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The Bank of Latvia Economics of Journal Baltic Volume 6 Number 2 Winter/Spring 2007

Articles

TheBankofEstonia Interdependence of Nordic and Baltic Stock Markets Ulf Nielsson

oue6Nme itrSrn 2007 Winter/Spring 2 Number 6 Volume Euro Introduction Effects on Individuals’ Economic Decisions: Testing the Presence of Difference Assessment Account among Lithuanian and Latvian Consumers Lineta Ramoniene and Dovydas Brazys

An Economic Analysis of the Inﬂuence of Different Vilnius University Attitudes Toward Game Animals: Emphasizing the Signiﬁcance of Large Carnivores Yukichika Kawata

Book Reviews

Constantine A. Stephanou (ed.) Adjusting to EU Enlargement. Recurring Issues in a New Setting by Mark Chandler

Published by EuroBaltic Centries of Excellence www.eurobaltic.lv/bje.html The Baltic Journal of Economics Information for Contributors to the Baltic Journal of Economics

Articles submitted to the BJE must be typed in double spacing throughout. Articles should be no more than 10,000 words in length, including notes and references. An abstract of up Jorgen Drud Hansen to 125 words should precede the main text, accompanied by up to five key words and JEL Editor in Chief classification. Raul Eamets Editor Tides and section headings should be clear and brief. Lengthy quotations (exceeding 40 words) University of Tartu should be displayed, indented, in the text. Submitted articles must be of good English language quality using British or American spelling. Kenneth Smith Editor Tables and figures should have concise and descriptive titles. All footnotes to tables and their Millersville University source should be typed below the tables. References cited in the text should read: Smith (1998: 66–9), Kroncke and Smith (1988, 1999). Use ‘et al.’ when citing a work by more than Mark Chandler two authors, e.g., Smith et al. (1996). The letters a, b, c, etc., should be used to distinguish Managing Editor citations of different works by the same author in the same year, e.g., Smith (1975a). All Stockholm School of Economics in Riga references cited in the text should be listed alphabetically and presented using the following style: The Baltic Journal of Economics (ISSN-140X-099X) is published twice a year in January and July by EuroBaltic Centres of Excellence, Raina Blvd. 19, Riga LV-1586, Latvia. ARTICLES IN JOURNALS: Cotton, Jeremiah C. (1988). “On the Decomposition of Wage Differentials”, The Review of Information for subscribers: Copy requests, orders and other enquiries should be addressed to Economics and Statistics 70(2), 236–43. the BJE at the address above, or by email to [email protected]. BOOKS: Becker, Gary S. (1971). The Economics of Discrimination. Chicago: University of Chicago The Baltic Journal of Economics is published by EuroBaltic Centres of Excellence and ref- Press. ereed by internationally recognized scientific standards. The journal is intended to provide a publication medium for original research in economics for scholars working in the Baltic states ARTICLES IN BOOKS: or those who are working on topics relevant to the Baltic states. Papers may be theoretical, Vinokur, Aaron and Ofer, Gur (1987). “Inequality of Earnings, Household Income, and Wealth empirical or political economy in emphasis. Papers with policy relevance or which combine in the Soviet Union in the 1970s”, in James Millar (ed.), Politics, Work, and Daily Life in the economic theory with empirical findings are particularly welcome. The Journal aims to stim- USSR: A Survey of Former Soviet Citizens. Cambridge: Cambridge University Press, pp. ulate a dialogue between scientists in social science, policy makers as well as other decision 13–38. makers involved with economic development in the Baltic States. In order to make the journal relevant to a wide audience of academics trained in social science the articles should be pre- UNPUBLISHED WORKS: sented in a form where explanations and the intuition behind the conclusions should be given Peters, Davis (2001). “Wage Differentials in Riga”, Unpublished Manuscript, University of priority above technical derivations. Latvia.

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Baltic Journal of Economics: [email protected] Baltic Journal of Economics Board of Reviewers

Articles submitted to the Baltic Journal of Economics are double blind reviewed by two independent reviewers from our international board of reviewers. The Editorial Board is grateful to these individuals for their generous service and support of the Journal.

Daunis Auers University of Latvia Andreas Cornett University of Southern Denmark, Sonderborg Ģirts Dimdiņš SSE Riga/University of Latvia Vyacheslav Dombrovsky BICEPS/SSE Riga Morten Hansen BICEPS/CETS/SSE Riga Jörgen Hansson SSE Riga Katerina Hellström Stockholm School of Economics Peter Högfeldt Stockholm School of Economics Andrejs Jakobsons BICEPS/Riga Business School Jan Guldager Jørgensen University of Southern Denmark, Odense Rasmus Kattai Bank of Estonia Roberts Ķīlis SSE Riga Gustav Kristensen University of Southern Denmark, Odense Charles Kroncke College of Mount Saint Joseph Virmantas Kvedaras Vilnius University Jens Larsen Danish Embassy, Brussels Jørgen Lauridsen University of Southern Denmark, Odense John Lewis De Nederlandsche Bank Anders Ljung Cepro Management Consultants Erik Strøjer Madsen Aarhus School of Business Jaan Masso University of Tartu Soren Bo Nielsen Copenhagen Business School Finn Olesen University of Southern Denmark, Esbjerg Olga Rastragina BICEPS/University of Latvia Rūta Rodzko Bank of Lithuania Priit Sander University of Tartu Phillip Schröder Aarhus School of Business Morten Skak University of Southern Denmark, Odense Jeff Sommers SSE Riga Niels Karl Sørensen University of Southern Denmark, Esbjerg Karsten Staehr Bank of Estonia Ott Toomet University of Tartu Lenno Uusküla European University Institute Igor Vetlov Bank of Lithuania Lisa Wilder Albright College Ekaterina Zhuravskaya New Economic School/CEFIR

Contents

3 Foreword Mark Chandler

5 Summary of Quantitative Results from the Survey of Subscribers to the BJE

7 Baltic Journal of Economics Board of Reviewers 2005–2006

Articles

9 Interdependence of Nordic and Baltic Stock Markets Ulf Nielsson

29 Euro Introduction Eﬀects on Individuals’ Economic Decisions: Testing the Presence of Diﬀerence Assessment Account among Lithuanian and Latvian Consumers Lineta Ramoniene and Dovydas Brazys

57 An Economic Analysis of the Influence of Different Attitudes Toward Game Animals: Emphasizing the Significance of Large Carnivores Yukichika Kawata

Book Reviews

79 Adjusting to EU Enlargement. Recurring Issues in a New Setting, Constantine A. Stephanou ed. Mark Chandler Foreword

The Baltic Journal of Economics has been through a substantial number of improvements over the last six months, hence I will try to summarise them here. Firstly, I want to thank those of you who participated in the survey of our readership that I distributed over the summer. The answers you provided formed the basis of several positive changes in the way we manage the BJE. I should also thank Una Chandler, whose research for an MBA thesis on the development of the BJE propelled many of the changes that we are benefiting from today. So what have been these improvements? They include the following: • Indexing in EconLit, the Journal of Economic Literature on CD-ROM and e-JEL. • Indexing in EBSCO and inclusion of articles there in full text. • Upgrade of the layout quality and consistency. • Commencement of retail at the Faculty of Economics and Management, University of Latvia book shop. Now that the BJE is listed by officially recognised institutions publications in the BJE will carry greater weight, both for academic recognition/promotion and in terms of the expanded audience they will reach. It is immensely pleasing that economists throughout the world, when they search topics on EconLit, will find abstracts of articles in the BJE and, if they have access to EBSCO, will immediately find the full text of the article. We will also continue to make the BJE available on our web site. I am sure you will agree that these sum up to substantial improvements in the BJE’s operations. However, I would also like to invite you to send me any additional suggestions for improvement either to my personal email or to the BJE email given on the inside cover. I will include two appendices to this foreword. The first is a summary of some of the quantitative responses we received from the survey of subscribers. Is should be emphasised, however, that survey responses also included many helpful qualitative suggestions. The second is a list of our long suffering reviewers, who generously give of their time and effort. We thank them for their invaluable service to the Baltic Journal of Economics and hope they will also be pleased that the Journal is now reaching greater heights.

Mark Chandler Managing Editor January 2007 Summary of Quantitative Results from the Survey of Subscribers to the BJE

Survey conducted: 10th August – 10th September 2006. Note: students here refers to postgraduate students on the mailing list, thus it primarily covers doctoral students.

The number of responses was 63, a response rate of 23 per cent.

Table 1. Respondents by occupation % Professor 46 Researcher 23 Lecturer 3 Doctoral student 20 Other 8

Table 2. Respondents by country % Denmark 17 Estonia 26 Finland 3 Germany 5 Latvia 12 Lithuania 13 USA 10 Other 14

Table 3. Responses to survey questions % Yes % No % Other % not answered Involved in the social sciences 93 7 0 0 Professional requirement to publish 85 15 0 0 Interest in the Baltic economic area 76 18 4 2 Personal interest in the Baltics 76 17 7 0 Aware of the BJE 80 17 0 3 Willing to publish in the BJE 58 15 22 5 Could accept advertising in the BJE 75 12 8 5 Baltic Journal of Economics Board of Reviewers 2005–2006

Andreas Cornett University of Southern Denmark, Sonderborg Girts Dimdinš SSE Riga/University of Latvia Vyacheslav Dombrovsky BICEPS/SSE Riga Morten Hansen BICEPS/CETS/SSE Riga Jörgen Hansson Stockholm University Katerina Hellström Stockholm School of Economics Peter Högfeldt Stockholm School of Economics Andrejs Jakobsons BICEPS/Riga Business School Jan Guldager Jørgensen University of Southern Denmark, Odense Rasmus Kattai Bank of Estonia Gustav Kristensen University of Southern Denmark, Odense Charles Kroncke College of Mount Saint Joseph Virmantas Kvedaras Vilnius University Jens Larsen Danish Embassy, Brussels Jørgen Lauridsen University of Southern Denmark, Odense John Lewis De Nederlandsche Bank Anders Ljung Cepro Management Consultants Erik Strøjer Madsen Aarhus School of Business Jaan Masso University of Tartu Soren Bo Nielsen Copenhagen Business School Finn Olesen University of Southern Denmark, Esbjerg Olga Rastragina BICEPS Ruta¯ Rodzko Bank of Lithuania Priit Sander University of Tartu Phillip Schröder Aarhus School of Business Morten Skak University of Southern Denmark, Odense Niels Karl Sørensen University of Southern Denmark, Esbjerg Karsten Staehr Bank of Estonia Ott Toomet University of Tartu Lenno Uusküla European University Institute Igor Vetlov Bank of Lithuania Lisa Wilder Albright College Interdependence of Nordic and Baltic Stock Markets

Ulf Nielsson *

Department of Economics, International Aﬀairs Building, 118th Street and Amsterdam Avenue, NY, 10027, USA

Abstract: The interdependence of the Nordic and Baltic stock markets is explored in light of increased merger activity of stock exchanges over the sample period, 1996–2006. The results show surprisingly little interdependence between the Nordic and Baltic stock indices. In the short run, the response of each market to a shock in another is insigniﬁcant. In the longer term there is limited evidence of integration and only weak indication of convergence within the sample period. The stock markets seem no more integrated than they were at the outset of recent merger activity, suggesting that the levels of cooperation between the Nordic and Baltic exchanges have not been deep enough to produce increased interdependence.

Keywords: Interdependence, integration, convergence, stock exchange merger, stock markets

JEL codes: F36, G15

1. Introduction

It has been established that the major stock markets of the world have been converging over the long run and becoming more interdependent.1 In recent years there has also been a tendency for stock market integration at a deeper level than price convergence. In Europe, these developments include the Euronext merger, the consolidation of the OMX group and ongoing discussions of consolidations of the New York Stock Exchange and Euronext, and potentially NASDAQ and London Stock Exchange.2 In light of this trend, it is natural to explore the relationship between increased merger activity and stock market integration (such as price convergence). Further, it is interesting to examine how the effect on international price interdependence differs depending on the form and level of stock exchange cooperation. This paper presents a case study which explores the relationships between the national stock markets of the Nordic and Baltic countries. The paper investigates whether these markets exhibit similar price characteristics and are converging over time, or if they are perhaps already fully integrated. The level of market integration has important implications for (i) the gains of international diversification and (ii) the effect of increased stock market

* E-mail: [email protected]. Reykjavik University and PhD candidate at Columbia University. The author wishes to thank Alexei Onatzki and two anonymous referees for very helpful comments and suggestions. Any remaining errors are my own. 1 See e.g. Taylor and Tonks (1989), Corhay et al. (1993), Fraser and Oyefeso (2005), Masih and Masih (2001), Chelley-Steeley et al. (1998), Bessler et al. (2003), Kim et al. (2005), Phylaktis (1999). 2 Euronext is the merged stock exchange of former national exchanges in Belgium, France, Netherlands and Portugal. OMX owns and operates 7 exchanges based in the Nordic and Baltic countries. NASDAQ is an American stock exchange with headquarters in New York. Baltic Journal of Economics 6(2) (2007): 9–27 10 merger activity on market adjustments.3 For example, if prices of national stock markets co-move, there are limited gains to be made from international diversification which may have implications on the ability to attract global capital. In the extreme case of full stock market integration, risk adjusted stock returns will in fact be equal in all countries. Fraser and Oyefeso (2005) argue that this implies that the time may be right for increased merger activity between national stock exchanges, i.e. if stock markets are fully integrated then increased merger activity requires no stock market adjustments. If, however, markets do not trend together, it implies that stock markets must adjust while adapting to institutional changes that accompany increased merger activity. Hence, studying the level of market integration and interdependence is particularly interesting in light of recent merger activity and cooperation among stock exchanges. The econometric tools and techniques used to study the interdependence of stock markets have developed rapidly in the last two decades, partly explaining the extensive amount of research on the topic. Taylor and Tonks (1989) presented early evidence indicating that major stock markets of the world are converging, at least over the long-term. Since then the major stock exchanges of the United States and Europe have been analyzed in detail, and recently there has also been a lot of work done on the integration of stock exchanges in e.g. Africa and Asia.4 Although results differ in terms of direction of causation, short vs. long term effects, etc., there seems to be a broad agreement on some degree of convergence of most stock markets in the last decade. This paper adds to the current literature by presenting a case study of the Nordic and Baltic countries, i.e. the countries of Iceland, Norway, Denmark, Sweden, Finland, Latvia, Estonia and Lithuania. These countries have undergone increased institutional and operational cooperation in recent years, both in terms of the NOREX and OMX consolidations.5 Despite this active process of increased cooperation, the Nordic and Baltic countries have been subject to limited research. Earlier literature on the region includes the study of Malkamäki et al. (1992) who found no cointegration among stock indices in Scandinavia in 1988–90, but found that the Swedish stock market Granger caused other Scandinavian markets. Mathur and Subrahmanyam (1990, 1991) come to the same conclusion, but these studies present little evidence of Scandinavian markets significantly influencing outside markets. Malkamäki (1992) examines the interdependence of stock markets in Sweden, Finland and their biggest trading partners in the period 1974–89 and finds that the Scandinavian markets seem to be led by the German and the UK market. Interestingly, the influence of these stock markets on the two Scandinavian markets seems stronger than the influence of Sweden and Finland on each other. In short, these paper suggest that pre 1990 the interdependence of Scandinavian markets was limited. But due to recent developments in econometric techniques and in light of the recent increased merger activity, previous literature on the sample countries has become dated.

3 Taylor and Tonks (1989) also argue that the existence of cointegration in a speculative market implies a violation of market eﬃciency. This follows from the existence of an error correction mechanism, i.e. from being able to use past prices to improve forecasts of current prices. More recent literature has however rejected this arguement of market ineﬃciency, since if fundamentals are cointegrated, then so are stock prices (Fraser and Oyefeso, 2005). 4 Hearn and Piesse (2002), Arshanapalli et al. (1995), Bessler et al. (2003), Bessler and Yang (2003), Yang et al. (2003b), Phylaktis (1999). 5 All the Nordic and Baltic stock exchanges are members of the NOREX cooperation, which facilitates the usage of a joint trading system and harmonization of regulations. The OMX consolidation goes beyond that, towards a formal merger of exchanges. 11 Interdependence of Nordic and Baltic Stock Markets

This paper updates and extends existing research in response to these changes. Also, since the Nordic and Baltic stock markets are relatively small in size, accompanied with thin trading and potentially low efficiency, the behavior of these markets may be quite different from that of other, larger markets. The study adds the Baltic region to the analysis, since the Baltic countries have been involved in the merger activity initiated by the Nordic exchanges. Furthermore, examining the interdependence of Nordic and Baltic stock markets with respect to the depth of the stock exchange integration (e.g. NOREX versus OMX) provides not only answers to whether these stock markets are converging, but also helps addressing the question of why stock markets may be converging – e.g. if increased merger activity may be a driving factor. The paper extends on econometrics tools previously used to examine the sample countries. In particular, the study applies the generalized impulse response analysis of Koop et al. (1996) and Pesaran and Shin (1998) to estimate short-term causal linkages across stock markets. This methodology has the advantage over the more traditional orthogonalized approach (such as Cholesky factorization) that it is invariant to the ordering of variables when estimating a vector autoregressive model. Long run dynamics are explored using cointegration tests, principal component analysis and common factor models. The results show surprisingly little interdependence of the Nordic and Baltic markets. In the short run, the response of each market to a shock in another is insignificant. In the longer term there is some evidence of integration, although there is no indication of convergence within the sample period. The stock markets therefore seem no more integrated than they were at the outset of recent merger activity, suggesting that the levels of cooperation between the Nordic and Baltic exchanges have not been deep enough to produce increased interdependence. This lack of interdependence is surprising given the vast literature showing increased convergence and integration of other stock markets throughout the world. The paper proceeds in the next section by introducing the data and describing the cooperation networks of the Nordic and Baltic countries. Section 3 introduces the methodology used to analyse the long-run interdependence of stock markets, as well as the methodology applied in the short-run analysis. Section 4 outlines the results of the analysis and Section 5 concludes.

2. Data and Background Information

The choice of sample countries is based on both geography and cooperation levels. There are primarily two stages of cooperation in the Nordic and Baltic countries. First, there is the NOREX alliance which was established in 1998 by the Swedish and Danish stock exchanges. Today all the Nordic and Baltic stock exchanges are members of the alliance. NOREX’s main area of cooperation is the usage of a joint trading system and the harmonization of regulations for trading and membership on the stock exchanges. The NOREX alliance is therefore concerned with facilitating interaction between exchanges, but without any implicit or formal merger activity. For example, the alliance encourages listed companies to list their securities on only one NOREX exchange, but still drives to provide easy access to all listed companies no matter where an investor may be located (The NOREX vision, 2006). The second form of cooperation among Nordic and Baltic stock exchanges is based on the Swedish–Finnish ﬁnancial services company named OMX, which came into play in 2003 by a merger between the Swedish and Finnish stock exchanges. OMX now owns and operates six stock exchanges, i.e. the stock exchanges of Sweden, Finland, Denmark, Estonia, Latvia and Baltic Journal of Economics 6(2) (2007): 9–27 12

Table 1. Date of membership in NOREX and OMX NOREX OMX Iceland Jun. 2000 Norway Oct. 2000 Denmark Jan. 1998 Dec. 2004 Sweden Jan. 1998 Sep. 2003 Finland Dec. 2003 Sep. 2003 Estonia* Dec. 2003 Sep. 2003 Latvia* Dec. 2003 Sep. 2003 Lithuania** May 2005 May 2004 *In 2001 the Finnish stock exchange acquired ownership of the Estonian exchange and later, in August 2003, of the Latvian exchange. As a result these exchanges were automatically incorporated into NOREX and OMX along with the Finnish stock exchange. **On May 30th 2005 the joint trading platform become operational in the Vilnius Stock Exchange. But it should be noted that membership in NOREX more or less follows from membership in OMX since most cooperation within NOREX is also eﬀective in OMX.

