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This study investigates the determinants of Catalan trade flows by ex- amining the impacts of Preferential Trade Agreements, Bilateral Investment Treaties , and the activities of the Catalan governmental agency, ''Accio'', using a comprehensive dataset spanning from 1995 to 2020. The findings reveal that both PTAs and BITs generally foster trade. Furthermore the role of ''Accio ''as a facilitator of internationalization is also particularly significant, especially for differentiated goods. | This study investigates the determinants of Catalan trade flows by ex- amining the impacts of Preferential Trade Agreements, Bilateral Investment Treaties , and the activities of the Catalan governmental agency, ''Accio'', using a comprehensive dataset spanning from 1995 to 2020. The findings reveal that both PTAs and BITs generally foster trade. Furthermore the role of ''Accio ''as a facilitator of internationalization is also particularly significant, especially for differentiated goods. | ||
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'''Keywords: '''Regional Trade, Trade policy, Gravity Equation | '''Keywords: '''Regional Trade, Trade policy, Gravity Equation | ||
| − | + | ==1 Introduction== | |
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Trade plays a pivotal role in the economic development and growth of regions, en- abling them to leverage their comparative advantages and access broader markets. In this context, trade-promoting policies are of utmost importance. In recent years, especially since the 1990s, the proliferation of both Preferential Trade Agreements (PTAs) and Bilateral Investment Treaties (BITs) has been well documented by stud- ies such as Baier and Bergstrand (2004), Hofmann et al. (2017), Egger et al. (2023), and Egger and Masllorens (2024). | Trade plays a pivotal role in the economic development and growth of regions, en- abling them to leverage their comparative advantages and access broader markets. In this context, trade-promoting policies are of utmost importance. In recent years, especially since the 1990s, the proliferation of both Preferential Trade Agreements (PTAs) and Bilateral Investment Treaties (BITs) has been well documented by stud- ies such as Baier and Bergstrand (2004), Hofmann et al. (2017), Egger et al. (2023), and Egger and Masllorens (2024). | ||
This study investigates the determinants of Catalan trade flows by ex- amining the impacts of Preferential Trade Agreements, Bilateral Investment Treaties , and the activities of the Catalan governmental agency, Accio, using a comprehensive dataset spanning from 1995 to 2020. The findings reveal that both PTAs and BITs generally foster trade. Furthermore the role of Accio as a facilitator of internationalization is also particularly significant, especially for differentiated goods.
Keywords: Regional Trade, Trade policy, Gravity Equation
Trade plays a pivotal role in the economic development and growth of regions, en- abling them to leverage their comparative advantages and access broader markets. In this context, trade-promoting policies are of utmost importance. In recent years, especially since the 1990s, the proliferation of both Preferential Trade Agreements (PTAs) and Bilateral Investment Treaties (BITs) has been well documented by stud- ies such as Baier and Bergstrand (2004), Hofmann et al. (2017), Egger et al. (2023), and Egger and Masllorens (2024).
However, it is important to note that regions often lack the authority to negotiate such treaties, as these are typically established at the state or supranational level, such as through the European Union. Consequently, regional entities must rely on specific local policies to promote international trade, such as trade delegations.
Some studies have focused on the role of similar entities (embassies and con- sulates) in trade. For instance, Bagir (2020) examines the Turkish foreign embassy network and finds a positive and significant impact on differentiated products trade flows. Similarly, Rose (2007) finds a positive effect of embassies on trade for 22 countries in a cross-sectional sample.
The current study, however, shifts the focus to a particular region, namely Cat- alonia, and its extensive network of trade delegations. Catalonia is a small, open nation keen to trade internationally. In recent decades, the region’s commitment to commerce has yielded remarkable growth, with data from the early 1990s revealing a dramatic surge in Catalan international trade. Notably, in 2023 alone, Catalan exports increased by over 6% compared to the previous year. Over the past 15 years, Catalonia has consistently maintained a positive trade balance, often exceeding 10% of its Gross Domestic Product (GDP). Impressively, exports have emerged as one of the most important Catalan economic activities, accounting for nearly a third of the nation’s GDP in 2023. Such figures stand out, especially when compared with neighboring countries like Spain, France, and the broader European Union.
