Journal of Political Economy Fang

8/14/2019 Journal of Political Economy Fang

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IS WAGE INEQUALITY LIMITED THROUGH FIRM HUMAN RESOURCE

MANAGEMENT PRACTICES?1

John C. Dencker

University of Illinois at Urbana-ChampaignSchool of Labor and Employment Relations

504 E. Armory Avenue

Champaign, IL 61820Tel: (217) 333-2383

e-mail: [email protected]

Chichun Fang

University of Illinois at Urbana-ChampaignSchool of Labor and Employment Relations

504 E. Armory AvenueChampaign, IL 61820

Tel: (217) 265-0954

e-mail: [email protected]

January 15, 2009


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Electronic Submission ID: 12291

IS WAGE INEQUALITY LIMITED THROUGH FIRM HUMAN RESOURCE

MANAGEMENT PRACTICES?

ABSTRACT

We examine the dynamics of wage patterns in a large firm, focusing on the role of HRM systems

and compensation practices in shaping wage patterns and inequality through corporate

restructuring. We conclude that the firms 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 firms institutional power on wage determination was undermined

after corporate restructuring as the wage inequality within the firm increased. After restructuring,

the firm became more inclined to reward high performance workers, and reduced entry-level

wages for a number of workers.

Key Words: Compensation, Corporate Restructuring, Wage Inequality


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INTRODUCTION

The increase of wage inequality in U.S. labor market during 1980s is well documented.

In brief, the wage structure polarized (Morris, Bernhardt, & Handcock, 1994), as the upper-tail

(the difference between the 90th

and 50th

percentiles) inequality increased steadily and the lower

tail (the difference between the 50th

and 10th

percentiles) inequality rose sharply in early 1980s

but stopped increasing thereafter (Autor, Katz, & Kearney, 2008).

While the increase of wage inequality in 1980s is studied early and a lot in labor

economics (Autor et al., 2008; Autor, Katz, & Krueger, 1998; Borjas & Ramey, 1995; Card &

DiNardo, 2002; Chay & Lee, 2000; DiNardo, Fortin, & Lemieux, 1996; Katz & Murphy, 1992;

Lemieux, 2006; Murphy & Welch, 1992), the roles of the firm and its human resource policies in

contributing to such increase is surprisingly not widely studied. Since wages are paid by the firm,

which makes pay decisions relying on its human resource management practices and

compensation policies, such dramatic change in wage inequality should be closely related with

change in human resource management practices.

The relationship between human resource management practices and firm performance

has early been established (Huselid, 1995; Ichniowski, Shaw, & Prennushi, 1997; MacDuffie,

1995; Youndt, Snell, Dean, & Lepak, 1996). Among all the human resource practices,

compensation policies have been related with various outcomes such as individual incentives

(Guzzo, Jette, & Katzell, 1985; Jenkins, Gupta, Gupta, & Shaw, 1998; Stajkovic & Luthans,

1997), turnover (Shaw & Gupta, 2007; Shaw, Gupta, & Delery, 2005), and promotion (Gerhart


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theory and seen as a practice rather the outcome of a firms compensation policy (Cowherd &

Levine, 1992). In this study, we view wage dispersion as the outcome of compensation policies

and examine the impact of a firms compensation rules on its wage dynamics. More specifically,

we discuss how the change in compensation policies caused by corporate restructuring influences

wage patterns and distribution. We also explore how the firms ability to limit wage inequality is

reduced through restructuring.

We find systematic and consistent wage patterns within the firm before and after

corporate restructuring. Cohort and tenure effects are significant; employees entered the firm in

different years may receive different entry wages depending on market conditions, but the wage

growth patterns conditional on respective entering wages are very similar. The effects of firm

restructuring, however, varies by types of employees. For blue collar workers, the post-

restructuring wage distribution is more polarized than the pre-restructuring distribution. For

white collar clericals and managers, the distribution shifts rightwards and becomes more

dispersed in the right 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.

