Estimating the probability of a match using microeconomic data for the youth labour market

Labour Economics 8 Ž2001. 335–357 www.elsevier.nlrlocatereconbase

Estimating the probability of a match using microeconomic data for the youth labour market Martyn J. Andrews a,) , Steve Bradley b, Richard Upward c a

School of Economic Studies, UniÕersity of Manchester, Manchester, M13 9PL, UK b Lancaster UniÕersity, Lancaster, UK c UniÕersity of Nottingham, Nottingham, UK

Received 20 July 1999; received in revised form 18 August 2000; accepted 22 February 2001

Abstract In this paper, we estimate the probability of a match for contacts between job seekers and vacancies. We relate the determinants of a match to the characteristics of the job seeker, the vacancy, and labour market conditions. Our main results are: ethnic minorities are discriminated against, but women are not; employers ‘cream’ the market and job seekers are ranked by their labour market state; high wage offers have a lower probability of a match; the probability of filling a job vacancy falls with vacancy duration, the higher stock of unemployed youths in a labour market, and the larger Careers Service; the probability of a match increases with job seeker duration. q 2001 Elsevier Science B.V. All rights reserved. JEL classification: J41; J63; J64 Keywords: Matching probability; Two-sided search

1. Introduction There has been a resurgence of interest in recent years in the matching, or hiring, function because of the potential insights it provides into the operation of the labour market and, in particular, the dynamics of unemployment. However, most of the previous work in this area has been highly aggregated and has focused on the estimation of the matching rate, which is the product of the contact rate and )

Corresponding author. Tel.: q44-161-275-4874. E-mail address: [email protected] ŽM.J. Andrews..

0927-5371r01r$ - see front matter q 2001 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 7 - 5 3 7 1 Ž 0 1 . 0 0 0 3 5 - 5

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the probability of a match. This paper represents a substantial departure from the existing literature, insofar as we estimate the determinants of the probability of a match using microeconomic data for a particular labour market in the UK. The data we use are the computerised records of the Lancashire Careers Service Žhereafter LCS. and contain information on agents from both sides of the Žyouth. labour market, as well as the interaction between firms and youths in the form of over 80,000 contacts between job seekers and vacancies. We observe every vacancy submitted by employers in Lancashire to the Careers Service between March 1988 and June 1992. For each vacancy, we observe every contact between the employer and every job seeker during the sample period, and for each contact, we observe whether or not a successful match is made. There are only a handful of studies that use similar microeconomic data, and none for the UK ŽTeyssiere, ` 1996; van Ours and Lindeboom, 1996; Russo and van Ommeren, 1998.. Clearly, there is considerable behavioural content in the matching probability, and our data enable us to shed light on a wide range of issues related to the operation of the youth labour market. Specifically, the following questions are addressed. Ž1. How does the probability of a match vary with the stock of unemployment and vacancies in the labour market? Is the probability of a match pro- or counter-cyclical? How do job and training sub-markets interact: does the stock in one sub-market affect the matching probability in the other? Ž2. How does the probability of a match vary with the size of the Careers Service? Ž3. What is the relationship between the matching probability and the wage? Are high wage vacancies more likely to match? Does it matter whether wages are set by negotiation between firms and job seekers or whether wages are fixed in advance? Ž4. How does the probability of a match vary with the duration of the vacancy and the duration of job search? Does the matching probability vary by current labour-market state? Ž5. Do firms select the ‘best’ job seekers regardless of their selection criteria Žhereafter referred to as ‘creaming’.? Is creaming a feature of both sub-markets? Ž6. Are ethnic minorities, women or the disabled discriminated against in the matching process? Ž7. Is there equal access to all types of youth training programmes? What type of programme fulfills government policy of providing a ‘guaranteed’ place on the Youth Training Scheme Žhereafter YTS. for 16–17 year-old youths? The paper is organised as follows. In Section 2, we present a brief model of the matching function and the role of the matching probability in such a model. In Section 3, we discuss the institutional background to the youth labour market, the Careers Service, and describe the main features of our data. Model specification is discussed in Section 4, and in Section 5 we discuss our findings. Section 6 summarises and concludes.

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2. A framework for interpreting the probability of a match In two-sided search models Žsee, for example, Pissarides, 1990; Burdett and Wright, 1998., in a given labour market, the determinants of the flow of new hires per period Ždenoted by H . are the flow of contacts between job seekers and firms per period Ždenoted by M . and the probability that each contact is successful Ždenoted by m .. This decomposition of the hiring function is written as H s H Ž S,V . s m Ž S,V . M Ž S,V . ,

Ž 1.

where S is the stock of job seekers Žnot all of whom are unemployed. and V is the stock of vacancies. It is standard to assume that the contact rate M is a function of both S and V, via a contact technology M Ž S,V . that has the same properties as a production function. The matching probability m may also depend on S and V, as is discussed below. The resulting hiring function H Ž S,V ., synonymously known as the matching function, has been estimated extensively in the literature, but usually with aggregate time-series data. In two-sided search models, m is the joint probability that a job seeker finds an employer acceptable, mw, and an employer finds a job seeker acceptable, me. The first of these probabilities Žthe ‘acceptance’ probability. will depend on the reservation utility of the job seeker R w, which in turn depends on labour market tightness VrS, the wage on offer w, the time the job seeker has been in hisrher current labour-market state t w, the costs of search and the average quality of relevant jobs.1 The last two variables are both proxied by the characteristics of the job seeker x w and the characteristics of the employer x e. Similar arguments imply that the employer’s ‘offer’ probability will depend on the same variables, with t e being vacancy duration. This gives the following general specification of the matching probability function:

m s m Ž VrS,w,t w ,t e ,x w ,x e . s me Ž VrS,w,t e ,x w ,x e . mw Ž VrS,w,t w ,x w ,x e . . Ž 2. We discuss the specification of x e and x w in Sections 4 and 5. Here, we are interested in the predicted effects of labour-market tightness, VrS, the wage, w, and elapsed duration, t e and t w, on the matching probability. This is complicated by the fact that we do not observe which party was responsible for whether or not a contact results in a match. That is, we observe m , but not me or mw. From the employer’s viewpoint, an increase in VrS means that on average, there are fewer job seekers per vacancy. The employer responds by lowering his 1

If the contact function does not exhibit constant returns to scale, the specifications would be S and V, separately.

