Wage Structure

The Impact of Corporate Restructuring on Wage Distributions*

Chichun Fang [email protected] 217-265-0954

John C. Dencker [email protected] 217-333-2383

School of Labor and Employment Relations University of Illinois at Urbana-Champaign 504 E. Armory Avenue Champaign, IL 61820

January 14, 2009

*

Submitted to American Sociological Association Annual Meeting, 2009

ABSTRACT We examine the dynamics of wage patterns over a twenty-five year time frame using personnel records and corporate documents from a large U.S. firm. We focus on the role of the firm—and its HRM systems, practices, and policies—in shaping wage patterns and wage inequality. We find significant cohort effect, in which entry wages differ each year depending on the market conditions, and tenure effect, in which employees follow similar wage growth patterns and are shielded from the fluctuation of economic cycle once they are hired. The firm’s ability to control wage inequality was undermined through corporate restructuring and market pressure in late 1980s. After restructuring, the firm became more inclined to reward high performance workers, and reduced entry-level wages for a number of workers. We conclude that the firm’s HRM system and its internal labor market mechanism governed the wage patterns and limited wage inequality considerably prior to the onset of restructuring. However, the firm’s institutional power on wage determination was reduced after corporate restructuring as the wage inequality within the firm increased.

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INTRODUCTION Recent sociological studies have explored various institutional determinants of wage outcomes, including cross-occupational difference (Kalleberg and Mouw 2006; Kim and Sakamoto 2008), the firm’s performance appraisal mechanism (Castilla 2008), and the opportunity structure of discrimination within a firm (Petersen and Saporta 2004). These results suggest the importance of institutions in wage dynamics. In this paper we focus on another institutional factor, the role of the firm’s HRM system and compensation policy, in changing wage patterns and limiting wage inequality. In the discussion of wage patterns and distributions, the role of the firm should not be overlooked because wages are, after all, paid to the employees by the firm. The firm decides employees’ entry wages upon hiring and how wages are adjusted later in their careers. Moreover, the firm can decide how to allocate its payroll budgets among employees through its compensation policies and potentially influence wage inequality. We argue the firm’s compensation policies, accompanying with the impacts of deregulation and corporate restructuring, plays a key role in the change of wage inequality. We find significant cohort and tenure effects; employees entered the firm in different years received different entry wages depending on market conditions, but the wage growth patterns conditional on respective entering wages were very similar. The effects of firm restructuring, however, varies by types of employees. For blue collar workers, the wage distribution was more polarized after restructuring than before. For white collar clericals and managers, the distribution shifted rightwards and became denser in the upper tail, indicating both real wage level and wage dispersion increase after restructuring. This suggests the firm was losing its ability to control wage inequality within the firm after corporate restructuring.

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THE FIRM’S ROLE IN SHAPING WAGE DISTRIBUTION While the increase of wage inequality in 1980s is studied early and a lot in labor economics (Autor, Katz, and Kearney 2008; Autor, Katz, and Krueger 1998; Borjas and Ramey 1995; Card and DiNardo 2002; Chay and Lee 2000; DiNardo, Fortin, and Lemieux 1996; Katz and Murphy 1992; Lemieux 2006; Murphy and Welch 1992), the role of the firm in contributing to such increase is surprisingly understudied. We argue that the firm, its human resource practices, and its compensation policies play an important role in shaping the wage distribution. Studies show that human resource practices modify the effect of technological changes on hourly workers (Fernandez 2001) and influence the link between performance appraisal and pay structure (Castilla 2008). The firm’s own compensation policy reflects its own internal labor market structure and will induce both cohort and tenure effects in wage patterns (Baker, Gibbs, and Holmstrom 1994a; Baker, Gibbs, and Holmstrom 1994b) and downward wage rigidity (Gibbs and Hendricks 2004; Harris and Holmstrom 1982; Seltzer and Merrett 2000). Through its internal labor market, the firm can provide employees job securities and opportunities for career development, and it also shields employees from labor market fluctuations (Doeringer and Piore 1971; Osterman 1984; Sørensen and Kalleberg 1981). Wage inequality within the firm can be limited through formal wage patterns and the internal labor market as well. Since wages are attached to jobs under internal labor market and career tournament (Lazear 1995; Lazear and Rosen 1981), the wage dispersion in a firm can be fixed in a certain range as long as the structure of the firm does not change dramatically. While the employees are moved among positions, the overall wage structure and wage policies of the

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firm are guided by the job levels and tenure effect. The firm’s compensation policies hence play the key role of shaping wage patterns and distributions and controlling wage inequality.

