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Craig E Landry, Andreas Lange, John A List, Michael K Price, Nicholas G Rupp

Cited by*: 18 Downloads*: 17

This study develops theory and conducts an experiment to provide an understanding of why people initially give to charities, why they remain committed to the cause, and what factors attenuate these influences. Using an experimental design that links donations across distinct treatments separated in time, we present several insights. For example, we find that previous donors are more likely to give, and contribute more, than donors asked to contribute for the first time. Yet, how these previous donors were acquired is critical: agents who are initially attracted by signals of charitable quality transmitted via an economic mechanism are much more likely to continue giving than agents who were initially attracted by non-mechanism factors.

Craig E Landry, Andreas Lange, John A List, Michael K Price, Nicholas G Rupp

Cited by*: 160 Downloads*: 21

This study develops theory and uses a door-to-door fundraising field experiment to explore the economics of charity. We approached nearly 5000 households, randomly divided into four experimental treatments, to shed light on key issues on the demand side of charitable fundraising. Empirical results are in line with our theory: in gross terms, our lottery treatments raised considerably more money than our voluntary contributions treatments. Interestingly, we find that a one standard deviation increase in female solicitor physical attractiveness is similar to that of the lottery incentive--the magnitude of the estimated difference in gifts is roughly equivalent to the treatment effect of moving from our theoretically most attractive approach (lotteries) to our least attractive approach (voluntary contributions).

Daniel Henderson , John A List, Daniel L Millimet, Christopher Parmeter , Michael K Price

Cited by*: 1 Downloads*: 4

Nonparametric estimators provide a flexible means of uncovering salient features of auction data. Although these estimators are popular in the literature, many key features necessary for proper implementation have yet to be uncovered. Here we provide several suggestions for nonparamteric estimation of first-price auction models. Specifically, we show how to impose monotonicity of the equilibrium bidding strategy; a key property of structural auction models not guaranteed in standard nonparametric estimation. We further develop methods for automatic bandwidth selection. Finally, we discuss how to impose monotonicity in auctions with differering number of bidders, reserve prices, and auction-specific characteristics. Finite sample performance is examined using simulated data as well as experimental auction data.

Craig E Landry, Andreas Lange, John A List, Michael K Price, Nicholas G Rupp

Cited by*: 4 Downloads*: 0

Several recent laboratory experiments have shown that the use of explicit incentives--such as conditional rewards and punishment--entail considerable "hidden" costs. The costs are hidden in the sense that they escape our attention if our reasoning is based on the assumption that people are exclusively self-interested. This study represents a first attempt to explore whether, and to what extent, such considerations affect equilibrium outcomes in the field. Using data gathered from nearly 3000 households, we find little support for the negative consequences of control in naturally-occurring labor markets. In fact, even though we find evidence that workers are reciprocal, we find that worker effort is maximized when we use conditional--not unconditional--rewards to incent workers.

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

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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.

Matilde Giaccherini, David H Herberich, David Jimenez-Gomez, John A List, Giovanni Ponti, Michael K Price

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This paper uses a field experiment to estimate the effects of prices and social norms on the decision to adopt and efficient technology. We find that prices and social norms influence the adoption and decision along different margins: while prices operate on both the extensive and intensive margins, social norms operate mostly through the extensive margin. This has both positive and normative implications, and suggests that economics and psychology may be strong complements in the diffusion process. To complement the reduced form results, we estimate a structural model that points to important household heterogeneity: whereas some consumers welcome the opportunity to purchase and learn about the new technology, for others the inconvenience and social pressure of the ask results in negative welfare. As a whole, our findings highlight that the design of optimal technological diffusion policies will require multiple instruments and a recognition of household heterogeneity.

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

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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.