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Asymmetric cost pass-through and consumer search: empirical evidence from online platforms


Prices often react stronger to rising than to falling costs. This asymmetric cost pass-through is still not fully understood, but recent theories suggest that asymmetric adjustments of consumers’ search efforts to rising and to falling prices may be one explanation for this pattern. I use novel panel data to investigate the interaction of consumer search intensity, pricing and cost pass-through of residential electricity tariffs on online price comparison sites. I find that consumers search slightly more when prices rise but drastically decrease search efforts when they fall. Moreover, I find direct evidence that cost pass-through heavily depends on consumers’ search efforts in that cost increases are passed-through less to the consumer when search intensity is high while cost decreases are passed-through more when search intensity is high. This finding may help upstream firms to better understand how their price changes will translate into retail price adjustments.

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  1. While Lewis (2011) uses a behavioral model to explain why search increases following a price increase, Cabral and Gilbukh (2020) assume rational buyers that hold correct beliefs regarding seller prices.

  2. Gugler et al. (2018) focus on how consumer search affects price dispersion. Another current paper using panel data of a direct measure of consumer search is Dong et al. (2021). The paper by Dong et al. (2021) studies the estimation of heterogenous preferences in markets where consumers engage in costly search to learn product characteristics.

  3. Byrne and De Roos (2017) do not adress endogeneity concerns in their paper and Lewis and Marvel (2011) instrument for national average gasoline prices via wholesale prices which is potentially related to demand/quantity.

  4. There may be some form of product differentiation, such as certification of a tariff with a “green” label etc. Thus, in the present application I exclude all search queries that exclusively consider eco-label tariffs (4% of all search queries) in order to eliminate price effects related to product differentiation. However, the results remain fully robust when these searches are also included.

  5. By law, the incumbent is the local electricity retailer with the largest customer base. Theoretically, an entrant may become the new incumbent because of that. However, this did not translate into practice due to low switching rates and the original incumbents retained their positions. The only exceptions where the incumbent changed were due to a few mergers of municipal utilities in the past two decades.

  6. Moreover, a household that moves to another zip code is also automatically supplied by the local incumbent at its default tariff again.

  7. To appreciate the magnitude of this amount: the wholesale electricity price for 3,500 kWh only accounted for on average 16% of the incumbents’ default tariffs in that year.

  8. Bundesnetzagentur (2015).

  9. Before the rise of price comparison websites consumers had to search sequentially for electricity tariffs by contacting electricity retailers one after another or they were sequentially approached by door-to-door salesmen. See Giulietti et al. (2014).

  10. Ellison and Ellison (2009).

  11. Search costs are 17 times higher than switching costs in Honka (2014). An exception in the literature is a recent working paper by Dressler and Weiergräber (2019) who argue that the consumer’s hassle of going through the switching process may cause switching costs in the Belgium electricity market. However, as discussed above such a hassle does not exist in the setup here as the switching process is conducted completely by the new supplier.

  12. Indeed, two of the bigger alternative providers went bankrupt in 2011 (Teldafax) and 2013 (Flexstrom), respectively.

  13. In a recent working paper Dressler and Weiergräber (2019) analyze switching in the Belgium electricity market and also find that brand effects only play a minor role compared to search costs.

  14. See Gugler et al. (2018).

  15. This is a major advantage compared to other industry studies such as competition between gas stations or supermarkets.

  16. Consumers may also opt for monthly contracts in some tariffs, but this is rarely done. According to a market report by the German regulatory authority [p. 150 Bundesnetzagentur, (2013)], the average contract period is 10 months, suggesting that the majority choose yearly contracts. During the term of the contract, consumers can only switch if their supplier changes under specific circumstances, for instance if they move to another zip code in which their current retailer is not active. Consumers then have an extraordinary termination right. Also, while electricity prices are generally fixed for the duration of a contract, a retailer is allowed to change prices within the duration of a contract under extraordinary circumstances such as unexpected cost shocks. Consumers also have an extraordinary termination right then.

