Land Markets in the EU Candidate Countries of Croatia, Former Yugoslav Republic of Macedonia and Turkey. Factor Markets Working Paper No. 1, September 2011

The paper provides an overview and a comparison of land markets covering the three candidate countries for European Union membership: Croatia, the Former Yugoslav Republic (FYR) of Macedonia and Turkey. It analyses and compares agricultural land structures and factors driving land markets, based on the available cross-section and time-series evidence on agricultural land structures and land productivity (yields). The land productivity measured by production per hectare of agricultural land varies between the three countries. Agricultural land structures are the result of historical evolution in land markets and land-leasing developments with additional different institutional environments and agrarian and land reforms.

A Spatial Analysis of Agricultural Land Prices in Bavaria. Factor Markets Working Document No. 50, June 2013

This paper empirically analyses a dataset of more than 7,300 agricultural land sales transactions from 2001 and 2007 to identify the factors influencing agricultural land prices in Bavaria. We use a general spatial model, which combines a spatial lag and a spatial error model, and in addition account for endogeneity introduced by the spatially lagged dependent variable as well as other explanatory variables. Our findings confirm the strong influence of agricultural factors such as land productivity, of variables describing the regional land market structure, and of non-agricultural factors such as urban pressure on agricultural land prices. Moreover, the involvement of public authorities as a seller or buyer increases sales prices in Bavaria. We find a significant capitalisation of government support payments into agricultural land, where a decrease of direct payments by 1% would decrease land prices in 2007 and 2001 by 0.27% and 0.06%, respectively. In addition, we confirm strong spatial relationships in our dataset. Neglecting this leads to biased estimates, especially if aggregated data is used. We find that the price of a specific plot increases by 0.24% when sales prices in surrounding areas increase by 1%.

RED vs. REDD: Biofuel Policy vs. Forest Conservation. Factor Markets Working Document No. 41, May 2013

This paper assesses the complex interplay between global Renewable Energy Directives (RED) and the United Nations programme to Reduce Emissions from Deforestation and forest Degradation (REDD). We examine the interaction of the two policies using a scenario approach with a recursive-dynamic global Computable General Equilibrium model. The consequences of a global biofuel directive on worldwide land use, agricultural production, international trade flows, food prices and food security out to 2030 are evaluated with and without a strict global REDD policy. We address a key methodological challenge of how to model the supply of land in the face of restrictions over its availability, as arises under the REDD policy. The paper introduces a flexible land supply function, which allows for large changes in the total potential land availability for agriculture. Our results show that whilst both RED and REDD are designed to reduce emissions, they have opposing impacts on land use. RED policies are found to extend land use whereas the REDD policy leads to an overall reduction in land use and intensification of agriculture. Strict REDD policies to protect forest and woodland lead to higher land prices in all regions. World food prices are slightly higher overall with some significant regional increases, notably in Southern Africa and Indonesia, leading to reductions in food security in these countries. This said, real food prices in 2030 are still lower than the 2010 level, even with the RED and REDD policies in place. Overall this suggests that RED and REDD are feasible from a worldwide perspective, although the results show that there are some regional problems that need to be resolved. The results show that countries directly affected by forest and woodland protection would be the most economically vulnerable when the REDD policy is implemented. The introduction of REDD policies reduces global trade in agricultural products and moves some developing countries to a net importing position for agricultural products. This suggests that the protection of forests and woodlands in these regions reverses their comparative advantage as they move from being land-abundant to land-scarce regions. The full REDD policy setting, however, foresees providing compensation to these countries to cover their economic losses.

Influence of the Common Agricultural Policy and Heterogeneous Land Quality on Land Rent and Land Allocation. Factor Markets Working Paper No. 38, March 2013

Against the background of the current discussion about the EU’s common agricultural policy (CAP) after 2013, the question of the impact of government support on land prices is crucially important. Validation of the CAP’s success also hinges on a proper assessment of a choice of policy instruments. This study therefore has the objective of investigating on a theoretical basis the effects of different government support measures on land rental prices and land allocation. The different measures under consideration are the price support, area payments and decoupled single farm payments (SFPs) of the CAP. Our approach evaluates the potential impact of each measure based on a Ricardian land rent model with heterogeneous land quality and multiple land uses. We start with a simple model of one output and two inputs, where a Cobb-Douglas production technology is assumed between the two factors of land and non-land inputs. In a second step, an outside option is introduced. This outside option, as opposed to land use of the Ricardian type, is independent of land quality. The results show that area payments and SFPs become fully capitalised into land rents, whereas in a price support scheme the capitalisation depends on per-acreage productivity. Moreover, in a price support scheme and a historical model, the capitalisation is positively influenced by land quality. Both area payments and price supports influence land allocation across different uses compared with no subsidies, where the shift tends to be larger in an area payment scheme than in a price support scheme. By contrast, SFPs do not influence land allocation.

