Adjustment, trade policy and investment slumps: evidence from Africa

ELSEVIER

Journal of Development Economics Vol. 52 (1997) 121-137

JOURNAL OF Development ECONOMICS

Adjustment, trade policy and investment slumps" evidence from Africa David Fielding 1 Department of Economics, University of Nottingham, University Park, Nottingham, NG7 2RD, UK

Received 15 June 1994; revised 15 July 1995

Abstract This paper presents a model of investment in six African economies over the 1970s and 80s, paying particular attention to the impact of the policy reforms which have accompanied structural adjustment programmes. A priori, the impact of trade reform on investment can be positive or negative; in practise, there is some evidence that it might be negative. JEL classification: E2; F1; O1 Keywords: Investment; Trade policy

1. Introduction The 1980s saw a substantial decline in the ratio of investment to income in many parts o f Africa, and a corresponding slowdown of economic growth. One of the aims o f W o r l d Bank and I M F structural adjustment programmes (SAPs) was to improve this record. Existing studies of the impact of SAPs on developing countries suggest that whatever their benefits in terms of reducing balance of payments deficits and inflation, their impact on investment was, if anything, negative (World Bank, 1990; Harrigan and Mosley, 1991). This is an important failing for policy strategies whose aims include the promotion o f long-term economic growth.

1 I am grateful to an anonymous referee, David Greenaway and Chris Milner for comments on earlier drafts of this paper. The usual disclaimers apply. 0304-3878/97/$17.00 Copyright © 1997 Elsevier Science B.V. All rights reserved. PII S0304-3878(96)00437-3

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The theoretical literature suggests that measures which lower the relative price of imports ought to stimulate investment, since so many capital goods in developing countries are imported (Buffie, 1986). However, there is very little evidence concerning the mechanisms by which policy reform affects investment. The closest that existing empirical studies come to addressing this issue is either to use dummy variables to capture multifaceted changes in policy regimes (Bleaney and Greenaway, 1993) or to use the real exchange rate as an explanatory variable in an investment equation (Musalem, 1989; Faini and de Melo, 1990). 2 The following paper is an attempt to fill this gap, using evidence from six African economies over the 1970s and 80s. In Section 2, we develop a theoretical model of private investment in a developing country, identifying the main parameters which are likely to affect investment. Some of these will be policy variables (such as tariffs and government expenditure) which change during structural adjustment; others will be part of the economic environment. In Section 3, this theoretical model will be used to construct and estimate an econometric model of investment.

2. A theoretical model of investment in the small open economy In this section we sketch out how investment in a developing country might be related to other key macroeconomic variables; this will provide a context for the interpretation of the coefficients of the model estimated in the next section. Private expenditure on capital goods (Ip) is determined by a function of the following form:

Ip = I( k "_Q,k+PN,m+Px, r_)

(1)

where Q is the price of capital goods, PN the price of non-traded consumer goods, Px the border price of exports and r the real interest rate. Border import prices are used as a numeraire, so PN is a real exchange rate and Px the (exogenous) terms of trade. Domestic agents will respond not to border prices but to post-tax prices; hence the inclusion of k = (1 + t) -1 and m = k. (1 - v ) , where t represents an import tariff and v an export tax. Rises in the two output prices increase the marginal revenue product of capital and so investment; rises in Q and r increase the marginal cost of capital and so reduce investment, lp is a composite of expenditure on both imported capital equipment and non-traded capital (such as buildings and land improvement). Data constraints prevent us from disaggregating investment and capital goods prices in the econometric model, but it will be useful

2 The latter approach is problematic since the real exchange rate is unlikely to be an exogenous variable.

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to do so here. The share of imported capital in total investment, a, will determine the capital goods price index: 3 Q = a . QM + (1 - a ) "QN

(2)

