Abstract
We propose a new explanation for the decoupling of official and perceived inflation based on relative consumption concerns. In presence of high inequality, when the consumers’ reference point of consumption is more distant to reach, a tight budget constraint is likely to be misperceived as a currency’s loss of purchasing power. Using data from a set of 15 European countries in the period 1990-2008, we estimate the effect of inequality on inflation perception. Our research design exploits the exogenous variation in inequality induced by the reduction in social expenditure that accompanied the implementation of the convergence criteria set up by the Maastricht treaty, in the years preceding the Euro changeover. Our results confirm that an increase in inequality significantly affects the deviation of inflation perceptions from actual inflation.
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Notes
See also King et al. (2001).
Results are not reported but are available upon request.
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Appendices
Appendix A: Multiply imputed data estimations
The SWIID data on inequality is unique in providing information on inequality in different countries, calculated upon a uniform set of assumptions and definitions on harmonized microdata. The data construction is subject to a trade-off between comparability across countries and coverage of country/years cells, resolved through a multiple-imputation procedure (Solt 2009). Such procedure introduces some noise in the inequality data, but one of the advantages of the SWIID dataset is that the researcher can appreciate the extent of such noise through the availability of 100 different imputed time series for inequality in each country. As a consequence, we can perform our analysis on SWIID data using two different approaches. The uncertainty introduced by multiple imputation can be resolved by simply averaging the 100 inequality series available for each country, as done in the tables above. Alternatively, we also estimated all models using Rubin (1987)’s approach, i.e. by estimating one separate model on each of the 100 inequality series, through multiple Monte Carlo simulations, and then pooling our set of 100 estimates by averaging the outcomes.Footnote 1 Our findings are strongly consistent under the two approaches, and we do not find any appreciable difference in our estimates. If anything, the estimates obtained though Monte Carlo simulations tend to deliver larger point estimates and t-statistics. The F-statistics of weak instruments tend instead to be slightly smaller, but well above the threshold of 10 indicated by Staiger and Stock (1997) as the criterion for testing for instrument weakness when the parameters are just identified.Footnote 2
Appendix B: Additional tables
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Filippin, A., Nunziata, L. Monetary effects of inequality: lessons from the euro experiment. J Econ Inequal 17, 99–124 (2019). https://doi.org/10.1007/s10888-018-9395-9
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DOI: https://doi.org/10.1007/s10888-018-9395-9