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Mismatch between Education and the Labour Market in the Netherlands: Is It a Reality or a Myth? The Employers' Perspective

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Title: Mismatch between Education and the Labour Market in the Netherlands: Is It a Reality or a Myth? The Employers' Perspective
Language: English
Authors: Cabus, Sofie J., Somers, Melline A.
Source: Studies in Higher Education. 2018 43(11):1854-1867.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 14
Publication Date: 2018
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Employee Attitudes, Labor Market, Higher Education, Education Work Relationship, Educational Attainment, College Graduates, Corporations, Foreign Countries, Skilled Workers, Job Skills, Supply and Demand, Employment Qualifications, Personnel Selection
Geographic Terms: Netherlands
DOI: 10.1080/03075079.2017.1284195
ISSN: 0307-5079
Abstract: This study examines whether the expansion in higher education over the past 20 years has contributed to better education-job matches on the labour market. In particular, we relate changes in the average formal schooling level of workers on the regional labour market to the educational attainment of the recruited staff within companies operating on that regional labour market. Hereby, it is acknowledged that companies most often recruit from a pool of workers available on the regional labour market. Next, we estimate the effects of changes in the level of schooling of the staff owing to the increased supply of higher educated graduates on the regional labour market on mismatch. Data from the Dutch Labour Demand Panel are used covering 7451 unique companies over the period 1991-2011. The results indicate that a one-month increase in companies' workforce average schooling level decreases the probability that companies report mismatch with -3.0 percentage points.
Abstractor: As Provided
Number of References: 52
Entry Date: 2018
Accession Number: EJ1197243
Database: ERIC
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  Value: <anid>AN0133105218;she01nov.18;2018Nov21.10:42;v2.2.500</anid> <title id="AN0133105218-1">Mismatch between education and the labour market in the Netherlands: is it a reality or a myth? The employers’ perspective </title> <p>This study examines whether the expansion in higher education over the past 20 years has contributed to better education-job matches on the labour market. In particular, we relate changes in the average formal schooling level of workers on the regional labour market to the educational attainment of the recruited staff within companies operating on that regional labour market. Hereby, it is acknowledged that companies most often recruit from a pool of workers available on the regional labour market. Next, we estimate the effects of changes in the level of schooling of the staff owing to the increased supply of higher educated graduates on the regional labour market on mismatch. Data from the Dutch Labour Demand Panel are used covering 7451 unique companies over the period 1991-2011. The results indicate that a one-month increase in companies’ workforce average schooling level decreases the probability that companies report mismatch with −3.0 percentage points.</p> <p>Keywords: Education; employer; historical trends; mismatch; skills</p> <hd id="AN0133105218-2">1. Introduction</hd> <p>Many OECD countries have witnessed a continued rise in the supply of tertiary education graduates over the recent decades (Autor, Katz, and Krueger 1998; Goos, Manning, and Salomons 2009; OECD 2014). The higher education expansion coincided with a significant increase in public investments in education and has raised several questions concerning its implications for the labour market (OECD 2014). One of the consequences potentially brought forth by this expansion pertains to a mismatch between the skill supply and skill demand on the labour market. While various studies propose that the developments in higher education have resulted in an over-supply of college graduates (e.g. Di Pietro and Urwin 2006; Hartog 2000; Mason 1996), ample evidence points out that the supply of high-skilled workers failed to keep pace with the demand for skilled labour (Bound and Johnson 1992; Katz and Murphy 1992). Conclusions on the labour market implications of the expansion in tertiary education therefore remain inconclusive and mostly limited to the perspective of employees (see e.g. Groot and Maasen van den Brink 2000; McGuinness 2006). Consequently, the overall picture we have on mismatch may be biased, or at least incomplete.</p> <p>This paper provides a different perspective on the implications of the higher education expansion and examines whether employers have benefitted from the increased supply of high-skilled graduates. We explore how employers' perception on the (mis-) match between skill demand and skill supply within their companies has developed over the last two decades and link this to the educational attainment of companies' workforce. Our analyses are performed for the Netherlands that like most other OECD countries has witnessed a growing number of enrolments in higher education as well as an increase in public expenses on education (Leuven and Oosterbeek 2000; Jacobs and Webbink 2006; CBS, 2011; OECD 2014; van den Berge and ter Weel 2015). Hence, the Netherlands provides an interesting case study to explore the consequences of the higher education expansion beyond the perspective of employees.</p> <p>Only a small number of studies have attempted to evaluate the consequences of the increased supply of skilled labour from the perspective of employers (Tsang 1987; Tsang, Rumberger, and Levin 1991; Büchel 2002; Belfield 2010; Kampelmann and Rycx 2012). These studies have investigated through various approaches how firm productivity is affected by the presence of employees whose acquired level of education exceeds the required level for the job. One approach relies on standard human capital theory and derives productivity effects of over-education from workers' wages. This approach argues that over-qualified workers are more productive than their well-matched colleagues as over-educated workers generally receive a wage premium over their adequately allocated colleagues (e.g. Rumberger 1987; Groot and Maasen van den Brink 2000). Other studies have examined the effect of over-education on productivity related factors such as job satisfaction and job turnover and found mixed results. While some studies found a negative relation between over-education and job satisfaction (Tsang 1987; Hersch 1991; Verhaest and Omey 2009; Belfield 2010), other studies did not find that over-educated workers report lower levels of job satisfaction in comparison to their well-matched colleagues (Büchel 2002; Verhaest and Omey 2006). Regarding job turnover, Hersch (1991) and Tsang, Rumberger, and Levin (1991) provided evidence that the turnover rate is higher among over-qualified male workers, whereas Büchel (2002) indicated that over-educated workers show longer firm tenure than their adequately educated colleagues. Only a few studies attempted to estimate the impact of over-qualification on direct measures of firm productivity. Tsang (1987) showed that over-education is negatively and significantly related to job satisfaction which is in turn positively and significantly related to firm output. In contrast, a more recent study of Kampelmann and Rycx (2012) found that additional years of over-qualification have a positive effect on firm productivity.