What difference does education make? Education expansion and rapid cohort fertility decline in Thailand,

1 What difference does education make? Education expansion and rapid cohort fertility decline in Thailand, 1970 2010 Thananon Buathong 1,3 Wiraporn Pothisiri 2 Raya Muttarak 3 Mujaheed Shaikh 4 Abstract Most previous studies have shown that increased female education is one of the most important factors explaining the rapidly fertility transition. Little is known about the changing association between education and cohort fertility in developing countries which have already experienced the rapid fertility transition. Using data from Thailand Population and Housing Censuses for the years 1970 to 2010 based on the sample of ever-married women aged 45-54 years, we analyze completed marital fertility of women born between 1916 and 1965. Results show that completed marital fertility declined sharply from 6.42 for the 1916 birth cohort to 2.06 for the 1956 birth cohort because of the family planning policy in 1970s. Meanwhile, the proportion of women in the sample with no education declined sharply from 50% to 6%, but the highest educational level of the majority in the sample is less than primary. The changes in education-specific cohort fertility rate accounted for 85% of the change in completed marital fertility between the oldest and youngest birth cohorts, while only 15% is attributable to the expansion of female education attainment, the decline in the proportion of women with no education. However, the effect of educational composition change became more important, accounted for 25% of the change in cohort fertility between the two recent cohorts. Multivariate analysis confirms the pattern of fertility differentials by education level appear to the permanent-differences model (i.e. educational differentials persist throughout the transition) rather than the leader-follower model (i.e. diminishing educational differentials as the transition advances). Keyword: Education differentials, Cohort fertility, Decompose, Thailand 1 Chulalongkorn university, Thailand 2 College of Population studies, Chulalongkorn university, Thailand 3 Wittgenstein Centre (IIASA, VID/ÖAW, WU), Austria 4 Vienna University of Economics and Business, Austria

2 1. Introduction Thailand experienced one of the most rapidly fertility decline of any developing countries in the late 20 th century (Jones, 2011). The number of children ever-born over the reproductive life of Thai women dropped from six on average in 1970 to the replacement level in two decades later. After that the period TFR sustain declined to 1.5 children per woman (UN World population prospect 2015). Previous study indicated that the rapid reduction in the number of children ever-born to ever-married women was the main driver of the period fertility rate dropped to replacement level in Thailand (Hirschman, Tan, Chamratrithirong, & Guest, 1994). Meanwhile, family planning and contraceptive use program in the 1970s was the main cause of marital fertility drop in Thailand (Knodel, Chamratrithirong, & Debavalya, 1987). In the literature on fertility transition, effect of education attainment was extensively studied (Kravdal, 2002). An increase in the education level of women is regarded as one of the key factors explaining the decline in fertility level (Berrington & Pattaro, 2014). There were two plausible paths regarding how the educational differentials in fertility changes over time in developing countries i.e. 1) the permanent difference model; or 2) the leader-follower model (Bongaarts, 2003). The permanent-difference model was based on the microeconomic perspective. According to these, fertility was influenced by socioeconomic conditions and fertility decline is a natural response to incompatibility of education and work under the changing social and economic structures. The main mechanism underlying negative relationship between education and fertility was related to relative disadvantage of highly educated women in childbearing (Basu, 2002; Cleland, 2002). Therefore, in this context, educational differentials remain significant throughout the fertility transition. On the other hand, the leader-follower model holds that fertility differentials by level of education were a transient phenomenon and fertility level will converge to the same level at the end of the fertility transition (Bongaarts, 2003; Cleland, 2002). This model was based on the theory of diffusion whereby the most educated women make the fertility transition earlier than the least educated, but eventually the norm of small family size and birth control use would become widespread and then diffuse to all segments of the society (Casterline, 2001; Rosero-Bixby & Casterline, 1993). Evidences based on summarizes patterns of educational differentials at different stages of the fertility transition in less developed countries indicated that educational differentials still exist

