WHY DO WE HAVE SKILL GAPS? An analysis of the determinants of the skill gaps in Europe

Transcription

1 WHY DO WE HAVE SKILL GAPS? An analysis of the determinants of the skill gaps in Europe by Federica Origo * and Cristiana Zanzottera (First draft; not to be quoted) August 2002 Abstract Aim of this paper is to analyse the determinants of the skill gaps in Europe. Due to the relevance of this issue in the growth potential of European countries and s policy implications, a deeper insight into the causes of labour demand and supply mismatches at the European level is urgent. However, to our knowledge specific research on the impacts of the instutional factors on the skill gaps is still lacking in the current mainstreaming lerature on the econometrics of unemployment and instutions. This paper contributes to the issue by assessing the role of different factors (distinguishing between market and instutional factors) affecting the skill gaps. Econometric estimates based on panel data for 11 European countries from 1990 to 2000 reveal that instutions by themselves poorly explain the variance of the skill gaps across countries and over time, but they are important in determining the effect of labour demand and supply factors on skill gaps. Looking at the effect of instutions, once controlling for labour market factors, most of our estimates show that skill gaps are posively correlated wh the rate of unionisation and negatively correlated wh education and training expenses, bargaining co-ordination and employment protection. Among labour market factors, the incidence of employment in small firms and the incidence of long term unemployment significantly increase the skill gaps, while the latter decrease wh the level of education of the labour force. Keywords: skill gap, labour market factors, instutions JEL Codes: J60, J63, J64 * Istuto per la Ricerca Sociale (IRS), Milan and Catholic Universy of Milan. origo@hsn. Istuto per la Ricerca Sociale (IRS), Milan. czanzottera@hsn. Why do we have skill gaps? page 1

2 1. Introduction The European labour market is characterised by a relatively high and persistent structural unemployment, low participation rates, low labour mobily and a difficulty to adjust rapidly to changing economic and technological condions. Related to the question of unexploed labour force potential are some "tradional" crical aspects of the matching process - such as the gender dimension, the long-term unemployment, the regional imbalances - as well as emerging and increasing difficulties in terms of labour shortages and of skills shortages and skills gaps. Labour and skill shortages have been underlined by several EU Member States in their National Action Plans for Employment, even if a comprehensive policy framework to deal wh these issues is still lacking. There are however differences whin EU countries due to different posions in the business cycle and in their productive specialisation and differences in the instutional framework regulating the labour market and the education and training systems. Wh the economic recovery job vacancies have become harder to fill in low unemployment countries/areas; while even in high unemployment countries/areas there are mismatches both in high skilled and low skilled posions, often filled up by immigrant workers. The role played by the Information Technology (IT) in creating new occupations and in shifting upwards general and specific skill requirements for a wide range of occupations (even outside s domain) is a key issue in the analysis of the skill gap. This type of skills gap deserves careful attention for s economic and social implications: on the one hand there is a strong relation between education and social inclusion and this implies a "group targeted" effect of skill mismatches, on the other hand we have also to consider the issue of wage inequaly. Furthermore, the presence of skill gaps might be extremely costly for both firms (for example, in terms of loss of production due to the presence of unfilled jobs) and individuals (in terms of loss of earnings or skills, especially when unemployed for a long time). At the macro level, skill gaps can cause sever costs to the society as a whole, such as higher expenses in unemployment benefs and re-training policies or lower long run economic growth. In order to avoid these costs and to properly intervene to reduce the skill gaps, is then crucial to understand what are the main factors underlying s existence. This paper is aimed to analyse the determinants of the skill gaps in Europe. The need for an overview of the labour shortages and skill gaps suation at the European level and of a comprehensive analytical framework is urgent, due to the relevance of this issue in the growth potential of European countries and s policy implications. However, to our knowledge in the current mainstreaming lerature on the econometrics of instutions (see, for example, Blanchard and Wolfers, 2000) there is not an insight into the impacts of the instutional factors on the skill gaps. This paper contributes to the issue by assessing the role of different factors (instutional features, labour demand and supply factors) affecting the skill gaps. Our analysis starts wh an assessment of the definion of skills and skill gaps, an overview of the indicators used in the lerature to measure skill gaps and skill shortages and some empirical evidence on skill gaps indicators in Europe. In section 3 and 4 we then introduce the analytical and econometric model considered, in order to identify and test the main research hypothesis in relation to the determinants of skills gaps. In section 5 we present the main features of the panel data-set used to estimate our model, whose Why do we have skill gaps? page 2

