Working capital dynamics
TL;DR: In this paper, the authors assess short run variations in firms' working capital allocations in light of finding persistence in these allocations in the recent literature and study the extent and speed of me...
Abstract: We assess short-run variations in firms’ working capital allocations in light of finding persistence in these allocations in the recent literature. Specifically, we study the extent and speed of me...
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TL;DR: In this paper, the authors use a multivariate framework to draw inferences from the marginal impact of working capital and its components on firm value while controlling for asset utilization, and find that, after accounting for asset utilisation, the marginal impacts of working assets and their components on the firm value is quite weak.
Abstract: PurposeThe article highlights potential mismeasurement in working capital allocations among academicians and practitioners and revisits the relationship between firms' working capital and productivity, as evident from their values.Design/methodology/approachThe research design acknowledges the relative role of firms' working capital vis-a-vis other assets in generating revenue, thereby effectively accounting for the overall asset efficiency in influencing firm value. The authors use a multivariate framework to draw inferences from the marginal impact of working capital and its components on firm value while controlling for asset utilization.FindingsThe authors find that, after accounting for asset utilization, the marginal impact of working capital and its components on firm value is quite weak. The results are consistent with the hypothesis that firms' trade-off between short-term and long-term assets per se should not have any value implications. After controlling for their asset turnovers, the authors find that higher allocations to working capital relative to other assets are not necessarily value-destructive. The findings contrast with the past literature.Research limitations/implicationsThe article, through its analytical and empirical insights, suggests that working capital allocations should be measured by managers and academicians relative to firms' other asset rather than their sales. Firm values should, therefore, be compared based on firms' overall asset utilization rather than inter-temporal allocations to short-term versus long-term assets.Originality/valueContrary to the existing literature so far, the article explicitly acknowledges the relative role of firms' other assets, and hence the overall asset utilization, to infer the marginal impact of working capital on firm value.
4 citations
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TL;DR: In this paper , the authors investigated the impact of working capital level on firm values and risks between group affiliated and standalone firms, and found that standalone firms are more financially constrained than group affiliated firms.
Abstract: PurposeThis study aims to primarily investigate two vital questions: First, the authors examine whether group-affiliated firms are more (less) financially constrained vis-à-vis standalone firms. The authors estimate working capital investment (WCI) to cash flow sensitivity to understand the nature of financial constraints. Second, the authors further investigate the impact of working capital level on firm values and risks between group-affiliated and standalone firms.Design/methodology/approachThis paper uses balanced panel data set from the year 2012–2019. The authors employ propensity score matching to ascertain comparable firm attributes from business group and standalone firms. This process yields 280 firms (140 in each group) after controlling the firm heterogeneity between these two groups. All the models are estimated using fixed-effect regression.FindingsThe authors find that group affiliated firms are less financially constrained than standalone firms. The results show that WCI to cash flow sensitivity is higher in standalone firms vis-a-vis group-affiliated firms, implying that standalone firms are more financially constrained than group-affiliated firms. Second, the authors find that firm values are more sensitive to working capital level in standalone firms versus group-affiliated firms. Furthermore, the authors document that the risk of the standalone firms is less sensitive to working capital level than that of group-affiliated firms.Originality/valueMost recent studies exploring the role of group affiliation in financing constraints have not controlled for heterogeneity among group-affiliated firms vis-à-vis standalone firms, which may arise due to variation in firm characteristics. Unlike prior studies, this research design ascertains comparable firm attributes between business group and standalone firms, implying firms belonging to these two groups differ by the exogeneous affiliation (business group and standalone firms). The authors document that group-affiliated firms are less financially constrained than standalone firms controlling firm-level heterogeneity between group-affiliated and standalone firms. To the best of the authors' knowledge, no such work has been previously done in general (specifically in India).
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TL;DR: In this article , the impact of the COVID-19 pandemic on firms' working capital management (WCM) and eventually, firms' performance of 4513 China and 1049 United Kingdom (UK) publicly listed firms was examined.
Abstract: This study examines the impact of the COVID-19 pandemic on firms' working capital management (WCM) and, eventually, firms’ performance of 4513 China and 1049 United Kingdom (UK) publicly listed firms. Static panel data analysis was used to achieve the objective of this study. By using the cash conversion cycle (CCC) as a proxy for WCM, we discover that COVID-19 has a negative effect on the WCM of Chinese firms. We also found a statistically significant negative relationship between WCM and Chinese firms’ performance. This suggests that when firms are affected by COVID-19 uncertainty, Chinese firms will be compelled to reduce their account receivables, inventory levels, and seek increased credit terms from suppliers. Contrary to Chinese firms, we discover the positive relationship between COVID-19 and WCM for UK firms. Further, the relationship between WCM and UK firms’ performance is positively associated. The greater investment in WCM by UK firms during the COVID-19 period generated a higher firm performance.
References
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01 Jan 2001
TL;DR: This is the essential companion to Jeffrey Wooldridge's widely-used graduate text Econometric Analysis of Cross Section and Panel Data (MIT Press, 2001).
Abstract: The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.
28,298 citations
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TL;DR: In this article, the generalized method of moments (GMM) estimator optimally exploits all the linear moment restrictions that follow from the assumption of no serial correlation in the errors, in an equation which contains individual effects, lagged dependent variables and no strictly exogenous variables.
