Help Wanted: The Politicization of Job Loss
in High-Unemployment Contexts
Matthew B. Incantalupo1
Joint Degree Program in Politics and Social Policy
Princeton University
mincanta@princeton.edu
This is a work in progress. Please do not cite or share without permission.
Abstract: When Americans are laid off from work, who should be mainly responsible for
helping them? I develop a new theory, Hardship-in-Context, which argues that citizens form
political perceptions about their personal problems using relevant contextual information.
Unemployed Americans perceive their hardship as individualized in low-unemployment contexts
and as politicized in high-unemployment contexts. I examine several surveys over a period of
time that includes both high and low unemployment and find support for this claim. When and
where the unemployment rate is low, jobless Americans believe they are mainly responsible for
helping themselves. When and where it is high, jobless Americans believe that government and
employers are responsible for helping people who are laid off from work. Employed Americans
do not display the same pattern of individualization in low-unemployment contexts and
politicization in high unemployment contexts. I support these results using evidence from a
recent longitudinal survey as well. Overall, I find strong evidence that the interaction of personal
experiences and the broader context in which they occur helps to shape political attitudes.
This paper is based on chapters 1, 2 and 3 of my dissertation project, “Help Wanted: Unemployment and the
Politicization of Personal Economic Hardship.” I would like to thank Amy Lerman, Christopher Achen, Martin
Gilens, Benjamin Bishin, Scott Abramson, Sarah Brayne, Kevin Collins, and Steve Rogers for helpful feedback and
support. All remaining errors and omissions are my own.
1
1
When, in a city of 100,000, only one man is unemployed, that is his personal trouble, and for its
relief we properly look to the character of the man, his skills, and his immediate opportunities. But
when in a nation of 50 million employees, 15 million men are unemployed, that is an issue, and
we may not hope to find its solution within the range of opportunities open to any one individual.
The very structure of opportunities has collapsed. Both the correct statement of the problem and
the range of possible solutions require us to consider the economic and political institutions of the
society, and not merely the personal situation and character of a scatter of individuals. (Mills 1959,
9)
In this paper, I advance and test the claim that individuals in high-unemployment
contexts politicize the personal hardship of unemployment and perceive it as a socially-centered
political problem.2 I argue that the effects of personal experience with job loss on how
Americans perceive unemployment vary as a function of the broader economic context,
specifically labor market conditions. This implies that out-of-work citizens view unemployment
as a personal problem in low-unemployment contexts and as a political problem in highunemployment contexts. When unemployment becomes politicized, jobless Americans shift their
foci of expectations away from themselves and look to government for solutions and assistance
(Brody and Sniderman 1977). I test this implication using a series of surveys merged with statelevel unemployment data. To exploit as much variation in contextual factors as possible, I
examine multiple samples that utilize the same survey instrument over an eight-year period that
spans both good and bad economic times to understand how individuals’ perceptions of
unemployment vary as a function of the broader context in which they experience this form of
economic hardship. I also support these findings using a recent panel survey of Americans who
lost their jobs in the Great Recession.
I find support for my theory; unemployed Americans in low-unemployment contexts are
likely to report that unemployed workers are responsible for helping themselves while
2
High-unemployment contexts refer to circumstances or places when and where the unemployment rate is high.
Alternatively, I define low-unemployment contexts to refer to circumstances or places when and where the
unemployment rate is low and near “full” employment.
2
unemployed individuals in high-unemployment contexts believe that government and employers
should help individuals who lose their jobs. Furthermore, unemployed individuals in high
unemployment contexts are unlikely to believe that the unemployed should be mainly
responsible for helping themselves. The politicization and deindividualization of unemployment
as a function of local economic context is unique to jobless Americans; employed respondents do
not exhibit this pattern of responses to the same extent as out-of-work respondents. This is strong
evidence that unemployment is best understood in context, particularly with respect to how it can
shape political beliefs and ultimately contribute to mobilization and political participation
(Incantalupo 2012a).
Background
In general, Americans do not expect government to help them deal with their personal
problems and hardships. “Citizens are likely to think the government has some responsibility to
help only if the type of problem of most concern to them is manifestly beyond the capacity of
any one person to deal with all by himself. Otherwise, they are overwhelmingly likely to insist
that it is up to them to cope with the problem on their own” (Brody and Sniderman 1977, 340).
This rugged individualism and the belief that individuals are personally responsible for their
economic successes and failures shape Americans’ attitudes towards economic and social policy
(Free and Cantril 1968; Huber and Form 1973; Kluegal and Smith 1986). Americans rely
primarily on themselves to cope with the personal hardships that they face and expect
government to take on socially-located problems that are perceived as not amenable to individual
influence, such as monetary inflation or natural disasters (Sniderman and Brody 1977).
Building off of this personal-social dichotomy, we can consider a continuum that ranges
from fully self-located to fully socially-located on which Americans place their personal
3
concerns (Brody and Sniderman 1977). Socially-located concerns become politicized and affect
political attitudes as well as influence participation and voting. Self-located concerns do not link
up to political behavior, and may in fact inhibit participating in politics or paying attention to
current events because they are distracting. For example, job loss and financial strain place
significant psychological (Schlozman and Verba 1979), familial (Feather 1989; Schlozman and
Verba 1979), and even health burdens (Warr 1987) on those who experience them, which make
Americans less likely to think about or participate in politics. Writing about unemployment,
Rosenstone concludes, “When a person experiences economic adversity his scarce resources are
spend on holding body and soul together – surviving – not on remote concerns like politics”
(1982, 26).
I begin with this personal-social continuum in developing a new theory of how
Americans politicize their personal hardships. However, existing theories argue that citizens
believe government has a responsibility to help if the type of problem that most concerns them is
one that they cannot manage on their own (Brody and Sniderman 1977; Sniderman and Brody
1977). This implies that citizens locate problems along the personal-social continuum based on
their content, which is problematic because it requires too much from citizens, most of whom
spend little time thinking about politics and more time worrying about their problems. Americans
do not possess a great deal of political information (Delli Carpini and Keeter 1996 and may lack
the necessary schemas to connect what is troubling them to politics (Lodge and Hamill 1986).
Citizens often punish incumbents for things politicians cannot control or influence so long as
they can connect their problems to government through a “folk story” (Achen and Bartels 2002).
They can also be happy with government despite poor performance based on recent good
4
economic performance (Bartels 2008) or even more banal events like sports victories (Healy,
Malhotra, and Mo 2010).
In contrast, I argue that individuals locate their personal problems along this continuum
based on the broader context in which they experience these hardships. By incorporating
contextual information into the process of addressing and coping with their personal problems,
Americans determine how widespread these problems are, the extent to which they are amenable
to individual influence, and the degree to which they should shift their foci of expectations away
from themselves and towards government. In this way, two citizens who are experiencing the
same hardship can reach two different conclusions about whether their problems should be
addressed by individual or government action if they experience them in disparate social and
economic contexts.
Considering the role of context in determining how Americans respond to personal
hardship allows us to reconcile the fact that problems that seem very personal by their content
sometimes lead to political action. Having or caring for someone with a serious illness would be
characterized as an individually-centered problem at face value (Brody and Sniderman 1977;
Sniderman and Brody 1977). However, state and local health departments receive over 1,000
requests to investigate suspected cancer clusters each year (Thun and Sinks 2004). Actual cancer
clusters are very rare, and most suspected cancer clusters are simply unfortunate coincidences
reported by cancer patients or their loved ones who believe that they have noticed an unusual
pattern of cancer incidences (Trumbo 2000; Robinson 2002). But, in the proper context, a
5
hardship that usually would not be politicized, a cancer diagnosis, contributes to political action
that in some cases can continue for years.3
The same can be said for numerous other hardships. A foreclosure sale on a home is a
self-located concern, but when hundreds of homes in a single county are facing foreclosure,
citizens will perceive their own foreclosure as part of a socially-located problem. Organizations
like the Foreclosure Working Group, an offshoot of Occupy Greensboro, boast hundreds of
members experiencing foreclosure who are now engaging in political action and protests against
major banks. As of June 2012, Guilford County, which contains Greensboro, had the fourthhighest foreclosure rate in North Carolina, and borders the county with the second-highest rate in
the state.4 And just as we observe “Occupy Homes” groups springing up in response to high
foreclosure rates, we observe “99ers” groups forming to advocate for extended unemployment
benefits to jobless Americans who have exhausted their unemployment compensation after a
maximum of 99 weeks.