QM is exogenous, but QN is determined by the demand for non-traded capital goods (and hence the arguments of Eq. (1)) and their supply. If the allocation of resources to non-traded capital good production depends positively on the price of non-traded capital and negatively on other producer prices, then we may write:

Q N = k -' . Q ( k . QM,k. P N , m . P x , r ) --

+

(3)

+

The other endogenous price is that of non-traded consumer goods. Demand for these is likely to depend negatively on their price and the interest rate, and positively on factor income (Y) and non-factor income (i.e. transfers from abroad, T); supply wilt depend positively on the real exchange rate and negatively on the other producer prices, Px and QN' Hence:

There is also a savings function, showing how higher income and interest rates may increase consumer saving:

)

(5>

If we assume that income is exogenous (an assumption which will be tested later), then there are two ways in which we might close the model. If domestic capital markets are integrated with world markets, then (for a given rate of inflation and nominal exchange rate depreciation) the domestic real interest rate, r, will be determined by the international interest rate. Domestic agents will have access to international capital markets and their net saving decisions, manifested as the current account, will be unconstrained. However, there is little evidence for such integration, and most studies suggest that domestic agents cannot borrow unlimited quantities of funds on international capital markets (see for example Bevan et al., 1990). A more plausible approach is to allow an endogenous domestic real interest rate and to specify a constraint on the economy's total net saving:

3 o~ might be endogenous (for example, it might depend on tially alter the model.

QN/QM ),

but this does not substan-

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where F is a given level of financial inflows from abroad and B is net government borrowing (measured in units of domestic currency and deflated by import prices). Both the government's budget deficit and the quantity of funds foreign donors are willing to lend to the country are assumed to be a function of domestic factor income, f 0 is the ratio of the private agents' consumer price index to import prices. In this model, there are four exogenous variables: Y, T, Px and QM. Before proceeding to estimate investment as a function of these variables, it may be useful to spell out the ways in which they might affect investment, and how these relationships might be affected by economic reform. 1. Income (Y, T). Higher income will raise both gross domestic saving and capital inflows from abroad, putting downward pressure on the interest rate and stimulating higher investment. This may be offset by increases in public borrowing. Since it increases aggregate demand, higher income will also push up non-traded consumer goods prices (and hence, indirectly, non-traded capital goods prices). The impact of this on investment is ambiguous, since higher PN increases investment (both directly and by increasing the value of savings) whilst higher QN reduces investment. 2. Imported capital goods prices (QM). Higher capital goods prices will increase the marginal cost of capital and so reduce investment. In addition, they will mean higher nominal levels of borrowing for a given real level of investment. This will put upward pressure on the interest rate, which will also increase the marginal cost of capital. 3. The terms of trade (Px). An improvement in the terms of trade pushes up non-traded commodity prices, since factors of production are switched out of non-traded output and into exports. So the effects of an increase in Px on investment via PN and QN are similar to those of an increase in Y. There is also a positive direct effect, reflecting the responsiveness of investment in the export sector to changes in export prices. If this effect is weak, then the impact of the terms of trade on investment will be ambiguous. 4. Trade reform. Any increase in k or m (i.e. any reduction of tariffs or export taxes) will increase the magnitude of the direct effects on investment of the different relative prices: Olp/OQ, OIp/OPN and OIp/OP x will all rise. However, the impact on the value of dIp/dQM and d I p / d P x is less certain: for example, d I p / d Px may rise or fall. Suppose that d I p / d Px is positive ex ante. The direct effect of the trade reform is to increase the magnitude of this effect, as OIp/OP x rises. However, if OQN/OP x rises and OPN/OP x falls, this will be offset by an increase in the magnitude of the negative indirect effects. 5. Other kinds of reform. Any improvement or deterioration in efficiency brought about by microeconomic or macroeconomic reforms is likely to shift the marginal efficiency of capital schedule. This might manifest itself as a shift in the intercept of the investment function. However, if the shift in the m.e.c. schedule is not parallel, then the responsiveness of investment to prices and output costs (and hence to their determinants) is likely to change.

