Spain: Selected Issues-Labor Market Polices and Unemployment Dynamics

International Monetary Fund

doi:10.5089/9781451812077.002.A001

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Spain

Selected Issues-Labor Market Polices and Unemployment Dynamics

Author: International Monetary Fund

Publication Date:: 08 Jul 1996

eISBN:: 9781451812077

Language:: English

Keywords:: ISCR; CR; employment equation; replacement ratio; equation dynamics; wage growth; reservation wage; policy model; real wage equation; wage determination equation; wage shock

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This Selected Issues paper analyzes labor market policies and unemployment dynamics in Spain. It provides a brief overview of economic developments and the institutional evolution of the Spanish labor market. The paper presents a basic model of the labor market, which is estimated for 1971–93. The regression results establish the basic underlying relationships among employment, labor force participation, and real wages. They also illustrate the importance of lags in the econometric specification and allow the identification of key structural changes in the labor market.

Abstract

1. Introduction

No country in Europe has as great an unemployment problem as Spain. From below 5 percent in the mid-1970s, the unemployment rate has peaked at above 20 percent in each of the last two economic slowdowns, without dropping below 15 percent in times of strong growth. From an analytical standpoint, the Spanish case is a fascinating extreme example of the pan-European unemployment problem. From the policy perspective, it is essential to understand and attack labor market problems successfully in Spain if the EU unemployment crisis is to be tackled, especially since the number of jobless in Spain in 1995 was higher than in the much larger EU countries of France, Italy, and the U.K., and nearly as high as Germany.

Broadly speaking, two competing strains of thought have existed in analyses of European unemployment over the last 20 years. One approach is to focus primarily on cyclical factors in generating unemployment, the implication being that macroeconomic shocks have caused unemployment to deviate from a (low) “natural” or non-accelerating inflation rate of unemployment (NAIRU). ^1/ Studies in this vein look to a series of adverse macroeconomic shocks to explain the high and persistent unemployment rates in Europe since the 1970s. The oil crises of the 1970s and the recession of the early 1990s are seen as triggers for increased European unemployment, exacerbated by high real interest rates which reduced investment. ^2/ At the other extreme is the hysteresis theory invoked by Blanchard and Summers (1986) and others which argues that most of the unemployment increase is due to an increase in the NAIRU rather than in deviations therefrom. Indeed, in its most extreme form, hysteresis implies that every change in unemployment becomes an equilibrium, as structural features of the labor market translate temporary shocks into permanent changes in the natural rate of unemployment.

In Spain, where unemployment has not only shown large cyclical swings (rising nearly 9 percentage points during the last recession), but has also demonstrated remarkable persistence at very high levels, the traditional NAIRU concept loses much of its usefulness. Can one really argue that an estimated NAIRU of 18-20 percent (as some economists have recently calculated) is a meaningful indication of what unemployment rate is “natural” for Spain? At the same time, however, the full hysteresis argument ignores the undeniably large cyclical movements in unemployment, while implicitly arguing for an even higher (albeit path-dependent) natural rate of unemployment.

For these reasons, the analytical approach taken in this paper is something of an intermediate position between the extreme NAIRU view that unemployment has a clearly defined (relatively low) equilibrium rate to which it returns after macroeconomic shocks, and the extreme hysteresis view that unemployment is a random walk, with the equilibrium rate equal to the current unemployment rate in each period. A simple three equation model of the labor market--a labor force equation, wage determination equation, and an employment equation is presented. By permitting several lags in the system of equations--and by allowing full interaction among the lags in the different equations--the model permits an examination of the degree to which unemployment is persistent, while allowing the identification of the sources of persistence in the different equations. This model structure implicitly assumes that the true nature of unemployment dynamics is a subtle combination of factors generating persistence, and forces pushing towards equilibrium. On the one hand, the structural nature of the system implies that there is indeed some underlying “equilibrium” level of unemployment in the economy, thus rejecting the extreme hysteresis view. On the other hand, by allowing for long and interactive lags, the issue of what the precise equilibrium rate becomes less crucial than the structural features of the economy which produce the pattern of lags. ^1/ Long lags have profound implications for the actual rate of unemployment; once the period of adjustment exceeds the average time between shocks (or the length of the average economic cycle), shocks can compound their effects and feed back on each other, generating unemployment persistence far beyond what one would expect from a simplistic analysis of the natural rate of unemployment vis-à-vis the economy’s position in the economic cycle. Before one shock has worked its way through the labor market another has already arrived, producing a complex, dynamic evolution that may have little correlation with the underlying NAIRU. Indeed, the emphasis on the impact of labor market institutions on unemployment behavior focuses more on how structure affects the adjustment process (i.e., the nature of the lags) rather than on structure as a determinant of some underlying natural unemployment rate.

The paper is organized as follows. Section 2 provides a brief overview of economic developments and the institutional evolution of the Spanish labor market. Section 3 presents a basic model of the labor market which is estimated for the period 1971-1993. The regression results establish the basic underlying relationships among employment, labor force participation, and real wages. They also illustrate the importance of lags in the econometric specification and allow the identification of key structural changes in the labor market. Building on the basic model, Section 4 considers an expanded model which incorporates a number of crucial policy variables in an attempt to directly capture the effect of change in labor market institutions on employment and wages. This expanded model is estimated over the period 1982-1993. The results indicate that changes in labor market policies have indeed had important effects on the Spanish unemployment. The regression results of both models are used in Section 5 in simulations which permit a direct evaluation of the importance of the intricate lag structure in generating persistence and imperfect responsiveness of unemployment to economic shocks. Section 6 concludes the paper, while a brief Appendix discusses the development of summary indicators of the dynamic properties of the results obtained.

2. The Spanish labor market since the mid-1970s

The structure of the labor market changed more profoundly in Spain than in any other western European country in the past twenty years. No other country has seen its unemployment rate rise as dramatically and stay so persistently high. These two facts do not represent mere coincidence--in the profound transformation in the structure of employment relations (and the transformation of the Spanish economy more generally) lies much of the explanation for Spain’s dismal unemployment rate. While buffeted by the same macroeconomic shocks as the remainder of Europe in the 1970s, these shocks alone do not provide a satisfactory causal explanation of the rise in unemployment from under 5 percent in 1975 to 24 percent in 1994. This section provides a brief description of the evolution of labor market structure and unemployment performance over the past two decades with a view towards pinpointing key stylized facts which may provide clues about the driving forces behind the sharp rise in unemployment in recent years.

a. Employment, unemployment, and the labor force

The performance of the labor market in Spain from 1975 through 1994 can be divided into three cyclical periods. During the first period, in the late 1970s and early 1980s, the second oil crisis produced several years of weak economic growth which in turn led to a sharp decline in employment. The unemployment rate rose sharply, rising from 7 percent in 1978 to over 20 percent in 1984, while the size of the labor force was relatively stagnant, growing at an average rate of only 0.5 percent per year. Despite the increase in unemployment, real wages continued to rise at nearly 1 percent per year.

The second period began in 1985 with preparations to enter the EC. Spanish accession to the EC (in 1986) sparked a major economic recovery, with growth averaging 4.5 percent a year during 1986-1990. This expansion, plus the government’s introduction of flexible temporary labor contracts in 1984 (see below) fueled an increase in employment averaging 3 percent per year. The unemployment rate fell from over 21 percent in 1985 to 16 percent in 1990. This drop in unemployment was smaller than might be expected from such strong employment growth due to a sharp acceleration in the growth of the labor force to 2.1 percent per year, primarily because of a significant increase in the participation rate for women. Real wage growth continued, albeit at a slower pace of 0.6 percent per year.

The overall changes in employment and unemployment do not do justice to the depth of the changes in the labor market, since they mask a profound shift in the nature of employment. The progressive opening of the economy accelerated a major transformation in the economic structure which had already begun in the 1970s. The role of agriculture and basic industry (e.g., coal, steel, shipbuilding) declined sharply, while modern industry and the services sector (particularly tourism and financial services) surged. ^1/

The economy slowed in 1991 and entered into recession in the second half of 1992. The unemployment rate climbed rapidly to 24.6 percent by the third quarter of 1994--a peak-to-trough variation of more then 8 percentage points in less than three years. While labor force growth decelerated (to an average of 0.7 percent) most of the increase in unemployment came from a sharp drop in labor demand. Employment fell by seven percent between 1991 and 1994. Until labor market reforms began to bite in 1994, real wages continued an unabated rise despite the enormous slack in the labor market.

b. The structure of the labor market

During the Franco period, Spain had a rigidly controlled labor market. Trade union activism was prohibited and the social security benefits of the modern welfare state were largely absent. In their place was a set of labor regulations which rigidly defined working conditions and provided social protection by making firing workers difficult and providing generous severance pay for dismissals.

After General Franco’s death in 1975, the country underwent a major economic transformation which paralleled the political transition to democracy. The economy modernized rapidly, with sharp declines in traditional agricultural and basic industrial activity and the rise of modern manufacturing and services. The economy also opened to further international competition, culminating in accession to the EC in 1986.

Similarly profound changes occurred in the labor market, affecting every aspect of labor relations. The tight regulations on working conditions with their attendant restrictions on geographical and functional mobility were continued, but they were combined with the labor relations systems and the social protection of a modern welfare state. Trade unions became both legal and extremely active. Although union membership remains relatively low, the coverage of union-negotiated agreements was well in excess of 80 percent of all salaried workers by the late 1980s. After a series of national wage pacts in the late 1970s which kept industrial action and wage increases under control, collective bargaining moved largely to the sectoral level. Union activism surged, with Spain consistently among the European countries with the largest number of days lost to strike activity.

While the legal structure of dismissals did not change radically from the Franco era, the effective real costs of dismissals rose due to the unions’ ability to negotiate collectively for better severance payments and due to government-supported schemes to support workers on temporary redundancies and to help pay severance costs of those permanently dismissed. Average severance payments grew from just over 4.5 months of pay in 1981 to over 12 months of pay by 1993.

To this severance system was added an increasingly complete social protection system providing relatively generous unemployment benefits for dismissed workers and pensions for those injured, disabled or retiring. Whereas in 1983-84 fewer than 30 percent of non-agricultural workers were eligible for unemployment compensation, by 1993 over 60 percent were receiving compensation. The size of unemployment benefits also grew substantially. Per person benefits grew by 30 percent in real terms between 1984 and 1993. These high benefits levels reflected a system under which workers were entitled to unemployment compensation with a generous replacement ratio of the previous salary, particularly during the first year of joblessness. The period of work required to become eligible for benefits was also quite short--six months work entitled one to three months of benefits, with the same 2:1 ratio holding for longer periods on the job.

Not all developments in the 1980s increased the rigidity of the labor market. Whereas during the 1970s the minimum wage grew by 55 percent in real terms (an average real growth rate of 4.5 percent per year), that growth leveled off in the early 1980s, and there was actually a 6 percent real decline in the level of the minimum wage during the decade. In 1984, in response to the sharply rising unemployment rate, the government liberalized the use of temporary contracts, permitting temporary workers (on contracts of up to three years in length) to do essentially the same work as permanent workers. Since temporary workers were not subject to the same hiring and firing conditions and their contracts effectively granted the firms greater functional and geographical mobility, this step significantly reduced rigidities for those firms using temporary workers. The growing number of temporary workers increased the dualism of the labor market, as the labor force became increasingly segregated into permanent and temporary “castes.”

As the Spanish economy slowed in 1991 and 1992 and employment again soared above 20 percent, it became increasingly clear that the labor market was in need of more profound reforms. In 1993 and 1994, the government undertook a series of reforms designed to reduce unemployment compensation, facilitate workplace mobility, and reduce firing costs. Early results of these reforms appear to be favorable, but given the long response time in the labor market, it is premature to evaluate whether they will make a major contribution to the reduction of unemployment over the medium and long term.

In summary, the analysis of the causes of high and persistent unemployment in Spain must look to the interaction of two sets of factors. First at the macroeconomic level, profound changes in the structure of the economy as a whole (opening to international trade, accession to the EU, the decline of agriculture and basic industry and the rise of modern manufacturing and services) as well as socio-demographic changes in the size of the working age population and the rise of female participation in the labor force, have affected the labor market at least as profoundly as the macroeconomic shocks of the oil crises and the rise in real interest rate which are often cited as the source of European unemployment. ^1/ Second, at the level of the labor market itself, Spain has experienced profound changes in the structure of labor market institutions which could have a major impact of the level and persistence of unemployment. During the past 20 years, Spain has seen the resurgence of trade union activism and reformed legal framework for the labor market, and the rise of the protections of a modem social welfare state.

3. The basic model

In this section, a basic 3 equation labor market model is constructed and estimated for the period 1971-1993. This model contains variables designed to measure the interactions between labor supply, labor demand, and real wages. In keeping with the focus on examining not just the equilibrium relationship, but also the adjustment process, each equation uses a set of lags on both the dependent and independent variable to capture the dynamics of the labor market. This model will be used to determine the basic relationships among the key variables, as well as to pinpoint structural breaks which could be identified with known changes in labor market institutions. Unfortunately, good time series data on many important policy variables over the entire sample period are lacking, so the estimations over this period are conducted on a simple specification. Making virtue out of necessity, however, these results provided interesting contrasts with those of the policy model estimated over the 1980s and 1990s in Section 4.

a. Structure of the model

The empirical specifications used here are based on an underlying right-to-manage type of wage and employment setting process. ^2/ Potential workers decide unilaterally whether or not to enter into the labor market based on the wage they can get if employed, the probability of employment, and socio-demographic factors exogenous to the model. To incorporate adjustment lags, lagged values on both the endogenous and exogenous variables are permitted. Thus, the labor supply equation is as follows:

\begin{matrix} \ln L F_{t} = α + Σ_{i = 0}^{n} β_{i} U R_{t - i} + Σ_{i = 0}^{n} γ_{i} \ln W_{t - i} + Σ_{i = 1}^{n} δ_{i} \ln L F_{t - i} + θ_{i} X_{t} + ϵ & (1) \end{matrix}

where LF is the labor force, W is the real wage, UR is the unemployment rate, and X is a vector of variables exogenous to the model (including unemployment benefits) which could affect the labor supply. For the basic version of the model estimated in this section, the only exogenous variable included is the working age population.

