Michael Demoussis

Nicholas Giannakopoulos

Keywords: J31 -Wage Level, Structure, Differentials by Skill, Occupation, etc. &lt, J3 -Wages, Compensation, and Labor Costs &lt, J -Labor and Demographic Economics, J61 -Geographic Labor Mobility|Immigrant Workers &lt, J6 -Mobility, Unemployment, and wage differentials, immigration, occupational selectivity, Greece wage di¤erentials, immigration, occupational selectivity, Greece. JEL Codes: J31, J61, O52

Introduction

In the beginning of the 1990's and following the collapse of the communist regimes in Eastern Europe, Greece has experienced a massive in ‡ux of illegal immigrants (e.g., [START_REF] Angrist | Protective or counter-productive? Labour market institutions and the e¤ect of immigration on EU natives[END_REF]. The country had no previous experience on the 'receiving end' of immigration ‡ows and she was unprepared to deal e¤ectively with this new phenomenon. Even today, more than 15 years since the …rst immigrants arrived, Greece lacks an integrated and coherent immigration policy. And this despite the fact that immigrants comprise a signi…cant minority of Greece's population (7-10%) and labor force (5-10%) and an important part of Greek economic, social and cultural life (OECD, 2005). The integration of immigrants into the Greek economy and society is nowadays a major political issue that arouses heated public discussions. In this paper we investigate only one facet of economic integration, namely the native-immigrant wage di¤erential1 . [START_REF] Borjas | The impact of immigrants on native earnings and employment[END_REF] argues that the existence of a positive native-immigrant wage gap does not necessarily mean that immigrant labour is cheaper but that this di¤erential could arise if there were systematic labour market discrimination against foreign-born workers. Using data from the most recent Greek Household Budget Survey (GHBS) we identify the group of immigrants, according to their country of birth and compare their wages to native Greeks. Typical wage generating functions are estimated and suitable decomposition techniques are applied. Selectivity issues with respect to the apparently important occupational segregation are also addressed. This, to our knowledge, is the …rst micro-level study of native-immigrant wages in Greece that employs nationally representative cross-sectional data.

The existing literature on the economic aspects of immigration in Greece is rather limited. The newness of the phenomenon and the lack of reliable nation-wide micro data bases have limited its investigation to descriptive aggregate macro-level analysis and a few case studies. [START_REF] Glytsos | Problems and policies regarding the socioeconomic 16 Page 16 of 27[END_REF] presented demographic data for four groups of immigrants and described the related policy framework adopted and applied by the Greek government. Lianos et al. (1996) utilized sample data for 4 prefectures of Northern Greece and concluded that illegal immigrant wages are 40-60 per cent lower than native ones and that immigrants are mostly employed in occupations forsaken by Greeks. [START_REF] Markova | The performance of Bulgarian illegal immigrants in the Greek labour market[END_REF] concentrated on illegal Bulgarian immigrants and examined entry conditions, sectors of employment, living and working conditions as well as their relations to natives and local authorities. The study of Sarris and Zographakis (1999) employed a theoretical and empirical approach regarding the e¤ects of illegal migration in Greece. They utilized a small CGE model and reached the conclusion that immigration exerts a detrimental e¤ect on the wages of unskilled labor and a positive e¤ect on the wages of skilled workers. In the process, they assumed a ‡at immigrant-native wage di¤erential of 40 per cent and that all immigrants o¤er unskilled work. [START_REF] Lianos | Contemporary immigration in Greece: an economic appraisal, Centre for Economic Programming and Research[END_REF] investigated the demographic characteristics of immigrants, their regional distribution and employment status and explored the impact of immigrants on the employment of Greeks, using aggregate data from three distinct Greek statistical sources. [START_REF] Kasimis | The multifunctional role of migrants in the Greek countryside: implications for the rural economy and society[END_REF] focused on immigrants employed in Greek agriculture and described their impact on farm operations and local labor markets. Finally, [START_REF] Rovolis | Ethnic characteristics and geographical distribution of immigrants in Greece[END_REF] examined the geographical distribution of immigrants in Greece and identi…ed disparities between di¤erent ethnic groups with regard to their educational level and occupational characteristics.

A preliminary inspection of the GHBS data base reveals that indeed foreign-born individuals comprise an important minority, which comes to 7.8 per cent of Greece's population. The share of immigrants from former communist Eastern European Countries (EEC) comes to a notable 73.5 per cent, from Asia, Africa and the Middle East (AAME) to 11.1 per cent, from EU-15 to 8.2 per cent, and from Other Countries-Americas and Oceania-(OC) to 7.2 per cent. The relative importance of the EEC group is profound and it is worth pointing out that roughly 47 per cent of all immigrants in Greece come from Albania. Furthermore and with regard to their employment status, prime-age immigrants, in the [18-64] age category, exhibit substantially higher labor force participation rates than native Greeks (60.97 per cent), especially those from EEC and AAME (70.95 and 70.69,respectively). This …nding holds particularly for male immigrants in these groups, who record participation rates almost 12 percentage points higher than natives (see Tables 1 and 2). Figure 1 depicts the densities of observed (ln) hourly wages for native Greeks and two immigrant groups (EEC and AAME+EU-15+OC). We observe that the densities of immigrant wages, those for the EEC block in particular, show greater concentration around their mean, which is clearly lower than the average native wage. Thus, prima facie, the observed wage di¤erentials seem to corroborate the commonly held view that immigrants are discriminated against in the Greek labour market, as far as labor earnings are concerned.

-Insert The paper is organized as follows. In the next section we discuss the sources and the descriptive statistics of the key variables for the two groups of interest, i.e., natives and immigrants. We also perform a basic background analysis of observed wage di¤erentials and present the results of routine decomposition exercises. Section 3 presents the econometric methodology that deals explicitly with occupational choice. The issue of discriminatory practices, as they relate to the occupational distribution of immigrants and an augmented version of the typical Blinder-Oaxaca decomposition technique, that incorporates occupational segregation, are also presented in this section. Section 4 presents and discusses the estimation results. The paper concludes with a summary of the basic …ndings, policy implications and suggestions for further research.

Data and background analysis

Our analysis uses data collected in the GHBS, a nationally representative sample of some 6,555 households, containing approximately 17,386 persons and covering the period February/2004-January/2005 2 . The survey questionnaire draws information about immigrant and labour market status, gender, marital status, region of residence, income and earnings, hours of work, job and family characteristics, education, sector of employment, occupations, housing tenure and conditions, etc. We extracted data for prime-age individuals, i.e., in the 18-64 age bracket and excluded retirees, students, military personnel and self employed. The latter category was excluded because, as a rule, self-employed do not report earnings, when they do they tend to underreport them and furthermore, reported income by the self-employed includes returns to capital as well. Finally, we constructed an hourly wage measure for natives and immigrants from data on monthly earnings and weekly hours of work. The estimating sample comes to a total of 6273 individuals of which 897 are immigrants. The employment rate of the selected sample is 68.1 per cent, i.e., 4274 individuals.

Table 3 presents sample statistics for the major correlates, for the whole sample and separately for the immigrant and native sub-samples. We observe that the average native wage is higher than the immigrant wage by 39.8 percent. It is worth noticing that 87.6 per cent of employed immigrants work in the private sector (vs. 62.1 per cent for the native population), that the percentage of uninsured immigrants is substantially higher than the native one, that the share of immigrants holding temporary jobs is also higher and, …nally, that immigrants are primarily employed as 'unskilled workers', 'craft and related workers'and 'service and shop-sales workers' 3 . In contrast, natives are employed mainly as 'clerks', 'service and shop-sales workers', 'craft and related workers'and 'professionals'. Overall, we observe that with regard to demographic characteristics the two groups do not di¤er substantially. Sig-ni…cant di¤erences appear to exist however with regard to job characteristics and occupations. With regard to the latter, we have calculated a congruence index, as suggested by [START_REF] Welch | In defence of inequality[END_REF] and [START_REF] Borjas | The labour demand curve is downward sloping: reexamining the impact of immigration on the labour market[END_REF], as an indicator of the degree of similarity in the occupational distributions of immigrants and natives 4 . The calculated value of the index turned out to be equal to minus one, a …nding that suggests clustering of immigrants and natives in di¤erent occupations.