The proliferation of jobless advocacy groups following the most recent economic
recession indicates that unemployment can meaningfully affect political behavior, at least if
unemployment is sufficiently high and widespread. This is in contrast to Schlozman and Verba
(1979), which finds that the unemployed are largely disorganized, in part due to a lack of class
consciousness in the United States, but does not consider how the effects of unemployment may
vary depending on the social and economic contexts in which Americans experience it.
Economic context seems to affect attitudinal measures as well. The belief that hard work pays
3
A confirmed cancer cluster at Marine Corps Base Camp Lejeune has been the subject of over a decade of
investigation, recently concluding in federal legislation to provide medical care for dozens of military personnel and
their family members who have developed cancer from contaminated drinking water.
Ordonez, Franco, and Barbara Barrett, (McClatchy), "Obama Signs Law Giving Health Care To Lejeune TaintedWater Victims", Raleigh News & Observer, 7 August 2012
4
These data are available at http://www.npr.org/templates/story/story.php?storyId=111494514 and come from
www.realtytrac.com, which bills itself as “the most trusted source of foreclosure information.”
6
off, an important pillar of American political culture, increases following periods of economic
growth and falls during recessions (Pew Research Center 2012). More locally, mass layoffs
positively associate with voter turnout at the county level (Healy 2009; Margalit 2011); In the
Canadian case, neighborhood-level unemployment negatively associates with support for free
trade (Cutler 2007). Thus, there exists ample evidence in the existing literature that Americans’
attitudes related to work and unemployment can be shaped by broader economic conditions.
I argue that personal experience with unemployment is perceived as a self-located
problem that is best remedied through individual action in low-unemployment contexts. When
relatively few Americans are unemployed, the hardship of unemployment is individualized and
jobless Americans are less likely to believe that the government should be responsible for
helping them.5 When and where the economy is struggling and the unemployment rate is high,
unemployed Americans are more likely to perceive unemployment as a social problem and
believe that government is responsible for helping them deal with it.
I define individuals’ perceptions of unemployment as politicized to the extent that they
believe that government bears responsibility for helping people who are unemployed. Citizens’
perceptions of unemployment are individualized to the extent that they believe that the
unemployed are primarily responsible for helping themselves. I expect gainfully employed
Americans to be more likely to perceive unemployment as individualized than as politicized in
high and low-unemployment contexts. I expect unemployed Americans’ attitudes about
unemployment to associate with unemployment context in the following manner: in lowunemployment contexts, unemployed Americans should individualize the hardships associated
5
That is not to say that the unemployed do not collect unemployment benefits from the government. Unemployment
benefits, temporary relief for laid-off workers, are generally less maligned than more controversial safety net
programs such Food Stamps (Gilens 1999).
7
with being out of work. In high-unemployment contexts, unemployed Americans should
politicize this experience and believe that government should step in and help.
Cross-Sectional Evidence
I make use of multiple editions of the Work Trends Poll, a survey of labor force
participants administered by the John J. Heldrich Center for Workforce Development at Rutgers
University. The advantages of using this poll are its focus on members of the labor force and
emphasis on public attitudes related to work, employers, the government, and economic issues.
The polls feature attitudinal measures related to work not included on surveys more familiar to
political scientists, such as the American National Election Studies. Unfortunately, relying upon
these polls has its disadvantages as well. The polls do not contain many measures that typically
accompany attitudes about the government and the economy, such as a measure of political
ideology. Still, I believe these are the best available data to test if perceptions of who is
responsible for helping the unemployed are shaped through the interaction between personal
experience with unemployment and the broader context in which unemployment takes place.
I use the following survey question from the Work Trends Poll series to measure the
politicization of job loss: “When people are laid off from work, who should be mainly
responsible for helping them? Is it government, employers, or workers themselves?” This is a
very strong measure of the politicization of unemployment for several reasons. First, it asks a
specific question about people who lose their jobs without adding additional information about
deservingness or the circumstances under which they are laid off from work. This is a significant
contrast from a measure such as asking respondents to agree or disagree with a statement like “It
is the responsibility of government to take care of people who cannot take care of themselves,”
which removes any ambiguity about individual efficacy and agency in solving a personal
8
hardship. Additionally, it forces respondents to choose who they feel are mainly responsible for
helping individuals who lose their jobs, as opposed to asking them the extent to which they
believe government, employers, or workers themselves should help the unemployed as
individual measures. Given Americans’ complicated beliefs about individualism and
government’s role in the economy, it would not be surprising to find a large proportion of
Americans believes that both government and individuals are responsible for dealing with the
problem of unemployment simultaneously if asked about their roles separately. This question
wording allows for a very straightforward test that unemployment becomes a politicized problem
when experienced in a high-unemployment context. Unemployed individuals in highunemployment contexts should be likely to believe that government is mainly responsible for
helping the jobless.6
This question is repeated using the same wording in six cross-sectional Work Trends
Polls, spanning from June 2003 through July 2011.7 I present some basic information about the
polls used in this analysis in Table 1.
[Table 1 here]
Table 1 serves as a useful summary of the sample sizes for each Work Trends Poll, as well as a
summary of the economic context during each poll’s fielding. Perhaps the most glaring detail
contained in Table 1 is the fact that the national unemployment rate increased by four points in
just one year from May 2008 to May 2009. For each poll in this analysis, I also indicate the
6
In some administrations of the Work Trends Poll, respondents can voluntarily offer more than one choice (usually
coded as “a combination”) or say that all three are equally responsible for helping people who are laid off. I remove
these individuals from my analysis, as I am unable to determine who they believe should help the unemployed.
7
I include a discussion of some earlier Work Trends Polls with alternative question wordings in Appendix A to this
paper. While the evidence in Appendix A cannot be treated as conclusive, it suggests that question wording does
significantly affect who Americans believe should be responsible for helping the unemployed. One alternative
question wording mentions workers who are laid off “through no fault of their own.” In this wording, I observe an
increase in the belief that employers should be responsible for helping the unemployed. Another alternative wording
mentions workers who are laid off in times of economic downturn. Here, I observe an increase in the belief that
government should be responsible for helping the unemployed.
9
highest and lowest state unemployment rates. Particularly in the July 2010 poll, we observe
considerable variation in unemployment at the state level.
As a simple first step, I plot respondents’ answers to my dependent variable, which asks
who should be responsible for helping people who are laid off from work. I disaggregate the
responses by employment status and poll to allow us to see if my expectations hold up under a
very simple and descriptive analysis and present the results in Fig. 1. For each poll, I perform a
simple chi-squared test to see if the pattern of responses is different between employed and
unemployed respondents.
[Fig. 1 here]
This is good early evidence in favor of Hardship-in-Context. Over the course of the 6
polls, we observe unemployed respondents becoming more likely to say that government should
be responsible for helping the unemployed and considerably less likely to say that the
unemployed should help themselves. We observe a similar pattern but to a lesser extent among
the employed. Even in July 2010, with the economy continuing to suffer under very high
unemployment, employed respondents’ modal response is that people who are laid off from work
should be responsible for helping themselves. In 4 of the 6 polls (June 2003, May 2008, May
2009, and July 2010), the chi-squared test is statistically significant at the 90% level or better,
indicating that the pattern of responses we observe between employed and unemployed
respondents is unexpected if employment status has no effect on this outcome and the responses
we observe are due to chance alone. Now that we see that this pattern holds in the most basic
sense, we can incorporate additional rigor into this analysis.
10
Explaining Politicization
I operationalize personal experience with unemployment using respondents’ stated
employment statuses. As Work Trends Polls sample only those in the labor force, I code
employment status using a binary variable that indicates whether a respondent is unemployed
(out of work and currently looking for work) or employed. I include several demographic
controls in my analysis as well, including an indicator variable for female respondents, who tend
to be more favorable towards economic redistribution and social welfare policies than men are
(Gilens 1999). I include another binary indicator for married respondents, control for
respondents’ age in years, and include a factor variable for a respondents’ race or ethnicity. I
indicate if respondents identify as black, Hispanic or Latino, or some other racial or ethnic group,
with white respondents set aside as an excluded category.
I control for educational attainment using a 4-point scale (no high school diploma, high
school diploma, some college, college degree or more). This is especially important because of
the disparity in unemployment between Americans with low educational attainment and
Americans with high educational attainment. Members of the labor force who lack college or
high school degrees are significantly more likely to be unemployed than workers who hold
degrees.8 While controlling for educational attainment by no means erases our concerns over
selection bias into who experiences unemployment, it does help to adjust for one of the
demographic differences between the employed and unemployed.
