Fertility, female labor participation and income in East Asia

Yoko Nakagaki (JICA Research Institute, Tokyo, Japan)

International Journal of Development Issues

ISSN: 1446-8956

Publication date: 3 April 2018

Abstract

Purpose

This study aims to apply two recently discovered relationships that describe fertility recovery in developed countries to East Asia: the U-shaped relationship between fertility and FLP (female labor participation) and the inverse-J-shaped relationship between fertility and income.

Design/methodology/approach

It uses a panel data set of 176 countries including 13 East Asian countries from 1990 to 2014. Pooled ordinary least squares, fixed-effects and random-effects models are tested.

Findings

The main findings are the following points concerning East Asia: The U-shape and the inverse-J-shape are confirmed, suggesting that fertility recovery could be realized if both FLP and income are high enough and increasing; in the region, the U-shape is peculiar. Lower-income countries’ data move from the upper-right to the bottom, whereas higher-income countries’ data move from the upper-left to the bottom; no country in the region has reached the stage where both FLP and income are high enough.

Originality/value

This is the first paper on East Asia to show the U-shape and the inverse-J-shape concerning fertility recovery and the peculiarity of the U-shape in East Asia. It explains the background of low fertility using the relationship between fertility, FLP and income.

Keywords

Citation

Nakagaki, Y. (2018), "Fertility, female labor participation and income in East Asia", International Journal of Development Issues, Vol. 17 No. 1, pp. 69-86. https://doi.org/10.1108/IJDI-06-2017-0106

Download as .RIS

Publisher

:

Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited


1. Introduction: declining fertility in developing East Asia

1.1 Declining fertility in developing countries

The fertility rate in developing countries started to decline in the 1960s. The TFR (total fertility rate[1]) in low- and middle-income countries declined from 5.8 in 1965 to 2.6 in 2015 [World Development Indicators; World Bank (2017)].

In 1965, TFR by region, excluding high-income countries, was over 6 in the Middle East and North Africa, sub-Saharan Africa and East Asia and the Pacific. In South Asia and Latin America and the Caribbean, TFR was also nearly 6. Among these regions, TFR declined most steeply in East Asia and the Pacific, dropping to 1.8 in 2015, below the population replacement level of around 2.1. TFRs in the other regions in 2015 were 2.1 in Latin America and the Caribbean, 2.5 in South Asia, 2.9 in the Middle East and North Africa and 4.9 in sub-Saharan Africa.

Developing countries in East Asia can be regarded as a forerunner in fertility decline among developing countries. The medium variant of the 2017 revision of the World Population Prospects projected that global TFR would decline from 2.5 in 2010-2015 to 2.2 in 2045-2050 and 2.0 in 2095-2100 (United Nations, 2017b). The report projected a steep decline in fertility especially in the least developed countries.

According to the traditional demographic transition theory, socioeconomic changes reduced the benefits and increased the costs of having children (Notestein, 1953). Since the 1980s, another stream of theories flourished, which focused on the “diffusion” of information, ideas and behaviors, and “social interaction” whereby attitudes and behaviors of individuals influenced one another (Bongaarts, 2006). The relative importance of these two theories has been discussed (Bongaarts, 2002; Bryant, 2007).

1.2 Low fertility in developing East Asia

The decline in fertility in developing East Asia[2] was regarded to be the success of fertility control by the governments, as well as the results of socioeconomic changes. The degree of the effect of fertility control was debated (Jones and Leete, 2002). The government programs did facilitate the diffusion of knowledge about contraception and provided access to contraceptive methods (Bongaarts, 2006). The programs also promoted a new attitude toward smaller families (Caldwell et al., 2002). The governments of China and Vietnam in particular introduced forceful policy measures.

At this point, TFR has fallen below the population replacement level in China, Thailand and Vietnam, as well as in Japan, Hong Kong, Singapore and Korea. Recently, studies have focused on issues beyond fertility control. Jones (2007) referred to factors which had a negative impact on fertility, such as delayed marriage, uncertain employment and conflicts between work and family.

