Life and Job Satisfaction in China: Exploring Longitudinal Analysis with Mplus (Quality of Life in Asia, 19) 3031486943, 9783031486944

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Quality of Life in Asia  19

Lukasz Czarnecki Delfino Vargas Chanes

Life and Job Satisfaction in China Exploring Longitudinal Analysis with Mplus

Quality of Life in Asia Volume 19

Editor-in-Chief Daniel T. L. Shek, Department of Applied Social Sciences Hong Kong Polytechnic University Hunghom, Hong Kong Series Editors Alex C. Michalos, University of Northern British Columbia Prince George, BC, Canada Doh Chull Shin, University of California California, MO, USA Ming-Chang Tsai, Department of Sociology Academia Sinica Taipei, Taiwan

This series, the first of its kind, examines both the objective and subjective dimensions of life quality in Asia, especially East Asia. It unravels and compares the contours, dynamics and patterns of building nations by offering innovative works that discuss basic and applied research and emphasizing inter- and multi-disciplinary approaches to the various domains of life quality. The series appeals to a variety of fields in humanities, social sciences and other professional disciplines. Asia is the largest, most populous continent on Earth, and it is home to the world’s most dynamic region, East Asia. In the past three decades, East Asia has been the most successful region in the world in expanding its economies and integrating them into the global economy, offering lessons on how poor countries, even with limited natural resources, can achieve rapid economic development. Yet while scholars and policymakers have focused on why East Asia has prospered, little has been written on how its economic expansion has affected the quality of life of its citizens. This series publish several volumes a year, either single or multiple-authored monographs or collections of essays.

Lukasz Czarnecki • Delfino Vargas Chanes

Life and Job Satisfaction in China Exploring Longitudinal Analysis with Mplus

Lukasz Czarnecki University of the National Education Commission Krakow, Poland

Delfino Vargas Chanes National Autonomous University of Mexico Mexico City, Mexico

ISSN 2211-0550     ISSN 2211-0569 (electronic) Quality of Life in Asia ISBN 978-3-031-48694-4    ISBN 978-3-031-48695-1 (eBook) https://doi.org/10.1007/978-3-031-48695-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland Paper in this product is recyclable.

Authorship Statement

The participation of both authors in the book is equal (50-50 percent), and the authorship only reflects the alphabetical order of their corresponding last names. For this reason, Lukasz Czarnecki is before Delfino Vargas Chanes.

v

Foreword

This timely work provides a practical introduction to longitudinal analysis in incremental steps using a real-world example of job satisfaction and life satisfaction and the Mplus statistical package. Social and behavioral researchers have used latent growth curve (LGC) and auto-regressive modeling approaches in structural equation modeling (SEM) frameworks to describe and analyze changes in individual attributes, such as behaviors, psychosocial characteristics, relationships, and health outcomes, over time. LGC models focus on intraindividual change, while auto-­ regressive models focus on residual change over time. When analyzing more than one co-varying or time-varying attribute, researchers have extended LGC models to parallel LGC models and auto-regressive models to cross-lagged (CL) auto-­ regressive models. The authors explain these conventional models well using illustrative examples of job satisfaction and life satisfaction. The CL approach for two time-varying variables is consistent with the time-­ sequential processes between two consecutive time points resulting in a change in the rank order of those attributes. Parallel LGC is consistent with the parallel intraindividual change process (interlocking trajectories of attributes). The authors also use illustrative examples to explain these extended models well in this book. However, the change process may occur via both these processes simultaneously—time-sequential processes between two consecutive time points resulting in a change in rank order (CL) and parallel intraindividual change process (interlocking trajectories of attributes) resulting in an absolute change in attributes (parallel LGC). The authors thoroughly explain how these two simultaneous change processes can be integrated within the structural equation modeling framework. In addition, using illustrative examples, the authors explain how the potential acceleration of the change process (change proportional to the lagged level) can also be incorporated into these integrated models. Furthermore, the conventional latent growth curve assumes that all individuals come from a single, homogeneous population and have the same pattern of growth. However, heterogeneity may exist in the study population. Growth mixture models (GMM), an extension of an LGM, can be employed to identify potential unobserved

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viii

Foreword

heterogeneity in a study population or clustering/grouping of individual trajectories. The authors also explain such growth mixture models using illustrative examples. It is worthy of mentioning that GL and parallel LGC approaches illustrated in this book can be used to analyze dyadic data using time-varying parallel attributes of couple members (e.g., husbands and wives). These approaches incorporate the dependency between couple-members within the SEM modeling framework. To facilitate understanding, the authors provide equations, figures with Mplus syntax, and an interpretation of the results for all the models. This book would be an excellent resource for researchers conducting analyses using time-varying variables (attributes within a person or between dependent partners). This book could also function as an ideal supplement for use in advanced courses on longitudinal data analysis and structural equation modeling and for workshops on data analysis. Professor Emeritus, University of Georgia Athens, GA, USA 7 Oct 2023

Kandauda (A. S.) Wickrama, PhD

Preface

Lukasz Czarnecki The original idea of this book stems from 2019, when I did my research stay at the Shanghai Academy. It was there that I decided to write a book on changing Chinese society using the China Family Panel Studies. Afterwords, I visited the Peking University, the Institute of Social Sciences Survey, where I was helped accessing the data. The book is dedicated to scholars, both social scientists and statistitians in order to understand multiple faces of the Chinese social tranformation. Delfino Vargas Chanes In 1998, I began exploring longitudinal data analysis at the Center for Family Research at Iowa State University under the guidance of Rand Conger, Fred Lorenz, Wickrama K.A.S., and Xiao-Ge. To further my knowledge, I attended multiple courses offered by Beng Muthen, Keneth Bollen, Joop Hox, and Ellen Hamaker on Mplus. This education coupled with my experience in this field allowed me to write this book, which is addressed to practitioners in the Humanities. Krakow, Poland Mexico City, Mexico

Lukasz Czarnecki Delfino Vargas Chanes

ix

Acknowledgments

Lukasz Czarnecki It was a long process, and without the help of many people, this manuscript could not be written. Especially, Li Peilin, vice president of the Chinese Academy of Social Science, Li Youmei, vice president of the Shanghai Academy; and president of the Chinese Sociological Association my special thanks to Zhao Kebin, executive vice president of the Shanghai Academy, Chinese Academy of Social Sciences (CASS) for hosting me at the Shanghai Academy in 2019. Delfino Vargas Chanes Over the past 20 years, my prior training in data analyses and current practice at the National Autonomous University of Mexico and with the encouragement of researchers such as Fernando Cortes have enabled me to apply my experience to analyzing longitudinal data within economics and sociology. I hope readers will find this book as a helpful resource.

xi

Contents

1

 Introduction to Chinese Society and the Study ��������������������������������������   1 1.1 Subjective Well-being in China����������������������������������������������������������   1 1.2 Chinese Society: Confucius, Hukou, and Guanxi������������������������������   4 1.3 Data and Methodology������������������������������������������������������������������������   7 References����������������������������������������������������������������������������������������������������  10

2

 Life Satisfaction Over Time, 2010–2018��������������������������������������������������  13 2.1 Background on Life Satisfaction��������������������������������������������������������  13 2.2 Variables and Data������������������������������������������������������������������������������  15 2.3 The Model������������������������������������������������������������������������������������������  17 2.4 Model Fit Indices��������������������������������������������������������������������������������  18 2.5 How Many Trajectory Classes?����������������������������������������������������������  20 2.6 Discussion ������������������������������������������������������������������������������������������  23 2.7 MPLUS Codes for Life Satisfaction ��������������������������������������������������  24 References����������������������������������������������������������������������������������������������������  31

3

Job Satisfaction and Guanxi ��������������������������������������������������������������������  35 3.1 Job Satisfaction: Some Theoretical Considerations����������������������������  35 3.2 The LGC Model for Job Satisfaction��������������������������������������������������  38 3.3 How Many Classes Do We Have for Job Satisfaction?����������������������  38 3.4 Discussion ������������������������������������������������������������������������������������������  42 3.5 MPLUS Codes for Job Satisfaction����������������������������������������������������  45 References����������������������������������������������������������������������������������������������������  50

