The Effects of Incomplete Employee Wage Information: A Cross-Country Analysis. Solomon W. Polachek. and. Jun (Jeff) Xiang *

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1 The Effects of Incomplete Employee Wage Informaton: A Cross-Country Analyss Solomon W. Polachek and Jun (Jeff) Xang * Department of Economcs State Unversty of New York at Bnghamton Bnghamton, New York 139 (Ths Revson: May 5) ABSTRACT In ths paper, we defne a tractable procedure to measure worker ncomplete nformaton n the labor market. The procedure, whch makes use of earnngs dstrbuton skewness, s based on econometrc fronter estmaton technques, and s consstent wth search theory. We apply the technque to eleven countres over varous years, and fnd that ncomplete nformaton leads workers to receve on average about 3-35% less pay than they otherwse would have earned, had they nformaton on what each frm pad. Generally marred men and women suffer less from ncomplete nformaton than the wdowed or dvorced; and sngles suffer the most. Women suffer more from ncomplete nformaton than men. Schoolng and labor market experence reduce these losses, but nsttutons wthn a country can reduce them, as well. For example, we fnd that workers n countres that strongly support unemployment nsurance (UI) receve wages closer to ther potental, so that doublng UI decreases ncomplete nformaton and results n 5% hgher wages. A more dense populaton reduces search costs leadng to less ncomplete nformaton. A more ndustral economy dssemnates wage nformaton better, so that workers exhbt less ncomplete nformaton and hgher wages. Fnally, we fnd that foregn worker nflows ncrease ncomplete nformaton, and at the same tme reduce average wage levels, at least n the short-run. * We are especally ndebted to Hung-Jen Wang for provdng a STATA verson of the fronter estmaton program, and to Subal Kumbhakar for valuable dscusson. A verson of ths paper was presented at the Bar-Ilan Conference n Honor of Tkva Lecker. We thank partcpants of ths conference for valuable comments and suggestons. All remanng errors are our own.

2 Introducton The paper goes back to George Stgler s (1961, 196) path breakng work on ncomplete nformaton, It apples earnngs functon concepts along wth fronter estmaton technques to devse a measure of ncomplete nformaton, whch we parameterze as the degree employees end up recevng wages less than ther potental, gven ther sklls. Then, once we estmate ncomplete nformaton, we explore how nsttutonal factors, such as unemployment nsurance and foregn worker nflows, affect ncomplete nformaton across eleven countres. Incomplete nformaton leads workers to accept wages below what they could have attaned had they full nformaton about each potental employer s pay. 1 Workers could mprove ther wage by prolongng job search, but nformaton about avalable jobs s costly. To fnd a job, workers search the market, but normally termnate ther search before dscoverng the very hghest payng job avalable. As explaned by Nelson (197), Mortonsen (197), McCall (197) and others, ndvduals set a reservaton wage, and search untl offered a job at least equal to ths reservaton wage. On average, the hgher the reservaton wage the longer the search, but nvarably the accepted wage s almost always less than the best possble market wage avalable for a person of ther skll level. Recevng a wage less than the maxmum possble wage (gven one s skll) s an mportant phenomenon because t llustrates an effect of ncomplete nformaton that arses from costly search. Collectvely, over the whole economy, t reflects foregone gross natonal product, snce so many wthn the economy are smlarly recevng less than they could potentally earn. Therefore n the aggregate, ths wage gap reflects ncomplete nformaton s cost to the economy. Measurng the effects of ncomplete nformaton s sgnfcant for at least two reasons. Frst, as just noted, one can gauge the overall economc losses assocated wth costly nformaton. Second, havng a measure of these losses, one can assess approprate polces needed to reduce search costs, thereby ncreasng effcency wthn the economy. One result regardng polcy seems pretty much unversal n past lterature: Unemployment nsurance (measured by the replacement rate) subsdzes employee search, whch lengthens unemployment duraton (e.g., Mofftt and Ncholson (198) and Meyer (199)). The resultng extra search enables workers to obtan more nformaton and hgher wages (e.g., Ehrenberg and Oaxaca, (1976)). A ggantc body of lterature corroborates these fndngs regardng UI, both for the U.S. and other countres (e.g., Jurajda and Tannery (3), Fourgere, Pradel, and Roger (1998), Van den Berg and van der Klaauw (1), Mcklewrght and Nagy (1995), Card and Levne (1998), Beach and Kalsk (1983), Ham and Rea (1987), Arellano, Bentolla and Bover (1998), Belzl (1995) and Gonzalo, Mara Teresa Gonzalo ()). Ths paper dffers from past emprcal research n three ways. Frst, rather than concentrate on 1 Katz and Zderman (1986) argue that ncomplete nformaton also affects the complete wage package ncludng non-wage benefts. Gven unobserved worker characterstcs, employers provde specfc non-wage amentes to attract worker wth desred but unobserved characterstcs, thereby affectng the equlbrum wage. Unfortunately we do not have suffcent data to consder non-wage frnges. 1

