Tag Archives: insecurity

What happens next

Wouldn’t you like to know.

“The pandemic has exposed the messiness of science. … We all want answers today, and science is not going to give them. … Science is uncertainty. And the pace of uncertainty reduction in science is way slower than the pace of a pandemic.” —Brian Nosek

Microsoft PowerPoint - games of chance.pptx

click to enlarge

The math of probability is manageable, up to a point. In principle, you could calculate the odds of, for example (clockwise from top left) getting heads on the fake Trump coin, a novel coronavirus linking up to human proteins, surviving a round of Russian roulette, having someone with COVID-19 at your planned event, rolling 6-6-4-8-20 on the dice, have all the marbles fall under a normal curve on a Galton board, and then surviving a flight from New York to Los Angeles without hitting one of the thousands of other planes in the air. But that doesn’t mean we can tell where humanity will be a year from now.

Of course things are always too complicated to predict perfectly, but “normally” we bracket uncertainty and make simplifying assumptions so we can work with forecasts of say, tomorrow’s weather or next quarter’s economic growth — which are strongly bounded by past experience. These are “the models” we live by. The problem now is not that reality has become objectively harder to predict, it’s that the uncertainties in the exercise (those most relevant to our lives) involve events with such catastrophic consequences that a normal level of uncertainty now includes outcomes so extreme that we can’t process them meaningfully.

Once nominally predicable events start influencing each other in complex ways, the uncertainty grows beyond the capacity of simple math. Instead of crunching every possibility, we simplify the assumptions, based on past experience and the outcomes we consider possible. Today’s would-be predictions, however, involve giant centrifugal forces, so that small deviations can lead to disintegration. For example, if the pandemic further tanks the economy, which provides more unemployed people to populate mass protests, leading to more military crackdown and turning more people against Trump, it might make him lash out more at China, and then they might not share their vaccine with us, and an epidemic wave could overwhelm the election and its aftermath, giving Trump a pretext for nullifying the results. And so on.

To make matters more ungraspable, we personally want to know what’s going on at the intersection of micro and macro forces, where we don’t have the data to use even if we knew what to do with it.

Examples

For example, as individuals try to ballbark their own risks of covid-19 infection, and the likelihood of a serious outcome in that event — given their own health history — they might also want to consider whether they have been exposed to tear gas at the hands of police or the military, which might increase the chance of infection. In that case, both the individual and the state are acting without quantifiable information on the risks. For another example, Black people in America obviously have a reasonable fear of police violence — with potentially immeasurable consequences — but taking the risk to participate in protests might contribute to political changes that end up reducing that risk (for themselves and their loved ones). The personal risks are affected by policy decisions and organizational practices, but you can’t predict (much less control) the outcomes.

Individual risks are affected by group positions, of course, creating diverging profiles that splinter out to the individual level. Here’s an example: race and widowhood. We all know that as a married couple ages, the chance that one of the partners will die increases. But that relationship between age and widowhood differs markedly between Blacks and Whites, as this figure shows:

widowhood probability

Before age 70, the annual probability of a Black woman being widowed is more than twice the chance that White women face. (After that, the odds are higher, but not as dramatically so.) Is this difference big enough to affect people’s decision making, their emotions, their relationships? I think so, though I can’t prove it. Even if people don’t map out the calculation at this level (though they of course think about their own and their partner’s specific health situation), it’s in there somewhere.

For most people, widowhood presents a pretty low annual probability of a very bad event, one that might turn your life upside down. On the other hand, climate change is certain, and observable over the course of a contemporary adult’s lifetime (look at the figure below, from 1980 to 2020). But although climate change presents potentially catastrophic consequences, the risks aren’t easily incorporated into life choices. If you’re lucky, you might have to think about the pros and cons of owning beachfront property. Or you could be losing a coal job, or gaining a windmill job. But I think for most people in the U.S. it’s in the category of background risk — which might motivate political participation, for example, but doesn’t hang over one’s head as a sense of life-threatening risk.

