NON-WORK AT WORK, UNEMPLOYMENT AND LABOR PRODUCTIVITY

Transcription

1 Frst Verson: February 205 Ths Verson: October 206 NON-WORK AT WORK, UNEMPLOYMENT AND LABOR PRODUCTIVITY Mchael C. Burda, Kate R. Genadek and Danel S. Hamermesh* Abstract: We use the Amercan Tme Use Survey ATUS to estmate tme spent n non-work on the job. Non-work s substantal and vares postvely wth local unemployment. Tme spent n non-work condtonal on any postve amount rses, whle the fracton of workers reportng postve values declnes wth unemployment. Both effects are economcally mportant, and are consstent wth a model n whch heterogeneous workers are pad effcency wages. That model correctly predcts the relatonshp between the ncdence of non-work and unemployment benefts n state data lnked to the ATUS, and s consstent wth estmated occupatonal dfferences n non-work ncdence and ntensty. JEL Codes: J22, E24 Keywords: tme use, non-work, effcency wages, labor productvty *Humboldt Unversty Berln, CEPR and IZA; Unversty of Mnnesota Twn Ctes; Royal Holloway Unversty of London, IZA, NBER and Unversty of Texas at Austn. We thank Dan Black, Peter Egger, Albrecht Gltz, Mathas Hoffmann, Chrstan Merkl, Rchard Murphy, 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.

2 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 thoughthstorcal dscusson of ths concept. The noton that unemployment ncentvzes workers to work harder to avod layoff the dea of effcency wages was descrbed formally n the nowclassc study by Shapro and Stgltz 984, and goes back to wrtngs of Kaleck 943 and even to the reserve army of the unemployed descrbed by Marx 867 n Chapter 23 of Das Kaptal. It mples counter-cyclcal changes that labor productvty and effort rse 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. In ths study we frst lay out the patterns of non-work on the job, and show how the amount of non-work and ts ncdence and condtonal duraton vary wth labor market A recent socologcal study, Paulsen 205, presents cases llustratng the role and reasons for people loafng on the job. Lazear et al 203 and Pencavel 204 analyze changes n effort and productvty n sngle frms. 2 Hamermesh 990 examnes cross-sectonal dfferences n the allocaton of tme on the job. 2

3 condtons, as measured by the unemployment rate. Havng demonstrated that these varatons are statstcally sgnfcant and economcally mportant, we construct a model that accounts for these regulartes. We then test some addtonal predctons of that model and examne the mplcatons of our fndngs for aggregate labor productvty and macroeconomc behavor. 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 an examnaton of some cyclcal aspects of tme use. It thus allows the frst study 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 analyss 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 on employees. 3 Obtanng responses about what the respondent was dong at each moment of the dary day, the day before the dary was completed, the ATUS then codes them nto over 400 dstnct 3 The ATUS collected more dares n ts frst year, generatng about 450 usable dares each month n

4 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 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. The ATUS does not collect nformaton on secondary actvtes of ths nature, so we cannot measure tme spent n non-work whle also workng, such as readng the news whle the employee s prmary actvty s workng on a conference call. Lkewse, ths measure of non-work tme may not capture very short ncrements of non-work tme, such as checkng socal meda webstes for two mnutes, because respondents are less lkely to remember short events compared to long events even though the ATUS survey collectors ask about actvtes for every mnute of the day. Non-work tme does not nclude any tme spent away from the workplace durng the workday. 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 4 Tme spent workng ncludes tme spent n Work Related Actvtes or ATUS codes Workrelated actvtes nclude socalzng and eatng as a part of the job. 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. 4

5 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 n non-work prmary actvtes, amountng to thrty-four mnutes per day. Roughly half of ths tme s spent eatng; the other half s spent n lesure, exercse, cleanng and other non-work actvtes. These latter three actvtes are so rare that henceforth we concentrate on the twofold dvson between eatng and non-work non-eatng 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 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 non-work tme on the job. 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, The dfference may arse because we restrct the workday to the tme respondents spend at the workplace n any actvty. 5

6 An mportant queston s what reported non-work actually represents. Ths s a measure of not workng whle at work and s what the respondent beleves t to be, just as reported hours of work n household surveys underlyng the mmense lterature on labor supply represent actual work tme. Unlke recall about past weekly hours n those surveys, non-work tme n the ATUS s specfcally lmted to and anchored by the tme an ndvdual spends at the workplace on the randomly selected dary day. These data are based on one-day recall, and errors should thus be fewer than those n the one-week recall of hours of work that are used n most laborforce surveys. Whether there are bases that are correlated wth the forcng varables on whch we focus s a more dffcult queston. If workers wllngness to report non-work vares wth unemployment, t mght fall as unemployment rses, basng the estmated mpact of unemployment downward toward zero. Ths effect would only arse, however, f people felt that ther confdental tme dares were known to ther employers, whch does not seem lkely. Throughout ths study, the central forcng varable s the local unemployment rate, measured as the jobless rate n the state where the worker resdes. 7 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. Partly because of the Great Recesson, there s substantal varaton n unemployment, whch allows us to examne how non-work responds to changng local labormarket 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. Ths s because workers and ther employers have dfferent nterests n non-work. If ntated by the worker, non-work mght be nterpreted 7 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. Replacng the most recent three-month average unemployment rate wth a lagged three-month average yelded smlarly weaker fts but no qualtatve change n the estmates, and smlarly for nonlnear transformatons of the unemployment rate. 6

7 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 joblessness s hgh. To avod unemployment, 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 resources. In a temporary economc downturn, a layoff may be an nferor choce to mantanng employment, possbly even at standard hours. Labor hoardng by frms s often assocated wth the assgnment of workers to unproductve tasks such as cleanng, mantenance, pantng, etc. or even tolerate more worker-ntated non-work. 8 A. Central Results 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. 9 In the 8 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 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. 7

