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

[email protected] 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

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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

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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,subject toK− 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

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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

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4. Database

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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

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5. Selection of farm and country subgroups

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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 + + + 0

510

15

-200 -150 -100 -50 0 50 100 150 200Shadow price of fixed capital (%)

Mixed

05

1015

Freq

uenc

y (%

)


Field crops

05

1015


Constrained Unconstrained

Dairy

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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


no no yes yes yes yes


yes yes no yes yes yes

Effect on shadow price 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

05

1015

Freq

uenc

y (%

)


Field crops

05

1015


Mixed

05

1015

Freq

uenc

y (%

)


Field crops

05

1015



Dairy

05

1015


Mixed

05

1015


Mixed

DK DK 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


yes no no yes no no no


no yes no no low debt only

no no

Effect on shadow price 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

05

1015

Freq

uenc

y (%

)

-200 -150 -100 -50 0 50 100 150 200Shadow price of working capital (%)

Field crops

05

1015


Mixed

05

1015

Freq

uenc

y (%

)


Field crops0

510

15



Dairy

05

1015

Freq

uenc

y (%

)


Field crops

05

1015

Freq

uenc

y (%

)


Field crops

05

1015

Freq

uenc

y (%

)


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

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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

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drivers of agricultural capital productivity in selected eu member sates

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