Business Cycle Research Articles

The Business Cycle and the Financial Cycle: Granger Causality for the Peruvian Economy, 2000 – 2019

It is relevant to know the relationship between the financial and business cycles to prevent the appearance of financial crises and their negative effects on the macroeconomy. In this sense, a better understanding of the links between financial cycles and the business cycle of Peru can provide valuable information for economic and macroprudential policy decisions. This study examines the causal relationship between Peru's financial and business cycles. The dynamic factors methodology is used to measure the financial cycle, taking four representative indicators of the financial sector: Volume of loans to the private sector (credit market), the Lima Stock Exchange General Index (stock market), Embig Peru (bond market), and the Exchange Rate (currency market) (Ramos, 2019). On the one hand, using the Granger causality test in the frequency domain, it was found that the financial cycle causes the business cycle at frequencies greater than 10 quarters (2.5 years). In contrast, the business cycle does not cause the financial cycle.

Do tax revenues track economic growth? Comparing panel data estimators

Determining how economic growth affects tax revenues is crucial for fiscal sustainability, economic stabilization, and policy design. The current literature on tax buoyancy presents contrasting estimates, highlighting the need for a systematic discussion of the trade-offs associated with different estimators. This paper provides new empirical evidence by reviewing a range of panel data estimators in a large sample of 172 countries from 1990 to 2019. We find evidence of lower estimates for tax responses to economic activity in the short term relative to previous literature, suggesting a limited automatic stabilization power of tax systems. The heterogeneity in our results within and across income groups underscores the importance of choosing the appropriate estimator. Our results remain broadly unchanged when we introduce new control variables to disentangle discretionary from automatic tax revenue variations, indicating that economic cycles do not significantly influence the timing of tax policies.

The Role of Race in Deaths of Despair in Brazil: Is it a White People Problem?

A new paradigm has been discussed regarding the impact of economic cycles on the mortality pattern from specific causes. These causes are called deaths of despair, and they selectively impact specific demographic groups. Also, low- and middle-income countries are most affected due to their economic and social instability. In this sense, the objective of study was to compare the magnitude of disparity in deaths from despair according to sex, age, and race in Brazil. We performed Poisson regression modeling to estimate the magnitude of the association between sex, age group, race, and deaths from despair. Also, we estimated the relationship of time as a proxy of economic crisis phase and deaths of despair. We found an association between mortality from despair and male sex (PR = 6.15, 95%CI 6.09-6.22); emphasis on the age groups from 40 to 49years old (PR = 2.45, 95% CI 2.41-2.48) and 50 to 59years old (PR = 2.39, 95% CI 2.36-2.43); and brown (PR = 1.21, 95% CI 1.20-1.22) and black race (PR = 1.36, 95% CI 1.34-1.37). The present study preliminarily presents the effect of the economic crisis and mortality in the population, with demographic differences. Association with race was opposite to that verified in the original study in the USA, which suggests that this variable should be analyzed in the light of structural context.

A feasibility study on the use of glycerol as a solution to waste renewable energy storage in the United Kingdom

This study has analysed to feasibility of incorporating a biologically derived liquid organic hydrogen carrier (LOHC) into the energy grid as a method of reducing waste renewable energy.With this goal in mind, a scenario has been devised in using glycerol to transport hydrogen for release at domestic homes for use in PEM fuel cells. Through this, a packed bed reactor can fulfil the average load with a glycerol conversion of 94 %. The peak load can be fulfilled by using 3 of these reactors at each home and reducing the temperature to decrease conversion when the energy is not needed. Through the dehydrogenation, many side products are formed, including methanol and ethanol which can be used to regenerate glycerol in a transesterification reaction. For a target conversion of 98 %, a 7.08 m3 reactor vessel would be required to handle the alcohol output of 100 houses.After these simulations, an economic analysis and life cycle assessment was performed. The economic showed operating costs to be around 76.5 $/h, and the capital cost per home to be $33,000. 60% of this investment is the cost of the catalyst used in the dehydrogenation of glycerol. An alternative catalyst is required to reduce this total cost. The life cycle assessment showed very high effectiveness in reducing carbon missions, however, the other pollutants severely damper its viability.