The real wage is a variable jointly determined by bargaining between employers and trade unions. This bargain is affected by past real wages, by the unemployment rate, by labor productivity, and by a vector of exogenous variables (such as the reservation wage determined by unemployment benefits). The empirical specification of the real wage equation is as follows:

\begin{matrix} \ln W_{t} = α + Σ_{i = 0}^{n} β_{i} U R_{t - i} + Σ_{i = 1}^{n} δ_{i} \ln W_{t - i} + Σ_{i = 0}^{n} ρ_{i} \ln \Pr o d_{t - i} + θ_{i} X_{t} + ϵ & (2) \end{matrix}

where Prod is labor productivity. For the basic version of the model estimated in this section, the only exogenous variable included is the minimum wage, under the assumption that minimum wage increases may have played a role in setting expectations for wage increases in the private sector. ^1/

In accordance with the right-to-manage literature, once wages are determined in collective bargaining, it is assumed that employers are free to set employment levels so as to maximize profits subject to the legal and institutional constraints of the Spanish labor market. Employment is thus dependent upon past employment, real product wages (i.e., the real wage of the worker plus social contributions paid by the employer), and a vector of exogenous variables as follows:

\begin{matrix} \ln E_{t} = α + Σ_{i = 0}^{n} β_{i} \ln W_{t - i} + Σ_{i = 1}^{n} δ_{i} \ln E_{t - i} + Σ_{i = 0}^{n} ρ_{i} \ln T_{s o c \sec} + θ_{i} X_{t} + ϵ & (3) \end{matrix}

where E represents employment, where T_socsec is social security taxation. For the empirical specification in this section, the only “exogenous” variable included is GDP.

The model is closed by the following identities:

\begin{matrix} W p r o d = W + T_{s o c \sec} & (4) \end{matrix}

\begin{matrix} U R = 1 - \frac{E}{L F} & (6) \end{matrix}

\begin{matrix} \ln \Pr o d = \frac{\ln E}{G D P} & (5) \end{matrix}

where W_prod is the product wage. ^1/ As shown in equation (5), the unemployment rate term in the labor force and real wage equations indirectly incorporate the effects of the employment on labor supply and of employment and labor supply on real wages. Although GDP is not explicitly modelled, it is treated as an endogenous variable and a simple GDP equation is included in the simulations in section 5.

b. Characteristics of the data

The empirical analysis was conducted using quarterly data from 1971 through 1994. ^2/ Data used were obtained from the databases of the Bank of Spain, the Ministry of Economy and from the OECD Analytical Database. In the case of GDP data, for which quarterly information was not available for the entire period, interpolations developed by the OECD were used. The data used are not seasonally adjusted; rather, seasonal dummies are included in all of the regressions. All variables, except for the unemployment rate, are in logs.

The most important feature of the variables under consideration is their stationarity (or lack thereof). Hysteresis theories of unemployment imply that unemployment is a nonstationary variable, raising the issue of non-stationarity of both employment and unemployment. It therefore is an essential first step to examine the variables to be used in the model for unit roots. Table 1 presents the results of ADF tests. As can be seen from the table, essentially all of the variables included in the model can be treated as nonstationary. From the unit root tests on the differenced variables, it appears that the variables may be treated as I(1), although there is some question about the Working Population variable. ^1/

c. Estimation results

After initial exploratory regressions in OLS, the model was estimated as an Autoregressive Distributed Lag (ARDL) model in levels using instrumental variables in order to find a cointegrating long run relationship. ^2/ The model was then estimated in error correction formulation in differences using instrumental variables when necessary to control for endogeneity. ^3/ In estimation, a strictly empirical approach was taken as regards the structure of the lags. Up to eight lags of each endogenous variable were included in initial specifications, with only lags with robust significance being retained in the chosen specifications. In this respect, the model differs from a structural VAR model, since only significant lags were retained in the final specifications, compared to a VAR, where all variables would contain the same number of lags. Chow tests were undertaken used to for structural breaks in the model, and in preparation for estimating the policy model in Section 4, the basic model was estimated separately for the two subperiods of 1971-1980 and 1981-1993.

In addition to an examination of the regression coefficients of the error correction model, diagnostics are presented to analyze the dynamic response of each estimated equation to changes in explanatory variables and to shocks. In addition, a series of diagnostic statistics on the dynamics is calculated. First, the “cross-persistence” of temporary shocks and the “cross-responsiveness” to permanent shocks of each equation is examined, building of the measures of persistence and responsiveness developed by Snower and Karanassou (1995) for unemployment. These measures basically measure the sum of the deviations of the dependent variable from its equilibrium; in other words they are a normalized version of the integral of the impulse response curve. The measures used, and how they differ from those developed by Snower and Karanassou are discussed in detail in Appendix. Second, information on the “half-life” of impulse responses is presented. The “half-life” of a permanent change is defined as the time required for one half the change to be transmitted through to the dependent variable. For a temporary shock, the half-life is the time required for the dependent variable to reach one-half of its maximum deviation and its original (and final value).

(1) The labor force equation

Economic models of the labor force are notoriously difficult, since many noneconomic factors affect labor force participation. The results presented here are no exception. Table 2 shows the long run cointegrating relationships of the model, while Table 3 presents the preferred specification for the labor force equation in error correction form. The long run relationship shows a positive coefficient on the Working Population variable, but a negative one on the real wage. While somewhat surprising, the negative relationship is not inconsistent with rational utility maximization in the decision to participate in the labor force, as higher wages among primary wage earners may lead secondary household members to participate less via an income effect. ^1/ The unemployment rate does not figure in the long-run relationship, since its coefficient was not robustly significant and the equation failed to cointegrate when it was included.

Turning to the error correction specification, several features of the regression results stand out. First, it is interesting that the coefficient on the error correction term, while significant and correctly signed, is quite small, implying relatively slow adjustment to the long-run relationship. Second, unemployment, which did not participate in the long run relationship, plays a dynamic role. Lagged changes in unemployment on balance have a slightly negative effect on labor force participation growth, as do lagged changes in the rate of labor force growth.

A clearer idea of the overall impact of the different explanatory variables on the labor force can be obtained by examination of the indicators of equation dynamics presented in the table below. As can be seen, permanent wage and population shocks take a long time to manifest their full effects on the equilibrium labor force. For a real wage shock, after 8 quarters only one half of the final effect has been transmitted, while for a population shock, it takes 35 quarters. A one-time shock to the labor force itself will be reversed over time as the long-run equilibrium relationship reasserts itself, but this process is a long one. It takes 13 quarters for one half of the adjustment to occur. The data on responsiveness show that the accumulated deviations from long-run equilibrium from permanent changes are quite significant in the case of real wages, where the difference between the long run equilibrium labor force and the sum of the actual values is 2.6 percent.

Temporary shocks to wages and unemployment have relatively small maximum effects on the labor force, while a (totally unrealistic) temporary jump in the working age population has a large temporary effect on the labor force. Of course, since the unemployment rate is not in the long-run relationship, it has only temporary effects. In all of these cases, it takes 1-2 years for the effects of a these temporary shocks to dampen down to half of their maximum level. A temporary shock to the labor force itself has a half-life of 14 quarters.

Indicators of Labor Force Equation Dynamics, Basic Model

Response of a one percent shock to:	Wage	UR	WorkPop	Labor Force
Permanent shock:
LR elasticity	-0.1727	0	1.149	0
Half-life (quarters)	8	--	35	13
Responsiveness (%)	-2.62	0	-1.059	-0.02
Temporary shock:
Maximum deviation	-0.037	-0.0028	-1.075	1
Half-life (quarters)	5	8	7	14
Persistence (%)	-0.1744	0	1.149	-1.009

Indicators of Labor Force Equation Dynamics, Basic Model

Response of a one percent shock to:	Wage	UR	WorkPop	Labor Force
Permanent shock:
LR elasticity	-0.1727	0	1.149	0
Half-life (quarters)	8	--	35	13
Responsiveness (%)	-2.62	0	-1.059	-0.02
Temporary shock:
Maximum deviation	-0.037	-0.0028	-1.075	1
Half-life (quarters)	5	8	7	14
Persistence (%)	-0.1744	0	1.149	-1.009

Chow tests on the labor force equation show clear evidence of structural breaks, so the model was reestimated for the sample divided at 1981. Both halves of the sample now show a negative relationship between wages and labor force participation, as well as a negative link between unemployment and labor force participation. There was also an important drop in the coefficient on the error correction term between the first and second half of the sample, suggesting a lengthening of the time required for the labor force to adjust to shocks.

(2) The real wage equation

The long-run cointegrating relationship for real wages (Table 2) shows a strongly positive relationship between real wages and labor productivity, as one would expect. Unemployment has a small negative impact on wages which is only marginally significant statistically, suggesting that wages are largely insensitive to labor market conditions. There is a strong correlation between minimum wages and average wages in the long run, which, although it may be a statistical artifact, may also indicate that general wage increases follow trends set in increases in the minimum wage.

The error correction specification (Table 4) also shows a significantly positive relationship between productivity growth and wage growth. The relationship with the minimum wage also remains positive in differences. Changes in the unemployment rate fail to be significant and are excluded from the preferred specification. Lagged changes in the real wage exert a strongly negative effect on wage growth in the current period, reflecting a strong tendency to revert to trend after variations in wage growth rates. The coefficient on the error correction term is highly significant and relatively large. As was the case with the labor force variable, there is clear evidence of structural breaks in the equation.

The table of equation dynamics shown below indicates that, as expected from the higher coefficient on the error correction term, the adjustment to a permanent shock is faster in the wage equation than in the labor force equation. Productivity shocks are the slowest in transmission into wages, with a half-life of more than three years. This lag is also reflected in the large negative responsiveness number, which indicates that wages are 21 percent lower after a productivity shock than they would be if adjustment were instantaneously complete. Temporary shocks in the wage equation work themselves out relatively quickly, with half-lives of 5 quarters or less from shocks in all of the variables.

Indicators of Real Wage Equation Dynamics, Basic Model

Response of a one percent shock to:	Product	MinWage	UR	Wage
Permanent shock:
LR elasticity	0.7185	0.9247	0.003	1
Half-life (quarters)	13	5	6	6
Responsiveness (%)	-20.95	-6.133	-6.605	-6.623
Maximum deviation	-1.76	0.277	-0.001	1
Half-life (quarters)	5	5	3	2
Persistence (%)	-0.74	-0.92	-.0034	-3.315

Indicators of Real Wage Equation Dynamics, Basic Model

Response of a one percent shock to:	Product	MinWage	UR	Wage
Permanent shock:
LR elasticity	0.7185	0.9247	0.003	1
Half-life (quarters)	13	5	6	6
Responsiveness (%)	-20.95	-6.133	-6.605	-6.623
Maximum deviation	-1.76	0.277	-0.001	1
Half-life (quarters)	5	5	3	2
Persistence (%)	-0.74	-0.92	-.0034	-3.315

(3) The employment equation

The long run employment equation is presented in Table 2. As expected, it shows a strongly positive relationship between GDP and Employment (although the fact that the coefficient is larger than one is surprising). Overall, the product wage has a negative effect on employment, but this is decomposed into a strongly positive relationship between wages and employment and a negative and even stronger effect from social security contributions. As suggested above, this may be because the social security contribution variable is acting as a proxy for other negative effects of government intervention in the labor market not included.

The preferred specification for the error correction form of the model is presented in Table 5. Changes in GDP growth have a positive impact on employment growth. On balance, the evolution of the real product wage has a positive impact as well, in contrast to the long-run relationship. On balance, lagged values of the dependent variable have a slightly negative impact, with a long adjustment process implied (the sixth lag proves to be significant). The error correction term is significant and correctly signed but the coefficient is quite small (only 0.1), which means that the adjustment to long term equilibrium is quite weak. As with the other two equations, there is strong evidence of structural breaks in the model.

The dynamics of the employment equation is intermediate between the relatively quick adjustment displayed by the real wage equation and the slow adjustment of the labor force equation. As can be seen from the table below, half-life adjustments to shocks generally take 1.5-2.5 years, with the adjustment to output shocks somewhat quicker. Of particular interest are the dynamic of an adjustment in the product wage (wage plus social contributions). This composite variable demonstrates the correct sign, unlike the simple real wage variable. ^1/ It also demonstrates relatively slow adjustment to shocks, with the responsiveness to permanent shocks being particularly poor.