A particularly interesting feature of Catalonia is the governmental agency Accio, whose primary mission is to enhance the competitiveness of Catalan firms by promot- ing innovation, supporting internationalization, and attracting foreign investment.
A key aspect of Accio is its extensive network of International Trade and In- vestment delegations, established in 41 foreign countries. According to the Accio website, the main objective of these delegations is:
to promote the connection of Catalan enterprises in the world and advise on setting up in new markets and discovering global business opportunities, as well as helping in the process of internationalization.
Importantly, opening a new delegation does not change tariffs or any trading regulations; instead, they reduce other trade barriers, such as information asymme- try. The assistance provided often includes introducing new potentially beneficial business contacts, navigating the legal framework, or simply arranging meetings.
This study, therefore, examines the effect of new Accio delegations while also controlling for classic trade policy instruments (trade and investment agreements) negotiated at the state level. Overall, it is found that all three trade-promoting tools (PTAs, BITs, and Accio delegations) positively impact trade. Furthermore, when focusing on Accio delegations, the effect is only significant for differentiated goods.
Overall, this study contributes to the understanding of informal trade policies, such as Accio delegations. Moreover, it provides useful insights at the regional level, where these informal policies are particularly relevant, given that regions lack the authority to negotiate BITs or PTAs.
This article draws on a Catalan trade dataset to analyze trade flows. In addition to the trade data, other databases are used to obtain explanatory and control variables, such as the presence of trade agreements, diplomatic relations, and country-specific economic indicators.
The primary dataset for this analysis are Catalan trade flows, obtained from the statistical agency Idescat through web scraping. The final dataset is a panel covering the period from 1995 to 2020, that contains trade flows where Catalonia is either the origin (for exports) or the destination (for imports). This panel includes 186 trading partners (Spain excluded) and 1,283 products categorized at the HS 4-digit level. As is standard in gravity model literature (see Felbermayr and Yotov, 2021), the dataset incorporates zero-valued trade flows.
After filling in the missing trade flows with zero values, the dataset contains 474,710 observations per year, resulting in a total of 12,342,460 observations. Of these, 10,354,315 are zero-valued flows, reflecting the sparsity typical in international trade data. A typical observation in this dataset is represented as Xijk,t, where i refers to the origin (exporter country), j to the destination (importer country), k to the product, and t to the year.
Given that the primary objective of this study is to explore the determinants of Catalan trade, several additional datasets are incorporated into the analysis. These datasets provide essential explanatory and control variables that help to account for the various factors influencing trade flows.
Preferential Trade Agreements (PTAs) are one of the central tools available to gov- ernments for promoting international trade. In this study, data on PTAs is sourced from the CEPII gravity database, and every PTA in which Spain is a signatory (of- ten through its membership in the European Union) is also attributed to Catalonia. PTAs are represented as a binary variable:
ij,t
= 1 if a PTA is in force between i and j at year t,
0 otherwise.
It is important to note that, due to the fixed effects structure described in sec- tion 3, the analysis only captures the effects of PTAs that came into force during the period under study (1995-2020). For Catalonia, this includes 59 new PTAs that were implemented during this time frame. Table A1 in the Appendix lists the coun- tries with which Catalonia established a new PTA, as well as the year in which each agreement was enacted.
Bilateral Investment Treaties (BITs) are agreements primarily aimed at enhancing investment between the signatory countries or jurisdictions. However, recent research has shown that BITs can also have significant effects on trade flows (see Heid and Vozzo, 2020), making them relevant for inclusion as explanatory variables in this study.
