Research returns redux: a meta‐analysis of the returns to agricultural R&D

Abstract

A total of 289 studies of returns to agricultural R&D were compiled and these provide 1821 estimates of rates of return. After removing statistical outliers and incomplete observations, across the remaining 1128 observations the estimated annual rates of return averaged 65 per cent overall ö 80 per cent for research only, 80 per cent for extension only, and 47 per cent for research and extension combined. These averages reveal little meaningful information from a large body of literature, which provides rate-of-return estimates that are often not directly comparable. This study was aimed at trying to account for the di¡erences. Several features of the methods used by research evaluators matter, in particular assumptions about lag lengths and the nature of the research-induced supply shift.

The Enigmas of TFP in China: A meta-analysis

“…Regarding the different results in primary studies, Alston et al (2000) categorized all factors that might account for the variation in primary studies into five broad groups: (1) characteristics of the results in primary studies (e.g., real or nominal, marginal or average); (2) characteristics of the analysts (e.g., published or unpublished); (3) characteristics of the research (e.g., geographic region); (4) evaluation characteristics (e.g., ex post or ex ante, method); (5) random measurement errors. Nelson and Kennedy (2009) suggest that heterogeneities between primary studies can be attributed to two basic causes: Factual factors and methodological factors.…”

“…Peer-review process and the flavor of an academic journal might also account for the variation in estimated TFPG (Alston et al, 2000). For instance, the studies that generate TFPGs that fall outside the range of "conventional wisdom" prevailing in the profession at the time may be discriminated in the publication process, thus published work and unpublished work may have different estimations.…”

Input, Output, and Outcome Metrics for Assessing Energy Technology Innovation

“…In these fields, innovation assessments commonly use econometric or regression techniques to evaluate either net social returns on R&D, or productivity gains. In agriculture, for example, such techniques have long been used to examine the effectiveness of particular structures and roles in the innovation process (Evenson et al, 1979), and to account for the often long time lags between innovation inputs and outputs (Alston et al, 2000).…”

“…With respect to the evaluation of research, "problems arise because not everyone perceives the same purpose" (p14, Alston et al, 2000). Is this a particular problem with assessments of energy technology innovation?…”

Agricultural Biotechnology in Developing Countries

“…Several recent reviews have surveyed hundreds of Green Revolution economic impact studies conducted over the last 30 years in all parts of the world (Alston et al, 2000;Evenson and Gollin, 2003). Although these studies were carried out using a variety of different methods, they showed considerable consistency.…”