2.1. Factors affecting the price of tea and other economic crops

Tea prices can vary widely due to differences in terroir (soil and climate conditions), plant varieties, and complex processing methods that influence quality. Many earlier studies focused on long-term price movements and market efficiency in tea rather than on specific quality-related price determinants. For instance, Dharmasena (Reference Dharmasena2003) compared forecasting models and found that a simple random walk performed as well as more complex approaches for predicting tea prices, suggesting that tea markets might be weak-form efficient. Dang and Lantican (Reference Dang and Lantican2011) examined international tea market integration and noted that current prices are significantly influenced by lagged price movements, indicating strong temporal linkages in price formation. Viknesh (Reference Viknesh2011) similarly applied a Vector Autoregression (VAR) model to forecast Sri Lankan tea auction prices. Krishnarani (Reference Krishnarani2013) explored the role of climatic and regional factors in tea price volatility (e.g., rainfall, drought, pest outbreaks) but obtained mixed results without clearly isolating which factors dominate. While these works improved the understanding of tea price dynamics, they did not identify the intrinsic or extrinsic attributes of tea that drive price differences at a given point in time. This highlights a gap in the literature regarding granular price determinants for tea.

Another strand of research has started examining the intrinsic quality attributes and extrinsic cues that could shape tea prices. Padamali and Fernando (Reference Padamali and Fernando2016) found that consumers are willing to pay a premium for Ceylon tea mainly due to factors signaling quality – such as the quality of fresh leaves, specific manufacturing processes, and certifications of origin. Chiu (Reference Chiu1990) documented that key elements influencing the quality (and thus value) of tea include the cultivar of the tea plant, the elevation at which it is grown, the degree of fermentation, and other processing techniques. In addition to these natural and processing factors, Haq et al. (Reference Haq, Shahbaz and Boz2020) emphasized that production conditions (soil management, use of inputs) and human factors (skilled labor in plucking and processing) affect the costs and output of tea, thereby indirectly influencing its market price.

More recently, researchers have begun using innovative analytical methods to link measurable attributes to tea pricing. For example, Wang et al. (Reference Wang, Feng, Liu, Li, Zhang and Zhang2023) employed near-infrared spectroscopy combined with multivariate analysis to predict the purchase price of a famous green tea (Enshi Yulu), demonstrating that objective quality indicators can have a strong, quantifiable correlation with market price. Likewise, Yang et al. (Reference Yang, Chen, Lin, Zhou and Wang2021) conducted a choice experiment on oolong tea and found that Chinese consumers place significant value on extrinsic quality cues, particularly organic certification labels, which substantially increased consumers’ willingness to pay.

In parallel, there is also a growing body of research applying advanced machine learning (ML) techniques to hedonic pricing models in agricultural markets, aiming to capture more complex attribute–price relationships than traditional linear methods. For instance, Brentari et al. (Reference Brentari, Zuccolotto and Levaggi2011) applied a random forest approach to Italian red wines, revealing that latent sensory and chemical quality factors carried appreciable implicit values in explaining price. Similarly, Niklas and Rinke (Reference Niklas and Rinke2020) showed that a machine-learning-based hedonic model achieved slightly higher explanatory power than a conventional log-linear regression in German wines, highlighting additional determinants such as nuanced alcohol effects across categories. These methodological advances underscore that while our study employs a traditional econometric hedonic framework, we recognize the potential of ML-based approaches to improve predictive accuracy and capture non-linear attribute effects, which represents a promising avenue for future tea research.

