education and signaling in hong kong
TRANSCRIPT
Education and Signaling in Hong Kong
By
Lui Siu Hei
07005407
Applied Economics Major
An Honours Degree Project Submitted to the
School of Business in Partial Fulfilment
of the Graduation Requirement for the Degree of
Bachelor of Business Administration (Honours)
Hong Kong Baptist University
Hong Kong
April 2009
Acknowledgement
I want to give a big thanks to Dr. Y.C. Ng for her patience and her every help when I was
struggled in doing this paper. From model specification to computer programs, this paper is
impossible to finish without her help. It’s precious to have her guidance. I also want to thank my
family including my girlfriend Fanny Lok. I am always overwhelmed by their unlimited love and
support. I do have to appreciate my schoolfellows especially the “Econmates” who like the battle
companions during these last days in school.
Content:
I. Abstract P.1
II. Introduction P.2
III. Literature Review P.6
IV. Methodology P.11
V. Data P.16
VI. Empirical Results P.17
VII. Limitations P.26
VIII. Conclusion P.28
Reference
1
I. Abstract
This paper investigates the Hong Kong Labor market for the validity of the human capital
theory as well as the screening hypothesis by comparing the return of education of the employed
as the presumed screened and the self-employed as presumed unscreened. The Mincerian
earnings equation is used to estimate the return of education of the screened jobs and the
unscreened jobs by genders. And Heckman two-step procedure is introduced to correct the
selection bias of the earnings functions. The results shows that human capital theory does explain
part of the return of education and the rest of it can be explained by the “signal” suggested by
screening hypothesis.
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II. Introduction
Many empirical labor economic studies have confirmed a strong positive relationship
between education levels and earnings of an individual. However, different economists have
different arguments on the causes of the way that higher education level leads to higher earnings.
There are two major opinions on the causes of the positive relationship: human capital theory
and screening hypothesis.
The human capital theory suggests education is a form of investment in human capital.
Becker (1993) states that knowledge, experiences, skills and health are different kinds of human
capital which are not able to separate from the individual. Education directly adds to productivity
and expenditure on education, training, medical care, etc, are in fact investments in human
capital that generate future returns such as raised earnings. Education as a kind of investment
endows an individual with more knowledge and better skills that positively related to one’s job-
related productivity. Therefore, employee with higher education level implies he/she would be
better equipped than his/her co-worker who has lower education level regardless other factors
determines their earnings. Individuals will only enter college if they can generate higher net
lifetime income.
3
Employee with higher education level in general earns more because of his/her higher
productivity endowed by education. In the view of employers, employees with higher education
level and hence higher productivity should be rewarded and worth higher salaries. The human
capital theory also suggests that education is an important source of economic growth (Denison,
1985) because it improves the quality as well as productivity of the labor force. Therefore
investment in education benefits to the whole society and government may have to invest heavily
on it.
Different from human capital theory, the screening hypothesis suggests that education is
not a kind of investment on human capital actually. The screening hypothesis suggests that
education is only or primarily used as a screening device that sort individua ls according to how
much should be paid to the individuals from highest to the least. Moreover, the screening
hypothesis supports that education is a filter that individuals have less talent would be screened
out or leave the education system as cost of the education outweigh benefit of it and those
remained that is better talented would be advanced to a higher level. Eventually, the higher the
education level an individual has, the better the talent he/she is.
In the point of view of employers, education is only a “signal”. When making hiring
decision to an individual, employer does not have full information about a candidate even though
4
the employer have interviewed and/or tested the candidate several times. The employer faces
risks of employing an individual with low productivity. In order to increase the probability of
hiring a suitable one, employer may simply use “signals” to determine whether to hire an
individual or not. As education level is a reliable signal to reflect the connate ability of an
individual, it is reasonable to believe that the more productive individual is the one who attain
highest education level (Riley, 1979). Therefore, it is the “sheepskin” that is rewarded rather
than the productivity which is suggested by the human capital theory (Layard & Psacharopoulos,
1974 and Chiswick, 1973).
