Cultural goods are distinguished not primarily by their material form but by their artistic, symbolic and intellectual value (Bernier 2005). This inherent distinctiveness renders the trade in cultural goods qualitatively different from that of conventional commodities. Unlike standard goods, the exchange of cultural products is deeply influenced by subjective tastes, social meanings and cultural preferences, posing significant challenges for analysis within the framework of conventional trade theory. Traditional economic models often marginalise the role of cultural factors, treating them as negligible in the long-term determination of trade flows (Acheson & Maule 2007).

The trade in cultural goods offers notable advantages for exporting countries, particularly within the context of globalisation and trade liberalisation, which can enhance productive capacity through international specialisation and broaden the range of goods available for consumption, thereby contributing to overall welfare gains (Marvasti 1994). Beyond its immediate economic impact, Marvasti (1994) goes on to mention that returns from cultural trade play a role in developing wealth creation and supporting sustainable economic development. Moreover, the reciprocal nature of cultural exchange and the inherent diversity of cultural products contribute to the expansion of intra-industry trade, facilitating broader market integration and economic dynamism. Since 2020, it has been estimated that the average trade in cultural goods from the BRICS trade bloc has exceeded that of the world trade average in cultural goods, as per the data derived from Savina et al. (2019) and presented in Figure 1.

Figure 1 illustrates the trajectory of the value of exported cultural goods expressed as a percentage of the value of all exported goods from 2004 to 2020 for each of the member countries of BRICS (UNESCO 2025). Overall, the data reveal a consistent upward trend across all countries, with varying degrees of growth and volatility. Between 1990 and 2025, China and Russia demonstrate the most pronounced increases, particularly after the mid-1990s, surpassing the world aggregate by a significant margin. Russia’s growth in trade accelerates notably post-2000, while China’s rise begins slightly earlier, around the late 1990s. Brazil and South Africa exhibit more modest but steady increases, generally aligning with or slightly trailing the world aggregate. India, initially lagging, shows a marked rise in cultural trade volumes after 2015, eventually overtaking the global trend. The world aggregate, represented by the dashed line, remains relatively stable throughout the period.

The box plot provides a comparative overview of the supply of trade in cultural goods among the BRICS countries, alongside the world aggregate (WLD) (see Figure 2). Each box represents the interquartile range (IQR), with the red line indicating the median trade index for each country. China and Russia exhibit the widest spreads, indicating significant variability in their cultural trade volumes over time, with China showing the highest overall range. Both countries have high upper quartiles, suggesting that in more recent years, their trade volumes have been among the highest within the BRICS group. India also shows a broad range, although its median is lower, reflecting later and more recent growth in cultural trade. In contrast, South Africa and Brazil indicate narrower IQRs, with South Africa’s median trade index slightly above the world aggregate and Brazil’s falling below it. The WLD shows the least variation, with a relatively tight distribution for that period.

Additional factors influencing these trends include the global reconfiguration of trade dynamics, marked by a shift in economic influence from the Global North to the Global South and from Western to Eastern regions. This transition has been accompanied by improved market access, technological advancement, the adoption of export-oriented development strategies and a surge in international tourist arrivals. The growth of the emerging middle class has intensified domestic demand for cultural goods (Li 2019). Moreover, economic liberalisation, particularly in India and China, along with the growth of digital and financial platforms and progressive reforms aimed at enhancing market accessibility, has contributed to attracting additional investment to the region (Lissovolik & Vinokurov 2019).

The study aims to explore the structural factors that influence cultural trade flows and assess how these dynamics contribute to broader patterns of regional and global economic cooperation. This analysis is particularly relevant in the context of shifting geopolitical alignments and ongoing disruptions in world trade, all of which highlight the need for alternative frameworks for understanding economic integration through non-traditional sectors. The central objective of this study is to examine the extent to which the trade in cultural goods can function as a channel of economic integration and specifically, the relationship between cultural trade and key economic drivers such as the arrival of tourists, the changing pattern of economic investment and digitalisation.

