Causes and Characteristics of Population Aging

: Evidence from Korea among OECD Countries*

Kyounghoon Park**

May 2018

The contents of this paper represent the personal opinions of the author and do not necessarily reflect the official view of the Bank of Korea. Any report/citation of this paper should specify the name of the author.

* This version is built upon the published paper in the Bank of Korea Monthly Bulletin (June 2017), The Effect of Population Aging and the Policy Challenge: ChapterⅡ-1 (September 2017) in Korean, and in the Bank of Korea Quarterly Bulletin (September 2017) in English.

** Economist, Macroeconomic and Fiscal Research Team, Research Department, Bank of Korea (Tel: +82-2-759-4427, Email: [email protected])

The author would like to express gratitude to Deputy Director Jaerang Lee of the Bank of Korea Economic Research Institute (BOKERI) and Hwankoo Kang, head of the Financial and Monetary Economics Team at the Bank of Korea (BOK), both of whom were of great help in writing this paper. The author would also like to thank Professor Jinill Kim of Korea University, Director Jong Ku Kang at the BOK, Senior economist Kiho Kim of the Macroeconomics Team at the BOK, Economist Sungyub Chung of the Industry and Labor Research Team of the Research Department at the BOK (who provided useful comments), Research associate Junseok Park (who assisted in classifying the materials), participants of the BOKERI’s seminars and the bilateral meeting of the BOK and the Deutsche Bundesbank, and anonymous reviewers. The author claims full responsibility for any errors or mistakes that remain in this paper. Causes and Characteristics of Population Aging : Evidence from Korea among OECD Countries

Abstract

Korea’s level of population aging remains lower than the OECD average. However, the pace of population aging in Korea is faster than that of many other member countries, as its total fertility rate is the lowest among OECD countries while its life expectancy exceeds the OECD average. Using panel data from OECD member countries, we categorize the common causes of population aging in OECD countries into declining fertility rate and increasing life expectancy, and analyze these causes of aging mainly in terms of factors influencing the declining fertility rate. We find that declines in fertility rate are attributable mainly to socioeconomic factors, including wedding and childcare expenses and labor market conditions that limit the division of labor in housework, and sociocultural factors, such as changes in education levels and gender equality. Increases in life expectancy are found to be positively correlated to welfare policies and income levels. Next we compare descriptively the characteristics of population aging in Korea with those in major countries. As Korea industrialized rapidly, the aging of its population has progressed rapidly as well. The factors driving this rapid aging include: historical characteristics, such as the decline in potential fertility, caused in part by Korea’s birth control policy; sociocultural characteristics, such as the decline in the fertility rate due to high wedding and childcare expenses, emergence of an environment in which it is difficult to achieve work-family balance, and gender inequality in the division of labor in housework; and demographic characteristics, such as the surge in the proportion of elderly people as the baby boomers age. To cope with this aging, policies are urgently needed to help ease the burdens of wedding and childcare expenses, for example by stabilizing the housing market and, reducing private education expenses, and create working conditions that ensure work-family balance and gender equality in the division of labor in housework. More fundamentally, it is necessary to establish social consensus on the need for a gender-equal society. As the recovery of fertility rates and slowing of the pace of population aging take place over several generations, policies aimed at achieving these goals should be implemented consistently and continuously based on a long-term perspective and in conjunction with policies that aim to improve education and employment conditions for young people. Finally, to address poverty among the elderly, comprehensive measures such as elderly employment policies, pension schemes and welfare systems will be important policy tasks going forward.

Key words: population aging, declining fertility rates, life expectancy JEL classification: J10, J13, J14 I. Introduction

Population aging refers to a demographic phenomenon in which simultaneously declining fertility and mortality rates cause the proportion of elderly citizens in a population to rise, ultimately leading to population decline. Statistics Korea announced the median values of its population estimates in 2006, 2011, and 2016, which showed that after peaking sometime around 2030, Korea’s population would begin decreasing. Population estimates are adjusted every five years within the scopes of low- and high-value scenarios. Overall, population estimates have been adjusted upward slightly based on median value scenarios. These adjustments are attributable largely to prediction errors, but are also partly attributable to the influx of foreign workers and the effects of the population policies 1 that have been implemented every time the population estimates are announced (Refer to Figures 1 and 2).

Figure 1. Population Estimate Trends in Korea1)

Note: 1) Based on median values of population estimates made in 2006, 2011, and 2016 Source: Statistics Korea, Future Population Estimates

1 This natural population increase is a result of the weakening effects of the birth control policies that were implemented from the 1960s to 1990s but abolished in the 2000s. Korea’s birth control policies are explained in detail in Chapter IV. Figure 2. Population Estimate Trends in Korea1)

Note: 1) Comparison of population estimates made for a period between 2005 and 2030, based on median values of population estimates made in 2006, 2011, and 2016 Source: Statistics Korea, Future Population Estimates

Economically, on the other hand, population aging causes the working age population to shrink, which in turn leads to declines in labor supply. In addition, the average propensity to consume of the elderly, whose proportion of the population is increasing, may change consumption patterns and affect total savings and investment. According to the future population estimates announced by Statistics Korea, people aged 65 or above accounted for 12.8% of Korea’s total population as of 2015, and that proportion is expected to reach 28.7% by 2035 and 42.5% by 2065, growing to take up nearly half the entire population (Refer to Figure 3).

Figure 3. Korea’s Elderly Population and Its Share of the Total Population1)

Panel A. Elderly Population,2) 1965-2065 Panel B. Its Share of the Total Population,3) 1965-2065

Note: 1) Based on median values of population estimates made in 2006, 2011, and 2016 Source: Statistics Korea, Future Population Estimates

Accordingly, the working age population aged 15 to 64 peaked in 2016, but is expected to decline rapidly at an average rate of more than 300,000 people per year until it reaches 20.62 million, which is 55.1% of the size of the working age population in 2015. The working age population’s proportion of the entire population was 73.4% in 2015, but is expected to fall to 60% by 2035 and 47.9% by 2065, accounting for slightly less than half the total population. By age bracket, the proportion of the working age population aged 25 to 49 will drop from 52.8% in 2015 to 49.2% in 2065, while the share of the working age population aged 50 to 64 will rise from 29.1% to 36% over the same period (Refer to Figure 4).

Figure 4. Size and Structure of Korea’s Working Age Population1)

Panel A. Working Age Population, 1965-2065 Panel B. Structure of Working Age Population,2) 1965-2065

Notes: 1) Based on 2016 median value estimate 2) Proportions of working age population aged 25 to 49 and 50 to 64 to the working age population aged 15-64 Source: Statistics Korea, Future Population Estimates

If the above projections are true, a declining working age population would lead to decreases in savings and investment, which would in turn reduce Korea’s economic growth potential in the long run (Kim, 2016; IMF, 2004).2 Furthermore, as the declining working age population erodes the government’s tax revenue, the government will be forced to increase its public expenditures for such as pensions and medical costs, resulting in the deterioration of the government’s fiscal soundness. These trends will make it difficult for the government to maintain balanced budgets for family and child support programs and may increase inter- generational income transfers between the younger and older generations. Considering the problems posed by these demographic changes, we need to gain an overall understanding of the causes and characteristics of population aging and establish fundamental countermeasures for addressing them. Studies on the causes of population aging and countermeasures for addressing them have been carried out for quite some time already. This study aims to promote a comprehensive understanding of the causes and characteristics of population aging and suggest implications for the development of countermeasures by first reviewing related literature and addressing matters that such studies have not dealt with. First, we added the gender division of labor in housework to the explanatory variables used in existing studies, in consideration of the rising labor force participation rate of women, and analyzed its impacts on fertility rates through its interaction with the working hours of men. We also examined the impacts gender income disparity would have on fertility rates based on the assumption that the education levels of women will continue to rise.

2 However, a recently released study (Acemoglu and Restrepo, 2017) argues that population aging would not necessarily reduce labor supply, savings and investment, and would thus not degrade economic growth potential. It also indicates that the effect of labor being replaced by technological advances should be considered. Second, we enhanced the explanatory power of our model by adding variables that can explain socioeconomic and policy factors, including childcare costs such as housing prices, policies supporting childcare costs, and the share of public pensions. Third, we attempted to reflect economic structural changes, such as the growing uncertainty after the global financial crisis, by increasing the number of sample countries subject to our analysis and including time periods after the global financial crisis in our analysis. Finally, this study classifies OECD member countries in terms of the extent of their population aging and aims to promote a better understanding of Korea’s population aging by presenting a descriptive account of its peculiarities in comparison with other member countries. In Chapter II of this study, we introduce the status of population aging in Korea and OECD member countries and the related literature on the causes of the phenomenon. In Chapter III, we categorize the common causes of population aging in OECD countries into declining fertility rates and increasing life expectancy and conduct an empirical analysis of determinants of the two causes using the panel data of OECD member countries. In Chapter IV, we provide a descriptive account of the characteristics of population aging in Korea in comparison with other member countries. Finally, in Chapter V, we give a brief summary of this study and highlight its implications.

II. Current Status of Population Aging and Related Literature

1. Current Status of Population Aging

In this chapter, we present the status of population aging based on major indicators of OECD member countries. The indicators of population aging used here include the proportion of the elderly population, dependency ratio, fertility rate, and life expectancy. We also provide a comparison of these sub-indicators of Korea with those of OECD member countries. Generally, the extent of population aging is measured by the proportion of a country’s population aged 65 and older. If this proportion of a country’s population falls into the 7 to 14% range, it is considered an aging society; if it falls into the 14 to 20% range, it is an aged society; and if it falls into the 20% or more range, it is a super-aged society. As of 2015, the average proportion of populations aged 65 and older among OECD member countries was 16.8%, indicating that most OECD countries can be classified as aged societies. According to a publication by the UN, the proportion of Korea’s population aged 65 and older was 13.1%,3 placing Korea together with Mexico, Turkey, and Chile in the group of aging societies. However, Korea joined the ranks of aged societies in 2017.4 As of 2015, the only super-aged societies are Japan, Italy, Greece, and Germany (Refer to Figure 5).

3 According to data published by Statistics Korea, the proportion of the population aged 65 and older was 12.8% in 2015 and 13.2% in 2016.

4 According to data published by Statistics Korea, the proportion of the population aged 65 and older reached 14.2% in 2017, placing Korea in the group of aged societies. Figure 5. Proportion of the Elderly Population1)2)

Notes: 1) Proportion of the population aged 65 and older 2) As of 2015 Source: UN World Population Prospects, 2015 Revision

Next, another indicator used to measure the extent of population aging is the dependency ratio, which is defined as the ratio of the elderly population (aged 65 and older) to the working age population (aged 15 to 64). As an alternative measure, there is the aging index, which is the ratio of the elderly population to the infant and adolescent population (aged 0 to 14). However, as it is difficult to obtain the aging index for each country, this paper uses the dependency ratio as a representative index. OECD member countries have seen rapid increases in their dependency ratios. As of 2015, Korea’s dependency ratio was 18%, falling short of the OECD average (25.7%) and making Korea the country with the fourth lowest ratio among the 35 OECD members. However, Korea’s ratio has been rising at a rapid pace since the 1990s. Among OECD member countries, Japan has the highest ratio, at 43.3%, followed by Italy, Greece, Finland, and Germany, all of which are super-aged societies. Meanwhile, Korea, Mexico, Turkey, Chile, and Israel are countries whose population aging has not yet progressed as far comparatively (Refer to Figure 6).

Figure 6. Trends of Dependency Ratio and Dependency Ratio by Country

Panel A. Trends of Dependency Ratio1) Panel B. Dependency Ratio by Country1)2)

Notes: 1) Ratio of the population aged 65 and older to the working age population (aged 15 to 64) 2) As of 2015 Source: World Bank, World Development Indicators On the other hand, population aging is directly related to declining fertility rates. Fertility rates are generally measured by the total fertility rate (TFR), which is the ratio of the number of newborns to the average number of women of childbearing age (15 to 49 years old) (OECD, 2005). The total fertility rate necessary to maintain the current population is called the population replacement rate or replacement fertility level, which currently stands at approximately 2.1 newborns, according to OECD and UN standards. While OECD countries show TFRs below the population replacement rate, on average, Korea’s TFR was 1.21 newborns 5 as of 2014, which is the lowest among OECD member countries and far below the OECD average of 1.68 newborns (Refer to Figure 7).

Figure 7. Trends of Total Fertility Rates and Total Fertility Rates by Country Panel A. Trends of Total Fertility Rates1) Panel B. Total Fertility Rates by Country1)2)

Notes: 1) Ratio of newborns to the average number of women of childbearing age 2) As of 2014, except for Canada (as of 2012) and Chile (as of 2013) Source: OECD Family Database

Life expectancy at birth, another factor that accelerates the rate of population aging, has been increasing among OECD member countries overall. Korea’s life expectancy began exceeding the OECD average after 2005. As of 2014, it stood at 82.1, which is 1.7 years longer than the OECD average of 80.4 years and the eighth highest among OECD member countries (Refer to Figure 8).