8
For more information, see the graph of unemployment rates by education attainment in Appendix A to this paper.
Generally speaking, the ratio of unemployed Americans with college degrees to unemployed Americans without
college degrees is fairly stable at the national level. From 1992, when unemployment rates by educational attainment
are first available, to 2012, the ratio of the unemployment rate for people without college degrees to the
unemployment rate for people with college degrees ranges between a minimum of 1.63 in December, 2001 and a
maximum of 2.56 in August, 2006. The average ratio over roughly 20 years of monthly observations is 2.05.
11
I also control for respondents’ household income. Because income is measured
differently across Work Trends Polls, I am forced to use a somewhat imprecise measure of it in
my analysis. Respondents are coded on a three-point scale, corresponding to low (less than
$30,000 per year), medium ($30,000 – $75,000 per year), and high (more than $75,000 per year).
I include a separate category for respondents who refuse to report their household incomes.9
Economic context
Local contextual information is an important determinant of public opinion (Cutler 2007).
I expect unemployed Americans in high-unemployment contexts to perceive job loss as a
socially-centered problem that requires government help and to be unlikely to perceive it as a
personal problem that should be fixed through individual action. I use a very straightforward
measure to operationalize local economic context: the seasonally-adjusted unemployment rate in
a respondent’s state of residence at the time when he or she is interviewed as part of a Work
Trends Poll.10 This metric reflects the economic circumstances in which a jobless individual
must cope with the problem of his or her unemployment and seek out a remedy for his or her
personal hardship.
I summarize this measure of economic context for each year in my sample using boxand-whisker plots in Figure 2. Since the purpose of this graph is to describe local economic
9
I present a summary of the basic demographic information about the respondents in my sample in the Appendix A
to this paper.
10
All unemployment rates are from the Bureau of Labor Statistics. It is not possible to use a more local measure of
unemployment, such as county or metro area-level unemployment. Work Trends Polls do not record these
geographical measures. Furthermore, unemployment rates at these very local levels are measured with more error
than at the state level. I have also considered using alternative measures of economic context. The Bureau of Labor
Statistics estimates and publishes alternative measures of the unemployment rate, including one that counts
“discouraged” workers and people who report working part time for economic reasons among the unemployed. This
measure is known as the U6 unemployment rate and is regarded by some as the “true” measure of unemployment,
because it includes additional individuals who are experiencing economic hardship. However, it does not contribute
additional information beyond what is contained in the more commonly-cited U3 unemployment rate. Monthly
observations of the U3 and U6 at the national level from January 1992 through the July 2013 have a correlation
coefficient of .995. Using the U6 instead of the U3 does not change any of the results in this paper in a meaningful
way. Additionally, using mass layoffs data from the BLS in place of the U3 does not change any of the results in this
paper.
12
context during each administration of the Work Trends Poll that I use, my unit of analysis in Fig.
2 is the state. Therefore, each boxplot is generated using 51 observations, one for each state and
the District of Columbia.11
[Fig. 2 here]
The important takeaway from the information presented in Fig. 2 is that we observe
considerable variation in economic context both across states at a given point in time and over
time as the economy experiences growth and recession. A central contribution of this project is
to incorporate this variation into the study of how unemployment affects political behavior. Both
the information presented in Fig. 2 (and the right half of Table 1) make it abundantly clear that
some states, such as North Dakota and South Dakota, can enjoy relatively low levels of
unemployment while other states experience a devastating shortage of jobs, like we observe in
Nevada in 2010. This additional information would be obscured and unused if we look only at
the national unemployment rate. Before I explain the statistical model I will use to test if this
variation in economic context across both time and space affects the relationship between
employment status and attitudes about who should help people who lose their jobs, I first need to
discuss how I incorporate party identification into this framework.
Party Identification
The Work Trends Polls do not provide ideal measures of party identification. In five of
the six polls, respondents are asked if they identify as Democrats, Republicans, or Independents.
Unfortunately, the polls do not measure the strength of these partisan attachments. Even more
unfortunately, respondents who identify as Independents or express no partisan attachment are
not asked whether they lean closer to one of the two major political parties following the first
11
Since Work Trends Polls use nationally-representative samples, the distribution of economic context at the
individual level in the sample I use for my analysis is roughly the same but slightly more cluttered, so I choose to
present the graph that is easier for the reader to comprehend.
13
question about partisanship. For these polls, I simply code whether respondents identify as
Democrats or Republicans and leave Independents and those without a party preference as an
excluded category.
Most problematically, the July 2010 Work Trends Poll does not contain a measure of
party identification. This means that I cannot simply include it in any analysis in which I would
like to control for the effects of partisanship on citizens’ beliefs about who should help the
unemployed or would just have to ignore the effects of partisanship. Since Americans’ political
views are heavily influenced by their partisan identifications (Campbell, Converse, Miller, and
Stokes 1960), I believe that this would introduce damaging omitted variable bias into my
analyses. To avoid ignoring the July 2010 poll or ignoring the effects of partisanship, I employ
two solutions.
First, I use multiple imputation to estimate partisan identification for July 2010
respondents using the measured partisanship of respondents in other Work Trends Polls, and
demographic measures that bridge across surveys. Because the July 2010 poll neglects to ask
about party identification, I can treat party identification as missing at random, as respondents
did not choose to skip or refuse to answer the question. I perform multiple imputation twenty
times, which allows me to incorporate a great deal of uncertainty into this measure of party
identification. In this way, it is a very conservative solution to my missing data problem.
In order to validate my use of imputation and to avoid relying entirely upon simulated
data in my analysis, I employ an additional solution to this missing data problem. I proxy for
party identification by using a question on the June 2010 poll that I believe reflects respondents’
partisan loyalties without directly asking about partisanship. The poll asks “Who do you trust to
do a better job handling the economy?” Answer choices are “President Obama,” “Republicans in
14
Congress,” and a third category I created by combining the “both” and “neither” responses.
Respondents were coded as Democrats if they chose President Obama, Republicans if they chose
Republicans in Congress, and Independents if they chose both or neither. While this is not a
perfect measure of partisanship, it is very plausible to assert that this question will be heavily
influenced by partisan attitudes.12 I report the cross-tabulations of this proxy measure by
employment status in Table 2.
[Table 2 here.]
Two important things jump out from the data presented in Table 2. First, the chi-squared
statistic is highly significant, indicating that employed and unemployed respondents significantly
differ in who they trust to handle the economy. Unemployed respondents are considerably more
likely than employed respondents to trust Pres. Obama and only one in eight unemployed
respondents trust Republicans in Congress more to handle the economy. Second, more than half
of all respondents, both employed and unemployed, answer both or neither to this economic trust
question. This almost certainly overstates the true number of Independents in the sample.
Individuals who offer one of the partisan responses are more likely to be extreme partisans than
weak partisans, which means that this measure of party identification is probably overdetermined
by the strength of a respondents’ true partisan attachments. This is not very problematic, since I
also have an imputed measure of partisanship. Taken together, the “true” partisanship of the
sample is probably somewhere in between the imputed and proxy measures. Still, I prefer to use
a pair of imperfect measures of partisanship in order to avoid having to either disregard
observations in my sample or disregard the effects of party on attitudes about who should help
the unemployed.
12
I validated this measure as part of a survey conducted in June 2013 for another project by asking the proxy
question alongside the standard party identification questions. In that sample, the proxy measure associates strongly
with partisan self-identification. The details of this validation are contained in Appendix C to this paper.
15
Cross-sectional Model
Because my outcome variable of interest -- a respondent’s belief about who is responsible
for helping people who are laid off from work -- is categorical, I estimate a multinomial logit
model.13 This is the most appropriate model for evaluating the predictions of Hardship-inContext, because it will allow me to estimate the probability that a respondent will offer each of
the three responses and then see if these estimated probabilities vary as a function of
employment status and local unemployment context.14 I estimate these probabilities as functions
of several factors: The first is employment status, measured using an indicator variable for if the
respondent is unemployed. Since Work Trends Polls only include individuals in the labor force
in their samples, the excluded category for this variable is composed entirely of people who are
employed. Therefore, the coefficient attached to this indicator variable is the effect of being
unemployed on the probability that a respondent chooses one option over the baseline option.
Second, I include economic context, measured using the unemployment rate (U3) in a
respondent’s state at the time of his or her interview.
