Not Working at Work: Loafing, Unemployment and Labor Productivity

Transcription

1 DISCUSSION PAPER SERIES IZA DP No Not Workng at Work: Loafng, Unemployment and Labor Productvty Mchael C. Burda Kate R. Genadek Danel S. Hamermesh June 205 Forschungsnsttut zur Zukunft der Arbet Insttute for the Study of Labor

2 Not Workng at Work: Loafng, Unemployment and Labor Productvty Mchael C. Burda Humboldt Unversty Berln, CEPR and IZA Kate R. Genadek Unversty of Mnnesota Danel S. Hamermesh Royal Holloway Unversty of London, Unversty of Texas at Austn, IZA and NBER Dscusson Paper No June 205 IZA P.O. Box Bonn Germany Phone: Fax: E-mal: Any opnons expressed here are those of the author(s) and not those of IZA. Research publshed n ths seres may nclude vews on polcy, but the nsttute tself takes no nsttutonal polcy postons. The IZA research network s commtted to the IZA Gudng Prncples of Research Integrty. The Insttute for the Study of Labor (IZA) n Bonn s a local and vrtual nternatonal research center and a place of communcaton between scence, poltcs and busness. IZA s an ndependent nonproft organzaton supported by Deutsche Post Foundaton. The center s assocated wth the Unversty of Bonn and offers a stmulatng research envronment through ts nternatonal network, workshops and conferences, data servce, project support, research vsts and doctoral program. IZA engages n () orgnal and nternatonally compettve research n all felds of labor economcs, () development of polcy concepts, and () dssemnaton of research results and concepts to the nterested publc. IZA Dscusson Papers often represent prelmnary work and are crculated to encourage dscusson. Ctaton of such a paper should account for ts provsonal character. A revsed verson may be avalable drectly from the author.

3 IZA Dscusson Paper No June 205 ABSTRACT Not Workng at Work: Loafng, Unemployment and Labor Productvty * Usng the Amercan Tme Use Survey (ATUS) , we estmate tme spent by workers n non-work whle on the job. Non-work tme s substantal and vares postvely wth the local unemployment rate. Whle the average tme spent by workers n non-work condtonal on any postve non-work rses wth the unemployment rate, the fracton of workers who report tme n non-work vares pro-cyclcally, declnng n recessons. These results are consstent wth a model n whch heterogeneous workers are pad effcency wages to refran from loafng on the job. That model correctly predcts relatonshps of the ncdence and condtonal amounts of non-work wth wage rates and measures of unemployment benefts n state data lnked to the ATUS, and t s consstent wth observed occupatonal dfferences n non-work. JEL Classfcaton: J22, E24 Keywords: tme use, non-work, loafng, shrkng, effcency wage, labor productvty Correspondng author: Danel S. Hamermesh Royal Holloway Unversty of London Department of Economcs 24 Horton Buldng Egham, Surrey TW20 0EX Unted Kngdom E-mal: Danel.Hamermesh@rhul.ac.uk * We thank Peter Egger, Albrecht Gltz, Mathas Hoffmann, Chrstan Merkl, Slva Sonderegger, Jeff Woods and partcpants n several semnars as well as Matthew Notowdgdo for provdng the fles on states unemployment nsurance programs. Hamermesh thanks the Humboldt Foundaton for fnancal support.

4 I. Introducton The relatonshp between labor-market slack and worker effort s a hoary topc n macroeconomcs and labor economcs. The noton of labor hoardng retanng workers durng tmes of low product demand even though ther labor nput s reduced goes back at least 50 years and has been adduced as an explanaton for pro-cyclcal changes n labor productvty productvty fallng as unemployment rses. (See Bddle, 204, for a thought-hstorcal dscusson of ths concept.) The noton that unemployment provdes workers an ncentve to expend extra effort to avod frng the dea of effcency wages was descrbed formally n the now-classc study by Shapro and Stgltz (984). More generally t perhaps even goes back to the reserve army of the unemployed mpled by Marx (867) n Chapter 23 of Das Kaptal. It mples counter-cyclcal changes that labor productvty and effort rse wth as unemployment rses. Both of these strands n economc thought descrbe the relatonshp between unemployment n a labor market and worker effort (and presumably labor productvty). Yet ther mplcatons are contradctory. A large emprcal lterature has nferred from lags n employment adjustment behnd shocks to output that labor hoardng s mportant (Hamermesh, 993, Chapter 7). A much smaller lterature has used the theory of effcency wages to examne how wages respond to workers opportuntes (e.g., Cappell and Chauvn, 99). No study to date has examned drectly how effort at work responds to dfferences or changes n unemployment. 2 The reason s smple: Untl very recently no large-scale data set has been avalable detalng what workers do on the job and provdng such nformaton as unemployment vares. Ths paper asks and answers the followng questons: ) On what do Amercan workers spend tme on the job when they are not workng? 2) Does non-work vary sgnfcantly across demographc groups? 3) Most mportant, how does non-work vary wth local labor market condtons, as measured by the unemployment rate? 4) Is there a model whch can account consstently for any regulartes that we A recent socologcal study, Paulsen (205), presents cases llustratng the role and reasons for people loafng on the job. Pencavel (204) and Lazear et al (205) analyze changes n effort and productvty n sngle frms. 2 Hamermesh (990) dd, however, examne cross-sectonal dfferences n the allocaton of tme on the job. 2

