Pub Date : 2021-01-01DOI: 10.17323/1813-8691-2021-25-1-9-41
S. Belev, V. Veterinarov, O. Suchkova
{"title":"Priority Development Areas and Productivity Growth in Russian Cities","authors":"S. Belev, V. Veterinarov, O. Suchkova","doi":"10.17323/1813-8691-2021-25-1-9-41","DOIUrl":"https://doi.org/10.17323/1813-8691-2021-25-1-9-41","url":null,"abstract":"","PeriodicalId":37657,"journal":{"name":"HSE Economic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67835962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.17323/1813-8691-2021-25-2-177-195
Mikhail Makushkin, V. Lapshin
There are many different models for estimation of a yield curve from bond market quotes. These models are well suited for developed markets with high liquidity level and market data readily available. However, this is not always the case for developing markets that are characterized by infrequent trading, heterogeneous liquidity and frequent missing data.In this article we provide a review of the existing and theoretically possible solutions to the problems arising in the process of yield curve construction in developing markets. Our review shows, that all these problems can be effectively tackled by adapting traditional yield curves models to the observer liquidity level of developing market.Heterogeneous liquidity can be addressed by introducing liquidity-based weights into a yield curve model and by removing observations with atypical liquidity from the dataset. To solve missing data problem, we suggest using dynamic yield curve models or recreating missing observations with help of a supplementary model. In special cases when there are not enough bond issues on the market one is recommended to simplify yield curve model and use the data from other markets (e.g. derivative market).The article might be of a great use for market practitioners who operate on developing bond markets as well as for quants who are engaged in construction of yield curves. It also serves as a starting point for a further academic research in the area of term structure modelling in illiquid bond markets.
{"title":"Yield Curve Estimation in Illiquid Bond Markets","authors":"Mikhail Makushkin, V. Lapshin","doi":"10.17323/1813-8691-2021-25-2-177-195","DOIUrl":"https://doi.org/10.17323/1813-8691-2021-25-2-177-195","url":null,"abstract":"There are many different models for estimation of a yield curve from bond market quotes. These models are well suited for developed markets with high liquidity level and market data readily available. However, this is not always the case for developing markets that are characterized by infrequent trading, heterogeneous liquidity and frequent missing data.In this article we provide a review of the existing and theoretically possible solutions to the problems arising in the process of yield curve construction in developing markets. Our review shows, that all these problems can be effectively tackled by adapting traditional yield curves models to the observer liquidity level of developing market.Heterogeneous liquidity can be addressed by introducing liquidity-based weights into a yield curve model and by removing observations with atypical liquidity from the dataset. To solve missing data problem, we suggest using dynamic yield curve models or recreating missing observations with help of a supplementary model. In special cases when there are not enough bond issues on the market one is recommended to simplify yield curve model and use the data from other markets (e.g. derivative market).The article might be of a great use for market practitioners who operate on developing bond markets as well as for quants who are engaged in construction of yield curves. It also serves as a starting point for a further academic research in the area of term structure modelling in illiquid bond markets.","PeriodicalId":37657,"journal":{"name":"HSE Economic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67835971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.17323/1813-8691-2021-25-3-379-402
A. Dmitrienko
{"title":"Factors of Pricing in Russian Public Higher Education Institutions","authors":"A. Dmitrienko","doi":"10.17323/1813-8691-2021-25-3-379-402","DOIUrl":"https://doi.org/10.17323/1813-8691-2021-25-3-379-402","url":null,"abstract":"","PeriodicalId":37657,"journal":{"name":"HSE Economic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67835986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.17323/1813-8691-2021-25-4-610-627
L. Hoang, M. Chatterjee, K. T. Nguyen, D. A. Nguyen
{"title":"Classifying Countries in Terms of Government Expenditure: A Multi-criteria Approach","authors":"L. Hoang, M. Chatterjee, K. T. Nguyen, D. A. Nguyen","doi":"10.17323/1813-8691-2021-25-4-610-627","DOIUrl":"https://doi.org/10.17323/1813-8691-2021-25-4-610-627","url":null,"abstract":"","PeriodicalId":37657,"journal":{"name":"HSE Economic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67836784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.17323/1813-8691-2021-25-1-147-164
Vranda Jain, Tavishi Tewary, B. Gopalakrishnan
