Agricultural and Food Science, Vol. 18(2009): 283-301 A G R I C U L T U R A L A N D F O O D S C I E N C E Vol. 18 (2009): 283–301. 283 © Agricultural and Food Science Manuscript received February 2009 Productivity growth on Finnish grain farms from 1976–2006: a parametric approach Sami Myyrä1, Pekka Pihamaa2 and Timo Sipiläinen1 1MTT Agrifood Research Finland, Economic Research, Luutnantintie 13, FI-00410 Helsinki, Finland, 2Ministry of Agriculture and Forestry, Finland, 1email: firstname.lastname@mtt.fi In the long term, productivity and especially productivity growth are necessary conditions for the survival of farms and the food industry in Finland. The natural handicap and small farm size are challenges, but farmers are further challenged by the decoupling of supports and their transformation into direct income payments. Additionally, farmers’ actions are limited by some institutional settings that substantially reduce incentives to improve productivity. Technical progress was found to drive the increase in productivity on grain farms in Finland. The scale had only a moderate effect and for the whole study period (1976–2006) the effect was close to zero. Total fac- tor productivity (TFP) increased, depending on the model, by 0.6–1.7% per year. The results demonstrated that the increase in productivity was hindered by the policy changes introduced in 1995. The cumulative increase in TFP over the study period was at the same level as the measured yearly changes in TFP. The results highlight the nature of grain farming in Finland as well as the challenges in simultaneously taking into account the general trend and yearly variation in TFP. Key-words: technical change, scale effect, production function, time trend, general index. Introduction Although Finns prefer Finnish food, most Finnish products have to compete on the market with for- eign alternatives at a fairly uniform price level. The same price linkage applies to primary production, which provides raw materials for the food industry. Therefore, in the long term, productivity and espe- cially productivity growth are necessary conditions for the survival of farms and the food industry on the market, and in general for the continuation of agriculture in Finland. A G R I C U L T U R A L A N D F O O D S C I E N C E Myyrä, S. et al. Productivity growth on Finnish grain farms 284 A G R I C U L T U R A L A N D F O O D S C I E N C E Vol. 18 (2009): 283–301. 285 Profitability at the farm level is determined by prices, subsidies and the productivity at which inputs are transformed to outputs. Prices and sub- sidies are to a large extent exogenous for farmers. However, farmers are able to adjust productivity at the farm level. This takes place according to the incentives given in prices and subsidies. One could say that increased productivity is the farmer’s con- tribution to the future success of Finnish agricul- ture. The problem in Finland is that productivity has remained low and productivity growth has stag- nated for several reasons. First, productivity has been low because of the natural handicap result- ing from the unfavourable climate and the small size of farms. Second, the political tendency of decoupling market-distorting price supports and transforming them into direct income payments has challenged farmers to reach the productivity goals of the CAP under Nordic production condi- tions. Once support payments are decoupled from production decisions, the economic incentives for productivity improvements diminish if farmers find that production costs will in any case remain higher than marginal returns. Third, farmers’ incentives are also limited by some institutional settings that substantially cut incentives to improve productiv- ity. Land tenure insecurity is one of the most im- portant of these institutional questions. The combination of prices, subsidies and pro- ductivity provides tools for agricultural policy. In the case of declining productivity in agriculture, the Finnish government has to put more money into subsidies in order to sustain farm profitability and the running of Finland’s primary industry. In the case of increasing productivity, the situation is reversed and becomes attractive for consumers and taxpayers. Productivity analysis is needed both to provide information about current productivity trends in agriculture and to guide policy makers when they decide on the most appropriate policy measures. This gives justification for the present study. Finnish agriculture has experienced a rapid structural change within the