Perception of Family and Work in Low-Fertility East Asia (SpringerBriefs in Population Studies) 9819938589, 9789819938582

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SpringerBriefs in Population Studies Population Studies of Japan Junji Kageyama · Eriko Teramura   Editors

Perception of Family and Work in Low-Fertility East Asia

SpringerBriefs in Population Studies

Population Studies of Japan Editor-in-Chief Toshihiko Hara, Professor Emeritus, Sapporo City University, Sapporo, Hokkaido, Japan Series Editors Shinji Anzo, Tokyo, Japan Hisakazu Kato, Tokyo, Japan Noriko Tsuya, Tokyo, Japan Toru Suzuki, Chiba, Japan Kohei Wada, Tokyo, Japan Hisashi Inaba, Tokyo, Japan Minato Nakazawa, Kobe, Japan Jim Raymo, New Jersey, USA Ryuichi Kaneko, Tokyo, Japan Satomi Kurosu, Tokyo, Japan Reiko Hayashi, Tokyo, Japan Hiroshi Kojima, Tokyo, Japan Takashi Inoue, Tokyo, Japan

The world population is expected to expand by 39.4% to 9.6 billion in 2060 (UN World Population Prospects, revised 2010). Meanwhile, Japan is expected to see its population contract by nearly one third to 86.7 million, and its proportion of the elderly (65 years of age and over) will account for no less than 39.9% (National Institute of Population and Social Security Research in Japan, Population Projections for Japan 2012). Japan has entered the post-demographic transitional phase and will be the fastest-shrinking country in the world, followed by former Eastern bloc nations, leading other Asian countries that are experiencing drastic changes. A declining population that is rapidly aging impacts a country’s economic growth, labor market, pensions, taxation, health care, and housing. The social structure and geographical distribution in the country will drastically change, and short-term as well as long-term solutions for economic and social consequences of this trend will be required. This series aims to draw attention to Japan’s entering the post-demographic transition phase and to present cutting-edge research in Japanese population studies. It will include compact monographs under the editorial supervision of the Population Association of Japan (PAJ). The PAJ was established in 1948 and organizes researchers with a wide range of interests in population studies of Japan. The major fields are (1) population structure and aging; (2) migration, urbanization, and distribution; (3) fertility; (4) mortality and morbidity; (5) nuptiality, family, and households; (6) labor force and unemployment; (7) population projection and population policy (including family planning); and (8) historical demography. Since 1978, the PAJ has been publishing the academic journal Jinkogaku Kenkyu (The Journal of Population Studies), in which most of the articles are written in Japanese. Thus, the scope of this series spans the entire field of population issues in Japan, impacts on socioeconomic change, and implications for policy measures. It includes population aging, fertility and family formation, household structures, population health, mortality, human geography and regional population, and comparative studies with other countries. This series will be of great interest to a wide range of researchers in other countries confronting a post-demographic transition stage, demographers, population geographers, sociologists, economists, political scientists, health researchers, and practitioners across a broad spectrum of social sciences.

Junji Kageyama · Eriko Teramura Editors

Perception of Family and Work in Low-Fertility East Asia

Editors Junji Kageyama Faculty of Economics Meikai University Urayasu-shi, Chiba, Japan

Eriko Teramura Faculty of Economics Meikai University Urayasu-shi, Chiba, Japan

ISSN 2211-3215 ISSN 2211-3223 (electronic) SpringerBriefs in Population Studies ISSN 2198-2724 ISSN 2198-2732 (electronic) Population Studies of Japan ISBN 978-981-99-3858-2 ISBN 978-981-99-3859-9 (eBook) https://doi.org/10.1007/978-981-99-3859-9 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

One demographic feature that characterizes today’s East Asian societies is the lowestlow fertility. The total fertility rate (TFR) has reached a level far below replacement, and the sustainability of societies, from local to national levels, is now in question. A potential cause of this phenomenon is the difficulty in reconciling parenthood and work. In particular, the friction between traditional Confucian values, which demand women to be fully responsible for household affairs, and rapid socioeconomic transformation, which pushes both women and men to engage exhaustively in productive activities, puts women in a conflicting position, supposedly, making them hesitant about family formation. In order to inquire into this issue, this book employs subjective well-being (SWB) data and evaluates the emotional returns/burdens derived from parenthood and work. In doing so, we aim to unravel the intricacies involved in balancing parenthood and work in low-fertility East Asia. For this purpose, this book puts forward five chapters, which are independent of one another. Thus, the reader can start with any chapter. Chapter 1, by Kageyama and Matsuura, treats five East Asian countries/territories (countries, hereafter), China, Hong Kong, Japan, South Korea, and Taiwan, as a single region and explores common features in the region. To do this, the authors employ World and European Values Surveys, which are repeated cross-sectional data sets, and compare the East Asian situation to that in Western countries. The main findings are as follows. First, parenthood and work remain penalties for women’s life satisfaction in East Asia, with the situation deteriorating in recent years. This presents a sharp contrast to Western countries, in which neither parenthood nor work yields a penalty for women’s life satisfaction in recent years. Second, no clear pattern regarding the impact of parenthood on men’s life satisfaction is found in East Asian and Western countries. This is again not the case among East Asian women. These results are consistent with the idea that gender-based role divisions in East Asia make reconciling family and work difficult for women. The following three chapters respectively focus on three East Asian democracies, Japan, South Korea, and Taiwan. The advantage of focusing on a particular country lies in the availability of a panel data set, which allows the authors to control for v

vi

Preface

the individual fixed effects. By employing better-structured data sets, these three chapters reveal if the common features found in the first chapter are detected in each country. Chapter 2, by Hagiwara, uses the Japan Household Panel Survey (JHPS/KHPS) to analyze the gender difference in happiness by marital, parental, and employment status. The results show that for men, marital, parental, and employment status positively affect their happiness. For women, marriage positively affects their happiness but parental and employment status have significant negative effects. Based on these results, the author asserts that a division of labor based on strong gender roles remains in Japan and parenthood does not contribute to the happiness of most women. In Chap. 3, Kageyama employs the Korean Labor and Income Panel Survey to examine the impact of parenthood and work on life satisfaction and the satisfaction in six domains of life, i.e., family relations, household income, leisure activities, housing environment, relations with relatives, and social relations. The main findings are as follows. First, the returns of having a job in life and almost all domains of satisfaction increased for younger generations of women. Second, having children no longer improves life or any domain satisfaction for younger generations of both women and men, while it used to raise satisfaction, at least in the family domain. These results suggest that working conditions have become less gender-discriminatory for younger generations, but the emotional burdens of having children have increased. Chapter 4, by Teramura, uses the Panel Study of Family Dynamics in Taiwan and examines the impact of employment types and parenthood on job and family-life satisfaction. The results show that among women, job satisfaction is not affected by employment type, but family-life satisfaction varies across employment types. The results further demonstrate that parenthood lowers job and family-life satisfaction for women. Based on these results, the author argues for the importance of considering both job and family-life satisfaction when exploring the relationship between SWB and reproductive behaviors. Finally, Chap. 5, by Sato and Teramura, directs attention to India to compare the East Asian situation to that in another Asian country. To do this, the authors employ the Preference Parameters Study of Osaka University and obtain the following results. First, parenthood does not negatively affect the happiness of married women, which contrasts with the findings in East Asia where parenthood lowers parental SWB. Second, having a job negatively affects the happiness of married women. Third, happiness is highest among non-working mothers with two children or fewer. These results indicate that preferences consistent with traditional gender-based role division remain strong in India. In conclusion, the findings in this book support the idea that the friction between persistent gender-based role divisions and socioeconomic transformation in East Asia makes it difficult for women to balance family and work, prompting fertility decline to the lowest-low level in the region. All the East Asian studies included in the book demonstrate that the impact of parenthood on women’s SWB is negative in one way or another. At the same time, the impact of work is non-positive except in Taiwan, in which the effect is positive for some employment types, presumably because the non-working category includes both voluntary and involuntary unemployment.

Preface

vii

These results suggest that women are not necessarily willingly trading parenthood for work. Rather, they are forced to take full responsibility for both activities. Reconciling family and work remains a challenging task for women in East Asia. In closing, we would like to thank the editorial board of the Population Studies of Japan, in particular Toshihiko Hara, for their encouraging comments and Yutaka Hirachi at Springer Nature, Japan for editing this book. The studies in this book are supported by Grant-in-Aid for Scientific Research from JSPS in Japan (17KT0037). Urayasu-shi, Japan Chiba, Japan

Junji Kageyama Eriko Teramura

Contents

1 How Do People in East Asia Feel About Parenthood and Work? . . . . . Junji Kageyama and Tsukasa Matsuura

1

2 Persistent Gender-Based Division in Japan . . . . . . . . . . . . . . . . . . . . . . . . 19 Risa Hagiwara 3 Deteriorating Family-Work Balance in South Korea: Evidence from Life and Domain Satisfaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Junji Kageyama 4 Subjective Well-Being and Women’s Employment in Taiwan . . . . . . . . 57 Eriko Teramura 5 The Association Between Subjective Well-Being, Parenthood, and Work of Married Women: Evidence from Longitudinal Data from Urban India . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 Kazuma Sato and Eriko Teramura

ix

Editors and Contributors

About the Editors Junji Kageyama is a Professor of Economics at Meikai University. He received his Ph.D. in economics from Osaka University and served as a visiting scholar at the Max Planck Institute for Demographic Research, the Vienna Institute of Demography, and the Department of Demography at the University of California, Berkeley. His research interests lie in the areas of demography, behavioral economics, subjective well-being, and bioeconomics, with a current focus on introducing happiness studies into demography and economics. His published research includes analyses of savings and lifespan, time discounting behavior, happiness and life expectancy, the financial burden of children, and the age-trajectories of dissatisfaction and preferences. He is also a member of the editorial board of the Journal of Population Studies. Eriko Teramura is a professor at Meikai University in Japan. She received her Ph.D. in Social Sciences from Ochanomizu University. Her areas of expertise are human resource management, labor economics, and gender studies. Her experience in the private sector has given her a unique perspective regarding family dynamics, demographics, and corporate labor issues. Her main interest is in examining gender disparity within the private sector. In addition to this book, she has co-authored “Wellbeing and Policy: Evidence for Action” (2023 (in press), Routledge), and other Japanese books such as “Women’s work and Japanese workplaces: workplace atmosphere and women’s work styles after the Equal Employment Opportunity Law” (2022, Koyo Shobo).

Contributors Risa Hagiwara Meikai University, Chiba, Japan Junji Kageyama Meikai University, Chiba, Japan xi

xii

Tsukasa Matsuura Chuo University, Tokyo, Japan Kazuma Sato Takushoku University, Tokyo, Japan Eriko Teramura Meikai University, Chiba, Japan

Editors and Contributors

Chapter 1

How Do People in East Asia Feel About Parenthood and Work? Junji Kageyama and Tsukasa Matsuura

Abstract This paper treats five East Asian countries/territories, China, Hong Kong, Japan, South Korea, and Taiwan, as a single region and explores common features in the region. To do this, we employ World and European Values Surveys and compare the East Asian situation to that in Western countries. The main results are as follows. First, parenthood and work remain penalties for women’s life satisfaction in East Asia, with the situation deteriorating in recent years. This presents a sharp contrast to the West, in which neither parenthood nor work yields a penalty for women’s life satisfaction in recent years. Second, we find no clear pattern regarding the impacts of parenthood on men’s life satisfaction in East Asia and the West. This is again not the case among East Asian women. Third, we find no significant correlation between financial satisfaction and parenthood or work for either women or men in East Asia and the West. These results are consistent with the idea that gender-based role divisions in East Asia make reconciling family and work difficult for women and cause them to hesitate about family formation.

1.1 Introduction Consciously or unconsciously, gender-based social norms often influence people’s perceptions, attitudes, and behaviors. It is particularly so in East Asia, where Confucianism has traditionally been prevalent. This paper assesses how people, especially women, in East Asia feel about parenthood and work and how these perceptions have changed in recent years. We pay particular attention to women and focus on parenthood and work because gender-based role divisions in East Asia, which demand women to be fully responsible for household tasks, make balancing parenthood and work extremely difficult J. Kageyama (B) Meikai University, 1 Akemi, Urayasu-Shi, Chiba 279-8550, Japan e-mail: [email protected] T. Matsuura Chuo University, 742-1 Higashinakano, Hachioji-Shi, Tokyo 192-0393, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Kageyama and E. Teramura (eds.), Perception of Family and Work in Low-Fertility East Asia, Population Studies of Japan, https://doi.org/10.1007/978-981-99-3859-9_1

1

2

J. Kageyama and T. Matsuura

for women.1 By assessing the perceptions regarding parenthood and work, we study how women’s heavy household burden in East Asia affects parenthood, work, and even fertility behavior. We employ subjective well-being (SWB) to capture emotional states. Specifically, we assess how parenthood and work relate to life and financial satisfaction. Using life satisfaction, we investigate how people perceive the impacts of parenthood and work on their overall lives. Similarly, we assess the impacts of parenthood and work in the financial domain using financial satisfaction. Previous studies have shown that financial satisfaction reflects the financial burden of having children (Blanchflower & Clark, 2021; Kageyama & Matsuura, 2018; Plagnol, 2011; Pollmann-Schult, 2014; Stanca, 2012). Besides, by comparing the impacts of life and financial satisfaction, we assess whether the impact of parenthood stems from the financial burden or other household-related challenges. In East Asia, we focus on five countries/territories (countries, hereafter), China, Hong Kong, Japan, South Korea (Korea, hereafter), and Taiwan, in which the data are available in World and European Values Surveys (WVS/EVS). WVS/EVS investigate SWB-related issues across a number of countries and enable us to make comparative analyses across countries. Employing the WVS/EVS dataset in these five countries, we explore the common features in East Asia regarding how people feel about parenthood and work.2 For comparison, we assess the situation in Western countries. There are two reasons to compare East Asia to the West. First, gender is likely to matter less in the West. We may be able to capture this difference using SWB data. Second, while both regions are known for fertility decline, fertility has been relatively stable in the West. Figures 1.1 and 1.2, respectively, present the TFR trends in five East Asian countries and the cross-country averages of both five East Asian countries and six selected Western countries, Australia, Germany, New Zealand, Spain, Sweden, and the United States, in which the data are available in WVS/EVS.3 The TFR data are from the United Nations (2022). As presented, fertility decline is more severe in East Asia than in the West, especially in the last 20 years. China’s TFR, which had been relatively high among East Asian counties, has also dropped in recent years and converged to a level similar to other East Asian countries. We suspect that difficulties associated with balancing 1

Various gender gap indicators point to the existence of strong gender-based role divisions in East Asia. According to the Global Gender Gap Index (GGGI) published by World Economic Forum (2022), China, Japan, and South Korea respectively ranked 102nd, 116th, and 99th out of 146 countries. Focusing on the economic participation and opportunity score, which constitutes a part of GGGI, we find that these three countries respectively ranked 37th, 121st, and 115th positions. Similarly, according to the ranking of Women, Business and the Law Index (WBL index) published by the World Bank (2022), China, Hong Kong, Japan, South Korea, and Taiwan were respectively placed at 117th, 33rd, 103rd, 61st, and 35th positions. 2 On the downside, WVS/ESV are repeated cross-sectional data sets. Thus, endogeneity bias remains an important issue, particularly for investigating the impacts of parenthood and work on financial satisfaction. Keeping this issue in mind, this paper prioritizes exploring the common features in the region. Please refer to Kageyama and Matsuura (2018) for further discussion. 3 Please refer to Sect. 1.3 for the criteria for selecting these countries.

1 How Do People in East Asia Feel About Parenthood and Work?

3

3.5 3 2.5 2 1.5 1 0.5

Hong Kong Taiwan

Japan

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

0

China Korea

Fig. 1.1 TFR trends in East Asia 2.5 2 1.5 1 0.5 Average (East Asia)

Average (West)

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020

0

Fig. 1.2 TFR trends in East Asia and the West

family and work, especially for women, relate to ultra-low fertility in East Asia, as discussed in many other studies (e.g., Cheng, 2020; McDonald, 2009; Raymo et al., 2015; Tsuya, 2019). We assess if this is truly the case by comparing the two regions. The structure of the paper is as follows. The next section reviews the literature. Sections 1.3 and 1.4, respectively, address the methods and the results. Section 1.5 concludes.

4

J. Kageyama and T. Matsuura

1.2 Literature Review One interesting finding in the happiness literature is that parenthood lowers SWB. For example, in his review article, Hansen (2012) asserted that the folk theory that having children make people happier is simply a myth. Various studies focused on this negative impact of parenthood and investigated how parenthood and SWB interact with other variables (e.g., Nomaguchi & Milkie, 2020, for a review). These variables include gender (Bernardi et al., 2017; NelsonCoffey et al., 2019), the ages of parents and children (Margolis & Myrskylä, 2011; Myrskylä & Margolis, 2014), income (Le Moglie et al., 2019), employment status (Ugur, 2020), governmental policies (Glass et al., 2016), and work-family conflict (Matysiak et al., 2016). In addition, the financial burden (Blanchflower & Clark, 2021; Kageyama & Matsuura, 2018; Plagnol, 2011; Stanca, 2012) plus time costs (Pollmann-Schult, 2014) are also considered important factors that lead to a reduction in parental SWB. Focusing on East Asia, we find that the region’s gender-based role divisions are considered moderating factors that explain the negative impact of parenthood on happiness and, further, its gender difference. Along this line, Kaufman and Taniguchi (2010) compared the gender difference in parental SWB between Japan and the United States and found no significant difference. In both countries, having young children raises men’s happiness but not women’s. Qian and Qian (2015) used Chinese data and demonstrated that parenthood has no impact on happiness for either women or men, whereas the traditional male bread-winner model remains in urban Chinese societies.4 Hori and Kamo (2018) investigated the situations in China, Japan, Korea, and Taiwan and found no significant impact of parenthood on happiness for either women or men. Sato (2022) examined the effect of employment status and parenthood on SWB in Japan and showed that non-working non-mothers are happier than those working or parenting. As in Sato (2022), the gender difference in the impacts of working status on parental SWB also attracts academic attention as gender-based role divisions intertwine with working status and are considered a source of difficulty when balancing family and work (Brinton & Oh, 2019; Chao & Glass, 2020; Oishi et al., 2015; Yamashita et al., 2016). Following these studies, the present study addresses SWB in relation to parenthood and work in the low-fertility East Asian context. In particular, we treat East Asia as one region and compare the results in East Asia to those in the West.

4

Qian and Qian (2015) combined minor children and (elder) parents.

