Pitfalls and solutions in the application of spatial computable general equilibrium models for transport appraisal

Transcription

1 Pitfall and olution in the application of patial computable general equilibrium model for tranport appraial L.A. Tavazy, TNO Inro M.J.P.M. Thien, Univerity of Groningen A.C. Muken, TNO Inro J. Ooterhaven, Univerity of Groningen Contact: Abtract The ue of patial equilibrium model for aeing the economic impact of tranport project i one of the key item on the reearch agenda for project appraial in the Netherland. Thee model are particularly uitable to analye indirect effect of tranport project through linkage between the tranport ector and the wider economy (i.e. the tranport uing ector). Potentially, according to the literature, thee impact can turn out to be up to 40% in magnitude of the direct impact. There i, however, no general indication that indirect effect are alway of thi magnitude - thi ha to be proven on a cae-by-cae bai. After two year of application of SCGE model for tranport appraial, we found that the conventional pecification of patial equilibrium model can lead to problem in project appraial in term of inaccuracie in the aement of impact. Thi paper dicue how to fine-tune thee model to allow an accurate aement of thee indirect effect. Thee idea hould be of value for thoe practitioner or reearcher who are developing SCGE application for ue in tranport appraial. 1

2 1 Background and objective of the paper In the lat decade, the ue of patial computable general equilibrium (SCGE) model for aeing the economic impact of tranport project ha become one of the key item on the reearch agenda for project appraial, world-wide. Thee model are particularly uitable to analye indirect effect of tranport project through linkage between the tranport ector and the wider economy (i.e. the tranport uing ector). Potentially, according to the literature, thee impact can turn out to be up to 40% in magnitude of the direct impact. There i, however, no general indication that indirect effect are alway of thi magnitude - thi ha to be proven on a cae-by-cae bai. After two year of application of SCGE model for tranport appraial, we find that the conventional pecification of patial equilibrium model can lead to problem in project appraial in term of inaccuracie in the aement of impact. Our paper dicue thee iue and propoe way of improving thee model. Thee idea hould be of value for thoe practitioner or reearcher who are developing SCGE application for ue in tranport appraial. After a hort introduction to SCGE modelling and it ue for tranport policy analyi (Section 2) we dicu ome pitfall and, where appropriate, propoe alternative pecification (Section 3-6). We ummarie our finding and recommendation in ection 7. 2 The advantage of SCGE modelling for tranport appraial 2.1 Introduction There i a large amount of literature on the economic impact of infratructure (ee Blonk, 1979, Rietveld and Bruinma, 1998, for overview) a well a a large variety of method to etimate thee impact (ee Ooterhaven, Sturm and Zwaneveld, 1998, Rietveld and Nijkamp, 2000, for overview). The method mot ued are the following: micro urvey with firm, etimation of quai production function, partial equilibrium potential model 2

3 macro and regional economic model, land ue/tranportation interaction (LUTI) model, and patial computable general equilibrium (SCGE) model. SCGE model typically are comparative tatic equilibrium model of interregional trade and location baed in microeconomic, uing utility and production function with ubtitution between input. Firm often operate under economie of cale in market with monopolitic competition of the Dixit-Stiglitz (1977) type. The few empirical application of thi approach are Venable and Gaiorek (1996) and Bröcker (1998). Intereting theoretical imulation with a SCGE model with a land market are found in Fan et al. (1998). Thee model are part of the new economic geography chool (Krugman, 1991, Fujita, Krugman and Venable, 1999) and have been around for le than a decade. In other word, we are comparing a mature methodology, poibly at the end of it life cycle, and a new methodology that i till in it infancy. The preent, till young SCGE model have oppoite propertie, namely a lack of detail and ound empirical foundation, but a ophiticated theoretical foundation and rather complex, non-linear mathematic. The latter i preciely the reaon why SCGE model are able to model (di)economie of cale, external economie of patial cluter of activity, continuou ubtitution between capital, labour, energy and material input in the cae of firm, and between different conumption good in the cae of houehold. Moreover, monopolitic competition of the Dixit-Stiglitz type allow for heterogeneou product implying variety, and therefore allow for cro hauling of cloe ubtitute between region. Due to the fact that SCGE model are comparative tatic model, their main trength in tranport appraial lie in the comparion of outcome of different equilibrium tate, uch a: Benefit of generalied tranport cot reduction due to changing price, production, conumption and trade, while holding the number of worker per region contant; howing what could be labelled a the hort-run effect, or the planned effect conidering the government houing policy. 3

