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Effectiveness of selective credit policies: Alternative framework of evaluation
World Development. Vol. 16, No. 8, pp. 913-919, Printed in Great Britain.
Effectiveness Alternative
0305-750)(/8X $3.00 + 0.00 @ 1988 Pergamon Press plc
1988
of Selective Framework
Credit Policies: of Evaluation
M. 0. ODEDOKUN
University of Ilorin, Nigeria Summary. - Consequent upon the general criticisms of the conventional approaches used in evaluating the effectiveness of selective credit controls, we have suggested other approaches. The most emphasized of these is then applied to quarterly data for Nigeria over the period 1970/I to 198301. The focus is on the selective credit controls on export supply, import demand, manufacturing production and mining production. The controls are found to be effective in influencing only manufacturing production
there is real expenditure substitution which is the extent of substitution among various expenditure categories or commodity markets in utilizing a given source of finance, e.g., substitution between expenditures on consumer durables and other expenditures in the utilization of consumer loans. He devised a model of evaluating the existence of each type of substitution. This model has, however, been subjected to criticisms, e.g., by Maris (1981) who suggested and used Sim’s causality test which has also been criticized by Molho (1983) who offered no alternative framework. One objective of this study, therefore, is to integrate the Cohen approach with the causality test approach of Maris in such a way as to avoid the criticisms levied against each of the two. Also, by using data for Nigeria at the empirical stage, an additional evidence (the paucity of which has hardly been challenged) will be available on the effectiveness of selective credit policies in the setting of developing countries. the previous In Section 2, we survey approaches that we intend to build upon. Section 3 discusses our model. We briefly discuss, in Section 4, the applications and objectives of selective credit policies in the country (Nigeria) whose data are used in empirical estimation. Section 5 presents and discusses the empirical results while Section 6 gives the summary and conclusion.
1. INTRODUCTION Selective credit policies include those adopted by the authorities so as to channel a given flow of credit into the desired sectors and activities. A proximate goal of such policies is to re-channel credit flows in order to correct for the imperfections in the credit market. The efficacy of the policies in this regard can be tested by evaluating the degree of substitution among various types of credit in the lenders’ (banks’) portfolio and sometimes in the borrowers’ portfolio as typified by Silber’s (1970) study. Because this is outside the scope of the present study, we will not discuss it further. But it is one thing for an amount of credit flow to go to the desired borrowers and another thing for the borrowers to use the credits for the intended purpose. In other words, the real expenditure or real sector objective is not necessarily achieved by merely directing credit flows to a particular direction. The efficacy of selective credit controls in influencing expenditure flows is customarily tested for by evaluating the existence and extent of substitution among various sources and uses of finance in the portfolio of the ultimate spenders, i.e., borrowers. The greater the substitution, the less the effectiveness of the policies. Two aspects of the substitution have been identified by Cohen (1968; 1970). First, there is the substitution among sources of funds or financial markets in financing a particular type of expenditure or commodity market, e.g.. substitution between consumer loans and other loans in financing expenditures on consumer durables. This is called financial substitution. Second,
2. PREVIOUS In this section, 913
APPROACHES
we shall discuss both the substi-
WORLD
914
tution model as suggested by Cohen and causality model as suggested by Maris.
DEVELOPMENT
the
In evaluating the first type of substitution. i.e., between various sources of funds in financing a particular type of expenditure or financial substitution as discussed earlier, Cohen specifics a linear expenditure equation of the form: 7 E; = a(,; + aI;Ci + a>,Y
(1)
where E, and C, are the sectoral expenditure of type i and external financial source which custo-marily finances E,, respectively. while Y is a measure of internal finance which is income. He then substitutes total external finance C for C; thus: E, = bo; + b,,C + bz,Y
(2)
If the t-value of bIi in equation (2) is not less than the t-value of alj in equation (l), it means that the relationship between Ei and C, is not greater than that between E; and C which means that in financing E,, borrowers do not discriminate between Ci and (C-C;). This implies there is a high substitution between Cj and (C -CJ in the process of financing Ei and this weakens the effectiveness of selective credit controls. To test for the second type of substitution. i.e.. real expenditure substitution in the utilization of a particular source of fund, he specifies another equation of the form: E =
~0; +
C,;Ci + C,,Y
(3)
where E is the total capital/durable expenditure of which E, is just a component. Again, if the t-value of cl; in equation (3) is not less than the t-value of al, in equation (I), it means the relationship btween the expenditure category i and the “related” financial source Ci is not greater than the relationship between all the categories of expenditure E and the same financial source C,. This is a high level of real expenditure substitution as it suggests that borrowers do not discriminate between Ei and (E - E,) in the utilization of the financial source Ci. This also serves to weaken the effectiveness of selective credit controls. In each of tbe above cases, the interpretation would be reversed if the l-value of b,, (in the first case) and the r-value of cIi (in the second case) are each less than the r-value of al;. But as mentioned earlier, this approach has been criticized first by Maris (lY81) and later by
Molho (lY83), among others. One of the criticisms is that the relationship identified in equations (1) to (3) above might be due to the effects of expenditures on the credit flows instead of the other way round. This could be so if causation runs from expenditure flows to credit flows.’
