Implementation of Socio-Economic Planning Models in Federal Agencies

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IMPLEMENTATION OF SOCIO-ECONOMIC PLANNING MODELS IN FEDERAL AGENCIES S. Dickhoven G .\! f) -I wlill/I,'

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Abstract. Planning models are increasingLy becoming a stand~rd Jart of the pollcy process; hence it lS lmportant to.determlne ~he conditions for effectlve implementatlon. of modellng Gtech nolo in public organizations. In a JOlnt Amerlcan- erm~n rese2 ~ ch project we have e x amined this questlon, asklng . speclflcall ' What a re the cha r acteristics of . tne envlronment, or g a ~ ;zation, technology and translfer.pollcl~slt~at ~::1n~0 t~: successful implementation of p.annlng mo e s. d l research on a reanaL y sis of eXlstlng survey databan s~~er~ case studies our research revealed a llst of a ou.t lr y variables that seem to be critical to successful model lmplementati o n, and ga v e insightsinto the dynamlcs of thls lmptem~~­ tation process in pollc y makln g envlronment~. In contrast 0 e re c ent implementation Literature wlth modellng appllcatlons as well as to o ur own assumpti o ns deliberate transfe~ pollCle~ O{ t h e mode l de v e Lopers do not be long to the mOl e 1 mpor an varia b Les Furthermore modeling appllcatlons In thls contT x t ~re clearL y d;minat e d b y the users and serve both a managerla an a political ideology of model use. Ke y words. Im p lementation, ModeLing, Models, GovernmentaL Plannlng, Human Fa c t o rs.

INTRODUCTION This research pr o ject grew out of a lnte r est on the part of mutual rese a rchers at t he Institut fUr Planu n gs- un d Entsch e idungss y steme of the Ge selLs c haft few Mathematik und Daten v erar b ei tung (GMD) in Bonn, West German y and the Public Po lic y (PPRO) of the Re search Org a nization Uni v ersit y of CaLifornia, Irvine, to stud y the processes by which comp u t er i zed plan n ing mod e ls are a do pted im p le mented and used in national g o ver nm ent po lic y ma k ing. The stud y 19 8 1 and part t oo k p la c e during 19 8 0, of 1982, a nd in vo l v ed Sie g fried Dickho v en from the GMD, and Kenneth L. John Leslie King, Susan Kr ae mer, FalLows, and Cecelia Ca mp beLl-Klein of PPRO. The results I'Jill be published this y ear ( Kraemer, K.L., S. Dickho v en, S.E. FaLLows, J.L. King). CONCEPTUAL FRAMEWORK Based upon re v iew of the literature and e x pLorat o r y casew o rk ~Je have dev eloped t he fra me wor' sh o l·m in Flgure 1 to illustrate the i mpo r tant di mensions. This simple model serves as the structure upon which subsequent anal y sis is based. Briefly, we view the outcomes of model imple mentation as a function of a set of t r ansfer policies operatin g withi n a br oad milieu which includes characte r istics o f the models the msel v es, the organizations and the technology in v olved in modeling and transfer eff o rts, and the e x ternal environment. 129

Organizational Attr'ib u tes Environmental Pr' econditi o ns

Fai lure or Success

Transfer Polic y

Featul' es of the Techn o log y Fig. 1:

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Problem

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the

Research

APPROACH TO THE RESEARCH PROBLEM Fro m the lite r ature and f r om our e xkne w that there was some p erie n ce we evi d ence that man y or e v en most of our different variables identified wit h in Jur conceptual framework could be of interest and even of importance for implementation aspects of pLanning models. In our research we wanted to stud y the relevance of single variables or groups of variabLes within our framework and to find eventualLy ad d itional important ones, and we tried to achieve at least a better feeling for the relative weights of single variables or groups of v ariables in terms of importance or

