ISSN 1691-3078
ISBN 978-9934-8304-6-4
ECONOMIC SCIENCE FOR
RURAL DEVELOPMENT
Proceedings of the International
Scientific Conference
No. 30
Production and Cooperation in Agriculture
Finance and Taxes
T. Balezentis et al.
7KH7UHQGVRI7HFKQLFDODQG$OORFDWLYH(I¿FLHQF\LQ/LWKXDQLDQ)DPLO\)DUPV
THE TRENDS OF TECHNICAL AND ALLOCATIVE EFFICIENCY IN
LITHUANIAN FAMILY FARMS
Tomas Balezentis1, Irena Krisciukaitiene, Dr
Lithuanian Institute of Agrarian Economics
Alvydas Balezentis, Prof. Dr
Mykolas Romeris University
Abstract.7KHHFRQRPLFRUFRVWHI¿FLHQF\FDQEHGHFRPSRVHGLQWRWHFKQLFDODQGDOORFDWLYHHI¿FLHQFLHV7KHWHFKQLFDO
HI¿FLHQF\LVUHODWHGWRIDUPDELOLW\WRWUDQVIRUPLQSXWVWRRXWSXWVZKHUHDVWKHDOORFDWLYHHI¿FLHQF\LVWKHUDWLRRIWKH
observed and the optimal cost, and measures farm’s ability to choose an optimal input-mix. The paper employed the
&RVW0DOPTXLVW,QGH[WRPHDVXUHWKHFKDQJHVLQHFRQRPLFHI¿FLHQF\DVZHOODVWKHWHFKQRORJLFDOFKDQJH7KHUHIRUH
the total factor productivity change was estimated for the sample of 200 Lithuanian family farms covering the period
of 2004–2009. The results indicated that the cost productivity decreased by some 8%, whereas the total factor
SURGXFWLYLW\ ± E\ GXULQJ WKH SHULRG 'HFOLQHV LQ ERWK SXUH WHFKQLFDO HI¿FLHQFLHV ZHUH WKH PDLQ GULYHUV RI WKH
decrease, and the scale effect had almost no impact.
Key words:HI¿FLHQF\WRWDOIDFWRUSURGXFWLYLW\0DOPTXLVW,QGH[GDWDHQYHORSPHQWDQDO\VLV/LWKXDQLD
JEL code: &&&44
Introduction
The accession to the European Union in 2004 rendered
PDQ\ VLJQL¿FDQW WUDQVIRUPDWLRQV IRU WKH /LWKXDQLDQ
DJULFXOWXUH 6SHFL¿FDOO\ WKH SURGXFWLRQ DQG HTXLSPHQW
VXEVLGLHV JDYH D PRPHQWXP IRU PRGHUQL]DWLRQ 2Q WKH
RWKHU KDQG WKH ÀXFWXDWLRQV RI WKH UHODWLYH SULFHV RI
the agricultural production resulted in the decreasing
prevalence of the livestock farming and, to some extent,
in farm expansion. These transformations obviously
reshaped the technologies of the agricultural production.
Family farms produce the largest share of the
agricultural output in Lithuania. As for 2004–2009, some
75–71% of the gross agricultural output was produced in
the family farms, whereas the remaining part came from
the agricultural enterprises. Although the agricultural
FRPSDQLHVPDLQO\VSHFLDOL]HLQFURSIDUPLQJWKHVKDUHRI
livestock production there did not decrease to the same
extent as it occurred in the family farms.
One of the key features describing the performance
RI DJULFXOWXUDO VHFWRU LV LWV SURGXFWLYH HI¿FLHQF\ 7KH
LVVXHV RI DJULFXOWXUDO HI¿FLHQF\ DUH WKRVH RI SDUWLFXODU
LPSRUWDQFH LQ WKH &HQWUDO DQG (DVW (XURSHDQ &((
VWDWHV WKDQNV WR WKHLU HFRQRPLF VWUXFWXUH LQÀXHQFHG E\
the historical turmoil during the 20th century (Gorton M.,
'DYLGRYD 6 $OYDUH] $ $ULDV & Henningsen A., Kumbhakar S., 2009; Henningsen A.,
(I¿FLHQF\ PHDVXUHV DUH WKH SULPDO WRROV IRU WKH
HFRQRPLF VFLHQFH DQG SROLF\PDNLQJ 6SHFL¿FDOO\ RQH
FDQ FRQVLGHU FHUWDLQ W\SHV RI HI¿FLHQF\ HJ WHFKQLFDO
HI¿FLHQF\7(DOORFDWLYHHI¿FLHQF\$(FRVWHI¿FLHQF\
&( DQG SUR¿W HI¿FLHQF\ 7KLV SDSHU IRFXVHV RQ WKH
WHFKQLFDO HI¿FLHQF\ PHDVXUHPHQW ZKHUHRI LQYROYHV QR
SULFHLQIRUPDWLRQDQGFRVWHI¿FLHQF\ZKLFKUHTXLUHLQSXW
price data.