5 observe? 5) What are the mplcatons of these fndngs for aggregate labor productvty and macroeconomc behavor generally? II. Data and Descrptve Statstcs Snce 2003, the Amercan Tme Use Survey (ATUS) has generated tme dares of large monthly samples of ndvduals showng what they are dong and where they are located. (See Hamermesh et al, 2005, for a descrpton of these data, and Aguar et al (203) for use of them to examne some cyclcal aspects of tme use.) It thus allows the frst examnaton of how workers spend tme on the job, ts relatonshp to ther demographc and job characterstcs, and ts varaton wth dfferences and changes n unemployment. Throughout ths study we use varous sub-samples from the ATUS, whch over the perod collected 36,960 monthly dares of former Current Populaton Survey (CPS) respondents actvtes on one partcular day between two to fve months after ther fnal rotaton n the CPS. Because we are concentratng on actvtes whle the respondent was at work, the only dares ncluded are those for days when a respondent reported some tme at the workplace. Snce half the dary days n the ATUS are on weekends when relatvely few respondents are workng, ths restrcton cuts the sample greatly, leavng us wth 4, usable dares. Moreover, snce our focus s on employee productvty, for most of ths study we exclude the self-employed (most of the remanng excluded observatons) and those dares wthout nformaton on usual weekly hours of work, whch reduces the sample to 35,548 usable observatons. Thus for a typcal month n the sample perod after 2003 we have around 250 observatons. 3 Obtanng responses about what the respondent was dong at each moment of the dary day, the ATUS then codes them nto over 400 dstnct actvtes. Respondents also note where they were whle performng each actvty, wth one of the possble locatons beng at the workplace. We focus on prmary actvtes performed at that locaton, defnng total tme at work as all tme spent at the workplace. We then dvde tme at the workplace nto tme spent workng and tme spent not workng. 4 The latter s dvded nto tme spent eatng, at lesure and exercsng, cleanng, and n other non-work 3 The ATUS collected more dares n ts frst year, generatng about 450 usable dares each month n Tme spent workng ncludes tme spent n Work Related Actvtes or ATUS codes Work-related actvtes nclude socalzng and eatng as a part of the job. 3

6 actvtes. 5 In the ATUS, eatng at work can be a prmary actvty, a secondary actvty to workng or a part of the job. Non-work tme at work whch s spent eatng corresponds to a response of eatng as a prmary actvty at the workplace. Non-work tme at work also ncludes actvtes that mght be vewed as nvestment n future productvty but that are not currently productve, such as cleanng and perhaps exercsng, as well as others such as gosspng, web-surfng and chattng that are less lkely to be productve. Table presents sample means and ther standard errors of the proportons of tme spent at the workplace n these four actvtes and n actual work, along wth the tme spent at work and other varables that are central to our analyss. All the statstcs are calculated usng the ATUS samplng weghts, thus accountng for dsproportonate samplng across days of the week, for standard CPS weghtng and for dfferental non-response to the ATUS by former CPS partcpants. The frst thng to note s that the typcal day at work lasts about eght hours and twenty mnutes, a statstc that yelds a fve-day workweek of 4.74 hours, whch s consstent wth the mean usual weekly hours of 4.38 hours reported retrospectvely by employees n the sample. 6 Sample respondents report spendng nearly seven percent of tme at the workplace not workng, amountng to thrty-four mnutes per day. Roughly half of ths tme s spent eatng at the workplace, the other half s spent n lesure, exercse, cleanng and other non-work actvtes. These three latter actvtes are so rare that henceforth we concentrate on the twofold dvson between eatng and non-work noneatng tme at work. Whle thrty-four mnutes per day at the workplace not workng seems low, most eatng reported durng the work day as a prmary actvty probably occurs away from the workplace and thus s not specfcally assgnable to the job n these data. To the extent that eatng away from the 5 In the orgnal data, non-work tme on the job s dvded nto the followng broad prmary actvtes: Personal care; household producton; care-gvng; educatonal actvtes; shoppng; servces; eatng, lesure, exercsng and sport, and volunteerng and relgous actvtes. Several of these are observed so nfrequently as to prevent them from beng analyzed separately, so that we combne them nto the ffth (other) category of non-work tme on the job. Household producton s not consdered an act at work. 6 Ths near-equalty dffers from the result n the lterature that recall weekly hours exceed dary hours (Juster and Stafford, 99; Frazs and Stewart, 2004). The dfference may arse because we restrct the workday to the tme respondents spend at the workplace n any actvty. 4

7 workplace durng work hours vares cyclcally, t wll be reflected n cyclcal varatons n the length of the day at the workplace. As Fgure shows, there are a substantal number of zeros n the responses, 33.7 percent of the sample, and much of our subsequent analyss focuses on ths fact. The condtonal mean amount of postve non-work tme s slghtly over 50 mnutes per day. Beyond that, the dstrbuton s skewed to the rght, wth a tny fracton of respondents even reportng not workng the entre tme on the job percent of the respondents reported eatng at the workplace but no other non-work tme, 4.6 percent reported other non-work but no eatng on the job, and 20.9 percent reported both eatng and other nonwork tme on the job. Throughout ths study, the central forcng varable s the local unemployment rate, measured as the jobless rate n the state where the worker resdes. 8 The average unemployment rate n the sample s 6.6 percent, but t vares over a wde range from barely two to over fourteen percent. Mostly because of the Great Recesson, there s substantal varaton n unemployment, whch allows us to examne how non-work responds to changng local labor-market condtons. III. Non-work and ts Relatonshp wth Unemployment over Tme and Space Before presentng evdence on the cyclcal behavor of non-work tme at work, t s mportant to remember that economc theory s ambguous about the sgn of the relatonshp between non-work and busness cycle condtons proxed by, say, the local unemployment rate. Ths s because workers and ther employers have dfferent nterests n non-work. If ntated by the worker, non-work mght be nterpreted as loafng, shrkng or goofng off on the job. A raft of theores predcts a negatve relatonshp between local labor market condtons and shrkng. The most promnent of these are Calvo (98), Akerlof (982), Shapro and Stgltz (984) and Bowles (985). In ths ven, hgh unemployment sgnals a lower value of utlty n the state of unemployment, ether because the ncdence or duraton (or both) of 7 We cannot exclude the.3 percent of respondents who reported loafng all day from the basc results, snce that would nvolve truncatng on the dependent varable. In unreported estmates, however, none of the conclusons changes qualtatvely f ths group s excluded. 8 Experments wth the one-month unemployment rate consstently yelded weaker fts, so we lmt the reported results to those based on the three-month average. 5