-Insert Table 3 about here-In general, native-immigrant wage di¤erentials are investigated within the human capital paradigm (e.g., [START_REF] Long | The e¤ect of americanization on earnings: some evidence for women[END_REF]Kee, 1985;Coulon, 2003). Typical Mincerian wage generating functions are estimated …rst for the two sub-samples [START_REF] Mincer | Schooling, experience and earnings[END_REF][START_REF] Chiswick | The e¤ect of americanization on the earnings of foreign-born men[END_REF] and a typical Blinder-Oaxaca decomposition analysis is performed in the second stage (e.g., Reimers, 1983;Kee, 1985;[START_REF] Darity | Ethnicity, race and earnings[END_REF][START_REF] Butcher | The immigrant and native-born wage distributions: evidence from United States censuses[END_REF]. The advantage of the Blinder-Oaxaca decomposition is that it breaks up and measures the contribution of observable characteristics to the estimated average wage differential. As a starting point and for comparison purposes we follow this typical approach.

Table 4 presents the estimated coe¢ cients of the 'immigrant' dummy variable for three alternative wage-generating model speci…cations 5 . The typical Mincerian wage-generating function, corrected for sample selection, 3 Borjas (1999) argues that the entrance of immigrants in non-traditional occupations in the host country constitutes a source of faster economic assimilation. 4 The index takes the value of one when the two groups have identical occupation distributions and minus one when they are clustered in completely di¤erent occupations.

5 Following the empirical tradition of [START_REF] Chiswick | The e¤ect of americanization on the earnings of foreign-born men[END_REF], we estimate …rst a typical human capital earnings function using the pooled sample, i.e., immigrants and natives. An indicator variable for 'immigrant status' is included as an additional argument. Borjas (1985) extended this empirical model by including controls for plausible cohort e¤ects and his fully-speci…ed model incorporates assimilation, cohort and period e¤ects. However, information on 'years of residence'in Greece and 'years since migration'are not available in the GHBS data base and thus, are not included in the present application. Nevertheless, it appears that the basic human capital model explains adequately the earnings distribution of workers in the Greek labour market. constitutes the base-line speci…cation (column 1)6 . Twelve regional and 16 sector-of-employment dummies are included as additional arguments in the second speci…cation (column 2). The third speci…cation includes in addition eight occupational dummies (column 3). We observe that the estimated coe¢ cient of immigration on wages is negative and statistically signi…cant across all model speci…cations, while sample selection with regard to participation appears to be present. The selectivity criterion lambda is negative and statistically signi…cant, indicating that wages of participants are estimated to be lower than those of non-participants who have the same values of the exogenous variables. Notice also that the e¤ect of immigration on wages is substantially reduced when we control for occupations, from -0.12 (2 nd speci…cation) to -0.07 (3 rd speci…cation). This …nding demonstrates that immigration may correlate with certain occupations.

Typical Blinder-Oaxaca decompositions of the estimated wage di¤erentials for the three model speci…cations are presented at the bottom of Table 4. The wage di¤erentials were estimated from separate estimations of native and immigrant wage generating functions. The obtained decomposition results are quite appealing. The predicted wage di¤erential is higher than the observed raw wage di¤erential between natives and immigrants, across the three speci…cations, indicating that non-participants have higher reservation wages than participants. Thus, if non-participants were to become participants then the required increase in o¤ered wages would have resulted in an increase in the estimated native-immigrant wage di¤erential. However, this wage gap is reduced substantially when the wage equation is controlled for regional, sectoral and occupational di¤erences. We observe for example that the distance between observed and predicted wage di¤erential has been signi…cantly reduced in the 3 rd speci…cation, implying that the occupational distribution of native and immigrant workers does matter. Speci…cally, the inclusion of occupational variables has caused a notable increase in the share of the estimated wage di¤erentials due to observed characteristics, from 51.0 per cent to 66.1 per cent. This …nding provides additional support in favor of the hypothesis that native-immigrant wage di¤erentials do correlate with certain occupations.

-Insert Table 4 about here-These background results provide su¢ cient motivation for an explicit analysis of the occupational choices made by immigrants in Greece. One could reasonably argue that immigrant wages are lower because they work exclusively in the private sector and their vast majority are employed and paid as 'unskilled labor'. Thus, observed native-immigrant wage di¤erentials may manifest nothing more than existing inter-occupational wage di¤erentials in the Greek labor market. Of course, the fact that immigrants are "attracted" in certain low-paid occupations and seriously under-represented in high-paid ones may entail systematic selectivity issues (Constant and Zimmerman 2003;Newman and Oaxaca, 2004;[START_REF] Liu | Occupational segregation and wage di¤erentials between natives and immigrants: evidence from Hong Kong[END_REF]). In other words, the presence of statistical discrimination cannot be ruled out (Bruce, 1995;[START_REF] Oettinger | Statistical discrimination and the early career evolution of the black-white wage gap[END_REF]. Since immigration is a relatively new phenomenon in Greece, employers lack the experience and the capability to judge the quali…cations of immigrants and their capacity to meet the demands of jobs with greater potential for advancement and higher wages (Bruce, 1995). Thus, in the short term, employers classify immigrants as 'unskilled labor'just to be on the safe side and o¤er the 'good'jobs to natives. In the long term, as more information become available and the real capabilities of immigrants are made known, employers have every reason to pursue a more e¢ cient matching of occupations and employee skills and quali…cations. However, if an immigrant, at an early stage, attaches a low probability to the event that he/she will be eventually hired into a high-paid job, where ability and skill are essential factors, then he/she has reduced motivation to invest in related skill acquisition. Thus, even in the long term, this structure of incentives could create a vicious circle that systematically pushes immigrants into low-paid occupations [START_REF] Greenwood | A model of the skill composition of US immigration[END_REF][START_REF] Gross | Impact of immigrant workers on a regional labour market[END_REF][START_REF] Hammarstedt | The predicted earnings di¤erential and immigrant self-employment in Sweden[END_REF][START_REF] Tasiran | Early labour-market experiences of second-generation immigrants in Sweden[END_REF].

Indeed, the recent (2005) OECD report on the economic impact of immigration in Greece and the related empirical evidence indicate that immigrant workers tend to be concentrated in agriculture, household services and construction, performing tasks …t mostly to unskilled or low-skilled labor. This is attributed to the stringent laws of the formal labour market in Greece, i.e., minimum wages for unskilled workers, and to the substantial size of the Greek underground economy, which evidently exhibits an insatiable demand for low-paid labour. The OECD report also asserts that immigration allowed "at least some Greeks to move to higher level jobs". In such a setting, the intra-occupational wage di¤erentials are not expected to be important, i.e., the immigrant -native wage di¤erential within the "unskilled"group for example is expected to be small or non-existent. Furthermore, the fact that immigrants are clustered into the "unskilled" group and under-represented in the "skilled"one, brings into centre-stage the issue of occupational segregation. The accumulated empirical evidence suggests that occupational choice cannot be ignored when earnings di¤erentials are investigated. This is because occupations determine earnings and therefore the existence of discrimination in occupational choices is directly transformed into discrimination in earnings [START_REF] Schmidt | The prediction of occupation using multiple logit models[END_REF]Strauss, 1975, Constant andZimmerman 2003). Brown et al.1980 suggested an appropriate methodology for assessing inter and intra-occupation wage e¤ects, by explicitly modelling occupational choice. This methodology was applied widely in the investigation of gender and race wage di¤erentials and more recently [START_REF] Liu | Occupational segregation and wage di¤erentials between natives and immigrants: evidence from Hong Kong[END_REF]) for analyzing nativeimmigrant wage di¤erentials in Hong-Kong.

In the present paper we follow closely Brown's methodology. We re-coded the eight occupational categories, available in the GHBS, for two reasons. First, because certain occupations in the estimating sample are reported by a very small number of immigrants and second, because some of the existing occupational categories appear to be perfect or very close substitutes. This re-coding, which resulted into four broad occupational categories (see Table 5), facilitates the estimation of occupation-speci…c wage equations and ensures that the available alternative choices are more or less distinct, (see Constant and Zimmerman, 2003).

Following this, wage equations for both, native and immigrant workers are speci…ed for each of the four occupational categories as follows:

ln w N ij = X N ij N j + e N ij ; j = 1; :::; J (1) ln w I ij = X I ij I j + e I ij ; j = 1; :::; J
where, w ij is the hourly wage rate for the i th worker in the j th occupation, X ij is a vector of observed independent correlates that determine occupation-speci…c wages, j is the vector of parameters to be estimated and e ij is the error term. The indicators N and I denote natives and immigrants, respectively. Taking into account the proportions of natives and immigrants in each occupation, P N j and P I j , respectively, the sample mean wage di¤erential [START_REF] Liu | Occupational segregation and wage di¤erentials between natives and immigrants: evidence from Hong Kong[END_REF] becomes:

ln w N ln w I = J X j=1 P N j ln w N j P I j ln w I j (2)
The average wage di¤erential is in essence a weighted average of occupational wage di¤erentials, the weights being the proportions of immigrants and natives in each occupational category. Of course, the occupational distribution of immigrants may be di¤erent from the distribution of natives, for reasons unrelated to their observed characteristics, i.e., discrimination. In other words, the observed distribution of immigrants in each occupation needs to be compared with the distribution that would have resulted in the absence of discrimination. Hence, the estimation of occupation-speci…c wage regressions (1) should account for the possible divergence of the observed from the non-discriminatory occupational structure of immigrants. This in turn creates the need for an explicit treatment of occupational choice.

