Russian Agricultural Industry under Sanction Wars
The motivation for focusing on economic sanctions is the mixed evidence of their effectiveness. We assess the role of sanctions on the Russian international trade flow of agricultural products after 2014. We use a differences-in-differences model of trade flows data for imported and exported agricultural products from 2010 to 2020 in Russia. The main expectation was that the Russian economy would take a hit since it had lost its importers. We assess the economic impact of the Russian food embargo on agricultural commodities, questioning whether it has achieved its objective and resulted in a window of opportunity for the development of the domestic agricultural sector. Our results confirm that the sanctions have significantly impacted foodstuff imports; they have almost halved in the first two years since the sanctions were imposed. However, Russia has embarked on a path to reduce dependence on food imports and managed self-sufficient agricultural production.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Sanction or Financial Crisis? An Artificial Neural Network-Based Approach to model the impact of oil price volatility on Stock and industry indices
In this paper, we model the impact of oil price volatility on Tehranstock and industry indices in two periods of international sanctions and post-sanction. To analyse the purpose of study, we use Feed-forward neural net-…
Are sanctions for losers? A network study of trade sanctions
Studies built on dependency and world-system theory using network approaches have shown that international trade is structured into clusters of 'core' and 'peripheral' countries performing distinct functions. However, fe…
Geo-political conflicts, economic sanctions and international knowledge flows
We address the question how sensitive international knowledge flows respond to geo-political conflicts taking the politico-economic tensions between EU-Russia since the Ukraine crisis 2014 as case study. We base our econ…
Large increases in public R&D investment are needed to avoid declines of US agricultural productivity
Increasing agricultural productivity is a gradual process with significant time lags between research and development (R&D) investment and the resulting gains. We estimate the response of US agricultural Total Factor Pro…
Forecasting Russian Equipment Losses Using Time Series and Deep Learning Models
This study applies a range of forecasting techniques,including ARIMA, Prophet, Long Short Term Memory networks (LSTM), Temporal Convolutional Networks (TCN), and XGBoost, to model and predict Russian equipment losses dur…