Lithuania.6 Although the exchanges still retain their business names, they are not marketed as separate brands (Riga Stock Exchange History, 2006). OMX therefore steps further towards a full merger of stock exchanges than NOREX does and is in many ways an extension of the NOREX cooperation. The membership of each country in these two consolidations is summarized in Table 1. The table shows that the process of integration between the stock exchanges has been gradual within the sample period 1996–2006. The process of uniﬁcation is likely to continue in the near future, since OMX is slowly extending the operational integration of its members and, for example, in September 2006 it was announced that the Iceland Stock Exchange intends to merge into OMX and, also, 3 weeks later OMX acquired a 10% stake in the Oslo Stock Exchange. Weekly data on the main stock exchange indices of the sample countries is obtained from Thomson Financial (Datastream) for a 10 year period, i.e. from May 1996 to May 2006.7 Each index in the sample thus consists of 520 observations, but due to limited data availability on Lithuania and Latvia, only Estonia is included in the sample of the 3 Baltic countries. Further description on the indices, summary statistics and correlation coeﬃcients are reported in Appendix A and the index values over the sample period are depicted in Figure 1. Figure 1 hints towards partial co-movement of stock indices, at least in the long run. The correlation of index values (reported in Appendix A) varies considerably, the highest being between the stock indices of Sweden and Finland (0.953) and the lowest between Sweden and Estonia (0.232). Since Figure 1 gives limited insight into short run dynamics, it is informative to remove the permanent component from the stock indices and graph only the transitory component. Decomposing the stock market price series into their permanent and temporary component can provide insight into whether markets are driven by a common

6 On September 19, 2006 it was announced that the Icelandic Stock Exchange would merge into OMX. Since the sample period ends in May 2006, the paper considers the Iceland Stock Exchange to be a NOREX member only. 7 The complete analysis in the paper was repeated using monthly data and no major changes in results arised. Also note that using weekly data (instead of daily) may alleviate problems of serial correlation due to thin trading of stocks (Lo and Mackinley, 1998). This may very well be important given the relatively small and illiquid stock markets in the sample. 13 Interdependence of Nordic and Baltic Stock Markets

Figure 1. Index values. factor. It may also shed light on whether the extent and duration of temporary deviations from trend is relevant for diversiﬁcation purposes. The transitory components of the stock indices are plotted in Figure 2 by applying a band-pass ﬁlter based on the Baxter and King (1995) methodology.8

8 Some data points are lost with this procedure, but it should be noted that applying the Hodrick–Prescott ﬁlter to the series (which maintains the whole sample) yielded similar results. The frequency chosen for the band-pass ﬁlter is 1 (lower) and 6 (upper) weeks, with a MA component of 1 year. For intuition, the choice of frequency relates to the cycle length of weekly stock prices (rarely longer than 6 weeks) and the longer the MA component the smoother the output series. Baltic Journal of Economics 6(2) (2007): 9–27 14

Figure 2. Transitory components of stock indices.

We observe in Figure 2 that there still seems to remain some level of interdependence of the stock indices, although perhaps less so than in Figure 1. Perhaps most noteworthy are transitory movements accompanying the boom in the late 90’s and the subsequent fall in 2000-01. The highest correlation between transitory components of the stock indices is between Sweden and Finland (0.906) and the lowest is between Estonia and Iceland (0.117). Taken together, Figures 1 and 2 suggest that there may be some degree of interdependence between the stock markets and the following sections are concerned with quantifying it. 15 Interdependence of Nordic and Baltic Stock Markets

3. Methodology

3.1. Long Run Relationship

The long run relationship between stock indices is explored by (a) testing for cointegration and (b) extracting unobservable (common) factors. To test for cointegration, the Johansen test is applied to determine the number of cointegrating vectors. The Johansen test asks how many common stochastic trends – or equivalently, how many cointegrating vectors – there are across the stock indices in the sample. The identiﬁcation of the number of cointegration vectors is undertaken simultaneously to estimation (by maximum likelihood) of the short- run dynamics between indices. In other words, the test estimates multivariate cointegrating systems based on an error correction mechanism of a vector autoregressive (VAR) model, which we can generally write as

Yt = A1Yt−1 +···+AkYt−k + εt ,t= 1, 2,...,T. (1)

In this setup the vector Y follows an autoregressive process of order k with Gaussian errors, where Ak is a n × n coeﬃcient matrix. This can be rewritten in error correction form as

k−1 Yt = iYt−i + Yt−k + εt , (2) i=1 where the n × n matrix =− k A represents the short-run dynamics and the n × n i j=i+1 j = k − matrix i=1 Ai I represents the long-run impact matrix. The rank of determines the number of cointegrating vectors, i.e. it reveals the extent of integration across stock markets in the sample. The Johansen test statistic tests the null hypothesis of at most r cointegrating vectors and the process is sequentially repeated for r = 1,...,n− 1 until it fails to reject, where n is the number of stock indices in the sample. One advantage of the Johansen test is that efficiency of estimation is increased by simultaneously estimating the VAR system and the cointegrating relationship (Johansen, 1991). The long run relationship is also investigated by factor/component analysis. This involves using the correlation or covariance matrix of returns to extract unobservable factors from the series, which count for most of the variation in the data. There are various methods available for factor extraction. The two most common ones in financial analysis are principal component analysis and common factor analysis. The principal component analysis involves extracting those linear combinations (components) from the dataset that contribute most to its variance. The principal component analysis differs from common factor analysis in that it analyzes the total variance, whereas the latter analyzes common variance. In other words, the common factor model analysis is covariance oriented, i.e. it finds linear combinations of subsets of variables that share maximum common variation. = The principal component analysis defines yi wir, where r is a k-dimensional vector of returns and wi is a k-dimensional vector that is chosen such that yi and yj are uncorrelated for i = j and the variance of yi is as large as possible. In other words, the ith first principal = component of r is the linear combination yi wir that maximizes variance of yi subject to = = − the constraint wiwi 1 and covariance of yi and yj being zero for j 1,...,i 1. It can be shown that the proportion of the total variance in r explained by the ith principal component Baltic Journal of Economics 6(2) (2007): 9–27 16 is the ratio between the ith eigenvalue and sum of all eigenvalues of the covariance matrix of returns (Tsay, 2005). A disadvantage of the principal component analysis is that the number of components needed to explain variation in the data cannot be tested. A common factor model improves on the principal component analysis by allowing for such inference. A common factor model takes the general form

= + rt βf t εt , (3) where rt is a k-dimensional vector of returns, β is a matrix of factor loadings (with βij being the loading of the ith variable on the jth factor), f t is a k-dimensional vector of factors and εit is the specific error of rit and i = 1,...,k. Equation (3) can be estimated by a maximum likelihood (ML) procedure, assuming joint normality of the common factors and specific errors. The ML method has desirable asymptotic properties and produces better estimates than principal component analysis in large samples. Again, the methodology also allows for testing the number of common factors and obtaining standard errors and confidence intervals for factor loadings (Tsay, 2005).

3.2. Short Run Relationship

The short relationship between stock markets is examined by applying the generalized impulse response analysis, introduced by Koop et al. (1996) and Pesaran and Shin (1998). This differs from the traditional, orthogonalized impulse response analysis in one important way, i.e. the generalized approach is invariant to the ordering of the variables in the VAR system. The traditional impulse response analysis, such as the one based on Cholesky factorization for orthogonalization of VAR innovations, lacks this property, and hence yields different results depending on the ordering. This property of the generalized impulse analysis is therefore particularly useful in studies as this one, where economic theory gives little guidance on how to order the variables. Impulse response functions measure the time profile of the effect of shocks on the expected future values of variables in a dynamic VAR system. In other words, the impulse responses outline the reaction of one stock index to a shock in another. When the VAR system in Equation (1) can be rewritten as an infinite moving average process we get

∞ Yt = Ciεt−1,t= 1, 2,...,T, (4) i=0 where Ci is a matrix of moving average coeﬃcients obtained recursively from Ai in Equa- tion (1). Taking derivatives of this expression with respect to ε at a certain point in time gives an innovation term, i.e. the impulse response. Pesaran and Shin (1998) show that the generalized impulse response function, which measures the eﬀect of one standard error shock to the jth equation at time t on expected values of Yt+p,is

g = −1/2 = ψj (p) σjj Cpej ,p0, 1, 2,..., (5) 17 Interdependence of Nordic and Baltic Stock Markets where σjj is the jjth element in the variance-covariance matrix and ej is a n × 1 vector with unity as its jth element and zeros elsewhere. This is diﬀerent from the more traditional impulse response function,

o = = ψj (p) CpPej p 0, 1, 2,... (6) which is obtained by using the Cholesky decomposition PP = , where P is an n×n lower triangular matrix. The generalized impulse response functions are shown for the Nordic and Baltic stock indices in Section 4.2.

4. Results

4.1. Long Run Relationship

As described above, the long run relationship between stock indices is explored by testing for cointegration and by extracting unobservable (common) factors. But first the augmented Dickey–Fuller test is used to verify that the series are indeed non-stationary, i.e. the test fails to reject non-stationarity for all series, but rejects non-stationarity after taking first differences (details reported in Appendix B). The appropriate lag length of the vector autoregressive (VAR) system is determined by the multivariate generalizations of the Akaike and Schwarz information criteria, which suggest a model with 3 or 1 lags, respectively. The analysis below proceeds with a VAR specification of 3 lags. The results of the Johansen cointegration test are reported in terms of both local currencies and the Swedish krona in Table 2. Both tables give Johansen trace statistics assuming an intercept but no deterministic trend in the cointegrating relationship (results are robust to including a linear trend). Since the development of stock exchange cooperation has been gradual throughout the sample period, there is no clear cut date at which to test for a structural break in the data. Also due to limited observations, the sample period is simply to short for it to be broken into more than two parts. Hence the sample is split into half at May 2001. At this point four countries were members of NOREX, but the OMX group was established 2 years later. The number of cointegrating vectors in each period is indicated in Table 2 by bold letters. The results show that there are 2 cointegrating vectors over the

Table 2. Johansen test of cointegration

Ho: No. of Local currency Swedish krona Critical value coint. Former Latter Whole Former Latter Whole (5%) vectors period period period period period period None 106.74 91.34 117.10 101.93 97.95 126.73 94.15 At most 1 66.32 54.42 75.47 58.27 54.33 74.97 68.52 At most 2 40.68 30.47 46.66 36.18 25.87 44.04 47.21 At most 3 23.14 14.79 23.65 20.07 12.85 20.47 29.68 At most 4 13.05 6.45 9.10 10.09 4.73 9.65 15.41 At most 5 4.11 0.47 0.03 3.97 0.67 0.00 3.76 No. of obs. 256 256 516 256 256 516 Start date 07/03/1996 06/27/2001 07/03/1996 07/03/1996 06/27/2001 07/03/1996 End date 05/23/2001 5/17/2006 05/17/2006 05/23/2001 5/17/2006 05/17/2006 Baltic Journal of Economics 6(2) (2007): 9–27 18

Table 3. Principal component analysis of returns Eigenvalues Cumulative proportion of variation explained by components Former Latter Whole Former Latter Whole period period period period period period 1 component 2.31 2.66 3.10 0.39 0.44 0.52 2 components 0.17 1.01 1.00 0.58 0.61 0.68 3 components 0.92 0.96 0.91 0.73 0.77 0.84 4 components 0.81 0.84 0.41 0.87 0.91 0.90 5 components 0.46 0.31 0.36 0.94 0.96 0.97 6 components 0.34 0.23 0.21 1.00 1.00 1.00 No. of obs. 259 260 519 259 260 519 Start date 6/12/1996 5/30/2001 6/12/1996 6/12/1996 5/30/2001 6/12/1996 End date 5/23/2001 5/17/2006 5/17/2006 5/23/2001 5/17/2006 5/17/2006 whole period. Further, there is 1 cointegrating vector in the first period, while only 0 or 1 in the second period. There is therefore no indication of increased integration of the stock markets over the sample period. It is noteworthy that not only is there no evidence of increased integration, but there are only 2 cointegrating vectors among the 6 stock indices over the whole sample. This does not strike one as a particularly deep level of integration. For comparison, Bessler et al. (2003) use weekly data to find only 1 cointegrating vector among the markets of USA, S.Africa, Egypt, Morocco, Nigeria and Zimbabwe, while Fraser and Oyefeso (2005) use monthly data to find full integration of 8 cointegrating vectors among stock markets of USA, UK, Germany, France, Italy, Belgium, Spain, Denmark and Sweden. Of the few studies that focus on Scandinavian markets, Malkamäki et al. (1992) find no cointegration among indices when using daily returns over the 1988–90 period. The results of the principal component analysis are shown in Table 3. Over the whole period it can be seen that 4 factors are needed to explain over 90% of the variation in the data. Splitting the sample in half as before, shows that 5 factor are needed to explain over 90% of the variation in the former period, but 4 suffice in the latter. Comparing the explanatory power of each component in the two periods hints towards increased common variability among stock index returns, but the extra explanatory power in the latter period is only a few percentage points higher for each component. In short, the results slightly lean towards increased comovement of stock indices, but by no means strongly support it. Using monthly returns produces very similar results (not reported). A common factor model, such as the one described in Section 3.1, is also estimated. Unfortunately, for this particular data set, estimation of the common factor model produces communality estimates (portion of variance in returns contributed by the common factors) exceeding 1. This mathematical peculiarity, which is referred to as an ultra-Heywood case, renders a factor solution invalid. This happens when one or more eigenvalues, which are the variances of the factors, are negative in value.9 There are no available routes to address

9 More speciﬁcally, a Heywood case occurs when returns are perfectly correlated with a linear combination of the factor returns so that the unique variance of a series is equal to zero (negative in ultra-Heywood case). The ML method is especially prone to Heywood cases since during the iteration process, a variable with high communality is given a high weight, which tends to increase its communality, which increases its weight, and so on. But here 19 Interdependence of Nordic and Baltic Stock Markets

Table 4. Common factor analysis of returns Eigenvalues Cumulative proportion of variation explained by components Former Latter Whole Former Latter Whole period period period period period period 1 component 2.473 2.837 1.043 0.820 0.799 0.262 2 components 0.375 0.542 2.756 0.944 0.952 0.955 3 components 0.168 0.170 0.177 1.000 1.000 1.000 2 * Ho: No. of factors χ statistic At most 1 13.89 23.21 24.34 At most 2 2.54 1.29 2.31 No. of obs. 259 260 519 259 260 519 Start date 6/12/1996 5/30/2001 6/12/1996 6/12/1996 5/30/2001 6/12/1996 End date 5/23/2001 5/17/2006 5/17/2006 5/23/2001 5/17/2006 5/17/2006 *Degrees of freedom are 9 for testing at most 1 common factor and 4 for testing at most 2 factors. The corresponding critical values are 3.325 and 0.711. a Heywood case, other than dropping factors associated with negative eigenvalues. Table 4 reports the results of the common factor analysis, where the factor space is reduced to 3 dimensions due to negative eigenvalues associated with other factors.10 The results are therefore of limited value and clearly not comparable to the results of the principal component analysis. Table 4 shows no evidence of increased explanatory power of common factors in the latter period of the sample and thus no indication of increased interdependence of the Nordic and Baltic stock markets in sample period. To sum up the long term relationship between indices, there seems to be limited interdependence among stock indices and there is no evidence for increased cointegration in the period of merger activity. It seems that any current cointegrating relationships were already established before 1996. A principal component analysis yields similar results, although there is some (weak) support of increased interdependence in variation of returns. 4.2. Short Run Relationship

The short-run interdependence is more relevant for short term investors who are e.g. looking for diversiﬁcation of risks. The short run dynamics are explored by e.g. analyzing the speed at which shocks are transmitted from one market to another, which indicates responsiveness of markets and the eﬃciency with which new information is transmitted between markets. Again the leading question is whether the increased merger activity and deeper level of cooperation has translated into contemporaneous co-movement of stock indices.

the problem persists for other estimation techniques as well, such as an unweighted least-squares estimation. Using monthly data does not bypass Heywood cases either. Heywood cases actually occur fairly frequently in practice, see e.g. Cho and Taylor (1987), Cho et al. (1984) and Diamond and Simon (1990). See also Sentana (2000) for a nice mathematical description. 10 These results are produced using weekly returns. Note that the common factor analysis assumes no serial correlation in returns, which is violated with weekly returns. However, in such cases one can remove the linear dynamic dependence of the data and apply factor analysis to the residual series. Fitting a VAR(4) model to returns removes the serial dependence and has hardly any eﬀects on the corresponding correlation matrix (see Appendix B). Therefore the factor analysis can in this case be applied directly to the return series (Tsay, 2005). Baltic Journal of Economics 6(2) (2007): 9–27 20

As described in Section 3.2, the methodology used to analyse the short run relationship of stock indices is the generalized impulse response analysis, credited to Koop et al. (1996) and Pesaran and Shin (1998). But additionally, it is useful first to briefly look at short run dynamics by testing for Granger causality between the series. This is both informative in terms of comparison to previous studies of the Nordic stock markets and it also has implications on what indices one might want to include in the vector autoregressive model previously presented. The results (reported in Appendix B) show that the Swedish stock index was most often a significant predictor of other stock indices in the sample period. This is consistent with previous work, which typically found the Swedish stock exchange to be leading other Scandinavian markets. Also, the Granger causality analysis indicates that overall the stock indices seem interrelated and no stock market is completely independent of other markets. This supports using all indices when specifying the vector autoregressive (VAR) model. The generalized impulse response functions (IRF), i.e. the response of one stock index to a shock in another, are obtained for all 6 stock indices. These responses to 1 standard deviation change in other stock indices are plotted in Figure 3. There are mainly two things to notice about Figure 3. First, the responses show long- lasting effects on stock indices following a shock in other markets. After half a year the stock indices have in many cases not settled back to its pre-shock level. These long lasting effects are consistent with results of other studies (e.g. Masih and Masih, 2001). But secondly, even though the impact is generally long-lived, all responses are very small in scale, using a conventional definition of significant responses as those that exceed 0.20 unit standard deviations.11 Figure 3 indicates that not a single response can be deemed significant using this cutoff value. For example, the Finnish stock market responds most strongly to changes in the Swedish stock market (excluding the Finnish stock market itself, of course), peaking in the third week with a response value of around 0.10 units of standard deviations following a 1 unit innovation in the Swedish market. It is therefore of little value to wade through the shape of the responses, since the level of impact is in all cases smallish.12 It is informative to convert the responses into percentage changes of index values. For example, the 0.10 unit response of the Finnish stock market to the Swedish one, is equivalent to roughly a 2.6% change in the Finnish stock index. This may perhaps seem a significant, weekly response, but it is important to note that the stock indices have been quite volatile in the sample period and thus 1 standard deviation is a fairly large innovation (see summary statistics, Appendix A). In fact, a one standard deviation innovation in the Swedish market corresponds to roughly a 29% change in its stock index (using average values), making the 2.6% response of the Finnish market seem miniature. It should be noted that even though the stock markets hardly react to changes in other markets in the short run, these small responses accumulate over the long run. The responses of the four Scandinavian stock markets to each other become cumulative significant (above 0.2 units of st.dev.) after 3–5 weeks. So from this point of view the markets can be considered interdependent. But in general the responses are nevertheless low, there is for example stronger short-run interdependence between five emerging African stock markets as shown by Bessler et al. (2003).

11 This cutoﬀ is suggested and used by e.g. Dekker et al. (2001), Bessler et al. (2003), Masih and Masih (2001) and Yang et al. (2003b). 12 This result also holds true when using returns in Swedish krona terms. 21 Interdependence of Nordic and Baltic Stock Markets

Figure 3. Generalized IRFs to 1 st.dev. innovation in stock mkt. indices.

As in Section 3, the sample is split in half to investigate whether the short run dynamics across stock markets have changed over the sample period. The results indicate that neither subperiod has signiﬁcant responses. Hence there is no indication of increased interdependence in the short run as the stock exchanges have increased the level of cooperation. Baltic Journal of Economics 6(2) (2007): 9–27 22

5. Concluding Remarks Given that the results indicate limited interdependence of stock markets and lack of strong evidence of convergence in times of increased cooperation, the obvious question to ask is what determines interdependence and convergence? Kim et al. (2005) find an unidirectional causality from the introduction of the European Monetary Union on European stock market integration. One can speculate if this may help to explain the lack of interdependence between the sample countries in this study, since only one country (Finland) in the sample is a member of the EMU. Masih and Masih (2001) postulate several other determinants of the interrelationship and integration among stock markets, such as deregulations, influence of multinational corporations, innovations in financial products and technology, etc. Determining the exact causes of stock market interdependence is an interesting (but tricky) battle left for future analysis. Future work may use a similar framework as is applied here to examine whether other stock exchange interactions of a deeper level have induced increased interdependence. For example, the Euronext merger between the stock exchanges of France, Belgium, Netherlands and Portugal is an interesting case, where the level of cooperation has been taken towards a fully formalized merger. It would be interesting to see if the interdependence has consequently become greater within the Euronext members and compare such an analysis to the Nordic and Baltic markets. Outside Europe there are further interesting, unexplored examples of increased merger activity. Besides ongoing discussions of transatlantic consolidations of the New York Stock Exchange or NASDAQ with either Euronext or the London Stock Exchange, many within country mergers have taken place, e.g. in Colombia, Japan, India, etc. With regards to the Nordic and Baltic markets, the weak interdependence of stock indices implies that there is still room for international diversification in the area. One might expect considerable market adjustment between the stock markets if the merger activity continues to deepen, since with further unification of exchanges the stock prices are bound to adapt to institutional changes. 23 Interdependence of Nordic and Baltic Stock Markets

Appendix A

Table 5. Data description Datastream Symbol Currency Description Denmark COSEASH DKK The OMX Copenhagen all-share index. Estonia ESTALSE EUR OMX Tallinn Index. Prices of shares listed in the Main and Investor lists of the Tallinn Stock Exchange. The base date of the Index is June 3, 1996 and the base is 100. Finland HEXPORT EUR All stocks listed on the Main list of the Helsinki Stock Exchange. Iceland ICEXALL ISK Comprises all ICEX listed equities. Has been cal- culated and published daily since January 1993 (31 December 1997 = 1000). Norway OSLOASH NOK This is a capital-weighted yield index with a base of 100 on January 1st 1983. It comprises of all shares on the main list. Sweden SWSEALI SEK The OMX Stockholm all-share index. The indices are in local currency terms, except Finland and Estonia are reported in Euros. Start of sample period is due to data only being available through Datastream on 3 indices prior to 1996.

