The Poor But Efficient Hypothesis Economics Essay

The Poor But Efficient Hypothesis Economics probeIn Chapter ace we set in motion the purpose for this research and explain to the reviewer the essence of quantifying the amount the ho procedure of goods and serviceshold is departing to pay for abating malaria both in the present and in the future. In this chapter we go a step get on by reviewing literature in this ara. This chapter is important because it provides the reader with a part of history into this area of research. It also gives the reader an opportunity to understand where our research stands vis--vis early(a) researches in this area. Obtaining a value for the marginal effect of malaria on farmers proficient cleverness is unmatchable of the live wires on which precise estimates for our Willingness-To-Pay depend. We therefore want to start by reviewing literature in the area of force measurement afterwards, we allow for research into literature in the area of Willingness To Pay.Before we go ahead we gamey light the purpose of measuring skillful ability to the reader.Technical susceptibility in general enables virtuoso to understand the relationship amidst input used and the getup (total harvested array). It also enables us to measure the per defecateance of individual farms in an industry as well as provide an index for the average performance of the overall industry. This then leads us to propose policy recommendations that could help shift the labor marge- the supreme attainable harvest from each input- of the farm closer to the industry term at the prevailing technology. As we progress in this research the reader pass on further appreciate this concept and the reason why it is sensation of the most talked about concepts in learning/resource sparings.At the moment, our aim is to examine somewhat literature that relates to our area of research. We therefore start Section 2.1 by reviewing literature relating to the poor nevertheless approach-effective hypothesis of Schultz (1964). Section 2.2 reviews some tillage-establish literature on strength and health. In doing this we divide the study on inefficiency into twain the Frequentist (Section 2.2.1) and the Bayesian (Section 2.2.2) studies. Using another manner of classification, we classify the study of efficiency into single output studies (Section 2.2.3) and multiple output studies (Section 2.2.4). This puts us in good standing to review literature on Willingness-To-Pay in Section 2.4.Productivity/Efficiency Studies in gardeningThe Poor but Efficient HypothesisThe huge volume of research on efficiency in agriculture draws motivation from Schultz (1964) book Transforming Traditional Agriculture. In the book he explains why rural farmers are efficient in the management and allocation of resources. He ad caravances a hypothesis popularly called the poor but efficient hypothesis. Researchers try to verify this hypothesis quantitatively in doing this, a lot of issues come to the fore, part of which is the best way to measure productivity. Before the climax of the de conditioninistic measure of productivity pioneered by Aigner and Chu (1968), and, Afriat (1972) researchers attempt to measure efficiency. Of great importance to us in this area are the works of Welsch (1965), Chennareddy (1967) and Lipton (1968) because they specifically screen the validity of Schultzs poor but efficient hypothesis.Chennareddy (1967) utilizes the linear regression summary on a information of one hundred and four rice and baccy farmers in South India victimization a Cobb-Douglas production function. His findings were in accord with Schultz hypothesis. He recommends that South Indian farmers should adopt modern technology and extension gentility in order to move to a higher frontier. Lipton (1986)1disagrees with this recommendation. He argues that if Schultzs findings are correct then the rural farmers do not need any sharp advice to improve their productivity in other words moving to a higher frontier should not be a problem for them. He further queries Schultzs assertion believing that it only works under a neo-classical theory of perfect competition he affirms that if Schultz uses linear programming to analyse his data his findings would try that the rural farmer is inefficient.Welsch (1965) in his study on Abakaliki rice in Eastern Nigeria makes use of the linear regression to affirm that peasant farmers respond to economic inducement by allocating efficiently among several resources at their disposal. Hence, he supports Schultzs hypothesis. nonpareil thing we want the reader to note in the above groups of literature is the writers who contain with Schultzs assertion use parametric techniques to arrive at their conclusion piece of music Lipton (1968) employs a non-parametric linear programming technique that assumes at least one reckon is not fully employed.Just as the argument is about to cease, Sauer and Mendoza-Escalante (2007) involve themselves i n it. Their work serves to reconcile these diametrically opposing tames of thought. It puts to use a parametric normalized generalized Leontief (GL) profit function technique to analyse joint production of Cassava flour and maize by small-scale farmers in Brazil. The small-scale farmers are allocatively efficient, they assert, but they show