This paper pioneers the identification of artificial intelligence (AI) enablers like technology feasibility, sophistication, data integrity, interoperability and per ceived benefits that can boost operational efficiency of firms in Indian food processing industry. With the food processing industry contributing significantly to domestic gross value added and generating an export earning of close to USD 40 billion from agricultural and processed food exports, the study examines the role of AI in overcoming the existing inefficiencies of firms, particularly the small and medium enterprises (SMEs) involved in food processing. For this, questionnaire wascirculated to 500 respondents comprising of IT and supply chain professionals, managers of food processing companies and academicians working in this do main, of which 341 complete responses were received. These responses were then analysed using PLS-SEM modeling, through which the relationship between AI adoption and operational efficiency of firm was established. The study found a significant relationship between AI adoption and operational efficiency. The R square and Q square values substantiate the predictive power of the model used in the study. The research has significant implications for supply chain professionals as technology adoption would boost resilience, integration and transparency of these firms. The study is also relevant for addressing issues pertaining to food security, employment generation, enhancing industrial output and export growth. Policy makers can also get perspectives on harnessing the benefits of AI technology while creating an enabling environment for different supply chain partners.
{"title":"Unlocking Technology Adoption for a Robust Food Supply Chain: Evidence from Indian Food Processing Sector","authors":"Vranda Jain, Tavishi Tewary, B. Gopalakrishnan","doi":"10.17323/1813-8691-2021-25-1-147-164","DOIUrl":"https://doi.org/10.17323/1813-8691-2021-25-1-147-164","url":null,"abstract":"This paper pioneers the identification of artificial intelligence (AI) enablers like technology feasibility, sophistication, data integrity, interoperability and per ceived benefits that can boost operational efficiency of firms in Indian food processing industry. With the food processing industry contributing significantly to domestic gross value added and generating an export earning of close to USD 40 billion from agricultural and processed food exports, the study examines the role of AI in overcoming the existing inefficiencies of firms, particularly the small and medium enterprises (SMEs) involved in food processing. For this, questionnaire wascirculated to 500 respondents comprising of IT and supply chain professionals, managers of food processing companies and academicians working in this do main, of which 341 complete responses were received. These responses were then analysed using PLS-SEM modeling, through which the relationship between AI adoption and operational efficiency of firm was established. The study found a significant relationship between AI adoption and operational efficiency. The R square and Q square values substantiate the predictive power of the model used in the study. The research has significant implications for supply chain professionals as technology adoption would boost resilience, integration and transparency of these firms. The study is also relevant for addressing issues pertaining to food security, employment generation, enhancing industrial output and export growth. Policy makers can also get perspectives on harnessing the benefits of AI technology while creating an enabling environment for different supply chain partners.","PeriodicalId":37657,"journal":{"name":"HSE Economic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67834401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.17323/1813-8691-2021-25-2-309-346
I. Niftiyev
This paper investigates the increased role of extractive industry, particularly oil and gas, within the Dutch disease model between 2000-2018 in the Azerbaijan economy. Dutch disease phenomenon befalls when the national economy produces and exports a single commodity or a particular sector becomes the booming sector. Increased exports of particular goods and spendings of the accumulated mineral revenue appreciate the national currency, decreasing the competitiveness and thus the production or export volume of the non-booming sectors. Azerbaijan is an oil and gas-rich country which naturally actualizes the existence of Dutch disease syndrome . Therefore, the purpose of this research is to separately reveal the Dutch disease effects of resource movement and spending effects based on the theoretical frame work constructed from the core theory. The paper contains a comprehensive literature review and overall macroeconomic screening of the Azerbaijan economy to describe the preconditions of Dutch disease. Then, the study employs 42 multivariate linear ordinary least squares (OLS) estimations. The estimated models illustrate the presence of indirect de-industrialization (one form of resource movement effect) and the spending effect of the Dutch disease hypothesis. However, the paper does not find a direct negative influence of booming sectors on aggregated lagging (i.e., manufacturing and agriculture) and non-tradable sectors (services). Moreover, variables such as oil price growth rates, real effective exchange rate (REER), nominal effective exchange rate (NEER), and economic crisis periods failed to significantly explain the employment and real wages dynamics. However, these variables described certain influence channels in output and returns on capital growth rates. This paper sheds light on the interconnections between the Azerbaijan economy’s labor resources, government spending, and monetary channels. These interconnections indicate that the Dutch disease hypothesis holds true for Azerbaijan. Of the estimated OLS coefficients, 90.5% were highly stable, which suggests that the results are reliable. This study mainly tests the general theoretical expectations of the original Dutch disease model and presents a common ground to conceptualize the possible harmful effects of the booming oil and gas sectors in Azerbaijan. Any causal derivations should be handled carefully.
