Input interactions and Structural Dynamics in Greek agriculture productivity

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Input interactions and Structural Dynamics in Greek agriculture productivity

https://doi.org/10.21203/rs.3.rs-3469316/v1

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This paper aims to analyze the impact of fixed capital, variable inputs and labor and their interaction to the volume of the agricultural output in Greece, for the period 1981 - 2021, using the estimation equation of a Cobb-Douglas equation form. At a second level, an attempt is made to investigate possible changes in this relationship over time, using the Bai-Perron structural changes method and their effect on the observed relationship between agricultural inputs and output. The existence of a structural break in 2006 was confirmed, which is accompanied by differences in elasticities before and after this year. During the period 1981-2005, positive elasticities of labor and fixed capital were observed, indicating that the increase in these factors contributed positively to agricultural production, but with decreased returns to scale. In contrast, after 2006, negative elasticity of labor and fixed capital was detected, while the elasticity of variable factors of production remained positive. This change in elasticities indicates the inefficiency in the use of fixed capital and labor after 2006 and the increased influence of variable production factors on agricultural production. These findings highlight the need for continuous monitoring and analysis of factors affecting agricultural production, in order to enhance the competitiveness of the corresponding sector and ensure its sustainability in the future, offering new perspectives for the design and implementation of policies that will enhance agricultural development.

JEL classification: O130, Q100, R150

Agriculture productivity growth

input-output analysis

Cobb - Douglas function

Greek agriculture

Agricultural production process requires, except of optimal use and combination of inputs, also the effective utilization of other aspects such as technological innovations, the experience and ability of those employed in the agricultural sector, but also the consideration of elements related to the economic circumstance that characterize economies both individually and globally. Moreover, a list of unprecedented economic, social and environmental conditions, such as financial crisis phenomena, farmers and consumers income inequalities and climate change, have shaped a complex environment for agricultural production. Based on the above, decisions are made regarding the use of the appropriate quantities, but also the type of production factors, with the ultimate goal of economic efficiency of the agricultural sector. These choices on production factors are not just technical decisions, but refer to the adaptation of agriculture to ever-changing challenges. As farmers face extensive changes in market conditions, technological capabilities offered and political regulations, the selection of production factors must be performed, considering their interaction.

In this context, capital is a decisive factor for agricultural production, performing a dual role as a source of financing and as a shaper of production processes. Technological progress and innovation require frequent investments in fixed capital, such as equipment and machinery, with the aim of improving efficiency along with the use of sustainable production methods (Crespi & Zuniga, 2012; Gann, 2000) in the most sectors, hence also in agriculture. Moreover, labor productivity, is also a crucial factor for the efficient implementation of agricultural activities, affecting the output quantity and quality but also production costs, which are besides affected by the nature of materials used (Akram, 2019), such as fertilizers, pesticides and seeds, costs of renting land, wages of dependent labor (Jayachandran, 2006) and intermediate consumption elements in general. Nevertheless, the selection of the amount of labor and variable inputs used in the production process is carried out on the basis of the given potential provided by the existing fixed capital, creating a relationship of interaction with it.

The Cobb-Douglas production function is considered as a useful tool for modeling the interaction between factors of production as it expresses the relationship between the quantity of production outputs and the inputs used in it. The efficiency of the Cobb-Douglas function is based on the fact that it can provide a reliable representation of the production process by estimating the contribution of each input to the total output produced, the corresponding returns to scale, the marginal productivity of production factors used and their marginal rate of substitution (Fuss & McFadden, 1978). The application of the Cobb-Douglas production function to agricultural production has been a subject of intense academic research in exploring the relationship between inputs and agriculture output, as is one of the most widespread production functions in economic logic, as it offers simplicity and flexibility in the econometric modelling.