(b) Causality model In order to identiFy whether C, causes E,, E, causes C, or a two-way causation between C, and E, exists, Maris (1981) has proposed Sim’s (1972) test which is a variant of causality tests. The essence of the Maris model can be discussed with equations (4) and (5) below which are the typical equations for performing the Sim’s test. E, = Ej (present values of Ci)
and past values
of C,; future (4)
C, = Cj (present values of E;)
and past values
of E,; future (5)
where E, and Ci are expenditures on consumer durables and consumer loans, respectively. In this type of formulation, Ci causes E; only if the future vaiues of Cj in equation (4) are insignificantly different from zero as a group in a situation when the present and past values of C, are significantly different from zero as a group. Similarly, Ei causes C, only if the future values of E; in equation (5) are insignificantly different from zero as a group whereas the present and past values of Ei are significantly different from zero as a group. By identifying a causation from C; to E, (in fact two-way causation is identified), Maris jumped to the conclusion that selective credit control on consumer loans is effective. Some of the fallacies contained in this approach have been identified by Molho (1083). In particular, the approach has only succeeded in demonstrating that C, is used in financing Ei but has not thrown a light on the more relevant issues of: (a) whether other forms of credit (C - C,) are as equally used as C, in financing E, in which case E, is being financed from a common or general pool of funds and (b) whether spenders do not discriminate between E, and other expenditure categories (E - E,) in utilizing C, in which case Ci is being used in financing a general pool of expenditures. In other words, by simply establishing that c’; causes E,, one has just demonstrated one of the necessary conditions for the effectiveness of
EFFECTIVENESS
selective credit ture flows.
controls
in influencing
3. THE PRESE,NT
OF SELECTIVE
expendi-
MODEL
(a) Cuusulity test The first step we propose is to identify the direction of causation between the objective variable E; and the associated credit variable C’, as has been done by Maris. To do this. we employ Granger’s (1969) causality test. (Of course, Sim’s causality test could as well be used, depending on which one is found simpler.) In performing the test, we regress the current value of the objective variable E; on its past values and the present and past values of the credit variable Ci as in equation (6) below. Causation from Ci to Ei is identified only if the present and past values of C, in equation (6) are significantly different from zero as a group. The appropriate significance test is the conventional F-test. In order to obviate the weakness of the causality tests arising from a consideration of only one causal variable without considering the influence of a series of other possible explanatory or causal variables, we have included trend variable T and seasonal dummy variables S,. ‘J‘Land S3 (S, = I for ith quarter and S; = 0 otherwise) in line with previous practices, e.g., Cuddington (1981) and Mixon, Pratt and Wallace (1979). This will capture some or most of the effects of the excluded causal variables. ~5, = E, (T, S,, Sz, &, past values of E,: present and past values of C,) (6) Next, depending on the results of equation (6) as discussed in Section 3b, we widen the scope of C, by substituting the total credit flow C for C; in equation (6) to get equation (7): E, = Ej (T, S,, &, S3, past values of Ej: present and past values of C) (7) Finally, in order to test for real expenditure substitution this time, we widen the scope of E; in equation (6) by substituting the total expenditure E for Ej as we have in equation (8): E = E (7’, SI, &. Sj, past values of E; present and past values of C;) (8)
CREDIT
915
POLICIES
(b) Cornnlents
on the possible outcomes
If the present and past values of C, in equation (6) are insignificantly different from zero as a group, this IS a conclusive indirect evidence that selective conlrols on C, are not effective in influencing 15,. No further test is required. If the present and past values of C, in equation (6) are significantly different from zero as suggested by our F-test. there is the possibility that the selective controls on Cj are effective in influencing I?,. To confirm or refute this possibility. we estimate equation (7) in order to examine financial substitution and equation (8) in order to test for the real expenditure substitution. If the F-value for the present and past values of C as a group in equation (7) is less than the F-value for the present and past values of Cl as a group in equation (6) this in interpreted to mean that the causal relationship of Cj on E; as identified in equation ((I) exceeds the causal relationship of C on Ei as identified in equation (7). Thus, there is little or no financial substitution in financing E,, i.e., spenders prefer C, to (C- Ci) in financing E,. The interpretation would be reversed if the F‘value in equation (7) is not less than the F-value in equation (6). Similarly, if the F-value for the present and past values of Cj as a group in equation (8) is less than the F-value for the present and past values of C’; as a group in equation (6), this implies that the causal relationship of C, on E; as identified in equation (6) exceeds the causal relationship of C, on total expenditure E as identified in equation (8). Therefore, there is little or no real expenditure substitution in the process of utilizing C,, i.e., borrowers prefer to use Ci on E, instead of (E - E,). Again, the interpretation would be reversed if the F-value in equation (8) is not less than the F-value in equation (6). Selective credit controls on C, would be most effective in influencing 6; when there is little or none of the financial and real expenditure substitution and only half as effective when there is much of one of the two types of substitution.
(c) Otherpossible
approaches
In addition to the model we have suggested above, the existing approaches can be adapted in order to avoid the criticisms levied against them in the following ways which we are, however, not going to estimate empirically in the present study: (i) Causality model The causality model discussed in Section 2b can be modified simply by substituting the vari-
WORLD
916
&k EjE equation
DEVELOPMENT-
and C,/C for Ei and C;, respectively, (4) to get equation (9):
EjE = F (present values of CJC)
in
and past values of CilC: future (9)
In this kind of model, we can infer whether or not an increase in the ratio of a particular source of external finance Cj to the total external finance C denoted by Ci/C does increase the ratio of the “associated” or “related” expenditure E, to the total expenditure E denoted by EdE. If there is a causation from Ci/C to E/E, the efficacy of selective credit controls on C, can be inferred outright without any further recourse to an examination of the presence or absence of financial and real expenditure substitutions. This approach does not suffer from the criticisms of the causality model discussed in Section 2b. (ii) Substitution model The substitution model discussed in Section 2a using equations (1) to (3) ceases to be valid in the presence of a causation running from Ej to C; in equation (l), from Ei to C in equation (2) and/or from E to Cj in equation (3). The obvious correction for this defect is to use a variant of simultaneous equation methods. Some empirical studies on selective credit controls using this approach can fortunately be spotted, e.g., Bitros’ (1981) study using the two-stage least square method of estimation.
