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Incidence and wage effects of overschooling and
underschooling in Hong Kong
Elchanan Cohn
a,*, Ying Chu Ng
baDepartment of Economics, The Darla Moore School of Business, University of South Carolina, Columbia, SC 29208, USA bDepartment of Economics, Hong Kong Baptist University, Kwonloon Tong, Kwonloon, Hong Kong
Received 19 June 1998; accepted 11 January 1999
Abstract
Data from the 1986 Hong Kong By-census and the 1991 Hong Kong Census were used to study the following issues: (1) What is the incidence of adequate schooling, overschooling and underschooling in Hong Kong, and has it changed between 1986 and 1991? (2) What are the wage consequences of adequate schooling, overschooling and underschooling, and have they changed over time? Also, are the results influenced by potential labor-market experience? The empirical results are discussed in the context of recent changes in the structure of the Hong Kong economy and the labor market. [JEL I21, J31]2000 Elsevier Science Ltd. All rights reserved.
Keywords: Earnings; Labor markets
1. Introduction
It is by now well known that a comprehensive analysis of wage determination requires inclusion in a wage equ-ation not only the actual level of schooling completed by a worker (SCHOOL), but also the extent of the “mis-match” (over- or underschooling) between a worker’s SCHOOL and the “required” or “adequate” level of schooling associated with her or his occupation. Research results confirm the above observation for a number of countries (see the survey by Hartog, 2000, in this issue). The typical result found was that the econ-omic return to an extra year of overschooling is positive but smaller than the return to an extra year of adequate schooling. In contrast, the economic return to an extra year of underschooling is typically negative. On the other hand, workers tend to have lower wages in jobs for which they are overschooled compared with the wages they would have received in a job for which they are adequately schooled. In contrast, workers tend to have
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higher wages in jobs for which they are underschooled compared with the wages that would have been received in a job for which they are adequately schooled.
Although the occupational mobility theory suggests that overschooling for a given individual might represent a temporary mismatch because overschooled workers prepare to move to higher-level jobs or to be promoted (Rosen, 1972; Sicherman & Galor, 1990), the com-petition model proposed by Thurow (1975) and the job-screening model discussed by Spence (1973) suggest that overschooling can be a persistent phenomenon. If it is a persistent phenomenon, overschooling might have an adverse effect on a worker’s productivity and may also cause a misallocation of resources in the labor market.
Another implication of the job-competition model and Sicherman (1991)’s work is the existence of a trade-off between overschooling/underschooling and other forms of human capital investment, such as experience. If workers are able to substitute experience for schooling, allocation of human resources in the labor market may yet be optimal.
Kong. Data obtained from the 1986 Hong Kong By-cen-sus and the 1991 Hong Kong CenBy-cen-sus allow us to study the labor market behavior of overschooled and under-schooled workers in Hong Kong.
There are two main purposes for the present study. First, the incidence of overschooling and underschooling in the Hong Kong market for 1986 and 1991 will be examined. Second, the study will address the effects of overschooling and underschooling on earnings and how these effects change over time. Finally, we will also examine the effect that labor-market experience might have on the economic returns to overschooling and underschooling.
2. Methodology
Two principal methods have been employed in the literature for classifying individuals as overschooled or underschooled: the “subjective” and the “objective” methods. In the subjective method, a worker’s self-evalu-ation of the required schooling or skill for adequate job performance is observed. An individual’s actual school-ing level is compared with the self-evaluated schoolschool-ing requirement to determine whether s/he is overschooled or underschooled.
The objective method is employed when data on required schooling for a job are unavailable. Such a methodology is found in Verdugo and Verdugo (1989), and several other studies. The criterion for the classi-fication of overschooling and underschooling is based on the years of schooling completed (SCHOOL) by individ-uals within occupations disaggregated at 2-digit or 3-digit levels. In the Verdugo and Verdugo scheme, when an individual’s SCHOOL falls within plus or minus one standard deviation of the mean value for the occupation, s/he is considered to be adequately schooled. Those whose SCHOOL is higher than one standard deviation above the mean for the specific occupation are said to be overschooled. Conversely, underschooled individuals are those whose SCHOOL is less than one standard devi-ation below the mean for the specific occupdevi-ation.