One well-established approach to disentangle price determinants is the hedonic pricing model, which explains a product’s price as the sum of implicit values of its attributes. This framework, originating from Lancaster’s consumer theory and formalized by Rosen (Reference Rosen1974), has been widely applied to agricultural and food products to quantify the value of both tangible and intangible quality attributes. Hedonic price analysis has evolved over time, expanding from simple models of a few attributes to more complex specifications incorporating sensory ratings, certifications, and producer characteristics. Wine is perhaps the most extensively studied product using this method. Núñez et al. (Reference Núñez, Martín-Barroso and Velázquez2024) provide a recent systematic review of hedonic price studies in the wine market, highlighting that the methodological choices, such as which attributes are included, how the product market is defined, data sources, and the econometric techniques, can significantly affect a model’s explanatory power. They propose a hierarchical classification of wine attributes: (i) objective attributes (e.g., grape variety, vintage, alcohol content), (ii) quality and reputation signals (e.g., expert scores, appellation status), and (iii) factors related to production and market environment (e.g., brand reputation, winery size, economic conditions). This mirrors the evolution of hedonic models to include not only product-intrinsic traits but also external reputation and market context factors. Empirical findings in the wine literature consistently show that certain attributes command price premiums. For example, wines from renowned regions or with prestigious appellations and higher expert ratings tend to fetch higher prices (Angulo et al., Reference Angulo, Gil, Gracia and Sánchez2000; Carew and Florkowski, Reference Carew and Florkowski2010).

In an emerging wine market context, the relative importance of attributes can differ: Romo-Muñoz et al. (Reference Romo-Muñoz, Monje-Sanhueza, Garrido-Henríquez and Gil2020) examined bottled wine prices in Chile and found that objective quality signals such as national and international quality awards have a greater impact on price than terroir-based attributes like specific region of origin. They suggest this is because Chilean consumers, in a younger market, rely more on visible quality certifications, whereas traditional attributes (e.g., vineyard location or valley designation) showed little or even negative price effect until the market matures. Their study also showed that firm-level business strategies (branding, distribution, etc.) significantly influence prices, underlining that beyond product characteristics, supply-side strategies can shape market values.

To provide conceptual clarity, it is useful to distinguish between intrinsic and extrinsic attributes of tea. This distinction later informs our hedonic framework (see Section 3.2). Intrinsic attributes refer to the inherent qualities or physical characteristics of the tea itself, such as the tea variety (cultivar), the degree of fermentation or oxidation, leaf grade and appearance, aroma profile, and other chemical or sensory properties that arise from the tea’s growing conditions and processing methods. Extrinsic attributes, in contrast, encompass external factors related to how the product is presented or perceived, including the region of origin (for instance, a high-mountain locale versus a lowland area), any certification labels (e.g., organic or geographical indication of origin), brand reputation, and packaging or other marketing information.

Hedonic pricing research on other high-value crops and foods further illustrates how diverse attributes influence prices. In specialty coffee auctions, studies have found that sensory quality scores, origin (country or region), variety of coffee bean, farm size, and even lot rankings are key determinants of price premiums (Donnet et al., Reference Donnet, Weatherspoon and Hoehn2008; Teuber and Herrmann, Reference Teuber and Herrmann2012; Wilson et al., Reference Wilson, Conley, Harris and Lafone2012; Wilson and Wilson, Reference Wilson and Wilson2014). For cacao (and its derivative, chocolate), Bedell and Reed (Reference Bedell and Reed2017) showed that certain flavor attributes (e.g., presence of fruit or nut flavor notes) yield different price premiums, while Villacis et al. (Reference Villacis, Alwang, Barrera and Dominguez2022) demonstrated that bean variety and post-harvest processing characteristics (such as fermentation methods) significantly affect cacao bean prices. Rousseau and Vranken (Reference Rousseau and Vranken2013) specifically examined organic labeling in chocolate and found that an “organic” claim positively influenced consumer choice, reflecting a willingness to pay more for organic chocolate. In the olive oil market, numerous hedonic analyses have identified both product attributes and branding factors that drive prices. Karipidis et al. (Reference Karipidis, Tsakiridou and Tabakis2005) reported that packaging size and label information are important in the Greek olive oil market, with smaller bottles often commanding higher unit prices. Carlucci et al. (Reference Carlucci, De Gennaro, Roselli and Seccia2014), analyzing Italian extra-virgin olive oils sold online, found that the presence of a Protected Designation of Origin (PDO) certification was the most influential quality attribute boosting price premiums. Cabrera et al. (Reference Cabrera, Arriaza and Rodríguez-Entrena2015) observed in the Spanish market that clearly indicating olive variety or a low acidity level on the label, as well as carrying a regional quality seal (such as Andalusia’s quality certification), were associated with significantly higher prices. Conversely, that study and others noted that olive oils sold under private retailer labels or in plastic packaging tend to have lower prices, indicating consumers discount those attributes. Interestingly, Romo-Muñoz et al. (Reference Romo Munoz, Lagos Moya and Gil2015) found that even within the “extra virgin” category, imported olive oils with slightly higher free acidity levels obtained higher prices in Chile – a counterintuitive result possibly because acidity was linked with certain desirable flavor profiles or because imported brands with higher acidity had other prestige factors. These examples demonstrate the broad applicability of hedonic models in revealing how both intrinsic characteristics (like chemical composition, flavor, or variety) and extrinsic indicators (like certifications, brand, and packaging) contribute to price formation across various agro-food products.

