Employers size wage differential: does investment in human capital matter?
Nasir, Zafar Mueen ; Iqbal, Nasir
Wage differential due to employer size is one of the key areas of
interest in labour market research because a strong positive
relationship between employer size and wages has been observed in
developed and developing countries. It is, however, relatively neglected
area of research in Pakistan. The purpose of present study is to
investigate the employer size wage differential by looking at human
capital factors. The study is based on standard methodology and
estimates earning functions on Labour Force Survey (LFS) data for year
2007-08. Results clearly show that human capital investment has a bigger
role in determining wages in the larger firms as compared to smaller
firms. The main policy implications emanating from the analysis are the
higher investment in skill which increases opportunities for workers in
the labour market for higher wages and for jobs with good
characteristics especially in large sized firms. The government policy
towards education and skill formation needs serious reforms and better
allocation of funds so that people get chance to enhance their skill
level hence wages.
JEL classification: J31, J40, J24
Keywords: Wage Differential, Human Capital, Labour Market
I. INTRODUCTION
Wage differential due to employer size is one of the key areas of
interest in the labour market research. A strong positive relationship
between employer size, measured as firm of plant size, and wages has
been found both in developed and developing countries [Masters (1969);
Pugal (1980) and Criscuolo (2000)]. (1) In literature various
theoretical explanations are forwarded to support the existence of wage
differentials across the employer size. Neoclassical school of thought
explains the existence of wage differential in the context of human
capital theory within the framework of the standard competitive model.
According to labour quality hypothesis, the large employers hire workers
of higher quality thus pay higher wages. There are a number of other
explanations also for the existence of employer size wage differential.
The larger firms pay higher wages to compensate workers for bad working
conditions; earn abnormal profits because of more market power and share
their excess profits with their workers; avoid unionisation, and
substitute high monitoring cost with wage premium [Lallemand, et al.
(2005)]. Moreover, larger firms require large number of workers
therefore pay higher wages to attract better quality employees with
required qualifications [Waddoups (2007)].
A number of reasons explain why larger firm look for higher quality
workers for employment. Griliches (1969) and Hamermesh (1980) argue that
larger firms are capital intensive so they need qualified and skilled
labour. To promote research and development activities, large firm need
labour with higher skill and education [Tan and Barra (1997)]. Shapiro
and Stiglitz (1984) argues that large firm pay higher wages to attract
labour with higher productivity due to existence of strong positive
correlation between wages and productivity. Incentive for hard work
[Shapiro and Stiglitz (1984)], to decrease the rate of turnover and
associated cost of recruiting and training [Salop (1979) and Oi (1983)],
complementarities between entrepreneurial and worker ability [Lucas
(1978)], and advance technology adopted by large firm [Kremer (1993)]
are other reasons for large firms to employ higher quality labours.
In Pakistan, various studies have confirmed the positive
relationship between human capital and income of the individuals and
have shown that education enhance the earning potential of individuals.
(2) However, there are few studies that have analysed the existence of
wage differentials. These studies have mainly investigated gender,
region and sector-specific aspects of wage differentials. Nasir (1999,
2000) and Hyder and Reilly (2005) examine the wage differential across
the public and private sector and found that public sector workers earn
higher wages as compare to private sector. Their findings support the
hypothesis of human capital theory. Ashraf and Ashraf (1993a, 1993b, and
1996) estimated gender earnings gap and concluded that education
explained the major part of earning differentials across gender. There
is so far no study which explains the employer size wage differential in
Pakistan.
The purpose of the present study is to investigate the employer
size wage differential by looking at human capital factors to explain
the difference. The study is based on the standard methodology and
estimates earning functions on Labour Force Survey (LFS) data for the
year 2007-08. The study is an important step to enhance our
understanding about human capital theory in explaining the employer size
wage differentials in Pakistan. It is organised in the following manner.
Conceptual framework is presented in Section 2. Section 3 discusses the
data and methodology. Results ate discussed in Section 4 and conclusions
are summarised in the last section.
II. HUMAN CAPITAL AND WAGE DIFFERENTIALS:
A CONCEPTUAL FRAMEWORK
Becker (1962) defines the human capital as the skills, education,
health, and training of individuals. These endowments are considered
capital because of their similarity with physical capital which yields
returns. "All such qualities of a person, such as knowledge,
health, skills and experience that affect his or her possibilities of
earning current and future money income, psychological income, and
income in kind are called human capital" [Kooreman and Wunderlink
(1997)]. Neoclassical theory explains that wages are paid on the basis
of the marginal product of labour and human capital is a component to
judge the productivity of the individual. Human capital theory seeks to
explain wage differentials as a consequence of differing human capital
stocks that determine an individual's marginal productivity.