Occupational choice is usually approached empirically through a reduced form multinomial logit model [START_REF] Brown | Incorporating occupational attainment in studies of male-female di¤erentials[END_REF][START_REF] Green | Immigrant occupational attainment: assimilation and mobility over time[END_REF]Constant and Zimmerman, 2003;[START_REF] Liu | Occupational segregation and wage di¤erentials between natives and immigrants: evidence from Hong Kong[END_REF]). The model predicts the probability that an individual, with known characteristics, will choose a certain occupation from a set of several un-ordered alternative occupations. Thus, the choice probability P ij of the i th individual for the j th occupation, conditional on the set of observed individual correlates Z i becomes:

P ij = Pr (y = j j Z i ) = e ( j Z i) P J k=1 e ( j Z i) ; j = 1; :::; J (3)
where, k is the vector of coe¢ cients of the set of independent variables Z i , corresponding to the k th occupation. For identi…cation purposes we impose the restriction that the vector of the estimated coe¢ cients of the reference occupation is equal to zero (Greene 2003, p. 721). The model is estimated using a) the native sub-sample and b) conventional maximum likelihood methods (MLE). The resulting parameter estimates are unbiased, consistent and asymptotically e¢ cient. The estimated coe¢ cients of the occupational choice model describe the structure of the labor market under investigation, with respect to the occupational choices made by native workers. Then, using the observed characteristics of the immigrant sub-sample we can predict for every immigrant the probability of choosing a speci…c occupation, P I j . If the estimated probabilities of occupational choice P I j di¤er from the observed proportion P I j then the allocation of immigrants into each occupation may not be random. If this is the case, then the occupational wage equations for immigrants and natives should be adjusted for occupation-speci…c selectivity bias. The adjustment utilizes information from the estimation of the occupational choice probabilities of equation (3). Thus, the occupation-speci…c wage equation conditional on occupation j being chosen is:

ln w ij = X ij j + ^ ij j + ij (4)
where, j = j % j , j is the standard deviation of the error term in (4) and % j is the correlation between the error terms of ( 4 , ' [ ] is the standard normal density function, ( ) = 1 (F ), is the standard normal distribution function and F is the distribution function of the multinomial logit probabilities de…ned in (3). Occupational selectivity bias is controlled for by including ^ j in the estimated regression. Equation ( 4) is estimated separately for the native and immigrant sub-samples.

Recall that the typical Blinder-Oaxaca decomposition, applied in section 2, with occupational dummies included as independent variables, is based on the assumption that occupational choice is random. However, if the distribution of immigrants across occupations is shaped by discriminatory factors and this is ignored then the estimated native-immigrant wage di¤erential will be biased. The legitimacy of the decomposition that is based on biased estimated wage di¤erentials is also questionable. Thus, the typical decomposition method of the estimated average wage di¤erential needs to be augmented, in order to account for the possible presence of occupational selectivity. In this case, the overall average wage di¤erential, as it is described in (3), can be decomposed into four distinct components as follows:

ln w N ln w I = (5) J X j=1 P I j ^ N j X N j X I j + J X j=1 P I j X I j ^ N j ^ I j + J X j=1 ln w N j P N j P I j + J X j=1 ln w N j P N j P I j
The …rst and third components account for the 'explained' part of the di¤erential and the second and fourth components for the 'unexplained'one. The 'explained' part captures the portions of the wage di¤erentials due to di¤erences in observed characteristics between natives and immigrants. The 'unexplained' part of the wage di¤erential captures di¤erences in returns that could be due to discrimination. Furthermore, the …rst two components account for the intra-occupational wage di¤erential and the last two for the inter-occupational wage di¤erential. Finally, the last component, i.e., interoccupational 'unexplained' part, provides an indication for the equality of access between natives and immigrants to various occupations, [START_REF] Liu | Occupational segregation and wage di¤erentials between natives and immigrants: evidence from Hong Kong[END_REF]. With regard to the occupational achievement equation (3), the key explanatory variables in Z include potential experience and its squared term 7 , education, gender and an urban/non-urban dummy 8 (e.g., Constant and Zimmerman, 2003). The results presented in Table 6 are based on the estimation of the multinomial logit model, using the native sub-sample. The …rst two columns present the actual occupational distribution of natives and immigrants. We observe that indeed immigrants are over-represented in the last two occupation categories ('production workers'and 'unskilled labour') and under-represented in the …rst two categories. The third and fourth columns present the predicted by the model distribution of natives and the predicted non-discriminatory occupational distribution of immigrants. It can be seen that the model predicts accurately the distribution of natives, while the results for the immigrant sub-sample indicate that a signi…cant occupational reallocation would have resulted in a non-discriminatory setting. Given their observed characteristics, immigrants would shift away from the categories of 'unskilled labour'and 'production workers'and move into the other two categories. Furthermore, the last three columns of Table 6 indicate that the di¤erences in observed native-immigrant occupational distributions cannot be explained by di¤erences in observed characteristics (Z) but rather they are caused by unobserved factors (unexplained part). The fact that P I j and P I j diverge so extensively clearly implies that the labour market in Greece treats immigrants di¤erently than natives 9 .

-Insert Table 6 about here-Table 7 presents the estimated results of the occupation-speci…c wage generating functions, for natives and immigrants, when occupational selfselection is taken into consideration and corrected for. Overall, the model …ts the data adequately, with R 2 values ranging from 0.26 to 0.53. Several 7 Data on 'actual' experience are not available and for this reason we use 'potential' experience, i.e., education-corrected age. We assume also that the ratio of 'actual' to 'potential' experience does not vary across ethnic groups, not an unrealistic assumption given the ethnic composition of the immigrant labour force (e.g., 73.5% from Eastern European Countries).

8 Due to small-sample restrictions, instead of regional dummies we include an indicator which identi…es residence in urban regions (e.g., [START_REF] Gabriel | The e¤ect of occupational segregation on the relative earnings of black males[END_REF]. The same indicator is used during the estimation of occupation-speci…c wage equations.

9 [START_REF] Greenwood | The supply of immigrants to the United States[END_REF] argue that occupational skills are not perfectly transferable between countries. However, they con…rm that educational background does play a role and furthermore, immigrants with higher educational attainment may also …nd substantial demand for their skills in the host country. points are worth making. The e¤ects of potential experience and its squared term reveal the existence of an inverse U-shaped relationship (in all occupations but 'unskilled labour') but this pro…le is statistically valid only for the native sub-sample. Educational attainment is important for natives but only in the "service workers" and "production workers" occupations. For immigrants, tertiary education exerts a positive e¤ect on the wages of "production workers"and a negative one on "unskilled labour", when compared to those that attended only primary school. Married native workers gain higher wages, with the exception of unskilled labourers, while this relationship could not be con…rmed for the immigrant sub-sample. Working in the previous year a¤ects positively and signi…cantly wages for both natives and immigrants across occupations. For the native sub-sample, the impact of this variable is very important for service and unskilled workers, implying that 'persistence'acts as 'experience'that generates higher current returns. This e¤ect is also consistent across occupations regarding the immigrant subsample of workers. Working in the private sector, negatively a¤ects native wages, while the same does not seem to hold for immigrants, with the exception of service workers. Holders of temporary work contracts, natives and immigrants, seem to receive lower wages [START_REF] Zorlu | The e¤ect of immigration on wages in three European countries[END_REF], while part-timers appear to enjoy higher hourly wages.

-Insert Table 7 about here-In addition, the occupational selectivity parameter ^ j is statistically sig-ni…cant in …ve out of eight wage regressions. Speci…cally, occupational selfselection is present for the immigrant sub-sample in all but the …rst occupational category. Furthermore, the identi…ed selectivity exerts a positive e¤ect on the immigrant wage of 'service workers'and 'unskilled labour'and a negative one on 'production workers'. This clearly implies that the choice of these occupations by immigrants is not random. The selectivity parameter for the native sub-sample is statistically signi…cant in the "service workers" and "production workers" occupations. Its e¤ect is positive in the …rst case and negative in the second one. This …nding implies that the unobserved factors that push natives into various occupations correlate negatively or positively with their wages. The di¤erentiated e¤ects of occupational selectivity, among occupations and between natives and immigrants, which are observed in the present study, were also identi…ed in the related international literature (e.g. [START_REF] Liu | Occupational segregation and wage di¤erentials between natives and immigrants: evidence from Hong Kong[END_REF].

