Based on the two articles provided summarize and answer the following questions for each article for factors related to pay differences. Use APA format and cite the articles. One page per article 1. What were the main findings in the article? 2. To whic

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Home >Applied Sciences homework help >Based on the two articles provided summarize and answer the following questions for each article for factors related to pay differences. Use APA format and cite the articles. One page per article 1. What were the main findings in the article? 2. To whic

207 © Blackwell PublishingLtd and theDepartment ofEconomics,University ofOxford, 2010.PublishedbyBlackwell PublishingLtd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA02148, USA.

OXFORD BULLETIN OF ECONOMICSAND STATISTICS, 73, 2 (2011) 0305-9049 doi:10.1111/j.1468-0084.2010.00613.x

Preferences,ComparativeAdvantage, and CompensatingWageDifferentials forJob Routinization*

Climent Quintana-Domeque

Departament de Fonaments de l’Anàlisi Econòmica, Universitat d’Alacant, Campus de Sant Vicent del Raspeig, 03690, Alacant, Spain (e-mail:[email protected])

Abstract Iattempt toexplainwhythere isnotmuchevidenceoncompensatingwagedifferentials for job disamenities. I focus on the match between workers’preferences for routine jobs and thevariability in tasksassociatedwith the job.UsingdatafromtheWisconsinLongitudinal Study, I find that mismatched workers earn lower wages and that both male and female workers in routinized jobs earn, on average, 5.5% and 7% less than their counterparts in non-routinized jobs. However, once preferences and mismatch are accounted for, this differencedecreases to2%formen,and4%forwomen,not statistically significant inboth cases.

I. Introduction Formore than30years, labour economists havebeen trying tofindevidenceofwagepre- miumsfor jobs that involvesuchdisamenitiesasphysicaleffort, routinenatureof thework or job insecurity.According to the theory of compensating wage differentials, which goes back to Adam Smith and involves the framework of analysis outlined by Rosen (1974),

ÅThis article is a revised version of Chapter 1 of my Ph.D. dissertation at Princeton University. I would like to thank my advisor,Alan Krueger, who has always been exceptionally generous with his advice. I am also extremely grateful to Jesse Rothstein for his insights and suggestions. I am particularly indebted to Carlos Bozzoli and Marco Gonzalez-Navarro for their many thoughtful remarks. Thanks also go to the Editor Beata Javorcik and the seminar participants at Princeton University, Universitat d’Alacant, Universitat de les Illes Balears, SAE 2007 Meetings in Granada and Universidad Pablo de Olavide. Special thanks go to an anonymous referee of the Oxford Bulletin of EconomicsandStatisticswhosecomments, insightsandsuggestionsmade theconceptual frameworkshorter, clearer and neater. I also want to thank Erik Plug for providing me with the codes used in his previous work. Financial support from theRafael delPinoFoundation, theBankofSpain and theSpanishMinistryofScience and Innovation (ECO2008-05721/ECON) is gratefully acknowledged. The usual disclaimers apply. Previous versions of this paper are circulated as Industrial Relations Section Working Paper 525, Princeton University, and IVIE Working Paper WP-AD 2010-06. This research uses data from the Wisconsin Longitudinal Study (WLS) of the University of Wis- consin-Madison. Since 1991, theWLS has been supported principally by the National Institute onAging (AG-9775 andAG-21079), with additional support from the Vilas Estate Trust, the National Science Foundation, the Spencer Foundationand theGraduateSchoolof theUniversityofWisconsin-Madison.Apublicusefileofdata fromtheWLS is available from the University ofWisconsin-Madison, 1180 Observatory Drive, Madison,Wisconsin 53706 and at http://www.ssc.wisc.edu/∼wls/data/. The opinions expressed herein are those of the authors. JELClassification numbers: J3, J31.

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workersmust receiveawagepremiumforsuffering fromjobdisamenities, ceterisparibus. However, a surveyof theevidencehasconcluded that ‘testsof the theoryofcompensating wage differentials are inconclusive with respect to every job characteristic except risk of death’(Borjas, 2005, Ch. 6, p. 224). It is obvious that on-the-job risk of death is an undesirable job characteristic, and the

availableempirical evidence indeedsuggests thatwagesarepositivelyassociatedwithon- the-jobriskofdeath(ViscusiandAldy,2003).However,manyother jobcharacteristicsare not regarded as intrinsically undesirable by all workers. Instead, the desirability of a large number of job attributes depends crucially on individual workers’ tastes or personalities. Smith(1979)notes that theheterogeneityofworkers’tastesmaketestingforcompensating wage differentials difficult. At first glance, preference heterogeneity may seem consistent with mixed results for

repetitivework.Forexample,Lucas (1977)findsevidenceof significant compensation for repetitivework,whileBrown(1980) reportsanegativeestimate.Almost20years later, the mixed results are even more striking. Daniel and Sofer (1998) present some such results in their paper. One straightforward way to account for preference heterogeneity when looking for

compensating wage differentials is to run separate wage regressions for workers with different preferences. Still, as I show in section II, non-routine-preferring workers earn lower wages in routinized jobs, which is contrary to what the theory of compensating wage differentials would predict. Therefore, preference heterogeneity by itself does not explain the puzzle of compensating wage differentials. Why,evenafteraccountingforpreferenceheterogeneity,arecompensatingwagediffer-

entialsnotobservedor incorrectlysigned?What ifworkers’preferences forone typeof job (or job attribute) are related to their productivity in performing that type of job?Workers’ tastes for a certain job attribute may correlate with their comparative advantage in such jobs. This is not the same as saying that preferences can have a direct effect on wages, independent of the type of job; i.e. workers with different preferences may have differ- ent absolute advantages in performing any job. Rather, the key insight here is that when workers’ preferences do not match job attributes, they are less productive. For example, non-routine-preferring workers are likely to be more productive in non-routinized jobs than routine-preferring workers. By the same token, routine-preferring workers are likely to be more productive in routinized jobs than non-routine-preferring workers. If matching were perfect and each worker was assigned to a job according to com-

parative advantage, then the marginal routine-preferring worker would be willing to pay for working in a routinized job. Similarly, the marginal non-routine-preferring worker would need to be compensated for working in a routinized job. This would be consistent with the compensating wage differentials theory. However, as Lang and Majumdar (2004) pointed out, both casual empiricism and

researchshowthatmatchingis imperfect.Morerecently,Shimer(2007)acknowledgesthat skills and geographical location of workers are poorly matched with the skill requirement and locationof jobs: unemployedworkers are attached to anoccupation and ageographic location where jobs with their skills are currently scarce. Here, a similar point can be made. As I will show, a mismatch between workers’ preferences and job attributes does exist, and must be taken into account when looking for compensating wage differentials.