Singapore, Japan, Korea, Thailand and China have already changed their policy stances to increase fertility [World Population Policies Database; United Nations (2017a)]. Park (2012) stressed the importance of reversing or slowing the decline in fertility in developing East Asia, as the root cause of population aging (an increase in the share of the elderly population), which had already become a major policy issue, was a sharp decline in fertility. However, the World Bank Group (2016) observed that fertility recovery in low-fertility East Asian and the Pacific countries seemed to be more difficult than in Organisation for Economic Cooperation and Development (OECD) countries, because of the less flexible labor market, the lack of public support and more traditional attitudes toward gender roles.

1.3 Contents of this study

Considering the steep decline in fertility in developing countries in the past and in the future, the contemporary framework to address low fertility in developing countries should be discussed.

For the purpose, this study focuses on East Asia, where fertility decline has been most notable among developing countries.

This study is based on previous studies concerning the low fertility and the recovery of fertility in OECD countries in Europe and in North America. In those countries, fertility has remained below the population replacement level since the 1980s. To address the situation, policy measures which were expected to have a positive impact on fertility have been introduced. As of the late 1990s, most of those countries experienced a modest recovery of fertility. Many studies were conducted to explain the phenomenon.

This study aims to apply two recently discovered relationships that describe fertility recovery in developed countries to East Asia: the U-shaped relationship between fertility and FLP (female labor participation) and the inverse-J-shaped relationship between fertility and per capita income.

Section 2 provides an overview of previous studies and explains the contribution of this study. Section 3 describes the methodology for examining the U-shaped and the inverse-J-shaped relationships using data from East Asia and the world. Section 4 shows the results and discusses the implications. Section 5 concludes this study.

2. Literature review and contribution

2.1 Studies on declining fertility in developed countries

Modern economics has had a major impact on the theories on declining fertility in developed countries since the latter half of the twentieth century. Among the economic models of fertility pioneered by Becker (1960, 1965), the negative relationship between FLP and fertility was especially highlighted (Hotz et al., 1997). Galor and Weil (1996) presented a general equilibrium model which explained the relationship among declining fertility, higher outputs per worker and higher relative wages for women.

Examining fertility below the population replacement level, Kohler et al. (2002) pointed out five factors contributing to “lowest-low fertility (below 1.3 in TFR)” in Europe: demographic distortion of period fertility (explained at the beginning of the next part, Section 2.2), socioeconomic changes, social interactions, institutional settings and reductions in complete fertility where childbearing was delayed. “The second demographic transition theory” stressed the impacts of autonomous shifts of people’s preference (Lesthaeghe, 2014).

2.2 Studies on fertility recovery in developed countries

At the same time, many studies have been conducted to explain fertility recovery. Bongaarts and Feeney (1998) and Bongaarts and Sobotka (2012) explained that the rebound was due to the slowdown of the postponement of childbearing. During the postponement period, fewer young women had children than their previous generations did, whereas elder women had few children because they were already past the childbearing stage. This situation caused the lower TFR compared to complete fertility of each cohort (demographic distortion of period fertility). The TFR recovery could be observed when the postponement slowed down.

Beyond the slowdown of the postponement of childbearing, two other factors contributing to the recovery have been frequently discussed: further socioeconomic development and gender equality.

2.2.1 Further socioeconomic development and fertility recovery.

As Myrskylä et al. (2009) stated, the negative association between fertility and socioeconomic development was one of the most solidly established regularities in the social sciences. If the relationship was depicted in the figure using fertility as the explained variable and socioeconomic development as the explanatory variable, it was expected to have a downward slope, which was true of the data in 1975. However, the authors showed the inverse-J-shaped relationship between TFR and the HDI (Human Development Index) using data from over 100 countries in 2005. The finding revealed that the relationship between TFR and development changed from negative to positive at the higher stages of development. This proposition has been reexamined by other authors. Furuoka (2009, 2013) argued that even in countries with relatively high HDI levels, the relationship between TFR and the HDI was either slightly negative or had a flatter slope. Harttgen and Vollmer (2014) found very little support for simple interpretations that fertility would automatically start to increase beyond a certain level of development.