An alternative method to measure overschooling and underschooling has been proposed by Kiker et al. (1997). Instead of defining adequate schooling (ADSCH) by plus or minus one standard deviation around the mean of SCHOOL for a given occupation, Kiker et al. suggest that (ADSCH) is equal to the modal value of SCHOOL for each occupation. That is, an individual is said to be adequately schooled if his/her SCHOOL equals the modal value for one’s occupation. Those with SCHOOL greater than the modal level of schooling for their spe-cific occupation are classified as overschooled. Similarly, individuals whose SCHOOL is lower than the modal value of the SCHOOL for their specific occupation are said to be underschooled. Since the Hong Kong data only
permit employment of the objective method, we utilize the objective method (modal value) for all of the calcu-lations shown in this study.1
To investigate the effects of overschooling and under-schooling on earnings, a standard Mincer-type wage equ-ation is used. Following Sicherman (1991), two basic specifications are used in the present study.
lnINCit5Xitb 1 a1ADSCHit1a2OVERSCHit (1) 1a3UNDERSCHit1mit
and
lnINCit5Xitd 1 g1SCHOOLit1g2OSCHit (2)
1g3USCHit1eit
where lnINCit is the natural logarithm of labor income
of individual i at time t and X is a row vector of inde-pendent variables including experience, experience squared, marital status, and industry dummy variables.2
ADSCH is the number of years of adequate schooling for a given job defined according to the objective method discussed above. SCHOOL is the actual number of years of schooling of the individual. OVERSCH is the number of years of overschooling, that is, OVERSCH 5
SCHOOL 2ADSCH, for SCHOOL > ADSCH. Simi-larly, UNDERSCH 5 ADSCH 2 SCHOOL, for SCHOOL , ADSCH, indicating the number of years of underschooling.
In the second specification, dummy variables are used for overschooling and underschooling, and ADSCH is replaced by SCHOOL. OSCH indicates that an individ-ual is overschooled, and has a value of 1 if OVERSCH > 0, and 0 otherwise. Likewise, USCH (being underschooled) has a value of 1 if UNDERSCH > 0, and 0 otherwise.
We also wish to examine whether the wage effects of overschooling and underschooling interact with experi-ence. We therefore add interaction terms for experience (EXP) and the schooling variables. Thus we have,
lnINCit5Xitb 1 a1ADSCHit1a2OVERSCHit
1The definitions of adequate schooling, overeschooling and
underschooling are based on 2-digit level occupational codes.
To test whether there are significant differences between the effects of overschooling and underschooling on earnings between 1986 and 1991, the two samples of observations are pooled. All independent variables are interacted with the year dummy variable, T91 (which has a value of 1 for observations of the 1991 sample, and 0 otherwise), and both the original variables and the inter-action terms are included in the new earnings regressions. The dependent variable (lnINC) for both years is expressed in constant (1990) Hong Kong dollars. Also, a Chow test is performed to test whether the struc-tural equations for earnings differ between the two years.
3. Data
As indicated earlier, data from the 1986 Hong Kong By-census and the 1991 Hong Kong Census are employed in this study. Only employees in the age range 15–60 years are included in the analyses. Workers in the agriculture, fishing, mining and quarrying industries, and those without reported monthly earnings from the main employment are excluded from the sample. Since infor-mation on the number of years of schooling an individual attained is not available, years of schooling completed (SCHOOL) are derived from the highest level of edu-cational attainment reported, such as Form 5 (equivalent to Grade 11 in the US), or Diploma/Certificate courses in Technical Institutes/Polytechnics. Since actual experi-ence is not available in these two data sets, potential experience (EXP5Age 2SCHOOL-6) and its square (EXPSQ) are used in the regressions instead.
