Discussion Paper Series

University of Tokyo

Institute of Social Science
Panel Survey
東京大学社会科学研究所 パネル調査プロジェクト
ディスカッションペーパーシリーズ

The Associations of Class and Status with Life
Chances and Life Choices: Causation or Selection?

Sho FUJIHARA（The University of Tokyo）

February 2022
No.150東京大学社会科学研究所
INSTITUTE OF SOCIAL SCIENCE UNIVERSITY OF TOKYOInstitute of Social Sciences
University of Tokyo
Panel Survey Project Discussion Paper Series No. 150
January 2022

The Associations of Class and Status with Life Chances and Life Choices:
Causation or Selection?*

Sho Fujihara (The University of Tokyo)

Abstract
Previous studies have shown that individuals’ class positions are more strongly related to economic
security, prospects, and life chances than status positions, whereas individuals’ status positions are
more strongly related to cultural consumption than class positions. This paper addresses whether
class and status are associated with life chances and life choices after controlling for unobserved
heterogeneity by using data from longitudinal surveys started in 2007 in Japan. The result indicatesthat class is associated with economic security and income more strongly than status, even after
controlling for the unobserved heterogeneity. Status positions are associated with cultural
consumption and health-related behaviors. However, after controlling for the unobserved stablecharacteristics, the associations with cultural consumption disappeared, but those with healthrelated behaviors remained.

Keywords
class, status, causality, panel surveys* This research was supported by KAKENHI Grant-in-Aid for Specially Promoted Research,
Scientific Research (S and B), and Challenging Research (Pioneering) (Grant Numbers

JP25000001, JP18H05204, JP18103003, JP22223005) from the Japan Society for the Promotionof Science (JSPS). The research support in conducting the panel surveys was obtained from theInstitute of Social Science, The University of Tokyo, and The Outsourcing, Inc, without any
involvement in the design, data collection, data analysis, or data interpretation of this study. The
Research Ethics Review Board at the authors’ institution approved the JLPS study. The
permission to use the panel data is obtained from the Management Committee of the JapaneseLife Course Panel Surveys. Direct correspondence to Sho Fujihara, Institute of Social Science,
The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033 Japan. Email:sho.fujihara@iss.u-tokyo.ac.jp

INTRODUCTION
Social stratification is one of the major topics of sociological research, and many scholars haveinvestigated the characteristics and consequences of social stratification. However, theconceptualization and measurement of social stratification have been controversial—generating
considerable dispute among scholars not only in sociology (Featherman and Hauser 1976;
Treiman 1977; Bourdieu 1984; Prandy 1990; Erikson and Goldthorpe 1992; Ganzeboom et al.
1992; Hauser and Warren 1997; Sørensen 2000; Weeden and Grusky 2005; Wright 2005) but
also in related fields such as epidemiology (Krieger et al. 1997; Bartley et al. 1999; Sacker et al.
2000; Galobardes et al. 2006a, 2006b; Geyer et al. 2006; Hiyoshi et al. 2013). Recentsociological studies have suggested a return to the traditional conceptualization and measurementof social stratification, namely, Weber’s distinction between class and status (Chan 2010; Chanand Goldthorpe 2004, 2007a; Goldthorpe 2012; Ridgeway 2014; Weber 1968, 2010). However,whether the established associations are the result of causation, selection, or both has not been
extensively examined. This study addresses whether the associations of class and status withindividuals’ life chances and life choices can be observed even after controlling for unobserved
stable characteristics.

DISTINCTION BETWEEN CLASS AND STATUS
Weber (1968:937) succinctly delineated the difference between class and status1 as follows:

With some over-simplification, one might thus say that “classes” are stratified according totheir relations to the production and acquisition of goods; whereas “status groups” arestratified according to the principles of their consumption of goods as represented by special“styles of life.”
An “occupational group” is also a status group. For normally, it successfully claims social
honor only by virtue of the special style of life which may be determined by it. The
differences between classes and status groups frequently overlap…
1

Weber observes that the situation of status is partially based on the class situation and that itpartially determines a class situation. Thus, class and status are interrelated, but are conceptuallydifferent, and they form two qualitatively different types of social stratification (Hechter 2004;
Goldthorpe 2012). The argument over the distinction between class and status suggests that they
have independent influences on life chances and life choices through different mechanisms(Breen 2005). However, sociological research has rarely distinguished between class and statusin empirical analyses (Archer and Orr 2011; Chan and Goldthorpe 2007a) until Chan andGoldthorpe launched a project to construct a status scale and analyze the effects2 of class andstatus on life chances and life choices in several studies.
Chan and Goldthorpe (2004) constructed a status scale for England based on the analyticalframework of Laumann (Laumann 1965; Laumann and Guttman 1966), which is similar to thescales developed by Stewart, Prandy, Lambert, and their associates (Stewart et al. 1980; Prandy
1990; Prandy and Jones 2001; Prandy and Lambert 2003).3 Chan and Goldthorpe used the scalealong with social class (Erikson-Goldthorpe-Portocarero (EGP) class scheme) to analyze
individuals’ life chances and life choices: long-term unemployment, annual earnings, music(theater, dance, cinema), visual arts, newspaper readership, party choice, and political attitudes(Chan and Goldthorpe 2005, 2006, 2007a, 2007b). In general, these studies found that whileclass positions are related to long-term unemployment and earning prospects rather than status
positions, the effects of status on cultural consumption are stronger than those of class, evencontrolling for other relevant variables such as income and educational attainment. These results
concur with Weber’s claim that class positions are more strongly related to economic security,prospects, and life chances than status positions, whereas status positions are more strongly
related to cultural consumption than class positions.
Following these arguments, studies of social stratification have begun to use both class andstatus in their analyses of cultural consumption, Internet use, political attitudes, long-term
unemployment, mortality, and children’s educational attainment, particularly those focusing on
2

European societies such as France, Hungary, the Netherlands, Sweden, Norway, and Greece(Bukodi 2007; Bukodi et al. 2018; Bukodi, Dex, and Goldthorpe 2011; Bukodi, Erikson, andGoldthorpe 2014; Bukodi and Goldthorpe 2013; Chan 2010; Chan et al. 2011; Coulangeon andLemel 2007; Kraaykamp et al. 2007; Lindblom and Räsänen 2017; Torssander and Erikson
2009, 2010). A similar analysis has also been conducted in non-European countries such as the
U.S. (Alderson et al. 2007), Chili (Torche 2007), and Japan (Fujihara 2020). Meraviglia,
Ganzeboom, and De Luca (2016) found that the effect of occupation on cultural consumption is
captured more strongly by the International Cambridge Scale (ICAMS) than by the International
Socio-Economic Index (ISEI) and Standard International Occupational Prestige Scale (SIOPS).
As such, these studies illustrate the existence of status orders and the usefulness of Weber’s
distinction between class and status to explain social stratification in contemporary societies.
There are some criticisms and debates on the analytical framework of Chan and Goldthorpe(Stewart et al. 1980; Peterson 2007; Wuggenig 2007; Freeland and Hoey 2018; Bihagen andLambert 2018; Flemmen et al. 2019; Laurison 2019). These criticisms have tended to focus
mainly on conceptualization and measurement of status and types of cultural consumptions.4 In
the present article, I present another problem to deepen further the theoretical and empirical
discussions on Weber’s argument of the distinction between class and status and the analyticalframework of Chan and Goldthorpe. Instead of proposing alternative methods or scales of status,
as in previous studies, this study addresses how class and status are associated with life chancesand life choices.

CAUSATION OR SELECTION?
Although previous studies have shown associations of class and status with several outcomes
related to an individual’s life chances and life choices, Chan and Goldthorpe cautioned againstinterpreting the results of their analyses (Chan and Goldthorpe 2007a:528):

3

The various associations reported in this article between cultural consumption, politics, andattitudes on the one hand, and status or class on the other, might arise partly as a result of
self-selection. That is, people with certain unobserved dispositions might choose to take up
particular occupations and thereby acquire a specific status (and class). At the same time,such dispositions might be associated with particular lifestyles or attitudes. Having said that,class and status might also shape attitudes and other life choices (Kohn 1977). Both causal
and self-selection processes could be at work.

The association between status and cultural consumption may be partially causal and partiallyconfounded by unobserved dispositions, as with many other associations (Greenland et al. 1999).
These unobserved dispositions may include socio-economic background characteristics,
cognitive traits, personality traits, or disposition of being apt to have a high status or ambition
that leads to higher economic attainment and forms the style of cultural consumption. Whether
an observed association is produced by causal or selection processes is a fundamental issue insociology research as well as other social science disciplines (Winship and Mare 1992; Winshipand Morgan 1999; Elwert and Winship 2014; Morgan and Winship 2014).5 Because the analysisof class and status was mainly based on data from the cross-sectional social surveys, theunobserved characteristics, which are related to both class (and/or status) and several outcomes,
but are not adequately controlled for, possibly produced the observed associations. Many
previous studies controlled for relevant confounders such as age, gender, marital status, having achild, and education, but it is difficult to operationalize, measure, and control for unobserved
dispositions. Therefore, there may still exist a bias in the estimated effect due to unobserved
dispositions. Although Chan and Goldthorpe (2007a) mention this bias in their paper, it isindispensable to empirically determine whether the observed association was created by causal
relationship, selection, or both. Moreover, some studies also controlled for income levels, butclass and status affect income levels, which produces an over-control bias (Westreich andGreenland 2013; Elwert and Winship 2014; Acharya et al. 2016; Keele et al. 2020). While4

previous studies that criticized the approach of Chan and Goldthorpe stimulated debates about
the distinction between class and status, none of them have mentioned or examined the causal
effects of class and status. This argument led us to the following research question:

RQ: Do class has causal effects on life chances and status on life choices?