An econometric approach is employed to explore these structural factors by incorporating OLS models, panel data analysis and time series forecasting techniques, including ARIMA and GARCH models. This methodological design supports both cross-sectional and longitudinal examinations of trade behaviour across the BRICS countries over the 52-year period. The OLS and panel models provide insights into country-specific and aggregate effects of the selected explanatory variables, while the ARIMA and GARCH models allow for the exploration of temporal patterns and volatility in trade.

Cultural trade is defined as the exchange of goods embodying artistic, intellectual and heritage-based value. This component within the broader cultural and creative industries contributes to both economic output and the preservation of cultural identity (Mazurkevych et al. 2024). Cultural economics literature has emphasised that cultural goods possess dual value, both economic and symbolic. The attributes of cultural goods that underpin their cultural value include aesthetic qualities, spiritual significance, symbolic meaning, historical importance, influence on artistic trends, authenticity, integrity and uniqueness. From an economic perspective, adopting a Lancastrian approach to demand suggests that these same attributes shape consumer preferences for cultural goods. This alignment indicates that the economic value individuals assign to cultural goods is often closely linked to the very characteristics that confer their cultural value (Throsby 2003). This enables cultural goods to develop cross-border connections, through trade, while reinforcing local distinctiveness (Klamer 2004).

Economic integration, classically framed by Balassa and Stoutjesdijk (1975), involves the reduction of trade barriers, policy harmonisation and the establishment of cooperative institutional frameworks among countries. While economic integration is often studied through the lens of conventional goods, emerging research highlights the cultural trade as an instrument of economic integration by facilitating flows of goods that carry both economic and cultural significance, yet according to Lizardo (2008), the global cultural economy is divided into a hierarchical structure where connectivity serves as the primary distinguishing factor. In this system, the ‘core’ comprises high-consumption, high-production cultural economies that predominantly engage in cultural trade with other similarly positioned economies (Lizardo 2008). Within the BRICS context, studies have explored cultural cooperation as a soft power strategy and a tool for regional cohesion (Mansoor 2024), yet empirical analysis linking cultural trade to measurable indicators of integration remains limited.

This article addresses this gap by focusing on cultural trade using the BRICS countries as a case study. By linking cultural trade with key economic drivers, namely tourist arrivals, investment and technology, and considering the institutional role of BRICS, this study examines how cultural trade contributes to the broader processes of economic integration among emerging economies.

This study draws upon annual time series data spanning from 1970 to 2021, obtained from the 2023 edition of the KOF globalisation index, published by the KOF Swiss Economic Institute. The index encompasses a broad spectrum of globalisation indicators, covering areas such as goods and services trade, financial flows, cultural exchange and international tourist arrivals.

The analysis incorporates several key variables, including the trade in cultural goods, the movement of tourists, changing investment and access to digitalisation. Key indicators were selected based on previous research by Baur (2018), which analysed preference formation and choice in cultural-artefact markets, with tourist arrivals as an economic proxy. The independent variables selected for this study, namely tourist arrivals, investment and digitalisation, were chosen based on their centrality in the literature on cultural trade and economic integration. Tourism serves as both a driver of demand for cultural goods and a channel for cultural exchange. Investment in this is critical for building production capacity within the economy, promoting the growth of innovation and supporting creative entrepreneurship. Digitalisation, particularly digital infrastructure, has become increasingly relevant in facilitating the production, dissemination and consumption of cultural goods across borders.

Dummy variables for global shocks, including the COVID-19 pandemic and the Ukraine conflict, were included to capture the disruptive impacts of external crises on cultural trade flows. To account for these exogenous disruptions, a series of dummy variables is employed to capture the effects of major economic shocks, specifically, the global financial crisis, COVID-19 and the conflict in the Ukraine. In addition, the formation of the BRICS trade bloc during the 1990s is captured using a proxy dummy construct, acknowledging its potential influence on economic integration and its relevance to the dynamics explored in this study (Rodriguez-Triocci 2024). The approach will be to examine the impact using an OLS approach as a descriptive analysis and a panel data approach to explore the overall impact using a linear mixed-effects model.