-Insert The upper part of Table 8 presents the mean values of the variables in the X matrix, for natives and immigrants, which were employed for the estimation of the occupation-speci…c wage generating equations, (presented at Table 7). Using these values together with the estimated ^ N j , ^ I j (Table 7) and P I j ; P I j ; P N j ; P N j (Table 6), we can proceed with the four-way decomposition of the average native-immigrant wage di¤erential as described in (5).

The results appear at the lower part of Table 8. First, recall that the average (ln)wage di¤erential between natives and immigrants comes to 0.335. Second, the decomposition shows that 51.9 per cent of this di¤erential can be attributed to endowment di¤erences between natives and immigrants, i.e., observed correlates, while the remaining 48.1 per cent is unexplained. Third, the explained part, i.e., 51.9, is primarily attributed to within-occupations endowment di¤erences (46.3 points) and to a much lesser extent to betweenoccupations di¤erences (5.7 points). Fourth, the unexplained part, i.e., 48.1, concerns primarily between-occupations di¤erences (43.3 points) and substantially less within-occupations di¤erences (4.8 points). In other words, we could conclude that the unexplained part of the observed native-immigrant wage di¤erential relates primarily to inter-occupational unobserved factors rather than to intra-occupational ones. This …nding may in turn imply that immigrants, unlike natives, face di¢ culties in gaining access to certain occupations. That is, if we assume that the observed wage di¤erentials reveal discrimination, then this discrimination relates primarily to unequal occupational access between Greek and foreign-born workers rather than unequal treatment within a given occupation. In other words, once an immigrant gains access to a certain occupation then the issue of discrimination, as far as earning di¤erentials are concerned, becomes relatively unimportant.

Summary and policy implications

In this paper we investigated, for the …rst time, native-immigrant wage differentials in the Greek labour market. We have employed the most recent GHBS (2004-05) data base, we considered four alternative occupations and we explicitly accounted for occupational selectivity. Occupational choice was approached through a multinomial logit model. Controlling for the usual individual and job-related characteristics and for occupational selectivity, we estimated occupation-speci…c wage equations for representative samples of employed native and immigrant workers. Following this, an augmented decomposition technique was utilised in order to allocate unexplained nativeimmigrant wage di¤erentials to within-and between-occupations unobserved factors. Our results demonstrate that native workers enjoy signi…cantly higher wages than immigrants and that this di¤erential is to a large extent, around 48 per cent, unexplained by di¤erences in observed characteristics of the two worker-groups. Furthermore, the decomposition analysis reveals that roughly 90 per cent of the unexplained part can be attributed to betweenoccupations di¤erences and 10 per cent to within-occupations ones. This …nding implies in turn that the underlying cause of observed wage di¤erentials is the asymmetrical occupational access of natives and immigrants and not their unequal treatment within a given occupation. Indeed, it is found that immigrants are over-represented in low-paid jobs ("production workers" and "unskilled labour") and under-represented in high-paid ones (legislators, managers, professionals, assistants and "service workers"). Moreover, the utilized occupational-choice model predicts that large numbers of immigrants will move out of low-paid jobs and into high-paid ones, in a labour market setting that would provide equal occupational access and opportunities.

The obtained results could be helpful in formulating a more appropriate immigration policy, aiming at a smoother assimilation of immigrants into the Greek economy. Closing of the native-immigrant wage di¤erential, i.e., a major aspect of assimilation, requires policies that will address two major issues: a) the asymmetrical occupational access of natives and immigrants and b) the within-occupations wage di¤erentials. The results of the present study indicate that (a) is much more important than (b). In other words, the under-representation of immigrants in high-skilled and paid occupations is the result of systematic factors rather than random ones and indicates the existence of invisible barriers ('glass ceiling') that prevent them from moving into high-wage occupations. Thus, policies need to be designed to improve the access of immigrants to high-paid jobs in the public and private sectors of the Greek labour market. For example, hiring quotas for qualifying immigrants in the public sector need to be considered. Similarly, an active labour market policy that assists employers to achieve better matching between job requirements and employee skills and quali…cations could also improve access to high-paid jobs by immigrants. It is evident that if technical and professional skills of immigrants are ignored then Greece is not using e¢ ciently its available workforce. On the other hand, continuous onthe-job training, speci…c and general, of immigrants could also be helpful in alleviating within-occupation native-immigrant wage di¤erentials.

The results of the background analysis (Section 2) revealed the presence of selectivity with respect to labour market participation. Selectivity implies that the native -immigrant 'wage-o¤er' di¤erential is larger than the observed wage di¤erential, (Reimers, 1983;Blau and Beller, 1988;Miller, 1987;[START_REF] Kee | Native-immigrant wage di¤erentials in the Netherlands: discrimination?[END_REF]. In other words, if currently non-participating natives were to become participants then the immigrant-native earnings di¤erential would have been larger, ceteris-paribus. Thus, the simultaneous treatment of participation and occupational selectivity, along perhaps the lines suggested by Dolton et. al., (1989), seems like a natural extension of the present study. This is certainly a more meaningful exercise for female workers, native and immigrant, who exhibit signi…cantly lower participation rates than males.

15

F

  ) and (3) and where, ^ j = 9 Zj^ j )] F (Zj^ j )

  10

  13

  Table 1 about here--Insert Table 2 about here-

	-Insert Figure 1 about here-
	F o r
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	r
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Table 8

 8

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	12

Densities of observed (ln) hourly wages for native Greeks and two immigrant groups (Eastern European countries and all other countries)

		1
	Density values for ln(wage)	.2 .4 .6 .8
	o r P e e r F o r P e e r F o r P e e r F o r P e 0 2 ln(wage) Greece All other countries o r P e Figure 1. F 0 -2 e e r r	4 European Eastern Countries	6
		R e v i e w R e v i e w R e v i e w R R e e v v i e w i e w

Table 1 .

 1 Ethnic composition of immigrants in Greece

	% of total	% of total
	population	immigrant population
	EU-15 1	0.6	8.2
	EEC 2	5.7	73.5
	AAME 3	0.9	11.1
	OC 4	0.6	7.2
	Total Immigrants	7.8	100
	Native Greeks	92.2	-
	TOTAL population	100	-
	1 European Union-15, 2 Former Communists Eastern
	European Countries, 3 Asia, Africa and the Middle East,
	4 Americas and Oceania
	Source: Own calculations, GHBS, 2004-2005

Table 2 .

 2 Employment status of immigrants and natives, [18-64] age bracket

		EU-15 1	EEC 2	AAE 3	OC 4	Native	Sample
						Greeks averages
	Total Sample
	Employed	59.55	70.94	70.69	60.52	60.97	61.79
	Unemployed	8.99	5.71	4.31	2.63	5.73	5.72
	Retired	3.37	1.17	3.45	1.32	7.37	6.79
	Inactive 1a	28.09	22.18	21.55	35.53	25.93	25.7
	TOTAL	100	100	100	100	100	100
	Men
	Employed	81.82	87.43	86.77	70.37	75.03	76.09
	Unemployed	15.15	6.83	4.41	3.7	5.21	5.37
	Retired	0	1.37	2.94	0	9.1	8.36
	Inactive 1a	3.03	4.37	5.88	25.93	10.66	10.18
	TOTAL	100	100	100	100	100	100
	Women
	Employed	46.42	56.05	47.91	55.1	47.61	48.29
	Unemployed	5.36	4.69	4.17	2.04	6.23	6.05
	Retired	5.36	0.99	4.17	2.04	5.71	5.31
	Inactive 1a	42.86	38.27	43.75	40.82	40.45	40.35
	TOTAL	100	100	100	100	100	100
	1a pupils, students, soldiers, housewives, incapacitated,
	1 European Union-15, 2 Former Communists Eastern European Countries,
	3 Asia, Africa and the Middle East
	Source: Own calculations, GHBS, 2004-2005

Table 3 .

