Baltic Journal of Economics
Volume 10
Number 1
Spring 2010
Policy paper Where is demography leading Latvian higher education? Zane Cunska
Articles
Volume 10 Number 1
The impact of EU Cohesion policy on environmental sector sustainability in the Baltic states Sirje Pädam, Üllas Ehrlich, Koidu Tenno Credible commitment and cartel: the case of the Hansa merchant in the guild of late medieval Tallinn Kaire Põder Industry relocation, linkages and spillovers across the Baltic Sea: extending the footloose capital model Ole Christiansen, Dirk H. Ehnts, Hans-Michael Trautwein
Spring 2010
Book review Economic prosperity recaptured: the Finnish path from crisis to rapid growth Seppo Honkapohja, Erkki A. Koskela, Willi Leibfritz, Roope Uusitalo José R. Sánchez-Fung
PhD news
Baltic Journal of Economics Editors Alf Vanags, Baltic International Centre for Economic Policy Studies Anders Paalzow, Stockholm School of Economics in Riga Editorial Board Raul Eamets, Tartu University Kenneth Smith, Millersville University Torbjörn Becker, Stockholm Institute of Transition Economics Karsten Staehr, Bank of Estonia Konstantins Benkovskis, Bank of Latvia Igor Vetlov, Bank of Lithuania Nerijus Maciulis, ISM University of Management and Economics Managing Editor Zane Cunska, Baltic International Centre for Economic Policy Studies Publisher: Baltic International Centre for Economic Policy Studies jointly with Stockholm School of Economics in Riga. Editorial address: Baltic International Centre for Economic Policy Studies, Strelnieku iela 4a, Riga, LV-1010, Latvia; www.biceps.org/bje; e-mail:
[email protected]. The Baltic Journal of Economics is a semi-annual refereed scientific journal in economics. The Journal is intended to provide a publication medium for original research in economics and selected fields for scholars working in the Baltic countries or those who are working with topics relevant for the Baltic countries or for transition economies in general. Papers may be theoretical or empirical in their emphasis and with relevance to the Baltic countries. Papers with policy relevance or which combine economic theory with empirical findings are particularly welcome. Abstracting and indexing: EconLit, the Journal of Economic Literature electronic edition/ CD-ROM, EBSCO, Scopus, GESIS, SSCI, Cabell’s directory and RePec. Submissions: electronic submissions of manuscripts should be addressed to:
[email protected]. Instructions for authors see on www.biceps.org/bje. Subscriptions: For subscriptions please contact the Managing Editor on
[email protected]. ISSN 1406-099X
Content Editorial Policy paper
Where is demography leading Latvian higher education? Zane Cunska
3
5
Articles
The impact of EU cohesion policy on environmental sector sustainability in the Baltic states Sirje Pädam, Üllas Ehrlich, Koidu Tenno
23
Credible commitment and cartel: the case of the Hansa merchant in the guild of late medieval Tallinn Kaire Põder
43
Industry relocation, linkages and spillovers across the Baltic Sea: extending the footloose capital model Ole Christiansen, Dirk H. Ehnts, Hans-Michael Trautwein
61
Book review
Economic prosperity recaptured: the Finnish path from crisis to rapid growth Seppo Honkapohja, Erkki A. Koskela, Willi Leibfritz and Roope Uusitalo José R. Sánchez-Fung
79
PhD news
Management by values: analysis of influencing aspects and its theoretical and practical implications Krista Jaakson
83
Manifestations of organizational culture based on the example of Estonian organizations Anne Reino
85
3
Editorial This issue continues our series of policy papers on higher education in the Baltic countries that started with the 2009 spring issue. This issue’s paper examines the policy implications and responses of the demographic shock that is about to hit Latvian higher education. This is a topic of high relevance not only in Latvia but all over Europe and the paper offers an interesting contrast between the Latvian and Estonian responses. We have a mix of regular papers: an interesting interpretation of the Hansa guild in terms of game theory; an application of the footloose capital model; and an evaluation of the impact of cohesion policy on environment sector sustainability in the Baltic states. We would also like to thank our benefactors, the Anne-Marie and Gustav Anders stiftelse för medieforskning and the Bank of Latvia for their continued support of the Journal for 2010. Anders Paalzow
Alf Vanags
Where is demography leading Latvian higher education?
5
Where is demography leading Latvian higher education? Zane Cunska1
1. Introduction Higher education in Latvia in the last two decades has been characterized by major expansion, often referred to as the “massification” of higher education. Thus by the early 2000s the number of students per 10000 population had more than tripled as compared to the early 1990s. So far the growth in enrolment rates has been associated with both positive demographic trends and increasing accessibility of higher education (via access to study loans, wide selection of study forms and programmes). However, demographic development poses growing concerns about the future of higher education in all developed countries. Most European countries are facing an unprecedented ageing of their populations, with ageing and depopulation hitting the Eastern European countries, including Latvia, especially hard. In the years to come, significant expansion of the younger population is not projected in any European country (Eurostat, EUROPOP2008). Quite the opposite – the younger cohorts are decreasing in size. As a consequence, an impact on the education system is inevitable. Little seems to have been done to investigate the consequences of demographic decline on the higher education system and to identify the actual scope of the problem. The aim of this article is to analyze the demographic potential of higher education in Latvia and to sketch the most likely enrolment volume in the medium term future. Associated policy issues are described. This paper is organized as follows. The second section gives background information – demographic facts and enrolment trends in recent years. The third section presents enrolment projections for Latvia, suggesting three scenarios that represent a set of plausible alternative outcomes based on changing environment and circumstances. The fourth section outlines policy issues arising for higher education and recommendations for addressing them. The last section concludes.
2. Facts and figures The demographic situation in Latvia is characterized by a negative natural rate of increase and by ageing. Depopulation started in the early nineties and still continues. In particular, the size of younger age cohorts has decreased. This is connected to the fact that at the beginning of the nineties the birth rate fell sharply. Eighteen to twenty years later the smaller youth population is about to enter the higher education system and the labour market. As evident
1 Baltic International Centre for Economic Policy Studies and University of Latvia, Strelnieku iela 4a, Riga, LV-1010, email:
[email protected].
Baltic Journal of Economics 10(1) (2010) 5-21
6
from the population projections (Figure 1), the population aged 15-24 will fall by about 40% in the coming 10 years, and will remain low in the foreseeable future. This fact has to be seen in the context of previous experience of a rising younger population associated with high birth rates in the nineteen eighties. Figure 1. Youth population (15-24 years) projections for Latvia 40000 15
35000
16
30000
17 18
25000
19 20
20000
21
15000
22 23
10000 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030 2032 2034 2036 2038 2040 2042 2044 2046 2048 2050 2052 2054 2056 2058 2060
24
Source: Eurostat population projections (EUROPOP2008)
Latvian higher education has already started to experience this decline. In the 2009/2010 academic year the Latvian HE system for the first time experienced a significant fall in numbers of students. Total enrolment decreased by 10%, with the number of first year bachelor students down by 26% compared to the year before. According to statistics on the number of students per 10 thousand inhabitants – 492 in 2009/2010 (Figure 2), Latvia is among the highest in the world together with Finland, the UK, and Canada. There were as many as 566 students per 10 thousand inhabitants in 2006/2007,
Source: Ministry of Education and Science, Latvia
2009/2010
2008/2009
2007/2008
2006/2007
2005/2006
2004/2005
2003/2004
2002/2003
2001/2002
2000/2001
1999/2000
1998/1999
1997/1998
1996/1997
1995/1996
1994/1995
1993/1994
1992/1993
1991/1992
556 562 566 552 545 600 496 530 492 453 500 386 400 314 342 300 227 264 183 172 173 158 138 152 200 100 0 1990/1991
Number of students
Figure 2. Number of students per 10 000 inhabitants in Latvia
Where is demography leading Latvian higher education?
7
and this indicator has been increasing since 1993 when the expansion of HE started. Some decrease was seen in the 2008/2009 school year, but a significant decline is clearly visible in 2009/2010. This HE expansion motivated creation of a great number of HE institutions, both public and private. There are now 60 HE institutions in Latvia (2009), which is very high for just 2.2 million inhabitants (27 per 1 million inhabitants). This compares with Estonia (29) and Denmark (32), which are also small countries, but significantly exceeds the US (14), the UK (15), the Netherlands (10) and Germany (8), which, in contrast to Latvia, host many foreign as well as home students. From age specific enrolment ratios, i.e. the ratio of students in the respective age population group in Latvia, we see that naturally the highest proportion of students is in the 19-24 age cohorts² (see Figure 3). Starting from age 23 and older, age specific enrolment rates gradually decrease with every older cohort – for the 25-28 age cohorts it is in the 8-13% area, for 29-39-year-olds the ratio is 5%, but in the older age groups 40-plus – slightly above 1%. The observed expansion of HE has happened in both the younger groups, and the older groups. In particular, the 29-39-age cohort that started as low as 1% in 1998 (there were virtually no adults above 30 in HE) has grown to 5% in 2010. Additionally, the number of students over Figure 3. Age-specific student ratios in LV (1998-2010) 45.0 45.0
% of age group
35.0
17
30.0
18 19 20
25.0
21 22 23 24 25 26 27 28
20.0 15.0
29-30 40 plus
10.0 5.0 0.0
1998
1999 2000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Source: Ministry of Education and Science and Eurostat, author’s calculations 2 The changes in the 18-year-old enrolment rate are connected to structural changes in the secondary education system and the transition from 11 to 12 years schooling (primary + secondary) starting in 1991. As a consequence, schooling before tertiary education takes longer, and the number of 18-year-old students has decreased.
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Baltic Journal of Economics 10(1) (2010) 5-21
40 has risen from 0.3% in 1998 to slightly above 1% in 2010, but still very few students are over 40. Between 1998-2008, enrolment trends showed unambiguous stable growth both in absolute and relative terms. In the 2009/2010 academic year the situation has changed – enrolment has fallen in all age groups. Naturally, this raises the question of what developments to expect in the future.
3. Evidence from elsewhere According to World Bank estimates (Chawia, 2007), by 2025 in Latvia the number of pupils in primary schools will shrink by 25%, in secondary schools by 20%, but the most significant fall is expected in the number of students in higher education – by 40%. Mizikaci (2007) has examined the phenomenon of the shrinking youth population in Europe and the associated effects on higher education. She notes that the severest declines will be observed in Estonia, Latvia, and Slovenia, where more than half of the 18-23 age group in 2005 will disappear by 2050. For those countries, immigration would not be enough to compensate for the natural decline, especially because currently they record negative net migration (i.e. emigration for Latvia, Lithuania, Poland, zero net migration for Estonia). In all former Eastern bloc countries, higher education is at risk because of low fertility rates and emigration, as well as failure to enrol significant numbers of foreign students. Following discussions at the Salzburg seminar on the future of higher education, Baumgartl (2007) states that due to shifting demographics in Europe some HE institutions will suffer from lack of students in the very near future, and that “the present and future body of HE population should be examined”. Before 2009 the effects of demography on the tertiary education system in Latvia had not been explicitly studied. Within the EU Structural Funds funded Ministry of Welfare labour market research programme (2005-2006) one project studied graduate life paths and study outcomes, another project modelled labour market developments, while another project studied conformity of HE programmes with labour market requirements, but no explicit attention was paid to investigating demographic impacts. In the spring of 2009, the Ministry of Education and Science communicated that in the nearest seven to ten years the number of students will continue to diminish, and in the 2015/2016 academic year the number of students will decrease by a third compared to recent years (LETA, May 26 2010). Occasionally the issue of demographic effects on the higher education system has been mentioned in the media, where (most often) university representatives are cited expressing concerns about the falling number of secondary school graduates. Overall, these are the same higher education establishments where the issue is raised and discussed, usually in the form of guessing, since it is crucially important for their development strategies. In the context of writing the Latvian sustainable development strategy, some analytical discussions on the issue have taken place over 2008-2010. None of them has been based on or resulted in a research paper. The most comprehensive analysis of tertiary education demography has been performed by the OECD, which in a report (OECD, 2008) concludes that “demography has only recently become a concern in debate on higher education policy, and past growth of systems in OECD
Where is demography leading Latvian higher education?
9
countries has had little to do with demographic changes. The increase in rates of admission to higher education has been of greater importance than the size of age cohorts.” (Teichler and Bürger, OECD 2008, Chapter 5). Among other things, the report concludes that: (1) student participation will continue to expand and will in most cases be evident from growth in the size of higher education systems. Contraction will affect only a small number of countries; (2) women will be in the majority in the student population; (3) the mix of the student population will be more varied, with, e.g., greater numbers of international students, older students, and those studying part-time; (4) the social base in higher education will probably continue to broaden. Latvia, not being an OECD country, is not analyzed in the report. With domestic knowledge about the Latvian HE system, we have reason to think that Latvia may be among the countries affected by contraction, but this will be analyzed later in the paper.
4. Methods For projecting future developments, the approach taken in this paper is the enrolment-ratio method, which is common for estimating sub-populations and uses two components –readily available population projections and enrolment rate development trends both (1) extrapolated from statistically observed ratios, and (2) estimated based on expert opinions and peer experience. For discussion regarding choice of projection method, see Cunska (2010). The OECD (2008) report on the future of higher education uses similar trend extrapolation methodology and argues that it is the turning points that in fact play the most important role in demographic trends, concluding that demographic trends cannot be extrapolated directly, but only explored through forward-looking scenarios incorporating political and economic factors. The projection approach used in the OECD report uses UN population projections as a basis and calculates enrolment with the extrapolated trends. Ahuja and Filmer (1995) adopted a very similar approach by taking existing UN population projections and superimposing onto them an educational distribution estimated for two broad age groups (ages 6-24 and 25+) from a given set of enrolment ratios and UNESCO projections. Three development trend scenarios are developed here: a stable enrolment ratio scenario, a global education trend scenario, and a crisis scenario. The different scenarios represent a set of plausible alternative outcomes based on changing environment and circumstances. The first two variants can be thought of as rather statistical, whereas the third scenario relies mostly on expertise and knowledge in the area. For cohort size, the Eurostat population projections (EUROPOP2008) convergence scenario is used. This describes possible future demographic developments assuming that across European countries fertility and mortality converge to those of the “forerunners” by 2150. The period for projections used is 2010 – 2020. The projections already take account of birth, death, and migration rates, and we assume rates equal for population in and outside tertiary education. The model inherits all the assumptions made for the projections.
Baltic Journal of Economics 10(1) (2010) 5-21
10
5. Scenarios The stable enrolment ratio (SER) scenario represents a situation in which tertiary education develops smoothly into the future. The only changes arise from differences in cohort size. Observed trends over the years 1998-2010 are extrapolated for the following 10 years, assuming that: • The proportion of students in the respective age cohort will continue to change at the same average speed and direction as previously. • Transition rates and dropout rates will change as previously. • Growth converges to zero when time converges to infinity. All calculated trends are positive or virtually constant (Appendix, Figure A1). Growth is expected in the ratio of younger students (20-23) and of the non-traditional age group, (29-39). The proportions of students in the 24-28 and 40-plus age groups are assumed to remain stable at their 2010 levels. Figure 4. Observed (1998-2010) and projected (2011-2020) number of students in tertiary education – stable enrolment ratio scenario 140000 120000 100000 80000 29 plus
60000
25-28 17-24
40000 20000
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
0
Source: Eurostat, author’s calculations
This scenario suggests that the total number of students in tertiary education will decrease from 113 thousand in 2010 to 92 thousand in 2020, while enrolment in 2020 would be 80% of that of 2010 (Figure 4). The most severe decline will be observed in the traditional student age groups (17-24) – by 44%, whereas the size of older student age groups (29 plus) will remain stable and would even slightly increase compared to the 2010 level as a result of positive enrolment ratio trends and slightly increasing cohort size. The share of older students (over 29) will increase from 24% to 44%.
Where is demography leading Latvian higher education?
11
The global education trend (GET) scenario takes into account schooling patterns across European countries and assumes that: • In the years 2010 – 2020 the enrolment ratio age structure in Latvia converges to the EU27 average. • The speed of convergence depends on the size of the difference between the rate in the previous period and the target value (EU-27 average). Enrolment ratios in the EU-27 have been gradually rising during 1998-2005, and have stabilized since 2005. They are generally lower than the Latvian 2010 rates, so that all but 25 and 26 year-old rate trends are negative (Figure A2 in Appendix). Figure 5. Observed (1998-2010) and projected (2011-2020) number of students in tertiary education – global education trend scenario 140000 120000 100000 80000
29 plus 25-28
60000
17-24
40000 20000
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
0
Source: Eurostat, author’s calculations
According to the GET scenario, a decline in higher education participation at all ages is expected (Figure 5). Total enrolment in 2020 is expected to approach the level of 1998 at around 70 thousand students – a decline of 38% compared to 2010 enrolment. It also entails a reduction of over 50% in traditional age student numbers. The older cohorts (29 plus) are not yet declining by 2020, and the fall in enrolments is only affected by convergence to the lower EU enrolment rate assumption. This results in a 13% fall in enrolment. As a result, the student population will be older and the proportion of non-traditional students in the total student population will increase to 50% in contrast to 36% in 2010. The crisis (CRI) scenario is designed to capture possible other effects that do not follow from statistics but can be concluded from the literature on historical development in other countries, the author’s observations of the situation, and suggested developments by experts. This
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Baltic Journal of Economics 10(1) (2010) 5-21
scenario is the most subjective of the three and is intended to sketch general developments on top of those directly flowing from data. During recession, some individuals invest in graduate education to position themselves for a better job when the economy revives. Often people change their life plans to apply for Master or PhD programmes earlier than planned because of unfavourable labour market conditions and because alternatives to schooling are less attractive. This behaviour can be observed from two relatively recent historical trends for recessionary periods in the global economy: 19911993 and 2000-2002. It was observed that enrolment grew more rapidly during and after recessions, while the largest dips happened in boom years. However, a slowdown in enrolment was observed at the very beginning of recession (Moody’s International Public Finance (2009), data on Canada, France, Italy, Spain, the UK, and the US). In its report, Moody’s outlines that universities are expected to experience some stress but be more sheltered than other sectors from the global recession. “This is due to their countercyclical business aspects, government support, and growing role in economic development and rebuilding.” However, many face the conflicting pressures of rising demand for their services while also needing to adjust to a weaker funding outlook. Hazarika (2002) investigated the effects of regional recessions on enrolments in the US and found that wealthier students are more likely to attend college in a recession, whereas those from less wealthy families are affected by credit constraints and less likely to attend college. Access to financing therefore plays a role in enrolment decisions. In Finland in the 1990 crisis period, applications for higher education grew by about 25%, and participation in entrance examinations by 42% (Kivinen and Rinne, 1996). The increased interest, though, was not supported by a sufficient increase in the supply of study places so actual enrolments remained stable. The impact on a particular country and particular institutions may vary. In the Latvian situation some additional institutional and behavioural aspects would play a role: • Participation in tertiary education will be a function of people’s beliefs on the speed of recovery of the economy. If people believe in a fast recovery (2-3 years), i.e., believe they will have job, they are willing to invest in education and probably even bear considerable personal cost. In the opposite situation, where people believe in slow recovery or stagnation, they may leave the country for study or work. The emigration alternative is relatively easy given the open EU labour market. • Completion of some degree of tertiary education is already a minimum standard for certain types of employment (government sector, schools), and therefore enrolment (and graduation) rates were very high by international standards even before the recession. There is hardly more room for growth in enrolment rates due to a saturated local market. • In Latvia, simultaneous work and study practice is common (Auers, Rostoks, Smith, 2007), often resulting in prolonged study time (academic breaks, longer programmes). With loss of employment or fewer working hours, study time may actually shorten, so that total enrolment will be lower. • Reasons for not continuing studies are financial problems and inability to pay study fees; shortage of money can also prolong study time as students may be forced to take study
Where is demography leading Latvian higher education?
13
breaks because of inability to pay tuition fees. • A popular view among the Latvian general public is that an increase in qualifications and skills levels does not significantly contribute to economic growth and hence people may not be willing to invest in education under budgetary pressure (DnB NORD Latvijas barometrs, March 2010). We take into account that individual behaviour is affected by changes in the labour market in a recession – a loss of job can serve as a catalyst for career decisions. Unchanged education policy in the country is assumed in this scenario: higher education still relies on local demand and active foreign student attraction does not take place; no further significant cuts in financing to HE take place, but also no new investment. We assume people believe the economy will return to growth in three years. In this scenario, projections are made for more aggregated age groups (Table 1). According to this scenario, the crisis would have a short-term positive impact on enrolment rates, which will slightly increase above the 2010 level and stay there between 2011 and 2013 (Figure A3 in Appendix). A rise would be expected in the 25-28 age group. After 2013 enrolment rates will start to fall to the EU-27 level. The total number of students in the period 2011-2013 would increase compared to 2009 and 2010 levels, but would not reach the 2006 peak of 131 thousand students. The 25-28 year student group would remain roughly the same size throughout the entire period 2000-2020. After 2013 enrolment rates are expected to converge to the EU average, and the demographic Table 1. Summary of reasoning and assumptions regarding educational behaviour for separate age groups Age
Rationale
Assumption
17-24
Most mobile of all groups, also the most free in terms of family commitments. Under crisis: the highest proportion leaving the country (for study and/or work) compared to other groups. Employment (traditionally popular among students in Latvia) increasingly difficult to find for younger people without experience and degree. People staying in Latvia invest time in education in the belief of recovery, may be more selective regarding the study area and more demanding.
The two effects (emigration and difficulty in finding a job) offset each other, enrolment rate is at the pre-crisis level (2008) for 3 years, converges to EU27 average after 2013, i.e., falls.
25-28
More commitments (family, social, work), consequently emigration is more complicated. More prone to stay and use all local opportunities. In case of loss of job, ready to invest in education but selective regarding the programme. Could choose good quality business education, probably looking for shorter 2-3 year executive education. Those who have dropped out could go back and finish their degree. Those who postponed a decision on second level higher education may start now.
For the first three years enrolment rate increases by 15% compared to 2008, converges to EU-27 average after 2013.
29 plus
This group is most settled of all. They may see little return on investment in a degree, but are probably more likely to attend qualification courses to build on previous education. Some proportion may consider second level tertiary education but with emphasis on professional skills.
Enrolment rate remains constant (for different reasons than for 17-24 population) over the first 3 years, converges to EU-27 average after 2013.