considerable inefficiency in the scale of operation. At this juncture, we remind the reader that our digression is intentional. Our aim is to show how Schultzs assertion has brought an upsurge in the second of efficiency studies in agriculture with special focus on the developing economies of the world. We like to say that the work not only instigates research in development/resource economics but it also prompts research in anthropology and sociology (see Adams, 1986 and the review by Michelena, 1965 pp. 540-541).Proper measure of productivity starts with Aigner and Chu (1968), Afriat (1972) and Richmond (1974) where they propose a determinist ic method of frontier measurement. Though their studies are obsolete they however underscore the popularity of the Cobb-Douglas functional form in the early literature to show the relationship surrounded by input and output. Aigner, Lovell and Schmidt (1977), Meeusen and van den Broeck (1977), and, Battese and Corra (1977)2introduce the modern random frontier analysis as we know it today simultaneously. Their position apart from incorporating the efficiency term into the deterministic warning it also includes the effect of random shock, hence, the name stochastic. Lau and Yotopoulos (1971) also introduce a dual profit function model to measure efficiency but their method is not as popular in production analysis because it only yields efficiency measures for a group of farms while the frontier method gives efficiency values for individual farms in the industry (Frsund et al 1980).The reader should note that the linear regressions of Chennareddy (1967) and Welsch (1965) give the shape of the technology of an average farm in the industry while the stochastic frontier model gives the shape of the technology of the most productive farm in the industry against which the efficiency of every other farm is measured (Coelli 1995). In other words, Chennareddy (1967) and Welsch (1965) use an average response model for their analysis.The specification of a functional form and/or distributional confidence confers on a technique the nomenclature parametric while the non-specification of a functional/distributional form confers on a technique the non-parametric nomenclature. The non-parametric nomenclature means, in the words of Koop (2003), you are letting the data speak. This he says is very difficult to achieve as even in the non-parametric system, but like in the parametric, one need to impose certain structure on a particular problem in order to achieve ones objectives.The use of the Data Envelopment abridgment (DEA) (another technique is the Free Disposal Hull, FDH) overshadows every other technique in the non-parametric class. Charnes, Cooper and Rhodes (1978) introduce this technique and gave it the name as we know it today. The data natural enclosure analysis technique uses the linear programming method to generate a piece-wise envelop over the data points. The technique is widely used in adept efficiency studies but it has the shortcoming of not incorporating randomness in measuring efficiency. Also, the envelop curve is not everywhere variousiable. Our focus in this research is the parametric technique.The parametric technique has progressed so much in the literature that there are now two different econometric schools of thought for estimating efficiency. The first school of thought are the Frequentists who dominate this field since its inception and the second school of thought are the Bayesians into which our research belongs.The Frequentist StudiesThe first set of Frequentist study is deterministic in nature and use the technol ogical structure of the mathematical programming approach (see Aigner and Chu, 1968 Timmer, 1971 and, Frsund and Hjalmarsson, 1979 for exposition on mathematical/goal programming). Richmond (1974) introduces the Modified mediocre least Square (MOLS) approach to analyse the efficiency of Norwegian manufacturing industries specifying a Cobb-Douglas production function. Richmond (1974) is a modification of the Corrected Ordinary Least Squares (COLS) approach. Winsten (1957) introduces this model by assuming a distribution (such as half normal or exponential) for the disturbance term. The Corrected Ordinary Least Square technique involves a two step process. The first step involves the use of the Ordinary Least Squares to observe unchanging and un predetermine estimates of the marginal effect parameters on the contrary, the intercept parameters are consistent but bias. The second step involves the shifting of the intercept upwards so the frontier envelops the data from above.Greene ( 1980) takes Richmond (1974) work a step further as he assumes a gamma distribution for the random error term using the maximum likelihood approach. He uses the data from Nerlove (1963) which is a sample of one hundred and fifty five firms producing electricity in the united States in 1955. Apart from replicating the results of Aigner and Chu (1963), Greene (1980) tries to explain the statistical relevance of his model. The reader should note that Greene (1980)s model is deterministic.One of the early applications of the deterministic frontier were Shapiro and Mller (1977), Shapiro (1983), Belbase and Grabowski (1985). Shapiro and Mller (1977) attempt to estimate the proficient efficiency