The objective of this model is to examine country-specific factors affecting the trade in cultural goods. The empirical strategy specifies a parametric linear relationship estimated by OLS. Where scale effects or skewness warranted, variables were transformed, most commonly through natural logarithms, so that the specification remains linear in parameters despite nonlinearities in levels. The estimation uses annual observations spanning 1970–2021. Inference rests on the classical Gauss–Markov regularity conditions: regressors of full rank, a disturbance with zero conditional mean, and homoscedastic, serially uncorrelated errors. With an intercept included, the sample orthogonality conditions obtain (implying residuals sum to zero), and under these assumptions, the OLS estimator is the best linear unbiased estimator (BLUE) (Puntanen & Styan 1989). All the variables used in this model are tested and deemed consistent with the Gauss–Markov theorem.

The model will take the form of the OLS approach using the following descriptive format: TradeCG ~ 1 + β₁.Tourism_(t) + β₂.Investment_(t) + β₃.Technology_(t) + β₄.BRICS_(t) + β₅.GShock_(t) + u_(t).

The dependent variable in this model focuses on the world trade in cultural goods, and the independent variables include the Global Tourism, Global Investment, Global Technology and a series of dummy variables to indicate global economic shocks, including COVID-19, financial shocks and Ukraine. Including these shocks as dummy variables in the model allows for the assessment of how such systemic disruptions influence the trade in cultural goods beyond regular economic fluctuations, providing some insights into the vulnerability of the cultural trade sector under conditions of global uncertainty.

Table 1 represents the world trade in cultural goods as the dependent variable and the global variables for tourist arrivals, investment, technology and economic shocks as the independent variables. The R-squared value measures how closely the data fit the regression line. The coefficient of determination for multiple regression is assessed by the adjusted-R² value. The adjusted-R² value of 0.99 indicates that the model has a significant fit. The F-statistic indicates that the model with independent variables better fits the data than a model without them. As the F-statistic has a near-zero value, this indicates that the model is a very suitable descriptive model.

Pre-estimation diagnostics addressed the stochastic properties of the series and core specification assumptions. Stationarity was assessed using the Augmented Dickey–Fuller (ADF) test, which evaluates the null of a unit root, and the Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test, which takes stationarity as the null against a unit-root alternative. Across variables, the series were stationary in levels (after standard transformations), validating time-series inference under covariance-stationarity. To gauge distributional distinctness across regressors, a one-way analysis of variance (ANOVA) was also implemented; the rejection of equal means is consistent with the regressors arising from populations with differing central tendencies. Model adequacy checks further examined regressor dependence. Finally, residuals were found to be approximately normal, supporting the validity of the associated t and F inferences.

Tourist arrivals, digitalisation and economic shocks were positively correlated with the world trade in cultural goods, while change in investment showed a negative relationship. This was consistent with work carried out by Baur (2020), indicating the negative relationship between investment flows and the trade in cultural goods, as cultural goods appear to have a hedging relationship between investment and market uncertainty. Furthermore, according to Li and Yang (2020), a study examining the impact of cultural goods on cross-border mergers and acquisitions found that the trade in cultural goods significantly increases the volume and realised economic gains of mergers and acquisitions (M&As) from importing to exporting countries. However, the research also highlights that cultural imports can mitigate the adverse effects of cultural distance on merger outcomes, suggesting that in the absence of such mitigating factors, investment flows may not positively impact trade in cultural goods.

The role of the trade bloc at this stage did not show any significance in the model and will be rediscussed in the panel data analysis in the next section.