Earlier research by the Hong Kong Census showed only minor deviations in hours worked. Therefore, the Hong Kong Census did not collect data on hours of work. Since labor earnings are provided on a monthly basis, it was not possible for us to convert these earnings into an hourly basis. Therefore, the dependent variable of the wage equations used here is the natural logarithm of monthly earnings from a worker’s principal employ-ment (lnINC). Other independent variables include industry dummy variables (CONSTR, WHOLES, TRANSP, FINANCE, and SERVICE—the reference group is manufacturing), and the marital status of an individual (MAR). The definitions of the variables are presented in Appendix A (Table 6) while Appendix B (Table 7) provides basic sample statistics.
4. Results
4.1. Incidence of overschooling and underschooling
Employing the objective method (following the Kiker et al. variant, described earlier), the distributions of indi-viduals by adequate schooling (ASCH), overschooling
(OSCH) and underschooling (USCH), for the years 1986 and 1991, are shown in Table 1. The data suggest that the incidence of over- and underschooling was fairly stable between 1986 and 1991. In addition, females were more likely to be adequately schooled than males and were less likely to be both over- and under-schooled. Finally, for both sexes and years, the incidence of overschooling is greater than the incidence of underschooling.
4.2. Basic statistics: years of actual schooling, adequate schooling, over- and underschooling, age, and experience
Table 2 provides means and standard deviations for these variables, cross classified by group (ASCH, OSCH and USCH). Note that the number of years of overschoo-ling is lower for overschooled males than for females, but that the incidence of adequate, over- and underschoo-ling has been fairly stable over time. Note also that the mean years of actual schooling for this group is fairly high (around 13 years). Concerning underschooled work-ers, the data indicate that the number of years of under-schooling is greater for females than for males. In con-trast to overschooled workers, the mean of years of actual schooling is much lower for underschooled work-ers (around 8 years).
We also find that the mean of ADSCH for adequately-schooled persons has increased by one year for females and by two-thirds of a year for males. This suggests at least a small secular rise in educational requirements for jobs in Hong Kong, especially for women. In addition, male workers tend to be somewhat older than females, especially among the underschooled and adequately schooled groups. Also, the 1991 group is slightly older than the 1986 group. Underschooled workers are older than either overschooled or adequately schooled work-ers.
Males have more labor-market experience than females, especially among adequately schooled workers, although differences in work experience by sex appear to be smaller than one might have expected. Finally, underschooled workers have more labor-market experi-ence than other workers.
Table 1
Percentage Distribution of Individuals by Adequate-, Over- and Under-schooling
1986 1991
Malea Female Male Female
ASCH 35 44 35 44
OSCH 38 32 37 31
USCH 28 24 28 25
Note: See Appendix A for definitions of variables.
Table 2 Basic Statistics
Over-schooled Individuals Under-schooled Individuals Adequately-schooled Individuals
1986 1991 1986 1991 1986 1991
Male Female Male Female Male Female Male Female Male Female Male Female
OVERSCH 3.37 3.75 3.29 3.68 – – – – – – – –
(1.75) (2.36) (1.74) (2.21)
UNDERSCH – – – – 3.92 4.16 3.77 4.02 – – – –
(2.44) (2.99) (2.42) (2.71)