4. APPLICATIONS AND OBJECTIVES OF SELECTIVE CREDIT POLICIES IN NIGERIA An understanding of the variables employed in estimating our model would require some knowledge of the applications and objectives of selective credit policies in the country whose data are employed.
(a) Applications
of the policies
Selective credit controls are applied by the government in the following three ways: First, the Central Bank applies selective credit controls on lenders through portfolio restrictions whereby commercial and merchant banks are given sectoral credit guidelines requiring them to give certain minimum percentages of their loans to the preferred or “productive” sectors like agriculture, manufacturing, mining and quarrying, real estate and construction, exports and service
sectors at the expense of non-preferred or “unproductive” activities like imports. Second, the Central Bank applies the controls on the credit itself by pegging the rates of interest on the loans meant for the preferred sectors below the rates of interest on other loans. This is to encourage the demand for the loans meant for the preferred sectors. Finally, the government sets up specialized financial institutions like the Federal Mortgage Bank, Agricultural and Cooperative Bank, and Industrial Development Bank which channel credits to the housing, agricultural and industrial sectors, respectively.
(b) The objectives of the policies By encouraging credit flows to the preferred sectors like manufacturing, agriculture, estate and construction, mining and quarrying etc., an obvious goal of the policies is to increase the outputs or productions resulting from these sectors at the expense of the others. This has to do with sectorai balanced growth in the economy. Second, by encouraging credit flows to export activities coupled with diversion of credit flows from import activities, the objectives of external balance, domestic employment etc. can be served. For these objectives to be accomplished, selective controls should influence the various sectoral outputs as well as export supply and import demand.
5. EMPIRICAL (a) Time domain,
RESULTS
dutN und their meusuremmts
According to what we have discussed in Section 4 concerning the objectives of selective credit policies, we have identified and will test the effectiveness of the selective credit policies on the following goal variables: index of manufacturing production; index of mining activities; import demand; and export supply. In other words, except in the case of import demand, we have substituted a measure of production or output flow for a measure of expenditure flow. Apart from the non-availability of data on the capital expenditure flows for manufacturing and mining sectors, it is apparent that the true goals of the selective credit policies are the sectoral outputs rather than the capital expenditures that only partly contribute to the outputs. An increase in the capital expenditures does not necessarily bring about an increased output. We. however, wish to indicate that the number and types of the
EFFECTIVENESS
OF SELECTIVE
goal variables covered in this study are seriously limited by data non-availability. The data are all measured as monthly averages for each quarter. For example, let xi be the variable for the ith month of a quarter, the variable used is X = % (x, + x2 + x3). For the credit variables, the first-difference of 2 is used throughout so as to convert the stock of credit to the flow of credit. For the goal variables, both the level form and the first-difference form are used. All the primary data are obtained from the various issues of Economic and Financial Review and Annual Report and Statement of Accounts both of which are published by the Central Bank of Nigeria. The study covers the period 1970/I to 198301 giving SO quarterly observations.
(b)
A discussion
of the results
We now discuss the results taking each of the goal variables in turn. As the F-statistics presented in the first column in Table 1 move in line with those presented in the second column (for first-difference of the goal variables), our discus-
Table
1. Empiricul
Export supply Export supply Import demand Import demand Import demand Index of manufacturing production Index of manufacturing production Index of industrial production Index of mining production Index of mining production Index of industrial production
sion focuses column. (i)
Export
POLICIES
on only
917
one
column
-
the first
supply
The F-statistic in equation (10a) indicates that export credit is not a causal variable on the export supply. This is a sufficient condition for impotence of selective controls on export credit. Even if we cast the insignificance of the F-statistic aside, a comparison of the F-statistics in both equations (lOa) and ( IOb) shows that export credit is not more causally related to export supply than are other forms of credit, i.e., no financial discrimination in favor of export credit in the process of financing export supply. The same conclusion can be reached by comparing the F-statistics in equations (lOa) and (ll), i.e., export credit is not more causally related to export supply than are other forms of commercial credit (commercial credit is the sum of credits for export, import and domestic trade).2 (ii) Import demand The results and the comments/interpretations
results of causal influence of the credit flows on goal variables
Goal variable
Export supply
CREDIT
No. of lags for the goal variable
Credit variable
Calculated F-statistic*
Export credit Total credit Commercial credit Import credit Total credit Commercial credit
Level 0.88 1.11 1.50 0.45 2.86$ 1.72
Firstdifference 1.57 1.28 2.89$ 0.69 4.OO(i 1.56
Manufacturing
1.32
Total
credit
Manufacturing Mining Total Mining
credit
credit
credit credit credit
5
Degrees of freedom
Equation number
v,t
vz
7 7 7 7
37 37 37 36
(lOal (1Obj (11) (12a)
7
36
‘::3”,
1.24
7
36
(14a)
1.12
1.06
7
36
(14b)
0.97
0.68
7
37
(14c)
3.265
3.649:
7
37
(15a)
4.149
4.059:
7
37
(15b)
1.69
2.05
7
37
(15c)
*The result for the undifferenced or level form of goal variables is presented in the first column while the result for the first-difference form is presented in the second column. tThe V, degree of freedom also equals the number of lags (including the current values) for the related credit variable. $Significant at 5% level. 9Significant at 1% level.
01x
WORLD
are exactly as in the case of export scribed.