The screening hypothesis also suggests that most job skills are not obtained from
education but are acquired by on-the-job training (Thurow, 1975). And what employers want is
the fastest learner with least training cost. Therefore, education is used by individuals as a self-
selection process that individuals signal their trainability. Slower learners would sift out as their
opportunity cost of achieve higher education is greater. The screening hypothesis implies
education yield higher private return to individuals than social benefits as education does not
really increase the productivity of workers which may result in over- investment in education
(Chatterji, Seaman & Singell, 2003) and hence suggests government to save money to invest in
other areas to generate higher return instead of invest so heavy in education and ask government
to simplify the screening device such as public examinations, IQ tests, etc.
5
To provide stronger evidence for determine the human capital theory and screening
hypothesis, a less regulated labor market should be investigated. It is because the higher the
regulated labor market the less the difference of wages as wages setting is centralized or
unionized (Addison & Siebert, 1997) which in turn provide less evidence for determining the
human capital theory and the screening hypothesis. Therefore, the labor market in Hong Kong
would be investigated in this project after Heywood and Wei (2004) with the same reasons that
as Hong Kong is one of the most competitive labor markets with least labor legislation
restrictions when compare to other developed cities like Singapore. Therefore the effect of
education to earnings of labors would be better reflected.
A common method to investigate this topic is to compare the return of education on
earnings of the presumed screened group and that of the presumed unscreened group which is
also the method used in this project and it will discuss later in this paper. And as the earnings
functions estimation of males and females are different, the samples would then separated by
gender to evaluate.
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III. Literature review
Education increases earnings. Human capital theory suggests the reason of increase in
earnings because of education directly increases productivity of an individual. However,
supporters of screening hypothesis argue that education only or primarily act as a signal to the
learning speed and ability of the individual. The debate between the two thoughts still exists.
Although many economists have done many great works on the issue, neither side can reason
down another. Some empirical studies support the former one and others support the later. All
these papers aim to investigate the true reason behind the positive relationship between educa tion
and earnings. Table 1 below summarizes results of some literatures.
Table 1
Authors Country/city
studied Supporting
side
Riley, 1979 United States S
Tao, 2006 United States H
Chatterji, Seaman & Singell, 2003 United Kingdom S
Miller & Volker, 1984 Australia S
Groot & Oosterbeek, 1994 Dutch H
Gullason, 1998 Global S
Heywood & Wei, 2004 Hong Kong S
S: Examines the screening hypothesis,
H: Examines the human capital theory
As noticed in Table 1, different literatures support different views. In fact, different
economists use different methods to investigate the issue.
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Riley (1979) studies the U.S. labor market, and compares the presumed screened
occupations versus the presumed unscreened occupations. He finds that the occupations with low
earnings functions and high mean education is better fitted to the presumed unscreened
occupations. This result supports the screening hypothesis as return on earnings of education for
unscreened occupations is less than that of screened occupations.
Tao (2006) also investigates the U.S. labor market. He wonders that if the extreme
screening hypothesis is true, there would be no significant different of productivity and hence
life-time earnings for those who has the same innate ability no matter the individual enter college
or not as he/she may have same productivity. Tao finds that the human capital accumulation
factor over time has significant impact on the earnings equation. And this finding supports the
human capital theory over the extreme screening hypothesis as it implies that human capital
actually adds to productivity.
Chatterji, Seaman and Singell (2003) study the U.K. labor market by comparing the
return of education of those presumed screened employees with those presumed unscreened self-
employed. They find that the “signal” has a positive rate of return to earnings of workers. When
it is eliminated from the earnings equation, the return of education is underestimated. That means
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the signaling effect actually enhance the return to education and the authors believe that the
signaling does occur in U.K., especially females rely more on it than males.
Miller and Volker (1984) compare the college graduates of two majors: economics and
science in Australia, to see whether there is significant different in starting salaries of graduates
who work in relevant field of their studies compare to those who work in other fields. They
conclude that the screening hypothesis alive and well in Australia as for the economics majored
graduates, there is no significant different in earnings between those who work in relevant fields
and those who are not. However, the conclusion is not that strong because the salaries of the
science majored graduates who work for relevant field is significantly higher than those who
work for other fields, especially for the males graduates.
Groot and Oosterbeek (1994) investigate the case in Dutch where has a flexible education
system. This education system allows Groot and Oosterbeek to divide the education years into
effective years, repeated years, skipped years, inefficient years and dropout years and include
these years separately into the earnings function. The screening hypothesis assumes that
individual who is signaled as a quicker learner should be rewarded and skipped years, which is
the number of years that an individual “jump over” because of his/her outstanding academic
performance, imply an individual has greater trainability and instead repeated years imply less
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trainability. However the result overthrows the screening hypothesis by concluding that there are
a negative effect of skipped years and a neutral effect of repeated years to earnings. Besides, the
result build an even stronger stand for human capital by demonstrate the positive effect of
dropout years to earnings, which is the number of years that an individual studied but dropped
out without a certificate. The positive relationship contradicts to the “sheepskin” effect of the
screening hypothesis.
