Rationalizing Rural Area Classifications for the Economic Research Service A Workshop Summary (2016) / Chapter Skim
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8 Evaluating the Reliability and Validity of Rural Area Classifications
8 Evaluating the Reliability and Validity of Rural Area Classifications
Pages 109-120
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From page 109... ...
prepared a commissioned paper, Evaluation of Rural Area Classifications Using Statistical Modeling, for the workshop. Goetz described their results, and Mark Shucksmith (New Castle University)
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From page 110... ...
Goetz said that evaluation using a few other variables showed that RUCA Codes perform very well on outcome measures such as population density, percentage rural population, and percentage farm area.
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From page 111... ...
The second way to look at labor markets is proximity to potential jobs as possibly introducing more economic opportunity to commuters. Higher gross payroll in a commuting destination ensures access to more potential income (jobs)
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From page 112... ...
Goetz reiterated that they are using neither population, adjacency, nor metropolitan/nonmetropolitan status to develop their code. Their code is based purely on commuting flows.
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From page 113... ...
, consumption countryside, diversified with strong secondary sector, and diversified with strong market sector. The analysis revealed that the least successful areas were those most dependent on agriculture, while those doing best in terms of economic performance were the consumption countryside and those where the tertiary sector now dominates.
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From page 114... ...
This rural definition was also useful in many other spheres, he said, such as in many official datasets by the Commission for Rural Communities and in annual reports titled The State of the Countryside. Shucksmith questioned the course of action if an analysis contradicts ground truths.
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From page 115... ...
But she said it is possible that places that look the same at a particular point in time due to one measure, like population size, behave differently over time based upon one of these other factors. When thinking about an evaluation using statistical modeling, the selection of the dependent variables is very important because they address different concepts of "validity" and "reliability," Patrick said.
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From page 116... ...
She pointed out other methods for evaluation using statistical methods include calculating parametric estimates of current and proposed components of classification systems to determine relative explanatory power, or doing nonparametric work and letting the data speak. If the purpose can be articulated in terms of what the classification is to explain and the variables to be predicted, then nonparametric methods like kernel density estimation or locally weighted regression can be used, she suggested.
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From page 117... ...
Comparing specifications for different numbers of thresholds, it is possible to see how much additional explanatory power comes from adding categories and choosing the optimal number of groupings. Ground-truthing and qualitative methods could verify validity of thresholds identified by nonparametric and grid source methods.
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From page 118... ...
In previous work in New England, he found a typical New Englander in the 1970 Census lived in the commuting sheds of four to seven major metropolitan areas. He suggested that perhaps Goetz should try using a different data source, for example the Longitudinal Employer-Household Dynamics (LEHD)
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From page 119... ...
Brown also said the issue of retirement migration is about life course transitions, which he said has been missing from the discussion. He asked how understanding microprocesses at the household and individual level fit into changing macro social structures.
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