DEVELOPMENT
supply just de-
(iii) In&x of munufacturing production The F-statistic in equation (14a) shows that manufacturing credit is just a rather weak causal production. If, variable on manufacturing however, we compare the F-statistics in equations (14a) and (14b), we find that manufacturing credit is more causally related to manufactunng production than are other forms of credit combined, i.e., little or no financial substitution is found. Similarly, if we compare the F-statistics in equations (14a) and (14c), we find that manufacturing credit is more causally related to manufacturing production than it is (insignificantly) related to total industrial production (consisting of manufacturing, mining and electricity/power productions), i.e., little or no real (production) substitution is spotted. The absence of financial and real (production) substitutions suggests that selective controls on manufacturing credit can influence manufacturing production. (iv) Index of mining production The F-statistic in equation (Isa) shows that mining credit is a causal variable on mining production. However, on comparing the F-statistics in equations (15a) and (Eb), we find that the causal relationship of mining credit to mining production is not greater than the causal relationship of other forms of (non-mining) credit to mining production, i.e., no financial discrimination in favor of mining credit in the process of financing mining production. On the other hand, if we compare equations (15) and (1%). we find that mining credit is more causally related to mining production than it is related to total industrial production. This implies that there is little or no real (production) substitution. On the whole, the absence of financial discrimination in favor of
mining credit casts much doubt on the effectiveness of selective conlrols on mining credit.
(c) Any cnraal influence
of goal vuriuhles credit flo ws?
Ott
In Table 2, we report the results of testing for causality of the goal variables (level form only, no first-differencing) on credit flows. There, it can be seen that in most cases, causality runs from goal variables to credit variables. This implies that the naive form of (Cohen) substitution model as presented in Section 2a would be unsuitable in describing and testing the effectiveness of selective credit policies. The credits traded in the market are largely demanddetermined
6. SUMMARY
AND CONCLUSION
The paper focuses on the effectiveness of seleccredit real tive controls in influencing expenditures/outputs. We discussed the conventional models used in evaluating the effectiveness together with the criticisms often levied against them. We therefore suggested alternative approaches that are not susceptible to the criticisms. The most emphasized of the approaches was subsequently applied to Nigerian quarterly data over the period 1070/I to lOX?/II so as to examine the efficacy of the selective controls on export, immanufacturing and mining credits in port. influencing export supply, import demand, manufacturing production and mining activity, respectively. The empirical findings suggest that, except for the controls on manufacturing credit, the controls are not effective.
NOTES I.
The
same
criticism applies to the expenditure the efficacy of selective credit conby the work of Hamburger and Zwick
model of evaluating trots as typified (1979). 2.
We do not examine
the issue of real (expenditure)
substitution in the cases of export supply and import demand as it is inappropriate to do so, considering the non-existence of relevant data. We arc unable to widen the scope of export supply and import demand to total supply and total demand in the economy, respectively, as would he required.
REFERENCES Bitros, G. C., “The fungibility factor in credit and the question of the efficacy of slective controls,” Oxford Economic Papers, No. 3 (Nov. 1981). pp. 4.59-477. Cohen, J., “Integrating the real and financial via the
linkage of financial flow,” The Journal of Finrmce, Vol. 23 (March 196X). pp. 2-27. Cohen, J., “Direct versus indirect controls as instruments of monetary policy,” The Quarrerly Review of
EFFECTIVENESS
Table
Export credit Total credit Commercial credit Import credit Total credit Commercial credit Manufacturing credit credit
Manufacturing Mining Total Mining
credit credit credit
credit
CREDIT
019
POLICIES
2. Causality of goal variables on credit variables
Goal variable
Credit variable
Total
OF SELECTIVE
No. of lags for the credit variable
Export supply Export supply Export supply Import demand Import demand Import demand Index of manufacturing production Index of manufacturing production Index of industrial production Index of mining production Index of mining production Index of industrial production
Calculated F-statistic*
Degrees of freedom
Equation number
v,
vz
1.66 4.63t s.17”r 3.53t 2.64 3.15t
6 6 6 7 7 7
37 37 37 36 36 36
(16) (17j (18) (19) (20) (21)
6
4.721-
7
36
(22)
6
3.62i
7
36
(23)
6
3.s3t
6
37
(24)
6
0.99
6
37
(25)
6
1.23
6
37
(26)
6
1.54
6
37
(27)
*For level form of goal variables. tsignificant at 1% significance level. Economic.s and Business, Vol. 10. No. 3 (October 1970), pp. 25.-34. Cuddington. J. T., “Money. income and causality in the UK: An empirical reexamination,” Journal of Money, Credit and Banking, Vol. 13 (Aug. 1981). pp. 342-35 I. Granger. C. W. J., “Investigating causal relationships by Econometric models and cross-spectral methods,” Econometrica, Vol. 37 (1969), pp. 424-438. Hamburger, M. J., and B. Zwick, “The efficacy of selective credit policies: An alternative test,” Journal of Money, Credit and Banking, Vol. 11 (Feb. 1979). pp. 10~110. Maris, B. A., “Indirect evidence on the efficacy of selective credit controls: The case of consumer
credit.” Journul of Money. Credit and Bunking, Vol. 13 (August, 1981), pp. 38&3YO. Mixon, B., L. J. Pratt, and M. S. Wallace, “Cross national money to income causality: U.S. money to U.K. income,” Journul of Money, Credit und Banking, Vol. 11 (November 1979), pp. 419-426. Molho, L. E., “On testing the efficacy of selective credit controls: A comment,” Journal of Money, Credit and Banking. Vol. 15 (Feb. 1983), pp. 120-122. Silber, W. L., Portfolio Behaviour of Financial Institutions (New York: Holt, Reinhart and Winston, 1970). Sims, C., “Money, income and causality,” American Economic Revieul, Vol. 62 (Sept. 1972), pp. 54&552.