ADSCH 9.14 8.98 9.74 10.09 12.03 11.79 11.93 11.65 11.22 11.15 11.85 12.15 (2.30) (4.22) (2.47) (2.63) (3.54) (3.01) (2.98) (3.05) (3.51) (3.71) (3.41) (2.69) SCHOOL 12.50 12.73 13.03 13.77 8.11 7.63 8.18 7.63 11.22 11.15 11.85 12.15
(2.66) (3.34) (2.80) (2.78) (4.11) (4.64) (3.87) (4.57) (3.51) (3.71) (3.41) (2.69) AGE 30.86 30.69 33.79 32.41 39.04 35.37 38.98 36.84 34.25 29.88 35.42 31.29
(9.59) (9.70) (9.85) (8.59) (11.85) (12.09) (11.62) (11.66) (10.36) (9.41) (10.32) (8.89) EXP 12.35 11.96 14.76 12.65 24.93 21.73 24.80 23.21 17.02 12.73 17.57 13.14
(9.55) (10.66) (10.12) (9.22) (14.06) (15.24) (13.69) (14.76) (11.40) (11.63) (11.58) (10.15) N 62795 32456 67103 37050 46126 24861 50560 30704 57728 45377 62226 53023
4.3. Distribution of over- and underschooling by experience
Table 3 provides information on the distribution of over- and underschooling by years of potential experi-ence. One observation is that a fairly large proportion of overschooled persons have very little experience (0– 5 years). In general, the percentage of overschooled per-sons declines as experience increases. Also, the percent-age of underschooled workers, both men and women, is greater than the percentage of overschooled workers for the high-experience groups (21 years or more), and especially so for the highest-experience group (36 years or more). Further, the percentage of underschooled male
Table 3
Percentage Distribution of Over- and Under-schooling by Years of Potential Experience
EXP in years 1986 1991
Male Female Male Female
Over Under Over Under Over Under Over Under
0–5 28.22 7.43 34.68 14.84 20.43 7.88 25.34 12.39
6–10 23.30 10.66 22.91 15.24 19.59 8.81 23.64 11.82
11–15 16.93 12.54 12.65 13.21 18.40 11.38 17.66 11.46
16–20 11.94 13.40 9.11 11.32 14.04 13.12 12.13 11.36
21–25 8.31 10.55 7.47 8.94 10.91 14.06 9.88 11.89
26–30 5.32 8.51 5.24 6.97 7.74 11.36 6.54 10.38
31–35 3.44 8.47 3.61 6.36 5.01 8.40 3.11 7.61
361 2.54 28.44 4.33 23.12 3.88 24.99 1.70 23.09
N 62795 46126 32456 24861 67103 50560 37050 30704
Note: Over5individuals classified as over-schooled; Under5individuals classified as under-schooled.
workers who have relatively low experience (0–15 years) is lower than the respective percentage of females. Finally, although we observe some changes over time (for example, in the proportion of both over- and under-schooled males and females with 0–5 year of experience), overall the 1986 and 1991 distributions are very similar.
4.4. Effects of overschooling and underschooling on earnings
under-Table 4
Selected regression coefficients and t-ratios (in parentheses) for lnINC: Alternative Models, 1986 and 1991a
Independent 1986 1991
variables
Model 1 Model 2 Model 1 Model 2
Male Female Male Female Male Female Male Female
ADSCH 0.11 (254.24) 0.09 (136.71) 0.14 (186.71) 0.14 (121.98) 0.13 (290.54) 0.15 (238.17) 0.17 (202.63) 0.19 (164.88) OVERSCH 0.04 (66.57) 0.05 (56.04) 0.11 (89.62) 0.10 (67.55) 0.05 (75.76) 0.04 (47.14) 0.11 (93.28) 0.08 (63.02) UNDERSCH 20.04 (62.70) 20.06 (272.42) 20.13 (293.01) 20.10 (254.42) 20.04 (258.54) 20.05 (268.17) 20.12 (280.55) 20.14 (282.00) ADSCH*EXP – – 20.001 (238.03) 20.003 (254.45) – – 20.002 (243.02) 20.002 (235.94) OVERSCH*EXP – – 20.004 (264.62) 20.002 (238.36) – – 20.004 (263.31) 20.003 (242.27) UNDERSCH*EXP – – 0.003 (69.03) 0.002 (31.39) – – 0.003 (58.80) 0.003 (55.76)
R2(Adjusted) 0.43 0.36 0.46 0.38 0.44 0.45 0.46 0.47
N 166649 102694 166649 102694 179889 120777 179889 120777
aThe equations also include controls for potential experience, dummy variables for being married and one digit industry codes.
schooling on earnings, other things equal. Results for two models are shown in Tables 4 and 5.
