Exploring the Effect of Retirement on Health in Japan (Population Studies of Japan) 9811626375, 9789811626371

Citation preview

SPRINGER BRIEFS IN POPULATION STUDIES POPULATION STUDIES OF JAPAN

Masaaki Mizuochi

Exploring the Effect of Retirement on Health in Japan 123

SpringerBriefs in Population Studies

Population Studies of Japan Editor-in-Chief Toshihiko Hara, School of Design, Sapporo City University, Sapporo, Hokkaido, Japan Series Editors Shinji Anzo, Tokyo, Japan Hisakazu Kato, Tokyo, Japan Noriko Tsuya, Tokyo, Japan Toru Suzuki, Tokyo, Japan Kohei Wada, Tokyo, Japan Hisashi Inaba, Tokyo, Japan Minato Nakazawa, Kobe, Japan Jim Raymo, Madison, USA Ryuichi Kaneko, Tokyo, Japan Satomi Kurosu, Chiba, 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.

More information about this subseries at http://www.springer.com/series/13101

Masaaki Mizuochi

Exploring the Effect of Retirement on Health in Japan

Masaaki Mizuochi Faculty of Policy Studies Nanzan University Nagoya, Aichi Japan

ISSN 2211-3215 ISSN 2211-3223 (electronic) SpringerBriefs in Population Studies ISSN 2198-2724 ISSN 2198-2732 (electronic) Population Studies of Japan ISBN 978-981-16-2637-1 ISBN 978-981-16-2638-8 (eBook) https://doi.org/10.1007/978-981-16-2638-8 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 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

For decades, most developed countries have been facing a rise in the aging population. Highly aged population causes serious challenges and strains to social security systems, including public pensions and medical insurance, because working-age people must sustain the retired people in the system. To sustain the social security system, the retirement age has been delayed by policy reform through increasing the eligible age for pension benefits. This reform causes older adults to work longer than ever before; however, such policy reform might result in adverse effects on medical care finance. That is, if retirement from the labor force has a beneficial effect on health, delayed retirement could worsen older adults’ health, raising public health expenditure. Thus, understanding the effect of retirement on health is important in an increasingly aging society. In terms of individual well-being, examining the effect of retirement on health is also crucial. After a long working life, can we enjoy a healthier retired life, or do we suffer from worsening health? Many people would agree that retirement is a relief from a life of stressful and often physically demanding work; thus, retirement should improve our health in later life. On the other hand, following retirement, we will lose work-related mental and physical activity, social networks, and sense of purpose. In this case, retirement might worsen our health. While a large body of literature has investigated the effects of retirement on health, the answer to this question is still unclear. And there are further questions that should also be addressed: Does retirement affect health immediately or is there a delay? Does the lifestyle before retirement matter for post-retirement health? Which is better for health, retiring early or later? Determining the answers to these questions will help us to prepare more properly for an improved quality of retired life. To answer these questions, I have worked on a research project “The relationship between the change in involvement in work, community activity, and family activity before and after retirement and the health of older adults” (JSPS KAKENHI Grant Number 17K03782). To this end, this research project exploits a Longitudinal Survey of Middle-aged and Elderly Persons (LSMEP) conducted by the Japanese Ministry

v

vi

Preface

of Health, Labour and Welfare (MHLW). The data is provided by the MHLW with a special permission.1 This book is one of the outcomes of this research project and reveals new evidence in Japan. Chapter 1 introduces the background, theoretical framework, and empirical methodology applied in the study. The chapter also summarizes the conflicting findings on the effect of retirement on overall health, physical health, mental health, cognitive functioning, mortality, and health care use in the previous literature. Chapter 2 examines the short- and long-term effects of retirement on health. In addition, a comparison of results of different estimation methods is shown. Chapter 3 presents the different effects of retirement based on lifestyle prior to retirement, which includes health-related behaviors and usual activities. Chapter 4 examines the different effects of early and normal (late) retirement. The manuscript was written during my research visits to the Center for Demography of Health and Aging (University of Wisconsin-Madison) and the Office of Population Research (Princeton University) in 2019–2021. I appreciate both institutes for accepting my visits, and I am also deeply grateful to James Raymo (Princeton University) for his support for my research activity while in the US. I also appreciate Toshihiko Hara (Editor-in-Chief) for his helpful comments on my manuscript and Nanzan University for giving me this research opportunity. I will be extremely pleased if the findings of this book are helpful for labor and health policymakers and individuals’ healthy post-retirement lives in Japan and other developed countries facing population aging. Princeton, NJ, USA

1 As

Masaaki Mizuochi

for the ethical approval, since this book uses the existing data (the LSMEP) that contains only deidentified information from the respondents, no ethical approval is needed.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 The Aim of the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Aging and Institutional Settings in Japan . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 Rapid Aging Society . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.2 Public Pension System and Labor Policy . . . . . . . . . . . . . . . . . 1.3 Analytical Framework: Economic Theory and Empirical Method . . . 1.3.1 Theoretical Consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3.2 Empirical Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 Empirical Findings in the Previous Literature . . . . . . . . . . . . . . . . . . . . 1.4.1 Data in the Previous Literature . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Definitions of Retirement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.3 Effect on Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Data and Variables Used in the Book . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 The Longitudinal Survey of Middle-Aged and Elderly Persons (LSMEP) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.2 Health Measure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.3 Retirement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.4 Age Trajectory of Retirement and Health . . . . . . . . . . . . . . . . . 1.5.5 Problem of Attrition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 1 3 3 7 9 9 12 14 14 14 15 19 19 20 21 21 21 23 23

2 Short- and Long-Term Effects of Retirement on Health . . . . . . . . . . . . . 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Effects of Retirement in the Short Term and Long Term . . . . 2.1.2 Overview of This Chapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 How Short- and Long-Term Effects Are Examined . . . . . . . . . . . . . . . 2.2.1 Data and Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Health Change Comparison Before and After Retirement . . . 2.2.3 Estimation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Average Effect of Retirement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

27 27 27 29 30 30 32 34 35 35 vii

viii

Contents

2.3.2 Estimation Results of Average Effect of Retirement . . . . . . . . 2.4 Short- and Long-Term Effects of Retirement . . . . . . . . . . . . . . . . . . . . 2.4.1 Main Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Estimation on Subsamples: Gender, Education, and Occupational Characteristics . . . . . . . . . . . . . . . . . . . . . . . . 2.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

36 38 38

3 Does Lifestyle Prior to Retirement Matter? . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 How to Estimate Different Effects of Retirement by Lifestyle Prior to Retirement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Data and Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Estimation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Health Trajectory by Lifestyle Before Retirement . . . . . . . . . . 3.3 Estimation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Estimation Based on Health Behavior . . . . . . . . . . . . . . . . . . . . 3.3.2 Estimation by Usual Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

49 49

4 Effect of Retirement Timing on Health . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.1 Delaying Retirement Timing . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1.2 Retirement Timing and Health Outcomes . . . . . . . . . . . . . . . . . 4.2 How the Effect of Retirement Timing Is Estimated . . . . . . . . . . . . . . . 4.2.1 Data, Health Measures, and Instruments . . . . . . . . . . . . . . . . . . 4.2.2 Retirement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Estimation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Effects of Retirement Timing on Health . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Main Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.2 Estimation on Subsamples: Gender, Education, and Occupational Characteristics . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

41 45 46

52 52 55 55 60 60 62 65 71 73 73 73 75 78 78 79 79 81 81 85 89 92

Epilogue . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Chapter 1

Introduction

Abstract This chapter introduces the background of the book. Section 1.1 presents the aim of this book, describing the importance of examining the effect of retirement on health. Section 1.2 provides an overview of Japan’s rapid population aging and high labor participation of older adults, as well as the institutional settings related to older adults, including the public pension and mandatory retirement systems and related law. Section 1.3 introduces the health capital model as a theoretical framework, demonstrating that retirement can both improve and worsen health. Introducing the empirical method, the reason why the endogeneity problem between retirement and health causes an inconsistent estimate and how the instrumental variables approach solves the problem is discussed. Section 1.4 summarizes the conflicting findings of the effect of retirement on several health measures in the previous literature. In addition, the fact that definitions of retirement vary in the previous literature is introduced and discussed as one of the reasons for conflicting findings. Section 1.5 presents detailed information about the longitudinal data and variables used in this book and illustrates descriptive statistics of retirement and health measures. Keywords Aging society · Health capital model · Endogeneity · Heterogeneity · Instrumental variables approach · Longitudinal data

1.1 The Aim of the Book Population aging is a common and serious problem in many developed countries. In terms of the public pension system, which is financially sustained by working-age people, population aging threatens the stability of the society. In an attempt to address the problem, these countries have already raised the retirement age or plan to raise it by increasing the age of eligibility for public pension benefit (Organization for Economic Co-operation and Development [OECD], 2017). Such pension reforms aim to sustain the pay-as-you-go public pension system with an increase in contributions and the reduction of payouts. This policy can actually serve to strengthen the sustainability of the public pension system; however, it may have a negative impact on both medical and long-term care systems. That is, if retirement has a beneficial health effect, a prolonged working life can possibly worsen the health of older © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Mizuochi, Exploring the Effect of Retirement on Health in Japan, Population Studies of Japan, https://doi.org/10.1007/978-981-16-2638-8_1

1

2

1 Introduction

adults, thus increasing the medical care costs. Therefore, understanding the effect of retirement on health is crucial in the highly aged society. Several studies have examined the causal effect of retirement on the health of older adults; however, the results are still conflicting as will be summarized in Sect. 1.4. Due to conflicting research results regarding the effect of retirement on health, governments face challenges in planning an effective labor policy. In addition, the contradictory findings suggest that more detailed and careful investigation is required. Therefore, it is crucial to examine the effect of retirement on health from various perspectives to unravel these conflicting findings. To this end, this book focuses particularly on the heterogeneous effects of retirement. A large body of literature has investigated the effect of retirement on health— the effect averaged over different durations of retirement, different pre-retirement lifestyles, and different timing of retirement. However, the effects of retirement can differ on those points, as some studies suggest. Thus, this book focuses on the heterogeneous effects of retirement based on these three aspects. Specifically, Chapter 2 examines whether the effects of retirement differ in the short and long run. Chapter 3 reveals the different effects of retirement based on pre-retirement lifestyle. Chapter 4 compares the difference in the effects of early and normal retirement. The implication of these analyses will provide a better understanding of the causal effect of retirement on health. As mentioned above, if retirement has a beneficial effect on health, working in old age might have an adverse effect on health following retirement. To simply confirm it, the relationship between the ratio of working older adults and life expectancy is examined here using OECD country-level data. Figure 1.1 presents a scatter plot of the labor force participation rate for those aged 65 or over and life expectancy at age 65 by gender. The figure demonstrates a positive correlation; that is, the longer that older adults work into common retirement age, the longer the life expectancy in old age becomes. The correlation coefficients are 0.192 and 0.108 for men and women, respectively. The correlation for men is stronger than for women; however, both correlation coefficients are not significantly different from zero. The figure implies that retirement has an adverse effect on subsequent health. However, the relationship presented here is not a causal one but just a correlation. That is, both paths are possible: good health encourages older adults to work longer and working in old age contributes to good health. This book empirically investigates the latter causal relationship applying a rigorous estimation method. The rest of this chapter is organized as follows. Section 1.2 introduces the situation of increasing longevity, population aging, and labor force participation of older adults in Japan and developed countries. The section also introduces the institutional settings related to older adults in Japan. Section 1.3 presents the theoretical framework and the empirical method applied in the study for this book. Section 1.4 summarizes the findings on the effects of retirement, considering several health measures highlighted in previous literature. Section 1.5 concludes this chapter.

1.2 Aging and Institutional Settings in Japan

3

Fig. 1.1 Relationship between working and health condition in old age (Source Labor force participation rate data, OECD Employment and Labour Market Statistics [2015–2018].1 Life expectancy data, OECD data [2015–2018]2 )

1.2 Aging and Institutional Settings in Japan 1.2.1 Rapid Aging Society The longevity of Japanese people compared with people from other developed countries is remarkable, as illustrated in Fig. 1.1. The change is also noteworthy. Figure 1.2 illustrates life expectancy at birth in Japan and “more developed regions” since the 1950s. More developed regions are defined by the United Nations as European countries, Northern American countries, Australia, New Zealand, and Japan. Japan’s life expectancy for both men and women was almost the same or below that of more developed regions in the 1950s; however, these figures are now far above those regions. Japan’s life expectancy has risen from 61.0 years (1950–1955) to 81.3 years (2015–2020) for men and from 64.6 years (1950–1955) to 87.5 years (2015–2020) for women. Notably, in 2015–2020, the life expectancy of Japanese men is 5.1 years 1 OECD.Stat. LFS by sex and age - indicators. (https://stats.oecd.org/viewhtml.aspx?datasetcode= LFS_SEXAGE_I_R&lang=en. Accessed October 20, 2020). 2 OECD iLibrary, Life expectancy at 65 (https://doi.org/10.1787/0e9a3f00-en. Accessed November 23, 2020).

1 Introduction

60

70

Years old

80

90

4

1950-1955

2015-2020 Year

Japan (men) Japan (women)

More developed regions (men) More developed regions (women)

Fig. 1.2 Change in life expectancy at birth 1950–2020 (Note More developed regions comprise European countries, Northern American countries, Australia, New Zealand, and Japan. Source United Nations, World Population Prospect 2019)3

older than that of men in more developed regions; for women, Japan’s life expectancy is 5.2 years older than that of more developed regions. Prolonged life itself reflects a beneficial aspect of the safe and healthy life available in Japan. However, since social security systems, including public pension, medical care, and long-term care systems, are essentially sustained through the contributions of working-age people, the massive rise in the ratio of retired people poses serious challenges to the sustainability of the systems. In addition to the remarkable increase in life expectancy over the last 70 years, an extremely low fertility rate has also accelerated the speed of population aging in Japan. Japan is known as an exceptionally low fertility country in addition to a country of notable longevity. According to the Vital Statistics from Japan’s Ministry of Health, Labour and Welfare (MHLW), the total fertility rate (TFR) dropped from 3.65 in 1950 to 1.36 in 2019 and the TFR has been below the replacement rate, roughly 2.1, since 1974.4 3 World Population Prospects 2019, Mortality data (https://population.un.org/wpp/Download/Sta ndard/Mortality/. Accessed October 19, 2020). 4 Portal Site of Official Statistics of Japan (https://www.e-stat.go.jp/stat-search/files?page=1& layout=datalist&toukei=00450011&tstat=000001028897&cycle=7&year=20190&month=0&tcl ass1=000001053058&tclass2=000001053061&tclass3=000001053064&result_back=1&tclass 4val=0. Accessed December 15, 2020). TFR refers to total number of children born or likely to be

1.2 Aging and Institutional Settings in Japan

5

Fig. 1.3 Change in old-age dependency ratio (65 + /20–64) 1950–2100 (Note More developed regions comprise European countries, Northern American countries, Australia, New Zealand, and Japan. Projections using medium-fertility variant are illustrated from 2020. Source United Nations, World Population Prospect 2019)5

Figure 1.3 illustrates the old-age dependency ratio for Japan and more developed regions from 1950 as projected into 2100. The ratio is calculated as the number of people aged 65 or older to the number of people aged 20–64. The ratio of Japan was just 9.9% in 1950, meaning that it required 10 working-age persons to sustain one older adult in a pay-as-you-go social security system. The burden borne by working-age people does not seem to be so heavy for that time. However, since this time, Japan’s old-age dependency ratio has rapidly increased, reaching 52.0% around 2020, indicating that, currently in Japan, two working-age persons are required to sustain one older adult. In more developed regions, the ratio was 32.7% in 2020. By the year 2050, the old-age population ratio of Japan is projected to reach 80.7%, whereas it will reach 53.3% in more developed regions. Japan will become a society in which less than two working-age persons must sustain one older adult’s life. As has been shown, it is easy to understand that this burden on working-age people will continue to increase and potentially reach a hazardous level in the future. born to a woman in her lifetime if she were subject to the prevailing rate of age-specific fertility in the population, according to the World Health Organization. 5 World Population Prospects 2019, Population data (https://population.un.org/wpp/Download/Sta ndard/Population/. Accessed October 19, 2020).

6

1 Introduction

Pension reforms of the Japanese government caused by rapid aging, financial pressure in later life by the increased life expectancy, and the high work motivation of older adults, have resulted in many older adults working in old age in Japan. Figure 1.4 illustrates the labor force participation rate of people aged 65 years or older for some developed countries in the OECD in 2019. Japan’s labor force participation is the highest among these countries for both men and women: 34.9% and 18.0%, respectively. This indicates that working in old age in Japan is much more common than in other developed countries. With regard to the motivation to work in old age, Japanese older adults express a much higher motivation for work (Seike & Yamada, 2004). For example, according to an international comparative survey, around 44.9% of people in Japan aged 60 years or older indicate a willingness to work, whereas this figure is 39.4% for the US, 22.7% for Germany, and 36.6% for Sweden (Cabinet Office, 2018). The facts presented here suggest that Japan has already reached a hazardous level in terms of the social security system managed by the pay-as-you-go system; however, the high ratio of working older adults appears to be sustaining the system. Thus, the determination of whether retiring from the labor market in old age is

Fig. 1.4 Labor force participation rate of older adults 65 + (2019) (Source OECD Employment and Labour Market Statistics)6

6 OECD.Stat.

LFS by sex and age – indicators. (https://stats.oecd.org/viewhtml.aspx?datasetcode= LFS_SEXAGE_I_R&lang=en. Accessed October 20, 2020).

1.2 Aging and Institutional Settings in Japan

7

beneficial or negative for health, and as a result, for the social security system, is of vital importance for policymakers in health and labor and researchers alike.

1.2.2 Public Pension System and Labor Policy This section will introduce the public pension system and labor policy for older adults in Japan. These factors are important to this book for two reasons. First, public pension system and labor policy obviously have a large impact on the retirement decisions of older adults. Specifically, if the eligibility age for pension benefit were increased or longer-term employment were guaranteed as a policy reform, many older adults would work longer, which could affect health in later life. Second, these reforms provide ideal instruments for the instrumental variables (IV) approach. As will be described in Sect. 1.3, since estimations of the effect of retirement on health usually suffer from an endogeneity problem, a rigorous causal estimation, such as the IV approach, is required to obtain an unbiased estimate of the effect of retirement. Change in the eligibility age for pension benefit and guaranteed age for continued employment will serve as good instruments. The public pension system for employees in Japan consisted of two plans during the survey period of the data used in this book: (1) Employee Pension (EP) insurance for employees of private companies and (2) Mutual Aid Pension (MAP) insurance for employees of the public sector and private schools. The EP and MAP offer almost the same schemes.7 The EP/MAP is based on a two-tier benefit system: (1) a flat-rate benefit, which is paid based on the number of months the individual paid in contribution and (2) an earnings-related benefit, which is paid in amounts proportional to the income earned prior to retirement. As shown in Table 1.1, the age of eligibility for the earnings-related benefit is lower than that for the flat-rate benefit. Therefore, employees can retire at the age of eligibility for the earnings-related benefit and claim the partial benefit, and then claim the full benefit at the eligible age for the flat-rate benefit a few years later. According to the MHLW (2017), the average monthly pension benefit in 2015 for the partial benefit was about 76,000 JPY (about 630 USD, based on the exchange rate in 2015) and the average monthly benefit for the full benefit was about 157,000 JPY (about 1,310 USD, based on the exchange rate in 2015). To strengthen the financing of the public pension, the Japanese government has gradually raised the eligibility age for the EP/MAP benefit from 60 to 65, a reform that will be completed in 2025 for men and in 2030 for women. Table 1.1 presents the birth cohorts used in this book and their eligibility age for pension benefit in the top panel. Due to the gradual nature of the reform, the age at which individuals can claim the full pension benefit varies across cohorts. Subsequently, the age at which individuals can claim the partial benefit also differs across cohorts; however, this difference is not very large. The exogenous variation in the age of eligibility 7 The

EP and MAP were consolidated into the EP in 2015.

8

1 Introduction

Table 1.1 Age of eligibility for pension benefit (top panel) and guaranteed age for continuous employment (bottom panel) Birth cohort

Men, Women (MAP)

Women (EP)

Flat-rate benefit (full)

Earnings-related benefit (partial)

Flat-rate benefit (full)

Earnings-related benefit (partial)

04/02/1945–04/01/1946

63

60

60

60

04/02/1946–04/01/1947

63

60

61

60

04/02/1947–04/01/1948

64

60

61

60

04/02/1948–04/01/1949

64

60

62

60

04/02/1949–04/01/1950

65

60

62

60

04/02/1950–04/01/1952

65

60

63

60

04/02/1952–04/01/1953

65

60

64

60

04/02/1953–04/01/1954

65

61

64

60

04/02/1954–04/01/1955

65

61

65

60

04/02/1955–04/01/1956

65

62

65

60

Birth cohort

Age for guaranteed employment by ASEEP

04/01/1946–03/31/1947

63

04/01/1947–03/31/1949

64

04/01/1949–

65

Source Adapted from Ministry of Health, Labour and Welfare (2011)

is considered useful as an instrument for the retirement decision-making in the IV approach, as eligibility is a strong determinant of the decision to retire. A characteristic of the Japanese labor market is a prevalent mandatory retirement system. Most companies have a mandatory retirement system, requiring a retirement age of 60. As of 2015, among companies with 30 or more employees, 92.6% of companies had a mandatory retirement system and 98.1% of them had a uniform retirement age. Among those companies with a uniform retirement age, 60 was set as the mandatory retirement age for 80.5% (MHLW, 2015a). However, as a result of the pension reform introduced above, most employees in their early 60s are no longer eligible for the full benefit, although they reached the mandatory retirement age. To fill the gap between the mandatory retirement age and the age of eligibility for the full pension benefit, the government passed a revised Act on Stabilization of Employment of Elderly Persons (ASEEP), which mandated that companies maintain employment for their employees up to the age of eligibility for full pension benefit starting in 2006. The age for such guaranteed employment is shown at the bottom of Table 1.1. Under the revised ASEEP, companies must take any one of the following three measures: (1) raise the mandatory retirement age, (2) introduction of a continuous employment system in which companies re-employ or continue to employ older

1.2 Aging and Institutional Settings in Japan

9

workers who are currently employed after the mandatory retirement age, or (3) abolition of the mandatory retirement system. Most companies chose option (2) “continuous employment” (81.7%), and a small number of companies chose (3) “abolition of mandatory retirement” (2.6%) (MHLW, 2015b). Approximately 90% of companies with continuous employment systems have chosen to re-employ mandatorily retired employees; among re-employed workers, 92.1% work full-time, but almost half of them work in a position that is inferior to their previous work (National Personnel Authority, 2016). As a result, many older workers are mandated to retire and then re-employed by their career employers to continue work with less responsibility (and lower pay) but the same hours. In terms of the IV approach, the effect of raising the age of eligibility for partial benefit might not be so strong on the decision to retire, as most workers are guaranteed employment until the full benefit age. Nonetheless, these policy reforms are considered to have a crucial influence on older adults’ decision to retire.