Luci-Greulich and Thévenon (2014) confirmed another inverse-J-shaped relationship, between TFR and per capita gross domestic product (GDP) in OECD countries. The study used panel data and focused on intracountry variations estimated through the fixed-effects model. Lacalle-Calderon et al. (2017) examined the relationship through a quintile panel data regression with data from 151 countries from 1970 to 2010. The study confirmed the inverse-J, at the stage when a certain level of economic development was attained.

2.2.2 Importance of gender condition.

Both the authors of the aforementioned studies, Myrskylä et al. (2009) and Luci-Greulich and Thévenon (2014), stressed that fertility recovery would not be accomplished without progress toward gender equality. Myrskylä et al. (2011) pointed out that highly developed countries could reap the fertility dividend of their development only if the increase in HDI levels went hand in hand with a decrease in the gender gap. Luci-Greulich and Thévenon (2014) also showed that fertility recovery was likely to happen in those countries where economic development came along with increases in female employment. McDonald (2013) concluded that the positive association between higher fertility and higher income was the result of higher FLP.

The correlation coefficients between TFR and FLP in the cross-country data of OECD countries moved from negative to positive by the 1990s. Many studies focused on this change. Ahn and Mira (2002) argued that the increasing income effect of female labor and the decline in the relative price of childcare caused the changes. Studies by Esping-Andersen (1999), Brewster and Rindfuss (2000) and Kögel (2004) stated that the increase in FLP itself would result in the decline in TFR even after the 1990s; however, there might be exogenous factors (i.e. reduced conflicts between work and family) which had positive impacts on both fertility and FLP.

Feyrer et al. (2008) agreed with the conclusion of previous studies and suggested that the relationship between TFR and FLP was U-shaped over time. According to the study, when FLP was below 50-60 per cent, there was a steep negative relationship between TFR and FLP. However, when FLP was above 50-60 per cent, TFR would increase modestly along with the increase in FLP. The study observed that, when FLP began to increase to some extent without a corresponding improvement in their status in the family, disincentives to having additional children was strongest. In contrast, at the next stage when labor market opportunities began to equalize between both sexes, women’s bargaining power in the family increased. In this stage, men’s participation in the family increased and disincentives for women to have children were reduced.

Other studies, such as that by McDonald (2000), also focused on this lag between the progress toward gender equality in society and in the family. Many studies confirmed that men’s increasing involvement in homemaking and childcare might potentially increase fertility (Goldscheider et al., 2015). McDonald (2013) stressed that countries with a positive association between higher fertility and higher income focused on policies to support balancing work and family.

The U-shaped relationships between fertility and gender-related variables other than FLP have also been examined. Hazan and Zoabi (2015) showed that the cross-sectional relationship between TFR and women’s education in the USA had become U-shaped. The study found that highly educated women were recently able to have more children and work longer hours because of the decrease in the relative cost of childcare. Esping-Andersen and Billari (2015) and Arpino et al. (2015) discovered the U-shaped relationship between TFR and people’s values concerning gender roles. Esping-Andersen and Billari (2015) concluded that gender egalitarianism became increasingly more compatible with having children.

As described in the next section, this study examines the U-shaped relationship between fertility and FLP, which was suggested by Feyrer et al. (2008) for developed countries, using the data from East Asia. However, in advance of the analysis, this study preliminarily examines the U-shaped relationship using the data from 19 OECD countries for reference. The U-shape is observed as shown in Figure 1. Each country’s data for the most part move along the U-shape from the upper-left, via the lower-middle to the upper-right.

2.3 Relationship between FLP and income (feminization-U)

There are also many studies on the relationship between FLP and per capita income. Goldin (1995) showed the U-shaped relationship between FLP and per capita GDP, which is known today as the feminization-U. (In this article, the term “feminization-U” is used for this relationship to distinguish it from the U-shape between fertility and FLP.) The U-shape means that further economic development would, at a certain point, reverse the declining FLP trends observed during the initial stage of development. The author explained that the initial decline of FLP was due to the strong income effect, but, at some point, the effect diminished and the substitution effect was strengthened.