The data on BITs is sourced from the United Nations Conference of Trade and Development (UNCTAD) Investment Policy Hub. Similar to the treatment of PTAs, a BIT is considered to include Catalonia as a signatory if Spain is a signatory. BITs are also modeled as binary variables:
ij,t
= 1 if a BIT is in force between i and j at year t,
0 otherwise.
It is important to emphasize that the empirical specification used in this study only captures the effects of Bilateral Investment Treaties (BITs) that came into
force during the analysis period (1995–2020). For Catalonia, this includes 48 BITs. A detailed list of the countries that entered into new BITs with Catalonia, along with the year each agreement came into force, is provided in Table A2 in the Appendix.
A potential concern when including both PTAs and BITs as explanatory vari- ables is the possibility of overlap between the two. In fact, for 22 countries, both a PTA and a BIT with Catalonia came into force during the sample period. How- ever, it is worth noting that for none of these cases did the PTA and BIT come into effect in the same year. This temporal separation helps to mitigate concerns about confounding effects, allowing the study to more accurately isolate the impact of each type of agreement on trade flows.
Accio is a Catalan governmental agency whose primary mission is to enhance the competitiveness of Catalan firms by promoting innovation, supporting internation- alization, and attracting foreign investment.
For the purposes of this study, the presence of Accio delegations will be captured using a binary variable:
Accioij,t
= 1 if there is a Accio delegation between i and j at year t,
0 otherwise.
Of the full network of 41 Accio delegations, this study is only able to estimate the impact of 13 delegations that were opened during the analysis period (1995–2020). These newly established delegations allow for a before-and-after comparison, helping to isolate the effect of Accio on trade flows. A complete list of the countries where new Accio delegations opened, along with the corresponding years, is provided in Table A3 in the Appendix.
The gravity dataset is sourced from CEPII, a widely-used resource for obtaining key gravity model controls such as distance between trading partners, GDP, contiguity (whether countries share a border), and diplomatic disagreement. These variables are mainly used as controls.
In addition, OECD membership is included as a proxy for distinguishing between developed and developing countries, allowing for an examination of the heterogene- ity in trade effects based on the level of economic development. Furthermore, the product classification system from Rauch (1999) is used to differentiate between homogeneous and differentiated goods.
This section presents the summary statistics of the dependent variable and the ex- planatory variables, as shown in Table 1.
The dependent variable, the trade flows between country i and j1, in sector k at year t, denoted as Xijk,t, has an average value of 222 thousand euros. However, it exhibits a large variance due to the significant number of zero trade flows in many observations. This high proportion of zero flows justifies the use of a count-data model for the analysis.
The main variables of interest, PTAij,t, BITij,t and Accioij,t, are binary. Their mean values are 0.30, 0.32, and 0.16, respectively, reflecting the proportion of obser- vations where these policies are in place.
| Table 1: Summary Statistics | |
| VariableMean | Std. Dev. |
| Trade flows (Xijk,t)222.447 | 6658.136 |
| Diplomatic Disagreementij,t1.275 | 0.747 |
| PTAij,t0.309 | 0.462 |
| BITij,t0.322 | 0.467 |
| Accioij,t0.165 | 0.371 |
| Distanceij6,015.731 | 3,708.545 |
| Contiguityij0.027 | 0.162 |
| GDPi,t724,000,000 | 1,050,000,000 |
| OECDi0.089 | 0.285 |
| Differentiated Productk0.653 | 0.476 |
Note: PTAij,t, BITij,t, Accioij,t, OECDi, Contiguityij and Differentiated Productk are binary variables; GDPi, t is expressed in thousands of US dollars ; Trade flows (Xijk,t) is expressed in thousands of euros; Distanceij is expressed in kilometers.
The main empirical specification is based on the structural gravity model as de- scribed in Anderson and Van Wincoop (2003). A key aspect to highlight is that the dependent variable, representing trade flows, is a count variable. Consequently, the
1Note that by definition, either i or j is always Catalonia
estimation employs a Pseudo-Poisson model to account for the nature of the data. Additionally, given the high-dimensional index structure of the dependent variable, the model includes fixed effects to control for unobserved heterogeneity across coun- tries and products.