of forty farms in Geita district of Tanzania. They follow Timmer (1971) method of analysing expert efficiency by applying the linear programming to a Cobb Douglas production frontier. Their result which is similar to that of Chennareddy (1967) shows that the traditional farmer can improve his te chnical efficiency by adopting modern farming practices through easy access to information. This, they say, will be at the expense of non-economic costs like the farmer being branded unsociable by his community. Shapiro (1983) working in the same district as Shapiro and Mller (1977) tries to confirm the poor but efficiency hypothesis but identifys the hypothesis whitethorn not be applicable in terms of peasant agriculture in Tanzania because their output could still be annexd if all farmers had the same efficiency as the most efficient farmer in the sample. These assertions echo the conclusion of Lipton (1968). He uses the same model and method of analyses as Shapiro and Mller (1977).Belbase and Grabowski (1985) introduce a technique that is different from the other two stated above. They apply the Corrected Ordinary Least Square (COLS) approach of Winsten (1957) on cross-sectional sample of farms in Nuwakot district of Nepal. They record an average technical efficiency value of 8 0% for joint production of rice, maize, millet and wheat. The average technical efficiency value for individual frontier calculation for rice and maize is given as 84% and 67% in that order. They find correlation between technical efficiency and other variables which are nutritionary level, income and education. Technical efficiency is however not correlated with farming experience.Some studies investigate the impact of certain agricultural policies on productivity. A priori one expects these policies to actually increase productivity but this is not always the case. One of such study Taylor, Drummond and Gomes (1986) use a deterministic production function and control the World Bank sponsored credit programme PRODEMATA did not impact positively on the technical efficiency of farmers in Minas Gerais, Brazil. Their result shows that there is no digression between the technical efficiency of farmers who participate in the programme and those that did not participate. This paper i s one of the few that oppose both the results of the Corrected Ordinary Least Square and the maximum likelihood approaches. Unexpectedly, the participant farmers in the PRODEMATA programmes have slightly lesser allocative efficiency than non-participant farmers. The researchers also favour Schultzs hypothesis.We want the reader to note that the deterministic frontier is still popular in the literature for example, Alvarez and Arias (2004) use Lau and Yotopoulos (1971) dual profit function model to measure the effect of technical efficiency on farm surface using data from one hundred and ninety-six dairy farms in Northern Spain. They introduce technical efficiency as a parameter to estimate in a simple production function. They observe a positive relationship between technical efficiency and farm size after they control for output prices, input prices and quasi- situated inputs. Also Amara et al (1999) use the deterministic frontier to discover the relationship between technical ef ficiency and the credence of conservation technologies by potato farmers in Quebec. They be that farming experience and the word meaning of conservation technologies have positive influence on technical efficiency.Croppenstedt and Demeke (1997) use a fixed-random coefficients regression to analyse data for small-scale farmers growing cereal in Ethiopia. They observe that land size is a major constraint to crop production and large farms are relatively less productive than small farms other things being equal. They note that most of the farms are inefficient. They also observe inefficiency in the use of inputs especially turn over and fertiliser. Share cropping is positively correlated to technical efficiency.Karagiannis et al (2002) propose an alternative for separating technical change form cadence change technical inefficiency. Their proposition uses the general formulation index to model technical change (Karagiannis et al 2002 cites Baltagi and Griffin 1988). They also mod el technical change as quadratic function of time. Their proposition does not assume any distributional assumption for the one sided stochastic error term. They then apply their proposition to the United Kingdom dairy sector from 1982 to 1992 using a translog production frontier. They obtain a mean technical efficiency value of about seventy-eight per cent for the dairy industry with this period.One major disadvantage of the deterministic frontier model is that it over-values our inefficiency estimates. For example, Taylor and Shonkwiler (1986) discover the deterministic frontier gives over seventy per cent inefficiency while the stochastic frontier gives twenty per cent value for inefficiency.At present, a lot of papers utilize the stochastic frontier model in their analysis. Coelli et al (2003) makes use of the stochastic frontier to calculate the total operator productivity for a panel data of crop agriculture in Bangladesh. The data contains cardinal observations put oned be tween 1960/61 and 1991/92 from 16 regions and the result