temperature anonamlies trend

If not imminent fear, however, climate change undoubtedly contributes to a climate of uncertainty about the future. Interestingly, there is a robust debate about whether and how climate change is also increasing climate variability. Rising temperatures alone would create more bad storms, floods, and droughts, but more temperature fluctuation would also have additional consequences. I was interested to read this paper which showed models predicting greater change in temperature variability (on the y-axis) for the rest of the century in countries with lower per-capita income (x-axis). When it comes to inequality, it rains and it pours. And for people in poorer countries of the world, it’s raining uncertainty.

tempvar

What comes next

I wrote about unequal uncertainties in April, and possible impacts on marriage rates, and I’ve commented elsewhere on fertility and family violence. But I’m not making a lot of predictions. Are other social scientists? My impression they’re mostly wisely holding off. My sense is also that this may be part of a longer-term pattern, where social scientists once made more definitive predictions with less sophisticated models than we do now that we’re buried in data. Is it the abundance of data that makes predicting seem like a bad business? I don’t think that’s it. I think it’s the diminished general confidence in the overall direction of social change. Or maybe predictions have just become more narrow — less world revolution and more fourth quarter corn prices.

One of the books I haven’t written yet, crappily titled Craptastic when I pitched it in 2017, would address this:

My theory for Craptastic is that the catastrophic thinking and uncontrollable feelings of impending doom go beyond the very reasonable reaction to the Trump shitshow that any concerned person would have, and reflect a sense that things are turning around in a suddenly serious way, rupturing what Anthony Giddens describes as the progress narratives of modernity people use to organize their identities. People thought things were sort of going to keep getting better, arc of the moral universe and all that, but suddenly they realize what a naive fantasy that was. It’s not just terrible, it’s craptastic. …

I suspect that if America lives to see this chapter of its decline written, Trump will not be as big a part of the story as it seems he is right now. And that impending realization is one reason for the Trump-inspired dysphoria that so many people are feeling.

Social science is unlikely anytime soon to be the source of reassurance about the future some people might be looking for — not even the reassurance that things will get better, but just confidence that we know what direction we’re headed, and at what speed. I don’t know, but if you know, feel free to leave it in the comments. (Which are moderated.)

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Job turnover and divorce (preconference preprint)

As I was prepared to discuss Alison Pugh’s interesting and insightful 2015 book, The Tumbleweed Society: Working and Caring in an Age of Insecurity, on an author-meets-critics panel at the American Sociological Association meetings in Montreal next week (Monday at 4:30), I talked myself into doing a quick analysis inspired by the book. (And no, I won’t hijack the panel to talk about this; I will talk about her book.)

From the publisher’s description:

In The Tumbleweed Society, Allison Pugh offers a moving exploration of sacrifice, betrayal, defiance, and resignation, as people adapt to insecurity with their own negotiations of commitment on the job and in intimate life. When people no longer expect commitment from their employers, how do they think about their own obligations? How do we raise children, put down roots in our communities, and live up to our promises at a time when flexibility and job insecurity reign?

Since to a little kid with a hammer everything looks like a nail, I asked myself yesterday, what could I do with my divorce models that might shed light on this connection between job insecurity and family commitments? The result is a very short paper, which I have posted on SocArXiv here (with supporting data and code in the associated OSF project shared here). But here it is in blog form; someday maybe I’ll elaborate it into a full paper.


Job Turnover and Divorce

Introduction

In The Tumbleweed Society, Pugh (2015) explores the relationship between commitments at work – between employers and employees – and those at home, between partners. She finds no simple relationship such that, for example, people who feel their employers owe them nothing also have low commitment to their spouses. Rather, there is a complex web of commitments, and views of what constitutes an honorable level of commitment in different arenas. This paper is inspired by that discussion, and explores one possible connection between work and couple stability, using a new combination of data from the Current Population Survey (CPS) and the American Community Survey (ACS).

In a previous paper I analyzed predictors of divorce using data from the ACS, to see whether economic indicators associated with the Great Recession predicted the odds of divorce (Cohen 2014). Because of data limitations, I used state-level indicators of unemployment and foreclosure rates to test for economic associations. Because the ACS is cross-sectional, and divorce is often associated with job instability, I could not use individual-level unemployment to predict individual-divorce, as others have done (see review in Cohen 2014). Further, the ACS does not include any information about former spouses who are no longer living with divorced individuals, so spousal unemployment was not available either.