The baseline regression is specified as follows:
Xijk,t = exp(PTAij,tβPT A + BITij,tβBIT + Accioij,tβAccio + Diplomaticij,tβDiplomatic
+ βXXi,t + βzZj,t + ηi + γj + νk + ϵijk,t), (1)
where Xijk,t are trade flows from country i to country j in sector k at year t. The variables PTAij,t, BITij,t, Accioij,t and Diplomaticij,t represent the main policy variables of interest, which are expected to influence trade flows. Xi,t and Zj,t capture control variables that account for country-specific characteristics that may vary over time.
All parameters β are regression coefficients reflecting the impact of the respective variables. The model incorporates fixed effects denoted by ηi for the origin country, γj for the destination country, and νk for the product-specific effects. Finally, ϵijk,t is an idiosyncratic disturbance term.
It is important to remark that the current study examines trade flows exclusively from a single country perspective, specifically focusing on Catalonia as the origin for exports and the destination for imports. This unique framework implies that some of the traditional gravity based fixed effects either cannot be included or are already nested in the fixed effects of equation 1.
For instance, when conducting a regression on Catalan exports, it holds that i = Catalonia for all i. This implies the following: (1) it is not necessary to include i-specific fixed effects; (2) it is impossible to include destination-time fixed effects and, at the same time, obtain coefficients on the variables of interest; (3) the country- pair fixed effect that is often used in the literature is equivalent to the inclusion of j-specific fixed effects.
Given these structural characteristics, the parameters β in equation 1 capture the time variation of the variables of interest. In other words, this translates to a before- and-after comparison within the same destination country (for Catalan exports) or origin country (for Catalan imports).
This section presents the key findings of the study. It begins with the baseline results, which provide an overall view of the effectiveness of the trade-promoting tools under consideration. Following this, some results related to country and product heterogeneities are introduced, offering deeper insights into how these tools perform across different contexts.
The analysis of country and product-level heterogeneities helps to unravel the underlying mechanisms of the trade-promoting policies. By understanding how these tools vary in effectiveness depending on the characteristics of the trading partner (such as their level of development) and the nature of the products (differentiated or homogeneous), the study provides a better understanding of the mechanisms behind these policies.
This section presents the baseline results derived from estimating equation 1. Ini- tially, a naive approach is employed in Table 2, where the equation is estimated without incorporating any fixed effects. While this approach may introduce bias in some of the coefficients (as noted by Felbermayr and Yotov, 2021), it remains a valuable exercise. The absence of fixed effects allows for the estimation of effects for time-invariant variables, which would otherwise be absorbed when fixed effects are included. This provides an initial understanding of the relationships at play before refining the model with more robust methods.
Table 2 is organized into three columns. The first column reports results for all trade flows (Catalan imports and exports combined), while the second and third columns focus exclusively on Catalan imports and exports, respectively.
Due to the omission of fixed effects, Table 2 includes results for time-invariant controls like Distance and Contiguity. As expected, Distance has a negative impact on trade, while Contiguity positively affects trade, both in line with gravity theory and existing literature. Additionally, the GDP of both the origin country (i) and the destination country (j) positively influences trade flows, reinforcing the idea that larger economies trade more.