1.3 Analytical Framework: Economic Theory and Empirical Method 1.3.1 Theoretical Consideration In economic studies, the health capital model proposed by Grossman (1972) based on the human capital model is widely used to predict the effect of retirement on health. The basic model is introduced here in reference to Grossman (2000). In this model, health directly increases utility as a consumption good and increases earnings by reducing sick time. A typical individual maximizes an intertemporal utility function as follows: U = U [ f (Ht ), Z t ], t = 0, 1, · · · , n,

(1.1)

where Ht is the health stock and f (Ht ) is total consumption of health services produced by the health stock at time t. More specifically, the stock of health yields a flow of healthy days. Z t is the consumption of other commodities. Initial health stock deteriorates with age, in old age in particular, but can be increased by the investment in health. That is, the health stock at time t + 1 equals the gross investment in time t, It , plus the remaining health stock between times t and t + 1: Ht+1 = It + (1 − δt )Ht ,

(1.2)

10

1 Introduction

where δt is the rate of health deterioration. The rate can be affected by age.8 If the health deterioration surpasses the gross health investment, health stock decreases in the next period. Gross investment in health and other commodities are produced by the following household production function: It = It (Mt , T h t ; E),

(1.3)

Z t = Z t (X t , T z t ; E),

(1.4)

where Mt and X t are vectors of purchased market goods, T h t and T z t are time inputs, and E is the stock of human capital other than health capital. That is, the productivity of health depends on the stock of human capital, for example, in terms of educational level. A budget constraint is as follows: n n   pt M t + q t X t wt T w t = + A0 t (1 + r ) (1 + r )t t=0 t=0

(1.5)

where pt and qt are the prices for Mt and X t , respectively. wt is the wage rate per hour, T w t is hours of work, r is the interest rate, and A0 is initial assets. The individual purchases market goods and services with labor income and initial assets. To consider the life of older adults, pension benefit should be included on the right-hand side of Eq. (1.5) as non-labor income. The time constraint is as follows: T w t + T h t + T z t + T l t = Tt ,

(1.6)

where T l t is time lost from market and nonmarket activities due to illness or injury and Tt is a total amount of time available at time t. To keep or increase both health investment and other commodities, the individual has to retain sick time T l t as minimum as possible. Sick time is inversely related to the health stock as follows: T l t = Tt − f (Ht ).

(1.7)

How can the effect of retirement on health in old age be explained based on the health capital model? Retirement results in the time for paid work becoming zero, signifying no labor income, whereas the individual after retirement can claim pension benefit instead. As a result, retirement substantially decreases opportunity cost. Following retirement, it is much easier for the individual to dedicate more time to health investment in Eq. (1.3); for example, exercising for physical health, 8 Muurinen

(1982) proposes a model in which higher education lowers the rate of health deterioration.

1.3 Analytical Framework: Economic Theory and Empirical Method

11

meditating for mental health, or solving crossword puzzles for cognitive health. An increase in the investment in such health activities can lead to the improvement or preservation of health in Eq. (1.2). In contrast, the individual is not compelled to keep good health following retirement because he or she can obtain pension benefits without working. Moreover, as the amount of pension benefits are less than that the individual earned prior to retirement, the market goods input in health investment, for example, medical services or a healthy diet, would decrease compared to the time prior to retirement. These changes lead to a decrease in health investment and thus declining health post-retirement. In sum, the net effect of retirement is ambiguous based on the theory. The causal relationship between retirement and health cannot be fully explained with the health capital model. In addition to the economic theory, most papers predict the effect of retirement using other causal links referencing relevant papers. For example, some papers argue that retirement is a relief from a stressful work (e.g., Gorry et al., 2018) and creates a new social network (e.g., Heller-Sahlgren, 2017), thus leading to better health. Other papers argue that retirement results in a loss of ambition (e.g., Insler, 2014), a reduction of mental and physical activity (e.g., Bound & Waidmann, 2007), an increase in health-adverse habits (Hernaes et al., 2013), a disruption to individuals’ accustomed activities (e.g., Behncke, 2012), and social isolation (e.g., Hagen, 2018), and retirement itself presents a stressful life event (e.g., Coe & Lindeboom, 2008), thus leads to a declining health. To comprehensively understand the link between retirement and health, the effect of retirement on health-related behaviors, such as smoking, drinking, and exercise is also examined in this book. This effect can be considered both positive and negative. Following retirement, healthy behavior is not necessarily considered to be imperative because the individual does not have to stay healthy in order to earn money. Nevertheless, the individual can use newly free retirement time for health-enhancing activities exploiting low opportunity cost. It is important to note here that the individual could obtain a utility from smoking and drinking itself. To compensate for the reduction of health services due to retirement, the individual might increase the amount of smoking and drinking to maximize his or her utility in Eq. (1.1). In this case, the deterioration in health and the increase in unhealthy behavior will possibly be observed simultaneously. Therefore, this result supports the idea that healthy behavior improves health in later life; however, it might be a reverse causal relationship if it was obtained from a reduced form estimation. Therefore, the mechanism linking retirement and health through health behavior must be interpreted with great care. This book also examines short- and long-term effects separately. It has been demonstrated that the effect of health investment may appear with a lag (Gorry et al., 2018; Heller-Sahlgren, 2017). Thus, the effect of retirement might not be observed in the short term but will instead be observable in the long term. In Atchley’s (1976) retirement stages, retirees are expected to experience a honeymoon stage; thus, the effect of retirement would first appear as beneficial in the short term. In contrast, retirees may experience a dramatic and irreversible change in their environment (Fé & Hollingsworth, 2016). In this case, retirement can worsen health in the short

12

1 Introduction

term. The heterogeneous effects of retirement based on individuals’ characteristics and lifestyles preceding retirement are also analyzed in this book. Individual characteristics and circumstances of retirement have an influence on the effect of retirement (Behncke, 2012; Grøtting & Lillebø, 2020). As mentioned in the health capital model, educational attainment affects the productivity of health investment or the rate of health deterioration (Grossman, 2000). This mechanism suggests that considerations, such as gender, occupational characteristics, and usual activity prior to retirement, can also affect the impact of retirement.

1.3.2 Empirical Method 1.3.2.1

Endogeneity Problem

Designing any rigorous examination of the effect of retirement on health is not straightforward because retirement is endogenous to health. A basic empirical model in this book to examine the effect of retirement on health for individual i at the survey time t is shown as follows: Hit = Xit β + δ R it +u it ,

(1.8)

where Hit is a health condition or a health behavior, Xit is a vector of exogenous controls including intercept, and u it is an error term. Rit is a dummy variable indicating whether individual i is retired or not at the survey time t. If a simple ordinary least squares (OLS) regression is applied to Eq. (1.8), the estimate of δ would be inconsistent because retirement is endogenous to health condition per the reasons described below. To address the endogeneity problem, a rigorous causal estimation has to be performed. There are mainly two sources of endogeneity in terms of retirement. First, a reverse causality possibly causes the endogeneity problem. While this book examines the causal effect of retirement on health, as shown in Eq. (1.8), health may also affect the decision to retire. For instance, unhealthy workers are more likely to retire than their healthy counterparts in addition to retirement affecting health. Put differently, retirement and health are determined simultaneously. In this case, the error term u it correlates with the retirement Rit , resulting in the inconsistent estimate of retirement. Second, unobserved confounders affecting health and retirement decision-making also cause an endogeneity problem. For instance, individuals’ unobserved characteristics, such as innate health or health shock not reported in the survey, for example, a family illness, can affect the retirement decision and subsequent health condition. The fact that unobserved confounders are included in the error term in the estimation leads to a correlation between the error term and retirement. As a result, the simple OLS estimation biases the estimate of retirement, which is known as an omitted variable bias.

1.3 Analytical Framework: Economic Theory and Empirical Method

13

To obtain a consistent estimate of the causal effect of retirement, the correlation between retirement and the error term has to be removed. One useful method for the solution is the IV approach.

1.3.2.2

Instrumental Variable Approach

Among the possible causal estimation methods, the IV approach is used the most often, and is applied in this book.9 The IV approach consists of two-stage estimations. In the first-stage, retirement is regressed on instruments and exogenous controls as follows: Rit = Xit γ+Zit θ + eit ,

(1.9)

where R is a retirement dummy and Z is a vector of the instruments. To obtain a consistent estimate, the instruments are required to have no correlation with the error term u in Eq. (1.8), called instrument exogeneity. In addition, the instruments have to have correlation with retirement R, called instrumental relevance (Wooldridge, 2019). The IV approach in this book uses two instruments: (1) dummy variable taking 1 if the respondent’s age is equal to or older than the age of eligibility for partial pension benefit, or 0 otherwise; (2) dummy variable taking 1 if the respondent’s age is equal to or older than the age of eligibility for full pension benefit, or 0 otherwise. As for the instrument relevance, weak identification tests based on the standard F-statistics, Sanderson-Windmeijer F-statistics (Sanderson & Windmeijer, 2016), and Kleibergen-Paap rk Wald F-statistics (Kleibergen & Paap, 2006) are performed depending on the estimation model. With regard to the instrument exogeneity, there is no reason to believe that discrete age threshold for pension eligibility should affect health directly, as the previous literature shows. Using the result of Eq. (1.9), a predicted value of retirement is calculated. In the second stage, the predicted value of retirement R is used as a regressor in the outcome equation as follows: 



Hit = Xit β + δ R it +u it ,

(1.10)



Because the variation in R is provided by the exogenous (to the error term u) instruments, it has no correlation with the error term, thus Eq. (1.10) can identify the consistent estimate of retirement. That is, the basic idea of the IV approach is that an exogenous part of the variation in an endogenous regressor is extracted using instruments, and then the health condition is regressed on the extracted part. Therefore, note that the effect of retirement estimated by the IV approach is the Local Average Treatment Effect (LATE), but not the Average Treatment Effect on the Treated (ATET) 9 Regression

discontinuity design and difference-in-differences are also used often in the previous literature. See Chapters 12–13 of Stock and Watson (2018), for more details about causal estimation.

14

1 Introduction

(Angrist & Pishcke, 2009); meaning that the LATE is the result of compliers who retired because they reached the eligibility age for pension benefit, but not that of all retirees.

1.4 Empirical Findings in the Previous Literature 1.4.1 Data in the Previous Literature A large body of literature has investigated the causal effect of retirement on health. Many of these studies use longitudinal survey data in a country or region. For example, the Health and Retirement Study (HRS) in the US (e.g., Gorry et al., 2018), the Survey of Health, Aging, and Retirement in Europe (SHARE) in continental Europe (e.g., Coe & Zamarro, 2011), and the English Longitudinal Survey of Aging (ELSA) in the UK (e.g., Behncke, 2012). An advantage of longitudinal data is that unobserved invariant characteristics, one of the sources of retirement endogeneity, can be removed through fixed effects estimation. In general, analysis with survey data is vulnerable to sample selection bias because individuals with certain characteristics are less likely to participate in a survey or more likely to drop from the sample, possibly leading to a biased result. To address the potential for bias in the sample, some studies use administrative or registered data, which covers an entire population in a specific area, for example, in Norway (e.g., Hernaes et al., 2013), Sweden (e.g., Hallberg et al., 2015), and the Netherlands (e.g., Bloemen et al., 2017). One possible disadvantage of this sort of data is the limitation of available health measures due to the aim of data collection. As the findings in the previous literature are somewhat conflicting, the definition of retirement and the effects on each health measure are introduced below. Major health measures include self-rated health, physical health, mental health, cognitive health, mortality, and health care use. While most health measures have scores with a certain range, some studies directly use the score, whereas others code the score into a dichotomous variable. Moreover, a few studies derive a composite score from a battery of health status measures.

1.4.2 Definitions of Retirement The definition of retirement varies in the literature. In general, retirement implies a withdrawal from the labor market, leading some studies to define individuals as retired if they have no paid work (e.g., Coe & Zamarro, 2011). In terms of the departure from a career, those who still work with less hours or inferior roles after leaving their career (partial retirement) can also be defined as retired (e.g., Gorry et al., 2018). Studies using administrative data, since determining whether the individual actually

1.4 Empirical Findings in the Previous Literature

15

retired or not is difficult, usually use information regarding the start of pension benefit (e.g., Hernaes et al., 2013). The definition of retirement is also dependent on the aim of the study. For example, retirement including partial retirement might not make a significant difference in physical health between retirees and non-retirees because many retirees are still engaging in physical activity in their work. However, losing one’s career can be a stressful event, even if retirees are still working. In this case, retirement including partial retirement, possibly makes a significant difference between retirees and non-retirees in terms of mental health. Most studies use a retirement dummy as an indicator of retirement, which usually takes 1 if they are retired or 0 otherwise. Among the studies using a retirement dummy, as will be discussed in Chapters 2 and 4, many studies have examined the average effect of retirement on health. However, since the effect of retirement might differ based on retirement duration or retirement timing, retirement simply defined in terms of being retired or not, may include heterogeneous effects. For example, retirement demonstrates different effects in the short and long term (e.g., Insler, 2014). Moreover, early and normal retirement also shows different effects (e.g., Calvo et al., 2013). Thus, to comprehend the mechanisms linking retirement and health correctly, the heterogeneity must be considered. Other than a retirement dummy, some studies use years from retirement as the retirement duration (e.g., Bertoni & Brunello, 2017), and other studies use retirement age to determine the retirement timing (e.g., Hernaes et al., 2013). Working hours in old age are also used (e.g., Neuman, 2008). In addition, the effect of retirement may differ by individual characteristics, for example, gender, educational attainment, and job characteristics (Dave et al., 2008). While many studies have examined the different effects of these characteristics, few studies focus on the lifestyle prior to retirement. From the perspective of the health capital model, pre-retirement lifestyle is relevant to health in later life. Chapter 3 examines these aspects of the effect of retirement. The effect of retirement on various health measures is roughly summarized below. However, the following summary of the empirical findings in the previous literature does not take the definition of retirement described here into account.

1.4.3 Effect on Health 1.4.3.1

Self-Rated Health

In many studies, a five-point Likert scale measure, for example, from very poor (1) to very good (5), is used as a raw score or a dichotomized variable (poor or good). However, since self-rated health is a subjective measure, it suffers from justification bias (Coe & Zamarro, 2011). That is, retirees tend to report their health worse than actual condition to justify their retirement decision. Thus, some studies (e.g., Behncke, 2012) construct a health index using self-rated health following Bound and Waidmann (1999), regressing the self-rated health on the other health measures, for example, mobility limitations, obesity, and depression, and then generate a predicted

16

1 Introduction

Table 1.2 The effect of retirement on self-rated health Effect

Studies

Beneficial

Bound & Waidmann, 2007; Neuman, 2008; Coe & Lindeboom, 2008; Johnston & Lee, 2009; Coe & Zamarro, 2011; Insler, 2014; Eibich, 2015; Zhu, 2016; Hessel, 2016; Oshio & Kan, 2017; Gorry et al., 2018; Shai, 2018; Messe & Wolff, 2019; Grøtting & Lillebø, 2020; Rose, 2020; Mountian & Diaz, 2020; Gorry & Slavov, 2021

Adverse

Calvo et al., 2013; Mazzonna & Peracchi, 2017

No significance Behncke, 2012; Atalay & Barrett, 2014

value of the self-rated health using the estimation results. Thus, self-rated health is reconstructed through actual health conditions only, which is considered to be free from individuals’ justification bias. The effects of retirement on self-rated health including the predicted health index are summarized in Table 1.2. As the table shows, most studies find a beneficial effect of retirement, while a few studies find adverse or no significant effect.

1.4.3.2

Physical Health

In previous literature, physical health is basically measured by functional limitations, diseases, and chronic conditions. Functional limitations include, for example, difficulties with activities in daily life, large muscle functions, mobility limitations, and the physical aspects of the SF-36.10 Diseases and chronic conditions include, for example, diabetes, stroke, arthritis, cancer, body mass index, and high blood pressure. Since each study investigates the effect of retirement on multiple physical health measures and finds mixed results regarding the significance and the signs of retirement, summarizing those results is difficult. Thus, if the results are mixed within a study, the study is listed in all corresponding categories in Table 1.3. Table 1.3 indicates that both beneficial and no significant retirement effect is observed with the same frequency; the adverse effect is not much observed in the previous literature. Moreover, the most frequent pattern of the results in a study is that retirement has a beneficial effect on some measures but no significant effect on others.

10 SF-36 (36-Item Short-Form Health Survey) is a self-reported health inventory. The questions are,

for example, “In general, would you say your health is: excellent, very good, good, fair, poor?”, “How much bodily pain have you had during the past 4 weeks?”, and “How much of the time during the past 4 weeks have you been very nervous person?”.

1.4 Empirical Findings in the Previous Literature

17

Table 1.3 The effect of retirement on physical health Effect

Studies

Beneficial

Bound & Waidmann, 2007; Neuman, 2008; Atalay & Barrett, 2014; Hallberg et al., 2015; Hessel, 2016; Horner & Cullen, 2016; Zhu, 2016; Gorry et al., 2018; Hagen, 2018; Shai, 2018; Messe & Wolff, 2019; Rose, 2020; Kuusi et al., 2020; Mountian & Diaz, 2020; Gorry & Slavov, 2021

Adverse

Bound & Waidmann, 2007; Behncke, 2012; Gorry et al., 2018; Gorry & Slavov, 2021

No significance Bound & Waidmann, 2007; Coe & Lindeboom, 2008; Neuman, 2008; Johnston & Lee, 2009; Behncke, 2012; Atalay & Barrett, 2014; Hallberg et al., 2015; Eibich, 2015; Hessel, 2016; Horner & Cullen, 2016; Gorry et al., 2018; Hagen, 2018; Messe & Wolff, 2019; Rose, 2020; Gorry & Slavov, 2021

1.4.3.3

Mental Health

In the previous literature, the effect of mental health is measured by various scales: the European Depression (Euro-D) scale, the Center for Epidemiologic StudiesDepression (CES-D) scale, the General Health Questionnaire-12 (GHQ-12), the Kessler Screening Scale for Psychological Distress (K6/10), and the mental portion of the Short Form (SF-12) Survey. These indicators consist of questions about the respondents’ mental condition, for example of K6, “During the last 30 days, how often did you feel anxious?” and “During the last 30 days, how often did you feel hopeless?” The respondents rate each question by a five-point Likert scale and the scores are summarized by researchers. These validated instruments have an established criterion to determine if the respondent has a mental disorder, and are thus used to produce a dichotomous variable in many cases (e.g., Oshio & Kan, 2017). A few studies generate a composite score from some questions using principal component analysis (e.g., Bertoni & Brunello, 2017) and others simply ask respondents whether they feel depressed or not (e.g., Coe & Zamarro, 2011). Table 1.4 shows the results in the literature on the effect of retirement on mental health. The summary shows that the beneficial effect is observed the most frequently, followed by no significant effect. A few studies identified an adverse effect. Table 1.4 The effect of retirement on mental health Effect

Studies

Beneficial

Charles, 2004; Johnston & Lee, 2009; Atalay & Barrett, 2014; Eibich, 2015: Gorry et al., 2018; Oshio & Kan, 2017; Rose, 2020; Kuusi et al. 2020; Picchio & Ours, 2020; Gorry & Slavov, 2021

Adverse

Calvo et al., 2013; Bertoni & Brunello, 2017; Heller-Sahlgren, 2017

No significance Coe & Lindeboom, 2008; Neuman, 2008; Coe & Zamarro, 2011; Behncke, 2012; Fé & Hollingsworth, 2016; Hagen, 2018

18

1 Introduction

Table 1.5 The effect of retirement on cognitive health Effect

Studies

Beneficial Adverse

Rohwedder & Willis, 2010; Behncke, 2012; Mazzonna & Peracchi, 2012, 2017; Bonsang et al., 2012; Kajitani et al., 2017; Celidoni et al., 2017

No significance

Coe & Zamarro, 2011; Mazzonna & Peracchi, 2012; Coe et al., 2012; Rose, 2020

1.4.3.4

Cognitive Health

Cognitive function is one of the major health measures in the literature investigating the effect of retirement, and is measured through various tests to evaluate the multidomain of cognition. The most widely used test is word recall. In this test, respondents are usually asked to listen to 10 words, for example, tree, home, and letter, read by the interviewer and to say these words immediately (immediate word recall) and a few minutes later (delayed word recall). The number of correct answers is the score of this test. A typical test of numeracy is a serial 7s subtraction, wherein respondents are asked to subtract seven from 100 five times. The number of the correct answers is the score of this test. Orientation is usually measured by some questions about, for example, the current place and time, and the number of the correct answers is the score of this test. In verbal fluency test, the respondents are required to name, for example, as many animals as possible in one minute. The number of the items named is the score of this test. While these test scores are usually used in a separate estimation equation, some studies derive a composite score from cognitive battery tests using principal component analysis (e.g., Mazzonna & Peracchi, 2017). Table 1.5 shows the results of the effect of retirement on cognitive function. The results show that most studies find an adverse effect on cognition and some studies find no significant effect. No studies found a beneficial effect.

1.4.3.5

Mortality

Mortality is usually examined in the microstudies using administrative or registered date because it is difficult to correctly observe the occurrence of death with a conventional survey. Most of the studies, except for Bound and Waidmann (2007), shown in Table 1.6, use administrative or registered date in Norway, Sweden, Austria, or the Netherlands. Table 1.6 shows the effect of retirement on mortality, indicating many studies find no significant effect, whereas a few studies found a beneficial or an adverse effect.

1.4 Empirical Findings in the Previous Literature

19

Table 1.6 The effect of retirement on mortality Effect

Studies

Beneficial

Bloemen et al., 2017

Adverse

Kuhn et al., 2020; Barban et al., 2020

No significance

Bound & Waidmann, 2007; Hernaes et al., 2013; Hagen, 2018; Grøtting & Lillebø, 2020; Kuhn et al., 2020

Table 1.7 The effect of retirement on health care use Effect

Studies

Beneficial (Decrease)

Hallberg et al., 2015; Eibich, 2015; Gorry et al., 2018, Bíró & Elek, 2017; Shai, 2018

Adverse (Increase) No significance

1.4.3.6

Eibich, 2015; Horner & Cullen, 2016; Gorry et al., 2018; Hagen, 2018; Grøtting & Lillebø, 2020; Barban et al., 2020

Health Care Use

Health care use includes, for example, hospital stay, doctor visits, and drug subscription. Theoretically, while health care use is considered partly as health investment, the effects on it are presented here as one of the health outcomes by retirement. Since each study investigates the effect of retirement on multiple health care uses and find the mixed results on the significance and sign of retirement, summarizing those results is difficult as with the results of physical health. Thus, if the results are mixed within a study, the study is listed in all corresponding categories in Table 1.7. Table 1.7 shows the results of the effect of retirement on health care use. The summary shows that beneficial and no significant effect is observed with almost the same frequency. No studies have noted an adverse effect.

1.5 Data and Variables Used in the Book 1.5.1 The Longitudinal Survey of Middle-Aged and Elderly Persons (LSMEP) The empirical analysis in this book uses the Longitudinal Survey of Middle-aged and Elderly Persons (LSMEP). The LSMEP is a nationally representative longitudinal survey in Japan that has been conducted annually (in November) by the MHLW. The survey collects information on work, health, usual activities, family, and livelihood from men and women who were aged 50–59 as of the first interview in 2005. The 15th survey was conducted in 2019. As for the survey method, a door-to-door survey

20

1 Introduction

was conducted from 2005–2008 and a mail survey was conducted during 2009–2019. There is no additional cohort until the latest survey.11 This book uses the respondents from the 11 waves (2005–2015) of the LSMEP. The initial number of individuals was 34,240 (response rate 83.8%) in 2005 and decreased to 22,595 (response rate 96.2%) in 2015. The observations used in this book are restricted in some ways. First, respondents who worked as full-time regular (called seiki in the questionnaire12 ) employees at the first wave are used. Many previous studies use both those who were full-time and part-time employees as the analytic sample, and some include the self-employed as well. However, the effects of retirement and retirement incentives are considered to be substantially different between those employment statuses. To remove the heterogeneity in the sample, this restriction is performed. Second, respondents whose work status is continuously observed from the first wave to a certain wave are used to capture accurate work history. Third, respondents who reentered the labor force after full retirement are excluded, as the effect of reentering the labor force on health is not symmetrical with withdrawal from the labor force. Consequently, the final sample used in the estimation is at most 10,694 individuals; however, the number of individuals in each estimation varies depending on the missing values for the variables used.