The frontier methods are the primal tools for distance
IXQFWLRQ HVWLPDWLRQ DQG PHDVXUHPHQW RI WKH HI¿FLHQF\
(Samarajeewa S. et al., 2012). The productivity indices
___________________________
based on the distance functions can then be employed
WR PHDVXUH WKH WRWDO IDFWRU SURGXFWLYLW\ &RHOOL 7-
Rao D.S.P., 2005; Ippoliti R., Falavigna G., 2012;
Tohidi G. et al., 2012; Epure M. et al., 2011). The total
factor productivity change can also be decomposed
into the different terms identifying the causes thereof
(Malmquist S., 1953; Fare R. et al., 1992; Fare R. et al.,
1994; Maniadakis N., Thanassoulis E.,, 2004).
Whereas Vinciuniene V. and Rauluskeviciene J.
5LPNXYLHQH ' HW DO DQG %DOH]HQWLV 7
HWDODWWHPSWHGWRHVWLPDWHWKHHI¿FLHQF\RIWKH
Lithuanian agricultural sector, however the total factor
productivity changes and cost productivity changes are
VWLOOWRSLFDOLVVXHVIRUWKHVFLHQWL¿FUHVHDUFK%DOH]HQWLV7
(2012) employed the cost Malmquist Index to
analyse the trends of the cost productivity change in
Lithuanian agriculture. This particular paper is based
RQ WKH PHWKRGRORJ\ GHYHORSHG E\ %DOH]HQWLV 7 HW DO
(2012). In the present study, the authors will further
analyse the results across different farming types.
This paper therefore seeks to assess the changes in
FRVWHFRQRPLFHI¿FLHQF\RIWKH/LWKXDQLDQIDPLO\IDUPV
during the period of 2004–2009. The non-parametric
IURQWLHU WHFKQLTXH YL] GDWD HQYHORSPHQW DQDO\VLV
'($ LV HPSOR\HG DORQJVLGH ZLWK WKH &RVW 0DOPTXLVW
Index to measure the cost productivity change. The
micro data covering some 200 family farms for the
period 2004–2009 were obtained from the Farm
Accountancy Data Network (FADN).
Preliminaries for the cost Malmquist
Productivity Index
Measurement of the total factor productivity (TFP)
of certain DMU involves measures for both technological
DQG ¿UPVSHFL¿F GHYHORSPHQWV $V %RJHWRIW 3 DQG
2WWR / SXW LW ¿UP EHKDYLRXU FKDQJHV
&RUUHVSRQGLQJDXWKRUTel.: + 370 5 261 81 45; fax: +370 5 261 45 24.
E-mail address: [email protected].
1
Economic Science for Rural Development
ISSN 1691-3078
No. 30, 2013
91
T. Balezentis et al.
7KH7UHQGVRI7HFKQLFDODQG$OORFDWLYH(I¿FLHQF\LQ/LWKXDQLDQ)DPLO\)DUPV
over time should be explained in terms of special initiatives as well as technological progress. The
EHQFKPDUNLQJ OLWHUDWXUH %RJHWRIW 3 DQG 2WWR / &RHOOL 7 5DR '63 VXJJHVWV WKH 0DOPTXLVW
Productivity Index as the most celebrated TFP measure. Hence, this section describes the preliminaries of
the Malmquist Index.