The table also highlights the role of diplomatic factors and trade policies: Diplo- matic disagreement acts as a deterrent to trade, while PTAs and Accio delegations promote trade. Interestingly, the results for Bilateral Investment Treaties (BITs) show a negative coefficient, suggesting that BITs might decrease trade. However, this is likely due to the naive approach used here, which is susceptible to omitted
| Trade flows (Xijk,t) | All | Imports | Exports |
| Distanceij | −0.000∗∗∗ | −0.000∗∗∗ | −0.000∗∗∗ |
| Contiguityij | (0.000)
0.637∗∗∗ |
(0.000)
0.317∗∗∗ |
(0.000)
0.900∗∗∗ |
| Diplomatic disagreementij,t | (0.023)
−0.485∗∗∗ |
(0.033)
−0.539∗∗∗ |
(0.030)
−0.391∗∗∗ |
| PTAij,t | (0.017)
1.071∗∗∗ |
(0.027)
0.803∗∗∗ |
(0.013)
1.388∗∗∗ |
| Accioij,t | (0.026)
1.805∗∗∗ |
(0.038)
1.843∗∗∗ |
(0.024)
1.779∗∗∗ |
| BITij,t | (0.020)
−0.435∗∗∗ |
(0.026)
−0.342∗∗∗ |
(0.032)
−0.611∗∗∗ |
| GDPi,t | (0.018)
0.000∗∗∗ |
(0.027)
0.000∗∗∗ |
(0.019)
0.000∗∗∗ |
| GDPj,t | (0.000)
0.000∗∗∗ |
(0.000)
0.000∗∗∗ |
(0.000)
0.000∗∗∗ |
| (0.000) | (0.000) | (0.000) | |
| Obs. | 11,013,272 | 5,506,636 | 5,506,636 |
| R2 | 0.283 | 0.250 | 0.344 |
Robust standard errors are reported in parentheses.
included.
In Table 3, the focus is on time-variant trade policies, with the results suggesting that PTAs positively impact overall trade, particularly Catalan exports. This is evident from the positive and highly significant PTA coefficients, with the coefficient in the exports column being larger than that in the imports column, indicating that PTAs have a stronger effect on exports. Similar patterns are observed for BITs and new Accio delegations, which also show positive effects on trade flows.
The coefficients in this table should be understood in a before-and-after context,
2In the imports column, only the country of origin fixed effect is included, as adding destination fixed effects would be redundant since Catalonia is the sole destination. Similarly, in the exports column, only destination fixed effects are used.
| Trade flows (Xijk,t) | All | Imports | Exports |
| Diplomatic disagreementij,t | −0.202∗∗∗ | −0.244∗∗∗ | −0.086∗ |
| PTAij,t | (0.045)
0.466∗∗∗ |
(0.068)
0.242∗∗∗ |
(0.048)
0.486∗∗∗ |
| (0.038) | (0.065) | (0.036) | |
| Accioij,t | 0.139∗∗∗ | 0.002 | 0.226∗∗∗ |
| (0.043) | (0.062) | (0.043) | |
| BITij,t | 0.318∗∗∗ | 0.135 | 0.257∗∗∗ |
| (0.060) | (0.087) | (0.033) | |
| GDP controls | ✓ | ✓ | ✓ |
| Origin-Country FE | ✓ | ✓ | |
| Destination-Country FE | ✓ | ✓ | |
| Product FE | ✓ | ✓ | ✓ |
| Obs. | 10,961,768 | 5,463,716 | 5,472,300 |
| R2 | 0.721 | 0.701 | 0.806 |
Robust standard errors are reported in parentheses.
comparing the impact of these policies over time. Since all variables are binary indi- cators, the coefficients can be directly interpreted as semi-elasticities. For instance, the 0.226 coefficient for Accio in the exports column suggests that opening a new Accio delegation in a specific country is associated with a 22.6% increase in exports to that country. A similar interpretation applies to the coefficients for PTAs and BITs.
Interestingly, the magnitudes of the coefficients for traditional trade and invest- ment policy instruments (PTAs and BITs) align with those found in previous studies that report global averages (see Heid and Vozzo, 2020; Nagengast and Yotov, 2023). This consistency reinforces the validity of the findings and suggests that these policies have a comparable effect on trade in the Catalan context.
Overall, Table 3 suggests that PTAs, BITs, and Accio delegations all have a positive effect on trade. The results highlight their effectiveness in promoting both imports and exports, with a particularly strong impact on Catalan exports.