3 at the post stage of transition and the educational composition remains a key predictor of overall fertility in late transitional countries (Bongaarts, 2003). However, the results from rapid cohort fertility transition in South Korea shown that the leader-follower model was the suitable model and the widespread of norm of small family size and birth control used from highly educated to less educated was the possible explanations (Yoo, 2014). Yet, little is known about the possible changing association between female education and cohort fertility in Thailand, a middle-income country with rapid fertility decline and substantial educational expansion. To this end, this paper aimed to investigate the association between female education and completed marital fertility in Thailand and how this association has changed during the fertility transition. It was particular importance to gain more knowledge about the changing association between female education attainment and cohort fertility in developing countries where female education has been rising constantly over time while fertility level has been declining. Moreover, observed changing association, if any, will be valuable for population projection, especially in terms of fertility assumption by level of education. 2. Social and economic development during fertility transition in Thailand In this section provide contextual overview, in order to understand the process of fertility transition in Thailand that might drove changes in preexisting conditions that contributed to the drastic fertility decline in the country. Before 1960, Thailand was characterized by high child mortality, high fertility and low-income country. After that Thailand also experienced a sharp decline in child mortality from 148 per 1,000 live births in 1960 to 95 per 1,000 live births a decade later owing to the expansion of primary health-care infrastructure nationwide (Vapattanawong et al., 2007). Consequently, the annual natural population growth rate was considerably high (approximately 3%). The country had been ruled by military viewed on fertility. In 1970, the Thai government launched the National Family Planning Program following the World Bank recommendations to reduce the population growth rate by promoting voluntary modern contraceptive use. At the same time, in the Third Social and Economic Development Plan aimed to transform Thailand from agricultural to an industrial society. In the short term, the plan focused on infrastructure investment and emphasized elementary and vocational education to produce skilled workers. In 1977, there is the change in education policy in Thailand. Since 1977/1978, compulsory education had extended to six years, covering complete primary education. It was the first

4 time that had been strictly implemented throughout the whole country. Primary schools were transferred back from the Ministry of Interior Affairs to the Ministry of Education, and most major movement within the reform was the establishment of primary schools in every single village for the first time. A sharp increase of the supply of primary schooling by the state in 1977/78 is evidently reflected by a sharp rise in the number of teachers per 1000 children during the period of 1970 to 2000 (Chankrajang & Muttarak, 2017). In late 1980s, Thailand became a middle-income economy and period fertility rate dropped to replacement levels. Rapid social and economic development and successful government-run family planning programs together with a unique cultural setting contributed to radical fertility in Thailand was the key of fertility decline. Generally, childbearing decisions making in Thailand was taken by couples themselves, with minimal influence by other family members and absence of strong parental and kin pressure to have large families. Likewise, the teachings of Thai Buddhism a religion practiced by 95% of the Thai population, posed no major barriers to contraceptive use (Knodel et al. 1986; Skirbekk et al. 2015). Figure 1: Population pyramid by education level, Thailand, 1970-2050 Thailand 1970 Observed Thailand 2010 Observed Thailand 2050 Projected Source: Wittgenstein Centre Data Explorer, accessed September 8, 2017. http://witt.null2.net/shiny/wic/ The effect of social and economic development on fertility was increased the direct cost of children and reduced benefit of children as labor in farm. Consequently, Thais parent tend to reduce the desired family size and trade off by invest more education for their kid (Knodel et al., 1987). Recent literature shows rapidly education expansion in Thailand (see Figure 1) and if the enrolment rates remained constant as in the year 2000, next three decades, more than 70% of Thai would graduate at least secondary school (KC et al., 2010).

5 Thailand became an upper-middle income economy since 2011, moving from a low-income country to an upper-income country in less than a generation. As of 2015, fertility rate has declined to 1.5 children per woman (UN world population prospect 2015), even lower than some advanced societies in Europe, example United Kingdom, Denmark, Netherlands, Finland, Belgium, Norway, Sweden and France.