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3. T h e e c o n o m e t r i c m o d e l 4 3.1. Variable definition and model structure

In this section, we will estimate a reduced form 5 equation for investment, using income, the terms of trade and capital goods prices as explanatory variables. Most of these are reported only annually in developing countries, and so it is not possible to construct separate investment equations for each country. Instead we will pool the data from several countries assuming that the structure of the investment equation is approximately the same in each country and allowing for country-specific parameters only on the intercept and trend terms of the investment equation. Such an assumption is unlikely to be valid for a wide range of countries. Investment and savings behaviour has varied enormously across Africa over the last 20 years. Ratios of domestic investment to GDP have ranged between 3% (Ghana) and 62% (Cape Verde Islands); domestic savings ratios have varied between - 9 9 % (Lesotho) and 66% (Gabon). 6 We will restrict ourselves to pooling those countries in which investment and savings ratios have remained within a narrower band. The six countries chosen are those for which investment and savings have always been between 5% and 40% of GDP over the last 20 years: Kenya, Nigeria, Zimbabwe, C6te d'Ivoire, Cameroon and Mauritius. This does not guarantee that the chosen countries will exhibit homogenous price and investment functions. However, existing studies on five of the countries (Kenya, Nigeria, C6te d'Ivoire, Cameroon and Mauritius) do suggest that consumption and investment respond both to the terms of trade and to the real exchange rate, and that non-traded goods prices are reasonably flexible (Devarajan and de Melo, 1987; Bevan et al., 1992, 1993; Milner and McKay, 1994). Statistical robustness of the estimated model will also provide circumstantial evidence for the validity of our homogeneity assumption. Of the variables appearing in the theoretical reduced form model, national income (Y) and external terms of trade ( P x ) are reported for all six of the countries in our sample in Worm Tables (World Bank, 1992), covering the years 1970-88. This gives us 19 observations for each country, and 114 observations for each variable. Aggregate capital goods prices (Q) are also reported (in the form of investment deflators), but not the price of imported capital. The aggregate capital goods price series is used as a proxy for the import price. Industrial investment figures (Ip) are taken from National Accounts Statistics (United Nations, 1970-91 )

4 Equations are estimated in PC-GIVE8.0 and LIMDEP6.0. 5 Estimation of a structural model of investment is difficult because some of the endogenous variables appearingin Section 2 (in particular the budget deficit) are not reported for all of our sample countries. 6 Figures are taken from World Tables (World Bank, 1992).

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and African Statistical Yearbook (United Nations, 1970-88). These include investment by publicly owned firms, but exclude central and local government capital expenditure. More details of these figures are available on request. The non-factor income term (T) is defined as the flow of net overseas development assistance reported in OECD (1976-91), converted into real domestic currency units using the official exchange rate and the CPI. (This measure includes loans accompanied by policy reform conditions; the impact of the conditions per se will be measured separately, as described below.) In order to estimate the investment equation, we need to assume some kind of functional form. In the absence of any strong theoretical presumption in favour of a particular form, and beating in mind that macroeconomic time series are more likely to be integrated processes when represented in logarithms (Banerjee et al., 1992), we begin by assuming that the equation can be approximated by a log-linear representation, and calculate the standard test statistics for the validity of such a restriction. The basic reduced form equation is: I n ( I ) z,t = az,o + gz t + az,l I n ( I ) z.,-, + Xbz.i In(Y) z,t-i + Zcz. i In( Px ) z,t-i + Zdz,i In(T) z.,-i + Xfz,i In(Q) z,,-i +uz, '

i=0,1; z=l ..... 6

(7)