Figure 8. Trends of Life Expectancy and Life Expectancy by Country Panel A. Trends of Life Expectancy1) Panel B. Life Expectancy by Country1)2)

Notes: 1) Life expectancy at birth 2) As of 2014 Source: World Bank, World Development Indicators

5 1.24 as of 2015, and 1.17 as of 2016 (Statistics Korea) Generally, considering Korea’s rate of population aging or dependency ratio, the level of Korea’s aging is lower than the OECD average. However, as Korea’s TFR is the lowest among OECD members and its life expectancy is above the OECD average, Korea’s population aging is progressing at a significantly rapid pace.

2. Related Literature

Previous research on population aging has focused mainly on the factors driving the decline in fertility rates. In addition, there are economic studies that outline the causes of the declining marriage rates, which have been pointed to as a fundamental factor of the low fertility rates. Meanwhile, there seem to be few studies that analyze, in detail, the causes of longer life expectancy, as it is generally believed that people can intuitively understand the causes, and related policy measures are limited. First of all, there are a significant number of domestic and foreign studies that view low fertility as a major cause of population aging and analyze the factors that influence fertility rates. Overall, these studies point to the increasing labor force participation rate of women (caused by women’s attainment of higher education), macroeconomic conditions, characteristics of social environments and policies, and sociocultural ambience as major causes of the low fertility rates. Below, we refer primarily to Sangho Yi and Sangheon Lee (2011), Sam-Sik Lee et al. (2012), and Chulhee Lee and Sunyoung Jung (2015) and outline the relevant previous research. Studies that point to women’s attainment of higher education as the major cause of low fertility rates include: Andersson (2005), Beets (1997), Sungho Chung (2010), Tai-Hun Kim, Sam-Sik Lee, and Dong-Hoy Kim (2006), Yujin Oh and Sung-Joon Park (2008), Byung Woo Kim (2010), and DeCicca and Krashinsky (2016). They explain that the higher education women attain, the higher their labor force participation rate and the more opportunities they gain in the labor market and society, eventually causing the low fertility rates to drop even further. Doo-Sub Kim (2007), who deals with the effects of macroeconomic conditions on fertility rates, showed that the fertility rates of low income groups in Korea plunged as the country was coping with the foreign exchange crisis. Sukhui Choi and Jeongwu Kim (2005) show that economic uncertainty after the unification of East and West Germany was a major cause of the declining fertility rates in East Germany at the time. Another case in point is Poland, which saw its fertility rates plummet after 1991, when the socialist block collapsed and transition economies emerged (Philipov and Kohler, 1999). Laroque and Salanié (2005) point to the characteristics of social environments and certain policies as major factors determining fertility rates. They point out that France’s expansion of its childcare allowance boosted the country’s fertility rates. Youn-Kyoung Min and Myungsuk Lee (2013) show that fertility rates are higher in regions that have fiscally sound local governments, are economically better off, have pleasant residential environments, and offer cultural benefits such as leisure and welfare. On the other hand, Huicheol Min et al. (2007) and Yunjeong Shin and Jihye Lee (2009) show that high childcare and education costs are factors that curb childbirth. Finally, Feyrer et al. (2008) and Yamaguchi (2010) conduct studies on sociocultural ambience and imply that men’s inability to balance work and family is related to low fertility rates. Sumi Park (2008) shows that the more time husbands spend doing housework, the more working moms have a second child. Sam-Hyun Yoo (2006) suggests that people’s degree of awareness of gender equality is one of the factors that influences the demographic structure of low fertility. Besides studies on determinants of low fertility, there are studies that point to increases in the median age of people at first marriage and reasons for remaining single as causes of declining marriage rates. These studies view unstable employment and high wedding expenses as the major causes of declining marriage rates. Using census data of the United States, Blau et al. (2000) show that while women’s marriage rates decrease in favorable labor market conditions, men’s marriage rates decrease in poor labor market conditions. Becker (1973), Becker et al. (1977), Oppenheimer (1988), and Loughran (2002) use a marriage market model to explain that while labor market conditions favorable to women lead to increases in women’s reservation values, labor market conditions disadvantageous to men cause gender income disparity. In addition, there are studies that look into the correlation between income levels and marriage, such as Wilson (1987) and Wood (1995). In Korea, the study conducted by Sangho Yi and Sangheon Lee (2011) is a representative one that examines the economic factors that influence marriage decisions. They point out that rising employment instability and increases in housing prices result in higher wedding expenses, which serve as a primary factor of the declining marriage rates. Jinbaek Park and Jaehee Lee (2016) examine how housing price fluctuations caused by economic cycles affect fertility rates. In their study, they consider housing prices as childcare expenses. On the other hand, there have been recent studies on factors influencing fertility rates from a perspective of gender equality. The study by Young-Mi Kim (2016) is a case in point. Such studies show that “highly educated women in the labor force who hold a gender equality attitude” tend to have fewer children, but this inverse relationship is sensitively linked to the degree of gender equality of social institutions in the given society. Therefore, these studies also show that if countries make their labor markets more gender equal and implement family welfare systems, these markets and systems may have a positive impact on the fertility rate among women who are sensitive to gender equality. Myrskylӓ et al. (2013) show that socioeconomic development in society leads to rising fertility rates, and that advanced countries are experiencing rising fertility rates among older women. The study also explains that even though countries may be advanced in terms of health, income, and education, those with low gender equality suffer from continuous decreases in fertility rates. Our study is largely in agreement with Young-Mi Kim (2016) and Myrskylӓ et al. (2013).

III. Analysis on Causes of Population Aging

So far, population aging is known to be the universal process of demographic transition as a country or region develops from a pre-industrial to an industrialized economic system (Kyungsoo Choi et al, 2003; Lucas, 2002). A country’s population growth rate rises during the process of its industrialization. However, when its industrialization enters a mature stage, the rate declines, causing the country’s population to age rapidly. In this chapter, we categorize the causes of population aging, occurring in line with the maturation of industrialization, into (1) declining fertility rates and (2) longer life expectancy, based on existing studies. In addition, human migration is considered to have a material impact on the progress of population aging. 6 In particular, as many of the OECD member countries are multiracial countries with large numbers of immigrants, immigration can be viewed as ameliorating the progress of population aging and its economic effects. However, as population immigration is influenced by exogenous factors, such as immigration policies or

6 The representative population projection method is a cohort component method. The basic equation of this method is as follows: t n t - - (where represents population; , number of births; , number of deaths; , number of immigrants; and E, number of emigrants). P + =P +B D+I E P B D I the influx of refugees, this study does not conduct an analysis of determinants of migration.

1. Declining Fertility Rates

Declining fertility rates are a consequence of declining marriage rates and increasing average ages at first marriage and first childbirth. Women who delay or avoid their first childbirth may find it difficult to give birth to more than two children in their lifetime. Regarding the causes of the declining marriage rates and people’s tendency to avoid having more than two children, existing studies suggest economic factors, such as income and labor market conditions; sociocultural factors, such as education and changes in values concerning gender roles; and policy factors, such as family and welfare policies. In this section, we look at the correlation between fertility rates and several factors that have been pointed to as major causes of declining fertility rates, such as changes in people’s concept of marriage, income, opportunity costs, labor market conditions, education and socioeconomic factors, and other policy and institutional factors.

(1) Major Causes

A. Changes in People’s Concept of Marriage

In a traditional society, where marriage is a precondition of childbirth, a decline in the marriage rate translates into a decrease in the fertility, because the fertility rates of married women are higher than those of single women. The marriage rates of OECD member countries have been declining, on average, in terms of the number of marriages per 1,000 people. Korea’s marriage rate, however, is higher than the OECD average (Refer to Figure 9).

Figure 9. Trends of Marriage Rates and Marriage Rates by Country Panel A. Trends of Marriage Rates1) Panel B. Marriage Rates by Country1)2)

Notes: 1) Number of marriages per 1,000 people 2) As of 2014, except for some countries: Austria, Chile (as of 2013), Belgium, France, Ireland and Israel (both as of 2012), Iceland and the UK (both as of 2011), and Canada (as of 2008) Source: OECD Family Database

However, the correlation between marriage rates and fertility rates has grown gradually weaker as industrialization has progressed and sociocultural environments have changed. The weakening of this correlation is confirmed by the fact that share of births outside of marriage have been growing. In particular, OECD member countries saw their share of births outside of marriage increase since the 1980s, with births outside of marriage accounting for 35% of total births among OECD countries in the 2010s. Marriage rates are therefore expected to have a weaker correlation to birthrates. In contrast, countries that attach great importance to marriage, such as Korea, Japan, Turkey, Israel, and Greece, have low share of births outside of marriage, making the correlation between marriage rates and birthrates significant (Refer to Figure 10). Accordingly, we examine whether marriage rates still show a significant correlation to birthrates despite these changes, leading us to include marriage rates as an explanatory variable in our empirical analysis.

Figure 10. Trends of Share of Births Outside of Marriage and Share of Births Outside of Marriage by Country Panel A. Trends of Share of Births Outside of Panel B. Share of Births Outside of Marriage by Marriage1) Country1)2)

Notes: 1) Proportion of births outside of marriage of total births (%) 2) As of 2014, except for some countries: Chile and Norway (as of 2013), Austria, Belgium, Canada, Estonia, France, UK, Iceland, and Ireland (as of 2012) Source: OECD Family Database

On the other hand, changes in people’s concept of marriage throughout the process of industrialization are reflected in the rising mean age at first marriage. Women who get married for the first time later in life are likely to have comparatively fewer childbirth opportunities, and women’s mean age at first marriage has been increasing around the world. Women’s mean age at first marriage in Korea was lower than the OECD average in the 1990s, but that gap began closing in the 2010s (Refer to Figure 11).

Figure 11. Trends of Women’s Mean Age at First Marriage and Women’s Mean Age at First Marriage by Country Panel A. Trends of Women’s Mean Age at First Panel B. Women’s Mean Age at First Marriage by Marriage Country1)

Note: 1) As of 2014, except for some countries: Chile and Israel (as of 2013), Austria and UK (as of 2012), France and Iceland (as of 2011), Belgium and Ireland (as of 2010), and Canada (as of 2008). Source: OECD Family Database

Along with the rising mean age of women at first marriage, the mean age of women at first birth has been going up as well (Refer to Figure 12). With the increasing share of births outside of marriage, among total births, women’s mean age at first marriage has surpassed their mean age at first birth, making the correlation between the two variables different across countries. In countries where women’s mean age at first marriage is higher than their mean age at first birth, such as Sweden, Denmark, Norway, and France, births outside of marriage tend to occur first, followed by marriage later. In contrast, in countries with lower share of births outside of marriage, such as Korea and Japan, women’s mean age at first birth is higher than their mean age at first marriage (Refer to Figure 13).

Figure 12. Trends of Women’s Mean Age at First Birth and Women’s Mean Age at First Birth by Country Panel A. Trends of Women’s Mean Age at First Panel B. Women’s Mean Age at First Birth by Country1) Birth

Note: 1) As of 2014, except for Canada (as of 2011) Source: OECD Family Database

Figure 13. Difference Between Women’s Mean Age at First Birth1) and Women’s Mean Age at First Marriage2) by Country

Notes: 1) As of 2014, except for Canada (as of 2011) 2) As of 2014, except for some countries: Chile and Israel (as of 2013), Austria and UK (as of 2012), France and Iceland (as of 2011), Belgium and Ireland (as of 2010), and Canada (as of 2008) Source: OECD Family Database

B. Income and Costs for Marriage, Childbirth, and Childcare

Generally, it is predicted that high-income households have more resources to cover the costs of marriage, childbirth, and childcare, which has a positive impact on fertility. On the other hand, if the wife in a dual-income household has to stop working due to childbirth, the couple might choose to give birth to fewer children, considering the opportunity cost of shrinking household income. As seen above, the relationship between income7 and fertility is

7 The extent of income inequality may affect fertility rates as well. The lower high income-earners’ fertility rate endogenous and inconsistent (Barlow, 1998). The correlation between GDP per capita and fertility rates in OECD member countries has changed over the course of their industrialization. Before the 1990s, the higher a country’s GDP per capita, the lower its fertility rates. Since the 2000s, however, the inverse relationship between these two variables has not been significant (Refer to Figure 14). 8 Therefore, the correlation between income and fertility rates needs to be empirically verified. In our empirical analysis, we include income as an explanatory variable.

Figure 14. Correlation between Income1) and TFR2) during Sub-periods

Notes: 1) Log-transformed values of GDP per capita 2) Ratio of newborns to the average number of women of childbearing age (15 to 49 years old) by year Source: World Bank, World Development Indicators

On the other hand, wedding expenses and child education expenses are major variables influencing decisions regarding marriage and number of children. When education expenses increase, parents generally tend to reduce the number of childbirths to ensure that their children receive high-quality education. 9 This is consistent with the findings of studies (Sangho Yi and Sangheon Lee, 2011; Jinbaek Park and Jaehee Lee, 2016) that showed housing prices, considered as part of wedding and childcare expenses, could have a negative impact on fertility rates. In our empirical analysis, we use the rate of increase in housing prices as an index that represents wedding and private education expenses.

is, the wider the income gap becomes, resulting in a cycle where the next generation’s fertility rate continues to fall. On the other hand, as the fertility rate among low-income groups increases, their childcare expenses will grow, aggravating income inequality (OECD, 2005, and IUSSP, 1998).

