curacy of the model is examined as the data become more remote from the serious
problem date. The SP sample results, as expected, show a drop in the accuracy of the
models. We utilized the weights from the model constructed with Year 1 data and in-
serted the variable measures for Years 2 and 3 prior to the SP date. Year 2 data pro-
vided accuracy of 84.2% (16 of 19 correct). Year 3 data provided lower accuracy of
77.8% (14 of 18 correct) classifications. Therefore, in only four cases were errors ob-
served in classification based on data from three (or more in some cases) years prior
to the SP date.
(c) Implications of Results for Brazil. The implications and applications of models
designed for assessing the potential for serious financial problems in firms are many.
This is especially true in a developing country, where an epidemic of business fail-
ures could have drastic effects on the strength of the private sector and on the econ-
omy as a whole. Most observers of the Brazilian situation would agree on the merit
of preserving an equilibrium among private enterprises, state-owned firms, and
multinationals. Such equilibrium would be jeopardized if the domestic private sector
were weakened by an escalation of liquidations. If a model such as the one suggested
is used to identify potential problems, then in many cases preventive or rehabilitative
action can be taken. This should involve a conscious internal effort, by the firms
themselves, to prevent critical situations as soon as a potential problem is detected.
Besides internal efforts, a program of financial and managerial assistance, more than
likely from official external sources, is a potential outcome.
Many economists, including the writers, have argued that significant government
assistance for the private sector is an unwise policy except where the system itself is
jeopardized. One can rationalize government agencies’ attempts to stabilize those in-
dustries where a significant public presence or national security is involved, for in-
stance, commercial and savings banks or the steel industry. In developing countries,
the distinction between high public interest sectors and the fragile private sector is
more difficult to make, and limited early assistance is advocated.
10.15 INDIA
(a) Bhatia (1988). The author has developed a discriminant analysis model for
identifying “sick” companies. Sick companies in India refer to companies that con-
tinue to operate (or more accurately are kept in operation even after their economic
value is in question) even after incurring losses. The definition used by the Industrial
Development Bank of India for sickness is if a company suffers from any of the fol-
lowing ills:
- Cash losses for a period of two years, or if there is a continuous erosion of net
worth, say 50% - Four successive defaults on its debt service obligations
- Persistent irregularity in the use of the credit lines
- Tax payments in arrears for one to two years
The sample consisted of 18 sick and 18 healthy companies all of which are pub-
licly traded. Data used pertained to the period 1976–1995. The healthy companies
were paired with the sick ones based on the type of product and gross fixed assets.
10.15 INDIA 10 • 35