Download Income in Greek Agriculture

ASSESSING INCOME DISPARITIES BETWEEN AGRICULTURAL AND
NON - AGRICULTURAL HOUSEHOLDS
by
Anastasios V. Semos
Associate Professor in the Department of Marketing, Agricultural Policy and Cooperatives, Aristotle University of Thessaloniki, Greece

ASSESSING INCOME DISPARITIES BETWEEN AGRICULTURAL AND
NON - AGRICULTURAL HOUSEHOLDS.
Summary
The main objective of this study is to identify the income trends and to
investigate the income changes and disparities in the Greek economy. In
particular, the relationship between agricultural and non-agricultural per
capita income is examined and causes of certain patterns of relation are
sought for through statistical techniques and by computing certain indices.
Results reveal that through per capita income in Greece has been
significantly increased for the reported period (1985-2002), agricultural per
capita income failed to follow suit. The income disparity between agriculture
and non-agriculture population exacerbates the long prevailing problems of
the agricultural sector. Although a considerable decline of the agricultural
population has recorded providing more resources to fewer farmers,
agricultural income decline still insists.
Key Words: income, agricultural income, measurement of agricultural
income, inequality.
Introduction
Agricultural in Greece comprises twelve percent of the GDP and
sixteen percent of the total employment (2004) while in EU the respected
numbers are three and six percent. Long before Greece’s accession to EU,
several policy measured had been targeted farm income support. Thus the
efficacy of various employed policy measures were under scrutiny in several
studies. Questions regarding income fluctuations, income support and
income discrepancies, among farmer and non farmer were examined.
(Bennett, 1969, Cordts et al., 1984, Mishra and Goodwin, 1997, Allanson et
al., 1999, Hill 1999, Hegrenes et al., 2001, Allanson, 2003 Phimister et al.
2004). Nevertheless, questions on income disparities have not yet resolved
and additional investigation is required on specific country cases.
However, this work is concentrated on the differences between
agricultural household income, earned exclusively from farm activities, and
non – agricultural household’s income. In this context, agricultural income is
defined as the income earned only due to farm activities. Thus, a
1
comparison of per capita income between laborers in agricultural sector and
non-agricultural sectors is performed, as income data on farm and
individual level are not available. In addition trends in income convergence
(divergence) are explored. In the next section a cursory review of income
measuring techniques is presented followed by a demonstration on applying
income disparities measure on the Greek agriculture. Finally an attempt is
made to draw some conclusions.
Income measuring background.
The so called “farm income problem”, common in most countries, is
not only referred to per capita income disparities between agriculture and
non-agriculture sectors but also to income fluctuations from year to year
due to unstable weather conditions. Thus, measuring techniques of the
agricultural income and interpreting income magnitude requires a special
attention. A few of encountered impediments very often mentioned in
computing income indices are the following: (a) income per capita
represents average level and individual deviation from the average are not
reflected. (b) Income earned from non – agricultural activities cannot easily
identified and counted. (c) Several income elements either are not
accounted for (e.g. farm house value) in the national income accounts or
could not be valued in monetary terms (self- consumption).
Real
agricultural
income,
in
a
national
accounts
framework,
corresponds to the actual net value added at factor cost. Net value added at
factor
cost
is
calculated
by
subtracting
the
value
of
intermediate
consumption, the consumption of fixed capital and production taxes from
the value of agricultural output and finally adding the value of the
production subsidies.
A very meaningful estimate is the per capita income of the
agricultural population. This estimate (AR) is expressed as ratio of Gross
Agricultural Product (GAP) to Total Agricultural Population1 (TAP):
1
The agricultural population includes all persons depending for their livelihood on
agriculture and everyone whose main economic activity is agricultural (crops,
livestock and fishing). All other persons are regarded as non – agricultural
population.
2
AR 
GAPt 
TAPt 
(1)
Agricultural per capita income (AR) compared to the relative estimates for
non-agricultural sectors provides useful insights on the differences between
agricultural
and
non-agricultural
income
distribution.
This
estimate
somehow represents average income gap between agricultural and nonagricultural (or even national income). Furthermore, another estimate, lowincome gap (ALG) is computed as the difference between the average
national per capita income and the average per capita agricultural income
over the average national per capita income (OECD, 2001):
ALG 
TR  AR
TR
(2)
Where:
TR = Average national per capita income
AR = Average per capita agricultural income
The ALG can be used as a criterion for the level of relative standard
of living for a particular group of employees. Thus, if the ALG demonstrate
an upward trend this can be interpreted as a welfare decline in agricultural
areas.
Measuring income inequality
Accurate measures of income inequality and trends over time are
complex procedures requiring huge data sets. Income inequality is very
often measured by using survey samples collection in depth information
over a limited number of households for a certain period of time. In such
techniques several problems are encountered regarding data availabilities
and reliabilities.
Widely used income inequality estimates, based upon national income
data, have been supported by FAO and they can be calculated by the
following formula (Bellerby, 1956):
3
Y
R A
PA
Y1
P1
(3)
Where R = ratio of per capita income in agriculture to per capita income in
the rest of economy; ΥΑ = agricultural GDP;
Υ1 = non- agricultural GDP;
ΡΑ = agricultural population;
Ρ1 = non- agricultural population.
