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2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Luca Ferri ◽  
Alessandra Allini ◽  
Marco Maffei ◽  
Rosanna Spanò

Purpose This study aims to investigate the readability of financial risk disclosure divulged by listed banks of the first five European countries according to gross domestic product. Design/methodology/approach This study adopts the management obfuscation hypotheses and tests data gathered for a sample of 790 observations from listed banks in Europe covering the 2007–2018 period. This study uses a readability index (Gunning’s fog index) as the dependent variable for measuring the readability of banks’ mandatory financial risk disclosures. Moreover, it relies on a completeness index, discretionary accruals and several control variables for identifying the determinants of risk disclosure readability using ordinary least square regression for testing the hypotheses. Findings The findings show the existence of a positive relation\nship between readability and completeness of risk disclosure. In contrast, a negative relationship exists between readability and banks’ discretionary accruals. Originality/value This study expands the stream of accounting literature analyzing the lexical characteristics of narrative risk disclosure, and, by focusing on the financial risk disclosure of banks, it extends the readability-related debate, which has primarily concentrated on other types of disclosure to date. This study is relevant to regulators and policymakers for fostering reflections as actions for improving the financial risk disclosures readability. This study is also of potential interest for investors to better delve into the questions surrounding risk disclosure.


2022 ◽  
Vol 14 (3) ◽  
pp. 1
Author(s):  
Edward Alabie Borteye ◽  
Williams Kwasi Peprah

The study confirms the debate on whether stock market development correlates to economic growth. The dimensions used for the stock market development consisted of market liquidity, size, and capitalization. Economic growth was represented by the real gross domestic product (GDP) growth rate. Based on secondary data obtained from the Ghana Stock Exchange (GSE) and Ghana Statistical Service from 2014 to 2018, a correlational research design was adopted to analyze the data with SPSS 20v by using bivariate and regression. The study found that there is a high positive relationship between market liquidity and economic growth, a moderate negative relationship between market size and economic growth, and a moderate positive relationship between market capitalization and economic growth. Also, the stock market development of market liquidity, size, and capitalization predict 95.7 percent of economic growth. The study summarized that there is a high positive association between stock market development and economic growth as a confirmatory revelation, but all the relationship results were not statistically significant. The result points to the casualty of the relationship between stock market development and economic growth. The study recommends that more firms must be encouraged to be listed on GSE to enhance economic growth in Ghana.


2022 ◽  
Vol 5 ◽  
Author(s):  
Ingrid Fromm

Coffee is an important agricultural sector in Central American, directly employing over 1.2 million people in Guatemala, Honduras, El Salvador, Nicaragua, and Costa Rica. Although export revenues from coffee trade have an overall positive effect on the gross domestic product (GDP) of these countries, poverty still prevails. The COVID-19 pandemic has placed additional pressure on the sector which is vulnerable to fluctuations in the international coffee prices, low productivity levels, and climate change effects and damages caused by pest and diseases. This paper examines the effects of the COVID-19 pandemic and analyzes if the sector is resilient to withstand unexpected external shocks such as the pandemic and the hurricanes which impacted the region in the last months of 2020. The capacity to absorb, adapt, and/or transform to these shocks was assessed from the perspective of small-scale coffee farmers, traders, exporters and the entire sector in two time periods—immediately after the start of the pandemic and after the coffee harvest. Although the actors in the coffee value chain absorbed these shocks and could withstand them, adaptation to the disruptions has been challenging for small-scale farmers. Despite the vulnerability to unexpected external shocks, results indicate that a long-term transformation of the sector to build resilience is likely to be slow.


2022 ◽  
Vol 9 (1) ◽  
pp. 51-58
Author(s):  
Gregory T. Papanikos

This paper evaluates the effects of the Olympic Games of 2004 hosted in Athens on Greece’s Gross Domestic Product (GDP), as estimated in Papanikos (1999). The estimates were made in 1997 for a period of fourteen years, 1998-2011, based on various scenarios. During this period two events have had a great impact on GDP that could have been predicted in 1997. Firstly, Greece adopted the euro in 2002, and even though this was pretty much a possibility in 1997, but not of course a certainty, the most important effect of the euro would have come from its exchange value vis-a-vis major currencies of countries with Greece was trading. This included tourism. Despite what many economists thought at the time, the introduction of the euro was not accompanied by a devaluation, but by unprecedented overvaluation. This had a negative impact on Greek GDP. Secondly, the Great Recession hit the Greek economy hard starting in 2008. These two effects had a negative impact on Greek GDP, wiping out the expected positive effects of the Olympic Games. Keywords: Olympic Games, GDP, Athens 2004, euro, great recession