In recent years, studies referred to the differences between the feminization-U in developed and developing countries. The World Bank (2012) showed the upper shift of the feminization-U between 1980 and 2008, which meant that FLP increased over time at all income levels. Olivetti (2014) found that the feminization-U was more muted for countries developing after 1950. Gaddis and Klasen (2014) stated that the feminization-U seemed to have little relevance for most developing countries today; instead, historically contingent initial conditions appeared to be more important drivers of FLP.

2.4 Convergence in TFR

Studying the convergence in fertility, Wilson (2001) found that social and demographic changes progressed far more rapidly than economic development. The aforementioned work by Feyrer et al. (2008) confirmed the absolute convergence of TFRs of 110 countries between 1970 and 2000[3]. The study stressed that convergence in TFRs occurred despite a lack of convergence in income.

On the contrary, Dorius (2008) argued that fertility began to converge only rather recently. Regarding the background of fertility convergence, the study referred to Bryant (2007), who stressed the importance of socioeconomic changes between two sets of theories for declining fertility: socioeconomic changes and diffusion of ideas.

2.5 Contribution of this study

This study examines the U-shaped relationship between fertility and FLP in East Asia, which includes both developed and developing countries. The estimation is based on Feyrer et al. (2008), who suggested the relationship in developed countries.

This study also examines the inverse-J-shaped relationship between fertility and per capita income in East Asia. The estimation is based on Luci-Greulich and Thévenon (2014), who confirmed the inverse-J-shaped relationship in OECD countries, and Lacalle-Calderon et al. (2017), who confirmed the inverse-J-shaped relationship in economies that attained a certain level of development.

This study then investigates the background of low fertility in East Asia through the framework of the feminization-U, and the absolute convergence in fertility, FLP and per capita income. Because of the range of stages of socioeconomic development among East Asian countries, the difference in the feminization-U between developed countries and developing countries (World Bank, 2012; Olivetti, 2014; Gaddis and Klasen, 2014) is expected to be clearly shown. The convergence in fertility and in FLP in East Asian countries has not been the focus of previous research.

3. Data and methodology

3.1 Data

A panel data set is used for this study. The data set comes from 176 countries around the world, including 13 countries in East Asia, collected between 1990 and 2014.

The data set contains three annual variables:

  1. TFR (total fertility rate);

  2. FLP (female labor participation rates of women aged between 15 and 64); and

  3. PCGDP (logarithm, per capita GDP [PPP, constant 2011 international $]).

All the data are extracted from the World Development Indicators (World Bank, 2017).

Table I and Table II summarize the data.

3.2 Methodology

Using the data set described above, this study examines the U-shaped relationship between TFR and FLP and the inverse-J-shaped relationship between TFR and PCGDP. Then, this study tests the estimation using the two explanatory variables (FLP, PCGDP) together.

Following previous studies such as those by Luci-Greulich and Thévenon (2014), this study adopts the quadratic function for the estimations.

All the estimations are conducted with three models: pooled OLS (ordinary least squares), FE (fixed effects) and RE (random effects). FE models focus on variation within each country, whereas RE models focus on variation within each country and between different countries. To choose the most appropriate model from the three, Hausman tests, F tests and Breusch and Pagan tests are[4] conducted. The results of the most appropriate model and the results of the three tests are shown in the tables.

The U-shaped relationship between TFR and FLP is tested with equation (1), where i stands for country i and t stands for year t. Zi is the specific effect for the country i and ε is the error term:

(1) TFRit=α0+α1FLPit2+α2FLPit+(Zi)+εit

Then, the inverse-J-shaped relationship between TFR and PCGDP is tested using equation (2):

(2) TFRit=α0+α1PCGDPit2+α2PCGDPit+(Zi)+εit

Finally, the estimation which considers those two variables together is examined with equation (3):

(3) TFRit=α0+α1PCGDPit2+α2PCGDPit+α3FLPit2+α4FLPit+(Zi)+εit

The expected signs for the coefficients are FLP2 > 0, FLP < 0, PCGDP2 > 0 and PCGDP < 0.

