In the econometric analysis based on survey responses, the variable to be ex- plained is the qualitative self-classification of borrowers. At first glance, the classification into several subgroups would suggest a model with a multinomial dependent variable. However, the groups of completely rejected and discouraged respondents turned out to be unimportant in the sample (see section 4.1.1, pp. 163 et seq.). I therefore merged these two categories with the group of partially constrained borrowers. As a result, the credit rationing status is expressed by a dichotomous or binary variable. It can be analysed by the following stochastic model:
i i
i u
k *=γ'z + (3-1)
ki* is a latent variable denoting an excess credit demand. We observe ki, which is
a dichotomous (1, 0) variable indicating whether observation i is a credit-
constrained borrower or not. represents a vector of explaining variables (such as household and production characteristics).
i z
γ is a vector of parameters, while
ui is a random error term. (3-1) is hence a model with a discrete dependent vari-
able (see AMEMIYA 1981). A plausible and frequently employed interpretation of
this type of models is to regard its predictions as probabilities that observation i
belongs to the constrained subgroup. Unfortunately, running an OLS regression on (3-1) would not ensure that the predictions are in fact bound in the (0, 1) in- terval. It is therefore usual practice to transform and in this way constrain it to the desired interval. A commonly used transformation is the standard normal distribution, which gives rise to the Probit model(GREENE 2000, pp. 811-849).
i
z
γ'
In the following, the Probit model is employed to analyse the determinants of credit rationing as given by the subjective assessment of the respondents. I esti- mate a general and a specific model, the latter being specifically tailored to the
requirements of the output supply model investigated later. Probit models are non-linear in parameters, they are therefore usually estimated by Maximum Likelihood (ML). Furthermore, the reported coefficients do not provide the mar- ginal effects of a change in zi on ki*. In non-linear models, marginal effects de-
pend on the specific values of the regressors. They are therefore usually reported for the sample means, separately from the coefficients. Note that the Probit model implies a very general functional form. Since the exact functional rela- tionship may be quite complex and is generally unknown, the Probit specifica- tion can only be regarded as a rough approximation to the real relationship (see the discussion in section 3.1.2, pp. 103 et seq.).
The choice of explanatory variables in the Probit model reflects the discussion of chapter 2 as summarised in sections 2.2.4, p. 74, and 2.3.3, p. 100. In particu- lar, the following considerations were taken into account: (a) the presence of credit rationing is determined both by supply and demand, observable character- istics that guide the lending decision of the bank should hence be included, (b) this is specifically important for factors that are likely to mitigate or worsen ef- fects of asymmetric information, for example collateral availability or the repu- tation of the borrower (see the discussion in section 2.2.2, pp. 55 et seq.), (c) on the other hand, consumption choices of household members are equally likely to affect the perceived rationing status of the household (see the discussion in sec- tions 2.3.1 and 2.3.2, pp. 77 et seq.).
I first explain the specific model and then the general model. This is due to the fact that the specific model is further used in the household production analysis. The latter precedes the investment analysis, which employs the general Probit model.
In the specific model to be further used in production analysis, the dependent variable is credit rationing (yes/no) with regard to short-term loans taken during the production year 1998/1999. The specifity is that only partially rationed and completely rejected short-term borrowers are considered in this model, i.e. short-term borrowers who obtained a positive loan volume in 1998/1999 but not as much as desired and applicants for short-term loans who were fully rejected.76 This decision was made in order to maintain consistency with the output supply model which is based on constrained short-term applicants only (see section 3.3.4, p. 149). It implies that borrowers of long-term loans (rationed or not) are considered as non-borrowers in this model.
76
For completely rejected applicants the purpose of their loan application was known. This made their classification possible.
The following explanatory variables were chosen (expected signs are given in parentheses). Land owned (-) and land rented from private persons (+) were taken as indicators of the volume of collateralisable wealth, which is expected to play a key role in the presence of asymmetric information on the loan market. The age of newest tractor (+) was used as a simple measure of the quality of the collateralisable wealth. The years of farming practice indicate the experience of the farmer. They were also included in quadratic form, to allow an age depend- ent effect. Usually one would expect that up to a certain age, more years of farming experience imply a lower probability of being credit-rationed. A vari- able measuring the educational degree of the farmer (+) takes a value between one for the lowest and five for the highest degree. A dummy indicating the ex- pressed habit of regularly conversing with neighbours (-) is used as a measure of village-internal information flow. Conversation with neighbours might reduce the probability of being credit-rationed due to improved information availability for the local bank. It is taken as a proxy for how well the respondent is known in the village. On the consumption side, the number of adult males and females were taken as household characteristics. The effect of the number of adults in the household is indeterminate since a higher number of household members may both increase (via increased consumption) and decrease (via generation of unearned income) the liquidity shortage. The separate inclusion of males and females is motivated by the fact that Polish women tend to benefit more from social transfer payments than men (WORLD BANK 1995, p. 117). In addition, the
number of males or females may take on a signalling function for the bank. For example, more men in the households’ labour force may indicate that more re- sources are devoted to actual farm production as opposed to household work, and hence may imply a higher creditworthiness. All explanatory variables are assumed to be exogenous or predetermined at the time of loan application.
The general model directly employs the definition of subjective credit rationing
as given above, independently of any loan term. As the investment model dis-
cussed below, it covers a period of three years (1997-1999), which is neverthe- less treated as a single decision period. A problem with investment and borrow- ing events is that they occur relatively rarely, as was illustrated for short-term borrowing. Confining the anlysis to one calendar year, as done for the produc- tion model, would have resulted in a lot of zero observations. The chosen ap- proach of taking into account a period of three years considerably mitigates this effect. However, it has a number of consequences for the definition and choice of variables as compared to the specific Probit model presented previously. First, it was not necessary to restrict the analysis to a certain class of applicants only.
The dependent variable hence indicates by one all farm households which self- classified as credit-rationed according to the definition given in section 3.3.2, p. 145, irrespectively of the term structure of the loan. This decision led to an in- crease of the sample size of the investment model (see below). Second, the choice of explanatory variables was slightly modified. For land owned, I used the nominal value of land owned by the farm in the beginning of the investment period expressed in thousand zł, which was calculated by subtracting land in- vestment carried out in the period 1997-1999 from the stated value of owned land in 1999. Land quality is hence captured as well, at least as long it is re- flected in monetary land values. However, since the variable is based on farm- ers’ own assessments of their land values, individual biases are possible. The volume of land rented from private persons was excluded from the equation be- cause it is unplausible to regard this as exogenous and stable over a period of three years. The age of the youngest tractor was also regarded als endogenous in the investment model and hence excluded. Because they did not have significant effects, the educational and years of farming practice variables were excluded as well. However, a dummy was added indicating a previously rescheduled loan (+), which illustrates the credit history of the borrower. The rescheduled loan variable was taken from the interviews where respondents were asked whether they rescheduled the repayment of a loan in the past. This is regarded as evi- dence for a relatively poor reputation of the borrower. Furthermore, two dum- mies indicating the year in which the loan was approved by the bank were added to the model.77
Descriptive statistics of the variables and the estimation results are presented in subchapter 4.1.