8 joblessness s hgh. To avod landng n the state, workers exert hgher effort when employed, n order to curry favor wth ther employers, to ncrease ther productvty, or to reduce the probablty of detecton when they do shrk. Because effort s unobservable and/or montorng s costly, frms accept ths outcome passvely, wth few or no layoffs of shrkers occurrng n equlbrum. Alternatvely, frms may fnd non-work by workers n certan states of the world to be desrable. Frms face varable and mperfectly forecastable demand for ther products, whle producng wth workers who represent substantal nvestments n human captal, search effort and other consderable resources. In an economc downturn whch s perceved as temporary, t s easy to show that a layoff, even f temporary, s an nferor choce to mantanng employment, possbly even at standard hours. 9 Ths behavor s often referred to as labor hoardng. In ths case, frms assgn workers to unproductve tasks such as cleanng, mantenance, pantng, etc. or even tolerate more non-work ntated by ther workers In Table 2 we present evdence on the cyclcal behavor of non-work n the Unted States based on the ATUS. Ths cyclcal behavor s measured by the response of non-work tme at the workplace to varatons n local labor-market condtons. We assume that workers take those condtons as gven, and we note that they vary across both tme and space. 0 In the data most of the varaton n unemployment s across tme: Temporal movements account for two-thrds of the varance. Only twelve percent of the varance n unemployment rates s dosyncratc at the state and month level. The ntal least-squares estmates, the results shown n Column (), smply relate the proporton of non-work tme at work to the state unemployment rate. There s a hghly sgnfcant postve 9 Burda and Hunt (20) showed that whle frms n Germany retaned workers durng the Great Recesson, hours worked declned by less than would be expected gven the declne n output, so that hourly productvty fell n a recesson for the frst tme snce 970. In the U.S. Gordon (98) documented a declne n labor productvty n every postwar recesson up to that pont n tme; Galí and van Rens (204) fnd that the correlaton between the busness cycle and productvty snce 990 has become negatve. 0 We cannot rule out that workers mght self-select va mgraton, effectvely choosng regons n whch unemployment s lower and thus affectng the condtons under whch they work. The same argument apples to employers and captal moblty. Ths possblty would bas the estmated mpact of unemployment toward zero. All the results n ths secton reman qualtatvely dentcal f we use mnutes of the varous types of non-work tme rather than ther proportons of the workday as the dependent varables. Smlarly, usng more flexble representatons of usual weekly hours and tme spent at work does not alter the results, nor does deletng the quadratc n tme at work from the estmates n Columns (2) and (3) change the central conclusons. 6

9 assocaton of unemployment wth non-work tme on the job. Over the entre range of unemployment observed n the data, the estmate suggests that the proporton of non-work tme wll vary by 0.03 (on a mean of 0.069). The estmates n Column () fal to account for the possble co-varaton of tme spent n nonwork wth the amount of work performed and wth workers demographc characterstcs. The equaton underlyng the estmates n Column (2) ncludes quadratc terms n usual weekly hours and tme at work on the dary day; ndcators of race and ethncty; a vector of ndcators of educatonal attanment; a quadratc n potental experence (age educaton 6), ndcators of gender and martal status and ther nteracton, and an ndcator of metropoltan resdence. 2 A longer usual workweek sgnfcantly ncreases the fracton of work tme not workng up to 42 usual weekly hours, wth decreases thereafter. Condtonal on usual hours, however, spendng more tme at work n a day decreases the proporton of tme spent n non-work actvtes, but only up to 5.8 hours of work tme per day. Beyond that, and thus for 85 percent of the sample, addtonal tme on the job ncreases the share of tme spent not workng. Whether because of boredom, fatgue or somethng else, the margnal effect of addtonal work tme on non-work actvtes s ncreasng for most employees as the workday lengthens. 3 The estmates on the ndcators of gender and martal status, and ther nteracton, are small and ndvdually and jontly nsgnfcant here and n all subsequent estmates, so we do not report them n later tables. Afrcan-Amercans and Hspancs, however, report spendng hgher fractons of ther tme at work not workng than do other workers wth otherwse dentcal demographc characterstcs and wth workdays and workweeks of the same length. Not only are these effects hghly sgnfcant statstcally, they are also large, mplyng a proporton of non-work tme 5 to 20 percent above the average. 2 In the Table we only report those estmates that are economcally nterestng. One should also note that, unsurprsngly, the estmates vary neglgbly f we use a quadratc n age nstead of potental experence. The estmates usng the latter do, however, descrbe varatons n tme not workng slghtly better than do those ncludng age. 3 Ths fndng s consstent wth older evdence from the scentfc management lterature chartng workers productvty over the work day (Florence, 958). 7

10 Addtonal schoolng attanment monotoncally decreases the reported proporton of work tme spent not workng. Here too the dfferences are substantal, wth the proporton among employees wth graduate degrees beng about 30 percent below that of hgh-school dropouts. Whle the estmated mpact of unemployment does change wth the addton of these covarates, ther unsurprsngly very weak correlaton wth state unemployment rates guarantees that ther ncluson does not qualtatvely alter the estmated effect of unemployment on non-work tme. 4 The nference may understate the magntude of ths effect: As unemployment rses, even holdng demographc characterstcs constant, workers who retan ther jobs may be those who report less non-work at work, creatng a compostonal effect that negatvely bases the estmated mpact of unemployment on non-work tme. In Column (3) we add vectors of fxed effects for occupaton, ndustry, state and month to the estmatng equaton n Column (2). Each of the four vectors of ndcators s jontly statstcally sgnfcant: There are substantal dfferences across occupatons, ndustres and states n the (condtonal) proporton of tme at work spent not workng. Even wth these addtons, however, over half of the estmated postve effect of unemployment on tme not worked remans. 5 These estmates have aggregated all non-work tme at work; yet one mght expect dfferent responses to changng unemployment of the partly bologcal actvty, eatng at work, and the broader category, other non-work tme on the job. We thus re-estmate the basc model, frst usng the proporton of tme at work spent eatng as the dependent varable, then usng the proporton of tme at work spent n other non-work tme. In each case we frst nclude the vectors of work tme and demographc measures that were added to the estmates shown n Column (2), then add the same four vectors of fxed effects ncluded n the estmates shown n Column (3). 4 The covarates ncluded n Column (2) descrbe 0.92 percent of the varaton n state unemployment rates over tme. 5 Almost the entre drop n the estmate arses from the ncluson of state fxed effects. Re-estmatng the model excludng state effects, the estmated mpact of unemployment s essentally unchanged from that n Column (2). 8