Human Capital Theory is mainly based on education because it
imparts knowledge and skills [Tilak (1994)]. The direct effect of
education is measured in term of pecuniary benefits accrue to the
individual [Becker (1962); Mincer (1974); Hungerford and Solon (1987);
Tilak (1994); Zuluaga (2007)]. Investment in education increases the
ability of the individuals and makes them more productive and efficient
[Lockheed, et al. (1980) and Jamison and Lau (1982)]. Because in
competitive labour market wages paid according to their marginal
productivity therefore an individual with better marketable skills have
higher productivity and more opportunities in labour market. These lead
to higher earnings through good jobs of success in business projects.
Training and health are other important and integral parts of human
capital. Similar to education, training and health increase productivity
of individuals, hence their earnings [Schultz (1961) and Strauss and
Thomas (1995)]. Schultz (1961) attributes the difference in earnings
between people to the difference in access to education and health.
To explain the phenomenon of employer's size wage
differentials based on human capital theory, various explanations are
documented in the literature. Most plausible explanation in this context
is based on the labour quality theory. Large firms employ workers of
higher quality thus pay higher wages [Chuang and Hsu (2004)]. According
to this theory, larger firms are more capital intensive; therefore
require more skilled workers due to capital skill complementarity
[Hamermesh (1980)]. Oi (1983) argues that large firms, being more
innovative and capital intensive need more qualified and specialised
workers. Secondly, the higher levels of both human and physical capital
per worker at larger employers are believed to be due to scale economies
and/or preferential access to credit in imperfect capital markets.
Thirdly, Oi (1983) and Garen (1985) argue that large plants employ
higher quality workers to reduce monitoring costs per unit of labour
services. Fourthly, to large firm pay more to reduced the workforce
turnover [Oi (1983) and Idson (1996)]. Becker (1975) also argues that
firms may reduce their turnover by increasing wages above workers
alternative wage. Fifthly, the presence of more able entrepreneurs and
of complementarities between entrepreneurial and workers ability imply
higher quality workers at larger employers [Lucas (1978)]. Sixthly,
greater complexity of tasks induced by the more advanced technology
adopted by large employers induces greater skill complementarity between
workers and, therefore, higher returns to human capital [Kremer (1993)].
Underpinning all these reasons, there is a common positive relationship
between employer size human capital and wages.
The other explanation comes from the theory of compensation wage
differential. Large firms tend to be more rigid in organisational
structure and rely on rules to discipline their workers [Mellow (1982)].
Large firms also impose greater pressure on workers and thus suppress
worker's creativity [Lester (1967)]. As a result, the workers in
large firms earn a compensating wage differential for less satisfying
work [Masters (1969 and Waddoups (2007)].
III. METHODOLOGY
Following Becker (1964) and Mincer (1974), we begin with a human
capital earning function which indicates that the variation in earnings
arises from difference in investment in human capital defined as below:
[W.sub.i]=[alpha.sub.0]+[sigma][[beta].sub.i][x.sub.i] + u ... ...
... ... ... (1)
Where [W.sub.i] represents wage rate while vector [X.sub.i]
represents all possible human capital factors that affect wages and u
represents all unobservable variables. We extend our model by estimating
separate earnings functions for different firm sizes. A semi-log
earnings function defined below is estimated: Let the wage equation for
each employer size be:
[W.sub.ij] [[alpha].sub.j] + [[gamma].sub.ij] [X.sub.ij] +
[v.sub.ij] ... ... ... ... ... ... (2)
i = 1,2,3,.........., n j = 1,2,3,.........., m
Where i and j are indices for the ith individual and jth firm size,
respectively, [W.sub.ij] is the wage rate, [X.sub.ij] represents human
capital factor for the ith worker belongs to jth firm and v represents
all unobservable variables. Educational endowment is one of the main
factors that contribute in human capital enhancement. Education is
divided into different level because different levels of education
impart different skills and earnings. Five level of education i.e., 0-4,
5-7, 8-9, 10-13, and degree education are included in the earnings
function. Similar to formal education, technical education is also
included into the model because of its crucial role in shaping the stock
of human capital. Experience is ah important part of the human capital
but information on actual experience is missing in most of the surveys.