3 unemployment duraton, t examnes worker wages. It focuses on the extent to whch workers receve a wage less than what they could be pad based on ther skll level. Ths focus enables the paper to get a metrc defnng the monetary effect (and ndeed a measure of) ncomplete nformaton. Second, the paper examnes ncomplete nformaton from an nternatonal perspectve. Rather than examne ncomplete nformaton for one sngle country, t obtans measures for eleven countres over several tme-perods. Thrd, by lookng across countres, the paper s able to explan how nter-country nsttutonal dfferences affect ncomplete nformaton. In testng our model, we frst corroborate past fndngs on unemployment nsurance. We show that an employee s ncomplete nformaton s smaller where UI s a larger proporton of GDP. Then second, we test whether nsttutonal factors lead to dfferences n ncomplete employee nformaton. In ths context, we show that geographc consderatons as well as ndustral structure lkely affect search costs, and hence ncomplete nformaton. Thrd, the paper culmnates by examnng how foregn mgrant workers affect overall employee ncomplete nformaton. In ths context, we determne that an nflux of foregn workers nto an economy decreases the effectveness workers search, thereby ncreasng the degree of ncomplete nformaton wthn the labor market. Background A country s dstrbuton of wages defnes the benefts of job search. The more dspersed are wages, the greater are the gans from search. Hgher search gans lead to relatvely hgher reservaton wages, whch n turn leads to more search. But as a consequence, earnngs dsperson narrows, and the degree of ncomplete nformaton dmnshes. At the same tme the amount of search s related to search costs. Indvdual characterstcs, such as a worker s locaton vs-à-vs jobs or a worker s opportunty costs (whch would be hgher for those already at work) affect search costs. Hgher search costs dmnsh search, leadng to wder earnngs dsperson. The whole search process provdes job seekers wth wage (and amenty) nformaton, but clearly nformaton remans mperfect because search s costly. 3 Creatng an ndex of the degree of Beyond the scope of ths paper s how nsttutonal characterstcs affect employer (as opposed to employee) ncomplete nformaton. To answer ths latter queston, one would need to adopt a two-ter estmaton technque (Polachek-Yoon, 1987). However, at ths pont, the two-ter algorthm s not avalable to use wth LIS data, snce all statstcal analyss must be done on the LIS computer wth standard statstcal software (SAS, SPSS, and STATA). 3 At ths pont we are abstractng from lfe cycle consderatons, partcularly tranng and other opportuntes avalable on the job. As wll be llustrated later, the estmaton procedure accounts for these factors by ncludng lfe cycle varables. Dfferences n nformaton between nsde and outsde employees may also be a consderaton, but gettng at these s more dffcult. Not all nsde nformaton s acted upon, and hence t s more dffcult to measure. For example, the perspcacous peregrnator (Polachek-Horvath, 1977) searches on-the-job for more benefcal opportuntes. The nformaton he/she gathers s unobservable untl acted upon, whch doesn t occur untl the perspcacous peregrnator actually moves.

4 ncomplete nformaton s mportant because, as already mentoned, ncomplete nformaton leads to lost opportuntes and dmnshed GNP. But n addton, the level of ncomplete nformaton s an ndcator of market competton. Whereas prces collapse to a unque sngle equlbrum n purely compettve full-nformaton markets, ths s not the case n mperfect competton where there are multple prces. But even markets for homogeneous easly transferable commodtes contan prce varablty when there s mperfect nformaton. The amount of market mperfecton can also be related to nsttutonal factors. These nsttutons mght nclude nformaton networks, such as natonally based barganng unts (e.g., Germany) and the avalablty of unemployment nsurance (most developed countres), but they also could nclude other nsttutons, such as nflows of foregn workers (who mght have lttle nformaton) nto an economy. There s much research on how mgrants affect the labor market. These analyses nclude the effect of mmgrants on wages. Some of ths research looks at how quckly (n terms of number of generatons) mgrants acheve success equal to natves (Chswck, 1978). Other of ths research examnes how ths rate of assmlaton depends on mgrant qualty, partcularly concentratng on the sklls mgrants possess upon enterng a country (Borjas, 1985). However, almost no research assesses workers overall knowledge of wage offer dstrbutons. Smlarly, almost no research assesses the effects of foregn workers on ncomplete nformaton wthn a partcular labor market. Ths paper examnes both. It develops a metrc defnng the effects of ncomplete nformaton. It assesses the mpact of nsttutonal factors ncludng unemployment nsurance and populaton densty. It assesses how n-mgraton of foregn workers affects the amount of nformaton workers have regardng the labor market. All ths s done wth nternatonal labor market data on eleven countres obtaned from the Luxembourg Income Study (LIS). Incomplete Informaton There s a large lterature on the theory of equlbrum prces. Most s theoretcal, and most concerns defnng the condtons under whch there s an equlbrum prce dstrbuton (e.g., Renganum (1979), Burdett and Judd (1983), Bester (1986), Arnold (), and Kamya and Sato (4)). There s also a small but growng body of lterature that s emprcal. That lterature reles on overall prce varaton to measure the degree f ncomplete nformaton (e.g., Stgler (1961), Stgler and Kndahl (197), Lach and Tsddon (199), and Sorensen ()). As recognzed by Stgler and others, there s a major drawback of merely usng prce (or wage) dsperson as a measure of ncomplete nformaton. Prce dsperson can vary for many reasons other than ncomplete nformaton. These nclude dfferences n worker qualty, dfferences n worker-frm barganng power such as through unons, but they also can result from nosy data. Dsperson measures do not get at these consderatons because they don t net out these effects. Regresson models suffer the same bases because the dsperson measures they use as the dependent varable do not net out random prce varatons, nor do they dstngush between a worker s (seller s) and a frm s (buyer s) ncomplete nformaton. Thus these past more tradtonal measures do not reflect accurate estmates of ncomplete nformaton. One technque to get at ncomplete nformaton that can net out worker qualty as well as pure 3