Third, I include the interaction between personal experience with unemployment and
unemployment context. This allows for separate effects of unemployment context for employed
and unemployed respondents. Hardship-in-Context implies that the coefficient attached to the
interaction between unemployed status and the local unemployment rate, should be positive for
the probability that respondents choose the categorical outcome that government should help
jobless Americans. Fourth, I include several demographic controls (age, race, gender, marital
13
The choices of the government, employers, and workers themselves represent three discrete options that do not
logically fall along an ordered scale.
14
Recall that I exclude respondents who volunteer responses that a combination or none of the three choices should
help the unemployed from this analysis.
16
status, education, and income). Fifth, I include one of two measures of party identification.15
The first is imputed using demographic characteristics that bridge across Work Trends Polls, and
the second is the proxy measure that I outlined above. Finally, I include fixed effects for each
individual poll and for each individual state. The poll effects help to account for unmeasured
factors unique to the period of time in which each Work Trends Poll conducted its interviews,
such as national economic conditions. The state fixed effects help to control for state-level
factors that might affect responses, such as statewide ideology or the administration of
unemployment benefits by state governments. I present the results of this analysis in Table 3 in
the next section.
Cross-sectional Results
The results to the multinomial logit analysis are presented in Table 3. I estimate the
model once using my imputed measure of party identification and a second time using my proxy
measure for party identification. For both estimations, the first column of coefficients represents
the change in the odds of the dependent variable in favor of the choice of “government” over the
baseline choice of “workers themselves” associated with a one-unit change in each explanatory
variable. The second column represents the change in favor of “employers” over “workers
themselves” and the third column represents the change in favor of “government” over
“employers.”16
[Table 3 here]
In the first run of the model (with party identification imputed), only two of my key
explanatory variables attain an accepted level of statistical significance. The first is the effect of
15
Recall that I use party identification as measured for every poll except for the July 2010 poll, which is the only
poll that did not include a question about party identification.
16
To consider one of these choices in reverse, like the change in favor of “employers” over “government,” for
example, simply multiply the coefficients by -1.
17
being unemployed on the probability of choosing “employers” over “workers,” which is negative
and significant at the 90% level. The other is the coefficient on the interaction between personal
experience with unemployment and state economic context. This coefficient is positive and
significant at the 95% level. Before addressing the substantive significance of these results, I will
first look to see if they are robust to switching to my alternative measure of party identification,
the proxy measure.
The right-hand side of Table 3 presents the estimates for an identical model but using the
proxy measure of partisanship. Importantly, the coefficients in this model are fairly similar to the
coefficients for the model that uses multiply-imputed party identification. It is important to note
that the coefficients attached to party identification in this model are on average larger and
estimated with less uncertainty than the coefficients in the imputed model. This is likely due to
attenuation bias in the first run of the model owing to the presence of measurement error in
multiple imputations of party affiliation, and possibly due to the fact that the proxy measure of
party identification picks up on more intense partisan attitudes. In sum, these two measures of
partisanship seem to be free of major issues, and switching from one to the other does not
drastically change our estimates.
That said, we do see more of the coefficients attached to our variables of interest attain
statistical significance in the model that uses the proxy measure of party identification for July
2010 respondents. The interaction of personal unemployment with the state unemployment rate
is positive and significant at the 90% level for the choice of “government” over a baseline of
“workers themselves.” As in the previous run of the model, we see the coefficients for
unemployed status and unemployment context are negative and significant and positive and
significant, respectively, for the choice of “employers” over a baseline category of “workers
18
themselves.” Finally, we see that personal experience with unemployment has a positive and
weakly significant effect for the choice of “government” over a baseline of “workers
themselves.” These effects persist in the presence of demographic controls, controls for
partisanship, and state and poll fixed effects.
Multinomial logit coefficients are fairly ungainly, so I present a few graphs to better
explain the results contained in Table 3. Figure 3 displays the predicted probability that a
respondent will say that government should be responsible for helping people who are laid off
from work. I plot this separately for employed and unemployed respondents across the observed
values of local economic context, measured as the seasonally-adjusted unemployment rate in a
respondent’s state at the time of his or her interview.17
[Fig. 3 here]
While there is considerable overlap in the confidence intervals surrounding the two lines,
it is still possible to see that the predicted probability of choosing “government” when asked who
should help people who are laid off from work increases with the unemployment rate among
unemployed respondents and slightly decreases with the unemployment rate among gainfully
employed respondents.18 Thus, we have evidence that suggests that employed and unemployed
citizens react to economic context in different ways. While I do not graph these quantities, I
compute the marginal effects of unemployment on the probability of choosing government and
find that it has a significant and positive effects ranging from about .051 to .066 between
unemployment rates of 5 and 10 percent. When and where the unemployment rate is high,
unemployed respondents are more likely than employed respondents are to believe that
government should step in and help the jobless.
17
All other variables are held at their means.
I use the multinomial logit model with the proxy measure of party identification to generate the figures in this
section.
18
19
I plot the predicted probability that employed and unemployed respondents choose
“employers” as a function of unemployment context in Fig. 4.
[Fig. 4 here]
In this instance, we see strong support for Hardship-in-Context. Employed respondents are
virtually nonresponsive to the unemployment rate, barely more likely to respond that employers
should help the unemployed in high-unemployment contexts than they are in low-unemployment
contexts. On the other hand, we see strong contextual effects among the unemployed. In lowunemployment contexts, unemployed respondents are less likely than employed respondents to
offer “employers” as a response, with a predicted probability below 0.2. This relationship is
reversed in high-unemployment contexts, in which unemployed respondents are more likely than
employed respondents to believe that employers have a responsibility to help people who are laid
off. This is entirely due to an increase in the predicted probability of choosing employers among
the unemployed, which approaches 0.4 in high-unemployment contexts. Additionally, the
marginal effect of unemployment on the predicted probability of saying employers are
responsible to help the unemployed (not graphed) is negative and significant in lowunemployment contexts and positive and significant in high-unemployment contexts.
I plot the predicted probability of saying that laid off workers are responsible for helping
themselves as a function of employment status and unemployment context in Fig. 5.
[Fig. 5 here]
The pattern of results visible in Fig. 5 is a reversal of what we observe in Fig. 4. In this instance,
unemployed respondents are slightly more likely than employed respondents to say that workers
themselves are responsible for helping themselves in low-unemployment contexts but are
significantly less likely to offer the same response in high-unemployment contexts. When and
20
where the unemployment rate is high, employed respondents are more likely than jobless
respondents to believe that unemployed Americans need to provide their own assistance. Again,
we observe no sensitivity to economic context among employed Americans, but we do see that
unemployed Americans’ responses are meaningfully influenced by economic context.
Figs. 3-5 present results that support Hardship-in-Context. The experience of
unemployment is individualized and self-located in low-unemployment contexts, approximately
at the level of full employment. As outlined and hypothesized by Hardship-in-Context,
individuals who lose their jobs under moderate to high levels of unemployment perceive their
hardship as deindividualized and politicized and look to government to provide them with some
assistance. While about half of unemployed respondents consider endorse an individual-based
remedy to unemployment in low-unemployment contexts, this proportion falls to below one-fifth
under the blisteringly high unemployment states like Nevada experienced in the aftermath of the
2007 economic recession.
Longitudinal Evidence
Having found support for Hardship-in-Context using cross-sectional data, I turn to the
Work Trends Panel survey, a 4-wave longitudinal study of individuals who reported losing their
jobs between August 2008 and August 2009. This means that everyone in the sample
experienced unemployment during the Great Recession. This is important because it reduces the
possibility that any effects that we observe are due to selection into who is employed and who is
unemployed at a given point in time. The entire sample for this panel is drawn from a common
pool of individuals who have experienced unemployment during the same period of nationwide
economic contraction.
21
Some of the respondents remained unemployed while other respondents found new jobs
or left the labor force by the start of wave 1 in August 2009. As before, I consider only those
respondents who are either employed or unemployed and looking for work, so I exclude
individuals who are not in the labor force from my analysis.19 I summarize the number of
employed and unemployed respondents across all 4 waves in Table 4.
[Table 4 here]
A high proportion of the sample, nearly seven in ten respondents, is unemployed at wave 1,
administered in August 2009. This proportion declines to roughly one in three respondents by
wave 4, administered two years later. Of course, some of this is due to panel attrition, as nearly
half of the sample has left either the survey or the labor force by wave 4. Table B1 in the
appendix to this paper reports the transition probabilities from one employment state to another
across the four survey waves.
Once again, the dependent variable is measured using the question “When people are laid
off from work, who should be mainly responsible for helping them?” As before, the choices are
“government,” “employers,” or “workers themselves.” I report the percentage of the sample that
gave each response in Table 4, and graph these percentages by employment status in Fig. 6. Just
as in Fig. 1, I also report chi-squared statistics.