</p> <p>The contribution of this study to the literature is at least twofold. Our study is the first to examine how the match between job requirements and employees' skills has developed over time according to employers. Doing so, this paper is also the first to provide a measure of mismatch that is based on employer reports. The analyses are based on data from the Dutch Labour Demand Panel consisting of 11,817 observations from 7451 unique companies between 1991 and 2011. Due to the richness of the collected data, this paper presents an overall and representative picture of mismatch from the employers' perspective.</p> <p>The second contribution concerns our methodology. The composition of companies' workforce heavily relies on the endogenous recruitment process (Caldwell and O'Reilly 1990). Due to endogeneity issues, direct estimates of the effect of an increase in formal education within companies on mismatch only allow for a correlational interpretation. However, prior research proposes that graduates' educational attainment on the regional labour market can be considered a public good for a company operating within that region (Rauch 1993; Moretti 2004; Tilak 2008). An employer who seeks a good match with his vacancies heavily relies on the availability of workers in the area wherein business activities take place. In other words, the employer takes the worker availability within the regional labour market as given (i.e. exogenous), while the selection of workers into the company is endogenous. We take advantage of the consecutive steps an employer has to take to hire a worker (worker availability-recruitment-mismatch) by performing our analyses in two steps: (<reflink idref="bib1" id="ref1">1</reflink>) we relate the average formal schooling level of the regional labour force to the average schooling level of the recruited staff within companies operating on that regional labour market; and (<reflink idref="bib2" id="ref2">2</reflink>) we estimate the effect of changes in the schooling level of the staff owing to the increase of high-educated graduates on the regional labour market on mismatch. This approach is similar to the instrumental variables method (Angrist, Imbens, and Rubin 1996). In this paper, individuals' educational attainment is defined as the total years in formal education. Given that we focus on the relation between the increased supply of skilled labour and the match between employees' skills and job requirements, our definition of educational attainment excludes other forms of human capital such as job training and work experience. However, we do consider other elements of individuals' educational attainment as an important direction for future research.[<reflink idref="bib1" id="ref3">1</reflink>]</p> <p>The remainder of this paper proceeds as follows. Section 2 discusses the endogeneity issues regarding the educational composition of companies' workforce and addresses how we deal with those issues. Section 3 describes the data and presents descriptive statistics. The empirical framework is discussed in Section 4. Section 5 presents the results and Section 6 concludes.</p> <hd id="AN0133105218-3">2. Endogenous recruitment process</hd> <p>The educational composition of a company's workforce is determined endogenously owing to the recruitment process of the company. The selection of employees is not only based on the perceived match of employees' skills with the job requirements, but also with strategies that are heterogeneous across firms (Chatman 1991). Firm characteristics that determine the educational composition of its workforce are often unobservable in empirical analyses and may pertain to human resource management strategies or specific skill needs. For instance, Bartel (1994) showed that workplace training is mostly received by high-educated workers and is more likely to take place in technologically progressive industries. Moreover, technologies can be skill complementary and favour certain type of workers (e.g. Bartel and Lichtenberg 1987; Autor, Katz, and Krueger 1998; Machin and Van Reenen 1998). Companies also decide whether to acquire certain skills and competences on the market or to develop them internally (Cappelli 2008). Hence, companies sort themselves non-randomly into specific employee-company matches which, eventually, lead to different workforce compositions.</p> <p>We exploit information on the average years in formal schooling of graduates available on the (regional) labour market to create exogenous variation in the average years in schooling attained by companies' employees. From the worker's perspective, employment opportunities mainly arise at the regional level due to restricted spatial flexibility (van Ham, Hooimeijer, and Mulder 2001). The likelihood that employed individuals in the Netherlands search for another job or accept a job offer increases with restricted commuting time (van Ommeren, Rietveld, and Nijkamp 1998). Therefore, companies are likely to draw their workforce from the pool of workers available on the regional labour market (van Ham, Hooimeijer, and Mulder 2001). The empirical literature confirms that the job location is indeed one of the most important reasons for individuals to accept as well as to reject a job offer (Boswell et al. 2003).</p> <p>Moreover, we instrument the formal schooling of companies' workforce in time <emph>t</emph> with the formal schooling of the regional labour force in <emph>t-1</emph>. The timing of graduation and job arrival does often not occur simultaneously (van Ours and Ridder 1992). Workers are heterogeneous in terms of the skills they possess and the skill requirements differ across firms (Pissarides 2000). Furthermore, the location of the demand for certain skills does not always coincide with the location of the supply of these skills (Pissarides 2000). Consequently, it takes time and other resources for a worker to find a good job with a good wage, and for a firm to find a proper match between a vacancy and a worker (Stigler 1962; Rogerson, Shimer, and Wright 2005). Provided that labour markets do not clear automatically, changes in the composition of the labour force are reflected in changes in companies' workforce only after some amount of time.</p> <p>The educational composition of the regional labour force can be treated as an exogenous supply of labour from the perspective of a single company as educational choices are made at the individual level (Rauch 1993; Moretti 2004; Tilak, 2008). Early research already pointed at the economic theory that individuals make their human capital investment decisions according to their expected present value of education (Becker 1964; Mincer 1974). In addition, educational choices follow from information on the individual's ability, personality and occupational preferences (Weiss 1972; Holland, Gottfredson, and Power 1980). Moreover, while our instrument is argued to be a good predictor of the endogenous regressor, it is unlikely to directly affect mismatch. Our outcome variable is derived from employers' perception on the degree to which employees adequately perform their job. The educational attainment of workers in the local labour force can only affect perceived mismatch if a company actually recruits workers from the regional labour market and reflects on the skills possessed by those workers. Hence, our instrument only indirectly influences mismatch through the endogenous recruitment process of the company.</p> <p>However, considering the increase in the share of high-educated graduates in the Netherlands as exogenous to the company, we must exclude that companies select their location due to expected gains from available levels of human capital. This assumption implies that companies do not relocate to areas that supply the desirable levels of schooling according to their needs. Our data show that during the sample period almost 99% of the companies in our sample did not relocate (Section 3). Furthermore, the longer companies have been in a region, the less likely it becomes that those companies have been able to predict the supply of human capital in this region. As Section 5 will demonstrate, the results of our analyses remain unchanged once we account for location sorting behaviour.