MIGRATION OF THE CREATIVE CLASS:

Transcription

1 Daniel Voßen Institute for Economic and Cultural Geography Leibniz University Hanover 54th ERSA Congress, Saint Petersburg Ordinary Session G_M. Innovations in space; Wed_4_Room E 323; MIGRATION OF THE CREATIVE CLASS: EVIDENCE FROM GERMANY 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

2 Contents Theoretical considerations & Hypotheses Empirical approach & data Results Conclusion & Discussion Bibliography Appendix 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

3 THEORETICAL CONSIDERATIONS & HYPOTHESES 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

4 Jobs vs. Amenities Aggregated macro-level perspective: >>> Explaining regional migration rates with regional push and pull factors Source: Jan105.html Push and pull factors explaining migration (LEE, 1966) Neoclassical equilibrium theory (HICKS, 1963; LOWRY, 1966) >>> Hard location factors (wage level, job opportunities) Human capital theory (GLAESER, 1994; GLAESER et al., 2001) >>> Amenities (climate, nature, [high] culture, low crime rates) Creative class concept (FLORIDA, 2002b) >>> Climate of tolerance and openness and amenities (culture, nightlife) attract highly mobile Creative Class, even more than hard factors 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

5 Motivation / Research Gap Testing Florida`s hypotheses: Most previous studies* My approach Regional location factors (jobs / amenities) Correlations Regressions Regional shares / location quotients (settled populations) Conclusions Migration behavior and motives of the Creative Class??? Regional location factors (jobs / amenities) Correlations Regressions Regional net migration rates (mobile populations) Conclusions Migration behavior and motives of the Creative Class!!! * e.g. FLORIDA, 2002a; FLORIDA & TINAGLI, 2004; LORENZEN & ANDERSEN, 2007; CLIFTON, 2008; FLORIDA et al., 2008; BOSCHMA & FRITSCH, 2009; FRITSCH & STÜTZER, 2009; ANDERSEN et al. 2010; CHANTELOT et al th ERSA Congress, Saint Petersburg, Daniel Vossen,

6 Aim / Hypotheses Until now the literature has focused on correlation measures and indexes, while completely missing the indisputably central question about mobility what are the primary push and pull mechanisms behind migration of talent and the creative class? (HANSEN & NIEDOMYSL, 2009:193) Therefore, the overarching aim of this work is to study the extent and motives of the migration of the Creative Class in Germany Hypotheses: H1: Creative Class members are highly mobile / or at least more mobile than other (groups of) employees H2: Amenity-related location factors have a significant positive and stronger relative impact on the migration of the Creative Class than hard location factors H3: Tolerance-related related location factors have a significant positive impact on the migration of the Creative Class 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

7 EMPIRICAL APPROACH & DATA 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

8 Creative Class Defining the Creative Class (CC) CC identified by 45 of 340 occupations* including: Bohemians, scientists, engineers, architects, entrepreneurs, leading administrators, technicians, high-tech sectors services, financial / legal / business services, About 4.6 million CC employees** in 2010 = 16.3% of all employees** Creative Class* in Germany Absolute (million) Share of total employment Total Employment Share of total Creative Class Creative Class Super-Creative Core*** Bohemians Creative Professionals * Definition based on MÖLLER & TUBADJI, 2008 ** Employees subject to social insurance (about 80% of total workforce; self-employed, family workers and civil servants are not included) *** Without Bohemians 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

9 Dependent Variable Regional net migration rates Annual average migration balance of employees in creative occupations subject to social insurance divided by corresponding employment in per region* between 2003 and 2010 Migration is defined as the change of the region* of residence between two reference dates (June 30 of present and previous year) Source: SIAB ** Representative 2% random sample of the Integrated Employment Biographies (IEB) Contains i.a. registration data of employees subject to the social security system Panel structure: IEB of 1.6 million individuals documented in 40.5 million lines of data Variables: i.a. educational level, occupation, place of residence, * ** All96German Raumordnungsregionen (= ROR; spatial planning regions), mostly representing functional spatial economic entities in the sense of travel to work areas Sample of Integrated Labor Market Biographies (SIAB) provided by the Institute for Employment Research 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

