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Modeling Technology Adoption in Developing Countries

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Modeling Technology Adoption in Developing Countries NEWDEVELOPMENTS IN DEVELOPMENTt Modeling Technology Adoption in Developing Countries By TIMOTHY BESLEY AND ANNE CASE* Perhaps one of the main reasons for or a region must decide whether or not to studyingeconomic development is to under- adopt a particularagricultural technology. stand better how individuals are able to We are interested in understandingwhat make the transitionout of poverty.Technol- determines adoption of the technology ogy may be viewed as a means to this end. across space and time. We begin by review- Yet, while the developmentof higher-yield- ing three basic empirical approaches that ing varieties (HYV's) of many crops grown varyaccording to the type of data available. by poor farmershas enhanced this hope, it is essential to understand how new tech- I. EmpiricalApproaches to Analysisof nologies are adopted in practice if their TechnologyAdoption promise is to be fulfilled. In our collective understandingof technologyadoption, many A. Time-SeriesStudies questionsremain unanswered. For instance, to what extent are socially valuable tech- Much of what is known about the adop- nologies slow to realize their potential due tion of new technologies comes from time- to informationconstraints or to externalities series evidence. In these data, one observes that lead the privateand social value of new only an aggregatemeasure of adoption,such technologiesto diverge? as the percentageof farmersemploying the A prior step to answeringthese questions new technology at each date. The study of is to provide an adequate analysisof tech- hybrid corn in the United States by Zvi nology adoption decisions by poor farmers, Griliches (1957) is a classic study of this and the purpose of this paper is to review kind. In general, the aim is to capture the some possible empiricalmodels for studying shape of the time-series diffusion process, technology adoption. In so doing, we will and these studies tend to model the pattern belabor the issue of theoreticalconsistency. of adoption as a logistic-shapedfunction Can researchersensure that their empirical over time. Letting pi, denote the fractionof adoption models are consistent with an un- adopters in region i at date t, one can derlyingchoice-based model? What are the estimate equationsof the form costs of such consistency,measured in terms of data needs and model complexity, and + what are the benefits, measuredin terms of (1) Pit f (Pit-1) ?it understandingthe microeconomicfounda- tions of adoption? While it may be possible to parameterize The typical scenario we investigate is as the function f( ) by regionalcharacteristics, follows. Each of M farmerswithin a village the main purpose in such studies is often to estimate the intertemporal component of the relationshipin (1). While disaggregating by region and investigating the effect of tDiscussants: TimothyBesley, PrincetonUniversity; Anne Case, PrincetonUniversity; Adam Jaffe,Harvard regional characteristics on adoption give University. some insight into what might drive adop- *Research Program in Development Studies, tion, this approachis limited in what it can WoodrowWilson School of Public and International Affairs,Bendheim Hall, PrincetonUniversity, Prince- ton NJ 08544. We are gratefulfor helpful discussions with Angus Deaton tandAriel Pakes throughoutthis 1Ouraim here is not to surveythe existingtheoreti- project and for the financialsupport of the National cal and empirical literature. For that the reader is Science Foundation(SES-9121838). referredto GershonFeder et al. (1985). 396 VOL. 83 NO. 2 NEWDEVELOPMENTS IN DEVELOPMENT 397 say about the underlyingdynamic process at Some cross-sectionalsurveys contain in- work. formation, based on recall, about when a farmer adopted a technology. Under some B. Cross-Sectional Studies circumstances,recall data may provide a means for dealingwith the above problems. There are also many studies of technol- If, for example, the dynamicstructure was ogy adoption that use cross-sectionaldata. well representedby ifit = 8xtx, we can aug- Such data are broadly of two kinds. First, ment equation (2) above. Creatingfor each there are studies that take a snapshotof M farmera set of discrete choice observations, farmers'technology use at some date. The dit, equal to 1 if farmer i was using the gain to farmeri of using the new technology technologyat time t, t E [1, ...., r], and zero is typicallyparameterized as yxi + ui, where otherwise,we estimate a probit: are farm and farmer characteristicsand xi (3) ui is an independentlyand identically dis- Prob{dit=1} It is tributed farm specific ex ante shock. = 4((yxi + pT+ 8[TX xi])/ou). often assumed that these shocks are nor- mallydistributed, and the