Competitive Pressure and Productivity Growth: Florida Vegetable Industry - Research Paper Example

Competitive Pressure and Productivity Growth: The Case of the Florida Vegetable Industry Competitive Pressure and Productivity Growth: The Case of Florida Vegetable Industry Abstract The paper was basically aimed at establishing the relationship between competitive pressure and productivity growth sung a case study of Florida fresh vegetable industry. To be able to establish this relationship, an empirical research was carried out to provide empirical results. The time series data was obtained from the firm was regressed to provide the empirical results that could be used to make conclusions and generalizations. The results of the empirical research revealed that there is a significant convincing evidence that depicts the connection between competitive pressure and productivity growth to be positive. Recommendations and word of cautions are included at the conclusion section. Introduction Increased efficiency and technical change have been considered to be majorly contributed traditionally by the level of competitive market pressure. When considering the growth of productivity, its principal contributors are technical change and increased efficiency. As a result, when considering the two factors together, the degree of productivity growth and the competitive pressure in a particular market are expected to be positively related. For this reason, it can be aid that factors that affect market competition can also be expected to affect productivity growth. Reducing the competition levels in the market, is usually done by applying the government intervention measures that usually come in the form of Agricultural Policies. Price supports, and trade barrier policies such as quotas and import tariffs are the most policy intervention methods by the government to solve this issue. For this reason, it is possible for such government intervention to create the adverse effect on the agricultural productivity growth. An example is cited from the works of Antle and Capalbo (1988), who suggested that the government interventions may have the substantial effect on agricultural productivity. It is very important to understand the existing relationship between competitive pressure and productivity growth in order to streamline the long-term trends of productivity in all the segments existing in the U.S Agricultural sector. With all this importance, it is till surprising that the empirical analysis of the relationship between competitive pressure and productivity growth has been overlooked to a greater extent. The main purpose of this paper, therefore, is to investigate and present the empirical findings on the relationship that exists between productivity growth and competitive pressure the paper will be in form of a case study of the Florida fresh water vegetable industry within the periods of 1969-1982. In as much as we will not be able to use the results from this study on the other agricultural industries, there are several reasons that make the Florida vegetable industry to provide a good opportunity for the investigation of the relationship that exists between competitive pressure and productivity growth. One such reason is that, the vegetables produced by the Florida vegetables industry can be categorized into two different mutually independent groups that are usually done based on the differential levels of competitive pressure in particular markets. In this case, one set of such crops is directly competing with a group of the similar product that are imported from Mexico. In this first group of crops are cucumber, tomatoes, squash, peppers, etc. The intensity of such competition between Florida and Mexico is well documented. The second set of crops include crops such as cabbages, eggplant, leaf crops, celery, sweet corn, watermelons, radishes, and potatoes. These crops, on the other hand, have no foreign competition and only a limited domestic competition. The second reason is that, there were new technologies and cultural practices that were improved, were available for adoption for a part of the vegetables that were involved during this analysis period. As a result, no differences were expected in the supply of technological advances. At this stage, comparable rates of productivity growth could have been possible in the production of both sets of Florida crops. When these factors are considered, there is a comparative analysis about productivity growth rates that is established across these two set of crops. This gives an opportunity for the analysis to give more information on the relationship between competitive pressure and productivity growth. When it is found that these crops that face a good level of competitive pressure show relatively greater productivity growth rates than the other crops that undergo less intense competition, then the assumption that competitive pressure brings about productivity growth, is considered valid. The first section of this paper will briefly be reviewing some already existing literature on the relationship between competitive pressure and productivity growth. The second section will give an overview of measuring productivity using index numbers, and section three will involve data presentation and analysis of the results. On the last section of the paper, there will be a presentation of the concluding remarks about the whole analysis. Competition and Productivity It has generally been considered that increased competitive pressure in a given market has a positive relation to the economic level of efficiency of a firm. It assumes that firms were possessing market power usually has the likelihood of exploring the advantage much more by just avoiding to get near the position of maximum profits (Hicks, 2005). As stated by Leibenstein (2006), the competitive pressure degree is related positively to the technical efficiency (Leibenstein, 2006). In this case, it was assumed by the author that allocative efficiency is rather trivial. Leibensteins assertions have been supported by the empirical studies that were carried