Farmer finds the buyers: Electronic and traditional influences in Tomato marketing

Farmer finds the buyers: Electronic and traditional influences in Tomato marketing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Farmer finds the buyers: Electronic and traditional influences in Tomato marketing Akash Vilas Mhaskey, Mayanglambam Rajeshwor, Nilabja Ghosh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7480251/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract A policy that brings resilience of price to product arrivals would be beneficial for the farmers. The expanse of the market that farmers can reach out to and the prices they fetch mark the stretching of frontier of agricultural marketing. Marketing success is deeply associated with available intelligence on demand distribution that ICT can now bring to farmers. A study of Uttar Pradesh conducted for a perishable crop Tomato reveals imperfection of markets creating dispersions of prices among markets. Some of these markets are privileged with e-NAM that confers superiority in computerization. Comparison between the two types of markets found greater volatility in less privileged markets. Data suggests that farmers increasingly hold produce and release them months after harvest but among other variables, superiority of ICT is found to be an important influence in improving price, increasing Arrivals and reducing distress during post-harvest months when prices tend to fall. e-NAM Markets imperfection ICT Modelling Information Figures Figure 1 Figure 2 Figure 3 Introduction Supply chains in agriculture have caught the attention of Indian administration particularly because of their implications for farmers’ income and welfare, a major agenda of contemporary development programme. Success at this initial phase of a supply chain is manifest in the market assurance farmers enjoy and the prices they are able to fetch especially in cases of crops that are perishable. It can be conjectured that by conveying the intelligence on demand distribution, Information and Communication technology (ICT) through electronic means, can be one among other key solutions to make marketing easy. India’s agricultural marketing has historically been guided by a legislated institution in the form of regulated market or APMC market where auctions are held through open vocal bids. Following the reforms of 1990s, decadence of the Agricultural Produce Market Committee (APMC) markets noted (Acharya, 1998, Acharya et al., 2012) was brooked by policy. Though further legislation was politically difficult, market reforms did take off leading to the evolution of alternate channels, participation of corporate buyers and formation of farmers’ organization (Ghosh, 2013). Horticultural marketing, where pre-harvest contracts had been common is more flexible. Farmers and the food security they bring remain principal concerns even in the reforming agriculture with lessening rigidity. Regardless of the marketing practices, the exogenous advent of electronic power was certainly a milestone. Utilized by public policy to strengthen the rural markets and the APMC market in particular, the years following 2015 have been a watershed. Information is an important input for competitiveness of markets (Singh and Singh, 2023, Michler, 2020). As it pervaded the rural sector, ICT not only brought market intelligence within rural precincts but also educated farmers in what was once a backward economy. Outdated practices of traders gave way to more modern and informed ways as the internet brought rural and urban people closer. It began to link all the APMC markets enabling them to share their market information. Prices and volumes traded registered in various APMC markets offer useful knowledge on locations of potential sales and a larger database for benchmarking the prices at bidding. In a further step, electronic auctions were enabled by ‘e-NAM’ embedded in certain APMC markets. The common electronic market platform, called the electronic National Agricultural Market or e-NAM was launched in the year 2016 following a successful demonstration in Karnataka. This Central Sector Scheme is equipped with a portal that provides a single window for all APMC related information and service, including commodity arrivals, prices and bids. It offers online trading or e-trading facility for which physical movement of produce takes place through the mandis. Integrating all the APMC markets of the country, e-NAM is a move towards ‘one’ national market where ideally, price of a homogenous product would be same when corrected for transport costs. Product prices may differ in different states and regions for various reasons but within a perimeter the products are likely to be similar and fetch comparable prices. Price distinctions can then be attributable to market imperfections. Operating in e-NAM specifically, a farmer has the option to reach out to traders from distant places within or even outside the state of residence. Visits to such markets and interactions revealed differences in operations in APMC markets with the traders doubling up as commission agents in some but not all and commission agents serving in displaying and assaying products in e-NAMs alongside their own jobs of open bidding but not in all cases. While e-NAM is a submarket within an APMC market which is increasingly progressing in computerization anyway, presupposing spillover of information and exchanges of ICT proficiency within the market, the paper assumes an ICT advantage of the entire market that possesses an e-NAM over others. This paper uses this distinction of possessing e-NAM among APMC markets as a proxy for an ICT advantage. Objective, Data and Method Lack of information as a component of imperfection leads to inefficiency of markets. Varying imperfection among markets drive wedges in prices and offer unequal assurances of sales across markets. Expectedly, any market that has a lead in computerized linkages can be hypothesized to provide a larger assured market to farmers and/or help them to fetch higher prices. Success of marketing is therefore assessed by the price registered in the APMC market and by the volumes of product delivery or ‘Arrivals’ in the market by farmers which is treated as an indicator of farmers’ confidence. Market imperfection is likely to be implied by preferences and price differences dividing the markets within a state for the same product. Price is measured in value per unit weight and ‘Arrivals’ expressed in quantity units. This study, considering the state Uttar Pradesh (UP) and the horticultural product Tomato in India aims to look for the extent of inter-market and intra-year variations price and market Arrivals and to identify their determinants over time and across markets to assess in particular the role of information. An assumption is made that across the state tomato is of near-uniform quality and sold for similar purposes so that market efficiency would bring prices closer towards a ‘one’ price ideal. A sample of 20 APMC markets is drawn from UP a state, the sample stretching across east and west UP. Largest and most populous state of India, UP ranks first in net sown area (NSA) and agricultural workers in India. Though a relatively small producer of 4% of India’s production, a total of 73 out of its 75 districts grow tomato. Most intense growers are districts like Agra, Barabanki, Etah, Mainpuri and Unnao. Markets are chosen randomly but making sure the data is available without gaps and half of the sample markets have e-NAMs. The selected districts contribute only 25% of UP’s tomato production. From each market, a time series of monthly data is drawn for years 2018–2024. Data of prices and Arrivals are taken from AGMARKNET and other variables from varied official sources like Directorate of Economic and Statistics (DES), Indian Meteorological Department (IMD), Ministry Commerce and Industry (MCI), Statistical Abstract of Uttar Pradesh, of Government of Uttar Pradesh (GoUP). Prices vary from day to day and month to month. To avoid bias due to price spikes, modal price of a month is taken as the monthly average. Annual average price is however the arithmetic mean of the modal monthly prices. Variance (or coefficient of variation) of price is calculated across the sample markets in any month. Prices tend to go down after harvest and shoot up when crop is on field. Although crop calendars even in a state need not be constant, such growing stages refer to majority areas in the market catchment. A high price (High) is the average of the months of highest three annual prices and likewise the low price (Low) is calculated but the process involves identifying the months of High and Low prices using not only numerical assessment but also regression on dummies for months with a base (July) corrected for years. Intuitively, prices sellers get can be explained by varied factors such as ( 1 ) seasonality marked by the harvest months, ( 2 ) Arrivals measuring supply in the market ( 3 ) expectations during bidding, ( 4 ) size of the market arguably influencing its bargaining power, ( 4 ) roads available and distance from nearest urban centre where demand supposedly exists, ( 5 ) time measuring both inflation if any and other gradual effects, ( 6 ) informational advantage proxied by the content of an e-NAM connection. Arrivals in turn are decisions of farmers based on varied factors that create assurance of sales. Intuitively, both prices and Arrivals can also be subject to seasonality and can depend on similar factors. District level data paucity is a serious hindrance. Only with limited information available, approximations have to be made rationally, some data are gathered from 5-yearly Agricultural census 2015 only and indices over time and space are constructed. Arrivals in a month however precede price formation acting as the supply in the clearing process. To the extent that ICT has made a difference, Arrivals and prices may be structurally different between APMC markets with and without e-NAM though the challenge is to identify the particular effect of this feature separate from many other contributing effects. Estimation is made using monthly panel data of 1680 observation for a sample period 2018 January to 2024 December of 20 APMC markets of which only 10 possess e-NAM. The random Effect (RE) model is reported given the type of data coverage but for all specifications the Hausman test rejected the Fixed Effect (FE). Among the variables, the dependent is (i) PRICE: nominal price (Rs/Qtl or Indian rupees per 100Kg) registered in the mandis where farmers or their representatives (aggregators) sell and (ii) ARRV: Arrivals which are the quantity (quintal or 100Kg) of products the sellers bring to any market for sale. Explanatory variables are ( 1 ) 11 dummy variables D_MON for months (MON = Jan, Feb, ….., Dec) excepting July which is the base, ( 2 ) dummy variable for computerization index (D_ICT) where D_ICT = 1 if the market has an e-NAM connection and 0 otherwise, ( 3 ) and a number of quantified continuous variables (as in Table 1 ). The district in which the market is located is often treated as a reference. Climatic factors like rainfall, humidity and temperature, all mutually related, cause product degradation. UP has two meteorological regions East-UP and West-UP made up of 45 and 30 districts respectively and rainfall in both regions can affect marketing and the district specific rainfall with or without lags undermine the product quality. Seasonality is described by the months and trend by time (months in the entire sample). For assessing demand, three clusters are delineated with cities Delhi, Lucknow and Ghazipur representing urban centres for carving out locations, Delhi being the capital of India and the richest state. A few districts even fall within the National Capital Region (NCR). They are fairly urbanized with higher demand and greater purchasing power. Proximity to the urban centres makes certain APMC markets privileged over others. Distance to the urban centres and lengths of roads available in the districts are used to capture the demand advantage. Market size is approximated as the total arrivals in the three months identified as harvest months when pressure of supply is felt most strongly. Market sizes are indexed using all sample years with Lucknow Market as base. Certain other cross-sectional comparative indices are similarly created such as manure use (proxied by livestock size), production and distance from the urban centre. Tabulation, graphical illustration and econometric regression with panel data make up the method. To explain price and Arrival at any point (month) in the markets panel data regression is estimated as follows: $$\:{Y}_{it}=\:\alpha\:+\:{X}_{ijt}^{{\prime\:}}\text{}{\beta\:}+\:{a}_{i}+\:{\tau\:}_{t}+\:{u}_{it}$$ 1 ……………….. Where, Y = Price or Arrival in any market i = 1, 2, ……………20 (market) j = 1, 2,……..n where n is the number of explanatory variables t = monthly periods from 2018 (January) to 2024 (December) where t = 1, 2, 3….84. X ijt = j th explanatory variable in ith market in t-th time point \(\:{a}_{i}\) = unit (market) effect. \(\:{\tau\:}_{t}=\:\) time effect (monthly shocks common to all markets) and \(\:\alpha\:\) \(\:and\:\:{\beta\:}\) are intercept and slope parameters to be estimated. Table 1 Descriptive Statistics, Abbreviations and Units Variable Abbreviations Units Source Mean-Value Price PRICE ₹/Qtl Agmarknet 1687.21 Arrivals ARRV Quintal (Qtl) Agmarknet 756.13 East UP Rainfall RF_EUP Millimeter (mm) IMD 71.52 West UP rainfall RF_EUP Millimeter (mm) IMD 61.59 District Rainfall RAINFALL Millimeter (mm) IMD 65.98 Production PRODUCTION Metric Ton(MT) DES 912.32 Farm Size FRM_SIZE Hectare (Ha) Agriculture Census 0.73 Distance from Urban Centre DIST_URB Kilometer (Km) Google Map 126 Road Length ROAD_L Kilometer (Km) GoUP 1195.68 High Speed Diesel Price HSD Index (Base = 2011) MCI 129.93 Market Size MKT_SIZE Index (Base = Lucknow) Agmarknet 38.45 Manure MANR Index (Base = Lucknow) GoUP 124.78 Note: 1 Quintal (Qtl) = 100Kg Tomato Marketing in Indian regulated (APMC) markets India is a large producer of fruits and vegetables but their bulkiness, seasonality of production and perishable character make marketing complex (Manogna, 2025). Ensuring quality at sales, would require caution at cultivation with treatments for soil and pests, post-harvest handling with care and planning ahead of selling and above all in the choice of the marketing channel. Even after devoting land and costly inputs, if the products are not sold quickly after harvest, very poor or even negative returns may result. There is also huge waste, decline in quality, and a frequent mismatch between demand and supply, both spatially and over time (Subbanarasiah, 1991, Singh et al., 1985). Tomato in Indian agriculture World’s third largest vegetable crop after potato and onion, tomato is grown widely in India where it is consumed as an essential vegetable in the regular culinary diet for its taste and nutrition and as a fresh fruit in salad. It is also processed into preserves such as pickles, soups and sauces. Making up nearly 10% of India’s vegetables, tomato production has grown fast in the last two decades, making its marketing an important farm function. A summer crop in some places and a winter crop in others, tomato can be cultivated throughout the year with spatially varying calendars. Its red colour depends on the temperature at harvest. Although risks of trader collusions are attributed to open auctions of APMC markets (Manogna, 2025) and varied marketing channels are opening up, price discovery and the practiced expertise of commission agents and other traders especially in assaying the quality are advantages hard to replace as of now. Agricultural marketing rules differ from state to state but APMC markets remain a common feature though they keep developing in technology, infrastructure and administration. Integration with digital advancements has been a gradual process. UP has more numbers of mandis and most of them have been coming under e-NAM (Rao et al., 2020). Information Technology for Marketing Innovation with ICT, rather nascent in India, is influential for the evolving business model and trade dependencies (Timpanaro, 2023). ITC’s ‘e-Choupal, offering internet to connect rural farmers with buyers in the year 2000 was pioneering. ‘App’s started providing information to farmers about weather, dealers, plant protection and other expert advice, about soil health card, godown and cold storages and also market price. The Union government is actively promoting digitization. The main design of the India Digital Environment of Agriculture (IDEA), which will establish the federated farmers’ database structure is completed. Agricultural Marketing Information Network or AGMARKNET is an e-governance portal acting as a single window service to stakeholders like farmers, traders, researchers and government officials for monitoring and policy making on markets. Its main goal however is to empower farmers with timely and accurate information on agricultural markets across the country and help them to get the best price. E-NAM, another portal, enables electronic auctioning. UPAg, a centralized digital platform that integrates agricultural services, schemes and information, helps farmers with easy access to resources and support. Further, for e-NAM, a vast collection of information crucial for agricultural trade in India is stored in servers under the Small Farmers Agribusiness Consortium (SFAC). The data includes real time and historical records on commodity arrivals, prices and trade volumes from various markets. This rich dataset facilitates transparent price discovery and helps farmers to make informed decisions. Intuitively, market intelligence from around the country informs farmers of possible markets beyond the immediate precincts. Primary sellers, i.e. farmers, are expected to locate buyers easily and obtain a higher price when they sell products on the e-platform (Banker et al. 2011). Efforts are underway to examine the positive effect of computerization on farmers but the initial teething problems under present system make implementations difficult (Chand, 2016) and do not help the effort so that the academic literature gives mixed results on the impact of information technology on rural income. Rigorous evidence on the effect may be deficient. Even with the enormous potential, the role of ICT in bringing prices close to one another especially by enhancing prices in producing regions