Age and its squared term are therefore included in the specification as
the proxy for experience. The quadratic term of age in the basic human
capital model of Becker (1964) and Mincer (1974) captures the
diminishing returns to experience with time.
The wage structure may differ due to different endowment of health
of the workers. To capture the health of the workers, sick leave is used
as a proxy. A dummy variable representing the health of individual is
included in the model. Demographic characteristics such as sex and
marital status are also used in the model as dummy variables. In
addition, the area of residence is used to capture the variations in
geographic and regional economic development.
Decomposition of Wage Differentials
The difference in wages may arise due to two reasons; the
difference in endowment and productivity-related personal
characteristics of the workers which includes different levels of human
capital, occupational difference, and other endowments. More productive
workers will get higher compensation relative to the workers, who on
average have a lower level of productivity-related characteristics and
the wage structure across different sectors, i.e., employees with the
same endowments may get different remuneration in different sectors.
Blinder (1973) and Oaxaca (1973) developed a methodology to measure
the unequal treatment in wages. According to this framework,
discrimination or 'unequal treatment' is revealed by
differences in the estimated coefficients. To measure the wage
differential, the mean of log wages between different sectors is used in
calculations. The absolute difference [D.sub.ij] is calculated as:
[D.sub.ij] = Ln[W.sub.i] - Ln[W.sub.j] ... ... ... ... ... ... (3)
Where i is the high wage firm and j is low wage firm. Wage
differential equations across group i and j are:
Ln[W.sub.i] = [f.sub.i]([X.sub.i])=
[SIGMA][[beta].sub.i],[X.sub.i], ... ... ... ... ... ... (4)
Ln[W.sub.j] = [f.sub.j]([X.sub.j])= [SIGMA][[beta].sub.j],[X.sub.j]
... ... ... ... ... ... (5)
Where [X.sub.i] and [X.sub.j] are the mean values of the vectors of
characteristics of group i and j respectively. The gross difference can
be expressed as
[D.sub.ij] = Ln[W.sub.i] - Ln[W.sub.j] = [f.sub.i]([X.sub.i]) -
[f.sub.i]([X.sub.j]) + [f.sub.i]([X.sub.j]) - [f.sub.j]([X.sub.j]) ...
... (6)
Where [f.sub.i] ([X.sub.i]) is the mean wage that employees of
group j would receive if they were paid according to the wage structure
of group i.
[D.sub.ij] = [[SIGMA][[beta].sub.i][X.sub.i] -
[[SIGMA][[beta].sub.j][X.sub.j]] + [[SIGMA][[beta].sub.i][X.sub.j] -
[[SIGMA][[beta].sub.j][X.sub.j]] ... ... ... (7)
[D.sub.ij] = [[SIGMA][[beta].sub.i][[X.sub.i]] - [X.sub.j]] +
[SIGMA][[beta].sub.i]-[[beta].sub.j]][X.sub.j] ... ... ... ... (8)
This implies total wage differential is decomposed into two parts.
First, 'endowment differential' or 'explained
differentials' which occur due to difference in characteristics of
the individuals. Therefore the difference in the average logarithmic
earnings of the two groups of workers exists due to the difference in
the average amounts of earnings-related characteristics such as
education, experience, gender and martial status etc. Second,
'treatment differentials' of 'unexplained
differentials' due to difference in productivity characteristics of
the workers. Therefore difference in average logarithmic earnings of the
two groups exists due to the rate at which both group compensate their
workers having the same characteristics and often used as a measure for
discrimination. The size of this term will depend on the difference in
the values of the regression coefficients estimated from earnings
equations of the two groups. This strategy allows the determination of
the part attributable simply to a difference in the structure of pay and
a difference in the endowment of the workers which drive a wedge between
pay levels in different sectors of employment.