last thirty years. The number of active farms in 1976 was 242 682, while in 2006 it had declined to only 69 071 (TIKE 2006). This indicates an average annual exit rate as high as 4.1%. However, the number of grain farms has not decreased in conformity with the general trend, as from 1990 to 2006 the annual decrease was only 1.3%. Under EU membership the number of grain farms has fallen even less, by 0.27% an- nually, as many small- and medium-sized farms have ceased animal production but continued crop farming. Simultaneously, this transformation has often included moving to other occupations and to part-time farming. The relatively slow structural change in grain farming may have slowed down the rate of productivity growth at the sector level. The slow structural development in grain farm- ing as compared to other production lines is prob- ably a consequence of several policy measures im- plemented during the period from 1976 to 2006. Finnish agricultural policy has faced numerous urgent tasks in maintaining sufficient farm income. Within the period from 1976 to 1994, these tasks involved a variety of outcomes with respect to the objective of self-sufficiency in grain production. High output prices and good yields led to strong overproduction in 1976 and 1977. For example, wheat production exceeded domestic consumption by 50%. With policy measures including compul- sory set-aside and export duties on grain, self-suf- ficiency collapsed from 1978–1982 to an average level of 50% of total consumption. Since then, self- sufficiency in grains has remained between 75% and 125%, except in some years with extremely poor weather conditions. The policy reform caused by Finland’s entry into the EU in 1995 significantly reduced agricul- tural output prices. To counterbalance the effects of the price decrease, extensive policy measures were introduced with the objective of maintaining farm income through income support1 (per hectare and per animal payments) and policy measures such as investment aids that were expected to support pro- 1 The Finnish agricultural subsidy system has four components: the arable area payment under the Common Agricultural Policy (CAP), Less Favorable Area (LFA) sup- port, environmental support, and national aid and northern aid for agriculture. The subsidy system is complicated. In addition, the conditions for crop production and the subsidy system differ between southern and northern Finland (Niemi and Ahlstedt 2004). A G R I C U L T U R A L A N D F O O D S C I E N C E Myyrä, S. et al. Productivity growth on Finnish grain farms 284 A G R I C U L T U R A L A N D F O O D S C I E N C E Vol. 18 (2009): 283–301. 285 ductivity growth and structural development. De- spite the new policy measures, the marginal value product (MVP) of both short- and long-term land improvements2 significantly decreased with the re- form. If we assume increasing returns to scale, the reduced use of inputs is expected to depress the average product (i.e. productivity) of inputs (see e.g. Gravelle and Rees 1998, p. 186). On the other hand, a previous index-based productivity study suggested that over the long term the reduction in MVP has delayed land improvement, which is likely to slow down productivity growth especially on grain farms (Myyrä 2004). For example, the soils of Finland have been formed from acidic rock and the pH values in ag- ricultural soils of the country are commonly low. Therefore, liming is one of the basic ameliorative measures used to maintain good yields. A slight but steady increase in soil pH could be observed from the 1960s until the 1990s, but during the last dec- ade in particular this progress has stopped because of reduced liming. Institutions also have an influence on farmers’ incentives for productivity improvements. Despite the rapid decline in the number of active farmers, the number of farmland owners has not decreased at a similar rate, since many former farmers and their successors have kept the land in their owner- ship but leased it out to active farmers. Farming under the insecurity of land tenure caused by land leasing in strongly regulated3 land lease markets has, however, led to the neglect of land improve- ments. As land improvements are a necessary con- dition for improving productivity in grain farming, this neglect may also have a long term influence on productivity growth. In addition to land improve- ments, land consolidation and restructuring of field 2 Fertilization is an example of a short-term land improvement measure, while long-term land improvement