Within this framework, Armagan and Ozden (2007) argued that the Cobb-Douglas production function estimation is suitable for the efficient analysis of agricultural activities, as it is characterized by ease of calculations and capability of statistical testing of production flexibilities, pointing that this approach addresses the concept of productivity and efficient use of inputs through the integration of the output function based on the inputs used. This particular methodology was enthusiastically supported in a recent article by Faruq-Uz-Zaman (2021), who also highlights the advantage that it provides for the calculation of marginal productivity in production. Based on this and using data set related to crop production in Bangladesh, used land area coverage under cultivation, the proportion of the population engaged in agriculture, household spending as a proxy for capital input, the amount of fertilizer used per hectare of land, and the total cultivated land area under irrigation as inputs, in order to estimate its impacts on agriculture output. Their results showed a particular inefficiency of the use of inputs, noting negative returns on the scale with elasticity equal to -0.977, which is mainly due to the negative marginal productivity of cultivated land and labor, emphasizing the non-rational use of those production factors.

In fact, the negative effect of labor growth on agricultural production was clearly confirmed by Echevarria (1998), who used data for the period 1971 to 1991 for Canada and Cobb-Douglas equation, comparing the agricultural sector with those of services and industry, as she determines the less need of the agricultural sector for labor and the particular importance of using technological innovations, as they positively affect productivity. Similarly, Mundlak et al. (1999) use panel data of the period 1967 to 1992 for 57 countries, suggesting that agriculture production should rely to a greater extent on its extensive industrialization as output is relatively less influenced by changes in labor compared to capital used, indicating also that adopting more efficient production methods is linked to a reduction in labor, signifying a shift towards technology that reduces the need for labor.

Yuan (2011), uses data from 1999 to 2008 for Hebei Province China by estimating a Cobb-Douglas production function, where independent variables consist of cultivated land area, effective irrigation area, chemical fertilizer usage, agricultural machinery power, rural electricity consumption, and manpower. Effective irrigation area appears as the most influential factor on agricultural production, followed by chemical fertilizer usage and farm labor, with cultivated land area, electricity consumption, and farm machinery power showing less but also positive impact on output and overall input elasticity indicating decreasing returns to scale, with a value of 0.56.

Dawson and Lingard (1982), investigate the relationship between various input factors and total revenue in agricultural output, using a Cobb-Douglas production function. The study employs Ordinary Least Squares and covariance analysis to estimate the elasticities and marginal productivity of labor, total wage-bill, machinery costs, livestock costs, crop costs, general farming costs, rent, and land area which are set as independent variables, using total revenue as dependent variable. The results emphasize the special role of labor, but also of raw materials, as their increase leads to increased productivity, while a similar but less important is the effect of investments in fixed capital. Nevertheless, decreasing returns on the scale of variable inputs are observed, and thus a relatively reduced efficiency of the use of production factors is signaled.

A study by Ghoshal and Goswami (2017), referring to efficiency of agricultural production in regions of India, using Stochastic Frontier Analysis with the help of Cobb-Douglas production function, in panel data for the period 2005 to 2014, found matching results. The average annual efficiency ranged from 0.376 to 0.882, for the different regions of the country, also indicating inefficiency in the use of production inputs, with the variable inputs of the amount of fertilizers and pesticides being the most important in increasing output, which it is also positively affected by the presence of an extensive network of public infrastructure, such as total state road length per unit of cultivated area. In fact, this particular element was also observed by Mamatzakis (2003), who used data concerning the Greek agricultural sector for the period 1960 to 1995, estimating with the use of the I3SLS method a translog cost function. The study, fount that the improvements in public infrastructure reduce marginal variable costs, while on the contrary there is a positive impact of public infrastructure on the advancement of productivity in the Greek agricultural sector.

Competitiveness and Productivity Challenges in Greek Agriculture

Greece's accession to the European Union in 1981, had a catalytic impact on the subsequent evolution of Greek agriculture. The full integration and implementation of the Common Agricultural Policy created the conditions, through measures such as price guarantees and the application of structural policies, for the transition of the rural economy from primitive self-sustaining engagement to modern entrepreneurial agricultural economy. Tangible outcomes for farmers were already noticeable during the adjustment period in 1978 and onwards. Today, agriculture is fully governed by the rules of the CAP, shaping and controlling the entire framework of operation and activity in the agricultural sector. Over approximately four decades of CAP implementation, significant changes have been observed in the Greek rural economy. The most noteworthy of these changes concern employment, production structure, prices, and rural incomes. The implementation of CAP resulted in substantial improvements in producer incomes through subsidies and played a key role in development in rural areas of the country. However, the periodic revisions of CAP had a significant impact on the Greek economy, often imposing limitations on the external trade of agricultural and livestock products, while simultaneously redirecting production away from market demands.