The technology set and respective frontier are likely to shift from one period to another. Therefore, one needs
an appropriate measure to identify these changes. The Malmquist Productivity Index (Malmquist S., 1953) can be
HPSOR\HGWRHVWLPDWH7)3FKDQJHVRIDVLQJOH¿UPRYHUWZRSHULRGVRUvice versa), across two production modes,
strategies, locations etc. In this study, the authors will focus on the input–oriented Malmquist Productivity Index and
DSSO\LWWRPHDVXUHSHULRG±ZLVHFKDQJHVLQ7)37KHLQSXW±RULHQWHG0DOPTXLVW3URGXFWLYLW\,QGH[GXHWR&DYHV':
HWDOLVGH¿QHGDV
,
(1)
t
with indices t and t UHSUHVHQWLQJ UHVSHFWLYH SHULRGV DQG D, ,&56 being the Shepard distance function
for the period t DVVXPLQJ FRQVWDQW UHWXUQV WR VFDOH &56 7KH WZR WHUPV LQ EUDFNHWV IROORZ WKH VWUXFWXUH
RI )LVKHU¶V LQGH[ 7KHUHDIWHU D QXPEHU RI VWXGLHV )DUH 5 HW DO 5D\ 6& DQG 'HVOL ( 6LPDU / DQG :LOVRQ 3: :KHHORFN '& DQG :LOVRQ 3: DWWHPSWHG WR GHFRPSRVH WKH ODWWHU
LQGH[ LQWR GLIIHUHQW WHUPV HDFK H[SODLQLQJ FHUWDLQ IDFWRUV RI SURGXFWLYLW\ VKLIWV 6SHFL¿FDOO\ )DUH 5 HW DO
GHFRPSRVHG SURGXFWLYLW\ FKDQJH LQWR HI¿FLHQF\ FKDQJH Ʃ( RU FDWFKLQJ XS DQG WHFKQLFDO FKDQJH
Ʃ7RUVKLIWVLQWKHIURQWLHU
,
(2)
7KHWHUPƩ(PHDVXUHVWKHUHODWLYHWHFKQLFDOHI¿FLHQF\FKDQJH7KHLQGH[EHFRPHVJUHDWHUWKDQXQLW\LQFDVHWKH
¿UP DSSURDFKHV IURQWLHU RI WKH FXUUHQW WHFKQRORJ\ Ʃ7 LQGLFDWHV ZKHWKHU WKH WHFKQRORJ\ KDV SURJUHVVHG DQG WKXV
PRYHGIXUWKHUDZD\IURPWKHREVHUYHGSRLQW,QFDVHRIWHFKQRORJLFDOSURJUHVVWKHƩ7EHFRPHVJUHDWHUWKDQXQLW\
and that virtually means, that it is possible to produce more using fewer resources. Given the Malmquist Productivity
Index measures TFP growth, improvement in productivity will be indicated by values greater than unity, whereas
regress – by that below unity.
As one can note, the decomposition of Fare R. et al. (1992) does not take into account the variable returns to scale
956WHFKQRORJ\DQGFRQVHTXHQWO\VFDOHHI¿FLHQF\)DUH5HWDOWKXVIXUWKHUGHFRPSRVHGWKHƩ(FRPSRQHQW
LQWRWKHWZRIDFWRUVQDPHO\SXUHWHFKQLFDOHI¿FLHQF\FKDQJHƩ37DQGVFDOHHI¿FLHQF\FKDQJHƩ6(7KHUHIRUHWKH
Malmquist (M) Productivity Index was decomposed into three parts:
M,
ZKHUHWKHWHUPƩ7UHIHUVWR(TDQG
'PT
'SE
'E ˜ 'T { ('PT ˜ 'SE ) ˜ 'T ,
1
D,t ,956
xt 1 , yt 1 D,t ,956 xt , y t ,
1
1
§ D,t ,&56
xt 1 , yt 1 D,t ,956
xt 1 , yt 1 ·¸
¨
.
¨ D,t ,&56 x t , y t D,t ,956 x t , y t ¸
©
¹
(3)
(4)
(5)
7KHUHIRUH Ʃ37 DQG Ʃ6( PHDVXUHV ¿UPVSHFL¿F FKDQJHV LQ SURGXFWLYLW\ UHODWHG WR VKLIWV LQ WHFKQLFDO DQG VFDOH
HI¿FLHQF\ZKHUHDVƩ7LGHQWL¿HVVKLIWVLQWKHWHFKQRORJ\IURQWLHU
The
discussed
Malmquist
Productivity
Index
is
suitable
to
analyse
the
dynamics
of
WHFKQLFDO DQG VFDOH HI¿FLHQF\ ,Q RUGHU WR PHDVXUH WKH FKDQJHV LQ HFRQRPLF FRVW HI¿FLHQF\
0DQLDGDNLV1DQG7KDQDVVRXOLV(RIIHUHGWKH&RVW0DOPTXLVW,QGH[
92
Economic Science for Rural Development No. 30, 2013
ISSN 1691-3078
7KH7UHQGVRI7HFKQLFDODQG$OORFDWLYH(I¿FLHQF\LQ/LWKXDQLDQ)DPLO\)DUPV
T. Balezentis et al.
§ Zt [ t 1 & t ( \ t 1 , Zt ) Zt 1 [ t 1 & t 1 ( \ t 1 , Zt 1 ) ·
&0 { ¨
¸
t t
t
t
t
Zt 1 [t & t 1 ( \ t , Zt 1 ) ¹
© Z [ & (\ ,Z )
1/ 2
.
(6)
The cost ratio Z [ & ( \ , Z ) is a reciprocal of the Farrel’s measure, and measures the extent to which the
aggregate production cost in period t can be reduced while maintaining the output vector yt given the input price
t t
vector wt. This ratio measures the distance between the observed cost, namely w x DQGWKHFRVWIURQWLHUGH¿QHGE\
t
t
t
& (\ ,Z ).