was found limited in the initial stage, acting more as a baseline for future monitoring of market integration (Ghosh et al, 2020). A detailed study using paired t-statistics and difference of difference analysis (DID) of both secondary data of wholesale prices and field survey suggested positive effect of e-NAM market institution (Manogna, 2025). Another study (Rao et al., 2020) too using both secondary and primary data found higher prices being fetched by participants in e-markets. Both conclusions are drawn by comparing pre-e-NAM and post e-NAM prices from the APMC markets from AGMARKNET though the difference could be attributed to many reasons. Features of APMC Markets: The markets can be described by a range of features: Locational factors 80% of Indian farms of size 2 hectares or less produce 60% of total food grains and over half of fruits and vegetables. Though small on an average, Indian farm are varied in sizes and the average farm size is an indicator of economic affluence. Larger farmers enjoy many relative advantages including in marketing. With population growing on an inelastic land supply, development has hardly mitigated the shortcoming. Except Jhansi, all sample districts have farms with average sizes below 1 hectare, Jaunpur, Barabanki and Gorakhspur having the smallest (Table 2 a). The average fam size in only 0.72 hectare. Located in different districts of a diverse state in northern India, some markets are close to national capital Delhi. Nearest to Delhi is Ghaziabad and Noida though Meerut, Bulandshahar and Aligarh are other districts in the same cluster (Table 2 (a)). Supplying to markets nearer to the cities is appealing but its effectiveness depends on the transportation of physical products. Access to roads and distance from urban centres matter. Some markets are remote with distances 200 to 300Km from the urban centre. Most districts have over 1000 km of road ways. Assessed by length of roads and education EUP districts are relatively backward. Table 2 (a). Profile of Selected Tomato Markets in Uttar Pradesh Markets District Distance from nearest urban centre Total Road Length Farm Size Market Size Animal Rainfall Km Km Hectare Index Lakhs Milimetre Gorakhpur Gorakhpur 141.0 1926.0 0.49 119.2 1.5 68.21 Kanpur Kanpur 98.0 1012.0 0.72 117.4 1.0 77.27 Gaziabad Ghaziabad 24.2 783.0 0.72 112.4 0.6 41.09 Lucknow Lucknow 7.7 1399.0 0.60 100.0 1.3 64.92 Aligarh Aligarh 164.0 990.0 0.99 66.8 1.9 62.39 Meerat Meerat 86.2 886.0 0.99 47.2 1.4 55.66 Jaunpur Jaunpur 114.0 1691.0 0.46 46.4 3.0 82.35 Muradnagar Ghaziabad 53.6 783.0 0.72 34.1 0.6 41.09 Azamgarh Azamgarh 84.4 1648.0 0.54 26.1 2.3 64.92 Bareilly Bareilly 273.0 772.0 0.70 18.7 1.3 83.65 Gazipur Ghazipur 6.4 1535.0 0.65 17.5 1.9 67.40 Khalilabad Sant Kabir Nagar 174.0 1582.0 0.53 15.1 0.4 99.22 Sikandarabad Bulandshahar 82.2 822.0 0.86 9.7 1.9 51.54 Mehmoodabad Sitapur 52.5 1095.0 0.70 8.3 2.7 49.19 Jasra Prayagraj 224.0 1658.0 0.71 7.8 3.0 68.34 Bijnaur Bijnor 166.0 762.0 0.96 6.2 2.1 58.26 Rudauli Barabanki 92.8 1138.0 0.47 4.9 1.8 109.13 Pukharayan Kanpur Dehat 154.0 997.0 0.75 4.4 0.9 23.09 Chorichora Gorakhpur 161.0 1926.0 0.49 3.9 1.5 68.21 Moth Jhansi 358.0 509.0 1.47 3.1 1.2 83.66 Total 125.9* 23131 0.73* 38.5* 31.7 66.00* CV (%) 70.4 36.3 33.30 108.3 46.6 30.6 Note: *Average and others are Total districts. Table 2 (b): Sample Mean Values for Selected Districts of Uttar Pradesh Markets District Prices Arrivals Rainfall Zone Nearest Urban Centre Tomato Production Share Population Net Sown Area (NSA) Literacy Units Rs./Qtl Quintal % Million ‘000 Hectare % Muradnagar Ghaziabad 1665.36 581.37 WUP Delhi 1.00 3.30 44.50 78.07 Gazipur Ghazipur 1792.80 352.52 EUP Ghazipur 0.90 3.60 251.80 71.78 Rudauli Barabanki 1736.07 91.23 EUP Lucknow 5.60 3.30 259.70 61.75 Chorichora Gorakhpur 1764.82 77.40 EUP Ghazipur 0.30 4.40 222.80 70.83 Sikandarabad Bulandshahar 1309.64 185.56 WUP Delhi 0.30 3.50 284.70 68.88 Jasra Prayagraj 1607.20 155.76 EUP Lucknow 0.80 6.00 320.10 72.32 Pukharayan Kanpur Dehat 1743.99 84.86 EUP Lucknow 2.30 1.80 212.10 75.78 Moth Jhansi 1521.55 53.00 WUP Lucknow 0.10 2.00 321.20 75.05 Bijnaur Bijnor 1763.94 116.63 WUP Delhi 0.10 3.70 326.50 68.48 Mehmoodabad Sitapur 1792.63 160.99 EUP Lucknow 0.60 4.50 444.90 61.12 Bareilly Bareilly 1800.26 364.63 WUP Delhi 0.30 4.40 324.40 58.49 Khalilabad Sant Kabir Nagar 1466.52 291.60 EUP Ghazipur 0.00 1.70 116.50 66.72 Jaunpur Jaunpur 1726.19 901.31 EUP Ghazipur 1.00 4.50 287.70 71.55 Aligarh Aligarh 1630.60 1295.73 WUP Delhi 3.70 3.70 290.80 67.52 Kanpur Kanpur 1697.44 2346.85 EUP Lucknow 3.00 4.60 182.50 79.65 Meerat Meerat 1566.37 913.14 WUP Delhi 1.30 3.40 190.70 72.84 Gaziabad Ghaziabad 1783.04 2219.36 WUP Delhi 1.00 3.30 44.50 78.07 Lucknow Lucknow 1913.51 2024.46 EUP Lucknow 2.00 4.60 113.40 77.29 Azamgarh Azamgarh 1757.08 512.12 EUP Ghazipur 0.50 4.60 281.20 70.93 Gorakhpur Gorakhpur 1705.24 2394.19 EUP Ghazipur 0.30 4.40 222.80 70.83 Total 1687.20* 15122.7 23.10 72.00 4698.30 70.52* CV (%) 8.2 110.3 114.1 28.3 42.5 8.2 Note: *Average and others are Total districts. Situational and Quality driving factors Situations arise in individual markets with a strong influence on the arrivals and price. Lagged price (P (t−1) ) registered in the same market can influence the expectations at biddings. Lagged Arrivals can set an example to follow. Monthly arrivals in a market can represent the supply which is mostly an outcome of local cropping pattern, production while being a commercial decision of the farmers. Adding to supply in the market, it can suppress the price. A policy that brings resilience of price to ARRV would be beneficial for the farmers. Heavy or continuous rain in a marketing month can damage roads, add to handling cost and undermine the quality unless reparative actions are taken. There are two broad meteorological regions in UP, namely Eastern (EUP) and Western (WUP) UP, through which transportation of products to buyers take place. Average annual rainfall is 71.52 mm in EUP and 61.59 in WUP, maximum rainfall occurring in monsoon months July to September. Variation (CV) of rainfall across districts in a year is high 30% against an average of 66 mm. Experts in the market assess the quality of tomato by its color, shape, dents and spots caused by moisture, heat and pests though. Mechanical aids are also available. While even within a market the ‘lots’ of products may qualitatively vary, some markets may be superior to others in terms of the share of products deemed good. While there is no direct information of the product quality, some determinants of quality may be considered in lieu. Harvest season rainfall is generally detrimental and is measured by district specific rainfall with varying lags. Well drained soil, preferably black is known to produce good tomato. Districts of UP however are nearly all made of alluvial soil which is otherwise fertile and well drained. Organic manure is good for tomato as is controlled irrigation with drainage. Seasonality and Time trend Prices of farm products often show seasonal patterns because crops have geographical demands for water and temperature in the growing period. Choice of crops and their sowing and other operations are synchronized with the natural conditions of seasons in any region. Harvest tends to be a period of high supply and therefore lower prices. Harvest calendars may not be static over the long run as seed technology and irrigation can modulate the gestation period and confer climate resilience. These factors along with cold storage and improved transportation that mitigate spoilage tend to smooth out seasonality to an extent. Moreover, Tomato has many varieties, most of them developed by scientific research at PUSA. Therefore, prices may be staggered over a window of time within a season. Price and Arrivals can increase or decrease over a long period of time due to technical changes and shifts in tastes or diet consciousness. For nominal prices a time trend can be imposed by inflation. Prices of High-speed diesel (HSD), a driver of inflation plotted in Fig. 1 however seems much smoother than tomato prices. Market features All markets are not uniform in capacity and modernity. However, although APMC markets have been greatly improved with investment in recent decades, little information is published on their infrastructure. As a way out, the index of the total arrivals in the three lowest price months proxies the size of the market, assuming the physical size would be related to the biggest (harvest time) Arrivals that need to be accommodated. Among the study markets, Gorakhpur and Kanpur are found to be the largest in size (119 and 117) followed by Ghaziabad (171.3) while smaller sized markets include Chorichora (0.82) and Moth (1.09) all indices relatively to Lucknow. Table 1 (b) cannot fully support a conjecture that larger markets fetch higher prices. In the absence of secondary data related to numbers of computer and accessories, training and personnel, e-NAM connectivity is assumed to make a market information-enriched compared to others. Although computerization is only comparative between the e-NAM and non-e-NAM markets, markets that house e-NAM are designated ‘more’ computerized because they not only possess computers for e-NAM transactions but personnel better trained in operations, access to high level software’s and hardware maintenance facilities. Results Behavior of Price and Arrivals Plots of monthly prices (Fig. 2 (a)) show a rising tendency (also Table 4 ) but the volatility seems to have increased with time too. Both are especially high in 2023. Though by and large the prices in markets moved in sync, there are wide dispersions in their levels, reflected also by the CV which spikes up to 50% in 2018, 2021 and 2022 and to 80% in 2020, apart from a higher spike in 2023 (Fig. 2 (b)). Lack of uniformity of inter-market in prices needs explanation. A seasonal pattern is implicit (Fig. 2 (a)) in prices, the first half of the year being relatively low in prices that tend to rise from July. Further, a specific seasonality analysis conducted with regression correcting for year effects (Table. 3) found months January, February and March registering three lowest annual prices (their average taken as Low) while July, August and October register higher prices (their average taken as High). The peaks and troughs give an idea of growing calendars and the harvest months in the state. The year effects, that incorporate the inflation, suggest continuous rise of prices but a big spike in 2020 is followed by corrections. The annual average price of tomato across the 20 markets increased steadily from Rs 1065 in 2018 to Rs 2334 in 2024 (Table 4 ). The price grew by over 16% per year compared to annual average inflation rate of 6% during the same period and in line with the rise in HSD. Although High and Low prices behaved likewise, the average doubled while the High price rose much faster comparatively to the peak marketing season Low price. A spike is observed in 2023. The CV of average price declined from 15–10%. In each year barring 2023, the Low price is less than 60% of the average price but its CV tends to be higher than the average and High price. Mean of years of CV is 11.9%, 18.6% and 19.1% respectively for average, High and Low prices. Table 3 Regressions of Price and Arrival on Month dummies with year effects Variables Price Arrival Seasonality Coeff. Coeff. January -2,012.89 *** (-21.11) 410.27 *** (5.72) February -2,039.09 *** (-21.39) 348.10 *** (4.85) March -1,982.89 *** (-20.79) 308.89 *** (4.3) April -1,832.42 *** (-19.22) 201.54 *** (2.81) May -1,777.62 *** (-18.65) 223.71 *** (3.12) June -1,547.07 *** (-16.23) 71.78 (0.99) July base base August -608.52 *** (-6.38) 84.37 (1.18) September -1083.67 *** (-11.37) 165.01 *** (2.3) October -762.38 *** (-7.99) 147.48 *** (2.05) November -887.06 *** (-9.31) 235.92 *** (3.29) December -1,579.79 *** (-16.57) 465.72 *** (6.49) Year Trend 2019 483.07 *** (6.63) -79.40 (1.45) 2020 800.13 *** (10.98) -243.89 *** (4.45) 2021 411.52 *** (5.65) -149.21 *** (2.72) 2022 646.38 *** (8.88) 219.72 *** (4.01) 2023 745.54 *** (10.24) 223.34 *** (4.07) 2024 1269.02 *** (17.43) 170.48 *** (3.11) Constant 2407.76 *** (2.80) 2407.76 *** (2.62) Observations 1680 1680 Number of Market 20 20 Note: Parenthesis figures are t-statistics. Level of significance at *** is 1% ** at 5% and * at 10%. Table 4 Annual average Prices in Markets and Across the Markets Coefficients of Variation (CV) Prices (Rs./Quintal) CV (%) Sample Years Average High Low Average High Low 2018 1065.0 1591.6 810.2 15.1 21.6 15.6 2019 1548.1 2026.2 926.0 15.2 20.4 27.2 2020 1865.1 2920.4 1065.2 10.9 13.7 18.1 2021 1476.5 2011.3 781.9 9.8 17.6 17.6 2022 1711.4 2229.8 1086.8 10.5 14.7 17.8 2023 1810.5 3529.9 865.4 11.5 28.9 20.4 2024 2334.0 3701.9 1593.2 10.2 13.4 17.0 Growth rate (%)/Mean* 15.9 19.1 17.6 11.9 18.6 19.1 Note: *Annual average growth rate and the average of CV during in the sample years. Low and High are for Months January February, March and July, August and October. Arrivals at the average certainly do not show a clear trend (Table 5 ) with an annual growth rate of 6% while during High price period they grew faster at rate 18% and at only 7% in the Low-price months. The average Arrivals have a CV of about 50% consistently and likewise for High and Low-price months but the dispersion is low at 25% in Low price months. With the bulk of produce to dispose of before spoilage sets in, the Low price carries enormous significance for farmers in their effort to recover the cost incurred and make some profit. Lower the post-harvest price greater is the distress and post-harvest policy actions hardly help (Acharya, 1998, 2004). Data shows that High prices in the markets and Arrivals at those times increased fast over the entire period. Though Low prices have also risen, Arrivals at low prices have been very slow to rise and in fact remained almost static and near uniform. A possible role of storage facilities that helps to hold produce beyond harvest months may be suspected. Table 5 Annual average Arrivals in Markets and Across the Markets Coefficient of Variations (CV%) Arrivals (Qtls) CV (%) Years Average High Low Average High Low 2018 288.8 118.8 178.8 49.9 33.9 28.5 2019 232.9 157.5 173.0 52.1 40.5 34.8 2020 240.9 134.5 153.5 50.6 39.0 21.4 2021 289.0 192.6 124.5 50.0 38.8 20.3 2022 411.3 270.3 246.0 56.6 36.6 23.3 2023 464.8 380.0 191.9 56.9 80.3 22.3 2024 366.9 258.5 187.7 50.7 42.0 27.0 Growth rate (%)/Mean* 6.4 18.4 6.7 52.4 44.4 25.4 Note: *Annual average growth rate and the average of CV during in the sample years, Low and High are for Months January February, March and July, August and October e-NAM and Non e-NAM market prices and Arrivals: Any difference No remarkable difference is marked between price in e-NAM and non-e-NAM possessing APMC markets, both rising in July 2023 to a peak. The average price increased between 2018 to 2024, though mildly slower in e-NAM markets. In comparison, the price in the computerized market is far more stable than that in the other markets where volatility is high (Fig. 3 (a) and (b)). Regression analysis to explain Price differences To explain price differences, a large data-set is formed pooling data of prices from all 20 markets. Multiple specifications are tried to explain price at a point and time, using market wise monthly prices allowing for quadratic terms and interactions but only those found statistically significant are considered as relevant. The RE model is selected for estimation in all cases. Table 5 (a) takes D_ICT as a separate variable but its interactions with TIME and ARRV are included in Table 5 (b). Table 6 (a): Panel Regressions of Price (RE) Variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. D-MON July – (Highest) Base month D-MON January (Lowest) -2214 *** (-13.48) -2216 *** (-13.49) -2198 *** (-13.37) -2199 *** (164.5) -2199 *** (13.37) -2128 *** (-12.60) -2198 *** (-13.37) TIME 9.45 *** (10.75) 13.46 *** (3.76) 9.48 *** (10.79) 9.45 *** (10.56) 9.36 *** (10.43) - 9.476 *** (10.77) TIME 2 - -0.05 (-1.15) - - - - - DIS_URB -0.50 ** (-2.15) -0.49 ** (-2.12) -0.59 ** (-2.51) -0.588 ** (-2.42) -0.11 (-0.19) -0.54 ** (-2.19) -0.59 ** (-2.47) MKT_SIZE 208.34 *** (3.51) 206.76 *** (3.48) 238.09 *** (3.88) 238.4 *** (3.88) 239.62 *** (3.90) 233.0 *** (3.67) 241.3 *** (3.89) RF_EUP -2.05 *** (-3.70) -2.14 *** (-3.82) -2.03 *** (-3.67) -2.03 *** (-3.75) -2.04 *** (-3.68) -2.11 *** (-3.61) -2.03 *** (-3.67) RF_WUP -3.79 *** (-2.98) -4.05 *** (-3.14) -3.80 *** (-2.99) -3.81 *** (-2.992) -3.81 *** (-3.00) -2.77 ** (-2.09) -3.805 *** (-2.99) RF_WUP 2 0.02 *** (4.50) 0.02 *** (4.63) 0.02 *** (4.52) 0.017 *** (4.52) 0.02 *** (4.53) 0.015 *** (3.700) 0.017 *** (4.52) PRICE (t−1) 0.30 *** (12.86) 0.30 *** (12.89) 0.29 *** (12.86) 0.298 *** (12.85) 0.29 *** (12.81) 0.36 *** (15.55) 0.29 *** (12.85) D_ICT 87.59 * (1.85) 85.34 * (1.79) 130.95 ** (2.49) 128.7 ** (2.41) 128.15 ** (2.39) 116.1 ** (2.14) 132.4 ** (2.51) ARRV -0.16 *** (-5.51) -0.16 *** (-5.39) -0.27 *** (-4.17) -0.28 *** (-4.14) 0.28 ** (-4.15) -0.257 *** (-3.76) -0.28 *** (-4.17) ARRV 2 - - 0.000 * (1.89) 0.001 * (1.88) 0.01 * (1.89) 0.001 * (1.66) 0.001 * (1.91) ROAD_L - - - 0.0104 (0.233) 0.085 (1.01) - - ROAD_L x DIS_URB - - - - -0.001 (-1.04) - - HSD - - - - - 2.665 *** (5.15) - MNR - - - - - - 14.23 (0.41) Constant 2622 *** (15.44) 2,560 *** (14.06) 2,635 ** (15.52) 2,624 *** (14.87) 2552 *** (13.49) 2523 *** (13.76) 2615 *** (14.79) Diagnostics Wald Chi 1622.61 1624.28 1623.36 1627.9 1629.07 1461.34 1628.12 σ u 0 0 0 0 0 σ e 771.07 771.12 771.35 768.5 768.69 791.22 768.43 Note: Panel data regression estimated with STATA, Significance *** 1% ** 5% * 10%. Table 6 (b): Panel Regressions of Price of Tomato Variables Model 1 Model 2 Model 4 Model 5 Model 6 Model 7 D_MON July (Highest) Base month D_MON January (Lowest) -2,198 *** (-13.37) 2216 *** (-13.48) -2192 *** (-13.34) -2,190 *** (-13.33) -2,197 *** (-13.36) -2,191 *** (-13.34) TIME 9.51 *** (10.77) 16.32 *** (3.35) 9.49 *** (10.76) 9.51 *** (10.77) 9.54 *** (10.78) 9.4 *** (10.76) TIME x D_ICT - -5.69 (-0.87) - - - - DIST_URB -0.60 ** (-2.52) -0.49 ** (-2.12) -0.71 *** (-2.87) -0.59** (-1.97) -0.61 ** (-2.54) -0.71 *** (-2.91) MKT_SIZE 235.49 *** (3.81) 207.86 *** (3.49) 228.55 *** (3.69) 226.63 *** (3.65) 237.66 *** (3.87) 229.16 *** (3.70) RF_EUP -2.03 *** (-3.67) -2.14 *** (-3.82) -2.04 *** (-3.69) -2.04 *** (-3.69) -2.03 *** (-3.67) -2.04 *** (-3.69) RF_WUP -3.82 *** (-3.01) -4.05 *** (-3.14) -3.77 *** (-2.96) -3.76 *** (-2.95) -3.80 *** (-2.99) -3.77 *** (-2.96) RF_WUP 2 0.02 *** (4.53) 0.02 *** (4.63) 0.02 *** (4.45) 0.012 *** (4.44) 0.02 *** (4.52) 0.02 *** (4.45) PRICE(t-1) 0.29 *** (12.86) 0.29 *** (12.86) 0.29 *** (12.75) 0.29 *** (12.77) 0.29 *** (12.85) 0.29 *** (12.76) D_ICT 120.13 * (1.95) 178.26 (1.43) 113.96 (0.98) 130.32 (1.17) 178.96 * (1.79) 68.11 (1.03) ARRV -0.30 *** (-3.19) -0.16 *** (-5.38) -0.71 *** (-3.27) -0.68 *** (-3.08) -0.28 *** (-4.19) -0.71 *** (-3.29) ARRV 2 0.00 * (1.67) - 0.00 ** (2.29) 0.00 ** (2.17) 0.00 * (1.92) 0.00 ** (2.3) ARRV x D_ICT 0.45 ** (2.05) - 0.45 ** (2.06) 0.41 * (1.77) - 0.45 ** (2.05) ARRV 2 x D_ICT - - -0.00 ** (-2.11) -0.00 * (-1.95) - -0.00 ** (-2.11) DIST_URB x D_ICT - - - -0.36 (-0.69) - - FRM_SIZE x D_ICT - - -71.71 (-0.48) - -0.037 (-0.56) - Constant 2639.97 ** (15.48) 2514.04 *** (15.52) 2,706.47 *** (15.62) 2,684.39 ** (15.24) 2,634.27 *** (15.51) 2706.53 ** (15.62) Diagnostics Wald Chi 1628.01 1623.79 1635.32 1635.84 1628.18 1635.86 σ u 0 0 0 0 0 0 σ e 767.96 771.43 767.55 767.55 768.43 767.55 Note: Estimated with STATA, Significance *** 1% ** 5% * 10%. Seasonality effects are not much different from Table 3 after correcting for other influential factors. The lowest price prevails in January and highest in the base month July. A positive time trend is present in price but with no quadratic effect for acceleration. The lagged price (P (t−1) ) has a positive and highly significant coefficient proving the bidding effect of past. Time trend (TIME) is important. Arrivals of products in the month as the market proxy of supply is one of the most powerful variables affecting price but nonlinearity shows a limit to the effect. Among other variables rainfall in the month in EUP seems to disturb marketing with a significant coefficient. Rainfall in WUP too has a similar negative coefficient but only up to a point. Beyond a turning point calculated to be 71.52 mm greater rainfall may be raising the price. The adverse effect may be due to the transportation problem on roads especially in EUP but the quadratic effect of rainfall in WUP could be indicative of prompt repair work or storage responses or a realization of farmers and buyers the urgency to clear the market. The relative size (MKT_SIZE) is an important positive effect. Sold in a large sized market, tomato can fetch a higher price than that possible in a smaller market but the reason is not explicit and can be complex. Size may be associated with privileges of expertise, business acumen, equipment and brand image. D_ICT, standing for higher computerization has a positive significant effect on price, i.e., creation of e-NAM has helped the whole APMC market with price advantage. Farm size in the district and road facility show no effects. Distance from the urban centres (DIS_URB), confirms the disadvantage of remote markets. Extending the model to allow non-linearity, does give additional insights. Interactions suggest that the effect of D_ICT does not improve with time but effect on PRICE of ARRV responds significantly to the interaction with computerization (D_ICT). Markets having E_NAM markets in them respond less adversely to heavier Arrivals or to the supply pressure possibly due to broader access to demand even in remote places even though ICT variable simply shifts the response curve upwards. This must be a great benefit, relieving farmers from harvest time distress. Use of HSD price in lieu of time trend does not change the results much but though HSD price is a positive significant effect, using TIME and HSD both reduces the significance. Multicollinearity possibly vitiates the results. Regression analysis of Arrival December, is the month of highest month of Arrivals and June the lowest. They precede the price peaks. Among the determining variables are Market size (positive), lagged Arrivals (positive), computerization (Positive), Distance from urban centre (negative), district level rainfall (negative but with a quadratic effect turning to positive effect). Square of trend, farm size, road lengths, lagged price did not show any significant effect. Interaction with computerization does not significantly increase the Arrivals effects of any variable. Neither trend nor HSD price are confirmed to be positive effects. Table 7 (a): Panel Regressions of Arrivals Dependent variable = Arrival Variables Model 1 Model 2 Model 3 Model 4# Model 5 Model 6 Coeff. Coeff. Coeff. Coeff. Coeff. Coeff. D_MON December (Highest) 237.05 *** (3.53) 240.15 *** (3.57) 237.23 *** (3.53) 243.10 *** (3.62) 285.81 *** (2.97) 238.25 *** (3.54) D_MON June (Lowest) -101.31 * (-1.75) -99.35 * (-1.71) -101.10 * (-1.74) -96.82 * (-1.67) -37.55 * (-0.45) -100.15 * (-1.73) TIME 0.70 (1.55) -1.31 (-0.71) 0.70 (1.55) 0.75 * (1.65) 0.57 *** (3.79) 0.76 * (1.66) TIME² - 0.02 (1.12) - - - - MKT_SIZE 316.02 *** (9.65) 316.43 *** (9.66) 316.19 *** (9.65) 307.04 *** (9.28) 948.8 *** (23.60) 315.75 *** (9.64) ARRV (t-1) 0.76 *** (44.91) 0.76 *** (44.78) 0.76 *** (44.89) 0.75 *** (44.33) - 0.76 *** (44.89) PRICE (t-1) - - - - -0.03 (-1.60) - D_ICT 109.09 *** (4.12) 109.89 *** (4.15) 110.32 *** (4.11) 114.10 *** (4.30) 577.4 * (4.18) 111.73 *** (4.18) RF_DIST -0.74 ** (-2.28) -0.71 ** (-2.18) -0.75 ** (-2.29) -0.72 ** (-2.20) -1.44 ** (-3.08) -0.74 ** (-2.27) RF_DIST² 0.01 * (1.70) 0.01 (1.61) 0.01 * (1.71) 0.01 * (1.69) 0.01 ** (2.44) 0.01 * (1.71) FRM_SIZE - - 12.88 (0.28) - - - DIST_URB - - - -0.24 * (-1.88) -1.32 *** (-4.97) - ROAD_L - - - - - -0.02 (-0.70) Constant -21.41 (-0.35) 4.63 (0.07) -31.62 (-0.44) 5.02 (0.08) 174.2* (1.78) -6.53 (-0.10) Wald chi² 7798.86 *** 7801.38 *** 7794.55 *** 7814.39 *** 1481.53 *** 7809.59 *** σu 0 0 0 0 69.04 0 σe 425.63 425.40 425.63 425.63 574.11 425.61 ρ 0 0 0 0 0.10 0 Corr(u, xb) 0 0 0 0 0 0 Note: Estimated with STATA, Significance *** 1% ** 5% * 10%. Figures in Parentheses are t-stat. # is selected model. Table 7 (b): Panel Regressions of Arrivals Dependent variable = Arrival Variables Model 6 Model 7 Model 8 Coeff. Coeff. Coeff. D_MON December (Highest) 244.02 *** (3.63) 253.81 *** (3.78) 241.51 *** (3.59) D_MON June (Lowest) -96.12 (-1.66) -92.16 (-1.59) -97.78 * (-1.69) TIME 0.748 (1.66) 0.09 (0.15) MKT_SIZE 306.75 *** (9.27) 295.28 *** (8.78) 303.87 *** (9.17) ARRV (t-1) 0.75 *** (44.32) 0.75 *** (43.63) 0.75 *** (44.32) D_ICT 91.36 (1.48) 189.15 *** (4.08) 59.33 (1.31) Rainfall -0.72 ** (-2.20) -0.69 ** (-2.13) -0.73 ** (-2.23) Rainfall² 0.01 * (1.70) 0.01 * (1.68) 0.01 * (1.71) DIST_URB -0.25 * (-1.89) -0.09 (-0.57) -0.25 * (-1.92) HSD - 0.46 * (1.74) - D_ICT*FRM_SIZE 33.81 (0.41) - - D_ICT*DIST_URB - -0.52 * (-1.93) - D_ICT*TIME - - 1.32 (1.49) Constant 4.33 (0.07) -49.26 (-0.70) 35.61 (0.54) Diagnostics Wald chi² 7810.59 *** 7830.11 *** 7822.46 *** σu 0 0 0 σe 425.63 425.14 425.16 ρ 0 0 0 Note: Estimated with STATA, Significance *** 1% ** 5% * 10%. Figures in Parentheses are t-stat. Determinants Models 7 in Table 6 (b) and model 4 in Table 7 (a) are respectively selected to explained price and arrivals with panel data – RE, The Hausman test (χ² = 23.94, p-value = 0.091) rejecting the FE model. Price as an outcome of demand and supply is not uniform across regulated markets but depends on several factors connected with the markets, the area and the time. The regressions of price suggest that explanatory variables are the months and the time trend, market size, past price, Arrivals, remoteness from urban centres and computerization. Computerization not only enables higher pricing, but it mitigates the price decline due to heavy Arrivals. Arrival itself a market decision and therefore an endogenous variable, is affected by the seasonality of months but not with any confirmed time trend. Past arrivals, local rainfall, remoteness and possession of e-NAM draw higher Arrivals. Location in terms of local farm size, roads and distance and interactions have no effect. Conclusion A policy that bring resilience of price to arrivals would be beneficial for the farmers. The expanse of the market that farmers can reach out to and the prices they fetch mark the stretching of frontier of agricultural marketing. For crops that easily decay under inappropriate climatic conditions, marketing success is deeply associated with available intelligence on demand distribution at any time etching their ability to dispose of the product swiftly. A study of UP conducted for a perishable crop Tomato which has increased in nominal price and grown in supply with volatility, reveals imperfection of markets creating dispersions of prices among markets. Some of these markets are privileged with e-NAM that confers superiority in computerization. Comparison between the two types of markets found greater volatility in less privileged markets. The first three months of the year are identified as the post-harvest months of supply pressure when prices fall low. Although attempts to sell are concentrated at this time to avoid product wastage, data suggests that farmers increasingly release produce in nonsystematic ways in other months aided by better storage facilities. Superiority of ICT is found to be an important influence in improving price and increasing Arrivals by regression exercises given the significant coefficients for the presence of e-NAM in the market. More important, the privilege of the market goes with reduced distress during post-harvest months when prices tend to fall. Correcting for the various impacts, computerization intensified by e-NAM is seen to help the search process for higher prices, building confidence on sales towards bringing more products and overcome the effect of supply pressure in post-harvest season. Declarations Author Contribution Akash Vilas Mhaskey - Data Collection, Pre-processing and Data Analysis and ModellingMayanglambam Rajeshwor - Data Analysis, Modelling, EditingNilabja Ghosh - Conceptual framework and methodology, Modelling, Manuscript writing References Acharya, S. S. (1998). Agricultural marketing in India: Some facts and emerging issues. Indian Journal of Agricultural Economics , 53 , 311–332. Acharya, S. S. (2004). State of the Indian Farmer: A Millennium Study, Agricultural Marketing . Department of Agricultural and Cooperation, Ministry of Agriculture, Government of India, and Academic Foundation. Acharya, S. S., Chand, P. R., Birthal, S. K., & Negi, D. S. (2012). Market integration and price transmission in India: a case of rice and wheat with special reference to the world food crisis of 2007/08 . Food and Agriculture Organization. AGMARKNRT (Website). https://agmarknet.gov.in/ Banker, R. D., Mitra, S., Sambamurthy, V., & Mitra, S. (2011). The effects of digital trading platforms on commodity prices in agricultural supply chains. MIS Quarterly , 599–611. Chand, R. (2016). e-Platform for National Agricultural Market. Economic & Political Weekly , 51 , 28, 09. Directorate of Economic & Statistics (DES) (Website). https://desagri.gov.in/ . Ministry of Agriculture & Farmers Welfare, GoI. Ghosh, N. (2013). India’s Agricultural Marketing: Market Reforms and Emergence of New Channels . Springer. Ghosh, N., & Parmeet, R. M. K. V (2020). Towards One Agricultural Market in India: Does the ICT Help? Book (Eds.) by Dibyendu Maiti, Fulvio Castellacci, Arne Melchior, Digitalisation and Development Issues for India and Beyond . Springer. Government of Uttar Pradesh (GoUP) (various). Statistical Abstract of Uttar Pradesh. https://updes.up.nic.in/updes/s_abstract.html India Meteorological Department (IMD) (Website). https://mausam.imd.gov.in/ . Ministry of Earth Science, GoI. Manogna, R. L. (2025). Assessing the Farmers’ Participation and Challenges in Linking Agricultural Produce Market Committee (APMC) Markets by e-National Agricultural Market (e-NAM): Evidence from the State of Goa. NABARD Research Study – 56. Michler, D., & Jeffrey (2020). Agriculture in the process of development: A micro-perspective. World Development , Volume 129, May. Ministry of Commerce Industry (MCI) (Website). https://eaindustry.nic.in/ . Office of The Economic Adviser, Department For Promotion Of Industry And Internal Trade, GoI. Rao, C. S., Nuthalapati, Y., Bhatt, Susanto, K., & Beero (2020). Electronic National Agricultural Market (e-NAM) A Review of Performance and Prospects . Research Study Report Submitted to the Ministry of Agriculture and Farmers’ Welfare, Government of India. Singh, M., et al. (1985). Price Spread of Vegetables Marketing. Indian Journal of Agricultural Economics , 40 , 31985. Singh, P., & Abhishek, S. (2023). Introductory Agricultural Micro Economics . Rubicon. Subbanarasaiah, N. (1991). Marketing of Horticultural Crops in India . Anmol Publishing Co. Timpanaro, G. (2023). Agricultural Food Marketing, Economics and Policies. Agriculture , 13 (4), 761. https://doi.org/10.3390/agriculture13040761 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7480251","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506913679,"identity":"dd8f4a16-8837-47df-8ea5-d347e45641af","order_by":0,"name":"Akash Vilas Mhaskey","email":"","orcid":"","institution":"Institute of Economic Growth","correspondingAuthor":false,"prefix":"","firstName":"Akash","middleName":"Vilas","lastName":"Mhaskey","suffix":""},{"id":506913680,"identity":"3634db25-1e53-4503-aa96-5bc40b960a25","order_by":1,"name":"Mayanglambam Rajeshwor","email":"","orcid":"","institution":"Institute of Economic Growth","correspondingAuthor":false,"prefix":"","firstName":"Mayanglambam","middleName":"","lastName":"Rajeshwor","suffix":""},{"id":506913681,"identity":"7f2f4424-4424-438b-ac9a-505e739b4138","order_by":2,"name":"Nilabja Ghosh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDACZuYGhg8GDAxsDAwGB4jUwtjAOIM0LQyMDcw8EJYBcRrM2RkbP9sU2Nj1STdvPPCDwSZf3oGAFstmxmbpHIO05DaZYwUHexjSLDcScp7BYcYGoJbDyWwSOQYHeBgOGxg2ENbS/NvC4D9Yy8E/RGppkwaGlR1Iy2GQLfIEdIC1WPYYJCewSaQVHJYxSDMgGG4G5w8fvvHjj529/IzkzR/fVNgYyBNyGAwkQhQakBCh9nAW0baMglEwCkbBiAEA9jY9EoD2PZMAAAAASUVORK5CYII=","orcid":"","institution":"Institute of Economic Growth","correspondingAuthor":true,"prefix":"","firstName":"Nilabja","middleName":"","lastName":"Ghosh","suffix":""}],"badges":[],"createdAt":"2025-08-28 12:23:28","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7480251/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7480251/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90170668,"identity":"da9050ac-1daf-41b8-a181-a582925ad5a1","added_by":"auto","created_at":"2025-08-29 11:09:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76418,"visible":true,"origin":"","legend":"\u003cp\u003e(a): Trends of prices of Tomato and High-speed Diesel over time (Sample)\u003c/p\u003e\n\u003cp\u003e(b): Annual average price of Tomato in Markets by their Sizes (Sample)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7480251/v1/f5f27f43dd9d737ca962b2e1.png"},{"id":90170666,"identity":"db4afffd-1b92-43c9-8091-1edbdfc75ff3","added_by":"auto","created_at":"2025-08-29 11:09:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":130335,"visible":true,"origin":"","legend":"\u003cp\u003e(a). Market-wise Monthly Average Prices\u003c/p\u003e\n\u003cp\u003e(b). Across Market Coefficient of Variation (%)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7480251/v1/83c00538136ba79bd54ad0ef.png"},{"id":90170669,"identity":"e5703cb2-afeb-44a3-b20f-8c920bd29d2c","added_by":"auto","created_at":"2025-08-29 11:09:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81329,"visible":true,"origin":"","legend":"\u003cp\u003e(a). Monthly Prices (Index) in e-NAM and Non e-NAM Markets\u003c/p\u003e\n\u003cp\u003e(b): CV (%) of Price in e-NAM and Non-e-NAM possessing Markets\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7480251/v1/a0adfc4d2fbeb7aa95927e58.png"},{"id":90518507,"identity":"8469031f-031d-4e63-bd15-fea9d75234b5","added_by":"auto","created_at":"2025-09-03 15:02:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2049324,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7480251/v1/677ac363-b797-44be-a337-e16519691753.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Farmer finds the buyers: Electronic and traditional influences in Tomato marketing","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSupply chains in agriculture have caught the attention of Indian administration particularly because of their implications for farmers’ income and welfare, a major agenda of contemporary development programme. Success at this initial phase of a supply chain is manifest in the market assurance farmers enjoy and the prices they are able to fetch especially in cases of crops that are perishable. It can be conjectured that by conveying the intelligence on demand distribution, Information and Communication technology (ICT) through electronic means, can be one among other key solutions to make marketing easy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIndia’s agricultural marketing has historically been guided by a legislated institution in the form of regulated market or APMC market where auctions are held through open vocal bids. Following the reforms of 1990s, decadence of the Agricultural Produce Market Committee (APMC) markets noted (Acharya, 1998, Acharya et al., 2012) was brooked by policy. Though further legislation was politically difficult, market reforms did take off leading to the evolution of alternate channels, participation of corporate buyers and formation of farmers’ organization (Ghosh, 2013). Horticultural marketing, where pre-harvest contracts had been common is more flexible. Farmers and the food security they bring remain principal concerns even in the reforming agriculture with lessening rigidity.\u003c/p\u003e\n\u003cp\u003eRegardless of the marketing practices, the exogenous advent of electronic power was certainly a milestone. Utilized by public policy to strengthen the rural markets and the APMC market in particular, the years following 2015 have been a watershed. Information is an important input for competitiveness of markets (Singh and Singh, 2023, Michler, 2020). As it pervaded the rural sector, ICT not only brought market intelligence within rural precincts but also educated farmers in what was once a backward economy. \u0026nbsp;Outdated practices of traders gave way to more modern and informed ways as the internet brought rural and urban people closer. It began to link all the APMC markets enabling them to share their market information. Prices and volumes traded registered in various APMC markets offer useful knowledge on locations of potential sales and a larger database for benchmarking the prices at bidding. In a further step, electronic auctions were enabled by ‘e-NAM’ embedded in certain APMC markets.