Applied Economic Perspectives and Policy最新文献

Black farmers have historically been discriminated against in services from the federal government, including access to credit. Discrimination can take the form of delayed loan processing requests, which can affect timely planting, harvesting, feeding of livestock, and farm performance. This study uses nationwide, farm-level data from 2009 to 2021 from the Farm Service Agency's direct farm loan program to investigate racial discrimination in the farm loan program. Findings reveal loan processing times average longer for Black borrowers on operating loans, though with a substantial state-level variation. Specifically, it takes an average of more than two additional days for Black farmers' operating loan applications to be completed and another two additional days to be processed. However, there was no significant difference in time for farm ownership loans suggesting a more nuanced cause than outright racial discrimination. Other factors that increased loan processing time for borrowers included larger loan amounts, more complex loan types, a mix of collateral, and being a new borrower.

Beginning in mid-2021, U.S. food prices surged at the fastest pace in decades, due to pandemic-related supply chain and labor shortages, rising transportation costs and wages, food commodity and fertilizer shocks resulting from Russia's invasion of Ukraine, and perhaps demand-side effects of recent monetary and fiscal stimulus. We decompose the path of domestic food prices into explanatory factors, grouped by supply or demand orientation. Our findings indicate that although supply-side factors explain most of the observed price changes, the demand-side factors we studied—particularly the money supply—have a stronger correlation with recent food price increases than they have, historically.

Using panel data for the period between April 2019 and September 2021, this paper investigates how the Covid-19 pandemic-induced lockdowns imposed differential labor market shocks on different social identity groups. We find that while all caste groups lost jobs in the first 2 months of the lockdown, the job losses for lowest-ranked caste are greater by factor of more than two. The data shows that caste gaps in employment outcomes remain sizeable, even when we compare groups within the same industry, occupations, or those who have completed secondary schooling. These findings suggest that caste is not merely a proxy for class, and identity-based policies might be essential to overcoming these disparities.

Replicability is a cornerstone of all scientific disciplines. While agricultural economists often provide recommendations to stakeholders that inform, among others policymaking, we currently lack replication papers published in leading agricultural economics journals. This increases the risk that published results are not replicable, which potentially can lead to inefficient resource allocation. In this article, we provide a framework for replications in agricultural economics and discuss challenges and opportunities with the objective to foster a replication culture. We provide pathways on how to untap this potential and provide guidance for enabling a stronger emphasis on replications in the field of agricultural economics. We present the first special issue on replications in agricultural economics, which consists of 11 articles that replicate various empirical analyses presented in published articles and advance the analyses that were used in the original work to provide further insights.

Economic experiments have emerged as a powerful tool for agricultural policy evaluations. In this perspective, we argue that involving stakeholders in the design of economic experiments is critical to satisfy mandates for evidence-based policies and encourage policymakers' usage of experimental results. To identify advantages and disadvantages of involving stakeholders when designing experiments, we synthesize observations from six experiments in Europe and North America. In these experiments, the primary advantage was the ability to learn within realistic decision environments and thus make relevant policy recommendations. Disadvantages include complicated implementation and constraints on treatment design. We compile 12 recommendations for researchers.

We examine shocks experienced by rural Nepali households during the COVID-19 pandemic. Households primarily experienced income and price shocks during a government-imposed lockdown. During this time, households managed to effectively protect consumption, and mostly relied on credit (26%), asset sales (10%) and savings (8%). Debt levels nearly doubled, with limited changes to savings. We then leverage a long-term randomized control trial (RCT) to assess whether beneficiaries of a livestock livelihood program are more resilient. Program beneficiaries are 6 percentage points less likely to take out new loans.

Increasing agricultural productivity is a policy priority in many countries. O'Donnell (Am. J. Agric. Econ. 94(4): 873–890, 2012) decomposed productivity change in US agriculture using a Lowe total factor productivity (TFP) index. We replicate the original study, assess its robustness to alternative TFP indices, and extend the analysis to EU agriculture. We consistently find that productivity growth in US agriculture is mainly driven by technical progress. In EU agriculture, TFP growth is less pronounced, and both technical change and efficiency change contribute to productivity changes. In both US and EU agriculture, the magnitude of measured productivity change varies across indices, highlighting the need to rely on multiple indices for robust policy recommendations.

This is a case study comparing outcomes for a probability-based representative sample versus a non-probability convenience sample for the valuation of beach condition information among Gulf of Mexico residents. We test the efficacy of several techniques used to adjust for hypothetical bias and sample weighting to reduce hypothetical willingness to pay (WTP). Weighting makes the WTP between the two samples similar, but model equivalence with respect to the significance of explanatory variables is rejected. The results support the use of certainty follow-ups, which consistently reduced WTP, regardless of the sampling approach or weighting.

Reducing agricultural greenhouse gas (GHG) emissions is key to achieve overall climate policy goals. Effective and efficient policy instruments are needed to incentivize farmers' adoption of on-farm climate change mitigation practices. We compare action- and results-based policy designs for GHG reduction in agriculture and account for farmers' heterogeneous behavioral characteristics such as individual farming preferences, reluctance to change and social interactions. An agent-based bio-economic modeling approach is used to simulate total GHG reduction, overall governmental spending and farm-level marginal abatement cost of Swiss dairy and beef cattle farms under both action- and results-based policy designs. We find that total governmental spending associated with the compared policy designs depends on the cost and benefits of the considered measures as well as behavioral characteristics of farmers. More precisely, if farmers are reluctant to change, additional incentives are needed to increase adoption of a win-win measure. In such a case, targeting the payment on the cost of that particular measure (action-based design) instead of paying a uniform amount for abated emissions (results-based design) can lower governmental spending for agricultural climate change mitigation. Farm-level marginal cost of reducing GHG emissions are lower with results-based payments independent of the cost of measures. Moreover, we find that farmers' individual preferences and reluctance to change substantially lower the adoption of mitigation measures and hence overall GHG reduction potential of farms.

We compare water quality valuation results from a probability sample and two opt-in non-probability samples, MTurk and Qualtrics. The samples differ in some key demographics, but measured attitudes are strikingly similar. For valuation models, most parameters were significantly different across samples, yet many of the marginal willingness to pay were similar across samples. Notably, for non-marginal changes there were some differences by samples: MTurk values were always significantly greater than the probability sample, as were Qualtrics values for changes up to about a 20% improvement. Overall, the evidence is mixed, with some key differences but many similarities across samples.