8 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 results shown n Column smply relate the proporton of non-work tme at work to the state unemployment rate. 0 There s a hghly sgnfcant postve 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 vares by 0.03 on a mean of 0.069, a varaton whose extent, as we show later, substantally alters nferences about the cyclcal path of labor productvty. The estmates n Column fal to account for the possble co-varaton of tme spent n non-work wth the amount of work performed. The equaton underlyng the estmates n Column 2 ncludes quadratc terms n usual weekly hours and tme at work on the dary day. Also ncluded but not reported are 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. A longer usual workweek sgnfcantly ncreases the fracton of work tme not workng up to 43 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 5.9 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. 0 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. Ths fndng s consstent wth older evdence from the scentfc management lterature chartng workers productvty over the work day Florence,

9 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. 2 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. 3 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. 2 The covarates ncluded n Column 2 descrbe 0.92 percent of the varaton n state unemployment rates over tme. 3 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. 9

10 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 non-work tme.e., on lesure on the job. Eatng at the workplace s much less affected by varatons n unemployment. 4 Moreover, except for Hspancs and Asan- Amercans, 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 argues that estmatng a probt on the ncdence of nonwork 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 nonwork 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 columns 2 and 3 of Table 2. The dfferences between these results and those n Columns 2 and 3 from the uncondtonal regressons are remarkable. Whle hgher unemployment s postvely assocated 4 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. 0

11 wth the proporton of tme at work reported non-workng, t sgnfcantly reduces the probablty that a worker spends any tme not workng. Ths reducton s more than offset by the sgnfcant ncrease n the proporton of tme not workng as the unemployment rate rses by those who state that they spent some tme not workng. 5 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: Movng from the lowest to hghest unemployment rate n the sample, the probablty of not workng falls by 0.06 on a mean of 0.337, whle the fracton of tme spent not workng rses by on a condtonal mean of It was dffcult to construct a convncng scenaro why errors n reported non-work would be correlated wth unemployment; t seems even more mplausble to construct a story why errors would produce a negatve bas n the estmated mpact of unemployment on the ncdence of non-work, but a postve bas on ts estmated mpact on the condtonal amount. 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 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 dfference n the responsveness of eatng and other non-work actvtes to hgher unemployment that were shown n Table 2 result from the greater responsveness of the latter among those workers who report some non-work tme: The mpact 5 Yet another concern mght be that commutng tme affects the amount of non-work and s correlated wth the local unemployment rate. Although ths measure s obvously endogenous, whch s why we have excluded t, expermentng wth addng t to the equatons presented n Column 3 of Table 2 and Columns 2 and 4 of Table 3 ncreases the absolute values and statstcal sgnfcance of the coeffcent estmates on the unemployment rate.

12 on the ntensty of other non-work tme s three tmes as large as that on tme spent eatng at work. B. Robustness Checks Alternatve Samples and Specfcatons Our central fndng s that hgher unemployment s 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. The net effect s the outcome of an mportant and surprsng par of subsdary effects, namely 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. In ths subsecton, we assess the senstvty of our fndngs to alternatve specfcatons of the estmatng equatons and underlyng samples. Whle we have controlled for farly large vectors of occupaton 22 and ndustry 5 ndcators, one mght argue that these are nsuffcently fne to account for dfferences n the structure of labor demand. The ATUS provdes more detal on these measures, 53 occupaton and 259 ndustry categores. At the expense of some cells beng very sparsely populated or empty, we re-estmate the equatons n columns 3 of Table 2 and columns 2 and 4 of Table 3 ncludng these expanded vectors of ndcators. For ths experment and the others, Table 5 reports the parameter estmates of the effects of unemployment on the uncondtonal mean proporton of tme spent not workng at work, ts ncdence and ts ntensty. Ths expanson of the vector of controls generates a slght ncrease n the mpact of hgher unemployment on the condtonal mean, but only tny changes n ts mpact on the ncdence and ntensty of nonwork. Wth less cyclcalty n demand and less exposure to the rsk of job loss, we mght expect that publc-sector employees non-work wll be less cyclcally senstve. To examne ths possblty, n the second experment we delete the roughly /6 of the respondents who are publc employees. As the results lsted n the second row of Table 6 demonstrate, the expected 2

13 change s exactly what we observe. Prvate-sector employees non-work s more senstve to varatons n unemployment, but solely because ts ntensty s more varable; ts ncdence actually vares slghtly less wth unemployment than does that of the entre work force. The Great Recesson was a unque experence n post-war Amercan hstory; much of the varaton n unemployment durng our sample perod arose because of ths shock. To what extent are our results drven by responses to ths unusual event? In the thrd experment we delete observatons from December 2007 through June 2009, the peak to trough of the NBER datng of ths cycle. Remarkably enough, these deletons hardly alter the estmated mpacts of unemployment on the three outcomes. Indeed, the effect on the uncondtonal mean s slghtly hgher than that estmated over the entre 20 months, because the mpact on the ntensty of non-work s greater when observatons from the Great Recesson are excluded. Whle we ncluded gender n the basc specfcaton, we dd not allow for dfferent responses to unemployment by gender. In the fourth experment, we estmate the three basc equatons separately by gender, wth the estmated mpacts of unemployment shown n the fourth and ffth rows of Table 5. The average ncrease by men n tme spent non-work as unemployment rses exceeds women s, because men s probablty of postve non-work s less senstve than women s, whle ther condtonal mean non-work s more postvely responsve to hgher unemployment. 6 It could be argued that cyclcal responsveness of non-work dffers by payment method, even holdng constant dfferences n demographcs and occupatonal and ndustry attachment between hourly and salared workers. The former are more lkely to report some non-work than are salared workers. As the results n the sxth and seventh rows of Table 5 show, however, dfferences n the cyclcal responsveness of non-work on the job by payment method are small. 6 The presence of young age 3 or less chldren sgnfcantly reduces men s uncondtonal mean non-work whle not affectng women s. Ths result seems consstent wth ncome effects on marred men s efforts n households wth young chldren where the male s the major earner. 3

