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*: denotes to statistical significance at the 5% level Source: Author’s calculation based on VLSS2 Education and Experience Education, one of the most important human capital investments, has statistically significant, but small wage effects. Another year of schooling is rewarded about 4.76%. It equals to average return to schooling of the whole economy in the 1992-1993 (Moock et al. 1998). In a recent study, Liu (2002) shows that the return to schooling is about 4% for the private sector worker, and about 7.4% and 5.8% for government sector and SOEs, respectively. Then, it is safe to say that the return to education in light industry is not high as compared with the economy as a whole. The reason may be due to its characteristics. The average years of schooling in light industry is 8.6 while the whole economy is 9.1 (Bales, 2000). In this sense, the low return to education in light industry is partly due to the low average education level of workers. On the other hand, most jobs of many industries in the light industry sector require basic skills such as sewing in the footwear, garment and therefore the sector offers employment opportunities for workers with less educated, e.g. with primary education. However, both educated and less educated workers apply for, and then the more educated are often hired (Belser, 2000). As a result of the over-qualification of workers, education investments are underpaid. Differently, the not so high return to education discourages talents from taking part in its labor force. Other industries such as banking and service, which promise higher returns, have advantages over light industry to attract high education level workers. That is a challenge to the development of light industry towards further exploitation of comparative advantages As compared with education, experience pertains to lower returns. An additional potential experience raises wages by about 3.58%. The lower return of experience suggests that employers regard formal education more important than potential experience. It is entirely consistent with traditional ideology of Vietnamese. Formal education is always appreciated among Vietnamese society. The signs of coefficients associated with experience and experience squared confirm theoretical arguments. It means wages follow the parabolic shape with respect to experience, peaking somewhere at workers’ mid-working life. To investigate the experience level at which the log wage is the highest, one first differentiates the wage equation with respect to experience: ¶lnyi /¶exp= 0.0358-0.000652*2*exp Set this equal to zero, it is found that at the level of 28 potential years, log wages are maximized. Despite that it also depends on workers’ schooling, on average, 50-year-old workers with university completion enjoy the highest return. This result makes us feel confused as according to Labor Code the retirement age is 60 years in full for males and 55 years in full for females. Gender and Marital Status Not surprisingly, gender is statistically significant at 5% level with the high value of the coefficient, implying a substantial wage effect. Being a male raises workers’ wages by 26.2%. It says that the gender wage gap and possible discrimination have been in fact in light industry. It is of interest to further explore what are behind this. Then, in next part, we will focus more details on this issue. In contrast, marital status variable is not statistically significant. Although this result is contrary to empirical findings in other countries where marital status has wage effects (e.g. Paternostro & Sahn, 2000), it confirms our intuition that this variable has strong effects on participation decision rather than wages. Migration and Regional Location Migrant variable is not statistically significant. This result may not follow our expectation. It is interesting to explore what lies behind this. First, it is necessary to look at the government’s policy and regulation on labor mobility. Until in the early 1990s, free geographic labor moves were strongly regulated by central government. The government planned the number of migrants as well as migration process. In this sense, practical admittedly, migrants cannot be treated as a “disadvantaged” group. Moreover, local governments at large cities raise high barriers to migrants. For example, having a house as well as having a permanent job is must for those who intend to migrate into city. Meeting these requirements means that migrants have certain endowments that are no less than the urban residents’, or even higher. It is well in line with previous studies on migration in Vietnam (e.g. UNDP, 1998). Notably, in the late 1990s with the increasingly widening region gap as well as loosened regulations on geographic mobility, free geographic moves have been accelerated. Hopefully, the empirical findings in the T&G in the next part can capture this trend, and further explore the possible wage discrimination against migrants. Differently, the insignificance of migrant status is due to sample characteristics. As noted earlier, the VLSS 2 sample is based on either VLSS1 or in 1995 MPHS. If household had moved away, another was chosen randomly. In fact among households in the VLSS 1 sample, 300 households had moved away, of which 19 households were temporarily from commune. Thus, VLSS 2 cannot contain useful information on geographic migration at that time. The south dummy is included to capture the wage effects of local labor market. A substantial premium, 56.36%, is attached for the southern workers with the same endowment. The high premium is a strong incentive for north-south migration. It is in line with previous studies such as Moock