THE FIRMS ROLE IN SHAPING WAGE DISTRIBUTION

We argue that the firm and its HR practices play an important role in shaping the wage

distribution. Studies have shown that human resource practices modify the effect of

technological changes on hourly workers (Fernandez, 2001) and influence the link between


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rigidity (Gibbs & Hendricks, 2004; Harris & Holmstrom, 1982; Seltzer & Merrett, 2000).

Through its internal labor market, the firm can provide employees job security and opportunities

for career development, and it also shields employees from labor market fluctuations (Doeringer

& Piore, 1971; Osterman, 1984). While the employees are moved among positions, the overall

wage structure and wage policies of the firm are guided by the job levels and tenure effect. The

firms compensation policies hence play the key role of shaping wage patterns and distributions

and controlling wage inequality.

Nevertheless, the effectiveness of internal labor market might be weakened through a

sequence of deregulation and hostile acquisitions (Cappelli, 1992; Cascio, Young, & Morris,

1997) that necessitates economic efficiency in firm operations (Schleifer & Summers, 1998;

Useem, 1996). Employees will be exposed to external market conditions (Cappelli et al., 1997;

Eriksson & Werwatz, 2005; Lazear & Oyer, 2004), and the firm is also more inclined to reward

high performance employees (Cappelli et al., 1997; Mitchell, 1989; Zenger, 1992); both will

change the role of a firm in shaping the wage distribution and undermine the firms power to

limit wage inequality.

Cohort and Tenure Effects

Wages can increase with tenure for several reasons, such as the accumulation of firm-

specific human capital (Becker, 1975), attaining higher position in firm hierarchy (Lazear, 1995;

Lazear & Rosen, 1981), or a deferred compensation used as incentives for staying in the firm

(Gibbons & Waldman, 1999, 2006). In the absence of dramatic institutional changes, tenure


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overall wage patterns and wage distribution, unless the composition of employees within a firm

changes (as a counter example, the overall aging of baby boom generation in 1980s is believed to

be one of the many reasons why the upper tail of wage distribution thickened at that time.)

However, the firm might pay different entry wages to the employee cohorts who enter in

different years. This is especially likely when the firm is under the economic pressure and needs

to peg its entry wages to market clearing wages or when a two-tier wage structure is

implemented (Martin & Peterson, 1987). Cohort effect is expected to impact the variation of

wage patterns across cohorts (Baker et al., 1994b; Heckman & Robb, 1985; Lazear, 1995), even

when tenure effect applies uniformly to all employees. The discrepancy in entry wages of two

adjacent cohorts could still persist after more than 15 years of tenure (Baker et al., 1994b).

Downward Wage Rigidity

In a neo-classical economics setting where wages are set to be equal to the value of

marginal products according to spot contracts, wages should fluctuate both upwards and

downwards with market condition. However, implicit contract theory suggests that a risk-averse

employee will accept lower life-time earnings if their annual compensation are guaranteed not to

decrease during economic downturns (Azariadis, 1975; Baily, 1974; Klaas & Ullman, 1995).

And hence, once being employed, wages will be sticky (Okun, 1981) or downward rigid

(Harris & Holmstrom, 1982). On the other hand, the employer might be more inclined to adjust

compensation than employment (Craig & Pencavel, 1992) to provide job security. Empirical

results about downward wage rigidity are mixed: some report nominal wage cuts are not rare


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Downward wage rigidity, if any, provides not only job security but also a guaranteed

level of income. Comparing to the potential wage loss due to involuntary turnover and loss of

firm-or industry-specific human capital (Neal, 1995) that may take more than 5 years for a

displaced worker to re-attain his or her pre-displacement income level (Podgursky & Swaim,

1987)1, downward wage rigidity avoids wage loss and compresses the bottom tail of wage

distribution, since the very first wage offer to an employee receives will server as an anchor in

his or her future wage profile. In this sense, the firm plays an important role in limiting the

increase in wage inequality (especially in the lower tail of distribution) and guarantees

employee's quality of life when wages are downward rigid.

