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This paper investigates the impact of a monetary policy shock on the production of a sample of 312 industries in manufacturing, mining and utilities in the United States using a factor-augmented vector autoregression (FAVAR) model.

The authors use a FAVAR model that builds on Bernanke et al. (2005) and Boivin et al. (2009). The main assumption in this model is that the dynamics of a large set of macro variables are captured by some observed and unobserved common factors. The unobserved factors are extracted from a large set of macroeconomic data. The key advantage of using this model is that it allows extracting the impulse responses of a wide range of macroeconomic variables to structural shocks in the federal funds rate.

The results indicate that industries exhibit differential responses to an unanticipated monetary policy tightening. In general, manufacturing industries appear to be more sensitive compared to mining, and utility industries and durable manufacturing industries are found to be more sensitive than those within nondurable and other manufacturing industries to a monetary policy shock. While all industries respond to the policy shock, most of the responses are reversed between 12 and 22 months.

The implication of our results is that monetary policy can be used to impact most US industries for four years and beyond. The existence of disparate responses across industries underscores the difficulty of implementing a monetary policy that will generate the same impact across industries. As the effects of the policy are distinct, policymakers may want to attend to the unique impacts and implement industry-specific policy.

The study is important in the context of the current challenges in the US economy caused by the spread of coronavirus. For example, to tackle the current pandemic, the researchers are trying to come up with cures for COVID-19. A considerable response of the chemical industry that provides materials to pharmaceutical and medicine manufacturing to the monetary policy shock implies that an expansionary monetary policy may facilitate an invention and adequate supply of the cure later on. The same policy may not effectively stimulate production in apparel or leather product industries that are being hard hit by the pandemic.

The study contributes to the literature in broadly two aspects. First, to the best of our knowledge, this is the first paper that investigates the impact of a monetary policy shock on a sample of 312 industries in manufacturing, mining and utilities in the US. Second, to identify structural shocks and investigate the effects of monetary policy shocks on economic activity, the authors diverge from the literature's traditional approach, i.e. the vector autoregression (VAR) method and use a FAVAR method. The FAVAR provides a comprehensive description of the impact of a monetary policy innovation on different industries.

This paper aims to conduct an empirical study to investigate whether an industry’s position affects the transmission of information and economic shocks.

This paper conducts an empirical study of inventory performance based on a large panel of 71 industries in the manufacturing, wholesale and retail sectors over a 10-year period (2007–2016).

It is found that the position of a focal industry in the supply chain network moderates the impacts of macroeconomic uncertainty shocks and shocks from supplier/customer industries on the focal industry’s inventory. On the one hand, more central industries are more sensitive to macroeconomic uncertainty shocks as well as spillover shocks from their supplier and customer industries. On the other hand, uncertainty shocks from more central industries have higher impact on their partner industries than those from less central industries.

A manager needs to take into account the network positions of suppliers/customers in supply network when making inventory decisions. For example, when sharing information with partners, the network position of a partner affects how important its information is.

The key novelty of this paper is the introduction of network structure that represents the supplier–customer relationships in the entire economy, and the modeling of uncertainty shocks transmitted through the supply chain network.

As technical barriers gradually become the important tools of trade protection, it is important to understand whether intensified enforcement of border controls is adopted as a hidden tool of trade protectionism and differs across periods and industries.

This article applies a panel structural vector autoregression (PSVAR) model to investigate the potential role of trade protectionism motives in Food and Drug Administration (FDA) import refusals on China's agricultural exports, utilizing newly constructed monthly data at the industry level.

The results show that import refusal is mainly driven by the inspection history, highlighting the importance of the intrinsic product quality and maintaining an excellent inspection history in border inspection. The novel finding is that US employment contractions would also lead to a small increase in FDA import refusals, especially those taking place within ten months and made without sampling tests. Such an association is driven by industry-specific employment shocks and becomes stronger after the financial crisis. It is also more evident in industries where the US lacks competitiveness against China, being manufactured without mandatory safety regulations, and with negative skewness of employment growth.