3 main results are discussed in section 6. The last section presents some concluding remarks and policy implications of our results. 2. Skill and skill gaps: definion and indicators 2.1. Definion The possible mismatch between the skills workers possess and the skills employers demand has become an issue increasingly relevant. To analyse the determinants of the skill gaps, is firstly necessary to have a clear definion of skills. There is not a generally accepted and commonly used definion of skill in the lerature, although there have been various attempts to come to a common one. The term "skill" is a multidimensional concept, which generally refers to the qualifications needed to perform certain tasks in the labour market. In empirical work, researchers often use proxies based on education and occupation to identify skills, but also the definion and classification of educational and occupational levels may vary across countries, time and statistical sources. Furthermore the comparabily of data between countries is also needed because of the range of instutional diversy they represent. Comparisons between sectors and regions whin countries are needed as well, because they are affected by instutional settings, management practices and technological and competive shocks in different ways. While qualifications and occupations are identifiable and measurable (even if difficult to compare), skills are harder to grasp since there may be not a perfect correspondence between the occupational status and skills, nor between job status and skills used in the workplace. Moreover, in order to analyse the skill gap and to understand s determinants is also necessary to get to a clear definion of skill gaps. In this paper we will refer to skill gap as the mismatch between the demand side and the supply side in the labour market due to skills. The mismatch self may occur as a skill shortage or as an excess supply of skills. A skill shortage is a skill mismatch occurring when the demand for a particular skill is not satisfied by the current labour reserve. A skill shortage in a particular occupation could be indicated both by a high level of vacancies relative to unemployment and by a strong increase in vacancies relatively to unemployment or by an increase in relative wages. An excess supply of skills is, on the other hand, a skill mismatch occurring when the supply for a particular skill is not totally demanded. An excess supply of skills in a particular occupation could be detected in particular by high levels of unemployment. Then the analysis of the characteristics of skill gaps in labour markets deals firstly wh the lack of a generally accepted definion of skill and of skill gaps, the lack of comparable data and the lack of specific indicators of skill gaps Indicators Usually in the empirical lerature the skill gaps indicators deal wh the matching function and the relation between vacancies and unemployment. The use of these Why do we have skill gaps? page 3

4 indicators in analysing skill gaps has to face the problem of a low level of data disaggregation, especially for vacancies. In the lerature the presence of a mismatch in the labour market has been tradionally analysed through the v/u ratio where v=v/l and u=u/l 1. This indicator represents the degree of tightness in the labour market. In other words is a measure of the excess of labour demand, resulting in vacancies (V), compared to the excess of labour supply, resulting in unemployment (U). This measure of mismatch may be found in Sesto (1988, 1994), Gregg and Petrongolo (1997), Mocavini and Paliotta (2000), Petrongolo and Pissarides (2000), and s rationale comes from the matching lerature and the Beveridge curve. This indicator may have three possible outcomes: - equals 1 if v and u at a given skill level are the same, wh no particular implication on the skill mismatch. - is higher than 1 if v>u at a given skill level, implying a skill shortage. - is lower than 1 if v<u at a given skill level, implying an excess of supply for that skill. The indicator can be also analysed by skill levels (through the occupational or Vi / Li educational proxy). The indicator Ui/ Li is based on this approach, where vacancies (V), unemployment (U) and the labour force (L) are indexed and considered by skill level (i). Another measure of the skill gaps that deals wh the matching function is the sum of the vacancy and unemployment rates (v+u, where v=v/l and u=u/l). This indicator measures the overall mismatch existing in the labour market, regardless whether is determined by eher the excess of labour demand, resulting in vacancies (V), or the excess of labour supply, resulting in unemployment (U). This indicator, based on the sum of the two ratios, stresses more the total importance of the mismatch in the labour market, while the v/u ratio discussed above represents more an expression of tightness (and, for this reason, s level is not high if both vacancies and unemployment are high). In general, due to the lack of complete information on vacancies, the sum of vacancy and unemployment rates is driven by the relevance of the latter. An alternative approach in measuring mismatch followed by Layard, Nickell and Jackman (1991) focused on mismatches as the persistent and temporary imbalances between the supply and demand for labour across skill groups, regions, and age groups. They considered the variance of skill specific unemployment rates as the indicator for mismatch in the labour market. Analysing the relation between mismatch and the NAIRU the authors show how this indicator measures the proportion of unemployment that can be explained by the mismatch. ui 1/ 2Var u The indicator is, where u i is the unemployment rate relative to the i-th skill and u is the national unemployment rate. The skill proxies used may be eher education or occupation. 1 V stands for vacancies, U for unemployment and L for labour force. Why do we have skill gaps? page 4