Promotion and Fast-trackers

Although tenure and cohort effects that apply uniformly to employees who have the same

tenure and entered the firm in the same year, employees are treated differently in the firm. Since

wages are largely attached to job levels, the way an employer promotes/demotes employees will

also influence wage patterns. Studies have shown the existence of fast-trackers (Baker et al.,

1994a; Rosenbaum, 1979, 1984; Seltzer & Merrett, 2000; Treble et al., 2001), who are high

performance workers and are promoted at a much faster pace than others. Studies also show that

conditional on wages at the time t, wages at time t+1 and time t-1 are negatively correlated

(Chiappori, Salani, & Valentin, 1999), indicating employees who receive a higher raise this

year are more likely to receive a higher raise in the coming years as well.

Corporate Restructuring


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Mergers and hostile acquisitions that followed the deregulation in late-1970s and early-

1980s (Schleifer & Summers, 1998) ignited the firms pursuit of organizational efficiency

(Useem, 1996), which in turn led to restructuring, including reduction in forces (RIFs) and

changes in wage policies (Cappelli et al., 1997; Cascio et al., 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 & Werwatz, 2005; Lazear & 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.

Corporate restructuring can limit the firms ability to reduce wage inequality. Wage

inequality within a firm is limited through cohort and tenure effects that govern the behavior of

group wages, and 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

the wages of low wage earners and increases the wages of high wage earners indicates the

reducing effectiveness of a firms internal labor market mechanism. The firms ability to control

wage inequality within the firm is hence weakened.

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


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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 employees past performances, and hence, all promotions and pay raises

would have to be re-earned 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 et al., 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 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


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were covered by a labor union in any given year. 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),

non-exempted 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

employee. To transform the data into a yearly panel, we keep only the last one record for each

employee in each year. If an employee has no records 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


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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

We look at the wage patterns and wage distribution in the twenty-five year period, and

hence, wage is the major 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 wage distributions 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

We use different independent variables in our analysis. 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, we focus our discussion on

both yearly wage patterns and pre- and post-restructuring comparison. We also regress wage at

time t+1 on wage at time t-1 controlling for wage at time tto test the existence of fast trackers.

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


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wage patterns across types. For each employee, we have his or her gender, race, level of

education, and union coverage. An employees 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 destroyed all performance measures once raise and promotion decisions were made.

Methods

The methods we use for empirical analysis are based on a set of both group- and

individual-level regressions (Baker et al., 1994b) and a semi-parametric variance decomposition

(DiNardo et al., 1996). We give an overview of the quantitative techniques we use here and will

explain the details when we present the results.

We largely follow the strategies in Baker, Gibbs, and Holmstrom (1994b) to test for

cohort effects, downward wage rigidities, and fast trackers. Similar procedures for firm-level

data analysis have been widely adopted in previous studies (Gibbs & Hendricks, 2004; Lin,

2005). A model suggested in Chiappori, Salani, and Valentin (1999) is also tested.

We further 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

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 effect across different segments of the wage distribution. This enables us to assess

different effects of firm restructuring at various points across wage distribution.


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------------------------------------------

Insert figures 1, 2, and 3 about here

------------------------------------------

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

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.

------------------------------------------


------------------------------------------

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


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(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.

In the following sections, we analyze the cohort and tenure effect of wages at group level,

downward rigidity, promotion and faster-trackers, and effect of firm restructuring on wage

distribution.

Cohort and Tenure Effects

We focus only on the behavior of group wages in this section. Wages at individual level

are determined by other more complicated factors which will be explored in the following

sections. More specifically, we cluster the employees who entered the firm in the same year and

have the same firm tenure as one group and look at the mean wages of each group. It is

reasonable that group wage can be influenced by when the group entered the firm (cohort effect),

how long the group has stayed with the firm (tenure effect), and in which year the wage is

recorded (year effect). Statistically it is impossible to test these three effects separately, since

any two of these three variables (entering year, firm tenure, and year of observation) can jointly

decide the other. For example, if we observe the wage in 1990 of a cohort who entered in 1980,

this cohort must have stayed with the firm for 10 years. And hence, a regression equation with

wages as the dependent variable and entering year, firm tenure and year of observation as the

independent variables is not identifiable (Heckman & Robb, 1985). Alternatively, we follow a

similar strategy as the one in Baker, Gibbs and Holmstrom (1994b) to impose a parametric


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In table 2, we test cohort effects for three types of employees separately (blue collar,

white collar clericals, white collar managers) using the same identification strategy. In model 1,

group wages are regressed on both tenure dummies and year of observation dummies. In model

2, we remove all tenure dummies and add polynomials of firm tenure sequentially (linear,

quadratic, cubic, etc.) until model 1 is no longer statistically different than model 2. Unlike the

linear trend of tenure effect found in Baker, Gibbs and Holmstrom (1994b), we fit our wage data

with both linear and quadratic terms of tenure for white collar workers, and all linear, quadratic,

and cubic terms are used to fit the tenure trend for blue collar workers (see table 2).

