Deep preferential trade agreements and export efficiency in Ghana: Do institutions matter?

Obeng, Camara Kwasi; Boadu, Michael Tutu; Ewusie, Ewura-Adwoa Article Deep preferential trade agreements and export efficiency in Ghana: Do institutions matter? Research in Globalization Provided in Cooperation with: Elsevier Suggested Citation: Obeng, Camara Kwasi; Boadu, Michael Tutu; Ewusie, Ewura-Adwoa (2023) : Deep preferential trade agreements and export efficiency in Ghana: Do institutions matter?, Research in Globalization, ISSN 2590-051X, Elsevier, Amsterdam, Vol. 6, pp. 1-12, https://doi.org/10.1016/j.resglo.2023.100112 This Version is available at: https://hdl.handle.net/10419/331045 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). Deep preferential trade agreements and export efficiency in Ghana: Do institutions matter? Camara K. Obeng * , 1 , Michael Tutu Boadu 2 , Ewura-Adwoa Ewusie 3 Department of Economic Studies, School of Economics, University of Cape Coast, Cape Coast, Ghana ARTICLE INFO Keywords: Deep PTAs Export Export efficiency Ghana ABSTRACT Preferential trade agreements with environmental provisions (Deep PTAs) have introduced a new layer of costs to bilateral trade. For a country seeking to grow its exports to generate sufficient foreign exchange for economic transformation, it is essential to investigate the effects of the expanded preferential trade area on its export efficiency. Therefore, this paper, employs the stochastic frontier gravity model to investigate Ghana’s bilateral export efficiency using a panel of 44 of her export destination countries for the period 2007 to 2019. The paper finds that Ghana’s bilateral export trade is inefficient, implying huge potential exists. It further reveals that PTAs with environmental provisions reduce Ghana’s export efficiency. The reduction in export efficiency lessens with improvement in institutional quality. The paper recommends that the regulatory roles of Ghana Standards Authority and the Environmental Protection Agency should be strengthened to ensure that exporters meet target market regulations to continue to benefit from the bilateral PTAs. Given the negative effect of the exchange rate on export efficiency, the Bank of Ghana and the Ministry of Finance should urgently enhance macroeconomic stability to provide a stable platform which should promote the country’s export value and volume. Introduction Governments enter into preferential trade agreements (PTAs) with trading partners to increase trade, promote economic growth and reduce poverty (Winters & Martuscelli, 2014). Traditionally, PTAs have focused on removing tariff and non-tariff barriers between trading partners. In recent times, however, the number of PTAs has ballooned. Their scope has expanded beyond the removal of tariffs and other trade-related frictions to include non-trade components such as investment, competition policy, labour standards and protection of the environment (Horn, Mavroidis, & Sapir, 2010). These add-ons are referred to as deep PTAs. Despite the prolific nature of such agreements, which are meant to increase trade, other hindrances remain and continue to impede the proportion of extractable trade potential existing in a country (efficiency) (Khan & Kalirajan, 2011). Therefore, with the increase in expanded PTAs, this paper focuses on bilateral export efficiency and argues that once a country gains knowledge of its export efficiency with a preferential trading partner, it will fashion policies to increase efficiency and enhance trade performance. The issue of trade efficiency has become pertinent due to disparate evidence surrounding the adoption and implementation of deep PTAs. In this paper, deep PTAs refer to PTAs with environmental provisions. While deep PTAs are suggestively positive for environmental protection (Brandi, Bruhn, & Morin, 2019; Kolcava, Nguyen, & Bernauer, 2019), they can also reduce trade (Berger, Brandi, Morin, & Schwab, 2020). Moreover, evidence from Brandi, Schwab, Berger, and Morin (2020) suggests that although deep PTAs curtail dirty exports, they encourage export of green products. It has also been observed that many environmental provisions fall outside the World Trade Organisation (WTO) mandate (Morin, Dür, and Lechner, 2018), thus, raising concerns about their real intent. Arguably, these are augmenting trade requirement pressures faced by developing countries. To meet such demands, exporting countries would require a robust institutional framework to devise and implement a green production policy which safeguards the environment. Extant literature argues that countries with institutions, which ensure that firm activities do not harm the environment and meet expected standards, can cope in the global export market when confronted * Corresponding author. E-mail addresses: [email protected] (C.K. Obeng), [email protected] (M.T. Boadu), [email protected] (E.-A. Ewusie). 1 Orcid: https://orcid.org/0000-0003-2475-0300. 2 Orcid: http://orcid.org/0000-0003-2907-7717. 3 Orcid: https://orcid.org/0000-0001-5450-1087. Contents lists available at ScienceDirect Research in Globalization journal homepage: www.sciencedirect.com/journal/research-in-globalization https://doi.org/10.1016/j.resglo.2023.100112 Received 10 November 2022; Received in revised form 4 January 2023; Accepted 4 January 2023 Research in Globalization 6 (2023) 100112 2 with these environmental provisions in PTAs (Fiorini & Hoekman, 2018; G´ omez-Mera & Varela, 2021). Therefore, regulatory quality, which we define as the “ability of the government and its agencies to formulate and implement sound policies and regulations that promote private sector development”, will ensure that firms obey environmental regulations and can extract more export potential in partner countries (G´ omez-Mera & Varela, 2021, p. 614). Another critical factor is the institutional capability to enforce environmental provisions. Some provisions are also considered restrictive, which may limit a country’s ability to maximise its export capacity with a bilateral trading partner. Consequently, regulatory quality, enforcement and the effect of restrictive provisions are crucial factors in analysing deep PTAs and export efficiency. These require urgent attention and make the issue of deep PTAs, their supporting institutions, and their effect on export potential an empirical problem. Ghana is well suited for this study because its trade was liberalised extensively during the 1980s economic reforms. It has since signed onto many trade agreements such as the interim Economic Partnership Agreement (iEPA) between the European Union (EU) and the African, Caribbean and Pacific (ACP) countries, the African Growth Opportunity Act (AGOA), and the African Continental Free Trade Area (AfCFTA) agreement. Despite the liberalisation, evidence suggests that such agreements may not yield the desired results. In the context of AfCFTA, Boakye et al. (2022) reveal that Ghana’s export efficiency was low from 2007 – 2019. Specifically, export inefficiency was highest in the European