Baltic Journal of Economics 10(1) (2010) 5-21
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Figure 6. Observed (1998-2010) and projected (2011-2020) number of students in tertiary education – crisis scenario 140000 120000 100000 80000 29 plus
60000
25-28 17-24
40000 20000
2020
2019
2018
2017
2016
2015
2014
2013
2012
2011
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
0
Source: Eurostat, Author’s calculations
decline is even more prominent. As a result, the total number of students in 2020 will fall to 81 thousand with less than half (47%) being in the traditional age group (17-24). Figure 7. Comparison of estimated trends – total number of students in tertiary education according to the three alternative scenarios 140000 130000
Total SER Total GET Total CRI Actual enrolment
120000 110000 100000 90000 80000 70000
Source: Eurostat, author’s calculations
2020
2019
2018
2017
2016
2014 2015
2012 2013
2010 2011
2009
2008
2007
2006
2004
2005
2003
2002
2001
2000
1998
50000
1999
60000
Where is demography leading Latvian higher education?
15
In comparison, all variants indicate a very similar future enrolment situation despite relying on different assumptions about enrolment rate future development. All variants suggest a significant fall in total enrolment – by 18% in SER, by 38% in GET, and by 28% in the case of the CRI scenario compared to 2010 (Figure 7). The crisis scenario is the only case where enrolment is expected to increase in the short term, and it may turn out to be the ‘best’ case for the higher education system in the nearest future. The other common characteristic concerns changes in student age structure. The number of traditional age students will decrease to somewhere between 44% and 50%. Consequently the traditional age students would be a minority in the student population. In contrast, the proportion of adult students will rise from 24% in 2010 to somewhere between 33% and 44% in 2020 (by a factor of three or four compared to 11% in 1998). Table 2. Comparison of scenarios: total number of students and proportions of wider age groups Year
1998
2010
2020
2020
2020
Actual
Actual
SER
GET
CRI
Proportions of age groups in total number of students 17-24
73%
64%
44%
50%
47%
25-28
16%
13%
12%
16%
17%
29 plus Total
11%
24%
44%
33%
36%
70233
112555
92152
69434
80841
Source: Eurostat, author’s calculations
6. Implications for policymaking The higher education system has to adjust to two imminent changes arising from demographic changes – a decrease in total enrolment volume and a change in the age structure. In its current form the present size of higher education system is not sustainable. Clearly, there are no solutions to increase the size of cohorts as a way to rescue the higher education system, at least not in the nearest future. Demographic processes are inert compared to financial markets and the economy, so there are no quick solutions in demography. The first question to be asked is: Should the current system be rescued by preserving the current volume of higher education? Furthermore, is it a problem that there are fewer students, there would be fewer universities, fewer academic staff, less taxes paid, but also less public expenditure on education? Not necessarily. Higher education may be viewed as a service that is in less demand and therefore over-supplied, analogous to photo film development services with the introduction of digital cameras, or typewriting when computers appeared. In other words, the higher education sector is like any other sector of the economy be subject to a demand side shock reducing the demand for its services to where it was in the 1990s. Among other things this would also imply cost savings to the state budget or alternatively the spending per student can increase without increasing the overall higher education budget. An obvious way to adjust would be to cut the supply, i.e. to reduce the number of higher education institutions. Here it makes sense to distinguish between private and state institu-
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Baltic Journal of Economics 10(1) (2010) 5-21
tions, respectively. The private institutions, being to a large extent directly subject to the market mechanisms, might adjust by itself when some might go out of business due to lack of students whereas others might develop new programmes or targeting non-traditional students. The state-funded institutions, on the other hand, are not directly affected by the market mechanisms since their development to a large extent is determined by political decisions. Based on the demographic changes the state-funded should be restructured, with the lowest performing programmes and institutions simply being closed down. From a political point of view, this might be easier said than done since higher education policy usually are considered as an integral part of other policy programmes, e.g. regional development. Furthermore, the current financing model of state-funded higher education institutions might further complicate the necessary restructuring. Given the financial incenctives provided by the current system where each student enrolled represent a substantial source of income, there is a great risk that the institutions will lower standards in order to enrol and keep as many students as possible. The alternative approach to cutting the supply would naturally be to find ways to increase the demand for the services that could be provided by the Latvian higher education sector. This require somewhat of a change of the mindset of policy makers as well as university leaders – to stop thinking of higher education as a cost to bear, but perceive it as a productive and competitive sector of the economy, capable not only of educating people for the Latvian labour market, but also of exporting its services and possibly also educating foreign young people for the Latvian labour market. The demographic decline not only affects the student population, but also the labour market, and working age people will be needed to cope with an increasing old-age dependency problem where opening up the Latvian labour market to foreigners could be at least part of the solution. The issue of increasing the demand for higher education in Latvia is partly addressed in the work of a specially established group which in December 2009 published an Informative Reporton the structural reforms in higher education and science needed to increase Latvian international competitiveness (Ministry of Economics, 2009). The proposed reforms have three aims: (1) to produce internationally competitive graduates, (2) to supply education that corresponds to the needs of the economy, and (3) to ensure that internationally competitive scientific results are successfully transmitted to the Latvian economy. According to the Report, one of the indicators of successful structural reforms is “proportion of foreign students exceeds 10% of student numbers” by 2015 (p15). This seems to be a very ambitious target in the light of international experience. In this context an Action Plan was developed by the Ministry of Education and Science in the spring of 2010 to implement the above reforms. One of the four main action directions is “internationalization of higher education and increasing its export capacity”, i.e. directly related to increasing the demand. It recognizes the importance of and need to attract foreign students as a way to improve the situation in the higher education sector. The Action Plan is less ambitious than the Report and aims for just a 3% share of foreign students (p15). Is even this target realistic? In 2009/2010 there were 1715 3 Informatīvais ziņojums par nepieciešamajām strukturālajām reformām augstākajā izglītībā un zinātnē Latvijas starptautiskās konkurētspējas paaugstināšanai, Ministry of Economics, Riga, December 2009. 4 Pasākumu plāns nepieciešamajām reformām augstākajā izglītībā un zinātnē 2010.-2012.gadam, Ministry of education and science of Latvia, available at http://www.mk.gov.lv/lv/mk/tap/?pid=40173173.
Where is demography leading Latvian higher education?
17
registered foreign students in Latvia (1.5% of the total number of students). Of those, 816 - or 48% - held Russian, Ukrainian, or Lithuanian passports. Clearly, the biggest proportion of those students is likely to be Latvian residents who have lived all their lives in Latvia and acquired a secondary education in Latvia. The term ‘foreign student’ is therefore somewhat inappropriate. Some 400 Erasmus students studied in Latvia in 2009/2010. These of course are genuine foreigners, but do not bring income to Latvian institutions. The real ‘de facto’ foreign student number is therefore much less than reported. Even nominally (in contrast to real foreigners arriving to study), reaching the goal of 10% or even 3% foreign students may be tricky. In European Higher Education Area countries (Bologna countries) on average there are 3.5% foreign students, and 6.6% is the EU-27 average. Countries with the highest proportion and total number of foreign students are those with historically established university traditions (2006: UK – 18.3%, Austria – 15.6%, France – 14.6%, Belgium – 14.3%, Germany – 12.8%) and where education is in global languages (English, French, German). Other countries are lagging behind, and there is none except Sweden where the proportion of foreign students exceeds 10%. It seems that the call for the necessity for English language programmes is finally being heard, as this is a tool to make studies in Latvia accessible to foreigners (see report and action plan mentioned above).Currently there is at least a discussion that provision of programmes and courses in English should be expanded. But is it enough?As noted earlier, shrinking generations is not a uniquely Latvian problem. Similar developments can be observed throughout Central and Eastern Europe. Other countries (like Estonia) are acting fast. For example, while in Latvian Bachelor programmes only basic English is taught, the University of Tartu, from academic year 2010/2011, has launched a new bachelor study programme in Business Administration that is entirely taught in English, and there are twelve master degree programmes in English (including joint degrees). Estonians have also been active in promoting Estonian education in China. As a result, the proportion of foreign students in Estonia already exceeded 4% in 2008. It is entirely possible that Tartu will attain a critical mass of foreigners and become a regional education centre leaving no space for an alternative centre in Riga. For economics and business studies Latvia is, with few exceptions, already far behind. Little attention has been paid to addressing the other demographic effect – the ageing population and the following old age-dependency problem. Here the demographics at least to some extent work in favour of higher education. An ageing population will most likely increase the demand for further higher education during an individual’s work life, i.e. Life Long Learning, since there will be less young people entering the labour market bringing in the most recent knowledge etc. Furthermore, an ageing population will most likely put pressure on the policy makers to increase the retirement age and thereby increasing the number of years an individual is active in the labour market, which in turn should further increase the demand for higher education among the non-traditional groups. In the analysis of the different scenarios above, it was assumed that the enrolment rate for the group “29 plus” would converge to the EU 27 average after 2013. However, this is something that could definitely be influenced through policy making. An active Life Long Learning policy with the aim, not to ‘rescue’ higher education, but with the aim to strengthen the competitiveness of the Latvian economy in the light of its rapidly changing demographics, would certainly increase the enrolment rate among the non-traditional (29 plus) cohorts. One immediate consequence would be that the
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Baltic Journal of Economics 10(1) (2010) 5-21
student body will not be dominated by young kids fresh out of school. They will be mature people, probably more confident and demanding, looking for more practical and applicable knowledge. Needless to say, this will require rethinking the curricula as well as of the way studies are organized. If successful, the concept of Life Long Learning will acquire real meaning and substance. A key issue is whether the state funded institutions have the flexibility to accommodate this new ‘market’ or whether the lion’s share of it will be captured by the private instituions with their more flexible organisations and governance structures.
7. Conclusion This paper discusses the implications for higher education in Latvia arising from changing demographics. So three possible variants based on different assumptions were presented. Higher education in Latvia is facing big changes due to the rapidly changing demographics in the years to come. Exact predictions are impossible given the number of different nondemographic impact factors and the unclear economic situation. All the analysis suggests that under any development scenario the total enrolment will fall. Enrolment will in the foreseeable future never be as high as it was in the early 2000s. By 2020 the number of students in higher education will decrease by 18-38 percent under the alternative scenarios. This implies that the current number of higher education institutions cannot be sustained. Most likely, tertiary education will continue to rely on local demand for education. Even if the export of higher education is stimulated via accessible programmes (especially the language of instruction), legislation changes and marketing, keeping the present enrolment level is unlikely to be feasible. To illustrate, in order to compensate for shrinking local demand (for example as given by the Stable Enrolment Scenario), by 2020 Latvia would have to attract some 20 thousand foreign students. This means that the number of foreigners in universities would have to rise by a factor of 12 as compared with 2009. Nearly all developed countries are experiencing the ageing of their populations and shrinking youth cohorts (although at a less dramatic rate than in Latvia); therefore the competition for students internationally is becoming more severe. The real issue therefore is not about competition between universities in Latvia, but about the competitiveness of Latvian higher education internationally in order to keep talented Latvian students in Latvia (to prevent a brain drain) and to attracted talanted foreign students. Informed policy decisions will be required in order to cope with the foreseen oversupply of higher education. There will, for example, be a need to restructure higher education by closing down certain universities, merging programmes, and concentrating resources to attain better quality. There is no (fast) medicine for treating the effects stemming from the rapidly diminishing cohorts. However, there are ways to prevent the higher education system from total collapse following the rapid fall in the demand for its services. A natural way would be to consider the higher education sector beaing a service sector like any other and hence try to identify new markets and services (programmes) to be provided by the educational sector. In this case the ageing population provides an opportunity since it most likely will put more of a policy focus on Life Long Learning. However, Life Long Learning as such is not the remedy – it has to be accompanied by insightful and forward looking policy making accompanied by a willingness from the side of the higher education institutions to adopt to the new
Where is demography leading Latvian higher education?
19
demographic environment and hence to the demands of the ‘non-traditional’ students whose share of the student body will incarease in the future.
References Ahuja V., Filmer D. (1995). Education Attainment in Developing Countries. New Estimates and Projections Disaggregated by Gender, World Bank Policy research working paper No.1489 (WPS-1489), The World Bank Auers D., Rostoks T., Smith K. (2007). Flipping burgers or flipping Pages? Student Employment and Academic Attainment in Post-Soviet Latvia,Communist and Post-Communist Studies, 2007, 40, pp.477-91 Baumgartl, B.(2007) Relevant Terms of Discussion in Eastern Europe. From Here to There: Mileposts of European Higher Education, Baumgartl, B.Mizikaci, F., Owen D. (eds.). Navreme publications, Vol7b, March 2007, pp 20-21 (available at www.navreme.net) Chawia, M. et al. (2007). From Red to Grey. Washington, DC: The World Bank, pp 217-261 Cunska, Z. (2010). Augstākajā izglītībā studējošo skaits un prognozes Latvijai, Latvijas Universitātes doktorantu rakstu krājums Dombrovsky V. (2009). Is anything wrong with higher education in Latvia? Baltic Journal of Economics, Vol9 No2, Autumn 2009, Riga Eurostat, methodological note on EUROPOP2008 convergence scenario, http://epp.eurostat. ec.europa.eu/cache/ITY_SDDS/EN/proj_08c_esms.htm (accessed 26/10/2009) Hazarika G. (2002). The Role of Credit Constraints in the Cyclicality of College Enrolments,Education Economics; August 2002, Vol. 10 Issue 2, p133-143, 11p Higher Education to 2030, Volume 1, Demography, Centre for Educational Research and Innovation, OECD, 2008 Kivinen O., Rinne R. (1996). Higher Education, Mobility and Inequality: The Finnish Case, European Journal of Education, Vol. 31, No. 3, Access to Higher Education 1985-95: An Extraordinary Decade (Sep., 1996), pp. 289-310 (22 pages) LETA, May 26 2010, LETA News agency, http://www.leta.lv (accessed 26/05/2010) Mizikaci, F. (2007). Demography: Risks and Opportunities for European Higher Education. From Here to There: Mileposts of European Higher Education, Baumbgartl, B.Mizikaci, F., Owen D. (eds.). Navreme publications, Vol7a, March 2007, pp 71-85 (available at www.navreme.net) Moody’s International Public Finance (2009). Global Recession and Universities: Funding Strains to Keep Up with Rising Demand, Special Comment on Higher Education, June 2009. Available at http://www.universityworldnews.com/filemgmt/visit.php?lid=45 (accessed 20/03/2010)A view of Lithuania 2001-2008
Baltic Journal of Economics 10(1) (2010) 5-21
20
Appendix Figure A1. Observed (1998-2010) and projected (2011-2020) age-year specific enrolment ratios in Latvia – SER scenario 45 40 35
17
30
18 19 20
25
21 22 23 24 25 26 27 28
20 15
29-30 40 plus
10 5
2019
2020
2017
2018
2016
2015
2013
2014
2011
2012
2009
2010
2008
2006
2007
2005
2003
2004
2001
2002
1999
2000
1998
0
Source: Eurostat, author’s calculations
Figure A2. Observed (1998-2010) and projected (2011-2100) age-year specific enrolment ratios in Latvia – GET scenario 45 40 35
% of age group
17 30
18
25
19 20 21 22
20
23 24 25 26 27 28 29-39
15 10
40 plus
5
1998 2001 2004 2007 2010 2013 2016 2019 2022 2025 2028 2031 2034 2037 2040 2043 2046 2049 2052 2055 2058 2061 2064 2067 2070 2073 2076 2079 2082 2085 2088 2091 2094 2097 2100
0
Source: Eurostat, author’s calculations
Where is demography leading Latvian higher education?
21
Figure A3. Observed (1998-2010) and projected (2011-2020) age-year specific enrolment ratios in Latvia – CRI scenario 30
25
20 17-24
15
25-28 10
29 plus
5
0 1998
2000
2002
2004
2006
Source: Eurostat, author’s calculations
2008
2010
2012
2014
2016
2018
2020
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The impact of EU Cohesion policy on environmental sector sustainability in the Baltic states
23
The impact of EU Cohesion policy on environmental sector sustainability in the Baltic states Sirje Pädam, Üllas Ehrlich, Koidu Tenno1
Abstract This article analyses investment from European Union cohesion policy funds into the Estonian, Latvian, and Lithuanian environmental sectors during the budget period 2007-2013. Total investment from these funds in Estonia, Latvia, and Lithuania during that period will be about 14.7 billion euros, of which about 18 percent covers the environmental sector. The purpose is to analyse whether allocation of expenditure to the environment is sustainable. In their analysis the authors apply sustainability criteria based on the cost-benefit rule and the Environmental Performance Index (EPI). The main finding is that the Baltic States allocate least environmental funds to those fields found to be most relevant to sustainability. Keywords: environmental investment, EU funding, sustainability JEL classification: H59; Q20; Q28; Q58
1. Introduction Vincent and his co-authors (2002) note that despite strong reasons for analysing public expenditure and the environment, only limited literature is available within this field. So far, most analyses concerning public expenditure on the environment have been undertaken by the World Bank and the OECD. This paper aims to fill the gap and offers a novel perspective into the study of allocation of public expenditure to the environment by comparing EU cohesion policy fund allocation to the environment in three countries of similar size and corresponding economic prerequisites. The analysis concerns the structure of EU cohesion policy funding for the environment in Estonia, Latvia, and Lithuania during the period 2007-2013. Since all countries eligible for funding are subject to the same regulations, it is expected that funding choices will be similar. However, country specific time schedules for fulfilling EU directives agreed on during membership negotiations can be a source of differences. The overall purpose of the analysis is to assess whether budgetary allocation to the environment according to funding plans supports sustainability of the environmental sector. Funding plans, the outcome of negotiations 1 Tallinn School of Economics and Business Administration, Tallinn University of Technology, Akademia tee 3, 12618 Tallinn, Estonia, corresponding author Sirje Pädam, e-mail:
[email protected]. Acknowledgement: this article was written with the support of the Estonian Ministry of Education and research foundation project No 0142697As05.
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Baltic Journal of Economics 10(1) (2010) 23-41
between each beneficiary country government and the EU Commission, are documented in National Strategic Reference Frameworks 2007-2013 and Operational Programmes.2 The Operational Programmes for Estonia, Latvia, and Lithuania represent the primary source of data of this paper. We begin by describing the theoretical framework for defining an efficient and environmentally sustainable resource allocation. Based on the theoretical framework, we then present an outline for step-wise assessment of sustainability of budgetary allocation to the environmental sector. In the following section we present the outcome of cohesion policy fund allocation in the three Baltic States. After that we carry out step-wise assessment and, based on the results, we classify spending priorities according to their relevance to sustainability. The conclusions are presented in the last section.
2. Theoretical framework Environmental regulations and public expenditure directed to the environment are generally justified by efficiency reasons. This is because unregulated markets pay too little attention to environmental protection, i.e. environmental quality. Supply of environmental goods may be insufficient since they are public goods, while oversupply of activities that give rise to negative externalities can also occur. The role of government expense on the environment is thus to redirect tax income to provision of public goods and to tax activities that give rise to negative externalities. To some extent, environmental protection can be self financing if taxes and charges paid by polluters are directed to rehabilitation and pollution control. In this paper we deal with supra-national funding where Member State payments are reallocated among EU countries. For this reason, the concept of fiscal federalism can be applied to allocation of public expenditure. Fiscal federalism addresses the problem of vertical allocation of economic responsibilities by level of government. Efficient allocation assigns the responsibility to the territorial authority where beneficiaries correspond to that of taxpayers (see Pitlik, 2007). If the benefits of public goods spill over to a neighbouring territory or country, this gives reason to centralize responsibility. Fiscal federalism would thus predict that EU funding to the environmental sector is devoted to environmental issues with cross border characteristics. In addition, efficiency reasons would motivate higher levels of funding when neighbouring countries benefit from improvements. Pitlik (2007) finds that almost half the financial resources of the EU budget are allocated to spending categories in which EU responsibilities are questionable from the viewpoint of fiscal federalism. Since one of the intentions of cohesion funding is to reduce disparities among Member States, regions, and individuals³, it is likely that the concept of fiscal federalism is not applicable for our purpose. Our main focus of environmental spending involves sustainability. Analogous to sustainable development, sustainability represents resource use that meets human needs while preserving the environment so that needs can be met for both present and future generations. The literature suggests a close relationship between efficiency and sustainability (see e.g. 2 See list of references. ³ See COM 2008/301 (2008).
