From the SelectedWorks of amarnath tripathi 2008 Estimation of Agricultural Supply Response Using Cointegration Approach amarnath tripathi Available at: https://works.bepress.com/amarnath_tripathi/4/
ESTIMATION OF AGRICULTURAL SUPPLY RESPONSE BY COINTEGRATION APPROACH A Report Submitted under Visiting Research Scholar Programme 2008 Amarnath Tripathi Indira Gandhi Institute of Development Research Gen. A. K. Vaidya Marg, Film City Road Goregaon (E), Mumbai- 400065
CERTIFICATE 2
ACKNOWLEDGMENT I would like to thank Indira Gandhi Institute of Development Research, Mumbai for providing me visiting scholarship and all the facilities required for the implementation of my study. I am extremely grateful to Dr. G. Mythili, Associate professor, Indira Gandhi Institute of Development Research, Mumbai for her kind guidance and sincere support during my stay at IGIDR. I extend my warm thanks to Professors J. Sarkar, S. Thamos, and C. Verramanai for giving me valuable suggestions during the course of my study. I would like to express my deepest sense of gratitude to Dr. A. R. Prasad, Professor, Banaras Hindu University, Varanasi my PhD supervisor at Banaras Hindu University without whose help and encouragement this report would not have been completed. 3
CONTENTS 1. List of Tables...(05) 2. Abstract..(06) 3. Introduction...(07) 4. A Brief Literature Review on Supply Response.(09) 5. Basic Framework and Approaches in Estimation..(19) a. Indirect Approach...(19) b. Direct Approach..(20) c. Nerlove and ECM Model (21) 6. Empirical Framework.(23) a. Model..(23) b. Estimation Procedure.(25) c. Selection of States..(26) d. Period of Study (26) e. Data Sources (27) 7. Results and Discussion (28) 8. Conclusion and Policy Implication (41) 9. References (43) 4
List of Tables Table 1: Earlier Studies in Indian Context...(13) Table 2: Share of Agricultural Income in total income for all major states..(26) Table 3: The Mean Values of Selected Variables...(27) Table 4: Descriptive Statistics...(28) Table 5: Correlation Matrix.(28) Table 6: Results of ADF Test.(30) Table 7: Results of ADF Test.(31) Table 8: Bivariate Analysis for All India...(32) Table 9: Bivariate Analysis for High Agricultural Based States..(32) Table 10: Bivariate Analysis for Medium Agricultural Based States..(33) Table 11: Bivariate Analysis for Low Agricultural Based States...(33) Table 12: Multivariate Analysis for All India..(34) Table 13: Multivariate Analysis for High Agricultural Based States...(34) Table 14: Multivariate Analysis for Medium Agricultural Based States (34) Table 15: Multivariate Analysis for Low Agricultural Based States...(35) Table 16: Results of ECM for All India (36) Table 17: Results of ECM for High Agricultural Based States.(37) Table 18: Results of ECM for Medium Agricultural Based States...(37) Table 19: Results of ECM for Low Agricultural Based States..(37) Table 20: Aggregate Agricultural Elasticities w.r.t. Agricultural TOT (38) 5
ABSTRACT The issue of agricultural supply response is a very important one as it has an impact on growth, poverty, and environment. The size of agricultural supply response is expected to improve after removing some of the constraints that farmers were facing before. Though many constraints have been removed from agrarian system and many incentives have been provided to farmers, still the supply response for Indian agriculture is price inelastic. Hence the question why supply response is price inelastic becomes relevant. The present study is an attempt to find supply response through cointegration approach and to see if the response has been better at the all India level in comparison to previous studies. Further, it also focuses on the question whether there is difference in the supply response among highly agricultural based, medium agricultural based, and low agricultural based states. The study indicates that aggregate agricultural output elasticity with respect to agricultural TOT is very low and not statistically different from zero. Key Words: Agricultural Supply Response, Cointegration Analysis, and Error Correction Model JEL Classification: C22, Q11 6
INTRODUCTION One of the most important issues in agricultural development economics is supply response since the responsiveness of farmers to economic incentives * largely determines agriculture s contribution to the economy. Further, the response elasticities are also important for policy decision regarding agricultural growth. That is why it has been a debatable issue among economist and policy makers in both India and abroad for the last five decades. Agricultural Supply Response represents change in agricultural output due to a change in agricultural output price. The concept of supply response is dynamic and different from supply function which is the static concept. The supply function describes a price quantity relation, where all other factors are held constant. The response relation is more general concept; it shows the change in quantity with changes in prices as well as supply shifters and, therefore, approximates to the long run, dynamic concept of supply theory. In India, agriculture has made remarkable growth after initiation of new agricultural strategy in mid-sixties. Indian agriculture has progressed not only in output and yield terms but the structural changes have also contributed. Government provided more incentive such as remunerative prices to protect farmers interest, availability of credit facilities, improving irrigation facilities, improving markets of both output and input, more investment in agricultural research and extension services, etc. to farmers to produce more. After these developments, it is expected that farmers would become more price responsive. The previous studies as well as recent studies (Krishna, 1980 Palanivel, 1995, Rao, 2003, etc) have shown that Indian agriculture is low price responsive. Then, the question arises why is the supply response low for Indian agriculture? The present study is an attempt to reexamine supply response through better approach as compared to the previous studies. Further, it also focuses on the question whether there is difference in the supply response among highly agricultural based, medium agricultural based, and low agricultural based states. * Economic incentives are offered to encourage people to make certain choices or behave in a certain way. Except product own price there are several factors that influance supply; as for example, ralative prices of the substitute products, climatic conditions, technological, progress (improvement in the art of production), changes in the institutional and policy variables and even attitude of the producers. In economic terminology these factors are called supply shifters. 7
