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Lottery Incentives and Resource Management: Evidence from the Agricultural Data Reporting Incentive Program (AgDRIP)

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Abstract

To manage resources effectively in an agri-environmental context, policymakers need information about on-farm management practices and ecological conditions. This information is often accessible to agricultural producers but not to policymakers. However, little is known about how best to structure incentives for voluntary reporting. In other contexts, lotteries are often used to provide an incentive for voluntary data reporting. This article provides evidence about the efficacy of lottery (stochastic) incentives relative to fixed (deterministic) incentives. Based on two field experiments embedded in a data reporting program for agricultural producers, we estimate that lottery incentives reduced program enrollment between 28% and 62% relative to fixed incentives. A novel feature of our study is a comparison between fixed incentives and actuarially equivalent lotteries with explicitly communicated probabilities, which allows us to rule out an effect size of actuarially equivalent lotteries larger than +15% relative to fixed incentives.

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Notes

  1. Appendix A expands on the theoretical motivations introduced here.

  2. Within Georgia and Colorado, the regions added in 2019 were very similar agriculturally to the regions present in both 2018 and 2019, with slightly more reliance on withdrawals for irrigation in the added regions in both states. See Appendix Tables B.12 and B.13.

  3. Average annual precipitation from 2000 through 2020 at Burlington Carson Airport and Southwest Georgia Regional Airport, calculated from data obtained from noaa.gov.

  4. There was particular interest in Georgia about the potential for more widespread data collection using the mobile app. The first thing users saw after logging into the app was a data entry screen that resembled a water meter but with blank spaces where the numbers would be. Users had access to two other “screens”: (1) a tools screen where they could customize nicknames for their water meters and the appearance of the meter on the data entry screen, and (2) a history screen where they could view the meter readings they had submitted in prior months.

  5. We chose to increase the size of the bonus because registration was lower than we anticipated. All 2018 registrants were given $100 for registration even if they registered when the advertised bonus was only $30.

  6. The prepaid Visa card was chosen as a payment delivery channel to be as close as administratively feasible to cash. Previous research has found that substitutes for cash elicit lower response rates than cash with the same face value (Teisl et al. 2006). Since the producers who chose to participate in 2018 also received an invitation in 2019, they were asked to keep their prepaid Visa cards from the prior year, and their 2019 invitation letters did not enclose a card.

  7. The lottery incentive thus includes a fixed component. We are not aware of previous literature examining mixed incentives with both fixed and lottery components.

  8. The 2019 experiment thus incentivized reporting on two meters, whereas the 2018 experiment incentivized reporting on only one meter. Simplicity was prioritized in the design of the 2018 experiment, and for the 2019 experiment the research team felt that the potential boost in incentivized data collection was worth the added complexity of the incentive structure.

  9. Postal mailings tend to produce low response rates with agricultural producers, on the order of five percent or less (Weigel et al. 2020). On the other hand, we expected compliance to be strong as a result of feelings of reciprocity following the registration payment, similar to the operative mechanism with prepaid survey instruments (Singer and Kulka 2002; Singer and Ye 2013).

  10. We include the blocking variables as covariates to adhere to the registered pre-analysis plan and to follow Bruhn and McKenzie (2009), who recommend that analysis of random experiments controls for the method of randomization.

  11. Appendix Figure C.4 reports results from the compliance experiments (prepaid versus postpaid), noting that our power analysis concluded that the compliance experiments were underpowered.

  12. The direction of the smaller measured effect in 2019 than in 2018 is consistent with the logic of Fehr-Duda et al. (2010); stakes were larger in 2018, and lottery incentives elicited a lower response rate relative to fixed incentives in 2018.

  13. Participation in AgDRIP in 2018 may have influenced 2019 participation decisions. We do not attempt to examine the causal impact of previous participation, and the fact that some producers in the 2019 sample participated in 2018 does not interfere with the identification of the treatment effects that we are examining. In all regressions that include the 2019 sample, we include a prior-year participation indicator as a covariate to adjust for differences between producers who previously participated in AgDRIP and producers who did not. The inclusion of the prior-year participation variable results in a notable increase in the model R squared in the models that include 2019 data.

  14. There were differences between the 2018 experiment and the 2019 experiment, including the size of the registration bonus payment and the expected value of the monthly payments. We do not attempt to examine the causal impact of those differences, and those differences do not interfere with the identification of the treatment effects that we are examining. In the pooled regression, we include year as a covariate to adjust for differences between the 2018 experiment and the 2019 experiment.

  15. Appendix Figure C.5 shows the reports per registered meter separately by each of the five treatment arms. None of the differences are statistically significant at the 5% level. Appendix Figure C.6 shows that producers with two registered meters submitted fewer readings per meter than producers with only one registered meter.

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Acknowledgements

The authors gratefully acknowledge financial support from the USDA Economic Research Service, Grant #59-6000-4-0064; the USDA National Institute of Food and Agriculture, Grant #2019-67023-29854; the USDA National Institute of Food and Agriculture, Grant #2017-67024-26278; and the USDA Office of the Chief Economist, Cooperative Agreement #58-0111-18-003. We thank Maddi Valinski and Linda Means for coordinating app developers and managing AgDRIP customer service respectively. We thank Carlos Estrada, James Geisler, Erick Tepale, Cecil Edens Jr, and Danylo Hirnyj for their contributions to development of the AgDRIP app. We thank Paul Feldman for helpful comments on the manuscript. We thank Dong-Woo Seo for research assistance.

Funding

This paper was supported financially by the USDA Economic Research Service, Grant #59-6000-4-0064; the USDA National Institute of Food and Agriculture, Grant #2019-67023-29854 and NIFA Grant #2017-67024-26278; and the USDA Office of the Chief Economist, Cooperative Agreement #58-0111-18-003.

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Correspondence to Ben S. Meiselman.

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The experiments reported in this paper were approved by the Institutional Review Board at Albany State University under the title “Understanding Agricultural Water Use Behavior Through Randomized Controlled Trials.” IRB 00004776, project 1041657.

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Meiselman, B.S., Weigel, C., Ferraro, P.J. et al. Lottery Incentives and Resource Management: Evidence from the Agricultural Data Reporting Incentive Program (AgDRIP). Environ Resource Econ 82, 847–867 (2022). https://doi.org/10.1007/s10640-022-00690-1

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