucial issue for research into cost efficiency. This is particularly true for banks as there is no agreement on appropiate inputs and outputs in this industry6 . As [START_REF] Berger | Efficiency of financial institutions: international survey and directions for future research[END_REF] point out, although there is no perfect approach, the intermediation approach may be more appropiate for evaluating entire financial institutions because this approach is F o r P e e r R e v i e w inclusive of interest expenses, which often account for one-half to two-thirds of total costs. Thus, following the empirical literature (Molyneux et al., 1996;[START_REF] Mester | A study of bank efficiency: taking into account risk-preferences[END_REF][START_REF] Yeh | The application of data envelopment analysis in conjunction with financial ratios for bank performance evaluation[END_REF], the intermediation approach is used, which views banks as mediators between the demand and supply of funds. Therefore, two banks' outputs are used in the current study: total loans (k 1 ) and interest income and commissions received (k 2 ).

In the input side, three variables are selected: total deposits (k 3 ), interests expenses and commissions paid (k 4 ) and personnel and administration expenses (k 5 ) 7 . Table 2 shows the descriptive statistics of the outputs and inputs considered in the design of performance models. = total loans; K 2 = interests income and commissions received; K 3 = total deposits; K 4 = interests expenses and commissions paid; K 5 = personnel and administration expenses (variables are expressed in million €).

IV. EMPIRICAL RESULTS

DEA efficiency scores

To get an overview of the efficiency annual measures, Table 3 summarises the results for three different models. The first one is based on the hypothesis of constant returns to scale (CRS), while the two remaining models assume the hypothesis of 7 To run the DEA model, personnel expenses and administration expenses have been summarised into a single variable in order to avoid an excessive number of inputs so that the proposed model can be accepted regarding the total number of variables [START_REF] El-Mahgary | Data Envelopment Analysis: visualizing the results[END_REF]. All models show high efficiency scores for all years, which implies that the process

Malmquist Index estimates

The results from the efficiency measures analysis support that efficiency scores have increased slightly over our sample period. However, this trend in mean efficiency values cannot be used to infer productivity growth because they do not take into account shifts in the efficiency frontier, that is, shifts in best practice firms.

Considering this issue, Malmquist total factor productivity change (TFP) is computed to evaluate productivity growth. We further decompose TFP into its two components: a) technological change (TC), or change in best practice, and b)

'catching-up' or technical efficiency change (TEC), where TFP= TC x TEC. Table 4 summarises geometric means of results for individual banks for each two-year period -also depicted in Figure 2-and the last row shows the geometric mean of the annual means for the entire period (2000)(2001)(2002)(2003)(2004). Our results reveal a productivity growth in the sample period (+2.2%), which is almost entirely attributable to technological change (+2.1%). This finding is consistent with the results obtained by [START_REF] Mukherjee | Productivity growth in large US commercial banks: The initial post-deregulation experience[END_REF] for the US case and

Malmquist Index Technical Change Efficiency Change

Relationship between shareholder value and change in efficiency and productivity

Our main hypothesis is that those banks characterised by a higher efficiency change and a higher total productivity change should have a higher shareholder value, proxied by total shareholder return [START_REF] Rappaport | Creating shareholder value: The new standard for business performance[END_REF]. Total shareholder return (TSR) is computed for each bank as follows:

1 1 + = it it it it it P d P P TSR ( 5
)
where P it is price of bank i's shares at the end of year t, P it-1 is price of bank i's shares at the beginning of year t and d it are dividends paid by bank i in year t. Therefore, TSR captures the two sources of value creation for a shareholder, i.e., the increase in share price during the year and the dividends received in that year.

To test our hypothesis, we first perform a cluster analysis in order to form two groups of banks according to their values of the technical efficiency change variable (TEC) and the total productivity change variable (TFP). Then we compute TSR for each group of banks and test whether the difference in TSR between the two groups is statistically significant. 5 reports the results for the k-means cluster analysis, which clearly differentiates two groups of banks. The first cluster includes those banks with growth in total productivity (mean TFP= 1.077) and with an increase in efficiency with respect to best practice frontier (mean TEC= 1.028), while the second cluster groups those banks with a decline in total productivity (mean TFP= 0.906) and with a decrease in efficiency with respect to best practice frontier (mean TEC= 0.945).

As hypothesised, it can be observed in Table 5 that the TSR is higher for those banks pertaining to cluster 1 (15.56%) as compared to those pertaining to cluster 2 (4.03%), this difference been statistically significant at a 5% level (p-value = 0.0223).

