Economic and Social Research Institute Cabinet Office Tokyo, Japan

Transcription

1 ESRI Dscusson Paper Seres No.34 Nonproft Wage Premums n Japan s Chld Care Market: Evdence from Employer-Employee Matched Data by Haruko Noguch, Satosh Shmzutan, and Wataru Suzuk May 003 Economc and Socal Research Insttute Cabnet Offce Tokyo, Japan

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3 Nonproft Wage Premums n Japan s Chld Care Market: Evdence from Employer-Employee Matched Data By Haruko Noguch, Satosh Shmzutan and Wataru Suzuk May 003 Please address correspondence to: Haruko Noguch, Toyo-Ewa Unversty, Yokohama, Japan Emal: hnoguch@newage3.stanford.edu Satosh Shmzutan, Cabnet Offce, Tokyo, Japan Emal: satosh.shmzutan@esr.cao.go.jp Wataru Suzuk, Osaka Unversty, Osaka, Japan Emal: suzuk@ospp.osaka-u.ac.jp Ths research orgnated n a study on Japan s chld care conducted by the Prce Polcy Dvson of the Cabnet Offce. We thank Mko Kawa, Naohro Yashro, Reko Kanda, Heather Montgomery and semnar partcpants at the ESRI for ther useful comments. The vews expressed n ths paper do not necessarly represent those of the Economc and Socal Research Insttute or of the Japanese government.

4 Abstract Ths paper nvestgates nonproft wage premums n Japan s chld care labor market, an area that has not yet been studed. We take advantage of a unque, large, and hgh-qualty data set on chld care workers collected n the summer 00 to evaluate nonproft wage premums after controllng for selecton bas and varous characterstcs of employers and employees. Our fndngs demonstrate that wage premums n the nonproft sector relatve to subsdzed for-proft centers were clearly observed n Japan s chld care ndustry; the estmated hourly wage dfferental s 8.3 percentage ponts (398 yen n real value), even after controllng for dfferences n subsdes for each area and faclty. Second, nonproft frms are more lkely to value workers wth respect to age, market experence, educaton, qualfcatons, and the sze of frms than are subsdzed for-proft frms. Thrd, more experenced workers wth hgher levels of educaton and qualfcatons, who therefore may provde hgher qualty of care, tend to choose workng n nonproft centers because they are more lkely to be valued n the not-for-proft sector compared to the propretary sector. Fourth, sgnfcant amounts of nonproft wage premums n the chld care market are most lkely to be caused by the hgh value on not-for-proft workers rather than on the presence of a larger number of workers who have unmeasurable comparatve advantage n the nonproft sector. To mprove the effcency of chld care management and break through the bottleneck of chld care supply, therefore, the polcy should shed lght on the lavsh and neffcent rate of return to workers n the not-for-proft sector.

5 . Introducton Ths paper nvestgates nonproft wage premums n the Japan s chld care labor market, an area that has not yet been studed. Although both for-proft and nonproft centers have been operatng n the chld care ndustry n Japan, not-for-proft centers have been rather domnant n the sector. These organzatons nclude both publcly owned and prvate nonproft centers run mostly va socal welfare cooperaton. They are approved by local governments and enjoy preferable treatments through subsdes. On the other hand, for-proft facltes are not lcensed, and most of them are small n sze and managed by ndvduals. In 000, the Japanese government began to encourage prvate companes to enter the lcensed chld care market. It s well known that the undersupply of chld care servces s serous n Japan, especally n urban areas. Usually, nonproft centers are consdered to be much more costly than for-proft counterparts (Cabnet Offce (003)). Snce the chld care ndustry s seen to be labor ntensve, hgher costs reflect hgher wages. Thus, research on wage determnaton n the market s urgent for mprovng the effcency of chld care management and breakng through the bottleneck of chld care supply. Surprsngly, however, there have been no emprcal studes on the wage dfferentals between the for-proft and not-for-proft sectors n ths market. There are competng hypotheses accountng for the sectoral wage dfferences n prevous theoretcal lterature. Those who support nonproft wage premums emphasze the non-dstrbutonal constrant, whch s pecular to nonproft nsttutons, and managers preferences toward the qualty of servces. On the other hand, those who nsst that wage dfferentals are more preferable for the propretary sector assume that employees value non-wage compensaton more than monetary rewards. To examne emprcally the wage dfferentals, we conducted a unque employer-employee matched survey on chld care workers n the summer of 00. Ths s Accordng to the Mnstry of Health, Labor and Welfare, the number of those who are on watng lsts s estmated to be 5,447 as of Aprl,, 00. However, ths number s usually consdered to be sgnfcantly underestmated.

6 the frst study to assess nonproft wage premums n the chld care market n Japan. Our fndngs demonstrate that wage premums n the nonproft sector relatve to the subsdzed for-proft centers were clearly observed. The estmated hourly wage dfferentals are 8.3 percentage ponts (398 yen n real value), even after controllng for dfferences n subsdes for each area and faclty. Second, nonproft frms are more lkely to value workers wth respect to age, market experence, educaton, qualfcatons, and the sze of frms than are subsdzed for-proft frms. Thrd, more experenced workers wth hgher levels of educaton and qualfcatons, who therefore may provde hgher qualty of care, tend to choose workng n nonproft centers because they are more lkely to be valued n the not-for-proft sector compared to the propretary sector. Fourth, sgnfcant amounts of nonproft wage premums n the chld care market are most lkely to be caused by the hgh value of not-for-proft workers rather than the presence of a larger number of workers who have unmeasurable comparatve advantage n the nonproft sector. To mprove effcency n chld care management and break through the bottleneck of chld care supply, the polcy should therefore shed lght on the lavsh and neffcent rate of return to workers n the not-for-proft sector. Ths paper s organzed as follows. The next secton brefly revews some theoretcal background and prevous research on the nonproft earnngs dfferentals. Secton 3 descrbes the emprcal specfcatons to be used n ths paper. Secton 4 descrbes the data for our emprcal work. Sectons 5 and 6 present and dscuss the results. The last secton concludes.. Prevous Research There s a large body of lterature on non-proft wage dfferentals n the Unted States. Most of these studes test the competng hypotheses that support ether the wage premum n the nonproft sector or that n the for-proft sector, derved from dfferent behavoral characterstcs of enterprses n these sectors. 3