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reLevance for modeL impLementation compared to the other variabLes within (and without) our framework. Data and r'1ethods The Largest body of empiricaL information in this context so far was the FI'omm, HamiLton, HamiLton (197S) survey study of federaLLy supported mathematical modeLs, focussing on differentiaLs in model use with respect to the modeL characteristics and the model deveLopment process. It incLuded a vast number (about 350) of non defense modeLs, a third of which were not onLy federaLLy funded but aLso intended for use in federaL agencies, the Locus of appLication we wanted to study in our research. Aside being very cost-effective the reanaLysis of an existing data base was expected to allow us to test a great number of preliminary hypotheses regarding the relative reLevance of certain variabLes and variabLes' groups for implementation success, that couLd be derivated from actual and measured modeL use. Additionally we performed three case studies on model impLementation efforts to incLude the recent and actuaL deveLopments in this context and to get more detai led information about the process &nd the dynamics of implementation under different environmental and impLementation poLicy conditions. The Cases chosen The folLowing and extremeLy successfuL modeLs were chosen: The TRIM/MATH - Model. The 'Transfer Income r10deL (TRIM)' I'esp. the modeL 'Micro Analysis of Transfer to Households (MATH)' are genel'aLized microanaLytic modeLs that try to anaLyse simuLtaneousLy the effects of a number of different weLfare programs fOI' individuaLs (citizens) or househoLds. Both modeLs have a predecessor modeL in common, that has been deveLoped during the late 1960's by an interagency task force of the U.S. FederaL Government. out of this unsuccessfuL predecessor named RIM a non for profit research organization deveLoped the TRIM modeL, which came into governmentaL use in 1973 and which is sti LL in use by the administration and by the deveLoper organization for contracted poLicy anaLysis tasks. In 1973 a major part of the deveLopment team Left this organization, took the modeL, that so far was deveLoped with federaL money and hence not proprietary to the first organization, joined a for profit research organization, improved the modeL technicaLLy and marketed it to federaL cLients with considerabLe success. Both modeLs are stiLL used at different pLaces in the federaL govel'nment incLuding the LegisLative branches and jointLy are the most wideLy used generaLized micromodeL. As these two deveLopers organizations considerabLy differ in their marketing styLes, this case was expected to bring better informations about the controL variabLes ('impLementation poLicies') of our framework. Furthermore in this case the users' interests are highLy organized, which makes it different from other experiences. The DRI-ModeL. The U.S.-Macroeconometrlc modeL of Data Resources Inc. (DRI-US-Macro) is the most wideLy used econometric modeL in the worLd and In U.S. federaL agencies as weLL with an

horizon of experience of over 10 years. DRI itseLf is an extremeLy marketing oriented company and its monthLy economlC outLooks for the U.S. meanwhiLe have become a pubLic institution which is wldeLy cited and accepted in aLL news media. Within federaL agencies it is used not onLy by users, whose mission requires instruments Like this modeL, but aLso by others who just want to controL these primary users. The BAFPLAN ModeL. The BAf6G-PLanungssystem (BAFPLAN) is a microanaLytic modeL for anaLyslng the effects and dynamlcs of the German FederaL Student Aid program (the BAf6G) and is one out of onLy a handfuL modeLs in the German FederaL Government, that are used over a considerabLe timespan (over 5 years) and on an extremeLy high LeveL of actuaL use (about 200 simuLation runs per year). It is bound to a transfer reguLation that was accompanied wlth anaLytic instruments from its very beginning. SUCCESSFUL IMPLEMENTATION AND UTILIZATION OF POLICY MODELS Our research has reveaLed a Long List of variabLes that seem to be criticaL to successfuL modeL impLementation and use in federaL agencies. These findings, which wiLL not be discussed in detaiL here, can be grouped into four major cLusters: the generaL environment of poLicy modeLing, the characteristics of modeLing technoLogies, the organizations invoLved in poLicy modeLing, and the poLicies specificaL Ly adopted by modeLel's to faci Litate modeL transfer. Given our conceptuaL framework and grouping the variabLes discussed in thls chapter as variabLes reLated to sucessfuL modeL impLementation into this framework we come to the underLying structure of Figure~. Adding to aLL these variabLes a dichotomous weight expressing onLy an impression Like higher or Lower importance to impLementation success and doing the same for the different groups of variabLes according to our conceptuaL framework we come to the foLLowing resuLt: Regarding the bLocks of variabLes as reLated to our framework we flrst have to concede that the bLock of our controL variabLes, nameLy the transfer poLicies, does not beLong to the more lmportant, above average bLocks. Furthermore the second bLock of varlabLes that is under direct controL and .lnfLuence, the organizationaL attrlbutes of the modeL deveLopers' organlzatlon, lS nelthel', ('i hi Le the other bL0ck that can be Less infLuenced Gy the modeL deveLopers themseLves are rated as above average. Our Judgement stems from two major reasons. First when we started this research we probabLy started with the notlon to overemphasize these transfer poLicy variabLes, and we might be somewhat disappointed when these varlabLes turned out to be Less promlnent than we had hoped or expected. This disappointment again mlght Lead us to some underemphasis of the poLicy variabLes, now. But there 1 s st 1 L L anothel' reason for OUI' j udgement. In our case stUdies (~e primarl Ly found more user agencies with definitlve mlSSlons for anaLysis than secondary user agencies with derivated modeL. use misslons, e.g. for controLLlng another agency or to be abreast of (pubLic) economic poLicy Issues. Though transfer poLicies even