4

 Life and Job Satisfaction in Parallel��������������������������������������������������������  51 4.1 Theory Approximation������������������������������������������������������������������������  51 4.2 Variables����������������������������������������������������������������������������������������������  53 4.3 Methods and Sample Population��������������������������������������������������������  53 4.4 How to Model a Parallel Process?������������������������������������������������������  53 4.5 Descriptive Statistics��������������������������������������������������������������������������  57 4.6 A Single Latent Growth Curve for Each Outcome ����������������������������  57 4.7 How Many Trajectory Classes?����������������������������������������������������������  58 xiii

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Contents

4.8 What Explains These Trajectory Classes?������������������������������������������  60 4.9 Discussion ������������������������������������������������������������������������������������������  61 4.10 Conclusion������������������������������������������������������������������������������������������  62 4.11 Mplus Codes ��������������������������������������������������������������������������������������  62 References����������������������������������������������������������������������������������������������������  67 5

 Interplay Between Life and Job Satisfaction������������������������������������������  71 5.1 The Cross-Lagged Model ������������������������������������������������������������������  69 5.2 The LCS Model����������������������������������������������������������������������������������  74 5.3 Testing Covariates on Job and Life Satisfaction ��������������������������������  78 5.4 Discussion ������������������������������������������������������������������������������������������  81 5.5 Mplus Codes ��������������������������������������������������������������������������������������  82 5.5.1 Scripts for Cross-Lagged Models ������������������������������������������  82 5.5.2 Scripts for the LCS Model������������������������������������������������������  85 References����������������������������������������������������������������������������������������������������  88

6

General Conclusion������������������������������������������������������������������������������������  91 References����������������������������������������������������������������������������������������������������  94

Index��������������������������������������������������������������������������������������������������������������������  95

About the Authors

Lukasz  Czarnecki is a research professor at the University of the National Education Commission, Krakow, Poland. He holds a PhD in Sociology at the National Autonomous University of Mexico (2012), University of Strasbourg (2015), and Juris Doctoris at tha Jagiellonian University of Krakow (2019). He focuses on inequalities, subjective wellbeing, and mixed-method research. Delfino  Vargas  Chanes has been a full professor at the National Autonomous University of Mexico (UNAM) since 2010. He earned PhD in Sociology, MS in Statistics, and MS in Sociology at Iowa State University, Ames, and BS in Mathematics at UNAM, México. His areas of interest are the study of poverty, inequality, and advanced methods in statistics applied to social sciences, such as structural equation models, multilevel modeling, and machine learning.

xv

Abbreviations

AIC Akaike Index Criterion BIC Bayesian Index Criterion CFA Confirmatory factor analysis CFI Comparative fit index CFPS China Family Panel Studies CL Cross-lagged LCA Latent class analysis LCS Latent change score LGC Latent growth change LGMM Latent growth mixture model LMR Lo-Mendell-Rubin test LMR Low-Mendel-Rubin test MLM Multilevel longitudinal model RMSEA Root mean square error Approximation TLI Tukey-Lewis index

xvii

List of Figures

Fig. 1.1 Components of subjective well-being. (Source: Own extrapolation)������������������������������������������������������������������� 3 Fig. 1.2 China GDP, 1980–2020, percent. (Source: own elaboration based on the World Bank data, https://data.worldbank.org/)�������������������������������������������������������������������� 4 Fig. 1.3 Marital status, 2010–2018. (Source: The authors extrapolate based on CFPS (2010, 2012, 2014, 2016, 2018a, b))����������������������������� 7 Fig. 2.1 Life satisfaction in China, 2010–2018, in percent. (Source: Based on CFPS (2010, 2012, 2014, 2016, 2018))������������������ 16 Fig. 2.2 Latent growth mixture model for five time points with a linear trend. (Source: Own elaboration based on Bollen and Curran (2006))��������������������������������������������������������������� 19 Fig. 2.3 Linear model with four latent trajectory classes of life satisfaction. (Source: Own extrapolations)��������������������������������������������������������������� 21 Fig. 2.4 LGC model for life satisfaction������������������������������������������������������������ 25 Fig. 2.5 SEM corresponding to the LGMM������������������������������������������������������� 27 Fig. 3.1 Guanxi model. (Source: Own elaboration based on Bedford (2011, p. 155))��������������������������������������������������������������������������������������� 36 Fig. 3.2 Job satisfaction in China, 2010–2018. (Source: Author’s extrapolation based on CFPS (2010, 2014, 2016, 2018a, b))��������������������������������������������������������������� 37 Fig. 3.3 Linear model with four latent trajectory classes of job satisfaction. (Source: own elaboration)��������������������������������������������������������������������� 40 Fig. 3.4 Guanxi, 2014 and 2018. (Source: Own extrapolation based on CFPS (2014, 2018a, b))������������������������������������������������������������������� 43 Fig. 3.5 Guanxi in terms of job class analysis, 2014 and 2018. (Source: Own extrapolation based on CFPS (2014, 2018a, b))������������ 44 Fig. 4.1 Represents Eqs. (4.1), (4.2), and (4.3)�������������������������������������������������� 54 Fig. 4.2 Latent growth model for five time points with a linear trend for life satisfaction. The intercept of life satisfaction xix

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Fig. 4.3

Fig. 4.4 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 5.4 Fig. 5.5

Fig. 5.6 Fig. 5.7 Fig. 5.8

List of Figures

is denoted as πL0 and the slope as πL1. (Source: Adapted from Bollen and Curran (2006))����������������������������������������������������������� 55 Latent growth mixture model as a parallel process for job and life satisfaction. Note that the variable of job satisfaction is not available in 2012, but the model can still be fitted. (Source: Own elaboration based on Masyn (2013) and adapted from Bollen and Curran (2006))�������������������������������������������������������������������������������������� 56 Predicted trajectories of job and life satisfaction as a parallel process������������������������������������������������������������������������������ 60 Cross-lagged model for job and life satisfaction. (Source: Own elaboration is based on McArdle (1986) and McArdle and Epstein (1987))��������������������������������������������������������� 72 Cross-lagged model of job and life satisfaction. Model 1. (Source: An elaboration of our own)����������������������������������������������������� 73 Cross-lagged model of job and life satisfaction. Model 2. (Source: An elaboration of our own)����������������������������������������������������� 74 Cross-lagged model of job and life satisfaction. Model 3. (Source: An elaboration of our own)����������������������������������������������������� 74 Bivariate latent change score model for job and life satisfaction. (Source: Own elaboration, adapted from Duncan (1969, 1975) and Ferrer and McArdle (2003) and Barker et al. (2014))����������������������������������������������������������������������� 77 Bivariate trait latent change score model for job and life satisfaction. (Source: Adapted from Klopack et al. (2019))������������������������������������������������������������������ 78 Latent change score model for analyzing the relationships between job satisfaction and life satisfaction. (Source: Own elaboration, adapted from Klopack et al. (2019))������������������������ 78 Latent change score model for job and life satisfaction. (Source: An elaboration of our own)����������������������������������������������������� 79

List of Tables

Table 1.1 Administrative units of China for 2010, 2012, 2014, 2016, and 2018����������������������������������������������������������������������������������������������� 8 Table 1.2 Models and description used in this book������������������������������������������ 10 Table 2.1 Control variables used in the study, 2010-2018��������������������������������� 16 Table 2.2 Descriptive statistics and correlations for job and life satisfaction����������������������������������������������������������������������������� 17 Table 2.3 Fit statistics for latent growth curve model for life satisfaction������������������������������������������������������������������������������ 19 Table 2.4 Latent growth mixture model fit statistics for linear model to determine the number of classes���������������������������������������������������� 20 Table 2.5 Parameter estimates for the four-class latent growth mixture model (LGMM)���������������������������������������������������������������������������������� 21 Table 2.6 Odds ratios for the ordered logistic regression models for life satisfaction CFPS data, 2010–2018���������������������������������������� 22 Table 2.7 Fit statistics for latent growth curve model for life satisfaction������������������������������������������������������������������������������ 26 Table 2.8 Parameter estimates for the four-class growth mixture model for life satisfaction������������������������������������������������������������������������������ 31 Table 3.1 Job satisfaction measured in the CFPS����������������������������������������������� 37 Table 3.2 Job satisfaction, in percent (%) year 2018������������������������������������������ 38 Table 3.3 Fit statistics for latent growth curve model for job satisfaction������������������������������������������������������������������������������ 39 Table 3.4 Latent growth mixture model fit statistics to determine the number of classes������������������������������������������������������������������������� 39 Table 3.5 Parameter estimates for the four-class growth mixture model for job satisfaction������������������������������������������������������������������������������ 40 Table 3.6 Odds ratios for ordered logistic regression models for Job satisfaction CFPS data, 2010–2018���������������������������������������� 41 Table 3.7 Descriptive statistics for guanxi��������������������������������������������������������� 44 Table 3.8 Guanxi, 2014 and 2018���������������������������������������������������������������������� 45 xxi