{"title":"Dutch Disease Effects in the Azerbaijan Economy: Results of Multivariate Linear Ordinary Least Squares (OLS) Estimations","authors":"I. Niftiyev","doi":"10.17323/1813-8691-2021-25-2-309-346","DOIUrl":"https://doi.org/10.17323/1813-8691-2021-25-2-309-346","url":null,"abstract":"This paper investigates the increased role of extractive industry, particularly oil and gas, within the Dutch disease model between 2000-2018 in the Azerbaijan economy. Dutch disease phenomenon befalls when the national economy produces and exports a single commodity or a particular sector becomes the booming sector. Increased exports of particular goods and spendings of the accumulated mineral revenue appreciate the national currency, decreasing the competitiveness and thus the production or export volume of the non-booming sectors. Azerbaijan is an oil and gas-rich country which naturally actualizes the existence of Dutch disease syndrome . Therefore, the purpose of this research is to separately reveal the Dutch disease effects of resource movement and spending effects based on the theoretical frame work constructed from the core theory. The paper contains a comprehensive literature review and overall macroeconomic screening of the Azerbaijan economy to describe the preconditions of Dutch disease. Then, the study employs 42 multivariate linear ordinary least squares (OLS) estimations. The estimated models illustrate the presence of indirect de-industrialization (one form of resource movement effect) and the spending effect of the Dutch disease hypothesis. However, the paper does not find a direct negative influence of booming sectors on aggregated lagging (i.e., manufacturing and agriculture) and non-tradable sectors (services). Moreover, variables such as oil price growth rates, real effective exchange rate (REER), nominal effective exchange rate (NEER), and economic crisis periods failed to significantly explain the employment and real wages dynamics. However, these variables described certain influence channels in output and returns on capital growth rates. This paper sheds light on the interconnections between the Azerbaijan economy’s labor resources, government spending, and monetary channels. These interconnections indicate that the Dutch disease hypothesis holds true for Azerbaijan. Of the estimated OLS coefficients, 90.5% were highly stable, which suggests that the results are reliable. This study mainly tests the general theoretical expectations of the original Dutch disease model and presents a common ground to conceptualize the possible harmful effects of the booming oil and gas sectors in Azerbaijan. Any causal derivations should be handled carefully.","PeriodicalId":37657,"journal":{"name":"HSE Economic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67835752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.17323/1813-8691-2021-25-1-42-64
V. Zuev, E. Ostrovskaya, V. Skryabina, H. Kalachyhin
{"title":"Features of the EAEU Strategy for the Formation of an FTA Network","authors":"V. Zuev, E. Ostrovskaya, V. Skryabina, H. Kalachyhin","doi":"10.17323/1813-8691-2021-25-1-42-64","DOIUrl":"https://doi.org/10.17323/1813-8691-2021-25-1-42-64","url":null,"abstract":"","PeriodicalId":37657,"journal":{"name":"HSE Economic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67835876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.17323/1813-8691-2021-25-2-292-308
Yurii Ivanov, A. Ponomarenko, Aleksandra Sil'chuk
Currently, several similar but not identical statistical indicators characterizing the income of the population/households in Russia are published and all of these indicators are more or less official. Namely these are the following indicators: (a) an indicator of the real disposable monetary income of the population, published in all major statistical datasets, usually in the form of corresponding indices and officially used by government agencies;(b) an indicator of gross disposable income of households published in the official statistical yearbook «National accounts of Russia» as an absolute value at current prices; (c) an indicator of real disposable household income published in form of index in the statistical yearbook of the international organization (OECD) based on data for Russia received from Rosstat. The difference between them is that