Partner: United States Agency for International Development (USAID)
Year: 2014
The research aimed to highlight sectors with the greatest potential, which have the capacity to drive sustainable growth and development of the Serbian economy through improved export activities and competitiveness in foreign markets. Additionally, this research will point out which sectors have obvious market potential but are simultaneously facing difficulties in achieving their goals and fully realizing that potential. The findings of this research should motivate policymakers to devise various actions through which adequate support would be provided to sectors with the greatest potential, while simultaneously removing – or at least minimizing – the limitations faced by those sectors.
This study is CEVES's foundational research on the Serbian real sector, and most importantly, it is the only one of its kind in Serbia. Through this study, CEVES has laid the groundwork and contributed to the creation of fact- and evidence-based policy. A particular achievement is the identification of sectors with potential, based on small and medium-sized enterprises, which were significantly underestimated at the time. This research introduced a series of innovations in the analysis of sectors in Serbia: the introduction of the analysis of the performance of the average/medium company within a sector (not just the overall generated sector performance), bridging different international classifications, products, and activities, which enabled the analysis of the performance of both companies and international competitiveness in trade of goods, etc. CEVES invested time in cleaning the database of the Business Registers Agency (APR) and identifying bona fide companies (separating those that „truly“ operate and earn from it, from those that are not creditworthy and/or have irregular business reporting) and conducted a business assessment solely on them. The analysis included the preparation of time series of various indicators, which were statistically analyzed using multivariate principal component analysis (PCA).
