The Educational Attainment of Second-Generation Mainland Chinese Immigrants in Taiwan

Wen-Jen Tsay ∗

This draft: June 2005 Abstract The social status and well-being of political immigrants’ children are seldom touched upon in the literature. This paper focuses on the impact of refugee experience on the relative educational attainment of second-generation immigrants in Taiwan. In contrast with the results in van Ours and Veenman (2003) and Riphahn (2003) who showed that second-generation immigrants lag behind their native counterparts, this paper’s principle finding is that the father’s immigration status can help his children achieve a higher educational qualification than native Taiwanese after controlling the relevant determinants of educational attainment, including parental background and the neighborhood where the interviewee grows up. In addition, women born in the earlier cohort benefit more by their fathers’ immigration status than their male counterparts do. However, Taiwanese schooling advances across generations are impressive, whereby the gap in schooling attainment between second-generation immigrants and native Tai- wanese is found to decline over time.

JEL classiﬁcation: J24, J62, I21

Key words: Second-generation immigrants, educational attainment, political immigrants, language assimilation

∗Tsay: Institute of Economics, Academia Sinica. 128 Academia Road, Sec. 2, Taipei, TAIWAN. Correspondence: [email protected]. We would like to thank An-Chi Tung for generously providing the data about the decomposition of Taiwan’s population and Chi-Hung Lin for her excellent research assistance. We are also grateful for the valuable comments and suggestions of two anonymous referees and the Editor, Klaus F. Zimmermann.

1 I Introduction

It is well known that formal educational qualifications are crucial to life- time labor market success, and the economic mobility of immigrants depends heavily on their human capital investment. Chiswick (1988), Borjas (1992), and Leslie and Drinkwater (1999) conducted interesting studies on the difference in educational attainment between immigrants and natives. Since second-generation immigrants constitute an increasing share of many countries’ population, there is an expanding literature on the educational attainment of second-generation immigrants.1 Most of the studies concerning second-generation immigrants share a common feature, i.e., they typically fail to distinguish between the cause of immigration. As documented in Borjas (1982, p.345), the determinants of the immigration decision are the same, but the consequences of immigration are likely to be different between political refugees and economic immigrants. Brenner and Kiefer (1981) argued that the refugee experience alters an individual’s perceptions of the value of general and specific human capital. This led Borjas (1982, p.346) to add that: Thus the refugee experience is likely to make clear to the individual the importance of general human capital investments in providing flexibility for dealing with unexpected political changes.2 Based on the preceding arguments, ceteris paribus, a refugee is likely to invest more in general human capital or formal education than someone without a similar experience. This conjecture is supported by Borjas (1982) who demonstrated that the economic success of Cuban (political) immigrants over that of other Hispanic (economic) counterparts is partly due to the fact that the Cuban immigrants have invested more in U.S. schooling than other Hispanic groups. As Borjas (1982) did not confine his investigation to second-generation immigrants, we have no idea about the intergenerational transmission of the

2 refugee experience. In this paper we fill the gap in the literature about the educational attainment of second-generation political immigrants. To what extent the parental refugee experience has an effect on children’s educational attainment is our major concern and is investigated through Taiwanese data. There are several advantages of using Taiwanese data. First, since 1949 when Chiang Kai-Shek’s forces were defeated by the People’s Liberation Army on mainland China, over one million mainland Chinese emigrated to Taiwan. The 1949 failure of Chiang Kai-Shek’s forces can be viewed as a clearly-defined dating point to distinguish who are first-generation immigrants.3 Secondly, an anonymous referee points out the finding documented in Hatton (2004) that the twenty-fold increase in asylum seekers/refugees over the past three decades in the European Union urgently calls for studies on the welfare of political immigrants. As compared to the three-decade history of the asylum seekers/refugees in the European Union, we have more scope to collect information about the socio-economic characteristics of political immigrants’ children from the five-decade history of mainland Chinese immigrants in Taiwan. Finally, unlike other second-generation studies involving several ethnic groups, the political immigrants in Taiwan are from the same source, mainland China. The cultural difference between these immigrants and native Taiwanese is much less than that observed in other second-generation immigrant studies.4 The similar cultural background faced by the mainland Chinese immigrants and native Taiwanese is unique and cannot be observed in the data used by Borjas (1982). Before discussing the dataset used for our study in the next section, let us point out that we cannot distinguish whether first-generation mainland Chinese immigrants came to Taiwan for political or economic causes, although we know that the share of economic immigrants among these first- generation immigrants is very small. Indeed, we can say that they are all

3 political refugees no matter whether they moved to Taiwan for economic or political purposes in that all of them were unable to go back to mainland China freely after 1949. The situation faced by them is almost identical to Cuban immigrants in the U.S. after Fidel Castro took control of Cuba in the 1960s. Therefore, we only consider the data for interviewees who were born after 1949 so as to facilitate an accurate analysis about the impact of the refugee experience on the relative educational attainment of Taiwan-born second-generation immigrants. Accordingly, in contrast with van Ours and Veenman (2003) and Riphahn (2003) who compared second-generation economic immigrants with their corresponding native counterparts, this paper assesses the relative educational attainment of second-generation political immigrants to native Taiwanese. The remainder of this paper is arranged as follows. In Section 2 we discuss the data and the summary statistics of second-generation immigrants and those of native Taiwanese. The econometric specification and the variable definitions are presented in Sections 3. In Section 4 we discuss the empirical results generated from the econometric analysis. The robustness of empirical findings is considered in Section 5. Section 6 concludes.

II Data and Summary Statistics

After the end of World War II when Japan returned control of Taiwan to the Republic of China (R.O.C.), many mainland Chinese moved to Taiwan for either political or economic reasons. At the end of 1946, the share of immigrants among Taiwan’s civilian population was 0.52 percent, rising to 1.87 percent at the end of 1948. After Chiang Kai-Sheik lost control of mainland China, the number of immigrants increased tremendously in Tai- wan, as immigrants jumped to 5.63 percent of Taiwan’s civilian population by the end of 1949. If we take the 600,000 military personnel into account, then at the end of 1949 roughly 12.71 percent of Taiwan residents were from

4 mainland China.5 Before proceeding with our analysis, we have to be precise about the definition used in this paper. Adapting the definition used by Smith (2003), we define second-generation immigrants as those who were born in Taiwan after 1949 from at least one parent who came as a mainland Chinese immigrant. Under this set-up, the educational systems faced by second-generation immigrants and native Taiwanese are identical to each other. We thus have a better standing point in making a fair comparison between these two groups’ schooling success. To facilitate an accurate analysis on the relative educational attainment of Taiwan-born second-generation immigrants, we employ a recent survey in Taiwan, entitled Panel Study of Family Dynamics (PSFD), to conduct our empirical analysis. The data used in this paper are from the R-I 1999, R-I 2000, and R-I 2003 of PSFD.6 The number of observations for the R-I 1999, R-I 2000, and R-I 2003 is 994, 1959, and 1152, respectively, but only 2144 observations are included in our analysis.7 For ease of comparison, following the classification employed in van Ours and Veenman (2003), we divide the educational level into 5 categories:

Level 0 = No education. Level 1 = Primary education. Level 2 = Lower secondary education. Level 3 = Intermediate education. Level 4 = Higher education.