The impact of EU Cohesion policy on environmental sector sustainability in the Baltic states
25
Pädam, 2003). Efficiency implies resource allocation that considers peoples’ preferences and accounts for resource constraints. By allowing for reallocation of resources in case human needs are not met and by adopting a dynamic perspective, efficiency will overlap with sustainability. According to the interpretation by Stavins et al. (2003), sustainability can be understood as dynamic efficiency along a feasible consumption path. Sustainability entails non-wastefulness, implying that the choice of a consumption path is such that the economy is on the Pareto frontier. Following Stavins et al.’s application of a Ramsey type of presentation, welfare, W, of such a path can be evaluated over time as:
∞
W (t ) ≡ ∫ U (C (τ )) e −r (τ −t ) dτ
(1)
t
where U denotes the utility function which depends on consumption, C, including both direct consumption and enjoyment of non-market goods and services. Time is denoted by τ and t (τ, t ≥ 0) and the time horizon is taken to be infinite. The utility discount rate is denoted by r. Since C contains two types of goods, the argument of the utility function can be rewritten as:
C = f ( x(τ ), , z (τ ))
(2)
where x(τ) denotes market goods and z(τ) denotes non-market goods, including environmental goods and services. In order to be sustainable, current decision making must consider the perspective of inter-temporal public goods and inter-temporal externalities. Securing future supply of environmental goods and services implies production of inter-temporal public goods, which need to be provided so as to include the preferences of future generations. Stavins et al. formulate a condition of intergenerational equity requiring non-decreasing welfare:
dW(t)≥ 0 dt
(3)
The requirement that the stream of welfare does not decline over time implies that future generations will not be worse off. Although constant consumption at no more than subsistence level could in principle meet the definition of sustainability, Stavins et al. (2003) argue that this definition would not be accepted as meeting reasonable social goals. For evaluating sustainability they propose a decision rule similar to the Kaldor-Hicks criterion, i.e. that those who are made better off by a policy in theory can fully compensate those who are made worse off. A policy that fails the Kaldor-Hicks test cannot pass the stricter Pareto test. In a dynamic context, intergenerational transfers could be applied to achieve non-declining welfare. This is the justification for their proposal to use dynamic efficiency as a criterion to find policies that are potentially sustainable. Although intuitively appealing, the approach of Stavins et al. (2003) disregards two central issues: one is the implicit assumption they make about natural capital and the other is the preferences of future generations. The implicit assumption that they make about natural capital is that natural environments and ecosystems can be represented by equations that are convex sets and that are at least twice differentiable. However, this need not be the case. The reason is that regeneration paths
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Baltic Journal of Economics 10(1) (2010) 23-41
of natural environments and ecosystems tend to exhibit nonlinear dose-response relations, implying that marginal changes in anthropogenic pressure may result in irreversible effects (see Dasgupta and Mäler 2004). Therefore no guarantee exists that equation (3) is non-negative or that the inequality can be defined in a meaningful way. In an analysis of policy reforms in imperfect economies, Arrow et al. (2004) show that social welfare might or might not be sustained between two periods. Reasons why an imperfect economy is incapable of sustaining welfare over time include e.g. scarcity of resources and limited substitution possibilities among capital assets. At the same time, Arrow et al. (2004) show that the general cost-benefit rule holds for guiding sustainable investment decisions in an imperfect economy. But, in order to certify that the cost-benefit rule produces correct estimates, it will become necessary to derive proper accounting prices, which can to a large degree differ from market prices (ibid). In the absence of proper accounting prices, the need arises to find other ways to consider scarcity and the need for preservation of key natural resources. Finding information about proper accounting prices is not only hindered by lack of know ledge about non-linear dose response relations of natural environments. Another difficulty in determining sustainable development over a long period or even more so over an infinite time span is lack of information about the preferences of future generations. Current decisions that affect sustainable development would need to take into account estimates of willingness to pay by unborn persons in the distant future. Taking a closer look at decision making, we can see that people do not tend to give up decision making in those cases where their decisions tend to have an impact on future generations. In several cases people even include the welfare of their children or grandchildren in their decisions. Monchareva and Gudas 2009 report that a large portion of respondents declare that improving the water quality in the Nevezis river basin is important “for children and for future generations’ wealth”. This implies that current generations have the capacity to represent future generations. Assuming that the preferences of current generations contain the requests of future generations on the natural environment implies that willingness to pay estimates based on generations now alive can be approximated as representative of the preferences of future generations. However, we cannot expect to find the whole answer from willingness to pay estimates based on generations now alive. The failure of humans to put an accurate value on critical natural assets is due to the inherent complexity of the natural environment. Taking into account that human preferences cannot correctly sense when ecosystems are at risk implies a need to use knowledge of ecological science in order to identify critical environmental assets.
3. Combined approach Since it may prove impossible to collect proper accounting prices by estimating willingness to pay (WTP) for natural environments and ecosystems, i.e. the accounting prices of z(τ) in equation (2), from generations now alive, the implication is that a need exists for a combined approach to assess the sustainability of environmental spending. In our analysis we will consider the cost-benefit rule in the first step for assessing sustainability and in the second step we will use ecological knowledge in order to certify that investments will be undertaken in critical fields of z(τ). For the purpose of the second step we use the Environmental Performance Index (EPI), (see Esty et al. 2008). This index is based on empirical data about the
The impact of EU Cohesion policy on environmental sector sustainability in the Baltic states
27
environment in 149 countries and allows cross country comparisons. The index has been developed by first identifying specific environmental targets and then measuring the distance between the target and current national achievement (ibid). Although the authors identify several data gaps, EPI is a comprehensive measure based on ecological knowledge. In terms of our purposes, EPI is no substitute for WTP estimates. Instead we need EPI in order to complement the information of the cost-benefit rule. Since EPI is available for a broader range of environmental issues than WTP estimates, we will use EPI as an indicator for suggesting additional policy implications when WTP estimates are missing. However, EPI cannot assess the range of required investment and cannot measure whether a certain level of investment passes the cost-benefit rule.
4. Budget allocation to the environmental sector Cohesion policy funding included by the Convergence Objective during the programming period 2007-2013 amounts to about 346 billion euros. Among the Baltic States, funding per country is between 3.4 and 6.8 billion euros. Estonia obtains less than Latvia, and Lithuania receives more than the two other Baltic states. The ranking of the contribution to the environmental sector shows similar positions between countries. Lithuania devotes most, then Latvia, while Estonia assigns least funds to the environment, see Table 1. Table 1. Allocation of cohesion policy funding to the environment, in total and per country 2007-2013, euros current prices** Priority theme Euro, million Environment Total Euro, per capita Environment Total Percent Environment Total
Estonia
Latvia
Lithuania
Community Wide*
781.3 3,403.5
792.7 4,530.4
1,053.4 6,775.5
46,735.9 346,150.8
582 2,535
347 1,986
311 2,002
270 1,997
23.0 100.0
17.5 100.0
15.5 100.0
13.5 100.0
Sources: Operational Programmes, COM 2008/301(2008) annex 1 and Eurostat (2008). Population data for January 2007: Estonia 1,342,409, Latvia 2,281,305, and Lithuania 3,384,879. *Community wide covers Member States and regions falling under the convergence objective covering 35 percent of the Union’s population. ** All amounts expressed in current prices. To accommodate inflationary expectations during 2007–2013, EU countries agreed to adjust financial framework ceilings (expressed in 2004 prices) by using a yearly 2 percent price deflator between 2004 and 2013.
The primary reason why funding differs between countries is due to country size. Dividing funding by population puts these figures into another perspective. The per capita allocation of cohesion policy funding to the environment is highest in Estonia and lowest in Lithuania. In comparison to the community wide allocation of cohesion funding that falls under the convergence objective, all three Baltic states devote more to the environment than is directed by cohesion funding on average.
Baltic Journal of Economics 10(1) (2010) 23-41
28
The definition of community funding devoted to the environmental sector includes 12 out of a total of 86 priority themes. The chosen priority themes include all but one theme of the category “Environmental protection and risk prevention” and two priority themes of “Tourism”: see EU (2006) for a complete list of priority themes. Our definition of the environmental sector is closely connected to fields commonly included in environmental protection expenditure of the general government budget. The fields used in general government expenditure include waste management, waste water management, pollution abatement, protection of biodiversity and landscape, and R&D in environment protection. Expenditure to reduce contribution to climate change is not explicitly included in our definition other than forming part of pollution abatement. One reason is the choice to follow the fields in general government expenditure. Another reason for not including climate change is that the Baltic states have different starting points depending on major differences in energy supply between countries. Leaving out investment in energy efficiency, renewable energy, and environmentally friendly transportation thus allows for a more equivalent base when making cross country comparisons between the Baltic states. In addition, a comparison of impacts of EU cohesion funding on climate change has been made elsewhere (see CEE Bankwatch Network 2007). Table 2 shows allocation of funding by priority theme of the three Baltic states and a comparison with community wide allocation for Member States and regions falling under the Convergence Objective. Table 2. Cohesion policy funding for the environment, per priority theme 2007-2013, euros per capita current prices** and percent Priority theme Management of household and industrial waste Management and distribution of water (drinking water) Water treatment (waste water) Air quality Integrated prevention and pollution control Mitigation and adaptation to climate change Rehabilitation of industrial sites and contaminated land Promotion of biodiversity and nature protection (including Natura 2000) Risk prevention (including drafting and implementing plans and measures to prevent and manage natural and technological risks) Other measures to preserve the environment and prevent risks Promotion of natural assets Protection and development of natural heritage Environmental sector, total
Estonia
Latvia
Lithuania
52 152
8.9% 26.1%
57 123
16.4% 35.5%
82 61
26.5% 13.0%
Community wide* 36 13.4% 47 17.3%
152 10 0 0
26.1% 1.7% 0.0% 0.0%
123 0 0 0
35.5% 0.0% 0.0% 0.0%
41 51 0 0
19.6% 16.3% 0.0% 0.0%
81 6 4 2
29.9% 2.2% 1.6% 0.7%
103
17.7%
21
6.2%
4
1.4%
20
7.4%
16
2.7%
11
3.2%
26
8.3%
16
5.8%
29
5.0%
11
3.2%
0
0.0%
34
12.6%
50
8.6%
0
23
7.5%
10
3.6%
9 9 582
1.6% 1.6% 100.0%
0 0 347
0.0% 23 7.5% 0.0% 0 0.0% 100.0% 311 100.0%
7 2.5% 8 3.0% 270 100.0%
Source: Authors’ calculations based on Operational Programmes, COM 2008/301(2008) annex 1 and Eurostat (2008). *Community wide covers Member States and regions falling under the convergence objective covering 35 percent of the Union’s population. ** All amounts expressed in current prices. To accommodate inflationary expectations during 2007–2013, EU countries agreed to adjust financial framework ceilings (expressed in 2004 prices) by using a yearly 2 percent price deflator between 2004 and 2013.
The impact of EU Cohesion policy on environmental sector sustainability in the Baltic states
29
Not all priority themes have been covered by the Baltic states. It is interesting to note that no Baltic state will invest in the priority themes of “Integrated Prevention and Pollution Control” or “Mitigation and Adaptation to Climate Change”. Moreover, community wide investment in these two priority themes is low. Estonia is the only Baltic state to allocate funds to “Protection and Development of Natural Heritage”. The other priority themes are covered by at least two Baltic states. In total, Estonia’s funding covers 10 priority themes, Latvia’s 6, and Lithuania’s 8. All Baltic states prioritise drinking water distribution and waste water treatment. These two priority themes are top priorities in Estonia and Latvia. Lithuania puts top priority on waste management, while drinking water and waste water treatment come at numbers two and four, respectively. Air quality is the third priority for Lithuania, while Latvia’s third is waste management. Estonia’s third priority is rehabilitation of contaminated land. Ranking of priority themes by expenditure is relatively similar in Estonia and Latvia for common fields, while Lithuania shows another ranking of priorities in that it includes a relatively large share of promotion of biodiversity and natural assets. Community wide priorities rank waste water treatment as top priority, followed by drinking water supply, and waste management. Notwithstanding comparable economic prerequisites and similar country size, funding plans for Estonia, Latvia, and Lithuania reveal larger differences than were expected. One reason for greater focus on drinking water in Estonia and Latvia may be that that Estonia and Latvia were granted transitional periods for fulfilling the directive on drinking water quality, while Lithuania was expected to fulfil the requirements on accession. In addition, Estonia’s funding plans cover a larger number of priority themes than Latvia’s and Lithuania’s and shows larger per capita spending on the environment. These differences may be due to the fact that Estonia’s production of electricity gives rise to significant pollution and that environmental protection was a major issue during the struggle to regain independence. Latvia has chosen fewer priority themes than its Baltic neighbours, but will spend more per capita than Lithuania. In Lithuania, biodiversity and natural assets receive a larger share of funding than in the other Baltic States.
5. Assessment of sustainability The observations above raise questions about whether more investment into the environmental sector is better from the viewpoint of sustainability and how the various priority themes add to sustainable development. Based on our initial discussion, sustainability can be assessed by the cost-benefit rule. However, since human preferences cannot correctly sense when ecosystems are at risk, WTP estimates might not produce proper accounting prices. Therefore, we will need to assess sustainability in two steps.
5.1. Cost-benefit rule Applying efficiency motivations to spending priorities, market failure can motivate all priority themes that were included in the environmental sector. Several priority themes deal with alleviating negative externalities including waste management, waste water treatment, and
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pollution control. Other priority themes can be motivated by reasons of provision of public goods, including air quality, rehabilitation of contaminated land, and promotion of natural assets. Drinking water infrastructure is not a public good, but its provision can be classified as market failure since the supply of drinking water infrastructure is characterised by increasing returns to scale. Existence of market failure is not sufficient to conclude that a certain priority theme needs funding for reasons of efficiency. In addition, the cost-benefit rule requires that total benefits exceed total costs, i.e. that willingness to pay (WTP) for the services or goods in question covers costs. Unfortunately, we know very little about whether willingness to pay covers the costs associated with the priority themes. However, some evidence exists for four priority themes.
5.1.1. Waste management and sewerage services Bluffstone and De Shazo (2003) report estimates of willingness to pay for two priority themes in Lithuania. They estimated the cost of implementing EU directives on waste management and urban waste water treatment and conducted contingent valuation studies among Lithuanian households in Ukmerge municipality 40 kilometres north of Vilnius. The population is approximately 34,000 and the average monthly household income is close to the national median (see Bluffstone and De Shazo, 2003). In interviews with households, the benefits of improved landfill construction and closure of old landfills were described in terms of avoiding pollution to surface and ground water and that after closure old landfills would be sealed and replanted to avoid future contamination. Respondents who indicated they had no access to the sewerage network were surveyed for their WTP to be connected to the municipal sewerage system. The benefits of municipal sewerage services were described in terms of there being no need to service their private septic system or pit toilet and no smell once connected to the municipal system. The authors found that at least 50 percent of respondents would be willing to pay 0.62 euros (2.73 litas) more per person and year for landfill upgrade and that half of respondents were willing to pay an additional 0.51 euros (2.24 litas) per person and per year for sewerage services. The average household size in Ukmerge is 2.67, thus producing household WTP of 1.7 and 1.4 euros respectively. Assuming that the households studied are representative of Lithuania, the authors estimated that national WTP covers between 80-90 percent of the costs of improving waste management practices, but that WTP for sewerage services covers only 10 percent of the costs (ibid). This implies that neither of the directives produces benefits large enough to cover costs. However, one limitation of the benefit estimation of sewerage services is that benefits from improved environmental conditions are missing. These include, for example, benefits that arise from improved water quality in local surface water bodies, enhanced fisheries, and improved recreation opportunities. Since 1.4 euros per household covers only 10 percent of costs, the improved environmental conditions of water bodies resulting from the urban waste water treatment directive must cover at least the remaining 90 percent of the 14 euros (i.e. 12.6), in order to pass the cost-benefit rule.
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5.1.2. Water quality In a recent article, Monarcheva and Gudas (2009) review three contingent valuation studies that have presented monetary WTP estimates for improving the water status in the river basins of the Nevezis (Lithuania), the Ludza (Latvia), and the Valgejogi (Estonia). The Lithuanian and Latvian studies measured the WTP for improving water quality from poor to good, while the purpose of the Estonian study was to estimate the value of restoration of salmon and other rare fish species in the Valgejogi River. These studies differ in certain respects. Firstly, the Lithuanian and Latvian studies focus on water quality and the Estonian on restoration of fish populations. Secondly, the authors mention a significant socio-economic difference between the Latvian study and the other two, as the Latvian study area has low population density and low income levels. Since WTP estimates are generally strongly correlated to income, it is reasonable to expect that the Latvian WTP is lower than the Lithuanian. The results, expressed in annual WTP in euros per household, are reported in the table below. The values in brackets represent estimates when zero bidders are included. Table 3. Willingness to pay (WTP) for improving water quality of river basins in the Baltic states, euro per year Environmental good
WTP per year per household, euros
Restoration of salmon and other rare fish species in Valgejogi river (Estonia)
22.8 (22.8)
Improving the water quality of Lake Ludzes and the upper part of the river Ludza river basin (Latvia)
13.7 (6.2)
Water quality improvement of the Nevezis River basin (Lithuania)
20.5 (13.3)
Source: Monarcheva and Gudas (2009)
In order to use the WTP for water quality estimates we would like to know whether implementation of EU directives on urban waste water management will result in improvements that have been valued by the Estonian, Latvian, and Lithuanian studies. The Estonian estimate concerning restoration of fish stocks seems less suitable for our purpose. The Latvian and Lithuanian studies seem to be more in line with expected impacts from improved sewage treatment. Including zero bidders, the Latvian and Lithuanian estimates produce a span of WTP for water quality improvements ranging from 6.2 to 13.3 euros annually per household. Assuming that the Latvian and Lithuanian WTP estimates approximately relate to the water quality benefits of the EU directive, this suggests that benefits might not be sufficient to cover the remaining 90 percent of the costs of about 12.6 euros.
5.1.3. Drinking water Experience from Poland implies that public willingness to pay for municipal services is higher for drinking water than for waste water services: see Stanek (2002). This seems logical based on the fact that people pay relatively more for safe drinking water, such as bottled water. However, a high WTP for drinking water does not seem to be the case for the Ukmerge municipality. One explanation may be that the WTP for safe drinking water only concerns a limited quantity of the water consumption of an average household. In the background documentation of the Ukmerge study, DEPA and DANCEE (2001) report the WTP for water
32
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supply. According to DEPA and DANCEE (2001), the quality of tap water distributed to households in Ukmerge municipality is checked regularly, but due to insufficient water pipe maintenance, households from time to time receive tap water with an orange/red colour or an odour. Respondents in Ukmerge were asked to value the benefit of upgrading the water supply pipes to ensure that the water supply system would be safe and so that no colour or odour would be present. The WTP was estimated to be 1.44 litas per person, per year, which corresponds to approximately 74,880 litas per year for the whole municipality. The estimated annual cost of upgrading the water supply pipe in Ukmerge municipality was estimated at approximately 10 million litas, suggesting that benefits cover less than 1 percent of investment.
5.1.4. Promotion of biodiversity Ehrlich et al. (2008) compared the costs and benefits of biodiversity enhancement by expanding the area covered by semi-natural plant communities in Estonia. Semi-natural plant communities, such as meadows, were developed by scythe, axe, fire, and grazing. These landscapes can persist only with support from human activity, such as mowing, grazing, and brush cutting. In 2007, semi-natural plant communities covered approximately 10,000 hectares in Estonia and the area is declining. Since these semi-natural plant communities are a prerequisite for richness in biodiversity and for migrating birds, the decline of traditional farming activities has put biodiversity under threat. Ehrlich et al. (2008) estimated that annual WTP was 265 euros per hectare of semi-natural plant communities. This amount was derived from the annual WTP estimate of 11.8 euros per person of the working age population. Based on an inventory covering all 31 protected areas in Estonia, the costs were collected for extending preservation of semi-natural plant communities to all Natura 2000 areas. This inventory was a base for Estonia’s funding plan for the priority theme promotion of biodiversity and nature protection. The present value of costs for extending the preservation areas to 19,334 hectares was estimated at 56.3 million euros. The cost estimate includes both running costs and investment costs during a 30 year period using a discount rate of 5 percent. The present value of benefits was found to be 89.0 million euros. The results indicated that willingness to pay for biodiversity enhancement exceeded costs by 58 percent.
5.1.5. Benefit transfer The cost-benefit rule can only be applied to four priority themes, and this scattered evidence gives point estimates for Lithuania in three out of four cases and in one case for Estonia. Benefit transfer from one country to another is a relatively common practice in literature and for policy purposes. However, differences in socio-economic characteristics and in the physical characteristics of study sites influence WTP. Generally, income is the most important variable affecting WTP estimates. The authors of the Lithuanian studies (see DEPA and DANCEE, 2001 and Bluffsone and De Shazo, 2003) assume that their results can be transferred to Lithuania as a whole. This
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is based on the fact that the socio-economic characteristics of Ukmerge are assessed as representative of the whole country. The Estonian study is based on a representative sample of the working age population (see Ehrlich et al. 2008). There are some differences in GDP per capita between the Baltic States, but from an EU perspective the income levels are similar. In addition, EU directives have imposed comparable requirements on the Baltic States. Assuming that it is possible to transfer the above results between the Baltic states implies that too many funds will be devoted to improving drinking water supply. Waste management funding probably also receives more funds than desirable. Connection to the municipal sewerage system and upgraded waste water treatment can only be motivated if the benefits of improved water quality are added to the benefit estimate. The scattered evidence further suggests that promotion of biodiversity receives too little funding. The results thus imply that from an efficiency point of view the funding plans will oblige the Baltic states to invest more than is socially desirable in drinking water and waste management. The implication is thus that support to drinking water and waste management should be reduced, while financing of biodiversity should be increased.
5.1.6. Cross border benefits Prior to arriving at conclusions concerning the first step of the assessment, it will be important to assess potential cross border benefits. We expect that more expenditure is allocated to priority themes that give rise to cross border benefits than can be motivated by national benefits. The priority themes of the environmental sector that can motivate costs exceeding national benefits are those that have significant cross border impacts. Potential priority themes include pollution control in those cases when air and water pollutants spread on a regional scale. Reduction of environmental risk could have cross border benefits if environmental damage spreads across national borders. Promotion of biodiversity, natural assets, and natural heritage might also have cross border benefits if citizens in other countries express use value or non-use value for preservation. Probably the most important cross border benefits are those that concern the water quality of the Baltic Sea. In the mid 1990s an extensive inter-disciplinary study on the state of the Baltic Sea was carried out by Turner et al. (1999). The authors simulated a 50 percent nitrogen and phosphorus reduction scenario. According to Turner et al. this corresponds approximately to nutrient levels of the Baltic Sea in the 1960s before its drastic deterioration. Cost effective policies for reducing nitrogen levels were found to include increased waste water treatment capacity at sewage treatment plants, reduction of use of nitrogen fertilisers, and construction of wetlands. Sewage treatment was proposed as a relatively low cost reduction option for reduction of phosphorous. On the other hand, benefits of waste management and of improvement of drinking water are geographically limited and high funding levels cannot be motivated by cross border benefits. The costs of nutrient reductions were compared to the benefits. Two WTP surveys were carried out: one in Poland and the other in Sweden, asking the adult population in each country
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34
for their willingness to pay for a 20 year action plan to reduce eutrophication in the Baltic Sea. The action plan would be financed by introduction of an extra environmental tax. The willingness to pay estimates were transferred to the other countries around the Baltic Sea by adjusting WTP estimates to the levels of GDP per capita. The Polish values were transferred to the formerly planned economies and the Swedish values were transferred to the other countries: see Turner et al. (1999). In order to achieve a better fit to current circumstances, WTP estimates for improving the status of the Baltic Sea have been updated and new estimates have been derived by using meta-regression analysis based on a large number of willingness to pay studies for improved water quality: see Huhtala et al. (2009). The authors found an average WTP of 60 euros per person and per year. This was then converted to country specific estimates by using country specific data on GDP per capita. The results are shown in Table 4. The population figures represent an estimate of the adult population in the Baltic Sea drainage basin of each country. Table 4. Distribution of benefits between Baltic Sea countries based on meta-regression results, benefits in euros 2007 Country Estonia Latvia Lithuania Denmark Finland Germany Poland Russia Sweden Total
Average annual WTP per person 45.2 38.8 40 71 68 66.2 36.6 33.5 72.6
Population (in millions) 1.05 1.78 2.42 3.58 3.86 2.45 25.85 7.00 6.78 54.77
Benefits per year (million euros) 47 69 97 254 262 162 946 235 492 2 564
Percentage of total benefits 1.8% 2.7% 3.8% 9.9% 10.2% 6.3% 36.9% 9.2% 19.2% 100.0%
Source: Huhtala et al. (2009)
Clearly, the WTP estimates for sea water quality in the Baltic States are higher than the WTP estimates for water quality in river basin areas reported in Table 3. This is in line with the findings of Huhtala et al. (2009) who report that the type of water body is influential in determining willingness to pay values. If the affected water body is a sea area, willingness to pay is on average 31–42 euros higher than for other water bodies. Although improved waste water treatment represents only one of the measures for achieving better Baltic Sea water quality, the WTP estimates in the table suggest that the benefits are substantial for all countries that border the Baltic Sea.
