Peer Irrigators and the Choice of Field Management and Water Control Practices for Irrigation in Arkansas

We examined how irrigation techniques in use by family and friends influence the use and share of land utilizing different irrigation techniques by Arkansas producers. A bivariate sample selection model simultaneously estimated how farm characteristics determine the use and explain the share of a farm that utilizes an irrigation technique. We found that the irrigation techniques in use by family and friends do affect the irrigation techniques a producer uses and the share of acres utilizing different irrigation techniques. A producer with a family or friend that uses end-blocking irrigation is 41% more likely to use end-blocking themselves. Having a family or friend who uses pivot irrigation technology tends to decrease the share of irrigated acres that utilizes end block irrigation by 0.211. We also found that when the irrigation techniques in use by family and friends interact with variables such as location and participation in a regional conservation partnership program, the effects on the producer’s decision vary. The share of irrigated acres that use cutback irrigation decreases by 0.21 for a producer who has a peer that uses irrigation scheduling. However, if the producer lives along Crowley’s Ridge and has a peer that uses irrigation scheduling, the share of irrigated acres that use cutback irrigation decreases by an additional 0.54.

Estimating COVID-19 Prevalence in the United States: A Sample Selection Model Approach

SummaryBackgroundPublic health efforts to determine population infection rates from coronavirus disease 2019 (COVID-19) have been hampered by limitations in testing capabilities and the large shares of mild and asymptomatic cases. We developed a methodology that corrects observed positive test rates for non-random sampling to estimate population infection rates across U.S. states from March 31 to April 7.MethodsWe adapted a sample selection model that corrects for non-random testing to estimate population infection rates. The methodology compares how the observed positive case rate vary with changes in the size of the tested population, and applies this gradient to infer total population infection rates. Model identification requires that variation in testing rates be uncorrelated with changes in underlying disease prevalence. To this end, we relied on data on day-to-day changes in completed tests across U.S. states for the period March 31 to April 7, which were primarily influenced by immediate supply-side constraints. We used this methodology to construct predicted infection rates across each state over the sample period. We also assessed the sensitivity of the results to controls for state-specific daily trends in infection rates.ResultsThe median population infection rate over the period March 31 to April 7 was 0.9% (IQR 0.64 1.77). The three states with the highest prevalence over the sample period were New York (8.5%), New Jersey (7.6%), and Louisiana (6.7%). Estimates from mod-els that control for state-specific daily trends in infection rates were virtually identical to the baseline findings. The estimates imply a nationwide average of 12 population infections per diagnosed case. We found a negative bivariate relationship (corr. = -0.51) between total per capita state testing and the ratio of population infections per diagnosed case.InterpretationThe effectiveness of the public health response to the coronavirus pandemic will depend on timely information on infection rates across different regions. With increasingly available high frequency data on COVID-19 testing, our methodology could be used to estimate population infection rates for a range of countries and subnational districts. In the United States, we found widespread undiagnosed COVID-19 infection. Expansion of rapid diagnostic and serological testing will be critical in preventing recurrent unobserved community transmission and identifying the large numbers individuals who may have some level of viral immunity.FundingSocial Sciences and Humanities Research Council.

Selection Without Exclusion

It is well understood that classical sample selection models are not semiparametrically identified without exclusion restrictions. Lee (2009) developed bounds for the parameters in a model that nests the semiparametric sample selection model. These bounds can be wide. In this paper, we investigate bounds that impose the full structure of a sample selection model with errors that are independent of the explanatory variables but have unknown distribution. The additional structure can significantly reduce the identified set for the parameters of interest. Specifically, we construct the identified set for the parameter vector of interest. It is a one‐dimensional line segment in the parameter space, and we demonstrate that this line segment can be short in practice. We show that the identified set is sharp when the model is correct and empty when there exist no parameter values that make the sample selection model consistent with the data. We also provide non‐sharp bounds under the assumption that the model is correct. These are easier to compute and associated with lower statistical uncertainty than the sharp bounds. Throughout the paper, we illustrate our approach by estimating a standard sample selection model for wages.