4. Unemployment insurance covers national insurance provisions earned without income-testing. Sick pay covers benefits paid in the event of short-term nonoccupational illness or injury. This includes provisions for mandatory private (employer-paid) benefits in addition to public insurance. Public pensions include only mandatory public programs (except for the nominally private Finnish earnings-related fund). Data are also provided for replacement rates of minimum pensions for those without work history. Replacement rates, eligibility criteria, and duration of benefits are calculated for a notional average production worker in manufacturing who is 40 years old and has been working for the 20 years preceding the loss of income or the benefit period. Two different household types are accounted for: single (living alone, no children, or other dependents) and family (cohabiting with a spouse with no earnings, two children aged 7 and 12). Replacement rates are calculated by annualizing the first half year of benefits (i.e., calculating the benefit for the first 26 weeks and multiplying by 2). The reference wage for replacement rates is the “average production worker wage.” General government cash transfers are accounted for when calculating the net wage; the replacement rate for families refers to income that includes child/family benefits (Scruggs, 2014a and 2014b).
5. A more elaborate example of a probability-estimate measure is the “Retirement Risk Index” developed by Munnell and colleagues to study changes in US economic security (Munnell, Webb, and Delorme, 2006). This measure is designed to capture the probability that working-age Americans who have yet to claim public and private retirement benefits will retire without adequate income. In essence, this risk measure marries the buffer approach discussed previously with the group-specific probability estimates currently under consideration. It does so by calculating available wealth for retirement (based on present wealth and its forecasted growth, plus expected public and private benefits) and then comparing this household-specific total with the amount needed to purchase an actuarially fair annuity that offers an income-replacement rate judged sufficient by established models of retirement planning. What makes it a group-specific risk measure is that these numbers are then used to calculate the future probability of inadequate retirement income as just defined for various educational and income groups and age cohorts. (The main finding is that retirement preparedness has declined sharply overall, particularly so for younger and poorer Americans.) Thus, this measure is conceptually equivalent to Rehm’s—though it uses forecasted income rather than observed unemployment to assign probabilities.
6. Another example of such measure is the index of labor market security used by the OECD in the context of its OECD Job Quality Framework. This index is measured as the product of unemployment risk (the monthly probability of becoming unemployed times the average expected duration of completed unemployment spells in months) and (one minus) unemployment insurance (the coverage of the unemployment insurance/assistance times the replacement rates of public transfers received by the unemployed). See Cazes, Hijzen, and Saint-Martin (2015).
7. Just how discretionary medical spending truly is remains a major topic of analysis, which is not discussed here; suffice it to say that the largest out-of-pocket expenditures are likely to be the least within the immediate control of individual patients.
8. Another volatility-related hybrid measure is the approach of Bossert and D’Ambrosio (2013), who measure economic security as a weighted sum of household wealth and its past volatility. Like other volatility measures, this approach provides a household-level estimate of economic insecurity (which can be translated into an individual-level estimate by assuming equal distribution of household-size-equivalized resources—the common approach in the volatility literature). Bossert and D’Ambrosio apply their approach to the US PSID and to the Italian Survey of Household Income and Wealth (SHIW). Without going into the precise characteristics of this measure—and, indeed, Bossert and D’Ambrosio say that researchers presently do not know exactly how to weight current wealth and past wealth volatility—its main strength is its integration of wealth levels and changes into a single measure. Its main weaknesses are, first, panel studies with high-quality wealth data are rare, certainly when compared with income data; and, second, there is limited evidence that changes in net worth are, by themselves, a major source of economic insecurity. In part, this is because wealth can change without any direct material hardship if asset prices fluctuate but individuals are not required to liquidate their wealth; in part, it reflects the aforementioned issue of how losses should be treated relative to gains (Bossert and D’Ambrosio are agnostic on this question, though they say that losses should be weighted at least as heavily as gains). By contrast, there is considerable evidence that large income losses make people feel less secure.
9. The SIPP has a shorter panel structure that allows assessment of whether the specific annual accounting period—i.e., what dates are considered as start and end points of a year—makes a significant difference to the results; it does not.