\u003c/p\u003e\n\u003cp\u003eThe common electronic market platform, called the electronic National Agricultural Market or e-NAM was launched in the year 2016 following a successful demonstration in Karnataka. This Central Sector Scheme is equipped with a portal that provides a single window for all APMC related information and service, including commodity arrivals, prices and bids. It offers online trading or e-trading facility for which physical movement of produce takes place through the mandis. Integrating all the APMC markets of the country, e-NAM is a move towards ‘one’ national market where ideally, price of a homogenous product would be same when corrected for transport costs. Product prices may differ in different states and regions for various reasons but within a perimeter the products are likely to be similar and fetch comparable prices. Price distinctions can then be attributable to market imperfections.\u003c/p\u003e\n\u003cp\u003eOperating in e-NAM specifically, a farmer has the option to reach out to traders from distant places within or even outside the state of residence. Visits to such markets and interactions revealed differences in operations in APMC markets with the traders doubling up as commission agents in some but not all and commission agents serving in displaying and assaying products in e-NAMs alongside their own jobs of open bidding but not in all cases. \u0026nbsp;While e-NAM is a submarket within an APMC market which is increasingly progressing in computerization anyway, presupposing spillover of information and exchanges of ICT proficiency within the market, the paper assumes an ICT advantage of the entire market that possesses an e-NAM over others. This paper uses this distinction of possessing e-NAM among APMC markets as a proxy for an ICT advantage.\u0026nbsp;\u003c/p\u003e"},{"header":"Objective, Data and Method","content":"\u003cp\u003eLack of information as a component of imperfection leads to inefficiency of markets. Varying imperfection among markets drive wedges in prices and offer unequal assurances of sales across markets. Expectedly, any market that has a lead in computerized linkages can be hypothesized to provide a larger assured market to farmers and/or help them to fetch higher prices. Success of marketing is therefore assessed by the price registered in the APMC market and by the volumes of product delivery or \u0026lsquo;Arrivals\u0026rsquo; in the market by farmers which is treated as an indicator of farmers\u0026rsquo; confidence.\u003c/p\u003e\u003cp\u003eMarket imperfection is likely to be implied by preferences and price differences dividing the markets within a state for the same product. Price is measured in value per unit weight and \u0026lsquo;Arrivals\u0026rsquo; expressed in quantity units. This study, considering the state Uttar Pradesh (UP) and the horticultural product Tomato in India aims to look for the extent of inter-market and intra-year variations price and market Arrivals and to identify their determinants over time and across markets to assess in particular the role of information. An assumption is made that across the state tomato is of near-uniform quality and sold for similar purposes so that market efficiency would bring prices closer towards a \u0026lsquo;one\u0026rsquo; price ideal.\u003c/p\u003e\u003cp\u003eA sample of 20 APMC markets is drawn from UP a state, the sample stretching across east and west UP. Largest and most populous state of India, UP ranks first in net sown area (NSA) and agricultural workers in India. Though a relatively small producer of 4% of India\u0026rsquo;s production, a total of 73 out of its 75 districts grow tomato. Most intense growers are districts like Agra, Barabanki, Etah, Mainpuri and Unnao. Markets are chosen randomly but making sure the data is available without gaps and half of the sample markets have e-NAMs. The selected districts contribute only 25% of UP\u0026rsquo;s tomato production. From each market, a time series of monthly data is drawn for years 2018\u0026ndash;2024. Data of prices and Arrivals are taken from AGMARKNET and other variables from varied official sources like Directorate of Economic and Statistics (DES), Indian Meteorological Department (IMD), Ministry Commerce and Industry (MCI), Statistical Abstract of Uttar Pradesh, of Government of Uttar Pradesh (GoUP).\u003c/p\u003e\u003cp\u003ePrices vary from day to day and month to month. To avoid bias due to price spikes, modal price of a month is taken as the monthly average. Annual average price is however the arithmetic mean of the modal monthly prices. Variance (or coefficient of variation) of price is calculated across the sample markets in any month. Prices tend to go down after harvest and shoot up when crop is on field. Although crop calendars even in a state need not be constant, such growing stages refer to majority areas in the market catchment. A high price (High) is the average of the months of highest three annual prices and likewise the low price (Low) is calculated but the process involves identifying the months of High and Low prices using not only numerical assessment but also regression on dummies for months with a base (July) corrected for years.\u003c/p\u003e\u003cp\u003eIntuitively, prices sellers get can be explained by varied factors such as (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) seasonality marked by the harvest months, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) Arrivals measuring supply in the market (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) expectations during bidding, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) size of the market arguably influencing its bargaining power, (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) roads available and distance from nearest urban centre where demand supposedly exists, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) time measuring both inflation if any and other gradual effects, (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e) informational advantage proxied by the content of an e-NAM connection. Arrivals in turn are decisions of farmers based on varied factors that create assurance of sales. Intuitively, both prices and Arrivals can also be subject to seasonality and can depend on similar factors. District level data paucity is a serious hindrance. Only with limited information available, approximations have to be made rationally, some data are gathered from 5-yearly Agricultural census 2015 only and indices over time and space are constructed. Arrivals in a month however precede price formation acting as the supply in the clearing process. To the extent that ICT has made a difference, Arrivals and prices may be structurally different between APMC markets with and without e-NAM though the challenge is to identify the particular effect of this feature separate from many other contributing effects.\u003c/p\u003e\u003cp\u003eEstimation is made using monthly panel data of 1680 observation for a sample period 2018 January to 2024 December of 20 APMC markets of which only 10 possess e-NAM. The random Effect (RE) model is reported given the type of data coverage but for all specifications the Hausman test rejected the Fixed Effect (FE). Among the variables, the dependent is (i) PRICE: nominal price (Rs/Qtl or Indian rupees per 100Kg) registered in the mandis where farmers or their representatives (aggregators) sell and (ii) ARRV: Arrivals which are the quantity (quintal or 100Kg) of products the sellers bring to any market for sale. Explanatory variables are (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) 11 dummy variables D_MON for months (MON\u0026thinsp;=\u0026thinsp;Jan, Feb, \u0026hellip;.., Dec) excepting July which is the base, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) dummy variable for computerization index (D_ICT) where D_ICT\u0026thinsp;=\u0026thinsp;1 if the market has an e-NAM connection and 0 otherwise, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and a number of quantified continuous variables (as in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The district in which the market is located is often treated as a reference.\u003c/p\u003e\u003cp\u003eClimatic factors like rainfall, humidity and temperature, all mutually related, cause product degradation. UP has two meteorological regions East-UP and West-UP made up of 45 and 30 districts respectively and rainfall in both regions can affect marketing and the district specific rainfall with or without lags undermine the product quality. Seasonality is described by the months and trend by time (months in the entire sample). For assessing demand, three clusters are delineated with cities Delhi, Lucknow and Ghazipur representing urban centres for carving out locations, Delhi being the capital of India and the richest state. A few districts even fall within the National Capital Region (NCR). They are fairly urbanized with higher demand and greater purchasing power. Proximity to the urban centres makes certain APMC markets privileged over others. Distance to the urban centres and lengths of roads available in the districts are used to capture the demand advantage. Market size is approximated as the total arrivals in the three months identified as harvest months when pressure of supply is felt most strongly. Market sizes are indexed using all sample years with Lucknow Market as base. Certain other cross-sectional comparative indices are similarly created such as manure use (proxied by livestock size), production and distance from the urban centre.\u003c/p\u003e\u003cp\u003eTabulation, graphical illustration and econometric regression with panel data make up the method. To explain price and Arrival at any point (month) in the markets panel data regression is estimated as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{it}=\\:\\alpha\\:+\\:{X}_{ijt}^{{\\prime\\:}}\\text{}{\\beta\\:}+\\:{a}_{i}+\\:{\\tau\\:}_{t}+\\:{u}_{it}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..\u003c/p\u003e\u003cp\u003eWhere,\u003c/p\u003e\u003cp\u003eY\u0026thinsp;=\u0026thinsp;Price or Arrival in any market\u003c/p\u003e\u003cp\u003e\u003cem\u003ei\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1, 2, \u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;20 (market)\u003c/p\u003e\u003cp\u003ej\u0026thinsp;=\u0026thinsp;1, 2,\u0026hellip;\u0026hellip;..n where n is the number of explanatory variables\u003c/p\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;monthly periods from 2018 (January) to 2024 (December) where t\u0026thinsp;=\u0026thinsp;1, 2, 3\u0026hellip;.84.\u003c/p\u003e\u003cp\u003eX\u003csub\u003eijt\u003c/sub\u003e = j\u003csup\u003eth\u003c/sup\u003e explanatory variable in ith market in t-th time point\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{i}\\)\u003c/span\u003e\u003c/span\u003e\u003cem\u003e=\u003c/em\u003e unit (market) effect.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\tau\\:}_{t}=\\:\\)\u003c/span\u003e\u003c/span\u003etime effect (monthly shocks common to all markets) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:and\\:\\:{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e are intercept and slope parameters to be estimated.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive Statistics, Abbreviations and Units\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbbreviations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eUnits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMean-Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePRICE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e₹/Qtl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgmarknet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1687.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArrivals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eARRV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQuintal (Qtl)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgmarknet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e756.13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEast UP Rainfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRF_EUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMillimeter (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e71.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWest UP rainfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRF_EUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMillimeter (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistrict Rainfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRAINFALL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMillimeter (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIMD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e65.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProduction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePRODUCTION\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMetric Ton(MT)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDES\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e912.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFarm Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFRM_SIZE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHectare (Ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgriculture Census\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance from Urban Centre\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDIST_URB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKilometer (Km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGoogle Map\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoad Length\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eROAD_L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKilometer (Km)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGoUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1195.68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Speed Diesel Price\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndex\u003c/p\u003e\u003cp\u003e(Base\u0026thinsp;=\u0026thinsp;2011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMCI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e129.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarket Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMKT_SIZE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndex (Base\u0026thinsp;=\u0026thinsp;Lucknow)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAgmarknet\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e38.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eManure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMANR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndex\u003c/p\u003e\u003cp\u003e(Base\u0026thinsp;=\u0026thinsp;Lucknow)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGoUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e124.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eNote: 1 Quintal (Qtl)\u0026thinsp;=\u0026thinsp;100Kg\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eTomato Marketing in Indian regulated (APMC) markets\u003c/h3\u003e\n\u003cp\u003eIndia is a large producer of fruits and vegetables but their bulkiness, seasonality of production and perishable character make marketing complex (Manogna, 2025). Ensuring quality at sales, would require caution at cultivation with treatments for soil and pests, post-harvest handling with care and planning ahead of selling and above all in the choice of the marketing channel. Even after devoting land and costly inputs, if the products are not sold quickly after harvest, very poor or even negative returns may result. There is also huge waste, decline in quality, and a frequent mismatch between demand and supply, both spatially and over time (Subbanarasiah, 1991, Singh et al., 1985).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eTomato in Indian agriculture\u003c/h2\u003e\u003cp\u003eWorld\u0026rsquo;s third largest vegetable crop after potato and onion, tomato is grown widely in India where it is consumed as an essential vegetable in the regular culinary diet for its taste and nutrition and as a fresh fruit in salad. It is also processed into preserves such as pickles, soups and sauces. Making up nearly 10% of India\u0026rsquo;s vegetables, tomato production has grown fast in the last two decades, making its marketing an important farm function. A summer crop in some places and a winter crop in others, tomato can be cultivated throughout the year with spatially varying calendars. Its red colour depends on the temperature at harvest.\u003c/p\u003e\u003cp\u003eAlthough risks of trader collusions are attributed to open auctions of APMC markets (Manogna, 2025) and varied marketing channels are opening up, price discovery and the practiced expertise of commission agents and other traders especially in assaying the quality are advantages hard to replace as of now. Agricultural marketing rules differ from state to state but APMC markets remain a common feature though they keep developing in technology, infrastructure and administration. Integration with digital advancements has been a gradual process. UP has more numbers of mandis and most of them have been coming under e-NAM (Rao et al., 2020).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInformation Technology for Marketing\u003c/h3\u003e\n\u003cp\u003eInnovation with ICT, rather nascent in India, is influential for the evolving business model and trade dependencies (Timpanaro, 2023). ITC\u0026rsquo;s \u0026lsquo;e-Choupal, offering internet to connect rural farmers with buyers in the year 2000 was pioneering. \u0026lsquo;App\u0026rsquo;s started providing information to farmers about weather, dealers, plant protection and other expert advice, about soil health card, godown and cold storages and also market price. The Union government is actively promoting digitization. The main design of the India Digital Environment of Agriculture (IDEA), which will establish the federated farmers\u0026rsquo; database structure is completed.