11 The results, presented n Columns (4)-(7) of Table 2, are strkng. The overwhelmng majorty of the effect of changng unemployment on non-work tme at work operates through ts mpact on other nonwork tme.e., on lesure on the job. Eatng at the workplace s affected much less. 6 Moreover, the effects of dfferences n workers demographc characterstcs on non-work tme also operate manly through other non-work tme, not through eatng at work. As Table and Fgure showed, there are many zeros n these data. That fact mght suggest estmatng these models usng tobt, but that s problematc for two reasons: ) There s no reason to assume that the mpacts of unemployment (or of any of the other regressors ncluded n Table 2) on the probablty of non-work and ts condtonal mean work n the same drecton. That dffculty suggests usng a more free-form technque, ether the all-n-one approach suggested by Cragg (97), or separate treatment of the probablty of non-work and ts mean condtonal on ts occurrence; 2) The zeros may result partly from the lmtaton of the dares to a sngle day (Stewart, 203), who argues that estmatng a probt on the ncdence of non-work and a regresson on the amount of non-work among those non-zero observatons crcumvents ths dffculty. Snce that approach handles both problems, we follow t here. Table 3 presents the probt dervatves of the varables mpacts on the probablty of non-work on the job, and regresson coeffcents descrbng ther effects on the amount of non-work for the two-thrds of the sample respondents who report postve non-work. The ndependent varables are the same as those ncluded n the regressons n Table 2. The probt and the condtonal regresson results both exclude and nclude vectors of fxed effects descrbng occupatons, ndustres, states and months of the year. The dfferences between these results and those n Columns (2) and (3) from the uncondtonal regressons are remarkable. Whle hgher unemployment s postvely assocated wth the proporton of tme at work reported non-workng, t reduces the probablty that a worker spends any tme not workng. Ths 6 One mght be concerned that employees change the amount of non-work mult-taskng that they do as unemployment changes. The ATUS does not provde nformaton on secondary actvtes n most months; but for 2006 and 2007, as part of the Eatng and Health Module, t collected nformaton on secondary eatng, ncludng at work. Of the employees n our sample n those years, 4 percent report some secondary eatng and/or drnkng at work. Among those who do, the average amount of tme spent n these secondary actvtes s almost exactly two hours per day. Although ths actvty s mportant, re-estmates of the models n Table 2 show that varatons n secondary eatng are ndependent of dfferences n unemployment rates across states and over these two years. 9

12 reducton s more than offset by the ncreased proporton of tme not workng by those who state that they spent some tme not workng as the unemployment rate rses. Unlke n the uncondtonal regressons, the negatve mpact on the probablty of not-workng and the postve mpact on the condtonal mean are robust to the ncluson of all the vectors of fxed effects. Moreover, the effects are economcally mportant: Over the range of unemployment n the sample, the probablty of not workng falls by 0.06 on a mean of 0.337, whle the fracton of tme not workng rses by on a condtonal mean of Our results also show that the demographc dfferences observed n Table 2 stem mostly from demographc dfferences n the probablty of reportng non-work tme as opposed to the amount of tme spent by those reportng non-work. That s especally the case for dfferences n educatonal attanment; but even the racal/ethnc effects, whle sgnfcant on both the probablty and the condtonal mean, are much stronger on the former. 8 One possble explanaton for the dfferences between mnorty and majorty workers s that relatve (to the dscrmnatory market wage) the reservaton wages of mnortes exceed those of majorty workers (as shown by Holzer, 986). Ths would reduce the ncentve for mnortes to avod shrkng and lead to the observed effect of mnorty status showng up chefly on the extensve margn of shrkng. Alternatvely, legal protectons for mnortes mght reduce the rsk of frng when a mnorty shrks, thus ncreasng the ncentves for shrkng. The results dsplayed n Table 2 showed that the postve effect of hgher unemployment on tme spent not workng was manly on other non-work tme rather than on tme spent eatng at work. Table 4 presents estmates of effects on the probabltes of eatng at work and engagng n other non-work, and on ther condtonal means. In all cases we present only the specfcatons expanded to nclude all the vectors 7 If we delete the roughly /6 of the respondents who are publc employees, the coeffcents on the unemployment rate n the regressons n column (3) of Table 2 and column (4) of Table 3 rse substantally n absolute value, as one mght expect. The probt dervatve n column (2) of Table 3 rses too, but only slghtly. 8 Do these effects arse from a greater wllngness of mnortes and the less educated to report non-work tme on the job? We cannot be sure, of course; but Afrcan-Amercan (Hspanc) workers report 0.52 (0.00) fewer dfferent actvtes per day than otherwse dentcal workers, on a mean of 9 actvtes. Those wth graduate degrees (college degrees) report.76 (.44) more actvtes per day than hgh-school dropouts. In short, the lkelhood of reportng an actvty s opposte the mpact of these demographcs on the propensty to report on-the-job loafng. 0