Union and the United States, which are identified as the nerve centres of PTAs with environmental provisions. To this effect, the aching question is, how have all these trade agreements affected Ghana’s bilateral export efficiency? Particularly, the role of expanded PTAs in ensuring optimal export efficiency. This paper fills a vital gap in the empirical literature by answering the following research questions: First, what is the level of efficiency of Ghana’s bilateral exports? Second, what is the effect of deep PTA on export efficiency and is the number of PTAs in place relevant for trade efficiency? Third, to what extent do existing institutions affect the relationship between deep PTAs and export efficiency? This paper answers these questions using 44 of Ghana’s bilateral trade partners and disaggregated exports [Agricultural, Forestry & Fishery (AFoF), Minerals, and Manufactured products] for the period 2007 – 2018. To our best knowledge, no country-specific study has been conducted for Ghana, and for a more efficient analysis, we employ the Stochastic Frontier Gravity Model (SFGM). Related export efficiency studies identified include Kumah (2017) and, Adam and Tweneboah (2008). The former studied the level of trade integration among countries in the West African Monetary Zone (WAMZ), while the latter employed the usual traditional gravity model to predict the trade potential of Ghana’s trading partners. The paper’s uniqueness is its focus on the effect of expanded PTAs on export efficiency in Ghana. Crucially, it examines the extent to which the quality of institutions promotes trade efficiency by interacting institutions (proxied by regulatory quality) with deep PTAs. It also examines the moderating effect of enforcement capability and the role of restrictive provisions in either improving efficiency by reducing inefficiencies or vice versa. The paper makes these important findings: First, Ghana’s export efficiency is low for the disaggregated exports to all trading partners, implying that considerable unexploited potential exist in these markets. Second, deep PTAs reduce Ghana’s export efficiency, and third, the extent of reduction lessens with improvement in institutions. The rest of the paper is organised as follows: Section two is devoted to a review of the literature. Section three presents the methodology. The results and discussion are provided in section four and section five concludes the paper. Literature review Theoretically, traditional PTAs remove tariffs to reduce trade costs and offer free access to expanded markets with the aim of promoting trade among members. This results in overall trade benefits for participating countries due to increased trade created in response to PTAs, or, from trade diversion to such favourable agreements (Viner, 1950). PTAs also ensure the survival of export firms, as encapsulated in the heterogeneous firm model of Melitz (2003) and espoused in Nkansah, Takyi, Sakyi, and Adusah-Poku (2022). The model argues that economic integration agreements (EIAs) remove trade barriers and reduce costs among member countries. The ensuing competition between firms leads to the more efficient firms surviving while the less profitable firms exit the export market. Survival of efficient export firms implies the ability of the country to extract more of the export potential existing in a particular market (enhance export efficiency). Another conduit by which firms, and hence, countries, enhance their export performance in a regional liberalisation environment is the formation of regional value chains. PTAs provide a product hub to which members contribute components as their comparative advantage dictates. This way, countries without the capacity to enter the global export market for whole products can participate through regional value chains (Razeq, 2022). Another channel through which PTAs enhance export efficiency is through the attraction of foreign direct investment (FDI). Here, the expanded market derived from the creation of the PTA attracts FDIs that desire to take advantage of the large market created. The enhancement of the scope of modern PTAs to include investment, property rights, competition, labour laws, and environmental provisions serve as a further attraction to FDIs. This is because investors are sure member countries will be committed to implementing the requirements of the agreements, thereby, securing their investments and improving their confidence (Medvedev, 2012). While increased trade is the ideal outcome for both parties of a PTA, the broadening of modern PTAs to include non-trade protocols such as, investment, labour, laws and environmental provisions have resulted in additional implications in terms of compliance and its attendant trade costs. This additional increase in trade costs has the potential to deter highly environmentally polluting firms from producing environmentally-dirty products for export, thereby reducing exports in line with the pollution haven hypothesis (Bommer, 1999; Brandi et al., 2020). From a positive perspective, the rising compliance cost will motivate innovative firms to explore more environmentally-friendly ways of production, and therefore, export more to increase exports in line with the Porter hypothesis (Porter & Van der Linde, 1995; Porter, 1991). Most crucially, the literature suggests that a vital channel through which PTA impacts exports is the policy credibility that accrues as a country joins a PTA (Büthe & Milner, 2008). Thus, upon joining a PTA, a government commits to implementing all its requirements. This totemic support is vital for deep PTAs with environmental provisions which require robust domestic regulatory attention. Thus, domestic institutions with strong administrative, regulatory, and technical capacities are essential for implementing trade agreements (Chauffour & Kleimann, 2012; Chayes & Chayes, 1993; Cole, 2016; Guzman, 2002). Gray (2014) highlights the mediating role of quality domestic institutions in facilitating the capacity of PTAs to enhance export performance, and hence, export efficiency. The literature identifies two channels through which quality domestic institutions augment the impact of PTAs on export performance: the implementation channel and the enforcement mechanism. First, comprehensive PTAs have numerous policy requirements that, when implemented, bestow the full benefits of an agreement on a country. Therefore, it takes stronger and quality domestic institutions to ensure that a country implements all these policy requirements. The second channel through which institutions moderate the effect of PTAs on export performance, and hence, export efficiency, is the enforcement of the agreement. When a country adopts the policy requirements of a PTA, it can only realise the full benefits if firms apply these requirements to their production activities. It takes only a strong and quality domestic C.K. Obeng et al. Research in Globalization 6 (2023) 100112 3 institution to enforce the policy requirements that have been agreed on (Büthe & Milner, 