Consequently, model 2 for three types of workers is slightly different. Model 2 hence controls

for both year and tenure effects as model 1 but uses a single trend for tenure effect rather than a

full set of tenure dummy variables.

-----------------------------


-----------------------------

In the final step, cohort dummies are add to model 2, and the regression results are shown

in model 3. Since tenure effect is controlled without using the full set of tenure dummies, the

perfect multicollinearity problem is eliminated and model 3 is identifiable. Model 3 is

significantly different when tested against model 2 for all three types of workers, implying a

statistically significant cohort effect. When the employees entered the firm will impact their

wage levels, everything else being equal. Combining with the significant polynomial of tenure


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Starting from this section, we switch our focus to individual wages. We have shown that

wages at group level are determined by cohort effect, year effect, and a uniform tenure effect.

We now examine some other factors that influence wages at individual level and could have

heterogeneous effects on workers in the same cohort-tenure group. We start with testing

downward wage rigidity.

Percentages of blue collar workers, white collar clericals, and white collar managers who

received zero nominal wage increases and negative real wage increases are tabulated in tables 3

through 5, respectively. Negative nominal increases are very rare and usually results of

demotions, so the frequencies of negative nominal increases are not listed in the table.

-----------------------------------------

Insert tables 3, 4, and 5 about here

-----------------------------------------

It should be first noticed that a non-trivial amount of workers receive zero nominal

increases throughout the 25-year period. Blue collar workers seem to be more likely to receive a

zero increase than other white collar workers. With a positive inflation rate, a zero nominal

increase actually implies a decline in real wage. As the numbers in the third columns of tables 3

through 5 would indicate, real wages are anything but downward rigid. Real wage decreases

occur because of two different reasons: a zero (or even negative) nominal wage increase, or a

positive nominal wage increase but with a growth rate lower than inflation. A brief inspection of

tables 2 through 4 lead to an observation that percentages of workers who received real wage


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percentage of workers who received zero nominal wage increase did not deviate much from

other years but percentage of those who suffered from a real wage decline rocketed. Overall, the

firm that we study did not seem to provide its employees enough protection from inflation,

especially in the years of oil crisis when inflations were high. Most of workers suffered real

wage declines.

The last columns in tables 3 through 5 provide another unique feature of the firms wage

policy. These numbers are the probabilities of an employee receiving a zero nominal wage

increase this year conditional on him or her receiving zero nominal increase in the previous year.

It is clear than an employee who received a zero nominal increase is more likely to receive zero

increase as well in the following year, since the conditional probability is usually larger than the

unconditional probability of receiving zero nominal increase in the same year. In other words,

wage increases are awarded to a certain group of workers repetitively rather than to all workers

uniformly, and these workers are more likely to be given other increases in their tenures with the

firm. This leads to our next section of analysis on promotion and fast-trackers.

Promotion and Fast-trackers

In the previous section we argue wage increases are not uniformly awarded to all

employees. An employee who received wage increase in the previous year is more likely to

receive another increase in the future. In this section we show that not only the likelihood of

wage increase is serially correlated but also the amount of wage increase.

There are nine sets of regression output in table 6. For each type (blue collar, white


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union status, current salary grade level, tenure at current salary grade level, and rate of promotion

are included as control variables. In model 3, the year when an employee entered the firm and

the year when the wage is recorded are also added as control variables.