Rather than examine the association between individual job change and divorce, this paper tests the association between turnover at the job level and divorce at the individual level. It asks, do people who work in jobs that people are likely to leave themselves more likely to divorce? The answer – which is yes – suggests possible avenues for further study of the relationship between commitments and stressors in the arenas of paid work and family stability. Job here turnover is a contextual variable. Working in a job people are likely to leave may simply mean people are exposed to involuntary job changes, which is a source of stress. However, it may also mean people work in an environment with low levels of commitment between employers and employees. This analysis can’t differentiate potential stressors versus commitment effects, or identify the nature (and direction) of commitments expressed or deployed at work or within the family. But it may provide motivation for future research.

Do job turnover and divorce run together?

Because individual (or spousal) job turnover and employment history are not available in the ACS, I use the March CPS, obtained from IPUMS (Flood et al. 2015), to calculate job turnover rates for simulated jobs, identified as detailed occupation-by-industry cells (Cohen and Huffman 2003). Although these are not jobs in the sense of specific workplaces, they provide much greater detail in work context than either occupation or industry alone, allowing differentiation, for example, between janitors in manufacturing establishments versus those in government offices, which are often substantially different contexts.

Turnover is identified by individuals whose current occupation and industry combination (as of March) does not match their primary occupation and industry for the previous calendar year, which is identified by a separate question (but using the same occupation and industry coding schemes). To reduce short-term transience, this calculation is limited to people who worked at least 20 weeks in the previous year, and more than 20 hours per week. Using the combined samples from the 2014-2016 CPS files, and restricting the sample to previous-year job cells with at least 25 respondents, I end up with 927 job cells. Note that, because the cells are national rather than workplace-specific, the size cutoff does not restrict the analysis to people working in large workplaces, but rather to common occupation-industry combinations. The job cells in the analysis include 68 percent of the eligible workers in the three years of CPS data.

For descriptive purposes, Table 1 shows the occupation and industry cells with the lowest and highest rates of job turnover from among those with sample sizes of 100 or more. Jobs with low turnover are disproportionately in the public sector and construction, and male-dominated (except schoolteachers); they are middle class and working class jobs. The high-turnover jobs, on the other hand, are in service industries (except light truck drivers) and are more female-dominated (Cohen 2013). By this simple definition, high-turnover jobs appear similar to precarious jobs as described by Kalleberg (2013) and others.

t1

Although the analysis that follows is limited to the CPS years 2014-2016 and the 2015 ACS, for context Figure 1 shows the percentage of workers who changed jobs each year, as defined above, from 1990 through 2016. Note that job changing, which is only identified for employed people, fell during the previous two recessions – especially the Great Recession that began in 2008 – perhaps because people who lost jobs would in better times have cycled into a different job instead of being unemployed. In the last two years job changing has been at relatively high levels (although note that CPS instituted a new industry coding scheme in 2014, with unknown effects on this measure). In any event, this phenomenon has not shown dramatic changes in prevalence for the past several decades.

f1

Figure 1. Percentage of workers (20+ weeks, >20 hours per week) whose jobs (occupation-by-industry cells) in March differed from their primary job in the previous calendar year.

Using the occupation industry codes from the CPS and ACS, which match for the years under study, I attach the job turnover rates from the 2014-2016 CPS data to individuals in the 2015 ACS (Ruggles et al. 2015). The analysis then uses the same modeling strategy as that used in Cohen (2014). Using the marital events variables in the ACS (Cohen 2015), I combine people, age 18-64, who are currently married (excluding those who got married in the previous year) and those who have been divorced in the previous year, and model the odds that individuals are in the divorced group. In this paper I essentially add the job turnover measure to the basic analysis in Cohen (2014, Table 3) (the covariates used here are the same except that I added one category to the education variable).

One advantage of the ACS data structure is that the occupation and industry questions refer to the “current or most recent job,” so that people who are not employed at the time of the survey still have job characteristics recorded. Although that has the downside of introducing information from jobs in the distant past for some respondents, it has the benefit of including relevant job information for people who may have just quit (or lost) jobs as part of the constellation of events involved in their divorce (for example, someone who divorces, moves to a new area, and commences a job search). If job characteristics have an effect on the odds of divorce, this information clearly is important. The ACS sample size is 581,891, 1.7 percent of whom reported having divorced in the previous year.