</p> <hd id="AN0133105218-4">3. Data and descriptive statistics</hd> <p></p> <hd id="AN0133105218-5">3.1. Labour demand</hd> <p>The analyses are based on data from the Dutch Labour Demand Panel (Arbeidsvraagpanel) and the Dutch Labour Supply Panel (Arbeidsaanbodpanel) which are available at the Netherlands Institute for Social Research (scp.nl). The Labour Demand Panel survey is conducted biannually among Dutch employers and contains information on the composition of the workforce and employees' competencies. The dataset covers the period 1991-2011. On average, companies participated twice in the survey, yielding 33,601 observations. About 26,530 employers answered the question on mismatch which was formulated as: 'In your opinion, is your workforce sufficiently equipped to meet the job task requirements of the coming years?' As from 2003, this question slightly changed to: 'In your opinion, is your workforce not sufficiently equipped to meet the job task requirements of the coming years?' The answers were coded into a binary variable, taking the value '1' if the answer was 'mismatch, not sufficiently equipped', and '0' if the answer was 'no mismatch, sufficiently equipped'.[<reflink idref="bib2" id="ref4">2</reflink>] The mismatch question was not included in the year 1995.</p> <p>About 17,498 employers (52%) indicated the share of employees whose highest level of education fell into each of the following categories of Dutch diplomas: (<reflink idref="bib1" id="ref5">1</reflink>) university (WO) or higher professional education (HBO), (<reflink idref="bib2" id="ref6">2</reflink>) vocational education (MBO), general secondary education (HAVO) or pre-university education (VWO), (<reflink idref="bib3" id="ref7">3</reflink>) pre-vocational secondary education (VMBO) and (<reflink idref="bib4" id="ref8">4</reflink>) primary education. Based on the nominal study duration of each level of education, we translated the educational attainment of companies' staff into average months as well as into years in formal schooling. After removing the observations with missing answers on the variables included in our analyses, our sample size is reduced to 11,817 observations (7451 unique companies).[<reflink idref="bib3" id="ref9">3</reflink>] Table 1 shows that the average mismatch rate between 1991 and 2011 is 31.2% when no survey weights are used and 27.7% when survey weights are used. For the remaining analyses in this paper, the samples are always weighted in order to obtain a better representation of the population from which companies were drawn. The average years of schooling equals 13.4 years (or 160.5 months). A large pool of workers (32% VMBO; and 7.7% only primary education) would nowadays be classified as 'school dropouts', that is, individuals without a secondary school-leaving certificate who are not in formal education.</p> <p>Summary statistics of the main variables.</p> <p> <ephtml> <table border="1" cellpadding="6"><tr><td align="left" /><td align="center">N</td><td align="center">Mean</td><td align="center">Std. dev.</td><td align="center">Min</td><td align="center">Max</td></tr><tr><td align="left" colspan="6">Mismatch</td></tr><tr><td align="left">Mismatch (1 = yes, 0 = no) unweighted</td><td align="left">11,817</td><td align="char">0.3120</td><td align="char">0.4630</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left">Mismatch (1 = yes, 0 = no) weighted</td><td align="left">11,817</td><td align="char">0.2770</td><td align="char">0.4480</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left" colspan="6">Highest educational level attained</td></tr><tr><td align="left">Categories (%)</td><td /><td /><td /><td /><td /></tr><tr><td align="left"> WO; HBO</td><td align="left">11,817</td><td align="char">24.9</td><td align="char">17.1</td><td align="char">0</td><td align="char">100</td></tr><tr><td align="left"> MBO; HAVO; VWO</td><td align="left">11,817</td><td align="char">35.4</td><td align="char">30.3</td><td align="char">0</td><td align="char">100</td></tr><tr><td align="left"> VMBO</td><td align="left">11,817</td><td align="char">32.0</td><td align="char">25.8</td><td align="char">0</td><td align="char">100</td></tr><tr><td align="left"> Primary education</td><td align="left">11,817</td><td align="char">7.7</td><td align="char">29.8</td><td align="char">0</td><td align="char">100</td></tr><tr><td align="left">Education (in years)</td><td align="left">11,817</td><td align="char">13.4</td><td align="char">1.9</td><td align="char">7</td><td align="char">20</td></tr><tr><td align="left">Education (in months)</td><td align="left">11,817</td><td align="char">160.5</td><td align="char">23.0</td><td align="char">86</td><td align="char">243</td></tr></table> </ephtml> </p> <p>Note: The categories denote: (<reflink idref="bib1" id="ref10">1</reflink>) university (WO) or higher professional education (HBO), (<reflink idref="bib2" id="ref11">2</reflink>) vocational education (MBO), general secondary education (HAVO) or pre-university education (VWO), (<reflink idref="bib3" id="ref12">3</reflink>) pre-vocational secondary education (VMBO), and (<reflink idref="bib4" id="ref13">4</reflink>) only primary education.</p> <p>The mismatch rates for the period 1991-2011 are plotted on Figure 1. The linear trend shows that employers' view on the match between employees' skills and the job requirements significantly improved between 1991 and 2011. In 1991, almost 1 out of every 2 employers reported a degree of mismatch, while in 2011, only about 1 out of every 4 employers did so. There appears to be a cyclical pattern in the share of companies reporting mismatch. To observe this, we have fitted a second order polynomial function to the data and took 2003, when economic activity in the Netherlands was at its lowest point, as a breaking point.</p> <p>PHOTO (COLOR): Figure 1. Employers' self-reported mismatch rates 1991-2011.</p> <p>Employers' self-reported mismatch rates are also analysed per sector. While the downward sloping trend is observed for each sector in our dataset, the degree to which companies experience mismatch differs across sectors (Table 2). Whereas the transport sector has a historical low rate of mismatch equal to 18.0%, the education sector (36.7%) and the government sector (42.1%) have always been suffering from relatively high rates of mismatch. The downward sloping mismatch trend from 1991 to 2011 is relatively small for the government sector (−4.0%). Regarding the average years of schooling of companies' workforce, one could rank the nine sectors from least attracting to most attracting (Table 3). The construction sector would be given rank 1, while the sectors industry and agriculture and transport receive second and third place. The education sector gets rank 9, followed by the sector business services and the government sector. The level of formal schooling increased for each sector, except for the sector other services.</p> <p>Mismatch rates 1991-2011 by sector.</p> <p> <ephtml> <table border="1" cellpadding="7"><tr><td align="left">Sector</td><td align="center">N</td><td align="center">Mean</td><td align="center">Std. dev.