Panel Data
Measures that look at fluctuations in individuals’ economic standing over time generally require panel data. Historically, this has been a key constraint on the development of better metrics on insecurity.
To be sure, panel data are not always necessary. Cross-sectional data can be used to develop a number of the measures discussed in this chapter, including assessments of the buffers that individuals and households have and estimates of the cross-sectional incidence of key economic shocks, such as unemployment. The challenge, however, is that such data can only provide insight into the point-in-time incidence of shocks, rather than their over-time prevalence. Moreover, it is generally not possible to use cross-sectional data to estimate how buffers change for particular households or individuals, or whether shocks are concentrated among specific groups rather than broadly distributed across the population.
Fortunately, a wide range of panel data sets have been inaugurated in the last two decades (see sidebar, “Major Panel Data Sources”). One weakness of many of these sources, however, is their lack of simultaneous data on income, consumption, and wealth. In addition, most do not contain questions that allow researchers to assess perceived economic security alongside observed economic security. Finally, differences in question wording and survey design can make it difficult to compare results across different panels. The recommendations presented at the end of this chapter aim to remedy these problems, and to increase the number of countries for which panel data are available.
MAJOR PANEL DATA SOURCES
For measuring economic insecurity, we would like multiple observations on income over a long period of time at a relatively high frequency. Typically, we have instead a short panel with high-frequency data collection, or a long panel with lower frequency.
In the United States, the PSID has measured incomes on an annual basis back to 1968, but has not been refreshed to stay representative in every year, and it switched to biennial reporting of incomes in 1997. The SIPP has measured monthly incomes every four months since 1984, but starts new panels every few years and switched to annual reporting in 2016. The CPS has annual income in March (matchable to the prior or subsequent March for a fraction of the sample) but does not follow movers. The Health and Retirement Survey and National Longitudinal Surveys are lower frequency and do not represent the full population.
Long panel surveys are available for many developed countries, including the Household, Income and Labour Dynamics (HILDA) in Australia, the Canadian Survey of Labour and Income Dynamics (SLID), the German Socio-Economic Panel (GSOEP), the Korea Labour and Income Panel Study (KLIPS), the Swiss Household Panel (SHP), and the British Household Panel Study (BHPS), plus harmonized country panel data sets in the Cross-National Equivalent File (CNEF) and the EU-SILC. China and many less developed countries have or are soon to have panel household surveys as well.
Source: Courtesy of Austin Nichols.
Administrative Data
New data sets linking tax and program data are becoming available in a handful of countries. These can be superior to panel data because of the well-known problems of recall bias and attrition in panel data. However, most administrative data sets remain limited, and they suffer from their own problems, including significant restrictions on their use by researchers (see sidebar, “Major Administrative Data Sources”). A promising new source of data is the large data sets being created by financial institutions. These data allow very fine-grained analysis of economic dynamics, but suffer from some serious problems, including limited availability, limited scope (often encompassing only transactions conducted through a single financial institution), and lack of representativeness of the individuals/households included.
MAJOR ADMINISTRATIVE DATA SOURCES
Administrative data from tax records are sometimes hard to access, but offer the possibility of longer panels with annual measures of income. In the United States, for example, the Longitudinal Employment and Household Dynamics (LEHD) data set, available in secure Census data facilities, links multiple administrative records on people and firms. Similarly, the Social Security Administration (SSA) hosts the Master Earnings File (MEF) containing all earnings reported to the SSA.
These sources offer long series of annual income thought to be of high quality because they are subject to auditing. Recently, efforts have been made to merge multiple administrative records with government survey data, as in the “Gold Standard File” created by the US Census and the SSA. An important point to bear in mind with matched data sources is that merge rates are never 100%, and matched samples may be less representative of the population of interest.
Federal government sources offer large sample sizes, but so too does a new generation of corporate data sources, both from regular clients and special surveys such as those conducted by AC Nielsen. Credit reporting agencies and major banks have in some cases better coverage of annual totals for both household income and expenditures, thanks to aggregating transaction-level data for account holders. Many of these sources can also track income across country boundaries and may include transactions not subject to third-party reporting and therefore missing from government files. Nevertheless, many low-income or unbanked individuals will never be observed in these files.
Source: Courtesy of Austin Nichols.
Questions About Economic Security in Conventional Opinion Surveys
As noted in the discussion of perceived security measures, opinion surveys sometimes include questions about economic security, particularly job security. These questions typically ask respondents to assess how likely it is that they will experience specific adverse economic events, or how well protected they would be if they experienced such events. Cross-nationally comparable survey questions are relatively rare, however, and overwhelmingly concern economic security in the domain of employment. Moreover, even within countries, panel surveys are relatively rare, making it difficult to assess the causes of changing perceptions of economic security at the individual level.