5 This is an indicator of the variance of relative unemployment rates. A low index means that unemployment rates are similar among skill levels, while a high index means that unemployment rates differ widely by skill The empirical evidence on skill gap indicators In the previous section, we discussed three main different types of skill gap indicators: the vacancies/unemployment ratio; the sum of vacancy and unemployment rates and the LNJ indicator based on the variance of unemployment rates by skill. Available data allows to calculate the first two indicators only at the aggregate (national) level, not by skill. Major limations are caused by the lack of comparable data at the EU level on vacancies by skill. The Vacancies/Unemployment indicator In figure 1, we plot the vacancies/unemployment ratio (the V/U indicator) in the Nineties for those EU countries publishing data on vacancies 2. According to the value of the indicator in 2000 and s evolution over time, we can divide the countries into three main groups. At the top of the ranking we have the Netherlands and Luxembourg, which are characterised by very tight labour markets and highly increasing V/U ratios, mainly in the second half of the Nineties 3. The second group of countries (UK, Austria, Germany, Belgium and Sweden) share V/U values that are between 0,15 (Sweden) and 0,25 (UK) in They are however characterised by que different rates of growth during the Nineties: for example, in the period this indicator significantly increased in UK and Belgium, while slightly dropped (by 18%) in Germany. The last group of countries (France, Portugal, Spain and Finland) present relatively loose labour markets, wh still low V/U ratios (between 0,05-0,1), even if this indicator grew considerably in the period considered. It is worth to point out that, looking at aggregated data, is anyway hard to detect how much of the changes in the V/U ratios might be due to (increasing) skill mismatch and how much is determined by other factors, such as composional effects, the business cycle or labour market instutions. For example, the tightness of the Dutch labour market in the Nineties is more the result of a significant reduction in the number of the unemployed (55% less in 1999 than in 1989) rather than a really exceptional increase in the number of jobs posted (which increased at the same rate as in Germany). This might be a sign of increasing mismatch specifically labour shortages, but might 2 Vacancies figures are based on OECD Main Economic Indicators. Since they are taken from administrative records held by Public Employment Services, these figures likely underestimate the actual number of total jobs posted, mainly for high skilled non manual occupations. Data are not available for Denmark, Greece, Ireland and Italy. Recent years for the UK were based on Nickell et al. (2001), because they appear more reliable. 3 Note that the Netherlands is also the EU country wh the highest share of employment in high skilled occupations. Why do we have skill gaps? page 5

6 be as well the result of the labour market reforms implemented in the Netherlands in the same period. The sum of vacancy and unemployment rates Figure 2 depicts the evolution of the sum of vacancy and unemployment rates over the Nineties in the eleven EU countries considered in the following econometric analysis (see sections 4-6). This indicator is in most cases driven by the unemployment rate, probably also because, as we ve already mentioned, the information on vacancies is likely incomplete. For this reason, the size of the skill gap measured by this indicator appears particularly high in those countries (such as Spain, Finland and France) where unemployment is high. On the contrary, is relatively low where unemployment is (or has recently become) a less worrying issue (such as in most of the Nordic countries, the Netherlands, the UK and Portugal). In the same way, this indicator has been decreasing over time mainly in those countries where unemployment has been most successfully reduced. However, in some cases (especially the Netherlands and the UK) s reduction has been partly off-set by a significant increase in the vacancy rate. Figure 2 The sum of vacancy and unemployment rates over time by country, AT BE DE DK ES FI FR NL sumuv 0 30 PT SE UK year Graphs by country Why do we have skill gaps? page 6

7 The Layard, Nickell and Jackman indicator based on unemployment rates Table 1 reports the Layard, Nickell and Jackman indicator based on unemployment rates (that is, the variance of relative unemployment rates by skill) for each EU country in the Nineties. The level of skill is proxied by the educational attainment. The average of this indicator over time shows that many of the continental countries (Belgium, Germany, Luxembourg, the Netherlands) and Ireland are characterised by high variance of unemployment levels by skill. The oppose is true in the Mederranean countries (Greece, Italy, Portugal and Spain): the LNJ indicator is usually relatively low, meaning that the national unemployment rate is less affected by the existence of skill gaps than in the previous group of countries. The suation is less clear cut in the Nordic Countries and the UK, which are characterised by intermediate values of this indicator. Table 1 Layard, Nickell & Jackman indicator by educational attainment (ISCED 1976) Avg Austria Belgium Denmark Finland France Germany Greece Ireland Italy Netherlands Portugal Spain Sweden Uned Kingdom EU The analysis of more disaggregated data based on national sources (EC, 2001) shows some evidence of occupation-specific skill mismatches. Even if increased labour demand can be observed for technicians and ICT-related occupations in most EU countries, the demand for other occupations differs significantly between the countries considered. For example, in Sweden and Finland there is an increasing demand for occupations in the health and teaching sectors 4, while in Austria vacancies have been increasing in tourism-related occupations. In France, according to Public Employment Services data referred to December 2000, the most requested occupations are some specific skills in the agricultural sector (like gardeners and wine-growers) and unskilled workers in the wood sector. German firms seems to be looking especially for ITC high skilled professionals, but estimates based mainly on firms survey data are very diverse and unstable over time. In Italy specialised blue collars and assembly machine operators are the occupations that firms are more looking for, but the demand is also increasing for managers and scientific professionals. 4 As pointed out by the European Commission, this is probably due to the relatively low wage levels in these occupations, more than structural skill mismatches. Why do we have skill gaps? page 7