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List of Tables

Table 4.1 Descriptive statistics and correlations������������������������������������������������ 57 Table 4.2 Fit statistics for latent growth curve models��������������������������������������� 58 Table 4.3 Fit statistics of parallel process growth mixture models (n = 26,332)���������������������������������������������������������������������������������������� 59 Table 4.4 Parameter estimates for the three-class growth mixture model������������������������������������������������������������������������������������� 59 Table 4.5 Logistic model for parameterization using reference class 1 “high and increasing”������������������������������������������������������������� 61 Table 5.1 Descriptive statistics for job and life satisfaction������������������������������� 72 Table 5.2 Estimated coefficients for all cross-lagged models (n = 26,332)���������������������������������������������������������������������������������������� 75 Table 5.3 MLM results for the effects of guanxi on job satisfaction and life satisfaction for 2014 and 2018���������������������������������������������� 81 Table 5.4 MLM with all sociodemographic variables for job satisfaction and life satisfaction for years 2010–2018������������������������������������������� 82

Chapter 1

Introduction to Chinese Society and the Study

1.1 Subjective Well-being in China Subjective well-being (SWB) is about how people think about themselves in relation to the effects of the domains of social life, development, and public policies (Vargas, 2018; Graham et al., 2018). It includes a general satisfaction from income, health, education, family life, and job, among others (Erdogan et al., 2012; Selezneva, 2011). SWB “includes various dimensions such as happiness, satisfactions, positive effects, and flourishing, according to how people think they are. Well-­being is influenced by people’s feelings, emotions and experiences” (Burke & Page, 2017, p. 4). SWB is a “psychological construct concerned not with what people have or what happens to them but with how they think and feel about what they have and what happens to them” (Maddux, 2018, p. 3–4). SWB resembles the Aristotelian concept of Eudaimonia. According to Diener (2000, p. 34), subjective well-being is “an all-­ inclusive entity and can be best considered as a latent variable, a higher-order construct composed of wide spectrum of people’s cognitive and affective evaluations of their lives.” This broad concept includes both happiness and satisfaction; the first is an affective assessment, while the latter is a cognitive assessment of life (Huang et al., 2019). SWB is an umbrella construct and evaluates the condition of the welfare state. The idea of a modern welfare state comes from Sir William Beveridge’s report published in 1942. Since then, the idea of a strong state in social domains such as social services, insurance, health, and education has been developed. However, this was the case for the western hemisphere. In China, the official communist regime based on Marxism-Leninism was implemented since 1949. The system promotes socialism. However, the paradigm of welfare state was pushed out from the discourse and practice from the 1980s when a new paradigm, neoliberal one, was gradually implemented during Reagan-Thatcher terms and then in developing countries, from Latin America, Central-East Europe, to China.

© The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Czarnecki, D. Vargas Chanes, Life and Job Satisfaction in China, Quality of Life in Asia 19, https://doi.org/10.1007/978-3-031-48695-1_1

1

2

1  Introduction to Chinese Society and the Study

Evaluation of SWB could be a difficult task (Gullone & Cummins, 2012). It has direct relations to the life evaluation, based on the information that person contributes to the happiness that life offers by his/her circumstances and conditions (Rojas, 2008; Orviska et al., 2014). The indicators can also be used as a proxy for quality of life, since they include “not only objective domains of standard of living such as health, comfort, or wealth, but are also related to how people feel about their own lives” (Gori-Maia, 2013). Quality of life impacts the life cycle. For Elder, the life cycle is “a sequence of social roles that bear upon stages of parenthood, from the birth of children to their departure from the household and their eventual transition to the role of parent, setting in motion another life cycle” (Elder, 1998, p. 1). The Western and Chinese values are different in terms of life satisfaction. As Erdogan observed, “in individualistic nations, it is reasonable to expect that much of individual behaviour is performed with the intent to pursue happiness” (Erdogan et al., 2012, p. 1040). In China, a different kind of approach was emerging related to social relationships that play an important support for one’s life. Some literature has found that higher subjective well-being has been associated with better social relationships (Diener et al., 2018). Some studies show that there exists an association between social relationships and high levels of subjective well-being, but the effect of existence is particularly weak (Lucas & Dyrenforth, 2006). Obviously, to better understand the link between social relationships and subjective well-being, we have to examine it in much more various contexts. Taking China as a case study, which has a typical relational society, will greatly help to deepen the understanding of these relationships in this research. We examine whether subjective well-being in China depends on guanxi (关系), which is based on the network building approach. The analysis of subjective well-being in China has different meaning for Western societies. Confucian ideas have a profound impact for the analysis of social networks, playing a great importance in building nonfamily relations (Kwang, K.-H. (2018). However, we address the gap in research that focuses primarily on life satisfaction through job satisfaction, without guanxi analysis. Making evaluations in China requires the analysis of social networks (guanxi), which are very important in the building of nonfamily relationships. As far as the Chinese economic model is concerned, it resembles the reproduction of the development capitalist paradigm of great industrialization in the nineteenth century, with accumulation of capital, privatization that transferred public assets, including land, to private hands, corruption, growing regional inequalities, which all concluded with falling happiness (Brockmann et  al., 2009; Wedeman, 2012). There are few studies on subjective well-being in China; Cheng and Lam (2010) pay attention to a particular group of population of street children; Miao and Wu (2016) make a comparison of urban population between Hong Kong, mainland China, and Taiwan. However, in the present analysis, subjective well-being will be traced through the analysis of multiple concepts, i.e., job satisfaction, life satisfaction, trust, social networking, and happiness. Confucius described himself how he was looking for a job, which is a condition to be fulfilled with life: “When I was young, my condition was low and therefore I acquired my ability in many things, but they were mean matters” (Yetts, 1943, p. 9).

1.1  Subjective Well-being in China

3

Job satisfaction Subjetive Wellbeing Life satisfaction

Fig. 1.1  Components of subjective well-being. (Source: Own extrapolation)

One must search and develop oneself considering different scopes of abilities, under one of the main virtues, which should be benevolence (in Chinese, ren). To better understand subjective well-being in the context of the Chinese society, unobserved factors including job satisfaction and life satisfaction are shaped within the socioeconomic development of the country (Fig. 1.1). The SWB is shaped by economic transformation. In terms of Chinese economic development, despite the huge economic growth registered each year, the turbulent times of the COVID-19 pandemic provoke a general response from governments to boost economies. Nevertheless, China’s recovery to economic growth in the third quarter of 2020 was more rapid and stronger than anticipated.1 China suffered the external shock of the financial crisis that emerged in 2007 and 2008 in the US and other advanced economies but was less intense than other countries (Fig. 1.2). GDP lost 5 percentage points between 2007 and 2009; in 2010 economy recovered. In 2011, a slow but steady slowdown in growth began. Macroeconomic conditions can have individual impacts on families, but these impacts are more likely to be heterogeneous, as we observe in this study. The burden of social costs is huge, including wage differences, the presence of huge regional inequalities between the developed east and the underdeveloped west, and corruption, among others.