1 How Do People in East Asia Feel About Parenthood and Work?

5

1.3 Data and Methods We employ the Integrated Values Surveys (IVS) dataset 1981–2021, which consists of the EVS Trend File 1981–2017 (EVS, 2021) and the WVS Time-series 1981– 2021 dataset (Haerpfer et al., 2021). The IVS dataset is a repeated cross-sectional dataset with 645,249 observations in 115 countries across seven waves. This dataset is suitable for our analyses since it contains questions on life and financial satisfaction across countries in various time periods. The wordings of life and financial satisfaction questions are as follows: All things considered, how satisfied are you with your life as a whole these days? and How satisfied are you with the financial situation of your household? Respondents are asked to choose an answer between dissatisfied (1) and satisfied (10). Using these questions, we examine how people feel about parenthood and work in East Asia and compare the results to those in the West. We leave country-specific analyses to other papers since more detailed country-specific datasets are available for such analyses. Specifically, we regress life and financial satisfaction on parenting and working conditions controlling for demographic and socio-economic characteristics, respectively, for East Asia and the West. We include country and wave dummies to control for country-specific and time-specific effects. As mentioned in Sect. 1.1, East Asia includes China, Hong Kong, Japan, Korea, and Taiwan, and the West includes Australia, Germany, New Zealand, Spain, Sweden, and the United States. The choice of these countries is merely technical and based on data availability. The variables necessary for the analyses, such as life satisfaction, financial satisfaction, and demographic and socio-economic variables, are available only in these countries within East Asia and western Europe, Australia, Canada, New Zealand, and the United States. Regarding time periods, we are able to access four waves of observations in the 2000s and 2010s for East Asia and five waves in the 1990s, 2000s, and 2010s for the West. We combine two waves in the 2000s and in the 2010s to retain a sufficient number of observations. Using the observations in multiple time periods, we can detect changes in the impacts of parenthood and work. At the methodological level, we employ the OLS model with cluster-robust standard errors that deal with country-wave differences. The results remain the same when we apply the ordered logit model. We present the OLS results because the interpretation of coefficients is more straightforward under the OLS model. To assess the impacts of parenthood and work on satisfaction, we follow Sato (2022) and separate parenting and working conditions into the following four categories, non-working non-parents (NW_NP), working non-parents (W_NP), nonworking parents (NW_P), and working parents (W_P). Note that working individuals include those working full-time and part-time and self-employed, while non-working

6

J. Kageyama and T. Matsuura

individuals are those categorized as housewives. As for men, we focus on two categories, working non-parents (W_NP) and working parents (W_P), as non-working men are quite rare in our sample. The other explanatory variables are marital status (never married and married/living together as married), age, age squared, education (lower, middle, and upper levels), and relative income. We focus on individuals at child-rearing ages and thus restrict the sample to those aged between 25 and 49. Tables 1.1 and 1.2 present descriptive statistics of these variables.

1.4 Regression Results 1.4.1 Life Satisfaction Figure 1.3 presents the impacts of parenting and working conditions on women’s life satisfaction.5 The reference group is non-working non-parents, NW_NP. The dots and lines, respectively, represent the coefficients and the 95% confidence intervals. In East Asia, the coefficients for working non-parents, W_NP, are significantly negative, about −0.7, in both the 2000s and the 2010s. This result demonstrates that the penalty of work remains for non-mothers.6 With respect to parenthood, the coefficients for both non-working parents, NW_P, and working parents, W_P, turned significantly negative, around −0.4, in the 2010s from insignificant negative in the 2000s. This points to the possibility that the burden of parenthood has increased in recent years. However, we do not observe any significant accumulated effects of parenthood and work as the coefficients for working non-parents, W_NP, non-working parents, NW_ P, and working parents, W_P, are not significantly different. These results imply that parenthood and work remain stressful tasks for women and that the situation has deteriorated in the 2010s. As a result, non-working nonparents, such as housewives with no children, are the group that is most satisfied with their overall lives in East Asia. Turning to the West, we observe different patterns. The impacts of both parenthood and work have continuously improved in the last three decades, although all related coefficients are insignificant. However, again, we observe no accumulated effects of parenthood and work. These results indicate that having children or having a job has no impact, or potentially even a positive impact in recent years, on life satisfaction in the West. This trend presents a sharp contrast to East Asia. For men, we find no clear pattern for both East Asia and the West, as presented in Fig. 1.4.

5

Please refer to the tables in Appendix for details of regression results. We should be careful when interpreting coefficients related to work because women with lowearning partners are likely less satisfied with their overall lives and tend to work more, as demonstrated by Douglas-Arisawa Law. If it is the case, the estimated coefficients underestimate the actual impacts of work. The same issue applies to financial satisfaction.

6

6.01

1 (lowest)–10 (highest)

Financial Satisfaction

0.198 0.267

Japan

South Korea

0.443

0.399

0.326

0.326

0.120 0.121

0.455

0.293

China

Taiwan ROC

0.493 2.06

0.413 4.93

Upper

Relative Income

Hong Kong

0.384 0.491

0.179 0.407

513.32

6.9

0.342

0.499

Middle

Education

1441.4

37.3

0.865

0.536

0.445

0.382

0.120

2.30

Lower

1 (lowest)–10 (highest)

0 never married, 1 married

25 to 49

Married

Age

Age squared

Working parent

W_P

0.272

0.177

Working non-parent

Non-working parent

W_NP

NW_P

0.015

Non-working non-parent

NW_NP

Work and Parental Status

2.13

0.167

0.160

0.148

0.148

0.377

4.77

0.442

0.395

0.163

1471.3

37.7

0.820

0.548

0.206

0.236

0.010

6.25

6.99

2010s

0.373

0.366

0.355

0.355

0.485

1.85

0.497

0.489

0.369

521.38

7.0

0.384

0.498

0.404

0.425

0.099

2.03

1.80

S.D

Mean

0.279

0.196

0.111

0.134

0.280

5.01

0.506

0.385

0.108

1438.1

37.3

0.792

0.718

0.282

5.80

6.45

2000s

6.69

Mean

2000s

1 (lowest)–10 (highest)

S.D Men

Life Satisfaciton

Mean Women

Explanations

Variables

Table 1.1 Descriptive statistics (East Asia)

0.449

0.397

0.314

0.340

0.449

2.02

0.500

0.487

0.311

515

6.9

0.406

0.450

0.450

2.21

2.07

S.D

0.185

0.171

0.129

0.156

0.359

4.78

0.525

0.354

0.121

1473.3

37.7

0.757

0.686

0.314

6.16

6.85

2010s

Mean

(continued)

0.388

0.376

0.336

0.363

0.480

1.88

0.499

0.478

0.326

528.61

7.1

0.429

0.464

0.464

2.00

1.83

S.D

1 How Do People in East Asia Feel About Parenthood and Work? 7

Explanations

2,317

2000s

Women

Mean

S.D

3,259

2010s

Mean

S.D

1,983

2000s

Men

Mean

S.D

2,914

2010s

Mean

Working: full-time and part-time employees and self-employed. Non-working: housewives. Married includes living together as married

Obs

Variables

Table 1.1 (continued) S.D

8 J. Kageyama and T. Matsuura

1,364

Age squared

0.105

Spain

0.307

0.353

0.419

0.227

0.146

Germany

New Zealand

0.427

0.240

Australia

0.485

2.40

0.378

6.17

Upper

Relative Income

0.350

0.500

0.143

0.479

Middle

511.35

6.9

0.358

0.498

Lower

Education

0.849

36.3

Married

Age

0.546

W_P

0.412

0.411

0.214

0.216

W_NP

NW_P

0.151

0.023

2.22

6.24

0.235

0.114

0.200

0.171

6.00

0.487

0.350

0.163

1,473

37.7

0.836

0.544

0.200

0.246

0.011

6.39

7.56

0.424

0.318

0.400

0.376

2.30

0.500

0.477

0.369

524.05

7.0

0.371

0.498

0.400

0.431

0.104

2.08

1.60

S.D

0.065

0.118

0.210

0.201

5.47

0.536

0.409

0.055

1417.8

36.9

0.794

0.525

0.149

0.315

0.010

6.47

7.58

2010s

Mean

0.246

0.323

0.408

0.400

1.86

0.499

0.492

0.229

547.16

7.3

0.404

0.499

0.357

0.465

0.102

2.18

1.64

S.D

Mean

0.090

0.123

0.212

0.243

6.45

0.409

0.488

0.103

1,413

36.9

0.796

0.671

0.329

6.43

7.50

1990s

1.75

7.65

2000s

Mean

1990s

S.D Men

Mean

Women

NW_NP

Work and Parental Status

Financial Satisfaction

Life Satisfaciton

Variables

Table 1.2 Descriptive statistics (West)

0.286

0.329

0.409

0.429

2.32

0.492

0.500

0.304

522.5

7.0

0.403

0.470

0.470

2.08

1.68

S.D

0.293

0.116

0.161

0.142

6.13

0.473

0.371

0.156

1461.9

37.6

0.746

0.614

0.386

6.48

7.50

2000s

Mean

0.455

0.320

0.368

0.349

2.32

0.499

0.483

0.363

524.51

7.0

0.436

0.487

0.487

1.99

1.60

S.D

0.082

0.084

0.247

0.136

5.58

0.526

0.406

0.068

1463.7

37.6

0.757

0.603

0.397

6.52

7.45

2010s

Mean

(continued)

0.275

0.277

0.432

0.343

1.82

0.499

0.491

0.252

535.23

7.1

0.429

0.489

0.489

2.08

1.62

S.D

1 How Do People in East Asia Feel About Parenthood and Work? 9

0.116

0.167

1,553

United States

Obs

Refer to Table 1.1

Mean

S.D

Mean

0.373

0.320 1,547

0.177

0.103

2000s 0.382

0.304 2,583

0.344

0.063

2010s 0.475

0.243 1,442

0.193

0.138

1990s

S.D

1990s

Mean Men

S.D

Women

Mean

Sweden

Variables

Table 1.2 (continued)

0.395

0.345

S.D

1,289

0.148

0.140

2000s

Mean

0.355

0.347

S.D

2,176

0.372

0.079

2010s

Mean

0.484

0.269

S.D

10 J. Kageyama and T. Matsuura

1 How Do People in East Asia Feel About Parenthood and Work?

East Asia

11

West

W_NP

NW_P

W_P

-1

0

1

2

1990s 2010s

-1

0

1

2

2000s

Fig. 1.3 Life satisfaction (women)

East Asia

West

W_P

-.5

0

.5

1990s 2010s

Fig. 1.4 Life satisfaction (men)

-.5

2000s

0

.5

12

J. Kageyama and T. Matsuura

East Asia

West

W_NP

NW_P

W_P

-2

-1

0

1

1990s 2010s

-2

-1

0

1

2000s

Fig. 1.5 Financial satisfaction (women)

1.4.2 Financial Satisfaction The results for women’s financial satisfaction appear in Fig. 1.5. In East Asia, we find no significant impacts among three parenting and working categories in both the 2000s and 2010s. The 95% confidence intervals are quite wide, suggesting that parenthood or work does not correlate to satisfaction in the financial domain. At the same time, we observe that the coefficients related to work increased slightly in the 2010s. Looking at the West, we again find steady improvement in both parenthood and work. The coefficients for three parenting and working categories turned from negative to positive in the last three decades, although still insignificant in the 2010s. These results are similar to those obtained for life satisfaction. For men, we again find no clear pattern in East Asia and the West, as presented in Fig. 1.6. One noteworthy result is that the coefficient of working parents, W_P, turned significantly negative, −0.2, in the 2010s. The reason should be investigated in future research.

1 How Do People in East Asia Feel About Parenthood and Work?

East Asia

13

West

W_P

-.5

0

.5

1

1990s 2010s

-.5

0

.5

1

2000s

Fig. 1.6 Financial satisfaction (men)

1.5 Concluding Remarks Using SWB data, we measure how people perceive parenthood and work in East Asia, where gender-based social norms remain prevalent. The main results relating to life satisfaction are as follows. First, parenthood and work remain penalties for women’s life satisfaction in East Asia, and the situation has deteriorated in recent years. This presents a sharp contrast to the West, in which neither parenthood nor work yields a penalty for women’s life satisfaction in recent years. Second, we find no clear pattern regarding the impacts of parenthood on men’s life satisfaction in both East Asia and the West. In East Asia, this also presents a sharp contrast to the women’s case. Parenthood relates to only women’s life satisfaction but not men’s life satisfaction. With respect to the financial domain, we could not confirm the impact of parenthood or work on women’s or men’s financial satisfaction in East Asia and the West.

14

J. Kageyama and T. Matsuura

These results are consistent with the idea that the difficulties associated with reconciling family and work in East Asia contribute to mothers’ low life satisfaction and, further, give rise to ultra-low fertility. As presented in Fig. 1.2, TFR is lower and more rapidly declining in East Asia than in the West.7 Policy-wise, these results highlight the importance of flexible work and household environments in which women feel less responsible for household tasks and can easily adjust their time usage based on their child-rearing and working needs. An appropriate family and work balance would remove a source of dissatisfaction among women, make family formation easier, and positively impact fertility behavior. Finally, we address the limitations of the present study. First, we aggregate multiple countries to investigate common features in the regions. This would potentially result in overgeneralization. Second, we use a repeated cross-sectional dataset, which is prone to endogeneity bias. To circumvent these issues, we should look at country-level studies that can make use of panel data. Such studies would complement the present study. Acknowledgements This research is supported by Grant-in-Aid for Scientific Research from JSPS in Japan (17KT0037).

Appendix See Tables 1.A1 and 1.A2.

7

The gap seems to have shrunk in the middle of the 2010s. This is due to the irregular movement observed in China. The gap continues to widen over a longer time span.

2010s

0.000 (0.001)

0.105 (0.113)

0.078 (0.045)

−0.001 (0.001)

0.200 (0.144)

−0.074 (0.068)

0.001 (0.001)

Married

Age

Age squared

3,259

2,317

0.076

Obs

R-squared 0.083

1,553

0.087** (0.031) 0.057

1,547

0.123*** (0.031)

0.157 (0.121)

0.160 (0.122)

0.000 (0.001)

−0.054 (0.050)

0.425** (0.140)

0.365 (0.500)

0.390 (0.526)

0.274 (0.464)

0.094

2,583

0.189*** (0.029)

0.265 (0.169)

0.204 (0.159)

−0.001* (0.001)

0.091* (0.048)

0.560*** (0.086)

0.536 (0.342)

0.669 (0.396)

0.436 (0.352)

0.081

1,983

0.241*** (0.039)

0.341 (0.338)

0.256 (0.273)

0.001 (0.001)

−0.123 (0.097)

0.578** (0.235)

0.125 (0.182)

0.093

2,914

0.207*** (0.025)

0.086 (0.166)

−0.098 (0.142)

0.000 (0.001)

−0.026 (0.042)

0.644*** (0.177)

0.087 (0.075)

2010s

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Country and wave dummies are included

0.100

0.262*** (0.031)

0.248*** (0.025)

Relative Income

0.247 (0.168)

−0.080 (0.142)

0.366 (0.219)

Upper

0.037 (0.149)

0.820*** (0.162)

−0.233 (0.340)

−0.255* (0.126)

0.262 (0.191)

Middle

Education (Ref: Lower)

−0.005 (0.066)

−0.575*** (0.120)

−0.310 (0.289)

W_P

0.026 (0.343)

−0.428** (0.178)

−0.147 (0.216)

NW_P

−0.008 (0.192)

−0.656*** (0.193)

−0.710** (0.205)

W_NP

Work and Parental Status (Ref: NW_NP for women, W_NP for men)

2000s

East Asia 2000s

West 1990s

East Asia

2000s

2010s

Men

Women

Table 1.A1 Regression results (life satisfaction) West

0.086

1,442

0.092* (0.038)

0.268 (0.266)

0.248 (0.289)

0.002*** (0.000)

−0.151*** (0.032)

0.807*** (0.100)

0.036 (0.107)

1990s

0.096

1,289

0.127*** (0.024)

0.191* (0.081)

0.059 (0.165)

0.001* (0.001)

−0.115** (0.044)

0.651*** (0.090)

0.158** (0.043)

2000s

0.091

2,176

0.209*** (0.024)

0.149 (0.266)

0.129 (0.232)

0.001 (0.001)

−0.066 (0.066)

0.492*** (0.093)

0.008 (0.056)

2010s

1 How Do People in East Asia Feel About Parenthood and Work? 15

0.145** (0.062)

−0.002* (0.001)

−0.011 (0.123)

−0.000 (0.002)

Age

Age squared

3,259

0.362*** (0.044)

2,317

0.123

Relative Income

Obs

R-squared

Refer to Table 1.A1

0.444*** (0.023)

0.188 (0.217)

Upper

0.175

0.125 (0.121)

−0.075 (0.154)

Middle

−0.095 (0.102)

0.192 (0.155)

0.148 (0.275)

Married

Education (Ref: Lower)

−0.043 (0.339)

−0.312 (0.515)

W_P

0.149

1,553

0.303** (0.085)

0.603** (0.174)

0.358** (0.103)

0.002 (0.001)

−0.104 (0.107)

0.708*** (0.132)

−0.862** (0.301)

−0.582 (0.372)

−0.013 (0.275)

0.127 (0.441)

NW_P

−0.240 (0.357)

0.074 (0.212)

−0.036 (0.391)

W_NP

1,547 0.160

0.239

2,583

0.524*** (0.044)

0.532** (0.211)

−0.070 (0.171) 0.369*** (0.046)

0.352 (0.193)

−0.001* (0.000)

0.078* (0.035)

0.295** (0.102)

0.192 (0.126)

0.193 (0.248)

0.266 (0.147)

−0.114 (0.185)

−0.000 (0.001)

0.034 (0.063)

0.063 (0.108)

−0.302 (0.359)

−0.282 (0.324)

−0.151 (0.348)

Work and Parental Status (Ref: NW_NP for women, W_NP for men)

0.107

1,983

0.342*** (0.050)

0.239 (0.405)

0.098 (0.329)

0.002 (0.001)

−0.160 (0.115)

0.084 (0.256)

0.208 (0.231)

2000s

East Asia 2010s

1990s

2000s

2000s

West

East Asia

2010s

Men

Women

Table 1.A2 Regression results (financial satisfaction)

0.151

2,914

0.379*** (0.042)

0.197 (0.127)

0.030 (0.114)

0.000 (0.001)

−0.011 (0.070)

0.242 (0.170)

−0.005 (0.177)

2010s

0.088

1,442

0.231** (0.079)

0.648* (0.281)

0.376* (0.148)

0.002* (0.001)

−0.167 (0.084)

0.114 (0.236)

−0.110 (0.124)

1990s

West

0.136

1,289

0.303*** (0.035)

0.485*** (0.116)

0.337* (0.156)

−0.000 (0.001)

0.196

2,176

0.477*** (0.030)

0.277 (0.203)

0.248 (0.198)

−0.001 (0.001)

0.114* (0.059)

0.107 (0.113)

−0.103 (0.204) 0.036 (0.057)

−0.235*** (0.062)

2010s

−0.247 (0.184)

