This study conducted an analysis to identify the vriables among the sub-dimensions of the OECD’s Trade Facilitation Indcx and the inocme level of trading countries, categorized as low, lower-middle, upper-middle, and high income, that affect the export amounts differentiated by firm size: large, medium sized, and small-to-medium sized enterprises. To achieve this, both Stepwise Regression and Lasso Regression analyses were applied, with the export amount serving as the dependent variable, and the results from both methods were compared. The primary findings revealed that the variables commonly selected by both Stepwise Regression and Lasso Regression analyses include information availability, participation in the trading community, and internal cooperation for the exports of small and medium sized enterprises. For large corporations, the variables determining the export amount were identified as information availability and internal cooperation. Additionally, the Stepwise Regression analysis on the income level of trading countries identified that exports of small and medium sized enterprises are significantly associated with upper-middle, lower-middle, and low income countries. Contrastingly, the Lasso Regression analysis indicated a reduction in the number of optimal variables compared to the Stepwise Regression analysis, suggesting a more concise model. This comparative analysis underscores the importance of specific trade facilitation factors and the economic status of trading countries in influencing export amounts, with a noted efficiency in model complexity reduction through Lasso Regression.