$FFRUGLQJWR0DQLDGDNLV1DQG7KDQDVVRXOLV(WKH&RVW0DOPTXLVW&0,QGH[FDQEHGHFRPSRVHGLQWR
WKHWZRFRPSRQHQWVYL]RYHUDOOHI¿FLHQF\FKDQJHƩ2(DQGFRVWWHFKQLFDOFKDQJHƩ&7
t
t
t
t
t
&0
'2( ˜ '&7 ,
(7)
where
Zt 1 [t 1 & t 1 ( \ t 1 , Zt 1 )
'2( {
,
Zt [t & t ( \ t , Zt )
and
(8)
§ Zt [ t 1 & t ( \ t 1 , Zt )
Zt [ t & t ( \ t , Zt ) ·
'&7 { ¨ t 1 t 1 t 1 t 1 t 1 t 1 t t 1 t t 1 ¸
© Z [ & (\ ,Z ) Z [ & (\ ,Z ) ¹
1/ 2
.
(9)
7KHUHE\Ʃ2(PHDVXUHV¿UPVSHFL¿FFKDQJHVLQFRVWHI¿FLHQF\UHODWHGWRLQSXW±PL[DQGƩ&7FDWFKHVWKHFRPELQHG
HIIHFWRIFKDQJHVLQLQSXWSULFHVDQGWHFKQRORJ\ERWKRIZKLFKDUHRXWRI¿UP¶VFRQWURO
%\UHODWLQJFRPSRQHQWVRIWKH&0WRWKRVHRIWKH0,QGH[RQHFDQIXUWKHUGHFRPSRVHWKHWZRWHUPVRIWKH&0
)LUVWO\Ʃ2(FDQEHGHFRPSRVHGLQWRHI¿FLHQF\FKDQJHƩ(DQGDOORFDWLYHHI¿FLHQF\FKDQJHƩ$(7KHIRUPHUWHUP
FDQEHHVWLPDWHGE\HPSOR\LQJHLWKHU(TRU(TVDQGZKHUHDVƩ$(LVREWDLQHGE\WKHYLUWXHRIWKHIROORZLQJ
computations:
'AE {
1
Zt 1 [t 1 & t 1 ( \ t 1 , Zt 1 ) ',t ,&56
( [ t 1 , \ t 1 ) Zt [ t & t ( \ t , Zt ) ',t , &56 ( [ t , \ t ) .
(10)
6HFRQGO\Ʃ&7FDQEHGHFRPSRVHGLQWRWHFKQLFDOFKDQJHƩ7DQGSULFHHIIHFWƩ37KHƩ7WHUPLVREWDLQHGDFFRUGLQJ
ZLWKWKH(TZKLOHƩ3LVGH¿QHGLQWKHIROORZLQJZD\
§ Zt [ t 1 & t ( \ t 1 , Zt ) ',t , &56 ( [ t 1 , \ t 1 ) Zt [ t & t ( \ t , Zt ) ',t ,&56 ( [ t , \ t ) ·
¸
'P { ¨ t 1 t 1
1
t 1
t 1
t 1 t
t 1
t
t 1
t 1
t
t
¨ Z [ & t 1 ( \ t 1 , Zt 1 ) ',t ,&56
¸
(
,
)
(
,
)
(
,
)
[
\
Z
[
&
\
Z
'
[
\
,
,
&56
©
¹
1/ 2
.
(11)
)LQDOO\WKH&RVW0DOPTXLVW3URGXFWLYLW\,QGH[FDQEHGHFRPSRVHGLQWRWKHVHFRPSRQHQWV
&0
'
37
˜ '6(
7
˜ '
3 { 0 ˜ '$( ˜ '3 .
˜ '$( ˜ '
'AE
'E
(12)
'2(
7KH&RVW0DOPTXLVW,QGH[FRXOGEHIXUWKHUGHFRPSRVHGLQWKHVSLULWRI5D\6&DQG'HVOL(6LPDU/DQG
:LOVRQ3::KHHORFN'&DQG:LOVRQ3:KRZHYHUWKHVHFRPSXWDWLRQVDUHRXWRIVFRSHRIWKLVSDSHU
Preliminaries for DEA
7KH GLVWDQFH IXQFWLRQV IRU UHVSHFWLYH FRPSRQHQWV RI WKH &RVW 0DOPTXLVW ,QGH[ FDQ EH REWDLQHG E\ HPSOR\LQJ
1,2,..., K DMUs, each producing j 1,2,..., n outputs from i 1,2,..., m
DEA. Suppose that there are k
inputs. Hence, DMU kH[KLELWV)DUUHOLQSXW±RULHQWHGWHFKQLFDOHI¿FLHQF\ T k ZKHUHDV6KHSDUGWHFKQLFDOHI¿FLHQF\LVD
reciprocal number, 1/ T k .
l ,t
l ,t
The distance function for the lWK¿UPSRVVHVVLQJLQSXW±RXWSXWEXQGOH ( x , y ) in terms of the technology set
of the period t may be obtained by solving the following multiplier DEA program 2:
2
Indeed, Maniadakis N. and Thanassoulis E. (2004) used
D, , &56 ( x , y )
min TO , i. e. the Shepard
measures. These, however, would invert the interpretation of the Malmquist Index Tl , Ok presented in this paper,
thus making it less intuitive.