IV. DATA CHARACTERISTICS AND DESCRIPTIVE STATISTICS
The data are taken from the Labour Force Survey (LFS) 2007-08. It
is a regular feature of the Federal Bureau of Statistics (FBS) since
July, 1963. These data provide comprehensive information on
characteristics of the workers. The information on earnings, age,
education levels, sex, marital status, regions, employer size,
occupation, and employment status is particularly important for this
study. To capture the number of employees working in the firm, following
question is asked to the respondent "How many persons are engaged
in the enterprise (including working proprietors, unpaid family workers,
and paid employees)?" Respondent chooses one option from the
following four options: (i) Number of person upto 5; (ii) 6 to 9; (iii)
10 to 19; and (iv) 20 and more. This is the only information available
on the number of employees working in particular enterprises. By using
this information; this study develops two categories bases on the firm
size i.e. small firm and large firm. In small firm, first two options
are merged therefore this category consists of those firms which has
maximum nine employees. Large firms category is based on the 10 and more
employees. This division is important because of the registration of
larger firms (having ten or more workers) with taxation social security
related departments. The sample of the study includes only wage
employees and not the casual and peace rate workers.
Descriptive statistics shows that the final sample of employed
workers with positive earnings consists of 12,913 individuals in which
11,595 (89 percent of the total) individual works in small firms while
1,318 (11 percent of the total) individual works in large firms (Table
1). The data on earnings include both cash and payments in kind. The
current value of the in-kind benefits such as free or subsidised housing
and transportation is included in the overall earnings reported in the
survey. Average monthly earning of the all individual is Rs 4,831 while
average monthly earning of the individuals works in small firm (Rs
4,694) is less than the average monthly earning of the workers employed
in large firm (Rs 6,031). The statistics reveals that the average age of
the sample is about 30.25 years (30.26 and 30.11 for small and large
firms respectively). Literacy rate is higher among the worker employed
in large firms (0.62 percent) as compare to small firms (0.56 percent).
Human capital variables show very interesting results. Proportion
of individuals with low level of education like primary, middle and
matric is high in small firm (0.04, 0.20 and 0.12) respectively as
compare to large firm (0.02, 0.14 and 0.11 respectively) while the
proportion of individuals with high level of education like
intermediate, degree and professional degree is higher is larger firms
(0.14, 0.06 and 0.12 respectively) as compare to smaller firms (0.12,
0.03 and 0.03 respectively). Very few professional are working in small
firms. These findings confirms the human capital theory with the
hypothesis that large firm employed worker with higher quality. In
literature one of the main reasons that explain the wage differentials
concept among the employer size is the quality of the worker. Worker
with higher quality capital works in large firm and rewarded on the
basic of their marginal productivity. Training, ah important component
of human capital, indicates that the proportions of workers with
training are higher in larger firms than smaller firms (Table 1).
V. WAGE DIFFERENTIALS AND HUMAN CAPITAL
Wage differentials among the workers are calculated on the basis of
their human capital potentials for both categories of firms. Wage effect
of education is higher in larger firm than smaller firm. With the
similar education, individual earn more in larger firm as compare to
smaller firms. Earning is also function of successive level of
education. Results shows that earnings increase when the education of
the individual increase from one category i.e. primary to other category
i.e. middle. Wage effect is twice when an individual has some
professional degree as compare to primary pass workers (Figure 1).
[FIGURE 1 OMITTED]
Experience significantly affects the earnings of individuals and
the association of earnings with age signifies the role of experience
for higher earnings. It is interesting to note that although there are
significant differences in compensation for workers across the employer
size, yet the age earnings profiles follow the life-cycle pattern in
both categories where income increases with age for some time, reaches
at the peak and then declines. Some interesting observations can be made
on the basis of these age- earnings profiles. The workers in the larger
firms start at a higher level of earnings and reach a higher peak as
compared to the smaller firms. They attain the highest level of earnings
(Rs 7371) in the age group 51-60. The highest increase in earnings is
observed when workers jump from age group of 21-30 to age group of 31-40
in larger firms. The sharp decline in the earnings experienced by the
all employees afterwards after age group of 51-60, is due to the
retirement benefits, which are much lower than the regular job benefits
(Table 2).
The age earning profiles of workers, in the smaller firms, shows
lower earnings at the start and remain low than bigger firms for all age
group. In small firm, earnings increase very smoothly with the
successive age group till the age group 51-60, when their earnings reach
the peak and start declining afterwards. The decline in the earnings of
the workers employed in smaller firms is sharp unlike the workers
employed in larger firms (Table 2).
Wage differentials are calculated for various types of individual
characteristics based on employer size. Results shows that male worker
earn more in large firms as compare to small firm. Similarly, monthly
earning of male worker is also higher than the female worker. Married
workers earn more as compare to their unmarried counterpart. Married
workers enjoy higher salaries in larger firms than smaller firms. Person
with technical education earn higher wages in both type of firms but
comparatively higher in larger firms (Table 3).