This research question is not easy to answer. Because changes in individuals’ class and status
positions do not occur at random, and we cannot conduct an experiment by randomly assigningindividuals to class and status groups, it is challenging to directly estimate the causal effects ofclass and status on attitudes and life choices. Therefore, to adjust for unobserved dispositions, weneed to adopt other identification strategies that utilize observational studies.
There are several methods to control for unobserved characteristics and estimate causal
effects (Gangl 2010; Morgan and Winship 2014). In the following analysis, I utilized the panel
data analysis method, especially fixed-effects (FE) models, which are widely used for causal
inference (Imai and Kim 2019). Panel data analysis uses information on changes within
individuals over time to identify the causal effects. In this case, the analysis uses information
about changes in class and status and outcomes related to life chances and life choices. As
research on intragenerational mobility has shown, both class and status are not fixed; theychange with time or over the life course, which will influence the life chances and life choices ofindividuals and their families. This study advanced the framework of distinction between classand position to the analytical framework that considers time and tested whether class and statushave causal effects on several outcomes. I used data from longitudinal surveys conducted inJapan, where previous studies found that class and status matter (Chiavacci 2008; Ishida andSlater 2009; Fujihara 2020).

5

CLASS AND STATUS IN JAPANJapan is an interesting illustrative case for the research question proposed above. Although both
structural and cultural settings may differ, class matters in Japan as well as in other industrial
societies (Kohn et al. 1990; Ishida et al. 1991, 1995; Ishida 2007; Chiavacci 2008; Ishida andSlater 2009; Fujihara and Ishida 2016). The pattern of intergenerational mobility in the relative
terms in Japan was similar to that found in Europe (Ishida et al. 1991), and the strength ofintergenerational mobility has been constant (Ishida 2018), whereas some European societies
experienced increases in social fluidity (Breen 2004). Moreover, class inequality in educational
attainment has persisted (Fujihara and Ishida 2016), and occupational returns to education havealso been stable, especially for men (Sato and Arita 2016; Hannum et al. 2019).
Regarding the distinction between class and status, a recent study in Japan shows that there
exists not only class but also status, and they function as in European societies (Fujihara 2020).
That is, status is associated with cultural consumption such as attending classical music concerts,
museums, and libraries and participating in sports in Japan, after controlling for education,
income, and class.6 This finding indicates the relevance of Weber’s distinction between class andstatus in an East Asian country (Kerbo 2011; Goldthorpe 2012; Fujihara 2020). Therefore, theanalysis of class and status in Japan contributes to the generalization of the findings to othersocieties. However, none of the previous studies that empirically addressed the distinction
between class and status have utilized techniques of causal inference and could adequately
distinguish associations from causal relationships, limiting the discussion of the mechanism of
the effect of the two forms of social stratification. This study is the first attempt to investigate thecausal effects of both class and status on life chances and life choices.

HYPOTHESES
To investigate how class and status are associated with life chances and life choices, I set threehypotheses. Class positions influence individuals’ life chances, especially economic security andprospects (Chan and Goldthorpe 2007a; Goldthorpe and McKnight 2006; Sørensen 2000). Status6

positions impact individuals’ life choices, such as cultural consumption and health-related
behaviors. I extend the analysis of cultural consumption to the analysis of health-related
behaviors because health lifestyles are regarded as a form of consumption, and sociologists aswell as social epidemiologists are interested in the relationship between social stratification andhealthy lifestyle (Bourdieu 1984; Cockerham 2005; Phelan et al. 2010). Previous studies insociology and epidemiology used both class and status (Cambridge scale) for the analysis ofcardiovascular risk factors (e.g., poor diet, smoking, non-light drinkers, and no sport) andmortality (Bartley et al. 1999; Sacker et al. 2000; Torssander and Erikson 2009, 2010), but thecausal relationship has not been well examined. Therefore, the following hypotheses were
derived:Hypothesis 1: Individuals’ class positions have an effect on their economic life chances rather
than status positions.
Hypothesis 2: Individuals’ status positions have an effect on cultural consumption rather thanclass positions.
Hypothesis 3: Individuals’ status positions have an effect on health-related behaviors than classpositions.

Note that the term “effect” here refers to the association of interest after controlling (adjusting)for relevant variables. In the following section, I test whether these hypotheses hold even when
the impacts of the observed and unobserved characteristics are considered.

METHODS
Data
This study used data from the Japanese Life Course Panel Survey (JLPS). The JLPS is anationally representative panel survey being conducted annually in Japan since 2007. The
respondents were sampled from Japanese residents aged 20 to 40 years (born between 1966 and71986) using the electoral register and the basic resident register. The areas in Japan werestratified by the combination of 10 regional blocks and population (16 major cities, cities with apopulation of over 200,000, other cities, and towns and villages), resulting in 271 samplingareas. In each area, the respondents were stratified according to the combination of sex and age
group (age 20–24, 25–29, 30–34, and 35–40) and were randomly sampled. The valid response
rate of the first survey conducted in 2007 was approximately 35% for the young cohort (20–34)
and 40% for the middle-aged cohort (35–40). In total, there were 4,800 respondents in 2007.
However, the number of respondents decreased to 3,319 (69%) in 2011 due to sample attrition.
Therefore, additional respondents from the same cohort (born between 1966 and 1986) wereadded in 2010 (N = 963). The valid response rate from the additional respondents wasapproximately 32% and 31% for the young and middle-aged cohorts, respectively. In 2019, newrespondents (aged 20 to 31 years) were added to the survey to study a younger generation (N =2,383, the response rate was 36.1%), but this sample was not used in the following analysis.
There were 3,194 respondents in 2020. I used the data from the 2007 to 2020 surveys (13 waves
of panel data that include data of the additional samples from 2011) and the unbalanced panel
data, where not all individuals have data for all time points. To examine the effects of class andstatus, the sample for the analysis was limited to those employed at the time of the survey. The
valid sample sizes in the following analyses ranged from 3,276 (income) to 4,384 (anxiety about
unemployment and bankruptcy).

Independent Variables: Class and Status
I used both class and status as time-varying independent variables. Both variables were based on
occupational titles, job, and employment-related status of the respondents at the time of thesurveys. Although social status can be based on non-occupational attributes such as race andethnicity, occupation is an important indicator of social status (Chan, 2019). For class, the sevencategory version of the EGP (Table 1) was used in the analysis (Erikson et al. 1979; Ishida2018). It consists of I: Higher-grade professionals and managersmanagerials; II: Lower-grade8

professionals and managerials; IIIab: Routine-nonmanual; IVab: Petty bourgeoisie; IVc+VIIb:
Farmers and farm workers; V+VI: Skilled workers; and VIIa: Semi- and unskilled workers.

[Insert Table 1 here]

I used a scale for status in Japan constructed by applying the RCII model to a huge two-way
table of 231 occupational titles for husbands and wives (Fujihara 2020). Table 2 indicates the
ranking of the scale for the status of the 56 sub-major groups of occupations. The score for thefull occupational titles (231 occupational titles) is shown inAppendix A. As in other Westerncountries, occupations are ordered according to the degree of “manuality,” and professionals are
more likely to have higher ranks than managerials (Chan and Goldthorpe 2004; Chan 2010,2019). I assigned the status scores to occupations of the respondents and ranked each observation
based on the status scores (0 < Status < 1, Mean = 0.500, standard deviation [SD] = 0.288,
SDBetween = 0.267, SDWithin = 0.117) for the ease of interpretation of the regression coefficient. To
use social status as a categorical variable, individuals were classified into seven categories
according to status scores so that the number of people in each status category wasapproximately equal.

[Insert Table 2 here]

Dependent Variables
I used 14 dependent variables and divided them into three groups. For details, see Appendix B.
The first group (Group A) is about economic life chances and consists of four variables: (1)anxiety about unemployment and bankruptcy (4 points scale), (2) unemployment risk (binary),and (3) the logarithms of current individual annual income. The variables employed here aredifferent from those of Chan and Goldthorpe (2007a). The first two variables are related toeconomic security and are measured subjectively and objectively. We can expect that if
9individuals are in the more advantaged class positions such as I and II, they feel less anxious
about unemployment and bankruptcy, have less unemployment risk, and earn more income.
The second group (Group B) is about cultural consumption, and there are five variables: (1)visiting museums, (2) visiting libraries, (3) reading novels, (4) playing pachinko (a gaming
device like a pinball and slot machine in Japan), and (5) reading tabloids. These variables were
employed by the Social Stratification and Social Mobility (SSM) surveys, which are large
nationally representative repeated cross-sectional surveys of Japan, to investigate thestratification of cultural consumption. Responses were scored on a four-point scale (0 = never, 1= once a year or so, 2 = about once a month, 3 = one or more times per week) and were treated ascontinuous variables. Visiting museums and libraries, and reading novels can be characterized asa high culture while playing pachinko and reading tabloids as popular culture in Japan (Nakai
2012). Status positions are expected to affect cultural consumption in such a way that if status
positions of individuals are high, they more frequently visit museums and libraries and readnovels and are less likely to play pachinko or read tabloids (Nakai 2012; Fujihara 2020).
The third group (Group C) is related to health behaviors, which is a concept that has not beenaddressed within the framework of the distinction between class and status. There are six
variables: (1) drinking alcohol, (2) smoking cigarettes, (3) eating fast food, (4) doing exercise,(5) eating three meals a day, and (6) eating balanced meals. “Drinking alcohol” is a six points
scale; “smoking cigarettes” is a three points scale; and “eating fast food,” “doing exercise,”“eating three meals a day,” and “eating balanced meals” are five points scales. Status positions
are assumed to have a positive effect on doing exercise, eating three meals a day, eating balanced
meals, and a negative effect on drinking alcohol, smoking cigarettes, and eating fast food.
Table 3 shows descriptive statistics of the dependent variables used in the analysis.