Gullason (1998) studies the screening hypothesis versus the human capital theory from a
global perspective. He compares two different formulated variables of years of schooling:
exogenous formulated and endogenous formulated. He finds that the exogenous formulated one
is able to explain the earnings functions but the endogenous formulated does not. And it implies
that employers simply use education as a device to assess the productivity of the candidates. It is
in the line of the screening hypothesis that employers do not has full information of the potential
employees and instead using education level as a indicator to determine the hiring decision.
Heywood and Wei (2004) investigate the case in Hong Kong which has a highly
competitive labor market. They separate the labor market into employed which is presumed
screened group and self-employed which is presumed unscreened group. And compare the return
to earnings of education levels either attended or completed. They conclude that the return on
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education levels of presumed unscreened (self-employed) group is significantly smaller when
compare to the presumed screened (employed) group. Moreover, the result refutes the human
capital as the increase in return of education on the presumed screened group is concentrated on
the completed rather than the attended level which in turn is actually the “sheepskin” effect.
To conclude, it is unable to simply summarize neither the human capital theory nor the
screening hypothesis stand out. As Heywood and Wei (2004) mentioned that different cultures,
institutions and nature of labor markets may result in different evidences of either sides of
opinion and I will follow their work investigate the case in Hong Kong.
11
IV. Methodology
As mentioned before, Heywood and Wei (2004) investigated the labor market in Hong
Kong by separating the presumed unscreened and the presumed screened. The reason behind is
that the self-employed group need not to be screened by employers which the employed does.
This method is adopted in this project. The sample is divided into self-employed which is
presumed unscreened and employed which is presumed screened. Then the two samples are
estimated by semi- log earnings equations separately taking into account of the self-selection bias
of being self-employed versus being employed. The return of education in earnings for females
and males is believed to be different. The earnings function is then separated by gender, in order
to estimate whether signaling effect exists in different genders or not. And if it exists, which
gender would be stronger affected.
The Mincerian earnings equation (Mincer, 1974) is used
For the employed sample:
Lnmearn = β0 + β1(male) + β2(mar) + β3(exp) + β4(exp2) +
β5(lowsec) + β6(upsec) + β7(postsec) + β8(univ) + ε
For the self-employed sample:
Lnmearn = γ0 + α1(male) + γ2(mar) + γ3(exp) + γ4(exp2) +
γ5(lowsec) + γ6(upsec) + γ7(postsec) + γ8(univ) + µ
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where: Lnmearn = nature log monthly earnings of the observed individual, which is the dependent variable that when independent variable change by 1 unit, the βs and γs will
indicate the changes in monthly earnings in a percentage term.
β0 = constant term for the employed sample
γ0 = constant term for the self-employed sample
male = dummy variable of whether the respondent is male or not
mar = dummy variable of whether the respondent married or not
exp = years of experience of the respondent in labor market which is calculated by age
minus 6 and the number of years of schooling
exp2 = years of experience of the respondent in labor market which is calculated by age minus 6 and the number of years of schooling squared
lowsec = dummy variable of the highest education attended is lower secondary level
upsec = dummy variable of the highest education attended is upper secondary level
postsec = dummy variable of the highest education attended is postsecondary level
univ = dummy variable of the highest education attended is university or above levels
The reference for the highest education level attended is primary level or lower which is
omitted to act as conditions to be compared in the regression. ε and µ = random error
terms which may also capture a selection bias that will talk about later.
Heckman (1979) mentions that using non-randomly selected samples to estimate
behavioral relationships as an ordinary specification would cause sample selection bias. In this
project, it is clear that the employed and self-employed themselves are non-randomly selected
and hence selection bias may be an issue. As the choice of individuals to be employed or self-
employed is endogenous which means individuals may have different characteristics making
him/her more likely to be self-employed. These characteristics are not included in the estimation
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of the earnings function (De Wit & Van, 1989). The value of the omitted characteristics can be
estimated and included as regressor to eliminate the bias (Heckman, 1979). Therefore,
Heckman’s two step procedure is adopted to capture the bias. The basic idea is:
Firstly, a probit regression for capturing the characteristics of self-employed is estimated:
P(Self) = Φ(Zi)
where: Zi = α0 + α1(A40) + α2(MT7) + α3(OSE) + α4(HKB)
Self indicates self-employed (Self = 1 if the individual is self-employed and Self = 0
otherwise). Φ is the cumulative distribution function. α0 is the constant term.
