Jacob S. Hacker is Director of the Institution for Social and Policy Studies and Stanley B. Resor Professor of Political Science at Yale University. The author wishes to thank Austin Nichols and especially Philipp Rehm—a true partner in conducting much of the research on which this chapter is based—as well as participants in the HLEG workshop on “Economic Insecurity: Forging an Agenda for Measurement and Analysis,” held in New York on March 4, 2016, organized in collaboration with the OECD, the Washington Center for Equitable Growth, and the Yale Institution for Social and Policy Studies. Thanks to the Ford Foundation for funding and hosting the event. The author also thanks the HLEG members who commented on prior drafts. The opinions expressed and arguments employed in the contributions below are those of the author and do not necessarily reflect the official views of the OECD or of the governments of its member countries.
Introduction
In the wake of the financial crisis of the late 2000s, hundreds of millions of people in OECD countries faced significant economic dislocations, including unemployment, income volatility, and sharp drops in housing wealth and other assets. Opinion surveys showed a spike in people’s worries about these and other economic risks, as well as a deterioration in their confidence that political leaders and public policies could effectively address them. In a phrase, citizens of crisis-affected countries grew more concerned about their “economic security,” i.e., the degree to which they were vulnerable to hardship-causing economic losses. This heightened concern, in turn, influenced everything from their consumer and investment decisions, to their choices about family formation and geographic mobility, to their political behavior.
This chapter examines what we know about economic security and what we still must learn. The 2009 Stiglitz-Sen-Fitoussi report devoted relatively limited attention to economic security, whether as an influence on subjective well-being or as an objective feature of individuals’ economic lives. It mentioned job security, health and medical spending security, and retirement security, but did not present measures of these phenomena. Its short section on economic security closed by arguing for a more comprehensive approach:
The many factors shaping economic security are reflected in the variety of approaches used to measure them. Some approaches try to quantify the frequency of specific risks, while others look at the consequences of a risk that materialises and at the means available to people to protect themselves from these risks (especially resources provided by social security programmes). A comprehensive measure of economic security would ideally account for both the frequency of each risk and its consequences, and some attempts in this direction have been made. A further problem is that of aggregating across the various risks that shape economic security, as the indicators that describe these risks lack a common metric to assess their severity. A final, even more intractable problem is that of accounting for the long-term consequences for quality of life of the various policies used to limit economic security (through their effects on unemployment and labour-force participation). (Stiglitz, Sen, and Fitoussi, 2009, pp. 201–202)
It is fair to say that the recommendations of the Stiglitz-Sen-Fitoussi report with regard to economic security—unlike its recommendations with regard to income inequality and subjective well-being—did not spark the investments in statistics, theory, and research necessary to achieve these ambitious goals. Given advances in concepts and data since 2009, however, the subject is ripe for a re-examination.
Key Features of Extant Measures
Before examining these measures, a few final points are in order. First, most measures focus on economic shocks that are unexpected and largely beyond the control of individuals. In practice, however, it is often difficult to know whether shocks are unexpected or involuntary. One approach is to focus on consumption, on the assumption that unexpected and involuntary shocks will have larger consumption effects. Yet consumption data are not widely available, nor are consumption drops the only possible measure of involuntary losses.
Second, while analysts frequently refer to the “risk” of economic loss, which implies a prospective outlook, most measures are in fact retrospective (the exception is measures of perceived security that ask respondents to offer their own assessment of various future events). In these cases, the risk faced by individuals or households is assumed to reflect past experience—their own, that of people like them, or a combination of the two.
Finally, and related, some measures are specific to people or households, while others can only be used to examine aggregate outcomes (for example, levels of security within regional or occupational groupings or among specific types of households). Perceived security is usually measured at the respondent level. By contrast, many measures of observed security are available only for aggregate groups, as they require observing the incidence of outcomes within defined populations.
This last point raises a final issue: the proper unit of analysis for measures of economic security. In general, security is an individual-level phenomenon. People experience insecurity, not groups. However, many of the most important forces that shape economic security, including the buffers people have against it, operate at the household level or even at higher levels of aggregation (communities, firms, regions, countries, and so on). Most measures are built up from either individual-level data or household-level data, with the choice depending on the specific measure used.1
What Is the State of Existing Statistics on Economic Security?
This section reviews the leading measures used to chart observed and perceived economic security, moving back and forth between concepts and data. Because data limitations are such a significant constraint, each measure is discussed in the context of the major data sources that are required to produce it. The section begins with measures of perceived security; it then turns to measures of observed security.