The results of the OLS models investigating the trade in cultural goods specific to each country are presented in Table 1. The sign, either positive or negative, indicates the relationship, and the adjusted-R² value indicates the degree of fit for each country. A positive sign indicates that the dependent and the independent variable move in the same direction. In other words, for South Africa as an example, an increase in tourist arrivals will increase the trade in cultural goods, while an increase in digitalisation in South Africa will have a decreasing effect on the trade in cultural goods. There are various reasons for these relationships, but a detailed analysis of these relationships is beyond the scope of this research.

Across the BRICS countries, the relationships between the independent variables (Tourism, Investment, Digitalisation, Global Shocks and the BRICS Trade Bloc) and the dependent variable, namely the trade in cultural goods (country-specific), may vary (see Table 2). Tourism generally shows a positive impact in most countries except Russia and Brazil, where no relationship is indicated. Investment impacts are mixed, with positive relationships in South Africa, Brazil and Russia, but a negative relationship in India. Technology tends to have a positive relationship to the trade in cultural goods except in South Africa, where technology appears to have a negative relationship, possibly explained by declining infrastructural issues, such as the irregular supply of electricity. The perceptions of the BRICS countries are positively related to the trade in cultural goods within South Africa and Brazil, while Global Shocks positively influence the local trade in cultural goods in all countries except for South Africa and China. The nature of the market economy may significantly affect this relationship in South Africa and China, but further analysis is still required at this stage.

A consistently high adjusted-R² values across the country-specific models indicate strong in-sample fit, with the specifications explaining a substantial share of variation in the dependent variable. To analyse the association between the covariates, while controlling for unobserved heterogeneity across countries and over time, a panel-data framework was subsequently employed in this analysis.

Panel data combines cross-sectional data (data across different entities, such as countries) and time-series data (data over time), allowing for the analysis of dynamic changes and persistent patterns. This approach enables the study to capture how the trade in cultural goods evolves over time and varies across the BRICS countries, offering deeper insights into temporal trends and country-specific effects. Furthermore, the panel data approach can control for unobserved heterogeneity, such as factors that vary across countries but are constant over time, by using fixed or random effects models and, in so doing, may isolate the impact of the key variables.

In order to understand the combined effect of the variables to both control for unobserved heterogeneity and to analyse the overall impact of the cultural trade across BRICS, I will construct a panel data model with both fixed effects (see Table 3) and random effects (see Table 4), respectively, with the analysis depending on the Hausman test results. I will be using the same dependent and independent variables as in the OLS models: TradeCG ~ β₁.Tourism_(t) + β₂.Investment_(t) + β₃.Technology_(t) + β₄.BRICS_(t) + β₅.GShock_(t) + u_(t) + (1 | code).

The panel data analysis conducted on the trade in consumer goods (TradeCG) across various economic indicators – tourist arrivals, investment, Internet access (IntAcc) and several types of shocks (GShockC, GShockU, GShockF) and the formation of the BRIC trade bloc GShockB. This model provides insight into the dynamics influencing trade activities. Two linear mixed-effects models were provided to measure the robustness of the analysis and using the Akaike information criterion (AIC), the best linear fixed effects model proved to be a suitable explanation of the relationships.

For robustness, the theoretical likelihood ratio test comparing Model 1 (which includes random slopes for the year within the group code) and Model 2 (which includes only random intercepts) yielded a highly significant result (p = 0). This suggests that Model 1, with a more complex structure accounting for variability in year effects across different groups, provides a significantly better fit to the data. Model 1 has lower AIC (1405) and Bayesian Information Criterion (BIC) (1446.8) values compared to Model 2 (AIC: 1548, BIC: 1582.8), indicating a more parsimonious model despite its higher complexity.