2008; Gray, 2014). The main drawback of PTAs on exports is the growing number that developing countries join, and the enormity of rules and regulations member countries have to commit to and comply with. This requires a huge state commitment, institutional capacity and effectiveness and appropriate infrastructure for a country to manage them successfully. Given that most developing countries do not have these capacities, they are often unable to implement the PTA requirements and do not accrue the best out of them. The inability of member countries to implement the fully required protocols of PTAs raises issues of policy credibility and institutional quality, which should improve the export efficiencies of participating countries (Bown, Lederman, Pienknagura, & Robertson, 2017). Drivers of export inefficiency Empirically, trade agreements and their modern variants have been widely studied due to their increasing relevance for trade performance. The Stochastic Frontier Gravity Model (SFGM) has been used in most studies, including those focused on individual nations, regions, trade agreements, bilateral relationships, and alternative scenarios. Some country-level research solely examined the factors that affect the efficiency and performance of exports. While these studies are useful for gauging how close a country is to its potential, they offer no evidence regarding the factors that contribute to inefficiency. For instance, Hassan (2017) examined the factors affecting Bangladesh’s export performance and efficiency in its bilateral trade with 40 countries. The author used the SFGM and likelihood estimation techniques on panel data from 2008–2011. He found that GDP, population, trade agreements, and exchange-rate depreciation all positively affect exports, while tariff levels and distance between Bangladesh and its partner countries adversely affect trade. The findings also demonstrated that customs procedures, port inefficiencies, and corruption are issues that impede trade. The results reveal a great deal of untapped export potential that may be harnessed by eliminating behind-the-border limitations to ensure a better integration with the world economy. For the Philippines, Deluna and Cruz (2013), also employed the SFGM to assess the efficiency and potential of bilateral exports between the country and its trade partners from 2009 to 2012. Results of the research indicate that distance, partner’s market size, and income all contribute to export performance. An additional intriguing conclusion from their research shows that the efficiency level varies from 38 % to 42 % between the Philippines and her trading partners, suggesting that inefficiency in the former’s exports is relatively significant. The results also suggest that the Philippines’ involvement in trading blocs like ASEAN, APEC, and the WTO, as well as the reduction of corruption, improvement in the importing country’s labour market conditions, and a common language, all contribute to greater export efficiency. It has also been observed that country-specific commodity/service studies that used SFGM are increasingly emerging (see Barma, 2017, Nasir & Kalirajan, 2014; Atif, Haiyun, & Mahmood, 2017). For instance, Barma (2017) examined the effectiveness of India’s bilateral agricultural exports to 112 trading partners between 2000 and 2013. The study, which employed the SFGM and maximum likelihood estimation (MLE) on panel data, demonstrated that while GDP, population, and business freedom all positively affect India’s bilateral agricultural exports, distance, being landlocked, the real exchange rate, trade freedom, and freedom from corruption all adversely affect India’s exports. Finally, there were considerable variations in the efficiency levels of the country’s agricultural exports with trading partners. Accessing data from 1995 to 2014, Atif et al. (2017) employed SFGM and MLE to assess Pakistan’s agricultural exports. The findings show that the factors driving agriculture exports were exchange rates, tariff rates, shared borders, shared cultures, colonial history and PTAs. They conclude that a huge untapped potential exists between Pakistan and its neighbouring Middle Eastern and European countries. The aforementioned works, however, simply take into account the countries’ capacity for trade; they say nothing about what causes the level of inefficiencies currently being experienced. Within the context of the SFGM, studies on regional blocs have also surfaced. Stack, Pentecost, and Ravishankar (2018) analysed the efficiency of bilateral trade between 18 Western European countries and the 13 new EU member countries. The authors used the SFGM and MLE techniques to analyse a panel of current EU member-to-new member states’ bilateral trade from 1995–2022. The researchers found that trade was positively affected by factors like income and per capita income differential, shared borders, languages, colonial ties, and regional integration, but negatively impacted by distance and landlocked status. The findings also demonstrated a significant increase in trade integration among new members. According to trade literature, a more effective logistics system lowers trade costs and promotes international trade. Recent studies have employed the World Bank logistic performance index to confirm the beneficial impact of logistics on trade flows. These include Martí, Puertas, and García (2014), Gani (2017), and Celebi (2017). Similarly, macroeconomic instability (inflation) lowers trade flows by making exports less competitive (Irshad, Xin, Hui, & Arshad, 2018). Demir, Bilik, and Utkulu (2017) employed the stochastic frontier gravity model to estimate the effect of competitiveness on trade efficiency for a subset of Asian economies between 2006 and 2015. The disclosed comparative advantage index was standardised to serve as a surrogate for competitiveness. The findings highlighted the importance of factors like GDP per capita, distance, population, proximity, a shared official language, a common coloniser, free trade agreements, and the global financial crisis in determining export performance. They suggested that, to strengthen their comparative advantages, nations should invest more in research and development (R&D) and human resources to boost their production technology. For developing countries, Kaushal (2022) used the SFGM to investigate how regional trade agreements impact India’s export efficiency. The findings demonstrate that India’s increased export efficiency is a direct effect of the country’s participation in regional trade accords. Similarly, Hai and Thang (2017) examined ASEAN Free Trade Agreement on Vietnam’s trade efficiency. The authors indicate that, ASEAN membership and trading partners’ economic freedom induced trade efficiency by bridging the gap between actual and potential trade. Kumah (2017) also used SFGM and panel data on exports for 45 countries over the period 2000–2014 to examine the trade efficiency of the West African Monetary Zone (WAMZ). This study focused on trade integration among WAMZ member countries using the Battese and Coelli (1988), and Kumbhakar (1990) models. The author discovered that trade among monetary zone members is extremely low, suggesting that a significant trade potential currently exists. There is also a more recent study by Boadu, Obeng, Dasmani, and Brafu-Insaidoo (2021). Employing the SFGM and a panel of 61 trading partners, the authors found that GDP and population had a positive effect, while distance had a negative effect on Ghana’s bilateral exports. On drivers of export efficiency, the authors found that members of trade agreements, language, corruption, trade freedom and institutional quality enhance export efficiency. On the other hand, tax burden, electricity supply and financial development raised export inefficiency. Finally, the results revealed a huge unexploited export potential among Ghana’s trading partners. To further investigate the reasons behind the considerable unexploited export capacity, it is vital to examine the role of trade agreements and how they impact export efficiency. These agreements have all been directed at liberalising trade to remove rigidities that might impede the potential for growth. The existing literature on deep PTAs point to the role of institutions in attracting foreign direct investment (FDI) (G´ omez-Mera & Varela, 2021) and promoting service trade (Fiorini & Hoekman, 2018). G´ omezMera and Varela (2021) argued that stronger institutions signal to a C.K. Obeng et al. Research in Globalization 6 (2023) 100112 4 government’s commitment to protect and respect investment agreements, and enhance the country’s ability to attract more FDI. Additionally, Fiorini and Hoekman (2018), posit that the welfare benefits of service trade liberalisation are enhanced in countries with high regulatory quality. However, there is no study that has investigated how institutional quality moderates the effects of deep PTAs on export efficiency. Crucially, there is no study on the moderating role of institutions (regulatory quality) on the effect of environmental PTAs and export efficiency in Ghana, and the contributing effects of enforcement and restrictive provisions. This study, therefore, extends the extant literature by exploring the mediating role of regulatory quality on the effects of deep PTAs on export efficiency using Ghana as a case study. Methodology This part of the work presents the theoretical and empirical models, measurements, descriptions, and reasons for using certain variables and data sources. Theoretical model Three factors slow down the flow of exports from the country of origin to the destination country: (1) natural constraints, such as distance and transportation costs; (2) “behind-the-border” restrictions, such as the exporter’s rigid institutions and infrastructure; and (3) “beyond-the-border” constraints, such as the importer’s rigid institutions and infrastructure. Beyond the boarder restrictions manifest in two ways: explicit and implicit limitations. Tariffs and exchange rates are the most obvious cross-border constraints. Contrarily, implicit beyond-the-border restrictions resulting from the importer’s institutional and regulatory rigidities are particularly challenging to identify and quantify because they are typically not within the exporter’s control. Without these limitations, the exporter will achieve its full export potential (Bhattacharya & Das, 2014; Boadu et al., 2021; Khan & Kalirajan, 2011; Obeng, 2022). Following Kalirajan (2007), the formal presentation of the aforementioned theoretical framework, which is a version of the SFGM created by Aigner, Lovell, and Schmidt (1977) and Meeusen and van Den Broeck (1977), is presented below: lnExpijt =f(lnZijt;γ)exp(− ω ijt, η ijt)(1) where lnExpijt is the log of bilateral exports from one country to another; lnZijt is the augments of potential bilateral exports; γ is the estimated parameters; η ijt is the stochastic error term and − ω ijt = (0,1)represents the bilateral exports inefficiency levels. It is presumed that − ω ijt follows a truncated (at 0) normal distribution, with a mean “mu” and constant variance. If − ω ijt =0, then bilateral exports reach its optimum level. When − ω ijt >0<1, it signifies that the actual level of exports falls short of potential exports, implying that behind-the-border constraints limit the attainment of full export potential. Using the notion of export efficiency as the ratio of realised to potential exports by Battese and Coelli (1988), we calculate efficiency as follows: Exportefficiencyi=lnf(lnZijt;γ)exp(− ω ijt, η ijt) lnf (lnZijt;γ)exp( η ijt)=exp(− ω ijt)(2) where all variables are as defined earlier. Export efficiency estimates vary from zero to one. A value of zero for export efficiency means that real export levels need increase (i.e., inefficiency), while a value of one for export efficiency means that current export values and potential export values are the same. Following from that, the export inefficiency is expressed as: ω ijt =f( ψ ijt,φ)+ϖijt (3) From Eq. (3), ω ijt is export inefficiency; ψ ijt represents plausible determinants; φ is the parameter to be predicted; ϖijt represents the random error. To test for the robustness of the stochastic frontier model, we estimated the gamma coefficient as specified in equation γ= σ ε 2 ( σ ε 2+ σ v2)(4) The gamma coefficient (γ)tests the robustness of the stochastic frontier gravity model in Eq. (1) and so provides insight into whether or not “beyond the border” limitations contribute to the inefficiency of Ghana’s bilateral exports. If γ is significant, then Ghana’s bilateral exports are heavily influenced by “behind the border” constraints. Put differently, the discrepancy between potential and actual export is attributable to the influence of behind-the-border limitations if gamma is significant. We also evaluated the likelihood ratio test statistics proposed by Kumbhakar, Wang, and Horncastle (2015) in addition to the gamma, which serves as a robustness test. Eq. (5) was used to calculate the likelihood ratio test. λ= − 2{ln[L(Ho) ] − ln[L(H1) ] } (5) where L(H0)is the restricted model computed from ordinary least squares (OLS) and L(H1)represents the unrestricted model estimated from the stochastic frontier. L(H0)and L(H1)are the values of the loglikelihood under the null and alternative hypotheses, H O ; there are no technical inefficiencies and H 1 : there are technical inefficiencies. The computed value of lambda (λ)was then compared with the mixed distribution of critical values obtained from Kodde and Palm (1986). Empirical model Implementation of specific objective one of the study required a simultaneous estimation of the frontier and the drivers of the inefficiency of bilateral exports. Following Simar, Lovell, and van den Eeckaut (1994) and Wang and Schmidt (2002) the empirical model for this exercise was specified as the Stochastic Frontier Gravity model below: ln Exportijt = α 0+ α 1lnGDPit + α 2lnGDPjt + α 3lnPopulationjt + α 4lnDistanceijt + η ijt − ω ijt (6) t=1,⋯,44&t=2007,⋯,2019;i=exporter;j=importer Eq. (6.1) shows