4 Benefit when the number of worker i allowed to change too, howing the longrun effect of new tranport infratructure. Below we dicu the baic characteritic of a typical SCGE model developed in the Netherland (ee Ooterhaven et al, 2001). 2.2 The RAEM model Following recommendation from the Dutch OEEI tudy (Eijgenraam et al, 2000) concerning guideline for Cot Benefit Analyi of tranport project, we have recently developed a new patial CGE model (RAEM) for the Netherland, tailored toward application in tranport project appraial. Below we give the baic pecification of the model baed on (Ooterhaven et al, 2001). Further in the paper, we return to pecific part of thi model, which deerve additional commenting. We how how the pecification hould be interpreted and how it can be improved. In the RAEM model we aume that all market are of the monopolitic competition type and each firm in each indutry produce one and only one variety of the product of that indutry. In all production and utility function the varietie x i are added to an aggregate Q j with the following CES-function (ee Dixit and Stiglitz, 1977): 1/(1 1/ σ ) 1 1/ σ Q = n j x i (4) i= 1 In (4) σ repreent the elaticity of ubtitution among the n different varietie of indutry j. All utility and production function have a Cobb-Dougla pecification. The production function only ue intermediate input and labour: 1 α m α γ j Y j = L j Qi (5) i= 1 In (5) parameter α control the diviion between labour and the total of the intermediate input and ector. γ i give the relative weight among the intermediate input from different 4

5 In the equilibrium all price are a function of all other price. In thi olution the complement of the quantity aggregate (4) i the following price index function: ( p p ) 1/(1 σ ) 1 σ G 1,..., = n j j nj pij (6) i= 1 In (6) p ij i the price of variety i in ector j. Thi price index varie acro different region, a thee purchaing price are incluive of the tranport and communication cot of delivering the product. In the monopolitic competition equilibrium, price are a mark-up over marginal cot, including the tranport cot. Thu, the way in which tranport cot are included in the price i deciive for the functioning of our model. We have followed tandard practice and introduce tranport cot a a mark-up over the regular f.o.b. price. Specifically, in view of the problem at hand, RAEM ue a new bi-modal (people/freight) tranport cot mark-up: p * = π 1 π [ f d )] [ f ( d )] p g ( (7) g p p In (7) π give the importance of freight tranport for the tranportation cot of the ector at hand. Information on thi parameter proved to be carce. Hence, expert judgement wa ued to guetimate the 14 ectoral π needed. In (7) f follow the uual pecification of iceberg tranport cot (ee e.g. Bröcker, 1998): f ω ( d) = 1+ ϑ d (8) In (8) υ and ω are parameter to be etimated and d i the ditance between the producer and the cutomer. For freight, imple road kilometre ued a ditance do not change in the application. A new railway link for paenger tranport i modelled a a decreae in 'people-ditance' d p. 2.3 Typical problem in the development of RAEM After two application of the multiector RAEM model to major Dutch tranport infratructure cheme, a number of leon have emerged with repect to the applicability of uch model to tranport appraial. Thee leon concern, in broad 5

6 term, the pecification of the relation between the tranport ytem and the patial economic ytem of production, conumption and trade. More pecifically, they have to do with Interfacing problem between SCGE and tranport model The modelling of the influence of tranport cot on ectoral production The intepretation of the conventional, micro-level pecification of product variety in aggregate application The problem of irrational agglomeration effect in economic activitie We treat thee iue in more detail within Section 3-6 of the paper. 3 On the interface between SCGE and tranport model 3.1 Introduction The tranport ytem enter the patial economy through the cot of tranport ervice. Typically, in tranport evaluation practice tranport model are applied to feed SCGE model with cot change in the tranport ector a a reult of policy meaure. Thi ection treat practical difficultie that can arie when linking SCGE model with tranport model. Our aumption i that SCGE model treat patial interaction between region, baed on a decription of their production and conumption, and do not decribe the choice made with repect to alternative ervice offered within the tranport ytem. Thi i the main reaon for complementarity between tranport and SCGE model. We alo decribe ome problem that haven t yet been olved and cannot be on the hort term due to limitation in data availability. Our aim here i to gain clarity in conceptual term of how thee modelling difficultie arie and to raie a dicuion about how thee could be olved. 6