5.2. Environmental Performance Index The efficiency criterion in terms of the cost-benefit rule carries information about the desirability of investment in a sustainability perspective. However, the problem of finding proper accounting prices necessitates collection of inputs from other sources about the state of the environment. For this reason and since available evidence of the cost-benefit rule is rather
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narrow, we have chosen an empirical source that allows cross country comparison about environmental status. The Environmental Performance Index (EPI) gives input by assessing current national achie vement towards environmental targets (see Esty, et al. 2008). The two overarching environmental objectives of EPI include: reducing environmental stress to human health (i.e. environmental health) and promoting ecosystem vitality and sound natural resource management. These objectives and the overall ranking of the eight Eastern European countries that joined the EU at the same time as Estonia, Latvia, and Lithuania plus Switzerland is shown in the figure below. The reason for including Switzerland is that this was the country with the highest EPI in 2008. Latvia, with an index of 89, scores the highest value of the index among the East European countries that joined the EU together with the Baltic states. Lithuania and Estonia have 86 and 85 as index values, placing them top after Latvia and Slovenia. Based on Figure 1, it is also possible to conclude that the problems of environmental performance in Eastern Europe involve ecosystem vitality and sound natural resource management rather than environmental stress to human health (i.e. environmental health). This might be taken as an additional indication of over-investment in such priority themes as drinking water supply. The ecosystem vitality index is further decomposed into four indicators. Figure 2 takes a closer look at this index of the Baltic States. Three of the indicators show the status of threats to ecosystems, such as water and air pollution and climate change, and the fourth indicator Figure 1. Environmental performance index of countries that became EU members in 2004 plus Switzerland, EPI 2008 100
Maximum, inbex=100
90 80
62
60
98
95 86
86
85
96
95
99 92
89
86
82
77
73
73
70
67
99
98
84
80
77
70
98
94
92
75
50 40 30 20 10
Ecosystem Vitality
Latvia
Slovenia
Lithuania
Slovakia Environmental Health
Awitzerland
Environmental Performance Index (EPI)
Source: Esty et al. (2008)
Estonia
Hungary
Poland
Czech Rep.
0
Baltic Journal of Economics 10(1) (2010) 23-41
36
measures the state of ecosystems including aspects such as species protection, forest, agricultural, and fishery re-productivity. The Baltic states score ranks lowest in biodiversity and productivity of natural resources. Estonia has a low position in climate change depending on large-scale use of oil shale in its energy sector. Figure 2. Ecosystem vitality index decomposed into four fields (maximum index=100), Baltic States 2008. 98
100
95
95
99
98 87
90 80
79 71
70
89
73 65
60
62
50 40 30 20 10 0 Water Quality Estonia
Latvia
Air Pollution
Biodiversity & productivity of natural resources
Climate Change
Lithuania
Source: Esty et al. (2008)
The policy implications of EPI are that the Baltic states should pay more attention to biodiversity (e.g. conservation of habitats) and productivity of natural resources (e.g. fishery and cropland intensity). Both efficiency motivations and EPI thus suggest that more funds should be allocated for biodiversity and less for drinking water provision. In addition, EPI proposes that more attention is paid to enhancement of natural resource productivity, i.e. fisheries and cropland. In terms of priority themes, rehabilitation of industrial lands, promotion of biodiversity, and promotion of natural assets are put forward by EPI. Although water quality does not stand out as being at risk, productivity of fisheries needs attention, thus indicating the importance of upgrading waste water treatment.
6. Relevance to sustainability The analysis in the previous section suggests some implications for sustainability of cohesion fund allocation to the environmental sector in the Baltic states in 2007-2013. However, it was not possible to include all priority themes in the analysis because of gaps in knowledge about the benefits and costs of several priority themes. Waste water treatment and promotion of biodiversity passed the cost-benefit rule. Management of household and industrial waste did not pass the criterion, although benefits were not far from covering costs. Drinking water supply was rejected.
The impact of EU Cohesion policy on environmental sector sustainability in the Baltic states
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EPI was used as a complementary input, and supported the scattered evidence of the costbenefit rule. In addition, EPI suggested that rehabilitation of industrial lands, promotion of biodiversity, and promotion of natural assets would be important from an ecological point of view. In order to arrive at an overall assessment we will take the analysis one step further by classifying all priority themes under study into four fields (see matrix in Table 5). Table 5. Classification of priority themes 2007-2013 Biodiversity and resource productivity Rehabilitation of contaminated land Biodiversity and nature protection Promotion of natural assets Protection of natural heritage
Pollution control Waste management Waste water treatment Air quality Integrated prevention and pollution control
Preventive measures Mitigation and adaptation to climate change Risk prevention (plans and measures to prevent and manage natural and technological risks) Other measures to preserve the environment and reduce risks
Incidental environmental expenditure Management and distribution of drinking water
Biodiversity enhancement in Estonia was supported by the cost-benefit rule. In addition, EPI further highlighted the need to promote biodiversity and resource productivity in the Baltic States. Four priority themes are classified as enhancing biodiversity and resource productivity and these will be classified as highly relevant for sustainability of the environmental sector. The theoretical framework emphasized the long run perspective and proposed that sustainability concerns future generations into an infinite future. This long term perspective has so far not been highlighted by the analysis. Since preventive measures allocate funding to future environmental problems, this category will be classified as highly relevant to sustainability. Three priority themes are included among preventive measures: mitigation and adaptation to climate change, risk prevention, and other measures to reduce risks. Four priority themes aim at reducing pollution. These include waste management, waste water treatment, air quality, and integrated pollution control. According to EPI, the status of water and air pollution is at satisfactory levels in Latvia and Lithuania. At the same time, WTP estimates for improving the water quality of the Baltic Sea show significant benefits. The level of funding of waste management did not pass the cost-benefit rule. These considerations imply that investment in pollution control can be considered as having medium to high relevance for sustainability. The remaining expenditure is classified as incidental environmental expenditure. This classification follows Vincent et al. (2002) who classify incidental environmental expenditure as expenditure undertaken for non-environmental reasons. Drinking water infrastructure falls under this category. Neither the cost-benefit rule nor EPI suggests that drinking water is important from the perspective of sustainability. Incidental environmental expenditure is thus classified as having low relevance from a sustainability perspective. Table 6 below shows funding support from EU cohesion funds according to the four fields defined above. The table shows that the Baltic States have allocated 10-24 percent of cohesion funds to biodiversity and resource productivity. Preventive measures receive 3-14 percent of funds. This
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implies that the two fields found to have high relevance for sustainability have been allocated less than half of the cohesion policy funding directed to the environmental sector. Latvia devotes least funds for investment in fields that will add most to sustainability (12.6 percent). Estonia allocates 37.1 percent and Lithuania 24.6 percent of environmental cohesion funding to these two fields. Funds for reducing pollution were found to have medium to high relevance for sustainability. Pollution control receives more than half of the funds devoted to the environmental sector in Latvia and Lithuania and a little more than one third in Estonia. In per capita terms, funding is on a similar level in the three countries and will receive about 200 euros per capita in each country during the period 2007-2013. Incidental environmental expenditure, the field classified as having least relevance to sustainability, will receive between one quarter and one third of funding to the environmental sector. It is evident that priority themes classified as having highest relevance to the sustainability perspective receive less funding than priority themes found to be of low and medium/high relevance to sustainability. Estonia, with its larger per capita contribution to the environment, also shows higher investment both in absolute terms and in percentages to fields highly relevant to sustainability. Lithuania, which ranks lowest according to its per capita funding, shows a better position than Latvia concerning allocation to fields highly relevant to sustainability. Table 6. Cohesion funding for the environmental sector classified by relevance to sustainability, euros million current prices, euros per capita and percentages 2007-2013 Euro, million Biodiversity and resource productivity Preventive measures Pollution control Incidental environmental expenditure Total Euro, per capita Biodiversity and resource productivity Preventive measures Pollution control Incidental environmental expenditure Total Percent Biodiversity and resource productivity Preventive measures Pollution control Incidental environmental expenditure Total
Estonia 184.2 105.5 287.8 203.9 781.3
Latvia 75.0 25.2 411.0 281.5 792.7
Lithuania 180.7 78.6 656.6 137.4 1,053.4
Total 439.9 209.2 1,355.4 622.8 2627.4
137 79 214 152 582
33 11 180 123 347
53 23 194 41 311
63 30 193 89 375
23.6% 13.5% 36.8% 26.1% 100.0%
9.5% 3.2% 51.8% 35.5% 100.0%
17.2% 7.5% 62.3% 13.0% 100.0%
16.7% 8.0% 51.6% 23.7% 100.0%
The impact of EU Cohesion policy on environmental sector sustainability in the Baltic states
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7. Conclusion The purpose of this paper is to evaluate sustainability of investment plans of EU cohesion policy funds for the environment in Estonia, Latvia, and Lithuania during the budget period 2007-2013. Theoretical literature shows that the efficiency criterion, i.e. the cost-benefit rule, is applicable to identify sustainable investment. But since natural environments are complex, proper accounting prices may be hard to find when relying on human preferences. With these difficulties in mind, we applied a step-wise assessment to identify sustainability. Economic efficiency was considered by using the cost-benefit rule. In the second step we used the Environmental Performance Index (EPI) as a complementary indicator and to identify whether critical fields of investment in the environmental sector had been left out. Use of the cost-benefit rule requires information on benefits and costs of planned investment. This information was only available for four out of twelve priority themes. Assuming that benefit transfer is possible between the Baltic states, available evidence suggests that too much funding is devoted to investment in drinking water infrastructure. Neither did management of household and industrial waste pass the cost-benefit rule, though benefits were not far from covering costs. Investment in sewerage services and waste water treatment were not possible to motivate unless benefits from environmental impacts on water bodies were included. Another implication is that investment in biodiversity protection could be extended since benefits significantly exceed costs. The complementary input of EPI supported the scattered evidence of the cost-benefit rule. EPI showed that the Baltic States have no serious concerns related to environmental stress to human health, which might be taken as an additional indication that allocation of cohesion funds represents over-investment in drinking water infrastructure. The implication of the environmental performance index is that more attention should be paid to biodiversity (e.g. conservation of habitats) and productivity of natural resources (e.g. fishery and cropland intensity). Although water quality did not stand out as being at risk, productivity of fisheries was suggested by EPI to be at a low level, thus indicating the importance of upgrading waste water treatment. Both steps of our analysis had similar implications, but neither was detailed enough to enable an assessment of all priority themes. In order to obtain an evaluation of all priority themes, we classified them into four fields. These fields were categorized according to their relevance to sustainability. The main finding is that the Baltic States allocate least investment to those fields of the environmental sector found to be most relevant to sustainability, i.e. preventive measures, and biodiversity and resource productivity. Investment in drinking water was assessed as too large from the sustainability perspective. Having as an objective to reduce disparities between Member States, distributional considerations may have guided funding plans. It is not clear, though, how extensive investment in drinking water infrastructure promotes this purpose. Another finding is that the three Baltic states, having large similarities concerning recent history, level of economic development, and natural environment, show significant differences concerning their priorities. Estonia has the highest per capita contribution to the environ-
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mental sector and also larger investment in fields with the highest relevance to sustainability. Lithuania ranks lowest according to its per capita funding, but shows a better position than Latvia concerning highly relevant fields. It is possible that Estonia’s significant environmental problems stemming from oil shale based energy production have made the country more inclined than its Baltic neighbours to invest in the environmental sector and also more ready to direct investment into fields with high relevance to sustainability.
References Arrow, Kenneth J., Dasgupta, Partha and Mäler, Karl-Göran (2004) “Evaluating Projects and Assessing Sustainable development in Imperfect Economies” in Partha Dasgupta and Karl-Göran Mäler (eds) The Economics of Non-Convex Ecosystems. Kluwer Academic Publishers, pp 149-187. Bluffstone, Randall and De Shazo, J.R. (2003) “Upgrading municipal environmental services to European Union levels: a case study of household willingness to pay in Lithuania” Environment and Development Economics 8, 637-654. CEE Bankwatch network and Friends of the Earth Europe (2007) “EU Cash Climate Clash: How the EU funding plans are shaping up to fuel climate change”, Budapest, April 2007. COM 2008/301(2008) Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions on the results of the negotiations concerning cohesion policy strategies and programmes for the programming period 2007-2013, Brussels. Dasgupta, Partha and Mäler, Karl-Göran (2004) “The Economics of Non-Convex Ecosystems: Introduction” in Partha Dasgupta and Karl-Göran Mäler (eds) The Economics of Non-Convex Ecosystems. Kluwer Academic Publishers, pp. 1-27. DEPA and DANCEE (2001) “Case Study in Municipal Financing: Ukmerge Lithuania” Annex 6 in Environmental Financing Strategy, Danish Environmental Protection Agency and Danish Cooperation for Environment in Eastern Europe, pp. 101-156. Available at: www.unep.org/.../INF.19.RS_Lithuania_Environmental_Financing_Strategy. pdf, accessed 25 March 2010. Ehrlich, Üllas, Pädam, Sirje and Tenno, Koidu (2008) “Monetary Equivalent of Non-Market Value of Habitats as an Economic Argument for Their Financing: Case of Estonian SemiNatural Plant Communities”, in Jüri Sepp and Dean Frear (eds), Globalization and Institutional Development. Wilkes-Barre: Congress of Political Economists International, pp. 495-508. Esty, Daniel C., M.A. Levy, C.H. Kim, A. de Sherbinin, T. Srebotnjak, and V. Mara. 2008, “Environmental Performance Index” New Haven: Yale Center for Environmental Law and Policy. Available at http://epi.yale.edu, accessed 8 June 2008. EU (2006) Commission Regulation No 1828/2006. Eurostat (2008) “Population and social conditions” Data in Focus 3/2008, Eurostat. Huhtala, Anni et al. (27 co-authors in total) (2009) “The economics of the state of the Baltic Sea” Pre-study assessing the feasibility of a cost-benefit analysis of protecting the Baltic Sea ecosystem, Sektoritutkimuksen neuvottelukunta, Kestävä kehitys 2-2009. Available on the internet, accessed 20 February 2010 http://www.minedu.fi/export/sites/default/ OPM/Tiede/setu/liitteet/Setu_2-2009.pdf.
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Monarchova, Julija and Mindaugas Gudas (2009) “Contingent Valuation Approach for Estimating the Benefits of Water Quality Improvements in the Baltic States” Environmental Research, Engineering and Management, 2009 No 1 Volume 47. pp. 5-12. Operational Programme for the Development of Economic Environment Republic of Estonia CCI number: CCI 2007EE 161PO001, 21 June 2007. Operational Programme for the Development of Human Resources Republic of Estonia CCI number: CCI 2007EE051PO001, 21 June 2007. Operational Programme for the Development of Human Resources 2007-2013, Vilnius, 30 July 2007. Operational Programme for the Development of the Living Environment (2007) Republic of Estonia, CCI number: 2007EE161PO002, Ministry of Finance, 21 June 2007 CCI number: 2007EE161PO002 21 June 2007. Operational Programme for Economic Growth for 2007–2013, 15 July 2007, Vilnius. Operational Programme “Entrepreneurship and Innovations” Draft Ministry of Finance, Republic of Latvia, Riga July 2007. Operational Programme “Human Resources and Employment” CCI: 2007LV051PO001 Ministry of Finance, Republic of Latvia, Riga October 2007. Operational Programme “Infrastructure and Services”, (Darbibas programma “Infrastruktura un Pakalpoluni”) (2007) Ministry of Finance, Republic of Latvia, CCI: 2007LV161PO002, Riga October 2007. Operational Programme for Promotion of Cohesion 2007-2013 (2007) Ministry of Finance, Vilnius July 5 2007. Pitlik, Hans (2007) “Spending Priorities in the EU Budget 2007–2013: The Perspective of Fiscal Federalism” Austrian Economic Quarterly 1, pp. 11-24. Pädam, Sirje (2003) “Sotsiaalmajanduslik tasuvusanalüüs ja jätkusuutlik areng transpordi näitel” (“Social cost benefit analyses and sustainable development in transport”), Master’s thesis, Tallinn University of Technology June 2003. Stanek, Rafael (2002) “Poland Brief overview Part 1” in Financing environmental protection infrastructure in Poland, Lithuania, Latvia and Estonia: Implementing European Union Directives in Waste Water Treatment and Waste Management, Institute for environmental tax reform and CEE Bankwatch network, ISBN 83-89230-05-4, pp.7-21. Stavins, Robert N., Wagner, Alexander F. and Wagner, Gernot (2003) “Interpreting sustainability in economic terms: dynamic efficiency plus intergenerational equity”, Economics Letters 79, pp. 339-343. Turner, Kerry; Stavros Georgiou, Ing-Marie Gren, Fredric Wulff, Scott Barrett, Tore Söderqvist, Ian J. Bateman, Carl Folke, Sindre Langaas, Tomasz Z;ylicz, Karl-Göran Mäler and Agnieszka Markowska, (1999). “Managing nutrient fluxes and pollution in the Baltic: an interdisciplinary simulation study”. Ecological Economics 30, pp. 333-352. Vincent, Jeffrey R; Jean Aden, Giovanna Dore, Magda Adriani, Vivianti Rambe and Thomas Walton (2002) “Public Environmental Expenditures in Indonesia”, Bulletin of Indonesian Economic Studies 38(1), pp. 61-74.
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Credible commitment and cartel: the case of the Hansa merchant
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Credible commitment and cartel: the case of the Hansa merchant in the guild of late medieval Tallinn Kaire Põder1
Abstract This paper contributes to the ongoing debate of institutional research in economics and the methodological debate over the plausibility of using analytic narratives, in social sciences in particular. Using a single historical case we argue that in Tallinn by and large the merchant guild solved a commitment problem in the Hanseatic League and the organisation-institution of the guild was meant for efficient enforcement of inter-city trade. We show that this argument holds in the late medieval period by using an extensive form of punishment and sanctions game. We also argue that after the breakup of the Hanseatic League, guilds turned into protectionist and rent-seeking cartels. Keywords: economic history, credible commitment, analytic narratives, reputation mechanism, Hanseatic League JEL Classification: C72, D81
1. Introduction This paper contributes to the ongoing debate about the role of the guilds in the late medieval city-state, and most of all to institutional research using the analytic narrative. First we state that territorial craft guilds efficiently solve coordination problems in the early days of the Hanseatic League2 and are thus economic growth-enhancing organizations, partly substituting missing markets. The second argument is that merchant guilds create growth by generating trade through credible commitment to honest trade. In this case a credible threat is created through the “reputation mechanism”. The third argument is that in later periods – starting from the declining era of the Hanseatic League – guilds started to perform as rent seeking organizations or cartels. Historical empirics or narratives concentrate on one specific case – one of the most important city-states in eastern trade: Tallinn (known in history as Reval). Institutions are rules of the game, writes North (1990). Institutions set the standards, structure society, and limit our freedom. The study of institutions seeks to answer at least two ques1 Tallinn University of Technology,
[email protected]
² The word Hansa (hanse, hense) has German roots, originally used in the sense of warrior band, later meaning tribute
paid by merchants, sometimes a group of merchants abroad (Dollinger 1970: xix). Here, Hansa indicates the institutional arrangement of the inter-city union or federation of cities called the Hanseatic League.
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tions: why institutions are needed, and how institutions are enforced. Our paper investigates governance of the medieval town through the institutional framework. We ask why guilds were needed, whom they benefited, and how they structured the medieval world. Neither economists nor historians completely agree upon the role of the market in medieval society. Moreover, the question of institutions as substitutes for or complements to markets is still open to debate. We intend to show that institutions can be market-, trade-, and growth- supporting, as well as restricting. Medieval society appreciated the status quo. Appreciation of a static world, including the relevant symbols, social structure, and social norms, can be expected in societies where uncertainty and risks prevail. Fostering the status quo was enforced through the social structure, where materialistic well-being was not an objective, rather a social status-related privilege (Le Goff 2000: 308). However, there is an exception: European economic growth between the tenth and the fourteenth centuries was facilitated by the “commercial revolution of the Middle Ages” – the re-emergence of Mediterranean and European long-distance trade (Lopez 1976). This European growth was not a general phenomenon but was rather geographically concentrated in the “clubs” of city-states (in Italian and other Mediterranean cities) and intercity unions (e.g. the Hanseatic League). Starting from the Cliometric “revolution” in the 1950s, economic historians began to utilize econometrics for assessing the functioning of markets in many historical episodes. However, altering the methodology to study case-specific or comparative institutions is relatively new. In most literature about medieval Tallinn, the fact of prosperous international trade during the Hanseatic League is largely assumed without much formal testing. However, as Greif (1995) asserts: [...] the neo-classical approach to the study of institutions through economic history established that contrary to the claims of traditional historians, it is not true that the governance of exchange markets is a very recent phenomenon. Furthermore, by revealing the economic rationale beyond various contractual relations and patterns of ownership it tends to support the Coasian view of non-market institutions as substitutes for the markets (Greif 1995:5).
The New Institutional Economics attempts to explain even more: to show “why institutions that produce poor economic (and political) performance can persist” (North 1993:12). In most cases, economic outcomes depend on efficient institutional change. The most intensively studied historical institutional tracks are property rights, and institutions which enabled technological change. In medieval studies, North and Thomas (1973) investigated the spectacular economic expansion of the late medieval period. Greif (1995) asserts that many questions which then remained unanswered demanded methodological improvement or change, which was provided by clear concepts of institutions and game theory. Organizations are non-technologically determined constraints (other than expectations) that impact behavior by introducing a new player (the organization itself), changing the information available to players, or changing payoffs associated with certain actions. The court, the regulator, the credit cooperative, the credit bureau, the firm, and the merchant gild are examples of such organizations (Greif 1995:8).