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reservation utility, R e , so that me goes up Ži.e. the employer is less selective.. From the job seeker’s viewpoint, R w increases and mw falls. In Burdett and Wright Ž1998., R w depends on R e negatively Ža less selective employer generates even fewer offers to job seekers. and R e depends on R w negatively, and so, in equilibrium, these responses are even stronger than the initial ‘one-sided’ responses. A priori, the effect of labour-market tightness on the matching probability is ambiguous. However, Burdett and Wright Ž1998. show that it is possible to have an equilibrium where job seekers are ‘especially easy’ in the sense that they accept all job offers. This could happen in a very slack labour market.2 There is evidence that job seekers rarely refuse job offers from employers ŽBarron et al., 1987; Holzer, 1988; Barron et al., 1997; van den Berg, 1990; Manning, 2000.. If, indeed, it is the case that the job seeker’s acceptance probability is close to unity, then the matching probability function can be interpreted as representing employers’ search behaviour, with m V r S being positive. In general, as the market gets tighter, it can be shown that the function m Ž VrS . is inverted-U, turning when the market has roughly equal numbers of employers and job seekers. In short, it is an empirical issue as to whether the partial derivative is positive or negative; also the homogeneity restriction that forms labour-market tightness is easily testable. The effect of the wage is absolutely standard in search theory Žsee, for example, Mortensen, 1986.: if the mean of the offer distribution Žin utility terms. increases, the optimal response of the job seeker is to increase R w, but by not as much as the shift in the distribution, and so mw increases Žthe job seeker is less selective.. By symmetry, the employer is more selective and so me falls. Again, the employer’s response should dominate in slack labour markets. It is possible that the matching probability will be a function of both the vacancy duration Ž t e . and the time the job seeker has been in hisrher current labour-market state Ž t w .. For the employer, casual empiricism and some evidence suggests that the initial arrival rate of job seekers Ž le . is high and tails off quickly, for which Coles and Smith Ž1998. provide a convincing explanation. Once a given stock of employers and job seekers have contacted and subsequently rejected each other, then employers will only search the flow of new arrivals of job seekers, which necessarily lowers the rate at which they contact each other. The optimal response of the employer is to become less selective Žandror possibly increase search intensity.. However, if the average quality of the job seeker also falls, because the most suitable arrive first, then the employer becomes more selective. The overall effect of t e on the matching probability me Ž t e , . . . . mw Ž t w , . . . . is ambiguous.3 Similar arguments from the job seeker’s point of view suggest that it

2

Note that Burdett and Wright Ž1998. normalise V rSs1. The effect of t e on the employer’s hazard, he , is also ambiguous, as seen by he Ž t e . s mw Ž t w . me Ž t e . le Ž t e .. However, it should be stressed that in this paper, we are estimating a model of the matching probability, not vacancy duration. 3

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is an empirical issue as to whether job seekers become more or less selective as t w passes. Finally, it is worth noting that there are only three other studies that estimate the probability of a match using microeconomic data. Teyssiere ` Ž1996. uses French data on a sample of contacts between job seekers and firms for a single labour market in Marseilles. van Ours and Lindeboom Ž1996. estimate models of the contact rate, the probability of a match and the hiring function, using Dutch data collected from a variety of different sources. Russo and van Ommeren Ž1998. also use Dutch vacancy data on the number and gender of job seekers. Comparisons between the findings in these papers and our own are noted later.

3. Institutional background The collapse of the youth labour market in the UK in the early 1980s led to the introduction of the Youth Training Scheme ŽYTS. in 1983. It has remained in place ever since, although in several disguises. The YTS is not a homogeneous programme; it can be seen as a route to a wide variety of skilled occupations, or seen as a work-experience programme designed to mop-up the excess supply of youth labour. Since its introduction, youths can choose between four labour-market activities at the age of 16: different types of YTS, continue their education, get a job or become unemployed. Employers can also choose whether to recruit youths via the YTS or directly into a job. The Careers Service fulfills a similar role for the youth labour market as Employment Offices and Job Centres provide for adults. Its main responsibilities are to provide vocational guidance for youths and to act as an employment service to employers and youths. The latter includes a free pre-selection service for employers. Use of the Careers Service is voluntary for employers with job vacancies, whereas notification of YTS vacancies is compulsory, so that the government offer of a guaranteed place for all 16–17-year-old youths can be monitored. Having notified the Careers Service of the type of vacancy—the occupation, the wage, a closing date for applications and selection criteria—job seekers are selected for interview. In other words, a contact is made. Either a match occurs or the pair each continue their search. The data we use are the computerised records of the Lancashire Careers Service ŽLCS.. The LCS holds records on all youths aged between 15 and 18, including those who are seeking employment. We observe every vacancy notified by employers to the Careers Service between March 1988 and June 1992. All YTS vacancies and about 30% of job vacancies are notified to the Careers Service. Job vacancies for which the Careers Service is not the method of search are not included in the data. Job vacancies require both high- and low-quality job seekers, and are representative of all entry-level jobs in the youth labour market. It follows

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that our data are representative of all job seekers, because we observe all contacts between notified job vacancies and job seekers. This is not an issue for YTS vacancies because all of them are notified to the Careers Service. The structure of the YTS is more complex than is commonly recognised. At one extreme is the large firm who provides work experience and in-house training. At the other extreme are Training Providers, who act as umbrella organisations, recruit trainees, and provide the off-the-job training, but do not offer work experience directly. Therefore, one of the main roles of the Training Provider is to coordinate the off-the-job training and the work experience for trainees and participating firms Žtypically small firms.. Training Providers receive a ‘fee’ from government for the service they provide, whereas all firms that participate in the YTS received a subsidy towards wage costs. The ‘fee’ was approximately £100 per trainee for the sample period Ž1988–1992., whereas the subsidy to firms varied. The government set the minimum for the YTS allowance paid to the trainee, which was, during the sample period, approximately £27.50 per week and increasing to £35.00 per week after 1-year training. A single firm recruiting young people through the YTS received all of the subsidy. Training Providers obtained a contribution towards the allowance from participating firms. This was typically low, and very small firms therefore had the greatest incentive to participate in YTS because the wage costs of an additional worker constitute a substantial proportion of total variable costs. There are also differences in the quality of training within the YTS. First, there are employee-status programmes, where the participants receive the rate of pay for the job and have greater job security; these are different from trainee-status programmes, which offer a standard allowance and a fixed contract. Employeestatus programmes are also more likely to be part of a longer training scheme provided by the firm, such as a 3- or 4-year apprenticeship. Competition for places on employee-status programmes is consequently greater, but firms are also more likely to be selective. Second, at the lower end of the YTS market, there are special programmes, usually provided by the voluntary sector, which receive extra funding to deal with the training needs of the less able. Special programmes are less selective, implying that special programmes bear the brunt of the Government’s policy of a guaranteed place for all youths. Throughout our empirical analyses, the data are split between job vacancies and YTS vacancies, since they represent two distinctly different types of sub-market.