[Insert Table 3 here]

10

Control Variables
The time-varying control variables were marital status, having a child, and survey year. I do notcontrol for household income or employment status (standard/non-standard) because our main
focus is to estimate the total causal effects of class and status instead of identifying the direct andindirect causal effects.7 Conditioning on income or employment status, variables on the pathway
between a treatment variable (class and status) and an outcome variable would then produce
overcontrol bias (Westreich and Greenland 2013; Elwert and Winship 2014; Acharya et al. 2016;
Keele et al. 2020), leading to misreporting and misinterpretation of results. In the betweeneffects (BE) models, the time-invariant individual’s characteristics such as age and age squaredat the time of surveys, gender, self-reported grade point average in 9th grade (a self-reported fivepoint grade relative to the cohort in the 3rd year of junior high school), educational attainment(10 categories), parental class (father’s EGP. If father’s EGP is missing, mother’s EGP is used),
and the father’s and mother’s education (four categories) are also controlled for. For details, see
Appendix C (C1 and C2).

Statistical ModelI employ models for panel data to control for unobserved heterogeneity (Allison 2009; Vaisey
and Miles 2017):%!!" = µ" + % #$β + '%# γ + υ! + ϵ!" ,(1)

for each 0 = 1,2, … , 3 and 4 = 1,2, … , 5. !!" denotes a dependent variable of 0th individual attime 4. µ" is an intercept that may differ across time periods. % !" indicates a set of observed
time-varying variables (e.g., a respondent’s class and status) and 6!" is a set of observed timeinvariant variables (e.g., gender and education). β and γ are vectors of the coefficients for % !"and 6!" , respectively. Here, we are interested in parameter β. 8! is the unobserved effect foreach individual that is constant across time (time-invariant), and ϵ!" is the residuals. If theunobserved variables 8! and ϵ!" are not handled properly, the estimated parameters will bebiased. The biased estimated parameter may indicate associations produced by both unobserved
11heterogeneity (confounder) and the causal effect, if any, and thus, cannot be considered as an
estimate of the causal effect. Both random-effects (RE) models and FE models can be used toestimate the parameters in Equation (1), but the assumption about the relationship between % &'and υ& : “RE models assume that the observed predictors in the model are not correlated with υ&
while FE models allow them to be correlated” (Vaisey and Miles 2017, p. 46). FE models
effectively control for the unobserved effect, but RE models do not. Therefore, FE models can beused to estimate causal effects (Angrist and Pischke 2008; Allison 2009; Gangl 2010; Vaisey andMiles 2017; Mummolo and Peterson 2018; Imai and Kim 2019). Although FE models have some
limitations (Vaisey and Miles 2017; Imai and Kim 2019), they are standard in panel data analysis
and are more appropriate than the standard linear regression models for the purpose of this study.
The FE model enables us to adjust for unobserved, unit-specific, and time-invariant confounders
by assuming that there is no dynamic causal relationship among the treatment, outcome, andobserved time-varying confounders (Imai and Kim 2019).8 In this study, I assume that timeinvariant unobserved confounders are more crucial than the dynamic causal relationship.
Therefore, I applied the simple FE model.9 Although our main concerns are to observe the effectof time-varying variables (class and status), I also employ the BE models to determine theimportance of unobserved heterogeneity. Previous studies relied on cross-sectional data andcompared between individuals, and thus, did not control for the unobserved heterogeneity;
therefore, the estimated parameters from the BE models correspond approximately to the resultsfrom previous studies.
In the following analysis, I compare the estimates from the BE and within-effects (FE)models. The BE model includes class and status as independent variables, and age, age squared,
gender, survey years, marital status, having at least one child, educational attainment, parental
class, and father’s and mother’s education as control variables. The FE models include class andstatus as independent variables and controlled for time-varying covariates (marital status, having
at least one child, survey years). In the FE models, cluster robust standard errors were used. The
other basic models for panel data analysis are the pooled regression model, the RE model, the12lagged dependent variables model, and the hybrid model (Allison 2009). If I do not adequately
control for all confounding variables, these methods will produce bias (Rabe-Hesketh andSkrondal 2012; Vaisey and Miles 2017). Our interest can be addressed by the hybrid model, but I
estimated the BE model and within-effects (FE model) separately. Although some of thedependent variables have only four categories, a linear FE model was applied to all of thedependent variables except for unemployment risk because the simple linear fixed model shows
the same results as more complex methods (binary or ordered fixed logit models) and is easy tocompute (Riedl and Geishecker 2014). I conducted the same analysis using percentile rank
scores of the dependent variables and found almost the same results.

RESULTS
Intragenerational Class and Status Mobilities
Before estimating the parameters from the BE and FE models, I showed descriptive statistics ofclass and status and intragenerational class and status mobility. Table 4 shows the distributionsof class and status (as seven categories) and between and within components of frequencies.

[Insert Table 4 here]

Table 5 indicates the class and status mobilities between years t and t + 1 (transition
probability). The intragenerational class mobility rates in a year were 13.3%, and theintragenerational status group mobility rates between t and t+1 were 15.5%.10 Mobilities of status
can also be measured using the intra-class correlation (ICC), which indicates the ratio of
between-individual variance to the total variance of status scores (the sum of the betweenindividual variance and the within-individual variance). Thus, if an ICC is close to one, the status
differs substantially between individuals and does not change significantly within individuals.
The estimated ICC of status was very high (ICC = 0.809), but there was a slight variation within
individuals.
13

[Insert Table 5 here]

Relationship between Class and Status
Next, I present the relationship between class and status. Figure 1 shows the distribution of status
within and between classes using boxplots. There are good deals of overlap of status scores
between classes, and the association between class and status is far from perfect (Chan 2011).
The 9( , which indicates the strength of the association between a categorical variable (class) anda continuous variable (status), was 0.499. As expected, the value was smaller than that of thesocioeconomic index (SEI) (9( = 0.636) and occupational prestige (9( =0.594). This evidence isessential for the following multivariate analysis because both class and status can be used as
independent variables in a regression model without multicollinearity being an issue.11[Insert Figure 1 here]

Variations of the Dependent Variables within Individuals
To determine the change and stability of the dependent variables, I estimated the ICC. The ICCs
are shown in the last column of Table 3.12 The ICCs of smoking cigarettes, individual annual
income, drinking alcohol, and playing pachinko were high and greater than 0.7.13 These
characteristics were relatively consistent and did not change within individuals very much. In
contrast, the ICCs of anxiety about unemployment and bankruptcy, unemployment risk, visiting
museums, and doing exercise were relatively low (from 0.266 to 0.464). These characteristics
changed more frequently within individuals.
Within each group of variables, there were some variations in ICCs. Regarding the variables
related to economic life chances, the ICC of the individual annual income was high (0.834), andthat for unemployment risk was low (0.266). The ICC for anxiety about unemployment andbankruptcy was located between the two (0.413). The ICCs for the variables related to cultural
14

consumption ranged from 0.457 to 0.766, and those for the variables related to health-related
behaviors ranged from 0.464 to 0.806. By using variables with different degrees of change
within individuals, this study examined the effect of class and status on economic life chancesand life choices.

The Effects of Class and Status on Economic Life ChancesThis section investigates the effects of class and status on economic life chances. Table 6 showsthe estimated effects of class and status on economic life chances: (1) anxiety about
unemployment and bankruptcy, (2) risk of unemployment, and (3) annual income. The baseline
category of the class was Class I (higher-grade professionals and managerials). Following Keeleet al. (2020) and Westreich and Greenland (2013), I do not show the estimated coefficients of thecontrol variables in the following analyses.
Regarding anxiety about unemployment and bankruptcy, the BE model shows that comparedto Class I, the other classes (II, IIIab, IVab, IVc+VIIb, V+VI, and VIIa) have higher anxiety.
Anxiety is especially high in classes IVab and VIIb. In contrast to clear differences by class,there were no differences in social status. In the FE model, which controls for the stableunobserved heterogeneity, the estimated coefficients of the class are significant only for ClassIVab (petty-bourgeoisie), and that of status is not significant. Thus, class position, not status
positions, had a causal impact on individuals’ anxiety about unemployment and bankruptcy.
Being self-employed raises the fear of unemployment and bankruptcy.
The effect of class positions can be observed with regard to unemployment risk, whichindicates objective economic security. Because unemployment risk is a binary variable, I
employed binary logistic regression models. All time-varying independent variables wereentered into the analysis, with a lag of one year. The FE logistic regression analysis was notconducted here because this analytic design is not suitable for examining the impact of class andstatus on the risk of being unemployed. Therefore, pooled and RE logistic regression models

15were applied. The results indicate that compared to Class I, Classes IIIab, V+VI, and VIIa had ahigher risk of unemployment.
Regarding an individual’s annual income, the BE results show that there are substantive
effects of both class and status on an individual’s income. The result of the FE model, which
controls the unobserved characteristics, indicates much smaller effects of class compared to the
BE model, but they are still significant at the 5% level. The coefficient of status also became
smaller than that of the BE model; however, it was still significant at the 5% level. The result
indicated that both class and status have an effect on an individual’s income, even after
controlling for unobserved stable characteristics. To compare the magnitude of the effect of classwith that of status, I divided the status scale into seven status categories of approximately equal
size (1st to 7th tiers of status groups) and conducted the analysis using the FE model. The result
indicated that there was no status effect from the 2nd to 6th tiers of status groups compared to the
1st tier (|;| < 0.075, @ > 0.05). The income levels of 7th tiers of the groups were significantly
lower than those of the 1st tier (; = −0.107, CD = 0.045, @ < 0.05). From the comparisons of
the coefficients of class and status, we can conclude that class has a stronger effect on income
than status.
Although the observed class effects are much smaller when the unobserved stablecharacteristics are controlled for, these results are generally consistent with those of Chan andGoldthorpe (2007a): Class positions have an effect on economic life chances, but status positions
do not have any effect.

[Insert Table 6 here]

The Effects of Class and Status on Cultural Consumption
Next, I investigate the effect of class and status on cultural consumption. The results are
summarized in Table 7. The BE model shows that status position is more associated with cultural
consumption than class positions. Individuals who engage in high-status occupations go to16

museums and libraries and read novels more frequently than those who engage in low-status
occupations. Individuals who engage in high-status occupations are less likely to engage inpachinko or read tabloids. This result is consistent with Chan and Goldthorpe’s arguments. The
estimates based on the FE model, however, showed that the effects of status on cultural
consumption are much smaller than those from BE (some of the signs are reversed) and are notstatistically significant except for reading tabloids. The association between status and cultural
consumption disappeared after controlling for unobserved stable characteristics. This suggeststhat the observed associations between status and cultural consumption are yielded byunobserved dispositions that affect both status and cultural consumption.

