Macroeconomic Statistics Research Articles

Financial resource curse and firms' accessibility to financing in resource-based countries

This study delves into the paradox of the financial resource curse, exploring how the abundance of natural resources in a country paradoxically constrains firms' accessibility to financing. Despite the potential economic boon natural resources represent, evidence suggests that they can lead to less diversified economies, making it challenging for firms outside the resource sector to access financing. Our research aims to dissect this phenomenon by analyzing microeconomic statistics on the financial accessibility of enterprises, juxtaposed with macroeconomic statistics across 170 countries, encompassing over 10,000 firms surveyed from 1990 to 2022. The panel regression analysis allows us to control for both time-invariant country characteristics and global economic trends, providing insights into the causal relationship between resource dependence and financial access for firms. The results are striking, revealing that, indeed, countries with significant natural resource wealth tend to exhibit reduced financial accessibility for firms outside the resource extraction sector. The panel regression models indicate a robust negative correlation between the extent of a country's resource wealth and the ease with which non-resource firms can access financial capital. This suggests that the financial resource curse is not only a real phenomenon but also one that has significant implications for economic diversification and sustainable development. Moreover, findings underscore the need for targeted policy interventions. Countries with abundant natural resources should implement strategies that foster economic diversification, enhance the financial infrastructure to support a broader range of industries, and encourage the development of financial instruments tailored to the needs of non-resource sectors.

Neural network analysis in time series forecasting

Objectives. To build neural network models of time series (LSTM, GRU, RNN) and compare the results of forecasting with their mutual help and the results of standard models (ARIMA, ETS), in order to ascertain in which cases a certain group of models should be used.Methods. The paper provides a review of neural network models and considers the structure of RNN, LSTM, and GRU models. They are used for modeling time series in Russian macroeconomic statistics. The quality of model adjustment to the data and the quality of forecasts are compared experimentally. Neural network and standard models can be used both for the entire series and for its parts (trend and seasonality). When building a forecast for several time intervals in the future, two approaches are considered: building a forecast for the entire interval at once, and step-by-step forecasting. In this way there are several combinations of models that can be used for forecasting. These approaches are analyzed in the computational experiment.Results. Several experiments have been conducted in which standard (ARIMA, ETS, LOESS) and neural network models (LSTM, GRU, RNN) are built and compared in terms of proximity of the forecast to the series data in the test period.Conclusions. In the case of seasonal time series, models based on neural networks surpassed the standard ARIMA and ETS models in terms of forecast accuracy for the test period. The single-step forecast is computationally less efficient than the integral forecast for the entire target period. However, it is not possible to accurately indicate which approach is the best in terms of quality for a given series. Combined models (neural networks for trend, ARIMA for seasonality) almost always give good results. When forecasting a non-seasonal heteroskedastic series of share price, the standard approaches (LOESS method and ETS model) showed the best results.

Socio-Economic Development of Russia in 2023–2025: Growth Trend Continues

The article examines the results of the socio-economic development of Russia in 2023, analyzes trends and provides forecasts for the strategic development of the national economy as a whole and its leading industries for 2024–2025, using mathematical and statistical methods.Based on macroeconomic statistics, a comparative analysis of development trends in the Russian and world economies is carried out, primarily in terms of such key development indicators as gross domestic product (GDP), industrial production, investment, inflation, and foreign trade. The authors identified bottlenecks and assessed the impact of new strategic initiatives on forecasts of socio-economic development in the period under review. The shortcomings of the current management system are analyzed, and measures aimed at overcoming the structural crisis are proposed. The need to improve state socio-economic policy is substantiated.It is noted that the Russian economy, despite the strengthening of sanctions by the collective West, has increased the sustainability of its development and the level of technological and scientific sovereignty. The article highlights shortcomings and a number of unresolved issues of import substitution and imperfect logistics of production and foreign economic relations, which create threats of decline of GDP and the level of technological sovereignty in the future.The paper substantiates the possibility of transforming all sectors and spheres of the national economy when implementing the plan presented in the Address of the President of the Russian Federation to the Federal Assembly.

Is Thailand Attractive to Japanese Companies?

