Очеловечивание текста — Анти ИИ-детектор

Abstract The essence of this project is to identify and assess financial and non-financial characteristics of firm performance among Russian pharmaceutical small and medium-sized enterprises (SMEs) via ordinary least squares (OLS) regression models using cross-sectional firm-level data from SPARK-Interfax database. The expected outcome of this sector-specific SME research is to provide practical implications for financial management, benchmarking and risk assessment of pharmaceutical economic sector. Introduction Background. The pharma industry plays an important role in Russian economics owing to its bond with public health, supply security and capital intensity. Over the time, the industry has shown the increase of market volume. The fastest growth is demonstrated by the generics segment, accompanied by intensified competition across manufacturing, distribution and retail pharmacy chains. Nevertheless, the market growth does not equal to individual firm improvement. Furthermore, high level of competition, regulatory requirements and indirect and secondary costs may amplify performance differences especially among SMEs. In terms of SMEs market presence, they participate in production (including contract manufacturing and packaging), wholesale distribution and retail segments. In comparison with major market players, SMEs apparently have more limited access to long-term financing, lower bargaining power in procurement and higher sensitivity to liquidity disruptions. These factors make SMEs viability dependent on working-capital structure, debt burden and the ability to scale efficiently. In addition, there are some industrial complexities, such as storage requirements affecting inventory management, shelf life, product traceability, compliance. Even though pharmaceuticals industry plays an integral role in economics and social policy, the empirical evidence on Russian pharmaceutical SMEs remains limited. The majority of international literature focuses on SMEs across broad industry sets or on large listed pharmaceutical firms in developed markets. Researches based on up-to-date microdata of Russian pharmaceutical SMEs are in short supply. This project addresses the gap by providing a cross-sectional, industry-focused analysis of 2024 firm-level data. Problem statement. The main objective of the thesis is to identify and quantify determinants of financial performance of Russian pharmaceutical SMEs. The research is focused on four questions: What financial factors are significantly associated with profitability among pharmaceutical SMEs? What non-financial characteristics explain heterogeneity in profitability? Do determinants differ between production and trade segments aligned with the differences in business models and operating cycles? Do financial variables explain a larger share of profitability variation than structural variables? In order to come to the answers, the following objectives have to be fulfilled: Analyze the environment of the SME segment, as well as the pharmaceutical industry in Russia and around the world. Study domestic and foreign approaches to determining the effectiveness of companies, as well as the factors that influence it. Develop the set of the most suitable criteria and factors. Based on the criteria, form a relevant sample of SME companies in the pharmaceutical industry. Identify the determinants of effectiveness via econometric modeling. Interpret the results and formulate practical conclusions on improving the financial performance of Russian SME pharmaceutical companies. Practical value. The results of this paper can be used for benchmarking and assessment of the financial stability of companies in the industry. In terms of internal usage, the findings can be applicable in making management decisions on debt policy and working capital management. The results are also useful for investors and creditors to evaluate the risks and quality of a business based on observable financial metrics. Literature Review In vast majority of empirical studies, firm performance or efficiency is measured through financial indicators derived from accounting statements. The most commonly used proxies are Return on Assets (ROA), Return on Equity (ROE), and Return on Sales (ROS), as they capture different dimensions of profitability: ROA reflects how efficiently a company uses its asset base to generate profit. ROE measures the return generated for shareholders and incorporates the effect of financial leverage. ROS indicates operating profitability and reflects the efficiency of core business activity. In SME research, performance is often modeled as a function of financial structure, working capital management, firm size, and structural characteristics. Ahmeti (2022) examine the impact of working capital management on SME profitability using financial statement data of 98 SMEs in Kosovo over the period 2010–2020. The dependent variable in the study is