8 This is consistent with the findings of Myrskylӓ et al. (2013), which shows that socioeconomic development coupled with advances in gender equality leads to increasing fertility rates. OECD (2005) shows that GDP per capita and fertility rates are positively correlated, based on countries’ cross section data as of 2000.

9 Under such circumstances, if the government bears a larger share of public education expenses, the pressure on parents regarding their choice concerning the number of children would be reduced. C. Labor Market Conditions and Women’s Labor Force Participation

There is some controversy over the impact of unemployment rates, which represent overall labor market conditions, on marriage and childbirth. If many young people facing high unemployment rates were to delay getting jobs, extend their studies, or decide to stay with their parents, they would also be expected to delay marriage and childbirth (OECD, 2005). Meanwhile, as high unemployment rates lower the opportunity cost of childbirth, they can have a positive impact on people’s decisions regarding childbirth (Gauthier and Hatzius, 1997; OECD, 2005). Accordingly, the correlation between unemployment rates and fertility rates requires empirical analysis. In the 1980s, the unemployment and fertility rates of OECD member countries showed a slightly positive correlation. After the 1990s, however, they showed a negative correlation, indicating that unemployment rates had a negative impact on childbirth (Refer to Figure 15).

Figure 15. Correlation between Unemployment Rates1) and TFR2) during Sub-periods

Notes: 1) Ratio of the unemployed to the total working age population (%) 2) Ratio of newborns to the average number of women of childbearing age (15 to 49 years old) by year Source: World Bank, World Development Indicators

Meanwhile, in the early stages of industrialization, women tend to get married and give birth to children later in life, as their participation in economic activities begins to increase. However, the recent statistics indicate changes in this general trend. From the 1980s to 1990s, the female employment rates and fertility rates in OECD member countries showed a negative correlation. Since the 2000s, however, they have started showing a positive correlation10 (OECD, 2005; Ahn and Mira, 2002; and Young-Mi Kim, 2016) (Refer to Figure 16).

10 The correlation between the fertility rates and labor force participation rates of women, which had previously been negative, became positive after the 2000s. As it is consistent with Myrskylӓ et al. (2013), this change implies that fertility rates are related not only to socioeconomic development but also to sociocultural and policy factors that promote women’s involvement in the labor force and childcare. Figure 16. Correlation between Female Employment Rates1) and TFR2) during Sub-periods

Notes: 1) Ratio of the female employment to female population aged 15 and older (%) 2) Ratio of newborns to the average number of women of childbearing age (15 to 49 years old) Source: World Bank, World Development Indicators

D. Sociocultural Factors, such as Female Education Levels and Gender Equality

Compared to the past, female education levels have become higher on average across OECD countries. As the advancement of women in tertiary education has become universal, their education periods have been getting longer. In addition, it is highly likely that women’s stronger drive for vocational achievement will replace their maternal desire to have children and provide childcare. Therefore, in the 1980s, women’s education levels11 and fertility rates showed a negative correlation. Since the 2000s, however, the correlation between the two seems to have weakened12,13 (Refer to Figure 17). In addition to the change in this correlation, the enrollment rates of women in tertiary education have changed as well. In other words, compared to the 1980s, the enrollment rates of women in tertiary education increased on average after the 2000s. This means that a growing number of women have received higher education, and human capital of the same quality has been accumulated across the economy, thus making the impact of women’s education levels on the fertility rates less and less pronounced.

11 As in OECD (2005), we adopt the enrollment rate of women in tertiary education after graduation from high school, which represents women’s education periods, as a variable for measuring women’s education levels.

12 OECD (2005) show that the correlation between women enrollment rates in tertiary education and total fertility rate became positive after the 1990s, based on countries’ annual cross-section data.

13 In contrast, a case study conducted in Canada (DeCicca and Krashinsky, 2016) shows that education compresses fertility. Figure 17. Correlation between Female Enrollment Rate in Tertiary Education1) and TFR2) during Sub-periods

Notes: 1) Female enrollment rate in tertiary education after graduation from high school 2) Ratio of newborns to the average number of women of childbearing age (15 to 49 years old) by year Source: World Bank, World Development Indicators

Meanwhile, it should be noted that, together with the rising education levels of women, people’s awareness of gender equality has increased as well. Favorable sociocultural factors, such as men’s participation in housework and gender equality in working conditions, can have positive impacts on fertility.14 As more women participate in economic activities during the course of industrialization, the typically long working hours of men is anticipated to have a negative impact on fertility. As men lack the time to participate in housework, women are forced to take sole responsibility for childcare, thus leading them to avoid childbirth. The share of men who work over 40 hours a week 15 among OECD member countries showed no significant correlation to fertility rates before the 1990s. After the 2000s, however, as the share decreased, fertility rates rose (Refer to Figure 18). This implies that sociocultural factors, such as shorter working hours among men, which allows them to participate more in housework, have positive impacts on fertility.

14 Myrskylӓ et al. (2013) point out that an advanced country in terms of income or education but with low gender equality would have low fertility rates.

15 The proportion of men working over 40 hours a week among the total number of employed men Figure 18. Correlation between Share of Men Working over 40 Hours a Week1) and TFR2) during Sub-periods

Notes: 1) Share of men working over 40 hours a week among the total number of employed men 2) Ratio of newborns to the average number of women of childbearing age (15 to 49 years old) by year Source: OECD Family Database

The gender wage gap16 is an index that measures the extent of gender equality in terms of working conditions. 17 The index expresses the inequality that can exist in the economic opportunities men and women have in the labor market. With the overall rise in education levels, the wide income disparity between the two sexes translates into significant gender inequality in terms of working conditions. A society that fails to realize gender equality in working conditions is likely to find it difficult to foster a working environment where people can balance work and family 18 (Refer to Figure 19). In fact, the correlation between the gender wage gap and TFR was not significant before the 1990s, but became negative in the 1990s (Refer to Figure 19).

16 The gender wage gap is defined as the difference between the median earnings of men and women, relative to the median earnings of men (OECD Family Database).

17 Sam-Sik Lee et al. (2012) used the Gender Parity Index (GPI), which is calculated as the ratio of women’s enrollment rate in tertiary education to men’s (World Bank). However, as university education for women has become universal and thus human capital has been accumulated, the ratio has had diminishing explanatory power as a measure of gender equality. Before the 1990s, there was an inverse relationship between the GPI and fertility rates, but after the 1990s, the correlation between the two did not appear significant. Sam-Sik Lee et al. (2012) also adopted the Gender-related Development Index (GDI, UNDP) and Gender Empowerment Measure (GEM, UNDP). There are comparatively fewer observations for these indexes, and thus they have been excluded from this study.

18 Regarding this, the study by Young-Mi Kim (2016) shows that the higher women’s education levels become, the more negative impacts gender income inequality have on fertility. Figure 19. Correlation between Gender Wage Gap1) and TFR2) during Sub-periods

Notes: 1) Ratio of the difference between median earnings of men and women to median earnings of men 2) Ratio of newborns to the average number of women of childbearing age (15 to 49 years old) by year Source: OECD Family Database

E. Other Policy and Institutional Factors

(Childcare support policies)

Generally, people believe that work and childbirth & childcare are mutually exclusive. Therefore, a country with well-established childbirth promotion or childcare support policies, such as maternity leave and childcare facilities, is expected to have more stable fertility rates. The scope of childcare support policies is measured by the ratio of public family benefits to GDP. The more public family benefits, such as childcare allowances, maternity leave pay, and other forms of childcare support, a country offers, the higher its fertility rates are likely to be. In the case of OECD member countries, public family benefits and fertility rates show a positive correlation in the 2000s (Refer to Figure 20). On the other hand, as the government can support the stability of childbirth and childcare through medical insurance schemes, the proportion of health expenditures can also have a positive impact on childbirth. Figure 20. Correlation between Public Family Benefits1) and TFR2) during Sub-periods

Notes: 1) Ratio of public family benefits, such as childcare allowances, maternity leave pay, and other forms of childcare support, to GDP (%) 2) Ratio of newborns to the average number of women of childbearing age (15 to 49 years old) by year Source: OECD Family Database

(Pension schemes)

Existing studies19 have shown that the expansion of pension schemes lowers fertility rates. Too much public pension spending may conflict with income distribution between generations, thus having a negative impact on fertility rates. On the other hand, if a country puts in place proper public pension systems and guarantees an acceptable quality of life for the elderly, people will be better able to secure the resources necessary to have and raise more than two children. Thus, to some extent, public pension systems can have a positive impact on fertility. Therefore, the effects of public pension systems on fertility rates need to be empirically analyzed. As high public pension spending reflects the existence of an already aged demographic structure, the latter cannot be seen as a direct effect of the former. Accordingly, we need to consider time gaps in conducting analyses on the relationship between the two.

(2) Empirical Model and Data

A. Empirical Model

We considered the limits on data collection and observable variables that arise from the multicollinearity between variables and the characteristics of regions and cultural areas and established a shortened empirical model made up of the explanatory variables we discussed

19 These include Cigno and Rosati (1996), Cigno et al. (2003), Boldrin et al. (2005), and Gyehyung Jeon (2015). earlier. The empirical model on determinants of fertility rates is represented by the following equation (1).20

' i t i t- ui ei t (1)

F , =α+X , 1β+ + , 21 The dependent variable i t refers to TFR per country; i t , the explanatory variable vector ( x ) by country; u , country fixed effects; i , the country; and t , the year. The i t i , , definitions and sources of Fthe major variables are explainedXin Table 1 (Definitions and Sources o,f Variables). Considering that the issues of simultaneity between variables or reverse causality can arise in a contemporaneous period, we applied a one-period lag to the explanatory variables, xi t. We conducted a Hausmann test for panel data of countries using fixed effects and random effects models, which showed significant structural differences between the two models., It turned out that the fixed effects model is more suitable than the random effects one. Therefore, in this study, we conduct analyses based on the fixed effects model. Sam-Sik Lee et al. (2012), which analyzed a fertility rate projection model, conducted a static panel analysis using a fixed effects model. On the other hand, OECD (2005) and Gauthier and Hatzius (1997) conducted a dynamic panel analysis using the Generalized Method of Moments (GMM), considering the endogeneity of the variables and dynamic effects. For example, women’s employment affects childbirth and women’s decision to give birth, which in turn influences their decision to work, creating endogeneity between variables. However, national panel data based on macroeconomic aggregate data are expected to impose limits on any detailed analysis of the endogeneity between variables, such as the interaction between individuals’ decision to work and give birth. Therefore, even though the GMM is used, there are analytic limits that blur the distinction between endogenous and exogenous variables. In addition, as changes in fertility rates are a long-term variable that persists over several generations, analyzing determinants of short-run changes through dynamic panel analysis can be limited. Considering the peculiarities of national aggregate data, we used lagged variables. And we adopted the static panel analysis in estimating the long-term effects of the explanatory variables on fertility rates, while selecting variables to minimize the multicollinearity caused by the correlation between variables. As the time series of samples collected annually is comparatively short, meaning that the number of observations per variable within a country is quite small, it should be noted that the information from cross sectional data between countries can account for much of the explanatory power in interpreting the results of our analysis.

20 As TFR, a dependent variable, always shows a positive value in formulating the empirical model, the necessity of a censored regression model or Tobit model can be discussed. However, it is difficult to assume that the reason the TFR collected by each country is always positive is due to respondents’ censoring in surveys just like surveys on personal income. This is because the TFR of each country is calculated by summing the age- specific fertility rates of women of childbearing age (ASFR = number of newborns per woman in a specific age group / number of women in a specific age group). In other words, it is calculated as either a (per one-year age group) or × a (per five-year age group). TFR= ASFR 21 Considering the skewTnFesRs=o5f the ATSFFRRd5istribution, we use the log-transformed values in our analysis. Table 1. Definitions and Sources of Variables Name of Variable Unit Definition Source 1. Dependent variable Ratio of newborns to the average TFR Logarithm number of women of World Bank childbearing age (aged 15 to 49) 2. Explanatory variables

Number of marriages per 1,000 Marriage rate Number OECD, Family Database people Income per capita Logarithm Real GDP per capita World Bank Rate of increase in Year-on-year rate of increase in % OECD housing prices housing prices Proportion of female Female employment rate Logarithm employment among the female World Bank population aged 15 and older Proportion of female labor force Female labor force (employed and unemployed) Logarithm World Bank participation rate among the female population aged 15 and older Proportion of the unemployed Unemployment rate Logarithm among the total working age World Bank population Women’s enrollment rate in Female university Logarithm tertiary education after World Bank enrollment rate graduation from high school Proportion of men working over Share of men working Logarithm 40 hours a week among the total OECD, Family Database over 40 hours a week number of employed men Difference between the Difference between the shares of men and OECD, Family Database %p proportions of men and women women working over 40 author’s calculation working over 40 hours a week hours a week Difference between the median earnings of men and women Gender wage gap Logarithm OECD, Family Database relative to the median earnings of men Ratio of public spending on family benefits, such as childcare Public family benefits % allowances, maternity leave pay, OECD, Family Database and other forms of childcare support, to GDP Ratio of public pension spending Public pension spending % OECD, Family Database to GDP B. Data

Concerning the basic data used for our empirical analysis, we established panel data from 1960 to 2015 for 35 OECD member countries, mainly using the OECD’s Family Database and World Bank’s World Development Indicators. We analyzed the data of 32 countries whose variables, subject to our analysis, could be obtained for the period from 1992 to 2012. 22 In addition, to strengthen the robustness of our analysis results, we analyzed 17 advanced countries, classified as such by the IMF, which are expected to experience greater magnitudes of population aging as their industrialization progresses. The basic statistics of each variable used in our analysis are provided in Table 2.

