In addition to the shock-specific measures, Osberg and his colleagues calculate an aggregate index of economic security, weighting each component based on the share of the population in each year presumed to face the relevant risk (i.e., everyone aged 15 to 64 years in the case of unemployment; the entire population in the case of sickness; married women with children under 18 in the case of single-parent poverty; and adults aged 45 to 64 in the case of old-age poverty).
Figure 8.3. Osberg’s Index of Economic Security, Selected OECD Countries, 1980 and 2014
Source: Centre for the Study of Living Standards (2016), Database of the Index of Economic Well-Being for Selected OECD Countries and Alberta, 1980–2014, www.csls.ca/iwb/FinalIEWBAlbertaandOECD2014.xlsx. StatLink 2 http://dx.doi.org/10.1787/888933839905.
For ease of exposition, this aggregate measure is used in Figure 8.3 to compare all 14 countries, with the dark bar indicating the beginning of the series (1980) and the lighter bar the end (2014).
Figure 8.3 points to several notable features.12 First, countries differ substantially in their overall level of economic security. In 2014, economic security was greatest in Norway, with a value of 0.83 (the indices are constrained to be between 0 and 1), followed by Denmark with a value of 0.81. By contrast, the United States had the lowest score at 0.48, followed by Spain (at 0.59).
Second, in most nations, the index does not change much between 1980 and 2014. Only three nations experienced a change in the index greater than 10%: Canada (12% increase), Spain (18% decline), and the United States (a 62% increase in economic security).
The relative stability of the aggregate index reflects relatively little change in most of the shock-specific indices.13 The most notable trend in the shock-specific indices is an overall increase in “security from single-parent poverty” and “security in old-age poverty.” These indices improve because of reductions in the prevalence and severity of poverty among single-parent households and the elderly, respectively.
Whether poverty is a good measure of economic security is an important question, and one that will be taken up in the conclusions. For now, it is simply worth noting that levels of poverty are not necessarily the same as the risk of poverty. Assessing risk requires looking at the probability that any individual or group will enter poverty over some defined interval, which in turn requires considering income dynamics. It is to the topic of income dynamics—and specifically the prevalence of large income drops—that we now turn.
The Prevalence of Major Income Drops
The second set of findings reviewed here is based on the Economic Security Index. Recall that the ESI is a hybrid measure constructed using panel data that captures the share of individuals experiencing a 25% or greater year-over-year decline in family-size-adjusted real household income net of out-of-pocket medical spending. The ESI excludes from this share both the very small portion of the population entering retirement between one year and the next and the larger (but still relatively small) proportion possessing liquid financial wealth sufficient to self-finance the loss.14
Unfortunately, neither out-of-pocket medical spending nor liquid financial wealth is reliably reported in many panel studies. Thus, what is presented here is a limited version of the ESI without the medical spending or wealth adjustments. This measure captures the share of the adult population experiencing a 25% or greater loss in (household-size-adjusted) individual income from one year to the next.15 It can be thought of as a measure of income risk, whether for the population as a whole (when looking at the prevalence of 25% or greater losses nationwide) or for specific sub-groups (when looking at the prevalence among regional, occupational, or demographic groupings).
This measure is simple, feasible given existing data, and scalable, with a larger absolute loss required as income rises. Given this last characteristic, it is also comparable across countries with different average per-capita income levels. As noted previously, the 25% threshold was based on US survey questions that asked how long respondents could go without their income before experiencing hardship. Whether responses to this sort of question would be similar in other countries is unclear. It is worth reporting, however, that within a plausible range neither overtime trends nor the rankings of countries are particularly sensitive to the exact threshold chosen.
This measure can be constructed for almost forty countries, reflecting the growth of reliable panel studies in the past two decades. Since panel data are a crucial precondition for any approach that tracks individuals or households over time, it makes sense to begin with a brief summary of the data. The next section discusses the changing scope and character of panel data in greater depth.