et al. (1998). They showed that workers in the South earned 50 % more than those in the North despite the fact that had a third less schooling. They also argued that it was due to the higher productivity with better machinery equipments in the south. Moreover, it is clear that in the South the marketisation degree may be higher than in the rest. Then, employers in the South have incentive to compensate workers at the wage rates reflecting more truly their human capital than the North. Ownership Sector variable is not statistically significant at any conventional confidence level(i.e. 10% or better) but its coefficient shows the positive sign. Perhaps, quantitatively, sector has no wage effects. The main reason lies behind this is, to a certain extent of marketisation degree, the private sector, which pursuits profit maximization, has not enough incentive to truly compensate for human capital. At the same time, the SOEs aim at not only profit maximization but also employee satisfaction. In this sense, they have no reason to lower workers’ wages and salaries even in the case that they are loss-making. Another study by Bales and Rama (2001) argue that the public sector workers are overpaid based on ‘worker approach” but based on “job approach” the finding is on contrary despite that they use the same dataset, namely VLSS2. In a nutshell, in Vietnam, the public - private wage gap is not clear, especially in the early transition phase. Yet, it is no doubtful that increasing degree of marketisation is widening this gap. Thus, further study on possible public - private wage gap can be done only with more recent dataset. Occupation Regarding occupation dummies included, the professional dummy is statistically significant while the skilled is on contrary. Quantitatively, the occupational choice between managerial and professional jobs and other jobs becomes critical to wage determination. On average, managers and professionals earn 46.28 % more than others. Practically, the high premium of being professionals entails the indirect returns to human capital as to be a manager or professional, one obviously must certain productive characteristics. In this way, the high premium of being professional is a monetary benefit that compensates for “white-collar” workers with high educational level. Constant term The negative sign of constant term may suggest that light industry workers do not enjoy premium. However, it is statistically insignificant at 5% level. Quantitatively, it is difficult to get more information on this.
In conclusion, the estimates of wage equation in light industry prove that human capital investment are compensated. The literature argues that the clearer earnings-human capital patterns reflect the labor market efficiency. In this sense, the human investment reward is a positive sign in the labor market for light industry. Yet, the low rates of return suggest that the reward is still modest, relative to other industries. This finding may do not satisfy some economists who wonder about the heterogenousity of light industry. They may assume that there is a difference between the wage structure of two groups in the sector: industries with export orientation (garment, electronics...) and the rest (tobacco...). In other words, the hypothesis suggests that export oriented industries tend to pay higher for human capital. We try to test this hypothesis by using ttest but in fact the difference is statistically insignificant. Thus the message here is more convincible: the impressive growth of light industry does not bring monetary benefits to workers as expected.
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|
Gap |
Percent |
|
|
Log wage differential |
0.2502 |
100 |
|
Discrimination |
0.2204 |
93.62 |
|
Of which: |
|
|
|
Male advantages |
-0.4339 |
|
|
Female disadvantages |
-0.0690 |
|
|
Environment gap |
0.7232 |
|
|
Productivity gap |
0.0299 |
6.38 |
Source: Author’s Calculation based on VLSS2
Discrimination
is entirely determined by pure premium or environment gap. As can be seen in
Table 2, it represents the log wage differential of 0.7232. The
message here is clear: the existing condition favors male workers, and therefore
pays higher to them. Despite that there is no discriminatory behaviors of
employers, men overpayment still persists, causing discrimination in the labor
market
Productivity gap is only small with the log wage differential of 0.0299. It is evident that the differential between productive characteristics of males and females is not large. Also, it shows that the access to opportunity for human capital investment is not different on the basis of gender. It may be the result of economic history, especially resource allocation as well as education policy of the government under the centrally planned system.
In short, discrimination is a major determinant of the gender wage gap in light industry despite that there is no signs of discriminatory behaviors. Environment gap is the sole source of discrimination, placing female workers at a disadvantage. In this sense, discrimination is not solved without great effort emphasized on changing social norms and perception bias against females.
3.4. Evidence from the T&G
It is of interest to look at sectoral choice equations. SOEs sector is regarded as comparison group because in fact there is a distinction between SOEs and non-SOEs. To make a decision on participating in which sector, individuals consider expected pecuniary and non-pecuniary benefits offered by different sectors. In this sense, SOEs sector is quite different from others because they pursuit employee satisfaction rather than profit maximization like PEs and FIEs. Also, the government use jobs in SOEs as income transfer (Bales and Rama, 2002).