7 The representatve argument on the nonproft wage premum s Hansmann (980). He emphaszes the pecular nsttuton to the nonproft enterprses, the non-dstrbutonal constrant under whch the nonprofts are prohbted from dstrbutng net earnngs. More concretely, the non-dstrbutonal constrant provdes two knds of hypotheses on non-proft management to explan the wage premum. The frst explanaton, termed phlanthropc wage-settng (Feldsten (97)) or attenuated property rghts (Borjas et. al (983)), argue that nonproft managers have less ncentve to lower wages, snce they do not have to make profts. The other explanaton s that managers have less ncentve to lower the qualty provded, snce an ncrease n qualty rases ther utlty n the not-for-proft sector (Newhouse (970)). Moreover, other research attrbutes nonproft wage premums to the organzatonal characterstcs of the nonproft sectors such as exempton of corporate taxes or preferental treatment by the government (Frank and Salever (994)), or soft budget constrants that allow neffcent management of nonproft enterprses. On the contrary, there are some arguments to support the theoretcal hypothess that nonproft wages are less than ther counterparts n the proft sectors. The representatve of ths vew s the labor donaton story, whch states that employees n nonproft sectors place lower value on money and hgher value on non-monetary benefts such as workng condtons or socal responsblty (Rose-Ackerman (996), Preston(989)). Another explanaton s that nonproft enterprses are concentrated n less-proftable sectors so that wages are lower n the nonproft compared to the for-proft sector (Lakdawall et. al. (998)). Despte a large body of emprcal lterature on the wage dfferentals, the results are nconclusve 3. 3 Ruhm and Borkosk (000) provde a compact survey on emprcal studes. One stream of emprcal research s to nvestgate overall and wthn-ndustry dfferentals. Leete (00) fnds that zero or slghtly postve economy-wde wage dfferentals between nonproft and for-proft employees. The other stream s to nvestgate the wage dfferentals n specfc sectors. Wesbrod (983) and Goddeers (988) nvestgate wage dfferentals between prvate-proft and publc-nterest lawyers. As for nursng homes and day care ndustres, Borjas et al. (983) report that, based on the OLS estmates, the non-proft premum n nursng homes s consstent wth the property rght explanaton. Preston (988) fnds t n federally regulated nonproft day care centers. However, Holtman and Idson (993) offer a counterargument on the prevous fndngs. Ther fndng mples that the nonproft wage premum can be nterpreted as compensaton for unobserved qualty dfferences, rather than as the comparatve advantage theory, whch states that employees follow the crteron of choosng the sector that offers the hghest 4

8 As regards the chld care market, Mocan and Tekn (000) perform an emprcal study on the nonproft wage premum. They use an employer-employees matched data set to address the wage dfferentals between for-profts vs. nonprofts and full-tme vs. part-tme. Ther evdence, whch uses,000 chld care workers, supports the labor donaton hypothess, but at the same tme ndcates that the exstence of nonproft wage and compensaton premums, whch supports the property rghts hypothess. Mocan and Vola (997) also fnds the postve nonproft premum, though the statstcal sgnfcance dsappears once sector breakdowns are controlled. Despte ts academc and practcal mportance, there s lttle research on the nonproft wage dfferentals n Japan. The only related research n Japan s Noguch and Shmzutan (00), whch demonstrates that the nonproft wage premum s observed n Japan s long-term care labor market. Ths s partly explaned by the unavalablty of mcro-level data on wages and employee characterstcs. As prevous research ponted out, t s crucal for evaluatng wage dfferentals after controllng for the characterstcs of employees and facltes due to heterogenety. To our knowledge, ths study s the frst attempt to assess non-proft wage dfferentals n the chld care market n Japan. 3. Emprcal Specfcaton Our basc specfcaton n ths paper s the followng Mncer type sem-logarthmc wage equaton frequently used n labor lterature. () W = X ß + D + τ X ncludes varous factors determnng the th worker s wage rate, such as human captal characterstcs. ß s a parameter to be estmated, and D s an ndcator varable for wage. 5

9 prvate status. τ shows unobservables that would nfluence hs or her wage rates. However, t s well-known that smple lnear regresson ncludng a sector allocaton dummy varable s not approprate for examnng the sectoral wage dfferentals because the estmaton yelds unrelable based estmates, owng to the endogenety problem such that the sector afflaton of workers s nfluenced by unobservable varous characterstcs that also affect wage rates. The nature of characterstcs of workers who decde to work n the publc or prvate sector s alleged to be systematcally very dfferent, and owners n these dfferent sectors tend to hre workers wth dfferent specfc types of characterstcs. To address ths selecton bas, we wll apply Heckman s two-stage approach to obtan unbased estmates of parameters (Lee (978), Heckman (979), Wlls and Rosen (979), Greene (98)). Frst, we use the maxmum lkelhood method for the probt regresson equaton to estmate the parameter vector γ for workers tendency to choose publcly owned facltes n the followng selecton rule. * () Z = Y γ + u Z = f Z * 0 or u Y γ Z = 0 f Z * < 0 or u > Y γ where u ~ N(0, σ ) u Then, we wll perform regresson for sector, separately, as follows: W on X and λ n the for-proft and nonproft (3.) (3.) W W = X δ = X δ + λ θ + λ θ + ω + ω where E( ω ) E( ω ) = 0 = λ = φ Y γ / σ ) / Φ(Y γ / σ ) and λ = φ Y γ / σ ) /[ Φ(Y γ / σ )] ( u u ( u u θ = σu / σ u = cov( ε, u ) / σ u and θ = σ u / σ u = cov( ε,u ) / σ u 6

