Longitudinal Principal Component and Cluster Analysis of Azerbaijan’s Agricultural Productivity in Crop Commodities

Abstract

Understanding long-term agricultural productivity is essential for designing agricultural policies, planning and targeting other economic policies (e.g., industrial policy), and managing agricultural business models. In a developing and oil-rich country such as Azerbaijan, agriculture is among the limited opportunities to diversify oil-based value added and address broad welfare issues, as farmers and agricultural workers account for a large share of total employment and the labor force. However, previous studies have not focused on an empirical assessment of the long-term and subsectoral productivity of crop commodities. Rather, they have used a highly aggregated and short-run perspective, focusing mainly on the impact of the oil sector on agricultural sectors. Here, we applied principal component analysis and hierarchical cluster analysis to identify similarities and differences in the productivity of specific crop commodities (e.g., cotton, tea, grains, tobacco, hay, fruits, and vegetables) between 1950 and 2021. We show that some crops are similar in terms of their variation, growth rates, and transition from the Soviet era to the post-Soviet period. Although the dynamics of change are different for food and non-food crops and for high- and low-productive commodities, it is still possible to narrow down specific subsectors that could reach the same productivity levels. This helps map out the productivity levels of crop commodities over time and across different subsectors, allowing for better policy decisions and resource allocation in the agricultural sector. In addition, we argue about some outlier commodities and their backward status despite extensive government support. Our results provide a common basis for policymakers and businesses to focus specifically on productivity and profitability from an economic standpoint.