Tea is one of the world’s oldest and most widely enjoyed non-alcoholic beverages that contains caffeine (Gogoi et al., 2025). Assam is the second largest producer of it and tea industry is one of crucial industries in this state which gives significant employment opportunities to unskilled and semi-skilled labours. Tea cultivation and usage in India have a deep-rooted history. The rich history of tea in India dates back to 1823, when Army Colonial officer Robert Bruce discovered tea plants in the Upper Assam Forest during his trade expedition for the East India Company. Initially tea cultivation was completely owned and controlled by the big tea estate, but in last few decades, there has been notable shift in the tea cultivationscale. Tea cultivation in small holding, a new addition to tea industry of Assam was initiated in 1978 by Late Soneswar Bora (the then agricultural minister of Assam) to utilize available fallow land and attract rural youth to agriculture sector and thereby to solve unemployment problem (Lama, 2016) and the term, small tea grower is become an essential component of this industry who contributes almost 50% of total state production of tea. However, small tea growers are plagued by numerous challenges, casting doubt on their sustainability. Unfair low-price realization has been regarded as core issue of small tea growers of Assam since 1990’s. Green tea leaves pricing is very crucial as it can estimate if small tea growers stand to gain financially to shift the tea cultivation on small scale towards sustainability. In India, small tea growers receive low price with an average USD 0.16 per k.g. in 2020-2022 due to which the small-scale tea farmers protested hunger and demanded fair pricing from Indian Tea Board. Setting lowest price for green tea leaves in Assam, India has been proposed, but it is not implemented due to non-cooperation of the buyers (Preethi, 2023; SAAPE, 2022; Dhar et al., 2022). The price mechanism of green tea leaves is impacted by many factors like status of organization of STGs, role of leaf commission agents, price formulas, green tea leavesquantity and price movements of tea in auction and international market and functioning of Indian Tea Board (Kalita, 2014). In this complex situation, assessment of the influence of these factors on price of green tea leaves will be fruitful to track the price decision process of green tea leaves. In the present study, the influence of two factors namely auction price of made tea and production done by small tea growers is assessed by examining direction of causality among real price of green tea leaves, real auction price at Guwahati tea auction center and tea production done by small tea growers in Assam using VAR model.

Review of literature
 
According to Wachira et al., (2013) tea is a member of the subgeneric group Thea of the genus Camellia and includes several interbreeding species, complicating its taxonomy. The beverage is mainly produced from Camellia sinensis, whereas other Camellia species are also cultivated in China. Cultivated tea plants are classified on the basis of leaf traits into three main types: The small-leafed China variety (C. sinensis var. sinensis), the large-leafed Assam variety (C. sinensis var. assamica) and the Cambod race (C. assamica ssp. lasiocalyx).

Sen and Nath (2012) states that small tea growers are officially recognized by Tea Board of India during the eight Five year plan (1992-97) and it was defined as an individual or entity managing a farm area up to 10.12 hectares (25 acres or 75 bighas) which is considered as a acceptable definition of STGs by different agencies associated with tea industry.

According to Das (2010), since 1999, small tea growers in Assam have faced reduced profitability due to failing green tea. In 2000, they were remarkably low at Rs. 4 per kg, even below the cost of production, due to an oversupply caused by the proliferation of small tea holdings.

Kalita (2014) finds in his study that, the price mechanism of green tea leaves is impacted by many factors like status of organization of STGs, role of leaf commission agents, price formulas, green tea leaves quantity and price movements of tea in auction and international market and functioning of Indian Tea Board.

Kakoty and Kaurintal (2021) state that product of STGs, i.e. green tea leaves, is perishable and pricing decision of it is very complex mechanism. According to them, green tea leavespricing decision is controlled by the cartel of BLF’s collection agents and large tea estate factories. Small tea growers have little involvement in price decision process and have less bargaining power because of weak organizational formation.