Compensating wage differentials for job routinization 209

Indeed, my findings indicate that not accounting for mismatch in wage equations could bias compensating wage differentials estimates. I propose a simple assignment model with Nash bargaining over wages for analysing

the role of mismatch when looking for compensating wage differentials. Assuming that observed workers are not in long-run market equilibrium, all workers, no matter what their preferences are, need to be compensated if working in the sector with a shortage of workers in the absence of pay differentials. However, only mismatched workers, who are less productive because their sectors do not match their preferences, are penalized. This simple frameworkoffers a rationale for theexistenceofmixedestimates for com-

pensatingwagedifferentials. Indeed, in the literature, thestandardestimatesmayconfound theeffectonwagesofthejobattributebeinganalysedwiththeoneattributabletomismatch. Thispaper focusesonjobroutinization(i.e. jobs involvingrepetitiveandroutine tasks).

I consider this isan important jobattribute tostudybecauseestimates for it in the literature aremixed (e.g.Lucas, 1977;Brown, 1980;Daniel andSofer, 1998). So, this analysismay shednewlighton thesourcesof thesemixed results.Furthermore,Table1shows that29% of male workers and 36% of female workers report that ‘being able to do different things rather than thesamethingsoverandover’is ‘muchmore important thanhighpay’. Indeed, the table indicates that variability of tasks is one of the most highly valued characteristics on the job for workers. This suggests that it should be easier to find compensating wage differentials for job routinization than for other job attributes. Using data from the Wisconsin Longitudinal Study (WLS), I find that mismatched

workersearn lowerwages.Myresultsalso indicate thataccountingformismatch is impor- tant in obtaining more reliable estimates of compensating wage differentials. On average, male workers in routinized jobs are paid 5.5% less than workers in non-routinized jobs, after accounting for: differences in completed years of education, IQ measured at high school, highschool rank, adult cognition, tenure,occupationandfirmsize.Thisdifference decreases to4.5%after accounting fordifferences in thepreference for routinework.Fur- thermore, controlling formismatch reduces thedifference inaveragewagesbetweenmale workers in routinizedvs.non-routinized jobs to2%,and this isnot statistically significant. For female workers, the difference decreases from 7% to 4%. This paper is laid out as follows. Section II briefly describes the puzzle. It presents a

brief review of the compensating wage differentials literature, offers a description of the WLS dataset, and takes a first look at the data. Section III presents a model that sheds

TABLE 1

Percentage of currently employed individuals reporting that job characteristic is much more important than high pay, WLS 1992–93

Job characteristic Men Women Being able to do different things rather than the same things over and over 29 36 Being able to work without frequent checking by a supervisor 22 27 Having the opportunity to get on-the-job training 18 25 Having a job that other people regard highly 7 11 Being able to avoid getting dirty on the job 2 6

Source: Table 2 inAndrew et al. (2006), page 51.

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light on the puzzle. Section IV offers the empirical model. My results are in section V. SectionVI offers some robustness checks. Finally, sectionVII concludes.

II. The puzzle A brief review

More than twocenturies ago,AdamSmithnoted thatworkerswith the same level of com- petence should be paid different wages if their working conditions are different. Rosen (1974) formalizesAdam Smith’s ideas showing that, under perfect competition, identical workers need to be compensated for job disamenities. The standard method for testing the prediction of this theory is to estimate a wage

equation with characteristics of the job (z) and personal characteristics (p). In general, the equation is of the form:

ln(w)= � + �z + �p+ �. (1) The estimation of equation (1) using cross-sectional data identifies a market relationship between ln(w) and z. If the market relationship is linear, then � measures the marginal cost of the disamenity for any worker who is in his most preferred job in long-run market equilibrium. For an undesirable job attribute, the theory predicts that �>0. This iswhat is predictedby the standard theoryof compensatingdifferentials:workers

have heterogeneous preferences, and firms are heterogeneous with respect to the costs of providing good working conditions; in long-run equilibrium workers who value good conditions most are matched with firms that have the lowest costs of providing them, and in thesematchesconditionsaregoodandwagesare low.Conversely,highwagesandpoor conditionsareobservedinmatchesofworkerswhocarelesswithfirmsthathavehighcosts. This long-run relationship is the only thing that the standard theory gives us.1 However, theempirical evidenceoncompensatingwagedifferentials ismixed for jobcharacteristics other than the risk of death [see Rosen (1986) for a classical discussion on the theory of equalizing differences]. There have been several previous attempts at solving this puzzle. First, omitted vari-

ables can lead to biased estimates because of the correlation between unobserved skills, preferences for the job attribute under study, individual productivities and the quality of workingconditions(e.g.Brown,1980;DuncanandHolmlund,1983;Garen,1988;Kostiuk, 1990; Hwang, Reed and Hubbard, 1992). Second, when working conditions are reported by the workers themselves, the estimates are likely to suffer from simultaneity bias (e.g. McNabb,1989).Further, ifanswerstosurveyquestionsaboutworkingconditionsaregiven in subjective terms, then the estimates are likely to suffer from subjectivity biases (e.g. McNabb, 1989). Finally, when worker conditions are defined using average occupation (or industry)characteristicsand thenmatched to individualworkers,misclassificationbias may arise. From an empirical perspective, in this paper I take into account most of these biases.

First, I control for preferences for the job attribute under study, and use IQ measured at

1I thank an anonymous referee of the Oxford Bulletin of Economics and Statistics for clarifying this issue.

Compensating wage differentials for job routinization 211

high school and high school rank as proxies for unobserved skills and individual produc- tivities, and occupation and size of firm dummy variables to account for characteristics other than job routinization (the job attribute under study) that may be related to worker productivities. Second, job routinization is measured by time spent doing monotone tasks in order to circumvent the problem of subjectivity biases due to the use of answers given in subjective terms. Last but not least, I measure working conditions at the worker level, not at the occupation level, to avoid misclassification bias. From a theoretical point of view, this paper can be thought of as looking at the conse-

quenceof thepossibility thatobservedworkersarenot inalong-runequilibrium,providing anaccountof short-runwagedeterminationwhenworkers arenotperfectlymatched.This is the gap that this study aims to fill. I present a very simple model: workers are randomly assigned to jobs and wages are determined by Nash bargaining. The model highlights the effect of mismatch on wages, which must be taken into account when looking for compensating wage differentials. Istartbypresentingtheimplicationsofpreferenceheterogeneity(about theattractiveor

unattractive features of performing a job task) for estimates of compensating wage differ- entials. Suppose there are two types of workers: those who enjoy z (x =1) and those who havedistasteforz (x =0).Inthatcase, totest thetheoryofcompensatingwagedifferentials, the following regressions should be run:

ln(w)= �0 + �0z + �0p+ �0, if x =0 (2)

ln(w)= �1 + �1z + �1p+ �1, if x =1. (3)

If the theory is correct, I should find evidence on �0>0 and �1<0: workers who have distaste for z (x =0) are compensated for working in a job involving high levels of z, while workers who enjoy z (x =1) are willing to pay for working in a job involving high levels of z. With these predictions at hand, I can assess the existence of compensating wage differentials for job routinization depending on workers’preferences. Before taking a first look at the data, I provide a description of the dataset used in this paper.