6 3. Data and method 3.1 Data This study makes used of series of Thailand Population and Housing Censuses for the years 1970, 1980, 1990, 2000 and 2010 conducted by the National Statistical Office of Thailand. The Thai census is conducted every ten years and includes basic information on household and individual characteristics including information on fertility and mortality such as the number of children ever born and number of child death. We accessed to 1% sampling of Thailand Population and Housing Censuses for the years 1970-2000 from IPUMS and for the year 2015 from Thailand National Statistic Officer. For this present study was restricted to ever-married women aged 45-54 years which is the end of reproductive period in order. Such restricted will enable the calculation of completed marital fertility of ten-year birth cohorts that reflects the census interval of ten years making it easier to compare the change in level of fertility overtime. Table 1: Analytical sample by birth cohort Year of census Age in 1970 Year of Birth Total sample size Analytical sample size 1970 45-54 1916-1925 21,868 19,741 1980 35-44 1926-1935 15,030 14,217 1990 25-34 1936-1945 21,675 20,712 2000 15-24 1946-1955 34,582 32,089 2010 5-14 1956-1965 42,125 39,303 Table 1 shown how to define the birth cohort by the year of census and sample size. The family planning program in 1970 was likely to affect reproductive behavior of women born in the 1926-35 birth cohort or later (aged 44 or younger in 1970). After checking for internal consistency and excluding missing responses as well as the respondents reporting the number of children ever-born and/or number off living children more than 15, the analytical sample consist of 14,217 to 39,303 ever-married women in each birth cohort and the nonresponse rate in the five birth cohorts (censuses) ranged from 4.7 to 9.7%.

Percentage 7 3.2 Measures Dependent variable Our dependent variable was the completed marital fertility which was measured as the number of children ever-born. Figure 2 shown distribution of number of children ever-born by birth cohort. Majority of mother had at least one child, reflecting that having a child remains important in Thai society. However, the proportion of childless women rose from around 2% for the earlier birth cohorts to 10% for the 1956-65 birth cohort. Focusing on the distribution of number of children ever-born, the most frequency of number of children ever-born shift to the two child norms. Figure 2: Distribution of number of children ever-born by birth cohort 50 45 40 35 30 25 20 15 10 5 - Birth cohort 1916-25 Birth cohort 1926-35 Birth cohort 1936-45 Birth cohort 1946-55 Birth cohort 1956-65 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 number of children ever-born Explanatory Variables The main explanatory variables include age, level of educational attainment, marital status, religion, experience of child loss, place of residence, working status and household wealth quintile. All covariates referred to the census year and were coded, recoded and aggregated in a harmonized way to improve comparability over time. The distribution of sample by the explanatory variables was presented in Table 2. Women s education level was categorized as no formal education, some primary education, completed primary education and beyond primary education. Age was incorporated as a categorical variable: aged 45-49 and aged 50-54. Marital status is measured as a categorical variable indicating whether the women was currently married or formerly married. Place of residence was coded using three categories: urban, rural and Bangkok, which was necessary because Bangkok was unique in social and economic development. Religion

8 identified four groups: Buddhist, Muslim, Christian and other religion. Working status was coded into four categories: not working, own account, work in formal sector and work in family without pay. The work in formal sector category also includes government employees, government enterprise employees and private employees. Table 2: Socio-demographic characteristics of the sample Socio-demographic characteristics Birth Cohort 1916-25 1926-35 1936-45 1946-55 1956-65 Age group Aged 45-49 56.09 55.57 52.86 57.37 53.41 Aged 50-54 43.91 44.43 47.14 42.63 46.59 Marital status Currently married 76.85 81.29 81.81 85.71 82.76 Widowed / Divorced / Separate 23.15 18.71 18.19 14.29 17.24 Religion Buddhist 94.52 95.32 95.55 95.89 95.43 Muslim 4.45 3.94 3.64 3.40 3.62 Christian 0.48 0.56 0.49 0.68 0.89 Other 0.55 0.17 0.32 0.03 0.06 Place of residence Bangkok 6.63 9.06 9.88 11.62 11.94 Rural 87.98 84.14 82.08 67.24 56.22 Urban 5.40 6.80 8.04 21.15 31.85 Experience of child loss No 45.28 64.18 84.95 91.80 96.57 Yes 54.72 35.82 15.05 8.20 3.43 Employment status Not work 19.57 15.86 17.19 17.25 16.34 Own account 20.49 21.33 21.54 24.66 30.42 Formal worker 5.00 7.06 10.83 17.63 24.59 Work in household without paid 54.94 55.75 50.45 40.46 28.65 Household wealth quintile Lowest 22.73 22.87 24.10 21.74 20.92 Low 18.04 17.13 16.61 18.78 19.34 Medium 21.66 20.48 19.33 19.50 19.79 High 17.06 19.90 21.11 20.01 20.14 Highest 19.01 19.62 19.85 19.97 19.82 Experience with child loss was measured with the following survey question: How many children ever born who had died? and we coded this variable as dummy (0 = no child loss, 1= experience with child loss) because of low frequency of respondents who experienced the death of two children.

