Week 6 - Assignment: Examine the Impacts of Policies Implemented During the Great Recession and Week 7 - Assignment: Measure the Effects of Social Policies

RESEARCH ARTICLE

A growing socioeconomic divide: Effects of the

Great Recession on perceived economic

distress in the United States

Dana A. GleiID 1*, Noreen Goldman2, Maxine Weinstein1

1 Center for Population and Health, Georgetown University, Washington, District of Columbia, United States

of America, 2 Office of Population Research, Princeton University, Princeton, New Jersey, United States of

America

* [email protected]

Abstract

We demonstrate widening socioeconomic disparities in perceived economic distress among

Americans, characterized by increasing distress at the bottom and improved perceptions at

the top of the socioeconomic ladder. We then assess the extent to which hardships related

to the Great Recession account for the growing social disparity in economic distress. Based

on the concept of loss aversion, we also test whether the psychological pain associated with

a financial loss is greater than the perceived benefit of an equivalent gain. Analyses are

based on longitudinal survey data from the Midlife Development in the US study. Results

suggest that widening social disparities in perceived economic distress between the mid-

2000s and mid-2010s are explained in part by differential exposure to hardships related to

the Great Recession, the effects of which have lingered even four to five years after the

recession officially ended. Yet, auxiliary analyses show that the socioeconomic disparities in

economic distress widened by nearly as much (if not more) during the period from 1995–96

to 2004–05 as they did during the period in which the recession occurred, which suggests

that the factors driving these trends may have already been in motion prior to the recession.

Consistent with the loss aversion hypothesis, perceptions of financial strain appear to be

somewhat more strongly affected by losses in income/assets than by gains, but the magni-

tude of the differentials are small and the results are not robust. Our findings paint a dismal

portrait of a growing socioeconomic divide in economic distress throughout the period from

the mid-1990s to the mid-2010s, although we cannot say whether these trends afflict all

regions of the US equally. Spatial analysis of aggregate-level mortality and objective eco-

nomic indicators could provide indirect evidence, but ultimately economic “despair” must be

measured subjectively by asking people how they perceive their financial situations.

Introduction

The Great Recession (December 2007 to June 2009) undoubtedly contributed to the growing

socioeconomic divide in the United States; the evidence suggests that those with lower levels of

PLOS ONE | https://doi.org/10.1371/journal.pone.0214947 April 4, 2019 1 / 24

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Citation: Glei DA, Goldman N, Weinstein M (2019)

A growing socioeconomic divide: Effects of the

Great Recession on perceived economic distress in

the United States. PLoS ONE 14(4): e0214947.

https://doi.org/10.1371/journal.pone.0214947

Editor: M. Harvey Brenner, University of North

Texas Health Science Center, UNITED STATES

Received: November 6, 2018

Accepted: March 23, 2019

Published: April 4, 2019

Copyright: © 2019 Glei et al. This is an open access article distributed under the terms of the Creative

Commons Attribution License, which permits

unrestricted use, distribution, and reproduction in

any medium, provided the original author and

source are credited.

Data Availability Statement: All the MIDUS data

used in this analysis are publicly available from the

Inter-university Consortium for Political and Social

Research (ICPSR, M1 Wave: https://www.icpsr.

umich.edu/icpsrweb/ICPSR/studies/2760; M2

Wave: https://www.icpsr.umich.edu/icpsrweb/

ICPSR/studies/4652; M3 Wave: https://www.icpsr.

umich.edu/icpsrweb/ICPSR/studies/36346).

Funding: This work was supported by the National

Institute on Aging [https://www.nia.nih.gov/, grant

numbers P01 AG020166 & U19AG051426 to Carol

D. Ryff]; the Eunice Kennedy Shriver National

socioeconomic status (SES) were hit harder than their more advantaged counterparts [1–4].

Taylor, Morin & Wang [5] found that 55% of Americans “lost ground” during the Great

Recession (based on cluster analysis of various hardships including unemployment, missed

rent/mortgage payments, and reduced income), whereas the remaining 45% remained largely

unscathed; the fraction suffering multiple recession-related hardships declined with education,

income, and wealth. Similarly, Bricker et al. [6] reported that, during 2007–09, more than 60%

of families experienced a decline in wealth and one-quarter lost more half their wealth, but

another quarter of Americans gained more than 25% in terms of wealth; those with higher

income and more wealth in 2007 were less likely to experience a subsequent decline in wealth.

Surprisingly, Taylor et al. [7] found that some of those hardest hit by the recession (i.e.,

blacks, young adults, Democrats) were more upbeat about future recovery for themselves and

the national economy than less affected groups (i.e., whites, older adults, Republicans). This

result suggests that there may be discordance between objective and subjective measures of

economic distress. Leininger & Kalil [8] argued that such discordance may be increasing: per-

ceived financial strain during the Great Recession might be less connected with individual eco-

nomic conditions than in the past. Furthermore, hardships suffered during the Great

Recession could have a lingering effect on perceptions long after apparent economic recovery.

As Wilkinson [9] noted (p. 746), “Reminiscent of Elder’s (1974) children of the Great Depres- sion, the Great Recession may leave an imprint on the lives of many older adults.”

Prior research on the effects of the Great Recession on inequality has been based primarily

on objective measures such as unemployment, income, and wealth [1, 2, 4, 6] rather than on

perceptions of economic status. Grusky et al. [10] argue that the social costs of the recession

may extend beyond the obvious economic costs. In a recent study, Glei et al. [11] suggested

that perceptions may be as (or even more) important than objective economic indicators for

understanding the social and health consequences of widening inequality. Based on cross-sec-

tional surveys fielded in the mid-1990s and the early 2010s, they found that the socioeconomic

disparity in perceived economic distress widened over this period even more than would have

been expected based on changes in reported economic and employment circumstances [11].

This widening gap in perceived economic distress appears consistent with the idea that

increased “deaths of despair” [12–14]—which include suicides as well as alcohol- and drug-

related deaths (SAD)—are a symptom of a deeper malaise. According to this hypothesis, the

cumulative effects of changes in labor market opportunities available to successive cohorts as

they enter the work force and its ramifications for marriage and family formation have trig-

gered increased levels of despair for many Americans [15]. Increased rates of SAD mortality

have been blamed for three consecutive years of declining life expectancy in the US [16–19].

In this paper, we consider the consequences of economic gains and losses during the Great

Recession on within-individual changes in perceived economic distress. We use longitudinal

data that span this critical time period and comprise a large number of individuals of working

and retirement ages. We begin the analysis by demonstrating that, consistent with the previous

cross-sectional findings [11], the longitudinal data reveal widening social disparities in per-

ceived economic distress between 2004–05 and 2013–14. Next, we assess the extent to which

hardships related to the Great Recession account for observed widening SES disparities in per-

ceived economic distress. We anticipate that,

H1: Individuals with low SES were harder hit by losses during the Great Recession than their

more advantaged counterparts, thereby exacerbating the growing socioeconomic divide in

distress.

Furthermore, we examine the association between within-individual changes in income

and assets over this time period and the corresponding changes in perceived current financial

Effects of the Great Recession on perceived economic distress

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Institute of Child Health and Human Development

[https://www.nichd.nih.gov/, grant number

P2CHD047879], and the Graduate School of Arts

and Sciences, Georgetown University [https://grad.

georgetown.edu/]. Noreen Goldman would like to

thank the Russell Sage Foundation for their

support while she was a Visiting Scholar in 2018-

2019. The funders had no role in study design, data

collection and analysis, decision to publish, or

preparation of the manuscript.

Competing interests: The authors have declared

that no competing interests exist.

strain. Prospect theory [20–22], which is associated with the concept of loss aversion, suggests

that the psychological pain associated with a financial loss is greater than the perceived benefit

of an equivalent gain. Thus, we hypothesize that the responses to gains and losses are asym-

metric (i.e., the direction of change matters):

H2: The negative effect of a loss in income/assets on perceived financial strain is greater in

magnitude than the positive effect of an equivalent gain in income/assets.

Widening social disparities in perceived economic distress in the U.S. over the previous

decade appear to be explained in part by differential exposure to hardships related to the Great

Recession. Yet, auxiliary analyses demonstrate that the growing socioeconomic divide in eco-

nomic distress began well before the Great Recession. There may have been pre-existing vul-

nerabilities among those at the lower end of the socioeconomic spectrum that were

exacerbated by the Great Recession.

Methods

Data

We use longitudinal data from the Midlife Development in the US study (MIDUS). In 1995–96

(Wave M1), MIDUS conducted phone interviews with a national sample of non-institutionalized,

English-speaking adults aged 25–74 in the coterminous United States [23]. National random digit

dialing with oversampling of older people and men was used to select the main sample (N = 3487) and a sample of twin pairs (N = 1914). The study also included a random subsample of siblings of individuals in the main sample (N = 950) and oversamples from five metropolitan areas in the U. S. (N = 757). The response rate for the phone interview ranged from 60% for the twin subsample to 70% for the main sample. Among those who completed the phone interview (N = 7108), 6329 (89%) also completed mail-in self-administered questionnaires (SAQ).

During 2004–05 (Wave M2), a follow-up telephone interview was conducted with 4963 of

the original MIDUS cohort (75% of survivors), 4041 (81%) of whom also completed the mail-

in SAQ. At Wave M3 (fielded May 2013-June 2014), the MIDUS cohort was re-contacted for a

second follow-up telephone interview (which was completed by N = 3293, 74% of survivors) and SAQ (N = 2717, 83% of those who completed the phone interview).

