Results and Interpretation.

The econometric estimates on a time series basis show that aid does have an

effect on fiscal behaviour, but mainly through private investment and the dynamic

linkages stemming from lagged government consumption and income44. Tables 3.4.2.1,

3.4.2.2 and 3.4.2.3 present the results for the three alternative models: Model I uses

grants and loans, Model II bilateral and multilateral aid, and Model in total aid.

Diagnostic test are reported in appendix 5. Tables 3.4.2.4, 3.4.2.5 and 3.4.2.6 report the

calculated reduced form coefficients for each model expressed as percentage changes

following a 1% shock in aid45. These tables show aid’s effect on the budget, on GDP

and on private consumption on impact and after the second period, when dynamic

linkages have started to operate (i.e. short run and medium run effects). The full long

run dynamics of the estimated model is presented in the next paragraph.

Grants and loans do not exhibit a significant direct impact on fiscal behaviour.

However, the interaction between fiscal behaviour and private investment decisions

channels an indirect effect. Reduced form coefficients show that grants tend to

discourage taxation efforts and to deflect resources away from public investment and to

44 Instrumental Variables estimations were carried out with PcGive 8.0 while 3SLS estimations were implemented with PcFiml 8.0.

45 The corresponding reduced form coefficients in levels are reported in Appendix 3.

government consumption. On the contrary, loans positively affect government revenues

and investment, although in the short run the impact on taxation is negative. However,

the correct interpretation of the results should be based on statistically significant

parameters, as pointed out in paragraph 3.2.3. The tables show both sets of results to

highlight how misleading it would be to rely on all parameters, significant and

insignificant.

In fact, if we consider significant parameters only, there is no evidence of a

short term impact either on fiscal behaviour or on income and consumption. Once

dynamic linkages initiate a multiplier process, statistically significant effects emerge.

At the end of the second period following the aid shock, a 1% increase in grants

discourages taxation efforts and leads to a 4% drop in revenues. Similarly, on the

expenditure side, investment and consumption will be curtailed, although investment

will be reduced by less than consumption. Thus, grants seem to partly finance a

tightening in fiscal policy aimed at reducing the size of government and exhibit a pro-

investment bias. As for loans, the effect is reversed. A 1% rupiah increase in loans

results in 1.7% increase in tax revenues leading to higher public investment and

consumption. Even if public consumption grows more than investment, investment still

increases more than proportionately with respect to the increase in loans. This implies

that loans pro-consumption bias is not high enough to cause fungibility.

An explanation for the opposite effects of grants and loans may lie in the

disincentive effect of receiving non-repayable grant money, which are used by policy

makers to reduce the tax burden. Given the budget constraint, reduced finances result in

reduced outlays and a general depressing effect on the whole economy. On the other

that they induce an intensification of tax collection efforts. Although increased

revenues will finance public investment less than public consumption, the whole

economy will benefit as a result of the multiplier process. As shown in table 3A.2.4 to

3.4.2.6, the effect on income and private consumption are both negative in the grant

case and positive in the loans case. The size of the coefficients in general is due to the

small figures for aid as a proportion of GDP, which ranged from 1% to 6.2%. In

particular, grants never exceeded 1% of GDP and loans ranged between 1% and 5.2%.

Interestingly, the use of the GOV dataset, yields different results. The impact of

aid is direct and not channelled via private investment. Grants have a significant

negative impact, while loans exhibit insignificant effects.

Model II and Model IE perform less well than Model I. The impact effect of aid

is only direct as coefficients for bilateral, multilateral and total aid in the private

investment equation are insignificant. Moreover, there is no evidence of long run

effects on the budget, on income and on private consumption from multilateral aid. In

both models, bilateral and total aid negatively affect, on impact, only taxation, public

consumption and income, while there is no statistical evidence for their effects on

public investment and private consumption. However, at the end of the second period,

not only T and Gc but also Ig will be curtailed as a consequence of an increase in

bilateral or total aid. In both cases, there is a pro-investment bias, given that

government investment will be reduced by .8% and .9%, respectively, against a fall in

public consumption of 1.3% and 1.2%, respectively.

Once again results from the corresponding regressions using the GOV dataset

differ. Government taxation efforts are significantly influenced by bilateral and

multilateral aid. Bilateral aid has a direct and negative impact only. On the contrary,

multilateral aid positively affects taxes, both directly and indirectly. Surprisingly, public

investment does not interact with private investment decisions and the only aid effect

comes from bilateral aid and is negative. When we turn to total aid results the only

significant effect is the direct negative impact on taxation efforts.

