FORMULATING OPTIMAL TARIFF AND QUOTA POLICIES FOR ENHANCING EFFICIENCY IN FOREIGN TRADE OPERATIONS

Аннотация

The relationship between foreign trade and tariffs is complex, and economists have not reached a full consensus on how they influence each other. This study investigates the issue by analyzing the impact of tariffs on foreign trade in four European economies over the period from 2005 to 2023. To explore these relationships, regression analysis was conducted using the STATA-14 software, employing the Ordinary Least Squares (OLS) method and the Autoregressive Distributed Lag (ARDL) model. The results indicate that all observed relationships are positive. Keywords:Ordinary Least Squares (OLS) Method, Foreign Trade, Tariff, Import Duties, AutoregressiveDistributed Lag(ARDL), Breusch–Godfrey Test, Breusch–Pagan Test Introduction. Import duties—also referred to as import taxes or tariffs—are taxes imposed primarily by a country's government on goods brought in from abroad. These are collected by the importing country’s customs authorities. There are two main reasons why countries implement customsduties: To increase government revenue, as customs duties constitute an important source of incomefor many nations; To protect domestic industries, as making imported goods more expensive can enhance thecompetitiveness ofdomestically produced products. The consistent fulfillment of the profitable part of the state budget largely depends on the completeness and timeliness of customs payments, among which tariffs play a key role. The multifaceted nature of customs duties reflects their interpretation through different academic lenses: they are viewed as part of the non-tax revenues of the budget system, a specific type of customscharge, and as a toolof customs-tariff policy. Linking collected tariffs to the import value allows for the calculation of applicable tariff rates, taking into account all pricing components and their usage. In many countries, the import of goods from certain nations is restricted through quota regimes, and the customs duty payment procedures may vary depending onthecountry of origin oftheproducts. According to preliminary data from the national statistical agency, Uzbekistan’s foreign trade turnover reached USD 4.2 billion in January 2024. The top partner countries in terms of tradevolumewere as follows (in USD): Country Trade Volume (USD Share Exports (USD Imports (USD million) (%) million) million) China 1100.0 27.2 185.0 956.0 Russia 767.0 18.3 240.0 526.0 Kazakhstan 271.2 6.5 76.3 194.9 Turkey 213.9 5.1 83.4 130.5 SouthKorea 180.2 4.3 3.3 176.9 Germany 96.3 2.3 5.5 90.8 page2208 INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE ISSN:2692-5206,ImpactFactor:12,23 AmericanAcademicpublishers,volume05,issue06,2025 Journal: https://www.academicpublishers.org/journals/index.php/ijai Japan 90.3 2.2 1.3 89.0 France 78.3 1.9 66.8 11.5 India 67.6 1.6 3.4 64.2 Czech 64.0 1.5 4.3 59.7 Republic The volume of foreign trade turnover decreased by USD 870.1 million, or 17.2%, compared to the corresponding period of 2023. In January 2024, Uzbekistan conducted trade relations with 137 countries worldwide. During the reporting month, exports amounted to USD 1.1 billion, marking a 47.1% decline, while imports totaled USD 3.1 billion, reflecting a 4.1% increase. As a result, thenegative trade balance stood at USD1.9billion. [1] Methodology. The statistical data for this study were obtained from the World Development Indicators (WDI) database maintained by the World Bank. The dataset covers the period 2005–2023 and includes four economically active countries in the Asian region: Turkey, Mexico, Syria, and Greece. To achieve the objectives outlined above, the primary task was to construct an appropriate econometric model and collect relevant statistical data. The formation of the econometric model necessitated the use of socio-economic indicators as emphasized in the reviewed literature. Accordingly, the structure of themodel, the variables included, their sources, definitions, and the academic references from which they were derived are presented in the following sections. (1.1) tTroadbeeitg=in βex0a+mβin1in×gtathrieffiitn+dicuaittors incorporated into the model, it is important to consider the variable representing the foreign trade activity of a country. For this purpose, data were obtained from the World Bank’s World Development Indicators, specifically the number ofnewly initiated trade operations within agiven year foreach country. This indicator is reported in absolute numbers, and its descriptive statistics are presented following the introduction of all model variables. In the STATA software environment, this variable is labeled as “foreign trade” (or simply “trade”) to facilitate the ease of command executionand result interpretation. In the context of the model, foreign trade volume serves as the primary dependent variable. Table1.Descriptive Statistics ofthe Variables Variable Obs Mean Std. Dev. Min Max tariff 53 2.618 2.145 1.39 14.18 page2209 INTERNATIONAL JOURNAL OF ARTIFICIAL INTELLIGENCE ISSN:2692-5206