An econometric analysis of market integration in Telangana maize markets

Introduction

Maize (Zea mays L.) is one of the most versatile emerging crops having wider adaptability under varied agro-climatic conditions. Globally, maize is known as the queen of cereals because it has the highest genetic yield potential among the cereals. It is cultivated in about 164 countries having wider diversity of soil, climate, biodiversity and management practices. The United States of America is the largest producer of maize contributing nearly 29.96% of the total production in the world. Other important growing countries are China, Brazil, Argentina, India, Mexico, Ukraine, Indonesia and South Africa. In India, maize is grown throughout the year. It is predominantly a kharif crop with 87% of the area under cultivation in the season. Maize is the third most important cereal crop in India after rice and wheat. It accounts for around 10% of total food grain production in the country. During 2024-25, area under maize was 120.91 lakh hectares as compared to 112.41 lakh hectares during 2023-24. Major maize growing states in India are Madhya Pradesh, Karnataka, Maharashtra, Rajasthan, Bihar, Uttar Pradesh, Telangana, Tamil Nadu and Gujarat. According to the final advance estimates 2024-25, Government of India, Maize crop is estimated at 434.09 lakh tonnes as compared to 376.65 lakh tonnes in 2023-24. The leading states of final advance estimates in 2024-25 are Madhya Pradesh 66.42 lakh tonnes followed by Karnataka 61.90 lakh tonnes, Bihar 54.68 lakh tonnes, Maharastra 47.49 lakh tonnes, Telangana 33.92 lakh tonnes, West Bengal 28.36 lakh tonnes, Rajasthan 26.78 lakh tonnes, Tamil Nadu 26.63 lakh tonnes and Andhra Pradesh 21.48 lakh tonnes. In addition to staple food for human beings and quality feed for animals, maize serves as a basic raw material as an ingredient to thousands of industrial products that include starch, oil, protein, alcoholic beverages, food sweeteners, pharmaceutical, cosmetic, film, textile, gum, package and paper industries etc. In Telangana, during 2024-25 area under maize was 5.79 lakh hectares as against 4.90 lakh hectares during 2023-24. Major maize growing districts in Telangana include Siddipet, Nagarkurnool, Rangareddy, Vikarabad, Mahabubnagar, Kamareddy, Nizamabad, Karimnagar and Jagtial contributing nearly 85% of total maize production. According to Final Advance Estimates of Production of Food grains for 2024-25, Maize production estimate was 33.92 lakh tonnes in Telangana State. An indirect means of analyzing market efficiency is to test for market integration. Three types of market integration are identified in the literature, inter-temporal, vertical and spatial. Inter-temporal market integration relates to the arbitrage process across periods. Vertical market integration is concerned with stages in marketing and processing channels. Spatial integration is concerned with the integration of spatially distinct markets i.e., if price changes in one market are fully reflected in an alternative market then these markets are said to be spatially integrated. The concept of market integration has normally been applied in studies involving spatial market inter-relatedness. Market integration is a central issue in many contemporary debates concerning the issues of market liberalization. Market integration is perceived as a precondition for effective market reform in developing countries. The high degree of market integration means the markets are quite competitive www.extensionjournal.com and provide little justification for extensive and costly government intervention designed to improve competitiveness to enhance market efficiency. Markets that are not integrated may convey an inaccurate picture about price information that might distort production decisions and contribute to inefficiencies in markets, harm the ultimate consumer and lead to low production and sluggish growth. In India, there exist several studies, which have analyzed market integration in food grain crops such as wheat, rice etc. (Ghosh, 2003; Ghosh, 2011; Ghoshray and Ghosh, 2011; Acharya, et al., 2012; Ghosh, 2012; Sekhar, 2012) [5,6,7,1] . The existing literature on market integration in horticultural crops is quite scanty (Basu, 2006; Beag and Singla, 2014; Wani, et al., 2015) [2,3,9] , while no such studies except Sendhilet al., (2014) [10] and Reddy et al. (2012) [11] exist in onion, which analyze market integration and price transmission in spatially separated markets. Both the studies were conducted in pre-2011 period during which the prices of onion were generally stable and as such impact of price shocks in one market was not visible in other markets. The formulation of valid study on the market integration in onion has potential application for the development of agricultural markets. Against this backdrop, the existing study analyses market integration in onion and its price transmission analysis in selected markets of India. We find a substantial body of research literature on the issue of agricultural markets integration in the developing countries (Goletti and Babu, (1994) [16] , Dercon (1995) [15] , Baulch, B. (1997) [13] , Dawson and Dey (2002) [14] , Jhaet al., (2005) and Bakhshoodeh and Sahraeiyan (2006)) [12] . Tahir Mukhtar and Muhammad Tariq Javed. 2007 [17] .

