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<!-- you can have any number of categories here --> [[Category:Douglas Webber]] [[Category:Market Power]] [[Category:The Workplace]] [[Category:Minimum Wage]] [[Category:Unionization]] [[Category:Laissez Faire]] <!-- 1 URL must be followed by >= 0 Other URL and Old URL and 1 End URL.--> {{URL | url = http://economics.cornell.edu/uploads/Webber_Firm_Monopsony_Inequality.pdf}} <!-- {{Other URL | url = }} --> <!-- {{Old URL | url = }} --> {{End URL}} {{DES | des = [[Market Power|Market power]], a form of [[Market Failure|market failure]], produces a positive relationship between a firm's labor supply elasticity and the earnings of its workers. This paper provides empirical evidence measuring market power and showing that employers with more power pay lower wages. Especially at lowest incomes. | show=}} <!-- DPL has problems with categories that have a single quote in them. Use these explicit workarounds. --> <!-- normally, we would use {{Links}} and {{Quotes}} --> {{Quotations|The Impact of Firm Market Power on the Earnings Distribution|quotes=true}} {{Text | The Impact of Firm Market Power on the Earnings Distribution Douglas A. Webber ∗† 8-13-11 Abstract Using the Longitudinal Employer Household Dynamics (LEHD) data from the United States Census Bureau, I compute rm-level measures of labor market (monop- sony) power. To generate these measures, I extend the semistructural model proposed by Manning (2003) and estimate the labor supply elasticity facing each private non-farm rm in the US. While a link between monopsony power and earnings has traditionally been assumed, I provide the rst direct evidence of the positive relationship between a rm's labor supply elasticity and the earnings of its workers. I also contrast the semistructural method with the more traditional use of concentration ratios to measure a rm's labor market power. In addition, I provide several alternative measures of labor market power which account for potential threats to identication such as endogenous mobility. Finally, I construct a counterfactual earnings distribution which allows the eects of rm market power to vary across the earnings distribution. My ndings suggest that there is signicant variability in the distribution of rm market power across US rms, and that the semistructural method is superior to the use of concentration ratios in evaluating a rm's labor market power. I nd that a one-unit increase in the labor supply elasticity to the rm is associated with wage gains of 3-5 percent. Furthermore, I nd that the negative earnings impact is strongest in the lower half of the earnings distribution, and is a determinant of earnings inequality. ∗This research uses data from the Census Bureau's Longitudinal Employer Household Dynamics Program, which was partially supported by the National Science Foundation Grants SES-9978093, SES-0339191 and ITR-0427889; National Institute on Aging Grant AG018854; and grants from the Alfred P. Sloan Foundation. Any opinions and conclusions expressed herein are those of the author and do not necessarily represent the views of the U.S. Census Bureau, its program sponsors or data providers, or of Cornell University. All results have been reviewed to ensure that no condential information is disclosed. †I have greatly beneted from the advice of John Abowd, Francine Blau, Ron Ehrenberg, Kevin Hallock, George Jakubson, and Alan Manning. I would also like to sincerely thank Henry Hyatt, J. Catherine Maclean, Matt Masten, Erika Mcentarfer, Ben Ost, and Michael Strain for their many helpful comments. All errors are mine. 1 Introduction There is good reason to believe that some rms have non-trivial power in the labor market, that not all rms act as price takers and pay their employees the prevailing market wage. Intuitively, most would not switch jobs following a wage cut of one cent, and we would not expect a rm which raises wages by a small amount to suddenly have an innite stream of workers. Empirically, a number of results point toward rms having some degree of power in setting the wage. The existence of signicant rm eects in wage regressions, even after controlling for detailed person and industry characteristics, is cited as strong suggestive evidence of rm market power (Abowd et al., 1999; Goux and Maurin, 1999). For instance, Goux and Maurin (1999) conclude that on average rm eects alter an individual's wage by more than 20 percent. Furthermore, they nd these rm eects are related more to rm characteristics such as size rather than productivity, implying that the rm eects are not simply absorbing workers' unmeasured marginal product of labor. Estimating the degree of wage competition in the labor market is important for both theoretical research and policy analysis. Since perfect competition is a standard feature in many models of the labor market, evidence of signicant distortions in the labor market would suggest labor economists should reevaluate the perfect competition assumption and its implications in their models. From a policy perspective, the degree of imperfect competition can drastically change the eects of institutions such as the minimum wage (Card and Krueger, 1995) or unions (Feldman and Scheer, 1982). While the industrial organization literature has theoretically and empirically modeled similar frictions in the product market, there has been comparatively less work done to ac- count for distortions of the labor market. This is primarily due to the comparative lack of rich labor market data (such as linked employer-employee data) versus product market data. Most of the theoretical work done on this topic resides in the search theory literature, with major contributions coming from Burdett and Mortensen (1998) and Shimer (2005) to name a few1. This line of research has given rise to a "new monopsony" literature, popular- ized by Alan Manning's (Manning, 2003) careful analysis of labor-related topics absent the assumption of perfect competition. The new monopsony model of the labor market views a rm's market power as derived from search frictions rather than solely geographic power as in a classic monopsony model. These search frictions originate from imperfections in the labor market such as imperfect information about available jobs, worker immobility, or heterogeneous preferences. Even if the existence of monopsony power is accepted, estimating the degree of market power possessed by a rm is not a simple task. Economists since Bunting (1962) have searched for empirical evidence of monopsony, with the predominant method being the use of concentration ratios, the share of a labor market which a given rm employs. The most commonly examined market in the empirical monopsony literature has been that of nurses in hospitals (Hurd, 1973; Feldman and Scheer, 1982; Hirsch and Schumacher, 1995; Link and Landon, 1975; Adamache and Sloan, 1982; Link and Settle, 1979). This market lends itself to monopsony because nurses have a highly specic form of human capital and there are many rural labor markets where hospitals are the dominant employer. Despite the relatively large literature on this narrow labor market, the concentration ratio approach has yielded mixed results and no clear consensus. More recently, studies have attempted to directly estimate the average slope of the labor supply curve faced by the rm, which is a distinct concept from the market labor supply elasticity2. Studying the market for nurses, Sullivan (1989) nds evidence of monopsony using a structural approach to measure the dierence between nurses' marginal product of labor and their wages. Examining another market commonly thought to be monopsonistic, the market for schoolteachers, Ransom and Sims (2010) instrument wages with collectively bargained pay scales and estimate a labor supply elasticity between 3 and 4. In a novel 1See Mortensen (2003) or Rogerson et al. (2005) for a review of this literature 2The market labor supply elasticity corresponds to the decision of a worker to enter the labor force, while the labor supply elasticity to the rm corresponds to the decision of whether to supply labor to a particular rm. This paper focuses on the rm-level decision. 