Three features of Figure 8.1 stand out. First, there is considerable overlap between income and asset poverty, though the degree of coincidence varies across countries. Most of the income poor are also asset poor. However, second, many of those who are not income poor lack adequate liquid financial wealth to weather economic shocks. Indeed, economic vulnerability is typically at least three times as high as income poverty. This suggests that vulnerability to economic shocks is a much broader phenomenon than economic deprivation. Third, levels of economic vulnerability vary greatly across countries. In Greece, for example, more than half the population lacks enough liquid financial wealth to maintain a poverty-level income for three months. By contrast, the share is roughly 1 in 5 in Austria and Norway.
Like other assessments of buffers, measures of asset sufficiency contain no information about the severity or character of the shocks that individuals or households face. Similar households with the same level of liquid wealth could experience very different levels of security based on their exposure to economic risks. Beyond this, asset measures do not typically take into account either borrowing capacity or informal sources of support, such as inter-household transfers. In principle, such measures could incorporate borrowing capacity through, for example, the use of credit ratings or (returning to perceived security) estimates of one’s ability to borrow in the event of adverse shocks. But so far the focus has been squarely on household wealth.
Another weakness is that, in their emphasis on self-insurance, these measures leave out various types of formal insurance against major risks, such as health insurance and retirement pensions—both public and private. The OECD and the International Labour Organization (ILO) collect extensive data on the breadth of such protections, including the level of benefits, scope of coverage, duration of support, and stringency of qualifying conditions. Typically, these measures are based on statutory program rules or administrative data (or both). On the basis of these measures, analysts have produced indices of social program generosity that allow for comparison across countries.
Similarly, many countries collect data on the prevalence and characteristics of private insurance, such as commercial life and health insurance. These data are often collected at the household or individual level in surveys of income and wealth, and at the aggregate level in surveys of national savings and production (or in commercial statistics on the breadth and characteristics of particular types of insurance).
Figure 8.1. Income and Asset-Based Poverty
Note: Countries are ranked in ascending order of the share of individuals who are “economically vulnerable.” The OECD average is the simple country average. The “income poor” are those with equivalized income below 50% of median income in each country. The “income and asset poor” are those with equivalized income below 50% of the median 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 wealth below 25% of the income poverty line. Income poverty rates refer to household disposable income for Australia, Canada, Chile, Denmark, Finland, Italy, the Netherlands, New Zealand, Norway, and the United States, and to household gross income for the remaining countries. Liquid financial wealth includes cash, quoted shares, mutual funds, and bonds net of liabilities of own unincorporated enterprises.
Source: OECD (2017), OECD Wealth Distribution Database, https://stats.oecd.org/Index.aspx?DataSetCode=WEALTH. StatLink 2 http://dx.doi.org/10.1787/888933839848.
For all their value, however, measures of formal insurance protection exhibit two salient weaknesses. First, with the exception of estimates based on household surveys, they are not usually available as individual- or household-level data that allow for comparison of levels of economic security across individuals or households, rather than across countries (or other geographic units).
Second, the tendency to focus on formal policy characteristics leaves these measures open to substantial slippage between assessed and actual risk protection. For example, not all benefits are “taken up” by those with formal coverage, nor do public and private implementers always carry out program instructions faithfully. With private benefits in particular, eligibility for specific benefits may be extremely complex to determine, as evidenced by US research on “surprise medical bills” (Cooper and Morton, 2016; Garmon and Chartock, 2016). Though actual protections are likely to be close to formal rules, important differences in economic security may still be missed, particularly when those most likely to experience economic dislocations—the poor, the young, the less educated—are most likely to fall through the gaps between promised and provided benefits.
Despite these caveats, measures of social program generosity provide crucial information about the extent to which public policies are designed to increase economic security. These indicators can be used to assess how broad and deep such protections are, as well as—alongside other measures of observed or perceived insecurity—how effective they are at achieving their purposes. Table 8.1 shows the most recent results (2010) for one broadly used collection of indicators of social program generosity, the Comparative Welfare Entitlements Dataset. The table shows the level (relative to the nation’s average production wage), duration, and coverage for two important benefits: sickness pay and unemployment insurance. It orders the countries based on the data set’s index of the comparative generosity of sickness pay, which normalizes these generosity indicators based on their historical (since 1980) and cross-country variation, equally weighting benefit levels, duration, and coverage.4
Shock Probabilities
Many students of political economy have examined discrete economic risks by looking at the cross-sectional prevalence of key economic shocks, most often unemployment. The goal is to calculate fine-grained measures of the risk of these shocks for particular groups, such as workers in different occupations. Rehm (2016), for example, develops “occupational unemployment rates,” which are “calculated exactly as national unemployment rates are, except that the calculations are performed for detailed occupations,” by dividing workers into up to 27 groups based on the International Standard Classification of Occupations (ISCO) developed by the ILO.