Effectiveness Alternative
0305-750)(/8X $3.00 + 0.00 @ 1988 Pergamon Press plc
1988
of Selective Framework
Credit Policies: of Evaluation
M. 0. ODEDOKUN
University of Ilorin, Nigeria Summary. - Consequent upon the general criticisms of the conventional approaches used in evaluating the effectiveness of selective credit controls, we have suggested other approaches. The most emphasized of these is then applied to quarterly data for Nigeria over the period 1970/I to 198301. The focus is on the selective credit controls on export supply, import demand, manufacturing production and mining production. The controls are found to be effective in influencing only manufacturing production
there is real expenditure substitution which is the extent of substitution among various expenditure categories or commodity markets in utilizing a given source of finance, e.g., substitution between expenditures on consumer durables and other expenditures in the utilization of consumer loans. He devised a model of evaluating the existence of each type of substitution. This model has, however, been subjected to criticisms, e.g., by Maris (1981) who suggested and used Sim’s causality test which has also been criticized by Molho (1983) who offered no alternative framework. One objective of this study, therefore, is to integrate the Cohen approach with the causality test approach of Maris in such a way as to avoid the criticisms levied against each of the two. Also, by using data for Nigeria at the empirical stage, an additional evidence (the paucity of which has hardly been challenged) will be available on the effectiveness of selective credit policies in the setting of developing countries. the previous In Section 2, we survey approaches that we intend to build upon. Section 3 discusses our model. We briefly discuss, in Section 4, the applications and objectives of selective credit policies in the country (Nigeria) whose data are used in empirical estimation. Section 5 presents and discusses the empirical results while Section 6 gives the summary and conclusion.
1. INTRODUCTION Selective credit policies include those adopted by the authorities so as to channel a given flow of credit into the desired sectors and activities. A proximate goal of such policies is to re-channel credit flows in order to correct for the imperfections in the credit market. The efficacy of the policies in this regard can be tested by evaluating the degree of substitution among various types of credit in the lenders’ (banks’) portfolio and sometimes in the borrowers’ portfolio as typified by Silber’s (1970) study. Because this is outside the scope of the present study, we will not discuss it further. But it is one thing for an amount of credit flow to go to the desired borrowers and another thing for the borrowers to use the credits for the intended purpose. In other words, the real expenditure or real sector objective is not necessarily achieved by merely directing credit flows to a particular direction. The efficacy of selective credit controls in influencing expenditure flows is customarily tested for by evaluating the existence and extent of substitution among various sources and uses of finance in the portfolio of the ultimate spenders, i.e., borrowers. The greater the substitution, the less the effectiveness of the policies. Two aspects of the substitution have been identified by Cohen (1968; 1970). First, there is the substitution among sources of funds or financial markets in financing a particular type of expenditure or commodity market, e.g.. substitution between consumer loans and other loans in financing expenditures on consumer durables. This is called financial substitution. Second,
2. PREVIOUS In this section, 913
APPROACHES
we shall discuss both the substi-
WORLD
914
tution model as suggested by Cohen and causality model as suggested by Maris.
DEVELOPMENT
the
In evaluating the first type of substitution. i.e., between various sources of funds in financing a particular type of expenditure or financial substitution as discussed earlier, Cohen specifics a linear expenditure equation of the form: 7 E; = a(,; + aI;Ci + a>,Y
(1)
where E, and C, are the sectoral expenditure of type i and external financial source which custo-marily finances E,, respectively. while Y is a measure of internal finance which is income. He then substitutes total external finance C for C; thus: E, = bo; + b,,C + bz,Y
(2)
If the t-value of bIi in equation (2) is not less than the t-value of alj in equation (l), it means that the relationship between Ei and C, is not greater than that between E; and C which means that in financing E,, borrowers do not discriminate between Ci and (C-C;). This implies there is a high substitution between Cj and (C -CJ in the process of financing Ei and this weakens the effectiveness of selective credit controls. To test for the second type of substitution. i.e.. real expenditure substitution in the utilization of a particular source of fund, he specifies another equation of the form: E =
~0; +
C,;Ci + C,,Y
(3)
where E is the total capital/durable expenditure of which E, is just a component. Again, if the t-value of cl; in equation (3) is not less than the t-value of al, in equation (I), it means the relationship btween the expenditure category i and the “related” financial source Ci is not greater than the relationship between all the categories of expenditure E and the same financial source C,. This is a high level of real expenditure substitution as it suggests that borrowers do not discriminate between Ei and (E - E,) in the utilization of the financial source Ci. This also serves to weaken the effectiveness of selective credit controls. In each of tbe above cases, the interpretation would be reversed if the l-value of b,, (in the first case) and the r-value of cIi (in the second case) are each less than the r-value of al;. But as mentioned earlier, this approach has been criticized first by Maris (lY81) and later by
Molho (lY83), among others. One of the criticisms is that the relationship identified in equations (1) to (3) above might be due to the effects of expenditures on the credit flows instead of the other way round. This could be so if causation runs from expenditure flows to credit flows.’