8 3. The theoretical framework In an economic perspective, the skill gaps (SK) are related to the interactions between three main groups of factors: labour demand side factors (LD), supply side factors (LS) and the instutional setting (I) of the labour market. Formally: SK = f(ld, LS, I) [1] a. Demand side factors. These factors are related to structural and cyclical aspects. Structural factors relate to the effects of rapid technological and economic change: the skills demanded by employers do not match wh those currently possessed by the existing labour force. In a context of rapid technological progress and increasing international competion 5, this effect is particularly relevant across and whin sectors and concentrates on the lack of high-skilled workers. Cyclical factors relate to the impact of the economic cycle on workers bargaining power: in periods of recovery this turns out in upward pressures on wage differentials. b. Supply side factors. The adequacy of the educational and training attainment of labour supply to demand needs depends on individual choices and on the capacy of the schooling system and of training and retraining programmes to provide adequate education and training incentives in periods characterised by rapidly changing condions. Individual choices relating to investment in human capal depend on the presence of incentives and returns to investment in the form of eher wage differentials or employment stabily and occupational upgrading. Enterprises decisions in on the job training are instead linked to returns in the form of productivy gains. These incentives and returns are strongly dependent on the instutional framework and the characteristics of the productive system prevalent in each country: in some cases (such as in Southern European countries), the rise in the level of education is often not matched by a rise in the level of skills and qualification demanded by firms, due to the industrial and dimensional structure of the productive system. In addion, the rise in educational levels is producing, in some countries, a rise in social and employment/income expectations that reduces the supply for low qualified posions which are then filled up by immigrant labour. c. Instutional factors. The instutional framework regulating the labour market, the cred market and education and training is considered by most researchers an important dimension in explaining the difficulties of European economies to adapt rapidly to changing condions. Instutions affect workers and firms incentives to invest in skills, they affect the qualy of schooling and training systems and also the capacy of these systems to adapt rapidly to technological or economic shocks. Due to policy interactions and complementaries is the overall instutional framework in each country which is relevant. Not only the education and vocational training systems have to be considered, but also their interactions wh the wage bargaining system, the regulation of employment contracts, the capal and cred markets, the industrial and R&D systems. 5 According to some economic research, increasing trade integration across countries may explain about 10-20% of the global decline in demand for unskilled work at the basis of the increasing US wage differentials or the increasing unemployment in European countries. Why do we have skill gaps? page 8

9 In order to get some indications on how to interpret the econometric estimates, is useful to better state what relation we should expect between all the factors mentioned above and the skill gap. Table 2 summarises our testable predictions. In particular, wh regard to labour demand factors (LD): - the more an industrialised country is open to international trade (OPENNESS), the more likely is that will specialise in the production of capal and knowledge-intensive goods and services, buying labour-intensive products from less developed countries. This will cause a relative decline in the demand for unskilled workers. Skill gaps, in particular skill shortages, should then increase wh the degree of market openness; - skill biased technological progress (INNOV) requires more skilled labour and multi-task workers: skill gaps should then be posively correlated wh the degree of technological innovation; - the presence of incomplete or costly information on the number and the characteristics of the available jobs and workers should increase the skill gaps. Getting this kind of information might be particularly difficult when the productive system is fragmented into a myriad of small firms (SMALL FIRMS): small employers usually use less formal and more local recrument channels and can invest less in both advertising their vacancies and screening the potential workers. We should then expect a posive relation between the incidence of employment in small firms and the skill gaps. However, skills needs might be relatively lower and more standard in small firms, so that should be easier for them to find a suable worker for a vacant job. In light of these two oppose effects (costly information vs lower skill requirements), the relation between skill gap and the incidence of employment in small firms is a priori ambiguous to determine. On the labour supply side (LS): - skill gaps should be negatively correlated wh the skill level of the labour force (HIGHEDU). If skills are correctly measured by the level of education, a more educated labour force should cause the skill gap to decline; - the longer the unemployment spells, the more likely is that workers loose their technical and professional skills, mainly if they don t undertake training courses while they are looking for a job. Since firms might use the duration of unemployment as a screening device to hire new workers (and long term unemployment might have a depressing effect on individuals, inducing them to put less effort in looking for a job), the incidence of long term unemployment (LTU/U) should be posively associated wh the level of skill gaps; - the higher is the rate of mobily of the workforce (EUMIGR), the more likely is that a certain vacancy is filled wh the right worker. Skill gaps should then decrease when the rate of mobily of the labour force increases. Finally, looking at labour market instutions (I): - a well functioning education and training system (EXP_TR&EDU) should be associated to lower levels of skill gaps, as long as is able to rapidly adjust, in Why do we have skill gaps? page 9