out by Bergsman, and Martin and Page. According to the model developed by Bergsman that was meant for estimating the effects of measures of protective trade on both the technical and allocative efficiency in selected six developing countries, it was established that limiting competition in those countries led to a significant amount of welfare costs in that are associated with technical inefficiencies (Bergsman, 2004). Page and Martin on the other hand made a computation of indices through the use of a frontier production function approach while carrying out a cross-sectional analysis of some firms in two Ghanaian subsidized industries (Martin & Page, 2003). At the same time, there are also the related differences in the estimated efficiency levels in the firms that either have or do not have subsidies of payments. There were lower levels of technical efficiencies in the subsidized firms from both industries than from unsubsidized firms. Page and Martin explained this result as a reflection of income effect where, when subsidy is received, the managers relax and deploy their efforts into a quiet life. There has also been a relation between competitive pressure and technical change, established. Cochrane (2008) asserts, in the agricultural treadmill hypothesis that, as technological innovation gets to availability state that makes firms adopt various improved technologies, here is usually an experience of an increase at both the firm and industry levels. With inelastic market demands, an increase in the output level results in the reduced real output prices. As a result, the firms operating at the high-cost levels are forced to either become innovative or exit the industry. When the innovative cycle theory by Kislev and Schchori-Bachrach (2003) is considered, similar positions are also developed. On the other side, when we consider situations where international trade is important, parallel arguments have value. With a situation where a low-cost foreign competitor decides to enter a market that is in equilibrium, the output prices will be reduced due to the additional product that comes to the market with it. It means, therefore, in situations where there are no trade barriers; there is a need for the domestic producers to become innovative. Given the fact that efficiency improvement, as well as technical change, have a positive relationship with productivity growth, it then follows that the arguments above will be pointing that competitive pressure is also positively related to productivity growth. Antle, however, suggests that an opposite view may hold. In fact, Antle has continued to add that the technical change in the dairy production has continued beyond its profitability when the dairy price supports are absent. It is thus, suggested that, those price-support policies that tend to reduce competitive pressure in any given market, can sometimes have a positive effect on the technical change that then also creates a positive change on the productivity growth. The argument is similar to that made by Schultz (2009), which suggested that the government protected and overpriced commodities of agriculture are most likely to show higher productivity growth when he government policies chances of uncertainty in price as there are incentive for technical change in the form of high prices. The studies above show that, it is unanimously agreed that competitive pressure, together with the influencing institutional arrangement, has a significant effect on productivity growth. It is, however, not distinct whether the degree of competitive pressure in any particular market has an enhancing or inhibiting effect on productivity growth. Productivity Measurement and Total Factor Productivity Total factor productivity (TFP) measures have recently been used to replace the partial productivity measures such as per acre yield and manpower output when technical progress is to be measured. It implies that, any action whose intention is to increase output while holding inputs constant will increase the total factor productivity. This may swell show correspondence to the shift in production surface that may be brought about by technical change. This shows that the total factor productivity is used to measure disembodied technical change. Let an equation: yt = f(ẍ;t) be a linearly homogenous production function with a concave twice differentiable and non-decreasing aggregate. In the equation, ẍ denotes a vector of inputs while t denotes technology state. While assuming technical growth to be neutral and using Solows derivation, the following equation can be used to measure TFP: (i) TFP = - ; In this function, a variable with a dot over it shows time derivative while Si represents the elasticity of output with respect to the ith production factor. With this illustration, the above equation (i) explains that the percent change in output resulting because of technical change is equal to the difference in the percent change in total output and the inputs elasticity-weighted percent change. As expressed in equation (i) above, productivity growth and technical change can be interchangeably used. An assumption that must be applied when this correspondence is used is that, all inputs are used in a technically efficient manner. However, when this efficiency assumption is relaxed, TFP is applied to measure both technical change and efficiency growth. This study does not assume a continuous technical efficiency. As a result, TFP is taken to measure both technical change and technical efficiency change. In case the production factors are paid their marginal value products, the summation of Si will be equal to 1; i.e. Si=1. When this expression is integrated with equation (i) above, the cumulated index of TFP growth from time t=0 to t=T is yielded. As a result, we have an expression that looks like this; (ii) TFP = The Divisia index of the input growth between t=0 and t=T is represented by the right-hand side denominator. As the right-hand side of equation (ii) involves the observable variables, it becomes possible to estimate the technical change index in principle. To