</td><td align="center">1991</td><td align="center">2011</td><td align="center">Change (%)</td></tr><tr><td align="left">Industry and agriculture</td><td align="char">2466</td><td align="char">0.3202</td><td align="char">0.4667</td><td align="char">0.4060</td><td align="char">0.2714</td><td align="char">−.3315</td></tr><tr><td align="left">Construction</td><td align="char">1024</td><td align="char">0.2199</td><td align="char">0.4144</td><td align="char">0.2786</td><td align="char">0.1330</td><td align="char">−.5227</td></tr><tr><td align="left">Trade, catering, repair</td><td align="char">1578</td><td align="char">0.2113</td><td align="char">0.4083</td><td align="char">0.3338</td><td align="char">0.1294</td><td align="char">−.6122</td></tr><tr><td align="left">Transport</td><td align="char">672</td><td align="char">0.1798</td><td align="char">0.3843</td><td align="char">0.2229</td><td align="char">0.1798</td><td align="char">−.1937</td></tr><tr><td align="left">Business services</td><td align="char">1439</td><td align="char">0.2573</td><td align="char">0.4373</td><td align="char">0.3390</td><td align="char">0.1990</td><td align="char">−.4130</td></tr><tr><td align="left">Life sciences and health</td><td align="char">1861</td><td align="char">0.3429</td><td align="char">0.4748</td><td align="char">0.4582</td><td align="char">0.2451</td><td align="char">−.4651</td></tr><tr><td align="left">Other services</td><td align="char">840</td><td align="char">0.2217</td><td align="char">0.4157</td><td align="char">0.3172</td><td align="char">0.1855</td><td align="char">−.4152</td></tr><tr><td align="left">Government</td><td align="char">829</td><td align="char">0.4212</td><td align="char">0.4940</td><td align="char">0.4414</td><td align="char">0.4236</td><td align="char">−.0404</td></tr><tr><td align="left">Education</td><td align="char">1108</td><td align="char">0.3668</td><td align="char">0.4821</td><td align="char">0.4668</td><td align="char">0.3551</td><td align="char">−.2391</td></tr></table> </ephtml> </p> <p>Years in formal education 1991-2011 by sector.</p> <p> <ephtml> <table border="1" cellpadding="7"><tr><td align="left">Sector</td><td align="center">N</td><td align="center">Mean</td><td align="center">Std. dev.</td><td align="center">1991</td><td align="center">2011</td><td align="center">Change (%)</td></tr><tr><td align="left">Industry and agriculture</td><td align="char">2466</td><td align="char">12.3</td><td align="char">1.3</td><td align="char">11.9</td><td align="char">12.6</td><td align="char">.0605</td></tr><tr><td align="left">Construction</td><td align="char">1024</td><td align="char">12.1</td><td align="char">1.3</td><td align="char">11.8</td><td align="char">12.6</td><td align="char">.0674</td></tr><tr><td align="left">Trade, catering, repair</td><td align="char">1578</td><td align="char">12.6</td><td align="char">1.3</td><td align="char">12.0</td><td align="char">13.1</td><td align="char">.0919</td></tr><tr><td align="left">Transport</td><td align="char">672</td><td align="char">12.3</td><td align="char">1.5</td><td align="char">12.0</td><td align="char">12.7</td><td align="char">.0587</td></tr><tr><td align="left">Business services</td><td align="char">1439</td><td align="char">14.1</td><td align="char">1.9</td><td align="char">13.1</td><td align="char">15.0</td><td align="char">.1430</td></tr><tr><td align="left">Life sciences and health</td><td align="char">1861</td><td align="char">13.8</td><td align="char">1.6</td><td align="char">13.3</td><td align="char">14.3</td><td align="char">.0710</td></tr><tr><td align="left">Other services</td><td align="char">840</td><td align="char">13.6</td><td align="char">1.8</td><td align="char">13.8</td><td align="char">13.5</td><td align="char">−.0196</td></tr><tr><td align="left">Government</td><td align="char">829</td><td align="char">14.0</td><td align="char">1.4</td><td align="char">13.1</td><td align="char">14.5</td><td align="char">.1040</td></tr><tr><td align="left">Education</td><td align="char">1108</td><td align="char">16.5</td><td align="char">1.1</td><td align="char">16.3</td><td align="char">16.5</td><td align="char">.0067</td></tr></table> </ephtml> </p> <hd id="AN0133105218-6">3.2. Labour supply</hd> <p>The Labour Supply Panel is biannually conducted and contains data on various aspects of the labour situation of employed and unemployed individuals aged between 16 and 66 years. On average, respondents participated three times in the survey. Questions typically deal with labour mobility, education and search behaviour for (other) jobs. The panel is used to construct the variable 'years and months in formal education on the regional labour market'. Between 1990 and 2010, 56,122 individuals were asked to report their highest attained level of formal education. As discussed in Section 2, we will use the educational attainment of the local labour force in year <emph>t-1</emph>, to instrument for the educational attainment of companies' employees in year <emph>t</emph>. For each NUTS3[<reflink idref="bib4" id="ref14">4</reflink>] (in Dutch: COROP) region and for each year in our dataset, we determined the average years/months individuals have been in formal schooling. There are 40 NUTS3 regions in the Netherlands, providing us 440 observations (i.e. 40 NUTS3 regions × 11 years). To obtain more information on how the educational attainment of men and women in the regional labour force relates to the human capital available within companies, we created two additional variables that distinguish between the formal schooling acquired by women and men.</p> <p>Table 4 presents summary statistics of the educational attainment for the full sample. The educational composition of the labour force is plotted on Figure 2 by gender and by highest level of education attained. The average years in formal schooling of the labour supply equal 13.2. Figure 2 shows that a rising share of individuals has obtained a professional higher education - (HBO) or a university degree during the last two decades. Also the proportion of individuals obtaining a vocational education - (MBO), a general secondary education - (HAVO) or a pre-university education degree (VWO) has increased over time. Moreover, the share of individuals in the labour force with primary education as the highest attained level of education or school dropouts from secondary education (VMBO) has declined.</p> <p>PHOTO (COLOR): Figure 2. Educational composition of women and men on the labour market 1990-2010.</p> <p>Years in formal education on the regional labour market by gender.</p> <p> <ephtml> <table border="1" cellpadding="6"><tr><td align="left" /><td align="center">N</td><td align="center">Mean</td><td align="center">Std. dev.</td><td align="center">Min</td><td align="center">Max</td></tr><tr><td align="left">All respondents</td><td /><td /><td /><td /><td /></tr><tr><td align="left">Education (in years)</td><td align="char">440</td><td align="char">13.2</td><td align="char">0.7</td><td align="char">11.0</td><td align="char">14.9</td></tr><tr><td align="left">Education (in months)</td><td align="char">440</td><td align="char">158.1</td><td align="char">8.9</td><td align="char">132.0</td><td align="char">178.3</td></tr><tr><td align="left">Female respondents</td><td /><td /><td /><td /><td /></tr><tr><td align="left">Education (in years)</td><td align="char">440</td><td align="char">13.0</td><td align="char">0.8</td><td align="char">10.3</td><td align="char">14.9</td></tr><tr><td align="left">Education (in months)</td><td align="char">440</td><td align="char">156.4</td><td align="char">9.2</td><td align="char">123.0</td><td align="char">178.2</td></tr><tr><td align="left">Male respondents</td><td /><td /><td /><td /><td /></tr><tr><td align="left">Education (in years)</td><td align="char">440</td><td align="char">13.3</td><td align="char">0.8</td><td align="char">10.9</td><td align="char">15.0</td></tr><tr><td align="left">Education (in months)</td><td align="char">440</td><td align="char">159.8</td><td align="char">9.5</td><td align="char">130.5</td><td align="char">180.3</td></tr></table> </ephtml> </p> <p>Note: Data collapsed by NUTS3 region and year.