Drivers of agricultural capital productivity in selected EU member sates

Drivers of agricultural capital
productivity in selected EU
member states
Mathias Kloss
Leibniz Institute of Agricultural Development in Central & Eastern Europe (IAMO)
Halle (Saale), Germany
kloss@iamo.de
All results based on Petrick/Kloss 2012, FM Working Paper No. 30

Contents
1.  Summary and Introduction
2.  The credit-constrained farm
3.  Issues in empirical implementation
4.  Database
5.  Selection of farm and country subgroups
6.  Results
7.  Conclusions
2

1. Summary and Introduction
•  Assessment of capital productivity in European agriculture is a matter of
controversy
•  Findings are not consistent over time and regions
•  Explanations:
•  Low capital productivities due to non pecuniary benefits or the wish to
provide safeguards against production risks
•  Aurbacher et al. (2011) argue that small agricultural structures may be
unable to coordinate on machinery sharing à hold inefficiently high
stocks of machinery
•  High capital productivities as a result of constrained access to capital
3

1. Introduction - Agenda
Ø  Empirical analysis of the marginal return on fixed and working capital
in agriculture
Ø  Emphasis on the detection of credit market imperfections
Ø  Idea: Estimate farm group-specific estimates of the shadow interest
rate and use these to analyse the drivers of on-farm capital use in EU
agriculture
Ø  Denmark, France, Germany (West and East), Italy, Poland, Slovakia,
United Kingdom
Ø  Sectors: field crops, specialised dairy farms, mixed farms
Ø  Main message: EU agriculture dominated by overcapitalisation rather
than by credit constraints
4

2. The credit-constrained farm
•  Assume profit maximisation subject to some credit constraint
•  One output and one variable input (fully credit funded)
(1)
(2)
(3)
5
Maxπ = pf x,z( )− 1+r( )x,subjectto
K − x ≥ 0,
p∂f ∂x =1+r*
>1+r, with r*
≡ r + η

Determinants of the shadow interest rate
•  Indebtedness
•  capital markets restriction (+)
•  Preference for overcapitalisation due too liberal banking practice
(-)
•  Farm assets (e.g. land) as collateral (-)
•  Corporate farms assumed to be less attractive clients for lenders,
e.g. liability restrictions (+)
•  Age of farmer
•  Longer credit history (-)
•  uncertainties concerning the farm successor (+)
6

3. Issues in empirical implementation
•  How to measure the a priori credit rationing status?
a)  Assume that all borrowers are credit constrained
b)  Direct elicitation of credit constraints
c)  Grouping the data into a priori credit constrained and
unconstrained farms
•  Production function approach
•  Unobserved heterogeneity
•  Functional form
7

Steps
1)  Subgroups of farms formed based on country, farm type & a
priori credit rationing status
2)  Cobb Douglas production functions for the subgroups
estimated, allowing for varying technology parameters in
each subgroup.
3)  Based on the estimated production elasticities, shadow
prices of fixed & working capital calculated
4)  The drivers of capital productivity analysed in a second-
stage regression of shadow prices; separately for countries,
farm types & fixed vs. working capital
8

4. Database
9
FADN code Variable description
Outputs
SE131 Total output (€)
Inputs
SE011 Labour input (hours)
SE025 Total utilised agricultural area (ha)
SE275 Total intermediate consumption (€) = working capital
SE360 Depreciation (€) = fixed capital
SE085 Dairy cows (livestock units; in dairy and mixed farms)
Determinants of shadow prices
SE485/SE436 Debt-to-asset ratio
SE025-SE030 Owned land (ha)
A18 Corporate farm (1/0)
C01YR Age of manager (years)
Source: FADN data.

5. Selection of farm and country subgroups
•  Subgroups were drawn according to
•  member state,
•  farm type, and
•  field crops (TF1), specialised dairy farms (TF5) and
mixed farms (TF8)
•  a priori credit rationing status
10

5. Selection of farm and country subgroups
11
Country Agricultural structure
(as represented in the data)
Agricultural finance
Denmark (DK) Medium-scale farms, highly
commercialised
Liberal lending; high investment and
financial leverage in agriculture
France (FR) Medium-scale family farms Centralised, cooperative banking
sector; preferential lending rates for
agriculture
Germany East
(DEE) and West
(DEW)
Small- to medium-scale family farms
(West); large corporate farms (East)
Mixed cooperative, savings and
commercial banks; lower debt
capacity in the East
Italy (IT) Small-scale family farms Very low financial leverage
Poland (PL) Small-scale family farms Cooperative banking sector, emerging
commercial banks, preferential
lending rates, low investment levels
Slovakia (SK) Large corporate farms Specialised agricultural bank, low
investment levels
United Kingdom
(UK)
Medium-scale farms, highly
commercialised
Lending primarily by non-specialised
commercial banks; traditionally a
focus on overdraft loans
Sources: Authors based on Pietola et al. (2011); Benjamin and Phimister (2002); European
Commission (2010); FADN data