2 approach using German administrative data, Schmieder (2010) nds evidence of a positive sloping labor supply curve through an analysis of new establishments. Using a semistructural approach similar to this study, Ransom and Oaxaca (2010) and Hirsch et al. (2010) both separately estimate the labor supply elasticities to the rm of men and women, each nding strong evidence of monopsonistic competition. Ransom and Oaxaca (2010) use data from a chain of grocery stores, and nd labor supply elasticities of about 2.5 for men and 1.6 for women. Hirsch et al. (2010) uses administrative data from Germany to estimate elasticities ranging from 2.5-3.6 and 1.9-2.5 for men and women respectively. Applying this approach to survey data, Manning (2003) nds labor supply elasticities ranging from 0.68 in the NLSY to 1.38 in the PSID. Utilizing data from the US Census Bureau's Longitudinal Employer Household Dynamics (LEHD) program, I estimate the market-level average labor supply elasticity faced by rms in the US economy, similar to the Hirsch et al. (2010) study using German data. I then extend the approach to estimate rm-level labor supply elasticities. This is accomplished through a semistructural estimation of the labor supply curve to the rm following the search model proposed by Burdett and Mortensen (1998) and similar to the empirical strategy proposed by Manning (2003). This method allows me to examine the eects of monopsonistic competition on the earnings distribution in great detail, and contributes to the existing literature in a number of ways. First, it is the rst examination of monopsony power using comprehensive administrative data from the US. Second, my particular empirical strategy allows me to examine the distribution of monopsony power which exists in the US, and to provide direct evidence on the negative impact of a rm's market power on earnings. I compare the performance of the market power measures derived in this study to that of the more traditional concentration ratio to illustrate the signicant contribution of the new monopsony models. Finally, I construct a counterfactual earnings distribution in which rms' market power is reduced in order to demonstrate the impact of imperfect competition on the shape of the earnings distribution. 3 I estimate the average labor supply elasticity to the rm to be approximately 0.92. Esti- mates in this range are robust to various modeling assumptions and corrections for endoge- nous mobility. Furthermore, I nd evidence of substantial heterogeneity in the market power possessed by rms, ranging from negligible to highly monopsonistic. While a link between monopsony power and wages has traditionally been assumed (Pigou, 1924), I provide the rst direct evidence of a positive relationship between a rm's labor supply elasticity and the earnings of its workers, estimating that a one-unit increase is associated with a decrease of .05 in log earnings. I demonstrate that the eect of monopsony power is not constant across workers: unconditional quantile regressions imply that impacts are largest among low paid and negligible among high paid workers. Finally, implications in the inequality literature are addressed through the construction of a counterfactual earnings distribution, which implies that a doubling of each rm's labor supply elasticity would decrease the variance in earnings by 5 percent. The paper is organized as follows, Section 2 describes the denition of market power utilized in this study. Section 3 lays out the theoretical foundation for this study. The data and methods are described in Section 4. Section 5 presents the results and sensitivity analyses, and Section 6 concludes. 2 Discussion of Monopsony Power The concept of monopsony was rst dened and explored as a model by Robinson (1969). In her seminal work, Robinson formulated the analysis which is still taught in undergraduate labor economics courses. Monopsony literally means one buyer, and although the term is most often used in a labor market context, it can also refer to a rm which is the only buyer of an input. It should be pointed out that in the new monopsony framework, the word monopsony is synonymous with the following phrases: monopsonistic competition, imperfect competition, 4 nite labor supply elasticity, or upward sloping labor supply curve to the rm. While the classic monopsony model is based on the idea of a single rm as the only outlet for which workers can supply labor, the new framework denes monopsony as any departure from the assumptions of perfect competition. Additionally, the degree of monopsonistic competition may vary signicantly across labor markets, and even across rms within a given labor market. Although it is tempting to include oligopsony in the new monopsony denition, they are distinct concepts. Oligopsony implies collusion among the rms, whereas the new monopsony framework emphasizes an equilibrium as the result of search frictions rather than collusion. In order to think about what determines a rm's monopsony power, we must consider why we do not observe the predicted behavior from a perfectly competitive model. What gives a rm exibility in oering a wage rather than being forced to oer the market wage? Put another way, why do we not observe workers jumping from job to job whenever they observe a higher paying opportunity for which they are qualied? One of the most prominent reasons is that the typical worker does not have a continuous stream of job oers (this point will be discussed further in the theoretical model section). This source of monopsony power has roots in the classic monopsony framework in that, all else held constant, workers in labor markets with more rms are likely to have a greater number of oers. However, this idea takes an overly simplistic view of the boundaries of a given labor market. Most employers are likely operating in many labor markets at any given time. A prestigious university may be competing in a national or international labor market for professors, a regional labor market for its high-level administrators and technical sta, and a local labor market for the low-level service workers. Even if the arrival rate of job oers were the only source of monopsony power, it seems that geographic modeling alone would do a poor job of measuring that power. Another source of monopsony power is imperfect information about job openings (McCall, 1970; Stigler, 1962), which is not completely distinct from the arrival rate of job oers since a decrease in information can 5 cause a reduction in job oers. This is a particularly compelling example since studies such as Hoer and Murphy (1992) and Polachek and Robst (1998) estimate that imperfect information about job prospects depresses wages by approximately ten percent. The costs (both monetary and psychic) associated with changing jobs can also be thought of as giving market power to the rm. Moving costs are typically thought of as a short run cost, particularly when a worker is young. However these costs can grow signicantly when a worker has a family and roots in a community. Consider the scenario of a dual-career family. Two job oers will be needed to induce either of the partners to move, a fact which gives signicant bargaining power to the employers of each partner, particularly the one who is paid less. Additionally, changing jobs means that workers must adjust to a new system which will require at least a small degree of learning on the job. Firm specic human capital also can be thought of as giving market power to the rm, since there is in eect a barrier to leaving a rm when an individual's rm specic capital is large relative to their general human capital. Possibly the biggest cost to changing jobs, however, is the cost to a worker's reputation. Potential employers would be very suspicious of hiring a worker who changes jobs the moment he is oered any wage increase. For all of these reasons, and likely many more, workers must be selective with the wage oers they choose to accept, thus leading to a labor market with substantial frictions. As discussed in Manning (2010), another way to think about imperfect competition in the labor market is in terms of the rents received by the employee and the employer. On the worker's side, the rents to a given job match would be the dierence between the current wage (utility) and the worker's opportunity cost, either a wage (utility) from a dierent rm or unemployment benets. Studies such as Jacobson et al. (1993) implicitly estimate these rents by exploring the impacts of exogenous job destruction. This literature estimates wage losses of 20-30 percent, implying signicant rents to employees from a given job