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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 Allanson, P. (2003). The Distribution of Agricultural Support in Scotland. Scottish Economic Policy Network Stirling: University of Stirling. 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(1999). Farm Household Incomes: Perceptions and Statistics, Journal of Agricultural Studies, 15(3):345-358. Mishra, A.K. and Goodwin, B.K. (1997). Farm Income Variability and the Supply of Off-farm Labour, American Journal of Agricultural Economics 79(3):880-887. OECD (2001) Low Incomes in Agriculture in OECD Countries, Working Party on Agricultural Policies and Markets, Directorate for Food, Agriculture and Fisheries Committee for Agriculture (2001). Phimister, E. Roberts, D., Gilbert A.(2004) The Dynamics of Farm Incomes: Panel data analysis using the Farm Accounts Survey, Journal of Agricultural Economics. Volume 55, Number 2. July 2004. Pages 197-220 9