14 The net mpact s slghtly greater among hourly workers, manly because ther condtonal mean amount of non-work s more responsve to varatons n unemployment. As wth the central results, for both groups the ncdence responds sgnfcantly negatvely to ncreases n unemployment, whle the condtonal mean responds sgnfcantly postvely. The lnk between non-work and the demand for part-tme workers mght dffer from those for workers more closely attached to the labor market. As shown n the eghth row of Table 5, the results do not dffer greatly when we lmt the sample to full-tme workers. Whle the net effect s smaller than n Table 2, both the ncdence and ntensty effects are statstcally sgnfcant, of opposte sgn, and dffer very slghtly from those shown n Table 3. Fnally, deletng those workers who report not workng the entre workday means truncatng the sample on the dependent varable, thus basng any results. The last row of Table 5 dsplays how the results change f we exclude ths one percent of the sample. The net mpact of unemployment among the remanng group s nearly zero; but the ncdence of non-work responds to hgher unemployment even more negatvely than n the entre sample, whle the ntensty of non-work responds sgnfcantly postvely, as before, but less strongly than n the entre sample. Overall, ths array of alternatve specfcatons wth samples truncated temporally or by workers characterstcs support the central conclusons that we draw from Tables 2 and 3. There s a small net postve effect of hgher unemployment n the amount of non-work n the workplace, a net effect that s composed of a sgnfcant negatve mpact on the ncdence of non-work and a more mportant sgnfcant postve mpact on the amount of non-work by the roughly two-thrds of workers who report any non-work at all. IV. A Model of Non-Work as 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. We wll show that an effcency wage model wth heterogeneous 4

15 preferences can reconcle these apparently contradctory fndngs. In that model, non-work s best thought of as worker-ntated loafng, wth employers playng a passve role. A. Prelmnares We consder 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. Workers are heterogeneous and, n the sprt of Shapro and Stgltz 984, face a bnary decson to spend a fracton of workng tme n non-work or exert full effort. 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 > lj 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 valuaton of non-work l s drawn when the job begns and lasts for ts duraton. It has tmenvarant expected value El. In each perod, worker chooses between loafng e< wth ncome equvalent w+l, and not loafng at all e= and recevng w. Wth exogenous probablty, management montors workers; f they are found loafng, they are fred. Employment relatonshps also end exogenously wth probablty. 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 5

16 6 exogenous labor force, s determned endogenously by a steady-state condton 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 wrte steadystate valuatons of the three possble labor-force states/strateges: Employment wthout any loafng V N, employment wth loafng V S, and unemployment V U : N U N V r V r r w V, S U S V r V r r w V, 2 U E U V r f V r f r b V, 3 where N S e E V V E V, max, 4 represents the expected value of employment from the perspectve of an unemployed person who does not know her future value of l, 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 noloafng wage w. If pad above ths wage, the worker s valuaton of not loafng at all domnates that of loafng: 5 Smlar to Shapro and Stgltz 984, ths no-loafng condton NLC, defnes the cutoff or mnmal threshold wage at whch worker s ndfferent between loafng and not loafng,.e.,. In the Appendx, the NLC wage for worker s shown to be:. S N V V S N V V

17 w r b f E r E 6 The NLC wage depends postvely on ncome n unemployment b, the nterest rate r, exogenous job turnover, 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 negatvely on, the probablty of detecton. C. Aggregate Loafng and the Steady-State Flow Equlbrum E. It depends The NLC wage represents a threshold wage above whch a worker wll not loaf at all. Invertng 6 yelds the dentty of the margnal loafer who s ndfferent between loafng her preferred amount and not loafng at all when the wage s w: w b fe 7 r At any common wage w, all workers for whom w w or, equvalently, wll spend tme loafng 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. The followng aggregate measures are mpled by 7, contngent on w: Fracton of workers loafng, w : g d G ; 2 Aggregate loafng, w : w g d 3 Aggregate effort, ew: e w w ; 4 Condtonal mean loafng, w : ; g d g d. G E 0 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, wherel s the exogenous labor force and L s employment. The mass 7

18 of workers who flow nto unemployment equals that of workers who lose ther jobs through exogenous separaton L plus those montored and caught loafng L G L. The flow rate out of unemployment f s: f L u L L. 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 8. Yet lower f wll ncrease the expected duraton of unemployment and the cost of loafng. Ths second-order effect reduces the fracton of loafers and lowers the nflow nto unemployment of those 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 general equlbrum, the outflow rate f s decreasng n the unemployment rate: f/u<0. In the Appendx we show that a suffcent condton for ths assumpton s that the elastcty of the ncdence of loafng wth respect to the outflow rate s less than unty;.e., f / / f. D. Predctons Wth these results and mposng f / u 0, we can establsh the followng propostons proofs of whch can be found n the Appendx, 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. Proposton s a partal-equlbrum relatonshp whch holds all other factors constant, ncludng the wage. A rse n unemployment lowers the outflow rate, and thereby rases the no- 8