In summary, the literature suggests that while tea has a rich array of quality factors, the rigorous quantification of their price impacts is less developed compared to products like wine, coffee, or olive oil. The evolution of hedonic pricing research in those other markets, ranging from basic attribute–price correlations to more nuanced analyses that incorporate reputation and strategy effects, provides a valuable roadmap. Drawing on these insights, our study aims to fill the gap by systematically evaluating how different attributes of tea (from origin and certifications to flavor and chemical indicators) are capitalized into price. By doing so, we not only address the call for deeper analysis of tea price determinants and econometric modeling but also contribute novel evidence to the growing body of hedonic pricing literature in the domain of specialty crops.

2.2. Tea production areas and types in Taiwan

Tea production in Taiwan is characterized by numerous smallholder farmers and many independent producers, resulting in a competitive market with no single entity able to dictate prices (Tea Journey Magazine, 2023). This decentralization means tea farmers typically operate on small plots (often 1–2 ha or less), and large corporate plantations are virtually absent – a situation that prevents monopolistic control of the market (Tea Journey Magazine, 2023). Such a decentralized structure, combined with the diversity of teas produced, effectively supports a market setting in which product attributes are capitalized into price – a key assumption of hedonic pricing models.

Production areas are among the important factors influencing market prices (Chen, Reference Chen2021; Das, Reference Das2006; Wang et al., Reference Wang, Liang, Ko and Lin2022b). In Taiwan, there are 13 geographical indications for tea production areas established by the Ministry of Agriculture, such as Sun Moon Lake Black Tea, Alishan High-Mountain Tea, and Wenshan Baozhong (Liu, Reference Liu2014). Regions produce tea with variations in a wide variety, as depicted in Figure 1 (Ministry of Agriculture, 2024).

Figure 1. The main types of tea produced in different regions in Taiwan. Source: Minister of Agriculture (2024).

While geographical indication plays a crucial role in tea valuation, the production process is another key factor that influences market prices. Using exactly the same tea leaves, different production process may yield different tea. The production process could be categorized by three types: non-fermented, partially fermented, or fully fermented. Non-fermented tea, like green tea, involves fixation, rolling, and drying to retain fresh flavors. Partially fermented teas, such as oolong, are sun-withered, indoor-withered, and stirred to initiate partial oxidation. Processes like rolling, fixation, drying, and roasting further shape their unique flavor. High-mountain oolong undergoes additional roasting for complexity, while heavier roasts, such as Tiehguanyin, develop bold flavors. Fully fermented teas, like black tea, are processed through controlled fermentation, fixation, rolling, and drying to achieve their rich, robust taste. The detailed production process is as shown in Figure 2 (Ministry of Agriculture, 2023).

