Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
The association between food insecurity and mental health during the COVID-19 pandemic
2021167 citationsDi Fang, Rodolfo M. Nayga et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Rodolfo M. Nayga
Since
Specialization
Citations
This map shows the geographic impact of Rodolfo M. Nayga's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Rodolfo M. Nayga with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Rodolfo M. Nayga more than expected).
Fields of papers citing papers by Rodolfo M. Nayga
This network shows the impact of papers produced by Rodolfo M. Nayga. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Rodolfo M. Nayga. The network helps show where Rodolfo M. Nayga may publish in the future.
Co-authorship network of co-authors of Rodolfo M. Nayga
This figure shows the co-authorship network connecting the top 25 collaborators of Rodolfo M. Nayga.
A scholar is included among the top collaborators of Rodolfo M. Nayga based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Rodolfo M. Nayga. Rodolfo M. Nayga is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Rickertsen, Kyrre, Geir Wæhler Gustavsen, & Rodolfo M. Nayga. (2017). Consumer willingness to pay for genetically modified vegetable oil and salmon in the United States and Norway. 20(2). 1–11.11 indexed citations
10.
Kemper, Nathan, et al.. (2016). The Effects of Honesty Oath and Consequentiality in Choice Experiments. RePEc: Research Papers in Economics.1 indexed citations
11.
Lawless, Lydia J.R., Rodolfo M. Nayga, & Andreas C. Drichoutis. (2013). Time preference and health behaviour: A review. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich).2 indexed citations
Stachtiaris, Spiros, et al.. (2011). Can religious priming induce truthful preference revelation. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich).1 indexed citations
15.
Drichoutis, Andreas C., et al.. (2011). Examining Projection Bias in Experimental Auctions: The Role of Hunger and Immediate Gratification. Munich Personal RePEc Archive (Ludwig Maximilian University of Munich).2 indexed citations
16.
Nayga, Rodolfo M., et al.. (2007). On the Use of Cheap Talk in New Product Valuation. Economics bulletin. 2(1). 1–9.25 indexed citations
17.
Kyureghian, Gayaneh, Rodolfo M. Nayga, George C. Davis, & Biing‐Hwan Lin. (2007). Food Away from Home Consumption and Obesity: An Analysis by Service Type and by Meal Occasion. RePEc: Research Papers in Economics.1 indexed citations
18.
Drichoutis, Andreas C., Panagiotis Lazaridis, & Rodolfo M. Nayga. (2006). Heteroskedasticity, the single crossing property and ordered response models. Economics bulletin. 3(1). 1–6.2 indexed citations
Brumfield, Robin G., et al.. (1995). The Economic Feasibility of a New Jersey Fresh Tomato Packing Facility: A Stochastic Simulation Approach. Journal of food distribution research. 26(1). 2–8.2 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.