7 policy Tranport ector Cot to uer Tranport uing ector ervice infratructure Tranport model SCGE production trade Model and market Figure 1: Linking cheme for the tranport and SCGE model Linking thee model can reveal problem that are not viible when one only conider one type of model. They are however in part well known to the Land Ue-Tranport Interaction or LUTI type model (ee e.g. Wilon, 1998). In part, a thee model do not hare the rigorou economic framework of CGE model (ee Ooterhaven et al, 2001 for a dicuion). We treat the following common problem: difference in linkage required between freight and paenger tranport the choice of a correct pecification of the cot of tranport poible inconitencie between SCGE and tranport model in the decription of trade pattern 3.2 Tranport cot by ector Paenger and freight tranport are linked to tranport uing ector by different mechanim. Freight tranport i needed to acquire good and i thu directly linked to ectoral input. Paenger tranport i a complex of different motive: buine traffic for the delivery of ervice (we ditinguih 2 type of ervice: thoe ourced directly by the firm e.g the cleaning company and thoe related to good delivered to the firm, e.g. the traveling aleman), commuting traffic of employee. Apart from the general quetion about the degree to which efficiency gain in tranport are made productive in the tranport uing indutry, there i a much more commonplace problem that deerve attention: the contribution of tranport cot to product value. For freight tranport thee cot are well identifiable and exiting tatitic indicate that depending on the ector thee lie between 5% and 25% of the value of the product. A a ignificant hare of firm ue tranport on an own account bai (in NL thi hare i etimated at 30% for low valued good and 60% for high valued good), we cannot in general rely on 7

8 aggregate indutry tatitic a own account tranport i not noted in I/O table a a eparate flow, the input to indutry from the tranport ector which appear in thee tatitic imply doe not give the full picture. For commuting, thee relationhip between tranport pending and ectoral turnover can be identified uing labour cot tatitic per ector. Buine traffic i the mot difficult category. For ervice ourced directly by firm, a imilar problem a with freight tranport arie in term of own account tranport, which i uually the cae with ervice. Additional ervice that go with the acquiition of good (adviory ervice, ale) are to a large part conidered a an overhead to the cot of production and delivery of good. No general indicator exit, however, on the proportion of the cot of buine trip in the product cot. A the pending on tranport ervice concern a key aumption in the application of SCGE model for tranport appraial, we recommend that additional reearch i undertaken in thi area to produce relevant and repreentative indicator 3.3 Which tranport cot? The meaning of tranport cot varie acro dicipline. In tranport appraial, where the tranport engineering and regional economic dicipline collide, the definition of tranport cot for SCGE modelling can in many cae be too wide or too narrow: Firtly, the cot that the tranport uing ector incur have nothing to do with generalied cot (a e.g. a weighted um of cot and time) of tranport. The market price that firm pay for tranport a tructural relationhip between the tranport ector T and the tranport uing ector TU - i omething different than the hadow price of ervice aumed in tranport choice model a behavioural relationhip between T and TU. It hould be clear what i included in the value of time ued in tranport model (driver wage? capital lo in tranport? cot of fulfillment downtream?) in order to avoid double counting. Secondly, on the other hand, we mut take care not to limit ourelve to tranport cot only. In broad term, it i the cot of interaction between region that interet u, i.e. the cot to get good in the right hape, in the right quantitie and on the right time between A and B. Thi include eaily dicarded, but highly relevant categorie like phyical ditribution cot or border croing cot. 8