19 shrkng wage and reduces the fracton of workers for whom shrkng s the more attractve opton. Proposton 2: Loafng, wages and unemployment ncome. Holdng the outflow rate f constant, the fracton of workers who loaf depends negatvely on the wage and postvely on ncome n unemployment: 0 w and 0. b Proposton 2 holds that, ceters parbus, an ncrease n the wage wll deter some workers who were prevously enjoyng loafng tme from dong so. Smlarly, an ncrease n unemployment ncome wll ncrease the mass of workers who are loafng. 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 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 n the wage and decreasng n ncome n unemployment: 0 w and 0. b Propostons 3 and 4 have mportant mplcatons about condtonal effects for those observed wth postve non-work. Proposton 3 mples that under the condtons assumed, an ncrease n unemployment wll rase the average amount of loafng by those observed wth any postve value. Intutvely, those who stll loaf wll have stronger preferences for t and ths shows n the mean of those who stll choose non-work. In contrast, an ncrease n the wage or a decrease n unemployment benefts ncreases average non-work observed for those wth postve non-work. 9

20 E. An Extenson Our theoretcal model, whch gnores labor hoardng, can explan both the negatve nfluence of unemployment on, the fracton of workers who report any loafng at all, and the postve nfluence on, the mean value of ther loafng condtonal on nonzero values. Yet our emprcal fndngs show that the response to unemployment at the ntensve margn the volume of non-work of each loafer domnates that at the extensve margn ncdence, mplyng a postve overall dependence of uncondtonal mean loafng,, 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. 7 A varable ntensve margn can be readly ncorporated nto our model whle contnung to eschew any explct reference to the frm s decson although such effects could also be operatve and we dscuss them below. Assume that loafng also mposes a fxed costu>0 on the worker, ndependent of dentty, wth u<el. Ths cost reduces the utlty of loafng and reduces preferred loafng tme. It could be assocated wth peer effects the stgma of beng observed goofng off, for example. Although the sgn of the dependence on unemployment could be postve or negatve, t s more lkely that ths cost declnes n the unemployment rate, u<0, mplyng a smaller stgma n recessons when unemployment s hgh, even as the potental cost of a layoff ncreases. In addton to heterogenety n the effect of loafng on utlty, the preferred level of loafng can thus vary ndependently of the loafers denttes; local labor-market slack s assumed to mpart a peer effect to 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 7 The partal effect of unemployment on aggregate effort at constant wage w s e w f g, whch u f u s strctly postve, snce both / f and / u are negatve. 20

21 the preferred 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. 8 The addtonal effect of unemployment works as follows: hgher unemployment lowers f, whch rases the loafng threshold at any wage and thereby reduces loafng, but at the same tme t also reduces the socal cost peer effect, whch rases the amount of loafng by the amount, condtonal on dong any at all. Aggregate effort at wage w s now u g d e w. In the Appendx we prove: Proposton 5: In the presence of a pro-cyclcal fxed cost of non-work, the net overall effect of unemployment on overall non-work s ambguous. The followng toy model hghlghts ths ambguty. Preferred loafng s unformly dstrbuted: g, G. Postve loafng s assocated wth a fxed cost 0-u, wth 0 and both postve and small, so that u E ½. We also mpose 0 w b½ f r f u 0 r 0 to ensure meanngful outcomes. It follows that: r f w b b f r f r ½ ½ 0 u w ½ 0 u r b½ f r f w 0 u r and 8 The mportance of peer effects at work was noted eghty years ago by Mathewson 93 and recently demonstrated by Mas and Morett It would be straghtforward to model ths n a more drect fashon, allowng l to depend postvely on - salent loafng by colleagues provdes a fllp to one s own loafng. An equvalent outcome would arse f frms hoard labor over the busness cycle Fay and Medoff 984 and accept slack n bad tmes, whch s taken up unformly by workers choosng to loaf, regardless of the amount. 2

22 w b½ f r f 0 u ½. r Total non-work s gven by: w ½ 0 u w b½ f r f r u and s represented as the trapezod ABCD n Fgure 2. Total avalable labor s and total effort s e w w ; although e / w / w 0 unambguously, / u cannot be sgned. Holdng the wage constant, hgher unemployment leads to a lower outflow rate f, whch means fewer workers are loafng; but among those who are, the average amount of non-work s hgher, 0 2, f snce by assumpton 0 u and df 0 u ½ r d du du r f 0. 9 Fgure 2 llustrates the mpact of an ncrease n unemployment whch matches that found emprcally n Secton III,.e., wth the ntensve margn domnatng the extensve margn. F. An Alternatve Interpretaton of the Emprcal Results Our model takes a supply-sde vew of non-work drven by workers motves to loaf. Our data do not allow us to test ths model aganst demand-drven explanatons of non-work based on labor-hoardng motves,.e., that frms contrbute actvely to countercyclcal nonwork. In ths sub-secton, we brefly sketch such a model and leave t to future research based on rcher datasets wth extensve employer nformaton to dstngush between labor hoardng and shrkng motves. Suppose workers have dfferent productvtes based on ther endowments of frmspecfc human captal, and suppose that the state of demand for frms s perfectly negatvely correlated wth the unemployment rate. In downturns, when demand s low, frms lay off some 9 Recall that u u E ½ was mposed. It should be emphaszed that even ths expanded model 0 s only partal equlbrum. In Burda et al. 206 we endogenze the wage by embeddng the model n a general equlbrum framework. 22

23 workers, but due to the costs of nvestng n frm-specfc captal they prefer to part wth workers wth lower levels of pror human captal frst. In terms of our model, ths could be subsumed by allowng the control and layoff probablty to depend explctly on l. Workers understand ths and know that f they are caught shrkng they wll be lad off. Because layoffs wll be concentrated but not lmted to workers wth low frm-specfc human captal, these workers have an ncentve to reduce non-work and lower ther layoff probablty, whle those wth more frm-specfc captal wll tend to take advantage of t. Overall, we would observe a reducton of non-work n recessons, but an ncrease of those for whom t s a low-rsk actvty, gven frm-specfc nvestments. V. Further Implcatons The model n Secton IV provdes several testable mplcatons that we can examne usng the ATUS data. The results n Secton III also 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 The valdty of Propostons 2 and 4 n Secton IV can be assessed by expandng the specfcatons 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 the usual weekly earnngs that are reported n the ATUS. 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 most recent year s pattern of earnngs and employment to state-specfc regulatons that are revsed annually. The ATUS lacks workerspecfc earnngs hstores, so we expermented wth two measures of the UI ncome that mght represent the average beneft avalable to an unemployed worker. The frst, the annual state- 23