(b) Causality model In order to identiFy whether C, causes E,, E, causes C, or a two-way causation between C, and E, exists, Maris (1981) has proposed Sim’s (1972) test which is a variant of causality tests. The essence of the Maris model can be discussed with equations (4) and (5) below which are the typical equations for performing the Sim’s test. E, = Ej (present values of Ci)
and past values
of C,; future (4)
C, = Cj (present values of E;)
and past values
of E,; future (5)
where E, and Ci are expenditures on consumer durables and consumer loans, respectively. In this type of formulation, Ci causes E; only if the future vaiues of Cj in equation (4) are insignificantly different from zero as a group in a situation when the present and past values of C, are significantly different from zero as a group. Similarly, Ei causes C, only if the future values of E; in equation (5) are insignificantly different from zero as a group whereas the present and past values of Ei are significantly different from zero as a group. By identifying a causation from C; to E, (in fact two-way causation is identified), Maris jumped to the conclusion that selective credit control on consumer loans is effective. Some of the fallacies contained in this approach have been identified by Molho (1083). In particular, the approach has only succeeded in demonstrating that C, is used in financing Ei but has not thrown a light on the more relevant issues of: (a) whether other forms of credit (C - C,) are as equally used as C, in financing E, in which case E, is being financed from a common or general pool of funds and (b) whether spenders do not discriminate between E, and other expenditure categories (E - E,) in utilizing C, in which case Ci is being used in financing a general pool of expenditures. In other words, by simply establishing that c’; causes E,, one has just demonstrated one of the necessary conditions for the effectiveness of
EFFECTIVENESS
selective credit ture flows.
controls
in influencing
3. THE PRESE,NT
OF SELECTIVE
expendi-
MODEL
(a) Cuusulity test The first step we propose is to identify the direction of causation between the objective variable E; and the associated credit variable C’, as has been done by Maris. To do this. we employ Granger’s (1969) causality test. (Of course, Sim’s causality test could as well be used, depending on which one is found simpler.) In performing the test, we regress the current value of the objective variable E; on its past values and the present and past values of the credit variable Ci as in equation (6) below. Causation from Ci to Ei is identified only if the present and past values of C, in equation (6) are significantly different from zero as a group. The appropriate significance test is the conventional F-test. In order to obviate the weakness of the causality tests arising from a consideration of only one causal variable without considering the influence of a series of other possible explanatory or causal variables, we have included trend variable T and seasonal dummy variables S,. ‘J‘Land S3 (S, = I for ith quarter and S; = 0 otherwise) in line with previous practices, e.g., Cuddington (1981) and Mixon, Pratt and Wallace (1979). This will capture some or most of the effects of the excluded causal variables. ~5, = E, (T, S,, Sz, &, past values of E,: present and past values of C,) (6) Next, depending on the results of equation (6) as discussed in Section 3b, we widen the scope of C, by substituting the total credit flow C for C; in equation (6) to get equation (7): E, = Ej (T, S,, &, S3, past values of Ej: present and past values of C) (7) Finally, in order to test for real expenditure substitution this time, we widen the scope of E; in equation (6) by substituting the total expenditure E for Ej as we have in equation (8): E = E (7’, SI, &. Sj, past values of E; present and past values of C;) (8)
CREDIT
915
POLICIES
(b) Cornnlents
on the possible outcomes
If the present and past values of C, in equation (6) are insignificantly different from zero as a group, this IS a conclusive indirect evidence that selective conlrols on C, are not effective in influencing 15,. No further test is required. If the present and past values of C, in equation (6) are significantly different from zero as suggested by our F-test. there is the possibility that the selective controls on Cj are effective in influencing I?,. To confirm or refute this possibility. we estimate equation (7) in order to examine financial substitution and equation (8) in order to test for the real expenditure substitution. If the F-value for the present and past values of C as a group in equation (7) is less than the F-value for the present and past values of Cl as a group in equation (6) this in interpreted to mean that the causal relationship of Cj on E; as identified in equation ((I) exceeds the causal relationship of C on Ei as identified in equation (7). Thus, there is little or no financial substitution in financing E,, i.e., spenders prefer C, to (C- Ci) in financing E,. The interpretation would be reversed if the F‘value in equation (7) is not less than the F-value in equation (6). Similarly, if the F-value for the present and past values of Cj as a group in equation (8) is less than the F-value for the present and past values of C’; as a group in equation (6), this implies that the causal relationship of C, on E; as identified in equation (6) exceeds the causal relationship of C, on total expenditure E as identified in equation (8). Therefore, there is little or no real expenditure substitution in the process of utilizing C,, i.e., borrowers prefer to use Ci on E, instead of (E - E,). Again, the interpretation would be reversed if the F-value in equation (8) is not less than the F-value in equation (6). Selective credit controls on C, would be most effective in influencing 6; when there is little or none of the financial and real expenditure substitution and only half as effective when there is much of one of the two types of substitution.
(c) Otherpossible
approaches
In addition to the model we have suggested above, the existing approaches can be adapted in order to avoid the criticisms levied against them in the following ways which we are, however, not going to estimate empirically in the present study: (i) Causality model The causality model discussed in Section 2b can be modified simply by substituting the vari-
WORLD
916
&k EjE equation
DEVELOPMENT-
and C,/C for Ei and C;, respectively, (4) to get equation (9):
EjE = F (present values of CJC)
in
and past values of CilC: future (9)
In this kind of model, we can infer whether or not an increase in the ratio of a particular source of external finance Cj to the total external finance C denoted by Ci/C does increase the ratio of the “associated” or “related” expenditure E, to the total expenditure E denoted by EdE. If there is a causation from Ci/C to E/E, the efficacy of selective credit controls on C, can be inferred outright without any further recourse to an examination of the presence or absence of financial and real expenditure substitutions. This approach does not suffer from the criticisms of the causality model discussed in Section 2b. (ii) Substitution model The substitution model discussed in Section 2a using equations (1) to (3) ceases to be valid in the presence of a causation running from Ej to C; in equation (l), from Ei to C in equation (2) and/or from E to Cj in equation (3). The obvious correction for this defect is to use a variant of simultaneous equation methods. Some empirical studies on selective credit controls using this approach can fortunately be spotted, e.g., Bitros’ (1981) study using the two-stage least square method of estimation.