4.4.1. Returns to years of adequate, over- and underschooling
Results shown in Table 4 for Model 1 are similar in many respects to those obtained earlier for the US (Duncan and Hoffman, 1981; Sicherman, 1991; Cohn and Khan, 1995). We observe high rates of return to adequate schooling (between 9 and 15 percent), lower but positive rates of return to overschooling (between 4
Table 5
Selected regression coefficients and t-ratios (in parentheses) for lnINC: Alternative Models, 1986 and 1991a
Independent 1986 1991
variables
Model 1 Model 2 Model 1 Model 2
Male Female Male Female Male Female Male Female
SCHOOL 0.09 0.08 0.14 0.13 0.10 0.11 0.17 0.17
(219.06) (127.30) (191.96) (117.48) (239.32) (171.22) (211.28) (146.73)
OSCH 20.19 20.15 20.10 20.08 20.23 20.29 20.17 20.29
(266.18) (238.71) (221.45) (213.34) (280.22) (280.24) (233.92) (249.03)
USCH 0.16 0.03 0.03 0.08 0.19 0.12 0.11 0.05
(50.97) (6.06) (5.39) (11.06) (55.54) (28.24) (17.38) (7.09)
SCHOOL*EXP – – 20.002 20.003 – – 20.003 20.003
(274.45) (254.13) (289.44) (257.72)
OSCH*EXP – – 20.007 20.007 – – 20.004 20.003
(224.35) (219.20) (215.89) (27.73)
USCH*EXP – – 0.005 20.003 – – 0.002 0.003
(19.17) (210.50) (8.80) (8.23)
R2(Adjusted) 0.38 0.35 0.42 0.37 0.38 0.34 0.42 0.37
N 166649 102694 166649 102694 179889 120777 179889 120777
aThe equations also include controls for potential experience, dummy variables for being married and one digit industry codes.
and 5 percent), and negative rates of return to under-schooling (between24 and26 percent). All of these results are highly statistically significant, as are all of the coefficients in the table. Note that while the 1986 and 1991 results are very similar, the rate of return to adequate schooling for females increased from 9% in 1986 to 15% in 1991.
2 we observe negative coefficients for the interaction of experience with ADSCH and OVERSCH, indicating that more experienced male and female workers tend to have a lower rate of return to adequate schooling or over-schooling. These results might represent a vintage effect: more recent graduates received schooling that is more useful in current production, hence more recent school-ing is rewarded more generously. The results for over-schooling, moreover, are consistent with the notion that extra schooling might be a good substitute for (especially) early labor-market experience. What the present findings suggest, however, is that the wage bene-fits of overschooling decline as one gains more labor-market experience.
In addition, we find positive coefficients for the inter-action of experience with UNDERSCH. The negative coefficient for UNDERSCH is reduced as one gains more experience, suggesting that experience is a substi-tute for schooling in so far as wages are concerned, albeit an imperfect substitute (43.3 [50] years of experience are required to fully offset the negative coefficient on UNDERSCH for males [females] in 1986; for 1991, the respective figures are 40 and 46.7).
4.4.2. Returns to being over- and underschooled
Results in Table 5 show the returns to actual school-ing, and for being overschooled and underschooled. As emphasized in Cohn (1992), results from this table should not be used to compute rates of return to over-and underschooling. Rather, as Sicherman (1991) points out, one can determine from these regressions whether overschooled or underschooled workers have wages that are lower or higher than the wages they would have earned in a job for which they are adequately schooled. Results using Model 1 are generally consistent with those of other studies (Verdugo and Verdugo, 1989; Sicherman, 1991; Cohn and Khan, 1995): overschooled (underschooled) workers have wages that are lower (higher) than the wages they would have earned in a job for which they are adequately schooled. The difference between earlier results for the US and the present find-ings for Hong Kong is the magnitude of these wage dif-ferences: in Hong Kong these wage differences are quite substantial.
