3 Technical efficiency and finance constraints
3.5 Concluding remarks
this sector, ICR affects positively technical efficiency while DAR does not. This result can be explained by reminding that firms in this sector can have access to external financial resources (thanks to government incentives) aimed in particu- lar at the relaxing of the long-run constraint. So firms cannot really be con- sidered debt-constrained and therefore tightening of the budget constraint cannot have an impact on efficiency. To test the significance of either DAR or ICR, I use the likelihood ratio testing procedure. The hypothesis test has been con- ducted with the unrestricted translog production function as the model under the null hypothesis. The likelihood ratio tests (in Table 3.6) show that both DAR and ICR are significant apart from the Tobacco sector.
The coefficients of either DAR or ICR do not tell directly the magnitude of the effect of changes in the DAR or ICR upon technical efficiency. Therefore, I have computed the partial derivatives of the technical efficiency predictor (or marginal effects) with respect to DAR first and ICR afterwards using the formula in Coelli (2003). They measure how reactive firms in the sector are in reducing their inefficiencies when there is a negative shock to their finances. The marginal effects are listed in Table 3.7. It is possible to see that, for instance, 1 per cent increase in DAR will increase the average technical efficiency score by 0.29 points in the Textiles sector. While in most sectors variations in finance constraints affect positively technical efficiency, it is interesting to notice that the size of the effect varies across sectors and within the same sector according to the whether the long-run or the short-run measure of finance constraints is considered. Consider the example of Food: a 1 per cent increase in ICR will increase efficiency by 1.17 points, while the same increase in DAR has a negli- gible impact on technical efficiency. This difference can be explained by looking at the composition of the sector. Small and medium-sized firms with scarce interest in expansion dominate the Food sector: these fund their activities mostly with internal resources and therefore they are very sensitive to changes in ICR.
The same argument applies to the Leather sector but in the opposite direction: as larger firms with more cash-flow and more interest into expansion populate the sector, changes in ICR do not necessarily trigger an internal re-organization to release internal resources. However, the tightening of the long-run constraint may have an adverse impact on the potential expansion of firms and therefore start a process of technical inefficiency reduction.
itself, this is not a bold proposition: indeed, as mentioned in Chapter 2, there already exists some empirical evidence suggesting a positive correlation between total factor productivity and increasing financial pressure (Nickell and Nicolitsas, 1999), although these previous papers have not clarified the exact mechanisms that underpin the empirical evidence. On the contrary, in this chapter, I have tried to identify theoretically the channels that allow increasing financial constraints to have a positive impact on productivity. More specifically, I make two hypotheses: first, I claim that increasing financial pressure aligns managers’ interests with those of the firm’s ownership; second, I assume that increases in productivity are due to reductions in technical inefficiency: as the firm cannot have access to additional resources to improve its technology (and therefore they do not experience technical change) it will try to improve the effi- ciency over time to improve productivity over time.
My starting point is the assumption that there exists an internal mechanism in the firm that generates the slack and so makes the firm appear technically ineffi- cient. Therefore in the theoretical model, I consider a firm which is technically inefficient because of the mismatch of preferences between the managers and the ownership. Indeed, managers value leisure while the ownership is obviously interested in maximizing the firm’s profits. As managers are paid only a fraction of their profits, this creates the potential for the hold-up problem where man- agers do not supply what would be the optimal effort from the standpoint of the ownership. My assumption is that this problem can only be mitigated when the external environment the firm operates in will change. So for instance changes in the financial position of the firm with the prospect of increasing financial pressure implies a reduction in the surplus generated by the firm and then a decrease of the share of surplus that managers can appropriate ex post. This mechanism may induce managers to re-align their incentives to those of the ownership with a reduction of the hold-up problem and then an increase of the firm’s technical efficiency.
I have tested these predictions empirically by using a panel of 1,124 firms, covering the period 1989–1994 and belonging to the main eight sectors from Italian manufacturing. The results show that there is support for the hypothesis that technical efficiency may be affected by the availability of external financial resources; more precisely, once finance constraints get tighter, then firms experience an improvement in technical efficiency over time to guarantee gains in productivity and so positive increases in profits. One limitation of the empiri- cal analysis is that I test a reduced form relationship between technical effi- ciency and financial pressure and therefore I cannot test whether the specific mechanism described in the theoretical model is at work in this sample of firms against alternative mechanisms. However, in spite of this, the empirical results are still of some value as they are a first step towards a more complete empirical analysis of the relationship between technical efficiency and credit constraints.