\u003c/p\u003e\u003cp\u003eAgricultural Marketing Information Network or AGMARKNET is an e-governance portal acting as a single window service to stakeholders like farmers, traders, researchers and government officials for monitoring and policy making on markets. Its main goal however is to empower farmers with timely and accurate information on agricultural markets across the country and help them to get the best price. E-NAM, another portal, enables electronic auctioning. UPAg, a centralized digital platform that integrates agricultural services, schemes and information, helps farmers with easy access to resources and support. Further, for e-NAM, a vast collection of information crucial for agricultural trade in India is stored in servers under the Small Farmers Agribusiness Consortium (SFAC). The data includes real time and historical records on commodity arrivals, prices and trade volumes from various markets. This rich dataset facilitates transparent price discovery and helps farmers to make informed decisions.\u003c/p\u003e\u003cp\u003eIntuitively, market intelligence from around the country informs farmers of possible markets beyond the immediate precincts. Primary sellers, i.e. farmers, are expected to locate buyers easily and obtain a higher price when they sell products on the e-platform (Banker et al. 2011). Efforts are underway to examine the positive effect of computerization on farmers but the initial teething problems under present system make implementations difficult (Chand, 2016) and do not help the effort so that the academic literature gives mixed results on the impact of information technology on rural income. Rigorous evidence on the effect may be deficient.\u003c/p\u003e\u003cp\u003eEven with the enormous potential, the role of ICT in bringing prices close to one another especially by enhancing prices in producing regions was found limited in the initial stage, acting more as a baseline for future monitoring of market integration (Ghosh et al, 2020). A detailed study using paired t-statistics and difference of difference analysis (DID) of both secondary data of wholesale prices and field survey suggested positive effect of e-NAM market institution (Manogna, 2025). Another study (Rao et al., 2020) too using both secondary and primary data found higher prices being fetched by participants in e-markets. Both conclusions are drawn by comparing pre-e-NAM and post e-NAM prices from the APMC markets from AGMARKNET though the difference could be attributed to many reasons.\u003c/p\u003e\n\u003ch3\u003eFeatures of APMC Markets:\u003c/h3\u003e\n\u003cp\u003eThe markets can be described by a range of features:\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLocational factors\u003c/strong\u003e\u003cp\u003e80% of Indian farms of size 2 hectares or less produce 60% of total food grains and over half of fruits and vegetables. Though small on an average, Indian farm are varied in sizes and the average farm size is an indicator of economic affluence. Larger farmers enjoy many relative advantages including in marketing. With population growing on an inelastic land supply, development has hardly mitigated the shortcoming. Except Jhansi, all sample districts have farms with average sizes below 1 hectare, Jaunpur, Barabanki and Gorakhspur having the smallest (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The average fam size in only 0.72 hectare.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eLocated in different districts of a diverse state in northern India, some markets are close to national capital Delhi. Nearest to Delhi is Ghaziabad and Noida though Meerut, Bulandshahar and Aligarh are other districts in the same cluster (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a)). Supplying to markets nearer to the cities is appealing but its effectiveness depends on the transportation of physical products. Access to roads and distance from urban centres matter. Some markets are remote with distances 200 to 300Km from the urban centre. Most districts have over 1000 km of road ways. Assessed by length of roads and education EUP districts are relatively backward.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e(a). Profile of Selected Tomato Markets in Uttar Pradesh\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarkets\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDistance from\u003c/p\u003e\u003cp\u003enearest urban centre\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTotal Road\u003c/p\u003e\u003cp\u003eLength\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFarm\u003c/p\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMarket\u003c/p\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAnimal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eRainfall\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eKm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHectare\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIndex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLakhs\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMilimetre\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGorakhpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGorakhpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e141.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1926.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e119.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e68.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKanpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKanpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e98.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1012.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e117.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e77.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGaziabad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGhaziabad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e783.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e112.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e41.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLucknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLucknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1399.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e100.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e64.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAligarh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAligarh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e164.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e990.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e62.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeerat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeerat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e86.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e886.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e47.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e55.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJaunpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJaunpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e114.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1691.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e46.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e82.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuradnagar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGhaziabad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e53.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e783.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e34.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e41.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAzamgarh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAzamgarh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1648.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e26.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e64.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBareilly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBareilly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e273.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e772.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGazipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGhazipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1535.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e67.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKhalilabad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSant Kabir Nagar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e174.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1582.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c2\"\u003e\u003cp\u003eJhansi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e358.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e509.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e83.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e125.9*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.73*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.5*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e31.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e66.00*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e108.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e46.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e30.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eNote: *Average and others are Total districts.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e(b): Sample Mean Values for Selected Districts of Uttar Pradesh\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarkets\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDistrict\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePrices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArrivals\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRainfall\u003c/p\u003e\u003cp\u003eZone\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eNearest\u003c/p\u003e\u003cp\u003eUrban Centre\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eTomato\u003c/p\u003e\u003cp\u003eProduction\u003c/p\u003e\u003cp\u003eShare\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNet Sown Area\u003c/p\u003e\u003cp\u003e(NSA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eLiteracy\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRs./Qtl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQuintal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMillion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lsquo;000 Hectare\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e%\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMuradnagar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGhaziabad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1665.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e581.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDelhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e44.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e78.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGazipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGhazipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1792.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e352.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGhazipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e251.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e71.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRudauli\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBarabanki\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1736.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e91.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLucknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e259.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e61.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChorichora\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGorakhpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1764.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e77.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGhazipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e222.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e70.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSikandarabad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBulandshahar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1309.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e185.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDelhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e284.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e68.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJasra\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrayagraj\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1607.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e155.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLucknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e320.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e72.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePukharayan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKanpur Dehat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1743.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLucknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e212.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e75.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMoth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJhansi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1521.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLucknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e321.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e75.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBijnaur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBijnor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1763.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e116.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDelhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e326.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e68.48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMehmoodabad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSitapur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1792.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e160.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLucknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e444.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e61.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBareilly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBareilly\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1800.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e364.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDelhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e324.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e58.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKhalilabad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSant Kabir Nagar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1466.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e291.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGhazipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e116.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e66.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJaunpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJaunpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1726.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e901.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGhazipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e287.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e71.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAligarh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAligarh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1630.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1295.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDelhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e290.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e67.52\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKanpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKanpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1697.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2346.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLucknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e182.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e79.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeerat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMeerat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1566.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e913.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDelhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e190.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e72.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGaziabad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGhaziabad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1783.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2219.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eWUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDelhi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e44.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e78.07\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLucknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLucknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1913.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2024.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLucknow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e113.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e77.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAzamgarh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAzamgarh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1757.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e512.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGhazipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e281.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e70.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGorakhpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGorakhpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1705.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2394.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGhazipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e222.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e70.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1687.20*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15122.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e72.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4698.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e70.52*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e114.