This study examines the relationship between the business sentiment of Japanese companies regarding promising or potential countries for investment and macroeconomic statistics, such as economic or population growth in Thailand, using data from the Survey Report on Overseas Business Operations by Japanese Manufacturing Companies from 1992 to 2022. Although investing in Thailand has been popular among Japanese companies since the late 1980s, it has seemingly become relatively inactive in recent years. The present study’s results are summarized as follows: First, the business sentiment of Japanese companies has some relationships with relatively short-term economic growth and the business cycle in the short run. Second, business sentiment depends on long-term trends, and this stance may have changed after 2020. Third, other elements, such as minimum wage or fewer young people, do not necessarily have a relationship with business sentiment. Although more studies including capital accumulation or the global value chain should be conducted, improving the sentiments of Japanese businesspersons is desirable.

Non-Profit Institutions in National Accounts

Since the outset of economic transformation in the 90th of the 20th century, the sphere of non-profit institutions in the Czech economy has been expanding. This evolution poses a challenge for macroeconomic statistics and its compilers to reflect adequately the operation of non-profit segment as well as to treat non-profit institutions properly in terms of their sector classification. The paper thus aims to discuss not only the treatment of non-profit institutions in national accounts but also the way non-profit institutions are identified in the regulatory environment of the Czech economy and classified following the rules laid down in the national accounts methodology. The paper also highlights major obstacles the compilers are facing when dealing with this specific economic sphere.

Implementation of bagging in time series forecasting

Objectives. The purpose of the article is to build different models of bagging, to compare the accuracy of their forecasts for the test period against standard models, and to draw conclusions about the possibility of further use of the bagging technique in time series modeling.Methods. This study examines the application of bagging to the random component of a time series formed after removing the trend and seasonal part. A bootstrapped series combining into a new random component is constructed. Based on the component thus obtained, a new model of the series is built. According to many authors, this approach allows the accuracy of the time series model to be improved by better estimating the distribution.Results. The theoretical part summarizes the characteristics of the different bagging models. The difference between them comes down to the bias estimate obtained, since the measurements making up the bootstraps are not random. We present a computational experiment in which time series models are constructed using the index of monetary income of the population, the macroeconomic statistics of the Russian Federation, and the stock price of Sberbank. Forecasts for the test period obtained by standard, neural network and bagging-based models for some time series are compared in the computational experiment. In the simplest implementation, bagging showed results comparable to ARIMA and ETS standard models, while and slightly inferior to neural network models for seasonal series. In the case of non-seasonal series, the ARIMA and ETS standard models gave the best results, while bagging models gave close results. Both groups of models significantly surpassed the result of neural network models.Conclusions. When using bagging, the best results are obtained when modeling seasonal time series. The quality of forecasts of seigniorage models is somewhat inferior to the quality of forecasts of neural network models, but is at the same level as that of standard ARIMA and ETS models. Bagging-based models should be used for time series modeling. Different functions over the values of the series when constructing bootstraps should be studied in future work.

China’s macroeconomic performance affects trading partners: How can their policies respond?

Utilizing expanded data analysis, this study aims to determine how China's macroeconomic performance affects the global economy. Employing the Global Vector Auto Regression (GVAR) model, we investigate the impact of China's macroeconomic variables on the economies of its selected trading partners throughout the world. For 2000Q1–2019Q4, developed countries (the US, Hong Kong), developing Europe countries (Russian Federation and Poland), and two developing Asia countries (Vietnam and India) show that Chinese macroeconomic performance has a greater impact than middle east and central Asia, Sub-Saharan Africa, and Western hemisphere. The Chinese GDP shock hits emerging Europe harder because of its extensive Russian production network. Results reveal that Chinese depreciation impacts developing Europe and Asia, while the Chinese stock market shock hits advanced economies more. Advanced and developing Asian economies are greatly affected by TV shock. Chinese FDI affects the Middle East and Central Asia, because of the China-Pakistan Economic Corridor. China trading partners use macroeconomic statistics to assess their economic priorities, industrial strengths, and weaknesses to create national interest plans. Currency adjustment, trade policies, domestic stimulus, and market diversification can minimize negative effects or capitalize on possibilities from China's macroeconomic conditions.