6. Results – How to handle
DEW-
f
DEW-
d
DEW-
m
Capital elasticity
identified
yes yes yes
Mean shadow price
high debt > low debt
yes yes no
Excessive average
shadow price level
no no no
Effect on shadow price
Debt-to-asset ratio ! + +
Owned land (ha) + + +
Corporate farm (1/0) + n.a. n.a.
Age of manager ! ! !
Age square + + +
051015
-200 -150 -100 -50 0 50 100 150 200
Shadow price of fixed capital (%)
Mixed
051015
Frequency(%)
-200 -150 -100 -50 0 50 100 150 200
Field crops
051015
-200 -150 -100 -50 0 50 100 150 200
Constrained Unconstrained
Dairy
12

Fixed capital
DK-f DK-m DEE-f DEE-d IT-m UK-m
Capital elasticity identified yes yes no high
debt only
yes yes
Mean shadow price high
debt > low debt
no no yes yes yes yes
Excessive average
shadow price level
yes yes no yes yes yes
Debt-to-asset ratio ! ! + + + +
Owned land (ha) + + + + + !
Corporate farm (1/0) + + ! 0 + +
Age of manager ! 0 ! 0 0 +
Age square + 0 + 0 + !
Source: Authors’ estimations based on FADN data. 13

14
051015
Frequency(%)
-200 -150 -100 -50 0 50 100 150 200
Field crops
051015
-200 -150 -100 -50 0 50 100 150 200
Mixed
051015
Frequency(%)
-200 -150 -100 -50 0 50 100 150 200
Field crops
051015
-200 -150 -100 -50 0 50 100 150 200
Dairy
051015
-200 -150 -100 -50 0 50 100 150 200
Mixed
051015
-200 -150 -100 -50 0 50 100 150 200
Mixed
DKDK DEE
DEE IT UK

Working capital
DK-f DK-m FR-f DEW-d IT-f PL-f UK-f
Capital elasticity
identified
yes yes yes yes yes yes yes
Mean shadow price
high debt > low debt
yes no no yes no no no
Excessive average
shadow price level
no yes no no low debt
only
no no
Debt-to-asset ratio ! + ! + ! ! !
Owned land (ha) + + ! ! ! ! +
Corporate farm (1/0) + 0 0 n.a. + 0 0
Age of manager + 0 0 0 + + +
Age square 0 0 0 + ! ! !
Source: Authors’ estimations based on FADN data. 15

16
051015
Frequency(%)
-200 -150 -100 -50 0 50 100 150 200
Shadow price of working capital (%)
Field crops
051015
-200 -150 -100 -50 0 50 100 150 200
Mixed
051015
Frequency(%)
-200 -150 -100 -50 0 50 100 150 200
Field crops
051015
-200 -150 -100 -50 0 50 100 150 200
Dairy
051015
Frequency(%)
-200 -150 -100 -50 0 50 100 150 200
Field crops
051015
Frequency(%)
-200 -150 -100 -50 0 50 100 150 200
Field crops
051015
Frequency(%)
-200 -150 -100 -50 0 50 100 150 200
Field crops
DK DK FR
DEW IT PL
UK

7. Conclusions
•  Overuse of working capital across countries and farm types
•  Overuse of fixed capital is less often the case
•  There is only a small number of sectors where fixed capital is severely
constrained
•  Relationship between farm financial indicators and the estimated shadow
prices varies considerably across countries and sectors
Ø  EU agriculture dominated by overcapitalisation rather than by credit
constraints
Ø  Policy Implication: downsize the importance of capital subsidies
17

Limitations and further research
•  Cobb Douglas Technology
•  Estimation approach
•  Control function approach (Olley and Pakes (1996),
Levinson and Petrin (2003), Ackerberg et al. (2006))
•  Limits of the conceptual framework
•  Drivers of overcapitalisation in EU agriculture need further
research
18

Drivers of agricultural capital productivity in selected EU member sates

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