1.2 Chinese Society: Confucius, Hukou, and Guanxi According to Fei Xiaotong (费孝通), one of the most prominent Chinese sociologists and the founding father of Chinese sociology, the basic structure of Chinese society is the “diversified model of social organization” (差 序 格局, chaxugeju).2

 IMF, 2020. World Economic Outlook, October 2020: A Long and Difficult Ascent, p. xiii. https://www.imf.org/en/Publications/WEO/Issues/2020/09/30/world-economic-outlookoctober-2020. 2  Fei Xiaotong was Bronislaw Malinowski’s student in the UK. 1

4

1  Introduction to Chinese Society and the Study

16.0 14.0

15.2

12.0 10.8

10.0

9.0

8.0 6.0

11.7 11.2

5.1

4.0

9.3

8.9

7.8

14.2

14.2 13.9 13.0

13.4

12.7 10.111.4

11.0 9.9 9.2

10.0 9.1 8.58.3 7.87.7

10.6 9.79.4 9.67.9

7.0 6.9 7.87.4 6.8 6.7 6.1

4.23.9

2.0 2018

2016

2014

2012

2010

2008

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

0.0

Fig. 1.2  China GDP, 1980–2020, percent. (Source: own elaboration based on the World Bank data, https://data.worldbank.org/)

He analyzed an agricultural society of China.3 This model is about creating a morality based on personal relationships. This constitutes a different mode of association. In Western civilization, different moral principles are based on the relationship between the organization and the individual. Organization is an entity that transcends the individual. The idea of the one God present, and the Church as a corporation, was born in the West.4 For Confucius, kindness is the basis (benevolence, 仁 ren). Therefore, Confucius will say that “everything under heaven returns to ren” (天下归仁, tien xia gui ren). This responsibility is the basis of a family relationship, which must be based on the principles of respect for the elderly (filial piety), fraternal duty, loyalty and sincerity (忠, zhong) and based on proper conduct—righteousness (义, yi). At the higher level of the organization, there is a relationship between subordinates and the ruler. Mencius, the follower of Confucius’ thought, introduces the term “ren government” (仁 政, renzheng). Furthermore, in the Chinese association system, the most important characteristic is certainly “self-mastery and observance of rites” (克己复礼, ke ji fu li). This is an analogue idiom that makes reference to “restrain yourself and return to the rites.”  F. Xiaotong (1992). 乡土中国. Beijing: Foreign Language Teaching and Research Press.  According to Art. XVI of the Universal Declaration of Human and Citizen Rights of 1789, the constitution provides for the recognition of human rights and the introduction of the separation of powers. This will become the foundation of the legal orders in the European civilization circle. The rule of law comprises a system of rights and obligations under the constitution. Also, the US Declaration of Independence is a document referring to “obvious” truths from the Scriptures. In both legal systems—European and North American—the law is adopted “without deliberation or communication.” 3 4

1.2  Chinese Society: Confucius, Hukou, and Guanxi

5

The law in the Chinese system means on the one hand 礼 (li), which means unwritten rituals, and on the other hand, 法 (fa) which means written law as a form of organization. The rule of law in the sense of Eurocentric civilization means that “the citizens use the law to govern.” The law is something external that regulates the relations between citizens. The enforcement of rights depends on the power of political support and the people. The difference between ruling by people and ruling by laws lies not in the words “people” and “rights,” but rather in the force used to maintain order and in the nature of social norms. In the Chinese tradition, Confucius and his Analects influenced the shaping of relations within the State. In addition, Laotze, the father of Taoism, played a role in shaping relationships in society. Tao is like “the empty pitcher.” The awareness of transformation and unity with the other world is contained in the story of Zhuangzi, Laotze’s parables: Once upon a time I dreamed that I was a butterfly flying here and there. I was only aware of my happiness as a butterfly, not knowing that I was myself. Soon I woke up and was me again.

With Laotze, we have deep empathy for the reality that surrounds us. The legal order resulting from the teachings of Confucius, Mengzi, and Laotze is based on a special force that results from the will to communicate a posteriori. In other words, the social order can be maintained without external force, that is, a priori law as a form of organization of the compulsion of society. The form of 礼 (li), i.e., rituals, unwritten rites, and the written law 法 (fa), refer to two different orders in the relationship between the individual and a law-based society. The Eurocentric law is a system of repression that is related to the idea of revealed rights. Another kind is law in the Chinese order—there is no idea of revealed truths and the protection of individual human rights because there is no need to do so. The law is based on rituals and rituals on how to relate to the other person. According to Fei Xiaotong, in Chinese civilization, social relations between women and men are based on different principles. They are not based on emotion ( 感情, ganqing), but on understanding (了解, liaojie) marriage is a relationship based on differences, not similarities. There must be some distance between men and women. There are only differences between men and women. Understanding your differences guides the relationship within marriage. There are two categories important for the evaluation of subjective social well-­ being evaluation in China, i.e., 户口 (hukou) and 关系 (guanxi). Hukou is the household registration policy introduced by Mao Zedong at the time of the planification economy and society. From the implementation of hukou, the modern transformation in China began. Hukou refers to the household registration system that divided the population into two categories: agricultural registered in rural areas and non-agricultural (urban) in metropolitan areas, established under the regulations on Hukou registration in 1958 by the National People’s Congress (NPC). According to Hu (2016), the status of the hukou of parents also plays a central role in marital

6

1  Introduction to Chinese Society and the Study

classification, as an individual’s father and father-in-law tend to have the same rural or urban hukou. Using the CFPS data, in 2010, 70.5% of the inhabitants have agricultural registration, and only nearly 29% are non-agricultural. The 2016 data show that 87.5% of the population did not change their hukou and 5.7% did. Hukou points out to what extent Chinese society is characterized by social mobility. Hukou will be the basis for life satisfaction analysis. Tsang (2016, p. 8) observes that “it was an effort to protect progress toward collectivization in rural areas and control food shortages in urban China.” The objective was to regulate the internal movement of the Chinese. A legal domicile was created for all people and bounded each person to that domicile. The information included details of births, deaths, marriages, divorces, and movements of all members of the family. Hukou was abolished de iure in 2003 as a consequence of the constitutional ruling in the Sun Zhigang Case.5 This case showed the impacts of changing society on the old institutions of stratification such as hukou. For rural migrant workers, living in urban cities can be difficult, since urban hukou have more privileges than rural migrant workers, but this is the only option to improve their living conditions. Wang (2005, p. xii–xiii) observes that “people are treated differently in accordance with where their legal residency is, and the change and relocation of any citizen’s legal residency must be approved by the government.” On the other hand, guanxi means informal nonfamily relations developed by people. This term can be compared with the degree of social cohesion. Guanxi is the basis for establishing professional relations in the sphere of labor relations and informal relations in the neighborhood. Guanxi makes Chinese well-being so characteristic. The composition of social networks, social attachment, perceived social support, and the volume of social resources are believed to be significantly and positively associated with life satisfaction. In the literature, the idea of social participation plays a significant role, such as in the happiness of the elderly (Miao & Wu, 2016). Guanxi is rooted in Confucianism and nonfamily-based relations. This concept raises an important question not only about the way of doing business in China, but also about how guanxi affects job satisfaction and subjective well-being. Guanxi, literally interpersonal connections, is a network of stakeholders based on resource coalitions and plays a key role in achieving business success in China (Su et al., 2007). Moreover, guanxi shapes the legal framework on investment law in contemporary China (Czarnecki, 2020). Traditionally, understanding the guanxi circle is crucial to understanding Chinese society (Gold et al., 2002). It is so different compared to Western societies, for example, according to Ambler et al. (2016), p. 83),  Sun Zhigang Case (孙志刚事件) in 2003. On March 20, 2003, 27-year-old Sun Zhigang died in the medical clinic of a detention center (拘留所) in Guangzhou. He had been detained after being unable to show his three identifications: temporary living permit (暫住証), his identity card, and his residence permit (hukou) which was with his family in Hubei. Three jurist scholars: Dr. Xu Zhiyong, Dr. Yu Jiang, and Dr. Teng Biao prepared an official replay on the basis of violation of Constitutional principle, as the only legislative body is the NPC (Biao, 2013). 5

1.3  Data and Methodology

100.0

7

79.6

80.0

72.4

60.0 40.0 20.0 12.9 0.0

12.5

Never married

0.3 Married 2010

2012

Cohabitation 2014

1.9 5.9

0.4 1.3

2016

Divorced

5.2

Widowed

2018

Fig. 1.3  Marital status, 2010–2018. (Source: The authors extrapolate based on CFPS (2010, 2012, 2014, 2016, 2018a, b))

“family and social context define the individual as distinct from the western view in which the individual defines the context.” This concept refers to the development of social relationships and connections without family networks. Is there any effect of hukou and guanxi on life and job satisfaction? The one-child policy, which started in the 1980s, brought substantial costs, including political costs, human rights concerns, a more rapidly aging population, and an imbalanced sex ratio resulting from a preference for sons (Zhang, 2017). It caused a falling youth dependency ratio in the short term and a rising elderly dependency ratio in the long term, as many authors observed. The harm produced by the policy is long-term and irreparable in terms of not only aging, but also sex ratio, as Feng (2016, p.  84) observed that “China has had three decades of abnormal sex ratios at birth, as couples first resorted to female infanticide and then to sex-­selective abortion.” The period of analysis is interesting as it includes this shift from one- to two-child official policy, which will have fertility consequences in the long term. Based on data from five waves of the Chinese Family Panel Studies, comparing the period 2010 to 2018, married couples represented 72.4% in 2018 (Fig.  1.3). However, marital status shows a different change in the decrease in the number of married couples in the period studied. Changes are associated with structural transformations in the development of China.