The previous studies on agricultural supply response in India used time series and panel data. Most of the studies applied Nerlovian Framework ** (1958). However, most economic time series data are trended overtime and regression between trended series may produce significant results with high R 2 s, but may be spurious (Granger and New Bold, 1974). So, we use cointegration analysis and error correction model (ECM) to overcome the problem of spurious regression. The ECM takes into account the partial adjustment in production and the mechanism used by farmers in forming expectations. These are the fundamentals of agricultural supply response model. So, ECM can be used in this context. We found one study related to India and many studies for other countries based on application of ECM in estimating agricultural supply response. The present study is organized as follows: Section (II) presents review of literature related to India agriculture. Section (III) discusses fundamentals and approaches of estimation of agricultural supply response. This section also shows the relevance of ECM in estimating agricultural supply response. Section (IV) discusses methodology and estimation procedure used in the study. Section (V) gives the results and findings. Section (VI) makes some observations on the causes of low supply response. Section (VII) concludes the whole study, gives limitation of the study and suggests policy implication. A time series is a set of observations on the values that a variable takes at different times. Panel data is a special type of pooled data in which the same cross-section unit is surveyed over time. ** Nerlove proposed a dynamic distributed lag model in which he introduced a dynamic element into the response equation by creating expectation. Cointegration is an economectric property of time series variables. If two or more series are themselves nonstationary, but a linear combination of them is stationary then the series are said to be cointegrated. Error correction model is useful for representing the short run relationships between variables. Sometimes we expect no relationship between variables, yet a regression of one on the other variable often shows a significant relationship. This satiation is called spurious regression. 8
A BRIEF REVIEW OF LITERATURE ON SUPPLY RESPONSE There are two types of studies found in the existing literature; one is at individual crop level and the other is at the aggregate output level. First type of studies shows change in the composition of agricultural output to a change in the relative price of individual agricultural commodities. On other hand, second type of studies shows change in total agricultural output due to change in the relative price of agricultural commodities compared with industrial goods, referred to terms of trade in the literature. A review of relevant literatures follows. Raj Krishana s (1963) study is the first study among those studies that used Nerlove framework in Indian Context. He used pre-independence data for Punjab (Undivided) and concluded that the Punjab farmers have responded to economic stimuli. Jai Krishna and Rao (1965, 1967) tried quite a few price formulations to specify the price variables and those on which farmers base their expectations. They found that traditional regression models give satisfactory results if not superior results compared with those obtained by using adjustment lag model as far as proportion of explained variation in wheat acreage is concerned. Parikh s (1967) work of supply response through the distributed lag models comes very close to the Nerlovian framework. He has used both adjustment lag and price expectation models along with the distributive lags. After analyzing different formulations with the data given in the appendices of Dharam Narain s study (1965), Parikh arrived at the result that non-price factors are quite important as compared to the price factors. It is worth noting here that the data set covered the period prior to 1939. Herdt, (1970) has evaluated supply response at aggregate output level for period 1907 to 1964 for Punjab agriculture. He divided the reference period into two sub-periods: 1907 to 1946 and 1947 to 1964. He concluded that the 1907-1946 period tends to support the hypothesis of a positive, although small, aggregate supply response of agriculture. The results for 1951-1964 suggest the opposite, or at least do not support the hypothesis. He used disaggregated approach given by Tweeten and Quance (1963) to measure supply elasticites. 9
Madhavan (1972) utilized a production function framework to arrive at the desired demand for land. A production function under the assumption of constant ratios of elasticity is maximized to obtain the factor demand equation. The estimated equation indicated that actual planted area of a crop in the given period is a function of the log of relative price of the crop, the expected yield of the crop under consideration as well as that of the competing crop and the weather index. Cummimgs (1975) examined supply responsiveness of the Indian farmers for post- Independent period by using Nerlove model. This work was followed by another study which is the joint work of Askari and Cummings (1976). They used a slightly modified version of the Nerlove model with maximum-likelihood estimation procedure to overcome the problem of serial correlation and non-consistency of the estimates. Narayana et al. (1981) estimated supply response crop-wise for Indian agriculture. They formulated expectation function for each crop by isolating stationary and random components in past prices and attach suitable weights for both in prediction. The method is based on ARIMA technique combined with BOX-Jenkins procedure. Bapna et al. (1984) derived a system of output supply and factor demand equation from the profit function. They began with monotonically increasing profit function and derived output supply and factor demand curves. Two systems are derived from the maximization of the profit function, namely, Generalized Leonteif and Normalized Quadratic Systems. The authors pooled the time series and cross section data by following error component model. It is confirmed by the authors that a Maximum-Likelihood Estimation procedure would be better than the three-stage least-squares procedure. The results were obtained for 96 districts spread over semi-arid tropical regions of the country. The authors indicated that 25 out of the 32 own elasticities had the anticipated sign and also demonstrated remarkable extent of price responsiveness of the semi-arid tropical farmers. Very high supply elasticity was noted for sorghum despite the small proportion of its marketed surplus. Hazell, et al (1995) used national and state level data for estimating the aggregate supply elasticities for Indian agriculture. The results from the national and state level analyses are very similar, and show that aggregate supply is inelastic. Both levels of analyses showed that 10