Table 2. Basic Statistics on Variables1) Name of variable Unit Observations Mean S.D. Min Max TFR Logarithm 1,923 0.70 0.34 0.07 1.92 Marriage rate Number 1,718 6.38 1.66 2.90 12.80 Income per capita Logarithm 1,674 10.00 0.75 7.01 11.61 Rate of increase in % 973 1.80 7.61 -33.18 50.25 housing prices Female employment Logarithm 1,063 3.80 0.25 3.02 4.35 rate Female labor force Logarithm 1,098 3.89 0.23 3.07 4.38 participation rate Unemployment rate Logarithm 840 1.92 0.51 0.41 3.30 Female university Logarithm 1,284 3.50 0.86 -0.09 4.73 enrollment rate Share of men working over 40 Logarithm 922 4.24 0.37 2.83 4.58 hours a week Difference between the shares of men and %p 922 23.67 10.91 2.05 48.48 women working over 40 hours a week Gender wage gap Logarithm 600 2.87 0.54 -0.96 3.97 Public family benefits % 1,025 1.83 1.03 0.03 4.45 Public pension % 933 6.86 3.12 0.17 15.85 spending Note: 1) OECD member countries from 1960 to 2015 Sources: OECD Family Database, World Bank, World Development Indicators

22 Please refer to Table 3 (Classification of Sample Countries) regarding the classification of OECD member countries. Table 3. Classification of Sample Countries OECD OECD member Advanced Region1) Names of country member country subject to country3) country2) our analysis East Asia (2) Japan ○ ○ ○ Korea ○ ○ ○ West Asia (2) Israel ○ ○ ○ Turkey ○ X X Eastern Europe (4) Czech Republic ○ ○ ○ Hungary ○ ○ X Poland ○ X X Slovakia ○ ○ X Northern Europe (9) Denmark ○ ○ ○ Estonia ○ ○ X Finland ○ ○ X Iceland ○ ○ ○ Ireland ○ ○ X Latvia ○ X X Norway ○ ○ ○ Sweden ○ ○ ○ United Kingdom ○ ○ ○ Southern Europe (5) Greece ○ ○ X Italy ○ ○ ○ Portugal ○ ○ X Slovenia ○ ○ X Spain ○ ○ X Western Europe (7) Austria ○ ○ X Belgium ○ ○ X France ○ ○ ○ Germany ○ ○ ○ Luxemburg ○ ○ X Netherlands ○ ○ X Switzerland ○ ○ ○ Central America (1) Mexico ○ ○ X South America (1) Chile ○ ○ X North America (2) Canada ○ ○ ○ United States ○ ○ ○ Oceania (2) Australia ○ ○ ○ New Zealand ○ ○ ○ Sum 35 32 17 Notes: 1) Based on UN classification 2) As of 2016 3) Based on IMF classification The correlations between the variables are shown in Table 4 below. As several variables may have significantly overlapping impacts on one another, we conducted analyses that took into account the multicollinearity between them (Samsik Lee et al., 2012). Of particular note, there is a high correlation between the women’s employment rate and women’s labor force participation rate. In our empirical analysis, we established the model to minimize the multicollinearity between variables using the variance inflation factor.

Table 4. Correlations between Variables1) Rate of Female labor Income increase Female Marriage force Unemployment TFR per in employment rate participation rate capita housing rate rate prices TFR 1.0000

Marriage rate 0.1724 1.0000

Income per capita 0.1619 -0.0664 1.0000 Rate of increase in 0.0429 -0.0464 0.1096 1.0000 housing prices Female employment 0.3790 0.2426 0.5229 0.0625 1.0000 rate Female labor force 0.3990 0.1738 0.5303 0.0438 0.9730 1.0000 participation rate Unemployment rate -0.0260 -0.4019 -0.2153 -0.1030 -0.5078 -0.3204 1.0000 Female university 0.1305 0.0494 0.2966 0.0674 0.3666 0.4503 0.1541 enrollment rate Share of men working over 40 -0.1985 0.2353 -0.5389 -0.0971 -0.2062 -0.2203 0.0520 hours a week Difference between the shares of men and women working 0.2168 -0.1089 0.3866 0.0986 0.2029 0.1971 -0.0516 over 40 hours a week Gender wage gap -0.1594 0.5451 -0.1478 0.0193 0.0755 0.0131 -0.2390 Public family 0.3668 -0.5190 0.4297 0.0857 0.3322 0.3922 0.1763 benefits Public pension -0.3436 -0.4663 0.1479 -0.0657 -0.4785 -0.3905 0.4975 spending Note: 1) Based on OECD member countries from 1960 to 2015 Sources: OECD Family Database, World Bank, World Development Indicators Table 4. Correlations between Variables (continued)1) Difference between the Share of shares of Female men men and Public Public university working Gender women family pension enrollment over 40 wage gap working benefits spending rate hours a over 40 week hours a week Female university 1.0000 enrollment rate Share of men working over -0.1298 1.0000 40 hours a week Difference between the shares of men and women -0.1124 0.2155 1.0000 working over 40 hours a week Gender wage gap -0.2274 0.3026 0.0821 1.0000

Public family benefits 0.2538 -0.4985 0.2073 -0.4425 1.0000

Public pension spending 0.0167 -0.1168 -0.0421 -0.2341 0.1210 1.0000

Note: 1) Based on OECD member countries from 1960 to 2015 Sources: OECD Family Database, World Bank, World Development Indicators

(3) Results of Empirical Analysis

In Table 5, we report the results of our empirical analysis on the determinants of fertility rates of 32 OECD countries for which we were able to obtain indicators. The first column shows the results of our baseline model. The second column shows the results of the verification of the following: whether panel data of OECD member countries would corroborate the findings of existing studies (Myrskylӓ et al., 2013; Young-Mi Kim, 2016), namely, that while conditions for gender equality in housework became important amid women’s rising labor force participation during the course of industrialization, fertility rates became more sensitive to gender equality in working conditions with women’s rising education levels. We selected an interaction term between two variables—female labor force participation rate and the difference between the proportions of men and women working over 40 hours a week—as a substitute indicator for the conditions of gender equality in housework. In addition, we used an interaction term between women’s university enrollment rate and the gender wage gap as a substitute indicator for gender equality in working conditions. The third and fourth columns show the results of robustness by removing the extreme upper and bottom one-percent of values of the data used in our model to eliminate the effects of such extreme values on our analysis. First of all, the results of our analysis show that despite the growing proportions of births outside marriage, marriage rates still have a positive correlation with fertility rates.23 Income per capita has a negative but statistically insignificant correlation with fertility rates. Our

23 To examine the effects of births outside of marriage, we conducted an analysis of a model that included the proportions of births outside of marriage, marriage rates, and an interaction term between the two. In the above case, the births outside of marriage and marriage rates showed significantly positive correlations with fertility rates, while the interaction term showed a negative but statistically insignificant correlation. However, we excluded marriage rates and the interaction term from our analysis due to concerns over multicollinearity. findings are contrary to the findings of OECD (2005), which shows that the economic well- being of families has a positive impact on childbirth and childcare. These contradictory findings imply that, as the sampling period of our study (1992-2012) is more recent than that of the OECD’s study (1980-1999), there has been a structural change in the effects of income levels on fertility rates.24 On the other hand, as Myrskylӓ et al. (2013) show, it should also be considered that high-income countries may have different fertility rates depending on the level of gender equality achieved by the given country. The rate of increase in housing prices appeared to have a significantly negative impact on fertility rates. This is consistent with our expectation and the findings of existing studies (Sangho Yi and Sangheon Lee, 2011; Jinbaek Park and Jaehee Lee, 2016), that is: the rate of increase in housing prices, which is recognized as a substitute indicator of wedding and childcare expenses, has a negative impact on fertility rates. However, the impact turned out not to be large. Our analysis shows that unemployment rates, which reflect the overall conditions of the labor market, have a negative impact on fertility rates, which is consistent with the findings of existing studies (OECD, 2005). Meanwhile, contrary to people’s overall expectation that the women’s labor force participation rate would have a significantly negative correlation with fertility rates, the former turned out to have a significantly positive correlation with the latter. This seems to be partially explained by the existence of an environment that provides the sociocultural or policy support people need to balance work and childcare. In addition, countries with higher women’s labor force participation rates tend to offer more gender equal environments for housework, allowing people to better prepare for childbirth and childcare. While anticipating that longer working hours among men may discourage more gender equality in the division of labor in housework, we examined the effects of men’s working hours on fertility rates. Contrary to our expectations, the first column showed that longer working hours among men had a positive impact on fertility rates potentially by increasing family income. However, as mentioned above, we examined the effects of the interaction term between the women’s labor force participation rate and the difference between the proportions of men and women working over 40 hours a week on fertility rates. The result showed that increased economic activity among women and comparatively longer working hours among men had a negative impact on fertility rates, although the degree of impact turned out not to be large. The results of our analysis show that women’s enrollment rate in tertiary school has a positive but statistically insignificant impact on fertility rates. This differs from the findings of existing studies (e.g. DeCicca and Krashinsky, 2016), which show that the correlation between the two is negative. In addition, we examined the effects of the gender wage gap, as a proxy for gender equality in working conditions, on fertility rates, which shows that higher wages among men has a negative impact on fertility rates. In the second column, we examined the effect of the interaction term between women’s university enrollment rate and the gender wage gap on fertility rates in order to figure out whether a high awareness of gender equality among both sexes would have an increasing impact on fertility rates when women’s education levels increase, as argued in the study by Young-Mi Kim (2016). The