where Xz, t represents the xth variable in the zth country in year t and Uz. t is white noise. 7 This equation does not allow for the impact on investment of economic reform. As we saw in the previous section, reform of various kinds could affect the structure of the investment equation. Now it is not possible to model changes in policy in terms of polychotomous explanatory variables: with regard to trade policy, data on effective rates of protection do not exist for every country in every year of the sample; moreover, many other policy reforms are of a qualitative rather than a quantitative nature. The usual approach to this problem is to introduce a dummy variable for periods following the introduction of a SAP in a country. The drawback of this approach is that structural adjustment, if it is carried out, may entail a wide range of policy changes, including adjustment of taxation and public expenditure, but also, for example, reforms to the structure of individual industries. A significant coefficient on a structural adjustment dummy does not demonstrate a relationship between investment and a single economic policy variable. However, the dating of structural adjustment loans is not the only available source of information on economic reform. The IMF (1992, Chapter 5) provides data which allows us to identify years in which trade reforms actually took place.

7 There are enough observations for a longer lag structure than this, but no variable lagged by more than 2 years was significant in any of the estimated equations.

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If we allow structural breaks for these periods as well as for periods in which a SAP was introduced, then the SAP dummy can be interpreted as measuring the impact of adjustment policies other than trade reform. In addition, we can allow structural breaks for periods in which a non-SAP World Bank loan was granted. Such loans accompany conditions relating to improvement of the performance of particular industries or sectors, which are largely microeconomic in character. The three dummy variables together will bear some economic interpretation, which is not possible using one dummy to represent all kinds of reform. SAPs were introduced in Kenya (1980), Mauritius (1981) and Crte d'Ivoire (1982). Other adjustment loans were made to Zimbabwe (1983, export industrial policy loan), Nigeria (1984, fertiliser sector loan), Kenya (1986, agricultural sector loan) and Mauritius (1987, industrial sector loan). The IMF reports "comprehensive" or "partial" trade reform in Crte d'Ivoire (1984) and Kenya (1988). 8 The dummy variables are defined as follows: qbA = 1 in the years following that in which a structural adjustment loan was introduced, = 0 else @B = 1 in the years following that in which another kind of World Bank loan was introduced, = 0 else @c = 1 in the year of trade reform indicated by IMF (1992), and in subsequent years, = 0 else The equation to be estimated is of the form: I n ( I ) z,t = [1 + ~ j . z a ]

" [az.0 + gz t + az.1 I n ( I ) z,t-, + ~bz,i In(Y) ~-.t-i

+ Xcz,i l n ( P x ) z , t - i + 2dz.~ In(T)...t-, + Xf:.i ln(Q) :,, i +Uz,t]

i = 0 , 1 ; z = l . . . . . 6; j = A , B , C

(8)

where q~j,z,t is a dummy variable indicating whether the zth country has undergone reform of type j in period t. This specification allows reform to alter both the intercept and the slopes of the investment function. qbc is a pure measure of trade reform. In so far as the IMF data are accurate, all the effects of changes in trade policy will be captured by this variable, and any significant coefficient on q~A variables must be attributable either to some other macroeconomic regime change or to the relaxation of the external financing

8 "Comprehensive" reform involves the complete or almost complete elimination of quantitative restrictions, possibly acccompanied by a reduction in tariff rates and coverage; "partial" reform involves a "greatly improved...transparency and price sensitivity of the trade regime". The latter definition is somewhat subjective, but its main component appears to be the partial elimination of quantitative restrictions. In these definitions, no attempt is made explicitly to calculate effective rates of protection, or to produce a general index of liberalization along the lines of Papageorgiou et al. (1990). However, given the poor coverage given to Africa in trade liberalization studies (for example, not a single African country is included in the Papageorgiou study), the IMF estimates are among the best available.

