This study examines the relationship between logistics performance and export competitiveness across 20 Latin American and Caribbean (LAC) economies during the Panama Canal expansion (PCE) era (2010–2018). Specifically, it investigates how the six components of the Logistics Performance Index (LPI), together with key macroeconomic factors, are associated with export performance across different income groups. Using a two-level Hierarchical Linear Model (HLM), the study evaluates changes in these relationships before and after the PCE. The objective is to provide empirical evidence that informs trade facilitation, customs modernization and maritime logistics policies aimed at enhancing regional export competitiveness.
This study employs a two-level HLM to analyze annual panel data for 20 LAC countries from 2010 to 2018. Repeated yearly observations (Level 1) were nested within countries (Level 2) to account for temporal and cross-country variation. Export performance was modeled as the dependent variable, while the six LPI components, along with gross domestic product, trade freedom (TRFR), industrialization and foreign direct investment, served as explanatory variables. Separate analyses were conducted for the pre-PCE (2010–2014) and post-PCE (2016–2018) periods to assess changes in logistics and export relationships across income groups.
The findings indicate that customs efficiency was the logistics component most strongly associated with export performance during the post-PCE period. The incremental explanatory power (R2) of LPI customs increased from 1.3% in the pre-PCE period (2010–2014) to 8.3% in the post-PCE period (2016–2018), reaching 10.1% in 2016 and 7.0% in 2018. High-income LAC economies exhibited statistically significant negative associations between customs performance and exports, while no significant relationships were observed for upper-middle- and lower-income economies. TRFR and industrialization consistently showed positive associations with export performance across the models.
This study is exploratory and identifies statistically significant associations rather than causal relationships between logistics performance and export competitiveness. The analysis is limited to 20 LAC countries over the 2010–2018 period and relies on secondary indicators, which may not capture all the institutional and operational dimensions that influence exports. Future research should incorporate longer time series, additional trade and governance variables, and causal inference methods to examine the mechanisms underlying these relationships. Despite these limitations, the findings provide a robust empirical basis for future multilevel studies on logistics performance, customs modernization and regional trade competitiveness.
The findings provide evidence for policymakers, customs authorities and port administrators seeking to strengthen export competitiveness through targeted logistics reforms. Prioritizing customs modernization, digital trade facilitation and administrative efficiency can improve the effectiveness of maritime supply chains, particularly in the post-PCE environment. The results also support investments in institutional capacity, streamlined border procedures and trade facilitation initiatives alongside broader industrialization and trade liberalization strategies. For LAC economies, these measures can enhance regional integration, improve logistics performance and increase resilience and competitiveness in global maritime trade and international export markets.
Improved logistics performance and customs efficiency can generate broader social benefits by facilitating trade, supporting economic growth and creating employment opportunities throughout the LAC region. More efficient border procedures and maritime logistics systems can reduce trade costs, enhance the availability of goods and strengthen regional connectivity, benefiting businesses and consumers alike. By promoting institutional modernization and more transparent customs administration, governments can improve public sector efficiency and investor confidence. These improvements contribute to more inclusive and sustainable economic development, helping countries enhance resilience, reduce trade barriers and expand opportunities for participation in global value chains.
This study contributes to the literature by providing one of the first multilevel analyses of the relationship between logistics performance and export competitiveness in LAC economies during the PCE era. Applying a two-level HLM captures both temporal and cross-country variation that conventional regression approaches often overlook. The study also distinguishes pre- and post-expansion periods and examines differences across income groups, offering new insights into the evolving role of customs performance and trade facilitation. These findings provide a valuable empirical foundation for future research on logistics and maritime trade.
1. Introduction
Efficient logistics systems are integral to sustaining economic growth and global competitiveness. In the context of globalization, logistics provides the backbone of international trade by linking producers and consumers across diverse geographies. Rodrigue (2020) asserts that the performance of logistics networks directly influences a country’s capacity to trade efficiently and expand its market access. The interdependence between transportation and logistics creates a multiplier effect on productivity, employment and economic resilience (Miller and Clayton, 2023). For developing economies, especially those within the Latin American and Caribbean (LAC) region, logistics modernization has emerged as a strategic imperative for export diversification and economic transformation.
Although the liberalization of trade barriers has increased market access for LAC countries, structural weaknesses in logistics infrastructure and customs administration persist (Islam et al., 2019). These weaknesses amplify the cost and complexity of trade, thereby reducing competitiveness. The Organisation for Economic Co-operation and Development (OECD, 2014) emphasized that the productivity of logistics operations directly contributes to a nation’s competitiveness by enabling faster and more reliable trade flows. Conversely, inefficiencies in infrastructure, poor port connectivity and outdated customs procedures can delay shipments, discourage investment and erode export performance.
Investing in logistics capabilities yields measurable benefits, including reduced transaction costs, improved supply chain reliability and enhanced export growth (Rodrigue, 2020; Miller and Hyodo, 2021). Logistics also fosters regional integration by connecting small island economies to larger trade networks. Yet, as highlighted by Gani (2017), disparities in logistics performance between developed and developing countries reflect persistent inequality in global trade participation. For instance, high-income economies tend to exhibit superior logistics efficiency, enabling them to benefit disproportionately from trade liberalization, while developing economies face systemic constraints related to customs bureaucracy, port congestion and underinvestment in technology.