Table 6. Summary statistics Mean St.dev. Skewness Kurtosis JB stat. Index points Denmark 216.75 60.74 0.83 3.51 Estonia 256.52 171.00 1.29 3.52 Finland 3200.94 808.60 0.67 3.09 Iceland 1843.61 1209.62 1.71 5.03 Norway 187.59 73.92 1.92 6.62 Sweden 221.08 63.46 0.67 2.98 Returns Denmark 0.23% 2.26% −0.54 6.16 240.63 Estonia 0.36% 4.59% −0.48 10.32 1178.68 Finland 0.19% 2.90% −0.65 5.94 222.70 Iceland 0.38% 1.86% −0.28 5.72 166.67 Norway 0.23% 2.73% −0.79 5.32 170.93 Sweden 0.19% 3.20% −0.53 5.34 143.97

Table 7. Correlation of prices (index values) Denmark Estonia Finland Iceland Norway Sweden Denmark 1 Estonia 0.742 1 Finland 0.723 0.334 1 Iceland 0.857 0.898 0.505 1 Norway 0.939 0.859 0.650 0.921 1 Sweden 0.724 0.232 0.953 0.421 0.587 1 Baltic Journal of Economics 6(2) (2007): 9–27 24

Table 8. Correlation of returns on stock indices Denmark Estonia Finland Iceland Norway Sweden Denmark 1 Estonia 0.179 1 Finland 0.647 0.202 1 Iceland 0.090 0.067 0.134 1 Norway 0.636 0.196 0.662 0.071 1 Sweden 0.642 0.202 0.785 0.085 0.636 1

Appendix B

Table 9. Augmented Dickey-Fuller test statistic for price series Levels 1st diﬀerence Denmark −0.993 −22.529 Estonia −1.030 −11.543 Finland −1.456 −22.291 Iceland 0.798 −8.467 Norway 1.284 −21.160 Sweden −1.490 −24.603 The 5% critical value is −3.418503 and the null hypothesis of non-stationarity is rejected if the test statistic is lower than critical value. In all cases the speciﬁcation includes a constant and a time trend.

Table 10. Lag length speciﬁcation for VAR model (prices) Lag Akaike Schwarz 0 70.065 70.114 1 47.678 48.025* 2 47.610 48.256 3 47.569* 48.513 4 47.616 48.858 5 47.621 49.161 6 47.638 49.476 7 47.661 49.797 8 47.706 50.139 *Indicates lag order selected by the criterion. Included observations: 512. 25 Interdependence of Nordic and Baltic Stock Markets

Table 11. Correlation matrix for residuals after ﬁtting a VAR(4) model to returns Denmark Estonia Finland Iceland Norway Sweden Denmark 1 Estonia 0.159 1 Finland 0.635 0.173 1 Iceland 0.059 0.016 0.105 1 Norway 0.641 0.171 0.667 0.053 1 Sweden 0.652 0.193 0.803 0.066 0.643 1 This can be compared to Table 8 which shows correlation of returns.

Table 12. Granger causality test statistic – returns Denmark Estonia Finland Iceland Norway Sweden causes. . . causes. . . causes. . . causes... causes. . . causes...... Denmark? – 1.802 0.416 0.375 0.235 1.778 ...Estonia? 5.661 – 1.165 0.123 3.512 3.639 . . . Finland? 0.025 1.217 – 1.358 0.000 4.910 ...Iceland? 5.424 0.662 1.850 – 2.745 3.249 . . . Norway? 0.176 1.919 0.317 0.553 – 0.143 . . . Sweden? 0.082 0.330 0.074 0.011 0.433 –

Bold letters indicate that one variable Granger causes another (10% sign. level). The null hypothesis is that x does not Granger cause y, i.e. that x does not contain information that helps to predict y. Rejection of this hypothesis is presented here as x causing y (knowing x helps predicting y). Granger causality is tested using a model specification with 1 lag (1 week forecasting horizon), determined by applying the Akaike and Schwarz criteria (see Table 10). In Table 13, the Granger causality test is presented using prices instead of returns. This is done in the spirit of Sims et al. (1990), who argue that differencing variables before carrying out the Granger causality tests may not be necessary. Now there are more significant causation relationships between indices, with the Danish and the Icelandic stock indices being the most frequent predictors of other stock indices in the sample period (here 3 lags are used, again determined by applying the Akaike and Schwarz criteria).

Table 13. Granger causality test statistic – prices Denmark Estonia Finland Iceland Norway Sweden causes. . . causes. . . causes. . . causes... causes. . . causes...... Denmark? – 3.028 0.560 9.316 1.485 1.203 . . . Estonia? 1.348 – 1.448 2.078 1.427 1.336 . . . Finland? 0.463 1.054 – 2.626 0.791 3.288 ...Iceland? 3.227 1.952 1.108 – 0.554 1.474 ...Norway? 2.753 2.764 0.964 11.118 – 0.476 . . . Sweden? 3.723 0.528 4.152 2.158 1.184 – Baltic Journal of Economics 6(2) (2007): 9–27 26

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Lineta Ramoniene * and Dovydas Brazys

ISM University of Management and Economics Lecturer, Lithuania Christian College

Abstract: This paper uses the theory called Difference Assessment Account to hypothesize on Euro introduction effects on Lithuanian and Latvian consumers’ behavior. Difference Assessment Account itself describes how consumers perceive value of transactions when nominal value of currency is varied, and how this perception affects their economic decisions. The paper explains Difference Assessment Account and tests this theory with an experiment, which’s participants are Lithuanian and Latvian consumers. The results of the experiment serve as a basis for the discussion of the assumptions on Euro introduction effects on Lithuanian and Latvian consumers.

1. Background and Rationale Mark, Lira, Peseta, Punt, Drachma. Until the year 2002, these words carried the significant meanings. Frank – France. Mark – Germany. Lira – Italy. Peseta – Spain. Punt – Ireland. Drachma – Greece. They represented each state’s unique position in the international trade area in terms of having different monetary units. Today these words carry only a historical significance. On January 1, 2002 when the Euro became a single currency of a group of EU member states, the former currencies’ notes and coins lost their international validity. Lita and Lat. These words still represent two EU member countries’ currencies. Lita – Lithuanian. Lat – Latvian. These countries still use their national currencies but plan to introduce Euro in the near future. The intentions are to introduce the Euro as soon as the countries meet the EU required economic criteria. Both countries expect to join Euro zone in 2008. Euro introduction topic raises a lot of discussion in the media at the present time. Economists, government officials and journalists try to identify and forecast possible Euro introduction effects on countries’ economies. However there is a lack of scientific discussion of how currency change will affect consumers’ behavior. Therefore, in this paper we look at the issue of currency change by taking the perspective of consumers. Mainly, the paper looks at how/if consumers’ perceptions of economic transactions will change when the country introduces Euro. Will consumers be willing to spend more or less? Currently people are familiar with sets of nominal values in their national currencies, namely sets in Litas and Lats. But the currency change will result in all monetary transactions’ presentation in different nominal values. Soman et al. argues that in the situation of currency change consumers’ perception of value of transactions is altered by the currency

* Corresponding author. E-mail: [email protected] Baltic Journal of Economics 6(2) (2007): 29–55 30 numerosity. According to the theory called Difference Assessment Account, consumers perceive the value of transactions by evaluating the difference between the budget (the amount of money they possess) and the price. Is it really true in the Lithuanian and Latvian contexts? This paper explains the Difference Assessment Account (DAA) and provides experimental evidence supporting the theory. The experiment, conducted with Lithuanian and Latvian consumers, tests the prevalence of Difference Assessment Account. Finally, the paper discusses possible currency change effects on consumers’ spending behavior. The study is delimited to defining one model of decision making (DAA), applying it to a real world setting and discussing its implications. It is delimited to the discussion and testing of the model of reasoning that consumers use to asses the value of transactions, and thus to make decisions. Whereas several theories of how consumers make their buying decisions exist, this study concentrates on questioning the assumption that consumers tend to rely on this particular model when making their economic decisions. The study takes perspective on consumers’ behavior in the situation of national currency change in two Baltic region countries, Lithuania and Latvia. It does not try to argue for pros and cons of introducing a single currency in Lithuania and Latvia. The study includes the experiment to test the DAA existence among Lithuanian and Latvian consumers. Due to financial and time restrictions, the experiment participants’ number is delimited to 58 (20 of them being Latvian, the 38 Lithuanian citizens), all of them being students of a private college in Lithuania, Lithuania Christian College (LCC). Other specific limitations of this research are discussed later in the paper as the experiment results are revealed.

2. Literature Review

2.1. Brief History: Lithuania, Latvia and European Union

Lithuania and Latvia are countries of the Baltic region of Europe that have similar historical backgrounds. Both of them were a part of Soviet Union until regaining their independence in 1991. This was an event, which led the countries towards new route of political and economic governance. The development of democratic states with a free market economy was the initial goal of new governments. Both countries, Lithuania and Latvia were developing throughout the 90’s and transitioned from planned economy very successfully, exhibiting significant economic growth since 2000 (see Table 1). Tremendous later economic growth and efforts to achieve economic convergence with EU member countries have qualified Lithuania and Latvia as strong EU candidates. The accession to the EU was achieved on May 1st 2004. Being members with a current rapid economic growth, countries get positive future forecasts.

Table 1. GDP Growth in Lithuania and Latvia % Gross Domestic Product average annual growth % 1990–2000 2000–2004 Latvia −1.6 7.4 Lithuania −2.7 7.5 Source: World Bank “2006 World Development Indicators”. 31 Euro Introduction Eﬀcts on Individuals’ Economic Decisions

Full integration within the EU is achieved when countries converge in terms of economic policies’ and governance aspects. One of the important convergence aspects is member countries’ participation in the Economic and Monetary Union (EMU). The EMU is the phenomenon where EU member countries establish a single currency among them (“Economic and Monetary Union”). The single currency, Euro, was first introduced on January 1, 2002. Twelve countries that shifted from their old currencies to the Euro were Austria, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal and Spain (“Euro”). One of the conditions for Lithuania to enter the European Union was that Lithuania should become a member of EMU as soon as the country is qualified for this event. The fact that Lithuania is the EU member actually means that sooner or later the country will have to replace its existing currency Litas with a new currency, Euro. Therefore, soon after Lithuania was accepted to the EU, the publications concerning the Euro introduction appeared in Lithuanian media. Lithuanian government planned to introduce Euro as early as 1st January 2007 however the country did not meet the inflation requirements as set in the Mastricht agreement. So now the most optimistic date for introducing the Euro is 1st January 2008. Latvia also plans to join the EMU 1st January 2008. The main reason for the delay in this country was the requirement to peg its currency to the Euro first, which happened on January 1, 2005 (“Bank of Latvia Pegs Lat to Euro”). According to the president of the Bank of Latvia, EU institutions should begin the country’s economic assessment in 2007 and give the response concerning the introduction of Euro. Due to positive economic performance indicators Latvia has displayed over recent years, the president is optimistic. He predicts that “that the response will be positive, because even now Latvia’s economy conforms to all the criteria for introduction of the common European currency” (“Latvia Likely to Introduce Euro as of 2008: Central Bank”). Most publications in Lithuanian mass media, in which the issues of Euro introduction were addressed, exhibited a few trends. They showed the trend that country’s industrialists, banks and government were in favor of Euro introduction. The reason for them to favor the upcoming currency change was the expected positive Euro introduction effects to the Lithuanian economy and businesses. Lithuanian Confederation of Industrialists listed the main arguments as: vanished costs of conversion of Euro and Litas would allow Lithuanian businesses to save significant amounts of capital; the interest rates for borrowing would decrease; and the risk of financial instability because of the currency fluctuations would decrease as well (“Public report of the Lithuanian Confederation of Industrialists concerning the launch of Euro in Lithuania”). The Bank of Lithuania advertised the same advantages of Euro introduction and stated that currency change would contribute to the growth of international trade and economic prosperity of the country (“Benefits of the Adoption of the Euro”). However there is lack of scientific discussion on how currency change will affect consumers which leads us towards the theory of consumers’ psychology of decision-making. 2.2. Consumer Theory in Economics

Neo-classical economic theory of the consumer predicts that consumers’ decisions are based on rational reasoning and they decide to maximize their utility (Economics: Principles and Policy). The theory suggests that consumer choices are inﬂuenced by the prices of the goods and services, consumers’ income levels and consumers’ tastes or so called preferences. Consumers’ incomes determine consumers’ budgets. Therefore, it can be restated that the Baltic Journal of Economics 6(2) (2007): 29–55 32

Figure 1. Basic model of how consumers arrive at their decisions. interaction between the prices, budgets and tastes or preferences lead consumers to their economic decisions (Figure 1). The factor of consumers’ tastes or preferences varies with every individual consumer; therefore this factor is assumed to be constant and will not be discussed in the course of the study. Thus, prices and budgets in this discussion remain the only factors in the consumers’ decision-making process. Price, or the amount of money the good or service costs, serves as a measure of value of this good or service. Budget serves as a reference standard. Studies on mental budgeting show that consumers budget their incomes for different spending categories. They evaluate prices against their budgets and use the budget as a reference standard for the purchase to take place (“Mental Budgeting and Consumer Decisions”). Therefore, consumer decision depends on the size of price of a good or a service and on the size of the budget at consumer’s disposition. Further discussion will include the importance of the perceived value of prices and budgets to the decision-making and how the decisions are affected when consumers re-value their prices and budgets when converted into different currencies. Soman et al. in the study on consumers’ perception of value of money claimed that consumers use a numerosity heuristic in the process of decision making and that they rely on the nominal value of money when judging the value of transactions. This claim was a basis for their decision making model called DAA.

2.3. The Concepts of Numerosity and Numerosity Heuristic

Numerosity is the number of units into which a stimulus is divided (Pelham et al., 1994: 103). It is not the same concept as quantity. For example, we could divide a 500 g cake into four 125 g pieces or into ten 50 g pieces. In both cases the quantity of the cake remains the same i.e. we would still have 500 g of the cake. What differs in each case is the numerosity. We would have either four or ten pieces of the cake. Research in psychology by Pelham et al. showed that “people are especially sensitive to numerosity as a cue for judging quantity.” They sometimes evaluate the amount “on the basis of the number of units into which a stimulus is divided without fully considering other important variables (e.g. the size of units)” (Pelham et al., 1994: 103). This is called numerosity heuristic. Specifically, they tend to overestimate the total quantity of stimulus, when they are presented with the stimulus divided into units. In general, numerosity heuristic is a false belief that if it is many, it is also much. People evaluate the object (the stimulus) as bigger or more valuable if it is divided into more units. If we would use the example with the cake to illustrate numerosity heuristic, it would turn out that one should judge a cake as 33 Euro Introduction Effcts on Individuals’ Economic Decisions bigger when it is divided into ten pieces than when it is divided into four pieces. Therefore, “the number of units of a given stimulus plays a larger role than the size of each unit in influencing individual behavior” (Soman et al., 2002: 7). The concept of numerosity could also be applied to other quantities, e.g. money. Let it be that we own 100 EUR and exchange them into 345 LTL (exchange rate: 1 EUR = 3.45 LTL). In such a case the quantity of money does not change; we still have the same real amount. What changes is the numerosity of the stimulus. The same amount expressed in Litas becomes more numerous than expressed in Euros (345 > 100). Soman et al. proposed that consumers apply numerosity heuristic when making economic decisions. “Specifically, their decisions are biased by the numerosity of the currency, within which they are made” (Soman et al., 2000: 8). As numerosity plays a role in people’s evaluations of quantity, the same amount expressed in more numerous currency, the Lita would appear as bigger than expressed in Euros. Besides numerosity heuristic, there is another factor, which biases people’s perception of value of money. This phenomenon, called “Money Illusion”, is a person’s bias towards the nominal value of money.

2.4. Money Illusion – The Role of Nominal Value of Money in Decision Making

In economics, there are two concepts of the value of money. Nominal and real values of money differ from each other. Nominal or so-called face value is the current value indicated on the currencies’ notes. For instance, nominal value of a ten Litas note would be ten, whereas its real value might be different. Because of inflation, the real value of money changes over time. In economic theory, the assumption exists that people rely more on nominal than real value of money when making economic decisions. The “focus on nominal rather than real value when making economic decisions was described in the economics literature over 70 years ago by Fisher” (Soman et al., 2002: 4), who called this phenomenon “Money Illusion”. In the study of Shafir et al. (1997) the prevalence of money illusion among people was explored. “Economic transactions can be represented either in nominal or in real terms” (Shafir et al., 1997: 347). Whereas economic literature provides with methods to assess the real value of transactions, in every day situations people tend to deal with nominal representations, as they face nominal prices of goods and services. Nominal representation (goods priced in nominal prices) is easily accessible, the simplest and most obvious representation of value. When people are presented with nominal values, it is different to adjust for the real values, as it requires application of economic knowledge, which obviously is not possessed by everyone. To find out the real value of the goods and services, on the other hand, one has to calculate it by adjusting for the inflation over the periods of years. This cannot be done without specific information and without particular economic data. It can be assumed that the proportions of people who have economic background and who can reason in economic terms are relatively small among populations. Also, it can be assumed that a relatively small number of consumers know that there are such concepts as real and nominal values. On the other hand, even if “people are generally aware of that there is a difference between real and nominal values, but because at a single point of time, or over a short period, money is a salient and natural unit, people often think of transactions in predominantly nominal terms” (Shafir et al., 1997: 347). Baltic Journal of Economics 6(2) (2007): 29–55 34

The study by Shafir, Diamond and Tversky shows a high probability of these assumptions holding true, as the study shows that money illusion is an existing phenomenon among people. It was illustrated in a series of experimental studies of North American people. Surveys were distributed to more than thousand people in U.S. airports, malls and universities. In one of the experiments people were now less likely than before to buy an armchair whose price was $400 six months ago and rose to $500 now. It is important to note that the inflation in that six-month period was 25%, which indicates that the real value of an armchair did not change. In the same experiment people were more likely to sell an antique desk now for $500 than for $400 six months ago. It shows that nominal value plays an important role in consumers’ valuation of transactions. The value of the good is judged based on nominal value, which is most easily noticeable and accessible. This study supports the proposition that consumers rely on nominal value rather than real value when making economic decisions. “Money Illusion primarily refers to the focus on the numerical face value of a given amount of money without regard to its real value (adjusted for inflation). But there are also situations in which the nominal value of a sum of money may change as a function of external factors” (Soman et al., 2002: 4). Such a factor could be a currency change. 2.5. Decision-Making in the Context of Currency Change This section joins the importance of numerosity and the significance of nominal value in the consumers’ decision-making process in the context of the currency change. This relationship was first studied by Soman et al. in their paper “Currency Numerosity Effects on the Perceived Value of Transactions”. The analysis of the decision making process becomes more complex, as it includes currency change. Are consumers’ perceptions of the value of a good and service and thus, their decisions, likely to change when the good or service is priced in a different currency? “Re- search in cognitive psychology indicates that alternative representations of the same situation can lead to systematically different responses” (Shafir et al., 1997: 345). In this study the “situation” is: “the good’s or service’s nominal price”. Situation’s “alternative representation” is: “the same good priced in a different currency”. For example, the “situation” is: “a sweater costs 125 LTL”. Then, the alternative representation would be: “a sweater priced in Euros costs 36.2 EUR”. (Exchange Rate: 1 EUR = 3.45 LTL.) In such a setting, the real value of the good (a sweater) would not change. But there would be a change in the nominal (face) value of the good – the nominal value of the good would change from 125 to 36.2. Also, this example illustrates how the numerosity of a price changes when the same price is presented in two different currencies. It comes out that the price in LTL is more numerous than the price in EUR (125 > 36.2). Assuming that people rely on numerosity heuristic and the nominal value when making decisions, what could be consumers’ responses to this alternative representation? Is it possible that decision maker perceived the same good, priced in a different currency, as of a different value? If yes, what could be the reasoning pattern used by consumers that would explain the relationship of numerosity and nominal values in decision making process? 2.6. Difference Assessment Account D. Soman, K. Wertenbroch and A. Chattopadhyay were the first to notice this relationship and proposed a model consumers follow to arrive at their decisions. The model, called DAA, assumes that (1) consumers compare prices to their budgets; (2) the nominal value of currencies and their numerosity play the major role in making the decision. 35 Euro Introduction Effcts on Individuals’ Economic Decisions