Materials and Methods

For price integration, simple bivariate correlation coefficients measure price movements of a commodity in different markets. This is the simplest way to measure the spatial price relationships between two markets. Early inquiries on spatial market integration, for example Lele (1967) [18] and Jones (1968) [19] have used this method. However, this method clearly has some limitations, as it cannot measure the direction of price integration between two markets. The cointegration procedure measures the degree of price integration and takes into account the direction of price integration. This econometric technique provides more information than the correlation procedure, as it allows for the identification of both the integration process and its direction between two markets.

Market Integration Test

Market integration is tested using the cointegration method, which requires that Two variables, say Pit and Pjt are non-stationary in levels but Stationary in first differences i.e. Pit ~ I (1) and Pjt ~ I (1). There exists a linear combination between these two series, Which is stationary i.e. ʋit (=Pit Pit) ~ I (0). So the first step is to test whether each of the univariate series is stationary. If they are both I (1) then we may go to the second step to test cointegration. The Engle and Granger (1987) procedure is the common way to test cointegration.

Unit root test

The regression analysis of non-stationary time series produces spurious results, which can be misleading (Ghafoor, et al., 2009) [20] . The most appropriate method to deal with non-stationary time series for estimating long-run equilibrium relationships is cointegration, which necessitates that time series should be integrated of the same order. Augmented Dickey-Fuller (ADF) and Phillips-Perron test (PP) are used to verify the order of integration for each individual series. The ADF test, tests the null hypothesis of unit root for each individual time series. The rejection of the null hypothesis indicates that the series is non-stationary and vice-versa. The number of the appropriate lag for ADF is chosen for the absence of serial correlation using Akaike Information Criterion (AIC). The ADF test is based on the Ordinary Least Squares (OLS) method and requires estimating the following model.

Where, P the price in each market, Δ is the difference parameters (i.e., ΔP1 = Pt-Pt-1, Pt-1 = Pt-1 -Pt-2 and Pn-1= Pn-1 -Pn-2) and so on, is the constant or drift, t is the time or trend variable, q is the number of lags length and is a pure white noise error term.

Johansen Cointegration

The maximum likelihood (ML) method of cointegration is applied to check long-run wholesale prices relation between the selected markets of India (Johansen, 1988; Johansen and Juselius, 1990) [21,22] . The starting point of the ML method is vector autoregressive model of order (k) and may be written as:

Where, (n*1) denotes the vector of non-stationary or integrated at order one, i.e., I (1) prices series. The procedure for estimating the cointegration vectors is based on the Vector error correction model (VECM) representation given by: Where,

Both Γi and Пi are the n*n matrixes of the coefficient conveying the short and long run information respectively, μ is a constant term, t is a trend, and is the n-dimensional vector of the residuals that is identical and independent distributed. The vector ΔPt is stationary Pt is integrated at order one I (1) which will make unbalance relation as long as П matrix has a full rank of k. In this respect, the equation www.extensionjournal.com can be solved by inversing the matrix П -1 for Pt and as a linear combination of stationary variable. The stationary linear combination of the Pt determines by the rank of Π matrix. If the rank r of the matrix П r = 0 the matrix is the null and the underlying series is stationary. If the rank of the matrix П is such that 0 < rank of (П) = r < n then there are n × r cointegrating vectors. The central point of the Johansen's procedure is simply to decompose Π into two n × r matrices such that Π=αβ'. The decomposition of Π implies that the β'Pt are r stationary linear combination. Johansen and Juselius, (1990) [22] proposed two likelihood ratio test statistics (Trace and Max Eigen test statistics) to determine the number of cointegrating vectors as follows:

Where, r is the number cointegrated vector, is the eigen value and is the (r + 1) th largest squared eigen value obtained from the matrix П and the T is the effective number of observation. The trace statistics tested the null hypothesis of r cointegrating vector (s) against the alternative hypothesis of n cointegrating relations. The Max Eigen statistic tested the null hypothesis (r =0) against the alternative (r + 1).