The Panama Canal expansion (PCE), completed in 2016, represents a transformative milestone in global maritime logistics. By allowing the passage of Neo-Panamax vessels, the PCE has reshaped global shipping patterns and altered the logistics landscape across the Americas (Liu et al., 2016; Wang, 2017). The expansion has increased trade flows between Asia, the Caribbean and the Atlantic, providing new opportunities for LAC ports to serve as transshipment hubs. However, the extent to which this transformation has translated into improved export performance remains uncertain. While the canal has enhanced capacity and connectivity, LAC countries continue to struggle with institutional inefficiencies that undermine the potential benefits of physical infrastructure investments (Rodrigue, 2012; Merk, 2018; Percoco, 2014; Miller and Hyodo, 2021).
This paper examines how the relationship between logistics performance and export outcomes evolved during the PCE era. Using the Logistics Performance Index (LPI) as a benchmark, the study evaluates how its six sub-components, Customs (Logistics Performance Index: Customs efficiency [LPCUST]), Infrastructure (Logistics Performance Index: Tradeand transport-related infrastructure [LPINFRA]), Logistics Quality and Competence (Logistics Performance Index: Quality of logistics services and competence [LPQLS]), Tracking and Tracing (Logistics Performance Index: Tracking and tracing [LPTT]), Timeliness (Logistics Performance Index: Timeliness of shipments [LPTL]) and International Shipments (Logistics Performance Index: Ease of arranging international shipments [LPIS]), are associated with export performance across LAC economies. The analysis further examines these relationships across different income classifications to capture heterogeneity in trade performance and logistics efficiency.
By employing a two-level Hierarchical Linear Model (HLM), the study examines both temporal and structural variation in the relationship between logistics performance and export competitiveness, providing evidence-based insights for regional policymakers. Although extensive research has examined logistics performance and trade facilitation, relatively few studies have investigated how the relationships between logistics efficiency and export competitiveness evolved across different income groups in LAC economies during the PCE era. Moreover, few studies have applied a multilevel hierarchical modeling framework to evaluate temporal and structural heterogeneity in these relationships across the pre- and post-expansion periods. This study addresses this gap by applying a two-level HLM to examine how specific LPI components are associated with export performance across LAC economies during the PCE era.
Accordingly, this study is intended as an exploratory multilevel analysis of logistics performance and export competitiveness during the PCE era. Rather than estimating the causal effect of the PCE, the analysis examines how the associations between logistics performance and export competitiveness evolved across LAC economies over this period. The findings should therefore be interpreted as evidence of changing relationships rather than definitive evidence that the PCE itself caused the observed differences in export performance.
2. Literature review
2.1 Logistics, economic growth and trade
Logistics has evolved from a purely operational function into a strategic enabler of global competitiveness and economic growth. It facilitates the efficient allocation of resources, ensures timely delivery of goods and reduces market uncertainty. Sezer and Abasız (2017) demonstrated that logistics efficiency has a strong multiplier effect on gross domestic product (GDP) growth, particularly in developing economies, where improvements in trade logistics can significantly enhance export competitiveness. Similarly, Hausman et al. (2013) found that countries with better logistics systems tend to integrate more effectively into global value chains.
Logistics also contributes to structural transformation by supporting the movement of intermediate goods and enabling value addition. The United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP, 2018) noted that logistics infrastructure improvements contribute to productivity growth in the manufacturing sector, particularly in small economies that depend heavily on trade. In LAC, however, logistics underperformance remains a major constraint on export diversification. The World Bank (2020) reported that logistics costs in some Caribbean nations account for as much as 25–30% of product value, compared to 10% in Organisation for Economic Co-operation and Development (OECD) countries.
Hilda (2020) highlighted the logistics sector’s ability to generate employment, stimulate innovation and attract foreign direct investment (FDI). Yet, as Devlin and Yee (2005) argued, without adequate institutional support, logistics improvements may not yield the desired economic benefits. The interplay between trade policy, infrastructure investment and governance is therefore crucial. According to Behar et al. (2009), improvements in trade logistics disproportionately benefit developing nations by reducing the marginal cost of exporting, enabling them to compete in global markets.
2.2 The Logistics Performance Index
The LPI serves as a comprehensive diagnostic tool that captures the quality of trade and transport-related infrastructure. Developed by the World Bank, it provides a standardized measure of logistics efficiency across six dimensions: customs, infrastructure, international shipments, logistics service quality, tracking and tracing and timeliness (World Bank, 2019). These dimensions collectively capture both the physical and institutional components of logistics systems.
Research consistently indicates that logistics performance is positively associated with a range of economic and trade outcomes. Çemberci et al. (2015) established a correlation between the LPI, the Global Competitiveness Index and GDP per capita, suggesting that countries with high logistics performance exhibit stronger macroeconomic stability. Barakat et al. (2018) showed that among Middle Eastern and African economies, logistics reforms led to significant export gains, particularly when coupled with policy reforms in customs and trade facilitation.
Infrastructure remains the foundation of logistics performance. Bensassi et al. (2015) identified infrastructure quality as a key determinant of trade costs, noting that countries with advanced port and road systems experience greater integration into global value chains. The importance of tracking and tracing has also increased in recent years, as global trade networks demand transparency and reliability. Technologies such as the internet of Things and blockchain are transforming supply chain visibility, offering opportunities for LAC countries to modernize their logistics networks (Rezaei et al., 2018).