In the next two sections, country and product heterogeneities are examined to bet- ter understand the mechanisms behind the success of these treaties and delegations. By exploring how these tools perform differently depending on the characteristics of the trading partner and the nature of the goods being traded, the analysis will
provide deeper insights into why and how these policies work.
This section investigates country-level heterogeneities, examining how the three trade- promoting tools (PTAs, Accio delegations, and BITs) may have varying effects de- pending on the level of development of the trading partner. The main idea is that these tools could influence trade differently depending on whether the trading part- ner is more or less developed. To capture this, the level of development is measured by whether the trading partner is a member of the OECD.
Table 4 presents the heterogeneous effects of the trade-promoting tools by in- teracting each tool with the OECD membership of the trading partner. The most critical components of Table 4 are, therefore, these interaction terms, which should be interpreted relative to the excluded category, i.e., non-OECD members.
For PTAs, the results reveal a negative and highly significant coefficient, indicat- ing that PTAs are more effective in boosting trade when the trading partner is not an OECD member. In other words, PTAs appear to generate a larger increase in trade with non-OECD countries.
As for BITs, the results do not show any significant heterogeneity based on the development level of the trading partner, suggesting that BITs have a uniform effect regardless of whether the partner is an OECD member or not.
Finally, the Accio delegations exhibit a notable asymmetry: they have a stronger effect in promoting Catalan exports to OECD countries, while they have a greater impact on imports from non-OECD countries. This suggests that Accio delegations are particularly useful for facilitating exports to developed countries, but they play a different role when it comes to imports.
This section explores product-level heterogeneities, focusing on how the three trade- promoting tools, namely, PTAs, Accio delegations, and BITs, may exert varying effects depending on certain product characteristics. The central premise is that these tools may influence trade differently based on whether a product is differentiated or homogeneous.
Specifically, the product differentiation classification proposed by Rauch (1999) is employed to distinguish between differentiated and non-differentiated products. The underlying idea is that differentiated products possess unique characteristics, which complicate the assessment of their quality or fair prices. In contrast, homogeneous
products are those with established reference prices or those traded on organized exchanges, making their evaluation straightforward.
Table 5 presents the results for this exercise by means of interaction terms. The interaction terms are particularly interesting and a close examination reveals that both PTAs and Accio delegations show positive and significant coefficients. This indicates that these two tools play a crucial role in enhancing trade, especially for differentiated goods.
Interestingly, the coefficients related to new Accio delegations are substantially larger than those for PTAs. This could be explained by the nature of Accio delega- tions, which, unlike PTAs, do not modify tariffs or introduce new trading regulations. Instead, they function as key providers of information. As a result, their influence is particularly pronounced in the trade of differentiated goods, where access to detailed information is essential.
Lastly, Table 5 shows no significant heterogeneity in the effects of BITs based on product characteristics, suggesting that BITs do not exhibit a differentiated impact across various types of goods.
In conclusion, this study provides valuable insights into the effectiveness of trade- promoting tools such as Preferential Trade Agreements (PTAs), Bilateral Investment Treaties (BITs), and Accio delegations for the Catalan economy. The results indicate that all three instruments have a positive impact on trade flows.
The study’s findings are consistent with gravity theory and prior research, re- inforcing the validity of the results and the relevance of trade-promoting tools in contemporary economic interactions. The coefficients associated with PTAs and BITs align closely with global averages reported in previous studies, suggesting that the Catalan context reflects broader trends observed in international trade.
Moreover, the exploration of product and country-level heterogeneities shows the mechanisms underlying the effectiveness of these trade policies. The findings suggest that PTAs are particularly beneficial when engaging with non-OECD countries, high- lighting their role in reducing trade barriers in less developed contexts. Conversely, Accio delegations appear to significantly enhance exports to OECD countries.
Furthermore, the analysis of differentiated goods reveals critical insights into how trade-promoting tools function in relation to product characteristics. The results indicate that PTAs and Accio delegations have a more pronounced effect on trade flows for differentiated products compared to homogeneous ones. This is particularly
important, as differentiated goods often present challenges in terms of quality assess- ment and pricing, making the provision of information and reduced trade barriers essential for successful market entry.
