Di Fang

Bio: Di Fang is an academic researcher from University of Arkansas. The author has contributed to research in topics: Childhood obesity & Product (category theory). The author has an hindex of 5, co-authored 27 publications receiving 99 citations. Previous affiliations of Di Fang include Arizona State University & Agricultural & Applied Economics Association.

The association between food insecurity and mental health during the COVID-19 pandemic.

Abstract: To explore the association between food insecurity and mental health outcomes among low-income Americans during the COVID-19 pandemic We conducted a survey of 2714 low-income respondents nationwide from June 29, 2020 to July 21, 2020 A proportional odds logit model was employed to estimate the associations between food insecurity and anxiety and between food insecurity and depression Food insecurity is associated with a 257% higher risk of anxiety and a 253% higher risk of depression Losing a job during the pandemic is associated with a 32% increase in risk for anxiety and a 27% increase in risk for depression Food insecurity caused by the pandemic was associated with increased risk of mental illness The relative risk of mental illness from being food insecure is almost three-fold that of losing a job during the pandemic Public health measures should focus on getting direct subsidies of food purchases to poor families, especially families with children They should also reduce the stigma and shame that is associated with accepting charitable foods

Food insecurity during the COVID-19 pandemic: evidence from a survey of low-income Americans.

Abstract: The COVID-19 pandemic significantly increased food insecurity despite emergency legislation that put more resources into food assistance programs, increased unemployment benefits, and provided stimulus payments. We conducted a survey in the US on food insecurity among low-income Americans during the early months of the pandemic. While we cannot estimate causal effects, we are able to show important associations between food insecurity and nutritional and economic assistance that highlight the need to ensure that those newly at risk for food insecurity are able to connect to resources. For example, our results indicate that those who lost jobs due to the pandemic reported the highest level of food insecurity and also the lowest engagement with food assistance programs. The SNAP expansion appears to be important only among groups with higher levels of income stability including non-minority households and those not experiencing a job loss. Thus, the SNAP expansion may not have had a meaningful impact on those most at risk for food insecurity. Finally, our data highlight the importance of school meal programs during normal times. Those who took advantage of school meals before the outbreak are more likely to have experienced food insecurity during the pandemic-related school closures.

On the Use of Virtual Reality in Mitigating Hypothetical Bias in Choice Experiments

Abstract: Choice experiments have been extensively used in many stated preference studies and disciplines. In order to give external validity to stated preferences, participants must behave the same in an experiment setting as they do in reality. The difference between a decision made in a real experiment and a hypothetical experiment is commonly attributed to “hypothetical bias.” In this paper, we investigate whether virtual reality can reduce hypothetical bias in choice experiments. We conducted a set of experiments using the Food and Drug Administration's new Nutrition Facts Label for yogurts. Participants were randomly assigned to non‐hypothetical and hypothetical experiments, which differed in terms of visual presentations of the product in question: text, picture, and a virtual grocery store. Given the absence of real markets for the new label at the time of the research, we approximate the real market with a binding choice in our non‐hypothetical experiments. Our results suggest that although the hypothetical bias with virtual reality is not statistically significantly different from those using text or picture, it is the smallest. Among participants who did not exhibit high simulator discomfort, results suggest that virtual reality can significantly reduce hypothetical bias in choice experiments.

