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2017 | OriginalPaper | Buchkapitel

8. Agricultural R&D, Technology, and Obesity

verfasst von : Julian M. Alston, Abigail M. Okrent

Erschienen in: The Effects of Farm and Food Policy on Obesity in the United States

Verlag: Palgrave Macmillan US

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Abstract

Agricultural R&D has contributed to obesity by making food more abundant and cheaper, and some commentators have proposed that the agricultural research portfolio could be tilted more in favor of healthy foods, and away from less-healthy foods. We review these ideas in principle, and present a review of prior work and new evidence on the effects of past research investments and on the likely costs of changing the R&D portfolio as a way of fighting obesity in the United States. Our analysis suggests that redirecting agricultural research priorities would be a generally ineffective and highly expensive way of fighting obesity.

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Fußnoten
1
See for instance Alston et al. (2006), Lakdawalla and Philipson (2002, 2009), Lakdawalla et al. (2005), Miller and Coble (2007, 2008), Mazzochi et al. (2009), and Popkin (2011).
 
2
The Specialty Crop Competitiveness Act of 2004 (PL 108-465) defines specialty crops as: fruits and vegetables, tree nuts, dried fruits, and nursery crops, including floriculture.
 
3
Details in this section are drawn from Pardey et al. (2013c).
 
4
These authors used the World Bank classifications of countries according to gross national income per capita, available at http://​data.​worldbank.​org/​news/​new-country-classifications-2015.
 
5
Partial factor productivity (PFP) measures refer to total output per unit of a particular input, such as land or labor. Multifactor productivity (MFP) measures refer to total output per unit of an aggregate of all inputs, where the aggregation uses appropriate weights for the different categories of inputs. PFPs and MFPs can refer to output of a particular product (e.g., wheat) or to an aggregate across all products. A special case of MFP is total factor productivity (TFP) in which all outputs and all inputs are taken explicitly into account. In practice, a very broadly based MFP measure is the best we can do, since we cannot always measure every input and every output.
 
6
Significant among these are substitution of other inputs and machines for increasingly expensive labor in many places, and increased use of synthetic fertilizers, especially nitrogen, and irrigation. Although not the direct result of agricultural R&D per se, these “modern” inputs were developed and applied in a complementary fashion with varietal and other technologies that were the direct result of agricultural R&D.
 
7
As discussed by Alston et al. (2006), some of the price increases for specialty crops might reflect premia for changes in quality, variety, or seasonal availability, which might not have been fully addressed in the indexing procedure.
 
8
AAJP (2010b, 2011) used a gamma lag distribution to capture the relationship between research investments in each of the past 50 years and today’s knowledge stock with a peak at a lag of 24 years. AMO (2016a) adapted this approach to model how knowledge stocks affect commodity prices, and we borrow from their work in what follows.
 
9
The estimated multiplier, or elasticity, was restricted to be the same for all groups, and estimated as –2.385. This estimate indicates that a 10% increase in a commodity-specific stock of knowledge in a particular year, say 2007 (e.g., from a 10% increase in research spending in each of the preceding 50 years) would cause a 24% decrease in the price of that commodity in the same year (i.e., 2007).
 
10
Popkin (2009) refers to policies “nurturing agriculture” at least as early as 1850 (which he mentions explicitly), prior to the creation of the USDA and the land grant colleges. One wonders, does he include the Homestead Act among these policies that pushed the food system in an unhealthy direction?
 
11
McAfee (1983) provides an amusing demonstration of the challenge of defining counterfactuals and using them to measure consequences of large changes.
 
12
The average male in the United States was 69.3 inches tall and weighed 195.5 pounds, for a BMI of 28.6 (= 703 × [195.5/(69.32)]) (Centers for Disease Control and Prevention 2012). To get to a BMI of 25, the average male would have to weigh 170.8 pounds, or 24.7 pounds less.
 
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Metadaten
Titel
Agricultural R&D, Technology, and Obesity
verfasst von
Julian M. Alston
Abigail M. Okrent
Copyright-Jahr
2017
DOI
https://doi.org/10.1057/978-1-137-47831-3_8