___________________________
Economic Science for Rural Development
ISSN 1691-3078
No. 30, 2013
t
l ,t
l ,t
1
93
T. Balezentis et al.
7KH7UHQGVRI7HFKQLFDODQG$OORFDWLYH(I¿FLHQF\LQ/LWKXDQLDQ)DPLO\)DUPV
(13)
Meanwhile, the distance function, when the input–output bundle of one period t LV FRPSDUHG WR WKH HI¿FLHQF\
frontier of another period, may be obtained by solving the following multiplier DEA program:
(14)
,Q (TV DQG WKH FRHI¿FLHQWV O k are weights of the peer DMUs. Noteworthy, this model
SUHVXPHV H[LVWLQJ FRQVWDQW UHWXUQV WR VFDOH &56 ZKLFK LV D UDWKHU DUELWUDU\ FRQGLWLRQ &56
indicates that the manufacturer is able to scale the inputs and outputs linearly without increasing or
GHFUHDVLQJ HI¿FLHQF\ 7KH YDULDEOH UHWXUQV K WR WKH VFDOH PRGHO KHQFH FDQ EH ZULWWHQ E\ VXSSOHPHQWLQJ
O 1.
Eqs. 13 and 14 with a convexity constraint,
k 1 k
According to Thanassoulis E. et al. (2008), in case of considering the input–output bundle and the input
costs of the tWK SHULRG WKH PLQLPXP FRVW FDQ EH REWDLQHG E\ WKH YLUWXH RI WKH IROORZLQJ OLQHDU FRVW PLQLPL]DWLRQ
model:
¦
(15)
l ,t
where wi
are the input prices for the l–th DMU. This model yields the minimum cost, which is
FRPSDUHG ZLWK WKH DFWXDO FRVWV ZKHQ FRPSXWLQJ WKH &RVW 0DOPTXLVW ,QGH[ ,Q FDVH LI RQH ZDQWV
to obtain the minimum cost with respect to technology of a different period, the following model is
implemented:
94
Economic Science for Rural Development No. 30, 2013
ISSN 1691-3078
T. Balezentis et al.
7KH7UHQGVRI7HFKQLFDODQG$OORFDWLYH(I¿FLHQF\LQ/LWKXDQLDQ)DPLO\)DUPV
(16)
7KH GLVFXVVHG OLQHDU SURJUDPPLQJ PRGHOV SURYLGH WKH EDVLV IRU FRPSXWDWLRQV RI WKH FRPSRQHQWV RI WKH &RVW
Malmquist Index.
Data Used and Results
7KH WHFKQLFDO DQG VFDOH HI¿FLHQF\ ZDV DVVHVVHG
in terms of the input and output indicators commonly
employed
for
agricultural
productivity
analyses
(Bojnec S., Latruffe L., 2011; Douarin E., Latruffe L.,
0RUH VSHFL¿FDOO\ WKH XWLOL]HG DJULFXOWXUDO DUHD
(UAA) in hectares was chosen as a land input variable,
annual work units (AWU) – as a labour input variable,
intermediate consumption in Litas, and total assets in
Litas as a capital factor. On the other hand, the three
output indicators represent crop, livestock and other
outputs in Litas, respectively. Indeed, the three output
indicators enable to tackle the heterogeneity of production
technology across different farms.
7KH FRVW HI¿FLHQF\ ZDV HVWLPDWHG E\ GH¿QLQJ
respective prices for each of the four inputs described
earlier. The land price was obtained from the Eurostat
and assumed to be uniform for all farms during the
same period. The labour price is an average salary in
the agricultural sector taken from Statistics Lithuania.
The price of the capital is depreciation plus interests
per one Litas of assets. Meanwhile, the intermediate
ȴPT
ȴSE
ȴT
consumption is directly considered as a part of
total costs.
The data for 200 farms selected from the FADN sample
cover the period of 2004–2009. Therefore, a balanced
panel of 1200 observations is employed for analysis.
The analysed sample covers relatively large farms
(mean UAA – 244 ha). As for labour force, the average
was 3.6 AWU.