1.5.2 Health Measure Three health measures are used in this book: poor self-rated health, difficulty with activities of daily living (ADLs), and depression. Self-rated health is used as a measure of overall health condition. Respondents are asked to rate their health condition on a six-point Likert scale at the survey time: very good, rather good, good, poor, rather poor, very poor. A dichotomous variable is generated indicating poor health, taking 1 if respondents’ health is poor/rather poor/very poor, or 0 otherwise. Difficulty with ADLs, a physical health measure, is a dichotomous variable that takes 1 if respondents have any difficulty among the following ten activities: walking, getting up out of bed or off floor, sitting down on and standing up from chair, taking clothes on and off, washing hands and face, eating, toileting, bathing, climbing and descending stairs, and carrying purchases, or 0 otherwise. Depression, a measure of mental health, is generated from the score on the Japanese version of the Kessler Psychological Distress Scale (K6). The K6 consists of six questions: “During the past 30 days, about how often did you feel nervous?,” “… feel hopeless?,” “… feel restless or fidgety?,” “… feel so depressed that nothing could cheer you up?,” “… feel that everything was an effort?,” and “… feel worthless?” Respondents rate each question from 0 (none of the time) to 4 (all of the time), and the total score ranges from 0 (good) to 24 (bad). A dichotomous variable 11 MHLW 12 Seiki

(https://www.mhlw.go.jp/english/database/db-ls/ls.html for more detail). workers basically work full-time with an undefined-term employment contract.

1.5 Data and Variables Used in the Book

21

indicating depression symptoms takes 1 if the total score equals to 5 or higher, or 0 otherwise (Sakurai et al., 2011). It is recognized that all of these measures are subjective and not objective, such as diagnoses by professionals. Thus, these measures might include some biases, which is one of the identified limitations of this book.

1.5.3 Retirement As noted previously, there is no consensus on the definition of retirement, and it varies among studies. In this study, respondents are defined as full retirement if they have no paid work. Those who reentered the labor force from full retirement are excluded from the sample, as described above. To examine the heterogeneous effect of retirement, full retirees are divided into two groups based on the duration of retirement in Chapters 2 and 3: those retired between the last and the present survey (short-term effect) and those retired before the last survey (long-term effect). In Chapter 4, full retirees are divided by instruments: those retired because they reached the eligibility age for partial pension benefit (early retirement) and those retired because they reached the eligibility age for full pension benefit (normal retirement).

1.5.4 Age Trajectory of Retirement and Health To examine the relevance of pension eligibility regarding the decision to retire, the relationship between retirement and age is illustrated in Fig. 1.5. The left panel shows the ratio of retirees by age and the right panel shows the change in the ratio of retirees by age. From two figures, some jumps emerge at ages 60, 61, 65, and 70. Ages 60 and 61 are the eligibility age for the partial benefit for many respondents and 65 is also the eligibility age for the full benefit for many respondents. This finding strongly suggests that pension eligibility actually affects the retirement decision and is thus an effective instrument in the IV approach. Figure 1.6 illustrates the age trajectory of health condition. The dots show the ratio of those reporting poor health condition for each age. The figure indicates that self-rated health and ADLs deteriorate with age, whereas depression improves with age. There seems no clear jump at the pension eligibility ages. In terms of the form of age function, a quadratic function seems to be suitable.

1.5.5 Problem of Attrition As briefly mentioned in Sect. 1.4.1, panel attrition might cause a sample selection problem. That is, respondents who are in poor health may be more likely to drop

22

1 Introduction

Fig. 1.5 Age trajectory of retirement (Note The ratio is calculated from respondents used in the estimation of poor self-rated health [N = 90,933]. The change in ratio is calculated as follows: ratio of retirees [age + 1] − ratio of retirees [age]. Source LSMEP 2005–2015)

from the sample, thus resulting in a bias with regard to the findings on the effect of retirement on health. To confirm the effect of health conditions on attrition, a fixed effect logit estimation was performed using those who worked as full-time regular employees at the first wave. In the estimation model, attrition (=1 if the respondents dropped from the sample or 0 otherwise) in time t is regressed on health status, age, work status, marital status, household composition, and survey year in time t − 1. Estimation results show that with a 5% significance level, difficulty with ADLs and depression have no significant effect on attrition among both men and women; selfrated health has a significant (but not significant at 1% level) effect among men but no significant effect among women. Accordingly, although we have to note the interpretation of the effect of retirement on self-rated health among men, the issue of attrition in this sample is not serious. One of the reasons for the result is that the respondents in this sample are relatively young and healthy, so health conditions do not have much effect on attrition.

1.6 Conclusion

23

Fig. 1.6 Age trajectory of health (Note The dots mark the ratio of having poor health condition by each age using respondents used in the estimation on each health measure. Observations used are as follows: poor self-rated health [N = 90,933], difficulty with ADLs [N = 88,935], and depression [N = 88,557]. Source LSMEP 2005–2015)

1.6 Conclusion This chapter introduced the background of the book, in particular, exposing the fact that previous findings of the effect of retirement on health are mixed and contradictory. Thus, further investigation from a different perspective is required. To this end, this book focuses on the heterogeneous effect of retirement. In addition, although it was not clearly described, findings about Japan are scarce thus far. Japan has both a high ratio of working older adults and high life expectancy; thus, the findings about the retirement–health relationship in Japan would be undoubtedly helpful for other developed countries facing population aging. Moreover, this chapter explained how the endogeneity problem of retirement occurs, how the problem is addressed in this book, and the limitation of the IV approach.

References Atalay, K., & Barrett, G. F. (2014). The causal effect of retirement on health: New evidence from Australian pension reform. Economics Letters, 125(3), 392–395.

24

1 Introduction

Atchley, R. C. (1976). The Sociology of Retirement. John Wiley and Sons. Angrist, J. D., & Pishcke, J.-S. (2009). Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton University Press. Behncke, S. (2012). Does retirement trigger ill health? Health Economics., 21(3), 282–300. Barban, N., de Luna, X., Lundholm, E., Svensson, I., & Billari, F. C. (2020). Causal effects of the timing of life-course events: Age at retirement and subsequent health. Sociological Methods & Research, 49(1), 216–249. Bertoni, M., & Brunello, G. (2017). Pappa Ante Portas: the effect of the husband’s retirement on the wife’s mental health in Japan. Social Science & Medicine, 175, 135–142. Bíró, A., & Elek, P. (2017). How does retirement affect healthcare expenditures? Evidence from a change in the retirement age. Health Economics, 27(5), 803–818. Bloemen, H., Hochguertel, S., & Zweerink, J. (2017). The causal effect of retirement on mortality: Evidence from targeted incentives to retire early. Health Economics, 26(12), 204–218. Bonsang, E., Adam, S., & Perelman, S. (2012). Does retirement affect cognitive functioning? Journal of Health Economics, 31(3), 490–501. Bound, J., Schoenbaum, M., Stinebrickner, T. R., & Waidmann, T. (1999). The dynamic effects of health on the labor force transitions of older workers. Labour Economics, 6(2), 179–202. Bound, J., & Waidmann, T. (2007). Estimating the health effects of retirement. Working Paper 168, University of Michigan, Michigan Retirement Research Center. Cabinet Office. (2018). Koreisha no Seikatsu to Ishiki ni Kansuru Kokusai Hikaku Chosa [International Comparative Survey on Life and Attitude of Older Adults]. https://www8.cao.go.jp/kou rei/ishiki/h27/zentai/index.html. Accessed January 3, 2020. Calvo, E., Sarkisian, N., & Tamborini, C. R. (2013). Causal effects of retirement timing on subjective physical and emotional health. Journal of Gerontology Series b: Psychological Sciences and Social Sciences, 68(1), 73–84. Celidoni, M., Dal Bianco, C. D., & Weber, G. (2017). Retirement and cognitive decline. A longitudinal analysis using SHARE data. Journal of Health Economics, 56, 113–125. Charles, K. K. (2004). Is retirement depressing?: Labor force inactivity and psychological well-being in later life. Research in Labor Economics, 23, 269–299. Coe, N. B., & Lindeboom, M. (2008). Does retirement kill you? evidence from Early Retirement Windows. IZA Discussion Paper Series 3817. Institute for the Study of Labor, (IZA). Coe, N. B., & Zamarro, G. (2011). Retirement effects on health in Europe. Journal of Health Economics, 30(1), 77–86. Coe, N. B., von Gaudecker, H.-M., Lindeboom, M., & Mauer, J. (2012). The effect of retirement on cognitive functioning. Health Economics, 21(8), 913–927. Dave, D., Rashad, I., & Spasojevic, J. (2008). The effects of retirement on physical and mental health outcomes. Southern Economic Journal, 75(2), 497–523. Eibich, P. (2015). Understanding the effect of retirement on health: Mechanisms and heterogeneity. Journal of Health Economics, 43(1), 1–12. Fé, E., & Hollingsworth, B. (2016). Short- and long-run estimates of the local effects of retirement on health. Journal of the Royal Statistical Society Series A, 179(4), 1051–1067. Gorry, A., Gorry, D., & Slavov, S. N. (2018). Does retirement improve health and life satisfaction? Health Economics, 27(12), 2067–2086. Gorry, D., & Slavov, S. N. (2021). The effect of retirement on health biomarkers. Economics and Human Biology, 40, 100949. Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80(2), 223–255. Grossman, M. (2000). The human capital model. In A. J. Culyer & J. P. Newhouse (Eds.), Handbook of Health Economics (pp. 347–407). Elsevier. Grøtting, M. W., & Lillebø, O. S. (2020). Health effects of retirement: Evidence from survey and register data. Journal of Population Economics, 33(2), 671–704. Hagen, J. (2018). The effects of increasing the normal retirement age on health care utilization and mortality. Journal of Population Economics, 31(1), 193–234.

References

25

Hallberg, D., Johansson, P., & Josephson, M. (2015). Is an early retirement offer good for your health? Quasi-experimental evidence from the army. Journal of Health Economics, 44, 274–285. Heller-Sahlgren, G. (2017). Retirement blues. Journal of Health Economics, 54, 66–78. Hernaes, H., Markussen, S., Piggott, J., & Vestad, O. L. (2013). Does retirement age impact mortality? Journal of Health Economics, 32, 586–598. Hessel, P. (2016). Does retirement (really) lead to worse health among European men and women across all educational levels? Social Science & Medicine, 151, 19–26. Horner, E. M., & Cullen, M. R. (2016). The impact of retirement on health: quasi-experimental methods using administrative data. BMC Health Services Research, 16, 1–9. Insler, M. (2014). The health consequences of retirement. Journal of Human Resources, 49(1), 195–233. Johnston, D. W., & Lee, W. S. (2009). Retiring to the good life? The short-term effects of retirement on health. Economics Letters, 103(1), 8–11. Kajitani, S., Sakata, K., & Mackenzie, C. (2017). Occupation, retirement and cognitive functioning. Aging & Society, 37(8), 1568–1596. Kleibergen, F., & Paap, R. (2006). Generalized reduced rank tests using the singular value decomposition. Journal of Econometrics, 133(1), 97–126. Kuhn, A., Staubli, S., Wuellrich, J.-P., & Zweimüller, J. (2020). Fatal attraction? Extended unemployment benefits, labor force exits, and mortality. Journal of Public Economics, 191, 104087. Kuusi, T., Martikainen, P., & Valkonen, T. (2020). The influence of old-age retirement on health: Causal evidence from the Finnish register data. Journal of the Economics of Ageing, 17, 100257. Mazzonna, F., & Peracchi, F. (2012). Ageing, cognitive abilities and retirement. European Economic Review, 56(4), 691–710. Mazzonna, F., & Peracchi, F. (2017). Unhealthy retirement? Journal of Human Resources, 52(1), 128–151. Messe, P. J., & Wolff, F. C. (2019). The short-term effects of retirement on health within couples: Evidence from France. Social Science & Medicine, 221, 27–39. MHLW. (2011). Shakai Hosho Shingikai Nenkin Bukai Siryo October 11, 2011. [Material for the Pension Subcommittee of the Social Security Council October 10, 2011]. https://www.mhlw.go. jp/stf/shingi/2r9852000001r5uy-att/2r9852000001r5zf.pdf. Accessed October 10, 2019. MHLW. (2015a). Shuro Joken Sogo Chosa [General Survey on working condition]. https://www. mhlw.go.jp/toukei/itiran/roudou/jikan/syurou/15/dl/gaikyou.pdf. Accessed October 19, 2020. MHLW. (2015b). Konenreisha no Koyo Jokyo [Employment Status of Elderly People] https://www. mhlw.go.jp/stf/houdou/0000101253.html. Accessed September 10, 2020. MHLW. (2017). Kosei Nenkin Hoken, Kokumin Nenkin Jigyo no Gaiyo [Overview of Employees’ Pension Insurance and National Pension] https://www.mhlw.go.jp/file/06-Seisakujouhou-125 00000-Nenkinkyoku/H27.pdf. Accessed October 9, 2020. Mountian, A. G., & Diaz, M. D. M. (2020). Effects of retirement on the health of elderly people in São Paulo. Brazil Applied Economics, 52(28), 2991–3003. Muurinen, J.-M. (1982). Demand for health: A generalised Grossmann model. Journal of Health Economics, 1(1), 5–28. National Personnel Authority. (2016). Minkan Kigyo no Kinmu Joken Seido To Chosa Kekka no Gaiyo [Overview of the Survey Result on Work Condition and System of Private Company] https://www.jinji.go.jp/kisya/1609/h28akimincho_bessi.pdf. Accessed on January 22, 2021. Neuman, K. (2008). Quit your job and get healthier? The effect of retirement on health. Journal of Labor Research, 29(2), 177–201. OECD. (2017). Pensions at a glance 2017: OECD and G20 indicators. OECD Publishing. Oshio, T., & Kan, M. (2017). The dynamic impact of retirement on health: Evidence from a nationwide ten-year panel survey in Japan. Preventive Medicine, 100, 287–293. Picchio, M., & van Ours, J. C. (2020). Mental health effects of retirement. De Economist, 168, 419–452.

26

1 Introduction

Rohwedder, S., & Willis, R. K. (2010). Mental retirement. Journal of Economics. Perspectives, 24(1), 119–138. Rose, L. (2020). Retirement and health: Evidence from England. Journal of Health Economics, 73, 102352. Sakurai, K., Nishi, A., Kondo, K., Yanagida, K., & Kawakami, N. (2011). Screening performance of K6/K10 and other screening instruments for mood and anxiety disorders in Japan. Psychiatry and Clinical Neurosciences, 65(5), 434–441. Sanderson, E., & Windmeijer, F. (2016). A weak instrument F-test in linear IV models with multiple endogenous variables. Journal of Econometrics, 190(2), 212–221. Seike, A. and Yamada, A. (2004). Koreisha Shugyo no Keizaigaku [Economic Analysis of Labor Supply of the Elderly]. Nihon Keizai Shimbun sha, Tokyo. Shai, O. (2018). Is retirement good for men’s health? Evidence using a change in the retirement age in Israel. Journal of Health Economics, 57, 15–30. Stock, J. M., & Watson, M. W. (2018). Introduction to Econometrics (4th ed.). . Pearson Education. Zhu, R. (2016). Retirement and its consequences for women’s health in Australia. Social Science & Medicine, 163, 117–125. Wooldridge, J. M. (2019). Introductory Econometrics (7th ed.). . Cengage.

Chapter 2

Short- and Long-Term Effects of Retirement on Health

Abstract This chapter examines the short- and long-term effects of retirement on health in addition to the conventional average effect of retirement using a nationally representative longitudinal survey conducted in Japan. Theories predict that the shortand long-term effects can differ. Causal estimation applying an instrumental variables (IV) approach reveals that simple estimation suffers from the endogeneity problem of retirement. The IV estimate indicates that the average effect of retirement has a beneficial or no significant impact on health. Conversely, the short- and long-term effects show influences in opposing directions. The short-term effect demonstrates a beneficial impact on overall and mental health but no significant impact on physical health, whereas the long-term effect indicates an adverse impact on overall and mental health, and no significant impact on physical health. That is, retirement is essentially good for health in the short term but detrimental in the long term. These opposite short- and long-term effects may result in no significant impact or only a small impact in the average effect. Moreover, retirement does not improve health behavior; rather it appears to worsen it in the long term. This is possibly the source of the long-term adverse effect of retirement. To examine the heterogeneous impact by individual characteristics, estimation on subsamples is performed. Roughly summarizing the findings, the health of men, those with high education, and those who had a whitecollar job is likely to deteriorate after retirement in the long term. Keywords Endogeneity bias · Fixed effects model · Average effect · Immediate and delayed effects · Heterogeneous effects by individual characteristics

2.1 Introduction 2.1.1 Effects of Retirement in the Short Term and Long Term Does retirement influence health immediately or a few years later? Are the directions of the short- and long-term effects of retirement the same or do they differ? These questions are important to the understanding of the conflicting results of studies on the effect of retirement on health in the literature as well as for policymaking in social security. Several studies have examined the average effect of retirement; however, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Mizuochi, Exploring the Effect of Retirement on Health in Japan, Population Studies of Japan, https://doi.org/10.1007/978-981-16-2638-8_2

27

28

2 Short- and Long-Term Effects of Retirement on Health

the difference in the effect based on the duration of retirement is less understood. The effect of retirement may depend on the length of the observed period of the data or differences in the impacts of short- and long-term effects. As a result, the average effect of retirement on health tends to vary based on each study design in the literature. This has led to misunderstandings regarding the effect of retirement on health and incorrect implications for social policy. Therefore, it is imperative to divide the assessment of the effects of retirement into short- and long-term considerations. Grossman (1972) proposes a health capital model in which health condition is determined by the difference in size between gross health investment and health deterioration with age in the previous time. The differential form is described in Eq. (2.1). H ealth t = Gr oss health investment t−1 − H ealth deterioration t−1 (2.1) If the gross health investment surpasses the health deterioration at time t − 1, health improves at time t. The health investment includes purchased medical care or other health-promoting activities (Case & Deaton, 2005). The rate of health deterioration is affected by age or individual characteristics such as education (Muurinen, 1982). As discussed in Chapter 1, retirement can exert both beneficial and detrimental impacts on health. Following retirement, since the opportunity cost substantially decreases, retirees can invest more time in health, which leads to better health. On the other hand, retirees might not invest in health because they no longer have to keep their health and productivity in order to earn money. This leads to a deteriorating health.1 Regarding short- and long-term effects, the effect of health investment may lag in appearance (Heller-Sahlgren, 2017). Thus, the effect of retirement might not be observed in the short term but in the long term. Retirees are also expected to experience a honeymoon stage, leading to a beneficial short-term effect (Atchley, 1989). In contrast, retirees may experience dramatic and irreversible change in their environment (Fé & Hollingsworth, 2016), leading to diminishing health in the short term. The health capital model also suggests that the short- and long-term effects can differ. For example, in preparation for the possible reentry into the labor force or due to a relatively comfortable economic situation, retirees may increase health investment at retirement and maintain or even improve their health. However, since the possibility of reentering the labor force and the stability of economic situation decreases with age, retirees may reduce their health investment in the long term, leading to a deteriorating health condition. While a large body of the literature has examined the effect of retirement on health, not many studies have focused on the different impacts of retirement based on duration. Some recent studies indicate that the short- and long-term effects may differ. The results of causal studies focusing on the retirement duration are summarized in Table 2.1. Insler (2014) identifies a beneficial impact of long-term effect on selfrated health, while finding no significant short-term effect on health in the US. Fé 1 See

Chapter 1, for more detailed discussion about the effect of retirement on health.

2.1 Introduction Table 2.1 Short- and long-term effects of retirement on health in the previous literature

29

Insler

(2014)a

Short-term

Long-term

Not significant

Beneficial

Fé and Hollingsworth (2016)a

Not significant

Not significant

Heller-Sahlgren (2017)a

Not significant

Adverse

Oshio and Kan (2017)b

Beneficial

Beneficial

Mazzonna and Peracchi (2017) b

Not significant

Adverse

Gorry et al. (2018)c

Adverse

Beneficial

Note Definitions of short-term after retirement in each study are as follows: a 0–2 years, b 0–1 year, and c 0–4 years

and Hollingsworth (2016) find no significant impact of both effects on physical and mental health in the UK. Heller-Sahlgren (2017) claims an adverse impact of long-term effect on mental health, whereas no significant short-term effect is found in continental Europe. Oshio and Kan (2017) highlight beneficial effects on selfrated health and mental health in both the short and long terms in Japan. Mazzonna and Peracchi (2017) observe an adverse impact of long-term effect on overall and cognitive health, whereas no significant short-term effect is found in continental Europe. Gorry et al. (2018) assert the beneficial impact of long-term effect on physical health, while finding an adverse short-term effect in the US. Roughly speaking, shortterm effect appears to demonstrate no significant impact and long-term effect seems to indicate a beneficial impact; however, the results remain in conflict, necessitating further investigation.

2.1.2 Overview of This Chapter This chapter first estimates the conventional average effect of retirement on health to compare these estimates with the short- and long-term effects derived later in this chapter. Identifying a difference in the effects would indicate the necessity to distinguish the effects of retirement into short and long terms. Next, the effect of retirement on health usually suffers from the endogeneity problem due to a reverse causality from health to retirement decision or unobserved confounders affecting both health and retirement. A rigorous causal estimation is required to obtain a consistent estimate of retirement. To address this challenge, this chapter demonstrates how the result changes based on the application of the estimation method between a simple estimation, an estimation that partly addresses the endogeneity problem, and an estimation that fully addresses the endogeneity problem. Differences in the estimates of retirement would indicate the necessity of applying a rigorous causal approach. This study uses the instrumental variables (IV) approach, endeavoring to fully address the endogeneity problem.

30

2 Short- and Long-Term Effects of Retirement on Health

The short- and long-term effects of retirement in Japan were previously examined by Oshio and Kan (2017); however, some issues remain. First, the researchers’ findings regarding the change in rate of health deterioration cannot be directly compared with the long-term effect in the literature. Second, they do not examine the effect of retirement on physical health, which is also a crucial health measure. Third, while they examine the heterogeneous effects by gender, they do not focus on other important individual characteristics, for example, educational level and previous occupational characteristics. These characteristics can change the effect of retirement, as suggested by the health capital model. Thus, this chapter contributes to the literature by examining these remaining issues. In addition, to understand the correlation between retirement and health from the perspective of the health capital model, the effect of retirement on health-related behavior such as exercise, smoking, and drinking is also examined. The rest of this chapter is organized into four sections. Section 2.2 introduces the data, variables, and estimation method applied and presents the results of descriptive analysis. Section 2.3 presents the estimation results for the average effect of retirement on health through a comparison of the estimates derived from the different estimation methods. Section 2.4 introduces the causal estimation results for the short- and long-term effects of retirement on health and health behavior. Section 2.5 discusses the findings in this chapter.

2.2 How Short- and Long-Term Effects Are Examined 2.2.1 Data and Variables This chapter uses 11 waves (2005–2015) of the previously introduced Longitudinal Survey of Middle-aged and Elderly Persons (LSMEP).2 The LSMEP is a nationally representative survey in Japan. The sample used here is restricted in some ways. First, respondents who worked as full-time regular (called seiki in the questionnaire3 ) employees at the first wave are used. Since the impact of retirement for part-time employees and self-employed workers seems to be substantially different from full-time regular employees, part-time employees and self-employed workers are excluded from the analytic sample. Second, only respondents whose work status is continuously observed from the first wave to a certain wave are used to capture accurate retirement duration. Third, respondents who reentered the labor force after full retirement are excluded from the analytic sample, as the health effect of withdrawal from and subsequent reentry into the labor force is not considered to be symmetrical. The final sample used in the estimation is at most 10,694 individuals.

2 See

Chapter 1 for more details. workers basically work full-time with an undefined-term employment contract.