Most of our analyses are restricted to respondents who completed the SAQ at both the M2

and M3 waves (N = 2569). For some of the auxiliary analyses, we analyze the sample of respon- dents who completed all three waves (N = 2525). In order to include as much data as possible in the analysis, we followed standard practices of multiple imputation to handle missing data

[24, 25]. The variables with the highest percentage of missing data were income sources (up to

37% missing at M3 for other household income for family members apart from the respondent

or spouse/partner), assets (15% at M2, 25% at M3), and ratings of future and current work sit-

uations (6% at M2, 12% at M3). For the multiple imputation process, we use information from

all three waves for all the analysis variables as well as auxiliary variables (e.g., physical health

and mental status, employment status).

The MIDUS study obtained written, informed consent from all participants and received

human subjects approval from the Educational and Social/Behavioral Science IRB (institu-

tional review board) at University of Wisconsin, Madison [#SE-2011-035].

Measures

Subjective measures of economic distress. The subjective outcomes include a measure of

current financial strain and two measures related to employment uncertainty. Our index of

current financial strain is based on the following five questions from the SAQ:

Effects of the Great Recession on perceived economic distress

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1. “Using a scale from 0 to 10 where 0 means ‘the worst possible financial situation’ and 10 means `the best possible financial situation,’ how would you rate your financial situation these days?”

2. “Looking ahead ten years into the future, what do you expect your financial situationwill be like at that time?” [using the same 0–10 scale]

3. “Using a 0 to 10 scale where 0 means `no control at all’ and 10 means `very much control,’ how wouldyou rate the amount of control you have over your financial situation these days?”

4. “In general, would you say you (and your family living with you) have moremoney than you need, just enough for your needs, or not enough to meet your needs?”

5. “How difficult is it for you (and your family) to pay your monthly bills?” [response categories: very difficult, somewhat difficult, not very difficult, not at all difficult]

Each item was standardized based on the distribution at M1 (so the scores are comparable

over time) and coded so that higher values indicate more financial strain. [Note: We use the

same standard (mean and SD at M1) to standardize the values of a given item across all survey

waves. Thus, the standardized value of that item has a mean of 0 and a SD of 1 at M1, but its

value at later waves may be higher or lower depending on how scores on that item compare

with the values at M1.] Then, we computed the mean across the five items for each wave

(Cronbach’s α = 0.83 at M2, 0.86 at M3). We measure employment uncertainty using the respondent’s ratings of his/her current

work situation (“Please think of the work situationyou are in now, whether part-time or full- time, paid or unpaid, at home or at a job. Using a scale from 0 to 10 where 0 means `the worst possible work situation’ and 10 means `the best possible work situation,’ how would you rate your work situation these days?”) and his/her expected work situation 10 years in the future (“Looking ahead ten years into the future, what do you expect your work situation will be like at that time?”, using the same scale). These two questions were asked of all respondents regardless of whether they were employed at the time of the survey. We reverse-coded these measures so

that higher values indicate worse evaluations (i.e., more uncertainty).

All outcomes measures are standardized based on the distribution at baseline. Because the

three outcome variables are measured on different scales, we standardize in order to compare

effect size across outcomes. Again, we use the same standard to standardize the outcome values

at all three survey waves so that we can compare levels across waves. For example, the mean

level of current financial strain was 0.1 at M3 versus 0.0 at M2, indicating that the average level

of financial strain increased by one-tenth of a SD between waves M2 and M3.

Objective measures of socioeconomic status. Socioeconomic status (SES) is typically

measured based on education, occupation, income, and/or wealth and can be specified in

either absolute or relative terms. We create a summary measure of relative SES (percentile

rank within each survey wave) based on baseline measures of the respondent’s (and spouse’s)

education (degree completion, measured in 12 categories); respondent’s (and spouse’s) current

or most recent occupation (recoded into four categories); annual household income; and net

assets of the respondent and spouse. Because income and assets are positively skewed, we

apply a square root transform to those two items. We then standardize the six items and com-

pute the average across relevant items (e.g., six items if married/partnered and both respon-

dent and spouse/partner have ever been employed; three items if not married/partnered and

respondent has never been employed; Cronbach’s α = 0.75). Finally, we convert the SES index score to a percentile rank where 0 represents someone in the first percentile of the SES distri-

bution and 1 denotes someone in the 99th percentile. A one-unit change represents the

Effects of the Great Recession on perceived economic distress

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difference between the bottom and the top percentile. (See S1 Text for more details regarding

the construction of relative SES.) For comparison, we also test the effect of the respondent’s

educational attainment alone.

Exposure to hardships related to the Great Recession. At M3 (2013–14), respondents

were asked to evaluate retrospectively whether they had experienced various economic hard-

ships since the recession began in 2008. Based on these responses, we include dichotomous

variables indicating exposure to five home-related hardships: 1) lost a home to foreclosure or

some other reason [Note: Very few respondents in this cohort reported losing their home to

foreclosure (2% of those interviewed at M3). Another 2.2% reported losing their home “due to

something other than foreclosure.” We combined these two groups]; 2) had family/friends

move into respondent’s home or respondent moved in with family/friends’ home to save

money (i.e., “doubled up”); 3) missed a mortgage or rent payment; 4) sold home at a loss; 5)

threatened with foreclosure or eviction. We also include dummy variables for three job-related

hardships: 1) lost a job; 2) took a job below the respondent’s education or experience level; and

3) started a new job the respondent did not like or took an additional job. Finally, we test six

dummy variables measuring financial-related hardships: 1) cut back on spending; 2) borrowed

money or increased credit card debt; 3) sold some possessions to make ends meet; 4) missed a

credit card payment or some other debt payment; 5) exhausted unemployment benefits; and

6) declared bankruptcy.

Changes in income and assets. To test the loss aversion hypothesis, we measure changes

over time in (inflation-adjusted) household income and assets (see S1 Text for details). Because

there is an inverse relationship between the level of income/assets at baseline and changes over

time in income/assets (see S2 Text for details), we also control for the level of income/assets at

baseline. In order to distinguish between possible asymmetric effects of gains versus losses in

income/assets, we create separate variables measuring increases (coded 0 if decreased) and

decreases (coded as a positive value and coded to 0 if increased) in income/assets.

We test both untransformed and log-transformed values for income/assets. For respon-

dents with no income/assets, we recode their values to $1 prior to applying the log transforma-

tion. To allow for a discontinuity in the linear relationship between log income/assets and

economic distress, our models also include dummy variables indicating that the respondent

reported no income/assets at M2 or at M3. For completeness, we include these same dummy

variables in the models using untransformed values of income/assets. Fredrickson [26] notes

that, according the prospect theory (a.k.a., loss aversion), losses and gains are referent depen-

dent (i.e., judged in relative rather than absolute terms). Thus, our main models use log-trans-

formed values, which assume the psychological effects are proportionate (e.g., the effect of a

$1,000 increase for someone with $10,000 in income at baseline is similar to the effect of a

$10,000 increase for their counterpart with $100,000 in income at baseline). We also explore

models that use untransformed (absolute) values, which assume the psychological effect of a

$1,000 change is the same regardless of the starting level of income/assets.

Demographic factors. We also control for the following potential confounders: sex

(based on interviewer observation), age (computed by subtracting date of birth from date of

interview), race/ethnicity, and marital status at baseline. Because there are few racial/ethnic

minorities in MIDUS, we use a dummy variable that distinguishes non-Latino whites from all

other groups. Race was based on self-report (“What race do you consider yourself to be?”). Latino/Hispanic origin is based on reported countries of ethnic origin (“Other than being

American, what are your main ethnic origins? That is, what countries or continents are your

ancestors from?”). We classified respondents as Latino if they reported a country of origin in

Mexico, Central America, Cuba, Dominican Republic, Puerto Rico, South America (including

Brazil), or Spain. For respondents who were missing information regarding race/ethnicity

Effects of the Great Recession on perceived economic distress

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from the M1 wave (5% of the sample), we used information from the M2 wave. At M2, respon-

dents were asked to identify the race with which they most closely identify (“Which do you feel best describes your racial background? White, Black orAfrican American, American Indian or Alaska Native, Asian, or Native Hawaiian or Pacific Islander?”); Latino/Hispanic origin was also based on self-report (“Are you of Spanish, or Hispanic or Latino descent, that is, Mexican, Mexican American, Chicano, Puerto Rican, Cuban or some other Spanish origin?”). Marital sta- tus is coded as dichotomous, indicating those who were married or living with a partner at

baseline.

Analytical strategy

We start with descriptive statistics (Table 1), followed by bivariate analyses (Table 2) that com-

pare reported hardships related to the Great Recession and changes in income and assets

between M2 and M3 by education (recoded into HS degree or less; some college; college grad-

uate) and by relative SES (recoded into tertiles). To test for significant differences by SES in

exposure to recession hardships, we estimated a logit model regressing each of the Great

Recession hardship variables on sex, age (and its quadratic), minority status, marital status,

and the measure of SES (education or relative SES; S1 Table).

We begin by evaluating whether social disparities in perceived economic distress widened

between 2004–05 (M2) and 2013–14 (M3). Using a lagged dependent variable model, we

implicitly model changes in economic distress (between M2 and M3) by regressing each of the

outcome measures at M3 on the lagged dependent variable at M2. The first model includes

education as an indicator of SES (Model 1), whereas the second model substitutes our sum-

mary measure of relative SES (Model 2; Tables 3–5 corresponding to the three outcomes). All

models are fit using linear regression and control for sex, age (and its quadratic to allow for

non-linear age patterns), race/ethnicity, and marital status. Because the sample, in some cases,

includes multiple individuals from the same family (e.g., twins, siblings), we use a robust esti-

mator of variance to correct for intra-family correlation. Our main analysis sample (N = 2569) includes 1,100 respondents from the main national sample, 786 from the twin pairs subsample,

450 from the siblings subsample; and 233 from the city oversamples.