Producing these estimates required assembling various panel studies that cover more than a few years and have been more or less continuous since their creation. For many of these studies, the period that can be analyzed is relatively brief. With the exception of a few pioneering panel studies—notably, the German Socio-Economic Panel (GSOEP), which starts in 1984, and the US PSID, which starts in 1968—country-specific panel studies mostly date from the 1990s. These encompass Australia, Canada, Korea, Sweden, Switzerland, and the United Kingdom. Another wave of studies began in the early 2000s with the launch of the European Union’s Survey of Income and Living Conditions (EU-SILC), eventually encompassing more than 20 countries.
For the United States, in addition to the PSID, panel data can be obtained from the SIPP and from the matched CPS files. Though the SIPP, PSID, and CPS were all used to construct the Economic Security Index, the estimates presented here use matched CPS files, which produce the largest sample. The main drawback of matched CPS results is their short (two-year) panels, but they are sufficient to measure year-over-year income drops.16
Figure 8.4 provides a simple picture of the prevalence of large income losses in all the countries for which data are available, as well as changes over the most recent period encompassing the financial crisis (roughly from the early 2000s until the end of the data series). The length of the bars shows the range of prevalence estimates across the years (indicated next to the country abbreviation). The point marker shows that average for these estimates within each country across these years. Finally, if there is a trend in the data over the most recent period, this is indicated by an arrow showing the direction of the change.
Thus, the figure shows the average, range, and recent evolution of income risk in these countries.
As Figure 8.4 shows, the prevalence of large income losses varies substantially both across countries and within countries over time. Within the group considered by Figure 8.4, the countries with the greatest range and highest average prevalence of large income losses are generally those that were hit hardest by the financial crisis, including Spain, Greece, and Iceland. The countries with the lowest average prevalence of large income losses include the Netherlands, Sweden, Switzerland, Denmark, Norway, and Finland. The United States—the country that has been the focus of most analyses of income volatility—has a relatively high prevalence of large income losses.
Moreover, the ranking of countries is fairly consistent with the “named risk” approach, especially its measure of security in the event of unemployment. Figure 8.5 shows the correlation between this measure, averaged for 2006–08, and the average prevalence of large income losses over the same period in the 14 countries for which both estimates are available.
Finally, while the prevalence of large income losses generally rose between the early and late 2000s, this trend was largely cancelled out as countries recovered from the crisis. Indeed, only a handful of countries—most notably the United States—have experienced a secular rise in the prevalence of large income losses. Most countries have seen little or no increase in the incidence of large income losses, and a few (e.g., Switzerland, Norway, and Austria) witnessed a decline. Since these data encompass the period of the financial crisis and the slow recovery that followed, this is a notable finding, suggesting that some countries were able to reduce the fallou
t of the crisis and its aftermath, at least when it comes to income risk.
This hypothesis can be put to a more direct test by parsing the components of household income. In the literature on income inequality, researchers commonly distinguish between inequality before and after taxes and transfers, and use the difference between the two as a rough measure of how much taxes and transfers reduce inequality. More specifically, the standard approach is to calculate the difference between the Gini coefficient of the distribution of market income [Gini(MI)] and the Gini coefficient of the distribution of disposable income [Gini(DI)].17 Because Gini coefficients are summary indicators, this measure is necessarily calculated at the aggregate level.
Figure 8.4. Average, Range, and Evolution of the Incidence of Large Income Losses
Notes: Based on the following panel data collections: ECHP, EU-SILC, CPS, CNEF (BHPS, SOEP, HILDA, KLIPS, SHP, SLID). For each country, the period covered is indicated on the horizontal axis. Arc-changes, unlike percentage changes, treat gains and losses symmetrically (e.g., an income gain from $50 to $100 implies a 100% change but a 67% arc-change; while an income loss from $100 to $50 implies a 50% change but a 67% arc-change); they are bound between +2 and -2. StatLink 2 http://dx.doi.org/10.1787/888933839924.