Therefore, it seems that the shareholder value creation is higher in those banks characterised by higher efficiency and productivity changes from one period to another. However, it could be the case that those banks with higher efficiency and productivity changes have higher shareholder returns just because they have higher return on assets (ROA) -which is a common accounting-based measure of banks' performance-. In order to verify whether our measures of efficiency and productivity change are significantly related to TSR after controlling for the effect of ROA we estimate for our panel data set the following three models: As hypothesised, ROA, technical efficiency change (TEC) and productivity change (TFP) are positively and significantly related to TSR, being the latter (TFP) the most significant variable (p-value = 0.010). Moreover, the two variables analysed in our study (TEC and TFP) are significantly associated with TSR, even after controlling for the effect of ROA, leading to an increase in R 2 of 3.58% and 9%, respectively with respect to model ( 6). Therefore, we can assert that the impact of both efficiency and productivity change on shareholder value does not stem from the fact that they are subsumed in the ROA standard measure of banks' performance. Moreover, as expected, the total productivity change variable (TFP) has a greater impact on TSR with respect to the efficiency change variable (TEC) (0.54 vs 0.43), since the former also takes into account technological change over time.

TSR it = 0 + 1 ROA it + u it (

V. CONCLUSIONS

Productivity change and shareholder value have been analysed in the banking sector in the last few years. However, these two important aspects have been studied

separately. In this regard, the main contribution of our study is to link these two lines of research by verifying whether those banks characterised by higher levels of Our results for a sample of listed Spanish banks in the period 2000-2004 confirm that those banks with higher efficiency and productivity changes have a higher shareholder value, even after controlling for the impact of traditional measures of performance, such as return on assets.

  of productivity change is referred to as technical efficiency change 2 , and measures the change in the efficiency of a decision making unit (DMU) relative to the best practice frontier, that is, the change in the DMU's distance to the production frontier. Thus, a value greater than 1 indicates an increase in the efficiency relative to the frontier while a value less than 1 indicates a decline in efficiency with respect to the frontier. The second component, referred to as technological change, is due to the variation of the production frontier between two periods, and thus reflects the improvement or deterioration of best practice DMUs. A value greater than 1 indicates technological progress while a value less than 1 means technological deterioration. The usefulness of this decomposition is that it provides information on the sources of the overall productivity change.

  scale (VRS) under a double specification (input/output orientation). A value of the DEA score equal to one means efficiency while a value less than one means inefficiency.

  of mergers and acquisitions occurred in Spain during the 1990s have resulted in an improvement of the efficiency of the resulting banks. For the CRS model, the average efficiency score shows a slight improvement in the efficiency level over time (+0.5%). Regarding the VRS model, the improvement in the efficiency level over time is very similar (+ 0.8% for the output-based model and +1.1% for the inputbased model). Figure1depicts the evolution of DEA efficiency scores over the sample period.

Figure

  Figure 1. DEA efficiency Scores (2000-2004)

Asmild

  Figure 2. Malmquist Index descomposition in S panishBanks (2000Banks ( -2004) )

Table 5 .

 5 Total shareholder return differences between banks grouped according to technical efficiency change (TEC) and total productivity change (TFP) by means of a of means differences in TSR across the two clusters= 0.0223

  change have a higher shareholder value, proxied by total shareholder return[START_REF] Rappaport | Creating shareholder value: The new standard for business performance[END_REF]. To measure changes in efficiency and productivity we use the Malmquist non-parametric technique, which is calculated from efficiency scores based on Data Envelopment Analysis (DEA) linear programming approach. This Malmquist index technique enables separation of the 'catching up' or 'efficiency change' effect, i.e, changes over time in the technical efficiency of each decision making unit with respect to best practice frontier, from 'technological change', i.e., the shift of best practice frontier over time due to technological progress.

  , in their analysis of productivity change in European banking

	D	t 0	(	x	t	,	y	t	)	=	inf{	:	(	x	t	,	y	t	/	)	S	t	}
	F o r
					P
								e
										e r
												R e v
																	i e
																			w
																							4

Table 2 . Summary Descriptive Statistics for outputs and inputs

 2

			K 1	K 2	K 3	K 4	K 5
	2000	Mean	28348	4752	29260	2811	1138
		Median	4805	412	4493	196	143
		St.dev.	53862	10363	56719	6547	2348
	2001	Mean	30825	5076	31810	2728	1293
		Median	5547	498	4840	243	152
		St. dev.	56461	10716	60854	6022	2689
	2002	Mean	30513	4277	29638	2078	1158
		Median	6340	473	5223	172	159
		St. dev.	52712	8792	54833	4533	2319
	2003	Mean	33452	3527	29411	1509	1044
		Median	7754	480	5886	142	162
		St. dev.	55554	6875	52174	3171	2033
	2004	Mean	49141	3730	40808	1581	1083
		Median	9866	550	6958	163	171
		St. dev.	93430	7222	82097	3312	2072
	Notes: K 1

Table 3 . DEA efficiency scores Constant returns to scale Variable returns to scale

 3

	Output-based	Input-based

Table 4 . Malmquist Index decomposition (summary of annual geometric means)

 4

		Technical	Technological	Total Factor
		Efficiency Change	Change	Productivity Change
	Years	(TEC)	(TC)	(TFP)
	2000/2001	1.034	0.989	1.022
	2001/2002	0.976	1.049	1.024
	2002/2003	0.990	1.068	1.058
	2003/2004	1.006	0.981	0.987
	2000/2004	1.001	1.021	1.022

Table 6 . Panel data estimation of models (6), (7) and (8).