Second, we test H1 (did the Great Recession exacerbate the socioeconomic divide?) by add-

ing the Great Recession variables to our regression models to determine the extent to which

exposure to such hardships may account for widening of social disparities in economic dis-

tress. Losing one’s home to foreclosure or some other factor is likely to have been the culmina-

tion of many other financial hardships (e.g., increased debt, cutting back on spending, selling

possessions, missing payments). Thus, in Model 3, we test the effects of losing one’s home

(which comprises less than 4% of the sample) by itself. Then, in Model 4, we add the remaining

hardship variables.

In the final model (Model 5, Table 3), we evaluate the loss aversion hypothesis by testing

variables measuring increases and decreases in income and assets between M2 and M3 in the

model predicting changes in current financial strain. We focus on this particular outcome

because we expect it to be more strongly affected by monetary gains and losses than the work

uncertainty outcomes. We include controls for the level of income and assets at M2 in this

model—including dichotomous indicators for those who reported no income/assets—and use

log-transformed values of income/assets to measure changes in relative terms, based on the

notion that individuals evaluate the magnitude of their gains and losses proportionately to

their previous level rather than in absolute numbers of dollars. To test the robustness of our

results, we re-estimate Model 5 by: 1) excluding respondents for whom income/assets was top-

coded; and 2) using untransformed values of income/assets (S1 Table).

Effects of the Great Recession on perceived economic distress

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Table 1. Descriptive statistics for analysis variables.

Completed SAQ at M2 & M3

(N = 2569) Demographic characteristics

Female, % 56.0

Age at M2 (30–84), mean (SD) 55.6 (11.2)

Minority (Latino or non-white), % 9.8

Married or living with a partner at M2, % 76.2

Objective economic/employment measures at M2

Educational degree (1–12), a

mean (SD) 7.5 (2.5)

Current/previous occupation

Never employed, b

% 0.2

Farming/labor/military, % 16.2

Service/sales/administrative, % 35.7

Management/business/financial, % 22.0

Professional, % 26.0

Household income (0–695), c

mean (SD) 64.7 (56.1)

No assets or a deficit, % 18.2

Net assets (0–1440), c

mean (SD) 260.2 (375.4)

Spouses’ educational degree (1–12), a,d

mean (SD) 6.8 (2.5)

Spouses’ current/previous occupation d

Never employed, b

% 1.0

Farming/labor/military, % 21.3

Service/sales/administrative, % 33.6

Management/business/financial, % 19.0

Professional/, % 25.2

Great Recession hardships

Home-related Lost home to foreclosure or something else, % 3.7

Doubled up, % 14.4

Missed mortgage/rent payment, % 5.4

Sold home for a loss, % 4.1

Threatened with foreclosure/eviction, % 4.1

Job-related Lost a job, % 11.9

Took a job for which respondent was overqualified, % 10.0

Started new job respondent did not like or additional job, % 12.6

Financial-related Cut back on spending, % 59.7

Borrowed money or increased credit card debt, % 29.0

Sold possessions to make ends meet, % 12.6

Missed credit card or other debt payment, % 10.5

Exhausted unemployment benefits, % 7.0

Declared bankruptcy, % 2.9

Change in income/assets between M2 and M3

Change in household income, c

mean (SD) -3.2 (5.8)

Change in net assets, c

mean (SD) 31.3 (323.9)

Perceived economic distress

Index of current financial strain at M2 (-1.7 to 3.5), mean (SD) 0.0 (1.0)

(Continued)

Effects of the Great Recession on perceived economic distress

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Finally, to further test the loss aversion hypothesis, we use a modeling strategy that implic-

itly controls for time-invariant characteristics of respondents that may affect their perceptions

of financial strain. Here, we use data from all three waves (M1, M2, & M3) to fit a fixed effects

linear regression model (S3 Table). Among the respondents who completed the SAQ at all

three waves (N = 2525), we examine changes in income/assets during the two survey intervals:

M1!M2 and M2!M3. About half the sample experienced an increase in income/assets in

one interval and a decrease (or no change) in the other survey interval. Because a relatively

small proportion of the sample experienced an increase in one interval and a decrease in the

other survey interval for both income and assets, we fit separate models to test the effects of

changes in income and changes in assets. In each case, we restrict the analysis sample to the

subset of respondents with an increase in one interval and decrease in the other interval for the

measure in question. Then, we regress current financial strain at time t+1 on the lagged depen- dent variable (the analogous variable measured at time t) and the variables representing increases and decreases in income/assets between t and t+1 controlling for age (and its qua- dratic) and the level of income/assets at time t. As in the previous models, we test two specifica- tions of the model: one using the log-transformed values of income/assets and the other using

untransformed values.

See S3 Text for more details regarding the statistical models.

Results

Table 1 shows descriptive statistics for all of the analysis variables. On average, inflation-

adjusted household income declined by just over $3,200 between M2 (2004–05) and M3

(2013–14) among this sample, but net assets of the respondent and spouse combined increased

by an average of $31,300 over the same period. Yet, the mean change in assets is deceptive

because the distribution of assets is so skewed. For example, the percentage who reported no

net assets actually increased from 18% at M2 to 20% at M3, and the median change in net

assets was zero (see S4 Text for more details regarding changes in assets).

Table 1. (Continued )

Completed SAQ at M2 & M3

(N = 2569) Index of current financial strain at M3 (-1.7 to 3.5), mean (SD) 0.1 (1.1)

Current work situation at M2 (0–10 = worst), mean (SD) 2.4 (2.1)

Current work situation at M3 (0–10 = worst), mean (SD) 2.5 (2.4)

Expected future work situation at M2 (0–10 = worst), mean (SD) 2.3 (2.3)

Expected future work situation at M3 (0–10 = worst), mean (SD) 2.9 (2.8)

Note: The M2 survey wave was fielded in 2004–05 (about 2–3 years before the Great Recession began) and the M3

wave in 2013–14 (about 4–5 years after the Great Recession officially ended). a

In the MIDUS survey, education is measured in terms of degree completion, with categories ranging from 6th grade

or less (= 1) to completion of a professional degree (e.g., PhD, MD, JD, etc.) (= 12). See S1 Text for more details. b

A small number of respondents (N = 4) and spouses (N = 20) had never been employed. For the purposes of modeling, occupations for these respondents/spouses are coded to the reference group for occupation (farming/

labor). c

Expressed in thousands of 1995 dollars. d

Among those who were married or living with a partner (N = 1957). For the purposes of modeling, spouse’s education is coded as high school graduate and spouse’s occupation is coded to the reference group (farming/labor)

for those who were not married/partnered (N = 612).

https://doi.org/10.1371/journal.pone.0214947.t001

Effects of the Great Recession on perceived economic distress

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Table 2. Exposure to hardships related to the Great Recession and changes in income/assets between M2 and M3, by socioeconomic status at M2.

Educational Attainment Relative SES

H.S. Graduate or less Some

College

College

Graduate

Bottom

Tertile

Middle

Tertile

Top

Tertile

Great Recession hardships

Home-related Lost home, % 5.1 3.8 2.6 6.0 3.5 1.4

Doubled up, % 16.2 16.6 11.9 19.9 14.7 8.6

Missed mortgage/rent payment, % 6.6 5.3 4.7 8.4 5.6 2.2

Sold home for a loss, % 4.3 3.8 4.1 4.2 4.7 3.4

Threatened with foreclosure/eviction, % 5.0 4.8 3.1 6.4 4.9 1.0

Job-related Lost job, % 12.4 12.1 11.4 14.3 14.0 7.2

Took a job for which respondent was overqualified, % 7.3 10.4 11.7 9.7 12.0 8.4

Started new job respondent did not like

or took an additional job, %

11.0 12.1 14.0 12.9 12.6 12.3

Financial-related Cut back on spending, % 65.3 65.2 52.2 70.2 64.6 47.0

Increased credit card debt, % 29.3 31.2 27.5 33.3 28.2 25.4

Sold possessions, % 14.6 14.2 9.8 17.8 12.2 7.1

Missed debt payment, % 12.1 12.1 8.5 15.4 11.0 5.0

Exhausted unemployment, % 9.9 6.7 5.1 10.3 6.6 3.8

Declared bankruptcy, % 4.1 3.4 1.7 4.9 2.6 1.1

Change in income/assets between M2 and M3 a

Change in household income, b

mean (SD) -6.5 (46.4) -3.3 (51.7) -0.8 (68.2) 0.3 (34.3) 3.3 (43.0) -6.6 (84.4)

Any assets at M2 and/or M3

None (or deficit) at both M2 and M3, % 14.1 10.7 5.8 19.4 6.8 2.3

None at M2, positive assets by M3, % 12.1 9.0 6.0 15.0 7.3 3.5

Positive assets at M2, but none by M3, % 11.8 11.5 8.0 13.4 11.9 4.9

Positive assets at both M2 and M3, % 62.0 68.8 80.2 52.2 74.1 89.3

Change in net assets, b

mean (SD) 8.0 (228.4) 4.6 (260.9) 64.9 (404.4) 10.8 (142.5) 35.5 (297.7) 48.4 (455.8)

a The M2 survey wave was fielded in 2004–05 (about 2–3 years before the Great Recession began) and the M3 wave in 2013–14 (about 4–5 years after the Great

Recession officially ended). b

Expressed in thousands of 1995 dollars.

https://doi.org/10.1371/journal.pone.0214947.t002

Table 3. Coefficients from linear regression models predicting changes (M2!M3) in perceived current financial strain, N = 2569.