Data

I use data from the WLS of the University of Wisconsin-Madison. The sample contains information on 10,317 men and women who graduated from Wisconsin high schools in 1957,approximatelyone-thirdofall seniors inWisconsinhighschools in1957. It contains a rich set of self-reported information fromsamplemembers, siblings andparents, aswell asadministrativedata, collected inaseriesof surveys:1957(graduates),1964(graduates), 1975 (graduates), 1977 (siblings), 1992–93 (graduates), 1993–94 (siblings) and 2003–05 (graduates and spouses). I focuson the1992–93waves,when respondentswere in their early50s.Thisdecision

is based on both informational requirements and sample (size and selectivity) consider- ations.Firstofall, informationonworkers’preferencesisnotavailablepriortothe1992–93 waves.Second,participationinthelabourmarket ishigherforpeople intheir50s(1992–93

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waves) than in their 60s (2003–05 waves): 92.4% of men were employed in 1992 while only47.8%ofthemwereemployedin2004.Finally, thishelpsmetominimizenon-random attrition problems. TheWLSdatasetoffers anopportunity for exploring the roleofmismatch inobserving

compensating wage differentials. It contains a set of individual characteristics obtained from the (graduate) respondents, such as IQ score measured at high school, high school rank, adult cognition, education, tenure, preference for job routinization, hourly wages, hours of work, number of hours performing different tasks on the job, etc. Moreover, the sample is quite homogeneous (high school graduates from Wisconsin high schools in 1957), which makes any concerns about omitted variables less important. Mysampleisrestrictedtoworkerswhowereemployedin1992.Unfortunately,employ-

mentstatusismissingfor1,824individuals.Thisimpliesadramaticdecreaseintheoriginal sample size from 10,317 to 8,493. There are 7,196 individuals employed in 1992. After restricting our sample size to those individuals having a positive hourly wage rate, the number of observations decreases to 6,756. Focusing only on Wisconsin residents, the sample decreases to 4,696.The sample also excludes individuals who were: working less than20hoursperweek, self-employed, employeesof their owncompanyor familywork- ers. Farm workers and members of the military also are excluded from my sample.After applying these restrictions, my working sample is left with approximately 3,800 observa- tions. The presence of extreme values in the wage distribution was detected accidentally through the comparison of average wages for men and women. To avoid the estimates being driven by extreme values in the wage distribution, I trim the tails of the log-wage distribution at both the 3% bottom and the 3% top. Finally, after dealing with missing observations for thevariablesused in theanalysis, theworkingsample size is about3,200. The next subsection presents the definition of the main variables used in the empirical analysis.

Definition of the main variables

The main variables in this paper are job routinization; worker’s preference for routine; and mismatch, i.e. the discrepancy between job routinization and worker’s preference for routine. In this subsection, I discuss how these variables are measured. The job routinization indicator (z) – whether a job is classified as routinized or non-

routinized – is constructed using the fraction of working time doing the same things over and over: job routinization is measured as 1 (routinized job) if the fraction of working time doing the same things over and over is equal to or higher than 0.5. Sensitivity anal- yses with alternative definitions of job routinization will be performed in the robustness checks section. I compute this fraction as the ratio of the number of weekly hours doing the same things over and over on the job to the total number of weekly working hours. Note that the reportednumberofhours canbecomparedacross individuals; this addresses standard subjectivity bias concernsdue toworkers’subjective assessments aboutworking conditions.Moreover, the fact that thenumberofhoursworked is reportedby theworkers themselves confronts the misclassification bias that is attributable to imprecise matching of average job (occupationor industry) characteristics to individualswhose jobcharacter- isticsmaydepart (byand large) fromtheaveragecharacteristicswithin theiroccupationor

Compensating wage differentials for job routinization 213

industry.Ofcourse, as inprevious studies, simultaneitybiasesmayexist:workerswhoare unhappy with earnings that they receive may also respond negatively when asked about job attributes (McNabb, 1989). The worker’s preference for routine indicator (x) – whether a worker is classified as a

routine-preferringworkeroranon-routine-preferringworker–ismeasuredbytheresponse to this question: ‘To what extent do you see yourself as someone who prefers work that is routine and simple?’ The possible answers to this question are: agree strongly, agree moderately, agree slightly, neither agree nor disagree, disagree slightly, disagree moder- ately, disagree strongly.This is oneof thequestions asked in scoring thefive-factormodel of personality structure, and it is included in the personality section of the 1992–93 ques- tionnaire, separate from jobhistoryor current/last jobcharacteristics.Hence, thepotential concerns about framing effects are minimized. For workers who agree strongly, moder- ately or slightly, preferring work that is routine and simple, x =1. Sensitivity analyses with alternative definitions of worker’s preference for routine will be performed in the robustness checks section. Finally, mismatch between job routinization and worker’s preference for routine and

simplework ismeasuredas theabsolutevalueof thedifferencebetween z and x,m(z,x)= |z −x|. I adopt this approach because absolute value seems to be the most intuitive way of thinking about the discrepancy between two variables. Note that for binary indicators, the absolute-value deviation is equivalent to the quadratic deviation.

Descriptive statistics

Table2presents themaindescriptive statistics of theWLSsample for currently employed individuals (1992–93). A first glance at the table shows that, on average, male workers in non-routinized jobs earn $18.09 per hour, while male workers in routinized jobs earn $15.21: a difference of approximately $3 in the hourly wage. Women in non- routinized jobs earn $11.41 per hour, while women in routinized jobs earn $9.33. Although these are unadjusted averages, workers do not seem to be compensated for job routinization. The tablealsoshowsthat themajorityofmen(52%)work innon-routinized jobs,while

the majority of women work in routinized jobs (64%). At the same time, the fraction of workers who prefer routine and simple work is higher for women than for men: 0.24 vs. 0.18.Thefact thatworkers innon-routinizedjobsarenotcompensatedfor jobroutinization is evenmore strikinggiven that the supplyof routine-preferringworkers seems tobevery low(24%ofmaleworkers,18%of femaleworkers) incomparison to thedemandfor them (48% of male workers, 64% of female workers). Can mismatch explain the apparent lower wages in routinized jobs? The percentages

of well-matched workers (according to job routinization and preference for routine and simple work) are 62% and 53% for men and women, respectively. Hence, mismatch is higher forwomen (47%) than formen (38%).Forbothmenandwomen,mismatch isvery high.Moreover,mismatchmayberesponsible for (partof) thedifference inaveragewages between routinized and non-routinized jobs: mismatched men are paid $15.51 per hour while those who are well-matched are paid $17.44 per hour. For women the difference is smaller: $9.61 vs. $10.53.