4. Data and model specification To recap, for each vacancy we observe every interview between an employer and a job seeker during the sample period; hereafter, this unit of observation is referred to as a contact. The sample consists of m s 86,978 contacts. In the line

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with the notation developed in Section 2, there are five sorts of information which describe each contact: 1. Whether each contact is successful Ž h i s 1. or not Ž h i s 0.. 2. Information about the job seeker Žx wi ., including hisrher current labourmarket state Žunemployed, in a job, on a training scheme, or in school., and duration in that state Ž t iw .. 3. Information about the employer Žx ei ., vacancy duration Ž t ie ., and other characteristics of the vacancy, including the wage Ž wi .. 4. Information on the selection criteria of the firm, from which we compute the ‘distance’ between job seeker characteristics and vacancy characteristics, denoted by
i s 1, . . . ,m,

Ž 3.

which, given that F denotes the normal distribution function, is estimated as a Probit regression. Aggregating over all contacts in the data, the raw matching probability is the total number of hires in the data Ž h ' Ýh i . divided by the number of contacts: m ˆ s hrm. Table 1 shows the total number of contacts, hires and the aggregate matching probability, stratified by the two sub-markets. Although there are approximately the same number of applications made to jobs as to YTS vacancies, only 1 in 10 applications to job vacancies results in a match, whereas nearly one in

Table 1 Sample size and raw matching probability, 1988–1992

Total number of contacts Ž m. Total number of hires Ž h. Raw matching probability Ž m ˆ s h r m. Total number of job seekers, of which: One contact is made Two or more contacts Total number of firms, of which: One contact made Two or more contacts Total number of vacancies, of which: One contact made Two or more contacts

Jobs

YTS

42,698 4364 0.102 11,093 3175 7918 3159 528 2631 7315 1987 5328

44,280 10,452 0.236 12,949 3480 9469 947 125 822 2637 322 2315

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Fig. 1. Average matching probability by month.

four applications to YTS vacancies are successful. Table 1 also shows the number of job seekers, firms and vacancies. Because the unit of observation is a contact, and because firmrjob seeker pairs do not recontact each other, there is no panel element in these data. However, the majority of job seekers, firms and vacancies are observed more than once. Finally, Fig. 1 plots the raw matching probabilities over the sample period. Recruitment is lower between March and May, just before the majority of school-leavers enter the labour market, and is much higher in June, July and September. A similar picture arises for YTS vacancies, although more pronounced.

5. Results Table 2 reports estimates of Eq. Ž3., estimated separately for contacts to job vacancies and contacts to YTS vacancies. Marginal effects, p-values and sample means are reported Žsee Table 2, tablenote a.. Standard errors are robust and are corrected for intra-district correlation between contacts. In Section 1, we raised

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Table 2 Probit results Ždependent variable h i . Job vacancies Marginal effect a Labour market characteristics (Si , Vi )c Log unemployed -18 y0.0175 Log job vacancies in the same y0.0052 occupationd Log YTS vacancies in the same 0.0015 occupationd Log density 0.0008 Log ŽCareers Service staffr y0.0258 population. Employer and Õacancy characteristics Žx ei . Wage offer wi Di Ž1y Ni . y0.0395 wi Di Ni y0.0681 Di Ni 0.0117 Ž1y Di . Ni y0.0056 Ž1y Ni . Di Duration of vacancy Ž t ie . Vacancy open for -1 month Vacancy open for 1–2 months y0.0130 Vacancy open for 2–3 months y0.0275 Vacancy open for 3–6 months y0.0349 Vacancy open for 6–12 months y0.0321 Vacancy open for )12 months y0.0650 Firm size -11 employees 11–30 employees 0.0070 30–100 employees 0.0029 )100 employees 0.0060 Firm activity ŽSIC. Agriculture 0.0048 Energy and water supplies y0.0199 Extraction of minerals, metals 0.0007 Metal goods, engineering 0.0058 Other manufacturing 0.0043 Construction 0.0026 Distribution, catering and hotels Transport and communication y0.0126 Banking, finance 0.0119 Other services 0.0056 Training agent Location Firm in town centre y0.0038 Firm outside town centre

P-valueb

YTS vacancies Mean

Marginal effect

P-value

Mean

w0.000x w0.010x

5.112 2.447

y0.0098 y0.0045

w0.480x w0.333x

5.117 1.981

w0.225x

4.040

y0.0060

w0.068x

4.258

w0.832x w0.000x

1.984 y9.169

y0.0056 y0.0217

w0.686x w0.110x

2.036 y9.224

w0.000x w0.001x w0.382x w0.362x

£1.49 e £1.42 e 0.042 0.134 0.824

y0.0225 0.0076 y0.0040

w0.214x w0.795x w0.791x

£0.76 e £0.63 e 0.040 0.000 0.960

w0.001x w0.000x w0.000x w0.000x w0.000x

0.500 0.266 0.090 0.104 0.026 0.014

y0.0009 y0.0047 0.0183 0.0491 y0.0257

w0.929x w0.755x w0.380x w0.030x w0.495x

0.290 0.156 0.178 0.378 0.153 0.039

w0.143x w0.598x w0.256x

0.383 0.206 0.192 0.219

y0.0367 y0.0302 y0.0227

w0.014x w0.019x w0.046x

0.290 0.274 0.158 0.278

0.007 0.007 0.009 0.118 0.160 0.064 0.305 0.027 0.141 0.161 0.000

0.1086 0.0031 y0.0142 0.0055 0.0858 0.0641

w0.025x w0.853x w0.720x w0.841x w0.000x w0.202x

0.0030 0.0037 y0.0029 0.0210

w0.934x w0.853x w0.867x w0.441x

0.508 0.492

0.0076

w0.328x

w0.714x w0.091x w0.951x w0.187x w0.401x w0.755x w0.001x w0.002x w0.323x

w0.319x

0.001 0.023 0.007 0.067 0.031 0.029 0.203 0.038 0.051 0.235 0.317 0.466 0.534

(continued on next page)