According to Das (2019), the product market of green tea leaves has characteristics of monopsony, where a cartel of large tea estate companies and bought leaf factories play as price fixers and small tea growers are only playing as price takers.
 
Research question
 
Do the quantity of output produced by small tea growers and auction prices of made tea influence the price of green tea leaf?
 
Objective of the paper
 
The paper aims to study influences of the quantity of output produced by small tea growers and auction prices of made tea on price of green tea leaf.

There are different economic and non-economic factors which lowers the price of green tea leaves. But it is often cited to the STGs that the green leaf price is fixed on the basis of market price of made tea at auction and excessive production of green tea leaves lowers price of green tea leaves. According to Singh et al., (2006) price of green tea leaves is decided not by the producers or buyers but by the bidders in auction centre. Das (2010) pointed out excessive production due to unplanned expansion of small tea cultivation in Assam caused serious price fall in the year. There has been a continuous increase in the green tea leaves production in Assam and its compound growth rate is higher than the national average (Das, 2019). Therefore, two factors, i.e. average annual auction price of Guwahati auction centre, Assam and total yearly green tea leaf production by small tea growers, are taken for further study. In the present study, the influence of two factor namely auction price of made tea and production done by small tea growers are assessed by examining the direction of causality among real price of green tea leaves, real auction price at Guwahati tea auction center and tea production done by the small tea growers in Assam using Vector Autoregressive (VAR) model.

The data needed is gathered from both published and unpublished reports of TBI, AASTGA (All Assam Small Tea Growers’ Association). Some data are collected from various data sources, such as NEDFI (North Eastern Development Finance Corporation), Government of Assam survey reports, Statistical Handbook of Assam, various published and unpublished research works. Study duration is from year 2000 to 2023. Some missing data regarding price and green tea leaf production using exponential growth trend. The real green tea leaves price and real auction price are calculated from collected nominal prices by using consumer price index provided by Directorate of Statistics of Assam is used. The old series i.e. from 2000-2011 is rebased using the base (2012) from the new series i.e. from 2012-2023. The formula used for converting nominal price into real price is:
 

Where,  
RPt = Real price.
 
Tool for data analysis
 
Multivariate time series models are useful to analyze the relation among these interdependent variables.VAR model is one of these models, which was introduced by C.A. Sims, is a popular statistical tool, used to capture linear interdependencies among several time series (Olanrewaju et al., 2015).  In current research, VAR model is utilized to gain valuable insights into dynamic relationships between key variables, including real price of green tea leaves, real auction price and production volume to assess impact of auction price and production on real green tea leaves price. It will test direction of causality among real green tea leavesprice, real auction price at Guwahati tea auction center and tea production done by small tea growers in Assam.

The trade reforms manifest a crisis in tea plantation sector in India and auction tea price declined from 1998-2003. The decline in international tea prices severely impacted India’s tea sector, causing a sharp drop in exports (Viswanathan and Shah, 2013). The tea crisis was triggered by a sharp decline in international and domestic prices driven by Kenya’s production recovery and loss of Iraqi market due to war (Hayami and Damodaran, 2004). But, in this crisis period, Small tea cultivation in Assam has expanded without proper planning which results in mushrooming of small tea growers as well as leads to green tea leavesoverproduction. Excessive tea production again leads to the price drop, leaving farmers with non-remunerative prices. Since 1999, minor tea growers in Assam have faced reduced profitability due to failing green tea. In 2000, they were remarkably low at Rs. 4 per kg, even below the cost of production, due to an oversupply caused by the proliferation of small tea holdings (Das, 2010).