Based on the preceding ﬁve scale levels, in Table 1 we calculate the distribution of interviewees’ educational attainment. Among 1,908 children of native Taiwanese, 29.25 percent of them attained a Level 4 education, the highest level of education considered in this paper. However, 52.97 percent of second-generation immigrants achieved the same level of schooling.

5 When both parents are immigrants, the share of interviewees who stood at Level 4 increases to be 62.34 percent. Consequently, the results in Table 1 are completely opposite to those found in most ethnic minority studies. In particular, van Ours and Veenman (2003) documented that the educational level of immigrant groups is lower than that of native Dutch people, and Riphahn (2003) showed that German-born children of immigrants significantly lag behind those of German natives in educational attainment. How- ever, Card, DiNardo, and Estes (1998) presented that second-generation children continue to achieve a higher educational qualification than children of comparable U.S.-born parents. Although the preceding results concerning the educational attainment of second-generation immigrants in different countries are mixed, all the above-mentioned studies are intrinsically different from ours, because they do not focus on the educational attainment of second-generation political immigrants as we do. To take a closer look at the distribution of second-generation immigrants’ average educational level, in Table 2 we divide the interviewees into 6 groups by their birth cohorts and gender. Similar to Table 1, Table 2 indicates that second-generation immigrants are better-educated than native Taiwanese. When both parents are from mainland China, the schooling success of second-generation immigrants is even more revealing, no matter whether the interviewees are males or females. However, Taiwanese schooling advances across generations are impressive, because the gap in the average educational level between these two groups declines over time in Table 2. Since parental human capital has long been recogized as the most important element in determining children’s educational attainment,8 we thus present the average educational level of the interviewees’ parents in Table 3 to better understand the driving forces behind the schooling success of second-generation immigrants. For every combination of children’s birth co-

6 hort and parental gender considered in Table 3, first-generation immigrants indeed possess higher average educational levels than their native Taiwanese counterparts do. This finding is consistent with that found in Haveman and Wolfe (1995). Tables 1, 2, and 3 jointly suggest that there is a positive correlation between the educational attainment of first-generation immigrants and that of their children. However, as there are many other factors behind an individual’s educational attainment, a statistical analysis will shed more light on the intergenerational transmission of the refugee experience on children’s schooling success. The details of the statistical model are presented in the next section.

III Econometric Analysis and Variable Deﬁnitions

Following van Ours and Veenman (2003) and Riphahn (2003), we apply the ordered probit model to address the major theme of this paper:9 Do the preceding ﬁndings about the schooling performance of second-generation immigrants remain intact after controlling the relevant determinants of educational attainment including parental background? Are there changes in the gap of educational attainment between these two groups over time? We assume here that the attained level of education y∗ of individual i depends on his/her observed characteristics xi, i.e.,

∗ 0 yi = xiβ + i, (1) where i = N(0, 1) and is independently distributed across observations. We employ ﬁve education scales in describing an individual’s educational level

7 and thus  ∗  yi = 0 if y ≤ 0,  i  ∗  yi = 1 if y ∈ (0, µ1],  i  ∗ yi = 2 if yi ∈ (µ1, µ2], (2)   ∗  yi = 3 if yi ∈ (µ2, µ3],   ∗  yi = 4 if yi > µ3, where

µ2 = µ1 + 4µ2, µ3 = µ2 + 4µ3, 4µ2 > 0, and 4 µ3 > 0. (3)

Accordingly, we have  0  Prob(yi = 0) = Φ(−x β),  i  0 0  Prob(yi = 1) = Φ(µ1 − x β) − Φ(−x β),  i i  0 0 Prob(yi = 2) = Φ(µ2 − xiβ) − Φ(µ1 − xiβ), (4)   0 0  Prob(yi = 3) = Φ(µ3 − xiβ) − Φ(µ2 − xiβ),   0  Prob(yi = 4) = 1 − Φ(µ3 − xiβ).

Throughout this paper, the parameters of interests, β, µ1, 4µ2, and 4µ3, are jointly estimated by the maximum likelihood estimator (MLE) through the numerical optimization method. The explanatory variables selected in our analysis are listed in Table 4. To facilitate a comparison, these variables are chosen based on the discussion in Riphahn (2003, p.718). For the socio-demographic control variables, we consider an interviewee’s birth cohort and sex. For the parent characteristics variable, we include the educational attainment of parents so as to cover the loss of detailed information about the wealth of parents when the interviewee was born. The ages of parents when the interviewee was born are also included in the model, i.e., FatherAge and MotherAge. We conjecture that the variable FatherAge can capture the wealth of a family, because the wage or money-making ability of the father is likely to increase with his age by the

8 well known age-earning profile considered in the labor literature. Moreover, household jobs are traditionally handled by women in Chinese society, even though they may also participate in the labor market. Therefore, a mother plays a more important role over her husband in monitoring her children’s process of education. Consequently, we also expect a positive relationship between the variable MotherAge and the children’s educational attainment. To control the regional fixed effect on the transmission of human capital, we follow the suggestion of one anonymous referee to include a variable NeighborSize representing the 2003 population within the zip code where the interviewee spent most of his/her time by age 16.10 The reason that we do not adopt the population of Taiwan’s different 366 zip codes at the time of the survey is mainly due to the observation that education is provided when a person is a little child, and the transmission of human capital is especially important during early childhood. As a result, the information concerning a person’s early childhood will be much more useful than the one obtained at the time of the survey in controlling the effects of neighborhoods on the transmission of human capital. The 16-year time span of the period implicit in the variable Neighbor- Size forces us to use the population of different zip codes in a specific year for computing the value of NeighborSize. As mentioned previously, the cal- endar year chosen for computing NeighborSize is 2003 and seems arbitrary at first sight. Nevertheless, Taiwan is an old society and its history can be traced back to at least 800 years ago, and the clustering of the population into specific locations of Taiwan has been quite stable for a long time. In effect, the percentage change of the population within each zip code of Tai- wan is very small across recent years. Thus, the use of 2003’s population of different zip codes in constructing the variable NeighborSize should not be misleading in representing the size of neighborhoods where interviewees grew up. Based on the arguments in Borjas (1995, p.365), we expect a