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Table 2 Ž continued . Job vacancies Marginal effect Employer and Õacancy characteristics Žx ei . Involvement in YTS schemes Provides YTS placements y0.0067 No YTS placements Occupational type Skilled occupation y0.0077 Unskilled occupation Non-manual occupation y0.0203 Manual occupation Training provided AIn-houseB training 0.0059 Day release training 0.0016 Apprenticeship y0.0074 No or little training provided Type of YTS scheme YTS employee Special funding Standard training programme Application method Written application required y0.0735 No written application required Log size of vacancy order 0.0387

YTS vacancies

P-value

Mean

w0.106x

0.378 0.622

w0.136x

0.637 0.363 0.581 0.419

w0.001x

w0.212x w0.732x w0.309x

w0.000x w0.000x

0.066 0.140 0.175 0.619

Marginal effect

P-value

Mean

1.000 0.000 y0.0188

w0.029x

y0.0226

w0.154x

0.635 0.365 0.531 0.469 0.000 0.594 0.406 0.000

y0.0266

w0.012x

y0.0314 0.0906

w0.021x w0.008x

0.302 0.018 0.680

0.457 0.543 0.313

y0.0543

w0.000x

0.0788

w0.000x

0.610 0.390 2.049

Žx wi .

Job seeker characteristics Ethnicity Non-white White Gender Female Male Female=proportion of females f Proportion of females f Health Poor health Normal health Number of occupational choices 1 2 3 4 Areas in which job seekers are willing to work Local district only Anywhere in the district Anywhere in Lancashire

y0.0279

w0.002x

0.049 0.951

y0.0585

w0.000x

0.046 0.954

y0.0053

w0.691x

y0.0210

w0.281x

0.0483 y0.0242

w0.041x w0.072x

0.467 0.533 0.315 0.470

0.0442 y0.0021

w0.066x w0.937x

0.426 0.574 0.297 0.423

y0.0029

w0.471x

y0.0063

w0.200x

y0.0047 y0.0056 y0.0019

w0.162x w0.029x w0.685x

0.172 0.279 0.281 0.268

y0.0283 y0.0452 y0.0644

w0.000x w0.000x w0.000x

0.194 0.277 0.232 0.297

y0.0160 y0.0100

w0.052x w0.141x

0.120 0.779 0.102

y0.0515 y0.0387

w0.000x w0.000x

0.211 0.719 0.070

0.146 0.854

0.147 0.853

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345

Table 2 Ž continued . Job vacancies

Job seeker characteristics Žx wi . Special training needs Special training needs No training needs Cohort Left school in 1988 Left school in 1989 Left school in 1990 Left school in 1991 Left school in 1992 Duration in current state Ž t iw . F1 month in FE )1 month in FE F 3 months in YTS ) 3 months in YTS F 3 months in employment ) 3 months in employment F1 month in unemployment 1–2 months in unemployment 2–3 months in unemployment ) 3 months in unemployment Labour market history g Log months in FE Log months in YTS Log months in unemployment Log months in employment Last school attended Single sex school Mixed sex school School GCSE performance h

Marginal effect

P-value

y0.0230

w0.000x

0.0055 0.0018 0.0045 y0.0025

w0.269x w0.712x w0.607x w0.855x

y0.0233 0.0200 0.0076 0.0327 0.0854 0.1013 y0.0619 0.0047 0.0151 0.0031

YTS vacancies Mean

Marginal effect

P-value

y0.0101

w0.345x

0.270 0.289 0.251 0.144 0.047

0.0725 0.1345 0.2277 0.1419

w0.000x w0.000x w0.000x w0.000x

0.210 0.228 0.254 0.193 0.115

w0.012x w0.001x w0.136x w0.000x w0.000x w0.000x w0.000x w0.227x w0.022x w0.449x

0.024 0.049 0.023 0.052 0.023 0.018 0.344 0.087 0.065 0.106

y0.0940 0.1157 0.0642 0.1667 0.1678 0.2549 y0.1388 0.0097 0.0380 0.0164

w0.000x w0.000x w0.011x w0.000x w0.000x w0.000x w0.000x w0.509x w0.030x w0.505x

0.012 0.019 0.012 0.016 0.009 0.007 0.290 0.046 0.031 0.049

0.0014 y0.0011 y0.0004 0.0007

w0.122x w0.112x w0.634x w0.347x

y2.949 y2.357 y1.221 y2.460

0.0073 0.0065 0.0118 0.0010

w0.046x w0.000x w0.000x w0.677x

y3.289 y3.052 y2.464 y3.107

0.0090

w0.263x

y0.0095

w0.409x

y0.0035

w0.739x

0.051 0.949 0.339

0.0262

w0.236x

0.058 0.942 0.344

y0.0093

w0.116x

y0.0092

w0.562x

0.0079

w0.003x

0.033 0.939 0.028

y0.0155

w0.219x

y0.0152

w0.001x

y0.0333

w0.000x

0.0110

w0.001x

0.140 0.465 0.395

0.0231

w0.000x

0.165 0.388 0.447

y0.0080 y0.0088

w0.114x w0.105x

0.321 0.329 0.351

0.0136 0.0588

w0.023x w0.000x

0.350 0.291 0.358

0.041 0.959

Mean

0.048 0.952

characteristicsŽ
Matching Age Age of applicant lower Age of applicant same Age of applicant higher Qualifications Applicant qualifications lower Applicant qualifications same Applicant qualifications higher Occupation No match Preference matches to two-digits Preference matches to three-digits

0.036 0.944 0.020

(continued on next page)

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Table 2 Ž continued . Job vacancies Marginal effect Matching characteristicsŽ
x bˆ f ŽxXbˆ . F ŽxXbˆ . N Log L x2 McFadden’s pseudo-R 2 Count R 2

YTS vacancies

P-value

Mean

Marginal effect

w0.127x

0.183 0.817

y0.0178

w0.005x

0.301 0.699

w0.145x

0.192 0.808

0.0046

w0.508x

0.237 0.763

y0.0130 y0.0854 y0.1339 y0.1125 y0.0366 0.1116 0.0907 0.0753 0.0331 y0.0269 y0.0417

w0.428x w0.000x w0.000x w0.000x w0.113x w0.000x w0.000x w0.001x w0.224x w0.094x w0.013x

y0.0483 y0.1138 y0.1677 y0.1864

w0.023x w0.000x w0.000x w0.000x

0.0053 y0.0273 y0.0250 y0.0199 0.0060 0.0241 y0.0047 0.0075 0.0065 y0.0042 y0.0111

w0.530x w0.001x w0.000x w0.010x w0.531x w0.007x w0.611x w0.387x w0.511x w0.612x w0.168x

y0.0138 y0.0233 y0.0418 y0.0421

w0.017x w0.001x w0.000x w0.000x y1.512 0.127 0.065 42,698 y11,856.981 497.788 0.158 0.899