1.3 Data and Methodology The present study takes into account the dataset from the China Family Panel Study, which is considered a representative national survey. However, the baseline CFPS of the 2010 sample covers 25 provinces/municipalities/autonomous regions, excluding Hong Kong, Macao, Taiwan, Xinjiang, Xizang, Qinghai, Inner Mongolia, Ningxia, and Hainan, representing 95% of the Chinese population (Table 1.1). The

1  Introduction to Chinese Society and the Study

8

Table 1.1  Administrative units of China for 2010, 2012, 2014, 2016, and 2018 Chinese Province Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi huanga Hainan Chongqing Sichuan Guizhou, China Yunnan Tibet Shaanxi Gansu Qinghai Ningxia Hui Autonomous Xinjiang Uygur Autonomous Region Missing Total

Year 2010 230 225 1737 1564 0 3129 691 1193 3162 646 471 543 377 594 1404 3732 614 956 3070 669

% 0.68 0.67 5.17 4.65 0 9.31 2.06 3.55 9.41 1.92 1.4 1.62 1.12 1.77 4.18 11.11 1.83 2.85 9.14 1.99

Year 2012 202 230 1949 1562 0 3069 582 1048 2388 748 592 741 453 754 1554 4346 526 1023 3512 684

% 0.57 0.64 5.46 4.37 0 8.59 1.63 2.93 6.69 2.09 1.66 2.07 1.27 2.11 4.35 12.17 1.47 2.86 9.83 1.91

Year 2014 288 230 2050 1662 10 3223 630 1121 2274 784 682 804 510 679 1641 4594 537 1053 3471 812

% 0.78 0.62 5.52 4.47 0.03 8.68 1.7 3.02 6.12 2.11 1.84 2.16 1.37 1.83 4.42 12.37 1.45 2.83 9.34 2.19

Year 2016 325 226 1997 1579 23 3151 655 967 2128 792 759 776 454 766 1638 4272 568 1051 3599 843

% 0.88 0.61 5.41 4.28 0.06 8.54 1.78 2.62 5.77 2.15 2.06 2.1 1.23 2.08 4.44 11.58 1.54 2.85 9.76 2.29

Year 2018 260 223 1961 1370 26 2688 541 910 1843 628 735 638 425 692 1503 3710 480 895 3128 707

% 0.8 0.68 6.01 4.2 0.08 8.24 1.66 2.79 5.65 1.93 2.25 1.96 1.3 2.12 4.61 11.38 1.47 2.74 9.59 2.17

0 363 1777 1053

0 1.08 5.29 3.13

0 354 1648 1234

0 0.99 4.61 3.45

5 335 1825 1119

0.01 0.9 4.91 3.01

13 391 2007 1253

0.04 1.06 5.44 3.4

17 331 1899 1004

0.05 1.02 5.82 3.08

991 0 705 3704 0 0

2.95 0 2.1 11.02 0 0

1026 0 709 4615 1 3

2.87 0 1.98 12.92 0 0.01

1204 0 829 4755 0 3

3.24 0 2.23 12.8 0 0.01

1033 5 849 4691 7 15

2.8 0.01 2.3 12.72 0.02 0.04

1098 10 685 4086 10 19

3.37 0.03 2.1 12.53 0.03 0.06

0

0

2

0.01

17

0.05

49

0.13

88

0.27

0 0 33,600 100

164 0.46 35,719 100

0 0 37,147 100

10 0.03 36,892 100

Source: own extrapolation, CFPS (2010, 2012, 2014, 2016, 2018a, b) a Autonomous region

0 0 32,610 100

1.3  Data and Methodology

9

2010 baseline survey interviewed a total of 14,960 households and 42,590 individuals (CFPS, 2017). The CFPS implemented its baseline survey in 2010 and four waves of full sample follow-up surveys in 2012, 2014, 2016, and 2018. The data from the last survey was delivered in the second half of 2019. To address the research question, the manuscript has been divided into four chapters. On the same basis, the first chapter focuses on life satisfaction, followed by the second chapter on job satisfaction, and the third and fourth chapters, respectively, focus on job and life satisfaction. In all chapters, we analyze the individual trajectories of life satisfaction scores from a longitudinal perspective. Each individual in the sample is followed over time and contributes with a single score assessed over five waves of observations. Our strategy begins with a Latent Growth Mixture Model (LGMM) that “permits straightforward examination of intraindividual (within-person) change over time, as well as interindividual (between-person) variability in intraindividual change” (Preacher et al., 2008, p. 2). With this approach, we can identify a single trajectory for all individuals over time. Furthermore, we identified trajectory classes, a group of trajectories categorized into classes that provides a new story of the evolution of life satisfaction in China from 2010 to 2018; with this approach, we incorporate the heterogeneity of growth and identify four trajectory classes. The Chap. 2 of this book focuses on a subset of the population (i.e., only on Economic Active Population). The item used was “How satisfied are you with your job” we again proceed with the same LGMM methodology (Bollen & Curran, 2006) to fit a model and identified four trajectory classes again for job satisfaction using a Latent Growth Mixture Model (LGMM). A similar heterogeneity of growth was found during the same period. In Chap. 3, we added another approach to explore the heterogeneity of growth for both outcomes when they are explored simultaneously. In that case, we want to address the question of whether we can identify classes of trajectory curves of job and life satisfaction simultaneously. To answer this question, we used latent growth curve mixture models for a parallel process (McArdle & Grimm, 2010; Lu et al., 2021). The findings indicate that there is a great deal of heterogeneity; we found that a three-class solution fits well. One group shows an increasing growth for both outcomes during the years of measurement, another group showed a similar trend but a lower lever, and finally a third group showed a steady decrease for both outcomes during the study period. In Chap. 4, we focus on the two processes of life satisfaction and job satisfaction in connection. We explore how both processes relate to each other in two ways. That is, does life satisfaction lead to job satisfaction over time? Or is job satisfaction the one that leads to life satisfaction? In this regard, we look forward to answering these questions using cross-lagged models (CL) and latent change score models (LCS) (McArdle & Grimm, 2010). In this chapter, we added a Multilevel Longitudinal Model (MLM) to explain how Guanxi and other variables can explain both life and job satisfaction trajectories. Our contribution includes two aspects of analysis: (1) to explore several dimensions of well-being for having a better understanding on how job and life

10

1  Introduction to Chinese Society and the Study

Table 1.2  Models and description used in this book Models LGC LGMM single LGMM parallel

Cross-lagged Latent change scores Multilevel longitudinal model

Chapter Description 2 and 3 Analysis of job and life satisfaction in a single trajectory 2 and 3 Analysis of job and life satisfaction fitting multiple trajectory classes over time 4 Analysis of job and life satisfaction fitting multiple trajectory classes over time as a parallel process, using the active economic population only 5 Analysis of the interplay of job and life satisfaction over time 5 Analysis of the cross-lagged effects of the leading factor (job or life satisfaction), imposing further longitudinal constrains 5 Covariates that explain changes over time

satisfaction can be studied from a non-Western perspective. The reader will find that there are different explanations of well-being in China. (2) Additionally, the reader will find how to analyze two outcomes (job and life satisfaction) from a longitudinal perspective using longitudinal techniques (e.g., LGMM, LCS, cross-lagged models) that are related to structural equation modelling (SEM) approach (Muthén & Muthén, 2017). We perform our analysis in Stata 13 and Mplus 8.7 (Muthén & Muthén, 2017 software). In addition, each chapter will have both a dataset and Mplus programs available for the readers who want to replicate our results. Table 1.2 shows the models presented in sequence in this study, so that the readers can easily find the applications of each model and the Mplus scripts for each chapter.