  17. Because I observe some extreme outliers in some zip code apparently resulting due to web scraping bots I drop the 2% of the observations with the highest values.

  18. According to a survey 80% of the switchers searched online in 2011 (Kearney (2012).

  19. Prices in each zip code are observed on a due date each month and transformed into year averages.

  20. Price changes of the incumbents’ default tariffs show are highly correlated with price changes of other suppliers (the correlation coefficient is 0.87).

  21. These are basically the former incumbents’ supply areas, however, some former incumbents had to sell their grids in due course of the unbundling legislations.

  22. This choice was affected by talks with electricity retailers saying that they purchase Phelix Base one year ahead future to procure wholesale electricity.

  23. Combined Heat and Power (CHP) plants generate heat and power simultaneously on the basis of cogeneration.

  24. Duso and Szücs (2017).

  25. In addition I also have data on consumer search at Verivox – another major price comparison platform in Germany – for the year 2014; however the data are only provided as percentages of search in a respective zip code relative to the overall search in Germany, which is why we cannot merge these data with our actual search data. We find a correlation coefficient of 85% between search intensity at Verivox and the platforms we use here, indicating that search at Verivox does not seem to differ much from search observed in our dataset.

  26. I use the change in regionally varying costs ΔCv as instrument instead of the change in total costs ΔC because certain national cost components are probably known to the consumers and may also affect their search efforts. For instance, the yearly adjustment of the EEG cost apportionment attracts considerable media attention. This may affect consumers’ search decisions and thus ΔC may not be a valid instrument as it may also affects search intensity directly. By contrast, consumers are not aware of the regionally varying costs, i.e. grid charges and concession fee. The variation in regionally varying costs thus should only affect consumer search efforts through their impact on price.

  27. In case of a single endogenous variable the Kleibergen-Paap F statistic is equivalent to the first stage F statistic.

  28. As the annoucement of price adjustments for the next year usually takes place around mid-November, consumers searching between November 15 and December 31 may search as a reaction to future prices instead of current prices. 24% of all searches take place in this period of the year. As a robustness check I transfer search queries that occured between November 15 and December 31 to the next year’s searches and re-estimate Table 2. The results are similar and are reported in Table 7 in the Appendix.

  29. In a robustness check I also control for additional household characteristics such as unemployment rate, the share of freelancers and the share of people working in jobs where social security contributions are mandatory. The results only change marginally and are reported in Table 8.

  30. A technical description of the required assumptions of the Lewbel (2012) IV and a brief description on the procedure itself are provided in Section B in the Appendix.


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Correspondence to Sven Heim.

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I thank ene’t ( for providing data on consumer search queries and electricity tariffs and cost components. I also thank Acxiom ( for providing data on household characteristics. For helpful comments and discussions I thank Pierre Fleckinger, Christos Genakos, Daniel Herrera, Matthew Lewis, Nathan Miller, Andras Niedermayer, Kathleen Nosal, Bettina Peters, Dennis Rickert, Nic de Roos, Philipp Schmidt-Dengler, Nicolas Schutz, Joel Stiebale and Michael Waterson, and the audiences at the European Economic Association Conference 2017, Jornadas de Economia Industrial 2017, CRESSE 2019, EARIE 2019, MaCCI conference 2019, and seminars at the University of Mannheim, Mines ParisTech and the OECD. Financial support by the State Government of Baden-Württemberg, Germany, through the research program “Strengthening Efficiency and Competitiveness in the European Knowledge Economies (SEEK)” is gratefully acknowledged.


