Assessing temporary product-specific subsidies: a time series intervention analysis

Abstract.

We propose an analytical forecasting framework to quantify the distortionary effects of product-specific subsidies. The novelty consists of a dual approach combining intervention analysis and a state-space modeling approach to construct and assess counterfactuals. Our pure intervention model approach faces challenges in identifying policy effects because, in certain sectors, these are weak and align with the crisis. We complement this approach by fitting a bivariate state-space model excluding the policy and creating counterfactual forecasts for periods of crisis. We then compare intervention analysis estimates of counterfactual series to those of observed series. Subsidizing one industry during global recessions seems unjustified and non-competitive. In our application, we use a time series of foreign and domestic order book levels during and after the temporary installation of an unprecedentedly generous car scrappage scheme in 2009. We assess implied disruptions in the automobile sector and eleven competing manufacturing industries in Germany. We find stimulus effects to be just mild, some evidence of intertemporal payback, and consumers’ windfall gains to come at the expense of other industries.

Keywords:

• Automobile industry
• Kalman filter
• intervention analysis
• counterfactual analysis

Acknowledgments

We thank the editor-in-chief, Milan Stehlik, two anonymous referees, Jesus Crespo Cuaresma, Christian Hutter, Christian Merkl, Willi Semmler, Thomas Steger, Roman Stöllinger, Marco Sunder, Timo Trimborn, Enzo Weber, and participants of the IWH Workshop on Fiscal Policy and the Great Recession, of the Recent Developments in Macroeconomics Conference at ZEW Mannheim, the ‘Modelling regime changes’ session of the International Conference on Computational and Financial Econometrics (CFE), and of seminars at Vienna University of Economics and Business, IAB/University of Erlangen-Nuremberg, and the University of Leipzig for many valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. However, in international terms, the dip was rather modest. Only car sales growth in Poland and China reacted even less negatively than the German one from 09/08 to 01/09; see Haugh et al. (2010, p. 95).

2. Ultimately, the circumstances allow only for a net effect to be quantified. We are indebted to one of the anonymous referees for drawing our attention to this aspect.

3. In the simple underlying model detailed in the Appendix this is due to Equation (A.1.9(A.1.9) $$\begin{array}{l}\frac{dV}{ds}=p{f}^{\prime }\frac{d{R}_x}{ds}+\underset{>0}{\underbrace{(g^{\prime }-k^{\prime })}}\frac{d{R}_y}{ds}.\end{array}$$(A.1.9) ).

4. Only recently, Menchetti et al. (2022) proposed a refined framework similar to intervention analysis by Box and Tiao (1975) but anchored in the potential outcomes framework. Like intervention analysis, their approach is based on ARIMA models and is therefore suited to complement existing methodologies like DiD or synthetic controls.

5. The demarcation between ITS analysis and intervention analysis in the sense of Box and Tiao (1975) is not clear-cut. For example, Schaffer et al. (2021) acknowledge that ITS traditionally uses segmented regression which may limit applications in case of time series with a more complex autocorrelative structure. They propose to augment ITS by allowing ARIMA components and transfer functions. Although they do not mention Box and Tiao (1975), the proposed approach is more like intervention analysis than traditional ITS and blends both methods.

6. We choose June 2012 as endpoint of our sample period as it generally marks a point of discontinuation of the official statistics on industrial new orders and corresponding sectoral series in European countries. See de Bondt et al. (2013) for detail.

7. Different from the situation at the time in the US, where subprime auto credit had been unsustainably inflated by the preceding housing and credit bubble (Goolsbee and Krueger 2015), neither credit crunch in the sense of households lacking access to credit and, thus, postponing car purchases nor the liquidation risk of the “big 3” car producers were given for Germany (Haugh et al. 2010).

8. Parameter estimates are obtained by Maximum Likelihood (ML) estimation.

9. It is noteworthy in this context that the German scrappage scheme preceded all comparable temporary programs in the OECD apart from the one installed in France. However, the latter represents the smallest among the five largest schemes for 2009 amounting to less than 8 percent of the German subsidization (Schweinfurth 2009; Haugh et al. 2010).

10. The impact of relying on the AIC instead of, for example, the BIC is shown in Figures A.5, A.6, and A.7 in Appendix A.9.

11. Considering only periods around the crisis, it looks as if the domestic textile sector was affected permanently. However, Figure 3 reveals the series has returned to its long-run trend.

12. This result is somewhat sensitive with regard to the underlying noise model. When repeating this step with a BIC-based noise model, we find the clothing, electronics, cars, pharmaceutics, machines, and other vehicles sectors to be significantly affected by the policy. See Appendix A.9.

13. The null of the model capturing the dependence structure of the time series cannot be rejected at a 5% level of significance in all but four cases.

14. As can be seen, foreign orders in the pharmaceutics sector declined already in 08/2007 while unaffected by later crisis events.

15. A graphical depiction of sample autocorrelation functions and residual histograms, given in Figures A.1 and A.2 in Appendix A.7, underscores this argument. Moreover, Figures A.3 and A.4 in Appendix A.8 show the model results when shifting the intervention period to 08/2007 – 08/2008 and analyzing the financial crisis directly. Qualitatively different from the aftermath of the Lehman Brothers collapse in September 2008, the German economy before appeared to be flourishing rather than declining.

16. To get an impression see the upper left panel in Figure A.5.

17. The positive part in the lower right panel in Figure 9 is not robust against changes in the noise model. The negative finding of a lasting negative effect, however, is still supported; see Figure A.6 in Appendix A.9.

18. This is closely linked to a “free-rider” problem, which we describe in more detail later.

19. Microeconomically, one route of reasoning for the substantially delayed rebound of consumer demand for autos might be that some households planned the acquisition of two low-price segment products for 2010 or later, and due to the subsidy not only pulled forward their purchasing but also changed their plan to acquire only one (subsidized) product from the premium price-segment. However, this contrasts with international experience suggesting a boost of low-margin segments (Haugh et al.2010). Our volume index series does not allow us to test such hypotheses.

20. Although we use the terms “counterfactual,” “effect,” and “impact,” it should be kept in mind that this is kind of a stretch. Since our analytical approach is not firmly grounded in an established causal framework, we basically borrow this wording to formulate our interpretation.

21. Thus, the values for the missing observations are estimated using the Kalman filter by making use of the information incorporated in a second series, i.e., foreign orders, which is assumed to be related to the series with missing observations. This method is advocated by Harvey and Chung (2000).

22. Note, since we do not use the Kalman Smoother at any point, our approach does not violate the non-anticipation assumption of casual analysis (Rubin 1978). Other than the Kalman Smoother, the Kalman filter always uses past and present observed values only; see Appendix A.13.2.

23. The superscript (.) ^(F) denotes Kalman filter values.

24. Compare, for example, Figure A.5 and A.6 in Appendix A.9 with Figures 8 and 9.

25. We can state that both models use the same underlying noise model n_t as all data points except for the ones between January and September 2009 are identical. Moreover, by limiting the artificially created gap of missing values to the period from January to September 2009, we also exclude the onset of the European debt crisis as a potential confounder.

26. The results in this sector are implausible, see Figure A.9 in Appendix A.11.

27. Above results are based on stretching the argument on the mirror image property of foreign and domestic orders in the automobile sector.