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Empirical Methods for Modelling Economic Insecurity

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Advances in Economic Measurement

Abstract

This chapter presents an overview of some of the technical methods used to measure Economic Insecurity (EI). We discuss conceptual challenges associated with measurement and provide a basic conceptual model for characterizing anxiety stemming from economic risk. Surveyed methods include (i) subjective indices, (ii) axiomatic methods derived from microeconomic theory, (iii) micro-econometric approaches and (iv) macro-level or aggregate methods. Some illustrations are provided using Australian panel data. We show that there is considerable heterogeneity in outcomes across different measurement concepts—it is common for markers to be uncorrelated or even negatively associated across our sample. More work is needed integrating alternative risk concepts within the broader framework of EI. Despite this ambiguity, two robust results still emerge. Across a suite of different measures, EI is (i) correlated with other markers of social disadvantage, and (ii) predictive of diminished health and well-being, even after conditioning on current socioeconomic status.

The unit record data from the HILDA Survey were obtained from the Australian Data Archive, which is hosted by The Australian National University. The HILDA Survey was initiated and is funded by the Australian Government Department of Social Services (DSS) and is managed by the Melbourne Institute of Applied Economic and Social Research (Melbourne Institute). The findings and views based on the data, however, are those of the author and should not be attributed to the Australian Government, DSS, the Melbourne Institute, the Australian Data Archive or the Australian National University and none of those entities bear any responsibility for the analysis or interpretation of the unit record data from the HILDA Survey provided by the author

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Notes

  1. 1.

    https://www.pewtrusts.org/en/research-and-analysis/issue-briefs/2015/02/americans-financial-security-perceptions-and-reality.

  2. 2.

    For example, future joblessness is unlikely to be a source of EI if it reflects an underlying preference or can be planned for well in advance (e.g. a decision to retire).

  3. 3.

    For example, a recently graduated university student may have a historically volatile income stream and hence appear potentially insecure, even if they are in the process of moving into a stable and well-paying job.

  4. 4.

    A relative measure based upon income volatility would focus on percentage changes to income (e.g. a 20% reduction from one year to the next) rather than unit changes (a $20,000 reduction from one year to another).

  5. 5.

    Note that the intermediate stream \(y\left(t\right)=\left(\mathrm{0,0},\mathrm{0,0}\right)\) also needs to yield an intermediate insecurity score.

  6. 6.

    Other allowances can be made for the presence of safety nets, such as health insurance. See Hacker (2006).

  7. 7.

    Nicholls and Rehm (2014) perform a similar decomposition that they explicitly interpret in terms of EI.

  8. 8.

    Note that we exclude current income as a control in the Bossert measure as this appears directly in the EI measure.

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Acknowledgements

We thank Prasada Rao for his ongoing support, and Alannah Pocknall for research assistance

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Authors and Affiliations

  1. Department of Accounting, Finance and Economics, Griffith Business School, Southport, QLD, Australia

    Nicholas Rohde

  2. University of Luxembourg, Esch-sur-Alzette, Luxembourg

    Conchita D’Ambrosio

  3. University of New Brunswick, Fredericton, NB, Canada

    Barry Watson

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  1. Nicholas Rohde
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  2. Conchita D’Ambrosio
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Correspondence to Nicholas Rohde .

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Editors and Affiliations

  1. Caulfield, VIC, VIC, Australia

    Duangkamon Chotikapanich

  2. St Lucia, QLD, Australia

    Alicia N. Rambaldi

  3. Southport, QLD, Australia

    Nicholas Rohde

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Rohde, N., D’Ambrosio, C., Watson, B. (2022). Empirical Methods for Modelling Economic Insecurity. In: Chotikapanich, D., Rambaldi, A.N., Rohde, N. (eds) Advances in Economic Measurement. Palgrave Macmillan, Singapore. https://doi.org/10.1007/978-981-19-2023-3_6

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  • DOI: https://doi.org/10.1007/978-981-19-2023-3_6

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