The logic—for which Rehm provides strong backing—is that “using a worker’s occupation as a reference group and approximating the probability of job loss by the unemployment rate of his or her occupation provides a good measure of risk exposure” in the employment domain. Figure 8.2 shows, for instance, that these rates are strongly correlated with individuals’ perceived job security. The figure pairs data from the US labor force survey and the US General Social Survey, showing the relationship between occupational unemployment rates (calculated for 27 occupation groups using labor force data) and whether a worker says it is “likely or very likely” they will lose their job in the coming year (calculated from the General Social Survey, with occupation groups assigned using the same ISCO standards). Given the imprecision of the survey question, the main point of interest is not the close numerical correspondence between the observed rate and perceived risk but the near-linear increase as occupational unemployment rates rise.
So far, unemployment has been the main focus of such inquiries, but similar group-specific measures of risk could in principle be constructed for other outcomes, such as reductions in pay or hours, entry into poverty, and reported sickness.5
These probability measures have the virtue of being readily understandable; moreover, in many cases they do not require panel data and thus can build on the large number of cross-sectional surveys already used by policy-makers. Indeed, labor force surveys are among the most widely available and reliable sources of data for inter-temporally and cross-nationally comparable indicators.
For all their virtues, however, these probability estimates can only be calculated for specific outcomes, and many important outcomes (such as large out-of-pocket medical expenses) may be more difficult to see in standard economic data than is t
he case for unemployment. Furthermore, they can only be justified as estimates of the probability of these outcomes for a given individual or household with additional theory or evidence that supports the assumption that all members of an occupation or other group face risk more or less equal to the group’s average probability.
Table 8.1. Welfare State Generosity, 2010
Note: Country characteristic scores are standardized using z-scores with mean and distribution based on a reference period. (Extreme values, including unlimited benefit duration, are dropped and assigned a maximum or minimum z-score.) These standardized characteristics for each program (unemployment, sick pay) are summed and the sum is multiplied by the coverage ratio. Thus, the sub-index is computed as: (Program generosity score) = [2*z(Benefit Replacement rate)+ z(ln(Benefit Duration weeks)) + z(ln(Benefit Qualification weeks)+ z(Waiting days)+12.5]*Insurance Coverage.
Source: Scruggs L. (2014), Comparative Welfare Entitlements Dataset (CWED2), University of Connecticut, and D. Jahn, University of Greifswald, http://cwed2.org/. StatLink 2 http://dx.doi.org/10.1787/888933839867.
Finally, because these measures are based on cross-sectional data—and, indeed, such fine-grained measures require samples of a size rarely achieved by panel studies—they cannot speak to the cumulative character of economic shocks. A group-specific unemployment rate, for example, only captures the share of a group that is out of work at any given time or during any given period. Yet the experience of unemployment is very different if particular individuals or households experience it again and again over time, as opposed to its incidence being spread broadly across the population.
Indices
Until now, the focus has been on discrete indicators, but a major class of measures used in time series and cross-national analysis attempts to bring together multiple indicators to “sum up” observed economic security. These indices range from the relatively simple measures of program generosity just discussed to relatively elaborate measures that try to capture both the probability of shocks and the strength of buffers within specific domains.
Figure 8.2. Occupational Unemployment Rates and Perceptions of Job Security, United States, 1972–2010
Note: The dashed line is the 45-degree line; the solid line shows the predicted probability of respondent saying he or she is likely or very likely to lose his or her job within the next 12 months, based on the US Current Population Survey (occupational unemployment rates) from 1972 to 2010 and the US General Social Survey (perceptions of job security within occupational groups) from 1972 to 2010, controlling for income, age, gender, work status, education, race, region (South versus outside South), and church attendance.
Source: Rehm, P. (2016), Risk Inequality and Welfare States: Social Policy Preferences, Development, and Dynamics, Cambridge University Press, Cambridge, MA. StatLink 2 http://dx.doi.org/10.1787/888933839886.
Among the latter, the work of Lars Osberg stands out as exemplary of domain-specific indices, which he calls the “named risks” approach. Though its implementation has evolved, the basic idea is that economic security is a product of (1) the probability of an adverse event and (2) the average consequence of that event, which is mediated by (3) the strength of the buffers available to protect people. Thus, Osberg’s approach embodies the threefold distinction between shocks, losses, and buffers common to most definitions of economic security.