Nonetheless, a number of cross-sectional surveys do include questions regarding perceived job security. These include:
• International Social Survey Program (ISSP) Work Orientations I–III: “For each of these statements about your (main) job, please tick one box to show how much you agree or disagree that it applies to your job. My job is secure” (1989, 1997, 2005).
Figure 8.8. How Insecure Is My Job?
Note: “I might lose my job in the next 6 months.” Percentage of respondents who “strongly agree” or “agree” (versus “neither agree nor disagree”/“disagree”/ “strongly disagree”).
Source: Eurofound (2010), European Working Conditions Surveys (EWCS), 2005/2010, www.eurofound.europa.eu/surveys/european-working-conditions-surveys. StatLink 2 http://dx.doi.org/10.1787/888933840000.
• European Social Survey (ESS): “Using this card, please tell me how true each of the following statements is about your current job. My job is secure” (2004, 2010).
• European Quality of Life Survey (EQLS): “Using this card, how likely or unlikely do you think it is that you might lose your job in the next 6 months?” (2003, 2007, 2011/12).
• Eurobarometer (EB): “Here is a list of statements about your current job. For each of them, please tell me if it is very true, quite true, a little true, or not at all true? My job is secure” (1996, 2009).
Figure 8.8 shows the country-year averages for one such question in the European Working Conditions Surveys (EWCS): “How much do you agree or disagree with the following statements describing some aspects of your job? I might lose my job in the next 6 months” (2005 and 2010):
Conclusions
This chapter has reviewed and extended the growing literature on economic security, outlining a range of measures and data that are improving analysts’ and policy-makers’ understanding of this critical aspect of economic life. This final section provides a series of suggestions for promoting more and better research on economic security and improvi
ng the base of data on which this work rests.
The core point is that researchers, national statistical agencies, and key international organizations should work together to improve and augment existing measures. No one doubts the fundamental importance of economic security. Yet few measures of this vital phenomenon are widely used and accepted. The goal of this chapter has been to lay out a small number of such measures that are consistent with available theory and evidence, can be relatively easily produced for multiple countries using extant data, and can guide policy-makers seeking to safeguard and increase economic security.
The task going forward will be to refine these measures to produce a small number of reliable indicators of economic security that can help experts and policy-makers evaluate economic well-being and social progress. These measures will necessarily be imperfect, and no single one will suffice on its own. Together, however, a small set of measures could provide a baseline for assessing and comparing economic security while creating the foundation for improved measures in the future.
Improving and Encouraging Research on Economic Security
The starting point for these efforts is a stronger conceptual foundation. Research on economic security requires a major collaborative program spanning policy-makers, researchers, and theorists to refine both theory and measurement. With regard to theory, the concept of economic security remains in its infancy. Substantial progress in the study of economic security will require its further refinement, with particular attention to the role of individual psychology. With regard to measurement, the development of stronger indicators will require grappling with the intertwined role of income, consumption, and wealth; the causes of losses (including the difficult question of how to establish causality given the ubiquity of confounders in this realm); and the improvement of measures of perceived security. It will also require better integration of measures of perceived and observed security within major data sources, the next focus.
Improving and Augmenting Data Related to Economic Security
Refinements in theory and measurement will only bear fruit if they rest on an adequate base of high-quality data. In particular, there is a pressing need for cross-national data efforts that bring together researchers and National Statistical Offices to develop panel data that are comparable across countries. These data should include perceived security measures alongside traditional panel economic variables, as is now done in a handful of panel studies. In addition, efforts should be made to link panel data with administrative data that incorporates public tax and program data sets.
At the same time, researchers and National Statistical Offices, as well as interested private survey firms, should work together to create a small subset of “security monitors” that could be incorporated into opinion surveys. On the whole, data on the subjective experience of economic security that could allow for monitoring of over-time or cross-country trends remain sparse. Fielding better cross-nationally comparable surveys is therefore imperative. Prior to that, however, extant measures should be compared with measures of observed security to see how closely they correspond. (For that purpose, including more questions about perceived security in panel data sets would again be invaluable.)
Any security monitors based on opinion surveys must be closely tied to the best available research on how to gauge security through a limited number of survey instruments. Here, survey designers should draw on the experience gained by the large community of researchers working on subjective well-being. Moreover, questions should be designed to cover a broad range of risks, not just those related to employment—including risks related to retirement and family dissolution, as well as access to and affordability of food, housing, and health care.