[Insert Table 7 here]

The Effects of Class and Status on Health-Related Behaviors
Finally, I estimated the effect of class and status on health-related behaviors. Table 8 summarizes
the results. Class positions are associated with the level of alcohol consumption from the BEmodel, but these effects become smaller and not significant when I use the FE model. Status
positions are not related to the level of alcohol consumption in both the BE and FE models. Bothclass and status were associated with cigarette smoking in the BE model, but no significant
effects of class or status were observed in the FE model.
Class and status were significantly related to healthy behaviors in the BE model. Although
the results of the FE model show that the effects of the class are not statistically significant, there
are significant effects of status on doing exercise, eating a balanced diet, and eating fast food.
The higher the individuals’ status positions, the more frequently they exercise and eat a balanced
diet, and the less frequently they eat fast food.

[Insert Table 8 here]17

Robustness Check
Instead of the status scale, I used the SEI and the occupational prestige scale to conduct arobustness check. The SEI and occupational prestige scores were both ranked as the scores ofstatus (Mean = 0.50, SD = 0.29). The results are shown in Appendix D (Table D1-D6). Because
the SEI is a composite variable of education and income levels of occupations, we expect that
SEI is strongly related to income. The result of the FE model indicates that individuals’ SEIs
have an effect on their income. When SEI was used instead of status, the effect of class became
smaller because SEI is strongly related to class positions, as shown before (Relationship between
Class and Status). However, there are some class differences between Class I and Classes II and
IIIab. Similarly, individuals’ prestige had a causal effect on their income. However, socioeconomic positions had no effect on unemployment axiety or risk. Occupational prestige had noeffect on unemployment axiety, but had a negative effect on the risk of unemployment.
Regarding cultural consumption, neither BE nor FE models showed a significant effect of socioeconomic positions on individuals’ visiting museums and reading novels and tabloids. The BEmodel indicated that socioeconomic positions are positively associated with playing pachinko,
and the FE model showed that this association is not a causal relationship. In the FE model, thecoefficient of occupational prestige was significant, but the sign was negative, indicating that
individuals’ high prestigious occupations have a negative causal effect on visiting museums andlibraries.
Finally, I estimated the effect of socioeconomic status on health-related behaviors. There wasno effect of socioeconomic status on smoking, drinking, eating fast food, or doing exercise in the
FE model, but socioeconomic status positions had a negative effect on eating three meals a day
and a balanced diet. Although SEI and prestige are associated with economic life chances, they
are not clearly associated with cultural consumption and health behaviors in either the BE or FE
models. Status, in contrast, is not associated with economic life chances, but is certainly
associated with cultural consumption and health behavior in the BE model. In the FE model,
status is also associated with health behavior, suggesting that status has a causal effect.
18

CONCLUSIONS AND DISCUSSIONS
Returning to Weber’s distinction between class and status, Chan and Goldthorpe (2004, 2007a)argued the validity of the concept and demonstrated its empirical relevance in contemporary
British society. Following their analytic frameworks, many studies found that while classpositions are associated with individuals’ economic security and prospects, status positions areassociated with cultural consumption. However, the causality of these relationships has rarely
been empirically tested. The analysis of longitudinal data provides new insights into theirfindings.
This study not only examined the association of class and status with life chances and life
choices, but also empirically explores the causal effects of class and status. The BE model, which
includes class and status as independent variables and a rich set of relevant control variables, but
not controlled for unobserved stable characteristics, showed that class positions are associatedwith life chances and status positions are associated with life choices. Thus, this study replicatesthe results of Chan and Goldthorpe with a richer set of control variables.14
Moreover, I controlled for unobserved characteristics that may produce the observed
associations using the FE model to reveal how class and status are associated with life chancesand life choices. The main results regarding our research question and three hypotheses can besummarized as follows: First, class positions affect individuals’ economic life chances, but status
positions do not or less so, which supports Hypothesis 1. In addition to substantial associations
between class and economic life chances, the causal relationship was observed as Goldthorpe
and McKnight (2006) argued: the economic situations of individuals are, to some extent, thecausal consequence of their class positions. However, since the number of variables examined
here is very limited, future analyses will need to examine the causal effects of class positions ona larger number of variables related to economic life chances.
Second, status positions are strongly related to cultural consumption, but when unobserved
stable dispositions are controlled for, there is no status effect on cultural consumption. This result
19suggests that the observed associations between status and cultural consumption are mainly
produced by unobserved dispositions and do not reflect the causal effect of status positions on
cultural consumption. Therefore, Hypothesis 2 is rejected: status positions are associated with
cultural consumption but do not have a causal impact on it.
Third, status positions are associated with health-related behaviors, and even after controllingfor unobserved characteristics, the association still exists, implying that status positions have acausal effect on health-related behaviors such as doing exercises, eating balanced diets, and noteating fast food. This result partially supports Hypothesis 3.
These results indicate that class positions have a causal effect on an individual’s economic
life chances, and status positions have a causal effect on life choices, especially health-related
behaviors, although much of the observed associations are also produced by the unobserved
traits. The most important finding of this study is that the effect of status on cultural consumption
disappeared after controlling for the unobserved stable dispositions of individuals. Thus, I canconclude that status positions themselves do not shape the style of cultural consumption. As
Chan and Goldthorpe (2007a) recognized, it is highly possible that certain unobserved
dispositions affect both status attainment and cultural consumption. Unfortunately, we cannot
capture the exact nature of such unobserved characteristics from the FE model, which makes it,
to some extent, mysterious (Angrist and Pischke 2008). There may be a view that it is inevitable
to discuss the relationship between status and cultural consumption because there is no causal
relationship. However, the fact that such certain unobserved characteristics create an association
between status and cultural consumption is of substantive importance and interest inunderstanding social stratification. The basic patterns and levels of individuals’ cultural
consumption are shaped by their experiences and socialization during childhood and youth, andare likely to be less affected by changes in socioeconomic circumstances. As the ICC shows,cultural consumption is not constant but changes within the same individual (ranging from 0.457
to 0.766), and this change can be interpreted as a rather random fluctuation or measurement error(Hout and Hastings 2016; Kiley and Vaisey 2020).
20

This study also demonstrated the existence of a causal effect of status positions on healthrelated behaviors, especially on doing exercises, eating balanced diets, and not eating fast food.
Health behaviors, health-related behaviors, and cultural consumption may be influenced by avariety of early experiences and socialization. However, it may be susceptible to change by new
information and the surrounding network. Using network data from the Framingham Heart
Study, Christakis and Fowler (2007) showed that obesity spreads through social ties, that is, thepossibility that an individual becomes obese increases when his or her friend, sibling, and spousebecome obese. They explain that the mechanism through which obesity in a close person affects
obesity in an individual is related to psychosocial processes such as altering the individual’s
norms about obesity and changing the individual’s healthy lifestyle, such as food consumption.
Individuals’ status positions may have a causal impact on their health-related behaviors in asimilar way. Social interactions with people in similar status positions encourage an individual toreceive and follow a specific (healthy or unhealthy) lifestyle of these status positions. Drinking
and smoking may be more addictive, and thus, these habits may be less susceptible to suchchanges. However, individuals’ eating and exercise habits may be more susceptible to the
influence of the people around them.
By showing not only associations but also causal effects, this study contributes to theongoing debate about the distinction between class and status. Two forms of stratification, classand status, are still useful in understanding social stratification and inequality in contemporarysocieties. In addition, this study demonstrated the importance of status positions rather than classpositions in comprehending the inequality of health-related behaviors (Goldthorpe 2012). This
result emphasizes the continuity of research on the relationship between social status andnetworks (or groups) and its consequences.

Limitations and Future Tasks
This study had several limitations. First, intragenerational mobility differs by individual
characteristics (Kalleberg and Mouw 2018), and class and status do not change at random.
21

Because the FE model relies on the information of changes, the results can be applied topopulations where the independent variable has changed, thus raising the issue of external
validity (Allison 2009; Hill et al. 2020). Second, time-varying unmeasured or unobserved
confounding variables could not be controlled. Third, there is a possibility of reverse causation.
This study examined the effect of class and status on life chances and life choices, but cultural
consumption may affect status attainment. More rigorous tests of causality are required for future
analyses (Allison 2009; Vaisey and Miles 2017). Fourth, the mechanisms by which status has acausal effect on health-related behaviors (health lifestyle) are not fully understood, which
requires theories and hypotheses. Moreover, to test these hypotheses, research designs thatenable us to utilize causal mediation analysis, which has attracted much attention in sociologicalresearch in recent years (Wodtke and Parbst 2017; Brand et al. 2019), are required in the future.
Moreover, we do not know whether the results presented in this study, especially on thecausal effects, can be generalized to other societies. As explained before, class and status matterin Japan as well as other industrial societies, and the associations of class and status with life
chances and life choices are similar to those found in European societies. Therefore, it is highly
possible that the results presented in this study can be found in other societies.