2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zeinab Rahimi Rise ◽  
Mohammad Mahdi Ershadi

PurposeThis paper aims to analyze the socioeconomic impacts of infectious diseases based on uncertain behaviors of social and effective subsystems in the countries. The economic impacts of infectious diseases in comparison with predicted gross domestic product (GDP) in future years could be beneficial for this aim along with predicted social impacts of infectious diseases in countries.Design/methodology/approachThe proposed uncertain SEIAR (susceptible, exposed, infectious, asymptomatic and removed) model evaluates the impacts of variables on different trends using scenario base analysis. This model considers different subsystems including healthcare systems, transportation, contacts and capacities of food and pharmaceutical networks for sensitivity analysis. Besides, an adaptive neuro-fuzzy inference system (ANFIS) is designed to predict the GDP of countries and determine the economic impacts of infectious diseases. These proposed models can predict the future socioeconomic trends of infectious diseases in each country based on the available information to guide the decisions of government planners and policymakers.FindingsThe proposed uncertain SEIAR model predicts social impacts according to uncertain parameters and different coefficients appropriate to the scenarios. It analyzes the sensitivity and the effects of various parameters. A case study is designed in this paper about COVID-19 in a country. Its results show that the effect of transportation on COVID-19 is most sensitive and the contacts have a significant effect on infection. Besides, the future annual costs of COVID-19 are evaluated in different situations. Private transportation, contact behaviors and public transportation have significant impacts on infection, especially in the determined case study, due to its circumstance. Therefore, it is necessary to consider changes in society using flexible behaviors and laws based on the latest status in facing the COVID-19 epidemic.Practical implicationsThe proposed methods can be applied to conduct infectious diseases impacts analysis.Originality/valueIn this paper, a proposed uncertain SEIAR system dynamics model, related sensitivity analysis and ANFIS model are utilized to support different programs regarding policymaking and economic issues to face infectious diseases. The results could support the analysis of sensitivities, policies and economic activities.Highlights:A new system dynamics model is proposed in this paper based on an uncertain SEIAR model (Susceptible, Exposed, Infectious, Asymptomatic, and Removed) to model population behaviors;Different subsystems including healthcare systems, transportation, contacts, and capacities of food and pharmaceutical networks are defined in the proposed system dynamics model to find related sensitivities;Different scenarios are analyzed using the proposed system dynamics model to predict the effects of policies and related costs. The results guide lawmakers and governments' actions for future years;An adaptive neuro-fuzzy inference system (ANFIS) is designed to estimate the gross domestic product (GDP) in future years and analyze effects of COVID-19 based on them;A real case study is considered to evaluate the performances of the proposed models.


2022 ◽  
Vol 5 (1) ◽  
pp. 31-48
Author(s):  
Suraj Sharma

Objective: The purpose of the present study is to revisit the export-led growth hypothesis in the wake of globalization. This will help in trade policy decisions and make it possible to standpoint whether the export promotion is a good idea to accelerate economic growth.  Design: The ELG hypothesis is examined for 107 countries through panel data analysis using cointegration and panel regression tests from 1990 to 2018. The study finds strong support for the long-run relationship between exports and gross domestic product and the export-led growth hypothesis in a two-variable regression framework. Findings: It is evident from the long-run coefficient of dynamic ordinary least squared that a 1.0 percent increase in real exports increases the real gross domestic product by 0.53 percent. The long-run coefficient of real exports for the Global South (0.55) is found higher than that of the Global North (0.51), which indicates that in the wave of globalization, the evidence of export-led growth hypothesis is stronger for comparatively poor Global South than the richer Global North. Practical Implications: The results indicate implications for export promotion policy in the Global South countries to accelerate economic growth and increase real gross domestic product. Originality: The study is the first to explore the ELG hypothesis using a big pool of 107 countries, including the global north-south divide.


2022 ◽  
Author(s):  
Miquel Oliu-Barton ◽  
Bary SR Pradel ◽  
Nicolas Woloszko ◽  
Lionel Guetta-Jeanrenaud ◽  
Philippe Aghion ◽  
...  