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e28.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e42.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eNote: *Average and others are Total districts.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSituational and Quality driving factors\u003c/strong\u003e\u003cp\u003eSituations arise in individual markets with a strong influence on the arrivals and price. Lagged price (P\u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e) registered in the same market can influence the expectations at biddings. Lagged Arrivals can set an example to follow. Monthly arrivals in a market can represent the supply which is mostly an outcome of local cropping pattern, production while being a commercial decision of the farmers. Adding to supply in the market, it can suppress the price. A policy that brings resilience of price to ARRV would be beneficial for the farmers.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eHeavy or continuous rain in a marketing month can damage roads, add to handling cost and undermine the quality unless reparative actions are taken. There are two broad meteorological regions in UP, namely Eastern (EUP) and Western (WUP) UP, through which transportation of products to buyers take place. Average annual rainfall is 71.52 mm in EUP and 61.59 in WUP, maximum rainfall occurring in monsoon months July to September. Variation (CV) of rainfall across districts in a year is high 30% against an average of 66 mm.\u003c/p\u003e\u003cp\u003eExperts in the market assess the quality of tomato by its color, shape, dents and spots caused by moisture, heat and pests though. Mechanical aids are also available. While even within a market the \u0026lsquo;lots\u0026rsquo; of products may qualitatively vary, some markets may be superior to others in terms of the share of products deemed good. While there is no direct information of the product quality, some determinants of quality may be considered in lieu. Harvest season rainfall is generally detrimental and is measured by district specific rainfall with varying lags. Well drained soil, preferably black is known to produce good tomato. Districts of UP however are nearly all made of alluvial soil which is otherwise fertile and well drained. Organic manure is good for tomato as is controlled irrigation with drainage.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSeasonality and Time trend\u003c/strong\u003e\u003cp\u003ePrices of farm products often show seasonal patterns because crops have geographical demands for water and temperature in the growing period. Choice of crops and their sowing and other operations are synchronized with the natural conditions of seasons in any region. Harvest tends to be a period of high supply and therefore lower prices. Harvest calendars may not be static over the long run as seed technology and irrigation can modulate the gestation period and confer climate resilience. These factors along with cold storage and improved transportation that mitigate spoilage tend to smooth out seasonality to an extent. Moreover, Tomato has many varieties, most of them developed by scientific research at PUSA. Therefore, prices may be staggered over a window of time within a season. Price and Arrivals can increase or decrease over a long period of time due to technical changes and shifts in tastes or diet consciousness. For nominal prices a time trend can be imposed by inflation. Prices of High-speed diesel (HSD), a driver of inflation plotted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e however seems much smoother than tomato prices.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMarket features\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eAll markets are not uniform in capacity and modernity. However, although APMC markets have been greatly improved with investment in recent decades, little information is published on their infrastructure. As a way out, the index of the total arrivals in the three lowest price months proxies the size of the market, assuming the physical size would be related to the biggest (harvest time) Arrivals that need to be accommodated. Among the study markets, Gorakhpur and Kanpur are found to be the largest in size (119 and 117) followed by Ghaziabad (171.3) while smaller sized markets include Chorichora (0.82) and Moth (1.09) all indices relatively to Lucknow. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e(b) cannot fully support a conjecture that larger markets fetch higher prices.\u003c/p\u003e\u003c/p\u003e\u003cp\u003eIn the absence of secondary data related to numbers of computer and accessories, training and personnel, e-NAM connectivity is assumed to make a market information-enriched compared to others. Although computerization is only comparative between the e-NAM and non-e-NAM markets, markets that house e-NAM are designated \u0026lsquo;more\u0026rsquo; computerized because they not only possess computers for e-NAM transactions but personnel better trained in operations, access to high level software\u0026rsquo;s and hardware maintenance facilities.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eBehavior of Price and Arrivals\u003c/h2\u003e\u003cp\u003ePlots of monthly prices (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a)) show a rising tendency (also Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e) but the volatility seems to have increased with time too. Both are especially high in 2023. Though by and large the prices in markets moved in sync, there are wide dispersions in their levels, reflected also by the CV which spikes up to 50% in 2018, 2021 and 2022 and to 80% in 2020, apart from a higher spike in 2023 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e(b)). Lack of uniformity of inter-market in prices needs explanation. A seasonal pattern is implicit (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e(a)) in prices, the first half of the year being relatively low in prices that tend to rise from July.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurther, a specific seasonality analysis conducted with regression correcting for year effects (Table. 3) found months January, February and March registering three lowest annual prices (their average taken as Low) while July, August and October register higher prices (their average taken as High). The peaks and troughs give an idea of growing calendars and the harvest months in the state. The year effects, that incorporate the inflation, suggest continuous rise of prices but a big spike in 2020 is followed by corrections.\u003c/p\u003e\u003cp\u003eThe annual average price of tomato across the 20 markets increased steadily from Rs 1065 in 2018 to Rs 2334 in 2024 (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The price grew by over 16% per year compared to annual average inflation rate of 6% during the same period and in line with the rise in HSD. Although High and Low prices behaved likewise, the average doubled while the High price rose much faster comparatively to the peak marketing season Low price. A spike is observed in 2023. The CV of average price declined from 15\u0026ndash;10%. In each year barring 2023, the Low price is less than 60% of the average price but its CV tends to be higher than the average and High price. Mean of years of CV is 11.9%, 18.6% and 19.1% respectively for average, High and Low prices.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegressions of Price and Arrival on Month dummies with year effects\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePrice\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eArrival\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSeasonality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eJanuary\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2,012.89\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-21.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e410.27\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(5.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFebruary\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2,039.09\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-21.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e348.10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(4.85)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMarch\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1,982.89\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-20.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e308.89\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(4.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eApril\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1,832.42\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-19.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e201.54\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eMay\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1,777.62\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-18.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e223.71\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eJune\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1,547.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-16.23)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.78\u003c/p\u003e\u003cp\u003e(0.99)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eJuly\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003ebase\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003ebase\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eAugust\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-608.52\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-6.38)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84.37\u003c/p\u003e\u003cp\u003e(1.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eSeptember\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1083.67\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-11.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e165.01\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eOctober\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-762.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-7.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e147.48\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eNovember\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-887.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-9.31)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e235.92\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.29)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eDecember\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-1,579.79\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-16.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e465.72\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(6.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003e\u003cem\u003eYear Trend\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e483.07\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(6.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-79.40\u003c/p\u003e\u003cp\u003e(1.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e800.13\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(10.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-243.89\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(4.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e411.52\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(5.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-149.21\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.72)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e646.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(8.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e219.72\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(4.01)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e745.54\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(10.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e223.34\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(4.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1269.02\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(17.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e170.48\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.11)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2407.76\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.80)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2407.76\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.62)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObservations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1680\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1680\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of Market\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u003cp\u003eNote: Parenthesis figures are t-statistics. Level of significance at *** is 1% ** at 5% and * at 10%.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnnual average Prices in Markets and Across the Markets Coefficients of Variation (CV)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003ePrices (Rs./Quintal)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSample Years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1065.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1591.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e810.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e21.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1548.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2026.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e926.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1865.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2920.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1065.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1476.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2011.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e781.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1711.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2229.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1086.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1810.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3529.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e865.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e28.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2334.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3701.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1593.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrowth rate (%)/Mean*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e19.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eNote: *Annual average growth rate and the average of CV during in the sample years. Low and High are for Months January February, March and July, August and October.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eArrivals at the average certainly do not show a clear trend (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e) with an annual growth rate of 6% while during High price period they grew faster at rate 18% and at only 7% in the Low-price months. The average Arrivals have a CV of about 50% consistently and likewise for High and Low-price months but the dispersion is low at 25% in Low price months. With the bulk of produce to dispose of before spoilage sets in, the Low price carries enormous significance for farmers in their effort to recover the cost incurred and make some profit. Lower the post-harvest price greater is the distress and post-harvest policy actions hardly help (Acharya, 1998, 2004). Data shows that High prices in the markets and Arrivals at those times increased fast over the entire period. Though Low prices have also risen, Arrivals at low prices have been very slow to rise and in fact remained almost static and near uniform. A possible role of storage facilities that helps to hold produce beyond harvest months may be suspected.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAnnual average Arrivals in Markets and Across the Markets Coefficient of Variations (CV%)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eArrivals (Qtls)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYears\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAverage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e288.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e178.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e49.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e33.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e28.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e232.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e157.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e173.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e40.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e34.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e240.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e134.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e153.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e289.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e192.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e38.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e411.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e270.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e246.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e36.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e23.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e464.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e380.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e191.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e80.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e366.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e258.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e187.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e50.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e42.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrowth rate (%)/Mean*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e52.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e44.