Measures of Perceived Economic Security
A wide range of surveys ask questions regarding people’s perceived economic security. The main benefit of such surveys is that they directly capture individuals’ perceptions of their personal and household economic experiences. To the extent that economic security is seen as synonymous with—or at least closely related to—individuals’ psychological response to economic risks, surveys provide information about subjective perceptions that data on individuals’ material circumstances cannot provide. And even if these perceptions are viewed “merely” as an effect or correlate of observed security, there is simply no way to understand the link between observed and perceived security without delving into these subjective responses.
Two key constraints, however, limit the utility of existing surveys for assessing perceived security. First, many survey questions are not asked repeatedly over any significant span of time, limiting the ability to examine changes in perceived security. Second, surprisingly few questions are replicated with similar wording in multiple national surveys, limiting the ability to examine cross-national differences in perceived security. The discussion that follows focuses on survey instruments that are available both over time and across at least a small subset of countries.
General Assessments of the Economy
Three broad categories of survey questions about perceived economic security appear regularly in major surveys. The first category comprises questions about how one feels about the economy or aspects of one’s economic situation in the past or present. The most famous are the questions on one’s own financial situation and the national financial situation, which are used to compile the University of Michigan Consumer Sentiment Index (Carroll, Fuhrer, and Wilcox, 1994).
The most common of these questions, however, seem weakly related to economic security—in particular, its forward-looking aspect. In addition, substantial research has shown that many of these general economic perceptions are heavily colored by assessments of incumbent parties, with partisans of the incumbent party offering more favorable assessments (Duch, Palmer and Anderson, 2000).
Perceptions of Buffers
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A second category of questions, one that offers greater promise, concerns individuals’ perceptions of their own ability to weather future economic shocks. Economic data often contain people’s reports of specific levels of wealth or whether they have particular forms of insurance coverage. These questions, however, do not assess perceived security—they are simply designed to elicit information about households’ material circumstances. Still, a number of surveys do include questions that involve a subjective component. The most common of such items concern the length of time the respondents believe they could remain economically comfortable if they were the victim of adverse economic shocks. Typically, these surveys find that the majority of individuals believe they could go only a limited amount of time before experiencing hardship—a reflection of the limited liquid wealth that many households have.
Similarly, some surveys ask people to assess whether they could rely on certain sources of support in the event of economic shocks, a line of questioning that also involves a subjective, forward-looking component. An example is the European Union’s Survey of Income and Living Conditions (EU-SILC) 2015 ad hoc module’s question on the possibility of the respondents relying on relatives, friends, or neighbors in case help (including financial help) is needed. Other examples of such questions are included in the EU-SILC—most notably, respondents are asked about their ability to make ends meet (current), their ability to face unexpected expenses (current, future), and their unmet needs (past) for medical and dental examination or treatment.
These questions contain a subjective component, and they provide an important indicator of individuals’ preparation against major economic risks. Still, they are best thought of as measures of buffers rather than of security more generally. They are most useful, therefore, when they are accompanied by questions about the likelihood that individuals will experience economic shocks.
Expectations Regarding Future Shocks
This brings us to the third and final category of survey measures—namely, questions about one’s likely economic situation in the future. These forward-looking measures seem closer to the concept of economic security than either of the survey items just discussed. More important, they provide something that measures of observed security cannot: individuals’ own estimate of the risks they face. Forward-looking questions fall into two broad subgroups.
• Questions that ask about individuals’ worry or anxiety with regard to specific risks. Prior research shows that expressed worry is closely correlated with the expected probability of economic shocks; it also captures an emotional element of individuals’ responses that other measures tend to miss. Unfortunately, surveys with these sorts of questions tend to be country-specific—the most notable examples are US surveys, such as the Survey of Economic Risk Perceptions and Insecurity (Rehm, Hacker, and Schlesinger, 2012; Hacker, Rehm, and Schlesinger, 2013). Country-specific surveys obviously cannot be used to examine differences across nations, though, when repeated, they can be used to look at changes over time.
• Questions that ask individuals about the likelihood of economic loss. Typically, these questions have asked people to rate the chance of an economic event occurring on the basis of an ordinal scale (for example, 1 to 5, with 5 denoting the most insecure status). The most common of these types of questions concern the risk of job loss. For example, the International Social Survey Program has repeatedly asked respondents to express how much they agree with the statement “my job is secure,” with four answer options ranging from “strongly agree” to “strongly disagree.” Several high-profile surveys, however, ask respondents to estimate the precise numerical probability that they or someone like them will experience a particular adverse economic shock (Manski, 2004; Hacker, Rehm, and Schlesinger, 2013; Hendren, 2017).
Can We Believe Respondents’ Estimates?
It might be wondered whether respondents can come up with meaningful estimates at the high level of precision that a 0–100 scale requires. To address this concern, some surveys use categorical or more limited ordinal scales. However, Hacker, Rehm, and Schlesinger (2013) show that with the appropriate survey instruments—in their work, a sliding scale that shows visually the proportion of the population that each percentage level represents—it is possible to get finegrained estimates that do not exhibit substantial clumping at 0% or 50%.
