Kim P. Huynh

Age Effects, Leverage and Firm Growth

Recent theories of firm dynamics emphasize the role of financial variables as determinants of firm growth. Empirically examining these relationships has been difficult, since there is a lack of financial data on the small, young, and private firms. Using a unique administrative data set, this paper considers the growth of new firms in Canadian manufacturing from a financial perspective. We find that financial factors, such as leverage and initial financial size, impact growth rates for new firms. Further, the inclusion of leverage has little impact on the economic significance of the conditional age and size relationships with firm growth.

The Impact of Initial Financial State on Firm Duration Across Entry Cohorts

Recent theories of industry dynamics emphasize the role of financial frictions in determining post entry performance of firms. Testing these theories has been difficult because of the lack of financial data on small, young and private firms. Using a unique data set, T2LEAP, this paper considers the survival of new firms in Canadian manufacturing from a financial perspective. Duration analysis quantifies the effects of firm, industry and aggregate factors. Findings show that nonlinear effects are found with firm leverage. Finally, likelihood decompositions offer insights into the contributing factors to firm hazard for nine entry cohorts during the period 1985–1997.

Functional Principal Component Analysis of Density Families with Categorical and Continuous Data on Canadian Entrant Manufacturing Firms

This article investigates the evolution of firm distributions for entrant manufacturing firms in Canada using functional principal components analysis. This methodology describes the dynamics of firms by examining production variables, size, and labor productivity, and a financial variable, leverage (debt-to-asset ratio). We adapt the canonical functional principal components analysis to allow for the inclusion of qualitative information in the form of discrete variables, industry, and region, to capture market structure differences, which is shown to change the dynamics of firm size and labor productivity distributions only. We also perform various tests with the null hypothesis that the distributions are equal across time. When accounting for industry and regional categories, there is a substantial fall in the number of rejections of the null hypothesis of equality for size and labor productivity, which is not the case for leverage. These results show the importance of including qualitative information to account for potential heterogeneity when applying functional principal component analysis to firm-level data. Finally, the methodology finds a correlation between the evolution of variable distributions and macroeconomic factors. This article has supplementary material online.

Nonlinear Difference-in-Difference Treatment Effect Estimation: A Distributional Analysis

This chapter uses the nonlinear difference-in-difference (NL-DID) methodology developed by Athey and Imbens (2006) to estimate the effects of a treatment program on the entire distribution of an outcome variable. The NL-DID estimates the entire counterfactual distribution of an outcome variable that would have occurred in the absence of treatment. This chapter extends the Monte Carlo results in Athey and Imbenss (2006) to assess the efficacy of the NL-DID estimators in finite samples. Furthermore, the NL-DID methodology recovers the entire outcome distribution in the absence of treatment. Further, we consider the empirical size and power of tests statistics for equality of mean, medians, and complete distributions as suggested by Abadie (2002). The results show that the NL-DID estimator can effectively be used to recover the average treatment effect, as well as the entire distribution of the treatment effects when there is no selection during the treatment period in finite samples.

The Distributional Efficacy of Collaborative Learning on Student Outcomes

This study addresses self-selection and heterogeneity issues inherent in measuring the efficacy of voluntary training programs. We exploit data collected from Indiana Universitys introductory microeconomics course. In conjunction with their class, undergraduates were given the choice to participate in a voluntary training program called Collaborative Learning (CL), which is designed to encourage a self-discovery learning style. To address self-selection and heterogeneity in the effectiveness of CL, program evaluation methods were used to measure student performance. We find, amongst other things, that CL produces heterogeneous results e.g., the bottom 40 percentile of CL participants improved their performance the most, and that students at the higher end of the grade distribution achieve greater improvement in topic understanding. The latter is greater than can be associated with superior innate ability alone. Finally, parametric and non-parametric sensitivity analysis confirmed that the sign of the calculated treatment effects is robust to potential violations of the underlying assumptions.

Average Derivative Estimation with Missing Responses

This chapter proposes a simple procedure to estimate average derivatives in nonparametric regression models with incomplete responses. The method consists of replacing the responses with an appropriately weighted version and then use local polynomial estimation for the average derivatives. The resulting estimator is shown to be asymptotically normal, and an estimator of its asymptotic variance–covariance matrix is also shown to be consistent. Monte Carlo experiments show that the proposed estimator has desirable finite sample properties.

R&D Spending and Knowledge Spillovers

Understanding firm constraints in R&D expenditures is a key component to addressing broader economic goals. We investigate the role of local intensity of university-industry knowledge spillovers on the amount of firm R&D expenditure. To investigate this issue we use firm-level dataon R&D expenditures from Italy. We find that geographical variation in the R&D intensity of higher education sector, which is shown to be a good proxy for the local intensity of knowledge spillovers, plays an important role for the amount of R&D expenditures financed by the local business sector. We argue that our findings have important policy implications.