13 of fxed effects. The negatve mpacts of unemployment on the probabltes of eatng at work and engagng n other non-work are essentally dentcal. The dfferences observed n Table 2 result from the greater responsveness of other non-work tme to dfferences n unemployment among those who report other non-work than from the responses of tme spent eatng: The mpact on other non-work tme s three tmes as large as that on tme spent eatng at work. 9 To summarze our central fndng, hgher unemployment s robustly assocated wth a greater fracton of tme at work spent not workng, wth most of the effect comng from greater tme at work n lesure, cleanng up, etc. Snce hgher unemployment, other thngs equal, s also assocated wth less tme at work (n ths sample each extra percentage pont of unemployment reduces the workday by 2.4 mnutes per day), ths fndng suggests that employers are allowng workers to spend smaller fractons of the workday actually workng. An mportant and surprsng par of subsdary fndngs s that hgher unemployment reduces the lkelhood of non-work, whle ncreasng the condtonal amount of non-work suffcently to generate the net postve relatonshp between unemployment and non-work on the job. IV. A Suggestve Model of Non-Work (Loafng) Our results mply contradctory and offsettng motves for non-work on the job over the busness cycle or at dfferent states of the labor market. Workers engage n non-work less frequently n bad tmes (when the rate of unemployment s hgher), but gven that they do so, they tend to do more of t. An effcency wage model wth heterogeneous preferences s consstent wth these apparently contradctory fndngs. A. Prelmnares We envson an envronment n whch workers effort cannot be montored perfectly, but ther aggregate productvty s an observable outcome of the state of the busness cycle or the local labor market as well as of the fracton of ther tme spent n non-work actvtes. In our model, workers are 9 The demographc effects are generally n the same drectons on both the ncdence and ntensty of the two types of non-work, but there are some changes from the overall patterns observed n Table 3. That s especally true for the racal/ethnc ndcators. Afrcan-Amercans greater amounts of both types of non-work arse entrely from ther greater lkelhood of eatng at work and dong other non-work; among Hspancs the effect arses from ther greater lkelhood of eatng at work and a hgher condtonal mean amount of other non-work n whch they engage.

14 heterogeneous and, n the sprt of Shapro and Stgltz (984), can choose to spend a fracton of workng tme n non-work. Indvdual workers are rsk-neutral and receve utlty from consumpton goods purchased wth ther wages, as well as from lesure on the job (non-work). Each worker s endowed wth one unt of tme and, f employed, receves a wage w plus the monetary equvalent of tme spent n lesure on the job (non-work or loafng ), denoted as l. Workers are ndexed by [0,] n ncreasng order of l, so > j mples l > l j for all and j. Wthout loss of generalty, the ndex could represent the percentle of the worker n the dstrbuton of preferred loafng tmes; n our example, the ndex s the name of the ndvdual worker n queston and equals the preferred loafng tme (as a fracton of total avalable labor effort). If undetected, worker s assumed to prefer enjoyng ths fxed amount of non-work l and exertng work effort e =- l to exertng full effort e =. The worker retans her valuaton of non-work l for the duraton of the job. In each perod, a worker chooses between loafng and recevng ncome equvalent w + l, or not loafng at all and recevng w. Wth exogenous probablty θ management montors workers; f they are found loafng, they are fred. Employment relatonshps also end exogenously wth probablty δ. Unemployed workers receve ncome equvalent n value to b and are ndstngushable from other workers on the bass of employment or non-work hstory. They fnd jobs at rate f, whch, gven the stock of employment, a separaton rate, and an exogenous labor force, s determned endogenously by a steadystate condton to be descrbed below. B. To Loaf or Not to Loaf: That s the Queston For an arbtrary worker (0,) earnng wage w, t s straghtforward to compute steady-state valuatons of the three possble labor-force states/strateges: Employment wthout any non-work (V N ), employment wth non-work (V S ), and unemployment (V U ): V N w δ = + V + r + r U δ + V + r N, () V S = w + + r θ + δ + V + r U θ δ + V + r S, (2) 2

15 V U = b + r f + V + r E + + f r V U, (3) where V E = E max V S, V N { } { 0, } (4) represents the expected value of employment from the perspectve of an unemployed person who does not know her future value of l j, but knows that she wll choose the strategy that maxmzes expected utlty gong forward. Gven ths set of behavoral assumptons, each worker s characterzed by a no-loafng wage. If pad above ths wage, the worker s valuaton of not loafng at all domnates that of loafng: V N V S. (5) As n Shapro and Stgltz (984), ths no-loafng condton (NLC), defnes the cutoff or mnmal N threshold wage w at whch worker s ndfferent between loafng and not loafng,.e., V = V Appendx A the NLC wage for worker s shown to be: S. In w E E = b + ( r + δ + f ) + ( r + δ ) (6) θ θ The NLC wage depends postvely on ncome n unemployment b, the nterest rate r, exogenous job turnover δ, the outflow rate from unemployment f, and the worker s expected valuaton of loafng E as well as the devaton of her current value from the mean, E. It depends negatvely on θ, the probablty of detecton. 3

16 C. Aggregate Loafng and the Steady-State Flow Equlbrum The NLC wage represents a threshold above whch a worker wll not loaf at all. Invertng (6) yelds the dentty of the margnal loafer for whom preferred amount and not loafng at all: w = w,.e., who s ndfferent between loafng her ( w b) θ fe =. (7) r + δ At wage w, all workers for whom w > w (or, equvalently, > ) wll spend tme n non-work on the job. Let g( ) be the densty of workers on the support of preferred loafng tme [0,] and G be the assocated c.d.f. Usng (7), we can derve the followng, all contngent on the common wage w: ) The fracton of workers loafng, γ, gven by the mass of workers whose NLC wage exceeds w: γ = g( ) d = G( ). ( w) = g( ) d 2) The aggregate volume of loafng, (w), : e( w) = g( ) d 3) Aggregate effort, e(w): 4) The condtonal mean amount of loafng for those loafng, φ : E[ > 0] g( ) d φ = =. G( ) In the steady state, the outflow rate f, expressed as a fracton of the unemployed, endogenously equates gross outflows and nflows nto unemployment. Outflows are the product of f and ( L L), where L and L are the exogenous labor force and employment respectvely. The mass of workers who flow nto unemployment equals that of workers who lose ther jobs through exogenous separaton (δl) plus those who are montored and were loafng (for whom w > w ), wth mass of θγl = θ ( G( )) L. The flow rate out of unemployment f s therefore gven by: 4