“According to the difference assessment account, consumers make an approximate judg- ment of the difference between price and a reference standard such as their income when evaluating a purchase decision” (Soman et al., 2002: 19). According to the DAA, the more numerous the difference between the budget and the price, the more consumers are willing to spend. To illustrate this we will use an example of a pair of jeans, priced in two different currencies, Euros and Litas (1 EUR = 3.45 LTL). The consumer’s budget is 300 EUR or 1035 LTL. Accordingly, the pair of jeans costs 50 EUR or 172 LTL. According to the findings, the person given a budget of 300 EUR would be less willing to pay 50 EUR for the jeans than given a budget of 1035 LTL, jeans costing 172 LTL. Even though the real price was the same (50 EUR = 172 LTL), person would be more likely to buy jeans with the nominal price of 172 LTL than 50 EUR, because the nominal difference between the budget and the price was more numerous [(1035 − 172 = 863)>(300 − 50 = 250)]. This example shows the change of nominal values of goods, when they are priced in two different currencies. The real value of the pair of jeans is always the same i.e. 50 EUR or 172 LTL. Even though the real values of goods did not change, they would perceive the same good as of a different value because of differing numerosity of currencies. This would affect the decision. It shows how the same situation represented in different ways can lead to different responses. If assuming that people use DAA as a decision making tool, the prediction is that the price would appear less expensive when it is shown in a more numerous currency. It would be the case because then the difference between the budget and price would be more numerous (863 LTL, rather than 250 EUR). When comparing the price of a good to their budget, people would be more willing to spend in this situation. In the experiments, conducted by Soman et al., the Difference Assessment Account was tested among the universities’ students in the U.S. and Hong Kong. “As predicted only by the difference assessment account, the numerosity of the target currency increased participants’ stated purchase likelihood. Hence, the more numerous both nominal prices and incomes, the more likely consumers are to purchase a discretionary product” (Soman et al., 2002: 19). The scenario in the experiments of Soman and his colleagues is very similar to the situation Lithuania and Latvia are going to face in the upcoming years when they introduce Euro. Soman et al. used imaginary countries and imaginary currencies in the scenario of the experiments. But the prevalence of DAA was never tested in a real world setting; therefore we set the goal in testing DAA in a situation of Lithuania and Latvia changing their currencies from Lita and Lat to Euro. When giving the first example with a pair of jeans, we already related Difference Assessment Account to the scenario of Lithuania changing its currency from the Lita to the Euro. In this case, prices of goods and services in Litas would be more numerous than prices in Euros (1 EUR = 3.45 LTL). In the situation of Latvia, the numerosity would change the opposite way because the Lat is less numerous than Euro (1 EUR = 0.7 LVL). What could be the Lithuanian and Latvian consumers’ responses to a similar experiment? Would the results of the experiment, which uses real currencies in its scenario, accompany the results of previous experiments by Soman, Wertenbroch and Chattopadhyay? These questions are answered in the next section of this paper. Although this paper explains and is limited solely to the DAA theory, other consumer behavior theories also exist. Although there has not been much research done in the field of consumers’ psychology regarding their perception of transactions in the situation of currency change, there are few alternative theories to DAA. Baltic Journal of Economics 6(2) (2007): 29–55 36

2.7. Alternative Consumer Behavior Theories

In a study on price valuation in foreign currencies by Raghubir and Saristava (2002), authors suggest that consumers’ behavior when evaluating prices in foreign currencies is anchored to the home currency consumers are used to. The simple explanation of this anchoring process could be given by using the example of American, who spends in Canada or in United Kingdom. Canadian dollar is worth less than US dollar; therefore nominal prices are higher in Canada than in the U.S. Thus, an American consumer in Canada would underspend because of being used to US dollar prices and perceiving Canadian prices as higher than they actually are. Contrarily, UK pound is worth more than US dollar and nominal prices in UK are lower than those in the U.S. Then, in UK the American consumer would perceive lower nominal prices as lower real prices and thus overspend. It is important to mention that anchoring and adjustment is a competing theory to DAA, as it predicts opposite effects to what DAA suggests. Romani and Dalli (2003) in their study based on one product category tested this theory on university students. Consumers perceived the price of a product expressed in Euro as of lower value than when expressed in Lira (1 EUR = 1936.27 Liras), which corresponds to anchoring process. However, another study showed that these effects did not last long because consumers quickly adapted to the new currency (Romani and Dalli, 2003: 1). Cannon and Cipriani (2002) challenged this theory by conducting a natural experiment on church collections in Italy, Ireland and other Europe countries where Euro was introduced. Authors collected and analyzed data on church collections before and after Euro introduction; this was non-laboratory experiment, therefore there were no bias in people behavior. The results suggested that the effect of DAA outweighed effects of other possible behavioral patterns. Cannon and Cipriani (2003) suggest that there may be other ways than numerosity through which changes in nominal prices could influence behavior; they suggest the concept called psychological threshold. “Certain prices are more “attractive” than other prices and there may be a “1 Euro” effect: the unit of the new currency might become a new psychological threshold or base price. For example, in Italy 1000 Lira, the smallest note in circulation, was considered a psychological threshold, and this role might be taken by the 1 Euro coin, worth twice as much.” (Cannon and Cipriani, 2003: 2). Relating this to the study on church collections, the typical donation in Italy was 1000 liras. If the 1 Euro coin replaced 1000 Lira note, the church collections would double, argued authors. On the other hand, if the usual donation of 10 Franks in French churches were to be replaced by 1 Euro in France, there would be a drop of 35% in overall donations. Since it is known from the marketing theory of pricing that people tend to perceive particular prices as more attractive, the concept of psychological threshold is very much related to price rounding. For example, now it is usual to pay 2 or 3 LTL for a cup of coffee in the bar in Lithuania. And it seems very likely that we might expect a price of 1 EUR for the same coffee after new currency introduction. But price rounding is already another topic in Euro introduction case and will not be explored in the paper. Although there were cases of currency change in countries before, Euro introduction was the first step of such magnitude. It has inspired a wave of academic discussion because of importance of the effects to the economies of the Europe countries and the rest of the world. But until now there is not much information already available on the topic of consumers’ perception of value in the situation of currency change because this field of research is new. 37 Euro Introduction Effcts on Individuals’ Economic Decisions

2.8. The Approach of Experimental Economics

Experimental economics is a relatively new approach of seeking answers to economic questions. Probably, the most important question is: can we rely on the results, obtained from experiments? Can experimental evidence be used to confirm or refute theoretical predictions? At minimum, this evidence can give a basis for the discussion. On the other hand, the whole science of psychology is built on experimentation. Marketing is also a sphere, which to a great extent is based on the information accumulated from surveying. This is because the laboratory or surveying are the only ways of testing theories of how people behave. Experiments can be valuable in economics as well. “Though economists need to be wary of generalizing from the results of laboratory experiments that involve small groups of subjects to aggregate macroeconomic activity”, there is a goal to do the testing “because the predictions of these macroeconomic models stand on their assumptions of how individuals behave” (Duffy, 2004: 9). In considering the importance of this research and what could be the weaknesses of Differ- ence Assessment Account, one of the co-authors of this theory was contacted. The response was that theory authors’ difficulty was that they “cannot conclusively separate numerosity from anchoring and adjustment as the theoretical mechanism that drives the currency perception effect” (Wertenbroch, 2004). In their study Soman et al. mentioned anchoring and adjustment concept as a part of theoretical backgrounds that could have explained consumers’ bias towards the nominal value (also called money illusion effect). But authors proposed that “potential explanation for the money illusion effect could be the so-called numerosity heuristic (Pelham et al., 1994) rather than anchoring and adjustment process” (Soman et al., 2002: 7). In this work the correctness of the latter proposition is assumed i.e. that the numerosity heuristic prevails among consumers. Therefore, the research will be limited to the discussion of importance of numerosity heuristic in the process of decision making and other theories of thinking patterns will not be discussed. This study is an example of experimental economics as it uses a relatively small experiment sample for the discussion of the possible macro-level effect of new currency introduction on the countries. The next section describes the design of the experiment, which is used to test whether the DAA is present among Lithuanian and Latvian consumers. The data drawn from this experiment is intended to serve as a basis for the discussion of overall effect on countries’ consumers.

3. Design of the Experiment

The method of the experiment was based on that used by Soman, Wertenbroch and Chat- topadhyay to test the robustness of DAA among consumers. 3.1. Testing the Robustness of DAA – The Initial Experiment

Aiming to examine the robustness of DAA, in one of the experiments Soman et al. tested consumers’ willingness to pay (WTP) in a situation of nominally varied prices and budgets. Participants were a group of 35 undergraduate students, who participated in two separate sessions of the experiment. The independent variable was currency numerosity i.e. the experiment authors varied nominal budgets and prices. The dependent variable was consumers’ willingness to pay (WTP) for different spending categories. WTP was a measure of purchasing power of the hypothetical currencies that were used in the experiment. The Baltic Journal of Economics 6(2) (2007): 29–55 38 experiment was intended to test whether consumers’ perceptions of purchasing power of currencies differed when currencies’ numerosity was varied (Soman et al., 2002: 21). The method of the experiment conducted by Soman et al. was described in their study “Currency Numerosity Effects on the Perceived Value of Transactions”: Participants were 35 English speaking undergraduates from a large Hong Kong university. They participated for course credit. The dependent variable was participants’ stated willingness to spend per month in each of several discretionary spending categories. We used two conditions in a scenario, one concerning Hong Kong and another concerning a hypothetical country called Tristania. All participants took part in two sessions, the first presenting the familiar Hong Kong scenario, the second the unfamiliar Tristania scenario. Each session comprised a number of unrelated tasks, in which one task involved the present experiment. The design was a 2 × 2 mixed factorial, with country (within subjects) and currency numerosity (between subjects) as the two factors. In the first session, participants were asked to imagine that they had recently graduated and started working on a new job. Their monthly post-tax salary was HK$ 9,000. They were told that their essential expenses (rent, groceries, lunch, transportation, utility and phone bills) were on average HK$ 4,860 a month. They were also told to imagine that they would like to save money to buy a new car and other household durable goods. They were asked: “After spending HK$ 4,860 each month on these essentials, you have no other essential expenses. However, you would probably want to budget some money for non-essential expenses like eating out (e.g., dinner, or visiting a bar with friends), shopping (e.g., clothes or accessories) or entertainment (e.g., tickets to a movie or music concert). Keeping in mind your income, expenses, and other financial obligations, how much do you think you would budget for each of these types of expenses per month?” In the second session (conducted two weeks later), participants read a similar scenario with the modification that their job required them to move to the foreign country of Tristania. Participants were assigned either to a low numerosity condition where T$ 1 = HK$ 18, and in which the salary was T$ 500 and expenses were T$ 270, or to a high numerosity condition where T$ 1 = HK$ 1/18, in which the salary was T$ 162,000 and essential expenses were T$ 87,480. Willingness to pay was elicited in T$ in this second session. Participants also saw the following pricing information to help them with their budgeting: Low numerosity Control High numerosity Dinner at a fancy restaurant T$ 12 HK$ 215 T$ 3, 870 A beer and food at bar T$ 6 HK$ 110 T$ 1, 980 Movie ticket T$ 3 HK$ 54 T$ 972 Ticket to a sport event or rock concert T$ 10 HK$ 180 T$ 3, 240 A pair of branded casual pants T$ 30 HK$ 540 T$ 9, 720 T-shirt suitable for daily wear T$ 8 HK$ 145 T$ 2, 610 (Soman et al., 2002: 21–22). 3.2. New Experiment and its Distinctiveness The same method was used to construct the experiment in order to collect the data for the research on Lithuanian and Latvian consumers (initial experiment author’s permission was obtained). Although the same model for the experiment was used, the new experiment differed from previous one in several aspects. The scenario of the experiment was aimed 39 Euro Introduction Effcts on Individuals’ Economic Decisions to correspond to the real world setting as much as it was possible. The scenario of the experiment contained real currencies (Litas, Lats and Euros) and the real exchange rates were used when converting budgets and prices into different currencies. The experiment participants were students from Lithuania Christian College, a private college in Klaipeda,˙ Lithuania. The participants of the experiment were citizens from two Baltic region countries: the sample consisted of 38 Lithuanian and 20 Latvian students. The economic and historic profiles of both countries gave a unique opportunity to test Difference Assessment Account in a real world setting. The Lithuanian currency Lita is more numerous than the Euro (1 EUR = 3.45 LTL). Contrarily, the Latvian currency, Lat, is less numerous than Euro (1 EUR = 0.69 LVL). Therefore, these two countries gave a unique opportunity to apply and test the robustness of Difference Assessment Account. According to the DAA, the Euro introduction effects in both countries should be adversative because in the situation of Euro adoption in both countries, the numerosity of currencies in both countries would change to opposite directions. In Lithuania the currency would become less numerous; in Latvia the numerosity of the currency would increase. Moreover the real values of prices of most consumer products in Lithuania are very similar, almost identical, to the prices in Latvia. For example, a beer and food at the bar cost approximately 18 LTL in Lithuania and 3.6 LVL in Latvia (the real values are equal: 18 LTL = 3.6 LVL = 5.2 EUR); Ticket to a movie costs 10 LTL or 2 LVL (again, the real values are equal: 10 LTL = 2 LVL = 2.9 EUR). These similarities allowed for the easier and more precise comparison of the results of the experiment. 3.3. The Scenario of the Experiment

Appendix 1 contains the summary of the experiment. In this experiment both groups of participants were taking part in two sessions. In each session participants were asked to fill in a questionnaire. Although questionnaires contained multiple tasks, only the responses from the tasks with budgeting were used in the analysis. 3.3.1. Session 1 In the first session both groups, Lithuanians and Latvians, were given budgets in their country’s currencies. Lithuanians were given a budget of 2000 LTL and Latvians – a budget of 400 LVL. The purchasing power of each budget, and thus the budgets’ real values, were equal (2000 LTL = 400 LVL), as the conversion was made using the real exchange rates. Current exchange rates for Litas and Lats to Euro are as follows: 1 EUR = 3.45 LTL = 0.7 LVL. Given the budgets and prices in the currencies they were accustomed to, participants were asked to allocate amounts they could spend on different categories of goods. Participants (who were undergraduate college students) were asked to imagine that they had already graduated and had a job, with a monthly post-tax salary of 2000 LTL (for the Lithuanian group) or 400 LVL (for the Latvian group). They were told that their essential expenses (rent, groceries, lunch, transportation, utility and phone bills) were on average 50% of their budget, accordingly 1000 LTL and 200 LVL. They were also told that they would like to save some money to buy a new car. Finally, they were asked the question: “After spending 1000 LTL (or 200 LVL) on these essentials, you have no other essential expenses. However, you would probably want to budget some money for non-essential expenses like eating out (e.g., dinner, or visiting a bar with friends), shopping (e.g., clothes or accessories) or entertainment (e.g., tickets to a movie or music concert). Keeping in mind your income, expenses, and other financial obligations, how much do you think you would budget for each of these types Baltic Journal of Economics 6(2) (2007): 29–55 40 of expenses per month?” Refer to Appendices 2 and 3 for the examples of questionnaires distributed to participants of the session 1 experiment.

3.3.2. Session 2 In the second session, the scenario was similar as in the first session. The difference was that participants were now asked to imagine that they lived in an EU country in which the currency was Euro. Therefore, the questionnaires now contained budgets and prices in Euros. The aim was to find out if the perception of value of money (or the perception of purchasing power) differed when the nominal values of budgets and prices were presented in a different currency. In the second session of the experiment both groups had to evaluate budgets and prices in Euros. The real value of budgets and prices was again the same: 580 EUR = 2000 LTL = 400 LVL. Just the nominal values were different: 580 < 2000; 580 > 400.The sample of Lithuanian sample became a low numerosity condition as Lithuanian currency changed from more numerous to less numerous. Contrarily, Latvian currency changed from less to more numerous.

3.4. Remarks and Predictions before the Experiment

The new experiment allowed comparing results in two different ways. It was possible to compare data after both sessions. In session one, two groups of participants were given to evaluate prices and budgets in two different currencies, Litas and Lats. Both groups were presented with tasks, which involved their national currencies. The numerosity of these currencies is different. Therefore, it was possible to compare how these two groups (Lithuanians and Latvians) budgeted their money for spending. Even after session one, it was possible to apply DAA theory. Using the DAA theory, the prediction was that people would budget more to the expense categories when presented with transactions in more numerous currency and vice versa. Thus Lithuanians’ sample should have budgeted more than Latvians’ sample if the thinking pattern of DAA was present among participants. Another point of comparison took place after the data from second session of the experiment was collected. Then, it was possible to compare how participants’ budgeted amounts differed after they were presented with transactions in higher numerosity at first and lower numerosity later for the Lithuanian sample or lower numerosity at first and higher numerosity later for the Latvian sample. According to the DAA, when presented with transactions in Euros, Lithuanians should have budgeted less than they did in the session one. This should have happened because the numerosity has shifted downwards: from higher to lower. Contrarily, Latvian sample should have budgeted more money on spending categories as the numerosity has changed upwards, from lower to higher. For the purpose of a better comparison, both sample groups contained very similar respondents. There was the same number of respondents in terms of age, gender and college year. All respondents were from the same socio-cultural background i.e. all of them were college students. In order not to bias the results, respondents were not informed about the real purpose of the experiment, nor were they informed about the theory of Difference As- sessment Account. They were also not told that they would be asked to participate in a second session of the experiment. Together with the task of budgeting, they were given other questions to answer as well. Therefore, the attention of respondents was not situated only on the task with budgeting. 41 Euro Introduction Effcts on Individuals’ Economic Decisions

4. Results of the Experiment 4.1. Session 1 Results and Comparison Assuming that Difference Assessment Account holds true in a real setting, it was expected that the Lithuanian sample of participants of the experiment will budget larger amounts than Latvians due to the fact that Lithuanian currency, Litas, is more numerous than Latvian Lats. The theory was confirmed by the results of the experiment. For the purpose of a better comparison and analysis the amounts in Litas and Lats were converted into Euros. We used the current real exchange rate of 1 EUR = 3.45 LTL = 0.7 LVL. In all three spending categories (Eating Out, Entertainment and Shopping) Lithuanian sample budgeted significantly larger amounts than Latvians; the means (averages) of budgeted amounts between Lithuanian and Latvian respondents differed by more than 100%. The means of budgeted amounts by the Lithuanian sample were as follows: 36.05 EUR for Eating out; 30.38 EUR for Entertainment; and 48.14 EUR for Shopping, comprising a total mean budgeted amount of 114.57 EUR. The means of amounts budgeted by Latvian participants were 15.83 EUR for Eating out, 13.39 EUR for Entertainment and 23.07 EUR for Shopping, comprising total mean budgeted amount of 52.28 EUR. Appendix 5 shows complete distribution of the responses. The results are summarized in Table 2. Statistical hypothesis test, with a hypothesis being “consumers spend significantly more in more numerous currency” showed extremely strong statistical evidence of holding true among population. Comparison of totals of budgeted amounts showed P -value of 0.00009 and t-statistics of −4.26. Refer to Appendix 9 for statistical analysis. The results showed that nominal value of the currency has an effect to the consumers’ perception of its value. These compared results support the argument that people tend to spend more in general when transactions are made in the currency, which is more numerous and they tend to spend less when presented with transactions in currency of lower numerical values. The evidence agrees with Difference Assessment Account, and gives support for the assumption that people tend to assess the value of transactions by referring to the difference between their budgets and the prices of the goods or services. 4.2. Session 2 Results and Comparison The results of the sample of Latvian respondents provided a strong support for DAA theory. As predicted by DAA, participants budgeted less when presented with a budgeting task with less numerous currency, Lat. Two weeks later, presented with the same task in more numerous currency, Euro, they indicated significantly larger amounts for all 3 of the spending categories. Statistical hypothesis test for the hypothesis “holding the real values constant, consumers spend less in currency with lower numerosity (Lat) and spend more in higher numerosity

Table 2. Session 1 – mean amounts expressed in EUR High numerosity: Sample – Lithuanians; currency – Litas Eating out Entertainment Shopping Total 36.05 30.38 48.14 114.57 Low numerosity: Sample – Latvians; currency – Lats Eating out Entertainment Shopping Total 15.83 13.39 23.07 52.28 Baltic Journal of Economics 6(2) (2007): 29–55 42 currency (Euro)” clearly proved the hypothesis. Analysis showed P -value of 0.00002 and t-statistics of −5.36 (Appendix 10). For the task with budgets and prices in Lats participants budgeted on average 15.83 EUR for Eating out, 13.39 EUR for Entertainment and 23.07 EUR for Shopping, which made up the average total of 52.28 EUR. In the task with Euros participants’ mean budgeted amounts have increased. They budgeted on average 30.3 EUR for Eating out, 21.38 EUR for Entertainment and 42.16 for Shopping, which comprised a total mean amount of 93.84 EUR. Appendix 6 displays whole data and Table 3 below summarizes the results of comparison. No other theory was found that could explain such a phenomenon when an increase in currency numerosity increases the spending. These results clearly illustrate the eﬀect of numerosity in consumers’ economic decisions. Although the comparison of results of Latvians’ sample supported DAA, that was not the case with Lithuanian sample. The comparison showed only a slight diﬀerence between the mean budgeted amounts in the responses of session 1 and session 2 assignments. DAA would suggest that Lithuanians would budget less in Euros than in Litas. When given a task containing more numerous currency, Litas, participants budgeted more on average only in two of three spending categories (Eating out and Entertainment). There was a slight decrease in mean budgeted amounts in Eating out and Entertainment category, from 36.05 EUR to 31.50 EUR and from 30.38 EUR to 29.30 EUR respectively. The mean total budgeted amount was also lower when the currency was less numerous; 114.57 EUR and 109.55 EUR respectively. Refer to the Appendix 7 and Table 4 for the compared results. Statistical analysis did not prove one of the hypothesis “holding the real values constant, consumers spend more in currency with higher numerosity (Lita) and spend less in lower numerosity currency (Euro)”. The results of statistical analysis are shown in Ap- pendix 11. Having in mind that part of the sample (Latvian respondents) showed DAA holding true, and that comparison of Lithuanian responses has not obviously complied

Table 3. Session 1 vs. session 2 Latvians’ mean amounts compared and expressed in EUR Session 1 results: Latvians’ budgeted amounts in Lats Eating out Entertainment Shopping Total 15.83 13.39 23.07 52.28 Session 2 results: Latvians’ budgeted amounts in Euros Eating out Entertainment Shopping Total 30.30 21.38 42.16 93.84

Table 4. Session 1 vs. session 2 Lithuanians’ mean amounts compared and expressed in EUR Session 1 results: Lithuanians’ budgeted amounts in Litas Eating out Entertainment Shopping Total 36.05 30.38 48.14 114.57 Session 2 results: Lithuanians’ budgeted amounts in Euros Eating out Entertainment Shopping Total 31.50 29.30 48.75 109.55 43 Euro Introduction Eﬀcts on Individuals’ Economic Decisions

Table 5. Session 1 vs. session 2 Lithuanians’ mean amounts compared; amounts in EUR Session 1 results: Lithuanians’ budgeted amounts in Litas Eating out Entertainment Shopping Total 25.93 22.55 58.64 107.12 Session 2 results: Lithuanians’ budgeted amounts in Euros Eating out Entertainment Shopping Total 22.74 20.11 48.97 91.82 with DAA, the experiment was extended to test DAA with an additional Lithuanian sample.