ESPÍRITO SANTO MOBILIZATION FOR INNOVATION (MCI): CHARACTERIZATION AND CONTRIBUTIONS TO THE INNOVATION ECOSYSTEM OF ESPÍRITO SANTO STATE

In 2018, it was organized Espírito Santo Mobilization for Innovation (MCI) 2018. It is an aligned set of local actors to create conditions that foster innovation in Espírito Santo State, contributing to the emergence of a new economic cycle and prosperity for the society of Espírito Santo State. The objective of this article is to present the main characteristics of MCI and its contributions to the Espírito Santo Innovation Ecosystem. The article carried out and the evaluation of a program financed with resources from FUNCITEC/MCI, the Support Program for Innovative Projects in the Areas of Logistics, Food and Beverage (LAB). We argue that MCI has carried out important actions for the consolidation of the innovation ecosystem in Espírito Santo, has clear goals for the 2020-2030 period and has financed important projects, especially in the period of the coronavirus pandemic, being an important institutional arrangement for the innovation ecosystem of Espírito Santo.

Assessing robustness of consequential LCA: Insights from a multiregional economic model tailored to the cement industrial symbiosis

AbstractOne of the key challenges in conducting consequential life cycle assessment (LCA) is to identify and quantify what is likely to be affected by changes in market behavior—so‐called marginal effects. Although the critical importance of uncertainty and sensitivity assessments in attributional LCA is recognized, they are rarely conducted in consequential studies. Thus, this paper aims to address two objectives: first, to examine the uncertainty and robustness of marginal effects in consequential LCA and second, to identify the most influential group of inputs contributing to the overall variance in climate change impact scores. To accomplish this, the study employs an enhanced consequential LCA framework that integrates a multiregional economic optimization model with consequential LCA tools. The framework is applied to assess the climate implications of a 6% increase in clinker substitution within average Eastern North American cement production by 2030, relative to 2020 levels. Through this framework, a Monte Carlo simulation is conducted to evaluate the dispersion of the results. Subsequently, a variance‐based one‐at‐a‐time sensitivity analysis is performed to rank the most influential groups of inputs affecting the robustness of result across macroeconomic, material flow, economic, transport, and consequential life cycle inventory databases. Afterward, a conceptual framework is proposed to guide practitioners in prioritizing areas for testing consequential LCA results based on region‐specific information. This work aims to contribute to decision‐makers' access to more reliable information to support the development of effective environmental regulatory measures.

Job search intensity and wage rigidity in business cycles

Search intensity, as well as the aggregate economic condition, is a crucial factor that determines unemployed workers’ success in job searches. Their search intensity is predicted to be procyclical in standard search and matching models. However, many empirical studies show that search intensity is countercyclical. This study examines the job search behavior of unemployed workers over business cycles using a search and matching model that incorporates wage rigidity and a generalized matching function. Unlike previous studies, the proposed model can generate both procyclical and countercyclical search intensities by introducing wage rigidity. This study calibrates the model to the U.S. economy and provides various impulse response analyses. The numerical exercises show that the model successfully and simultaneously reproduces countercyclical search efforts and sizable labor market fluctuations.

How important are trend shocks? The role of the debt elasticity of interest rate

We study how financial frictions affect the importance of trend productivity shocks for macroeconomic fluctuations. Using long-run data from 17 small open economies (SOEs), we compare two variants of a workhorse SOE real business cycle model featuring a debt-elastic interest rate (DEIR), a measure of financial frictions. The first variant estimates the DEIR parameter, while the second fixes it to 0.001, effectively abstracting from financial frictions. On average, ignoring financial frictions doubles the contribution of trend shocks to output fluctuations. This suggests that a proper assessment of the quantitative effects of trend shocks requires reasonable DEIR values.