9 Household wealth was measured in quintiles following the methodology described by (Filmer & Pritchett, 2001). Principle component analysis is used to estimate the weight of each component and uses information about household characteristics, ownership of real estate, land and expensive consumer items belonging to one of the household members, such as a computer, refrigerator, microwave, mobile phone, washing machine, motorbike, car, small truck. 3.3 Method and Analytical strategy We performed the analysis in four steps. First, we presented changes in cohort fertility and educational composition. We measured cohort fertility level with completed marital fertility rate (CFR) that was measured by the sum of education-specific marital fertility rate multiplied by the proportion of corresponding educational group, the average number of children ever-born of ever-married women (see equation 1, employed from Yoo (2014)) and interpreted its change between the 1916-25 birth cohort and the 1956-65 birth cohort. By adopting a cohort perspective we did not need to discuss about timing distortions that affected to period fertility (Zeman, Beaujouan, Brzozowska, & Sobotka, 2017). At the same time, the distribution of level of female education was also presented to show the education expansion during rapid fertility transition in Thailand. CF a j = (r a i p a i i ) (1) CF a represents the completed marital fertility for the birth cohort a i is the educational-level index, 1-4 a r i is the marital completed fertility rate of group i for the birth cohort a a p i is the proportion of the group i for the birth cohort a Second, we analyzed the role of changing first, second, third and fourth and higher-order birth rates in driving the decline in completed marital fertility in Thailand. We employed the cohort parity progression ratios by education level from Yoo (2014). Parity progression ratios are parity-specific birth probabilities and have been used to capture the proportion of women who have another child (parity k+1) after a certain number of children (parity k) (Preston, Heuveline, & Guillot, 2001). The cohort parity progression ratios by the level of education attainment were measured as follows: PPR k,k+1 = Number of women at parity k+1 or more Number of women at parity k or more (2)

10 Third, we adopted the demographic-decomposition technique from Yoo (2014) to distinguish the effect of change in educational composition (composition effects) to cohort fertility decline from those effect of change in education-specific fertility rate (rate effects). The difference in cohort fertility between the two birth cohorts was decomposed into change in the rates of the cohort fertility and change in the composition of education as follows: j CF = CF a CF b = (r a i p a i ) (r b i p b i i i ) (3) j CF is the change in completed marital fertility between cohort a and b To compare the two cohorts, we evaluated the effect of change in educational composition on standard population by averaging education-specific fertility rate across both populations and averaging educational composition to estimate the effect of change in education-specific cohort fertility rate as well. Finally, we investigated a suitable model pattern for fertility transition by level of education. There are two plausible paths regarding educational differentials in cohort fertility at the end of fertility transition. The leader-follower model, the most educated women made the fertility transition earlier than the least educated. The less educated follow the pattern of the more educated with a lag. Levels of fertility at the beginning and the end of the fertility transition are the same in all education groups, but during the transition fertility is inversely associated with level of education. An alternative pattern of educational differentials, can be called the permanent difference model. In this pattern, the differences exist at all stage of the fertility transition (Bongaarts, 2003). To identify which model was appropriate for the pattern of educational differentials in cohort fertility through the rapid fertility transition in Thailand. Bongaarts (2003) used the trend in educational-specific fertility rate to determine whether the pattern was close to the leaderfollower model or to the permanent difference model. In this study, we used the average number of children ever-born of ever-married women aged 45-49 years old by level of education to identify the suitable model pattern for fertility transition. Next, the multivariate regression models are employed to examine educational differentials in completed marital fertility rate between the birth cohorts. Since our dependent variable was not continuous variable, but a count variable with over-dispersion problem and only 2%-10% are childless, Negative binomial regression model seemed more appropriate for the 1916-25

