Features of the Formation of Technological Expectations in Russia: Analysis of the Results of Business Tendency Surveys of Digital Transformation of Manufacturing Enterprises

The purpose of the article is to analyze the technological expectations of managers of Russian enterprises from the manufacturing industry in the face of changes in the external economic environment. Firstly, the authors have calculated a specially developed «index of fulfillment» of technological expectations, reflecting the ratio of the expectations of the introduction of digital technologies formed in the previous period and an increase in the real level of implementation. Secondly, using regression analysis, the authors investigated the mechanisms of the formation of technological expectations of managers in various conditions: non-crisis 2018, pre-crisis 2019, and crisis 2020. The influence of the three mechanisms was tested in intertemporal context: the «inertial» one, which presupposes the preservation of the expectations formed in the past in the current period; «adaptive» which involves adjusting expectations in accordance with the current dynamics of technology implementation; «predictive» which implies the connection of expectations with the future level of implementation.The basis for empirical calculations was the data of annual business tendency surveys of digital activity of Russian manufacturing enterprises for 2018–2020. The aggregate sample of surveyed enterprises for three years included more than 3000 enterprises from 23 manufacturing industries. The paper studied patterns of implementation of 19 digital technologies, most of which, according to specialists, belong to Industry 4.0.The results obtained indicate that technological expectations are characterized by great heterogeneity in terms of feasibility. Regression analysis showed that all three identified mechanisms can play a role, but their influence varies. In particular, when a crisis occurs, the adaptive mechanism plays a key role, and the inertial mechanism becomes irrelevant.The results of this study indicate that Russia is characterized by the initial and transitional nature of digital transformation in the manufacturing industry, with technological development achieved through «breakthroughs» rather than a steady process of modernization. The main conclusion of the work is that external uncertainty greatly affects the evolution of technological expectations, destroying their continuity from previous plans and negatively affecting the predictive capabilities.

Forecasting with Business and Consumer Survey Data

In a context of growing uncertainty caused by the COVID-19 pandemic, the opinion of businesses and consumers about the expected development of the main variables that affect their activity becomes essential for economic forecasting. In this paper, we review the research carried out in this field, placing special emphasis on the recent lines of work focused on the exploitation of the predictive content of economic tendency surveys. The study concludes with an evaluation of the forecasting performance of quarterly unemployment expectations for the euro area, which are obtained by means of machine learning methods. The analysis reveals the potential of new analytical techniques for the analysis of business and consumer surveys for economic forecasting.

Stress Testing in Statistical Modeling of Business Activity in Conditions of Market Shocks

The authors proposed an original method of stress testing in statistical modeling of business activity based on the results of business tendency surveys to study possible scenarios for the development triggered by external unforeseen supply and demand shocks, as in the case of the COVID-19 pandemic. The article also provides an overview of existing approaches in the field of stress testing and the construction of stress indices with an emphasis on methods based on vector autoregressive models and their various modifications. Thus, the article aims to adapt existing methods of macro-level stress testing for their use based on the results of business tendency surveys.The basis for empirical calculations was the data from business tendency surveys of the leaders of Russian manufacturing enterprises, reflecting their combined estimates of the current state of business activity. The methods used in the article included: firstly, the formation of four composite indices based on the results of business tendency surveys from 2008 to March 2020, reflecting various aspects of business activity of enterprises (demand index, production index, financial index and employment index); secondly, the construction of the BVAR (Bayesian vector autoregression) model and its application for studying and comparing various forecast scenarios of index reactions to market shocks.The results of the study, forecasts of the dynamics of indices were obtained as a reaction to four possible shock scenarios: short-term, V-, W-, and U-shaped. Moreover, for each of the scenarios, cases of shocks from the side of demand, production and their simultaneous impact are presented.The conclusions based on the results of this study point to the key role of demand in the dynamics of all the considered indices and to the relatively greater sensitivity of the employment index in relation to the demand index and the finance index in relation to the production index. W-shaped shock was the worst of the four scenarios considered.Conclusions based on the study results indicate the vital role of demand in the dynamics of all the indices under consideration, the Wshaped shock, as the worst of the considered scenarios, as well as the relatively higher sensitivity of the employment index to the demand index and the finance index to the production index.

RESPONSE DYNAMICS IN BUSINESS TENDENCY SURVEYS: EVIDENCE FROM POLAND

In this paper, trends, business cycle correlates and macroeconomic patterns in response rates are explored. Two groups of respondents taking part in the RIED (Research Institute for Economic Development of the Warsaw School of Economics) economic tendency survey are taken into consideration: industrial enterprises and households. Empirical analysis indicates that household response rates rise slightly with consumer price index, and decline during current expansion phase of the economy. Gender, geographical location and city / country residence are not factors in determining household response rate dynamics. In case of industrial enterprises, willingness to answer seems to rise when business conditions deteriorate, and vice versa, although this effect is small in terms of absolute values of correlation coefficients. Non-response is found to be higher when economy expands but the relationship is weak.