Results from two multivariate regression analyses are presented in Table 2. The first model predicts the turnover rate in the ACS respondents’ job, using OLS regression. It shows that, ceteris paribus, turnover rates are higher in the jobs held by women, younger people (the inflection point is at age 42), people married more recently, those married few times, those with less than a BA degree, Blacks, Asians, Hispanics, and immigrants. Thus, job turnover shows patterns largely similar to labor market advantage generally.

Most importantly for this paper, divorce is more likely for those who most recent job had a higher turnover rate, as defined here. In a reduced model (not shown), with just age and sex, the logistic coefficient on job turnover was 1.39; the addition of the covariates in Table 2 reduced that effect by 39 percent, to .84, as shown in the second model. Beyond that, job turnover is predicted by some of the same characteristics as those associated with increased odds of divorce. Divorce odds are lower after age 25, with additional years of marriage, with a BA degree, and for Whites. However, divorce is less common for Hispanics and immigrants. (The higher divorce rates for women in the ACS are not well understood; this is a self-reported measure, not a count of administrative events.)

t2

To illustrate the relationship between job turnover and the probability of divorce, Figure 2 shows the average predicted probability of divorce (from the second model in Table 2) for each of the jobs represented, with markers scaled according to sample size and a regression line similarly weighted. Below 20 percent job turnover, people are generally predicted to have divorce rates less than 2 percent per year, with predicted rates rising to 2.5 percent at high turnover rates (40 percent).

job changing effect 2015 ACS-CPS

Figure 2. Average predicted probability of divorce within jobs (from logistic model in Table 2), by turnover rate. Markers are scaled according to sample size, and the linear regression line shown is weighted by sample size.

Conclusion

People who work in jobs with high turnover rates – that is, jobs which many people are no longer working in one year later – are also more likely to divorce. A reading of this inspired by Pugh’s (2015) analysis might be that people exposed to lower levels of commitment from employers, and employees, exhibit lower levels of commitment to their own marriages. Another, noncompeting explanation would be that the stress or hardship associated with high rates of job turnover contributes to difficulties within marriage. Alternatively, the turnover variable may simply be statistically capturing other aspects of job quality that affect the risk of divorce, or there are individual qualities by which people select into both jobs with high turnover and marriages likely to end in divorce. This is a preliminary analysis, intended to raise questions and offer some avenues for analyzing these questions in the future.

References

Cohen, Philip N. 2013. “The Persistence of Workplace Gender Segregation in the US.” Sociology Compass 7 (11): 889–99. http://doi.org/10.1111/soc4.12083.

Cohen, Philip N. 2014. “Recession and Divorce in the United States, 2008–2011.” Population Research and Policy Review 33 (5): 615–28. http://doi.org/10.1007/s11113-014-9323-z.

Cohen, Philip N. 2015. “How We Really Can Study Divorce Using Just Five Questions and a Giant Sample.” Family Inequality. July 22. https://familyinequality.wordpress.com/2015/07/22/how-we-really-can-study-divorce/.

Cohen, P. N., and M. R. L. Huffman. 2003. “Individuals, Jobs, and Labor Markets: The Devaluation of Women’s Work.” American Sociological Review 68 (3): 443–63. http://doi.org/10.2307/1519732.

Kalleberg, Arne L. 2013. Good Jobs, Bad Jobs: The Rise of Polarized and Precarious Employment Systems in the United States 1970s to 2000s. New York, NY: Russell Sage Foundation.

Pugh, Allison J. 2015. The Tumbleweed Society: Working and Caring in an Age of Insecurity. New York, NY: Oxford University Press.

Steven Ruggles, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. Integrated Public Use Microdata Series: Version 6.0 [dataset]. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D010.V6.0.

Sarah Flood, Miriam King, Steven Ruggles, and J. Robert Warren. Integrated Public Use Microdata Series, Current Population Survey: Version 4.0. [dataset]. Minneapolis: University of Minnesota, 2015. http://doi.org/10.18128/D030.V4.0.

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