VI. RESULTS AND DISCUSSION
Table 4 displays the traditional log wage regression with firm size
dummy and separate regression by firm-size groups. The results of column
1 show that even after controlling for the worker's attributes,
human capital attributes and regional dummy, the firm-size variable
remains positive and highly significant. The Chow test is also used
which reveals that there are structural differences in these categories
and a single equation does not explain the differences in earnings. For
this reason, separate equations ate estimated for both types of firms.
Estimated coefficients display importance of human capital which varies
across firm-size. Overall results show that education does have a
positive and significant effect which increases with firm size.
Attainment of five year education rather than no education has positive
but insignificant impact on earning across the firm size. There are very
interesting findings on return to education at different level of
education between the both categories. Wage effect of middle education
is higher in small size firms (22 percent and significant) than large
size firm (20 percent and insignificant). Similarly, for matric and
intermediate, returns on education are higher in small firms than large
firms (Table 4). But, return to education is higher in larger firm with
degree and professional education than smaller firms. This supports the
argument that large firms require workers of high quality and often
regard education as an indicator of potential productivity. Hence, they
tend to hire and pay greater rewards to educated workers. This confirms
the human capital theory which envisage bigger role for education and
training in larger firms.
The coefficients of variable age (proxy for experience) in all
three categories are statistically significant but its square term bears
negative and statistically significant coefficient, implying diminishing
returns on experience after a specific age. However the effect of
experience is greater in small size firms than large size firms. This
means that firm specific human capital is more important than general
human capital across the firm size. Health of the workers also plays
significant role on earnings of worker according to the firm size. The
higher earnings associated with age, education, and health provides
clear support to the human capital theory in the firm size [Becker
(1964) and Mincer (1974)]. As consistent with most studies, male or
married workers in general earn higher wages than female or single wage
workers.
VII. DECOMPOSITION ANALYSIS
The decomposition analysis presented in Table 5 reveals very
important results. The positive sign of 'explained of endowment
differentials' shows the better characteristics of the workers
implied in large firms. This is quite oblivious because those with
better human capital variables and characteristics take the initiative
to join large firms. The treatment differentials or unexplained part has
positive sign and larger in magnitude.
VIII. CONCLUDING REMARKS AND POLICY OPTIONS
The study on employer size wage differential based on LFS 2007-08
data clearly shows that human capital investment has a bigger role in
determining wages in the larger firms as compared to smaller firms. The
human capital is measured as investment on education, training,
experience and health. When the wage differential between large and
small firms of 0.2537 is decomposed into difference due to endowment and
due to wage structure, the human capital factors explained almost 6
percent difference in the earnings. This clearly indicates the
importance of human capital investment for larger firms. It may be noted
here that larger firms not only pay higher wages but also provide higher
benefits such as social security and paid holidays. The main policy
implications emanating from the analysis are the higher investment in
skills which increase opportunities for workers in the labour market for
higher wages and for jobs with good characteristics especially in large
size firms. The main reason is the higher productivity associated with
skills due to human capital. The government policy towards education and
skills formation needs serious reforms and better allocations of funds
so that people get chance to enhance their skill level.
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(Discussions Paper Series (DPS) 07.02).
(1) Besides, a number of other studies also reported employer size
wage gap in different countries like United State [Brown and Medoff
(1989)], Germany [Gerlach and Schmidt (1990); Schmidt and Zimmermann
(1991); Gerlach and Huebler (1998)], Great Britain [Main and Reilly
(1993)], Japan [Rebick (1993)], Cariada [Morissette (1993)], Taiwan
[Chuang and Hsu (2004)] and Australia [Waddoups (2007)].
(2) Nasir and Nazli (2000); Qureshi and Arif (2001); Haq (2005);
Jamal (2005): Kurosaki and Khan (2006); Arif and Iqbal (2009) and Awan
and Iqbal (2010).
Zafar Mueen Nasir <zfrnasir@yahoo.com> and Nasir
Iqbal<nasir@pide.org.pk> are Chief of Research and Staff Economist
at Pakistan Institute of Development Economics, Islamabad, respectively.
Authors' Note: The authors are thankful to Dr Asma Hyder,
Assistant Professor at NUST Business School NUST Islamabad, for her
valuable suggestions.