Persistence of Overeducation Among Young Workers and Business Cycle

Persistence of Overeducation Among Young Workers and Business Cycle

Using Data Anonymization in big data analytics security and privacy

Big Data and Analytics mean an enormous and complex collection of very diverse information, which is processed with various technologies and methods to produce and deliver useful and valuable insights. Analytics is the science of using data, or information to extract useful and actionable insights, facts and knowledge from a collection of data it could be stated that Big Data Analytics is the best thing since every commercial data system ever built, although everybody with a more optimistic vision of technology would like to take note that there is a fine line where Everything Data crosses the boundary to something else, especially with regard to privacy and security of the world as we know it. Privacy and security are two distinct but closely related phenomena. Whereas privacy refers to the control over access to the individual, security refers to the stability or strength of controls designed to protect the individual’s privacy. There are many obvious considerations and obstacles when attempting to securely share data. During big data analytics, many invasive techniques such as data fusion, cross-correlation, and algorithm training are often conducted over shared data, which can lead to severe privacy leaks. This means that every enterprise, organization, and individual maintaining large data repositories are in danger of being breached. Our study teaches us that security, privacy, and ethical concerns in big data analytics do not exist in parallel to the business cycle, but must be wisely and ethically managed in coherence throughout all emerging processes of the big data and information systems.

Business News and Business Cycles

ABSTRACTWe propose an approach to measuring the state of the economy via textual analysis of business news. From the full text of 800,000 Wall Street Journal articles for 1984 to 2017, we estimate a topic model that summarizes business news into interpretable topical themes and quantifies the proportion of news attention allocated to each theme over time. News attention closely tracks a wide range of economic activities and can forecast aggregate stock market returns. A text‐augmented vector autoregression demonstrates the large incremental role of news text in forecasting macroeconomic dynamics. We retrieve the narratives that underlie these improvements in market and business cycle forecasts.

Minijobs as stepping stones to regular employment: overall trends and the role of Midijob reforms

Minijobs are subsidized small jobs below a fixed earnings threshold. Since a reform in 2003, they are viewed as stepping stones to the first labor market. However, the Minijob subsidy generates a ‘Minijob trap’ that causes vast bunching at the Minijob earnings threshold. Therefore, Midijobs were designed to reduce this bunching and to eliminate the ‘Minijob trap’. Midijobs are employments that earn between the Minijob earnings threshold and the Midijob earnings threshold. In this range, Midijobs subsidize social insurance contributions on a sliding scale. This paper describes time trends in the propensity to leave Minijobs for regular employment and studies the role of Midijobs for transitions out of Minijobs. We find a strong increase in transitions from Minijobs to regular employment over time. However, there is no convincing evidence that Midijobs are connected to this development. Instead, behavioral changes and aggregate developments such as the business cycle and a booming labor market may have contributed to this development.

The Resilient Employment of Cooperative Societies in Relation to SDG-8

The interaction between employment and the economic cycle allows us to observe the behavior of cooperatives in Spain and to discuss the achievement of SDG-8. This analysis aimed to determine the employment-generating potential of Spanish cooperatives. An inductive methodology was used through automation in three analyses. The results found that decent and resilient cooperative employment is concentrated in four regions: Catalonia, Galicia, Extremadura, and Murcia. Over the period (2005-2020), cooperatives generate or destroy less employment than all Spanish firms during financial crises. Conversely, during a health crisis, the total of companies generates or destroy less employment than cooperatives. The forecast is that all national regions, except Ceuta and Melilla, will generate more cooperative employment in the short term.

Business Cycle Variations in Manager and Investor Sentiment Indices

I find that the highly regarded investor and manager sentiment indices demonstrate both cyclical and persistent variations. The presence of cyclicality in the orthogonalized indices suggests the existence of feedback loops connecting sentiment with economic or market outcomes. For example, optimistic sentiment can stimulate increased consumer spending and investment, thereby fostering economic growth and further boosting sentiment. These feedback loops have the potential to amplify cyclical sentiment trends and increase the volatility of economic and market cycles, leading to persistent cycles driven by feedback mechanisms. Importantly, my results remain robust against potential mean reversion resulting from heuristic behaviors such as herding and overreaction, as well as against random behavior arising from intermittent bubbles characterized by near-rational learning and potential overextrapolation bias.