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constraint resulting from the structural adjustment loan. (/)B coefficients measure either the impact of a relaxation of the financing constraint or the impact of microeconomic reforms on the structure of the investment equation. 3.2. Tests f o r stationarity and exogeneity Since the data set to be used incorporates time series, it is appropriate to test for the stationarity of the variables of interest. If more data were available, then we could explore the times series properties of each variable in each country individually. Although the sample period we have is quite long (19 years), variables are observed only annually. With only 19 observations per country per variable, the power of the relevant test statistics for individual countries is very low, and previous studies of investment in LDCs have made no attempt to explore the time series properties of the data. In order to construct a test for the stationarity of the variables of interest, we need to make some a priori assumptions about the characteristics of the data. By analogy with the assumptions underlying the multivariate regression (and with the same justifications), we proceed under the assumption that every variable can be represented by the same ARIMA process in each country, except possibly for different values of the trend and intercept terms. Each variable can then be treated as a single time series with a number of pre-identified structural breaks (as we move from one country to another). Using the Perron/Dickey-Fuller methodology (Dickey and Fuller, 1981; Perron, 1989), we will construct a regression equation of the form:

AXz,t=az+flzt+yXz,t_l+~iAxz,t_i

i=1

.....

n

(9)

where the lag order n is just large enough to ensure that there is no residual autocorrelation. The stationarity test statistic, under the null hypothesis that x is non-stationary, is the t-ratio on y (with a non-standard t distribution). If y is significantly less than zero, then we can reject the null in favour of stationarity. If the intercept and trend terms are insignificant, then the power of the test is increased by their omission from the regression. It has been noted (Leybourne, 1995) that the power of the Dickey-Fuller test is greatly increased if we run the regression in reverse; that is, we also estimate: --Axz,t=Odz + fl~t+ y ' X z , t + ~8;Axz,t+i

i = 1.....

n

(10)

If the smaller of y and y' is significantly less than zero, then rejection of the null is much less likely to be erroneous. 9 The stationarity tests presented in Table

9 The critical values correspondingto this test are somewhat smallerthan the critical values for the one-way test.

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Table 1 Stationarity tests Variable In(Ip) ln(lp)' In(Y) In( Y)' In( Px ) In( Px ) ln(Q) In(Q)' In(T) In(T)'

DF statistic - -

-

5.02 3.87 4.25 3.23 5.01 4.28 5.08 5.76 3.44 3.47

Intercepts

Trends

X X X X X X X X

X

X

X X X X X X

The figure for In(I) corresponds to the t-ratio on 7 in Eq. (9), the figure for In(l)' corresponds to the t-ratio on y' in Eq. (10), and so on. The regressions for In(T) and In(T)' also include a dummy variable for 1980 in Zimbabwe. No lags were required to ensure residual independence. See Appendix A for variable definitions.

1 therefore include t-ratios on both y and y ' , indicating in each case whether trend, intercept or lag terms have been included in the regression. In the case of ln(T), a residual outlier occurs in both versions of the regression, corresponding to Z i m b a b w e in 1980 (in this year, sanctions were lifted, and there w a s a very large increase in capital inflows). For this reason, the statistics reported for this variable are from a regression which includes a dummy variable for this observation. The reported statistics show that the null of non-stationarity can be rejected for every variable at at least the 10% level. On this basis, we proceed by assuming that the variables of interest are stationary, and equations are estimated by least squares techniques. In estimating the model, it will also be important~ to test for the exogeneity of the explanatory variables. Now it is quite likely that some of the explanatory variables are not strongly exogenous. In particular, income may depend on past investment, and transfers from abroad m a y depend on past income and investment. However, the strong exogeneity o f income and aid flows is not of direct interest, since we will not be performing any kind of counterfactual analysis. However, weak exogeneity is essential if we are to draw valid inferences from our regression. Another variable which may be weakly endogenous is Q. Although QM is exogenous in the theoretical model, its proxy Q might not be since it includes endogenous non-traded capital goods prices. In addition, it will be useful to know which of the parameters in our model are invariant. So we require a test of super-exogeneity (i.e. weak exogeneity plus invariance) for all of the explanatory variables.