In game theory we are after organizations which are self-enforcing. This makes multiple equilibria games fascinating tools which can be enriched with historical data. The multipli city and indeterminacy of equilibria in strategic situations indicate that details of the his-
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torical context are potentially important in the selection of institutions, the implications of a particular institution, and institutional path dependence (Greif 1995:9). In order to construct such an enriched game theoretic model, it is vital to capture the choice-specific details of the historical situation. In ideal cases, we need a micro level study for model specification. Historical institutional studies using microeconomic or game-theoretic tools have a relatively short history. In Greif’s (1989, 1993) analysis of “Maghribi traders”, contractual relations between merchants and their overseas agents in eleventh-century Mediterranean trade are analyzed, to show how to motivate merchants to participate in sanctions when necessary. A created institution can be called a coalition, which made reciprocal information transmission and collective punishment self-enforcing. Similarly, Milgrom et al. (1990) argued that the use of merchant courts in the Champagne Fairs during the twelfth and thirteenth centuries can be analyzed as an institution that created proper incentives for gathering information, honouring agreements, and reporting disputes. All these lowered transaction costs and allowed reliance on markets. There are also other game theoretic studies; for example, Greif (1998) analyses agency relations in twelfth-century Genoa, showing the rationality of creation of inter-clan cooperation and enforcement of external governance institutions called the podestá. Using an infinitely repeated complete information game, Greif et al. (1994) examined the operation of an organization that enabled late medieval rulers to commit to the property rights of alien merchants. This study is of particular interest to us, because it uses the merchant guild as an example of a particular organization that supported a multilateral reputation mechanism. A multilateral reputation mechanism can potentially overcome the commitment problem at the efficient level of trade, but only when an organization exists with the ability to coordinate the responses of all merchants to abuses against any merchant (Greif 1995:18). Greif (1995:748) also states that the argument concerns merchant guilds and not craft guilds. In the latter, the “common knowledge” or monopolization argument (e.g. Hickson and Thompson 1991, Gustafsson 1987, Ekerlund and Tollisson 1981) still holds. We may say that the literature provides evidence of growth-enhancing and retarding institutions or organizations. Our methodology is the analytic narrative. The analytic narrative is a combination of rational choice based game and historical-anthropological or qualitative study. Our narrative combines various sources of historical material. Compared to Scandinavian and German merchant guilds, some unique documents are available about Tallinn merchant guilds, but no comprehensive study has been undertaken. Thus a synthesis of the narrative is of value in itself. This of course opens us to criticism if the narrative is not well presented. However, we are sure that the case of Tallinn is too interesting to leave aside. And in this study historical narrative is used for explanatory purposes – to show that the guild was a solution to the collective action problem. The analytical part of the narrative derives from analysis of choice rules and payoffs of individuals using an extensive form game. The paper proceeds as follows. Section 2 reports the relevant pre-knowledge about the Hanseatic League’s history. Our study covers approximately 100 years, from the foundation of the merchant guild in approximately 1363, until the closing of the Novgorod office in 1478. Section 3 introduces the problem of credible commitment. Then, in section 4 we propose the model of the agency which enforced unilateral sanctions as a credible threat. Later we discuss the historical context in the case of the Tallinn merchant guild, showing that the guild was the only mediator of trade and that the threat of punishment was accompanied by the guild’s regulations. Finally, we follow the guild’s further development into a rent-seeking institution.
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The last section concludes the paper by considering the subsequent history of the decline of the Hanseatic League, and proposes a course for further studies including elaboration of a theoretical framework from neighbouring disciplines.
2. Introduction to the narrative: the Hanseatic League Long distance trade in late medieval Europe was based on exchange of goods through different towns or fairs located in geographically or politically favourable places. Yet the gains from trade were insecure not only because of technological constraints (North and Weingast 1989), but also because of many political and institutional constraints: wars, piracy, cheating. The narrative will show that in response to these uncertainties, two possible risk-pegging mechanisms were available: 1) military action or 2) a mechanism for sanctioning shirkers. Which mechanism was more cost-effective? It is argued (Greif et al. 1994:751) that before the fifteenth century, defensive technology was superior to an offensive one. Thus, compared to military action, “diplomatic” action or even “trade sanctions” such as embargos were costeffective measures for enforcement of mutual benefits from trade. We will argue that the merchant guild, which enforced a unilateral reputation mechanism, helped to protect trade from shirkers. In 1189, the Hanseatics – Germans and Gotlanders – signed the oldest known treaty with the Russian Prince Jaroslav, stipulating similar privileges to Russian and German merchants. In 1205, merchants created the Peterhof – a base for traders in Novgorod, which then created the first trade organization called a kontor (Dollinger 1970). In 1229, free trade was re-confirmed under the “Gotland Community” (Sartorius 1830). Starting from 1280, Lübeck was called the capud et principium of all Hanseatic towns (Christensen 1957:107). The first trading routes to Novgorod crossed Tallinn via Lake Ladoga and the rivers Neva and Volga. “Eastward expansion” created a profitable trading route for the Hanseatic League and Tallinn was a trading establishment for the east-bound trade. In 1230, two hundred German merchants accompanied by Danes and Swedes settled in Tallinn. Tallinn became a base for operations and assembly-point for German merchants travelling to Novgorod by sea. Figure 1. Overview of the Hanseatic League’s history and our study (1363-1478)
In Germany, cities emerged through a political process (Greif 1995:21) that led to the establishment of relatively small cities. Hence, the community or organization of the Hansa – or as Greif (1995) states, an inter-city merchant guild – emerged to govern relations between Ger-
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man merchants. Trade started only after negotiating appropriate safety arrangements (Dollinger 1970). The organizational unit for this coordination abroad – the office or kontor – was the conditional residence of Hansa merchants in a particular town. Any “common merchant” who arrived in a non-Hanseatic town could join the local kontor, which coordinated disputes and financial obligations. Hanseatic privileges were conditional on citizenship of the member town. The number of active Hanseatic member-towns varied between 55 to 80, but more than 180 towns were somehow related to Hansa trade (Dollinger 1970:88). Active members had to attend annual conferences or diets (held in Lübeck): the only official organizational tool for central planning, since the Hanseatic League had no official administrative, military, or fiscal apparatus. From 1356 onwards the regularly held annual Hansetag, the general assembly of Hanseatic towns, was the only controlling organ of the league. Assemblies were mainly held in Lübeck and because of the heavy cost of travel, not all members participated. Delegates from the town councils voted over regulations of the Hansa under a simple majority rule (Dollinger 1970:95), although Lübeck, being always present, played a vital role in Hansa affairs. Starting from 1347, in the Hansa statutes the three “thirds” of the cities were mentioned – Tallinn was one of the leading figures in the Gotland-Livonia “third” (Dollinger 1970:95). Several times yearly, local or regional assemblies were also held: these sent their deputies to the general diet and decided “more local affairs”: It [the nature of the Hanseatic League] was neither a society, nor a college, nor a corporate body, but a permanent federation of towns owing allegiance to various princes, having no common institution – even the Hanseatic diet was not admitted as such – and consequently not responsible for the acts or undertakings of any of its members (Dollinger 1970:106-107).
Thus the question arises – how could this loose “organization” expand and create growth for many centuries? Greif (1995) believes that this could have only been accomplished through mutually beneficial cooperation. Merchant guilds emerged and supported trade expansion and market integration “[…] and their function was to ensure the coordination and internal enforcement required to make the threat of collective action credible” (Greif 1995:19). Was it really so? According to Dollinger (1970), first, in the case of conflicts, the matter had to be discussed in the circle of neighbouring towns (any participation by the territorial ruler in this mediation had to be avoided). Second, if this was unsuccessful, the matter was brought up in the Hansa diet, which made the final decision – in the most severe cases, exclusion of the town from commercial privileges, never military action. In some cases, exclusion could apply to individuals, but then sentence was pronounced by the town where the merchant was a burger or by the kontor – including confiscation of goods (in the case of smuggling), or exclusion from the rights of a “common merchant”. Third, only in extreme cases were more severe sanctions used such as suspension of trade by embargo or war. An embargo or a war is clearly damaging for all sides, making enforcement in a loose organization even more complicated. The ultimate sanction – war – was used only against piracy, not for economic domination, and even then a great number of towns tried to evade the burden because of heavy military expenses (Dollinger 1970:112). Thus the “optimal” institution of the Hansa was neither able nor willing to provide the public good with military action for protection but provided the public good with economic growth through trade.
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3. Setting up the problem: credible commitment As Bardhan (2005) points out, literature on the economic analysis of social and political institutions has focused mainly on the role of those institutions as protectors of property rights. A more neglected role of institutions is to correct coordination failures or commitment problems that sometimes plague the most basic type of economic interaction. In our study, the merchants’ welfare was dependent on efficient trade. “International prices” signalled relative scarcity and gave information about efficient exchange, but huge risks were involved in trade. Formal business organization of the merchants was practically non-existent – a merchant was an entrepreneur with full responsibility over risks taken. Risks in inter-city trade, especially in the thirteenth century, were remarkable. At the same time, increase of trade volume would have benefited both foreign and local merchants. Therefore it was in the common interest to decrease the risks of being cheated, attacked by pirates, or constrained by local rulers. Later, credit risks were included. However, as in the case of many social situations, a single merchant has neither the incentives nor the means to make investments for provision of the public good in the shape of security of trade. Without an external enforcer, we are faced with the classic problem of an empty promise of punishing the cheated party even in the case of perfect information (see Figure 2). In Figure 2, Merchant I has two alternatives – to cheat (C) or not (N), and Merchant II in the second stage of the game has two options: to punish (P) by ostracizing or prohibiting further trade or not (N). A simple extensive form game with perfect information indicates the payoff profiles, and it is evident that the strategy “never punish” strictly dominates over “always punish” and weakly dominates over “punish when cheated and not punish when not cheated”. Thus cheating is the optimal strategy of the first player. Figure 2. Punishment is not a credible threat
Although evolutionary games (Axelrod 1984) justify the emergence of reciprocity and cooperation in repetitive situations, we assume that independently operating merchants may not belong (at least initially) to the network of repetitive interactions (there were approximately 180 trading places in the Hanseatic League, each having dozens of independent merchants). However, if punishment can be enforced as a social norm in the case of cheating, then Merchant I will change his behaviour. Obviously, this commitment to punish is beneficial for the second player, and therefore it is very likely that the second player is ready to bear the considerable cost (enforcement costs) of making the threat of punishment credible.
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Greif et al. (1994:756) state that the merchant guild’s strategy is conditioning future trade on adequate past protection, using ostracism to achieve security (rather than privileges or lower prices). We will argue that there is one more important strategy – reputation building. “The war of everybody against everybody” created the precondition of the emergence of an organizational form like a merchant guild. On the one hand, a guild was caused by individualistic organisation of “common merchants” as there were no alternative corporations for risk backing and trade mediation. On the other hand, it was caused by the need to enforce credible commitment by “the community label”. This label could make all the merchants from a certain community responsible for the damage created by any member of the community. Thus, as Greif et al. (1994) show, the reputation building of the community was vital and inside the community there was keen surveillance over the behaviour of traders, which made intracommunity enforcement mechanisms support inter-community exchange.
4. The solution: the agency model We will show that agency is an institutional structural solution to the credible commitment problem. Later, we will demonstrate how the narrative supports our argument. But first we have to admit that forming an organization (like a merchant cartel), which will transfer information, stand for the members in conflicts with local rulers, and most of all, commit sanctions towards its own members, is costly. The benefits from the efficient ‘community label’ or quality mark are related to the volume of trade accompanied by decreased risks of being cheated. Let us assume that each community member faces a dilemma – the ability to credibly commit to punishment will increase trade volume Σ x(p), where p is the number of people committed and x is the value of traded goods for each merchant. Compared to the model in Figure 2, now we add a local guild brother (who is also a merchant), thus his benefits also come from honest trade. Sanctions (or Axelrod (1997) metanorms) to make punishment a credible threat are used by the local guild brother. To keep the game as simple as possible, we assume that sanctions are costly. The cost of sanctions is cs. If the discount factor is δ, indicating preferences over time, and the game is repeated infinitely, then total benefits from sanctioning the x(p)
guildbrother are 1-δ -x[(p)-x(p-1)+c³] . This means that sanctions will benefit future trade but hurt current trade, and of course they are costly. In Figure 3 we substitute the latter three arguments by u, so u=x(p)-x(p-1)+cs, which indicates the one time difference of trade volume because of punishment plus the costs of sanctions. If sanctions are not enforced, future trade volume will be infinitely lower, so the guild brother benefits stay at x( p − 1) . 1−δ
Thus, in the final stage, the guild brother compares two possible discounted payoffs and chooses to sanction if x( p) − (1 − δ )u > x( p − 1) . The value of the left side of the equation is dependent on the discount factor – the more patient agents are (the bigger the discount factor), the higher the probability of sanctions. Although in economics this conclusion seems trivial, due to the static nature of the medieval worldview, it could be that traders were not just after short term profits. However, the difference in trade volume x(p)-x(p-1) also matters considerably. In Figure 3, the local merchant’s cardinal preference ordering is also given (the bigger number indicates higher preferences). If we assume that sanctions are beneficial to the guild brother, then “punishment” (P) is also
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Figure 3. Agency in the sanctions and punishing game
x( p) −u 1−δ
x( p − 1) 1−δ
beneficial, so that sanctions must be a credible threat to make agency self-enforcing. Assuming that foreign merchants’ benefits are X, Y and y accordingly and X >Y (and this is independent of y) a foreign merchant chooses “not cheating” (NC) over “cheating” (C), and the game will end in the first stage. Honest trade is established. The costs of sanctions (cs) must also be considered – the lower they are, the more probable that “sanctions” is a dominant strategy. The cost of sanctions depends on compliance with the group and this is also dependent on group size. Thus we may argue that restricted access to the merchant guild in early periods was not caused by the cartelization argument, but rather by the information argument – the more optimal the size of the group, the easier it was to enforce sanctions, and to obtain information about possible misbehaviour by members. Due to cost considerations, certain rules (like membership by nationality) could be justified. To an extent, it defined communities and fostered their internal organization similarly to other norms of identification such as clothing and rituals. This observation is consistent with North’s (1990) claim that “groups of individuals bound by some common purpose of achieving objectives […] come into existence and […].evolve [in response to] the institutional framework”. Will the result of our model – credible threat – find any empirical evidence? Although the number of sources is limited, we can combine a narrative to show that considerable evidence exists to support our model.
5. Back to the narrative: Tallinn Merchant guild in the 14th century Up to the mid-thirteenth century, a Hansa merchant was an itinerary trader, who travelled in groups and traded by barter (Dollinger 1970:163). Later, this tradition was replaced by an independent entrepreneur in charge of his own firm, who conducted business from his office
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at home and used representatives or clerks for travelling. A merchant was the owner in many partnerships and this was the standard form of commercial enterprise. Partnerships involved a small number of associates, for a limited period, and for a specific project: […] there was no single commercial firm, permanent, centralized, having its headquarters in a special building, with subsidiary firms, its own clerk and agents and surviving through several generations – the sort of business represented for example in Italy […] or in south Germany […] (Dollinger 1970:168).
Transaction costs (costs of using inter-city markets) were therefore relatively low (which is definitely not the case at the beginning of the trade) and it seems that transaction costs could be lowered by using institutions alternative to the market – a guild or a kontor. The risks in the first period of the Hanseatic League were more related to piracy and security of sea travel; later, credit-risk was included. A guild is a common organizational feature of all Hanseatic towns, mainly dealing with organizing overseas trade (Hammel-Kiesow 2008). Its relative importance and role varied from case to case, depending on the social power of the princes and the patriciate, on the size of the population, and on a guild’s relative wealth. Our study focuses on the town of Tallinn in the 14th century (see also Figure 1) and we show that in this case the merchant guild (Grosse Gilde) was a cooperative agency which enforced honest trade. From 1346-1561 Tallinn was ruled by the Teutonic Order. The ruler of the town was not the Grand Master alone but rather the Order as a corporation (Kreem 2002:20). While the relationship between the Order and the Town may not be as unimportant as portrayed by Pullat (1976) and Margus (1939), we consider merchants to be independent players. This follows from the fact that in 1346 the Teutonic Order confirmed all the privileges of the citizens granted by the Danish kings as a part of medieval routine (Kreem 2002:39). Tallinn’s total trade in 1368 amounted to 99 294 marks (the price of the average stone-built house in Tallinn was 60-80 marks at that time (Kaplinski 1980)). Furs – sable, beaver, lynx, squirrel, and rabbit – all from Novgorod, were in great demand in the Hanseatic League and the amounts imported were impressive – 300 000 pelts between 1403-1415 , 30% of which came through the merchants of Tallinn, the rest through Tartu, Pärnu, and Riga (Dollinger 1970:325). The town remained small. According to sources from the 14th century, the total population of the lower town was approximately 4000 (Mänd 2004). By adding together approximate numbers of participants from the Christmas and Shrovetide festivals based on Mänd (2004: 138-139) we propose that there were altogether more than 200 merchants in the town. If only guild brothers are included (bachelors and non-citizens are not included) the number would have likely been between 70-150 merchants in 1510-1550. Also, it may be assumed that the number of guild brothers increased considerably over time (Mänd 2005), and that it was smaller during our period of interest. By the 15th century, the social career of the merchant in late medieval Tallinn was well formalised. First the non-citizens and bachelors were accepted into the Brotherhood of Black Heads. On average, a “blackhead” spent five years in the corporation before being accepted into the Grosse Gilde. Only merchants could be members of that guild. Although all schras (regulations) of the guild had to be accepted by the city council (magistrate), the guild was
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rather independent in its everyday “club-life”. The Grosse Gilde (probably founded in 1363) was the only merchant corporation in Tallinn. Increasing institutionalization (also formalisation due to increasing literacy) is characteristic of that time. In later periods (in the 15th century), as successful merchants became members of the magistrate, increasingly more initiative in merchant affairs is taken over by the magistrate. The Hansetag in Lübeck gradually became a gathering of the altermen (members of the assembly) of the magistrate, accompanied by the guild brothers. The magistrate consisted of two (Kotter 1991:8) or four (Kala 1998:31) burgomasters and fourteen aldermen procured for their lifetime. Aldermen and burgomasters received no material reward or exemptions for their service. Only the members of the Grosse Gilde could be elected to the magistrate. Aldermen were divided into 11 different sub-courts (Kala 1998:31), which dealt with town affairs in provision of public goods such as the court, military defence, diplomatic relations, minting, and various constructions. Kotter (1999:76) describes the cost structure of the magistrate during 1433-1705, showing that 70% of the town revenues were spent on salaries (usually non-monetary rewards like clothing and footwear) of the magistrate-craftsmen and on other services (such as musicians), court expenses, defence, “foreign affairs” (such as travel costs, gifts, official dinners, letters and translations), building, and renovation. Already in 1282, the Lübeck town law of Tallinn stated that each burgher had to contribute an annual personal tax (Kala 1998: §108). The tax consisted of two parts – the poll tax (a fixed tax of 1/12 of a Riga mark) and property tax approximately 4-5% of the value of property. The largest part of revenues came from excise taxes on beer (levied in 1454 (Jatruševa 1986:36)) and on export of wine and stones (Kotter 1999). This shows that the town earned directly 15-20% of total revenues on export excise. The wealth of the burgher (the value of his property) was dependent on efficient trade relations, while reliance on trade in town fiscal affairs is obvious. Over time, the magistrate increasingly started to coordinate the internal and external affairs of the town, while social ties between the magistrate and merchant guilds were tight. Dollinger (1970:135) states that government in some towns was practically a family business. This is not true in our case; in Tallinn it was prohibited to elect brothers, fathers, or sons to the magistrate. The aldermen received no salary for their duties, thus had simultaneously to maintain their merchant activities. Aldermen were elected from among the grosse gilde brothers, mostly from among brothers who had been an alderman or an assessor of the guild (this indicates that before election they had been members of the merchant guild approximately twenty years (Mänd: 2005:180)). An alderman of the magistrate remained a member of the guild; he did not participate in all of the social events of the guild, although he did participate in the guild’s most important festivals and feasts. It can be assumed that in the early days of the Hanseatic League, merchants gave an oath of commitment to honest trade, including to pay all customs and tariffs (Hammel-Kiesow 2008). Eventually, certain trade norms called Schras were agreed upon. All of the exclusive rights of “common merchant” were granted by citizenship of Hanseatic towns. Becoming a burgher (citizen) simultaneously meant becoming a member of the guild (Mänd 2005:141, and also Lübeck town Rights 1282). No earlier Schras (if they existed at all) are preserved, but the 1395 schra does not indicate any direct trading principles of the guild, but rather states the requirements for guild brotherhood (and sisterhood). “Everyone who belongs to our guild must be honest and trustworthy” (Nottbeck 1885:40); moreover, it is mentioned that un-
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til all conflicts between guild brothers or others (it can be assumed that “others” included Hansa merchants) are resolved, a person would not belong to the guild. The Schra states that those who “desecrate” contracts will be ostracized. A 1541 Schra includes a paragraph stating that “if somebody violates general trading principles of the guild, he has to be expelled from it” (Nottbeck 1885:61§93). This is worth stressing, as the rights of “common merchant” related to town citizenship and membership in the guild. The Schra also indicates that a ban was imposed on any illicit trading by non Hansa merchants, and guild members could not belong to other guilds in the same town (Nottbeck 1885:§57). The implication is that the “guild label” was seen as a guarantee of honesty of trade. Ostracising problematic local traders and acceptance (or at least an invitation to guild “club-activities” (Mänd 2005:170)) of all Hansa merchants from other cities justifies our model. The oral culture enforced institutions based on credibility and trust, and reputation building was the most important aspect in efficient enforcement. Most legal procedures relied on oral witnesses, on a social network based on reciprocity of credible commitment to “tell the truth”. Although only more recent (from the 17th century) cashbooks of the Grosse Gilde have been preserved and although the Schra from 1395 does not indicate fees other than penalties (mainly fines) related to misbehaviour, it still may be assumed that guild brothers paid an entrance fee and a quarterly lump sum membership fee (poll tax). “Project specific” payments (e.g. related to building the guild house 1406-1417 as well as fees for annual feasts and festivals) were also probable. As the cost of building the guild house, organizing feasts, social security and care, was considerable, it can be assumed that the membership fee was relatively high. The next section will overview the gradual decline of the Hanseatic League for “political” reasons. As the benefits from Hansa trade declined, the guild brothers’ income decreased and the intrinsic reasons for the existence of the Grosse Gilde changed. Protection of the town’s interests against the interests of Hansa merchants or even against other guilds in the town become more important, and membership become increasingly exclusive.