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eNote: *Annual average growth rate and the average of CV during in the sample years, Low and High are for Months January February, March and July, August and October\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003ee-NAM and Non e-NAM market prices and Arrivals: Any difference\u003c/h2\u003e\u003cp\u003eNo remarkable difference is marked between price in e-NAM and non-e-NAM possessing APMC markets, both rising in July 2023 to a peak. The average price increased between 2018 to 2024, though mildly slower in e-NAM markets. In comparison, the price in the computerized market is far more stable than that in the other markets where volatility is high (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003e(a) and (b)).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eRegression analysis to explain Price differences\u003c/h3\u003e\n\u003cp\u003eTo explain price differences, a large data-set is formed pooling data of prices from all 20 markets. Multiple specifications are tried to explain price at a point and time, using market wise monthly prices allowing for quadratic terms and interactions but only those found statistically significant are considered as relevant. The RE model is selected for estimation in all cases. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e(a) takes D_ICT as a separate variable but its interactions with TIME and ARRV are included in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e(b).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e(a): Panel Regressions of Price (RE)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModel 6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eModel 7\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eD-MON\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eJuly \u0026ndash; (Highest)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c8\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBase month\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD-MON\u003c/p\u003e\u003cp\u003eJanuary (Lowest)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2214\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-13.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2216\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-13.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2198\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-13.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2199\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(164.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2199\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(13.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2128\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-12.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-2198\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-13.37)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.45\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(10.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.46\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.48\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(10.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.45\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(10.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.36\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(10.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e9.476\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(10.77)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIME\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003cp\u003e(-1.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIS_URB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.50\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.49\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.59\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.588\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.42)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.11\u003c/p\u003e\u003cp\u003e(-0.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.54\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.19)\u003c/p\u003e\u003c/td\u003e\u003ctd 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colname=\"c5\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.91)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eROAD_L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0104\u003c/p\u003e\u003cp\u003e(0.233)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003cp\u003e(1.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eROAD_L x\u003c/p\u003e\u003cp\u003eDIS_URB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.001\u003c/p\u003e\u003cp\u003e(-1.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.665\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(5.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMNR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.23\u003c/p\u003e\u003cp\u003e(0.41)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2622\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(15.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,560\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(14.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,635\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(15.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2,624\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(14.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2552\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(13.49)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2523\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(13.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2615\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(14.79)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiagnostics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWald Chi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1622.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1624.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1623.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1627.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1629.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1461.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1628.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eu\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e771.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e771.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e771.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e768.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e768.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e791.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e768.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003eNote: Panel data regression estimated with STATA, Significance \u003csup\u003e***\u003c/sup\u003e1% \u003csup\u003e**\u003c/sup\u003e5% \u003csup\u003e*\u003c/sup\u003e10%.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e(b): Panel Regressions of Price of Tomato\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModel 7\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eD_MON\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eJuly (Highest)\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003e\u003cem\u003eBase month\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD_MON\u003c/p\u003e\u003cp\u003eJanuary (Lowest)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-2,198\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-13.37)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2216\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-13.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2192\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-13.34)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2,190\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-13.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2,197\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-13.36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2,191\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-13.34)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(10.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.32\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.49\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(10.76)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(10.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.54\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(10.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.4\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(10.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIME x D_ICT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-5.69\u003c/p\u003e\u003cp\u003e(-0.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIST_URB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.60\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.49\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.71\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.87)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.59**\u003c/p\u003e\u003cp\u003e(-1.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.61\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.54)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.71\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.91)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMKT_SIZE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e235.49\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.81)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c2\"\u003e\u003cp\u003e-2.03\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-3.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-2.14\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-3.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-2.04\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-3.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-2.04\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-3.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-2.03\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-3.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-2.04\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-3.69)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRF_WUP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-3.82\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-3.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-4.05\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-3.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-3.77\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.96)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-3.76\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-3.80\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-3.77\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.96)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRF_WUP \u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(4.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(4.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(4.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.012\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(4.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(4.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.02\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(4.45)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRICE(t-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(12.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(12.86)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(12.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(12.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(12.85)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.29\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(12.76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD_ICT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e120.13\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e178.26\u003c/p\u003e\u003cp\u003e(1.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113.96\u003c/p\u003e\u003cp\u003e(0.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.68\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-3.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-4.19)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.71\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-3.29)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARRV\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.92)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARRV x D_ICT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.45\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.45\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.41\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.45\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARRV\u003csup\u003e2\u003c/sup\u003ex D_ICT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.00\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.00\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-1.95)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.00\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.11)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIST_URB x D_ICT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.36\u003c/p\u003e\u003cp\u003e(-0.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFRM_SIZE x D_ICT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-71.71\u003c/p\u003e\u003cp\u003e(-0.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.037\u003c/p\u003e\u003cp\u003e(-0.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2639.97\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(15.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2514.04\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(15.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2,706.47\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(15.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2,684.39\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(15.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,634.27\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(15.51)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2706.53\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(15.62)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiagnostics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWald Chi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1628.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1623.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1635.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1635.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1628.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1635.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003eu\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eσ\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e767.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e771.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e767.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e767.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e768.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e767.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eNote: Estimated with STATA, Significance \u003csup\u003e***\u003c/sup\u003e1% \u003csup\u003e**\u003c/sup\u003e5% \u003csup\u003e*\u003c/sup\u003e10%.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSeasonality effects are not much different from Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e after correcting for other influential factors. The lowest price prevails in January and highest in the base month July. A positive time trend is present in price but with no quadratic effect for acceleration. The lagged price (P\u003csub\u003e(t\u0026minus;1)\u003c/sub\u003e) has a positive and highly significant coefficient proving the bidding effect of past. Time trend (TIME) is important. Arrivals of products in the month as the market proxy of supply is one of the most powerful variables affecting price but nonlinearity shows a limit to the effect. Among other variables rainfall in the month in EUP seems to disturb marketing with a significant coefficient. Rainfall in WUP too has a similar negative coefficient but only up to a point. Beyond a turning point calculated to be 71.52 mm greater rainfall may be raising the price. The adverse effect may be due to the transportation problem on roads especially in EUP but the quadratic effect of rainfall in WUP could be indicative of prompt repair work or storage responses or a realization of farmers and buyers the urgency to clear the market.\u003c/p\u003e\u003cp\u003eThe relative size (MKT_SIZE) is an important positive effect. Sold in a large sized market, tomato can fetch a higher price than that possible in a smaller market but the reason is not explicit and can be complex. Size may be associated with privileges of expertise, business acumen, equipment and brand image. D_ICT, standing for higher computerization has a positive significant effect on price, i.e., creation of e-NAM has helped the whole APMC market with price advantage. Farm size in the district and road facility show no effects. Distance from the urban centres (DIS_URB), confirms the disadvantage of remote markets.\u003c/p\u003e\u003cp\u003eExtending the model to allow non-linearity, does give additional insights. Interactions suggest that the effect of D_ICT does not improve with time but effect on PRICE of ARRV responds significantly to the interaction with computerization (D_ICT). Markets having E_NAM markets in them respond less adversely to heavier Arrivals or to the supply pressure possibly due to broader access to demand even in remote places even though ICT variable simply shifts the response curve upwards. This must be a great benefit, relieving farmers from harvest time distress. Use of HSD price in lieu of time trend does not change the results much but though HSD price is a positive significant effect, using TIME and HSD both reduces the significance. Multicollinearity possibly vitiates the results.\u003c/p\u003e\n\u003ch3\u003eRegression analysis of Arrival\u003c/h3\u003e\n\u003cp\u003eDecember, is the month of highest month of Arrivals and June the lowest. They precede the price peaks. Among the determining variables are Market size (positive), lagged Arrivals (positive), computerization (Positive), Distance from urban centre (negative), district level rainfall (negative but with a quadratic effect turning to positive effect). Square of trend, farm size, road lengths, lagged price did not show any significant effect. Interaction with computerization does not significantly increase the Arrivals effects of any variable. Neither trend nor HSD price are confirmed to be positive effects.