9 positive relationship between the variable NeighborSize and the children’s educational attainment, because a larger value of NeighborSize fosters more social contacts and economic opportunities that affect people through their lives. According to the gender of the interviewee, the descriptive statistics for the variables in Table 4 are presented in Tables 5.1, 5.2, and 5.3, respectively. These descriptive statistics are pretty similar across samples, and demonstrate the quality of our dataset. The results in Tables 5.1, 5.2, and 5.3 also confirm the prediction of the referee that immigrants tend to live around urban centers as the average value of the variable NeighborSize is much larger for the immigrants than for the corresponding native Taiwanese. Furthermore, these tables show that a male interviewee on average attains a higher educational level than a female counterpart does. This phenomenon is widely perceived in traditional Chinese society and strongly supported by the estimation results obtained from several model specifications in the next section.

IV Empirical Results

Table 6 contains the estimation results using the total sample. The rela- tively robust variables are Coh50-59, FemaleSex, and NeighborSize which remain qualitatively intact across all three specifications considered in Ta- ble 6. Particularly, the educational attainment of Taiwanese improves over time, as we note that the coefficient of the variable Coh50-59 is significantly negative and the reference cohort is the latter birth cohort 1960-1976. This result is compatible with the observation that the average educational level from the 2,144 interviewees is 2.24, 2.92, and 3.25 for people born in the 1950-58, 1959-67, and 1968-76 cohort, respectively. Moreover, based on the t ratios associated with the variable FemaleSex in Table 6, other things being equal, a man achieves a better educated position than a woman. This result

10 corresponds closely to the finding that the average educational level from the 2,144 interviewees is 2.68 and 2.86 for the female and male respondants, respectively. In Table 6 we also find that an interviewee growing up in a larger neighborhood attains a higher educational level. This result is similar to that in Riphahn (2003) who find that those living in smaller cities have lower educational levels. The variables describing parental education are also robust variables in Table 6, and they are always significantly positive once they are included into the control variables. Furthermore, the hypothesis that parental education has no effect on children’s schooling attainment is strongly rejected with a likelihood ratio test using the estimation results in columns 2 and 3 of Table 6.11 Therefore, in the following discussions we will not consider the results in column 2 of Table 6 where the coefficients of the parental education variables are set to be zero. Among the remaining two specifications in Table 6, we divide the discussions into two cases based on whether we assume the effects of parental immigration status on offsprings’ educational attainment remain constant across children’s birth cohorts.

4.1 Model with ﬁxed parental immigration eﬀect across children’s birth cohorts

Column 3 of Table 6 presents the estimation results when the influence of parental immigration status on offsprings’ schooling success is assumed to be fixed across children’s birth cohorts. We find that the immigration status of a father and that of a mother are both positively related with their children’s educational level even though we control the socio-economic characteristics of the interviewee. Other than the social contacts and cultural factors pertaining to the difference between native Taiwanese and immigrants’ families,12 the preceding findings are consistent with the psycholog-

11 ical interpretation suggested by Reuben Kessel, Brenner and Kiefer (1981), and Borjas (1982) who argued that the refugee experience of parents induces them to invest more mobile human capital on their children. In addition to the aforementioned psychological interpretation, we propose another insti- tutional interpretation to answer why parental immigration status can help children attain a higher educational level. This second explanation is more related to the political environment in Taiwan. It is well known that language assimilation is an important factor to the schooling success of immigrants. Before the end of World War II, Taiwanese were required to speak and learn Japanese, even though some of them were still learning the Chinese writing system and literature.13 After the end of World War II, formal education in Taiwan was ordered to be given under the official language of mainland China, Mandarin Chinese. Under this cir- cumstance, language assimilation became a serious problem to most native Taiwanese. Obviously, the value of education obtained by Taiwanese during the ruling era of Japan is likely to be discounted when the language system was exogenously changed.14 Consequently, parental immigration status might capture the effect of the exogenous change in the language system and explain why we observe the better educational performance of second- generation immigrants over native Taiwanese. The effects of the conventional psychological interpretation and the language assimilation problem of native Taiwanese proposed in this paper on the relative educational attainment of second-generation political immigrants are compounded together, and there exists no easy way to disentangle these two effects cleanly. However, based on the preceding arguments, there is a common feature shared by these two interpretations, i.e., they both pre- dict a positive relationship between parental immigration status and their children’s educational attainment. This implies that the impact generated from the sudden language change and the insecurity feeling on children’s

12 educational attainment will be mitigated over time, if we can find that the effect of parental immigration status on children’s schooling success is significant only for the interviewee born in the earlier cohort. In fact, this is exactly what we observe in the last column of Table 6 and discuss in the next subsection. Column 3 of Table 6 also reveals that an interviewee’s schooling success is both positively related to his/her mother’s and father’s ages when the interviewee was born. However, the variable MotherAge is not significant at the 0.05 level in a two-tailed test, and the relationship between an interviewee’s educational level and his/her father’s age when he/she was born is even weaker. Our explanation is that the effect of the variable FatherAge for proxying the wealth of a family has already been captured by the variables describing a father’s educational level, which is widely recognized as a good proxy for a family’s resources. Similarly, the effect of the variable MotherAge for proxying earning income in the market and engaging in home production has been captured by a mother’s educational attainment. In the next subsection we also find that both FatherAge and MotherAge are not significant even at the 0.10 level in a two-tailed test under a more flexible specification.