THE CONSEQUENCES OF A FORCED MIGRATION SHOCK FOR THE ECONOMY OF A COUNTRY AT WAR: THE CASE OF UKRAINE

The full-scale war in Ukraine, which started with the Russian invasion in February 2022, has led to an unprecedented forced migration crisis not seen in Europe since the Second World War. The current Ukrainian forced displacement, which has no analogues in terms of its scale, geographical coverage, quantitative indicators and timeframe, represents an exceptional case to study in terms of its impact on the economic environment of the country of origin. The purpose of this paper is to identify and analyse the short- and long-term effects of the forced migration shock on the economy of a combatant country in the context of a full-scale war in Ukraine. Methodology. To address the task of classifying the economic consequences of a forced migration shock from the perspective of the country of origin, the publication uses the matrix diagram method. The matrix developed is based on two key characteristics that indicate the durability of the impact (short-term or long-term) and the nature of the impact (explicit or implicit). The analysis of the short-term and long-term effects of forced migration on the Ukrainian economy is based on macroeconomic statistics (key macroeconomic indicators, labour market indicators, public finance indicators, external sector statistics) in the pre-war period, the war period since 2022, and forecasts for 2024-2025. Results. Unlike regular labour migration, forced migration has a predominantly negative impact on the economy of a country of origin, both in the short and long term. The explicit effect of forced migration on the economic system of the country of origin is to damage the labour market. The implicit effects include a decrease in remittances, a reduction in production facilities, an increase in the budget deficit and regional budget imbalances, an increase in the level of external and internal debt, increased dependence on international aid, a decrease in consumption, investment and GDP growth rates. Practical implications. The practical results provide a basis for further research into policy measures to address the negative aspects of increasing migration processes in Ukraine, particularly with a view to the country's future post-war reconstruction. Value/Originality. The matrix developed to assess the impact of the forced migration shock provides a deeper understanding of the multifaceted effects of this phenomenon on the socio-economic system of a country at war.

RJDemetra, a promising tool for the seasonal adjustment of official statistics

Seasonal adjustment (SA) is a crucial factor in the process of producing official macroeconomic statistics. The most important SA methods, X-13Arima-Seats and Tramo-Seats, are currently included into JDemetra+, a universal open-source environment, which is available on several platforms and operating systems, as a result of adoption of Java programming language for source codes, and Xml metalanguage for the definition of input specifications. This paper focuses on the potentials of RJDemetra, the R library developed for JDemetra+ suite. Its structure and functionalities will be illustrated with several examples, reporting the associated R scripts. In addition, a new operational practices will be suggested, exposing an alternative procedure to enhance interactive time-series updating in SA revision policies step, and also to ensure consistency checking in input system, in order to improve and to speed up the SA estimation process, providing greater security and efficiency. Finally, the interaction between two very different environments such as SAS-IML, and R will be displayed through a new SAS-R procedure available for estimating Quarterly Accounts SA series.

Social Accounting Matrices: Challenges and Prospects of Use in Macroeconomic Statistics

The paper presents results of the first stage of research on the construction of social accounting matrices (SAMs) for the Russian Federation as part of the HSE University study on statistical assessment of households’ economic behaviour. The author analyses the methodological foun- dations of SAM compilation, their connection with the System of National Accounts (SNA) and their use in macroeconomic analysis. Special attention is paid to the application of SAM in evaluating exogenous impacts, including multiplier effects, arising in all spheres of the national economy at governing decision-making and implementation of economic policy projects.The paper reviews aggregated social accounting matrices for the Russian Federation for the year 2020 that were compiled according to the SNA methodology and based on the G. Pyatt and A. Roe modified model. The author proposes the scheme of accounts partial trans- formation enhancing the reflection of direct income flows between institutional sectors. The properties of SAM as a static economic impact evaluation model are reviewed, and for illustration purposes, results of experimental computation of the effect of changed demand from the Rest of the World are considered.Analysis of areas of SAM application and its development as a tool for macroeconomic research highlights the importance of development methods of a detailed representation of the structure of household incomes and expenditures in social accounting matrices. It will foster solutions to poverty, inequality and connected social problems. It is noted that, with increased attention to the social aspects of macroeconomic accounting, SAM can become a major tool for measuring well-being and household income formation and distribution processes in macroeconomic statistics.