The estimates of multinomial logit model produce plausible results. First, it confirms that more educated workers are more inclined to be in SOEs. Much of empirical work in Vietnam agrees with this result (e.g. Moock et al, 1998). Yet, it is important to note that while all variables reflecting education in PEs equation are significant, only university education dummy in FIEs is significant at 5% level. Second, experience has effect on sectoral choice between SOEs and PEs. Workers with more experience are more inclined to participate in PEs. Not surprisingly, it is due to the recruitment policy of PEs that favors workers with more experience level, which is in turn a result from their aims of training cost minimization. In contrast, experience plays insignificant role in sectoral choice between SOEs and FIEs. Third, male workers are found to prefer to work in SOEs than other sectors. This result also agrees with findings from other studies. For example, MOLISA(1998) reveals that the predominance of male workers in SOEs despite the fact that labor force participation rates do not vary with respect to gender. Fourth, non-migrants are likely to be in SOEs rather than non-SOEs sector. In reality, there are a few observations of migrant in sample SOEs firms. Next, the results from regression show that sectoral choice depends on residence of workers. Workers who live in the south are much more inclined to be in non-SOEs. This trend is clearer in workers’ decision on FDI sector participation. In the same fashion, individuals living in HCMC or Ha Noi are likely to be PEs workers rather than SOEs. Yet, the insignificance of this variable in FIEs participation equation implies that it may have no effect on sectoral choice between SOEs and FIEs. Last, marital status is likely to play no role in determining to participate SOEs or non-SOEs.
Despite that participation equations produce convincing results, one still wants to compare wage equation with and without selectivity correction. Table 3 reports results for different sectors with and without selectivity correction. Convincingly, the Mincerian equations with selectivity correction yield more plausible results. Moreover, the correction terms (lambda) in all three equations are significant at 1% level, implying the selection bias in the OLS estimation of the wage function. With such results, we focus on investigating wage determinants in different sectors based on results from wage equation with selectivity correction.
As can be seen in the Table 3, the statistical significance of coefficients on correction terms confirms the presence of selection bias. Moreover, the negative sign of lambda in SOEs wage equation implies unobserved characteristics of SOEs workers, which determine their sectoral choice, have adverse impact on their earnings. Meanwhile, PEs and FIEs workers experience the positive correlation between unobserved characteristics and earnings.
Table 3 reveals that all three sectors reward human capital. Yet, return to human capital varies across sectors. Moreover, some variables become critical in determining wage in a particular sector but show insignificant role in others. Then, it is of interest to examine each wage determinant in each sector to exatract more information.
|
|
SOEs |
PEs |
FIEs |
|||
|
|
Without |
With |
Without |
With |
Without |
With |
|
|
Coef. |
Coef. |
Coef. |
Coef. |
Coef. |
Coef. |
|
Secondary Education |
-0.13 |
0.13* |
0.25*** |
0.27*** |
-0.02 |
0.01 |
|
Technical Training |
0.23*** |
0.49*** |
0.11 |
0.15* |
0.02 |
0.03 |
|
University |
0.74*** |
0.78*** |
0.24*** |
0.28*** |
0.44*** |
0.45*** |
|
Experience |
0.02 |
0.02 |
0.02*** |
0.02*** |
0.02*** |
0.02*** |
|
Experience squared |
-0.06 |
-0.07** |
-0.05*** |
-0.05*** |
-0.07*** |
-0.07*** |
|
Gender |
-0.08 |
0.11* |
0.08* |
0.09** |
0.10** |
0.11*** |
|
Migrant status (1) |
|
|
0.14*** |
0.17*** |
0.13*** |
0.21*** |
|
South |
0.80*** |
-0.18 |
0.68*** |
0.64*** |
0.23*** |
0.24*** |
|
Managers/Professionals |
0.33*** |
0.21* |
0.25*** |
0.25*** |
0.64*** |
0.63*** |
|
Skilled |
0.47*** |
0.41*** |
0.17*** |
0.16*** |
0.14*** |
0.14*** |
|
Lambda |
|
-2.23*** |
|
0.36*** |
|
0.43*** |
|
Constant |
0.65*** |
1.77*** |
0.30*** |
0.09 |
0.94*** |
0.64*** |
|
# Obs. |
320 |
608 |
272 |
|||
|
Adj. R2 |
0.4931 |
0.6159 |
0.4241 |
0.4290 |
0.4176 |
0.4308 |
***,**,* denote significant at 1%, 5%, 10% level respectively.
(1): Migrant dummy is dropped in SOEs wage equation because lack of observations.