The four domain-specific indices developed by Osberg and his colleagues are meant to describe economic security for residents of a given country with regard to unemployment, family breakup, medical costs, and poverty in old age. In practice, the precise indices mix (1), (2), and (3) in different ways. With regard to unemployment, for example, the focus is on (1) and (3): “security from employment” is defined as a product of the unemployment rate (1) and the breadth of and fraction of prior wages provided by unemployment benefits (3).6 With regard to family breakup, the focus is instead on (1) and (2): “security from single-parent poverty” is a product of the divorce rate (1) and the incidence and depth of poverty among single-female-parent families (2). In two cases—“security in the event of sickness” and “security from poverty in old age”—the index is based solely on (2): economic security with regard to medical costs is conceived of as (the inverse of) the average share of income spent on un-reimbursed medical expenses (2); and economic security in old age is conceived of as (the inverse of) the incidence and depth of poverty within a country among those older than 65.
The next section on key results that emerge from current evidence will consider these indices more fully, as well as nonindex integrated measures that attempt to capture economic security with a single indicator, such as the various measures of income volatility that have become so central to economic research over the last generation. For now, it suffices to lay out briefly the strengths and weaknesses of index measures in general and the index measures proposed by Osberg and his colleagues in particular (Osberg, 2015, 2010; Osberg and Sharpe, 2014, 2009).
A major strength of the domain-specific index approach is its ability to reflect the leading priorities embodied in public policy. That is, in this approach, deciding which domains to cover and how to conceptualize security within them can—and, indeed, must—be tailored to the goals of specific policies. Osberg bases his list of “named risks” on Article 25 of the United Nations’ 1948 Universal Declaration of Human Rights, which reads, “Everyone has the right to … security in the event of unemployment, sickness, disability, widowhood, old age or other loss of livelihood in circumstances beyond his control.” Consequently, Osberg’s measures focus on economic security with regard to “unemployment,” “sickness,” “widowhood,” and “old age.”
This strength, however, is counterbalanced by a potential weakness. Basing measures on the priorities of public policy increases the chance that they will be relevant to policy-makers, but reduces the ability to assess whether those priorities are aligned with the realities of economic security. Implicitly, this is acknowledged in the substitution of “single parenthood” for “widowhood” in the index of family-related security: in an age in which most mothers work and family breakup is common, death of the (male) family breadwinner is no longer seen as the only or even most salient financial risk that parents face.
A concern common to all indices is the appropriateness of weighting. Domain-specific indices of economic security weight at two levels. The first is weighting done to construct the domain-specific index itself. In recent iterations of the index of “security in the event of unemployment,” for example, Osberg and his colleagues have weighted the unemployment rate more heavily than unemployment benefits on the grounds that it has a larger effect on well-being. While this justification has merit, it moves the index away from a relatively simple measure—the joint product of the unemployment rate, the average replacement rate, and the share of workers covered—toward one that is more complex and potentially ad hoc.
Second, to the extent that these domain-specific measures are to be combined into a comprehensive index, there is the question of how to weight these different and possibly even incommensurable measures. At least four options present themselves: equal weighting (as in the Human Development Index); weighting on the basis of some measure of policy priority, such as spending on particular social benefits; weighting on the basis of the impact of each domain on household well-being; and weighting on the basis of measures of perceived security, such as the importance that individuals ascribe to security within a given domain, or the impact of shocks within that domain on perceived security. The last two approaches have considerable merit, but for the most part the evidence they require is lacking. If that evidence were stronger, moreover, it might well allow for more direct measures of observed security than the indices under consideration.
Another great strength of these domain-specific indices is that they rely on easily obtained and generally reliable data. The flipside is that these aggregate-level data may not correspond closely with individual
experience or lend themselves to finer-grained comparisons across groups. The potential slippage between formal benefit rules and actual implementation has already been discussed. Also problematic is the inconsistent treatment of public and private buffers. Some of the measures proposed—un-reimbursed medical expenses, old-age poverty—are designed to measure outcomes in the context of all public and private protections. In the case of unemployment, however, only public benefits are considered, even though personal savings, spousal labor supply, and other private buffers may be an important source of additional support.
Finally, these indices were designed to measure economic security at the country level and are not always easy to apply to less highly aggregated groups. Certainly, it is not possible to use them to say that particular individuals or households are economically secure since, like estimates of the probability of specific shocks, they require a reference group. Thus, they are less useful for calculating the distribution of economic security within countries than they are for comparing levels of security across countries.
Volatility
Over the past generation, a large literature on income and consumption volatility has accumulated, driven by advances in both data and theory (e.g., Gottschalk et al., 1994; Moffitt and Gottschalk, 2002; Hacker, 2008; Hacker and Jacobs, 2008; Dynan, Elmendorf, and Sichel, 2008; Acs and Nichols, 2010; Nichols and Zimmerman, 2008; Nichols and Rehm, 2014; Kopczuk, Saez, and Song, 2010; Gorbachev, 2011; Dogra and Gorbachev, 2016). With regard to data, the growth of high-quality panel studies has made it feasible to examine the over-time fluctuation of economic outcomes at the individual or household level in a way that was rarely possible in the past.
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