growth in agricultural output in India in recent decades is largely attributable to increased irrigation. Palanivel, (1995) estimated the aggregate agricultural supply response by using more appropriately constructed variables for the period 1951-52 through 1987-88. The model was developed within the basic Nerlovian partial adjustment framework. He constructed his own agricultural terms of trade (TOT) ***. He used farm harvest prices and retail prices in estimating the index for prices received and the index for prices paid by farmers, respectively. He argued that since farmers sell bulk of their products immediately after the harvest, farm harvest prices seemed to be a better approximation for the prices received. Similarly, he also pointed that as farmers purchase their requirements (particularly family consumption items) from retail markets, rural retail prices seemed to be a better approximation for the prices paid. His results indicated that the elasticity of aggregate agricultural output with respect to TOT is positive and statistically significant but non-price factors are equally, if not more, important. Mishra, (1998) attempted to assess the impact of economic reforms initiated in 1991 along with price and non-price factors on aggregate supply in the post-green revolution period. His results assured that the aggregate supply measured either through aggregate output or marketable surplus does respond significantly positively to terms of trade. Surajit Deb, (2003) explored the presence of long-run relationship between TOT and agricultural output by using cointegration analysis and error correction model. The bivariate results between TOT and output level in agriculture reflect no statistically significant cointegration. The non-cointegratedness indicates that no direct long-run relationship exists between TOT and output level in Indian agriculture. This, in turn, would suggest that a favourable TOT structure alone may not be effective in sustaining higher agricultural growth. Further, he included irrigation ratio as a technology between TOT and output. Then he found that variables are cointegrated. Finally he concludes that growth in agricultural output may respond better if specific structural variables are suitably combined with the price variables. Chandrasekhara Rao, (2004) examined agricultural supply response at aggregate level for Andhra Pradesh by using Nerlove Partial Adjustment Model. He found the short-run *** The agricultural terms of trade is defined as the ratio of prices received and prices paid by farmers. 11
elasticites of output with respect to TOT for aggregate agriculture vary from 0.20 to 0.29 and the long-run elasticities vary from 0.21 to 0.31. The results also indicated that non-price factors are more important determinants in aggregate agricultural supply than price related factors in the state of Andhra Pradesh. Mythili, (2008) estimated supply response for major crops during pre-and post-reform periods using Nerlovian adjustment-cum-adaptive expectation model. Estimation is based on dynamic panel data approach with pooled cross section - time series data across states for India. The study found no significant difference in supply elasticities between pre-and postreform periods for majority of crops. This study also indicated that farmers increasingly respond better through non-acreage inputs than shifting the acreage. This includes better technology, use of better quality of inputs and intensive cultivation. Many more studies (Thamarajaki, 1977; Krishna, 1982; Mungekar, 1997; Desai and Namboodiri, 1997) are also available in the context of Indian agriculture. Thamarajakshi, (1977) reported a statistically significant and effective relation between aggregate farm output and non-price factors (irrigation) but the author could not detect any statistically significant impact of TOT on agricultural output. Krishna, (1982) also drew same conclusion but difference between both the studies was that Krishna observed a marginal impact of TOT on agricultural output. Mungekar, (1997) and Desai & Namboodiri, (1997) reported negative impact of TOT on agricultural output. Both studies also observed significant impact of nonprice factors such as irrigation, rainfall, area under high yielding verity seeds, fertilizer use, rural roads and other infrastructure facilities. After reviewing these studies, some common inferences can be drawn. The supply response of individual crop is well documented but study related to aggregate output remains to be a less researched area. Most of the studies have followed same methodology given by Nerlove in original form or with some modification. Most of the studies have reported low supply response. They also indicated that non-price factors are relatively more important than price factors. However, there has been controversy as to whether aggregate agricultural supply is really not responsive. Schiff and Montenegro (1997), argued that aggregate agricultural supply response is, in fact, high but that there are other constraints such as financing that hinder this response such that a low elasticity is found. A lot of methodological questions 12
have been raised on the previously used model (Nerlove Model) and the estimation techniques applied. For instance, the Nerlove Model is unlikely to capture the full dynamics of agricultural supply (Thiele, 2000), this model has inability to give adequate distinction between short-and long-run elasticities (McKay et.al., 1998), it uses integrated series, which poses the danger of spurious regression (Granger & Newbold, 1974), etc. Therefore, an alternative of Nerlove model is required. For this particular case, the suitable techniques are the general co-integration methodology and the ECM (detailed in the next section). Indeed, one work (Surjit Dev, 2003) has been done based on cointegration approach but his special emphasis was on long-run relationship between agricultural output and price. The present study is an attempt to fill this gap. This study has done the analysis at the aggregate all India as well as at the diaggregated state level. Supply response at the disaggregated level can throw more light since aggregation at the all India level might have hidden the variation that could explain the response better. This study has classified states into low, medium and high agricultural based states and to see if the response varies significantly between them. 13