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e(a): Panel Regressions of Arrivals\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e\u003cp\u003eDependent variable\u0026thinsp;=\u0026thinsp;Arrival\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eModel 4#\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eModel 5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eModel 6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD_MON\u003c/p\u003e\u003cp\u003eDecember (Highest)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e237.05\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e240.15\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e237.23\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.53)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e243.10\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e285.81\u003csup\u003e***\u003c/sup\u003e (2.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e238.25\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD_MON\u003c/p\u003e\u003cp\u003eJune (Lowest)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-101.31\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-1.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-99.35\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-101.10\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-96.82\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-1.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-37.55\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-0.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-100.15\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-1.73)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003cp\u003e(1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-1.31\u003c/p\u003e\u003cp\u003e(-0.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003cp\u003e(1.55)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.57\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.76\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.66)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIME\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003cp\u003e(1.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMKT_SIZE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e316.02\u003csup\u003e***\u003c/sup\u003e (9.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e316.43\u003csup\u003e***\u003c/sup\u003e (9.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e316.19\u003csup\u003e***\u003c/sup\u003e (9.65)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e307.04\u003csup\u003e***\u003c/sup\u003e (9.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e948.8\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(23.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e315.75\u003csup\u003e***\u003c/sup\u003e (9.64)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARRV (t-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.76\u003csup\u003e***\u003c/sup\u003e (44.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.76\u003csup\u003e***\u003c/sup\u003e (44.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.76\u003csup\u003e***\u003c/sup\u003e (44.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.75\u003csup\u003e***\u003c/sup\u003e (44.33)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.76\u003csup\u003e***\u003c/sup\u003e (44.89)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePRICE (t-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003cp\u003e(-1.60)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD_ICT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109.09\u003csup\u003e***\u003c/sup\u003e (4.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e109.89\u003csup\u003e***\u003c/sup\u003e (4.15)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110.32\u003csup\u003e***\u003c/sup\u003e (4.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e114.10\u003csup\u003e***\u003c/sup\u003e (4.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e577.4\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(4.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e111.73\u003csup\u003e***\u003c/sup\u003e (4.18)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRF_DIST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.74\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.71\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.18)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.75\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.72\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.44\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-3.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.74\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.27)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRF_DIST\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003cp\u003e(1.61)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.71)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.69)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(2.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.71)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFRM_SIZE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.88\u003c/p\u003e\u003cp\u003e(0.28)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIST_URB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-0.24\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-1.88)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-1.32\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-4.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eROAD_L\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003cp\u003e(-0.70)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-21.41\u003c/p\u003e\u003cp\u003e(-0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.63\u003c/p\u003e\u003cp\u003e(0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-31.62\u003c/p\u003e\u003cp\u003e(-0.44)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.02\u003c/p\u003e\u003cp\u003e(0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e174.2*\u003c/p\u003e\u003cp\u003e(1.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-6.53\u003c/p\u003e\u003cp\u003e(-0.10)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWald chi\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7798.86\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7801.38\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7794.55\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7814.39\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1481.53\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7809.59\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eσu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e69.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eσe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e425.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e425.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e425.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e425.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e574.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e425.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eρ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCorr(u, xb)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eNote: Estimated with STATA, Significance \u003csup\u003e***\u003c/sup\u003e1% \u003csup\u003e**\u003c/sup\u003e5% \u003csup\u003e*\u003c/sup\u003e10%. Figures in Parentheses are t-stat. # is selected model.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e(b): Panel Regressions of Arrivals\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eDependent variable\u0026thinsp;=\u0026thinsp;Arrival\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModel 6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModel 7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModel 8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCoeff.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD_MON\u003c/p\u003e\u003cp\u003eDecember (Highest)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e244.02\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e253.81\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e241.51\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(3.59)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD_MON\u003c/p\u003e\u003cp\u003eJune (Lowest)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-96.12\u003c/p\u003e\u003cp\u003e(-1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-92.16\u003c/p\u003e\u003cp\u003e(-1.59)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-97.78\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-1.69)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTIME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.748\u003c/p\u003e\u003cp\u003e(1.66)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003cp\u003e(0.15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMKT_SIZE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e306.75\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(9.27)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e295.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(8.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e303.87\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(9.17)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eARRV (t-1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.75\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(44.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(43.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.75\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(44.32)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD_ICT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e91.36\u003c/p\u003e\u003cp\u003e(1.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e189.15\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(4.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.33\u003c/p\u003e\u003cp\u003e(1.31)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRainfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.72\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.69\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.73\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-2.23)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRainfall\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.68)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.71)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDIST_URB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.25\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-1.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.09\u003c/p\u003e\u003cp\u003e(-0.57)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.25\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-1.92)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.46\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(1.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD_ICT*FRM_SIZE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33.81\u003c/p\u003e\u003cp\u003e(0.41)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD_ICT*DIST_URB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.52\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(-1.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD_ICT*TIME\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003cp\u003e(1.49)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.33\u003c/p\u003e\u003cp\u003e(0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-49.26\u003c/p\u003e\u003cp\u003e(-0.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35.61\u003c/p\u003e\u003cp\u003e(0.54)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiagnostics\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWald chi\u0026sup2;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7810.59\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7830.11\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7822.46\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eσu\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eσe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e425.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e425.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e425.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eρ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\u003cp\u003eNote: Estimated with STATA, Significance \u003csup\u003e***\u003c/sup\u003e1% \u003csup\u003e**\u003c/sup\u003e5% \u003csup\u003e*\u003c/sup\u003e10%. Figures in Parentheses are t-stat.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eDeterminants\u003c/h2\u003e\u003cp\u003eModels 7 in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e6\u003c/span\u003e(b) and model 4 in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e7\u003c/span\u003e(a) are respectively selected to explained price and arrivals with panel data \u0026ndash; RE, The Hausman test (χ\u0026sup2; = 23.94, p-value\u0026thinsp;=\u0026thinsp;0.091) rejecting the FE model. Price as an outcome of demand and supply is not uniform across regulated markets but depends on several factors connected with the markets, the area and the time. The regressions of price suggest that explanatory variables are the months and the time trend, market size, past price, Arrivals, remoteness from urban centres and computerization. Computerization not only enables higher pricing, but it mitigates the price decline due to heavy Arrivals. Arrival itself a market decision and therefore an endogenous variable, is affected by the seasonality of months but not with any confirmed time trend. Past arrivals, local rainfall, remoteness and possession of e-NAM draw higher Arrivals. Location in terms of local farm size, roads and distance and interactions have no effect.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA policy that bring resilience of price to arrivals would be beneficial for the farmers. The expanse of the market that farmers can reach out to and the prices they fetch mark the stretching of frontier of agricultural marketing. For crops that easily decay under inappropriate climatic conditions, marketing success is deeply associated with available intelligence on demand distribution at any time etching their ability to dispose of the product swiftly. A study of UP conducted for a perishable crop Tomato which has increased in nominal price and grown in supply with volatility, reveals imperfection of markets creating dispersions of prices among markets. Some of these markets are privileged with e-NAM that confers superiority in computerization. Comparison between the two types of markets found greater volatility in less privileged markets. The first three months of the year are identified as the post-harvest months of supply pressure when prices fall low. Although attempts to sell are concentrated at this time to avoid product wastage, data suggests that farmers increasingly release produce in nonsystematic ways in other months aided by better storage facilities. Superiority of ICT is found to be an important influence in improving price and increasing Arrivals by regression exercises given the significant coefficients for the presence of e-NAM in the market. More important, the privilege of the market goes with reduced distress during post-harvest months when prices tend to fall. Correcting for the various impacts, computerization intensified by e-NAM is seen to help the search process for higher prices, building confidence on sales towards bringing more products and overcome the effect of supply pressure in post-harvest season.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAkash Vilas Mhaskey - Data Collection, Pre-processing and Data Analysis and ModellingMayanglambam Rajeshwor - Data Analysis, Modelling, EditingNilabja Ghosh - Conceptual framework and methodology, Modelling, Manuscript writing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcharya, S. S. (1998). Agricultural marketing in India: Some facts and emerging issues. \u003cem\u003eIndian Journal of Agricultural Economics\u003c/em\u003e, \u003cem\u003e53\u003c/em\u003e, 311\u0026ndash;332.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAcharya, S. S. 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Agricultural Food Marketing, Economics and Policies. \u003cem\u003eAgriculture\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(4), 761. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agriculture13040761\u003c/span\u003e\u003cspan address=\"10.3390/agriculture13040761\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"e-NAM, Markets imperfection, ICT, Modelling, Information","lastPublishedDoi":"10.21203/rs.3.rs-7480251/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7480251/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA policy that brings resilience of price to product arrivals would be beneficial for the farmers. The expanse of the market that farmers can reach out to and the prices they fetch mark the stretching of frontier of agricultural marketing. Marketing success is deeply associated with available intelligence on demand distribution that ICT can now bring to farmers. A study of Uttar Pradesh conducted for a perishable crop Tomato reveals imperfection of markets creating dispersions of prices among markets. Some of these markets are privileged with e-NAM that confers superiority in computerization. Comparison between the two types of markets found greater volatility in less privileged markets. Data suggests that farmers increasingly hold produce and release them months after harvest but among other variables, superiority of ICT is found to be an important influence in improving price, increasing Arrivals and reducing distress during post-harvest months when prices tend to fall.\u003c/p\u003e","manuscriptTitle":"Farmer finds the buyers: Electronic and traditional influences in Tomato marketing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-29 11:08:39","doi":"10.21203/rs.3.rs-7480251/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4ff8dbda-f307-413b-84cb-17625565c060","owner":[],"postedDate":"August 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-03T14:53:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-29 11:08:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7480251","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7480251","identity":"rs-7480251","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}