As for general diagnostics, the three model perform well when estimated with

the IMF-OECD dataset. The estimated equations (restricted reduced forms of the

structural model) appear to parsimoniously encompass their respective unrestricted

reduced forms. There is also no evidence of problems of autocorrelation and

misspecification. On the contrary, for all the three models estimated with GOV dataset,

diagnostic tests do not allow us to accept the null of the equations parsimoniously

encompassing the unrestricted reduced forms. There are also problems of

autocorrelation in both the private and the public investment equations when bilateral

and multilateral aid are used.

The comparison with 3SLS results in all cases tend to confirm the

corresponding results from IV estimations. However, these model perform badly in

terms of some diagnostic tests. The most serious problem is the rejection in all cases of

the null that the reduced model parsimoniously encompasses the system. There are

Finally, it is worth noting the robustness of the private consumption equation.

The long run estimated marginal propensity to consume out of disposable income is

0.62 for the IMF-OECD dataset and 0.60 for the GOV dataset46.

Table 3.4.2.I. Model I. Grants and Loans (IMF-OECD Dataset)

Constant I imf _*D out,., r «e,Himf A, A,

rpimf _2661.0**

(1239.7) .37“(.07) .46”(.19) .65“(.28) -3.04(3.24) (1.07)-.75 1 imf

** -932.6

(876.5) (.05).09’ .31“(.13) .60"(.20) -2.78(2.29) 1 .0 2(76) Instruments used: Ag,,.i, A|,t.i, O Ft_i, Y u , Dcred.

Constant Oil tv, r _'Jc.l-limf A, A, A«.i-i A|,m OF,., Y vi Dcred

■ imf _-10098**

(1793.6) -.03(22) -.50(.41) (3.72)6.14 (1.30).30 -14.3“(5.68) 3.67“(1.48) -.80"(27) .37“(.06) .15“(04) Private Consumption Equation: Long Run

C = 10990“ Constant +.62“ Yd‘mf

(2460) (.05)

*, ** denote significance at 10% and 5% respectively. Standard errors in parentheses.

46 Woo, Glassburner and Nasution (1994) obtain a 0.63 elasticity of the private consumption deflator with respect to the GDP deflator. This is loosely comparable with our estimated marginal propensities to consume, 0.62.

Table 3.4.2.2. Model II. Bilateral and Multilateral Aid (IMF-OECD Dataset)

Constant I « o u t,., P _«C.Uimf Ab Am

rjiimf _{2893.4" .41" .47" .47' -1.63" 3.47}

(1200.1) (.07) (.18) (.26) (.74) (315)

I ,“"' -634.2 .08 .34" .49" .26 .005

(903.2) (.05) (.14) (.20) (.56) (2.37)

Instruments used: Ab,,_i, OF,.,, Y,.i, Dcred.

Constant Oilt,., g~* t-1imf Ab Am Ab.t-i Ani,|.i O F,., Y ,., Dcred

■ imf

*P -6858.3**_(1915.4) -.0 2

(.25) (61).23 -1.16(1.44) (6.93)3.99 (1.65)2.49 -9.78(7.87) -.52(32) .23"(.08) .17"(05) Private Consumption Equation: Long Run

C = 11040* Constant +.62** Yd1"*

(2581) (.05)

*, ** denote significance at 10% and 5% respectively. Standard errors in parentheses.

Table 3.4.2.3. Model III. Total Aid (IMF-OECD Dataset)

Constant _i.“ o u t,., G«,,.,“ A

rjiiltlf _{2989.3" .38" .47" .56" -1.28'}

(1214.7) (.07) (18) (.26) (72)

i f * -719.5 .08 .34" .50" .28

(881.5) (.05) (13) (.19) (53)

Instruments used: A,.,, OF,., Y u , Dcred.

Constant out,., r imf A A,., O F,., Y i-i Dcred

« imf

*P -8178.7**

(1693.3) -.04(25) -.33(.44) (1.19).0 2 (1.25)1 .2 1 -.69"(.30) .31"(.06) .14"(.05) Private Consumption Equation: Long Run

c = 10980** Constant +.62** Yd“"'

(2449) (.05)

Percentage Changes Following a 1% Increase in Aid.

Table 3.4.2.4. Model I. Grants and Loans (IMF-OECD Dataset (.Estimated Impacts on the Budget, on GDP and on Consumption.