In summary, this research highlights the significant role of PTAs, BITs, and Accio delegations in enhancing trade. By understanding how these tools function across different contexts, policymakers can better design and implement trade agreements and initiatives that effectively boost economic activity. Future research could expand on these findings by exploring additional variables or contexts, further enriching the discourse on international trade policy.
Table 4: Catalan trade determinants: Trading partner OECD membership
| Trade flows (Xijk,t) | All | Imports | Exports |
| Diplomatic disagreementij,t | −0.210∗∗∗ | −0.254∗∗∗ | −0.089∗ |
| PTAij,t | (0.045)
0.799∗∗∗ |
(0.068)
0.740∗∗∗ |
(0.048)
0.590∗∗∗ |
| PTAij,t × Exporter OECDi | (0.040)
−0.830∗∗∗ |
(0.074)
−0.852∗∗∗ |
(0.034) |
| PTAij,t × Importer OECDj | (0.109)
−0.269∗∗∗ |
(0.131) | −0.197∗∗∗ |
| (0.074) | (0.073) | ||
| Accioij,t | 0.252∗∗∗ | 0.277∗∗ | 0.090 |
| Accioij,t × Exporter OECDi | (0.071)
−0.163∗ |
(0.139)
−0.245 |
(0.059) |
| (0.097) | (0.154) | ||
| Accioij,t × Importer OECDjr | 0.077 | 0.179∗∗ | |
| BITij,t | (0.089)
0.279∗∗∗ |
0.088 | (0.079)
0.288∗∗∗ |
| (0.069) | (0.097) | (0.036) | |
| BITij,t × Exporter OECDi | 0.234 | 0.278 | |
| (0.159) | (0.171) | ||
| BITij,t × Importer OECDj | 0.004 | −0.136∗ | |
| (0.105) | (0.080) | ||
| GDP controls | ✓ | ✓ | ✓ |
| Origin-Country FE | ✓ | ✓ | |
| Destination-Country FE | ✓ | ✓ | |
| Product FE | ✓ | ✓ | ✓ |
| Obs. | 10,961,768 | 5,463,716 | 5,472,300 |
| R2 | 0.721 | 0.701 | 0.806 |
Robust standard errors are reported in parentheses.
| Trade flows (Xijk,t) | All | Imports | Exports |
| Diplomatic disagreementij,t | −0.202∗∗∗ | −0.245∗∗∗ | −0.086∗ |
| (0.045) | (0.066) | (0.048) | |
| PTAij,t | 0.268∗∗∗ | 0.070 | 0.360∗∗∗ |
| PTAij,t × Differentiated Productk | (0.043)
0.304∗∗∗ |
(0.070)
0.279∗∗∗ |
(0.045)
0.173∗∗∗ |
| Accioij,t | (0.037)
−0.291∗∗∗ |
(0.051)
−0.618∗∗∗ |
(0.038)
0.198∗∗∗ |
| (0.047) | (0.065) | (0.058) | |
| Accioij,t × Differentiated Productk | 0.653∗∗∗ | 1.020∗∗∗ | 0.038 |
| (0.038) | (0.050) | (0.051) | |
| BITij,t | 0.300∗∗∗ | 0.110 | 0.260∗∗∗ |
| (0.069) | (0.097) | (0.042) | |
| BITij,t × Differentiated Productk | 0.030 | 0.051 | −0.004 |
| (0.036) | (0.052) | (0.033) | |
| GDP controls | ✓ | ✓ | ✓ |
| Origin-Country FE | ✓ | ✓ | |
| Destination-Country FE | ✓ | ✓ | |
| Product FE | ✓ | ✓ | ✓ |
| Obs. | 10,961,768 | 5,463,716 | 5,472,300 |
| R20.723 | 0.706 | 0.806 | |
Robust standard errors are reported in parentheses.