10 Independent Variables Problem: Multitude number of possible independent variables Abit Arbitrary selection of variables representing regional characteristics ti such as labour market conditions, cultural / consumer amenities, climate of tolerance and openness, cost of living, What fits best??? For example labour market conditions: Unemployment rate (e.g. CLIFTON, 2008; CLIFTON & COOKE, 2009) Wage level (e.g. FLORIDA & MELLANDER, 2010; SCOTT, 2010) GDP per capita (e.g. FLORIDA & TINAGLI, 2013) Absolute number of employees* (e.g. ALFKEN et al., 2013) Share of employees* (e.g. GLAESER, 2005; ALFKEN et al., 2013) Location quotient of employees* (e.g. SCOTT, 2010; MIGUEL-MOLINA et al., 2012) Employment* growth (e.g. BOSCHMA & FRITSCH, 2009; BUCH et al., 2012) Increasing multicollinearity of independent variables * Total employment or sectoral / occupational / educational subgroups 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

11 Reducing the dimensionality Principal Component Analysis (PCA) PCA extracts the common statistical variance of a set of variables Approach: Generate only one Principal Component (PC) at the same time out of a set of variables which are likely to approximate the same (underlying) regional factor* (= confirmatory factor analyses) PCA requires adequate: Kaiser-Mayer-Olkin-Index** (KMO): Tests if dataset (all variables) is adequate for PCA Measure of Sampling Adequacy** (MSA): Tests if certain variable is adequate for PCA Eigenvalues: Share of total variance of all variables explained by the PC Commonality: Share of variance of one variable explained by the PC * ** Derived from theory and / or previous empirical studies Kaiser criterion, KMO / MSA: < 0.5 = unacceptable; = miserable; = mediocre; = middling; = meritorious; = marvelous 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

12 Principal Components 1/2 KMO Index % of variance explained Principal Component (Label) JOBS CULTURE TOLERANCE ACCESSIBILITY COSTS Variable* (Label) MSA Commonality employment growth wage level unemployment rate labour market conditions valued as satisfactory visitors of museums and theatres absolute number of bohemians share of bohemians cultural offers valued as satisfactory share of foreigners share of mixed marriages share of non christians share of same sex marriages access agglomerations by car access agglomerations by train access european agglomerations access airport land price price index cost of living valued as problematically availability of accommodation valued as problematica * For details (variables definition, periods, and data sources) see appendix 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

13 Principal Components 2/2 KMO Index % of variance explained Principal Component (Label) UNIVERSITY TOURISM NATURE CRIME Dependent / single control variable ibl (Label) MIGRATION CLIMATE DENSITY Variable* (Label) MSA Commonality share of young inhabitants students per capita graduates of cultural subjects per capita graduates of all other subjects per capita tourist beds per capita tourist overnight stays per capita recreation and holiday homes per capita preventive and rehabilitation facilities per capita state of nature valued as satisfactory size of recreation, forest and water area recreation, forest and water area per capita share of recreation, forest and water area domestic burglary per capita theft from vehicles per capita street robbery per capita public safety valued as problematically Dependent / single control variable* net migration rate climatic conditions population density * For details (variables definition, periods, and data sources) see appendix 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

14 RESULTS 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

15 Migration rates per group all employees creative class bohemians other creative core creative professionals highly qualified * (19.58) (27.12) (41.19) (32.24) (20.40) (35.88) * Annual average volume of migration of employees divided by corresponding employment in in the period from 1999 to th ERSA Congress, Saint Petersburg, Daniel Vossen,