reveals technical change is convex in nature with increase starting about the time of the introduction of the green revolution varieties in the 1970s. Technical efficiency reduces at an annual rate of 0.47 per cent during the period they investigate. This has an effect on the total factor productivity which declines at the rate of 0.23 per cent per annum with the rate of reduction increasing in later years. This, they say, raises questions of food security and increase in agricultural productivity in Bangladesh. They point out the non-use of price data in their analysis which makes their work different from other authors (Coelli et al 2003 cites Pray and Ahmed, 1991, and, Dey and Evenson, 1991). Wadud and White (2000) compare the stochastic frontier approach with the data envelopment analysis and discover both methods indicate efficiency is significantly affected by irrigation and environmental degradation. in that location ar e a few papers that attempt to analyse technical, allocative and economic efficiencies at once in a single research. Bravo-Ureta and Pinheiro (1997) carry out a frontier analysis using the self-dual Cobb-Douglas production function to analyse farm data from Dominican Republic. They justify the use of the Cobb Douglas production function because the method they adopt requires both the use of the production and cost frontiers. Their research is important because they use the maximum likelihood technique to emphasize the essence of not only estimating the technical efficiency but also, the allocative and economic efficiency. Another paper that follows in this light is that of Bravo-Ureta (1994) who attempts to measure the technical, allocative and economic efficiencies of cotton and cassava farmers in eastern Paraguay. He estimates economic efficiency for cotton and cassava farmers to be around forty per cent and fifty-two per cent respectively.There could be spatial differences in th e technical efficiencies of different farms based on ecological differences, farm size and interactions between these two variables. Tadesse and Krishnamoorthy (1997) set out to investigate this in their research on paddy rice farmers in the state of Tamil Nadu, India. They remark that the farmers still have opportunity of increasing their efficiency by seventeen per cent. They observe significant sport in the variation of mean technical efficiency in the four zones that make up Tamil Nadu. They use a two floor approach where the first proletariat is to obtain farm-specific technical efficiency and then use a Tobit model to compare the differences in the technical efficiencies of each region and zone. Wang and Schmidt (2002) note a bias in the results obtained by this process and they went ahead to use the Markov chain Monte Carlo technique to prove that there is serious bias at every stage of the procedure.Chen et al (2009) also examine the technical efficiency of farms in four regions of China. The four regions are North, North-East, East and South-West. They observe that different inputs need to be put to efficient use in the different regions. For example, inefficient use of industrial input is the main problem in the East while in the North it is capital. They assert that farms in the North and North-East are relatively more efficient than farms in the East and South-West. They recommend a change in the land tenure system to eliminate land fragmentation in China.Other researchers have used the stochastic production frontiers to investigate the impact of governing body programmes on farmers efficiency. For example, Seyoum et al (1998) use the Battese and Coelli (1995) stochastic production function to compare between farmers that participate in Sasakawa-Global 2000 project and those who do not in Ethiopia. They collect twenty samples from two different districts (Keresa and Kombolcha) of eastern Ethiopia and show the difference in the levels of product ion in these two districts by use of a dummy for one district. The data is panel in nature which justifies their use of the Battese and Coelli (1995) model. Battese and Coelli (1995)3is a panel data extension of the Kumbhakar et al (1991) research work. Seyoum et al (1998) recommend that policy makers should fill out the Sasakawa-Global 2000 project as farmers who participated have better output, productivity and efficiency than farmers that did not.Still on the impact of government programmes on efficiency, Abdulai and Huffman (2000) look at the impact of the geomorphological Adjustment Programme on the efficiency of rice farmers in Northern Ghana using a stochastic profit function. Their results show rice producers in the region are highly responsive to market prices for rice and inputs. They support the introduction of the structural adjustment programme because it makes the farmers more market oriented. Also, Ajibefun and Abdulkadri (1999) find the Cobb-Douglas production func tion as being adequate to represent the efficiency of Nigerias National Directorate of Employment Farmers Scheme. They reject the half-normal distribution assumption for the inefficiency term. Ajibefun (2002) simulates the impact of policy variables on the technical efficiency of small-scale farmers in Nigeria. He discovers that increase in education level and the farming experience would significantly