9 3.4 Conitency with 4 tep tranport model Mot tranport model are not limited to the market of tranport ervice but alo decribe the pattern of trade between region. Thi introduce a ource of inconitencie between SCGE and tranport model. The fact that, conceptually at leat, the mot common model form for decribing thee pattern, i implicitly alo part of the SCGE mechanim (the gravity model), i of little comfort. SCGE model are fundamentally different in the ene that in tranport modelling term the production and attraction rate are elatic. The total flow leaving or entering a region will thu differ between the two type of model. Thee elaticitie are endogenou in SCGE model which place limit on the tranferability to tranport model. Thi problem can in principle be olved by attempting to let the two model reach convergence in thee patial pattern, by feeding back patial pattern of tranport flow from the SCGE model to the tranport model. 4 Modelling the influence of tranport cot on ectoral production 4.1 Introduction Samuelon' (1952) iceberg approach i commonly ued in regional general equilibrium model. The approach, in which it i aumed that tranport cot can be modeled a produce melting while being tranported, i theoretically elegant for one-ector model but inappropriate in cae of multi-ector model. The iceberg approach will caue a evere mi-pecification of the production cot in the tranport ector. Moreover, a argued in Ooterhaven and Knaap, (2002), the iceberg approach mixe up volume and price effect and may even lead to incorrect pervere model reult. 4.2 Tranport production The firt mi-pecification of modeling the tranport ector uing the iceberg approach in a multi-ector framework i due to the implicit production function that i ued in producing tranport. The iceberg approach implicitly aume that the tranport of good i produced in the ame way a the product tranported. For, tranport i expreed in unit of the product tranported. One only ha to think of the mining ector to undertand that thi i a fundamental mi-pecification, which may have evere 9

10 conequence for factor ue in the economy. In a one ector economy it i aumed that all product have the ame production function. Obviouly, the iceberg approach will not lead to any (additional) mi-pecification in a one ector model. 4.3 Price and volume effect Reducing iceberg tranport cot implie that the upplier need to produce le to atify the ame level of demand on the part of the cutomer. Hence, conumption i able to increae more than production. Thi may even lead to pervere effect where a reduction in the tranport cot lead to increaed conumption of a good, while production actually decline. Thi i caued by the baic aumption of the iceberg approach that a reduction in tranport cot may be modeled a an increae in the amount of produce that arrive in a certain region. When a macro SCGE i ued, thi property doe not poe a eriou problem a the macro economic output i incluive of tranportation output that doe (implicitly) reduce. In a multi-ectoral SCGE, however, thi iceberg type tranport cot imply a eriou mi-pecification a they lead to an underetimation of the output effect in the non-tranport ector, epecially in thoe ector for which tranport cot reduce mot, wherea the oppoite hould be the cae. 5 On the interpretation of micro level variable 5.1 Introduction In the pecification of SCGE model, the firm i the baic entity whoe behaviour we want to decribe. At thi level, which we will call the micro level, the SCGE framework i unambiguou. The interpretation of variable related to firm and product are, however, le eay to interpret when we apply the SCGE framework on the meo level, i.e. regional or ectoral level. Such application are neceary for tranport project appraial, a we will typically want to know about the behaviour of an aggregate et of firm, e.g. all firm within a region. In thee application, we will typically not have the empirical data available to decribe (or etimate) the characteritic of individual firm or product. The micro level variable will thu need to be re-interpreted a variable at the meo level. The quetion i whether thi conflict in definition force u to 10

11 reconider the pecification of our model. We anwer thi quetion in the remainder of thi ection. 5.2 The number n: varietie, firm and geographical cale In the literature there i much confuion about the interpretation of the variable n in the Krugman tyle regional equilibrium model (Fujita, 1999). i Normally n i aociated with the number of varietie and the number of firm in a region. In thi chapter we will argue that, although the relative ize of n compared to different region tand into a relation to the number of firm in a region, it hould not be interpreted a the number of firm or varietie in a region. Aociating n with the number of firm i a mileading implification. Moreover, it i argued that the exact interpretation of n i not clear and of only little importance. 5.3 Calibrating n: the ubtitution elaticity and fixed cot To undertand what i the meaning of n we have to look a bit more careful to the model and the way n i determined. In general n i etimated baed on the flow of good d between region r and rr of a ector good. Thi flow of good i a function of the relative price of a good in region rr vi-à-vi the price of a good in region r, the ubtitution elaticity σ between varietie of thi good in ector and the aborption A in receiving region rr. Thi i mathematically decribed in equation (0.1). (0.1) p rr, drr, = f, σ, A rr, p The price in a region i a function of the price in all region, the variable n and the ubtitution elaticity ubtitution elaticity the following two equation. σ. The variable n i a function of the fixed cot ψ the σ and the production in a region drrr,, rr (0.2) P = f ( n, p, σ ) (0.3) n = f σ, ψ, drrr,, rr. Thi i decribed in 11