[Fig. 6 here]
Most interestingly, the proportion of unemployed respondents who believe that
employers should be responsible for helping people who are laid off increases over time, nearly
approaching the proportion of jobless respondents who believe that government should be
responsible for helping by wave 4. While the pattern of responses for employed respondents
19
I only exclude them while they are not in the labor force. If respondents reenter the labor force, I include them in
the analysis for the survey waves during which they are in the labor force.
22
stays fairly similar across survey waves, we observe some shifting among the unemployed. Table
B2 in the appendix reports transition properties on this dependent variable measure across the 4
survey waves.
Longitudinal Model
Omitted variable bias is one potential concern that might arise from the cross-sectional
analysis presented in the previous section. It is possible that factors that we do not observe, such
as the circumstances under which an individual loses his or her job, or his or her attachment to
that job before losing it, might affect the extent to which an unemployed American views his or
her job loss as a personal or a social problem. Fortunately, I can utilize the longitudinal design of
the Work Trends Panel to reduce potential concerns about omitted variable bias. I do this by
employing an individual fixed-effects model, which accounts for factors that are unique to
respondents in my sample that do not change over time. Therefore, I am able to estimate beliefs
about who should be responsible for helping the unemployed as a function of measures that
change over time, namely employment status and unemployment context while controlling for
any individual-level time-invariant characteristics, observed or unobserved.
Unfortunately, this means I must abandon using a multinomial logit model, as
implementing such a model with individual fixed-effects is currently not possible but is under
development (Pforr 2011). I recode my dependent variable of interest into three binary variables
that capture whether a respondent chose “government,” “employers,” “or workers themselves.”20
I run three separate fixed-effects regressions for each binary choice. Although my dependent
variable is binary, I use OLS regression as it does not require me to discard observations that do
not change across the 4 survey waves, which is necessary in a fixed-effects or conditional logit
For example, I code respondents who say “government” as 1 for my “government” variable and code respondents
who say “employers” or “workers themselves” as 0. I repeat this for separate variables for “employers” and for
“workers themselves.”
20
23
model. My estimates are substantively unchanged if I switch to a fixed-effects logit model, so I
choose to present the results of an OLS regression with individual fixed effects. I also include an
indicator variable for each survey wave, which accounts for any wave-specific variation such as
national economic conditions or political events such as electoral campaigns. The results for this
fixed-effects model are presented in Table 5.
Longitudinal results
[Table 5 here.]
In the fixed-effects model, which accounts for all individual-level variation that remains
constant over time, we see support for Hardship-in-Context in that the belief that workers should
be responsible for helping themselves decreases among the unemployed as we move from a
relatively low-unemployment context to a high-unemployment context. While we do not see any
significant results for the government or employers outcomes, this negative result is still
noteworthy, because I have confirmed at least one of my findings from the cross-sectional
analysis using a very strong test in the form of a fixed effects model on a unique survey sample
that all experienced job loss during the same period of economic turmoil. In sum, we see
evidence that Americans who experience the personal hardship of unemployment are more likely
to believe that they should help themselves in low-unemployment contexts and less likely to
believe that they should help themselves in high-unemployment contexts. In high-unemployment
contexts, the jobless believe that government and employers should step in to help the
unemployed. Using both cross-sectional and longitudinal data, I find evidence that unemployed
Americans shift their foci of expectations away from themselves and towards government as a
function of the context in which they experience job loss. In the next section, I discuss these
findings in more detail.
24
Discussion and Conclusion
In this paper, I provide a substantial amount of evidence that the effects of unemployment
on attitudes about who should help the unemployed vary as a function of the severity of
unemployment, measured locally at the state level. One potential counterargument to this paper
and Hardship-in-Context more broadly is the claim that individuals who experience job loss in
low-unemployment contexts are systematically different and therefore incomparable to
individuals who lose their jobs during severe economic recessions. I do as much as I can to
control for demographics and still observe significant differences between the unemployed and
employed as a function of local unemployment rates. While I cannot fully rule it out in my crosssectional sample, I do not observe clear evidence that cohort selection into who is unemployed is
driving the pattern of results we observe.21 Most importantly, my use of panel data and
implementation of a fixed effects model allow me to identify changes in attitudes as a function of
changes in employment status, unemployment context, and the interaction between the two while
controlling for any time-invariant characteristics that are unique to the individual respondents.
This includes demographics and any personal characteristics, psychological orientations, beliefs,
or habits, insofar as they do not change over time. The sample design of the Work Trends Panel,
composed entirely of individuals who recently lost their jobs, and the estimation techniques I
employ help to minimize potentially confounding selection effects.
21
I also do not think we observe this pattern of results because of demographic imbalances in who is unemployed in
low versus high-unemployment contexts. I report demographic characteristics across surveys in Appendix A to this
paper and while they do vary at times, this variation is not remarkable and not likely to be producing the pattern of
results we observe. I also include a graph of unemployment rates by educational attainment in the appendix to this
paper in order to make the point that the composition of the unemployed stays roughly the same with respect to
education over time.
25
Implications for additional research
The main finding of this paper, that unemployment is viewed as a politicized problem
and not as an individualized problem in high-unemployment contexts, provides an important link
to other research in how personal experience with unemployment affects political attitudes and
behaviors as a function of unemployment context. This finding joins a growing literature on the
effects of unemployment on political attitudes. Paolino (2010) finds that long-term
unemployment undermines support for individualist values in Americans. Additionally, new
research finds that the economic “shock” of job loss increases support for unspecified social
welfare spending (Margalit 2013). This effect is significantly larger among Republicans than it is
among Democrats. My paper is similar to these recent studies in that it makes use of a
longitudinal survey of the unemployed in order to reach conclusions about how job loss
meaningfully affects public attitudes. However, my paper is novel in that it provides a new
theory to explain the divergent effects of job loss on political attitudes, incorporates contextual
factors into our understanding of how personal experiences shape political beliefs, and examines
who Americans believe is responsible for helping the unemployed, not simply how much help
Americans believe the unemployed should receive. It is also an example of how we can better
understand how economic hardship influences political behavior through purposeful sampling
and repeated surveys of understudied groups of Americans.
In a separate paper (Incantalupo 2012a), I find that unemployment affects voter turnout
as a function of broader economic conditions. Job loss and unemployment are a demobilizing
influence on voter turnout in low-unemployment contexts and a mobilizing influence on turnout
in high-unemployment contexts. This finding seems especially plausible in light of what we now
know about individualized and politicized responses to job loss as a function of local
26
unemployment context. Taken together, these findings have important implications for research
in political participation and economic voting. Despite a body of research that emphasizes
“sociotropic” factors in structuring political behavior to a greater extent than “pocketbook”
factors (Kinder and Kiewiet 1982; Mutz 1992), I find that individual and contextual factors can
interact to affect public attitudes.
In another paper (Incantalupo 2012b), I examine whether local unemployment context
affects blame and attribution following job loss. I find that unemployed Americans are less warm
towards immigrants (documented and undocumented), more likely to blame immigrants for their
own job loss, and more likely to accuse them of taking jobs away from native-born Americans in
high-unemployment contexts than they are when the economy is doing relatively well. This is
another promising application of Hardship-in-Context and has implications for research into
attitudes towards immigrants (Hainmueller and Hiscox 2010), as well as theories of group
conflict and attribution theory in social psychology. In summary, Hardship-in-Context is a rich
theory of how Americans perceive and react to the very serious personal hardship of job loss and
provides a framework by which we can understand other hardships.
Finally, the United States remains in a prolonged period of high unemployment.
Understanding how unemployment (and other economic hardships that can become politicized in
context, such as foreclosures) affects political behavior sheds additional light on a phenomenon
that affects millions of citizens and could contribute to better policies for providing assistance or
relief to out-of-work Americans. For example, state unemployment offices could tailor the kinds
of unemployment benefits they give based on the broader state of the economy, which affects
how unemployed Americans view their circumstances. States, aware that job loss in lowunemployment contexts is typically viewed as an unemployed person’s problem to fix on his or
27
her own, can divert resources to job training programs and other policies to help people who
believe they are mainly responsible for helping themselves. During periods of high
unemployment, governments could provide more direct relief in the forms of increased
unemployment benefits or create government jobs as a solution to high unemployment that
constituents perceive as a socially-centered problem that government should address. Elected
officials who ignore the very probable reality that millions of unemployed Americans view their
personal hardships as part of a much broader politicized problem that government is mainly
responsible to remedy do so at their own risk, particularly since job loss in high-unemployment
contexts has a mobilizing effect on an individual’s probability of turning out to vote (Incantalupo
2012a).
