Assessing inflation expectations anchoring for heterogeneous agents: analysts, businesses and trade unions

ABSTRACT

Forecasts of agents who are actively involved in the setting of prices and wages are less readily available than those of professional analysts but may be more relevant for understanding inflation dynamics. Here we compare inflation expectations anchoring between analysts, businesses and trade unions for one country for which comparable forecasts are available for almost two decades: South Africa. Forecasts are modelled as monotonically diverging from an estimated long-run anchor point, or ‘implicit anchor’, towards actual inflation as the forecast horizon shortens. We find that the estimated inflation anchors of analysts lie within the 3–6 percent inflation target range of the central bank. However, those for businesses and trade unions, which our evidence suggests may be the most relevant for driving the inflation process, have remained above the top end of the official target range. Our results point to challenges for central banks seeking to gain credibility with agents whose decisions directly influence inflation.

KEYWORDS:

• Inflation expectations
• inflation anchoring
• decay function
• inflation targeting

JEL CLASSIFICATION:

• E58
• E31

Acknowledgments

We are grateful to the staff of the South African Reserve Bank and Bureau for Economic Research South Africa for providing data and guidance. We thank, without implication, an anonymous referee, Ana Lucía Coronel, Hèndré Garbers, Gaston Gelos, Federico Grinberg, Thomas Harjes, Aaron Mehrotra, Montfort Mlachila, Chris Papageorgiou, Axel Schimmelpfennig, Pierre Siklos, Filiz Unsal, Holly Wang and the participants of the 2018 IMF Article IV Consultation Workshop, particularly Rudi Steinbach as discussant, the IMF African Department Monetary Policy Network seminar and BIS seminar for helpful comments. The views expressed here are those of the authors, and are not necessarily shared by the BIS or the IMF.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ Lanzafame (2016). The relationship may also work in reverse: improved fiscal conditions tend to lead to lower inflation expectations (Celasun, Gelos, and Prati 2004).

² See, also, Binder (2015). For a related study on the effects of demographic factors on inflation expectations in South Africa, see Reid, Siklos, and Du Plessis (2018).

³ Comparable surveys are available for few countries. In a survey of 21 central banks reported in Sousa and Yetman (2016), only the central banks of Columbia and South Africa reported making use of the inflation forecasts of trade unions, while forecasts of (non-financial) businesses tended to be available at only short horizons of no more than one year (versus up to 12 quarters in our data). See also the discussion of available forecasts in Coibion et al. (2018b).

⁴ Shorter horizon expectations of households are also collected by BER, but are not examined here. They are analyzed in Reid, Siklos, and Du Plessis (2018).

⁵ Conceptually, the approach is similar to Kozicki and Tinsley (2012).

⁶ We also assess the inflation expectations of analysts going back as far as 1993 based on Consensus Forecasts, and report the results in Appendix 2. We can use these to assess changes in the implicit inflation anchor around the introduction of IT. The results in the body of the paper focus primarily on expectations of heterogeneous agents, which are only available after the introduction of IT.

⁷ Inflation expectations of households are only collected at one horizon in each survey: before 2011 this was for the current calendar year, and thereafter for the following 12 month period. In Figure 1, we display these in the top-left panel (containing the current calendar year forecasts for other groups) for all years.

⁸ Total non-agricultural wages and non-agricultural labour productivity.

⁹ Mehrota and Yetman (2014) consider a more restricted version of the model (with $c=1$) for a group of Asian economies. We estimated the restricted model on a portion of our samples and it was always rejected in favour of our more general model.

¹⁰ Households are not included as data are available only at short horizons. Average expectations over the next five years are not used as these are only available beginning 2011, and are on a different basis than the other forecasts (the average inflation rate over a five year period, rather than being for a specific future year). If expectations converge to an anchor at long horizons, average expected inflation will be disproportionately affected by short horizon expectations so that the ‘effective’ horizon may be relatively short. In our dataset, for example, average five-year expectations across all agents are more highly correlated with one-year ahead expectations (0.82) than two-year ahead expectations (0.76), despite having an ‘average’ horizon of 2.5 years.

¹¹ The SARB was established in 1921. By the middle of the 1960s, credit controls, credit ceilings and deposit rate control were adopted. After the end of the Bretton Woods agreement in 1971, South Africa pegged or managed its currency. The rand was pegged to the pound sterling, the US dollar, a basket of currencies, and again to the US dollar, in turn. It was formally devalued twice, in December 1971 and September 1975. A managed floating regime was introduced in January 1979. The money supply growth targets adopted in 1986 were replaced with money supply growth guidelines in the early-1990s. In 1995, the financial rand, an investment currency for non-residents, was abolished. This was followed by the gradual relaxation of exchange controls on residents.

¹² See public lecture by Governor Kganyago (2016): ‘The Influence of South Africa’s Price-setting Environment on Monetary Policy Trajectory,’ https://www.bis.org/review/r160908a.pdf.

¹³ Despite the change in the target from CPIX to CPI inflation, we focus on CPI inflation throughout since CPI inflation expectations have been surveyed consistently by BER beginning 2000 (whereas those of CPIX ended when the official inflation target changed).

¹⁴ We report equivalent results but based on Consensus Forecasts, as are often used in this literature, in Appendix 2.

¹⁵ We use rolling samples to allow for the evolution of the estimates over the sample. An alternative approach would be to explicitly test for breaks. However, there are no clear focal points for this: our sample begins after the introduction of IT in 2000, for example. Moreover, the estimates for all eight-year rolling windows (available upon request) do not exhibit any clear trend over the different samples. This stands in contrast to Yetman (2017) who, using the same approach on US data, finds a gradual increase in anchoring over the rolling samples.

¹⁶ To further illustrate the fit of the model, Figure A1 in Appendix 1 displays the forecasts, fitted values and model errors for the different BER expectations for the final rolling sample of 2010–17.

¹⁷ We are grateful to the SARB for providing this data.

¹⁸ The approximation of a fixed four-quarter horizon forecast made up of forecasts of calendar-year inflation is a weighted average of the current and next year forecast such that the weighted average of the horizons is equal to four quarters: ${\pi }_{t+4}\approx \frac{k}{4}{\pi }_{t+k}+\frac{4-k}{4}{\pi }_{t+k+4}$ . See, for example, Siklos (2013).