4. APPLICATIONS AND OBJECTIVES OF SELECTIVE CREDIT POLICIES IN NIGERIA An understanding of the variables employed in estimating our model would require some knowledge of the applications and objectives of selective credit policies in the country whose data are employed.
(a) Applications
of the policies
Selective credit controls are applied by the government in the following three ways: First, the Central Bank applies selective credit controls on lenders through portfolio restrictions whereby commercial and merchant banks are given sectoral credit guidelines requiring them to give certain minimum percentages of their loans to the preferred or “productive” sectors like agriculture, manufacturing, mining and quarrying, real estate and construction, exports and service
sectors at the expense of non-preferred or “unproductive” activities like imports. Second, the Central Bank applies the controls on the credit itself by pegging the rates of interest on the loans meant for the preferred sectors below the rates of interest on other loans. This is to encourage the demand for the loans meant for the preferred sectors. Finally, the government sets up specialized financial institutions like the Federal Mortgage Bank, Agricultural and Cooperative Bank, and Industrial Development Bank which channel credits to the housing, agricultural and industrial sectors, respectively.
(b) The objectives of the policies By encouraging credit flows to the preferred sectors like manufacturing, agriculture, estate and construction, mining and quarrying etc., an obvious goal of the policies is to increase the outputs or productions resulting from these sectors at the expense of the others. This has to do with sectorai balanced growth in the economy. Second, by encouraging credit flows to export activities coupled with diversion of credit flows from import activities, the objectives of external balance, domestic employment etc. can be served. For these objectives to be accomplished, selective controls should influence the various sectoral outputs as well as export supply and import demand.
5. EMPIRICAL (a) Time domain,
RESULTS
dutN und their meusuremmts
According to what we have discussed in Section 4 concerning the objectives of selective credit policies, we have identified and will test the effectiveness of the selective credit policies on the following goal variables: index of manufacturing production; index of mining activities; import demand; and export supply. In other words, except in the case of import demand, we have substituted a measure of production or output flow for a measure of expenditure flow. Apart from the non-availability of data on the capital expenditure flows for manufacturing and mining sectors, it is apparent that the true goals of the selective credit policies are the sectoral outputs rather than the capital expenditures that only partly contribute to the outputs. An increase in the capital expenditures does not necessarily bring about an increased output. We. however, wish to indicate that the number and types of the
EFFECTIVENESS
OF SELECTIVE
goal variables covered in this study are seriously limited by data non-availability. The data are all measured as monthly averages for each quarter. For example, let xi be the variable for the ith month of a quarter, the variable used is X = % (x, + x2 + x3). For the credit variables, the first-difference of 2 is used throughout so as to convert the stock of credit to the flow of credit. For the goal variables, both the level form and the first-difference form are used. All the primary data are obtained from the various issues of Economic and Financial Review and Annual Report and Statement of Accounts both of which are published by the Central Bank of Nigeria. The study covers the period 1970/I to 198301 giving SO quarterly observations.
(b)
A discussion
of the results
We now discuss the results taking each of the goal variables in turn. As the F-statistics presented in the first column in Table 1 move in line with those presented in the second column (for first-difference of the goal variables), our discus-
Table
1. Empiricul
Export supply Export supply Import demand Import demand Import demand Index of manufacturing production Index of manufacturing production Index of industrial production Index of mining production Index of mining production Index of industrial production
sion focuses column. (i)
Export
POLICIES
on only
917
one
column
-
the first
supply
The F-statistic in equation (10a) indicates that export credit is not a causal variable on the export supply. This is a sufficient condition for impotence of selective controls on export credit. Even if we cast the insignificance of the F-statistic aside, a comparison of the F-statistics in both equations (lOa) and ( IOb) shows that export credit is not more causally related to export supply than are other forms of credit, i.e., no financial discrimination in favor of export credit in the process of financing export supply. The same conclusion can be reached by comparing the F-statistics in equations (lOa) and (ll), i.e., export credit is not more causally related to export supply than are other forms of commercial credit (commercial credit is the sum of credits for export, import and domestic trade).2 (ii) Import demand The results and the comments/interpretations
results of causal influence of the credit flows on goal variables
Goal variable
Export supply
CREDIT
No. of lags for the goal variable
Credit variable
Calculated F-statistic*
Export credit Total credit Commercial credit Import credit Total credit Commercial credit
Level 0.88 1.11 1.50 0.45 2.86$ 1.72
Firstdifference 1.57 1.28 2.89$ 0.69 4.OO(i 1.56
Manufacturing
1.32
Total
credit
Manufacturing Mining Total Mining
credit
credit
credit credit credit
5
Degrees of freedom
Equation number
v,t
vz
7 7 7 7
37 37 37 36
(lOal (1Obj (11) (12a)
7
36
‘::3”,
1.24
7
36
(14a)
1.12
1.06
7
36
(14b)
0.97
0.68
7
37
(14c)
3.265
3.649:
7
37
(15a)
4.149
4.059:
7
37
(15b)
1.69
2.05
7
37
(15c)
*The result for the undifferenced or level form of goal variables is presented in the first column while the result for the first-difference form is presented in the second column. tThe V, degree of freedom also equals the number of lags (including the current values) for the related credit variable. $Significant at 5% level. 9Significant at 1% level.
01x
WORLD
are exactly as in the case of export scribed.
