Search Papers

Steffen Andersen, Alec Brandon, Uri Gneezy, John A List

Cited by*: 6 Downloads*: 51

Perhaps the most powerful form of framing arises through reference dependence, wherein choices are made recognizing the starting point or a goal. In labor economics, for example, a form of reference dependence, income targeting, has been argued to represent a serious challenge to traditional economic models. We design a field experiment linked tightly to three popular economic models of labor supply-two behavioral variants and one simple neoclassical model--to deepen our understanding of the positive implications of our major theories. Consistent with neoclassical theory and reference--dependent preferences with endogenous reference points, workers (vendors in open air markets) supply more hours when presented with an expected transitory increase in hourly wages. In contrast with the prediction of behavioral models, however, when vendors earn an unexpected windfall early in the day, their labor supply does not respond. A key feature of our market in terms of parsing the theories is that vendors do not post prices rather they haggle with customers. In this way, our data also speak to the possibility of reference-dependent preferences over other dimensions. Our investigation again yields results that are in line with neoclassical theory, as bargaining patterns are unaffected by the unexpected windfall.

Alec Brandon, Paul J Ferraro, John A List, Robert D Metcalfe, Michael K Price, Florian Rundhammer

Cited by*: 4 Downloads*: 95

This study examines the mechanisms underlying long-run reductions in energy consumption caused by a widely studied social nudge. Our investigation considers two channels: physical capital in the home and habit formation in the household. Using data from 38 natural field experiments, we isolate the role of physical capital by comparing treatment and control homes after the original household moves, which ends treatment. We find 35 to 55 percent of the reductions persist once treatment ends and show this is consonant with the physical capital channel. Methodologically, our findings have important implications for the design and assessment of behavioral interventions.

Alec Brandon, John A List, Robert D Metcalfe, Michael K Price, Florian Rundhammer

Cited by*: None Downloads*: None

This study considers the response of household electricity consumption to social nudges during peak load events. Our investigation considers two social nudges. The first targets conservation during peak load events, while the second promotes aggregate conservation. Using data from a natural field experiment with 42,100 households, we find that both social nudges reduce peak load electricity consumption by 2 to 4% when implemented in isolation and by nearly 7% when implemented in combination. These findings suggest an important role for social nudges in the regulation of electricity markets and a limited role for crowd out effects.

Alec Brandon, John A List, Robert D Metcalfe, Michael K Price, Florian Rundhammer

Cited by*: None Downloads*: None

This study considers the response of household electricity consumption to social nudges during peak load events. Our investigation considers two social nudges. The first targets conservation during peak load events, while the second promotes aggregate conservation. Using data from a natural field experiment with 42,100 households, we find that both social nudges reduce peak load electricity consumption by 2 to 4% when implemented in isolation and by nearly 7% when implemented in combination. These findings suggest an important role for social nudges in the regulation of electricity markets and a limited role for crowd out effects.

Alec Brandon, Christopher M Clapp, John A List, Robert D Metcalfe, Michael K Price

Cited by*: None Downloads*: None

Smart-home technologies have been heralded as an important way to increase energy conservation. While in vitro engineering estimates provide broad optimism, little has been done to explore whether such estimates scale beyond the lab. We estimate the causal impact of smart thermostats on energy use via two novel framed field experiments in which a random subset of treated households have a smart thermostat installed in their home. Examining 18 months of associated high-frequency data on household energy consumption, yielding more than 16 million hourly electricity and daily natural gas observations, we find little evidence that smart thermostats have a statistically or economically significant effect on energy use. We explore potential mechanisms using almost four million observations of system events including human interactions with their smart thermostat. Results indicate that user behavior dampens energy savings and explains the discrepancy between estimates from engineering models, which assume a perfectly compliant subject, and actual households, who are occupied by users acting in accord with behavioral economists' conjectures. In this manner, our data document a keen threat to the scalability of new user-based technologies.

Pradhi Aggarwal, Alec Brandon, Ariel Goldszmidt, Justin Holz, John A List, Ian Muir, Gregory Sun, Thomas Yu

Cited by*: None Downloads*: None

Prior research finds that, conditional on an encounter, minority civilians are more likely to be punished by police than white civilians. An open question is whether the actual encounter is related to race. Using high-frequency location data of rideshare drivers operating on the Lyft platform in Florida, we estimate the effect of driver race on traffic stops and fines for speeding. Estimates obtained across traditional and machine learning approaches show that, relative to a white driver traveling the same speed, minorities are 24 to 33 percent more likely to be stopped for speeding and pay 23 to 34 percent more in fines. We find no evidence that these estimates can be explained by racial differences in accident and re-offense rates. Our study provides key insights into the total effect of civilian race on outcomes of interest and highlights the potential value of private sector data to help inform major social challenges.