11 to the 1936-45 birth cohorts, while Poisson regression model for the 1946-55 to the 1956-65 birth cohorts (Long & Freese, 2006; Pandey & Kaur, 2015). Provided applied to account for the survey sampling design and distribution of.. The weights were used to adjust for the distribution of the sample. Moreover, the variance inflation factor is used as a diagnostic statistic to ensure that the model has no multi-collinearity problem. 4. Main finding 4.1 Cohort fertility decline and education expansion in Thailand The number of children ever-born by women s education level and cohort shown in Table 3 indicated that completed fertility sharply declined from 6.23 for the 1916-25 birth cohort to 1.88 for the 1956-65 birth cohort that were covered from early stage to post stage of fertility transition (Bongaarts, 2003). At the same time, completed marital fertility decreased from 6.42 for the 1916-25 birth cohort to 2.06 for the 1956-65 birth cohort. The gap between the two fertility indicators was only 3% for the 1916-25 birth cohort and 10% for the 1956-65 birth cohort. Meanwhile, proportion of permanent celibacy increased from 5% in the 1916-25 to 9% in the 1956-65 birth cohort. It indicated that the rapid reduction in the number of children ever-born to ever-married women was the main cause of the sharp cohort fertility decline in Thailand (Hirschman & Guest, 1990; Hirschman, Tan, Chamratrithirong, & Guest, 1998; Knodel et al., 1987). Table 3: Mean number of children ever-born by birth cohort Birth cohort 1916-25 1926-35 1936-45 1946-55 1956-65 Completed fertility (1) 6.23 5.27 3.81 2.53 1.88 Completed marital fertility (2) 6.42 5.51 3.99 2.72 2.06 % difference of (2) from (1) 3.05 4.55 4.72 7.51 9.57 Stage of fertility transition Early Early/Mid Mid/late Late Post As seen in Table 3, the completed marital fertility declined sharply between the 1936-45 birth cohort and the 1946-55 birth cohort (observed change in completed marital fertility was approximately -1.52 and -1.28 children per women). Family planning program was the key of marital fertility changes. In 1970, modern contraceptive program was implemented in Thai society and Thai women were well-receptive to the modern contraceptive (Hirschman et al., 1994; Knodel et al., 1987). Hence, the family planning program in 1970 was likely to affect to reproductive behavior of women born in the 1936-45 birth cohort or later (aged 34 or younger

12 in 1970). Opposite to the 1916-25 birth cohort, when they were at childbearing age, they did not experience with any major changes that the Thai society has underwent - neither the reduction in infant and child mortality in 1960s or family planning program in 1970s, nor economic prosperity in the 1990s. Figure 3: educational distribution by birth cohort 100 90 80 70 60 50 40 30 20 10 0 1916 1926 1936 1946 1956 Birth cohort no education some primary primary completed Secondary and beyond Meanwhile, cohort fertility rate dropped between the 1916-25 and the 1956-65 birth cohort. Figure 3 educational distribution by birth cohort shown that expansion of female education was slow during the fertility transition period in Thailand because most of them were belong to the birth cohort that did not get affect by education policy reform in 1977. The proportion of sample with no education decreased sharply from 50% in the 1916-25 birth cohort to 6% in the 1956-65 birth cohort, while the proportion of sample with higher education rose slowly. Consequently, highest education of the majority in the sample was still less than primary level. It was interesting to note that Thailand reached the post stage of fertility transitional before becoming an upper-middle income country in the 1990s. Moreover, the country completed the process of cohort fertility transition in approximately four decades. Fertility transition in Thailand has been one of the most rapid among Asian countries (Jones, 2011). Remarkable, the social and economic development may contribute to the fertility transition in Thailand. Table 2 shown that proportion of sample who experience with child loss dropped from 55% to 4%, while proportion of sample who worked in household without paid decrease from 55% to 29%.

13 4.2 The contribution of changing parity progression ratio in cohort fertility decline Using parity progression ratios, our result indicated that the completed marital fertility decline in Thailand was mainly attributable to the transition from having three or more children to having two children in all education level. The fall in parity progression to parity three (PPR2) and parity four (PPR3) were substantial for all educational level since the 1936-45 birth cohort, the first cohort who got benefit from family planning program in 1970s. At the same time, the parity progression to first and second births remained high and stable. The results indicated that the role of modern contraceptive use in Thai society was to limit the supply of children at two children. This patterns are similar to the cohort fertility transition in Europe or East Asia (Zeman et al., 2017). Remarkable, highly educated tend to have lower completed marital fertility rate than their lower educated counterparts since the early stage of cohort fertility transition in Thailand. When parity progression to parity three (PPR2) and parity four (PPR3) of no education and some primary education continuously declined, the parity progression to third and fourth births of secondary education and beyond stabilized at low level. Figure 4: Cohort parity progression ratio (PPRs) by education level and birth cohort

