Applied Economic Perspectives and Policy最新文献

Agricultural and applied economists have maintained a public discourse at the Agricultural and Applied Economics Association (AAEA) meetings and subsequently published papers discussing the mission of land-grant institutions and the role of AAEA members in that mission. With a content analysis of 4001 Invited Papers and Presidential Speeches, we find agricultural and applied economists questioned their profession's purpose and role within the land-grant system. The reflective questions still apply to land-grant institutions and the agricultural and applied economics profession. We argue that AAEA members are crucial in addressing the food and agricultural challenges connected to society's deepest needs today and into the future.

We provide an application of machine learning to identify the distributional consequences of climate change in Malawi. We compare climate impact estimates based on drought indicators established objectively from the k-means algorithm to more traditional measures. Young women affected by drought were 5 percentage points more likely to be married by 18 than those living in nondrought areas. Our approach generates robust results when varying the number of clusters and definition of treatment status. In some cases, we find the design using k-means to define treatment is more likely to satisfy the assumptions underlying the difference-in-differences strategy than when using arbitrary thresholds. Projections from the estimates indicate future drought risk may lead to larger declines in labor productivity due to women's engagement in early age marriage than other factors affecting their participation rates. Under the extreme representative concentration pathway scenario, drought exposure encourages the exit of 3.3 million women workers by 2100.

Meta-analyses are widely used in various academic fields, including applied economics. However, the high labor intensity involved in paper searching and small sample sizes remain two dominant limiting factors. We conducted a meta-analysis of studies on consumer preferences for plant-based and lab-grown meat alternatives using machine-learning techniques at both the data collection and the data analysis phases. We demonstrated that machine learning reduces the workload in the manual title-abstract screen phase by 69% accounting for 24% of total workload in data collection. We also found that machine learning improves out-of-sample of sample prediction accuracy by 48–78 percentage points when compared to econometric model. Notably, we showed that integrating machine learning can also improve the predictive performance of econometric methods, thereby improving their out-of-sample predictions. Our empirical findings further revealed that demand for meat alternatives is higher among younger consumers, especially when the products displayed benefit information.

This paper provides a novel approach to integrate farmers' behavior in spatially explicit agricultural land use modeling to investigate climate change adaptation strategies. More specifically, we develop and apply a computationally efficient machine learning approach based on reinforcement learning to simulate the adoption of agroforestry practices. Using data from an economic experiment with crop farmers in Southeast Germany, our results show that a change in climate, market, and policy conditions shifts the spatial distribution of the uptake of agroforestry systems. Our modeling approach can be used to advance currently used models for ex ante policy analysis by upscaling existing knowledge about farmers behavioral characteristics and combine it with spatially explicit environmental and farm structural data. The approach presents a potential solution for researchers who aim to upscale information, potentially enriching and complementing existing land use modeling approaches.

Economic freedom, a measure of the degree of freedom from government intervention in the economy, has been found to be associated with many positive economic outcomes, such as lower unemployment rates, and higher growth of income, employment, and population. One area that remains unexplored is the relationship with food insecurity. Areas with more government intervention may be expected to have higher food insecurity because those interventions can create greater impediments to people's ability to prosper economically. One specific example of that is the minimum wage, which may make it harder for inexperienced low-skilled workers to obtain employment. We provide the first state-level examination of the relationship between food insecurity and economic freedom and find higher values of economic freedom (lower levels of intervention) are associated with lower food insecurity. We also examine one specific component of that economic freedom measure, the minimum wage, and find some limited evidence that higher minimum wages are associated with higher food insecurity.

Agricultural transformation involves the transition from subsistence agriculture marked by cultivating crops for auto-consumption to cultivating crops for output markets. This transition from subsistence agriculture to market-oriented agriculture can be a key policy boost to economic development, but evidence on the key entry points to increasing smallholder commercialization remains scarce. We examine the relationship between the adoption of improved maize varieties (IMVs), inorganic fertilizers, and smallholder commercialization. We model commercialization as a two-step decision process involving market participation and the extent of participation (sales quantity) conditional on participation. Given these two related steps, we estimate a double-hurdle model in both linear and non-linear forms. Employing a three-wave panel dataset from Ethiopia, we use the household fixed effects and correlated random effects model with the control function approach. We find the adoption of IMVs to be significantly associated with both market participation and the extent of participation. This relationship is also true for fertilizers, where we show a positive association between fertilizer use and commercialization. Given the seeming complementarity in the use of both IMVs and fertilizers, we further estimate their joint adoption. We use the multinomial endogenous switching regression model where we show greater commercialization gains under joint adoption. These findings are in line with a growing literature supporting the bundling of agricultural technologies. Given these insights, we provide empirical and policy support to the scaling of agricultural technologies as they have the potential to induce agricultural transformation by unlocking market opportunities.

In this paper, we discuss the reasons why agricultural and applied economics and similar departments are often stand-alone academic units. The factors that affect and shape the relationship of agricultural and applied economics faculty and departments with those from general economics departments are discussed. We present case studies of three universities having different relationships with general economics faculty at their respective universities: a merged unit, an unmerged unit, and a never-merged unit. We conclude with rationale for the existence and future trajectory of agricultural economics and related academic units at Land Grant Universities.

This study uses statistical learning methods to identify robust coverage alternatives for the Pasture, Rangeland, Forage (PRF) insurance program. Shrinkage and ensemble learning techniques are adapted to the context of the PRF coverage selection process. The out-of-sample performance of the proposed methods is evaluated on 116 representative grids throughout Texas during 2018–2022. Ensemble learning methods generated more stable coverage choices compared with the other selection strategies considered. Depending on the target return, a reduction in the prediction error between 5% and 14% was observed. Furthermore, the proposed coverages can provide a broader protection than current coverage choices made by farmers.

Forecasts are common in agricultural settings where they are routinely used for decision-making. The advent of the computer age has allowed for rapid generation of individual forecasts that can be updated in real time. It is well known that the selection and use of a single forecast can expose the forecaster to serious error as a result of model mis-specification. Forecast combination avoids this problem by combining information from different forecasts. Although forecast combination can be as simple as averaging across forecasts, advances in machine learning have made it possible to combine forecasts according to more complicated weighting schemes and criteria. We provide an overview of forecast combination techniques, including those at the frontier of current practice and involving machine learning. We also provide a retrospective on the use of forecast combination in agricultural economics and prospects for the future. Several of the techniques are illustrated in an application to forecasting nationwide corn and soybean planted acreage and we demonstrate how forecast combination can improve expert USDA projections.

National food security data have been vital in raising public awareness and motivating policy. This paper uses cross-survey multiple imputation as a flexible way to assess within-state food security patterns that cannot be measured well with the Current Population Survey. Using a CPS-based model, we impute food security status to households in the much larger American Community Survey. We illustrate the value of this approach by showing how grouping households by demographic or geographic attributes can provide insight into disparities within states by race and ethnicity, as well as variation in food security across substate geographies.