4.2 Model with varying parental immigration eﬀect across children’s birth cohorts

In this subsection we consider whether the eﬀect of refugee experience has changed over time. As mentioned previously, this question is important to test whether the impact generated from the sudden language change and the insecurity feeling on the children’s educational attainment will eventually fade away. The outcome of our investigation also sheds new light on the studies concerning the asylum seekers/refugees in the European Union and globally. To address this question, we operationally add two additional

13 interaction terms of parental immigration status and children’s birth cohort into the model implicit in column 3 of Table 6. The estimation results are illustrated in column 4 of Table 6, where native male Taiwanese born in the latter cohort, 1960-1976, make up the referee group for comparison. Before discussing the empirical results from the more flexible specification, let us point out that, based on the likelihood ratio statistic, the hypothesis that immigration status has an identical impact on second-generation immigrants’ schooling success over time is rejected at the 0.01 level of sig- nificance in a one-tailed χ2 test with 2 degrees of freedom.15 Accordingly, the model implicit in column 4 of Table 6 is most appropriate in describing the educational attainment of Taiwanese among the three specifications considered in Table 6. Based on the t statistics associated with the four variables, FatherMain, MotherMain, Coh50-59×FatherMain, and Coh50-59×MotherMain, we have two observations: Firstly, we find no significant difference between native Taiwanese and second-generation immigrants when the interviewees were born in the latter cohort, 1960-76. Secondly, second-generation immigrants achieve a significantly better-educated position only for those born in the earlier cohort and whose father also holds an immigration status at the same time. To permit a visual interpretation of the magnitude of interaction effects, we follow the suggestion of an anonymous reader to evaluate the marginal effect of the variable Coh50-59×FatherMain on the educational attainment of Taiwanese using the procedure suggested in Greene (2000, p.879). For the interviewee born in the earlier cohort and when his/her father holds an immigration status, the probability for such a second-generation immigrant to attain a college level of education is 25.15% higher relative to the one who is not in the same group. This magnitude of change that results when the variable takes its two different values with other variables held at their sample means is not trivial, and it is consistent with the findings

14 generated from column 4 of Table 6. These two observations above can be similarly explained by the above- mentioned refugee experience of immigrants and language assimilation problems of native Taiwanese, but with little modification. For the first observation, adapting the arguments of Borjas (1982, p.346) we note that the incentives for parents to invest general human capital on their children is strong when the feeling of insecurity is fresh, while the feeling of insecurity gradually weakened as Taiwan survived from the threat of the People’s Lib- eration Army. Moreover, the loss of parental human capital resulting from the sudden change of a language system is most relevant to the native Tai- wanese born near the time of change. Again, as time goes by, most native Taiwanese adapted to the new system gradually and the discount in the value of their education is subsequently mitigated. Since parental immigration status captures both the effect of the exogenous change in the language system and the insecurity feeling inherent in an immigrant’s family, no wonder a significant difference between native Taiwanese and second-generation immigrants only presents itself when the interviewees were born in the earlier cohort. The first aforementioned observation is also interesting in its own right, i.e., it indicates that the intergenerational transmission of refugee experience does not last forever, and the children of political refugees gradually assimilate into the society where they live. Most importantly, the first observation provides an additional support of the idea that the asylum seekers/refugees resulting from armed conflicts can be admitted on humanitarian grounds, because eventually their children assimilate into the host society. Please see Zimmermann (1995) and Hatton (2004) on the debates concerning different policy regimes for asylum seekers. We now modify the aforementioned refugee experience of immigrants and language assimilation problems of native Taiwanese to decipher the second

15 observation that only a father’s immigration status is of significant value in improving his children’s educational attainment. First of all, similar to the assumption adopted in Gang and Zimmerman (2000, p.557) in explaining that the father’s education has a more positive impact than the mother’s education on children’s educational attainment in German, in a traditional Chinese family we also note that the father’s effect on the educational attainment of his children is mostly through the income he earns in the market, while mothers can affect their children’s educational attainment both by earning income in the market and by directly engaging in home production. As a result, the loss of a mother’s human capital resulting from the change of a language system is not as important as the loss of a father’s human capital to his offspring’s educational attainment when we apply the result implied in the Beckerian allocation-of-time model adopted by Gang and Zimmermann (2000). Secondly, the refugee experience of the father has a much stronger impact on his children’s educational attainment than that of the mother, because the father is known to be the major decision maker of a traditional Chinese family. Again, the preceding second observation can be explained by the refugee experience of immigrants and language assimilation problems of native Taiwanese with a little modification.

V Sensitivity Analysis

In this section we first check the robustness of the results in column 4 of Table 6 by relaxing the constraint that the effects of the control variables on the dependent variable are the same across different genders of interviewees. To achieve this goal, we apply the procedure described in Greene (2000, p.288 and p.289) by allowing the coefficients β in (1) to be different across gender groups, i.e.,  ∗   0      yi,f xi,f 0 βf i,f   =     +   , (5) ∗ 0 yi,m 0 xi,m βm i,m

16 where the subscripts f and m denote female and male, respectively, and both xi,f and xi,m contain all the variables in the last column of Table 6, except the variables FemaleSex, µ1, 4µ2, and 4µ3. A Wald statistic is employed to test the following hypothesis,

H0 : βf = βm, (6) using the critical values obtained from a χ2 distribution with 17 degrees of freedom, because 17 restrictions are required to test the hypothesis in (6). Since the resulting Wald statistic is 61.6130 with a corresponding p value less than 0.0001, we shall replicate our analysis for female and male samples separately, in order to check the robustness of the findings presented in the last section. The estimation results for the female and male samples are displayed in Table 7. On the one hand, Table 7 shows that the testing results concerning the variables FatherAge, MotherAge, Coh50-59, FatherMain, MotherMain, and the ones characterizing parental education levels remain qualitatively intact as we find for the total sample in Table 6. Accordingly, the robustness of the findings concerning these variables in column 4 of Table 6 is further enhanced. On the other hand, Table 7 provides us a different picture about the impact of a father’s immigration status on his children’s schooling success which cannot be revealed by the total sample. That is, a woman born in the earlier cohort is found to benefit significantly by her father’s immigration status, but a father’s immigration status is no longer of significant value to his son’s schooling success. After computing the marginal effect of the variable Coh50-59×FatherMain for the female sample as we have done for Table 6, we find that the probability for a second-generation immigrant to attain a college degree is 40.92% higher if she was born in the earlier cohort as compared to one who is outside of this group. However, other things being equal, the marginal effect of the variable Coh50-59×FatherMain for