-----------------------------


-----------------------------

It is clear from table 6 that after the wage in the current period is controlled, the wage in

the next period and the wage in the previous period is negatively correlated. Conditional on an

employees current wage, the lower his/her wage in the previous year, the higher his/her wage in

the next year will be. Hence, not only are wage increases given unevenly to all employees, so

are the amounts of wage increases. The evidences from table 6 and last columns in tables 3

through 5 suggest there are fast-trackers in the firm.3

Some employees receive wage increases

more likely than others, and some among those who receive wage increases more frequently

receive larger raises than the rest.

Effect of Corporate Restructuring

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.4 A semi-parametric approach

(DiNardo et al., 1996) is used to construct a counterfactual pre-restructuring wage distribution


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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.

------------------------------------------


------------------------------------------

Figures 7 through 9 graphically show the results of 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 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.


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observe a straight line in the bottom-left panel in figure 7. We observe some positive difference

when logarithm of real wage is around 10 (equivalent to $22,026 in 2007 dollars) and around 11

(equivalent to $59, 874 in 2007 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 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.

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 1981the 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

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 et al., 1998;

Fernandez, 2001). After corporate restructuring, wage inequality increased for all types of

employees.

DISCUSSION


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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. Real wages were not downward rigid,

and the firm seemed not be able to keep wage growth with inflation rates especially when

inflations were high. Employees who received zero nominal wages increases in the past were

more likely to receive other zero nominal increases later in their career. The amount of wage

raises an employee received was serially correlated, suggesting the existence of fast-trackers.

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 employeeslow-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 wage inequality increased after restructuring, indicating

the firms 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


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idiosyncrasies in wage policies and how wage patterns are influenced; we do not intend to

conclude whatwill

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, we tried two indirect strategies.

First, 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. Alternatively, we tried to implement a

proxy of performance. Assuming wage changes are caused by the change in job characteristics

(such as salary grade level) and change in performances. The residual in the regression of wage

changes on change of job characteristics should be composed of both changes in performance

and regression errors, which then can be seen as a noisy measure of performance change. We do

find the returns to this noisy measure increased after corporate restructuring. This is our second

indirect way to test the effect of the new compensation system on wages.

Conclusion

In this study we address the changing of a firms role in shaping wage patterns, especially


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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 firms 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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Figure 1: Salary Paths by Cohort, Blue Collar Workers

2

0000

30000

400

00

50000

60000

1970 1975 1980 1985 1990 1995Year

Salary Paths for Each Cohort (Blue Collar Workers)

Figure 2: Salary Paths by Cohort, White Collar Clericals

35000

40000

45000

50000

55000

Salary Paths for Each Cohort (White Collar Clericals)



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Figure 3: Salary Paths by Cohort, White Collar Managers

6000

0

80000

100000

120000

1970 1975 1980 1985 1990 1995Year

Salary Paths for Each Cohort (White Collar Managers)

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

9.5

10

10

.5

11

Log

Rea

lAnnua

lWage

Wage Dispersion of Blue Collar Workers



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

10

10

.2

10.4

10

.6

10

.8

11

Log

Rea

lAnnua

lWage

1970 1975 1980 1985 1990 1995Year

10th Percentile 30th Percentile50th Percentile 70th Percentile

90th Percentile

Wage Dispersion of White Collar Clericals

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

10

.8

11

11.2

11

.4

11

.6

11

.8

Log

Rea

lAnnua

lWage

Wage Dispersion of White Collar Managers



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

0

1

2

3

4

Dens

ity

9 10 11 12Log Real Wages

1981 Weighted 1981

0

1

2

3

Dens

ity


1990 Weighted 1981

-.4

-.2

0

.2

.4

D

ifference

inDens

ities


DFL Decomposition for Blue Collar Workers

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

0

.5

1

1.5

Dens

ity

9.5 10 10.5 11 11.5Log Real Wages

1981 Weighted 1981

0

.5

1

1.5

Dens

ity

9.5 10 10.5 11 11.5Log Real Wages

1990 Weighted 1981

.2

.4

Dens

ities

DFL Decomposition for White Collar Clericals



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

0

.5

1

1.5

Dens

ity


1981 Weighted 1981

0

.5

1

1.5

Dens

ity


1990 Weighted 1981

-.4

-.2

0

.2

D

ifference

inDens

ities


DFL Decomposition for White Collar Managers



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35

Table 1: Descriptive Statistics

Blue Collar Workers White Collar Clericals White Collar ManagersYear Real

WageTenure(Year)