4. Results and discussion

4.1 Results

The U-shaped relationship between TFR and FLP is observed in East Asia and the world [“East Asia (1)” and “World (1)” of Table III]. R2 is 0.5565 (overall) for East Asia, which is higher than 0.1893 (within) for the world.

What should be noted is the peculiar movement of the data from each country in East Asia. As shown in Figure 2, data move from the upper-left to the bottom of the U in some East Asian countries (blue arrow), whereas in other countries, data move from the upper-right to the bottom (orange arrow). That seems to be the reason why the RE model is chosen for the estimation, whereas the FE model is chosen for the world estimation, as well as for the OECD estimation [Figure 1, “OECD (1960-)” of Table III].

The inverse-J-shaped relationship between TFR and PCGDP is also observed in East Asia and the world [Figure 3; “East Asia (2)”and “World (2)” of Table III]. R2 is 0.4886 (overall) for East Asia, higher than 0.2046 (within) for the world.

The relationships persist when estimating two variables together [“East Asia (3)” and “World (3)” of Table III]. For the East Asian estimation, the FE model is chosen and its R2 reaches 0.6668 (within), which is higher than 0.3475 (within) for the world.

Those results suggest that the correlation between both TFR and FLP and TFR and PCGDP could change from negative to positive at the highly advanced stage of development in East Asia. In other words, fertility recovery could be achieved, if both FLP and PCGDP are high enough and increasing, which is consistent with the findings of Myrskylä et al. (2011), McDonald (2013) and Luci-Greulich and Thévenon (2014). However, as of this point, no remarkable recovery of fertility has yet been observed in the region.

4.2 U-shaped curve between FLP and PCGDP

To analyze the background of low fertility in East Asia, this study examines the feminization-U in East Asia using equation (4). Figure 4 and Table IV show the results.

(4) FLPit=α0+α1PCGDPit2+α2PCGDPit+(Zi)+εit

Figure 4 depicts the division of East Asian countries into two groups by the blue line that stands for the estimation result of the feminization-U in the region. The group below the blue line has a relatively higher income and lower FLP. The group above the blue line comprised countries with lower income and higher FLP. R2 increases if the estimation is conducted only in higher-income countries [0.5168 (within)], compared with the estimation in all East Asia [0.3221 (within)]. The U-curve for the higher income group (brown line) is located below the blue line. On the contrary, the feminization-U is not observed in lower-income countries. Such results are consistent with the findings of the World Bank (2012), Olivetti (2014) and Gaddis and Klasen (2014).

In Figure 4, solid red lines stand for turning points of the quadratic function estimated in “East Asia (3)” of Table III, whereas dotted red lines stand for turning points of the quadratic function estimated in “World (3)” of Table III. Fertility recovery might be expected if the country is in the upper-right part of Figure 4, where both FLP and PCGDP are high enough. However, no country in East Asia is included in the area. In contrast, among OECD countries, countries such as Sweden and Denmark have recently been situated in that part (Table II). At that stage, women are expected to balance work and family without much difficulty.

4.3 Convergence in TFR, in FLP and in PCGDP

Figure 5 shows that all three variables examined in this study (TFR, FLP and PCGDP) converged in East Asia between 1990 and 2014 in an absolute sense. The peculiarity in the U-shape between TFR and FLP shown in Figure 2 might be the result of the convergence in TFR and in FLP.

The division of East Asian countries into two groups (lower-income countries and higher-income countries) by the blue line in Figure 4 and the difference in the time-series movements of the data between two groups in the figure are also related to the convergence of FLP as well as income. FLP in the initial term was polarized between higher-income countries and lower-income countries. Therefore, convergence in FLP results in the decline in FLP for the lower-income group, whereas it increases FLP for the higher-income group. Along with the converging process of FLP, the data of lower-income countries moved more notably from the left to the right than the data of higher-income countries, which represents the convergence of income.

4.4 Diverse situations concerning fertility and FLP in East Asia

The situation concerning fertility varies among East Asian countries.

Cambodia and Laos are in the upper-left part of Figure 4. Their TFRs are still much higher than the population replacement level, and their policy stances aim to lower fertility. FLP is also very high in those countries because they are still in the initial stage of development.