Semi log trend was used to calculate decadal growth rates of actual prices of green tea leaves and coefficient of variation was used to measure variability.While analyzing the findings, decadal comparisons are done among 2000s, 2010s and 2020s. However, the study duration is 2000 to 2023, for sake of comparison, averages for 2000s, 2010s and 2020s are measured. Table 1 shows a clear pattern of fluctuations, indicating distinct peaks as well as troughs of green tea leaves price over the study period, i.e. from the year 2000 to 2023. Indian tea industry was facing a crisis since 1990s. Emergence of small tea gardens began in the early 1990s and reached its peak in the late 1990s. In the period 2000 to 2009, the nominal prices decreased and real prices gradually increased, reflecting nice growth rate (7.15%) due to low inflation rate than two later decades. Tea production sector has faced a crisis since 2014 because ofincreasing production costs as well as stagnant exports. Auction Assam tea prices remain between Rs. 153 and Rs. 156.43 per kg, while production costs have risen to Rs. 200 per kg. Over 90% of tea sells below Rs. 200, with 60% fetching just Rs. 150. Prices grew by only 1%, while input costs surged by 6-7% and worker wages in Assam increased by 22%. Production rose from 979 million kg in 2009 to 1,339 million kg in 2018, driven by small tea growers, but domestic consumption, at 786 grams per capita, lags, leading to market oversupply. Small tea growers face lower prices due to arbitrary pricing by large companies. Growth rate of real price is -5.66 and price fluctuation is highest in this period that is reflected by coefficient of variation (30.04%).The nominal prices have increased in 2020 and onwards likely driven by supply chain, affecting both production as well as distribution because of imposition of covid-19 pandemic lockdown measures on March 24, 2020. But, the real prices are moderate and have a negative trend due to high inflation rate.


 
Autoregressive conditional heteroskedasticity (ARCH) test for volatility checking
 
Autoregressive conditional heterosekedasticity (ARCH) test is used to identify the presence of heterosekedasticity (changing variance) over time or to check volatility of time series data. In Table 2, the F-statistic is 0.031997 and the associated p-value (Prob. F) is 0.8598. Therefore, results show that residuals do not have heteroskedasticity, green tea leafprices are not volatile.

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Modeling the influence of auction price of made tea and production done by small tea growers on price of green tea leaf
 
Stationarity of the series is the pre condition to apply VAR model. ADF (augmented dickey fuller) test is utilized to check presence of a unit root in a time series of the variables. Here, PGTL stands for real green tea leavesprice, APTG for real auction tea price at Guwahati Auction Centre and PRGTL stands for production of green tea leaves by the STGs.The results in Table 3 show the three original series are non-stationary making it stationary by become stationary after taking the first difference.

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Test of co-integration
 
The results of co-integration test in Table 4 show that no co-integrating equation were there. The Vector Autoregressive model will be utilized for further study because of lack of co-integration among the variables.

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Identification of VAR model
 
All the criteria have unanimously selected a lag order of 1 in Table 5. This means that including one lag of endogenous variables in VAR model is optimal for capturing relationships among these variables without over fitting. Hence, VAR (1,1) is considered appropriate model for future analysis.

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Estimation of VAR (1, 1)
 
VAR (1, 1) is selected as appropriate model for future analysis after satisfying the lag selection criterion. The VAR (1, 1) equations are:
 

Logreal price = C(1)*Logreal price(-1) + C(2)*Logreal auction price(-1) + C(3)*Log production(-1) + C(4)          ...(i)
 
Logreal auction price = C(5)*Logreal price(-1) + C(6)* Logreal auction price(-1) + C(7)*Log production(-1) + C(8)             ...(ii)
 
Log production = C(9)*Logreal price(-1) + C(10)*Logreal auction price(-1) + C(11)*Log production(-1) + C(12)                ...(iii)

The vector autoregression (VAR) estimates are shown in Table 6. It shows regression results for 3 different dependent variables- Real green tea leavesprice, real auction price and Production, each with lagged values of the dependent as well as independent variables. In real green tea leaves price equation, real green tea leaves price (-1) has positive as well as significant coefficient (0.433770) with a t-statistic of 2.12416 and probability (0.0380). This shows that lagged value of real green tea leaves price has positive as well as statistically significant effect on its own on current price value. The two independent variables seem to be insignificant.