Vector Error Correction Model (VECM)

If price series are I (1), then one could run regressions in their first differences. However, by taking first differences, we lose the long-run relationship that is stored in the data. This implies that one needs to use variables in levels as well. Advantage of the vector error correction model (ECM) is that it incorporates variables both in their levels and first differences. By doing this, VECM captures the short-run disequilibrium situations as well as the long-run equilibrium adjustments between prices. Even if one demonstrates market integration through cointegration, there could be disequilibrium in the short-run i.e. price adjustment across markets may not happen instantaneously. It may take some time for the spatial price adjustments. VECM can incorporate such short-run and long-run changes in the price movements. A VECM formulation, which describes both the short-run and long-run behaviors of prices, can be formulated as: (4) In this model, is the impact multiplier (the short -run effect) that measures the immediate impact tha t a change in will have on a change in . On the other hand, is the feedback effect or the adjustment effect that shows how much of the disequilibrium is being corrected, that is the extent to which any disequilibrium in the previous period effects any adjustment in the period of course and therefore from this equation we also have being the long-run response.

Granger Causality Test

If a pair of series is cointegrated then there must be Granger causality in at least one direction, which reflects the direction of influence between series (in our case prices). Theoretically, if the current or lagged terms of a time-series variable, say , determine another time-series variable, say , then there exists a Granger causality relationship between and , in which is Granger caused by . Bessler and Brandt first introduced this test into research on market integration to determine the leading market. From the above analysis, the model is specified as follows:

The following two assumptions are tested using the above two models to determine the Granger causality relationship between prices.

Results and Discussion

Our price data consist of monthly modal prices of maize (Rs/qtl) in five major markets viz. Badepally, Nagarkurnool, Karimnagar, Siddipet and Warangal of the Telangana State using monthly maize prices over the period from January 2020 to December 2024. The data were taken from the websites of agriculture marketing government of Telangana http://tsmarketing.in/ and https://agmarknet.gov.in/. The maize modal price trend of all the selected markets is presented in Fig. 1, which shows the symmetric behavior in the movement of prices in all the selected markets except Warangal, the maximum modal price of 2880.00 Rs/ quintal prevailed in Nagarkurnool and the minimum price was found in Nagarkurnool 1193.00 Rs/ quintal followed by Karimnagar 1228 Rs/ quintal.

Descriptive Statistics

The summary statistics indicate that maize prices have been highly volatile in Nagarkurnool, followed by Warangal, as shown by their high coefficients of variation. The Badepally market, being the largest producer of maize in Telangana, experiences price fluctuations primarily influenced by the demand in other markets. Among the markets, the highest average price for maize were observed in Siddipet, whereas the lowest average prices were recorded in Karimnagar and Badepally, as presented in Table Notes: * denote significance at 1% levels of significance and Δ denote the first difference of the time series.

Co-integration Analysis

Johansen's Co-integration test for selected maize markets for the long-run co-integration was performed. If two series are potentially co-integrated, at least one co-integration relationship exists. Co-integration may be affected by some factors, such as transportation cost, tariffs, and so on. The two tests, i.e., trace and max Eigen statistics of Johansen's approach based on the vector autoregressive model (VAR) were put into the application to analyze the cointegrating vectors between the selected maize markets. The results of Johansen's maximum likelihood tests (Maximum eigenvalue and trace test) are reported in Table 3. The first null www.extensionjournal.com hypothesis of maximum eigen-value and trace test, tests the no cointegration (r = 0) against the alternative hypothesis (r ≥ 1) of at least one cointegrated equation prevailed in the VAR system. Both, the maximum eigen-value and trace test reject the null hypothesis of no cointegration. The rejection/acceptance of the null hypothesis is decided by the trace max-eigen test statistic values against their critical value and corresponding probability value which is less than test statistic in the first null hypothesis. Similarly, the null hypotheses from r ≤ 1 to r ≤ 2 for both the statistics were rejected against their alternative hypotheses from the r ≥ 1 to r ≥ 3 as their critical values are less than the test statistics and the corresponding probability values are also less than 0.05. This implies that Johansen co-integration test indicate that the maize markets in Telangana exhibit significant long-run co-integration. There are three co-integrating relationships among the five markets, suggesting that they are closely linked and move together over time. These findings have important implications for understanding the market integration and price transmission mechanisms in the region. Policymakers, traders, and stakeholders can use this information to design strategies that promote market efficiency and stability, ensuring that price changes in one market are reflected across others in a predictable manner. Notes: in represent the natural logarithm and * denote the rejection of null hypothesis at 5% level of significance Table 4 shows the results of bivariate Trace and Maximum Eigen-value tests. For all the Maize markets pairs, the first null hypothesizes of r = 0 are rejected as the critical value is less than both from the trace and max Eigen statistics, and corresponding probability value is also less than 5% level of significance. Some market pairs (e.g., Badepally-Nagarkurnool, Nagarkurnool-Siddipet, Nagarkurnool-Warangal and Karimnagar-Nagarkurnool) do not show further co-integration at r ≤ 1, r ≥ 2. Markets like Siddipet-Warangal, Badepally-Karimnagar, Badepally-Warangal, Karimnagar -Siddipet, Karimnagar -Warangal and Badepally-Siddipet exhibit the strongest co-integration relationships, reflecting high interdependence. Hence, it can be concluded that only most of the Maize markets (like Badepally-Nagarkurnool, Nagarkurnool-Siddipet, Nagarkurnool-Warangal and Karimnagar-Nagarkurnool) in the Telangana state are efficiently functioning. Moreover, the Johansen's Trace and Maximum Eigen-value tests signify that the modal prices in these maize markets are competitive. In the competitive markets, the movements of the prices are closely associated. The study has conformity with most of the regional research work, which also revealed that the major maize markets are efficiently functioning and prices are well transmitted and cointegrated (Reddy et al., 2012; Sendhil et al., 2014) [11,10] . The null hypotheses for both the trace and trace max Eigen statistics are no cointegration equation against the alternative hypothesis of at least one cointegration equation are rejected in all the cases. The acceptance and rejection of the null hypothesis are based on the critical test value and corresponding probability value.