As Heywood and Wei (2004) mentions that as experiences and personal network are
needed, people who are more mature and/or reside in Hong Kong a longer period would be more
likely to be self-employed. Also certain positions and occupations are more likely to be self-
employed. Therefore, three independent dummy variables are included which indicate whether
an individual aged equal to or above 40 or not, reside in Hong Kong for 7 years or more or not
and is born in Hong Kong or not. Moreover, a dummy variable OSE is included to indicate
whether an individual is in certain positions and occupations indicating that they are more likely
to be self-employed. Those positions and occupations are small business manager, life science
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and health professionals, salespersons and models or drivers and mobile machine operators.
Although the included variables are imperfect, the benefits of using them are that they are
excluded from the earnings equation. Inverse Mills ratio evaluate at Zi is later included in the
earning functions in the second step to account for the selection bias.
Secondly, the selection bias variables are introduced into the Mincerian earnings equation
(Mincer, 1974):
For the employed sample:
Lnmearn = β0 + β1(male) + β2(mar) + β3(exp) + β4(exp2) +
β5(lowsec) + β6(upsec) + β7(postsec) + β8(univ) + ρσελ(Zi) + ε’
For the self-employed sample:
Lnmearn = γ0 + γ1(male) + γ2(mar) + γ3(exp) + γ4(exp2) +
γ5(lowsec) + γ6(upsec) + γ7(postsec) + γ8(univ) + ρ’σε’λ’(Zi) + µ’
where: ρ and ρ’ = the correlation between the characteristics (A40, MT7, HKB
and OSE) propensity to self-employed for the employed and self-
employed samples respectively and random error terms (ε and µ) of the
15
earnings functions defined earlier. σε and σε’ are the standard deviations of
ε and µ respectively, and λ and λ’ are the inverse Mills ratios evaluated at
Zi for the employed and self-employed respectively. The definition of
other variables can be found in page. 12
Theoretical expectations
On one hand, screening hypothesis suggests that the return to education for the screened
group, the employed in this project, would be greater than that of those unscreened. It is because
the employer adds value on education level achieved to the earnings of employees, however this
is impossible for self-employed to do so which results in a higher return of education of screened
than unscreened. On the other hand, the human capital theory suggests that the return to
education level for the two groups would have no significant difference, as the education
enhance the productivities of the two groups at a same rate and the return to education should be
the same.
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V. Data
The data from Hong Kong 2006 Population By-census 1% sample dataset is used. The
sample enquires on a broad range of demographic and socio-economic characteristics of the
population. The variables such as gender, marital status, age, schooling year are provided.
Individuals who are in the labor force with identifiable highest fields of study and occupations,
and age between 15 and 60 are included.
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VI. Empirical Result
Sample statistics
Table 2a Females sample Females Employed Females Self-employed
Mean S.D. Mean S.D. Mean S.D.
Monthly earnings 12234.0780 13362.8040 12248.9044 13374.8819 11442.0455 12699.8515
Age 36.4863 10.2654 36.4155 10.2433 40.2689 10.7473
Married 0.5203 0.4996 0.5191 0.4997 0.5833 0.4939
Years of experience 19.1382 11.8368 19.0638 11.8127 23.1136 12.4477
Primary or below 0.1145 0.3184 0.1142 0.3181 0.1288 0.3356
Lower secondary 0.1600 0.3666 0.1600 0.3666 0.1629 0.3700
Upper secondary 0.4622 0.4986 0.4622 0.4986 0.4583 0.4992
Postsecondary 0.0531 0.2243 0.0535 0.2250 0.0341 0.1818
University 0.2102 0.4075 0.2101 0.4074 0.2159 0.4122
Sample size 14367 14103 264
Table 2b Males sample Males Employed Males Self-employed
Mean S.D. Mean S.D. Mean S.D.