According to Hellenic Statistics Authority data, the 1980s represent the most prosperous decade for Greek agriculture in terms of real producer prices and agricultural production value. During the 1990s, production volume stabilized, while real prices progressively declined. Labor productivity during the 1980s increased at an annual rate of 4.9%, and in the 1990s, it stabilized at slightly higher levels despite a significant reduction in the workforce, which decreased from 31.6% of the total workforce in Greece in 1980 to 18% in 2000. After 2000 until 2010, employment and Gross Value Added in the Greek agricultural sector exhibited a steady decline, resulting in a significant reduction in agricultural GDP. Simultaneously, the 2007 crisis, coupled with the decrease in private investments in agriculture, resulted in low overall performance and competitiveness of the primary sector (Kassimis & Papadopoulos, 2013). These challenges have persisted in the long term, leading the country to exhibit a deficit in the trade balance of agricultural products throughout the 2010s and up to date. As a result, today, Greek agriculture contributes only 3.6% to the country's national GDP, with the total value of Greek agricultural production amounting to 12 billion euros, indicating that it lags behind in competitiveness in the international market.

Hence, enhancing competitiveness and productivity in the agricultural sector remain key challenges for the future of Greek agriculture, as they determine its ability to penetrate international markets. However, as producer prices generally show a downward trend and considering the departure of a significant portion of the workforce from the agricultural sector, the further contraction of production is not unlikely unless there are drastic improvements in the productive structures of the Greek agricultural sector and the modernization of all other factors involved in the production and marketing of agricultural products.

Data were collected from the European Commission's Directorate General for Economic and Financial Affairs (AMECO) database, on an annual basis, which refers from 1981 to 2021. The used regression model is based on a production function that reflects the impact of labor and production cost on agricultural output. These aspects interact separately with capital, creating a framework for analyzing the relationship between these factors in the production process, formulating a modified Cobb – Douglas production function expressed by the following equation:

$$Y=A{\left(LK\right)}^{aL}{\left(CK\right)}^{aC}$$

The terms LK and CK represent the interaction effect between labor and capital and cost and capital respectively on agricultural output produced, while the exponents αL and aC, determine the labor and cost elasticity under their interaction with capital.

Το estimate the impact of labor, production cost and capital on agricultural output the modified Cobb – Douglas production function is transformed to a log-linear production model of the form:

$\text{ln}\left(Y\right)={\beta }_{0}+{\beta }_{1}\left(lnL+lnK\right)+{\beta }_{2}\left(lnE+lnK\right)+u$ , where:

Y defines agriculture output in current prices

K stands for the agriculture sector fixed capital in current prices

L represents labor in terms of employed persons

C defines agriculture sector variable costs as the aggregate of land rental expenses, dependent labor wages and intermediate consumption and

u represents the disturbance term

The model is estimated with the use of the OLS regression method and the coefficients attribute the elasticities of the inputs to the output of production and as a result, the estimates of β₁ and β₂ are interpretive indicators of the interactions between the production factors to the agricultural output. Nevertheless, the most important element is that the coefficients β₁ and β₂, reveal the dynamic systemic interactions between the capital and the other aforementioned production factors as the increase or decrease of it can affect the performance of the rest and therefore agricultural output.

Additionally, given the fact that in the agricultural sector, there are frequent changes of a complex nature in the relationship between agricultural outputs and production inputs, an element due to factors related to rapid technological progress, climate change and other environmental factors, the application of differentiated policies for agriculture and the differentiation of demand needs, it is considered necessary to study and detect the presence of structural changes in it. In this direction, the Bai – Perron Multiple Structural Changes Linear Estimation (Bai & Perron, 2003), is applied, which detects the presence of endogenous structural brakes. Initially, the hypothesis of zero versus a fixed number of up to 5 breaks and zero versus up to five undetermined number of brakes (Bai & Perron, 1998) is tested, followed by testing l versus 1 + l breaks (Bai & Perron, 2003), a process which is suitable for small samples, as presented in detail by (Souto et al., 2008). The structural change points were detected using the Bayesian Information (BIC) (Yao, 1998) and modified Schwarz (LWZ) (Liu, 1997) criteria, in order to achieve the optimal choice of the number of structural changes incorporated in the regression model. The final selection of the optimal model is achieved based on the criterion that provides the lowest critical value threshold, and then the linear regression model is estimated for each possible period of structural change.