16 Regional net migration rates Raumordnungsregionen (ROR; spatial planning regions; N = 96) 101 Schleswig Holstein Mitte 806 Neckar Alb 102 Schleswig Holstein Nord 807 Nordschwarzwald 103 Schleswig Holstein Ost 808 Ostwürttemberg 104 Schleswig Holstein Süd 809 Schwarzwald Baar Heuberg 105 Schleswig Holstein Süd West 810 Stuttgart 201 Hamburg 811 Südlicher Oberrhein 301 Braunschweig 812 Unterer Neckar 302 Bremen Umland 901 Allgäu 303 Bremerhaven 902 Augsburg 304 Emsland 903 Bayerischer Untermain 305 Göttingen 904 Donau Iller (BY) 306 Hamburg Umland Süd 905 Donau Wald 307 Hannover 906 Industrieregion Mittelfranken 308 Hildesheim 907 Ingolstadt 309 Lüneburg 908 Landshut 310 Oldenburg 909 Main Rhön 311 Osnabrück 910 München 312 Ost Friesland 911 Oberfranken Ost 313 Südheide 912 Oberfranken West 401 Bremen 913 Oberland 501 Aachen 914 Oberpfalz Nord 502 Arnsberg 915 Regensburg 503 Bielefeld 916 Südostoberbayern 504 Bochum/Hagen 917 Westmittelfranken 505 Bonn 918 Würzburg 506 Dortmund 1001 Saar 507 Duisburg/Essen 1101 Berlin 508 Düsseldorf 1201 Havelland Fläming 509 Emscher Lippe 1202 Lausitz Spreewald 510 Köln 1203 Oderland Spree 511 Münster 1204 Prignitz Oberhavel 512 Paderborn 1205 Uckermark Barnim 513 Siegen 1301 Mecklenburgische Seenplatte 601 Mittelhessen 1302 Mittleres Mecklenburg/Rostock 602 Nordhessen 1303 Vorpommern 603 Osthessen 1304 Westmecklenburg 604 Rhein Main 1401 Oberes Elbtal/Osterzgebirge 605 Starkenburg 1402 Oberlausitz Niederschlesien 701 Mittelrhein Westerwald 1403 Südsachsen 702 Rheinhessen Nahe 1404 Westsachsen 703 Rheinpfalz 1501 Altmark 704 Trier 1502 Anhalt Bitterfeld Wittenberg 705 Westpfalz 1503 Halle/S. 801 Bodensee Oberschwaben 1504 Magdeburg 802 Donau Iller (BW) 1601 Mittelthüringen 803 Franken 1602 Nordthüringen th Hochrhein Bodensee ERSA Congress, Saint Petersburg, 1603 Ostthüringen Daniel Vossen, Mittlerer Oberrhein 1604 Südthüringen 16

17 Results multiple linear regression Independent variables / PCs (1) (2) (3) Dependent variable: Net migration rate (4) (5) (6) (7) (8) JOBS (++) *** (++) *** (++) *** (++) *** (++) *** (++) *** (++) *** (++) *** CULTURE (+) *** (+) *** (+) *** (+) *** (+) *** (+) *** (+) *** (+) *** TOLERANCE ( ) *** ( ) * ( ) *** ( ) *** ( ) *** ( ) *** ( ) *** DENSITY O ACCESSIBILITY COSTS UNIVERSITY TOURISM NATURE CLIMATE ( ) *** ( ) ** ( ) ** ( ) ** ( ) *** ( ) ** ( ) ** ( ) ** ( ) ** ( ) *** ( ) *** ( ) *** (+) ** (+) O O *** CRIME (+) ** (+) Observations R squared Adj. R squared F test Prob > F *** p<0.01, ** p<0.05, * p<0.1, O not significant, Beta Coefficients: (++) most positive, (+) positive, ( ) most negative, ( ) negative ** * For details (Beta Coefficients; t-statistics) see appendix 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

18 CONCLUSION & DISCUSSION 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

19 Testing Florida`s hypotheses H1: Creative Class is indeed more mobile than the total workforce, but only marginal? Ø 27 (CC) vs. Ø 20 (All)? Highly Qualified Persons are more mobile than Creative Class Members H2: Amenity-related location factors (especially cultural amenities) indeed have a significant positive impact on CC migration But hard location factors (especially labour market conditions but also low cost of living) have a stronger impact on CC migration H3: Tolerance-related location factors have indeed a significant impact on CC migration But this impact seems to be negative (under control of JOBS and CULTURE factors) FLORIDA: 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

20 Daniel Voßen Institute of Economic and Cultural Geography Leibniz University Hanover hannover.de THANK YOU FOR YOUR ATTENTION 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