S.No. Author Title of the Paper Objective/ Objectives of the Study 1. Raj Krishana (1963) Farm Supply Response in India-Pakistan: A Case Study of the Punjab Region 2. Dharam Narain The Impact of Price Movements on Areas under Selected Crops in India 3. Rao & Krishana (1965, and 1967) 4. Krishana and Rao (1967) Price Expectation and Acreage Response for Wheat in Uttar Pradesh Dynamics of Acreage Allocation for wheat in Uttar Pradesh: A Study in Supply Response 5. Parikh (1967) Market Responsiveness of Peasant Cultivaters: Some Evidence from Pre-War India 6. Subbarao (1969) Farm Supply Response: A Case Study of Sugarcane in Andhra Pradesh Table 1: Earlier Studies in Indian Context To estimate supply elasticities To evaluate impact of price changes on decision of acreage allocation To formulate alternative price expectation model and to study their impact on acreage response coefficients To examine the acreage and output response of sugarcane in Andhra Pradesh to changes in its relative prices Data Base/ Period of Study & Methodology i. Time series ii. 1914/15 1945/46 iii. OLS Estimation Procedure i. Time Series ii. 1900-1939 iii. Simple ratio i. Time series ii. 1951/51 1962/63 iii. OLS estimation procedure i. Time series ii. 1951/51 1962/63 iii. OLS estimation procedure i. Time series ii. 1900-1939 iii. OLS estimation procedure i. Time series ii. 1952/53 1964/65 iii. OLS estimation procedure Estimated Model Results Remarks Nerlovian adjustment model Tabular analysis Traditional regression model i. Nerlovian adjustment model ii. Traditional regression model Distributed lag model Traditional Regression Model Positive but low supply elasticities w.r.t. price Found positive relationship between changes in price and acreage of crops Acreage response changed drastically with change in expected price used. Non-price factor is quite important in comparison to price factor. Changes in relative acreage under sugarcane are positively associated with changes in its relative price. It is the first study among those studies that examined agricultural supply response in Indian Context This study was discussed only coovement of changes in price and acreage. Especially focused on price expectation behaviour of farmers This study was an extension and modification of study reported earlier. The used data set covered period prior to 1939 The study introduced relative profitability as the most appropriate factor in allocation of acreage. The relative profitability was measured by relative prices and relative gross income per hectare.
S.No. Author Title of the Paper Objective/ Objectives of the Study 7. Herdt (1970) A Disaggregated Approach to Aggregate Supply 8. Maji et.al (1971) Dynamic Supply and Demand Models for Better Estimation and Projections: An Econometric Study for Major Foodgrains in the Punjab Regions 9. Kaul and Sadhu (1972) Acreage Response to Prices for Major Crops in Punjab: An Econometric Study 10. Madhavan (1972) Acreage Response of Indian Farmers: A Case Study of Tamil Nadu To evaluate supply response of aggregate agricultural output To estimate supply and demand elasticities To estimate acreage response with respect to change in relative prices and risk variable. To examine whether and to what extent Indian farmers influenced in their production decisions by economic stimuli Data Base/ Period of Study & Methodology i. Time series ii. 1907-1964 iii. OLS estimation procedure i. Time series ii. 1948/49 1965/67 iii. OLS estimation procedure i. Time series ii. 1960/61 1969/70 iii. OLS estimation procedure i. Time series ii. 1947/48 1964/65 iii. Production function approach iv. OLS estimation procedure Estimated Model Results Remarks Multiple Regression Model using disaggregated approach given by Tweeten and Quance Nerlovian adjustment model. Nerlovian adjustment model Multiple regression model The study found positive supply response for period 1907-1946 and insignificant supply response for period 1951-64. Price coefficient was found significant only Found inelastic supply response for each selected crops Tamil Nadu farmers respond with change in relative prices but the degree of response differs from crop to crop It is the first study among those studies that examined agricultural supply response at aggregate output level in Indian Context First time introduced risk variable in Indian context This study was the modification over Maji et.al. s study. In this study cofficent of variation was used to measure risk variable instead of standard deviation. This study yield better results in comparison to earlier studies based on Nerlove framework both in terms of statistical significance of the coefficients and the explanatory power of the equation 15
S.No. Author Title of the Paper Objective/ Objectives of the Study 11. Cummings (1975) The Supply Responsiveness of Indian Farmers in the Post-Independence Period To examine supply responsiveness of Indian farmers Data Base/ Period of Study & Methodology i. Cross-section ii. Maximum likelihood estimation procedure Estimated Model Results Remarks Nerlovian adjustment model The study showed high degree of supply response. The study also confirmed that there is large variation in supply response elasticities across regions. Discussed the causes of variation in supply elasticites across regions. 12. Narayana et.al. (1981) Estimation of Farm Supply Response and Acreage Allocation 13. Bapna et.al.(1984) Systems of Output Supply and Factor Demand Equations for Semi-Arid Tropical India 14. Hazell et.al. (1995) Role of Terms of Trade in Indian Agricultural Growth: A national and State level Analysis To investigate how do farmers form their expectations and how do their expectations affect their crucial decisions about land allocation? To estimate supply elasticities To analyze relative contributions of terms of trade and non-price variables in explaining agricultural growth i. Time series ii. 1950-1974 iii. Box-Jenkins estimation procedure i. Panel Data ii. Error component model iii. Maximum likelihood estimation procedure i. Time series (for national level analysis) and panel data (for state level analysis) ii. 1952 1988 (for national level) 1971-1988 (for state level iii. OLS and 2SLS estimation procedure Autoregressive integrated moving average model Output supply and input demand equation derived from profit function Multiple regression model The study gives more satisfactory results in comparison to Nerlove model. Demonstrated remarkable extent of price responsiveness of the semi-arid tropical farmers. The result from the national and state level analyses was very similar and shown that aggregate supply response is inelastic. The study modified the conventional econometric techniques to overcome a limitation of the traditional Nerlovian model. But the study losses long-run information because of using Box-Jenkins method. This study applied system approach. The study used gross terms of trade as a indicator of terms of trade. 16