4.3. Additional Data Collection

Additional data was collected using the same experiment model. The participants were 18 Lithuanian 1st year college students. They participated in 2 sessions, as previously described in the paper. The results obtained from collection of data were very similar to those obtained before from the ﬁrst sample of Lithuanians. Although mean results show that in all three spending categories the sample respondents indicated that they wish to spend more in Litas than in Euros, there was not enough of statistical diﬀerence that this hypothesis would work on the whole population. The summary of the compared responses is shown in Table 5; Appendix 8 gives the full range of responses; Appendix 12 shows statistical analysis.

4.4. Discussion of Findings

The part of the results of the experiment showed the trends towards the presence of DAA in the process of currency valuation and making economic decisions. Results drawn from the Latvian sample provided significant support for DAA, whereas Lithuanian sample results were not obvious in confirming the predictions raised by DAA. It is noteworthy to mention that the experiment did not yield any evidence against DAA i.e. it was not the case that Lithuanian sample showed spending trends opposite of what DAA claims. The Latvian sample results showed the effect of currency numerosity on the perception of value of money, which was reflected in consumers’ spending decisions. Consumers showed trends of spending more in a currency which is more numerous and spending less when the currency is less numerous. These findings suggest that the larger amount is left after the purchase of particular product or service, the more affordable the purchase seems, and thus the consumers are more willing to spend. Contrarily, the smaller amount is left on hand after the purchase; the lower is the possibility that consumers will commit the transaction. There could be multiple reasons of why Lithuanian sample results did not give significant support for DAA. It could be attributed to the idea that numerosity has the effect only in the situation when currency shifts from less to more numerous (Latvia’s case). Also it might be that exactly this Lithuanian sample was very different from the whole population of Lithuania in terms of spending habits and that the sample used is too small to generalize on the whole population. Moreover, it is possible that additional factors influencing consumption such as inflation and/or media opinion are stronger in Lithuania than in Latvia. Baltic Journal of Economics 6(2) (2007): 29–55 44

In short, the research suggested these Euro-introduction effects on Lithuanian and Latvian consumers: (1) Lithuanian consumers are most likely to perceive high price rises. As predicted by DAA, the consumption in Lithuania should decrease. (2) Assuming that DAA holds true among Latvian population, Latvians should perceive prices as lower in Euros than in Lats. Therefore, it is predicated that there will be a temporary increase in consumption level. This research concentrated only on testing DAA, which predicts very obvious changes of trends in consumption when the currency numerosity is altered. But the reliability of these predictions is limited as there are many other factors forming consumers’ perceptions and their behavior. For example, the effect of inflation or the influence of the media when forming consumers’ opinions were not included in the study. This was the first step to test DAA in real setting with countries that are in the midst of changing their currencies and to observes how this currency change might affect consumption levels in these countries.

5. Conclusion In this paper we presented the theory on consumers’ behavior in terms of currency valuation and making economic decisions in the situation of currency change. The theory, called Difference Assessment Account, proposes that consumers’ perception of value of currencies is based on currencies’ nominal values and their numerosity. We explained the theoretical model of DAA, then provided with an empirical test for it. The empirical test was suited to be applied to the situation of Lithuania and Latvia changing their national currencies to the Euro. The main question, raised at the beginning of the study was “What effect(s) could Lita’s and Lat’s replacement by Euro have on Lithuanian and Latvian consumers’ economic decisions?” The specific questions of the study were “What could be the changes in consumers’ perceptions of economic transactions when transactions were made in Euro currency? And what consequences could currency change have upon Lithuanian and Latvian consumers’ spending behavior?” all these questions were raised in the context of DAA. The assumptions based on DAA were that Lithuanian consumers would perceive prices as less affordable after Euro introduction; Latvians, contrarily, would perceive prices as more affordable. The change in perception would reflect in consumers’ spending behavior. The consumption level should decrease in Lithuania, whereas the increase in consumption is expected in Latvia. These hypotheses were tested empirically through the experiment on two samples of participants. Results drawn from Latvian sample provided significant support for DAA, whereas Lithuanian sample results were not obvious in confirming the predictions raised by DAA. This research showed that consumers’ perceived situation is often different from the real situation observed. Consumers’ perceived value of transactions might be biased by the nominal values of currencies, thus impacting consumers’ spending decisions. 45 Euro Introduction Effcts on Individuals’ Economic Decisions

Appendix 1. The Design of the Experiment

Note: • Purchasing power – amount of goods and services given amount of money can buy. • In the experiment real purchasing power of the budgets is equal: 2000 LTL = 400 LVL = 580 EUR.

Appendix 2. A Copy of Questionnaire Used for Lithuanian Respondents in Session 1

I. What is your age? (Circle)

17 18 19 20 21 22 23 24 25

II. What is your college year? (Circle)

I II III IV

III. What is your gender? (Circle)

Male Female Baltic Journal of Economics 6(2) (2007): 29–55 46 IV. Please state your opinions on these questions. Situations in Questions 1, 2, 3 and 4 are NOT related. 1. Imagine within this year the prices of all goods and services in Lithuania went up by 30%. Now you earn and spend 30% more than before. A year ago you bought a set of furniture, which price was 3500 LTL. Within the period of the year, the price of a set went up to 6000 LTL. Would you be more likely to buy the set now or the year before? Now Year before 2. Imagine that you have recently graduated from the university. You rent an apartment and have a job, where you get a monthly salary of 2000 LTL. For your essential expenses (such as food, rent, transportation, utilities and phone bills) you spend on average 1000 LTL a month. You would also like to save some money to buy a new car. After spending 1000 LTL each month on these essentials, you have no other essential expenses. However, you would probably want to budget some money for non-essential expenses like eating out (e.g., dinner, or visiting a bar with friends), shopping (e.g., clothes or accessories) or entertainment (e.g., tickets to a movie or music concert). Approximate prices for the expenses are given below: Eating out Dinner at a fancy restaurant 30 LTL A beer and food at bar 18 LTL

Entertainment Movie ticket 10 LTL Ticket to a sport event or a concert 20 LTL

Shopping A pair of branded casual pants 80 LTL T-Shirt suitable for daily wear 20 LTL Keeping in mind your income, expenses, and having in mind that you have to save for a new car, how much do you think you would budget a month for these spending categories? (Indicate the amount.) Eating out ______Entertainment ______Shopping ______3. Consider a situation of two Lithuanians, Stasys and Mantas, who work in diﬀerent companies of similar proﬁles. Stasys started with a monthly salary of 2500 LTL in the company where average starting salary was 3000 LTL. Mantas started with a monthly salary of 2250 LTL in a company where average salary was 2000 LTL. Which one of these employees would you think is happier with his salary? Circle one of these answers: Stasys Mantas

4. Suppose you bought 20 bottles of good Bordeaux wine in the market for 50 LTL a bottle. This wine now sells in the auction for 175 LTL a bottle. You have decided to drink the bottle of wine with your dinner. Which of these answers best reﬂects your opinion of the cost to you of drinking this bottle? Circle a, b or c. 47 Euro Introduction Eﬀcts on Individuals’ Economic Decisions

A. I don’t feel that it costs to me anything because I paid for this bottle many years ago and now I probably do not even remember how much exactly I paid for it. B. Drinking this bottle feels like I’m losing 125 LTL a bottle. It’s because I paid only 50 LTL for a bottle and now I could sell this bottle for 175 LTL and make a proﬁt of 125 LTL. C. Drinking this bottle feels like I’m saving 125 LTL because now I can drink a 175 LTL bottle for which I only paid 50 LTL.

Appendix 3. A Copy of Questionnaire Used for Latvian Respondents in Session 1 I. What is your age? (Circle)

17 18 19 20 21 22 23 24 25

II. What is your college year? (Circle)

I II III IV

III. What is your gender? (Circle)

Male Female

IV. Please state your opinions on these questions. Situations in Questions 1, 2, 3 are 4 NOT related. 1. Imagine that within this year the prices of all goods and services in Latvia went up by 30%. Now you earn and spend 30% more than before. A year ago you bought a set of furniture, which price was 900 LVL. Within the period of the year, the price of a set went up to 1200 LVL. Would you be more likely to buy the set now or the year before? Circle one of those answers: Now Year before

2. Imagine that you have recently graduated from the university. You rent an apartment and have a job, where you get a monthly salary of 400 LVL. For your essential expenses (such as food, rent, transportation, utilities and phone bills) you spend on average 200 LVL a month. Youwouldalsoliketosavesomemoneytobuyanewcar. After spending 200 LVL each month on these essentials, you have no other essential expenses. However, you would probably want to budget some money for non-essential expenses like eating out (e.g., dinner, or visiting a bar with friends), shopping (e.g., clothes or accessories) or entertainment (e.g., tickets to a movie or music concert). Approximate prices for the expenses are given below:

Eating out Dinner at a fancy restaurant 3 LVL A beer and food at bar 3.6 LVL

Entertainment Movie ticket 2LVL Baltic Journal of Economics 6(2) (2007): 29–55 48

Ticket to a sport event or a concert 4LVL

Shopping A pair of branded casual pants 16 LVL T-Shirt suitable for daily wear 4LVL

Keeping in mind your income, expenses, and having in mind that you have to save for a new car, how much do you think you would budget a month for these spending categories? (Indicate the amount.)

Eating out ______Entertainment ______Shopping ______

3. Consider a situation of two Latvians, Kaspers and Janis, who work in diﬀerent companies of similar proﬁles. Kaspers started with a monthly salary of 500 LVL in the company where average starting salary was 600 LVL. Janis started with a monthly salary of 450 LVL in a company where average salary was 400. Which one of these employees would you think is happier with his salary? Circle one of these answers:

Kaspers Janis

4. Suppose you bought 20 bottles of good Bordeaux wine in the market for 10 LVL a bottle. This wine now sells in the auction for 35 LVL a bottle. You have decided to drink the bottle of wine with your dinner. Which of these answers best reﬂects your opinion of the cost to you of drinking this bottle? Circle a, b or c. A. I don’t feel that it costs to me anything because I paid for this bottle many years ago and now I probably do not even remember how much exactly I paid for it. B. Drinking this bottle feels like I’m losing 25 LVL a bottle. It’s because I paid only 10 LVL for a bottle and now I could sell this bottle for 35 LVL and make a proﬁt of 25 LVL. C. Drinking this bottle feels like I’m saving 25 LVL because now I can drink a 35 LVL bottle for which I only paid 10 LVL.

Appendix 4. A Copy of Questionnaire Used for Latvian and Lithuanian Respondents in Session 2 I. What is your age? (Circle)

17 18 19 20 21 22 23 24 25

II. What is your college year? (Circle)

I II III IV

III. What is your gender? (Circle)

Male Female IV. Imagine that you live in a country, which is a member of EU. The currency in your country is Euro. Please state your opinions on these questions. 49 Euro Introduction Eﬀcts on Individuals’ Economic Decisions

1. A year ago you bought a set of furniture for 400 EUR. Imagine within this year the prices of all goods and services in your country went up by 25%. Now you earn and spend 25% more than before. Today the same set of furniture costs 500 EUR. You would be more likely to buy the set: Now Year before There is no diﬀerence 2. Imagine that you have recently graduated from the university. You rent an apartment and have a job, where you get a monthly salary of 580 EUR. For your essential expenses (such as food, rent, transportation, utilities and phone bills) you spend on average 290 EUR a month. Youwouldalsoliketosavesomemoneytobuyanewcar. After spending 290 EUR each month on these essentials, you have no other essential expenses. However, you would probably want to budget some money for non-essential expenses like eating out (e.g., dinner, or visiting a bar with friends), shopping (e.g., clothes or accessories) or entertainment (e.g., tickets to a movie or music concert). Approximate prices for these expenses in your country are given below:

Eating out Dinner at a fancy restaurant 8.7 EUR A beer and food at bar 5.2 EUR

Entertainment Movie ticket 2.9 EUR Ticket to a sport event or a concert 5.8 EUR

Shopping A pair of branded casual pants 23.2 EUR T-Shirt suitable for daily wear 5.8 EUR Keeping in mind your income, expenses, and having in mind that you have to save for a new car, how much do you think you would budget a month for these spending categories? (Indicate the amount). Eating out ______Entertainment ______Shopping ______3. Imagine that you are about to sign a 3-year contract in the international company in sales department. You have to choose between two options of your salary payment plan. Which one would you choose?

A. Constant 500 EUR per month. B. Base salary of 400 EUR per month plus premium every month, which depends on your accomplishment. Baltic Journal of Economics 6(2) (2007): 29–55 50

Appendix 5. Session 1 Results: Comparison of Budgeted Amounts in Respondents’ Home Currencies (Litas vs. Lats)

# Gender Age College year Eating out Entertainment Shopping Total LT LV LT LV LT LV LT LV 1 m192 29 12.614.55.60 2.843.521 2 m202 43.52.143.51.48711.2 174 14.7 3 m202 87 7 58 14 87 35 232 56 4 m203 52.22817.47 43.5 21 113.156 5 m213 29 5 87 2.8295.6 145 13.4 6 m213 58 14 14.5145.81478.342 7 m224 58 21 58 14 29 28 145 63 8 f181 13.98.48.74.22922.451.635 9 f191 5.22111.61423.2354070 10 f191 14.517.511.615.82917.555.150.8 11 f182 116 10.52910.5 58 28 203 49 12 f202 29 14 43.5 14 116 35 188.563 13 f212 14.5498.75672.53595.7 140 14 f203 11.68.414.51.472.52.898.612.6 15 f203 52.22158145835168.270 16 f213 43.53514.517.5 58 35 116 87.5 17 f213 14.57 292.114.510.55819.6 18 f224 11.610.511.63534.8425887.5 19 f224 29 7 16 7 58 10.5 103 24.5 20 f224 8.717.55817.5 58 35 124.770 Means (averages) 36.05 15.83 30.38 13.39 48.14 23.07 114.57 52.28 All amounts are expressed in EUR at the exchange rate of 1 EUR = 3.45 LTL = 0.7 LVL.

Appendix 6. Comparison of Latvians’ Budgeted Amounts. Currency Changes from Less (Lats) to More Numerous (Euros)

# Gender Age College year Eating out Entertainment Shopping Total LT LV LT LV LT LV LT LV 1 m192 12.6525.6292.823.2 21 104.2 2 m202 2.16 1.43 11.2614.715 3 m202 720141035105640 4 m203 28 50 7 10 21 60 56 120 5 m213 5162.8125.64013.468 6 m213 14 60 14 15 14 30 42 105 7 m224 21 50 14 15 28 30 63 95 8 f181 8.4204.211.622.4303561.6 9 f191 21 20 14 10 35 40 70 70 10 f191 17.51015.83017.51550.855 11 f182 10.54010.520284049100 12 f202 14 40 14 20 35 50 63 110 13 f212 49 50 56 60 35 50 140 160 14 f203 8.4121.42 2.8412.618 15 f203 21 40 14 50 35 100 70 190 16 f213 35 25 17.550357587.5 150 17 f213 7252.11510.52519.665 18 f224 10.5 20 35 25 42 100 87.5 145 19 f224 7 20 7 10 10.51524.545 20 f224 17.53017.5 30 35 100 70 160 Means (averages) 15.83 30.30 13.39 21.38 23.07 42.16 52.28 93.84 All amounts are expressed in EUR at the exchange rate of 1 EUR = 0.7 LVL. LV 1 – Latvians session 1; LV 2 – Latvians session 2. 51 Euro Introduction Eﬀcts on Individuals’ Economic Decisions

Appendix 7. Comparison of Lithuanians’ Budgeted Amounts. Currency Changes from More (Litas) to Less Numerous (Euros)

# Gender Age College year Eating out Entertainment Shopping Total LT LV LT LV LT LV LT LV 1 m192 29 40 14.5200 0 43.560 2 m202 43.52543.5 50 87 15 174 90 3 m202 87 30 58 30 87 60 232 120 4 m203 52.26017.43043.5 30 113.1 120 5 m213 29 20 87 100 29 50 145 170 6 m213 58 15 14.55 5.85 78.325 7 m224 58 50 58 50 29 40 145 140 8 f181 13.9128.7 6 29 25 51.643 9 f191 5.25 11.61023.2204035 10 f191 14.54011.620294055.1 100 11 f182 116 30 29 0 58 20 203 50 12 f202 29 50 43.5 40 116 100 188.5 190 13 f212 14.5158.71572.57095.7 100 14 f203 11.62014.53072.55098.6 100 15 f203 52.25058505890168.2 190 16 f213 43.52014.5 20 58 40 116 80 17 f213 14.570295014.5 100 58 220 18 f224 11.61811.61034.8405868 19 f224 29 40 16 20 58 150 103 210 20 f224 8.72058305830124.780 Means (averages) 36.05 31.50 30.38 29.30 48.14 48.75 114.57 109.55 All amounts are expressed in EUR at the exchange rate of 1 EUR = 3.45 LTL. LT 1 – Lithuanians session 1; LT 2 – Lithuanians session 2.