Monthly earnings 16636.7752 18398.5793 16892.2267 18698.2568 12373.3273 11569.9790
Age 39.0376 10.8732 38.7299 10.8835 44.1722 9.3086
Married 0.6164 0.4863 0.6080 0.4882 0.7560 0.4297
Years of experience 21.8934 12.4076 21.5313 12.4055 27.9373 10.7807
Primary or below 0.1167 0.3210 0.1134 0.3171 0.1710 0.3768
Lower secondary 0.2254 0.4179 0.2221 0.4157 0.2805 0.4495
Upper secondary 0.3984 0.4896 0.3996 0.4898 0.3786 0.4853
Postsecondary 0.0520 0.2221 0.0532 0.2245 0.0319 0.1759
University 0.2075 0.4055 0.2117 0.4085 0.1380 0.3451
Sample size 15514 14637 877
Table 2a and 2b present the summarized statistics for the females sample and males
sample respectively. The total number of workers is 29881 (14367 are females and 15514 are
males) and 1141 are self-employed (264 are females and 877 are males). The average monthly
18
earnings of females is 12234 dollars and that of males is 16637 dollars. The two tables show that
the average monthly earnings of males is greater than that of females in the employed samples as
well as the self-employed samples. The average year of experience is 19 years for females and
22 years for males. There are a greater average number of years of experience for males than
females. The average age of the female respondents is 36 and that of male respondents is 39.
And 52% of the female respondents are married and 62% of male respondents are married. The
average age of respondents for males is older than that of females and more males is married
than that of females.
The percentages of female individuals with highest education levels attended are 11.5%,
16%, 46%, 5.3% and 21% for primary level or lower, lower secondary level, upper secondary
level, postsecondary level and university level or above respectively. These percentages of male
individuals are 11.7%, 22.5%, 39.8%, 5.2% and 20.8%. It is important to notice that there is a
higher percentage of females (72.6%) than males (65.8%) attended upper secondary or above
education level. Average monthly earnings of the employed are higher than the self-employed,
and the mean age for employed is younger than the self-employed for both females and males.
For the employed of both female sample and male sample 72.6% females and 66.45% males
with highest education levels attended for upper secondary or above level higher than that of
70.8% females and 54.9% males for the self-employed.
19
Riley (1979) mentioned that if education level is used as a signal for screening, it is
reasonable to believe that it should be an efficient signal. From Table 2a, we can see that the
percentage of presumed screened to obtain higher education level, namely upper secondary level,
postsecondary level and university level or above, is greater than these percentage of the
presumed unscreened. The screened jobs may require higher education (may be overly higher)
and individual who are choosing to be unscreened self-employed may have less education level
as they need not to “signal” themselves. So the higher education level of the screened jobs
supports the screening hypothesis.
As the employed have to be screened, their earnings are expected to be corrected
eventually overtime and hence their earnings would spread wider than the unscreened. In Table
2a and 2b the standard deviation of the employed are greater than the standard deviation of the
self-employed.
The employed generally obtain higher education level to “signal” themselves. Moreover,
the employed has a wider spread of earnings distribution when compare to self-employed. The
sample statistics consist with Riley’s mind as if the “signal” exists.
20
Chow test
In order to test whether earnings functions for males and females are different or not in
this project, Chow test is introduced to test whether the two earnings functions are equivalent.
For the employed sample:
F = 239.6412
Fc = 2.02
F > Fc
As F > Fc, we should reject the null hypothesis that the earnings functions of males and
females for the employed samples are equivalent.
For the self-employed sample:
F= 2.6497
Fc = 2.02
F > Fc
As F > Fc, we should reject the null hypothesis that the earnings functions of males and
females for the self-employed samples are equivalent.
For females and males, either the employed or the self-employed, should be estimated
separately as the Chow test concludes they are not equivalent.
