PRODUCTIVITY AND THE EVOLVING NORWEGIAN DAIRY QUOTA SYSTEM. Abstract

Transcription

1 185 PRODUCTIVITY AND THE EVOLVING NORWEGIAN DAIRY QUOTA SYSTEM Tibor Marton 12, Thomas Heckelei 2, Øyvind Hoveid 1 1 Norwegian Agricultural Research Institute, Olso, Norway 2 Institute for Food and Resource Economics, University of Bonn, Germany Abstract In recent decades the Norwegian Dairy Industry has undergone substantial changes and experienced extensive restructuring. Notably, the restrictive output quota scheme first implemented in 1983 to address overproduction has changed over time. The move in 1996 to the quota buy and sell scheme management system was sought to achieve greater allocative efficiency and create a more desirable quota distribution in the industry as well as overcome the previous issues of inflexibility and unfair features of the strict quota system. Detecting the relationship between dairy productivity and the evolving quota rules in intertemporal manner is of interest. This paper estimates dairy total factor productivity dynamically in order to observe the potential dynamic linkages of production decisions and statistically tests the dependence between differentiated quota systems and farm-level productivity by utilizing Chi Square test of independence. The empirical application focuses on three distinctive five-year panel data periods of no quota, restrictive output quota and transferable quota system; , , , respectively. We found that productivity increased significantly when operating under the strict quota system (2.02%) followed by positive but much smaller productivity increase in the subsequent buy and sell quota scheme (0.03%). We identified that the transferable quota system has an overall positive effect in terms of both productivity increase and structural change (because statistically significantly more firms attained productivity increase than under any of the previous quota regulations). Keywords: Luenberger indicator, productivity, quota system 1. Introduction 1.1. Background In recent decades the Norwegian Dairy Industry has undergone substantial changes and experienced extensive restructuring. Most notably, the strict quota based regulatory scheme was first implemented in 1983 to reduce overproduction in the late 1970 s and early 80s. Since then it has evolved into a transferable quota system. The quota system has been investigated in the academic literature by JERVELL and BORGEN (2000) who qualitatively explored the quota design and the evolution of the regulation. More recently, KUMBHAKAR et al. (2008) calculated the impact on output growth by decomposing the output growth rate into output, input, socioeconomic and technical change components. They utilized a modified distance function approach in a static manner. Finally, ATSBEHA (2012) wrote a PhD dissertation on the economics of dairy production in the Norwegian and Icelandic dairy sectors. The restrictive output quota system was first introduced to curb overproduction in the Norwegian dairy industry. Production in the five years prior to 1983 had grown by 10% even though the government had issued incentives to operators to relax their output quantity. The substantial production increase was combined with contracting demand led to a dairy surplus of 300 million liters in 1982 and it was evident that production regulation was required (JERVELL and BORGEN, 2000). Individual dairy operators were allocated strict non-tradable production quotas based on the previous two years production volumes. After its implementation several minor changes have been made because powerful Norwegian dairy co-operatives lobbied for relaxing of the strict rules. In the empirical application on this paper we made a simplification and July ISBN Congress Proceedings Page 1 of 8