match. From the employer's perspective, the rents from the ith job match are the dierence between 6 (MPi − wi) and (MPj − wj), where j is the next worker who would be hired if worker i leaves the rm. This is a harder quantity to measure empirically, but can be approximated (assuming that the marginal product is the same for workers i and j) by hiring and training costs. The estimates of hiring and training costs as a fraction of total wages paid tend to be in the range of 3-7 percent (Oi, 1962; Abowd and Kramarz, 2003). The ratio of worker rents to employer rents can be thought of as a measure of the rm's market power. If the worker's opportunity cost is high relative to her employer's opportunity cost, then the employer will be able to extract a large amount of the surplus from the job match. However, if the converse is true, the worker will be in the position of power. A relatively new branch of labor economics which focuses on the initial labor market conditions when a worker enters the labor market may also provide insight into the mobility of workers. A number of studies (Oyer, 2006, 2008; Genda and Kondo, 2010; Kahn, 2010) nd persistent and negative wage eects from entering the labor market in a bad economy, lasting for at least 20 years. These persistent eects provide further evidence that there are signicant long-run frictions in the economy. Finally, while a worker's earnings represent an important market outcome, it is important to remember that wages make up only a part of the total compensation to the worker. The true quality of a job match has many dimensions, such as benets, working conditions, and countless other variables. The interaction of monopsony with these non-wage goods should be explored in future research. 3 Theoretical Model A central feature of perfect competition is the law of one wage, that all workers of equal ability should be paid the same market clearing wage. In an attempt to explain how wage dispersion can indeed be an equilibrium outcome, Burdett and Mortensen (1998) develop a model of the economy in which employers post wages based on the wage-posting behavior 7 of competing employers. Even assuming equal ability for all workers, wage dispersion is an equilibrium outcome as long as one assumes that the arrival rate of job oers is positive but nite (perfect competition characterizes the limiting case, as the arrival rate tends to innity). The model for this study will primarily be derived from the Burdett and Mortensen model, with important contributions from Manning (2003). See Kuhn (2004) for a critique of the use of equilibrium search models in a monopsony context. The Burdett and Mortensen model of equilibrium wage dispersion Assume there are Mt equally productive workers (where productivity is given by p), each gaining utility b from leisure. Further assume there are Me constant returns to scale rms which are innitesimally small when compared to the entire economy. A rm sets wage w to maximize steady-state prots π = (p-w)N(w,F) where N(w,F) represents the supply of labor to the rm and F represents the distribution of wage oers observed in the economy. All workers within a rm must be paid the same wage. Employed workers will accept a wage oer w' if it is greater than their current wage w, and non-employed workers will accept w' if w'b where b is their reservation wage. Wage oers are drawn randomly from the distribution F(w), and arrive to all workers at rate λ. Assume an exogenous job destruction rate δ, and that all workers leave the job market at rate δ to be replaced in nonemployment by an equivalent number of workers. Burdett and Mortensen (1998), or alternatively Manning (2003), show that in this econ- omy, as long as λ is positive and nite, there will be a nondegenerate distribution of wages even when all workers are equally productive. As λ tends to zero, the wage distribution will collapse to the monopsony wage, which in this particular economy would be the reservation wage b. As λ tends to innity the wage distribution will collapse to the perfectly competitive wage, the marginal product of labor p. We can recursively formulate the supply of labor to a rm with the following equation, 8 where R(w,F) is the ow of recruits to a rm and s(w,F) is the separation rate. Nt+1(w, F ) = Nt(w, F )[1 − st(w, F )] + Rt(w, F ) (1) This simply formalizes the denitionally true statement that a rm's employment next period is equal to the fraction of workers this period who stay with the rm plus the number of new recruits. In steady state, we must have N(w,F)=R(w,F)/S(w,F). This steady-state assumption means that the quantities estimated in this paper represent the long-run labor supply elasticity to the rm. Taking the natural log of each side and dierentiating we can write the elasticity of labor supply ε as a function of the elasticities of recruitment and separations. ε = εR − εS (2) We can further decompose the recruitment and separation elasticities in the following way ε=θRεER +(1−θR)εNR −θSεES −(1−θS)εNS (3) Where the elasticity of recruitment has been broken down into the elasticity of recruit- ment of workers from employment (εER) and the elasticity of recruitment of workers from nonemployment (εNR ). Similarly the elasticity of separation has been decomposed into the elasticity of separation to employment (εES ) and the elasticity of separation to nonemploy- ment (εNS ). θRand θS represent the share of recruits from employment and the share of separations to employment respectively. The following theoretical results will be useful for estimating the above quantities, formal proofs of these results can be found in Manning (2003). First, Manning (2003, p. 99) shows that the elasticity of separations to employment is equal to the negative of the elasticity of recruitsfromemployment,εES =−εER.Notethattheimplicitassumptioninthisrelationship is that the ow of separations to employment equal to the ow of recruits from employment. This is true by construction with our data, and is considerably less restrictive than the 9 assumption made in most of the previous literature that the total ow of recruits is equal to the total ow of separations. Next, Manning (2003, p. 100) notes that εNR = εER − wθ‘R(w)/θR(w)(1 − θR(w)) (4) ThisisderivedfromthesimpledenitionofθR,whichimpliesRN =RE(1−θR)/θR,where RN and RE are the recruits from nonemployment and employment respectively. Taking the log of each side of this relation and dierentiating yields the denition of the elasticity of recruitment from nonemployment. The second term on the right-hand side of Equation (4) can be thought of as the bargaining premium that an employee receives from searching while currently employed. Thus, the labor supply elasticity to the rm can be written as a function of both separation elasticities and the premium to searching while employed. This is important because there are established methods for estimating these quantities. By contrast, if we wanted to directly estimate the recruitment elasticities we would need data on all employment oers received by each individual. In an economy where the arrival rate of job oers is nite (and thus the labor supply elasticity is nite) rms are not bound by market forces to pay workers their marginal product of labor. The model presented above implies that, even in a world where all rms and individuals are identical, a decrease in the arrival rate of job oers will both lower the average wage and increase inequality. To see how a rm's labor supply elasticity aects the wage it pays, consider a prot-maximizing rm which faces the following objective function: MaxΠ = pQ(L) − wL(w) (5) w P is the price of the output produced according to the production function Q. The choice of wage w determines the labor supplied to the rm L. Taking rst order conditions, substituting ε = w ∂L(w) , and solving for w yields: L(w) ∂w 10 w = pQ′(L) (6) 1+1 ε The numerator in Equation (6) is simply the marginal product of labor, and ε is the labor supply elasticity faced by the rm. It is easy to see that in the case of perfect competition (ε = ∞) that the wage is equal to the marginal product of labor, but the wage is less than then marginal product for all 0 < ε < ∞. Every empirical study in the new monopsony literature attempts to estimate the labor supply elasticity to the rm at the market level. In other words, they measure the (rm-size weighted) average slope of each rm's supply curve in the market. In a highly competitive market we would expect these elasticities to be very large numbers. Among the contributions of this paper is to separately estimate each rm's labor supply elasticity rather than a market average. 