21
Result of probit regression
Table 3a Marginal effect for dummy variable is P(Self)|1 – P(Self)|0 for females
Variables Coefficient Standard Error
Constant -0.0982* 0.0053
MT7 0.0120* 0.0021
A40 0.0493* 0.0062
OSE 0.0070* 0.0021
HKB -0.0014 0.0022
* means the coefficient of variable is statistically significant at 1% level
Table 3b Marginal effect for dummy variable is P(Self)|1 – P(Self)|0 for males
Variables Coefficient Standard Error
Constant -0.2224* 0.0148
MT7 0.0316* 0.0049
A40 0.1273* 0.0068
OSE 0.0310* 0.0032
HKB -0.0147* 0.0038
* means the coefficient of variable is statistically significant at 1% level
The results of probit regression are presented in Table 3a for females and Table 3b for
males. The results show that except the coefficient of females who born in Hong Kong all
variables are statistically significant at 1% level. It can be concluded that if the females
individual residence in Hong Kong for 7 years or more, age equal to or greater than 40, and is in
specific occupations mentioned before, she would be 1.2%, 4.9%, and 0.7% more likely to be
self-employed respectively. And if the male individuals residence in Hong Kong for 7 years or
more, age equal to or greater than 40, is in specific occupations mentioned before, and is not
born in Hong Kong he would be 3.1%, 12.7%, 3.1% and 1.5% more likely to be self-employed
respectively.
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The expected Zi of the individuals can be calculated after obtaining the coefficients above.
Then the λ(Zi) can also be obtained by inverse Mills ratio, which is:
λ(Zi) = ф(Zi)/[1 - Φ(Zi)]
where ф is the standard normal density function and Φ is the standard normal cumulative
distribution function.
23
Result of Mincerian earnings equation (Mincer, 1974)
The result of sample selected earnings regressions is shown on Table 4. λ is introduced as
a variable to control for selection bias.
Table 4. Sample selected earnings regressions by gender
Females Males
Variables Employed Self-employed Employed Self-employed
Constant 7.7442* 8.5301* 7.9944* 8.6407*
(0.0268) (0.4670) (0.0243) (0.1952)
Married 0.0323# -0.1036 0.1984* 0.1158^ (0.0129) (0.1126) (0.0133) (0.0596)
Years of experience 0.0588* 0.0640* 0.0748* 0.0364*
(0.0019) (0.0157) (0.0017) (0.0093)
Years of experience squared -0.0010* -0.0012* -0.0013* -0.0007* (0.0000) (0.0003) (0.0000) (0.0002)
Lower secondary 0.0843* 0.1405 0.0944* 0.0201
(0.0233) (0.2026) (0.0199) (0.0733)
Upper secondary 0.5556* 0.2871 0.4405* 0.1284^ (0.0222) (0.2040) (0.0201) (0.0760)
Postsecondary 0.9762* 0.4711 0.7993* 0.1411
(0.0326) (0.3193) (0.0291) (0.1421)
University 1.3675* 0.6904* 1.2636* 0.5303* (0.0253) (0.2298) (0.0228) (0.0958)
Λ -2.2206* -0.1829 0.1735 * -0.0211
(0.1678) 0.1515) (0.5660) (0.0588) Adjusted R-squared 0.3001 (0.1043 0.3830 0.0836
Sample size 14103 264 14637 877
* means the coefficient of variable is statistically significant at 1% level
# means the coefficient of variable is statistically significant at 5% level
^ means the coefficient of variable is statistically significant at 10% level
24
The result shows that the return to experience of both employed and self-employed
groups for both males and females increase at a decreasing rate. One may also find that for males,
marriage would increase the earning of both employed and self-employed. However, marriage
shows less significant effect on females. The variables of years of experience are significantly
positive. In detail, for employed the return of experience for males is greater than that of females.
In contra, the return of experience for females is greater than that of males in the self-employed
sector.
From the result we can see that the higher the education level, the higher the earnings for
both samples. As the human capital theory suggests that education enhance productivity of the
individuals and hence the monthly earnings. The result supports the human capital theory as
education increase earnings of both samples, especially the self-employed sample as it is
presumed not affected by signaling effect.
However, the return of education levels increase more in the employed for both genders
than the self-employed which in the line of signaling does has positive effect on earnings. (Riley,
1979 and Chatterji, Seaman and Singell, 2003)
25
We can also see that the higher education the less increase in additional return for self-
employed than that of employed such as university or above level for the females, the additional
return is 137% for employed but only 69% for the self-employed which is only about a half of
the return of earnings to education of the employed. For the employed of both genders, achieving
higher education level brings much greater returns of education than the self-employed which is
supporting the screening hypothesis as education as a signal does gain positive return to earnings.
Therefore, although education does enhance productivity of individuals, using education as a
signal does exist in Hong Kong. (Heywood & Wei, 2004).