7KH G\QDPLFV RI WKH &RVW 0DOPTXLVW 3URGXFWLYLW\
Index and its components is given in Fig. 1. The
DOORFDWLYH HI¿FLHQF\ DQG SULFH FKDQJH HLWKHU SOD\HG
D UDWKHU LQVLJQL¿FDQW UROH GXULQJ PRVW RI WKH SHULRGV
or moved to the opposite directions (e.g. during
2005–2006). Therefore, the difference between the
0DOPTXLVW3URGXFWLYLW\,QGH[0DQGWKH&RVW0DOPTXLVW
3URGXFWLYLW\ ,QGH[ &0 UHPDLQHG FORVH WR QRXJKW
Obviously, the TFP was decreasing during most of the
periods save those of 2006–2008. As for 2006–2007, the
TFP change was mainly driven by the outward movement
of the production frontier, which indicated the recovery
after unsuccessful year 2006.
ȴAE
ȴP
CM
M
Logged rate of change
0.60
0.40
0.20
0.00
-0.20
-0.40
2004-2005
2005-2006
2006-2007
2007-2008
2008-2009
Source: designed by the authors.
Fig. 1. Dynamics of the cost Malmquist Index and its components, 2004–2009
Economic Science for Rural Development
ISSN 1691-3078
No. 30, 2013
95
T. Balezentis et al.
7KH7UHQGVRI7HFKQLFDODQG$OORFDWLYH(I¿FLHQF\LQ/LWKXDQLDQ)DPLO\)DUPV
Logged rate of change
0.20
0.15
0.10
0.00
-0.10
0.00
-0.08
-0.08
-0.12
-0.20
-0.02
-0.20
-0.30
CM
ȴPT
M
ȴSE
ȴT
ȴAE
ȴP
Source: designed by the authors.
Fig. 2. The cumulative change in the Cost Malmquist Index and its components, 2004–2009
(rectangles encompass the two productivity indices and their components)
Crop
Livestock
Mixed
Rate of change (%)
10
5
0
-5
-10
-15
CM
M
ȴPT
ȴSE
ȴT
ȴAE
ȴP
Fig. 3. The cumulative change in the cost Malmquist Index and
its components across farming types, 2004–2009
Overall, the decrease in the TFP of some 20% was
observed for the whole sample taking into account the
period of 2004–2009. Thanks to a positive price change
HIIHFW Ʃ3 WKH FRVW SURGXFWLYLW\ GHFUHDVHG WR D PDUJLQ
RI VRPH 7KH SXUH WHFKQLFDO HI¿FLHQF\ GHFUHDVHG
by some 12%, and thus constituted the main source
RI GHFUHDVH LQ WKH 7)3 7KH FXPXODWLYH VFDOH HI¿FLHQF\
FKDQJHƩ6(VXPPHGXSWR]HURWKXVLQGXFLQJDSUHVHQFH
of the underlying constant returns to scale technology.
0HDQZKLOH WKH DOORFDWLYH HI¿FLHQF\ FKDQJH Ʃ$( ZDV
QHJDWLYH DOEHLW TXLWH LQVLJQL¿FDQW 7KHUHIRUH WKH
LQSXWPL[ DGMXVWPHQWV ZHUH QRW HI¿FLHQF\LQFUHDVLQJ
either.
In order to assess the farming-type features of the
TFP change, the Fig. 3 exhibits the mean values for the
Malmquist Productivity Indices across crop (crop output
constituted at least 2/3 of the total output), livestock
(livestock output constituted at least 2/3 of the total
output), and mixed farms. The analysis showed that the
crop farms had suffered to the highest extent in terms of
the TFP losses. However, the loss in the cost productivity
was alleviated by the lowest decrease in productivity
FDXVHGE\FKDQJHLQWKHDOORFDWLYHHI¿FLHQF\7KHODWWHU
¿QGLQJ LPSOLHV WKDW WKH FURS IDUPV ZHUH WKRVH PRVW
HI¿FLHQWO\ DGMXVWLQJ WKHLU LQSXWPL[HV 7KH PL[HG IDUPV
experienced both the highest gains from the input price
change and the highest losses induced by the decreasing
DOORFDWLYHHI¿FLHQF\
7KH VFDOH HI¿FLHQF\ FKDQJHG UDWKHU LQVLJQL¿FDQWO\
as regarding the livestock farms, whereas the
two remaining farming types experienced a slight
decrease therein. The steepest decrease in the pure
WHFKQLFDO HI¿FLHQF\ ZDV REVHUYHG IRU WKH FURS IDUPV
whereas the production frontier moved inwards to
the highest extent with respect to the livestock and
mixed farms.