 6 TSR it : Total shareholder return of bank i for period t; ROA it : Return on assets of bank i for period t; TEC: Technical efficiency change of bank i for period t; TFP: Total productivity change of bank i for period t. The results correspond to the random effects specification since the[START_REF] Hausman | Specification tests in econometrics[END_REF] test indicates that this specification is better than the fixed effects model for the three models.

	6)

Notes: Figures in parentheses represent

the p-values. (*)Significant at the 10% level; (**)Significant at the 5% level;(***)Significant at the 1% level.

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

Banco Bilbao Vizcaya Argentaria, SA Banco de Andalucía, SA Banco de Castilla, SA Banco de Crédito Balear, SA Banco de Galicia, SA Banco de Sabadell, SA Banco de Valencia, SA Banco de Vasconia, SA Banco Español de Crédito, SA Banco Guipuzcoano, SA Banco Pastor, SA Banco Popular Español, SA Banco Santander Central Hispano, SA Bankinter, SA

The DEA models can take two different orientations. The first one, called input orientation, seeks to identify technical inefficiency as a proportional reduction in inputs usage. The second one, referred to as output orientation, seeks to identify technical inefficiency as a proportional increase in output production. To date, the theoretical literature is inconclusive as to the best choice among these two alternatives. These two orientations yield equal values under CRS, but not when VRS is assumed[START_REF] Thanassoulis | Introduction to the Theory and Application of Data Envelopment Analysis[END_REF].

The most debated issue regards the role of deposits. Under the production model, which views banks as service-producing organisations, deposits are considered as an output. Under the intermediation approach, banks are viewed as financial intermediaries whose primary business is to borrow funds from savers and lend those funds to customers to obtain profits. Hence, in this case deposits are regarded as inputs.

where:

TSR it : Total shareholder return of bank i for period t.

ROA it : Return on assets of bank i for period t. TEC: Technical efficiency change of bank i for period t.

TFP: Malmquist's total productivity change of bank i for period t.

We hypothesise that ROA, TEC and TFP should be positively and significantly associated with TSR. Table 6 shows the results from the estimation of models ( 6), ( 7) and ( 8). As we work with panel data, we can have two alternative specifications, i.e., the fixed effects model and the random effects model. The former assumes differences in intercepts across firms, whereas the latter assumes differences in the error term. The [START_REF] Hausman | Specification tests in econometrics[END_REF] specification test compares the fixed versus random effects under the null hypothesis that the individual effects are uncorrelated with the other regressors in the model. If the null is rejected (i.e., there is correlation among individual effects and regressors), the random effect model produces biased estimators, so a fixed effect model is preferred. In our case, the Hausman test (1978) reveals that the random effects specification (which is estimated using Generalized Least Squares) is better than the fixed effects specification for the three models.


Angeles Mª

Díaz

Rosario Sánchez
email: rosario.sanchez@uv.es.

Keywords: labour market, gender, temporary contract, permanent contract, panel data labour market, gender, temporary contract, permanent contract and panel data JEL: J21 and J29

In this paper we analyse the determinants of temporary employment through a balanced panel of workers from 1995 to 2000. Firstly, we estimate a panel with 1267 individuals with ages ranging from 16 to 65 years. We obtain that the probability of having a temporary contract increases for people younger than 46 years old. Secondly, we estimate separately the sample of people younger than 46 years old and we obtain that the probability of temporality increases for young people with university level of education. More interestedly, the probability of being in a temporary contract is smaller for young women that for young men in Spain.

Introduction

The persistence of the Spanish unemployment spurred, in 1984, the implementation of a major reform package to enhance labour market flexibility and to help entrance to the labour market 1 . One of the most labour market innovations was the modification of temporary contracts that allow firms to hire employees performing regular activities. Before this, permanent work contracts represented 90% of all contracts, with the remaining temporary contracts being mainly of seasonal nature. These contracts became attractive to employers because of both, their short duration and low severance payment. Consequently, from these greater incentives, the new temporary contracts immediately enjoyed a degree of success that far exceeded expectations.

The difference in severance payments between temporary and permanent contracts is a key for understanding its success (see [START_REF] Dolado | Drawing lessons from the boom of temporary jobs in Spain[END_REF]. The number of temporary contracts was disproportional after the reform of 1984[START_REF] Bentolila | Lessons from flexibility in Spain[END_REF], 1997[START_REF] Amuedo-Dorantes | From temp to perm, promoting permanent employment in Spain[END_REF] took place new reforms, trying to achieve a more balanced situation by restricting the use of temporary contracts and by reducing mandatory firing costs under new permanent contracts. Afterwards, these reforms were not effective to reduce the rate of temporality that still remains around the level of 30%.