In Fig. 1, time trends of output fixed capital, employed persons and variable costs for Greek agriculture sector (1980–2020) are presented. As observed, initially, a significant increase in agricultural output and fixed capital of Greek agriculture sector is observed during the 1980s and up to 1995, probably related to the introduction of new technologies and improvements in the management of European resources, indicating the adoption of strategies for the development of the agricultural sector and the industrialization of production leading to the transformation of capital / labor ratio. More specifically, the average growth rate of the agricultural output for the period 1981–1995 is equal to 2.24%, while its overall percentage change in 1995 compared to 1981 reaches 34.69%. Additionally, the average and total increase in fixed capital for the same time period is equal to 1.84% and 28.21% respectively, signaling possible presence of increasing returns to scale in the relationship between invested capital and agricultural output. The trend of increasing capital intensity and decreasing labor intensity in agricultural production is confirmed by the decreased the number of individuals employed in agriculture sector at an average rate of 2.24% per year leading to an overall decrease in the total number of farmers by 27.80%, which corresponds to 292.20 thousand individuals. On the contrary, production costs increase significantly by 3.09% per year and 50.30% in overall by 1995 compared to 1981.

During the 1996–2006 period, there is a reversal of the agricultural output growth trend, which shows a total decline of 10.22%, which is mainly due to the pre-crisis reduction of the agricultural product in 2006 compared to 2005 by 14.41%, but also a negative average change of 0.95%. In contrast, the capital shows a decelerated average annual growth equal to 1.34% and a total growth of 16.95%, as employment is furtherly decreasing by 27.35% in 2006 compared to 1996 and at a constant rate of 2,90% annually. On the contrary, production costs continued their upward trend, inflated by 35.11% in 2006 compared to 1996.

In 2007, as the global economic crisis emerged a decline in agricultural output to its average levels during the 1990s and a total reduction of 6.04%. Besides, during the years 2007–2009 an increase in fixed capital by 11.52% is observed in relation to the pre-crisis period, while the variable costs were proportionally less decreased, in comparison to the corresponding output reduction, reflecting the negative effects of the crisis in the agricultural sector.

After the crisis period, the agricultural output is observed to recover with a slow growth rate of 0.95% per year, failing to reach pre-crisis levels. These recovery efforts are accompanied by a reduction in fixed capital by 11.66% in 2021 compared to 2009 and 1.12% per year and an average increase in production costs of 1.26% annually. Thus, the compensation of the fixed capital and variable inputs of production, is linked to the stabilization of the produced agricultural output. Finally, the number of individuals employed in the agricultural sector, shows relative stability after the negative economic condition of the period 2007–2009, as it decreases by 15.50 thousand individuals in 2021 compared to 2007 with a rate of 2.92%, demonstrating the long-term effect of the economic crisis on employment.

The results presented through Spearman correlation matrix (Table 1), highlight the presence of generally statistically significant linear relationships between agricultural production, fixed capital, employment and variable costs. Initially, the positive correlation of agriculture output and capital (rho = 0.338, p = 0.031), suggests that the increase of the latter contributes to the growth of agricultural productivity. Nevertheless, it appears that the increase in the agricultural output is also associated with an increase in variable production costs (rho = 0.447, p = 0.003) and a decrease in the number of individuals employed in the agricultural sector (rho=-0.432, p = 0.005). The negative correlation between capital and labor and (rho=-0.663, p < 0.001) confirms the inversion of the capital-labor ratio. These results suggest that technological development and automation of the production process bring about a steady increase in production, but also that improvements in equipment and know-how allow labor to be more productive, regardless of the number of workers, with production costs increasing given increased investment in equipment (rho = 0.653, p < 0.001). Simultaneously, as the number of people employed in the agricultural sector decreases, production costs increase (rho=-0.783, p < 0.001), as a result of changes in the production process, with the integration of more industrialized but also costly practices driving part of the rural population to abandon the agriculture sector.