(1)

Education

(2)

SES

(3)

Lost Home

(4)

Recession

(5)

Income/Assets

Current financial strain at M2 0.63��� 0.59��� 0.59��� 0.47��� 0.48���

Female 0.05 0.05 0.04 0.02 -0.01

Age—40 -0.01 -0.01 -0.01 0.0008 0.00

(Age—40) 2

0.00 0.00 0.00 0.0002� -0.00

Minority -0.13� -0.14� -0.14� -0.16�� -0.24���

Married/partnered at M2 -0.11� -0.01 -0.02 -0.01 0.05

Socioeconomic status at M2

Education a

-0.47��� — — — —

(Continued)

Effects of the Great Recession on perceived economic distress

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Table 3. (Continued )

(1)

Education

(2)

SES

(3)

Lost Home

(4)

Recession

(5)

Income/Assets

Relative SES b

— -0.60��� -0.58��� -0.43��� —

Great Recession hardships

Home-related

Lost home — — 0.47��� -0.11 —

Moved in with family/friends — — — 0.06 —

Missed mortgage/rent payment — — — 0.19 —

Sold home for a loss — — — -0.05 —

Threatened with foreclosure/eviction — — — 0.06 —

Job-related

Lost job — — — 0.06 —

Took a job for which respondent was overqualified — — — 0.25��� —

Started new job respondent did not like or additional job — — — -0.05 —

Financial-related

Cut back on spending — — — 0.38��� —

Increased credit card debt — — — 0.25��� —

Sold possessions — — — 0.34��� —

Missed debt payment — — — 0.24��� —

Exhausted unemployment — — — 0.20� —

Declared bankruptcy — — — 0.10 —

Income/Assets

No household income at M2 or M3 c

— — — — -0.72�

Log Household income at M2 d

— — — — -0.14���

Decrease in log income (M2!M3) — — — — 0.12��� ,e

Increase in log income (M2!M3) — — — — -0.09��

No assets or deficit at M2 or M3 c

— — — — -0.77���

Log assets at M2 d

— — — — -0.13���

Decrease in log assets (M2!M3) — — — — 0.11��� ,f

Increase in log assets (M2!M3) — — — — -0.06���

Constant g

0.47��� 0.43��� 0.40��� -0.19�� 0.19���

��� p<0.001

�� p<0.01

� p<0.05

Note: Both the outcome and the lagged dependent variable are standardized based on the distribution of the outcome at M2. Age at M2 is centered at 40. a

Education is scaled from 0 (6th grade or less) to 1 (professional degree: PhD, MD, JD, etc.) so that the coefficient can be interpreted as the difference between a person

with highest and lowest educational attainment. b

Relative SES is scaled from 0 (1st percentile) to 1 (99th percentile) so that the coefficient represents the difference between a person in the bottom 1% and the top 1% of

the SES continuum at M2. c

At M2 or M3 (but not necessarily both), the respondent reported no income/assets. d

For respondents with no income/assets, we recode their values to $1 prior to applying the log transformation. e

The absolute value of the coefficient for an decrease in log income does not differ significantly from the coefficient for an increase in log income based on a Wald test. f The coefficient associated with a decrease in log assets is significantly greater (p<0.05) than the absolute value of the coefficient for an increase in log assets.

g The constant represents the change in current financial strain between M2 and M3 in SD units for an individual in the reference group for categorical variables (i.e.,

male, non-Latino white, not married nor partnered, no exposure to Great Recession-related hardships) and values of zero for all continuous measures (i.e., mean level of

financial strain at M2, age 40, 6 th

grade or less education, bottom percentile of relative SES, mean income & assets at M2, no change in income/assets between M2 and

M3).

https://doi.org/10.1371/journal.pone.0214947.t003

Effects of the Great Recession on perceived economic distress

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The most common hardships related to the Great Recession (as assessed at M3) were cut-

ting back on spending (60% of the sample) and increased debt (29%). The most extreme hard-

ships (i.e., declaring bankruptcy, losing one’s home, selling one’s home at a loss) were reported

by less than five percent of the sample. Between M2 and M3, there was a slight increase in

mean levels of current financial strain and current work uncertainty and a larger increase in

future work uncertainty, but those averages hide considerable heterogeneity.

Table 4. Coefficients from linear regression models predicting changes (M2!M3) in current work uncertainty, N = 2569.

(1)

Education

(2)

SES

(3)

Lost Home

(4)

Recession

Current work uncertainty at M2 0.32��� 0.31��� 0.30��� 0.27���

Female -0.01 -0.01 -0.02 -0.04

Age—40 -0.02��� -0.02��� -0.02��� -0.02���

(Age—40) 2

0.00��� 0.00�� 0.00�� 0.00���

Minority -0.02 -0.01 -0.01 0.00

Married/partnered at M2 -0.25��� -0.16�� -0.16�� -0.16��

Socioeconomic status at M2

Education a

-0.49��� — — —

Relative SES b

— -0.54��� -0.51��� -0.35���

Great Recession hardships

Home-related

Lost home — — 0.48�� 0.21

Moved in with family/friends — — — -0.02

Missed mortgage/rent payment — — — -0.27

Sold home for a loss — — — -0.12

Threatened with foreclosure/eviction — — — 0.04

Job-related

Lost job — — — 0.45���

Took a job for which respondent was overqualified — — — 0.13

Started new job respondent did not like or additional job — — — -0.10

Financial-related

Cut back on spending — — — 0.19���

Increased credit card debt — — — -0.01

Sold possessions — — — 0.19�

Missed debt payment — — — 0.07

Exhausted unemployment — — — 0.41���

Declared bankruptcy — — — 0.14

Constant c

0.68��� 0.58��� 0.55��� 0.23��

��� p<0.001

�� p<0.01

� p<0.05

Note: Both the outcome and the lagged dependent variable are standardized based on the distribution of the outcome at M2. Age at M2 is centered at 40. a

Education is scaled from 0 (6 th

grade or less) to 1 (professional degree: PhD, MD, JD, etc.) so that the coefficient can be interpreted as the difference between a person

with highest and lowest educational attainment. b

Relative SES is scaled from 0 (1st percentile) to 1 (99th percentile) so that the coefficient represents the difference between a person in the bottom 1% and the top 1% of

the SES continuum at M2. c

The constant represents the change in current work uncertainty between M2 and M3 in SD units for an individual in the reference group for categorical variables (i.e.,

male, non-Latino white, not married nor partnered, no exposure to Great Recession-related hardships) and values of zero for all continuous measures (i.e., mean level of

current work uncertainty at M2, age 40, 6 th

grade or less education, bottom percentile of relative SES).

https://doi.org/10.1371/journal.pone.0214947.t004

Effects of the Great Recession on perceived economic distress

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Were those with low SES hit harder by the Great Recession?

Table 2 shows exposure to hardships related to the Great Recession by education and by SES at

M2. Those with less education or lower SES were more likely to report negative consequences

of the Great Recession than their more advantaged counterparts. For example, 6% of those in

the bottom tertile of SES lost their home since the Great Recession versus 1.4% of those in the

top tertile. Differentials by relative SES were generally larger than those by education.

Table 5. Coefficients from linear regression models predicting changes (M2!M3) in future work uncertainty, N = 2569.

(1)

Education

(2)

SES

(3)

Lost Home

(4)

Recession

Future work uncertainty at M2 0.40��� 0.38��� 0.38��� 0.37���

Female -0.04 -0.04 -0.05 -0.06

Age—40 -0.00 0.00 0.00 0.00

(Age—40) 2

0.00� 0.00 0.00 0.00�

Minority -0.12 -0.11 -0.11 -0.09

Married/partnered at M2 -0.11 0.00 0.00 0.00

Socioeconomic status at M2

Education a

-0.62��� — — —

Relative SES b

— -0.67��� -0.64��� -0.51���

Great Recession hardships

Home-related

Lost home — — 0.38�� 0.10

Moved in with family/friends — — — -0.01

Missed mortgage/rent payment — — — -0.03

Sold home for a loss — — — -0.04

Threatened with foreclosure/eviction — — — 0.02

Job-related

Lost job — — — 0.19�

Took a job for which respondent was overqualified — — — 0.04

Started new job respondent did not like or additional job — — — -0.20��

Financial-related

Cut back on spending — — — 0.15��

Increased credit card debt — — — -0.01

Sold possessions — — — 0.34���

Missed debt payment — — — 0.00

Exhausted unemployment — — — 0.26�

Declared bankruptcy — — — 0.08

Constant c

0.62��� 0.48��� 0.46��� 0.24��

��� p<0.001

�� p<0.01

� p<0.05

Note: Both the outcome and the lagged dependent variable are standardized based on the distribution of the outcome at M2. Age at M2 is centered at 40. a

Education is scaled from 0 (6 th

grade or less) to 1 (professional degree: PhD, MD, JD, etc.) so that the coefficient can be interpreted as the difference between a person

with highest and lowest educational attainment. b

Relative SES is scaled from 0 (1st percentile) to 1 (99th percentile) so that the coefficient represents the difference between a person in the bottom 1% and the top 1% of

the SES continuum at M2. c

The constant represents the change in future work uncertainty between M2 and M3 in SD units for an individual in the reference group for categorical variables (i.e.,

male, non-Latino white, not married nor partnered, no exposure to Great Recession-related hardships) and values of zero for all continuous measures (i.e., mean level of

future work uncertainty at M2, age 40, 6 th

grade or less education, bottom percentile of relative SES).

https://doi.org/10.1371/journal.pone.0214947.t005

Effects of the Great Recession on perceived economic distress

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Based on logit models that controlled for demographic variables, most measures of expo-

sure to the Great Recession differed significantly by SES (S1 Table). The biggest differences

were in the likelihood of declaring bankruptcy (OR = 10.7 for bottom 1% of SES vs. top 1%),

being threatened with foreclosure/eviction (OR = 8.6), missing a mortgage/rent payment

(OR = 7.7), and losing one’s home (OR = 7.3).