24 A country’s absolute income represents its income relative to the incomes of several other countries and shows the effects of the difference between the relative incomes of the countries in the cross section data. In addition, to examine the effects of relative income within a country, we analyzed the effects of the Gini-index and the interaction term between the index and income levels, which showed that the income inequality index within a country does not have a significant correlation with fertility rates. However, due to concerns over multicollinearity, we did not include the index in our analysis. In addition, we conducted a regression analysis that excluded the income level variable, which did not change the effects of other variables. result of our examination shows that a high women’s university enrollment rate and wide gender wage gap have a significant negative impact on fertility rates, which implies that the higher the level of education women attain, the greater the impact that gender equality in working conditions will have on fertility rates. In terms of policies, the results of our analysis show that the more money is spent on public family benefits, the greater positive impact it will have on fertility rates. Finally, people generally believe that public pension spending would have a negative impact on fertility rates, but our analysis found otherwise. We assumed that public pension spending on the elderly would put financial pressure on the younger generations, making it difficult for them to give birth and provide childcare, and thus fostering conflicts of interest between generations. Contrary to this assumption, however, we found that more public pension spending resulted in a higher TFR. This finding implies that guaranteeing stable income for people in old age would have a positive impact on childbirth and childcare in the long term. Our findings show that the results of the first and second columns remain robust with respect to the signs and significance in the third and fourth columns with winsorized variables, which had the one-percent extreme values at the high and low ends removed In addition, we narrowed down the subjects of our analysis to 17 IMF advanced countries that are more industrialized and have more aged populations, and conducted a test to see whether the above findings would also be observed in our analysis of IMF countries (Refer to Table 6). According to the results, most of the variables in Table 6 showed consistent effects with those in Table 5 in terms of the signs and significance. In Table 6, the extent of gender equality in working conditions had an insignificant impact on the TFR but the same sign. Amid a rising women’s university enrollment rate, a growing gender wage gap still had a negative impact on fertility rates. In addition, the effect of the interaction term between the women’s labor force participation rate and the difference between the ratio of men’s working hours to women’s, as an indicator of gender equality in the division of labor in housework, still had a significant impact on fertility rates. The above finding implies that, concerning 17 IMF advanced countries, with women’s growing involvement in economic activities, fostering conditions conducive to gender equality in the division of labor in housework by reducing men’s working hours would be more effective than narrowing the gender wage gap as a means of raising fertility rates. The above findings of our analysis imply that fertility rates would be positively affected by reducing wedding and childcare expenses by stabilizing the housing market, improving employment conditions as a means of using women’s human capital to promote economic activities, and forming a society-wide consensus on gender equality in the division of labor in housework and the concept of gender equality. Furthermore, enabling people to better prepare for old age by increasing welfare benefit expenditures and operating public pension schemes for the elderly is expected to have a positive impact on fertility rates in the long-run. Table 5. Determinants of Fertility Rates of OECD Countries1) (3) (4) Fertility rates (t) (1) (2) Winsorized2) Winsorized2) Marriage rate (t-1) 0.063** 0.064*** 0.058** 0.058*** (0.023) (0.019) (0.021) (0.018) Income per capita (t-1) -0.066 -0.051 -0.081 -0.071 (0.128) (0.118) (0.128) (0.121) Rate of increase in housing prices (t-1) -0.001*** -0.001*** -0.001*** -0.001*** (0.0004) (0.0004) (0.0005) (0.0005) Unemployment rate (t-1) -0.055*** -0.053** -0.056*** -0.054** (0.018) (0.022) (0.018) (0.021) Female labor force participation rate (t-1) 0.311** 0.436*** 0.349*** 0.467*** (0.123) (0.087) (0.119) (0.091) Share of men working over 40 hours a week 0.105* 0.270** 0.116** 0.264*** (t-1) (0.060) (0.099) (0.060) (0.095) [Female labor force participation rate x -0.001* -0.001* Difference between the shares of men and (0.0008) (0.008) women working over 40 hours a week](t-1) Women’s university enrollment rate (t-1) 0.021 0.050 0.027 0.059 (0.054) (0.055) (0.055) (0.058) Gender wage gap (t-1) -0.047* -0.046* (0.024) (0.025) [Women’s university enrollment rate -0.011* -0.011* x gender wage gap] (t-1) (0.006) (0.006) Public family benefits (t-1) 0.050** 0.043** 0.049** 0.042** (0.021) (0.018) (0.021) (0.018) Public pension spending (t-1) 0.016** 0.020** 0.012* 0.016** (0.007) (0.008) (0.007) (0.008) Constant -0.851 -2.195* -0.849 -2.066 (1.072) (1.184) (1.084) (1.266) Number of observations 305 305 305 305 Number of countries 32 32 32 32 Country fixed effects Yes Yes Yes Yes Within R-sq 0.4900 0.5264 0.4917 0.5256 Notes: 1) Based on 32 OECD member countries from 1992 to 2012 2) Winsorized data, removing extreme values at the high and low ends at the one-percent level 3) Figures within parentheses refer to standard errors that are robust to heteroscedasticity by country. 4) *** represents a significance level of 1%; **, 5%; and *, 10%. Table 6. Determinants of Fertility Rates of IMF’s Advanced Countries1) (3) (4) Fertility rates (t) (1) (2) Winsorized2) Winsorized2) Marriage rate (t-1) 0.077*** 0.069*** 0.070*** 0.062*** (0.021) (0.016) (0.020) (0.016) Income per capita (t-1) -0.031 -0.080 -0.060 -0.112 (0.187) (0.134) (0.190) (0.150) Rate of increase in housing prices (t-1) -0.002*** -0.002*** -0.002*** -0.002*** (0.001) (0.001) (0.001) (0.001) Unemployment rate (t-1) -0.062** -0.068** -0.061** -0.068** (0.022) (0.024) (0.022) (0.024) Female labor force participation rate (t-1) 0.524** 0.695*** 0.589*** 0.740*** (0.199) (0.194) (0.180) (0.163) Share of men working over 40 hours a week 0.021 0.160 0.040 0.155 (t-1) (0.117) (0.105) (0.104) (0.098) [Female labor force participation rate x -0.002** -0.002* Difference between the shares of men and (0.001) (0.001) women working over 40 hours a week] (t-1) Women’s university enrollment rate (t-1) 0.020 0.077 0.037 0.091 (0.061) (0.056) (0.063) (0.061) Gender wage gap (t-1) -0.082 -0.067 (0.059) (0.057) [Women’s university enrollment rate -0.021 -0.017 x gender wage gap] (t-1) (0.012) (0.012) Public family benefits (t-1) 0.033 0.020 0.034 0.021 (0.025) (0.021) (0.024) (0.019) Public pension spending (t-1) 0.038*** 0.051*** 0.032*** 0.042*** (0.007) (0.009) (0.009) (0.009) Constant -1.782 -2.547* -1.856 -2.402* (2.064) (1.278) (1.992) (1.368) Number of observations 200 200 200 200 Number of countries 17 17 17 17 Country fixed effects Yes Yes Yes Yes Within R-sq 0.5692 0.6210 0.5685 0.6115 Notes: 1) Based on 17 IMF’s advanced countries from 1992 to 2012 2) Winsorized data, removing extreme values at the high and low ends at the one-percent level 3) Figures within parentheses refer to standard errors that are robust to heteroscedasticity by country. 4) *** represents a significance level of 1%; **, 5%; and *, 10%. 2. Longer Life Expectancy

Generally, life expectancy is defined as life expectancy at birth. As the causes of increased life expectancy can be explained intuitively and related policy measures are limited, few studies have been done on the topic. In our study, we examine the overall correlations between variables and life expectancy using an analysis of descriptive statistics, instead of an analysis based on an empirical model. In theory, welfare policies, the development of medical insurance schemes, and overall trend of growing incomes can be pointed to as major determinants of increases in life expectancy. Furthermore, technological development and capital accumulation in the health and medical fields, including the number of medical personnel and medical institutions, such as public health centers and hospitals, can be considered as well. As infant and elderly mortality rates decline with increases in health and welfare expenditures, such expenditures are expected to contribute to increasing life expectancy. The effects of income on life expectancy are quite complex. In a study on individuals, high income was attributable to people’s ability to spend more on medical expenses, thus increasing their life expectancy. However, individuals with high income are more likely to fall ill due to their long working hours and high stress levels, meaning that the negative effects of high income may offset the positive effects. Meanwhile, in a study on countries, developing countries with low but rapidly increasing income levels were found to have shorter life expectancies due to their poor working environments and medical systems. In contrast, advanced countries with high levels of income but comparatively lower rates of increase in income were found to have longer life expectancies. In Figure 21, we illustrate the correlations between life expectancy and several variables through scatter plots. As expected, health/welfare spending and income per capita showed positive correlations with longer life expectancy. In contrast, the higher the rate of increase in GDP per capita, the shorter the life expectancy. The correlation between life expectancy and the variable related to medical institutions, measured as the number of hospital beds per 1,000 people, turned out not to be relatively significant. However, the number of doctors per 1,000 people appeared to be positively correlated with longer life expectancy. On the other hand, as aged societies with longer life expectancies had very low TFR, life expectancy and the TFR showed a negative correlation. As life expectancy increases, it will become more difficult for the elderly to save income after retirement, and poverty issues among the elderly are expected to become more serious, forcing the elderly to continue earning income through re-employment after retirement in order to supplement their savings or pension. Therefore, implementing measures to increase income for the elderly, such as elderly employment policies and pension schemes, will be an important policy task going forward (Keunkwan Ryu and Gyehyung Jeon, 2013). Figure 21. Correlations between Life Expectancy1) and its Determinants2)

Notes: 1) Life expectancy at birth 2) Health spending (proportion of public health benefit spending, %), GDP per capita (log- transformed value), rate of increase in GDP per capita (year-on-year, %), medical institutions (number of hospital beds per 1,000 people), medical personnel (number of doctors per 1,000 people), TFR (ratio of newborns to the average number of women of childbearing age (15 to 49) by year Sources: OECD Family Database, World Bank, World Development Indicators

IV. Characteristics of Korea’s Population Aging: Comparison with Major Countries

In this chapter, we compare Korea with major countries in terms of the status and causes of population aging and take an in-depth look at the characteristics of the process of population aging in Korea. First, through a comparison with major countries, we show that Korea’s population aging is progressing at a pace significantly faster than the aging of other major countries. Next, we look into the historical, sociocultural, and demographic characteristics of Korea’s population aging in comparison with the general causes of population aging discussed in Chapter III. In addition, we briefly discuss the problems that are expected to arise due to the peculiarities of Korea’s situation. In particular, in our analysis, we must consider the fact that OECD countries are undergoing a heterogeneous demographic transition (OECD, 2005).

1. Rapid Pace of Population Aging

One major feature of Korea’s population aging is that it is progressing at a rapid pace, in proportion to the pace at which its industrialization progressed. On average, it took 45 years for major advanced countries to make the transition from aging societies to aged societies, while it took approximately 17 years for Korea (and 24 years for Japan) to complete the same transition 25 . Going forward, it is expected to take approximately 30 years for advanced

25 According to data provided by Statistics Korea, Korea reached aged societies already in 2017. countries to transition from aged societies to super-aged societies, while it is expected to take only seven years for Korea (and 12 years for Japan) to make the same transition (Refer to Figure 22).

Figure 22. Proportion of the Elderly Population 1) Estimates2)

Notes: 1) Proportion of population aged 65 and older 2) Based on median values of estimates Source: UN World Population Prospects: 2015 Revision

(1) Rapidly Declining Fertility Rates

To understand why Korea’s population aging is progressing at such a rapid pace, we examined fertility rate trends by extent of population aging,26 as shown in Figure 23. As of 2015, the TFRs of countries in aging societies 27 generally came close to the population replacement rate (2.1), while the TFRs of countries in aged societies, 28 excluding some countries, were below the population replacement rate. The TFRs of all countries in super- aged societies29 fell far below the population replacement rate, showing an average of around 1.5. While Korea is classified as an aging society, its TFR plummeted, falling well below the population replacement rate and coming close to the average TFR of super-aged societies.

26 If the proportion of a country’s population aged 65 and older falls within the range of 7 to 14%, it is classified as an aging society; if it falls within 14 to 20%, it is classified as an aged society; and if it exceeds 20%, it is classified as a super-aged society.

27 This includes Mexico, Turkey, Chile, Israel, Korea, Ireland, Iceland, and Slovakia.

28 This includes Luxemburg, the United States, New Zealand, Australia, Poland, Canada, Norway, the United Kingdom, Hungary, Slovenia, Switzerland, the Czech Republic, Belgium, the Netherlands, Austria, Estonia, Spain, Denmark, France, and Latvia.

29 This includes Sweden, Finland, Portugal, Germany, Greece, Italy, and Japan. Figure 23. Trends of TFR1) by Extent of Population Aging2)

Notes: 1) Ratio of newborns to the average number of women of childbearing age (15 to 49 years old) 2) Aging society (proportion of population aged 65 and older: 7 to 14%), aged society (proportion within 14 to 20%), and super-aged society (proportion exceeding 20%) Source: World Bank, World Development Indicators

The low fertility trap hypothesis is offered as an explanation for the rapidly declining fertility rates. This hypothesis refers to the self-reinforcing mechanism of social changes that continue to bring down the TFRs of countries that fall below a certain threshold (Lutz and Skirbekk, 2005; Lutz, Skirbekk, and Testa, 2006). McDonald (2006) classifies a TFR of 1.5 or below as the threshold level below which countries may fall into the trap.30 Lutz (2008) explains that the low fertility trap may be triggered by the age structure of the population and ideal personal family size but is largely influenced mainly by social norms. This means that if individuals accept the current fertility rate as a social norm while living in a society with low numbers of children for a significant period of time, a certain ideal personal family size becomes fixed in a normative way, triggering the low fertility trap. Accordingly, when a society falls into this trap, its ideal family size shrinks and its desired fertility rates keep falling. The explanation related to ideal fertility rates is discussed later in this paper. Lutz (2008) explains that although it was too early to conclude that the hypothesis is convincing, there was no particular reason to exclude it. The TFRs of aged and super-aged societies that have achieved high levels of industrialization and are experiencing rapid population aging fell below the population replacement rate in the 1990s. After hitting their lows, the TFRs of these countries rebounded to near the replacement rate (e.g. France, Sweden, and Norway). There are countries whose TFRs were below the replacement rate but have slowly increased (e.g. Germany, Italy, Spain, and Japan). Meanwhile, European countries whose TFRs rebounded have seen the recovery momentum of their TFRs weaken due to the shocks of the global financial crisis. However, Germany’s TFR and the TFRs of Asian countries, such as Korea and Japan, appeared to be comparatively unaffected by the shocks of the global financial crisis. Recently, Japan has

30 McDonald (2006) argues that if a country’s TFR is slightly below the population replacement rate of 2.1, the population can be easily buoyed by immigration policies; however, if a country’s TFR is below 1.5, massive migration is required. He classifies countries with a TFR of more than 1.5 but less than 2.1 as being in a “safety zone.” seen its TFR rebound slightly. For simplicity, we classified countries with an average TFR of more than 1.5 over the past 10 years, which include France, Sweden, Norway, Denmark, and Finland, as countries with rebounding TFRs (Refer to Figure 24), and countries with an average TFR of less than 1.5, including Germany, Italy, Spain, Japan, and Korea, as countries with low TFRs (Refer to Figure 25). We then examined the trends of aging by each group of TFRs.