In this paper, I set out to examine whether citizens politicize the personal hardship of
unemployment. The answer, of course, is that it depends. Americans perceive unemployment as
a self-located problem or as a socially-located problem depending on the context in which it is
experienced. The analyses I have presented in this paper demonstrate that there exists a strong
relationship among personal experience, social and economic context, and political beliefs that
has heretofore not been discussed and therefore constitutes a valuable contribution to our
understanding of the oftentimes complex linkages between the personal, the contextual, and the
political.
28
References
Achen, Christopher H. and Larry M. Bartels. 2002. “Blind Retrospection: Electoral Responses to
Droughts, Flu, and Shark Attacks." Paper presented at the Annual Meeting of the
American Political Science Association, Boston, MA.
Bartels, Larry M. 2008. Unequal Democracy: The Political Economy of the New Gilded Age.
Princeton: Princeton University Press.
Brody, Richard A. and Paul M. Sniderman. 1977. "From Life Space to Polling Place." British
Journal of Political Science 7: 337-360.
Campbell, Angus, Philip E. Converse, Waren E. Miller, and Donald E. Stokes. 1960. The
American Voter. New York: John Wiley & Sons, Inc.
Cutler, Fred. 2007. “Context and Attitude Formation: Social Interaction, Default Information, or
Local Interests?” Political Geography 26: 575-600.
Delli Carpini, Michael X., and Scott Keeter. 1996. What Americans Know About Politics and
Why It Matters. New Haven: Yale University Press.
Feather, Norman T. 1989. The Psychological Impact of Unemployment. New York: SpringerVerlag.
Free, Lloyd A., and Hadley Cantril. 1968. The Political Beliefs of Americans. New York:
Simon & Schuster.
Gilens, Martin. 1999. Why Americans Hate Welfare: Race, Media, and the Politics of AntiPoverty Policy. Chicago: University of Chicago Press.
Gordus, Jeanne Prial, Paul Jarley, and Louis A. Ferman. 1981. Plant Closings and Economic
Dislocation. Kalamazoo: W.E. Upjohn Institute for Employment Research.
Hainmueller, Jens, and Michael J. Hiscox. 2010. “Attitudes toward Highly Skilled and Low
Skilled Immigration: Evidence from a Survey Experiment.” American Political Science
Review 104: 61-84.
Healy, Andrew J. 2009. “Individual Unemployment, Layoffs, and Voting in U.S. Presidential
Elections.” Working Paper. Available from:
http://130.203.133.150/viewdoc/summary?doi=10.1.1.144.7276
29
Healy, Andrew J., Neil Malhotra, and Cecilia Hyunjung Mo. 2010. “Irrelevant Events Affect
Voters' Evaluations of Government Performance." Proceedings of the National Academy
of Sciences 107: 12804-12809.
Huber, Joan., and William H. Form. 1973. Income and Ideology: An Analysis of the American
Political Formula. New York: Free Press.
Incantalupo, Matthew B. 2012a. “The Effects of Unemployment on Voter Turnout in U.S.
National Elections.” Working paper. Princeton University.
Incantalupo, Matthew B. 2012b. “Blame and Attribution Following Job Loss” Working Paper.
Princeton University.
Kinder, Donald R. and D. Roderick Kiewiet. 1981. “Sociotropic Politics: The American Case.”
British Journal of Political Science 11:129-161.
Kluegal, James R. and Elliot R. Smith. 1986. Beliefs about Inequality: Americans’ Views of
What Is and What Ought to Be. New York: Aldine de Gruyter.
Lodge, Milton and Ruth Hamill. 1986. “A Partisan Schema for Political Information
Processing.” American Journal of Political Science 80: 505-520.
Margalit, Yotam. 2011. “Costly Jobs: Trade-related Layoffs, Government Compensation, and
Voting in U.S. Presidential Elections.” American Political Science Review 105: 166-188.
Margalit, Yotam. 2013. “Explaining Social Policy Preferences: Evidence from the Great
Recession.” American Political Science Review 107: 80-103. Forthcoming.
McBride, Bill. 2012. “Employment Graphs: Construction Employment, Unemployment by
Education.” Calculated Risk. Updated 10 September.
http://www.calculatedriskblog.com/2012/09/employment-graphs-construction.html (Sept. 10,
2012).
Mills, C. Wright. 1959. The Sociological Imagination. New York: Oxford University Press.
Mutz, Diana C. 1992. “Mass Media and the Depoliticization of Personal Experience.” American
Journal of Political Science 36: 483-508.
Paolono, Philip. 2011. “Long-Term Unemployment and Individualist Values.” Presented to the
2011 Annual Meeting of the Midwest Political Science Association. Chicago, IL. March
31-April 3.
30
Pew Research Center. 2012. “The Lost Decade of the Middle Class.” 22 August. Available from:
http://www.pewsocialtrends.org/2012/08/22/the-lost-decade-of-the-middle-class/ (1
September 2012).
Pforr, Klaus. 2011. “Implementation of a Multinomial Logit Model with Fixed Effects”
Presented at the Ninth German Stata Users Group Meeting. Bamberg. Germany.
Robinson D. 2002. “Cancer Clusters: Findings vs. Feelings.” MedGenMed 4: 16.
Rosenstone, Steven J. 1982. “Economic Adversity and Voter Turnout.” American Journal of
Political Science 26: 25-46.
Schlozman, Kay Lehman, and Sidney Verba. 1979. Injury to Insult: Unemployment, Class, and
Political Response. Cambridge: Harvard University Press.
Sniderman, Paul M. and Richard A. Brody. 1977. "Coping: The Ethic of Self-reliance."
American Journal of Political Science 21: 501-521.
Thun, Michael. J. and Thomas Sinks. 2004. “Understanding Cancer Clusters.” CA: A Cancer
Journal for Clinicians 54: 273–280.
Trumbo, Craig W. 2000. “Public Requests for Cancer Cluster Investigations: A Survey of State
Health Departments.” American Journal of Public Health 90: 1300-1302.
Warr, Peter. 1987. Work, Unemployment, and Mental Health. New York: Oxford University
Press.
31
Tables and Figures
Poll
Jun. 2003
N (employed)
758
N (unemployed)
86
National U3
6.3%
Low U3
High U3
3.6%
8.6%
(ND, SD)
(OR)
Feb. 2004
678
66
5.6%
3.4%
7.6%
(ND)
(AK, OR)
May 2008
389
50
5.4%
2.9%
7.7%
(SD, WY) (MI)
May 2009
446
72
9.4%
4.3%
13.5%
(ND)
(MI)
Jul. 2010
322
259
9.5%
3.9%
14.9%
(ND)
(NV)
Jul. 2011
183
144
9.1%
3.5%
13.5%
(NE)
(NV)
Table 1: Summary of cross-sectional Work Trends Polls (2003-2011). “U3” refers to the
seasonally-adjusted unemployment rate as measured by the Bureau of Labor Statistics.
32
Employed
Unemployed Total
Democratic Proxy
21.5%
36.7%
28.3%
(President Obama)
Republican Proxy
21.8%
12.35%
54.1%
(Republicans in Congress)
Independent Proxy
56.7%
51.0%
17.6%
(Both/Neither)
χ2 (2) = 19.84; p < .01
Table 2: Proxy measure of Party Identification (July 2010 Work Trends Poll) by employment
status. Survey question is “Who do you trust to do a better job handling the economy?”
Percentages are given by column and may not add up to 100% due to rounding.
33
Choice
Base Category
Unemployed
Unemployment Rate (U3)
Unemployed*U3
Female
Married
Black
Latino
Other
Age
Income
Income (Ref.)
Education
Democrat (imputed)
Republican (imputed)
Government
Workers
-.106
(.432)
.001
(.061)
.064
(.049)
.205***
(.094)
-.086
(.112)
.535***
(.170)
.384**
(.172)
.136
(.196)
-.011***
(.004)
-.242***
(.077)
-.568
(.246)
-.190***
(.055)
.549***
(.130)
-.380***
(.142)
Employers
Workers
-.802*
(.473)
.023
(.064)
.117**
(.054)
.482***
(.096)
-.190*
(.109)
.377**
(.175)
.196
(.189)
.192
(.197)
-.004
(.004)
-.191**
(.077)
-.792***
(.273)
-.063
(.056)
.406***
(.133)
-.263*
(.142)
Government
Employers
.697
(.471)
-.023
(.069)
-.054
(.053)
-.275***
(.105)
.104
(.119)
.159
(.165)
.188
(.186)
-.057
(.213)
-.007
(.004)
-.052
(.084)
.224
(.298)
-.126**
(.059)
.143
(.144)
-.118
(.167)
Democrat (proxy)
Republican (proxy)
Cons.