4 Data and Methodology Data The Longitudinal Employer Household Dynamics (LEHD) data are built primarily from Un- employment Insurance (UI) wage records, which cover approximately 98 percent of wage and salary payments in private sector non-farm jobs. Information about the rms is constructed from the Quarterly Census of Employment and Wages (QCEW). The LEHD infrastructure allows users to follow both workers and rms over time, as well as to identify workers who share a common employer. These data also include demographic characteristics of the worker and basic rm characteristics, obtained through administrative record and statistical links. For a complete description of these data, see Abowd et al. (2009). My sample consists of quarterly observations on earnings and employment for 46 states between 1985 and 20083. I make several sample restrictions in an attempt to obtain the 3The states not in the sample are California, Connecticut, Massachusetts, and New Hampshire. Not all 11 most economically meaningful results, analyses without these restrictions are presented as a robustness check later in the paper. These restrictions are necessary in large part because the earnings data are derived from tax records, and thus any payment made to an individual, no matter how small, will appear in the sample. As a consequence, there are many job spells which appear to last only one quarter, but are in fact one-time payments which do not conform with the general view of a job match between a rm and worker. First, I only include an employment spell in the sample if at some point it could be considered the dominant job, dened as paying the highest wage of an individual's jobs in a given quarter4. I also remove all spells which span fewer than three quarters. This sample restriction is related to the construction of the earnings variable. Since the data do not contain information on when in the quarter an individual was hired/left, the entries for the rst and last quarters of any employment spell will almost certainly underestimate the quarterly earnings rate (unless the individual was hired on the rst day or left employment on the last day of a quarter). Thus, in order to get an accurate measurement of the earnings rate I must observe an individual in at least one quarter other than the rst or last of an employment spell. Additionally, I limit the analysis to rms with 100 total employment spells of any length over the lifespan of the rm. For the full-economy monopsony model, these sample restrictions yield a nal sample of 130,937,872 unique individuals who had 295,131,926 total employment spells at 572,740 dierent rms. Additionally, for analyses using the rm-level measure of the labor supply elasticity, only rms which have greater than 25 separations to employment, 25 separations to unemployment, and 25 recruits from employment over the lifespan of the rm are considered. This reduces the analysis sample to 104,381,863 unique individuals having 234,163,233 employment spells at 279,251 unique rms. states are in the LEHD infrastructure for the entire time-frame, but once a state enters it is in the sample for all subsequent periods. 4This formulation allows an individual to have more than one dominant job in a given quarter. The rationale behind this denition is that I wish to include all job spells where the wage is important to the worker. Restricting the dominant job denition to only allow one dominant job at a given time does not alter the reported results. 12 Empirical Strategy The primary reason for the small empirical literature on monopsony is a lack of high quality data. In order to identify a rm's market power, the researcher must have a credible rm- level instrument for each rm studied or detailed employer-employee linked data to identify worker ows. I employ the latter approach in this study since nding a credible instrument for nearly every rm in the US is unlikely. The construction of the market power measures most closely represents an augmented rm-level implementation of the methodology proposed in Manning (2003). I rst describe in detail how the market power measures are calculated, followed by a description of how they are used to examine the US earnings distribution. Location-Based Measures I construct an overall measure of the percent of the industry-specic labor market that each rm employs (Number of workers at rm i/number of workers in rm i's county and in rm i's industry) using North American Industry Classication System (NAICS) industry denitions. While this variable is far from a perfect measure of an employer's power to set wages, it has several advantages over the semistructural measures to be used later in the paper. Both the construction of these measures and the regression estimates using them are transparent. Endogeneity, misspecied equations, etc. are of less concern in the construction of these labor concentration measures, and the interpretation of the regression coecients on these variables is straightforward. This analysis corresponds to the traditional concentration ratio approach of analyzing labor market power. Semistructural Measure The simplest way to estimate the labor supply elasticity to the rm would be to regress the natural log of rm size on the natural log of rm wages. However, even when controlling for 13 various demographic characteristics, this is deemed to produce a potentially biased estimate5. I therefore rely on estimating parameters presented in the theoretical section which are plausibly identied, and then combine them using results from Manning (2003) and equation (3) to produce an estimate of the labor supply elasticity to the rm. To my knowledge, only Hirsch et al. (2010) has used a similar, but considerably more restrictive, method with administrative data which yielded an economy-wide estimate of the average labor supply curve facing the rm. Manning (2003) also estimates an economy-wide measure of the degree of monopsony using surveys such as the National Longitudinal Survey of Youth (NLSY) 1979. One of the major contributions of this paper is that I estimate the labor supply elasticities for each rm, rather than the average over the whole economy. Estimating the labor supply elasticities at the rm level does have several advantages. First, the estimation of each of the elasticity components is much more exible than even the least constrained specications of Hirsch et al. (2010). Second, I will be able to use the measures as an explanatory variable, and can test a number of dierent models. Finally, I will be able to examine the eect of market power on earnings at each point in the market power distribution, rather than examining only the average eect. This is particularly important because theory predicts signicant nonlinear eects relating to the labor supply elasticity and a rm's ability to mark down wages (Pigou, 1924). However, this strategy has the drawback that I am unable to estimate the relevant parameters, and thus the labor supply elasticity, for the smallest rms (sample restrictions are discussed in the data section). In order to compare my results to the prior literature I will also present the results using the same method as Hirsch et al. (2010). According to the results presented in the theoretical model section, three quantities must be estimated in order to construct the labor supply elasticity measure, (εES ), (εNS ) and (wθ‘R(w)/θR(w)(1 − θR(w))). Each of the following models will be run separately for 5The rm size-wage premium is a well known result in the labor economics literature, and is often attributed to non-monopsony related factors such as economies of scale increasing the productivity, and thus the marginal product, of workers at large rms 14 every rm in the sample (as well as on the whole sample for comparison purposes), where the unit of observation is an employment spell, thus one individual can appear in multiple rm's models. Looking rst at the separation elasticities, I model separations to nonemployment as a Cox proportional hazard model given by λN (t|βN,seplog(earnings)i + XiγN,sep) = λ0(t) exp(βN,seplog(earnings)i + XiγN,sep) (7) where λ() is the hazard function, λ0 is the baseline hazard, t is the length of employment, log(earnings) is the