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TABLE 2

Descriptive statistics, WLS 1992–93

Men Women

Obs. Mean SD Obs. Mean SD Hourly wage routinized jobs 800 15.21 4.98 1,111 9.33 3.46 Hourly wage non-routinized jobs 865 18.09 6.10 637 11.41 4.19 Job routinization (z =1 if fraction of weekly worked 1,665 0.48 0.50 1,748 0.64 0.48 hours doing the same things over and over is equal or higher than 0.5, z =0 otherwise)

Routine-preferring worker (Preference for routine 1,656 0.18 0.38 1,743 0.24 0.42 and simple work: x =1 if strongly/moderately/slightly agree, x =0 if strongly/moderately/slightly/disagree or neither agree nor disagree)

Mismatch, |z −x| 1,656 0.38 0.49 1,743 0.47 0.50 Fraction of weekly worked hours doing the 1,665 0.48 0.38 1,748 0.61 0.37 same things over and over

Preferences for routine and simple work Strongly agree 94 0.06 – 108 0.06 – Moderately agree 162 0.10 – 238 0.14 – Slightly agree 35 0.02 – 48 0.03 – Neither agree nor disagree 6 0.00 – 18 0.01 – Slightly disagree 59 0.04 – 68 0.04 – Moderately disagree 447 0.27 – 503 0.29 – Strongly disagree 853 0.52 – 760 0.44 –

Hourly wage mismatched workers 636 15.51 5.08 821 9.61 3.52 Hourly wage well-matched workers 1,020 17.44 6.04 922 10.53 4.12 Hourly wage 1,665 16.71 5.77 1,748 10.09 3.87 IQ (measured at high school) 1,665 98.95 14.35 1,748 100.10 13.89 High school rank 1,543 41.59 27.03 1,636 57.04 27.21 Education (years of completed education) 1,665 13.44 2.19 1,748 12.93 1.71 Adult cognition score (WAIS) 1,653 7.47 2.78 1,739 7.62 2.63 Tenure 1,659 19.34 11.00 1,744 12.09 9.00

Notes:Author’s calculations.

As expected, men are paid higher hourly wages than women: $16.71 vs. $10.09. Not surprisingly, given the cohort under study, born around 1940, women on average are less educated than men. Table 3 shows the distribution of workers (by their preferences for routine and simple

work) across jobs (by routinization) and the average hourly wages by worker-job type. Among men, 42% of non-routine-preferring workers are mismatched into routinized jobs (567/1359×100),while thispercentage is57 forwomen(758/1331×100).Forbothmen and women, the percentage of mismatched workers is lower in non-routinized jobs. This is consistent with the fact that the majority of men and women are non-routine-preferring workers (76%ofmen, and82%ofwomen).Regarding the averagehourlywage, the table describes an interesting feature of my data: there are no differences in average wages betweenmismatchedandwell-matched routineworkers. Indeed, thedifferencesare found only for non-routine-preferring workers.

Compensating wage differentials for job routinization 215

TABLE 3

Distribution of workers across jobs and average hourly wages by worker-job type, WLS 1992–93

(Number of observations) z =0 z =1 Male x =0 48% 34%

18.4 15.7 (792) (567)

x =1 4% 14% 14.3 14.0 (69) (228)

Female x =0 33% 43%

11.8 9.7 (573) (758)

x =1 4% 20% 8.4 8.5 (63) (349)

Notes:Author’s calculations.

TABLE 4

Fraction of weekly worked hours doing the same things over and over by occupational category, WLS 1992–93

Occupational category Men Women Professional and technical specialty occupations 0.27 0.46 Executive, administrative and managerial occupations 0.27 0.41 Sales occupations 0.54 0.70 Administrative support occupations (including clerical) 0.63 0.65 Precision production, craft, and repair occupations 0.44 0.83 Operators and fabricators 0.79 0.86 Service occupations 0.64 0.79 Handlers,equipmentcleaners,helpers, labourers, farmoperators 0.73 0.90 farm workers and related occupations

Source:Author’s calculations.

A first look at the data

I start bymeasuring job routinizationas the fractionof timeatworkdoing the same things over and over. Routine-preferring workers (x =1) are defined as those individuals who strongly agree, moderately agree, slightly agree or neither agree nor disagree, with the statement ‘I see myself as someone who prefers work that is routine and simple’. Table 4 reports the degree of job routinization by occupational category for men and

women, respectively. As the table makes clear, ‘Professional and Technical Specialty Operations’, and ‘Executive,Administrative, and Managerial’occupational categories on average involve less routinization, while occupations such as ‘Operators and Fabricators’ involve more routinization of tasks. Another interesting feature that emerges from this table is that female workers tend to spend a higher fraction of time than male workers

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TABLE 5

Job routinization and wages by workers’ preferences. OLS estimates for men and women Men Women

Workers’ preferences Workers’ preferences Routine Non routine Routine Non routine (1) (2) (3) (4)

Job routinization 0.035 −0.082 0.004 −0.096 (0.052) (0.024) (0.046) (0.016)

Completed years of education 0.044 0.032 0.042 0.016 (0.016) (0.005) (0.015) (0.006)

IQ measured at high school 0.002 0.002 0.002 0.004 (0.002) (0.001) (0.002) (0.001)

High school rank 0.000 0.000 −0.000 0.000 (0.001) (0.005) (0.001) (0.000)

Adult cognition score −0.005 0.004 −0.002 0.001 (0.007) (0.003) (0.006) (0.004)

Tenure 0.009 0.008 0.016 0.013 (0.002) (0.001) (0.002) (0.001)

R2 0.25 0.32 0.39 0.38 Number of observations 270 1,253 378 1,243 Notes: Dependent variable is log(hourly wage). Routine-preferring worker equals to 1 if

worker agrees strongly, moderately or slightly, preferring work that is routine and simple. Job routinization is the fraction of weekly worked hours doing the same things over and over. Heteroskedasticity robust standard errors are reported in parentheses.All regressions include occupation dummy variables.

doing the same things over and over. In other words, women tend to do more routinized tasks than men within occupational categories. The results from Table 5 show evidence contrary to the theory of compensating wage

differentials:workerswithlowerpreferencesforroutineandsimpleworkearnlowerwages in the routinized jobs.Columns(2)and(4)showthatbothnon-routine-preferringmaleand female workers do not appear to be compensated for working in routinized jobs; rather, if anything, they appear to be penalized. For routine-preferring workers, columns (1) and (3), I find a positive but not statistically significant association between job routinization and hourly wages. The bottom line of Table 5 is that preference heterogeneity clearly matters, but in a

surprisingly opposite way to what one would have expected from a selection-bias explan- ation: workers with lower preference for routine and simple work earn lower wages in routinized jobs.This paper provides an explanation for such a finding. Note that the implicit assumption behind the prediction of a positive association

betweenjobroutinizationandwagesfornon-routine-preferringworkersisthattheymustbe compensatedbecauseof theirhigherdisutilitywhenworking in routinized jobs.However, non-routine-preferringworkers are likely to be less productive in routinized jobs. In other words, workers’preferences are likely to reflect two things that are equally important for wage determination: their disutility from working, which will be higher as the discrep- ancy between preferences and job attributes (characteristics or job tasks) increases; and

Compensating wage differentials for job routinization 217

their comparative advantage on the job, which will be lower as the discrepancy between preferences and job attributes increases. If matching were perfect, and each worker was assigned to a job according to her

comparative advantage, then the productivity effect of comparative advantage would not play any role: productivity would be the same for every worker, because every worker would be assigned to a job where her comparative advantage was maximized. However, matching is far from perfect, and neglecting its influence on wages is likely to confound thecompensatingwagedifferentialsestimates. Inotherwords,equations (2)and(3)would be mis-specified if mismatch also matters. Thus, a potential explanation for the puzzling results in Table 5 is that preferences for

performinga joband theworker’s comparativeadvantage inperforming it are (positively) correlated. If this is the case, then workers with lower preference for routine and simple work will earn lower wages in routinized jobs, not because they are not compensated for taking such jobs but because they are less productive in performing them.