Corporate Restructuring However, the effectiveness of internal labor market and the firm’s ability to limit wage inequality can be weakened through corporate restructuring. Mergers and hostile acquisitions that followed the deregulation in late-1970s and early1980s (Schleifer and Summers 1998) ignited the firm’s pursuit of organizational efficiency (Useem 1996), which in turn led to restructuring, including reduction in forces (RIFs) and changes in wage policies (Cappelli, Bassi, Katz, Knoke, Osterman, and Useem 1997; Cascio, Young, and Morris 1997). Firm engaging in RIF undermined job security provided under internal labor markets, and continuing employment were more influenced by market factors (Cappelli 1992; Eriksson and Werwatz 2005; Lazear and Oyer 2004). Also, changing the compensation scheme to a reward or performance based system made pay more variable than it would have been in the previous seniority-based system (Cappelli et al. 1997; Mitchell 1989). The firm will be more inclined to reward high performance employees (Cappelli et al. 1997; Mitchell 1989; Zenger 1992). And hence, firm restructuring should lead to a more wide-spread wage distribution and decreasing returns to seniority. All of these weaken the role of a firm in shaping the wage distribution. Corporate restructuring can limit the firm’s ability to reduce wage inequality. Wage inequality within a firm is limited through cohort and tenure effects that govern the behavior of group wages; downward rigidity of wages, if any, anchors the lower tail of wage distribution. Oppositely, a more dispersed wage distribution due to corporate restructuring that both decreases

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the wages of low wage earners and increases the wages of high wage earners indicates the reducing effectiveness of a firm’s internal labor market mechanism. The firm’s ability to control wage inequality within the firm is hence undermined. The firm that we study experienced several waves of restructuring. Two waves of reduction in force (RIF) were undertaken. The first RIF tool place in early- to mid-1980s during the time of hostile takeovers.1 The second RIF occurred in early 1990s following the regulatory changes that limited takeovers. Between two waves of RIF, the firm had a hiring freeze and also transitioned its performance management and compensation system, in which a seniority-based pay scheme was changed to a performance-based one. The firm sought to make performance goals more measurable and relevant, which pay decisions can be made upon. Additionally, the firm destroyed the performance evaluation records once promotion and pay raise decisions were made. The purpose of this was to make sure performance evaluation would not be biased by an employee’s past performances, and hence, all promotions and pay raises would have to be “reearned” each year.

ORGANIZATIONAL SETTING, DATA, AND METHODS Organizational Setting The firm that we study is a Fortune 500 firm in energy sector. Like most other large firms studied over the same period of the time (Baker, Gibbs, and Holmstrom 1994a), this firm also had an internal labor market composed of hierarchically-ranked salary grade levels (SGLs) to which jobs and salaries were attached. Non-exempt employees (clerical, secretarial, and support staff) ranked in SGL 1 through 9, and exempted workers (managers and professionals) were in SGL 7 through 24. Additionally, roughly 25% of employees were paid on hourly basis and did 1

The firm itself was not taken over during the time. Neither was there any evidence that it was a target of takeover.

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not belong to a salary grade level. We use the terms “white collar clericals”, “white collar managers” and “blue collar workers” to denote these three broad categories, respectively. An employee hired as an hourly worker could be promoted to a white collar clerical later in the career, so could a white collar clerical become a white collar manager. Neither the salary grade level system nor job requirements attached to each level were significantly changed through the time of study. Employees in the firm that we study were paid relatively well. For those who were paid by hours, hourly wages were two to three times higher than federal minimum wages throughout the time our data. The average union coverage was not high; at most 10% blue collar workers were covered by a labor union. Table 1 shows the descriptive statistics that summarize the employee characteristics. ---Insert table 1 about here---

Data Set Career records of 25% randomly sampled U.S. employees between 1969 and 1993 are provided by the firm. We examine the change of wage patterns and distribution by three broad categories of employees: blue-collared workers (who do not belong to any salary grade level), nonexempted white collar clericals, and exempted white collar managers. Only employees hired after 1969 are included in our analysis in order to avoid potential bias caused by left censoring due to incomplete career information of employees hired before 1968 (Petersen 1995). We only include full time employees in our analysis. In the original data set provided by the firm, a new record is added whenever there is a “career change” (such as hiring, salary change, promotion, demotion, transfer, etc.) for each

