Jaana Korhonen, 3rd International Congress on Planted Forests Porto, Portugal May 18, 2013
Objectives Data Methods Results Conclusions
Identify factors that influence investments in forest plantations Macroeconomic Institutional Forestry Comparision between OECD90 and non- OECD90 Examine implications for investors, countries seeking investments Influential factors may be linked to expected profits Relate to concept of competiveness
IAIF Forest investment attractiveness SUPRAsectoral factors Profitability of forest investment INTRAsectoral factors INTERsectors factors IADB, 2010
1000 ha, Source FAO 80000 70000 60000 50000 40000 30000 20000 140000 120000 100000 80000 60000 40000 10000 20000 0 1990 2000 2005 2010 0 1990 2000 2005 2010 Data for Portugal and South Korea are missing for 1990 OECD countries Non-OECD countries
OECD 90 Non-OECD 90 Asia North America Japan South Korea Europe Belgium Oceania Denmark Finland France Germany Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland United Kingdom Canada United States Mexico Australia New Zealand Africa South Africa Asia China India Indonesia Malaysia Thailand Central America Costa Rica Guatemala Nicaragua Panama Mexico Europe Russian Federation South America Argentina Bolivia Brazil Chile Colombia Ecuador Paraguay Peru Uruguay Venezuela
Cross-country panel data Four datapoints 1990, 2000, 2005 and 2010 Or closest year possible for a few data sets Data collection 6 months effort open information from public sources Dependent variable Area of planted forests From FAO
Dependent variable: the area of planted forests Macroeconomic indicators Gross Domestic Product Credit provided by a banking sector Foreign direct investments Institutionals indicators Tariff rate Corruption Unemployment Forest sector indicators Roundwood production Forest productivity
1) The pooled OLS model Y it =a+ K k=1 2a) The fixed effects model Y it = ai + 2b) The random Effects Model Y it = ai + K X kit + uit β X + k kit uit k=1 K k=1 β X + k kit (uit + vi ) Y it = area of planted forest for individual i at time t a= constant β k = coefficient, individual effect of country k X it = variable value for individual i at time t u it = random error of the model that varies over observations i and time t v i = random disturbance characterizing the ith observation
For OECD countries, the Hausmann test unexpectedly favored the random effects model Pooled OLS Non-OECD countries the most preferable model was fixed effects model Results differ for OECD and non-oecd countries
GDP positively affects the area of planted forests Coefficients of banking credit and FDIs are positive, but not significant. Tariffs have a positive effect Unemployment and corruption were not significant Roundwood production is highly significant and positively affects the development of planted forests. Productivity also showed a positive, if not quite significant, effect on the area of planted forests. Pooled OLS Variable Coefficient (Std. error) GDP 1.46** (0.58) Banking credit 0.69 (0.48) FDI 0.13 (0.10) Tariff 1.07** (0.34) Corruption -0.37 (1.29) Unemployment 0.17 (0.35) Productivity 0.41 (0.27) Roundwood production 0.24** (0.10) Constant ** indicates significance at 5% and * at 10 % confidence level Number of observations 52 Df 43 R 2 0.86 Adj R 2 0.84
GDP showed a positive and significant elasticity in the fixed effects model Banking credit was significant in none of the models Countries with an overall high level of foreign direct investment also had larger areas of planted forests Tariffs had a negative and, at a 10% level, almost significant impact in the fixed effects model. The effects of corruption proved to be a significant institutional factor Unemployment insignificant Forest productivity insignificant Roundwood production had a positive and significant effect This underscores the importance of resource availability and the capacity of processing wood for industrial needs. Non-OECD countries Fixed effects Variable Coefficient (Std. error) GDP 0.14** (0.06) Banking credit -0.03 (0.10) FDI 0.05 (0.03) Tariff -0.15 (0.09) Corruption -0.36** (0.15) Unemployment -0.03 (0.09) Productivity -0.17 (0.18) Roundwood production 0.18** (0.08) Constant ** indicates significance at 5% and * at 10% confidence level Number of observations 52 Df 25 R 2 1.00 Adj R 2 0.996
Macroeconomic factors important Institutional factors less investments occur despite country investment challenges Forestry factors significant Results differ for OECD and non-oecd countries Resource availability and well functioning national roundwood markets key competiveness factors in OECD countries In non-oecd countries the land availability problems are likely to increase and investors need to tolerate more international risks
IADB (2010) accessible at http://www.iadb.org/en/aboutus/about-the-inter-american-developmentbank,5995.html FAO. 2011. FAO: State of the world's forests 2010. Rome, Italy. 2006. Global forest resource assessment 2005: Progress towards sustainable forest resource assessment. 147.