natural log of individual i's quarterly earnings, and X is a vector of explanatory variables including gender, race, age, education, and year control variables (in- dustry controls are also included in the full-economy model). While the entire sample will be used, workers who transition to a new employer or who are with the same employer at the end of the data series are considered to have a censored employment spell. In this model, the parameter β represents an estimate of the separation elasticity to nonemployment. In an analogous setting, I model separations to employment as λE(t|βE,seplog(earnings)i +XiγE,sep)=λ0(t)exp(βE,seplog(earnings)i +XiγE,sep) (8) with the only dierence being that the sample is restricted to those workers who do not have a job transition to nonemployment. As before, β represents an estimate of the sep- aration elasticity to employment. To estimate the third quantity needed for equation (3), wθ‘R(w)/θR(w)(1 − θR(w)), Manning (2003) shows that this is equivalent to the coecient on log earnings when estimating the following logistic regression Prec = exp(βE,reclog(earnings)i + XiγE,rec) (9) 1 + exp(βE,reclog(earnings)i + XiγE,rec) 15 where the dependent variable takes a value of 1 if a worker was recruited from employment and 0 if they were recruited from nonemployment. The same explanatory variables used in the separation equations are used in this logistic regression. At this point the results listed in the theoretical section can be used (along with calculating the share of recruits and separations to employment) in conjunction with equation (3) to produce an estimate of the labor supply elasticity facing each rm. One concern with this methodology is that I am only able to estimate a time-invariant long-run labor supply elasticity to the rm. Since rms, particularly young ones, certainly see changes in their market power, the interpretation of the labor supply elasticities derived in this paper is not straightforward. These values may not represent the true elasticities at any point in time, but can rather be thought of as an average of many short-run elasticities. If you assume that a rm's market power grows with the rm, then the estimated elasticities will be conservative (less monopsonistic) estimates of the current level of market power which rms possess. Additionally, this could be seen as measurement error in the earnings equations presented in Tables 7 and 8, which would likely bias the coecients toward zero. To provide some intuition on the models being estimated, consider the analysis of sepa- rations to employment. A large (in absolute value) coecient on the log earnings variable implies that a small decrease in an individual's earnings will greatly increase the probability of separating in any given period. In a perfectly competitive economy, we would expect this coecient to be innitely high. Similarly, a very small coecient implies that the em- ployer can lower the wage rate without seeing a substantial decline in employment. One concern with this procedure is that this measure of monopsony power is actually proxying for high-wage rms, reecting an eciency wage view of the economy where rms pay a wage considerably above the market wage in exchange for lower turnover. This is much more of a concern in the full economy estimate of the labor supply elasticity to the rm found elsewhere in the literature than in my rm-level estimation since the models in this paper are run separately by rm. The logic behind this dierence is that in the full economy 16 model cross-sectional variation in the level of earnings is used to identify the labor supply elasticity. In a rm-specic model, however, the labor supply elasticity of rm A does not mechanically depend on the level of earnings at rm B. This eciency wage hypothesis will be directly tested. Analysis In addition to the full-economy models of monopsony, I include the concentration ratio and rm-level labor supply elasticity measures in earnings regressions. This provides direct evidence of the eect of rm market power on earnings, a feature not possible in the full- economy models. Additionally, it serves as a test of the eciency wage hypothesis, which predicts that rms with low estimated labor supply elasticities will pay the highest wages. The main focus of this paper is on this model, explicitly written as: log(quarterly earningsij) = βmarketpowerj + γXij + δYj + θZi + εij (10) The dependent variable is the natural log of individual i's quarterly earnings in employ- ment spell j. The market power variable represents rm j's estimated labor supply elasticity or the share of the local working population employed at the rm. X is a vector of person characteristics, which may vary by the employment spell, including age, age-squared, tenure (quarters employed at rm), tenure-squared, education6, gender, race, ethnicity, and year ef- fects. Y is a vector of rm characteristics which includes indicator variables for the two-digit NAICS sector and the size (employment) of the rm. Z is a vector of person xed-eects and ε is the error term. Time-invariant characteristics in X are excluded in models with person xed-eects. 6Reported educational attainment is only available for about 10 percent of the sample, although sophis- ticated imputations of education are available for the entire sample. The results presented in this paper correspond the the full sample of workers (reported education and imputed education). All models were also run on the sample with no imputed data, and no substantive dierences were observed. In particular, since the preferred specication includes person xed-eects, and thus educational attainment drops out of the model, this is of little concern. 17 Finally, to examine whether there is a disproportionate impact of imperfect competition on workers near the bottom of the earnings distribution, I construct a counterfactual earnings distributions in which each rm's labor supply elasticity is increased. The counterfactual distribution is constructed according to the unconditional quantile approach decomposition suggested in Firpo et al. (2010). Unconditional quantile regression, rst introduced in Firpo et al. (2009), estimates the parameters of a regression model as they relate to the quantiles of the dependent variable. This contrasts with traditional quantile regression, which estimates parameters corresponding to the conditional (on the included regressors) quantiles of the dependent variable. The unconditional quantile approach is most advantageous in models with relatively low R-squared (i.e. all wage regressions) since the quantiles of y are most likely to diverge from the quantiles of y-hat (predicted dependent variable) in this scenario. Under this approach, unconditional quantile regressions are performed on every 5th quan- tile of the earnings distribution using the same model as Equation (10). The estimated coecients on the labor supply elasticity variable from each regression will then be used to simulate the impact of a one unit increase in the labor supply elasticity to the rm on earnings in the associated quantile. 5 Results Summary Statistics Table 1 reports summary statistics from my analysis sample. Since the unit of observation is the employment spell, and only dominant jobs are included, some statistics deviate slightly from typical observational studies of the labor market (such as a nearly even split of job spells between men and women). The average employment spell lasts about two and a half years, with a little over eighty percent of spells resulting from a move from another job. The quarterly nature of the LEHD data make it dicult to precisely identify7 whether 7The denition used in this paper requires an individual to have no reported earnings for an entire quarter following an employment spell to be dened as a separation to nonemployment, with all other separations 18 an individual separated to employment or nonemployment, and therefore the proportion of separations to employment is slightly higher than comparable statistics reported in Manning (2003). Also of note are the employment concentration ratios, with the average rm employing roughly 9 percent of their county's industry specic labor force. Location-Based Measure As previously noted, many studies have attempted to search for evidence of monopsony in the labor market through the use of concentration ratios. While this approach was the best available given prior