By M3 (May 2013-June 2014), the Great Recession had officially been over for 4–5 years

and the U.S. was in the midst of an economic boom that began in 2010: GDP growth was

between 1.6 and 2.6% throughout 2010–14 [27], and unemployment in the U.S. had steadily

declined from a high of 10.0% in Oct 2009 to 6.1% in June 2014 [28]. Yet, the economic

boom may not have benefited everyone to the same degree. As shown in Table 2, respon-

dents with a high school degree or less experienced the largest average decline in household

income between M2 (2004–05) and M3 (2013–14), while college graduates fared the best.

Differences by relative SES are in the opposite direction: in fact, the largest mean decline in

household income occurred among those in the top tertile of SES. These results may seem

contradictory but there is a strong inverse relationship between the level of income at base-

line and the change in income between M2 and M3 (see S2 Text). Individuals with little or

no income do not have much to lose; only those with high incomes are likely to experience a

big loss.

In terms of changes in wealth, less educated respondents and those with lower relative SES

fared much worse than their more advantaged counterparts. The proportion with no assets (or

a deficit) at both M2 and M3 was higher for those with a high school degree or less (14%) and

those in the bottom tertile of SES (19%) than for college graduates (6%) and those in the top

tertile of SES (2%). Similarly, the percentage with positive assets at M2, but no assets or a defi-

cit by M3 was also inversely related to SES (e.g., 13% for those in the bottom tertile vs. 5% for

the top tertile of SES). The mean change in net assets was highest for college graduates (+

$64,900) and lowest for those with a high school degree or less (+$8,000).

Did social disparities in perceived economic distress widen between M2

and M3?

Across all three measures of economic distress, individuals with lower SES—whether mea-

sured by education or relative SES—exhibited bigger increases in economic distress than their

more advantaged counterparts (Models 1 and 2, Tables 3–5). For example, the coefficients

from Model 1 of Table 3 imply that the education differential (between someone who com-

pleted 6th grade or less versus someone who completed a professional degree: PhD, MD, JD,

etc.) in perceived current financial strain widened by 0.47 SD. Similarly, the socioeconomic

divide as measured by relative SES also widened (Model 2, Table 3). The first set of bars in Fig

1 (demographic-adjusted) shows the predicted change in current financial strain for an indi-

vidual in the bottom 1% of relative SES (+0.41 SD) versus a person in the top 1% (-0.19 SD),

assuming mean levels for all other covariates. Thus, these results imply a 0.6 SD widening of

the SES differential in perceived financial strain between those at the bottom and those at the

top of the SES spectrum.

The educational (Model 1) and SES differentials (Model 2) in current work uncertainty

(Table 4) and future work uncertainty (Table 5) followed a similar pattern, but widening of the

education/SES gap was somewhat greater for future (0.62 SD for education; 0.67 SD for SES)

than for current work uncertainty (0.49 SD and 0.54 SD, respectively).

In sum, among this longitudinal cohort, perceived economic distress deteriorated over this

approximately nine-year period more (by over a half SD) for those at the bottom of the SES

continuum (at M2) than for those at the top.

Effects of the Great Recession on perceived economic distress

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Is exposure to Great Recession hardships associated with increased

economic distress?

We first test whether those who lost their home to foreclosure or another factor experienced a

bigger increase in economic distress than those who did not lose their home during the crisis

(Model 3, Tables 3–5). In every case, we find a strong, significant association: losing one’s

home was associated with a 0.38–0.48 SD increase in economic distress. Yet, as revealed by a

comparison of the coefficient for relative SES in Models 2 versus 3, loss of a home accounts for

very little of the widening SES differential in economic distress (<5%).

In Model 4, we add the remaining Great Recession variables, which greatly attenuate the

effects of losing one’s home. The following recession-related hardships were associated with

substantial increases in economic distress even many years after the recession officially ended

[Note: M3 was fielded 4–5 years (May 2013-June 2014) after the Great Recession officially

ended (June 2009)]: cutting back on spending, having to sell possessions, and exhausting

unemployment benefits.

Other exposures (i.e., incurring more debt, missing debt payments, and taking a job for

which the respondent was overqualified) were associated with increased financial strain, but

not significantly related to work uncertainty. In contrast, losing one’s job had a big effect on

work uncertainty, but little effect on current financial strain.

Fig 1 demonstrates the estimated change in current financial strain for individuals who

reported none of the recession-related hardships (which comprises slightly more than one-

quarter of the sample); those who both cut back on spending and increased their debt level

(23% of the sample); and the most common combination of three hardships: cut back on

spending, increased debt, and sold possessions (7% of the sample). There is almost a full SD

difference in the change in financial strain for this last group (e.g., +0.86 increase for those in the bottom 1%) compared with their counterparts who encountered no recession-related

hardships (-0.10 decrease in strain).

Did differential exposure to the Great Recession contribute to the growing

socioeconomic divide in economic distress?

As hypothesized (H1), reported hardships related to the Great Recession account for a notable

share of the widening SES differential in current financial strain: the 0.60 SD widening of the

SES differential in financial strain (Model 2, Table 3) is diminished by 28% (to 0.43 SD, Model

4) after controlling for exposure to hardships related to the Great Recession (see also Fig 1).

Similarly, the recession variables account for 35% of the widening social disparity in current

work uncertainty (Table 4) and 25% of the widening socioeconomic divide in future work

uncertainty (Table 5). Thus, widening of the SES differential in economic distress appears to

be attributable in part to the fact that those with low SES were harder hit by lingering effects of

the Great Recession.

In general, exposure to the Great Recession appears to account for all of the increase in

financial strain, but only part of the increase in work uncertainty. Our estimates suggest that in

the absence of the Great Recession (predicted scores for “No recession hardships” in Fig 1),

levels of financial strain might have improved slightly (0.1 SD decline) even for those at the

bottom of the SES distribution. Among those in the top 1% of relative SES, the coefficients

imply that financial strain would have declined by 0.53 SD in the absence of economic hard- ships stemming from the Great Recession. That is, those at the top of the SES spectrum still

fared much better than those at the bottom, but the differential was smaller (0.43 vs. 0.60

before adjusting for exposure to the Great Recession). In the case of work uncertainty, control-

ling for the Great Recession variables greatly diminished the predicted increase in work

Effects of the Great Recession on perceived economic distress

PLOS ONE | https://doi.org/10.1371/journal.pone.0214947 April 4, 2019 14 / 24

uncertainty for the least advantaged Americans (as indicated by the reduction in the constant

in Models 4 compared with Models 2, Tables 4 & 5).

Is the negative effect of a loss in income/assets greater in magnitude than

the positive effect of an equivalent gain?

In Model 5 (Table 3), we test the loss aversion hypothesis (H2) for the current financial strain

outcome. As expected, a decline in log assets appears to be more detrimental to perceived

financial strain than the benefit associated with an equivalent gain in log assets. Fig 2 shows

the estimated change in financial strain associated with various changes in log assets. Almost

one-quarter of the observed sample exhibited a 50% or greater reduction in assets (i.e., �0.7

decrease in log assets), while 28% of the sample reported at least doubling of assets (i.e., �0.7

increase in log assets). The increase in financial strain (+0.08 SD) associated with a 50% loss in

assets is about twice the decrease in strain (-0.04 SD) of a doubling of assets, but the difference

Fig 1. Predicted change in current financial strain between M2 (2004–05) and M3 (2013–14) by relative SES. Demographic-adjusted scores are based on Model 2

(Table 3), while the remaining scores are based on Model 4. Predicted scores are computed using the coefficients from the linear regression model and setting the

covariates to the following values: financial strain at M2 (2004–05) is fixed at the mean; relative SES at 0 (bottom 1%) or 1 (top 1%); indicators for exposure to Great

Recession-related hardships as specified; and all other covariates (sex, age, minority, marital status) are fixed at the mean for our sample. Thus, scores represent the

estimated change in current financial strain between M2 and M3. Error bars indicate the 95% confidence interval for each estimate.

https://doi.org/10.1371/journal.pone.0214947.g001

Effects of the Great Recession on perceived economic distress

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in the absolute magnitude of those effects is very small (0.04 SD). A much smaller proportion

of the sample exhibited a big loss (10% reported �5 decrease in log assets, 99% of whom ended

up with no assets at M3) or a big gain (9% exhibited a gain of similar or greater magnitude,

97% of whom had no assets at M2). In these more extreme cases, the differential between the

detriment associated with a big loss of assets (+0.55 SD increase in strain) and the benefit of a

big gain in assets (-0.32 SD decrease in strain) is still only 0.23 SD. We find no significant dif-

ference between the absolute coefficients for increases versus decreases in log income.