Appendix 8. Additional Data: Comparison of Lithuanians’ Budgeted Amounts. Currency Changes from More (Litas) to Less Numerous (Euros)

# Gender Age College year Eating out Entertainment Shopping Total LT LV LT LV LT LV LT LV 1 m191 43.520.82911.672.5 58 145 90.4 2 m191 22.6402630297077.6 140 3 m191 20.120.814.529582992.678.8 4 m251 27.83017.4 18 58 90 103.2 138 5 f191 29 20 17.4 50 58 100 104.4 170 6 f191 58 50 58 50 174 100 290 200 7 f191 29 30 20.3 20 58 30 107.380 8 f191 29.6125.81434.82070.246 9 f191 14.55.28.72.9875.8 110.213.9 10 f191 14.515293043.5508795 11 f191 14.510.411.62.914.55.840.619.1 12 f191 15.7262.95.8292947.660.8 13 f191 5.8108.7 4 58 40 72.554 14 f191 43.550586072.5 90 174 200 15 f191 43.5 20 58 3 87 30 188.553 16 f191 29 19.123.25.85833.8 110.258.7 17 f221 14.5105.8 5 29 50 49.365 18 f221 11.62011.62034.8505890 Means (averages) 25.93 22.74 22.55 20.11 58.64 48.97 107.12 91.81 All amounts are expressed in EUR at the exchange rate of 1 EUR = 3.45 LTL. LT 1 – Lithuanians session 1; LT 2 – Lithuanians session 2. Baltic Journal of Economics 6(2) (2007): 29–55 52

Appendix 9. Session 1 Comparison of Results

Statistical Test and Analysis

F-test: Two-sample for variances Lats Litas Mean 52,28 114,565 Variance 1005,736421 3264,0298 Observations 20 20 df 19 19 F 0,308127221 P(F ≤ f) one-tail 0,006846503 F Critical one-tail 0,461201089 t-test: Two-sample assuming unequal variances Lats Litas Mean 52,28 114,565 Variance 1005,736421 3264,0298 Observations 20 20 Hypothesized Mean Diﬀerence 0 df 30 – t Stat 4,262814358 P(T ≤ t) one-tail 0,00009230 t Critical one-tail 1,697260851 P(T ≤ t) two-tail 0,000184599 t Critical two-tail 2,042272449

Appendix 10. Comparison of Latvians’ Budgeted Amounts in Lats and Euros – Comparison of Totals

Statistical Test and Analysis

t-Test: Paired two sample for means Lats Euros Mean 52,28 93,84 Variance 1005,73642 2466,12 Observations 20 20 Pearson Correlation 0,72023896 Hypothesized Mean Diﬀerence 0 df 19 tStat −5,3580824 P(T ≤ t) one-tail 0,0000180 t Critical one-tail 1,72913279 P(T ≤ t) two-tail 3,5956E-05 t Critical two-tail 2,09302405 53 Euro Introduction Eﬀcts on Individuals’ Economic Decisions

Appendix 11. Comparison of Lithuanians’ (First Sample) Budgeted Amounts in Litas and Euros – Comparison of Totals

Statistical Test and Analysis

t-Test: Paired two sample for means Litas Euros Mean 114,565 109,55 Variance 3264,0298 3542,05 Observations 20 20 Pearson Correlation 0,3041522 Hypothesized Mean Diﬀerence 0 df 19 t Stat 0,3258375 P(T ≤ t) one-tail 0,3740543 t Critical one-tail 1,7291328 P(T ≤ t) two-tail 0,7481086 t Critical two-tail 2,093024

Appendix 12. Comparison of Lithuanians’ (Second Sample) Budgeted Amounts in Litas and Euros – Comparison of Totals

Statistical Test and Analysis

t-Test: Paired two sample for means Litas Euros Mean 107,1222 91,816667 Variance 3752,328 3145,385 Observations 18 18 Pearson Correlation 0,566679 Hypothesized Mean Diﬀerence 0 df 17 t Stat 1,184757 P(T ≤ t) one-tail 0,126206 t Critical one-tail 1,739607 P(T ≤ t) two-tail 0,252413 t Critical two-tail 2,109816

References “Adoption of the Euro in Lithuania.” Bank of Lithuania. 20 Jan. 2005. 12 Mar. 2005. . “Bank of Latvia Pegs Lat to Euro.” Ministry of Foreign Aﬀairs of Latvia. 30 Dec. 2004. 12 Mar. 2005. . Baltic Journal of Economics 6(2) (2007): 29–55 54

Baumol, William and Alan Blinder (2003). Economics: Principles and Policy. Versailles: Thomson. “Benefits of the Adoption of the Euro.” Bank of Lithuania. 16 Sep. 2004. 12 Nov. 2004. . Budvytiene,˙ Giedre.˙ “Euro Can Provoke a Consumption Boom”. Kauno Diena. 24 Mar. 2005. 2 Apr. 2005. . Cannon, Edmund and Giam Pietro Cipriani (2003), “Euro-illusion: A Natural Experiment.” Department of Economics, University of Bristol. Disscussion Paper No. 03/556. Carter, Richard, “No New Member State Ready to Join Euro, Says Brussels”. Euobserver. 2004, 20 Oct. 2004, 10 Nov. . Duffy, John. “Monetary Theory in the Laboratory.” Federal Reserve Bank of St. Louis Review. 1998. 12 Nov. 2004. . “Economic and Monetary Union.” Wikipedia. 2004. 10 Nov. 2004. . “Euro.” Wikipedia. 2004. 10 Nov. 2004. . Gaiotti, Eugenio and Francesco Lippi. “Pricing behavior and the introduction of the euro: evidence from a panel of restaurants.” Repec Research Paper Series. Feb. 2005. 10 Mar. 2005. . Heath, Chip and Jack B. Soll. (1996). “Mental Budgeting and Consumer Decisions.” Journal of Con- sumer Research 23, 13. 15 Nov. 2004. EBSCO Host Research Database. . Jonas, Eva. “Introduction of the Euro – Goodbye to the Deutchmark.” Presentation Paper in Workshop “Euro: Currency and Symbol” (IAREP) Vienna, Austria. 3–5 July, 2003. . Jonas, Eva Tobias Greitemeyer, Dieter Frey and Stefan Schulz-Hardt (2002). “Psychological Effects of the Euro – Experimental Research on the Perception of Salaries and Price Estimation.” European Journal of Social Psychology 32, 147–169. “July 2002 Euro-zone annual inflation up to 1.9%.” Eurostat. News Release 97/2002. 19 Aug. 2002. 10 Mar. 2005. . “July 2003 Euro-zone annual inflation down to 1.9%.” Eurostat. News Release 94/2003. 19 Aug. 2003. 10 Mar. 2005. . “Latvia Likely to Introduce Euro as of 2008: Central Bank.” Ministry of Foreign Affairs of the Republic of Latvia. 11 Jun. 2003. 15 Dec. 2004. “Lithuania Country Brief 2004.” Worldbank. 2004. 10 Nov. 2004. . Mastrobuoni, Giovanni (2004). “The Effects of the Euro-Conversion on Prices and Price Perceptions”. Princeton University. September. CEPS Working Paper No. 101. Pelham, Brett W., Tin Tin Sumarta and Laura Myakovsky (1994). “The Easy Path from Many to Much: The Numerosity Heuristic”. Cognitive Psychology, pp. 103–133. “Public report of the Lithuanian Confederation of Industrialists Concerning the Launch of Euro in Lithuania.” Lithuanian Confederation of Industrialists. 8 July 2004. 10 Nov. 2004. . Raghubir, Priya and Joydeep Srivastava (2002). “Effect of Face Value on Product Valuation in Foreign Currencies.“ Journal of Consumer Research 29. 55 Euro Introduction Effcts on Individuals’ Economic Decisions

Romani, Simona and Daniele Dalli (2003). “Effects of the Transition from Lira to Euro on Buyers’ Product Evaluation. An Analysis before and after the Introduction of the New Currency.“ Presen- tation Paper in 30th International Research Seminar in Marketing, La Londe les Maures, France. 11–13 June. Shafir, Eldar, Peter Diamond and Amos Tversky (1997). “Money Illusion,” Quarterly Journal of Eco- nomics, 112, 342–374. Soman, Dilip, Klaus Wertenbroch and Amitava Chattopadhayay (2002). “Currency Numerosity Effects on Perceived Value of Transactions.” INSEAD Working Paper Series. 124/MKT. “The Lithuanian Embassy Newsletter.” Embassy of the Republic of Lithuania. Feb. 23. 2004. 11 Nov. 2004. http://www.ltembassyus.org/press/Newsfiles/Newsfile20040307.html>. Wertenbroch, Klaus. E-Mail. 12 Nov. 2004. An Economic Analysis of the Influence of Different Attitudes Toward Game Animals: Emphasizing the Significance of Large Carnivores

Yukichika Kawata∗ Faculty of Economics, Keio University 2-15-45, Mita, Minato-ku, 108-8345, Japan

Abstract: This paper investigates the inﬂuence of a change in attitude toward the sustainable resource use of wild animals that exist under a prey–predator relationship. We build a theoretical model in which use value (price value) and non-use value (for example, existence value) are incorporated; we then conduct a numerical simulation to examine several cases with varying values. The primary results are as follows. Firstly, we reaﬃrm that it is important for people to value prey as well as predators in order to maintain a viable population. Secondly, as the willingness to pay for the prey increases relative to the price of the prey, the amount of the resource will be prioritized over the amount of the harvest. Thirdly, the minimum/maximum price may be required for resource conservation and conservation rather than protection is required even if the willingness to pay for the prey increases. Finally, the existence of the predator is desirable in that it improves the optimal resource level of the prey.

Keywords: Predator–prey model, pest predator, willingness to pay, non-use value, attitude toward game animals

JEL codes: Q27, Q56, Q57

1. Introduction

Generally speaking, large carnivores such as wolves and lynx have often been persecuted for the following reasons. Firstly, these carnivores cause trouble for human beings and have been perceived as pest wild animals. For example, they kill domestic and wild animals, which have economic value for breeders and hunters. Secondly, these carnivores provide fewer hunting trophies; therefore, they are considered to have a relatively lower economic value than their prey such as deer and wild boar. For example, their skins may have some value, but most of their bodies, such as the ﬂesh and viscera, do not serve any purpose, whereas their prey are full of trophies such as meat, some viscera (liver and heart) and horns or antlers. Thirdly, while their prey possesses both harmful and useful aspects, these carnivores appear to have almost no positive eﬀects on human economic activities, as is described above (pest wild animals with lower economic values). Therefore, hunters may realize the positive value of these carnivores only when they conquer them and share the tale with their

* E-mail: [email protected] Baltic Journal of Economics 6(2) (2007): 57–78 58 fellow hunters. As a result, large carnivores have often been persecuted and several were nearly exterminated all over the world1. Although scientists understood the role of carnivores in nature some time ago, this real- ization did not extend to most people. Their role has recently been reaffirmed and recognized more than before, and some laws and/or local norms that contain provisions to protect these carnivores have been instituted2. To consider an example, it has been pointed out that wolves attack the infirm members of a herd, those that suffer from illness, senile deterioration, and/or handicap; as a result, wolves help to maintain the healthy condition of the prey population3. In addition, Andersone (2003) points out that in some parts of Latvia, beavers are one of the main foods for wolves during the summer; this is beneficial to hunters because many of them are foresters whose forests have suffered as a result of beavers. Few studies examine the role of large carnivores from an economic viewpoint. Therefore, this paper examines the ways in which the attitude of the economic entity, a representative person whom we will assume later, influences the optimal resource levels of prey and predator. We will distinguish the following economic values when we examine the different attitudes of the economic entity: (1) the economic value (use value) of the prey and predator and (2) the economic entity’s willingness to pay for the conservation of the prey and predator (non-use value). With regard to previous studies4, Alexander (2000) examines the optimal resource management of a single pest species (African elephant) when considering existence value (non-use value). Finnoff and Tschirhart (2003) treat the case in which the Steller sea lion and the fish exist in a predator–prey relationship. The latter is harvested by fishermen, and the former competes with fishermen (they are a pest for the fishermen); as a result, the former becomes an endangered species. Kawata (2003) treats the predator–prey case and includes the existence value of the pest predator, which utilizes economically valuable prey. Hoekstra and van den Bergh (2005) also examine this case when including the non-use value of the predator in the predator–prey model. These studies reveal that as long as the non-use value is considered, (near) extermination will never be appropriate even if these animals (predators)

1 Many wolves have (nearly) become extinct all over the world. For example, wolves in Latvia (Canis lupus)were on the verge of extinction in the 1960s (Ozoli¸nš and Andersone, 2003). Moreover, they were persecuted late in the 1990s under the anti-predatory campaign (Andersone, 2003, Ozoli¸nš et al., 2005). In Japan, the Honshu wolf (C.l. hodophilax) and the Hokkaido wolf (C.l. hattai) have been classified as extinct species by the Ministry of the Environment, Japan (http://www.biodic.go.jp/rdb/rdb_f.html). Based on the IUCN (International Union for Conservation of Nature and Natural Resources) red list, the Falkland Island wolf (Dusicyon australis)isalso classified as an extinct species, and the red wolf (C. rufus) and the Ethiopian wolf (C. simensis) are classified as threatened species (http://www.redlist.org/search/search-basic). Further, in North America, the following animals have already become extinct: the Kenai Peninsula wolf (C.l. alces), the Newfoundland wolf (C.l. beothucus), C.l. bernardi in Victoria Islands and other areas, C.l. fuscus in the Cascade Mountains, C.l. mogollonensis in Arizona and New Mexico, C.l. monstrabilis in Texas and New Mexico, the Great Plains wolf (C.l. nubilus) and the Southern Rocky Mountain wolf (C.l. youngi) (http://www.wolfcountry.net/information/WolfSpecies/north_america.html). 2 With regard to the Latvian and EU cases, see, for example, Ozoli¸nš (2002), Ozoli¸nš and Andersone (2003) and Ozoli¸nš (2003). 3 For example, see the following site provided by Croatia’s State Institute for Nature Protection (http://www.life- vuk.hr/biology_of_wolf.htm) and the site provided by Mech et al. (http://www.mnforsustain.org/wolf_mech_etal_ patterns_of_prey_selection_in_denali.htm). Since neither site provides sources for the list of references, the author cannot confirm the references. 4 Studies in this field may be rooted in the extermination problem, which was first treated seriously by Clark (1973). Although Clark (1973) shows that extermination can be optimal under a static setting, he does not assure that extermination is socially optimal. 59 Influence of Different Attitudes Toward Game Animals are regarded as pests. However, these studies do not consider the alteration in the attitude toward prey and predators mentioned above. Further, we will also treat the case in which the predator has been exterminated. As we have already seen in the footnote, many wolves all over the world have (nearly) become exterminated. Therefore, it is significant to examine the impact of the predator’s absence. The rest of the paper is organized as follows. In the second section, a basic bioeconomic model is built. In the third section, numerical simulations are presented. In the fourth section, the requirement for market intervention on the part of the authority is examined. Finally, our conclusions are put forward in the fifth section.

2. Model

2.1. Predator–Prey Dynamics Without Hunting

We will discuss a pair consisting of a large carnivore and an ungulate, which exist in a prey– predator relationship. For the sake of simplicity, we assume that they have no relationships with other animals. We use the following variables: X1(t): biomass of the prey, X2(t): biomass of the predator, r1: instantaneous growth rate of the prey, r2: instantaneous growth rate of the predator, 5 K1: carrying capacity of the prey, mi : constants (i = 1,...,3). X1(t) and X2(t) are functions of time t and are hereafter denoted without the. Firstly, we build a Schaefer model6 to describe the dynamics of the prey without incor- porating predator eﬀects as follows: dX1 X1 = r1 1 − X1. (1) dt K1

To facilitate a comparison with Eq. (1), we present the prey model when considering predator eﬀects as follows: dX1 X1 = r1 1 − X1 − m1X1X2. (2) dt K1

On the other hand, we describe the predator dynamics corresponding to Eq. (2) by the following equation: dX2 X2 = r2 1 − + m2X1X2. (3) dt m3X1

5 It is the stable equilibrium that is reached when there is neither prey nor hunting. See for example, Clark (2005). 6 The Schaefer model and its modiﬁcations are often used in resource economics. For greater detail, see for example Clark (2005). Baltic Journal of Economics 6(2) (2007): 57–78 60

Equations (2) and (3) describe the dynamics of predator–prey, where the carrying capacity of the prey is restricted by the constant value K1, whereas that of the predator is restricted by m3 times the biomass of the prey, or m3X1. If the biomass of the predator is zero, Eq. (2) will coincide with Eq. (1) and the biomass of the prey will be restricted by the carrying capacity K1. This formulation seems to be realistic as is demonstrated, for example, by Japanese deer7. By setting zero in the r.h.s of Eq. (2), we obtain the following equations:

X1 = 0, (4) r1 X1 X2 = 1 − . (5) m1 K1

Similarly, from Eq. (3), we obtain,

X2 = 0, (6)

m3X1 X2 = [r2 + m2X1]. (7) r2

From Eqs. (5) and (7), we acquire the following stationary point, which takes a positive value: 2 − + + 4m1m2m3r1r2 r1 X1 X1, X2 = , 1 − , (8) 2m1m2m3 m1 K1 where = r1r2/K1 + m1m3r2. Figure 1 depicts the vector ﬁeld diagram for our predator–prey model, where the variables are set as follows: r1 = 0.1, r2 = 0.05, K1 = 10,000, m1 = 0.001, m2 = 0.00001 and m3 = 0.001. 2.2. Bioeconomic Model with Hunting

For the sake of analytical simplicity, we assume the existence of a representative economic entity whose purpose is to maximize the social net benefit. In reality, prey and predators cause damages to agricultural crops, forestry products, the natural environment (vegetation, etc.) and other elements (traffic accidents, etc.), and a variety of people may be concerned with each of these damages. However, we will proceed as if the representative economic entity were the one to suffer from these different damages and enjoy the benefits, such as satisfaction from obtaining the trophies of the prey, for example, meat (use value), and satisfaction from their existence (non-use value). We also assume that the representative economic entity manages the local population and that there are many such local populations. Harvests from each local population are sold at the same market; therefore, each representative economic entity is a price taker. This

7 Japanese wolves are believed to have preyed on Japanese deer and were exterminated around 1900 as a result of intensive hunting by humans and for other reasons. Currently, almost no predators prey on Japanese deer, and the Japanese deer population has been restricted mainly by the food supply during the winter. 61 Inﬂuence of Diﬀerent Attitudes Toward Game Animals

Figure 1. Vector field diagram. Note: r1 = 0.1, r2 = 0.05, K1 = 10, 000, m1 = 0.001, m2 = 0.00001, m3 = 0.001. situation is analogous to that of sole ownership proposed by Gordon (1954), which is often discussed in the context of fishery economics8. The economic entity may perceive two economic values for both the prey and predator. One is the economic value of the hunted bodies and/or the hunting activity of the prey and predators themselves; this value is reflected in the prices (hereafter referred to as price values). The other economic value is not reflected in the prices; it is referred to as the willingness to pay for the conservation of the prey and predator (WTP). This WTP can be interpreted in many ways: as the existence value for many naturalists, as the value of improving the prey population for hunters (in the case of predators like wolves) and as the value of biological diversity for the government. In this paper, we will not distinguish between these aspects; instead, we will assume that the representative economic entity recognizes all of them. We denote the amount of prey and predator hunting by h1 and h2, respectively. Further, we assume the following elements: H1(h1): total revenue from the prey hunting, H2(h2): total revenue from the predator hunting, E1(X1): total willingness to pay for the prey, E2(X2): total willingness to pay for the predator, C1(X1): total cost of the prey hunting, C2(X2): total cost of the predator hunting, D1(X1): total damages caused by the prey, D2(X1,X2): total damages caused by the predator. For the sake of analytical simplicity, we will hereafter eliminate C1(X1) and C2(X2). The problem that the economic entity will solve can be given as follows:

8 Sole ownership differs from a monopoly in that the price is given. In a fishery, resources (fish and/or shellfish) are also managed by a local unit, which is often called a local stock rather than a local population. Baltic Journal of Economics 6(2) (2007): 57–78 62 ∞ Max exp(−δt) H1(h1) + H2(h2) + E1(X1) h ,h 1 2 0 + E2(X2) − D1(X1) − D2(X1,X2) dt (9) s.t. dX1 X1 = r1 1 − X1 − m1X1X2 − h1, (10) dt K1 dX2 X2 = r2 1 − + m2X1X2 − h2, (11) dt m3X1 where δ is the discount rate. The current-value Hamiltonian Hc of this maximization problem is given as:

Hc = H (h ) + H (h ) + E (X ) + E (X ) − D (X ) − D (X ,X ) 1 1 2 2 1 1 2 2 1 1 2 1 2 X + µ r 1 − 1 X − m X X − h 1 1 K 1 1 1 2 1 1 X2 + µ2 r2 1 − X2 + m2X1X2 − h2 , (12) m3X1 where λi is the Lagrange multiplier and µi equals exp(δt)λi . If we assume an interior solution, some of the conditions for optimality are given by the following partial derivatives: (Maximum principle conditions)

∂Hc dH1(h1) = − µ1 = 0, (13) ∂h1 dh1

∂Hc dH2(h2) = − µ2 = 0, (14) ∂h2 dh2

(Portfolio balance conditions)

dE1(X1) dD1(X1) ∂D2(X1,X2) −˙µ1 + δµ1 = − − dX1 dX1 ∂X1 ∂G1(X1,X2) ∂G2(X1,X2) + µ1 + µ2 , (15) ∂X1 ∂X1

dE(X2) ∂D2(X1,X2) ∂G(X1,X2) ∂G2(X1,X2) −˙µ2 + δµ2 = − + µ1 + µ2 , (16) dX2 ∂X2 ∂X2 ∂X2 where G (X ) and G (X ) are equal to r [1 − X1 ]X − m X X and r [1 − X2 ]+ 1 1 2 2 1 K1 1 1 1 2 2 m3X1 m3X1X3, respectively, and µ˙ denotes the partial differential of time t. At the steady state, µ˙ will be equal to zero since the shadow price will not change. By substituting the condition (µ˙ = 0) as well as Eqs. (13) and (14) into Eqs. (15) and (16), we obtain two equations that constitute the golden rule equations for this predator–prey model. 63 Influence of Different Attitudes Toward Game Animals

The combinations of the X1 and X2 values, which simultaneously satisfy the golden rule equations, are the steady-state solutions under the condition of hunting. We will present the golden rule equations after specifying their functional forms. 2.3. Speciﬁcations of the Functional Forms

In order to proceed with the numerical simulation in the next section, we will specify the functional forms as follows. total revenue from hunting (H 1(h1) and H 2(h2)): We assume the following equation.