Several studies have applied the LPI in regional contexts. Martín et al. (2017) found that logistics scores were significantly higher in developed countries, while Latin American economies exhibited lower scores, particularly in customs and infrastructure. This finding underscores the structural gap between developed and developing nations in logistics performance. Moreover, Park (2020) emphasized that transport infrastructure and logistics quality constitute a comparative advantage for trade-oriented economies.
2.3 The Panama Canal expansion and regional trade
The PCE has been a game-changer in global maritime logistics. Completed in 2016, it enabled the transit of Neo-Panamax vessels nearly three times larger than the original Panamax ships, thus doubling the canal’s capacity (Liu et al., 2016). The expansion altered global shipping routes, shifting significant cargo flows from the US West Coast to the East Coast and the Caribbean Basin (Rodrigue, 2020). This realignment enhanced the strategic significance of Caribbean ports and positioned them as critical nodes in global supply chains.
The PCE’s economic and logistical impact has been the subject of extensive research. Wang (2017) reported that the canal’s expansion boosted Panamanian GDP through increased toll revenues, while also reducing global shipping costs by up to 12%. Cosco (2017) and Miller and Hyodo (2021) observed that the PCE spurred a wave of infrastructure investments across LAC ports, including Kingston, Cartagena and Colón, as nations sought to capitalize on new trade opportunities.
However, the benefits of the expansion have not been uniformly realized. Merk (2018) noted that institutional inefficiencies, bureaucratic delays and corruption continue to hinder logistics performance in the region. The International Transport Forum (2019) further reported that despite infrastructure expansion, operational inefficiencies in customs clearance processes remain among the leading causes of trade delays in LAC. This aligns with the findings of Rodrigue and Ashar (2016), who argued that regional competitiveness depends not only on physical expansion but also on institutional capacity to manage larger cargo flows.
2.4 Exports, income classification and logistics performance
The relationship between export performance, income classification and logistics capability has attracted growing scholarly interest. According to the World Bank (2020), higher-income LAC countries such as Chile, Panama and Uruguay consistently outperform lower-income nations in logistics indicators. The disparity reflects differences in both institutional quality and access to capital. Naanwaab and Diarrassouba (2013) demonstrated that trade liberalization and economic freedom positively correlate with export growth, but that institutional quality mediates this effect.
Empirical studies linking logistics performance to export outcomes remain scarce for the LAC region. Behar et al. (2009) found that improved logistics performance enhances export capacity, particularly in developing countries with limited infrastructure. Liu et al. (2016) and Hausman et al. (2013) similarly emphasized that efficient customs and transport systems contribute significantly to export competitiveness. Yet, as Celebi (2017) highlighted, the impact of logistics varies across income levels: while low-income countries benefit most from logistics efficiency, high-income countries derive diminishing returns due to already optimized systems.
Theoretical perspectives such as the GVC framework and the New Economic Geography (NEG) also provide insights into logistics’ role in trade. The GVC framework posits that efficient logistics enables deeper integration into production networks, allowing countries to move up the value chain (Gereffi and Fernandez-Stark, 2016). NEG theory, on the other hand, emphasizes that spatial proximity and connectivity reduce transaction costs and promote regional agglomeration (Krugman, 1991). Applying these perspectives to the LAC region underscores how improved logistics infrastructure and administrative efficiency are widely associated with the development of regional trade hubs and enhanced economic integration.
While previous studies generally support a positive relationship between logistics performance and trade competitiveness, important debates remain regarding whether physical infrastructure or institutional quality exhibits a stronger association with export performance. Some studies emphasize transport infrastructure and port connectivity as the primary determinants of competitiveness, whereas others argue that customs efficiency, governance quality and administrative modernization generate greater long-term trade gains. These inconsistencies highlight the need for an exploratory multilevel regional analysis capable of distinguishing temporal and income-based variations in the associations between logistics performance and export performance during the PCE era. Accordingly, this study adopts an exploratory multilevel perspective to examine how the relationships between logistics performance and export competitiveness evolved across LAC economies during the PCE era. Rather than estimating the causal effect of the PCE, the analysis focuses on identifying statistically significant associations and temporal patterns using a two-level HLM.
3. Methodology
This study adopts a quantitative research design using a two-level HLM to examine the relationship between logistics performance and export outcomes across LAC countries during the PCE era. The multilevel modeling framework was selected because the dataset contains repeated observations across time nested within countries, thereby allowing the analysis to account for both temporal variation and country-level heterogeneity. The analysis integrates macroeconomic variables and logistics performance indicators to evaluate their combined and incremental associations with export performance. The study focuses on the period 2010–2018, corresponding with the pre- and post-PCE phases.
3.1 Data sources and sampling
The dataset consists of 20 LAC countries with complete observations across five LPI survey years: 2010, 2012, 2014, 2016 and 2018.
Countries with substantial missing observations, including Barbados, Belize and Suriname, were excluded to maintain dataset consistency and comparability across model estimations.
Data were obtained from:
The World Bank LPI database;
World Development Indicators;
International trade and macroeconomic databases.
The dependent variable was export performance measured in US dollars. Independent variables included the six LPI subcomponents:
Customs efficiency (LPCUST),
Infrastructure quality (LPINFRA),
International shipments,
Logistics service quality (LPQLS),
Tracking and tracing (LPTT),
Timeliness (LPTL).