2000s

16 J. Kageyama and T. Matsuura

1 How Do People in East Asia Feel About Parenthood and Work?

17

References Bernardi, L., Bollmann, G., Potarca, G., & Rossier, J. (2017). Multidimensionality of well-being and spillover effects across life domains: How do parenthood and personality affect changes in domain-specific satisfaction? Research in Human Development, 14(1), 26–51. Blanchflower, D. G., & Clark, A. E. (2021). Children, unhappiness and family finances. Journal of Population Economics, 34, 625–653. Brinton, M. C., & Oh, E. (2019). Babies, work, or both? Highly educated women’s employment and fertility in East Asia. American Journal of Sociology, 125(1), 105–140. Chao, S. Y., & Glass, J. (2020). Parental happiness and social policy in Asia. Asian Population Studies, 16(2), 123–144. Cheng, Y. H. A. (2020). Ultra-low fertility in East Asia. Vienna Yearbook of Population Research, 18, 83–120. EVS. (2021). EVS Trend File 1981–2017. GESIS Data Archive, Cologne. Glass, J., Simon, R. W., & Andersson, M. A. (2016). Parenthood and happiness: Effects of workfamily reconciliation policies in 22 OECD countries. American Journal of Sociology, 122(3), 886–929. Haerpfer, C., Inglehart, R., Moreno, A., Welzel, C., Kizilova, K., Diez-Medrano, J., et al. (Eds.). (2021). World values survey trend file (1981–2022) cross-national data-set. Madrid, Spain & Vienna, Austria: JD Systems Institute & WVSA Secretariat. Hansen, T. (2012). Parenthood and happiness: A review of folk theories versus empirical evidence. Social Indicators Research, 108, 29–64. Hori, M., & Kamo, Y. (2018). Gender differences in happiness: The effects of marriage, social roles, and social support in East Asia. Applied Research in Quality Life, 13, 839–857. Kageyama, J., & Matsuura, T. (2018). The financial burden of having children and fertility differentials across development and life stages: Evidence from satisfaction data. Journal of Happiness Studies, 19, 1–26. Kaufman, G., & Taniguchi, H. (2010). Marriage and happiness in Japan and the United States. International Journal of Sociology and Social Policy, 36(1), 25–48. Le Moglie, M., Mencarini, L., & Rapallini, C. (2019). Does income moderate the satisfaction of becoming a parent? In Germany it does and depends on education. Journal of Population Economics, 32, 915–952. Margolis, R., & Myrskylä, M. (2011). A global perspective on happiness and fertility. Population and Development Review, 37, 29–56. Matysiak, A., Mencarini, L., & Vignoli, D. (2016). Work–family conflict moderates the relationship between childbearing and subjective well-being. European Journal of Population, 32, 355–379. McDonald. (2009). Explanations of low fertility in East Asia: A comparative perspective. Ultra-Low Fertility in Pacific Asia. Myrskylä, M., & Margolis, R. (2014). Happiness: Before and after the kids. Demography, 51, 1843–1866. Nelson-Coffey, S. K., Killingsworth, M., Layous, K., Cole, S. W., & Lyubomirsky, S. (2019). Parenthood is associated with greater well-being for fathers than mothers. Personality and Social Psychology Bulletin, 45(9), 1378–1390. Nomaguchi, K. M., & Milkie, M. A. (2020). Parenthood and well-being: A decade in review. Journal of Marriage and Family, 82, 198–223. Oishi, A. S., Chan, R. K. H., Wang, L. L., & Kim, J. (2015). Do part-time jobs mitigate workers’ work–family conflict and enhance well-being? New evidence from four east-Asian societies. Social Indicators Research, 121, 5–25. Plagnol, A. C. (2011). Financial satisfaction over the life course: The influence of assets and liabilities. Journal of Economic Psychology, 32(1), 45–64. Pollmann-Schult, M. (2014). Parenthood and life satisfaction: Why don’t children make people happy? Journal of Marriage and Family, 76(2), 319–336.

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Qian, Y., & Qian, Z. (2015). Work, family, and gendered happiness among married people in urban China. Social Indicators Research, 21, 61–74. Raymo, J. M., Park, H., Xie, Y., & Yeung, W. J. J. (2015). Marriage and family in East Asia: Continuity and change. Annual Review of Sociology, 41, 471–492. Sato, K. (2022). Who is happier in Japan, a housewife or working wife? Journal of Happiness Studies, 23, 509–533. Stanca, L. (2012). Suffer the little children: Measuring the effects of parenthood on well-being worldwide. Journal of Economic Behavior and Organization, 81, 742–750. Tsuya, N. O., Choe, M. K., & Wang, F. (2019). Convergence to very low fertility in East Asia: Processes, causes, and implications. Springer, Japan. Ugur, Z. B. (2020). Does having children bring life satisfaction in Europe? Journal of Happiness Studies, 21, 1385–1406. United Nations. (2022). The 2022 Revision of World Population Prospects. World Bank. (2022). Women, Business and the Law 2022. World Economic Forum. (2022). The Global Gender Gap Report 2022. Yamashita, T., Bardo, A. R., & Liu, D. (2016). Are east Asians happy to work more or less? Associations between working hours, relative income and happiness in China, Japan, South Korea and Taiwan. Asian Journal of Social Psychology, 19, 264–274.

Chapter 2

Persistent Gender-Based Division in Japan Risa Hagiwara

Abstract This study investigates the gender gap in happiness by marital, parental, and employment status and the difference in the effects of status on happiness between young and old cohorts in Japan. The results suggest that for men, marital, parental, and employment status positively affect their happiness. For women, marriage positively affects their happiness but parental and employment status have significant negative effects. These results suggest that a division of labor based on strong gender roles remains in Japan and children do not increase the happiness of most women. As women enter the labor market, married women who are working with children are in a dilemma when choosing between work and house chores. The situation of having to multi-task causes unhappiness among many women. However, most men devote themselves to only their work. Such differences give rise to the gender gap in happiness.

2.1 Introduction Is there a gender gap in happiness by marital (having a spouse), parental (having children), and employment (having a job) status? Does the happiness response to these factors vary between young and old cohorts in Japan? This study aims to answer these questions. Japan is a country with significantly low birth and marriage rates. According to the National Institute of Population and Social Security Research, the total fertility rate (TFR) was 1.30 in 2021, and the marriage rate (per population of 1,000) was 4.1. In the past, Japan had a period when the TFR exceeded 2.0 known as a “baby boom.” Marriage and childbirth were common, and gender role division of labor, with men working outside their homes and women concentrating on house chores, was entrenched in society. However, these attitudes toward marriage and childbirth gradually changed in accordance with the increase in female labor force participation. Despite women’s social advancement, house chores remained exclusively women’s tasks and, until today, gender role attitudes have not changed. R. Hagiwara (B) Meikai University, 1 Akemi, Urayasu-Shi, Chiba 279-8550, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Kageyama and E. Teramura (eds.), Perception of Family and Work in Low-Fertility East Asia, Population Studies of Japan, https://doi.org/10.1007/978-981-99-3859-9_2

19

20

R. Hagiwara

Therefore, one working woman who wants to have a family but faces difficulty in continuing her job may decide to leave the labor market after marriage or childbirth. Other working women who prioritize their working lives over their private lives may decide to continue their job but not get married or give birth. Such a trade-off between work and family life is considered a reason for the low birth and marriage rate. The gender role division of labor also causes the current gender gap in the labor market, despite the education gender gap shrinking (a 57.70% 4-year colleges and universities enrollment rate for men and 50.94% for women in 2020, based on data from the School Basic Survey conducted by the Ministry of Education, Culture, Sports, Science and Technology).1 According to the World Economic Forum (2022), Japan’s overall score on gender gap is 0.650, ranking it 116th in 146 countries in 2022 (120th in 156 countries in 2021).2 Japan’s score in the area of the economy is 0.564 (previously 0.604). The details are as follows: the score of labor force participation is 0.750 (ranked 83rd); the score of wage equality for similar work is 0.642 (ranked 76th); and the score of legislators, senior officials, and managers is 0.152 (ranked 130th). Compared with the previous survey, both scores and ranking remained almost unchanged. The above scores are the lowest among developed countries, and are lower than South Korea, China, and the Association of Southeast Asian Nations (ASEAN) countries among Asian countries. As seen above, gender role division of labor remains and causes harmful effects on marriage and childbirth. Although it might affect individual happiness through the conflict of balancing work and family, this study investigates the gender gap in happiness by marital, parental, and employment status in Japan. Additionally, to examine whether the effects of status on happiness change and whether the gender role attitudes ease, this study compared young and old cohorts. The rest of the paper is organized as follows. Section 2.2 presents the literature review, Sect. 2.3 explains the data and method of the study, and Sect. 2.4 shows the trend of happiness and the factors which affect happiness, such as time allocation, wage, and income. Section 2.5 shows the estimation results, and finally, Sect. 2.6 concludes the study.

2.2 Related Literature As noted in Sect. 2.1, women face difficulties when attempting to balance having children and enhancing their careers, and trying to achieve both simultaneously often results in an inevitable trade-off and a difficult choice. Therefore, the opportunity for 1

Including the vocational school and junior college, the gender gap is very small (the percentages are 58.71% for men and 58.56% for women in 2020). In addition, the percentage of women is sometimes higher than that of men, depending on the year. 2 This index is based on data from four areas: economy, politics, education, and health, with 0 indicating perfect inequality and 1 indicating perfect equality. Each index consists of more detailed indices.

2 Persistent Gender-Based Division in Japan

21

promotion is often difficult for them to secure (Zhou, 2015; Hara, 2016). The decisions to quit their job and become solely a housewife are significant problems that affect their subjective well-being (SWB), according to Treas et al. (2011), Berger (2013), Beja (2014), and Hamplová (2019). In response to the difficulty of maintaining women’s jobs, studies related to gender inequality in the labor market and SWB have also been accumulated (Clark, 1997; Booth & van Ours, 2009; Bas, levent & Kirmano˘glu, 2017; Brinton, 2017). Attitudes toward work, marriage, and childbirth have become more diverse, and having a family is no longer necessarily considered the preferred option. Compared with the decision itself, the mechanism related to family formation and SWB is not empirically analyzed in detail in previous research, particularly on the gender gap in Japan (Nomaguchi & Milkie, 2003; Kohler et al., 2005; Shiraishi & Shiraishi, 2007; Lee & Ono, 2008; Umberson et al., 2010; Hansen, 2012; Myrskylä & Margolis, 2014; Pollmann-Schult, 2014; Brinton, 2017; Ugur, 2020; Sato, 2022). Sato’s (2022) study is the one most similar to this study. He examined the interaction effects of having children as well as a wife’s employment status on happiness using the Japanese Panel Survey of Consumers in 1993–2014. He clarified the SWB of married women by parental and employment status. However, he focused only on young married women aged in their twenties and thirties. In contrast, this study considers both men and women, aged between their twenties and forties, by marital, parental, and employment status. Additionally, to confirm whether the effects of status on happiness change, and whether the gender role attitudes ease, this study compares young and old cohorts. Following the above analysis, this study discusses why marriage and childbirth are not popular in Japan.

2.3 Data and Method This study uses the Japan Household Panel Survey (JHPS/KHPS), 2004–2020, from the Panel Data Research Center at Keio University. JHPS/KHPS is an annual Japanese national-representative household repeated survey and consists of two surveys. Keio Household Panel Survey (KHPS), which was based on 7,000 individuals aged 20 to 69 years nationwide was implemented in 2004. New samples were supplemented in 2007 and 2012 to enlarge the sample size and to reduce the sample attrition problem. In 2009, the Japan Household Panel Survey (JHPS), which included 4,000 individuals aged above 20 years nationwide began, but almost all survey questions were the same as KHPS. JHPS/KHPS surveyed basic characteristics such as gender, age, final education attainment, employment status, time allocation, wage/income, and family characteristics such as marital status (whether survey subjects have a spouse), number of children of the survey subjects and the spouse of subjects, if married. Additionally, JHPS/KHPS also included SWB such as happiness. Thus, JHPS/KHPS was suited for this study.

22

R. Hagiwara

The estimation method in this study is the fixed-effects ordered logit (FEOLogit) model to control for the individual invariant effects.3 The estimation model is as follows: Hit∗ = Sit β + X it γ + u i + εit ,

(2.1)

Hit = k if τik < Hit∗ ≤ τik+1 .

(2.2)

Subscript i (i = 1, . . . , N ) and t (t = 1, . . . , T ) indexes the individual and survey round respectively. Hit∗ is the latent variable and Hit is the observed ordered variable through the thresholds τik (k = 1, . . . , K ) and refers to happiness (the scale is from 0  to 10; 0 indicates unhappiness and 10 indicates happiness). Sit is the vector of status of whether to get married, have children, and have a job. Specifically, this study separates the status into the following six categories: non-working unmarried (reference group), working unmarried, non-working married without children, working married without  children, non-working married with children, and working married with children. X it is the vector of control variables. β and γ are the vectors of estimated parameters, and β refers to the status effects. u i refers to unobservable individual heterogeneities which is assumed to simultaneously affect happiness and status, and εit is the error term. This study focuses on β to compare each status’s effects on happiness level. FEOLogit is popular for estimating happiness levels because the variable of happiness is usually an ordered variable. This study assumed that the spacing between the thresholds is different for all individuals. This study then obtained the blowup and cluster (BUC) estimator, following Baetschmann (2012), Baetschmann et al. (2015, 2020).4 This study also added control variables to analyze the happiness levels, such as age, subjective health, hourly wage (yen per hour), working hours (hours per week), housework hours (hours per week), and spouse’s annual income (ten thousand yen per year, which is zero if unmarried). This study focuses on men and women aged between 20 and 49. However, the effects of marital, parental, and employment status might be different between birth cohorts because the environment surrounding survey subjects also changes. For this reason, this study divides the sample in half to compare the old cohort, who were born between 1962 and 1976, and the young cohort, who were born between 1977 and 1991. The descriptive statistics are shown in Table 2.1. This study excluded the sample of unmarried individuals with children 3

This study also estimated the fixed-effects ordinary least squared model and examined the estimation results, which did not differ significantly from the model in the FEOLogit. According to Ferrer-i-Carbonell and Frijters (2004), assuming cardinality or ordinality is relatively unimportant to estimation results but important to considering time-invariant unobserved factors. 4 The null hypothesis that the BUC-τ estimator assuming constant thresholds for all individuals and BUC estimator assuming different thresholds for all individuals are statistically the same is rejected in the Hausman test. It indicates that this study should use the BUS estimator instead of the BUC-τ estimator. The null hypothesis is rejected at the 0.1 percent level for men aged 20 to 49 (χ 2 (23) = 342.32 and Prob > χ 2 = 0.0) and the 0.5 percent level for women aged 20 to 49 (χ 2 (23) = 38.23 and Prob > χ 2 = 0. 0.0241).

2 Persistent Gender-Based Division in Japan

23

because of a small sample size. After excluding outliers and missing values, 6,933 observations for men and 7,474 observations for women were obtained in our basic specification.

2.4 Preliminary Analysis Before estimating the FEOLogit model, this section observes the descriptive statistics of happiness and the factors, such as time allocation, wage, and income, which affect happiness by marital, parental, and employment status.

2.4.1 Happiness Profiles by Marital, Parental, and Employment Status Figure 2.1 shows the happiness profiles from 2004 to 2020. The happiness level of women is higher than men. Married men are happier than unmarried men except for those men who are married but without children and jobs. The happiness levels of working men are higher than that of men without a job. It should, however, be noted that the number of men from the sample who were not working was very small; this can also be seen in the percentage of employment types by age in Fig. 2.2. Therefore, the happiness of men without a job might be influenced by other factors. This study assesses the true trend in the analysis, which control other factors as discussed in Sect. 2.5. The happiness level of women also depends on their marital status. However, the effects are smaller than in the case of men. It can be seen that the happiness level of married women is also higher than that of unmarried women; however, the difference between married and unmarried women is smaller than that of men. The happiness levels of working married women without children and non-working married women with children are almost the same since 2011. In the case of men, work positively affects their happiness. However, in the case of women, the positive effects of work are not clearly observed. Happiness in women is affected by various factors. For example, the effect of employment on women is twofold: work enables women to earn money, which increases their happiness level, but simultaneously, it involves physical and mental fatigue, which decreases their happiness level. Additionally, marriage and having children entail a household burden and are generally women’s tasks in gendered societies.