14 4.3 The importance of educational composition effect to cohort fertility decline In this section, we aimed to investigate the effect of change in educational composition to cohort fertility decline in Thailand. Shown in Table 4, effect of change in education-specific fertility rate (rate effects) of all education groups accounted for approximately 85% of the observed cohort fertility change between the 1916-25 and the 1956-65 birth cohort (-3.71 children per woman). Meanwhile, approximately 15% of cohort fertility dropped in Thailand was attributable to the effect of educational composition change (-0.66 children per woman). Result indicated that change in fertility behavior of Thai women was the key of cohort fertility transition in Thailand. However, the effect of change in education composition became more important for fertility decline especially for recent cohorts. Birth cohort Table 4: Decomposition of change in completed marital fertility (CF) Observed change in CF Rate effect Change attributable to Educational composition effects 1916-25 and 1926-35 -0.92-0.88 95.85% -0.04 4.15% 1926-35 and 1936-45 -1.52-1.45 95.79% -0.06 4.21% 1936-45 and 1946-55 -1.28-1.17 91.74% -0.11 8.26% 1946-55 and 1956-65 -0.66-0.50 75.68% -0.16 24.32% 1916-24 and 1956-65 -4.37-3.71 84.80% -0.66 15.20% Focusing on how and to what extent education as a whole and education level contributed to the change in cohort fertility between the 1916-25 birth cohort and the 1956-65 birth cohort in terms of rate effects and educational composition effects (see Table 5), we can see that contribution of lower educational groups was pronounced. The rate effects of no education accounted for roughly one-third of the decline in marital fertility between the two cohorts, while approximately 60% of the change in marital fertility was attributable to the reduction of proportion of no education women (educational compositional effects). When we combine the rate effects and composition effects of no education and some primary education, it was seen that the changes in these two educational categories are mainly responsible for the changes in completed marital fertility between the 1916-25 and the 1956-65 birth cohort. The decline in education-specific fertility rate and the proportion of these two educational groups leads to the narrowing gap of completed marital fertility rate across levels of education. The results confirmed that the effect of family planning policy and contraceptive use are mainly change in fertility behavior of low educated women and effect to the fertility rate of them (Cleland, 2002).

Mean number of children ever-born 15 Table 5: Decomposition of change in completed marital fertility (CF) by education level Education level Change in Change attributable to Standardized CF Rate effect Composition effects Cohort 1916 / C1956-4.37 100.00% -3.71 84.80% -0.66 15.20% No education -3.93 89.87% -1.33 30.43% -2.50 57.47% Some primary -1.11 25.43% -1.85 42.51% 0.74-16.97% Primary completed 0.25-5.76% -0.18 4.10% 0.41-9.40% Secondary and beyond 0.42-9.53% -0.23 5.33% 0.59-13.46% 5.4 Educational differentials in completed marital fertility In this sub-section, we investigate suitable models explaining the fertility decline in Thailand. Focusing on educational differentials by birth cohort, highly educated women have lower completed marital fertility rate than their lower educated counterparts from early stage to post stage of fertility transition in Thailand. Since the 1936-45 birth cohort, there was fertility difference between no education and some primary education and the differential between some primary education and secondary and beyond was narrower. The relationship between cohort fertility and women s education level in Figure 5 was in part attributable to other factors such as experience with child loss, household wealth, rural-urban residence and working status that associated with completed marital fertility rate. However, after controlling for the effect of such factors in multivariate analysis, results as shown in Table 6 indicated that women education level remained statistically negative correlation with completed marital fertility and it was strongest indicator, compare to other covariates. Figure 5: mean number of children ever-born by education level Cohort 1916 Cohort 1926 Cohort 1936 Cohort 1946 Cohort 1956 No education 6.46 5.57 4.25 3.41 2.65 Some primary 6.44 5.54 4.04 2.75 2.19 Primary completed 4.61 4.26 2.97 2.22 1.94 Secondary and beyond 3.96 3.81 2.57 1.97 1.69