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In 3^rd equation, green tea leaves production (-) has positive as well as significant coefficient (0.917752) with t-statistics of 14.2090 and probability (0.000).

In the Table 7 it is seen that R- Squared and adjusted R-squared of 1^st and 2^nd quation are moderate, which shows that independent variables in model explain moderately the variation in dependent variable prices i.e. real green tea leaves price and real auction price. The production data is well-fitted by model, with both R-squared (0.964976) and Adjusted R-squared (0.959446) values being high which implies that the independent variables in model explain most of variation in production of tea. The values of Durbin-Watson statistics are more than 2 for all the variables, so it could be said that no autocorrelation in data was present.

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Checking Joint effect of real auction price of made tea and production done by small tea growers on price of green tea leaf
 
Wald test is utilized to test joint effect of real auction price and production on real price of green tea leaves. The results of Table 8, 9 and 10 show that real auction price and production have no joint and individual effect.

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Diagnostic checking of the VAR (1) model
 
VAR (Vector autoregressive) model is powerful tool that is utilized to analyse relationships among several time series variables. The Granger causality test as well as impulse response analysis are done to leveraging VAR model analysis.

The results shown in Table 11 indicate that all null hypotheses cannot be rejected at 5 per cent level of significance. It indicates that past values of 1 variable do not help predicting future values of other variable.

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Impulse response function
 
In Fig 1, the solid blue lines represent the point estimates of the responses and the dashed red lines indicate the confidence intervals, typically at a 95% confidence level. The first row of the figure of impulse response analysis indicates how shocks in real price of green tea leaves, real auction price and tea production affect the real price of green tea leaves. It represents the response of real green tea leaves price towards the shocks in said variables. The first graph in 1^st raw shows a shock to previous the real green tea leaves prices has strong effect on its own value which temporary and do not lead to long run changes. The middle graph in 1^st raw shows the real green tea leaves prices reacts initially to the real auction prices but gradually converging to equilibrium. The third graph in 1st row indicates that shock in tea production has little to no effect on real green tea leaves price.



Green tea leaves, the product of small tea growers have complex price mechanism. As per reviewed studies, the price mechanism is demand sided and control by a cartel of bought leaf factories and big tea estates. The sellers, i.e. small tea growers, have little involvement in price determination. In current study, two factors real auction price of made tea and tea production are assumed to have an impact on decision of green tea leaves price. The current research finds that the two factors, which are said to have influence on real green tea leaves’ price are statistically insignificant. According to Das (2019) Product market of green tea leaves has characteristics of monopsony, where a cartel of big tea estate companies and bought leaf factories acts as price fixer. The Buyers of green tea leaves enjoy supreme power in price mechanism because of their strong organizational status and excessive control on processing of blending and package. The agents also have a significant role and gap and earn handsome amount of commission. Whereas, the STGs have weak organizational status and are not capable to detect market signals due to a gap between marginal farmers and price fixing bodies. Green tea leaves are perishable and have to be sold within a few hours after plucking because of lack of proper storage facilities. This creates such a market for green tea leaves in which price is controlled by demand side and the suppliers, i.e. the STGs, have to accept whatever price is given by price fixers. 

The study finds that auction price of made tea and production done by small tea growers aren’t significant factors in price decision process of green tea leaves. Small tea growers should take initiatives to enhance leaf quality by practicing fine plucking and rational chemical fertilizers and pesticides use to get a better price. Smart farming technology (SFT) may enhance productivity quality of tea and reduce cost of production (Katherasala and Thaduru, 2025). Diversification of farms will also increase farm income (Jose and Ponnusamy, 2024). Indian Tea Board should implement properly price sharing formula and minimum benchmark price to cushion small tea growers and also actions should be taken to control monopsony nature of processing units.
 
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We declare that we have no conflicts of interest related to the publication of this article.