DEVELOPMENT
supply just de-
(iii) In&x of munufacturing production The F-statistic in equation (14a) shows that manufacturing credit is just a rather weak causal production. If, variable on manufacturing however, we compare the F-statistics in equations (14a) and (14b), we find that manufacturing credit is more causally related to manufactunng production than are other forms of credit combined, i.e., little or no financial substitution is found. Similarly, if we compare the F-statistics in equations (14a) and (14c), we find that manufacturing credit is more causally related to manufacturing production than it is (insignificantly) related to total industrial production (consisting of manufacturing, mining and electricity/power productions), i.e., little or no real (production) substitution is spotted. The absence of financial and real (production) substitutions suggests that selective controls on manufacturing credit can influence manufacturing production. (iv) Index of mining production The F-statistic in equation (Isa) shows that mining credit is a causal variable on mining production. However, on comparing the F-statistics in equations (15a) and (Eb), we find that the causal relationship of mining credit to mining production is not greater than the causal relationship of other forms of (non-mining) credit to mining production, i.e., no financial discrimination in favor of mining credit in the process of financing mining production. On the other hand, if we compare equations (15) and (1%). we find that mining credit is more causally related to mining production than it is related to total industrial production. This implies that there is little or no real (production) substitution. On the whole, the absence of financial discrimination in favor of
mining credit casts much doubt on the effectiveness of selective conlrols on mining credit.
(c) Any cnraal influence
of goal vuriuhles credit flo ws?
Ott
In Table 2, we report the results of testing for causality of the goal variables (level form only, no first-differencing) on credit flows. There, it can be seen that in most cases, causality runs from goal variables to credit variables. This implies that the naive form of (Cohen) substitution model as presented in Section 2a would be unsuitable in describing and testing the effectiveness of selective credit policies. The credits traded in the market are largely demanddetermined
6. SUMMARY
AND CONCLUSION
The paper focuses on the effectiveness of seleccredit real tive controls in influencing expenditures/outputs. We discussed the conventional models used in evaluating the effectiveness together with the criticisms often levied against them. We therefore suggested alternative approaches that are not susceptible to the criticisms. The most emphasized of the approaches was subsequently applied to Nigerian quarterly data over the period 1070/I to lOX?/II so as to examine the efficacy of the selective controls on export, immanufacturing and mining credits in port. influencing export supply, import demand, manufacturing production and mining activity, respectively. The empirical findings suggest that, except for the controls on manufacturing credit, the controls are not effective.
NOTES I.
The
same
criticism applies to the expenditure the efficacy of selective credit conby the work of Hamburger and Zwick
model of evaluating trots as typified (1979). 2.
We do not examine
the issue of real (expenditure)
substitution in the cases of export supply and import demand as it is inappropriate to do so, considering the non-existence of relevant data. We arc unable to widen the scope of export supply and import demand to total supply and total demand in the economy, respectively, as would he required.
REFERENCES Bitros, G. C., “The fungibility factor in credit and the question of the efficacy of slective controls,” Oxford Economic Papers, No. 3 (Nov. 1981). pp. 4.59-477. Cohen, J., “Integrating the real and financial via the
linkage of financial flow,” The Journal of Finrmce, Vol. 23 (March 196X). pp. 2-27. Cohen, J., “Direct versus indirect controls as instruments of monetary policy,” The Quarrerly Review of
EFFECTIVENESS
Table
Export credit Total credit Commercial credit Import credit Total credit Commercial credit Manufacturing credit credit
Manufacturing Mining Total Mining
credit credit credit
credit
CREDIT
019
POLICIES
2. Causality of goal variables on credit variables
Goal variable
Credit variable
Total
OF SELECTIVE
No. of lags for the credit variable
Export supply Export supply Export supply Import demand Import demand Import demand Index of manufacturing production Index of manufacturing production Index of industrial production Index of mining production Index of mining production Index of industrial production
Calculated F-statistic*
Degrees of freedom
Equation number
v,
vz
1.66 4.63t s.17”r 3.53t 2.64 3.15t
6 6 6 7 7 7
37 37 37 36 36 36
(16) (17j (18) (19) (20) (21)
6
4.721-
7
36
(22)
6
3.62i
7
36
(23)
6
3.s3t
6
37
(24)
6
0.99
6
37
(25)
6
1.23
6
37
(26)
6
1.54
6
37
(27)
*For level form of goal variables. tsignificant at 1% significance level. Economic.s and Business, Vol. 10. No. 3 (October 1970), pp. 25.-34. Cuddington. J. T., “Money. income and causality in the UK: An empirical reexamination,” Journal of Money, Credit and Banking, Vol. 13 (Aug. 1981). pp. 342-35 I. Granger. C. W. J., “Investigating causal relationships by Econometric models and cross-spectral methods,” Econometrica, Vol. 37 (1969), pp. 424-438. Hamburger, M. J., and B. Zwick, “The efficacy of selective credit policies: An alternative test,” Journal of Money, Credit and Banking, Vol. 11 (Feb. 1979). pp. 10~110. Maris, B. A., “Indirect evidence on the efficacy of selective credit controls: The case of consumer
credit.” Journul of Money. Credit and Bunking, Vol. 13 (August, 1981), pp. 38&3YO. Mixon, B., L. J. Pratt, and M. S. Wallace, “Cross national money to income causality: U.S. money to U.K. income,” Journul of Money, Credit und Banking, Vol. 11 (November 1979), pp. 419-426. Molho, L. E., “On testing the efficacy of selective credit controls: A comment,” Journal of Money, Credit and Banking. Vol. 15 (Feb. 1983), pp. 120-122. Silber, W. L., Portfolio Behaviour of Financial Institutions (New York: Holt, Reinhart and Winston, 1970). Sims, C., “Money, income and causality,” American Economic Revieul, Vol. 62 (Sept. 1972), pp. 54&552.