2.247

Std. Dev

6.800

0.072

0.005

0.575

38.853

Working married without children

Non-working married with children

Working married with children

Age

0.484

0.375

0.319

Normal

Somewhat good

0.466

0.311

0.012

0.109

Somewhat bad

0.109

0.070

Bad

Health status:

0.494

0.003

0.258

0.456

0.058

Working unmarried 0.296

0.218

0.050

Non-working married without children

Non-working unmarried

0.325

0.360

0.116

0.012

38.718

0.442

0.204

0.056

0.016

0.250

0.032

6.203

Std. Dev

0.468

0.480

0.320

0.107

6.980

0.497

0.403

0.230

0.124

0.433

0.176

2.218

0.319

0.401

0.112

0.012

42.102

0.686

0.006

0.071

0.004

0.197

0.037

5.861

Mean

Mean

5.854

Mean

Marital, parental, and employment status:

Happiness

Old cohort Men

Women

All samples

Men

Table 2.1 Descriptive statistics

0.466

0.490

0.316

0.110

4.353

0.464

0.075

0.257

0.060

0.398

0.188

2.184

Std. Dev

0.321

0.377

0.127

0.011

42.058

0.532

0.213

0.053

0.018

0.162

0.023

6.127

Mean

Women

0.467

0.485

0.333

0.107

4.449

0.499

0.409

0.224

0.132

0.368

0.149

2.206

Std. Dev

0.321

0.310

0.099

0.012

30.437

0.286

0.003

0.073

0.003

0.550

0.085

5.834

Mean

0.467

0.462

0.299

0.109

4.332

0.452

0.056

0.260

0.051

0.498

0.279

2.403

Std. Dev

Young cohort Men

0.335

0.314

0.087

0.012

30.017

0.208

0.182

0.065

0.010

0.480

0.055

6.400

Mean

Women

(continued)

0.472

0.464

0.282

0.109

4.395

0.406

0.386

0.246

0.098

0.500

0.229

2.237

Std. Dev

24 R. Hagiwara

0.363

Std. Dev 0.163

Mean 0.370

Std. Dev 0.258

Mean 0.438

Std. Dev 0.252

Mean

0.434

Std. Dev

6,933

144.283

22.963

7,474

384.619

22.529

19.309 343.018

18.254

The sample includes men and women aged between 20 and 49

Observations

Spouse’s annual 86.537 income (Unmarried = 0)

4.557

18.984

5,002

102.588

2.771

44.713 152.472

4.566

18.468

5,401

450.120

25.004

21.652 346.625

17.489

18.419

1,931

44.959

3.104

40.303 110.156

4.526

19.915

2,073

213.961

16.078

26.382

266.299

18.629

21.082

2.863

0.156

Mean

Women

Housework hours

0.391

Std. Dev

Young cohort Men

2419.700 3132.198 1158.882 1719.903 2642.224 3361.752 1123.759 1674.664 1843.279 2343.558 1250.391 1829.788

0.188

Mean

Women

43.485

0.388

Std. Dev

Old cohort Men

Hourly wage

0.185

Mean

Women

Working hours

Good

Men

All samples

Table 2.1 (continued)

2 Persistent Gender-Based Division in Japan 25

26

R. Hagiwara

Happiness

Men

Women

10

10

9

9

8

8

7

7

6

6

5

5

4

4

3

3

2

2

1

1

0

0

2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Non-working unmarried Working unmarried Non-working married without children Working married without children Non-working married with children Working married with children 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

Fig. 2.1 Happiness profiles by marital, parental, and employment status. The sample includes men and women aged between 20 and 49. The questions about happiness are included from JHPS/KHPS 2011. Sources JHPS/KHPS 2011–2020

Percentage of employment types

Men

Women

100%

100%

80%

80%

60%

60%

40%

40%

Self-employed

20%

20%

Non-working

0%

0% 20222426283032343638404244464850525456586062646668

Regular employee Non-regular employee

20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 52 54 56 58 60 62 64 66 68

Fig. 2.2 Changes in employment types by age. The sample includes men and women aged between 20 and 69. Regular employee includes a full-time employee with and without position and manager; non-regular employee includes a part-time employee; self-employed includes a worker not categorized in the above employment types; non-working includes unemployed, housewife/househusband, students, retired, individuals who take leave, and others remaining at home. Sources JHPS/KHPS 2004–2020

2.4.2 Changes in Employment Types by Age Figure 2.2 shows the changes in employment types by age. In the case of men, the percentage of regular employees rapidly increases around the age of 25, which is the average graduation age in 4-year colleges and universities. The percentage of self-employed also increases, but at a slower rate. Comparatively, the percentages of non-regular employees and non-working individuals decrease around this age. This indicates that almost all graduates work as regular employees after they graduate. The percentage of regular employees decreases after the age of 60, which is the retirement age in Japan.5 On the other hand, the case of women is very different from that of men. The percentage of regular employee women increases before the age of 25; however, it decreases before the age of 30. Instead, the percentages of nonregular employees and non-working women increase. In particular, the percentage of non-working women is the highest among women with ages in the early thirties. A significant proportion of women giving birth retire from their job and return to work

5

Japan is currently in the transition period in which the retirement age has increased to 70 years in order to maintain the pension system in Japan.

2 Persistent Gender-Based Division in Japan

27

as non-regular employees after their children grow up. The change in the percentage of self-employed women is similar to that of men and increases slowly. The change in the employment rate of Japanese women is represented by an Mshaped curve, as shown in Fig. 2.2. However, if employment types are divided into regular and non-regular employees, the first peak in the curve describes the top of the regular employee rate line, and the second peak in the curve describes the top of the non-regular employee rate line. The trough in the M-shaped curve represents women with ages in the early thirties. At this age, women tend to get married and have children. This trend is observed in a country where it is difficult to balance work and personal life. On the contrary, the change in the employment rate of Japanese men is represented by an inverse U-shaped curve. Figure 2.2 shows that almost all men work as regular employees.

2.4.3 Employment Types by Marital and Parental Status

Women

Men

100%

100% 80%

80% 60% 40%

60%

Unmarried

40%

Married without children

20%

Married with children

Regular employee

Non-regular Self-employed Non-working employee

Non-working

0%

Self-employed

20%

Non-regular employee

0% Regular employee

Percentage of employment types

Figure 2.3 shows the employment types by marital and parental status. In the case of men, the percentage of regular employees is the highest on average. Other employment type percentages are less than 20% of the total. The percentage of employment types varies with marital status. The difference in parental status is small for married men. Married men with children tend to work as regular employees and avoid being non-regular employees or non-working individuals. In the case of women, the percentages of non-regular employees and non-working women are higher than other employment types, particularly married women with children. Unmarried women tend to work as regular and non-regular employees. Married women are unlikely to work, but married women with children tend to work as non-regular employees.

Fig. 2.3 Employment types by marital and parental status. The sample includes men and women aged between 20 and 49. Regular employee includes a full-time employee with and without position and manager; non-regular employee includes a part-time employee; self-employed includes a worker not categorized in the above employment types; non-working includes unemployed, housewife/ househusband, students, retired, individuals who take leave, and others remaining at home. Sources JHPS/KHPS 2004–2020

28

R. Hagiwara

2.4.4 Time Allocation by Marital and Parental Status Figure 2.4 shows the time allocation by marital and parental status. Time usage is separated into the following three categories: working hours, housework hours, and childcare hours (note that housework hours do not include childcare hours). In the case of men, the working hours per week are 37.8 h for the unmarried, 46.1 h for the married without children, and 48.1 h for the married with children. Due to these long working hours, the hours for housework and childcare available for men are very short: housework hours per week are 3.3 h for the unmarried, 3.0 h for the married without children, and 2.3 h for the married with children. Childcare hours per week are only 4.1 h for the married with children. On the contrary, women holistically spend their time on work, housework, and childcare. For women, working hours per week are 32.8 h for the unmarried, 26.5 h for the married without children, and 17.4 h for the married with children. Housework hours per week are 9.0 h for the unmarried, 18.0 h for the married without children, and 29.8 h for the married with children. Childcare hours per week are 26.7 h for the married women with children. Total hours of working and housework are almost the same for unmarried (41.1 h for men and 41.8 h for women). The same is true for married without children (49.1 h for men and 44.5 h for women). However, the total hours of working, housework, and childcare for married women with children (74.0 h) are longer than that for married men with children (54.4 h). The total hours for working, housework, and childcare of married men increases by only five hours when they have children, whereas that of women increases by thirty hours. Figure 2.4 shows that the traditional gender division of labor no longer persists, at least for women. Married women with children bear the burden of both, working inside, and outside the house. Women

Hours per week

Men 60

60

50

50

40

40

30

30

20

20

10

10

Unmarried Married without children Married with children

0

0 Working

Housework

Childcare

Working

Housework

Childcare

Fig. 2.4 Time allocation by marital and parental status. The sample includes men and women aged between 20 and 49. The unit of hours is hours per week. Working hours include overtime hours. The hours of childcare are calculated only for married people with children. Sources JHPS/KHPS 2004–2020

2 Persistent Gender-Based Division in Japan

29

2.4.5 Hourly Wage and Annual Income by Marital and Parental Status Figure 2.5 shows the hourly wage and annual income by marital and parental status. The difference between men and women who get married and have children increases in terms of their hourly wage and annual income; notably, the difference of those married with children is the largest. The difference in hourly wage is 1,477 yen (about 14.77 U.S. dollars at the exchange rate of 1 dollar = 100 yen), and the difference in annual income is 3.93 million yen (about 39,300 U.S. dollars at the exchange rate of 1 dollar = 100 yen) in the case of a married couple with children. The reason for both, hourly wage and annual income of women being lower than those of men, is evidently that women tend to work as non-regular employees, and their working hours are shorter than those of men. The above descriptive statistics show that the happiness level of women is higher than that of men, and that the happiness level of married men is higher than that of unmarried men. Married men tend to work as regular employees and their working hours are long, but their housework and childcare hours are short. Their wage and income are also high. The happiness level of married women is also high. Married women, especially married women with children, often decide to be non-working or non-regular employees even if their total hours spent on work, housework, and childcare are long and the wage and income are low. These results indicate that men follow the breadwinner-type gender model while women follow the caretakertype gender model. Following these gender roles raise happiness levels. In the next section, this study controls for other factors that affect happiness and observe how marital, parental, and employment status affect happiness. Hourly wage

Annual income 600

2500

500

Ten thousand yen

3000

Yen

2000 1500 1000 500 0

400 300

Men

200

Women

100 0

Unmarried

Married without children

Married with children

Unmarried

Married without children

Married with children

Fig. 2.5 Hourly wage and annual income by marital and parental status. The sample includes men and women aged between 20 and 49. Sources JHPS/KHPS 2004–2020

30

R. Hagiwara

2.5 Estimation Results 2.5.1 Main Results Table 2.2 shows the estimation results of the male and female samples. Columns (1) and (2) show the case of all samples. As presented in columns (1) and (2), happiness depends on the marital, parental, and employment status. Focusing on significant results, the highest happiness level is seen for working married men with children, the second highest happiness level is seen for working married men without children, and the third highest happiness level is seen for non-working married men with children. Namely, the happiness of men having a spouse, children, and a job is higher than that of men not having them. In the case of women, looking at significant results, the highest happiness level is shown for non-working married women without children, the second highest happiness level is shown for non-working married women with children, and the third highest happiness level is shown for working married women without children. The positive effects on happiness levels of having a spouse for women are similar to those for men. However, having a job and children has negative effects on happiness for women. These results corroborate the findings of Sato (2022). Working out of the house and having children, combined, have a negative impact on the happiness level of women. Among married women, the happiness level of those working while having children is the lowest. At present, women tend to be expected to work both, inside and outside the house. This dual burden can exert extra stress on women. As a result, even if women follow the behaviors outlined by the prevalent gender norms, they tend to experience a lower level of happiness. On the contrary, among married men, the happiness level of working men with children is the highest. Our findings show this to be the distinctive difference between men and women. To further analyze the potential burden of jobs in and out of the house, this study also estimates the effects of working hours and housework hours. For men, effects of both are significantly negative, but the results for women are insignificant. Men are not evidently affected greatly by having a job and children, although the length of working hours and housework hours has a negative impact on their happiness levels. Women, however, are not evidently affected by working hours and housework hours significantly.

2.5.2 Comparison Results in Young and Old Cohorts This study also confirms the change in the effects of status on the happiness of men and women by comparing the results of young and old cohorts. In Table 2.2, columns (3) and (4) show the case of the old cohort sample, and columns (5) and (6) show the case of the young cohort sample. For both young and old cohorts of men, the same status is ranked from first to third, although they are ranked in a different

2 Persistent Gender-Based Division in Japan

31

Table 2.2 Estimation results of the fixed-effects ordered logit model All samples

Old cohort

Young cohort

Men

Women

Men

Women

Men

Women

(1)

(2)

(3)

(4)

(5)

(6)

Marital, parental, and employment status: Ref. Non-working unmarried 3.445***

1.52

3.425***

2.522**

3.715***

1.549

(0.921)

(0.479)

(1.381)

(1.128)

(1.385)

(0.692)

Non-working married without children

2.252

3.435***

0.599

4.306**

4.388

4.714**

(2.731)

(1.474)

(1.447)

(2.686)

(5.634)

(3.132)

Working married without children

14.66***

2.541**

16.56***

3.814**

11.58***

2.645*

(5.444)

(1.007)

(9.647)

(2.205)

(5.790)

(1.529)

Non-working married with children

9.527***

2.700***

9.130***

2.603*

15.99***

3.655**

(5.027)

(1.008)

(6.590)

(1.273)

(16.060)

(2.225)

Working married with children

19.51***

1.822

22.14***

1.91

15.79***

2.083

(7.139)

(0.695)

(12.570)

(0.926)

(7.648)

(1.336)

Age

0.967**

0.970**

0.960**

0.954***

0.99

0.983

(0.013)

(0.013)

(0.015)

(0.015)

(0.027)

(0.033)

2.658***

1.720**

2.996***

1.646

1.879

2.256**

(0.760)

(0.464)

(0.972)

(0.610)

(1.060)

(0.764)

5.359***

3.389***

6.459***

3.674***

3.306**

2.699***

(1.648)

(0.906)

(2.274)

(1.365)

(1.954)

(0.802)

7.513***

5.427***

9.145***

5.839***

4.543**

4.465***

(2.355)

(1.497)

(3.304)

(2.226)

(2.701)

(1.429)

10.58***

7.686***

11.80***

7.832***

7.347***

6.649***

(3.435)

(2.179)

(4.502)

(3.050)

(4.411)

(2.253)

1.000

1.000

1.000

1.000

1.000

1.000***

Working unmarried

Health status: Ref. Bad Somewhat bad Normal Somewhat good Good Hourly wage

(0.00001) (0.00002) (0.00001) (0.00003) (0.00003) (0.00003) Working hours Housework hours

0.994**

0.998

0.995*

1.002

0.992**

0.992*

(0.002)

(0.003)

(0.003)

(0.004)

(0.004)

(0.005)

0.987*

1.000

0.992

0.998

0.976*

1.004

(0.007)

(0.003)

(0.009)

(0.003)

(0.013)

(0.005)

Spouse’s annual income 1.000 (Unmarried = 0) (0.0005)

1.000

0.999

1.000

1.001

1.000

(0.0002)

(0.001)

(0.0002)

(0.001)

(0.001)

Observations including copies

27,863

18,244

19,852

7728

8011

25,972

(continued)

32

R. Hagiwara

Table 2.2 (continued) All samples

Old cohort

Young cohort

Men

Women

Men

Women

Men

Women

(1)

(2)

(3)

(4)

(5)

(6)

Observations

6933

7474

5002

5401

1931

2073

Number of ID

1249

1329

840

877

409

452

Pseudo R2

0.0477

0.0329

0.0423

0.036

0.0681

0.0474

Log likelihood

−9451

−10,252

−6673

−7275

−2755

−2910

The sample includes men and women aged between 20 and 49. *** 1% significance level; ** 5% significance level; and * 10% significance level. Odds ratios are shown in the upper rows and cluster-adjusted standard errors at the individual level are shown in parentheses

order in both young and old cohorts. Focusing on significant results, the highest happiness level is shown for working married men without children, the second highest happiness level is shown for non-working married men with children, and the third highest happiness level is shown for working married men with children. The happiness level of married men with children is higher than that of married men without children in the young cohort. For both young and old cohorts of women, looking at significant results, the highest happiness level is shown for non-working married women without children. The second highest happiness level is shown for working married women without children, and the third highest happiness level is shown for non-working married women with children. The rank of the happiness level of working married women without children and non-working married women with children switches in each female sample. The happiness level of those nonworking married women with children is higher than that of those working married women without children in the young cohort. In either case, having both, a job and children decreases the happiness level for women but increases it for men. These results further indicate that having children increases happiness levels in the young cohort for both, men, and women. This result is worth noting since the birth rate has been declining. A possible reason for this is that the happiness level of working married women with children remains low in both young and old cohorts, and balancing family and work remains a demanding task for women.

2.5.3 Robustness Check From the descriptive statistics in Table 2.1, the average age in the old cohort sample is twelve years higher than that in the young cohort sample. Additionally, the compositions of marital, parental, and employment status are also different due to the differences in age distribution. Therefore, it is possible that this study is perceived to merely compare people of different ages. However, the objective of this study is to compare the effects of marital, parental, and employment status on the happiness

2 Persistent Gender-Based Division in Japan

33

levels of men and women facing the problem of finding a balance between work and house chores. Hence, the presented analysis focuses on the age group between 20 and 49, because this age group tends to be at the life stage of family formation. Nevertheless, to assess the impact of age distribution, this study regresses the model focusing on individuals between the ages of 30 and 39. The results, presented in Table 2.3, are quite similar to the results of this study using a sample with the age group between 20 and 49. Thus, this study extracts results using the sample aged between 20 and 49 to increase the sample size.

2.6 Conclusions This study investigates the gender gap in happiness by marital, parental, and employment status, and the change in the effects of status on happiness between young and old cohorts in Japan, by estimating the FEOLogit model. The results suggest that marital, parental, and employment status positively affect happiness levels of men. For women, marriage positively affects happiness levels but parental and employment status have significant negative effects. These results suggest that strong gender role division of labor remains in Japan and that children do not improve the happiness of most women. As women enter the labor market, married women who are working with children are in a dilemma between work and house chores. The situations of having to multi-task tend to cause unhappiness among women. However, many men devote themselves to only their work. Such differences give rise to the gender gap in happiness. From a policy perspective, the presented results point to the importance of eliminating the traditional gender-based division of labor. As indicated in the presented results, the young cohort of women is more willing to have children. Thus, if the policies remove the traditional gender-based division of labor, balancing family and work will become easier and working women will feel happier.

34

R. Hagiwara

Table 2.3 Estimation results of the fixed-effects ordered logit model (only for the sample of those with age in the thirties) All samples

Old cohort

Young cohort

Men

Women

Men

Women

Men

Women

(1)

(2)

(3)

(4)

(5)

(6)

Marital, parental, and employment status: Ref. Non-working unmarried Working unmarried Non-working married without children Working married without children Non-working married with children

5.711***

1.144

5.028**

0.998

8.373***

1.595

(2.958)

(0.665)

(3.962)

(0.935)

(5.950)

(1.155)

0.991

3.552*

8.04e-08***

1.792

8.217**

7.530**

(2.080)

(2.505)

(0.00000009)

(1.673)

(7.223)

(7.695)

5.300***

2.631

9.146**

2.187

4.629*

3.938

(3.353)

(1.808)

(8.657)

(2.078)

(4.150)

(3.767)

31.53***

3.779*

33.06***

3.277

39.26***

5.610*

(28.080)

(2.566)

(42.800)

(2.825)

(54.300)

(5.424)

Working married with children

14.22***

1.867

16.84***

1.289

16.57***

3.595

(8.809)

(1.311)

(16.170)

(1.166)

(13.600)

(3.653)

Age

0.950*

0.942**

0.945

0.927**

0.948

0.946

(0.026)

(0.029)

(0.036)

(0.035)

(0.037)

(0.044)

Health status: Ref. Bad 2.629*

2.386**

3.582**

2.471

2.080

2.779**

(1.321)

(0.862)

(2.136)

(1.725)

(1.573)

(1.181)

Normal

5.415***

5.116***

9.708***

7.485***

3.231

3.633***

(2.930)

(1.917)

(6.303)

(5.491)

(2.527)

(1.393)

Somewhat good

8.182***

8.264***

11.30***

13.43***

6.775**

4.820***

(4.493)

(3.251)

(7.356)

(10.120)

(5.436)

(2.017)

Somewhat bad

Good Hourly wage Working hours

10.71***

11.37***

13.00***

18.50***

9.476***

6.213***

(6.126)

(4.678)

(9.077)

(14.360)

(7.804)

(2.808)

1.000

1.000

1.000

1.000

1.000

1.000

(0.00002)

(0.00003)

(0.00003)

(0.00004)

(0.00003)

(0.0001)

0.996

0.999

1.002

1.005

0.990**

0.993

(0.004) Housework hours 0.991 (0.014) Spouse’s annual income (Unmarried = 0) Observations including copies

(0.005)

(0.005)

(0.007)

(0.005)

(0.006)

0.995

0.983

0.988**

0.992

1.009

(0.005)

(0.019)

(0.006)

(0.020)

(0.006)

1.001

1.000

0.999

1.000

1.003***

0.999

(0.001)

(0.0004)

(0.001)

(0.001)

(0.001)

(0.001)

8219

8763

4377

4926

3842

3837 (continued)

2 Persistent Gender-Based Division in Japan

35

Table 2.3 (continued) All samples

Old cohort

Young cohort

Men

Women

Men

Women

Men

Women

(1)

(2)

(3)

(4)

(5)

(6)

Observations

2421

2548

1314

1457

1107

1091

Number of ID

567

572

307

320

260

252

Pseudo R2

0.0544

0.0541

0.0463

0.0826

0.0883

0.0412

Log likelihood

−2915

−3122

−1562

−1701

−1317

−1387

The sample includes men and women aged between 20 and 49. *** 1% significance level; ** 5% significance level; and * 10% significance level. Odds ratios are shown in the upper rows and cluster-adjusted standard errors at the individual level are shown in parentheses

Acknowledgements This study was supported by the Japan Society for the Promotion of Science (JSPS) Topic-Setting Program to Advance Cutting-Edge Humanities and Social Sciences Research (Global Initiatives) 17KT0037 and Grant-in-Aid for Young Scientists 18K12799. This study used the data from the Japan Household Panel Survey (JHPS/KHPS), 2004–2020, provided by the Panel Data Research Center at Keio University (PDRC). The author of this study would like to appreciate JSPS and PDRC for financial support and data supply. Needless to say, all remaining errors are the author’s.