5 measurement error, yet get a measure of the effect of ncomplete employee nformaton, s gven n Hofler-Polachek (1985), and Polachek-Yoon (1987). The technque s relatvely smple to mplement. Bascally, t measures worker ncomplete nformaton as a parameter obtaned from estmatng an earnngs functon usng fronter estmaton technques. Essentally, ths parameter depcts the degree workers receve wages that are less than they could obtan, had they known the specfc frms payng the hghest wages. Hofler and Murphy (199) employ ths technque to compute the average worker shortfall usng the 1983 U.S. Current Populaton Survey (CPS). Gaynor and Polachek (1994) apply the technque to compute ncomplete nformaton about physcan prces. Daneshvary et al. (199) use the technque to get at assmlaton of foregn workers n the U.S., and Lang (4) uses t to get at assmlaton of German mmgrants. Groot and Oosterbeek (1994) corroborate the valdty of the approach usng a 1985 Dutch natonal sample of employees. Fnally Polachek and Robst (1998) confrm the technque s power by showng how the technque s ncomplete nformaton measures match those ndependently obtaned from the World of Work test admnstered to a group of workers n the 1966U.S.Natonal Longtudnal Survey of Young Men (NLSYM). But to date, no one has systematcally studed how the degree of ncomplete nformaton dffers across countres. Part of the problem s the paucty of data for enough countres to be able to draw vald conclusons. Comparatve cross-country mcro-data were smply not avalable. However, new data are now obtanable from the Luxembourg Income Study (LIS) for a suffcent number of countres to get meanngful results. Ths paper apples the technques mentoned above to estmate the amount of employees ncomplete nformaton. It then looks at dfferent nsttutonal factors across countres to determne how these nsttutonal factors affect ncomplete nformaton. Approach We use Luxembourg Income Study (LIS) data to examne ncomplete nformaton by country. The LIS s a collecton of household data compled from ongong statstcal surveys n approxmately 9 countres wdely spread across Europe, Amerca, Asa and Oceana. The data are standardzed n order to facltate comparatve research. The LIS began n 1983 and s now jontly sponsored by the Luxembourg government and the Centre for Populaton, Poverty and Polcy Studes (CEPS), the Centre Unverstare (CU) de Luxembourg. Data nclude country-specfc labor force surveys over varous labor market structures. These data provde demographc, ncome and expendture nformaton on three dfferent levels: household, person and chld. We concentrate on extractng earnngs, educaton and age data from the LIS person fles for ten OECD countres, and Israel. For each country we use an econometrc fronter estmaton technque developed by Agner, Lovell and Schmdt (1977) and Meeusen and van den Broeck (1977), orgnally employed by Hofler and Polachek (1985) to estmate a numerc ndex of ncomplete worker nformaton. Next we look at nsttutonal factors wthn countres to establsh whch of these nsttutons affect ncomplete nformaton. Frst standard nsttutons such as UI are consdered. Then, we concentrate on populaton densty, the proporton of rural employment, the proporton of ndustral employment, 4

6 and fnally the nflux of foregn workers nto an economy. Modelng Incomplete Informaton Assume a labor market wth a wage offer dstrbuton such that wages for a worker of gven skll vary from a low of w w mn to a maxmum of max. 4 A worker enterng ths job arena s motvated to seek a job at the frm payng the hghest possble wage. The only problem s that the worker does not know whch frms pay hgh wages. Indeed, gven that frm heterogenety s not unform across jobs (so that some frms may pay hgh wages for one partcular skll, but low wages for another), wages vary across frms n uneasy to detect ways, a pror. Gven ths ncomplete nformaton, the worker must search. To do ths, the worker sets a reservaton wage R w, whch s the mnmum wage the worker wll accept when searchng. Ths reservaton wage s based on perceved costs and benefts of search. On average, a worker searches more the hgher the reservaton wage, and less the lower the reservaton wage. The greater the search, the more lkely the worker acheves a wage closer to the maxmum w max. Suppose worker fnds a job payng a wage w, such that w w max R w. The gap between wmax and w wll vary across ndvduals, dependng upon relatve success n the worker s job search. The greater one s knowledge of the labor market the more lkely the chance w s closer to w max, barrng the mpact of luck (whch we ll talk about shortly). Let u be the (logarthmc) gap between w and the wage one actually receves ( w ). Ths gap represents the proporton by max 4 Wage level w s dependent on the worker s skll level x and a varable θ that wll be defned shortly, so that w = w( x, θ ). Ths wage can vary somewhat because companes dffer n how they use comparably sklled workers. Some, perhaps wth good management and/or hgher physcal captal (both ndexed by θ ) pay more, whle other less effcent companes pay lower amount for comparable workers. Ths results n a dstrbuton of potental wages, for example W ~ N ( w, θ ), gven the frms heterogenety of utlzaton of workers of gven qualty x. Accordngly, there are hgh wage frms, the maxmum wage beng w ( x), and low wage frms, the mnmum beng w ( x ). For now, snce we max 5 mn are talkng about a worker of a partcular skll level, we suppress the vector of worker characterstcs (x), defnng these avalable wages as w mn and w max.

7 whch one could enhance one s current wage through contnued search for the hghest possble wage. The varable u can be expressed as u = ln wmax - ln w + ω (1) and reflects the cost to a worker of ncomplete nformaton. In (1) ω reflects luck, whch for the sake of assumpton can be normally dstrbuted wth zero mean. The average u = u / N, over all workers (N) n the market, depcts the mean effect of worker ncomplete nformaton. Note that snce E( ω ) = luck cancels out when computng average ncomplete nformaton for all workers. Usng smlar logc, a frm pays more than necessary to the th worker, snce t pays w despte the worker s wllngness to work at w. The gap (ln w - ln R R w ) reflects the effect of the frm s ncomplete nformaton regardng worker (neglectng luck). But, n realty, worker need not be the lowest reservaton waged worker, so that the true effect of the frm s ncomplete nformaton s η = ln w - ln w mn, where w mn s the mnmum reservaton wage for all comparable workers who satsfy the job s requrements. The average η = η mean effect of frm ncomplete nformaton. / N, across all workers (N), depcts the Polachek and Yoon (1987) tested search theory s mplcatons by computng average u and η for varous groups of workers. They used the Panel Study of Income Dynamcs (PSID) for the U.S. n 1981, and found smaller u values for workers who receved UI benefts before ther current job. Ths fndng llustrated the effcency UI brngs about through enhanced worker search. Smlarly, usng the January 1983 Current Populatons Survey (CPS), Hofler and Murphy (199: 516) found that workers n areas payng relatvely hgher unemployment benefts exhbt a smaller u. Groot and Oosterbeek (1994) found that males have more labor market nformaton than females [whch s] probably caused by the greater market attachment of males, whch makes the opportunty costs of gnorance [.e., ncomplete nformaton] greater for males than females (p. 388). They also found that employees n the publc sector possess more market nformaton than workers n the prvate sector probably due to the fact that wage polces n the prvate sector are n general less publc knowledge and more ndvdually based (p. 388). Polachek and Robst (1998) found that workers scorng hgh on World of Work tests had more nformaton than those wth lower World of Work scores. Fnally Gaynor and Polachek (1994) appled the technque to the medcal professon, and there too found that measured ncomplete nformaton s hgher when search costs are hgher (p.83). But to date no one has done a comprehensve study across more than one country. 6

