Search results

Despite recent advances, neither organizational studies nor the scholarship on economic resilience has systematically addressed how the ecologies of organizations that populate local economies can serve as infrastructures for responding proactively to economic shocks. Using county-level data, this study analyzes relationships between the prevalence of organizational alternatives to shareholder value-oriented (SVO) corporations within a particular locality and its unemployment levels during and after the Great Recession. The results support the hypothesis that the presence of such alternative organizations can enhance the capacities of local economies to resist and recover from recession shocks. Cooperative, municipal, and community-based enterprises, research universities, and nonprofits more generally were associated with greater resistance to the recession shock and stronger recoveries – specifically, lower surges in unemployment rates from 2007 to 2010 and greater reductions in unemployment rates from 2010 to 2016. By contrast, SVO corporations were associated with greater surges in unemployment and perhaps weaker recoveries. Providing a proof of concept, this study opens up new lines of inquiry for organizational studies by linking organizational ecologies to the promotion of collective efficacy and a more broadly shared prosperity in economic life.

A state space representation of a linearized DSGE model implies a VAR in terms of observable variables. The model is said be non-invertible if there exists no linear rotation of the VAR innovations which can recover the economic shocks. Non-invertibility arises when the observed variables fail to perfectly reveal the state variables of the model. The imperfect observation of the state drives a wedge between the VAR innovations and the deep shocks, potentially invalidating conclusions drawn from structural impulse response analysis in the VAR. The principal contribution of this chapter is to show that non-invertibility should not be thought of as an “either/or” proposition – even when a model has a non-invertibility, the wedge between VAR innovations and economic shocks may be small, and structural VARs may nonetheless perform reliably. As an increasingly popular example, so-called “news shocks” generate foresight about changes in future fundamentals – such as productivity, taxes, or government spending – and lead to an unassailable missing state variable problem and hence non-invertible VAR representations. Simulation evidence from a medium scale DSGE model augmented with news shocks about future productivity reveals that structural VAR methods often perform well in practice, in spite of a known non-invertibility. Impulse responses obtained from VARs closely correspond to the theoretical responses from the model, and the estimated VAR responses are successful in discriminating between alternative, nested specifications of the underlying DSGE model. Since the non-invertibility problem is, at its core, one of missing information, conditioning on more information, for example through factor augmented VARs, is shown to either ameliorate or eliminate invertibility problems altogether.

The global slack hypothesis is central to the discussion of the trade-offs that monetary policy faces in an increasingly more integrated world. The workhorse New Open Economy Macro (NOEM) model of Martínez-García and Wynne (2010), which fleshes out this hypothesis, shows how expected future local inflation and global slack affect current local inflation. In this chapter, I propose the use of the orthogonalization method of Aoki (1981) and Fukuda (1993) on the workhorse NOEM model to further decompose local inflation into a global component and an inflation differential component. I find that the log-linearized rational expectations model of Martínez-García and Wynne (2010) can be solved with two separate subsystems to describe each of these two components of inflation.

I estimate the full NOEM model with Bayesian techniques using data for the United States and an aggregate of its 38 largest trading partners from 1980Q1 until 2011Q4. The Bayesian estimation recognizes the parameter uncertainty surrounding the model and calls on the data (inflation and output) to discipline the parameterization. My findings show that the strength of the international spillovers through trade – even in the absence of common shocks – is reflected in the response of global inflation and is incorporated into local inflation dynamics. Furthermore, I find that key features of the economy can have different impacts on global and local inflation – in particular, I show that the parameters that determine the import share and the price-elasticity of trade matter in explaining the inflation differential component but not the global component of inflation.

Pesaran and Smith (2011) concluded that Dynamic Stochastic General Equilibrium (DSGE) models were sometimes a straitjacket which hampered the ability to match certain features of the data. In this chapter, the authors look at how one might assess the fit of these models using a variety of measures, rather than what seems to be an increasingly common device – the Marginal Data Density. The authors apply these in the context of models by Christiano, Motto, and Rostagno (2014) and Ireland (2004), finding they fail to make a match by a large margin. Both of these models feature more shocks than observed variables, resulting in the empirical shocks having a singular density, and so making them correlated. When correlated one can neither interpret impulse responses nor perform variance decompositions. Against this, there is a strong argument for having a straitjacket, as it enforces some desirable behavior on models and makes researchers think about how to account for any non-stationarity in the data. The authors illustrate this with examples drawn from the SVAR literature and also more eclectic models such as Holston, Laubach, and Williams (2017) for extracting an estimate of the real natural rate.