Abstract In the COVID-19 pandemic, governments have used various interventions,1,2 including COVID certificates as proof of vaccination, recovery, or a recent negative test, required for individuals to access shops, restaurants, and education or workplaces.3 While arguments for and against COVID certificates have focused on reducing transmission and ethical concerns,4,5 the effect of the certificates on vaccine uptake, public health, and the economy requires investigation. We construct counterfactuals based on innovation diffusion theory6 and validate them with econometric methods7 to evaluate the impact of incentives created by COVID certificates in France, Germany, and Italy. We estimate that from their announcement during summer 2021 to the end of the year, the intervention led to increased vaccine uptake in France of 13.0 (95% CI 9.7–14.9) percentage points (p.p.) of the total population, in Germany 6.2 (2.6–6.9) p.p., and in Italy 9.7 (5.4–12.3) p.p.; averted an additional 3,979 (3,453–4,298) deaths in France (i.e., 31.7%), 1,133 (-312–1,358) in Germany (5.6%), and 1,331 (502–1,794) in Italy (14.0%); and prevented gross domestic product (GDP) losses of €6.0 (5.9–6.1) billion in France, €1.4 (1.3–1.5) billion in Germany, and €2.1 (2.0–2.2) billion in Italy. Notably, the application of COVID certificates substantially reduced the pressure on intensive care units (ICUs) and, in France, averted surpassing the occupancy levels where prior lockdowns were instated. Overall, our findings are more substantial than predicted8 and may help to inform decisions about when and how to employ COVID certificates to increase vaccination and thus avoid stringent interventions, such as closures, curfews, and lockdowns, with large social and economic consequences.


Risks ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Matteo Foglia

The purpose of this work is to investigate the influence of macroeconomics determinants on non-performing loans (NPLs) in the Italian banking system over the period 2008Q3–2020Q4. We mainly contribute to the literature by being the first empirical article to study this relationship in the Italian context in the recent period, thus providing fresh evidence on the macroeconomic impact on NPLs, i.e., on the credit risk of Italian banks. By employing the Autoregressive Distributed Lag (ARDL) cointegration model, we are able to investigate the short and long-run effects of macroeconomic factors on NPLs. The empirical findings show that gross domestic product and public debt have a negative impact on NPLs. On the other hand, we find that the unemployment rate and domestic credit positively influence impaired loans. Finally, we find evidence of the “gamble for resurrection” approach, i.e., Italian banks tend to support “zombie firms”.


Author(s):  
Razana Alwee ◽  
Siti Mariyam Hj Shamsuddin ◽  
Roselina Sallehuddin

Features selection is very important in the multivariate models because the accuracy of forecasting results produced by the model are highly dependent on these selected features. The purpose of this study is to propose grey relational analysis and support vector regression for features selection. The features are economic indicators that are used to forecast property crime rate. Grey relational analysis selects the best data series to represent each economic indicator and rank the economic indicators according to its importance to the property crime rate. Next, the support vector regression is used to select the significant economic indicators where particle swarm optimization estimates the parameters of support vector regression. In this study, we use unemployment rate, consumer price index, gross domestic product and consumer sentiment index as the economic indicators, as well as property crime rate for the United States. From our experiments, we found that the gross domestic product, unemployment rate and consumer price index are the most influential economic indicators. The proposed method is also found to produce better forecasting accuracy as compared to multiple linear regressions.


2022 ◽  
pp. 42-49
Author(s):  
Kamelia Assenova

The pandemic of COVID-19 influences all sectors of the economy. It caused decreasing in produced Gross domestic product (GDP) and higher unemployment. As it is known, to overcome this negative tendency, it is possible to put in practice monetary and fiscal instruments. During the pandemic, the government tried to slow down negative economic results through public spending. With them, the government looks to be increased aggregate demand in the economy and as a result-GDP raises and unemployment reduces. The research is based on created original model for testing the impact of total public spending, capital, salary, social insurance and care, for maintenance by a consolidated fiscal program on the value of GDP. The changes of GDP measure the effectiveness of public spending. The period of research is before and during the COVID-19 crisis (2019-2020) in the case of Bulgaria. Before the pandemic the analysis shows coefficient of determination for capital spending is more significant compare with all other types of public expenditure and these cost predetermine economic growth. During the pandemic of COVID-19 public spending has used as the main instrument to overcome the negative results for the economy. For this period it found an extremely strong impact of labor costs and social care expenditure on aggregate demand. They bring more positive results to be solved health issues, but not for faster recovery of the economy.


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