Macroeconomic control variables included:
GDP,
Trade freedom (TRFR),
Industrialization (IND),
FDI.
All continuous variables were log-transformed to stabilize variance, reduce Heteroscedasticity and improve model normality.
3.2 Hierarchical model structure
This study uses a two-level HLM to reflect the nesting of repeated observations within countries over time. This multilevel approach captures both within-country change over time and between-country differences in logistics performance and export outcomes.
Level 1 comprises annual observations for each country drawn from the World Bank LPI survey years (2010, 2012, 2014, 2016 and 2018). Level 1 variables include exports and the six LPI subcomponents.
Level 2 captures country-level differences across 20 LAC economies. Country-level covariates include GDP, industrialization, TRFR and FDI.
Rather than estimating six hierarchical levels, the study fits six sequential model specifications. Each model adds one additional LPI component to evaluate its incremental contribution to explaining export performance.
The general HLM specification is
where
Exportit represents export performance for country i at time t;
LPIit represents logistics performance indicators;
Xit represents macroeconomic control variables;
ui captures country-specific random effects;
ϵit represents the residual error term.
3.3 Model estimation and diagnostics
The HLM estimation procedure involved sequentially introducing the LPI subcomponents into the regression framework to assess their incremental explanatory power (ΔR2) on export performance. Model estimation was conducted using Stata and R statistical software packages.
Robust standard errors were employed to minimize potential heteroscedasticity concerns. Several diagnostic assessments were conducted to validate model assumptions and ensure robustness of the estimations, including
Residual normality assessment;
Multicollinearity testing using variance inflation factor (VIF);
Examination of heteroscedasticity patterns;
Correlation analysis among explanatory variables.
The study reports coefficient estimates, F-statistics, R2 values and incremental changes in explanatory power across the six sequential model specifications. Diagnostic results indicated acceptable model stability. VIF values remained below conventional multicollinearity thresholds. Residual assessments suggested no major violations of normality assumptions following logarithmic transformation. Robust standard errors were retained to minimize potential heteroscedasticity effects.
Preliminary clustering diagnostics indicated meaningful between-country heterogeneity in export performance, supporting the application of the two-level HLM. Due to the exploratory and comparative nature of the study, formal intraclass correlation coefficient, akaike information criterion and Bayesian information criterion reporting were not emphasized; however, future studies may incorporate these measures for expanded model validation and comparison.
3.4 Pre- and post-PCE comparative framework
To explore differences in logistics–export relationships across the PCE era, the dataset was divided into
Pre-PCE era (2010–2014),
Post-PCE era (2016–2018).
This comparative framework allows the analysis to examine whether logistics performance indicators demonstrated stronger associations with export outcomes following the operationalization of the expanded Panama Canal and the introduction of Neo-Panamax vessel capacity.
Additionally, income classification models were estimated to evaluate whether the relationship between logistics performance and exports differed across
high-income LAC economies,
upper-middle-income LAC economies,
lower-middle-income LAC economies.
The HLM framework provides a useful multilevel approach for analyzing repeated observations across countries and years; however, the study does not establish definitive causal relationships between logistics performance and export outcomes. Potential endogeneity may exist because higher export performance can itself contribute to improvements in logistics systems and institutional quality. Additionally, the relatively small sample size and exclusion of countries with incomplete observations may introduce sample-selection bias. Although fixed-effects estimation, interaction-based models and structural break analyses may provide additional robustness, these approaches were beyond the scope of the present exploratory analysis and are recommended for future research. Future research may also incorporate dynamic panel approaches, instrumental variable techniques and additional post-2018 data to strengthen causal inference and improve longitudinal analysis.
4. Results and analysis
4.1 Correlation results
Pearson’s correlation analysis revealed strong relationships among logistics components. Economic variables like GDP, TRFR, industrialization and FDI showed positive correlations with export performance, while customs inefficiencies were negatively linked to export values, as shown in Figure 1. These relationships suggest that logistics quality and economic strength mutually support one another.
A heat map representing the correlation between various variables. The heat map is structured as a grid with both axes labeled with variable names. The x-axis and y-axis both list the same set of variables: Exp, FDI, GDP, IND, LOG, LPCUST, LPINFRA, LPQLS, LPTL, LPTT, OLPI, TRFR. The color scale ranges from blue to red, indicating the strength and direction of the correlation, with red representing higher positive correlations and blue representing negative correlations. The values range from -0.25 to 1.00. Notable regions include high correlations between LPCUST and LPINFRA (0.89), LPCUST and LPQLS (0.89), and LPCUST and LPTL (0.93). There are also high correlations between LPTL and LPTT (0.82), and between OLPI and TRFR (0.82). Some variables show negative correlations, such as GDP and LPTL (-0.25). The heat map provides a visual representation of how these variables interact with each other, highlighting areas of strong positive and negative correlations.Correlation heatmap of variables. Source(s): Author’s elaboration
A heat map representing the correlation between various variables. The heat map is structured as a grid with both axes labeled with variable names. The x-axis and y-axis both list the same set of variables: Exp, FDI, GDP, IND, LOG, LPCUST, LPINFRA, LPQLS, LPTL, LPTT, OLPI, TRFR. The color scale ranges from blue to red, indicating the strength and direction of the correlation, with red representing higher positive correlations and blue representing negative correlations. The values range from -0.25 to 1.00. Notable regions include high correlations between LPCUST and LPINFRA (0.89), LPCUST and LPQLS (0.89), and LPCUST and LPTL (0.93). There are also high correlations between LPTL and LPTT (0.82), and between OLPI and TRFR (0.82). Some variables show negative correlations, such as GDP and LPTL (-0.25). The heat map provides a visual representation of how these variables interact with each other, highlighting areas of strong positive and negative correlations.Correlation heatmap of variables. Source(s): Author’s elaboration
The Pearson correlation results indicate moderate-to-strong relationships among several logistics performance indicators, reflecting the interconnected nature of logistics systems and trade facilitation processes. Although some variables exhibited relatively high pairwise correlations, multicollinearity diagnostics using VIF assessments remained within acceptable thresholds. Consequently, the observed correlations were not considered sufficiently severe to invalidate the regression estimations or hierarchical model interpretations. Figure 1 visually illustrates the correlation structure among the variables, while Appendix Table A1 provides the corresponding Pearson correlation coefficients.