model is then run where T is a set of ir - 1 year indicators and as a probit, so that Tx xi are interactionterms that allow the influenceof field and farm characteristicsto (2) Prob{adoptionby farmeri} change over the diffusion process. While = more flexible than (2), this structureis also (4yxi /au) extremelylimiting. It is necessary to main- where 4(Q) is the distributionfunction of tain the assumption that influential farm the standardnormal. The intention of this and farmervariables xi do not change over line of research is often to measure the time. In general, this seems unreasonable impact of xi on adoption decisions. since farmer wealth and credit-worthiness However,this model is problematicif, as are apt both to influence and to be influ- the time-series analysis suggests, there is enced by adoption choices taken. If these some dynamicstructure to the adoption de- data are availableonly at the time when the cision. The cross section provides only a survey was done, their use in (3) will bias snapshot;at that point, the technologymay the parameters of interest. It also seems be incompletelydiffused throughthe popu- unlikely that the dynamicsof adoption can lation. This confoundsthe interpretationof be well captured by allowing time-varying the coefficients in (2). For example, there coefficients on variables whose values are may be a time-dependent element in the assumed to be constant over time. Thus, adoption decision, so that the expected while one might be able to do better by profits are yx1 +it, where if= having informationabout the year in which Dt-1, xit)+ it, where Dt-1 is a history of the technologywas adopted, there are still the new technology'suse up to period t -1. problems.2 There are many possible interpretationsof qIit,including (i) a farmer's knowledge about C. Panel-Data Studies the new technologyor (ii) evolvingcosts of adoption,which vary with credit availability. If panel data detailing farm and farmer Either way, it may depend upon farm and characteristics and the adoption choices farmer characteristicsat time t. In these cases, hit will bias the parameter estimates of y. Thus, upon completion of the adop- 2There are other possible ways of using recall data. tion process, cross-sectionalstudies of this For example, the researcher may wish to stratify the kind may be able to provideinsight into the sample into individuals who always used the technology farm and farmer characteristicsassociated during the recall period, those who adopted during the period, and those who never used it. Predicting with ultimatelyaccepting the new technol- adopters in such circumstances may be interesting, but ogy. However,these data are of limited use is still subject to the criticism that the xi's may be in exploringthe adoptionprocess itself. endogenous. 398 AEA PAPERS AND PROCEEDINGS MAY 1993 made at each point in time are available, namic choice as being governedby a recur- then a number of the criticismsraised for sive problem: time-series and cross-sectionaldata can be met. Here, for example, the researchercan (5) Vti(kit allow for householdeffects and state-depen- dence. Thus, consider a model in which the = + underlyingdynamic component is well rep- Max{rrjt(djt,ki) E[V/ 1(ki+1)lkki,dt]} resented by where VO(k) is farmer i's value function at time t, n-it(d ,,kt) is some current payoff, (4) it =,I H dit- + at + Ai+ uit. and 8 reflects the discount rate. Several i C N(i) different interpretationsof the state vari- able are possible: James Heckman (1981) offers an excellent survey of methods for handling such mod- (a) Credit availability.-The variable k' may els, where Ai is either a randomor a fixed represent current assets that are avail- effect. able to pay for implementationof the However, the availabilityof panel data new technology. forces researchers to think harder about (b) Learning.-The variable k' may repre- reasonable dynamic specifications for dis- sent the stock of knowledge about the crete choice. For, while there is a rich set of new technology which evolves through empirical models available for panel data, time. there is a real question about how these (c) An Irreversible Investment.-The vari- relate to the underlyingchoice problemthat able k' equals 1 if the investmentwas individuals face. For policy analysis, the ever undertakenpreviously and 0 other- abilityto returnto the underlyingmicroeco- wise. nomic foundations of adoption is impera- tive. This will lead us, in Section II, to think In any of these cases, current choices once again about theory. have future consequences,and any current decisionsought to weigh these. In designing II. DynamicChoice an econometric specification for studying technologyadoption, we would formulatea So far, we have said very little about the model whose likelihood function comprised dynamicchoices that generate the data. In
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