References Ambler, T., Witzel, M., & Xi, C. (2016). Doing business in China. Routledge. Bollen, K. A., & Curran, P. J. (2006). Latent curve models: A structural equation perspective. Wiley. Brockmann, H., Delhey, J., Welzel, C., & Yuan, H. (2009). The China puzzle: Falling happiness in a rising economy. Journal of Happiness Studies, 10(4), 387–405. Burke, R. J., & Page, K. M. (2017). Research handbook on work and wellbeing. Edward Elgar Publishing. Biao, T. (2013). Xu, Zhiyong, Jiang Yu, and Teng Biao: An official response centered on constitutional principles coverage, highlighting that the legal legislative power rests solely with the NPC: Oficial State Agency. CFPS. (2010). China Family Panel Survey from Peking University. Institute for Social Studies Survey. CFPS. (2012). China Family Panel Survey from Peking University. Institute for Social Studies Survey. CFPS. (2014). China Family Panel Survey from Peking University. Institute for Social Studies Survey. CFPS. (2016). China Family Panel Survey from Peking University. Institute for Social Studies Survey.

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CFPS. (2018a). China Family Panel Survey from Peking University. Institute for Social Studies Survey. CFPS. (2018b). Chinese Family Summary Questionnaire. Peking University, Institute for Social Studies Survey. In Chinese. Cheng, F., & Lam, D. (2010). How is street life? An examination of the subjective wellbeing of street children in China. International Social Work, 53(3), 353–365. https://doi. org/10.1177/0020872809359863 Czarnecki, L. (2020). The 2020 Foreign Investment Law of China. Confucianism and new challenges for social development. Contemporary Central & East European Law, 1(133), 94–103. https://doi.org/10.37232/cceel.2019.08 Diener (Ed.). (2000). Subjective wellbeing: The science of happiness and a proposal for a national index. American Psychologist, 55(1), 34. Diener, E., Oishi, S., & Tay, L. (2018). Advances in subjective wellbeing research. Nature Human Behaviour, 2(4), 253–260. Elder, G. (1998). The life course as developmental theory. Child Development, 69(1), 1–12. https:// doi.org/10.2307/1132065 Erdogan, B., Bauer, T. N., Truxillo, D. N., & Mansfield, L. R. (2012). Whistle while you work: A review of the life satisfaction literature. Journal of Management, 38(4), 1038–1083. https://doi. org/10.1177/0149206311429379 Feng, W., Gu, B., & Cai, Y. (2016). The end of China’s one-child policy. Studies in Family Planning, 47(1), 83–86. Gold, T., Gold, T. B., Guthrie, D., & Wank, D. (2002). Social connections in China: Institutions, culture, and the changing nature of guanxi. Cambridge University Press. Gori-Maia, A. (2013). Relative income, inequality and subjective wellbeing: Evidence for Brazil. Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 113(3), 1193–1204. Graham, C., Laffan, K., & Pinto, S. (2018). Wellbeing in metrics and policy. Science, 362(6412), 287–288. Gullone, E., & Cummins, R. (2012). The universality of subjective wellbeing indicators: A multi-­ disciplinary and multi-national perspective (Vol. 16). Springer Science & Business Media. Hu, Y. (2016). Marriage of matching doors: Marital sorting on parental background in China. Demographic Research, 35, 557–580. Huang, X., Western, M., Bian, Y., Li, Y., Côté, R., & Huang, Y. (2019). Social networks and subjective wellbeing in Australia: new evidence from a national survey. Sociology, 53(2), 401–421. Kwang, K.-H. (2018). Enriching social network theory with theoretical model of confucian five virtues. Archaeology and Culture, 1(1), 68–86. Lu, S., Liu, Y., Guo, Y., Chak-Ho, H., Song, Y., Cheng, W., Kwan-Chui, C. H., Chan, O. F., Webster, C., Chiu, R. L. H., & Lum, T. Y. S. (2021). Neighbourhood physical environment, intrinsic capacity, and 4-yea late-life functional ability trajectories of low-income Chinese older population: A longitudinal study with the parallel process of latent growth curve modelling. EClinical Medicine, 35. https://doi.org/10.1016/j.eclinm.2021.100927 Lucas, R.  E., & Dyrenforth, P.  S. (2006). Does the existence of social relationships matter for subjective wellbeing? In K. D. Vohs & E. J. Finkel (Eds.), Self and relationships: Connecting intrapersonal and interpersonal processes (pp. 254–273). The Guilford Press. Maddux, J. E. (2018). Subjective wellbeing and life satisfaction. In J. E. Maddux (Ed.), An introduction to conceptions, theories, and measures (pp. 3–31). Routledge/Taylor & Francis Group. https://doi.org/10.4324/9781351231879-­1 McArdle, J. J., & Grimm, K. J. (2010). Five steps in latent curve and latent change score modeling with longitudinal data. In Longitudinal research with latent variables (pp. 245–273). Springer. Miao, J., & Wu, X. (2016). Subjective wellbeing of Chinese elderly: A comparative analysis among Urban China, Hong Kong, and Taiwan. PSC Research Report, 16, 868. Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (7th ed.). Muthén & Muthén.

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Orviska, M., Caplanova, A., & Hudson, J. (2014). The impact of democracy on wellbeing. Social Indicators Research, 115(1), 493–508. Preacher, Kristopher J., Wichman, Aaron L., MacCallum, Robert C., & Briggs, Nancy E. (2008). Latent growth curve modeling: . Rojas, M. (2008). The measurement of quality of life: Conceptualization comes first. A four-­ qualities-­of-life conceptual framework and an illustration to Latin America. Selezneva, E. (2011). Surveying transitional experience and subjective well-being: Income, work, family. Economic Systems, 35(2), 139–157. Su, C., Mitchell, R. K., & Sirgy, M. J. (2007). Enabling guanxi management in China: A hierarchical stakeholder model of effective guanxi. Journal of Business Ethics, 71(3), 301–319. Tsang, S. (2016). Consolidating political and governance strentgh. In S. Tsang & H. Men (Eds.), China in the Xi Jinping Era (pp. 17-40). Springer Nature. Vargas, D. (2018). Evaluación de las dimensiones de la satisfacción con la vida. Un enfoque metodológico. In R.  Millán & C.  Cereceda (Eds.), Bienestar subjetivo en México (pp. 85–116). México. Wang, F. L. (2005). Organizing through division and exclusion: China’s Hukou System. Stanford University Press. Wedeman, A. (2012). Double paradox: Rapid growth and rising corruption in China. Cornell University Press. Xiaotong, F. (1992). From the soil. Foreign Language Teaching and Research Press. Yetts, W. P. (1943). The legend of Confucius / by W. Percevel Yetts. The China society. Zhang, J. (2017). The evolution of China’s one-child policy and its effects on family outcomes. Journal of Economic Perspectives, 31(1), 141–160.

Chapter 2

Life Satisfaction Over Time, 2010–2018

The concept of satisfaction in life has different meanings in China, compared to that developed in the western hemisphere (the Global North), where we can argue that satisfaction in life is related to the concept of agency of Amartya Sen. Amartya Sen introduced a capability approach based on individual right that shape human development and impacts personal well-being (Sen, 1996). In contrast, satisfaction with Chinese life is absent from the individual human rights approach. Instead, it is based on the Confucian idea of hierarchy, social responsibility, filial piety, and ethical pleasure that shape social as well as state-person relations (Luo, 2019).