how the inefficiency effect model takes into account important factors that lead to Ghana’s bilateral export inefficiency. Exp.Ieffijt =exp[γ1PTAdummyiit +γ2PTAdepthjt +γ3enforcementPTAjt +γ4restrictiveprovisionsjt +γ5LANGjt +γ6Landlockedjt +γ7corruptionit +γ8corruptionjt +γ9Inflationit +γ10exchangeratesit +γ11TradeFreedomijt +γ12Logisticsit +γ13Logisticsjt +γ14Regulatory Qualityit +γ15PTAdepth*Requlatoryqualityijt +∊ijt ] (6.1) t=1,⋯,44&t=2007,⋯,2019;i=exporter,j=importer where Exportij represents disaggregated bilateral exports of Ghana to her trading partners (agricultural, forestry and fishery, mineral products and manufactured products); Exp.Ieffijt is export inefficiency; GDPit is the GDP of Ghana while GDPjt is the GDP for country j at time t;Populationjt is the population of country j at time t;Distanceijt refers to geographical distance between the capital cities of country i and j measured in kilometres; PTA dummy, PTA depth, enforcement, and restrictive provisions indicates preferential trade agreements, depth of preferential trade agreements, enforcement of environmental provisions in preferential C.K. Obeng et al. Research in Globalization 6 (2023) 100112 5 trade agreements, and restrictive provisions in preferential trade agreements, respectively. LANG shows that importing country speaks the same official language as Ghana. Landlocked means importing country is landlocked; Logisticsi and Logisticsj are the logistic performance index of the exporter and the importer taken from the World Bank, corruption represents the freedom from corruption; PTAdepth* Regulatoryquality is the interaction term between depth of preferential trade agreements and regulatory quality; − ω ijt represents the inefficiency level of bilateral exports; η ijt and ∊ijt is the random error term ℓ n denotes natural logarithm. Definition and sources of data The study utilised panel data consisting of 44 of Ghana’s trading partners (see Appendix A for the list of countries) from 2007 to 2019. The selection of this sampling period was motivated by the availability of Ghana’s bilateral export statistics and its trading partners around the world. Table A1 (see Appendix B) presents the variables used, measurements, a priori signs, and sources of data. Estimation techniques This study adopted the maximum likelihood estimation technique for the stochastic frontier analysis since it is distinct when the OLS residuals have the right skewness, as suggested by the works of Kalirajan and Paudel (2015), Ravishankar and Stack (2014), and Atif et al. (2017). It is a well-known statistical approach that is used to fit a mathematical model to real-world data. The maximum likelihood estimates of an unknown parameter are defined as the parameter value that optimises the chances of arbitrarily reflecting a certain sample of observations. The likelihood function is maximised via an iterative optimisation technique that entails selecting beginning values for the unknown parameters and extensively upgrading and amending them until the values that maximise the log-likelihood function are determined. Different from and superior to OLS estimates, a routine solution for the stochastic frontier emerges. The stochastic frontier gravity model’s method of estimating efficiency is better than other methods because it can examine both the underlying export potential and changes in technical efficiency over time. To determine the adequacy of the SFGM, the least square residuals were tested for negative skewness. The presence of negative skewness subsequently validated the use of SFGM, and STATA was used for all estimations. Results In this section, the estimation results are presented. We first present the descriptive statistics, followed by presentation of the export inefficiency results, the frontier analysis estimates and export efficiency scores. Descriptive statistics The descriptive statistics of the variables used in this study are presented in Table 2. Table 2 shows that Ghana exported agriculture, forestry, and fishery commodities worth an average of US $30889.98 million, with a minimum of US $ 0.044 million and a maximum of US $1070000 million over the studied period. Ghana exported mineral products worth an average of $43903.32 million USD. Ghana exports roughly $ 23,230 million in manufactured products on average. This means that, on average, mineral commodities were Ghana’s most exported items. The standard deviations of the goods shipped are larger than their means, which indicate that the data points are spread out over a wider range of values. Furthermore, Ghana’s average GDP for the period is around US $31.7 billion, whereas its partner country’s average GDP is about US $1.1 trillion. Ghana’s GDP has a minimum and maximum value of about US $5 billion and US $65.6 billion, respectively. The partner’s GDP has a minimum and highest value of around US $487 million and US $ 20 trillion, respectively. Throughout the study period, the average population of the partner countries was around 100 million people. Likewise, the statistics show that the average distance between Ghana and all trading partners was around 5864.057 km. This means that Ghana’s commodities travel an average distance of 5864.057 km to reach its trading partners. The mean logistic performance index for Ghana is 2.505 while the mean logistic index of its trading partners is 4.373. This implies that goods are easily and more efficiently moved to Ghana’s partner trading countries. The minimum and maximum values for Ghana’s logistic performance index are 2.16 and 2.66, while the minimum and maximum values of partner’s logistic performance index are 2.96 and 5.50, respectively. Table 2 Descriptive Statistics. Variable Obs Mean Std. Dev. Min Max Agriculture, forestry, and fishery (SIC 0) 585 30889.98 75157.2 0.044 1,070,000 Mineral commodities (SIC 1) 585 43903.32 221,000 0.002 4,300,000 Manufactured commodities (SIC 2) 585 23230.01 68289.28 0.016 833,000 GDP of exporter 585 317209** 207926** 498300** 655600** GDP of importer 585 114507*** 255670*** 487000* 205000*** Population of importer 585 100057* 261322* 1320* 1393** Distance 585 5864.057 3851.618 306.96 15472.97 Trade freedom of exporter 585 6.652491 0.1953403 6.25 6.92 Trade freedom of importer 585 7.155583 0.8752686 4.59 9.11 Freedom from corruption of exporter 585 39.469 4.677 33 48 Freedom from corruption of Importer 585 55.103 23.993 10 94 Inflation of exporter 585 13.08631 3.767191 7.12635 19.25072 Effective Exchange rate 585 86.48745 11.94544 68.3678 106.9446 Regulatory Quality 585 −0.0125304 0.1095471 −0.2325097 0.1281278 PTA dummy 585 0.6222222 0.4852466 0 1 PTA number 585 1.822 1.902 0 5 Depth of PTA 585 0.735 1.001 0 2.286 Enforcement environmental provisions in PTAs 585 0.244 0.43 0 1 Restrictive environmental provisions in PTAs 585 4.089 5.35 0 13 Logistics of exporter 585 2.505 0.149 2.16 2.66 Logistics of importer 585 4.373 0.608 2.96 5.5 Source: Authors’ construct. NB *, **, *** are in millions, billions, and trillions respectively. C.K. Obeng et al. Research in Globalization 6 (2023) 100112 6 Table 3 Frontier and Export inefficiency estimates. (1) (2) (3) (4) (5) (6) Agricultural, Forestry & Fishery Agricultural, Forestry & Fishery Mineral Products Mineral Products Manufactured Products Manufactured Products Frontier Estimates Log GDP of exporter 0. 