Juan Páez-Farrell
email: paez-farrellj@cf.ac.uk.

Mitchell Approach

ASSESSING STICKY PRICE MODELS USING THE BURNS AND MITCHELL APPROACH

Keywords: New Keynesian Models, Correlations, Burns and Mitchell JEL Classification: E32, E52, E58 New Keynesian Models, Business Cycles, Correlations, Burns and Mitchell JEL Classification: E32, E52, E58 Sticky price Models, Business Cycles, Correlations, Burns and Mitchell

This paper evaluates sticky-price models using the methods proposed by Burns and Mitchell, focusing on the monetary aspects of the business cycle. Recent research has emphasised the responses of models to shocks at the expense of its systematic component. Whereas sticky-price models have been successful at replicating impulse response functions from VARs, this paper highlights that they are unable to mimic the data for nominal variables. Moreover, the results are robust to the specification of the Phillips curve, including its backward-looking variant, calibrated values and the inclusion of fiscal policy shocks. Since being able to mimic the data is the lowest hurdle a model must pass, these results pose a challenge for sticky price models.

Beginning with [START_REF] Kydland | Time to build and aggregate fluctuations[END_REF] real business cycle (RBC) macroeconomics has led to sweeping changes in the way macroeconomics is conducted. There is greater emphasis on building models with strong microeconomic foundations, with the aim of overcoming the Lucas critique; the supply side was considered the economy's driving force and traditional econometric techniques were eschewed in favour of a more a-theoretical approach that attempted to construct models which were able to replicate their empirical counterparts' second moments.

The current paradigm in macroeconomics, New Keynesian macroeconomics, builds on many elements from RBC theory, but has placed greater emphasis on nominal rigidities and the nominal causes of output fluctuations -so that consequently, less importance has been attached to technology shocks -and models are often evaluated by their ability to replicate the impulse responses obtained from vector autoregressions (VARs). But this focus on the effects of shocks, to the neglect of a model's systematic components, could potentially lead researchers to incorrectly conclude that their model performs well, as only one aspect of the model's characteristics is observed. The traditional assessment procedure used to evaluate RBC models proposed by Kydland and Prescott, which built on the work of [START_REF] Burns | Measuring business cycles[END_REF], focused on the co-movement of variables as the defining features of the business cycle. Nevertheless, this approach has been increasingly discarded due to identification problems. The purpose of this paper is to argue that the RBC model evaluation methodology can still provide useful insights and that this is an area that should not be neglected. Evaluating a model solely on the basis of the cross correlations it yields and the standard deviations of the variables has its limitations; simply because a model is able to mimic the data does not mean that it can explain it. As [START_REF] Summers | Some skeptical observations on real business cycle theory[END_REF] argued, "Many theories can approximately mimic any given set of facts; that one theory can does not mean that it is even close to right". Indeed, in the context of RBCs, one of the biggest limitations was the ability of many models to mimic the data and the Burns-Mitchell methodology was unable to discriminate alternative models. As [START_REF] King | Real business cycles and the test of the Adelmans[END_REF] found, one cannot distinguish between a Keynesian (Klein-Goldberger) and an RBC model when using the methods of Burns and Mitchell. As a result, to this author's knowledge, there has been no general attempt to assess sticky price models using the Burns-Mitchell methodology. To the extent that alternative models are able to mimic the data, this exercise will yield few insights. However, if different model specifications result in clearly distinguishable co-movements in the variables or if the models are unable to mimic the data, then it is possible to gain additional information on the models that satisfy this minimum of benchmarks. In effect, if matching the data's co-movements is a necessary but not sufficient condition for explaining the data, then sticky price models that are unable to do so can be rejected on the grounds that they do not satisfy this basic criterion. This paper focuses on the implications of New Neoclassical Synthesis (NNS) 2 models for the behaviour of output, inflation and interest rates using RBC methods.

The purpose of this paper is to determine whether different sticky price models lead to clearly distinguishable co-movement in the models' variables with output, and if so, to determine which models are able to replicate the data.

The paper proceeds as follows. Section II will present the cyclical characteristics of a small subset of key macroeconomic variables for the US economy that are at the core of most small models used for monetary policy analysis, so that the theoretical models can be evaluated using RBC methods. Section III will then present a small New Keynesian (NK) macro model representative of the literature for analysing monetary policy. Section IV discusses the calibrated values used and section V then evaluates this benchmark model, as well as also assessing alternative variants commonly found in the literature. Section VI considers the role of fiscal policy as a source of fluctuations and section VII analyses the importance of technology shocks and operational monetary policy in sticky price business cycle models. Section VIII concludes with a discussion on the consequences of RBC analysis for sticky price models.

II. SOME BUSINESS CYCLE FACTS

The study of the stylised facts of economic fluctuations has already been well documented 3 . Therefore this section will provide a brief description of the variables of interest, focusing on a limited number of real and nominal variables that feature prominently in modern monetary policy analysis. These are output (y), the inflation rate ( ) and the nominal interest rate (R). Since the relationship between real and nominal variables is likely to be unstable with changes in monetary policy regime -3 See among others, [START_REF] Els | Real business cycle models and money: a survey of theories and stylised facts[END_REF], [START_REF] Fiorito | Stylised facts of business cycles in the G7 from a real business cycle perspective[END_REF] [START_REF] Millard | The labour market over the business cycle: can theory fit the facts?[END_REF]. and hence the term "stylised fact" would be inappropriate -this paper will focus on the period 1987:3-2002:2, which covers Greenspan as chairman of the Fed. 4 The data have been de-trended using the HP filter 5 on the grounds that this paper is focusing on fluctuations of 32 quarters or less, which is exactly what the HP filter yields, as argued by [START_REF] King | Resuscitating real business cycles[END_REF]; furthermore, using a band pass filter that discards high frequency fluctuations does not change the main conclusions of this paper.

[TABLE I]

The second column presents the standard deviation for each variable, while in the third column these are stated as a proportion of the volatility of output. 1 denotes the first order autocorrelation coefficient and the remaining columns present the correlation coefficient between each variable (at time t+i) with output at date t. A large number in (absolute terms) appearing in column t + i (t -i) indicates that the series lags (leads) the cycle by i quarters. If the absolute value of the crosscorrelation is highest at i = 0, then the variable will be defined to move contemporaneously with the cycle. Additionally, for the whole sample period the 4 As in [START_REF] Judd | The Taylor rule and the Fed: 1970-1997[END_REF] and [START_REF] Clarida | Monetary policy rules and macroeconomic stability: evidence and some theory[END_REF], there are reasons to believe that the Fed's reaction function was stable during the Volcker-Greenspan chairmanship of the Fed. 5 With a value of =1600. I indicate that all variables are procyclical, with inflation and the nominal interest rate lagging the cycle by three quarters and one quarter, respectively. Furthermore, the inflation rate displays a phase shift, as it is initially countercyclical and then procyclical.

These results are not new and well known in the RBC literature, but what has not been determined is how well sticky price models can fit these facts.

III. A STANDARD NEW KEYNESIAN MODEL

Most current models used for monetary policy analysis 7 are derived from optimising behaviour that can be simplified into three equations. Let y, R and denote output, a short-term nominal interest rate and inflation, respectively. f y represents flexible price output and t E is the mathematical expectations operator conditional on information up to period t. The shocks are given by a cost-push shock, , a shock to the monetary policy rule, t v and a technology shock, t z . 8

Then, writing the model in deviations from steady state the system is given by ( )

1 1 + + = t t t t t t E R y E y (1)
6 See [START_REF] Mccandless | Some monetary facts[END_REF] or [START_REF] Hoel | Introduction to mathematical statistics[END_REF]. The standard deviation of the correlation coefficient can be computed as: , where n is the sample size. 7 Representative among these are Walsh (2003, Ch. 5), [START_REF] Galí | New perspectives on monetary policy, inflation and the business cycle[END_REF] and McCallum and Nelson (1999a). 8 Both the cost-push and monetary policy shocks are assumed to be i.i.d disturbance with constant variance, whereas the technology shock is assumed to follow an AR(1) process.

F

+ = + 1 1 (2) ( )[ ] t t t t t v R x R + + + = 1 3 2 1 3 1 µ µ µ µ (3) ( ) ( ) t f t z y + + + = 1 1 (4) f t t t y y x =
(5) where 0 > , ( )

1 , 0 , 0 1 > , 2 0 µ , 3 0 µ and 1 1 µ < . is labour's share of
income in a Cobb-Douglas production function and 1 represents the intertemporal elasticity of labour supply. 9

Equation (1) represents an expectational IS equation, relating output to its expected future value and depends negatively on the rate of interest, 10 with the parameter being an index of the curvature of the utility function and in this case -since output equals consumption -also represents the interest elasticity of aggregate demand.

Equation ( 2) is the New Keynesian Phillips curve derived from [START_REF] Calvo | Staggered prices in a utility maximising framework[END_REF] pricing. Current period inflation depends on expected future inflation, the output gap 11 and the cost-push shock, with being the subjective time preference factor. This Phillips curve formulation is the most widely used due to its tractability and 9 A lucid discussion on the importance of intertemporal substitution of labour supply in business cycle analysis is provided by Blanchard and Fischer (1989, p. 338). 10 Assuming that money enters the utility function in separable form has the consequence that the quantity of money can be ignored in the present model. 11 In principle the NK Phillips curve would relate inflation to real marginal costs, but in the absence of endogenous state variables, as in the present model, there is a one-to-one correspondence between the output gap and real marginal costs, so one can use the former directly. microeconomic foundations, but as it has been subject to substantial criticism alternatives will also be discussed below.