12 It i normal procedure to calibrate n (Bröcker, 1995 and Elhort et al., 2000). Thi implie looking for value of σ and ψ uch that the imulated value for the trade flow are a cloe a poible to the oberved trade flow, given the aborption in all region. The actual calibration involve only the ubtitution elaticity and the fixed cot a equation (0.3) can be eaily ubtituted into equation (0.2). It hould be noted that the ytem of equation (0.1), (0.2) and (0.3) i independent with repect to the ector. In other word, the parameter can be calibrated equentially for all ector becaue there are no inter ector flow. ii The calibrated parameter are therefore ector pecific. They are and not region pecific becaue in that cae the ytem would be underdetermined. 5.4 What i n? The variable n i baed on a the fixed cot of making a variety and the ubtitution elaticity for thi variety vi-à-vi other varietie and the regional production. Firt, we define N a the aggregate of n over the region. iii Thi N depend only on the national production level and i equal to the total production divided by the (optimal) production for a variety. Thu, N i the number of varietie in the economy. Thi number of varietie (or thi combination of σ and ψ ) fit bet the oberved flow of good between region for thi product category. It hould be emphaized that thi number of varietie i an average of the combination of actual oberved varietie and their agglomeration effect. It i not poible to decompoe thi variety concept into oberved varietie and the agglomeration effect. Thi leave u with the commonly ued variable n on the regional level. The only meaning of thi variable i the hare of thi region production in the production of a variety. In other word, varietie have no regional component but are calibrated on the aggregate level only (for the economy a a whole). The quetion remain however whether the variable n tell u omething about the agglomeration effect in thi region. 12

13 The agglomeration effect in a region i a function of the upply in thi region and in all neighboring region. Thi i bet decribed by the price of an aggregate good in a region a defined in equation (0.2). The variable n i the weight in thi CES price aggregation and tell u only omething about the region own contribution to the regional agglomeration effect. However, the exact interpretation of n i not obviou becaue of the non-linearity in the aggregation function. It hould therefore be concluded that n tell u little about the regional agglomeration effect. 5.5 Condition for N and n The variable n i continuou and hould be larger than 0. The actual ize of n, depend on the geographical cale of the analyi. The argumentation i traightforward. Given the ubtitution elaticity and the fixed cot of producing a variety there i an amount of varietie N in the economy. The more region you ditinguih within thi economy, the maller your n will be. r In general it i argued that N hould be larger that 1. Thi i directly derived from the CES Price aggregation and i related to the aumption that every firm produce one variety. In thi cae N hould be much higher than 1 becaue we aume monopolitic competition a a prerequiite for the model with agglomeration effect. However, if we aume that a variety i made by many firm, which i not unlikely in the empirical cae given the variety concept ued (ee above), there eem to be no reaon for N to be larger than 1. In thi cae N i a continuou variable and hould imply be larger than zero. 6 Irrational agglomeration effect 6.1 Introduction Change in land ue are imulated by SCGE model through change in the volume of regional production and conumption. One can experience problem with the traditional pecification of thee function, however, if contraint upon change in land ue are neglected. More pecifically, uch irrational agglomeration effect can occur in SCGE model if hyterei and locational boundedne i not adequately taken into account. 13

14 Thi may take everal form: Hyterei: Pat deciion affect the future. Setting up a new plant in another location intead of extending an exiting plant may for ome ector be very cotly. Invetment in the pat hould in thi cae be een a 'unk cot' in the production proce and hould be treated in that way if compared to new invetment. Locational boundedne due to locational input; in other word, production factor may be only locally available. An example i the availability of natural reource. In thi cae one can think about natural ga, but alo about the factor land in the agricultural ector. Locational boundedne due to locational output: thee are mainly governmentregulated product. For intance, ervice upplied by municipalitie cannot be ubtituted. That i, one ha to conume municipality ervice from one own municipality. Thi i exogenou local production. 6.2 Preventing irrational agglomeration effect in RAEM Locational boundedne can bet be modeled by explicitly taking factor market into account, or by fixing ome of the production y iv. Hyterei, however, ak for a more ophiticated approach. Hyterei affect the productivity of production, becaue it i inefficient to produce a different amount than the normal capacity of the firm y. In other word: cot have to be made to reduce or increae production in a region. Thi capture the unk cot idea. It i uually more cotly to build a new factory than it i to improve an exiting factory. Thi implie that although it would be more efficient to produce in other region thi will not take place becaue it i cotly to move a plant. It can be argued that in the very long run thee effect will be zero. However, over the period of policy analyi RAEM (a period of approximately 25 year) thee effect are definitely not equal to zero. Of coure thee effect are ector pecific and depend on the footlooene of a ector λ (varie between 0.1 and 1). Given a normal Cobb-Dougla production factor with caling parameter A, we propoe the following function to capture hyterei: 14