Socio-Economic Planning Models in Federal Ag e ncies

for prlmary mission users are not unimportant, we learned that this part of the modellng market cl~arly is demand-orlented and hence actlve s~p­ ply side policies are not that lmportant and effective. This differs considerably with secondary use~s. Looking at this part of the modellng market only, transfer policy variables and the organizational attributes of the developers must be regarded as very important. Thus ranklng these variables in the B-category here, lS due to the circumstance that we found more primary (immediate mission oriented) use of the models in our study. And as we expe~t that the market of secondary use wlll lncrease over time, we also expect that these hiO groups of variables \~ill become as important as the other groups. Variables that are expected to become more important for secondary users, are marked with an + in figure 2.

ENVIRONMENTAL PRECONDITIONS SUBSTANTIVE THEORY FUNDING FOR MODEL DEVELOPMENT AND TRANSFER STATUS OF COMPUTING TECHNOLOGY AVAILABILITY AND QUALITY OF DATA + Saliency of Policy Issues. . + Competiveness among Organlzatlons Flexible Personnel Practlces + Pr'edisposition to Technlque ORGANIZATIONAL ATTRI BUTES Features of the Modeler Or'ganization ~'ODELER'S REPUTATION MODELER'S IDENTIFICATION WITH THE MODEL r"ODELER'S POLITICAL NEUTRALITY + Organizational Type

~E~~~~~~~~~

__________

FEATURES OF THE USER ORGANIZATION AGENCY'S NEED FOR ANALYSIS FIT OF ~'OD EL TO AGENCY'S MISSION INTERNAL RESOURCES AVAILABLE FOR MODELING USER'S FAMILIARITY WITH THE TECHNIQUE COOPERATIVE ARRANGEMENTS FOR MODEL TMPiFMENTATION FEATURES OF THE TECHNOLOGY NATURE OF MODEL ERROR CHARACTERISTICS OF THE DATA A NTAI NAB I LI TY OF THE ~'OD EL OF KNO\~LEDGE + Cost of Model Acquisition and Use + Complexity of the Model + Intended Purpose of the Mode l: Product vs. Research ~, I ACCUr~ULATION

,

Tr' ansfer Policies

MODEL PACKAGING ~'ar' ket i ng Styles and Strategies + Tr'ansfer Agent + Pr'icing + User Gr'oups +

ABC abc +

Fig. 2:

Higher Weighting Lower Weighting Possibly more important for secondary model users Weightings of Importance of Study Variables for Successful ~'odeL

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CRITICAL VARIABLES FOR IMPLEr~ENTATION