Indicators of Employment Equation Dynamics, Basic Model

Response of a one percent shock to:	GDP	Real Wage	Social Security Taxes	Product Wage	Employment
Permanent shock:
LR elasticity	1.316	1.078	-1.411	-0.333	1
Half-life (quarters)	3	6	8	10	6
Responsiveness (%)	-7.85	-9.81	1.411	-15.29	-9.43
Temporary shock:
Maximum deviation	0.35	0.128	-0.194	-0.124	1.172
Half-life (quarters)	3	7	7	11	7
Persistence (%)	1.31	1.073	-1.403	-0.331	9.43

Indicators of Employment Equation Dynamics, Basic Model

Response of a one percent shock to:	GDP	Real Wage	Social Security Taxes	Product Wage	Employment
Permanent shock:
LR elasticity	1.316	1.078	-1.411	-0.333	1
Half-life (quarters)	3	6	8	10	6
Responsiveness (%)	-7.85	-9.81	1.411	-15.29	-9.43
Temporary shock:
Maximum deviation	0.35	0.128	-0.194	-0.124	1.172
Half-life (quarters)	3	7	7	11	7
Persistence (%)	1.31	1.073	-1.403	-0.331	9.43

d. Discussion of the results of the basic model

Together, the results of the basic model lead to a number of important conclusions. The most telling conclusion from this preliminary analysis is the strong evidence that long lags in the adjustment of the labor market to shock play a crucial role in the sustaining high unemployment. The half-life numbers presented suggest that it takes between 3 and 35 quarters for one half of the final effect of a permanent shock in an explanatory variable to be felt in the corresponding dependent variable. The numbers are even more striking as regards the time taken for 90 percent adjustment. For example, a permanent output shock takes over 6 years to manifest 90 percent of its final effect on employment; a permanent working population shock (e.g., the Spanish baby boom) takes 63 quarters (15.75 years) to manifest 90 percent of its final effect on the labor force; a permanent wage shock takes 4 years to show 90 percent of its final effect on the wages themselves, over 8 years to show 90 effect on employment, and nearly 10 years for the labor force equation. Chart 1 shows graphically the adjustment process for some of these key permanent shocks to give an idea of the full dynamics of adjustment in each equation.

The second interesting insight from the basic model concerns the relative importance of macroeconomic shocks themselves (as opposed to their propagation mechanisms) on labor market evolution. While output shocks clearly have played a role in the generation of unemployment in Spain (as can be seen from the large and significant coefficients on the GDP variable in the employment equation, and on productivity in the real wages equation), they clearly account for only a small part of the overall labor market story. An attempt to capture the effects of output shocks directly within the model was completely unsuccessful. Variables like the oil price, the real interest rate, and the real exchange rate showed no significance when tried in the model, suggesting that they only effect employment indirectly via GDP. ^1/ Even an inflation variable was insignificant, although one might expect to have a strongly negative relationship with employment if unemployment is being generated as part of a disinflation process via the Phillips curve. A related issue is the striking lack of sensitivity of the labor market to the unemployment rate. Unemployment has little impact on the labor force and a minuscule effect on real wages.

Even in this basic model, socio-demographic factors, and policies and institutions of the labor market itself play a very important role in labor market outcomes. The increase in the working age population, coupled with changes in attitudes toward female labor force participation which produce the larger than unity coefficient on the population variable, have clearly contributed to the rise in unemployment. In the two policy variables included in the basic model (the minimum wage and the tax wedge of social security contributions) there is strong evidence that institutional factors have also played an important role in pushing up product wages and reducing employment.

Finally, the instability of the coefficients of the basic model highlights the importance of structural changes in the labor market in Spain. This phenomenon will be explored in more detail in the next section.

4. The policy model

The results of the basic model, while providing some general information about the behavior of the Spanish labor market, are unsatisfactory for several reasons. First, the model is econometrically deficient due to clear structural breaks (undoubtedly produced by the changes in the structure of the labor market since the early 1970s). Second, from a more conceptual standpoint, there is little in the basic model which explains the “why” of Spanish unemployment. The institutional features of the labor market discussed in Section 2 as potential factors in generating high unemployment are not well modelled, and the traditional concept of European unemployment rising in the 1970s and 1980s as a result of macroeconomic shocks does not seem to be sustained in the results. These weaknesses motivate the development of another version of the model where the structural problems and unanswered causal questions of the basic model can be addressed.

a. Additional variables included in the policy model

The ‘policy’ model has the same general form as the basic model estimated above but with the addition of a series of variables designed to capture explicitly some of the institutional features of the labor market which could have played a role in generating persistent unemployment in Spain. The inclusion of the aspects of the labor market structure makes the model less susceptible to structural breaks in the coefficients. The model is estimated only over the period 1981-1993; this shorter time series also makes it less likely to suffer from structural breaks than the 1972-1993 period used in the basic model, since it excluded the Franco era and democratic transition in the 1970s. Of course, this restricted sample has the disadvantage of excluding the oil shocks in 1973-4 and 1979-80, but the data for most of the key policy variables included do not reach back into the 1970s.

The basic labor force equation in the policy model includes two variables in addition to those in the basic model--the average level of disability pensions, and the replacement ratio of unemployment compensation. It would be expected that disability pensions would decrease the labor force directly since people granted disability benefits leave the labor force, but beyond that, a negative relationship is to be expected between the level of benefits and the labor force, due to the fact that pensions have been used in Spain as an alternative to redundancies by some firms. The disability pension variable may also capture some of the effect of retirement pensions on the labor force. ^1/ The inclusion of the replacement ratio variable reflects the incentives a person may have to remain in the labor force (or enter) despite high unemployment. High unemployment benefits could prevent discouraged workers from leaving the labor force, or could provide incentives for entering the labor market. ^2/

The wage equation includes two new policy related variables--the replacement ratio of unemployment benefits and the share of temporary workers in the labor force. The replacement ratio reflects the reservation wage of workers, and hence should have a positive impact on collectively bargained wages. It is not clear ex ante whether the presence of temporary workers would increase or decrease average wages. On the one hand, since temporary workers tend to be paid less than their permanent counterparts, there is a composition effect which would cause the average wage to decline as the share of temporary workers increases. On the other hand, it is possible that the presence of temporary workers will exacerbate insider-outsider problems by making permanent workers less susceptible to redundancies. ^1/ Finally, the minimum wage is included as with the basic model.

In estimating the employment equation, three policy variables were added to the variables included in the basic model. Two variables relate to labor market relations between worker and employers, one measuring days lost to strike action, and another measuring the coverage of trade union agreements. The strike activity variable reflects a clear non-wage cost to employers which could negatively affect their level of employment. The coverage of union agreements may also affect employment levels by constituting a direct labor cost, or indirectly via wages. The third policy variable, severance pay, measures the real value of severance pay settlements as a means of exploring the impact of dismissal costs on employment levels. Of course, social contribution costs remain included and constitute another policy variable with an expected negative impact on employment.

b. Estimation results

As with the model in the previous section, the policy model was initially explored in OLS, with regressions subsequently run in error correction form using (where necessary) instrumental variables, and including cointegrating long-run relationships. The long-run relationships are shown in Table 6, with the error correction models of the individual equations shown in Tables 7-9.

(1) The labor force equation

As seen in Table 6, in the long-run wages and the labor force show a significant positive relationship, in contrast to the negative sign given in the basic model. The idea that higher wages draw more labor force participation is more intuitively satisfactory than the negative sign found previously. The positive long run relationship between working age population and the labor force is also strongly significant, as expected. The fact that the size of the coefficient is even larger than in the basic model is probably reflecting the accelerating trend of the incorporation of women into the labor force in the 1980s compared to the 1970s. Turning to the policy variables, there is a small but significant positive effect of the replacement ratio on labor force participation, while the generosity of disability pension benefits holds the expected negative correlation with participation. The unemployment rate showed no significant relationship, and was excluded from the preferred specification. ^1/

The error correction version of the model is shown in Table 7. Wage growth has the expected significant positive effect on the labor force. The two policy variables also hold significant signs in the expected direction, with increasing disability pensions decreasing the labor force, while the growth replacement ratio increases it. The error correction term also has the expected sign. In contrast, short run fluctuations in the working age population paradoxically decrease the labor force. As in the basic model, changes in the unemployment rate have virtually no net effect.

An examination of the table of summary statistics on equation dynamics shows that the half-lives of the responses to permanent shocks are smaller than those for the Basic Model, while the speed of adjustment to temporary shocks is somewhat longer. It is notable that the speed of adjustment to shocks in the labor force itself seems to have improved significantly. This phenomenon appears for all of the policy model equations, as well as for the dynamics of the system as a whole (see below). This apparent improvement in adjustment does not necessarily reflect better real adjustment. ^2/

Indicators of Labor Force Equation Dynamics, Policy Model

Response of a one percent shock to:	Wage	UR	Work. Pop.	Replace Ratio	Pension	Labor Force
Permanent shock:
LR elasticity	0.237	0	1.452	-0.333	-0.17	1
Half-life (q)	6	--	11	10	3	5
Responsiveness (%)	-5.62	--	-13.3	-15.29	-3.35	-4.91
Temporary shock:
Maximum deviation	0.0482	0.0031	-2.27	-0.124	-0.054	1
Half-life (q)	7	10	8	11	2	5
Persistence (%)	0.236	7*10^-8	1.478	-0.331	-0.169	4.887

Indicators of Labor Force Equation Dynamics, Policy Model

Response of a one percent shock to:	Wage	UR	Work. Pop.	Replace Ratio	Pension	Labor Force
Permanent shock:
LR elasticity	0.237	0	1.452	-0.333	-0.17	1
Half-life (q)	6	--	11	10	3	5
Responsiveness (%)	-5.62	--	-13.3	-15.29	-3.35	-4.91
Temporary shock:
Maximum deviation	0.0482	0.0031	-2.27	-0.124	-0.054	1
Half-life (q)	7	10	8	11	2	5
Persistence (%)	0.236	7*10^-8	1.478	-0.331	-0.169	4.887

(2) The real wage equation

The long-run determinants of wages (Table 6) show the expected strong positive correlation between productivity and wage growth. In contrast to the basic model, the coefficient is less than one, implying that not all of productivity improvements are translated into wages. This is consistent with the general increase in profit margins experienced in Spain in the 1980s. Unemployment has a significantly negative effect on wages, although the size of the effect is small. The minimum wage continues to have a positive effect on real wages; however, the size of this pass-through effect is smaller than in the basic model. Turning to the policy variables introduced in this version of the model, increases in the replacement ratio increase wages are expected. Interestingly, the share of temporary workers in the labor force on balance exerts a moderating effect on wages, suggesting that the composition effect of lower wages paid to temporary workers dominates the insider-outsider effect that more temporary workers could have on wage bargaining.

The error correction version of the real wage equation, shown in Table 8, provides stronger results than those of the labor force equation. Real wage increases tend to perpetuate themselves into the future, as demonstrated by the net positive impact lagged wage growth has on current wage increases. The error correction coefficient is large and highly significant, suggesting a rapid adjustment to the long-run real wage path. Changes in the replacement ratio have a significantly positive impact on wage growth, as in the long run relationship. The share of temporary workers in the workforce has a short-run positive effect compared to its long-run negative effect. Changes in the unemployment rate, in productivity, and in the minimum wage have short-run effects which also run counter to their long-run relationship with real wages. Unemployment growth lagged one quarter is positively linked to wage increases, while the coefficient on lagged productivity is of only marginal significance statistically.

As with the labor force equation, the real wage equation of the policy model shows faster adjustment than with the basic model as demonstrated by the shorter half lives and less negative responsiveness numbers (see table below). This is particularly true of the response of the real wage to a real wages shock. The situation with temporary shocks is the reverse--they tend to have longer half lives than in the basic model. This is a result of the functional form of the equation, which induces a behavior which oscillates around the final values. These oscillations take time to settle down, hence the long half lives despite very small net persistence statistics.

Indicators of Real Wage Equation Dynamics. Policy Model

Response of a one percent shock to:	Productivity	Min. Wage	UR	Replace Ratio	Temp. Share	Wage
Permanent shock:
LR elasticity	0.8892	0.368	-.0044	0.031	-.00146	1
Half-life (q)	6	4	15	1	9	2
Responsiveness (%)	-2.55	-3.314	-4.518	0.508	-2.867	-1.52
Temporary shock:
Maximum deviation	-0.927	0.242	0.011	0.031	0.002	1
Half-life (q)	16	16	11	6	13	2
Persistence (%)	0.897	0.371	-.0043	0.031	-0.0015	1.512

Indicators of Real Wage Equation Dynamics. Policy Model

Response of a one percent shock to:	Productivity	Min. Wage	UR	Replace Ratio	Temp. Share	Wage
Permanent shock:
LR elasticity	0.8892	0.368	-.0044	0.031	-.00146	1
Half-life (q)	6	4	15	1	9	2
Responsiveness (%)	-2.55	-3.314	-4.518	0.508	-2.867	-1.52
Temporary shock:
Maximum deviation	-0.927	0.242	0.011	0.031	0.002	1
Half-life (q)	16	16	11	6	13	2
Persistence (%)	0.897	0.371	-.0043	0.031	-0.0015	1.512

(3) The employment equation

One of the most striking results of the long-run regression on the employment equation is the lack of a significant relationship between wages and employment (Table 6). Although the variable has the expected negative sign (in contrast to the case of the basic model) it is not significantly different from zero. Social security contributions, in contrast, demonstrate a strongly negative effect on employment. GDP holds a positive relationship with employment of approximately one-to-one, implying little or no long run productivity growth. The three policy related variables included in the regression all maintain a significant relationship with employment. Severance pay is negatively related with employment, as is strike activity. The coverage of collective bargaining agreements is also related negatively with employment.

The error correction version of the employment equation is presented in Table 9. As with the real wage equation, the error correction term is large and highly significant. Changes in employment tend to persist over time, as evidenced by the positive net relationship between current employment growth and its lagged values. Social security taxes retain the negative relationship with employment in differences which they have in the long run regression, while wages have a very small negative effect on employment growth. GDP growth has a unexpected negative relationship with employment growth over the short run. Changes in the coverage of collective bargaining agreements has a negative impact on employment growth, while strike action has a very small positive effect. Severance pay was not significant in the short-run regressions and was excluded from the preferred specification.