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employee. To transform the data into a yearly panel, we keep only the last record for each employee in each year. If an employee has no record in a given year, his/her record in the previous year is used to fill forward in the missing year. Although we delete all records but the last one for each employee in any given year, all information regarding promotion, demotion, and bonus awarded occurred during the year are still preserved. And hence, we have a “snapshot” of all employees in the firm in the end of each year (Gibbs and Hendricks 2004). We perform our analysis based on these end-of-year “snapshots” rather than event histories that could possibly occur at any given time during the year. Our final sample includes a total number of 22, 187 employees: 6,555 blue collar workers (34,808 employee-year records), 9,051 white collar clericals (46,173 records), and 6,551 white collar managers (67,276 records).

Dependent Variable Wage is our dependent variable of interest. Provided in the data are the nominal annual wages, which generates some potential problem. Since the level of annual wage depends on both the level of hourly wage and the number of hours worked for those who are paid on hourly basis, we only include full time employees in our study to eliminate labor supply effect. In order to make wages observed at different time comparable, all wage data are deflated to 2007 U.S. dollars. We also take the logarithm transformation when necessary. Both real wage and log of real wages in 2007 U.S. dollars are used in this study.

Independent and Control Variables The occurrence of corporate restructuring is the major independent variable in this study, as we look at how wage patterns and distributions changed through restructuring. More specifically,

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we focus our discussion on both yearly wage patterns and pre- and post-restructuring comparison. Other control variables are defined as the following. As noted, the firm has 24 formal salary grade levels, with additional 25% employees paid on hourly basis and do not belong to any salary grade level. We divide all employees into three types: blue collar workers, white collar clericals, and white collar managers, depending on whether they belong to a salary grade level, their exempt status, and which salary grade level they are in. We analyze these three types of employees separately to allow different career and wage patterns across types. For each employee, we have his or her gender, race, level of education, and union coverage. An employee’s firm tenure and tenure at current salary grade level can be easily constructed given the nature of our data. Also, we calculate the frequency of promotions that an employee has received. This is an important proxy for job performance since the firm did not keep performance measures in its personnel data. The firm’s HR manager told us in an interview that the firm would like to eliminate any potential evaluation bias caused by previous performance evaluations, and hence performance measures were destroyed each year once the raise and promotion decisions were made. Following the common practices in other sociological literatures (Castilla 2005; Castilla 2008), we also estimate the likelihood of employment separation using a Cox proportional model and use the estimate to control for possible sample selection, although controlling for likelihood of separation does not qualitatively change our results.

Methods We use a semi-parametric variance decomposition method proposed in DiNardo, Fortin and Lemieux (1996) that can be applied to the whole distribution. Comparing to Oaxaca

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decomposition (Oaxaca 1973) that assumes that the effect of treatment (in this case firm restructuring) is constant across the whole wage distribution, the method we use allows different treatment effects across different segments of the wage distribution. This enables us to assess different effects of firm restructuring at various points across wage distribution. In order to separate the effect of firm restructuring on wage distribution from those of other factors, we ask the question “what the pre-restructuring wage distribution would have been had employee characteristics attained the post-restructuring level.”2 A semi-parametric approach (DiNardo, Fortin, and Lemieux 1996) is used to construct a “counterfactual” pre-restructuring wage distribution in which each individual employee is weighted in a way that overall employee characteristics, such as age, gender, race, schooling, tenure, and union coverage, resemble those in the post-restructuring level. Such “counterfactual” distribution is then compared to the actual post-restructuring wage distribution to obtain the pure effect of firm restructuring on wages. And hence, the discrepancy between the “counterfactual” and the actual distributions can be interpreted as the change in wage distribution due to factors other than employee characteristics.

RESULTS Figures 1 through 3 replicate figure 2 in Baker, Gibbs, and Holmstrom (1994b) and show the group wage patterns of cohort entered since 1969. The solid baseline in each graph connects the mean starting wage of the cohort entered in each year. There is a different line deviating from the baseline every year, each of these lines indicates the year-by-year mean wages of that specific cohort. For example, the top-most dashed line in figure 2 indicates the year-by-year mean wages of white collar clericals who entered the firm in 1969. Two observations emerged 2

This question can be asked in an opposite way as “what the post-restructuring wage distribution would have been had employee characteristics remained at the post-restructuring level”. This, however, only influences which distribution is weighted and will not alter the final result.