</p> <p>Although the gap between men's and women's perspectives on the labour market has closed, there are still some notable gender differences. Figure 2 shows that between 1990 and 2010, about 27% of the male respondents acquired a university or professional higher education degree, compared to 22% of the female respondents. The largest gender differences are found for university education with completion rates of 8.2% for men and 4.7% for women. However, we observe a steep increase in the percent of women with a higher education degree, indicating that women are catching up. As from 1996, the share of women in the labour force without a secondary school-leaving certificate (i.e. school dropouts) declined from almost 45% to 26% in 2010.</p> <hd id="AN0133105218-7">3.3. Control variables</hd> <p>Table 5 summarises a set of control variables that may affect the likelihood that companies report mismatch. We account for workforce characteristics including the average age of companies' workforce and the type of contracts employees hold. We also control for company characteristics including the size of the company, the cyclical sensitivity and whether the company has a collective bargaining agreement. Finally, we account for respondents, job function, and for companies' location sorting behaviour. To account for location sorting behaviour, we include variables that deal with the year of start-up and with whether relocation took place during the sampling period.</p> <p>Summary statistics of the control variables.</p> <p> <ephtml> <table border="1" cellpadding="6"><tr><td align="left" /><td align="center">N</td><td align="center">Mean</td><td align="center">Std. dev.</td><td align="center">Min</td><td align="center">Max</td></tr><tr><td align="left" colspan="6">Workforce characteristics</td></tr><tr><td align="left">Average age of employees</td><td align="char">11,817</td><td align="char">38.561</td><td align="char">6.04</td><td align="char">17.5</td><td align="char">63.2</td></tr><tr><td align="left">Employees with temporary contracts (1 = yes, 0 = no)</td><td align="char">11,817</td><td align="char">0.3866</td><td align="char">0.4870</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left" colspan="6">Company characteristics</td></tr><tr><td align="left">Company size</td><td /><td /><td /><td /><td /></tr><tr><td align="left"> 0-19 employees</td><td align="char">11,817</td><td align="char">0.3790</td><td align="char">0.485</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left"> 20-49 employees</td><td align="char">11,817</td><td align="char">0.1970</td><td align="char">0.398</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left"> 50-99 employees</td><td align="char">11,817</td><td align="char">0.1380</td><td align="char">0.345</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left"> 100-199 employees</td><td align="char">11,817</td><td align="char">0.1400</td><td align="char">0.347</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left"> >200 employees</td><td align="char">11,817</td><td align="char">0.1460</td><td align="char">0.353</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left">Status of respondent</td><td /><td /><td /><td /><td /></tr><tr><td align="left"> Respondent is HR manager (1 = yes, 0 = no)</td><td align="char">11,817</td><td align="char">0.1890</td><td align="char">0.3920</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left"> Respondent is HR officer (1 = yes, 0 = no)</td><td align="char">11,817</td><td align="char">0.0690</td><td align="char">0.2540</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left">Cyclical sensitivity</td><td /><td /><td /><td /><td /></tr><tr><td align="left"> Strong</td><td align="char">11,817</td><td align="char">0.2600</td><td align="char">0.4390</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left"> Somewhat</td><td align="char">11,817</td><td align="char">0.3920</td><td align="char">0.4880</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left"> Barely</td><td align="char">11,817</td><td align="char">0.3480</td><td align="char">0.4760</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left"> Collective bargaining agreement (1 = yes, 0 = no)</td><td align="char">11,665</td><td align="char">0.8494</td><td align="char">0.3577</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left">Location sorting</td><td /><td /><td /><td /><td /></tr><tr><td align="left"> Company switched region (1 = yes, 0 = no)</td><td align="char">11,817</td><td align="char">0.0143</td><td align="char">0.1187</td><td align="char">0</td><td align="char">1</td></tr><tr><td align="left"> Age company</td><td align="char">10,829</td><td align="char">30.278</td><td align="char">29.508</td><td align="char">0</td><td align="char">600</td></tr></table> </ephtml> </p> <hd id="AN0133105218-8">4. Empirical estimation</hd> <p>We estimate the effect of the human capital available to company <emph>c</emph> in year <emph>t</emph> on employers' perception on the match between skill demand and skill supply within the company. We then may write: where is equal to 1 if company reports mismatch in year <emph>t</emph>, and 0 otherwise; constitutes a vector of company characteristics; and represent, respectively, region and sector fixed effects and is the error term.</p> <p>First, Equation (<reflink idref="bib1" id="ref15">1</reflink>) will be estimated by using a pooled ordinary least squares (OLS) regression without control variables (model a).[<reflink idref="bib5" id="ref16">5</reflink>] Subsequently, five extended regressions will be estimated by adding the available control variables in the following order: (b) average age of employees, (c) whether the company uses temporary contracts, (d) the size of the company and the job function of the respondent, (e) cyclical sensitivity and (f) whether the company is subject to a collective bargaining agreement, whether the company changed location during the sample period, and the age of the company. The region and sector fixed-effects specification will be used across all models to control for region- and sector-specific trends. All models cluster the standard errors at the level of the unique identification number of the company. Robust standard errors, controlling for heteroscedasticity, are presented.</p> <p>Using pooled OLS estimation might yield biased estimates due to endogeneity. As discussed in Section 2, this paper addresses this endogeneity problem by instrumenting the educational composition of companies' workforce with the average schooling level of the regional labour force in a two-stage estimation approach. First, we estimate a regression using the endogenous regressor as the dependent variable, and the instrument as the independent variable. The first-stage regression can be written as follows: From Equation (<reflink idref="bib2" id="ref17">2</reflink>), fitted values can be computed and plugged into the second-stage regression (see Equation (<reflink idref="bib3" id="ref18">3</reflink>)). While denotes the average years in formal schooling of female workers, represents the average years in formal schooling of male workers, both measured at the level of the regional labour market <emph>r</emph>. The second-stage regression estimates the effect of a one-month increase in companies' human capital stock that can only be explained by increasing the levels of formal education of men and/or women on the regional labour market, on mismatch. This effect is captured by the parameter in Equation (<reflink idref="bib3" id="ref19">3</reflink>).</p> <hd id="AN0133105218-9">5. Results</hd> <p></p> <hd id="AN0133105218-10">5.1. OLS results</hd> <p>Table 6 presents the results of the pooled OLS regression as defined in Equation (<reflink idref="bib1" id="ref20">1</reflink>) of Section 4. The estimates with respect to companies' human capital stock () are significantly negative in model (a) until (e). In model (e), the coefficient of interest gets closer to zero and in model (f) the coefficient loses significance.