Source: Author’s calculation based on the 2001 T&G firms survey
Education and Experience
With respect to wage effect of formal education achievements, both SOEs and PEs reward for any education level introduced while FIEs do not follow the same pattern. In FIEs wage equation; only coefficient on university education is significant at 10% level. Generally, the higher educational level pertains to the higher return for all sectors. Notably, returns to formal education in SOEs are the highest. Is it evidence of stronger marketisation degree of SOEs as compared with non-SOEs? Or is it a proof of the fact that SOEs aim at employee satisfaction rather than profit maximization? To answer, more information is required but the latter is likely to be more convincing.
More specifically, the low returns to high school completion do not
surprise us. As a common perception, in the light of “option value” concept,
workers with secondary education are benefited from further study rather than
private return to education. Regarding technical training, SOEs workers
experience the highest return. Meanwhile, the wage effect of this variable in
PEs is significant at 10% and in FIEs is insignificant at even 10% level. This
phenomenon may be due to low quality of vocational education system. In
addition, return to university education is the highest in the SOEs and lowest
in PEs. The university degree doubles SOEs workers’ wages but raises wages by
32.3%, and by 56.8% for PEs and FIEs workers, respectively. Moreover, it is
noted that students must complete secondary education before attending
universities. In this sense, workers with the university degree may raise
wages by more than 150%, 73% and 58% for workers in SOEs, PEs and FIEs.
Compared with other studies, e.g. Nguyen (2002), the returns to formal education
in the T&G are lower than the average rates of the whole economy.
Variables related to experience are found to be significant, except for experience level for SOEs workers. Importantly, the signs of all variables are as expected: positive sign of linear term in experience, and the negative sign of squared experience. It confirms that return to experience follows the parabolic shape. It is observed that the wage experience profile is steeper in SOEs and FIEs, implying higher impact of an additional experience year in SOEs and FIEs. Return to experience peaks at 29 years of experience for SOEs and FIEs workers while it peaks at 40 years of experience in PEs. Again, the low returns to experience makes us feel confused.
Gender
Gender wage gap is evident in non-SOEs sectors. A male worker in FIEs is overpaid by 11.6%. At the same time, a male in PEs enjoys 9.4 % increase in earnings. Despite that over-representation of females in T&G, the gender wage gap is still a problem. It is noting the gender wage gap is not evident in SOEs sector. Coefficient on this variable in SOEs equation is insignificant at 5% level. Moreover, the sign of this coefficient in the equation without selectivity correction contradicts that in the equation with selectivity correction. Then, it is still question of whether gender wage gap exists in SOEs sector. Moreover, if gender wage is in fact in non-SOEs, what accounts for this gap? The next section will provide an answer.
Migration
The wage differential between migrants and non-migrants is observed for non-SOEs sector. 23.4% and 18.5% increases in earnings are for FIEs and PEs non-migrant workers. Likely, it suggests that there might be wage discrimination on the basis of migrant status. It is noting that migration is considered as human capital investment in the sense that workers sacrifice the present benefits for the higher expected benefits. At the first stage, migrants often face wage discrimination but for the long term the expected benefits will be greater regardless whether they know wage discrimination at destination. Moreover, their wages at destination are far much higher than those in the their countryside. Then, it is the determinant of geographic migration. Calculation on the T&G dataset indicates that the difference between wages before and after migration is significant at 1% level with the mean monthly wage differential is about 450 thousand dongs. Such the high wage differential is strong incentive for geographic labor moves despite that wage discrimination might exist.