Figure 2. The production process of different types of tea. Source: Minister of Agriculture (2023).

2.3. Organic, origin, and traceability certification in Taiwan

Taiwan has implemented three major certification schemes in its tea industry: organic certification, origin certification, and traceability certification. Each scheme is administered by government-authorized bodies and is intended to signal specific quality attributes to consumers. Organic certification denotes that a tea has been produced according to strict organic farming standards. Under Taiwan’s Organic Agriculture Promotion Act (effective 2019), only products that have been inspected and certified by an accredited third-party agency may be labeled and sold as “organic” (Organic Agriculture Promotion Center, 2019). This means the tea is grown without synthetic pesticides, with no chemical fertilizers or genetically modified organisms (GMOs) allowed – only naturally approved inputs are used in cultivation. Obtaining this certification is not a one-time process – farmland must undergo a two- to three-year conversion period to organic management, and even after certification farmers face annual inspections to ensure continued compliance (Organic Agriculture Promotion Center, 2021). Certified organic teas carry the official “Taiwan Organic” mark, indicating compliance with these rigorous standards (Organic Agriculture Promotion Center, 2019). For consumers, the organic label serves as a credibility signal of natural and safe cultivation practices, which can enhance the tea’s appeal. Many consumers are willing to pay a premium for organic products due to perceived health and environmental benefits, so we anticipate organic certification to positively influence tea prices. Indeed, evidence suggests organic tea commands a premium value: Taiwanese farmers are willing to invest more in organic tea cultivation versus conventional farming in expectation of higher returns (Liu et al., Reference Liu, Chiang, Yap and Lin2024), and globally, organic teas have been reported to fetch higher average market prices than non-organic teas in niche markets (Hajra, Reference Hajra2017).

Origin certification in Taiwan refers to the system of certifying and labeling teas from specific renowned producing regions, functioning similarly to geographical indication (GI) protections in other countries. Taiwan’s Council of Agriculture, through the Tea Research and Extension Station and the Agriculture and Food Agency, oversees a Certificate of Origin Label program for premium local teas (Agriculture and Food Agency, 2022a). For a tea to receive an origin label, the producer must prove the tea’s geographic origin and adhere to defined quality standards. Certification units verify that the tea was indeed grown and processed in the claimed locale and often conduct sensory evaluations (taste, aroma, liquor color) to ensure it meets the region’s quality reputation. They also test for safety (e.g., pesticide residues) to uphold the standard (Agriculture and Food Agency, 2022a). Only tea batches that pass these checks can carry the region’s official certification mark. This origin labeling system gives consumers assurance of authenticity. It also implies a certain level of quality tied to the origin’s terroir and traditional craftsmanship.

Traceability certification is the third key scheme, focused on food safety and supply-chain transparency. Taiwan’s traceability program issues the Traceable Agricultural Product (TAP) label to products that are produced under monitored conditions with full record-keeping. To obtain a TAP certification, a tea farmer must implement Taiwan Good Agricultural Practices (TGAP) and maintain detailed logs of all farming and processing activities. A government-approved certification body audits the farm’s records and practices; only if the production process meets the standards is the traceability label granted (Agriculture and Food Agency, 2022b). Each TAP-certified tea is sold with a unique traceability code on its packaging. Consumers can enter this code into an official web portal to instantly retrieve information on that tea’s origin, the farm and farmer, cultivation methods, harvest date, processing steps, and even test results for safety (Agriculture and Food Agency, 2022b). In essence, the traceability certification guarantees that the product is safe and its history is transparent, from leaf to cup.

