This article is devoted to the studying of rules of international agreements concluded between the subjects of the Russian Federation and foreign states or their subjects, and experience of European Union in using such kind of instruments. The article focuses on analyse of already existed agreements, necessity of harmonization and elaboration of framework agreement.

By Presidential decree No. 215 of 15 may 2018 “On the structure of Federal Executive bodies”, the Ministry of education was transformed into the Ministry of education of the Russian Federation and the Ministry of science and higher education of the Russian Federation (hereinafter - the Ministry of education). One of the structural divisions of the Ministry of education is the Department of international cooperation (hereinafter-DIC). One of the projects supervised by the DIC is the program of external academic mobility, as a result of which Russian citizens have the opportunity to fully or partially undergo training (scientific training) in more than 20 foreign countries (hereinafter - the program of the EAM). Cooperation is carried out in accordance with intergovernmental agreements, orders of the Government of the Russian Federation, interdepartmental agreements of the Ministry of education of the Russian Federation, concluded within the framework of international scientific and educational cooperation programs. This article presents the results of the analysis of the effectiveness of the program for the last 5 years, as well as the results of the analysis of industry statistics on the financing of research in the framework of international scientific and educational cooperation.

This article presents the results of the analysis of the current state of international scientific and educational cooperation with Italy through the Ministry of Science and Higher Education of the Russian Federation.

In this article has been analyzed the current state of education in Azerbaijan, and the role of education in poverty reduction and the formation of the labor market has been researched. Also, the assessment of sectoral and macroeconomic impacts of capital investments in the formation of the education system in accordance with the requirements of the labor market was considered.

The purpose of the study is to evaluate the volume and designation of investment in education, including macroeconomic impacts on its economic activities as a whole, in terms of investment programs.

The research database consists of official state documents related to the volume of capital investments and the inter-sectoral balance of production and distribution of products and services developed by the State Statistical Committee of the Republic of Azerbaijan.

The assessments were made using the Social Accounts Matrix and its general multiplier model, based on the intersectoral balance of production and distribution of products and services.

As a result, the economic and macroeconomic effects of capital investments in education were evaluated by simulations.

It is important to understand the effect of Small and medium enterprises (hereinaftercalled SME) on economic development and to find out whether International Organizations’ policy recommendations are “Kicking Away the Ladder” policies or not. Therefore, in this study, we seek to evaluate the causal effect of SME’s on growth of 11 different sectors (as suggested by IMF) of economy. To this end, we started with a simple regression model to check whether there is a positive association between SMEs and economic growth at all. The toy regression model we employed was based on the regression of sectoral per capita GDP on the SME growth variable (measured by the labor employed by SMEs in each sector). Furthermore, we checked the robustness of such positive association in different environments determined by the fixed and random effects assumptions and interestingly, we obtained statistically significant positive association no matter which assumption about fixed and random effects were made.

The data used in this study are obtained from the website of the State Statistical Committee of the Republic of Azerbaijan (“The State Statistical Committee of the Republic of Azerbaijan,” 2019). We did initial data cleaning and transformations to make the data serve our purposes and these manipulations are given in the paper. The interesting side of the paper is it uses panel data to reveal the relationship of SMEs and growth.

To the best of our knowledge, no paper has employed macroeconomic panel data to assess the effect of SMEs on each sector of a single country so far. To this end, we use Arellano-Bond estimator which is also called Generalized Method of Moments Instrumental Variable estimator. The essence of this methodology is that current values of the dependent variable cannot have a causal impact on the past values of the endogenous regressors (Arellano and Bond, 1991).

Relying on this fact, we construct instruments for possible endogenous SME growth variable using its own past values. After filtering out the variation (noise) in SME growth variable in current period which is not associated with its own lagged values we hope that there will not be any signal left in the predicted values of SME growth variable which can be affected by current period GDP contemporaneously. After doing so, we believe that any positive regression coefficient obtained is purely due to causality flowing from SME growth to GDP and not vice versa. Moreover, we use three different fixed effects model for robustness check purposes and find that the relationship between SME growth variable and GDP in each sector stays positive and statistically significant which makes our results more convincing.

In the presented article, the analysis and assessment were made through the regional intersectoral balance model for the Azerbaijani economy. Thus, based on official data provided by the State Statistical Committee of the Republic of Azerbaijan, a regional inter-sectoral balance model for Ganja-Gazakh economic region of Azerbaijan was established. The built model allows to evaluate both sectoral and overall impact on the 19 economic sectors.

Basically, as a result of the change in the output of the final product by economic sectors , the change in economic sectors separately and gross output of the economic region as a whole were assesed. As a result, multiplier coefficients were calculated for 19 economic sectors.

As a result, in economic sectors such as agriculture, forestry and fishing, processing industry, construction, trade; repair of transportation, financial and insurance activities, administrative and support services, and other services the economic multiplier ratios were sufficiently high.