10 The superscrpts stand for afflates, that s, s for prvate and 0 for publc centers. For the above equatons for truncated mean λ, φ, and Φ ndcate the standard normal probablty densty and the standard normal cumulatve densty functons, respectvely. θ and θ measure the relaton between wage rates and the unobservable characterstcs that nfluence the sector allocaton. θ > 0 mples a postve selecton bas among * workers n the prvate sector, such that E[W Z 0] E(W ), or those who earn greater > than predcted wages derved from observed factors of workers n prvate homes are more lkely to be workng n the prvate sector than the predcted probablty. In contrast, θ < 0 ndcates a negatve selecton bas among publc workers, such that E[W Z < 0] E(W ), or those who earn greater than the observed predcted wages n * > the publc sector, are less lkely to be allocated to the prvate sector than the expected prospect (Wlls and Rosen (979), Holtmann and Idson (993)). Suppose that a worker n the chld care labor market chooses to work n the sector that rewards hgher hs or her personal characterstcs, such as ablty, talent, educaton, and experence. Ths argument suggests the condton such that θ > 0 and θ < 0, whch means that the employee wth the hghest earnngs who s able to provde the best qualty of servces n the prvate sector would also gve the best performance wth the greatest wages n the prvate sector, along wth a strctly herarchcal self-allocaton pattern based on ablty 4. 4 Instead of the partcular drectons for θ, Trost (98) clams that θ > θ, or the relaton between the unobservable characterstcs that nfluence the sector allocaton and wage rates, s greater n the for-proft than nonproft sector, as the necessary condton for the comparatve-advantage hypothess. * * Explctly, E[W Z 0] > E[W Z 0], for-proft workers wth greater than condtonal mean wages of nonproft employees f they decde to work n the for-proft sector are more lkely to be allocated to for-proft homes than the expected probablty. On the other hand, * * E[W Z < 0] > E[W Z < 0], not-for-proft workers wth greater than condtonal mean wages of for-proft workers f they choose to work n the nonproft sector are less lkely to be allocated to the for-proft sector than the expected prospect. 7

11 4. Data The emprcal analyss of ths study s based on two dfferent surveys on the chld care centers: non-lcensed and lcensed. Both surveys were conducted by the Prce Polcy dvson of the Cabnet Offce, Japanese government, n the summer of 00. These two data sets are, to our knowledge, the largest and most comprehensve ones on non-lcensed and prvate lcensed chld care centers currently avalable n Japan. We were able to perform the survey wth specal cooperaton and deep understandng by local governments. Before the survey, we explaned the purpose of our research and consulted wth offcals n charge of chld care polces at local governments. We presented them wth our detaled questonnare, consderng the feasblty of the survey and precseness of response. Surprsngly, mcro-level wage data on chld care workers had not been prevously avalable. Thus, those were the frst surveys on chld care workers n Japan. They were also unque. The sample of the survey on non-lcensed chld care facltes comprses those operatng n Tokyo Prefecture, facltes that had the unfortunate experence of havng the largest number of chldren on a watng lst of all the chld care centers n our survey. The data n Tokyo were randomly collected by the local government, and were regarded as beng for-proft. 5 However, due to the lack of necessary ndcators for examnng sectoral wage dfferences, note that only chld care workers from nonlcensed facltes subsdzed by local governments are ncluded n our study. Therefore, n ths study, for-proft facltes represent propretary centers fnancally supported by local governments. To adjust for the wage dfferences owng to the amounts of government subsdes between not-for proft and subsdzed for-proft sectors, we nclude area and faclty dummes nto our regresson analyss. We have a rch set of nformaton on both employees and employers as descrbed below, whch enables us to control the characterstcs of facltes to nvestgate the 5 Non-lcensed centers are concentrated n urban areas, where the chld care supply s nsuffcent and where excess demand for care s observed. Note that all non-lcensed chld care centers are recognzed by local governments. 8

12 nonproft wage premums. Obvously, we also need a data set on workers and ther wages n nonproft counterparts to evaluate the nonproft wage premums. The second data set we use n ths paper s that of workers n prvate and lcensed nonproft facltes collected n Tokyo Prefecture. Most of them are operated wth the cooperaton of socal welfare organzatons, and are not consdered to be propretary. Ths survey was performed through cty/town/vllages n Tokyo Prefecture. They collected detaled nformaton on all chld care workers n each faclty, from 40 percent of lcensed prvate facltes wthn the area of ndvdual local governments, whch were randomly selected 6. As n the non-lcensed data set, we are able to match employer and employees n each chld care center, whch enables us to study nonproft wage premums whle controllng for varous faclty-related factors. Focusng on the wage dfferentals between for-proft and nonproft chld care centers, we choose sx muncpal areas wthn Tokyo Prefecture, where the data on care workers n both non-lcensed for-proft and prvate lcensed not-for-proft facltes are avalable. For elmnatng any factors affected by regonal dfferences between urban and rural areas, therefore, we ncluded only the muncpal areas where both non-lcensed and lcensed facltes data are accessble. Moreover, we elmnated the samples of whch the data necessary for our estmaton are mssng. We ended up wth 9 (84 percent) and 73 (6 percent) workers n nonproft and for-proft centers, both subsdzed by the local government, respectvely. Before explanng the basc statstcs, we have two remnders. In ths study, we focus on the wage dfferences between prvate nonproft and prvate for-proft centers; we exclude publc facltes. Noguch, Shmzutan, and Suzuk (003) observe substantal dfferences n age of workers between publcly owned and prvate centers, and the wage age profles are much steeper n publc facltes, whch are not related wth worker productvty. Ther fndngs suggest that the labor market for chld care workers s segregated between the publc and prvate sectors. 6 Each local government collected data on chld care workers from at least one authorzed prvate center, even f the number of facltes n the area s small. 9