AgeRealWage

Tenure(Year)

AgeRealWage

Tenure(Year)

Age

1969 $44,072 0.62 42 $35,426 0.60 31 $73,869 0.62 401970 $45,630 1.13 42 $35,177 1.16 31 $73,926 1.38 391971 $49,870 1.81 41 $38,045 1.81 32 $80,341 2.22 391972 $50,899 2.51 41 $38,551 2.43 32 $82,410 3.01 391973 $51,593 3.01 39 $38,121 2.60 31 $82,533 3.60 39

1974 $49,960 3.27 36 $37,681 2.70 31 $85,092 3.96 381975 $53,014 3.57 35 $38,302 3.28 31 $82,517 4.63 371976 $54,422 3.73 34 $38,825 3.56 31 $84,393 4.85 371977 $54,279 3.86 33 $37,856 3.78 31 $83,222 5.20 361978 $55,512 4.21 33 $37,690 3.92 31 $83,174 5.42 351979 $59,398 4.33 33 $36,807 4.01 31 $80,569 5.58 351980 $50,749 4.41 32 $34,915 3.91 31 $75,768 5.49 341981 $50,319 4.53 33 $35,607 3.86 31 $78,053 5.47 34

1982 $51,019 5.03 33 $37,660 4.43 32 $83,540 5.96 341983 $51,510 5.73 34 $38,589 5.05 32 $86,327 6.72 351984 $49,928 6.19 35 $37,648 5.52 33 $86,503 7.62 351985 $46,155 6.17 34 $37,691 5.85 34 $87,692 8.02 361986 $45,417 6.69 35 $38,739 6.68 34 $89,830 8.84 361987 $44,816 7.06 35 $38,551 7.05 35 $89,264 9.35 361988 $43,748 7.08 35 $38,212 6.55 35 $89,406 9.43 371989 $43,179 7.44 36 $37,822 6.49 35 $88,416 9.49 37

1990 $43,446 7.74 36 $38,620 6.71 35 $88,265 9.70 371991 $44,140 8.36 37 $39,557 6.95 36 $90,218 10.06 371992 $45,180 9.14 38 $40,189 7.70 37 $91,902 10.79 381993 $46,207 9.32 39 $40,894 7.83 37 $93,341 11.04 38



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36

Table 2: Testing Cohort Effect5

Dependent Variable: Mean Wage by Entering Cohort and Tenure GroupsBlue Collar Workers White Collar Clericals White Collar Managers

Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3

Intercept44072.1**(3017.72)

44072.1**(3022.17)

44072.1**(1922.20)

35426.0**(2284.89)

35426.0**(2249.96)

35426.0**(1729.68)

73869.5**(3803.29)

73869.5**(3724.93)

73869.5**(2288.59)

Tenure --3411.10**(208.06)

3573.34**(150.79)

--1619.23**

(72.92)1534.66(91.09)

--3470.67**(120.73)

3080.84**(120.53)

Tenure Squared ---214.74**

(23.72)-206.62**

(15.98)--

-28.38**(3.54)

-31.57(2.81)

---46.42**

(5.87)-50.40**

(3.72)

Tenure Cubed -- 4.86**(0.74) 4.94**(0.50) -- -- -- -- -- --

Year of RecordDummies

Yes Yes Yes Yes Yes Yes Yes Yes Yes

TenureDummies

Yes No No Yes No No Yes No No

Entering CohortDummies

No No Yes No No Yes No No Yes

R-Squared 0.8381 0.8253 0.9348 0.8934 0.8884 0.9391 0.9453 0.9433 0.9803

Change in R2

Compared toModel 2

0.0128 -- 0.1095** 0.0050 -- 0.0507** 0.0020 -- 0.0370**

F-statisticsCompared to

Model 2

1.04 -- 18.13 0.59 -- 9.97 0.45 -- 22.37

5 Standard errors are in the parentheses. ** if p


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37

Table 3: Probability of Zero or Negative Wage Increases, Blue Collar Workers

Percent receiving a:Year

Zero nominal increase Negative real increaseConditional Probability6

1970 13.73 49.54 --1971 10.39 11.54 46.931972 10.33 11.96 76.661973 11.92 13.12 69.731974 11.03 71.42 60.241975 13.20 23.38 70.121976 17.20 18.26 78.63