Abstract: This paper presents specification tests that are applicable after estimating a dynamic model from panel data by the generalized method of moments (GMM), and studies the practical performance of these procedures using both generated and real data. Our GMM estimator optimally exploits all the linear moment restrictions that follow from the assumption of no serial correlation in the errors, in an equation which contains individual effects, lagged dependent variables and no strictly exogenous variables. We propose a test of serial correlation based on the GMM residuals and compare this with Sargan tests of over-identifying restrictions and Hausman specification tests.
26,580 citations
"Working capital dynamics" refers methods in this paper
...Arellano and Bond (1991) and Blundell and Bond (1998) suggest the generalized method of moments (GMM) and the system (two-step) GMM approach, respectively, and use lagged dependent variables and lagged first differences as instruments....
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TL;DR: In this paper, two alternative linear estimators that are designed to improve the properties of the standard first-differenced GMM estimator are presented. But both estimators require restrictions on the initial conditions process.
19,132 citations
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28 Apr 2021
TL;DR: In this article, the authors proposed a two-way error component regression model for estimating the likelihood of a particular item in a set of data points in a single-dimensional graph.
Abstract: Preface.1. Introduction.1.1 Panel Data: Some Examples.1.2 Why Should We Use Panel Data? Their Benefits and Limitations.Note.2. The One-way Error Component Regression Model.2.1 Introduction.2.2 The Fixed Effects Model.2.3 The Random Effects Model.2.4 Maximum Likelihood Estimation.2.5 Prediction.2.6 Examples.2.7 Selected Applications.2.8 Computational Note.Notes.Problems.3. The Two-way Error Component Regression Model.3.1 Introduction.3.2 The Fixed Effects Model.3.3 The Random Effects Model.3.4 Maximum Likelihood Estimation.3.5 Prediction.3.6 Examples.3.7 Selected Applications.Notes.Problems.4. Test of Hypotheses with Panel Data.4.1 Tests for Poolability of the Data.4.2 Tests for Individual and Time Effects.4.3 Hausman's Specification Test.4.4 Further Reading.Notes.Problems.5. Heteroskedasticity and Serial Correlation in the Error Component Model.5.1 Heteroskedasticity.5.2 Serial Correlation.Notes.Problems.6. Seemingly Unrelated Regressions with Error Components.6.1 The One-way Model.6.2 The Two-way Model.6.3 Applications and Extensions.Problems.7. Simultaneous Equations with Error Components.7.1 Single Equation Estimation.7.2 Empirical Example: Crime in North Carolina.7.3 System Estimation.7.4 The Hausman and Taylor Estimator.7.5 Empirical Example: Earnings Equation Using PSID Data.7.6 Extensions.Notes.Problems.8. Dynamic Panel Data Models.8.1 Introduction.8.2 The Arellano and Bond Estimator.8.3 The Arellano and Bover Estimator.8.4 The Ahn and Schmidt Moment Conditions.8.5 The Blundell and Bond System GMM Estimator.8.6 The Keane and Runkle Estimator.8.7 Further Developments.8.8 Empirical Example: Dynamic Demand for Cigarettes.8.9 Further Reading.Notes.Problems.9. Unbalanced Panel Data Models.9.1 Introduction.9.2 The Unbalanced One-way Error Component Model.9.3 Empirical Example: Hedonic Housing.9.4 The Unbalanced Two-way Error Component Model.9.5 Testing for Individual and Time Effects Using Unbalanced Panel Data.9.6 The Unbalanced Nested Error Component Model.Notes.Problems.10. Special Topics.10.1 Measurement Error and Panel Data.10.2 Rotating Panels.10.3 Pseudo-panels.10.4 Alternative Methods of Pooling Time Series of Cross-section Data.10.5 Spatial Panels.10.6 Short-run vs Long-run Estimates in Pooled Models.10.7 Heterogeneous Panels.Notes.Problems.11. Limited Dependent Variables and Panel Data.11.1 Fixed and Random Logit and Probit Models.11.2 Simulation Estimation of Limited Dependent Variable Models with Panel Data.11.3 Dynamic Panel Data Limited Dependent Variable Models.11.4 Selection Bias in Panel Data.11.5 Censored and Truncated Panel Data Models.11.6 Empirical Applications.11.7 Empirical Example: Nurses' Labor Supply.11.8 Further Reading.Notes.Problems.12. Nonstationary Panels.12.1 Introduction.12.2 Panel Unit Roots Tests Assuming Cross-sectional Independence.12.3 Panel Unit Roots Tests Allowing for Cross-sectional Dependence.12.4 Spurious Regression in Panel Data.12.5 Panel Cointegration Tests.12.6 Estimation and Inference in Panel Cointegration Models.12.7 Empirical Example: Purchasing Power Parity.12.8 Further Reading.Notes.Problems.References.Index.
10,363 citations
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6,503 citations
"Working capital dynamics" refers background in this paper
...Furthermore, Nickell (1981) suggests that within-firm transformation by mean differencing to control for firm fixed effects introduces a correlation between the transformed dependent variable and the error term....
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