Moreover, their work and recent research by Rehm (2016) and by Hendren (2017) shows that individuals do a reasonably good job forecasting the likelihood of major economic shocks in the following year. A number of studies also show that shocks affect the probabilities that individuals attach to future economic losses, as well as their attitudes toward economic security more broadly. These findings strongly suggest that measures of perceived and observed security may not be as distinct as some analysts believe, at least when individual responses are averaged across larger groups.
Hendren’s analysis also shows that measures of perceived and observed security can be used in conjunction with each other to assess the welfare loss associated with economic shocks. In brief, he looks at the drop in consumption that occurs not only when individuals lose their job, but also when individuals come to believe they have a high probability of losing it. The former measure is often used as a proxy for the welfare loss of unemployment, but as Hendren points out, if individuals cut back their consumption prior to unemployment because they expect to become unemployed, this approach will understate the negative welfare effects of unemployment—according to Hendren, by a substantial amount. Though Hendren’s work does not consider the additional disutility associated with the fear or anxiety that such knowledge might produce, it does point to the promise of measures that utilize both perceived and observed security simultaneously.
Measures of Observed Economic Security
While measures of perceived security rely on individuals’ own perceptions of their economic situation, measures of observed security draw on economic data that capture their material circumstances. Of course, much of this data comes from surveys of national populations (rather than, say, administrative sources). The difference is that these survey items are not designed to elicit information about respondents’ perceptions but rather about their experiences and circumstances.
Four classes of measures of observed security are prevalent in the literature: (1) measures of household and individual buffers; (2) estimates of the probabilities of economic shocks; (3) indexes of observed security; and (4) various measures of over-time economic (in)stability.
Some measures are hybrids of these. For example, Bossert and D’Ambrosio present an indicator of insecurity that is a weighted combination of wealth levels (buffers) and past wealth dynamics (stability), while Hacker and his colleagues present a measure of the joint risk (probabilities) of income loss and medical spending shocks (stability) that includes a correction for household wealth (buffers). These hybrid measures are generally close to one of the major types (Bossert and D’Ambrosio, 2013; Hacker et al., 2014).2
Buffers
This class of measures looks at the adequacy of individuals’ or households’ savings, insurance, or other buffers against major economic shocks.
A major category of these measures defines and assesses asset sufficiency, i.e., whether households could maintain an adequate standard of living for a specified period merely by drawing down their wealth (usually, but not always, excluding housing because it is relatively illiquid and households need a place to live). In effect, these are measures of households’ capacity for self-insurance. “Adequate” is defined differently in different studies, but a common metric in US analyses is the federal poverty line (a very low standard), with the specified period usually being 3 months (again, a low bar). Thus households are defined as “asset poor” when they have so little wealth they would not be able to support themselves at the US federal poverty line for at least three months if they lost their sources of income.
Cross-national analyses of asset poverty are rare, however, in part beca
use of limited data. With regard to data, the Luxembourg Wealth Study has augmented the pool of cross-national wealth data, drawing on National Statistical Offices’ micro-data regarding income and wealth. At the same time, the OECD and Eurostat have launched a project on household income consumption and wealth statistics, linking data on the distribution of each item based on micro-data. Yet these data are not always fully comparable cross-nationally, nor are high-quality wealth measures generally integrated into panel data, so wealth levels and vulnerability to shocks often cannot be examined inter-temporally side by side. Moreover, these wealth data generally do not do a good job measuring wealth at the top of the distribution—for that, specialized surveys that over-sample high-net-worth individuals like the US Survey of Consumer Finances are best. However, this is not a significant problem when looking at asset poverty, since the focus is on the lower part of the distribution.3
In addition to data challenges, conceptual issues bedevil cross-national analysis. These include how to set the minimum standard of living that assets are expected to provide, including whether it should be relative to the national income distribution or an absolute standard that is common across countries. No less vexing is whether to include housing wealth in asset measures, as housing valuation and prevalence differ substantially across countries, as does the ease of unlocking housing wealth when experiencing economic shocks (by, for example, borrowing against accumulated home equity).
Nonetheless, measures of asset sufficiency contain valuable information and are an important part of measuring economic security across countries, especially since they can be developed with existing data. To illustrate their value, Figure 8.1 compares income and asset poverty and “economic vulnerability” across OECD countries, using a relative poverty standard and adjusting income for household size (“equivalizing”). The “income poor” are those with equivalized income below 50% of the median income in each country. The “income and asset poor” are those with equivalized income below 50% of the median income and equivalized liquid financial wealth below 25% of the income poverty line (3-month buffer). The “economically vulnerable” are those who are not income poor but have equivalized liquid financial wealth below 25% of the income poverty line.
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