Figure 24. Trends of TFRs1) of Countries with Rebounding TFRs2)

Notes: 1) Ratio of newborns to the average number of women of childbearing age (15 to 49 years old) 2) Countries with an average TFR of more than 1.5 over the past 10 years Source: World Bank, World Development Indicators

Figure 25. Trends of TFRs1) of Countries with Low TFRs2

Notes: 1) Ratio of newborns to the average number of women of childbearing age (15 to 49 years old) 2) Countries with an average TFR of less than 1.5 over the past 10 years Source: World Bank, World Development Indicators (2) Exponential Increase in the Proportion of the Elderly Population

Excluding Finland, countries with rebounding TFRs experienced declining rates of increase in the proportion of their elderly population until the early 2000s. However, during the course of the global financial crisis, the decline in those rates of increase subsided, leading to accelerated population aging (Refer to Figure 26).

Figure 26. Trends of the Elderly Population Ratio1) of Countries with Rebounding TFRs2)

Notes: 1) Proportion of population aged 65 and older, % 2) Countries with a TFR of more than 1.5 over the past 10 years Source: World Bank, World Development Indicators

In contrast, countries with low TFRs, including Korea but excluding Germany and Spain, showed rapid population aging. In particular, the proportion of the elderly population in Korea has shown a steep curve similar to that of an exponential function (Refer to Figure 27).

Figure 27. Trends of the Elderly Population Ratio1) of Countries with Low TFRs2)

Notes: 1) Proportion of the population aged 65 and older, % 2) Countries with a TFR of less than 1.5 over the past 10 years Source: World Bank, World Development Indicators 2. Background of the Rapid Pace of Population Aging in Korea

In this section, we discuss which factor has played a larger role in the rapid progress of population aging in Korea, among the common causes of population aging discussed earlier, and see whether any other unique factors have come into play in Korea by presenting a descriptive account of Korea’s case in comparison with major countries. First of all, the historical factor influencing the changes in Korea’s fertility rates is the fact that Korea’s potential fertility plummeted following the introduction of the government’s birth control policies. As for sociocultural factors, it was found that the difficulty of maintaining work- family balance, the difficulty of achieving gender equality in the division of labor in housework, and wedding expenses, such as housing prices, have had more profound impacts on the fertility rates in Korea than in other countries, serving as factors that restrain people’s decision to marry and their choice over the number of children to have. Finally, the demographic factor specific to Korea is the rapid aging of the country’s baby boomers.

(1) Historical Background: Birth Control Policies and Declining Potential Fertility

While it was due to the progress of industrialization and sociocultural factors that European countries saw their fertility rates naturally fall below the population replacement rate, Korea’s birth control policies were a primary cause of its rapidly declining fertility rates. During the early stages of its industrialization, Korea increased its income per capita by actively curbing any rapid increase in its population and achieving high economic growth, which inevitably resulted in the decline of its potential fertility. Therefore, during the period when the populations of countries naturally decline following industrialization, Korea experienced larger population declines than other advanced countries. Korea’s birth control policies were first implemented in the 1960s and ended in the mid- 1990s, when its TFR fell far below the population replacement rate. Korea’s TFR fell halfway down, from approximately 3 in the late 1970s to around 1.5 in the late 1980s, since which time it has remained more or less unchanged. Even though the Korean government changed its population policies in response to several challenges posed by low fertility in the 2000s, its TFR decreased not only due to exogenous factors but also naturally, in line with the progress of industrialization, as in advanced countries, and has thus been falling at a pace greater than that of any other country31 (Refer to Figure 28).

31 Meanwhile, Korea’s TFR declined further during the Asian financial crisis. It increased slightly at certain times due to people’s preferences for giving birth in certain years, such as the turn of the millennium (2000), year of the World Cup (2003), the year of the Golden Pig (2007, occurs every 600 years), and the year of the White Tiger (2010, occurs every 60 years). Figure 28. Trend of Korea’s TFR1)

Note: 1) Ratio of newborns to the average number of women of childbearing age (15 to 49 years old) Sources: World Bank, National Archives of Korea

Table 7. Korea’s Population Policies per Period 1940-1950s 1960-1980s 1990-2000s Population welfare and pro- Goals Population growth Population control natalist policies – Korean War and refugees – Family planning campaign – Countermeasures for low – Beginning of the baby – Establishment of maternal fertility and population boom and child health policies aging Major – Pursuit of emigration characteristics – Continuance of policies designed to curb population growth – "We should have five – "If you give birth without – "The best gift for your children: three sons and any planning, you will end child is a younger two daughters." up living like a beggar." sibling." – "Let’s have two children – "The best inheritance you and raise them well can bequeath to your regardless of their sex." child is a younger – "Bless one child and raise sibling." Major slogans him/her well with love." – "The more children you – "The first promise of give birth to, the more newlyweds is to conduct hope and happiness you family planning with a will have." smile." – "A country where the children are happy has a hopeful future." Source: National Archives of Korea, Population Policies per Period

On the other hand, Korea has seen its proportion of men reaching the age of majority and older increase more than any other country, except during the time of the Korean War in the 1950s, due to people’s preference for male children over female children. Advanced countries, high-income countries, and Japan, all of which show smaller gender imbalances than Korea, have seen their fertility rates decline more slowly than that of Korea. This implies that if the effects of Korean people’s preference for male children over female children subsides and the sex ratios of the generations born afterward move closer to equality, the negative effects on Korea’s declining fertility rates would be offset (Refer to Figure 29).

Figure 29. Trends of Sex Ratios of Adult Men to Adult Women and TFRs per Period Panel A. Sex Ratios of Adult Men to Adult Panel B. Trends of TFRs per Period Women1) per Period

Note: 1) Ratio of the number of men aged 20 to 64 to the number of women in the same age bracket Source: UN, World Population Prospects (2015)

(2) Korea’s Unique Sociocultural Factors: Country-Specific Determinants of Marriage and the Number of Children Couples Have

As mentioned in the previous chapter, Korea’s birthrates outside of marriage are so low that the correlation between marriage rates and fertility rates is quite high. Korea’s declining fertility rates can therefore be explained by its falling marriage rates. For this reason, the analysis of factors driving the decreases in the marriage rates can complement that of factors driving the decreases in fertility rates. Existing studies show that countries with rebounding TFRs and those with low TFRs exhibit different sociocultural characteristics in terms of awareness of gender equality, acceptance of various types of families, gender wage gap as a proxy for gender equality in the labor market, housing prices as a measure of wedding and childcare expenses, and policies designed to ensure work-family balance. (Refer to Table 8). The findings of these studies show that countries with greater awareness of gender equality, higher acceptance of various types of families (such as couples with children born outside of marriage), more gender equality in the labor market, lower housing costs, and well-established policies that enable people to balance work and family tend to succeed in making their TFRs rebound. This is consistent with our findings regarding the common determinants of declining fertility rates discussed in the previous chapter. In connection with these findings, we divide Korea’s particular sociocultural factors into: (1) legal marriage-based family types, (2) high wedding, childcare, and education costs, and (3) an environment that makes it difficult to balance work and family and achieve gender equality in the division of labor in housework. We then examine each of these factors. Table 8. Comparison of the Characteristics of Countries with Rebounding TFRs and Those with Low TFRs Countries with Countries with Low Rebounding TFRs1) TFRs2) Whether accommodating various types of families, such as couples giving birth outside of Accommodating Not accommodating marriage Housing costs Low High Gender equality Good Lacking Gender wage gap Low High Policies to ensure work-family balance Good Lacking Notes: 1) France, Sweden, Norway, Denmark, and Finland 2) Germany, Italy, Spain, Japan, and Korea Source: Huicheol Min (2007), Third Basic Plan for Low-Fertility Aging Societies

A. Legal Marriage-based Family Types

Countries with rebounding TFRs, such as France, Norway, Sweden, Denmark, and Finland, are known to have sociocultural conditions that promote the acceptance of various types of families, such as cohabitation and single-parent families, and comparatively high proportions of births outside of marriage (Refer to Figure 30). In contrast, countries with low TFRs including Korea have low proportions of births outside of marriage and maintain the traditional concept of family, where legal marriage is considered the norm.

Figure 30. Trends of Proportions of Births outside of Marriage1) by Country

Panel A. Trends of Proportions of Births outside Panel B. Trends of Proportions of Births outside of Marriage in Countries with Rebounding TFRs2) of Marriage in Countries with Low TFRs2)

Notes: 1) Proportion of births outside of marriage to total births, % 2) As of 2014, excluding France (as of 2012) Source: OECD Family Database

Under the above mentioned sociocultural environment, births outside of marriage in Korea have caused problems in terms of the children requiring protection.32 During the periods of high fertility rates, Korea sent large numbers of infants, such as war-time orphans, overseas for adoption. Even in the 1970s and 1980s, the overseas adoption of children requiring protection surged, resulting in the outflow of large numbers of infants to foreign countries (Refer to Figure 31).

32 Children requiring protection refer to children who are not raised by their parents but need to be protected by having them stay at childcare facilities or through other means, such as adoption. Meanwhile, as more unmarried women who lack the resources necessary to raise children are giving birth to children requiring protection, the number of Korean children being adopted is increasing, which means that the government needs to provide childcare support for them. In particular, the sociocultural factors in Korea that emphasize legal marriage-based family types are posing structural problems that are preventing children born outside of marriage and requiring protection from receiving the support they need. In addition, the number of intentional deaths of infants and toddlers caused by abandonment has increased.

Figure 31. Trends of the Number of Overseas Adoptees and Children Requiring Protection Panel A. Trend of the Number of Overseas Panel B. Trend of the Number of Children Adoptees in Korea Requiring Protection in Korea

Sources: Ministry of Health and Welfare, KOSIS

B. High Wedding, Childcare, and Education Expenses

In 2015, the Korea Institute for Health and Social Affairs conducted a questionnaire survey of single men and women regarding their reasons for not marrying (Refer to Table 9). Among all respondents, the high cost of married life was the fifth most common reason for not marrying, while among male respondents, it was the second most common reason. 33 However, it should be noted that besides the high cost of married life, the drastic change in people’s sociocultural concept of marriage has become a major cause of the declining marriage rates as well.

Table 9. Reasons for Not Marrying1) Total Single Single respondents men women Never had any intention to marry 22.2 20.0 23.6 Unable to find someone who satisfies my requirements 11.5 9.1 12.9 Do not want to be bound by a partner 10.4 9.1 11.2 Want to be devoted to my work rather than marriage 9.0 4.5 11.8 High cost of married life 7.3 14.5 2.8 Low income 6.3 7.3 5.6 Worried that marriage may disrupt my social life 4.2 1.8 5.6 (Other responses omitted) Number of respondents 288 110 178 Note: 1) Proportions of each reason for not marrying cited by single men and women (%) Source: Korea Institute for Health and Social Affairs, 2015 National Survey on Fertility and Family Health and Welfare

33 Korea Institute for Health and Social Affairs, 2015 National Survey on Fertility and Family Health and Welfare. In terms of education expenses, the institute asked married women about their monthly average childcare expenses. The survey results showed that single- and two-child families spent an average of KRW 0.65 million and KRW 1.29 million, respectively, of which private education expenses took up the largest proportions, 18.5% and 43.3%, respectively. 34 In addition, both single men and women and married women were asked about social changes that would make them comfortable enough to give birth to and raise children (Refer to Table 10). The respondents commonly cited the importance of changes in the childcare environment, such as reductions in private education expenses, creation of safe environments for raising children, and expansion of high-quality childcare facilities.