96
Economic Science for Rural Development No. 30, 2013
ISSN 1691-3078
T. Balezentis et al.
7KH7UHQGVRI7HFKQLFDODQG$OORFDWLYH(I¿FLHQF\LQ/LWKXDQLDQ)DPLO\)DUPV
Conclusions, proposals,
recommendations
The paper estimated the total factor productivity
change for the sample of 200 Lithuanian family farms
covering the period of 2004–2009. The results indicated
that the cost productivity decreased by some 8%,
whereas the total factor productivity – by 20% during the
SHULRG 'HFOLQHV LQ ERWK SXUH WHFKQLFDO HI¿FLHQFLHV ZHUH
the main drivers of the decrease, and the scale effect had
almost no impact.
The crop farming should draw an immediate
attention in terms of modernisation of this farming type,
experienced the lowest input price effect and the highest
GHFUHDVH LQ WKH SXUH WHFKQLFDO HI¿FLHQF\ 2Q WKH RWKHU
hand, the very production frontier moved inwards to a
lower extent, if compared to the remaining farming types.
Although the livestock and mixed farms did not exhibit
WKHVDPHVWHHSGHFUHDVHLQWKHSXUHWHFKQLFDOHI¿FLHQF\
their production frontiers mowed inwards to a higher
extent, and thus resulted in the decreased total factor
productivity.
Bibliography
1.
2.
3.
4.
5.
6.
7.
8.
9.
Malmquist, S. (1953). Index Numbers and
Indifference Surfaces. Trabajos de Estatistica,
Volume 4. Issue 2, pp. 209–242.
$OYDUH] $ $ULDV & 7HFKQLFDO (I¿FLHQF\
DQG )DUP 6L]H $ &RQGLWLRQDO $QDO\VLV Agricultural
Economics. Volume 30, pp. 241–250.
%DOH]HQWLV 7 7KH &RVW 0DOPTXLVW
Index decomposition for analysis of the total
factor productivity change in Lithuanian family
farms. Zemes ukio mokslai, Volume 19, Issue 3,
pp. 168–179.
%DOH]HQWLV 7 .ULVFLXNDLWLHQH , %DOH]HQWLV $
)DUPLQJ (I¿FLHQF\ DFURVV WKH (8 0HPEHU
States and Farming Types: Frontier Benchmarking.
Economic
Science
for
Rural
Development:
3URFHHGLQJV RI WKH ,QWHUQDWLRQDO 6FLHQWL¿F
Conference, Rural Business and Finance, Volume 28,
pp. 20–24.
Bogetoft, P., Otto, L. (2011). Benchmarking with
DEA, SFA, and R. International Series in Operations
Research and Management Science, Vol. 157.
Springer. p. 351.
%RMQHF 6 /DWUXIIH / )DUP 6L]H DQG
(I¿FLHQF\GXULQJ7UDQVLWLRQ,QVLJKWVIURP6ORYHQLDQ
Farms. Transformations in Business & Economics,
Volume 10, Issue 3, pp. 104–116.
&DYHV ' : &KULVWHQVHQ / 5 'LHZHUW : (
(1982). The Economic Theory of Index Numbers
and the Measurement of Input, Output, and
Productivity. Econometrica, Volume 50, Issue 6,
pp. 1393–1414.
&RHOOL 7 - 5DR ' 6 3 7RWDO
Factor Productivity Growth in Agriculture: A
0DOPTXLVW ,QGH[ $QDO\VLV RI &RXQWULHV
1980–2000. Agricultural Economics, Volume 32,
pp. 115–134.
Douarin, E., Latruffe, L. (2011). Potential Impact of
the EU Single Area Payment on Farm Restructuring
DQG(I¿FLHQF\LQ/LWKXDQLDPost-Communist Studies,
Volume 23, pp. 87–103.
Economic Science for Rural Development
ISSN 1691-3078
No. 30, 2013
10. Epure, M., Kerstens, K., Prior, D. (2011).
Bank Productivity and Performance Groups:
A Decomposition Approach Based upon the
Luenberger
Productivity
Indicator.
European
Journal of Operational Research, Volume 211,
pp. 630–641.
11. Fare, R., Grosskopf, S., Lindgren, B., Roos, P.
3URGXFWLYLW\&KDQJHVLQ6ZHGLVK3KDUPDFLHV
1980–1989: A Non-parametric Malmquist Approach.
Journal of Productivity Analysis, Volume 3, pp. 85–
101.
12. Fare, R., Grosskopf, S., Norris, M., Zhang, Z.
(1994). Productivity Growth, Technical Progress,
DQG
(I¿FLHQF\
&KDQJH
LQ
,QGXVWULDOL]HG
&RXQWULHV American Economic Review, Volume 84,
pp. 66–83.