References Bas, levent, C., & Kirmano˘glu, H. (2017). Gender inequality in Europe and the life satisfaction of working and non-working women. The Journal of Happiness Studies, 18, 107–124. Baetschmann, G. (2012). Identification and estimation of thresholds in the fixed effects ordered logit model. Economics Letters, 115(3), 416–418. Baetschmann, G., Staub, K. E., & Winkelmann, R. (2015). Consistent estimation of the fixed effects ordered logit model. Journal of the Royal Statistical Society. Series A, (Statistics in Society), 178(3), 685–703. Baetschmann, G., Ballantyne, A., Staub, K. E., & Winkelmann, R. (2020). Feologit: A new command for fitting fixed-effects ordered logit models. Standards in Genomic Sciences, 20(2), 253–275. Beja, E. L., Jr. (2014). Who is happier: Housewife or working wife? Applied Research Quality Life, 9(2), 157–177. Berger, E. M. (2013). Happy working mothers? Investigating the effect of maternal employment on life satisfaction. Economica, 80(317), 23–43. Booth, A. L., & van Ours, J. C. (2009). Hours of work and gender identity: Does part-time work make the family happier? Economica, 76(301), 176–196. Brinton, M. C. (2017). Happiness at work? Marital happiness among Japanese housewives and employed wives. In B, G. Holthus & W. Manzenreiter (Eds.), Life course, happiness and wellbeing in Japan (pp. 138–157). Nissan Institute, Routledge Japanese Studies Series. Clark, A. E. (1997). Job satisfaction and gender: Why are women so happy at work? Labor Economics, 4(4), 341–372. Ferrer-i-Carbonell, A., & Frijters, P. (2004). How important is methodology for the estimates of the determinants of happiness? The Economic Journal, 114(497), 641–659. Hamplová, D. (2019). Does work make mothers happy? The Journal of Happiness Studies, 20(2), 471–497.

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Hansen, T. (2012). Parenthood and happiness: A review of folk theories versus empirical evidence. Social Indicators Research, 108(1), 29–64. Hara, H. (2016). Glass ceilings or sticky floors? An analysis of the gender wage gap across the wage distribution in Japan. RIETI Discussion Paper Series 16-E-099. Kohler, H. P., Behrman, J. R., & Skytthe, A. (2005). Partner + children = happiness? An assessment of the effect of fertility and partnerships on subjective well-being in Danish twins. Population and Development Review, 31(3), 407–445. Lee, K. S., & Ono, H. (2008). Specialization and happiness in marriage: A U.S.–Japan comparison. Social Science Research, 37(4), 1216–1234. Myrskylä, M., & Margolis, R. (2014). Happiness: Before and after the kids. Demography, 51(5), 1843–1866. Nomaguchi, K. M., & Milkie, M. A. (2003). Costs and rewards of children: The effects of becoming a parent on adults’ lives. Journal of Marriage and Family, 65(2), 356–374. Pollmann-Schult, M. (2014). Parenthood and life satisfaction: Why don’t children make people happy? Journal of Marriage and Family, 6576(2), 319–336. Sato, K. (2022). Who is happier in Japan, a housewife or working wife? The Journal of Happiness Studies, 23, 509–533. Shiraishi, S., & Shiraishi, S. (2007). Female happiness decision factor related with child care: A non-linear panel analysis. ESRI Discussion Paper Series No.181. Treas, J., Lippe, V. D., & ChloeTai, T. (2011). The happy homemaker? Married women’s well-being in cross-national perspective. Social Forces, 90(1), 111–132. Ugur, Z. B. (2020). Does having children bring life satisfaction in Europe? The Journal of Happiness Studies, 21, 1385–1406. Umberson, D., Pudrovska, T., & Reczek, C. (2010). Parenthood, childlessness, and well-being: A life course perspective. Journal of Marriage and Family, 72(3), 612–629. World Economic Forum. (2022). The Global Gender Gap Report 2022. Zhou, Y. (2015). Career interruption of Japanese women: Why is it so hard to balance work and childcare? Japan Labor Review, 12(2), 106–123.

Chapter 3

Deteriorating Family-Work Balance in South Korea: Evidence from Life and Domain Satisfaction Junji Kageyama

Abstract This paper assesses how Korean people feel about parenthood and work and how such feelings have changed over the past quarter century. For this purpose, we measure emotional returns/burdens derived from parenthood and work using life satisfaction and the satisfaction in six domains of life available in the Korean Labor and Income Panel Survey (KLIPS), i.e., family relations, household income, leisure activities, housing environment, relations with relatives, and social relations. The main findings are as follows. First, for younger generations of women, the returns of having a job in life and almost all domains of satisfaction increased. Second, having children no longer improves life or any domain satisfaction for younger generations of both women and men, while it used to raise satisfaction, at least in the family domain. These results indicate that working conditions have become less gender-discriminatory for younger generations, but the emotional burdens of having children have increased. These changes are consistent with the ongoing social transformation characterized by the rise in women’s labor participation rate and the decline in fertility.

3.1 Introduction South Korea (Korea, hereafter) is characterized by its rapid economic growth. Figure 3.1 presents GDP per capita (constant 2015 US$) and its growth rate since 1960 (World Bank, 2022). Although the pace of growth has slowed, GDP per capita exceeded $30,000 in 2017. Rapid economic growth has accompanied extensive social changes that affect family life. Figure 3.2 presents the trends of total fertility rate (TFR) and women’s labor force participation rate, two examples of social changes in family-related domains. The figure shows that TFR and women’s labor force participation rate have respectively decreased and increased steadily since 1980. In particular, TFR in J. Kageyama (B) Meikai University, 1 Akemi, Urayasu-Shi, Chiba 279-8550, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Kageyama and E. Teramura (eds.), Perception of Family and Work in Low-Fertility East Asia, Population Studies of Japan, https://doi.org/10.1007/978-981-99-3859-9_3

37

J. Kageyama

5 0

-5

0

growth rate (%)

20000 10000

GDP per capita

10

30000

15

38

1960

1980

2000

2020

Year GDP per capita

growth rate (%)

Fig. 3.1 GDP per capita and its growth rate (World Bank, 2022)

1

1.5

TFR 2

2.5

3

45 50 55 40 Women's labor force participation rate (%)

Korea dropped sharply and reached 0.837 in 2020, the lowest in the world, followed by Hong Kong, Puerto Rico, and Singapore (World Bank, 2022).

1980

1990 TFR

2000 Year

2010

2020

Women's labor force participation rate (%)

Fig. 3.2 TFR and women’s labor force participation rate (World Bank, 2022)

3 Deteriorating Family-Work Balance in South Korea: Evidence from Life …

39

While these behavioral changes are observable and well investigated, attitudes or, more broadly, the way of thinking associated with these behavioral changes are unobservable and thus attract little attention. The present paper sheds light on this issue. Particularly, we investigate how Korean people feel about family and work and how such feelings have changed in the course of economic development and social transformation. Specifically, we measure emotional returns/burdens derived from parenthood and work using life satisfaction and the satisfaction in six domains of life available in the Korean Labor and Income Panel Survey (KLIPS), i.e., family relations, household income, leisure activities, housing environment, relations with relatives, and social relations. The structure of the paper is as follows. The next section reviews the literature. Section 3.3 discusses the data and methods, and Sect. 3.4 presents the results. Section 3.5 concludes.

3.2 Literature Review One distinctive demographic feature that characterizes Korean society is ultra-low fertility. While TFR started to recover in Western countries in the 2000s (Myrskylä et al., 2009), it remained low in Korea as well as in other East Asian countries. Furthermore, in the late 2010s, Korea experienced a further plunge in TFR, making it the world’s lowest TFR country in 2016. Various factors have been proposed to explain this trend. The tempo effect is one of them (Yoo & Sobotka, 2018). The prevalence of Confucianism that imposes excessive burdens on women in the household sector is another. Economic factors such as high educational and housing costs, economic hardship, instability, and insufficient institutional support are also considered to contribute to ultra-low fertility. The agreement in the literature is that not a single factor but a mixture of these factors is responsible for ultra-low fertility in Korea (Anderson & Kohler, 2013, 2015; Atoh et al., 2004; Brinton & Oh, 2019; Cheng, 2020; Eun, 2007; Jones, 2019; Kan & Hertog, 2017; Kan et al., 2019; H.S. Kim, 2014; K. Kim, 2017; Lim, 2021; Ma, 2013, 2014, 2016; McDonald, 2000, 2009, 2013; Ogawa et al., 2009; Raymo et al., 2015; Suzuki, 2008, 2013; Tsuya et al., 2000; Tsuya et al., 2019). For example, McDonald (2009) points to the importance of three factors: the lack of work-family balance and gender equity, a sense of economic risk among young people, and a relative absence of family support provided by governments and employers. Similarly, Raymo et al. (2015) argued that low fertility and associated family issues in East Asia reflect “tension between rapid social and economic change and limited change in family expectations and obligations.” In the meantime, only limited studies employed subjective well-being (SWB) data for investigating people’s feelings toward family in the context of ultra-low fertility in Korea. Chao and Glass (2020) are one of them. They showed that public policies, such as those promoting work flexibility, moderate the happiness gap between parents and non-parents in Asia. Another study in this direction is done by Cho and Jung (2022).

40

J. Kageyama

Focusing on the Korean stratified labor market, they showed that life satisfaction of parents is generally lower than those without children controlling for firm size and standard/non-standard employment status. Concerning family values, Cheung and Kim (2018) showed that women’s time spent on domestic labor reduces the marital satisfaction of younger wives when they do not hold traditional family attitudes. At the global level, Kageyama and Matsuura (2018) pointed to the importance of the financial burden in explaining low fertility. Nevertheless, no study has yet to scrutinize feelings toward family and work using life and domain satisfaction in the ultra-low fertility context in Korea. Bernardi et al. (2017), which employed domain-specific satisfaction to investigate the impact of parenthood on SWB in Germany, is closest to the present study in this respect. The present study fills this gap and examines how emotional returns/burdens derived from parenthood and work have changed over the past quarter century.

3.3 Data and Methods The data are taken from the Korean Labor and Income Panel Survey (KLIPS), Waves 1–23 (Korean Labor Institute, 2023). The survey was first conducted in 1998 for 13,321 individuals in 5,000 households. The survey tracks both original and branch households and added 1,415 households in Wave 12 and 5,044 households in Wave 21. Wave 23 was conducted in 2020. Along with information on the individual’s demographic and socioeconomic characteristics, the data set includes information on overall life satisfaction and the satisfaction in six domains of life, i.e., family relations, household income, leisure activities, housing environment, relations with relatives (from Wave 3 onward), and social relations (from Wave 3 onward). All satisfaction measures are coded between 1 (very dissatisfied) and 5 (very satisfied). SWB data in KLIPS have been used to assess family- and work-related issues (Kim & Do, 2013; Rudolf, 2014; Bethmann & Rudolf, 2018) and to investigate satisfaction across life domains (Barrington-Leigh, 2014). Since we are interested in parenting and work perceptions, we use data on married individuals at child-rearing age (between 25 and 49). Figures 3.3 and 3.4 present the trends of life and domain satisfaction among married individuals aged between 25 and 49, respectively, for women and men. No marked gender difference appears in the satisfaction trends. While life satisfaction and satisfaction in the domains of household income, leisure activities, and housing environment show upward trends, satisfaction in family, relative, and social relations remain stagnant. These results suggest that the Korean society has successfully improved materialistic conditions in the last two decades but has failed to enhance human relations. In the following, we perform regression analyses to investigate the impacts of parenthood and work on life and domain satisfaction. We examine women and men separately as gender matters in parenting and work behaviors. We first regress all cohorts together to understand the characteristics of Korean society and then split the

41

2

2.5

3

3.5

4

3 Deteriorating Family-Work Balance in South Korea: Evidence from Life …

2000

2005

2010 year Life Income Housing Social

2015

2020

Family Leisure Relatives

2

2.5

3

3.5

4

Fig. 3.3 Trends of life and domain satisfaction (women)

2000

2005

2010 year Life Income Housing Social

Fig. 3.4 Trends of life and domain satisfaction (men)

2015 Family Leisure Relatives

2020

42

J. Kageyama

cohorts in the middle to investigate the change in parenting and work perceptions. The earlier sample includes the birth cohort of 1971 and earlier (Cohort 1), and the latter sample includes the birth cohort of 1972 and later (Cohort 2). The dependent variables are life and six domain satisfaction. The main independent variables are parenthood-work dummies. Following Sato (2022), we separate parenthood and work statuses into the following four categories: non-working nonparents (NW_NP), working non-parents (W_NP), non-working parents (NW_P), and working parents (W_P). With these dummies, we measure the returns/burdens of parenthood and work in the satisfaction scale. Note that, for men, we focus on working individuals since non-working men are quite rare in our sample. The model controls for age, age squared, income, home ownership dummies, place of residence dummies, and wave dummies. Following previous studies such as Ferrer-iCarbonell and Frijters (2004), we employ a fixed-effects OLS model. The definition and descriptive statistics of variables employed in this study appear in Table3.1.

3.4 Results 3.4.1 Women To begin with, we regress all cohorts together. The results for women appear in Fig. 3.5. In each graph of the figure, the dots represent the coefficients of parenthoodwork dummies, and the lines illustrate the 95% confidence intervals. The reference group is non-working non-parents, NW_NP. The details of the regression results concerning parenthood-work dummies are reported in Table 3.A1 in Appendix. The findings relating to the impacts of work and parenthood on satisfaction are as follows. First, regarding life satisfaction, having a job has no impact for non-mothers. This result appears in the coefficient of W_NP, which represents the impact of having a job for non-mothers, as NW_NP, the reference group, and W_NP are respectively non-working and working non-parents. As the coefficient of W_NP is positive but insignificant, we conclude that having a job has no impact on non-mothers. Hereafter, we interpret the coefficients in this way. For parenthood, the result shows that being a mother has a negative impact. This result appears in the coefficients of NW_P and W_P, which are both significantly negative. Furthermore, parenthood and work together seem to add an extra burden as the coefficient of W_P is the smallest, whereas the difference between NW_P and W_P is insignificant.1 1

Please refer to Table 3.A1 to separately assess the impacts of parenthood and work. Concerning work, the coefficients of W_NP reflect the impacts of having a job for non-parents, and the differences between NW_P and W_P present the impacts for parents. For parenthood, the coefficients of NW_P and the differences between W_NP and W_P respectively show the impacts of having children for non-working and working individuals. The same arguments apply in the following analyses.

3.495 3.468

Relations with relatives

Social relations

28,184 23,394

Obs (except satisfaction in relative and social relations)

Obs (satisfaction in relative and social relations)

0.440 0.329

0.263 0.123

Rent with deposit only

0.698

Rent with a monthly fee and other

Own

Home ownership

7.509

5.4

0.499

0.495

0.143

0.136

0.585

0.595

0.761

0.487

Log of real annual household income divided by the root of household size

Income

0.528 41.7

0.807 0.813

0.614

Work & parent

Cohort 1: 27–49. Cohort 2: 25–48

W_P

Age

0.432

Child/family care & parent

NW_P

0.019 0.021

Child/family care & non-parent

Work & non-parent

NW_NP

W_NP

Main activity during last week & parenthood status

3.280

Housing environment

Parenthood-work status

2.746 2.920

Household income

Leisure activities

0.677 0.618

3.245

26,783

27,042

0.149

0.307

0.544

7.732

35.7

0.366

0.503

0.077

0.054

3.577

3.619

3.531

3.248

3.087

3.788

3.542

Mean

0.356

0.461

0.498

0.587

5.5

0.482

0.500

0.266

0.226

0.554

0.561

0.643

0.707

0.714

0.527

0.559

S.D

Women Cohort 2

Mean

S.D

Women Cohort 1

3.711

1 (very dissatisfied) - 5 (very satisfied)

Description

Family relations

Life overall

Satisfaction

Variable

Table 3.1 Descriptive statistics

21,781

25,805

0.122

0.288

0.590

7.556

42.0

0.952

0.048

3.542

3.568

3.342

2.966

2.823

3.776

3.330

Mean

0.327

0.453

0.492

0.637

5.1

0.214

0.214

0.570

0.574

0.724

0.786

0.771

0.584

0.645

S.D

Men Cohort 1

19,550

19,611

0.150

0.322

0.528

7.771

37.0

0.844

0.156

3.633

3.653

3.581

3.278

3.119

3.827

3.587

Mean

0.357

0.467

0.499

0.544

5.1

0.362

0.362

0.535

0.542

0.601

0.672

0.670

0.493

0.536

S.D

Men Cohort 2

3 Deteriorating Family-Work Balance in South Korea: Evidence from Life … 43

44

J. Kageyama

Life

Family

Income

Leisure

Housing

Relatives

W_NP NW_P W_P

W_NP NW_P W_P

W_NP NW_P W_P -.4

-.2

0

.2

Social W_NP NW_P W_P -.4

-.2

0

.2

Fig. 3.5 Regression results (women)

Second, in the family domain, having a job lowers satisfaction for non-mothers as the coefficient of W_NP is significantly negative. On the other hand, being a mother has no impact, as the coefficient of NW_P is negative but insignificant. The impacts of both parenthood and work seem to add up as the coefficient of W_P is significantly negative and smallest, although at the 10% level. Here, it is interesting that being a mother does not raise satisfaction, even in the family domain. Children are often considered invaluable elements for fulfilling family life. However, we could not confirm such effects in the present analysis. Third, in the household income domain, we obtain the result that having a job raises satisfaction since the coefficient of W_NP is significantly positive. This result makes sense as work generates income. On the other hand, parenthood has no impact, as the coefficient of NW_P is negative but insignificant. Thus, we cannot confirm the idea that parental dissatisfaction primarily arises from the heavy financial burden of children. The coefficient of W_P suggests that the impacts of parenthood and work cancel out each other in the income domain. Forth, in the domain of leisure activities, both parenthood and work reduce satisfaction as the coefficients of W_NP and NW_P are significantly negative. The negative impact is greater for parenthood, indicating that parenthood considerably requires sacrificing leisure activities. The negative impacts of parenthood and work add up as the coefficient of W_P is very close to the sum of those of W_NP and NW_P. The burden for working mothers is substantial in this domain.