3 Seiki

2.2 How Short- and Long-Term Effects Are Examined

31

Health measures include poor self-rated health, difficulty with activities of daily living (ADLs), and depression. Poor self-rated health is used as the overall health measure. Respondents are asked to rate their current health condition on a six-point Likert scale: very good, rather good, good, poor, rather poor, very poor. A dichotomous variable indicating poor health is generated, taking 1 if respondents’ condition is poor/rather poor/very poor, or 0 otherwise. Difficulty with ADLs is used as a physical health measure. A dichotomous variable is generated, taking 1 if respondents answered that they have any difficulty among the following ten activities: walking, getting up out of bed or off floor, sitting down on and standing up from chair, taking clothes on and off, washing hands and face, eating, toileting, bathing, climbing and descending stairs, and carrying purchases, or 0 otherwise. Depression is used as a mental health measure. The LSMEP asks respondents about their mental condition with the Japanese version of the Kessler Psychological Distress Scale (K6). The K6 consists of six questions: “During the past 30 days, about how often did you feel nervous?,” “… feel hopeless?,” “… feel restless or fidgety?,” “… feel so depressed that nothing could cheer you up?,” “… feel that everything was an effort?,” and “… feel worthless?” Respondents rate from 0 (none of the time) to 4 (all of the time) for each question; the total score ranges from 0 (good) to 24 (bad). A dichotomous variable indicating depression symptoms takes 1 if the total score equals to 5 or higher, or 0 otherwise (Sakurai et al., 2011). To examine the mechanisms of the effect of retirement on health through health investment, usual health behaviors, specifically, exercise, smoking, and drinking, are also used as dependent variables in this chapter. All behaviors are applied as dichotomous variables. Exercise takes 1 if the respondents had done medium-intensity exercise at the survey time, or 0 if they had not.4 Smoking takes 1 if the respondent smoked at the survey time, or 0 if they did not smoke. Drinking takes 1 if the respondents consumed alcohol at least a few times in a month, or 0 if they had consumed less alcohol than that. Respondents are defined as retirees if they have no paid work at the survey time. In this chapter, retirement is distinguished into two phases; short-term dummy takes 1 if respondents retired between the last and the present survey, or 0 otherwise. Short term in this book is defined as a year or less. The long-term dummy takes 1 if respondents retired before the last survey, or 0 otherwise. As instruments in the IV approach, eligibility for partial benefit (EPB) and eligibility for full benefit (EFB) for public employee pension are used here.5 The EPB takes 1 if the respondent’s age is equal to or older than the eligibility age for earningsrelated benefit, or 0 otherwise. The EFB takes 1 if the respondent’s age is equal to or older than the eligibility age for flat-rate benefit, or 0 otherwise. The eligibility for pension benefits is considered to be useful as an instrument in the IV estimation because it is institutionally fixed and expected to affect the decision to retire but not health directly (Oshio & Kan, 2017).

4 Medium-intensity 5 See

exercise is an exercise that produces slight shortness of breath. Chapter 1 for more details.

32

2 Short- and Long-Term Effects of Retirement on Health

2.2.2 Health Change Comparison Before and After Retirement To see the health change in old age or considering the distance from retirement, two figures are illustrated here. Figure 2.1 illustrates the relationship between age and the ratio of poor health condition for non-retirees and retirees using the sample used in the estimation. All retirees’ health measures are basically worse than those of non-retirees. The difference is substantial before the age of 55, and then the difference becomes smaller but is still large at ages 55–60. After the age of 60, which is a typical age of mandatory retirement, the difference becomes much smaller than before the age of 60. These facts indicate that retirement at an earlier age may be the result of poor health condition, which leads to a reverse causality problem in the simple estimation for the effect of retirement on health. As illustrated in Chapter 1, since many individuals begin to retire from the age of 60, the impact of reverse causality on the estimation of the effect of retirement caused by early retirement seems to be inconsequential. However, this reverse causality may indeed exist. The IV approach using eligibility for pension benefit as an instrument can avoid the effect of reverse causality in the estimation.

Fig. 2.1 Age trajectory of health for non-retirees and retirees (Note Analytic sample is used. Source LSMEP 2005–2015)

2.2 How Short- and Long-Term Effects Are Examined

33

Next, the relationship between poor health condition and the years before/after retirement is illustrated in Fig. 2.2. The ratio of poor health condition and its 95% confidence interval is depicted in the figure. The sample used here is restricted to those who experienced retirement during the survey period. Negative (positive) numbers of years indicate years to (from) retirement, and zero year indicates the retirement year. The figure demonstrates that all three health conditions are declining prior to retirement. The deterioration rate of self-rated health is the highest among the three measures. At retirement, a little jump toward a worse condition is found on difficulty with ADLs, while the jump in any direction is unclear for self-rated health and depression. Post-retirement, all health conditions show little change for 4–5 years. Following this, while self-rated health and difficulty with ADLs deteriorate with time, depression shows improvement with time. These results suggest that investigating the effects of retirement by its duration is necessary to capture the accurate effect of retirement on health.

Fig. 2.2 Change in health before/after retirement (Note Negative [positive] numbers of years indicate before [after] retirement and zero year indicates the retirement year. Of the analytic sample, only those who experienced retirement are used. Source LSMEP 2005–2015)

34

2 Short- and Long-Term Effects of Retirement on Health

2.2.3 Estimation Method To compare the results, the average effect of retirement is estimated prior to the estimation for short- and long-term effects. The outcome equation for the average effect of retirement is defined as follows: Hit = Xit β + δ Rit + μi + λt + εit ,

(2.2)

where H is one of the health measures or health behaviors and the subscripts i and t indicate individual and time, respectively. X is a vector of covariates including age and age squared. Since there is no reason to believe that discrete age threshold for pension eligibility should affect health directly beyond the quadratic age trend, the covariates include age and age squared only. μ is an individual fixed effect, λ is a time fixed effect, ε is an idiosyncratic error term. For the time fixed effect, a wave dummy is used; that is, Eq. (2.2) is the fixed effects (FE) model. R is a retirement dummy that takes 1 if respondents are retired, or 0 otherwise; thus, the estimate δ provides an average effect of retirement. Equation (2.2) is estimated as a linear probability model for ease of interpretation (Coe & Zamarro, 2011; Oshio & Kan, 2017). In the first-stage estimation of the IV approach, R is regressed on the instruments and covariates as the FE model. Then, a predicted value of retirement is calculated using the first-stage result. In the second stage, Eq. (2.2), including the predicted value R instead of R, is estimated as the FE model; that is, the fixed effects instrumental variables (FE-IV) model is the main model applied here. Since the variation of R is provided by the exogenous (to the error term) variable, the estimate of retirement is consistent. The Negative sign for the estimate of δ indicates health improvement. This chapter also examines the short- and long-term effects of retirement using the following equation:





Hit = Xit β + δ1 Rs it + δ2 Rl it + μi + λt + εit ,

(2.3)

where Rs is a short-term dummy and Rl is a long-term dummy as explained above. To address the endogeneity problem of retirement, the IV approach is applied to this model as well. In the first-stage estimation of the IV approach, Rs and Rl are separately regressed on the instruments and covariates as the FE model. Then, predicted values of retirements are calculated using the first-stage results. In the second stage, Eq. (2.3), including the predicted values Rs and Rl instead of Rs and Rl, is estimated as the FE model. The next section presents the results of ordinary least squares (OLS) estimation, ignoring both individual fixed effects and the endogeneity of retirement, and the results of the FE estimation, partly ignoring the endogeneity of retirement, to compare the difference in the effect of retirement by estimation methods. 



2.3 Average Effect of Retirement

35

Table 2.2 Summary statistics by retirement status Non-retirees Observations

Retirees Mean

Observations

Mean

Health measures Poor self-rated health

77,312

0.1565

13,621

0.2373

Difficulty with ADLs

75,657

0.0611

13,278

0.1362

Depression

75,192

0.2489

13,365

0.2699

EPB

77,912

0.3588

13,733

0.8911

EFB

77,912

0.1173

13,733

0.5326

Instruments

Demographics Age

77,912

58.302

13,733

63.190

Men

77,912

0.7633

13,733

0.6578

More than high school

76,329

0.3946

13,608

0.3440

White collar

77,139

0.6566

13,504

0.6604

2.3 Average Effect of Retirement 2.3.1 Summary Statistics This section presents the estimation results of the average effect of retirement. Table 2.2 shows the summary statistics of the variables by retirement status. As for health conditions, for all health measures, retirees show worse conditions than non-retirees. 15.7% of non-retirees report poor self-rated health, compared to 23.7% of retirees; 6.1% of non-retirees report difficulty with ADLs, compared to 13.6% of retirees. However, the difference in depression is rather small, 24.9% of non-retirees and 27.0% of retirees report depression. In terms of the instruments, the ratio of those who are eligible for partial/full benefit is higher among retirees than non-retirees. Among non-retirees, 35.9% and 11.7% of them are eligible for partial and full benefit, respectively, while among retirees, 89.1% and 53.3% of them are eligible for those, respectively. While demographic variables other than age are not used as independent variables in this chapter, estimation on subsamples by gender, education, and initial (at the first wave) job characteristics is performed. Thus, the summary statistics of the demographics are also shown in the table. The ratios of men are 76.3% and 65.8% for non-retirees and retirees, respectively. The ratio of highly educated individuals is higher among non-retirees, at 39.5%, than retirees, at 34.4%. The difference in the initial job characteristics is not much large, 65.7% of non-retirees had a white-collar job and 66.0% of retirees had white-collar jobs.6 6 In

this study, white collar includes administrative and managerial workers; professional and engineering workers; clerical workers; and sales workers. As another category, blue collar includes

36

2 Short- and Long-Term Effects of Retirement on Health

2.3.2 Estimation Results of Average Effect of Retirement Estimation results of the effect of retirement for Eq. (2.2) by OLS, FE, and FE-IV are presented in Tables 2.3–2.5. Table 2.3 presents the results for poor self-rated health. The results of OLS and FE indicate that retirees have 9.2% and 2.7% higher probability of reporting poor health than non-retirees. By controlling the individual fixed effects, the magnitude of the estimate of FE becomes much smaller than that of OLS, suggesting that unobserved individual characteristics cause a positive bias in OLS. For FE-IV, the result of the first-stage estimation shows that both instruments significantly increase the probability of retirement. The EPB increases the probability of retirement by 7.7% points and the EFB increases it by 8.7% points. F-statistics for the instruments, 189.51, is well above the rule of thumb of 10 for the critical value for instrument relevance by Stock et al. (2002). This confirms that the results by FE-IV are reliable. In the second stage, the estimate of retirement shows a negative sign; however, the magnitude of the estimate is small and not statistically different from zero. While the effect of retirement is not significant, the sign of estimate turns from positive in FE to negative in FE-IV. This suggests that positive endogeneity bias caused by reverse causality or unobserved time-varying shock is consequential, even after controlling individual fixed effects by FE. The results for difficulty with ADLs are presented in Table 2.4. The results of Table 2.3 Average effect of retirement on poor self-rated health OLS

FE

FE-IV

Retirement

0.0919*** (0.0078)

0.0271*** (0.0058)

Age

0.0374*** (0.0106)

0.0136 (0.0095)

− 0.2167*** (0.0123)

0.0033 (0.0164)

Age squared / 100

− 0.0321*** (0.0091)

− 0.0044 (0.0079)

0.2107*** (0.0112)

0.0056 (0.0151)

1st stage

2nd stage − 0.0052 (0.0415)

EPB

0.0767*** (0.0048)

EFB

0.0872*** (0.0065)

Observations

90,933

90,933

90,933

90,933

Individuals

10,694

10,694

10,694

10,694

F-statistics for instruments

189.51

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1 service workers; security workers; agriculture, forestry, and fishery workers; transport and machine operation workers; manufacturing process workers; and other workers.

2.3 Average Effect of Retirement

37

Table 2.4 Average effect of retirement on difficulty with ADLs OLS

FE

FE-IV 1st stage

2nd stage

Retirement

0.0659*** (0.0062)

0.0302*** (0.0046)

Age

0.0036 (0.0076)

− 0.0111 (0.0070)

− 0.2154*** (0.0123)

− 0.0128 (0.0130)

0.0169** (0.0059)

0.2089*** (0.0112)

0.0185 (0.0121)

Age squared / 100 − 0.0007 (0.0067)

0.0251 (0.0328)

EPB

0.0777*** (0.0048)

EFB

0.0875*** (0.0065)

Observations

88,935

88,935

88,935

88,935

Individuals

10,646

10,646

10,646

10,646

F-statistics for instruments

191.36

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1

OLS and FE indicate that retirees have 6.6% and 3.0% higher probability of having difficulty with ADLs than non-retirees. By controlling the individual fixed effects, the estimate of FE becomes much smaller than OLS, as with the estimation on self-rated health, suggesting that unobserved individual characteristics cause a positive bias in the simple estimation of difficulty with ADLs too. For the FE-IV model, the results of the first stage show that both instruments significantly increase the probability of retirement, and the F-statistics for the instruments, 191.36, is well above the rule of thumb of 10. In the second stage, the estimate of retirement shows a positive but is imprecisely estimated. While the effect of retirement is not significant, the magnitude of the estimate of retirement becomes smaller from those of OLS and FE, suggesting that positive endogeneity bias is consequential. Finally, Table 2.5 presents the results for depression. The results of OLS indicate that retirees are 5.9% more likely to report depression than non-retirees. After controlling the individual fixed effects, the estimate of retirement by FE becomes much smaller than that of OLS and is not significantly different from zero. As with the other health measures, the result shows that unobserved individual characteristics cause a positive bias in the OLS estimation. For the FE-IV model, the results of the first stage show that both instruments significantly increase the probability of retirement, and the F-statistics for the instruments, 192.37, is well above the rule of thumb of 10. In the second stage, retirees have a 7.7% lower probability of reporting depression. The negative sign of retirement indicates that positive endogeneity bias caused by reverse causality or unobserved time-varying shock cannot be ignored for depression, even after controlling individual fixed effects.

38

2 Short- and Long-Term Effects of Retirement on Health

Table 2.5 Average effect of retirement on depression OLS

FE

FE-IV 1st stage

2nd stage − 0.0773* (0.0451)

Retirement

0.0590*** (0.0083)

0.0061 (0.0063)

Age

0.0013 (0.0121)

0.0276** (0.0105)

− 0.2144*** (0.0124)

0.0011 (0.0176)

0.0001 (0.0086)

0.2089*** (0.0112)

0.0258 (0.0161)

Age squared / 100 − 0.0111 (0.0103) EPB

0.0779*** (0.0048)

EFB

0.0881*** (0.0066)

Observations

88,557

88,557

88,557

88,557

Individuals

10,611

10,611

10,646

10,611

F-statistics for instruments

192.37

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1

2.4 Short- and Long-Term Effects of Retirement 2.4.1 Main Results In this section, the estimation results for the short- and long-term effects of retirement on health and health behavior are presented. Table 2.6 shows the health condition Table 2.6 Health condition by retirement phases

Observations

Mean

Non-retirees

77,312

0.1565

Short term

3,152

0.2398

Long term

10,469

0.2365

Non-retirees

75,657

0.0611

Short term

3,080

0.1377

Long term

10,198

0.1358

Non-retirees

75,192

0.2489

Short term

3,071

0.2797

Long term

10,294

0.2670

Poor self-rated health

Difficulty with ADLs

Depression

2.4 Short- and Long-Term Effects of Retirement Table 2.7 First-stage results for the estimation of poor self-rated health

39 Short term

Long term

EPB

0.0575*** (0.0026)

0.0192*** (0.0040)

EFB

0.0187*** (0.0042)

0.0685*** (0.0065)

Age

0.0739*** (0.0061)

− 0.2906*** (0.0122)

Age squared / 100

− 0.0521*** (0.0053)

0.2628*** (0.0112)

Observations

90,933

90,933

Individuals

10,694

10,694

Sanderson-Windmeijer F

284.08

87.10

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1

of non-retirees, short-term retirees (being retired 0–1 year), and long-term retirees (being retired 1 + years). Health conditions of retirees are worse than non-retirees, while the difference in depression is quite small. Among retirees, health reveals a slightly worse condition in the short term than in the long term, as 24.0% of retirees report poor self-rated health in the short term, whereas 23.7% report poor condition in the long term. The results indicate that health deteriorates at retirement, however, is preserved or slightly improved during long retirement life. The results of the first-stage estimation for poor self-rated health are presented in Table 2.7. Since the results for the other two health measures are almost the same, as shown in Tables 2.3–2.5, they have not been presented here. The instruments significantly increase the probability of both retirement phases. The EPB has a larger effect on the short term than the EFB, the marginal effects are 5.8% and 1.9%, respectively. In contrast, the EPB has a smaller effect on the long term than the EFB, the marginal effects are 1.9% and 6.9%, respectively. The difference in the marginal effects seems to be reasonable. As for the instrument relevance of an individual endogenous regressor, since the standard F-statistics is no longer sufficient in case that multiple endogenous regressors are used, the weak identification is tested by Sanderson and Windmeijer F-statistics (Sanderson & Windmeijer, 2016). All Sanderson and Windmeijer Fstatistics shown in the last row in the table, 284.08 and 87.10, are well above the critical value of 10 (Stock et al., 2002). Thus, the instruments are not weak for each endogenous variable. The results indicate that the first-stage mechanism is properly captured and thus the second-stage results are reliable. Table 2.8 presents the result of short- and long-term effects for three health measures. Weak identification F-statistics (Kleibergen-Paap, 2006) for multiple endogenous regressors in each equation, 48.472, 45.597, and 45.307, are well above

40

2 Short- and Long-Term Effects of Retirement on Health

Table 2.8 Short- and long-term effects on health Poor self-rated health

Difficulty with ADLs

Depression

Short-term effect

− 0.1490* (0.0760)

0.0674 (0.0546)

− 0.3219*** (0.0864)

Long-term effect

0.1676* (0.0865)

− 0.0258 (0.0655)

0.2221** (0.0964)

Age

0.0723** (0.0340)

− 0.0331 (0.0256)

0.1205*** (0.0381)

Age squared / 100

− 0.0545* (0.0300)

0.0363 (0.0228)

− 0.0788** (0.0337)

Observations

90,933

88,935

88,557

Individuals

10,694

10,646

10,611

Kleibergen-Paap Fa

48.472

45.597

45.307

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. a Stock and Yogo (2005) weak identification test critical values: 10% maximal IV size 7.03; 15% maximal IV size 4.58; 20% maximal IV size 3.95; 25% maximal IV size 3.63

the critical value (Stock & Yogo, 2005), once again indicating that the instruments are not weak. Retirees have 14.9% and 32.2% lower probability of reporting poor self-rated health and depression than non-retirees in the short term, respectively. For the difficulty with ADLs, retirees have no significant difference with non-retirees in the short term. In the long term, contrary to the short-term effect, retirees have 16.8% and 22.2% higher probability of reporting poor self-rated health and depression, respectively. However, retirees have no significant difference in difficulty with ADLs in the long term. As described in the Introduction in this chapter, change in health behavior caused by retirement is a possible source of change in health. Thus, the effects of retirement on exercise, smoking, and drinking are examined here. Table 2.9 presents the estimation results. Weak identification F-statistics for multiple endogenous regressors in each estimation are well above the critical value. Short-term retirees are 43.0% more likely to exercise than non-retirees; however, no significant difference in smoking or drinking is found. In contrast, long-term retirees are 14.9% more likely to smoke, whereas there is no significant difference in exercise or drinking. These results indicate that health investments decrease or do not increase following retirement in the long term. This might cause health deterioration in the long term.

2.4 Short- and Long-Term Effects of Retirement Table 2.9 Short- and long-term effects on health behavior

41 Exercise

Smoking

Drinking

Short-term effect

0.4302*** (0.0994)

0.0563 (0.0642)

0.0439 (0.0572)

Long-term effect

0.0883 (0.1144)

0.1494** (0.0639)

− 0.0171 (0.0622)

Age

− 0.0217 (0.0451)

0.0679*** (0.0260)

0.0245 (0.0247)

Age squared / 100

0.0331 (0.0399)

− 0.0785*** (0.0230)

− 0.0274 (0.0219)

Observations

90,151

91,331

90,641

Individuals

10,640

10,689

10,676

Kleibergen-Paap Fa

47.087

48.766

48.889

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. a Stock and Yogo (2005) weak identification test critical values: 10% maximal IV size 7.03; 15% maximal IV size 4.58; 20% maximal IV size 3.95; 25% maximal IV size 3.63

2.4.2 Estimation on Subsamples: Gender, Education, and Occupational Characteristics The effect of retirement is potentially heterogeneous based on respondents’ characteristics. For example, the degree of contribution of work-related activity to health may differ by gender. If men’s health depends more on their work than women’s, the effect of retirement would be larger among men. The level of education potentially has a different effect of retirement on health. Specifically, highly educated retirees can produce health investment effectively or lower the rate of health deterioration. Thus, highly educated retirees are less likely to have a detrimental health effect from retirement. Occupational characteristics may also have a different effect on retirement health. For example, those who had a mentally or physically demanding job would experience a larger impact from retirement than those who did not. Thus, the estimation of subsamples by gender, education, and occupational characteristics is performed here.

42

2 Short- and Long-Term Effects of Retirement on Health

Table 2.10 presents the results by gender; the top panel for men and the bottom panel for women. Weak identification F-statistics for multiple endogenous regressors in each equation are well above the critical value. Short-term male retirees are 28.4% less likely to report depression than non-retirees, but there is no significant difference in poor self-rated health and difficulty with ADLs. Long-term retirees have 66.1% and 68.3% higher probability of reporting poor self-rated health and depression, respectively; however, there is no significance in difficulty with ADLs. Short-term female retirees are 20.3% more likely to have difficulty with ADLs but have no significant difference in poor self-rated health or depression. Long-term female retirees have no significant difference in any of the health measures. Table 2.11 presents the results by education; the top panel represents those with more than high school education, the bottom panel represents those with high school Table 2.10 Short- and long-term effects on health by gender Poor self-rated health

Difficulty with ADLs

Depression

Short-term effect

− 0.0633 (0.0892)

− 0.0334 (0.0546)

− 0.2840*** (0.0952)

Long-term effect

0.6614** (0.2605)

− 0.0418 (0.1608)

0.6826** (0.2831)

Age

0.2484** (0.0972)

− 0.0392 (0.0609)

0.2772*** (0.1067)

Age squared/100

− 0.2151** (0.0879)

0.0456 (0.0551)

− 0.2268** (0.0966)

Observations

67,971

66,600

66,314

Individuals

8,104

8,065

8,039

Kleibergen-Paap Fa

9.711

9.177

8.955

Short-term effect

0.0210 (0.0955)

0.2028** (0.0807)

− 0.1130 (0.1132)

Long-term effect

− 0.0573 (0.0884)

0.0522 (0.0740)

0.0443 (0.1003)

Age

0.0038 (0.0382)

− 0.0138 (0.0316)

0.0901** (0.0428)

Age squared/100

0.0014 (0.0332)

0.0072 (0.0276)

− 0.0412 (0.0368)

Observations

22,962

22,335

22,243

Individuals

2,590

2,581

2,572

Kleibergen-Paap Fa

30.317

31.090

29.180

Men

Women

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. a Stock and Yogo (2005) weak identification test critical values: 10% maximal IV size 7.03; 15% maximal IV size 4.58; 20% maximal IV size 3.95; 25% maximal IV size 3.63

2.4 Short- and Long-Term Effects of Retirement

43

Table 2.11 Short- and long-term effects on health by education Poor self-rated health

Difficulty with ADLs

Depression

Short-term effect

− 0.2432* (0.1401)

0.0816 (0.0989)

− 0.4344*** (0.1599)

Long-term effect

0.2723** (0.1197)

− 0.0787 (0.0868)

0.2344* (0.1359)

Age

0.1112** (0.0448)

− 0.0479 (0.0327)

0.1409*** (0.0518)

Age squared/100

− 0.0926** (0.0399)

0.0465 (0.0293)

− 0.1003** (0.0462)

Observations

34,556

33,986

34,177

Individuals

3,735

3,735

3,734

26.574

28.378

25.395

Short-term effect

− 0.1044 (0.0934)

0.0384 (0.0672)

− 0.2907*** (0.1060)

Long-term effect

0.0968 (0.1213)

0.0279 (0.0939)

0.2367* (0.1351)

Age

0.0520 (0.0490)

− 0.0089 (0.0377)

0.1240** (0.0547)

Age squared/100

− 0.0350 (0.0431)

0.0171 (0.0333)

− 0.0805* (0.0481)

Observations

54,688

53,352

52,826

Individuals

6,062

6,059

6,047

Kleibergen-Paap Fa

24.345

22.798

22.412

More than high school

Kleibergen-Paap

Fa

High school or less

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. a Stock and Yogo (2005) weak identification test critical values: 10% maximal IV size 7.03; 15% maximal IV size 4.58; 20% maximal IV size 3.95; 25% maximal IV size 3.63

education or less. Weak identification F-statistics for multiple endogenous regressors in each equation are well above the critical value. For those with more than high school education, short-term retirees have 24.3% and 43.4% lower probability of reporting poor self-rated health and depression than non-retirees, respectively, but have no significant difference in difficulty with ADLs. Long-term retirees with more than high school education have 27.2% and 23.4% higher probability of reporting poor self-rated health and depression, respectively, but have no significant difference in difficulty with ADLs. For those with high school education or less, short-term retirees are 29.1% less likely to report depression than non-retirees but have no significant difference in poor self-rated health or difficulty with ADLs. Long-term retirees with high school education or less are 23.7% more likely to report depression but have no significant difference in poor self-rated health or difficulty with ADLs.