This research is one of the preliminary attempts to understand whether the de facto border controls are worked as a hidden tool of protectionism to agricultural products, and what the specific trajectory and duration of the impacts at the monthly level. This study provides empirical evidence showing the role of protectionism motives in FDA import refusals and is heterogeneous across industries, which generate new insights and policy implications to predict and cope with additional barriers on agricultural trade.

The purpose of this study is to examine how auditors would react when there are exogenous negative shocks to their client portfolios.

Using a sample of 31,256 firm-year observations (2001–2016), the authors investigate whether industry shocks to a subset of an auditor’s clients distract the auditor and affect the professional skepticism applied in the audits of other clients.

The authors find that clients of distracted auditors are more likely to meet or beat analyst consensus forecasts, suggesting that auditors’ professional skepticism is compromised by distractive events. The cross-sectional analyses reveal that the negative impact of the distractive events on audit quality is more pronounced when the distracted auditors audit less important clients, face lower third-party legal liabilities and experience higher growth. Using an alternative measure of audit quality, the additional analysis shows that clients of distracted auditors exhibit a higher probability of restating their earnings in subsequent years. Overall, the empirical evidence suggests that when distracted, auditors render lower quality audit.

The study complements recent work by Cassell et al. (2019), which shows that the 2008–2009 financial crisis affected the quality of the audits of nonbank clients of bank-specialized auditors. While Cassell et al. (2019) focus on one shock (financial crisis) to one industry (i.e. the financial services industry), the study examines more frequent shocks over a wide range of industries to identify the potential effects of distractive events, improving the generalizability of the findings to all industries and all auditors (specialist and nonspecialist) in nonrecession periods.

The purpose of this paper is to examine the effects of exchange rate and global industry shocks on the relative performance of global industries.

In addition to SUR approach, we also use GARCH approach to control for heteroskedasticity.

Using industry data from Japan and the USA, the authors find that although both exchange rate and global industry shocks are statistically significant in explaining the performance of these industries relative to their domestic markets, economically the global industry shock plays the major role in determining this performance.

The authors' findings are only based on two countries, the USA and Japan, so future researchers can use the authors' empirical models to test if their results hold using data from other countries.

Investors should focus more on the performance of global industries instead of exchange rate changes when creating their portfolios.

Our empirical results may explain the poor performance of the regression models in Griffin and Stulz ten years ago where they fail to control for the global industry shock.

The purpose of this paper is to develop a theoretical model of price transmission from the farm to the retail sector, allowing not only for an interaction between oligopoly power, oligopsony power and non‐constant returns to scale in industry technology, but also allowing for the market power conduct parameters to vary in response to an industry‐wide exogenous shock. Also, the degree of price transmission under imperfect competition relative to that under perfect competition is evaluated.

Conjectural variations are used to parameterize both seller and buyer market power conduct of the industry and then the equilibrium displacement approach is applied to solve a system of six structural equations which describe the demand for and supply of industry retail output and farm and marketing inputs.

First, it is found that given empirical values of retail output demand elasticity, of farm and marketing inputs supply elasticities, of market power conducts, and of the returns to scale measure, the degree of price transmission under imperfect competition is greater than that under perfect competition. Second, it is found that the relative degree of price transmission under imperfect competition could be greater or smaller under the assumption of a varying market power conduct than one under the alternative assumption of a constant market power conduct, depending on whether market conduct is falling or rising, respectively.

The paper makes two original contributions to the literature. First, it allows for an interaction between oligopoly power, oligopsony power and industry technology. Second, it allows both oligopoly and oligopsony power parameters to vary in response to industry‐wide exogenous shocks.

The purpose of this paper is to measure the systemic importance of industry in the world economic system under the system-wide event – the crisis of 2008-2009, by viewing this system as a weighted directed network of interconnected industries.

First, the authors investigate this crisis at three different levels based on network-related indicators: the “macro” global level, the “meso” country level, and the “micro” industry level. This investigation not only provides evidence for the systemic influence, that is, systemic risk, of the crisis, but also reveals the contagion mechanism of the crisis, which supports the stress testing. Second, the authors use a network-related business intelligence algorithm, the combined hyperlink-induced topic search (HITS) algorithm, to measure the contribution of a given individual industry to the overall risk of the economic system or, in other words, the systemic importance of the individual industry.

