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Time-Varying Predictability of Labor Productivity on Inequality in United Kingdom

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Abstract

In this paper, we analyze time-varying predictability of labor productivity for growth in income (and consumption) inequality of the United Kingdom (UK) based on a high-frequency (quarterly) data set over 1975:Q1 to 2016:Q1. Results indicate that the growth rate of an index of labor productivity has a strong predictive power on growth rate of income (and consumption) inequality in the UK. Interestingly, the strength of the predictive power is found to be higher towards the end of the sample period in the wake of the global financial crisis. In addition, based on time-varying impulse response function analysis, we find that inequality and labor productivity growth rates are in general negatively associated over our sample period, barring a short-lived positive impact initially.

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Notes

  1. The data is downloadable from: https://discover.ukdataservice.ac.uk/series/?sn=200016 and https://discover.ukdataservice.ac.uk/series/?sn=2000028.

  2. We would like to thank Professor Haroon Mumtaz for kindly sharing the inequality data.

  3. The data is available for download from: https://fred.stlouisfed.org/series/ULQELP01GBQ661S.

  4. Complete details of the various unit root tests (ADF (Dickey and Fuller 1979), PP (Phillips and Perron 1988), Kwiatkowski et al. (KPSS 1992), Elliot et al. (ERS, 1996), Ng and Perron (NP, 2001)) have been presented in Table 2 in the Appendix of the paper.

  5. Again, the constant parameter-based Granger causality test could not detect causality running from GI1 to GLP even at the 10% level of significance, though ExpW, MeanW, and SupLR tests all overwhelmingly rejected the null of no time-varying predictability at the highest level of significance, but the Nyblom test statistic could not do so event at the 10% level of significance. Complete details of these results are available upon request from the authors.

  6. This line of reasoning is further corroborated by the Bayesian Markov-switching quantile regression model (see, Yamaka et al. (2019) for further technical details) in Table 2 in the Appendix of the paper, with the results highlighting the importance of accounting for regime-changes in standard quantile regression models, when trying to deduce the correct inference, i.e., the negative relationship between GI1 and GLP, especially when inequality growth is conditionally high. Note the quantile regression model involves two lags of GLP with and without regime-switching across two states of low (indicated by regime-0) and high (indicated by regime-1)-GI1 states.

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Acknowledgements

We would like to thank two anonymous referees for many helpful comments. However, any remaining errors are solely ours. David Gabauer would like to acknowledge that this research has been partly funded by BMK, BMDW and the Province of Upper Austria in the frame of the COMET Programme managed by FFG.

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Appendix

Appendix

See Figs. 5, 6, 7, 8, 9, 10, 11, Tables 2, 3 and 4.

Fig. 5
figure 5

Data plots. Note: GIj, j = 1,..6, corresponds to the growth rate six measures of income and consumption inequality (Gini coefficient, the standard deviation, and the difference between the 90th and 10th percentile) respectively; GLP: growth of labor productivity index; DUR: change in unemployment rate; GITGDP: growth of income tax to nominal GDP ratio

Fig. 6
figure 6

Time-varying Wald statistics with VAR(2) under SIC, testing whether GI1 Granger-causes GLP. Note: See Notes to Fig. 1; t: corresponds to quarterly data; and the vertical axis measure the test statistic

Fig. 7
figure 7

Time-varying Wald statistics with VAR(2) under SIC, testing whether GLP Granger-causes GI2. Note: See Notes to Figs. 1 and 3

Fig. 8
figure 8

Time-varying Wald statistics with VAR(2) under SIC, testing whether GLP Granger-causes GI3. Note: See Notes to Figs. 1 and 3

Fig. 9
figure 9

Time-varying Wald statistics with VAR(2) under SIC, testing whether GLP Granger-causes GI4. Note: See Notes to Figs. 1 and 3

Fig. 10
figure 10

Time-varying Wald statistics with VAR(2) under SIC, testing whether GLP Granger-causes GI5. Note: See Notes to Figs. 1 and 3

Fig. 11
figure 11

Time-varying Wald statistics with VAR(2) under SIC, testing whether GLP Granger-causes GI6. Note: See Notes to Figs. 1 and 3

Table 2 Unit root results
Table 3 The estimation results for the quantile regression model
Table 4 The estimation results for the Markov-switching quantile regression model

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Gabauer, D., Gupta, R., Nel, J. et al. Time-Varying Predictability of Labor Productivity on Inequality in United Kingdom. Soc Indic Res 155, 771–788 (2021). https://doi.org/10.1007/s11205-021-02622-w

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