use such a calculation method, time series data is usually required, but it does not exist in practice. Discrete data is usually used to approximate the continuous expression in equation (ii). Various indices such as Fishers Ideal, Laspeyres, Tornqvist-Theil index and Paasche have been used as Divisia index discrete approximations (Diewert, 1980). Choosing which approximation to use for the Divisia index was considered ad hoc for very many years. The notion of precise and superlative index numbers was however introduced by Diewart (1980) to tie the form of the selected specific forms of production functions. In one way, when f(.) is of the homogeneous translog, form, (iii) ln f(xt) = α0 + ln xit + ij ln xit ln xjt the Tornqvist-Theil index quantity is applied in discrete framework n providing the exact growth measure of TFP between the base and the period. This index form is provided by; (iv) TFP = When equation (iv) is rewritten in a log-linear form, it emphasizes the fact that we can measure the rate of productivity growth as the residual of output growth over one which is attributable to input growth: (v) Ln = ln - ln Empirical Results For a uniform and nonbiased comparison between the two sets of crops in Florida vegetable industry, there is a requirement of use of similar measures on those crops that exhibit limited domestic competition. Data on yield per acre, the cost and quantity of yield is required to calculate the TFP indices for each crop. Production and yield data were borrowed from Brooke Taylor and Taylor and Wilkowske (1983). The categories of input that were used to calculate TFP indices included fertilizer, seed, agricultural chemicals, capital services, energy, labor and other miscellaneous category of costs. By the use of the regional price indices from the Agricultural Prices, the implicit indices for input quantity were generated. Fishers weak-data reversal test was also employed to generate a corresponding production cost data. Equation (v) was applied to calculate the TFP indices for each crop. From that, a simple regression analysis was used to derive the average annual productivity change. This method accounted for major weather related conditions. Interestingly from the results, it was discovered that the difference in the growth rates of productivity is insensitive to the used method of calculating the annual average rate of growth. Even without accounting for weather effects, t was still discovered that the crops facing a considerable extent of import competition had their productivity growth rates exceeding the growth rates in the crops that have limited domestic competition. When the differential in productivity was calculated using the arithmetic, endpoint-average and geometric methods, it was established that the differences between the two groups of crops, in as far as the average yearly rate of growth productivity is concerned, were 3.4, 3.9 and 3.7 percent respectively. With the regression results, it was implied that the average difference in productivity growth rates was about 3.5 percent every year. Conclusion This research paper investigate the relationship between competitive pressure and productivity growth in the form of a case-study of the Florida fresh winter vegetables industry. From the empirical results, here are a fairly convincing results that explain the existence of a relationship between the productivity growth rate and the competitive pressure level. It was vivid from the results that the crops that exhibited the significant amount of pressure in the form of Mexican importation showed higher rates of productivity growth than those crops that the crops that underwent limited domestic competition. There are a fairly well-delineated groups of crops at the Florida vegetable industry that can be defined on the basis of differential levels of competitive pressure with a minimum level of government intervention. This makes it possible, to a greater extent, to isolate the relationship between competitive pressure and productivity growth. However, there are other factors remaining that can be used to potentially explain the differences that were observed between the productivity growths across the two sets of crops. Lastly, it would be wise to take caution that the number of observations that were in obtaining the regression estimates was small. This leads to some question marks about the precision of the parameter estimated. Another observation is that, it was unfortunate that it was not possible to extend the data to cover more recent observations as the way in which the vegetable cost was collected was from the format of a survey to that of a technical budget by around 1983. The two series are not compatible, so they could not be used together to gather data for analysis. References Antle, J., & Capalbo, S. (1988). Introduction and Overview," in Agricultural Productivity: Measurement and Explanation, S.M. Capalbo, and J.M. Antle. Washington:: Resources for the Future. Bergsman, J. (2004). Commercial Policy, Allocative Efficiency, and X-efficiency. Quart. J. Econ., 88, 409-433. Cochrane, W. (2008). Farm Prices: Myth and Reality. Minneapolis, MN: University of Minnesota Press. Diewert, W. (1980). Aggregation Problems in the Measurement of Capital." The Measurement of Capital. D. Usher, ed. Chicago: University of Chicago Press. Hicks, J. (2005). Annual Survey of Economic Theory: Monopoly. Econometrica, 3, 1-20. Kislev, Y., & Shchori-Bachrach, N. (2003). The Process of Innovation Cycle. Am. J. Agr. Econ., 55, 28-37. Leibenstein, H. (2006). Allocative Efficiency vs X-efficiency. Am. Econ., 56, 392-415. Martin, J., & Page, J. (2003). The Impact of Subsidies on X-efficiency in LDC Industry: Theory and an Empirical Test. Rev. Econ. Stat, 5, 608-617. Schultz, T. (2009). Distortions of Agricultural Incentives. Bloomington: : Indiana University Press,. Taylor, T., & Wilkowske, G. (1983). Costs and Returns from Florida Vegetable Crops, Season 1981-82 with Comparisons." Gainesville: Food and Resource Economics Department, Economic Information Report 186. Florida: University of Florida, . Read More