2 186 TIBOR MARTON, THOMAS HECKELEI, ØYVIND HOVEID considered the strict quota system as a homogenous period. The next significant change occurred in 1996 when the inefficiencies of this strict system eventually led to a relaxing on the system to the Quota buy and sell scheme which allowed quotas to be traded amongst dairy farmers at an administratively set uniform price. This occurred because the market with fixed prices could not be expected to balance quota supply and demand and there was a need to comply the requested fairness and create the positive structural changes. The reallocation (scale) of quota had to be administered in accordance with certain rules. The country was divided into six trade regions in order to avoid quota accumulation in a few more productive areas and maintain the regional structure of production (JERVELL and BORGEN, 2000). It was hypothesized that the liberalized quota reallocation system would support a more efficient industry structure and thereby will lead to greater productivity amongst the Norwegian diary producers (ROMSTAD, 1995 in JERVELL and BORGEN, 2000, p. 364). This research study investigates the effects of the changing quota conditions with a focus uniquely on farm-level productivity. This is a preliminary study to a future comprehensive dynamic paper analyzing farmers production decisions (e.g. investments and policy environment relation) in a capital intensive agricultural industry, like dairy. This research study contributes to the literature by utilizing the primal dynamic Luenberger TFP growth indicator at the farm-level for Norwegian dairy. The empirical application uses a non-parametric method (Data Envelopment Analysis) to estimate the dynamic directional functions. We presume that the implemented quota systems, particularly the buy and sell system, generate an increase in the number of productive firms due to more efficient reallocation of the quotas The Norwegian dairy industry For Norway, in Northern Europe, dairy farming is the most significant and dominant agricultural industry and accounts for 30% of total farm revenue. Direct farm subsidies make up a large part of total farm receipts and ensure that Norwegian dairy farming remains profitable for farmers despite lower production costs elsewhere in the world. Norwegian dairy farms are typically family operated businesses operating at a small scale. However, between 1976 and 2012 the average dairy farm size is expanded from hectares with on average milking cows (authors own calculations based on Norwegian Farm Accountancy Survey). Since the structure of Norwegian dairy is small scale the implemented supply quota systems which were intended to withdraw milk from the market, affected the majority of the farmers with different interests (JERVELL and BORGEN, 2000). Buying quotas become a precondition to develop farms, even though the availability is very limited and upper bounded. The allocation and redistribution of quotas between producers emerges as a question of distributing a scarce good. This implies that the pressure from and the perceived uncertainties by farmers who need to make investment decisions for the future, has increased. Some farmers in situation where important long-term decisions must be made, opt to sell their quota and exit dairying altogether. Due to the implementation of buy and sell scheme, the quotas become a new asset in the capital structure and by definition influences investment. 2. Methodology and estimation The total factor productivity of dairy operations over each separate five year period was calculated. The periods ( , and ) were selected based on reducing potential production bias. All periods were investigated in an identical manner in order to facilitate meaningful comparison between the quota system arrangements. In previous studies, single output specifications were used in several agricultural related applications (e.g. FAN and PARDEY, 1997; TVETERÅS, 1999; KARAGIANNIS and TZOUVELEKAS, 2005). This is not appropriate for our study given the multiple input output nature of the dairy industry. In addition, the single output specification can lead to aggregation problems (KUMBHAKAR et al., 2008) because of the different components of the utilized goods in the technology set. Thus, the model specification has to be tailored to multi input-output production technology at the farm level. The use of a distance function formulation assists July ISBN Congress Proceedings Page 2 of 8