10. How much is required to buffer a loss is determined by using the PSID to determine how long it takes for the median household with similar characteristics experiencing a similar-sized drop to return to their pre-drop income level, and then summing the cumulative income shortfall over this period.
11. This could be done either by adjusting household income at retirement or by accounting for the full range of work-related nondiscretionary expenditures that retirees need not incur. At present, those retiring in the previous year are excluded from calculation of the ESI, so as not to confuse entrance into retirement with an adverse shock.
12. The next three paragraphs draw heavily on Osberg and Sharpe, 2014.
13. These trends in poverty, in turn, raise two questions: First, should poverty be assessed using country-specific poverty levels; and second, should these levels be absolute (i.e., the same across time or space) or relative (e.g., less than 50% percent of median income, the standard used by Osberg and his colleagues)? For comparing across countries, there is a strong argument for using country-specific thresholds. Poverty is commonly understood as deprivation relative to other members of a given society. It would be difficult, for example, to use the same standard for old-age poverty in Bangladesh as in Belgium; there would either be no poverty in the latter or near-universal poverty in the former. For over-time comparison, however, there is a stronger argument for using absolute poverty levels—for example, by fixing poverty levels for each nation at the beginning of a series—so as to separate out trends in economic security from trends in median income. This is particularly true when looking at short time intervals during which it is plausible to argue that the income levels that define poverty remain relatively constant.
14. Technically, the cumulative loss that occurs before income returns to its pre-drop level for a typical household with similar characteristics and a similarly sized loss.
15. Technically, the threshold is a 25 percent or greater arc-percent change. Arc-percent changes are calculated as 2*(Income[t]-Income [t-1])/[(Income[t] + Income[t-1])]. Unlike percent changes, arc-percent changes are bound by minus and plus 2, and they treat gains and losses symmetrically. For example, a respondent doubling her income from $50 to $100 experiences a 100 percent change (but a 67 arc-percent change). A respondent with a change in income from $100 to $50 experiences a 50 percent change (but a 67 arc-percent change). The arc-percent approach treats the change of $50 in a symmetric fashion.
16. All data were cleaned and standardized following established conventions. To deal with outliers as well as different top-coding and bottom-coding rules, income values were bottom-coded at 1 national currency unit (NCU) and top-coded at the 98th percentile. The main income variable used is total household income after taxes (including all cash benefits), adjusted by family size.
17. Usually, researchers calculate the percentage difference between the two [Gini(MI)-Gini(DI)]/Gini(MI)], though sometimes they use the absolute difference.
18. This approach
raises issues that can only be touched upon here. The biggest one is the common assumption—embodied in the convention of calling pre-tax, pre-transfer income “market” income—that income before taxes and transfers is a pristine reflection of market forces, while disposable income after taxes and transfers captures the effects of government policy. In fact, taxes and transfers can greatly affect labor and capital markets, hence “market” income as well as disposable income. In addition, governments can and do attempt to shape market income through a range of tools besides taxes and transfers, including regulatory and macro-economic policies. For these reasons, it is best to think of the difference between market and disposable income as suggestive of the role of taxes and transfers but not, by itself, as offering a complete or definitive assessment.
19. The sample consists of respondents aged 25–60 with 10 consecutive years of nonmissing information on income drops (most recent spell). Weights from the final year of the spell are applied.
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9.
Measuring Sustainability
Marleen De Smedt, Enrico Giovannini, and Walter J. Radermacher
This chapter outlines the principles of the capital approach and of the systems approach to measuring sustainable development. In the capital approach, human, social, natural, and economic capital are considered separately, with indicators presented on their stocks and how they change over time. While significant progress has been achieved in operationalizing this approach to sustainability, this approach, argue the authors, implicitly assumes the independence of these stocks, and does not easily lend itself to considering interactions between different parts of the systems that underpin human well-being and functioning ecosystems. The chapter considers how the systems approach should be taken forward to move from theoretical considerations to empirical applications. It explains the key notions underpinning the systems approach, including risk, vulnerability, and resilience, arguing that sustainability remains the ultimate objective. The chapter proposes a measurement agenda, suggesting steps to improve consideration of economic, human, and natural capital in the capital approach; and to improve the measurement of resilience and other aspects of the systems approach.
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