Other vars. (not shown)
N
-.463
(.701)
-2.01***
(.670)
State and Poll
3183
1.54**
(.785)
Government
Workers
-.359
(.435)
-.005
(.061)
.086*
(.050)
.204**
(.096)
-.075
(.113)
.479***
(.174)
.363**
(.175)
.103
(.206)
-.012***
(.004)
-.231***
(.077)
-.636**
(.260)
-.200
(.056)
Employers
Workers
-1.12**
(.481)
.022
(.065)
.148***
(.055)
.494***
(.097)
-.185*
(.111)
.406**
(.176)
.171
(.088)
.210
(.200)
-.003
(.004)
-.179**
(.076)
-.681**
(.278)
-.059
(.056)
Government
Employers
.764*
(.486)
-.028
(.071)
-.062
(.054)
-.290***
(.106)
.110
(.121)
.073
(.166)
.192
(.190)
-.107
(.221)
-.008**
(.004)
-.052
(.085)
.045
(.310)
-.140
(.059)
.727***
(.116)
-.470***
(.131)
-.975
(.817)
.380***
(.119)
-.429***
(.126)
-2.05***
(.671)
State and Poll
3091
.347***
(.124)
-.042
(.150)
1.07
(.880)
Table 3: Results for combined Work Trends Polls with partisan identification (2003-2011). Cell
entries are multinomial logit coefficients with robust standard errors in parentheses.
*p < .10; *p < .05; ***p < .01
34
Wave (Date)
Employment Status
1 (Aug. 2009)
2 (Mar. 2010)
3 (Nov. 2010)
4 (Aug. 2011)
Employed
Unemployed
Responsible for helping the
unemployed
31.0%
69.0%
47.1%
52.83
58.2%
41.81%
64.60%
35.40%
Government
41.1%
43.2%
40.6%
36.6%
Employers
21.0%
23.1%
32.5%
28.1%
Workers Themselves
37.9%
33.7%
27.0%
35.3%
1111
808
636
544
N
9.6%
9.8%
9.8%
9.1%
National U3
Table 4: Summary of Work Trends Panel (2009-2011). “U3” refers to the seasonally-adjusted
unemployment rate as measured by the Bureau of Labor Statistics.
35
Government Employers Workers Themselves
-.034
-.161
.195**
(.097)
(.113)
(.098)
-.007
.020
-.013
Unemployment Rate (U3)
(.017)
(.017)
(.016)
.004
.018
-.021**
Unemployed*U3
(.010)
(.011)
(.010)
.462***
.007
.531***
Cons.
(.173)
(.181)
(.164)
Wave
Wave
Wave
Other vars. (not shown)
1163
1163
1163
N
3099
3099
3099
N*T
2.7
2.7
2.7
T (avg.)
Table 5: Longitudinal results for Work Trends Panel. Cell entries are fixed-effects OLS
coefficients with robust standard errors in parentheses. The dependent variable is noted at the top
of each column and if a binary variable created from “When people are laid off from work, who
should be mainly responsible for helping them?”
*p < .10; **p < .05; ***p < .01
Y=1
Unemployed
36
Jun. 2003
Feb. 2004
2
(2)
=4.65*
May 2008
2
(2)
=2.15
=7.98**
0
20
40
60
2
(2)
Gov.
Emp.
Self
Gov.
Emp.
Self
Gov.
Emp.
Self
May 2009
Emp.
Self
Gov.
Emp.
Self
2
(2)
=9.76***
Gov.
Emp.
Self
Emp.
Self
Jul. 2011
2
(2)
=34.15***
=4.01
0
20
40
60
2
(2)
Gov.
Jul. 2010
Gov.
Emp.
Self
Employed
Gov.
Emp.
Self
Unemployed
Gov.
Emp.
Self
Employed
Gov.
Emp.
Self
Unemployed
Gov.
Emp.
Self
Employed
Gov.
Unemployed
Fig. 1: “When people are laid off from work, who should be mainly responsible for helping
them?” Graphs by Work Trends Poll and employment status. “Gov.” refers to “government,”
“Emp.” refers to “employers,” and “Self” refers to “workers themselves.” Combined responses
(volunteered) are not included.
*p < .10; *p < .05; ***p < .01
37
15
10
5
0
Jun. 2003
Feb. 2004
May 2008
May 2009
Jul. 2010
Jul. 2011
Fig. 2: The distribution of the seasonally-adjusted unemployment rate (U3) by state (plus the
District of Columbia) for each administration of the Work Trends Poll I use in my crosssectional analysis.
38
.5
.4
.3
.2
.1
3
4
5
6
7
8
9
10
State Unemployment Rate
Employed
11
12
13
14
15
Unemployed
Fig. 3: Predicted probability of answering “government” to “When people are laid off from
work, who should be mainly responsible for helping them?” by employment status and
unemployment context. The coefficients used to generate this graph are presented in in Table 3
(right-hand side).
39
.6
.5
.4
.3
.2
.1
3
4
5
6
7
8
9
10
State Unemployment Rate
Employed
11
12
13
14
15
Unemployed
Fig. 4: Predicted probability of answering “employers” to “When people are laid off from work,
who should be mainly responsible for helping them?” by employment status and unemployment
context. The coefficients used to generate this graph are presented in in Table 3 (right-hand side).
40
.8
.6
.4
.2
0
3
4
5
6
7
8
9
10
State Unemployment Rate
Employed
11
12
13
14
15
Unemployed
Fig. 5: Predicted probability of answering “workers themselves” to “When people are laid off
from work, who should be mainly responsible for helping them?” by employment status and
unemployment context. The coefficients used to generate this graph are presented in in Table 3
(right-hand side).
41
Wave 1 (Aug. 2009)
2
Wave 2 (Mar. 2010)
2
= 5.87*
(2)
= 2.76
0
10
20
30
40
50
(2)
Gov.
Emp.
Self
Gov.
Emp.
Self
Gov.
Emp.
Wave 3 (Nov. 2010)
2
Gov.
Emp.
Self
Emp.
Self
Wave 4 (Aug. 2011)
2
= 6.81**
(2)
= 12.29***
0
10
20
30
40
50
(2)
Self
Gov.
Emp.
Employed
Self
Gov.
Emp.
Unemployed
Self
Gov.
Emp.
Employed
Self
Gov.
Unemployed
Fig. 6: When people are laid off from work, who should be mainly responsible for helping
them?” Graphs by Work Trends Panel wave and employment status. “Gov.” refers to
“government,” “Emp.” refers to “employers,” and “Self” refers to “workers themselves.”
*p < .10; *p < .05; ***p < .01
42
Appendix A: Supplementary information (Cross-sectional)
Question Wording
Poll
N emp. N unemp.
Aug. 1998
778
52
When workers get laid off from their jobs through no fault of their own, who do you
think should be primarily responsible for providing financial support for them until they
find another job? (No fault wording)
Aug. 1999
798
55
Oct. 2001
617
62
In times of economic downturn, many companies experience large layoffs. Who should
be primarily responsible for providing services to workers who have been laid off from
their job? (Economic downturn wording)
Jun. 2003
758
86
When people are laid off from work, who should be mainly responsible for helping
them? (Laid off wording)
Feb. 2004
678
66
May 2008
389
50
May 2009
446
72
Jul. 2010
322
259
Jul. 2011
183
144
Table A1: Work Trends Poll question wordings. The shaded cells (laid off wording) are used in the cross-sectional analysis in the
main paper. Earlier administrations of the Work Trends Poll used different question wordings. I plot the responses to these polls by
employment status in Fig. A1
43
Aug. 1998
2
Aug. 1999
2
= 4.77*
(2)
Oct. 2001
2
=1.60
(2)
=1.70
0
20
40
60
(2)
Gov.
Emp.
Self
Gov.
Emp.
Self
Gov.
Emp.
Self
Jun. 2003
2
Emp.
Self
Gov.
Emp.
Self
Feb. 2004
2
=4.65*
(2)
Gov.
Emp.
Self
Gov.
Emp.
Self
Gov.
Emp.
Self
May 2008
2
=2.15
(2)
=7.98**
0
20
40
60
(2)
Gov.