3 PRODUCTIVITY AND THE EVOLVING NORWEGIAN DAIRY QUOTA SYSTEM 187 in overcoming the problems of single-output specifications allowing for various inputs and outputs (KUMBHAKAR et al., 2008; WALDEN et al. 2012). Furthermore, the implementation of distance functions theoretically eliminates the need for price information and instead means that the observed quantities can be used for estimating productivity measures for individuals (WALDEN et al. 2012). CHAMBERS et al. (1996) proposed the directional input distance function in which technology was represented by input correspondences as a maximal translation of the input set along a defined line in the direction of 1. The Luenberger productivity indicator employs directional input distance functions and provides a ratio-based index number. The Luenberger indicator can be specifically used for dynamic calculations, by utilizing the property of weak disposability of inputs. The indicator provides an arithmetic average of productivity change measured by the technology at time and the productivity change measured by the technology at time for each farm in the samples between sequential years (CHAMBERS et al. 1996, WALDEN et al. 2012, LANSINK et al. 2015). The indictor can be decomposed into technical inefficiency change and technical change assuming the technology constant. By the decomposition we allow to quantify the source of productivity changes. KAPELKO et al. (2012) relaxed the technology assumption and permitted variable returns to scale, which is more realistic considering interactions between research decisions. The technical inefficiency component can be further decomposed into scale inefficiency component and technical inefficiency component assuming VRS. Besides calculating the productivity measures for various time periods, the aim of the paper was to test the hypothesis whether there is a relationship between quota system and productivity. A contingency table was constructed, where the quota impacts on individuals were investigated by performing either positive or negative productivity changes at the five-year average. The Chi-squared test for independence was conducted to find out whether the observed proportional differences are likely to have occurred by chance or not. The Chi-squared test by definition is: ( where is denoted as the observed individuals and is denoted as expected outcome. A significant number would justify our a priori expectation that the quota system has effects on productivity; furthermore it significantly encourages it. 3. Data The data for calculating the productivity indicator was sourced from the Norwegian Farm Accountancy Survey, an unbalanced panel data set collected by Norwegian Agricultural Economics Research Institute (NILF). The survey is representative of different regions and dairy farm sizes. The data include farm production and economic data, collected annually from about farms. The data sets used in the analysis are medium size balanced panel, each captured for five-year period blocks. As a result of balancing a number of observations were lost. The final datasets consist 392, 312, 174 farms that operated in Norway consecutively over the examined five-year periods; thus in total each period contained 1960, 1560 and 870 observations, respectively. 1 CHAMBERS et al. (1996): is a vector of inputs and is a vector of outputs, the technology is represented by, which defines the input set: (. The maximal translation of the input set is the straightforward definition of the input distance function (. This input distance function was expanded with a line in the direction of g ( which is a fixed vector towards the input set. ( ( With words, the directional input distance function is the maximal translation of the input set along that permits keeping feasible. The relationship between the input vector and input set leads to the feasibility issue that will be discussed in a later version if this paper. July ISBN Congress Proceedings Page 3 of 8

4 188 TIBOR MARTON, THOMAS HECKELEI, ØYVIND HOVEID Dairy farms produce multiple outputs with primary output milk and by-products such as beef and some other products. This is why the multiple outcomes are substituted by the carefully deflated revenue data (in 1000 Norwegian Kroner, NOK), keeping 1976 prices constant 2. The purchased feed and fertilizers were measured in NOK, the labor inputs were measured in working hours, and the aggregated other inputs measure was constructed as implicit quantity index; measured also in NOK such as variable input materials. Capital was considered as a quasi-fixed input; index number was created which takes into consideration the annual interest rate. Investment, by definition the first difference of capital multiplied by 1 minus the annual depreciation rate, even so in the calculation the observed survey investment data was used (all costs and values were deflated to 1976 prices). Table 1 provides the summary statistics included in the estimation. Table 1 Summary Statistics Variable Period Mean SD Min Max Feed (Purchased in 1000 NOK) Fertilizer (Purchased in 1000 NOK) Hired labor (1000 hrs) Family labor (1000 hrs) Other input (Purchased in1000 NOK) Capital (1000 NOK) Investment (1000 NOK) Subsidy (1000 NOK) Consumer Price index, 1976 = 100%; revenue data does not contain the annual direct payments. July ISBN Congress Proceedings Page 4 of 8

5 PRODUCTIVITY AND THE EVOLVING NORWEGIAN DAIRY QUOTA SYSTEM 189 Output (1000 NOK) Results Primal dynamic Luenberger indicator was calculated for each farm in the examined sample periods. Table 2 presents the computation of annual average productivity changes for pairs of consecutive years and also the aggregated period averages (1977/1982, 1991/1995, 2008/2012). The Luenberger indicator (LTFP) is not bounded in either direction (upper or lower). If it is negative then the firms on average experienced a productivity decrease between consecutive periods. If the indicator is greater than zero the farms on average achieved a productivity increase. In theory, the LTFP could be zero, meaning no productivity change, although in this paper, for simplicity we assume farmers constantly make at least minor changes in their production decisions (e.g. changes in the input set) in order to become more efficient. This is why we only differentiate positive or negative change even though sometimes the change is marginal. Table 2 Primal dynamic Luenberger productivity growth Year LTFP SD Min Max July ISBN Congress Proceedings Page 5 of 8

