Applied Economic Perspectives and Policy最新文献

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.

This study uses social accounting matrix multipliers and structural path analyses to estimate effects of COVID-19 and related fiscal stimuli on five household groups. The COVID-19 lockdown increased poverty in Pakistan by 15%, which was addressed using a $1.5 billion, digitally implemented Ehsaas Emergency Cash (EEC) program that reached 14.8 million poor households. The study's models show that the largest multipliers from Ehsaas program finance were in agriculture, as a 1 Rupee shock adds 0.225 Rupee income to households. About 30% of that gain was estimated to go to poor farm families. In contrast, our models find that construction and trade growth added three times as much income to poor nonfarm and urban households as to farm households. However, those sectors added only one third as much total income as agriculture. From the structural path analysis, the importance of capital assets in generating income was seen, as was the possibility of greater poverty reduction from sectors with proportionally fewer intermediate inputs and more value added.

We replicate the findings of Emlinger and Guimbardr (ERAE, 2021) on the heterogeneous effects of per-unit tariffs on trade patterns for developed and developing countries. Analyzing import and export data from 2001 to 2013, they confirm the Alchian-Allen conjecture that per-unit trade costs induce higher export unit values. However, the effects are more pronounced for developed country exporters. Understanding the effects of per-unit trade costs vis-a-vis ad valorem tariffs is important to level the playing field of trade negotiations that involve pricing and non-pricing policies. We extend the original study with data for 191 exporting (190 importing) countries, and 670 HS6 digit products, covering the period 2001–2019 period. The general findings of the original study hold, with remarkable differences. First, using a data set that is constructed in a replicable way and introducing highly relevant bilateral fixed effects reduce effect sizes and the level of statistical significance. Second, the Alchian-Allen effect is not clearly separated by the economic development dimension of the exporter, but rather dependent on the price levels of the traded goods. These results have important policy implications as they call for deeper investigation of countries' industrial structures of exports to better shape the international debate on trade negotiations.

Because replicability is an important part of every scientific endeavor, this research deals with comparing and contrasting parameter estimates, standard errors, and p-values from the estimation of five commonly encountered nonlinear models in applied econometrics. Commonplace software packages indigenous to econometrics and statistics are used, namely EVIEWS 11.0, SAS 9.4, Stata 17, and R 4.1.2 in five replication exercises to determine potential differences, if any, in empirical results. The hypothesis that mainstream software packages generate different empirical results in the estimation of nonlinear models is confirmed for the polynomial distributed lag (PDL) model and the GARCH(1,1) model. For the probit model and the Barten synthetic demand system model, the differences in parameter estimates, standard errors, and p-values are less evident across the four commonly used software packages. For the Tobit model, the respective sets of parameter estimates, standard errors, and p-values are nearly identical across the respective software packages. Economic analysts should not just accept estimation results uncritically, but instead, conduct sensitivity analyses involving the use of at least two software packages. The agricultural economics profession should adopt this recommendation as standard practice.

In the aftermath of the COVID-19 pandemic, interest has grown in what kinds of assistance protect household food security during shocks. We study rural and urban Bangladesh from 2018 to 2019 to late 2021, assessing how pre-pandemic access to social safety net programs and private remittances relate to household food insecurity during the pandemic. Using longitudinal data and estimating differences-in-differences models with household fixed effects, we find that pre-pandemic access to social protection is associated with significant reductions in food insecurity in all rounds collected during the pandemic, particularly in our urban sample. However, pre-pandemic access to remittances shows no similar protective effect.

Low-income households and minority communities in the US are considered highly vulnerable to diet-and nutrition-related adverse health effects. Households' food-related decisions may entail real or perceived tradeoffs among price, preference, access and availability, and other factors. We use a sample of low-income households, mostly from African American communities, from the Nashville metropolitan area of Tennessee to assess fruit and vegetable purchasing behavior. We found that the vast majority of household shops for fruits and vegetables (F&Vs) less than two to three times per month. Using probit and nested logit models, we analyzed purchase decisions and investigated reasons and factors determining “not to buy” F&Vs by low-income households.

This paper examines the employment effects of an emergency assistance package by the Indian government, the Garib Kalyan Rozgar Abhiyaan that had the sole objective of providing employment to returning migrants. It was targeted to 116 districts that had seen returning migrants in excess of 25,000, was limited in duration to 4 months, and was directed at top-up funding to public works and 25 other target sectors in rural areas. Using a sharp RD approach, we find that the intervention had substantive impacts on employment and in reducing rationing in public works and that it did so in a cost-effective manner. In contrast to the widespread impression of a slow-moving bureaucracy, these results point to an administrative machinery that was able to successfully implement this project within a relatively short period of time.