16 The educational differentials in cohort fertility remained exist in all cohorts and the gap between some primary women and primary completed women is diminishing. Meanwhile, the differences between no education and some primary has changed over time. Since the 1936-45 birth cohort, no educated woman tended to have a higher number of children ever-born compared with some primary. The results showed that pattern of fertility differentials by level of education remain from the birth cohort in early stage to the birth cohort in post stage of the cohort fertility transition in Thailand. This study leaded the support the permanent-differences model rather than the leader-follower model (Bongaarts, 2003; Cleland, 2002). Table 6: Result of Poisson regression model and Zero-inflated Poisson regression model, IRR Educational differences Birth cohort 1916-25 1926-35 1936-45 1946-55 1956-65 No education 1.01 0.99 1.02 * 1.14 *** 1.15 *** Some primary (reference) 1.00 1.00 1.00 1.00 1.00 Primary completed 0.87 *** 0.92 ** 0.85 *** 0.89 *** 0.92 *** Secondary and more 0.84 *** 0.83 *** 0.76 *** 0.85 *** 0.84 *** Note: Controlling for effect of age group, marital status, religion, place of residence, experience of child loss employment status and household wealth Negative binomial regression model for the 1916-25 to 1936-45 birth cohorts Poisson regression model for the 1946-55 to 1956-65 birth cohorts * Significant at 5%, ** significant at 1%, *** significant at 0.1%

17 6. Discussion and Conclusion The aim of this paper was to investigate the role of education expansion on rapid cohort fertility transition in Thailand. Results showed that the changes in educational composition accounted for only 15% of cohort fertility decline between the 1916-25 and the 1956-65 birth cohort. However, the educational composition effects became more important for fertility decline especially for the recent cohorts who were having children prior to the point when replacement level was reached (see Table 4). The results supported the hypothesis that improvement in women education are not a necessary condition for fertility decline (Yoo, 2014). In Thailand, the role of education expansion to cohort fertility transition was not only reducing the proportion of no education and some primary education, but also changing the fertility behavior of these two lowest educational groups. Consequently, reduction of no education and some primary education is a key element in the cohort fertility transition (see Table 5). However, it is difficult to conclude that female education expansion was the main driver of completed marital fertility decline during the Thai fertility transition. The change in fertility behavior in every social group accounted for approximately 85% of cohort fertility transition in Thailand (see Table 4) and the transition from having three or more children to having two children was mainly attributable to the cohort fertility transition (see Figure 3). However, highly educated tended to have lower completed marital fertility rate than their lower educated counterparts since the early stage of cohort fertility transition, before implemented family planning policy in 1970s (see Figure 4). The pattern of cohort fertility differentials by level of education in Thailand appears to have confirmed the permanent-differences model rather than the leader-follower model (see Table 6). Educational differentials in completed marital fertility still remain. It is clear that there were negative correlations between education level and completed marital fertility from the early stage to the post stage of cohort fertility transitional in Thailand. These patterns were similar to previous studies in less developed societies (Abbasi-Shavazi, Lutz, Hosseini- Chavoshi, & KC, 2008; Bongaarts, 2003, 2010), except in cohort fertility transition of South Korea (Yoo, 2014). The convergence of educational differentials at the post stage of cohort fertility transition was described through the theory of diffusion and social interactions (Bongaarts, 2003). Family planning programs are the main mechanism to reduce the fertility gap across social strata by helping them to meet the latent demand of modern contraceptive use and the modern contraceptive use will spread from highly educated to less educated (Yoo, 2014). However,