The table of equation dynamics below shows an increase in the speed of adjustment in the policy model compared to that of the employment equation in the basic model. The time for one half of the adjustment is less than 1.5 years, although the oscillations in employment caused by shocks in several of the explanatory variables (e.g., social security taxes, collective bargaining, strike days) led to considerable overshooting which is not reflected in the summary statistics.

Indicators of Employment Equation Dynamics, Policy Model

Response of a one percent shock to:	GDP	Real Wage	Soc. Sec. Tax	Product Wage
Permanent shock:
LR elasticity	1.016	-0.1019	-0.295	-0.397
Half-life (quarters)	2	5	4	4
Responsiveness (%)	-2.28	-1.338	-1.251	-1.273
Temporary shock:
Maximum deviation	0.438	-0.04	-0.261	-0.301
Half-life (quarters)	7	5	6	6
Persistence (%)	1.014	-0.101	-0.293	-0.394

Indicators of Employment Equation Dynamics, Policy Model

Response of a one percent shock to:	GDP	Real Wage	Soc. Sec. Tax	Product Wage
Permanent shock:
LR elasticity	1.016	-0.1019	-0.295	-0.397
Half-life (quarters)	2	5	4	4
Responsiveness (%)	-2.28	-1.338	-1.251	-1.273
Temporary shock:
Maximum deviation	0.438	-0.04	-0.261	-0.301
Half-life (quarters)	7	5	6	6
Persistence (%)	1.014	-0.101	-0.293	-0.394

Response of a one percent shock to:	Collective Bargaining	Strike Days	Severance Pay	Employment
Permanent shock:
LR Elasticity	-0.0157	-0.0026	-0.0673	1
Half-life	2	3	3	3
Responsiveness	-0.295	-0.26	-1.453	-1.451
Temporary Shock
Max. Deviation	-0.0164	-0.00261	-0.0298	1
Half-life	3	4	5	4
Persistence	-0.0156	-0.0026	-0.067	2.253

Response of a one percent shock to:	Collective Bargaining	Strike Days	Severance Pay	Employment
Permanent shock:
LR Elasticity	-0.0157	-0.0026	-0.0673	1
Half-life	2	3	3	3
Responsiveness	-0.295	-0.26	-1.453	-1.451
Temporary Shock
Max. Deviation	-0.0164	-0.00261	-0.0298	1
Half-life	3	4	5	4
Persistence	-0.0156	-0.0026	-0.067	2.253

c. Discussion of the results of the policy model

One important conclusion of the three equations in the policy model is that labor market structure does indeed pay an important role in both the underlying long run equilibria of employment, real wages, and the labor force, and also in the dynamics. In addition to confirming the fact that increases in the minimum wage do contribute to upward pressure on overall real wages and that the level of social security contributions by employers decreases employment, there is also evidence that other key social and labor market policies of the government affect employment, wages and the labor force. The policy effects can be summarized as follows:

- Higher minimum wages push up real wages overall;
- Higher average disability pensions reduce the labor force;
- Increases in the generosity of unemployment benefits contribute to higher unemployment by increasing the labor force; they also help generate higher real wages;
- The liberalization of temporary contracts has dampened wage increases;
- Higher severance pay reduces employment;
- More labor conflicts (as measured by strike days) reduce employment;
- Greater coverage of collective bargaining agreements reduces employment.

Each of these effects is statistically significant. The impression that institutional variables are crucial to understanding the Spanish labor market is reinforced by the fact that the policy model equations are much more econometrically stable than those of the basic model. A comparison between the basic model run over the same sample period with the policy model confirms that much of the slow adjustment captured in the basic model is due to the effects of the institutional variables included in the policy model.

The second key insight emerging from the policy model is the lack of responsiveness of the Spanish labor market to traditional market clearing forces. Unemployment has little effect on the decision of potential workers to enter the labor force, nor does it have much impact on moderating wages (the coefficient is significant but small). Even more striking is the fact that the expected negative effect of real wages on employment is not strongly present. The variable has the expected significant sign, but it is not statistically significant. Thus, not only are real wages insensitive to the level of slack in the labor market, but employment itself does not unambiguously respond to the wage.

As with the basic model, a number of variables were tested for inclusion in an attempt to capture directly the effects of macroeconomic shocks. The policy model is also notable as much for the variables not found to be significant as for those included in the chosen specification. Two sets of variables in particular are conspicuous by their absence. Beyond GDP (included in the employment equation) and productivity (in the wage equation), no variables related to real economic shocks were found to be significant in any of the equations. Neither the oil price nor real interest rates were found to be significant. Furthermore, a series of fiscal variables also proved to be insignificant in explaining Spanish unemployment. Regressions were run using variables representing overall government spending, social spending, overall taxation, direct and indirect taxation. None of these variables proved significant, which suggests that the size of government in the economy has not per se had a negative and positive effect on unemployment or wages in Spain. Of course, the use of the product wage in the employment equation includes the tax wedge, which does prove to be significant, but it is striking that overall fiscal pressure has no independent effect.

5. Structural change and unemployment: persistence and responsiveness

So far in this paper, the static and dynamic characteristics of each equation in the three equation model have been analyzed separately. This exercise yields interesting information, but it ignores the possibility that changes in one equation could feed back into other equations in the model, prolonging the adjustment process. The specifications chosen for both the basic model and the policy model provide several channels for such interequation effects: changes in employment generate effects in wages via the unemployment rate; changes in the labor force affect unemployment, which feeds into the wages; changes in real wages could affect both the labor force and employment, and so on. Recognizing the endogeneity of GDP to this labor market model, additional avenues of feedback become apparent. Output and employment are simultaneously determined and via labor productivity they affect wages. To test the importance of these feedback effects and obtain a more complete idea of the overall speed of adjustment of the labor market to shocks, simulations were conducted for the system as a whole for both the basic and policy models following the methodology of Snower and Karanassou (1995).

To simulate the model as a complete system, it was necessary to develop a simple equation for GDP. A simple Cobb-Douglas style output equation was estimated as follows:

\begin{matrix} \ln G D P = a_{o} + 0.75 \ln E + 0.25 \ln K + a_{1} t & (\begin{matrix} 7 \end{matrix}) \end{matrix}

where K is the capital stock and T is a simple trend term. For the purposes of the simulations, it is assumed that increases in real wages affect both the consumption wage and the level of social security contributions (effectively assuming that social security contribution rates remain constant).

Using this four equation system, the model was shocked to observe the persistence and responsiveness measures for the full system. In these simulations the employment equation was shocked by one percent and the Snower-Karanassou (S-K) measures of persistence and responsiveness for the level of unemployment itself were calculated. For the basic model, a temporary shock yields a persistence of 32.6, with a half life of 32 quarters. ^1/ A permanent shock has a persistence measure of -1439, with a half-life of 22 quarters (Chart 2). The chart confirms that a significant proportion of the adjustment takes place several years after the initial shock. Indeed, for a permanent shock, it takes 80 quarters (20 years) for 90 percent of the final impact of the shock to appear, while for a temporary shock it takes nearly as long--75 quarters (18.7 years). With the recovery period from shocks lasting considerably longer than the average business cycle, the dynamics of unemployment become extremely complex, with the unemployment effects of one recession beginning before the effects of the previous one have become fully manifest. With this dynamic pattern, the whole concept of “natural” rate of unemployment can be called into question.

The results of the basic model confirm the basic assertion of this paper--that feedback both within and between equations produces a situation where the adjustment to labor market shocks is extremely slow in Spain. The additional delays in adjustment from the interaction among equations can be clearly seen by comparing the 32 quarter half-life for an employment shock as a whole with the seven period half-life of a temporary employment shock on the employment equation alone. The shock to employment affects output which feeds back in employment and wages (via a countercyclical increase in productivity); higher wages affect the labor force and (via product wages) feed back into employment, maintaining the high unemployment rate.

The adjustment to shocks is not only slow relative to the adjustment of the individual equations, it is also slow relative to other European countries. For a temporary shock, a comparison of the persistence and half-life measures with those of the other major European countries (Henry and Snower, 1995) shows that the half-life is longer than those for Germany (6.5 years), Italy and the U.K. (5 years) and France (3 years). The persistence measure is also the highest, tied with the U.K. and well above those for the other major EU countries. For a permanent shock, the same conclusion holds--the adjustment time for Spain is significantly longer than for the other major European economies.

Turning to the policy model, there appears to be either a contradiction with the basic model or a marked improvement in the speed of adjustment of the Spanish labor market in the 1980s and early 1990s compared with that in the 1970s (reflected in the basic model). A persistence of 10.3 with a half-life of 13 quarters for a temporary shock. As shown in Chart 3, the pattern of both the permanent and temporary shock dynamics is similar to the basic model, but the size of the reactions is smaller and the time scale is compressed. For a permanent shock, responsiveness is -102.4, with an adjustment half-life of 11 quarters. While it remains true that adjustment is slower for the system as a whole than for the individual equations (13 quarters versus 4 for the employment equation alone), the Spanish results now compare reasonably well with those for the other major European countries. This does not mean that adjustment is quick. To achieve 90 percent recovery from a temporary shock, it still takes 8.5 years, while 90 percent adjustment to a permanent shock takes 6 years. These speeds of adjustment, while considerably faster than in the basic model, are sufficiently slow that they do not alter the qualitative conclusions of the basic model. Furthermore, as argued above, the contradiction is more apparent than real. Under the policy model adjustment to “equilibrium” unemployment is indeed more rapid than under the basic model, but it is an adjustment to an equilibrium level which is driven higher by the inexorable increase during the 1980s of labor market policies which induced unemployment to persist. In other words, the basic model indicates that unemployment persists in Spain; the policy model indicates that this persistence is due to the persistence of labor market institutions (unemployment benefits, severance pay, minimum wages, social contributions, etc.). ^1/

6. Conclusions

This paper has presented two main contentions. First, the traditional idea that European unemployment is the result of macroeconomic shocks does not adequately explain the situation in Spain. The shocks faced by the Spanish economy reach far beyond the simply macroeconomic, extending to the profound structural changes in the economy and socio-demographic factors. This is confirmed by the results obtained in the empirical analysis. While variables associated with macroeconomic shocks (beyond GDP itself) proved to be insignificant as determinants of employment or the labor force, demographic factors (as measured by the significance and greater-than-unity size of the coefficient on the population variable) are highly significant, as are structural features of the labor market (such as collective bargaining, minimum wages, and the share of temporary contracts). Furthermore, the evidence on the importance of policy variables in the regressions suggests that policies themselves have played a role in generating and sustaining high unemployment beyond any external shocks.

The second major argument of this paper is that the dynamics of labor market adjustment constitute an important part of the explanation of the size and persistence of unemployment. This is amply demonstrated by the results of the basic model, where an external shock can take eight years just for one-half of the eventual effects to manifest themselves--the slowest adjustment rate among major European countries. The comparison of the basic model with the policy model suggests that the persistence of unemployment observed in the basic model is due to the persistence of certain variables linked to the structure and policies governing the labor market in Spain. Only a significant change in these features of the labor market generating and sustaining high unemployment can lead to a substantial reduction in Spanish unemployment. The government made a start in 1993-94 with significant reforms in unemployment benefits, changes in certain rigidities in hiring and firing, and improvements in the flexibility in using the workforce. The effects of these reforms are slowly bearing fruit in terms of lower unemployment and more flexible real wages, but it is likely that many additional changes will have to be undertaken if unemployment in Spain is to fall to the European average.

APPENDIX: Simple Measures of “Cross-persistence” and “Cross-responsiveness”

Snower and Karanassou (1995) define unemployment persistence as the sum of the deviations of unemployment from its initial value from an employment shock as follows:

π = Σ_{t = 1}^{\infty} \frac{u_{t}^{/} - u_{t}}{Δ ϵ 0}

where Δϵ_o is the shock in period 0. To obtain a measure of persistence for shocks in explanatory variables, then this indicator must be modified, because it is unclear what the denominator would be since the shock is the domain of a different variable than the reaction. To solve this, it is normalized by the equilibrium value of the dependent variable rather than by the shock itself. Thus, the “cross-persistence” measure of deviations in the dependent variable, y, due to a shock in variable x, is as follows:

π_{y x} = Σ_{t = 1}^{\infty} \frac{y_{t}^{/} - y_{t}}{y 0}

where y_o is value of dependent variable before the shock to x. Where persistence is measured due to a shock in the dependent variable, π_yy is related to Snower-Karanassou persistence (π_sk) by the relationship: π_yy = ((y₁-y_o)/y_o)π_sk.

For responsiveness to a permanent shock, the Snower-Karanassou measure is:

σ = Σ_{t = 0}^{\infty} \frac{u_{t}^{/ /} - \bar{u_{t}^{/ /}}}{Δ ϵ 1}

where uʺ bar is the long-run equilibrium value. Here again, the S-K measure has to be modified to measure the “cross-responsiveness” effect of one variable on another, although here the modification is more minor. The difference between the initial and final equilibrium values is used to normalize as follows:

σ_{x y} = Σ_{t = 0}^{\infty} \frac{y_{t}^{/ /} - \bar{y_{t}^{/ /}}}{y_{o} - \bar{y^{/ /}}}

For responsiveness to shocks in the dependent variable in a single equation error correction model, this measure would be identical to the corresponding S-K responsiveness measure (although this might not be true for a system of equations where feedback effects from other equations could cause the long run change in the dependent variable to be larger than the shock.