6 190 TIBOR MARTON, THOMAS HECKELEI, ØYVIND HOVEID 1977/ / / Note: The indicated years indicate pairs of years with the first year of the pair presented. The results show that the highest productivity increase occurred under operating restrictive quota system (2.017%). The no quota and tradable quota systems also indicated positive productivity by 0.28% and 0.028%, respectively. The annual changes fluctuated the most under perfect competition (first period) between -2.89% and 6.63% by leading to the largest standard deviation (under any of the quota systems these fluctuation is significantly smaller). In the second period the average productivity growth of 2.017% indicates that every year during the sample period ( ), Norwegian dairy farmers produced the same output level with 2.017% less inputs. Unfortunately we do not have the data of the quota level for individuals. Consequently, we assume that all farmers are maximizing their output production up to their quota level. This is the reason we considered the output constant in the interpretation and assuming changing inputs, where the decreasing level indicates productivity increase. During the estimation procedure the investigated datasets lost many observations. For the periods (in chronological order) we computed 791, 647 and 318 feasible Luenberger productivity indicators. The weak stability of the results is caused because the indicator turns out to be impossible to compute under certain weak conditions (BRIEC and KERSTENS, 2009). For further analysis, searching for interaction between productivity change and operated quota systems, these feasible numbers will be used. The contingency table examines the no quota, strict quota and transferable quota systems relation to farm productivity which is sorted out to each quota system whether the farm performed positive or negative productivity change in the examined five year average. The first number in the contingency table denotes the observed frequencies ( (in parenthesis their proportion to total number of observations), while the second number is the calculated expected frequency (. Table 3 Contingency table 3X2 Negative productivity Positive productivity Total No quota Strict quota Transferable quota 445 (56.4%) 346 (43.6%) (54.1%) 297(45.9%) (39.3%) 193(60.7%) Total 920 (52.4%) 836 (47.6%) 1756 July ISBN Congress Proceedings Page 6 of 8

7 PRODUCTIVITY AND THE EVOLVING NORWEGIAN DAIRY QUOTA SYSTEM 191 We observed that the proportional changes of both quota systems are in favor of positive productivity. Under no quota system 44% of the farms operated with positive productivity. Under the strict system it has increased by two percent and after the structural changes operating under transferable quota system 61% of the sampled farms produced the same output with less input then earlier. Consequently, we presume that the quota system encourages efficiency and thus leads to the increase of the number of efficient farms that will experience positive productivity change over time. This relation was statistically tested by the Chi-square test of independence. The test was found the value of (df 5 =2), which is significant at 1% level of significance. Thus, we could reject the null hypothesis of independence in favor the alternative and conclude that the quota system statistically significantly increases productivity in the Norwegian Dairy industry. The contingency table can be decomposed into more simple relations, in which we only measure the statistical dependences between the no quota system and the strict quota system, as well as the strict quota system and transferable quota system. These simple relations have been also detected and found out that there is no statistically significant relationship between the productivity and quota system in the first two periods (, p-value=0.412), but there is a strong relation between the second and third period (, p-value=0.000). These results prove the hypothesis that the liberalization of the quota system has a statistically significant positive effect on farm efficiency and on productivity. The low productivity increase in the third period can be explained in several ways. For instance, the quota becomes a tradable asset and a precondition of expanding the production. More productive firms in order to increase their scale must also buy quotas besides cows and land for example, which leads to higher overall investment costs. It will also be tested how investments and productivity correlates in a dynamic investigation, because mostly the beneficial effects of an investment turn up in subsequent periods. This is called adjustment cost. Presumably, in the third period the more implemented investments will lead to higher positive productivity change later on. Finally, we identify that even though the productivity increase was found to be the lowest in the third period under tradable quota system, it was in this period that there was also statistically significantly the largest proportion in number of farms with positive average productivities were found in this period. This possibly indicates positive structural change and increasing future productivity after diminishing adjustment costs. 5. Conclusion This research paper investigated the relationship between successive Norwegian dairy quota arrangements and productivity changes of the dairy industry. Primal dynamic Luenberger productivity growth indicator was utilized to calculate the annual productivity changes in the constructed datasets referring to the quota arrangements (no quota, ; restrictive quota, ; buy and sell quota ). Productivity at five-year averages generally increased in all periods, although the largest average increase was achieved in the second period under restrictive output quota system. Therefore, we presumed that there are some dependencies between quota systems and the evolution of productivity. A simple statistical test was performed on the results (Chi-squared test for independence) to find validation for our hypothesis. The test proved our surmise at 1% level of significance calculating the overall contingency table.(the decomposition of the contingency table revealed there is no statistical significance between the no quota system and restrictive quota system, but there is a very strong one between the restrictive and tradable systems in favor of productivity. In the first step, our estimation procedure could be refined in several ways. By the construction of the balanced panel structure we lost many observations from the original unbalanced data. If we do not bind all the five years together and let the data unbalanced, we will not lose important observations, which lead to more stable and reliable results. Furthermore, hopefully other significant parts of the quantity data will be 5 df = Degrees of freedom July ISBN Congress Proceedings Page 7 of 8

