Using these data, scholars have proposed a range of innovative measures of such over-time fluctuations—most of them falling into the broad category of “volatility” estimates. Generally, volatility estimates are meant to describe the magnitude of inter-temporal fluctuation of some economic variable at the household or individual level. Typically, these measures focus on earnings or household income but sometimes they examine consumption. Moreover, they usually treat volatility as variance relative to longer-term trends, such as lifetime growth in earnings. Thus, volatility measures provide a rough estimate of income or consumption risk. The “Measures of Volatility” sidebar provides a more in-depth look at various volatility measures.
The primary implication of the burgeoning body of research on volatility is that income (and, to a lesser extent, consumption) varies enormously over time. Though most of the existing work has been done on the United States—which, it is becoming increasingly clear, has higher levels of volatility compared with other affluent countries—the conclusion holds across all countries for which panel data exist. People’s economic circumstances change a great deal over time—so much so that, according to one recent calculation using the US Panel Study of Income Dynamics (PSID), more than half of adult Americans between the ages of 25 and 60 experienced at least one year below 150% of the federal poverty line (around $12,000 for an individual in 2015) in the period 1968–2009 (Rank, Hirschl, and Foster, 2014). This means, in turn, that many more people utilize social benefits aimed at cushioning major economic shocks during their lives than a static snapshot of the income distribution might suggest.
Whether income or consumption volatility has risen over time is a different question, which will be taken up in the next section. But the fact of volatility, hitherto hidden from view by the near-universal reliance on one-shot cross-sectional samples, is not in dispute.
Nonetheless, volatility is not the same as economic security for at least two reasons.
• Volatility measures treat gains and losses as symmetrical (except insofar as models assume diminishing marginal utility of income). Yet economic security is focused on losses, which, again, likely loom much larger in subjective well-being than do comparable gains.
• Volatility measures do not as a rule distinguish between different sources of losses (or gains). Yet not all losses threaten economic security. Losses that are freely incurred are different from less controllable shocks. For example, economic security is likely to be much more compromised by an involuntary job loss than by a planned hiatus from the workforce.
With regard to the source of economic shocks, some studies of volatility focus on outcomes that are unlikely to be voluntary, such as large shifts in consumption that are not explained by predictable changes in household needs. Most, however, treat all income or consumption changes similarly. Distinguishing voluntary changes from involuntary ones is difficult, since many life events involve elements of both. Early retirement, for example, is often associated with sickness or disability or with firms’ efforts to shed older workers. Even divorce, almost by definition a choice for at least one partner, involves involuntary aspects, as the large literature on the negative consequences of divorce (particularly for women) suggests. Thus, determining voluntariness with regard to particular shocks can be difficult, especially with existing data. And since volatility analysts are usually interested in changes over time rather than absolute levels, putting to the side this thorny issue does not compromise their findings so long as the relative proportion of voluntary versus involuntary changes remains stable over time.
The issue of voluntariness should also be distinguished from the question of whether economic shocks are foreseeable. Foreseeability, too, is a matter of degree, and economic security is almost certainly not related to it in any unvarying manner. We know that people dislike uncertainty (Knight, 1921)—that is, situations in which they cannot even assign probabilities to future outcomes—and will pay to reduce that uncertainty (e.g., Ellsberg, 1961; Camerer and Weber, 1992; Di Mauro and Maffioletti, 2004). Thus, less foreseeable risks may well pose a greater threat to economic security, or at least to perceived security, all else equal.
MEASURES OF VOLATILITY
Many approaches to measuring volatility decompose variance in incomes into “persistent” components that are relatively stable over time (variance in long-run incomes across people, or inequality), and “transitory” components measuring inter-temporal variability or “volatility.” These studies differ on many dimensions, including whether they focus on earnings or household income and whether they use administrative or panel data. A key difference is whether they use parametric or nonparametric models.
• Nonparametric Models: The seminal nonparametric computations of permanent and transitory variation from Gottschalk et al. (1994) have been followed by “error components models” (ECMs) or dynamic variance components models with persistent and transitory shocks. After the early work looking primarily at male earnings (often white male earnings in US data), numerous authors decomposed changes in household income inequality into persistent and transitory parts. Many of these ECMs identify a smaller role for transitory variation or volatility than for long-term or persistent differences across people; but even when transitory variation is small relative to persistent variation, the increase in transitory variation is often large.
