1. Staying Power Department of Social & Economic Geography Umeå University Sweden The role of related experience and geographic proximity for firm survival in tourism Patrick Brouder & Rikard Eriksson

2. Setting the scene Area approx. the same size as Great Britain but with a population of about 900,000 (2/3 in coastal areas) Public sector is chief employer in inland and primary sector (mining, timber) are the largest private employers Public sector has traditionally compensated for relative underdevelopment but emphasis is now shifting towards improving market competitiveness Tourism employment up to 6% in some municipalities “a residuum of leftover core activities” (Anderson, 2000) “a Swedish pleasure periphery” (Müller, 2008)

3. Theoretical framework Linking tourism and economic geography (Ioannides, 2006, Tourism Geographies) Studies of entrepreneurs often focus on individual characteristics (Ateljevic & Doorne, 2000; Matlay & Fletcher, 2000; Russell & Faulkner, 2004; Timmermans, 2010; among others) Evolutionary approach has routines/skills as focus (Boschma & Frenken, 2006, Journal of Economic Geography) Transfer of knowledge between firms and individuals (Eriksson, 2011; Boschma, Eriksson & Lindgren, 2009) Reasoning from industrial cases to service cases? Beyond survival?

4. Data and variables Data: ASTRID: Longitudinal micro-database containing matched annual data on all workers and workplaces in Sweden 133 cases of new tourism entrepreneurs (1999-2001) 440 time points (richer analysis) Dependent variable: Firm failure over a 7 year period Key variables (dummies): tourism related work experience (based on SIC-codes) local experience (working in same municipality previously) regional specialisation (many employed in tourism) Controllers: employment previous year, education, gender, age, regional roots, marital status, local unemployment, Gross Regional Product levels, number of local micro-firms, number of local tourism firms, share of local employees in micro-firms, overnight stays, non-tourism management experience, and, level of benefits received before and at startup

5. Results I: Kaplan-Meier (a) Overall survival(b) Related experience (c) Local experience (d) Regional specialisation

6. Results II: Cox hazard models Model 1Model 2Model 3Model 4 Success factors Related experience 56%* (0.191) (0.197) Local experience 31%** (0.166) (0.164) Regional specialization 96%87% (0.228)(0.212) Model score (Prob > Chi2) 0.096*0.054*0.2640.047** Notes: n=133; percentages in model are hazard ratios standard errors are in parentheses; *=sig. at 0.1 level, **=sig. at 0.05 level Note: Control variables omitted from table, only lodging was significant for survival.

7. Contribution to the local economy? 17% of new firms surviving after 7 years Median turnover of new firms increases (from €35,000 to €180,000) Mean employees per new firm increases (from 0.15 to 1.5 employees per firm) Performance of extant firms over seven years (at higher levels but lower rates of increase) Smart thinking in entrepreneurial support can help to optimise survival of new firms in tourism (how to overcome the experience deficit?)