III. Conceptual framework In this section, I present a simple assignment model with Nash bargaining to show the effect of mismatch on the wage rate. The main purpose of the model is to highlight the importance of the mismatch productivity effect on the wage rate, and its relevance for understanding estimates of compensating wage differentials. There are two types of workers x ∈ {0,1}, defined by their preferences for a job attri-

bute (x =0 fornon-routinepreferringworkers,x =1 for routine-preferringworkers) anda continuumoffirms’types z∈[0,1], definedby the jobattribute (z =0 for completelynon- routinized jobs, and z =1 for completely routinized jobs).Eachfirm is randomlymatched witheachworker: (z,x) foreachfirm-workerpair.Then, thefirm zandtheworkerxbargain over the division of the match surplus to decide the optimal wage. The profit function of the firm is given by

� =A(m(z,x))−w, (4) whereA isgross revenue(production),whichdependsnegativelyonmismatchm(z,x), and w is the wage rate.The negative relationship between A and m is assumed on the grounds that the worker’s taste for a job attribute (e.g. routine-preferring worker) is likely to be positively correlated with his ability to perform well in a job with such an attribute (e.g. routinized job). In other words, a routine-preferring worker will tend to have a compara- tive advantage indoing repetitive things.Tinbergen (1975) sets a production function that depends on the extent to which a person’s abilities match those required in the execution of a job task. The utility function of the worker is given by

u=w− v(z,m(z,x)), (5) wherev is thedisutility fromwork,whichdependspositivelyonmismatchm(z,x)between the job characteristic (z) and the worker’s preference for such a job characteristic (x), and on the job characteristic (z).

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This randomassignmentsettingcanbeunderstoodbyassumingthatdue tofrictions the market is not in long-run equilibrium. This is a plausible assumption as the data suggest thatmismatch is substantial:18%ofmaleworkersareclassifiedas routine-preferworkers, while48%of themareworking in jobs involvinghalf (ormore)of theirweekly timedoing the same thingsover andover.Hence, I assume that the routinized sector is the sectorwith a shortage of workers in the absence of pay differentials,

∂v(z,m(z,x)) ∂z

>0.

The solution to the Nash bargaining problem is obtained from

max w

{��u1−�}, (6) where 0<�<1 measures the firm bargaining power. The FOC gives us the optimal wage rate:

wÅ(z,m(z,x))= �v(z,m(z,x)) +(1− �)A(m(z,x)). (7) The marginal effect of z holding m constant, which is the ‘standard’ compensating

wage differential, is

∂wÅ(z,m(z,x)) ∂z

= � ∂v(z,m(z,x)) ∂z

, (8)

which is positive given my previous assumption. However, the total effect of z holding x constant is

dwÅ(z,m(z,x)) dz

= ∂w Å(z,m(z,x))

∂z + ∂w

Å(z,m(z,x)) ∂m(z,x)

∂m(z,x) ∂z

, (9)

where ∂wÅ(z,m(z,x))/∂z>0 from equation (8), ∂m(z,x)/∂z>0 if x =0 (i.e. the higher is job routinization, the higher is the mismatch for a non-routine preferring worker), and ∂m(z,x)/∂z<0 if x =1 (i.e. the higher is job routinization, the lower is the mismatch for a routine preferring worker). Equation (9)givesusprecisely theeffectsbeingestimatedas �0 and �1 inequations (2)

and (3), in which m is omitted. What is the sign of ∂wÅ(z,m(z,x))/∂m(z,x)? The answer to this question is given by Proposition 1.

Proposition 1.When mismatch also affects gross revenue (output), it has an ambiguous effect on the wage rate. If the productivity effect dominates the disutility effect, then mismatchaffects thewage ratenegatively. If the reverse is thecase, thenmismatchaffects thewage rate positively. If both effects cancel eachother out, thenmismatchhasnoeffect on the wage rate.

Proof

∂wÅ(z,m(z,x)) ∂m

= � ∂v(z,m(z,x)) ∂m ︸︷︷︸

+(1− �)∂A(m(z,x)) ∂m ︸︷︷︸

. (10)

�

Compensating wage differentials for job routinization 219

Hence, givenProposition1,weconclude that the total effect of z holding x constant is ambiguous.2

IV. Empirical model Mymodelyields threeparameters that are captured inequation (11): a routine sectormain effect (the‘standard’compensatingwagedifferential, �); a routine-preferringworkermain effect (the absolute advantage of this type of worker, �); and a negative wage effect for workerswhoare ina sectorother than theone theyprefer (thenegativeproductivity effect due to mismatch, ).

ln(w)= � + �z + �x + m(z,x)+ �. (11) To identify the effects of m and z, I need to be aware of the possibility that the error

term � is correlated with m and/or z. First, mismatch (m) is likely to be correlated with worker’s ability: workers with worse skills are likely to be paid lower wages and to end up being mismatched. Second, the level of job routinization (z) could be correlated with worker’s skills and skills requirements of the job: routine jobs are perhaps those requiring unskilled workers. Imeasure relevantworker’s characteristics thatmaybe related tobothwages andmis-

match by education (completed years of education), IQ score measured at high school, high school rank and an adult cognition measure that is based on 8 of the 14 items from the WeschlerAdult Intelligence Scale (WAIS). To account for the relevant characteristics of the job thatmaybe related tobothwages and job routinization, I control for occupation dummyvariables (the8occupationalcategoriesaredescribed inTable4).Notice thatonce I control for occupation, the unique variation used to identify the wage premium/penalty associated with job routinization is within-occupation variation. Further, I also control for size of firm dummy variables. Given this rich set of control variables (C′), it seems plausible to identify the effects of m and z by means of equation (12):

ln(w)= � + �z + �x + m(z,x)+C′� +u. (12) Finally, although I have a rich set of control variables that helps me to identify the

effects of z and m, regression (12) contains worker’s preferences (x), which may well be endogenously determined and thus may compromise the interpretation of my estimates: workers’ preferences are likely to be affected by their labour market experience. More specifically, an individual’s working experience on a particular job (tenure) is likely to affect his preferences for such a job. Although I do not have suitable data for assessing whetherworkers’preferenceschangeover time, I try toovercomethisshortcomingbycon- trolling for tenure: keeping tenure constant, the effect of preferencesonwages is obtained net of the effect of tenure on preferences. Hence, C′ will also include tenure.

2Borghans et al. (2006) show that the effect of people skills on wages (in the equilibrium assignment) can be decomposed into two effects: first, workers with more people skills earn more because they generate higher (net) revenue(productivityeffect);second,workerswithmorepeopleskillstakejobswherepeopletasksaremoreimportant and these jobs pay less, all else equal (compensating wage differential effect).