1 For a deeply analysis of the legal framework of Spanish' temporary employment growth see [START_REF] Amuedo-Dorantes | From temp to perm, promoting permanent employment in Spain[END_REF]; [START_REF] Dolado | Drawing lessons from the boom of temporary jobs in Spain[END_REF] and Segura (2001). Several studies carried out for the Spanish case show that the average productivity of temporary workers is lower than that of permanent ones. For instance, Sánchez and Toharia (2000) find evidence in favour of this hypothesis. This evidence is explained by the negative effect over the worker's effort caused by differences in wages. In addition, [START_REF] Diaz-Mayans | Temporary employment and technical efficiency in Spain[END_REF] obtain, through a stochastic frontier analysis, that the high percentage of temporary contracts has affected negatively productivity. [START_REF] Thurow | Generating inequality[END_REF] model of job competition assumes that productivity is associated to jobs, not to workers. There are jobs with higher productivity than others, and firms allocate workers to jobs according to their educational profile. As employers rarely have direct evidence of the specific training costs for specific workers, they end up by ranking workers according to their educational attainment. In this sense, if job productivity is related with jobs as well as workers, in Spain the high augment of "bad jobs" could explain the negative effects in productivity. Also, Rodríguez-Gutierrez (2006) identifies the main determinants of the proportion of temporary workers at firms and show that this proportion has a countercyclical behaviour.

We built a balanced panel of 1267 workers from 16 to 65 years old to estimate a random effect probit model to analyse the determinants of the temporality in Spain. We obtain that temporality increases for employees younger than 46 years old. Given that temporality affects, over all, people younger than 46 years old we estimate separately two balanced panel of individuals, one for people younger of 46 and other for people older than 45 years old. We have found significant differences between those groups in gender, in education and in sectors of activity. To be a woman younger than 46 reduces the probability of temporality. This result is related with the sample selection imposed to obtain a group of people that works six years without any unemployment spell. It is more difficult for women than for men to persist in employment given the women's higher unemployment rate. Therefore, we can say that the young women of our sample have strongly succeeded in the labour market. Other interesting result is that young people with university level of education increases the probability of temporality. As we will show later, this result could be explained by the fact that during this period most new contracts were temporal. This new contract framework has affected young workers that face different labour market legislation than that found by people who arrived to the labour market before 1984. Firstly, because they faced a labour market with a job creation focused mainly in temporary contracts and secondly, because the labour market reform of 1997 introduced a new permanent contract with lower firing cost and one of the target groups were workers older than 45 years old. Therefore, it explains our findings that workers under 45 have a higher incidence of temporary work. The Spanish legal framework, after the labour market reforms, have developed a broad set of rules promoting permanent contracts as, for example, the existence of legal restriction to the excessive renewing of temporary contracts for the same workers, and some economic incentives to convert temporary contracts in permanent ones.

Even though the existence of a legal constraint 2 , in our sample, the average number of years with the same employer for temporary workers exceed the three 2 It is not allowed to renew a temporary contract to the same worker after three years. The paper is organised as follows: In section 2, we show the data and variables. Section 3 provides the discussion of results. Finally, in section 4 we present the concluding remarks.

Data and Variables

Data from the ECPH for Spain, conducted by the Spanish National Institute of Statistics (INE), is used to estimate three random effect Probit models to investigate the determinants of temporary work. We analyse a balanced panel of 1267 wage earners currently working 15 or more hours per week, from 1995 to 2000. This is a sample of people, which are employed during the six years of analysis. In the ECPH survey there are a number of questions (PE001A, PE002A, PE011, PI211M, PU001, and PU002) that allow us to discriminate between workers that have experienced some unemployment spell and those that have been working six years without interruption. Firstly, through variables of activity, and secondly, checking that individuals have obtained a monthly wage without interruption in this period.

The percentage of temporary workers in 1995 was of 23.8% while in 2000 it diminished until 10.4%. From these 1267 individuals we have 275 women, which represent the 21.7%. We have made this sample selection because we are interested in analysing the determinants of people that remain in temporary jobs even though they are working enough time to be in a permanent position. Table 1, shows the share of temporary and permanent workers included in the sample. From 1995 to 2000, 170 temporary workers have obtained a permanent contract, while 132 workers remain in a temporary contract. The 75% of these 132 temporary workers have seniority with their employers from 5 to 9 years while the 21.5% have seniority higher than 10 years. From this results we infer how difficult is to understand the concept of temporality in Spain.