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The test procedure follows Engle and Hendry (1993). 10 The explanatory variables are regressed on a set of instruments, and the resulting residual terms ( 7 / ( x ) ) are added to the investment equation. If any is significant, then x is not weakly exogenous, r l ( x ) t is also used to construct an estimate of the residual variance: V ( x ) t = Y _ , r l ( x ) 2 i . If this term is significant, then there is parameter invariance, and even if x is weakly exogenous, is not super-exogenous. In order for the model to be identified, we need to specify a priori variables which appear in the marginal model of x but not in the conditional model of investment on x. The equations representing the marginal processes are described below, and reported in Table 2. For the terms of trade ( P x ) , the marginal model includes dummy variables for Px in Nigeria representing the shocks to international oil prices in 1974, 1980 and 1986; and dummy variables for Px in Kenya, Cameroon and C6te d'Ivoire representing the temporary shock to coffee and cocoa prices in 1977. For the capital goods price (Q), the marginal model allows prices in one country in period t - 1 to affect prices in another country in t, reflecting some degree of market integration. In fact, there is only one significant relationship of this kind: past capital goods prices in Nigeria affect present prices in its smaller neighbour, Cameroon. For aid inflows (T), the marginal model includes a dummy variable for the lifting of sanctions against Zimbabwe-Rhodesia in 1980. For income (Y), the lag structure allowed in the marginal model is longer than that in the conditional model, reflecting the fact that present output will depend on the capital stock, which has been built up over many years. The residuals 7/(x) t from these equations will be used to test the exogeneity of the explanatory variables. Significance of r l ( x ) t in the investment model will indicate the violation of weak exogeneity for x; significance of V ( x ) t will indicate the violation of super-exogeneity. Note that the marginal models entail that neither Y, T not Q is strongly exogenous with respect to investment. Factor income depends positively on past investment and aid inflows depend positively on past factor income. If investment is a positive function of these variables, then the effect of any innovation which increases investment will be multiplied by the interaction of investment and output. There is another group of variables, the possible endogeneity of which must be taken into account: the reform dummies qbj. The timing of reform may not be independent of the current state of the macroeconomy. In this case, we have a "switching regression model with endogenous switching" (Maddala and Nelson,

10Note that the role of structural breaks in our model is rather different from that of the structural breaks in Engle and Hendry (1993). Here, we are not using dummy variables to test for changes in the 'deep' parameters of the data generating process (for example, breaks due to changes in expectations). The dummy variables here represent major movements in an explanatory variable for which data are unavailable. So dummies in our model appear both in the conditional model of investment on x and in the marginal model of x.

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Table 2 Marginal models of explanatory variables ( 1 9 7 1 - 1 9 9 0 ) ln(T) = 0.797 In(T) i - 0.726 In(Y)_ 1 + 2 . 2 3 1 D Z - 1.805DZ_ t (0.065) (0.254) (0.325) (0.361) ~2 = 0.97 L M ARCH: LM autocorrelation:

F(6, 86) = 0.50 [0.81] F(1, 9 7 ) = 0.73 [0.39]

In(Y) = 0.669 In(Y)_ 1 - 0.236 In(Y)_ 2 + 0.126 In(1)_ 1 (0.115) (0.096) (0.042) ~2 = 0.88 LM ARCH: F(6, 87) = 1.19 [0.37] LM autocorrelation: F(1, 98) = 0.21 [0.65] In(Q) = 0.449 In(Q)_ 1 - 0.220 ln(Y) t 1 + 0.239 In(Q c - N) 2 (0.089) (0.089) (0.116) ~,2 = 0.71 L M ARCH: F(6, 93) = 1.88 [0.09] LM autocorrelation: F(1, 104) = 0.02 [0.90] I n ( P x ) = 1.281 l n ( P x ) _ 1 - 0 . 6 0 7 ( P x ) - 1 + 0.591ADN1 + 0.382DN2 - 0.726DN3 + 0.264DK (0.211) (0. l 51) (0.104) (0.104) (0.099) (0.099) + 0.361DI + 0.331DC + 0.331DM (0.099) (0.099) (0.104) ~2 = 0.92 L M ARCH: F(6, 81) = 0.29 [0.94] L M autocorrelation: F(1, 92) = 0.00 [0.98]

