RDP 2021-09: Is the Phillips Curve Still a Curve? Evidence from the Regions Appendix C: Geographic Classifications and Robustness

James Bishop and Emma Greenland

September 2021

As discussed in Section 8.1, we examine if our baseline results are robust to the way we define ‘local labour markets’. In this appendix, we discuss the results of that robustness test in more detail.

First, we examine if our results are robust to using journey to work data from the 2016 Census to define local labour markets, rather than the 2011 Census as in our baseline. This accounts for any changes in commuting patterns between 2011 and 2016 due to, say, changed transport or communications infrastructure. The estimates of the key parameters are largely unaffected by this, suggesting that changes in commuting patterns are not influencing our baseline estimates (second column of Table C1).

Table C1: Robustness to Alternative Local Labour Market Classifications
Linear spline with kink at 4 per cent unemployment, 1998/99–2017/18
  Baseline 2016 Census SA4 FER (CofFEE) FER (PC) GCCSA State or Territory
Unemployment rate −0.734***
(0.206)		 −0.645***
(0.195)		 −0.804***
(0.240)		 −0.705***
(0.249)		 −0.713***
(0.253)		 −1.206***
(0.341)		 −0.883*
(0.484)
Linear spline term 0.561***
(0.200)		 0.490**
(0.194)		 0.640***
(0.223)		 0.487**
(0.243)		 0.499**
(0.253)		 0.961***
(0.356)		 0.612
(0.462)
Lagged wages growth 0.236***
(0.040)		 0.265***
(0.035)		 0.325***
(0.048)		 0.283***
(0.044)		 0.346***
(0.069)		 0.408***
(0.080)		 0.448***
(0.089)
Observations 5,639 5,404 1,745 2,620 1,679 300 160
Fraction of region-year observations with unemployment rate ≤4.0 per cent 0.23 0.23 0.19 0.18 0.08 0.06 0.05

Notes: Standard errors (in parentheses) are clustered by region; ***, **, and * denote statistical significance at the 1, 5, and 10 per cent levels, respectively; estimation is done using the Arellano-Bond estimator, and weighted by the number of employees in each region; all models include region and time effects

Sources: ABS; Authors' calculations; Centre of Full Employment and Equity; National Skills Commission; Productivity Commission

Our estimates are also robust to using SA4s to represent local labour markets (third column of Table C1). The main difference is in precision, with the standard errors in the SA4-level model 15–20 per cent larger than in the baseline. This may reflect that the number of cross-sectional units is more than three times larger in the latter.

Our baseline results are also robust to using ‘functional economic regions’ (FER) instead of our own approach to classifying local labour markets (fourth and fifth columns of Table C1). These alternative classifications were developed by the Centre of Full Employment and Equity (CofFEE) (Stimson et al 2016) and the Productivity Commission (2017) respectively, and are also based on the strength of commuting ties between areas.[52] While these classifications differ in a number of ways from our own, we find that using functional economic regions as the unit of analysis yields similar results to our baseline model. Again, however, estimation is less precise, which may reflect differences in sample size.

Using broad areas such as GCCSA or states and territories as the unit of analysis suggests the Phillips curve is steeper than in our baseline when the unemployment rate is both above or below 4 per cent (final two columns of Table C1). However, it is important to note that only around 5 per cent unemployment rate observations at the GCCSA or state level were below 4 per cent over our sample period. In contrast, 23 per cent of region–year unemployment rate observations were below 4 per cent using our baseline classification. Reflecting this, the null hypothesis that the Phillips curve is linear is not rejected at the 10 per cent level in the state-level model.

Overall, our estimates of the slope and curvature of the Phillips curve are remarkably insensitive to using different aggregations of the data, although precision is lower in several cases.

Footnote

These studies also apply hierarchical clustering algorithms to journey to work data from the 2011 Census. [52]