Очеловечивание текста — Анти ИИ-детектор

Methods To analyze the determinants of performance of Russian small and medium-sized enterprises in the pharmaceutical industry, a cross-sectional regression analysis will be conducted based on firm-level data for 2024. The empirical analysis will use multiple linear regression to identify the financial and non-financial indicators that have the most significant impact on company performance. The dataset will be constructed using information obtained from the SPARK–Interfax database. Sample Formation. The sample will include companies that meet the official criteria of small and medium-sized enterprises under Russian legislation. The type of economic activity will be determined according to the All-Russian Classifier of Economic Activities (OKVED-2). The selected activity codes are: • 21 – Manufacture of pharmaceutical products • 46.46 – Wholesale of pharmaceutical goods • 47.73 – Retail sale of pharmaceutical products. To ensure comparability and reliability of the analysis, the following additional selection criteria will be applied: 1) firms must have been operating for at least 5 years 2) firms must report positive revenue in 2024 3) firms must provide complete financial statements for the required indicators. Thus, the final dataset will represent actively operating pharmaceutical SMEs for the year 2024. Data Preparation and Preliminary Analysis. After exporting firm-level data from SPARK, several stages of preprocessing and primary analysis will be conducted. First, the dataset will be cleaned from duplicate entries and firms with incomplete or inconsistent financial statements. Companies with negative total assets, zero balance-sheet totals, or missing key variables will be excluded. Second, all financial ratios will be constructed based on standardized accounting formulas. Since SME financial data often contain extreme values due to small denominators or temporary losses, descriptive statistics (mean, median, standard deviation, minimum, maximum) will be calculated for each variable. Additionally, boxplots and distribution checks will be used to evaluate skewness and identify extreme observations. Third, a correlation matrix will be constructed to examine relationships between explanatory variables. Variance Inflation Factors (VIF) will be calculated to detect potential multicollinearity. If high collinearity is detected, alternative model specifications will be considered. This preliminary analysis will ensure the stability and interpretability of subsequent regression results. Dependent Variables. Firm performance will be measured using three indicators: • Return on Assets (ROA) – net profit to total assets • Return on Equity (ROE) – net profit to equity • Return on Sales (ROS) – operating profit to revenue. Using three performance measures allows for a more comprehensive assessment of efficiency and strengthens robustness. Independent Variables. The explanatory variables are divided into financial and non-financial factors. Financial Factors: • Leverage (total debt to total assets) • Net working capital to assets ratio • Liquidity (cash and equivalents to total assets) • Revenue growth rate • R&D expenditure. These variables reflect capital structure, liquidity position, growth dynamics, and innovation intensity. Non-Financial Factors: • Firm size • Firm age • Number of employees • Region (dummy variable) • SME category (small or medium) • Type of activity (production or trade) • Group affiliation (dummy variable). These variables capture structural and organizational characteristics that may influence performance. Results At this stage, the empirical analysis has not yet been completed, therefore no final results are available. However, based on the reviewed literature and theoretical considerations, several hypotheses are formulated that are expected to be confirmed or rejected during the econometric analysis. First, it is expected that not all explanatory variables will prove to be statistically significant. Financial indicators related to capital structure and liquidity are likely to demonstrate the strongest impact on performance. Second, leverage is expected to have a negative effect on profitability indicators (ROA and ROE). A higher debt burden increases financial risk and interest expenses, which may reduce net profitability, especially for SMEs with limited access to long-term financing. Third, indicators related to working capital structure are expected to have a significant impact. Net working capital and liquidity ratios may positively influence performance, as they reflect the firm's ability to maintain stable operations and manage short-term obligations effectively. Fourth, revenue growth rate is expected to positively affect return on sales and return on assets, as expanding firms are more likely to improve operational efficiency and scale effects. Fifth, R&D expenditure may not demonstrate a strong positive relationship with profitability in the short term. It is possible that innovation-related expenses reduce current financial results while contributing to long-term competitiveness. Sixth, structural characteristics such as firm size are expected to positively influence performance due to economies of scale and better access to financial resources. At the same time, variables such as legal form or group affiliation may have a weaker impact. Seventh, differences between production and trade companies are expected. For wholesale and retail firms, working capital and liquidity indicators may play a more significant role, while for production firms capital structure and asset-related indicators may be more important. The regression analysis will allow identification of the most influential determinants and evaluation of the explanatory power of the model. Conclusion The SME sector plays a significant role in the Russian economy, and the pharmaceutical industry is one of its most dynamic and strategically important segments. Since 2020, the industry has undergone substantial structural changes due to the pandemic, sanctions, supply chain disruptions, and regulatory adjustments. These changes have likely affected both the level of performance and the factors determining efficiency, particularly for small and medium-sized enterprises. The purpose of this study is to identify the key determinants of financial performance of Russian pharmaceutical SMEs using firm-level data for 2024. The analysis will focus on both financial and non-financial factors, allowing for a comprehensive evaluation of internal drivers of efficiency. The results of the study are expected to contribute to a better understanding of how capital structure, liquidity, working capital management, and structural characteristics influence SME performance in the pharmaceutical sector. The findings may also help distinguish the relative importance of financial versus organizational factors.