Concerning Model 2 results, negative interactions are found for all of the coefficients of SCHOOL*EXP and OSCH*EXP as well as for the coefficient of USCH*EXP for females in 1986. The interpretation of the results for the first two interaction terms follows the explanation in the preceding section. The negative coefficients of USCH*EXP for females in 1986 is puzzling, especially since the opposite effect has been found for 1991. (Interestingly, calculations based on the 1% Census sam-ple [N 5 10,661] produced a positive and significant coefficient for USCH*EXP for both 1986 and 1991.)
4.4.3. Pooled data for 1986 and 1991
To test whether regression results for 1986 differ sig-nificantly from those of 1991, we pooled the two cross sections and ran the regressions shown in Model 1 of Tables 4 and 5 with the addition of interaction time dummies (each of the relevant variables times T91, where T91 is 1 for 1991 and 0 for 1986). To save space, the full regressions are not reproduced here (they are available from the authors upon request).
To test the null hypotheses that, for each model/specification, by sex, the 1986 and 1991 models produce identical coefficients, we conducted a series of Chow (F) tests. The results indicate that all of the null hypotheses are rejected at the 5% level, suggesting that running separate equations for 1986 and 1991 or pooled equations with time interactions was justified.
Corresponding to Model 1 of Table 4, all of the rel-evant interaction coefficients are significant and positive, except for OVERSCH*T91 for females which is nega-tive (20.01). The results suggest a slight increase over the period in rates of return to adequate schooling, over-schooling and underover-schooling for males (0.02 for ADSCH*T91, 0.006 for OVERSCH*T91, and 0.003 for UNDERSCH*T91). For females, the results suggest a fairly large increase (0.07) for adequate schooling and a small increase for underschooling (0.01).
Corresponding to Model 1 of Table 5, all of the inter-action coefficients are statistically significant. Relatively modest positive coefficients were found for males, regarding actual schooling (0.01) and being under-schooled (0.02). The respective coefficients for females are also positive, but larger in absolute value (0.03 for actual schooling and 0.10 for being underschooled). Finally, for being overschooled, both for males and females, we find negative interaction coefficients (20.04 for males and20.14 for females), consistent with the results shown in Table 5.
5. Discussion
Sino-Bri-tish Joint Declaration. The estimated annual number of emigrants increased from 22,000 in 1985 to more than 60,000 by 1990 (Lam and Liu, 1995, Table 5). By the late 1980s, the labor shortage problem became acute, as indicated by the decrease in the annual rate of unemploy-ment from 4.5% in 1983 to 1.1% in 1989.
To tackle the labor shortage problem, the Hong Kong government adopted the policy of importing foreign workers. In contrast to a restrictive policy under which only 3000 skilled workers were allowed to enter in May 1989 (Lam and Liu, 1995), the government relaxed its restrictive policy and allowed more workers to enter dur-ing the 1990s. Consequently, labor shortages in semi-skilled and unsemi-skilled occupations were more acute in the 1980s than before or after (Chiu, 1996).
During the same period when labor shortages were acute, the Hong Kong economy was also undergoing a restructuring (Greenwood, 1990; Lui and Chiu, 1993; Mok, 1993). One of the consequences of restructuring was a shift in employment from manufacturing industries to the service sector. The rate of structural change in Hong Kong surpassed that of other countries, such as Singapore, Korea and Japan, between 1987 and 1992 (Suen, 1995). With the rapid change in the demand for different types of labor resulting from economic restruc-turing, and the persistent labor shortages between the mid 1980s and the early 1990s, one might expect a sub-stantial job mismatch (over- and underschooling) in the Hong Kong labor market. Moreover, the rapid change in the Hong Kong economy and labor market could be expected to alter the returns to adequate, over- and underschooling. Since several factors and policies (e.g., labor shortages, emigration policy, worker retraining programs) were operating simultaneously, it is not clear what the a-priori direction of change in such rates of return would be between 1986 and 1991.