8 Nor have they examned whether n-mgraton affects ncomplete nformaton. Estmaton Procedure The LIS data requre all computatons be done on the Luxembourg computer. At ths tme lmtatons on canned STATA and SAS software preclude estmatng both employer and employee nformaton smultaneously. However, Groot and Oosterbeek (1994) fnd employer ncomplete nformaton to be relatvely constant across economc sectors, as does Polachek and Yoon (1987). Ths result means that ncomplete employee nformaton vares far more than ncomplete employer nformaton. So, because ncomplete employer nformaton vares lttle wth nsttutonal characterstcs, we concentrate solely on ncomplete worker nformaton ( u ). To put meat on the dscusson, we now derve the econometrc specfcaton. However, frst a word regardng the underpnnngs of an ndvdual s maxmum possble wage, w ( x) defned earler n footnote 4. Varous models can descrbe the underlyng factors of an ndvdual s wage potental. For example, the human captal model argues that a worker s wage potental s determned by human captal acquston. Productvty enhancng contract models suggest contracts whereby a worker s ablty to supply effort affects wage. Fnally, collectve barganng mples that unon power s mportant when negotatng a vable contract. Each of these underpnnngs can be represented by a vector of an employee s own ndvdual and hs or her frm s characterstcs, whch max we denote as vector x. 5 As such, a worker s maxmum potental wage ( w ) s related to x, plus p measurement error. 6 In logarthmc form, ln w max x = ln w p = ln( g ( x )) + v () Takng a lnear model for g x ), ( ln w p = x β + v, (3) 5 The LIS data do not contan employer characterstcs. Thus, such nformaton s omtted from x. To the extent these omtted varables nfluence u, our worker ncomplete nformaton estmates may be based. However, dfferences n these measures across countres are unaffected by ths bas. These dfferences are akn to serally correlated errors n fxed-effects panel estmates beng better than cross-sectonal estmates. (See Bound and Krueger (1991).) Economy-wde nsttutonal varables are ncluded later n the analyss when comparng ncomplete nformaton across countres. 7

9 where the dependent varable represents the ndvdual s maxmum potental wage offer; x denotes a vector of ndvdual characterstcs defnng the worker ' s skll level; and v s a dsturbance dstrbuted as N(, σ ). Wth perfect nformaton regardng what each frm pays, a worker would be v able to locate the frm payng hs or her maxmum potental wage p w. However, n a world of ncomplete nformaton, where search s costly, the typcal worker defnes a reservaton wage R w below hs or her potental wage. As ndcated above, the offer wage one accepts w R w. Combnng (1) and (), a worker s observed wage can be represented as: ln w = ln w -u (4) p where u. Ths one-sded resdual u represents the extent ncomplete nformaton causes one to accept a wage lower than the market s potental wage. It reflects a monetary measure of ncomplete nformaton. Substtutng (3) nto (4) yelds ln w = x β + ε (5) where ε = v u. As already ndcated v N(, σ v ). We also assume that + u ( µ µ, σ ), as s typcal n fronter estmaton. 7 N µ Based on the above assumptons, the composte error ( ε = v u ) must be rghtward skewed for the approach to be vald. Therefore before proceedng, we test for skewness n ε. To do ths, we follow Schmdt and Ln (1984) to obtan resduals from OLS regressons of (5) for each country and 6 Ths s the measurement error omtted from past studes of ncomplete nformaton that smply use wage (or prce) dsperson measures to get at ncomplete nformaton. 7 It s not necessary to assume u N + ( µ µ, σ ) µ. Other common dstrbutons used n ths context nclude the exponental, the Gamma, and lognormal. However, past studes found lttle qualtatve dfference between results usng these dfferent dstrbutons. See Olson, Schmdt, and Waldman (198) and Byeong-Ho Gong and Robn C. Sckles (199). 8

10 year. The specfc test 8 we adopt was developed by Royston (1991) and s a test for normalty whch combnes two tests, one based on skewness and another based on kurtoss, nto an overall test statstc. In no country or year do the test results supports the normalty assumpton (wth p-value less than.1%). Therefore, our hypothess about the skewness of the resduals s strongly upheld justfyng that we can proceed wth estmaton of (5) usng fronter technques to dsentangle u and v. To do ths, we adopt the maxmum lkelhood approach to estmate (5) that ncorporates a composed error term frst proposed by Agner, Lovell, and Schmdt (1977) and Meeusen and van den Broeck (1977). It assumes a non-negatve one-sded error term, error term, v. To understand the approach rewrte (5) as: u, n addton to the tradtonal normally dstrbuted w = g( x ).exp{ u + v }, (6) where w s the actual observed earnngs, as already defned, and g x ) s the potental ( maxmum earnngs achevable barrng random errors v. Actual earnngs ( w ) are less than potental maxmum earnngs ( g x ) ) by the proporton (1- exp{ u } ). In other words, ( (1- exp{ u } ) s the effect of ncomplete nformaton. As s standard n the lterature we specfy the x vector to adhere to typcal earnngs functons. 9 Based on current lterature regardng functonal form of earnngs equatons (Mncer, 1974, Heckman and Polachek, 1974, and Murphy and Welch, 199), we express an ndvdual s potental wage as 8 The skewness and kurtoss test for normalty (sktest) s descrbed n the STATA reference manual, p As already noted, data on employers were not avalable, and hence omtted. However, n the latter cross-country analyss, the effect of these omtted varables are smaller, the more unformly dstrbuted these varables are across countres. Also, wthout data on tenure t s mpossble to examne ncomplete nformaton dfferences between nsder and outsder workers. However, were tenure data avalable one could compare nformaton measures of recently hred workers to workers wth longer tenure to ascertan the role on-the-job search of nsders wthn the frm. Prelmnary evdence on ths solely for the U.S. (Polachek-Yoon, 1987, Table ) ndcates that tenured employees have less ncomplete nformaton. Smlarly were enough panel data avalable one could employ the technques utlzed by Polachek-Yoon (1997) to net out person-specfc heterogenety. 9