Equation (3) represents the monetary policy rule, which is assumed to follow a

Taylor rule with persistence, as estimated by McCallum and Nelson (1999b). The central bank raises interest rates in response to the inflation rates being above its target and the output gap, and there is also dependence on past interest rates as generally found in empirical evidence. 12 A rule of this type could originate from the central bank possessing a quadratic loss function with the inflation and output gaps as its arguments -as used by e.g., Woodford (2001) -. [START_REF] Mayer | The macroeconomic loss function: a critical note[END_REF] has criticised loss functions of this form because of its symmetrical treatment of above target output and below target inflation as losses, but if this is what many central banks do follow under an inflation targeting regime 13 and one assumes that the Fed's behaviour is not that different from inflation targeting central banks, the quadratic loss function remains a useful approximation. The result of a rule such as (3)in the presence of an expectational Phillips curve is that the monetary authorities face a 'Taylor menu' as defined by [START_REF] Uhlig | The role of national central banks and of different policy cultures, in The Impact of EMU on Europe and the developing countries[END_REF], that is, the central bank faces a trade off between the volatility of inflation and the volatility of the output gap. 14 Equation ( 4) describes the behaviour of the flexible price level of output as a function of the technology shock, z, and is also affected by the model's parameters , and , the reason being that a technology shock not only raises flexible price output via its direct effect on the production function, but also induces 12 See for example, [START_REF] Clarida | Monetary policy rules and macroeconomic stability: evidence and some theory[END_REF]. 13 For example, the Bank of England's target is in symmetrical with a target of 2% for the inflation rate. 14 [START_REF] Cobham | The inflation variability trade-off: further evidence[END_REF] calculate this menu for a variety of developed economies.

zt t z t z z + = 1 ) 1 , 0 [ z (6)
and zt is assumed to be i.i.d.with mean zero and variance 2 z .

Equation ( 5) defines the output gap that is, the difference between cyclical output and its flexible price counterpart and it is one of the key variables of the model, entering the Phillips curve and being one of the target variables in the monetary policy rule. It is important to note that the monetary authority reacts to the gap between sticky-price output and its flexible-price counterpart, rather than cyclical output itself. The fact that central banks are aware of this distinction is evident in their publications and speeches where high productivity growth is not regarded as inflationary, 15 so that the use of this concept can be deemed as appropriate.

One should also note that there are three shocks in this model. Technology shocks, t z , affect potential output and therefore have a direct effect on the Phillips Curve and the monetary policy rule. Additionally, there are monetary policy shocks, t v , and cost-push shocks ( t ), both i.i.d. These are important in that they provide a theoretical rationale for the existence of a short term trade-off between inflation and output stabilisation, even if it is not clear how this shock originates in the model. 15 For a discussion on this issue from a central bank perspective see ECB (2000). A further issue that needs mention is the stability and uniqueness of the equilibrium in the model. These are considered at length by [START_REF] Mccallum | On non-uniqueness in rational expectations models: an attempt at perspective[END_REF][START_REF] Mccallum | The unique minimum state variable RE solution is E-stable in all well formulated linear models[END_REF] and Bullard andMitra (2002, 2005) for models such as that presented above, which incorporate a monetary policy rule. The model is solved using the minimum state variable (MSV) solution, which is unique by construction, 16 and [START_REF] Bullard | Determinacy, learnability and monetary policy inertia[END_REF] demonstrate that for monetary policy rules with inertia a stationary equilibrium exists as long as 2

µ is small and 1 1 > µ but not too large; for the values estimated by McCallum and Nelson (1999b) this is indeed the case. To prove this, the statespace representation of the model comprised of the benchmark model presented above is:

+ = t t t t t t t v z q mR R y 1 (7)
Using the calibrated values in section IV, m is given by: IV CALIBRATION 16 As a result, all the models can be solved as functions of the four shocks and the lagged interest rate only, as there are no endogenous state variables present. Given that the shocks are all stationary, stability of the equilibrium requires focus on the behaviour of interest rates. The calibrated values are shown in Table II and these are standard in the NK literature, where z and z are equal to 0.95 and 0.007, respectively; is set at 0.4 as in McCallum (2001a) and justifiable on the grounds that this model includes both consumption and investment. (the elasticity of demand) has been set at 6 17 and the volatility of the cost-push shock is the same as in McCallum (2001a). The values for v , 1 µ , 2 µ and 3 µ correspond to those in McCallum and Nelson (1999b) and is set to yield a value for the steady state real interest rate of 2%. Finally, labour's share in income, , is set to 1/3.

[TABLE II]

The paper will also present results for different values of 1 18 , given the considerable disagreement over the specific formulation of the Phillips curve.

V RESULTS OF BENCHMARK MODEL AND VARIANTS

Although the model presented above is representative of the NK literature on monetary policy analysis there is considerable disagreement on the specifics, 19 especially those regarding the Phillips Curve (Fuhrer, 1997, Galí andGertler, 1999) and the importance of technology.

17 Using the alternative value of 11 (implying a markup of 1.1) does not affect the main results in this paper. 18 Values of 1 =[0.05,0.1], often used in the literature were considered, but these do not alter the main conclusions of the paper. These results are available from the author on request. 19 See McCallum (2001a) for a lucid discussion of some of the issues.

+ + = + 1 1 0 1 0 ) 1 ( (2')
as in [START_REF] Fuhrer | Inflation persistence[END_REF], with a coefficient of 0.5 for both the backward and forward looking inflation coefficients. To contrast with the first model, model 3 embodies a predominantly backward looking Phillips Curve, 22 with 1 . 0 0 = . For these cases, the state-space representation (7') will yield:

= 1 1 1 1 0.2047 0.7187 0.4322 - 1.1195 - 0.7112 0.1964 - t t t t R R m
for the Fuher-Moore specification (model 2) and 20 In this paper the term NNS will be used to define all models embodying nominal rigidities, not just the New Keynesian Phillips curve. 21 All the model variants are described in Appendix A. 22 According to [START_REF] Mankiw | The inexorable and mysterious tradeoff between inflation and unemployment[END_REF], this formulation is superior to its forward looking variant as it better represents the economy's response to monetary shocks. for the backward-looking PC (model 3). In each case the models are clearly stable. 23 Figure 1 presents the autocorrelation coefficients for output, Y, which shows that all models exhibit greater persistence than found in the data and in this respect all models are virtually indistinguishable.

[Figure 1] These results are not surprising when one considers that RBC models possess similar features and that these sticky price models have the same underlying real structure.

In this regard, the main contribution of NK models is their ability to provide an account of the real effects of nominal variables, which is the emphasis of this paper.

Figure 2 presents the results for the cross correlations of output with inflation. A striking feature in all three models, including the backward looking Phillips Curve (PI3), is that they imply countercyclical inflation and moreover, it moves contemporaneously with the cycle, whereas in the data inflation is procyclical and lags the cycle. This result is stronger than that reported in [START_REF] Galí | Inflation dynamics: a structural econometric analysis[END_REF] 24 since it applies not only to the basic NK model (PI1) where inflation is forward looking, but also in model 3, with backward-looking inflation. 23 Results for all the other models considered in this paper are available from the author upon request. 24 Also, they treat de-trended output as the output gap, whereas in this paper that variable would be cyclical output and the gap would be the difference between this variable and its flexible price counterpart. The results for the nominal interest rate are even more pronounced and in all cases it is clear that the models are unable to capture the dynamics of the data, even qualitatively. 25 These results may seem counterintuitive, but their interpretation is straightforward. The model includes technology, cost push and monetary policy shocks. The first two lead to countercyclical inflation; that is, both move output26 and inflation in opposite directions. The monetary policy shock however, produces a procyclical relationship between the two variables.

There are two important features regarding the shocks. First, in terms of their volatility, monetary policy shocks only account for a very small proportion of total interest rate volatility, which is the reason McCallum (2001b) has argued in favour of emphasising the systematic component of monetary policy. In effect, the monetary policy shocks are small relative to the cost push and technology shocks.

The second feature is the way the shocks affect the model, that is, the model's structure. The cost push shock enters the Phillips curve one-for-one; the technology shock, by having a direct effect on the output gap27 has a direct effect on inflation and on the IS part of the model, whereas the monetary policy shock affects the IS only. Thus for these two nominal variables, interest rates and inflation, NNS models seem unable to explain their comovement with output, in other words, the business cycle. This is surprising, given the considerable amount of research and improvements in estimating monetary policy rules and robust estimates of the Phillips Curve. So this begs the question: how should these models be modified?

VI INCLUDING FISCAL POLICY SHOCKS

New Keynesian models tend to emphasise monetary policy issues and the role of nominal rigidities. Allowing for the inclusion of fiscal expenditure and assuming that government expenditures follow an AR(1) process, 28 the effect would be to rewrite equation (1) as:

( )

t g t t t t t t g Y G E R Y C y E y ) 1 ( 1 1 + = + + (1')
where Y G represents the steady state government expenditure-output ratio, set to 0.17 The results are consistent with the findings in [START_REF] Canzoneri | Price and wage inflation targeting: variations on a theme by Erceg[END_REF], whose impulse responses show that the impact of government spending shocks are negligible. This result holds despite the fact that fiscal policy shocks are as volatile as technology shocks, but the reason is clear: equation (1') shows that fiscal policy influences output but is scaled by the government spending-output ratio (which in the US is small) and the shock's persistence (which is very high). Consequently, the inclusion of fiscal policy in the NNS models seems to provide very little additional insight, 29 whether it is for inflation or interest rates.

However, it could be argued that the reason the results presented above are at odds with the data is not because IS shocks are too small, but because the role of technology shocks has been overstated.

VII. THE ROLE OF TECHNOLOGY SHOCKS AND OPERATIONAL MONETARY POLICY

Ever since [START_REF] Kydland | Time to build and aggregate fluctuations[END_REF] argued that technology shocks were central to understanding fluctuations, many economists (e.g., [START_REF] Summers | Some skeptical observations on real business cycle theory[END_REF] have argued that the role of technology has been overstated. More recently, [START_REF] Galí | Technology, Employment and the business cycle: do technology shocks explain aggregate fluctuations?[END_REF][START_REF] Galí | New perspectives on monetary policy, inflation and the business cycle[END_REF] has argued that technology shocks are much smaller than generally estimated. Could this 29 Including investment as in [START_REF] Canzoneri | Price and wage inflation targeting: variations on a theme by Erceg[END_REF] does not change this result. provide an explanation for the puzzles above? Taking the approach to an extreme, one could explore the effects of eliminating technology shocks altogether and this forms our third variant. Again, the three alternative NNS model specifications are presented in Figure 4 and the results remain largely unchanged. It is worth pointing out that model 7, embodying the NK Phillips curve, yields a slightly positive correlation coefficient between inflation and cyclical output but this improvement is counteracted by its worsening performance regarding the behaviour of the nominal interest rate, as its countercyclicality is now more pronounced. The reasoning behind this is straightforward: the source of the procyclicality of inflation in this model lies in the monetary policy shock being the driving for of the business cycle, consequently this will result in the interest being countercyclical as the monetary authority, rather than reacting to the cycle, is actually causing it.