527*** 0.650** 0.0561*** 0.0561*** 3.392*** 0.849** (0.118) (0.218) (0.00505) (0.00443) (0.598) (0.307) Log GDP of importer 0.831*** 0.863*** 0.242*** 0.242*** 0.454*** 0.150* (0.0907) (0.167) (0.00161) (0.00144) (0.137) (0.0531) Log Population of importer 0.0202 1.521*** 0.585*** 0.585*** 1.178*** 0.574*** (0.0957) (0.175) (0.00244) (0.00216) (0.153) (0.0812) Log Distance −0.576*** −0.584** −1.461*** −1.461*** −2.428*** −1.312*** (0.161) (0.214) (0.00216) (0.00193) (0.169) (0.145) Constant −19.57*** −17.094*** 7.758*** 7.758*** −56.78*** −14.02** (5.205) (4.092) (0.00131) (0.00116) (14.11) (4.703) Export Inefficiency Estimates PTA Dummy –22.44*** 1.266 −0.781** (4.801) (0.802) (0.283) Number of PTA −1.699*** (0.181) −0.742* (0.316) −2.757*** (0.743) Depth of PTA −16.35*** −5.204*** −3.489* −2.268** −0.775** −4.027** (3.593) (0.837) (1.714) (0.873) (0.260) (1.377) Landlocked 5.210*** 0.213* 1.586 2.211 1.253*** 6.116* (1.070) (0.101) (1.115) (1.138) (0.367) (3.029) Enforcement of Environmental Provisions in PTA −66.72*** −18.66*** −7.831*** −7.745*** −6.412*** −13.18** (12.92) (1.541) (1.171) (2.043) (1.179) (4.126) Restrictive environmental provisions in PTA 8.877*** 2.482*** 0.847** 0.461** 0.668*** 2.072** (1.829) (0.263) (0.517) (0.159) (0.194) (0.683) Trade Freedom of importer −1.302*** 1.507 −3.251*** −0.541** −1.754* −5.033* (0.170) (1.016) (1.063) (0.184) (0.778) (2.277) Trade Freedom of exporter 2.036*** 0.543 1.740** 1.928** −0.0436 1.504 (0.609) (0.300) (0.591) (0.600) (0.219) (0.814) Language −2.066** 0.164 −0.677 −0.886 −0.962*** −5.179** (0.764) (0.344) (0.714) (0.717) (0.241) (1.892) Corruption of exporter 0.211*** 0.0797** 0.100** 0.112* −0.0115 0.0455 (0.0512) (0.0263) (0.0341) (0.0558) (0.0176) (0.0453) Corruption of importer 0.0371** 0.0115 0.0636*** 0.0568** −0.0196*** −0.0477** (0.0127) (0.00895) (0.0180) (0.0178) (0.00580) (0.0174) Exchange rate of exporter 0.473*** 0.178** 0.0723** 0.0930*** 0.0679** 0.121 (0.0648) (0.0599) (0.0242) (0.0281) (0.0233) (0.0693) Inflation of exporter 0.114 0.0326 0.0122 0.0185 −0.0830** −0.00708 (0.0732) (0.0366) (0.0755) (0.0758) (0.0277) (0.0681) Logistics of exporter −6.801*** 1. 999*** −1.936** −2.141 10.40*** 8.211*** (-2.013) (0.480) (0.649) (3.143) (1.516) (2.583) Logistics of importer −4.330*** −5.634*** −3.181*** −3.095*** −2.819*** −3.328** (0.964) (0.599) (0.926) (0.932) (0.371) (1.197) Regulatory Quality −3.899*** −3.751*** −4.955** −0.500*** −2.548*** −0.636 (0.981) (0.628) (1.671) (0.135) (0.337) (2.890) PTA Dummy ×Regulatory Quality −70.29*** −17.15** 2.074 (18.64) (5.773) (2.286) Depth of PTA ×Regulatory Quality −49.60*** −4.421** −11.03** −4.441 −0.477*** −2.639*** (12.86) (1.517) (3.724) (14.04) (0.0341) (0.769) Enforcement of Env. Prov. in PTA ×Regulatory Quality −192.4** −25.50* −61.83** −35.10* −9.179* −18.82* (64.82) (11.68) (23.91) (14.77) (3.474) (7.301) Restrictive Evn. Prov. ×Regulatory Quality −27.94** −3.252** −8.130*** −4.366* −2.823* −2.200 (9.47) (1.094) (2.815) (2.124) (1.204) (3.201) PTA Number ×Regulatory Quality 2.920* 1.315* 1.752* (1.348) (0.588) (0.836) Lgtgamma constant 1.389*** 1.657*** 34.68*** 35.30*** 2.533** 0.532** (0.266) (0.256) (0.942) (0.119) (0.941) (0.162) lnsigma2 lnsigma2 constant 2.387*** 1.802*** 3.301*** 3.307*** 1.213*** 1.761*** (0.151) (0.0738) (0.0455) (0.0489) (0.0662) (0.171) Gamma 0.800 0.840 0.983 0.975 0.926 0.629 Likelihood ratio Test 311.721 341.247 136.311 139.651 145.701 338.269 Observation 585 585 585 585 585 585 Standard errors in parentheses. NB: Enforcement of Env. Prov. in PTA is enforcement of environmental provisions in PTA. * p <0.05. ** p <0.01. *** p <0.001. C.K. Obeng et al. Research in Globalization 6 (2023) 100112 7 Additionally, the statistics show that Ghana’s trade freedom is roughly 6.7, while that of its trading partners is approximately 7.2. This suggests that, on average, Ghana’s trading partners are more economically-free. Ghana’s average inflation rate is 13.1 percent, with minimum and maximum readings of around 7.1 and 19.3 percent, respectively. Ghana’s exchange rate is 86.48745 on average, with minimum and maximum values of 68.37 and 106.94, respectively. The mean for regulatory quality is −0.013, with a minimum of −0.23 and a maximum of 0.13. Finally, the average number of PTAs between Ghana and her trading partners at any particular time is 1.822, with a minimum and maximum values of 0 and 5, respectively. Furthermore, the average depth of PTA between Ghana and her partners is 0.735, with a minimum value of 0 and a maximum of 2.286. The average value for restrictive environmental provisions in PTAs is 4.089, with minimum and maximum values of 0 and 13, respectively. Estimation results The results in Table 3 are in two parts. The first part presents the frontier estimates, which capture the traditional variables for a trade gravity model, and, the second part submits the inefficiency estimates, which capture the determinants of Ghana’s export inefficiency. For the estimation of export inefficiency, four sets of variables were considered. The first set focuses on trade agreements, which comprises of preferential trade agreements (PTAs), the depth of preferential trade agreements, the enforcement of environmental provisions in PTAs, and environmental provisions in PTAs that are considered restrictive. On the principal variable, the absolute number of PTAs contracted between Ghana and her trading partners in a given year was used as a robustness check. This is to capture the membership of trade agreements on export efficiency. The second set of variables presents specific characteristics of trading partners, such as logistics, and, language and geographical location, which are natural barriers to export. For the third set, macroeconomic and market freedom variables such as inflation, exchange rate, economic freedom, and corruption were also included to capture economic stability. The final covariate is the institutional variable proxied by regulatory quality. This crucial variable determines whether domestic institutions matter for a country to benefit from signing trade agreements with environmental provisions. From Table 3, Columns (1), (3) and (5), we present results of the export inefficiency estimates for the determinants of technical efficiency with a focus on the PTA dummy. We highlight those estimations of the models in Columns (2), (4) and (6) were conducted to improve the rigour of the analysis. In these estimations, we replace the PTA dummy with the absolute number of PTAs in place between Ghana and her trading partners at a particular time. This is to ascertain whether the number of PTAs in place actually matters for Ghana’s export efficiency. For each variable discussed, parameter estimates are presented consecutively for all three