Regional Location
In addition, the wage effects of local market are impressive in non-SOEs sector. This result is well in line with previous studies, which confirm that offered wages in the south rank the top in the whole economy. More specifically, the gap between two regions in the PEs and FIEs is large. 89.6% and 27.1% are for whom work for PEs and FIEs in the south, respectively. The more favorable business environment and/or more efficiency at the firm level are the main reasons for such huge gaps? Is firm size wage effect valid in such a case? Bear this caveat in mind. In appendix, we have conducted some simple test on this concern. In practice, on average, the firm size in the south is larger. According to SMEs definition of the government (official letter No 681/CP), 93.1% of the sample in the south is considered as large firms while the north: 76.1%. Firstly, we use ttest to examine the difference in mean hourly wage between SMEs and large firms. Accordingly, the difference in mean hourly wage of workers in the large firm and in the SMEs is about 2.43 thousand dongs per hour. This test is significant at 1% level. This result confirms our hypothesis that there is a positive relation between firm size and wages. Secondly, we try to examine the correlation between workers’ wages and firm size. It is observed that there is positive correlation between workers’ wages and firms’ revenue. Likely, there is a sign of rent sharing (More details in Appendix).
Occupation
Coefficients on occupational dummies are statistically significant for all three sectors at 1% level. Not surprisingly, the wage differential across occupation for FIEs is the clearest FIEs have both capability and managerial autonomy to pay higher wages to managers and professionals. As such, it ensures that wages of managers are commensurate with their authority and responsibility. PEs follow the same pattern but their rewards for higher level are still low.
Constant term
It is also noted
about constant terms. The constant terms in both SOEs and FIEs are significant
and have the high value. It is evident that SOEs and FIEs offer high premium for
their workers.
|
By sector |
Return to capital (%) |
Labor Productivity* (mil.VND/a worker) |
|
SOEs |
11.79 |
215.31 |
|
FIEs |
20.78 |
100.70 |
|
PEs |
9.52 |
62.93 |
Source: Author’s calculation based on the 2001 T&G firm survey
In conclusion, the closer examination of wage determination in the all three sector sends a clear message: all three sectors reward for the most relevant human capital investment of workers. That is encouraging sign for the labor market regarding the T&G. However, the analysis also indicates that there might be unexplained wage gap, especially gender wage gap in non-SOEs sector. The following section will aim to explore these gaps.
The results from
Mincerian equations indicate that gender plays a significant role in determining
wages of non-SOEs workers. The Chow test also confirms that it is appropriate to
estimate wage equation for men and women in each sector, namely PEs and FIEs
separately.
To examine whether these gaps exist in reality, we calculate gaps between male and female workers at the mean values of explanatory variables. In other word, we examine the gaps between male and female workers who have similar endowments. For both sectors, FIEs and PEs, female workers’ wages are just over 90% of those of males despite the fact that they have similar endowment. Thus, it is clear that gender wage gap is in fact in non-SOEs sector.
Interestingly, the wage gaps in two sectors take the same value with the log wage differential of 0.14. However, the sources of gender wage gap vary with respect to sector.
|
|
PEs |
FIEs |
||
|
|
Gap |
Percent |
Gap |
Percent |
|
Log wage differential |
0.14 |
100 |
0.14 |
100 |
|
The unexplained gap |
0.08 |
57.1 |
0.10 |
71.4 |
|
Of which: |
|
|
|
|
|
Male advantages |
0.38 |
|
0.04 |
|
|
Female disadvantages |
0.06 |
|
-0.20 |
|
|
Environment gap |
-0.36 |
|
0.27 |
|
|
Productivity gap |
0.06 |
42.9 |
0.04 |
28.6 |
Source: Author’ s calculation based on the 2001 T&G firm survey
Table 5 shows that the gender wage gap in PEs sector is attributable to both the unexplained and productivity gap equivalently. It should be noted that the unexplained stands for wage discrimination. The unexplained gap contributes to 57.1% of gap while productivity gap 42.9%. Further examination of the unexplained gap reveals the existence of both favoritism toward men and pure discrimination against women. Specifically, male overpayment is 46% while female underpayment is 6%. Technically, this reflects the discriminatory behaviors of employers.
Further, one can wonder about the contribution of some most relevant variables, namely education and experience to calculation of the unexplained gap.
|
|
Male Advantages |
Female Disadvantages |
|
Secondary Education |
-0.06 |
-0.01 |
|
Technical training |
-0.01 |
0.0* |
|
University |
0.0* |
0.0* |
|
Experience |
0.60 |
0.13 |
*: denotes extremely small value
Source: Author’s Calculation based on the 2001 T&G firm survey
Table 6 provides an answer. In fact, with an additional year of experience, men enjoy much higher returns while women experience lower returns. In this sense, return to experience plays an important role in determining the unexplained. In contrast, education makes non-existent contribution to the calculation of the unexplained.