</p> <p>Results of the pooled OLS estimates (Equation 1).</p> <p> <ephtml> <table border="1" cellpadding="7"><tr><td align="left">y = mismatch </td><td align="center">Model 1(a)</td><td align="center">Model 1(b)</td><td align="center">Model 1(c)</td><td align="center">Model 1(d)</td><td align="center">Model 1(e)</td><td align="center">Model 1(f)</td></tr><tr><td align="left">HC within company </td><td align="center">−.001**</td><td align="center">−.001**</td><td align="center">−.001***</td><td align="center">−.001**</td><td align="center">−.000**</td><td align="center">−.000</td></tr><tr><td /><td align="center">(.000)</td><td align="center">(.000)</td><td align="center">(.000)</td><td align="center">(.000)</td><td align="center">(.000)</td><td align="center">(.000)</td></tr><tr><td align="left">Control variables (1)</td><td align="center">No</td><td align="center">Yes</td><td align="center">Yes</td><td align="center">Yes</td><td align="center">Yes</td><td align="center">Yes</td></tr><tr><td align="left">Number of clusters</td><td align="center">7451</td><td align="center">7451</td><td align="center">7451</td><td align="center">7451</td><td align="center">7451</td><td align="center">6760</td></tr><tr><td align="left">Number of obs.</td><td align="center">11,817</td><td align="center">11,817</td><td align="center">11,817</td><td align="center">11,817</td><td align="center">11,817</td><td align="center">10,686</td></tr><tr><td align="left">Adj. R<sup>2</sup></td><td align="center">.023</td><td align="center">.023</td><td align="center">.032</td><td align="center">.065</td><td align="center">.067</td><td align="center">.069</td></tr></table> </ephtml> </p> <p>Note: The control variables are added in the following order: Average age employees (model b), labour flexibility (model c), company size and respondent's job function (model d), cyclical sensitivity (model e), location sorting and collective bargaining agreement (model f).</p> <p>All models account for region- and sector fixed effects. Standard errors clustered at the level of the company unit are in parentheses. Asterisks indicate significance levels.</p> <p>* <emph>p</emph> < .10, ** <emph>p</emph> < .05, *** <emph>p</emph> < .01.</p> <hd id="AN0133105218-11">5.2. IV results</hd> <p></p> <hd id="AN0133105218-12">5.2.1. First-stage estimates</hd> <p>Table 7 presents the first-stage regression results as specified in Equation (<reflink idref="bib2" id="ref21">2</reflink>).[<reflink idref="bib6" id="ref22">6</reflink>] The human capital on the regional labour market has a positive relationship with the human capital within companies across all models. A one-month increase in the average months of education attained among female workers in the regional labour force in <emph>t-1</emph> is associated with a 0.222 months increase in the average months of schooling within companies in year <emph>t</emph>. For men, the estimated relation equals 0.154 months. The coefficient signs remain positive and significant after the inclusion of control variables. The educational attainment of women in the regional labour force appears to be a better predictor for the educational composition of companies' workforce. Working females commute less and are more likely to select a job location closer to their place of residence as they tend to put a higher value on time spent commuting due to household commitments (Turner and Niemeier 1997). Moreover, as women are more often secondary wage earners than men, seeking to increase the family budget, they tend to look for a job with a more casual attitude (Kain 1962).</p> <p>Results of the first-stage estimates (Equation 2).</p> <p> <ephtml> <table border="1" cellpadding="7"><tr><td align="left">y = HC within company </td><td align="center">Model 2(a)</td><td align="center">Model 2(b)</td><td align="center">Model 2(c)</td><td align="center">Model 2(d)</td><td align="center">Model 2(e)</td><td align="center">Model 2(f)</td></tr><tr><td align="left">HC on labour market females </td><td align="center">.222*** (.051)</td><td align="center">.200*** (.052)</td><td align="center">.181*** (.051)</td><td align="center">.147*** (.051)</td><td align="center">.147*** (.051)</td><td align="center">.124*** (.052)</td></tr><tr><td align="left">HC on labour market males </td><td align="center">.154*** (.021)</td><td align="center">.149*** (.052)</td><td align="center">.143*** (.051)</td><td align="center">.143*** (.051)</td><td align="center">.143*** (.051)</td><td align="center">.147*** (.051)</td></tr><tr><td align="left">Control variables (1)</td><td align="center">No</td><td align="center">Yes</td><td align="center">Yes</td><td align="center">Yes</td><td align="center">Yes</td><td align="center">Yes</td></tr><tr><td align="left">Number of clusters</td><td align="center">7451</td><td align="center">7451</td><td align="center">7451</td><td align="center">7451</td><td align="center">7451</td><td align="center">6760</td></tr><tr><td align="left">Number of obs.</td><td align="center">11,817</td><td align="center">11,817</td><td align="center">11,817</td><td align="center">11,817</td><td align="center">11,817</td><td align="center">10,868</td></tr><tr><td align="left">Adj. R<sup>2</sup></td><td align="center">.446</td><td align="center">.447</td><td align="center">.452</td><td align="center">.458</td><td align="center">.458</td><td align="center">.467</td></tr></table> </ephtml> </p> <p>Note: The control variables are added in the following order: Average age employees (model b), labour flexibility (model c), company size and respondent's job function (model d), cyclical sensitivity (model e), location sorting and collective bargaining agreement (model f).</p> <p>All models account for region- and sector fixed effects. Standard errors clustered at the level of the company unit are in parentheses. Asterisks indicate significance levels.</p> <p>* <emph>p</emph> < .10, ** <emph>p</emph> < .05, *** <emph>p</emph> < .01.</p> <hd id="AN0133105218-13">5.2.2. Second-stage estimates</hd> <p>The second-stage regression results of the analyses are presented in Table 8. Model (3f) shows that a one-month increase in employees' formal schooling reduces the probability that companies report mismatch with 3 percentage points (or −0.064 of one standard deviation). The coefficient of interest slightly increases in magnitude as we move from the basic model without control variables to model (3b). Model (3b) shows that companies with older employees are more likely to report mismatch, suggesting that older workers suffer from skill obsolescence which is not being offset by their job experience (de Grip and van Loo 2002). Also companies using temporary contracts are less likely to report mismatch as such contracts allow companies to more easily lay off workers with inadequate skills. With respect to the company size, large sized companies are more likely to report mismatch in comparison to the smallest companies. Model (3d) also includes two variables indicating whether the respondent is either an HR manager or HR officer. As HR managers or HR officers are considered responsible for bringing about a proper worker-company match, their response on the mismatch question may be biased. However, the inclusion of these variables leaves the sign and significance level of the coefficient of interest unchanged. Model (3e) shows that compared to companies that are strongly sensitive to cyclical changes, companies that are hardly sensitive to changes in the economy are less likely to report mismatch. Hiring people with the appropriate skills becomes more difficult in times of economic growth, as the competition for labour becomes fiercer during such periods (Nickell 1978). This especially holds for companies that are more sensitive to cyclical changes. Finally, model (3f) shows that our coefficient of interest remains negative and significant when controlling for whether companies are subject to a collective bargaining agreement and for companies' location sorting behaviour. Given that the share of employers reporting mismatch is rather dispersed across sectors (see Table 2), the results are most strongly driven by those sectors that have witnessed the greatest decline in the share of companies experiencing mismatch (see Section 3.1).