improve the small-scale farmers technical efficiency. Amaza and Olayemi (2002) investigate the technical efficiency of food crop farmers in Gombe State, Nigeria and arrive at similar mean technical efficiency as Ajibefun and Abdulkadri (1999). However, the difference between the minimum and maximum technical efficiency score for Amaza and Olayemi (2002) is seventy-six per cent while for Ajibefun and Abdulkadri (1999) is about sixty-six per cent.Jara-Rojas et al (2012) look at the impact of the adoption of soil and water conservation practices on productivity and they discover a positi ve relation between soil and water conservation and technical efficiency. They discover that an enhancement of the technical efficiency also improves the net returns on investment.The use of the stochastic frontier model to estimate the effect of health on farmers efficiency is also very important in the literature. Croppenstedt and Mller (2000) take up this challenge when they research into the role of the Ethiopian farmers health and nutritional status on their productivity and efficiency. They find that distance to the source of water as well as nutrition and morbidity affect agricultural productivity. Surprisingly, elasticities of labour productivity regarding their nutritional status are strong. They further affirm that this strong correlation continues with technology estimates and wage equations. However, they record considerable loss in production due to technical inefficiency even after accounting for health and nutrition of workers.Ajani and Ugwu (2008) look at the impact of adverse health on the productivity of farmers living in the Kainji wash-hand basin of North-Central Nigeria. Their study shows the health variable as being positive, large and statistically significant. They therefore come together that health capital is an essential input in agriculture.A paper that successfully combined the non-parametric technique of data envelopment analysis and an econometric model is Audibert et al (2003). They use a combination of the data envelopment analysis and the Tobit model to infer on the social and health determinants of the efficiency of cotton farmers in Northern Cte dIvoire. They use the high density of the malaria parasite in the blood of an individual as a proxy for the health of the household. They use a two step process firstly, they use the data envelopment analysis to arrive at relative technical efficiency values and then they regress this efficiency scores against factors they think will affect efficiency. The high density of malaria p arasite in the blood variable enters the model at the second stage. Their results show that malaria greatly reduces farmers technical efficiency. They further conclude that it is intensity of infection by the disease that is important rather than its presence. Our research collects data on the prevalence of the disease in an area rather than just hospital reported cases this we believe will give further credence to our results.Ajani and Ashagidigbi (2008) use numbers of days of incapacitation as a proxy for malaria incidence in Oyo State, Nigeria. Surprisingly, they ran a normal linear regression to investigate the effects of malaria on agricultural productivity. Their analysis shows that age and days of incapacitation are insignificant statistically. Olarinde et al (2008) research the factors that affect bee keepers technical efficiency in Oyo state, Nigeria. They observe that the bee keepers are efficient by about eighty-five per cent there is still room for to increase their eff iciency by fifteen per cent. They point out that some of the farmers do not take bee-keeping as their main occupation. This, they say, is a major determinant of efficiency. matrimonial status is also another variable that affects technical efficiency, they note. They observe that a farmer who is single is likely to be more efficient than a wed farmer.Mochebelele and Winter-Nelson (2000) examine the effect of migratory labour (to mine fields in South Africa) on farm technical efficiency. They try to establish if migrant labour actually complement farm production or not. They establish that households with migrant farmers have higher production and are more efficient than households without migrant farmers.In the use of the panel data for efficiency estimation, some researchers try to see if differences exist in efficiency values between the fixed effect model and the stochastic frontiers. Ahmad and Bravo-Ureta (1996) use panel data of ninety-six Vermont dairy farms between the peri ods 1971 to 1984. They carry out statistical tests to investigate the better model between the fixed effect model and the stochastic frontier model. The fixed effect model gave better results than the stochastic frontier model. Hence, they conclude that the fixed effect model involve to be considered in panel data analysis.Reinhard et al (1999) estimate the technical and environmental efficiency of a panel of dairy farms. They assume the production of two outputs dairy and excessive use of Nitrogen. They analyse their efficiencies separately. Their objective involves investigating whether farmers can both be technically and environmentally efficient. They also examine the compatibility of these two types of efficiencies. They obtain