Collaborating Institutions
University of Arkansas at Pine Bluff

Carole R. Engle

Asian Institute of Technology

Harvey Demaine

Objective
To develop estimates of social and economic returns attributable to PD/A CRSP technologies in Thailand.

Significance
The Pond Dynamics/Aquaculture CRSP is a global research activity directed toward improving the reliability and efficiency of pond aquaculture production. The ultimate benefit of this effort will be the economic and social returns that represent the impact from farmers adopting new technologies developed by the PD/A CRSP.

Technical progress has been modeled as a lagged function of research expenditures (Chavas and Cox, 1992). This study identified and measured the length of time required to fully translate public research expenditures into economic benefits and estimated internal rates of return for research expenditures. In the Chavas and Cox model, there were no restrictions on substitution possibilities among inputs, joint estimation of the production technology, technical change, and the effects of research on technical progress using very disaggregate inputs. This approach required only a standard linear programming algorithm. Ayer and Schur (1972), Ardito-Barletta (1971) and others estimated social rates of return to the investment in public research.

White (1985), in a study valuing research as an intangible capital in agriculture using Tobin’s q theory, estimated that the market value of public research capital to be 8.6 times higher than conventional assets. Private research capital was valued 5.2 times higher than conventional assets.

Fischer et al. (1996) used a random-effects within a Bayesian framework to analyze the effect of adoption of new wheat varieties in South Australia. Results showed that not all pieces of information added equally to knowledge about the innovation. It further showed that the acquisition of information was much slower than had been suggested by previous Bayesian models and could also explain laggards and partial adoption. Huang and Secton (1996) developed a general imperfect competition model to evaluate returns to a cost-reducing innovation. In an imperfectly competitive market structure, this study showed that farmers’ incentives to adopt a mechanical harvester for tomatoes in Taiwan were attenuated because the benefits were reduced by oligopsony power of processors.

Dorfman (1996) used a multinomial probit model to model adoption decisions faced by farmers when there are multiple technologies that can be adopted in varying combinations. Results showed that, for adoption of possible sustainable production technology bundles, that the adoption decisions are significantly influenced by off-farm labor supply.

Fuglie (1995) developed a multimarket model to explore equity and efficiency implications of improving crop storage technologies. The rate of return to research on potato storage in Tunisia was estimated to be between 44% and 75%.

Anticipated Benefits
Results of this study will be useful for the PD/A CRSP to justify continued funding by quantifying benefits and impacts of the research effort. This study will provide the first estimates of the global social and economic returns generated by the PD/A CRSP. The results of this project will document the contribution that the PD/A CRSP research has made and will continue to make over time in both social and economic terms. This is essential to justify continued funding for the CRSP in the US and for host country support. This project will not produce a field tool, but will provide the CRSP with a measure of the returns to AID funds invested in research.

Activity Plan
Location: The activity will be conducted by UAPB economists, but will be based on data from Thailand. No facilities other than computers at UAPB are required. The host country will be involved in supporting the data collection, but the analysis will be done at UAPB.

Data to Be Used Include: Much of the data required are secondary data. These include costs of the research, size of the industry, acreage, prices, total area in production, total production in ha, and average production. Primary data that will be needed include information on the rates of adoption of the specified technologies. The authors have been in touch with a researcher at AIT who has expressed interest in participating in the project.

Methods: This study builds upon work in the Eighth Work Plan in which the theoretical model was constructed to estimate the economic and social returns to technology and investment in CRSP research in Honduras. In this study, the same theoretical model will be used to estimate returns to the research investment in Thailand. This study defines a point in time when CRSP-developed technology was introduced. A measurement point is then defined. The equations estimated use time series data in functions and not data for any one given pint in time. The analysis is done for a specific technology, not necessarily a specific country. If the adoption of a given technology crosses national boundaries, it can be included in the analysis and the overall impact will be higher. The model presented below has been reviewed by economists at Purdue University and at Texas A&M University. It is similar to another impact study we are doing in collaboration with John Sanders at Purdue on the InterCRSP work. Supply and demand equations will be estimated to identify consumer surplus and producer surplus. Research that leads to the development and adoption of new technologies reduces the cost of production which further causes a reduction in the price to the producers. The combined changes in the net gain to producers and the net gain to consumers is the social gain from research. This study hypothesizes that there is a net increase in the economic surplus resulting from development and adoption of technologies produced from CRSP-funded research.