17 the male sample is only 7.92%. Thus, the results from the marginal effect and the statistical test coincide with each other. Furthermore, Table 7 shows that a woman benefits significantly by growing up in a larger society, but the size of the neighborhoods where a man spent his childhood is not that important to his educational attainment. Why does a woman born in the earlier cohort benefit by her father’s immigration status? Why can a woman achieve a higher educational level if she is raised in a larger society? Why is a man’s educational attainment not affected by his father’s immigration status and the size of the neighborhoods where he grew up? To explain these three phenomena, first note that the teaching of Confucius (551-479 B.C.) is strongly rooted in Chinese society, and Weber (1951, p.107) recognized that education in China is the yardstick of social prestige in a most exclusive fashion. Combined with the well known son-preference effect prevalent in Chinese society, a traditional Chinese family tries very hard to encourage their male offsprings to pursue schooling success even though their economic status is not that well. Therefore, the loss of parental human capital resulting from the change of a language system will not have too much impact on a son’s educational attainment, because the native Taiwanese family tries to compensate for this loss, no matter if they live in an urban center or not. That explains why we cannot find a significant relationship between a male’s educational attainment and his father’s immigration status or his neighborhoods. The preceding arguments also imply that the probability for a woman in a traditional Chinese family to pursue education is strongly related to her family resources, mainly through the son-preference effect prevalent in Chinese society. Since a woman raised in a larger society indicates that her family might have better economic opportunities than the one who grows up in a smaller society, no wonder a woman can achieve a higher educational level when she grows up in a larger society. Moreover, as shown previously,

18 the father’s immigration status represents an extra advantage to an immigrant’s family member born in the earlier cohort, no matter if it is due to a change of language or a feeling of insecurity. This argument explains why we observe that the female second-generation immigrants born in the earlier cohort benefit by their fathers’ immigration status. The preceding findings hinge on the set-up that we divide interviewees into two cohorts, i.e., 1950-1959 and 1960-1976. The sensitivity of these estimation results is also investigated via a different division of cohorts, 1950-1962 and 1963-1976. The estimation results show that all the preceding findings concerning the variables in Tables 6 and 7 remain intact qualitatively. To further confirm the robustness of our empirical results, we replicate the estimation procedures in Table 7, but based on a much finer division of cohorts, i.e., we divide the interviewees into five cohorts, 1950- 54, 1955-59, 1960-64, 1965-70, and 1971-1976. We want to check whether the preceding findings hold only when we divide the observations into two cohorts. The estimation results are presented in Table 8 and show that all the preceding findings concerning the variables in Table 7 remain intact qualitatively. Most interestingly, only a woman born in the earliest cohort, 1950-1954, benefits by her father’s immigration status, and a man is not significantly influenced by his family background, except for his parental educational attainment.

VI Conclusions

In contrast with van Ours and Veenman (2003) and Riphahn (2003) who compared second-generation economic immigrants with the corresponding native counterparts, this paper fills the gap in the literature to consider the relative educational attainment of second-generation political immigrants to native Taiwanese. We find that the father’s immigration status can help his children achieve a higher educational qualification than native Taiwanese

19 after controlling the relevant determinants of educational attainment, including parental background. Other than the social contacts and cultural factors pertaining to the difference between native Taiwanese and immigrants’ family, this phenomenon is compatible with the psychological interpretation which predicts that the refugee experience induces parents to invest more mobile human capital on their children and confirms the conjecture of Reuben Kessel cited by Stigler and Becker (1977, p.76), and that of Brenner and Kiefer (1981) as well as the arguments in Borjas (1982). An- other possible explanation is the sudden and exogenous change of the language system as Japan returned control of Taiwan to the R.O.C., because language assimilation became a serious problem to most native Taiwanese at that time. However, the gap in schooling attainment between second- generation immigrants and native Taiwanese is found to decrease over time, which is consistent with the declining feeling of insecurity as Taiwan survived on its own during post-World War II and native Taiwanese adapted gradually to the new educational system. This observation also indicates that the intergenerational transmission of refugee experience does not last forever, and the children of political refugees gradually assimilate into the society where they live. Most importantly, it provides an additional support of the idea that the asylum seekers/refugees resulting from armed conflicts can be admitted on humanitarian grounds, because eventually their children assimilate into the host society. Finally, women born in the earlier cohort are found to benefit by their fathers’ immigration status, but we cannot find such a significant relationship between fathers’ immigration status and their sons’ educational attainment, even though the males were also born in the earlier cohort.

20 Endnotes

1. Please see Riphahn (2003), van Ours and Veenman (2003), and the references therein.

2. Similarly, Stigler and Becker (1977, p.76) also cited the explanation of Reuben Kessel about why there are few Jews in farming: Since Jews have been persecuted so often and forced to ﬂee to other countries, they have not invested in immobile land, but mobile human capital — business skills, education, etc. — that would automatically go with them. . . .

3. There are some mainland Chinese who moved to Taiwan before 1949, but not many. For example, they only consisted of 0.52 percent of Taiwan’s civilian population at the end of 1946.

4. For example, the cultural diﬀerence between mainland Chinese immigrants and native Taiwanese is much less than that between Turkish immigrants and native Dutch people considered in van Ours and Veen- man (2003).

5. Data are from the Statistical Abstract of Taiwan Province, Republic of China, 1971. The data show that, at the end of 1949, there are 250,788 male and 165,909 female mainland Chinese immigrants among the 7,396,931 civilians in Taiwan. In fact, female immigrants only consisted of 2.07% of Taiwan residents at the end of 1949, provided we assume all the 600,000 military personnel are males. As a result, many male mainland Chinese immigrants had to marry native Taiwanese, if they want and could. Moreover, it is well known in Taiwan that many of the 600,000 military personnel remained single for the rest of their lives. These statistics explain why in Table 1 we ﬁnd only 77 individuals with both parents born on the mainland China. Indeed,

21 there are only 82 mothers who were born in mainland China. These statistics also explain why only 10.77% of fathers are from mainland China, and the share of mothers who are from mainland China is only 3.82% based on the 2,144 observations used for our empirical studies.

6. The survey data are the output of an attempt to develop a unique panel dataset in Chinese society. These data have been used in Wen-Jen Tsay and C.Y. Cyrus Chu (2005) to investigate the fertility behavior of married Taiwan women. The letter “R” represents respondant, and R-I 1999 denotes the ﬁrst of a sequence of surveys conducted in 1999 for a group of respondants born between 1953 and 1962. We have another two groups of respondants surveyed in 2000 and 2003, and their data are contained in R-I 2000 and R-I 2003, respectively. The respondants for R-I 2000 were born between 1933 and 1952, while the respondants for R-I 2003 were born between 1964 and 1976. Note that we do not have R-I 2001 and R-I 2002, but we do have R-II 2000 which represents the second of a sequence of surveys conducted in 2000 for the same group of respondants for R-I 1999, i.e., R-II 2000 is the ﬁrst follow-up of R-I 1999. For details, please see http://psfd.sinica.edu.tw.