3 Deteriorating Family-Work Balance in South Korea: Evidence from Life …

45

Fifth, in the domain of the housing environment, having a job lowers satisfaction since the coefficient of W_NP is significantly negative, although at the 10% level. This result indicates that having extra income does not necessarily lead to better housing conditions. Parenthood also lowers satisfaction as the coefficient of NW_P is significantly negative. This is consistent with the idea that raising children requires space. Again, the impacts of parenthood and work add up according to the coefficient of W_P. Sixth, work and parenthood have no impacts in the domain of relations with relatives, as the coefficients of W_NP and NW_P are insignificant. The coefficient of W_P is also insignificant. Finally, in the domain of social relations, having a job has no impact, and parenthood has a negative impact on satisfaction according to the coefficients of W_NP and NW_P. The coefficient of W_P is also significantly negative, presumably due to the impact of parenthood. These results indicate that having children does not enhance social life. On the contrary, it leads to isolation from society. In sum, we come to the following results. Concerning work, the positive impact on satisfaction is limited in the income domain. As for parenthood, having children accompanies heavy burdens in various domains. Indeed, the coefficients of NW_P are all negative, and the differences between W_NP and W_P are negative except for family satisfaction, indicating that having children lowers satisfaction in almost all domains for both non-working and working women. Next, we look at the differences between the two cohorts. The results appear in Fig. 3.6. The details are reported in Table 3.A2. Concerning work, the situation seems to have improved for women. For non-mothers, all coefficients of W_NP are larger for Cohort 2, whereas the differences in coefficients between Cohorts 1 and 2 are insignificant except in the domain of leisure activities according to the formal tests (not shown). These results suggest that work provides more satisfaction for the younger generations of non-mothers. We observe similar trends for mothers. Turning to parenthood, we observe opposite patterns. All the coefficients of NW_ P are smaller for Cohort 2, suggesting that the impacts of parenthood have worsened for non-working women, although the differences in coefficients between Cohorts 1 and 2 are again insignificant. Comparing the differences between W_NP and W_P, the negative differences have widened in all domains in Cohort 2, indicating that the impacts of parenthood have worsened for working women as well. The change in the coefficients in the family domain is symbolic. The impact of having children has turned from positive to negative for non-working and working women, although insignificant. These results indicate that children do not contribute to family satisfaction and that the situation has deteriorated. In sum, working conditions have become less gender-discriminatory for younger generations, but child-rearing conditions for women have deteriorated. Balancing parenthood and work remains to be a demanding task.

46

J. Kageyama

Life

Family

Income

Leisure

Housing

Relatives

W_NP NW_P W_P

W_NP NW_P W_P

W_NP NW_P W_P -.6

-.4

-.2

0

.2

Social W_NP NW_P W_P -.6

-.4

-.2

0

Cohort 1

.2

Cohort 2

Fig. 3.6 Regression results by cohort (women)

3.4.2 Men Figure 3.7 summarizes the results for men. The details are reported in Table 3.A3. The coefficient of W_P represents the impact of having children, as the reference group is W_NP. The results point to the existence of penalties for parenthood in various satisfaction. The coefficients are significantly negative, at least at the 10% level, except in the domains of relations with family and relatives. The magnitude is largest in the domain of leisure activities, as is the case of women. These results indicate that men also face difficulties with parenthood. As for the differences between the two cohorts, the results appear in Fig. 3.8 with the details in Table 3.A4. The results again point to the deteriorating conditions of parenthood. The coefficients of W_P are smaller for Cohort 2 in all satisfaction measures. Performing the formal tests, we obtain the results that the differences in coefficients between the cohorts are significant for life satisfaction and in the domains of family, housing environment, and social relations (not shown). Even in the domain of family relations, in which the coefficient is significantly positive for Cohort 1, the impact of having children has vanished for Cohort 2. These results indicate that, as is the case of women, having children no longer contributes to satisfaction in any domain for the younger generations.

3 Deteriorating Family-Work Balance in South Korea: Evidence from Life …

Life

Family

Income

Leisure

Housing

Relatives

47

W_P

W_P

W_P

-.2

-.1

0

.1

Social W_P

-.2

-.1

0

.1

Fig. 3.7 Regression results (men)

Life

Family

Income

Leisure

Housing

Relatives

W_P

W_P

W_P -.2

Social W_P -.2

-.1

0

.1

Cohort 1

Fig. 3.8 Regression results by cohort (men)

.2

Cohort 2

-.1

0

.1

.2

48

J. Kageyama

3.5 Concluding Remarks With life and domain satisfaction, this paper analyzed how Korean people feel about parenthood and work and how such feelings have changed over time. The main results are as follows. First, for younger generations of women, the returns of having a job in life and almost all domains of satisfaction increased. Second, for younger generations of both women and men, having children no longer contributes to life or any domain satisfaction. These results indicate that working conditions have become less genderdiscriminatory for younger generations, but the emotional burdens of having children have increased. Namely, the balance between parenthood and work has tilted toward work, as appears in the rise in women’s labor participation rate and the decline in fertility. These results reveal the pros and cons of the economy-oriented aspect of Korean society, which was strengthened by the 1997 Asian financial crisis. On the positive side, the economic reform after the crisis, together with the people’s strong work ethic, brought the economy back on track. Furthermore, it would have contributed to weakening the gender-discriminatory work ethics as productive efficiency became the utmost concern. The steady rise in life satisfaction in the 2000s could at least partly be attributed to this economy-first approach. However, economic success came at the expense of reproduction. The emotional penalty for parenthood has increased over the past quarter century. In other words, both women and men focus more on production and less on reproduction, pushing down its already low fertility even further. Policies that allow women and men to enjoy child-rearing would be necessary to change this situation. Namely, economy promotion policies should accompany policies that allow women and men to feel the joy, not the penalty, of parenthood. Future research should shed light on this point. In particular, the factors that make parenthood a penalty should be specified. Using the words of Raymo et al. (2015), we expect that these factors relate to “family expectations and obligations” that could not keep up with the rapid economic and social changes. We also expect that the rise in non-standard employment and the decline in the marriage rate are closely related. Future research should incorporate these issues to enhance our understanding of the cause of ultra-low fertility in South Korea. Acknowledgements An earlier version of this paper was presented at the seminar at the Study Group on Population Science in Japan. I would like to thank Toshihiko Hara, Ryuzaburo Sato, and other seminar participants for their helpful comments. I would also like to thank Joyup Ahn for sharing the information on the Korean Labor and Income Panel Study. Of course, any remaining errors are my own. This research is supported by Grant-in-Aid for Scientific Research from JSPS in Japan (17KT0037).

3 Deteriorating Family-Work Balance in South Korea: Evidence from Life …

49

Appendix See Tables 3.A1, 3.A2, 3.A3 and 3.A4.

Table 3.A1 Regression results (women) Parental and working status (Ref: NW_NP) Life/domain

W_NP

NW_P

W_P

Obs

Life

0.011 (0.021)

−0.065*** (0.020)

−0.077*** (0.021)

55,226

−0.077*** (0.019)

−0.089*** (0.019)

Diff. from W_NP

−0.012 (0.008)

Diff. from NW_P Family relations

−0.058*** (0.021)

Diff. from W_NP

−0.012 (0.021)

−0.041* (0.022)

0.046** (0.019)

0.017 (0.020) −0.029*** (0.008)

Diff. from NW_P Household income

0.089*** (0.026)

Diff. from W_NP

−0.036 (0.024)

−0.000 (0.025)

−0.125*** (0.023)

−0.089*** (0.023)

Diff. from NW_P Leisure activities

−0.130*** (0.027)

−0.234*** (0.025)

−0.387*** (0.026)

−0.104*** (0.023)

−0.257*** (0.024)

−0.044* (0.027)

Diff. from W_NP

−0.063*** (0.024)

−0.116*** (0.026)

−0.019 (0.023)

−0.071*** (0.023)

55,226

−0.053*** (0.010)

Diff. from NW_P

Diff. from W_NP

55,226

−0.153*** (0.011)

Diff. from NW_P

Relations with retatives

55,226

0.036*** (0.010)

Diff. from W_NP

Housing environment

55,226

−0.017 (0.022)

−0.014 (0.022)

−0.032 (0.023)

0.003 (0.020)

−0.015 (0.021)

50,177

(continued)

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J. Kageyama

Table 3.A1 (continued) Parental and working status (Ref: NW_NP) Life/domain

W_NP

NW_P

Obs

−0.018** (0.009)

Diff. from NW_P Social relations

W_P

(0.022) 0.010

Diff. from W_NP

−0.056*** (0.021)

−0.054** (0.022)

−0.066*** (0.019)

−0.064*** (0.020)

Diff. from NW_P

50,177

0.003 (0.009)

Robust standard errors in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. All equations include the control variables mentioned in the main text. Table 3.A2 Regression results by cohort (women) Parental and working status (Ref: NW_NP) Life/domain

W_NP

NW_P

W_P

Obs

−0.025 (0.054)

−0.039 (0.048)

−0.061 (0.049)

28,184

−0.014 (0.049)

−0.036 (0.049)

Women/Cohort 1 Life Diff. from W_NP

−0.022* (0.012)

Diff. from NW_P Family relations

−0.090* (0.051)

Diff. from W_NP

0.063 (0.051)

0.031 (0.052)

0.152*** (0.051)

0.121** (0.051) −0.031** (0.013)

Diff. from NW_P Household income

0.059 (0.057)

Diff. from W_NP

−0.012 (0.051)

0.003 (0.052)

−0.070 (0.055)

−0.056 (0.055)

Diff. from W_NP Diff. from NW_P

28,184

0.015 (0.014)

Diff. from NW_P Leisure activities

28,184

−0.218*** (0.064)

−0.179*** (0.056)

−0.385*** (0.057)

0.038 (0.060)

−0.167*** (0.060)

28,184

−0.205*** (0.015) (continued)

3 Deteriorating Family-Work Balance in South Korea: Evidence from Life …

51

Table 3.A2 (continued) Parental and working status (Ref: NW_NP) Life/domain

W_NP

NW_P

W_P

Obs

Housing environment

−0.084 (0.063)

−0.020 (0.057)

−0.100* (0.057)

28,184

0.064 (0.061)

−0.016 (0.061)

Diff. from W_NP

−0.080*** (0.014)

Diff. from NW_P Relations with retatives

−0.051 (0.052)

Diff. from W_NP

0.051 (0.054)

0.040 (0.055)

0.101* (0.053)

0.091* (0.054) −0.010 (0.013)

Diff. from NW_P Social relations

23,394

−0.059 (0.056)

Diff. from W_NP

−0.006 (0.052)

−0.005 (0.052)

0.053 (0.051)

0.054 (0.051)

Diff. from NW_P

23,394

0.001 (0.013)

Women/Cohort 2 Life

0.024 (0.023)

Diff. from W_NP

−0.083*** (0.022)

−0.082*** (0.024)

−0.108*** (0.020)

−0.107*** (0.021)

Diff. from NW_P Family relations

0.001 (0.012) −0.051** (0.023)

Diff. from W_NP

−0.029 (0.023)

−0.057** (0.025)

0.022 (0.021)

−0.006 (0.022)

Diff. from W_NP Diff. from NW_P

27,042

−0.028** (0.011)

Diff. from NW_P Household income

27,042

0.104*** (0.029)

−0.065** (0.027)

−0.001 (0.030)

−0.169*** (0.026)

−0.105*** (0.027)

27,042

0.064*** (0.015) (continued)

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Table 3.A2 (continued) Parental and working status (Ref: NW_NP) Life/domain

W_NP

NW_P

W_P

Obs

Leisure activities

−0.101*** (0.030)

−0.247*** (0.028)

−0.346*** (0.030)

27,042

−0.146*** (0.026)

−0.245*** (0.027)

Diff. from W_NP

−0.099*** (0.015)

Diff. from NW_P −0.030 (0.030)

Housing environment Diff. from W_NP

−0.073*** (0.027)

−0.097*** (0.030)

−0.043* (0.025)

−0.068*** (0.026)

27,042

−0.024* (0.014)

Diff. from NW_P −0.015 (0.025)

Relations with retatives Diff. from W_NP

−0.025 (0.024)

−0.051** (0.025)

−0.010 (0.022)

−0.035 (0.023)

26,783

−0.025** (0.012)

Diff. from NW_P Social relations

0.023 (0.024)

Diff. from W_NP

−0.067*** (0.023)

−0.064*** (0.025)

−0.090*** (0.021)

−0.088*** (0.023)

Diff. from NW_P

26,783

0.003 (0.011)

Refer to Table 3.A1. Table 3.A3 Regression results (men)

Parental and working status (Ref: W_NP) Life/domain

W_P

Obs

Life

−0.042*** (0.016)

45,416

Family relations

0.021 (0.016)

45,416

Household income

−0.062*** (0.020)

45,416

Leisure activities

−0.144*** (0.020)

45,416

Housing environment

−0.034* (0.019)

45,416 (continued)

3 Deteriorating Family-Work Balance in South Korea: Evidence from Life … Table 3.A3 (continued)

53

Parental and working status (Ref: W_NP) Life/domain

W_P

Obs

Relations with retatives

−0.024 (0.017)

41,331

Social relations

−0.041** (0.017)

41,331

Refer to Table 3.A1. Table 3.A4 Regression results by cohort (men)

Parental and working status (Ref: W_NP) W_P

Obs

Life

0.005 (0.033)

25,805

Family relations

0.138*** (0.032)

25,805

Household income

−0.016 (0.039)

25,805

Leisure activities

−0.108*** (0.040)

25,805

Housing environment

0.064* (0.038)

25,805

Relations with retatives

0.011 (0.036)

21,781

Social relations

0.012 (0.033)

21,781

Life/domain Men/Cohort 1

19,611

Men/Cohort 2 Life

−0.072*** (0.018)

19,611

Family relations

−0.028 (0.018)

19,611

Household income

−0.093*** (0.023)

19,611

Leisure activities

−0.156*** (0.023)

19,611

Housing environment

−0.075*** (0.021)

19,611

Relations with retatives

−0.040** (0.020)

19,550

Social relations

-0.064*** (0.019)

19,550

Refer to Table 3.A1

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Ma, L. (2016). Female labour force participation and second birth rates in South Korea. Journal of Population Research, 33(2), 173–195. McDonald, P. (2000). Gender equity in theories of fertility transition. Population and Development Review, 26(3), 427–439. McDonald, P. (2009). Explanations of low fertility in East Asia: A comparative perspective. In P. Straughan, A. Chan, & G. Jones (Eds.), Ultra-low fertility in Pacific Asia (pp. 23–39). Routledge. McDonald, P. (2013). Societal foundations for explaining low fertility: Gender equity. Demographic Research, 28, 981–994. Myrskylä, M., Kohler, H. P., & Billari, F. C. (2009). Advances in development reverse fertility declines. Nature, 460(7256), 741–743. Ogawa, N., Mason, A., Chawla, A., Matsukura, R., & Tung, A. C. (2009). Declining fertility and the rising cost of children. Asian Population Studies, 5(3), 289–307. Raymo, J. M., Park, H., Xie, Y., & Yeung, W. J. J. (2015). Marriage and family in East Asia: Continuity and change. Annual Review of Sociology, 41, 471–492. Rudolf, R. (2014). Work shorter, be happier? Longitudinal evidence from the Korean five-day working policy. Journal of Happiness Studies, 15(5), 1139–1163. Sato, K. (2022). Who is happier in Japan, a housewife or working wife? Journal of Happiness Studies, 23, 509–533. Suzuki, T. (2008). Korea’s strong familism and lowest-low fertility. International Journal of Japanese Sociology, 17(1), 30–41. Suzuki, T. (2013). Low fertility and population aging in Japan and Eastern Asia. In T. Suzuki (Ed.), Low fertility and population aging in Japan and Eastern Asia (pp. 1–87). Springer, Japan. Tsuya, N. O., Bumpass, L. L., & Choe, M. K. (2000). Gender, employment, and housework in Japan, South Korea, and the United States. Review of Population and Social Policy, 9(9), 195–220. Tsuya, N. O., Choe, M. K., & Wang, F. (2019). Convergence to very low fertility in East Asia: Processes, causes, and implications. Springer, Japan. World Bank. (2022). World Development Indicators. https://datatopics.worldbank.org/world-dev elopment-indicators/. Downloaded on June 18, 2022. Yoo, S. H., & Sobotka, T. (2018). Ultra-low fertility in South Korea: The role of the tempo effect. Demographic Research, 38, 549–576.

Chapter 4

Subjective Well-Being and Women’s Employment in Taiwan Eriko Teramura

Abstract This study examined the relationship between subjective well-being (SWB) and reproductive behaviors in Taiwanese women, specifically in terms of their job and family life satisfaction. Using panel data from a long-term survey of Taiwanese SWB, the study investigated the impact of employment status and family factors on women’s SWB. The analysis indicated that job satisfaction among Taiwanese women is not affected by employment type, but family life satisfaction is higher in business categories, such as self-employed with employees. Furthermore, the results revealed that Taiwanese women with children experience lower levels of job and family life satisfaction. This study provides valuable insights into the factors influencing SWB among Taiwanese women and underscores the importance of considering both job and family life satisfaction when exploring the relationship between SWB and reproductive behaviors.

4.1 Introduction This study examined the relationship between subjective well-being (SWB), employment, and reproductive behaviors of Taiwanese women. Despite Taiwan having achieved gender equality in education and labor force participation, it faces a severe decline in birth rates, unlike other developed countries. To address this, we examine the relationships between employment, family, and childbirth, and Taiwanese women’s job and family life satisfaction. Many studies suggest that gender inequality contributes to low fertility rates (McDonald 2000). However, studies show that the relationship between employment and birth is established through family factors (Brinton & Oh 2019). The low fertility rates in East Asia, including Taiwan, are related to the heavy burden of housework on women (Yu, 2009; Kan & Hertog, 2017).