 3 De…nition of variables and descriptives (' employed'sub-sample)

	Variabe	De…nition

22 of 27 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

							Submitted Manuscript
	Table 7. Estimated wage equations corrected for occupational self-selection:
	Table 4. Estimated e¤ects of immigrant status on (ln)wages Table 5. Occupational categories Table 6. Actual and predicted probabilities of occupational attainment natives (N) and immigrants (I)
	and decompositionof the estimated native-immigrant Categories Coding Occupation in GHBS by native (N) and immigrant (I) workers Highly Skilled Service workers	Production workers	Unskilled labour
	wage di¤erentials Highly skilled	N	1 Observed	Legislators, Managers, Administrators, Predicted Observed Explained I N I N	Unexplained I	N	I
	Intercept		(1) distribution 1.031 5.235 Senior O¢ cials, Professionals, (2) (3) distribution di¤ erence di¤ erence 0.161 0.508 0.694***	di¤ erence 0.85***	0.179	0.710*
			N (1.222)	coef. I (6.558)	coef. Associate Professionals coef. N I (0.119) (0.315)	(0.148)	(0.210)	(0.353)	(0.442)
	Immigrant status Service workers Exper	2 (1) 0.040***	-0.155 Clerks, Service and Shop-sales workers -0.124 -0.076 (2) (3) (4) (1)-(2) (1)-(4) 0.027 0.027*** 0.025 0.028***	(4)-(2) 0.026**	0.027	-0.010
	Production workers Highly skilled	(0.021) Skilled agriculture and …shery workers, (0.022) (0.021) 0.238 0.065 0.238 0.198 3 0.173 0.04 (0.011) (0.074) (0.005) (0.020) (0.008)	0.133 (0.011)	(0.017)	(0.028)
	Selectivity term Service workers Exper-squared	-0.271 0.442 0.215 0.442 0.478 -0.137 Craft and related workers, -0.139 0.227 -0.0005*** -0.0005 -0.0003*** -0.0004	-0.036 -0.0004*** -0.0005*** 0.263	-0.0003	0.0002
	(0.061) Production workers 0.234 0.377 0.234 0.245 (0.058) Plant and machine operators (0.056) -0.143 (0.0001) (0.0010) (0.0001) (0.0003)	-0.011 (0.0001)	-0.131 (0.0002)	(0.0002)	(0.0005)
	Unskilled labour Secondary	Oaxaca-type wage decomposition and assemblers 0.086 0.343 0.086 0.079 -0.257 -0.096 -0.999 0.293*** 0.106	0.007 0.221***	-0.264 0.095	0.011	-0.111
	Raw di¤erential Unskilled labour		4 (0.352)	0.335 (1.879)	0.335 Unskilled labour 0.335 (0.059) (0.201)	(0.040)	(0.073)	(0.093)	(0.101)
	Predicted di¤erential Tertiary 0.080	0.483 -3.462	0.392 0.345***	0.366 0.196	0.661***	0.371**	-1.336	-0.399***
	Due to endowments	(0.837)	0.204 (4.395)	0.201 (0.058)	0.242 (0.177)	(0.094)	(0.172)	(1.046	(0.139)
	% Due to endowments Married 0.139***	0.422 -0.004	0.511 0.085***	0.661 -0.050	0.179***	0.153*	-0.009	-0.002
	Due to discrimination (0.045)	0.279 (0.289)	0.192 (0.026)	0.124 (0.085)	(0.038)	(0.056)	0.071	(0.086)
	% Due to discrimination Hsize 0.118	0.578 0.169*	0.493 -0.051*	0.339 -0.051*	-0.022*	0.017	0.032	0.031
	F o r P e e r (0.013) (0.101) (0.008) (0.029) Robust standard errors in parentheses. Wage regressions corrected for sample selection. Exogenous variables included in Speci…cation (1): exper, exper-squared, secondary, tertiary, female, married, hsize, emplt-1, private, tempcont, uninsured, part-time, in Speci…cation (2): as in (1) plus 12 regional and 16 sectoral dummies, in Speci…cation (3): as in (2) F o r F o r F o r Empl. t-1 0.607*** 0.500* 0.845*** 0.762*** (0.113) (0.258) (0.068) (0.143) Private -0.167*** -0.111 -0.276*** -0.413*** -0.232*** (0.013) 0.742*** (0.076) (0.036) (0.164) (0.023) (0.129) (0.028) Temp. contract -0.151** -0.338* -0.214*** -0.214*** -0.136*** (0.076) (0.187) (0.044) (0.087) (0.049) F o r plus 8 occupational dummies. P e e r P e e r P e e r Uninsured -0.443* -0.0001 -0.120 -0.275** -0.622*** (0.237) (0.187) (0.158) (0.135) (0.249) Part-time 0.217** -0.196 0.391*** 0.577*** 0.294*** (0.112) (0.681) (0.077) (0.145) (0.104) Urban -0.053 0.514* 0.050* 0.200* 0.121*** P e (0.068) (0.240) (0.030) (0.105) (0.033) -0.105 -1.991 0.193*** 0.260** -0.325*** e (0.415) (2.146) (0.046) (0.119) (0.052) r	(0.020) 0.525*** (0.082) -0.137 (0.137) -0.118*** -0.263*** (0.021) 0.809*** (0.129) -0.236*** (0.068) (0.062) (0.097) -0.055 -0.117*** (0.010) (0.245) 0.424** 0.725*** (0.199) (0.137) 0.267*** 0.079 (0.096) (0.074) -0.375*** 0.020 (0.108) (0.013)	(0.028) 0.690*** (0.119) 0.022 (0.166) -0.075 (0.096) -0.038 (0.115) 0.373*** (0.122) -0.019 (0.088) 0.011*** (0.003)
	R e v R e v R e v R e v 8.33*** 30.74*** 49.1 51.7 149 822 R is the occupational selectivity term as described in the text. F-test 45.09*** 6.82*** 80.80*** R-squared 42.8 53.2 50.2 Observations 837 45 1551 e Asteriscs denote statistical signi…cance at, ***1%, **5% and *10% v	6.29*** 39.2 261	9.87*** 39.2 302	4.98*** 26.1 238
								i e w i e w i e w i e w i e w

Table 8 .

 8 Occupation-speci…c average observed characteristics and occupational native (N)-immigrant (I) wage di¤erential decomposition

		Highly Skilled	Service workers Production workers Unskilled labour
		N	I	N	I			N	I	N	I
	Hourly wage (in e)	2.153	2.002	1.64	1.367		1.588	1.482	1.387	1.209
	Exper	26.122	26.178	25.61	23.752	30.428	25.808	34.119	28.777
	Exper-squared	777.259	771.244 769.662	661.51	1075.443	773.962	1311.417 944.055
	Secondary	0.235	0.212	0.718	0.752		0.633	0.736	0.401	0.651
	Tertiary	0.749	0.756	0.191	0.221		0.047	0.111	0.007	0.155
	Married	0.659	0.556	0.549	0.631		0.646	0.671	0.616	0.693
	Household size	3.268	3.244	3.34	3.188		3.578	3.575	3.513	3.395
	Emplt-1	0.918	0.822	0.899	0.812		0.866	0.801	0.791	0.782
	Private	0.407	0.612	0.648	0.846		0.814	0.904	0.682	0.92
	Tempcont	0.122	0.222	0.147	0.154		0.176	0.241	0.351	0.42
	Uninsured	0.008	0.000	0.019	0.074		0.018	0.061	0.043	0.176
	Part-time	0.049	0.067	0.058	0.04		0.034	0.023	0.142	0.122
	Urban	0.871	0.844	0.814	0.893		0.685	0.862	0.666	0.811
		0.864	0.884	0.767	0.744		0.989	1.110	3.075	7.069
				Expressed					Expressed
				in logs				as % of the (WD)
	Within-occupations di¤ ential
	Endowments 1			0.155					46.3
	Unexplained 2			0.016					4.8
	Total			0.171
	Between-occupations di¤ ential
	Endowments 3			0.019					5.7
	Unexplained 4			0.145					43.3
	Total			0.164
	Total explained 5			0.174					51.9
	Total unexplained 6			0.161					48.1
	Observed wage di¤erential (WD)		0.335					100
	is the occupational selectivity term as described in the text. 1 P J j=1 P I j ^ N j X N j X I j ; 2 P J j=1 P I j X I j ^ N j ^ I j ; 3 P J j=1 ln w	N j	P N j	P I j ; 4 P J j=1 ln w	N j	P N j	P I j
	5 (1) + (3); 6 (2) + (4)

[START_REF] Borjas | Heaven's door: Immigration policy and the American Economy[END_REF] describes economic assimilation (or, integration) as the process of human capital accumulation that narrows the native-immigrant wage gap.

Since market wages are observed only for labour market participants but not for nonparticipants (whose reservation wage is higher than the o¤ered one), sample selection issues with respect to participation need to be addressed[START_REF] Heckman | Shadow wages, market wages and labor supply[END_REF][START_REF] Kee | Native-immigrant wage di¤erentials in the Netherlands: discrimination?[END_REF][START_REF] Shields | The earnings of male immigrants in England: evidence from the Quarterly LFS[END_REF] Blackby, et. al., 2002; Newman and Oaxaca, 2004; Blackby et. al., 2005).