11 g ( x ) = exp( α + α1s + α t + α 3t + α 4F ) (7) where, S s the ndvdual worker s completed hghest schoolng level, t s the ndvdual worker s potental workng experence, t s the quadratc term of s experence level, and F s the ndvdual worker s gender dummy, 1 for female and otherwse. The above equaton (7) gves a worker s potental wage n the absence of the ncomplete nformaton. In realty, because of the lmts of fnte search, a worker receves less than hs or her potental maxmum wage, and gets an observed wage obtan o w. To ncorporate ncomplete nformaton, substtute (7) nto (6) to o w = g( x ).exp{ u + v} = exp( α + α1s + α t + α 3t + α 4F ) exp( u + v ) (8) After takng the logarthm, t becomes: ln w α α S α t α t α F u + v (9) o = The common approach s to estmate (9) usng the (ln) lkelhood functon ln 1 β, ε (1) Π N N 1 1 L( w x λ, σ ) = N ln + N lnσ + ln[1 F( ε λσ )] = 1 σ = 1 proposed by Agner et al. (1977) and Meeusen and van den Broeck (1977), where σ = σ u + σ v σ u λ = σ v 1

12 and where f and F are respectvely the standard normal densty and dstrbuton functons. 1 The expected value of the composte error s E( ε) = E( u) = σ u (11) Π the mean of the one-sded error term. A problem wth the above approach s t assumes u to be unrelated to any of the ndependent varables. So, followng Wang s () strategy, we take account of how the ndependent varables can nfluence ncomplete nformaton by parameterzng u to be a functon of gender, martal status, race, schoolng and experence, because each these varables affects a worker s opportunty costs of search. Thus, we assume u ~ N + ( µ, σ ) u u (1) u + δ1f + δ M 1 + δ 3M + δ 4R + δ 5S δ 6t µ δ + = (13) σ u = exp{ γ + γ 1F + γ M 1 + γ 3M + γ 4R + γ 5S + γ 6t} (14) v ~ N(, σ ) (15) v where u s truncated normally dstrbuted wth mean µ and varance u σ u, M 1 s the ndvdual worker s martal status, 1 for marred and otherwse, M s the ndvdual worker s martal status, 1 for dvorced, wdowed, separated and otherwse, and R s the ndvdual worker s race, 1 for whte and otherwse. (For countres other than U.S., 1 for the majorty ethnc group and otherwse). 1 Note that based on Wensten (1964), the lkelhood functon s based on the ε dstrbuton beng ε 1 f ( ε) = f ( )[1 F( ελσ ). σ σ 11

13 The emprcal estmate of the u s obtaned through ts condtonal expectaton on the total error term ε, whch s defned as ε = u + v. (16) As noted by a referee, our choce of ndependent varables n (9), (13) and (14) s an mportant ssue. Omttng key earnngs determnants from (9) that are ncluded n (13) and (14) can bas our estmates of ncomplete nformaton ( µ ). Take an example: Suppose educaton affects earnngs postvely. Then, ncludng educaton n equaton (9) mples lower potental earnngs for the less educated, and hgher potental earnngs for the more educated. On the other hand, omttng educaton from equaton (9) mples a sngle potental earnngs measure for all. But gven the fronter estmaton adopted n lkelhood functon (1), potental earnngs n ths latter case are those of the most educated group. As a result, the gap between the potental wage and the actual wage s smaller for the more educated and larger for the less educated. Thus, ncorporatng educaton n (13) but not n (9) would mply a smaller µ for the most educated workers (hence less ncomplete nformaton) smply because the gap between the potental wage and the actual wage s smaller for the more educated when n realty fronter wages for the less educated should be lower than fronter wages for the more educated. In ths paper we adopt the Mncer earnngs specfcaton for (9). Here educaton, experence, and experence-squared dentfy potental earnngs. In addton, we ncorporate gender because gender s known to affect wage level. On the other hand, we nclude martal status and race n (13) and (14) but not (9) because martal status does not affect wage unformly. For women martal status appears to lower earnngs, whereas for men t appears to rase earnngs. Smlarly we ncorporate race n (13) and (14) but not (9). The LIS data (whch we descrbe later) ncludes majorty ethnc group rather than race. For the U.S. ths ethncty varable denotes race, but not for the other countres. Blacks n the U.S. earn less than whtes. However, there s no strong evdence outsde the U.S. that mnorty ethnc groups earn less than the majorty ethncty groups. Thus we hestate to nclude ths ethncty varable n the wage equaton. As for the ncomplete nformaton equaton, marrage and race are ncluded because marred, wdowed/dvorced, and majorty ethncty groups are assumed to have dfferent search behavors. We expect these groups to search more not only because ther margnal gans for an addtonal search are hgher, but also because ther search costs are lower. The determnants of search do not always perfectly match those of earnng patterns and therefore, the varables n the two equatons are not requred to be the same. We admt some bases can creep n because of dffculty n specfyng the varables that affect µ but not w, and vce versa. Mostly for ths reason, later n the paper, we adopt an addtonal approach to dentfy country nsttutonal factors that affect µ. In ths latter approach (comparable to dfferences-n-dfferences) the above type errors cancel out as long as they are unform across countres. 1

