Abstract
There is broad agreement about the importance of investments in public agricultural research and extension in the United States, but there is less agreement about the exact methods to be used in data collection, variable definitions, econometric model specification, and benefit-cost comparisons. This chapter reviews these issues and presents a summary and comparison of recent estimates of the rate of return to investments in US public agricultural research and extension. This chapter will be useful to graduate students, researchers, university administrators, and agricultural science policy advisors.
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Notes
- 1.
However, two state agricultural experiment stations operate independently of the local land-grant universities. These are the ones at Geneva, NY, and New Haven, CT.
- 2.
This change was facilitated by major advances in computer storage and software.
- 3.
In the United States, forestry is a minor activity on farms and ranches and generally excluded from agricultural productivity measures.
- 4.
In addition, in 1977, 30% of the gross investments in extension were allocated to 4-H (youth activities), but this share declined to 23% in 1992 and seemingly leveled off. The investment in these youth activities seems unlikely to contribute much to agricultural productivity.
- 5.
Since extension is largely information for current decision-making, 50% of its impacts—or timing weight—occurs within the year undertaken, and then the impact and weights decline to zero with obsolescence (See Huffman and Evenson 2006b, p. 272).
- 6.
In addition, they separate the USDA’s own research, which is largely conducted in the states, from that of the state institutions. Also, they ignore the livestock research undertaken by the colleges of veterinary medicine.
- 7.
Of course there are differences across states. Jin and Huffman (2016) report trends for California, Iowa, North Carolina, and Texas.
- 8.
- 9.
With lags of significant length, including expenditures rather than capital as regressors leads to estimated coefficients on successive lags that tend to oscillate in sign and be statistically weak and are impossible to rationalize. Griliches (2000) suggests that it is useful in these situations to impose some structure on the lag pattern. Since we do not know the “true” lag pattern, we are involved in constructing plausible proxy variables for stocks (Greene 2003).
- 10.
One could use different lag lengths for constructing within-state and spillover research stock variables. That issue might be useful to pursue in the future.
- 11.
- 12.
Ignoring the fact that two series are trending in the same or opposite directions can lead to a false conclusion that changes in one variable are actually caused by changes in another variable (Wooldridge 2013; Enders 2010). In many cases, two time series processes appear to be correlated, only because they are both trending over time for reasons related to other unobserved factors.
- 13.
This model does not include a measure of stochastic spatial correlation of spillovers. In private communication, Wayne Fuller suggested to me that spillover effects and stochastic spatial effects are most likely related. For example, an error in defining spillover regions could make the disturbances appear to be spatially correlated. More likely, however, is that plausible spillover measures dramatically reduce and perhaps eliminate significant stochastic spatial correlation.
- 14.
With 33 (or even 50) observations per state, Wayne Fuller does not recommend unit root tests for short time series (Dickey and Fuller 1979) because the test statistic has only good large sample properties and 33 or 50 observations are not large. Moreover, in small samples, he suggests that these unit root tests are unreliable, tending to create confusion.
- 15.
- 16.
In developing countries where rates of inflation may be high and variable and government budget constraints are severe, it becomes difficult to obtain a defensible measure of the real discount rates for evaluating investments in public agricultural research and extension.
- 17.
Because the relevant productivity elasticities used in these computations have their narrowest confidence interval at the sample mean of the data, this is an advantageous place to perform the evaluation. Evaluations of marginal products at each point of the data set suffer from the fact that the confidence interval differs for each point, being generally much larger at the beginning and end of the series. This type of evaluation seems unnecessary in a linear model of state agricultural productivity.
- 18.
Even though politicians may like sound bites that B/C ratios can generate, they are more problematic than IRR estimates. In computing the B/C ratio, one must have an estimate of the social opportunity costs of funds (interest rate) in each year of the project. Harberger (1972, pp. 29–30) discusses how it is difficult to do this accurately. Moreover, it is extremely arbitrary to assign a single value to this social opportunity funds for every year of the project, e.g., 3%, and it would make a big difference if the rate were twice this large for more distant dates. Evenson (2001, pp. 605–606) discusses some common problems in interpreting B/C ratios, including the gross misinterpretation of Griliches (1958) estimate of the B/C ratio for hybrid corn research.
- 19.
- 20.
The difference in the estimate of the IRR to productivity-oriented public agricultural research reported in Huffman and Evenson (2006a) and Jin and Huffman (2016) is due to a significant revision of the public agricultural research expenditure data set occurring over 2009–2010 (see Huffman 2010, 2015). The largest change came about in the methods used to extend public agricultural research expenditures backward over 1935–1970. This revision most likely reduced measurement error, which increased the estimated impact of public agricultural research on agricultural productivity. The panel of states was also extended 5 years to include 2000–2004.
- 21.
They also report what they call a marginal IRR of 10%, but it arbitrarily includes only 3% of the estimated benefits; the argument for doing this is not convincing, as mentioned above.
- 22.
Another complication is that they provide a separate estimate of the IRR from investing in intramural research of the USDA. In Alston et al. (2010, p. 1274), they report an estimate of 18.7%.
- 23.
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Huffman, W.E. (2018). Public Agricultural Research and Its Contributions to Agricultural Productivity. In: Kalaitzandonakes, N., Carayannis, E., Grigoroudis, E., Rozakis, S. (eds) From Agriscience to Agribusiness. Innovation, Technology, and Knowledge Management. Springer, Cham. https://doi.org/10.1007/978-3-319-67958-7_22
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