S.No Author Title of the Paper Objective/ Objectives of the Study 15. Palanivel (1995) The Aggregate Supply Response in Indian Agriculture 16. Mishra (1998) Economic Reforms, Terms of Trade, Aggregate Supply and Private Investment 17. Rao (2004) Aggregate Agricultural Supply Response in Andhra Pradesh 18. Sunil Kanwar (2004) Price Incentives, Nonprice Factors, and Crop Supply Response: The Indian Cash Crops 19. Mythili (2006) Supply Response of Indian Farmers: Pre and Post Reforms To estimate supply response of aggregate agricultural output in India To assess the impact of economic reforms initiated in 1991 along with price and nonprice factors on aggregate supply in the post-green revolution period To estimate supply response of aggregate agricultural output in Andhra Pradesh To estimate acreage and yield responsiveness of individual crop with respect to price and non-price factors To examine whether there is deference in supply response of major crops between pre- and post-reform period Data Base/ Period of Study & Methodology i. Time Series ii. 1951/52 1987/88 iii. OLS estimation procedure iv. NLS estimation procedure i. Time Series ii. 1966/67-1995/96 iii. OLS estimation procedure i. Time Series ii. 1980/81 1999/2000 iii. OLS estimation procedure I. Panel Data II. 1967/68-1999/2000 III. Random Effect Model IV. GLS estimation procedure i. Panel Data ii. 1970/71 2004/05 iii. Random effect model iv. Generalised method of moments Estimated Model Results Remarks Nerlovian expectation cum adjustment model Multiple regression model Nerlovian adjustment model Nerlovian adjustment model Nerlovian adjustment cum adaptive expectation model Inelastic supply response for each case; aggregate agricultural output, aggregate crop output The aggregate supply measured either through aggregate output or marketed surplus does respond significantly positively to the terms of trade (i e, relative prices). The supply elasticities with respect to terms of trade for aggregate agricultural output, crop output, foodgrain crop, and nonfoodgrain crops were found positive but statistically non-significant Non-price factors; irrigation, consumption of fertilizer, high yield variety seeds, and infrastructure are highly significant in higher agricultural growth The study found no significant difference in supply elasticities between pre and post reform periods for majority of crops. This study followed both linear and nonlinear least square estimation procedure and found similarity in results Focus on the interaction between terms of trade and technology In this study, agricultural terms of trade was calculated for the state level. Unlike earlier studies instead of using aggregated time series data, this study used panel data. There was a dearth of studies on supply response in post reform era. This study fills this gap. 17
S.No Author Title of the Paper Objective/ Objectives of the Study 20. Mythili (2008) Acreage and Yield Response for Major Crops in the Pre- and Post-Reform Periods in India: A Dynamic Panel Data Approach To examine whether there is deference in supply response of major crops between pre- and post-reform period Data Base/ Period of Study & Methodology i. Panel Data ii. 1970/71 2004/05 iii. Random effect model iv. Generalised method of moments Estimated Model Results Remarks Nerlovian adjustment cum adaptive expectation model With proper specification of the price variable, the acreage elasticity significantly increased by about 20 to 40% post reforms as compared to pre reforms for all crops, except cotton and groundnut. Mythili presented an improvement over her earlier studies with proper specification of price variables. 18
BASIC FRAMEWORK AND APPRAOCHES USED IN ESTIMATION In agriculture the observed prices are known after the production has occurred, while planting decisions are based on the prices expected to prevail later at the harvest time. Because of this time lag, producer price expectations play a key role in the analysis. Three alternative agricultural producer price expectations hypotheses commonly found in the literature are naïve expectation, adaptive expectation, and rational expectation. Naïve expectation means expected price is the actual price in the previous period. Adaptive expectations means that people form their expectations about what will happen in the future based on what has happened in the past. According to rational expectation an economic agent forecast his future event on the basis of all available information. Farmers, especially in the developing countries, are mostly low literate and hence it is hard to obtain all the relevant information. Therefore, rational expectation behavior is not relevant. The present study assumed that farmers learn from their past mistakes, and so in this situation, adaptive expectation becomes more relevant. Further, it is also important in the analysis of supply response that observed quantities may differ from desired ones because of the adjustment lag of variable factors. There are two approaches to estimation of agricultural supply response; indirect structural form approach and direct reduced form approach. (A) Indirect structural form approach This approach involves derivation of input demand function and supply function from the available data, information relation to production function, and individuals behaviours. This approach is more theoretically rigorous but fails to take into account the partial adjustment in production and the mechanism used by farmers in forming expectations. This approach requires detailed information on all the input prices. Moreover, the agricultural input markets are not functioning in a competitive environment in India, particularly land and labour markets. Market intervention in delivering material inputs to the farmers is a common practice. It is difficult to get information on price at which the inputs are supplied to the farmers. Keeping in view these aspects, most of the previous studies have chosen second approach; Direct reduced form approach.