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


Jessica Bennett

Assessing the

Keywords: J24 -Human Capital|Skills|Occupational Choice|Labor Productivity < J2 -Time Allocation, Work Behavior, and Employment Determination Structure; Differentials by Skill, Occupation, etc. < J3 -Wages, Compensation Skill Shortages, Productivity, High-tech Industries Skill Shortages, Productivity, High-tech Industries JEL Codes: J24, J30, J31

published or not. The documents may come   L'archive ouverte pluridisciplinaire

Introduction

Skills related issues are now centre stage in UK economic policy, having been identified as one of the five drivers of productivity performance. A critical aspect of government policy has been the attempt to alleviate skill shortages within key sectors of the economy which have led to the establishment of Sector Skills Councils which are charged with the responsibility of reducing skills gaps and shortages within their respective sectors. Thus, policy is underpinned by the assumption that skills shortages within the economy have serious negative impacts on productivity performance. There are certainly a number of reasons why we might expect skill shortages to adversely impact productivity levels, for instance, firms may be forced to lower their recruitment standards and fill positions with less productive workers, and / or workers in the affected occupations may exploit their bargaining power to disproportionately enhance their employment conditions; either way, we would expect the level of output per worker to fall. In the event of skill shortages becoming a wide spread phenomenon, it has been argued that the economy will move towards a low skills, bad jobs, lower wages equilibrium trap, characterised by persistent low productivity levels, under investment in training and few skilled jobs (Snower, 1996; Redding 1996). However, UK evidence supporting such views is limited and somewhat mixed, a factor which is at least partially related to a lack of available datasets linking skill shortages and firm level performance. Forth & Mason (2004) using the 1998 Technical Graduates Employers Survey (TGES) combined with Dun & Bradstreet data found no clear differences in the sales per employee levels of companies that experienced recruitment difficulties compared to those that did not.

Similarly, McGuinness & Bonner (2002) failed to find a link between unfilled vacancies and productivity levels within a sample of Northern Ireland (NI) IT firms, Nevertheless, there does exist some evidence to support the view that skill shortages incur real costs on the firm. Haskel and Martin (1993a) report that the increased skill shortages during the mid-1980s reduced productivity growth by around 0.7 per cent per annum whilst Nickell and Nicolatsis (1997) found that skilled labour shortages at industry level were significantly and negatively related to a number of productivity corollaries such as investment in fixed capital and R&D. This paper utilises a dataset constructed using information collected during three separate skill related surveys of firms in the NI IT, Electronic Engineering and Mechanical Engineering industries in order to assess the extent to which the performance of high-tech firms are being constrained as a result of skill shortages.

Data and Methods

The data was collected by the Priority Skills Unit (PSU) of the Economic Research Institute of Northern Ireland (ERINI) who work under the direction of the NI Skills Task Force, the body tasked with identifying sectors of importance to the NI economy that are potentially being constrained as a result of labour market shortages. The data consists of a merged dataset comprising information collected in three separate surveys of the electronic engineering sector (surveyed 2000), the mechanical engineering sector (surveyed 2001) and the IT sector (surveyed 2002). In each of the studies, the inter-departmental business register (IDBR) provided the sampling frame.

Each sample was skewed towards the larger firms in order to ensure the largest degree of employment coverage; however care was taken to ensure that sufficient numbers of 3 small and medium sized firms were also included to ensure the representativeness of each sample. Given that the surveys were collected at slightly different points in time and related to different sectors, the information contained within each dataset was not fully compatible, however, whilst some data was lost during the merger process, we were able to retain a lot of information on both skill composition and company performance 1 . At the end of the data reconciliation process we were left with information on 242 high-tech firms employing 39,328 workers. In terms of the employment breakdown 11 per cent of the sample was employed in the IT sector, 40 per cent in the electronic engineering sector and 49 per cent in the mechanical engineering sector. The achieved sample is broken down by firm size in Table 1 and the dominance of large firms becomes wholly apparent with the largest 20 per cent of firms accounting for over 80 per cent of employment. However, it is important to note that the distribution of employment in Table 1 is consistent with the observed facts for each of the sectors and, as such, the samples reflect the respective industry structures.

As stated, the IDBR provided the populations from which the respective samples were drawn with each sample selected on the basis of the Standard Industrial Classification 1992 (SIC92 Our first measure of skill shortage relates to hard-to-fill vacancies. Firms were asked to assess the degree of difficulty associated with recruiting staff within a set of occupational categories that could be standardized across the various sectors. It should be noted that this, along with the unfilled vacancy rate, is the standard measure of skill shortage used within the UK Skills Monitoring Surveys. As we would expect, the level of recruitment difficulty increases the more experienced and qualified are the staff concerned (Table 2). Just under 70 per cent of firms employing graduates with over two years experience, project leaders and senior managers described the recruitment of such staff as being difficult or very difficult. This is in contrast to the labour market for new graduates (those with no previous experience), which appears to be relatively well supplied with only 24 per cent of firms employing such staff perceiving the labour market to be tight. For all other occupational categories, recruitment appears much less problematic with over 50 per cent of employers describing the process as easy or very easy. In terms of the sectoral breakdown the pattern is generally similar however, some differences are observed. Whilst over half of electronics firms believed the recruitment of new graduates to be problematic, less than 15 per cent of IT and mechanical engineering respondent firms felt that recruitment at this level was difficult (Table 3). The recruitment of craft and technician level staff was much less of a problem for firms operating in the IT sector, with 16 per cent and 10 per cent of firms respectively describing this process as 6 difficult or very difficult compared to over half of firms in electronic and mechanical engineering sectors who described these labour markets as tight.

Whilst hard-to-fill vacancies is a useful measure of labour market difficulty it can also be problematic as such an approach cannot provide us with a concrete empirical measure of shortage nor is it possible to control for sources of subjective bias arising from differences in how respondents define a term such as "difficult". Consequently, information was also collected on the number of unfilled vacancies occurring in the twelve months preceding our surveys, thus enabling us to generate estimates of the actual rate of shortfall occurring within each of the various occupational groupings.

Of the 242 respondent firms, 60 firms reported a total of 420 unfilled vacancies in the twelve months prior to the surveys (Table 4), with 76 per cent of these vacancies occurring at below graduate level. In terms of raw numbers, recruitment problems were most acute at the operator and craft levels. When respondents were asked to attribute these unfilled vacancies to a range of problems, it was obvious that a lack of skilled and qualified workers, as opposed to an inability to pay were the key drivers of unfilled posts. Disaggregating the results by sector, some differences are observable with a lack of technical ability as the most important factor in explaining unfilled vacancies in both the electronics and IT sectors, whilst a shortage of qualified applicants was cited as the most significant problem for firms in the mechanical engineering sector 4 . Thus it is clear from this descriptive analysis that the variable used here is capturing the effects of skill shortage and if it transpires that the hard-tofill and unfilled vacancies variables are highly correlated, this will also provide support for the view that hard-to-fill vacancy information is a good proxy for skill shortages 5 . We then proceeded to calculate actual vacancy rates 6 and these were derived for each occupational aggregate by standardising the number of unfilled vacancies in each category by the number of persons employed in that category. The highest rate of shortage, at 4.5 per cent, was recorded for experienced graduates (more than two years experience) suggesting that the recruitment of staff to this level has been particularly difficult, a finding broadly consistent with the more subjective measure.

To a lesser extent, problems were also experienced in the recruitment of technician level staff (HNC/D) and new graduates with these categories recording an unfilled vacancy rate of 3.2 and 2.8 per cent respectively. Table 5 shows the comparative vacancy rates for each sector. The rate of shortfall for technician level staff is similar in both the electronic and mechanical engineering sectors, however the similarity ends here. Vacancy rates for all of the occupational groupings are much higher in the electronics sector with difficulties particularly acute for graduates with more than two years experience. Electronics vacancy rates are also very high for new graduates and craft level staff at 12.1 and 10.4 per cent respectively. significant negative effect on the wage variable on hiring difficulties 9 . In relation to female employment structure, there is no obvious explanation; however, it may be the case that as females are more likely to exit the labour market due to family reasons, this may in turn lead to higher levels of recruitment activity and consequently skill shortages. Our results also demonstrate that firms employing a lower proportion of new and inexperienced graduates will be more likely to report unfilled vacancies.

< Insert

This implies that firm level preferences and Human Resource Management (HRM)

practices are a significant driver of skill shortages, specifically, those firms setting (2002). Therefore, to conclude, whilst there are many similarities in the determinants of both measures of skill shortage, some differences are also present. On the grounds that both hard-to-fill and unfilled shortages may impact firms' behavior in different ways, it is also possible that both variables will generate distinct impacts with respect to productivity and therefore, whilst potentially correlated, they cannot be thought of as interchangeable.