6. Arise of rent-seeking guild: developments from the 1450s to the 1520s Although Tallinn was still flourishing in the fifteenth century, the period was already marked by the gradual decline of the Hanseatic community. During this period, monarchical power was being consolidated in Northern Europe, increasing the town’s cost of compliance with the Hansa. Ivan III annexed the great urban republic of Novgorod in 1478. This ended the functioning of the kontor because the Muscovite empire was hostile to foreigners. The private benefits that the Livonian towns and merchants individually gained from eastbound trade initiated the gradual monopolisation of trade. In 1422, Tartu started to control the kontor and decide who could trade with Novgorod, and in 1459 Riga stopped all foreigners, including Hansa merchants, from trading directly with Polotsk (Dollinger 1970:294). These changes in economic policy and foreign affairs created a bankruptcy wave in Tallinn before the Reformation (Margus 1939:87). Later, in the 1520s the Reformation began almost at the same moment in all the North German towns. At first, town councils were hostile to the new religion: in 1525, the diet of
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Lübeck even passed measures against teaching Luther’s doctrine, but only a few delegates approved it (Dollinger 1970:320). The second diet of the year proclaimed that in religious matters, each town had to decide for itself (Dollinger 1970:321). The Reformation made social relations in Tallinn more intense – in the 1530s, conflicts between merchants and artisans often took place (Põltsam 2003:21). In 1526, the craft guilds of Tallinn decided that all who did not support the new religion, and in some cases, even those who visited Catholic masses, were expelled from the guild (Põltsam 2003:22). The reformation, teaching equality of humans in the eyes of God, encouraged craftsmen to defend their rights against the magistrate and patriciate (Margus 1939:88). Guilds became increasingly nationally and socially segregated. This indicates that guilds gradually changed their role in the urban community and became unions to protect artisans’ economic interest against merchants and vice versa. Based on our model and sources (Hammel-Kieslow 2008; Pagel 1942), it can be assumed that, originally, merchant guilds did not restrict access by nationality. Over time, merchant affairs became increasingly institutionalized, merchants were no longer travelling much, and the merchant guild started accepting members from lower social classes to do the travelling for them. Access to town citizenship was, however, restricted for the lower social classes, as one had to live in the town for at least a year and have recommendations in order to become a citizen. In later periods, access to the guild became increasingly restricted and regulated. Estonians were not accepted into the Grosse Gilde; later, the restrictions applied to everyone who worked for a salary, and finally they also came to apply to local shopkeepers (Mänd 2005:167). Eventually, non-Germans or workers could not even visit the guild house (Mänd 2004). In the 1528 Schra, an additional paragraph stated that issues discussed in the guild house could not be shared with outsiders 1885:45). x( p − 1) x( p(Nottbeck )
−u
1 − δ embodied in the formal 1 −rules δ of town culture, the A century after the creation of social norms role of guilds in the social life of the town had become ever greater, and we can assume that compliance with guild regulations was high. Therefore the enforcement costs of sanctions Figure 4. Optimal versus insiders’ benefits from the protecting organization
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(cs) were low. Individual enforcement costs were now primary direct costs related to the membership fee. Thus average cost (c) was constant. The members’ total benefits from optimal group size nˆ are now smaller than the individually optimal group size n* (see Figure 4). Figure 4 indicates that each individual had to pay a membership fee (c) for entry to the club, and thus we are dealing with a fixed average cost. The total benefit G(n) is now a decreasing function due to the competitive provision of an almost private good now – the bigger the group, the less beneficial is the cartel to a single member. Thus each individual has an incenG tive to become a member of the guild as far as n +1 > c , and this condition is satisfied dG when we reach n*. The insiders’ total benefit is maximized when dn = c , meaning that the optimal size of the group is nˆ . As far as n* > nˆ , insiders have the incentive to limit entry to the guild, as in all monopolistic cases. Is there any additional evidence as to restrictions on entry and increasing secrecy to justify our interpretation? There is some, for example dress codes. Segregation by dress is one additional measure similar to entry restrictions and this was common. However, there is no evidence that dress norms created any tensions in Tallinn before the 15th century. The first sumptuary law regulating the costs of female jewellery was implemented at the end of the 15th century (Põltsam 2002). It is difficult to assess whether it was the relative cost of clothing that did not make sumptuary codes relevant earlier, or if it was relative compliance with informal dress-codes that was higher until the end of the 15th century. Dress-norms enforce segregation. It is clear that at the beginning of the sixteenth century, urban societies became increasingly formally regulated. However, after 1541 no additional paragraphs were added to the Schra of the merchant guild concerning honest trade.
7. Conclusions and discussion More than 100 years is a long period for a case study. Regarding the limited number of original sources and our ability to perceive the historical context, we assume that the Merchant guilds’ social and economic role in society changed considerably over time. Initially, it was a risk-pegging agency for itinerary travelling merchants, then increasing its social role and finally becoming a rent-seeking cartel. We demonstrated that initial institutionalisation of merchant affairs was self-enforcing by supporting trade and thus also economic growth. In our case we can say conclusively that by the middle of the thirteenth century the Hanseatics already held a near-monopoly in trade in two seas, and their commerce was organized around the great axis Novgorod-Tallinn-Lübeck-Hamburg-Bruges-London, also enlarging later to southern Germany, Italy, France, Spain, and Portugal. At the same time, the Hansa remained an anomalous institution which can puzzle contemporary political scientists and economists: It was not a sovereign power, for it remained within a framework of Empire and its members continued to owe some measure of allegiance to many different overlords, ecclesiastical or lay. It was an amorphous organization, lacking legal status, having at its disposal neither finances of its own nor an army or a fleet. It did not even have a common seal or officials and institutions on their own except for the Hanseatic diet or Hansetag, and even then met rarely, at irregular intervals and never in full strength (Dollinger 1970: xvii).
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In spite of the structural weaknesses and the conflicting interests inevitable in an association of towns so different and so distant from another, the Hanseatic League lasted for nearly five hundred years. In 1630, a closer alliance was set up between Lübeck, Hamburg, and Bremen as a substitute for the Hanseatic League. In 1669, the Hanseatic diet met for the last time, and a final attempt at restoration proved unsuccessful (Dollinger 1970: xix). The secret of its longevity is not to be found in coercion, but in the realisation of common interests binding the members of the community together. The common interest was based on voluntary cooperation, and was not easy to enforce under medieval technological, informational, and institutional constraints. Our study indicates that the guild acted as a substitute for formal state or legal institutions. While “common knowledge” among economists often views guilds as monopolistic cartels or rent-seeking organizations, Merges (2004) states that guilds were efficient information transferees solving the asymmetric information and quality assurance problems in medieval society. Greif et al. (1994) describe merchant guilds as organizations vital for efficient trade. Both the rent-seeking argument and the credible commitment argument demand that organizations fulfil certain criteria: (a) the segregation principle (differentiates insiders from outsiders); and (b) shared norms by insiders (Merges 2004:4). We have seen that the role of segregation increased in importance throughout the history of the merchant guild. Various social norms of segregation – by clothing, by profession, or by nationality – eventually appeared. In order to be self-enforcing, norms have to be beneficial to follow. Merchants benefited from an intercommunity exchange that included common merchants, but excluded all local shopkeepers and craftsmen, and eventually all non-Germans. Traders applied a principle of community responsibility that linked the conduct of a trader and the obligations of each and every member of the community. Through decreased trade volume, the “community label” made all the merchants from a certain community partly responsible for the damage created by anyone inside the circle. Greif et al. (1994) also confirm that communal punishment or sanctions became a credible threat and traders were able to use intra-community enforcement mechanisms to support the inter-community exchange. The system created the need for restrictions on membership due to cost considerations which led to constraints such as membership by nationality. The restrictions defined communities and fostered their internal organization like other norms of identification, such as clothing and rituals. Institutional studies reveal a variety of reasons that lead to institutional change, but we believe that a change in private benefits had an important role in this gradual change. In institutional literature, the most elaborated cause of change is called the “critical juncture” (Pierson 2002, Rittberger 2003), which can be an unanticipated technological change (Guinnane, 1994), political changes (Greif et al. 1994), or population increase (Hoffmann et al. 1994). In our case, private benefits changed mainly due to changes in the political situation brought on by the closure of the Novgorod kontor. Whereas most institutional studies concentrate rather on path-dependency or on the inability to change, we also note the gradual decline of the Hansa, and the gradual transformation of the function of the guilds. The most elaborated causes of path-dependency are “cultural beliefs” related to some institutional settlement (Greif 1995:23), and vested interest or assets specificity (Pierson 2002:205). When the guild as an organization was firmly established and functioning, then the new role of protection of economic rights was a logical continuum under changed economic and po-
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litical circumstances. The guild became beneficial for a narrow circle of insiders, partly at the expense of outsiders. The days of the positive-sum game ended for Livonia and Tallinn. The most severe criticism of institutional studies is presented by Clark (2007), who states that narratives do not allow for demonstrating universal knowledge or any policy recommendations, because everything depends on the specific cultural and historical context. This has also been duly noted by one of the promoters of the method, Margaret Levi (2002), who calls the (in)ability to make generalizations the Achilles’ heel of the method. Our case suffers from the same limitation, but we believe that although the specific game may not be totally portable it does yield explanations that can be tested in different settings. As Levi (2002:16) states, “The comparisons can be done by other area specialists, historians, and others who must conquer languages, archives and other sources to acquire in-depth authority over the subject matter”. That is why it can be said that demonstrating generalisability may rest on the wider community of scholars. We also encourage comparative studies about the impact of similar contemporary institutions. Will political change or the current turmoil in the world economic climate transform our current “guilds” – national or other – into organizations hostile to new entrants? What about international “guilds” like the EU?
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Industry relocation, linkages and spillovers across the Baltic Sea
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Industry relocation, linkages and spillovers across the Baltic Sea: extending the footloose capital model Ole Christiansen, Dirk H. Ehnts, Hans-Michael Trautwein 1
Abstract Studying the recent relocation of manufacturing industries from the Nordic countries to the Baltic countries, this paper provides an empirical application of the footloose capital model, a framework for spatial analysis. The model is extended to include input-output linkages and FDI spillovers. It is calibrated and applied, industry by industry, to a 3x3 matrix of Baltic countries and Nordic countries and to the pairing of these blocs. Simulation results are compared with ‘real world’ data and discussed in regard to testability restrictions of the footloose capital model. Incorporation of vertical linkages and spillovers can improve the goodness of fit, but while the predicted direction of industry relocation is often correct, predicted levels are not. Keywords: new economic geography, footloose capital model, linkages, spillovers JEL classification: F120, F140, R120
1. Introduction In the context of integration of the Central and Eastern European accession countries into the European Union, expectations are high that these countries will achieve faster economic development through inward foreign direct investment (FDI). It is generally argued that the relocation of productive activities from Western economies would create a more vertically differentiated structure of production in Eastern economies, with upstream and downstream linkages between the sectors. This process could generate economies of scale and scope that help to increase aggregate employment, income, and demand. It is also argued that productivity of local firms would be enhanced by FDI through technology spillovers that come with cooperation and imitation. In general, the ‘footloose capital’ (FC) model of location theory, developed by Martin and Rogers (1995) and extended by Ottaviano et al. (2002) and Baldwin et al. (2003, chs 3 and 5), is a suitable framework for studying the causes and effects of cross-border flows of direct investment. Unlike many other models in the domain of the ‘New Economic Geography’ (NEG) it is analytically tractable and amenable to empirical studies. However, the stan1 Fak.II-VWL, Carl von Ossietzky Universität Oldenburg, 26111 Oldenburg, Germany. Contact:
[email protected];
[email protected];
[email protected]; http:// www.uni-oldenburg.de/iw
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dard formulations of the FC model do not capture the linkages and spillover effects of FDI operationally. Following Venables (1996), intermediate goods are typically defined as CES aggregates of all varieties of the industrial good (see also Brakman et al. 2009, p. 151). For reasons of technical tractability, it may be practical to let all varieties of the final product be inputs in each others’ production. However, this procedure implies that imperfect substitutes are simultaneously treated as complementary goods, both upstream and downstream. This blurs the perspective on changes in output and productivity in a vertical production structure and is not compatible with input-output data. The first objective of this paper is therefore to extend the FC framework by modelling the structure of vertical linkages less restrictively and empirically more operationally. The second objective is to integrate FDI spillovers. In the NEG literature, spillovers have been modelled in the static terms of Marshallian externalities of regional specialization (Fujita and Thisse 2002). Here we set the focus on dynamic spillovers generated by cross-border movements of firms. While such effects have been examined in empirical literature outside the NEG domain (see, e.g., Kugler 2006, Javorcik and Spatareanu 2009), they have not - to our knowledge - been explicitly integrated in the FC framework before. The third objective of our paper is to study industry relocation in the Baltic region of the EU in terms of cross-border movements from the EU-Nordic countries (Finland, Sweden and Denmark) to the Baltic States (Estonia, Latvia and Lithuania). We have chosen these two blocs of countries because we think that empirical application of the FC model extended to include linkages and spillovers generates insights into the dynamics of economic development in the Baltic region. At the same time, the characteristic mix of similarities and differences within and between these blocs is particularly suitable for bringing out the dynamics at the core of an extended FC model that includes linkages and spillovers. Why is the Baltic region a suitable test case for application of the extended FC model? The six countries form two distinct groups of three in neighbouring regions around the Baltic Sea. They all have relatively small populations, compared to Russia, Poland, and Germany, the other neighbours on the Baltic shore. Until 1991, the two groups were quite strictly separated in political and economic terms. The Baltic States were part of the Soviet Union, and trade and investment flows between the Baltic States and the Nordic countries were small (Laaser and Schrader 2004). This changed during the 1990s, when the Baltic States (henceforth: the Baltics) regained their independence and started their transformation into Western-style market economies. At least two out of the three EU Nordic countries (henceforth: the Nordics) are now among the largest trade partners for each of the Baltics. And the picture is even more impressive in terms of FDI. The Nordics are the biggest investors in the Baltics. As of 2009, they together account for about 65 per cent of inward FDI stock in Estonia, nearly 27 per cent in Lithuania, and nearly 25 per cent in Latvia; outward FDI from the Baltics to the Nordics is negligible. All six countries are members of the European Union. At the beginning of the observation period (in the mid-1990s) all of them had at least an accession perspective. Despite some convergence in the integration process, both groups remain distinct subregions of the EU, especially with regard to their income levels. In 2007, the end of our observation period and the beginning of the great financial crisis, Sweden’s GDP alone was more than five times
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larger than the aggregate Baltic GDP. In the same year, the Nordic per capita income (PPPadjusted average) was roughly twice the Baltic per capita income. While the Nordics form the richest subregion in the EU, the Baltics are among the poorest subregions. However, between 1995 and 2007 per capita incomes rose by factor 4 in the Baltics, as compared to factor 1.8 in the Nordics. Finally it should be noted that, even though the outside world tends to regard the Nordics and the Baltics as homogeneous groups of countries, the industry structures and other characteristics (cultural and historical backgrounds) of the six economies are quite heterogeneous. What do these facts imply for analysis of industry relocation in NEG terms? Trade costs arise for exports of goods from one region to the other, but they are not likely to be prohibitive, as distances across and around the Baltic Sea are comparatively short. Nor are trade costs likely to make a strong difference between the countries. EU integration forced countries to bring their economies under the rule of the acquis communautaire. Political barriers to trade and investment - another component of trade costs that would complicate the picture - were thus relatively low and decreasing. Moreover, EU membership (or the prospects of it) should imply some coherence in the data sets. That the Baltic and Nordic countries have relatively small populations means that they are relatively similar in potential market size - at least in comparison with the other neighbours (Russia, Poland, Germany). This symmetry makes complete agglomeration of industries in one region or country less likely to occur.2 In terms of actual market size, on the other hand, the Baltics and the Nordics are quite different, as GDP and other income figures show - and the differences were even greater back in the 1990s. This indicates a high potential for capital flows from the Nordics to the Baltics. The dominant role that the Nordics play in the inward FDI stocks of the Baltics shows that such capital flows actually take place. Last but not least, the transition process of the Baltics can be considered as a policy experiment of opening markets. This beds for dynamics in location choices that one would not normally see in more tranquil times. All facts taken together suggest that the two regions should trade with each other to a degree that makes changes in trade composition and the spatial distribution of industrial activities observable. The Baltics and the Nordics appear to be an almost ideal case for testing the predictive powers of the FC model. The structure of the paper is as follows. In the second section we describe a linear version of the FC model, adapted from the earlier literature. Due to its highly stylized character and to limitations in the data bases, we cannot test the FC model with conventional econometric methods. Yet we use real data for the relevant variables to run simulations of relocation in the manufacturing industries, both in the Nordic-Baltic aggregate and in a 3x3 matrix for the Nordic-Baltic country pairs. In the next section we describe the simulations and present the results for relevant industries. In the fourth section we check on the predictions of the FC model by confronting simulation results with statistical indicators of industry relocation in terms of exports and FDI stock. In the next section we extend the basic setup of the FC model and simulation to include spillovers, and in the following section we do the same for linkages, 2 As explained in the next section, the footloose capital model is an appropriate framework for analysis of this constellation, since the exclusion of various non-linearities makes it less prone to extreme solutions than core-periphery models. Yet even in the FC model, strong differences in market size tend to generate extreme solutions.
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in order to see whether these extensions increase the predictive quality of the simulations. In the last section we conclude that positive FDI spillovers could not be detected, whereas the linkage effects appear to be significant.
2. A Linear Footloose Capital Model NEG has developed a variety of models to simulate location of industries. The choice is between models, where workers, capital owners, or just capital units are on the move. For our purposes, a model with capital flows appears to be the best choice, since factor movements in terms of capital appear to be much stronger than labour migration across the Baltic Sea (Kielyte and Kancs 2000). Two alternative frameworks with capital flows are the ‘footloose entrepreneur’ and the ‘footloose capital’ models. In the former setup, owners of firms move along with capital, i.e. they consume their income in the region where their firms are (re-)located. We rule this out on the basis of realism. Under present conditions, not many shareholders of multinational enterprises that invest in the Baltics move there. Hence we choose the FC model, in which only capital moves, but not its owners. Moreover, we use the linear setup of the FC model in order to avoid the problems that would come with bifurcations, hysteresis, and other complications of non-linearity. The framework permits us to deal with non-symmetric regions, such as the Nordics and the Baltics. Two opposite forces create incentives for profit-maximizing firms to move to or leave a region, with the additional influence of efficiency: the positive profit effect of market access and the negative profit effect of market crowding. That way firms will not (except under extreme conditions) agglomerate completely in one region. The smaller region might have a smaller market, but it offers a less competitive environment. Therefore, some firms find it more profitable to locate in a small region. Efficiency also influences firm location in so far as lower costs translate into higher profits. The FC model is a 2x2x2 model with two regions, two factors of production and two sectors - and hence two goods (Baldwin et al. 2003, ch. 5). In our case, the regions are called the Nordics and the Baltics. The two sectors are agriculture A and manufacturing M. Factors of production are labour L and capital K; their owners are in both cases assumed to be geographically immobile. Physical capital can be moved costlessly from one region to the other. So sn, the share of’ world’ capital employed in the Baltics, is allowed to differ from sK, the share of world capital owned by residents in the Baltics. sL is the share of the world endowment of L that is employed in the Baltics. Capital movements across the regions are generated by capital owners’ search for the highest nominal profit r. Since the owners spend their capital income in the region where they live, price index changes in the region where the capital is employed are of no direct concern. This means that even if more capital becomes available in one region through faster growth or a rise in saving, the decision where to put that capital to work is made by the workings of the FC model. Remember that the resulting sn gives us the relative and not the absolute share of industry. Capital flows are modelled through the standard ad hoc’ migration’ equation (see Baldwin et al. 2003, ch. 2). This yields a change in the Baltic share of employed capital, sn. In the following, Nordic variables are denoted by asterisks.
(1)
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The structure of the agricultural sector is kept as simple as possible. The sector supplies a homogeneous good, the A-good, under constant returns to scale, using labour as the only input. The sector is perfectly competitive, and unit costs are eAw, where eA is the technology parameter and w the wage rate. Agricultural wages in the Baltics w and Nordic w* are not equalized, but agricultural goods can nevertheless be traded. The agricultural good A serves as numeraire and it is assumed that eA =1. In the original linear footloose model, workers can switch between industries, which equalises wages. Here, we allow wages to differ between regions. The manufacturing sector is monopolistically competitive. A range of n firms produces a differentiated M-good of n varieties, such that the total number of varieties (across the two regions) can be written as nΩ = n + n*. The firms use K exclusively as a fixed-cost input F (e.g. for setting up a factory), while L is the sole variable input. The total cost of producing x units of a variety of the M-good can thus be expressed in terms of a linear function with fixed and variable costs, F + (w / eM) x, which captures increasing returns to scale. Exports of the M-good to the other region are costly. It is assumed that the costs of shipping one unit of the M-good between the Baltics and the Nordics are τ units of the A-good, and that they are paid by the exporting region. Preferences are described by the quasi-linear quadratic utility function:
(2)
where α > 0,β > δ > 0, xi is consumption of variety i of a manufactured good, and CA is consumption of the agricultural good. Preferences in the two regions are identical. Utility optimization produces linear demand for the typical variety j of the manufactured good:
(3)
with
(4)
Demand depends on the own price, ρj, and on the average price P of other firms. Income does not influence demand due to the special form of the utility function.3 Demand for the agricultural good is determined as a residual. Total demand is (3) multiplied by the number of consumers. The typical firm in the Baltics thus maximises total profits as follows:
(5)
3 Baldwin et al. (2003, ch. 5) argue that neglect of income effects has little impact on the logic of agglomeration, and that this form of utility function helps to avoid the exaggeration of market size differences that tends to occur in models with CES utility functions.