4.2 Hierarchical linear model results
The HLM results reveal year-specific variation in the associations between logistics performance indicators and export performance. As shown in Table 1, for 2010, infrastructure (LPINFRA) and logistics service quality (LPQLS) contributed significantly, accounting for 1.9% of the variance in exports. In 2014, tracking and timeliness (LPTT, LPTL) were significant, reflecting improvements in port technology and operational reliability. For 2016 and 2018, LPI customs exhibited the strongest statistical association with export performance, accounting for 10.1% and 7.0% of export variance, respectively. These findings suggest that the relative associations between logistics performance indicators and exports differed across the study period, with customs efficiency becoming more strongly associated with export performance in the later years.
Hierarchical linear model (HLM) for exports for LAC (2010, 2012, 2014, 2016, 2018)
| Year | Model | R2 | F(df) | P | R2 change | F(df) change | P |
|---|---|---|---|---|---|---|---|
| 2010 | 1 | 0.901 | 34.229(4,15) | 0.000 | |||
| 2 | 0.906 | 26.870(5,14) | 0.000 | 0.004 | 0.648(1,14) | 0.433 | |
| 3 | 0.919 | 24.665(6,13) | 0.000 | 0.014 | 2.193(1,13) | 0.161 | |
| 4 | 0.938 | 25.966(7,12) | 0.000 | 0.019 | 3.646(1,12) | 0.079* | |
| 5 | 0.939 | 21.124(8,11) | 0.000 | 0.001 | 0.147(1,11) | 0.708 | |
| 6 | 0.940 | 17.418(9,10) | 0.000 | 0.001 | 0.192(1,10) | 0.670 | |
| GDP (3.418)***, TRFR (32.42)***, infra (−7.212)**, QLS (3.051)*, N = 20 | |||||||
| 2012 | 1 | 0.906 | 36.319(4,15) | 0.000 | |||
| 2 | 0.908 | 27.518(5,14) | 0.000 | 0.001 | 0.187(1,14) | 0.672 | |
| 3 | 0.912 | 22.524(6,13) | 0.000 | 0.005 | 0.682(1,13) | 0.423 | |
| 4 | 0.914 | 18.212(7,12) | 0.000 | 0.002 | 0.240(1,12) | 0.632 | |
| 5 | 0.914 | 14.645(8,11) | 0.000 | 0.000 | 0.026(1,11) | 0.875 | |
| 6 | 0.916 | 12.102(9,10) | 0.000 | 0.002 | 0.206(1,10) | 0.659 | |
| GDP (3.404)***, TRFR (30.79)***, FDI (−1.886)*, N = 20 | |||||||
| 2014 | 1 | 0.757 | 11.674(4,15) | 0.000 | |||
| 2 | 0.772 | 9.494(5,14) | 0.000 | 0.015 | 0.945(1,14) | 0.346 | |
| 3 | 0.773 | 7.382(6,13) | 0.001 | 0.001 | 0.048(1,13) | 0.829 | |
| 4 | 0.786 | 6.297(7,12) | 0.003 | 0.013 | 0.725(1,12) | 0.410 | |
| 5 | 0.857 | 8.210(8,11) | 0.001 | 0.071 | 5.409(1,11) | 0.038** | |
| 6 | 0.908 | 11.028 | 0.000 | 0.052 | 5.672(1,10) | 0.036** | |
| TRFR (27.79)***, QLS (−6.960)**, LPTT (6.872)**, LPTL (−3.653)**, N = 20 | |||||||
| 2016 | 1 | 0.627 | 6.298(4,15) | 0.004 | |||
| 2 | 0.728 | 7.476(5,14) | 0.001 | 0.101 | 5.176(1,14) | 0.038** | |
| 3 | 0.735 | 6.006(6,13) | 0.003 | 0.007 | 0.360(1,13) | 0.558 | |
| 4 | 0.775 | 5.893(7,12) | 0.004 | 0.040 | 2.118(1,12) | 0.169 | |
| 5 | 0.781 | 4.899(8,11) | 0.009 | 0.006 | 0.311(1,11) | 0.588 | |
| 6 | 0.783 | 4.014 | 0.021 | 0.002 | 0.109(1,10) | 0.748 | |
| IND (11.669)**, LPCUST (−2.206)**, N = 20 | |||||||
| 2018 | 1 | 0.718 | 9.564(4,15) | 0.000 | |||
| 2 | 0.788 | 10.436(5,14) | 0.000 | 0.070 | 4.640(1,14) | 0.048** | |
| 3 | 0.820 | 9.895(6,13) | 0.000 | 0.032 | 2.310(1,13) | 0.151 | |
| 4 | 0.835 | 8.705(7,12) | 0.001 | 0.015 | 1.102(1,12) | 0.313 | |
| 5 | 0.837 | 7.056(8,11) | 0.002 | 0.001 | 0.098(1,10) | 0.760 | |
| 6 | 0.845 | 6.060(9,10) | 0.005 | 0.008 | 0.526(1,10) | 0.484 | |
Note(s): TRFR (14.91)**, IND (10.41)**, LPCUST (−2.56)**, N = 20, ***p < 0.01, **p < 0.05, *p < 0.1. Model 1 uses the general OLPI index (overall); model 2 uses the LPI customs index; model 3 uses the LPI infrastructure; model 4 uses the LPI quality of logistics services index; model 5 uses the LPI tracking and tracing index; model 6 uses the LPI timeliness. Coefficients are based on log-transformed variables and therefore reflect scaled associations rather than standardized beta coefficients. QLS = Quality of logistics services and IND = Industry or industrial sector indicator
