Search results

This paper analyses how firm births and deaths are influenced by previous firm births and deaths in related and unrelated sectors. Competition and multiplier effects are used as the theoretical lens for this analysis.

This paper uses 2008–2016 Irish business demography data pertaining to 568 NACE 4-digit sectors within 20 NACE 1-digit industries across 34 Irish county and sub-county regions within 8 NUTS3 regions. A three-stage least squares (3SLS) estimation is used to analyse the impact of past firm deaths (births) on future firm births (deaths). The effect of relatedness on firm interrelationships is explicitly modelled and captured.

Findings indicate that the multiplier effect operates mostly through related sectors, while the competition effect operates mostly through unrelated sectors.

This paper's findings show that firm interrelationships are significantly influenced by the degree of relatedness between firms. The raw data used to calculate firm birth and death rates in this analysis are count data. Each new firm is measured the same as another regardless of differing features like size. Some research has shown that smaller firms have a greater propensity to create entrepreneurs (Parker, 2009). Thus, it is possible that the death of differently sized firms may contribute differently to multiplier effects where births induce further births. Future research could seek to examine this.

These findings have implications for policy initiatives concerned with increasing entrepreneurship. Some express concerns that public investment into entrepreneurship can lead to “crowding out” effects (Cumming and Johan, 2019), meaning that public investment into entrepreneurship could displace or reduce private investment into entrepreneurship (Audretsch and Fiedler, 2023; Zikou et al., 2017). This study’s findings indicate that using public investment to increase firm births could increase future firm births in related and unrelated sectors. However, more negative “crowding out” effects may also occur in unrelated sectors, meaning that public investment which stimulates firm births in a certain sector could induce firm deaths and crowd out entrepreneurship in unrelated sectors.

This paper is the first in the literature to explicitly account for the role of relatedness in firm interrelationships.

One pertinent dynamic phenomenon in supply chains is the amplification of order variance, i.e. the bullwhip effect. Its continued significance is underscored in contemporary empirical research. While numerous publications have pinpointed various causes of the bullwhip effect, there remains a gap in their systematic consolidation. The purpose of this paper is to compile a comprehensive list of the causes of the bullwhip effect from existing literature and categorize them appropriately.

This study conducts a systematic literature review to offer a comprehensive overview of bullwhip effect causes addressed in the existing literature. The identified causes are categorized using a qualitative content analysis approach.

The study shows the diversity of the causes of the bullwhip effect and their interdependencies. In addition, this study demonstrates that, at the highest level of aggregation, causes of the bullwhip effect can be classified into four main categories: causes inherent in the system structure, causes related to uncertainty, causes related to misaligned incentives and causes related to inadequate cognition of the situation.

The work provides an extensive overview and categorization of bullwhip effect causes, offering valuable insights for both researchers and practitioners seeking a deeper understanding of this phenomenon. In addition, it underscores managerial implications and highlights future research opportunities.

The COVID-19 pandemic severely impacted tourism, and the hotel and restaurant industry was the most affected sector, which faced issues related to business uncertainty and unemployment during the crisis. The analysis of recovery time and the influence factors is significant to support policymakers in developing an effective response and mitigating the risks associated with the tourism crisis. This study aims to investigate numerous factors affecting the recovery time of the hotel and restaurant sector after the COVID-19 crisis by using survival analysis.

This study uses the quarterly value added with the observation time from quarter 1 in 2020 to quarter 1 in 2023 to measure the recovery status. The recovery time refers to the number of quarters needed for the hotel and restaurant sector to get value added equal to or exceed the value added before the crisis. This study applies survival models, including lognormal regression, Weibull regression, and Cox regression, to investigate the effect of numerous factors on the hazard ratio of recovery time of hotels and restaurants after the COVID-19 crisis. This model accommodates all cases, including “recovered” and “not recovered yet” areas.

The empirical findings represented that the Cox regression model stratified by the area type fit the data well. The priority tourism areas had a longer recovery time than the non-priority areas, but they had a higher probability of recovery from a crisis of the same magnitude. The size of the regional gross domestic product, decentralization funds, multiplier effect, recovery time of transportation, and recovery time of the service sector had a significant impact on the probability of recovery.