14 Maxmum lkelhood s used to estmate equaton (17), gven assumptons (1) to (16). 11 The log lkelhood functon for a sample of N workers s ln L( µ u µ u ε λ 1 β, λ, σ ) = lnσ ln Φ( ) + ln Φ( ) σ σ λ σ w C u ε + µ u ( ) σ (17) where σ + 1/ = ( σ u σ v ) (18) λ σ / σ = (19) u v As dscussed above, we use 1- E(exp{ } ) as a measure of a worker s ncomplete nformaton. u ε The advantage of ths measure s that t s bounded by (,1), whch s easly nterpreted as the proporton of the potental maxmum wage a worker gves up due to the ncomplete nformaton. So the bgger the 1- E(exp{ } ε ), the more ncomplete one s knowledge of avalable wages. The formula to obtan u each worker s ncomplete nformaton s 1 Φ[ σ ( ~ / )] ~ * µ σ * 1 1 E(exp{ u } ε ) = 1 ~.exp{( µ ) + σ * } () 1 Φ[( µ ) / σ ] where * σ = σ σ σ (1) * u v / ~ µ + = () ( σ uε µ uσ v ) / σ The estmate of ncomplete nformaton s the average of all workers ncomplete nformaton. So each country year s ncomplete nformaton s N j 1 µ j = [1 E(exp{ u} ε )] N j = 1 (3) where N j s the total number of workers for country year j. The Data 11 See Stevenson (198). 13

15 Our frst task s to compute each worker s ncomplete nformaton defned n equaton (), usng the maxmum lkelhood estmaton formulated n equaton (17). In the LIS data, there are three data fles for each country and year. We use the personal fle, whch has nformaton on work status, personal ncome, educaton as well as other basc ndvdual characterstcs. For each of the 9 countres, the number of avalable years dffers, rangng from one year for Estona to nne years for Canada and the Unted States. For two reasons, we use only part of the avalable countres for the analyss. Frst, our fnal goal s to understand how nsttutonal dfferences explan varatons n ncomplete nformaton across countres. However, crucal varables to test hypotheses related to nsttutonal perspectves are not avalable for all LIS countres and years. Generally LIS has suffcent data for many, but not all OECD countres. So for ths reason we frst restrct our analyss to the OECD countres and years, whch contan the full complement of demographc nformaton that we need for analyss. (We also nclude Israel our only non-oecd country because LIS contans the relevant nformaton, and because ths paper was also presented n Israel.) Second, we only nclude countres and years for whch the nonlnear maxmum lkelhood estmaton of equaton (17) converged. Of the 3 potental OECD countres (and Israel), ths left 11 countres for whch we have suffcent data. They are the Unted States, Canada, the Unted Kngdom, Germany, Sweden, Fnland, Ireland, Norway, Netherlands, Czech Republc and fnally Israel. They span North Amerca and all parts of Europe. Wthn Country Regresson Results Summary statstcs are provded n Table 1. For most countres and years, the average schoolng s around 11 to 1 years, and the average potental experence s between and 5 years. So, across these OECD countres, most workers have relatvely comparable educatonal backgrounds, and are smlar n age. Also, female workers consttute about half of the populaton ntervewed. (Though, not shown n the table, a greater proporton of women dd not work for pay.) The ethncty varable depcts the proporton of the populaton consttutng a country s majorty racal or ethnc group. In the, ths s the proporton whte. Marred workers are around 6% of the populaton, and dvorced or wdowed workers are n the 5-% range. Mean wages are contaned n the last column, and are defned ether as hourly wages or annual earnngs, dependng on the avalable data. We expect more varaton n annual earnngs because hourly wages fluctuate less as work hours vary. In the statstcal analyss to follow, we add a dummy categorcal varable to sgnfy country-years wth hourly wage data. Table contans the regresson results of equaton (9). Columns (1) and () gve the country and year; columns (3) through (6) gve the coeffcent values for schoolng, potental experence, potental experence-squared, and gender; and fnally column (7) gves estmates of the extent of ncomplete nformaton obtaned from equaton (3). We begn by explanng Columns (3) through (6) snce they reflect earnngs functon parameters typcally obtaned when estmatng Mncer earnngs functons. The schoolng varable coeffcent depcts the average rate of return for an addtonal year of school. For more than half of the countres, ths rate of return s ncreasng over tme. (Only a few countes 14

16 have decreasng rates of return.) Ths pattern s consstent wth other data sets as well as wth technologcal change. It mples that the more educated absorb advanced technology easly, and that ther rate of pay per year of schoolng s ncreasng secularly. 1 The experence coeffcents yeld concave earnng profles, shown by the consstently negatve squared-term. The female dummy varable s unformly negatve, suggestng that n all the countres (that we consder), women earn less than men, gven adjustments for schoolng and experence. Overall, these earnngs functon parameters are typcal of those found n the lterature. Estmates of ncomplete nformaton, based on equaton (3), are gven n Column 7. They range from.16 to.58, but average about.3. Ths means that ncomplete nformaton causes the average worker to get about 3% less than hs or her potental. An examnaton of the values ndcates strong consstency wthn countres, snce these values dffer more country-to-country than wthn countres. Wthn countres ncomplete nformaton measures do not reveal any apparent tme pattern. Incomplete nformaton seems to be ncreasng n Sweden, Czechoslovaka, Fnland, Germany, Netherlands and Israel, whle n Norway t s decreasng. On the other hand, tme trends are relatvely flat for the Unted States, the Unted Kngdom, Canada and Ireland. Usually technologcal mprovements mrror tme trends. The lack of a tme trend mght suggest that technologcal mprovements are not necessarly assocated wth decreasng ncomplete nformaton, as common sense mght have mpled. 13 What factors affect the level and dstrbuton of ncomplete nformaton wthn each country? To answer ths queston, we adopt Wang s () method to compute margnal effects of µ s covarates lsted n equaton (13) (Table 3) and σ u s covarates gven n equaton (14) (Table 4). 14 Begnnng wth Table 3, we see a number of trends. For example, the mostly negatve margnal effects of school ndcate that addtonal schoolng reduces a worker s ncomplete nformaton. Ths fndng s consstent wth Stephenson s (1976) argument that workers wth more educaton gather more wage nformaton by searchng more effcently. Smlarly, about three-quarters of the cases show marred workers, and two-thrds of the cases show that wdowed and dvorced workers, have more nformaton than sngles. Ths result s consstent wth hgher marred and wdowed labor u 1 As schoolng levels rse, the wage dstrbuton becomes wder. Although a wder earnngs dstrbuton ncreases search gans, t need not mply more nformaton snce search costs also rse. Perhaps ths s the reason we see no tme trends regardng ncomplete nformaton. 13 Whereas technologcal mprovement provdes the opportunty for more search, the whole search process can become more overwhelmng, makng the job choce decson more complcated. For nstance, by ntensvely searchng through the nternet, ndvduals decrease search costs. These decreased search costs mght mply more search but less real nformaton f, for example, wages are not posted. Thus technology could leave workers wth less real nformaton. 14 For the detals, see equaton (9) and equaton (1) n Wang s paper. 15

