https://orcid.org/0000-0003-3242-6172
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ABSTRACT
We combine panel household data with the introduction of an increasing block tariffs (IBT) for residential electricity in three experimental regions of Russia to analyze the relationship between the IBT and the propensity of households to purchase dirty fuels. Using a difference-in-differences empirical specification, we find that the propensity to purchase dirty fuels has increased in the regions where the IBT schemes were introduced. The size of the increase varies from 3.8 percentage points for the full sample of households to 13.4 percentage points when restricting the sample to households that do not have access to district heating networks.
Acknowledgments
Fnancial support was provided by the Grant Agency of Charles University (grant number 454120). This paper is part of a project that received funding from the European Union’s Horizon 2020 (GEOCEP) research and innovation programme under the Marie Skłodowska-Curie grant agreement No 870245. We also thank the participants of the 24th Annual Conference on Environmental Economics, Policy and International Relations VSE-UK in Prague, the 23rd Annual Conference on Finance and Accounting (ACFA 2022) in Prague, and the 43rd IAEE International Conference 2022 in Tokyo. Responsibility for any errors remains with the authors.
Disclosure Statement
No potential conflict of interest was reported by the authors.
Notes
1. More specifically, firewood, coal, peat, or kerosene.
2. More details on social norms are available in Section 2.
3. Usually in a form of hot water flowing through pipes supplied to installed radiators within the dwelling by the local boiler stations, or heat-and-power plants (Sorokina Citation2019).
4. The “Decree of the Government of the Russian Federation of July 22, 2013 No. 614” was updated on the 25th February of 2014 to exclude the oblast of Samara from the list of experimental regions. However, since the Samara oblast is not covered by RLMS-HSE this change does not affect our estimations.
5. It should be noted, however, that the social norms for the electricity consumption were not suspended in the pilot regions. Households in these regions still pay for the electricity consumption according to two-block tariff scheme based on their prescribed social norm.
6. The other pilot regions are Zabaykalsky Krai, Vladimir Oblast, Orlov Oblast, and Samara Oblast.
7. See, old.donland.ru (Citation2020); Ševcov (Citation2018), and “Social norm” (Citation2019).
8. Residential electricity tariffs were obtained from the Federal State Statistics Service (Rosstat) of Russia.
10. Excluding households observed only once during the survey period (singleton values) (about 15%), renters (about 10%), and households that report installing electric cooking stove, while also being connected to the district gas supply (about 1%).
11. The data for electricity consumption is available up to 2016.
12. Some households, however, neither have an access to the district delivery of gas, nor they use an electric stove for cooking. These households may opt to other sources of energy (like various dirty fuels, or bottled gas)s
for cooking. Thus, the observed differential tariffs, and the social norms for dwellings with installed electric stoves, and without in the Krasnoyarsk Krai.
13. In our case, we use a standard definition of the term propensity as the “frequency of any particular event of interest to occur”.
14. We control for the logarithm of heating degree days, logarithm of precipitation, logarithm of wind speed, and logarithm of humidity levels across all 38 regions under the study. The weather data was provided by www.meteoblue.com.
15. It is possible to estimate our model in the context of probit or logit models without the inclusion of household fixed-effects to avoid the “incidental parameters problem”. However, in this case the resulted coefficients will be biased due to the individual time-invariant unobserved household heterogeneity which is not accounted for if household fixed-effects are excluded.
16. The effects estimated by the regressions based on the observations on a common support estimated by the propensity scores tend to be 12–15% (depending on the specification) larger than compared to the regressions based on cem.
17. The elasticity in linear-logarithmic specification is obtained by: b(1/Y), where b is the regression coefficient and Y is the mean value of dependent variable.