Short run and long run behavior of market prices

Since the Johansen's multiple co-integration test results showed that selected maize markets have a long-run equilibrium relationship and there exists co-integration between them. Hence the Vector Error Correction model (VECM) among the selected markets of maize is employed to know the speed of adjustments for the prices of maize among selected markets, for short run and long run equilibrium of prices. The results of VECM are presented in Table 6. The estimates of the Vector Error Correction Model revealed that the co-integration equation values for Karimnagar and Warangal indicate that these markets rapidly attain short-run equilibrium. The one-month lagged price in the Badepally market significantly influences the current prices in Badepally, Siddipet, and Karimnagar. Similarly, the one-and two-month lagged prices in the Karimnagar market affect the current prices in the Warangal market. In Nagarkurnool and Siddipet, the one-and twomonth lagged prices impact their respective current prices. Furthermore, the one-month lagged price in the Warangal market influences the current prices in Karimnagar, while the two-month lagged price impacts the current prices in Nagarkurnool. This study examined the spatial market integration and price behavior of maize markets in Telangana State through cointegration analysis, utilizing modal monthly price data from January 2020 to December 2024. The findings revealed that all major maize markets in Telangana are highly integrated in terms of price movements. Agricultural markets play a pivotal role in enhancing agricultural marketing and production efficiency. A critical issue in analyzing policy reforms related to national agricultural markets is the degree to which domestic agricultural commodity markets respond to price changes. The overall performance of agriculture depends not only on production or supply efficiency but also on marketing efficiency, particularly the effectiveness of agricultural markets and price signals. Spatial market integration reflects the extent to which geographically distant markets (e.g., between regions) share common long-term price or trade information for a homogeneous commodity. The results of ADF unit root test indicated that price series are stationary in first differencing logarithm i.e., Badepally, Siddipet, Karimnagar and Warangal markets and except one market i.e., Nagarkurnool found to be integrated of zero order I (0). Results of Johansen's co integration test showed the price series as co integrated. The results of the granger causality test indicated that in the long -run there was a twodirection relationship between market prices. There is bidirectional causality affecting maize prices of Nagarkurnool-Badepally. While unidirectional causality is shown in the Badepally-Karimnagar, Siddipet-Karimnagar. Results of Vector Error Correction Model (VECM) showed that Karimnagar and Warangal markets reached short-run equilibrium rapidly. The one-month lagged price in the Badepally market significantly influences the current prices in Badepally, Siddipet, and Karimnagar. Similarly, the oneand two-month lagged prices in the Karimnagar market affect the current prices in the Warangal market. In Nagarkurnool and Siddipet, the one-and two-month lagged prices impact their respective current prices. Furthermore, the one-month lagged price in the Warangal market influences the current prices in Karimnagar, while the twomonth lagged price impacts the current prices in Nagarkurnool. It is clearly shown that there is huge scope for increasing maize prices in India and Telangana State. The projected 20-30% increase in feed demand and the rising ethanol demand driven by the government's 20% ethanol blending target by 2030 will significantly boost maize consumption in India. The present prices of Maize crop in India and Telangana State maize modal prices are on par with the Minimum support prices.

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