When looking at the prevalence of large income losses, an equivalent approach would distinguish between losses in income before and after taxes and transfers, and assess how many fewer citizens experience large income losses when taxes and transfers are taken into account.18 In other words, the reduction of risk would be calculated as the difference between the aggregate prevalence of market income losses [Risk(MI)] and the aggregate prevalence of disposable income losses [Risk(DI)], as follows: [Risk(MI)- Risk(DI)]/Risk(MI).
Figure 8.5. The Incidence of Large Income Losses versus Osberg’s Scaled Index of Economic Security
Note: Refer to Figure 8.3. Osberg’s Index of Economic Security, Selected OECD Countries. Sample: AUS 2001–14, BEL 1993–2014, CAN 1993–2010, DEU 1984–2014, DNK 1993–2014, ESP 1993–2014, FIN 1995–2014, FRA 1993–2014, GBR 1992–2014, ITA 1993–2014, NLD 1993–2014, NOR 2002–14, SWE 2003–14, USA 1985–2012.
This approach, however, is less precise than necessary. When comparing summary measures such as Gini coefficients, there is no way to determine who changes rank as the income measure switches from market income to disposable income. By contrast, with the current measure of income risk, it is possible to know who is prevented from experiencing a large income loss by taxes and transfers. In particular, individuals can be classified into four categories based on whether they experience a qualifying drop in market income, in disposable income, or in both, as illustrated by the two-by-two matrix in Table 8.2.
The matrix shows the four possible combinations of market and disposable income dynamics. Those in the top-left cell [1] do not experience large income losses in either market income or disposable income. Pooling the data for all countries and years, about 81% of adults belong to that category. At the other extreme, those in the bottom-right cell [4] experience large income losses in both disposable income and market income. On average, about 10% of adults are in this unfortunate situation.
The top-right cell [3] refers to individuals who experience a large drop in market income, but not in disposable income. For these individuals, about 7% of adults, the tax and transfer system serves as a safety net that keeps them from crossing the 25% threshold. Finally, the bottom-left cell [2] indicates the peculiar situation in which the tax and transfer system actually exacerbates income losses. Fortunately, these cases are rare, roughly 2% of observations.
Table 8.2. Incidence of Risk (Market Income versus Disposable Income)
Note: Based on the following panel data collections: ECHP, EU-SILC, CPS, CNEF (BHPS, SOEP, HILDA, KLIPS, SHP, SLID). Sample: AUS 2002–16, AUT 1995–2014, BEL 1994–2014, BGR 2006–14, CAN 1994–2010, CHE 2001–16, CZE 2005–14, DEU 1985–2015, DNK 1994–2014, ESP 1994–2014, EST 2004–14, FIN 1996–2014, FRA 1994–2014, GBR 1993–2015, GRC 1994–2014, HRV 2010–14, HUN 2005–14, IRL 1994–2014, ISL 2004–14, ITA 1994–2014, KOR 2004–14, LTU 2005–13, LUX 2004–14, LVA 2007–14, MLT 2006–14, NLD 1994–2014, NOR 2003–14, POL 2005–14, PRT 1994–2014, ROU 2007–14, SRB 2013–14, SVK 2005–14, SVN 2005–14, SWE 2004–14, USA 1986–2011. StatLink 2 http://dx.doi.org/10.1787/888933839943.
These cells suggest a more precise measure of the buffering role of the tax and transfer system: the share of adults who experience a large loss in market income but not in disposable income. The proportion is substantial: on average, the tax and transfer system mitigates large income drops for about 41% of individuals experiencing large income drops (7/(7+10) = 0.41). However, this number varies dramatically across countries and over time.
Figure 8.6 provides a summary picture of the role of taxes and transfers in reducing the prevalence of large income losses in our set of countries during the most recent period (again, roughly the early 2000s until the end of the relevant data series). The length of the bars shows the average share of adults who see their market income decline by 25% or more, but are prevented from experiencing a large disposable income loss by taxes and transfers. The arrows show whether this buffering role has increased or decreased over time.