Table 1
Summary Statistics
Characteristics All Small Firms Large Firms
Number of Observations 12,913 11,595 1,318
Mean
Monthly Income 4831 4694 6031
Log of Monthly Earning 8.2251 8.1992 8.4529
Personal Characteristics
Age (Number of Years) 30.2487 30.2636 30.1184
Sex (Male = 1) 0.8903 0.8915 0.8801
Marital Status (Married= 1) 0.5618 0.5619 0.5615
Literacy (Literate = 1) 0.5647 0.5588 0.6168
Human Capital Background
Primary (Primary =1) 0.0415 0.0434 0.0250
Middle (Middle = 1) 0.1952 0.2013 0.1419
Matric (Matric = 1) 0.1238 0.1248 0.1153
Intermediate (Intermediate 1) 0.1241 0.1219 0.1426
Degree (Degree =1) 0.0387 0.0356 0.0660
Professional Degree (Prof = 1) 0.0364 0.0265 0.1237
Training (Yes= 1) 0.0147 0.0140 0.0212
Health (No Sick Leave = 1) 0.7685 0.7837 0.6351
Region
Urban (Urban = 1) 0.6521 0.6530 0.6434
Source: LFS 2007-08.
Table 2
Average Monthly Earnings of Workers by Age Group
Age Group All Small Firm Large Firm
10 to 20 3512 3421 4545
21 to 30 4870 4750 5729
31 to 40 5671 5484 7205
41 to 50 5747 5605 7047
51 to 60 5839 5708 7371
61 and Above 5105 4963 6865
All 4831 4695 6032
Source. LFS 2007-08.
Table 3
Average Monthly Earnings of Workers by their Characteristics
Characteristics All Small Firm Large Firm
Male 5082 4958 6184
Female 2794 2528 4912
Married 5476 5346 6615
Unmarried 4005 3859 5285
Literate 5201 5028 6583
Illiterate 4351 4273 5144
Technical Training 5656 5482 6662
No Technical Training 4819 4683 6018
Urban 4981 4796 6641
Rural 4549 4505 4932
Source: LFS 2007-08.
Table 4
Coefficients of Ordinary Least Square Estimates for Different
Sectors (Dependent Variable = Log Monthly Earnings)
Variables Full Sample Small Firms Large Firms
Age 0.06 *** 0.06 *** 0.05 ***
(0.00245) (0.00255) (0.00908)
Age Square -0.0006 *** -0.0007 *** -0.0005 ***
-3.06e-05 -3.16e-05 (0.000117)
Sex (male =1) 0.78 *** 0.82 *** 0.53 ***
(0.0183) (0.0192) (0.0613)
Marital Status (Married= 1) 0.006 -7.87E-05 0.087 *
(0.0153) (0.0162) (0.0453)
Primary 0.044 0.034 0.158
(0.0289) (0.0297) (0.117)
Middle 0.088 *** 0.087 *** 0.081
(0.0154) (0.0160) (0.0564)
Matric 0.172 *** 0.176 *** 0.114 *
(0.0183) (0.0191) (0.0610)
Intermediate 0.114 *** 0.114 *** 0.107 *
(0.0185) (0.0194) (0.0590)
Degree 0.180 *** 0.156 *** 0.191 **
(0.0304) (0.0330) (0.0817)
Professional Degree 0.509 *** 0.456 *** 0.520 ***
(0.0326) (0.0385) (0.0725)
Health 0.00588 * 0.00963 ** 0.00311 ***
(0.0171) (0.0199) (0.0147)
Urban 0.0721 *** 0.0596 *** 0.190 ***
(0.0120) (0.0125) (0.0402)
Firm Size 0.196 ***
(0.0188)
Constant 6.236 *** 6.200 *** 6.764 ***
(0.0406) (0.0422) (0.145)
Observations 12913 11595 1318
R-squared 0.237 0.238 0.168
Standard errors in parentheses.
*** p<0.01, ** p<0.05, * p<0.1.
Table 5
Oaxaca Decomposition
Explained or
Wage Differentials Endowment Differentials
Ln[W.sub.LARCE]-Ln[W.sub.SMALL] 0.0562
Unexplained or
Wage Differentials Treatment Differentials Total
Ln[W.sub.LARCE]-Ln[W.sub.SMALL] 0.1975 0.2537