Given the nature of the PD/A CRSP projects and in conjunction with the different groups involved in the PD/A CRSP projects, it is necessary to evaluate the net social welfare resulting from the implementation of these projects. Although welfare economics is concerned with policy recommendations, it can also be used as an evaluation tool to determine the social impact of a given project. In an attempt to measure the PD/A CRSP impact, a function describing the net social benefits can be estimated. While the different groups involved in these projects are usually not mutually exclusive and in conjunction with the compensation criterion, social welfare can be measured as follows:

w =PQ + CSQ + PSQ + E - G

where W is net social benefits (positive or negative); P_Q is the profit or rent accruing to PD/A CRSP researchers; CS_Q is consumers surplus for the host country which can be measured as surplus for final consumers plus all forward rents; PS_X is producers surplus measured as rent inputs plus all backward rents plus surplus for raw materials; E is external benefits/costs; and G is the social overhead cost for PD/A CRSP programs.

Let DQ = Q₀' - Q₀ denote the change in total quantity observed due to research, k denote the vertical movement factor of the supply curve. Social gain (SG) can be expressed as:

SG = kQ₀ ± kDQ where + is used for ex-ante and - for ex-post evaluations.
Q is known; k and DQ are unknown and need to be estimated.

The necessary parameters to be estimated include the following: increase in productivity (DR) in kg/ha; adoption cost (DC) in terms of acreage moved from one activity to a new activity; adoption rate (t) in terms of % increase in acreage devoted to the activity (or in terms of new entrants); total area in production (S) in ha; total production (Q) in metric tons; average production/productivity (R = Q/S) in same as Q. The following will be estimated:

1. Let J =DR * t * S. J can be viewed as the total increase in production due to technology adoption, holding cost and prices constant. Let j be the change in supply or coefficient by which the supply curve has moved with the new technology, j = (DR * t)/R = J/Q.

2. I =DC * t/R. I is the increase in cost of inputs per unit necessary to achieve J. I can be calculated proportionally to observed price (P) such that c = I/P = (DC * t)/(R * P).

3. Let K = (b * J) - I, where b is the supply curve slope; K represents the net reduction in production cost due to technology (vertical movement of the supply curve). In fact, the coefficient b is not used, the supply elasticity (s) is used instead.

es =DQ/DP)*(P/Q) = (l/b)*(P/Q). This leads to es*b = P/Q, and b = (1/es) * (P/Q); therefore,
K = [(1/es) * (P/Q) * J] - I = (P * j/es *Q) - I. With respect to price (P),
k = K/P = [P * J/es * Q) - I]/P = (P * J/es * Q * P) - (I/P) = [(1/es) * (P * J/Q * P)] - (I/P)
k = (1/es) * j - c
when supply is inelastic (es < 1), an increase in production due to research has a relatively high economic value (k > j - c), possibly limited acreage. Elastic supply (es > 1), possibly abundant acreage, s reduces k (k < j - c). In this latter case, it is easy to increase production and research gains have little economic value.

Regional Integration
The Regional Plan for Asia includes the need to monitor the impact of the CRSP and its returns to both the research community and the local aquaculturists.

Schedule
Year 1 (5/1/99-4/30/00): Collect and analyze data from Thailand necessary to estimate the above- mentioned model.
Year 2 (5/1/00-4/30/01): Estimate the above-mentioned model with the data collected and prepare a manuscript for publication.

Report Submission
Year 1: Annual report will describe the data collection efforts.
Year 2: The final report will be completed by 4/30/01.

References
Ardito-Barletta, N. 1971. Costs and social benefits of agricultural research in Mexico. Ph.D. thesis, University of Chicago.
Ayer, H.W. and G.E. Schuh, 1972. Social rates of return and other aspects of agricultural research:
The case of cotton research in Sao Paulo, Brazil. American Journal of Agricultural Economics, 54:557-569.
Chavas, J.P. and T.L. Cox, 1992. A nonparametric analysis of the influence of research on agricultural productivity. American Journal of Agricultural Economics, 74(3):583-591.
Dorfman, J.H., 1996. Modeling multiple adoption decisions in a joint framework. American Journal of Agricultural Economics, 78:547-557.
Fischer, A.J., A.J. Arnold, and M. Gibbs, 1996. Information and the speed of innovation adoption. American Journal of Agricultural Economics 78:1073-1081.
Fuglie, K.O., 1995. Measuring welfare benefits from improvements in storage technology with an application to Tunisian potatoes. American Journal of Agricultural Economics, 77:162-173.
Huang, S.Y. and R.J. Sexton, 1996. Measuring returns to an innovation in an imperfectly competitive market: application to mechanical harvesting of processing tomatoes in Taiwan. American Journal of Agricultural Economics, 78:558-571.
White, F.C., 1995. Valuation of intangible capital in agriculture. Journal of Agricultural and Applied Economics, 27:437-445.