0.034 0.060 0.071 0.116 0.104 0.170 0.101 0.087 0.064 0.090 0.063 0.041 0.089 0.204 0.290 0.282 0.135

P-value

Mean

0.016 0.022 0.073 0.223 0.135 0.259 0.087 0.060 0.033 0.050 0.023 0.020 0.160 0.220 0.235 0.222 0.163

y0.942 0.256 0.173 44,280 y18,220.591 695.393 0.247 0.817

a Marginal effects are computed for continuous variables, whereas for dummy variables the ‘marginal effect’ compares the effect of switching the variable from 0 to 1. For each continuous X X variable, the ‘marginal effect’ and the underlying coefficient are in the ratio f Žx bˆ ., where x bˆ is the regression function evaluated at the means of the data. Regression includes constant Žnot shown.. b P-values refer to test of underlying coefficient being 0. Standard errors are robust and are corrected for intra-district correlation between contacts. c S and V vary by district and month, Careers Service staff varies by district and year, and population and area vary by district only. d Defined as the number of vacancies open in the same month, the same district and the same occupation. e Average real hourly wage rate for group defined by dummy variables. f Proportion of females applying to that occupation. g Log Ž1 dayqduration. used to avoid missing values at 0. Excludes duration in current state. h Proportion of year 11 pupils obtaining five or more GCSE grades A) –C.

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seven questions, and the results are discussed in relation to those questions. This discussion does not cover all the covariates reported in Table 2, the rest being a fairly standard set of controls. 5.1. The effect of the labour market See Table 2, Labour market characteristics. The basic theoretical prediction is that, in slack labour markets, labour-market tightness Ž VrS . has a positive effect on the matching probability. Both Fig. 1 and the estimates on the year dummies in the regressions suggest that there is, indeed, this pro-cyclical effect.4 Being only suggestive, we seek to detect whether the tightness of a particular market has an effect on the individual job seeker’s matching probability. Table 2 reports the effect of the stocks of job seekers and vacancies in a given labour market, together with the effect of three other ‘aggregate’ variables. Labour markets are defined by the 14 districts in Lancashire. Our measure of job seekers, S, is the stock of unemployed in each district-month who are under 18, taken from the National Online Manpower Information System database. The stock of job seekers includes a proportion of those not unemployed, typically those in jobs or on training schemes. As with all the aggregate studies, this is a potential source of misspecification. The implications and possible solutions are discussed in Burgess Ž1993. and Mumford and Smith Ž1999.. For example, in Burgess’s job-competition model, an increase in the proportion of on-the-job job seekers has a differential impact on the relative outflow rates out of unemployment and jobs, in favour of the latter. Aggregate studies are unable to detect whether this crowding-out is because the matching probability also depends on labour-market state, an effect we are able to estimate Žsee Section 5.3 below. even though we do not observe the aggregate stocks of each type of job seeker. Once we correct for the intra-district correlation between contacts, generally these aggregate variables are insignificant and have weak effects. However, the marginal effect of the district-level stock of the unemployed under 18 Ž S . converts to an elasticity of y0.172 Žy0.0175r0.102. in the jobs regression—the matching probability is about 1 percentage-point lower for a district with double unemployment—but is insignificant in the YTS regression.5 Our measures of V are taken from the LCS data. We count the number of job and YTS vacancies in each district-month. We also count the number of vacancies 4

The downward trend in the matching probability is faster for training schemes. This is consistent with higher staying-on rates into continued education over this period, generating more competition between jobs and YTS. As young people generally prefer jobs to schemes, the matching probability for the latter declines faster. 5 Throughout, an elasticity is calculated as a marginal effect divided by the raw matching probability.

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which ‘compete’ with the contact itself, in the sense of being in the same district-month and occupation. We can therefore examine whether the effect of the stock of similar vacancies on the matching probability is greater than an increase in aggregate vacancies. We find that the total stock of vacancies, whether or not split between job vacancies and YTS vacancies, has no significant effect on the matching probability. We therefore report a specification which uses the stock of competing vacancies, split between jobs and YTS. The ‘own effect’—the effect of the stock of job ŽYTS. vacancies on the probability of a job ŽYTS. contact resulting in a match—is significant but incorrectly signed, if we maintain the assumption that the market is slack and mw is close to 1. In other words, it is inconsistent with the negative effect of S above. It should, of course, be remembered that both the stocks of S and V are measured with error: we do not observe all vacancies notified to the Careers Service Žabout 30% of the total., and we do not observe all non-unemployed job seekers. The two other variables which vary at the district level are the number of staff in a given Careers Office, denoted by C, normalised on the population of each district, denoted by P, and the population density of each district, denoted by PrA Žwhere A is the area of each district.. Homogeneity is easily not rejected. The former is included because both contact rates and hiring rates may depend on the ‘efficiency’ of the Careers Service. If the matching probability is estimated as increasing in CrP, this suggests that those offices with more staff may be able to spend more time sifting the pool of searchers and select more suitable job seekers for interview. On the other hand, smaller offices typically deal with a smaller pool of firms and job seekers, and so their higher matching probability may reflect better information on both employers and job seekers. Population density is included because the rate at which agents contact and match with each other might be lower in more dispersed rural labour markets compared with those in cities ŽColes and Smith, 1996.. The population density variable is insignificant in both regressions. However, the number of Careers Service staff has a significant, negative effect on the probability of a match in both regressions. For the jobs regression, the elasticity is y0.253 Žy0.0258r0.102.. Although the effects are not large, this finding is consistent with the view that job seekers do benefit from personal counselling by Careers Service staff. From a policy viewpoint, this finding is interesting, although the implied policy of decentralising the Careers Service would, of course, cost more. 5.2. Wages and training allowances See Table 2, Employer and Õacancy characteristics. The relationship between the wage offer and the probability of a hire is of particular interest, especially in two-sided search models. There are three different