In comparison, the negative sign of environment gap in PEs sector implies that female workers not males earn substantial premiums. Does PEs share its rent with female workers?
(Unit: percent)
|
Productivity Gap |
6.2 |
|
Of which: |
|
|
Secondary Education |
4.0 |
|
Technical Training |
0.0 |
|
University |
2.0 |
|
Experience |
1.0 |
|
Manager/Professionals |
3.1 |
|
Others |
-2.9 |
Source: Author’s calculation based on the 2001 T&G firm survey
In addition to the unexplained, productivity gap has made substantial effects on gender wage gap in PEs. The male productivity advantage is estimated to be about 5%. Keep in mind that the productivity differential reflects pre-labor market discrimination. A closer examination on sub-components’ contribution to this differential leads to two interesting findings. First, male workers have higher education level as well as potential experience. Second, over-representation of men in “white-collar” jobs is evident (Table 7).
As compared with PEs, wage structure of FIEs is quite different. The unexplained gap is entirely attributable to environment gap. Due to environment gap, male workers are overpaid by 31%. Moreover, the positive sign of male advantages sub-component confirms the existence of nepotism. Male advantages portion contributes a small increase in a worker’s wage: 4%. On the contrary, the negative signs of female disadvantages suggest there is no sign of pure discriminatory behaviors against female. Generally, wage discrimination in FIEs is mostly determined by the nature of the labor market rather than discriminatory behaviors of employers.
Unit: percent
|
Productivity Gap |
6.2 |
|
Of which: |
|
|
Secondary Education |
0.0 |
|
Technical Training |
0.0 |
|
University |
2.0 |
|
Experience |
1.3 |
|
Manager/Professionals |
3.1 |
|
Other |
-0.2 |
Source: Author’s calculation based on the 2001 T&G firm survey
Productivity gap is smaller than in PEs. Consistent with findings in PEs, most of this gap is due to education, experience and over-representation of male workers at managerial/professional levels (Table 8). However, the difference in formal education attainment is smaller than that in PEs.
In short, the gender wage gap is evident in non-SOEs sector. However, due to different wage structures, the sources of the gaps vary across sectors. In PEs sector, there is a sign of discriminatory behaviors of employers. Meanwhile, in FIEs sector, the environment gap and nepotism are the determinants of the unexplained. For both sectors, productivity gap exists. Actually, male workers have higher educational level as well as over-represent in high occupational level.
3.5. A Comparison
This section is to analyze the trend of wage determination and discrimination in the light industry sector by comparing results obtained. Moreover, instead of comparing all variables, emphasis will be placed on the most distinctive features of labor markets.
Firstly, it is noted that evidence form VLSS2 does not show the statistical significance of ownership sector in light industry but it is on contrary with evidence from the 2001 T&G survey. It then suggests that sector choice has become increasingly critical to wage determination. Likely, higher degree of marketisation accounts for this.
Secondly, it is observed that return to experience is far lower than return to formal education, reflecting the common perception preferring to formal education achievements has long been rooted in Vietnamese society. The below box is an evidence.

Thirdly, gender wage gap has narrowed considerably. Perhaps, there are two main reasons for that. Firstly, it is considered as a good result of great efforts of the government coping with gender bias. Secondly, it is due to industry-specific characteristics. That is nature of the T&G characterized by over-representation of female workers (80%). Obviously, the first seems to be not convincing. It is said that the ever-increasing degree of marketisation has widened gender wage gap (Liu, 2002). It is not true for Vietnam only but for all transition economies. The second reason is, on the contrary, entirely believable explanation.