data constraints, it assumes that monopsony power is derived only from geographical constraints. Table 2 presents the estimated impact of a ten percentage point increase in the concentra- tion ratio in various specications of Equation (10). These results suggest that, in general, a rm's geographic dominance does not appear to signicantly alter the wage bill it pays. Note that when the models are run separately by North American Industry Classication System (NAICS) sector, as depicted in Table 3, there is evidence that rms with high concentration ratios in certain industries (such as the utilities sector) pay slightly lower wage bills. How- ever, the eect sizes are small relative to the observed distribution of concentration ratios. Given the small results, and the fact that the industry-specic eects seem to be centered around zero, it seems plausible to conclude that geographic constraints in the labor market play at most a small role in wage determination for the average worker. Full-Economy Model I rst compute the average labor supply elasticity to the rm prevailing in the economy by estimating Equations (7)-(9) on a pooled sample of all (dominant) employment spells, and coded as a separation to employment. This denition was chosen because it lead to the most conservative (least monopsonistic) results, although the dierences were small. The other methods tried involved imputing the time during the quarter at which employment stopped/started based on a comparison of the earnings reported in the last/rst quarter to a quarter in which I know the individual worked the entire quarter. 19 combining the results according to Equation (3). Table 4 presents the output of a several specications of the full-economy monopsony model. The estimated elasticities range from 0.55 to 0.61 depending on the specication (inclusion of xed eects, etc.). These elasticities are certainly on the small side, implying that at the average rm a wage cut of one percent would only reduce employment by .6 percent. However, this magnitude is still within the range observed by Manning (2003) in the NLSY79. Additionally, even the inclusion of xed- eects still puts many more restrictions on the parameter estimates than separate estimations for each rm. Based on a comparison of the full-economy model and the rm-level model presented in the next section, the failure to fully saturate the full economy model likely produces downward biased estimates. A detailed discussion of factors which may attenuate these estimates, as well as structural reasons we should expect these results from US data, is given in the Discussion and Extensions section. Firm-Level Measure Table 5 displays information about the distribution of rms' labor supply elasticities, and Figure 1 presents a kernel density plot of the market power measure8. This distribution is constructed by separately estimating Equations (7)-(9) for each rm. While the median supply elasticity (0.74) is close to the estimate from the full-economy model, there appears to be signicant variation in the market power possessed by rms. I estimate a mean labor supply elasticity of 0.92, however, there are many rms (about 3 percent of the sample) with labor supply elasticities greater than 5. It appears that while there is a nontrivial fraction of rms whose behavior approximates a highly competitive labor market, the majority of the distribution is characterized by signicant frictions.While not surprising, to my knowledge this is the rst documentation of the large discrepancy in rms' ability to set the wage. Table 6 reports average labor supply elasticities broken down by NAICS sector. The manufacturing sector appears to enjoy the least wage-setting power, with a labor supply 8For condentiality reasons, the long right tail of the kernel density plot has been suppressed 20 elasticity of 1.36. As manufacturing is likely the most heavily unionized of all sectors, this result is not surprising. By contrast, rms in the health care (0.67) and arts/entertainment (0.68) sectors seem to wield the greatest wage-setting power. This is consistent with the focus on the healthcare market among economists investigating monopsony power. The central focus of this paper is presented in Tables 7, which estimates various speci- cations of Equation (10) in order to measure the impact of market power on the earnings distribution. Unconditionally, a one unit increase in the labor supply elasticity increases earnings by .16 log points. With the addition of detailed rm characteristics and person xed eects, the eect declines to .05 log points. This is an important result for the new monopsony literature, since it rules out the possibility that their identication strategy is actually identifying high-wage rms whose employees do not often switch jobs due to the high wages. There are two main reasons why these results may be underestimated and instead inter- preted as lower bounds. First, each labor supply elasticity is a weighted average of many more precisely dened elasticities which would more accurately measure a rm's market power over a particular individual. For example, rms likely face dierent supply elasticities for every occupation, and potentially dierent elasticities across race and gender groups. From a measurement error perspective, regressing the log of earnings on the average labor supply elasticity to the rm would attenuate the estimates relative to the ideal scenario where I could separately identify every occupation specic elasticity. Secondly, the inclusion of tenure and its squared term in the model is likely overcontrol- ling, and introduces downward-biasing endogeneity into my results. To see why, consider a study which attempts to estimate the gender wage gap. The inclusion of occupational xed- eects is often seen as overcontrolling, and providing an underestimate of the gap, because in many cases occupation is at least partially caused by gender (particularly in the historical case). In the case of this study, the underlying model of monopsony and even denition of a labor supply elasticity implies that a rm's market power is a partial cause of tenure. 21 Counterfactual Distribution Table 8 details the disproportionate eect which rms' market power has on workers at the low end of the earnings distribution. Assuming a doubling of each rm's labor supply elasticity (the median rm moves from 0.74 to a still modest 1.48), the 10th percentile of the earnings distribution increases by 0.071 log points under the counterfactual assumption, while the median worker sees an increase of 0.035 log points and the 90th percentile remains unchanged. The nonlinear impacts are also clearly seen in the unconditional quantile regres- sion coecients, which are 4-5 times greater than the OLS coecient at lower quantiles and essentially zero at the upper end of the distribution. Standard measures of inequality are also reported in Table 8 for both the empirical and counterfactual distributions. A 100 percent increase in rms' labor supply elasticity is associated with a 5 percent reduction in the variance of the earnings distribution (0.93 to 0.48 log points). Similarly, we see considerable decreases in the 90-10 ratio (1.32 to 1.3), 50-10 ratio (1.18 to 1.16), and 90-50 ratio (1.12 to 1.11). These results could arise from a number of dierent scenarios, the examination of which is beyond the scope of the current paper. It may reect low-ability workers having few outside options for employment. This could be due to strict mobility constraints, a less eective job referral network (Ioannides and Loury, 2004), lower job search ability (Black, 1981), or simply being qualied for fewer jobs. Another mechanism through which a rm's market power might dierentially aect low wage workers is gender discrimination, as suggested by Hirsch et al. (2010) or racial discrimination. These questions deserve a much deeper treatment, and should be explored in future research. Figure 2 plots both the empirical earnings distribution and the counterfactual distribution under a more drastic assumption which more closely approximates perfect competition, that each rm's labor supply elasticity is increased by a factor of 10 (median elasticity goes from .74 to 7.4). The variance of the counterfactual distribution is considerably lower, with nearly all of the movement occurring in the lower half of the distribution. The striking fact about 22 Figure 2 is that the Burdett and Mortensen