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types of wage offer in the data.6 Ninety-six percent of YTS vacancies and 82% of job vacancies have a set pre-announced wage, where the wage is non-negotiable. The majority of these vacancies specify age and tenure profiles, which reflects the rigid institutional nature of wage setting in the youth labour market.7 Four percent of both YTS and job vacancies have a set pre-announced wage offer, but are still open to negotiation. The remaining 13% of job vacancies have a negotiable wage offer and no pre-announced wage. Clearly, for this third category, there is no wage recorded in the data. The important point is that both job seekers and employers take the wage as given when they decide whether or not to form a match. Our justification for focusing on the Burdett and Wright Ž1998. model in Section 2 is that they analyse what happens when the wage is not negotiable after agents meet, and assume that an agent cannot transfer utility to the other party by varying the wage or by other means.8 We argue that this is a very accurate characterisation of the youth labour market, given the vast majority of job and YTS vacancies in the data have a non-negotiable wage. We model these effects as follows. Ni is defined as a dummy variable indicating whether a vacancy has a negotiable wage offer, and Di as a dummy variable indicating whether the wage is pre-announced. Interacting the dummies with the log real hourly wage rate wi , where it exists, gives: Pr Ž Hi s 1 . s F Ž b 0 q . . . qb 1wi Di Ž 1 y Ni . q b 2 Di Ni q b 3 wi Di Ni qb4 Ž 1 y Di . Ni . . . . ,

Ž 4.

The four parameters are interpreted as follows:

Wage not negotiated Ž Ni s 0. Wage negotiated Ž Ni s 1.

Wage set Ž Di s 1.

Wage not set Ž Di s 0.

b 0 q b 1 wi b 0 q b 2 q b 3 wi

not applicable b 0 q b4

In this model, the predictions are clear cut. A high wage Žor training allowance. means that job seekers increase their probability of accepting a job offer Ž mw ., but employers decrease the offer probability Ž me .. In slack labour markets, the employer effect should dominate. The key parameter Žsee Eq. Ž4.. is b 1 , the impact of the wage on the matching probability for the large majority of job and training vacancies, whose wages are set in advance and are non-negotiable. For the 6

The term ‘wage’ also refers to the training allowance for YTS vacancies. Our own personal knowledge of this particular market leads us to believe that wage rates do not reflect supply and demand conditions Žsee Section 3.. 8 Note that in Pissarides Ž1990., utility is transferable, and the wage is determined by splitting the total surplus of both parties. 7

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jobs regression, bˆ1 converts to an elasticity of y0.387 Žy0.0395r0.102., whereas for the YTS regression, bˆ1 converts to y0.093 Žy0.0225r0.242. and is insignificant. It makes sense that jobs are more responsive, and implies that the employer effect does outweigh the job-seeker effect. The elasticity Žsee b 3 . is twice as strong for job vacancies that are negotiable, but is zero for YTS vacancies that are negotiable; however, these are only a very small proportion of the sample. 5.3. The length of search and labour-market state See Table 2, Job seeker characteristics and Employer and Õacancy characteristics. In this subsection, we address the issues raised in the fourth question of Section 1. The variables ‘duration of Õacancy’ non-parametrically model the effects of vacancy duration Ž t e . on the matching probability. For job vacancies, the results suggest that the longer the vacancy has been unfilled, the lower the probability of a match. The probability of a match falls significantly after 1 month Žby y0.0130., and again after 2 months Žy0.0275.. The negative duration effect is then quite similar for vacancies open anywhere from 2–12 months Žf y0.03., but there is another drop after 12 months. However, very few Ž4%. of job vacancies remain unfilled after 6 months. If it is the case that the arrival rate of job seekers declines with vacancy duration, then this is interpreted as a job-seeker quality effect. A different picture emerges for YTS vacancies. For vacancies unfilled between 6 and 12 months, the more likely it is that a match will be found, compared with shorter durations. This is consistent with the view that the programme absorbs any temporary excess supply of labour Žonly 4% of YTS vacancies survive beyond 12 months because a new batch of YTS vacancies are posted annually onto the market and old vacancies cannot compete.. Vacancies are often posted on to the market in multiple ‘orders’ Žsee Table 2, ‘log size of vacancy order’.. In fact, the average size of a YTS order is 19 vacancies, but only two for job orders. As firms will exploit economies of scale to search wherever possible, i.e. the costs of search per vacancy falls, we would expect the matching probability to decrease with the size of an order. On the other hand, the quality of the job seeker may also fall, and so firms would be less selective. In the data, it is this the latter effect that dominates—the elasticity in both regressions is about one-third. Turning to the other side of the market, we observe job seekers in one of five states: compulsory schooling Žthe base group., post-compulsory education Žhereafter FE., YTS, unemployment and employment. The variables ‘duration in current state’ non-parametrically model the effect of the time the job seeker has been in hisrher current labour-market state Ž t w . on the matching probability. More precisely, the duration in each state is classified into one of two categories: less than x months and greater than x months Žthis does not apply to compulsory schooling.. To get roughly equal proportions in the two categories, x s 1 for FE

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and x s 3 for YTS and employment; for unemployment, we use four categories. For applications to YTS vacancies, there is a clear ranking in terms of the probability of a match: employed job seekers are clearly at the front of the queue Ž0.1678., followed by those on YTS Ž0.0642., compulsory schooling Žbase., FE Žy0.0940., with the unemployed at the back of the queue Žy0.1388.. A similar picture emerges for applications to job vacancies, although the probability differentials are much smaller. Recall that Blau and Robbins Ž1990. and Holzer Ž1988. find that employed job seekers have a higher probability of finding a job when compared with the unemployed. There is no evidence whatsoever that the matching probability declines after x months for any of the four states: in the YTS regression, it goes up by 0.210 for FE Ž0.0940 q 0.1157., by 0.103 for YTS, by 0.087 for the employed and by 0.149 for the unemployed after 1 month. These are large differentials. The equivalent four numbers for job vacancies are 0.043, 0.025, 0.016, 0.067, i.e. are much smaller but still positive. The implication is clear. Any negative duration dependence in vacancy hazards to jobs or YTS vacancies is because the contact rate falls, not because the matching probability falls, supporting Coles and Smith’s Ž1998. stock-flow matching hypothesis. See the equation in footnote 3. There are various reasons why the matching probability rises. The first is a standard search theory response to a fall in the arrival rate of suitable vacancies, irrespective of labour-market state. Second, for those who are employed or on YTS, being in a state for longer than a month signals potential accrual of transferable skills which increases their employability. The third reason applies only to the unemployed, and might be the effect of short-term benefit payments received by 16- and 17-year-olds, which lasted for only 8 weeks, and may have made the short-term unemployed more selective in their job search. Once benefit entitlement is exhausted, reservation wages fall, and so the longer-term unemployed become less selective as the Careers Service encourage them to accept any job or YTS offers. This is why we used four finer categories. For the YTS regression, there is another clear rise of 0.028 after two months, before falling back after 3 months; similar effects are seen in the jobs regression. This suggests that policing the longer-term unemployed by requiring them to attend an interview with the Careers Service may have a positive effect on the outflow rate from unemployment. In addition to observing each job seekers’ current labour-market state and duration in that state, we are able to construct a set of variables which summarise their work history since leaving school. Workers with a poor work history may face an added disadvantage in the selection process, insofar as employers are less likely to make job offers to job seekers with repeated spells of unemployment Žan effect found by Teyssiere, ` 1996.. None of the four variables are significant for the jobs regression; however, there is a positive effect from all four labour-market states on the matching probability for training schemes, which suggests that we might be picking up some kind of age effect.