As illustrated in Table 9, the unexplained gap always makes up the larger portion. It is clear that discrimination against females has been rooted in the society. Also, the gaps in non-SOEs confirm the most socialist ideology of SOEs as compared with other sectors.
|
|
1997/98 |
2001 |
|
|
|
Light Industry |
T&G PEs |
T&G FIEs |
|
Log wage differential |
0.25 |
0.14 |
0.14 |
|
The unexplained gap |
0.22 |
0.08 |
0.10 |
|
Of which: |
|
|
|
|
Male advantages |
-0.43 |
0.38 |
0.04 |
|
Female disadvantages |
-0.07 |
0.06 |
-0.20 |
|
Environment gap |
0.72 |
-0.36 |
0.27 |
|
Productivity gap |
0.03 |
0.06 |
0.04 |
Source: Author’s Calculation based on VLSS2 and 2001 T&G firm survey
A closer look at components of the unexplained gap reveals that in 1997/98 the environment gap is the sole determinant while in 2001 there are contributions of male advantages or female disadvantages with respect to sectors. Since that time on, discriminatory behaviors on the basis of gender have been evident. Specifically, regarding PEs, both nepotism and pure discrimination are determinants of the unexplained gap. At the same time, with respect to FIEs, nepotism exists together with environment gap. It is, therefore, safe to say that the existence of gender wage discrimination has become more serious. It is well in line with increasing degree of marketisation.
Fourthly, wage effects of being migrants are found in 2001 despite that it is not evident in 1997-98 due to many reasons. Obviously, geographic labor moves are an increasing trend. It is also noted that there is a sign of wage gap between migrants and non-migrants.
Fifthly, occupation choices are increasingly critical to wage determination. In 1997-98, only the wage effect of being managers at any level or professionals is statistically significant. Meanwhile in 2001, the high and statistically significant rewards for managers, professionals and skilled labors are observed. It is a positive sign in Vietnam’s labor market. Because it reveals that workers are compensated in commensurate with their authorities and responsibilities. In this sense, this reward will be increasingly high with marketisation degree.
In short, the comparison produces plausible but not much informative results. The reason is due to different sample characteristics of two datasets. One is derived from household surveys while the other comes from firms’ survey. Moreover, the industry-specific characteristics may be taken into account. In this case care must be taken to make any comparison. Nonetheless, it is safe to conclude that: (i) sectoral location has become increasing significant to wage determination; (ii) formal education is still recognized among Vietnamese, and return to formal education is higher than return to experience; (iii) gender wage gap persists in Vietnamese society even in the industry characterized by female over-representation like T&G; (iv) there is a sign of wage gaps between migrants and non-migrants in 2001; and (v) occupational choice has increasingly exerted critical wage effects.
4. Conclusions and Policy Implications
The empirical work is an attempt to investigate quantitative dimension of not well-documented Vietnam labor market. Through exploring monetary benefits of workers in the high growth industry, it is possible to assess on-going government’s regulation on the labor market. Such assessments have become important to recommend policies.
In fact, the growth of light industry brings monetary benefits to them. As evidence, human capital investments have been compensated. This is an encouraging sign implying that earnings are more inclined to reflect truly price of labor in the market. In this sense, an expansion of trade with respect to the line of Vietnam’s comparative advantage should have positive impacts on their laborers. Therefore, further exploitation of its comparative advantage not only promises higher economic growth but also poverty reduction via significant employment effects in terms of monetary compensation.
Also, it is noted that labor-intensive industries can absorb workers with primary education, providing employment opportunities for the poor or near -poor. However, light manufacturing firms are mostly located in urban area or large cities. Therefore, government should further liberalize labor market in order to help the poor and near-poor can access employment opportunities offered by light industry. Firstly, loosening regulation on geographic labor mobility might be a good solution. Secondly, access to information on job opportunities should be of great concerns. In fact, it is argued that friends and relatives are the most effective channel of information in the labor market so far, and personal recommendation may influence the job decision of firms (Nguyen, 2002). It is a challenge to job creation and unemployment reduction across regions, particularly in the periphery. Then, policies should focus on creation and control of effective information channels in the labor market. Naturally, state and non-state job centres are expected to play much more effective information channels in the labor market.
The gender wage gap in light industry might not ask for government’ s economic policy to deal with. Instead, the important contribution of environment gap to the unexplained implies that changing social norms is more important. It is difficult task because the Confucian ideology has been rooted in Vietnam society. It is important to note that government should focus on two main points: (i) breaking “glass ceiling”, and (ii) solving occupational segregation. To attack these subtle barriers, government should lead by example and strengthen enforcement of laws. In addition, education system and mass media should be considered as effective tools to direct social perception towards more gender equality.