22

NOTES
1. In Weber (2010), translated by Waters et al., Stand (plural Ständ) in German was usedinstead of status or status group. See also Waters and Waters (2010, 2016).
2. Chan and Goldthorpe (2004) applied a multidimensional scaling technique to data on theoccupations of respondents and their close friends and found that the first dimension
extracted reflected the hierarchy of status. Stewart et al. (1980) used respondents’ andfriends’ occupational data to construct what is called the Cambridge scale. The Cambridge
scale was updated to the Cambridge Social Interaction and Stratification (CAMSIS) scale(Prandy 1990; Prandy and Jones 2001; Prandy and Lambert 2003). The CAMSIS scales wereconstructed from cross-tabulations of the occupations of friends or marriage partners. For
details, see Lambert (2018).
3. In this study, I use the term “effect” to refer to an association or an association adjusted forthe relevant observed covariates and the term “causal effect” to indicate the association
adjusted for the relevant observed covariates and unobserved stable characteristics.
4. See a special section of The British Journal of Sociology (Laurison 2019). For the theoreticalarguments of status in general, see Gould (2002) and Joel and Lynn (2009).
5. Elwert and Winship (2014) explain the three structural sources of association between two
variables: (1) causation, (2) confounding, and (3) endogenous selection.
6. There is no national version of the CAMSIS scale for East Asian societies(http://www.camsis.stir.ac.uk/versions.html#Flags) as of November 11, 2021.
7. Moreover, in order to estimate the direct causal effect, post-treatment and outcome
confounders should be considered (Zhou and Wodtke 2019).
8. The three key assumptions of the fixed-effects model are that (1) there is no unobserved
time-varying confounder, (2) past treatment (independent) variables do not directly affectcurrent outcomes, and (3) past outcomes do not affect current treatment (independent)
variables (Imai and Kim 2019).

23

9. The wfe package in R can relax the assumption of linearity, but the treatment variable should
be binary (Imai and Kim 2019).
10. These figures are similar to intragenerational class mobility in the U.S., although the mobility
study in the U.S. used the five-category version of the class (Jarvis and Song 2017).
11. The variance inflation factor (VIF) values were not high (less than 3) for all independent
variables and covariates.
12. Objective variables, such as demographic and religious characteristics, are found to be morereliable than subjective items such as attitudes (Hout and Hastings 2016).
13. Hout and Hastings (2016) found that the ICCs of logged income were between 0.75 and 0.80
in three-waves panel surveys of the General Social Survey.
14. Note that I did not control for income levels to avoid overcontrol bias (Elwert and Winship2014; Keele et al. 2020).

24

REFERENCES
Acharya, Avidit, Matthew Blackwell, and Maya Sen. 2016. “Explaining Causal Findings
Without Bias: Detecting and Assessing Direct Effects.” American Political Science
Review 110(03): 512–29.
Alderson, Arthur S., Azamat Junisbai, and Isaac Heacock. 2007. “Social Status and Cultural
Consumption in the United States.” Poetics 35(2–3): 191–212.
Allison, Paul David. 2009. Fixed Effects Regression Models. Los Angeles: SAGE.
Angrist, Joshua D., and Jörn-Steffen Pischke. 2008. Mostly Harmless Econometrics: An
Empiricist’s Companion. Princeton, NJ: Princeton University Press.
Archer, Patrick, and Ryan Orr. 2011. “Class Identification in Review: Past Perspectives andFuture Directions: Class Identification.” Sociology Compass 5(1): 104–15.
Bartley, M, A Sacker, D Firth, and R Fitzpatrick. 1999. “Understanding Social Variation in
Cardiovascular Risk Factors in Women and Men: The Advantage of Theoretically
Based Measures.” Social Science & Medicine 49(6): 831–45.
Bihagen, Erik, and Paul Lambert. 2018. “Can Class and Status Really Be Disentangled?”
Research in Social Stratification and Mobility 58: 1–10.
Bourdieu, Pierre. 1984. Distinction: A Social Critique of the Judgement of Taste. London, UK:
Routledge & Kegan Paul.
Brand, Jennie E., Ravaris Moore, Xi Song, and Yu Xie. 2019. “Why Does Parental Divorce
Lower Children’s Educational Attainment? A Causal Mediation Analysis.” Sociological
Science 6: 264–92.
Breen, Richard (ed.). 2004. Social Mobility in Europe. Oxford: Oxford University Press.
———. 2005. “Foundations of a Neo-Weberian Class Analysis.” Pp. 31–50 in Approaches toClass Analysis, edited by E. O. Wright. Cambridge: Cambridge University Press.
Bukodi, Erzsébet. 2007. “Social Stratification and Cultural Consumption in Hungary: Book
Readership.” Poetics 35(2): 112–31.

25

Bukodi, Erzsébet, Shirley Dex, and John H. Goldthorpe. 2011. “The Conceptualisation andMeasurement of Occupational Hierarchies: A Review, a Proposal and Some Illustrative
Analyses.” Quality & Quantity 45(3): 623–39.
Bukodi, Erzsébet, Ferdinand Eibl, Sandra Buchholz, Sonia Marzadro, Alessandra Minello,
Susanne Wahler, Hans-Peter Blossfeld, Robert Erikson, and Antonio Schizzerotto.
2018. “Linking the Macro to the Micro: A Multidimensional Approach to Educational
Inequalities in Four European Countries.” European Societies 20(1): 26–64.
Bukodi, Erzsébet, Robert Erikson, and John H Goldthorpe. 2014. “The Effects of Social Origins
and Cognitive Ability on Educational Attainment: Evidence from Britain and Sweden.”
Acta Sociologica 57(4): 293–310.
Bukodi, Erzsébet, and John H. Goldthorpe. 2013. “Decomposing ‘Social Origins’: The Effects of
Parents’ Class, Status, and Education on the Educational Attainment of Their Children.”European Sociological Review 29(5): 1024–39.
Chan, Tak Wing (ed.). 2010. Social Status and Cultural Consumption. Cambridge: Cambridge
University Press.
———. 2011. “Social Status.” SAGE.
———. 2019. “Understanding Social Status: A Reply to Flemmen, Jarness and Rosenlund.” The
British Journal of Sociology 70(3): 867–81.
Chan, Tak Wing, Gunn Elisabeth Birkelund, Arne Kristian Aas, and Øyvind Wiborg. 2011.
“Social Status in Norway.” European Sociological Review 27(4): 451–68.
Chan, Tak Wing, and John H. Goldthorpe. 2004. “Is There a Status Order in Contemporary
British Society? Evidence from the Occupational Structure of Friendship.” European
Sociological Review 20(5): 383–401.
———. 2005. “The Social Stratification of Theatre, Dance and Cinema Attendance.” Cultural
Trends 14(3): 193–212.
———. 2006. “Social Stratification and Cultural Consumption: Music in England.” European
Sociological Review 23(1): 1–19.
26

———. 2007a. “Class and Status: The Conceptual Distinction and Its Empirical Relevance.”
American Sociological Review 72(4): 512–32.
———. 2007b. “Social Status and Newspaper Readership.” American Journal of Sociology
112(4): 1095–1134.
Chiavacci, David. 2008. “From Class Struggle to General Middle-Class Society to Divided
Society: Societal Models of Inequality in Postwar Japan.” Social Science Japan Journal
11(1): 5–27.
Christakis, Nicholas A., and James H. Fowler. 2007. “The Spread of Obesity in a Large Social
Network over 32 Years.” New England Journal of Medicine 357(4): 370–79.
Cockerham, William C. 2005. “Health Lifestyle Theory and the Convergence of Agency and
Structure.” Journal of Health and Social Behavior 46(1): 51–67.
Coulangeon, Philippe, and Yannick Lemel. 2007. “Is ‘Distinction’ Really Outdated? Questioning
the Meaning of the Omnivorization of Musical Taste in Contemporary France.” Poetics
35(2): 93–111.
Elwert, Felix, and Christopher Winship. 2014. “Endogenous Selection Bias: The Problem ofConditioning on a Collider Variable.” Annual Review of Sociology 40(1): 31–53.
Erikson, Robert, and John H. Goldthorpe. 1992. The Constant Flux: A Study of Class Mobility inIndustrial Societies. Oxford: Clarendon.
Erikson, Robert, John H. Goldthorpe, and Lucienne Portocarero. 1979. “Intergenerational Class
Mobility in Three Western European Societies: England, France and Sweden.” The
British Journal of Sociology 30(4): 415–41.
Featherman, David L., and Robert M. Hauser. 1976. “Prestige or Socioeconomic Scales in the
Study of Occupational Achievement?” Sociological Methods & Research 4(4): 403–22.
Flemmen, Magne Paalgard, Vegard Jarness, and Lennart Rosenlund. 2019. “Class and Status: On
the Misconstrual of the Conceptual Distinction and a Neo-Bourdieusian Alternative.”
The British Journal of Sociology 70(3): 816–66.

27

Freeland, Robert E., and Jesse Hoey. 2018. “The Structure of Deference: Modeling Occupational
Status Using Affect Control Theory.” American Sociological Review 83(2): 243–77.
Fujihara, Sho. 2020. “Socio-Economic Standing and Social Status in Contemporary Japan: Scale
Constructions and Their Applications.” European Sociological Review.
Fujihara, Sho, and Hiroshi Ishida. 2016. “The Absolute and Relative Values of Education and theInequality of Educational Opportunity: Trends in Access to Education in Postwar
Japan.” Research in Social Stratification and Mobility 43: 25–37.
Galobardes, Bruna, Mary Shaw, Debbie A. Lawlor, John W. Lynch, and George Davey Smith.
2006a. “Indicators of Socioeconomic Position (Part 1).” Journal of Epidemiology &Community Health 60(1): 7–12.
———. 2006b. “Indicators of Socioeconomic Position (Part 2).” Journal of Epidemiology &Community Health 60(2): 95–101.
Gangl, Markus. 2010. “Causal Inference in Sociological Research.” Annual Review of Sociology
36(1): 21–47.
Ganzeboom, Harry B.G., Paul M. De Graaf, and Donald J. Treiman. 1992. “A Standard
International Socio-Economic Index of Occupational Status.” Social Science Research21(1): 1–56.
Geyer, Siegfried, Orjan Hemström, Richard Peter, and Denny Vågerö. 2006. “Education,
Income, and Occupational Class Cannot Be Used Interchangeably in Social
Epidemiology. Empirical Evidence against a Common Practice.” Journal of
Epidemiology and Community Health 60(9): 804–10.
Goldthorpe, John H. 2012. “Back to Class and Status: Or Why a Sociological View of Social
Inequality Should Be Reasserted.” Revista Española de Investigaciones Sociológicas
137: 201–16.
Goldthorpe, John H, and Abigail McKnight. 2006. “The Economic Basis of Social Class.” Pp.
109–36 in Mobility and Inequality: Frontiers of Research from Sociology and28