model predicts the exact same behavior of the earnings distribution as the arrival rate of job oers increases. Discussion and Extensions The labor supply elasticities reported in this paper imply that rms possess a high degree of power in setting the wage. For a variety of reasons, these elasticities are on the lower end of those present in the literature. In this section I address the factors which contribute to these results. First, it should be noted that the only other studies to estimate the labor supply elasticity to the rm with comprehensive administrative data used European data. Given the very restrictive (from the point of view of the employer) employment laws in place in many European countries, this result is not surprising. Assuming that job security accrues over time within rm but drops following a transition to a new rm, any law which makes it more dicult to re a worker eectively lowers the cost to the employee of switching jobs because job security is less of a factor. One potential criticism of the labor supply elasticities derived in this paper is that the data do not contain detailed occupation characteristics. This problem is mitigated by the fact that the measures are constructed at the rm level in that I am only comparing workers in the same rm in the construction of a rm's monopsony power. Additionally, previous studies such as Hirsch et al. (2010) and Manning (2003) nd that the addition of individual- level variables had little impact on the estimated labor supply elasticities and that it was the addition of rm characteristics which altered the results. As a further check of this problem, I compute the aggregate monopsony measures in the NLSY, as done in Manning (2003), both with and without detailed occupation characteristics. As shown in Table 9, I nd that the dierence between these labor supply elasticities is about 0.2 and is not statistically signicant. Keep in mind that even if this dierence were statistically signicant, the estimates in this paper are still a long way from implying perfect competition. Thus, I 23 conclude that the absence of occupation controls in the LEHD data will not seriously bias the results of this study. A potentially more serious problem in the estimation of the labor supply elasticity to the rm is endogenous mobility. Consider the standard search theory model with on the job search: A worker will leave their current job if they receive a higher wage oer from another rm. Their wage at the new rm is then endogenously determined since in eect it was drawn from a distribution truncated at the wage of the their previous job. In this sense, the earnings data for those individuals who were hired away from another job is biased upward, which will bias estimates of the labor supply elasticity to the rm downward. I deal with the endogenous mobility bias in several dierent ways. First, I estimate the average earnings premium an individual gets from moving to their nth job (where n is the job number in a string of consecutive employment spells). For instance, workers' earnings increase on average .19 log points when they move from their rst to their second jobs. I then reduce the earnings of all job movers by the average premium associated with a move from job n-1 to n. For example, all workers in their second jobs of a string of employment spells would have their earnings reduced by .19 log points.9 The rationale behind this adjustment is that I only observe workers moving from one job to another if they receive a higher wage oer (This is a typical assumption of on-the-job search models, and is overwhelmingly true in the data). Thus, the earnings I observe in the second job are endogenously determined, since they were in a sense drawn from a strictly positive oer distribution. Second, I recalculate the labor supply elasticities with a Heckman selection correction. In this model I dene the selected group as those who separate from one job to another, and use the number of new jobs in an individual's state and industry as the excluded variable. The logic behind this restriction is that the state-industry specic labor market should be highly correlated with the likelihood that an individual moves to a new job, but should 9Dene a string of employment spells as consecutive jobs an individual holds with no time spent outside the labor force. In other words, each job transition in a string of employment spells is dened as being a separation to, or recruitment from, employment. An observation takes a default value of 1, 2 if the employment spell is the second in a string of spells, etc. 24 be uncorrelated with that individual's unobserved ability to move. The inverse Mills ratio from the Heckman selection model is included as a regressor in each of the Equations (7)-(9). As noted in Table 9, each of these corrections leads to a trivial change in the labor supply elasticity distribution. One nal concern regarding endogenous mobility is that we do not observe the complete history of workers, only that within the time-frame of the LEHD infrastructure. Thus, any employment spells in progress at the beginning of our window which are the result of a hire from another rm may introduce bias into the results. To assess the degree to which this is a problem, I again employ the NLSY79. I use a Monte Carlo approach to compare the estimated labor supply elasticities using the complete worker histories and using only employment spells which occurred in the nal third of the sample window. This is the ideal comparison, where the rst calculation takes into account the entire work histories of each individual and the second calculation uses only those spells observed after an arbitrary date. The Monte Carlo analysis nds that using the complete worker histories leads to a statistically insignicant decrease of the estimated labor supply elasticity. This implies that the use of some partial histories in this study is not likely a problem, and at worst yields an underestimate of monopsony power. For the reasons mentioned in this section and probably many others, critics may claim that this paper does not accurately estimate the labor supply elasticity to the rm, and they could be right. As with any identication strategy, this study relies on assumptions, not all of which are testable. But while the average rm's labor supply elasticity may not be exactly .92, the variable which I call a supply elasticity is certainly some kind of weighted average highly correlated with mobility and individuals' responsiveness to changes in earnings. The fact that this measure is highly correlated with earnings, especially for those at the bottom of the distribution, tells us that our economy is far less competitive than we commonly assume. 25 6 Conclusion This study nds evidence of signicant frictions in the US labor market, although the severity of these frictions varies greatly between labor markets. I estimate the average rm's labor supply elasticity to be quite monopsonistic at 0.92, however there is a nontrivial fraction of rms who do appear to be operating in an approximately competitive labor market. While identifying the precise frictions which contribute to rms' labor market power is beyond the scope of this study, I can conclude that a rm's geographical dominance alone does not account for all or even most of their ability to aect the wage oer distribution. I extend the semistructural empirical strategy proposed by Manning (2003) to identify rm level labor supply elasticities. 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Sullivan, Monopsony power in the market for nurses, Journal of Law and Economics, vol. 32, pp. S135S178, 1989. 30 Figure 1 31 Figure 2 32 Table 1: Summary Statistics Variable Age Female White Hispanic High School Some College College Degree+ Tenure (Quarters) Log(Quarterly Earnings) Firm Concentration Firm Industry-Concentration Recruited from Employment Mean Std Dev 38 15.2 0.5 0.5 0.78 0.41 0.11 0.31 0.3 0.46 0.32 0.47 0.24 0.42 10.1 10.7 8.5 1 0.01 0.02 0.09 0.16 0.83 0.4 33 Impact of a ten percentage point increase in concentration ratio on log(earnings) Demographic and human capital controls Employer controls Tenure Controls State xed-eects R-Squared 0.0213 0.0053 No Yes No No No No No No 0.0013 0.2369 0.0109 0.0066 0.0114 Table 2: Impact of Firm Concentration on Earnings *A pooled national sample of all dominant employment spells is used in this set of regressions. The dependent variable is the natural log of quarterly earnings. Demographic and human capital controls include: age, age-squared, and indicator variables for gender, ethnicity, racial status, and education level. Employer controls include indicator variables for each of the 20 NAICS sectors and number of employees working at the rm. Tenure controls include the length (in quarters) of the employment spell, as well as its squared term. Year eects are included in all models. The results are not reported for the models with rm and person xed eects because the coecient was deemed to be biased due to severe multicollinearity. Standard errors are not reported because all t-statistics are greater than 50. 