10 terms of contents and qualy of the programs and courses offered, to new skill requirements of the labour demand. Assuming that quanty and qualy are in this case posively related, a higher incidence of expenses in public education and training should cause the skill gap to decline; - active labour market policies (EXP_ALMP), in particular employment services, are aimed at spreading information on vacancies and job-seekers readily available and reducing the mismatch between labour supply and demand. Expenses in active labour market policies should then be negatively related to skill gap; - more stringent laws regulating hiring and firing procedures (EP) make the labour market more rigid, so that is more difficult to adjust to eventual mismatches between labour demand and supply. On the other hand, in such a setting firms are not always free to hire or fire the workers they want and they might be induced to post a lower number of vacancies. It is then also possible that higher employment protection is negatively related to skill gaps and the actual effect is not unambiguously predictable; - the presence of strong unions (UNION) is usually associated to lower intrafirm wage differentials and more homogeneous pay systems, rarely based on individual abily and performance. Collective bargaining tradionally makes the wage structure more rigid, preventing wages from adjusting to changes in the relative labour demand for skill. If this is the case, a more unionised workforce should be posively correlated wh the level of skill gaps; - co-ordination in the wage bargaining system (COORD) should reduce eventual conflicts between the bargaining parties and facilate an overall view of the economy, incentiving the exploation of synergies and an efficient use of the available resources in trying to reach the common goals. Co-ordination might then help to solve also mismatches problems, so that should be negatively correlated wh the presence of skill gaps. Table 2 The determinants of the skill gap: theoretical considerations SKILL GAP SKILL GAP fl SKILL GAP? OPENNESS EXP_TR&EDU SMALL FIRMS INNOV EXP_ALMP EP LTU/U COORD EUMIGR HIGHEDU UNION 4. The econometric model To estimate the model discussed in the previous section, we start considering a simple linear quantative specification. Why do we have skill gaps? page 10

11 In particular, we want to explain the variance in the skill gap across European countries and over time using a set of variables capturing all the three elements (i.e., labour demand and supply; instutions) ced above. A reduced form of the model in the short run can be wrten as follows: SK = α + βd + δs + γi + ν + τ + ε [2] i t where SK is an indicator of the skill gap (or labour market tightness) in the i-th country in year t; D is a vector of demand side factors, S is a vector of supply side factors and I a vector of instutional features, mainly related to the functioning of the labour market. α, β, δ, γ are the vectors of parameters to be estimated, while ε is the usual error term. ν i and τ t are, respectively, country and time fixed effects. To take into account the possible existence of interactions between instutions and market factors, the linear model can be extended as follows: SK = α + βd + δs + γi + ηd * I + κs * I + ν + τ + ε [3] i t where each labour market factor is interacted wh each instution and η and κ are two further vectors of coefficients to be estimated. This specification allows for the effects of market factors to also depend on the specific labour market instutions of a country. A primary problem we have to deal wh in estimating this model is the lack of a direct measure of the skill gap. As a proxy of the true value of, we used a number of different indicators, capturing eventual mismatches eher on the labour supply or demand side or both. They are essentially some of the indicators usually found in the lerature and discussed in section 2, such as: 1. the sum of vacancy rate and unemployment rate (v+u); 2. the Layard-Nickell-Jackman indicator (0,5*var(ui/u)) 6. Since these variables are only proxies of the true measure of the skill gap, they are likely to be differently influenced by the labour market and instutional factors considered. For this reason, is important to interpret and compare all the results together: the presence of estimates that are consistent regardless of the dependent variable used is a good indicator of the robustness of the relation between the skill gap and s possible causes. The independent variables we used in the regressions are: 6 We estimated the model also separately for the vacancy rate and the unemployment rate and for the v/u ratio, mainly to get further information for interpreting the coefficients estimated using the sum of them as the dependent variable. Nonetheless the results were not significant. Why do we have skill gaps? page 11

12 a) demand side factors (vector D). We try to capture their effects using variables such as: - a measure of the degree of openness of an economy, given by the sum of imports and exports as percentage of the GDP at constant prices (OPENNESS); - enterprises R&D expenses (INNOV), as a proxy for innovation and technical progress; - the incidence of employment in small firms (SMALL_FIRMS), as an indirect measure of the eventual cost and availabily of information on both people looking for a job and jobs posted; - a set of time fixed effects to control for the economic cycle. b) Supply side factors (vector S). We proxy supply side effects on the size of the skill gap by: - the incidence of high educated people in the labour force, assuming that the level of education can be an indirect measure of the level of skills (HIGHEDU); - the incidence of long-term unemployed (LTU/U), assuming that those looking for a job longer are more likely to have lost their skills and not to have acquired the new required ones, mainly if they didn t participate to training programs in the meanwhile. - the incidence of EU immigrants in the labour force (EUMIGR), as a proxy of the degree of mobily of the population across Europe (and, consequently, an indirect indicator of the cost of mobily) 7 ; c) Instutional factors (vector I). Following the mainstream lerature on the econometrics of instutions (see, for example, Nickell, 1997; Blanchard and Wolfers, 1999; Lucifora and Dell Aringa 2000; Nickell et al., 2001), we capture the effect of the instutional setting using the following variables: - total expenses in educational instutions and training programs as percentage of GDP (EXP_TR&EDU), as a measure of the relevance of the schooling and training system; - total expenses in other active labour market policies (EXP_ALMP); - index of employment protection (EP), measuring the degree at which firms can hire/fire workers and so giving some indications on the fluidy of the workforce in and out of employment; - union densy (UNION), as a proxy of the wage bargaining system and wage differentials compression; - index of bargaining co-ordination (COORD), to capture the level of conflict among the bargaining parties and the eventual presence of common strategies in dealing wh problems in the labour market, including the existence of mismatches between labour demand and supply. 7 We used other proxies of the cost of mobily, such as the share of owners of a house in the population (as in Oswald, 1996) and the rate of migration between different regions whin a country (internal migration). However, the lack of time variabily did not allow us to use them in models including also country fixed effects. Why do we have skill gaps? page 12