Hi(hi) = pihi,i= 1, 2, (17) where p1 and p2 are constants that denote the prices of the carcasses of the prey and predator, respectively. These prices reﬂect the value of trophies such as game meat, some viscera (liver and heart) and/or horns or antlers. total willingness to pay (E1(X1) and E1(X2)): We assume the following equation.

Ei(Xi) = WTPiXi,i= 1, 2, (18) where WTP1 and WTP2 are constants that denote the WTP for the prey and predator, respectively. For example, when the economic entity does not recognize the non-use values of the predator, WTP2 will be zero; as he/she increasingly recognizes the value, the WTP2 will increase. total damages caused by the prey D1(X1): As the number of the prey increases, the amount of damage may also increase. Therefore, we assume the following equation.

= 2 D1(X1) d1X1,d1 > 0, (19) where d1 is a constant. total damages caused by the predator D2(X2): On the other hand, the damages caused by the predator will be inﬂuenced by the relative number of their prey. As the prey decrease, particularly when they face strong hunting pressure, the predator will have less food and may therefore need to kill domestic animals more frequently. Therefore, we assume the following equation.

X2 D2(X1,X2) = d2 ,d2 > 0, (20) X1 where d2 is a constant. 2.4. Speciﬁcation of the Golden Rule and the Steady-State Solutions

We can now specify the golden rule equations as follows. Baltic Journal of Economics 6(2) (2007): 57–78 64 d X 2r δp = WTP − d X + 2 2 + p r − 1 X − m X 1 1 2 1 1 2 1 1 1 1 2 X K2 1 r + p 2 X2 + m X , 2 2 2 2 2 (21) m3X1

m3X1 d2 X2 = p2[−δ + r2 + m2X1]+WTP2 − − p1m1X1 . (22) 2r2p2 X1

The pair of steady-state solutions X∗ and X∗ should simultaneously satisfy both Eqs. 1 2 ∗ ∗ (21) and (22). We can determine the solutions (X1,X2) using the following procedure. By setting the initial value of X1 in Eq. (22), we can obtain the corresponding value of X2. Then, we verify whether this pair of values satisﬁes Eq. (21). If it does not, we change the value of X1 until the pair of values satisﬁes Eq. (21). We also provide the golden rule equation for the case in which there is no predator. This equation can be reduced from Eqs. (21) and (22) by substituting p2 = 0 and X2 = 0:

WTP1 + p1[r1 − δ] X1 = . (23) 2[d1 + p1r1/K]

∗ At the steady state, the economic entity will have not only a pair of optimal populations X1 ∗ ∗ ∗ ∗ = ∗ ∗ − ∗ ∗ and X2 but also optimal harvests h1 and h2, which are given by h1 G(X1,X2) m1X1X2 ∗ = ∗ ∗ + ∗ ∗ and h2 G(X1,X2) m2X1X2 , respectively.

3. Numerical Simulations and Analysis

3.1. Parameter Settings

In this section, we will set the parameter values. As a result of the limitations in the empirical data, we set the parameter values in the following manner. Firstly, we assume that the instantaneous growth rates of the prey and predator – r1 and r2 – are 0.15/year and 0.1/year, respectively. This is because the prey is usually an r strategist while the predator 9 is a K strategist ; therefore, the value of r1 is higher than that of r2. Wealsoassumethat the carrying capacity of the prey K1 is 10,000 heads. We then set m1, m2 and m3. The predator–prey ratio can be 1 : 200. Therefore, we set m3 = 0.005 so that m3X1 : K1 = 10000m3 : 10000 = 1 : 200. m1 and m2 inﬂuence the population of both the prey N1 and the predator N2 (miX1X2, i = 1, 2). We assume that m1 = 0.0000001 and m2 = 0.000001. Next, we assume the economic parameter values. We set the social discount rate as δ = 0.01/year. We then set the price of the prey and predator. As is mentioned above, the economic value of the predator appears to have often been evaluated as being less than that of the prey. Moreover, because the skin, one of the most valuable parts of the predator, is becoming less attractive as a daily use material and/or a trophy, we assume that the value of p2 is considerably lower than that of p1. We select the values of p1 and p2 such that

9 In an evolution process, while r strategist lays emphasis on increasing r, K strategist lays emphasis on increasing K. For additional details, see r and K selection in some texts on basic population biology. 65 Inﬂuence of Diﬀerent Attitudes Toward Game Animals

Table 1. Parameter value assumption Notation Unit Value(s) Exposition

r1 /year 0.15 : instantaneous growth rate of the prey r2 /year 0.1 : instantaneous growth rate of the predator K1 heads/area 10,000 : carrying capacity of the prey m1 – 0.0000001 : constant in the equation of prey dynamics m2 – 0.000001 : constant in the equation of predator dynamics m3 – 0.005 : constant governing the predator’s carrying capacity p1 currency unit 100–1500 : price of the prey p2 currency unit 50–150 : price of the predator WTP1 currency unit 0–100 : willingness to pay for the prey WTP2 currency unit 0–100 : willingness to pay for the predator d1 – 0.001 : constant indicating the strength of the prey damage d2 – 10000 : constant indicating the strength of the predator damage δ /year 0.01 : social discount rate 2 Note: For example, K1 represents the number of heads per 10 km and p1 and p2 denote prices in lats.

10 they yield economically appropriate results . Subsequently, we select the values of d1 and d2 such that the magnitude of the damage caused by the prey and predator can be realistic in comparison with the revenue. We also set WTP1 and WTP2, which may be realistic in relation to the price values. The parameter values above are tabulated in Table 1. 3.2. Simulation Results and Analysis

We will show several results. As is mentioned above, the aim of this paper is to examine how the attitude of the economic entity influences the optimal resource level of the prey and predator. In general, the attitude toward the prey and predator seems to follow several stages. At the first stage, p1 and p2 are sufficiently high, but WTP1 and WTP2 are almost zero (STAGE 1). Then, as the economic condition improves, while the values of p1 and p2 decrease somewhat, WTP1 increases (STAGE 2). Finally, the significance of the predator is 11 recognized and WTP2 also assumes a positive value (STAGE 3) . Therefore, we arranged several pairs of parameter values in keeping with the change of attitude mentioned above, and the results are tabulated in Tables 2-1 to 2-612.

10 When we select the price value, we also need to adjust m1 and m2. This is because some variables will assume different values when human activities are and are not influential. In fact, once we incorporate human influence into the current model, some pairs of values (p1,p2) will not bring economically appropriate results without an adjustment of m1 and m2. These values have already been adjusted, as is shown above (m1 = 0.0000001 and m2 = 0.000001). 11 For example, in Latvia, ecotourism and/or green tourism has been launched recently, but there is a lack of awareness of large carnivores (Andersone and Ozoli¸nš, 2002, 2004). This situation may correspond to STAGE 2. Currently, while hunting activities appear to be less popular among young people (personal communication with the staff of the State Forest Service, Latvia), as we have already seen, the significance of carnivores has begun to be recongized among researchers as well as the public; this situation may correspond to STAGE 3. 12 ∗ ∗ The steady-state harvests h1 and h2 sometimes assume negative values in Tables 2-1 to 2-6. These negative values indicate that someone, such as the representative economic entity, should annually contribute to rather than harvest the prey and/or predator population in order to maintain the steady-state population level. However, this ∗ may not be realistic. For example, when WTP1 takes a high value and p1 alowvalue,X1 exceeds its carrying capacity K and h1 assumes a negative value. For instance, this negative h1 means that in order to maintain the population at the same level, they should raise fawn in an artificial deer farm and release them into their natural Baltic Journal of Economics 6(2) (2007): 57–78 66

Table 2.1a. (WTP1 = 0, WTP2 = 0) p2 = 50 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 2801 0.7 302 0.130,247 4 7,846 3 300 3819 1.0 354 0.1 106,223 5 14,585 3 500 4118 1.0 363 0.1 181,665 5 16,958 2 1500 4469 0.8 371 0.1 556,155 4 19,972 2 p2 = 100 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 2806 8.1 303 0.430,279 36 7,874 29 300 3820 7.0 354 0.5 106,233 47 14,592 18 500 4119 6.6 363 0.5 181,677 47 16,966 16 1500 4469 5.9 371 0.5 556,152 46 19,972 13 p2 = 150 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 2842 21.9 305 −1.130,514 0 8,077 77 300 3826 17.8 354 0.2 106,296 29 14,638 47 500 4122 16.7 363 0.4 181,715 58 16,991 41 1500 4469 15.1 371 0.6 556,146 83 19,972 34

3.2.1. Influence when the WTP and Price Change Firstly, we examine the case in which the WTP increases. In Table 3, we summarize the cases ∗ ∗ and results. When WTP1 increases, X1 increases but X2 decreases (compare, for example, Tables 2-1a and 2-2a with regard to the X∗ increase and Tables 2-2a and 2-3a with regard to ∗ 1 the X2 decrease). This result justifies the assertion that if WTP1 is not high, it is appropriate to hunt in order to reduce the number of carnivores, which are regarded as the pest animal. It also suggests that as awareness of carnivores increases, as is reflected in the increase of WTP1, the number of carnivores should be maintained at a higher level than before. Next, we examine the case in which the price increases. In Table 4, we summarize the results.

Increase of p1 or p2 at STAGE 1 ∗ ∗ When p1 increases at STAGE 1, X1 increases, but X2 chieﬂy decreases (for example, see 13 ∗ ∗ 14 Table 2.1a) . On the other hand, when p2 increases, both X1 and X2 increase .

environment when they become young deer. Because people have a substantial WTP for the prey, the cost may be covered, although the WTP for the prey is not always intended to cover this expenditure and/or it is not always actually collected. In fact, due to damages to commercial timber, maintaining the population above the carrying capacity may not be optimal. In addition, if we explicitly consider the sustainability of the vegetation within the deer habitat, it is untenable to maintain the population above the carrying capacity. We cope with this problem by introducing a maximum and a minimum price setting in the following section. 13 We include ‘chiefly’ because when price increases, the relationship may be reversed. This is because we consider a steady state, and if the steady-state biomass levels increase to the point where they exceed the point corresponding to MSY, the steady-state yields begin to decrease. We omit ‘chiefly’ in the text that follows. 14 ∗ X1 increases slightly. This may be attributed to the parameter setting. Since we assign small values to m1 and m2, the predator has a small effect on the dynamics of the prey. The author could not find the pair of values 67 Influence of Different Attitudes Toward Game Animals

Table 2.1b. (WTP1 = 0, WTP2 = 0) P1 = 100 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 2801 0.7 302 0.130,247 4 7846 3 100 2806 8.1 303 0.430,279 36 7874 29 150 2842 21.9 305 −1.130,514 0 8077 77

P1 = 300 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 3819 1.0 354 0.1 106,223 5 14,585 3 100 3820 7.0 354 0.5 106,233 47 14,592 18 150 3826 17.8 354 0.2 106,296 29 14,638 47

P1 = 1500 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 4469 0.8 371 0.1 556,155 4 19,972 2 100 4469 5.9 371 0.5 556,152 46 19,972 13 150 4469 15.1 371 0.6 556,146 83 19,972 34 ∗ ∗ Note: Because Hi(hi ) is the product of pi and hi ,whenhi assumes a negative value, Hi(hi ) also assumes a negative value. However, these negative value is values are not realistic (negative values suggests breeding, as is explained in the text), and they are replaced with zero in the series of the Table 2.

Table 2.2a. (WTP1 = 50, WTP2 = 0) p2 = 50 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 12,801 0.7 −538 0.1 0 4 163,866 1 300 8,364 0.8 205 0.161,576 4 69,956 1 500 7,059 0.9 311 0.1 155,704 5 49,829 1 1500 5,532 0.8 371 0.1 556,131 4 30,603 1 p2 = 100 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 12,801 2.9 −538 0.3 0 32 163,866 2 300 8,364 4.0 205 0.461,575 40 69,956 5 500 7,059 4.5 311 0.4 155,702 42 49,829 6 1500 5,532 5.0 371 0.4 556,128 44 30,603 9 p2 = 150 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 12,801 6.8 −538 0.7 0 104 163,866 5 300 8,365 9.5 205 0.861,543 122 69,973 11 500 7,060 10.9 311 0.8 155,669 124 49,844 15 1500 5,533 12.7 371 0.8 556,097 114 30,614 23

that assign higher values to m1 and m2 while maintaining a realistic model overall. This may be one of the unresolved issues of this paper. Baltic Journal of Economics 6(2) (2007): 57–78 68

Table 2.2b. (WTP1 = 50, WTP2 = 0) P1 = 100 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 12,801 0.7 −538 0.1 0 4 163,866 1 100 12,801 2.9 −538 0.3 0 32 163,866 2 150 12,801 6.8 −538 0.7 0 104 163,866 5

P1 = 300 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 8,364 0.8 205 0.161,576 4 69,956 1 100 8,364 4.0 205 0.461,575 40 69,956 5 150 8,365 9.5 205 0.861,543 122 69,973 11

P1 = 1500 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 5,532 0.8 371 0.1 556,131 4 30,603 1 100 5,532 5.0 371 0.4 556,128 44 30,603 9 150 5,533 12.7 371 0.8 556,097 114 30,614 23

Table 2.3a. (WTP1 = 50, WTP2 = 50) p2 = 50 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 12, 801 8.5 −538 0.8 0 42 163, 866 7 300 8, 365 12.8 205 1.061, 542 50 69, 973 15 500 7, 060 15.0 311 1.0 155, 668 48 49, 844 21 1500 5, 533 18.8 371 0.7 556, 092 35 30, 614 34

p2 = 100 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 12, 802 18.6 −538 1.6 0 155 163, 891 14 300 8, 367 27.9 205 1.261, 478 116 70, 007 33 500 7, 062 32.8 311 0.5 155, 600 46 49, 872 46 1500 5, 535 41.1 371 −1.8 556, 026 0 30, 636 74

p2 = 150 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 12, 805 30.2 −539 2.0 0 297 163, 968 24 300 8, 373 45.4 204 0.061, 292 0 70, 107 54 500 7, 070 53.3 311 −2.3 155, 344 0 49, 985 75 1500 5, 542 66.8 371 −9.1 555, 835 0 30, 714 121

Increase of p1 or p2 at STAGES 2 and 3 ∗ ∗ When p1 increases at STAGES 2 and 3, X1 decreases, but X2 increases. This is the opposite result from that observed in STAGE 1. On the other hand, when p2 increases, both ∗ ∗ X1 and X2 increase. This is the same result as that observed in STAGE 1. 69 Inﬂuence of Diﬀerent Attitudes Toward Game Animals

Table 2.3b. (WTP1 = 50, WTP2 = 50) P1 = 100 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 12, 801 8.5 −538 0.8 0 42 163, 866 7 100 12, 802 18.6 −538 1.6 0 155 163, 891 14 150 12, 805 30.2 −539 2.0 0 297 163, 968 24

P1 = 300 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 8, 365 12.8 205 1.061, 542 50 69, 973 15 100 8, 367 27.9 205 1.261, 478 116 70, 007 33 150 8, 373 45.4 204 0.061, 292 0 70, 107 54

P1 = 1500 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 5, 533 18.8 371 0.7 556, 092 35 30, 614 34 100 5, 535 41.1 371 −1.8 556, 026 0 30, 636 74 150 5, 542 66.8 371 −9.1 555, 835 0 30, 714 121

Table 2.4a. (WTP1 = 100, WTP2 = 0) p2 = 50 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 22, 801 0.4 −4, 378 0.1 0 3 519, 886 0 300 12, 910 0.6 −564 0.1 0 3 166, 668 0 500 10, 001 0.7 −00.1 0 4 100, 020 1 1500 6, 596 0.7 337 0.1 505, 187 4 43, 507 1 p2 = 100 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 22, 801 1.9 −4, 378 0.2 0 23 519, 886 1 300 12, 910 2.8 −564 0.3 0 31 166, 668 2 500 10, 001 3.4 −00.4 0 35 100, 020 3 1500 6, 596 4.3 337 0.4 505, 183 41 43, 507 7 p2 = 150 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 22, 801 4.3 −4, 378 0.5 0 76 519, 886 2 300 12, 910 6.6 −564 0.7 0 102 166, 668 5 500 10, 001 8.1 −00.8 0 114 100, 020 8 1500 6, 596 10.9 337 0.8 505, 177 120 43, 507 17

We obtain the opposite results concerning the increase of p1, as shown above. This can be attributed to the ratio between the price of the prey and the WTP for the prey. When WTP1 is suﬃciently high, as p1 decreases, the impact of WTP1 increases; as a result, the economic entity is more eager to maintain a higher X1 level, which results in a reverse relationship. On the other hand, if WTP1 is suﬃciently low, the economic entity does not place a large Baltic Journal of Economics 6(2) (2007): 57–78 70

Table 2.4b. (WTP1 = 100, WTP2 = 0) P1 = 100 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 22, 801 0.4 −4, 378 0.1 0 3 519, 886 0 100 22, 801 1.9 −4, 378 0.2 0 23 519, 886 1 150 22, 801 4.3 −4, 378 0.5 0 76 519, 886 2

P1 = 300 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 12, 910 0.6 −564 0.1 0 3 166, 668 0 100 12, 910 2.8 −564 0.3 0 31 166, 668 2 150 12, 910 6.6 −564 0.7 0 102 166, 668 5

P1 = 1500 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 6, 596 0.7 337 0.1 505, 187 4 43, 507 1 100 6, 596 4.3 337 0.4 505, 183 41 43, 507 7 150 6, 596 10.9 337 0.8 505, 177 120 43, 507 17

Table 2.5a. (WTP1 = 100, WTP2 = 50) p2 = 50 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 22, 801 4.8 −4, 378 0.6 0 29 519, 886 2 300 12, 910 8.4 −564 0.8 0 42 166, 668 6 500 10, 001 10.7 −00.9 0 47 100, 020 11 1500 6, 596 15.9 337 0.9 505, 172 46 43, 507 24

p2 = 100 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 22, 801 10.6 −4, 378 1.2 0 121 519, 886 5 300 12, 910 18.3 −564 1.5 0 155 166, 668 14 500 10, 001 23.4 −01.5 0 148 100, 020 23 1500 6, 598 34.6 337 0.1 505, 010 5 43, 534 53

p2 = 150 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 22, 801 17.4 −4, 378 1.9 0 281 519, 886 8 300 12, 912 29.9 −564 2.0 0 299 166, 720 23 500 10, 004 38.1 −11.3 0 193 100, 080 38 1500 6, 601 56.3 337 −3.6 504, 772 0 43, 573 85

∗ amount of emphasis on maintaining a high resource level; therefore, X1 will increase as p1 increases (usual relationship). It follows that there is a critical willingness to pay when the steady-state population–price relationship is reversed. In our case, this occurs when WTP1 = 9.33 and the corresponding ∗ X1 level is 4,667, regardless of the price (Table 5). 71 Inﬂuence of Diﬀerent Attitudes Toward Game Animals

Table 2.5b. (WTP1 = 100, WTP2 = 50) P1 = 100 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 22, 801 4.8 −4, 378 0.6 0 29 519, 886 2 100 22, 801 10.6 −4, 378 1.2 0 121 519, 886 5 150 22, 801 17.4 −4, 378 1.9 0 281 519, 886 8

P1 = 300 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 12, 910 8.4 −564 0.8 0 42 166, 668 6 100 12, 910 18.3 −564 1.5 0 155 166, 668 14 150 12, 912 29.9 −564 2.0 0 299 166, 720 23

P1 = 1500 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 6, 596 15.9 337 0.9 505, 172 46 43, 507 24 100 6, 598 34.6 337 0.1 505, 010 5 43, 534 53 150 6, 601 56.3 337 −3.6 504, 772 0 43, 573 85