When we restrict the sample to those individuals younger than 46 years old, the percentage of temporary workers rise to 30% in 1995 while in 2000 the percentage diminished until 11.4%; both percentages are higher than that obtained for the whole sample. The younger have a high proportion of temporality. Here we have 159 women and 552 men; then women represent the 22.36% of the sample.

Analysing the distribution of education by type of contract the sample shows that the 27.6% of permanent workers had university level in 1995 while from second level of secondary education and 12.9% from university level. The percentage of people with university education that became permanent is smaller than that obtained in other levels of education.

When we analyse the distribution of temporary and permanent workers by education for the restricted sample of individuals younger than 46 years old, we obtain that the percentage of workers with university level in 1995 rise to 26.5% while in 2000 it was of 24.9%. Along the period, the percentage of university studies in permanent contracts was smaller than that of the whole sample analysed above.

Concerning the distribution of education between men and women for workers younger than 46 years old, we obtain that 40.25% of women have a university level of education while only the 18.12% of men have this level. The condition of been during six years in a job, is stronger for women than for men, given the existence of gender discrimination in the labour market. Thus, we infer that we have made a positive sample selection bias in favour of women4 , and only the most qualified have survive to unemployment. Age4: This variable takes value 1 if the individual is from 56 and more years old and 0 otherwise.

Gender:

Women: This variable takes value 1 if the individual is a woman and 0 if it is a man.

Marriage:

Married: This variable takes value 1 if the individual is married and 0 otherwise.

Income:

(2004) for a good theoretical framework about gender discrimination and intergenerational Here we will analyse the results obtained by the estimation of three samples. Firstly, we will explain the results of the whole sample (Table 2), and secondly, we will make a sample selection to analyse separately the results obtained for individuals younger than 46 and older than 45 years old (Table 3).

The tables with the results of the estimated coefficients appear in the Appendix.

The variables used for estimation have been described in section 2.

The whole sample

We have estimated a balanced random effect Probit model to study the determinants of temporary contracts. In Table 2 of Appendix, we present the results. The dependent variable is a dummy variable that takes value one if the individual has a temporary contract, zero if the contract is permanent.

The age is a variable that affects the probability of been temporal. To be younger than 46 years old increases this probability with respect to be older.

There are not significant differences between men and women in temporality.

In addition, to be married reduces the probability of temporality with respect other situations as single or divorced.

As higher is the monthly net wage, smaller is the probability of temporality. Even though we have a significant coefficient, the impact of this variable is very small because its value vanishes to zero.

To belong to a firm with more than 500 workers reduces the probability of temporality with respect to a firm from 101 to 499 workers. The result, in terms of education, indicates that individuals with secondary or university education do not seem to make difference with respect individuals with primary education. When we analyse this sample with population from 16 to 65 years old of stable workers, the education level does not present significant differences in the probability of obtaining a permanent contract. This result reinforces the finding that temporary contracts have been used for all type of jobs.

Therefore, in Spain, in the last twenty years, the most part of job creation have been oriented to "bad" jobs. Consequently, the Spanish productivity has been one of the lowest of the European Union.

To work in sectors as chemical products, metallurgy, construction, hotels and transportation increases the probability of temporality with respect the financial management sector; while to work in sectors as real states activities, textile and clothing, fishing and agriculture or education do not present significant differences with respect to work in the financial management sector.

We have obtained significant differences before and after the reform of 1997. The probability of temporality augment in 1995 compared with 1997, which is the year of the second labour market reform and is the category of reference.

However, we do not found significant differences between the year 1998 and 1997, while in the years 1999 and 2000 the probability of temporality decreases with respect 1997. In this section, we will compare the results obtained from the estimation of two samples corresponding to individuals younger than 46 and older than 45, shown in Table 3.

As we have mentioned previously, temporality affects essentially young people. We have obtained that the probability of temporality, for younger than 25, is larger than for workers older than 24 years old.

When the sample is restricted to individuals younger than 46 to be women reduce the probability of been in a temporary contract. In Spain to persist in a job is difficult for women. So, in this selection we have the most qualified women as we have analysed in previous section. In fact, the percentage of university studies is higher for women than for men. In addition, we have to remark that since 1994 there have been some fiscal incentives to promote the transition of temporary to permanent people for some population groups and females being among these.

As higher is the monthly net wage lesser is the probability of temporality.

This result is related with the Spanish labour market segmentation between "good" and "bad" jobs. In fact, one third of workers are under very flexible employment contracts with low wages and low severance payment and two-thirds are under permanent employment contracts with high wages and very high employment protection.