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V. Results Empirical findings

Tables 6 and 7 present the results on the effect of job routinization on wages for men and women, respectively. Column (1) in Table 6 shows that, on average, male workers in routinized jobsearn11%less thanmaleworkers innon-routinized jobs.Once theworker’s preference for routine work is accounted for, this penalty is reduced to 10% [see column (2)]. Column (3) shows that routinized jobs on average pay 7% less than non-routinized jobs when mismatch is controlled; on average, mismatched workers earn 4% less than well-matched workers. Hence, if mismatch is not accounted for, the negative effect of job routinization on wages is overestimated. Indeed, once mismatch is included as a new variable in the wage regression, I can explain a substantial portion of the incorrectly- signed estimate for job routinization. While columns (1) to (3) control for worker heterogeneity, they do not account for

job heterogeneity. In columns (4)–(6), I add both occupation and size of firm dummy variables into the previous specifications in an attempt to account for both kinds of heterogeneity. Notice that controlling for occupation is crucial to account for different skill requirements of the job. The results in columns (4)–(6), are qualitatively similar to thoseincolumns(1)–(3):maleworkers inroutinizedjobsearn5.5%less thantheircounter-

TABLE 6

Mismatch and compensating wage differentials. OLS estimates for men

(1) (2) (3) (4) (5) (6)

Job routinization −0.107 −0.095 −0.068 −0.053 −0.045 −0.023 (0.016) (0.016) (0.022) (0.016) (0.016) (0.021)

Routine-preferring worker – −0.073 −0.091 – −0.056 −0.071 (0.020) (0.022) (0.019) (0.022)

Mismatch – – −0.037 – – −0.031 (0.022) (0.021)

Completed years of education 0.049 0.049 0.048 0.033 0.033 0.033 (0.003) (0.004) (0.004) (0.005) (0.005) (0.005)

IQ measured at high school 0.003 0.003 0.003 0.003 0.003 0.003 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

High school rank 0.000 0.000 0.000 0.000 −0.000 −0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Adult cognition score 0.006 0.005 0.005 0.003 0.002 0.002 (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Tenure 0.008 0.008 0.008 0.007 0.007 0.007 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Occupation dummy variables? No No No Yes Yes Yes Firm size dummy variables? No No No Yes Yes Yes

R2 0.27 0.28 0.28 0.36 0.36 0.37 Adjusted R2 0.27 0.27 0.28 0.35 0.36 0.36 Number of observations 1,523 1,523 1,523 1,520 1,520 1,520

Notes: Dependent variable is log(hourly wage). Job routinization equals to 1 if fraction of weekly worked hours doing the same things over and over is equal to or higher than 0.5. Heteroskedasticity robust standard errors are reported in parentheses.

Compensating wage differentials for job routinization 221

TABLE 7

Mismatch and compensating wage differentials. OLS estimates for women

(1) (2) (3) (4) (5) (6)

Job routinization −0.100 −0.083 −0.034 −0.072 −0.064 −0.038 (0.018) (0.018) (0.021) (0.016) (0.016) (0.020)

Routine-preferring worker – −0.114 −0.157 – −0.076 −0.099 (0.018) (0.021) (0.017) (0.019)

Mismatch – – −0.071 – – −0.037 (0.021) (0.019)

Completed years of education 0.047 0.046 0.045 0.022 0.022 0.021 (0.006) (0.006) (0.006) (0.006) (0.006) (0.006)

IQ measured at high school 0.005 0.005 0.005 0.004 0.004 0.004 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

High school rank 0.000 −0.000 −0.000 0.000 0.000 0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Adult cognition score 0.004 0.004 0.004 0.001 0.001 0.001 (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Tenure 0.015 0.015 0.015 0.012 0.012 0.012 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Occupation dummy variables? No No No Yes Yes Yes Firm size dummy variables? No No No Yes Yes Yes

R2 0.31 0.32 0.32 0.44 0.45 0.45 Adjusted R2 0.30 0.32 0.32 0.44 0.44 0.44 Number of observations 1,621 1,621 1,621 1,612 1,612 1,612

parts innon-routinized jobs [seecolumn(4)].Thispenaltydecreases to4.5%once I adjust for differences in preferences [see column (5)]. Finally, once workers’ preferences and mismatch are accounted for, this difference is reduced to 2% [see column (6)]. Moreover, this is not statistically different from zero. Table 7 reports similar results for women. Accounting for differences in preferences

slightly decreases the job-routinization wage penalty, from 10% to 8% [columns (1) and (2)], or from 7% to 6.5% [columns (4) and (5)].Again, adding mismatch into the model seems tobe important: theeffectof job routinizationdecreases from8%to3.5%[columns (2) and (3)], or from 6.5% to 4% [columns (5) and (6)]. In none of the cases, the job routinization effect on wages is statistically significant once both preferences for routin- ization and mismatch are accounted for. Mismatched female workers earn 4% less than well-matched female workers. Overall, two features of the data stand out. First, mismatch is negatively related to

wages.ThisisconsistentwithbothmyassignmentmodelandBorghansetal.(2008):people aremostproductive in jobs thatmatchtheirstyle,andtheyearn lesswhentheyhavetoshift to other jobs. Indeed, I find a mismatch effect after accounting for worker type (worker’s preference for routine work), job type (job routinization), and other observable character- istics at the worker, occupation and firm levels. Second, once mismatch is accounted for, the coefficient on job routinization is attenuated.The evident mismatch effect can explain

222 Bulletin

a substantial portion (but not all) of the incorrectly-signed compensating differential for jobroutinization indicated inpreviousanalyses. Indeed, in themodelswithoccupationand size of firm dummy variables, the compensating differential for job routinization cannot be statistically distinguished from zero. In the next section, I perform several robustness checks to the use of alternative measures and the presence of outliers. Before presenting the results of my sensitivity analyses, it is important to discuss my results.

Discussion Myresultsshowthataccountingformismatchexplainsasubstantialportion(butnotall)of the incorrectly signedcompensatingdifferential for job routinization indicated inprevious analyses. The fact that job routinization has still a negative sign could be reflecting that workers in routine jobsare lessproductive thanworkers innon-routine jobs.However,we control fordifferentproxies for individualproductivity suchaseducationand IQ.Further- more, in the most complete empirical models, the coefficient on job routinization is not statistically different from zero. Regarding the estimated effect of mismatch on wages, it must be recognized that this

could be picking up two different kinds of effects. On the one hand, mismatch can have a negative effect on productivity due to the discrepancy between worker’s preferences for routine jobs and the variability in tasks associated with the job [Tinbergen (1975) sets a production function that depends on the extent to which a person’s abilities match those required in the execution of a job task]. On the other hand, mismatch may reflect unobserved worker’s ability: mismatched workers could be less productive to start with. Unfortunately, I cannot disentangle these two effects in my paper. Nonetheless, the fact that mismatch must be accounted for in wage equations is an important one. Future research could benefit from such a framework using new and better data that

may help to disentangle these two effects by using quasi-experimental variation in mis- match.For example, plant closingcouldbeusedasan instrument formismatch to identify theeffectofmismatchonwages for ‘workerswhohavebeendisplaced fromanon-routine job to a routine one by plant closing’.