13 Table descrbes some basc statstcs of major characterstcs of both employees and facltes that could affect wages n nonproft and for-proft chld care centers. Mean hourly wages are, remarkably, 5.7 percent hgher n nonproft facltes (,604 yen n not-for-proft centers; the mean wage s,05 yen n for-proft centers) 7. We are able to observe the nonproft wage premums very clearly. The summary statstcs demonstrate that ndvdual employee characterstcs between both sectors are not heterogeneous n our samples. The dfferences n mean years of age and market experence between both sectors are subtle. Workers n the nonproft sector tend to be slghtly younger, but have about. years more market experence compared to employees n the propretary sector. Regardng educatonal backgrounds, two-year communty college graduates seem to occupy around 50 percent of all employees n both sectors. The share of unversty graduates s slghtly hgher n for-proft centers than not-for-proft facltes, by 4.4 percentage ponts. More than two-thrds of employees n the chld care centers are qualfed and the share of qualfed workers are 5. percentage ponts hgher n the not-for-proft sector than n the for-proft sector. As for worker status, the shares of regular workers n nonproft and for-proft centers are 67.8 percent and 7.8 percent, respectvely. Unfortunately, we do not have nformaton on martal and famly status, whch s controlled for n prevous works as proxy ndcators for employee stablty 8. Further, the data do not contan nformaton on non-wage benefts such as nsurance or professonal tranng. Contrary to the homogenety among employees, we observe the dfference n faclty-related characterstcs between nonproft and for-proft sectors. The number of employees ndcatng frm sze s apparently larger n nonproft centers than for-proft organzatons. Ths dstnct dfference n frm sze mght be accounted for by the postve relaton between the number of employees and wage rates that a large number of studes 7 Mean monthly and daly wages are also remarkably hgher n nonproft facltes, by 38.7 and 3.4 percentage ponts. The mean monthly wage s 6,634 yen n not-for-proft centers and 60,56 yen n for-proft centers. Also, the mean hourly wage s,63yen n nonproft centers and 854 yen n propretary facltes. 8 See Holtmann and Idson (993) and Mocan and Tekn (000). 0

14 have shown 9. Interestngly, not-for-proft centers are more lkely to provde care for extra hours; total hours of care are.4 hours longer than for-proft centers, whch s thoroughly contradctory to the general belef that non-lcensed centers are nclned to operate for much longer hours than lcensed facltes 0. In sum, the basc statstcs reveal that workers n nonproft centers enjoy hgher hourly wages by approxmately 34 percentage ponts than for-proft facltes. However, worker characterstcs are not seen to be varous among our samples n both sectors. Not-for-proft facltes are somewhat advantageous n employees years of market experence, two-year communty college educaton, and qualfcatons as chld caregvers, whle propretary centers are slghtly advantageous n worker age, four-year unversty educaton, and full-tme status. Regardng faclty characterstcs, nonproft facltes tend to operate for longer hours and to employ more workers, whle for-proft centers provde supplemental types of care such as temporary and post acute care. To examne the effects of these factors on wage determnaton, we frst run a smple sem-logarthmc human captal wage functon for a pooled full sample, usng a dummy varable that ndcates whether or not the ownershp s propretary. The results demonstrate that the dummy varable s large and statstcally sgnfcant; they ndcate that daly wages are hgher n nonprofts by 3.9 percent (37.5 percent and 5.7 percent for monthly and hourly wages, respectvely), after controllng for varous worker and faclty characterstcs. However, smple least square regressons are not approprate, snce the allocaton of workers wth varous characterstcs n our data to the for-proft and nonproft sectors s nonrandom. Therefore, applyng Heckman s two-stage approach (Lee (978), we decded that Heckman (979)) s an approprate procedure to obtan relable estmates on the effects of a varety of ndvdual and frm factors on the sectoral allocaton and wage 9 See Brown and Medoff (989), Idson and Feaster (990), Troske (999). Note that propretary facltes have larger frm sze and lower wages n the Japanese long-term care market (Noguch and Shmzutan (00)). 0 Regardng addtonal care, the shares of provdng temporary care (43.9 percent versus 0 percent) and post acute care (39.3 percent versus 3. percent) are much hgher n propretary facltes than n nonproft centers, but ths s not the case for dsablty care (34.7 percent versus 76.9 percent). Put dfferently, for-proft chld care centers play a complementary role to not-for-proft facltes usually provdng standard day-tme care servces.

15 rates. There are only a few studes that address self-selecton of workers nto dfferent sectors, n partcular, wth control for frm effects. 5. Results Tables -, -, and -3 show the estmated coeffcents of the for-proft selecton equaton and monthly, daly, and hourly wage equatons. Snce regular and non-regular workers are pad by these dfferent types of wages, we need these to standardze the results. The results of the sector allocaton probt regresson n the frst column bascally replcate unadjusted comparsons between the two sectors n Table. At the 5-percent-level sgnfcance, workers who are relatvely young and who have farly long labor market experence and a two-year communty college degree reman less lkely to self-select nto propretary homes. Also, we observe that both regular and nonregular employees wth long hours of work, at frms wth larger numbers of employees, and longer hours of care are sgnfcantly preferable for nonproft organzatons. The rest of the columns n these tables show the estmated coeffcents of wage equatons for propretary and not-for-proft centers, corrected for self-selecton bas. Focusng on hourly wages (Table -), male, older, and more experenced employees wth hgher degree of educaton and qualfcatons seem to be more apprecated n the not-for-proft sector than the propretary sector, where none of these factors are statstcally sgnfcant. Coeffcents of squared years of age and experence n Table - desgnate a famlar pattern of wage profle that does not lnearly ncrease along wth age and market experence n the not-for-proft sector. On the other hand, the proft-seekng sector rewards regular and non-regular workers wth long hours, more than the not-for-proft sector, whch s consstent to the propensty of these workers to self-allocate nto for-proft centers. Movng down to the effects of faclty characterstcs, larger propretary frms offer lower wage rates, whch s nconsstent wth prevous studes. In the not-for-proft sector, at the 5-percent-level sgnfcance, the effects of the frm sze on wage rates are postve, yet