15 (0.4) 2 yr, λ ln y = A e A Thi idea of productivity effect depending on a normal production level draw heavily form the tructuralit pot-keyneian tradition. In model imulation we may aume that the normal production i equal to the production in the bae-run. The function i plotted for the two extreme cae for λ in Figure 2. Thi function ha the property of being equal to 1 if the production i equal to the normal production. A become le than 1 if production deviate from the normal production. The productivity in the ector decline in when A become le than 1. Thi function can alo partly be ued to capture locational boundedne in cae of locally available production factor. v For example, given the amount of land available the production may be extended at the cot of a lo in productivity (more intenive ue of land) or reduced with a lo in productivity (taking valuable land out of production). Epecially in the cae of land there i a clear limit to the amount available. e 2 y λ ln y yr, y Figure 2 Scaling of production to capture hyterei 15

16 7 Concluion and recommendation In thi paper we dicu a number of complexitie in modelling change in the economy of region. We focu in particular on change that arie a a reult of change in the efficiency of tranport procee the modelling approach uing tranport and SCGE model. Thee problem in modelling have not yet received widepread attention, a the application of SCGE model for the appraial of tranport invetment and policie i a new phenomenon. We decribe 4 type of iue, explain the poible implication of neglecting thee iue and, where relevant, propoe approache for their reolution. Thee iue concern 1) interfacing problem between SCGE and tranport model, 2) the modelling of the influence of tranport cot on ectoral production, 3) the intepretation of the conventional, micro-level pecification of product variety in aggregate application and 4) the problem of irrational agglomeration effect in economic activitie. Our main concluion concerning thee point are a follow: 1. In order to have a conitent linkage between tranport and SCGE model, the main variable that form thi linkage tranport cot- deerve pecial attention. We firtly oberve that there i a evere lack of empirical data on the conumption of tranport ervice by variou ector of indutry. Secondly, we identify two cae of a poible mimatch in the definition of tranport cot, a they are produced by tranport model, and a they hould enter SCGE model. Thirdly, we decribe how the ue of 4 tep tranport model may introduce inconitencie in appraial reult. 2. The ue of iceberg tranport cot i theoretically convenient and empirically acceptable in the cae of a one-ector economy. In a multi-ector economy, it may lead to trange reult: an underetimation of the impact in preciely thoe ector that are mot enitive to the reduction of tranport cot at hand. Moreover, etimation of factor cot involved in tranport are baed on the wrong production function and therefore incorrect. 3. We find that the interpretation of the variable n i le traightforward for our purpoe of application than often preented in the literature. Thi variable eem to be merely a caling factor ueful in calculating the model outcome but of no great 16

17 importance in policy analyi. Moreover it wa found that thi variable hould only be larger than 0 and not larger than 1 becaue it ize only depend on the geographical cale of the analyi. 4. We propoe a new method to take hyterei and locational boundedne of production into account. Thi i neceary to have a more realitic policy analyi, particularly when it come to predicting change in patial pettern of production and conumption. A a concluding remark, we feel that uch a critical and, hopefully contructive, evaluation of the application of SCGE modelling for tranport appraial purpoe i a neceary tak for the reearch community. The advent of SCGE modelling, beide improving our inight in how regional economie interact, alo include a promie of improved quality of appraial reult for tranport invetment and policie. New reearch into the critical interface with tranport modelling i needed, however, for thi promie to materialize. NOTES i See among other Venable and Gaiorek (1996), Bröcker, J. (1999) and Elhort et al. (2000). ii In the model the relative ector price affect the demand for ector input. Thi i however not the cae in the calibration. iii Thu, N = n. r iv Note that in an empirical application of the model fixing production to zero will have large numerical conequence. Thee ector hould be completely removed from the model for thee region becaue otherwie price would reach infinity. v Note that the normal production may be to a mall figure if the production i zero. REFERENCES 17

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