Table 1:

Unit Root Tests

Variables in log Levels: ^1/

Augmented Dickey-Fuller Tests with Constant and Trend Included. Critical values: 5%=-3.461 1%=-4.065:

Table 1:

Unit Root Tests

Variables in log Levels: ^1/

Augmented Dickey-Fuller Tests with Constant and Trend Included. Critical values: 5%=-3.461 1%=-4.065:

variable	t-adf	σ	lag	t-lag	t-prob
Employment	-2.9236	0.0042795	7	4.2465	0.0001
	-2.0720	0.0055139	3	4.6742	0.0000
	-1.2809	0.0061676	2	0.08197	0.9349
	-1.2900	0.0061311	1	6.4553	0.0000
	-0.67922	0.0074549	0
Labor force	-1.6115	0.0030614	8	2.8375	0.0058
	-0.93532	0.0037966	3	-0.46107	0.6460
	-0.99135	0.0037786	2	-1.0523	0.2957
	-1.1372	0.0037810	1	0.36924	0.7129
	-1.1109	0.0037617	0
Real wage	-2.1970	0.023305	8	3.2161	0.0019
	-2.7267	0.028227	3	-14.909	0.0000
	-1.8818	0.054045	2	-11.403	0.0000
	-4.4334**	0.086069	1	0.89391	0.3739
	-4.4446**	0.085967	0
Real GDP	-3.5852*	0.0023928	5	3.3229	0.0013
	-2.8740	0.0025183	3	0.10950	0.9131
	-2.9383	0.0025036	2	-2.0423	0.0442
	-3.6888*	0.0025493	1	20.027	0.0000
	-2.5007	0.0060322	0
Productivity	-3.3226	0.0048599	5	-2.1035	0.0385
	-3.4663*	0.0051399	3	2.0173	0.0469
	-3.6074*	0.0052318	2	-3.0869	0.0027
	-3.3823	0.0054851	1	2.2849	0.0248
	-3.6169*	0.0056166	0
Working age population	1.8774	0.00045652	4	4.3548	0.0000
	3.5467	0.00050297	3	-5.7526	0.0000
	2.0160	0.00059034	2	-0.35274	0.7252
	1.9966	0.00058733	1	5.5767	0.0000
	3.6750	0.00068139	0
Real minimum wage	-2.5714	0.022230	4	3.9929	0.0001
	-3.2779	0.024127	3	-13.524	0.0000
	-2.0709	0.042752	2	-3.0324	0.0032
	-2.2601	0.044742	1	-4.1514	0.0001
	-3.0134	0.048738	0
Unemployment rate	-4.0555*	0.31338	4	5.7622	0.0000
	-2.2715	0.36859	3	4.0276	0.0001
	-1.5242	0.40023	2	0.68891	0.4928
	-1.4413	0.39901	1	7.0165	0.0000
	-0.74549	0.49746	0
Social security contributions	-1.9742	5.3380	4	2.2346	0.0281
	-1.6507	5.4634	3	-1.2426	0.2175
	-1.8799	5.4809	2	1.9048	0.0602
	-1.6311	5.5640	1	2.8696	0.0052

Table 1:

Unit Root Tests

Variables in log Levels: ^1/

Augmented Dickey-Fuller Tests with Constant and Trend Included. Critical values: 5%=-3.461 1%=-4.065:

variable	t-adf	σ	lag	t-lag	t-prob
Employment	-2.9236	0.0042795	7	4.2465	0.0001
	-2.0720	0.0055139	3	4.6742	0.0000
	-1.2809	0.0061676	2	0.08197	0.9349
	-1.2900	0.0061311	1	6.4553	0.0000
	-0.67922	0.0074549	0
Labor force	-1.6115	0.0030614	8	2.8375	0.0058
	-0.93532	0.0037966	3	-0.46107	0.6460
	-0.99135	0.0037786	2	-1.0523	0.2957
	-1.1372	0.0037810	1	0.36924	0.7129
	-1.1109	0.0037617	0
Real wage	-2.1970	0.023305	8	3.2161	0.0019
	-2.7267	0.028227	3	-14.909	0.0000
	-1.8818	0.054045	2	-11.403	0.0000
	-4.4334**	0.086069	1	0.89391	0.3739
	-4.4446**	0.085967	0
Real GDP	-3.5852*	0.0023928	5	3.3229	0.0013
	-2.8740	0.0025183	3	0.10950	0.9131
	-2.9383	0.0025036	2	-2.0423	0.0442
	-3.6888*	0.0025493	1	20.027	0.0000
	-2.5007	0.0060322	0
Productivity	-3.3226	0.0048599	5	-2.1035	0.0385
	-3.4663*	0.0051399	3	2.0173	0.0469
	-3.6074*	0.0052318	2	-3.0869	0.0027
	-3.3823	0.0054851	1	2.2849	0.0248
	-3.6169*	0.0056166	0
Working age population	1.8774	0.00045652	4	4.3548	0.0000
	3.5467	0.00050297	3	-5.7526	0.0000
	2.0160	0.00059034	2	-0.35274	0.7252
	1.9966	0.00058733	1	5.5767	0.0000
	3.6750	0.00068139	0
Real minimum wage	-2.5714	0.022230	4	3.9929	0.0001
	-3.2779	0.024127	3	-13.524	0.0000
	-2.0709	0.042752	2	-3.0324	0.0032
	-2.2601	0.044742	1	-4.1514	0.0001
	-3.0134	0.048738	0
Unemployment rate	-4.0555*	0.31338	4	5.7622	0.0000
	-2.2715	0.36859	3	4.0276	0.0001
	-1.5242	0.40023	2	0.68891	0.4928
	-1.4413	0.39901	1	7.0165	0.0000
	-0.74549	0.49746	0
Social security contributions	-1.9742	5.3380	4	2.2346	0.0281
	-1.6507	5.4634	3	-1.2426	0.2175
	-1.8799	5.4809	2	1.9048	0.0602
	-1.6311	5.5640	1	2.8696	0.0052

Variables in log Levels: ^1/

Critical values: 5%=-3.46 1%=-4.064; Constant and Trend includad unless indicated.

Variables in log Levels: ^1/

Critical values: 5%=-3.46 1%=-4.064; Constant and Trend includad unless indicated.

variable	t-adf	σ	lag	t-lag	t-prob
Employment ^2/	-2.6173**	0.0044143	8	2.1305	0.0362
	-1.6311	0.0051963	3	-3.8262	0.0002
	-2.5383*	0.0055888	2	-4.6530	0.0000
	-4.6518**	0.0062100	1	0.45974	0.6468
	-5.0554**	0.0061824	0
Labor force	-3.7147*	0.0032089	4	2.5170	0.0138
	-3.0277	0.0033105	3	-5.1984	0.0000
	-6.0310**	0.0037887	2	0.51942	0.6048
	-7.2654**	0.0037724	1	1.1063	0.2717
	-9.0158**	0.0037773	0
Real wage	-3.6934*	0.026224	4	-2.0450	0.0441
	-5.3825**	0.026722	3	-4.5226	0.0000
	-28.086**	0.029655	2	14.032	0.0000
	-21.769**	0.053909	1	13.337	0.0000
	-10.106**	0.094250	0
Real GDP ^2/	-2.1604*	0.0025402	4	-2.9227	0.0045
	-2.4653**	0.0026531	3	-0.18694	0.8522
	-2.5293*	0.0026378	2	-0.097111	0.9229
	-2.5827*	0.0026224	1	2.3860	0.0193
	-2.4181*	0.0026930	0
Productivity	-4.5270**	0.0049609	5	2.3244	0.0225
	-3.0602	0.0052618	3	-2.5360	0.0130
	-4.6068**	0.0054236	2	-2.3886	0.0191
	-7.8148**	0.0055667	1	3.1361	0.0023
	-7.0829**	0.0058365	0
Working age population ^3/	-0.89082	0.00060966	6	-3.6818	0.0004
	-1.8815	0.00065069	3	-2.2246	0.0288
	-2.3517	0.00066563	2	1.2231	0.2247
	-2.0771	0.00066755	1	-4.1870	0.0001
	-4.4102**	0.00072821	0
Minimum wage	-2.8192	0.023558	4	-1.1380	0.2583
	-3	-11.1809	0.023599	311.10*	0.0000
	-14.134**	0.036603	2	7.3485	0.0000
	-10.175**	0.046332	1	2.4460	0.0164
	-14.484**	0.047612	0
Unemployment rate ^3/	-2.1701*	0.33015	5	2.4653	0.0158
	-0.84717	0.34810	3	-4.3075	0.0000
	-1.8164	0.38275	2	-3.9074	0.0002
	-3.2439**	0.41363	1	-0.85778	0.3934
	-3.9895**	0.41299	0
Social security contributions	-3.8296*	5.3843	4	1.7528	0.0834
	-3.3908	5.4511	3	-1.9935	0.0495
	-4.7771**	5.5468	2	1.5110	0.1345
	-4.5669**	5.5885	1	-1.6287	0.1071
	-6.9535**	5.6419	0

Variables in log Levels: ^1/

Critical values: 5%=-3.46 1%=-4.064; Constant and Trend includad unless indicated.

variable	t-adf	σ	lag	t-lag	t-prob
Employment ^2/	-2.6173**	0.0044143	8	2.1305	0.0362
	-1.6311	0.0051963	3	-3.8262	0.0002
	-2.5383*	0.0055888	2	-4.6530	0.0000
	-4.6518**	0.0062100	1	0.45974	0.6468
	-5.0554**	0.0061824	0
Labor force	-3.7147*	0.0032089	4	2.5170	0.0138
	-3.0277	0.0033105	3	-5.1984	0.0000
	-6.0310**	0.0037887	2	0.51942	0.6048
	-7.2654**	0.0037724	1	1.1063	0.2717
	-9.0158**	0.0037773	0
Real wage	-3.6934*	0.026224	4	-2.0450	0.0441
	-5.3825**	0.026722	3	-4.5226	0.0000
	-28.086**	0.029655	2	14.032	0.0000
	-21.769**	0.053909	1	13.337	0.0000
	-10.106**	0.094250	0
Real GDP ^2/	-2.1604*	0.0025402	4	-2.9227	0.0045
	-2.4653**	0.0026531	3	-0.18694	0.8522
	-2.5293*	0.0026378	2	-0.097111	0.9229
	-2.5827*	0.0026224	1	2.3860	0.0193
	-2.4181*	0.0026930	0
Productivity	-4.5270**	0.0049609	5	2.3244	0.0225
	-3.0602	0.0052618	3	-2.5360	0.0130
	-4.6068**	0.0054236	2	-2.3886	0.0191
	-7.8148**	0.0055667	1	3.1361	0.0023
	-7.0829**	0.0058365	0
Working age population ^3/	-0.89082	0.00060966	6	-3.6818	0.0004
	-1.8815	0.00065069	3	-2.2246	0.0288
	-2.3517	0.00066563	2	1.2231	0.2247
	-2.0771	0.00066755	1	-4.1870	0.0001
	-4.4102**	0.00072821	0
Minimum wage	-2.8192	0.023558	4	-1.1380	0.2583
	-3	-11.1809	0.023599	311.10*	0.0000
	-14.134**	0.036603	2	7.3485	0.0000
	-10.175**	0.046332	1	2.4460	0.0164
	-14.484**	0.047612	0
Unemployment rate ^3/	-2.1701*	0.33015	5	2.4653	0.0158
	-0.84717	0.34810	3	-4.3075	0.0000
	-1.8164	0.38275	2	-3.9074	0.0002
	-3.2439**	0.41363	1	-0.85778	0.3934
	-3.9895**	0.41299	0
Social security contributions	-3.8296*	5.3843	4	1.7528	0.0834
	-3.3908	5.4511	3	-1.9935	0.0495
	-4.7771**	5.5468	2	1.5110	0.1345
	-4.5669**	5.5885	1	-1.6287	0.1071
	-6.9535**	5.6419	0

Table 2:

Long-Run Cointegrating Regressions for the Basic Model

Table 3:

Basic Model Results for the Labor Force Equation, 1972-1993

(Lags shown in parentheses)

Dependent Variable is DlnLF - Estimated by OLS ^1/

Table 3:

Basic Model Results for the Labor Force Equation, 1972-1993

(Lags shown in parentheses)

Dependent Variable is DlnLF - Estimated by OLS ^1/

Variable		Coefficient	Std. Error	t-value	t-prob	PartR²
	Constant	-0.0046171	0.0029604	-1.560	0.1230	0.0310
	DlnW(1)	0.025603	0.0094077	2.721	0.0081	0.0888
	DlnW(3)	-0.026833	0.010219	-2.626	0.0104	0.0832
	DlnW(A)	-0.027768	0.010260	-2.706	0.0084	0.0879
	DUR(5)	-0.0027475	0.00080911	-3.396	0.0011	0.1317
	DUR(8)	0.0029098	0.00098402	2.957	0.0041	0.1032
	DlnWorkPop(4)	-1.1142	0.52383	-2.127	0.0367	0.0562
	DlnWorkPop(6)	-1.0661	0.48717	-2.188	0.0317	0.0593
	Seasonal	0.0017203	0.0021740	0.791	0.4312	0.0082
	Seasonal_1	0.016041	0.0039911	4.019	0.0001	0.1753
	Seasonal 2	0.011635	0.0025946	4.484	0.0000	0.2092
	LFECM(1)	-0.054273	0.025020	-2.169	0.0332	0.0583
R² = 0.589644 F(11, 16) = 9.9277 [0.0000] σ = 0.00260946 DW = 1.83 RSS = 0.00051750381 for 12 variables and 88 observations
AR 1- 5F( 5, 71)= 0.56186 [0.7288]
ARCH 4 F( 4, 68)= 0.25429 [0.9061]
Normality Ch² (2)= 0.46904 [0.9647]
Xi² F(19, 56)= 0.46904 [0.9647]
RESET F( 1, 75)= 0.053158 [0.8183]

