Abstract
This paper investigates the price impact of the fuel discount in Germany, which was introduced between June and August 2022 to partially compensate increased energy costs. Using the augmented synthetic control method (ASCM) to construct the counterfactual, we provide quantitative evidence to the heated debate concerning the impact of this policy tool and find the fuel discount to have decreased consumer prices of petrol (diesel) by at least 0.30 euro per litre (0.10 euro per litre) on average. The results are robust to various sensitivity checks. Thus, oil companies and petrol stations decreased prices for consumers and passed on about 85 % (65 %) of the discount in case of petrol (diesel). Moreover, we do not find signs of excessive price increases in anticipation of the fuel discount.
Acknowledgment
This research did not receive any specific grant from funding agencies in the public, commercial or non-profit sectors. Part of this research was conducted while Volker Seiler was Senior Research at the Deutsche Bundesbank. The views expressed in this paper are those of the authors and do not necessarily reflect the views of the Deutsche Bundesbank or the Eurosystem. The authors would like to thank Jesus Crespo Cuaresma (Managing Editor) and an anonymous referee for their helpful comments. We thank Philipp Scheibe for helpful comments and suggestions. Any remaining errors are our own.
References
Abadie, A. 2021. “Using Synthetic Controls: Feasibility, Data Requirements, and Methodological Aspects.” Journal of Economic Literature 59 (2): 391–425. https://doi.org/10.1257/jel.20191450.Search in Google Scholar
Abadie, A., and J. Gardeazabal. 2003. “The Conomic Costs of Conflict: A Case Study of the Basque Country.” The American Economic Review 93 (1): 113–32. https://doi.org/10.1257/000282803321455188.Search in Google Scholar
Abadie, A., A. Diamond, and J. Hainmueller. 2010. “Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program.” Journal of the American Statistical Association 105 (490): 493–505. https://doi.org/10.1198/jasa.2009.ap08746.Search in Google Scholar
Abadie, A., A. Diamond, and J. Hainmueller. 2011. “Synth: An R Package for Synthetic Control Methods in Comparative Case Studies.” Journal of Statistical Software 42 (13): 1–17. https://doi.org/10.18637/jss.v042.i13.Search in Google Scholar
Abadie, A., A. Diamond, and J. Hainmueller. 2015. “Comparative Politics and the Synthetic Control Method.” American Journal of Political Science 59 (2): 495–510. https://doi.org/10.1111/ajps.12116.Search in Google Scholar
Amit, Y., and D. Geman. 1997. “Shape Quantization and Recognition with Randomized Trees.” Neural Computation 9 (7): 1545–88. https://doi.org/10.1162/neco.1997.9.7.1545.Search in Google Scholar
Athey, S., and G. W. Imbens. 2017. “The State of Applied Econometrics: Causality and Policy Evaluation.” The Journal of Economic Perspectives 31 (2): 3–32. https://doi.org/10.1257/jep.31.2.3.Search in Google Scholar
Athey, S., M. Bayati, N. Doudchenko, G. Imbens, and K. Khosravi. 2021. “Matrix Completion Methods for Causal Panel Data Models.” Journal of the American Statistical Association 116 (536): 1716–30. https://doi.org/10.1080/01621459.2021.1891924.Search in Google Scholar
Bach, S. 2022. “Übergewinnbesteuerung bei Öl und Gas sinnvoll, aber in Deutschland nicht zu machen.” DIW Wochenbericht (22): 354. https://doi.org/10.18723/diw_wb:2022-24-3.Search in Google Scholar
Barber, R. F., E. J. Candès, A. Ramdas, and R. J. Tishirani. 2021. “Predictive Inference with the Jackknife+.” Annals of Statistics 49 (1): 486–507. https://doi.org/10.1214/20-AOS1965.Search in Google Scholar
Ben-Michael, E., A. Feller, and J. Rothstein. 2021. “The Augmented Synthetic Control Method.” Journal of the American Statistical Association 116 (536): 1789–803. https://doi.org/10.1080/01621459.2021.1929245.Search in Google Scholar
Billmeier, A., and T. Nannicini. 2013. “Assessing Economic Liberalization Episodes: A Synthetic Control Approach.” The Review of Economics and Statistics 95 (3): 983–1001. https://doi.org/10.1162/REST_a_00324.Search in Google Scholar
Born, P., G. J. Müller, M. Schularick, and P. Sedláček. 2019. “The Costs of Economic Nationalism: Evidence from the Brexit Experiment.” Economic Journal 129: 2722–44. https://doi.org/10.1093/ej/uez020.Search in Google Scholar
Breiman, L. 1996. “Bagging Predictors.” Machine Learning 24: 123–40. https://doi.org/10.1007/BF00058655.Search in Google Scholar
Breiman, L. 2001. “Random Forests.” Machine Learning 45: 5–32. https://doi.org/10.1023/A:1010933404324.10.1023/A:1010933404324Search in Google Scholar
Brodersen, K. H., F. Gallusser, J. Koehler, N. Remy, and S. L. Scott. 2015. “Inferring Causal Impact Using Bayesian Structural Time-Series Models.” Annals of Applied Statistics 9 (1): 247–74. https://doi.org/10.1214/14-AOAS788.Search in Google Scholar
Bundeskartellamt. 2022a. “Bundeskartellamt Statement on Fuel Prices and the Reduction of the Energy Tax.” Press release, May 31, 2022.Search in Google Scholar
Bundeskartellamt. 2022b. “Exploitation of Energy Price Caps Prohibited – Bundeskartellamt Starts to Build the Required Organizational Structure,” Press release, December 20, 2022.Search in Google Scholar