24 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. 20 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 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 UI benefts 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 non-work tme; and we see n Columns and 4 of Table 6 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. 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 6 represent strong evdence n support of ths hypothess. Controllng for 20 These data represent an extenson of the sample used by Kroft and Notowdgdo

25 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 represents 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 2 mpled that the ncdence of non-work s ncreasng n unemployment ncome and decreasng n the wage rate. Proposton 4, n contrast, mples that the ntensty of non-work s decreasng n unemployment ncome and ncreasng n wages. The former mplcaton s supported by the results n Columns 3 and 6 of Table 6: Other thngs equal, ncludng the large vectors of demographc, ndustry and occupatonal characterstcs, the condtonal fracton of non-work s lower among workers 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 6 shows, UI benefts exhbt 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. 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 should behave qualtatvely and quanttatvely dfferently from employees. To test ths, we estmate the same equatons for the self-employed respondents who reported tme at the workplace, of whom there are 3347 wth complete nformaton on work-days n the ATUS

26 The estmates of the same expanded models that appeared n Column 3 of Table 2 and Columns 2 and 4 of Table 3 yeld parameter estmates on the unemployment rate of s.e.=0.005, s.e.=0.006 and s.e.= The extensve margn plays no sgnfcant role n non-work behavor of the self-employed, whle the mpact at the ntensve margn s much larger than that shown n Column 4 of Table 3. Although the model n Secton IV s not relevant for them, t s reasonable to expect that when unemployment s hgh and demand s slack, the self-employed mght spend less tme workng at work, nstead watng for work or for customers. Our fndngs mply much more cyclcalty of non-work tme at work among the self-employed than among employees. In the equatons presented n Tables 2 and 3, the parameter estmates of the vector of occupatonal ndcators were hghly sgnfcant statstcally. Fgures 3a, 3b and 3c present these estmates for the net mpact, the extensve margn and the ntensve margn respectvely, wth management as the excluded occupaton. As the equatons already hold constant a large vector of demographc characterstcs, the 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, producton, extracton and n constructon. 2 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 2 Vectors of nteractons of the occupatonal ndcators wth the unemployment rate were not statstcally sgnfcant. 26

27 output per pad worker-hour 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. Moreover, our model s partal equlbrum n scope. Burda et al. 206 dentfy four dstnct effects of an exogenous ncrease n labor productvty when the model s closed and wages are endogenous. Frst, a drect effect s greater productvty per worker, holdng workers constant. Second, frms expand producton and hre more labor n response, whch tends to reduce 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 boostng productvty. Fourth, unemployment falls, ncreasng labor turnover and reducng the cost of shrkng, wth the ncrease n loafng puttng a damper on productvty. The net result of these effects s ambguous. 22 Let Yt be output and Ht be total hours pad for n year t, as measured by the BLS, so that labor productvty n the BLS data can be wrtten as Pt = Yt/Ht. The Cocuba et al 202 correcton usng total pad hours created H t, mplyng productvty measured as P t = Pt[Ht/H t]. Burda et al 203 corrected H t to account for the dfference between employerpad hours and H 2 t, hours worked as reported by ATUS respondents, calculatng labor productvty as P 2 t = P t[h t/h 2 t]. Even P 2 t fals to measure total effort, the approprate denomnator to use n measurng output per unt of effort. We thus adjust t to obtan P 3 t = 22 In Burda et al 206, 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. 27

28 P 2 t/et, where et s the fracton of tme at work n perod t that the average worker s actually workng, based on our estmates n Table 2. Our estmates demonstrate that the mpact at the ntensve margn domnates that at the extensve margn, so that we would expect less pro-cyclcal labor productvty f we measure t as output per hour of effort on the job, P 3, whch s a closer approxmaton to the neoclasscal concept than others. 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 peak. 23 The frst row of Table 7 presents detals on P n the busness sector usng a base of 2003:I = 00 for these two quarters and ts peak-trough percentage change. The second row provdes the same nformaton on P, whle the thrd row lsts levels and changes n P 2. All of these measures confrm that labor productvty rose durng the Great Recesson. The fourth and followng rows of Table 7 shows the levels of and changes n P As the fnal column shows, accountng for the counter-cyclcalty of non-work tme at work sharply ncreases the estmated counter-cyclcalty of labor productvty. Indeed, t nearly doubles ts varaton durng the Great Recesson compared to P, and ncreases t by 25 percent compared to the BLS measure, P. Even wth much less counter-cyclcalty of non-work based on the estmates n Column 3 of Table 2, accountng for ths phenomenon n the sxth row of Table 7 stll shows substantal effects on the estmated change n labor productvty over the cycle. The man concluson s that the standard neoclasscal predcton holds up even more strongly when worker effort s measured correctly. 23 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 These are calculated so that they equal the measure n Row 3 at the average unemployment rate durng ths perod. Changng the bass does not change the estmated cyclcal change n ths adjusted ndex. 28