34 Yes Yes Yes Yes Yes Yes No Yes Yes No No Yes 0.3300 0.3438 0.3502 Table 3: Concentration Ratio Regressions by Industry Industry Agriculture Mining/Oil/Natural Gas Utilities Construction Manufacturing Wholesale Trade Resale Trade Transportation Information Finance and Insurance Real Estate and Rental Profession/Scientic/Technical Services Management of Companies Administrative Support Educational Services Health Care and Social Assistance Arts and Entertainment Accommodation and Food Services Other Services Public Administration Impact of a ten percentage point increase in concentration ratio on log earnings 0.0055 0.0071 -0.0760 -0.0157 0.0050 -0.0142 -0.0009 0.0361 -0.0308 -0.015 0.022 0.019 0.056 -0.01 -0.005 0.016 0.046 0.021 -0.129 -0.013 *A pooled national sample of all dominant employment spells is used in this set of regressions. The dependent variable is the natural log of quarterly earnings. Demographic and human capital controls include: age, age-squared, and indicator variables for gender, ethnicity, racial status, and education level. Employer controls include the number of employees working at the rm. Tenure controls include the length (in quarters) of the employment spell, as well as its squared term. Year eects are included in all models. 35 Table 4: Full-Economy Estimate of the Labor Supply Elasticity to the Firm Full sample Full sample with rm FE .55 .6 Only rms with an individually estimated elasticity .61 *These labor supply elasticities were obtained by estimating equations (7)-(9), on a pooled sample of all (dominant) employment spells. Each model contained age, age-squared, along with indicator variables for female, nonwhite, Hispanic, high school diploma, some college, college degree or greater, year, and each of 20 NAICS sectors. 36 Table 5: Distribution of Estimated Firm-Level Labor Supply Elasticities Percentiles Mean 10th 25th 50th 75th 90th 0.92 0.21 0.42 0.74 1.17 1.74 *Three separate regressions, corresponding to equations (7)-(9), were estimated separately for each rm in the data which met the conditions described in the data section. The coecients on log earnings in each regression were combined, weighted by the share of recruits and separations to employment, according to equation (3) to obtain the estimate of the labor supply elasticity to the rm. Demographic and human capital controls include: age, age-squared, and indicator variables for gender, ethnicity, racial status, and education level. Employer controls include number of employees working at the rm and industry indicator variables. Year eects are included in all models. 37 Table 6: Mean Labor Supply Elasticity by Sector Industry Agriculture Mining/Oil/Natural Gas Utilities Construction Manufacturing Wholesale Trade Resale Trade Transportation Information Finance and Insurance Real Estate and Rental Profession/Scientic/Technical Services Management of Companies Administrative Support Educational Services Health Care and Social Assistance Arts and Entertainment Accommodation and Food Services Other Services Public Administration Mean Labor Supply Elasticity 1.11 1.06 1.03 1.14 1.36 1.13 0.84 1.11 0.86 0.98 0.84 0.86 0.72 0.69 0.79 0.67 0.68 0.79 0.97 0.97 *The numbers in this table represent averages by NAICS sector of the estimated labor supply elasticity to the rm. Three separate regressions, corresponding to equations (7)-(9), were estimated separately for each rm in the data which met the conditions described in the data section. The coecients on log earnings in each regression were combined, weighted by the share of recruits and separations to employment, according to equation (3) to obtain the estimate of the labor supply elasticity to the rm. Demographic and human capital controls include: age, age-squared, and indicator variables for gender, ethnicity, racial status, and education level. Employer controls include number of employees working at the rm. Year eects are included in all models. 38 Coecient on labor supply elasticity Demographic and human capital controls Employer controls Tenure controls State xed-eects Person xed-eects R-Squared 0.16 0.12 0.05 0.03 0.03 0.05 No Yes Yes Yes Yes Yes No No Yes Yes Yes Yes No No No Yes Yes Yes No No No No Yes Yes No No No No No Yes Table 7: Impact of Firm Market Power on Earnings 0.009 0.238 0.317 0.335 0.341 0.783 *A pooled national sample of all dominant employment spells subject to the sample restriction described in the data section is used in this set of regressions. The dependent variable is the natural log of quarterly earnings. Demographic and human capital controls include: age, age-squared, and indicator variables for gender, ethnicity, racial status, and education level. Employer controls include the number of employees working at the rm and industry indicator variables. Tenure controls include the length (in quarters) of the employment spell, as well as its squared term. Year eects are included in all models. Standard errors are not reported because the t-statistics range from 500-1000, but are available upon request along with all other estimated coecients. 39 Table 8: Counterfactual Distribution Analysis Change (log points) in Quantiles of the Earnings Distribution Quantile 10th 25th Change in 0.071 0.055 log(earnings) Inequality Variance 90-10 measure Earnings .93 1.32 distribution Counterfactual .88 1.30 distribution 50th 75th 90th 0.035 0.016 0.00 50-10 90-50 1.18 1.12 1.16 1.11 *The counterfactual distribution was constructed by estimating unconditional quantile regressions at every fth quantile of the earnings distribution, and using the supply elasticity coecient from each regression to simulate the eect at each quantile of a doubling of the labor supply elasticity. Demographic and human capital controls include: age, age-squared, and indicator variables for gender, ethnicity, racial status, and education level. Employer controls include the number of employees working at the rm and industry indicator variables. Tenure controls include the length (in quarters) of the employment spell, as well as its squared term. Year eects are included in all models. 40 *Panel A: NLSY comparisons Bootstrapped dierence in labor supply elasticity Std Error **Panel B: Endogenous mobility corrections Median of distribution With versus without occupational eects 0.20 0.14 Uncorrected labor supply elasticity .74 Full history versus partial history -.46 .76 Earnings of job changers adjusted downward .73 Control for Heckman selection correction .75 Table 9: Robustness Checks *Panel A: Equations (7)-(9) were estimated on a sample of employment spells from the NLSY79 from 1979-1996 (the last year for which detailed information on recruitment and separation dates are available). The specications include the same variables available through the LEHD data: age, age-squared, year eects, along with gender, ethnicity, race, industry, and education indicators. The rst column compares the labor supply elasticities with and without the inclusion of occupational xed eects. The second column compares the labor supply elasticities with and without the assumption that only the last third of every individual's work history is known. **Panel B: The second column represents a recalculation of the labor supply elasticity in which workers who are recruited away from another job have their earnings adjusted downward by the average premium of moving from job n to job n+1. The third column represents a recalculation of the labor supply elasticity in which the inverse mills ratio of a Heckman selection model for mobility is controlled for in each of Equations (7)-(9). The omitted category in the Heckman model is the number of new local jobs in each workers current industry. 41 }}
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