[Figure 4a]

[Figure 4b]
Nevertheless, the values for inflation remain too low, and part of the reason for this is that ignoring technology shocks results in a greater emphasis of the cost-push shocks. 30 Hence it seems that in order to capture the cyclical properties of the data, it is necessary to focus not only on the shocks and their magnitudes, but also the models' structure. In this sense, one element that has been ignored until now is the operationality of monetary policy, that is, in setting interest rates the monetary 30 As is well known the inclusion of these shocks is often essential for NNS models if these are to argue that there is a tradeoff between output and inflation stabilisation. )[ ]

t t t t t t t v R x E E R + + + = 1 3 1 2 1 1 3 1 µ µ µ µ (3')
This will result in monetary policy not being able to respond contemporaneously to events (and therefore, agents' expectations of changes in interest rates), and the models' cyclical behaviour is now markedly different to those presented earlier.

Neglect of operationality in monetary policy is of substantial importance, 32 especially when trying to determine optimal monetary policy. The results, shown in Figure 5a, demonstrate the effect of operationality on the dynamics of inflation where, to emphasise the main results of this paper, technology shocks have been omitted.

[Figure 5a]

[Figure 5b]
The model with the NK Phillips curve (PI10) is again the closest when it comes to mimicking the dynamic behaviour of inflation, and this case it is vastly superior to all the previous alternatives considered. Inflation is now procyclical and, unlike in the data, it moves contemporaneously with the cycle. Moreover, the magnitude of the correlation coefficient is lower throughout most of the cycle than its empirical 31 Issues of uncertainty about the output gap is ignored in this paper. 32 See, for example, the arguments put forward by [START_REF] Mccallum | Issues in the design of monetary policy rules[END_REF]. But this improvement in the model's performance has its drawback in the performance of the nominal interest rate, as shown in Figure 5b -which presents the co-movement of output with interest rates-shows that NNS models still fail dismally. Hence, it is not clear that the model with operational monetary policy is superior. Moreover, given that the monetary policy rule used in this paper is an estimated one and representative of those commonly used in the literature, this truly represents a challenge for NNS models.

VIII. CONCLUSION

Modern macroeconomics emphasises a model's response to shocks compared to that of a VAR when assessing its performance. Although this approach has yielded many useful insights, it has neglected to consider the implications pertaining to the systematic components of the models. This paper has tried to determine to what extent NNS models are capable of capturing the cyclical comovements in the data, especially with regards to nominal variables. It has done so by using the Burns-Mitchell methodology proposed by [START_REF] Kydland | Time to build and aggregate fluctuations[END_REF] to a variety of NNS models, using both calibrated and estimated values commonly used in the literature. The result has been that these models have limited success at replicating the data. Indeed, in many cases they imply behaviour of inflation and interest rates completely at odds with their empirical counterparts. This is surprising for two reasons. Firstly, the Burns-Mitchell methodology has partly been neglected when analysing real business cycles because several different models could replicate the data, so one would expect a similar conclusion to be reached for an NNS model. Secondly, NNS models have been designed to explain monetary phenomena, so their inability to describe the behaviour of inflation and interest rates is surprising.

The model that best describes the cyclical behaviour of inflation is model 10, that is, a NK model with operational monetary policy and with no technology shocks.

However, this result is obtained at the expense of strongly counterfactual implications for the nominal interest rate (the contemporaneous correlation coefficient between output and inflation is -1.

Following [START_REF] Summers | Some skeptical observations on real business cycle theory[END_REF], one should reject models that are unable to replicate the data, but being able to "mimic the facts" does not imply that a model can explain it; that is, it is a necessary but not sufficient criterion. Following this argument, since the NNS models considered above cannot replicate the data this poses a serious challenge to the New Keynesian paradigm.

An additional conclusion that emerges from the results in this paper is that by analysing both a model's systematic components and their response to shocks provides further insights and understanding of the model, whereas simply focusing on shocks can result in models that face serious shortcomings.

  the correlation coefficients 6 is 0.13. The results from Table

  through the effects on the real wage. It is also assumed that technology follows an AR(1) process:

  . The presence of a positive government spending shock in an NNS model is to raise output and, because the flexible price level of output is not affected, this will have the effect of increasing the output gap and consequently the inflation rate, potentially overcoming the effects of the previous shocks. The models with fiscal policy are shown in Figure3, with model 4 being the standard NK model, model 5 28 This follows[START_REF] Canzoneri | Price and wage inflation targeting: variations on a theme by Erceg[END_REF], except that, as with output, government purchases are detrended using the Hodrick-Prescott filter. The resulting AR(1) coefficient and standard deviation of the shocks

  have up to date information on the current inflation rate and output gap.31 Therefore it should be their expected values that should enter the policy rule.McCallum and Nelson (1999b) estimated equation (3) as:

(

  , it is able to replicate the broad features present in the data.

  to Max Gillman, Patrick Minford, Laurence Copeland and seminar participants at Cardiff Business School, the Bank of England and the Money, Macro and Finance Conference for many helpful comments. All errors are my own. . (2003) Monetary theory and policy, Second Edition. Cambridge, MA: MIT Press. Woodford, M. (2001) The Taylor rule and optimal monetary policy. American Economic Review, 91(2), 232-237.

Fig. 1 .

 1 Fig. 1. Dynamic cross-correlations for output. Y represents actual GDP. Each suffix denotes a different model, described in Appendix A.

Fig. 2a .Fig. 2b .Fig. 3a .Fig. 3b .Fig. 4a .Fig. 4b .Fig. 5a .Fig. 5b .

 2a2b3a3b4a4b5a5b Fig. 2a. Dynamic cross-correlation between inflation at different leads and lags with output in the basic NNS model with alternative Phillips Curve specifications. PI denotes actual inflation. Each suffix denotes a different model, described in Appendix A.

Page 12 of 31 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  This section will assess New Neoclassical Synthesis (NNS) 20 models using three different variants, with each model being denoted by a different suffix. All the simulated data from the models is contained in the figures, which present the dynamic cross-correlations in graphic form. Each model is simulated 253 periods, discarding the first 53 periods in order to eliminate the fact that the simulations began from the model's steady states, and 1000 simulations were conducted using the programmes available from Bennett McCallum's website.The first model is the benchmark NK model presented above with the calibrated values described in TableII; model 2 only differs from the previous one in that the

						Submitted Manuscript
				F o
				r
	P Phillips curve, equation (2) is replaced by: 21
	t	E	t	t	t	e e r t t x +
						R e v i e w

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

It is worth pointing out that these results are robust to changes in parameter values, as often used in the literature.

It is important to note that this refers to cyclical output and not the output gap, which is a different concept in the present model.

The coefficient relating the flexible price level of output to the technology shock is generally close to one for standard calibrated values.

ACKNOWLEDGEMENTS

Page 21 of 31 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Appendix A: Model descriptions.

Model 1: Basic New Keynesian Model described in the text, with 05 . 0 = .

Model 2: Basic NNS model with Fuhrer-Moore Phillips Curve.

Model 3: Basic NNS model with predominantly backward-looking Phillips Curve.

Model 4: NK model with fiscal policy shocks.

Model 5: Fuhrer-Moore model with fiscal policy shocks.

Model 6: Backward-looking PC with fiscal policy shocks.

Model 7: NK model with fiscal policy shocks and without technology shocks.

Model 8: Fuhrer-Moore model with fiscal policy shocks and without technology shocks.

Model 9: Backward-looking PC with fiscal policy shocks and without technology shocks.

Model 10: NK Model with operational monetary policy.

Model 11: Fuhrer-Moore model with operational monetary policy.

Model 12: Backward looking PC operational monetary policy.


Markets Segmented by Regional-Origin Labelling with Quality Control

, the question under which conditions the generic promotion of regional-origin labelling results in a significant price mark-up for farmers remains largely unanswered. As the second pillar of the EU's CAP aims at strengthening the development of rural areas and providing farmers with diversified opportunities for the marketing of quality products, supporting regional promotion measures of produce from remote locations that feature quality-control systems could be of added significance. Given this background, it is the objective of this paper to provide a methodological framework for the analysis of regional marketing programs which include regional-origin labelling as well as quality assurance and control. An equilibrium-displacement model (EDM) for a segmented market with differential qualities is developed that extends most of the existing approaches in the generic advertising and country-of-origin literature. We investigate the extent to which a phased reduction of initial governmental support levels impacts farmer price premiums and welfare. It could be argued that with increasing producer cost shares for program participation farmers lack the incentive to participate in regional marketing activities that meet a growing consumer demand for high-quality foods from designated regional origins. Results suggest that full governmental support to regional-marketing programs provide significant price mark-ups at the farm level.

Our framework can be applied to a variety of regional marketing programs. An empirical application of the model is illustrated for a selected European case, i.e. "Certified Quality -Bavaria".

Introduction

The protection of the regional origin of foods is a major part of the EU's quality policy in agriculture. According to Council Regulation No. 2081[START_REF] Piggott | Some Old Truths Revisited[END_REF], "the promotion of products having certain characteristics could be of considerable benefit to the rural economy, in particular to less-favored or remote areas, by improving the incomes of farmers and by retaining the rural population in these areas" (Commission of the EU 1992). When the guidelines for State aid for advertising of products were established in 2001 (Commission of the EU 2001), only the promotion of those agricultural products can be supported which are protected designations of origin (PDO) as outlined in Council Regulation [START_REF]/92 of 14 July 1992 on the Protection of Geographical Indications and Designations of Origin for Agricultural Products and Foodstuffs[END_REF]/92 (Art. 2, Council Regulation No. 2081[START_REF] Piggott | Some Old Truths Revisited[END_REF] 1 . This decision implies that regional-origin labelling has to be associated with a quality-control system that leads to a superior quality, if the program is to be subsidised by the government.