The interpretation of fixed effects used in this model indicates that several predictors significantly impact the relationship between the BRICS countries’ variables and the world trade in cultural goods. Tourism (0.785, p < 0.001) and investment (0.132, p < 0.01) exhibit strong positive effects, suggesting that increases in tourist arrivals and investment are associated with higher trade in consumer goods. This aligns with economic theories that tourist arrivals boost demand for local products and services, while investment enhances production capacity and trade competitiveness. Internet access (0.493, p < 0.001) also shows a significant positive effect and the role of technology in facilitating trade by improving access to markets, information and efficient communication channels.

Among the shock variables, COVID-19 (GShockC) (3.784, p < 0.01) and the formation of the BRICS Trade Bloc (BRICS) (4.675, p < 0.01) are significant, indicating that these shocks, likely representing global and local economic disruptions, have a substantial positive impact on trade. The data may reflect adaptive mechanisms or policy responses that bolster trade in times of crisis. The impact of Ukraine did not appear to have a significant impact at this stage in the model, possibly because it was captured very late in the data for any significant conclusion to be drawn.

See Table 5 and Table 6 for fixed effect and random effect analysis.

A highly significant positive effect indicates that increases in tourist arrivals are strongly associated with higher trade in consumer goods. This aligns with the idea that tourist arrivals can boost local demand and trade activities. It is also significant, suggesting that higher investment levels are positively correlated with increased trade. Investment enhances production capacity and trade efficiency. A significant positive effect highlights the importance of technology in facilitating trade by improving access to markets and communication. Both the formation of BRICS and the impact of COVID-19 are significant with positive coefficients. Interestingly, the impact of financial shocks did not feature as a significant variable in this analysis. This could be because of the depth and reach of the respective financial crisis and its impact on each of the BRICS countries.

The fixed effects analysis (see Table 5) emphasises the role of tourist arrivals, investment and technology in driving trade in consumer goods. The significant positive impacts of global shocks suggest that trade sectors in these countries have adaptive mechanisms or benefit from supportive policies during economic disruptions. The random effects analysis (see Table 6) reveals substantial heterogeneity across countries, with specific nations such as Brazil and South Africa exhibiting notably different baseline trade levels and growth rates.

In this study, we use two time series models that are Box-Jenkins ARIMA and GARCH models (see Figure 3 and Figure 4) in modelling and forecasting the trade in cultural goods from the BRICS countries. The main objective of the ARIMA model is to analyse the time series properties and forecast the trade volumes and volatility of the trade in cultural goods. The main strengths of ARIMA models are their flexibility and ability to model a wide range of time series data (Mondal, Shit & Goswami 2014). The main strengths of GARCH models are their ability to model the volatility clustering in financial data and capture the long-memory effects in the data. It was found in another study by work highlighted by Miswan et al. (2014) that the GARCH model could not provide the best results because of the lower volatility of the trade in cultural goods. This was also relevant to the lower volatility of the trade in cultural goods within each of the BRICS countries. In contrast to the world aggregate, BRICS cultural goods trade exhibits more pronounced, time-varying volatility, thereby warranting a GARCH specification to capture conditional heteroscedasticity. In other words, the GARCH model remains more suitable for this study.

The Box-Jenkins ARIMA model has been extensively used in various fields of time series analysis. As one of the earliest models, its capability is frequently tested, and it is widely used as a benchmark against other time series models. The Box-Jenkins ARIMA model, also known as the ARIMA (p, d, q) model, includes p autoregressive (AR) terms, d differencing steps and q moving average (MA) terms. Autoregressive integrated moving average models typically assume that the data variance remains constant (Miswan et al. 2014).

The GARCH model is most often used for modelling heteroscedasticity, which means variability in variance over time. It has been extensively used in financial and business domains because of the high volatility typically observed in these data sets. The GARCH model is denoted as GARCH (q, p), where q represents the number of MA terms, and p represents the number of AR terms (Miswan et al. 2014). For each of the BRICS countries, an ARIMA (1, 2, 1) model is applied, and for the world trade in cultural goods, the GARCH (0, 1) model was selected. To determine the best model fitting (Miswan et al. 2014), AIC was used.

