Robert C.M. Beyer, Milagros Chocce and Martin Rama
The purpose of this paper is to present a new data set of comparable employment indicators for South Asian countries, constructed from more than 60 primary data sources from 2001…
Abstract
Purpose
The purpose of this paper is to present a new data set of comparable employment indicators for South Asian countries, constructed from more than 60 primary data sources from 2001 to 2017.
Design/methodology/approach
The main contribution of the paper is to curate the information provided by individual respondents to censuses and surveys, in a way that is consistent across countries and over time. The usefulness of the data set is illustrated by conducting a rigorous assessment of employment characteristics, of changes in employment over time and of the short- and long-run relationships between economic growth and employment growth in South Asia.
Findings
The exercise shows that agriculture still employs the majority of the working-age population across the region and, except in Sri Lanka, more than half of the employment is self-employment or unpaid family work. The paper also shows that employment rates are generally decreasing in South Asia, and that in some countries female employment rates are falling rapidly. Seasonal growth patterns are shown to affect the composition of employment, while non-seasonal changes in short-run growth affect the overall level of employment. The paper estimates that, in the long run, one percentage point growth of gross domestic product has led on average to a 0.34 per cent increase in employment.
Originality/value
This paper provides a new employment data set for South Asia, a rigorous assessment of employment trends and changes and an analysis for relationship between economic growth and employment (both quarterly and long-run).
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Manisha Chakrabarty and Subhankar Mukherjee
The purpose of this paper is to estimate the impact of the COVID-19 pandemic on the patterns of convergence/divergence among the districts in India. Specifically, this paper…
Abstract
Purpose
The purpose of this paper is to estimate the impact of the COVID-19 pandemic on the patterns of convergence/divergence among the districts in India. Specifically, this paper investigates if the impact is heterogeneous among different cohorts of districts (based on income distribution). The differential impact may lead to heterogeneous long-run growth paths, resulting in unbalanced development across regions within the country. A study of convergence can ascertain the possible trajectory of such development across regions. Investigation of this phenomenon is the primary aim of this study.
Design/methodology/approach
This paper uses the panel regression method for estimation. This paper uses high-frequency nighttime light intensity data as a proxy for aggregate output.
Findings
The authors observe a significant reduction in the convergence rate as a result of the pandemic. Across the cluster of districts, the drop in ß-convergence rate, compared to the pre-pandemic period, varied from approximately 33% for the poorer districts to close to zero for the richest group of districts. These findings suggest that the pandemic may lead to a wider disparity among different regions within the country.
Originality/value
This paper contributes to the literature in the following ways. First, to the best of the authors’ knowledge, this is the first paper to investigate the impact of COVID-19 on the convergence rate. A detailed look into the possible disparity in convergence among various regions is critical because a larger drop in convergence, especially among the poorer regions, may call for policy attention to attain long-term equitable development. The authors perform this exercise by dividing the districts into four quantile groups based on the distribution of night-light intensity. Second, while previous studies on convergence using nighttime light data have used a cross-sectional approach, this study is possibly the first attempt to use the panel regression method on this data. The application of this method can be useful in tackling district-level omitted variables bias. Finally, the heterogeneity analysis using different quantiles of the distribution of night-light intensity may help in designing targeted policies to mitigate the disparity across districts due to the shock.
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Claretha Hughes, Lionel Robert, Kristin Frady and Adam Arroyos
Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and…
Abstract
Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.
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Scott B. Beyer, J. Christopher Hughen and Robert A. Kunkel
The authors examine the relation between noise trading in equity markets and stochastic volatility by estimating a two-factor jump diffusion model. Their analysis shows that…
Abstract
Purpose
The authors examine the relation between noise trading in equity markets and stochastic volatility by estimating a two-factor jump diffusion model. Their analysis shows that contemporaneous price deviations in the derivatives market are statistically significant in explaining movements in index futures prices and option-market volatility measures.
Design/methodology/approach
To understand the impact noise may have in the S&P 500 derivatives market, the authors first measure and evaluate the influence noise exerts on futures prices and then investigate its influence on option volatility.
Findings
In the period from 1996 to 2003, this study finds significant changes in the volatility and mean reversion in the noise level and a significant increase in its relation to implied volatility in option prices. The results are consistent with a bubble in technology stocks that occurred with significant increases in noise trading.
Research limitations/implications
This study provides estimates for this model during the periods preceding and during the technology bubble. The study analysis shows that the volatility and mean reversion in the noise level are much stronger during the bubble period. Furthermore, the relation between noise trading and implied volatility in the futures market was of a significantly larger magnitude during this period. The study results support the importance of noise trading in market bubbles.