(B) Direct reduced form approach In this approach the supply response is directly estimated by including Partial Adjustment and Expectations Formation. This is also known as Nerlovian Model. Most of the existing studies on the agricultural supply response in India and abroad have applied the Nerlove method. According to Nerlove, desired output can be expressed as a function of expected price and exogenous shifters: where, Q* t is desired output, P* t is expected price, Z t is a set of exogenous shifters such as technology change, weather condition, etc. Actual output may differ from the desired ones because of the adjustment lags of variable factors. Therefore, it is assumed that actual output would only be a fraction δ of the desired output. where, Q t is actual output in period t, Q t-1 is actual output in period t-1, and δ is adjustment coefficients. Its value lies between 0 and 1. The farmers expected price at harvest time can be observed. So, we have to formally describe how decision makers form expectations based on the knowledge of actual and past price and other observable information. We may think that farmers maintain in their memory the magnitude of the mistake they made in the previous period and learn by adjusting the difference between actual and expected price in t-1 by a fraction λ Putting the value of P* t and Q* t from equation 2 and 3, in equation 1, the equation 1 becomes where, A0 is a d λ, A1 is b d λ, A2 is (1- d) +(1- λ), and A3 is c d. 20
This model is estimated from variables measured in levels and, should the suspicion of nonstationarity be confirmed, as it happens with most economic series, the statistical significance of the t tests of the estimated regressions lose sense. It is here where the use of advanced econometric methodologies becomes relevant. For this particular case, the suitable techniques are the general co-integration methodology and the ECM. The ECM allows examining existence of long-run and short-run relationship among variables. All the used variables are stationary and so the results are consistent. Instead of consistent results, the ECM also considers the partial adjustment and the mechanism used by farmers in farming expectations which are the fundamental in the analysis of agricultural supply response. In the next section we show that partial adjustment model is nested within the ECM. (C) Error Correction Model and Partial Adjustment Model Let us take two variables Q and P and assume that both are integrated of order 1. The longrun relationship between them can be expressed as: Given that Qt and Pt are cointegrated, say of order 1, the term ut will be stationary and an ECM-type representation exists for these two variables (Granger s Representation Theorem), which is expressed as: Putting the value of u t-1 from equation 5, 21
Compare equations (9) and (4). They are identical after ignoring exogenous additional variables - only if the term involving Pt is omitted from the ECM specification, which shows that the partial adjustment model is nested within the ECM model, which constitutes a more general specification of the problem. 22
EMPIRICAL METHODOLOGY (A) Model Agricultural supply depends on both output and input prices. An increase in output prices increases profit and increase in profit provides incentive to farmers to produce more. Similarly, an increase in input prices increases cost of production and increase in cost is disincentive to produce more. Thus, real output price, that is, output over input price ratio positively affects the agricultural production. Except for real output prices, there are several factors that affect agricultural production. Manmingi (1997) subsumed these factors under four different categories: rural infrastructure, human capital, technology, and agroclimatic conditions. All rural infrastructure services such as irrigation facilities, accessibility of roads, markets facilities, farmers access to credit, agro extension services, availability of fertilizer, high yields variety seeds and pesticides, communication, and transport facilities are expected to positively affect agricultural output through its effect on productivity and cost of production. Education, agricultural extension, agricultural research and other technological progress are supposed to have positive effect on agricultural production by improving efficiency and reducing cost. Among the agroclimatic factors, soil quality and the intensity and regularity of rainfall are likely to be most decisive for the supply response. We cannot incorporate all of these factors due to non-availability of data and quantification problems (some variables such as soil quality cannot be quantified). Considering these problems, long-run supply response is estimated using variables indicated in equation 10 below: where, Y t = is the dependent variable in year t. Agricultural output was used as dependent variables in this study. Agricultural output is measured by agricultural value added output(agdp) at constant prices (1999-2000) in crores of rupees TOT t = represents real output price in year t. Agricultural terms of trade was used to measure the relative prices. Agricultural terms of trade is ratio of prices received by farmers to prices paid by farmers. It captures changes in producer prices, intermediate input 23