Media advertising and ballot initiatives: The case of animal welfare regulation

Abstract: I. INTRODUCTION In 2008, the citizens of California voted in favor of Proposition 2, which bans the use of cages for housing egg-laying chickens. (1) Similar propositions in Arizona (Proposition 204 in November 2006) and other states mandate stall-free and crate-free housing for sows and calves for veal, respectively. Media advertising is used heavily in state-level propositions because of the narrow focus of the issue, the geographic concentration of likely voters, and the (typically) highly polarized nature of the campaigns. There is evident support for this issue among some consumers as cage-free eggs sell for a significant premium in retail stores (sometimes $1.75 per dozen or more). What is less clear, however, is whether voting in initiatives similar to Proposition 2 is driven by advertising-inspired mass-support of the issue at hand, or whether it is highly motivated support by a small segment of the population that cannot be influenced by advertising. In this study, we examine the role of media advertising in the initiative process using an experimental analysis of ads used by both supporting and opposing sides for Proposition 2 in California in November 2008. Polling voters' intentions is fraught with difficulties. Often, likely voters will tell the pollster what they think the pollster wants to hear. Second, more important for the narrow issues that are often the subject of public referenda, many voters simply do not care, or are unaware of the issue (Rothschild 1978). In the issue at hand, only a certain percentage of the population are even egg consumers or animal lovers so the issue seems abstract, at best. Third, voters sometimes lack a sense of consequentiality if they are asked their intent and they are not actually in the voting booth so put little thought into their response. In this study, we circumvent these problems by facing voters--consumers in this case--with real economic incentives in an experimental environment to determine their willingness-to-pay (WTP) for cage-free eggs. WTP, while important from a food marketing perspective, is also a valuable proxy for likely voting intentions and an accurate gauge of the importance consumer-voters place on an issue. Ultimately, if a policy is a good one and "deserves" a vote, it should be welfare-improving. Therefore, in this study we assess the value of political advertising not on how many votes it generates, but on how it impacts the way the vote should go, or how the ad affects economic welfare. WTP is not without limitations as a measure of the expected improvement in welfare because of a policy change. Hamilton, Sunding, and Zilberman (2003) argue that individuals vote in referenda in ways that reflect their choices over public goods, and not necessarily private goods, so their choices regarding public policy alternatives may not reflect their private WTP. Further, Lusk and Norwood (2009a, b) and Norwood and Lusk (2011) believe that conventional methods of estimating WTP for public-good attributes are subject to a significant "social desirability bias" whereby a consumer's stated valuation includes an estimate of what he/she thinks the researcher would regard as appropriate. These authors show that social desirability bias with respect to animal welfare issues can be mitigated through a method of "inferred valuation," while Lusk and Norwood (2010) and OLnyk, Tonsor, and Wolf (2010) address social desirability bias in an animal welfare context through indirect questioning, or asking survey respondents to express how they think the "average American" would respond. In the current study, we are not as concerned with social desirability bias as voting outcomes only depend upon whether the WTP for cage-free eggs is greater than for conventional eggs, not necessarily how much. Animal welfare is an important issue in its own right, and has become increasingly prominent as consumers become more conscious about what they eat, and where it came from. …

Joint modeling of correlated binary outcomes: The case of contraceptive use and HIV knowledge in Bangladesh.

Abstract: Recent advances in statistical methods enable the study of correlation among outcomes through joint modeling, thereby addressing spillover effects. By joint modeling, we refer to simultaneously analyzing two or more different response variables emanating from the same individual. Using the 2011 Bangladesh Demographic and Health Survey, we jointly address spillover effects between contraceptive use (CUC) and knowledge of HIV and other sexually transmitted diseases. Jointly modeling these two outcomes is appropriate because certain types of contraceptive use contribute to the prevention of HIV and STDs and the knowledge and awareness of HIV and STDs typically lead to protection during sexual intercourse. In particular, we compared the differences as they pertained to the interpretive advantage of modeling the spillover effects of joint modeling HIV and CUC as opposed to addressing them separately. We also identified risk factors that determine contraceptive use and knowledge of HIV and STDs among women in Bangladesh. We found that by jointly modeling the correlation between HIV knowledge and contraceptive use, the importance of education decreased. The HIV prevention program had a spillover effect on CUC: what seemed to be impacted by education can be partially contributed to one's exposure to HIV knowledge. The joint model revealed a less significant impact of covariates as opposed to both separate models and standard models. Additionally, we found a spillover effect that would have otherwise been undiscovered if we did not jointly model. These findings further suggested that the simultaneous impact of correlated outcomes can be adequately addressed for the commonality between different responses and deflate, which is otherwise overestimated when examined separately.

Farm size and the diffusion of green revolution technology ; On information and innovation diffusion : a bayesian approach

Abstract: Learning and information accumulation are hypothesized to play a major role in innovation diffusion. For instance, Hiebert argues that the probability distributions of new (and unfamiliar) technological parameters, as perceived by farmers, will shift over time due to learning and experience. Probabilities will be redistributed from lower to higher payoffs. This induces farmers to increase their use of the innovation, which was new seed varieties in the Hiebert model. The model presented by Kislev and ShchoriBachrach introduces in the new technology production function an efficiency factor which is positively related to learning. It is approximated by the cumulative (over time) output produced with the innovative technology. As learning increases, the innovation becomes advantageous for more and more producers who then adopt it. In a similar vein, Feder and O'Mara construct a diffusion process where uncertainty about an innovation (high-yielding varieties [HYV]) depends on cumulative area allocated to HYV. This represents experience. With the accumulation of experience, uncertainty declines, and the innovation is adopted by an increasing proportion of producers. These models produce sensible (and empirically valid) hypotheses about the dynamics of innovation diffusion. However, the accumulation of information, which is the basic driving element in these models, is not treated explicitly. Thus, the use of cumulative output, or a cumulative input as an index of learning, while plausible, still requires formal justification. One possibility is a Bayesian learning process. Indeed, the work by O'Mara (1971, 1981) and more recently by Lindner, Fischer, and Pardey have utilized Bayes theorem to characterize an individual farmer's adoption behavior.

An Introduction to Generalized Linear Models

Abstract: An Introduction to Generalized Linear Models. By A. J. Dobson. ISBN 0 412 31110 0. Chapman and Hall, London, 1990. 174 pp. £13.50.