Fierce international debates rage over whether trophy hunting is socially acceptable, especially when people from the Global North hunt well-known animals in sub-Saharan Africa. We used an online vignette experiment to investigate public perceptions of the acceptability of trophy hunting in sub-Saharan Africa among people who live in urban areas of the USA, UK and South Africa. Acceptability depended on specific attributes of different hunts as well as participants’ characteristics. Zebra hunts were more acceptable than elephant hunts, hunts that would provide meat to local people were more acceptable than hunts in which meat would be left for wildlife, and hunts in which revenues would support wildlife conservation were more acceptable than hunts in which revenues would support either economic development or hunting enterprises. Acceptability was generally lower among participants from the UK and those who more strongly identified as an animal protectionist, but higher among partic..., Data collected from an online vignette experiment hosted on the Qualtrics platform. Data analysed in R statistical software., R statistical software. Required packages called at the top of the accompanying R script., # Public perceptions of trophy hunting are pragmatic, not dogmatic

Data underpinning analyses presented in Hare et al (2024), ‘Public perceptions of trophy hunting are pragmatic, not dogmatic’.

Description of the Data and file structure

This data set includes all columns necessary to replicate model fitting, selection, and comparisons outlined in the manuscript.

Variable names mean:

• education = participant’s level of formal education
• people.animals = whether a participant would prioritise people or wild animals if their interests clash
• individuals.groups = whether a participant would prioritise individual wild animals or groups of wild animals if their interests clash
• hunter = whether a participant identifies as a hunter
• conservationist = whether a participant identifies as an advocate for environmental conservation
• animal.protectionist = whether a participant identifies as an advocate for animal protection
• human.rights = whether a participant i...

The Fish and Wildlife Service prescribes final late-season frameworks from which States may select season dates, limits, and other options for migratory bird hunting seasons. The effect of this final rule is to facilitate the States' selection of hunting seasons and to further the annual establishment of the late-season migratory bird hunting regulations. This dataset contains the following administrative waterfowl flyway delineations that are used by states in this process. Atlantic Flyway--includes Connecticut, Delaware, Florida, Georgia, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, North Carolina, Pennsylvania, Rhode Island, South Carolina, Vermont, Virginia, and West Virginia. Mississippi Flyway--includes Alabama, Arkansas, Illinois, Indiana, Iowa, Kentucky, Louisiana, Michigan, Minnesota, Mississippi, Missouri, Ohio, Tennessee, and Wisconsin. Central Flyway--includes Colorado (east of the Continental Divide), Kansas, Montana (Counties of Blaine, Carbon, Fergus, Judith Basin, Stillwater, Sweetgrass, Wheatland, and all counties east thereof), Nebraska, New Mexico (east of the Continental Divide except the Jicarilla Apache Indian Reservation), North Dakota, Oklahoma, South Dakota, Texas, and Wyoming (east of the Continental Divide). Pacific Flyway--includes Alaska, Arizona, California, Idaho, Nevada, Oregon, Utah, Washington, and those portions of Colorado, Montana, New Mexico, and Wyoming not included in the Central Flyway.

Historical trapping statistics in Quebec contain the number of furs traded for each species of fur animals in each fur management unit (UGAF) for each year since 1917. The species of fur-bearing animals are as follows: weasels, Canadian beaver, coyotes, squirrels, wolves, wolves, river otters, lynx, American marten, striped skunk, pekan, striped skunk, pekan, muskrat, muskrat, muskrat, raccoon, raccoon, arctic fox, red fox, and American mink. The trapping seasons extend from the fall of one year to the winter of the following year (e.g. October 2015 to March 2016), so the years are entered as follows: 2015-2016. Data available between 1917-1918 and 1983-1984 are compiled for the whole province only, not by fur management unit (UGAF). Starting from 1984-1985, the data is presented by UGAF. ### Warning The data does not represent fur harvesting but raw fur transactions. Catches from one year may be carried over to the following year or may be absent if the trapper keeps his furs for personal use. In addition, animals caught in the context of controlling intrusive animals (outside the trapping season) do not appear in these statistics either. It should be noted that the data has been verified and changes have been made to ensure validity. However, inconsistencies or errors could have crept in. Data deemed to be outlier (species whose location is clearly outside its known range (UGAF probably erroneous)) have been moved to the undetermined UGAF (UGAF 99). A “data not validated” column is included in the database. Here, there is no judgment whether the data is true or false, it has just not been validated. Thus, users should not take this information for a certain statement. This indication was added when there is a transaction located near the limit of the species' known range. ### Important information UGAF 99 does not exist in practice. Data that is not spatially located (UGAF unknown) is added there so that the compilation of total catches per year remains accurate. Several species can be grouped together in the trapping statistics: * Squirrels include all squirrel species, including the red squirrel (Tamiasciurus hudsonicus), the gray squirrel (Sciurus carolinensis) and the flying squirrel (Glaucomys sabrinus). * Weasels include both species of weasels: pygmy (Mustela nivalis) and long-tailed (Mustela frenata) as well as the ermine (Mustela erminea). * The arrival of the coyote (Canis latrans) being relatively recent in Quebec, until 1982-1983, wolves and coyotes did not were not differentiated in fur transactions. That is why, Loup-Coyote is listed in the “species” column between 1944-1945 (date of the first mention of coyote in Quebec) and 1982-1983. Then (starting from 1983-1984), the two species were dissociated. However, wolves have been considered absent south of the St. Lawrence River for about a hundred years. Despite this, several transactions attributed to wolves appear in the historical trapping database. It could be a typing error or an identification error based on how the fur was prepared, or maybe it was a real lone wolf. Considering the high rate of hybridization and the difficulty in identifying them on the basis of physical criteria, all wolf data attributed to the south of the St. Lawrence River were identified as “Loup-Coyote”. Data on the identity of wolves and coyotes should be considered carefully. * Arctic foxes include the different forms of coloring: white and blue. * Red foxes incorporate the different forms of coloring: red, crossed, silver. Data extraction date: 2025-02-24 Additional data from historical hunting statistics for caribou, white-tailed deer, wild turkey and moose, as well as those for black bear hunting and trapping can be found here: Historical statistics of big game hunting and black bear hunting/trapping in Quebec

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Hunt County. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Hunt County. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Hunt County, householders within the 45 to 64 years age group have the highest median household income at $88,588, followed by those in the 25 to 44 years age group with an income of $76,882. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $49,431. Notably, householders within the under 25 years age group, had the lowest median household income at $44,834.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Hunt County median household income by age. You can refer the same here