17 f ( δ + θγ ) L = = ( δ + θγ )( u ) L L. 20 (8) By nspecton, an ncrease n unemployment has two opposng effects on the outflow rate. In the frst nstance, t decreases f drectly va the steady-state unemployment flow condton. Yet lower f wll ncrease the expected duraton of unemployment and cost of loafng. Ths second-order effect reduces the fracton of loafers (γ) and lowers the nflow nto unemployment due to shrkers who are caught, and renders the overall effect on f of a rse n unemployment, strctly speakng, ambguous. Wthout further restrctng the model, we wll assume that the frst-order effect domnates: Assumpton: In equlbrum, the outflow rate f s decreasng n the unemployment rate: f/ u<0. In Appendx A we show that a suffcent condton for ths assumpton s that the elastcty of loafng wth γ f respect to the outflow rate n general equlbrum s less than unty;.e., <. f γ D. Predctons Wth these results and assumng f/ u 0, we can prove the followng propostons, whch wll help nterpret our fndngs and pont to further emprcal mplcatons for loafng: Proposton : Loafng and unemployment. Holdng the wage constant, the fracton of workers who loaf depends negatvely on the unemployment rate. Proof: Ths and subsequent proofs can be found n Appendx A. Proposton s a partal equlbrum relatonshp whch holds all other factors constant ncludng the wage. A rse n unemployment lowers the outflow rate out of unemployment, thereby rasng the noshrkng wage and reducng the fracton of workers for whom shrkng s the more attractve opton. Proposton 2: Loafng, wages, and unemployment ncome. Holdng the rate of outflow constant, the fracton of workers who loaf depends negatvely on the wage and postvely on ncome n γ unemployment: < 0 w γ and > 0. b 20 We assume that separatons occur exogenously before montorng. 5

18 Proposton 2 holds that an ncrease n the wage wll, ceters parbus, deter some workers who were prevously enjoyng non-work from dong so. Smlarly an ncrease n unemployment ncome wll ncrease the mass of workers who enjoy postve non-work. Proposton 3: Condtonal mean loafng and unemployment. Holdng the wage constant, the condtonal mean of non-work on the job by those wth postve non-work s ncreasng n the φ unemployment rate: > 0. u Proposton 4: Condtonal mean loafng, wages and unemployment ncome. Holdng the outflow rate constant, the condtonal mean of non-work on the job by those wth postve non-work s ncreasng φ n the wage and decreasng n ncome n unemployment: > 0 w φ and < 0. b E. An Example For the purpose of llustraton, consder the case of a unformly dstrbuted preferred level of loafng,.e., g( ) =. Then E = ½, G( )=, and the fracton of loafng workers s γ = = ( w b) θ ½ f, where (r+δ+½ f)/θ > (w-b) > 0 s mposed to ensure meanngful values. Total r + δ loafng at a wage w s ( w b) 2 ½ ( ) = θ f w d = ½, r + δ depcted as the unshaded area n Fgure 2. Snce total avalable labor s and non-work s the trapezod ABCD, the total amount of effort ( ) θ w b ½ f s gven by e ( w) = ½ + r + δ 2, so e (w)>0. The average loafng of a worker condtonal on any loafng s ( w b) d θ ½ f φ = = ½ + r + δ. Holdng the wage constant, hgher unemployment leads to lower outflows, whch means fewer workers are loafng; but among those who are loafng, the average amount of loafng s hgher. 6

19 F. Accountng for Countercyclcal Aggregate Loafng Loafng wth Peers Our theoretcal model, whch makes no recourse to arguments nvolvng labor hoardng, can explan both the negatve nfluence of unemployment on γ, the fracton of workers who report any loafng at all, and the postve nfluence on φ, the mean value of ther loafng condtonal on t beng postve. Yet we also found that changes at the ntensve margn (volume of loafng of each loafer) domnate those at the extensve margn (ncdence), mplyng a postve overall dependence of uncondtonal mean loafng, γφ, on the unemployment rate. In the model presented above ths s mpossble, snce for each ndvdual the amount of preferred shrkng, f postve, s fxed at l. 2 A varable ntensve margn can be readly ncorporated nto our model whle contnung to eschew any explct reference to the frm s decson (although we do not deny that such effects could also be operatve). Assume that, n addton to heterogenety n the levels of the gan n utlty from loafng, the preferred level of loafng vares ndependently of the loafers denttes. We assume that the level of local labor-market slack mparts a peer effect on the preferred level of loafng, perhaps resultng from the shame of beng seen by colleagues, frends and others goofng off on the job. Ths cost lnks the preferred 2 The effect of unemployment on aggregate effort at wage w s e( w) f = ( g( ) d ) = g( ), u u f u whch s strctly postve snce and f f are both negatve. u 7

20 8 amount of loafng negatvely to the state of the busness cycle. In busy tmes when the labor market s tght and others are workng hard, beng seen loafng by frends on the job s more lkely to be embarrassng. In contrast, n slack tmes, ths cost wll be smaller snce others are workng less too. 22 To capture ths behavoral feature n our model, we assume that ) (u ε = wth 0 ) ( > u ε and 0 ) ( < u ε. When unemployment rses, the preferred level of loafng of each ndvdual worker rses as well. The ndvdual s gan from non-loafng when the wage s w remans: N U N V r V r r w V = δ δ. ( ) The equaton for loafng becomes: S U S V r V r r u w V = ) ( δ θ δ θ ε. As before: U E U V r f V r f r b V =, where { } { } = N S E V V E V, max, 0 represents the expected value of employment from the perspectve of an unemployed person. The NLC now mples: b E f u r w θ ε θ δ ) (, and the no-loafng threshold becomes: ( ) δ ε θ + + = r fe b u w ) (. 22 Ths s consstent wth the mportance of peer effects at work, ponted out eghty years ago by Mathewson (93) and recently demonstrated by Mas and Morett (2009). It would be straghtforward to model ths n a more drect fashon, allowng l to depend postvely on φ - salent loafng by colleagues provdes a fllp to one s own loafng. An equvalent outcome would arse f frms hoarded labor over the busness cycle (Fay and Medoff 984) and tolerated slack n bad tmes, whch was taken up unformly by workers choosng to loaf, regardless of the extent.