When we exclude the 395 individuals (15% of the sample) for whom income or assets was

top-coded in either M2 or M3 (and thus, we do not have accurate measures of the changes in

Fig 2. Predicted change in current financial strain between M2 (2004–05) and M3 (2013–14) by changes in assets. Predicted scores are computed using the

coefficients from Model 5 (Table 3) and fixing financial strain at M2 (2004–05) at the mean; the level of assets at M2 at the median ($120,000 based on absolute values);

the change in assets between M2 and M3 at the specified values, and all other covariates (sex, age, minority, marital status, and household income at M2) are based on

the observed distribution in the sample. Thus, scores represent the estimated change in current financial strain between M2 and M3. The difference (M3—M2) in log

assets is equivalent to the log of the ratio of assets at M3 relative to M2. Thus, a 0.7 decrease in log assets represents a loss of 50% (24% of the observed sample exhibited a

loss of that magnitude or greater), whereas a 0.7 increase corresponds with an approximate doubling of assets (28% of the sample reported a gain of that magnitude or

greater). A decrease of 5 in log assets would be equivalent to a decline from $40,000 at M2 to $270 at M3 (10% of the sample reported a loss of similar or greater

magnitude, 99% of whom ended up with no assets at M3), while an increase in log assets of 5 would be equivalent to a gain from $500 at M2 to $74,000 at M3 (9% of the

sample exhibited a gain of similar or greater magnitude, 97% of whom had no assets at M2).

https://doi.org/10.1371/journal.pone.0214947.g002

Effects of the Great Recession on perceived economic distress

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income/assets), the differences in the absolute coefficients for losses versus gains in log

income/assets are not significant (S2 Table, Model S5a).

In Model S5b (S2 Table), we fit an alternative model using untransformed values of

income/assets. In this case, the increase in financial strain associated with a loss of income is

significantly greater than the decrease in financial strain associated with an equivalent gain in

income. Still, the differential in the effect of losses versus gains in income is modest. There is

no significant difference in the absolute coefficients for increases versus decreases in assets.

Alternative fixed effects modeling strategy

One weakness of the previous models is that persons experiencing financial gains over the

period are likely to differ in many ways from those suffering financial losses. Individual charac-

teristics (e.g., personality traits, optimism) or the strength of social networks may influence

perceptions of financial distress and bias the resulting estimates of the effects of gains versus

losses. Our final set of estimates is based on fixed effects models that control for unmeasured

time-invariant characteristics of the respondents. These models use information from two sur-

vey intervals (M1!M2 and M2!M3) to identify individuals that experienced both gains and

losses. Among those (N = 1388) who experienced an increase in household income in one sur- vey interval, but a decrease in the other interval, the effect of a loss in log income does not dif-

fer significantly from the absolute value of the effect of an equivalent gain (Model 1a, S3

Table). The results are similar if we further restrict the analysis to respondents who experi-

enced substantial changes in income: a $10,000 or greater gain (in 1995 dollars, which would

be equivalent to ~$16,600+ in 2018 dollars) in one interval and at least a $10,000 loss in the

other (Model 1b).

Among those (n = 1217) who experienced an increase in log assets in one survey interval,

but a decrease in the other interval, we again find no significant difference between the effects

of losses versus gains in assets (Model 2a, S3 Table). In fact, the relative magnitude of the coef-

ficients is contrary to our hypothesis (i.e., the coefficient for a decrease in log assets is smaller

than the absolute value of the coefficient for an increase). The results are similar if we further

restrict the analysis to respondents who experienced a $25,000+ gain (in 1995 dollars, which

would be equivalent to ~$41,500+ in 2018 dollars) in one interval and a $25,000+ loss in the

other (Model 2b).

If we use untransformed values of income/assets, we find that the coefficient for a loss in

income is significantly greater than the absolute coefficient for a gain in income (Model 1c, S3

Table), but there is no significant difference between the effects of losses versus gains in assets

(Model 2c).

Discussion

Widening social disparities in perceived economic distress between the mid-2000s and mid-

2010s in the U.S. can be explained in part by differential exposure to hardships related to the

Great Recession, the effects of which have lingered even four to five years after the recession

officially ended. Our results, which suggest that individuals with less education were much

harder hit by the Great Recession, are consistent with other findings based on MIDUS [29]

and other data [1–3]. Although the final wave in the MIDUS survey was fielded three to four

years after the start of an economic boom that followed the Great Recession, we find a growing

socioeconomic divide.

On the whole, individuals with less education and lower relative SES were harder hit by the

Great Recession and recovered less wealth during the early post-recession period (2010–14)

than their more advantaged counterparts. For those in the bottom 1% of SES, our estimates

Effects of the Great Recession on perceived economic distress

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suggest that levels of perceived financial strain improved slightly among those who experi-

enced no recession hardships, but financial strain dramatically worsened (by 0.86 SD) for indi-

viduals who endured the most common combination of three recession-related hardships (i.e.,

cut back on spending, increased debt, and sold some possessions). Yet the outcomes were

more favorable for those in the top 1% of SES: we estimate that perceived financial strain

declined by more than half a SD for those who experienced no hardships, while it increased by

less than half a SD for their counterparts who cut back on spending, increased debt, and sold

some possessions. In sum, exposure to the Great Recession had a big effect (almost a one SD

difference between no hardships versus the specified combination of three hardships) at all lev-

els of SES. Nonetheless, for any given level of exposure to recession-related hardships, those

with higher SES fared better than their less advantaged counterparts.

Prior literature has documented the effects of the Great Recession on standard economic

indicators such as unemployment, income, and wealth [4, 6, 10, 30], but to our knowledge, no

previous studies have examined the effects on perceived economic distress. Grusky et al. [10]

noted that, “The economic costs of the Great Recession, such as loss of income or wealth, cannot fully capture the social costs and hardships that individuals and family must endure in hard times. These social costs may be in the form of . . .despair and pessimism . . .” (p. 6). They further acknowledged that (p. 8), “We know surprisingly little as yet about the extent and variation in such behavioral and attitudinal responses to the recession.” We also note that an individual’s subjective ratings can be affected by the experiences of their peers. Wilkinson [9] argues that

the link between objective circumstances and subjective evaluations may seem counterintui-

tive: during periods of economic decline (i.e., when lots of people are suffering economic diffi-

culty), a person may downplay his/her own financial troubles.

Based on prospect theory and the concept of loss aversion, we hypothesized that the psychic

cost of financial losses over this nine-year period would be greater than the psychological bene-

fit associated with an equivalent gain. We find partial support for the theory in the sense that

all significant differences between the effects of losses versus gains are in the hypothesized

direction: in most cases, perceptions of financial strain appear to be somewhat more strongly

affected by losses in income/assets than by gains. Nonetheless, the magnitude of the differen-

tials is small and the results are not robust: many differences are not significant, they depend

on whether losses and gains are measured in absolute or in relative terms, and they differ for

income versus assets. It may be that the effects of losses/gains were muted because they did not

necessarily lead to big changes in relative social standing. Another possibility is that, given the

nine-year lag between survey waves, individuals had already adapted to changes in their eco-

nomic fortune.

Admittedly, we are testing loss aversion in a different context from most evaluations of

prospect theory, which typically focus on decision-making in the face of risk (e.g., gambling,

financial investment). Here we assess the effects of actual (experienced) economic losses and

gains, and our outcome is not a decision or choice but rather a subjective assessment after the

gain/loss has occurred. To our knowledge, no prior study has tested loss aversion in this con-

text. We found one study that evaluated prospect theory and the concept of loss aversion with

respect to the effect on saving behavior [26]. Compared with individuals who reported “nor-

mal” income for the current year, Fredrickson et al. [26] found those who reported below nor-

mal income were less likely to save, but they found no difference in saving behavior among

individuals who reported above normal income. This study did not directly test the effects of

longitudinal losses/gains in income or wealth, but rather relied on cross-sectional subjective

assessments of income in the past year (relative to a “normal” year) and whether the respon-

dent spent more than their income in the previous year.

Effects of the Great Recession on perceived economic distress

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While our results suggest that exposure to the Great Recession played a notable role in exac-

erbating the socioeconomic divide, it is fair to ask whether the Great Recession was the culprit

or whether there were factors already in play long before the Great Recession hit. To address

that question, we repeated the analysis for the interval between the M1 (1995–96) and M2

(2004–05) waves of MIDUS. Given that the Great Recession was still many years ahead on the

horizon, we would not expect to find growing socioeconomic disparities in economic distress

during this earlier period. Nonetheless, we found even greater widening of the educational dis-

parities in perceived financial strain between M1 and M2 (increased by 0.70 SD) than we did

for M2 to M3 (0.47 SD). When measured by relative SES, widening was also slightly larger in

the early period (0.67 SD for M1-!M2) than during the interval in which the Great Recession

occurred (0.60 SD for M2-!M3). For the measures of perceived work uncertainty, widening

of the educational gap was similar in the two intervals, but the differential by relative SES wid-

ened somewhat more in the later period compared with the earlier one, particularly for future

work uncertainty (increased by 0.58 SD for M1-!M2 versus 0.67 SD for M2-!M3).

In sum, we find that the socioeconomic gap in economic distress grew wider throughout

the entire period from the mid-1990s to the mid-2010s. The fact that educational/SES dispari-

ties in economic distress were already widening before the Great Recession hit suggests that

the factors driving the growing socioeconomic divide in perceived economic distress were

likely already in motion prior to the recession. For example, rising levels of economic distress

may relate to existing characteristics or vulnerabilities of individuals rather than being the

result of exogenous economic shocks. Nonetheless, the Great Recession almost certainly aggra-

vated the situation, by exacerbating existing vulnerabilities.