Generic promotion of agricultural products by EU member countries as well as regional marketing initiatives by federal states in line with these regulations have been widespread for years. However, despite the high -and possibly increasingvalue the EU addresses to the promotion of regional products, analytical work on the economic impacts of those initiatives is lacking. While the economic importance of regional-origin labelled products in consumer demand has been confirmed [BALLING 1 There are two kinds of regional origin that are registered and protected under Council Regulation No. 2081/1992: (i) Protected Designation of Origin (PDO) and (ii) Protected Geographical Indication (PGI). Under the first definition -being more advanced -foodstuffs have to be produced, processed and prepared in the designated region. A causal link has to be proven to exist between the regional origin and the quality, which has to be "essentially or exclusively due to a particular geographical environment with its inherent natural and human factors" (Art. 2, Council Regulation No. 2081[START_REF] Piggott | Some Old Truths Revisited[END_REF]. The PGI covers a product where at least one of the stages -production, processing, or preparationoccurs in the designated area. In a somewhat weaker formulation than for PDOs, quality, reputation or other characteristics are "attributable to that geographical origin" (ibid., Art. 2) for a PGI. (2002) analyse the implications of market power for the allocative and redistributive effects of generic promotion. Economic studies on European regional promotion programs are rare, but some do exist for Germany (e.g., HOFF/CLAES 1997,

HERRMANN/THOMPSON/KRISCHIK-BAUTZ 2002).

A broad literature on the economics of food labeling has developed in recent years much of which is surveyed in GOLAN/KUCHLER/ [START_REF] Golan | Economics of Food Labeling[END_REF]. This general literature is related to the theory of food labeling and the role of government in the labelling process. Within this field, regional-origin labelling and especially geographical indications have gained increasing attention among economists as a number of studies reveal high and increasing consumer appreciation for labels of origin in Europe and the U.S. [START_REF] Lusk | Consumer Behavior, Public Policy and Country-of-Origin Labeling[END_REF][START_REF] Roosen | Consumer Demand for and Attitudes Toward Alternative Beef Labeling Strategies in France, Germany, and the UK[END_REF].

In this context a growing literature has focused on the economic impacts of the country of origin applying models of segmented agricultural markets to measure the Moreover, an extensive literature has investigated the effects of the country of origin as a signal of quality to consumers. [START_REF] Becker | Bedeutung und Nutzung geschützter Herkunftszeichen. Gutachten im Rahmen des TA-Projektes "Entwicklungstendenzen von Nahrungsmittelangebot und -nachfrage und ihre Folgen" -Vertiefungsphase, vorgelegt dem Büro für Technikfolgenabschätzung[END_REF] notes that the regional origin is becoming one of the most important determinants of food demand in the EU. VAN DER LANS et al. (2001) and VERLEGH and STEENKAMP (1999), in a meta-analysis of country-of-origin studies, found origin labels to be a significant factor in consumer product evaluations. Recently, LUSK et al. (2006) differentiate between consumer motivations for origin labels from the economic and marketing literature to investigate the reasons behind "why" consumers want COOL and derive causes for public policy in the U.S.

Despite the numerous extensions in the generic promotion and origin-labelling literature, analyses were mainly carried out within models where one uniform price or cost measure at one stage of the marketing chain is determined. In the case of a regional marketing program that includes regional-origin labelling and additional costs for quality control, as is the case under the EU Council Regulation 2081/1992, different qualities have to be distinguished. Market segmentation occurs between a higher-quality market for the origin-labelled product and an average-quality residual market segment. However, a segmented-market approach that considers the regulations for governmental support of regional origin-labelling set by Council Regulation No. 2081/92 has not yet been developed. For this reason, a flexible approach to evaluating the price and welfare effects of state-financed regional-origin

The Model

The EDM methodology was originally proposed by [START_REF] Muth | The Derived Demand for a Productive Factor and the Industry Supply Curve[END_REF] and reviewed by [START_REF] Piggott | Some Old Truths Revisited[END_REF] and [START_REF] Alston | Science Under Scarcity: Principles and Practice for Agricultural Research Evaluation and Priority Setting[END_REF]. The objective of the EDM approach applied in this study is to model the producer price and welfare effects and their implications for state-financed regional-origin programs of qualitydifferentiated products and regionally segmented markets.

In our general model each region can produce for a uniform standard-quality market which we call the "mass" market. Each region can also incur additional program participation costs and produce for a high-quality market which is regionally labelled. The demand for these high quality regional products may be augmented by regional promotion expenditures borne within and outside the region.

As stated earlier, a linkage between improved product quality and regional-origin labelling is a justification for government-subsidised promotion efforts. So, we seek a model that will enable us to evaluate promotional programs designed to send quality signals based on regional origin. However, as shown by [START_REF] Kinnucan | A Note on Measuring Returns to Generic Advertising in Interrelated Markets[END_REF], when markets are interrelated, ignoring the cross-price and cross-advertising effects will yield biased measures of advertising effectiveness. Hence, the model explicitly accounts for various cross-price and cross-advertising elasticities. Given the nature of regional-origin labelling, the assumption of perfect product substitutability between "mass" and labeled products from different regions is resolved in favor of a more realistic assumption of differentiated products based on their regional origin and quality. We extend the existing work and present a general model which allows for interactions between "mass" and regional-origin markets with respect to price, regional advertising, supply response and differing cost structures.

Structure of the Model

A multi-equation market-equilibrium model for two regions engaged in regionalorigin labelling which are related in price, advertising and costs is specified as Supply:

( )

Z , C , P i j S i j S =
(1) Demand:

( )

X , A , P i j D i j D = (2) Market Equilibrium: i j D i j S = , (3)
where i = region A or B; j = mass-market product M, high-quality product A or B;

P is a vector of producer prices, A is a vector of regional advertising expenditures, C = additional producer cost of participation in the regional advertising program, and Z and X are exogenous supply and demand shifters. We assume competitive markets at the farm level. Prices and quantities are determined endogenously according to the market equilibrium (3).

We follow the general methods used by [START_REF] Kinnucan | Free-Rider Effects of Generic Advertising: The Case of Salmon[END_REF] and [START_REF] Piggott | Measures of Precision for Estimated Welfare Effects for Producers from Generic Advertising[END_REF], where dx/x=d lnx is the percentage change of any variable x. Then use of the logarithmic differential approximation to equations ( 1) -( 3) yields the following multi-equation EDM where the parameters are interpreted as elasticities.

Region A

Supply:

A A MA M A M A M P ln d P ln d S ln d + = (4) F o r P e e r R e v i e w 7 A C ln d 1 A P ln d A A A A S ln d + =
(5) Demand:

M P ln d A M A M D ln d = (6) A B A ln d A AB e A A ln d A AA e M P ln d A AM B P ln d A AB A P ln d A A A A D ln d + + + + = (7) A A ln d A BA e A B A ln d A BB e M P ln d A BM A P ln d A BA B P ln d A B A B D ln d + + + + = (8)

Region B

Supply:

B B MB M B M B M P ln d P ln d S ln d + = (9) B C ln d 2 B P ln d B B B B S ln d + = (10) Demand: M P ln d B M B M D ln d = (
Equilibrium Conditions i M D ln d Di M h i M S ln d Si M h = (14) B A D ln d DB A h A A D ln d DA A h A A S ln d + = (15) A B D ln d DA B h B B D ln d DB B h B B S ln d + = (16)
Superscripts denote the region (A or B), subscripts denote products (mass-quality product M, high-quality product A, or high-quality labelled product B), 's are ownand cross-price elasticities of supply; 's are own-and cross-price elasticities of demand, e's are the own-and cross-advertising elasticities, and C's represent the As we start from the idea of regional-origin labelling with quality control, this implies vertical product differentiation. Thus, the demand functions of model ( 4) to ( 16) do not include a substitutive relationship between the two quality levels.

Substitution effects occur at one given quality level only, i.e. between qualities A and B but not between either A or B as opposed to M (KINNUCAN/XIAO/HSIA 1996).

There also is substitution on the supply side between the two different qualities. A rising price in the high-quality market leads to a reduction of supply on the low-

quality market ) , ( B MB A MA 0 0 < < .
Given exogenous market shares, advertising quantities, and program-participation cost, the linear equation system ( 14) -( 16) can be solved for the three endogenous price-change variables d ln P j as, where the a matrix includes own-and cross-price elasticities of supply and demand as well as market shares, the b matrix captures own-and cross-advertising elasticities, and the c matrix includes parameters associated with the added cost of regional program participation. 2 We assume the components of X and Z are subsumed in the constant terms of equations ( 1) and (2).

+ = B B C ln d A A C
3 If for supply,

B M S A M S S + = , then SB M h B M S ln d SA M h A M S ln d S ln d + =
, where SA h and SB h are supply shares on the mass market originating from region A and B, respectively. This same relationship holds for markets segmented on the demand side. The solution to equation ( 17) can be used to evaluate the total and distribution of changes in producer welfare due to regional advertising. This can be accomplished by computing changes in producer surplus (PS) in each market, assuming parallel shifts in demand and supply.

i j i j S ln d 5 . 0 1 i j P ln d i j S i j p i j i j PS + = .
(20)

Possible Model Uses

The model presented above has been designed for a combined analysis of regionalorigin labelling and quality control. Accordingly, the implications of promotion expenditures for the labelled products can be elaborated as well as the consequences of increasing producer costs due to the instruments of quality control. The model allows for the general situation where competing high-quality products exist as well as a common non-competing lower-quality mass product. This is typical for the current situation in the EU where different regional labels have been introduced, e.g., for beef, advertising occurs for competing labels. A crucial task in the empirical application of the model is to define precisely (i) the competing high-quality products and (ii) the relevant market on which the products compete.

If strong competition between high-quality segments of the market does not exist, it would be necessary to restrict the model to distinguish only one regional label from Our model differs from these approaches in the literature in two major respects:

1. The modelling framework is applied to regional-origin labelling. None of the other modelling approaches has been used to study this issue. We now provide an application of the model to a regional-labelling and qualitycontrol scheme. The case study is related to the German program "Certified Quality -Bavaria".

An Empirical Application

Background

The origin of Bavarian regional-origin labelling dates back to 1985 when the 12 binding since the regional label may only be EU-supported when a superior quality is guaranteed.

The Bavarian Beef Market

The general model is specified to characterise the "Certified Quality -Bavaria"

program. The model structure consists of two regions (Bavaria and Rest of Germany -ROG), a single high-quality product (produced in Bavaria but sold in both regions)

and a common mass-market product (produced in both regions).