21 As before, an ncrease n unemployment lowers f, whch rases the loafng threshold at any wage and thereby reduces loafng, but at the same tme t reduces the socal cost ( peer effect ), whch rases the amount of loafng by the amount ε, condtonal on dong any loafng at all. Aggregate effort at wage w s now e( w) = ( ( u) ) g( ) d ε. Hgher unemployment thus has an ambguous effect on total loafng. 23 Fgure 3 shows ths phenomenon wthn the toy model that we llustrated n Fgure 2. A hgher unemployment rate ncreases loafng condtonal on postve loafng, whle reducng the mass of loafers (extensve margn) va a hgher threshold. Ths leads to an ambguous net effect on non-work and total effort dependng on the relatve szes of the two shfts The effect of unemployment on total effort holdng the wage constant, w ( u ) g d e( ) = ε ( ) ( ) w s f gven by ε + g ( ) + ε, whch has an ambguous sgn. f u ε 24 Even the expanded model s only partal equlbrum. In Appendx B, we endogenze the wage by embeddng the model n a general equlbrum framework. 9

22 V. Applcatons The model n Secton IV suggests several testable mplcatons that we can examne usng the ATUS data. Also, the results n Secton III have mplcatons for the ongong debate over the cyclcalty of labor productvty (see, e.g., Hagedorn and Manovsk, 20; Galí and van Rens, 204). We deal wth these two ssues n turn n ths Secton. A. Unemployment Insurance and Goofng Off Propostons 2 and 4 n Secton IV can be tested by expandng the equatons presented n Tables 2 and 3 to nclude proxes for the wage rate and unemployment ncome. Rather than addng the wage rate tself, whch would generate errors due to dvson bas, we add weekly earnngs. Snce a quadratc n weekly hours s already ncluded, weekly earnngs n ths context become a measure of the worker s hourly wage rate. The relevant measure of unemployment nsurance (UI) ncome for each worker depends on complcated formulas typcally lnkng her most recent year s pattern of earnngs and employment to state-specfc regulatons that are revsed annually. The ATUS lacks worker-specfc earnngs hstores; faute de meux we therefore experment wth two measures of the UI ncome that mght represent the average beneft avalable to an unemployed worker. The frst, the annual state-specfc maxmum weekly beneft amount (maxwba), s set legslatvely. Gven the relatvely low beneft celngs that characterze most states programs, roughly half of UI recpents receve maxmum benefts, so that ths measure could be a good proxy for the ncentves descrbed n the model of Secton IV. An alternatve measure s the average weekly beneft amount (averagewba) pad n each state each year. 25 We experment wth ths too, although t s not as clean a measure as maxwba, snce t depends partly on state-specfc varaton n unemployment. We re-estmate the models n Column (3) of Table 2 and Columns (2) and (4) of Table 3, addng each worker s usual weekly earnngs and sequentally the maxwba and averagewba n the state n the partcular year. For both maxwba and averagewba we present estmates of the determnants of the 25 These data represent an extenson of the sample used by Kroft and Notowdgdo (20). 20

23 uncondtonal mean of the percentage of non-work tme, the ncdence of non-work (the extensve margn) and ts condtonal mean (the ntensve margn). We measure these and weekly earnngs n thousands of dollars for ease of presentng the parameter estmates, notng that ther raw means are $384, $28 and $858 respectvely. Whle the results are very smlar for both measures of UI benefts, the explanatory power s slghtly hgher when we nclude maxwba. Secton IV generated no predctons about the determnants of the uncondtonal mean of nonwork tme; and we see n Columns () and (4) of Table 5 that the ncluson of nether maxwba nor averagewba has a sgnfcant mpact on ths outcome. Even wth all the demographc controls, however, condtonal on hours of work those wth hgher weekly earnngs (mplctly a hgher wage rate) spend a smaller fracton of ther tme at the workplace n non-work. 26 (Those whose earnngs are above a regresson lne are more lkely to be hgh-effort workers, although causaton n ths bvarate relatonshp s dffcult to determne.) Proposton 2 suggested that the ncdence of non-work wll fall wth ncreases n the wage rate and rse wth ncreases n ncome when unemployed. The estmates n Columns (2) and (5) of Table 5 represent strong evdence n support of ths Proposton. Controllng for educaton and other characterstcs, workers wth hgher wage rates are less lkely to engage n any goofng off on the dary day n the ATUS. Workers n states and at tmes where the maxmum (average) UI beneft s hgher condtonal on ther earnngs and hours are more lkely to spend part of ther day at work n non-work. Ths seems to be a strong confrmaton of the model n Secton IV and, more generally, of the role of ncentves to shrk n determnng workers and frms behavor. Proposton 4 mpled that the amount of non-work, condtonal on t beng postve, would ncrease n unemployment ncome and decrease n the wage rate. The former mplcaton s supported by the results n Columns (3) and (6) of Table 5: Other thngs equal, ncludng a large vector of demographc, ndustry and occupatonal characterstcs, the condtonal fracton of non-work s lower among workers 26 The ncluson of weekly earnngs and the UI varables does not qualtatvely alter any of the nferences about the effects of the demographc and other varables on the amount and ncdence of non-work tme that were based on the estmates n Tables 2 and 3. 2