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5.4. Do firms ‘cream’ the market? See Table 2, Matching characteristics. The role played by education in the labour market has been the subject of controversy in labour economics for many years. Human capital theory suggests that education has a direct effect on worker productivity, hence the positive correlation between educational level and wages. Theories of signalling suggest there is also an indirect correlation between education and wages, insofar as educational credentials signal unobservable differences in worker productivity ŽSpence, 1974.. Thurow Ž1975. argues that job seekers are ranked in a queue on the basis of their ‘trainability’, and workers with better characteristics are selected because they are cheaper to train. Both approaches therefore predict that the best-qualified job seekers receive job offers, irrespective of the requirements of the job.9 In view of this debate, we are interested in whether firms ‘cream’ the market by selecting the most qualified job seekers. As discussed in Section 4, because each contact contains information on both the job seeker and the vacancy to which the application is made, it is possible to construct measures of the distance between the characteristics of the job seeker and the characteristics of the vacancy Ždenoted
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match. In the YTS regression, it is the three-digit match that has the highest matching probability, by some 0.0588 points compared with the base category of no match. Curiously, where the travel-to-work intentions of job seekers and the location of the firm offering a YTS coincide, the probability of a match falls. 5.5. Discrimination in the matching process See Table 2, Job seeker characteristics. Discrimination can occur with respect to gender, race and health, although legislation regarding gender and racial discrimination has been more rigorously enforced. Conventionally, wage or employment discrimination has been estimated as a residual from a wage or employment regression after controlling for all relevant observables Žsee, for example, Leslie et al., 1998 for a collection of recent studies.. Our approach is more direct. We are able to investigate whether females or Asians Žmainly Pakistani–Bangladeshi in origin., conditional on a contact, have a lower probability of a match than their male or white counterparts; note that our controls include labour-market state, academic qualifications, occupational choices, mobility, and school background.10 There is clear evidence that job seekers from a minority ethnic background have a lower probability of a match, particularly in terms of applications to YTS vacancies Žan estimated differential of y0.0585 log-points.. This is an alarming finding in view of the stringent equal opportunity policy that has been implemented in the training market. If the probability of acceptance is close to 1, then the implication of our findings is that racial discrimination does indeed exist. Teyssiere ` Ž1996. finds the same effect. Turning to gender discrimination, to allow for gender segmentation in occupational choice, we calculate the proportion of female job seekers to each vacancy, denoted by x, the hypothesis being that jobs that are traditionally associated with women are less likely to match. For each of the 74 occupational codes, we also calculate the proportion of females among all job seekers.11 In contrast to Russo and van Ommeren Ž1998., we find that only 27% of vacancies receive applications from females only, suggesting a greater degree of competition between males and females in the youth labour market in the UK. This variable is also interacted with the female dummy, denoted by F. The results for YTS vacancies give a matching

10 Unlike some studies, our sample of non-white observations is sizable. Also, it is possible that there is discrimination in the process that generates the contact; if this were so, the degree of discrimination is underestimated. 11 Occupations are classified according to their Occupational Training Family. There are 11 two-digit families each of which contains several three-digit occupational categories.

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probability differential of y0.0210 q 0.0442 x for females compared with men; the higher the proportion of female job seekers, the higher the probability of a match. Where the proportion of women is greater than 0.0210r0.0442—roughly one-half—there is a positive differential in favour of women. For the jobs regression, we write the regression result as 0.0483Fx y 0.0242 x, giving differentials of y0.0242 x if the job seeker is a man and 0.0241 x if a woman. So, for jobs vacancies where x is low, there is no discrimination whatsoever; for vacancies where x is high, the matching probability is higher for women and, consistent with this, lower for men. In conclusion, for jobs and YTS vacancies where there is a high proportion of women job seekers, it is young men that lose out. The only evidence of discrimination against young women is for YTS vacancies dominated by male job seekers. A dimension of discrimination that has received less attention relates to health. The disabled may be discriminated against because employers focus on the job seeker’s ‘disability’ rather than their ‘ability’. Similarly, less severe health problems, such as being partially sighted or colour blind may preclude entry to some occupations. The evidence here suggests that such concerns are unfounded. Job seekers with a poor social background may also be stigmatised by employers, especially where they come from council estates with a bad reputation. Involvement in criminal activities may also reduce the likelihood of a job offer. The ‘special training needs’ variable reflects a Careers Service decision that a youth requires extra help under YTS, and is a composite measure designed to capture ‘disadvantage’ of these kinds. Our results clearly show that job seekers from a disadvantaged background have less chance of obtaining a job, but are not excluded from participating in YTS. 5.6. Access to YTS programmes and the ‘ guarantee’ See Table 2, Employer and Õacancy characteristics. In Section 3, we provided a detailed description of the institutional background to youth training in the UK. A number predictions were made which are borne out by our results. First, we can now see that these entry-level jobs are indeed both high and low in quality, as reflected by occupation Žskilled and unskilled., training provision Žnorlittle training versus apprenticeship. and the hourly wage. Second, it was claimed that very small firms are much more likely to partake in the YTS. This is clearly seen in Table 2: the probability of a match is substantially higher for the base group Žthat is, the smallest firms with up to 10 employees., with a differential of about 3 percentage points. Finally, there is evidence of unequal access to different types of YTS programmes. Employee programmes are more selective Žy0.0314., and special programmes very much more likely to help meet the guarantee Ž0.0906..