Economics, edited by S. L. Morgan, D. B. Grusky, and G. S. Fields. Stanford, CA:
Stanford University Press.
Gould, Roger V. 2002. “The Origins of Status Hierarchies: A Formal Theory and Empirical
Test.” American Journal of Sociology 107(5): 1143–78.
Greenland, Sander, Judea Pearl, and James M. Robins. 1999. “Causal Diagrams for
Epidemiologic Research.” Epidemiology 10(1): 37–48.
Hannum, Emily, Hiroshi Ishida, Hyunjoon Park, and Tony Tam. 2019. “Education in East AsianSocieties: Postwar Expansion and the Evolution of Inequality.” Annual Review ofSociology 45: 625–47.
Hauser, Robert M., and John Robert Warren. 1997. “Socioeconomic Indexes for Occupations: A
Review, Update, and Critique.” Sociological Methodology 27(1): 177–298.
Hechter, Michael. 2004. “From Class to Culture.” American Journal of Sociology 110(2): 400–
445.
Hill, Terrence D., Andrew P. Davis, J. Micah Roos, and Michael T. French. 2020. “Limitationsof Fixed-Effects Models for Panel Data.” Sociological Perspectives 63(3): 357–69.
Hiyoshi, Ayako, Yoshiharu Fukuda, Martin J. Shipley, Mel Bartley, and Eric J. Brunner. 2013.
“A New Theory-Based Social Classification in Japan and Its Validation Using
Historically Collected Information.” Social Science & Medicine 87: 84–92.
Hout, Michael, and Orestes Hastings. 2016. “Reliability of the Core Items in the General Social
Survey: Estimates from the Three-Wave Panels, 2006–2014.” Sociological Science 3:
971–1002.
Imai, Kosuke, and In Song Kim. 2019. “When Should We Use Unit Fixed Effects Regression
Models for Causal Inference with Longitudinal Data?” American Journal of Political
Science 63(2): 467–90.
Ishida, Hiroshi. 2007. “Japan: Educational Expansion and Inequality in Access to Higher
Education.” Pp. 63–86 in Stratification in Higher Education: A Comparative Study,

29

edited by Y. Shavit, R. Arum, A. Gamoran, and G. Menahem. Stanford: Stanford
University Press.
———. 2018. “Long-Term Trends in Intergenerational Class Mobility in Japan.” Pp. 41–64 in2015 SSM Research Report 3: Social Mobility and Health, edited by T. Yoshida.
Ishida, Hiroshi, John H. Goldthorpe, and Robert Erikson. 1991. “Intergenerational Class
Mobility in Postwar Japan.” The American Journal of Sociology 96(4): 954–92.
Ishida, Hiroshi, Walter Müller, and John M. Ridge. 1995. “Class Origin, Class Destination, and
Education: A Cross-National Study of Ten Industrial Nations.” The American Journalof Sociology 101(1): 145–93.
Ishida, Hiroshi, and David H. Slater (eds.). 2009. Social Class in Contemporary Japan:
Structures, Sorting and Strategies. New York, NY: Routledge.
Jarvis, Benjamin F., and Xi Song. 2017. “Rising Intragenerational Occupational Mobility in the
United States, 1969 to 2011.” American Sociological Review 82(3): 568–99.
Joel, Podolny, and Freda Lynn. 2009. “Status.” Pp. 554–65 in The Oxford Handbook ofAnalytical Sociology, edited by P. Hedström and P. Bearman. New York, NY: Oxford
University Press.
Kalleberg, Arne L., and Ted Mouw. 2018. “Occupations, Organizations, and Intragenerational
Career Mobility.” Annual Review of Sociology 44(1): 283–303.
Keele, Luke, Randolph T. Stevenson, and Felix Elwert. 2020. “The Causal Interpretation of
Estimated Associations in Regression Models.” Political Science Research and Methods
8(1): 1–13.
Kerbo, Harold. 2011. Social Stratification and Inequality. 8 edition. 8 edition. New York, NY:
McGraw-Hill Education.
Kiley, Kevin, and Stephen Vaisey. 2020. “Measuring Stability and Change in Personal Culture
Using Panel Data.” American Sociological Review 85(3): 477–506.
Kohn, Melvin L. 1977. Class and Conformity: A Study in Values. 2nd edition. 2nd edition.
Chicago,IL: The University of Chicago Press.
30

Kohn, Melvin L., Atsushi Naoi, Carrie Schoenbach, Carmi Schooler, and Kazimierz M.
Slomczynski. 1990. “Position in the Class Structure and Psychological Functioning in
the United States, Japan, and Poland.” American Journal of Sociology 95(4): 964–1008.
Kraaykamp, Gerbert, Koen van Eijck, Wout Ultee, and Kees van Rees. 2007. “Status and MediaUse in the Netherlands: Do Partners Affect Media Tastes?” Poetics 35(2): 132–51.
Krieger, N., D. R. Williams, and N. E. Moss. 1997. “Measuring Social Class in US Public HealthResearch: Concepts, Methodologies, and Guidelines.” Annual Review of Public Health18(1): 341–78.
Lambert, Paul. 2018. “CAMSIS: Social Interaction and Stratification Scale.”
http://www.camsis.stir.ac.uk/index.html. Accessed on January 7, 2019.
Laumann, Edward O. 1965. “Subjective Social Distance and Urban Occupational Stratification.”
American Journal of Sociology 71(1): 26–36.
Laumann, Edward O., and Louis Guttman. 1966. “The Relative Associational Contiguity ofOccupations in an Urban Setting.” American Sociological Review 31(2): 169–78.
Laurison, Daniel. 2019. “On Culture and Inequality: Distinction, Omnivorousness, Status andClass.” The British Journal of Sociology 70(3): 780–83.
Lindblom, Taru, and Pekka Räsänen. 2017. “Between Class and Status? Examining the Digital
Divide in Finland, the United Kingdom, and Greece.” The Information Society 33(3):147–58.
Meraviglia, Cinzia, Harry B.G. Ganzeboom, and Deborah De Luca. 2016. “A New International
Measure of Social Stratification.” Contemporary Social Science 11(2–3): 125–53.
Morgan, Stephen L., and Christopher Winship. 2014. Counterfactuals and Causal Inference:
Methods and Principles for Social Research. 2 edition. 2 edition. New York, NY:
Cambridge University Press.
Mummolo, Jonathan, and Erik Peterson. 2018. “Improving the Interpretation of Fixed Effects
Regression Results.” Political Science Research and Methods 6(4): 829–35.

31

Nakai, Miki. 2012. “Aspects of Lifestyle Inequality as Seen through Consumption.” Pp. 221–36
in Contemporary Stratified Society 1: Inequality and Diversity, edited by Y. Sato and F.
Ojima. University of Tokyo Press.
Peterson, Richard A. 2007. “Comment on Chan and Goldthorpe: Omnivore, What’s in a Name,
What’s in a Measure?” Poetics 35(4): 301–5.
Phelan, Jo C., Bruce G. Link, and Parisa Tehranifar. 2010. “Social Conditions as Fundamental
Causes of Health Inequalities: Theory, Evidence, and Policy Implications.” Journal of
Health and Social Behavior 51(1_suppl): S28–40.
Prandy, Kenneth. 1990. “The Revised Cambridge Scale of Occupations.” Sociology 24(4): 629–
55.
Prandy, Kenneth, and Frank L. Jones. 2001. “An International Comparative Analysis of
Marriage Patterns and Social Stratification.” International Journal of Sociology andSocial Policy 21(4/5/6): 165–83.
Prandy, Kenneth, and Paul Lambert. 2003. “Marriage, Social Distance and the Social Space: An
Alternative Derivation and Validation of the Cambridge Scale.” Sociology 37(3): 397–
411.
Rabe-Hesketh, Sophia, and Anders Skrondal. 2012. Multilevel and Longitudinal Modeling Using
Stata. Third Edition. Third Edition. College Station, Texas: Stata Press.
Ridgeway, Cecilia L. 2014. “Why Status Matters for Inequality.” American Sociological Review
79(1): 1–16.
Riedl, Maximilian, and Ingo Geishecker. 2014. “Keep It Simple: Estimation Strategies forOrdered Response Models with Fixed Effects.” Journal of Applied Statistics 41(11):2358–74.
Sacker, Amanda, David Firth, Ray Fitzpatrick, Kevin Lynch, and Mel Bartley. 2000.
“Comparing Health Inequality in Men and Women: Prospective Study of Mortality
1986-96.” BMJ 320(7245): 1303–7.