Which Global Cycle? A Stochastic Factor Selection Approach for Global Macro-Financial Cycles

Abstract Instead of assuming a certain factor structure, we statistically test for the factor structure driving common global dynamics in macroeconomic and financial data by employing a stochastic factor selection approach. Using a sample of 16 developed countries from 1996Q1 to 2019Q4, we present strong empirical evidence of a global macro-financial cycle and an independent global financial cycle. Moreover, the global macro-financial cycle we estimate is essentially the global business cycle identified in the literature. It captures the common global macroeconomic dynamics and drives a significant share of the comovement in the financial sector. The remaining commonality in financial variables is driven by separate global financial cycles: the global credit cycle and the global capital flow cycle.

Toward Net-Zero Greenhouse Gas Emission: Techno–Economic and Life Cycle Analyses of Routes for Triacetic Acid Lactone (TAL) Bioproduction

Toward Net-Zero Greenhouse Gas Emission: Techno–Economic and Life Cycle Analyses of Routes for Triacetic Acid Lactone (TAL) Bioproduction

The Emission-Reduction Effect of Green Demand Preference in Carbon Market and Macro-Environmental Policy: A DSGE Approach

Along with the new stage of prevention and control of the COVID-19 pandemic and the vision and goals of combatting climate change, the challenges of the transition to a green economy have become more severe. The need for green recovery of the economy, stability and security of energy production and consumption, and the coordination of low-carbon transformation and socio-economic development has become increasingly urgent. This paper proposes a new theoretical framework to study the effect of carbon emission reduction on the mutual application of the carbon market, fiscal policy and monetary policy under the non-homothetic preference of energy product consumption. By constructing an environmental dynamic stochastic general equilibrium (E-DSGE) model with residents’ non-homothetic preferences, this paper finds that coordinating the carbon market and macroeconomic policies can achieve economic and environmental goals. However, the transmission paths for each are different. The carbon market influences producers’ abatement efforts and costs through carbon prices. Monetary policy controls carbon emissions by adjusting interest rates, while fiscal policy controls carbon emissions by adjusting total social demand. Improving non-homothetic preferences will amplify business cycle fluctuations caused by exogenous shocks, thus assuming the role of a “financial accelerator”. Further research shows that non-homothetic preferences influence the heterogeneity of different policy mixes. Finally, this paper discovers that the welfare effects, the relative size and difference of long-term and short-term effects resulting from the different policy mixes, also depend on the level of non-homothetic preferences. The intertemporal substitution mechanism due to the improvement of non-homothetic preferences endows low-carbon production with “option” characteristics. Our study reveals the role of non-homothetic preferences on the effectiveness of policy implementation. It highlights the importance of matching monetary and fiscal policies with the carbon market based on the consumption and production side. It provides ideas for policy practice to achieve the goal of “dual carbon” and promoting coordinated socio-economic development.

Forecasting the architecture billings index (ABI) using machine learning predictive models