13 5. The data The data used for the econometric estimates are based on a panel data-set for 11 European countries from 1990 to European cross-country comparisons have been essentially based upon the use of the European Labour Force Survey (ELFS), the OECD data on Main Economic Indicators and the Nickell data-set on labour market instutions. The table in the Appendix provides specific details on the definion and the statistical source of each variable Summary statistics on market factors and instutions Tables 3 and 4 report some descriptive statistics on the labour market factors and instutions in the EU countries considered in the empirical analysis. As both the tables show, these countries are characterized by que different levels and evolutionary trends in most of the variables considered. Nonetheless, most of the countries have been experiencing significant changes in both labour market features and instutions over the Nineties. For example, on the labour supply side the incidence of high educated people in the labour force has been increasing in all the countries considered, mainly in those (Austria, Portugal and Spain) whose level was particularly low at the beginning on the Nineties. Almost all countries (wh the exception of Germany and France) have been experiencing increasing migration flows from other EU countries, usually accompanied by decreasing shares of long term unemployed among the job-seekers. The suation is less clear cut on the labour demand side: even if employment in small firms have been increasing almost all over Europe (also due to down-sizing and outsourcing trends in large companies), different trends emerge regarding the degree of trade openness and the incidence of R&D expenses. Table 3 The role of labour market factors by country, Openess R&D expenses % employment in small firms % high educated in the labour force % long term unemployed % migrants from other EU countries (a) (b) (a) (b) (a) (b) (a) (b) (a) (b) (a) (b) Austria Belgium Germany Denmark n.a Spain Finland France Netherlands n.a Portugal n.a Sweden n.a. n.a Uned Kingdom n.a. n.a (a) average in 2000 (b) % change Also labour market instutions, which are usually less time variant than market factors, experienced relevant changes during the Nineties across Europe. Expenses in training and education and/or in other active labour market policies have been increasing in most of the EU countries, as well as bargaining coordination. On the other Why do we have skill gaps? page 13

14 side, employment regulation has been decreasing almost everywhere. Evidence on the evolution of union densy is instead que mixed, even if the incidence of union members in the workforce have been slightly increasing in six of the eleven countries considered (namely, Belgium, Denmark, Spain, Finland, Portugal and Sweden). Table 4 The role of labour market instution by country, Expenses in training & educ. (% GDP) Expenses in ALMP (% GDP) Employment Protection Union densy Coordination (a) (b) (a) (b) (a) (b) (a) (b) (a) (b) Austria = Belgium = Germany = Denmark Spain Finland = France 0.34* = 1.7 Netherlands = 3.0 = Portugal 0.36** 0.55** Sweden Uned Kingdom = (a) average in 2000 (b) change wh respect to 1990 *1999 **1998 excluding expenses in training and education 5.2. Econometric and data problems Estimates of the model presented above can be biased by the presence of data and econometric problems that are usually common wh this kind of macro-panel data. These problems might also cause some misalignment between the econometric estimates and the theoretical expectations. Other then the lack of a direct measure of skill gap already discussed, this kind of data my be affected by: - mispecification, due to the omission of other relevant factors or the use of the wrong functional form; - measurement errors, mainly in the case of qualative and instutional indicators. Furthermore, most variables are not time variant in the short-medium run and are in general highly persistent; - endogeney of market and instutional factors: might be that some of the dependent variables and the skill gap are simultaneously determined or there could be a sort of recursive relation between skill gaps and other factors: the latter causes Why do we have skill gaps? page 14