Within the different variable groups I now will discuss our first conclusions about their relative weights. Transfer PoLicies As stated earLier this group in fact does not have the high welght that we originally expected. Nevertheless the weight of the whole varlable group .1S increasing. Regardlng the flve variabLes within that group we thlnk that 'Model Packaging' .1S and will be in the future the most.lmportant one. We therefore lift th1s varlable 1nto the A category, Leaving all other variables in the B category. We do so because in all cases we found that the users want the application of the technology and not the technology aLone. For example we found many subscribers of the DRI service who were just interested in getting DRI's monthly economic outlooks. And even the so calLed primary users of that model were all interested in the models as weLL as in the DRI's outLook and as in the availability of data. In the TRIM/MATH and the BAFPLAN-cases the users contracted with the developers organizations not only for the instruments but also for analyses with the instruments. Therefore the model, its supporting technology and the applicat10n of th1s 1nstrument must be regarded as the regular and necessary package. DRI expanded thlS minimal package conslderably. by courses, related models, data bank1ng, software and other extensions of its services and experienced additionaL success. On a smaller scale MPR's additional success partially comes from its packaging strategy. Compared to the importance of this variable the other variables of this group are less important, though we expect that their importance wiLL increase over t1me. And even if this conclusion telLs nothing but the trivial 'a good product sells itself', it is noted here, because in the OR/MS and model implementation literature the other variables are often regarded as more reLevant (see e.g. the transfer agent Literature, the user lnvoLvement literature etc.). The user gl'oup val'lable is not only a policy variable like it is when used by DRI or GMD to create future demand. It aLso is an environmental variable Like it is in the TRIM/MATH user groups (see the variable cooperative arrangements in the variabLes block organizational attributes of the user agency). Environmental Preconditions This group of variabLes for successfuL model impLementatlons 1S ranked 1nto the A-category because it contains a group of four variabLes (among the A variables within that group), WhlCh are absoluteLy crucial for any model implementation success... These variabLes, namely the Ava1 Lab1l1ty and QuaLiy of data and substantlve theories gained .by a considerable funding Gody and h1story for experlmental appLications. in res~arch environments, and posslbLe only \'i1th1n a certain level of technology, must be regarded as absolutely necessary prerequisits for successfuL model lmpLementation or institutionalization. It must be doubted that without all four variables which represent a sound body of t~eoretical, empirical, experimentaL and technical kno~ledge, serious and successfuL modeL1ng can take pLace at all. These variables therefore must be reaarded as

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necessary but not aLways aLready as sufficient prerequisits for success. The next three variabLes, poLiticaL saLiency, competitiveness among agencies and fLexibLe personeLL practices have proved to be of above average lmportance, but in our experience they beLong rather to the subcategory, 'sufflcient for success' than to 'necessary for success'. OnLy the variabLe 'predisposition to technique' in our view has not proved to be above average important. or2anizationaL Attributes of the ~'oc e Let's As stated earLier this group of variabLes has got a Lower score, what is due to the status we actuaLLy found, nameLy a reLative predominance of primary users of modeLs, for whom the variabLes out of this group are Less cruciaL. But as it aLready can be seen with the DRI case, which reaLLy can be regarded as the forefront of modeLing, this deficit of importance is expected to decrease. ReLated to the actuaL situation the variabLes 'reputation', 'identification' and 'poLicy abstinence' seem to be of higher importance than the other. It is not cLear, whether 'division of Labor' is a prerequisite or more a consequence of impLementation success. Therefore it ranks Low, 'OrganizationaL type' of the modeLer's organization seemed to be a very important var iabLe with a cLear edge for prof it o riented modeLers Like ORI and MPR over other organizationaL types Like TUI or GMO, but after our e xp erience with the new chaLLengers of the cLassicaL pragmatic macromodeLs, we beLieve that this variab Le must be seen from an environmentaL point of v iew, nameLy under professionaL s tan dards audited by research competitors or at Least auspiced by a professionaL or scientific community. Without that embedding the importance of the variabLe 'organizationaL type' seems to be of beLow average importance, at Least in the Long run. OrqanizationaL attributes of the uset' aqencles This group incLudes two variabLes, nameLy 'A gency 's mission' and 'Fit of ModeL to Agency's Mission', that teLLs nothing more than that the demand side i s dom lnating this business, and that modeLers have to meet the users' reaL needs. Though this essence is reaLLy triviaL, it is noteworthy to point heaviLy to this triviaLity, because it is often forgotten or is interpreted more in the sense 'Fit of the agencies mission to a modeL.' ALL cases I'Je s tudied (with a Low LeveL of exceptions in the ORI case) showed that the appLication, the poLicy and the poLitics are aLways dominating the modeLs. This can go as far as to a very criticaL point where the modeLers even consider t o Lea v e a path of professionaL pragmatism, as it seemed to happen IIJith the nel'J chaLLengers of the cLassicaL econometric modeLs. On the other hand this dominance of the appLications normaLLy guides the modeL to a pragmatic evoLvement, that is a necessary prerequisite for confidence and trust in this new anaLytic technoLogy. Compared with these two dominant variabLes the other seem to be of much Lower importance, though in our studies they reaLLy had infLuence on successes or on the absence of successes. TechnoLogy To understand the characteristics of this new 3nd high technoLogy and its