Finally, efforts should be directed at developing innovative new data sources that can be scaled up, including lab experiments that probe subjective risk perceptions and individuals’ willingness to pay to insure themselves against key economic risks. A central question here will be how to distinguish “economic security” from general risk aversion, since the former is broader than the latter. Collaboration with private businesses and other institutions that provide financial services should also be encouraged to obtain new data on individual economic dynamics that address the major current limits of proprietary sources. In all these efforts, data should be designed to capture the joint distribution of income, consumption, and wealth, as argued elsewhere in this volume.
Identifying a Small Number of Core Measures of Economic Security
In the meantime, researchers, policy-makers, and national statistical agencies should work to develop a handful of core measures of economic security that can be incorporated into a “dashboard” of indicators of well-being. In addition to refined ESI-style measures of income risk, these indicators should include one or a few perceived measures of economic security; one or a few measures of buffers (such as asset poverty); and one or a few “named-risk measures” (probability multiplied by severity) for a few major risks such as unemployment, uninsured medical expenses, and inadequate retirement income.
There is a strong case, however, for not aggregating these various measures into a single index, since the logic of current weighting schemes is relatively weak. When employing Osberg’s named-risk approach, moreover, it would be ideal to have all such measures focus on the same basic outcome: the uninsured income loss in the event of a particular adverse shock. Ideally, these estimates would also be based on panel data, reducing the gap between measures of income loss and the “named-risk” approach.
With regard to the ESI-style measure of income risk and the estimates developed, this chapter indicates that it should be possible to develop reliable measures that can be used to compare multiple countries. Nonetheless, several questions remain. For starters, is it possible to incorporate out-of-pocket medical spending into such estimates, as is done for the United States in the ESI? Out-of-pocket medical costs represent a substantial financial burden for many households, so ignoring their effect on economic security leaves an incomplete picture. However, rising out-of-pocket costs may reflect improvements in the quality or volume of care, so their welfare effects are ambiguous. How to treat medical costs is a question that research on economic security needs to better address.
Additional questions must be addressed as well. Perhaps the most pressing is whether wealth and consumption data can be integrated into measures of income loss. Should, for example, such measures subtract debt service or treat wealthy individuals differently from those without wealth, as is done in the ESI? Can consumption effects be used as a test of whether income losses are involuntary? Can measures better account for the differing consumption needs of different households?
A large set of questions also concern the causes of income losses. With present data, it is not always possible to link large losses to discrete changes in the standing of workers or their families. Improved data and measurement techniques should make it possible to pinpoint the causes of large losses more precisely. It is worth keeping in mind, however, that large losses are often the result of multiple simultaneous shocks, which presents difficulties for those interested in parsing their causes. Moreover, even with the best available data, it is always difficult to distinguish correlation from causation in observational studies.
Over the past few decades, economic security has gained heightened salience due to major changes in the workplace and the family, as well as mounting pressures on public programs and the erosion of key private protections. These changes—and the popular concerns they have generated—make it imperative that researchers and policy-makers better understand how economic security is changing, who is most affected by these changes, and what policies are best poised to address the resulting dislocations.
The findings and recommendations of this chapter point to major areas where our knowledge of economic security has improved. The chapter has also highlighted areas where work can and should be further advanced. Better concepts and measures would not only bene
fit economic research. They could also help policymakers evaluate how well existing policies protect economic security and guide them as they sought to improve those policies. Few tasks are more vital today.
Notes
1. One reason for looking at household data is that earnings—the main focus of individual-level analyses—is only one source of income for most people. With increased labor force participation of women, and reforms to social programs that increase the incentive to work, there is good reason to think that the covariance of earnings and transfers has increased over time, and that the inter-temporal covariance of spouses’ incomes has increased as well. Such increased covariances can result in higher family or household income variability even in the absence of increasing earnings volatility. It is important to note, however, that assigning household resources to individuals, as is common in insecurity research, requires assumptions about how household resources are shared within the family. An important agenda for this research—one essential to understanding the gendered character of insecurity—is to examine more closely how resources are actually pooled within the family.
2. Indices, by design, encompass multiple indicators that can, and often do, involve elements of measures (1), (2), and (4).
3. The OECD Wealth Distribution Database compiles data on wealth distribution sourced from surveys and administrative registers based on common definitions and classifications. OECD (2017) presents data on the distribution of wealth for 28 OECD countries and 3 emerging economies.
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