When comparing the effect of education level to earnings of different genders. Only the
return to education of lower secondary for employed males is greater than that of employed
females. For higher education levels, namely upper secondary, postsecondary, university or
above levels, females clearly earn higher return on it. This implies that females earn a greater
productivity from education than males do. Besides, females may need a stronger signal and earn
a greater portion of their return to education from using it as a signal. However, the stronger
signal needed by females does not means they are in disadvantageous in labor-market. It may be
a result of gender-differences of job natures (Chatterji, Seaman and Singell, 2003).
26
VII. Limitations
Firstly, this paper uses Hong Kong 2006 By-census 1% sample dataset which the full
sample contains 31022 individuals. However, the sample statistics show that only 1141
individuals are self-employed. The sample is small that makes many coefficient less significant
especially for the females self-employed sample which only include 264 individuals. Therefore,
if a larger sample such as 5% sample dataset can be used the result would be more representative.
Secondly, because of the limitations of the data, some characteristics and talents of the
individuals that may influence the education level as well as the earnings functions of the
individuals such as intelligence quotient have not been measured. Therefore, it is less able to
investigate whether education is used as a filter to screen out the less talented and eventually be
used by employers as a signal or education actually better equips and improves the productivity
of individual with same level of talent.
Thirdly, the variables used in the probit regression, which is used to capture the
characteristics of self-employed, are not perfect as mentioned before. However it is an alternative
because they do not alter the earning function. As a result the λ includes in the earnings functions
may not significant, but it is actually introduced as a variable to control for selection bias.
27
Lastly, this paper suggests although the signal does exists, education still enhance
productivity of an individual. Therefore, as education enhance productivity as well as generate
the private “signal”, it is not able to sure whether the public investments on education of
individuals generates greater social benefits than the social cost or not in this paper and more
work should be done by comparing the total social costs versus social benefits in order to
determine: “Is it worth us to invest so heavy on education or invest in the rest that could generate
more economic returns?”
28
VIII. Conclusion
In this project, Mincerian earning function is used for compare the education level to
earnings of both employed and self-employed by different genders. The Chow test is used to
provide evident that earnings functions of females and males is not equivalent which should be
estimated separately. The Heckman two-step procedure is used, in order to correct the selection
bias of the earnings functions. The result of probit regression states that individual reside in
Hong Kong for 7 years or more, age equal or greater than 40, and is small business manager, life
science and health professionals, salespersons and models or drivers and mob ile machine
operators would be more likely to be self-employed for both genders and if a male who is not
born in Hong Kong also would be more likely to be self-employed.
The result of the earnings functions show that higher education level would result in higher
monthly earnings either for the employed or the self-employed. It can be evident of human
capital theory that education enhances productivity of an individual. However, it is only part of
the picture. Another part of the picture supports the screening hypothesis by consist with the
thought of Riley (1979) as following:
Firstly, the sample statistics shows that the employed group which is presumed screened
has generally higher education level than that of the self-employed group which is presumed
29
unscreened. That means the screened jobs require higher education level, and people who choose
to be self-employed generally achieve lower education levels as the screened jobs require
individuals to signal themselves but individuals choose to be self-employed need not to do so.
Secondly, the employed has wider spread over than the self-employed in the earnings
distribution as the result of earnings correcting. It supports the screening hypothesis as the
employed have to be screened and their earnings are expected to be corrected eventually.
Thirdly, the additional return for self-employed for an additional education level achieved
generate less and less return eventually when comparing to those employed which implies
achieving higher education level generate greater and greater return for those who are employed
rather than those who are self-employed.
Besides, the result of this paper indicates that females generally attend higher education
level than that of males. And the return to education of females is greater than that of males
either the employed or the self-employed. From the self-employed, we can conclude that females
gain more productivity in education. Moreover, from the employed, the education levels for
females are used as a signal more intensively than that of males (Chatterji, Seaman and Singell,
2003).
30
All in all, this paper provides supports that use education level as signal does exists in
Hong Kong labor market. Employees may use it to signaling themselves and employers also rely
on it as a reference to determine the productivity of an employee or a potential employee.
However, the screening hypothesis does not explain everything of the earnings functions as the
positive return of education level to earnings for the self-employed. The rest of explanations of
the functions come to the human capital theory that education actually raises ones’ productivity.
And when comparing the return to education by genders, females gain more return on education
by both productivity and use it as a signal.
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