measures include liming and investments in drainage sys- tems. 3 The standard land lease contract in Finland is a short-term contract with a fixed duration and a fixed cash lease payment per year. About 40% of all lease contracts have a duration of five years. With only a few exceptions, the annual cash lease payment is fixed per hectare of land when the contract is signed. plots is especially needed under rapid structural development. Weather conditions are the main driver of inter- annual variability in productivity on Finnish grain farms. This is because most of the inputs are ap- plied in the spring on the basis of expected yields, but the output is strongly affected by the weather conditions during the growing season. Liu and Pi- etola (2005) showed that yield volatility is large and dominates price volatility in Finnish wheat production. Nauges et al. (2009) reported that between 1995 and 2003, the yield explained 80% of the variation in annual wheat revenues, while the price explained 18% and the acreage only 2%. Thus, large variation in productivity growth in se- quential periods typically occurs in Finnish data. This feature of the data challenges the methods of analysis: they should reveal the variation but still capture the long-term trends in productivity growth. This study has three main goals. The first goal is to determine the rate of productivity growth on Finnish grain farms over the 30-year period from 1976 to 2006. We apply both time trend and gen- eral index techniques in order to capture the pat- terns of technical change during the period. The second goal is to examine how technical change has evolved in different subsidy regions and in dif- ferent size classes of grain farms. The third goal is to clarify the role of the scale effect in productivity growth in general and especially in various farm size classes, but also in different subsidy regions. In earlier Finnish studies on productivity change, both econometric estimation (Hemilä 1982, Yläta- lo 1987, Ryhänen 1994, Sipiläinen and Ryhänen 2005, Sipiläinen 2007) and index numbers (Iha- muotila 1972, Sims 1994, Myyrä and Pietola 1999) have been applied. The majority of these studies are relatively old, and even fewer have separately examined grain production. The two exceptions in this respect are a study by Myyrä and Pietola (1999), in which the data covered almost the entire 1990s and index number techniques were applied, and Sipiläinen’s (2003) examination of productiv- ity growth on cereal farms, applying nonparametric methods (data envelopment analysis, DEA) and the Malmquist index. In the latter study, productivity A G R I C U L T U R A L A N D F O O D S C I E N C E Myyrä, S. et al. Productivity growth on Finnish grain farms 286 A G R I C U L T U R A L A N D F O O D S C I E N C E Vol. 18 (2009): 283–301. 287 growth from 1989 to 2000 was decomposed into technical change and technical efficiency change. The results suggested relatively moderate techni- cal change and only a minor change in technical efficiency. The same conclusion concerning the change in technical efficiency also applies to dairy farms (Sipiläinen 2008). Therefore, we concentrate in this article on technical change and on the scale component of productivity change. Returns to scale (RTS) are of particular inter- est in Finland, where the goals of the government include improvement in the productivity and profit- ability of production. If returns to scale are increas- ing, productivity can be increased by enlarging the scale of production. Conversely, if returns to scale are decreasing, productivity can be increased by shrinking the scale. Our study is carried out at the farm level. If re- turns to scale are increasing, structural change and the growth in scale should still continue because this would support productivity growth. Decreas- ing RTS should probably also not imply an im- mediate reduction in the scale of production, since even if we could improve average productivity by shrinking the scale this would not necessarily be profitable in economic terms. Technical change (TC) is usually the most important component of productivity improve- ment. Technical change means either a neutral or non-neutral shift in the production function over a period of years. Such a shift is the result of in- troducing new and more productive technology. This technical change effect has to be taken into account, especially in long-term analysis (Kumb- hakar and Lovell 2000, p. 107). In this study we apply a flexible functional form, which allows non- neutral technical