44

2 Short- and Long-Term Effects of Retirement on Health

Table 2.12 shows the results based on the pre-retirement job that the respondents had at the first wave; the top panel is for white collar and the bottom panel is for blue collar. Weak identification F-statistics for multiple endogenous regressors in each equation are well above the critical value. Short-term retirees who had a white-collar job are 40.9% less likely to report depression than non-retirees but have no significant difference in poor self-rated health or difficulty with ADLs. Long-term white-collar retirees are 25.2% more likely to report depression but have no significant difference in poor self-rated health or difficulty with ADLs. Short-term retirees who had a blue-collar job are 27.8% less likely to report poor self-rated health but have no significant difference in difficulty with ADLs or depression. Among long-term bluecollar retirees, there is no significant difference in any of the health measures. Table 2.12 Short- and long-term effects on health by initial job Poor self-rated health

Difficulty with ADLs

Depression

Short-term effect

− 0.0958 (0.0917)

0.0921 (0.0645)

− 0.4093*** (0.1063)

Long-term effect

0.1620 (0.1010)

0.0020 (0.0758)

0.2521** (0.1191)

Age

0.0666* (0.0403)

− 0.0212 (0.0300)

0.1334*** (0.0475)

Age squared/100

− 0.0485 (0.0358)

0.0226 (0.0268)

− 0.0931** (0.0421)

Observations

59,124

58,071

58,096

Individuals

6,839

6,812

6,802

Kleibergen-Paap F

32.658

32.098

30.445

Short-term effect

− 0.2782** (0.1383)

0.0094 (0.1015)

− 0.1382 (0.1520)

Long-term effect

0.1878 (0.1651)

− 0.0604 (0.1244)

0.1332 (0.1635)

Age

0.0933 (0.0628)

− 0.0468 (0.0476)

0.0974 (0.0638)

Age squared/100

− 0.0735 (0.0554)

0.0548 (0.0422)

− 0.0517 (0.0563)

Observations

30,824

29,921

29,531

White collar

Blue collar

Individuals

3,731

3,712

3,686

Kleibergen-Paap F

16.319

16.312

15.408

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. Stock and Yogo (2005) weak identification test critical values: 10% maximal IV size 7.03; 15% maximal IV size 4.58; 20% maximal IV size 3.95; 25% maximal IV size 3.63

2.5 Conclusion

45

2.5 Conclusion This chapter focused on the different effects of retirement on health based on the duration of retirement. Short- and long-term effects of retirement, as well as a conventional average effect, are examined using a nationally representative longitudinal survey, LSMEP 2005–2015, conducted in Japan. Causal estimation exploiting the longitudinal structure of the data and the IV approach provides new evidence on the effect of retirement. With regard to the average effect, the methods that do not fully address the endogeneity problem of retirement, the OLS and FE estimation, show a significant and adverse effect on health. In contrast, the FE-IV estimation, which fully addresses the endogeneity problem, reveals a beneficial or no significant impact on health. Change in the magnitude and sign of estimates between methods clearly show that simple estimation suffers from endogeneity bias, leading to an incorrect implication. Causal estimation of the average effect on health indicates that retirement is beneficial or not detrimental for health. As for the different effects based on the duration of retirement, examination of short- and long-term effects demonstrates the impacts in opposite directions by the FE-IV estimation. The short-term effect shows a beneficial impact on self-rated health and depression, whereas the long-term effect shows an adverse impact on those health measures. Moreover, retirement slightly increases healthy behavior in the short term but decreases it in the long term. Notably, as suggested in the health capital model, increased healthy behavior improves retirees’ health in the short term; however, decreased healthy behavior diminishes retirees’ health in the long term. With regard to the difficulty with ADLs, no significant impact of either effect is found, indicating physical health is less likely to be influenced by the advent of retirement. The opposite impacts between short- and long-term effects may cause a nonsignificant or small effect in the estimation on the average effect; that is, short- and long-term effects are offset. The finding that the short- and long-term effects have opposite impacts is in line with Gorry et al. (2018); however, the direction of the impact differs from their findings. This chapter uses the same data as Oshio and Kan (2017), confirming the same beneficial short-term effect. However, while they find that retirement decelerates the rate of health deterioration, this chapter finds that retirement worsens health in the long term. This inconsistent result might be caused by the sample composition, since they included part-time employees and selfemployed workers in their analytic sample and analyzed only those who experienced retirement. The adverse impact of long-term effect is in line with Heller-Sahlgren (2017) and Mazzonna and Peracchi (2017), however, contradicts the assertions of Insler (2014) and Gorry et.al. (2018). The latter conflicting result might be caused by the difference in the characteristics of labor market or social security system between Japan and the US. However, from the perspective of the health capital model, the result of this chapter and the results of Insler (2014) and Gorry et.al. (2018) are consistent. That

46

2 Short- and Long-Term Effects of Retirement on Health

is, Insler (2014) finds a beneficial impact of retirement on both health behavior and health, whereas this chapter finds an adverse impact of retirement on both health behavior and health in the long term, which means that we have potentially the same mechanism linking retirement and health through health behavior. The results above indicate that the average effect has a beneficial or non-significant impact on health, whereas the long-term effect has an adverse impact. The implications of these results are substantially different. The result of the average effect of retirement suggests that the delay of retirement timing is not beneficial for the stability of social security, whereas the result of the long-term effect of retirement suggests that prolonged working life is beneficial. Thus, distinguishing retirement’s effect into short- and long-term is crucial for advancing well-informed and accurate policymaking. In this chapter, estimation on subsamples was also performed to examine the heterogeneous effect of individual characteristics. As for gender, the beneficial impact of the short-term effect on depression and the adverse impact of the longterm effect on self-rated health and depression are observed among men, whereas women are barely influenced by retirement. In terms of education, the difference in the effect of retirement on health is not much large; however, the health of those with high education is more likely to deteriorate by retirement in the long term than less-educated counterparts. This result is contrary to the prediction. One possible explanation is that highly educated individuals might decrease health investment substantially because they do not have to keep high earnings (productivity). This impact of retirement is also observed in terms of occupational characteristics. The health of those who had white-collar jobs deteriorates more than those who had blue-collar jobs in the long term. Although undoubtedly more detailed investigation is called for, these results indicate that the effect of retirement varies based on individual characteristics and suggests that more refined and responsive labor and health policy are required. This chapter has some limitations. First, the effect of retirement estimated in this chapter is the Local Average Treatment Effect (LATE), but not the Average Treatment Effect on the Treated (ATET) (Angrist & Pishcke, 2009). That is, the LATE is the results of compliers who retired because they reached the pension eligibility age, but not that of all retirees. Second, this chapter only uses respondents who had a full-time work at the first wave, but not the entire workforce. The number of part-time workers has been increasing in Japan as well as other developed economies. The effect of retirement for such workers will become more important in the future.

References Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton University Press. Atchley, R. (1989). A continuity theory of aging. The Gerontologist, 29, 183–190.

References

47

Case, A., & Deaton, A. S. (2005). Broken down by work and sex: how our health declines. In D. A. Wise (Ed.), Analysis in the Economics of Aging (pp. 185–212). University of Chicago Press. Coe, N. B., & Zamarro, G. (2011). Retirement effects on health in Europe. Journal of Health Economics, 30(1), 77–86. Fé, E., & Hollingsworth, B. (2016). Short- and long-run estimates of the local effects of retirement on health. Journal of the Royal Statistical Society Series A, 179(4), 1051–1067. Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80(2), 223–255. Gorry, A., Gorry, D., & Slavov, S. N. (2018). Does retirement improve health and life satisfaction? Health Economics, 27(12), 2067–2086. Heller-Sahlgren, G. (2017). Retirement blues. Journal of Health Economics, 54, 66–78. Insler, M. (2014). The health consequences of retirement. Journal of Human Resources, 49(1), 195–233. Kleibergen, F., & Paap, R. (2006). Generalized reduced rank tests using the singular value decomposition. Journal of Econometrics, 133(1), 97–126. Mazzonna, F., & Peracchi, F. (2017). Unhealthy retirement? Journal of Human Resources, 52(1), 128–151. Muurinen, J.-M. (1982). Demand for health: A generalised Grossmann model. Journal of Health Economics, 1(1), 5–28. Oshio, T., & Kan, M. (2017). The dynamic impact of retirement on health: Evidence from a nationwide ten-year panel survey in Japan. Preventive Medicine, 100, 287–293. Sakurai, K., Nishi, A., Kondo, K., Yanagida, K., & Kawakami, N. (2011). Screening performance of K6/K10 and other screening instruments for mood and anxiety disorders in Japan. Psychiatry and Clinical Neurosciences, 65(5), 434–441. Sanderson, E., & Windmeijer, F. (2016). A weak instrument F-test in linear IV models with multiple endogenous variables. Journal of Econometrics, 190(2), 212–221. Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics, 20(4), 518–529. Stock, J. H., & Yogo, M. (2005). Testing for weak instruments in linear IV regression. In D. W. K. Andrews & J. H. Stock (Eds.), Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg (pp. 80–108). Cambridge University Press.

Chapter 3

Does Lifestyle Prior to Retirement Matter?

Abstract Lifestyle before retirement can be crucial to post-retirement health; however, few studies have paid attention to it. The health capital model suggests that maintaining a healthy and active lifestyle prior to retirement mitigates the adverse health effect of retirement. To address this gap, this chapter examines the heterogeneous effect of retirement on health, examining the involvement of health behavior and usual activity prior to retirement. The causal estimation using the instrumental variables (IV) approach indicates that a healthy and active lifestyle prior to retirement is key to preserving post-retirement health in the long term. Specifically, with regard to health behavior, retirement significantly worsens health for those who did not engage in exercise prior to retirement, while it has no significant effect for those who engaged in it. A similar effect is found in terms of alcohol consumption; retirement significantly worsens health for those who often consumed alcohol but has no significant impact on those who rarely consumed alcohol. However, there is no clear difference between smokers and non-smokers; retirement is shown to significantly worsen health for both smokers and non-smokers. As for usual activity, contrary to the prediction, retirement significantly diminishes the health of those who participated in leisure and social activity but improves the health of those who did not participate in these activities. The effect of retirement does not differ in terms of interaction with neighbors. As for housework, retirement worsens health among those who had not been involved in housework but does not worsen health among those who engaged in housework. The findings of this chapter indicate that participation in healthy behavior and in-home activity seems to be important to preserving health in later life. Keywords Exercise · Smoking · Drinking · Social participation · Leisure activity · Interaction with neighbors · Housework · Retirement · Health

3.1 Introduction Several studies have examined the effect of retirement on health behavior as well as the effect of retirement on health, seeking mechanisms linking retirement and health. For example, Insler (2014) finds a beneficial effect of retirement on health behaviors, specifically in terms of vigorous activity and smoking in addition to health condition, © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Mizuochi, Exploring the Effect of Retirement on Health in Japan, Population Studies of Japan, https://doi.org/10.1007/978-981-16-2638-8_3

49

50

3 Does Lifestyle Prior to Retirement Matter?

concluding that these health behaviors may affect health. Eibich (2015) and Oshio and Kan (2017) also find a favorable retirement effect on health behaviors, such as smoking, drinking, and exercise in addition to health condition, thereby supporting Insler’s (2014) finding. Chapter 2 in this book also finds that retirement has an adverse effect on health behavior and health condition. These studies suggest that change in health condition by retirement is partly caused by the change in health behavior. In contrast, Atalay and Barrett (2014) find no effect of retirement on health behavior, although they reveal a positive impact of retirement on health. In sum, the literature has identified a possible link between retirement, health condition, and health behavior; however, the mechanism remains unclear. This also suggests that investigation about other potential links between retirement and health is required to further understand its mechanism. In this regard, few studies have paid attention to the lifestyle prior to retirement. Change in lifestyle, including health behavior, by retirement requires some time to affect the health condition. On the other hand, pre-retirement lifestyle could determine the efficiency of post-retirement health production in advance. Thus, the effect of pre-retirement lifestyle can be larger than that of change in health behavior or lifestyle following retirement. Lacking this perspective may lead to an insufficient understanding of the link between retirement and health. For instance, the effect of retirement on health might largely depend on whether respondents engaged in exercise, or whether they participated in social activity prior to retirement. Individual characteristics and circumstances of retirement can impact the effect of retirement (Behncke, 2012; Grøtting & Lillebø, 2020). In terms of the health capital model, if mental and physical activities of work are the primary form of health investment prior to retirement, healthy and active pre-retirement life enables individuals to secure another source of health investment and thus to maintain health production post-retirement. As a result, those who had a healthy and active lifestyle before retirement are likely to experience less adverse health effects after retirement. Additionally, through becoming accustomed to exercising or participating in social activity prior to retirement, individuals can possibly increase the productivity of their health investment prior to retirement. As a result, retirees can produce health investments more efficiently, thus preventing health decline. While there is no causal study investigating the role of pre-retirement lifestyle, some studies focus on occupational characteristics or income prior to retirement. For example, Mazzonna and Peracchi (2017) find that retirement improves overall and cognitive health for those who had highly physically demanding jobs but had no effect on those who had less laborious jobs. The literature on cognitive health also indicates that occupational characteristics are an important consideration in terms of cognitive function after retirement. Some studies have examined the relationship between occupational complexity and cognitive decline after retirement; however, the findings are contradictory. Specifically, the high complexity of occupation causes a slower cognitive decline (Fisher et al., 2014), a more rapid decline (Finkel et al., 2009; Hyun et al., 2019), and there is no significant association with cognitive decline (Andel et al., 2016). These results suggest that occupational complexity prior to retirement should be taken into account

3.1 Introduction

51

to understand the link between retirement and health. However, these studies do not fully address the endogeneity of retirement and the results might be biased. The effect of retirement on health usually suffers from the endogeneity problem due to the potential reverse causality of health to the decision to retire or the existence of unobserved confounders affecting both health and retirement. Obtaining a consistent estimate of retirement requires a rigorous causal estimation. Does pre-retirement lifestyle have an association with the effect of retirement on health? To confirm this simply, the relationship between time spent for leisure activities for those aged 15–64 and life expectancy at age 65 is examined here using country-level data from the Organization for Economic Co-operation and Development (OECD). The time spent for leisure activities includes socializing; attending cultural, entertainment, and sports events; engaging in hobbies, games, and other pastime activities; participating in sports and outdoor activities; using mass media; performing other leisure activities. Figure 3.1 illustrates a scatter plot of time spent

Fig. 3.1 Relationship between leisure activity of working-age people and health of older adults (Source Leisure time of people aged 15–64 from OECD data [1999–2016].1 Life expectancy at age 65 from OECD data [2015–2018]2 ) 1 OECD.Stat,

Time use (https://stats.oecd.org/Index.aspx?DataSetCode=TIME_USE#. Accessed November 26, 2020). 2 OECD iLibrary, Life expectancy at 65 (https://doi.org/10.1787/0e9a3f00-en. Accessed November 23, 2020).

52

3 Does Lifestyle Prior to Retirement Matter?

for leisure activities and life expectancy by gender. These two factors show a positive relationship, the longer people spend leisure time in their working age, the longer the life expectancy at old age becomes. The correlation coefficients are 0.324 and 0.294 for men and women, respectively; the relationship for men is slightly stronger than for women. The correlation coefficient for men is statistically significantly different from zero, while it is not significant for women. Since this relationship is of course not a causal relationship but just a correlation, a rigorous causal estimation should be performed. Based on the above background, this chapter examines the relationship between retirement and health from the perspective of lifestyle prior to retirement. In particular, this chapter focuses on the outcome of the long-term effect of retirement; its importance is already established in Chapter 2. In addition, to address the endogeneity problem of retirement, a rigorous causal estimation applying the instrumental variables (IV) approach is performed. The rest of this chapter is organized into three sections. Section 3.2 introduces the data, variables, and estimation method applied here and shows some descriptive results. Section 3.3 presents the estimation results for the effect of retirement on health based on involvement in health behavior and usual activities prior to retirement. Section 3.4 discusses the findings obtained in this chapter.

3.2 How to Estimate Different Effects of Retirement by Lifestyle Prior to Retirement 3.2.1 Data and Variables This chapter uses 11 waves (2005–2015) of the previously introduced Longitudinal Survey of Middle-aged and Elderly Persons (LSMEP).3 The LSMEP is a nationally representative survey in Japan. The sample used here is restricted in some ways. First, respondents who worked as full-time regular (called seiki in the questionnaire4 ) employees at the first wave are used. Since the impact of retirement for part-time employees and self-employed workers is considered to be substantially different from full-time regular employees, part-time employees and self-employed workers are excluded from the analytic sample. Second, respondents whose work status is continuously observed from the first wave to a certain wave are used to capture the accurate retirement duration. Third, respondents who reentered the labor force after full retirement are excluded, as the effect of withdrawal from and subsequent reentry into the labor force on health is not considered to be symmetrical. The final sample used in the estimation is at most 10,694 individuals.

3 See

Chapter1 for more details.

4 Seiki workers are basically those who work full-time with an undefined-term employment contract.

3.2 How to Estimate Different Effects …

53

Health measures include poor self-rated health, difficulty with activities of daily living (ADLs), and depression. Poor self-rated health is used as the overall health measure. Respondents are asked to rate their current health condition on a six-point Likert scale: very good, rather good, good, poor, rather poor, very poor. A dichotomous variable is generated indicating poor health, taking 1 if respondents’ health is poor/rather poor/very poor, or 0 otherwise. Difficulty with ADLs is used as a physical health measure. A dichotomous variable is generated, taking 1 if respondents answered that they have any difficulty among the following ten activities: walking, getting up out of bed or off floor, sitting down on and standing up from chair, taking clothes on and off, washing hands and face, eating, toileting, bathing, climbing and descending stairs, and carrying purchases; or 0 otherwise. Depression is used as a mental health measure. The LSMEP asks respondents about their mental condition with the Japanese version of the Kessler Psychological Distress Scale (K6). The K6 consists of six questions: “During the past 30 days, about how often did you feel nervous?,” “… feel hopeless?,” “… feel restless or fidgety?,” “… feel so depressed that nothing could cheer you up?,” “… feel that everything was an effort?,” and “… feel worthless?.” Respondents rate from 0 (none of the time) to 4 (all of the time) for each question; the total score ranges from 0 (good) to 24 (bad). A dichotomous variable indicating depression symptoms takes 1 if the total score equals to 5 or higher, or 0 otherwise (Sakurai et al., 2011). Respondents are defined as retirees if they have no paid work at the survey time. In this chapter, retirees are distinguished into two phases, as with Chapter 2. The short-term dummy takes 1 if respondents retired between the last and the present survey, or 0 otherwise; that is, the short term in this chapter indicates a year or less, and the long-term dummy takes 1 if respondents retired before the last survey, or 0 otherwise. As a measure of lifestyle before retirement, health behavior and usual activity at the first wave are used. As for health behavior, light-intensity exercise, mediumintensity exercise, smoking, and drinking are used. All health behaviors are divided into two groups. Both levels of exercise take 1 if the respondent engaged in exercise in the first wave, or 0 if they did not.5 Smoking takes 1 if the respondents smoked at the first wave, or 0 if they did not. Drinking takes 1 if the respondent consumed alcohol at least a few times in a month at the first wave, or 0 if they drank less than that. The LSMEP asks respondents about their usual activities. Among those questions, four activities that are considered to be crucial as a lifestyle prior to retirement are chosen here: participation in leisure activity,6 participation in social activity,7 interaction with neighbors, and involvement in housework. The dummy variables for these activities take 1 if respondents participated in it at the first wave, or 0 otherwise. 5 Light-intensity

exercise is an exercise that does not produce shortness of breath; for example, stretching or light-intensity gymnastics. Medium-intensity exercise is an exercise that produces slight shortness of breath; for example, walking or jogging. 6 Leisure activity includes hobby and intellectual activity. 7 Social activity includes, for example, community events, volunteer work, or support for elderly people.

54

3 Does Lifestyle Prior to Retirement Matter?

Table 3.1 Summary statistics

Observations

Mean

Poor self-rated health

90,933

0.1686

Difficulty with ADLs

88,935

0.0723

Depression

88,557

0.2521

Engagement in light-intensity exercise

87,737

0.3228

Engagement in medium-intensity exercise

87,852

0.2850

Smoking

91,213

0.3699

Drinking

Health measures

Lifestyle measures

90,854

0.6583

Participation in leisure activity

88,168

0.6613

Participation in social activity

88,168

0.2216

Interaction with neighbors 88,168

0.5742

Involvement in housework 88,168

0.5408

Instruments EPB

91,645

0.4386

EFB

91,645

0.1795

Demographics Age

91,645

59.035

Male

91,645

0.7475

More than high school

89,937

0.3869

White collar

90,643

0.6571

As the instruments in the IV approach, eligibility for partial benefit (EPB) and eligibility for full benefit (EFB) for public pension are used here.8 The EPB takes 1 if the respondent’s age is equal to or older than the eligibility age for earnings-related benefit, or 0 otherwise. The EFB takes 1 if the respondent’s age is equal to or older than the eligibility age for flat-rate benefit, or 0 otherwise. The eligibility for pension benefits is considered to be useful as an instrument in the IV estimation because it is institutionally fixed and expected to affect the decision to retire but not health directly (Oshio & Kan, 2017). Table 3.1 presents the summary statistics of the analytic sample. Among the health behaviors, the ratio who consumed alcohol, 65.8%, is the highest, and those who had engaged in medium-intensity exercise, 28.5%, is the lowest. As for the usual activities, the participation rate for leisure activity, 66.1%, is the highest and that for social activity, 22.2%, is the lowest. 8 See

Chapter 1 for more details.

3.2 How to Estimate Different Effects …

55

3.2.2 Estimation Method As presented in Chapter 2, dividing retirement into short and long terms is crucial to correctly understand the mechanism linking retirement and health. The outcome equation is defined as follows: Hit = Xit β + δ1 Rs it + δ2 Rl it + μi + λt + εit ,

(3.1)

where H is one of the health measures and subscripts i and t indicate individual and time, respectively. X is a vector of covariates including age and age squared. Since there is no reason to believe that discrete age threshold for pension eligibility should affect health directly beyond the quadratic age trend, the covariates include age and age squared only. μ is an individual fixed effect, λ is a time fixed effect, ε is an idiosyncratic error term. For the time fixed effect, a wave dummy is used. Rs is a short-term dummy and Rl is a long-term dummy. Although all dependent variables are dichotomous, Eq. (3.1) is estimated as a linear probability model for ease of interpretation (Coe & Zamarro, 2011; Oshio & Kan, 2017). In the first stage estimation of the IV approach, Rs and Rl are separately regressed on the instruments and covariates by the fixed effects (FE) estimation. Following this, predicted values of retirement are calculated using the first stage results. In the second stage, Eq. (3.1), including the predicted values Rs and Rl instead of Rs and Rl, is estimated as the FE model. That is, the fixed effects instrumental variables (FE-IV) model is the model applied here. Because the variations of Rs and Rl are provided by the exogenous (to the error term) variable, the estimates of retirement are consistent. Negative signs for the estimates of δ1 and δ2 indicate health improvement. 