Table 10. Social Changes Necessary to Make Survey Respondents More Comfortable with Childbirth and Childrearing1) Respondents Necessary social changes Proportion of responses Single men (1) Stimulate the economy 15.1 (2) Eliminate academic cliques 13.1 (3) Reduce cost of private education 11.0 (4) Introduce changes in labor market 10.6 Single women (1) Stimulate the economy 15.9 (2) Foster safe environments for raising children 15.6 (3) Expand high-quality childcare support facilities 10.6 (4) Reduce cost of private education 8.5 Married women (1) Reduce cost of private education 17.9 (2) Foster safe environments for raising children 15.9 (3) Expand high-quality childcare support facilities 12.4 (4) Expand public education 8.5 Note: 1) Proportions of respondents who indicated that the given desirable social change would make them feel comfortable enough to give birth to and raise children (%) Source: Korea Institute for Health and Social Affairs, 2015 National Survey on Fertility and Family Health and Welfare

Accordingly, single men and women in Korea cited the provision of homes for newlyweds, provision of financial support to help cover the costs of children’s education, and provision of childcare and support for childcare education costs as the most urgently needed measures to promote childbirth and childcare (Refer to Table 11).35

Table 11. Most Urgently Needed Measures to Promote Childbirth and Childcare1) Single men Most needed measures Single women 26.2 Provision of homes for newlyweds 14.0 21.5 Provision of financial support for children’s education 13.1 14.3 Provision of childcare and support for childcare education costs 17.1 Note: 1) Proportions of respondents indicating that the given measure is urgently needed to promote childbirth and childcare (%) Source: Korea Institute for Health and Social Affairs, 2015 National Survey on Fertility and Family Health and Welfare

34 Korea Institute for Health and Social Affairs, 2015 National Survey on Fertility and Family Health and Welfare.

35 Korea Institute for Health and Social Affairs, 2015 National Survey on Fertility and Family Health and Welfare. As discussed earlier, the rate of increase in housing prices is a measure that is used to analyze wedding and childcare expenses in Korea, as it is generally recognized as having an influence on wedding and childcare expenses. Statistically, the rate of increase in housing prices and marriage and fertility rates are negatively correlated (Refer to Figure 32). The relationships between them are consistent with the findings of the empirical analysis of OECD member countries elaborated in the previous chapter. In the same context, existing studies show that as housing prices increase, marriage and fertility rates decrease significantly.36,37

Figure 32. Correlations between the Rate of Increase in Housing Prices1) and Marriage Rate2) and TFR3) in Korea

Notes: 1) Year-on-year rate of increase in real housing prices (%) 2) Number of marriages per 1,000 people 3) Ratio of newborns to the average number of women of childbearing age (15 to 49 years old) by year Sources: World Bank, OECD

As discussed above, even though single men and women feel great pressure due to costs of childcare, the support provided by the Korean government for childcare expenses has not lived up to people’s expectations. The Korean government’s support for childcare expenses as a share of the GDP stands at 0.7%, which exceeds the OECD average of 0.4%. However, the Korean government’s support for pre-primary education as a share of the GDP is only 0.1%, much lower than the OECD average of 0.5% (Refer to Figure 33). Countries with rebounding TFRs, such as Denmark, Sweden, France, and Norway, spent comparatively more, in terms of share of GDP, on childcare and pre-primary education than countries with low TFRs. This implies that the government should provide support for childrearing expenses that covers not only childbirth and infant care but also pre-primary education.

36 Sangho Yi and Sangheon Lee (2011), Jinbaek Park and Jaehee Lee (2016)

37 According to the 2015 survey by the Korea Institute for Health and Social Affairs, the average amount of loans extended to newlyweds for home purchases increased significantly, from KRW 20 million in the 1990s to KRW 50 million in the 2010s. Home prices, jeonse (a unique form of Korean rent system with a lump-sum rent payment at the beginning of a contract, which is for the purpose of rental fees and collaterals for a long-term contract lasting usually for 2 years or longer) amounts, and monthly rents have been rising continuously. Figure 33. Government Support for Childcare Expenses as Share of GDP by Country1)

Panel A. Countries with Rebounding TFRs Panel B. Countries with Low TFRs

Note: 1) Public spending on childcare and pre-primary education as share of GDP (%) as of 2011 Source: OECD Family Database

Countries with rebounding TFRs spend three to four percent of their GDP on public family benefits, while countries with low TFRs spend less than three percent. In particular, Korea’s spending on public family benefits is one of the lowest among countries with low TFRs. Cash transfers, such as family allowances, are expected to have larger effects than other types of policy support, such as services. 38 Korea’s public family benefits in the form of cash transfers as a share of GDP was less than one percent as of 2013, which was the lowest share among OECD countries (Refer to Figure 34).

Figure 34. Share of Public Family Benefits by Country1)

Panel A. Countries with Rebounding TFRs Panel B. Countries with Low TFRs

Note: 1) Share of public family benefits (%), as of 2011 Source: OECD Family Database

As discussed previously, wedding and childcare expenses aggravate the burden of marriage, thus having an indirect and negative impact on marriage rates. Besides wedding costs, single men and women pointed to youth unemployment, workplace disadvantages caused by marriage, Korea’s vain and extravagant wedding culture, and the widespread practice of working long hours as factors that make marriage difficult and need to be addressed by the government’s marriage support policy (Refer to Table 12). In particular, a large number of women expressed concern over the disadvantages in the workplace caused by marriage,

38 Meanwhile, the study by Young-Mi Kim (2016) revealed that while cash transfers had a larger impact on women who were not working, the rendering of services was more effective for working women. which shows that it is important to improve labor conditions for such women. We look into this issue in the next section.

Table 12. Issues that Marriage Support Policies Should Address1) Single Single Issues men women 32.6 Stabilization of youth employment 28.6 27.4 Support for the purchase of homes by newlyweds 24.1 23.0 Elimination of youth unemployment 16.5 6.5 Improvement of vain and extravagant wedding culture 5.6 6.0 Rooting out of practice of working long hours 4.9 4.4 Removal of disadvantages in the workplace caused by marriage 20.3 Note: 1) Proportions of single men and women who believe that the listed issues should be addressed by family policies (%) Source: Korea Institute for Health and Social Affairs, 2015 National Survey on Fertility and Family Health and Welfare

C. Environmental Factors that Make It Difficult to Balance Work and Family and Achieve Gender Equality in the Division of Labor in Housework

In general, most married women in Korea find it difficult to balance work and family, citing the burden of childcare and housework and lack of time to spend with their children as reasons.39 Meanwhile, the aspect of Korean culture that takes for granted that men will work long hours and women will take responsibility for childcare makes it difficult to achieve gender equality in the division of labor in housework. As women have been responsible for the majority of childcare and housework, that aspect of Korean culture makes it more difficult for people to balance work and family (Refer to Table 13). According to recent studies by the Ministry of Gender Equality and Family, Ministry of Employment and Labor, and the Korean Women’s Development Institute, Koreans prefer balancing work and family over taking parental leave. Survey respondents selected eliminating the practice of working long hours, 40 spreading flexible working hours, and improving social awareness and corporate culture as urgent tasks that need to be addressed in order to enable people to balance work and family successfully. In a survey conducted by the Korea Institute for Health and Social Affairs, married women in Korea pointed to financial support for childcare and support to help people balance work and family as the first and second priorities in assisting families with childbirth and childrearing.41

39 Korea Institute for Health and Social Affairs, 2015 National Survey on Fertility and Family Health and Welfare

40 In fact, Korea’s working hours (2,113 hours) are the second longest, after Mexico, among OECD member countries and are 400 hours longer than the OCED average (1,766 hours).

41 Korea Institute for Health and Social Affairs, 2015 National Survey on Fertility and Family Health and Welfare Table 13. Time Spent on Childcare1) by Gender Total Dual-income family Single-income family Women2) Weekdays 5.6 4.3 6.7 Weekends 7.2 6.6 7.8 Men3) Weekdays 1.2 1.3 1.2 Weekends 3.4 3.5 3.4 Notes: 1) Average hours 2) Married women with elementary school-aged children or younger (15 to 49 years old) 3) Husband of married women with elementary school-aged children or younger (15 to 49 years old) Source: Korea Institute for Health and Social Affairs, 2015 National Survey on Fertility and Family Health and Welfare

Amid the growing need for childcare support policies, Korea allocated the majority of its policy budget to parental leave benefits, which turned out to be the least effective policy measure. 42 As of 2014, women in Korea who received maternity leave payments equal to 100 percent of their average income accounted for only approximately 30% of all Korean women on maternity leave, placing Korea below the OECD average of 45.4%. Men who received paternity leave payments equal to 100 percent of their average income accounted for roughly 31 percent of all Korean men on paternity leave, far below the OECD average of 65.1%. We expect that even after the institutional implementation of parental leave, the effects of the policy measure would only materialize when people actually begin taking advantage of it. Korea has already introduced parental leave. However, as little time has passed since its implementation, people are still reluctant or find it difficult to take advantage of it. Among OECD member countries, countries with rebounding TFRs showed high rates of men taking parental leave (Refer to Figures 35 and 36). The period of paternity leave men are allowed to take in Korea was 52.6 weeks as of 2015, which was the longest among OECD member countries. However, few Korean men have actually opted to take the leave, which is attributable to the sociocultural environment in Korea, where men are not perceived as playing an equal role in childcare. As paternity leave was introduced in Korea only recently, there is much room for improving this scheme depending on the results of its operation considering the lagged effect of policy implementation. On the other hand, few women have taken maternity leave either, which reflects Korea’s corporate culture and working conditions, which are not accepting of women’s use of maternity leave (Refer to Figures 35 and 36).

42 Parental leave benefits currently account for 40% of people’s average monthly salary, up to a maximum of KRW 1 million, which falls below the minimum monthly income of a three-person household (KRW 1.43 million) as of 2016. Figure 35. Recipients / Users of Paid Parental Leave by Country1)

Panel A. Countries with Rebounding TFRs Panel B. Countries with Low TFRs

Note: 1) As of 2013 Source: OECD Family Database

Figure 36. Gender Share of Recipients / Users of Paid Parental Leave by Country1)

Panel A. Countries with Rebounding TFRs Panel B. Countries with Low TFRs

Note: 1) As of 2013 Source: OECD Family Database

In addition, the maternity protection measures stipulated in the Labor Standards Act 43 have not been complied with in many cases, especially in the case of temporary workers. Korea’s gender wage gap, which represents the extent of gender inequality in terms of working conditions, shows the highest level of gender discrimination among OECD member countries (Refer to Figure 37), implying that gender inequality in terms of working conditions could have a negative impact on childbirth (Young-Mi Kim, 2016).

43 These measures include the prohibition of overtime work during pregnancy, transfer of pregnant workers to less-demanding positions, prohibition of dangerous work, curtailment of working hours during pregnancy (reduction of daily working hours by two hours a day within 12 weeks and 36 weeks after pregnancies with high risks of miscarriage or stillbirth, since September 2014), and restrictions on night and holiday work for pregnant workers (Korean Women’s Development Institute). Figure 37. Gender Wage Gap by Country1)

Panel A. Countries with Rebounding TFRs Panel B. Countries with Low TFRs

Note: 1) Average difference between the median earnings of men and women relative to the median earnings of men Source: OECD Family Database

D. High Fertility-Rate Gap

Influenced by the above sociocultural factors, people in Korea who want to marry or give birth delay or avoid doing so in many cases due to practical difficulties. The extent of structural factors that restrain fertility can be partly verified by the gap between the ideal fertility rate and actually observed fertility rate (hereinafter called “fertility-rate gap”). The wider this gap, the stronger the extent to which structural factors restrain fertility. First of all, countries with rebounding TFRs, such as Sweden and France, have ideal fertility rates comparatively higher than countries with low TFRs, giving them a comparatively narrower fertility-rate gap than countries with low TFRs, such as Korea, Spain, and Germany (Refer to Table 14). Even though Korea’s TFR is the lowest among OECD member countries, its fertility-rate gap exceeds the OECD average. Accordingly, it can be inferred that structural factors restrain fertility in Korea more than they do in other OECD member countries. In particular, married women in Korea have a comparatively wider fertility-rate gap than in other countries with low TFRs, which implies that if government were to implement policies aimed at ameliorating the structural factors that restrain its fertility, there would be more room for improvement in Korea than in countries with TFRs that have rebounded to the population replacement level, resulting in small fertility-rate gaps or low ideal fertility rates. Table 14. Fertility-Rate Gap Ideal fertility rate Actual fertility rate3)4) Fertility-rate gap (A) (B) (A-B) Korea1) Married women 2.25 1.24 1.01 Single women 2.00 1.24 0.76 Single men 2.10 1.24 0.86 Countries with rebounding TFRs Finland2) 2.58 1.83 0.75 Denmark2) 2.48 1.75 0.73 Sweden2) 2.41 1.90 0.51 France2) 2.33 2.00 0.33 Countries with low TFRs Germany2) 2.12 1.39 0.73 Spain2) 2.11 1.34 0.77 Italy2) 2.01 1.39 0.62 OECD average2) 2.27 1.59 0.68 Notes: 1) As of 2015 2) Average of men and women, as of 2011 3) Ratio of newborns to the average number of women of childbearing age (15 to 49 years old) 4) Korea’s TFR provided by Statistics Korea, as of 2015 Sources: Korea Institute for Welfare and Social Affairs, 2015 National Survey on Fertility and Family Health and Welfare, OECD Family Database, and Statistics Korea

On the other hand, the ideal fertility rate is related to the low fertility trap hypothesis that was discussed earlier. Even though Korea’s TFR fell below 1.5, a threshold set by McDonald (2006), married women in Korea still have an ideal fertility rate slightly higher than those of their counterparts in countries with low TFRs, such as Germany. It is therefore too early to tell whether Korea’s social norms regarding family size have changed as those of other countries did. However, as Korea’s super-low fertility could persist and thus become accepted as a social norm, preparations to cope with such a scenario are critical at this stage.