21 BIBLIOGRAPHY 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

22 Bibliography ALFKEN, C.; BROEKEL, T. & STERNBERG, R. 2013: Factors explaining the spatial agglomeration of the Creative Class - Empirical evidence for German artists. In: Working Papers on Innovation and Space, Philipps-Universität Marburg. ANDERSEN, K.V.; BUGGE, M.M.; HANSEN, H.K.; ISAKSEN, A. & RAUNIO, M. 2010: One Size Fits All?: Applying the Creative Class Thesis onto a Nordic Context. In: European Planning Studies, 18(10), pp BOSCHMA, R. A.; FRITSCH, M. 2009: Creative Class and Regional Growth - Empirical Evidence from seven European Countries. In: Economic Geography, 85(4), pp BUCH, T.; HAMANN, S.; NIEBUHR, A. & ROSSEN, A. 2012: Jobs or Amenities - What determines the migration balances of cities? In: ERSA conference papers, ersa12p401, European Regional Science Association. CHANTELOT, S.; PERES, S. & VIROL, S. 2010: The Geography of French Creative Class: An Exploratory Spatial Data Analysis. In: Cahiers du GREThA 2010(16), pp CLIFTON, N. 2008: The "creative class" in the UK: An initial analysis. In: Geografiska Annaler Series B-Human Geography, 90(1), pp CLIFTON, N. & COOKE, P. 2009: Creative knowledge workers and location in Europe and North America: a comparative review. In: Creative Industries Journal, 2(1), pp FLORIDA, R. 2002a: Bohemia and economic geography. In: Journal of Economic Geography, 2(1), pp FLORIDA, R. 2002b: The Rise of the Creative Class. New York: Basic Books. FLORIDA, R.; MELLANDER, C. 2010: There Goes the Metro: How and Why Bohemians, Artists and Gays Affect Regional Housing Values. In: Journal of Economic Geography 10(2), pp FLORIDA, R.; MELLANDER, C. & STOLARICK, K. 2008: Inside the Black Box of Regional Development - Human Capital, the Creative Class and Tolerance. In: Journal of Economic Geography, 8(5), pp FLORIDA, R. & TINAGLI, I. 2004: Europe in the Creative Age. Demos FRITSCH, M. & STÜTZER, M. 2009: The Geography of Creative People in Germany. In: Int. J. Foresight and Innovation Policy, 5(1), pp GLAESER, E. L. 1994: Cities, Information and Economic Growth. In: Cityscape, 1(1), pp GLAESER, E. L. 2005: Review of Richard Florida s The Rise of the Creative Class. In: Regional Science and Urban Eco., 35(5), pp GLAESER, E. L.; KOLKO, J.; SAIZ, A. 2001: Consumer city. In: Journal of Economic Geography, 1(1), pp HANSEN, H.K. & NIEDOMYSL, T. 2009: Migration of the Creative Class: Evidence from Sweden. In: Journal of Eco. Geo., 9(2), pp HICKS, J. R. 1963: The theory of wages. (2nd). London: Macmillan. LEE, E. S. 1966: Theory of Migration. In: Demography, 3(1), pp LORENZEN M. & ANDERSEN, K.V. 2007: The Geography of the European Creative Class: A Rank Size Analysis. In: DRUID WP No LOWRY, I. S. 1966: Migration and metropolitan growth: Two analytical models. San Francisco, Calif.: Chandler. MIGUEL MOLINA, B.; HERVAS-OLIVER, J.-L.; BOIX, R. & MIGUEL MOLINA, M. 2012: The Importance of Creative Industry Agglomerations in Explaining the Wealth of European Regions. In: European Planning Studies, 20(8), pp MÖLLER, J. & TUBADJI, A. 2008: The Creative Class, Bohemians and Local Labor Market Performance A Micro data Panel Study for Germany In: ZEW Discussion Paper No SCOTT, A.J. 2010: Jobs or Amenities? Destination Choices of Migrant Engineers in the USA. In: Papers in Regional Science, 89(1), pp th ERSA Congress, Saint Petersburg, Daniel Vossen,