Bavaria (Region A)

Supply:

A A MA M A M A M P ln d P ln d S ln d + = (21) A C ln d 1 A P ln d A A A A S ln d + = (22) Demand: M P ln d A M A M D ln d = (23) A A ln d A AA e M P ln d A AM A P ln d A A A A D ln d + + = (24)

Rest of Germany (Region B)

Supply:

M P ln d B M B M S ln d = (25)
Demand: (29) Again, superscripts characterise regions A and B, and subscripts the high-quality product A and the mass product M. Bavaria is the largest exporter of beef among all German federal states. Bavarian exports occur both under the regional label and for unlabelled beef, i.e. for the high-quality and the mass market. Therefore, there is demand for Bavarian beef in the rest of Germany for both qualities (equations ( 26) and ( 27)). As exports from the region go to various regional markets in Germany, Bavarian beef competes with beef under various other labels as well as foreign beef.

M P ln d B M B M D ln d = (
There is no single competitor of regionally-labelled Bavarian beef in the high-quality market sector. Thus, we posit that the labelled product is of superior quality to that of the mass market. We distinguish only the regional label as the high-quality beef product from the mass (lower-quality) beef product.

In the Bavarian case, the high-quality price (P A ) is what wholesalers pay producers; it does not include deductions for advertising. The producer contribution to advertising is a cost which must be deducted from P A to obtain a net producer price P P . We derive P p from equation ( 5)

A C ln d 1 A P ln d A A A A S ln d + =
, where Here is the relative horizontal shift in the high-quality supply curve due to the added cost of producing high-quality beef. Substituting (30) into (5) yields

A C ln d 1 A C / A dC ) A A S / A C )( A C / A A S ( = ( 30
) ) A A S / A A S ( = = .
K A A S ln d ) A A 1 ( A P ln d = , ( 31
)
where

A A K =
is the relative vertical shift in the price direction. Further, the change in producer price (P P ) is defined when K = 0 as

A A S ln d ) A A 1 ( P P ln d = (32)
and the level of the producer price is given as

) 1 P P ln d ( M P P P + = . ( 33
)
The logic of our comparative-static analysis can be followed by referring to Figure 1.

With no advertising (and presumably no higher-quality product) we begin with the high-quality market-equilibrium point as well as reliable price data in the market segments or the additional producer costs due to participation in a program that combines quality standards and control with regional-origin labelling. However, there is much we do know. For instance, we have good statistical estimates of the responsiveness of labelled-product sales to advertising effort. Given this somewhat limited knowledge, simulations and sensitivity analyses are particularly important. Simulations can also be used to illustrate stronger changes of policy than those realised in the past. This is crucial when one is interested in the amount of additional advertising expenditures necessary to induce a defined price difference between the labelled and the non-labelled market.

Table 1 provides the parameters and elasticities of the empirical model. A key parameter of the model for evaluating the impacts of advertising for a regional-origin label is the advertising elasticity of demand. We take the econometric estimate of 0.04 by HERRMANN, THOMPSON and KRISCHIK/BAUTZ (2002) measured for the program "Quality from Bavaria". It is consistent with most studies from the genericpromotion literature that the advertising elasticity of demand is significantly positive but typically less than 0.1. In this same study, econometric estimates of the own-price elasticities of demand (-0.8 in the high-quality segment and -0.4 on the "mass" market) were found to be consistent with other estimates for beef demand in Germany. With respect to the importance of cross-price effects on the welfare implications of regional-origin promotion, the cross-price elasticity of demand between high quality and "mass" quality beef is 0.1. Some recent econometric studies based on demand systems indicate that our price and cross-price elasticities might be at the lower end, suggesting that the price elasticity of demand for beef has increased over time and might now be above unity [START_REF] Wildner | Die Nachfrage nach Nahrungsmitteln in Deutschland unter besonderer Berücksichtigung von Gesundheitsinformationen[END_REF].

Market simulation results can be particularly sensitive to both the advertising elasticities as well as the marginal cost of participation parameter. Given an advertising elasticity of 0.04 in both markets, we focus attention over the sensitivity of the market impacts of the cost parameter ( ). The price and quantity change effects are extended to producer welfare effects in each market segment.

Table 1: Parameters and Elasticities of the Empirical Model

Simulations

Our benchmark simulations are based on actual segmented-market data for the year 2003. At that time, the mass-quality price (P M ) averaged € 2.31. While high-quality certified Bavarian product price premium over the mass-market product varied considerably, the premium achieved could be as much as ten percent. In Bavaria, the annual production of labelled and mass market beef was 107,608 and 161,413 million kgs., respectively. We assume that there exists no competing regional-quality label in any state of ROG. In the ROG, no high-quality labelled beef was produced.

However, the production of mass-market beef was 1,160,523 kgs. Thus, the market shares of mass-market beef produced in Bavaria and ROG were 12 and 88 percent, respectively. Our benchmark assumes the existence of an ongoing promotion program in Bavaria which implies that the producer cost of participation are included in the existing supply function for the labelled product. Thus, shifts in the supply function are due to producer contributions associated with promotional labelling. In In Table 2 we show how increased regional quality advertising affects prices and quantities in the segmented markets and how these change as producers share the cost of advertising with the government. Suppose the situation is that the regional labelling of "Certified Quality -Bavaria" is subsidised by 100 percent governmental payments ( = 0). This is especially likely for the year 2003 where the regional quality label was revised by the EU commission and launched on the market 4 . Since that time, governmental support was scheduled to be reduced by 10% per year.

Bavarian Beef

The simulated advertising-induced outward demand shift increases both the wholesale price of Bavarian beef (P A ) and the producer price (P P ). Market interrelationships reveal that advertising Bavarian beef has a positive influence on the mass-market price. When the cost parameter = 0, all costs associated with the advertising-induced demand shift are borne by the government. In this situation, both the wholesale and producer price in the high-quality Bavarian market increase by 4.1 percent over the mass-market price. The positive supply response of the high-quality

Bavarian product shifts the mass-market supply leftward in Bavaria, while the marginally higher P M encourages a small positive supply response in the ROG mass market.

4 Annual Bavarian advertising expenditure on "Certified Quality -Bavaria" for beef was 0. 2.

Changes in producer surplus (PS) associated with the promotion of "Certified Quality -Bavaria" are shown in Table 3. Clearly, the overall change in producer surplus is greatest when the demand shift is entirely government subsidised ( = 0). However, for the profit-maximising producer, it makes sense to share in the cost of advertising because positive changes in PS continue as producers contribute up to a breakeven point, again where = 1.0; that is, where the change in producer surplus is zero. Also, producers are expected to contribute as scheduled government subsidies decrease. The breakeven point increases when the advertising contribution of the Bavarian producers rises. in PS is greatest in the "no cost" situation, where 0 = . The last column in Table 3 shows the sum of the high-quality and mass-market effects for Bavaria. On a perfarm basis clear gains to advertising are seen even as producers share in the cost of advertising. Note that these are changes in producer surplus, so it is profitable for producers to contribute to the advertising effort up to the point where . 0 = PS

Concluding Remarks

Quality signals of regionally produced products can be economically beneficial to producers. The benefits accrued are directly related to the effectiveness to which the demand for the high-quality product can be augmented with advertising, the cost associated with the advertising effort and, of course, the basic economic structural characteristics of the market segments under study.

Policy conclusions arise from the analysis. Quality control for regional products and regional-origin labelling are supposed to raise the income of farmers in rural areas and, thus, contribute to rural development. A pre-condition is that the income effect of rising demand outweighs that of increasing costs due to participation in the new programme. These results are based on computed benefit-cost ratios of regionalorigin labelling from the producers' point of view. Additional analyses are needed as to whether policies are successful under an extended regional objective function that includes changes in consumer surplus and additional regional expenditures for the labelling scheme. More research is necessary, too, regarding the aggregate assessment of regional-origin labelling from the federal or national point of view. In this paper we suggest a general economic framework that can be used to examine problems of this nature. We illustrate this framework with an empirical examination of the "Certified Quality -Bavaria" promotion program. This illustration includes two regions, Bavaria and Rest of Germany (ROG), both of which produce beef for the mass-market but only Bavaria produces the higher quality-labelled product of pure guaranteed Bavarian origin. We allow for trade in both products between regions.

The promotion of the Bavarian labelled product in Bavaria positively influences both regions and products. All market segments can gain. While clearly producer gains are great when the cost of the advertising the Bavarian labelled product is financed entirely by the government, it remains rational for profit-maximising producers to cofinance contributions as well.

We found our proposed analytical framework to be a flexible and easy-to-use tool to simulate market behaviour in response to promoting the Bavarian quality-labelled product. We believe it is generally applicable to examine a number of policy-related issues in segmented commodity markets.