This study contributes to the literature by examining the recovery time of the hotel and restaurant sector across Indonesian provinces after the COVID-19 crisis. Employing survival analysis, this study identifies the pivotal factors affecting the probability of recovery. Moreover, this study stands as a pioneer in investigating the multiplier effect of the regional tourism and its impact on the speed of recovery.

Our study aims to evaluate the impact of infrastructure and public investment on private investment in machinery and equipment in Brazil from 1947 to 2017. The contribution of our article to the existing literature lies in providing a more comprehensive understanding of the presence or absence of the crowding effect in the Brazilian economy by leveraging an extensive historical database. Our central argument posits that the recent decline in private capital accumulation over the last few decades can be attributed to shifts in economic policies – moving from a developmentalist orientation to nondevelopmental guidance since the early 1990s, which is reflected in the diminished levels of public investment and infrastructure since the 1980s.

We conducted a series of econometric regressions utilizing the autoregressive distributed lag (ARDL) model as our chosen econometric methodology.

Employing two different variables to measure public investment and infrastructure, our results – robust across various specifications – have substantiated the existence of a crowding-in effect in Brazil over the examined period. Thus, we have empirical evidence indicating that the state has influenced private capital accumulation in the Brazilian economy over the past decades.

Our article contributes to the existing literature by offering a more comprehensive understanding of the crowding effect in the Brazilian economy, utilizing an extensive historical database.

This study examines the possibility of asymmetric impact of inflation on the financial development (FD) in the case of Indian economy from 1980 to 2020. Moreover, the finance–growth hypothesis is also tested.

The authors incorporated the “Nonlinear Autoregressive Distributed Lag” (NARDL) model due to Shin et al. (2014) to investigate the asymmetric impact of inflation on financial development. Asymmetric cumulative dynamic multipliers are also used to track the traverse of any short-run distortion towards the long-run cointegration.

The results revealed that inflation impacts the financial development negatively whereas the economic growth (EG) and trade openness have a positive effect. However, the effect of inflation on financial development is not symmetric. Moreover, the findings support the demand-led growth hypothesis.

To the best of the authors' knowledge, this is the first study examining the asymmetric effects of inflation on financial development in the Indian context. In addition, instead of using a single proxy to measure financial development, an index for financial development encompassing different aspects of the financial system has been incorporated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-02-2023-0094

This paper seeks to explore the sensitivity of these parameters and their impact on fiscal policy outcomes. We use the existing literature to establish possible ranges for each parameter, and we examine how changes within these ranges can alter the outcomes of fiscal policy. In this way, we aim to highlight the importance of these parameters in the formulation and evaluation of fiscal policy.

The role of fiscal policy, its effects and multipliers continues to be a subject of intense debate in macroeconomics. Despite adopting a New Keynesian approach within a macroeconomic model, the reactions of macroeconomic variables to fiscal shocks can vary across different contexts and theoretical frameworks. This paper aims to investigate these diverse reactions by conducting a sensitivity analysis of parameters. Specifically, the study examines how key variables respond to fiscal shocks under different parameter settings. By analyzing the behavioral dynamics of these variables, this research contributes to the ongoing discussion on fiscal policy. The findings offer valuable insights to enrich the understanding of the complex relationship between fiscal shocks and macroeconomic outcomes, thus facilitating informed policy debates.

This paper aims to investigate key elements of New Keynesian Dynamic Stochastic General Equilibrium (DSGE) models. The focus is on the calibration of parameters and their impact on macroeconomic variables, such as output and inflation. The study also examines how different parameter settings affect the response of monetary policy to fiscal measures. In conclusion, this study has relied on theoretical exploration and a comprehensive review of existing literature. The parameters and their relationships have been analyzed within a robust theoretical framework, offering valuable insights for further research on how these factors influence model forecasts and inform policy recommendations derived from New Keynesian DSGE models. Moving forward, it is recommended that future work includes empirical analyses to test the reliability and effectiveness of parameter calibrations in real-world conditions. This will contribute to enhancing the accuracy and relevance of DSGE models for economic policy decision-making.