16 they reman almost the same magntude along wth the sze of frm. Workers n for-proft facltes provdng extra hours of care are valued more than those n the not-for-proft sector, although employees n facltes that actually operate for longer hours are less apprecated n both sectors. Fnally, t s mportant to pont out the coeffcents of truncated mean adjustment factors for selecton bas, both θ and θ and θ n the wage equatons. The estmates n ths study show that θ are sgnfcantly postve, whch mples that unobserved factors extensvely affect employee allocaton and wage rates. Although observed varables show that workers are not heterogeneous as dscussed above, θ > θ shows that the relatons between unobserved factors and both sectoral allocaton and wage rates are larger n the propretary than nonproft sectors. Our estmates show that a worker who has tendency to choose for-proft earns.8 percent hgher wages than a worker who s nclned to choose nonproft facltes f he s employed n for-proft center. He also earned more than hs counterpart f he works for a nonproft center, but the dfference s smaller. Therefore, our estmates demonstrate that there s a self-selecton pattern between two sectors along wth comparatve advantage among workers who tend to choose the sector offerng the hghest wages. Ths mplctly means that the mean wages of employees who are actually self-allocated nto proft facltes are greater than wages that would have been observed f they chose to work n not-for-proft facltes. On the contrary, the mean wages of employees who n fact chose to work n the nonproft sector are greater than wages that would have been observed f employees wth observed smlar characterstcs self-allocated nto the proft sector. 6. Factor Decomposton Analyss of Wage Dfferentals Between Two Sectors Based on the estmates from the wage equatons corrected for self-selecton bas n Tables -, -, and -3, we apply a smple decomposton analyss to the wage dfferental to dstngush the contrbutons of attrbutes from dfferental contrbutons n valuaton of 3

17 key explanatory factors between for-proft and not-for-proft centers to the nonproft wage premum. The total effects are broken down n the followng way: (8) W W = ( X δ + λ θ ) ( X δ + λ θ ) = { δ ( X ) ( )} { ( ) ( ) X + θ λ λ + δ δ X + θ θ λ } where { δ ( X ) + θ ( λ λ )} dfferental contrbutons and { ( δ δ ) X ( ) + θ θ λ } X s the summaton of each factor s qualtatve s the sum of dfferental contrbutons n valuaton of each explanatory varable. The results are shown n Tables 3 and 4. Lookng at the daly wage dfferences n the frst panel of Table 3, the summaton of hourly wage dfferentals between the nonproft and for-proft sectors over all explanatory varables s 8.3 percentage ponts (398 yen n real value) after controllng for unobserved self-selecton bas. The characterstcs of employees and facltes (attrbutes) and sector valuaton account for 44.8 percentage ponts (76 yen) and mnus 6.5 percentage ponts ( 36 yen) of the total, respectvely. Table 4 shows the breakdown of sectoral wage dfferentals n real value by factor, where postve (or negatve) values enlarge (or reduce) the total nonproft wage dfferental. The last column reports that employee demographc characterstcs, market experence, educaton, qualfcatons, and the sze of frms contrbute to the hgher observed wage n not-for-proft facltes. For all these factors other than gender, market experence, and educaton, the magntude of effect s largely due to the hgher value on characterstcs n the not-for-proft sector than the for-proft sector. In other words, not-for-proft facltes value these attrbutes much more than they do n the propretary centers. Therefore, as prevous studes found (Idson and Feaster (990) and Holtmann and Idson (993)), nonproft managers are less lkely to be cost-senstve n total under the non-dstrbutonal constrant. They are, therefore, more lkely to value worker productvty assocated wth the qualty of servces related to age, market experence, educaton, qualfcatons, and the sze of frms. Ths result s seen to be completely consstent wth the property-rghts hypothess. 4

18 Focusng on the selectvty, we found that the total effect of selectvty s negatve (-4 yen) and the net of selectvty (reducng the effect of selectvty from the total effect) s 440 yen. Also, snce the truncated mean s postve n the for-proft sector, the postve coeffcent on λ entals that wages for the average observed self-selected for-proft employees are hgher than would be observed f employees are randomly allocated across two sectors. Ths mples that a random allocaton of employees would lead to even lower wages n the for-proft sector relatve to wages n the nonproft sector, and would enlarge the wage dfferental between two sectors. In contrast to the results of Holtmann and Idson (993), our result shows that a large number of workers who actually chose to work n nonproft centers accordng to ther comparatve advantage are redstrbuted to for-proft centers by a random reallocaton procedure, decreasng for-proft relatve to nonproft wages. As shown n Table 4, ths negatve selectvty effect s mostly attrbuted to dfferental characterstcs rather than to sector valuaton of the selectvty. However, the magntude of the selectvty s not large enough to conclude that the presence of a larger number of workers who have unmeasurable comparatve advantage n the not-for-proft relatve to for-proft sectors s a serous cause of nonproft wage premum. Rather, to mprove effcency n chld care management and to break through the bottleneck of chld care supply, the polcy should shed lght on the lavsh and neffcent rate of return to workers n the not-for-proft sector. 7. Concluson The actvtes and enhanced presence of the nonproft sector are stll remarkable n varous Japanese ndustres. Those employed n the nonproft sector account for up to 3.8 percent of total employment n 000. An ncrease n the demand for chld care due to the rapd growth of female labor partcpaton rates would lead to a strong poltcal ncentve Matsunaga, Y. (00). Chapter : Macro estmaton. In Yamauch, N. (ed.), The Japanese Nonproft Almanac 00: Insghts nto Japanese Nonprofts from the Latest Data Set (pp.7-0). Osaka, Japan: NPO Research Project, Osaka School of Internatonal Publc Polcy. 5