1977 25.17 32.38 70.251978 19.00 22.94 56.221979 18.76 81.23 76.091980 19.28 89.92 83.901981 20.44 31.87 73.441982 21.99 26.52 66.661983 14.40 17.73 39.711984 17.25 60.45 57.67

1985 37.08 74.38 52.631986 50.11 53.86 37.691987 23.19 79.17 31.511988 13.52 69.35 31.251989 17.36 70.85 57.001990 12.73 40.27 32.501991 26.22 31.55 51.541992 23.26 25.64 19.56

1993 6.86 13.62 8.49

6 The probability of receiving a zero increase this year conditional on having received a zero nominal increase last year.



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38

Table 4: Probability of Zero or Negative Wage Increases, White Collar Clericals



1970 11.95 42.62 --1971 8.57 8.92 72.971972 17.06 18.90 38.961973 11.73 13.97 24.441974 8.97 39.48 43.661975 11.53 60.68 82.001976 12.56 13.27 56.36

1977 11.31 24.28 66.331978 9.47 16.25 66.661979 9.41 55.32 70.651980 9.28 75.20 75.301981 8.99 20.46 54.711982 8.50 10.27 69.131983 7.86 11.41 61.161984 22.79 53.02 76.04

1985 8.21 23.95 24.521986 13.50 15.28 43.021987 13.12 40.31 42.851988 7.72 19.32 30.951989 9.41 33.41 46.421990 6.67 23.40 33.651991 7.01 11.07 62.021992 14.15 24.46 52.32

1993 7.69 21.04 25.49




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39

Table 5: Probability of Zero or Negative Wage Increases, White Collar Managers



1970 10.49 18.13 --1971 11.59 11.99 58.061972 11.06 15.17 30.761973 11.88 15.76 48.931974 4.59 14.29 42.221975 11.21 56.75 88.001976 7.61 8.38 47.85

1977 6.97 16.95 70.001978 6.90 11.44 64.761979 5.34 42.58 65.761980 6.10 65.26 87.001981 5.26 13.27 50.001982 5.62 6.83 51.351983 6.57 10.32 45.311984 25.92 44.60 44357

1985 4.03 15.93 7.871986 11.23 11.76 40.951987 9.75 37.84 26.921988 4.47 13.50 18.241989 6.17 33.58 37.501990 4.44 18.40 27.681991 4.49 7.28 54.681992 12.30 19.54 50.40

1993 5.64 16.70 22.71




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Table 6: Testing Fast Trackers9

Dependent Variable: Logarithm of Wage at time t+1Blue Collar Workers White Collar Clericals White Collar Managers

Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3Logarithm of

Wage at time t-1-0.092**(0.011)

-0.076**(0.010)

-0.034**(0.012)

-0.082**(0.009)

-0.053**(0.009)

-0.037**(0.010)

-0.124**(0.007)

-0.069**(0.006)

-0.057**(0.007)

Logarithm ofWage at time t

Yes Yes Yes Yes Yes Yes Yes Yes Yes

IndividualBackgrounds

10

No Yes Yes No Yes Yes No Yes Yes

JobCharacteristics11 No Yes Yes No Yes Yes No Yes Yes

Entering CohortDummies


Year of RecordDummies


Number ofRecords

23,296 23,296 23,296 28,312 28,312 28,312 50,926 50,926 50,926

R-Squared 0.9390 0.9407 0.9485 0.9536 0.9544 0.9609 0.9697 0.9713 0.9758

Change in R2 -- 0.0017** 0.0078** -- 0.0008** 0.0065** -- 0.0016** 0.0045**

F-statistics -- 85.09 72.80 -- 34.86 79.73 -- 135.57 200.47

9 Clustered Huber-White standard errors are in the parentheses. ** if p

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