In this study, ARIMA and GARCH models are employed to analyse and forecast the trade in cultural goods. The ARIMA models are applied to each of the BRICS countries, while both ARIMA and GARCH models are utilised for world trade in cultural goods. The data are differenced to the first degree to ensure stationarity. This approach allows for the examination of both time series properties and volatility in the data.

The AR (1) and MA (1) terms are significant, indicating that past values are useful in predicting future values. The high significance of the MA (1) term suggests strong autocorrelation in the residuals. The low AIC value indicates a good model fit. The significant autoregressive conditional heteroscedasticity (ARCH) term indicates the presence of volatility clustering. The GARCH model is suitable for capturing the volatility in world trade in cultural goods. The slightly higher AIC compared to the ARIMA model suggests that the GARCH model may not fit as well as the ARIMA model, but it is useful to explain the high levels of volatility in the world trade in cultural goods. Both models predict increasing levels of volatility in the trade in cultural goods. From a global perspective, this could suggest that there is an increasing level of market uncertainty. Given that the available data period ends at the emergence of Ukraine, it could suggest that Ukraine, along with the impact of COVID-19, has heightened concerns in the market.

To ensure the appropriateness and robustness of the ARIMA models applied to the trade in cultural goods within the BRICS countries, several diagnostic tests were conducted. These tests included the ADF test for stationarity, the Ljung-Box Q-test for autocorrelation and the ARCH test for heteroscedasticity (Mishra & Mishra 2022).

The analysis presented in this article explores the mechanism underpinning the integration of the trade in cultural goods across member economies of the BRICS countries. The analysis utilised various econometric models, including OLS and panel data analysis, to examine country-specific factors and their impact on the trade in cultural goods. In addition, ARIMA and GARCH models were employed to analyse the time series properties and volatility of global and BRICS-specific trade in cultural goods. For time series analysis, models like ARIMA and GARCH are employed to understand the properties and volatility of trade data over time. For instance, a study on financial data analysis utilised ARIMA-GARCH models to forecast stock market returns, demonstrating the effectiveness of these models in capturing time-dependent volatility. The use of econometric models such as OLS and panel data analysis is well established in examining country-specific factors affecting trade. These models help to identify relationships between variables such as tourist arrivals, technology access and trade volumes (Grachev 2017).

Tourism and digitalisation were consistently found to have a positive impact on the trade in cultural goods. This was supported by a report submitted by the World Travel and Tourism Council (WTTC 2025), which stated that tourist arrivals significantly contributed to the trade in cultural goods by stimulating demand for cultural products and services. The WTTC reports that in 2024, travel and tourist arrivals’ contribution to global Gross Domestic Product (GDP) totalled US$10.9 trillion, representing 10% of the global economy. This substantial economic impact supports the findings that the role of tourist arrivals is a significant factor in promoting cultural exchange and the consumption of cultural goods (WTTC 2025).

This aligns with the notion that increased tourist arrivals boost local demand for cultural products, while technological advancements facilitate better access to markets and improve trade efficiency. Jiang and Phoong (2023) mention that technological advancements have played a role in facilitating the trade in cultural goods. The development of digitalisation has enriched cultural service products, such as museums, by enabling virtual tours and augmented reality experiences. This digital transformation diversifies cultural offerings and expands the reach to a broader global audience, thereby increasing trade in cultural goods.