ROA, while independent variables include: Inventory turnover period Receivables collection period Payables period Cash conversion cycle (CCC) Firm size Current ratio Sustainable growth Leverage. The authors estimate regression models using pooled OLS and assess multicollinearity through Variance Inflation Factors (VIF). The general form of the estimated model is: ROA_{it} = β_0 + β_1 INTP_{it} + β_2 TRCP_{it} + β_3 TPP_{it} + β_4 SIZE_{it} + β_5 CR_{it} + β_6 SGR_{it} + β_7 LEV_{it} + ε_{it} where, INTP — inventory turnover period; TRCP — trade receivables collection period; TPP — trade payables period; CR — current ratio; SGR — sustainable growth rate; LEV — leverage. The study finds that working capital components significantly affect profitability, and that liquidity and leverage are important control variables. A key methodological insight is the careful handling of multicollinearity among working capital indicators, especially the cash conversion cycle. Similarly, Dinku (2013) analyzes 67 micro and small enterprises in Ethiopia using a cross-sectional OLS regression model. The dependent variable is ROA, and independent variables include receivables period, inventory period, payables period, and cash conversion cycle. The general regression equation is specified as: ROA_i = β_0 + β_1 DAR_i + β_2 DINV_i + β_3 DAP_i + β_4 CCC_i + β_5 LNSALES_i + β_6 GEAR_i + ε_i where, DAR — days accounts receivable; DINV — days inventory; DAP — days accounts payable; CCC — cash conversion cycle; LNSALES — log(sales); GEAR — gearing. The results reveal a negative relationship between the length of the cash conversion cycle and profitability, highlighting the importance of efficient working capital management. These studies demonstrate that OLS-based models are widely used in SME research to quantify the relationship between working capital structure and performance. Akinola and Apps (2025) investigate small pharmaceutical firms listed on the Alternative Investment Market (AIM) in the United Kingdom. The sample includes firm-year observations from 2018 to 2022. The dependent variables are ROA and ROE. Independent variables include: Debt-to-equity ratio Short-term debt Long-term debt, Firm size (control). The authors estimate three types of models, which are pooled OLS, fixed effects (FE), random effects (RE). The general specification is: Y_{it} = β_0 + β_1 STD_{it} + β_2 LTD_{it} + β_3 DER_{it} + β_4 SIZE_{it} + ε_{it} where, STD — short-term debt; LTD — long-term debt; DER — debt-to-equity ratio; SIZE — market value. The results indicate a statistically significant negative relationship between short-term debt and profitability. The study concludes that capital structure plays a crucial role in determining the efficiency of small pharmaceutical firms. This research is particularly relevant because it focuses specifically on the pharmaceutical sector and confirms the importance of leverage in performance modeling. Elaborating on Russian context, Panova (2020) examines determinants of capital structure in Russian small and medium manufacturing enterprises using panel data from 2010–2018. The study applies a fixed effects (FE) panel model to account for firm-specific heterogeneity. The dependent variable is the debt ratio, while explanatory variables include: Liquidity Asset structure Profitability (ROA, ROS) Firm size The general form of the FE model is: LEV_{it} = α_i + β_1 LIQ_{it} + β_2 ASSTR_{it} + β_3 TAX_{it} + β_4 PROF_{it} + β_5 SIZE_{it} + ε_{it} where, LEV — debt ratio (leverage); LIQ — liquidity; ASSTR — assets structure; TAX — taxes to pay; PROF -profitability (ROA/ROS). The results show that liquidity and asset structure significantly influence leverage decisions, while profitability indicators are not always significant. Although Panova (2020) models leverage rather than profitability, the study highlights the interconnectedness of liquidity, debt structure, and financial sustainability in Russian SMEs. Therefore, these variables must be included when modeling performance. Suttipun and Insee (2024) analyze the relationship between R&D intensity and SME performance in Thailand. The dependent variable is firm performance measured by revenue and profitability indicators. The authors estimate multiple regression models including an interaction term: Performance_i = β_0 + β_1 RD_i + β_2 SIZE_i + β_3 (RD_i × SIZE_i) + ε_i where, RD — R&D intensity; SIZE —ln(employees). The study finds that R&D intensity does not always have a direct positive impact on performance. In some cases, the relationship is weak or negative in the short run, particularly for smaller firms. This suggests that innovation expenditures may reduce short-term profitability while contributing to long-term competitiveness. Based on this literature, the present study adopts a cross-sectional regression framework using 2024 data from SPARK–Interfax and includes financial and structural variables consistent with prior empirical research, while foc