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from a quick inspection of figures 1 through 3. First, there is a significant cohort effect, as huge discrepancies existed among cohorts who entered the firm in different years. A more notable trend is the decreasing of entry wages for blue collar workers between late-1970s and mid-1980s. Second, there is also a tenure effect, shown by the nearly parallel wage profiles of cohorts entered at different years, especially for white collar employees. This suggests wage growths are guided by similar rules throughout the time of study.3 ---Insert tables 1, 2, and 3 about here--Figures 4 through 6 give a graphic presentation of wage dispersion, again by three types of employees. The 10th, 30th, 50th, 70th, and 90th wages percentiles in each year are shown in each graph. Overall, it is clear that the lower tail inequality (the difference between the 50th and 10th percentiles) increased dramatically for blue collar workers. Combining the evidences from figure 1, such huge increase is likely to be caused by the decreasing entry wages. Both upper tail (the difference between the 90th and 50th percentiles) and lower tail inequality increased at similar paces for white collar clericals. For white collar managers, upper tail inequality increased faster than the lower tail side. ---Insert tables 4, 5, and 6 about here--Figures 7 through 9 graphically show the results of DiNardo, Fortin, and Lemieux (DFL) decomposition. We use the wage distribution in 1981 as “pre-restructuring” distribution and the one in 1990 as “post-restructuring” one. The selection of timing is somewhat arbitrary, and we choose 1981 and 1990 as the starting and end points of the whole process of corporate restructuring, which took place in 1980s. Using other start points (such as 1980 or 1982) and

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Both cohort and tenure effects are also statistically significant when tested under a parametric model. The results are not show here due to space limitation. Although it is a well know problem that it is impossible to test the effects of year, cohort, and tenure separately, we impose a polynomial constraint on tenure effect to make the model identifiable.

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ending points (1989 or 1991) yields similar results. There are three panels in each graph: the dashed line in the top-left panel denotes the actual 1981 real wage distribution, and the solid line in the same panel denotes a “weighed” real wage distribution, which is the distribution that would have prevailed if the employee characteristics in 1981 were the same as those in 1990. The solid line in top-right panel is the same as the solid line in top-left panel, and the dashed line is the actual real wage distribution in 1990. Finally, the difference of the two lines in top-right panel is shown in the bottom-left panel. If there is no difference in the two lines in the top-right panel, a horizontal line at zero should be observed in the bottom-left panel; this means employees across the whole wage distribution are paid in the same way in 1981 and 1990 conditional on their characteristics. However, we do not observe a horizontal line in the bottomleft panel in figure 7. We observe some positive difference when logarithm of real wage is around 10 (equivalent to $22,026 in 2007 U.S. dollars) and around 11 (equivalent to $59, 874 in 2007 U.S. dollars), and we also observe some negative difference when logarithm of real wage is between 10 and 11. This indicates, conditional on worker characteristics, less people are paid between $22,026 and $59,874 (in 2007 U.S. dollars) in 1990 compared to 1981; more people are paid around $22,026 and around $59,874 in 1990 than in 1981. Briefly, the wage distribution in 1990 is more polarized than in 1981, holding employee characteristics constant. ---Insert tables 7, 8, and 9 about here--Similar inspections in the bottom-left panels of figures 8 and 9 suggest the wage distributions for white collar workers are more skewed to the left in 1990 than in 1981—the firm is more likely to pay a higher wage than a lower wage to white collar workers when employee and job characteristics are held constant. Moreover, the discrepancy between weighted 1981 and actual 1990 distributions is larger for white collar managers (figure 8) than for white collar

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clericals (figure 9), implying the change in compensation system that inclines to reward top performers impacted white collar managers more than white collar clericals. Overall, the change of wage patterns for white collar workers under the semi-parametric decomposition show similar mechanisms as the well-documented skill-biased technological change (Autor, Katz, and Krueger 1998; Fernandez 2001). After corporate restructuring, wage inequality increased for all types of employees.

