Applied Economic Perspectives and Policy最新文献

Immigrants are vital to agricultural production in the United States, and nearly half the crop workforce is unauthorized. Previous attempts to reform the immigration system have not successfully legalized the farm workforce or caused substantive rise in farmworker incomes. Current proposed legislation would legalize unauthorized farmworkers, streamline the H-2A agricultural guest worker program, and provide a pathway to citizenship for H-2A workers while simultaneously requiring agricultural employers to check the immigration status of workers using E-Verify. This paper discusses proposed farm labor legislation in the context of current farm labor market conditions, outcomes of historical farm labor and immigration policies, and ongoing immigration trends.

Grant, Jason H., Shawn Arita, Charlotte Emlinger, Robert Johansson, and Chaoping Xie. 2021. “Agricultural exports and retaliatory trade actions: An empirical assessment of the 2018/2019 trade conflict.” Applied Economic Perspectives and Policy, 43(2): 619–640. https://doi.org/10.1002/aepp.13138.

In the acknowledgements section, the text “The findings and conclusions in this article are those of the authors and do represent any official U.S. Department of Agriculture or U.S. government determination or policy.” was incorrect.

This should have read: “The findings and conclusions in this article are those of the authors and do not represent any official U.S. Department of Agriculture or U.S. government determination or policy.”

We apologize for this error.

Impacted by both economic and political forces, agricultural research serves as a critical approach to alleviating the adverse effects of climate change. Focusing on public agricultural research in the United States, this paper provides a literature review on research and development from the perspectives of the market environment and political economy. It also examines the current assessment of agricultural research effectiveness in addressing the challenges of climate change. A bibliometric analysis is conducted to appreciate the knowledge dynamics in the nexus of agricultural research, political economy, and climate change. Future research directions related to public agricultural research are discussed.

Agricultural and applied economists make substantial positive contributions to the domestic economy. Defining a measure of the true total value of their contributions is likely impossible, because so much about their efforts is difficult to comprehensively observe and quantitatively document. In this paper, we adopt a conservative approach to generating an estimate of the contributions ag and applied economists make to U.S. economic output and the associated welfare of society through their teaching, research, and outreach efforts. To conduct the analysis, we implemented a nationwide survey of Agricultural and Applied Economics (AAE) departments and developed a framework to calculate the value of their contributions to national income, or Gross Domestic Product (GDP). We estimate that AAE departments increase overall U.S. GDP by $2.6 billion, annually. Through its efforts to improve the human capital of its graduates, AAE teaching raises the (expected) national income by $2.2–$2.3 billion, while we value direct research and outreach contributions at $207 million and $146 million, respectively. Because we do not observe the opportunity cost of the resources used to generate those contributions, we do not claim to estimate a true net economic impact but rather attempt to quantify the gross economic contributions of the professional services AAE departments currently offer the economy. The values we provide—especially the research and extension estimates which are exceedingly difficult to measure—likely underestimate the true benefits AAE offers to the nation.

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.