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.

Drivers of agricultural capital productivity in selected EU member states. Factor Markets Working Paper No. 30, September 2012

The aim of this Working Paper is to provide an empirical analysis of the marginal return on working capital and fixed capital in agriculture, based on data gathered by the Farm Accountancy Data Network from seven EU member states. Particular emphasis is placed on the detection of credit market imperfections. The key idea is to provide farm group-specific estimates of the shadow price of capital, and to use these to analyse the drivers of on-farm capital use in European agriculture. Based on Cobb Douglas estimates of farm-type specific production functions, we find that working capital is typically used in more than economically optimal quantities and often displays negative marginal returns across countries and farm types. This is less often the case with regard to fixed capital, but it is only in a small set of sectors where access to fixed capital appears severely constrained. These sectors include field crop and mixed farms in Denmark, dairy farms in East Germany, as well as mixed farms in Italy and the UK. The relationship between farm financial indicators and the estimated shadow prices of capital varies considerably across countries and sectors. Among the farms with a high shadow price for fixed capital in Denmark, high debt levels and little owned land tended to induce more intensive capital use, which may reflect the liberal Danish banking system. In East Germany, Italy and the UK, high debt levels made farmers more tightly capital constrained. Hence, in the latter group of countries, more traditional mechanisms of capital allocation based on debt capacity seemed to be at work. As a general conclusion, EU agriculture appears to be characterised by overcapitalisation rather than by credit constraints.

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.

Identifying Factor Productivity from Micro-data: The case of EU agriculture. Factor Markets Working Paper No. 34, January 2013

The classical problem of agricultural productivity measurement has regained interest owing to recent price hikes in world food markets. At the same time, there is a new methodological debate on the appropriate identification strategies for addressing endogeneity and collinearity problems in production function estimation. We examine the plausibility of four established and innovative identification strategies for the case of agriculture and test a set of related estimators using farm-level panel datasets from seven EU countries. The newly suggested control function and dynamic panel approaches provide attractive conceptual improvements over the received ‘within’ and duality models. Even so, empirical implementation of the conceptual sophistications built into these estimators does not always live up to expectations. This is particularly true for the dynamic panel estimator, which mostly failed to identify reasonable elasticities for the (quasi-) fixed factors. Less demanding proxy approaches represent an interesting alternative for agricultural applications. In our EU sample, we find very low shadow prices for labour, land and fixed capital across countries. The production elasticity of materials is high, so improving the availability of working capital is the most promising way to increase agricultural productivity.

Will improved access to capital dampen the need for more agricultural land? A CGE analysis of agricultural capital markets and world-wide biofuel policies. Factor Markets Working Paper No. 48, May 2013

This paper analyses the consequences of enhanced biofuel production in regions and countries of the world that have announced plans to implement or expand on biofuel policies. The analysis considers biofuel policies implemented as binding blending targets for transportation fuels. The chosen quantitative modelling approach is two-fold: it combines the analysis of biofuel policies in a multi-sectoral economic model (MAGNET) with systematic variation of the functioning of capital and labour markets. This paper adds to existing research by considering biofuel policies in the EU, the US and various other countries with considerable agricultural production and trade, such as Brazil, India and China. Moreover, the application multi-sectoral modelling system with different assumptions on the mobility of factor markets allows for the observation of changes in economic indicators under different conditions of how factor markets work. Systematic variation of factor mobility indicates that the ‘burden’ of global biofuel policies is not equally distributed across different factors within agricultural production. Agricultural land, as the pre-dominant and sector-specific factor, is, regardless of different degrees of inter-sectoral or intra-sectoral factor mobility, the most important factor limiting the expansion of agricultural production. More capital and higher employment in agriculture will ease the pressure on additional land use – but only partly. To expand agricultural production at global scale requires both land and mobile factors adapted to increase total factor productivity in agriculture in the most efficient way.