Gov.
Emp.
Self
Gov.
Emp.
Self
Gov.
Emp.
Self
May 2009
2
Emp.
Self
Gov.
Emp.
Self
Jul. 2010
2
=9.76***
(2)
Jul. 2011
2
=34.15***
(2)
=4.01
0
20
40
60
(2)
Gov.
Gov.
Emp.
Self
Employed
Gov.
Emp.
Self
Unemployed
No fault wording
Gov.
Emp.
Self
Employed
Gov.
Emp.
Self
Unemployed
Economic downturn wording
Gov.
Emp.
Self
Employed
Unemployed
Laid off wording
Fig. A1: Work Trends Polls with alternative question wordings. Notice that the modal response is employers in the “no fault wording” and government in the
“economic downturn wording.” Also the pattern of responses across employment groups is unremarkable. Because these question wordings prime particular
types of unemployment, I exclude them from my analysis and use only those with the “laid off” wording. While I cannot claim that these potential question
wording effects are fully robust since these data are from repeated cross-sections and not from a controlled, randomized survey experiment, the data suggest that
beliefs about who should help the unemployed might be affected by how job loss is framed.
44
Fig. A2: Unemployment Rate by Level of Education (McBride 2012). The educational composition of the unemployed stays roughly
the same over time with some minor perturbations. Periods of economic recession are indicated by a blue background.
45
Feb. 2004
May 2008
0
20
40
60
80
Jun. 2003
Male
Female
Male
Female
Male
Female
Female
Male
Female
Jul. 2010
Male
Female
Male
Female
Jul. 2011
0
20
40
60
80
May 2009
Male
Male
Female
Employed
Male
Female
Unemployed
Male
Female
Employed
Male
Female
Unemployed
46
Male
Female
Employed
Unemployed
Fig. A3: Gender by Employment Status and Work Trends Poll.
Feb. 2004
May 2008
0
20
40
60
Jun. 2003
Ref. Low Mid High
Ref. Low Mid High
Ref. Low Mid High
Jul. 2010
Ref. Low Mid High
Ref. Low Mid High
Jul. 2011
0
20
40
60
May 2009
Ref. Low Mid High
Ref. Low Mid High
Ref. Low Mid High
Ref. Low Mid High
Ref. Low Mid High
Ref. Low Mid High
Ref. Low Mid High
Fig A4: Income Group by Employment Status and Work Trends Poll. Individuals who refused to
indicate their household incomes are noted using the “Ref.” category. The “Low” income
category consists of indivduals with household incomes below $30,000 per year. The “Mid”
income category consists of indivduals with household incomes between $30,000 and $75,000
per year. The “High” income category consists of individuals with household incomes greater
than $75,000 per year.
47
Feb. 2004
May 2008
0
10
20
30
40
50
Jun. 2003
No HS
HS Some Col. Col.
No HS
HS Some Col. Col.
No HS
HS Some Col. Col.
No HS
HS Some Col. Col.
No HS
HS Some Col. Col.
Jul. 2010
No HS
HS Some Col. Col.
Jul. 2011
0
10
20
30
40
50
May 2009
No HS
HS Some Col. Col.
Employed
No HS
HS Some Col. Col.
Unemployed
No HS
HS Some Col. Col.
Employed
No HS
HS Some Col. Col.
Unemployed
No HS
HS Some Col. Col.
Employed
No HS
HS Some Col. Col.
Unemployed
Fig. A5: Educational Attainment by Employment Status and Work Trends Poll. The education
attainment categories noted in the graph are no high school diploma, high school diploma or an
equivalent certificate, some college (no degree), and a bachelor’s degree and any advanced
degrees.
48
Feb. 2004
May 2008
0
20
40
60
80
Jun. 2003
White Black Latino Other
White Black Latino Other
White Black Latino Other
White Black Latino Other
Jul. 2010
White Black Latino Other
Jul. 2011
0
20
40
60
80
May 2009
White Black Latino Other
White Black Latino Other
White Black Latino Other
White Black Latino Other
White Black Latino Other
White Black Latino Other
White Black Latino Other
Employed
Unemployed
Employed
Unemployed
Employed
Unemployed
Fig. A6: Race/Ethnicity by Employment Status and Work Trends Poll. The groups used in this
analysis are white, black, Latino, and an additional category for any other race/ethnic groups.
49
Feb. 2004
May 2008
0
20
40
60
Jun. 2003
Dem. Ind. Rep.
Dem. Ind. Rep.
Dem. Ind. Rep.
Dem. Ind. Rep.
Jul. 2010
Dem. Ind. Rep.
Jul. 2011
0
20
40
60
May 2009
Dem. Ind. Rep.
Dem. Ind. Rep.
Dem. Ind. Rep.
Dem. Ind. Rep.
Dem. Ind. Rep.
Dem. Ind. Rep.
Dem. Ind. Rep.
Employed
Unemployed
Employed
Unemployed
Employed
Unemployed
Fig. A7: Party Identification by Employment Status and Work Trends Poll. Note that the July
2010 Work Trends Poll did not ask party identification, so I use a proxy measure for this poll.
50
Appendix B: Supplementary Information (Longitudinal)
*p < .10; *p < .05; ***p < .01
Employed (Wave 2) Unemployed (Wave 2)
79.3%
20.5%
32.3%
67.7%
Employed (Wave 3) Unemployed (Wave 3)
86.8%
13.2%
Employed (Wave 2)
35.2%
64.8%
Unemployed (Wave 2)
Employed (Wave 4) Unemployed (Wave 4)
89.1%
11.0%
Employed (Wave 3)
26.0%
74.0%
Unemployed (Wave 3)
Table B1: Employment status transition probabilities across Work Trends Panel Waves.
Percentages are given by row. Respondents who leave the labor force or panel are not included
in this table.
Employed (Wave 1)
Unemployed (Wave 1)
51
Government (Wave 1)
Employers (Wave 1)
Workers Themselves (Wave 1)
Government (Wave 2)
72.2%
31.8%
19.7%
Employers (Wave 2)
17.1%
44.9%
17.4%
Workers Themselves (Wave 2)
10.7%
23.3%
62.9%
Government (Wave 2)
Employers (Wave 2)
Workers Themselves (Wave 2)
Government (Wave 3)
64.9%
27.4%
16.6%
Employers (Wave 3)
25.6%
58.1%
24.8%
Workers Themselves (Wave 3)
9.5%
14.5%
58.6%
Government (Wave 3)
Employers (Wave 3)
Workers Themselves (Wave 3)
Government (Wave 4)
66.3%
24.4%
11.7%
Employers (Wave 4)
18.5%
47.5%
13.3%
Workers Themselves (Wave 4)
15.2%
28.1%
75%
Table B2: Dependent variable transition probabilities across Work Trends Panel Waves.
Percentages are given by row. Respondents who leave the labor force or panel are not included
in this table.
52
Appendix C: Validation of Proxy Measure for Partisan Self-Identification
In June 2013, I conducted an online survey as part of another project and included the proxy
measure of partisanship that I use in place of the standard measure of party identification for the
June 2010 Work Trends Poll. The survey used Amazon Mechanical Turk as a subject
recruitment platform. Table C1 below reports the distribution of partisan self-identification,
recorded in the same manner that it was recorded on the June 2010 Work Trends Poll, by
respondents’ answers to the proxy measure of partisanship.
Partisan Self-identification
Democrat Independent Republican
Democratic Proxy
92.6%
5.43%
1.96%
(President Obama)
Independent Proxy
48.0%
28.2%
23.8%
(Both/Neither)
Republican Proxy
87.2%
7.47%
5.34%
(Republicans in Congress)
59.0%
17.6%
23.4%
Total
2
χ (4) = 1200; p < .001
Table C1: Validation of proxy measure for party identification. I report the proportions of
partisan-self-identification for respondents by on their answer to the proxy measure of
partisanship, “Who do you trust to do a better job handling the economy?” Answer choices are
“President Obama,” “Republicans in Congress,” and a middle category I created by combining
the “both” and “neither” responses. Percents are reported by row and may not add up to 100 due
to rounding.
The pattern of responses indicates that this economic trust question is a reasonable proxy
measure for partisan self-identification in this sample, which is composed of 2,466 adult Amazon
Mechanical Turk users who reported being either employed or unemployed. While I cannot
replicate this analysis for the June 2010 Work Trends Poll, and cannot assume that the
relationship between the two measure holds perfectly across both time and survey sample, I have
provided this as a robustness check on the proxy measure of party identification that I employ in
my analysis.
53