needs and probLems is absoLuteLy criticaL for impLementation success. In addition to the four necessary prerequisits for mOdeLin g nameLy theory, data, e xper ience 6ody, and status of computing, which were aLready mentioned as criticaL variabLes within the framework section 'environmentaL preconditions', two other aspects of modeLing are highLy important. The first is to understand the Limitations of modeLing for poLicy anaLysis. A thorough understanding of the nature of modeL error stemming from weaknesses in the underLying theories, representativeness of the modeL, actuaLization or extension Lags, or from communication probLems or other reasons HS weLL as a good notion of the characterist1cs and probLems of the underLying data incLuding the Lackin g ones 1S reaLLy necessary to be aware of the very Limited contributions o f mode Ling to poLicy making. But it is that awareness of modeLs' Limited potentiaLs that onLy can create the beLief and trust into the reaLLy substantiaL worth of this LittLe additionaL information for poLicy making. In our studies it were thos e peopLe with the most Limited (and therefore most reaListic) expectati on s of modeLs' contributions to poLicy making, who on the other hand va Luated th es e anaLytic instruments the highest. There is another aspect of the technoLogy that is very important for successfuL impLementati ons of modeLs in our context, nameLy that modeLs ( -at Least those modeLs that become institutionaLized as reguLar anaLytic tooLs in t he po Li cy making process, the modeLs we studied in our research -) are ever evoLving, changing, e xpa nding. This means that the y very quickLy can become outdated, not actuaL, not fittin~J to the appLications unLess they are steadiLy and considerabLy actuaLized, improved, changed etc. To start with a rather weak modeL in this appL i cation area probabLy is n ot as criticaL for impLementation as when there is no considerabLe and steady improvement and adaption to the appLication needs. The discussion of the variabLes 'maintenance costs' and ' accumu Lation of k n 0 I·J Led g e ' can iLL us t t' ate t h is s t t' 0 n g statement. ALthough we had expected this, it was reaLL y surpr i sing that aLL modeLs studied here or at Least their antecedents experienced severe pt'obLems I'Jith these issues, though simiLar probLems of comparab Le software systems are meanwhiLe wideLy reaLized and accepted. The comparabLy Li tt Le infLu ence of modeL costs and model complexity on implementation success at the first glance is somewhat counterintuitive, too. But we think that this is partiaLLy due to the fact, that in our studies the primary users of pLanning modeLs I'Jet'e the majot'ity . Fot' these users cost and compLexity must be regarded in terms of their anaLytic missions. Spending about ZOO to 400 thousand OM annuaLLy on BAFPLAN 1S nearLy negLectable for the BMBW as Long as they feeL that they improve the1r budgeting projections, their poLicy (their tar ae tin g of the program), or their defensive positions dur1ng budget cutting years by fifties or hundreds of milLions of OM. On the other hand the variabLes cost and compLexity are important success criteria at Least for secondary users, who we e xp ect to increas e in the future. This wi LL aLso 1ncreas e the reLat1ve we1ght of these two var iabLes.