Multiplication effects of investment in a publicly significant infrastructure project

The article features results of the assessment of the multiplier effects for the national economy from the implementation of construction project of the first stage of production facility of the Dniester HPP for the period 2010-2019. For this purpose, macroeconomic statistics were processes, on whose basis an information system of primary and calculated indicators was created using the data from the project documentation, which became the basis for calculations. A methodological approach was developed to assess multiplier effects at the macroeconomic level using the input-output balance, which is reflected in the symmetrical "input-output" table. The authors highlight the main indicators, through which the impact of the implementation of the construction project of the Dniester HPP on Ukraine’s economy is manifested, and which allow assessing its socio-economic significance: the multiplier of the output growth of the economic activity "Construction" at the expense of project investments, the GVA multiplier for all economic activities from project investments, and the GDP multiplier (public expenditure multiplier). According to the implemented approach, multiplier effects for the economy are calculated via additional output growth, additional value added growth, and GDP growth on project investments. Based on the obtained results, it is proved that: 1) the implementation of the construction project of the first stage of the production facility of the Dniester HPP had made it possible to accumulate a multiplier effect for Ukraine’s economy, confirming in practice the theory of multiplier effects and proving the efficiency of public capital investments; 2) to the greatest extent, the multiplier effect from the additional investment growth in this project is generated within the economic activity "Construction", which is explained by increased demand for domestically produced construction products.

High-frequency sectoral carbon and environmental analysis based on monthly input-output tables compilation during 2018–2021

This paper proposes a process for updating monthly input-output tables with monthly macroeconomic statistics and published input-output tables. Reasonable assumptions are set up and 48monthly input-output tables are prepared from 2018 to 2021 with the combination of the row range series method and nonlinear mathematical planning. The Weaver-Thomas composite index is used to analyze the role of the sector in the economic network, and the sectoral correlation indicators are used to analyze the correlation change of the sector's monthly electricity emissions to show an environmental application effect of the monthly input-output table. The results show that the monthly input-output tables can be prepared with acceptable accuracy, and they can reveal the sectoral network structure changes and sectoral carbon emissions changes in continuous monthly time series. The proposed approach contributes for the compilation of high-time-frequency input-output tables, so as to support high-frequency industrial environmental impact analysis.

Forecast of changes in the macroeconomic situation in Ukraine: smart economy of the future

Aims: to theoretical development of methodological tools for selecting national priorities for sustainable economic development based on the concept of a smart future economy. Methodology. This article uses a systematic approach to the study of change processes in the fields of macroeconomics, analysis and synthesis, induction and deduction, quantitative and qualitative scientific abstraction, comparison and analysis. Based on the modeling method, the article proposes a model for planning economic growth based on the indicators of socio-economic, investment, and innovation development of Ukraine and major countries of the world. Results. The paper proves the complexity of the problem of choosing the country's development priorities and its specialization, which is observed in many works of Ukrainian scientists and researchers from other countries. For this purpose, 40 scientific papers by foreign and domestic scholars were analyzed, and macroeconomic statistics data presented on official statistics websites were used. Based on the analyzed sources, the main problems of the socio-economic development of the country are determined, and the model provides a systematic approach to the formation and implementation of strategies for sustainable socio-economic development. The knowledge industry was developed, and a model was created for the selection of national innovative development priorities based on two main parameters: Overview of the national structure of innovative and specialized industries. The approach of information and communication platforms as a methodological tool for the formation of strategies for local socio-economic development is proposed. Scientific Novelty. With the help of the developed model, it is possible to determine the factors affecting the growth of Ukraine's GDP and forecast the growth of the country's economy. It is found that the transition to a smart economy and the use of the latest innovative smart technologies have the greatest impact on national economic growth. The proposed model for forecasting and evaluating the national socio-economic, investment, and innovation policy can provide the main directions for resource allocation and budgeting. Conclusion. The proposed proposals will contribute to the further development of an innovative program for socio-economic development of the country by applying the principles of the European concept of smart specialization to the Ukrainian economy. Implementation. The research materials can be used as methodological support for demonstrating the directions of regional socio-economic development.

THE LATEST TRENDS IN GEORGIA’S BUSINESS REGISTER

Lia Dzebisauri i 
 E-mail: lia.dzebisauri@tsu.ge 
 Associate Professor, Ivane Javakhishvili Tbilisi State University 
 Tbilisi, Georgia
 https://orcid.org/0009-0002-2604-9323
 