17 force partcpaton rates (Taubman, 1976), whch leads to larger margnal gans from search. Further, as hypotheszed (McCall, 1973) blacks exhbt lower labor force partcpaton and possbly hgher search costs, so they mght acqure less nformaton than whtes. In our nternatonal data, we show that on average mnorty ethnc groups have less complete nformaton than majorty groups, whch s an extenson of McCall s argument. Also, from Table 3, potental experence reduces workers ncomplete nformaton n over 8% of the observatons. Fnally, ncomplete nformaton s larger among female workers than male workers, agan consstent wth less lfetme female labor force partcpaton. Another observaton regardng these margnal effects n Table 3 s that the patterns are more consstent wthn a specfc country, than across countres. As such, any covarate s margnal effect s lkely to have a unform sgn wthn a partcular country, but not necessarly across countres. Ths unformty wthn countres underscores the mportance of usng cross-country nsttutonal dfferences to explan how ncomplete nformaton dffers from country-to-country. Recall from equaton (5) that σ u depcts the dsperson of ncomplete nformaton. How ndvdual characterstcs affect ths dsperson s parameterzed n equaton (15). The mpacts of these characterstcs on σ u are gven n Table 4. They ndcate the degree ncomplete nformaton fluctuates across socoeconomc groups. As an example, take race. From Table 3, ncomplete nformaton s smaller for whtes than for blacks. But, the negatve ethncty coeffcent n Table 4 mples that whtes exhbt smaller dsperson n ncomplete nformaton than blacks. Ths mples a relatvely wder range n ncomplete nformaton for blacks than whtes. Thus the varance of ncomplete nformaton s greater for blacks than whtes. Smlarly, though on average females garner less nformaton n total, they exhbt greater varance n the amount of nformaton they gather, as well. For the most part beng older (havng more potental experence) reduces ths dsperson, as does beng marred or wdowed, and as does havng more educaton (except for the Unted Kngdom, Germany, The Netherlands and Israel). Why Incomplete Informaton Dffers Across Countres? An Examnaton of Insttutonal Factors So far we have estmated ncomplete nformaton and examned how t vares wthn countres. We have seen that an employee s characterstcs, especally those characterstcs affectng one s ncentve to search, nfluence the amount of wage related nformaton one acqures. However, a country s nsttutons may also be mportant, but dentfyng the mpact of these nsttutons s dffcult to dscern wth a lmted number of cross-sectons for a gven country. For ths reason, we now do a comparatve analyss by contrastng nsttutonal dfferences across each of the eleven countres to explan nter-country dfferences n ncomplete nformaton. 16

18 UI and Incomplete Informaton Perhaps unemployment nsurance (UI) s the most studed nsttuton regardng nformaton and search. Ehrenberg and Oaxaca (1976), Jurajda and Tannery (3), and numerous other studes fnd that UI ncreases unemployment duraton, job search and post-unemployment wage. Hofler-Polachek (1985), Polachek-Yoon (1987) and Hofler-Murphy (199) corroborate ths result usng one of the fronter estmaton technques descrbed above. They fnd that havng receved UI leads to less ncomplete nformaton. As already mentoned, ths fndng s consstent wth search theory explanatons that UI subsdzes search costs leadng to longer search, better wages, and more nformaton (less ncomplete nformaton). Whereas these studes test ths proposton wth U.S. data, to the best of our knowledge, none examne other countres; nor do any do comparatve analyss across countres. In what follows, we perform a comparatve analyss usng the eleven countres mentoned above. We test whether varatons n UI are related to worker ncomplete nformaton. To acheve ths goal, we need UI nformaton for these countres. One measure, found n the OECD Employment Outlook, s a country s publc expendture as a percentage of GDP. The advantage of ths measurement s that we have each country s actual expendtures on UI, nstead of some nomnal beneft measure that mght not be mplemented exactly for each worker. The dsadvantage s that ths measure mght reflect a country s busness cycle rather than how t subsdes an ndvdual s search. Because UI (as a proporton of GDP) s narrowly dstrbuted (most of the UI expendtures are less than percent of countres GDP, wth a few over 3 percent), we convert the UI varable to a logarthm. Ths assumpton s consstent wth UI s mpact beng nonlnear. A logarthmc specfcaton mples a larger mpact when UI s ntally small, whch s what we expect. Other Insttutonal Factors Besdes UI, we examne several other nsttutonal factors that mght contrbute to the explanaton of workers ncomplete nformaton. These varables are populaton densty, proporton of employment n ndustry, rural populaton and the nflow of foregn workers. Informaton on each of these s obtaned from World Bank data. The frst three varables get at how nformaton s concentrated among the populaton. As such they reflect the costs of search, snce we presume that search costs rse when nformaton s dspersed more wdely and harder to fnd. So for example, a more dense populaton mples qucker access to nformaton networks. Also, a more dense populaton probably means that jobs are closer n proxmty. A large rural populaton mples the opposte, namely sparse harder to fnd nformaton, wth jobs spread over wder dstances. Sandell (198) suggests that geographcally concentrated opportuntes lowers search costs, whch prolongs search, and reduces ncomplete nformaton. In contrast to urban areas, rural regons are less concentrated wth job opportuntes, and therefore lkely result n more ncomplete nformaton. Unonzaton rates rse as a country s ndustral employment ncreases (Polachek, 4). Also 17