One limitation of our findings is that we cannot separate the effects of aging from a possible

period effect among our longitudinal cohort. A prior study indicated that economic distress

increased over time for those with low SES (i.e., period effect), yet those cross-sectional data

also suggested that levels of financial strain and current work uncertainty were lower at older

ages (i.e., among earlier cohorts) than at younger ages (i.e., among later cohorts) [11]. If eco-

nomic distress diminishes with age it could, to some degree, offset any increase in economic

distress attributable to a period effect. In this analysis, we cannot separate those two effects

because everyone in the cohort is aging at the same rate. For example, if current distress

increased by 10% because of a period effect, but declined 10% as a result of the respondent

aging nine years, then the net change would be zero. All we can say is that among our observed

cohort, social disparities in perceived economic distress widened considerably. It is possible

that the period effect of rising economic distress was even greater than these results suggest

because the period effect may have been tempered by the fact that the cohort was growing

older, accumulating wealth, and gaining job experience.

Another key limitation of this study is that it is based only on the select sample of respon-

dents who survived and participated in the M3 wave of MIDUS. Even at M1, the MIDUS sam-

ple under-represents less educated persons and minorities relative to the general population

(see Table 9 in [31]). Furthermore, mortality and loss-to-follow-up are inversely associated

with SES (see S5 Text for more details). In general, respondents who participated in all three

waves of MIDUS are likely to be more advantaged than the population as a whole. Conse-

quently, the SES differential might be even larger if not for selective attrition of the most disad-

vantaged individuals. That is, our data may over-estimate economic well-being and under-

estimate the level of perceived economic distress at the bottom of the SES spectrum.

We must also acknowledge our limited ability to draw causal inferences and our inability to

distinguish between different forms of wealth. It is difficult to establish causation based on the

relationship between retrospective measures of exposure to the Great Recession and SES dif-

ferences in perceived economic distress measured up to five years after the recession officially

Effects of the Great Recession on perceived economic distress

PLOS ONE | https://doi.org/10.1371/journal.pone.0214947 April 4, 2019 19 / 24

ended. Furthermore, we cannot distinguish between the effects of housing assets versus other

forms of wealth.

Conclusion

Our findings paint a dismal portrait of a growing socioeconomic divide in economic distress

throughout the period from the mid-1990s to the mid-2010s, one that mirrors recent increases

in psychological distress and decreases in well-being among those with lower relative SES [32].

These two trends are almost certainly linked and may relate to increasing economic and psy-

chological “despair” that have been hypothesized to be a root cause of increases in the deaths

of despair. Researchers have been struggling to understand these rising deaths rates and have

deemed simple explanations such as easier access to opioids and short-term declines in

employment and wages as insufficient. Deaton (p. 3, [33]) described deaths of despair “as sui-

cides in one form or another . . .that respond more to prolonged economic conditions than to

short-term fluctuations, and especially social dysfunctions, such as loss of meaning in the

interconnected worlds of work and family life, that come with prolonged economic distress”.

Although there have been no clear answers to the sources of increased despair, many schol-

ars agree that explanations for these increases in mortality are likely to involve complex social

and economic transitions. Yet, most researchers who purport to explore the despair hypothesis

rely on objective economic indicators without considering perceptions and without any

attempt to account for social dysfunction. For example, Ruhm [34] largely rejected the despair

hypothesis based on analysis of the relationship between county-level changes in economic

conditions and corresponding changes in drug mortality rates. Yet, he did not measure

“despair” per se (which is, by definition, subjective), nor did his measures of poverty rates,

household income, home prices, unemployment rates, and import exposure capture the type

of social dysfunction to which Deaton refers. Similarly, Masters et al. [35] dismissed the

despair hypothesis, although they did not test it directly.

Despair would appear to imply an extreme form of mental distress: a state of abject hope-

lessness that might lead someone to take his or her own life or engage in self-destructive behav-

ior that could eventually lead to the same end. While it may include an absence of “well-

being”, that is not likely to be sufficient to result in a death of despair. Cherlin [36] defined

despair “to mean a loss of heart that might lead to abuse of alcohol or drugs or even to an

attempt at suicide”, further noting that, “it is possible to be unhappy and dissatisfied with one’s

life without the utter loss of hope that despair connotes (p. 7176).” Economic despair, repre-

sented by high levels of perceived economic distress, could be part of the story, but we suspect

that despair is also characterized by social and psychological dysfunction such as a lack of pur-

pose in life, a sense of worthlessness, little hope or goals for the future, and perceived social

rejection by broader society.

Our analysis does not permit us to answer these pressing questions regarding the causes of

rising despair, nor can we ascertain whether these trends afflict all regions of the US equally.

Given data highlighting the huge geographic disparities in life expectancy at birth (e0) [37–41],

age-standardized mortality rates [42], gains in e0 [40, 43–45], drug-related mortality [46], and

SAD mortality more generally [47, 48], one might wonder how the spatial distribution of eco-

nomic, psychological, and social distress maps onto the observed patterns of mortality decline

(Preston, personal communication, 9/28/2018). Spatial analysis of SAD mortality and objective

economic indicators can provide indirect evidence about the “landscapes of despair” [49], but

ultimately “despair” must be measured subjectively, which means we need to ask people how

they perceive their own lives, their position in their community, and their role within US soci-

ety at large.

Effects of the Great Recession on perceived economic distress

PLOS ONE | https://doi.org/10.1371/journal.pone.0214947 April 4, 2019 20 / 24

Supporting information

S1 Fig. Change (M3—M2) in household income by level of household income at M2.

Household income is measured in thousands of 1995 dollars.

(DOCX)

S2 Fig. Change (M3—M2) in assets by level of assets at M2. Assets are measured in thou-

sands of 1995 dollars.

(DOCX)

S1 Table. Odds ratios for relative SES from logit models predicting exposure to specified

recession-related hardships.

(DOCX)

S2 Table. Linear regression models predicting changes (M2!M3) in perceived current

financial strain using alternative specifications.

(DOCX)

S3 Table. Fixed effects linear regression models for current financial strain at t+1. (DOCX)

S1 Text. Construction of relative socioeconomic status.

(DOCX)

S2 Text. Inverse relationship between income/assets at baseline and changes in income/

assets.

(DOCX)

S3 Text. Statistical models.

(DOCX)

S4 Text. Changes in assets between M2 and M3.

(DOCX)

S5 Text. SES and economic distress as predictors of mortality and loss-to-follow-up.

(DOCX)

Acknowledgments

This work was supported by the National Institute on Aging [https://www.nia.nih.gov/, grant

numbers P01 AG020166 & U19AG051426 to Carol D. Ryff]; the Eunice Kennedy Shriver

National Institute of Child Health and Human Development [https://www.nichd.nih.gov/,

grant number P2CHD047879], and the Graduate School of Arts and Sciences, Georgetown

University [https://grad.georgetown.edu/]. Noreen Goldman would like to thank the Russell

Sage Foundation for their support while she was a Visiting Scholar in 2018–2019. The funders

had no role in study design, data collection and analysis, decision to publish, or preparation of

the manuscript.

We are grateful to Germán Rodrı́guez for his advice regarding the modeling strategy. Fur-

thermore, we thank Andrew Cherlin, Samuel H. Preston, Lauren Gaydosh, and Peter Muennig

for their comments on the manuscript.

Author Contributions

Conceptualization: Dana A. Glei, Noreen Goldman, Maxine Weinstein.

Data curation: Dana A. Glei.

Effects of the Great Recession on perceived economic distress

PLOS ONE | https://doi.org/10.1371/journal.pone.0214947 April 4, 2019 21 / 24

Formal analysis: Dana A. Glei.

Funding acquisition: Maxine Weinstein.

Investigation: Dana A. Glei.

Methodology: Dana A. Glei, Noreen Goldman, Maxine Weinstein.

Project administration: Maxine Weinstein.

Supervision: Maxine Weinstein.

Visualization: Dana A. Glei.

Writing – original draft: Dana A. Glei.

Writing – review & editing: Dana A. Glei, Noreen Goldman, Maxine Weinstein.

References 1. Engemann KM, Wall H, J. The effects of recessions across demographic groups. Federal Reserve

Bank of St Louis Review. 2010; 92(1): 1–26.

2. Hipple SF. The labor market in 2009: recession drags on. Monthly Labor Review. 2010;March: 3–22.

3. Hoynes HW, Miller DL, Schaller J. Who suffers during recessions? The Journal of Economic Perspec-

tives. 2012; 26(3): 27–47.

4. Hurd MD, Rohwedder S. Effects of the financial crisis and Great Recession on American households.

Cambridge, MA: National Bureau of Economic Research; 2010

5. Taylor P, Morin R, Wang W. One Recession, Two Americas: Those Who Lost Ground Slightly Outnum-

ber Those Who Held Their Own. Washington, DC: Pew Research Center; 2010

6. Bricker J, Bucks BK, Kennickell A, Mach TL, Moore K. Drowning or weathering the storm? Changes in

family finances from 2007 to 2009. Cambridge, MA: National Bureau of Economic Research; 2011

7. Taylor P, Morin R, Kochhar R, Parker K, Cohn D, Lopez MH, et al. A balance sheet at 30 months: How

the Great Recession has changed life in America. Washington, DC: Pew Research Center; 2010

8. Leininger LJ, Kalil A. Economic strain and children’s behavior in the aftermath of the great recession.

Journal of Marriage and Family. 2014; 76: 998–1010.

9. Wilkinson LR. Financial Strain and Mental Health Among Older Adults During the Great Recession. J

Gerontol B Psychol Sci Soc Sci. 2016 Jul; 71(4): 745–54. https://doi.org/10.1093/geronb/gbw001

PMID: 26843395

10. Grusky DB, Western B, Wimer C, editors. The Great Recession. New York, NY: Russell Sage Founda-

tion; 2011.