4.3 Income classification analysis
Table 2 shows income-based models indicating that customs performance was significantly associated with export performance in high-income economies but is statistically insignificant for upper- and lower-middle-income countries. Industrialization exhibited a statistically significant positive association for upper-middle-income economies, while FDI was positively associated with export performance in lower-income economies. These findings suggest that the elasticity of logistics performance varies with income level and industrial development.
Hierarchical linear model (HLM) for exports per income classification
| Year | Model | R2 | F(df) | P | R2 change | F(df) change | p |
|---|---|---|---|---|---|---|---|
| High-income LAC | 1 | 0.917 | 55.030(4,20) | 0.000 | |||
| 2 | 0.928 | 49.199(5,19) | 0.000 | 0.012 | 3.072(1,19) | 0.095* | |
| 3 | 0.932 | 40.939(6,18) | 0.000 | 0.003 | 0.902(1,18) | 0.354 | |
| 4 | 0.933 | 33.948(7,17) | 0.000 | 0.002 | 0.386(1,17) | 0.542 | |
| 5 | 0.938 | 30.169(8,16) | 0.000 | 0.005 | 1.181(1,16) | 0.292 | |
| 6 | 0.942 | 27.137(9,15) | 0.000 | 0.004 | 1.117(1,15) | 0.306 | |
| TRFR (25.07)***, FDI (2.811)**, IND (11.982)**, LPCUST (−2.074)*, N = 25 | |||||||
| Upper-middle-income LAC | 1 | 0.967 | 351.577(4,45) | 0.000 | |||
| 2 | 0.969 | 272.099(5,44) | 0.000 | 0.001 | 2.089(1,44) | 0.155 | |
| 3 | 0.970 | 232.702(6,43) | 0.000 | 0.001 | 2.088(1,43) | 0.156 | |
| 4 | 0.970 | 194.904(7,42) | 0.000 | 0.000 | 0.018(1,42) | 0.895 | |
| 5 | 0.970 | 166.568(8,41) | 0.000 | 0.000 | 0.021(1,41) | 0.886 | |
| 6 | 0.972 | 153.486(9,40) | 0.000 | 0.002 | 2.428(1,40) | 0.127 | |
| GDP (0.8732)***, IND (0.482)**, N = 50 | |||||||
| Lower-middle-income LAC | 1 | 0.928 | 64,064(4,20) | 0.000 | |||
| 2 | 0.931 | 50.918(5,19) | 0.000 | 0.003 | 0.807(1,19) | 0.380 | |
| 3 | 0.931 | 40.672(6,18) | 0.000 | 0.001 | 0.197(1,18) | 0.662 | |
| 4 | 0.939 | 37.226(7,17) | 0.000 | 0.007 | 2.068(1,17) | 0.168 | |
| 5 | 0.939 | 30.720(8,16) | 0.000 | 0.000 | 5.409(1,16) | 0.862 | |
| 6 | 0.939 | 25.601(9,15) | 0.000 | 0.000 | 5.672(1,15) | 0.989 | |
Note(s): GDP (0.9885)***, FDI (0.2535)***, N = 25, ***p < 0.01, **p < 0.05, *p < 0.1. Model 1 uses the general OLPI index (overall); model 2 uses the LPI customs index; model 3 uses the LPI infrastructure; model 4 uses the LPI quality of logistics services index; model 5 uses the LPI tracking and tracing index; model 6 uses the LPI timeliness. Coefficients are based on log-transformed variables and therefore reflect scaled associations rather than standardized beta coefficients
4.4 Pre- and post-PCE analysis
Comparative results indicate differences between the pre- and post-PCE periods. Before the PCE (2010–2014), customs efficiency explained only 1.3% of export variance, while post-PCE (2016–2018), the figure rose to 8.3%. TRFR remained significant throughout, highlighting the role of policy liberalization. However, the overall Logistics Performance Index was significant only during the post-PCE period, accounting for 7.3% of export variance. These findings are consistent with the proposition that administrative efficiency may become increasingly important alongside infrastructure development, although the present analysis does not establish causality, as shown in Table 3.