DISCUSSION Summary We examine the role of the firm’s compensation practices in influencing wage patterns, wage distributions, and wage inequality. The firm can shape wage patterns and limit wage inequality through systematic wage polices. Such ability to limit wage inequality with the firm was undermined through corporate restructuring. We find significant cohort and tenure effects. Employees entered the firm in different years receive very different wages. Conditional on entering wages, wage growth follow similar patterns no matter when the employees entered the firm. A semi-parametric strategy is used to compare wage distribution before and after corporate restructuring and implementation of performance-based compensation system. Holding individual backgrounds and job characteristics consistent, higher wage earners were more likely to be paid even higher after restructuring. The effect of restructuring on low wage earners differ by types of employees— low-paid blue collar employees were paid even lower after restructuring, while the whole wage distribution shifted rightward with a flatter upper tail for white collar employees. In brief, the

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wage inequality increased after restructuring, indicating the firm’s power to reduce wage inequality was declining through corporate restructuring and market pressure.

Limitations The issue of external validity and generalizability inevitably rises when we draw conclusions from analysis of a single firm in such a special economic context. Two reasons help strengthen our results. First, our findings are largely consistent with other studies of large firms in the same period (Gibbs and Hendricks 2004; Lin 2005). Second, there certainly are firm idiosyncrasies in wage policies and how wage patterns are influenced; we do not intend to conclude what will happen in a certain context, alternatively, our findings provide possible explanations to what happened in the past and also can be used to infer what may happen under similar circumstances in the future. As noted before, the lack of performance measures in our data also handicapped our ability to quantitatively test how much of the change in the wage patterns can be attributed to the introduction of performance-based compensation. Nevertheless, preliminary results not reported here indicate that the impact of firm tenure on wages decreased after restructuring. This confirms that wages were less dependent on firm tenure after the firm introduced a performance-based compensation system. Also, holding explanatory powers of other control variables constant, a decreasing impact of firm tenure implies an increasing impact of performance on wage determination.

Conclusion

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In this study we address the changing of a firm’s role in shaping wage patterns, especially through corporate restructuring times. We find that, in additional to economic factors, there are institutional reasons why wage inequality increased in 1980s. Market deregulations and takeovers necessitated corporate restructuring and to some extent weakened the internal labor market within the firm, which in turn increased the effect of market forces on wage structure. While some features of internal labor market were preserved through corporate restructuring, we also observe a more polarized wage structure. The firm was more inclined to reward high performers and pay the new hires at lower wages. We conclude that the ability of the firm to limit wage inequality through its internal labor market mechanisms was substantially weakened through restructuring, as market power prevailed and altered the firm’s wage policies. Employment outcomes relied more on market conditions than on firm rules. Although firm hierarchies still was and continued to be an important factor that determines wage structure, we suggest there are increasing individual idiosyncrasies in wage patterns and employees were less likely to shielded from market risks and chances by the firm. Our findings also have potential implications on current economic crisis. Downsizing and wage cuts happened since mid-2007 resemble what occurred to the firm we study through reduction in forces and wage restructuring in the pressure of economic efficiency. On the one hand, we would hence expect an increasing wage inequality among the workers who manage to stay with the same employer through current economic crisis. On the other hand, albeit the public opinion on the media to re-establish and re-regulate economic order, we doubt if firms can regain its ability to limit wage inequality after market forces have penetrated into firm structures for so many years.

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Salary Paths for Each Cohort (Blue Collar Workers)

20000

30000

40000

50000

60000

Figure 1: Salary Paths by Cohort, Blue Collar Workers

1970

1975

1980

Year

1985

1990

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Figure 2: Salary Paths by Cohort, White Collar Clericals

30000

35000

40000

45000

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Salary Paths for Each Cohort (White Collar Clericals)

1970

1975

1980

Year

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Figure 3: Salary Paths by Cohort, White Collar Managers

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80000

100000

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Salary Paths for Each Cohort (White Collar Managers)

1970

1975

1980

Year

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1985

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Figure 4: Wage Dispersion of Blue Collar Workers, 1969-1993

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Log Real Annual Wage 10 10.5 11

Wage Dispersion of Blue Collar Workers

1970

1975

1980

1985

1990

1995

Year 10th Percentile 50th Percentile 90th Percentile

30th Percentile 70th Percentile

Figure 5: Wage Dispersion of White Collar Clericals, 1969-1993

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Log Real Annual Wage 10.2 10.4 10.6 10.8

