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The purpose of the paper is to study the influence of education on placement of production in the agro-industrial complex (AIC) in the conditions of Industry 4.0.

For studying the experience of modern Russia in the aspect of the influence of education on placement of production in the AIC, the author uses the methodology of economic statistics. The author performs analysis of the ratio of the number of companies of the AIC and the share of the employed with higher education in the federal districts of the Russian Federation in 2018. Two markets are distinguished in the structure of the AIC: agriculture and food industry. Also, the method of regression analysis is used for compilation of regression curves, which reflect the dependence of the number of companies in the AIC (in view of the distinguished markets) on the share of the employed with higher education.

The results of the performed research showed that during the third technological mode in Russia, accessibility and quality (level of education) of human resources are insignificant factors during decision-making on placement of production in the AIC. In the conditions of Industry 4.0, the situation will change, and education will have significant influence on placement of production in the AIC. The higher the level of education of a territory’s human resources, the lower the entrepreneurial risks (risk of equipment’s failure and production defects), the higher the probability of creation of know-how (possibility to save on R&D with high innovative activity) and the higher the opportunities for selling the products of AIC 4.0 on the territory.

Entrepreneurial structures are recommended to use the developed algorithm of selecting a territory for placement of production in the AIC in the conditions of Industry 4.0, in view of the level of education.

The purpose of the article is to study the recent tendencies of growth of Russia’s agro-industrial complex (AIC), determine the optimal scenario of its development, and develop recommendations in the sphere of state regulation for its practical implementation. While there are tendencies of growing production and increase in Russia’s export, against this background, there is a tendency of quicker increase of import of food – if it continues, positive balance of foreign trade of food products in 2018 will turn into negative balance in 2020–2024. Though efficiency of crop farming is peculiar for a tendency of quick growth, efficiency of animal breeding is stable, which does not allow overcoming the growing deficit of food in Russia, which grows under the influence of the tendency of wear of fixed funds and slow implementation of new fixed funds due to insufficient financing. Scenarios of mid-term (i.e., until 2024) growth of Russia’s AIC are compiled, of which the most optimal is scenario that requires technological advancements, due to which increase in the value of index of food security up to 85.00 points (27%) will be achieved and the set goals of growth and development of Russia’s AIC will be reached. For a successful optimal scenario of the growth of Russia’s AIC, we offer recommendations in the sphere of state regulation of its digital modernization: adoption of the national strategy of transition to AIC 4.0 within the program “Digital economy of the RF,” development of import substitution in the AIC with emphasis on B2B markets, preparation of the technological platform for transition to AIC 4.0, and sufficient financing for digital modernization of the AIC.

The agro-industrial complex (AIC) of Belarus is one of the priority sectors of the national economy, which performs economic, social, environmental and cultural functions. The main trends in the development of the industry on modern business conditions are presented in the section. The goals, objectives, and mechanisms for the implementation of the state programs of development of the AIC are analyzed. The directions and measures of state support for agriculture are reasonable, the actual structure of the “green box” and “yellow box” measures is presented. Approaches and mechanisms to ensure national food security are highlighted in the light of new conditions, goals, and objectives. The results of the foreign trade in agricultural products of Belarus and rural development and social infrastructure of the village are presented. Scenarios for the development of agriculture in Belarus, taking into account national priorities in the field of agricultural production, the domestic consumer market, foreign trade, have been developed.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.

The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.

The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.

A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.

While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.

This study estimates a Vector Error Correction Model (VECM) that incorporates the linkages among the agriculture, industry, construction, transport, storage and communication and service sectors for Bangladesh by using historical data from 1979 to 2009. Two cointegrating vectors confirm that all the different sectors moved together over the sample period, and therefore that their growth rates are interdependent. The long-run relationships of the industrial, construction, transport, storage and communication and service sectors to the agricultural sector were established, and the results show that the industrial and construction sectors contribute positively to the agricultural sector, the growing service sector contributes negatively and the transport, storage and communication sector shows mixed results. In addition, weak exogeneity for the agricultural sector is rejected and this underlines the fact that the agricultural sector should be considered by policymakers in any analysis of inter sector growth.