Lester and Wolff (2013) find little empirical support for the Austrian business cycle theory. According to their analysis, an unexpected monetary shock does not alter the structure of production in a way consistent with the Austrian view. Rather than increasing production in early and late stages relative to middle stages, they find the opposite – a positive monetary shock typically decreases production in early and late stages relative to middle stages. We argue that the measures of production and prices employed by Lester and Wolff (2013) are constructed in such a way that makes them inappropriate for assessing the empirical relevance of the Austrian business cycle theory’s unique features. After describing how these measures are constructed and why using ratios of stages is problematic, we use a structural vector autoregression to consider the effects of a monetary shock on each stage of the production process. We show that, with a clearer understanding of what is actually being measured by the stage of process data, the results are consistent with (but not exclusive to) the Austrian view.

The resistance of the energy sector in recent time has been tried by COVID-19 as the occurrence has added to the dampening down demand for crude oil which has resulted in volatility in prices and dwindling production of crude oil at the global crude oil market. Dwindling demand and price decline can also be perceived as a trend in the electricity sector, the electricity price (ELECTP) and consumption. The consequences of individual policy response by countries in relation to the socio-economic impact of COVID-19 is yet to be known, and recent studies conducted in the continent are yet to document the impact of the pandemic on the oil-producing African countries. It is in the light of this that this study determined the effect of socio-economic shocks activated through the COVID-19 pandemic on the energy sector performance and economic development of Africa. Monthly data were sourced from the United States Energy Information Administration, COVID-19 geographic distribution worldwide, World Development Indicator and Trading Economics from 2019 (m12) to 2020 (m10). Seventeen oil-producing African countries were selected across the region based on data availability. The structural panel vector auto regression (SPVAR) analytical technique was used in estimating COVID-19 pandemic and socio-economic shocks on the energy sector performance proxied by oil production (OILP), ELECTP and economic development proxied by gross domestic product per capita (GDPPC) of the countries. Findings revealed that the COVID-19 pandemic transmits a negative shock to OILP and GDPPC while a positive shock is transmitted to ELECTP. The socio-economic variables also transmitted both the positive and negative shocks to OILP, ELECTP and GDPPC. Therefore, the study recommended that policies should be directed towards putting in place a shock-absorbing mechanism so as to cushion the effect of the identified shocks on the performance of the energy sector and the economic development of the countries.

Purpose – The purpose of this study is to develop a numerical framework to predict the time-dependent probability of failure of a bridge subjected to multiple vehicle impacts. Specially, this study focuses on investigating the inter-relationship between changes in life-cycle parameters (e.g., damage size caused by vehicle impact, loss of initial structural capacity, and threshold intervention) and bridges probability of failure.

Design/Methodology/Approach – The numerical procedure using MATLAB program is developed to compute the probability failure of a bridge. First, the importance and characteristics of life-cycle analysis is described. Then, model for damage accumulation and life cycle as a result of heavy vehicle impacts is discussed. Finally, the probability of failure of a bridge subjected to vehicle impacts as a result of change in life-cycle parameters is presented.

Findings – The results of study show that damage size caused by both vehicle impacts and loss of initial structural capacity have a great impact on the long-term safety of bridges. In addition, the probability of failure of a bridge under different threshold limits indicates that the structural intervention (e.g., repair or maintenance) should be undertaken to extend the service life of a bridge.

Research Limitations/Implications – The damage sizes caused by heavy vehicle impacts are based on simple assumptions. It is suggested that there would be a further study to estimate the magnitude of bridge damage as a result of vehicle impact using the full-scale impact test or computational simulation.

Practical Implications – This will allow much better predictions for residual life of bridges which could potentially be used to support decisions on health and maintenance of bridges.

Originality/Value – The life-cycle performance for assessing the time-dependent probability of failure of bridges subjected to multiple vehicle impact has not been fully discussed so far.