Vietnam is an exceptional case, where forceful policy measures to control fertility had a strong impact, which has resulted in fertility below the population replacement level. FLP is relatively stable at the high level.

Singapore, Japan and Korea are in the lower-right portion of the figure. Those countries are known for low TFR and difficulties in balancing work and family.

Indonesia and the Philippines, where TFRs are above the population replacement level, are in the lower FLP stage.

Thailand and China have been developing rapidly. Their FLP is declining, though the level is relatively high. In both countries, TFR has fallen far below the replacement level. Both countries have already changed their policy stances to increase fertility. It is urgent to develop society in a way that women can balance work and family.

5. Conclusion

This study aims to apply two recently discovered relationships that describe fertility recovery in developed countries to East Asia: the U-shape between fertility and FLP and the inverse-J-shape between fertility and income.

The main findings of this study are as follows:

  • Both the U-shape and the inverse-J-shape are confirmed in East Asia, and the relationships persist when including two variables (FLP, income) into one estimation. This result suggests that fertility recovery could be achieved if both FLP and income are high enough and increasing.

  • In East Asia, the U-shape is peculiar. Lower-income countries’ data move from the upper-right to the bottom with both fertility and FLP declining, whereas higher-income countries’ data move from the upper-left to the bottom with declining fertility and increasing FLP. This peculiarity seems to be related to the convergence of fertility and FLP across countries.

  • The so-called feminization-U, which is another U-shaped relationship between FLP and income, is observed in East Asia. The shapes of the curve and the movements of the data are different between higher-income countries and lower-income countries in the region. The difference is related to the convergence of income and the initially polarized FLP.

  • No country in the region has reached the stage of the coexistence of high FLP and high income, where fertility recovery might be possible.

This study focused on East Asia where fertility decline was most notable among developing countries. However, considering the projection that global fertility is expected to continue to decline and that the decline is expected to be most notable in least developed countries, future research focusing on other regions could be a worthwhile next step.

Figures

Total fertility rate and female labor participation in 19 OECD countries (1960-2014)

Figure 1.

Total fertility rate and female labor participation in 19 OECD countries (1960-2014)

Total fertility rate and female labor participation in East Asia (1990-2014)

Figure 2.

Total fertility rate and female labor participation in East Asia (1990-2014)

Total fertility rate and per capita GDP in East Asia (1990-2014)

Figure 3.

Total fertility rate and per capita GDP in East Asia (1990-2014)

Female labor participation and per capita GDP in East Asia (1990-2014)

Figure 4.

Female labor participation and per capita GDP in East Asia (1990-2014)

Convergence of three variables in East Asia

Figure 5.

Convergence of three variables in East Asia

Summary of the data set

Variable name Source World East Asia
Mean SD Minimum Maximum Mean SD Minimum Maximum
TFR Total fertility rate WDI 3.27 1.74 0.83 8.61 2.31 1.08 0.90 6.15
FLP Female labor participation rate (aged between 15 and 64) WDI 56.0 17.6 9.7 91.9 63.1 12.4 44.5 84.7
PCGDP Per capita GDP (logarithm, PPP, constant 2011 international $) WDI 8.9 1.3 5.5 11.8 9.3 1.2 6.9 11.3
Number of countries 176 13
Number of observations 4262 321
Period 1990-2014 1990-2014
Note:

“WDI” stands for World Development Indicators

Summary of countries

Country name Country ID TFR FLP PCGDP
1990 2014 1990 2014 1990 2014
East Asia
Malaysia mys 3.52 1.94 45.3 47.2 9.3 10.1
Brunei brn 3.53 1.87 47.0 55.5 11.3 11.1
Philippines phl 4.32 2.98 49.2 52.8 8.3 8.8
Korea kor 1.57 1.21 49.7 55.6 9.4 10.4
Indonesia idn 3.12 2.46 51.9 53.5 8.4 9.2
Hong Kong hkg 1.27 1.23 53.3 60.4 10.2 10.9
Singapore sgp 1.87 1.25 54.6 65.3 10.4 11.3
Japan jpn 1.54 1.42 57.1 65.4 10.3 10.5
China chn 2.43 1.56 79.1 70.4 7.3 9.4
Thailand tha 2.11 1.51 80.0 71.0 8.8 9.6
Cambodia khm 5.60 2.64 80.8 82.1 8.0
Vietnam vnm 3.55 1.96 81.1 79.2 7.3 8.6
Laos lao 6.15 2.99 84.7 80.0 7.4 8.5
FLP from OECD Stat.
OECD     Start Year 2014
Spain esp 1.36 1.27 41.6 68.5 10.1 10.4 30.0 1972 69.8
Ireland irl 2.11 1.96 42.4 62.8 10.0 10.8 32.7 1961 62.5
Luxembourg lux 1.60 1.55 42.4 62.1 10.9 11.4 40.6 1983 64.2
Greece grc 1.40 1.30 43.1 58.7 10.0 10.1 39.2 1983 59.0
Italy ita 1.33 1.39 43.6 54.0 10.3 10.4 28.6 1970 55.2
Belgium bel 1.62 1.75 46.4 62.4 10.3 10.6 44.3 1983 63.0
The Netherlands nld 1.62 1.68 52.4 74.4 10.4 10.7 29.9 1971 74.0
Austria aut 1.46 1.44 55.3 70.6 10.3 10.7 61.3 1994 70.8
Germany deu 1.45 1.39 55.5 72.0 10.4 10.7 46.5 1970 72.9
France fra 1.77 1.99 57.7 66.8 10.3 10.5 56.4 1983 67.2
Portugal prt 1.56 1.21 58.8 70.2 9.9 10.2 48.3 1974 70.0
UK gbr 1.83 1.83 66.8 70.5 10.2 10.5 61.7 1984 72.1
USA usa 2.08 1.86 67.1 66.2 10.5 10.9 42.0 1960 67.1
Switzerland che 1.58 1.52 67.8 78.2 10.7 10.9 68.2 1991 79.0
Canada can 1.83 1.61 68.4 74.7 10.4 10.7 51.4 1976 74.2
Norway nor 1.93 1.78 69.8 76.0 10.7 11.1 49.1 1972 75.9
Iceland isl 2.30 1.93 76.5 82.3 10.3 10.6 76.8 1991 84.2
Denmark dnk 1.67 1.67 77.6 75.7 10.4 10.7 71.9 1983 75.0
Sweden swe 2.13 1.89 81.5 78.9 10.3 10.7 54.5 1963 79.3
Notes:

In principle, data are from World Development Indicators. FLP and PCGDP data start in 1990, whereas TFR data start in 1960, for each country; “FLP from OECD Stat.” shows the data from OECD Stat., which span over a longer period. These data are used for “OECD (1960-)” of Table III and Figure 1

Estimation results (1)

