Investment as the key to recovery in the euro area? CEPS Policy Brief No. 326, 18 November 2014

Investment has declined in the euro area since the start of the economic and financial crisis, but this does not mean that there is necessarily an ‘investment gap’, explains Daniel Gros in this CEPS Policy Brief. Investment was probably above a sustainable level due to the credit boom before 2007. Moreover, the fall in the euro area’s potential growth − due to a combination of a sharp demographic slowdown and lower total factor productivity (TFP) growth − should also lead to a permanently lower investment rate. Increasing the investment rate might thus be the wrong target for economic policy. The author advises that the aim of economic policy should be to increase consumption, rather than investment overall. Increasing infrastructure investment might be justified in some member countries, but it is not a ‘free lunch’ when efficiency levels are low, which seems to be the case in some of the financially stressed euro area countries.

Productivity and Credit Constraints: Firm-Level Evidence from Propensity Score Matching. Factor Markets Working Paper No. 3, September 2011

Drawing on a unique, farm-level panel dataset with 37,409 observations and employing a matching estimator, this paper analyses how farm access to credit affects farm input allocation and farm efficiency in the Central and Eastern European transition countries. We find that farms are asymmetrically credit constrained with respect to inputs. Farm use of variable inputs and capital investment increases up to 2.3% and 29%, respectively, per €1,000 of additional credit. Our estimates also suggest that farm access to credit increases total factor productivity up to 1.9% per €1,000 of additional credit, indicating that an improvement in access to credit results in an adjustment in the relative input intensities on farms. This finding is further supported by a negative effect of better access to credit on labour, suggesting that these two are substitutes. Interestingly, farms are found not to be credit constrained with respect to land.

Defining an Institutional Framework for the Labour Market. Factor Markets Working Paper No. 24, February 2012

This paper describes a conceptual framework for the empirical analysis of farmers’ labour allocation decisions. The paper presents a brief overview of previous farm household labour allocation studies. Following this, the agricultural household model, developed by Singh, Squire and Strauss (1986), which has been frequently applied to the study of labour allocation, is described in more depth. The agricultural household model, the theoretical model to be used in this analysis, is based on the premise that farmers behave to maximise utility, which is a function of consumption and leisure. It follows that consumption is bound by a budget constraint and leisure by a time constraint. The theoretical model can then be used to explain how farmers decide to allocate their time between leisure, farm work and off-farm work within the constraints of a finite time endowment and a budget constraint. Work, both farm and off-farm, provides a return to labour which in turn relaxes the budget constraint allowing the farm household to consume more. The theoretical model can also be used to explore the impact on government policies on labour allocation. It follows that subsidies that decrease commodity prices, such as reductions in intervention prices, mean that farmers have to work more (either on or off the farm) to maintain income and consumption levels. On the other hand, income support subsidies that are not linked to output or labour, such as decoupled subsidies, are a source of non-labour income and as such allow farmers to work less while maintaining consumption levels, known as the wealth effect.

Effects of Economic Factors on Adoption of Robotics and Consequences of Automation for Productivity Growth of Dairy Farms. Factor Markets Working Paper No. 32, December 2012

In the long term, productivity and especially productivity growth are necessary conditions for the survival of a farm. This paper focuses on the technology choice of a dairy farm, i.e. the choice between a conventional and an automatic milking system. Its aim is to reveal the extent to which economic rationality explains investing in new technology. The adoption of robotics is further linked to farm productivity to show how capital-intensive technology has affected the overall productivity of milk production. The empirical analysis applies a probit model and an extended Cobb-Douglas-type production function to a Finnish farm-level dataset for the years 2000–10. The results show that very few economic factors on a dairy farm or in its economic environment can be identified to affect the switch to automatic milking. Existing machinery capital and investment allowances are among the significant factors. The results also indicate that the probability of investing in robotics responds elastically to a change in investment aids: an increase of 1% in aid would generate an increase of 2% in the probability of investing. Despite the presence of non-economic incentives, the switch to robotic milking is proven to promote productivity development on dairy farms. No productivity growth is observed on farms that keep conventional milking systems, whereas farms with robotic milking have a growth rate of 8.1% per year. The mean rate for farms that switch to robotic milking is 7.0% per year. The results show great progress in productivity growth, with the average of the sector at around 2% per year during the past two decades. In conclusion, investments in new technology as well as investment aids to boost investments are needed in low-productivity areas where investments in new technology still have great potential to increase productivity, and thus profitability and competitiveness, in the long run.

Does land fragmentation affect farm performance? A case study from Brittany. Factor Markets Working Paper No. 40, April 2013

Agricultural land fragmentation is widespread and may affect farmers’ decisions and impact farm performance, either negatively or positively. The authors investigated this impact for the western region of Brittany, France, in 2007, regressing a set of performance indicators on a set of fragmentation descriptors. The performance indicators (production costs, yields, revenue, profitability, technical and scale efficiency) were calculated at the farm level using Farm Accountancy Data Network (FADN) data, while the fragmentation descriptors were calculated at the municipality level using data from the cartographic field pattern registry (RPG). The various fragmentation descriptors enabled the authors to account for not only the traditional number and average size of plots, but also their geographical scattering. They found that farms experienced higher costs of production, lower crop yields and lower profitability where land fragmentation (LF) was more pronounced. Total technical efficiency was not found to be significantly related to any of the municipality LF descriptors used, while scale efficiency was lower where the average distance to the nearest neighbouring plot was greater. Pure technical efficiency was found to be negatively related to the average number of plots in the municipality, with the unexpected result that it was also positively related to the average distance to the nearest neighbouring plot. By simulating the impact of hypothetical consolidation programmes on average pre-tax profits and wheat yield, the study also showed that the marginal benefits of reducing fragmentation may differ with respect to the improved LF dimension and the performance indicator considered. The analysis therefore shows that the measures of land fragmentation usually used in the literature do not reveal the full set of significant relationships with farm performance and that, in particular, measures accounting for distance should be considered more systematically.