change and non-constant returns to scale. Our model also allows heterogeneity of technical change between size classes of farms. In addition, we apply both time trend and general index methods in order to capture the long- and short-term variation in technical change. The results show that average returns to scale are less than 1 in all models that apply farm-spe- cific intercepts (fixed effect). This suggests that on average, no productivity gains can be obtained by increasing the scale of production. Productiv- ity mainly grows because of technical progress, which averages 1.7% per year in the time trend (TT) model and 0.8% per year in the general index (GI) model and is most rapid in the largest farm classes and in the south of Finland. The paper is structured as follows. In the next section the econometric model is derived. Section three presents the Finnish grain farm data and the variables used in the analysis. Section four pro- vides the results and econometric tests and section five the discussion and conclusions. The econometric model Modelling productivity change In the case of a logarithmic production function, following Denny et al. (1981) and Bauer (1990), the Divisia index of total factor productivity (TFP) growth can be defined as the growth in scalar out- put (y = f(x,t;α)), which cannot be explained by the growth in the input quantity index (vector X) over time (t): , (1) j j jj x C xw XwhenXyTFP ••••• ∑=−= where wjxj ___ C is the observed cost share on the input j (w is the price of the input x) and the dot indicates the rate of change. In a single output case, with constant returns to scale (CRS) and cost efficiency (CE), TFP growth equals technical change (TC). In our analysis, we assume efficiency but allow non-constant returns to scale. Thus, our production function is not a frontier function but an average production function, and technical change is measured in relation to the shifts in this average function. Taking the total differential of logarithmic y =f (x,t;) and adding it into (1) we obtain: (2) j j jj j j j j j j j j j j j jj j j x C xw xty x C xw tyTFP •• •• ∑ ∑∑ ∑∑ ∑           −           +           −+∂∂= −+∂∂= ε ε ε ε ε ε )1(ln )(ln A G R I C U L T U R A L A N D F O O D S C I E N C E Myyrä, S. et al. Productivity growth on Finnish grain farms 286 A G R I C U L T U R A L A N D F O O D S C I E N C E Vol. 18 (2009): 283–301. 287 where εj is the elasticity of the output with re- spect to input j, i.e. εj =∂f (x,t,α)/ ∂ln xj, when a logarithmic function is ∑ j εj applied is the sum of output elasticities of inputs, indicating returns to scale. When the sum is larger than one, returns to scale are increasing. A sum of one indicates con- stant returns to scale, and a sum less than one sug- gests that returns to scale are decreasing. If we assume allocative efficiency of production,4 we may drop the last part of equation 2, since the elasticity share and cost share must coincide. From this it follows that we only have two components left that can be derived from the production function: technical change and the scale effect on productivity growth. If production technology is time-invariant, no technical change occurs (∂ln y/∂t= 0). On the other hand, if CRS prevails, the scale component does not contrib- ute to productivity growth (see Kumbhakar and Lovell 2000). For this technical decomposition, no detailed price information is needed, although we often have to assume that farmers face equal prices in order to be able to estimate changes in TFP.5 The main challenge is thus to estimate a suitable production function. The main advantage of the parametric approach is that TFP growth can be easily decomposed into sub-components such as technical change and farm-specific returns to scale. The respective de- compositions can also be based on non-parametric estimation methods, such as data envelopment analysis, which are less restrictive with respect to assumptions about the production technology, but which usually do not take into account the stochas- tic nature of the production process. Time trend and general index models In our analysis, we apply two different models to capture technical change. In the time trend model (TT), the trend variable is used as a regressor along 4 We do not necessarily assume technically efficient production. The minimum assumption of our analysis is that technical inefficiency, if it occurs, must be time invariant. 