</p> <p>Results of the second-stage estimates (Equation 3).</p> <p> <ephtml> <table border="1" cellpadding="7"><tr><td align="left">y = mismatch </td><td align="center">Model 3(a)</td><td align="center">Model 3(b)</td><td align="center">Model 3(c)</td><td align="center">Model 3(d)</td><td align="center">Model 3(e)</td><td align="center">Model 3(f)</td></tr><tr><td align="left">HC within company </td><td align="char">−.024*** (.003)</td><td align="char">−.029*** (.003)</td><td align="char">−.033*** (.004)</td><td align="char">−.027*** (.004)</td><td align="char">−.027*** (.004)</td><td align="char">−.030*** (.005)</td></tr><tr><td align="left">HC within company , mismatch standardised</td><td align="char">−.052*** (.006)</td><td align="char">−.061*** (.007)</td><td align="char">−.070*** (.008)</td><td align="char">−.058*** (.008)</td><td align="char">−.057*** (.008)</td><td align="char">−.064*** (.010</td></tr><tr><td align="left" colspan="7">Controls</td></tr><tr><td align="left"> Average age employees</td><td /><td align="char">.007*** (.002)</td><td align="char">.009*** (.002)</td><td align="char">.007*** (.002)</td><td align="char">.007*** (.002)</td><td align="char">.008*** (.002)</td></tr><tr><td align="left"> Employees with temporary contracts (1 = yes, 0 = no)</td><td /><td /><td align="char">−.217*** (.022)</td><td align="char">−.143*** (.023)</td><td align="char">−.142*** (.023)</td><td align="char">−.150*** (.026)</td></tr><tr><td align="left" colspan="7">Company size</td></tr><tr><td align="left"> 0-19 employees</td><td /><td /><td /><td align="center">(.) (.)</td><td align="center">(.) (.)</td><td align="center">(.) (.)</td></tr><tr><td align="left"> 20-49 employees</td><td /><td /><td /><td align="char">.076*** (.021)</td><td align="char">.075*** (.021)</td><td align="char">.075*** (.023)</td></tr><tr><td align="left"> 50-99 employees</td><td /><td /><td /><td align="char">.035 (.030)</td><td align="char">.034 (.030)</td><td align="char">.028 (.033)</td></tr><tr><td align="left"> 100-199 employees</td><td /><td /><td /><td align="char">.097*** (.029)</td><td align="char">.096*** (.029)</td><td align="char">.121*** (.031)</td></tr><tr><td align="left"> >200 employees</td><td /><td /><td /><td align="char">.081** (.038)</td><td align="char">.080** (.037)</td><td align="char">.083** (.041)</td></tr><tr><td align="left">Respondent is HR manager (1 = yes, 0 = no)</td><td /><td /><td /><td align="char">.149*** (.029)</td><td align="char">.147*** (.029)</td><td align="char">.133*** (.031)</td></tr><tr><td align="left">Respondent is HR officer (1 = yes, 0 = no)</td><td /><td /><td /><td align="char">.118*** (.036)</td><td align="char">.115*** (.036)</td><td align="char">.101** (.040)</td></tr><tr><td align="left" colspan="7">Cyclical sensitivity</td></tr><tr><td align="left"> Strong</td><td /><td /><td /><td /><td align="center">(.) (.)</td><td align="center">(.) (.)</td></tr><tr><td align="left"> Some</td><td /><td /><td /><td /><td align="char">−.019 (.018)</td><td align="char">−.024 (.019)</td></tr><tr><td align="left"> Hardly any</td><td /><td /><td /><td /><td align="char">−.046** (.020)</td><td align="char">−.046** (.022)</td></tr><tr><td align="left">Collective bargaining agreement (1 = yes, 0 = no)</td><td /><td /><td /><td /><td /><td align="char">−.231*** (.043)</td></tr><tr><td align="left">Company switched region (1 = yes, 0 = no)</td><td /><td /><td /><td /><td /><td align="char">.280*** (.092)</td></tr><tr><td align="left">Age company</td><td /><td /><td /><td /><td /><td align="char">−.001*** (.000)</td></tr><tr><td align="left">Number of clusters</td><td align="center">7451</td><td align="center">7451</td><td align="center">7451</td><td align="center">7451</td><td align="center">7451</td><td align="center">6767</td></tr><tr><td align="left">Number of obs.</td><td align="center">11,817</td><td align="center">11,817</td><td align="center">11,817</td><td align="center">11,817</td><td align="center">11,817</td><td align="center">10,686</td></tr><tr><td align="left">Adj. R<sup>2</sup></td><td align="center">.047</td><td align="char">.049</td><td align="char">.061</td><td align="char">.083</td><td align="char">.084</td><td align="char">.082</td></tr></table> </ephtml> </p> <p>Note: The control variables are added in the following order: Average age employees (model b), labour flexibility (model c), company size and respondent's job function (model d), cyclical sensitivity (model e), location sorting and collective bargaining agreement (model f).</p> <p>All models account for region- and sector fixed effects. Standard errors clustered at the level of the company unit are in parentheses. Asterisks indicate significance levels.</p> <p>* <emph>p</emph> < .10, ** <emph>p</emph> < .05, *** <emph>p</emph> < .01.</p> <p>The pooled two-stage least squares estimator used to perform Equations (<reflink idref="bib2" id="ref23">2</reflink>) and (<reflink idref="bib3" id="ref24">3</reflink>) assumes that the effect of workers' educational attainment on mismatch is the same within each company. This assumption is only appropriate if company effects do not vary once we account for company and workforce characteristics. In the case that company effects are related to the educational composition of the company's workforce, variations in the effect of workers' formal schooling on mismatch need to be modelled in order to obtain unbiased estimates. We have performed Equations (<reflink idref="bib2" id="ref25">2</reflink>) and (<reflink idref="bib3" id="ref26">3</reflink>) once more using the generalised two-stage least squares random-effects estimator.[<reflink idref="bib7" id="ref27">7</reflink>] The second-stage results can be found in Appendix 2 and are comparable to the results of the pooled regression models. We conclude that if company specific effects exist, they are not systematic but distributed randomly across firms. As such, the results of the pooled regression models can be considered robust.</p> <hd id="AN0133105218-14">6. Conclusion</hd> <p>Using a rich and unique dataset for the Netherlands, this study explored whether the increased supply of tertiary graduates has improved the match between skill supply and job requirements within companies. Whereas prior studies focused on employees' perspective, we derived our measurement of mismatch from the perspective of employers. While almost 50% of the Dutch employers reported mismatch in 1991, in 2011, only 25% of the employers did so. Using a two-step empirical framework, we show that companies benefitted from an increased supply of skilled labour in the recent two decades. The first-stage estimates demonstrate that a one-month increase in formal schooling acquired by the regional labour force increases the educational attainment of companies' staff with almost 0.3 months. The second-stage results indicate that a one-month increase in companies' workforce average schooling level decreases companies' probability of experiencing mismatch with 3 percentage points. Our findings, therefore, show that firms have benefitted from the increasing supply of skilled labour.