23 APPENDIX 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

24 Variables Definition 1/4 Principal Component Variable (Label) employment growth Variable (Definition) Period Source compound annual growth rate of employees subjecttoto social insurance in % DESTATIS1 JOBS CULTURE TOLERANCE wage level annual average disposable income of private households in EUR per capita DESTATIS1 unemployment annual average share of unemployed persons among the civilian rate labour force in % BA labour market share of respondents satisfied with the labour market conditions among the conditions total respondents in % 2004/2005 PD visitors of museums per 100 inhabitants in relation to the corresponding key visitors of museums figure for Germany and visitors of theatres and concerts per household in and theatres relation to the corresponding key figure for Germany 2009 BS absolute number annual average absolute number of bohemians subject to social insurance as FDZ / IAB of bohemians well as freelance artists & KSK share of bohemians annual average share of bohemians subject to social insurance as well as FDZ / IAB freelance artists divided by total employment subject to social insurance in % & KSK cultural offers share of respondents satisfied with cultural offers among the total respondents in % 2004/2005 PD share of foreigners annual average share of foreigners among the total population in % DESTATIS1 share of annual average share of mixed marriages between nationals and foreigners mixed marriages among all marriages in % DESTATIS1 share of non christians share of non christians among the total population in % 2011 DESTATIS2 share of same sexsex marriages share of same sex marriages among all marriages in % 2011 DESTATIS2 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

25 Variables Definition 2/4 Principal Component Variable (Label) Variable (Definition) Period Source ACCESSIBILITY access agglomerations by car access agglomerations by train access european agglomerations access airport average traveling time by car to the next 3 of 36 agglomerations in Germany and neighbouring countries in minutes average traveling time by train to the next 3 of 36 agglomerations in Germany and neighbouring countries in minutes average traveling time by combined car and air transport to all 41 european agglomerations in minutes average traveling time by car to the next international airport in Germany in minutes 2012 BBSR BBSR BBSR BBSR1 land price land price per square meter in 2009/2010 BBSR1 COSTS UNIVERSITY price index regional price index; Bonn= BBSR2 cost of living availability of accommodation share of young inhabitants share of respondents not satisfied with the cost of living among the total respondents in % share of respondents not satisfied with the availability of housing accommodation among the total respondents in % annual average share of inhabitants between 25 and 30 years old among the total population in % 2004/2005 PD 2004/2005 PD BBSR1 students per capita annual average number of students per 1,000 inhabitants BBSR1 graduates of cultural subjects per capita graduates of all other subjects per capita annual average number of graduates of cultural subjects per capita DESTATIS3 annual average number of graduates of all other subjects per capita DESTATIS3 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

26 Variables Definition 3/4 Principal Component TOURISM NATURE CRIME Variable (Label) tourist tbeds per capita tourist overnight stays per capita recreation and holiday homes per capita preventive and reha. facilities per capita state of nature size of recreation, forest and water area recreation, forest and water area per capita share of recreation, forest and water area domestic burglary per capita theft from vehicles per capita street robbery per capita public safety Variable (Definition) annual average number of beds in tourist accommodation establishments per 1,000 inhabitants annual average number of overnight stays in tourist accommodation establishments per capita annual average number of recreation and holiday homes per 100,000 inhabitants t annual average number of preventive and rehabilitation facilities per 100,000 inhabitants share of respondents satisfied with the state of nature among the total respondents in % Period Source DESTATIS DESTATIS DESTATIS DESTATIS1 2004/2005 PD absolute size of recreation, forest and water area in hectare 2004 DESTATIS1 number of recreation, forest and water area in square metre per capita 2004 DESTATIS1 share of recreation, forest and water area among the total area in % 2004 DESTATIS1 number of domestic burglary cases per 100,000 inhabitants 2003 BKA number of theft from vehicles cases per 100, inhabitants t 2003 BKA number of street robbery cases per 100,000 inhabitants 2003 BKA share of respondents not satisfied with public safety among the total respondents in % 2004/2005 PD 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