3.2.3 Health Trajectory by Lifestyle Before Retirement 3.2.3.1

Health Behavior

Prior to estimating the effect of retirement, health trajectory based on health behavior prior to retirement is presented. The relationship between the ratio of poor health and the years before/after retirement using those who have experienced retirement during the survey periods are illustrated in Figs. 3.2–3.5. Negative (positive) numbers of years indicate years to (from) retirement, and zero year indicates the retirement year. Figure 3.2 presents the health trajectory based on engagement in light-intensity exercise. After retirement, poor self-rated health of those who did not engage in lightintensity exercise clearly deteriorates more than those who engaged in light-intensity exercise. For difficulty with ADLs and depression, those who did not engage in lightintensity exercise show a little worse health condition around retirement; however, the difference disappeared in a few years following retirement.

56

3 Does Lifestyle Prior to Retirement Matter?

Fig. 3.2 Health trajectory by engagement in light-intensity exercise at initial period (Note Negative [positive] numbers of years indicate before [after] retirement and zero year indicates the retirement year. Of the analytic sample, only those who experienced retirement are used. Source LSMEP 2005–2015)

The health trajectory based on engagement in medium-intensity exercise is depicted in Fig. 3.3. All three health measures show that the health condition of those who did not engage in medium-intensity exercise is already worse than that of those who had engaged in medium-intensity exercise prior to retirement. For difficulty with ADLs, those who did not engage in exercise show a little worse health conditions after retirement. Compared with the health trajectory of light-intensity exercise, clear differences in change in health after retirement are not observed. One of the reasons is that unhealthy individuals are less likely to engage in the exercise prior to retirement due to its intensity. The health trajectory based on smoking is illustrated in Fig. 3.4. The self-rated health of smokers is already worse than non-smokers before retirement, and the difference between smokers and non-smokers becomes slightly larger after retirement. For difficulty with ADLs and depression, there seems to be no difference between smokers and non-smokers after retirement. Figure 3.5 illustrates the health trajectory based on alcohol consumption. For poor self-rated health, there seems to be no clear difference between drinkers (those who had consumed alcohol at least a few times per month) and infrequent drinkers (those who had consumed alcohol less than a few times in a month). Difficulty with ADLs of infrequent drinkers becomes slightly worse after retirement. Moreover, the ratio

3.2 How to Estimate Different Effects …

57

Fig. 3.3 Health trajectory by engagement in medium-intensity exercise at initial period (Note Negative [positive] numbers of years indicate before [after] retirement and zero year indicates the retirement year. Of the analytic sample, only those who experienced retirement are used. Source LSMEP 2005–2015)

of depression among infrequent drinkers is slightly higher than drinkers. These two latter results are contrary to the prediction. These figures indicate that those who did not participate in healthy behavior basically show worse health than those who participated in healthy behavior, as predicted, except in the case of drinking behavior. As these findings are descriptive, a causal estimation is required to confirm them.

3.2.3.2

Usual Activity

Next, the health trajectory based on usual activities before retirement is presented. The relationship between the ratio of reporting poor health condition and the years before/after retirement using those who have experienced retirement during the survey periods is illustrated in Figs. 3.6–3.9. Figure 3.6 illustrates the health trajectory based on participation in leisure activity. Poor self-rated health and difficulty with ADLs of those who did not participate in leisure activity are slightly worse than those who participated in leisure activity before retirement. The differences in health condition become larger after retirement. For depression, the health condition of those who did not participate in leisure activity

58

3 Does Lifestyle Prior to Retirement Matter?

Fig. 3.4 Health trajectory by smoking at initial period (Note Negative [positive] numbers of years indicate before [after] retirement and zero year indicates the retirement year. Of the analytic sample, only those who experienced retirement are used. Source LSMEP 2005–2015)

is already worse than those who participated in leisure activity prior to retirement, and the difference becomes larger after retirement. The health trajectory based on participation in social activity is illustrated in Fig. 3.7. Prior to retirement, there seems to be no difference between those who did and did not participate in social activity for all health measures. After retirement, for those who did not participate in social activity, depression clearly shows worse condition than those who participated in social activity. Poor self-rated health and difficulty with ADLs seem to deteriorate a little among those who did not participate in the activity. Figure 3.8 illustrates the health trajectory based on interaction with neighbors. For poor self-rated health, those who did not interact with neighbors show worse condition than those who interacted with neighbors prior to retirement; however, the difference disappeared after retirement. For difficulty with ADLs, there seems to be no difference between those who did and did not interact with neighbors prior to retirement; however, the health of those who did not interact with neighbors becomes slightly worse following retirement. The trajectory of depression is similar to poor self-rated health; worse health for those who did not interact with neighbors disappears after retirement. The health trajectory based on involvement in housework is presented in Fig. 3.9. For poor self-rated health, while there seems to be no difference in health condition

3.2 How to Estimate Different Effects …

59

Fig. 3.5 Health trajectory by drinking at initial period (Note Negative [positive] numbers of years indicate before [after] retirement and zero year indicates the retirement year. Of the analytic sample, only those who experienced retirement are used. Source LSMEP 2005–2015)

prior to retirement, those who were not involved in housework clearly show worse condition following retirement. For difficulty with ADLs, those who were involved in housework show worse health prior to retirement; however, their health condition becomes better than those who were not involved in housework after retirement. For depression, while those who were involved in housework show worse health prior to retirement, there seems to be no difference following retirement. Descriptive results indicate that those who did not participate in usual activities basically show worse health than those who did participate in usual activities after retirement, in congruence with the results of health behavior. In the next step, these descriptive findings are confirmed through a rigorous causal estimation of the IV approach.

60

3 Does Lifestyle Prior to Retirement Matter?

Fig. 3.6 Health trajectory by participation in leisure activity at initial period (Note Negative [positive] numbers of years indicate before [after] retirement and zero year indicates the retirement year. Of the analytic sample, only those who experienced retirement are used. Source LSMEP 2005–2015)

3.3 Estimation Results 3.3.1 Estimation Based on Health Behavior Estimation results based on health behavior prior to retirement are shown in Tables 3.2–3.5.9 Table 3.2 presents the impact of retirement on health by engagement in light-intensity exercise. Weak identification F-statistics (Kleibergen-Paap, 2016) for multiple endogenous regressors in each estimation are well above the critical value shown at the bottom of the table (Stock & Yogo, 2005), indicating that the instruments are not weak. Thus, the results obtained here are reliable. Of those who had engaged in exercise, short-term retirees are 54.6% less likely to report depression than non-retirees, but there is no significant difference in any health measure in the long term. For those who did not engage in exercise, short-term retirees have 18.5% and 22.8% lower probability of reporting poor self-rated health and depression, respectively. Long-term retirees are 25.9% more likely to report poor self-rated health. 9 The

here.

first stage results are similar with those obtained in Chapter 2, thus the results are not shown

3.3 Estimation Results

61

Fig. 3.7 Health trajectory by participation in social activity at initial period (Note Negative [positive] numbers of years indicate before [after] retirement and zero year indicates the retirement year. Of the analytic sample, only those who experienced retirement are used. Source LSMEP 2005–2015)

Table 3.3 presents the effect of retirement on health based on engagement in medium-intensity exercise. Weak identification F-statistics for multiple endogenous regressors in each estimation are well above the critical value. For those who engaged in exercise, both short- and long-term retirees have no significant difference in any health measure with non-retirees. For those who did not engage in exercise, short-term retirees have 24.7% and 40.7% lower probability of reporting poor selfrated health and depression, respectively. However, long-term retirees have 20.4% and 30.2% higher probability of reporting poor self-rated health and depression, respectively. Table 3.4 presents the effect of retirement on health based on smoking status. Weak identification F-statistics for multiple endogenous regressors for smokers are not above the critical value of 7.03 for 10% maximal IV size; however, all F-statistics are well above the critical value of 4.58 for 15% maximal IV size. For non-smokers, short-term retirees are 31.5% less likely to report depression than non-retirees, and long-term retirees are 17.5% more likely to report depression. For smokers, shortterm retirees are 28.6% less likely to report depression, and long-term retirees are 67.2% more likely to report poor self-rated health. Table 3.5 presents the effect of retirement on health based on the frequency of alcohol consumption. Weak identification F-statistics for multiple endogenous regressors in each estimation are well above the critical value. For infrequent drinkers,

62

3 Does Lifestyle Prior to Retirement Matter?

Fig. 3.8 Health trajectory by interaction with neighbors at initial period (Note Negative [positive] numbers of years indicate before [after] retirement and zero year indicates the retirement year. Of the analytic sample, only those who experienced retirement are used. Source LSMEP 2005–2015)

short-term retirees have 27.6% and 21.5% lower probability of reporting poor self-rated health and depression than non-retirees, respectively. Short-term retirees are also 17.4% more likely to have difficulty with ADLs. In contrast, long-term retirees showed no significant difference in any health measure. For drinkers, shortterm retirees are 37.0% less likely to report depression, whereas long-term retirees have 26.8% and 37.7% higher probability of reporting poor self-rated health and depression, respectively.

3.3.2 Estimation by Usual Activity Next, estimation results based on usual activity prior to retirement are shown in Tables 3.6–3.9. Table 3.6 presents the effect of retirement on health in terms of participation in leisure activity. Weak identification F-statistics for multiple endogenous regressors in each estimation are well above the critical value. For those who participated in leisure activity, short-term retirees have 25.0% and 31.9% lower probability of reporting poor self-rated health and depression than non-retirees, respectively, whereas long-term retirees have 27.3% and 20.4% higher probability of reporting poor self-rated health and depression than non-retirees, respectively. Short-term

3.3 Estimation Results

63

Fig. 3.9 Health trajectory by involvement in housework at initial period (Note Negative [positive] numbers of years indicate before [after] retirement and zero year indicates the retirement year. Of the analytic sample, only those who experienced retirement are used. Source LSMEP 2005–2015)

retirees who did not participate in leisure activity are 38.6% less likely to report depression, and long-term retirees are 28.5% less likely to have difficulty with ADLs than non-retirees. Table 3.7 presents the effect of retirement on health based on participation in social activity. Weak identification F-statistics for multiple endogenous regressors in each estimation are well above the critical value. For those who participated in social activity, short-term retirees are 29.6% more likely to have difficulty with ADLs than non-retirees, and are 36.7% less likely to report depression. Long-term retirees are 33.4% more likely to report depression. For those who did not participate in social activity, short-term retirees have 18.8% and 31.8% lower probability of reporting poor self-rated health and depression, respectively. Long-term retirees are 13.3% less likely to have difficulty with ADLs. Table 3.8 presents the effect of retirement on health based on interaction with neighbors. Weak identification F-statistics for multiple endogenous regressors in each estimation are well above the critical value. For those who interacted with neighbors, short-term retirees have 24.8% and 38.0% lower probability of reporting poor self-rated health and depression than non-retirees, respectively, whereas, in contrast, long-term retirees have no significant difference in any health measures. Short-term retirees who did not interact with neighbors are 18.1% more likely to

64

3 Does Lifestyle Prior to Retirement Matter?

Table 3.2 Light-intensity exercise and the effect of retirement on health Poor self-rated health

Difficulty with ADLs

Depression

Those who had engaged Short-term effect

− 0.0730 (0.1337)

0.0268 (0.1102)

− 0.5458*** (0.1730)

Long-term effect

0.0302 (0.1270)

0.0268 (0.1000)

0.2351 (0.1617)

Age

0.0251 (0.0491)

− 0.0011 (0.0389)

0.1489** (0.0633)

Age squared / 100

− 0.0189 (0.0434)

0.0119 (0.0344)

− 0.0998* (0.0564)

Observations

28,120

27,626

27,615

Individuals

3,224

3,212

3,208

17.488

17.982

16.594

Kleibergen-Paap

Fa

Those who had not engaged Short-term effect

− 0.1849* (0.0966)

0.0885 (0.0646)

− 0.2275** (0.1011)

Long-term effect

0.2583** (0.1232)

− 0.0802 (0.0901)

0.1822 (0.1265)

Age

0.1088** (0.0483)

− 0.0575 (0.0352)

0.0900* (0.0500)

Age squared / 100

− 0.0839* (0.0429)

0.0567* (0.0315)

− 0.0534 (0.0443)

Observations

58,964

57,734

57,585

Individuals

6,967

6,941

6,927

Kleibergen-Paap Fa

28.026

26.696

25.740

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. a Stock and Yogo (2005) weak identification test critical values: 10% maximal IV size 7.03; 15% maximal IV size 4.58; 20% maximal IV size 3.95; 25% maximal IV size 3.63

have difficulty with ADLs and 27.4% less likely to report depression. Long-term retirees have no significant difference in any health measure. Table 3.9 presents the effect of retirement on health based on involvement in housework. Weak identification F-statistics for multiple endogenous regressors in each estimation are well above the critical value. Short-term retirees who were involved in housework have 32.3% and 30.4% lower probability of reporting poor self-rated health and depression than non-retirees, respectively. Long-term retirees have no significant difference in any health measure. Short-term retirees who were not involved in housework are 18.5% more likely to report poor self-rated health and 26.5% less likely to report depression. Long-term retirees have 41.7% and 53.8% higher probability of reporting poor self-rated health and depression, respectively.

3.4 Conclusion

65

Table 3.3 Medium-intensity exercise and the effect of retirement on health Poor self-rated health

Difficulty with ADLs

Depression

Short-term effect

0.0555 (0.1196)

0.0278 (0.0895)

− 0.1804 (0.1316)

Long-term effect

0.1067 (0.1457)

0.0737 (0.1085)

0.0252 (0.1537)

Age

0.0585 (0.0596)

0.0042 (0.0437)

0.0017 (0.0643)

Age squared / 100

− 0.0458 (0.0528)

0.0018 (0.0390)

0.0121 (0.0568)

Observations

24,864

24,420

24,465

Individuals

2,802

2,796

2,795

16.666

14.987

15.539

Those who had engaged

Kleibergen-Paap

Fa

Those who had not engaged Short-term effect

− 0.2472** (0.1020)

0.0927 (0.0715)

− 0.4074*** (0.1168)

Long-term effect

0.2037* (0.1165)

− 0.0842 (0.0865)

0.3017** (0.1322)

Age

0.0845* (0.0443)

− 0.0517 (0.0331)

0.1632*** (0.0505)

Age squared / 100

− 0.0636 (0.0393)

0.0540* (0.0295)

− 0.1095** (0.0448)

Observations

62,336

61,042

60,849

Individuals

7,401

7,370

7,352

Kleibergen-Paap Fa

28.273

28.941

26.517

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. a Stock and Yogo (2005) weak identification test critical values: 10% maximal IV size 7.03; 15% maximal IV size 4.58; 20% maximal IV size 3.95; 25% maximal IV size 3.63

3.4 Conclusion This chapter examined the heterogeneous effect of retirement on health based on lifestyle prior to retirement (at the first survey). The causal estimation using the IV approach indicates that maintaining a physically and mentally healthy lifestyle before retirement possibly mitigates the adverse effect of retirement on health in the long term. Specifically, with regard to health behavior, long-term retirement appears to significantly diminish health conditions for those who did not engage in exercise prior to retirement, while retirement has no significant impact for those who engaged in it. This result suggests that the habit of regular exercise prior to retirement can prevent health deterioration after retirement. A similar effect is found with regard

66

3 Does Lifestyle Prior to Retirement Matter?

Table 3.4 Smoking status and the effect of retirement on health Poor self-rated health

Difficulty with ADLs

Depression

Short-term effect

− 0.0830 (0.0850)

0.0746 (0.0640)

− 0.3154*** (0.0986)

Long-term effect

0.0949 (0.0831)

0.0099 (0.0672)

0.1748* (0.0943)

Age

0.0488 (0.0328)

− 0.0255 (0.0261)

0.0932** (0.0373)

Age squared / 100

− 0.0400 (0.0288)

0.0288 (0.0231)

− 0.0542* (0.0327)

Observations

57,028

55,859

55,732

Individuals

6,473

6,449

6,439

43.776

43.927

40.823

Short-term effect

− 0.2048 (0.1498)

0.0362 (0.0910)

− 0.2863* (0.1533)

Long-term effect

0.6716** (0.3347)

− 0.1300 (0.1995)

0.5321 (0.3349)

Age

0.2566** (0.1304)

− 0.0663 (0.0789)

0.2545* (0.1322)

Age squared / 100

− 0.2135* (0.1177)

0.0675 (0.0712)

− 0.2013 * (0.1192)

Observations

33,491

32,700

32,458

Individuals

4,161

4,139

4,117

Kleibergen-Paap Fa

6.270

5.907

6.015

Non-smokers

Kleibergen-Paap

Fa

Smokers

Note: Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. a Stock and Yogo (2005) weak identification test critical values: 10% maximal IV size 7.03; 15% maximal IV size 4.58; 20% maximal IV size 3.95; 25% maximal IV size 3.63

to drinking habits; while long-term retirement significantly worsens health for those who consumed alcohol often, retirement shows no significant effect for those who had rarely drunk. However, a clear difference between smokers and non-smokers is not found; long-term retirement significantly worsens health for both smokers and non-smokers. These results suggest that maintaining a healthy lifestyle prior to retirement is an effective approach for the prevention of health deterioration in retirement. As for usual activities, contrary to the prediction, long-term retirement significantly worsens health for those who participated in leisure and social activities, while health is improved for those who did not participate in leisure and social activities. One possible explanation for the latter beneficial health is that those who had health problems before retirement were not likely to participate in these activities, and as

3.4 Conclusion

67

Table 3.5 Frequency of drinking and the effect of retirement on health Poor self-rated health

Difficulty with ADLs

Depression

Short-term effect

− 0.2761** (0.1090)

0.1736** (0.0807)

− 0.2147* (0.1194)

Long-term effect

0.0748 (0.1002)

− 0.0653 (0.0748)

0.1307 (0.1025)

Age

0.0686 (0.0448)

− 0.0671** (0.0333)

0.1374*** (0.0456)

Age squared / 100

− 0.0545 (0.0392)

0.0647** (0.0293)

− 0.0831** (0.0397)

Observations

30,815

30,098

29,887

Individuals

3,592

3,576

3,563

27.764

29.304

26.834

Short-term effect

− 0.0122 (0.0969)

0.0226 (0.0674)

− 0.3696*** (0.1099)

Long-term effect

0.2683* (0.1570)

0.0225 (0.1203)

0.3771** (0.1884)

Age

0.0951* (0.0576)

− 0.0101 (0.0442)

0.1480** (0.0696)

Age squared / 100

− 0.0746 (0.0517)

0.0161 (0.0399)

− 0.1106* (0.0626)

Observations

59,358

58,133

58,004

Individuals

6,985

6,955

6,940

Kleibergen-Paap Fa

18.466

16.777

16.583

Infrequent drinker

Kleibergen-Paap

Fa

Drinker

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. a Stock and Yogo (2005) weak identification test critical values: 10% maximal IV size 7.03; 15% maximal IV size 4.58; 20% maximal IV size 3.95; 25% maximal IV size 3.63

a result, long-term retirement improved their health. A different effect of retirement is not found in terms of interaction with neighbors. As for housework, while longterm retirement worsens health among those who were not involved in housework, retirement does not worsen health among those who were involved in housework. These results suggest that adjustment or preparation for in-home activity following retirement is more important than activity outside of the home for the preservation of health in post-retirement life. In sum, this chapter demonstrated that lifestyle prior to retirement, including health behavior and usual activities, is crucial to preserving health condition following retirement. Moreover, health behavior seems to be more important than usual activity. As predicted, these results suggest that maintaining a healthy and

68

3 Does Lifestyle Prior to Retirement Matter?

Table 3.6 Participation in leisure activity and the effect of retirement on health Poor self-rated health

Difficulty with ADLs

Depression

Short-term effect

− 0.2497*** (0.0936)

0.0541 (0.0651)

− 0.3193*** (0.1032)

Long-term effect

0.2727** (0.1119)

0.0624 (0.0791)

0.2035* (0.1209)

Age

0.1165*** (0.0436)

0.0029 (0.0309)

0.1065** (0.0475)

Age squared / 100

− 0.0950** (0.0386)

0.0029 (0.0275)

− 0.0738* (0.0421)

Observations

57,889

56,876

56,813

Individuals

6,628

6,607

6,602

28.133

28.373

25.721

Short-term effect

0.0389 (0.1537)

0.1940 (0.1192)

− 0.3857** (0.1750)

Long-term effect

− 0.1203 (0.1584)

− 0.2854** (0.1362)

0.2414 (0.1743)

Age

− 0.0325 (0.0636)

− 0.1346** (0.0535)

0.1547** (0.0700)

Age squared / 100

0.0452 (0.0562)

0.1254*** (0.0477)

− 0.0957 (0.0617)

Observations

29,626

28,881

28,703

Individuals

3,562

3,548

3,527

Kleibergen-Paap Fa

18.098

17.415

16.603

Participants

Kleibergen-Paap

Fa

Non-participants

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. a Stock and Yogo (2005) weak identification test critical values: 10% maximal IV size 7.03; 15% maximal IV size 4.58; 20% maximal IV size 3.95; 25% maximal IV size 3.63

active lifestyle prior to retirement may increase the productivity of health investment or enable retirees to smoothly transfer to post-retirement life. The findings of this chapter also suggest that middle-aged and elderly workers must prepare prior to retirement in order to achieve a healthy post-retirement life. This chapter has some limitations. First, post-retirement change in health behavior and usual activity is ignored, while this is also considered to be a possible link between retirement and health.10 The effect of retirement on health behavior is examined in Chapter 2 as well as some previous studies (e.g., Motegi et al., 2016; Zhao et al., 2017), finding that retirees in Japan change health habits. Second, the effect of retirement estimated in this chapter is the Local Average Treatment Effect (LATE), but 10 Shiba

et al. (2017) examine the intermediate effect of social participation on the effect of retirement, although they do not fully address endogeneity of retirement.

3.4 Conclusion

69

Table 3.7 Participation in social activity and the effect of retirement on health Poor self-rated health

Difficulty with ADLs

Depression

Short-term effect

0.0584 (0.1652)

0.2964** (0.1286)

− 0.3673* (0.1937)

Long-term effect

0.2479 (0.1664)

0.1860 (0.1401)

0.3339 * (0.1823)

Age

0.1011* (0.0576)

0.0424 (0.0469)

0.1694*** (0.0648)

Age squared / 100

− 0.0841 (0.0522)

− 0.0345 (0.0421)

− 0.1208** (0.0581)

Observations

19,395

19,073

19,029

Individuals

2,097

2,093

2,093

18.067

16.667

17.307

Short-term effect

− 0.1877** (0.0917)

0.0863 (0.0646)

− 0.3176*** (0.1028)

Long-term effect

0.1128 (0.1044)

− 0.1330* (0.0789)

0.1775 (0.1179)

Age

0.0557 (0.0425)

− 0.0754** (0.0321)

0.1050** (0.0479)

Age squared / 100

− 0.0364 (0.0373)

0.0734** (0.0285)

− 0.0661 (0.0423)

Observations

68,120

66,684

66,487

Individuals

8,093

8,062

8,036

Kleibergen-Paap Fa

30.178

30.650

27.270

Participants

Kleibergen-Paap

Fa

Non-participants

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. a Stock and Yogo (2005) weak identification test critical values: 10% maximal IV size 7.03; 15% maximal IV size 4.58; 20% maximal IV size 3.95; 25% maximal IV size 3.63

not the Average Treatment Effect on the Treated (ATET) (Angrist & Pishcke, 2009); that is, the LATE is the result of compliers who are retired because they reached the pensionable age, but not that of all retirees. Third, this chapter uses the respondents who had a full-time work at the first wave, but not the entire workforce. The number of part-time workers has been increasing in Japan as well as other developed economies. The effect of retirement for such workers will become more important in the future.