</p> <p>Given that the Dutch college premium has continued to rise since the early nineties (Leuven and Oosterbeek 2000; Jacobs and Webbink 2006; van den Berge and ter Weel 2015), our results could indicate that the supply of tertiary education graduates has responded positively to an increasing demand for skilled labour. In the case that the demand for skilled labour has indeed outpaced the supply of college graduates as the development of the college premium proposes, a declining mismatch trend would suggest that the value of an additional year of schooling has increased over time.</p> <p>However, the findings of our study could also mask a situation in which the presence of over-educated workers at the workplace has intensified over the past decades. Despite the increasing college premium, previous literature indicates that the increased number of high-skilled jobs has not been able to absorb the rising supply of skilled labour (Muysken, Kiiver, and Hoppe 2003; Hartog 2000). Moreover, at current employment growth rates, the creation of high-skilled occupations is likely to fall behind the supply of high-skilled graduates in the next decade (Cedefop 2014). As a consequence, job seekers can be forced to accept jobs below their level of education, at least in knowledge-based sectors. Provided that prior studies have illustrated that the presence of over-qualified employees can affect firm output adversely as well as beneficially (Tsang 1987; Büchel 2002; Kampelmann and Rycx 2012), it remains unclear whether employers are willing or hesitant to hire over-educated workers. Hence, further research is needed to point out whether our findings merely reflect a rising incidence of over-education in the Dutch labour market.</p> <p>While this paper focussed on the match between job requirements and the increased supply of tertiary graduates, the skills supplied by college graduates and therefore also the labour market prospects are heterogeneous across college majors (Machin and McNally 2007). Hence, linking mismatch within companies to individuals' choice to enrol in a specific field of study provides interesting scope for future research. Future research could also explore how the observed mismatch trend relates to on-the-job training and the ability of school curricula to adjust to changing skill requirements.</p> <ref id="AN0133105218-15"> <title> Notes </title> <blist> <bibl id="bib1" idref="ref1" type="bt">1</bibl> <bibtext> One would also have to find additional valid instruments for job training and work experience in order to obtain unbiased estimates of the effects of these forms of educational attainment on mismatch. Based on the skill needs of the company, employers determine endogenously how much work experience their employees have through the recruitment process and how much and which types of training their employees should receive.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref2" type="bt">2</bibl> <bibtext> We test whether the parameter for mismatch on year is constant before and after 2003. The <emph>F</emph> statistic of the chow test for structural breaks yields 11.22 (<emph>p </emph>= .000). Hence, we have to reject the hypothesis of stable coefficients. However, we do not observe any abrupt change in the mismatch pattern after 2001 and therefore conclude that the change in the mismatch question is not a concern.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref7" type="bt">3</bibl> <bibtext> A two-sample <emph>t</emph>-test with equal variances indicates that the un-weighted mismatch rates of the full sample (32.18% mismatch) are highly comparable to the reduced sample (31.51% mismatch).</bibtext> </blist> <blist> <bibl id="bib4" idref="ref8" type="bt">4</bibl> <bibtext> The NUTS, the Nomenclature of Territorial Units for Statistics, is a geocode standard referencing the subdivisions of countries for statistical purposes. Depending on the size of the country, three levels of NUTS can be distinguished. Here, we use NUTS3 as a definition for the regions.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref16" type="bt">5</bibl> <bibtext> According to Angrist and Pischke (2008), the OLS is as adequate as a probit or logit model, at least if the 'right' non-linear model is unknown.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref22" type="bt">6</bibl> <bibtext> To confirm the validity of our instrument, we have also computed under-identification tests, weak identifications tests and over-identification tests. The results are presented in Appendix 1. Based on the Kleibergen-Paap rank LM statistics, we reject the null-hypothesis of under-identification. The obtained Cragg-Donald Wald <emph>F</emph> statistics indicate that we do not deal with problems of weak identification. Finally, the Hansen <emph>J</emph> statistics indicate that our instrument does not suffer from endogeneity problems.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref27" type="bt">7</bibl> <bibtext> In line with Clark and Linzer (2015), we prefer the random-effects specification over the fixed-effects specification as we have many units in our data (over 7000 unique companies), but few observations of each company (on average, each company appears twice in the data).</bibtext> </blist> </ref> <hd id="AN0133105218-16">Disclosure statement</hd> <p>No potential conflict of interest was reported by the authors.</p> <hd id="AN0133105218-17">Acknowledgements</hd> <p>This paper benefitted from discussions with Wim Groot, Henriëtte Maassen van den Brink, TIER seminar participants, participants of the XXIV Meeting of the Economics of Education Association, and participants of the Second Workshop on Education Economics. 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A. 2000. <emph>Equilibrium Unemployment Theory</emph>. 2nd ed. Cambridge : MIT Press.</bibtext> </blist> <blist> <bibtext> Rauch, J. E. 1993. " Productivity Gains from Geographic Concentration of Human Capital: Evidence from the Cities." <emph>Journal of Urban Economics</emph> 34 ( 3 ): 380 - 400. doi: 10.1006/juec.1993.1042</bibtext> </blist> <blist> <bibtext> Rogerson, R., R. Shimer, and R. Wright. 2005. " Search-theoretic Models of the Labor Market: A Survey." <emph>Journal of Economic Literature</emph> 43 ( 4 ): 959 - 88. doi: 10.1257/002205105775362014</bibtext> </blist> <blist> <bibtext> Rumberger, R. 1987. " The Impact of Surplus Schooling on Productivity and Earnings." <emph>Journal of Human Resources</emph> 22 ( 1 ): 24 - 50. doi: 10.2307/145865</bibtext> </blist> <blist> <bibtext> Stigler, G. J. 1962. " Information in the Labor Market." <emph>Journal of Political Economy</emph> 70 ( 5 ): 94 - 105. doi: 10.1086/258727</bibtext> </blist> <blist> <bibtext> Tilak, J. B. 2008. 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  Data: This study examines whether the expansion in higher education over the past 20 years has contributed to better education-job matches on the labour market. In particular, we relate changes in the average formal schooling level of workers on the regional labour market to the educational attainment of the recruited staff within companies operating on that regional labour market. Hereby, it is acknowledged that companies most often recruit from a pool of workers available on the regional labour market. Next, we estimate the effects of changes in the level of schooling of the staff owing to the increased supply of higher educated graduates on the regional labour market on mismatch. Data from the Dutch Labour Demand Panel are used covering 7451 unique companies over the period 1991-2011. The results indicate that a one-month increase in companies' workforce average schooling level decreases the probability that companies report mismatch with -3.0 percentage points.
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