For individuals with university degree the probability of been temporal is higher than for people with primary education, for the sample of younger than 46 years old. [START_REF] Casquel | The causes and consequences of temporary jobs in Spain: a theoretical-empirical approach[END_REF] have developed a matching model built on 15 [START_REF] Blanchard | The perverse effects of partial labor market reform: fixed duration contracts in France[END_REF]Landier (2002) andWasmer (1999). They have obtained, that for some type of workers "high productivity workers" temporary jobs act as stepping stones to permanent jobs, whereas other type of workers "low productivity workers" get stuck in temporary jobs. However, we have obtained, in a framework of stables workers, that people with university education get stuck in temporality. Even more, the percentage of conversion from temporary to permanent contracts is higher for workers with other levels of education below to that of university.

As we have mentioned in previous sections, after 1994, the new jobs' creation were essentially temporal. This restriction, have forced young individuals to start in the labour market with a temporary contract. The young people are more educated than older, usually with permanent contract because they came from other type of labour market regulation. In this context firms could choose the best workers from a pool of workers than exceed firm's labour demand. This type of policy has generated serious consequences on productivity, and, consequently, has reduced incentives for education in the long run.

The probability of temporality increases for jobs that belong to the industrial sectors of Fishing and Agriculture, Chemical products, Building and Hotels with respect to the management sector, for both samples. However only for the sample of workers younger than 46 to work in sectors as Food and drinking, Metallurgy, manufacturing and Government augment the probability of temporality with respect the management sector. The reform of 1997 has reduced the share of temporary jobs in private sector, but such a reduction was partially Finally, when we analyse the effect of the labour market reform of 1997 we observe that it was effective in the sample of younger than 46 while for people older than 45 years old there are not significant differences. This result reinforce our previous idea that young workers were the more affected by the change in labour market regulation. They were the most damaged by the use and abuse of temporary contracts and after the 1997 labour market' reform, they have been benefited by the restriction imposed by law and, overall, by the introduction of a new permanent contract. In any case, the reduction in temporality measured around 2 or 3% has questioned the efficacy of these labour market reforms. We have to point out that the temporality rate in Spain in 2006 is still around the 30%.

This result together with that obtained for the sectors of activity, show the segmentation of the Spanish labour market.

Concluding remarks

Temporary contracts have been used in Spain as a way to enter in the labour market. Therefore, as the old permanent contracts coexist together with those temporary modified after the reform of 1984, we have found that older and less educated individuals are more probable to have a permanent contract than young people, which are on average more educated. From a panel of working people with an age ranged from 16 to 65 years old, the gender does not affect to the probability of been temporal. However when we restrict the sample to individuals younger than 46 years old, to be women reduces the probability of temporality with respect to be men. Our selection implies a sample of young workers that are employed during six years without interruption. In Spain to persist in a job is difficult for women. So in this selection we have the most qualified women. In fact the percentage of university studies is higher for young women than for young men. In this sense we could conclude that is more difficult for Spanish women to obtain a job and also to keep it, than to obtain a permanent contract.

We have shown that the probability of temporality in Government increases with respect to the financial management sector for young people. After the 1997 labour market reform the temporality share augments in the public sector. This policy affects mostly the new entrants in the labour market that were predominantly young people.

Our results have shown that temporary contracts have been used in Spain for jobs and in sectors where the long run relations were important to enhance productivity. Now we need a more balanced situation to avoid the disincentives of a high rate of temporality in young entrants in the labour market. Age1 (16-24) 0.8293* 2.536 0.0222 ------------------------------Age4 (more than 56) ------------------------------0.0092 0.074 0.2807

F

Gender: the category of reference is man.

Woman

  law, showing that the Spanish firms have been making other arrangements with workers to avoid legal constraints.

  three Random Effect Probit model to study the differences, in terms of temporality, between people younger than 46 and people older than 45 years old. The dependent variable used for estimation is TC, temporary contracts, that takes value 1 when the individual has a temporary contract, 0 if the contract is permanent. This variable takes value 1 if the individual is from 16 to 24 years old and 0 otherwise.Age2: This variable takes value 1 if the individual is from 25 to 45 years old and 0 otherwise.Age3: This variable takes value 1 if the individual is from 46 to 55 years old and 0 otherwise.

  The monthly net wage.Number of workers of the firm:Less than 50 workers: This variable takes value 1 if the individual works for a firm with less than 50 workers and 0 otherwise. From 51 to 100: This variable takes value 1 if the individual works for a firm from 51 to 100 workers and 0 otherwise. From 101 to 499: This variable takes value 1 if the individual works for a firm from 101 to 499 workers and 0 otherwise. More than 500: This variable takes value 1 if the individual works for a firm with more than 500 workers and 0 otherwise. Education Classification: Primary: Takes value 1 if the individual has primary education and zero otherwise. Secondary 1st cycle: Takes value 1 if the individual has the first level of secondary education and vocational and zero otherwise, this education is obligatory. Secondary 2nd cycle: Takes value 1 if the individual has the second level of secondary education and vocational and zero otherwise. University: Takes value 1 if the individual has completed university education (three or five years) and zero otherwise. transmission of preferences.