E. Teramura (B) Meikai University, 1 Akemi, Urayasu-shi, Chiba 279-8550, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Kageyama and E. Teramura (eds.), Perception of Family and Work in Low-Fertility East Asia, Population Studies of Japan, https://doi.org/10.1007/978-981-99-3859-9_4

57

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E. Teramura

After martial law was lifted in Taiwan in 1987, companies’ privatization and rapid industrialization progressed, mainly among small- and medium-sized enterprises (Sumiya, 1992). Consequently, women’s labor force participation increased in Taiwan because of a rapid domestic labor shortage. In Taiwan, it is common for three generations to live together and share childcare with their families. Additionally, grandparents actively support housework and childcare because of a shortage of external resources, such as nursery schools (Teramura, 2021). However, Taiwan’s total fertility rate (TFR) has declined significantly during industrialization and women’s labor force participation, from 3.71 in 1970 to 0.89 in 2022 (Republic of China (Taiwan), 2022b). The following section provides an overview of Taiwanese women’s employment and childbirth behaviors, as well as their subjective well-being related to work and life, with a focus on the type of business in which they are employed.

4.2 Employment and Fertility of Taiwanese Women and Literature Review 4.2.1 Employment of Taiwanese Women In Taiwan, the electrical and electronic equipment industry’s rapid growth in the 1970s led to an outflow of labor from farming villages, and women became a major and inexpensive labor force (Sumiya, 1992). In the 1980s, large companies expanded, and the electronics industry became a leading industry in Taiwan (Sato, 2008). The Ushaped hypothesis of economic development and female labor force participation is well known (Goldin, 1995). In Taipei, the largest city in Taiwan, the tertiary industry ratio is expected to reach 75.6% by 2022 (Industrial Development Bureau, Taipei City Government, 2022). However, among Taiwanese women, the employment rate of young people was the highest, with a declining trend thereafter. No trend was confirmed for an increase in the employment rate among middle-aged and older workers. Figure 4.1 shows the employment rates by age and sex. The employment rate for women aged 25–29 was the highest at 84% (87% for men). Subsequently, the employment rate of women gradually declined, reaching 74% for those in the 45–49 age group and 63% for those in the 50–54 age group. The completion rate for men remains above 80% until they reach the age range of 50–54 years, indicating that the gender gap in labor participation widens as people age. Brinton et al. (1995) demonstrated that patriarchal cultural norms impose certain restrictions on the labor supply of married women in response to the labor demand for women accompanying rapid industrialization. Later, Chou and Staiger (2001) and Zveglich and Rodgers (2003), who indicated that the expansion of health insurance coverage in the early 2000s pushed down the labor supply for women, used data from Taiwan to demonstrate that restrictions on working hours reduced women’s working hours and labor supply. In contrast, the maternity leave system helped

4 Subjective Well-Being and Women’s Employment in Taiwan 100.0 90.0 80.0 70.0 60.0 50.0 40.0 30.0 20.0 10.0 0.0

87.0 84.1

93.5

94.6

82.8

79.5

91.0

90.5

75.5

74.3

59

84.3 71.2 63.0

52.2 50.8

44.4

51.2

25.2 9.4 7.0

15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 Male

Female

Fig. 4.1 The employment rate (%) by age group and gender. Sources Ministry of Labor, Republic of China (Taiwan) (2022)

increase women’s labor supply. It has also been pointed out that Corporate Social Responsibility (CSR) activities, including employee welfare, bring significant financial benefits in the long run (Lin & Yang, 2009) and that in Taiwan, as in mainland China, an increase in family size reduces maternal labor supply (Chen et al., 2021).

4.2.2 Reproductive Behavior of Taiwanese Women As mentioned earlier, Taiwan’s TFR has declined significantly; since the 2000s, it has hovered around 1.0. The TFR has declined further as a result of the COVID-19 pandemic, reaching 0.89 in 2022 (Republic of China (Taiwan), 2022b) (Fig.4.2). 4 3.5 3

3.71 3.09 2.46

2.5 2 1.5 1

1.88 1.81 1.78

1.68 1.12 1.1 1.05 1.03 1.07 0.9

1.27

1.07 1.18

0.89

0.5 0

Fig. 4.2 Shifts in Taiwan’s TFR. Sources National Statistics, Republic of China (Taiwan) (2022a)

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In addition to research showing that the decline in TFR has a large impact on the labor force participation rate of women (Bloom et al., 2009), there is also a study (Kan & Hertog, 2017) suggesting that the gender imbalance in housework and childcare time leads to low fertility. Factors behind the decline in Taiwan’s TFR have been examined from various angles, including Pan and Yang (2020), who demonstrated that the higher the volatility of household disposable income, the lower the TFR. Hsu et al. (2013) verified the relationship between TFR and Quality of life (QOL) in infertile couples.

4.3 Data and Methods 4.3.1 Data We used Taiwanese data based on the “Panel Study of Family Dynamics” (hereinafter referred to as PSFD) from Taiwan’s Academia Sinica. The PSFD collection rate for the first year was unknown, and the follow-up rates ranged from 80% to 90% in the second survey. The survey used a multistage hierarchical extraction method. The PSFD has been consistently surveyed since 1999; however, this study used data from 2007 to 2016, including questions on job satisfaction.1 To facilitate comparison, we focused on a male sample and individuals not currently working. Furthermore, we restricted our sample to individuals under the age of 60 to examine the effects of child-rearing and employment types on satisfaction. After removing missing values, the final sample size comprised 7,676 males and 5,375 females in Taiwan.

4.3.2 Methods We employed the following regression model: Sit = α + γ Wit + β Pit + θ Mit + δ X it + μi + εit Sit represents two types of SWB (job satisfaction and family life satisfaction),2 measured on a four-point scale ranging from 1 (very dissatisfied) to 4 (very satisfied). Wit refers to the type of business in which the respondents work, which consists of the following 10 categories: Self-employed without employees, Self-employed with employees, Employed by a private company, Employed by a public enterprise, 1

Surveys were not conducted in 2013 and 2015, so the data are missing from these years. The question on job satisfaction is: “Are you satisfied with your current job?” The question on life satisfaction is: “Are you satisfied with your current family life?”.

2

4 Subjective Well-Being and Women’s Employment in Taiwan

61

Fig. 4.3 Job and family life satisfaction by gender

Working for family business with regular, Government employee, Employee of a nonprofit organization, Working for a family business without pay, Partnership without employees, and not-working, including both voluntary and involuntary unemployed. Pit is parenthood (0 no child, 1 having children) and Mit is marital status (0 not married, 1 married).3 Xit represents the control variables, including education level, age, log of monthly income, and year dummies.

4.4 Results 4.4.1 Descriptive Analysis First, we examined two SWB measures, job satisfaction and family life satisfaction, by gender (Fig. 4.3). The job satisfaction score was 2.99 for women and 2.98 for men, with no statistically significant gender differences. However, family and life satisfaction was 3.26 for men and 3.20 for women, indicating that males were significantly more satisfied (1% level) than females. Sex differences in family life satisfaction were noteworthy. Although it is not possible to observe the time spent on housework and childcare by gender in this data, the score of those who answered “We should live with our parents after getting married” was 2.93 for women and 3.37 for men in the five-point scale. Many women in Taiwan live with or near their parents-in-law (Teramura, 2021). The score of those who answered “Family members have a good relationship with one another” was 4.72 for women and 4.70 for men in the five-point scale, and this was higher in females (significant at the 1% level), suggesting that in Taiwan, the gender difference 3

Unmarried includes single, cohabitation, separated, divorced, and widowed.

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Table 4.1 Employment type by gender

Male

Female

Self-employed without employees

6.7

4.6

Self-employed with employees

8.7

3.4

Employed by a private company

61.6

60.3

Employed by a public enterprise

2.5

0.9

Working for family business with regular

3.6

4.2

Government employee

10.2

11.5

Employee of a non-profit organization

1.6

5.5

Working for family business without pay

0.5

2.5

Partnership without employees

1.1

1.2

Not working

3.5

6.0

Total

100

100

in family and life satisfaction may have some influence on women’s intention to form a family. Table 4.1 shows the types of workplaces by gender. Employment by private companies accounted for the largest number of men and women, with 62% for men and 60% for women, so there was no significant difference. The secondlargest group comprised male government employees (10%) and female government employees (12%). In addition, 9% of men were self-employed with employees (3% for women) and 7% of men were self-employed without employees (5% for women). The percentage of men running their own businesses is higher than that of women. One of the reasons for the aforementioned difference in the employment rate between men and women is that many men are self-employed and do not have mandatory retirement. In contrast, many women are employees either of private companies or the government. Based on these results, we investigated the relationships between job satisfaction and family life satisfaction (SWB), employment, and childcare.

4.4.2 Empirical Analysis This section examines the relationship between Taiwanese women’s SWB (job and family life satisfaction), business type, and family factors (whether they have children). Table 4.2 presents the descriptive statistics for the main variables by sex. Regarding items other than SWB, approximately 60% of both men and women were married, and 56–62% of both men and women had children, making up the majority of the sample with children. The average age of the patients was 39.5 for women and 39.1 for men. We performed regression analysis using panel data and analyzed pooled regression models, fixed-effects models, and random effects models. After conducting tests such

4 Subjective Well-Being and Women’s Employment in Taiwan

63

Table 4.2 Descriptive statistics Male

Female

Mean Std.Dev Min Job Satisfaction

Max

Mean Std.Dev Min

Max

2.97

0.60

1

4

2.99

0.57

1

4

Family and Life Satisfaction 3.25

0.57

1

4

3.20

0.55

1

4

0.59

0.49

0

1

0.62

0.49

0

1

marriage Having children

0.56

0.50

0

1

0.62

0.48

0

1

age

39.06

9.01

26

60

39.46

9.41

27

60

log monthly income

10.58

0.99

0.00 14.88 10.08

1.72

0.00 13.53

Obs

7,676

5,375

as the Hausman test, F test, and Breusch and Pagan tests, all fixed-effects models were adopted. Table 4.3 presents the results of impact of employment type and birth on job satisfaction.4 The column (1) shows the results for males, and the column (2) shows the results for females. Taiwanese women with children have significantly lower job satisfaction. Job satisfaction was high among men employed by public enterprises and government employees. For females, none of the business categories significantly affected job satisfaction. Table 4.4 presents the results of impact of employment type and birth on family and life satisfaction. The column (3) shows the results for males, and the column (4) shows the results for females. Taiwanese women with children have significantly lower family and life satisfaction. And self-employed females had significantly higher satisfaction levels. The average working hours of full-time working women in Taiwan exceeded 40 hours per week as of 2006 (Teramura, 2021), so it is possible that selfemployed women, who have flexible working hours and places, are highly satisfied. Regarding other factors, our analysis revealed that marriage positively affects women’s job satisfaction and men’s family life satisfaction. However, having children significantly lowers women’s job and family life satisfaction. We also found that income had a positive effect on both men and women.

4

We exclude not-working for regressing job satisfaction.

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E. Teramura

Table 4.3 Impact of employment type and birth on job satisfaction Job Satisfaction

Having children Marriage

Male

Female

(1)

(2)

0.022

−0.082

(0.035)

(0.045)

0.006

0.116

(0.030)

(0.036)

* ***

Business Type (Ref: Self−employed without employees) 0.083

−0.042

(0.053)

(0.088)

Employed by a private company

0.020

−0.047

Employed by a public enterprise

0.282

Self-employed with employees

(0.049)

(0.068) ***

(0.109) Working for family business with regular

0.118

−0.101 (0.132)

*

(0.070)

−0.037 (0.090)

Government employee

0.170 (0.079)

(0.097)

Employee of a non-profit organization

0.122

0.090

(0.096)

(0.091)

Working for family business without pay

**

0.006

0.051

0.082

(0.252)

(0.200)

Partnership without employees

−0.010

0.021

(0.088)

(0.120)

Age

−0.024

0.212

(0.090)

(0.131)

ln monthly income

0.137

***

0.111

(0.022)

(0.027)

_cons

2.308

−5.907

(3.211)

(4.686)

Estimation method

Fixed Effect

Fixed Effect

Observations

7,384

4,960

sigma_u

0.493

2.054

sigma_e

0.471

0.454

rho

0.523

0.953

**

*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Standard errors in parentheses. Education dummies and year dummies were included as control variables

4 Subjective Well-Being and Women’s Employment in Taiwan

65

Table 4.4 Impact of employment type and birth on family and life satisfaction Family and life satisfaction

Having children Marriage

Male

Female

(3)

(4)

−0.006

−0.092

(0.027)

(0.043)

0.112 (0.027)

***

**

−0.028 (0.035)

Business Type (Ref: Not Working) Self-employed without employees

0.007

0.165

(0.063)

−0.073

Self-employed with employees

0.003

0.157

(0.063)

(0.081)

Employed by a private company

0.051

0.082

(0.053)

(0.054)

Employed by a public enterprise

0.071

0.060

(0.104)

(0.124)

Working for family business with regular

0.002

0.155

(0.071)

(0.077)

Government employee

0.009

0.072

(0.076)

(0.081)

−0.136

0.093

(0.089)

(0.077)

Working for family business without pay

−0.057

−0.040

(0.189)

(0.127)

Partnership without employees

−0.087

0.143

(0.091)

(0.109)

Employee of a non-profit organization

−0.027

0.030

(0.083)

(0.062)

ln monthly income

0.006

−0.004

(0.009)

(0.007)

_cons

4.024

2.112

(2.979)

(2.250)

Estimation method

Fixed Effect

Fixed Effect

Observations

7,676

5,375

sigma_u

0.501

0.536

sigma_e

0.440

0.444

Age

** **

*

(continued)

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E. Teramura

Table 4.4 (continued) Family and life satisfaction

rho

Male

Female

(3)

(4)

0.564

0.593

*, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively. Standard errors in parentheses. Education dummies and year dummies were included as control variables

4.5 Conclusion This study examined the relationship between SWB and reproductive behavior in relation to Taiwanese women’s employment and life satisfaction. To achieve this, we examined the relationship among Taiwanese women’s SWB, employment, and family factors using panel data from a long-term survey of Taiwanese SWB. The results of our study suggest that difficulties associated with having children and working full-time, especially among Taiwanese women, led to a decline in SWB. Specifically, we found that both job satisfaction and family life satisfaction decreased when women had children. For women, marriage resulted in higher family life satisfaction, but having children reversed this effect. Additionally, we found that self-employed Taiwanese women reported higher levels of satisfaction with their family life than unemployed women. However, this was not the case for women working in the largest number of private and public companies. We postulate that the long working hours of women working as employers in Taiwan affect their job satisfaction and family life satisfaction. In contrast, men working in the public sector reported higher job satisfaction. These findings suggest that the utility of having children differs between men and women in Taiwanese society, with women being disproportionately affected. Finally, we identified some research issues that should be addressed in future studies. There are differences in employee behavior and family formation in Taiwan between urban and rural areas; however, this study did not distinguish between them because of data limitations. We intend to address these issues in future research. Acknowledgements In preparing this study, we received the “Panel Study of Family Dynamics” (PSFD) loan from Academia Sinica, Taiwan. Ruoh-rong Yu and Tsung-Wei Hung also contributed to data construction. This research was supported by a Grant-in-Aid for Scientific Research from the JSPA in Japan (17KT0037) and a Research Grant-in-Aid from the Nomura Foundation (N224-W30-007, 2022).

References Bloom, D. E., Canning, D., Fink, G., & Finlay, J. E. (2009). Fertility, female labor force participation and demographic dividends. Journal of Economic Growth, 14, 79–101.

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Brinton, M. C., Lee, Y. J., & Parish, W. L. (1995). Married women’s employment in rapidly industrializing societies: Examples from East Asia. American Journal of Sociology, 100(5), 1099–1130. Brinton, M. C., & Oh, E. (2019). Babies, work, or both? Highly educated women’s employment and fertility in East Asia. American Journal of Sociology, 125(1), 105–140. Chen, C., Zhao, W., Chou, S. Y., & Lien, H. M. (2021). The effect of family size on parents’ labor supply and occupational prestige: Evidence from Taiwan and Mainland China. China Economic Review, 66, 101596. Chou, Y. J., & Staiger, D. (2001). Health insurance and female labor supply in Taiwan. Journal of Health Economics, 20(2), 187–211. Goldin, C. (1995). The U-shaped female labor force function in economic development and economic history. In T. P. Schultz (Ed.) Investment in women’s human capital and economic development (pp. 61–90). Chicago, IL: University of Chicago Press. Hsu, P. Y., Lin, M. W., Hwang, J. L., Lee, M. S., & Wu, M. H. (2013). The fertility quality of life (FertiQoL) questionnaire in Taiwanese infertile couples. Taiwan Journal of Obstetrics and Gynecology, 52(2), 204–209. Industrial Development Bureau, Taipei City Government. (2022). Business Services Overall Business Overview (in Chinese). Kan, M. Y., & Hertog, E. (2017). Domestic division of labour and fertility preference in China, Japan, South Korea, Taiwan. Demographic Research, 36, 557–588. Lin, W. I., & Yang, S. Y. (2009). From successful family planning to the lowest of low fertility levels: Taiwan’s dilemma. Asian Social Work Policy Review, 3(2), 95–112. McDonald, P. (2000). Gender equity, social institutions and the future of fertility. Journal of Population Research, 1–16. Ministry of Labor, Republic of China (Taiwan). (2022). International Gender Statistics. National Statistics, Republic of China (Taiwan). (2022a). Statistical Tables. National Statistics, Republic of China (Taiwan). (2022b). Population Estimates of the Republic of China. Pan, J. N., & Yang, Y. J. (2020). The impact of economic uncertainty on the decision of fertility: Evidence from Taiwan. American Journal of Economics and Finance, 54, 101090. Sato, Y. (Ed.). (2008). Firms and Industries in Taiwan, The Institute of Developing Economies, JETRO, IDE Research Series No.574 (in Japanese). Sumiya, M. (Ed.). (1992). Taiwan’s economy:Light and shadow of NIES, University of Tokyo Press (in Japanese). Teramura, E. (Ed). (2021). Work and family of highly educated women in Japan and Taiwan: Comparative study of ultra-low birthrate, Koyo-Shobo (in Japanese). Yu, W. H. (2009). Gendered Trajectories Women, Work, and Social Change in Japan and Taiwan, Stanford University Press. Zveglich, J. E., Jr, & Rodgers, Y. V. D. M. (2003). The impact of protective measures for female workers. Journal of Law Economics, 21(3), 533–555.

Chapter 5

The Association Between Subjective Well-Being, Parenthood, and Work of Married Women: Evidence from Longitudinal Data from Urban India Kazuma Sato and Eriko Teramura

Abstract This study aimed to examine the impact of parenthood and work on the subjective well-being (SWB) of married women in urban India. Empirical analysis using FE (fixed-effects) models revealed three findings. First, parenthood does not negatively affect the happiness of married women, which differs from the results of previous studies indicating the negative impact of having children. Second, the happiness of married women who work decreases. Third, happiness is high among women who have two children or fewer and are not working. These results are consistent with the gender role division in India. The results of this study indicate that gender role division remains strong in India.