This study is motivated by the aim to provide a deeper understanding of the roles macroeconomic model parameters play concerning responses to expansionary fiscal policies and the subsequent reactions of monetary authorities. Comprehensive reviews that encompass this breadth of relationships within a single text are rare in the literature, making this work a valuable contribution to stimulating discussions on macroeconomic policies.

The aim of this paper is to analyse the spillover effects of microcredit on the economy of Ecuador, with a particular focus on its potential as a poverty alleviation mechanism.

To address our research questions, we take into account the distance between cantons (Ecuador’s own administrative distribution) by adopting a spatial autoregressive (SAR) model. To this end, a database will be constructed with macroeconomic information about the country, broken down by canton (administrative division of Ecuador), and in a 2019 cross section, with a total of 1,331 microcredit operations in all 221 of Ecuador’s cantons.

We find a positive effect of microcredit on these neighbouring regions in terms of wealth generation.

We acknowledge that this study is limited to Ecuadorian cantons. Nonetheless, it is crucial to emphasize that focussing on an under-represented developing country like Ecuador adds significant value to the research.

Facilitating access to microcredit is one of the main solutions to address the goals proposed in the sustainable development goals (SDGs).

Microcredit activity contributes to the creation of value and wealth in Ecuador, exerting a spillover effect in neighbouring areas that can generate positive multiplier effects and alleviate poverty. For all of the above reasons, our proposal for the country is to support and promote microcredit as one of the main solutions to address the goals proposed in the SDGs.

The novelty of this study lies in the use of spatial econometrics to observe the indirect effects of microcredit on the regions bordering the canton in which it was issued, thus examining the spatial effects of microcredit on wealth distribution.

This study aims to investigate the effect of oil prices, economic growth and information communication technology (ICT) on investment into renewable energy transition (RET).

Based on six selected African countries (i.e. Algeria, Egypt, Angola, Ethiopia, South Africa and Nigeria), the study uses a nonlinear autoregressive distributed lag model over the period from 1995 to 2020.

The results show that increasing oil prices, by substitution effect, leads to increasing RET investment, while declining oil prices lead to decreasing RET investment in the short and long run. Furthermore, the results reveal that increasing real gross domestic product leads to increased RET investment, while declining real gross domestic product (GDP) leads to decreasing RET investment both in the short and long run. Simultaneously, the study shows that increasing ICT has a significant and positive impact on RET investment, while declining ICT has a significant negative impact on RET investment in the short and long run.

The findings of this study have advanced the understanding of which factors significantly influence RET investment and the need to concentrate efforts on strategically addressing those factors. The findings indicate that these countries are at the progressive stage in terms of renewable energy; though increasing oil prices contribute to rising RET investment, the countries can be more proactive by improving the full potential of ICT as well as facilitating the growth of their economies.

Joint research is pointed out by the literature as a potentially virtuous cooperation scheme to generate learning in the public sphere and beneficial effects in society. The purpose of this study, based on the Argentine experience in the COVID-19 pandemic, is to analyze the network of capacities, relationships and effects generated, over time, by a series of projects financed by the State in 2010, to clarify the link between learning effects and social effects.

A qualitative methodology focused on the multiple case study method was used. Each case covers joint R&D projects financed 10 years ago by the state that subsequently led to different solutions for COVID-19.

The work identifies a public learning process that integrates both industry’s contributions and the intellectual dimension of economic benefits and their translation into specific capabilities; conceptualizes the capacities accumulation process as a multiplier of social effects (direct and indirect) that emerge as knowledge is reused; identifies the articulation between different schemes as a condition for learning effects and social effects to manifest over time.

An aspect not studied in the literature is addressed, the relationship between the learning process induced by joint research, in terms of capabilities, and the social effects specifically generated over time. This is taking place in a context, such as the COVID-19 pandemic, where calls from the scientific and academic community to promote science–industry cooperation are multiplying.