 Marine (Maia) Mindorashvili 
 E-mail: maia.mindorashvili@tsu.ge 
 Associate Professor, Ivane Javakhishvili Tbilisi State University 
 Tbilisi, Georgia
 https://orcid.org/0009-0001-7177-9870
 Abstract .In this paper, we examine the Statistical Business Register (SBR) in Georgia, which serves as the foundation for economic statistics. To keep up with the demands of the modern digital economy, the SBR requires ongoing capacity-building efforts. It's a critical component in generating official statistics that inform evidence-based decision-making. In light of the ongoing globalization process, particularly in the wake of the COVID-19 pandemic, there's a growing demand for high-quality, comparable SBR and economic statistics at the international level. Geostat's BR is unique in that it allows for determining an entity's activity status. However, the current globalization process presents a challenge to creating a perfect SBR, increasing the need for an internationally comparable SBR in the post-COVID-19 pandemic world. Our paper highlights the significance of the SBR and investigates registered and active business entities in Georgia according to various parameters, such as economic activities, legal status, and territorial distribution by regions, and statistical trends of active companies during pre and pandemic periods. We also identify areas where COVID-19 had the most detrimental effect and where further recovery is necessary for the sustainable development of Georgia's economy. As the backbone for economic statistics, the SBR needs continued capacity-building activities in the modern digital economy. It plays a central role in producing official statistics, both in terms of how the statistics are produced and the quality of the statistics that draw on information from them. Our article, in particular, is devoted to improving the quality and modernizing the indicators of the statistical business register, which further contributes to producing high level business and macroeconomic statistics.

Forecasting Inflation using the ARIMA Approach (Case of Albania)

Traditionally, macroeconomic statistics have played a major role in creating the framework for analyzing economic phenomena. Price changes are one of the most worrying situations where individuals, firms, and government tend to keep in control as much as possible. Even if the economic effect could be negligible, the psychological effect could be more considerable. Inflation creates a touchable impact in the vast majority of economic sectors. Meanwhile, empirical studies of inflation have shown a very correlative relationship between inflation and other macroeconomic indicators such as unemployment, GDP growth, net exports, etc. Albanian economy has suffered from time to time from inflation consequences. Simultaneously, inflation in Albania has created a cyclical form and a significant trend. Due to these conditions, simple econometric models such as ARMA or ARIMA can be used to forecast future inflation, especially at the moment when inflation is the focus of the Albanian economy. This paper aims to create an ARIMA econometric model of inflation in the time frame from 2009-2022. It also creates a quantitative approach for forecasting inflation in the Republic of Albania. Furthermore, this paper tries to explain some phenomena linked with inflation giving some qualitative data. ARIMA model will be used to forecast future inflation in Albania. Lastly, as explained in the paper, it is shown that the ARIMA model should be taken under consideration in policymaking processes.

Tổng quan về ứng dụng dữ liệu lớn trong dự báo kinh tế

The term “big data” has been first appeared for nearly two decades and has quickly become a symbol for data analytics by helping to solve the most complex problems of research. One of the important applications of big data is to improve the timeliness and accuracy of economic forecasts. Before the advent of big data, policymakers needed to wait for the periodic release of macroeconomic statistics to forecast GDP and inflation. Today, high frequency economic time series allow researchers to make forecasts more frequently, faster, and, in some cases, significantly more accurately than traditional forecasting methods. This paper will examine the use of big data in economic forecasting by providing an overview of empirical studies on this topic. In addition, the article also provides some discussions on challenges and solutions in using big data related to investment in technical infrastructure for analysis and handling of unstructured data. as well as accessibility and privacy when using big data.

Мультиплікаційні ефекти від інвестування в суспільно значущий інфраструктурний проєкт

The article features results of the assessment of the multiplier effects for the national economy from the implementation of construction project of the first stage of production facility of the Dniester HPP for the period 2010-2019. For this purpose, macroeconomic statistics were processes, on whose basis an information system of primary and calculated indicators was created using the data from the project documentation, which became the basis for calculations. A methodological approach was developed to assess multiplier effects at the macroeconomic level using the input-output balance, which is reflected in the symmetrical "input-output" table. The authors highlight the main indicators, through which the impact of the implementation of the construction project of the Dniester HPP on Ukraine’s economy is manifested, and which allow assessing its socio-economic significance: the multiplier of the output growth of the economic activity "Construction" at the expense of project investments, the GVA multiplier for all economic activities from project investments, and the GDP multiplier (public expenditure multiplier). According to the implemented approach, multiplier effects for the economy are calculated via additional output growth, additional value added growth, and GDP growth on project investments. Based on the obtained results, it is proved that: 1) the implementation of the construction project of the first stage of the production facility of the Dniester HPP had made it possible to accumulate a multiplier effect for Ukraine’s economy, confirming in practice the theory of multiplier effects and proving the efficiency of public capital investments; 2) to the greatest extent, the multiplier effect from the additional investment growth in this project is generated within the economic activity "Construction", which is explained by increased demand for domestically produced construction products.