11

Wage Dispersion of White Collar Clericals

1970

1975

1980

1985

1990

1995

Year 10th Percentile 50th Percentile 90th Percentile

30th Percentile 70th Percentile

Figure 6: Wage Dispersion of White Collar Managers, 1969-1993

10.8

Log Real Annual Wage 11 11.2 11.4 11.6 11.8

Wage Dispersion of White Collar Managers

1970

1975

1980

1985

1990

Year 10th Percentile 50th Percentile 90th Percentile

18

30th Percentile 70th Percentile

1995

Figure 7: DFL Decomposition for Blue Collar Workers, 1981 versus 1990

3 0

0

1

Density 1 2

Density 2 3

4

DFL Decomposition for Blue Collar Workers

9

10 11 Log Real Wages

12

9

10 11 Log Real Wages

Weighted 1981

1990

12

Weighted 1981

Difference in Densities -.4 -.2 0 .2 .4

1981

9

10 11 Log Real Wages

12

Figure 8: DFL Decomposition for White Collar Clericals, 1981 versus 1990

1.5 Density .5 1 0

0

Density .5 1

1.5

DFL Decomposition for White Collar Clericals

9.5

10

10.5 11 Log Real Wages

9.5

Weighted 1981

10

10.5 11 Log Real Wages

1990

11.5

Weighted 1981

Difference in Densities -.4 -.2 0 .2 .4

1981

11.5

9.5

10

10.5 11 Log Real Wages

11.5

Figure 9: DFL Decomposition for White Collar Managers, 1981 versus 1990

1.5 Density .5 1 0

0

Density .5 1

1.5

DFL Decomposition for White Collar Managers

10

11 12 Log Real Wages

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Weighted 1981

10

11 12 Log Real Wages

10

11 12 Log Real Wages 1990

Difference in Densities -.4 -.2 0 .2

1981

13

19

13

Weighted 1981

Table 1: Mean Real Annual Wages in 2007 U.S. Dollars, Tenures, and Ages of All Employees Hired After 1969

Year 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993

Blue Collar Workers Real Tenure Wage (Year) $44,072 0.62 $45,630 1.13 $49,870 1.81 $50,899 2.51 $51,593 3.01 $49,960 3.27 $53,014 3.57 $54,422 3.73 $54,279 3.86 $55,512 4.21 $59,398 4.33 $50,749 4.41 $50,319 4.53 $51,019 5.03 $51,510 5.73 $49,928 6.19 $46,155 6.17 $45,417 6.69 $44,816 7.06 $43,748 7.08 $43,179 7.44 $43,446 7.74 $44,140 8.36 $45,180 9.14 $46,207 9.32

Age 42 42 41 41 39 36 35 34 33 33 33 32 33 33 34 35 34 35 35 35 36 36 37 38 39

White Collar Clericals Real Tenure Age Wage (Year) $35,426 0.60 31 $35,177 1.16 31 $38,045 1.81 32 $38,551 2.43 32 $38,121 2.60 31 $37,681 2.70 31 $38,302 3.28 31 $38,825 3.56 31 $37,856 3.78 31 $37,690 3.92 31 $36,807 4.01 31 $34,915 3.91 31 $35,607 3.86 31 $37,660 4.43 32 $38,589 5.05 32 $37,648 5.52 33 $37,691 5.85 34 $38,739 6.68 34 $38,551 7.05 35 $38,212 6.55 35 $37,822 6.49 35 $38,620 6.71 35 $39,557 6.95 36 $40,189 7.70 37 $40,894 7.83 37

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White Collar Managers Real Tenure Age Wage (Year) $73,869 0.62 40 $73,926 1.38 39 $80,341 2.22 39 $82,410 3.01 39 $82,533 3.60 39 $85,092 3.96 38 $82,517 4.63 37 $84,393 4.85 37 $83,222 5.20 36 $83,174 5.42 35 $80,569 5.58 35 $75,768 5.49 34 $78,053 5.47 34 $83,540 5.96 34 $86,327 6.72 35 $86,503 7.62 35 $87,692 8.02 36 $89,830 8.84 36 $89,264 9.35 36 $89,406 9.43 37 $88,416 9.49 37 $88,265 9.70 37 $90,218 10.06 37 $91,902 10.79 38 $93,341 11.04 38