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Table A1: New PTAs with Catalonia during 1995-2020 by country
| Year | Country | Year | Country | |
| 1996 | Andorra | 2009 | Saint Lucia | |
| 1996 | Turkey | 2009 | Trinidad and Tobago | |
| 1997 | Faeroe Islands | 2009 | Saint Vincent and the Grenadines | |
| 1998 | Tunisia | 2010 | Fiji | |
| 1999 | Slovenia | 2010 | Papua New Guinea | |
| 2000 | Israel | 2012 | South Korea | |
| 2000 | Morocco | 2013 | Colombia | |
| 2000 | South Africa | 2013 | Peru | |
| 2001 | Mexico | 2014 | Ukraine | |
| 2001 | Macedonia | 2015 | Cameroon | |
| 2002 | Croatia | 2015 | Costa Rica | |
| 2002 | Jordan | 2015 | Georgia | |
| 2002 | San Marino | 2015 | Guatemala | |
| 2003 | Chile | 2015 | Honduras | |
| 2003 | Lebanon | 2015 | Moldova | |
| 2004 | Cyprus | 2015 | Nicaragua | |
| 2004 | Egypt | 2015 | Panama | |
| 2004 | Malta | 2015 | El Salvador | |
| 2007 | Albania | 2017 | Ecuador | |
| 2009 | Antigua and Barbuda | 2018 | Armenia | |
| 2009 | Bahamas | 2018 | Canada | |
| 2009 | Bosnia and Herzegovina | 2018 | Romania | |
| 2009 | Belize | 2019 | Comoros | |
| 2009 | Barbados | 2019 | Japan | |
| 2009 | Cote d’Ivoire | 2019 | Madagascar | |
| 2009 | Dominica | 2019 | Mauritius | |
| 2009 | Grenada | 2019 | Seychelles | |
| 2009 | Guyana | 2019 | Zimbabwe | |
| 2009 | Jamaica | 2020 | Singapore | |
| 2009 | Saint Kitts and Nevis |
| Year | Country | Year | Country | |
| 1996 | Honduras | 2000 | Slovenia | |
| 1996 | Indonesia | 2001 | Gabon | |
| 1996 | Peru | 2002 | Jamaica | |
| 1996 | Mexico | 2003 | Bosnia and Herzegovina | |
| 1996 | Malaysia | 2003 | Uzbekistan | |
| 1996 | Pakistan | 2004 | Guatemala | |
| 1996 | Algeria | 2004 | Iran | |
| 1996 | El Salvador | 2004 | Syria | |
| 1996 | Dominican Republic | 2004 | Albania | |
| 1996 | Paraguay | 2004 | Trinidad and Tobago | |
| 1997 | Latvia | 2004 | Namibia | |
| 1997 | Lebanon | 2006 | Nigeria | |
| 1997 | Ecuador | 2007 | Macedonia | |
| 1997 | Venezuela | 2007 | Colombia | |
| 1998 | Panama | 2007 | Moldova | |
| 1998 | Croatia | 2008 | Kuwait | |
| 1998 | Estonia | 2009 | Equatorial Guinea | |
| 1998 | Turkey | 2009 | Libya | |
| 1998 | Bulgaria | 2011 | Senegal | |
| 1998 | India | 2011 | Vietnam | |
| 1999 | Costa Rica | 2013 | Bolivia* | |
| 1999 | South Africa | 2014 | Bahrain | |
| 2000 | Jordan | 2016 | Mauritania | |
| 2000 | Ukraine | 2016 | Saudi Arabia |
Year Country 1999 Brazil
2000 Canada
2003 Egypt
2012 Colombia
2012 South Korea
2014 Ghana
2014 Peru
2015 Israel
2015 Panama
2017 Croatia
2017 Kenya
2017 Netherlands
2017 Serbia
Published on 02/03/25
Submitted on 30/10/24
Volume L’economia catalana dins el nou marc geoestratègic global, 2025
Licence: CC BY-NC-SA license