70

3 Does Lifestyle Prior to Retirement Matter?

Table 3.8 Interaction with neighbors and the effect of retirement on health Poor self-rated health

Difficulty with ADLs

Depression

Those who had interacted Short-term effect

− 0.2476** (0.1102)

0.0444 (0.0803)

− 0.3803*** (0.1236)

Long-term effect

0.1053 (0.1066)

− 0.0129 (0.0802)

0.1755 (0.1187)

Age

0.0588 (0.0410)

− 0.0245 (0.0307)

0.1209*** (0.0460)

Age squared / 100

− 0.0366 (0.0360)

0.0273 (0.0270)

− 0.0768* (0.0404)

Observations

50,261

49,246

49,110

Individuals

5,633

5,622

5,607

29.156

28.935

26.848

Kleibergen-Paap

Fa

Those who had not interacted Short-term effect

− 0.0043 (0.1119)

0.1812** (0.0788)

− 0.2739** (0.1268)

Long-term effect

0.2064 (0.1566)

− 0.1213 (0.1209)

0.2751 (0.1771)

Age

0.0889 (0.0642)

− 0.0744 (0.0495)

0.1307* (0.0726)

Age squared / 100

− 0.0737 (0.0573)

0.0722 (0.0447)

− 0.0920 (0.0650)

Observations

37,254

36,511

36,406

Individuals

4,557

4,533

4,522

Kleibergen-Paap Fa

15.945

15.855

14.571

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. a Stock and Yogo (2005) weak identification test critical values: 10% maximal IV size 7.03; 15% maximal IV size 4.58; 20% maximal IV size 3.95; 25% maximal IV size 3.63

3.4 Conclusion

71

Table 3.9 Involvement in housework and the effect of retirement on health Poor self-rated health

Difficulty with ADLs

Depression

Short-term effect

− 0.3227*** (0.1099)

0.1094 (0.0817)

− 0.3035** (0.1226)

Long-term effect

0.0555 (0.0932)

− 0.0368 (0.0714)

0.1248 (0.1020)

Age

0.0460 (0.0390)

− 0.0417 (0.0292)

0.1077** (0.0425)

Age squared / 100

− 0.0264 (0.0340)

0.0396 (0.0256)

− 0.0620* (0.0368)

Observations

47,346

46,395

46,342

Individuals

5,368

5,355

5,345

33.121

32.424

30.865

Those who had involved

Kleibergen-Paap

Fa

Those who had not involved Short-term effect

0.1848* (0.1095)

0.1031 (0.0729)

− 0.2645** (0.1222)

Long-term effect

0.4166* (0.2491)

− 0.0904 (0.1711)

0.5380* (0.2886)

Age

0.1682* (0.0949)

− 0.0504 (0.0663)

0.2246** (0.1110)

Age squared / 100

− 0.1433* (0.0855)

0.0539 (0.0599)

− 0.1820* (0.1008)

Observations

40,169

39,362

39,174

Individuals

4,822

4,800

4,784

Kleibergen-Paap Fa

8.106

8.382

7.290

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1. a Stock and Yogo (2005) weak identification test critical values: 10% maximal IV size 7.03; 15% maximal IV size 4.58; 20% maximal IV size 3.95; 25% maximal IV size 3.63

References Andel, R., Finkel, D., & Pedersen, N. L. (2016). Effects of preretirement work complexity and postretirement leisure activity on cognitive aging. Journal of Gerontology Series B Psychological Sciences and Social Sciences, 71(5), 849–856. Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless Econometrics: An empiricist’s companion. Princeton University Press. Atalay, K., & Barrett, G. F. (2014). The causal effect of retirement on health: New evidence from Australian pension reform. Economics Letters, 125(3), 392–395. Behncke, S. (2012). Does retirement trigger ill health? Health Economics., 21(3), 282–300. Coe, N. B., & Zamarro, G. (2011). Retirement effects on health in Europe. Journal of Health Economics, 30(1), 77–86. Eibich, P. (2015). Understanding the effect of retirement on health: mechanisms and heterogeneity. Journal of Health Economics, 43(1), 1–12.

72

3 Does Lifestyle Prior to Retirement Matter?

Finkel, D., Andel, R., Gatz, M., & Pedersen, N. L. (2009). The role of occupational complexity in trajectories of cognitive aging before and after retirement. Psychology and Aging, 24(3), 563–573. Fisher, G. G., Stachowski, A., Infurna, F. J., Faul, J. D., Grosch, J., & Tetrick, L. E. (2014). Mental work demands, retirement, and longitudinal trajectories of cognitive functioning. Journal of Occupational Health Psychology, 19(2), 231–242. Grøtting, M. W., & Lillebø, O. S. (2020). Health effects of retirement: evidence from survey and register data. Journal of Population Economics, 33(2), 671–704. Hyun, J., Katz, M. J., Lipton, R. B., & Sliwinski, M. J. (2019). Mentally challenging occupations are associated with more rapid cognitive decline at later stages of cognitive aging. Journal of Gerontology Series B Psychological Sciences and Social Sciences. Insler, M. (2014). The health consequences of retirement. Journal of Human Resources, 49(1), 195–233. Kleibergen, F., & Paap, R. (2006). Generalized reduced rank tests using the singular value decomposition. Journal of Econometrics, 133(1), 97–126. Mazzonna, F., & Peracchi, F. (2017). Unhealthy retirement? Journal of Human Resources, 52(1), 128–151. Motegi, H., Nishimura, Y., & Terada, K. (2016). Does retirement change lifestyle habits? Japanese Economic Review, 67(2), 169–191. Oshio, T., & Kan, M. (2017). The dynamic impact of retirement on health: Evidence from a nationwide ten-year panel survey in Japan. Preventive Medicine, 100, 287–293. Sakurai, K., Nishi, A., Kondo, K., Yanagida, K., & Kawakami, N. (2011). Screening performance of K6/K10 and other screening instruments for mood and anxiety disorders in Japan. Psychiatry and Clinical Neurosciences, 65(5), 434–441. Shiba, K., Kondo, N., Kondo, K., & Kawachi, I. (2017). Retirement and mental health: Does social participation mitigate the association? A fixed-effects longitudinal analysis. BMC Public Health, 17, 526. Stock, J. H., & Yogo, M. (2005). Testing for weak instruments in linear IV regression. In D. W. K. Andrews & J. H. Stock (Eds.), Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg (pp. 80–108). Cambridge University Press. Zhao, M., Konishi, Y., & Noguchi, H. (2017). Retiring for better health? evidence from health investment behaviors in Japan. Japan and the World Economy, 42, 56–63.

Chapter 4

Effect of Retirement Timing on Health

Abstract This chapter reveals the effect of retirement timing on health. Retirement timing varies widely between individuals; some workers withdraw from the labor market as soon as they are eligible for partial pension benefits, other workers continue to work until they can claim the full pension benefits. If the work is stressful or physically demanding, early retirement may improve a retiree’s health. In contrast, if physical or mental activities related to work are a source of good health, later retirement could be beneficial to a retiree’s health condition. Due to policy reform in Japan, older workers tend to retire later than previous generations; however, the effects of retirement timing have not been well understood. The causal estimation in this chapter indicates that early retirement has a beneficial effect on overall health and mental health. In particular, the beneficial long-term effect on overall health is observed among retired men, those with high education, and those who retired from blue-collar jobs. In terms of mental health, the beneficial long-term effect is also observed among men, those with both educational levels, and those who had white-collar jobs. Conversely, normal retirement shows an adverse effect on overall health and mental health. Adverse long-term effect on overall health is basically observed among men and highly educated individuals, and the effect on mental health is observed among men, those with low education, and those who had white-collar jobs. The results obtained in this chapter suggest that retiring early is beneficial for the health of retirees, but normal retirement is detrimental. Thus, policy reform delaying the timing of retirement may have an adverse effect on medical and long-term care systems. Keywords Early retirement · Normal retirement · Health · Short- and long-term effect · Causal estimation

4.1 Introduction 4.1.1 Delaying Retirement Timing Which is better for health, retiring early or later? Many developed economies previously promoted early retirement to address the shortage of labor demand and its © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Mizuochi, Exploring the Effect of Retirement on Health in Japan, Population Studies of Japan, https://doi.org/10.1007/978-981-16-2638-8_4

73

74

4 Effect of Retirement Timing on Health

consequence for unemployment (Adam et al., 2013). However, those economies now tend to advocate the delay of retirement. This is because increasing life expectancy and the fiscal instability of social security, such as public pension, have altered the labor policy regarding older adults. Many developed economies that now face population aging have raised the retirement age or plan to raise it by increasing the eligibility age for pension benefit (Organization for Economic Co-operation and Development [OECD], 2017). Delayed retirement may benefit public pensions by reducing immediate benefits received; however, if retirement has a beneficial effect on health, such delayed retirement may impose fiscal burden on medical and long-term care systems and worsen individuals’ well-being in later life. Nevertheless, the issue of the timing of retirement has received less attention (Fisher, et al., 2016). The timing of retirement is largely determined by the eligibility for pension benefits; however, it is also the individual’s choice, and thus, the timing varies widely. For example, gradual retirement is prevalent in the US. After leaving full-time career employment, most older workers work for a new employer, work for the same employer with reduced work hours and effort, or reenter the labor market after a short period of retirement, and then later fully withdraw from the labor force (Cahill & Quinn, 2020; Cahill et al., 2018; Ruhm, 1990). In Japan, also, most older workers can continue to work as a non-full-time regular employee for the same employer following mandatory retirement before completely withdrawing from the labor force (Shimizutani & Oshio, 2010; Clark et al., 2015).1 That is, older adults can determine their retirement pathways and timing depending on their health and/or financial need. Thus, it is important to understand the different effects of retirement timing on health in terms of its effect on the stability of the social security system. With regard to the recent policy reform in Japan, the government passed the revised Act on Stabilization of Employment of Elderly Persons (ASEEP) in 2020 to guarantee the employment of older workers. From 2021, many companies must apply their best efforts to secure employment for their employees up to 70 years old. While Japanese older workers have already been secured for employment until 65 years old, retirement timing will now be further delayed. This policy reform intensifies the importance of investigating the effect of retirement timing on health. As an overview of retirement timing, let us first consider the average effective age of retirement in Japan and OECD. The average effective age of retirement is defined as “the average age of exit from the labour force for workers aged 40 and over” (OECD, 2019). Figure 4.1 illustrates the retirement age by gender. Notably, the average retirement age in Japan is much higher than that of the OECD average. In terms of the change in the age, for the OECD average, the retirement age of men and women, respectively, declined from 66.2 and 64.2 years in 1980 to 63.1 and 61.0 years in 2000. Since around 2000, the retirement age has steadily increased, reaching 65.4 and 63.7 years for men and women, respectively, in 2018. For Japan, though the retirement age fluctuated between increasing and decreasing from 1980 to 2010, the age did not change so much for men and women, respectively; from 1 A full-time regular employee is called Seiki in Japan. They basically work full-time with an undefined-term employment contract.

75

2018

2016

2014

2012

2010

2008

2006

2004

2000

2002

1998

1996

1994

1992

1990

1988

1984

1986

1982

1980

60

Age 65

70

4.1 Introduction

Year Men(Japan) Women(Japan)

Men(OECD) Women(OECD)

Fig. 4.1 Average effective age of retirement in Japan and OECD (Source OECD data [2018])2

71.0 and 66.6 years in 1980 to 70.1 and 67.0 years in 2010. However, since 2010, the retirement age has been rapidly increasing, reaching 70.8 and 69.1 years for men and women, respectively, in 2018. In particular, the increase in retirement age among women is remarkable. The figure shows that although past trends differ slightly between Japan and the OECD, recent policy-based retirement delays are obvious in both Japan and the OECD.

4.1.2 Retirement Timing and Health Outcomes While several studies have investigated the causal effect of retirement on health, many of them focus on the average effect of retirement (Behncke, 2012; Coe & Zamarro, 2011; Gorry & Slavov, 2021; Hessel, 2016; Neuman, 2008). Such studies ignore the heterogeneous retirement effects on health; for example, the effects of short- and long-term retirement may differ in the US (Gorry et al., 2018; Insler, 2014), in the UK (Fé & Hollingsworth, 2016), and in continental Europe (HellerSahlgren, 2017; Mazzonna & Peracchi, 2017). Chapter 2 in this book also finds that retirement has a beneficial effect on the self-rated health and mental health of 2 OECD,

Ageing and Employment Policies in OECD (https://www.oecd.org/els/emp/average-eff ective-age-of-retirement.htm. Accessed November 25, 2020).

76

4 Effect of Retirement Timing on Health

short-term retirees, while it has an adverse effect on those health conditions for longterm retirees in Japan. These findings suggest that the average effect of retirement contains heterogeneous effects, and thus, there remains a lack of information for understanding the relationship between retirement and health. From the perspective of retirement timing, some studies examine the difference in the effects of early and normal (statutory or on-time) retirement. Calvo et al. (2013) find that, in the US, retiring too early, prior to the traditional and statutory retirement age, worsens self-rated physical and mental health, whereas late retirement has no harmful effect on retirees’ health. The authors use an interaction term between a retirement dummy and current age to examine the effect of retirement timing. In Germany, Eibich (2015) indicates that the beneficial effects of retirement on subjective and mental health are driven by individuals who retired at around normal retirement age but not by individuals who retired at early retirement age. He separately estimates the effect of early and normal retirement using a fuzzy regression discontinuity design. Celidoni et al. (2017) point out that, in continental Europe, early retirement has a beneficial effect on cognitive health, whereas late retirement has an adverse effect on cognitive health in the long run. To distinguish the effect between early and late retirement, Celidoni and colleagues apply two sets of instruments, one set inducing early retirement and the other set inducing late retirement, and estimate the effect of early and late retirement in separate equations. Taken as a whole, these studies suggest that early and normal retirement have a different effect on the health of older adults.3 To date, there has been no study investigating the effect of the timing of retirement for older Japanese adults. Therefore, the effect of retirement timing on health in Japan also requires investigation. Does the different timing of retirement have a different effect on the health of retirees? If retirement presents a relief from a stressful and physically demanding working life, early retirement would be presumed to improve health condition. However, retirement may also lead to reduced mental and physical activity, loss of social networks, and health-adverse habits, suggesting that later retirement may be beneficial to the health condition (Hernaes et al., 2013). Moreover, in general, the older someone gets, the harder it is to adjust to new post-retirement life. For example, a retiree might have to create a new social network, face change in usual activities, or find a new sense of purpose in older age. As a result, later retirement may have a more harmful effect on health than early retirement. The health capital model (Grossman, 1972) can predict the detrimental aspect of later retirement with a similar logic. If the mental and physical activities of work are the primary form of health investment before retirement, later retirees would face difficulty in finding another source of health investment and thus be unable to effectively maintain health after full retirement. This may lead to a diminished health for later retirees. In addition, the timing of retirement is endogenous to health; that is, unhealthy individuals are more likely to retire early (reverse causality), or unobserved health 3 Some

less rigorous causal studies examine the effect of retirement timing on health. Jokela et al. (2010) find both early and statutory retirement improves physical functioning and mental health in the UK. Wu et al. (2016) find later retirement decreases the risk of mortality in the US.

4.1 Introduction

77

shock, for example, family health problem, can affect both retirement timing and the retiree’s health (unobserved confounders). This underlying mechanism creates a correlation between retirement timing and the error term in the outcome equation (unexplained aspect of health). This endogeneity makes the estimate of retirement inconsistent in an estimation using a retirement indicator as an exogenous variable. Thus, this chapter investigates the health effects of retirement timing using the instrumental variables (IV) approach to address the endogeneity problem. Is longer working life beneficial for health? To simply confirm it, the macro-level relationship between average effective age of retirement and life expectancy at age 65 are examined here. Higher average effective age of retirement means that the workers continue working longer in that country. Figure 4.2 shows the scatter plot of retirement timing and life expectancy by gender. Longer working life demonstrates a positive association with longer life expectancy for both men and women. The correlation coefficients are 0.179 and 0.203 for men and women, respectively. The relationship for women is slightly stronger than men, whereas the relationship is, of course, not a causal relationship.

Fig. 4.2 Length of working life and life expectancy (Source Average effective age of retirement, OECD data [2018]. Life expectancy at 65, OECD data [2015–2018]4 )

4 OECD

iLibrary, Life expectancy at 65 (https://doi.org/10.1787/0e9a3f00-en. Accessed 23 November 2020).

78

4 Effect of Retirement Timing on Health

Therefore, this chapter examines the effect of retirement timing on the health of older adults with a rigorous causal estimation. The remainder of this chapter is organized into three sections. Section 4.2 introduces the data, variables, and estimation method applied here. Section 4.3 presents the estimation results for the effect of retirement timing on health and some robustness checks. Section 4.4 discusses the findings in this chapter.

4.2 How the Effect of Retirement Timing Is Estimated 4.2.1 Data, Health Measures, and Instruments This chapter uses 11 waves (2005–2015) of the previously introduced Longitudinal Survey of Middle-aged and Elderly Persons (LSMEP), which is a nationally representative longitudinal survey in Japan that has been conducted annually by the Ministry of Health, Labour and Welfare (MHLW). The survey collects information on work, health, usual activities, family, and livelihood from men and women aged 50–59 as of the first interview in 2005. The sample used here is restricted in some ways. First, respondents who worked as full-time regular (called seiki in the questionnaire) employees at the first wave are used, as with other chapters. Since the impact of retirement for part-time employees and self-employed workers seems to be substantially different from full-time regular employees, part-time employees and self-employed workers are excluded from the analytic sample. Second, respondents whose work status is continuously observed from the first wave to a certain wave are used to capture the accurate retirement timing. Third, respondents who reentered the labor force after full retirement are excluded from the analytic sample, as the effect of withdrawal from and subsequent reentry into the labor force on health is not considered to be symmetrical. Health measures include poor self-rated health, difficulty with activities of daily living (ADLs), and depression. Poor self-rated health is used as the overall health measure. Respondents are asked to rate their current health condition on a six-point Likert scale: very good, rather good, good, poor, rather poor, very poor. A dichotomous variable is generated indicating poor health, taking 1 if respondents’ health is poor/rather poor/very poor, or 0 otherwise. Difficulty with ADLs is used as a physical health measure. A dichotomous variable is generated, taking 1 if respondents answered that they have any difficulty among the following ten activities: walking, getting up out of bed or off floor, sitting down on and standing up from chair, taking clothes on and off, washing hands and face, eating, toileting, bathing, climbing and descending stairs, and carrying purchases; or 0 otherwise. Depression is used as a mental health measure. The LSMEP asks respondents about their mental condition with the Japanese version of the Kessler Psychological Distress Scale (K6). The K6 consists of six questions: “During the past 30 days, about how often did you feel nervous?,” “… feel hopeless?,” “… feel restless or fidgety?,” “… feel so depressed

4.2 How the Effect of Retirement Timing is Estimated

79

that nothing could cheer you up?,” “… feel that everything was an effort?,” and “… feel worthless?.” Respondents rate from 0 (none of the time) to 4 (all of the time) for each question; the total score ranges from 0 (good) to 24 (bad). A dichotomous variable indicating depression symptoms takes 1 if the total score equals to 5 or higher, or 0 otherwise (Sakurai et al., 2011). As for the instruments in the IV approach, eligibility for partial benefit (EPB) and eligibility for full benefit (EFB) for public employee pension are used here.5 The EPB takes 1 if the respondent’s age is equal to or older than the eligibility age earningsrelated pension benefit, or 0 otherwise. The EFB takes 1 if the respondent’s age is equal to or older than the eligibility age for flat-rate pension benefit, or 0 otherwise. The eligibility for pension benefits is considered to be useful as an instrument in the IV estimation because it is institutionally fixed and expected to affect the decision to retire but not health directly (Oshio & Kan, 2017).

4.2.2 Retirement Respondents are defined as full retirement if they have no paid work at the survey. Those who reentered the labor force from full retirement are excluded from the sample, as described above. To distinguish the effect of retirement between early and normal timing, two compliers are used in the IV estimation following Celidoni et al. (2017); that is, early retirees are individuals who retired because they reached the eligibility age for partial benefit (EPB complier) and normal retirees are individuals who retired because they reached the eligibility age for full benefit (EFB complier). Specifically, only one of two instruments is used in a separate IV estimation. Figure 4.3 illustrates the distribution of full retirement age by gender, educational attainment, and type of employment. The retirement age concentrates on ages 60–61 in any attribute and a relatively high ratio is found at 65 years old. Among attributes, men, those with high education, and those who had a white-collar job indicate a higher retirement age than their counterparts. Therefore, subsample estimation by these attributes is required to confirm the effect of retirement timing on health.

4.2.3 Estimation Method The estimation model for the effects of retirement timing on health is as follows: Hit = Xit β + δ Rit + μi + λt + εit ,

(4.1)

where H is one of the health measures and the subscripts i and t indicate individual and time, respectively. R is an early or normal retirement dummy, it depends on the 5 See

Chapter 1 for more detail.

80

4 Effect of Retirement Timing on Health

Fig. 4.3 The distribution of retirement age by gender, educational attainment, and career (Note These figures are depicted using 3,175 individuals; those who experienced retirement during the survey period. Source LSMEP 2005–2015)

instrument used as explained above. X is a vector of covariates including age and age squared. Since there is no reason to believe that discrete age threshold for pension eligibility should affect health directly beyond the quadratic age trend, the covariates include age and age squared only. μ is an individual fixed effect, λ is a time fixed effect, and ε is an idiosyncratic error term. For the time fixed effect, a wave dummy is

4.2 How the Effect of Retirement Timing is Estimated

81

used. While the health measure is a dichotomous variable, a linear probability model is used for the ease of interpretation (Coe & Zamarro, 2011; Oshio & Kan, 2017). To address the endogeneity problem of retirement, the IV approach is applied here. In the first stage estimation of the IV approach, R is regressed on either EPB or EFB and exogenous covariates by the FE estimation. Rit = Xit γ1 +θ1 E P B it + φ1,i + η1,t + e1,it ,

(4.2)

Rit = Xit γ2 +θ2 E F B it + φ2,i + η2,t + e2,it ,

(4.3)

Next, using the first stage results of (4.2) and (4.3), linear predicted values of retirement timings are calculated. In the second stage, Eq. (4.1), including one of the two predicted values, Re (early retirement) or Rn (normal retirement) instead of R, is estimated as the FE model; that is, the fixed effects instrumental variables (FE-IV) estimation is performed here. Since the variations of Re and Rn are provided by the exogenous (to the error term) instrumental variables, the estimate of retirement is consistent. A negative sign for the estimate of δ indicates health improvement. In this chapter, the short- and long-term effect of retirement timing is also examined, as the previous literature suggests the importance of distinguishing these effects (e.g., Gorry et al. 2018), as explored in Chapter 2. Due to the limit of the number of instruments, short- and long-term effects are estimated separately following Calvo et al. (2013). For the short-term effect, all the observations subsequent to the wave when a respondent reported a transition to retirement for the first time are dropped. For the long-term effect, all the observations are used. 