8 192 TIBOR MARTON, THOMAS HECKELEI, ØYVIND HOVEID available for us and fewer necessary aggregations will be included in the input and output measures. In future, research into the decomposition of the Luenberger indicator into technical change, technical inefficiency change and scale efficiency change holds promise in the understanding of significant movements in the sector. 6. Acknowledgements The authors want to thank Prof. Maureen Kilkenny for valuable and helpful comments. Furthermore, we acknowledge the support of a Research Council of Norway grant: Space, land and society: challenges and opportunities for production and innovation in agriculture based value chains (AGRISPACE/23810). 7. References Atsbeha, D. M. (2012). The Economics of Dairy Production: Effects of Breeding and Marketing Quotas. UMB School of Economics and Business. Briec, W., & Kerstens, K. (2009). Infeasibility and Directional Distance Functions with Application to the Determinateness of the Luenberger Productivity Indicator. Journal of Optimization Theory and Applications, 141(1), Chambers, R. G., Chung, Y., & Färe, R. (1996). Benefit and Distance Functions. Journal of Economic Theory, 70, Fan, S., & Pardey, P. G. (1997). Research, productivity, and output growth in Chinese agriculture. Journal of Development Economics, 53(1), Jervell, A. M., & Borgen, S. O. (2000). Distribution of dairy production rights through quotas: The Norwegian case. In H. K. Schwarzweller & B. P. Mullan (Eds.), Dairy Industry Restructuring (Research i., pp ). Kapelko, M., Lansink, A. O., & Stefanou, S. (2012). Dynamic Productivity Growth in the Spanish Meat Industry. In 131th EAAE Seminar, Prague, Czech Republic (pp. 1 11). Karagiannis, G. (2005). Explaining output growth with a heteroscedastic non-neutral production frontier: the case of sheep farms in Greece. European Review of Agriculture Economics, 32(1), Kumbhakar, S. C., Lien, G., Flaten, O., & Tveterås, R. (2008). Impacts of Norwegian Milk Quotas on Output Growth: A Modified Distance Function Approach. Journal of Agricultural Economics, 59(2), Lansink, A. O., Stefanou, S., & Serra, T. (2015). Primal and dual dynamic Luenberger productivity indicators. European Journal of Operational Research, 241(2), Romstad, E. (1995). Omsettelige kvoter (Tradable quotas). In Landbruksøkonomisk forum 1/95 (pp ). Tveteros, R. (1999). Production Risk and Productivity Growth: Some Findings for Norwegian Salmon Aquaculture. Journal of Productivity Analysis, 12(2), Walden, J. B., Kirkley, J. E., Fare, R., & Logan, P. (2012). Productivity Change under an Individual Transferable Quota Management System. American Journal of Agricultural Economics, 94(4), July ISBN Congress Proceedings Page 8 of 8