15 the first, who subsequently influences the latter, etc. This is probably the case, for example, of R&D expenses: technical progress causes skill gaps but the latter, at least in the medium-long run, influence R&D expenses; for this reason in our regression we used the lagged values of the R&D expenses. We are aware of all these problems in estimating and interpreting the econometric results. However, given the lack of better qualy data, good instruments and a higher number of degrees of freedom, we are forced to estimate our model using OLS/GLS, controlling for the presence of heteroskedasticy. 6. Main empirical results Tables 5 and 6 report the estimates of equation [2] for the two dependent variables outlined above (i.e., the sum of vacancy and unemployment rates and the LNJ indicator based on the unemployment rates). In order to highlight the relative importance of market and instutional factors in influencing the skill gap, we estimate the model also using only market factors (columns 1 and 2) and only instutions (columns 3 and 4). We then try different specifications using both groups of independent variables (columns 5-10) 8. To test the robustness of the results, we estimate the model also wh and whout time fixed effects. Regardless of the dependent variable considered, our estimates reveal that instutions by themselves poorly explain the variance of the skill gaps across countries and over time, while the market factors alone have a higher explanatory power (up to 95% of total variance, according to the value of the R squared in the model estimated including both country and time fixed effects). However, both specific market factors and instutions are significantly correlated wh the presence of skill gaps. Most of our estimates show that skill gaps increases wh the incidence of employment in small firms (supporting the incomplete-costly information hypothesis), while is negatively correlated wh education and training expenses and coordination. The elasticies differ wh the dependent variable used: for example, a 10% increase of the employment share in the small firms causes an increase of the skill gap ranging from 8-9% in the case of the LNJ indicator, 2-3% when we use the sum of vacancy and unemployment rates as the dependent variable. In general, elasticies are higher when estimated wh the first dependent variable. Regardless of how skill gaps are measured, we obtain that the skill gap decreases wh the degree of employment protection (in this case confirming the lower turnover and vacancies posted hypothesis). When skill gaps are proxied by the sum of the vacancy and unemployment rates, we obtain, as we expected, that skill gaps increase wh the incidence of long term unemployment and decrease wh the level of education of the labour force. The effect of the first supply side factors is relatively more important, since a 10% increase in the incidence of long term unemployment causes the skill gap to go up by 6-8%. 8 Note that information on the share of employment in small firms ( small_firms ) is not available for any years in the case of Sweden and the UK. In columns 7 and 8 we then estimated the complete model including these two countries but excluding that variable. Why do we have skill gaps? page 15

16 Table 5 - The determinants of the skill gap Coefficients (robust standard errors in brackets) Dep. Variable: u_rate+v_rate * 8* 9 10 costant ** (2.01) (3.74) (21.78) (24.30) (12.67) (9.62) (11.78) (7.90) (9.17) (27.60) Demand factors: openness * (1.25) (0.86) (1.36) (0.79) (1.30) (0.90) (0.80) (1.45) innov ** ** ** * ** - - (1.38) (1.97) (0.90) (1.07) (2.38) (1.48) small_firms 7.37 ** 7.42 ** ** 9.55 ** ** 8.23 * (2.00) (2.79) (4.82) (3.39) (3.65) (4.57) Supply factors: high_edu * ** ** ** ** ** - (7.61) (11.01) (9.58) (6.52) (8.86) (5.13) (4.27) ltu/u ** ** ** 8.69 ** ** ** - (4.14) (6.69) (3.84) (5.31) (3.05) (2.08) (6.13) eu_migr * (118.19) (114.6) (104.03) (118.57) (157.74) ( ) Instutions: exp_tr&edu ** ** ** ** ** (3.16) (2.64) (2.54) (1.25) (2.70) (1.72) (0.59) (3.65) exp_almp (1.87) (1.21) (2.10) (1.64) (2.40) (1.89) ep ** ** * (6.02) (7.54) (4.69) (4.18) (4.88) (4.07) (3.89) (8.35) union * ** * (24.81) (19.01) (28.77) (21.10) (21.27) (17.65) (21.23) coord ** * ** 2.32 (5.27) (4.25) (5.23) (2.92) (4.81) (3.37) (2.45) (6.48) Fixed effects: country yes yes yes yes yes yes yes yes yes yes time no yes no yes no yes no yes yes yes R-squared Number of obs * these models include also Sweden and Great Brain as the small firms variable is not considered Note: * statistical significant at 10%; ** statistical significant at 5%; Note: Robust standard errors, allowing correlation whin countries (cluster)

17 Table 6 - The determinants of the skill gap Coefficients (robust standard errors in brackets) Dep. Variable: Lajard-Nickell-Jackman Indicator * 8* 9 10 costant 0.08 * 0.10 ** 0.41 ** ** 0.51 ** 0.55 ** 0.65 ** 0.51 ** 0.44 ** (0.04) (0.04) (0.11) (0.29) (0.12) (0.10) (0.14) (0.14) (0.09) (0.17) Demand factors: openness * ** * * (0.02) (0.01) (0.02) (0.01) (0.02) (0.01) (0.01) (0.01) innov ** - - (0.02) (0.01) (0.02) (0.02) (0.04) (0.04) small_firms 0.28 ** 0.28 ** ** 0.26 ** ** 0.32 ** (0.06) (0.06) (0.06) (0.05) (0.05) (0.05) Supply factors: high_edu (0.15) (0.20) (0.13) (0.19) (0.17) (0.28) (0.15) ltu/u (0.06) (0.08) (0.06) (0.05) (0.13) (0.14) (0.04) eu_migr (2.07) (1.67) (1.52) (1.33) (2.63) (3.20) Instutions: exp_tr&edu * * ** ** ** (0.02) (0.03) (0.02) (0.02) (0.03) (0.03) (0.01) (0.02) exp_almp (0.01) (0.02) (0.01) (0.01) (0.02) (0.02) ep ** ** * ** * (0.04) (0.1) (0.05) (0.05) (0.03) (0.06) (0.05) (0.06) union * * ** ** * - (0.25) (0.20) (0.15) (0.11) (0.19) (0.16) (0.11) coord ** ** ** ** (0.06) (0.07) (0.02) (0.02) (0.03) (0.04) (0.01) (0.04) Fixed effects: country yes yes yes yes yes yes yes yes yes yes time no yes no yes no yes no yes yes yes R-squared Number of obs * these models include also Sweden and Great Brain as the small firms variable is not considered Note: * statistical significant at 10%; ** statistical significant at 5%; Note: Robust standard errors, allowing correlation whin countries (cluster)

