A Stochastic Frontier Analysis of Financing Constraints on Investment: The Case of Financial Liberalization in Taiwan

Wang, Hung‐Jen

doi:10.2139/ssrn.353640

Cited by 43 publications

(82 citation statements)

References 35 publications

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“…This point has been made by Wang (2002Wang ( , 2003, though his list of special cases is not the same as ours. To address the testing question in some generality, let ξ = 0 denote the hypothesis of interest.…”

Section: Testing Scaling and Other Interesting Hypothesessupporting

confidence: 49%

Interpreting and Testing the Scaling Property in Models where Inefficiency Depends on Firm Characteristics

et al. 2006

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Let u ≥ 0 be technical inefficiency, let z be a set of variables that affect u, and let δ be the parameters of this relationship. The model satisfies the scaling property if u(z, δ) can be written as a scaling function h(z, δ) times a random variable u* that does not depend on z. This property implies that changes in z affect the scale but not the shape of u(z,δ). This paper reviews the existing literature and identifies models that do and do not have the scaling property. It also discusses practical advantages of the scaling property. The paper shows how to test the hypothesis of scaling, and other interesting hypotheses, in the context of the model of Wang, Journal of Productivity Analysis, 2002. Finally, two empirical examples are given. Copyright Springer Science+Business Media, LLC 2006Stochastic frontier model, Scaling property, Technical inefficiency, C12, C31, C52,

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Section: Testing Scaling and Other Interesting Hypothesessupporting

confidence: 49%

Interpreting and Testing the Scaling Property in Models where Inefficiency Depends on Firm Characteristics

et al. 2006

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“…A financially constrained firm will find it difficult to fund positive net present value projects due to the scarcity of internal funds and costly external finance. Later studies by Devereux and Schiantarelli (1990), Hoshi et al (1991), T. Whited (1992), Wang (2003), Almeida et al (2004), Denis and Sibilkov (2010), and Bhaumik et al (2012) confirmed the findings of Fazzari et al (1988). Carpenter et al (1998), who compare three measures of financial constraints (cash-flow sensitivity, cash stocks, and coverage ratio), find evidence confirming cash flow as the preferred variable to test for the presence of financing constraints.…”

Section: Literature Reviewmentioning

confidence: 64%

Financial constraints and investment decisions of listed Indian manufacturing firms

Kumar

Ranjani

2018

Financ Innov

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Background: The purpose of the study is to understand the role of cash flow sensitivity to investment as a measure of financial constraints among listed Indian manufacturing firms. It also analyses the role of tangibility in alleviating financial constraints. Further, the role of other financial factors in investment decisions is explored. Methods: The study is conducted using the generalized method of moments (GMM) estimator on dynamic panel data for the period of (2009-2015) on 768 listed manufacturing firms. Results: The analysis finds that cash flow sensitivity is a valid measure of financial constraints in the Indian manufacturing sector. Results according to splitting criteria found that investment decisions of standalone firms are more sensitive to cash flow than group affiliated firms. Further, splitting the firms according to market capitalization and tangible net worth reveals a higher degree of cash flow sensitivity by firms with lower market capitalization and asset tangibility. The results for the effects of tangibility of assets on easing financial constraint were found significant only in the case of firms with low tangible net worth and medium market capitalization. Conclusions: The study confirms cash flow sensitivity to investment as a valid measure of financial constraints. It will confirm pooling of internal funds by financially constrained firms to accept profitable investment opportunities in future. Further, it also reports that asset tangibility eases the financial constraints faced by firms.

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“…To accommodate possible heteroscedasticity of the error term υ it , we follow Hadri (1999) and Wang (2003) and make σ 2 υ = exp(h it ϕ); where h it is a vector of time and bank characteristics that may induce heteroscedasticity in υ it , and ϕ is a vector of parameters to be estimated. We include time, NPL, and leverage in h. For all stochastic frontiers we estimate, we reject the hypothesis of homoscedasticity at the 1% level of statistical significance.…”

Section: Homogeneity Properties Of the Nspfmentioning

confidence: 99%

“…15 Formally, we test if the variance of the inefficiency term in equation (20) is zero, σ u = 0. We use likelihood ratio tests following Coelli and Battese (1996) and Wang (2003). Under the null hypothesis, these tests follow a mixture Chi-squared distribution whose critical values are given in Kodde and Palm (1986, Notes: This table shows the average (mean), standard deviation (sd), and the 5, 25, 50, 75, and 95 percentiles of the estimated efficiency measures for all, big (assets > $1 billion), medium ($100 million < assets < $1 billion), and small (assets < $100 million) banks.…”

Section: Efficiency Estimatesmentioning

confidence: 99%

Profit Efficiency of U.S. Commercial Banks: A Decomposition

Restrepo-Tobón

Kumbhakar

2013

SSRN Journal

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This paper presents new evidence regarding the relation between profit, revenue, and cost efficiencies of U.S. commercial banks. Building on the widely used nonstandard profit function (NSPF) approach, we show (i) why estimation of NSPF would be wrong and (ii) how revenue and cost efficiencies contribute to profit efficiency. Using data from U.S. commercial banks from 2001 to 2010, we find that losses due to profit inefficiency represents about 8.2% of banks' equity of which 3.5% is due to revenue inefficiency and 4.7% to cost inefficiency.Cost efficiency weighs more than revenue efficiency in estimated profit efficiency. However, compared with cost inefficiency, revenue inefficiency affects more overall profitability.

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A Stochastic Frontier Analysis of Financing Constraints on Investment: The Case of Financial Liberalization in Taiwan

Cited by 43 publications

References 35 publications

Interpreting and Testing the Scaling Property in Models where Inefficiency Depends on Firm Characteristics

Interpreting and Testing the Scaling Property in Models where Inefficiency Depends on Firm Characteristics

Financial constraints and investment decisions of listed Indian manufacturing firms

Profit Efficiency of U.S. Commercial Banks: A Decomposition

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