• Parametric Models: A more recent body of work uses parametric decomposition, with most estimates informed by a series of papers by Gottschalk and Moffitt; the most prominent recent analysis using this approach is Kopczuk, Saez, and Song (2010). By contrast, Nichols and Zimmerman (2008), Acs and Nichols (2010) and Nichols and Rehm (2014) write income (not log income) as the sum of a permanent (time-invariant) component, a person-specific linear time trend, and transitory variability around trend. Persistent variance is the “inequality” (I) in longer-run incomes; variation in trends is called “mobility risk” (M); and variation around the trend is called “volatility” (V) or “inter-temporal variability around trend.” Using a wide variety of data, Nichols and Rehm (2014) document large increases in volatility in the United States relative to Canada and other countries. They also conclude that tax and transfer programs have lesser volatility-mitigating effects in the United States than in other countries, and that the United States is diverging from its peers in the extent of volatility mitigation of its tax and transfer system.
Persistent variation across individuals in family or household resources tends to be much larger than inter-temporal variation in family or household income. In the United States, however, both persistent and transitory variations seem to have increased at comparable rates in recent decades. The increase in inter-temporal variation in household income stems from many sources, and more work is needed to ascertain its welfare consequences, but a substantial rise in the prevalence of large income drops (paired with a much smaller rise in the prevalence of large income gains) indicates that welfare-lowering risk has increased over past decades in the United States. This view is supported by recent work on consumption volatility by Dogra and Gorbachev (2016), who show that unexplained consumption variation has increased alongside higher income volatility—with the rise driven by increases among households with liquidity constraints (proxied by zero or negative wealth).
Source: Courtesy of Austin Nichols.
However, we also know from Rehm’s research and related work (Rehm, 2016; Hendren, 2017) that people can more or less correctly anticipate many common economic shocks, at least to the extent that their perceptions of the likelihood correlate highly with observed measures of risk, and that they can update these perceptions on the basis of on new information or experiences. Indeed, one reason why commercial insurance against salient economic shocks is often inadequate or unavailable is such private information, which creates adverse selection (only high-risk individuals demand coverage), can destabilize or prevent the formation of viable private markets. This line of research suggests that economic security (or,
at least, perceived security) depends not only on foreseeability but also on what is foreseen, with those who see the chance of a shock as high being less secure than those who are less certain or foresee a lower probability.
The third and final reason why volatility estimates are not direct measures of economic security is that they generally look only at household income, ignoring household wealth and major nondiscretionary expenditures such as out-of-pocket medical costs.7 But liquid wealth is of course a major source of household protection against income volatility, and household well-being can be threatened by sharp spikes in nondiscretionary spending as well as by sharp drops in income. The small literature on consumption volatility is in part a response to these difficulties, but it suffers from its own weaknesses—most notably, the scarcity of high-quality consumption data.
Though economic security and economic volatility are not synonymous, the extensive and increasingly sophisticated literature on volatility provides crucial guidance for the measurement of economic security. One of the great virtues of this research has been its consistent focus on the refinement of individual-level measures that can be used for analysis at multiple levels, from the worker or household, to demographic or educational groups, to countries as a whole. This micro-level focus distinguishes volatility measures from some of the indices of economic security just considered, which are, by construction, limited to macro- or meso-level analysis.
In addition to offering crucial conceptual and methodological guidance, the literature on volatility also provides many valuable clues about the evolution of citizens’ economic security. These central findings and their implications are discussed in the next section.
Hybrid Measures
Responding to some of the shortcomings of volatility measures as measures of economic security, Hacker and his colleagues have developed an alternative measure called the Economic Security Index or ESI (Hacker et al., 2014).8 Despite the title, it is not truly an index; rather, it is a comprehensive measure of the incidence of large shocks to household economic standing that integrates multiple data sets covering income, wealth, and medical spending. This measure has been implemented using three major US panel data sources: the PSID, the Survey of Income and Program Participation (SIPP), and the Current Population Survey (CPS), which re-contacts households that remain in the same residence, allowing the formation of two-year mini-panels through an algorithm-based matching of households across adjacent years. All three of these sources show similar trends and demographic differences.
Unlike measures of volatility, the ESI focuses only on drops—in this case, a 25% or greater decline in “available household income” from one year to the next. The 25% threshold was chosen based on a separate opinion survey, which found that the median US household said they could go roughly three months without income before suffering “real hardship.” (Other thresholds show similar trends, albeit at different levels.) The “one year to the next” criterion reflects the annual structure of most panel data, as well as the annual reporting and receipt of most public taxes and many public benefits.9
There are two other notable features of the ESI: first, it accounts for liquid financial wealth; second, it accounts for two of the most important nondiscretionary expenditures faced by many households, i.e., out-of-pocket medical costs (including insurance premiums) and debt service. Liquid (not total) household wealth enters in the calculation of the ESI as an exclusion criterion: households who have adequate liquid financial wealth to fully buffer their cumulative expected losses are not treated as “economically insecure” even when they experience a 25% or greater year-over-year income drop.10 Medical costs and debt service enter in as constraints on income available for other consumption needs—i.e., these expenditures are subtracted from income when determining whether households experience a 25% or greater loss. Finally, household income is equivalized and then assigned equally to adult household members to provide an individual-level measure.