Explained variable TFR East Asia (1) East Asia (2) East Asia (3) World (1) World (2) World (3) OECD (1960-)
      *FLP from OECD Stat.
Explanatory variables Coef. z Coef. z Coef. t Coef. t Coef. t Coef. t Coef. t
PCGDP^2  0.208 6.59*** 0.418 12.70***   0.175 13.57*** 0.226 18.54***  
PCGDP  −4.62 −8.31*** −7.98 −13.98*** −3.84 −16.92*** −4.56 −21.37***  
FLP^2  0.00605 13.49***   0.00340 7.44*** 0.00133 19.94***   0.00107 17.66*** 0.000728 11.15***
FLP  −0.776 −13.48***   −0.510 −8.74*** −0.185 −25.10***   −0.156 −23.29*** −0.0979 −13.51***
Cons  26.3 14.40*** 26.9 11.09*** 57.8 17.87*** 9.05 45.62*** 23.3 23.48*** 30.6 32.45*** 4.87 25.04***
Model  Random effects   Random effects   Fixed effects   Fixed effects   Fixed effects   Fixed effects   Fixed effects  
Number of obs 321   321   321   4262   4262   4262   706  
Number of groups 13   13   13   176   176   176   19  
Prob > F     0.0000   0.0000   0.0000   0.0000   0.0000  
Prob > chi2 0.0000   0.0000              
R-square  0.5565 (overall)   0.4886 (overall)   0.6668 (within)   0.1893 (within)   0.2046 (within)   0.3475 (within)   0.3700 (within)  
Data period 1990-2014   1990-2014   1990-2014   1990-2014   1990-2014   1990-2014   1960-2014  
Turning point FLP 64.1, TFR 1.4   PCGDP 11.1, TFR 1.2   FLP 75.0 PCGDP 9.5   FLP 69.5, TFR 2.6   PCGDP 11.0, TFR2.2   FLP 72.9 PCGDP 10.1   FLPR 67.2, TFR 1.6  
Tests for choosing the model                      
F test Prob > F = 0.0000   Prob >F = 0.0000   Prob > F = 0.0000   Prob > F = 0.0000   Prob > F = 0.0000   Prob > F = 0.0000   Prob > F = 0.0000  
Breusch and Pagan test Prob > chibar2 = 0.0000   Prob > chibar2 = 0.0000   Prob > chibar2 = 0.0000   Prob > chibar2 = 0.0000 Prob > chibar2 = 0.0000   Prob > chibar2 = 0.0000 Prob > chibar2 = 0.0000  
Hausman test Prob > chi2 = 0.6808   Prob > chi2 = 0.5087   Prob > chi2 = 0.0097   Prob > chi2 = 0.0000   Prob chi2 = 0.0000   Prob > chi2 = 0.0000   Prob > chi2 = 0.0034  
Notes:
***

Significant at the 1 per cent level. For each estimation, a model is chosen from pooled OLS, FE and RE, through tests shown in the lower part of the table. OECD estimation on the right is shown as a reference. For this estimation, data from OECD Stat. (OECD, 2017) are used for FLP. Initial periods of FLP are different according to countries

Estimation results (2)

Explained variable East Asia East Asia (higher PCGDP) World
FLP
Explanatory variables Coefficient t Coefficient t Coefficient t
PCGDP^2 2.30 11.94*** 2.55 6.45*** 1.86 17.65***
PCGDP −40.4 −12.05*** −42.6 −5.44*** −29.2 −15.68***
Cons 237 16.36*** 223 5.76*** 165 20.25***
Model Fixed effects Fixed effects Fixed effects
Number of observations 321 199 4262
Number of groups 13 8 176
Prob > F 0.0000 0.0000 0.0000
R2 0.3221 (within) 0.5168 (within) 0.1209 (within)
Data period 1990-2014 1990-2014 1990-2014
Turning point PCGDP 8.8, FLP 60.0 PCGDP 8.4, FLP 45.1 PCGDP 7.8, FLP 50.4
Tests for choosing the model
F test Prob > F = 0.0000 Prob > F = 0.0000 Prob > F = 0.0000
Breusch and Pagan test Prob > chibar2 = 0.0000 Prob > chibar2 = 0.0000 Prob > chibar2 = 0.0000
Hausman test Prob > chi2 = 0.0000 Prob > chi2 = 0.0037 Prob > chi2 = 0.0000
Notes:

***Significant at 1% level; for each estimation, a model is chosen from pooled OLS, FE and RE, through tests shown in the lower part of the table; the estimation “East Asia (Higher PCGDP)” includes eight countries: Malaysia, Brunei, the Philippines, Korea, Indonesia, Hong Kong, Singapore and Japan

Notes

1.

TFR (total fertility rate): Average number of children who would be born to a woman in her lifetime if she were to experience the current age-specific fertility rate.

2.

The analysis of this study includes both Northeast and Southeast Asia in East Asia.

3.

Convergence of economies in an absolute sense means poor economies tend to grow faster per capita than rich ones (Barro and Sala-i-Martin, 2004).

4.

Hausman tests are used to test the null hypothesis that the preferred model is RE versus the alternative FE; F tests are used to test the null hypothesis that the preferred model is pooled OLS versus the alternative FE; and Breusch and Pagan tests are used to test the null hypothesis that the preferred model is pooled OLS versus the alternative RE.

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