Socio-Economic Planning Models In Federal Agencies

TYPES OF MODEL USE Federal agencies utilize computerized economic planning models for a variety of purposes, some conforming to a managerial ideology of model use, some fittIng more into a political ideology of modeling, and others serving both approaches. A managerial ideology of modeling presumes that the qualIty of decisions that emerge from the policy-making process depends at least in part upon the quality of the information entering the system, and that improvements in the information available may lead to improved policy choices. According to this ideology, computerized planning models deserve a place in the policy-making process because: they enhance the diversity of information available to analysts, managers and policy makers; they provide a systematic means to reduce uncertainty about the future, given certain identifiable assumptions about the present; and they provide detailed if not altogether scientific answers to "what wi lL happen if ... ?" questions. Many of the agency anaLysts who actively use (or have used) the models in our study cited such reasons for doing, for exampLe to make forecasts of future states of the U.S. economy, or projections of future costs for key entitLement programs in the welfare system. In most agencies these forecasts are part of larger information-coLLection processes where anaLysts attempt to increase the number of reputabLe sources of information avaiLabLe to them and to managers, and thereby reduce the risk of too-heavy reliance on any single information source. AnaLysts tend to temper the resuLts of their models' forecasts with other information, including their own judgement or intuition. Another major use of the modeLs is for simuLation to answer "What wiLL happen if ... ?" questions. This use is important for predicting the impacts of aLternative poLicy changes in variabLes of interest to policy makers, and for expLoring how sensitive different variabLes are to changes in assumptions or to forecast program costs and case Loads of incometransfer programs, using aLternative eLigibiLity ruLes and benefit LeveLs. These modeLing appLications contribute to a Larger process of poLicy design by enabLing anaLysts to expLore the marginaL impacts of aLternative policy configurations. AnaLysts often use these simuLation techniques to finetune poLicy proposaLs, or to determine the mix of program eLements (e.g., eligibiLity ruLes) that wouLd produce a desired program objective (e.g., totaL caseLoad size or program cost). ALL of these more or less technical appLications of models for forecasting, for poLicy simuLation and design, for maximizing available information support a managerial ideoLogy of modeLing. They provide extensive, detai Led and quantitative information about the costs and b~nefits of alternative public policie ~ , and once they have been implemented, they do so relatively efficiently, aLthough with an uncertain amount of error. These applications serve other purposes as welL, ones that support a political ideology of modeling. This ideoLogy notes that the policy-making process is a system that produces decisions about who wins and who loses

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as a consequence of governmentaL actions. It is therefore a political system whose members compete for power, at least as a proximate goal, uSIng whatever weapons they have at their disposition, including information. Information is useful for decisions to the extent that it provides answers to politically relevant questions: Who will be adversely or posItIveLy affected by a decision, and in what ways? Where do these people live? What policy options do different groups favor or oppose? How intenseLy do they care about them? Information can also be politicaLly useful to the extent that it is consistent with policy makers' biases and can therefore be used as arguments or justifications for their positions. Moreover, a politicaL ideology of modeling presumes that mechanisms that provide such information, such as computerized planning models, wiLL find support among members of the policymakIng community, who wiLL utilize these tooLs strategically to achieve poLitical objectives. The cases of model use by federal agencies reveaL numerous instances where modeling interacted with elements of the political and institutional environment to support a poLitical ideoLogy of modeL use. In the earLy 1970s, for e xampLe, weLfare analysts noted a distinct increase in the attention poLiticians paid to the resuLts of their microsimuLation anaLyses after they presented the data in charts showing the breakdown of income-maintenance program beneficiaries by region. Over the Long run, this treatment of data made the information generalLy more saLient to poLItIcIans and heLped to buiLd the constituency of individuaLs in favor of a methodoLogy that couLd produce such relevant detai Ls. In many instances modeL generated informations have been used to avert changes and cuts of the own program whIch were raIsed by other governmental branches, by the parLiament or by the pubLic. To be the soLe dispenser of hard evidence has been proved to be of great ad v antage over confL1ctlng Interests, and in most cases the own position remained unchaLlenged. Other instances of a political ideoLogy of modeling occur, as with "accountabiLity modeLing," where an agency impLements the same modeL another agency is using in order to keep that other agency "honest" to monitor the information avaiLabLe to the rivaL agency and the credibility of the numbers in the reports the rivaL agency issues. This can aLso be done by acquiring and using a model dIfferent from the one their 'opponents' use. Theses forms of 'watchdog'or 'countermodeLing' are of growing importance and can heLp to neutraLize the information inbaLances between executive and LegisLative branches.