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with
(6)
H and H* are the numbers of consumers in each region and p and p are the prices that Baltic firms charge in their home markets and in their Nordic export markets respectively. As firms are assumed to compete in prices, consumer prices of domestic and foreign varieties in the Baltics are:4
(7)
Equilibrium prices are not simply mark-up prices as in other NEG models, but depend on spatial distribution of firms. The reason is that trade costs protect local firms from foreign , such that competitors. It is assumed that trade costs fulfil the condition there can be two-way trade. Dividing firms’ operating profits by the invested F units of capital yields the rate of profit:
(8)
The equilibrium number of firms is determined by:
(9)
In the long run, capital is mobile between regions. As firms are defined by capital input, the distribution of capital and firms is identical. Long-run equilibrium is characterized by a noarbitrage condition, stating that there are no more profit incentives to move:
(10)
The first expression holds for interior equilibria where manufacturing firms are located in both regions, whereas in the other cases all industry would agglomerate either in the Baltics (n=1) or in the Nordics n*=1. Substituting (8) into (10), using (7), yields the profit differential:
(11)
Equation (11) closes the model. The profit differential determines the change in the Baltics’ share of employed capital as described by equation (1).
4 Admittedly this setting of prices implies the possibility of dumping; for a discussion see Ottaviano et al. (2002), p. 417.
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3. Simulation The FC model is a highly stylized story in which the world is represented by two regions (or countries), two types of goods, and two production factors. It may be doubted whether it makes sense to run ex-post simulations with this model using data from a sample of 22 out of 99 NACE sectors (group D - manufacturing), of 6 out of 27 EU countries (not to speak of the rest of the world), and with capital flows confined to FDI. There are at least two possible mechanisms of industry relocation. The first works via FDI from one region to the other (though not all statistically observed FDI implies relocation). The second works via ‘independent’ exits of firms in one region and entries of firms in the other; for example, firms in the Nordics would close down, because they cannot compete with firms in the Baltics. If the Baltic newcomers are financed with loans or portfolio investment from Nordic capital owners, we have a second variety of FC. This is considerably more difficult to identify in the data. Yet we think that our simulation exercises yield some valuable insights into the potential as well as the difficulties of carrying out empirical studies based on the FC model. We have run our simulations industry by industry, bloc-wise for the regions (Nordics - Baltics in the aggregate), but also for all Nordic - Baltic pairs of countries (which gives us nine cases). The partial-analysis character of the FC model, which derives from the suppression of income effects through the choice of a quasi-linear quadratic utility function, may be considered as a general drawback of the model. Yet here it is an advantage, because it permits us to look at changes in the Baltic shares of employed capital in each industry in isolation. The simulations are based on annual data, as no higher frequency was consistently available. The simulation results are driven by the parameters, some of which are permanent while others are time-specific. Table 1 lists the parameters of the FC model. The list is not complete, as some parameters are eliminated by way of normalization, while others are only of technical importance. Parameters a, b, and c, which are not listed above, determine the love for variety. The endogenous variable in the simulation is the relative share of capital employed in the Baltics, sn. Table 1. List of parameters
Parameter τ s K sL eM w K Ω L Ω
Explanation trade costs between regions Baltic share of owned capital, K Baltic share of employed labour, L productivity of Baltic manufacturing production (by year) wage in Baltic region (by year) absolute value of total capital absolute value of total labour
Source: own table
We do not change trade costs from year to year in our model in order to avoid results being driven by their changes. For the endowment parameters sK, sL, sn, KΩ, and LΩ, we have used data for capital formation and labour force that are available from national statistical offices. Table 2 shows Baltic shares of the combined labour force in 1995, pairwise, related to Nordic
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countries. Hence, the share of Estonia in the combined labour force of Estonia and Denmark is 18.4 per cent. Due to asymmetries in the size of populations in the two blocs, the calculated shares do not vary much from year to year; therefore, we take 1995 labour shares as the basis for all calculations, given that our simulations start with the following year. Table 2. Actual labour shares of the Baltics in the total of Nordics and Baltics, 1995 Denmark (DK)
Finland (FI)
Estonia (EE)
0.184
0.204
Sweden (SE) 0.127
Latvia (LV)
0.273
0.300
0.195
Lithuania (LT)
0.365
0.396
0.271
Source: Eurostat, own calculations
For analytical convenience we assume that the distribution of owned physical capital is equivalent to that of labour, sK = sL. There are, of course, large differences in owned capital stock between the Nordics and the Baltics, due to differences in both the sizes of their economies and their histories. Yet the purchasing-power adjusted incomes from capital, which matter here, may not be all that different. It might also be objected that differences in capital endowment actually induced the capital flows (FDI and others) that could be observed during the transition process. However, in the context of the FC model, spatial distributions of capital ownership and capital usage are detached from each other, and shares in capital ownership affect market size only weakly through consumer demand. The global endowment-value parameters, KΩ and LΩ, have also been simplified. KΩ was normalized to 1. In this way, sn becomes an expression for both the share and the absolute number of firms. LΩ is set to 15, so that the share of the manufacturing sector in total GDP amounts to about 20 per cent. Even though this setting is unrealistically low, it is required in order to keep the model open for the possibility of full agglomeration, so that the A-sector is big enough to pay for transport costs. Note that while endowments matter for the demand side, wages do not. Discussing the location of single industries in a partial-analysis perspective, it is assumed that wages matter only as a cost factor, and hence exclusively on the supply side. The last parameter is the industryspecific technology parameter eM. Together with wage w it determines comparative advantage in the model through Ricardian differences in technology. In the real world, wage differences between the Baltics and other EU regions have played a major role in explaining relocation of industries to the accession countries (EU Commission 2003). Data for wages and productivity are taken from Eurostat as available for the period 1997-2007. Industries are represented by NACE groups. Data gaps in some Baltic cases before 2000 were filled using estimates. Whenever plausible, we used average annual growth rates calculated from available values. In all other cases, gaps were filled by the productivity figures of the following year. Based on these parameter definitions, the simulation yields the Baltic share of employed capital, sn. Its changes over time indicate industry relocation. The variable sn can be calculated for every NACE group. We think it insightful to select one industry for evaluation of the model.
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We have chosen NACE 20, the wood industry,5 because it is the only sector that shows significant output in all six countries and significant FDI stock in all three Baltic States. This choice might seem odd, since endowments play a role in this industry. However, Moroney (1975) shows that labour costs and resource endowments are complementary explanations for trade. Endowments of wood are not exhausted but managed, since it is a renewable resource. Also, the wood industry comprises more than wood cutting (but stops short of furniture). Table 3. Equilibrium values of Sn in industry NACE 20 in per cent, projected and real (wood and wood products), Baltic aggregate Countries 1997 1998 projected 20.6 20.8 real 5.4 6.2 Source: own calculations
1999 20.8 7.2
2000 19.9 8.9
2001 21.5 10.0
2002 21.5 11.6
2003 21.2 15.5
2004 21.2 13.9
2005 21.2 15.1
2006 20.8 15.0
2007 20.6 15.6
Table 3 shows the results for the bloc simulation, where the Baltics are aggregated as one region and the Nordics as the other. Here sn denotes the aggregate Baltic share of total industry. Between 1997 and 2002, the equilibrium share of the wood industry in the Baltics rose from around 20.6 to 21.5 per cent. Given that the adjusted share of labour, sL, in the Baltics amounts to 26 per cent, the result does not seem to indicate the expected catching-up. The result does not compare too well with reality, which is shown in the second row. From 19972002 we see some catching-up, while from 2003-2007 the Baltic share of industry stagnates. Table 4 reports the results for the 3x3 country-wise simulations. These are shown in the upper half of the table. For example, the equilibrium industry share of Estonia in relation to Denmark is 9.4 per cent in 1997, rises slightly to 10.9 in 2002, and falls to 9.2 in 2007. The real share of industry is calculated by dividing the Baltic country’s production by the combined production of the two countries and is given in the lower half of the table. We see an increase in the share of the Baltic country vis-à-vis the Nordic country in all rows. This increase is much more pronounced in the real world data than in the simulated data. It should be kept in mind, though, that the simulations pertain to long-run equilibrium values, whereas the real data also reflect short-run fluctuations. A major cause for discrepancies between the simulated and real data is certainly to be found in the foreign borrowing that took place in the Baltics prior to the great financial crisis, which started in 2007. Foreign borrowing increased local demand and corresponding wage rises, almost a doubling of wage levels between 2002 and 2007, entered the simulations with the effect of pushing industry away from the Baltics, when in reality it tended to pull industry towards these countries due to a relative increase in demand. It should be noted that there is a certain trade-off between applicability of the FC model to real world data, as compared to NEG models that include demand effects in terms of circular causation, and the neglect of demand effects in the FC model. On the other hand, the statistical demand effect of 2003-2007 should not be overrated, since a large fraction may be of a transitory nature. The partly drastic adjustments in the external accounts (in particular of Latvia) that took place in the wake of the global credit crisis, may - once the post-2007 data become available - change the picture towards stronger parallelity in the real data and the simulation results. 5 NACE 20 includes manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials.
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Table 4. Simulated equilibrium and real values of sn in the industry NACE 20 (wood and wood products) in per cent Sn proj EE-DK EE-FI EE-SE LV-DK LV-FI LV-SE LT-DK LT-FI LT-SE Sn real EE-DK EE-FI EE-SE LV-DK LV-FI LV-SE LT-DK LT-FI LT-SE
1997 9.4 8.6 0.0 24.3 24.5 11.8 30.7 34.4 15.9 1997 11.9 4.8 3.1 17.2 7.2 4.7 9.4 3.7 2.4
1998 9.3 10.1 1.6 24.1 25.9 13.5 30.5 32.8 17.6 1998 13.8 5.7 3.9 19.7 8.5 5.8 8.6 3.5 2.3
1999 9.5 10.4 1.7 24.3 26.2 13.6 30.7 33.1 17.7 1999 16.3 6.6 4.5 22.9 9.7 6.8 10.3 4.0 2.7
2000 9.4 10.1 1.8 23.7 25.4 13.3 30.6 32.8 17.8 2000 18.5 7.3 5.2 28.2 12.0 8.7 13.7 5.2 3.7
2001 10.0 12.7 2.8 27.7 31.4 17.7 34.5 38.7 22.2 2001 21.9 8.9 6.7 27.8 11.9 9.0 16.4 6.4 4.8
2002 10.9 12.7 2.6 25.1 27.9 14.0 33.0 36.3 19.5 2002 27.3 11.0 8.2 31.5 13.2 10.0 19.9 7.6 5.6
2003 10.6 12.0 2.5 25.3 27.7 14.3 34.0 36.9 20.7 2003 29.3 11.7 8.8 33.7 14.0 10.6 22.2 8.3 6.2
2004 9.8 11.8 0.0 24.7 27.7 15.2 33.5 37.0 21.7 2004 31.5 13.1 9.8 35.9 15.5 11.7 23.8 9.3 6.9
2005 8.5 11.3 1.7 25.4 29.3 15.8 32.9 37.2 20.9 2005 32.8 14.7 10.9 35.9 16.5 12.3 25.5 10.8 7.9
2006 9.3 10.3 0.6 24.1 26.2 12.6 32.0 34.5 18.1 2006 33.9 13.6 11.1 37.8 13.2 12.8 26.4 9.9 8.0
2007 9.2 9.0 0.0 24.3 25.1 11.0 30.8 32.1 15.2 2007 35.3 14.2 11.0 40.0 16.8 13.1 28.6 10.8 8.3
Source: own calculations
With respect to the question of bloc versus country simulations, we find that results vary. If countries are grouped in a bloc, much valuable information is lost due to aggregation. The correlation coefficient is just 0.34 for the bloc-wise simulation, lower than any of the countrywise simulation coefficients. The country-wise simulations yield results that in most cases fit reasonably well.
4. Foreign Direct Investment and Exports To check the predictions of the FC model, we confront them with statistical indicators of industry relocation. As discussed in earlier sections, we assume that a positive change in sn corresponds with a significant inflow of FDI6 in the respective industry, and that a high value of sn correspondingly implies a relatively large stock of Nordic FDI in the Baltics. Moreover, an increase in sn should normally go along with a rise in exports of the same industry. Finally, comparing the changes in sectoral output in both regions should also give a clue about the validity of the model. Here we set the focus on the wood industry in Latvia, as sectoral FDI data were available only for this country, in time series that are consistent only until 2004. As Table 4 has shown, the model predicts industry relocation from all three Nordic countries to Latvia in the years 1997-2003. The extent of industry relocation seems to be the biggest in the case of Sweden. Firms from both Finland and Denmark are predicted to relocate at a lower rate. In absolute terms, industry relocation from Denmark to Latvia is most significant. 6 There are different types of FDI, such as greenfield FDI, M&A, and joint venture; not all of them are connected with industry relocation. However, disaggregated data were not available.
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If we compare this with the data displayed in Figure 1 we see that the model does not fare too badly, at least in regard to signs of change. Exports and FDI increased in relation to all the Nordic countries. Figure 1. Exports (continuous line, left scale) and FDI stock (dotted line, right scale) in the Latvian wood industry, in million euros7 250
net exports DK FDI stock DK
net exports FI FDI stock FI
net exports SE FDI stock SE
200
150
100
50
0 1996
1997
1998
1999
2000
2001
2002
2003
2004
Source: Central Statistical Bureau of Latvia
Rates of change, on the other hand, differ significantly. Danish FDI has increased more strongly than predicted, in line with exports to Denmark. Exports to Sweden rose much more (in absolute terms) than FDI from Sweden. Exports to and, in particular, FDI stock from Finland are much smaller than one would assume from the simulation. These differences are certainly explained by market structures not captured by the model, and probably also to some extent by calibration of various parameters. It is also quite plausible that Finnish FDI and trade have been concentrated in Estonia, due to proximity - not only in distance, but also in language and other cultural aspects. Accounting for these links by a reduction of relative trade costs between Finland and Estonia would improve the result of the prediction. Looking at the growth of simulated equilibrium values of sn between 1997 and 2003 in Latvian industry NACE 20, it is interesting that the value is highest in relation to Sweden (21.2 per cent, compared to 4.1 for Denmark, and 13.1 for Finland). This is reflected in the data. Swedish imports and FDI are rising faster than those of the other Nordics. The data also support the more general predictions about industry relocation from the Nordics to the Baltics. In table 5 we find that output growth in the Baltics outperforms that of the Nordics. Hence, we can conclude that there is a relative increase in production in the Baltics. Summing up, the example of the wood industry appears roughly to confirm the predictions of the FC model. It would be interesting to add spillovers and linkages to the model to see if interactions between multinationals and local firms have any effect on industry location.
7 Annual mean values for 1996-98 in terms of DM/LVL, recalculated by 1 EUR = 1.95583 DM.
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Table 5. Output in million euros, NACE 20 DK EE LV LT FI SE
1997 1,644 221 341 170 4,410 6,873
1998 1,745 290 428 165 4,603 6,946
1999 1,744 339 519 201 4,820 7,143
2000 1,864 423 734 296 5,387 7,686
2001 1,807 505 697 355 5,186 7,062
2002 1,748 656 805 435 5,303 7,338
2003 1,788 740 909 510 5,608 7,688
2004 1,865 859 1,043 582 5,679 7,883
2005 2,023 986 1,135 694 5,728 8,091
2006 2,174 1,116 1,322 782 7,120 8,981
2007 2,339 1,276 1,557 936 7,699 10,350
change 1.42 5.77 4.57 5.51 1.75 1.51
Sources: Eurostat, Central Statistical Bureau of Latvia, own calculations
5. Spillovers The occurrence of technology spillovers is a possible side effect of cross-border flows of capital. In such cases, technology is transmitted from newly arriving multinational enterprises to domestic firms. There is a large literature about spillovers from FDI, mostly based on standard neoclassical and endogenous growth theories (Javorcik 2004 provides an overview). The Baltics are quite small, so we treat them as regions. We limit the model to intra-regional spillovers and linkages since there are no data on inter-regional trade and input/output relations for the Baltics. The simplest and most direct case is a pure technological spillover from an arriving firm that affects the productivity of local firms. Productivity of local and newly arriving (foreign) firms should converge as more firms move from one region to the other. Inclusion of technology spillovers changes the dynamics of the model (of section 3) as circular causation becomes possible. A simple modification of the productivity measure that keeps the FC model tractable is necessary:
(12)
The change in hence depends on the change in sn, i.e. productivity reacts, with a lag, to relocation. Positive technological spillovers can be expected if sn has risen and foreign productivity eM* is higher than domestic productivity eM. This is of course only a first approximation, since the lag structure may differ in reality and other determinants may play a role. Moreover, we have assumed that wages do not immediately adjust. We tested for spillovers, starting from the case of Latvia again. In addition to NACE 20 (wood industry) we selected NACE 31 (electrical machinery and apparatus), since horizontal spillovers should be stronger in high-tech industries, as compared to low-tech sectors. Table 6 describes the changes in the location of industry in NACE 20 and 31 for the case of Latvia and Finland. There should be a feedback in the sense that simulation of the FC model including yields a different configuration of industry location sn. Nevertheless, if technology spillovers are included in the FC model (FCTS), location of firms in both NACE 31 and NACE 20 does not change significantly. Possibly our extended model underestimates the effects of spillovers, as the latter might not be linear functions of the presence of multinational firms. In NACE 31 the actual size of the industry in Latvia is so small that spillovers might be hard to detect, if they occur at all. Our time series may also be too short to account for the lags that typically exist between the arrival of FDI and their effects. Barrios et al. (2005) found that the net effect from the entry of multinational enterprises (MNEs) is negative at first
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Table 6. Simulated equilibrium and real values of Sn in industry NACE 20 (wood) and NACE 31 (electrical machinery and apparatus) Countries LV-FI NACE 20 LV-FI NACE 31
model FCTS FC real FCTS FC real
1999 0.26 0.26 0.10 0.25 0.24 0.02
2000 0.26 0.25 0.12 0.22 0.23 0.02
2001 0.32 0.31 0.12 0.27 0.26 0.02
2002 0.26 0.28 0.13 0.26 0.26 0.03
2003 0.28 0.28 0.14 0.28 0.27 0.03
2004 0.28 0.28 0.16 0.25 0.26 0.03
2005 0.29 0.25 0.17 0.24 0.25 0.04
2006 0.26 0.25 0.13 0.22 0.23 0.05
2007 0.25 0.25 0.17 0.20 0.21 0.04
Source: own calculations
and then turns positive as more and more foreign firms arrive; in addition to time, the number and share of MNE in the industry may thus play a role. In contrast to other studies, wages adjust to changes in productivity with a lag of one year and erase competitive advantages.8 In the end, we find no proof of horizontal technological spillovers from FDI. This is in line with the results of other studies, but it could also be due to limitations in the data.
6. Linkages Industries are not autonomous, but trade goods with each other. The output of one industry might be the input of another. Following Hirschman (1958), this constitutes a vertical linkage. The input-output flows through such vertical linkages might change when foreign MNEs arrive (see figure 2). Assuming that FDI takes place in industry B, there might be a rise in domestic demand in a sector that provides inputs to the MNE (backward linkage to industry A). On the other hand, the MNE might provide a cheaper input or new variety to a downstream sector (forward linkage to industry C). Since firms making investments abroad are on average more productive than domestic firms (cf. Melitz 2003), we expect inward FDI to have positive linkage effects. As pointed out in the introduction, existing versions of the FC model use CES aggregates of all varieties of the manufactured good as intermediate goods. In our view, this procedure does not (appropriately) take account of vertical linkages, as they can be identified from inputoutput tables and other relevant data, so we develop two different approaches to incorporate linkages into the FC framework. The first approach takes account of backward linkages, as described in figure 2 by the supplies of inputs for industry B by industry A. We assume that a rise in the Baltic share of , feeds back to industry A. Increased local production in industry B firms in industry B, raises demand for inputs from industry A, affecting the prices in that industry. For the sake of simplicity, we assume that the maximum rate of price change corresponds to the share of the deliveries of A to B in the total production of A. A higher price leads to higher profits in the region, which attracts firms.
8 A feedback to the demand side would result, which is not modelled here. It would be interesting to pursue this road in future research.
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Figure 2. Linkages and spillovers resulting from foreign direct investment
FDI inflow backward linkages input
industry A
forward linkages spillovers
output
industry B
industry C
Source: own graph
The new price of a variety is described by
(13)
The right-hand part in brackets represents the rise in price caused by changes on the demand side. ε, the price elasticity of demand for a specific input, affects pricing of the final good. We demonstrate this with an example. In Estonia, the wood industry (NACE 20) gets inputs from NACE 26 (manufacture of other non-metallic mineral products) that amount to about two percent of the latter sector’s total output. Now we need to make some assumption about how changes in demand translate into prices. Therefore, we posit that changes in NACE 20’s demand for a NACE 26 product can change the price of that product in the range of -2 to +2 per cent, so that є=0.02. If all the wood industry is located in the Nordic country Sn = 0, equation (13) yields the original price multiplied by 0.98. For the Nordic country, the multiplier would be 1.02 (see equation (7) for the original version). The bracket on the right of equation 13 is only there to get a multiplier of . If the wood industry is distributed symmetrically , the multiplier takes on unit value. A higher price is positive for the firms in the region, since its costs do not change. This means that profits rise. Table 7. Simulated equilibrium values of Sn, in per cent, industry NACE 26 (Manufacture of other non-metallic mineral products) Countries EE-FI
model FCBL FC real Source: own calculations
2000 7.5 10.4 6.3
2001 7.7 10.4 6.9
2002 8.5 11.1 7.7
2003 9.3 11.9 8.7
2004 9.5 12.1 9.8
2005 8.7 11.3 10.8
2006 9.0 11.8 14.5
2007 8.6 11.5 13.7
Table 7 shows the results for the case of Estonia and Finland. The extended model (FCBL) features backward linkages. It predicts an Estonian share in NACE 26 that is lower than that of the basic FC model. In 1997, the share of firms of NACE 26 locating in Estonia is predicted to be 7.8 per cent (FCBL) against 11.0 (FC) and 5.7 (reality). The reason for this discrepancy is the backward linkage: As firms in NACE 20 are predicted to be mostly located in Finland (and the other Nordics),9 that region enjoys the advantage of higher local demand
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from NACE 20. Firms in NACE 26 are more inclined to choose Finland as it is a larger region. The FCBL model consequently predicts a lower share of NACE 26 firms in Estonia than the benchmark FC model. This works at least as long as the Baltic share in NACE 20 is lower than 50 per cent. The second approach is based on the argument that the number of suppliers in one region is inversely related to the price of their product. We now consider the output of industry B as intermediate goods that serve as inputs for industry C. If the number of domestic producers in B, sn, is rising, firms in industry C should benefit from a decline in its input prices. To include this effect, a second manufacturing sector that produces an intermediate M-good is added, whereas the original sector produces a consumer M-good. For the sake of simplicity, the FC model’s original production function is minimally modified. An increase in sn in the intermediate industry translates into a lower price index in that region.