5 Many of the inputs are only recorded in monetary terms. Farm-specific input prices are not available. Thus, sector level price indices have to be applied for the deriva- tion of implicit quantities. with the input variables. It is a proxy variable rep- resenting the rate of technical change or the shift in the production function over time, and produces smooth technological changes. As a starting point, we allow a flexible translog functional form with non-neutral technical change and heterogeneous changes between size classes of farms.6 The time trend (TT1) model can be written as: (3) where v is the random noise term. The production function above is assumed to satisfy symmetry con- ditions; the regularity conditions can be tested. The price of smoothness in the measures of technical change in the TT model is that cyclical phenomena and short-term changes in productiv- ity or its components could not be revealed. This feature of the TT model is referred to as the “time- trend straitjacket” in Kumbhakar et al. (1999) In the general index model of Baltagi and Griffin (1988), the trend variable t is replaced by a vector of dummy time variables, where A(t) (t = 1,…,T) are parameters to be estimated. The time trend model results in a smooth shift in the produc- tion function over time, while time dummies cap- ture erratic changes over time. The latter model is thus less restrictive and preferable when capturing the variation in grain production in Finland. The yields are quite volatile, due to the climatic condi- tions. In this case we also allow non-neutral techni- cal change.7 Thus, the general index (GI) model of the production function can be written as: (4) 6 This heterogeneity is modelled by allowing different slope parameters on time according to the farm size class. 7 In this case we allow heterogeneity of technical change by introducing separate dummies for the farm size classes. ∑ ∑∑∑ +++ +++= j jjttt j k kjjkt j jj v,txt xxtxy ln lnlnlnln 2 2 1 2 1 0 αα αααα ∑ ∑∑∑ +++ ++= j jjt j k kjjk j jj vtAtx xxxy )(ln lnlnlnln 210 α ααα A G R I C U L T U R A L A N D F O O D S C I E N C E Myyrä, S. et al. Productivity growth on Finnish grain farms 288 A G R I C U L T U R A L A N D F O O D S C I E N C E Vol. 18 (2009): 283–301. 289 Technical change (TC) (derivatives with re- spect to time) in the time trend and general index models can be expressed as: (7) and (8) In the flexible translog production function, technical change is clearly not independent of the point at which it is calculated when continuous t is applied. This leads us to use the geometric mean between t and t+1 as follows (Coelli et al. 1998): (9) and (10) There is one restriction in the pure time trend model. With unbalanced panel data it is not clear whether the trend variable for a firm entering in pe- riod τ(1<τ |t| Intercept 0.917713 0.4335 2.12 0.0343 CAP dummy –0.00187 0.00483 –0.39 0.6982 CAP dummy^2 –0.00108 0.000479 –2.25 0.0243 d 1989 0.101015 0.0235 4.30 <.0001 d 1987 –0.29597 0.0252 –11.76 <.0001 d 2000 0.187878 0.0275 6.84 <.0001 l (xj) 0.102927 0.0314 3.28 0.0011 c (xj) 0.311363 0.0471 6.61 <.0001 cd (xj) 0.36096 0.0384 9.41 <.0001 t –0.02389 0.0217 –1.10 0.2715 lt (xjt) 0.00021 0.00160 0.13 0.8954 ct (xjt) –0.00178 0.00232 –0.77 0.4441 cdt (xjt) 0.002529 0.00208 1.22 0.2239 tt 0.002373 0.000478 4.97 <.0001 d2 (d2t) 0.003373 0.00135 2.50 0.0125 d3 (d3t) 0.006766 0.00170 3.98 <.0001 d4 (d4t) 0.010479 0.00199 5.26 <.0001 d5 (d5t) 0.01304 0.00238 5.47 <.0001 l = labour, c = capital, cd = variable cost, t = time and d1 … d5 are farm size class dummies. The model includes 404 fixed ef- fect dummies separately for each farm (405) which are not presented here. Equation DF Model DF Error SSE MSE Root MSE R-Square Adj R-Sq y 422 2856 185.6 0.0650 0.2549 0.9006 0.8860 A G R I C U L T U R A L A N D F O O D S C I E N C E Myyrä, S. et al. Productivity growth on Finnish grain farms 300 A G R I C U L T U R A L A N D F O O D S C I E N C E Vol. 18 (2009): 283–301. 301 General index model Equation DF Model DF Error SSE MSE Root MSE R-Square Adj R-Sq y 561 2717 144.1 0.0530 0.2303 0.9229 0.9070 Parameter Parameter Estimate Standard Error t Value Pr > |t| Intercept 1.20601 0.51996 2.32 0.0204 l (xj) 0.10530 0.02965 3.55 0.0004 c (xj) 0.24785 0.04878 5.08 <.0001 cd (xj) 0.41726 0.03703 11.27 <.0001 lt (xjt) –0.00042803 0.00150 –0.29 0.7756 ct (xjt) 0.00095802 0.00235 0.41 0.6834 cdt (xjt) –0.00065930 0.00199 –0.33 0.7407 There are also 5 (size class) × 29 (year) + 405 (farm) = 550 dummy variables in the general index model, which are not presented here. l = labour, c = capital, cd = variable cost, t = time. Introduction Results Conclusions References SELOSTE Appendix