4.3 Effects of Retirement Timing on Health 4.3.1 Main Results Table 4.1 presents the summary statistics of the variables by retirement status. As for the health condition, retirees show worse condition for all health measures in comparison to non-retirees. 15.7% of non-retirees report poor self-rated health, while 23.7% of retirees report it; 6.1% of non-retirees report difficulty with ADLs, whereas 13.6% of retirees report it. However, the difference in depression is rather minor; 24.9% of non-retirees and 27.0% of retirees, respectively, report depression. For the instruments, the ratio of those who are eligible for partial/full benefit is higher among retirees than non-retirees. Among non-retirees, 35.9% and 11.7% of them are eligible for partial and full benefit, respectively, while among retirees, 89.1% and 53.3% of them are eligible for those, respectively. While demographic variables other than age are not used as independent variables in this chapter, estimation on subsamples by gender, education, and initial (at

82

4 Effect of Retirement Timing on Health

Table 4.1 Summary statistics Non-retirees Observations

Retirees Mean

Observations

Mean

Health measures Poor self-rated health

77,312

0.1565

13,621

0.2373

Difficulty with ADLs

75,657

0.0611

13,278

0.1362

Depression

75,192

0.2489

13,365

0.2699

EPB

77,912

0.3588

13,733

0.8911

EFB

77,912

0.1173

13,733

0.5326

Instruments

Demographics Age

77,912

58.302

13,733

63.190

Men

77,912

0.7633

13,733

0.6578

More than high school

76,329

0.3946

13,608

0.3440

White collar

77,139

0.6566

13,504

0.6604

the first wave) job characteristics is performed. Thus, the summary statistics of the demographics are also shown in the table. The ratios of men are 76.3% and 65.8% for non-retirees and retirees, respectively. The ratio of highly educated individuals is higher among non-retirees, 39.5%, than retirees, 34.4%. The difference in the initial job characteristics is not much large, 65.7% of non-retirees had a white-collar job and 66.0% of retirees had it.6 Figure 4.4 illustrates the relationship between the years before/after retirement and the ratio of poor health conditions by retirement timing. The sample used here is restricted to those who retired at 60 or 65. Negative (positive) numbers of years indicate years to (from) retirement, and zero year indicates the year when they retired. The pre-retirement health condition of those who retired at 60 is worse than those who retired at 65. Additionally, all three health conditions show little change, or rather, deterioration before retirement among both groups. However, following retirement, the health condition of those who retired at 65 obviously deteriorates with time. Conversely, the health condition of those who retired at 60 shows little change (poor self-rated health and difficulty with ADLs) or improvement (depression). Although it does not imply a causal relationship, this descriptive analysis suggests that early retirement is better for health than later retirement. Table 4.2 presents the first stage result for both the short- and long-term models associated with poor self-rated health. As the first stage results for other outcomes are very similar to the table, those results are not presented here. The signs of instruments are mostly as predicted. Eligibility for partial benefit increases the probability of 6 White-collar workers include administrative and managerial workers; professional and engineering

workers; clerical workers; and sales workers. The blue-collar category includes service workers; security workers; agriculture, forestry, and fishery workers; transport and machine operation workers; manufacturing process workers; and other workers.

4.3 Effects of Retirement Timing on Health

83

Fig. 4.4 Retirement timing and the change in poor health condition before/after retirement (Note The number of individuals used in the figure is 3,175. Source LSMEP 2005–2015)

early retirement and eligibility for full benefit increases normal retirement in the short term. The effect on early retirement is larger than on normal retirement. In the long-term model, two eligibility dummies increase the probability of retirement as with in the short-term model. The marginal effects on two retirement choices are almost the same. These results suggest that this empirical model captures the first stage mechanism properly. Table 4.3 presents the results of the short-term effect. Weak identification Fstatistics for each equation are well above the critical value of 10, indicating the results are reliable (Stock et al., 2002). Early retirees have 11.9% lower probabilities of reporting poor self-rated health and depression than non-retirees, whereas no significant difference in difficulty with ADLs is shown. Normal retirement shows relatively large effect for all health measures; however, these are imprecisely estimated and thus not statistically significant. Therefore, it is confirmed that early retirement improves overall and mental health; however, the effect of normal retirement is statistically unclear in the short term. In addition, physical health seems unlikely to be affected by retirement. Table 4.4 presents the results of the long-term effect. Weak identification Fstatistics for each equation are well above the critical value. Early retirees have 10.1% and 23.4% lower probability of reporting poor self-rated health and depression, respectively, than non-retirees, whereas no significant difference in difficulty

84 Table 4.2 Results of the first stage estimation for poor self-rated health

4 Effect of Retirement Timing on Health Early retirement

Normal retirement

Short-term effect EPB

0.1403*** (0.0090)

EFB

0.0590*** (0.0141)

Age

− 0.1787*** (0.0226)

− 0.1963*** (0.0276)

Age squared / 100

0.2025*** (0.0201)

0.2309*** (0.0244)

Observations

21,783

21,783

Individuals

3,175

3,175

Long-term effect EPB

0.0649*** (0.0047)

EFB

0.0673*** (0.0065)

Age

− 0.3061*** (0.0114)

− 0.2518*** (0.0117)

Age squared / 100

0.2930*** (0.0103)

0.2473*** (0.0105)

Observations

90,933

90,933

Individuals

10,694

10,694

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1

with ADLs is shown. The magnitude of the effect on poor self-rated health is almost the same as that of the short-term model, whereas the long-term effect on depression is approximately twice as large as the short-term model. Thus, early retirement affects health both in the short- and long run. Moreover, the effect becomes larger in the long run. On the other hand, normal retirees have 15.0% and 18.2% higher probability of reporting poor self-rated health and depression, respectively, than non-retirees, and show no significant difference in difficulty with ADLs. The effect of normal retirement was insignificant for any health measure in the short-term, however, it becomes significant in the long-term for two health measures. This means that normal retirement does not immediately affect health but is delayed.

4.3 Effects of Retirement Timing on Health

85

Table 4.3 Results for short-term effect Poor self-rated health

Difficulty with ADLs

Depression

Early retirement

− 0.1186** (0.0547)

0.0402 (0.0396)

− 0.1192** (0.0584)

Age

− 0.0176 (0.0277)

− 0.0108 (0.0205)

0.0158 (0.0294)

Age squared / 100

0.0311 (0.0256)

0.0160 (0.0189)

0.0133 (0.0268)

Observations

21,783

21,356

21,169

Individuals

3,175

3,173

3,167

F-statistics for instruments

243.317

243.987

243.071

Normal retirement

0.2121 (0.1916)

− 0.1042 (0.1570)

0.2927 (0.2045)

Age

0.0698 (0.0558)

− 0.0487 (0.0451)

0.1226** (0.0605)

Age squared / 100

− 0.0655 (0.0596)

0.0577 (0.0481)

− 0.1040 (0.0635)

Observations

21,783

21,356

21,169

Individuals

3,175

3,173

3,167

F-statistics for instruments

17.486

15.781

17.868

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1

4.3.2 Estimation on Subsamples: Gender, Education, and Occupational Characteristics The effect of retirement is potentially heterogeneous by respondents’ characteristics. For example, the degree of contribution of work-related activity to health may differ by gender. If men’s health depends more on their work than women’s, the effect of retirement would be larger for men. Educational attainment potentially affects retirement differently. Specifically, highly educated retirees could possibly produce health investment more effectively or facilitate a lower rate of health deterioration. Thus, highly educated retirees are less likely to have a detrimental health effect from retirement. Occupational characteristics may also affect retirement differently. For example, those who had a mentally or physically demanding job would experience a greater impact from retirement than those who did not. Thus, the estimation of subsamples by gender, education, and occupational characteristics is performed here. First, Figs. 4.5 and 4.6 present the results on poor self-rated health. Figure 4.5 illustrates the estimates of the short-term effect. Estimates with weak instruments are not depicted in the figure (the same representation is used in the following figures); in this case, the effects of normal retirement among women, those with low education, and those who had a blue-collar job are not illustrated. All estimates of early retirement show negative effects on the probability of reporting poor self-rated health.

86

4 Effect of Retirement Timing on Health

Table 4.4 Results for long-term effect Poor self-rated health

Difficulty with ADLs

Depression

Early retirement

− 0.1006* (0.0591)

0.0527 (0.0430)

− 0.2342*** (0.0660)

Age

− 0.0271 (0.0216)

− 0.0040 (0.0161)

− 0.0487** (0.0237)

Age squared / 100

0.0350* (0.0203)

0.0100 (0.0151)

0.0740*** (0.0223)

Observations

90,933

88,935

88,557

Individuals

10,694

10,646

10,611

F-statistics for instruments

189.61

192.15

192.16

Normal retirement

0.1501* (0.0780)

− 0.0213 (0.0614)

0.1824** (0.0825)

Age

0.0528** (0.0263)

− 0.0275 (0.0207)

0.0836*** (0.0280)

Age squared / 100

− 0.0423* (0.0251)

0.0328* (0.0198)

− 0.0541** (0.0266)

Observations

90,933

88,935

88,557

Individuals

10,694

10,646

10,611

F-statistics for instruments

108.73

105.90

107.20

Note Standard errors clustered by individual are in parentheses. All regressions include wave dummies. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1

Significant effect is observed only among men, those with low education, and those who had a white-collar job. These marginal effects range from a 12.2% to 14.8% reduction. In contrast, all estimates of normal retirement show positive effect on the probability of reporting poor self-rated health. Only the effect among men is significant; normal retirees are 46.9% more likely to report poor self-rated health. Figure 4.6 illustrates the estimation results of long-term effect. All estimates of early retirement show negative effects on the probability of reporting poor selfrated health, as with the short-term model; however, significant effect is observed only among men, those with high education, and those who had a blue-collar job. Attributes showing significant effects are different from the short-term model. These marginal effects range from a 20.0% to 22.4% reduction. With regard to the effect of normal retirement, all estimates, except for women, show positive effect on the probability of reporting poor self-rated health. Significant effect is observed among men and those with high education, which are 38.5% and 21.7% more likely to report poor self-rated health, respectively. The difference in the effect between short and long term is not very large. Early retirement basically improves overall health, whereas normal retirement worsens it, in congruence with the main results. Next, Figs. 4.7 and 4.8 present the results on difficulty with ADLs. Figure 4.7 illustrates the estimation results of short-term effect. Estimates of normal retirement

4.3 Effects of Retirement Timing on Health

87

Fig. 4.5 Short-term effect for poor self-rated health (Note Parentheses show the number of [observations; individuals]. Estimates with weak instruments are not shown. All regressions include wave dummies, age, and age squared. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1)

among women, those with low education, and those who had a blue-collar job are not shown due to a weak instrument. All estimates of early retirement show positive effects on the probability of having difficulty with ADLs; however, there is no significant effect. These marginal effects are very small, ranging from 2.5% to 6.2%. With regard to the normal retirement, estimates for men and those with high education show negative effect on the likelihood of having difficulty with ADLs, however, positive effect is shown for those who had a white-collar job. These results are all insignificant, while the estimate for those with high education show relatively large effect of 18.0% reduction. Figure 4.8 illustrates the results of long-term effect. Estimates of early retirement for men and those who had a blue-collar job show a slightly negative effect on the likelihood of having difficulty with ADLs; both effects are small and statistically insignificant. For other attributes, among women, both educational levels, and those who had a white-collar job, early retirees are more likely to have difficulty with ADLs; however, the effect is significant only among women; early retirees are 13.8% more likely to have difficulty with ADLs. With regard to normal retirement, the effects are insignificant but negative on the likelihood of having difficulty with ADLs among both genders, those with high education, and those who had a blue-collar job. Among those with low education and those who had a white-collar job, normal retirees are

88

4 Effect of Retirement Timing on Health

Fig. 4.6 Long-term effect for poor self-rated health (Note Parentheses show the number of [observations; individuals]. Estimates with weak instruments are not shown. All regressions include wave dummies, age, and age squared. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1)

slightly more likely to have difficulty with ADLSs; however, these effects are all insignificant. As the results suggest, physical health is less likely to be affected by retirement, thus, the difference in the effect between short and long term is not very large. Both early and normal retirement basically have no effect on physical health, except in the case of women. Finally, Figs. 4.9 and 4.10 present the results on depression. Figure 4.9 illustrates the estimation results of short-term effect. All estimates of early retirement show negative effects on the probability of reporting depression. Significant effect is observed only among women; early retirees are 16.0% less likely to report depression. In contrast, all estimates of normal retirement show positive effect on the probability of reporting depression. The effect is significant only among men; normal retirees are 37.9% more likely to report depression. Figure 4.10 illustrates the estimation results of long-term effect. All estimates of early retirement show negative effects on the probability of reporting depression, as with the short-term model. Significant effect is observed among men, those with both educational levels, and those who had a white-collar job. Attributes that show significant effects differ from the short-term model. These significant marginal effects range widely, from 18.3% to 45.6% reduction. With regard to the effect of normal

4.3 Effects of Retirement Timing on Health

89

Fig. 4.7 Short-term effect for difficulty with ADLs (Note Parentheses show the number of [observations; individuals]. Estimates with weak instruments are not shown. All regressions include wave dummies, age, age squared. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1)

retirement, all estimates show positive effect on the probability of reporting depression. Significant effect is observed among men, those with low education, and those who had a white-collar job. The effects range from an 18.9% to 29.6% increase. The effects in the short and long term are similar; however, the long-term effect is larger than short-term effect. Additionally, early retirement basically improves mental health, whereas normal retirement worsens it, in agreement with the main results.

4.4 Conclusion This chapter examined the effects of the timing of retirement on health in later life. Causal estimation using the IV approach demonstrates that early and normal retirement interestingly have opposing effects; early retirement shows a significant and beneficial effect on self-rated health and depression, whereas normal retirement shows a significant and adverse effect on these health measures. The adverse effect of later retirement is in line with Celidoni et al. (2017) and Shai (2018). Both early and normal retirements show no significant effect on difficulty with ADLs. These results

90

4 Effect of Retirement Timing on Health

Fig. 4.8 Long-term effect for difficulty with ADLs (Note Parentheses show the number of [observations; individuals]. Estimates with weak instruments are not shown. All regressions include wave dummies, age, age squared. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1)

are basically observed in both the short- and long-term models; however, the longterm effect is larger than the short-term effect. Taken as a whole, the result indicates that early retirement is generally beneficial to health, thus delaying retirement causes a detrimental effect on health. The results also support the hypothesis that later retirement makes the adjustment to fully retired life difficult. To understand the adverse effect of normal retirement on health, estimation was performed on subsamples. The adverse effect on self-rated health is particularly observed among men and those with high education. One possible explanation is that men, those with high education are in general deeply involved with or attached to their high-status jobs and continue working beyond mandatory retirement, possibly resulting in the loss of the job worsening their health. This explanation is proposed by Celidoni et al. (2017). They find that those who are satisfied with the salary or freedom of their jobs tend to retire later and show a significant decline in cognitive health post-retirement. Moreover, the adverse effect on depression is particularly observed among men, those with low education, and those who had a white-collar job. These workers seem to engage in mentally demanding jobs, like walk-in sales; therefore, later retirement might worsen their mental health. As discussed above, this chapter offers new evidence on the heterogeneity of the effect of retirement. Specifically, retiring as late as possible does not result in better health. On the other hand, the Japanese government has been increasing the

4.4 Conclusion

91

Fig. 4.9 Short-term effect for depression (Note Parentheses show the number of [observations; individuals]. Estimates with weak instruments are not shown. All regressions include wave dummies, age, age squared. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1)

age of eligibility for pension benefit, resulting in older workers’ tendency to work longer due to financial pressure, making it difficult to evaluate the effect of retirement timing. While later retirement is good for individuals’ economic conditions and the public pension system, it is bad for individuals’ health conditions and the medical and long-term care system. Policymakers challenged by the aging society should consider this implication. This chapter has some limitations. First, this chapter does not examine what occurs during partial retirement. Normal retirees may experience partial retirement after mandatory retirement, given the continuous employment system introduced by the revised ASEEP. Individuals may alter their health-related behavior during partial retirement. To understand the link between retirement timing and health more comprehensively, additional investigation regarding the change in health and health behavior during partial retirement is required. Second, the effect of retirement estimated in this study is the Local Average Treatment Effect (LATE), but not the Average Treatment Effect on the Treated (ATET) (Angrist & Pishcke, 2009). That is, the LATE is the results of compliers who fully retired because they reached the eligibility age for pension benefit, but not that of all retirees. Third, this chapter uses the respondents who had a full-time work at the first wave, but not the entire workforce. The number of part-time workers has been increasing in Japan and in other developed economies.

92

4 Effect of Retirement Timing on Health

Fig. 4.10 Long-term effect for depression (Note Parentheses show the number of [observations; individuals]. Estimates with weak instruments are not shown. All regressions include wave dummies, age, age squared. Significance levels: *** p < 0.01, ** p < 0.05, * p < 0.1)

Thus, retirement timing is predicted to become more diverse in the future, and more detailed investigation into the effect of retirement timing is needed.

References Adam, S., Bonsang, E., Grotz, C., & Perelman, S. (2013). Occupational activity and cognitive reserve: Implications in terms of prevention of cognitive aging and Alzheimer’s disease. Clinical Interventions in Aging, 8, 377–390. Angrist, J. D., & Pishcke, J.-S. (2009). Mostly harmless econometrics: An empiricist’s companion. Princeton University Press. Behncke, S. (2012). Does retirement trigger ill health? Health Economics., 21(3), 282–300. Cahill, K. E., Giandrea, M. D., & Quinn, J. F. (2018). Is bridge job activity overstated? Work, Aging and Retirement, 4(4), 330–351. Cahill, K. E., & Quinn, J. F. (2020). The importance of gradual retirement in America today. Public Policy & Aging Report, 30(3), 107–112. Calvo, E., Sarkisian, N., & Tamborini, C. R. (2013). Causal effects of retirement timing on subjective physical and emotional health. Journal of Gerontology Series B: Psychological Sciences and Social Sciences, 68(1), 73–84. Celidoni, M., Dal Bianco, C. D., & Weber, G. (2017). Retirement and cognitive decline. A longitudinal analysis using SHARE data. Journal of Health Economics, 56, 113–125.

References

93

Coe, N. B., & Zamarro, G. (2011). Retirement effects on health in Europe. Journal of Health Economics, 30(1), 77–86. Clark, R. L., Matsukura, R., Ogawa, N., & Shimizutani, S. (2105). Retirement transitions in Japan. Public Policy & Aging Report, 25(4), 129–131. Eibich, P. (2015). Understanding the effect of retirement on health: Mechanisms and heterogeneity. Journal of Health Economics, 43(1), 1–12. Fé, E., & Hollingsworth, B. (2016). Short- and long-run estimates of the local effects of retirement on health. Journal of the Royal Statistical Society Series A, 179(4), 1051–1067. Fisher, G. G., Chaffee, D. S., & Sonnega, A. (2016). Retirement timing: A review and recommendations for future research. Work, Aging and Retirement, 2(2), 230–261. Gorry, A., Gorry, D., & Slavov, S. N. (2018). Does retirement improve health and life satisfaction? Health Economics, 27(12), 2067–2086. Gorry, D., & Slavov, S. N. (2021). The effect of retirement on health biomarkers. Economics and Human Biology, 40, 100949. Grossman, M. (1972). On the concept of health capital and the demand for health. Journal of Political Economy, 80(2), 223–255. Heller-Sahlgren, G. (2017). Retirement blues. Journal of Health Economics, 54, 66–78. Hernaes, H., Markussen, S., Piggott, J., & Vestad, O. L. (2013). Does retirement age impact mortality? Journal of Health Economics, 32, 586–598. Hessel, P. (2016). Does retirement (really) lead to worse health among Europeann men and women across all educational levels? Social Science and Medicine, 151, 19–26. Insler, M. (2014). The health consequences of retirement. Journal of Human Resources, 49(1), 195–233. Jokela, M., Ferrie, J. E., Gimeno, D., Chandola, T., Shipley, M. J., Head, J., Vahtera, J., Westerlund, H., Marmot, M. G., & Kivimäki, M. (2010). From midlife to early old age: Health tragectories associated with retirement. Epidemiology, 20(3), 284–290. Mazzonna, F., & Peracchi, F. (2017). Unhealthy retirement? Journal of Human Resources, 52(1), 128–151. Neuman, K. (2008). Quit your job and get healthier? The effect of retirement on health. Journal of Labor Research, 29(2), 177–201. OECD. (2017). Pensions at a glance 2017: OECD and G20 indicators. OECD Publishing. OECD. (2019). Pensions at a glance 2019: OECD and G20 indicators. OECD Publishing. Oshio, T., & Kan, M. (2017). The dynamic impact of retirement on health: Evidence from a nationwide ten-year panel survey in Japan. Preventive Medicine, 100, 287–293. Ruhm, C. J. (1990). Bridge jobs and partial retirement. Journal of Labor Economics, 8(4), 482–501. Sakurai, K., Nishi, A., Kondo, K., Yanagida, K., & Kawakami, N. (2011). Screening performance of K6/K10 and other screening instruments for mood and anxiety disorders in Japan. Psychiatry and Clinical Neurosciences, 65(5), 434–441. Shai, O. (2018). Is retirement good for men’s health? Evidence using a change in the retirement age in Israel. Journal of Health Economics, 57, 15–30. Shimizutani, S., & Oshio, T. (2010). New evidence on initial transition from job to retirement in Japan. Industrial Relations, 49(2), 248–274. Stock, J. H., Wright, J. H., & Yogo, M. (2002). A survey of weak instruments and weak identification in generalized method of moments. Journal of Business and Economic Statistics, 20(4), 518–529. Wu, C., Odden, M. C., Fisher, G. G., & Stawski, R. S. (2016). Association of retirement age with mortality: A population-based longitudinal study among older adults in the USA. Journal of Epidemiology and Community Health, 70(9), 917–923.

Epilogue

In this book, I have provided some evidence for the effect of retirement on health in Japan from various viewpoints. Chapter 2 shows that, on average, short-term retirement has a beneficial effect on health, while long-term retirement has an adverse effect. Chapter 3 posits that a healthy pre-retirement lifestyle can prevent health decline after retirement. Chapter 4 demonstrates that early retirement is beneficial to health, while normal (or late) retirement is detrimental to health. As summarized in Chapter 1, there are several other effects of retirement on health. The results obtained in this book show some of the effects of retirement. According to the findings of this book, considering the long-term adverse effect of retirement on health, individuals should exit from the labor force as late as possible. This book, however, also indicates that late retirement is detrimental for post-retirement health in the long term, while early retirement is beneficial even in the long term. One of the reasons for the long-term beneficial effects of early retirement is that early retirees were able to retire earlier because they had a sufficient retirement fund when retiring. As a result, they can spend a healthy retirement life with appropriate medical treatment and healthy diet. However, as suggested in Chapter 4, late retirees are likely to have less retirement funds even after having worked longer, because of which they have an unhealthy life after retirement. Therefore, retirement timing may not be the only issue; the determinants of retirement timing must also be investigated further. Retiring earlier is beneficial to health for older adults. Therefore, accumulating retirement funds as early as possible is important for post-retirement health. One possible measure for this is to change the wage system from a seniority-wage system to efficiency-wage system—that is, changing the wage curve to a flatter one. This enables working-age employees to manage their retirement fund earlier and thus may increase the fund more rapidly. In addition, the expansion of the defined contribution pension system may enhance the accumulation of the retirement fund. This book also suggests that a physically and mentally healthy lifestyle should be introduced prior to retirement. Specifically, individuals should engage in moderate exercise and housework more. In other words, good work–life balance during working age is beneficial for post-retirement health and well-being. However, employer and employee health © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021 M. Mizuochi, Exploring the Effect of Retirement on Health in Japan, Population Studies of Japan, https://doi.org/10.1007/978-981-16-2638-8

95

96

Epilogue

insurers generally have less incentive to promote the health of retirees because the retirees institutionally have to move to other health insurers after retirement. Therefore, a mechanism that boosts the incentive for employers and insurers to consider retirees’ health is required. I hope that the findings of the book would be helpful in moving toward a healthy aged society.