19 for the Japanese government to acheve market effcency n the chld care ndustry. Ths n turn wll lead to acceleratng the entry of for-proft frms nto the conventonal nonproft sector and to stmulatng competton between dfferent types of frms n the market. Under such a drastc change n soco-economc crcumstance, the emprcal assessment of the chld care ndustry, where both for-proft and nonproft sectors coexst, wll gve us sgnfcant clues regardng polcy mplcatons from varous aspects. In ths paper, we focus on the dfferences n economc behavor on wage settng between for-proft and nonproft facltes. We hypothesze that the nonproft wage premum n the chld care market may be accounted for by the segregated labor market between two sectors, provdng observed and unobserved dfferent qualty of servces owng to non-dstrbutonal constrants. Under non-dstrbutonal constrants, nonproft centers are prohbted from dstrbutng net earnngs, whch makes nonproft provders rase the qualty of servces to avod the opportunstc behavor often observed n for-proft facltes. Snce chld care s a labor-ntensve product, the dfference n qualty of workers between for-proft and nonproft facltes wll most lkely clarfy the qualty of the servce provded. We have an emprcal challenge n ths context: sample selecton bas caused by the endogenety problem such that both sector afflaton of workers and wage rates are correlated wth varous unobserved ndvdual and faclty characterstcs. We present three major emprcal fndngs. Frst, our emprcal results confrm that there s a sgnfcant magntude of nonproft wage premums n the chld care labor market n Japan before and after controllng for nonrandom unobserved self-selecton bas, even after controllng for dfferences n subsdes for each area and faclty. Second, nonproft frms are more lkely to value workers wth respect to age, market experence, educaton, qualfcatons, and the sze of frms than are for-proft frms. Thrd, the chld care market s segregated between for-proft and not-for-proft facltes n Japan as a consequence of the presence of comparatve advantage on worker self-allocaton procedures. More experenced workers wth hgher levels of educaton and qualfcatons, who therefore may provde a hgher qualty of care, tend to choose workng n nonproft centers because they are more lkely to 6

20 be valued n the not-for-proft sector compared to the propretary sector. Fourth, the sgnfcant amounts of nonproft wage premums n the chld care market are most lkely to be caused by the hgh value on not-for-proft workers rather than on the presence of a larger number of workers who have unmeasurable comparatve advantage n the nonproft sector. 7

21 References Borjas, George J., Frech III, H.E., and Gnsburg, Paul B.(983). Property Rghts and Wages: The Case of Nursng Homes. Journal of Human Resources, vol.8, pp3-46. Brown, C. and Medoff, James (989). The Employer Sze Wage Effect. Journal of Poltcal Economy, vol.0, pp Cabnet Offce. (003). Report on the Chld Care Market n Japan. Government of Japan. Frank, Rchard G. and Salkever, Davd S.(994). Nonproft Organzatons n the Health Sector. Journal of Economc Perspectve, vol.8, pp9-44. Feldsten, Martn (97). The Rsng Cost of Hosptal Care. Informaton Servces Press. Washngton D.C. Goddeers, John H. (988). Compensatng Dfferentals and Self-Selecton: An Applcaton to Lawyers. Journal of Poltcal Economy, vol.96, pp4-48. Greene Wllam (98). Sample Selecton Bas as a Specfcaton Error: Comment Econometrca 49, pp Hansmann, Henry (980). The Role of Nonproft Enterprse. Yale Law Journal, vol.89, pp Heckman, James (979). Sample Selecton Bas as a Specfcaton Error. Econometrca, vol.47, pp53-6. Holtmann, A.G., and Idson, Todd L.(993). Wage Determnaton of Regstered Nurses n 8

22 Propretary and Nonproft Nursng Homes. Journal of Human Resources, vol.8, pp Idson, Todd, and Danel J. Feaster (990) A Selectvty Model of Employer-Sze Wage Dfferentals. Journal of Labor Economcs, 8(), pp99-. Lakdawall, Darus and Phlpson, Tomas (998). Nonproft Competton and Producton. NBER Workng Papers, no Lee, Lung-Fe (978) Unonsm and Wage Rates: A Smultaneous Equatons Model wth Qualtatve and Lmted Dependent Varables. Internatonal Economc Revew, 9(), pp Leete, Laura (00). Whther the Nonproft Wage Dfferental? Estmates from the 990 Census. Journal of Labor Economcs, vol.9, pp Matsunaga, Y. (00). Chapter : Macro estmaton. In Yamauch, N. (ed.), The Japanese nonproft almanac 00:The nsghts nto the Japanese nonprofts from the latest data set (pp.7-0). Osaka, Japan: NPO Research Project, Osaka School of Internatonal Publc Polcy Mocan, H. Nac and Deborah Vola (997). The Determnants of Chld Care Worker s Wages and Compensaton: Sectoral Dfference, Human Captal, Race, Insders and Outsders, NBER Workng Papers, no.638. Mocan, H. Nac and Tekn, Erdal (000). Nonproft Sector and Part-Tme Work: An Analyss of Employer-Employee Matched Data of Chld Care Workers. NBER Workng Papers, no Montgomery, Mark and Cosgrove, James (995). Are Part Tme Women Pad Less? A 9

23 Model wth Frm Specfc Effects. Economc Inqury, vol.38, pp9-33. Noguch, Haruko and Satosh Shmzutan (00). Earnngs and Qualty Dfferentals n For-Proft versus Nonproft Long-Term Care: Evdence from Japan's Long-Term Care Market. ESRI Dscusson Paper Seres No.7, Cabnet Offce, Government of Japan. Noguch, Haruko, Satosh Shmzutan and Wataru Suzuk (003). The Wage Determnants and Age Profle n the Japanese Chld Care Industry: Evdence from Employee-level Data. mmeo, Economc and Socal Research Insttute, Cabnet Offce, Government of Japan. Preston, Anne E.(988). The Effects of Property Rghts on Labor Costs of Nonproft Frms: An Applcaton to the Day Care Industry. Journal of Industral Economcs, vol.36, pp Preston, Anne E.(989). The Nonproft Worker n a For-Proft World. Journal of Labor Economcs, vol.7, pp Ruhm, Chrstopher J., and Borkosk, Carey (000). Compensaton n the Nonproft Sector. NBER Workng Papers, no.756. Troske, Kenneth R. (999). Evdence on the Employer Sze-Wage Premum from Worker-Establshment Matched Data. Revew of Economcs and Statstcs, vol.8, pp5-6. Trost, Robert P. (98). Interpretaton of Error Covarances wth Non-Random Data: An Emprcal Illustraton of Returns to College Educaton. Atlantc Economc Journal, 9(3), pp

24 Wesbrod, Burton A. (983). Non-Proft and Propretary Sector Behavor: Wage Dfferentals Among Lawyers. Journal of Labor Economcs, vol., pp Wlls, J. Robert and Sherwn Rosen (979). Educaton and Self-Selecton. Journal of Poltcal Economy, 87 (supplement), pps7-s36.