In summary, these three certifications serve as signals of quality, origin, and safety in the Taiwanese tea market. Including them in our analysis is theoretically grounded in the idea that certified attributes resolve information asymmetries for consumers and create product differentiation. Prior research supports this view: products with certified organic, origin, or safety labels often achieve price premiums because consumers associate these labels with higher quality or lower risk (Erraach et al., Reference Erraach, Jaafer, Radić and Donner2021). We therefore treat organic, origin, and traceability certifications as central explanatory factors in our hedonic price model. By doing so, our study captures how credible certifications contribute to price formation.

3.1. Data and variable selection

The data used in this study is from an administrative survey, “Taiwan Tea Manufacturing Factory Operation Survey,” conducted by the Ministry of Agriculture, which investigated tea factories across Taiwan in 2019. The survey includes 2,214 individual tea products made by 1,340 tea factories. The dataset contains various factories’ and teas’ characteristics, such as production area, production process, workforce composition, sales channels, certification status, and geographic coordinates. Additionally, it provides detailed data on production environment such as facilities, labor allocation, particularly the distribution of full-time and part-time employees. Finally, the data also includes several certifications, such as traceability, organic, origin, and export.

Data processing was conducted in two phases. In the first, we removed observations with missing or erroneous values (e.g., invalid coordinates or cases where production volume or price was zero). To resolve inconsistent naming of tea products, we standardized the tea type classifications by grouping similar teas under unified categories. The original dataset contained seven tea categories – black tea, green tea, oolong tea, Baozhong tea, Oriental Beauty, Tieguanyin, and a few labeled Others. For consistency and to ensure adequate sample size per category, we consolidated these into five broader categories. Specifically, Tieguanyin (a subtype of semi-fermented oolong tea) was merged into the oolong category, and the small “Others” category was combined with Oriental Beauty. To ensure consistency, we standardized the naming of tea types by consolidating naming variants under five unified categories. For instance, specific red/black tea varieties (such as Tai-18, Tai-20, Tai-27, Wuhong, Ruby, and Assam) were grouped as ‘black tea.’ Likewise, different local labels including ‘Oolong,’ ‘Dongding Oolong,’ and related subtypes were uniformly categorized as ‘oolong tea. This standardization was applied to all tea categories in the dataset. This standardization ensured consistency across the dataset. Thus, the five tea-type categories used in our study are black tea, green tea, oolong, Baozhong, and Oriental Beauty (the last of which includes Oriental Beauty and other minor specialty teas). In the second phase, we processed the geographical data, specifically the geographic coordinates recorded in the survey. Using ArcGIS, we converted these coordinates into elevation data, thereby incorporating elevation as a continuous variable. After data cleaning, we obtained a final sample of 1,673 observations.

Additionally, following the Ministry of Agriculture tea production region classification (Ministry of Agriculture, 2008), we reclassified the tea production areas to better reflect their geographical environment and growing conditions, which are generally similar within the same county or city. The revised classification includes the Taoyuan/Hsinchu/Miaoli (Taoyuan City, Hsinchu County, Miaoli County), Northern area (Taipei City, New Taipei City, Keelung City, Yilan County), Central area (Taichung City, Changhua County, Nantou County, Yunlin County), Eastern area (Hualien County, Taitung County, outer islands), and Southern area (Chiayi County, Tainan City, Kaohsiung City, Pingtung County).

Production-related factors include land area, facilities, labor, and elevation. Tea production involves a variety of processing machines, which can be categorized into general facilities and specialized facilities based on their function, cost, and operational complexity. While general facilities are essential for standard tea processing and packaging, specialized facilities play a crucial role in refining tea quality, requiring higher capital investment and professional expertise. General facilities in tea production are machines used for basic processing, such as drying and packaging, making them fundamental to most tea factories, such as withering drum, type-B dryer, de-clumping machine, roasting machine, and vacuum packaging machine.

In contrast, specialized facilities are used for high-end tea processing, shaping, and refining, often requiring advanced skills to operate, such as withering trough, rolling machine, bagging machine, lotus machine, compression shaping machine, tofu machine, and type-A Dryer. These machines are critical in producing premium-quality tea. For example, withering trough and rolling machine directly influence tea oxidation and shaping in producing high-end teas with complex aromas and textures.

