Road transport plays a central role in supply chains; without efficient logistics, domestic and international markets cannot function smoothly. Transport and logistics costs affect final prices because changes in service tariffs and energy inputs are transmitted into production and distribution costs. This paper examines how road transport costs influence macroeconomic price formation through supply‑and‑demand mechanisms, focusing on the effects of fuel prices and demand for transport and logistics services on transport‑related price dynamics. The study uses monthly and quarterly secondary data from official sources: the Agency for Statistics of Bosnia and Herzegovina (BHAS), the Statistical Office of the Republic of Srpska (RZS RS), the International Road Transport Union (IRU), Eurostat, and BIHAMK. Data were analysed using descriptive statistics, correlation analysis, and a simple regression model (implemented in Excel) to assess the intensity and direction of the relationship between fuel prices and the Consumer Price Index (CPI). Results indicate a statistically significant relationship between fuel prices and CPI movements, while several logistics variables could not be fully examined due to limitations in data availability and inconsistent time frequencies. Multiple regression was not applied for the same reasons. The model explains 55.6% of CPI variability (R² = 0.556), highlighting transport costs as an important channel in macroeconomic price dynamics and motivating further empirical research with longer, frequency‑aligned logistics series.
Road transport plays a central role in supply chains; without efficient logistics, domestic and international markets cannot function smoothly. Transport and logistics costs affect final prices because changes in service tariffs and energy inputs are transmitted into production and distribution costs. This paper examines how road transport costs influence macroeconomic price formation through supply‑and‑demand mechanisms, focusing on the effects of fuel prices and demand for transport and logistics services on transport‑related price dynamics. The study uses monthly and quarterly secondary data from official sources: the Agency for Statistics of Bosnia and Herzegovina (BHAS), the Statistical Office of the Republic of Srpska (RZS RS), the International Road Transport Union (IRU), Eurostat, and BIHAMK. Data were analysed using descriptive statistics, correlation analysis, and a simple regression model (implemented in Excel) to assess the intensity and direction of the relationship between fuel prices and the Consumer Price Index (CPI). Results indicate a statistically significant relationship between fuel prices and CPI movements, while several logistics variables could not be fully examined due to limitations in data availability and inconsistent time frequencies. Multiple regression was not applied for the same reasons. The model explains 55.6% of CPI variability (R² = 0.556), highlighting transport costs as an important channel in macroeconomic price dynamics and motivating further empirical research with longer, frequency‑aligned logistics series.
Road transport plays a central role in supply chains; without efficient logistics, domestic and international markets cannot function smoothly. Transport and logistics costs affect final prices because changes in service tariffs and energy inputs are transmitted into production and distribution costs. This paper examines how road transport costs influence macroeconomic price formation through supply‑and‑demand mechanisms, focusing on the effects of fuel prices and demand for transport and logistics services on transport‑related price dynamics. The study uses monthly and quarterly secondary data from official sources: the Agency for Statistics of Bosnia and Herzegovina (BHAS), the Statistical Office of the Republic of Srpska (RZS RS), the International Road Transport Union (IRU), Eurostat, and BIHAMK. Data were analysed using descriptive statistics, correlation analysis, and a simple regression model (implemented in Excel) to assess the intensity and direction of the relationship between fuel prices and the Consumer Price Index (CPI). Results indicate a statistically significant relationship between fuel prices and CPI movements, while several logistics variables could not be fully examined due to limitations in data availability and inconsistent time frequencies. Multiple regression was not applied for the same reasons. The model explains 55.6% of CPI variability (R² = 0.556), highlighting transport costs as an important channel in macroeconomic price dynamics and motivating further empirical research with longer, frequency‑aligned logistics series.