25 Table : Key Varable defntons and summary statstcs Varable Defnton Total (n=,084) For-proft (n=73) Not-for-proft (n=9) Mean Standard Mean Standard Mean Standard devaton devaton devaton I. Employees' characterstcs month Monthly wage ncludng varous types of compensaton 45, (99,48.70) 60, (6,035.00) 6, (96, ) a day Daly wage ncludng varous types of compensaton, (4,83.69) 8, (, ), (4,.83) a hour Hourly salary ncludng varous types of compensaton, (5.665), (46.43), (54.49) a lgmonth Natural logarthm of monthly wage.35 (0.468).895 (0.459).395 (0.46) a lgday Natural logarthm of daly wage 9.35 (0.37) (0.300) (0.35) a lghour Natural logarthm of hourly wage 7.68 (0.330) (0.08) 7.33 (0.3) a male = f male (0.08) 0.07 (0.3) (0.9) a age Years of age (.683) (.949) (.63) sqage Years of age squared,9.438 (93.76),36.30 (933.38),79.36 (93.004) age0 = f age s equal to or greater than 0 and less than (0.500) (0.498) (0.500) age30 = f age s equal to or greater than 30 and less than (0.40) 0.08 (0.407) 0.00 (0.400) age40 = f age s equal to or greater than 40 and less than (0.389) 0.97 (0.399) 0.83 (0.387) age50 = f age s equal to or greater than 50 and less than (0.305) 0.7 (0.334) (0.99) age60 = f age s equal to or greater than (0.50) 0.03 (0.5) 0.03 (0.50) exp Years of experence 8.09 (8.75) 6.68 (7.803) 8.73 (8.3) a sqexp Years of experence squared (5.487) 05.8 (6.964) (56.868) c educ = f unversty graduate (0.69) 0.6 (0.3) 0.07 (0.58) a educ = f communty college graduate 0.55 (0.498) (0.50) (0.496) a educ3 = f professonal tranng school graduate 0.4 (0.48) 0.37 (0.46) 0.43 (0.49) educ4 = f others 0.9 (0.336) 0.68 (0.375) 0. (0.37) b q = f qualfed chld care worker (0.455) (0.473) 0.77 (0.45) q = f regular worker (0.464) 0.78 (0.446) (0.467) q = f nonregular worker wth long hours of work (0.34) 0.09 (0.9) 0.05 (0.) a q3 = f care worker wth short hours of work (0.36) (0.98) (0.4) q4 = f chef or drector of chld care workers (0.95) (0.45) (0.84) b II. Faclty characterstcs proft = f proft 0.60 (0.366) temply Number of employees.559 (0.5) (.448) (9.64) a emp = f number of employees s equal to or less than (0.086) 0.03 (0.5) (0.066) a emp = f number of employees s greater than 5 and equal to or less than 0.0 (0.33) (0.484) 0.0 (0.04) a emp3 = f number of employees s greater than (0.3) (0.477) (0.3) a q93x = f specal care provded (extra hours of care) (0.465) (0.477) 0.69 (0.46) q93x Total hours of care.44 (.53) 0.43 (5.356).668 (0.543) a lgq93x Natural logarthm of total hours of care.387 (0.447).04 (.043).456 (0.046) a lambda Selectvty for monthly wage (lambda proft) (0.65) 0.04 (0.067) lambda Selectvty for daly wage (lambda proft) (0.08) (0.039) lambda Selectvty for hourly wage (lambda proft) (0.05) 0.30 (0.05) Note a-c descrbes sgnfcant mean dfference between the for-proft and nonproft sectors at 5%, 0%, and 5% levels, respectvely, based on F-statstcs of ANOVA.

26 Table -: Hourly wage and for-proft and not-for-proft allocaton regressons Selecton nto for-proft For-proft Not-for-proft Varable Defnton Coeffcent Standard Coeffcent Standard Coeffcent Standard error error error male = f male (0.460) b 0.06 (0.0) (0.033) a age Years of age 0.00 (0.050) a 0.03 (0.00) (0.005) sqage Years of age squared (0.00) a (0.000) c (0.000) exp Years of experence (0.08) a (0.006) (0.003) a sqexp Years of experence squared (0.00) a (0.000) (0.000) a educ = f unversty graduate (0.85) 0.6 (0.056) a (0.033) a educ = f communty college graduate -0.7 (0.6) a 0.00 (0.044) 0.5 (0.06) a educ3 = f professonal tranng school graduate (0.46) a (0.049) b 0. (0.07) a q = f qualfed chld care worker (0.08) c -0.0 (0.039) (0.04) b q = f regular worker 0.45 (0.9) b (0.04) b 0.09 (0.05) a q = f nonregular worker wth long hours of work (0.69) a (0.055) -0. (0.037) a q4 = f chef or drector of chld care workers (0.379) (0.06) 0.7 (0.037) a emp = f number of employees s greater than 5 and equal to or less tha.069 (0.53) a (0.099) 0.46 (0.) a emp3 = f number of employees s greater than (0.50) a (0.0) a 0.4 (0.0) a q93x = f specal care provded (extra hours of care) 0.0 (0.55) (0.045) a (0.0) b lgq93x Natural logarthm of total hours of care -.8 (0.98) a (0.04) (0.) a Intercept ntercept (.084) 6.76 (0.04) a 4.73 (0.56) a sgma Selectvty for monthly wage (lambda proft) (0.04) a 0.00 (0.008) a Log-lkelhood Note Parameter estmates are reported wth standard errors n parentheses. a-c denote sgnfcance level of 5%, 0%, and 5%, respectvely.