7. The procedure for selecting the observations is mainly based on an exogenous historic event to distinguish who are ﬁrst-generation immigrants, i.e., we only include the individuals who were born in Taiwan after 1949, and they are not the third generation of mainland Chinese immigrants. In so doing, the number of observations decreases from 4,105 to be 2,730. Since all the data are asked retrospectively, we do not include the data of interviewees when the record is missing or incomplete as van Ours and Veenman (2003, p.744) used a net sample that contains information about the education of children and the education of their parents. As a result, the number of observations further reduces from 2,730 to be 2,144.

22 8. Please see Haveman and Wolfe’s (1995) overview about the determinants of children’s educational attainment.

9. Please see van Ours and Veenman (2003) and Riphahn (2003), or Greene (2000) for the details of ordered probit models.

10. The data about the population of each zip code are from Department of Household Registration Affairs, Ministry of the Interior, 2004. More- over, one anonymous referee kindly suggests that immigrants tend to concentrate in particular areas, which are typically found in urban centers, and this could have an effect on the transmission of human capital as documented in Borjas (1995) who used the 1/100 Neigh- borhood File of the 1970 Public Use Sample of the U.S. Census and a specially designed version of the National Longitudinal Surveys of Youth (NLSY) which groups workers into 1,978 zip codes. Moreover, the referee points out the findings in Hendricks (2004) showing that there can be considerable spatial differences in educational attainment within a country, and those in Riphahn (2003) who used two dummies for the size of city where the interviewee lives at the time of survey to control the regional fixed effect.

11. The likelihood ratio statistic is 2×(2812.3111−2590.6769) = 443.2684, and it is signiﬁcant at the 0.01 level in a one-tailed χ2 test with 8 degrees of freedom.

12. For example, please see the schooling ambitions, career planning, and orientation on return migration as mentioned in van Ours and Veen- man (2003).

13. Under the ruling era of Japan, it was not legal for Taiwanese to publicly learn a traditional Chinese education.

23 14. For research on the economic value of language, please see Angrist and Lavy (1997) and the references therein.

15. The likelihood ratio statistic is 2 × (2590.6769 − 2582.7866) = 15.7806.

24 References

Angrist, Joshua D. and Lavy, Victor. “The eﬀect of a change in language of instruction on the returns to schooling in Morocco,” Journal of Labor Economics, January 1997, 15(1), pp. S48-S76.

Borjas, George J. “The earnings of male Hispanic immigrants in the United States,” Industrial and Labor Relations Review, April 1982, 35(3), pp. 343-353.

Borjas, George J. “Ethnic capital and intergenerational mobility,” Quar- terly Journal of Economics, February 1992, 107(1), pp. 123-150.

Borjas, George J. “Ethnicity, neighborhoods, and human-capital exter- nalities,” American Economic Review, June 1995, 85(3), 365-390.

Brenner, Reuven and Kiefer, Nicholas M. “The economics of Dias- pora: discrimination and occupational structure,” Economic Develop- ment and Cultural Change, April 1981, 29(3), pp. 517-534.

Card, David, DiNardo, John and Estes, Eugena. “The more things change: Immigrants and the children of immigrants in the 1940s, the 1970s, and the 1990s.” National Bureau of Economic Research (Cam- bridge, MA) Working Paper No. 6519, May 1998.

Chiswick, Barry R. “Diﬀerences in education and earnings across racial and ethnic groups: Tastes, discrimination, and investments in child quality,” Quarterly Journal of Economics, August 1988, 103(3), pp. 571-597.

Gang, Ira N. and Zimmermann, Klaus F. “Is child like parent? Edu- cational attainment and ethnic origin,” Journal of Human Resources, Summer 2000, 35(3), pp. 550-569.

25 Greene, William H. Econometric Analysis. New York: Prentice Hall, 2000.

Haveman, Robert and Wolfe, Barbara. “The determinants of children’s attainments: a review of methods and ﬁndings,” Journal of Economic Literature, December 1995, 33(4), pp. 1829-1878.

Hendricks, Lutz. “Why does educational attainment diﬀer across U.S. states?,” CESIFO Working Paper No. 1335.

Hatton, Timothy J. “Seeking asylum in Europe,” Economic Policy, April 2004, 5-62.

Leslie, Derek and Drinkwater, Stephen. “Staying on a full-time education: Reasons for higher participation rates among ethnic minority males and females,” Economica, February 1999, 66(261), pp. 63-77.

Riphahn, Regina T. “Cohort eﬀects in the educational attainment of second generation immigrants in Germany: An analysis of census data,” Journal of Population Economics, November 2003, 16(4), pp. 711-737.

Smith, James P. “Assimilation across the Latino generations,” American Economic Review, May 2003, 93(2), pp. 315-319.

Statistical Abstract of Taiwan Province, 1946-1967. Republic of China, 1971.

Stigler, George J. and Becker, Gary S. “De Gustibus non est dis- putandum,” American Economic Review, March 1977, 67(2), pp. 76- 90.

Tsay, Wen-Jen and Chu, C.Y. Cyrus. “The pattern of birth spac- ing during Taiwan’s demographic transition,” Journal of Population Economics, 2005, forthcoming.

26 Van Ours, Jan C. and Veenman, Justus. “The educational attainment of second generation immigrants in the Netherlands,” Journal of Population Economics, November 2003, 16(4), pp. 739-753.

Weber, Max. The Religion of China Confucianism and Taoism, trans- lated and edited by Hans H. Gerth. The Free Press, 1951.

Zimmermann, Klaus F. “Tackling the European migration problem,” Journal of Economic Perspectives, Spring 1995, 9(2), pp. 45-62.

27 Table 1. Educational Level of Children by Parental Group

# of parents who are mainland 0 ≥ 1 2 immigrants

Education level of child

Level 0 52 (2.73) 0 (0.00) 0 (0.00) Level 1 313 (16.41) 3 (1.27) 1 (1.30) Level 2 355 (18.61) 19 (8.05) 4 (5.19) Level 3 630 (33.02) 89 (37.71) 24 (31.17) Level 4 558 (29.25) 125 (52.97) 48 (62.34)

Total 1908 (100.00) 236 (100.00) 77 (100.00)

Notes: Numbers in parentheses are the percentages of interviewees belonging to the corresponding category.