Table 3:

Basic Model Results for the Labor Force Equation, 1972-1993

(Lags shown in parentheses)

Dependent Variable is DlnLF - Estimated by OLS ^1/

Variable		Coefficient	Std. Error	t-value	t-prob	PartR²
	Constant	-0.0046171	0.0029604	-1.560	0.1230	0.0310
	DlnW(1)	0.025603	0.0094077	2.721	0.0081	0.0888
	DlnW(3)	-0.026833	0.010219	-2.626	0.0104	0.0832
	DlnW(A)	-0.027768	0.010260	-2.706	0.0084	0.0879
	DUR(5)	-0.0027475	0.00080911	-3.396	0.0011	0.1317
	DUR(8)	0.0029098	0.00098402	2.957	0.0041	0.1032
	DlnWorkPop(4)	-1.1142	0.52383	-2.127	0.0367	0.0562
	DlnWorkPop(6)	-1.0661	0.48717	-2.188	0.0317	0.0593
	Seasonal	0.0017203	0.0021740	0.791	0.4312	0.0082
	Seasonal_1	0.016041	0.0039911	4.019	0.0001	0.1753
	Seasonal 2	0.011635	0.0025946	4.484	0.0000	0.2092
	LFECM(1)	-0.054273	0.025020	-2.169	0.0332	0.0583
R² = 0.589644 F(11, 16) = 9.9277 [0.0000] σ = 0.00260946 DW = 1.83 RSS = 0.00051750381 for 12 variables and 88 observations
AR 1- 5F( 5, 71)= 0.56186 [0.7288]
ARCH 4 F( 4, 68)= 0.25429 [0.9061]
Normality Ch² (2)= 0.46904 [0.9647]
Xi² F(19, 56)= 0.46904 [0.9647]
RESET F( 1, 75)= 0.053158 [0.8183]

Table 4:

Basic Model Results for the Real Wage Equation. 1972-1993

(Lags shown in parentheses)

Dependent variable is DlnW - Estimated by Instrumental Variables

Table 4:

Basic Model Results for the Real Wage Equation. 1972-1993

(Lags shown in parentheses)

Dependent variable is DlnW - Estimated by Instrumental Variables

	Variable	Coefficient	Std. Error	t-value	t-prob
	Constant	-0.0084830	0.019519	-0.435	0.6650
	DlnW(1)	-0.30033	0.099220	-3.027	0.0033
	DlnW(2)	-0.34003	0.10596	-3.209	0.0019
	DlnW(3)	-0.34639	0.083345	-4.156	0.0001
	DlnPROD	1.7841	0.88210	2.023	0.0463
	DlnPROD(2)	-1.3308	0.51530	-2.583	0.0116
	DlnWMIN(3)	0.17398	0.063875	2.724	0.0079
	WECM(1)	-0.30078	0.057241	-5.255	0.0000
	Seasonal	-0.10570	0.020184	-5.237	0.0000
	Seasonal_1	0.093051	0.036693	2.536	0.0131
	Seasonal_2	0.037127	0.031524	1.178	0.2423
σ = 0.0221772 DW = 1.99
RSS = 0.04082159323 for 11 variables and 94 observations
2 endogenous and 10 exogenous variables with 12 instruments
Reduced Form σ = 0.0203942
Specification Chi² (1) = 3.317 [0.0686]
Testing ß = 0: Chi² (10) = 1610.9 [0.0000] **

Table 4:

Basic Model Results for the Real Wage Equation. 1972-1993

(Lags shown in parentheses)

Dependent variable is DlnW - Estimated by Instrumental Variables

	Variable	Coefficient	Std. Error	t-value	t-prob
	Constant	-0.0084830	0.019519	-0.435	0.6650
	DlnW(1)	-0.30033	0.099220	-3.027	0.0033
	DlnW(2)	-0.34003	0.10596	-3.209	0.0019
	DlnW(3)	-0.34639	0.083345	-4.156	0.0001
	DlnPROD	1.7841	0.88210	2.023	0.0463
	DlnPROD(2)	-1.3308	0.51530	-2.583	0.0116
	DlnWMIN(3)	0.17398	0.063875	2.724	0.0079
	WECM(1)	-0.30078	0.057241	-5.255	0.0000
	Seasonal	-0.10570	0.020184	-5.237	0.0000
	Seasonal_1	0.093051	0.036693	2.536	0.0131
	Seasonal_2	0.037127	0.031524	1.178	0.2423
σ = 0.0221772 DW = 1.99
RSS = 0.04082159323 for 11 variables and 94 observations
2 endogenous and 10 exogenous variables with 12 instruments
Reduced Form σ = 0.0203942
Specification Chi² (1) = 3.317 [0.0686]
Testing ß = 0: Chi² (10) = 1610.9 [0.0000] **

Table 5:

Basic Model Results for the Employment Equation, 1972-1993

(Lags shown in parentheses)

Dependent Variable lnE - Estimated by Instrumental Variables

Table 5:

Basic Model Results for the Employment Equation, 1972-1993

(Lags shown in parentheses)

Dependent Variable lnE - Estimated by Instrumental Variables

	Variable	Coefficient	Std. Error	t-value	t-nrob
	Constant	0.00061231	0.0012243	0.500	0.6185
	DlnE(1)	0.27658	0.088304	3.132	0.0025
	DlnE(6)	-0.37990	0.082863	-4.585	0.0000
	DlnW	0.071040	0.022843	3.110	0.0026
	DlnTSS	-0.19534	0.040882	-4.778	0.0000
	DlnTSS(1)	0.15814	0.038609	4.096	0.0001
	DlnTSS(3)	0.13946	0.034501	4.042	0.0001
	DlnGDP	0.36081	0.086105	4.190	0.0001
	EmpECM(1)	-0.10588	0.013703	-7.726	0.0000
	Seasonal	0.0081766	0.0035076	2.331	0.0224
	Seasonal_1	-0.016746	0.0031162	-5.374	0.0000
	Seasonal_2	0.0093842	0.0019501	4.812	0.0000
σ = 0.0032641 DW = 1.72
RSS = 0.0007990785297 for 12 variables and 87 observations
3 endogenous and 10 exogenous variables with 19 instruments
Reduced Form σ = 0.00344761
Specification Chi²(7) = 12.083 [0.0979]
Testing ß = 0: Chi² (11) = 355.71 [0.0000] **

Table 5:

Basic Model Results for the Employment Equation, 1972-1993

(Lags shown in parentheses)

Dependent Variable lnE - Estimated by Instrumental Variables

	Variable	Coefficient	Std. Error	t-value	t-nrob
	Constant	0.00061231	0.0012243	0.500	0.6185
	DlnE(1)	0.27658	0.088304	3.132	0.0025
	DlnE(6)	-0.37990	0.082863	-4.585	0.0000
	DlnW	0.071040	0.022843	3.110	0.0026
	DlnTSS	-0.19534	0.040882	-4.778	0.0000
	DlnTSS(1)	0.15814	0.038609	4.096	0.0001
	DlnTSS(3)	0.13946	0.034501	4.042	0.0001
	DlnGDP	0.36081	0.086105	4.190	0.0001
	EmpECM(1)	-0.10588	0.013703	-7.726	0.0000
	Seasonal	0.0081766	0.0035076	2.331	0.0224
	Seasonal_1	-0.016746	0.0031162	-5.374	0.0000
	Seasonal_2	0.0093842	0.0019501	4.812	0.0000
σ = 0.0032641 DW = 1.72
RSS = 0.0007990785297 for 12 variables and 87 observations
3 endogenous and 10 exogenous variables with 19 instruments
Reduced Form σ = 0.00344761
Specification Chi²(7) = 12.083 [0.0979]
Testing ß = 0: Chi² (11) = 355.71 [0.0000] **

Table 6:

Long-Run Colntegrating Regressions for the Policy Model

Table 7:

Policy Model Results for the Labor Force Equation, 1981-1993

(Lags shown in parentheses)

Dependent Variable is DLnLF - Estimated by OLS ^1/

Table 7:

Policy Model Results for the Labor Force Equation, 1981-1993

(Lags shown in parentheses)

Dependent Variable is DLnLF - Estimated by OLS ^1/

	Variable	Coefficient	Std. Error	t-value	t-prob	PartR²
	Constant	-0.0040603	0.0026636	-1.524	0.1353	0.0549
	DLnW(3)	-0.034878	0.016465	-2.118	0.0404	0.1009
	DUR(7)	-0.0022275	0.0012352	-1.803	0.0789	0.0752
	DUR(8)	0.0026690	0.0013203	2.021	0.0500	0.0927
	DLnWorkPop(3)	-2.4973	0.63070	-3.960	0.0003	0.2816
	DLnREPR(3)	0.0018743	0.0014475	1.295	0.2028	0.0402
	DLnREPR(4)	0.0035148	0.0015403	2.282	0.0279	0.1152
	DLnLPENSD	-0.053779	0.030614	-1.757	0.0866	0.0716
	LFECM2(1)	-0.20421	0.046137	-4.426	0.0001	0.3288
	Seasonal	0.0036653	0.0034986	1.048	0.3011	0.0267
	Seasonal_1	0.0075809	0.0041596	1.823	0.0759	0.0767
	Seasonal_2	0.016190	0.0032696	4.952	0.0000	0.3800
R² = 0.737506 F(11, 40) = 10.217 [0.0000] σ = 0.00239251 DW = 2.31
RSS = 0.0002289640177 for 12 variables and 52 observations
AR 1 - 4F( 4, 36) = 0.98292 [0.4291]
ARCH 4 F( 4, 32) = 0.18011 [0.9470]
Normality Chi² (2) = 3.7685 [0.1519]
Xi² F(19. 20) = 0.52641 [0.9161]
RESET F( 1, 39) = 0.54023 [0.4667]

Table 7:

Policy Model Results for the Labor Force Equation, 1981-1993

(Lags shown in parentheses)

Dependent Variable is DLnLF - Estimated by OLS ^1/

	Variable	Coefficient	Std. Error	t-value	t-prob	PartR²
	Constant	-0.0040603	0.0026636	-1.524	0.1353	0.0549
	DLnW(3)	-0.034878	0.016465	-2.118	0.0404	0.1009
	DUR(7)	-0.0022275	0.0012352	-1.803	0.0789	0.0752
	DUR(8)	0.0026690	0.0013203	2.021	0.0500	0.0927
	DLnWorkPop(3)	-2.4973	0.63070	-3.960	0.0003	0.2816
	DLnREPR(3)	0.0018743	0.0014475	1.295	0.2028	0.0402
	DLnREPR(4)	0.0035148	0.0015403	2.282	0.0279	0.1152
	DLnLPENSD	-0.053779	0.030614	-1.757	0.0866	0.0716
	LFECM2(1)	-0.20421	0.046137	-4.426	0.0001	0.3288
	Seasonal	0.0036653	0.0034986	1.048	0.3011	0.0267
	Seasonal_1	0.0075809	0.0041596	1.823	0.0759	0.0767
	Seasonal_2	0.016190	0.0032696	4.952	0.0000	0.3800
R² = 0.737506 F(11, 40) = 10.217 [0.0000] σ = 0.00239251 DW = 2.31
RSS = 0.0002289640177 for 12 variables and 52 observations
AR 1 - 4F( 4, 36) = 0.98292 [0.4291]
ARCH 4 F( 4, 32) = 0.18011 [0.9470]
Normality Chi² (2) = 3.7685 [0.1519]
Xi² F(19. 20) = 0.52641 [0.9161]
RESET F( 1, 39) = 0.54023 [0.4667]

Table 8:

Basic Model Results for the Real Wage Equation, 1971-1993

(Lags shown in parentheses)

Dependent variable is DlnW - Estimated by OLS ^1/

Table 8:

Basic Model Results for the Real Wage Equation, 1971-1993

(Lags shown in parentheses)

Dependent variable is DlnW - Estimated by OLS ^1/

	Variable	Coefficient	Std. Error	t-value	t-prob	PartR²
	Constant	0.013066	0.015687	0.833	0.4096	0.0162
	DLnW(3)	-0.23414	0.087457	-2.677	0.0105	0.1458
	DLnW(4)	0.34891	0.10150	3.438	0.0013	0.2196
	DLnPR0D(3)	-1.0042	0.52678	-1.906	0.0635	0.0796
	DUR(1)	0.013594	0.0047158	2.883	0.0062	0.1652
	DREPR	0.030792	0.0082032	3.754	0.0005	0.2512
	DLnWMIN(2)	-0.31806	0.097096	-3.276	0.0021	0.2035
	Dtempshare(3)	0.0021394	0.00096377	2.220	0.0319	0.1050
	LWECM2(1)	-0.66090	0.12613	-5.240	0.0000	0.3953
	Seasonal	-0.068184	0.022547	-3.024	0.0042	0.1788
	Seasonal_1	-0.0021800	0.022236	-0.098	0.9224	0.0002
	Seasonal_2	0.029393	0.020928	1.405	0.1675	0.0449
R² = 0.983182 F(11, 42) = 223.21 [0.0000] σ = 0.0132199 DW = 2.23 RSS = 0.007340122792 for 12 variables and 54 observations
AR 1- 4F( 4, 38) = 1.5174 [0.2166] ARCH 4 F( 4, 34) = 0.2956 [0.8788] Normality Chi² (2) = 1.2894 [0.5248] Xi² F(19, 22) = 0.34389 [0.9890] RESET F( 1, 41) = 3.1949 [0.0813]

Table 8:

Basic Model Results for the Real Wage Equation, 1971-1993

(Lags shown in parentheses)