Labor is distinguished by full-time and part-time positions in factories. Full-time employees are those who work on a regular basis with fixed contracts. In contrast, part-time employees are often hired on a seasonal or temporary basis, particularly during peak harvesting and processing periods. Elevation in our dataset is measured in meters above sea level and varies across different production areas, reflecting the diverse growing conditions of tea plantations.

Certifications play a crucial role in ensuring quality standards and market differentiation. The dataset includes four key certification categories: (1) traceability certification: allows consumers to verify the origin and processing history; (2) origin certification: confirms that the tea is produced in a specific region; (3) organic certification: verifies that tea is grown without synthetic pesticides or chemical fertilizers; (4) export status: indicates whether the tea is produced for domestic consumption or meets regulatory requirements for international trade.

Table 1 provides detailed definitions and descriptive statistics for these selected variables. We have 13 variables, including six categorical and seven continuous variables. The categorical variables include tea type, production area, certification of traceability, certification of organic, certification of origin, and export status. Meanwhile, the continuous variables cover tea price (dependent variable), land area (measured in hectare), number of general facilities, number of specialized facilities, elevation (meters), number of full-time employees, and number of part-time employees. For further analysis, tea gardens are also categorized by elevation: ground-level (below 600 m), mid-elevation (600 to 1,000 m), and high-elevation (above 1,000 m).

Table 1. Definitions of selected variables

Although some variables (e.g., elevation) are not directly observable by consumers at point of sale, we include them as proxies for underlying quality and certification signals. This approach is consistent with prior hedonic studies in food markets. For instance, wine studies incorporate expert scores and vineyard terroir variables as quality indicators that consumers rely on via reputation (Núñez et al., Reference Núñez, Martín-Barroso and Velázquez2024). Similarly, olive oil research has treated chemical measures (like acidity level) and production methods (e.g., cold extraction) as quality signals, especially when such information is communicated on labels or through appellation certifications (Cabrera et al., Reference Cabrera, Arriaza and Rodríguez-Entrena2015; Carlucci et al., Reference Carlucci, De Gennaro, Roselli and Seccia2014). They suggest that until consumers gain experience, tangible signals (e.g., certification) drive prices, whereas attributes such as elevation, soil, microclimate only command premiums once they are better understood. In short, even unseen characteristics can influence price when they serve as credible indicators of product quality.

3.2. Econometric approach

We employ the hedonic pricing model (Rosen, Reference Rosen1974) to analyze the determinants of tea prices. Cuevas et al. (Reference Cuevas, Pede, McKinley, Velarde and Demont2016), Dilaver and Dilaver (Reference Dilaver and Dilaver2022), Khanal and Dhoubhadel (Reference Khanal and Dhoubhadel2023), Outreville and Le Fur (Reference Outreville and Le Fur2020) have applied hedonic pricing to assess the value of agricultural products. Moreover, Martinez et al. (Reference Martinez, Boyer and Burdine2021) use hedonic price model and found that feeder cattle with certain desirable attributes earned significant premiums at auction.

In addition, Taiwanese teas exhibit considerable product heterogeneity, varying by cultivar, region (e.g., high-mountain versus lowland), processing method, and grade, which leads to differentiated products catering to diverse consumer preferences (Tea Research and Extension Station, 2022). With no government price controls or monopolistic pricing, prices in this sector largely reflect perceived quality and demand, satisfying the assumptions of a competitive market. Therefore, employing a hedonic pricing model is appropriate, as observed price differences can be interpreted as the implicit valuations of tea attributes under competitive equilibrium.

