Gender, Investment Financing and Credit Constraints

This paper provides the first evidence on gender differences in investment financing, credit application and credit denial rates in Germany. The empirical analysis is carried out on a sample of firms drawn from the KfW Mittelstandspanel, a representative survey of German SMEs for the period from 2003 to 2009. Our results suggest that in female-owned firms the share of internal capital in investment financing is higher and the share of external funds is lower than for male-owned firms. An analysis of the supply- and demand-side on the credit market shows that women are not more likely to be denied credit but the probability that they apply for credit is on average lower. Yet, this gender difference in the probability of credit application is only evident when considering firms with negative or neutral sales expectations. There is no significant gender difference in credit application rates of firms with positive sales expectations

Male vs. female business owners: Are there differences in investment behavior?

This paper analyzes gender differences in the investment activity of German small and medium sized enterprises (SMEs). The empirical analysis is carried out on a sample of firms drawn from the KfW Mittelstandspanel, a representative survey of German SMEs for the period from 2003 to 2009. We find evidence that female-owned firms are less likely to invest and if they invest, then their average investment rate is lower. These differences cannot entirely be explained by firm or owner characteristics. Furthermore, women’s investment is less sensitive to cash flow, which indicates that it is unlikely that their lower investment is driven by difficulties in acquiring external finance. An analysis of stated investment goals reveals that women have different preferences and attitudes towards investment. They indicate to a lesser extent aspiring and growth-orientated investment goals like sales increase, innovation/R&D or implementation of new products

Male vs. female business owners: Are there differences in investment behavior?

This paper analyzes gender differences in the investment activity of German small and medium sized enterprises (SMEs). The empirical analysis is carried out on a sample of firms drawn from the KfW Mittelstandspanel, a representative survey of German SMEs for the period from 2003 to 2009. We find evidence that female-owned firms are less likely to invest and if they invest, then their average investment rate is lower. These differences cannot entirely be explained by firm or owner characteristics. Furthermore, women’s investment is less sensitive to cash flow, which indicates that it is unlikely that their lower investment is driven by difficulties in acquiring external finance. An analysis of stated investment goals reveals that women have different preferences and attitudes towards investment. They indicate to a lesser extent aspiring and growth-orientated investment goals like sales increase, innovation/R&D or implementation of new products.Gender Economics; Female Entrepreneurship; Investment

Gender, Investment Financing and Credit Constraints

This paper provides the first evidence on gender differences in investment financing, credit application and credit denial rates in Germany. The empirical analysis is carried out on a sample of firms drawn from the KfW Mittelstandspanel, a representative survey of German SMEs for the period from 2003 to 2009. Our results suggest that in female-owned firms the share of internal capital in investment financing is higher and the share of external funds is lower than for male-owned firms. An analysis of the supply- and demand-side on the credit market shows that women are not more likely to be denied credit but the probability that they apply for credit is on average lower. Yet, this gender difference in the probability of credit application is only evident when considering firms with negative or neutral sales expectations. There is no significant gender difference in credit application rates of firms with positive sales expectations.Gender Economics; Female Entrepreneurship; Investment Financing

Change-Point Testing and Estimation for Risk Measures in Time Series

We investigate methods of change-point testing and confidence interval construction for nonparametric estimators of expected shortfall and related risk measures in weakly dependent time series. A key aspect of our work is the ability to detect general multiple structural changes in the tails of time series marginal distributions. Unlike extant approaches for detecting tail structural changes using quantities such as tail index, our approach does not require parametric modeling of the tail and detects more general changes in the tail. Additionally, our methods are based on the recently introduced self-normalization technique for time series, allowing for statistical analysis without the issues of consistent standard error estimation. The theoretical foundation for our methods are functional central limit theorems, which we develop under weak assumptions. An empirical study of S&P 500 returns and US 30-Year Treasury bonds illustrates the practical use of our methods in detecting and quantifying market instability via the tails of financial time series during times of financial crisis

Inference for Large Panel Data with Many Covariates

This paper proposes a novel testing procedure for selecting a sparse set of covariates that explains a large dimensional panel. Our selection method provides correct false detection control while having higher power than existing approaches. We develop the inferential theory for large panels with many covariates by combining post-selection inference with a novel multiple testing adjustment. Our data-driven hypotheses are conditional on the sparse covariate selection. We control for family-wise error rates for covariate discovery for large cross-sections. As an easy-to-use and practically relevant procedure, we propose Panel-PoSI, which combines the data-driven adjustment for panel multiple testing with valid post-selection p-values of a generalized LASSO, that allows us to incorporate priors. In an empirical study, we select a small number of asset pricing factors that explain a large cross-section of investment strategies. Our method dominates the benchmarks out-of-sample due to its better size and power