Dependent variable is DlnW - Estimated by OLS ^1/

	Variable	Coefficient	Std. Error	t-value	t-prob	PartR²
	Constant	0.013066	0.015687	0.833	0.4096	0.0162
	DLnW(3)	-0.23414	0.087457	-2.677	0.0105	0.1458
	DLnW(4)	0.34891	0.10150	3.438	0.0013	0.2196
	DLnPR0D(3)	-1.0042	0.52678	-1.906	0.0635	0.0796
	DUR(1)	0.013594	0.0047158	2.883	0.0062	0.1652
	DREPR	0.030792	0.0082032	3.754	0.0005	0.2512
	DLnWMIN(2)	-0.31806	0.097096	-3.276	0.0021	0.2035
	Dtempshare(3)	0.0021394	0.00096377	2.220	0.0319	0.1050
	LWECM2(1)	-0.66090	0.12613	-5.240	0.0000	0.3953
	Seasonal	-0.068184	0.022547	-3.024	0.0042	0.1788
	Seasonal_1	-0.0021800	0.022236	-0.098	0.9224	0.0002
	Seasonal_2	0.029393	0.020928	1.405	0.1675	0.0449
R² = 0.983182 F(11, 42) = 223.21 [0.0000] σ = 0.0132199 DW = 2.23 RSS = 0.007340122792 for 12 variables and 54 observations
AR 1- 4F( 4, 38) = 1.5174 [0.2166] ARCH 4 F( 4, 34) = 0.2956 [0.8788] Normality Chi² (2) = 1.2894 [0.5248] Xi² F(19, 22) = 0.34389 [0.9890] RESET F( 1, 41) = 3.1949 [0.0813]

Table 9:

Policy Model Results for the Employment Equation, 1981-1993

(Lags shown in parentheses)

Dependent Variable lnE = Estimated by Instrumental Variables

Table 9:

Policy Model Results for the Employment Equation, 1981-1993

(Lags shown in parentheses)

Dependent Variable lnE = Estimated by Instrumental Variables

	Variable	Coefficient	Std. Error	t-value	t-nrob
	Constant	-0.00013265	0.0025914	-0.051	0.9595
	DLnGDP	0.24073	0.13816	1.742	0.0910
	DLnGDP(3)	-0.61608	0.15212	-4.050	0.0003
	DLnW	-0.040610	0.019300	-2.104	0.0433
	DLnW(1)	0.035554	0.014486	2.454	0.0197
	DLnE(1)	0.39297	0.082871	4.742	0.0000
	DLnE(3)	0.21376	0.094120	2.271	0.0300
	DLnE(6)	-0.24854	0.057618	-4.314	0.0001
	DLnTSS	-0.26215	0.041517	-6.314	0.0000
	DLnTSS(1)	0.30460	0.039048	7.801	0.0000
	DLnTSS(3)	0.096461	0.046362	2.081	0.0456
	DLnTSS(4)	-0.16525	0.044189	-3.740	0.0007
	DLnSTRIKE(2)	-0.00095640	0.00033149	-2.885	0.0069
	DLnSTRIKE(4)	0.0014214	0.00033737	4.213	0.0002
	DLnCOVER(1)	-0.0094967	0.0024831	-3.825	0.0006
	EmpECM2(1)	-0.44459	0.052754	-8.428	0.0000
	Seasonal	-0.0089656	0.0045113	-1.987	0.0555
	Seasonal_1	0.013916	0.0041862	3.324	0.0022
	Seasonal_2	0.0073756	0.0019385	3.805	0.0006
Additional Instruments used:
DLnSEVER DLnTSS(2) DLnGDP(1) DLnCOVER DLnE(5) DLnW(2) DLnW(3) DLnSEVER(1)
σ = 0.00176823 DW = 2.02 RSS = 0.000100052243 for 19 variables and 51 observations 3 endogenous and 17 exogenous variables with 25 instruments Reduced Form σ = 0.00155334 Specification Chi² (6) = 10.587 [0.1020] Testing ß = 0: Chi² (18) = 1157.8 [0.0000]**
Testing for Error Autocorrelation from lags 1 to 4 Chi² (4) = 1.4692 [0.8321]
IV Error Autocorrelation Coefficients:
	Coeff.	Lag 1 -0.05538	Lag 2 0.1402	Lag 3 -0.1866	Lag 4 -0.1426
	ARCH 4 F( 4, 24) = 0.94264 [0.4564] Normality Chi² (2) = 0.40163 [0.8181]

Table 9:

Policy Model Results for the Employment Equation, 1981-1993

(Lags shown in parentheses)

Dependent Variable lnE = Estimated by Instrumental Variables

	Variable	Coefficient	Std. Error	t-value	t-nrob
	Constant	-0.00013265	0.0025914	-0.051	0.9595
	DLnGDP	0.24073	0.13816	1.742	0.0910
	DLnGDP(3)	-0.61608	0.15212	-4.050	0.0003
	DLnW	-0.040610	0.019300	-2.104	0.0433
	DLnW(1)	0.035554	0.014486	2.454	0.0197
	DLnE(1)	0.39297	0.082871	4.742	0.0000
	DLnE(3)	0.21376	0.094120	2.271	0.0300
	DLnE(6)	-0.24854	0.057618	-4.314	0.0001
	DLnTSS	-0.26215	0.041517	-6.314	0.0000
	DLnTSS(1)	0.30460	0.039048	7.801	0.0000
	DLnTSS(3)	0.096461	0.046362	2.081	0.0456
	DLnTSS(4)	-0.16525	0.044189	-3.740	0.0007
	DLnSTRIKE(2)	-0.00095640	0.00033149	-2.885	0.0069
	DLnSTRIKE(4)	0.0014214	0.00033737	4.213	0.0002
	DLnCOVER(1)	-0.0094967	0.0024831	-3.825	0.0006
	EmpECM2(1)	-0.44459	0.052754	-8.428	0.0000
	Seasonal	-0.0089656	0.0045113	-1.987	0.0555
	Seasonal_1	0.013916	0.0041862	3.324	0.0022
	Seasonal_2	0.0073756	0.0019385	3.805	0.0006
Additional Instruments used:
DLnSEVER DLnTSS(2) DLnGDP(1) DLnCOVER DLnE(5) DLnW(2) DLnW(3) DLnSEVER(1)
σ = 0.00176823 DW = 2.02 RSS = 0.000100052243 for 19 variables and 51 observations 3 endogenous and 17 exogenous variables with 25 instruments Reduced Form σ = 0.00155334 Specification Chi² (6) = 10.587 [0.1020] Testing ß = 0: Chi² (18) = 1157.8 [0.0000]**
Testing for Error Autocorrelation from lags 1 to 4 Chi² (4) = 1.4692 [0.8321]
IV Error Autocorrelation Coefficients:
	Coeff.	Lag 1 -0.05538	Lag 2 0.1402	Lag 3 -0.1866	Lag 4 -0.1426
	ARCH 4 F( 4, 24) = 0.94264 [0.4564] Normality Chi² (2) = 0.40163 [0.8181]

Friedman (1968) coined the term “natural rate of unemployment” which was subsequently used extensively in the so-called New Classical Economics school of thought.

Bianchi and Zoega (1994).

Karanassou and Snower (1993).

See Franks (1994) for a detailed discussion of the effects of these structural changes on the labor market in Spain.

See Bean (1994) for a review of explanations of European unemployment.

See Booth (1995) pp. 124-128 for an exposition of the right-to-manage model. Oswald and Turnbull (1985) provide empirical evidence in support of the right-to-manage assumption for Britain.

It should be recognized that minimum wages may not be a completely exogenous variable, since there is often an implicit or explicit linkage between average wages and the setting of the minimum wage. In the case of Spain, there appears to have been some effort to maintain the minimum wage as a share of the average wage in the mid-1970s, but not since then. Nevertheless, in the estimation of the model, the minimum wage variable was included with a lag to avoid a possible simultaneity problem. See Dolado et al 1996 for an in depth discussion of the effect of minimum wages on employment.

One could also consider estimating the model directly on the product wage, imposing the condition alpha=rho. As will be seen in the empirical results, this restriction is rejected by the data. This is possibly due to the fact that T_socsec may also capture other negative effects of government taxation (such as red tape) in addition to the direct labor cost.

Due to the lag structure incorporated into the estimates and missing data for some variables, the estimates were generally done for the period 1972-1993.

As is usually the case given the weakness of the unit root tests, the results are not unambiguous. Some of the variables show some signs of having I(2) properties. Nevertheless, the results reported in Table 1, together with the literature on models of this type, lead to a strong presumption that the variables are I(1). Stationarity tests on the differenced regressions with the error correction terms also confirmed this presumption.

Pesaran and Shin (1995).

The preferred specification was also estimated simultaneously using full information maximum likelihood techniques to compare with the instrumental variables results. The FIML results did not differ substantially from those of the IV ECM specification reported below.

Interestingly, when the sample is split and the same regression is run separately over 1972-1980 and 1981-1993, the negative relationship between wages and labor force participation disappears, particularly for the second half of the sample, implying that their may have been an exogenous change in the social attitudes regarding households’ work-leisure tradeoff.

In fact, the dynamics of the composite variable are much more representative of the true response to wage increases, since an increase in the real wage automatically raises the real value of social insurance contributions, since contributions are levied as a proportion of wages. Thus, the impulse response of the simple real wage variable must be thought of as the response to an increase in wages with a simultaneous reduction in social contribution rates. Seen from this perspective, the paradoxical positive sign is more understandable.

Dolado, Malo de Molina, and Zabalza (1986) examine unemployment in Spanish industry over the 1970s and early 1980s and find a significant role played by demand shocks.

Controls on disability pensions were fairly weak until the 1990s; there is substantial anecdotal evidence of firms using temporary disability classifications of workers as an alternative to redundancies for younger workers. For older workers, early retirements were often used with official acquiescence. This effect may also be captured by the disability pension variable.

First-time entrants into the labor force are not eligible for unemployment benefits. Nevertheless, the future availability of unemployment benefits will certainly have a positive impact on the expected value of entering the labor force, especially since there has been a large amount of rotation between temporary jobs and unemployment since temporary contracts were liberalized in 1984.

See Bentotlila and Dolado (1994); Jimeno and Toharia (1993).

It should be noted, however, that the long-run regressions do not unambiguously cointegrate. See the footnote to Table 6.

In the basic model, own variable persistence and responsiveness measures capture the adjustment effects of the structural features of the labor market which, in the policy model are separately modeled. To illustrate this effect, consider the comparison between a simple autoregression versus an autoregressive equation with additional structural variables. If one calculates the adjustment speed of the simple autoregressive model compared with the model including structural variables, the autoregressive model will show slower adjustment (i.e., higher coefficients on the lagged terms) because the autoregressive terms are picking up some of the effects of the persistence of the omitted structural variables.

The S-K persistence measure used here is slightly different from that used in the measures of persistence used for shocks to the individual equations in previous sections. See the Appendix for details.

The models of the other countries used in the comparison are actually more similar to the basic model than to the policy model, so that the basic is the more appropriate reference point.

Tests were conducted for all lags up to 8. Results reported are for the longest lag whose coefficient is significant, plus lags 0-3.

Tests were conducted for all lags up to eight. Results are reported for the first significant lag, plus for lags 0-4. Unsatisfactory results for some variables led to test without the inclusion of a constant and seasonal dummies rather than a constant and time trend.

Does not include constant or trend. Critical values: 5%=-1.944 1%=-2.589.

Constant and seasonal dummies used. Critical values: 5%=-2.893 1%=-3.502.

None of the endogenous variables was significant in contemporaneous variables, so the estimation was done with OLS rather than IV.

The critical value for this test is about 3.9, so there is not conclusive evidence of cointegration. Nevertheless, the same model without the inclusion of the dummy variables does unambiguously cointegrate. On this basis, and in light of the characteristics of the residuals to the regression, cointegration is accepted.

None of the endogenous variables was significant in contemporaneous variables, so the estimation was done with OLS rather than IV.