Hierarchical linear model (HLM) for exports during the pre- and post-era
| Year | Model | R2 | F(df) | P | R2 change | F(df) change | P |
|---|---|---|---|---|---|---|---|
| Pre-PCE era (2010,2012,2014) | 1 | 0.821 | 63.216(4,55) | 0.000 | |||
| 2 | 0.834 | 54.444(5,54) | 0.000 | 0.013 | 4.278(1,54) | 0.043** | |
| 3 | 0.835 | 44.659(6,53) | 0.000 | 0.000 | 0.128(1,53) | 0.722 | |
| 4 | 0.835 | 37.567(7,52) | 0.000 | 0.000 | 0.011(1,52) | 0.916 | |
| 5 | 0.842 | 34.066(8,51) | 0.000 | 0.007 | 2.413(1,51) | 0.126 | |
| 6 | 0.849 | 31.310(9,50) | 0.000 | 0.007 | 2.303(1,50) | 0.135 | |
| GDP (1.644)***, TRFR (25.335)***, LPCust (−1.112)**, N = 60 | |||||||
| Post-PCE era (2016,2018) | 1 | 0.668 | 17.625(4,35) | 0.000 | |||
| 2 | 0.751 | 20.506(5,34) | 0.000 | 0.083 | 11.295(1,34) | 0.002*** | |
| 3 | 0.751 | 16.614(6,33) | 0.000 | 0.000 | 0.043(1,33) | 0.838 | |
| 4 | 0.751 | 13.819(7,32) | 0.000 | 0.000 | 0.016(1,32) | 0.900 | |
| 5 | 0.754 | 11.890(8,31) | 0.000 | 0.003 | 0.350(1,31) | 0.558 | |
| 6 | 0.768 | 11.034(9,30) | 0.000 | 0.014 | 1.783(1,30) | 0.192 | |
| TRFR (15.481)***, IND (11.172)***, LPCust (−2.261)***, N = 40 | |||||||
| Pre-PCE era (OLPI) | 1 | 0.821 | 63.219(4,55) | 0.000 | |||
| 2 | 0.826 | 51.148(5,54) | 0.000 | 0.004 | 1.333(1,54) | 0.253 | |
| GDP (2.019)***, TRFR (25.91)***, N = 60 | |||||||
| Post-PCE era (OLPI) | 1 | 0.668 | 17.625(4,35) | 0.000 | |||
| 2 | 0.741 | 19.477(5,34) | 0.000 | 0.073 | 9.588(1,34) | 0.004 | |
Note(s): GDP (1.306)***, TRFR (15.733)**, IND (10.870)***, OLPI (−2.312)***, N = 40
***p < 0.01, **p < 0.05, *p < 0.1. Model 1 uses the general OLPI index (overall); model 2 uses the LPI customs index; model 3 uses the LPI infrastructure; model 4 uses the LPI quality of logistics services index; Model 5 uses the LPI tracking and tracing index; model 6 uses the LPI timeliness. Coefficients are based on log-transformed variables and therefore reflect scaled associations rather than standardized beta coefficients
5. Discussion
The results of this research provide substantial insights into the interaction between logistics performance and trade outcomes in the LAC region. The PCE coincided with substantial changes in maritime capacity and connectivity across LAC ports. Within this context, the findings suggest that institutional and administrative quality exhibited stronger statistical associations with export performance than measures of physical infrastructure within the study period.
The negative relationship between LPI customs and exports among high-income countries suggests that even advanced economies are not immune to bureaucratic inefficiencies. In some cases, higher levels of regulation, documentation requirements or trade compliance standards may increase transaction times and costs. For instance, higher-income LAC economies such as Chile, Panama and Uruguay operate under more comprehensive customs compliance regimes that may involve stricter documentation, inspection and security procedures. Although these measures strengthen governance and trade security, they may also increase administrative complexity and transaction costs. This finding aligns with the theory of institutional complexity, which posits that countries with dense regulatory environments may experience diminishing returns from logistics investments if administrative frameworks are not simultaneously streamlined (Rodrigue, 2020; Liu et al., 2016). Therefore, policy interventions must focus not only on building capacity but also on simplifying processes and improving transparency.
The institutional mechanisms discussed above should be interpreted as plausible explanations rather than empirically verified pathways. The present dataset does not include direct measures of customs processing times, inspection intensity, documentation burdens or regulatory complexity that would allow these mechanisms to be tested explicitly. Consequently, while the observed negative association is consistent with the institutional complexity literature (Rodrigue, 2020; Liu et al., 2016), future research incorporating these indicators, together with robustness analyses such as interaction models or fixed-effects estimation, would be necessary to determine whether the relationship reflects administrative complexity, measurement effects or other institutional factors.
For middle and lower-income economies, the insignificant impact of LPI components indicates persistent structural deficiencies. These nations often face logistical bottlenecks arising from underdeveloped port facilities, inadequate hinterland connectivity and a lack of digital systems for cargo tracking. As Merk (2018) and UNCTAD (2018) observed, inefficient customs procedures and limited coordination among border agencies lead to substantial trade delays. The absence of efficient multimodal logistics systems constrains the ability of these countries to benefit from the PCE’s economies of scale.