29 D. The Changng Cyclcal Behavor of Labor Productvty We can decompose the rse n productvty wth hgher unemployment nto the ncrease caused by the rse n non-work tme at the ntensve margn and the drop generated at the extensve margn. As the ffth and bottom rows of Table 7 show, movements at the ntensve margn generate effects on productvty per hour of actual effort that double the countercyclcalty of productvty as compared to the less comprehensve measures n Rows -3. The decomposton demonstrates that the cyclcalty of productvty wll depend crucally on the relatve mportance of movements along the extensve and ntensve margns. The ncrease n labor productvty n the wake of the Great Recesson stands n contradcton to an earler, conventonal wsdom that labor productvty and total factor productvty n general are pro-cyclcal e.g., Cooley and Prescott, 995, Gordon, 979; 2003, Ch. 8. Our estmates hnt that ths reversal n the correlaton could be due to changng strengths of behavor at the ntensve and extensve margns n the pre-990 perod, the ntensve margn may have domnated the extensve margn. Our model ponts to parameters such as worker turnover or f as well as the montorng ntensty or ncome whle unemployed as causal n ths regard and ndcates several drectons for future work. VI. Concludng Remarks We have focused on measurng changes n effort exerted by workers as the labor market loosens or tghtens. Ths would appear to be a smple measurement ssue, one that would have been reflected n offcal aggregate data for many years. It has not been. Wth the now thrteenyear-old large-scale study of tme use, the Amercan Tme Use Survey, we can begn to examne ths ssue n a way that was heretofore mpossble, snce that survey provdes nformaton on tme use on the job. In partcular, these new data allow us to measure the cyclcal varablty of tme not workng whle at the workplace. Whle non-work tme at work s counter-cyclcal, ths net result s the outcome of hghly sgnfcant but opposte-sgned mpacts of hgher unemployment. The role of shrkng s 29

30 reflected strongly n the pro-cyclcal varaton n the ncdence of non-work tme; but among those who shrk at all, the ntensty of ther non-work s strongly counter-cyclcal. These emprcal results can be ratonalzed by a model of nteractons between employers and workers n whch hgher unemployment reduces the ncentve for heterogeneous workers to shrk, so that those who choose to contnue shrkng as unemployment rses are those whose preferences for shrkng are strongest. The model also generates predctons about how addtonal external opportuntes, n the form of prospectve unemployment benefts, affect the ncdence and ntensty of non-work on the job. In general, they suggest that hgher unemployment benefts lead unsurprsngly to more non-work on the job, but they also lead those who do shrk to do less of t. These predctons are mostly supported when we match the tme-use data to varous parameterzatons of states unemployment nsurance systems. Other models mght be constructed that explan all of the phenomena we have documented and stll others, and that would be a worthwhle addtonal development. We use the estmates to develop a new measure of labor productvty, relatng t to changng unemployment and showng that, at least durng the perod encompassng the Great Recesson, labor productvty was even more counter-cyclcal than suggested by prevous estmates. No doubt there are many other applcatons of ths new approach to measurng effort on the job that mght be carred out. For example, we fnd strkng demographc dfferences n the share of work tme devoted to non-work. These mght be used to re-estmate hourly wage dfferentals among dfferent races/ethnctes; they mght be employed to adjust measures of the returns to educaton; or they could be used to re-examne the returns to on-the-job tranng. We leave ths large set of potental extensons and applcatons, along wth the dervaton of addtonal predctons from our model, to future work. 30

31 REFERENCES Mark Aguar, Erk Hurst and Loukas Karabarbouns, Tme Use durng the Great Recesson, Amercan Economc Revew, 03 Aug. 203: George Akerlof, Labor Contracts as a Partal Gft Exchange, Quarterly Journal of Economcs, 97 Nov. 982: Truman Bewley, Why Wage Don t Fall Durng a Recesson. Cambrdge, MA: Harvard Unversty Press, 999. Jeff Bddle, The Cyclcal Behavor of Labor Productvty and the Emergence of the Labor Hoardng Concept, Journal of Economc Perspectves, 28 Sprng 204: Samuel Bowles, The Producton Process n a Compettve Economy: Walrasan, Neo- Hobbesan, and Marxan Models, Amercan Economc Revew, 75 March 985: Mchael Burda and Jennfer Hunt, What Explans the German Labor Market Mracle n the Great Recesson? Brookngs Papers on Economc Actvty Sprng 20: Mchael Burda, Kate Genadek and Danel Hamermesh, Not Workng at Work: Loafng, Unemployment and Labor Productvty, NBER Workng Paper No. 2923, Jan Mchael Burda, Danel Hamermesh and Jay Stewart, Cyclcal Varaton n Labor Hours and Productvty Usng the ATUS, Amercan Economc Assocaton: Papers & Proceedngs, 03 May 203: Kenneth Burdett and Dale Mortensen, Wage Dfferentals, Employer Sze and Unemployment Internatonal Economc Revew, 39 May Peter Cappell and Keth Chauvn, An Interplant Test of the Effcency Wage Hypothess, Quarterly Journal of Economcs, 06 Aug. 99: Gullermo Calvo, Quas-Walrasan Theores of Unemployment, Amercan Economc Revew, 69 May 979: Smona Cocuba, Edward Prescott and Alexander Ueberfeldt, U.S. Hours and Productvty Behavor Usng CPS Hours Worked Data, 947:III- 20:IV, Unpublshed paper, Unversty of Western Ontaro, 202. Thomas Cooley and Edward Prescott, Economc Growth and Busness Cycles, n Thomas Cooley, ed., Fronters of Busness Cycle Research. Prnceton: Prnceton Unversty Press, 995. John Cragg, Some Statstcal Models for Lmted Dependent Varables wth Applcaton to the Demand for Durable Goods, Econometrca, 39 Sept. 97: Jon Fay and James Medoff, Labor and Output over the Busness Cycle: Some Drect Evdence, Amercan Economc Revew, 75 Sept. 985: P. Sargant Florence, Past and Present Incentve Study, Chapter n J.P. Davdson, ed., Productvty and Economc Incentves. London: Routledge,