Bundeskartellamt. 2022c. “Fuel Price Development Since the Introduction of the Tax Reduction,” Press release, June 7, 2022.Search in Google Scholar
Choi, H., and H. Varian. 2012. “Predicting the Present with Google Trends.” The Economic Record 88 (s1): 2–9. https://doi.org/10.1111/j.1475-4932.2012.00809.x.Search in Google Scholar
Dietterich, T. G. 2000. “An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting and Randomization.” Machine Learning 40: 139–57. https://doi.org/10.1023/A:1007607513941.10.1023/A:1007607513941Search in Google Scholar
Doudchenko, N., and G. W. Imbens. 2017. “Balancing, Regression, Difference-In-Differences and Synthetic Control Methods. A Synthesis.” In Working Paper.10.3386/w22791Search in Google Scholar
Duso, T. 2022. “Der Tankrabatt ist der einfachste und schnellste, aber nicht der beste Weg.” DIW Wochenbericht (23): 342. https://doi.org/10.18723/diw_wb:2022-23-3.Search in Google Scholar
Engel, E. 1857. “Die Productions- und Consumtionsverhältnisse des Königreichs Sachsen.” Zeitschrift des Sächsischen Ministerium des Inneren: 8–9.Search in Google Scholar
Fratzscher, M. 2022. “Konzertierte Aktion darf nicht mit faulen Kompromissen enden.” Statement, July 4, 2022.Search in Google Scholar
Freund, Y., and R. E. Schapire. 1996. “Experiments with a New Boosting Algorithm.” In Machine Learning: Proceedings of the Thirteenth International Conference, 148–56. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.6252.Search in Google Scholar
Ho, T. K. 1995. “Random Decision Forests.” In Proceedings of the 3rd International Conference on Document Analysis and Recognition, Vol. 1, 278–82.Search in Google Scholar
Ho, T. K. 1998. “The Random Subspace Method for Constructing Decision Forests.” IEEE Transactions on Pattern Analysis and Machine Intelligence 20 (8): 832–44. https://doi.org/10.1109/34.709601.Search in Google Scholar
Hoerl, A. E., and R. W. Kennard. 1970a. “Ridge Regression: Applications to Nonorthogonal Problems.” Technometrics 12 (1): 69–82. https://doi.org/10.1080/00401706.1970.10488635.Search in Google Scholar
Hoerl, A. E., and R. W. Kennard. 1970b. “Ridge Regression: Biased Estimation for Nonorthogonal Problems.” Technometrics 12 (1): 55–67. https://doi.org/10.1080/00401706.1970.10488634.Search in Google Scholar
ifo Institute. 2022. “Oil Companies Passing on 85 to 100 Percent of Fuel Discount in Germany.” Press release, June 14, 2022.Search in Google Scholar
Kearns, M. 1988. “Thoughts on Hypothesis Boosting.” In Unpublished Manuscript, Machine Learning Class Project.Search in Google Scholar
Kearns, M., and L. Valiant. 1994. “Cryptographic Limitations on Learning Boolean Formulae and Finite Automata.” Journal of the Association for Computer Machinery 41 (1): 67–95. https://doi.org/10.1145/174644.174647.Search in Google Scholar
Pinotti, P. 2015. “The Economic Costs of Organized Crime: Evidence from Southern Italy.” Economic Journal 125 (586): F203–32, https://doi.org/10.1111/ecoj.12235.Search in Google Scholar
Quenouille, M. H. 1949. “Problems in Plane Sampling.” The Annals of Mathematical Statistics 20 (3): 355–75. https://doi.org/10.1214/aoms/1177729989.Search in Google Scholar
Quenouille, M. H. 1956. “Notes on Bias in Estimation.” Biometrika 43 (3–4): 353–60. https://doi.org/10.1093/biomet/43.3-4.353.Search in Google Scholar
Runst, P., and J. Thomä. 2020. “Does Occupational Deregulation Affect In-Company Vocational Training? – Evidence from the 2004 Reform of the German Trade and Crafts Code.” Journal of Economics and Statistics 240 (1): 51–88. https://doi.org/10.1515/jbnst-2018-0059.Search in Google Scholar
Schapire, R. E. 1990. “The Strength of Weak Learnability.” Machine Learning 5: 197–227. https://doi.org/10.1007/BF00116037.Search in Google Scholar
Seiler, V., B. M. Gilroy, C. Peitz, and N. Stöckmann. 2022. “40 Years of Economic Reform – the Case of Pudong New Area Open Economic Zone in Shanghai.” Applied Economics 55: 1845–58. https://doi.org/10.1080/00036846.2022.2099803.Search in Google Scholar
Steinkraus, A. 2019. “A Synthetic Control Assessment of the Green Paradox: The Role of Climate Action Plans.” German Economic Review 20 (4): e545–70. https://doi.org/10.1111/geer.12176.Search in Google Scholar
Tibshirani, R. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society: Series B 58 (1): 267–88. https://doi.org/10.1111/j.2517-6161.1996.tb02080.x.Search in Google Scholar
Tukey, J. W. 1958. “Bias and Confidence in Not-Quite Large Samples.” The Annals of Mathematical Statistics 29 (2): 614. https://doi.org/10.1214/aoms/1177706647.Search in Google Scholar
Xu, Y. 2017. “Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models.” Political Analysis 25 (1): 57–76. https://doi.org/10.1017/pan.2016.2.Search in Google Scholar
Zou, H., and T. Hastie. 2005. “Regularization and Variable Selection via the Elastic Net.” Journal of the Royal Statistical Society – Series B: Statistical Methodology 67 (2): 301–20. https://doi.org/10.1111/j.1467-9868.2005.00503.x.Search in Google Scholar
Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/ger-2022-0108).
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