24 wth hgher hourly wages. The only part of Propostons -4 that s not supported by the data s the relatonshp between unemployment ncome and condtonal non-work tme: As Table 5 shows, unemployment ncome exhbts no correlaton wth condtonal non-work tme, only wth ts ncdence. B. Heterogenety of Loafng The estmates n Secton III suggest substantal heterogenety n loafng along demographc, geographc, occupatonal and ndustral dmensons. We explctly excluded the self-employed from our emprcal analyss, both because we wshed to concentrate on how the employment relatonshp expresses dfferences and changes n loafng, and because we wshed later to focus on causes of changng employee productvty. Yet the exstence of self-employed ndvduals who by defnton are not subject to effcency-wage consderatons suggests an addtonal test of the theory: They wll behave qualtatvely and quanttatvely dfferently from employees. To test ths, we estmate the same equatons for the selfemployed respondents who reported tme at the workplace, of whom there are 3347 wth complete nformaton on work-days n the ATUS The estmates of the same expanded models that appeared n Column (3) of Table (2) and Columns (2) and (4) Table 3 yeld parameter estmates on the unemployment rate of (s.e.=0.005), (s.e.=0.006) and (s.e.=0.0022). Most nterestng s the fact that the mpact at the extensve margn s smaller than that shown n Column (2) of Table 3 for employees. Ths should be expected n lght of the theoretcal dscusson: Havng no employer, the self-employed are not montored and do not face dsncentves to shrk when unemployment ncreases. On the other hand, the mpact at the ntensve margn s larger than that shown n Column (4) of Table 3. Ths too would be expected, although the model n Secton IV s not relevant here, as when unemployment s hgh and demand s slack, self-employed ndvduals mght spend less tme workng at work, nstead watng for work or for customers. In the equatons presented n Tables 2 and 3 the parameter estmates of the vector of occupatonal ndcators were hghly sgnfcant statstcally. Fgures 4a, 4b and 4c present these estmates for the net mpact, the extensve margn and the ntensve margn respectvely, wth management as the excluded 22

25 occupaton. Rememberng that the equatons already hold constant a large vector of demographc characterstcs, these parameter estmates suggest an nterestng pattern of heterogenety across occupatons. Frst, except for protectve servces, workers n all other occupatons loaf more on the job than do managers, other thngs equal. Second, and most strkng, the occupatonal dfferences n the net amount of loafng arse almost entrely from dfferences at the extensve margn. The pattern at ths margn s consstent wth what seem easly predctable occupatonal dfferences n the ease of montorng potental shrkers. It s plausble that the montorng technology s the weakest, and the tolerance of slack to be the hghest, n farmng, fshng and forestry, n producton and extracton and n constructon. C. Cyclcal Movements n Labor Productvty Snce 2003 The nature of cyclcal changes n labor productvty has been a focus of macroeconomc controversy for over half a century (e.g., Okun, 962). Labor-hoardng motves suggest that output per worker-hour pad wll fall n recessons, whle the reducton n shrkng ncentves coupled wth dmnshng margnal productvty suggests t wll rse when workers are lad off. Our model mples the outcome wll be ambguous, dependng on the relatve szes of the mpacts at the extensve margn (counter-cyclcal productvty per hour at work through greater ncentves aganst shrkng) and the ntensve margn (pro-cyclcal productvty per hour at work through ncreased loafng by retaned workers). In the model presented n Secton IV, there are four dstnct effects of an exogenous ncrease n labor productvty, characterzed n Appendx B. Frst, t rases labor productvty drectly, leadng to more hrng. Second, compettve frms wll expand producton and hre more labor n response, whch tends to reduce labor productvty at the margn. The thrd and fourth effects arse n a general equlbrum context. As employment rses unambguously, wages rse unambguously, enhancng effort and actng as a fllp to hgher productvty. Fourth, unemployment falls, ncreasng labor turnover and reducng the 23

26 cost of shrkng, wth the ncrease n loafng puttng a damper on productvty. The net result of these effects s ambguous. 27 Our estmates demonstrate that the mpact at the ntensve margn domnates that at the extensve margn, so that we would expect pro-cyclcal labor productvty f we measure t as output per hour on the job. To examne how our fndng mght alter measures of productvty change over the cycle, we extend the measures of labor productvty generated by Burda et al (203) to account for cyclcal varaton n non-work at work. Most crucally, n our adjustment we measure labor productvty as output per hour at work and workng.e., per hour of actual effort. We concentrate on labor productvty n 2006:IV, when the U. S. unemployment rate n our data reached ts cyclcal low, and 200:I, when t reached ts hgh. 28 The frst row of Table 6 presents the BLS ndex of labor productvty n the busness sector (usng a base of 2003:I = 00) for these two quarters and ts peak-trough percentage change. The second row lsts a calculaton based on a number of changes by Cocuba et al (202) usng detaled modfcatons of the BLS seres that account carefully for hours actually at work among cvlan workers. The thrd row shows calculatons that adjust the measures n the second row to account for tme at work as reported by ATUS respondents n ther dares, whch we vew as more relable than one-week retrospectve reports of tme on the job, based on Burda et al (203). All of these measures confrm that labor productvty rose durng the Great Recesson. Amendng these calculatons to account for non-work tme at work, the purpose of ths exercse, wll ncrease the estmated counter-cyclcalty of labor productvty, measured as output per hour of effort (snce we showed that the net mpact of hgher unemployment s to rase non-work tme at work), but the queston s: By how much? Takng the change n unemployment over ths cycle, and usng the estmates 27 In Appendx B we show that the partal dervatve of average productvty wth respect to a homothetc outward shft n the producton functon s ambguous for the reasons cted n the text. 28 Aggregate unemployment reached ts cyclcal mnmum of 4.4 percent n a number of months between October 2006 and May Its cyclcal maxmum was reached n October We use the mnmum and maxmum n the ATUS sample for convenence, although the use of these dates hardly alters the nferences qualtatvely. 24