(14)
The price index pI is formed by weighing the product prices of domestic (phh) and foreign (phf) intermediate goods in the domestic market. The same is done to calculate the price index in the foreign region pI*. It is an implicit assumption here that consumer good producers (industry C) need all varieties of intermediates (industry B). In our model, we have an external sector delivering intermediates, thus avoiding the problem with CES aggregates that we described in the introduction. The regular linear FC model suffices to generate numerical solutions of prices. The consumer goods producing sector is redefined by two equations. First, the price of the consumer good is now also dependent on the price index. Second, the return to capital is adjusted to include the costs of the intermediate good:
(15)
where sig is the share of the intermediate in total output. Using data from input-output tables, the resulting costs of intermediate goods can be calculated as:
(16)
(17)
It is thus possible to analyse the effects of FDI on forward linkages in the FC model. If the intermediate industry in one region expands, increased competition will drive down prices. This lowers production costs in the consumer goods industry. However, applying the FC model with forward linkages (FCFL) to our previous examples, industries NACE 20 and 31, does not change the results significantly, since only 20 and 27 per cent, respectively, of their output is used as inputs of other manufacturing industries. Hence a more obvious case of forward linkages was chosen: Textiles (NACE 17) make up for 38 per cent of the production 9 The share of firms of NACE 20 in the Baltic region that we used is an average of the three individual shares of the Nordic countries.
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of wearing apparel (NACE 18). This is a classic vertical linkage where one would expect an influence of the prices of one industry on the other. We have calibrated the FCFL model for Latvia in the period 2000-07 based on a 1998 input / output table from Latvia’s CSB 2003. Earlier data on productivity in the Baltics is not available. The results are given in table 8, where they can be compared to the original results of our simulation. Note that equilibrium values of sn in the FCFL model are lower than in the benchmark FC model. This is because the intermediate industry is located mostly in the Nordics, which lowers the input costs of that region’s intermediate products there. Only if the relatively small Baltic economies were to succeed in attracting a share of the textile industry that exceeds their share in total labour would they gain competitiveness in the wearing apparel industry. Table 8. Simulated equilibrium values of Sn in industry NACE 18 (wearing apparel) Countries LV-DK
LV-FI
LV-SE
model FCFL FC real FCFL FC real FCFL FC real
2000 0.13 0.20 0.14 0.19 0.24 0.18 0.06 0.13 0.26
2001 0.17 0.22 0.14 0.23 0.27 0.17 0.11 0.18 0.29
2002 0.12 0.18 0.15 0.18 0.23 0.17 0.07 0.15 0.26
2003 0.15 0.21 0.17 0.19 0.25 0.19 0.04 0.11 0.27
2004 0.15 0.21 0.20 0.18 0.23 0.22 0.00 0.08 0.30
2005 0.14 0.20 0.21 0.19 0.24 0.24 0.01 0.08 0.29
2006 0.14 0.20 0.27 0.19 0.24 0.26 0.00 0.08 0.36
2007 0.13 0.18 0.29 0.19 0.24 0.27 0.00 0.08 0.36
Source: own calculations
Inclusion of forward linkages tends to produce a less positive result for the Baltics because the home-market effect, which normally plays against them, gains in strength. This downward correction seems realistic from 2000-2003, at least in the case of Latvia versus Denmark and Finland. In 2004-2007 the FC model is the better fit. The case of Latvia versus Sweden fails spectacularly. We have discussed two extensions of the FC model. The first features backward linkages, the second forward linkages. Both work to the effect that the region with a higher share of industry in the upstream/downstream sector gains more industry than in the benchmark FC model. The transmission channel is prices, for inputs in the case of forward linkages, for final products in the case of backward linkages. Inclusion of vertical linkages creates an additional agglomeration force, as suggested by Venables (1996). In our case this new force works against the Baltics and lowers their share of industry compared to the basic FC model. Our empirical findings support some of the predictions from the model. The implication of the FCVL models is that the arrival of an MNE in the Baltics should have positive effects on upstream and downstream sectors through price effects. Most FDI would thus induce more firms to enter up- or downstream sectors. This follow-up movement would in turn generate another round of feedbacks. The effects are cumulative, as they are all advantageous to the receiving region’s industries. In the end, the arrival of a MNE could trigger a ‘FDI wave’ or a ‘relocation wave’. If threshold values can be identified, the model might
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be able to explain the volatility of FDI. Our time series are too short to provide any evidence in that respect.
7. Conclusion Some interesting results emerge from our attempt to apply the FC model to an empirical study. In general, the model is suitable for describing important aspects of the economic integration of the Baltics and the Nordics. There is continuous change in the distribution of firms between the two groups of countries. Some of it may be due to faster growth of new firms and/ or output per firm in the Baltic country, but much of it can be explained by the activities of MNEs in terms of FDI flows from the Nordics to the Baltics. We have applied the FC model in two ways: bloc-wise and pair-wise simulations. The country-by-country results were more pronounced and corresponded better to the real data. The bloc simulations delivered a ‘one size fits all’ result, suggesting that the Baltics were not treated very differently by firms. This does not accord with real data. Even though the Baltics are small, it seems important to treat them as separate entities when calculating the distribution of industry locations. We encountered various problems in the empirical application of the FC model. Due to data restrictions, relevant parameters were hard to come by. Empirical research on technology spillovers and so-called vertical spillovers is normally conducted by using a growth theory framework. We have come up with a new approach by using a NEG model which we amended to feature spillovers and linkages. The two categories of secondary effects of FDI can be separated analytically, and the simulations that have been redefined accordingly yield the expected results. As in most of the empirical literature on FDI spillovers, we have not detected any significant positive spillovers from FDI in the Baltics. This may also be due to limitations in our data, given that we have only short time series for the economies in question. The quality of our simulations can be improved by incorporating vertical linkages. The Baltic share was predicted to be lower in all industries due to the fact that the relevant downstream or upstream firms were mostly located in the Nordics. In industries where I/O linkages are significant, the arrival of MNEs is generally expected to create benefits for down- and upstream firms. However, our results lead to the tentative conclusion that the speed of such processes is slowed down by the relatively large weight of the linkages that MNEs have in their home countries. The supply-side effects of inward FDI flows and productivity growth in the Baltics are possibly limited by the size of their home markets. Our model indicates that, due to this limitation, expectations of faster economic development through FDI inflows to the Baltics might have to be modified. In the period 2002-07, the markets in the Baltics apparently grew at much higher rates than predicted by our long-run oriented model. Yet much of the market growth in the real data was fuelled by foreign borrowing exposed as unsustainable after the outbreak of the great credit crisis in 2007. It remains to be seen in the real-world data of the subsequent five years whether actual adjustments in the external relations of the Baltics (in particular Latvia) lead to a greater convergence with the model predictions in the long run.
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References Baldwin, Richard, Rikard Forslid, Philippe Martin, Gianmarco Ottaviano, and RobertNicoud, Frédéric (2003). Economic geography and public policy. Princeton: Princeton University Press Barrios, Salvador, Holger Görg and Eric Strobl (2005). “Foreign direct investment, competition and industrial development in the host country”, European Economic Review 49(7), 1761–1784 Brakman, Steven, Harry Garretsen and Charles van Marrewijk (2009). “The New Introduction to Geographical Economics“, Cambridge: Cambridge University Press Central Statistical Bureau of Latvia (2003). Input/output tables for Latvia 1998. Riga EU Commission (2003). “Impact of enlargement on industry”, Commission Staff Working Paper 234 Fujita, Masahisa and Jacques-François Thisse (2002). ‘Economics of agglomeration: cities, industrial location, and regional growth’, Cambridge: Cambridge University Press Hirschman, Albert O. (1958). ‘The strategy of economic development’, New Haven. Javorcik, Beata Smarzynska (2004). “Does Foreign Direct Investment Increase the Productivity of Domestic Firms? In Search of Spillovers Through Backward Linkages”. American Economic Review, 94(3), 605–27 Javorcik, Beata S. and Mariana Spatareanu (2009). “Tough Love: Do Czech Suppliers Learn from their Relationships with Multinationals?”. Scandinavian Journal of Economics,111(4), 811–833 Kielyte, Julda, and d’Artis Kancs (2002). “Migration in the Enlarged European Union: Empirical Evidence for Labour Mobility in the Baltic States”. Journal of Baltic Studies, 33, 3, 259–279 Kugler, Maurice (2006). “Spillovers from foreign direct investment: Within or between industries?”. Journal of Development Economics, 80(2), 444–477 Krugman, Paul (1998). “Space: The Final Frontier”. Journal of Economic Perspectives, 12(2), 161–174 Laaser, Claus Friedrich, and Klaus Schrader (2004). “The Baltic States’ Integration in the European Division of Labor”. Kiel Working Paper No. 1234, Kiel Institute of World Economics Martin, Roger and Carol Rogers (1995). “Industrial Location and Public Infrastructure”. Journal of International Economics, 39, 335–351 Melitz, Marc (2003). “The Impact of Trade on Intraindustry Reallocation and Aggregate Industry Productivity”. Econometrica, 71(6), 1695–1725 Moroney, John R. (1975). “Natural resource endowments and comparative labor costs: A hybrid model of comparative advantage”. Journal of Regional Science, 15(2), 139–150 Venables, Anthony J. (1996), ‘Equilibrium Locations of Vertically Linked Industries’, International Economic Review 37(2), 341–59
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Book Review Economic prosperity recaptured: the Finnish path from crisis to rapid growth. Seppo Honkapohja, Erkki A. Koskela, Willi Leibfritz, and Roope Uusitalo, 2009. MIT Press. Hardback: ISBN 978-0-262-01269-0. Finland’s economic performance from the mid-1990s has been remarkable in spite of its having suffered a severe financial crisis at the beginning of the 1990s leading to the economy’s worst recession. How can we explain Finland’s success in negotiating the perils of financial turmoil and concomitantly transforming itself into a high technology economy? Economic prosperity recaptured: The Finnish path from crisis to rapid growth tackles the topic head-on. Seppo Honkapohja, Erkki A. Koskela, Willi Leibfritz, and Roope Uusitalo write a concise volume building on the authors’ previous work (e.g. Honkapohja and Koskela, 1999) and also advancing fresh insights. The authors tell stories short and tall, meeting with considerable success in throwing light on Finland’s experience. The book contains seven chapters. Following the introduction in Chapter 1, Chapters 2 and 3 deal with the crisis and related policy mistakes. The country escaped the 1970s oil crisis relatively unharmed mainly due to agreements with the Soviet Union, and following a period of disinflation pursued liberalisation and deregulation policies during the 1980s. But reforming and liberalising led to an overheating economy – inflation doubled between the mid-1980s and 1990 - which subsequently collapsed at the beginning of the 1990s. The downturn was worsened by the parallel disintegration of the Soviet Union; the authors estimate that the Soviet demise accounts for roughly 3% of the 7% GDP decline in 1991. Finland’s collapse was followed by recovery and a successful transition to a high-tech, thriving economy from the mid-1990s. The Finnish crisis of the 1990s is rather similar to events in Mexico during the 1994-1995 ‘Tequila crisis’, Asia during 1997-1998 (beginning with the Thai baht’s devaluation in 1997), Russia in 1998, Brazil in 1999, Turkey in 2000, and Argentina in 2002. Finland’s crisis evolved from various factors, including an increasing private sector debt alongside capital flows encouraged by a higher domestic interest rate in relation to foreign rates. Finland also had debt and illiquidity problems. Furthermore, the country’s monetary policy stance gave the impression that the exchange rate could be kept stable, which was a further factor encouraging bubbles in real estate and other asset prices. But these monetary and exchange rate policies were unsustainable: Finland devalued the markka in November 1991 and floated the currency in September 1992, adopting an inflation targeting regime. In explaining
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historic monetary policy setting in Finland, the study could have benefited from running a formal econometric exercise. Empirically estimated reaction functions can provide valuable information for discussing monetary policy. The book does explain other important mechanisms operating during the crisis. Increasing bank lending tends to precede crises, as was also the case in Finland. The conclusion arises from empirically estimating a consumption function revealing that credit growth contributed to private consumption growth and collapse before and after the crisis. Econometric modelling indicates the likely existence of threshold effects in the impact from finance to consumption, but the analysis does not attempt to explore that feature of the data. The 1991 banking crisis ended up costing around 7.5% of GDP in 1992. Growing bank lending was not the problem itself, but regulation and supervision did not advance at the same pace, and banking surveillance only improved after 1991. As a result of the crisis, the banking system was reformed and wide-ranging restructuring took place. The stabilisation attempt also benefited from Finland’s centralised wage bargaining. The book also draws attention to the positive role of labour market training programmes in setting the stage for the economy’s recovery and subsequent take-off. Chapter 4 focuses on the growth and structural changes following the crisis. Globalisation is a theme in the chapter and Finland has benefited from opportunities arising from the increasing volume of global trade. The study explains productivity growth as resulting mainly from changing total factor productivity (TFP). TFP growth contributed to rapidly closing the output gap arising from the financial crisis. But not all recoveries from crises rest on higher productivity growth and economic transformation. For instance, the Dominican Republic, a small emerging market economy, suffered a banking crisis with events similar to those occurring in Finland (Sánchez-Fung, 2005). As is often the case, the subsequent recovery was mainly consumption-based and fuelled by an appreciating currency after initial overshooting in the 2003-2004 crisis. So what was behind Finland’s ability to recover from the crisis while concomitantly becoming more competitive on the basis of technological advance? Chapter 5 explains the importance of human capital in the Finnish recovery. The investigation explicates that Finland was prepared to benefit from the turning tide as it had a qualified labour force. In fact, educational attainment measured using average years of formal schooling has been increasing during the last three decades. Engineering education is of good quality and appears to be a factor in explaining the economy’s transformation. But, unlike countries such as India that also have a strong technology-focused tertiary education system, Finland has been preparing from more basic levels and the country performs very well in standardised tests. Results for 15-year old Finnish children in the Program for International Student Assessment (PISA) examinations consistently top the rankings in sciences and math. Chapter 6 focuses on the leap towards a technology-driven economy. Information and communication technology spearhead Finland’s growing prominence in the international economy and comprise a large amount of the country’s exports. Government-supported and private investment in research and development has been critical for the success of Finland’s techno
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logy ventures -the National Technology Agency (TEKES) has been an important institution in allocating funding. The case of Nokia is revealing: the company was successful in transforming from its origins manufacturing cables, metals, and rubber products to being a powerhouse with the largest share in the global cellular phones market. It is worth noting the extent to which the company has benefited from government financial backing. The book reports that TEKES contributed about 10% of Nokia’s research and development expenditure in 1991, even though the support has been declining over time – it was only 0.4% in 2004. The strategy of using government money to finance the private sector is paying off: Nokia contributes to the rest of the economy not only directly but also via its suppliers and the positive spillover from its research and development activities to other technology industries and to academia. Other sectors are actively endorsed, such as biosciences, but the results from that course of action are still to be seen. Chapter 7 concludes by highlighting the challenges that Finland faces going forward. Unemployment is relatively high for an otherwise fairly successful economy, remaining above pre-crisis levels. The authors also underline issues common to other rich economies: population aging and the implications of facing an increasingly more open and competitive global economy. The book should prove illuminating for anyone interested in economic growth and development. But potential readers should be warned that there is no recipe in the book as to how other countries could follow Finland’s path to consolidating a technology-driven economy.
References Honkapohja, Seppo, and Erkki A. Koskela (1999) The economic crisis of the 1990s in Finland, Economic Policy, vol. 29, pp. 401-436. Sánchez-Fung, José R. (2005) Exchange rates, monetary policy, and interest rates in the Dominican Republic during the 1990s boom and new millennium crisis, Journal of Latin American Studies, vol. 37, pp. 727-738. José R. Sánchez-Fung Kingston University, London, UK
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PhD news
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PhD news Management by values: analysis of influencing aspects and its theoretical and practical implications Krista Jaakson E-mail:
[email protected] The public defence of the dissertation took place at the Faculty of Economics and Business Administration of the University of Tartu on 12 November 2009 in Narva Rd. 4, Tartu The dissertation is available at the University of Tartu Library in Tartu, W.Struve 1, 50091 A full electronic version of the dissertation is available at http://dspace.utlib.ee/dspace/bitstream/10062/14186/1/jaakson_krista.pdf Management by values is a concept that is applied in organisational practices in Estonia as well as abroad. The roots of this management concept lie in the 1980s, when semi-scientific works were published in the US, where organisations with a strong culture were claimed to be successful on the basis that a strong culture leads to higher behavioural consistency, stronger goal alignment, and increased employee effort. The core element of organisational culture consists of organisational values, and management by values is focused on these. The author of this thesis defines management by values as a series of interconnected managerial activities to ensure the acceptance of relevant organisational values inside and outside the organisation. While plenty of theoretical literature exists on management by values, empirical research is scant. The aim of this thesis was to work out theoretical and practical implications for management by values based on the example of Estonian organisations. The thesis consists of four separate articles supplemented by an introductory overview of the research field and a discussion at the end. Four research questions were formulated: Which stakeholders, and how, should be engaged in defining values?, What socio-demographic variables affect the perception of values?, How are values and practices (using the example of corporate social responsibility) related?, and Why do values and practices diverge? Seventeen research propositions were set up to answer these questions. It was found that management by values is influenced by existing practices of the organisation, its stakeholders, availability of resources and employee empowerment, internal systems and organisational culture. In addition, the success of management by values is dependent on how well the values statement itself meets certain principles. Organisational members’ perception of values as a relevant process in management by values is also influenced by their socio-demographic variables such as gender and involvement in the organisation. Conclusions reached in this thesis help managers to better understand the essence of management
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by values and raise awareness of potential problems that may occur in the process. Although empirical data are based on Estonian organisations, the author asserts that the results are of interest to and suggestions applicable in any organisation wishing to implement management by values.
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Manifestations of organizationalculture basedon the example of estonian organizations Anne Reino E-mail:
[email protected] The public defence of the dissertation took place at the Faculty of Economics and Business Administration of the University of Tartu on November 6th, 2009 in Narva Rd 4, Oeconomicum, Tartu. The dissertation is available at the University of Tartu Library in Tartu, Str. W.Struve 1, 50091. A full electronic version of the dissertation is available at: http://dspace.utlib.ee/dspace/bitstream/10062/11016/1/reino_anne.pdf Organizational culture facilitates explanation of the essence and meaning of the organization and has been considered a powerful force influencing organizational behaviour and the overall performance of organizations. The relevance of organizational culture as a topic is important in unpredictable and rapidly changing economic conditions, where the human side of organizations may be critical for their survival. The aim of this dissertation was to outline regularities and patterns in manifestations of organizational culture using the example of Estonian organizations. In the scope of the dissertation the notion of an organizational culture pattern was defined as a cultural profile which characterizes organizational culture from two perspectives: firstly, it demonstrates the relative importance of organizational cultural types in a particular organization and secondly, it denotes the relationships between different organizational cultural types. The author of the dissertation analyzed what kinds of connection exist between types of organizational culture in Estonian organizations and how contextual factors such as national culture and industry influence organizational culture. The dissertation also analyzed the impact of organizational characteristics - age and size - on patterns of organizational culture. The dissertation aimed to broaden the scope of research into organizational culture in terms of factors influencing manifestations of organizational culture. It could be argued that a gap exists between theoretical discussions about the formation of organizational culture and influential forces in that process, and empirical research on the topic. Although in theory several contextual and organizational factors are seen as important determinants that influence manifestations of organizational culture, empirical research seldom focuses on those particular factors (national culture could be seen as an exception here). Moreover, research into organizational culture has long traditions in Western countries, but no systematic overview and analysis of the topic has been carried out in transition countries like Estonia. Research conducted on organizational culture in Estonia has been quite fragmented, both in terms of samples and methods. Therefore, extensive research covering variables not yet investigated
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and also considering the peculiarities of the local context is crucial to explore regularities in manifestations of organizational culture. In order to analyze the impact of contextual and organizational factors on patterns of organizational culture in Estonian organizations, a new tool for analyzing organizational culture – an Organizational Values Questionnaire - was developed by the author. The general framework of the questionnaire was based on the ideas of the Competing Values Framework. Empirical research brought out several regularities in the manifestation of organizational culture. Those regularities were discussed in the context of Estonia. Analysis of organizational culture showed a positive relationship between types of organizational culture. This finding demonstrates that although certain types of organizational culture encompass antagonistic values on theoretical grounds, those values tend to coexist in organizations. Therefore, organizational culture should be approached as a continuum, where changes in some aspects involve changes in other aspects of organizational culture. The relationships that exist between types of organizational culture may demonstrate basic assumptions held by people in and around the organization. For example, the current study indicated a moderate connection between the Rational Goal and Internal Processes types of organizational culture, a finding that differs from previous studies conducted in different cultural areas. This finding may be interpreted as a reflection of cultural values in Estonian society, where it is believed that competitiveness requires internal integration and formalization. Moreover this result is quite similar to findings from research conducted in Estonia supporting the idea that Estonian organizations tend to follow the principles of a well-oiled machine. But emphasis on results and relying on formalization may also be connected to the transitional era in society. First of all, the period of transition forced organizations to become more results-oriented, but it also put pressure on people’s attitudes and behavioural patterns, where significant changes were expected. New ways of operating expected from employees often also necessitated new standards and procedures, so that organizations going through important change processes became more formalized and bureaucratic. The analysis demonstrated similarities in patterns of organizational culture in Estonian organizations – it became evident that those types of organizational culture that value stability and control were dominant in patterns of organizational culture. Findings showed that the impact of the industry where the organization operated was a significant factor influencing the culture of the particular organization; moreover, the impact of the industry may be even more important compared to the influence of national culture. The research could not confirm the effect of organizational age on patterns of organizational culture. The results of the current research indicated that organizational age did not predict patterns of organizational culture. This finding may be related to the context of transitional countries because the distinction between old and new organizations is blurred due to radical changes in society and considering the impact of those changes on organizations. Unlike organizational age, the research revealed the impact of organizational size on organizational culture. The contribution of the dissertation is twofold: several implications of the dissertation appeared from the perspective of analysis of organizational culture, as well as from the viewpoint of management practices. Findings from the study could be taken into account when planning management action in organizations.
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Baltic Journal of Economics
Anne-Marie och Gustaf Anders stiftelse för medieforskning
Volume 10 Number 1 Spring 2010