The HITS algorithm considers both the market information and the interconnectedness of the industries. Based on the stress testing, the performance of the combined HITS is compared with the purely market-based systemic risk measurement. The results show that the combined HITS outperforms the baseline in finding the top N systemically important industries.

The combined HITS algorithm provides a novel network-based perspective of systemic risk measurement.

Measuring the systemic importance based on the combined HITS algorithm can help managers and regulators design effective risk management policies. In this respect, the work initiates a research direction of studying the systemic risk in a business system based on a network-related business intelligence algorithm because the business system can be viewed as an interconnected network.

The puzzle of hot and cold issue markets has attracted substantial interest in the academic community. The behavior of IPO volume and initial returns over time is well documented. Few studies, however, investigate the dynamic interrelationship between these two variables. This paper aims to fill this gap. In addition, the technological innovations hypothesis of hot issue markets is tested. Welch and Hoffmann‐Burchardi suggest that the clustering of new issues is caused by IPO volume spikes in industries that have recently experienced technological innovations or favorable productivity shocks.

This paper employs a sample of 8,160 initial public offerings filed in the USA between January 1972 and December 2001. A simultaneous equation approach is used to examine the endogenous relationship between IPO volume and initial returns. In addition, the paper analyzes the industry correlation matrix of new issue activity and estimates a fixed‐effects model based on industry‐level data to examine the impact of technological innovations on new issue activity.

It is found that higher IPO volume causes higher initial returns, but not vice versa. In addition, evidence is found against the technological innovations hypothesis. The findings suggest that economy‐wide rather than industry‐specific factors are responsible for the observed variations in IPO volume.

As with any empirical study, the results may be sample‐specific.

The paper extends the prior literature on the relationship between IPO volume and initial returns by applying two‐stage and three‐stage least squares models that go beyond prior methodological approaches used in the extant literature. In addition, the paper provides some of the first empirical evidence on the effect of technological innovations and productivity shocks on IPO activity.

Prior studies of the role of risk in executive compensation focus on market risk and firm risk, neglecting the role of industry risk in explaining executive compensation. We include industry risk and find that the portion of CEO compensation for bearing industry risk is greater than the portion of CEO compensation for bearing market risk. Consistent with the human capital of a CEO being non-diversifiable, CEOs also receive compensation for bearing firm-specific risk, in contrast to investors, who can diversify their risk over many assets. CEOs are compensated for bearing firm-specific risks through all the compensation tools we examine; salary, bonus, option grants and option exercises. CEOs are compensated for bearing market and industry risk primarily through stock option grants.

This study examines the value implications of oil price uncertainty for investors in diversified firms using a sample of 922 USA firms from 2001 to 2019.

Our study employs a panel dataset to examine the value implications of oil price uncertainty for diversified firm investors. We consider several alternative specifications to account for unobserved factors and measurement errors that could potentially bias our results. In particular, we use alternative measures of the excess value of diversified firms and oil price uncertainty, additional control variables, fixed-effects models, the Oster test, impact threshold for confounding variable (ITCV) analysis, two-stage least square instrumental variable (2SLS-IV) analysis and the system-GMM model.

We find that the excess value of diversified firms, relative to a benchmark portfolio of single-segment firms, increases with high oil price uncertainty. The impact of oil price uncertainty is asymmetric, as corporate diversification is value-increasing for diversified firm investors only when the volatility is due to positive oil price changes and amidst supply-driven oil price shocks. The excess value increases irrespective of diversified firms’ financial constraints and oil usage. Diversified firms become conservative in their internal capital allocations with high oil price uncertainty. Such conservatism is value-increasing for diversified firm investors, as it supports higher performance in response to oil price uncertainty.

Our study has three important implications: first, they are relevant to investors in understanding the portfolio value implications of oil price uncertainty. Second, they are helpful for firm managers while comprehending the value-relevant implications of internal capital allocations. Finally, our findings are policy relevant in the context of the future of diversified firms in developed markets.