Determinants Of The Instability Of The Russian Economic Growth

Based on a detailed comparative analysis of the concepts of "economic growth" and "economic development", the article provides arguments in favor of the thesis that from the post-default period to the present day, the Russian economy demonstrates not only steadily fading average annual GDP expansion rates, but also the so-called "impoverishing growth", "growth without development". The leading characteristic of the low-quality growth is recognized as the long-term evolution of the domestic economy in the vein of the historically futile natural resource export model, which, through the mechanisms of the "Dutch disease" and regular fluctuations in global energy prices, moves in an excessively cyclical form, inevitably making the socio-economic development of the country unstable. Significant determinants of its unstable growth, carefully concealed by the methods of misleading macroeconomic statistics presented in the article, are also uncovered being the dominance of extensive factors over the intensive ones associated with increasing macroeconomic efficiency and achieving accumulation rates close to the optimum values, the socially conflicting nature of GDP growth, accompanied by the excessive income inequality, both in terms of income and property, even in the relatively favorable conditions compared to global practice, and the resource-intensive and wasteful nature of the dynamics of the national economy.

Assessment of the Impact of Airlines External Environmental Factors on the Probability of Bankruptcy Risks in the Context of the COVID-19

The paper analyses which external environmental factors had the greatest impact on the probability of airline bankruptcy risk in the pre-pandemic and pandemic periods. In view of the strengths and weaknesses of the models selected and presented, the Altman Z 'score model was chosen for the study. It has been modified by recalculating the coefficients for the model's weighted indicators. The investigation included environmental macroeconomic statistics and financial data of 49 airlines in the airline sector over the span of ten years (2011-2021). The correlation method has identified the severity of external factors in connection with bankruptcy risks during the pre-pandemic and pandemic timeframe and has established a correlation between fluctuations in the risk of bankruptcy of the airline sector and environmental factors. Based on the results obtained, conclusions were drawn on the link between external environmental factors and changes in the sector’s bankruptcy risk score in the context of the COVID-19 pandemic. The results of the comparative analysis method showed that the influence of environmental factors analysed in Europe, North America, South America, and Oceania differs due to the predominance of business culture in the region. Overall, however, legal factors had the biggest negative impact on the sector before the COVID-19 pandemic (2011-2018), while other factors (passengers, competitors, labour force, and creditors) were weakly correlated with the likelihood of bankruptcy risk. During the COVID-19 pandemic, the most positive relationship between creditors intensified.

A cross-country analysis of macroeconomic responses to COVID-19 pandemic using Twitter sentiments.

The COVID-19 pandemic has had a devastating impact on the global economy. In this paper, we use the Phillips curve to compare and analyze the macroeconomics of three different countries with distinct income levels, namely, lower-middle (Nigeria), upper-middle (South Africa), and high (Canada) income. We aim to (1) find macroeconomic changes in the three countries during the pandemic compared to pre-pandemic time, (2) compare the countries in terms of response to the COVID-19 economic crisis, and (3) compare their expected economic reaction to the COVID-19 pandemic in the near future. An advantage to our work is that we analyze macroeconomics on a monthly basis to capture the shocks and rapid changes caused by on and off rounds of lockdowns. We use the volume and social sentiments of the Twitter data to approximate the macroeconomic statistics. We apply four different machine learning algorithms to estimate the unemployment rate of South Africa and Nigeria on monthly basis. The results show that at the beginning of the pandemic the unemployment rate increased for all the three countries. However, Canada was able to control and reduce the unemployment rate during the COVID-19 pandemic. Nonetheless, in line with the Phillips curve short-run, the inflation rate of Canada increased to a level that has never occurred in more than fifteen years. Nigeria and South Africa have not been able to control the unemployment rate and did not return to the pre-COVID-19 level. Yet, the inflation rate has increased in both countries. The inflation rate is still comparable to the pre-COVID-19 level in South Africa, but based on the Phillips curve short-run, it will increase further, if the unemployment rate decreases. Unfortunately, Nigeria is experiencing a horrible stagflation and a wild increase in both unemployment and inflation rates. This shows how vulnerable lower-middle-income countries could be to lockdowns and economic restrictions. In the near future, the main concern for all the countries is the high inflation rate. This work can potentially lead to more targeted and publicly acceptable policies based on social media content.