< Insert Table 7 Here >

Firms reporting unfilled vacancies were then asked to outline the main difficulties arising as a result of these recruitment shortfalls. The impact on company performance was believed to be very important with the majority of firms reporting that their development was being constrained in one or more ways (Table 8). Over half of affected firms asserted that their organizations ability to meet deadlines was severely impeded with a further 47 per cent of firm's experiencing lower productivity as a direct result of unfilled vacancies, thus providing preliminary evidence of a link between skill shortages and output. Reduced credibility and higher running costs were also reported to be a direct result of skill shortages. Generally speaking, the results for the individual sectors broadly reflected those reported in Table 8.

< Insert Table 8 Here >

Finally, in terms of the policies introduced by firms when attempting to alleviate skill shortages (Table 9), the majority of affected firms did appear to have undertaken some form of longer-term strategy. Sixty-three per cent of firms were prepared to increase salary, with a further 58 per cent choosing to train and up-skill their existing staff to fill these positions. Forty-seven per cent of firms were also willing to recruit staff from other backgrounds and train with a third of firms stating that they would be willing to adjust internal structures and practices. Therefore, in addition to the obvious response of increasing wages, additional training and flexible HRM practices appear to have a very important role in combating skill shortages within the high tech sectors. In relation to the sub-sectoral analysis, the up-skilling of existing staff was the most common reaction within the IT and electronics sectors whilst the offering of higher wages was the dominant strategy within mechanical engineering 10 .

< Insert Table 9 Here >

Productivity Impacts

Whilst the descriptive analysis indicates that just under 50 per cent of firms incurring unfilled vacancies believe that their productivity has been adversely affected, it is not always the case that such opinions are validated when tested within the context of econometric models (McGuinness & Bonner (2002)). Consequently, OLS models were estimated to assess the productivity / skills shortage relationship more formally both in the context of unfilled and hard-to-fill vacancies 11 . On the grounds that both variables were correlated and potentially highly endogenous with respect to each other, separate models were estimated on efficiency grounds. As before a parsimonious approach was adopted. Under both models it was found that productivity was lower the higher the R&D intensity of the firm, however, output per worker was higher if the firm was R&D intensive and large (Tables 10 and11). Thus the interaction term indicates higher returns to scale in relation to R&D investment whereas the negative effect may indicate that many small firms within the sectors, all of which were experiencing rapid growth at the time of survey, were still very much in the investment stage of the R&D cycle. Externally owned firms were found to have higher productivity within both models. Over and above the R&D and foreign ownership effects, the models yielded slightly different results with the model containing hard-to-fill vacancies somewhat better specified than that including unfilled vacancies. Within the hard-to-fill vacancies specification firms less than two years old had higher productivity whilst a higher proportion of female graduates tended to lower output; within the unfilled vacancy model there was some evidence to suggest that firms employing a higher proportion of new and inexperienced graduates were less productive. The finding relating to new and inexperienced graduates is consistent with the findings of McGuinness & Bonner (2002) and would perhaps explain why firms are apparently willing to exclude this type of labour from their recruitment pool despite the fact that such a strategy will lead to unfilled and hard to fill vacancies (Table 6). The finding in relation to female graduates is slightly more contentious as it tends to suggest substantially lower rates of human capital accumulation amongst female professionals due, for example, to more demanding vacancies reported that their productivity levels were being adversely affected, no statistical relationship was established. Conversely, we found that firms with hardto-fill vacancies had higher productivity 12 . This result runs contrary to ex ante reasoning and previous studies in this area and, given the early descriptive evidence of Table 9, may lead us to believe that the HRM policies and organizational restructuring introduced to combat hard-to-fill vacancies have had the additional impact of improving productivity performance.

However, we also know from the bivariate probit that the probability of a firm experiencing skill shortages is not randomly distributed and, if this proves to be the case with respect to productivity, the results observed in Tables 10 and 11 may be biased. Given the lack of any obvious instrument for skill shortages, treatment models were estimated following the framework developed by Heckman (1979). The adopted methodology gets around the problem of selection bias by estimating a two stage model; stage 1 involves a probit model describing the key characteristics of firms experiencing skill shortages whereas stage 2 involves the productivity OLS augmented with a control term, drawn from stage 1, that explicitly accounts for any differences in the characteristics of firms experiencing skill shortages. Assuming the error terms from both models are drawn from a bivariate normal distribution we can derive the following: x is a vector of explanatory variables, is the correlation coefficient between the stage 1 and stage 2 error terms, 1 is the standard deviation of the probit model and i is the Inverse Mills Ratio (IMR) given by: 13

1 1 1 1 ( / , 1) . i i i i i E P x m x = = + (1)
2 2 2 2 2 2 ( / ) ( / ) i i i x x = (2)
Where and are density and distribution functions of the standard normal distribution and 2 is the standard deviation of the OLS. In order to ensure that the models were properly identified (see Himler, 2001) we used the evidence from the bivariate probit and the parsed productivity regressions to select at least one variable that, whilst determining the likelihood of a shortage occurring, had no impact on productivity. Consequently, the electronics variable was omitted from the unfilled vacancy model whilst the electronics and perceived shortages variables were omitted from the hard-to-fill treatment models 14 .

The results of both treatment models are reported in Tables 12 and13 with the results generally consistent with the bivariate probits and OLS models reported earlier with the critical exception of the skill shortage impacts within the selection consistent productivity models. The results reveal that firms experiencing skill shortages, of whatever kind, have ex ante productivity levels approximately 50 per cent higher than the average and that these skill shortages substantially reduce productivity levels by 65 per cent in the case of hard-to-fill vacancies and 75 per cent in the case of unfilled vacancies. The analysis thus provides weight to the assertion that skill shortages incur real costs on firms within key economic sectors and demonstrate that a failure to account for non-randomness in the incidence of skill difficulties can produce highly misleading results.

Summary and Conclusions

This paper sought to examine the extent and nature of both hard-to fill and unfilled vacancies amongst a group of high-tech firms and the extent to which such skill shortages impacted on productivity performance. Not surprisingly, problems of skill shortage were more acute for experienced graduates across each of the technical disciplines with firms attributing unfilled vacancies to a combination of supply side shortages and the existence of skill gaps amongst applicants. Consistent with the finding of previous research the results indicate that the determinants of skill shortage vary depending upon the definitional approach adopted, nevertheless, a high level of correlation was found. In relation to the impacts of skill shortages on firm level performance, it was found that ex ante high productivity firms were much more likely to experience skill shortages and that such labour market constraints had the effect of reducing productivity levels by between 65 and 75 per cent. The results suggest that skill shortages can almost completely eradicate the productivity advantage of the best performing high-tech firms. However, on the grounds that the highest rates of shortage were found for experienced professionals it is not clear that the solution to the problem is be found by simply expanding higher education supply, this is particularly the case given that many firms appear to actively desist from employing new graduates and are of the view that they are not being adequately equipped with the requisite skills at university. In support of the skills gap hypothesis there was some evidence of a productivity cost associated with employing large numbers of new graduates. Finally, from a methodological perspective, the results highlight the importance of controlling for selection effects when estimating the impact of labour market constraints on firm level performance. 10 Here > < Insert Table 11 Here > < Insert Table 12 Here > < Insert Table 13 Here > 1 All financial information within the study is expressed in 2002 prices. 2 20 of our respondent firms were added by key informants and were classified within these SIC groups by IDBR. 3 In addition to the IDBR firms, a further 4 firms employing 200 persons were added to the sample by key informants giving us a total of 81 IT firms for whom we have full information. 4 Results available from the authors. 5 Unfortunately a question was not asked on the reasons behind unfilled vacancies. 6 The unfilled vacancy rate is calculated as (unfilled vacancies / employment + unfilled vacancies)*100 7 Where 1 = Very Easy, 2 = Quite Easy, 3 = Difficult and 4 = Very Difficult. 8 This variable was not restricted to professional level vacancies as only a few firms' reported unfilled vacancies at assembler level only; however, when the sample is restricted all subsequent findings hold. 9 The authors point out that this finding was not detected in their previous studies, for example, Haskel & Martin (1993b). 10 Results available from the authors. 11 Due to incomplete information the sample size was reduced to 227. This consists of 162 firms who provided turnover information and a further 65 whose financial data was obtained from databases held within ERINI. All information is given at 2002 current prices. 12 Given that unfilled vacancies and firms perceived hard-to-fill vacancies may be correlated and thus, in addition to being technically inefficient, may have been canceling each other out, we re-estimate the regression excluding one variable alternatively and we find our results are still robust (results available from authors). 13 It should be noted that equation five was estimated using a maximum likelihood procedure which solves the model simultaneously as opposed to adopting a two-step process. 14 The models were estimated in Stata using maximum likelihood.

