The Long-Run Dynamics of Electricity Demand: Evidence from Municipal Aggregation

Abstract

We study the dynamics of residential electricity demand by exploiting a natural experiment that produced large and long-lasting price changes in over 250 Illinois communities. Using a flexible difference-in-difference matching approach, we estimate that the price elasticity of demand grows from − 0.09 in the first six months to − 0.27 two years later. We find similar results with a dynamic model in which usage is a function of past and future prices. Our findings highlight the importance of accounting for consumption dynamics when evaluating energy policy. (JEL L94, L98, Q41, Q48)

“Wild” tariff schemes: Evidence from the Republic of Georgia

“…Whatever the explanation for this apparent drop in price elasticity (a short-or mediumrun elasticity), it remains much higher than estimates from recent quasi-experimental studies in the US, which is -0.08 to -0.09 in the short or medium-long run (Ito, 2014;Deryugina et al, 2020) and -0.27 in the long run (Deryugina et al, 2020). It is however similar to that in Ukraine (Alberini et al, 2019).…”

“…4 The implied price elasticity from our DID designs is -0.3. We interpret this as a short-run elasticity, noting that it is much stronger than the figures in recent quasi-experimental studies in the US (-0.09 in Deryugina et al, 2020, andin Ito, 2014). We also fit a demand function, relying on the changes in tariffs across places and over time, which points to a price elasticity that is even stronger (-0.49) and generally stable over our study period, except for the last three years.…”

Renewable Energy and Economic Performance in the Context of the European Green Deal

“…For Portugal, the target is 31%, the main RESs being represented by wind, sun, water and biofuels [46]. Weather conditions in this country influence the utilization of hydropower.…”

The impact of COVID-19 infection on labor outcomes of Mexican formal workers

“…Given that we do not have a natural experiment, we use two methods to identify the effect on labor market outcomes of testing positive versus testing negative. First, we use a nearest-neighbor matching algorithm with replacement ( Abadie and Imbens, 2006 , Abadie and Imbens, 2011 ) that has been widely used (see, for example, Campello and Graham, 2013 , Cicala, 2015 , Deryugina et al, 2020 , Fowlie et al, 2012 , Garip, 2014 ). Second, as a robustness check, we use a fixed effects regression.…”