All Parameters 1“ period 2nd Period Significant Parameters 1" Period 2nd Period a T / d A , A I T_{K R} -.7 -3.1 - -4.7 -5.0 -4.0 - -2.9 d Gc / d A g Ag /g c 3.3 -.9 - -5.4 a T I d A, A, I T -.8 .3 - 1.7 d l g I d A, A, l l g 3.3 3.0 - 1 .1 a Gc I d A, A, / Gc -1 . 2 .5 - 1.8 d Y / d A g Ag / Y 1 .0 -4.5 - -5.7 d Y I d A, A, / Y -.04 1.5 - 1.7 d C / d A g Ag 1C .4 -3.8 - -4.7 d C I d A, A, 1C .0 2 1 .2 - 1 .2

Table 3.4.2.5. Model II. Bilateral and Multilateral Aid (IMF-OECD Dataset). Estimated Impacts on the Budget, on GDP and on Consumption.

Percentage Changes Following a 1% Increase in Aid. All Parameters

1“ period 2nd Period

Significant Param eters

1” Period 2nd Period 3 T l d A h A J T -2.5 -2.4 -1.9 -2.4 d l g / d A h A j l g .5 -1 .0 - -.9 d Gc 1 d Ah Ah / Gc -2 . 1 -1 . 1 -1.0 -1.3 a T / d A m Am / T 3.6 3.3 - - d l , / d A m An I I g .7 5.4 - - 3Cc l d A m Am / G c 8 .1 4.5 - - a Y I d Ah A J Y -.5 .4 - . 2 -.4 d Y l d A m Am / Y 1.5 -2.7 - - d C / d A h Ah I C - . 1 .6 - - . 1 d C / d A m Am / C .2 -3.2 - - 127

Percentage Changes Following a 1% Increase in Aid.

Table 3.4.2.6. Model III. Total Aid (IMF-OECD Dataset). Estimated Impacts on the Budget, on GDP and on Consumption. Elasticities.

All Parameters 1“ period 2nd Period Significant Parameters 1" Period 2nd Period 3 T / d A A I T -1.4 -1.4 -1.4 -1 .8 a l t I d A A l l g .8 .2 - -.8 a Gc I d A ■ A I Gc -.9 -.5 -.4 -.7 a Y I d A A l Y -. 2 .1 - . 2 -.6 3 C / d A A I C .0 2 .3 - -.3 3.4.3. A Simulation Exercise.

The estimated parameters are also used to simulate the impact of grants and

loans on fiscal and macroeconomic variables in order to explore the full long run

dynamics of the estimated model. Graphs 3.2.3.1 to 3.4.3.6 show the simulated effects

that temporary and permanent shocks in grants, loans and joint grants and loans have

on all the endogenous variables of the system. We carried out simulations47 using

shocks of the size of the estimated standard errors from autoregressive regressions of

grants and loans, namely 69.32 billion rupiahs for grants and 279.27 billion rupiahs for

loans. Percentage changes are plotted against time.

Graph 3.4.3.I. Effect of a Temporary Shock in Grants.

Y;--- I p : ---- c:

Graph 3.4.3.2. Effect of a Temporary Shock in Loans.

Y:--- Ip:--- C:

% Change on the vertical axis; time on the horizontal axis.

Graph 3.4.3.3. Effect of a Temporary Joint Shock in Grants and Loans.

Y:--- Ip :--- C:

% Change on the vertical axis; time on the horizontal axis. Graph 3.4.3.4. Effect of a Permanent Shock in Grants.

Y:---- Ip:---- C:

Graph 3.4.3.S. Effect of a Permanent Shock in Loans.

Y :--- I p — C:

% Change on the vertical axis; time on the horizontal axis.

Graph 3.4.3.6. Effect of a Permanent Joint Shock in Grants and Loans.

Y;--- Ip :— C:

% Change on the vertical axis; time on the horizontal axis.

Simulations confirm the preceding discussion of the results. We note briefly the

overall negative impact of grant aid in contrast to the generally positive impact of loans.

Their joint impact is also positive, except for a 2 periods adjustment in taxation efforts.

What is striking is the size of the short period impact in all cases. This may be ascribed

once again to the relative little weight of grants and loans to GDP. In fact, the

temporary effects tend to stabilise to values not in excess of 2.5%. Not surprisingly,

private and public investment are the most sensitive to such shocks, given the structure

of the model. On the budget side, the policy maker in this simulated economy would

reduce taxation efforts for about one year as a response to increased loans and joint

loans and grants. Afterwards, tax revenues would rise again. The increase in public

investment would therefore be at the expenses of taxation efforts only in the very short

run. Grants alone would only harm the budget and the overall economy. Similar

behaviour can be inferred when considering the reactions to permanent shocks. Not

surprisingly, these cause a much higher impact on the economy, especially on

investment decisions, both private and public.

In document Aid, the public sector and the real exchange rate: the case of Indonesia (Page 135-146)