27 Exchange Rate Dynamics A New Open Economy Macroeconomic Perspective Edited by Jean-Oliver Hairault and Thepthida Sopraseuth. 32 Tools for Prosperity Fiscal Policy Reconsidered Edited by Per Gunnar Berglund and Matias Vernengo.
Acknowledgements
1 Introduction
The direct effect of these financial constraints is of course the increase in the cost of capital and thus a reduction in investment expenditure. In the first, I analytically show the specific circumstances under which increasing economic pressure can improve a firm's technical efficiency.
2 Credit constraints and economic outcomes
Introduction
In this chapter, I will present a selective review of the most relevant academic research that looks at credit rationing and at the impact of credit restrictions on both firms' demand for investment and the formation of new firms. In the second part of the chapter, I will review the literature on self-employment, financial constraints and gender.
Asymmetric information in the credit market and its impact on firms’ demand for investment: a brief survey
Not surprisingly, some research has been conducted to clarify the conditions under which the optimal properties of the credit market can be restored even in the presence of asymmetric information. It is assumed that borrower quality and choice of action are (partial) substitutes.
Self-employment choice, gender and financial constraints
For example, they may affect the individual's accumulation of human capital which in turn may also affect self-employment income through its impact on the skills of the self-employed. Dunn and Holtz-Eakin (2000) find that having a self-employed parent has a strong positive effect on men's probability of self-employment.
Conclusions
Most of the literature focuses on the financial constraints faced by women's businesses once they are established. Is it possible to draw an overall conclusion about the direction of the impact that financial constraints can have on economic outcomes.
3 Technical efficiency and finance constraints
Introduction
Inefficiency can also arise as a result of the bargaining process between employees and the owners of the company (Haskel and Sanchis, 2000). As the deterioration of financial conditions reduces the availability of capital, the manager may decide to increase his efforts, as long as this can offset the negative impact of the financial constraints on the firm's production level.
Technical efficiency and finance constraints: a partial equilibrium approach
Thus, this effect cannot affect the technical efficiency in period 1, but it will affect the efficiency in period 2. 1 Technical efficiency in period 1 is decreasing in the credit constraint and otherwise it is not decreasing. 2 Technical efficiency in period 2 is decreasing in the credit constraint and does not decrease otherwise.
This is the case where the interaction effect by itself is not sufficient to overcome the direct effect (and thus technical efficiency in period 1 is increased by the availability of more credit) while the combination of the effort effect and the interaction effect is sufficient to reduce technical efficiency in period 2.
The empirical analysis
The focus of this research is also on the development of the production system, as this is the subject of the empirical analysis carried out in the following sections. The decline in production that began in the fall of 1992 appears to have bottomed out in the summer due to a recovery in exports. Uit reflects the shortfall of firms relative to their own best practice in each period, where the firm's best practice is determined by a limit.
For the ICR, the picture is different: this is the ratio of the company's interest expenses to the sum of interest expenses plus cash flow.
The econometric results
The estimated coefficients (in absolute value) of the z variables are listed in the last rows of Table 3.7. It is important to note that in the context of Battese and Coelli's (1995) model, a negative (positive) sign of the DAR or ICR coefficient implies that firms with high DAR or ICR are more/less efficient. The coefficients of DAR or ICR do not directly indicate the magnitude of the effect of changes in the DAR or ICR on technical efficiency.
We can see that, for example, a 1% increase in DAR will increase the average technical efficiency score by 0.29 points in the textile sector.
Concluding remarks
Therefore, using the formula from Coelli (2003), I calculated the partial derivatives of the predictor of technical efficiency (or marginal effects) first with respect to DAR and then with ICR. Therefore, in the theoretical model, I consider a company that is technically inefficient due to a mismatch of preferences between managers and ownership. The Capitalia database (formerly known as the Mediocredito Centrale database) is one of the most valuable sources of information on Italian manufacturing available today.
Balance sheets contain quantitative information regarding the main financial indicators of companies for each of the three years considered in each wave of the survey.
4 Product market competition,
- Introduction
- The general framework
- The empirical analysis
- Concluding remarks
However, it differs from previous literature in that I focus on the role that the state of product market competition and financial pressure can play together in improving the technical efficiency of cooperatives. As in the previous chapter, financial pressure is measured by the debt-to-asset ratio (DAR). I also control for firm location with the SOUTH variable in the inefficiency model specification.