13. Gorton, M., Davidova, S. (2004). Farm Productivity
DQG (I¿FLHQF\ LQ WKH &(( $SSOLFDQW &RXQWULHV
A Synthesis of Results. Agricultural Economics,
Volume 30, pp. 1–16.
14. Henningsen, A. (2009). Why is the Polish Farm
Sector still so Underdeveloped? Post-Communist
Economies, Volume 21, pp. 47–64.
15. Henningsen,
A.,
Kumbhakar,
S.
(2009).
Semiparametric
Stochastic
Frontier
Analysis:
An
Application
to
Polish
Farms
during
Transition. Abstract from European Workshop on
(I¿FLHQF\ DQG 3URGXFWLYLW\ $QDO\VLV (:(3$ Pisa, Italy.
16. ,SSROLWL 5 )DODYLJQD * (I¿FLHQF\ RI WKH
medical care industry: Evidence from the Italian
regional system. European Journal of Operational
Research, Volume 217, pp. 643–652.
17. Maniadakis, N., Thanassoulis, E. (2004). A
&RVW 0DOPTXLVW 3URGXFWLYLW\ ,QGH[ European
Journal of Operational Research, Volume 154,
pp. 396–409.
18. 5D\ 6 & 'HVOL ( 3URGXFWLYLW\ *URZWK
7HFKQLFDO 3URJUHVV DQG (I¿FLHQF\ &KDQJH LQ
,QGXVWULDOL]HG &RXQWULHV &RPPHQW American
Economic Review, Volume 87, pp. 1033–1039.
19. Rimkuviene, D., Laurinaviciene, N., Laurinavicius, J.
(6VDOLX]HPHVXNLRHIHNW\YXPRLYHUWLQLPDV
>(YDOXDWLRQ RI $JULFXOWXUDO (I¿FLHQF\ LQ WKH (8
&RXQWULHV@ Vagos: LZUU mokslo darbai [Vagos:
6FLHQWL¿F 3DSHUV RI /8$@ 9ROXPH ,VVXH pp. 81–89.
20. Samarajeewa, S., Hailu, G., Jeffrey, S. R.,
%UHGDKO0$QDO\VLVRI3URGXFWLRQ(I¿FLHQF\
RI%HHI&DOI)DUPVLQ$OEHUWDApplied Economics,
Volume 44, pp. 313–322.
21. Simar, L., Wilson, P. W. (1998). Productivity Growth
in Industrialized Countries. Discussion Paper 9810,
,QVWLWXW GH 6WDWLVWLTXH 8QLYHUVLWp &DWKROLTXH GH
Louvain, Louvain-la-Neuve, Belgium.
22. 7KDQDVVRXOLV(3RUWHOD0&6'HVSLF2
Data Envelopment Analysis: The Mathematical
3URJUDPPLQJ $SSURDFK WR (I¿FLHQF\ $QDO\VLV ,Q
)ULHG + 2 /RYHOO & $ . 6FKPLGW 6 6 (GV
7KH 0HDVXUHPHQW RI 3URGXFWLYH (I¿FLHQF\ DQG
Productivity. New York: Oxford University Press,
pp. 3–91.
23. 7RKLGL * 5D]DY\DQ 6 7RKLGQLD 6 $ *OREDO &RVW 0DOPTXLVW 3URGXFWLYLW\ ,QGH[ XVLQJ
97
T. Balezentis et al.
7KH7UHQGVRI7HFKQLFDODQG$OORFDWLYH(I¿FLHQF\LQ/LWKXDQLDQ)DPLO\)DUPV
Data Envelopment Analysis. Journal of the
Operational
Research
Society,
Volume
63,
pp. 72–78.
24. Vinciuniene, V., Rauluskeviciene, J. (2009). Lietuvos
UHVSRQGHQWLQLž XNLQLQNX XNLX WHFKQLQLR LU PDVWR
efektyvumo neparametrinis vertinimas [Technical
DQG 6FDOH (I¿FLHQF\ RI )DPLO\ )DUPV LQ /LWKXDQLD
$ 1RQ3DUDPHWULF $SSURDFK@ Vagos:
LZUU
mokslo darbai >9DJRV 6FLHQWL¿F 3DSHUV RI /8$@
Volume 85, Issue 38, pp. 39–46.
25. :KHHORFN ' & :LOVRQ 3 : 7HFKQLFDO
3URJUHVV ,QHI¿FLHQF\ DQG 3URGXFWLYLW\ &KDQJH
in U.S. Banking, 1984–1993. Journal of Money,
Credit, and Banking, Vol. 31, pp. 212–234.
Acknowledgments
This research was funded by the European Social Fund under the Global Grant measure (VP1-3.1-ŠMM-07-K-03-002).
98
Economic Science for Rural Development No. 30, 2013
ISSN 1691-3078