CONCLUSIONS We think that modeling in both governments under consideration is here to stay, and that this application of information technology is and will be dominated by its users. Furthermore we expect a growing use of modeling in the poLicymaking process. The strongest indicator for this assump-

S. Dickhoven

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tion is a recentLy pubLished GAOSurvey on existing modeLs in U.S. agencIes. This 1982-survey identitlfies over 300 modeLs in active use by the executive agencies onLy, and DoD-modeLs are not incLuded. This is about six times as much as the Fromm, Hami Lton-survey stated for the year 1974, aLso excLuding the 000 appLications, but IncLudIng the LegIsLatIve branches of the U.S. government. The demand for modeLs continues to be fed by the fact that modeLing is becoming more refined, thus resuLting in modeLs that are more LikeLy to provide genuine anaLyticaL assistance to those Locked into difficuLt pLanning situations. As Long as modeLs continue to prove their vaLue as usefuL tooLs for pLanning, in the rationaListic sense of the term, theIr poLiticaL potency as weapons in debate wiLL remain, too. However, In cases where modeLs eventuaLLy prove to be no better than guesswork, the demand for modeLs wi LL eventuaLLy wane and dIsappear because their pr actical . and poLiticaL va Lue cannot be sustaIned. In our eyes therefore it is likeLy that if government interest In uSIng models were to wane, either because modeLs faiLed to show sufficient practical utiLity or' faiLed to retain their poLiticaL potency as weapons, It is doubtful that the concerted efforts of modeLing suppLiers couLd sustaIn this modeLing market.

REFERENCES Bendisch, J., P. Hoschka (Eds.): Mogllch ke lten und Grenze~ sozio6konomlscher PLanungsmodeLLe, GMD-Studien Bd.68, Bonn 1982 Dickhoven, S.: InstitutionaLization Without Model Transfer : The BAFPLANInternal Rep ort IPES.82.0206, Mode L, Bonn 1982 FaLlows, S., ImpLementation by PersonaL Tr'ansfer': The TRIM /~lA THModeL, Int erna L Report IPES.82.0207, Bonn 1982 Fr'omm , G., W. Hami lton, D. Hami Lton: FederaLL y Supported MathematicaL ModeLs: Survey and AnaLysis, Washington 1975 Gass, S. (ed.): UtiLity and Use of Large ScaLe MathematicaL ModeLs, Washington 1979 Gass, S., R. Sisson: A Guide to ModeLs in GovernmentaL PLanning and Operations, Washington, 1975 Gr'eenberger' , M., M. Cr' enson , B. Cr'issay: in the Policy ModeLs Process, Nel'l Yor'k 1976 Harris, R.: MicroanaLytic SimuLation ModeLs for AnaLyses of PubLic WeLfare policies, Washington 1978 King, J.L.: ImpLementation by CommerciaLization: The DRI-ModeL, InternaL Report IPES.82 . 0208, Bonn 1982 Kr'aemer', K.L., C. Cam pIJe LL-KLein: Factor's ReLated to ImpLementat ion :: rc cess : Survey ReanaLysis, InternaL Report IPES.82.0205, Bonn 1982 Kraemer, K.L., S. Dickhoven, S.E. FaLLows, J.L. King: Computers, PLanning ModeLs and PoLIcy MakIng; (ImpLementatIon of HIgh TechnoLogies in Fede ra L Agencies), (book manuscript, forthcoming)

Orcutt, G.H., Merz, J., Quinke, H.: MicroanaLytic SimuLation ModeLs to Support SociaL and FinanciaL PoLicy (book manuscript, forthcoming) U.S.-GeneraL Accounting Office: Uses of NationaL Economic ModeLs by FederaL Agencies, Washington 1979 U.S.-GeneraL Accounting Office : Ways to Improve Management of FederaLly ModeLs, Washington 1976 U.S.-GeneraL Accounting Office: Survey to Idenitify ModeLs Used by Executive Agencies in the PoLicymaking Process, Washington 1982