2.1 Background on Life Satisfaction Life satisfaction as a latent construct was developed by the Ed Diener team (Diener et al., 1985) at the beginning of the 1980s. Participants were asked to indicate how much they agree or disagree with cognitive judgments of life satisfaction, using a 7-point scale that ranged from strongly agree (7) to strongly disagree (1). Studies on life satisfaction were continued in psychology. Wang et  al. (2018) analyzed the Chinese context and proposed that the perception of successful agency is positively associated with life satisfaction. However, agency stems from the neoliberal discourse, especially from the Human Development Index by Amartya Sen, who considered agency a principal modus operandi of capabilities (Sen, 1985). According to Sen, the “promotion of the person” and “the pursuit of the person’s overall agency goals” are strongly interconnected; “The latter encompasses the goals that a person has reason to adopt, which can inter alia include goals other than the improvement of his or her own wellbeing” (Sen, 2007, p. 275). Crocker and Robeyns (2009) explained that Sen’s agency refers to someone who acts and brings about change through four conditions: (1) self-determination, (2) reason orientation and deliberation, (3) action, and (4) impact on the world. Needless © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 L. Czarnecki, D. Vargas Chanes, Life and Job Satisfaction in China, Quality of Life in Asia 19, https://doi.org/10.1007/978-3-031-48695-1_2

13

14

2  Life Satisfaction Over Time, 2010–2018

to say, Sen’s agency is based on individual freedom to make changes. Self-­ determination refers to the ability to individually perform an act for change; reason orientation refers to an agent who acts with purpose, goal, and reason; the third element, action, refers not only to the freedom to act, to decide, and norms for action, but it goes far beyond that. The agency decides whether to act or not. Finally, impact on the world refers to an agent’s ability to transform and make a difference in the world. We can consider that a high degree of agency entails a corresponding degree of life satisfaction. Sen speaks about the “agency role” of individuals, acting within an institutional framework to make transformations. Crocker and Robeyns concluded that due to the important links between well-being and agency, there is reason to introduce the concept of an “agency-focused capability approach” (Crocker & Robeyns, 2009: 86). In the literature, there is a common belief that life satisfaction, happiness, and subjective well-being are interchangeable concepts (Li & Raine, 2014). However, there are some differences. Life satisfaction is “partly the result of life circumstances and events, but also partly the result of general disposition or temperament” (Maddux, 2018: 17). The focus is on particular outcomes. Life satisfaction can be considered “a ‘cognitive’ conceptualization of happiness or subjective wellbeing” (Sirgy, 2012, p. 13). As we can see, life satisfaction closely interacts with subjective well-being and happiness. The contemporary notion of life satisfaction is based on autonomy, individualism, and subjective cognitive well-­ being (Germani et al., 2021). In the countries of the global north, life satisfaction is associated with individual achievements, emphasizing how people evaluate their lives rather than their current feelings (OECD, 2020). Furthermore, the concept of life satisfaction was implemented by mainly economic international organizations to show the viability of a lifestyle approach (Meisel, 2007). Moreover, occidental countries impose policies of lifestyle and individual life satisfaction on less developed countries. Furthermore, the Global North has a clear focus on caring for the well-being of the poorest in the South, which “offers the possibility of transforming millions of lives much more profoundly than we could by finding the recipe to increase growth from 2% to 2.3% in the rich countries” (Banerjee & Duflo, 2019: 242). In the countries of the Global South, life satisfaction developed from a broader perspective. As Mahali et al. (2018: 392) stated, the well-being in the Global South has four dimensions: “relationships between (a) the individual and the collective; (b) the people and the state; (c) the people and the environment; and (d) the people and power.” For example, Bolivia’s idea of suma qamaña is much broader than life satisfaction in the Global North, emphasizing the idea of harmonious relations between humans and nature (Artaraz & Calestani, 2015). The concept of life satisfaction is based on relations with Pachamama. In the Ecuadorian constitution, Pachamama is a legal entity and the rights of nature are recognized by the state. The concept of well-being from the Global South is based on the redistribution of power relations, as Mahali et al. (2018, p. 392–393) argued: “Established wellbeing theories largely neglect addressing the, admittedly, greater challenge of investigating

2.2  Variables and Data

15

power relations and redressing the structural inequities that impact people’s ability to be well.” According to Millán and Castellanos (2018), a critical paradigm of social development should be considered. The constructive approach was implemented by the Seminar on Subjective Satisfaction in Life and Society at the National Autonomous University of Mexico in 2013. As Vargas (2018) argued, life satisfaction has both subjective and objective categories. Although it is an intangible concept, it can be measured and conceptualized using latent variables that are the domains of life satisfaction, i.e., affective life, family life, economic situation, social life, and health. According to Vargas (2018: 110–111), “measurement of subjectivity implies the use of models that include latent (intangible) variables, as well as manifest (tangible) variables that involve measurement error. It is concluded that intangible concepts become measurable. There are few comparative studies on life satisfaction between China and the West (Stankov, 2013). The satisfaction of Chinese life for individuals shares some similarities with the Western world, but overall these concepts are different (Li et al., 2021). Life satisfaction is related to the Confucian idea of a harmonious society (和谐 社会, héxié shèhuì). The main idea stems from the consideration of life satisfaction not through individual rights but rather hierarchical relations (Xiaotong, 1992). Recently, life satisfaction was related to self-compassion (Li et al., 2021). A key component of life satisfaction refers to the social harmony that governs human relations, both on the interpersonal level and between the state and its citizens (Berthel, 2017). The ultimate objective is to shape the satisfaction of life by understanding one’s role and place in the hierarchical order of society.

2.2 Variables and Data In our analysis, we use life satisfaction as a general concept of satisfaction measured in the CFPS, where four variables refer to satisfaction: satisfaction with income, satisfaction with social status, satisfaction with life, and confidence in the future. There is a common pattern of overall satisfaction with life rising from 2010 to 2018. Figure 2.1 shows a transversal analysis showing growth, from very dissatisfied people to very satisfied people. This aggregate measure is one matter, but individual trajectories are another. We assume that there is a sort of heterogeneity here. There is a formation of different types of satisfaction; these typologies make up groups. Thus, latent social statuses can be separated. The question is from a longitudinal perspective, whether individual trajectories are missed? If not, how are these individual trajectories moving? Does an unobserved heterogeneity exist between them? The variables are shown in Table 2.1; “Province” refers to 1 of 32 administrative units in China. “Urban” is a dummy variable that refers to living in rural (0) or urban (1) environments. “Age” is a continuous variable that ranges from 16 to 120 years, divided into five categories as follows: 16–29 (1), 30–49 (2), 50–59 (3), 60–79 (4),

16

2  Life Satisfaction Over Time, 2010–2018 very dissatified

45.0

dissatified

35.3 32.2

35.0

29.2

17.5

20.0

0.0

29.1

10.1

11.9 6.0

4.7

2010

22.2

15.1

2.7 2012

34.6 30.8

28.6 25.1

24.7

25.0

5.0

very satisfied

33.1

33.1

30.0

10.0

satisfied

41.8

40.0

15.0

medium

5.8

2014

9.0 4.1

1.8 3.0 2016

2018

Fig. 2.1  Life satisfaction in China, 2010–2018, in percent. (Source: Based on CFPS (2010, 2012, 2014, 2016, 2018)) Table 2.1  Control variables used in the study, 2010-2018 Var. pid pr ur age

Description Personal ID Province Urban Age

Var. ms gn hu educ21

Description Marital status Gender hukou status Years of education

and 80–120 (5). “Gender” is a dummy variable divided into women (0) and men (1). “Marital status” refers to the following five categories: never married (1), married (with a spouse) (2), co-habiting (3), divorced (4), and widowed (5). “Years of education” refers to the total years of education, ranging from 0 to 22 years and formed into five groups: 1–6 (1), 7–9 (2), 10–12 (3), 13–15 (4), and 16–23 (5). Hukou () is the policy of household registration introduced by Mao Zedong in the time of the planned economy and society. The modern transformation in China began with the implementation of hukou. Hukou refers to a household registration system that divided the population into two categories: agricultural registered in rural areas and non-agricultural (urban) registered in metropolitan areas, established under the Regulations on Hukou Registration in 1958 by the National People’s Congress. According to Hu (2016), the status of the hukou of the parents also plays a crucial role in marital sorting, in that the father and father-in-law of an individual tend to have the same rural or urban hukou. Table 2.2 shows the matrix of correlation and descriptive statistics for all variables needed for the LGMM for life satisfaction. We observe a mean score increase in life satisfaction from 3.48 to 4.01 for the years 2010 and 2018, respectively. In the next section, we explore the linear model fitted.