  Takes value 1 if the individual works in this sector of activity and zero otherwise. Extractives industries: Takes value 1 if the individual works in this sector of activity and zero otherwise. Food, drinking and tobacco: Takes value 1 if the individual works in this sector of activity and zero otherwise. Textile, clothing and leather: Takes value 1 if the individual works in this sector of activity and zero otherwise. Wood and paper except furniture: Takes value 1 if the individual works in this sector of activity and zero otherwise. Chemical product, couch and plastic, non-metallic mineral products: Takes value 1 if the individual works in this sector of activity and zero otherwise. Metallurgy; fabricated metal products, industrial equipment: Takes value 1 if the individual works in this sector of activity and zero otherwise. Manufacturing of electronic material, office machinery: Takes value 1 if the individual works in this sector of activity and zero otherwise. Construction: Takes value 1 if the individual works in this sector of activity and zero otherwise.Retail industry: Takes value 1 if the individual works in this sector of activity and zero otherwise.

  Takes value 1 if the individual works in this sector of activity and zero otherwise.Transportation, telecommunications: Takes value 1 if the individual works in this sector of activity and zero otherwise.

Financial management :

 management Takes value 1 if the individual works in this sector of activity and zero otherwise. This is the category of reference.Real state activities:Takes value 1 if the individual works in this sector of activity and zero otherwise. Government administration: Takes value 1 if the individual works in this sector of activity and zero otherwise. Education: Takes value 1 if the individual works in this sector of activity and zero otherwise. Sanity service: Takes value 1 if the individual works in this sector of activity and zero otherwise. Other social activities: Takes value 1 if the individual works in this sector of activity and zero otherwise Private Sector: This variable takes value 1 if the individual works in the private sector zero otherwise.Language: Takes value 1 if the individual needs a foreign language in its job and zero otherwise.

  increase of the share in the public sector. The reasons are related to fiscal consolidation and to the implementation of active labour market policies that affects especially young workers that are the new entrants in the labour market.

TABLE 1 :

 1 DISTRIBUTION OF TEMPORARY AND PERMANENT WORKERS BY until the 25.4% in 2000. From 1995 to 2000, one hundred and seventy temporary workers became permanent. The 14.1% came from primary education, 44.7% from first level of secondary education 3 , 28.2%

	YEARS
	YEARS	TEMPORARY	PERMANENT		TOTAL
		Whole	Young	Whole	Young	Whole	Young
	1995	302	213	965	498	1267	711
	1996	262	175	1005	536	1267	711
	1997	218	140	1049	571	1267	711
	1998	192	119	1075	592	1267	711
	1999	134	84	1133	627	1267	711
	2000	132	81	1135	630	1267	711

Marriage the category of reference is not married (single, divorced.)

	Wasmer, E. (1999), Competence for jobs in a growing economy and the
	emergence of dualism. Economic Journal, 109, pp. 349-371. 6. Appendix: Maximum Likelihood Estimates of Random Effects
	Probit Model
	TABLE 2: RANDOM EFFECT PROBIT MODEL OF TEMPORALITY (1995-2000)
	Variable	Coefficient	T-Value	Mean of variable
	Constant	-0.9933 *		-3.831
	Age of individuals the category of reference is Age 3 (46-55) years.
	o r F o r F o r 1.388 * 0.6915 * F Age4 (more than 55) Age1 (16-24) Age2 (25-45) -0.0758 o r Gender the category of reference is woman	5.399 5.233 -0.426	0.0125 0.6390 0.0738
	Woman	P P P P -0.1548	-1.054	0.2170
	Married Monthly net wage	-0.2889 *	e e r e e r e e r e -2.865 e r	0.7499
	Wage	-0.00009*	R e v R e v R e v v e R -3.831	195527.97
				i e i e i e i e
				w w w w
				20 21

Average number of workers by firm, the category of reference is from 101 to 499.

	Less than 50	-0.467	-0.475	0.4672
	From 51 to 100	0.0968	0.802	0.0949
	More than 500	-0.3174*	-2.281	0.1793

Education, the category of reference is EDU1: Primary education.

	Secondary 1º cicle	0.0316	0.259	0.2853
	Secondary 2º cycle	-0.1509	-0.936	0.2139
	University level	0.3611	1.833	0.2382

Industrial sector classification, the category of reference is Sec13 (Financial Management)

	Hotels…	0.8143*	3.102	0.0399
	Transportation…	0.4658*	2.013	0.0656
	Real state activities	-0.3648	-0.943	0.0602
	Government	0.3331	1.421	0.0538
	Education	-0.2060	-0.974	0.1306
	Sanity	0.0068	0.023	0.0578
	Other social activities 0.1325	0.655	0.0843
	Private Sector,
	F o r
		P
		e
		e r
			R e v
			i e
			w
	Fishing and agriculture 0.4321	1.799	0.0322
	Extractive industries	0.3956	1.266	0.0263
	Food, drinking…	0.3964	1.498	0.0377
	Textile, clothing….	0.4363	1.159	0.0161
	Wood and paper…	0.1027	0.283	0.0292
	Chemical products	0.7816*	3.093	0.0418
	Metallurgy…	0.6469*	2.738	0.0567
	Manufacturing…	0.1462	0.610	0.0494
	Building…	1.780*	8.963	0.0895
	Retail industry	0.3893	1.814	0.0957
			22

the category of reference is public sector.