As shown in Table 1, the incidence of adequate, over-and underschooling, by sex, did not change substantially between 1986 and 1991, although one may notice a slight decline in the percentage of workers who were overschooled in 1991. Further, as shown in Table 2, there was a reduction in the number of years of adequate schooling for underschooled workers between 1986 and 1991, compared to an increase in the same variable for adequately schooled and overschooled workers. Also, there is almost no change in the actual level of schooling of underschooled workers, in contrast to an increase in schooling for both adequately schooled and overschooled workers. These phenomena might reflect the high flexi-bility of the Hong Kong labor market: the supply of skilled workers appears to have been unaffected by the restructuring of the Hong Kong economy and the labor shortages (which were more acute among the unskilled and semi-skilled workers). The greater opportunity to attaining tertiary education, on the other hand, may have been responsible for the overall increase in the schooling
level of the workforce between 1986 and 1991. These arguments are reinforced by the data shown in Table 3, indicating a reduction in the percentage of younger workers (with potential experience of 10 years or less) who are over- or underschooled.
Concerning the effect of schooling on earnings in Model 1 of Table 4, we obtain high rates of return to adequate schooling, lower but positive rates of return to overschooling, and negative rates of return to under-schooling. Consistent with the earlier argument that the Hong Kong labor market may be highly flexible and with Suen (1995)’s findings that sectoral shifts in the Hong Kong economy did not affect relative earnings among males, we find that the rates of return to over- and under-schooling remained relatively stable between 1986 and 1991. On the other hand, the rate of return to adequate schooling suggests an upward trend for the same period, especially for females. These results probably reflect the greater opportunities afforded to females to attain higher levels of schooling and the stronger commitment of females to the labor force in recent years (data provided by the Hong Kong Annual Digest of Statistics, 1993 indicate that the labor force participation rate of females aged 25–34 increased steadily during the late 1980s).
When interaction effects between potential experience and the schooling variables are considered in Model 2 of Table 4, we find a complementary effect between underschooling and experience. The opposite is found for over- and adequate schooling. The inclusion of inter-action terms also underscores the observed changes in rates of return to over- and underschooling for females (but not males). The lower rates of return to over- and underschooling for females may be the outcome of the slow-down in the Hong Kong economy around 1990 which apparently had uneven labor-market consequences for males and females. As Suen (1995) points out, min-ority groups and females are first to be affected by a disequilibrium in the labor market or changes in econ-omic conditions.
6. Summary and concluding remarks
The two main purposes of the present study were (1) to study the incidence of overschooling and underschoo-ling in the Hong Kong and US labor markets for 1986 and 1991, and (2) to address the effects of overschooling and underschooling on earnings and how these effects change over time.
over- and underschooled workers is remarkably similar for the two years. It was also found that the distribution of over- and underschooling is affected by labor-market experience. In particular, workers with 36 or more years of experience are much more likely to be underschooled, while workers with relatively few years of experience are more likely to be overschooled.
In regard to the wage effects of over- and underschoo-ling, we find that the rate of return to overschooling is positive but lower than the rate of return to adequate schooling, and that the rate of return to underschooling is negative. Also, in general, overschooled (underschooled) workers have wages that are substantially lower (higher) than the wages they would have earned in a job for which they are adequately schooled. In addition, we found that the rates of return to adequate schooling and overschooling (underschooling) decline (rise) as labor-market experience rises, for both males and females.
Although our analysis is based on excellent data sources, we recognize that all empirical findings are lim-ited by the nature of the inquiry, model specification,
measurement error, and other potential errors of omis-sion and commisomis-sion. In particular, we did not consider the issue of possible sample-selection bias in the wage equations. If overschooled and underschooled workers are not selected randomly, then the rates of return we obtain might be biased (Oosterbeek, 1993). Finally, it would be interesting to examine changes in the distri-butions of over- and underschooled persons as well as the rates of return to schooling for these groups over a longer time horizon.
Acknowledgements
Appendix A
Table 6
Definitions of Variables
Variable Definition
SCHOOL Actual years of schooling.
ADSCH Years of adequate schooling: the most frequently observed years of schooling for the individual’s occupation (two-digit census occupation codes).