costs, real exchange rates, and world market prices. Agricultural terms of trade was calculated using the following formula: TOT= IPD for agricultural sector / IPD for non-agricultural sector where, IPD for agricultural sector = implicit price deflator for agricultural sector. IPD for non- agricultural sector = implicit price deflator for non-agricultural sector. IRR t = is irrigation ratio in year t. Irrigation ratio is the share of total irrigated land in the total cropped area. It was calculated as a proxy for rural infrastructure. Tech t = indicates technology in year t. Previous studies have used either trend variable or irrigation ratio or total factor productivity index to capture technological improvement. Use of time trend as a technology is very old pattern. It also catches up effects of all other variables so when we already incorporated other variables, use of time trend is not viable. Likewise, productivity index captures the effect of improvement in technology, efficiency, rural infrastructure and weather. Use of high yield variety of seeds per hectare is the most appropriate variables for indicating technology but data related to this is not available for a long period so we used consumption of fertilizers (measured in kilogram) per hectare as a technology variable in this study. AAR t = represents annual average rainfall in year t. To incorporate weather condition in the model average annual rainfall (measured in millimeter) was used. e t = is error term Cointegration and error-correction techniques are applied in this study. These techniques are believed to overcome the problem of spurious regressions and to give consistent and distinct estimates of long-run and short-run elasticities that satisfy the properties of the classical regression procedure. This is because all variables in an ECM are integrated of order zero, I (0). Spurious regression and inconsistent and indistinct short-run and long-run elasticity estimates are major problems exhibited by traditional Adaptive Expectation and Partial Adjustment models (Hallam & Zanoli, 1993; McKay et al, 1998). Co-integration and ECMs have been used in agricultural supply response analysis in other countries by a number of researchers, namely Hallam & Zanoli, (1993); Townsend (1997); Schimmelpfennig et al. 24
(1996); Townsend & Thirtle (1994); Almu et al. (2003); McKay et al.(1998); Thiele (2003); and Avasola (2006). One major use of the co-integration technique is to establish long-run equilibrium relationships between variables. However, two conditions must be met for co-integration to hold. First, individual variables should be integrated of the same order. Second, the linear combination of these variables must be integrated of an order one less than the original variables (Engle & Granger, 1987). In other words, if the variables under consideration are integrated of order one, or I (1), the error term from the co-integrating relationship should be integrated of order zero, I (0), implying that any drift between variables in the short run is temporary and that equilibrium holds in the long run. If deviation from the long-run equilibrium path is bounded or co-integration is confirmed, Engle & Granger (1987) show that the variables can be represented in a dynamic errorcorrection framework. Therefore, in this paper, like similar studies elsewhere, supply response is modeled in two stages. First, a static co-integrating regression given by equation (10) is estimated for Indian agriculture and tests for co-integration are conducted. Second, if the null for no co-integration is rejected, the lagged residuals from the co-integrating regression are imposed as the error correction term in error correction model. An error correction model is shown below: where, represents first differencing, λ measures the extent of correction of errors by adjustment in Y t. β i measures the short-run effect on supply or short-run elasticities when the variables are measured in logarithm, while α i measure the long-run price elasticities. u t is error term. (B) Estimation Procedure Each of the series is tested for the presence of a unit root by estimating an Augmented Dickey Fuller (ADF) equation both with and without the deterministic trend. The number of lags in the ADF equation is chosen by using LR, AIC, and SBIC tests. After verifying that variables are stationary or not, we took first lag difference of all series and again estimated ADF equation both with and without the deterministic trend. The final stage is to test for 25
cointegration. We test for cointegration by using Engel Granger two-step procedure. In this approach, first we estimated long-run relationship if all variables are integrated in same order and obtained residuals. The residuals of this relationship is tested for the presence of a unit root. If the test reported presence of unit root in residuals, the variables used in long-run relationship are not cointegrated and if the test rejected null hypothesis, the variables used in long-run relationship are cointegrated. (C) Selection of States The study also focuses on whether there is difference in the supply response among highly agricultural based, medium agricultural based, and low agricultural based states. For this purpose, we divided all states into three categories. For classification, first we estimated the share of agricultural income in total state s income for each state 10 for the 3 base years 1980-81, 1990-91, and 2000-01, respectively (see Table 2). After then we categorized these states into three categories as mentioned above. The criterion used was, those states which have more than 30 percent agricultural share are highly agricultural based, which have less than 30 and more than 20 percent are medium agricultural based, and which have less than 20 percent agricultural share are low agricultural based states. Through this procedure finally we have chosen 14 states in which four states (Bihar, Hariyana, Punjab, and Uttar Pradesh) belong to the first category, six states (Andhara Pradesh, Karnataka, Madhya Pradesh, Orissa, Rajasthan, and West Bangal) belong to second category, and four states (Gujarat, Kerala, Maharashtra, and Tamil Nadu) belong to the third category. (D) Period of Study The study covered time span from 1970-71 to 2004-05 for all India level analysis and from 1980 to 2005 for regional analysis. The year 1970-71 is chosen as base year because Indian agriculture has made significant growth since late 60s. Further, during seventies agricultural and rural development was given more importance. Despite these, one more reason to leave 50s and 60s is during these periods Indian economy faced many structural changes. Due to these changes agricultural output has more variation in this period than study period (1970-71 to 2004-05). The year 2004-05 was chosen as the end period of the study because the published data on which this study is based is available up to 2004-05. 10 Northeast, small such as Goa etc, and new states were excluded in this procedure. 26