a mean output-technical efficiency of 0.894 while the input-oriented environment efficiency is 0.441. They remark that intensive dairy farming is both technically and environmentally more efficient than extensive dairy farming.Reinhard et al (2000) exa mine comprehensive environmental efficiency in Dutch dairy farms. This paper is a continuation of Reinhard et al (1999) paper. In this paper, apart from surplus Nitrogen which they use in their earlier work, they also investigate excess use of phosphate and total energy use of these farms. They compare efficiency scores in the stochastic frontier analysis with the data envelopment analysis. The mean technical efficiency values for the two methods of analysis are different. The stochastic frontier has an output technical efficiency value of eighty-nine per cent while the data envelopment analysis has an efficiency value of seven-eight per cent. There is significant difference between their environmental efficiencies also. The stochastic frontier analysis records a value of eighty per cent while the data envelopment analysis records a value of fifty-two per cent. It is evident from the result of the two efficiencies that the stochastic frontier method over-values efficiency scores.Bef ore we close this section we refer the reader to a work by Strauss (1986). The work is important because it attempts to investigate the effect of nutrition on farm labour productivity in Sierra Leone. He uses an average response model to capture this effect. He estimates a Cobb-Douglas production function which accounts for simultaneity in input and calorie choice. His exercise shows calorie using up has significant impact on labour productivity. He, however, places a caveat on this result because individual-level nutrient and anthropometric data are not included in the analysis. His result supports the nutrition productivity hypothesis to a great extent.In the last few pages we attempt to explain to the reader the preponderance of the Frequentist method of analysing the stochastic frontier especially in agriculture. We emphasize the diverse uses of the parametric method of efficiency measurement in agriculture. We believe that other literature in agriculture will fall into one of the categories we peruse above. Next, we take a look at the Bayesian econometrist view. The reader should note how few the literature is compared to the Frequentist method. Also, for a thorough perusal of the literature from the Frequentist perspective we refer the reader to Bravo-Ureta et al (2007) Delete.The Bayesian StudiesThe works of van den Broeck, Koop, Osiewalski and leaf blade (1994) Koop, Osielwalski and Steel (1994, 1997) Koop, Steel and Osielwalski (1992), and, Fernndez, Osiewalski and Steel (1997) herald the Bayesian technique for estimating the compose-error model.van den Broeck, Koop, Osiewalski and Steel (1994) is a primer for estimating a Bayesian cross-sectional composed-error data. They resolve the problem of choosing the best functional form experienced in classical econometrics by mixing over a number of distributions. They use the Bayesian model averaging to average over the results of the Jondrow et al. (1982) and Greene (1990). In other words van den Broeck , Koop, Osiewalski and Steel (1994) solve the problem of choosing the better distribution between the two. They also carry out predictive demonstration on their results using the Monte Carlo technique of importance sample.In continuation of van den Broeck, Koop, Osiewalski and Steel (1994) work Koop, Osielwalski and Steel (1994) show how to use the Gibbs sampling Monte Carlo method to arrive at estimates for the stochastic cost frontier model. They fit an asymptotically ideal price aggregator, non-constant returns to scale composed error cost frontier. They use Barnett, Geweke, and Wolfe (1991) method for generating the asymptotically ideal price aggregator (Koop, Osielwalski and Steel, 1994 cite Barnett, Geweke, and Wolfe 1999). They caution that care should be taken in the choice of functional form for frontier analysis. We believe the use of the Bayesian model Averaging technique should circumvent this problem. Also, they discover that imposing regularity condition on the price aggregator is found to reduce the spread of the Mntz- Szatz expansion.Koop, Steel and Osielwalski (1995) essentially show how to draw the different parameters in the composed-error model using the Gibbs sampler. They provide an algorithm to draw the different parameters of choice in the composed-error model. They show the ease with which this can be done using the Gibbs sampler. They also note the use of 0.875 as an informative prior for the inefficiency value. van den Broeck, Koop, Osielwalski and Steel (1994) propose this value.Fernndez, Osiewalski and Steel (1997) introduce the Bayesian method for estimating panel data using a class of non- or partly-informative prior. They assert that using this type of priors for a cross-sectional data will make its posterior inference unreliable and inaccurate. This is because the total number of parameters in the entire model is big than the sample size. They circumvent this problem in the panel data where the researcher can impose a struct ure on the inefficiency terms. Koop, Osielwalski and Steel (1997) take Fernndez et al (1997)