Table 2.6a. (WTP1 = 100, WTP2 = 100) p2 = 50 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 22, 801 9.2 −4, 378 1.1 0 53 519, 886 4 300 12, 910 16.1 −564 1.4 0 71 166, 668 12 500 10, 001 20.7 −01.4 0 71 100, 020 21 1500 6, 597 31.0 337 0.4 505, 085 19 43, 520 47 p2 = 100 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 22, 801 19.4 −4, 378 2.1 0 205 519, 886 9 300 12, 911 33.8 −564 2.0 0 205 166, 694 26 500 10, 003 43.4 −01.0 0 101 100, 060 43 1500 6, 601 64.9 337 −5.9 504, 764 0 43, 573 98 p2 = 150 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 22, 803 30.6 −4, 379 2.9 0 440 519, 977 13 300 12, 915 53.1 −565 1.6 0 244 166, 797 41 500 10, 009 68.0 −1 −1.8 0 0 100, 180 68 1500 6, 612 101.6 336 −20.4 503, 932 0 43, 719 154

3.2.2. The Case in which There Is no Predator Next, we examine the case in which there is no predator. Further, because we established that the basic model of the prey described by Eq. (2) is deduced from the Schaefer model Baltic Journal of Economics 6(2) (2007): 57–78 72

Table 2.6b. (WTP1 = 100, WTP2 = 100) P1 = 100 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 22, 801 9.2 −4, 378 1.1 0 53 519, 886 4 100 22, 801 19.4 −4, 378 2.1 0 205 519, 886 9 150 22, 803 30.6 −4, 379 2.9 0 440 519, 977 13

P1 = 300 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 12, 910 16.1 −564 1.4 0 71 166, 668 12 100 12, 911 33.8 −564 2.0 0 205 166, 694 26 150 12, 915 53.1 −565 1.6 0 244 166, 797 41

P1 = 1500 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p2 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 50 6, 597 31.0 337 0.4 505, 085 19 43, 520 47 100 6, 601 64.9 337 −5.9 504, 764 0 43, 573 98 150 6, 612 101.6 336 −20.4 503, 932 0 43, 719 154

Table 3. Qualitative inﬂuences of changes in WTP ∗ ∗ Stage WTP1 increases WTP2 X1 X2 Stages1to2 Tables2-1(WTP1 = 0)to2-2(WTP1 = 50) WTP2 = 0 +− Tables 2-1 (WTP1 = 0)to2-4(WTP1 = 100) WTP2 = 0 Stage 3 Tables 2-3 (WTP1 = 50)to2-5(WTP1 = 100) WTP2 = 50 ++

∗ ∗ Stage WTP2 increases WTP1 X1 X2 Stages2to3 Tables2-2(WTP2 = 0)to2-3(WTP2 = 50) WTP1 = 50 ++ Tables 2-4 (WTP2 = 0)to2-5(WTP2 = 50) WTP1 = 100 Tables 2-4 (WTP2 = 0)to2-6(WTP2 = 100) WTP1 = 100 Stage 3 Tables 2-5 (WTP2 = 50)to2-6(WTP2 = 100) WTP1 = 100 ++

Table 4. Qualitative inﬂuences of changes in price and WTP STAGE 1 STAGE 2 STAGE 3 (WTP1 = WTP2 = 0)(WTP1 > 0, WTP2 = 0)(WTP1 > 0, WTP2 > 0) ∗ ∗ ∗ ∗ ∗ ∗ X1 X2 X1 X2 X1 X2 p1 increase +−− + − + p2 increase (+) + (+) + (+) + Note: WTP values are set at zero or at positive values greater than 50.

(Clark, 2005) described by Eq. (1)15, we can compare the results of both equations in other scenarios. In Tables 6-1 to 6-3, we show the results for the cases in which the predator does not exist. By comparing them with those of Tables 2-1 to 2-6, it is evident that the

15 To be exact, a model is referred to as a Schaefer model when harvest is incorporated. 73 Inﬂuence of Diﬀerent Attitudes Toward Game Animals

∗ Table 5. The case in which p1 has no impact on X1 ∗ ∗ ∗ ∗ ∗ ∗ ∗ ∗ p1 X1 X2 h1 h2 H1(h1)H2(h2)D1(X1)D2(X2) 100 4667 1.1 373 0.137,334 5 21,781 2 300 4667 1.1 373 0.1 112,001 5 21,781 2 500 4667 1.0 373 0.1 186,668 5 21,781 2 700 4667 1.0 373 0.1 261,335 5 21,781 2 900 4667 0.9 373 0.1 336,003 5 21,781 2 1100 4667 0.9 373 0.1 410,670 4 21,781 2 1300 4667 0.9 373 0.1 485,337 4 21,781 2 1500 4667 0.8 373 0.1 560,004 4 21,781 2

Note: Parameters are set as WTP1 = 9.33, WTP2 = 0,andp2 = 50.

Table 6.1. (WTP1 = 0) ∗ ∗ p1 X1 h1 H1(h1)D1(X1) 100 2,800 302 30,240 7,840 300 3,818 354 106,215 14,579 500 4,118 363 181,661 16,955 1500 4,468 371 556,134 19,964

Table 6.2. (WTP1 = 50) ∗ ∗ p1 X1 h1 H1(h1)D1(X1) 100 12,800 −538 0 163,840 300 8,364 205 61,587 69,950 500 7,059 311 155,709 49,827 1500 5,532 371 556,134 30,602

Table 6.3. (WTP1 = 100) ∗ ∗ p1 X1 h1 H1(h1)D1(X1) 100 22,800 −4,378 0 519,840 300 12,909 −563 0 166,645 500 10,000 0 0 100,000 1500 6,596 337 505,206 43,504 steady-state population and harvest of the prey are larger when predators exists than when they do not. 3.2.3. Sensitivity Analysis We conducted a sensitivity analysis because the parameter values were assumed and there are both usual and reverse relationships. We separately change the values of r1, r2, m1, m2, m3, d1, d2 and δ by 10%. As we have already observed, the steady state in STAGE 1 (when WTP1 is less than 9.33) is diﬀerent from those in STAGES 2 and 3. In Table 7, we present two cases, one in which WTP1 and WTP2 are set at 0 and 0 (usual relationship, hereinafter called CASE 1), and the other in which they are set at 100 and 100 (reverse relationship, hereinafter called CASE 2). Other parameters are set as before (see Table 1), and p1 and p2 are set at 500 Baltic Journal of Economics 6(2) (2007): 57–78 74

Table 7. Results of the sensitivity analysis ∗ ∗ ∗ ∗ X1 X2 h1 h2 r1 10% up −++− ++ 10% down +−−+ −− r2 10% up 0 + 0 + 10% down 0 − 0 − m1 10% up 0 − 0 − 10% down 0 + 0 + m2 10% up 0 + 0 + 10% down 0 − 0 − m3 10% up 0 − 0 − 10% down 0 + 0 + d1 10% up −++− −− 10% down +−−+ ++ d2 10% up 0 − 0 − 10% down 0 + 0 + δ 10% up −++− −− 10% down +−−+ ++ Note 1: 0 indicate denotes the case where in which the values do not change or change quite only slightly. Note 2: WTP1 and WTP2 are set at 0 and 0 (CASE 1), and 100 and 100 (CASE 2), respectively; when they differ, CASE 1 is shown on top and CASE 2 on the bottom. and 50, respectively. In Table 7, when the results for CASES 1 and 2 differ, CASE 1 is presented on top and CASE 2 is presented on the bottom. There are three parameters that vary differently in CASES 1 and 2. Firstly, when the ∗ ∗ instantaneous growth rate of the prey r1 increases, in CASE 1, X1 and h2 decrease while they increase in CASE 2. Similarly, when the constant, which indicates the strength of the ∗ ∗ prey damage d1 and social discount rate δ, increases, X2 and h1 increase in CASE 1 and decrease in CASE 2. ∗ ∗ ∗ ∗ With regard to other parameter values, the signs of the changes of X1 , X2 , h1 and h2 are same for both cases or change only slightly.

4. Discussion In the following four subsections, we will discuss whether the minimum/maximum price16 should be required for resource conservation. There are a variety of reasons for which we

16 In this paper, we implicitly assume the existence of many local populations; the price can be regarded as the market price. The minimum and/or maximum price should be set by the authority in charge of game management. In the case of Latvia, this price can be interpreted as the price of a hunting permit. In this case, it is more 75 Influence of Different Attitudes Toward Game Animals should set a minimum/maximum price. For example, minimum and maximum prices help to maintain the population size, to moderate damages to agriculture or forestry, to maintain habitats in good condition and/or to avoid unrealistic correspondence in releasing artificially grown prey. We demonstrate these reasons below, under our parameter settings. Then, in the 5th subsection, we will compare the cases in which the predator does and does not exist.

4.1. The Minimum/Maximum Price Setting of p1 for the Prey when WTP1 < 9.33 ∗ When WTP1 < 9.33, the optimal resource level X1 decreases as the price decreases. There- fore, to maintain the population size above a critical point, such as that of the MVP (minimum viable population), the price should be maintained above this minimum price. However, a maximum price is not required to keep the population size under our parameter setting as 17 long as the p1 values remain between 100 and 1500 .

4.2. The Minimum/Maximum Price Setting of p1 for the Prey when WTP1 > 9.33

A minimum price setting should be required for the following reasons. When WTP1 > 9.33, ∗ the optimal resource level X1 increases as the price decreases. As the price decreases, the revenue decreases because of the reduced price and the corresponding reduced optimal resource level; also in addition, the cost increases due to the intensiﬁcation of the damages caused by the prey. As a result, the net revenue decreases; this is not preferable from a social viewpoint. This tendency becomes more prominent as WTP1 increases. The above suggests that the increase of WTP1 should be accompanied by the use of the 18 resource; therefore, the minimum price should be set . A higher WTP1 seems to suggest the necessity of protecting the resource; from the social point of view, it is practical that the resource be conserved. It is not a positive tendency that the increase in the GNP has resulted in a decrease in the use of wild animals as well as an impetus to campaign for the protection of wild animals, as is illustrated by some empirical examples19. There is another reason for this. The optimal harvest of the prey and predator sometimes takes negative values under our parameter settings. The negative harvest suggests that the representative economic entity should release the individuals he/she has bred into the local population of prey/predator in order to sustain the population level. However, this is somewhat unrealistic. ∗ ∗ The steady-state harvest of prey h1 assumes negative values when X1 exceeds the level of the carrying capacity K. Therefore, the steady state is not sustainable from the viewpoint of the sustainability of the habitat or vegetation. As a result of limitations such as food supply,

realistic to suppose that the permit price is a function of the amount of hunting. This is because the representative economic entity can be interpreted as an authority such as the State Forest Service, which manages most of the local population and has the authority to modify permit prices. 17 Needless to say, this result depends largely on the determination of our parameters. In these subsections, we demonstrate our reasons for setting the minimum/maximum prices. 18 A subsidy for hunting can be another policy option. This was pointed out by an anonymous referee, who I am grateful to. 19 Petty’s law suggests this tendency. One of the typical cases was seen during and after the high-growth period in Japan, where hunting pressure had drasticaly declined. Baltic Journal of Economics 6(2) (2007): 57–78 76 this scenario results in the collapse of the deer population, as has been observed in places across the world20. In this sense, a minimum price of the prey should be introduced. ∗ When WTP1 is suﬃciently large, the optimal resource level X1 decreases as the price increases. In this case, in order to sustain the population size above a critical point, such as the MVP, the price should be maintained below a given maximum price. However, with the establishment of our parameters, a maximum price is not necessarily required.

4.3. The Minimum/Maximum Price Setting of p2 for the Predator when WTP1 < 9.33

We now consider the case of the predator. When WTP1 < 9.33, h2 may assume a negative value as p2 increases and approaches p1.Ifh2 assumes a negative value, the maximum price should be set because it is not realistic to release the predator in order to sustain the resource level. On the other hand, the minimum price will not be required until the population size of the prey approaches the MVP.

4.4. The Minimum/Maximum Price Setting of p2 for the Predator when WTP1 > 9.33

When WTP1 > 9.33, h2 may assume a negative value as p1 increases if p2 also takes a relatively high value. If h2 assumes a negative value, the maximum price should be set in keeping with the reasons presented in Subsection 4.3. On the other hand, the minimum price will not be required until the population size of the prey approaches the MVP. This result is also same as that presented in Subsection 4.3. 4.5. The Case in Which There Is no Predator Many of the results in the above section also apply to the case in which there is no predator. As is mentioned above, the steady-state population and harvest of the prey are larger when the predator exists than when it does not exist21. The result suggests that, contrary to expectations, the existence of the predator results in a higher steady-state population of the prey. With regard to the real situation, this result may reﬂect the function of the predator, which is evaluated as the WTP for the predator. This result may support the reintroduction of the prey into areas where the prey has been exterminated22.

5. Concluding Remarks This paper presents four main results. Firstly, we reaﬃrm that it is important for people to value the prey as well as the predator in order to maintain a viable population. Secondly, as the WTP for the prey, WTP1, increases relative to p1, the amount of the resource will be prioritized above the amount of the harvest. Thirdly, the minimum/maximum price may be required for resource conservation and conservation rather than protection is required

20 Famous studies such as those of Klein (1968) and Caughley (1970) present a drastic eruption of the ungulate population, in which the population reduces to nearly zero; however, subsequent studies have not always supported their results (for example, Hanks, 1981). 21 The differences are substantially small. As we have already seen, this may be attributed to the parameter setting. 22 The reintroduction of the wolf into Yellowstone National Park is the most famous example. Thanks to the populations of other countries, reintroduction in Latvia seems to have occured naturally rather than artificially, and this reintroduction supports the population of Latvia (Andersone-Lilley et al., 2005). 77 Influence of Different Attitudes Toward Game Animals even when WTP1 increases. This is because high population levels result in significant vegetation damage, which may lead to the collapse of the prey. We should consider this result, particularly in the short term. In other words, we should recognize the need to utilize the wild animals. However, in reality, utilization of wild animals has decreased. Therefore, in the long run, it would be better to transition to a management system with minimal human intervention. Finally, the existence of the predator is desirable. Some issues remain to be discussed. Firstly, we assume the existence of the representative economic entity. This assumption enables us to avoid certain problems that are difficult to resolve and not necessarily essential for our analysis. However, this assumption is not realistic. Secondly, we eliminated the total cost of the prey and predator in the model. Thirdly, we treat only the steady state without uncertainty; however, a more desirable situation may exist if we consider, for example, pulse harvest23.

References

Alexander, R. R. (2000). “Modelling Species Extinction: The Case for Non-Consumptive Values”, Ecological Economics 35, 259–269. Andersone, Ž. (2003). “Wolves in Latvia: Past and Present”, Wolf Print 16, 13–14. Andersone, Ž. and J. Ozoli¸nš (2002). Investigation of the Public Opinion about Three Large Carnivore Species in Latvia—Brown Bear (Ursus arctos), Wolf (Canis lupus) and Lynx (Lynx lynx), WWF Latvia. Andersone, Ž. and J. Ozoli¸nš (2004). “Public Perception of Large Carnivores in Latvia”, Ursus 15(2), 181–187. Andersone-Lilley, Ž., L. Balciauskas, J. Ozoli¸nš and H. Valdmann (2005). “Would Baltic Wolves Vote for the EU?”, Wolf Print 23, 9. Caughley, G. (1970). “Eruption of Ungulate Populations, with Emphasis on Himalayan Thar in New Zealand”, Ecology 51(1), 53–72. Clark, C. W. (1973). “Proﬁt Maximization and the Extinction of Animal Species”, Journal of Political Economy 81, 950–961. Clark, C. W. (2005). Mathematical Bioeconomics: The Optimal Management of Renewable Resources, 2nd edition, Wiley. Finnoﬀ, D. and J. Tschirhart (2003). “Protecting an Endangered Species While Harvesting Its Prey in a General Equilibrium Ecosystem Model”, Land Economics 79(2), 160–180. Gordon, H. S. (1954). “The Economic Theory of a Common-Property Resource: The Fishery”, Journal of Political Economy 62, 124–142. Hanks, J. (1981). “Characterization of Population Condition”, in Fowler, C. W. and Tim D. Smith (eds) Dynamics of Large Mammal Populations, Wiley, pp. 47–73. Hoekstra, J. and J. C. J. M. van den Bergh (2005). “Harvesting and Conservation in a Predator–prey System”, Journal of Economic Dynamics and Control 29, 1097–1120. Kawata, Y. (2003). “Optimal Management of Targeted Species and Pest-Predator”, Papers on Environ- mental Information Science 17, 311–316 (in Japanese). Klein, D. R. (1968). “The Introduction, Increase, and Crash of Reindeer on St. Matthew Island”, Journal of Wildlife Management 32, 350–367.

23 Pulse harvest implies a discontinuous harvest. For example, the authority does not set the hunting limit annually but permits harvestation only when the population size exceeds the target size. Baltic Journal of Economics 6(2) (2007): 57–78 78

Ozoli¸nš, J. (2002). Management Plan for Eurasian Lynx (Lynx lynx) in Latvia, State Forest Service of the Ministry of Agriculture, Latvia. Ozoli¸nš, J. (2003). Management Plan for Conservation of Brown Bear (Ursus arctos) in Latvia, State Forest Service of the Ministry of Agriculture, Latvia. Ozoli¸nš, J. and Ž. Andersone (2003). Management Plan for Wolf (Canis lupus) in Latvia, State Forest Service of the Ministry of Agriculture, Latvia. Ozoli¸nš, J., N. Laanetu and E. Vilbaste (2005). Prospects of Integrated Game Management in the Trans-Border Area of North Livonia: Final Report. Book review

“Adjusting to EU Enlargement. Recurring Issues in a New Setting”, Constantine A. Stephanou ed., Edward Elgar, 2006. ISBN-13: 978 1 84542 604 0

This edited volume is based on a collection of papers prepared for 12th annual session of the Spetses European Summer Academy in July 2004. The book is divided into three sections. The first looks at the new economic setting after EU expansion. The second section tackles governance issues. The third section of the book turns outside the EU to discuss the evolution of its external relations. In the first chapter Victoria Curzon Price looks at trade and investment patterns in the enlarged union and paints an favourable picture of developments overall. She argues that trade creation is outweighing trade diversion with enlargement, despite the negative influence of the Common Agriculture Policy (CAP). However, her data regarding the Baltic countries appears erroneous. For example in Table 1.6 she reports Latvia’s exports to the EU made up 84 per cent of its exports in 1993. Data from the Latvian Central Statistical Bureau gives a figure of only 25 per cent for that year. Miroslav Javanovic provides an extensive survey of theoretical developments in spatial location and attempts some application to the present EU. Unfortunately his survey is quite repetitive and difficult to follow and his application seems rather weak; hardly using the theory. Nicholas Baltas provides a helpful overview of issues surrounding the combined impact on EU agriculture policy of enlargement and CAP reform. It is interesting to note that large support reductions are taking place for rye, durum wheat and rice. And perhaps even more significantly the basis of CAP payments has been moved away from output, allowing farmers to respond more to market incentives. Franco Praussello provides results on well chosen tests for an optimal currency area in the EU. His intuitively appealing test, based on gdp correlation, is surprisingly positive about the prospects for the stability of a euro zone encompassing all the current 27 EU members states. However, a few individual states could bear a high macroeconomic cost and Lithuania stands out in this regard. Turning to governance issues, Neill Nugent presents a clear overview of the key political dynamics associated with EU expansion. He argues that the new EU member states are unlikely to form a destabilising block, and indeed the hitherto stabilising EU pattern of cross-cutting cleavages seems likely to continue. In her own chapter Stephanou reviews regulatory adjustment in wider Europe. She argues that harmonisation pressures are stronger in fiscal policy than in social policy, but even in the area of company taxation the benefits of free riding tend to limit the scope of regulatory approximation. Panagiots Liargovas describes trends in cohesion funding including the move toward cohesion funding that also seeks to pursue objectives such as innovation. Carol Cosgrove-Sacks commences the section of the book on EU external relations with her chapter examining economic integration with the wider Europe. She finds significant the CIS commitment to adopt the UNECE ‘International Model for Technical Harmonisation’. Dimitris Xenakis argues that EU enlargement and the Euro-Mediterranean Free Trade Area can be mutually reinforcing projects, leading to the creation of a free trade zone of 800 million consumers. Yelena Kalyuzhnova and Maria Vagliasindi argue that Caspian gas will Baltic Journal of Economics 6(2) (2007): 79–80 80 be directed to the EU for both geographical and political reasons. René Schwok contends that although many saw the entry of East European states into the EU as an Americanisation it remains to be seen whether their pro-US stance will last with the change of generations, socialisation with other EU members and possible increased US unilateralism. Appropriately the book concludes with consideration of the prospects for further enlargement of the EU. It argues that, although Croatia may be able to enter, the three large countries now on the EU’s doorstep, Russia, Turkey and Ukraine, may not be able to. The 2004 round of enlargement appears to have been unpopular enough in the older EU members to stop the Constitutional Treaty in 2005. And thus further enlargement may have to be limited to creation of associate status for those countries.

Mark Chandler Stockholm School of Economics in Riga and Baltic International Centre for Economic Policy Studies (BICEPS)