Appendix A: Additional figures

Fig. 4
figure 4

Screenshot of a price comparison website (

Fig. 5
figure 5

Incumbent default tariffs (2012)

Fig. 6
figure 6

Total costs (2012)

Fig. 7
figure 7

Search intensity (2012)

Fig. 8
figure 8

Range of prices (2012)

Fig. 9
figure 9

Distribution of cost changes by year

Appendix B: First-stage results

Table 4 First-stage estimations of Table 2
Table 5 First-stage estimations of Table 3

Appendix C: Estimations of consumer search intensity without covariates

Table 6 IV estimation of consumer search – estimation without covariates

Appendix D: Estimations of consumer search intensity when search between November 15 to December 31 counts for the next year

Table 7 IV estimation of consumer search – search queries from November 15 to December 31 transferred to the next year

Appendix E: Estimations of cost pass-through with additional control variables

Table 8 Estimation of cost pass-through - additional control variables

Appendix F: Alternative consumption level

Table 9 Estimation of cost pass-through for a standard 5 person household (5,000 MWh/a)
Fig. 10
figure 10

Marginal effects of Δμ on the cost pass-through rate for a 5 person household (5,000 MWh/a)

Appendix G: Alternative fixed effects

Table 10 Estimation of cost pass-through with incumbent FE
Fig. 11
figure 11

Marginal effects of Δμ on the cost pass-through rate based on estimations with incumbent FE

Appendix H: Technical description of Lewbel’s (2012) IV method and results

1.1 H.1 Technical description

Consider the linear relationship Y = Xβ + Zγ + ε1, where Z is potentially endogenous (the interactions of Δμ and the two cost change variables here) and γ is the parameter we wish to estimate. The equation that determines Z is Z = Xα + ε2, where ε1 and ε2 may be correlated and no element of X can be used as an instrument, i.e. there is no outside instrument available. As usual, the requirement is that \(E\left (X\varepsilon _{1}\right ) =0\), E(Xiε2) = 0, and that \(E\left (XX^{\prime }\right ) \) is nonsingular. The additional assumptions for the identification in the absence of an outside instrument are that \(Cov\left (X,\varepsilon _{1}\varepsilon _{2}\right ) =0\) and that there is some heteroskedasticity in the error of the first-stage, \(Cov\left (X,{\varepsilon _{2}^{2}}\right ) \neq 0\). If these assumptions hold the variation in ε2 can be used to identify the model parameters. γ (and β) can then be estimated consistently by using interactions of the mean-centered control variables and the residuals \((\left (X-\bar {X}\right ) \hat {\varepsilon }_{2})\) to instrument for Z.

The estimation procedure is then as follows:

  1. 1.

    Estimate \(\hat {\alpha }\) by an OLS regression of Z on X to obtain \(\hat {\varepsilon }_{2}=Z-X\hat \alpha \).

  2. 2.

    Use the interactions of the residuals \(\hat {\varepsilon }_{2}\) and the mean-centered covariates \((X-\bar {X})\) as instruments for Z and estimate \(Z=X\alpha + \gamma \left (X-\bar {X}\right ) \hat {\varepsilon }_{2} + \varepsilon _{3}\).

  3. 3.

    Obtain \(\hat {\beta }\) and \(\hat {\gamma }\) by estimating Y =\(X \beta + \hat {Z} \gamma + \varepsilon _{4}\).

1.2 H.2 Results

As Lewbel (2012) shows, the model is identified if the errors from a regression of the endogenous variable on covariates from the main model are heteroskedastic and the variance of these errors is correlated with at least some of the covariates but not with the covariances of these errors and the second stage errors. I test the heteroskedasticity requirement based on the residuals of the first stage regression, using a modified Wald statistic for groupwise heteroskedasticity. The test rejects the null hypotheses of a constant variance as can be seen in Table 11.

The Kleibergen-Paap F-statistic suggests that the generated instruments are sufficiently strong to identify the endogenous variables in all estimations as the Stock and Yogo critical values are exceeded. Again, the results remain robust to this alternative IV.

Table 11 Lewbel (2012) IV estimation of cost pass-through
Fig. 12
figure 12

Marginal effects of Δμ on the cost pass-through rate based on Lewbel (2012) IV estimations

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Heim, S. Asymmetric cost pass-through and consumer search: empirical evidence from online platforms. Quant Mark Econ 19, 227–260 (2021).

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  • Consumer search
  • Cost pass-through
  • Rockets and feathers

JEL Classification

  • D83
  • L11