Investment in cultural sectors showed varied impacts across different BRICS countries. While it had a positive effect in some countries, indicating enhanced production capacity and trade competitiveness, it exhibited a negative relationship in others, possibly as a result of differing economic conditions and investment climates. This was supported by the works of Wang et al. (2025), indicating that BRICS countries have embarked on diverging trajectories in terms of innovation competitiveness. For instance, China and South Africa have shown higher rankings, while India has scored and ranked lower. These differences in innovation competitiveness can influence how effectively investments in cultural sectors translate into trade competitiveness. Furthermore, a study examining the relationship between foreign direct investment (FDI), economic growth and trade openness in BRICS countries found that while FDI exhibits a substantial interaction with economic growth in the short term, the long-term relationship remains weak. This suggests that in some BRICS countries, investments in cultural sectors may not consistently lead to enhanced trade competitiveness, possibly as a result of differing economic conditions and investment climates (Malik & Sah 2024).

The GARCH model highlighted significant volatility clustering in the world trade in cultural goods, reflecting increased market uncertainty, particularly with respect to recent global events such as the COVID-19 pandemic and the Ukraine conflict. According to Gherghina and Constantinescu (2025), the application of GARCH models has been instrumental in highlighting significant volatility clustering in world trade, particularly during periods of crisis. For instance, a study examining the impact of the COVID-19 pandemic and the Russia–Ukraine war on financial markets found that these events led to heightened volatility and market uncertainty. The research employed GARCH-type models to assess the persistence and dynamics of volatility during these periods, underscoring the models’ effectiveness in capturing the impact of global shocks on trade and financial systems.

These shocks had varying impacts on the BRICS economies, with some countries showing resilience because of robust fiscal measures and others experiencing prolonged economic downturns. The formation of the BRICS trade bloc did not have a significant impact on the trade in cultural goods globally, but was found to be significant on each of the BRICS countries independently, as per the panel data analysis. However, the formation of the BRICS trade bloc has influenced member countries differently. While the bloc’s establishment aimed to enhance economic cooperation, its impact on the trade in cultural goods globally has been limited. Panel data analyses suggest that BRICS formation has had significant effects on individual member countries, indicating that the bloc’s influence is more pronounced when considering the combined effect of multiple variables over time. For example, research on trade openness and economic growth in South Africa before and after joining BRICS found that trade openness substantially influenced GDP growth in the post-BRICS period (Monyela & Saba 2024).

The findings of this analysis indicate that the role of trade in cultural goods within the BRICS trade is characterised by greater stability of these trade flows. The ARIMA–GARCH estimates reveal both lower volatility and lower conditional variance for BRICS compared to the world benchmark. This indicates that there is a stabilisation effect that exists within the BRICS economies, which is economically robust to systemic shocks (COVID-19, financial shocks, Ukraine). This article builds upon previous research, which focuses on the trade of cultural products within the BRICS countries (Baur 2025). This suggests that the institutional coordination within the BRICS countries may reduce the effect of market fluctuations and further sustain cross-border cultural exchange. The findings, from within this perspective, indicate that the integration induced through the formation of this trade bloc is affected not just through the scale of trade, such as measurable through trade volumes, but also through the reduction in volatility.

Overall, the evidence presented here indicates that there are benefits to the trade in cultural goods through the formation of the BRICS trade bloc. The development of the empirical framework captures the mechanism underlying the role of the interdependent channels, including economic, technological and institutional. These channels facilitate long-run integration within the BRICS trade bloc. The analysis shows that this mechanism has an influence when considering the combined effect of multiple variables over time. The high adjusted R-squared values across all models indicate a strong fit, explaining a substantial portion of the variance in the trade in cultural goods is explained through the workings of the mechanism, which supports the trade in cultural goods within the BRICS countries.

While this study provides an econometric assessment of the relationship between cultural trade and economic integration within the BRICS context, several limitations should be acknowledged. Firstly, the reliance on secondary data may not fully capture informal or non-commercial cultural exchanges that contribute to cultural integration. Secondly, although panel data analysis controls for unobserved heterogeneity, there remain structural and contextual differences among BRICS countries that may influence cultural trade dynamics in ways not fully accounted for by the selected variables. Thirdly, the dummy variables applied to global shocks provide a broad proxy for crisis impacts but do not capture the intricacies of policy responses or sector-specific effects within each country.