Table 1

Spearman correlation matrix
	Output	Capital	Labor	Cost
Output	1
Capital	0.338 (0.031)	1
Labor	-0.432 (0.005)	-0.663 (< 0.001)	1
Cost	0.447 (0.003)	0.653 (< 0.001)	-0.783 (< 0.001)	1

Interpreting the results of the modified Cobb – Douglas production function model (Table 2), it is observed that b₁ is statistically significant, holding a value equal to 0.105, (p = 0.025) and signaling a positive combined effect of the increase in fixed capital and the amount of labor towards agricultural product. Also, the interaction between fixed and variable cost signifies a positive impact on agricultural output (b₂ = 0.103, p = 0.038). Based on the estimated coefficients, output elasticity to labor equals 0.105 and the elasticity of output with respect to variable inputs equals 0.103, with the output elasticity to capital being equal to 0.208. Consequently, an increase in fixed capital, labor and variable factors of production by 1% lead to a corresponding increase in agricultural output by 0.208%, 0.105% and 0.103% respectively making the role of fixed capital more important than that of labor and variable production factors. Finally, since the sum of the elasticities is 0.416, decreasing returns to scale occur.

Table 2

OLS estimation results
Dependent Variable: lnY
	b	s.e	t	p
Constant	6.972	3.121	2.234	0.031	*
lnK + lnL	0.105	0.046	2.333	0.025	*
lnK + lnC	0.103	0.048	2.142	0.038	*
R²	0.282
F (p)	7.673 (0.002)
* Significant at 5%

In Table 3, structural breaks testing is presented. Exploring the number of possible structural breaks in the relationship between production inputs and agricultural output, the presence of no breaks against the hypothesis of the presence of up to 5 separate breaks is initially tested. The corresponding sup F_Ts signal the presence of structural changes, as the null hypothesis of no structural breaks is in any case rejected. This is also confirmed through the Wald-Wolfowitz Test based on WD max criterion, which leads to rejection of the null hypothesis of absence of structural breaks in favor of the alternative hypothesis of up to five undetermined number of brakes.

Having established the presence of structural breaks in the relationship between production inputs and agricultural output, the determination of their exact number follows by testing the null hypothesis of l versus the alternative of l + 1 breaks. This procedure identified one significant break based on the supF_T(1|0) statistic, while all other supF(l + 1|l) were insignificant. Hence signs of the presence of a structural break are provided, a result that is also confirmed through the BIC and LWZ information criteria which select one structural break identified in 2006.

Table 3

Estimated structural breaks
0 vs unknown number of breaks	0 vs fixed number of breaks		l versus the alternative of l + 1		Information criteria			Estimated break dates
WDmax	Number of Breaks	sup F_T	Number of Breaks	Sup F_T(l + 1\|l)	Number of Breaks	BIC Criterion	LWZ Criterion
134.226^* (15.590)	1	44.741^* (13.980)	1\|0	44.741^* (13.980)	1	-6.479*	-6.123*	2006
	2	30.056^* (11.990)	2\|1	3.972 (15.720)	2	-6.433	-5.856	1990, 2006
	3	26.732^* (10.390)	3\|2	2.882 (16.830)	3	-6.408	-5.591	1987, 1994, 2006
	4	20.230^* (9.050)	4\|3	0.970 (17.610)	4	-6.152	-5.072	1987, 1994, 2006, 2015
	5	14.800^* (7.460)	5\|4	0.219 (18.140)	5	-5.820	-4.442	1987, 1993, 1999, 2006, 2015
* Significant at 5%

() denote Bai-Perron critical values.

Given the presence of a structural break dated in 2006, the regression model is re-estimated using standard regression techniques, after dividing the sample of observations into two sub-periods with the first covering the 1981 to 2005 and the second 2006 to 2021. The results presented in Table 4, show the existence of significant dissimilarities in the relationship between production inputs and agricultural output for the two sub-periods.