In this study, we classify the price determinants of tea into intrinsic attributes, extrinsic attributes, production factors, and regional characteristics. Let P_i represent the price of tea, which can be expressed through the following pricing model:

(1) $${P_i} = f\left( {{Z_i},{T_i},{X_i},{L_i}} \right)$$

where Z_i captures the regional characteristics of the tea. Intrinsic attributes (T_i) include tea types, such as black tea, green tea, oolong tea, Baozhong, and Oriental Beauty. Extrinsic attributes (L_i) refer to external quality signals, including certifications such as organic certification, traceability, and geographic indication, as well as export orientation. X_i are production factors such as tea plantation area, labor, and specialized facilities. The baseline model for this study is specified as equation (2):

(2) $$\eqalign {ln(Pric{e_i}) = \; & {\beta _0} + \mathop \sum \limits_{j = 1}^4 {\gamma _j} \times Are{a_{ij}} + \mathop \sum \limits_{k = 1}^4 {\delta _k} \times typ{e_{ik}} + {\boldsymbol {\theta ' X_i}} + {\tau _1}traceabilit{y_i} + {\tau _2}organi{c_i} \cr & + {\tau _3}origi{n_i} + {\tau _4}expor{t_i} + {\varepsilon _i}}$$

where ln(Price_i) is the natural logarithm of the i ^th tea price; Area_ij is the i ^th tea in the j ^th production area (Taoyuan/Hsinchu/Miaoli (as reference group), Northern area, Central area, Eastern area and Southern area); type_i is the i ^th tea belonging to the k ^th type (black tea (as reference group), green tea, oolong tea, Baozhong tea, Oriental Beauty and other)，X_i is a vector of variable associated with production factors (land area, general and specialized facilities, elevation, full-time and part-time employee), traceability_i represents the traceability certification, organic_i refers to the organic certification, origin_i denotes the geographic indication certification, export_i represents whether the tea is exported. The error term ϵ_i follows a normal distribution with a mean of 0 and a standard deviation of 1. γ_j, δ_k, θ′, τ ₁, τ ₂, τ ₃, τ ₄ are the estimated parameters. These parameters of interest capture the tea price determinants of the four categories.

To further analyze price variations across different regions, we extend the baseline model by segmenting the sample according to production areas. The model is specified as equation (3):

(3) $$\eqalign {ln(Pric{e_{ij}}) = \; & {\beta _0} + \mathop \sum \limits_{k = 1}^4 {\delta _k} \times typ{e_{ijk}} + {\boldsymbol {\theta '{X_{ij}}}} + {\tau _1}traceabilit{y_{ij}} + {\tau _2}organi{c_{ij}} \cr & + {\tau _3}origi{n_{ij}} + {\tau _4}expor{t_{ij}} + {\varepsilon _{ij}}}$$

We identified elevation as a potentially key determinant of tea prices, prompting further investigation of its impact. We extend the baseline model by introducing two dummy variables to distinguish mid-elevation and high-elevation production regions. The model is specified as equation (4):

(4) $$\begin{align}ln(Pric{e_i}) =&\;{\beta _0} + {\rho _1} \times midelevatio{n_i} + {\rho _2} \times highelevatio{n_i} + \sum\limits_{j = 1}^4 {{\gamma _j}} \times Are{a_{ij}} + {\boldsymbol {\theta '{x_i}}} \\&+ {\tau _1} \times traceabilit{y_i} + {\tau _2} \times organi{c_i} + {\tau _3} \times origi{n_i} + {\tau _4}expor{t_i} + {\varepsilon _i}\end{align}$$

where ρ ₁ and ρ ₂ are coefficients of interest, capturing the effect of mid-elevation (600 to 1,000 m) and high-elevation (above 1,000 m) on tea price with ground-level (below 600 m) as the reference. The rest of equation (4) follows the same specification as equation (2). Finally, since previous studies (Hajra, Reference Hajra2017; Liu et al., Reference Liu, Chiang, Yap and Lin2024) have demonstrated that organic certification leads to a price premium, we compare the sub-samples of organic versus non-organic teas.