28 Table 2. Average Educational Level of Children by Gender and Birth Cohort

# of parents Female Male who are mainland 0 ≥ 1 2 0 ≥ 1 2 immigrants

Birth year of child

1950-58 1.90 (369) 3.42 (33) 3.44 (25) 2.36 (362) 3.43 (37) 3.54 (26) 1959-67 2.76 (259) 3.30 (43) 3.80 (5) 2.89 (296) 3.71 (45) 4.00 (8) 1968-76 3.34 (288) 3.42 (36) 3.67 (3) 3.17 (334) 3.24 (42) 3.30 (10)

Total 2.60 (916) 3.38 (112) 3.52 (33) 2.79 (992) 3.47 (124) 3.57 (44)

Notes: Numbers in parentheses are the number of observations belonging to the corresponding category.

29 Table 3. Average Educational Level of Parents by Children’s Birth Cohort

Father Mother Immigrant Native Immigrant Native

Birth year of child

1950-58 2.16 0.90 1.40 0.47 1959-67 2.41 1.22 2.31 0.70 1968-76 2.29 1.57 2.25 1.09

Total 2.30 1.21 1.71 0.75

30 Table 4. Variable Deﬁnitions

V ariable

Education Level 0 (reference) No education Level 1 Primary education Level 2 Lower secondary education Level 3 Intermediate education Level 4 Higher education

FatherAge Father’s age when interviewee was born

MotherAge Mother’s age when interviewee was born

FemaleSex = 1 if interviewee is a female

FatherMain = 1 if interviewee’s father is a mainland Chinese immigrant

MotherMain = 1 if interviewee’s mother is a mainland Chinese immigrant

NeighborSize The (population/10000) of the zip code where the interviewee spent most of his/her time by age 16

CohAB-CD = 1 if interviewee was born between 19AB and 19CD = 0 otherwise

31 Table 5.1. Characteristics of the Variables for the Total Sample

Natives Immigrants

# of Observations 1908 (%) 236 (%)

Personal education Level 0 (reference) 52 (2.73) 0 (0) Level 1 313 (16.40) 3 (1.27) Level 2 355 (18.61) 19 (8.05) Level 3 630 (33.02) 89 (37.71) Level 4 558 (29.25) 125 (52.97) Father’s education Level 0 (reference) 476 (24.95) 24 (10.17) Level 1 941 (49.32) 55 (23.31) Level 2 205 (10.74) 43 (18.22) Level 3 187 (9.80) 57 (24.15) Level 4 99 (5.19) 57 (24.15) Mother’s education Level 0 (reference) 864 (45.28) 70 (29.66) Level 1 819 (42.92) 96 (40.68) Level 2 125 (6.55) 28 (11.86) Level 3 79 (4.14) 29 (12.29) Level 4 21 (1.10) 13 (5.51) Mean of FatherAge 30.68 37.69 Mean of MotherAge 27.29 26.33 NeighborSize 11.15 17.75 Coh50-59 731 (38.31) 70 (29.66)

Notes: Numbers in parentheses are percentages.

32 Table 5.2. Characteristics of the Variables for the Female Sample

Natives Immigrants

# of Observations 916 (%) 112 (%)

Personal education Level 0 (reference) 38 (4.15) 0 (0) Level 1 188 (20.52) 1 (0.89) Level 2 130 (14.19) 10 (8.93) Level 3 309 (33.73) 47 (41.96) Level 4 251 (27.40) 54 (48.21) Father’s education Level 0 (reference) 220 (24.02) 13 (11.61) Level 1 454 (49.56) 28 (25.00) Level 2 108 (11.79) 18 (16.07) Level 3 92 (10.04) 27 (24.11) Level 4 42 (4.59) 26 (23.21) Mother’s education Level 0 (reference) 404 (44.10) 36 (32.14) Level 1 404 (44.10) 42 (37.50) Level 2 72 (7.86) 14 (12.50) Level 3 28 (3.06) 14 (12.50) Level 4 8 (0.87) 6 (5.36) Mean of FatherAge 30.28 37.38 Mean of MotherAge 26.98 26.13 NeighborSize 11.12 16.84 Coh50-59 369 (40.28) 33 (29.46)

Notes: Numbers in parentheses are percentages.

33 Table 5.3. Characteristics of the Variables for the Male Sample

Natives Immigrants

# of Observations 992 (%) 124 (%)

Personal education Level 0 (reference) 14 (1.41) 0 (0) Level 1 125 (12.60) 2 (1.61) Level 2 225 (22.68) 9 (7.26) Level 3 321 (32.36) 42 (33.87) Level 4 307 (30.95) 71 (57.26) Father’s education Level 0 (reference) 256 (25.81) 11 (8.87) Level 1 487 (49.09) 27 (21.77) Level 2 97 (9.78) 25 (20.16) Level 3 95 (9.58) 30 (24.19) Level 4 57 (5.75) 31 (25.00) Mother’s education Level 0 (reference) 460 (46.37) 34 (27.42) Level 1 415 (41.83) 54 (43.55) Level 2 53 (5.34) 14 (11.29) Level 3 51 (5.14) 15 (12.10) Level 4 13 (1.31) 7 (5.65) Mean of FatherAge 31.05 37.98 Mean of MotherAge 27.57 26.52 NeighborSize 11.18 18.57 Coh50-59 362 (36.49) 37 (29.84)

Notes: Numbers in parentheses are percentages.

34 Table 6. Educational Attainment of Children in Taiwan

Total Sample

Parental education Father Level 1 - 0.6044 (9.010)∗∗ 0.6020 (8.954)∗∗ Level 2 - 0.8029 (8.150)∗∗ 0.7978 (8.061)∗∗ Level 3 - 1.1608 (11.320)∗∗ 1.1439 (11.158)∗∗ Level 4 - 1.1759 (8.139)∗∗ 1.1967 (8.269)∗∗ Mother Level 1 - 0.4225 (6.899)∗∗ 0.4223 (6.896)∗∗ Level 2 - 0.7352 (5.839)∗∗ 0.7262 (5.782)∗∗ Level 3 - 0.6302 (4.671)∗∗ 0.6599 (4.893)∗∗ Level 4 - 1.0218 (4.526)∗∗ 1.0778 (4.648)∗∗ FatherAge -0.0037 (0.649) 0.0029 (0.486) 0.0054 (0.899) MotherAge -0.0033 (0.501) 0.0124 (1.811)∗ 0.0102 (1.463) FemaleSex -0.1574 (3.290)∗∗ -0.1967 (4.038)∗∗ -0.1994 (4.085)∗∗ FatherMain 0.5100 (4.378)∗∗ 0.2959 (2.448)∗∗ 0.1295 (0.952) MotherMain 0.6628 (4.077)∗∗ 0.4328 (2.547)∗∗ -0.0950 (0.303) NeighborSize 0.0105 (5.221)∗∗ 0.0037 (1.753)∗ 0.0036 (1.719)∗ Coh50-59 -0.7858 (15.280)∗∗ -0.5929 (10.918)∗∗ -0.6468 (11.672)∗∗ Coh50-59 × - - 0.6708 (2.252)∗∗ FatherMain Coh50-59 × - - 0.3091 (0.702) MotherMain ∗∗ ∗∗ ∗∗ µ1 1.1934 (18.654) 1.3118 (19.006) 1.3218 (18.999) ∗∗ ∗∗ ∗∗ 4µ2 0.6432 (9.408) 0.7332 (9.972) 0.7386 (9.980) ∗∗ ∗∗ ∗∗ 4µ3 0.9519 (13.215) 1.0853 (14.086) 1.0889 (14.058) Constant 2.6477 (18.110)∗∗ 1.4654 (8.859)∗∗ 1.4888 (8.966)∗∗ -Loglikelihood 2812.3111 2590.6769 2582.7866 Observations 2144