The ESI is thus a hybrid of the buffer and volatility approaches that produces a number similar to the estimates of the probability of adverse economic shocks discussed earlier. It is also a hybrid of an income-based and a consumption-based measure, since key nondiscretionary expenditures are subtracted from income. Like occupational unemployment rates, the individual-level measure is binary (“drop” or “no drop”) and can only be turned into an estimate of economic security by looking at its incidence across a defined population. Though it would be possible to construct a truly individual-level measure with a long panel by looking solely at an individuals’ past history (e.g., Stettner, Cassidy, and Wentworth, 2016), a probability estimate better captures the core aspect of economic security, namely that it involves the risk (but not certainty) of adverse outcomes. Like all observed measures, however, it is inherently backward-looking: future risk is assumed to be similar to (recent) past risk.
To bolster this approach, the research team that developed the ESI conducted a panel opinion survey that asked extensive questions about economic security, including whether individuals experienced large household income drops. These results allow not just for an independent verification of the estimates of the incidence of large drops made using the SIPP, PSID, and CPS. More important, given that none of these data sources contain questions about perceived economic security, they were used to verify that individuals experiencing large drops did in fact express lower levels of security. As already noted, they did: large income shocks were associated with much higher levels of worry, higher estimates of future shocks, and greater support for public policies designed to buffer these shocks. Wealth drops did not have such consistent effects, though the relatively small size of the sample—between 2,000 and 3,000 respondents—may have contributed to this (non) finding.
Nonetheless, the ESI has its own significant weaknesses. Like nearly every other measure discussed, it does not distinguish between voluntary and involuntary changes (although it does focus on sizable losses of 25% or greater that are least likely to be expected or voluntary). Nor does it capture all economic threats, including other salient nondiscretionary expenditures, most notably the unavoidable costs that come with participation in the labor force, such as child care and transportation costs (although there is much less agreement about the extent to which these expenses are nondiscretionary than there is with regard to medical costs.)
To date, no measure has been proposed that truly integrates over-time volatility with long-term risks such as retiring without adequate income. The ESI is no exception, though it does exclude earmarked retirement savings from the calculations of liquid financial wealth, on the grounds that spending down such savings jeopardizes household’s economic security with regard to retirement. There is a large literature that looks at consumption drops at retirement (e.g., Banks, Blundell and Tanner, 1998; Aguiar and Hurst, 2005; Haider and Stephens Jr., 2007), and this focus might be integrated into the ESI approach. But such an integration would require accounting for the reduced income needs of retirees.11
Finally, in its focus on changes, the ESI does not treat people facing persistent but stable deprivation as economically insecure—a feature shared by all volatility measures. In practice, low-income individuals experience much greater instability of income and nondiscretionary spending than do higher-income individuals, and they have much more limited liquid financial wealth. Still, it remains true that volatility-based measures miss aspects of economic insecurity that do not involve economic instability.
In short, several measures have been proposed to assess economic security. Some of these measures are designed to capture only certain important aspects of economic security, such as the strength of household buffers, while others are closer to fully comprehensive measures. Even the latter, however, do not represent incontestable proxies, not least because the precise definition of economic security remains under discussion. Nonetheless, these measures of observed security clarify how analysts, policy-makers, and statistical agencies could better asses
s a critical feature of citizens’ lives and a fundamental influence on their well-being. The next section turns to what these measures tell us about the character and evolution of economic security in recent decades.
What Can We Say About Economic Security Based on the Available Evidence?
This section presents a small number of indicators that can be used to compare economic security across individuals, households, socio-demographic groups, and countries. It focuses on observed economic security, and more specifically on two measures of observed security that are available or can be developed for multiple countries and time periods: Lars Osberg’s “named risk” approach, a hybrid of the probability-of-shock and prevalence-of-buffers approach; and new estimates based on the approach embodied in the ESI, i.e., a volatility-based measure that focuses on large year-to-year income drops.
In presenting these results, this chapter draws on both published and unpublished research. In particular, the ESI-style estimates for countries other than the United States have been developed only recently and thus should be considered preliminary. Moreover, both these data and the evidence developed by Osberg and his colleagues are currently available only for affluent countries. As more and more countries develop high-quality economic data—and in particular panel data—it should become possible to develop similar measures for a broader range of countries.
The “Named Risk” Approach
Osberg’s “named risk” measure of economic security is designed to capture the overall prevalence of and degree of protection against major economic shocks listed in the UN Universal Declaration of Human Rights, specifically unemployment, sickness, single-parent poverty, and poverty in old age. Results are available from 1980 through 2009 for 14 affluent democracies: Australia, Belgium, Canada, Denmark, Finland, France, Germany, Italy, the Netherlands, Norway, Spain, Sweden, the United Kingdom, and the United States.
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