At the beginning period of Green Revolution, there were substantial inequalities in agricultural production among states of India (Das and Barua, 1996). Due to these inequalities, there might have occurred differences in supply response among states and results may be biased. To avoid this biasedness, the year 1980-81 is chosen as base for regional analysis. (E) Data Source Data related to agricultural gross value of output was collected from various issues of National Account Statistics published by Central Statistical Organization, Government of India, New Delhi. Gross sown area, and gross irrigated area were taken from Agricultural Statistics at a Glance (2007) published from Directorate of Economics and Statistics, Ministry of Agriculture, Government of India. Consumption of fertilizers was also collected from Agricultural Statistics at a Glance (2007). Average annual rainfall was calculated from monthly journal Agricultural Situation of India published by Directorate of Economics and Statistics, Ministry of Agriculture, Government of India. All the state level data for each variable are taken from various issues of Fertilizers Statistics published by Fertilizers association in India, New Delhi, and www.indiastat.com. Table 2: Share of Agricultural Income in total income for all major states Name of state 1980-81 1990-91 2000-01 SNDP ANDP/SNDP*100 ANDP SNDP ANDP/SNDP*100 ANDP SNDP ANDP/SNDP*100 ANDP Andhra Pradesh 315705 732395 43.11 441689 1358012 32.52 2141924 7707692 27.79 Bihar 302911 634922 47.71 396613 1025327 38.68 1347880 3136296 42.98 Gujarat 243408 654742 37.18 268421 1083915 24.76 921900 6257500 14.73 Haryana 163026 303195 53.77 257704 571921 45.06 936612 2888511 32.43 Karnataka 239327 558736 42.83 303450 911210 33.30 1852057 6213241 29.81 Kerala 129384 382273 33.85 176135 526234 33.47 544822 3396268 16.04 Madhya Pradesh 306072 701244 43.65 478591 1110721 43.09 1108250 4309917 25.71 Maharashtra 374908 1516258 24.73 546770 2722382 20.08 2125238 13671250 15.55 Orissa 161736 344269 46.98 148966 434470 34.29 521155 2027161 25.71 Punjab 215618 444925 48.46 357434 750493 47.63 1516910 3663590 41.41 Rajasthan 202801 412571 49.16 385419 847260 45.49 1111879 4566369 24.35 Tamil Nadu 177319 721816 24.57 271417 1242299 21.85 1393387 8045255 17.32 Uttar Pradesh 699614 1401182 49.93 957778 2277965 42.05 3317322 9168983 36.18 West Bengal 264060 959400 27.52 392967 1445781 27.18 1902846 7825403 24.32 Note: ANDP = State net domestic product from agriculture, and SNDP = State net domestic product. Source: State domestic product, EPW research foundation 27
RESULTS AND DISCUSSION (A) Descriptive Statistics Table 3 shows the mean value of all variables (used in absolute form) for all India and each category of states; high, medium, and low agricultural based states. From this table, the situation of agriculture in each category of states can be compared. The mean value of agricultural TOT is higher for medium agricultural based states in comparison both high and low agricultural based states. The mean value of agricultural TOT is almost same for high and low agricultural based states. The mean values of technology and irrigation ratio are the highest for highly agricultural based states. This shows that high agricultural based states are better in terms of infrastructure and technology than the other two groups of states. Tables 4 and 5 describe the descriptive statistics (all variables are used in natural logarithmic form in order to be consistent with the variables used in the statistical analysis) and correlation matrix, respectively. Table 3: The Mean Values of Selected Variables Group AGDP (In Rs TOT IRR TECH (in Kg AAR (in mm) Crore) per Hectare) All India 290805.1 95.12 32.78 53.75 1257.9 Category of States High 63813.98 96.05 65.78 100.26 876.2 Medium 73381.17 103.35 29.83 65.01 1277.7 Low 44758.65 93.07 25.32 72.75 1285.3 Note: 1. All variables are in absolute measure. 28
Table 4: Descriptive Statistics Variables AGDP TOT IRR TECH ARR All India (Period: 1970 2004) Mean 12.54 4.55 3.47 3.82 7.13 Std. Dev. 0.30 0.075 0.20 0.63.09 Maximum 12.99 4.77 3.73 4.57 7.28 Minimum 12.05 4.44 3.14 2.61 6.89 High Agricultural Based States (Period: 1980-2004) Mean 11.05 4.56 4.18 4.56 6.77 Std. Dev. 0.18 0.05 0.106 0.31 0.14 Maximum 11.33 4.66 4.31 4.93 6.94 Minimum 10.73 4.47 3.99 3.81 6.51 Medium Agricultural Based States (Period: 1980-2004) Mean 11.18 4.64 3.38 4.09 7.15 Std. Dev. 0.22 0.053 0.17 0.429 0.08 Maximum 11.48 4.76 3.67 4.65 7.31 Minimum 10.77 4.55 3.05 3.23 6.97 Low Agricultural Based States (Period: 1980-2004) Mean 10.69 4.53 3.22 4.24 7.15 Std. Dev. 0.21 0.05 0.12 0.32 0.13 Maximum 10.97 4.62 3.40 4.65 7.50 Minimum 10.35 4.44 3.02 3.56 6.88 Table 5: Correlation Matrix Variables AGDP TOT IRR TECH ARR All India (Period: 1970 2004) AGDP 1.0000 TOT -0.19 1.0000 IRR 0.98-0.23 1.0000 TECH 0.96-0.33 0.98 1.0000 ARR 0.020-0.004-0.09-0.08 1.0000 High Agricultural Based States (Period: 1980-2004) AGDP 1.0000 TOT 0.38 1.0000 IRR 0.95 0.46 1.0000 TECH 0.96 0.38 0.95 1.0000 ARR 0.003-0.09-0.05 0.04 1.0000 Medium Agricultural Based States (Period: 1980-2004) AGDP 1.0000 TOT 0.31 1.0000 IRR 0.96 0.27 1.0000 TECH 0.96 0.33 0.97 1.0000 ARR 0.13 0.27-0.02 0.09 1.0000 Low Agricultural Based States (Period: 1980-2004) AGDP 1.0000 TOT 0.25 1.0000 IRR 0.86 0.29 1.0000 TECH 0.85 0.36 0.86 1.0000 ARR 0.33-0.10 0.13 0.09 1.0000 29