  (see the contributions in KAISER 2003). Typically, the economic effects of generic advertising on food demand are statistically modelled and the redistributive and welfare impacts elaborated. Especially for the U.S., where generic advertising is financed by producer levies, cost-benefit ratios are calculated which relate additional revenues and costs for producers due to program participation. Studies have investigated the importance of cross-price effects for advertising effectiveness (KINNUCAN 1996, KINNUCAN/XIAO/HSIA 1996), the distribution of impacts of advertising within the marketing chain (KAISER/SCHMIT 2003) and across interrelated markets (KINNUCAN/MIAO 2000). ZHANG and SEXTON

  welfare and market effects of origin labelling (BRESTER/MARSH/ATWOOD 2004). The country-of-origin labelling (COOL) provision of the 2002 U.S. Farm Sector and Rural Investment Act requires from September 30, 2004 that retailers label the country of origin on fresh and frozen foods. CHUNG, ZHANG and PEEL (2004) review assessed market level costs and welfare effects of introducing COOL in the U.S. meat industry. LUSK and ANDERSON (2004) adapt a WOHLGENANT (1993) equilibrium-displacement model specification of vertically segmented markets to simulate producer and consumer welfare effects of COOL in the U.S. meat industry. Their results reveal that demand increases of two to three percent are sufficient to offset additional costs at the producer level.

  quality differentiated products and quality-assurance systems is developed below.

  the market share of the total demand for high-quality product A within region A (B).

  above model is needed to simulate how changes in own-and cross-region advertising expenditures and changes in program participation cost affect market prices, quantities and producer welfare. Empirical illustrations will likely necessitate restrictions to the general model to characterise the unique dimensions of any particular empirical application.

  quality market, but perhaps also allow for the possibility of trade between markets. Other problem-specific restrictions can be easily imposed.The general model may serve other purposes as well. The EDM model could be applied to other relevant issues where market segmentation plays a major role. Cases in point are strategies of country-of-origin labelling, differentiation of ecological as opposed to conventional farming and foods, or the labelling of foods that do not contain genetically modified organisms (GMOs). Some modelling approaches of these markets have already been provided. CHUNG,[START_REF] Chung | Will Country-of-Origin Labeling (COOL) be "Cool" for the U.S. Meat Industry[END_REF] PEEL (2004), LUSK and[START_REF] Lusk | Effects of Country-of-Origin Labeling on Meat Producers and Consumers[END_REF] and[START_REF] Kinnucan | Effects of Income Growth and Tariffs on the World Salmon Market[END_REF] distinguish between domestic and foreign product market segments in EDMs of vertical supply chains.However, their empirical models do not include cross-price effects between the labelled products. Products from ecological as opposed to conventional farming are analysed in a segmented equilibrium-displacement model by[START_REF] Hagner | Akzeptanz und wohlfahrtsökonomische Analyse von Extensivierungspolitiken in Deutschland -unter besonderer Berücksichtigung des ökologischen Landbaus[END_REF] and the impacts of governmental policies on the conventional and ecological markets are elaborated. MOSS, SCHMITZ and SCHMITZ (2004) use a partial-equilibrium segregation model in their study of how resistance to the introduction of genetically modified (GM) crops leads to segregated markets for GM and non-GM crops. Based on this model, they illustrate the welfare implications of market segregation and the relevance of segregation costs. To our knowledge the studies of JAMES and ALSTON (2002) and LUSK and NORWOOD (2005) are the only applications of EDMs that explicitly incorporate differentiated product qualities.

  individual papers go further in other respects than we do, none of the segmented-market models in the literature cover competition between highquality products as does our model with labelled goods of regions A and B.

  Figure 1: High-Quality Beef Market

  the baseline, we explore the effects of a 100 percent increase in the promotional expenditures for Bavarian quality-labelled beef.

  prices for the Bavarian high-quality product, as producers are asked to share in the cost of advertising ( increases), net producer price falls as the cost-induced supply function shifts leftward. Producer contributions act as a wedge between wholesale and producer prices. This wedge can increase until the added advertising cost exactly equals the benefits. This breakeven point is where the benefit-cost parameter | | = 1.0 and the increase in P A is 8.0 percent. Different changes in P A and P P are observed as the breakeven point ( ) moves "up or down" in Table

  Figure 1: High-Quality Beef Market

  11)

	d	ln	D	B B	=	B B	d	ln	B P	+	B BA	d	ln	A P	+	B BM	d	ln	M P	+	e	B BB	d	ln	A	B	+	e	B BA	d	ln	A	B A	(12)
	d	ln	D	B A	=	B A	d	ln	B P	+	B AB	d	ln	A P	+	B AM	d	ln	M P	+	e	B AA	d	ln	A	B A	+	e	B AB	d	ln	A	B	(13)

Table 2 : Price and Quantity Effects of Increased Advertising of High-Quality

 2

  5 million Euro in 2003. Total program expenditures of the state of Bavaria in 2003 were € 2.556 million, which includes advertising expenditures for other products and program costs.

Table 3 : Changes in Producer Surplus due to Increased Advertising of High-

 3 we calculated producer-surplus changes per beef farmer. For the participants of "Certified Quality -Bavaria" the actual number of participating beef producers in 2003 is used to calculate PS of "label members". Of course, the change

	Quality Bavarian Beef (millions of €)
	Welfare changes among markets also occur. Advertising of the Bavarian high-quality
	product in both regions (Bavaria and ROG) results in positive welfare changes in all

markets and regions. Even the ROG gains from Bavarian advertising albeit small. In fact, due to substitutability in supply, the mass-market welfare gain in ROG is relatively greater than that in the Bavarian mass market. Since the absolute sizes of

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

Q00 -General < Q0 -General < Q -Agricultural and Natural Resource Economics, Q18 -Agricultural Policy|Food Policy < Q1 -Agriculture < Q -Agricultural and Natural Resource Economics, Q17 -Agriculture in International Trade < Q1 -Agriculture < Q -Agricultural and Natural Resource Economics, Q13 -Agricultural Markets and Marketing|Cooperatives|Agribusiness < Q1 -Agriculture < Q -Agricultural and Natural Resource Economics, Q12 -Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets < Q1 -Agriculture < Q -Agricultural and Natural Resource Economics

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript


Interactive vs. non-interactive knowledge production by faculty members

In this article we develop a model of individual choice to study the determinants of faculty members allocation of effort between interactive and non interactive activities. This model is tested by using censored and discrete choice econometric models to estimate optimum effort allocated to interactive activities and real interaction. We conclude, first, that individual responds to non-monetary rather than monetary incentives and to the difficulty of producing non-interactive rather than interactive knowledge. Second, we detect the possible existence of rationing, since optimum effort and real interaction depend on different variables.

Introduction

The importance of increasing university-industry interaction (UII) in most developed Western economies motivates this research. UII takes place in the form of contract and collaborative research, industry-oriented student's curricula, practices for students in firms, consultancy, joint centres, scientist exchange, informal contacts, seminars for firms, patent licensing, etc. The reasons behind this growth of UII lie in the enhancement of technological innovation attributed to a more tangible orientation of academic activities.

Within innovation studies, several approaches have come to justify the interweaving of universities in the economy: [START_REF] Freeman | Technology Policy and Economic Performance: Lessons from Japan[END_REF] and [START_REF] Lundvall | Innovation as an interactive process: from user-producer interaction to the national system of innovation[END_REF] under the perspective of national systems of innovation, [START_REF] Gibbons | The New Production of Knowledge[END_REF] with their detection of new Mode 2 of knowledge production, [START_REF] Etzkowitz | Emergence of a Triple Helix of University-Industry-Government Relations[END_REF] with their ideas about the Triple Helix model, [START_REF] Clark | Creating Entrepreneurial Universities[END_REF] and his paradigm of an entrepreneurial university, etc. These approaches differ in the importance granted to universities in the innovation process, but do not question that some degree of interaction with firms should exist.

Other voices have been more critical. The economics of science approach recovers the Mertonian ideas that the mechanism of review by peers can efficiently assign R&D resources Applied Economics submission -after 1 st revision [START_REF] Dasgupta | Towards a new economics of science[END_REF]. It also emphasises that the promotion of UII responds to a narrow vision of the benefits of basic research, which leaves aside less tangible but equally beneficial links with innovation [START_REF] David | Analyzing the Economic Payoffs from Basic Research[END_REF]. Some of these benefits are increasing useful knowledge, training skilled graduates, creating new scientific instrumentation and methodology, forming networks and social interaction, increasing the capacity for scientific and technological problemsolving, creating new firms [START_REF] Salter | The economic benefits of publicly funded basic research: a critical review[END_REF], providing social knowledge and access to unique facilities [START_REF] Scott | The Economic Returns to Basic Research and the Benefits of University-Industry Relationships: A literature review and update of findings[END_REF].

The former conflicting views set an ongoing debate on whether the marginal value of interactive activities by universities is higher than the marginal value of non-interactive activities or vice versa. At least in public universities, faculty members are autonomous to decide whether to engage on interactive activities or not. The aim of this paper is to analyse the effort they allocate between interactive and non-interactive activities. What are the motivations behind faculty members' behaviour? Do monetary incentives matter more than non-monetary ones? Is it important that production of interactive knowledge may be less time-consuming than of noninteractive one?

Theoretical model

In order to study the professor interactive activities, we develop a model of individual choice in which a maximising utility agent allocates his scarce time between interactive and non-interactive activities. We consider an activity interactive when it involves a relationship with industrial partners outside the university and non-interactive in the opposite case 1 . This classification overlaps with the usual distinction among academic R&D and teaching [START_REF] Euwals | What matters most: teaching or research? Empirical evidence on the remuneration of British academics[END_REF] since both interactive and non-interactive activities may include these tasks. This approach draws from previous models by [START_REF] Beath | Optimal Incentives for Income-Generation within Universities: The Rule of Thumb for the Compton Tax[END_REF] and [START_REF] Jensen | The Academic Effects of Patentable Research[END_REF]. A difference with our model is that we do not introduce faculty members' time devoted to research in the utility function but faculty members' research output. Furthermore, we do not assume the distinction between basic research leading to prestige and applied research as a source 1 Interaction with non-industrial partners falls into the category of "non-interactive activities", e.g. cooperation with other academic colleagues. Applied Economics submission -after 1 st revision of income, but between non-interactive and interactive activities, both of them leading to prestige and the former also as a source of income. 2 We assume that the representative university professor -considered rational from an economic point of view-maximises the utility function defined by equation (1). Professor's utility level depends on two arguments: income (Y) and subjective prestige (M) that comes out of academic activities.

e -1 e Y M U =

(1)

Being e a parameter with 0 < e < 1

Notice that our notion of prestige is broader than the usual one, since we do not only refer to prestige stemming out from peer-review mechanisms but also from public image and social reputation, e.g. entering extra-academic networks, receiving gratitude from others or attention by mass-media, etc.

Therefore, subjective prestige, M, derives from two sources: the results of interactive and noninteractive activities. We assume that M is obtained as a weighted average of each type of contribution.

n t K ) - (1 K M δ δ + = (2)
Where K t -Professor contribution to knowledge from interactive activities.

K n -Professor contribution to knowledge from non-interactive activities.

δ -Parameter; 0 ≤ δ ≤ 1

With regards to individual income, it may be broken down in wage and revenue from the output of interactive activities, both given. Therefore Applied Economics submission -after 1 st revision Q -Net revenue from a unit of knowledge coming from interactive activities.

t K Q w Y + = (3)
Knowledge outputs, both interactive and non-interactive, are determined as linear functions of total time allocated in each activity.