32 Harley Frazs and Jay Stewart, What Can Tme-Use Data Tell Us About Hours of Work? Monthly Labor Revew, 27 Dec. 2004: 3-9. Jord Galí and Thjs van Rens, The Vanshng Procyclcalty of Labor Productvty, CEPR Dscusson Paper 9853, 204. Robert Gordon, The "End-of-Expanson" Phenomenon n Short-Run Productvty Behavor, Brookngs Papers on Economc Actvty 979:2: , Productvty Growth, Inflaton and Unemployment. New York: Cambrdge Unversty Press, Marcus Hagedorn and Iour Manovsk, Productvty and the Labor Market: Comovement over the Busness Cycle, Internatonal Economc Revew, 52 Aug. 20: Danel Hamermesh, Labor Demand. Prnceton Unversty Press, , Shrkng or Productve Schmoozng: Wages and the Allocaton of Tme at Work, Industral and Labor Relatons Revew, 43 Feb. 990: 2S-33S. Danel Hamermesh, Harley Frazs and Jay Stewart, Data Watch: The Amercan Tme Use Survey, Journal of Economc Perspectves, 9 Wnter 2005: F. Thomas Juster and Frank Stafford, The Allocaton of Tme: Emprcal Fndngs, Behavoral Models, and Problems of Measurement, Journal of Economc Lterature, 29 June 99: Mchal Kaleck, Poltcal Aspects of Full Employment, The Poltcal Quarterly, 4 943: Kory Kroft and Matthew Notowdgdo, Should Unemployment Insurance Vary wth the Unemployment Rate: Theory and Evdence, Natonal Bureau of Economc Research, Workng Paper No. 773, June 20. Edward Lazear, Kathryn Shaw and Chrstopher Stanton, Makng Do Wth Less: Workng Harder n Recessons, NBER Workng Paper No. 9238, 203. Alan Mannng, Monopsony n Moton: Imperfect Competton n Labor Markets. Prnceton, NJ: Prnceton Unversty Press, Karl Marx, Das Kaptal Band 867. Berln: Detz Verlag, 982. Alexandre Mas and Enrco Morett, Peers at Work, Amercan Economc Revew, 99 Jan. 2009: Stanley Mathewson, The Restrcton of Output Among Unorganzed Workers. New York: Vkng, 93. Arthur Okun, Potental GNP: Its Measurement and Sgnfcance, Proceedngs of the Busness and Economcs Statstcs Secton of the Amercan Statstcal Assocaton 962: Roland Paulsen, Empty Labor: Idleness and Workplace Resstance. New York: Cambrdge Unversty Press, 205. John Pencavel, The Productvty of Workng Hours, IZA Dscusson Paper No. 829,

33 Carl Shapro and Joseph Stgltz, Equlbrum Unemployment as a Worker Dscplne Devce, Amercan Economc Revew, 74 June 984: Robert Solow, Another Possble Source of Wage Stckness, Journal of Macroeconomcs, 979: Jay Stewart, Tobt or not Tobt, Journal of Economc and Socal Measurement, :

34 APPENDIX A. Dervaton of the NLC wagew In ths secton we derve the NLC-wagew, the wage level for worker wth value of preferred loafng, that mples ndfference between loafng and non-loafng, for the more general case of Secton IV.E.,.e. ncorporatng a fxed cost of shrkng u wth 0. The baselne model corresponds to 0. For any we mpose V N S E V V and obtan the followng four equatons n the NLC wage w, the expected value of employment when unemployed EV E gven the uncondtonal expectaton and the value of employment V E : E A U r V w u E U A2 r V w V U E A3 and r f V b fev E U E u A4 EV V. The soluton for the NLC wage s A5 w r f b E E, the value of unemployment V U, V r E and the NSC threshold gven wage actually pad w s solved for by settng nvertng: A6 w b fe. r A2. Condtons for f/u<0 and l / u >0 to hold n steady state equlbrum. w w and Proof. For f/u: Dfferentate equaton 8, whch defnes f u, wth respect to u to obtan df du 2 u, so for df/du<0 t s necessary and suffcent that d u df d u. Snce u=+/++f, ths expresson becomes df f d or df f d f df f. Because 0, t suffcent df for 0 du d f that. df 34

35 E f For l / u:, u r u df whch s unambguously postve because 0. du A3. Proofs of Propostons -4. Proposton : Loafng and unemployment. Holdng the wage constant, the fracton of d workers who loaf depends negatvely on the unemployment rate: 0. du Proof: Dfferentate G wth respect to u to obtan d d g du du, whch s negatve d because g>0 and s postve as shown n A2 above. du 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 unemployment: 0 and 0. w b Proof: Dfferentate G wth respect to w to, usng w b fe whch yelds r d g d g < 0; smlarly, > 0. dw r db r Proposton 3: Condtonal mean loafng and unemployment. Holdng the wage constant, the condtonal mean of non-work on the job for those wth postve non-work s ncreasng n the unemployment rate: 0. u Proof. Dfferentate E 0 g d g d wth respect to u usng G d g d g d d d d Lebnz s Rule:. Snce 2 0 from A2 and 0 by du du du du du Proposton, the frst term s negatve and the second term s postve. Use Proposton, d d g du du to rewrte ths as: 35

36 36, 2 du d g d g du d g d g du d g du d whch can be wrtten as du d g whch s unambguously postve. Because s the lower bound of for those who loaf,. 25 Proposton 4: Condtonal mean loafng, wages and unemployment ncome. Holdng the outflow rate constant, the condtonal mean of non-work on the job for those wth postve non-work s ncreasng n the wage and decreasng n ncome n unemployment: 0 w and 0 b. Proof. As wth the proof of Proposton 3, dfferentate d g wth respect to w and b: 0 2 r g dw d d g d g dw d dw d 0 2 r g db d d g d g db d db d The sgns of these dervatves follow from 0 dw d and, 0 db d as shown n Proposton 2, and from the fact that, establshed n the proof of Proposton 3. A4. Fxed costs of non-work and Proposton 5. Parallel to the orgnal model, we assume that the condtons obtan that yeld a negatve correlaton n general equlbrum between the unemployment rate u and the nflow rate f. Usng arguments analogous to those above, t s straghtforward to show that: Use ntegraton by parts to wrte d g as d G whch s postve for 0,. 26 A more detaled appendx descrbng ths extenson s avalable from the authors upon request.

