Data created in a controlled laboratory setting are a relatively new phenomenon to economists. Traditional data analysis methods using either parametric or nonparametric tests are not necessarily the best option available to economists analyzing laboratory data. In 1935, Fisher proposed the randomization technique as an alternative data analysis method when examining treatment effects. The observed data are used to create a test statistic. Then treatment labels are shuffled across the data and the test statistic is recalculated. The original statistic can be ranked against all possible test statistics that can be generated by these data, and a p-value can be obtained. A Monte Carlo analysis of t-test, the Mann-Whitney U-test, and the exact randomization t-test is conducted. The exact randomization t-test compares favorably to the other two tests both in terms of size and power. Given the limited distributional assumptions necessary for implementation of the exact randomization test, these results suggest that experimental economists should consider using the exact randomization test more often.

We use a comprehensive new dataset of asset-class returns in 38 developed countries to examine a popular class of retirement spending rules that prescribe annual withdrawals as a constant percentage of the retirement account balance. A 65-year-old couple willing to bear a 5 percent chance of financial ruin can withdraw just 2.31 percent per year, a rate materially lower than conventional advice (e.g., the 4% rule). Our estimates of failure rates under conventional withdrawal policies have important implications for individuals (e.g., savings rates, retirement timing, and retirement consumption), public policy (e.g., participation rates in means-tested programs), and society (e.g., elderly poverty rates).

The strategy frequency estimation method (Dal Bó and Fréchette in Am Econ Rev 101(1):411-429, 2011; Fudenberg in Am Econ Rev 102(2):720-749, 2012) allows us to estimate the fraction of subjects playing each of a list of strategies in an infinitely repeated game. Currently, this method assumes that subjects tremble with the same probability. This paper extends this method, so that subjects’ trembles can be heterogeneous. Out of 60 ex ante plausible specifications, the selected model uses the six strategies described in Dal Bó and Fréchette (2018), and allows the distribution of trembles to vary by strategy.

The canonical income process, including autoregressive, transitory, and fixed effect components, is routinely used in macro and labor economics. We provide a guide for its estimation using quasidifferences, cataloging biases in the estimated parameters for various $N$, $T$, initial conditions, and weighting schemes. Using Danish administrative data on male earnings, estimation in quasidifferences yields divergent estimates of the autoregressive parameter for different weighting schemes, which conforms to our simulation results when the variance of transitory shocks is higher than that of persistent shocks, true persistence is high, and the persistent component’s variance in the first sample year is nonzero. We further apply quasidifferences to the data from a calibrated lifecycle model and find significant biases in the persistence of shocks and their insurance. Estimation of the income process using quasidifferences is reliable only when the variance of persistent shocks is higher than that of transitory shocks and the moments are equally weighted.

A theoretically consistent structural model facilitates definition and measurement of use and non-use benefits of ecosystem services. Unlike many previous approaches that utilize multiple stated choice situations, we apply this conceptual framework to a travel cost random utility model and a consequential single referendum contingent valuation research design for simultaneously estimating use and non-use willingness to pay for environmental quality improvement. We employ Monte Carlo generated data to evaluate properties of key parameters and examine the robustness of this method of measuring use and non-use values associated with quality change. The simulation study confirms that this new method, combined with simulated revealed and stated preference data can generally, but not always, be applied to successfully identify use and non-use values of various ecosystems while consistency is ensured.

The United States has a long history of providing ad hoc disaster assistance to agricultural producers. The latest version – the Emergency Relief Program (ERP) – follows five consecutive years of appropriations for disaster assistance. In response to ongoing appropriations, there is growing interest in establishing a permanent disaster program. However, with that comes concerns over the impact it could have on the existing farm safety net, particularly crop insurance. In this paper, we characterize the likely effects on crop insurance coverage levels of a permanent authorization of ERP. We assume that corn and soybean producers choose a coverage level based on the effects of that choice on the distribution of future ending wealth reflecting crop revenue, insurance indemnities, and ERP payments. We find very modest effects on crop insurance coverage level choices and crop insurance premiums collected.

We study individuals' incentives to make investment decisions. Using data from a large pension system in Chile we find that individuals who are active in managing their investments have, on average, poor performance. We provide robust evidence suggesting that learning plays an important part in this phenomenon. Indeed, individuals who have made successful investment decisions in the past go on to trade more frequently. However, this result holds when using a naive definition for successful decisions. Also, average performance is negatively related to the number of investment decisions, casting doubt on the existence of market timing skills.

The addition of a set of cohort parameters to a mortality model can generate complex identifiability issues due to the collinearity between the dimensions of age, period and cohort. These issues can lead to robustness problems and difficulties making projections of future mortality rates. Since many modern mortality models incorporate cohort parameters, we believe that a comprehensive analysis of the identifiability issues in age/period/cohort mortality models is needed. In this paper, we discuss the origin of identifiability issues in general models before applying these insights to simple but commonly used mortality models. We then discuss how to project mortality models so that our forecasts of the future are independent of any arbitrary choices we make when fitting a model to data in order to identify the historical parameters.

As the field of modelling mortality has grown in recent years, the number and importance of identifiability issues within mortality models has grown in parallel. This has led both to robustness problems and to difficulties in making projections of future mortality rates. In this paper, we present a comprehensive analysis of the identifiability issues in age/period mortality models in order to first understand them better and then to resolve them. To achieve this, we discuss how these identification issues arise, how to choose identification schemes which aid our demographic interpretation of the models and how to project the models so that our forecasts of the future do not depend upon the arbitrary choices used to identify the historical parameters estimated from historical data.