VI. Robustness checks This section addresses some potential concerns about my previous estimates: the use of alternativemeasuresof job routinization, routine-preferringworker andmismatchand the sensitivity of OLS estimates to outliers.

Alternative measures

Thediscreteapproachtomeasuringjobroutinizationandworkers’preferencesisappealing because it is neat and clear cut. Unfortunately, it does not take full advantage of all the available informationcontained inmydata.Moreover, the thresholdsdefiningroutine jobs and routine-preferring workers are arbitrary. In this subsection, I start byexploiting thevariability inworkers’preferencesandmea-

sures of job routinization. Here, job routinization is measured as a continuous variable;

Compensating wage differentials for job routinization 223

workers’preferencesaremeasuredbyseveralbinary indicators;andmismatch ismeasured as it is in the rest of the paper. More specifically, the new job routinization variable is the fraction of working time doing the same things over and over on the job (as in Table 5). Workers’preferenceforroutineiscapturedbyseveralbinaryindicators:Routine-Preferring Worker 1 (equal to 1 for workers who disagree strongly or moderately with the statement ‘I see myself as someone who prefers work that is routine and simple’, zero otherwise); Routine-Preferring Worker 2 (equal to 1 for workers who agree slightly, neither agree nor disagree, or disagree slightly with the previous statement, zero otherwise); Routine- Preferring Worker 3 (equal to 1 for those workers who agree moderately or strongly with the previous statement, zero otherwise). Tables 8 and 9 present the new estimates using thesealternativemeasuresof jobroutinizationandworkers’preferences,wheretheomitted categoryisRoutine-PreferringWorker1.Thenewestimatesareverysimilar totheprevious ones: thenegativeassociationbetweenwagesand jobroutinizationdecreasesdramatically after accounting for worker’s preference and mismatch. The tables also reveal a negative association between mismatch and wages for both men and women: on average, both mismatched female and male workers earn 3% less than their well-matched counterparts. I also check the sensitivity of my estimates to the thresholds defining routine jobs and

routine-preferringworkers.Now, I classifya jobas routinized if the fractionof timedoing

TABLE 8

Mismatch and compensating wage differentials. OLS estimates for men. Alternative definitions

(1) (2) (3) (4) (5) (6)

Job routinization −0.144 −0.125 −0.086 −0.071 −0.058 −0.029 (0.021) (0.022) (0.027) (0.022) (0.022) (0.027)

Routine-preferring worker 2 – −0.052 −0.056 – −0.046 −0.049 (0.034) (0.034) (0.032) (0.032)

Routine-preferring worker 3 – −0.081 −0.104 – −0.061 −0.078 (0.022) (0.024) (0.021) (0.023)

Mismatch – – −0.046 – – −0.034 (0.020) (0.019)

Completed years of education 0.049 0.048 0.048 0.033 0.033 0.033 (0.004) (0.004) (0.004) (0.005) (0.005) (0.005)

IQ measured at high school 0.003 0.003 0.002 0.002 0.002 0.002 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

High school rank 0.000 0.000 0.000 0.000 −0.000 −0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Adult cognition score 0.006 0.004 0.004 0.003 0.002 0.002 (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Tenure 0.008 0.009 0.009 0.007 0.007 0.007 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Occupation dummy variables? No No No Yes Yes Yes Firm size dummy variables? No No No Yes Yes Yes

R2 0.27 0.28 0.28 0.36 0.36 0.36 Adjusted R2 0.27 0.28 0.28 0.35 0.35 0.36 Number of observations 1,523 1,523 1,523 1,520 1,520 1,520

Notes:Dependentvariable is log(hourlywage). Job routinization is the fractionofweeklyworkedhoursdoing the same things over and over. Heteroskedasticity robust standard errors are reported in parentheses.

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TABLE 9

Mismatch and compensating wage differentials. OLS estimates for women. Alternative definitions

(1) (2) (3) (4) (5) (6)

Job routinization −0.152 −0.126 −0.086 −0.106 −0.092 −0.068 (0.023) (0.023) (0.027) (0.022) (0.022) (0.025)

Routine-preferring worker 2 – −0.075 −0.086 – −0.047 −0.053 (0.030) (0.030) (0.028) (0.029)

Routine-preferring worker 3 – −0.110 −0.142 – −0.073 −0.092 (0.019) (0.022) (0.018) (0.020)

Mismatch – – −0.051 – – −0.029 (0.019) (0.018)

Completed years of education 0.045 0.045 0.044 0.021 0.022 0.021 (0.006) (0.006) (0.006) (0.006) (0.006) (0.006)

IQ measured at high school 0.005 0.005 0.005 0.004 0.004 0.004 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

High school rank −0.000 −0.000 −0.000 0.000 0.000 0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Adult cognition score 0.004 0.004 0.004 0.001 0.001 0.001 (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Tenure 0.015 0.015 0.015 0.012 0.012 0.012 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Occupation dummy variables? No No No Yes Yes Yes Firm size dummy variables? No No No Yes Yes Yes

R2 0.31 0.32 0.33 0.44 0.45 0.45 Adjusted R2 0.31 0.32 0.32 0.44 0.44 0.44 Number of observations 1,621 1,621 1,621 1,612 1,612 1,612

the same thingsover andover is above the third quartile on thedistributionof the fraction of time. And, a worker is classified as routine-preferring if his score on the preference for routine and simple work is above the third quartile on the distribution of preferences. The new mismatch measure is the absolute value of the difference between these new alternative measures. I provide new estimates with these alternative definitions for men and women inTable 10.The new estimates are very similar. Formen,column(1)showsthatworkers inroutinizedjobsonaverageearn7%less than

theircounterparts innon-routinizedjobs.Column(2)showsthataccountingfordifferences in preferences makes the wage penalty lower: almost 6%. Finally, adding mismatch into the model, column (3), decreases the wage penalty even further: 3%. Note too that being mismatched is associatedwith awagepenalty of 7%.Similar qualitative results are found for women in columns (4)–(6).