These results have interesting implications. Indeed, the notion that adverse financial shocks will negatively affect the firm does not always apply. This would be the case if the firm is efficient; however, firms will always have a certain
degree of organizational slack that can be used to counterbalance the negative impact of adverse financial shocks. So in this respect, being technically inefficient is not necessarily bad for a firm: indeed, it may provide a cushion that buffers the impact of negative productivity shocks on firms. Of course this does not mean that increasing financial pressure may always have a beneficial impact on a firm.
Indeed, it is important to recall that there are additional effects of financial con- straints (as reviewed in Chapter 2) that can very easily offset their benefits (in terms of increased technical efficiency). Finally, these results suggest a potential non-linearity in the relationship between technical efficiency and financial con- straints. Indeed, if a firm is relatively inefficient, then the impact of increasing financial pressure may be rather lenient as firms can absorb the negative shock by reducing technical efficiency. Less clear is the impact of an increase in the cost of external borrowing on firms that are on the frontier: as these do not have slack that can be used to reduce technical inefficiency, the impact on productivity is obviously not of the type predicted by the model. However, what it will be is open to speculations as the model contained in this chapter cannot predict what will happen in this case. This is therefore a point that warrants further investiga- tion and that can therefore be left to future research.
Appendix
The Capitalia database (formerly known as the Mediocredito Centrale database) is one of the most valuable sources of information about Italian manufacturing available nowadays. The Capitalia surveys consider an open panel of Italian manufacturing firms, with about 4,500 firms for each survey. It was initiated in 1989 by the Osservatorio delle piccole e medie imprese with the declared purpose of collecting and disseminating information about the manufacturing world with a special emphasis on the small and medium-sized firms.7The data- base has been built and is continuously updated using a periodic survey adminis- trated by the Osservatorio delle piccole e medie imprese. The survey belongs to the category of the so-called mixed survey; that is, the survey is sampling firms from 11 to 500 employees and it is exhaustive for firms with more than 500 employees. To construct the sample, the following sampling plan has been adopted: all firms with more than ten employees have been divided into homo- geneous groups according to the variability of the per capitaoutput. Then, from each group, a number of firms is selected proportionally. A supplementary list of about 8,000 firms has been constructed for each survey, in order to integrate by stratum the firms that had failed to reply. To ensure a good quality of the data, all the data collected from both the questionnaire and the balance sheets have been subject to a rigorous examination to detect eventual outliers. Emphasis has been given to the monitoring of the stratification variable; in this case, the number of firms within the sample has been compared with the actual number in each strata. When significant differences have been observed, there has been a re-sampling of the strata on the basis of the information drawn from the questionnaires.
Information is collected from two main sources: firms’ balance sheets and a questionnaire, distributed periodically by the Osservatorio delle piccole e medie imprese. The questionnaire is divided into eight sections and collects informa- tion about the firm’s internal organization, its prevailing economic activity, the previous education of employees, the amount of investment in R&D and the links with foreign markets.8 It also provides qualitative information ranging from general aspects (year when the firm was founded, legal form, reorganiza- tions, ownership and control, groups and consortia) to firms’ finance and finan- cial and fiscal incentives. Most of this qualitative information relates to the three-year period as a whole with only a small subset of information being avail- able for each year. The balance sheets contain quantitative information relating to the firms’ main financial indicators for each of the three years under consideration in each wave of the survey. The number of firms replying to the questionnaire is different from the number of firms providing the balance sheets:
indeed, 4,431 firms have returned the questionnaire in 1994, while 2,519 firms have provided information about their balance sheets for the period 1989–1994.
Given the amount of the collected information, the Capitalia database allows a researcher to get a more complete overview of the Italian manufacturing world than similar databases (like the ones from Mediobanca and from the Centrale dei Bilanci). To extract the data-sets, I have used the information contained in the 5th and 6th Surveys, published by the Osservatorioin 1997. Our data refer to the report published in 1992 that presented data relative to 4123 firms from 1989 to 1991. The second report was published in 1997 and collected the balance sheets of 2,519 firms from 1992 to 1994 and the replies to the question- naire of 4,431 firms relative to 1994. However, the information from the balance sheets has been made homogeneous so that it is possible to have the balance sheets of 2,519 firms from 1989 to 1994. More specifically, to build up the employed panel data-sets, I have used only the information provided from the balance sheets in the last published report to get an homogeneous data-set.