Notes: Numbers in parentheses are the absolute value of t ratios. ∗ denotes signiﬁcance at the .10 level in a two-tailed test. ∗∗ means signiﬁcance at the .05 level in a two-tailed test. Native male Taiwanese born between 1960 and 1976 are the reference group.

35 Table 7. Educational Attainment of Children in Taiwan by Children’s Gender

Female Sample Male Sample

Parental education Father Level 1 0.7659 (7.377)∗∗ 0.4634 (5.119)∗∗ Level 2 0.9664 (6.866)∗∗ 0.6619 (4.533)∗∗ Level 3 1.2682 (8.782)∗∗ 1.0504 (7.110)∗∗ Level 4 1.2725 (6.224)∗∗ 1.1436 (5.307)∗∗ Mother Level 1 0.3794 (4.274)∗∗ 0.4583 (5.366)∗∗ Level 2 0.6970 (4.243)∗∗ 0.7287 (3.608)∗∗ Level 3 0.5778 (3.214)∗∗ 0.7392 (3.468)∗∗ Level 4 1.2324 (2.605)∗∗ 0.9891 (3.095)∗∗ FatherAge 0.0089 (1.034) -0.0009 (0.109) MotherAge 0.0141 (1.406) 0.0096 (0.976) FatherMain 0.0174 (0.089) 0.2840 (1.482) MotherMain 0.0099 (0.016) -0.1495 (0.393) NeighborSize 0.0092 (2.843)∗∗ -0.0015 (0.529)

Coh50-59 -0.8348 (10.502)∗∗ -0.4608 (5.842)∗∗ Coh50-59 × FatherMain 1.1038 (2.554)∗∗ 0.2162 (0.509) Coh50-59 × MotherMain -0.1709 (0.235) 0.7022 (1.167) ∗∗ ∗∗ µ1 1.4057 (15.498) 1.2980 (10.794) ∗∗ ∗∗ 4µ2 0.5893 (6.201) 0.8991 (7.013) ∗∗ ∗∗ 4µ3 1.1839 (11.487) 1.0250 (7.761) Constant 1.0178 (4.205)∗∗ 1.8318 (7.631)∗∗ -Loglikelihood 1221.8143 1330.7792 Observations 1028 1116

Notes: Numbers in parentheses are the absolute value of t ratios. ∗ denotes signiﬁcance at the .10 level in a two-tailed test. ∗∗ means signiﬁcance at the .05 level in a two-tailed test. Native Taiwanese born between 1960 and 1976 are the reference group.

36 Table 8. Educational Attainment of Children in Taiwan by Children’s Gender

Female Sample Male Sample

Parental education Father Level 1 0.7355 (7.032)∗∗ 0.4464 (4.863)∗∗ Level 2 0.9470 (6.737)∗∗ 0.6400 (4.291)∗∗ Level 3 1.2572 (8.703)∗∗ 1.0178 (6.785)∗∗ Level 4 1.3058 (6.331)∗∗ 1.1856 (5.336)∗∗

Mother Level 1 0.3248 (3.611)∗∗ 0.4399 (5.075)∗∗ Level 2 0.6478 (3.940)∗∗ 0.7143 (3.539)∗∗ Level 3 0.4543 (2.495)∗∗ 0.6972 (3.225)∗∗ Level 4 1.0889 (2.026)∗∗ 0.9454 (2.851)∗∗

FatherAge 0.0073 (0.832) 0.0011 (0.123) MotherAge 0.0159 (1.566) 0.0063 (0.631) FatherMain -0.0721 (0.220) 0.1430 (0.474) MotherMain 2.7084 (0.0003) -0.4712 (0.599) NeighborSize 0.0085 (2.628)∗∗ -0.0010 (0.333)

Coh50-54 -1.2767 (10.541)∗∗ -0.6415 (5.870)∗∗ Coh55-59 -0.9086 (7.054)∗∗ -0.2997 (2.269)∗∗ Coh60-64 -0.6511 (4.862)∗∗ -0.1453 (1.116) Coh65-70 -0.1839 (1.378) -0.0604 (0.510)

Coh50-54 × FatherMain 1.6613 (2.062)∗∗ 0.0866 (0.123) Coh55-59 × FatherMain 0.8619 (1.474) 0.2688 (0.469) Coh60-64 × FatherMain 0.5455 (1.246) 0.5770 (1.337) Coh65-70 × FatherMain -0.0756 (0.182) -0.1732 (0.432)

37 Table 8. (continued)

Coh50-54 × MotherMain -2.9546 (0.0003) 1.0264 (0.969) Coh55-59 × MotherMain -3.0198 (0.0003) 1.4479 (1.327) Coh60-64 × MotherMain -3.1142 (0.0004) 4.2882 (0.0001) Coh65-70 × MotherMain -2.2689 (0.0003) 0.5746 (0.608)

∗∗ ∗∗ µ1 1.4189 (15.452) 1.3058 (10.773) ∗∗ ∗∗ 4µ2 0.6097 (6.297) 0.9115 (7.095) ∗∗ ∗∗ 4µ3 1.2200 (11.582) 1.0340 (7.817) Constant 1.3884 (5.454)∗∗ 1.9640 (7.951)∗∗

-Loglikelihood 1201.7595 1320.6554

Observations 1028 1116

Notes: Numbers in parentheses are the absolute value of t ratios. ∗ denotes signiﬁcance at the .10 level in a two-tailed test. ∗∗ means signiﬁcance at the .05 level in a two-tailed test. Native Taiwanese born between 1971 and 1976 are the reference group.