To support decision-makers considering adopting integrated pest management (IPM) cropping in Norway, we used stochastic efficiency analysis to compare the risk efficiency of IPM cropping and conventional cropping, using data from a long-term field experiment in southeastern Norway, along with data on recent prices, costs, and subsidies. Initial results were not definitive, so we applied stochastic efficiency with respect to a function, limiting the assumed risk aversion of farmers to a plausible range. We found that, for farmers who are risk-indifferent to moderately (hardly) risk averse, the conventional system was, compared to IPM, less (equally) preferred.

In this research, we analyze the economic effects across various crop insurance subsidy and policy scenarios to determine producer insurance choice response, total premium and subsidy payments and study their economic implications on dryland corn, soybean, and winter wheat producers. We rely on the expected utility maximization framework to rank policy combination sets that are available to a typical producer to analyze the impacts of crop insurance subsidy changes and elimination of certain insurance policies across the three crops. Several scenarios were analyzed across subsidy and policy options and were found to have noticeably different farmer behavioral responses and economic implications.

Inclusion of additional dimensions to population projections can lead to an improvement in the overall quality of the projections and to an enhanced analytical potential of derived projections such as literacy skills and labor force participation. This paper describes the modeling of educational attainment of a microsimulation projection model of the European Union countries. Using ordered logistic regressions on five waves of the European Social Survey, we estimate the impact of mother's education and other sociocultural characteristics on educational attainment and implement them into the microsimulation model. Results of the different projection scenarios are contrasted to understand how the education of the mother and sociocultural variables may affect projection outcomes. We show that a change in the impact of mother's education on children's educational attainment may have a big effect on future trends. Moreover, the proposed approach yields more consistent population projection outputs for specific subpopulations.

Adverse weather-related risk is a main source of crop production loss and a big concern for agricultural insurers and reinsurers. In response, weather risk hedging may be valuable, however, due to basis risk it has been largely unsuccessful to date. This research proposes the Lévy subordinated hierarchical Archimedean copula model in modelling the spatial dependence of weather risk to reduce basis risk. The analysis shows that the Lévy subordinated hierarchical Archimedean copula model can improve the hedging performance through more accurate modelling of the dependence structure of weather risks and is more efficient in hedging extreme downside weather risk, compared to the benchmark copula models. Further, the results reveal that more effective hedging may be achieved as the spatial aggregation level increases. This research demonstrates that hedging weather risk is an important risk management method, and the approach outlined in this paper may be useful to insurers and reinsurers in the case of agriculture, as well as for other related risks in the property and casualty sector.

In order to guarantee the success of the nascent cellulose-based biofuel industry, it is crucial to identify the most economically relevant components of the biofuel production path. To this aim, an original stochastic financial model is developed to estimate the impact that different feedstock production and biofuel conversion parameters have on the probability of economic success. Estimation of the model was carried out using Monte Carlo simulation techniques along with parametric maximum likelihood estimation procedures. Results indicate that operational efficiency strategies should concentrate on improving feedstock yields and extending the feedstock growing season.

One of the most popular risk management strategies for wheat producers is varietal diversification. Previous studies proposed a mean-variance model as a tool to optimally select wheat varieties. However, this study suggests that the mean–expected shortfall (ES) model (which is based on a downside risk measure) may be a better tool because variance is not a correct risk measure when the distribution of wheat variety yields is multivariate nonnormal. Results based on data from Texas Blacklands confirm our conjecture that the mean-ES framework performs better in term of selecting wheat varieties than the mean-variance method.

The 2002 Farm Bill creates several opportunities for landowners to adopt management practices that protect and improve soil and water quality. Landowners considering enrollment in conservation programs must compare the monetary and nonmonetary costs and benefits from removing land from production agriculture. The overall purpose of this invited paper session was to improve the understanding of the factors affecting a landowner's decision to enroll in conservation programs. Papers addressed the environmental benefits of conservation programs and compared the returns to enrolling in conservation programs to the returns from production agriculture.

A dynamic-stochastic model is developed to evaluate preferences among alternative countercyclical payment programs for representative farms producing corn or soybeans in Iowa and cotton or soybeans in Mississippi. Countercyclical payment programs are found to not necessarily be preferred to fixed payment programs.

The Financial and Risk Management (FARM) Assistance program created by Texas Cooperative Extension is a strategic analysis service offered to farmers and ranchers in Texas. The program serves as an example of large-scale, focused programming by extension agencies, as well as the implementation of technical stochastic simulation methods for use on the farm.

Conservation reserve program (CRP) payments amount to several billion dollars annually. Payments are allocated to both remove land from production and to help farmers pay for conservation improvements. However, research examining whether farmers increase their utility with CRPs is limited. This paper uses simulation analysis and certainty equivalents to compare farming income to payments under the CRP. Farming income is a combination of crop production and government payments as specified in the 2002 Farm Bill. This analysis focuses on farms in three different counties in Kentucky. Results indicate that CRPs are good choices for many farmers.

The provision for producers to update base acres and payment yields in the 2002 farm bill afforded an opportunity to test whether it was feasible to deliver a complex simulation model directly to producers. A Monte Carlo simulation model for assessing the economic impacts of the alternative base and yield options on individual farms was developed and made available to producers via the World Wide Web. The experiences and challenges from this collaborative extension and research effort are described, as well as the issues educators might consider before delivering complex software to a national audience via the Web.