(3) Demographic Characteristics: Aging of Baby Boomers

“Baby boom” refers to a massive increase in births during a certain period of time, while “baby boomers” refers to the people born in this period. The fertility rate or number of births is the criterion used to determine which birth cohort groups can be classified as the baby boom generation. Those born between 1946 and 1967 in France and Italy are classified as baby boomers in those countries; those born between 1946 and 1953, in Sweden; those born between 1947 and 1949, in Japan; and those born between 1955 and 1963, in Korea. The reason most advanced countries have become aged or super-aged societies is that a significant number of baby boomers in those countries have now joined the elderly population, which is a common process that both countries with rebounding TFRs and low TFRs have undergone, even though this process has occurred during different periods (Refer to Figure 38). Figure 38. Position of the Baby Boom Generation in the Demographic Structure of Major Countries1)

Panel A. Countries with Rebounding TFRs Panel B. Countries with Low TFRs

Note: 1) The position of the baby boom generation is indicated in dark blue. Sources: Tae Heon Kang (2016), OECD

In Korea, the baby boom generation joined the ranks of the elderly population 10 years later than it did in the above countries, which, combined with rapidly declining fertility rates, is accelerating the process of population aging in the country. As of 2015, baby boomers in Korea began entering the cohort of the population aged 60 years and older (Refer to Figure 39 and Table 15). This transition is expected to have a snowball effect on population aging in terms of labor, consumption, savings, pension, and finance.

Figure 39. Position of Korea’s Baby Boom Generation

Note: 1) The position of Korea’s baby boom generation is indicated in dark red. Sources: Tae Heon Kang (2016), OECD Table 15. Proportion of the Baby Boom Generation of Korea’s Population (%)

2004 2009 2014 2019 2024

Age 40-49 45-54 50-59 55-64 60-69

Working-age population1) 23.5 22.4 21.6 21.4 11.9

Key working-age population2) 39.4 20.8 - - -

Retirement-age population3) - - - 31.9 55.0

Elderly population4) - - - - 35.8 Notes: 1) Aged 15 to 64 2) Aged 25 to 49 3) Aged 60 and older 4) Aged 65 and older Sources: Tae Heon Kang (2016), OECD

Meanwhile, the children of the baby boom generation, born between 1979 and 1992, are called the “echo generation.” The proportion of women in this echo generation is much lower than that of men,44 but the size of the echo generation is much larger than that of the baby boom generation.45 Thus, if their fertility rates were to rebound, it would offset the effect of the baby boom generation’s aging to some extent. Accordingly, if the government implements effective policies targeting the echo generation, Korea’s population decline and aging may be slowed or reversed. The high youth unemployment rate and young people’s avoidance or postponement of marriage have had a direct impact on and changed the lifestyles of the echo generation in Korea46 (Statistics Korea, 2012). Therefore, the government needs to establish more customized policies targeting the echo generation. In particular, the number of young people who are unable to secure financial independence and continue to rely on their parents even after becoming adults (the so-called “Kangaroo Tribe” 47 ) is increasing, and more and more baby boomers are living together with and supporting their children, 48 all of which confirm that the number of young people who are

44 As of 2010, the ratio of men to women in the baby boom generation was 99.3, showing that the number of men (3.46 million) fell below that of women (3.49 million) by 30,000. The ratio of men to women in the echo generation was 107.8, indicating that the number of men (4.95 million) surpassed that of women (4.59 million) by 360,000 (Statistics Korea, 2012).

45 As of 2010, the sizes of the baby boom and echo generations were 6.95 million and 9.54 million people, respectively, accounting for 14.5% and 19.9% of the total population, respectively (Statistics Korea, 2012).

46 The echo generation is characterized by a high university enrollment rate (baby boomers: 12.5% → echo generation: 75.6%), increasing number of single women (proportion of married people among baby boomer population: 83.5% → proportion of single people among echo generation: 82.4%), high average age at first marriage among women (baby boomers: 24 years old → echo generation: 25.3 years old), low average number of newborns per married woman (baby boomers: 2.04 → echo generation: 1.10), and increasing number of single-person households (baby boomers: 580,000 households → echo generation: 1 million households).

47 In addition to the term “Kangaroo Tribe,” there are several newly coined words in the same vein, including: “boomerang kids,” who return to home to live with their parents after graduating from universities in the United States; “Umka Tribe” (Kangaroo Tribe using their moms’ credit cards) in Korea; “parasite singles” (members of the Kangaroo Tribe in their 20s and 30s who still rely on their parents in their 30s and 40s) in Japan; and “helicopter moms,” who control all aspects of their children’s lives from childhood, leading them to grow up without any sense of independence. European countries have seen an increase in these types of patterns among the echo generation since the global financial crisis as well. dependent on their parents is growing. The increasing proportion of young people who are financially reliant on their parents is not only a problem for these young people themselves but also poses a threat to the preparations being made by their parent’s generation to ensure financial stability later in their lives.

3. Problems Posed by Rapid Population Aging: Poverty among the Elderly

This unprecedented rate of population aging may pose several problems to the Korean economy in the future. Most of all, the rapid increase in life expectancy may aggravate poverty among the elderly. Despite longer life expectancy at birth, if healthy life expectancy, which is the age until which one is able to lead a healthy life, remains short, longer life expectancy will not lead to any substantial population aging. Accordingly, we need to look at healthy life expectancy at birth as an additional index. As of 2015, Korean men’s healthy life expectancy at birth was 70.8 years, which is the eighth highest in the world, after countries such as Japan and Italy, while Korean women’s healthy life expectancy at birth was 75.3 years, the second highest after Japan. Korea has risen rapidly through the ranks in terms of the healthy life expectancy of men and women since 2000, when it held 23rd and 15th place, respectively (Refer to Figure 40).

Figure 40. Healthy Life Expectancy by Country

Panel A. Healthy Life Expectancy of Men by Panel B. Healthy Life Expectancy of Country Women by Country

Source: WHO

48 As of 2010, of the 5.15 million households with baby boomers, 2.79 million households (54.2%) had members of the echo generation living with them (Statistics Korea, 2012). Accordingly, the government needs to take measures aimed at making better use of the elderly’s human capital and improving medical insurances and systems. In particular, the establishment of countermeasures for tackling poverty among the elderly is urgently needed, due to their declining wages and disposable income. As pension schemes have not become firmly established in Korea, the poverty rate among the elderly is now relatively high (Refer to Figure 41). Figure 41. Old-Age Poverty Rate by Country1)2)

Notes: 1) As of 2014 2) Share of the over 65 year-olds living with less than 50% of median equivalised household income by country Source: OECD Society at a Glance 2016

With an increasing number of the elderly people living alone (Refer to Table 16), the management of their health is becoming increasingly difficult (Refer to Figure 42). As a result, there is a growing need for the government to offer more livelihood assistance for the elderly and expand medical support policies.

Table 16. Proportion of Single-Person Elderly Households1)

Elderly households2) Single-person elderly households3) Number of total households Number of Number of Composition Composition households households 2000 14,312 1,734 12.1 543 3.8 2010 17,339 3,111 17.9 1,066 6.1 2015 19,111 3,720 19.5 1,223 6.4 Notes: 1) proportion of total households, % 2) Households with heads aged 65 and older 3) Single-person households with heads aged 65 and older Source: Statistics Korea Figure 42. Proportion of People Aged 65 and Older in Good Health1)2)

Note: 1) As of 2014, except for Japan as of 2013 2) Proportions of respondents by country who selected “Very Good” or “Good” on a five-point scale (Very Good, Good, Ordinary, Bad, and Very Bad) when asked about their physical and psychological health Source: OECD Society at a Glance 2016

V. Conclusion

With growing worldwide concern over population aging, Korea’s level of population aging remains lower than the OECD average. However, Korea’s pace of aging is faster than those of other members, as its TFR is the lowest among OECD members due to the rapid progress of its industrialization while its life expectancy exceeds the OECD average. This study analyzed the common causes of population aging among OECD member countries and provided a descriptive account of the characteristics of Korea’s population aging in comparison with major countries. We attempted to consider country-specific peculiarities based on panel data of the countries subject to our analysis, but there are limits to which the regional and cultural peculiarities of the countries can be explained. In addition, as we used national aggregate data that do not take into account individual traits, it should be kept in mind that any interpretation of the results of our analysis is also limited. Using the panel data from OECD members, this paper categorized the common causes of population aging among OECD countries into declining fertility rates and increasing life expectancy and analyzed the causes of population aging mainly in terms of the determinants of declining fertility rates. According to the results of the analysis, declines in fertility rates are attributable mainly to socioeconomic factors, including wedding and childcare expenses and labor market conditions that limit equality in the gender division of labor in housework, and socio-cultural factors such as changes in education levels and gender-equality values. In particular, amid the growing participation of women in economic activities, longer working hours among men has had a negative impact on fertility rates, while, with women’s rising education levels, greater gender equality in the workplace has had a positive impact on fertility rates. In addition, longer life expectancy showed a positive correlation with welfare policies and increases in income levels. Next, we examined which of the common factors has played a more important role in Korea’s population aging and looked into the existence of any other unique circumstances of the aging process. We then provided a descriptive account of Korea’s situation in comparison with other major countries. We divided the countries subject to our comparison into those with rebounding TFRs, which include France, Sweden, and Norway, and those with low TFRs, such as Germany, Italy, and Japan, depending on the extent of the recovery of their TFRs. As Korea has industrialized quite rapidly, its population has aged quickly as well. The factors driving the rapid pace of Korea’s aging include various historical, sociocultural, and demographic characteristics. First of all, the historical characteristics include declining potential fertility resulting in part from the government’s birth control policies, while the sociocultural characteristics include declining fertility rates due to the high costs of marriage and childcare, the difficulty of achieving work-family balance in Korea, and inequality in the gender division of labor in housework. These two types of characteristics are considered the main causes of the rapidly declining fertility rates in Korea. The demographic characteristics include a surge in the proportion of elderly people as Korea’s baby boomers age. All countries around the world have a baby boom generation. However, such a generation appeared in Korea 10 years later than in any other country, which, coupled with declining fertility rates, has caused the proportion of the elderly population to rise exponentially. Population aging is known to be a universal process of demographic change that accompanies industrialization. However, the pace of Korea’s population aging is so fast that it has been difficult for the country to prepare for it, which is expected to give rise to major socioeconomic side effects. Accordingly, it is necessary to establish measures for slowing the pace of the country’s aging and addressing its side effects based on a clear understanding of the causes of the process. In light of the findings of our analysis, in order to slow the pace of population aging in Korea by minimizing the economic and social factors that are responsible for the country’s declining fertility rates, family welfare policies are urgently needed to help ease the burdens of wedding and childcare expenses, such as stabilizing the housing market and reducing private education expenses, and create working conditions that ensure work-family balance and equality in the gender division of labor in housework. More fundamentally, the government needs to understand that only when people are able to engage in childbirth and childcare while also carrying out economic activities will they choose to give birth and provide childcare.49 It is necessary to build a social consensus on the need for a gender-equal society and develop legal and institutional frameworks that make it possible to realize such a society. It is also necessary to seek comprehensive measures to address the issue of poverty among the elderly, caused by the rapid pace of population aging, and support the post- retirement pension and welfare systems. 50 In addition, as the recovery of fertility rates and slowing of the pace of population aging take place over several generations, policies aimed at achieving these two goals should be implemented consistently and continuously based on a long-term perspective and in conjunction with policies that aim to improve education and

49 As highly educated, progressive, and employed women seem to be more sensitive to childbirth and childcare in countries that do not provide an environment conducive to achieving gender equality and balancing work and family, we need to note the findings of the study (Young-Mi Kim, 2016) that supports establishing a gender- equal social environment with the aim of reducing the gender wage gap in the labor market, expanding public services for families, and narrowing the gaps in the cultural concepts of the two sexes.

50 For example, after learning from Korea’s past demographic policies, including its birth control policies, Vietnam is now establishing measures for not only tackling low fertility but also setting up broader medical insurance and pension schemes based on policy advice from the UN (UNFPA, “Addressing the emerging challenges of fertility decline: Key to maintaining the country’s sustainable development,” March 27, 2017). employment conditions for young people. In particular, policies customized to young people, including the echo generation, need to be considered.51 As such institutional reforms and policy measures complement each other, they will become more effective when introduced and implemented collectively rather than individually. Therefore, these policies should be designed and implemented based on the premise that ensuring equality in the gender division of labor in housework, promoting work- family balance, and improving the educational and employment environment for the elderly and young people are interlocked with one another in the labor market and family welfare systems. Thus, the key to the success of these policies is to implement them in consideration of their interlocking nature. Korea’s population aging is progressing at a pace faster than those of other OECD member countries, giving rise to fairly grim forecasts of the country’s prospects. However, if preparations are made for the population aging process based on a sufficient understanding, its repercussions will be minimized on both society and the economy in the future.

51 Existing studies (Chulhee Lee and Sunyoung Jung, 2015) showed that certain policies implemented by Korean government (measures for tackling low fertility such as support for newlyweds as a means of facilitating marriage) had a positive impact on a certain group of people (fertility rates among married women). References

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