32

Sato, Yoshimich, and Shin Arita. 2016. “Inequality in Educational Returns in Japan.” Pp. 94–113
in Education, Occupation and Social Ori- Gin: A Comparative Analysis of theTransmission of Socio-Economic Inequalities, edited by F. Bernardi and G. Ballarino.
Cheltenham, UK: Edward Elgar.
Sørensen, Aage B. 2000. “Toward a Sounder Basis for Class Analysis.” American Journal of
Sociology 105(6): 1523–58.
Stewart, Alexander, Kenneth Prandy, and Robert M Blackburn. 1980. Social Stratification andOccupations. London. London: Macmillan.
Torche, Florencia. 2007. “Social Status and Cultural Consumption: The Case of Reading inChile.” Poetics 35(2–3): 70–92.
Torssander, Jenny, and Robert Erikson. 2009. “Marital Partner and Mortality: The Effects of theSocial Positions of Both Spouses.” Journal of Epidemiology & Community Health63(12): 992–98.
———. 2010. “Stratification and Mortality: A Comparison of Education, Class, Status, andIncome.” European Sociological Review 26(4): 465–74.
Treiman, Donald J. 1977. Occupational Prestige in Comparative Perspective. New York:
Academic Press.
Vaisey, Stephen, and Andrew Miles. 2017. “What You Can—and Can’t—Do With Three-Wave
Panel Data.” Sociological Methods & Research 46(1): 44–67.
Waters, Tony, and Dagmar Waters. 2010. “The New Zeppelin University Translation of Weber’s‘Class, Status, Party.’” Journal of Classical Sociology 10(2): 153–58.
———. 2016. “Are the Terms ‘Socio-Economic Status’ and ‘Class Status’ a Warped Form of
Reasoning for Max Weber?” Palgrave Communications 2(1): 1–13.
Weber, Max. 1968. Economy and Society. Berkeley and Los Angeles, CA: University ofCalifornia Press.
———. 2010. “The Distribution of Power within the Community: Classes, Stände, Parties.”
Journal of Classical Sociology 10(2): 137–52.
33

Weeden, Kim A., and David B. Grusky. 2005. “The Case for a New Class Map.” American
Journal of Sociology 111(1): 141–212.
Westreich, Daniel, and Sander Greenland. 2013. “The Table 2 Fallacy: Presenting andInterpreting Confounder and Modifier Coefficients.” American Journal of Epidemiology
177(4): 292–98.
Winship, Christopher, and Robert D. Mare. 1992. “Models for Sample Selection Bias.” Annual
Review of Sociology 18: 327–50.
Winship, Christopher, and Stephen L. Morgan. 1999. “The Estimation of Causal Effects from
Observational Data.” Annual Review of Sociology 25(1): 659–706.
Wodtke, Geoffrey T., and Matthew Parbst. 2017. “Neighborhoods, Schools, and Academic
Achievement: A Formal Mediation Analysis of Contextual Effects on Reading andMathematics Abilities.” Demography 54(5): 1653–76.
Wright, Erik Olin (ed.). 2005. Approaches to Class Analysis. Cambridge, UK: Cambridge
University Press.
Wuggenig, Ulf. 2007. “Comments on Chan and Goldthorpe: Pitfalls in Testing Bourdieu’s
Homology Assumptions Using Mainstream Social Science Methodology: Social
Stratification and Cultural Consumption: The Visual Arts in England.” Poetics 35(4):306–16.
Zhou, Xiang, and Geoffrey T. Wodtke. 2019. “A Regression-with-Residuals Method for
Estimating Controlled Direct Effects.” Political Analysis 27(03): 360–69.

34

Figure 1. Boxplot of distributions of social status within and between social class
Source: JLPS, 2007-2020.

35

Table 1 The Full Version of EGP Class Schema and the Seven-Category Version of EGP
Seven-category
version of EGP

Full version of EGP
Higher-grade professionals, administrators, and officials; managers in large
industrial establishments; large proprietors
Lower-grade professionals, administrators, and officials; higher-grade technicians;IImanagers in small industrial establishments; supervisors of non-manual employees
IIIa Routine nonmanual employees, higher grade (administration and commerce)
IIIb Routine nonmanual employees, lower grade (sales and services)
IVa Small proprietors, artisans etc., with employees
IVb Small proprietors, artisans etc., without employees
IVc Farmers and small holders; other self-employed workers in primary production
VIIb Agricultural and other workers in primary production
V
Lower-grade technicians; supervisors of manual workers
VI
Skilled manual workers
VIIa Semi- and unskilled manual workers (not in agriculture, etc.)
I

Source: Erikson and Goldthorpe (1992).

36

I
II
IIIab
IVab
IVc+VIIb
V+VI
VIIa

Table 2 The Ranking of Social Status Score of 56 Sub-Major Groups of Occupations inJapan
Occupational Titles(56 Sub-Major Groups)
1 (8) Legal Workers
2 (4) Researchers
Finance, and3 (9) Management,
Insurance Specialists
4 (12) Authors, Journalists, Editors
5 (14) Musicians, Stage Designers
6 (10) Teachers(15) Other Specialist
7 Professionals
8 (11) Workers in Religion
Government
9 (1) Management
Officials

Score

Rank

Score

41
42

Occupational Titles(56 Sub-Major Groups)(51) Electric Construction
Workers(47) Ship and Aircraft Operators(18) Production-Related Clerical
Workers(41) Machine Maintenance andRepair Workers(23) Merchandise Sales Workers(34) Public Security Workers(45) Railway Drivers(27) Care Service Workers(35) Agriculture Workers(56) Other Carrying, Cleaning,
Packaging, and RelatedWorkers(50) Construction and Civil
Engineering Workers(49) Stationary and Construction
Machinery Operators(29) Domestic Hygiene Service
Workers(53) Carrying Workers

71.9
70.6
66.0

29
30
31

66.0
66.0
65.6
63.0
62.3
62.2

32
33
34
35
36
37

of Companies and
10 (2) Officers
Organizations

62.1

38

11 (17) Accountancy Clerks

57.8

39

12 (5) Engineers

57.1

40

13 (6) Health Care Workers
14 (16) General Clerical Workers(13) Artists, Designers,
15
Photographers, Film
Operators(7)
Social
Welfare Specialist
16
Professionals

56.7
56.7
56.5

43

(42) Product Inspection Workers

42.3

54.5

44

42.2

17 (24) Quasi-Sales Workers

54.1

45

18 (33) Other Service Workers

54.1

46

Administrative and19 (3) OtherManagerial Workers
20 (19) Sales Clerks
21 (22) Office Appliance Operators

52.8
52.8
52.5

47
48
49

22 (28) Healthcare Service Workers

52.3

50

(40) Machine Assembly Workers(30) Food and Drink Preparatory
Workers(39) Product Manufacturing andProcessing Workers (Except
Metal Products)(31) Customer Service Workers(43) Machine Inspection Workers(52) Mine Workers(38) Product Manufacturing andProcessing Workers (Metal
Products)

52.1

51

(55) Packaging Workers

39.9

51.6
49.7

52
53

(48) Other Transport Workers(36) Forestry Workers

39.5
39.4

49.2

54

(54) Cleaning Workers

38.8

49.1

55

(46) Motor Vehicle Drivers

37.3

48.2

56

(37) Fishery Workers

36.4

Rank

23
24
25
26
27
28

(32) Residential Facilities, Office
Buildings and OtherManagement Personnel(25) Sales Workers(21) Transport and Post Clerical
Workers(26) Domestic Support Service
Workers(44) Manufacturing-Related andQuasi-Manufacturing
Workers(20) Outdoor Service Workers

37

47.9
47.8
47.5
47.2
47.0
46.4
45.6
45.2
43.7
43.5
43.3
43.2
43.0
42.4

41.9
41.7
41.5
41.3
40.9
40.8

Note: The score for the detailed occupational titles (231 occupational titles) is shown in theOnline Supplement (S1). The numbers in parentheses indicate the order of the 56 sub-majorgroups.

38

Table 3 Descriptive Statistics of the Dependent Variables
Variables

Collection

N

Obs.

Mean

Annual4,384

34,694

0.56

Unemployment risk

Annual3,330

24,302

Individual annual income (logged)

Annual3,276

Visiting museums

Biennial

Visiting libraries

SD

SDBetween

SDWithin

Min

Max

ICC

0.77

0.60

0.57

0

3

0.413

0.014

0.12

0.11

0.10

0

1

0.266

23,153

5.69

0.79

0.78

0.31 3.22

7.72

0.834

3,798

16,884

0.39

0.54

0.44

0.35

0

3

0.457

Biennial

3,795

16,889

0.66

0.85

0.73

0.49

0

3

0.564

Reading novels

Biennial

3,799

16,889

1.08

1.08

0.93

0.59

0

3

0.609

Playing pachinko

Biennial

3,800

16,896

0.29

0.74

0.71

0.32

0

3

0.766

Reading tabloids

Biennial

3,798

16,911

1.18

1.05

0.89

0.64

0

3

0.521

Smoking cigarettes

Biennial

4,322

17,992

0.43

0.82

0.82

0.33

0

3

0.806

Drinking alcohol

Biennial

4,323

18,010

2.44

1.96

1.80

0.81

0

6

0.772

Eating fast food

Annuala4,344

30,215

1.37

0.96

0.83

0.64

0

5

0.506

Doing Exercise

Annuala4,337

29,859

0.88

1.32

1.04

0.89

0

5

0.464

Eating three meals a day

Annuala4,342

30,181

4.08

1.56

1.45

0.87

0

5

0.659

Eating balanced meals

Annuala4,341

30,091

3.56

1.43

1.29

0.84

0

5

0.612

(A) Economic Life Chances
Anxiety about unemployment andbankruptcy(B) Cultural Consumption(C) Health-Related Behaviors

Source: JLPS-YM, 2007–2020.
Note: (a) 2007, 2009, and every year from 2011 to 2020. SDBetween: Between standard deviation, SDWithin: Within standard deviation.
39

40

Table 4 Distributions of Class and Status and Between and Within Components
Class

Overall

BetweenWithin

I

18.2

24.6

61.1

II

16.2

23.3

IIIab

32.6

IVab

StatusOverall

BetweenWithin1 (Top)

14.7

20.3

67.4

67.9

2

15.1

23.4

57.5

48.5

70.0

3

14.2

25.2

53.8

7.7

11.9

63.5

4

16.0

28.4

55.5

IVc+VIIb

1.1

1.6

66.3

5

8.0

14.3

56.7

V+VI

10.6

20.2

56.4

6

18.8

31.7

63.7

VIIa

13.7

26.3

58.1

7 (Bottom)

13.2

24.9

61.3

Total

100.0

156.2

64.0

Total

100.0

168.2

59.5

Source: JLPS-YM, 2007–2020.
Note: N = 4,393.