T A K t = (4) N B K n = (5)
Where T -Time allocated by the professor to interactive activities N -Time allocated by the professor to non-interactive activities A, B -parameters

We normalise professor total time available to interactive and non-interactive activities to one.

Therefore, the time constraint for professor will reduce to

N T 1 + = (6)
Substituting equations 2 to 6 into equation 1, utility can be expressed as a function of the amount of time devoted to interactive activities (T), being T the only decision variable,

( ) [ ] ( ) e - 1 e QAT w δAT T 1 δ)B (1 U + + - - = (7)
By maximising the latter expression with respect to T, we obtain the following first order condition that is the result of the professor's optimisation problem:

[ ] [ ] A - )B - (1 QA e A - )B - (1 w - e) - (1 B ) - (1 A Q T δ δ δ δ δ = (8)
Therefore, optimum time allocated to interactive activities only depends on the parameters of the model. Now, considering that T must lie between 0 and 1, we can define T* such that individual chooses

1 1 * 1 0 * 0 0 * > = ≤ ≤ = < = T if T T if T T T if T
The condition for an internal solution, i.e. for 0 < T < 1, is:

( ) ( ) ( ) ( ) ( ) we B QA e A B we QA B QA e δ δ δ δ - - < - - < + - - 1 1 1 1 1 (9)
We can rewrite it this way:

F o r P e e r R e v i e

w

Applied Economics submission -after 1 st revision

( ) ( ) ( ) we QA e B A we QA QA e - < - - < + - 1 1 1 1 δ δ (10)
One necessary condition for this former to be met is that ( )

A B δ δ 1 > - (11)
In other words, the contribution to prestige of one unit of time devoted to non-interactive activities (1-δ)B must be higher than the correspondent contribution from one unit of time in interactive activities (δA). This is easy to understand: since interactive activities provide income, the individual would devote the totality of his time to these activities if the contribution to prestige of one unit of time devoted to these activities were equal or higher. Moreover, the difference between these two terms must be enough as to compensate the income received. This minimum difference depends on the income the individual can obtain from a unit of time devoted to interactive activities (QA) and the relative preference for income in the utility function (1-e). On the other hand, the value of this difference has also a maximum. Were the difference too high, the individual would concentrate all his time in non-interactive activities.

To ensure that the solution in equation ( 8) corresponds to a maximum, we obtain the second order condition of this problem, which corresponds to the following equation Considering that an interior solution holds and differentiating (8), we obtain a comparative statics exercise:

( ) ( ) [ ]( ) ( ) ( ) [ ] ( ) ( ) [ ] ( )( ) ( ) ( ) ( ) [ ] ( ) ( ) [ ] ( ) 0 1 1 1 1 2 1 1 1 1 1 1 1 2 2 2 2 1 2 2 < ⎭ ⎬ ⎫ ⎩ ⎨ ⎧ + - - + - - - + + - + - - + - - ∂ - • + - - + ∂ - = ∂ ∂ - QAT w T B AT A B QA
0 QA e - w d T d < = (13) 0 A Q e w Q d T d 2 > = (14) [ ] 0 )B - (1 A A e) - (1 d T d 2 > = δ δ δ - B (15) [ ] [ ] { } 0 )B - (1 A QA )B - (1 A Q e w A Q e) - (1 ) - (1 A d T d 2 2 2 2 > + = δ δ δ δ δ δ - - (16) [ ] 0 )B - (1 A A e) - (1 ) - (1 - B d T d 2 < = δ δ δ δ F o r P e e r R e v i e w Applied Economics submission -after 1 st revision [ ] 0 A )B - (1 A] ) - w[(1 )B - QA(1 - de T d < ⎥ ⎦ ⎤ ⎢ ⎣ ⎡ - - + = δ δ δ δ δ QA B ( 18
)
We conclude that the individual will decide to allocate more time to interactive activities the higher revenue from a unit of knowledge from interactive activities (Q), the weight given to noninteractive activities in the determination of prestige (δ) and the amount of interactive knowledge produced by a unit of time devoted to such activity (A). On the other hand, the individual will be more reluctant to allocate time in interactive activities the higher his wage (w), the amount of noninteractive knowledge produced by a unit of time devoted to such activity (B) and the relative preference for prestige versus income (e).

Results

In Fernandez et al., 2001).

Therefore, it fits in the description of a region with low absorptive capacity, and we will have to consider it in the interpretation of the results3 , since universities produce economic effects on the local economy [START_REF] Bleaney | What does a University add to its local economy?[END_REF].

The population of the survey includes faculty members from the five public universities of the Valencian Community. We stratified it in three teaching scales: full professors, assistant professors Applied Economics submission -after 1 st revision and associate professors 4 . Selection was by means of simple random sampling. The sample was 10% of the population, or 872 individuals 5 . We obtained a response rate of 44%, so we could build a database with 382 observations.

As the questionnaire was not designed to match our theoretical model, we had to build some proxies departing from the existing information. The questionnaire included questions regarding the support for different objectives of UII, which we will use as a proxy for the optimum effort allocated to interactive activities (see appendix). As the optimum effort allocated to interactive activities may not coincide with the degree of real interaction (i.e. if exogenous constraints or rationing on individual behaviour arises), it would result in a degree of real interaction below the individual optimum. Consequently, we define another variable as a proxy for the real effort allocated to interactive activities (see appendix).

Table 2 contains the estimation results of our proxy variable for the optimum time allocated to interactive activities, the average support for the objectives of UII. The significance of the σ parameter across all columns indicates that there is actually censoring, thus the technique of estimation appears to be adequate. Column 1 is a base model with all the variables. We find that the intensity of the support for the objectives of UII depends positively on weight of interactive activities in the determination of prestige. Other coefficients do not have any significant effects, although their signs are as predicted by the theoretical model. For wage (w) and knowledge contribution by unit of time allocated to non-interactive activities (B) are correlated, we estimate separate regressions in Columns 2 and 3. When we remove one of the variables, the other one becomes significant, both with the expected negative sign. However, variable B shows a higher significant effect than w. Furthermore, when we follow a selection strategy to reduce the base model in column 1 to a model with significant variables only, w drops while B stands, as we can 4 The equivalence between the Spanish original categories and the three categories that we mention is not exact, but it uses more popular terms, it simplifies the exposition at the same time that captures the intuition behind the original categories. Applied Economics submission -after 1 st revision see in column 4. Therefore, B appears to reflect better the rationale of our model. In any case, the intensity of the support for the objectives of UII does not depend on income from interactive activities, knowledge contribution by unit of time allocated to interactive activities and parameters of the utility function. 6Table 3 shows the estimation of our proxy variable for real time allocated to interactive activities, the degree of R&D co-operation with firms. The significance of the µ parameter across all columns indicates that the dependent variable is actually ordered, thus the technique of estimation is adequate. According to the base model in Column 1, the frequency of R&D cooperation depends positively on wage, knowledge contribution by unit of time allocated to interactive activities and weight given to interactive activities in the determination of prestige. This frequency does not depend on knowledge contribution by unit of time allocated to non-interactive activities. Frequency of co-operation depends negatively on income from interactive activities and parameters of the utility function. Note that the sign for wage and income from interactive activities is different from the predicted by the theoretical model. When we drop variable w (column 2), it can be observed an increase in significance of variable B since both are positively correlated. However, when we drop B in column 3, w shows higher significance than B in column 2. The estimation in column 3 is also a reduced model with significant variables only. Therefore, we consider that column 3 reflects better the rationale of our theoretical model.

In Table 4 we analyse marginal effects of the reduced models from Table 2 andTable 3. For support, the scale factor is 0.91, so marginal effects in column 1 do not make much difference with full effects from Table 2. On the contrary, for cooperation they permit the interpretation of the estimated coefficients as elasticities and clarify the direction of change. According to Column 2, marginal effects show that an increase of wage (w) rises the probability of "some cooperation" by 6% and of "much/very much cooperation" by 8%. An increase of relative prestige from interaction (δ) rises the probability of "some cooperation" by 9% and of "much/very much cooperation" by 13%. An increase of efficiency in the production of interactive knowledge (A) rises the probability of "some cooperation" by 34% and of "much/very much cooperation" by 51%. An increase of revenue from interaction (Q) reduces the probability of "some cooperation" by 9% and of Applied Economics submission -after 1 st revision "much/very much cooperation" by 13%. An increase of the preference for income instead of prestige (e) reduces the probability of "some cooperation" by 9% and of "much/very much cooperation" by 14%.

Conclusions

We conclude that the relative weight given to interactive vs. non-interactive activities in the determination of prestige, measured through non-monetary incentives, is significant, whereas wage is less explicative and revenue from a unit of knowledge produced by interactive activities is not significant, i.e. monetary incentives, are not. This has one major implication: policies towards nonmonetary incentives are more difficult to implement and therefore promoting UII must be seen as a medium-long term target rather than a short-term one.7

Although less subject to public control, it is relevant to notice that the effect of knowledge contribution by unit of time allocated to non-interactive activities is significant, whereas it is not the case for interactive activities. This implies that UII is a question of how difficult is to produce non-interactive knowledge rather than how difficult is UII per se.

Our second conclusion is related to the possible existence of rationing, i.e. individual is unable to interact as much as desired since the real amount of interaction may be decided by industry. In this sense, we detect that the intensity of the support for the objectives of interaction and the degree of actual R&D cooperation differ. However, there may be a (non-exclusive) explanation, from the econometric perspective, i.e. a misspecification problem given that industry's decision may affect the results. To disentangle this issue will be subject to further research8 . It would be interesting because if UII is considered as desirable, policy initiatives might help to eliminate suboptimal situations arising from rationing.

These conclusions should only apply to regions like the one we have analysed, i.e. with low absorptive capacity. This can be seen either as a limitation or as an opportunity to study UII from a Applied Economics submission -after 1 st revision less common perspective, not focused as usually on high-tech regions. In a high-tech region, we expect lower levels of rationing, so we may wonder that the estimation of optimum and real effort would show less differences, but of course this makes it convenient to test the model with a sample from such a region. Each objective was ranked as follows: 0 ("no or weak support"), 1 ("some support") and 2

("strong support"). We assume that they have the same weight in the determination of their average. Support is therefore a quasi-continuous variable ranging between 0 and 2. Since we want the predictions of the model to fall between this range, we consider that support follows a distribution that is left-censored at 0 and right-censored at 2. The tobit model appears to be the adequate method of estimation given this consideration.