5.1 Introduction In this paper, we examine the relationship between female labor force participation, parenthood, and subjective well-being (SWB) in India. In India, gender role division remains strong and the total fertility rate (TFR) has declined, similar to Japan, Taiwan, and Korea (Fig. 5.1). However, the female labor force participation rate has also decreased since the mid-2000s, which is different from Japan, Taiwan, and Korea

K. Sato (B) Takushoku University, 3-4-14, Kohinata, Bunkyo Ku, Tokyo 112-0006, Japan e-mail: [email protected] E. Teramura Meikai University, 1 Akemi, Urayasu-Shi, Chiba 279-8550, Japan e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 J. Kageyama and E. Teramura (eds.), Perception of Family and Work in Low-Fertility East Asia, Population Studies of Japan, https://doi.org/10.1007/978-981-99-3859-9_5

69

70 35.0

K. Sato and E. Teramura

(Labor force participation rate)

(Fertility rate) 4.5 4.0

30.0

3.5 25.0

3.0

20.0

2.5

15.0

2.0 1.5

10.0 5.0

Labor force participation rate, female (% of female population ages 15+) Fertility rate, total (births per woman)

0.0

1.0 0.5

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

0.0

Fig. 5.1 Labor force participation rate and TFR in India. Source World Bank (2022)

(Fig. 5.1). This trend in India is interesting because the female labor force participation rate has increased in many OECD countries in recent decades (Kinoshita & Guo, 2015).1 The decreasing trends of female labor force participation and TFR in India have attracted significant interest, and several studies have examined the associated mechanisms (Kapsos et al., 2014; Klasen & Pieters, 2015; Amonker & Brinker, 2007; Spoorenberg & Dommaraju, 2012; Visaria & Visaria, 1995). However, few studies have considered the association between subjective well-being (SWB), and work and parenthood. Many previous studies based on data from Western countries highlight the negative impact of parenthood on SWB for female (Blanchflower & Clark, 2021; Di Tella et al., 2003; Margolis & Myrskylä, 2011; Sato, 2022; Stanca, 2012). However, the effect of employment on female SWB depends on the general conditions of female advancement in society (Shiraishi & Shiraishi, 2007; Lee & Ono, 2008; Booth & van Ours, 2009; Treas et al., 2011; Berger, 2013; Beja Jr, 2014; Bas, levent & Kirmano˘glu, 2017; Hamplová, 2019). In the case of India, which has strong gender role division, employment can negatively affect female SWB. In contrast, parenthood may have no effect or even a positive effect because the female employment rate is low and the burden of balancing work and family may be relatively small. Therefore, considering the interaction between parenthood and work, females who

1

In India, various factors such as gender disparity in education, ethnicity, caste, and religion contribute to female unemployment and employment in the informal sector (Cooke, 2010). Considering this background, the analysis of female employment and fertility in India, which has high economic growth and a large population, deserves attention.

5 The Association Between Subjective Well-Being, Parenthood …

71

have children and do not work are likely happier than in other cases. However, to the best of our knowledge, no previous research has examined this point. This study aimed to uncover the association between SWB, and parenthood and work for females in India using longitudinal data from urban regions. In India, there is a significant disparity between urban and rural areas, and people living in urban areas face various changes related to child-rearing and employment. We reveal how changes in child-rearing and employment in urban areas can affect the SWB of women. This study focuses on the impact of parenthood, work, and their interaction on SWB based on a sample of married women as marriage is nearly universal in India. The main results derived from using fixed-effects models are as follows. First, parenthood does not affect the happiness of married women, which differs from the findings in other countries. Second, the happiness of married women who work declines. Third, happiness is high among women who have two children or fewer and are not working. These results are consistent with the strong gender role division in India. The remainder of this article is organized as follows. Section 5.2 reviews related literature. Section 5.3 discusses the data and methods used in our analysis. Section 5.4 presents the results of our analysis. Section 5.5 summarizes our conclusions.

5.2 Literature Review 5.2.1 Parenthood and SWB In many countries, parenthood is believed to provide fulfillment and enrich human life. For example, several studies have shown that most people agree with the notion of “watching children grow up is life’s greatest joy” (Halle, 2002; ISSP, 2002; Koropeckyj-Cox & Pendell, 2007; NSD, 2002). Additionally, parenthood becomes more valuable at old age because children may provide both financial and nonfinancial support to their parents. These concepts suggest that having children can positively impact the SWB of parents. However, parenthood is also believed to require sustained physical, mental, and financial investment. For example, parents must devote significant time to caring for young children because they cannot live independently. This imposes many time constraints on parents, which can have a negative impact on their mental health. Additionally, as children grow, parents must bear the increasing financial costs of clothing, food, housing, and education. All of these costs can negatively impact the SWB of parents. Overall, having children can have both positive and negative effects on the SWB of parents. Therefore, the net SWB impact of parenthood is determined by the relative sizes of positive and negative effects. Many empirical studies examining this topic have shown that parenthood generally has a negative impact on SWB, meaning the adverse effects of having children outweigh the positive effects (Blanchflower &

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Clark, 2021; Di Tella et al., 2003; Margolis & Myrskylä, 2011; Sato, 2022; Stanca, 2012; Kageyama & Matsuura, 2018). Blanchflower and Clark (2021) indicated that the most significant adverse effects of parenthood are caused by the financial burden of having children.

5.2.2 Studies Related to SWB in India Several studies have examined SWB in India. For example, Agrawal et al. (2011) explored the determinants of SWB using an urban Indian sample (n = 1099). Their study indicated that SWB based on life satisfaction positively correlates with age, marriage, education, and working full-time job. Additionally, the results of step-wise regression analysis indicated that income, age, and education are significant predictors of life satisfaction. Zorondo-Rodríguez et al. (2016) focused on examining the impact of natural capital on SWB using data from Kodagu (Karnataka, India) (n = 171). They measured natural capital using both subjective and objective methods. In the case of subjective measures, respondents answered regarding their level of satisfaction with the local ecosystem and services. For objective measures, three natural assets, namely home garden diversity, agricultural diversity, and livestock ownership, were used. The estimation results indicate that natural capital using subjective methods has a positive association with SWB and that the size of the effect is clearly greater than that of economic capital. Linssen et al. (2011) examined the impact of relative income and conspicuous consumption on SWB using data from rural low-income households in India (n = 697). Their study indicated that conspicuous consumption negatively affects SWB and that an individual’s relative income position has no relationship with SWB. Bandyopadhyay (2020) compared the impact of education and employment on SWB between male and female using data from slums in Delhi (n = 1214). This study posited that although education and employment may lead to greater financial well-being, social norms restricting female autonomy may decrease the impact on female SWB. The results of empirical analysis indicate that education and employment are likely to decrease the SWB of female relative to male. Nevertheless, although research related to SWB has become a hot topic, previous studies have largely ignored the association between SWB, and parenthood and employment in India.

5.2.3 Decreasing TFR and Female Labor Force Participation Rate in India Several studies have examined the mechanisms of the decreasing TFR in India. For example, Visaria and Visaria (1995) indicated that decreasing fertility is caused by

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73

increased literacy, urbanization, industrialization, modern communication and transportation, and improved women’s status. Their study also highlighted regional differences in fertility and revealed faster fertility decreases in urban areas. Amonker and Brinker (2007) attempted to explain the declining fertility in India using demographic transition theory. The results of their analysis based on the National Family Health Survey (NFHS) are consistent with the demographic transition theory, indicating that overall socioeconomic development, including modernization and education, is likely to cause a decrease in fertility. Spoorenberg and Dommaraju (2012) focused on the regional fertility transition in India and compared regional fertility patterns based on the NFHS. Their study indicated that a reduction in third-and-higher-order births is the main reason for declining fertility. We review government statistics on women’s labor participation that cover a timeframe similar to that used in these previous analyses. According to the National Sample Survey’s 68th round (2011 to 2012), the proportion of employed males was 74.6% for class-1 cities (population of one million or more), 72.8% for class-2 towns (populations of 50,000 to one million), and 75.8% for class-3 towns (populations less than 50,000). The proportion of employed females was 19.9% for class-1 cities, 17.9% for class-2 towns, and 21.7% for class-3 towns (Ministry of Statistics and Program Implementation, Government of India, 2015). After this period, a decline in the employment rate of women was observed in some regions. Kapsos et al. (2014) explored the factors leading to the decline in India’s female labor force participation rate. Their study indicated that the decrease in the female labor force participation rate can be attributed to a shortage of employment opportunities, increased participation in education, and household income. Klasen and Pieters (2015) reported similar results.

5.3 Data and Methods 5.3.1 Data We use the Preference Parameters Study of Osaka University, which surveyed preferences in Japan, the United States, India, and China. The present study employs data in India which are available in years between 2009 and 2013. The survey in India began in 2009 with 1,857 respondents who were male and female between the ages of 20 and 69 years living in six major cities (Delhi, Mumbai, Bangalore, Chennai, Kolkata, Hyderabad). These data are suitable for examining the relationship between women’s employment/fertility and SWB in urban India. Respondents from 2009 continued to be surveyed annually through 2013. The response rates from 2010 to 2013 were 68.9%, 81.0%, 80.3%, and 47.1%, respectively. This study focuses on married women under 45 years of age because in India, 72% of women aged

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K. Sato and E. Teramura

15 to 49 years are currently married, and marriage is still practically universal and connected to childbirth.2

5.3.2 Methods The econometric model used to examine the relationship between SWB, and parenthood and work is defined as follows: Hit = α + β Pit + γ Wit + δ X it + μi + εit

(5.1)

Hit represents the subjectively rated happiness of individual i at time t based on a scale of 0 to 10, where “10” represents “very happy” and “0” represents “very unhappy.” Pit is a parenthood dummy variable that is equal to one if respondents have children and equal to zero otherwise. Pit also represents the number of children. Wit represents the working status, which is equal to if respondents are currently working and equal to zero otherwise. Xit represents individual attributes, including age, age squared, and the natural log of yearly household income. Model (1) is estimated using fixed-effects (FE) ordered logit model and FE ordinary least squares (OLS) to control for individual unobserved fixed effects. We mainly interpret the estimated results of the FE ordered logit model as the dependent variable is ordinal. We also estimate the following model, which considers the interaction between children and working status, as described by Sato (2022), using the FE ordered logit model and FE OLS: Hit = α + β P N Wit + γ N P N Wit + δ N P Wit + θ X it + μi + εit .

(5.2)

Here, PNWit is a dummy variable equal to one if respondents have children and do not work, and equal to zero otherwise. NPNWit is a dummy variable equal to one if respondents have no children and do not work, and equal to zero otherwise. NPWit is a dummy variable equal to one if respondents have no children and are working, and equal to zero otherwise. The reference group of these interaction variables defines the case where respondents have children and are working. In analysis, we also conduct the estimate using the interaction between the number of children and working status. Table 5.1 presents summary statistics for the analyzed sample of married women. The mean happiness value is 7.18. Approximately 95% of the sample have children and the average number of children is 1.97. Women with two children account for 50.7% of the sample and women with one or more than three children account for approximately 22%. Additionally, 61% of married women are currently working and their mean age is 35.2 years, indicating a large number of young women. Regarding 2

According to the National Family Health Survey (2019 to 2021), 72% of women and 60% of men aged 15 to 49 years are married in India. Additionally, only 1% of women aged 45 to 49 years have never been married.

5 The Association Between Subjective Well-Being, Parenthood … Table 5.1 Summary statistics

75

Mean

S.D

Happiness

7.18

1.70

Having children

95.41%

0.21

Number of children

1.97

0.96

Having no child

4.59%

0.21

Having one child

22.48%

0.42

Having two children

50.70%

0.50

Having more than three children

22.23%

0.42

Working

61.03%

0.49

Having children and working

58.57%

0.49

Having children and not working

36.83%

0.48

No children and not working

2.13%

0.14

No children and working

2.46%

0.15

Age

35.23

6.34

Age squared

1281.48

Log household income

11.82

Observations

1,219

440.88 0.66

the interaction variable for parenthood and work, women who have children and work account for 58.6% of the sample, making them the largest group. In contrast, only 36.8% of women have children and are not currently working. The proportion of childless women is small at less than 3% for both the working and non-working groups.

5.4 Results Table 5.2 presents the effects of parenthood and work on happiness as estimated using the FE ordered logit models and FE OLS. Columns (1) and (3) present the results of the FE ordered logit model, and columns (2) and (4) present the results of the FE OLS model. All coefficients for children are positive, but are not statistically significant, indicating that parenthood does not have a significant negative effect on female happiness in India. Although this result contrasts with the findings of previous studies, it is reasonable considering the scenario in India, where the female labor force participation rate tends to be small and the burden of balancing work and family is relatively small. In contrast, the coefficients of working status are negatively significant at a 10% level in the case of the FE ordered logit model in columns (1) and (3). This result indicates that the happiness of married women who work decreases. In India, where

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K. Sato and E. Teramura

Table 5.2 Impact of parenthood and work on the happiness of married women (1)

(2)

Having children

0.022

0.217

(Ref: Having no child)

(0.553)

(0.523)

Number of children

(3)

(4)

0.008

0.043

(0.135)

(0.098)

Working

−0.448*

−0.303

−0.448*

−0.304

(Ref: Not working)

(0.271)

(0.185)

(0.271)

(0.185)

Age

0.709

0.511

0.708

0.512

(0.449)

(0.318)

(0.445)

(0.318)

0.000

0.000

0.000

0.000

(0.005)

(0.004)

(0.005)

(0.004)

0.313*

0.236**

0.313*

0.238**

(0.163)

(0.119)

(0.163)

(0.118)

Estimation method

FE Ologit

FE OLS

FE Ologit

FE OLS

Observations

1020

1,219

1020

1,219

Age squared Log household income

R−squared Log likelihood

0.073 −1028.6028

0.072 −1028.6002

*,

**, and *** denote significance at the 10%, 5%, and 1% levels, respectively. Heteroskedasticity robust standard errors are reported in parentheses

there is strong gender role division, the burden of employment for married women is heavy, leading to a decline in happiness. Regarding the results of other variables, the coefficients of household income are positively significant, indicating that higher household income leads to increased happiness. Table 5.3 presents the results of the effects of the interaction between parenthood and work on happiness. Among the interaction variables, only the coefficient of women who have children and do not work is statistically significant. The happiness of women who have children and do not work is likely to be higher and this result is consistent with the perspective of gender role division. Table 5.4 presents the impact of the interaction between happiness, and parenthood and work, considering the dummy variable of the number of children. The reference group for these interaction variables is women who have one child and are currently working. Among the interaction variables in the FE ordered logit model, the coefficients of women who have two children and do not work, and women who have one child and do not work are positively significant. These results suggest that for married women in India, behavior consistent with the division of labor by gender, such as having children and not working, leads to increased happiness. However, considering that the coefficients of women with more than three children and who do not work are insignificant, having too many children may impose an undesirable burden on urban married women.

5 The Association Between Subjective Well-Being, Parenthood … Table 5.3 Estimated results for the effects of the interaction between parenthood and work on happiness

Having children and not working No children and not working No children and working

77

(1)

(2)

0.469*

0.327*

(0.271)

(0.184)

0.028

−0.444

(0.732)

(0.644)

0.426

0.201

(Ref: Having children and working)

(0.706)

(0.586)

Age

0.676

0.480

(0.457)

(0.322)

0.000

0.001

(0.005)

(0.004)

0.324**

0.243**

(0.165)

(0.119)

Estimation method

FE Ologit

FE OLS

Observations

1020

Age squared Log household income

R−squared Log likelihood

1,219 0.076

−1026.196

*,

**, and *** denote significance at the 10%, 5%, and 1% levels, respectively. Heteroskedasticity robust standard errors are reported in parentheses

5.5 Concluding Remarks This study aimed to examine the impact of parenthood and work on the SWB of married women in urban India. Empirical analysis using FE models revealed three findings. First, the results of Table 5.2 indicate that parenthood does not negatively affect the happiness of married women, which differs from the results of previous studies indicating the negative impact of having children. Second, the results of Table 5.2 show that the happiness of married women who work decreases. Third, the results of Table 5.4 present that happiness is high among women who have two children or fewer and are not working. These results are consistent with the gender role division in India. The results of this study indicate that gender role division remains strong in India. Therefore, even if economic growth continues in India in the future, women’s employment may be restrained. Furthermore, even if women’s employment increases in a scenario where gender role division remains strong, a tradeoff between female labor participation and fertility will be likely be observed in India, similar to Japan, which could lead to population decline. Our results point to the importance of reforming India’s gender role division for India’s future. When considering the labor situation in India, it is important to note that the informal sector accounts for an extremely large proportion of labor and that the

78

K. Sato and E. Teramura

Table 5.4 Estimated results for the effects of the interaction between parenthood and work on happiness considering the dummy variable of the number of children Having two children and working

(1)

(2)

0.371

0.293

(0.324)

(0.253)

Having more than three children and working

0.134

0.150

(0.407)

(0.314)

Having one child and not working

0.815*

0.553

(0.446)

(0.346)

0.719*

0.559*

Having two children and not working

(0.411)

(0.315)

Having more than three children and not working

0.574

0.427

(0.533)

(0.390)

No children and not working

0.246

−0.248

(0.766)

(0.695)

No children and working

0.610

0.387

(Ref: Having one child and working)

(0.777)

(0.656)

Age

0.732

0.513

(0.450)

(0.321)

Age squared

−0.000

0.000

(0.005)

(0.004)

0.328**

0.243**

(0.162)

(0.119)

Log household income Estimation method

FE Ologit

FE OLS

Observations

1020

1,219

R−squared Log likelihood

0.079 −1022.486

*,

**, and *** denote significance at the 10%, 5%, and 1% levels, respectively. Heteroskedasticity robust standard errors are reported in parentheses

number of employees in the organized sector is less than 10% of all workers (Ministry of Health, Labour, and Welfare, 2012). However, our data did not include a variable distinguishing between the two labor sectors, so we could not analyze the informal sector in isolation. Additionally, household income has a significant positive effect on SWB, but is separate from the main issue considered in this study (i.e., the SWB of female in affluent urban households is extremely high). The analysis of SWB from this perspective will be an important point of discussion in the future. Acknowledgements This research utilized data from the Preference Parameters Study of Osaka University’s 21st Century COE Program “Behavioral Macro-Dynamics Based on Surveys and Experiments,” its Global COE project “Human Behavior and Socioeconomic Dynamics,” and JSPS

5 The Association Between Subjective Well-Being, Parenthood …

79

KAKENHI 15H05728 “Behavioral-Economic Analysis of Long-Run Stagnation.” This research is supported by Grant-in-Aid for Scientific Research from JSPA in Japan (15K01937 and 17KT0037).

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