27 Variables Definition 4/4 Dependent / single control variable (Label) net migration rate climatic conditions Dependent / single control variable (Definition) annual average migration balance of employees in creative occupations subject to social insurance divided by corresponding employment in Klimafaktoren (KF) for energy performance certificate necessary for the construction, sale or new rental of a building; It takes account of both the temperatures and other climatic conditions in German regions. The higher the factor the better the climatic conditions (lower energy requirements). Period Source FDZ / IAB DWD population density annual average number of inhabitants per square kilometre BBSR1 Sources BA BBSR1 BBSR2 BS DESTATIS1 DESTATIS2 DESTATIS3 DWD FDZ / IAB KSK PD BKA Bundesagentur für Arbeit (Federal Employment Agency): Arbeitsmarktstatistik. Bundesinstitut für Bau, Stadt und Raumforschung (Federal Institute for Research on Building, Urban Affairs and Spatial Development): Indikatoren und Karten zur Raum und Stadtentwicklung [electronic resource: CD ROM], INKAR. Volume 2010, Bonn Bundesinstitut für Bau, Stadt und Raumforschung (Federal Institute for Research on Building, Urban Affairs and Spatial Development): Regionaler Preisindex, (=Berichte Berichte, Bd. 30, Anhang3) 3), Bonn Bertelsmann Stiftung: Deutscher Lernatlas Ergebnisbericht 2011, Gütersloh Statistischen Ämter des Bundes und der Länder (Statistical Offices of the Federation and the Länder): Regionaldatenbank Deutschland. Statistischen Ämter des Bundes und der Länder (Statistical Offices of the Federation and the Länder): Zensus Statistischen Ämter des Bundes und der Länder (Statistical Offices of the Federation and the Länder): Amtliche Hochschulstatistik. Deutscher Wetterdienst (German Weather Service): Klimafaktoren (KF). Forschungsdatenzentrum der Bundesagentur für Arbeit [BA] im Institut für Arbeitsmarkt und Berufsforschung [IAB] (Research Data Centre of the Federal Employment Agency at the Institute for Employment Research: Stichprobe der Integrierten Arbeitsmarktbiografien (SIAB) Künstlersozialkasse: Künstlersozialversicherung soziale Sicherung für Künstler und Publizisten. McKinsey & Company et al. 2004/2005: "Perspektive Deutschland Deutschland" study. Bundeskriminalamt (Federal Criminal Police Office): Polizeiliche Kriminalstatistik. 54th ERSA Congress, Saint Petersburg, Daniel Vossen,

28 Results multiple linear regression Independent Dependent variable: Net migration rate variables / PCs (1) (2) (3) (4) (5) (6) (7) (8) JOBS CULTURE TOLERANCE 0.416*** 0.357*** 0.496*** 0.698*** 0.589*** 0.375* 0.633*** 0.609*** 0.759*** 0.704*** 0.567*** 0.699*** 0.837*** 0.693*** 0.553*** 0.870*** 0.797*** 0.760*** 0.965*** 0.756*** 0.824*** 0.956*** 0.731*** (4.857) (4.167) ( 4.042) (6.605) 605) (6.014) ( 1.944) (4.755) (6.025) ( 5.013) (6.900) (5.978) ( 4.629) (7.110) (6.300) ( 3.814) (7.938) (7.555) ( 4.656) (7.870) (7.066) ( 5.240) (7.818) (7.299) DENSITY ( 0.811) ACCESSIBILITY COSTS 0.345*** 0.269** 0.311** 0.290** 0.278** 0.265** 0.395** 0.239** 0.461*** ( 2.789) ( 2.143) ( 2.541) ( 2.485) ( 2.441) ( 2.345) ( 2.473) ( 2.128) ( 3.156) UNIVERSITY TOURISM 0.336*** 0.224** 0.304*** 0.253*** 0.297*** ( 3.935) (2.423) ( 3.627) (2.777) ( 3.583) NATURE (1.547) CLIMATE CRIME (0.641) 0313** ** (2.511) (2.161) Observations R squared Adj. R squared F test t Prob > F Beta Coefficients; t statistics in parentheses; *** p<0.01, ** p<0.05, * p<0.1 54th ERSA Congress, Saint Petersburg, Daniel Vossen,