19 unons provde nformaton to employers and employees regardng wages and jobs (Polachek and Yoon, 1987) and unons compress wage dstrbutons (Freeman, 198). Thus because ndustralzed countres are more unonzed, we expect workers n more ndustral countres to be more nformed and have less ncomplete nformaton. Fnally, we examne the nflow of foregn workers. A consderable body of research examnes the relatve success of mmgrants. For example, past studes of U.S. mmgraton note that wages of newly arrved mmgrants lag behnd natve wages (Chswck, 1978), but that the assmlaton process can cause earnngs of the foregn born to eventually overtake U.S. natves (Chswck, 1986), though there remans some debate on the ssue (Borjas, 1985). In any case, the whole assmlaton process nvolves ncreased acquston of nformaton on domestc labor markets, and the use of ths nformaton n the search process (Daneshvary, et al., 199). Further, new lower-waged foregn workers, who come to a country wth less ntal knowledge of wage structures can affect the overall dstrbuton of wages. Snce generally, havng less nformaton mples that mmgrant workers receve lower wages than natves, wage dsperson ncreases, as does skewness. Thus the effect of foregn workers s lkely to be a more left-skewed earnngs dstrbuton, resultng from ncomplete nformaton (holdng sklls constant). But ths result s not always found. A study of German mmgrants, usng data from, fnds natves and mmgrants at about the same dstance from the fronter (Lang, 4). For ths reason, the effect of foregn n-mgraton on ncomplete nformaton s stll an open queston. Table 5 gves a detaled summary of these varables. Column (1) contans the varable name and defnton. Column () contans the number of country-year cells for whch data are avalable. Columns (3) (6) contan summary statstcs. Rows (1) through (7) contan statstcs for each of the nsttutonal varables just mentoned. Generally data are avalable for most of the tme perods and countres. The exceptons are for UI (whch s mssng eght of 45 observatons) and foregn worker nflows (whch s mssng 4 observatons). Ths latter restrcton necesstates runnng separate analyss of foregn worker nflow effects. Row (8) gves estmates of the ncomplete nformaton varable (obtaned from Table ). Because the effects of ncomplete nformaton are affected by usng annual nstead of hourly wages, we normalze those 18 observatons that were based on annual nstead of hourly wages. Ths normalzaton unfes the ncomplete nformaton measurement to make t consstent across countes so that nter-country comparsons can be made. These values are gven n row (1). 15 Cross-country Analyss Cross-country regresson results are gven n Table 6. Country (and year) specfc ncomplete nformaton measures derved from (3) are the dependent varables, and the country-specfc nsttutonal varables just descrbed serve as ndependent varables. We present fve models that explan ncomplete nformaton as a functon of these nsttutonal factors. Model (1) 15 We adjust the 18 ncomplete nformaton estmates by the hourly wage dummy coeffcent n a regresson of ncomplete nformaton on the hourly wage dummy varable. 18

20 concentrates on UI. Model () concentrates on the populaton densty and ndustral structure varables. Models (3) and (4) concentrate on the nflow of foregn workers. Fnally, Model (5) ntegrates most of the nsttutonal factors nto one model. 16 We present four parameters for each ndependent varable. The frst s the smple OLS coeffcent. The second s the t-statstc based on the robust standard error. The thrd s the margnal effect caused by a one standard devaton change n the ndependent varable. Fnally the fourth s an elastcty measure descrbng the percent change n ncomplete nformaton caused by a one percent change n the ndependent nsttutonal characterstc. To dstngush between ncomplete nformaton varables derved from annual versus ncomplete nformaton measures derved from hourly wage data, we adjust for hours of work. 17 The frst model focuses on how UI mpacts ncomplete nformaton. A one-unt ncrease n the logarthm of a country s UI expendtures (relatve to GDP) reduces ncomplete nformaton by.5. Ths means that a one extra logarthm unt of UI spendng (relatve to GDP) nduces workers to get 5% closer to ther maxmum attanable wage. The -.36 coeffcent mples that a one standard devaton ncrease n a country s logarthm of UI (relatve to GDP) causes workers to get 3.6% closer to ther potental wage. Fnally the -.48 elastcty measure ndcates that a doublng of the logarthm of UI spendng (from ts mean value) leads to 4.8 percentage decrease n worker ncomplete nformaton (from ts mean value). Most mportant, these coeffcents substantate search theory. Increasng UI reduces search costs. Lower search costs ncrease search leadng to more worker nformaton on the wage structure. The second model focuses on populaton densty, rural populaton, and ndustral employment. If, as we postulate, each of these nfluences search costs, then we should expect these varables to have an effect on worker ncomplete nformaton. Indeed, ths s precsely what we fnd. Both a more dense populaton and a hgh level of ndustral employment provde nformaton networks, and thus decrease ncomplete nformaton. A large rural populaton ncreases dstances, thereby ncreasng nformaton costs. Ths leads to decreases n employee nformaton, and hence a greater level of employee ncomplete nformaton. The margnal effect of a one standard devaton change n each of these three varables s very smlar n magntude (.3). Ths means that a one standard devaton ncrease n ether populaton densty or ndustral employment decreases a country s ncomplete nformaton by about 1%. 18 Analogously, one could look at ths another way: Incomplete nformaton of about.3 (the mean reported n Table 5) mples that employees on average earn about 7% of ther maxmum possble wage. A one standard devaton ncrease n populaton densty rases 16 Foregn worker nflows cannot be ntegrated because the lmted number of observatons on ths varable precludes suffcent degrees of freedom. In addton, we elmnated the ndustral employment varable because t becomes nsgnfcant. 17 Ths adjustment s omtted from Models (3) and (4) because each of the 1 observatons contans ncomplete nformaton measures derved solely from hourly wage data. 18 Accordng to Table 5, average adjusted employee ncomplete nformaton s about.3. A.3 change s about 1%. 19

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