Moreover, the analysis underscores the critical importance of TRFR and industrialization as complementary factors that are positively associated with export performance alongside logistics performance. Liberalized trade regimes encourage competition and innovation in logistics services, while industrial diversification increases export volume and resilience. Policymakers in LAC must therefore adopt an integrated approach, combining logistics reforms with industrial and trade policies to foster inclusive growth.
The findings also contribute to the ongoing debate about the relative roles of infrastructure and governance in trade facilitation. While physical investments such as port expansion and road upgrades are necessary, they are insufficient without efficient institutional frameworks. As demonstrated by the HLM results, the findings suggest that customs-related institutional factors exhibited stronger statistical associations with export performance than physical infrastructure measures within the study period. Although these results are consistent with literature emphasizing the importance of governance and administrative efficiency, the exploratory nature of this study does not permit causal conclusions regarding the relative effectiveness of customs reforms and infrastructure investment.
The role of technology cannot be overstated. The adoption of electronic data interchange systems, blockchain-enabled cargo documentation and port community systems (PCS) can significantly reduce clearance times and increase transparency. Such systems have been successfully implemented in economies like Singapore and South Korea, serving as potential benchmarks for LAC nations. Furthermore, the integration of artificial intelligence and predictive analytics in logistics operations can optimize route planning and inventory management, lowering operational costs.
Regional cooperation is another key takeaway from this study. The LAC region’s fragmented logistics landscape requires harmonized standards for trade documentation, customs procedures and data interoperability. Organizations such as the Inter-American Development Bank and the Economic Commission for Latin America and the Caribbean have emphasized the need for a regional logistics strategy to improve connectivity and competitiveness. A coordinated approach would allow smaller economies to pool resources and leverage shared platforms for digital trade facilitation.
Environmental sustainability also warrants consideration. The expansion of the Panama Canal has increased shipping traffic, raising concerns about carbon emissions and ecological impacts. Integrating sustainability metrics into logistics performance assessments can help LAC countries align with the International Maritime Organization’s decarbonization goals. Green logistics initiatives such as electrification of port equipment, adoption of alternative fuels and carbon offset mechanisms should be embedded within future infrastructure planning.
Finally, the study highlights the value of HLM as an analytical framework for logistics research. By accounting for multilevel variation across time and income groups, HLM provides a nuanced understanding of how economic and institutional factors are associated with trade outcomes across different LAC economies. This methodological approach can be extended to future research incorporating fixed-effects estimation, interaction-based models or quasi-experimental designs could further strengthen causal inference while building on the exploratory multilevel framework presented in this study.
5.1 Limitations
This study has several limitations. First, the sample size was constrained to 20 LAC countries due to data availability limitations within the World Bank LPI dataset. Countries with incomplete observations were excluded, which may introduce sample-selection bias. Second, the study covers only five observation years (2010–2018), limiting the ability to capture long-term structural adjustments following the PCE. Future studies should incorporate additional post-2018 observations as they become available. Third, while the HLM identifies statistically significant associations between logistics performance and export outcomes, causality cannot be definitively established. Potential endogeneity may exist because higher export performance can itself contribute to improvements in logistics infrastructure and institutional quality. Future research should estimate interaction effects between the PCE period and LPI customs, while also considering fixed-effects, difference-in-differences or other quasi-experimental approaches to more directly evaluate whether the PCE moderated the relationship between customs efficiency and export performance. Finally, the study focuses primarily on export outcomes and does not explicitly examine import dynamics, environmental externalities, shipping decarbonization impacts or port-level operational efficiency indicators.
6. Conclusion
This research provides empirical evidence of statistically significant associations between logistics performance, particularly customs efficiency, and export outcomes in LAC economies. During the PCE era, logistics modernization remained closely linked to export competitiveness, while institutional and administrative inefficiencies continued to appear as important constraints. The HLM approach demonstrates that customs processes explained a growing proportion of export variance across the study period, reinforcing the argument that administrative efficiency should be considered alongside infrastructure development in regional trade policy.
6.1 Maritime and port policy implications
The findings suggest that customs modernization within major maritime gateways such as Kingston, Colón, Cartagena and other regional transshipment hubs may strengthen trade facilitation during the PCE era.
Particular emphasis should be placed on
PCS;
Electronic customs clearance;
Maritime single-window systems;
Neo-Panamax vessel accommodation;
Multimodal maritime corridors;
Smart-port digitalization strategies.
These interventions may help reduce cargo dwell time, improve vessel turnaround efficiency and strengthen regional maritime competitiveness.
6.2 Policy recommendations
Digitize customs operations through e-clearance systems, risk-based inspections and single-window platforms.
Strengthen infrastructure integration by developing multimodal logistics corridors linking ports, highways and rail networks.
Promote regional harmonization through standardized customs procedures under regional trade frameworks.
Foster public/private partnerships to support logistics investment and operational efficiency.
Build capacity among customs and port personnel in modern trade facilitation techniques and digital systems.
Leverage data analytics to monitor trade flows and identify bottlenecks in real time.
Incorporate green logistics by aligning national logistics strategies with sustainability goals.
Future research should incorporate additional years of LPI data as they become available, include import dynamics and consider environmental impacts related to shipping efficiency. Future studies should also apply fixed-effects estimation, interaction-based models, difference-in-differences approaches or other quasi-experimental methods to strengthen causal inference and further examine whether the PCE moderated the relationship between logistics performance and export outcomes.
The supplementary material for this article can be found online.