Sensitivity to outliers

The OLS estimates are known to be sensitive to outliers. In my analysis, I trimmed both the bottom 3% and the top 3% of the wage distribution in order to avoid the influence

Compensating wage differentials for job routinization 225

TABLE 10

Mismatch and compensating wage differentials. OLS estimates for men and women. Alternative thresholds defining routine jobs and routine-preferring workers

Men Women

(1) (2) (3) (4) (5) (6)

Job routinization −0.071 −0.056 −0.030 −0.089 −0.072 −0.054 (0.018) (0.018) (0.019) (0.017) (0.017) (0.018)

Routine-preferring worker – −0.086 −0.070 – −0.116 −0.114 (0.019) (0.020) (0.018) (0.018)

Mismatch – – −0.067 – – −0.038 (0.019) (0.017)

Completed years of education 0.050 0.050 0.049 0.050 0.048 0.048 (0.004) (0.004) (0.004) (0.005) (0.005) (0.005)

IQ measured at high school 0.003 0.003 0.003 0.006 0.005 0.005 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

High school rank 0.000 0.000 0.000 0.000 −0.000 −0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Adult cognition score 0.006 0.005 0.005 0.004 0.004 0.004 (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)

Tenure 0.008 0.008 0.008 0.015 0.015 0.015 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

R2 0.26 0.27 0.27 0.30 0.32 0.32 Number of observations 1,523 1,523 1,523 1,621 1,621 1,621

Notes: The dependent variable is log(hourly wage). Job routinization equals to 1 if fraction of weekly worked hours doing the same things over and over is above the third quartile on the distribution of time. Routine-preferring worker equals to 1 if his score on the preference for routine and simple work is above the third quartile on the distribution of preferences. Heteroskedasticity robust standard errors are reported in parentheses.

of extreme values. Here, I go one step further and perform a median Quantile regression analysis tomake sure thatmypreviousOLSestimates arenot drivenbyextremevaluesof the wage distribution. The new (median) estimates reported in Tables 11 and 12 are robust to outliers and

very similar to my previous OLS estimates. In Table 11, column (1) shows that, at the median, male workers in routinized jobs earn 11% less than male workers in non- routinized jobs. Once the worker’s preference for routine work is accounted for, this penalty is reducedto9%[column(2)].Column(3)showsthat routinizedjobsat themedian pay 5% less than non-routinized jobs when mismatch is controlled. Mismatched work- ers earn 6% less than well-matched workers. Table 12 shows similar results for women, columns (1)–(3). To sum up, my results appear to be robust. Moreover, the rich set of covariates I

consider in the WLS (education, IQ at high school, high school rank, cognition score, preferences, tenure, occupation type and size of firm) helps me to control to some extent forbothworkers’and job’sheterogeneity.Nonetheless, it shouldbenoted that theabsence of comparable longitudinal information on job routinization and workers’preferences as wellas theabsenceofanyvalid instrumentspreventsmefromarguingthat theassociations I document are causal.

226 Bulletin

TABLE 11

Mismatch and compensating wage differentials. Quantile median estimates for men

(1) (2) (3) (4) (5) (6)

Job routinization −0.108 −0.088 −0.049 −0.044 −0.037 −0.016 (0.020) (0.021) (0.029) (0.021) (0.020) (0.027)

Routine-preferring worker – −0.074 −0.107 – −0.068 −0.087 (0.025) (0.030) (0.020) (0.026)

Mismatch – – −0.060 – – −0.029 (0.030) (0.025)

Completed years of education 0.053 0.052 0.052 0.034 0.034 0.035 (0.005) (0.005) (0.005) (0.005) (0.005) (0.005)

IQ measured at high school 0.003 0.003 0.002 0.003 0.003 0.003 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

High school rank 0.000 0.000 0.000 −0.000 −0.000 −0.000 (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Adult cognition score 0.007 0.005 0.006 0.001 0.003 0.003 (0.004) (0.004) (0.004) (0.004) (0.004) (0.003)

Tenure 0.008 0.008 0.008 0.006 0.006 0.006 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Occupation dummy variables? No No No Yes Yes Yes Firm size dummy variables? No No No Yes Yes Yes

Pseudo R2 0.16 0.17 0.17 0.22 0.22 0.22 Number of observations 1,523 1,523 1,523 1,520 1,520 1,520

Notes: Dependent variable is log(hourly wage). Job routinization equals to 1 if fraction of weekly worked hours doing the same things over and over is equal to or higher than 0.5. Bootstrapped standard errors (1,000 replications) are reported in parentheses.

Compensating wage differentials for job routinization 227

TABLE 12

Mismatch and compensating wage differentials. Quantile median estimates for women

(1) (2) (3) (4) (5) (6)

Job routinization −0.100 −0.089 −0.056 −0.070 −0.069 −0.037 (0.024) (0.022) (0.027) (0.022) (0.021) (0.024)

Routine-preferring worker – −0.134 −0.157 – −0.074 −0.111 (0.021) (0.027) (0.022) (0.025)

Mismatch – – −0.060 – – −0.052 (0.026) (0.022)

Completed years of education 0.064 0.063 0.063 0.032 0.032 0.030 (0.007) (0.007) (0.007) (0.007) (0.007) (0.007)

IQ measured at high school 0.006 0.005 0.006 0.004 0.004 0.004 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

High school rank 0.000 0.000 0.000 −0.000 −0.000 −0.000 (0.001) (0.001) (0.000) (0.001) (0.001) (0.001)

Adult cognition score 0.001 −0.003 −0.002 −0.001 −0.003 −0.002 (0.005) (0.006) (0.006) (0.004) (0.004) (0.004)

Tenure 0.016 0.017 0.017 0.013 0.014 0.014 (0.001) (0.001) (0.001) (0.001) (0.001) (0.001)

Occupation dummy variables? No No No Yes Yes Yes Firm size dummy variables? No No No Yes Yes Yes

Pseudo R2 0.20 0.21 0.21 0.30 0.30 0.30 Number of observations 1,621 1,621 1,621 1,612 1,612 1,612

VII. Conclusions In this paper, my goal has been to argue that previous estimates of compensating wage differentials are inconclusive because they do not account for the discrepancy between workers’preferences and job attributes. Both casual empiricism and research results sug- gest that this discrepancy indeed exists. In my sample, 38% of the men and 47% of the women appear to be mismatched. I propose a simple assignment model with Nash bargaining over wages for analysing

the role of mismatch when looking for compensating wage differentials. Assuming that observedworkersarenot in long-runmarketequilibrium,allworkers,nomatterwhat their preferences are, need to be compensated if working in the sector with a shortage of work- ers in the absence of pay differentials. However, only mismatched workers, who are less productivebecause their sectorsdonotmatch theirpreferences,arepenalized. Ifmismatch isnot accounted, then theassociationbetweenwagesand jobattributesmaybepickingup the correlation between job attributes, preferences and mismatch. My empirical analysis uses the WLS and focuses on job routinization (the fraction of

working time spent doing the same things over and over). I report several findings. First, mismatch is negatively related to wages, which is consistent with the negative mismatch productivityeffectdominating thepositivecompensatingwagedifferential effect.Second, for both men and women, I find that the negative relationship between wages and job

228 Bulletin

routinization is attenuatedoncemismatchandworkers’preferencesareaccounted for.The evident mismatch effect can explain a substantial portion (but not all) of the incorrectly- signed compensating wage differential for job routinization that previous analyses have indicated. In my view, this paper highlights the importance of accounting for mismatch when

looking for compensating wage differentials. Clearly, much more work needs to be done on the theoretical front, for instance,byendogenizingmismatch.Nevertheless, I anticipate that as long as there are search frictions that ensure that some workers remain in jobs that arenotoptimalgiventheexistingwagerates, theresultsof theassignmentmodelpresented here will generalize to a market setting. Given the substantial mismatch I find in the data, these sorts of frictions seem realistic.

Final Manuscript Received: July 2010

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