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6. Summary and conclusions This is the first UK paper to estimate the probability of a match, using microeconomic data relating to both sides of the labour market. Because of the richness of the data, which includes the characteristics of job seekers, vacancies and firms, we are able to shed light on how the youth labour market operates. We have therefore been able to present fresh evidence on a wide variety of issues that have occupied labour economists for many years. The main conclusions, which match the issues raised in Section 1, are enumerated below. Some of these confirm findings of studies using similar data, as shown. Ž1. There is a clear negative, i.e. procyclical, effect of the stock of unemployed in a labour market on the matching probability for jobs Želasticity y0.17.. There is no equivalent positive effect for the stock of vacancies. Ž2. After controlling for the size of the labour market Žpopulation and area., larger Careers Offices have a smaller matching probability Želasticity for jobs is y0.25.. Ž3. The majority of wages are set in advance by employers, and are non-negotiable. The effect of the wage is negative for jobs Želasticity y0.39., but insignificant for YTS vacancies Žsee also Teyssiere, ` 1996.. Ž4. Ža. The longer the vacancy has been open, the matching probability is lower for job vacancies, but higher for YTS vacancies; Žb. there is a clear ranking of the matching probability by job seekers’ labour-market state: the employed are at the front of the job queue and the unemployed are at the back; Žc. for all labour-market states, the matching probability increases after 1 or 3 months; Žc. is important because any negative duration dependence in vacancy hazards Žsee, for example, van Ours, 1990. is because the contact rate falls, not because the matching probability falls, supporting Coles and Smith Ž1998. stock-flow matching hypothesis. Ž5. Employers ‘cream’ the most qualified job seekers in the selection process, which may be regarded as evidence in support of the signalling or job competition models Žsee also Teyssiere, ` 1996; van Ours and Lindeboom, 1996.. Ž6. There is clear evidence of racial discrimination. Job seekers from the ethnic minorities have a lower probability of a match compared to their white counterparts Ždifferential of y0.059 for training schemes. Žsee also Teyssiere, ` 1996.. However, there is almost no evidence of discrimination in terms of gender and health Žsee also Russo and van Ommeren, 1998.. Ž7. Matching probabilities vary by the type of training offered within the YTS. Also, very small firms offering a training place have a much higher matching probability. One difficulty in interpreting these results arises because the matching probability is the product of the offer probability by firms and the probability of acceptance by job seekers. A common assumption is that job seekers are willing to

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accept any job offer for which they apply. While this is plausible in many cases, the probability of a job seeker being offered a training scheme is much higher than for a job, simply because one objective of government policy was to mop-up some of the excess supply of youth labour. Nonetheless, when taken together, our conclusions imply that we are observing the decisions of employers dominating those of job seekers.

Acknowledgements The authors thank The Leverhulme Trust Žunder grant Fr120rAS. for financial assistance. The data were kindly supplied by Lancashire Careers Service. The comments of three anonymous referees and Alison Booth are gratefully acknowledged, as are those from participants at various presentations. These include economics departments at Manchester and Stirling, and the 1999 Royal Economic Society Conference ŽNottingham.. The data used in this analysis are available on request.

References Barron, J., Black, D., Loewenstein, M., 1987. Employer size: the implications for search, training, capital investment, starting wages, and wage growth. Journal of Labor Economics 51, 76–89. Barron, J., Berger, M., Black, D., 1997. On the Job Training. W.E. Upjohn Institute for Employment Research, Kalamazoo, MI. Blau, D., Robbins, P., 1990. Job search outcomes for the employed and unemployed. Journal of Political Economy 98, 637–655. Burdett, K., Wright, R., 1998. Two-sided search with nontransferable utility. Review of Economic Dynamics 1, 220–245. Burgess, S., 1993. A model of job competition between unemployed and employed job seekers: an application to the unemployment outflow rate in Britain. Economic Journal 103, 1190–1204. Coles, M., Smith, E., 1996. Cross-section estimation of the matching function: evidence from England and Wales. Economica 63, 589–597. Coles, M., Smith, E., 1998. Marketplaces and matching. International Economic Review 39, 239–255. Holzer, H., 1988. Search method use of unemployed youth. Journal of Labor Economics 6, 1–20. Leslie, D., Blackaby, D., Clark, K., Drinkwater, S., Murphy, P., O’Leary, N., 1998. An Investigation of Racial Disadvantage. Manchester University Press, Manchester. Manning, A., 2000. Pretty vacant: recruitment in low-wage labour markets. Oxford Bulletin of Economics and Statistics 62 ŽSupplement 1., 747–770. Mortensen, D., 1986. Job search and labor market analysis. In: Ashenfelter, O., Layard, R. ŽEds.., Handbook of Labor Economics, vol. 2, North-Holland, Amsterdam, pp. 849–919. Mumford, K., Smith, P., 1999. The hiring function reconsidered: on closing the circle. Oxford Bulletin of Economics and Statistics 61, 343–364. Pissarides, C., 1990. Equilibrium Unemployment Theory. Blackwell, Oxford. Russo, G., van Ommeren, J., 1998. Recruitment methods and vacancy duration. Bulletin of Economic Research 50, 155–166.

M.J. Andrews et al.r Labour Economics 8 (2001) 335–357

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Singell, L.D., Seaman, P.T., Chatterji, M., 1997. A test of the signalling hypothesis. Department of Economic Studies Discussion Paper No. 80, University of Dundee. Spence, M., 1974. Market Signaling. Harvard University Press, Cambridge, MA. Teyssiere, ` G., 1996. Matching processes in the labour market: an econometric study. Labour Economics 2, 421–435. Thurow, L., 1975. Generating Inequality. Basic Books, New York. van den Berg, G., 1990. Search behaviour, transitions to non-participation and the duration of unemployment. Economic Journal 100, 842–865. van Ours, J., 1990. An empirical analysis of employers search. In: Hartog, J., Ridder, G., Theeuwes, J. ŽEds.., Panel Data and Labor Market Studies. North-Holland, Amsterdam, pp. 191–214. van Ours, J., Lindeboom, M., 1996. ASeek and ye shall findB: an empirical analysis of the matching of job seekers and vacancies. Paper Presented at Labour Market Changes and Income Dynamics Conference. van Ours, J., Ridder, G., 1995. Job matching and job competition: are lower educated workers at the back of job queues? European Economic Review 39, 1717–1731.