PurposeThis research aims to forecast the ABI as a leading indicator of U.S. construction activities, applying multivariate machine learning predictive models over different horizons and utilizing the nonlinear and long-term dependencies between the ABI and macroeconomic and construction market variables. To assess the applicability of the machine learning models, six multivariate machine learning predictive models were developed considering the relationships between the ABI and other construction market and macroeconomic variables. The forecasting performances of the developed predictive models were evaluated in different forecasting scenarios, such as short-term, medium-term, and long-term horizons comparable to the actual timelines of construction projects.Design/methodology/approachThe architecture billings index (ABI) as a macroeconomic indicator is published monthly by the American Institute of Architects (AIA) to evaluate business conditions and track construction market movements. The current research developed multivariate machine learning models to forecast ABI data for different time horizons. Different macroeconomic and construction market variables, including Gross Domestic Product (GDP), Total Nonresidential Construction Spending, Project Inquiries, and Design Contracts data were considered for predicting future ABI values. The forecasting accuracies of the machine learning models were validated and compared using the short-term (one-year-ahead), medium-term (three-year-ahead), and long-term (five-year-ahead) ABI testing datasets.FindingsThe experimental results show that Long Short Term Memory (LSTM) provides the highest accuracy among the machine learning and traditional time-series forecasting models such as Vector Error Correction Model (VECM) or seasonal ARIMA in forecasting the ABIs over all the forecasting horizons. This is because of the strengths of LSTM for forecasting temporal time series by solving vanishing or exploding gradient problems and learning long-term dependencies in sequential ABI time series. The findings of this research highlight the applicability of machine learning predictive models for forecasting the ABI as a leading indicator of construction activities, business conditions, and market movements.Practical implicationsThe architecture, engineering, and construction (AEC) industry practitioners, investment groups, media outlets, and business leaders refer to ABI as a macroeconomic indicator to evaluate business conditions and track construction market movements. It is crucial to forecast the ABI accurately for strategic planning and preemptive risk management in fluctuating AEC business cycles. For example, cost estimators and engineers who forecast the ABI to predict future demand for architectural services and construction activities can prepare and price their bids more strategically to avoid a bid loss or profit loss.Originality/valueThe ABI data have been forecasted and modeled using linear time series models. However, linear time series models often fail to capture nonlinear patterns, interactions, and dependencies among variables, which can be handled by machine learning models in a more flexible manner. Despite the strength of machine learning models to capture nonlinear patterns and relationships between variables, the applicability and forecasting performance of multivariate machine learning models have not been investigated for ABI forecasting problems. This research first attempted to forecast ABI data for different time horizons using multivariate machine learning predictive models using different macroeconomic and construction market variables.

Corrosion Behavior of 3104 Aluminum Cans When Used as Packaging for Chinese Liquor.

Aluminum cans are commonly used for packaging soft drinks and low-alcohol beverages due to their good recyclability. To enhance the economic cycle and expand the packaging of liquors, the feasibility of commercial 3104 aluminum cans for packaging Chinese liquor was studied. The aluminum's migration into alcoholic solutions was studied using inductively coupled plasma emission spectroscopy (ICP-OES). Electrochemical impedance spectroscopy (EIS) was used to study the corrosion process of epoxy coatings on the aluminum cans. Scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), infrared attenuated total reflection (IR-ATR), and X-ray diffraction (XRD) were used to determine the inner coatings and adhering surfaces of the cans and the corrosion process. The results showed that the maximum aluminum migration in Chinese liquor was 4.3572 mg/kg at 60 °C for 30 days. The epoxy coating was corroded enough to decrease the coating impedance and expose the metal substrate after 25 days. Permeation and aging degradation of coatings are the main factors to consider when packaging liquor.

Scars of the job market “ice-age”

Abstract Existing studies found that entering the labor market during a recession has persistent negative effects—the scarring effects—on employment and earnings for Japanese men. The cohorts who entered the labor market during the prolonged recession between 1993 and 2004 are called “the ice-age cohorts,” and the persistent gap between them and older cohorts who entered the labor market during the bubble economy is often interpreted as the scarring effect of business cycle conditions at entry. However, the existing studies in Japan use data covering cohorts who enter the labor market in the 1990s or earlier. Motivated by the lack of empirical evidence with updated data, this article revisits the scarring effects of a recession at labor market entry on employment and earnings using data covering graduation cohorts 1984–2013. Younger cohorts who enter the labor market during the recovery do not earn more or enjoy more stable employment than the cohorts who enter the bottom of the ice-age, and that the effects of unemployment rate at entry on employment and earnings are no longer statistically significant for cohorts who enter the labor market after the job market ice-age. I also discuss potential factors that may have weakened scarring effects.