The optimum effort allocated to interactive activities may not coincide with the degree of real interaction, i.e. if exogenous constraints or rationing on individual behaviour arises. It would result in a degree of real interaction below the individual optimum. Consequently, we define another variable as a proxy for the real effort allocated to interactive activities:

Cooperation: perceived degree of R&D cooperation with firms. We considered three answers, ranked as follows: 0 ("none"), 1 ("some") and 2 ("much" or "very much").

Given the qualitative and indexed nature of the response variables for cooperation we decided to use an ordered probit for estimating our econometric model.

Here is the list and description of the explanatory variables:

w: proxy for faculty wage. It is the average of the following variables, ranging from 0 to 3 10 : Age: 0 (less than 30 years), 1 (30-39 years), 2 (40-49 years), 3 (more than 49 years).

Teaching experience: 0 (less than 5 years), 1 (5-9 years), 2 (10-14 years), 3 (more than 14 years).

Teaching scale: 0 (associate professor), 1 (assistant professor), 2 (full professor 1), 3 (full professor 2). 11

9 For a separate treatment of each objective, see self-quotation #1. 10 We also tried to build an alternative variable for wage, w e , according to this formula (self-quotation #2: w e (two), 3 (more than two).

Holding a managerial position at the university: 0 (no), 3 (yes).12 Q: proxy for net revenue from a unit of knowledge produced by interactive activities. It is the average of the following variables, regarding the perceived influence of UII on some aspects of Technological content of faculty members' discipline: 0 (social sciences and humanities), 0.5 (exact and natural sciences), 1 (engineering and technology).

Being an associate professor: 0 (no), 1 (yes). The rationale under this variable is that associate professors in Spain are supposed to have previous experience in non-academic domains, e.g. industry.

Length of research abroad: 0 (0 months), 0.25 (between 0 and 5 months), 0.5 (between 6 and 24 months), 1 (more than 24 months). Here the assumption is that faculty members who do research abroad do so to improve their scientific skills. Hence, they will tend to travel to Applied Economics submission -after 1 st revision leading scientific countries with more to offer, especially if they are from regions with low absorptive capacity. Some of these leading countries also interact more with industry.

Perceived influence of the institutional policy of the respondent's university on cooperation with firms: 1 ("it favours cooperation"), 0 (otherwise).

B: proxy for knowledge contribution by unit of time allocated to non-interactive activities. It is the average of the following variables, ranging from 0 to 3: Faculty member's university age: 0 (two ten years old universities), 1 (a twenty years old university), 2 (a thirty-five years old university), 3 (a five hundred years old university).

Teaching experience: same as defined for w.

e: personal preferences in the utility function. We assume that they may vary according to gender, although we are limited to use a rough measure of gender -sex: 0 (male), 1 (female) 13 .

Table 1 offers descriptive statistics of the independent variables. Notice that the highest correlation corresponds to w and B, so we will have to consider it in the estimations. 13 The idea that gender differences may influence preferences is widely accepted by psychologists (e.g. [START_REF] Deaux | Putting gender into context: an interactive model of gender-related behaviour[END_REF].

5

  The questionnaire was sent by the research vice-rectorates of each university by electronic mail to the random sample of faculty members. Once filled in, faculty members could return the questionnaire by electronic mail, ordinary mail or fax. After a first stage of spontaneous response, a follow-up team was organised to make telephone contact with faculty members of the sample. This fieldwork took place between 22 nd May 2001 and 30 th June 2001.

  academic life. All take values 1 (positive) or 0 (otherwise): Perceived influence of UII on faculty members salary Perceived influence of UII on public resources for R&D projects Perceived influence of UII on scientific career (sexenia) δ: proxy for the relative weight given to interactive vs. non-interactive activities in the determination of prestige. It is the average of the following variables, with the same interpretation and range of those included in w: Perceived influence of UII on professional openings for students and collaborators Perceived influence of UII on exchange of relevant knowledge Perceived influence of UII on freedom of selection of research agenda A: proxy for knowledge contribution by unit of time allocated to interactive activities. It is the average of the following variables, ranging from 0 to 1: Technological content of faculty members' university: 0 (four non-polytechnic universities), 1 (one polytechnic university).

12 of 16 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  Some full professors in Spain are hired only for teaching. This is what we call "full professors 1". The rest have also time for research activities. This is what we call "full professors 2".

	Page Submitted Manuscript
	Applied Economics submission -after 1 st revision
	Number of Spanish six-year term research awards (so-called sexenia): 0 (none), 1 (one), 2
	F o r
	P
	e
	e r
	R e v
	i e
	w
	= exp [6.4738 + 0.0248*(faculty age) -0.0003*(faculty age 2 ) + 0.0048*(years of teaching experience) +
	0.1504*(teaching scale)]. It did not change the results.

11

Page 14 of 16 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

				Submitted Manuscript
			Applied Economics submission -after 1 st revision
	Tables
	Table 1. Descriptive statistics of independent variables
	Variable	Mean	Standard	Number	Correlation matrix
			deviation	of cases
					w	Q	δ	A	B	E
	w	1.23	0.80	357	1.00
	Q	0.51	0.31	380	0.06	1.00
	δ	0.63	0.29	380	-0.04	0.44	1.00
	A	0.39	0.20	367	-0.05	0.12	0.22	1.00
	B	1.70	0.90	378	0.67	-0.03	-0.08	-0.06	1.00
	e	0.28	0.45	380	-0.01	-0.07	-0.05	-0.18	0.08	1.00
		F o r
			P
				e
				e r
					R e v
						i e
							w

Table 2 .

 2 Tobit estimation of optimum time allocated to interactive activities (support) -censored (0,2)

		1	2	3	4
		Base model	w not included	B not included	Reduced model
	Number of observations	328	346	328	358
	Log likelihood function	-274.33	-295.37	-275.32	-308.01
		Coeff. (t-ratio)	Coeff. (t-ratio)	Coeff. (t-ratio)	Coeff. (t-ratio)
	Constant	0.22 (0.72)	0.12 (0.38)	0.15 (0.48)	0.93 (9.66)
	w	-0.03 (-0.61)		-0.08 (-2.12)
	Q	0.13 (1.21)	0.12 (1.12)	0.14 (1.32)
	δ	0.61 (5.23)	0.66 (5.6)	0.62 (5.26)	0.7 (6.55)
	A	0.03 (0.19)	0.06 (0.41)	0.03 (0.17)
	B	-0.06 (-1.41)	-0.09 (-2.62)		-0.09 (-2.76)
	e	-0.02 (-0.24)	-0.01 (-0.07)	-0.03 (-0.38)
	σ	0.53 (23.27)	0.54 (23.62)	0.53 (23.27)	0.54 (24.11)

Page 15 of 16 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK

  Applied Economics submission -after 1 st revision

	Submitted Manuscript
	F o r
	P
	e
	e r
	R e v
	i e
	w

Table 3 .

 3 Ordered probit estimation of real time allocated to interactive activities (cooperation)

		1	2	3
		Base model	w not included	B not included (reduced model)
	Number of observations	340	359	340
	Log likelihood function	-313.70	-339.92	-313.71
	Prob[χ 2 > value] =	0.00	0.00	0.00
		Coeff. (t-ratio)	Coeff. (t-ratio)	Coeff. (t-ratio)
	Constant	-2.92 (-4.35)	-2.34 (-3.66)	-2.94 (-4.44)
	w	0.36 (3.3)		0.35 (4.3)
	Q	-0.57 (-2.45)	-0.47 (-2.12)	-0.56 (-2.44)
	δ	0.53 (2.06)	0.51 (2.04)	0.53 (2.07)
	A	2.14 (6.15)	1.9 (5.74)	2.14 (6.15)
	B	-0.02 (-0.19)	0.17 (2.46)
	e	-0.58 (-3.83)	-0.62 (-4.25)	-0.59 (-3.85)
	µ	1.11 (12.39)	1.1 (12.88)	1.11 (12.39)

Table 4 .

 4 Marginal effects of the estimation of optimum and real time allocated to interactive activities

		1	2
		Tobit Model -censored (0,2)	Ordered Probability Model
	Dependent variable	Support	Cooperation
		Coeff.	Cooperation=0	Cooperation=1	Cooperation=2
	Constant	0.84	0.00	0.00	0.00
	w		-0.14	0.06	0.08
	Q		0.22	-0.09	-0.13
	δ	0.64	-0.21	0.09	0.13
	A		-0.85	0.34	0.51
	B	-0.08
	e		0.23	-0.11	-0.12

Page 16 of 16 Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

We follow[START_REF] Cohen | Absorptive Capacity: a new Perspective on Learning and Innovation[END_REF] definition of absorptive capacity: "a limit to the rate or quantity of scientific or technological information that firm can absorb". To justify the extension of the concept of absorptive capacity from firms to regions, see[START_REF] Niosi | The Absorptive Capacity of Regions[END_REF].

Editorial Office, Dept of Economics, Warwick University, Coventry CV4 7AL, UK Submitted Manuscript

We also made estimations using ordinary least squares and ordered probit models -in this latter case, taking the integer part of the original variable to transform it into an ordinal one. It did not change the results.

This is consequent with the long-standing idea from psychology that social reinforcement has greater influence on the determination of behaviour than material reinforcement (e.g.[START_REF] Bandura | Social learning theory[END_REF].

This research should start by the theoretical analysis of firm's use of university R&D, in the line of[START_REF] Cohen | Innovation and Learning: The Two Faces of R&D[END_REF], to end up with a general equilibrium model, following[START_REF] Gambardella | Proprietary vs. Public Domain Licensing of Software and Research Products[END_REF].

On the relationship between managerial positions within university and wage, see[START_REF] Baimbridge | Rewards to academia: the remuneration of vice chancellors and principals[END_REF].

Acknowledgements

To be included.

Appendix

The survey included questions regarding the support for different objectives of UII, which we will use as a proxy for the optimum effort allocated to interactive activities. The basic assumption is that the higher support for the objectives of UII, the higher propensity to allocate time to interactive activities. Hence, we define: