Swedish data Testing Trade Models with Labor Market Heterogeneity Anders Akerman, IFN

Introduction Good supply of firm, plant and individual level microdata Generally supplied by Statistics Sweden: MONA (Microdata Online Access) FIEF Database on Swedish MNEs collected by IFN

The Swedish context (1) A different setting than when using US data: Very open economy Source: World Development Indicators.

The Swedish context (2) Large government sector: Source: World Development Indicators.

The Swedish context (3) High FDI inflows: Source: World Development Indicators.

The Swedish context (4) High FDI outflows: Source: World Development Indicators.

Overview Individuals: Examples: Education, employment history, gender, age, wages, job tasks Firms: Examples: Capital, number of employees, trade pattern, intermediates, R&D, value added Matched firm-worker data Multinationals: Sales and employment by foreign affiliates of Swedish MNEs Plants: Examples: Employment and wage according to age, education and gender

Firm level data “Företagsdatabasen” / Firm level database < 1996: Selection subject to size cutoff ≥ 1996: All firms covered Includes much information on all firms: 1. Trade (import and exports with information on within/between firm trade, product, origin/destination country, value, weight, industry classification) 2. Production (inputs such as labour, capital, energy, raw materials, profits, R&D, value added) 3. Other: age, geography, foreign ownership (changes) About 300,000 firms since 1996.

Plant level employment data “Arbetställedatabasen” (RAMS) / Plant level database Selection criteria similar as firm level data, but complete coverage already from 1986 (compatible identification system) Geography, industry Employee characteristics: wages and employment according to skill level, age and gender etc. (based on November employment) About 400,000 plants since 1986.

Individual data (1) “Lönedatabasen” / Employment database (compatible with previous datasets) Years 1970 – Covers all employments in the public sector, random selection for private sector. Employment characteristics (individual id, wage full-time equivalent, education, type of job/profession). About 2 million individuals since 1975.

Examples Create a panel at the firm level with detailed information on production, trade patterns and employee characteristics for the years 1990 – In recent years about 170,000 firms can be linked to the plant level data giving information on skill composition, wage differences across age, education and gender. Or about 100,000 firms when linked to the individual level data, giving detailed demographic and educational information on all employees. Semiparametric estimates of firm productivity as in Levinsohn & Petrin (2003) and Olley & Pakes (1996). Using variation in firm trade patterns (including differentiating between trade inside and outside the firm, product information and origin/destination of import/exports) and linking this to employee characteristics.

“Do Entrenched Managers Pay Their Workers More?” (Henrik Cronqvist, Fredrik Heyman, Mattias Nilsson, Helena Svaleryd and Jonas Vlachos), forthcoming in Journal of Finance. “Inward FDI and Demand for Skills in Manúnfacturing Firms in Sweden” (Pär Hansson and Roger Bandick), forthcoming in Review of World Economics. "Is there Really a Foreign Ownership Wage Premium? Evidence from Matched Employer-Employee Data" (Fredrik Heyman, Patrik Gustavsson Tingvall and Fredrik Sjöholm), Journal of International Economics, 73(2), “Does Multinationality Matter? Evidence from Swedish Firm Data” (Patrik Karpaty), Applied Economics Quarterly, 52(2), “Skill Upgrading and Production Transfer within Swedish Multinationals” (Pär Hansson), Scandinavian Journal of Economics, 107(4), “Exports as an Indicator on or Promoter of Successful Swedish Manufacturing Firms in the 1990s” (Pär Hansson and Nannan Lundin), Review of World Economics, 140(3), Examples of published work using these databases

Individual data (2) “LOUISE” or “LISA”: Years 1990 – 2004, all individuals older than 15 Demographic variables (age, ethnic origin, immigrant status) Education Employment, plant and firm (November and source of largest income), sick leave, family information

IFN database on MNEs (1) Data on Swedish multinational enterprises and their affiliates: Distribution, sales, exports, employment, R&D, investment and capital, wages and skill-levels Both at the level of the parent firm and the foreign affiliate

IFN database on MNEs (2) Hakkala and Zimmerman, (2005)

Local sales of affiliates (B-form) Exports from affiliates (B-form) Export- from Sweden (A-form) China: Rest of the World (R): Sweden Affiliate Germany IFN database on MNEs (3)

Examples of published work using the IFN database on MNEs Articles: "Asymmetric Effects of Corruption on FDI: Evidence from Swedish Multinational Firms” (Katariina Hakkala, Pehr-Johan Norbäck and Helena Svaleryd), Review of Economics and Statistics. Accepted July “Multinational Enterprises and Wage Costs: Vertical FDI Revisited” (Henrik Braconier, Pehr-Johan Norbäck and Dieter Urban), Journal of International Economics, Vol. 67 (2005), Issue 2: “Reconciling the Evidence of the Knowledge Capital Model” (Henrik Braconier, Pehr-Johan Norbäck and Dieter Urban), Review of International Economics v13, No 4 (2005): “Multinational Firms, Technology and Location” (Pehr-Johan Norbäck), Journal of International Economics, Vol. 54 (2001): “Swedish Multinationals and Competition from High- and Low-Wage Locations” (Braconier, H. and K. Ekholm), Review of International Economics 8 (1999), “Effects of Overseas Production on Home Country Exports: Evidence based on Swedish Multinationals” (R. Svensson), Weltwirtschaftliches Archiv vl32, n2 (1996): Books: Braunerhjelm and Ekholm, editors (1998). The Geography of Multinational Firms. Boston: Kluwer Academic Publishers Andersson et al. (1996), Multinational Restructuring, Internationalisation and Small economies. The Swedish case. Routledge Studies in International Business and the World Economy. Swedenborg, B. (1979). The Multinational Operations of Swedish Firms: An Analysis of Determinants and Effects”, IUI, Stockholm