one of them focuses only on monetary incomes, while the other two use the classical interpretation of disposable income used in the system of national accounts, which also includes incomes in kind. There are also differences in approaches to deflation. But, objectively, the simultaneous use of all of these indicators is not so much due to the difference in their content or methodology, but rather to the peculiarities of the organization of the statistical system and the incomplete implementation of the system of national accounts in Russia. However, the simultaneous use of several indicators that are similar in terms of content leads to some confusion among users who do not have special statistical qualifications. The article examines the methodological differences between the indicators listed above, which lead to differences in their size and semantic content, as well as differences in the formats of their publication, and also provides recommendations for their correct use and interpretation.
{"title":"On the Interpretation of the Disposable Household Income Statistical Indicator","authors":"Yurii Ivanov, A. Ponomarenko, Aleksandra Sil'chuk","doi":"10.17323/1813-8691-2021-25-2-292-308","DOIUrl":"https://doi.org/10.17323/1813-8691-2021-25-2-292-308","url":null,"abstract":"Currently, several similar but not identical statistical indicators characterizing the income of the population/households in Russia are published and all of these indicators are more or less official. Namely these are the following indicators: (a) an indicator of the real disposable monetary income of the population, published in all major statistical datasets, usually in the form of corresponding indices and officially used by government agencies;(b) an indicator of gross disposable income of households published in the official statistical yearbook «National accounts of Russia» as an absolute value at current prices; (c) an indicator of real disposable household income published in form of index in the statistical yearbook of the international organization (OECD) based on data for Russia received from Rosstat. The difference between them is that one of them focuses only on monetary incomes, while the other two use the classical interpretation of disposable income used in the system of national accounts, which also includes incomes in kind. There are also differences in approaches to deflation. But, objectively, the simultaneous use of all of these indicators is not so much due to the difference in their content or methodology, but rather to the peculiarities of the organization of the statistical system and the incomplete implementation of the system of national accounts in Russia. However, the simultaneous use of several indicators that are similar in terms of content leads to some confusion among users who do not have special statistical qualifications. The article examines the methodological differences between the indicators listed above, which lead to differences in their size and semantic content, as well as differences in the formats of their publication, and also provides recommendations for their correct use and interpretation.","PeriodicalId":37657,"journal":{"name":"HSE Economic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67835903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.17323/1813-8691-2021-25-4-560-573
A. Tomaev
{"title":"Rail Freight Sector: Different Organizational Models and Possible Scenarios for Russia","authors":"A. Tomaev","doi":"10.17323/1813-8691-2021-25-4-560-573","DOIUrl":"https://doi.org/10.17323/1813-8691-2021-25-4-560-573","url":null,"abstract":"","PeriodicalId":37657,"journal":{"name":"HSE Economic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67836818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.17323/1813-8691-2021-25-4-595-609
I. Lyubimov, D. Mirakyan
{"title":"Export Transformation in Belarus: Results and Opportunities","authors":"I. Lyubimov, D. Mirakyan","doi":"10.17323/1813-8691-2021-25-4-595-609","DOIUrl":"https://doi.org/10.17323/1813-8691-2021-25-4-595-609","url":null,"abstract":"","PeriodicalId":37657,"journal":{"name":"HSE Economic Journal","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67836625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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