27 Table -: Daly wage and for-proft and not-for-proft allocaton regressons Selecton nto for-proft For-proft Not-for-proft Varable Defnton Coeffcent Standard Coeffcent Standard Coeffcent Standard error error error male = f male (0.460) b 0.00 (0.4) 0.07 (0.04) a age Years of age 0.00 (0.050) a (0.06) 0.08 (0.006) a sqage Years of age squared (0.00) a (0.000) (0.000) a exp Years of experence (0.08) a (0.009) (0.004) a sqexp Years of experence squared (0.00) a (0.0003) (0.000) a educ = f unversty graduate (0.85) 0.08 (0.075) 0.47 (0.043) a educ = f communty college graduate -0.7 (0.6) a (0.06) 0.48 (0.033) a educ3 = f professonal tranng school graduate (0.46) a (0.07) 0.4 (0.035) a q = f qualfed chld care worker (0.08) c 0.03 (0.053) (0.03) b q = f regular worker 0.45 (0.9) b 0.68 (0.063) a (0.033) a q = f nonregular worker wth long hours of work (0.69) a (0.078) a -0.4 (0.046) a q4 = f chef or drector of chld care workers (0.379) 0.03 (0.090) 0.5 (0.048) a emp = f number of employees s greater than 5 and equal to or less than.069 (0.53) a 0.04 (0.49).038 (0.56) a emp3 = f number of employees s greater than (0.50) a (0.5) a 0.9 (0.35) a q93x = f specal care provded (extra hours of care) 0.0 (0.55) 0.86 (0.049) a (0.08) a lgq93x Natural logarthm of total hours of care -.8 (0.98) a -0.4 (0.03) a -0.0 (0.77) Intercept Intercept (.084) 9.6 (0.34) a (0.688) a sgma Selectvty for monthly wage (lambda proft) (0.08) a 0.54 (0.006) a Log-lkelhood Note Parameter estmates are reported wth standard errors n parentheses. a-c denote sgnfcance level of 5%, 0%, and 5%, respectvely.

28 Table -3: Monthly wage and for-proft and not-for-proft allocaton regressons Selecton nto for-proft For-proft Not-for-proft Varable Defnton Coeffcent Standard Coeffcent Standard Coeffcent Standard error error error male = f male (0.460) b 0.77 (0.89) 0.9 (0.047) a age Years of age 0.00 (0.050) a (0.0) (0.007) sqage Years of age squared (0.00) a (0.0003) (0.000) exp Years of experence (0.08) a 0.00 (0.04) 0.05 (0.005) a sqexp Years of experence squared (0.00) a 0.00 (0.0004) a (0.000) a educ = f unversty graduate (0.85) (0.04) (0.048) c educ = f communty college graduate -0.7 (0.6) a 0.07 (0.095) 0.33 (0.039) a educ3 = f professonal tranng school graduate (0.46) a 0.09 (0.096) 0.40 (0.040) a q = f qualfed chld care worker (0.08) c (0.075) a (0.035) a q = f regular worker 0.45 (0.9) b (0.079) a 0.86 (0.038) a q = f nonregular worker wth long hours of work (0.69) a -0.0 (0.) (0.053) q4 = f chef or drector of chld care workers (0.379) (0.) 0.68 (0.054) a emp = f number of employees s greater than 5 and equal to or less tha.069 (0.53) a (0.80) a (0.8) a emp3 = f number of employees s greater than (0.50) a (0.7).06 (0.56) a q93x = f specal care provded (extra hours of care) 0.0 (0.55) (0.) (0.03) lgq93x Natural logarthm of total hours of care -.8 (0.98) a (0.07) (0.34) b Intercept Intecept (.084).506 (0.435) a 9.48 (0.78) a sgma Selectvty for monthly wage (lambda proft) (0.0) a 0.88 (0.007) a Log-lkelhood Note Parameter estmates are reported wth standard errors n parentheses. a-c denote sgnfcance level of 5%, 0%, and 5%, respectvely.

29 Table 3: Decomposton analyss of nonproft secotal wage dfferentals logarthmc value real value. Hourly wage dfference () The predcted wage of not-for-proft faclty () The predcted wage of for-proft faclty (3) The predcted wage dfferental due to attrbutes (4) The predcted wage dfferental due to valuaton (5) Total predcted wage dfferental between for-proft and nonproft facltes. Daly wage dfference () The predcted wage of not-for-proft faclty () The predcted wage of for-proft faclty (3) The predcted wage dfferental due to attrbutes (4) The predcted wage dfferental due to valuaton (5) Total predcted wage dfferental between for-proft and nonproft facltes 3. Monthly wage dfference () The predcted wage of not-for-proft faclty () The predcted wage of for-proft faclty (3) The predcted wage dfferental due to attrbutes (4) The predcted wage dfferental due to valuaton (5) Total predcted wage dfferental between for-proft and nonproft facltes Note: () () (3) (4) ( X δ +λθ ) ( X δ + λθ ) { δ ( X X ) + θ ( λ λ )} { ( δ δ ) X ( ) + θ θ λ} (5) ()-()=(3)+(4)

30 Table 4: Decomposton analyss of nonproft secotal wage dfferentals by factors Due to dfferentals Due to dfferentals Total wage of attrbutes a/ of valuatons b/ dfferentals c/. Hourly wage dfference Male Age Experence Educaton Qualfcatons Regular Nonregular wth long hours of work Sze Other Selectvty Intercept Total d/ e/. Daly wage dfference Male Age Experence Educaton Qualfcatons Regular Nonregular wth long hours of work Sze Other Selectvty Intercept Total d/ e/ 3. Monthly wage dfference Male Age Experence Educaton Qualfcatons Regular Nonregular wth long hours of work Sze Other Selectvty Intercept Total d/ e/ Note: a/ b/ c/ { δ ( X X ) + θ ( λ λ )} { ( δ δ ) X ( ) + θ θ λ } ( X δ + λ θ ) ( X δ + λ θ ) d/ Net of the ntercept e/ Net of selectvty