Search results

This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020.

The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while.

The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions.

This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.

The authors estimate the multiplier effect of government public infrastructure investment in Spain. This paper aims to use annual data of the 17 Spanish autonomous communities for the 1980–2016 period.

The authors use dynamic acyclic graphs and the heterogeneous panel structural vector autoregressive (P-SVAR) method of Pedroni (2013). This method is robust to cross-sectional heterogeneity and dependence, which are present in the data.

The findings suggest that an increase in the level of government public infrastructure investment generates a positive and persistent effect on the level of output. Five years after the fiscal expansion, the multiplier effects of government public infrastructure investment reach values above one. This confirms that government public infrastructure investment expansions have Keynesian effects. The authors also find that the multiplier effects differ between autonomous communities with above-average and below-average GDP per capita.

To the best of the authors’ knowledge, no research uses dynamic acyclic graphs and heterogeneous P-SVAR techniques to estimate fiscal multipliers of government public investment in Spain by using subnational data.

This study aims to examine the impact of external debt on economic growth in Bangladesh within a broader macroeconomic scenario.

In the process of doing so, it assesses the empirical cointegration, long-run and short-run dynamics of the concerned variables for the period of 1980–2017 applying the autoregressive distributed lag (ARDL) bounds testing approach to cointegration. First, debt-gross domestic product linkage explores the impact of external debt impact on economic growth using a set of macro and country risk variables, and then this linkage is also analyzed along with a newly formed macroeconomic policy (MEP) variable using principal component analysis.

The study results reveal the negative impact of external debt on GDP growth, but the larger positive impact of MEP index indicates that this adverse effect of debt can be mitigated or even nullified by sound MEP and appropriate human resource policy.

The dynamic effects of different shocks (external debt and macro policy variable) on economic growth by vector autoregression impulse response function also confirm our ARDL findings.

The impact of financial reforms and financial development on an economy has received considerable attention over the recent past. This paper aims to investigate whether financial liberalisation and financial development increase the likelihood financial crises in Southern African development community (SADC) countries.

Due to the binary nature of the dependent variable, the logit model is used for the analysis using data for the period 1990 to 2015.

The results showed that financial liberalisation captured by real interest rates reduces the likelihood of financial crises. Furthermore, regulatory quality strengthens this reductive effect of financial liberalisation on the probability of financial crises. On the other hand, financial development represented by bank credit increases the incidence of financial crises. The results also suggest that financial liberalisation may increase the likelihood of financial crises indirectly through financial development.

The study recommends that a sound regulatory and supervisory framework be established as well as institutional quality raised to curb the effect of financial development on the incidence of financial crises.

There is scant evidence on the role that financial liberalisation and financial development play in the incidence of financial crises in the SADC. This study incorporates the effect of institutional quality in the analysis which has been neglected by most studies on financial reforms in SADC countries. A number of recent studies in SADC countries conclude that financial development resulting from financial reforms, may hinder economic growth. Therefore, this study sheds light on this negative relationship.

One of the main theories regarding the relationship between government expenditure and gross domestic product (GDP) is Wagner’s law. This law was developed in the late-19th century by Adolph Wagner (1835–1917), a prominent German economist, and depicts that an increase in government expenditure is a feature often associated with progressive states. This paper aims to examine the validity of Wagner’s law in Egypt for 1960–2018. The relationship between real government expenditure and real GDP is tested using three versions of Wagner’s law.

To test the validity of Wagner in Egypt, law time-series analysis is used. The methodology used in this paper is: unit-root tests for stationarity, Johansen cointegration approach, error-correction model and Granger causality.

The results provide strong evidence of long-term relationship between GDP and government expenditure. Moreover, the causal relationship is found to be bi-directional. Hence, this study provides support for Wagner’s law in the examined context.

It should be noted, however, that there are some limitations to this study. For instance, in this paper, the government’s size was measured through government consumption expenditure rather than government expenditure due to data availability, which does not fully capture the government size. Moreover, the data available was limited and does not fully cover the earliest stages of industrialization and urbanization for Egypt. Furthermore, although time-series analysis provides a more contextualized results and conclusions, the obtained conclusions suffer from their limited generalizability.

This paper aims to specifically make a contribution to the empirical literature for Wagner’s law, by testing the Egyptian data using time-series econometric techniques for the longest time period examined so far, which is 1960–2018.

In this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation procedures and thus contribute to more reliable statements about the value of real estate.

The authors hypothesize that empirical error in the interpretation and qualitative assessment of visual content can be minimized by collating the assessments of multiple individuals and through use of repeated trials. Motivated by this problem, the authors developed an experimental approach for semi-automatic extraction of qualitative real estate metadata based on Comparative Judgments and Deep Learning. The authors evaluate the feasibility of our approach with the help of Hedonic Models.

The results show that the collated assessments of qualitative features of interior images show a notable effect on the price models and thus over potential for further research within this paradigm.

To the best of the authors’ knowledge, this is the first approach that combines and collates the subjective ratings of visual features and deep learning for real estate use cases.

Design, implementation, and evaluation are all important for integrated care. However, they miss one critical factor: education. The authors define “integrated care education” as meaningful learning that purposefully supports collaboration and the development of adaptive expertise in integrated care. The ECHO (Extensions for Community Health Outcomes) model is a novel digital health solution that uses technology-enabled learning (TEL) to facilitate, support, and model integrated care education. Using ECHO Concussion as a case study, the authors describe the effects of technology-enabled integrated care education on the micro-, meso-, and macro-dimensions of integrated care.

This case study was constructed using data extracted from ECHO Concussion from video-archived sessions, participant observation, and internal program evaluation memos. The research team met regularly to discuss the development of relevant themes to the dimensions of integrated care.

On the micro-level, clinical integration occurs through case-based learning and the development of adaptive expertise. On the meso-level, professional integration is achieved through the development of the “specialist generalist,” professional networks and empathy. Finally, on the macro-level, ECHO Concussion and the ECHO model achieve vertical and horizontal system integration in the delivery of integrated care. Vertical integration is achieved through ECHO by educating and connecting providers across sectors from primary to quaternary levels of care. Horizontal integration is achieved through the establishment of lateral peer-based networks across sectors as a result of participation in ECHO sessions with a focus on population-level health.

This case study examines the role of education in the delivery of integrated care through one program, ECHO Concussion. Using the three dimensions of integrated care on the micro-, meso-, and macro-levels, this case study is the first explicit operationalization of ECHO as a means of delivering integrated care education and supporting integrated care delivery.