	Private Sector	-0.1606	-0.118	0.6852
	Time,

the category of reference is 1997 (the year of the second labour market reform).

	1995	0.3639*	3.665	0.1667
	1996	0.2023	1.806	0.1667
	1998	-0.0817	-0.721	0.1667
	1999	-0.4197*	-3.584	0.1667
	2000	-0.3371*	-3.414	0.1667
	Language,

category of reference does not need foreign language in the job.

	F o r
	Foreign Language	-0.0303 P	-0.193	0.0947
	Rho	0.6285*	e	24.465
	Statistics N		1267 individuals r e	7602 observations
	Log likelihood function Restricted log likelihood Chi-squared Note: the coefficient with * are significant at one percent. -1864.075 -2247.634 767.1174 R e v
				i e
				w

TABLE 3 :

 3 RANDOM EFFECT PROBIT MODEL OF TEMPORALITY (1995-2000)

	Variable	YOUNGER THAN 46		OLDER THAN 45
		Coefficient T-Value Mean	Coefficient T-Value Mean
	Constant	-0.0107	-0.045	-0.3655	-0.815

Age of individuals the: category of reference is Age 2 (25-45) years old for younger than 46. For older than 45 the category of reference is age 3 (46-55).

Marriage the category of reference is not married (single, divorced.)

		-0.3334* -3.372	0.2273	0.2236	1.289	0.1951
	Married	-0.5441* -6.580	0.7022	0.1920	0.917	0.8173
	Monthly net wage
	Wage	-0.0001* -12.850	180989.65 -0.0001* -5.261	208211.68

Average number of workers by firm, the category of reference is from 101 to 499.

	Less than 50	0.0475	0.5138	0.5054	-0.1852	-1.003	0.4559
	From 51 to 100	0.0870	0.1211	0.0893	0.2182	0.704	0.0915
	More than 500	-0.1178	-0.709	0.1579	-0.4854	-1.017	0.1952
	Education,

the category of reference is EDU1: Primary education.

	Secondary 1º cycle	0.0796	0.811	0.3413	-0.2198	-1.201	0.1751
	Secondary 2º cycle	-0.0487	-0.372	0.2480	0.1980	0.853	0.0955
	University level	0.6037*	3.779	0.2445	-0.0501	-0.146	0.2237

Industrial sector classification, the category of reference is Sec13 (Financial Management)

	F o r
		P
		e
			e r
				R e v
					i e
	Fishing and agriculture 0.7313* Extractive industries 0.3682	3.091 1.167	0.0309 0.0295	0.6375* -5.782 w 2.970 0.001	0.0405 0.0185
	Food, drinking…	0.7465*	2.837	0.0389	-0.2435	-0.453	0.0455
	Textile, clothing….	0.4468	1.525	0.0159	0.6202*	2.046	0.0145
	Wood and paper…	0.5194	1.462	0.0236	-0.1878	-0.330	0.0370
	Chemical products	1.0113*	4.003	0.0321	0.6814*	2.854	0.0610
	Metallurgy…	0.7725*	3.229	0.0539	0.6642	1.747	0.0580
	Manufacturing…	0.5275*	2.024	0.0445	-0.2384	-0.436	0.0455
	Building…	2.040*	10.082	0.0895	1.5227*	7.584	0.0905
	Retail industry	0.5908*	2.953	0.1148	0.2276	0.724	0.0680
	Hotels…	0.6862*	2.810	0.0473	1.0202*	4.078	0.0285
	Transportation…	0.5245*	2.465	0.0682	0.3938	1.194	0.0620
	Real state activities	0.0226	0.0643	0.0635	-6.295	0.000	0.0435
	Government	0.5524*	2.367	0.0496	-0.0057	-0.015	0.0560
	Education	-0.0986	-0.4665	0.1303	0.2793	0.942	0.1401

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

In the secondary education variable is included the vocational studies, focused in specific subject, more devoted to the labour market.

Notice that in Spain the women unemployment rate is twice that of men. Usually, women have alternated temporary contracts and unemployment periods and then only the most qualified women have been in employment during the six years of our sample. See Escriche, Olcina and Sanchez

This study has been financed by the project of Science and Technology GV04B-086

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

Note: the coefficient with * are significant at one percent, those with ** are significant at five percent.