The conclusion suggested by this figure is that countries with high levels of income risk (as shown in Figure 8.4) do relatively little to cushion large market income losses through the tax and transfer system. Moreover, the recent evolution of this buffering role indicates that countries have, if anything, increased the degree to which they reduce large income losses during the recent crisis. That is to say, a growing share of those who experience large market income losses are prevented from experiencing large disposable income losses thanks to the tax and transfer system. Whether this reflects a conscious change in policy or, more likely, the resilience of countries’ safety nets during the economic crisis, it provides some reassurance to those who worry that national systems of social protection are becoming progressively less capable of covering contemporary risks to income. It remains to be seen whether this conclusion will continue to hold true once the deep employment effects of the crisis recede.
Finally, while all these estimates have looked at the adult population as a whole, they can be used to examine socio-demographic differences within countries. Figure 8.7 contains one such comparison looking at the average prevalence of large disposable income losses for adults with different levels of formal education. Not surprisingly, in most nations, income losses are much more likely among adults with limited formal education as compared with those with extensive formal education. Though not reported in Figure 8.7, income losses are also more prevalent among lower-income adults (when income is averaged across all available observations for each individual), workers who are not members of trade unions, heads of single-parent households, and younger adults.
Indeed, it is possible to look at more than just the differences across broadly defined groups. When panels are sufficiently long, it is also possible to construct individual-level or household-level measures for extended periods of time, as is done in the volatility literature.
Table 8.3, for example, shows the share of the working-age population experiencing one, two, three, or four or more large income losses over a decade.19 Over 10 years, as the table shows, only a minority of working-age adults in Australia, Germany, the United Kingdom, Switzerland, and the United States were fortunate enough to escape the experience of a year-over-year income loss of 25% or more. On the other side, roughly 23% of Americans experienced 2 drops, about 12% experienced 3 drops, and over 7% experienced 4 or more drops, indicating that close to half of Americans experienced 2 or more large income losses in that decade.
Figure 8.6. Effect of Taxes and Transfers on the Incidence of Large Income Losses
Notes: Based on the following panel data collections: ECHP, EU-SILC, CPS, CNEF (BHPS, SOEP, HILDA, KLIPS, SHP, SLID). For each country, the period covered is indicated on the h
orizontal axis. Arc-changes, unlike percentage changes, treat gains and losses symmetrically (e.g., an income gain from $50 to $100 implies a 100% change but a 67% arc-change; while an income loss from $100 to $50 implies a 50% change but a 67% arc-change); they are bound between +2 and -2. StatLink 2 https://doi.org/10.1787/888933839962.
Figure 8.7. Incidence of Income Losses by Education, Pre- to Post-secondary
Note: Based on the EU-SILC for 2012/2013.
Table 8.3. Number of Large Year-to-Year Income Drops Experienced by Individuals over a Decade
Note: Based on the CNEF panel data collection (PSID, BHPS, SHP, SOEP, HILDA). Sample: AUS 2002–16, CHE 2001–16, DEU 1985–2015, GBR 1993–2006, USA 1971–97. StatLink 2 http://dx.doi.org/10.1787/888933839981.
In sum, the prevalence of large income losses appears to be a sensible measure of economic security that varies across countries and individuals in ways generally consistent with other relevant findings (including, as discussed in the last section, measures of perceived economic security). Still, it remains incomplete in some key respects, and significant additional work will need to be done to develop a broader evidence base for measuring other elements of economic security (such as large consumption shocks) and extending these measures to a larger set of countries. The next section discusses this evidence base in greater depth.
Are Available Statistics Adequate to Inform Policy?
As is clear from the discussion thus far, the low availability of reliable and cross-nationally comparable data has been a crucial constraint on the development of improved measures of economic security. Three shortcomings of existing statistics stand out: the limited pool of long-term and cross-nationally comparable panel data; the weaknesses of most administrative data for tracing individuals over time; and the lack of regular questions about economic security in conventional random-sample opinion surveys and, to even greater extent, in panel data. Nonetheless, these data have been rapidly improving, as shown when reviewing each of these major sources.
For Good Measure Page 36