OVERSCH Years of over-schooling: the number of years of schooling exceeding ADSCH, if SCHOOL > ADSCH and 0 otherwise.
UNDERSCH Years of under-schooling: the number of years of schooling under ADSCH, if SCHOOL,ADSCH and 0 otherwise.
ASCH Dummy variable for adequately schooled individuals: 1 if the individual’s actual schooling equals adequate schooling and 0 otherwise.
OSCH Dummy variable for over-schooled individuals: 1 if the individual’s actual schooling is higher than adequate schooling and 0 otherwise.
USCH Dummy variable for under-schooled individuals, 1 if the individual’s actual schooling is less than adequate schooling and 0 otherwise.
LnINC Natural logarithm of monthly income. AGE Age of the individual.
EXP Years of labor market experience. (AGE-SCHOOL-6)
MAR Dummy variable: 1 if the individual is married and 0 otherwise.
MANUF Dummy variable: 1 if the individual works in the manufacturing industry and 0 otherwise (this is the omitted category).
CONSTR Dummy variable: 1 if the individual works in the construction industry and 0 otherwise.
WHOLES Dummy variable: 1 if the individual works in the wholesale and retail trade industries and 0 otherwise. TRANSP Dummy variable: 1 if the individual works in the transportation, communications, and other public utilities
industries and 0 otherwise.
FINANCE Dummy variable: 1 if the individual works in the finance, insurance, real estate and business services industries and 0 otherwise.
SERVICE Dummy variable: 1 if the individual works in the personal and social services industries and 0 otherwise. MANAG Dummy variable: 1 if the individual is a manager or administrator and 0 otherwise.
PROFES Dummy variable: 1 if the individual is a professional, technical or kindred worker and 0 otherwise. CLERK Dummy variable: 1 if the individual works in a clerical occupation and 0 otherwise.
SALES Dummy variable: 1 if the individual is a sales worker and 0 otherwise. SERVI Dummy variable: 1 if the individual is a service worker and 0 otherwise.
Appendix B
Table 7
Basic Statistics for the Entire Sample (means and standard deviation [in parentheses]
1986 1991
Male Female Male Female
SCHOOL 10.84 (3.84) 10.80 (4.30) 11.26 (3.89) 11.50 (4.05)
AGE 34.30 (11.02) 31.46 (10.45) 35.81 (10.74) 33.05 (9.85)
EXP 17.45 (12.63) 14.67 (12.96) 18.56 (12.40) 15.55 (12.12)
MAR 0.58 (0.49) 0.49 (0.50) 0.62 (0.49) 0.55 (0.50)
MANUF 0.34 (0.47) 0.46 (0.50) 0.28 (0.45) 0.32 (0.47)
CONSTR 0.10 (0.30) 0.01 (0.10) 0.11 (0.32) 0.01 (0.10)
WHOLES 0.19 (0.39) 0.18 (0.39) 0.19 (0.39) 0.22 (0.41)
TRANSP 0.13 (0.34) 0.03 (0.18) 0.15 (0.36) 0.05 (0.22)
FINANCE 0.07 (0.25) 0.08 (0.28) 0.11 (0.31) 0.12 (0.33)
SERVICE 0.17 (0.38) 0.24 (0.43) 0.16 (0.37) 0.28 (0.45)
MANAG 0.04 (0.19) 0.01 (0.12) 0.06 (0.24) 0.03 (0.17)
PROFES 0.09 (0.28) 0.11 (0.31) 0.16 (0.37) 0.15 (0.36)
CLERK 0.12 (0.32) 0.28 (0.45) 0.09 (0.29) 0.31 (0.46)
SALES 0.07 (0.26) 0.06 (0.24) 0.03 (0.18) 0.05 (0.22)
SERVI 0.19 (0.39) 0.17 (0.37) 0.23 (0.42) 0.23 (0.42)
PROD 0.50 (0.50) 0.37 (0.48) 0.43 (0.50) 0.23 (0.42)
N 166649 102694 179889 120777
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