37 37 b u E f r E r w r u f r fe b w. Wth these results, we can prove Proposton 5: In the presence of a pro-cyclcal fxed cost of loafng, the net overall effect of unemployment on overall loafng s ambguous. Proof: Overall total loafng s G u d g d g u w ; by ntegratng the frst term by parts, we can wrte G u d G G w. Dfferentate ths wth respect to u: u g u G u u g u w A3 Note that r f r u E u f u, so even f E u, a value of whch s suffcently close to zero s necessary for u >0. The sum of three terms n A3 has an ambguous sgn; the frst term s negatve, the second s postve and the thrd s postve.

38 Table. Descrptve Statstcs, ATUS Employees' Workdays, , N=35,548, Means and ther Standard Errors and Ranges for Several Varables Uncondtonal Mean/ Incdence Condtonal Mean Daly hours at work [0.02, 24] Proporton of tme at work Not Workng: Of whch: Eatng Non-work not eatng Of whch: Lesure and exercse Cleanng Other Usual weekly work hours [, 60] State unemployment rate 6.65 three-month average 0.02 [2.,4.4] 38

39 Table 2. Basc Estmates of the Fracton of Tme at Work Not Workng, ATUS , N=35,548 Parameter Estmates and Ther Standard Errors* Ind. Var. Non-Work _Eatng at Work Other State unemployment rate month average Usual weekly hours x x Usual weekly hours 2 / x x Hours at work x x Hours at work 2 / x x Demographc varables x x x x x x Occupaton fxed effects 22 x x x State fxed effects 5 x x x Month fxed effects x x x Adjusted R *x denotes the varable or vector s also ncluded. The demographc varables ncluded here and n Tables 3 and 4 are ndcators for beng Afrcan-Amercan, Asan-Amercan or Hspanc; gender, martal status and ther nteracton; a quadratc n potental experence, and an ndcator of metropoltan status. Standard errors n parentheses here and n Tables

40 Table 3. Probt Dervatves and Condtonal Regresson Estmates of the Fracton of Tme at Work Not Workng, ATUS * Ind. Var. Probt Dervatves Condtonal Regressons State unemployment rate month average Demographc varables x x Occupaton fxed effects 22 x x Industry fxed effects 5 x x State fxed effects 5 x x Month fxed effects x x Pseudo- or Adjusted R N 35,548 35,548 23,578 23,578 *x denotes the varable or vector s also ncluded. Each equaton also ncludes quadratcs n usual weekly hours, tme at the workplace, and potental experence; ndcators of gender, martal status and ther nteracton, and metropoltan resdence. 40

41 Table 4. Probt Dervatves and Condtonal Regresson Estmates of the Fracton of Tme Eatng at Work and Other Non-Work, ATUS * Ind. Var. Eatng at Work Other Non-Work Probt Condtonal Probt Condtonal Dervatves Regressons Dervatves Regressons State unemployment rate month average Occupaton fxed effects 22 X x x x Industry fxed effects 5 X x x x State fxed effects 5 x x x x Month fxed effects x x x x Pseudo- or Adjusted R N 35,548 8,40 35,548 2,62 *x denotes the varable or vector s also ncluded. Each equaton also ncludes quadratcs n usual weekly hours. Tme at the workplace, and potental experence; ndcators of gender, martal status and ther nteracton, and metropoltan resdence. 4

42 Table 5. Robustness Checks on Basc Equatons Descrbng Non-Work Tme: Estmated Impacts of Unemployment* Dependent Varable: Experment: Uncondtonal Incdence Intensty N=Total Mean Intensty More occupaton 53 and ,548 ndustry 259 ndcators ,578 Prvate-sector only , ,402 Exclude Dec June , ,09 Men and women separately: Men , ,808 Women , ,770 Workers separately by payment method: Hourly , ,644 Salared , ,932 Full-tme 35+ weekly hours , ,408 No 00-percent non-workers , ,089 *Each equaton also ncludes all the controls ncluded n Column 3 of Table 2, here and n Table 6. 42

43 Table 6. Estmates of the Effects of Earnngs and Unemployment Benefts on the Incdence and Intensty of Non-work, ATUS Maxmum Beneft Average Weekly Beneft Uncondtonal Uncondtonal Mean Incdence Intensty Mean Incdence Intensty State unemployment rate month average Weekly earnngs $ UI benefts $ Pseudo- or Adjusted R N 35,548 35,548 23,578 35,548 35,548 23,578 43

44 Table 7. Changes n Labor Productvty, 2006:IV 200:I 2003:I = 00 Row Productvty Measure Peak Trough Percentage Quarter Quarter Change Peak 2006:IV 200:I to Trough P BLS Busness Productvty P Cocuba et al Adjustment P 2 Burda et al Adjustment a P 3 based on maxmum non-work of whch: ntensve margn b P 3 based on mnmum non-work of whch: ntensve margn Unemployment Rate

45 0 5 Densty prop_notwrk_atwrk 5 Fgure. Dstrbuton of the Fracton of Non-work Tme, ATUS

46 46