In this study the aforementioned relation (3) is used to calculate the
agricultural income inequality over the time span 1985 to 2003. The Greek
ministry of Agriculture, the Eurostat and several EU publications are the
main data sources. Employing the equations (1) and (2) agricultural and
non-agricultural per capita income has been estimated (figure 1).
A glance at figure 1 can reveal some intriguing relations. Both agricultural
and non- agricultural per capita income exhibit a stable upwards trend.
Nevertheless, the rate of growth for non agricultural per capita income is
higher than the respective growth for the agricultural one. Therefore, the
distance
between
the
two
lines
respectively,
agricultural
and
non-
agricultural per capita income from year to year fall apart.
A furthermore analysis was performed to gain some additional
insights. Thus, thee linear equations were estimated, regarding the three
dependent variables (TR, AR, and UR) and using GDP as independent
variable, for the time span under investigation (1985-2003).
1)
TR = 61.43 + 0.92GDP
(61,72)
2
R  0.985
2)
D-W =1.85
UR = 42.06 + 0.98GDP
(36.78)
2
R  0.958
D-W =1.80
4
3)
AR = 455.8+ 0.5GDP
(29.26)
2
R  0.976
D-W =1.75
Figure 1.Per capita income trend.
10000
9000
Per capita income in (Euros)
8000
7000
6000
5000
4000
3000
2000
1000
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
0
Year
TR Total per capita income
AR Agricultural per capita income
UR Non-agricultural per capita income
Then, the Chow test was applied in order to check if the three equations
significantly differ (Chow 1960, Gujarati, 1995). Chow test is applied under
the following assumptions:
a) u1t ~ N(0, σ2)
and
u2t ~ N(0, σ2)
a) u1t and u2t are independently distributed
Under these assumptions the following ratio must be calculated:
5
F* 
S5 / k
S 4 /( n1  n2  2k )
Where: S5 = S1 – S4, S1= the residual sum of the square (RSS) for
the equation (1); S4 = S2 + S3, S2= the residual sum of the square (RSS)
for the equation (2); S3 = the residual sum of squares (RSS) for the
equation (3); k the number of the factors estimated and n1 and n2 the
number of observations. The F* follows the F distribution with dF = (k, n1 +
n2-2k) while the null hypothesis is that there is no difference in the
coefficients obtained by the regressions 2 and 3.
The F* value estimated as F*= 15.27 and the theoretical one F2,34=
4.51 at 99 per cent significance level. Since F*>F, the null hypothesis is
rejected and it can be admitted that the two equations are statistically
different. Hence, the growth path of the per capita income for agriculture
and non- agriculture substantially varies.
In addition, the income multiplier, was estimated using the formula
M 
1
. Replacing bi, the estimated coefficient for the respective
1  bi
equation by equations 1, 2 and 3, the calculated values for total, nonagriculture and agriculture multiplier are derived (M1 = 12.5, M2 = 50, M3 =
2). Thus, if a million euros increase in the GDP was assumed the per capita
income will increase by 12.5, 50, and 2 euros per year respectively for total,
non-agricultural and agricultural population.
The number of agricultural population is strictly associated with the
size of agricultural GDP. Thus, any decline of the agricultural population is
accompanied with a decline in agricultural GDP, as agricultural wage
constitute part of the agricultural GDP (figure 2).
However, any drops of the agricultural population will be followed by
a release of natural resources and production factors favouring the people
remained in the agricultural sector. Hence, it could have been expected a
rise in agriculture’s GDP followed suit by a rise in per capita income but this
sequence cannot be observed in the graph (Figure 2).
Overall,
examining
agricultural
and
non-agricultural
population
significant disparities have been recorded and illustrated in the graph; this
somehow is exhibited in a very succinctly way in figure 3.
6
0.25
0.2
0.15
0.1
0.05
0
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
GDP-of agriculture population of agriculture %
Figure 2. Changes in agricultural population and in gross domestic
product originated from agriculture
Year
PA changes of agricultural population
YA Changes in agricultural cross product
Figure 3. The trend of the R ratio (per capita income in agriculture to
the total per capita income). 1985-2002
1
Ratio R
0.8
0.6
0.4
0.2
19
85
19
86
19
87
19
88
19
89
19
90
19
91
19
92
19
93
19
94
19
95
19
96
19
97
19
98
19
99
20
00
20
01
20
02
0
Year
RA
Discussion and Conclusions
The main purpose of this work was to investigate the per capita income
trends and relations in the Greek economy utilizing various indices and
statistical techniques. The study was focused on the relationships between
agricultural and non-agricultural income and its affect upon the agriculture’s
future. Results indicate a stable upwards trend of both agricultural and non7
agricultural per capita income over the entire time span under investigation
and substantial income disparities between those two sectors.
Intriguingly, the agriculture’s acquisition of additional resources due to the
decline of the agricultural population failed to counterbalance the existing
income diversion though a slight income growth was observed. Insisting
disparities between agricultural and non-agricultural income signaled a
probable failure of the so far proposed income policies and constitute a
future challenge for devising and implementing new more effective rural
policies.
Definitely, it can be argued that such disparities have recorded in
other countries as well and the causes of these income variations have
extensively probed. Nevertheless, this line of work sheds new light on the
path of income growth in Greece and constitutes the baseline for further
research on the probable causes of income disparities and on the disparities
among various rural regions.
References
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