The Use of Regression Statistics to Analyze Imperfect Pricing Policies

Abstract

Corrective taxes can solve many market failures, but actual policies frequently deviate from the theoretical ideal because of administrative or political constraints. We present a method to quantify the efficiency costs of constraints on externality-correcting policies or, more generally, the costs of imperfect pricing, using simple regression statistics. Under certain conditions, the R 2 and the sum of squared residuals from a regression of true externalities on policy variables measure relative welfare gains from policies. We illustrate via four empirical applications: random mismeasurement of externalities, imperfect electricity pricing, heterogeneity in the longevity of energy-consuming durable goods, and imperfect spatial policy differentiation.

The Environmental Bias of Trade Policy*

“…An accounting identity states that each industry's gross output equals the value of its output used for intermediate goods in all industries plus the value of its output 5 One could also wonder how domestic or "behind-the-border" policies affect the choice among energy-consuming durable goods like cars or air conditioners. While Section 4.2 discusses sensitivity analyses designed to account for energy used in consuming these goods, a detailed analysis of energy consumption for these goods and associated policies is the topic of an active body of research that uses models specialized to these sectors (e.g., Bento et al 2009;Jacobsen et al 2019). used for final demand: x = Ax + d. Simple algebra then reveals the total amount of intermediate inputs (including both direct and indirect inputs) required to produce a dollar of final demand:…”

Moral Suasion and Economic Incentives: Field Experimental Evidence from Energy Demand

“…Finally, Schwartz et al (2013), after sending consumers postcards that stated their electricity usage was being observed, find that the treated customers had a 2.7 percent reduction in electricity usage relative to customers who received nothing. 13 When interpreting the size of the treatment effect, we want to emphasize again that our households were not a random sample of the population. While observable characteristics are not statistically different between the experimental sample and a random sample of the population in Table 1, there can be unobservable factors that are different between the two groups.…”

Political economy of renewable resource federalism

“…If federal one‐size‐fits‐all polices can capture most of the efficiency gains from state‐specific policies, federal management likely represents the preferred approach. Recent empirical work suggests that the efficiency costs of federal policies can be approximated using simple regression statistics (Jacobsen et al 2020). Adapting these methods for renewable resource management represents a potentially fruitful avenue for future work.…”