Finally, it can be seen from table 4.3 that the technical efficiency has increased for the cooperatives in the period considered.
5 Self-employment and gender
Introduction
This does not necessarily mean that they will be financially constrained when they first apply for external funding; it simply means that, based on observation of the aggregate variables (in this case, the small proportion of women and individuals from minority backgrounds who have access to external credit), they expect to be credit rationed based on both their gender and ethnic background. So, in the second model, I will try to quantify the extent to which our two main variables of interest (gender and ethnicity) only influence the likelihood that individuals experience financial constraints first, such that only those who do not financial constraints can then become independent. This is equivalent to estimating a two-stage Heckman model, where in the first stage I model the probability of experiencing economic constraints as a function of both gender and ethnic background, while in the second stage I model the independent choices of those who have survived the first phase as a function of variables such as ability, human capital, and so on.
Again this will involve the estimation of a two-stage Heckman model, where in the first stage the probability of access to external finance is influenced by both gender and ethnicity, while in the second stage I estimate the probability of self-employment for the model individuals. who have approached external funders based on their previous experience, training and so on.
The model
In the first model, I will test whether the individual's probability of becoming self-employed is affected by both (a) gender/ethnic background and (b) by the interaction terms. I assume that, ceteris paribus, the probability that an individual will become self-employed is positively influenced by a subjective positive attitude towards entrepreneurship;. In other words, I want to test to what extent the probability of being financially limited influences the individual's choice in self-employment.
In this equation, I will model the likelihood of becoming self-employed as a function of (among other things) previous experience (looking at entrepreneurial ability), employment status (employment status) (indirectly measuring foregone wage income), and attitudes toward compared to self-employment (as a measure of the useless costs associated with running an entrepreneurial project).
The Household Survey of Entrepreneurship (HSE), 2003
Doers appear to be concentrated in the 25 to 64 age group and there appears to be no difference by gender. Most doers (male and female) are located in the north and south of the country. In terms of location, most White Thinkers are located in the North and South.
A relatively large proportion of black thinkers are located in the South, while Asian thinkers are evenly distributed between North and South.
The results
In the first stage, the likelihood of a contractor is not significantly influenced by gender, but by previous experience and attitude towards entrepreneurship. Indeed, it would be interesting to design an empirical strategy that would allow testing whether this type of mechanism operates in the population. This point may simply reflect the proportion of the population of individuals from the Black and Asian community.
In other words, financial constraints may also depend on unobserved heterogeneity of individuals in the sample, and the use of cross-sectional data prevents us from controlling for all these additional factors that may affect financial constraints.
Concluding remarks
DEP2ST = this is a dummy variable that has a value of 1 if the respondent is a Contractor and 0 otherwise. DEP1ST = this is a dummy variable that takes the value 1 if the respondent wants to gain access to external finance and 0 otherwise. Financial constraints: This variable is a dummy variable with a value of 1 if the respondent had financial constraints and 0 otherwise.
Landlord: this is a dummy variable that takes the value of 1 if the respondent does not own a house and 0 otherwise.
6 Conclusions
In my model, I assume that managers contribute to the creation of the firm's surplus by exerting effort. Hence the interest in the relationship between the two mechanisms that underlies the study contained in Chapter 4. In the second case, financial pressure can be beneficial for the firm as it pressures managers and workers to align their interests with those of the firm.
This appendix is devoted to the illustration of the analytical methods for measuring technical efficiency, based on the concept of production frontier.
Notes
2 The calculation of the DEA model was carried out using the software Deap version 2.1 by Tim Coelli. For the remainder of the article, I will alternately refer to this sector as the wine sector or the wine industry. 5 The dataset does not provide information about the production mix of companies: companies can specialize in the higher or lower segment of the market, or they can produce both types of products.
1 In the rest of this chapter I will use "entrepreneur" and "self-employed" as synonyms.
1988) “Predicting Firm-Level Technical Efficiency with a Generalized Frontier Production Function and Panel Data”, Journal of Econometrics. A Study of Small Business Loans”, Journal of Small Business Management Gender and Entrepreneurship: An Ethnographical ApproachAbingdon and New York: Routledge. 1993) "A theoretical overview and extension of research on sex, gender and entrepreneurship", Journal of Business Venturing.
1988) “A stochastic frontier cost function for residential childcare”, Journal of Applied Econometrics, vol.
Index