Practical implications
Bloomfield, O'Hara and Saar (2009) find that noise traders lower bid–ask spreads and improve liquidity through increases in trading volume and market depth. Such improved market conditions could have positive effects on market quality, and this impact could be evidenced by lower implied volatility when noise traders are more active. Indeed, the results in this study indicate that the level and characteristics of noise trading are fundamentally different during the technology bubble, and this noise trading activity has a larger impact during this period on implied volatility in the options market.
Originality/value
This paper uniquely analyzes derivatives on the S&P 500 Index in order to detect the presence and influence of noise traders. The authors derive and implement a two-factor jump diffusion noise model. In their model, noise rectifies the difference of analysts' opinions, market information and beliefs among traders. By incorporating a reduced-form temporal expression of heterogeneities among traders, the model is rich enough to capture salient time-series characteristics of equity prices (i.e. stochastic volatility and jumps). A singular feature of the authors’ model is that stochastic volatility represents the random movements in asset prices that are attributed to nonmarket fundamentals.
John Antonakis and Robert J. House
In this chapter, we briefly trace the history of the neo-charismatic movement and review Bass and Avolio’s full-range leadership theory (FRLT). We present the FRLT as the flame…
Abstract
In this chapter, we briefly trace the history of the neo-charismatic movement and review Bass and Avolio’s full-range leadership theory (FRLT). We present the FRLT as the flame bearer of the movement, and argue that it should be used as a platform to integrate similar leadership theories. We identify conditions that may moderate the factor structure of the FRLT, and review the validity of the Multifactor Leadership Questionnaire – the instrument underlying the FRLT. Furthermore, we identify theoretical deficiencies in the FRLT and propose the addition of a broad class of behaviors labeled instrumental leadership, which, we argue, is distinct from transformational, transactional, and laissez-faire leadership. Finally, we discuss the utility of dispositional variables in predicting the emergence of leadership.
Scott Beyer, Luis Garcia-Feijoo, Gerry Jensen and Robert R. Johnson
The purpose of this paper is to analyze security-market returns relative to the political party of the president, the Federal Reserve’s monetary policy, the year of the…
Abstract
Purpose
The purpose of this paper is to analyze security-market returns relative to the political party of the president, the Federal Reserve’s monetary policy, the year of the president’s term, and the state of political gridlock. Contrary to prior studies, which evaluated the influences separately, the authors jointly evaluate these variables.
Design/methodology/approach
The analysis supports the notion that security returns are significantly related to shifts in Fed monetary policy, political gridlock, and the year of the presidential term; however, returns are generally invariant to the president’s political party affiliation. Overall, the findings suggest that investors should focus less attention on the party of the president and instead more closely monitor Fed actions.
Findings
It appears that political harmony should be welcomed by equity investors, but not debt investors. Finally, regardless of the political outcome, if the past serves as a guide, investors may have to wait until year three of the next presidential term to enjoy the fruits of the current political season.
Originality/value
The academic literature is rich with studies that consider the aforementioned political effects and the influence that monetary policy have on the markets. To date, however, these factors have not been jointly considered when examining returns. This paper considers several dimensions of the political landscape – the party of the president, the presence or absence of political gridlock, and the presidential term cycle effect – in conjunction with Fed monetary policy in examining long-term security returns. By examining the relationship between security returns and both political and monetary conditions, the authors provide robust evidence regarding the relationships.
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Robert E. Freeland, Lynn Smith-Lovin, Kimberly B. Rogers, Jesse Hoey and Joseph Quinn
Answering two questions: What do people believe is the gender makeup of different occupations? If there is a systematic difference between the actual and perceived gender…
Abstract
Purpose
Answering two questions: What do people believe is the gender makeup of different occupations? If there is a systematic difference between the actual and perceived gender composition what factors predict or mediate this difference?
Methodology/Approach
We integrate three occupation-level datasets: ratings of perceived gender composition and cultural sentiments (EPA ratings) for every 2010 Census occupation collected for this study, occupational characteristics from O*NET, and demographic characteristics from the 2015 to 2019 Current Population Survey. Regression models examine the association between sentiments and objective occupational traits on the perceived gender composition net of the actual gender composition.
Findings
While respondents underestimate extreme values, perceptions largely reflect actual composition. Gendered sentiments had a significant independent effect on gender composition perceptions. Examining the relationship between objective occupational features, sentiments, and perceptions allows scholars to better understand the links between structural conditions, gendered beliefs, and social action. If individuals underestimate the extent of gender segregation and view some occupations as more diverse than they are, they may be more willing to consider occupations inconsistent with their gender identity. On the other hand, if they misperceive gender composition because of cultural sentiments, they may choose an occupational course somewhat different from their intentions.
Originality/Value of the Chapter
Research on gender composition typically employs either a macro approach based on governmental statistics or a micro approach that examines a limited number of occupations. This is the first study to conduct a complete census of every Census occupation for perceived gender composition and cultural sentiments.