11. Glei DA, Goldman N, Weinstein M. Perception has its own reality: Subjective versus objective measures

of economic distress. Popul Dev Rev. 2018; 44(4): 695–722. https://doi.org/10.1111/padr.12183 PMID:

30828111

12. Khazan O. Middle-Aged White Americans Are Dying of Despair. Available: https://www.theatlantic.com/

health/archive/2015/11/boomers-deaths-pnas/413971/.

13. Case A. ’Deaths of despair’ are killing America’s white working class. Available: https://qz.com/583595/

deaths-of-despair-are-killing-americas-white-working-class/.

14. Monnat SM. Deaths of Despair and Support for Trump in the 2016 Presidential Election, The Pennsyl-

vania State University Department of Agricultural Economics, Sociology, and Education Research

Brief. State College, PA: Pennsylvania State University; 2016

15. Case A, Deaton A. Mortality and morbidity in the 21st century. Washington, DC: Brookings Institution;

2017

16. Muennig PA, Reynolds M, Fink DS, Zafari Z, Geronimus AT. America’s Declining Well-Being, Health,

and Life Expectancy: Not Just a White Problem. Am J Public Health. 2018 Sep 25: e1–6.

17. Murphy SL, Xu JQ, Kochanek KD, Arias E. Mortality in the United States, 2017. Hyattsville, MD:

National Center for Health Statistics; 2018

18. Kochanek KD, Murphy SL, Xu JQ, Arias E. Mortality in the United States, 2016. Hyattsville, MD:

National Center for Health Statistics; 2017

Effects of the Great Recession on perceived economic distress

PLOS ONE | https://doi.org/10.1371/journal.pone.0214947 April 4, 2019 22 / 24

19. Xu JQ, Murphy SL, Kochanek KD, Arias E. Mortality in the United States, 2015. Hyattsville, MD:

National Center for Health Statistics; 2016

20. Kahneman D, Tversky A. Choices, Values, and Frames. American Psychologist. 1984; 39(4): 341–50.

21. Kahneman D, Tversky A. Advances in prospect theory: Cumulative representation of uncertainty. Jour-

nal of Risk and Uncertainty. 1992; 5(4): 297–323.

22. Kahneman D, Tversky A. Prospect theory: an analysis of cecision under risk. Econometrica. 1979; 47

(2): 263–91.

23. Brim OG, Baltes PB, Bumpass LL, Cleary PD, Featherman DL, Hazzard WR, et al. National Survey of

Midlife Development in the United States (MIDUS), 1995–1996. ICPSR02760-v11. http://doi.org/10.

3886/ICPSR02760.v11. 2016

24. Schafer JL. Multiple imputation: a primer. Stat Methods Med Res. 1999 Mar; 8(1): 3–15. https://doi.org/

10.1177/096228029900800102 PMID: 10347857

25. Rubin DB. Multiple imputation after 18+ years (with discussion). J Am Stat Assoc. 1996; 91: 473–89.

26. Fredrickson JE. Prospect theory and saving behaviors during the Great Recession: 2009 SCF Panel

Survey results. Journal of Behavioral Studies in Business. 2013; 6: 1–17.

27. The World Bank. GDP Growth Rate. Available: https://www.google.com/publicdata/explore?ds=

d5bncppjof8f9_&ctype=l&strail=false&bcs=d&nselm=h&met_y=ny_gdp_mktp_kd_zg&scale_y=

lin&ind_y=false&rdim=country&idim=country:USA&ifdim=country&hl=en&dl=en&ind=false.

28. Bureau of Labor Statistics. Labor Force Statistics from the Current Population Survey, seasonally

adjusted unemployment rate, age 16 years and older. Available: https://data.bls.gov/pdq/

SurveyOutputServlet.

29. Song J, Mailick MR, Greenberg JS. The Impact of the Great Recession on Midlife and Older parents of

Individuals With a Mental Health Problem or a Developmental Disability. Gerontologist. 2018 May 8; 58

(3): 448–55. https://doi.org/10.1093/geront/gnw269 PMID: 28329847

30. Duque V, Pilkauskas NV, Garfinkel I. Assets among low-income families in the Great Recession. PLoS

One. 2018 Feb 5; 13(2): e0192370. https://doi.org/10.1371/journal.pone.0192370 PMID: 29401482

31. Brim OG, Baltes PB, Bumpass LL, Cleary PD, Featherman DL, Hazzard WR, et al. National Survey of

Midlife Development in the United States (MIDUS), 1995–1996. Technical Report on Methodology. Ann

Arbor, MI: Inter-university Consortium for Political and Social Research; 2016

32. Goldman N, Glei DA, Weinstein M. Declining mental health among disadvantaged Americans. Proc

Natl Acad Sci U S A. 2018 Jul 10; 115(28): 7290–5. https://doi.org/10.1073/pnas.1722023115 PMID:

29915079

33. Deaton A. Economic Aspects of the Opioid Crisis: Testimony before the Joint Economic Committee of

the United States Congress. 2017

34. Ruhm CJ. Deaths of Despair or Drug Problems? National Bureau of Economic Research; 2018

35. Masters RK. Explaining recent mortality trends among younger and middle-aged white Americans. Int J

Epidemiol. 2018; 47(1): 81–8. https://doi.org/10.1093/ije/dyx127 PMID: 29040539

36. Cherlin AJ. Psychological health and socioeconomic status among non-Hispanic whites. Proc Natl

Acad Sci U S A. 2018 Jul 10; 115(28): 7176–8. https://doi.org/10.1073/pnas.1808753115 PMID:

29925598

37. Wolf JK. Exploring the Power of County-Level Regional Classifications on Predicting Sex-Specific All-

Cause Mortality in the United States [dissertation]. West Virginia University; 2017

38. Van Dam A. Strawberry Capital of the World is the early death capital of the U.S.: lessons from a land-

mark dataset. Washington Post 2018 09-14-2018;Wonkblog

39. Arias E, Escobedo LA, Kennedy J, Fu C, Cisewski J. The U.S. Small-area Life Expectancy Estimates

Project: Methodology and Results Summary. Hyattsville, MD: National Center for Health Statistics;

2018

40. Dwyer-Lindgren L, Bertozzi-Villa A, Stubbs RW, Morozoff C, Mackenbach JP, van Lenthe FJ, et al.

Inequalities in Life Expectancy Among US Counties, 1980 to 2014: Temporal Trends and Key Drivers.

JAMA Intern Med. 2017 Jul 1; 177(7): 1003–11. https://doi.org/10.1001/jamainternmed.2017.0918

PMID: 28492829

41. Chetty R, Stepner M, Abraham S, Lin S, Scuderi B, Turner N, et al. The association between income

and life expectancy in the United States, 2001–2014. JAMA. 2016 Apr 26; 315(16): 1750–66. https://

doi.org/10.1001/jama.2016.4226 PMID: 27063997

42. Montez JK, Zajacova A, Hayward MD. Explaining Inequalities in Women’s Mortality between U.S.

States. SSM Popul Health. 2016 Dec; 2: 561–71. https://doi.org/10.1016/j.ssmph.2016.07.004 PMID:

27722192

Effects of the Great Recession on perceived economic distress

PLOS ONE | https://doi.org/10.1371/journal.pone.0214947 April 4, 2019 23 / 24

43. Elo I, Hendi A, Ho J, Vierboom Y, Preston SH. Trends in Non-Hispanic White Mortality in the United

States by Metropolitan-Nonmetropolitan Status and Region, 1990–2016. Popul Dev Rev. Forthcoming

(accepted subject to minor revision)

44. Ezzati M, Friedman AB, Kulkarni SC, Murray CJ. The reversal of fortunes: trends in county mortality

and cross-county mortality disparities in the United States. PLoS Med. 2008 Apr 22; 5(4): e66. https://

doi.org/10.1371/journal.pmed.0050066 PMID: 18433290

45. Wang H, Schumacher AE, Levitz CE, Mokdad AH, Murray CJ. Left behind: widening disparities for

males and females in US county life expectancy, 1985–2010. Popul Health Metr. 2013 Jul 10; 11(1):

8,7954-11-8. https://doi.org/10.1186/1478-7954-11-8 PMID: 23842281

46. Monnat SM. Factors Associated With County-Level Differences in U.S. Drug-Related Mortality Rates.

Am J Prev Med. 2018 May; 54(5): 611–9. https://doi.org/10.1016/j.amepre.2018.01.040 PMID:

29598858

47. Dwyer-Lindgren L, Bertozzi-Villa A, Stubbs RW, Morozoff C, Shirude S, Unutzer J, et al. Trends and

Patterns of Geographic Variation in Mortality From Substance Use Disorders and Intentional Injuries

Among US Counties, 1980–2014. JAMA. 2018 Mar 13; 319(10): 1013–23. https://doi.org/10.1001/

jama.2018.0900 PMID: 29536097

48. Monnat SM. Deaths of Despair from the Cities to the Hollers: Explaining Spatial Differences in U.S.

Drug, Alcohol, and Suicide Mortality Rates. 2017

49. Monnat SM, Brown DL. More than a rural revolt: Landscapes of despair and the 2016 Presidential elec-

tion. J Rural Stud. 2017; 55: 227–36. https://doi.org/10.1016/j.jrurstud.2017.08.010 PMID: 29269990

Effects of the Great Recession on perceived economic distress

PLOS ONE | https://doi.org/10.1371/journal.pone.0214947 April 4, 2019 24 / 24

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