< Insert Table

F

  Doyle (2005) came to the same conclusion in relation to the NI construction industry.

  higher entry requirements and thus recruiting a relatively low proportion of new and inexperienced graduates are the firms most likely to have vacancies that remain unfilled. Therefore, firms, by restricting their available pool of labour, through low levels of new graduate utilization, are leaving themselves more open to unfilled vacancies;McGuinness & Bonner (2002) also report this result. Finally, in keeping with our more descriptive analysis, skill shortages were found to be more common within the electronics sector.A number of variables are unique to one or other of the measures of shortage, however, this was particularly the case within the unfilled vacancy model. The location variable was significant indicating that firms located in central Belfast, the main urban conurbation in Northern Ireland, are less likely to suffer from unfilled vacancies. This is perhaps unsurprising given that the regions universities are both located in the Belfast area thus providing increased access to a source of highly skilled and educated labour. Firms reporting unfilled vacancies were also more likely labour market entrants from the regions educational and training institutions as being deficient in either, technical, interpersonal or business awareness skills. This negative perception is likely to influence the recruitment activities of firms and specifically, may result in firms restricting their available labour pool by holding out for more experienced staff which in turn increases the likelihood of an unfilled vacancy occurring. Again this result is consistent with the findings ofMcGuinness & Bonner

  periodic absences from the labour market. However, most significantly, despite the fact that almost half of firms (47 per cent) with unfilled

< Insert Table 1 Here > The Incidence of Skill Shortages

  ). The electronics sector was principally defined as those firms operating within divisions 30, 32 and 33. From our final sample of 125 firms we were left with

	SIC 92). A sample was drawn of 121 firms from which a total of 77 firms employing
	2,888 agreed to participate in our study giving a survey response rate of 76 per cent
	(measured in terms of employment) and a population coverage rate of 56 per cent 3 .
	Finally, the mechanical engineering industry was defined in terms of the
	amalgamation of five two-digit sectors (SIC 28, SIC 29, SIC 31, SIC 34, and SIC 35)
	where demand for mechanical engineering skills tends to be most heavily
	concentrated. Our final sample consisted of 160 firms employing 20,581 persons
	F o r from which a total of 98 firms employing 16,537 agreed to participate in the study giving a survey response rate of 80 per cent and a target population coverage rate of 77 per cent. F o r
	P P
	e Turning to the key variable within the dataset, it is important to note that the notion of e
	e r R e v i e w a skills shortage is subject to substantial ambiguity with different surveys using e r different methodologies, terminologies and phraseologies. The official definition from the Department for Education and Skills (DfES) defines skill shortage vacancies as a "situation where there is a genuine shortage in the accessible external labour R e v market of the type of skill being sought, and which leads to a difficulty in i e recruitment". This rather ambiguous definition reflects the fact that there is no clear cut approach to measuring skill shortages and generally, studies have tended to use w
	data on either unfilled vacancies or some measure of hard-to-fill vacancies. However,
	valid responses for 63 firms which gave us a survey response rate of 50.4 per cent, of it is far from certain that both measurement approaches will yield similar results given
	whom 43 were located in SIC groups 30, 32 and 33 2 . On the basis of our 43 that the evidence would tend to suggest that they have different determining factors.
	respondent IDBR firms, we had a population coverage rate of 99.5 per cent suggesting This study has the advantage of containing information on both unfilled and hard-to-
	fill vacancy data and as such both measures are tested against productivity as
	measured by the log of output per worker.
	4

that official sources had seriously underestimated the size of the NI sector. The IT sector was defined as consisting of the computer services industry (division 72 of the

Table 2 Here > < Insert Table 3 Here > < Insert Table 4 Here > < Insert Table 5 Here > < Insert Table 6 Here > Whilst

  To investigate this matter further we modeled the likelihood of hardto-fill and unfilled vacancies using standard econometric analysis. A composite measure of hard-to-fill vacancies was derived by attributing the level of assessed recruitment difficulty for each non assembler grouping a value between 1 and 4 7 and then calculating the average for each firm. If the firm reported an average recruitment difficulty level of greater than three then they were deemed to have been affected by hard-to-fill vacancies and the relevant variable was assigned the value one (and zero otherwise). In relation to the unfilled vacancy measure, the dependent variable takes the value 1 if the firm experienced an unfilled vacancy in the previous 12 months, and zero otherwise 8 . As both variables are binary in nature and likely to be highly

	F o r r o that in higher wage establishments there is a reduced probability of having skill driving factors. F shortages or hard-to-fill vacancies, whilst Haskel & Martin (2001) observed a
	P P
	e e
	e r e r
	R e v R e v
	i e i e
	w w
	8 9

both measures of skill shortage yield generally similar patterns, it is unclear to what extent these key measurement variables are correlated and subject to the same correlated we estimate a bivariate probit model, the use of binary variables is also useful as they will facilitate the estimation of selection controls within the productivity regressions. In terms of explanatory variables, we use a number of firm level characteristics which include employment structure, location, ownership, salary, age, size, R&D and sector.

Table

7

gives the results from the Bivariate probit and the first thing to note is that the model yields a positive and significant correlation coefficient indicating, not surprisingly, that that firms affected by shortage are likely to report both unfilled and hard-to-fill vacancies. In relation to the explanatory variables both measures of shortage share a number of determining factors. The coefficient on the wage variable and the female graduate share of total employment are both positive and highly significant. The wage impact is in line with the findings of

Dickerson (2003)

, who reported that higher local relative wages are associated with both higher vacancy incidence and higher vacancy propensity. In contrast to this,

Green et al (1998)

find

Table 1 : Size Distribution of All Firms

 1

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Table 2 : Ease / Difficulty of Recruitment -All Firms (%)

 2

		V Easy	Quite Easy	Difficult	V. Difficult	N
	Operators / Assemblers	22	32	29	17	144
	Non-Grad Tech Support -Level 3	26	32	29	13	120
	Non-Grad Tech Support -HNC/D	24	31	29	17	108
	Graduates with no experience	36	39	12	12	105
	Graduates with < 2 yrs experience	29	28	26	18	98
	Graduates with 2 + yrs experience	6	26	32	35	62
	Project Leaders	13	19	34	35	107
	Senior Managers	14	17	26	42	99
	Source: ERINI, 2005

Table 3 : Firms Describing Recruitment as Difficult / Very Difficult by Sector (%)

 3

				Mechanical
		Electronics	IT	Engineering
	Operators / Assemblers	42	-	49
	Non-Grad Tech Support -Level 3	57	16	52
	Non-Grad Tech Support -Level 4	51	10	59
	Grads no experience	57	12	14
	Grads < 2 yrs experience	78	24	48
	Grads 2 + yrs experience	80	-	59
	Project Leaders	82	58	66
	Senior Managers	72	62	71

Table 4 : Number and Rate of Unfilled Vacancies in last 12 months -All Firms (%)

 4

		No.	Total	Vacancy
	No. Firms	Vacancies	Employment	Rate

Table 5 : Comparative Vacancy Rates

 5

	Electronics	IT	Mechanical

Table 6 : Reasons for Unfilled Vacancies -All Firms (n= 60) 1

 6

		Shortage of	Lack of	Shortage of		Competition
	%	Qualified	Technical	Sufficient	Cannot Pay	from Other
		Applicants	Ability	Experience	Enough	Employers
	Very Important	60	67	53	16	59
	Important	20	21	33	30	21
	Not Important	20	13	13	54	21
	Source: ERINI, 2005

Table 7 : Bivariate probit -Hard-to-Fill and Unfilled Vacancies

 7 The figures are regression coefficients with the standard errors in parentheses. The significance of each coefficient is also noted, *** denotes significance at the 99% level; ** at the 95% level; and * at the 90% level.

	Had-to-fill	Unfilled

Table 8 : Difficulties Arising from Unfilled Vacancies (n=58)

 8

						Inability to
		Lower	Loss of	Lower	Higher	Develop	Failure to	Reduced
	%	Productivity	Orders	Quality	Running	New	Meet	Credibility
				Product	Costs	Products	Deadlines
	Very Important	47	34	28	43	31	57	43
	Important	33	33	26	19	22	24	28
	Not Important	21	33	47	38	47	19	29
	Source: ERINI, 2005

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Table 9 : Actions Taken Regarding Unfilled Vacancies -All Firms (%)

 9

	Total (n = 60)

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Table 11 : Results from Productivity Level Regressions -Unfilled

 11 The figures are regression coefficients with the standard errors in parentheses. The significance of each coefficient is also noted, *** denotes significance at the 99% level; ** at the 95% level; and * at the 90% level.

	Unparsed	Parsed

The percentages in each of these tables below vary as not all firms were able to rate the level of importance for each reason given.