disaggregated exports [Agricultural, Forestry & Fishery (AFoF), Mineral, and Manufactured products] to indicate the differential effects of a PTA on the three main export sectors. For the same variable, we present the statistically significant results for models (2), (4) and (6), to compare the differences between the results with the PTA dummy and those with the number of PTAs. This is followed by a discussion of the frontier estimates and efficiency scores. Export inefficiency model From our export inefficiency results in models (1), (3), and (5) of Table 3, we find that Ghana’s membership of a preferential trade agreement significantly reduces its inefficiency in the exports of agricultural, forestry, & fishery products (henceforth, AFoF) and exports of manufactured products. However, joining a preferential trade agreement has no effect on Ghana’s export efficiency of mineral products. Specifically, at 1 per cent level, Ghana’s membership of a PTA reduces its export inefficiency or increases export efficiency of AFoF and manufactured products by 22.44 and 0.781 units respectively. We replace the binary PTA with the number of PTAs in place [see models (2), (4), and (6)] to check that it is not just the existence of a PTA but also the total number of agreements in place can affect the extent of export inefficiency or efficiency. The results suggest that the number of PTAs significantly reduces inefficiencies in Ghana’s exports of AFoF, mineral, and manufactured products. Precisely, the results show that an additional PTA will increase export efficiency of AFoF and manufactured products by 1.699 and 2.757 respectively, and they are statistically significant at 1 percent. Additionally, the number of PTAs in place increases Ghana’s export efficiency of mineral products by 0.742, which is statistically significant at 10 percent. These findings reveal that, although belonging to a PTA has a greater effect on improving export efficiency of AFoF products, the total number of PTAs in place at a given period is also important for the performance of Ghana’s exports in all product categories. On the depth of PTAs, we find that it significantly reduces export inefficiencies in AFoF and manufactured products but the increase in efficiency for mineral exports is marginally significant. However, after considering the number of PTAs, the depth of trade agreements significantly reduces inefficiencies in all three product categories at 1 and 5 percent levels. It is important to note that the binary PTA has a greater effect on increasing Ghana’s export efficiency in AFoF products relative to the depth of PTAs. For instance, the effect of the PTA dummy on AFoF products is 6.09 units higher than those with expanded provisions. This can be attributed to the fact that deep PTAs increase Ghana’s obligations to fulfil its environmental provisions compared to simply signing on to any trade agreement. Consequently, deep PTAs will have a lower impact on increasing exports. Environmental provisions with restrictive policies and their enforcement can also affect Ghana’s disaggregated export performance. The results indicate that, consistent with common expectation, including restrictive provisions in preferential trade agreements significantly reduces export efficiency or increases export inefficiency for all three product categories. From models (1), (3), and (5), the results suggest that, restrictive environmental provisions in PTAs reduces efficiency levels of all categories of export products; however, inefficiencies are greater for AFoF products (8.877 units). We also find an increase in inefficiencies after controlling for the number of trade agreements. Therefore, for all models, having restrictive provisions in PTAs adversely affect export efficiency, which is consistent with earlier studies (Blümer, Morin, Brandi, & Berger, 2020; Brandi et al., 2020). This implies that signing a PTA with restrictive environmental provisions hurts Ghana’s overall export performance. Following Brandi et al. (2020), we categorise PTAs based on whether they contain a generic or specific dispute resolution process for environmental provisions to measure enforcement of such provisions in PTAs. The results revealed that PTAs that contain enforceable provisions or those that have specific dispute settlement mechanisms reduce export inefficiencies in all three product categories. Particularly, enforceability of environmental provision reduces inefficiencies significantly for agricultural-related products with an effect of 66.72 units and the result is highly statistically significant at the 1 percent level. The study also incorporated unique trading partner variables such as language and landlocked status. Language improves export efficiency of AFoF and manufactured products. From models (1), (3) and (5), the estimate’s negative sign indicates that Ghana’s exports to Englishspeaking countries increase efficiencies when compared to nonEnglish-speaking countries. In other words, Ghana’s export of agricultural-related and manufactured products to English-speaking countries boosts its export efficiency by 2.066 and 0.962 units, respectively. Language’s negative and significant coefficient shows that it has some predictive power, which eliminates communication barriers between trading partners and boosts export efficiency. This is consistent with the findings of Ravishankar and Stack (2014) and Boadu et al. (2021), who concluded in their studies that common language improves C.K. Obeng et al. Research in Globalization 6 (2023) 100112 8 bilateral trade flows significantly. In contrast, Deluna and Cruz (2013), discovered that language had no effect on export efficiency in their analysis of the Philippines’ trade potential and performance. As expected, the coefficient of the landlocked dummy is positive for all the product categories, but significantly increases the export inefficiency of only AFoF and manufactured products [see Columns (1) and (5)]. Specifically, if the trading partner is a landlocked country, it reduces the export efficiency of the aforementioned products by 5.210 and 1.253 units, respectively. These coefficients are highly significant at 1 percent. The geographic feature of being landlocked lowers trade, primarily because lack of access to the sea tends to increase transportation costs. The results confirm the findings of Ravishankar and Stack (2014). The study included trade freedom index to measure the openness of Ghana’s trade. The result revealed that the trade freedom of the importer significantly reduces inefficiencies in Ghana’s export of AFoF, mineral, and manufactured products [see Columns (1) to (6)]. Conversely, the exporter’s trade freedom increases export inefficiencies of agricultural-related and mineral products [see Columns (1) and (3)], but it has no significant effect on the efficiency of Ghana’s manufactured products. Additionally, in Columns (1) and (3), corruption in Ghana significantly increases inefficiency in the exports of AFoF and mineral products while results in Columns (1) to (6) suggest that corruption in partner countries limits the performance of Ghana’s exports in all product categories. Regarding the macroeconomic variables, we find that the exchange rate of the exporting country has a positive and significant effect on all three product categories [see Columns (1) to (5)]. However, the effect is greater on the exports of agricultural, forestry, & fishery products. Regarding the macroeconomic stability variables, the results suggest that the exchange rate of the exporting country has a positive and significant effect on all three product categories. For every percentage increase in the value of the cedi, we see a 0.473, 0.0723, and 0.0679 percent increase in the inefficiency of AFoF products, mineral exports, and exports of manufactured products, respectively [see Columns (1) to (5)]. However, the effect is greater for the exports of AFoF products. This implies that Ghana’s exchange rate appreciation vis-` a-vis its partner economy increases export inefficiency of agricultural, forestry, & fishery products. After replacing the PTA dummy with the number of PTAs in place, we find that exchange rate appreciation only matter for Ghana’s exports of agricultural-related and mineral products [see Columns (2) and (4)]. The inflation rate of the exporting country has a positive but insignificant effect on the exports of all product categories except for manufactured products. This variable is only responsive to the number of PTAs in place, which happens to be relevant for reducing inefficiencies for the exports of manufactured products [see Column (5)]. This implies that domestic inflation matter only for Ghana’s exports of manufactured products. The results in Column (1) to (6) of Table 3 further show that the logistics performance index of both exporter and trading partner has a statistically significant negative effect on export inefficiency of AFoF products, mineral products, and manufactured commodities. The statistically negative effect of the logistics performance index of both exporter and importer highlights logistics as an important determinant of export efficiency as improved trade-related logistics facilitate more trade and increase trade volume. This outcome corroborates the findings of Martí et al. (2014), Gani (2017) and Celebi (2017). The role of institutions We examined how institutional quality moderates the influence of the PTA dummy, depth of PTA, enforcement of environmental provisions and restrictive environmental conditions on Ghana’s disaggregated export efficiency. Our findings show that, the quality of domestic regulations reduces export inefficiencies in Ghana’s exports of AFoF, mineral, and manufactured products for both the PTA dummy and the number of PTAs in place [see Columns (1) to (5)]. However, we note that the result is not significant for the exports of manufactured products when the number of PTAs is considered. On PTA dummy and regulatory quality, we find that the interaction term is negative and statistically significant at 1 percent for agricultural, forestry, and fishery products in Column (1) and significant at 5 percent for mineral products in Column (3). This means that domestic regulatory quality is critical for Ghana to realise the potential impact of a PTA on its export efficiency in agricultural, forestry, and fisheries exports, as well as mineral products. When the effect of the PTA dummy and the interaction term between PTA and regulatory quality on AFoF exports are compared, it is clear that the interacting term has a greater effect on increasing efficiency in agricultural, forestry, and fishery exports the independent PTA dummy. This demonstrates the importance of regulatory quality in a country’s ability to benefit from preferential trade deals. Furthermore, the interaction term for the depth of PTA and regulatory quality is statistically significant and negative across models, indicating that the relationship between depth of PTA and all disaggregated exports is dependent on domestic regulatory quality. This means that strong domestic rules are essential for Ghana to boost its export efficiency in all product categories for PTAs with environmental provisions. The findings indicate that regulatory quality has a bigger impact on increasing the effect of depth of PTA on the export efficiency of manufactured goods, agricultural, forestry, and fishery products than on mineral products. When the interacted coefficients are compared to the depth of PTA coefficient, we discover that the interaction term has a stronger impact on enhancing Ghana’s export efficiency. This implies that when domestic-level regulatory quality is effective, the full benefits of expanded PTAs on export efficiency can be realised. Again, at the 5 percent level of significance, the interaction term for enforcement of environmental provisions in PTA and regulatory quality increases the export efficiency of agricultural, forestry, & fishery products, as well as mineral products, while only marginally increasing the export efficiency of manufactured products. As expected, interacting enforcement of environmental provisions in PTAs with regulatory quality has a bigger influence on boosting Ghana’s disaggregated export efficiency, implying that domestic-level regulatory quality is crucial. Finally, when restrictive environmental provisions in PTAs are interacted with regulatory quality, we find that at 5 percent, regulatory quality is statistically significant in reducing inefficiencies in agricultural, forestry, & fishery products, as well as mineral products while at 10 percent, the interaction term for restrictive environmental provisions in PTAs and regulatory quality reduces exports inefficiency of manufacturing products. Frontier estimates Although our focus is on the inefficiency results, we also present results of the traditional gravity model. In all the six Columns of Table 3, as expected, the GDP of both the exporting country and its trading partners have positive and significant impacts on the disaggregated exports of Ghana. The GDP of the exporting country has a significant effect on the exports of agriculture, forestry, & fishery products, mineral commodities, and manufactured commodities. In particular, in Column (1), (2), and (3), a percentage increase in Ghana’s GDP increases its exports of agriculture, forestry, & fishery products, mineral products, and manufactured products by 0. 527, 0.0561, and 3.392 percent, respectively. With respect to the GDP of the trading partner, a percentage rise increases Ghana’s exports of agricultural, forestry, & fishery products, mineral products, and manufactured products by 0.831, 0.242, and 0.454 percent, respectively, and all the coefficients are highly significant at 1 percent. The outcome on GDP validates the findings of Berma (2017) and Hassan (2017). Also, in Column (1), (2), and (3), the population of the partners, which measures the economic size of the partner country has a positive and significant effect on Ghana’s disaggregated exports. Specifically, a C.K. Obeng et al.