Table 4

OLS estimation results under structural breaks
Dependent Variable: lnY
1981–2005
	b	s.e	t	p
Constant	1.225	0.450	2.722	0.011	*
lnK + lnL	0.161	0.077	2.090	0.044	*
lnK + lnC	0.304	0.024	12.461	0.000	**
2006–2021
	b	s.e	t	p
Constant	10.161	0.931	10.903	0.000	**
lnK + lnL	-0.411	0.059	-6.996	0.000	**
lnK + lnC	0.304	0.056	5.429	0.000	**
R²	0.852
F (p)	40.514 (< 0.001)
* Significant at 5%
** Significant at 1%

Regarding the 1981–2005 period, the coefficient of capital and labor is statistically significant (p = 0.044), and equal to 0.161, while, for the combination of capital and variable inputs, the corresponding coefficient is 0.304 (p < 0.001). Therefore, it is indicated that the combined increase of fixed capital and labor and fixed capital and variable inputs of production by 1% implies an increase in agricultural output by approximately 0.161% and 0.304% respectively, expressing the contribution of combined efficiency on productivity. It is worth noting the presence of decreasing returns to scale, with the elasticities of the labor and variable cost coefficients being equal to 0.161 and 0.304, while the capital elasticity attributed to the sum of the aforementioned elements is equal to 0.465.

As for the 2006–2021 period, strongly differentiated results are observed both in relation to those of the period 1981–2005, and in compared to those of the period 1981–2021. The most result element is the negative sign of the coefficient of capital and labor as it equals − 0.411 (p < 0.001), suggesting that their combined increase leads to a decrease in agricultural output. In contrast, the coefficient for the of capital and variable inputs remains equal to 0.304 and significant (p < 0.001), indicating an increasing effect in agricultural production. The negative elasticity of labor indicates its negative effect of on production, while capital is also inelastic with an elasticity of -0.107. On the contrary, the positive elasticity of variable production inputs confirms their observed positive effect on the level of agricultural output.

Analyzing agricultural production in Greece and examining its relationship with production factors, opens new perspectives for understanding the driving forces shaping the agricultural economy of the country. The period after Greece's accession to the European Union in 1981 and the implementation of CAP, marked an era of change in the agricultural sector, which coincided with an increase in productivity following the general European trend. Nevertheless, this course was not stable as it is characterized by certain features which were clearly established in the present study, which provides insight into the evolution of agricultural production, revealing the consequent changes in output, employment, fixed capital and variable inputs.

The results of the study, demonstrate that the increase of capital and labor and the interaction between them, contribute to the increase of agricultural production, an outcome that agrees with the classical theory of production. Given the similar results concerning the interaction of variable inputs and capital, a two-way positive relationship between investments in fixed capital and the quality and quantity of production materials, that improves the productivity of agricultural holdings, also emerges. Nevertheless, the returns to scale which are decreasing are of particular importance, signaling the chronic under-utilization of inputs or alternatively over-investment in productive factors. Of course, this particular result shows significant changes over time, with the observed effects of input use changing significantly after 2006, a period when the efficiency of labor and capital decreases, perhaps due to the effects of the economic crisis, indicating change in production pattern. The negative elasticity of labor is interesting but more complex to explain and can be attributed to factors such as technological progress and automation, since as agricultural technology advances, less labor may be required, but productivity may also increase. Accordingly, the inelasticity of capital signals the reduced return on investment in fixed equipment, or its poor management, or the inadequacy of agricultural infrastructure. On the contrary, the consistently positive elasticity of the variable production factors, which recognizes their positive effect on the production of agricultural products, indicates their special importance if their use is combined with the efficient utilization of capital.

The above findings can form the basis for the implementation of proposals concerning Greek agriculture, which presented significant deficiencies after the mid-2000s. Thus, it is deemed necessary to change the model of investments in high-cost fixed capital without the use of computerized applications or even capabilities offered by artificial intelligence technology, with the aim of automating the production process and reducing both investment and production costs. Given that Greek agriculture is obliged to operate under the guidelines of the CAP, which move towards supporting agriculture with conditions of compliance with environmental and quality criteria, Greek agriculture can benefit in order to shift towards quality and the application of efficient production methods environmentally friendly. Increased and sustainable agricultural production, will only be possible with broader research and innovation efforts at all levels as significant innovations are not being implemented at the required scale.

Conflict of Interest

The authors declare no conflicts of interest related to this research.

Funding Statement

This research was not supported by any funding source

Acknowledgments

No acknowledgements

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No competing interests reported.

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Input interactions and Structural Dynamics in Greek agriculture productivity

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Abstract

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Introduction

Competitiveness and Productivity Challenges in Greek Agriculture

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