18 previous study in Thailand showed that the availability of family planning outlets increases the likelihood of contraceptive use to approaching those common in economically advanced countries but there was weakness positive relationship between level of education and likelihood of use (Entwisle, Hermalin, Kamnuansilpa, & Chamratrithirong, 1984). According to the leader-follower model, the cohort fertility rate of all education groups converges to the same value at the post stage of fertility transition and there was no effect of the distribution of population by level of education on overall fertility (Bongaarts, 2003). Whereas, results in Table 4 indicated that changes in education composition became more important for fertility decline. The permanent different mode, review of literatures by Cleland (2002) suggest that fertility was inversely related to level of education in all stage of fertility transition and the possible explanations included the effect of schooling on autonomy, opportunity costs of childbearing and exposure of Western value. However, given the complexity of this issues, researchers may never reach consensus (Bongaarts, 2003). The main mechanism underlying the negative relationship between education and fertility was related to relative disadvantage of highly educated women in childbearing. According to this microeconomic perspective, fertility was influenced by socioeconomic conditions and fertility decline was a natural response to incompatibility of education and work under the changing social and economic structures (McDonald, 2000). Meanwhile, there were changes in social and economic institutions related to child mortality, market employment and cost of living in through the process of cohort fertility transition in Thailand (see Table 2). Several points should be noted for this paper. First, these findings should be different from research based on period fertility because of the tempo-effect (Bongaarts & Feeney, 1998). Second, the time frame in this study covers only from early stage to post stage of fertility transition in Thailand to test hypothesis of Bongaarts (2003). However, empirical evidence indicated that the differentials in level of completed marital fertility between some primary and primary completed were narrower. Third, the results in this paper were based on retrospective approach about number of children ever born of women aged 45-54 in the year of census. Hence, the quality of data is doubtful especially for the census in 1970 because it was the second time of National statistical office Thailand to conduct the census. Finally, there were limitation of detailed information related to fertility behavior of women in the census. Cleland (2002) and Bongaarts (2003) mentioned about role of fertility behavior on the educational differentials in fertility level. In low fertility context, highly educated tend to postpone age at

19 first marriage and/or first birth and their actual fertility may be less than desired number of children at the end of fertile span. However, the postponement was not limit only to highly educated women and empirical study in Asia pacific show that women can avoid to marry until aged 30. After that the proportion of single dropped (Jones, 2007). At the same time, the difference in rate of unwanted pregnancy between highly and low educated women due to the lack of modern contraceptive use to limit their supply of children was the main mechanism to describe why educational differentials still exist in the post-transitional stage. 5. Acknowledgement We would like to thanks Sam Hyun YOO, Korea Institute for Health and Social Affairs for knowledge transfer and excellent comments. We are grateful to Thailand national statistics office for providing data. This paper was fully supported by a doctoral scholarship from the 100 th Anniversary Chulalongkorn University Fund. References Abbasi-Shavazi, M. J., Lutz, W., Hosseini-Chavoshi, M., & KC, S. (2008). Education and the world's most rapid fertility decline in Iran. Basu, A. M. (2002). Why does Education Lead to Lower Fertility? A Critical Review of Some of the Possibilities. World Development, 30(10), 1779-1790. doi:http://dx.doi.org/10.1016/s0305-750x(02)00072-4 Berrington, A., & Pattaro, S. (2014). Educational differences in fertility desires, intentions and behaviour: A life course perspective. Advances in life course research, 21, 10-27. Bongaarts, J. (2003). Completing the fertility transition in the developing world: The role of educational differences and fertility preferences. Population Studies, 57(3), 321-335. Bongaarts, J. (2010). The causes of educational differences in fertility in Sub-Saharan Africa. Vienna yearbook of population research, 31-50. Bongaarts, J., & Feeney, G. (1998). On the quantum and tempo of fertility. Population and Development Review, 271-291. Casterline, J. B. (2001). Diffusion processes and fertility transition: selected perspectives: National Academies Press. Chankrajang, T., & Muttarak, R. (2017). Green returns to education: Does schooling contribute to proenvironmental behaviours? Evidence from Thailand. Ecological Economics, 131, 434-448. Cleland, J. (2002). Education and future fertility trends, with special reference to mid-transitional countries. Completing the fertility transition, 187-202. Entwisle, B., Hermalin, A. I., Kamnuansilpa, P., & Chamratrithirong, A. (1984). A multilevel model of family planning availability and contraceptive use in rural Thailand. Demography, 21(4), 559-574. Filmer, D., & Pritchett, L. H. (2001). Estimating wealth effects without expenditure Data Or tears: An application to educational enrollments in states of india*. Demography, 38(1), 115-132. Hirschman, C., & Guest, P. (1990). Multilevel models of fertility determination in four Southeast Asian countries: 1970 and 1980. Demography, 27(3), 369-396. Hirschman, C., Tan, J., Chamratrithirong, A., & Guest, P. (1994). The path to below replacement-level fertility in Thailand. International Family Planning Perspectives, 82-107. Hirschman, C., Tan, J., Chamratrithirong, A., & Guest, P. (1998). Explaining the rapid fertility decline in Thailand.

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