2.3  The Model

17

Table 2.2  Descriptive statistics and correlations for job and life satisfaction 1 2 3 4 5

Life Satisfaction 2010 Life Satisfaction 2012 Life Satisfaction 2014 Life Satisfaction 2016 Life Satisfaction 2018 N Mean Sd Min Max Skewness Kurtosis

1 1 0.269 0.283 0.263 0.207 33,523 3.478 1.040 1 5 −0.367 2.752

2

3

4

5

1 0.321 0.297 0.222 31,975 3.312 1.056 1 5 −0.181 2.693

1 0.330 0.280 31,564 3.802 1.009 1 5 −0.561 2.877

1 0.330 33,228 3.615 1.080 1 5 −0.406 2.587

1 30,165 4.011 0.959 1 5 −0.773 3.247

2.3 The Model We used a latent growth mixture model (LGMM), which describes the trajectories of the observations according to the initial level (or intercept) and the rate of change over time (or slope) of latent constructs (Cortés & Vargas, 2016, p. 50). According to McArdle and Grimm (2010, p. 246), “The term growth curve analysis denotes the processes of describing, testing hypotheses, and making scientific inferences regarding growth and change patterns in a wide range of time-related phenomena.” An LGMM is placed in the context of models of structural equations, where the parameters (intersection and slope in the linear model) can be viewed as latent variables containing a fixed part and a random part (Bollen & Curran, 2006; Little, 2013). Satisfaction with life among Chinese families was measured by fitting the LGMM, with a linear and a quadratic model over time. However, a single model describes only overall growth and is inadequate. We need to identify particular growth typologies since some individuals in the sample increase (or decrease) at different rates over time. The equations associated with the LGMM are as follows:

yti   0 i   1i t   ti

(2.1)



 0 i   0  r0 i

(2.2)



 1i  1  r1i ,

(2.3)

where yti denotes life satisfaction score for the i-th individual in the sample at time t. The terms π0i and π1i denote the intercept and the slope for each individual, respectively. The terms in Eqs.  2.2 and 2.3 denote that both the intercepts and slopes,

18

2  Life Satisfaction Over Time, 2010–2018

respectively, are random, recognizing the existence of intervariability of individual trajectories. When we substitute Eqs. 2.2 and 2.3 into Eq. 2.1, we have the following expression, which is called a mixed model and contains a fixed and random components.

yti   0  1t   r0 i  r1i t   ti  .



(2.4)

The terms β0 + β1λt are known as the fixed part and the term r0i + r1iλt + εti is called the random component. The number of parameters to identify in the basic model are the mean, variances, and covariances among the y’s which are E(yti), Var(yti), and Cov(yti, yt  −  s, i), for s  >  0 and t  −  s  ≥  1. For the linear trajectories, the unknown parameters that we need to estimate are β0, β1, Var(εti), Var(r0) = ψ11, Var(r1) = ψ22, Cov(r0, r1) = ψ12, and λt.

2.4 Model Fit Indices The SEM model uses three indices to evaluate model fit: (1) the comparative fit index (CFI; Bentler, 1990), (2) the Tucker-Lewis index (TLI; Tucker & Lewis, 1973), and (3) the root mean square error of approximation (RMSEA; Rigdon, 1996). According to Hu and Bentler (1999), values of CFI and TLI greater than 0.95 and RMSEA less than 0.06 are considered a good fit; additionally, RMSEA less than 0.08 are considered an acceptable fit (Hooper et al., 2008). The analyses we conducted in this book used Mplus 8.5 (Muthén & Muthén, 2017). Because there are many parameters to estimate, to identify this model, we need to impose some restrictions for the intercept and the slope, which we can explain in Fig. 2.2. In this figure, we can identify two latent variables, namely, π0i and π1i; both can be seen as two latent constructs, such as a confirmatory factor analysis (CFA). The loading for the intercepts must be fixed to 1 and the loading for the slope are fixed to 0, 1, 2, 3, 4, and 5, representing a linear trend over time. The term εt represents the error term at each time point. On the other hand, we have the terms β0 and β1 representing the intercept and slopes of each individual trajectory. Whereas r0 and r1 represent the random errors associated with the intercepts and slopes, respectively. Finally, the term ψ12 represents the covariance between the intercepts and slopes. In our model in this chapter, the outcome variable is life satisfaction and corresponds to the measures made for the years 2010, 2012, 2014, 2016, and 2018. Note that in this study, we have a single indicator of life satisfaction, since this item was asked in the data set for all years. Table 2.3 shows the coefficients of the latent growth curve model for the life satisfaction curve. Additionally, this table shows the fit indices (e.g., RMSEA, CFI, and TLI) that are acceptable (Bentler, 1990; Browne & Cudeck, 1992; Hooper et al., 2008; Rigdon, 1996). The latent growth curve for life satisfaction is LifeSatij = ­3.37

2.4  Model Fit Indices

19

ε1

ε2

ε3

y1

y2

y3

1

1

1

2

ε5

y4

3

y5 1

1

1

0

ε4

4

Intercept πo β0

Slope π1

ro r1 ψ12

β1

Fig. 2.2  Latent growth mixture model for five time points with a linear trend. (Source: Own elaboration based on Bollen and Curran (2006)) Table 2.3  Fit statistics for latent growth curve model for life satisfaction Parameter Intercept Slope Variance intercept Variance slope Parameters RMSEA CFI TLI Deviance AIC BIC SSABIC

Life satisfaction 3.371 0.139 0.332 0.014 10 0.099 0.843 0.845 42,5474.04 42,5494.03 42,5580.01 42,5548.28

1 + 0.139 ∗ yearj; this means that the initial score is close to 3.371 for the year 2010, with a rate of increase for every 2 years of 0.139. Note that the year variable was recoded as 0, 1, 2, 3, 4, and 5 for the years 2010, 2012, 2014, 2016, and 2018, respectively.

20

2  Life Satisfaction Over Time, 2010–2018

2.5 How Many Trajectory Classes? Equation 2.4 shows a single growth in life satisfaction but describes only the overall growth. The next step is to explore the heterogeneity of growth, more likely there exist different trajectory classes; to identify them, we need to explore the formation of different typologies, so we use LGMM. First of all, we define the criteria used to determine the number of classes that are described as follows (Jones & Nagin, 2007): (1) the Bayesian index criterion (BIC), the lower the BIC, the better the fit (Schwarz, 1978; Sclove, 1987); (2) percentage of BIC change, comparing the amount of decrease from k to k + 1 class solution, the rule of thumb is to stop when the change shows a high percentage; (3) the entropy score, an entropy score close to 1 indicates a better fit; (4) all trajectory groups consisting of a reasonable proportion of the sample, at least 5% is a common rule of thumb; (5) the probabilities of the highest trajectory group membership being at least 0.85; (6) the Lo-Mendell-Rubin test; and (7) a low p-value indicates a better fit (Lo et al., 2001). Table 2.4 shows all the criteria fitted to decide the number of classes. The BIC index for one to five latent trajectories (column 2), the lower the BIC indicates a better fit. The percentage of decrease is indicated in column 3; we favor the model with the higher difference and lower BIC. In this table, we observe that a lower BIC is observed for the four-class solution and shows 10% change compared with the three-class solution. We select the four latent trajectory classes solution depicted in Fig.  2.2 and observe the LGMM with four trajectories over time (2010–2018) for life satisfaction. These changes constitute latent trajectories that could model the trajectory of individuals in each society (Liu, 2016). Table 2.5 shows the parameter estimates of the four-class solution, all intercepts are very similar, around 3.3 ± 0.1, and the slopes make the difference for each trajectory class. For class 1, it is named “high and increasing” and contains approximately 41.6% (n  =  20,786), and the slope is the highest, compared to the other Table 2.4  Latent growth mixture model fit statistics for linear model to determine the number of classes Bayesian Index Class Criterion

% of BIC change

Percent Prob. Class by Memberhsip Lo-MendelEntropy group (min-max) Rubin Test Prob

1 2

455,717.02 454,232.95

0.33% 0.509

3

452,365.47

0.41% 0.607

4

406,700.07 10.09% 0.672

5

4,06,214.83

0.12% 0.670

15.6– 84.4 2.4– 68.3 3.4– 41.1 1.7– 39.0

LL

0.72–0.87

1556.98

−227,848.512