18 In this models skill gaps also increase wh the rate of unionisation of the workforce and this effect looks particularly relevant, given the high level of the elasticy (around 2 in the models wh two fixed effects). In the case of the LNJ indicator, the size of the skill gaps also decreases wh the degree of market openness. Even if the last result does not meet our expectations, is worth pointing out that this measure of globalisation might capture other factors other than globalisation, such as the degree of competiveness of a certain economy. It might be that the normalised variance in unemployment rates by skill is lower the more competive is the economy. Table 7 summarises the main significant results obtained. The comparison wh the expected results reported in table 1 shows that our empirical model fs well the data and seems to predict in the right way most of the testable predictions of the economic theory. Table 7 - The determinants of the skill gap: empirical results (elasticies) SKILL GAP SKILL GAP fl e* u+v indicator e* LNJ indicator e* u+v indicator e* LNJ indicator OPENNES INNOV -0.36/-0.66 SMALL FIRMS +0.25/ /+0.85 HIGHEDU -0.44/-0.57 LTU/U +0.36/+0.57 EUMIGR EXP_TREDU /-0.23 EXP_ALMP EP UNION +2.13/+3.30 COORD /-2.10 * Elasticies range for the estimates of the 5th and the 6th column of tables 5 and 6 We report only factors that have resulted statistically significant at 5% or 10% 6.1. The effect of interactions In a policy perspective, skill gaps, in particular skill shortages, might be reduced by improving the matching between labour demand and supply and by adapting labour supply s skills to labour demand and, in the long run, to the potential needs of economic growth. These two goals can be achieved through the right mix of active labour market policies (mainly efficient public employment services) and education and training programs. We should also expect the impact of some market factors on the size of the skill gap to be influenced by the presence and the relevance of these instutions. For Why do we have skill gaps? page 16

19 example, long-term unemployment might be less harmful if people looking for a job can undertake (re)training courses; in the same way, the lack of proper information on the labour market faced by small firms might be less severe in presence of well-functioning public employment services. For these reasons, we estimated also a model that takes into account the possible existence of interactions between instutions and market factors, focusing our attention on the effects of training and active labour market policies expenses together wh the incidence of employment in small firms and long term unemployment 9. Tables 8 and 9 show the results of the estimates when all the demand and the supply factors (columns 1-2) and only some market factors (columns 3-4) are interacted wh the total expenses on education and training and the total expenses in other active labour market policies. Note that controlling for these interactions provides a further test of the robustness of our estimates. When skill gaps are proxied by the sum of the vacancy and unemployment rates (tab. 8), the sign of the impact of the relevant variables found in the linear model is confirmed: the skill gaps increase wh the incidence of employment in small firms and the incidence of long term unemployment, while they decrease wh the level of education of the labour force, the expenses in education and training and the degree of employment protection in the labour market. The interactions of the economic factors wh the total expenses on education and training turned out to work as expected: investing in a well educated and well trained labour force reduces the potential skill gaps arising from technological progress and the presence of long term unemployed. Expenses in other active labour market policies reduce the impact of long-term unemployment on the skill gap as well. When the LNJ indicator is used as a proxy of the skill gaps in the labour market (tab. 9), the explanatory power of the model estimated wh the interactions is lower than in the case of the sum u- v indicator. Nonetheless, most of the significant results obtained wh the model whout interactions are confirmed: the skill gaps are posively related wh the incidence of employment in small firms, and negatively related wh the expenses in education and training and the level of coordination in the labour market. As far as the interaction terms are considered, is worth noting the different effect on the skill gap of the expenses in education and in other active labour market policies when interacted wh the incidence of a high educated labour force. In fact, a higher presence of better educated people increases the mismatch when interacted wh the expenses in education and training, while decreases the gap when interacted wh the expenses in other active labour market policies. These results can be explained if we consider that, on the one side, further expenses in education and training could increase the excess of supply of high skilled workers, increasing the mismatch in this way. On the other side, the implementation of proper active labour market policies might favour 9 Estimates wh all the possibile interactions between market factors and instutions are available upon request. We have also estimated a non linear model in which all the market factors were interacted wh the whole set of labour market instutions (for an example of this specification, see Blanchard and Wolfer, 2000). However, this model did not perform well and most of the estimated interactions do not work as the theory predictions. Why do we have skill gaps? page 17