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Spain: Selected Issues-Labor Market Polices and Unemployment Dynamics

Author: International Monetary Fund

Volume/Issue:: Volume 1996: Issue 057

Publisher:: International Monetary Fund

ISBN:: 9781451812077

ISSN:: 1934-7685

Pages:: 46

DOI:: https://doi.org/10.5089/9781451812077.002

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Basic Model: Impulse Response Graphs for Permanent Shocks to Key Variables

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Basic Model: Impulse Response Graphs for the Model as a Whole

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Policy Model: Impulse Response Graphs for the Model as a Whole

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Labor Force Equation:
$\underset{(SE)}{\begin{matrix} \ln LF \end{matrix}} = \underset{(0.2336)}{1.137} - \underset{(0.04182)}{0.1727 \ln W} + \underset{(0.133)}{1.149} lnWorkPop - \underset{(0.0351)}{0.09056} Seasonal$
WALD test Chi²(3) - 147.08 [0.0000] **
Tests on the significance of each variable
Variable		F(num, denom)	Value	Probability	Unit Root t-test
	InLF	F( 1, 80) =	1316.2	[0.0000] **	-3.8009*
	InLW	F( 2, 80) =	8.069	[0.0006] **	-3.1943
	InWorkPop	F( 2, 80) =	11.332	[0.0000] **	3.7608
	Constant	F( 1, 80) =	11.473	[0.0011] **	3.3872
	Seasonal	F( 3, 80) =	14.518	[0.0000] **	-2.6918
Real Wage Equation:
$\begin{matrix} \underset{(SE)}{lnW} = - \underset{(0.5385)}{2.713} - \underset{(0.001998)}{0.003419} UR + \underset{(0.06656)}{0.7195} lnPROD + \underset{(0.05897)}{0.9247} \underset{(0.03558)}{lnWMIN - 0.3595 Seasonal} \end{matrix}$
WALD test Chi²(4) = 1229.5 [0.0000] **
Tests on the significance of each variable
Variable		F(nun.denom)	Value	Probability	Unit Root t-test
	InW	F( 2, 83) =	19.74	[0.0000] **	-6.0076**
	UR	F( 1, 83) =	3.802	[0.0546]	-1.9499
	InPROD	F( 1. 83) =	30.207	[0.0000] **	5.4961
	InWMIN	F( 2, 83) =	14.74	[0.0000] **	5.4275
	Constant	F( 1, 83) =	13.743	[0.0004] **	-3.7071
	Seasonal	F( 3, 83) =	17.304	[0.0000] **	-6.1764
Employment Equation:
$\begin{matrix} \begin{matrix} \underset{(SE)}{lnE =} - \underset{(1.723)}{7.366} + \underset{(0.2191)}{1.078} lnW - \underset{(0.2179)}{1.411} lnTSS + \underset{(0.171)}{1.316} lnGDP + \underset{(0.06799)}{0.3234} Seasonal \end{matrix} \end{matrix}$
WALD test Chi²(4) = 77.384 [0.0000] **
Tests on the significance of each variable
Variable		F(num, denom)	Value	Probability	Unit Root t-test
	lnE	F( 4, 73) =	709.45	[0.0000] **	-5.4854**
	Constant	F( 1, 73) =	24.3	[0.0000] **	-4.9295
	InW	F( 2, 73) =	16.685	[0.0000] **	5.3147
	InTSS	F( 3, 73) =	14.037	[0.0000] **	-5.8658
	InGDP	F( 2, 73) =	21.05	[0.0000] **	6.0038
	Seasonal	F( 3, 73) =	17.343	[0.0000] **	5.8042

Labor Force Equation:
$\underset{(SE)}{\begin{matrix} \ln LF \end{matrix}} = \underset{(0.2336)}{1.137} - \underset{(0.04182)}{0.1727 \ln W} + \underset{(0.133)}{1.149} lnWorkPop - \underset{(0.0351)}{0.09056} Seasonal$
WALD test Chi²(3) - 147.08 [0.0000] **
Tests on the significance of each variable
Variable		F(num, denom)	Value	Probability	Unit Root t-test
	InLF	F( 1, 80) =	1316.2	[0.0000] **	-3.8009*
	InLW	F( 2, 80) =	8.069	[0.0006] **	-3.1943
	InWorkPop	F( 2, 80) =	11.332	[0.0000] **	3.7608
	Constant	F( 1, 80) =	11.473	[0.0011] **	3.3872
	Seasonal	F( 3, 80) =	14.518	[0.0000] **	-2.6918
Real Wage Equation:
$\begin{matrix} \underset{(SE)}{lnW} = - \underset{(0.5385)}{2.713} - \underset{(0.001998)}{0.003419} UR + \underset{(0.06656)}{0.7195} lnPROD + \underset{(0.05897)}{0.9247} \underset{(0.03558)}{lnWMIN - 0.3595 Seasonal} \end{matrix}$
WALD test Chi²(4) = 1229.5 [0.0000] **
Tests on the significance of each variable
Variable		F(nun.denom)	Value	Probability	Unit Root t-test
	InW	F( 2, 83) =	19.74	[0.0000] **	-6.0076**
	UR	F( 1, 83) =	3.802	[0.0546]	-1.9499
	InPROD	F( 1. 83) =	30.207	[0.0000] **	5.4961
	InWMIN	F( 2, 83) =	14.74	[0.0000] **	5.4275
	Constant	F( 1, 83) =	13.743	[0.0004] **	-3.7071
	Seasonal	F( 3, 83) =	17.304	[0.0000] **	-6.1764
Employment Equation:
$\begin{matrix} \begin{matrix} \underset{(SE)}{lnE =} - \underset{(1.723)}{7.366} + \underset{(0.2191)}{1.078} lnW - \underset{(0.2179)}{1.411} lnTSS + \underset{(0.171)}{1.316} lnGDP + \underset{(0.06799)}{0.3234} Seasonal \end{matrix} \end{matrix}$
WALD test Chi²(4) = 77.384 [0.0000] **
Tests on the significance of each variable
Variable		F(num, denom)	Value	Probability	Unit Root t-test
	lnE	F( 4, 73) =	709.45	[0.0000] **	-5.4854**
	Constant	F( 1, 73) =	24.3	[0.0000] **	-4.9295
	InW	F( 2, 73) =	16.685	[0.0000] **	5.3147
	InTSS	F( 3, 73) =	14.037	[0.0000] **	-5.8658
	InGDP	F( 2, 73) =	21.05	[0.0000] **	6.0038
	Seasonal	F( 3, 73) =	17.343	[0.0000] **	5.8042

Labor Force Equation:
$\begin{matrix} \underset{(SE)}{LnLF} = & - \underset{(0.7534)}{3.017} + \underset{(0.107)}{0.237} LnW + \underset{(0.1376)}{1.452} LnWorkPop - \underset{(0.08463)}{0.1703} LnPens \\ + \underset{(0.004754)}{0.01395} LnRepR + \underset{(0.01834)}{0.02548} Seasonal \end{matrix}$
WALD test Chi² (5) = 1127.2 [0.0000] **
Tests on the significance of each variable
	variable	F(num, denom)	Value	Probability	Unit Root t-test
	LnLF	F( 1. 43) =	106.51	[0.0000] **	-3.8288 ^1/
	Constant	F( 1, 43) =	13.393	[0.0007] **	-3.6596
	LnW	F( 1, 43) =	7.6414	[0.0084] **	2.7643
	LnWorkPop	F( 1, 43) =	15.689	[0.0003] **	3.9609
	LnPensD	F( 1. 43) =	6.4561	[0.0147] *	-2.5409
	LnREPR	F( 1, 43) =	4.7291	[0.0352] *	2.1746
	Seasonal	F( 3, 43) =	13.341	[0.0000] **	1.6176
Real Wage Equation:
$\underset{(SE)}{InW} = \underset{(2.393)}{\begin{matrix} 2.2 \end{matrix}} - \underset{(0.001611)}{0.004407} UR + \underset{(0.13)}{0.8892} LnPROD + \underset{(0.1622)}{0.368} \begin{matrix} LnWMIN \end{matrix}$
	$+ \underset{(0.004906)}{0.03123} LnRepR - \underset{(0.0005428)}{0.001464} Tempshare \underset{(0.03824)}{- 0.08182} Seasonal$
WALD test Chi² (6) = 1212.5 [0.0000] **
Tests on the significance of each variable
variable		F(num, denom)	Value	Probability	Unit Root t-test
	InW	F( 3, 42) =	10.517	[0.0000] **	-7.8598**
	UR	F( 2, 42) =	17.506	[0.0000] **	-3.1633
	Constant	F( 1, 42) =	0.79076	[0.3789]	0.88925
	LnPROD	F( 1, 42) =	53.676	[0.0000] **	7.3264
	LnREPR	F( 1, 42) =	40.206	[0.0000] **	6.3408
	LnWMIN	F( 3, 42) =	1.8698	[0.1494]	2.2322
	Tempshare	F( 1, 42) =	9.9623	[0.0030] **	-3.1563
	Seasonal	F( 3, 42) =	16.758	[0.0000] **	-2.3641
Employment Equation:
$\begin{matrix} \underset{(SE)}{LnE} = & \underset{(0.7786)}{- 2.298} - \underset{(0.08087)}{0.1019} LnW - \underset{(0.06779)}{0.2949} LnTSS + \underset{(0.03561)}{1.016} LnGDP \\ \underset{(0.009573)}{- 0.06734} LnSEVER - \underset{(0.001331)}{0.002626} LnSTRIKE - \underset{(0.002264)}{0.01571} LnCoverage \\ \underset{(0.02735)}{- 0.01261} Seasonal \end{matrix}$
WALD test Chi² (7)= 1820.6 [0.0000] ** Tests on the significance of each variable
Variable		F(num, denom)	Value	Probability	Unit Root t-test
	LnE	F( 4, 37) =	12.62	[0.0000] **	-5.6891**
	LnW	F( 1, 37) =	1.3874	[0.2464]	-1.1779
	LnTSS	F( 3, 37) =	11.238	[0.0000] **	-4.4591
	LnGDP	F( 1, 37) =	34.985	[0.0000] **	5.9148
	LnCoverage	F( 1, 37) =	14.286	[0.0006] **	-3.7796
	LnSEVER	F( 1, 37) =	17.064	[0.0002] **	-4.1309
	LnSTRIKE	F( 1, 37) =	4.1322	[0.0493] *	-2.0328
	Constant	F( 1, 37) =	6.8657	[0.0127] *	-2.6203
	Seasonal	F( 3, 37) =	6.9233	[0.0008] **	-0.44946

Labor Force Equation:
$\begin{matrix} \underset{(SE)}{LnLF} = & - \underset{(0.7534)}{3.017} + \underset{(0.107)}{0.237} LnW + \underset{(0.1376)}{1.452} LnWorkPop - \underset{(0.08463)}{0.1703} LnPens \\ + \underset{(0.004754)}{0.01395} LnRepR + \underset{(0.01834)}{0.02548} Seasonal \end{matrix}$
WALD test Chi² (5) = 1127.2 [0.0000] **
Tests on the significance of each variable
	variable	F(num, denom)	Value	Probability	Unit Root t-test
	LnLF	F( 1. 43) =	106.51	[0.0000] **	-3.8288 ^1/
	Constant	F( 1, 43) =	13.393	[0.0007] **	-3.6596
	LnW	F( 1, 43) =	7.6414	[0.0084] **	2.7643
	LnWorkPop	F( 1, 43) =	15.689	[0.0003] **	3.9609
	LnPensD	F( 1. 43) =	6.4561	[0.0147] *	-2.5409
	LnREPR	F( 1, 43) =	4.7291	[0.0352] *	2.1746
	Seasonal	F( 3, 43) =	13.341	[0.0000] **	1.6176
Real Wage Equation:
$\underset{(SE)}{InW} = \underset{(2.393)}{\begin{matrix} 2.2 \end{matrix}} - \underset{(0.001611)}{0.004407} UR + \underset{(0.13)}{0.8892} LnPROD + \underset{(0.1622)}{0.368} \begin{matrix} LnWMIN \end{matrix}$
	$+ \underset{(0.004906)}{0.03123} LnRepR - \underset{(0.0005428)}{0.001464} Tempshare \underset{(0.03824)}{- 0.08182} Seasonal$
WALD test Chi² (6) = 1212.5 [0.0000] **
Tests on the significance of each variable
variable		F(num, denom)	Value	Probability	Unit Root t-test
	InW	F( 3, 42) =	10.517	[0.0000] **	-7.8598**
	UR	F( 2, 42) =	17.506	[0.0000] **	-3.1633
	Constant	F( 1, 42) =	0.79076	[0.3789]	0.88925
	LnPROD	F( 1, 42) =	53.676	[0.0000] **	7.3264
	LnREPR	F( 1, 42) =	40.206	[0.0000] **	6.3408
	LnWMIN	F( 3, 42) =	1.8698	[0.1494]	2.2322
	Tempshare	F( 1, 42) =	9.9623	[0.0030] **	-3.1563
	Seasonal	F( 3, 42) =	16.758	[0.0000] **	-2.3641
Employment Equation:
$\begin{matrix} \underset{(SE)}{LnE} = & \underset{(0.7786)}{- 2.298} - \underset{(0.08087)}{0.1019} LnW - \underset{(0.06779)}{0.2949} LnTSS + \underset{(0.03561)}{1.016} LnGDP \\ \underset{(0.009573)}{- 0.06734} LnSEVER - \underset{(0.001331)}{0.002626} LnSTRIKE - \underset{(0.002264)}{0.01571} LnCoverage \\ \underset{(0.02735)}{- 0.01261} Seasonal \end{matrix}$
WALD test Chi² (7)= 1820.6 [0.0000] ** Tests on the significance of each variable
Variable		F(num, denom)	Value	Probability	Unit Root t-test
	LnE	F( 4, 37) =	12.62	[0.0000] **	-5.6891**
	LnW	F( 1, 37) =	1.3874	[0.2464]	-1.1779
	LnTSS	F( 3, 37) =	11.238	[0.0000] **	-4.4591
	LnGDP	F( 1, 37) =	34.985	[0.0000] **	5.9148
	LnCoverage	F( 1, 37) =	14.286	[0.0006] **	-3.7796
	LnSEVER	F( 1, 37) =	17.064	[0.0002] **	-4.1309
	LnSTRIKE	F( 1, 37) =	4.1322	[0.0493] *	-2.0328
	Constant	F( 1, 37) =	6.8657	[0.0127] *	-2.6203
	Seasonal	F( 3, 37) =	6.9233	[0.0008] **	-0.44946

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Abstract

1. Introduction

2. The Spanish labor market since the mid-1970s

a. Employment, unemployment, and the labor force

b. The structure of the labor market

3. The basic model

a. Structure of the model

b. Characteristics of the data

c. Estimation results

(1) The labor force equation

(2) The real wage equation

(3) The employment equation

d. Discussion of the results of the basic model

4. The policy model

a. Additional variables included in the policy model

b. Estimation results

(1) The labor force equation

(2) The real wage equation

(3) The employment equation

c. Discussion of the results of the policy model

5. Structural change and unemployment: persistence and responsiveness

6. Conclusions

APPENDIX: Simple Measures of “Cross-persistence” and “Cross-responsiveness”

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