41

Table 5 Class and Status Mobilities
Class (t)

I

II

IIIab

Class (t+1)
IVab
IVc+VIIb

V+VI

VIIa

I

89.4

3.1

5.1

0.8

0.0

0.7

0.9

II

5.1

87.9

3.3

1.1

0.1

1.6

0.9

IIIab

4.9

1.7

87.5

1.3

0.1

1.4

3.2

IVab

1.6

1.9

4.7

87.1

0.0

2.8

1.8

IVc+VIIb

0.0

0.3

3.4

0.0

91.6

1.3

3.4

V+VI

2.0

2.8

4.4

2.3

0.2

81.9

6.5

VIIa

1.6

1.7

8.2

1.2

0.4

5.7

81.4

Total

19.3

16.1

32.1

7.8

1.1

10.5

13.3

6

7

Status (t)12Status (t+1)
43

5

1 (Top)

91.4

2.3

2.1

2.5

0.3

0.9

0.6

2

2.2

84.6

6.3

3.1

1.5

1.5

0.8

3

2.4

6.0

80.5

6.5

1.4

2.1

1.2

4

2.4

2.7

5.5

83.0

1.4

3.1

1.8

5

0.8

3.7

2.2

3.1

85.2

3.0

2.1

6

0.7

1.7

1.6

3.4

1.7

85.2

5.6

7 (Bottom)

1.1

1.4

1.8

2.5

1.8

7.9

83.5

Total

14.9

15.4

14.3

16.2

8.0

18.4

12.7

Source: JLPS-YM, 2007–2020.
Note: Observations = 30,821.

42

Table 6 The Effects of Class and Status on Economic Life Chances
Unemployment
Unemployment
Individual
Anxiety
Risk
IncomeBE
FE
Pooled
RE
BE
FE
Class
I (ref.)
0.000
0.000
0.000
0.000
0.000
0.000
------------II
0.097*-0.0150.411
0.392-0.430***-0.060*(0.041) (0.027) (0.268)(0.272)(0.044)(0.026)
IIIab
0.199*** -0.022 0.813*** 0.809** -0.566*** -0.087***(0.037) (0.021) (0.242)(0.247)(0.041)(0.017)IVab
0.387*** 0.101*-0.193-0.213-0.654*** -0.139***(0.048) (0.045) (0.400)(0.405)(0.052)(0.039)
IVc+VIIb
0.254*
0.016
0.11
0.090
-1.076***-0.337*(0.104) (0.142) (0.796)(0.820)(0.116)(0.171)
V+VI
0.262*** -0.036 0.862** 0.857** -0.672*** -0.079**(0.050) (0.036) (0.310)(0.317)(0.053)(0.029)VIIa0.339*** -0.062 0.985** 0.968** -0.851*** -0.165***(0.050) (0.036) (0.306)(0.311)(0.054)(0.031)
Status0.012-0.025-0.388-0.3990.206***
0.107*(0.053) (0.049) (0.314)(0.322)(0.058)(0.044)
Observations
34,694
34,69424,302
24,302
23,15323,153
N
4,384
4,384
3,330
3,330
3,276
3,276
* p < 0.05, ** p < 0.01, *** p < 0.001 (two-tailed tests).
Source: JLPS-YM, 2007–2020.
Note: Robust standard errors are shown in parentheses. Logistic regression models were used
to analyze the unemployment risk. BE: Between-effects model, FE: Fixed-effects model.

43

Table 7 The Effects of Class and Status on Cultural Consumption
Museums
Library
Novels
BE
FE
BE
FE
BE
FE
Class
I (ref.)
0.000
0.000
0.000
0.000
0.000
0.000
------------II-0.0050.029
0.169*** 0.017-0.0640.023(0.029)(0.025)(0.049)(0.038) (0.063) (0.041)
IIIab
0.000
-0.044*0.087
0.002-0.013-0.008(0.027)(0.019)(0.045)(0.028) (0.058) (0.034)
IVab0.087*
0.017-0.0020.080-0.109-0.039(0.034)(0.034)(0.057)(0.051) (0.074) (0.060)
IVc+VIIb-0.020-0.105-0.026
0.213*-0.0430.022(0.073)(0.083)(0.121)(0.105) (0.156) (0.149)
V+VI-0.004-0.0240.047
0.009-0.181*-0.044(0.035)(0.030)(0.059)(0.044) (0.076) (0.050)
VIIa
0.014
-0.0440.102
0.030-0.125-0.028(0.036)(0.029)(0.059)(0.043) (0.077) (0.049)
Status
0.194*** -0.055 0.281*** -0.074 0.260** -0.026(0.039)(0.040)(0.064)(0.062) (0.083) (0.065)
Observations
16,884
16,884
16,88916,88916,88916,889
N
3,798
3,798
3,795
3,795
3,799
3,799

Pachinko
BE
FE
0.000
---0.013(0.048)-0.043(0.044)0.021(0.056)-0.128(0.119)0.053(0.058)0.070(0.058)-0.170**(0.063)
16,8963,800

0.000---0.031(0.021)-0.020(0.021)
0.009(0.037)0.051(0.090)0.013(0.036)-0.020(0.034)0.007(0.037)
16,896
3,800

Tabloids
BE
FE
0.000
---0.008(0.062)-0.008(0.057)0.027(0.073)-0.126(0.155)-0.147(0.076)-0.118(0.076)-0.193*(0.083)
16,911
3,798* p < 0.05, ** p < 0.01, *** p < 0.001 (two-tailed tests).
Source: JLPS-YM, 2007–2020.
Note: Robust standard errors are shown in parentheses. BE: Between-effects model, FE: Fixed-effects model.
44

0.000
---0.034(0.042)-0.041(0.034)0.028(0.066)0.122(0.166)-0.013(0.056)0.038(0.054)0.143*(0.072)
16,911
3,798

Table 8 The Effects of Class and Status on Health-Related Behaviors
SmokingClass
I (ref.)
II
IIIab
IVab
IVc+VIIb
V+VI
VIIa
StatusObservations
N

Drinking

Exercise

BE

FE

BE

FE

BE

FE

0.000
---0.061(0.050)-0.020(0.046)0.157**(0.059)0.007(0.131)-0.053(0.060)-0.014(0.061)-0.305***(0.065)
17,9924,322

0.000--0.002(0.019)0.033(0.019)0.028(0.039)0.059(0.074)0.004(0.029)0.008(0.030)0.014(0.037)
17,9924,322

0.000---0.427***(0.118)-0.368***(0.107)-0.139(0.140)-0.506(0.310)-0.388**(0.142)-0.367*(0.144)-0.155(0.154)18,0104,323

0.000---0.041(0.057)-0.038(0.044)-0.097(0.080)0.157(0.274)-0.059(0.069)-0.066(0.069)0.012(0.091)18,0104,323

0.000--0.114(0.070)0.009(0.064)0.156(0.082)0.234(0.182)-0.049(0.085)0.066(0.085)0.204*(0.092)
29,8594,337

0.000---0.082(0.053)-0.103*(0.040)0.029(0.066)0.288(0.241)
0.036(0.057)-0.005(0.059)0.237**(0.080)29,8594,337* p < 0.05, ** p < 0.01, *** p < 0.001 (two-tailed tests).
Source: JLPS-YM, 2007–2020.

45

Eat Three Times
a Day
BE
FE
0.000
--0.060(0.096)-0.068(0.088)-0.242*(0.113)0.256(0.248)0.068(0.116)-0.082(0.117)0.268*(0.126)30,1814,342

0.000--0.029(0.046)-0.025(0.037)-0.138*(0.070)0.370(0.202)-0.062(0.060)-0.037(0.059)0.089(0.078)30,181
4,342

Eat aBalanced Diet
BE
FE
0.000
--0.228**(0.083)-0.021(0.076)0.097(0.099)0.661**(0.217)0.232*(0.101)0.001(0.102)0.321**(0.110)30,0914,341

0.000--0.036(0.044)0.001(0.036)0.054(0.064)0.418*(0.178)0.114*(0.057)0.104(0.058)0.247***(0.075)30,091
4,341

Fast food
BE

FE

0.000
--0.040(0.053)0.141**(0.049)0.000(0.063)-0.281*(0.141)-0.124(0.065)0.075(0.065)-0.429***(0.070)30,2154,344

0.000--0.051(0.032)0.020(0.024)-0.057(0.045)0.029(0.104)-0.059(0.042)-0.013(0.041)-0.155**(0.054)30,2154,344

Note: Robust standard errors are shown in parentheses. BE: Between-effects model, FE: Fixed-effects model.

46

東京大学社会科学研究所パネル調査プロジェクトについて
労働市場の構造変動、急激な少子高齢化、グローバル化の進展などにともない、日本社会における就業、結婚、家族、教育、意識、ライフスタイルのあり方は大きく変化を遂げようとしている。これからの日本社会がどのような方向に進むのかを考える上で、現在生じている変化がどのような原因によるものなのか、あるいはどこが変化してどこが変化していないのかを明確にすることはきわめて重要である。
本プロジェクトは、こうした問題をパネル調査の手法を用いることによって、実証的に解明することを研究課題とするものである。このため社会科学研究所では、若年パネル調
査、壮年パネル調査、高卒パネル調査、中学生親子パネル調査の４つのパネル調査を実施している。

本プロジェクトの推進にあたり、以下の資金提供を受けた。記して感謝したい。

文部科学省・独立行政法人日本学術振興会科学研究費補助金
基盤研究 S：2006 年度～2009 年度、2010 年度～2014 年度 基盤研究 C：2013 年度～
2016 年度 特別推進研究：2015 年度～2017 年度 若手研究 A：2015 年度～2018 年度
基盤研究 B：2016 年度～2020 年度 特別推進研究：2018 年度～2024 年度
厚生労働科学研究費補助金
政策科学推進研究：2004 年度～2006 年度
奨学寄付金
株式会社アウトソーシング（代表取締役社長・土井春彦、本社・静岡市）：2006 年度
～2008 年度

東京大学社会科学研究所パネル調査プロジェクト
ディスカッションペーパーシリーズについて
東京大学社会科学研究所パネル調査プロジェクトディスカッションペーパーシリーズは、
東京大学社会科学研究所におけるパネル調査プロジェクト関連の研究成果を、速報性を重視し暫定的にまとめたものである。

東京⼤学社会科学研究所 パネル調査プロジェクト
https://csrda.iss.u-tokyo.ac.jp/panel/