DZ = 1 in Zimbabwe from 1980, = 0 else QC- N = Nigerian capital goods price (in Cameroon only) DN1 = 1 in Nigeria from 1974, = 0 else DN2 = 1 in Nigeria from 1980, = 0 else DN3 = 1 in Nigeria from 1986, = 0 else DK = 1 in Kenya in 1977, = 0 else DI = 1 in C6te d'Ivoire in 1977, = 0 else DC = 1 in Cameroon in 1977, = 0 else DM = 1 in Mauritius in 1974, = 0 else All equations also include trend and intercept terms. See Appendix A for variable definitions.

1975). This is dealt with in the way described in Maddala (1983). First, we estimate probit models to explain the observed values of the ~j:

xz,,) + vz,,

(11)

where Vz, t is an error tenn. These equations are reported in Table 3. x includes the level of foreign debt ( D ) as an instrument. Note that all the explanatory variables Xz,t are lags of present variables, and therefore weakly exogenous to present investment. The (over-parameterised) investment equation is then estimated using the fitted values of ~j,z,t in place of the actual values. Fitted values of dP'()j,z, t are also added to the regression to allow for non-zero covariance between vt and the residuals in Eq. (8). 11

~1 This approach is not used for q'c, however. Trade reform happens so rarely that it is easy to data-mine and obtain a perfect fit for ~ c with the explanatory variables in the model. That ~ c appears directly in the estimated investment equation should be borne in mind as a caveat.

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Table 3 Probit models for timing of structural breaks (1971-1990) Variable

Coeff.

SE

t-value

prob.

'I'aO

ln(Y)_ 1 (%) In(Y)2 1 (%) ln(D)_ 1 (%) In(D) z_1 (%) rz_l (%) Trend Joint significance

--5.8773 0.0667 -4.8845 0.1350 - 1.6164 12.5190 X2(5) = 33.1664 [0.0000]

3.247 0.040 2.214 0.060 0.890 6.875

- 1.810 1.670 -2.206 2.256 - 1.815 1.821

0.0702 0.0950 0.0274 0.0241 0.0695 0.0686

ln(Y)Z_~ (%) r21 (%) Trend Trend2 Constant Joint significance

0.4395 3.5741 317.66 - 22.437 - 1482.8 X(4) = 11.5050 [0.0214]

0.2460 2.0400 176.50 12.680 824.80

1.787 1.752 1.800 - 1.770 - 1.798

0.0740 0.0798 0.0719 0.0767 0.0722

3.3. The i n v e s t m e n t m o d e l

The conditional model of investment reported in Table 4 is the result of omitting insignificant variables from the over-parameterised model corresponding to Eq. (8). Standard test statistics are reported, indicating that the estimated model is robust. The model shows that higher income and aid inflows stimulate investment, as predicted as the likely outcome of the theoretical model. The income elasticity (before economic reform) is around 2.5 in the long run, suggesting that investment is highly responsive to income, although in the reduced form estimated we cannot identify which of the mechanisms outlined in the theoretical model is responsible for this. This result confirms the positive link between income and investment found in Bleaney and Greenaway (1993), in contrast with the insignificance of income in Greene and Villanueva (1991). The income coefficient in other papers is typically much smaller, and the explanatory variable is often A In(Y), not In(Y) (which would be an inappropriate model specification for our data set, since income and investment are integrated to the same order), The elasticity on aid inflows (which do not appear in other empirical models) is around 0.075 before reform. This suggests that aid does have a significant effect on investment, but that the impact of a 1% increase in aid flows is much smaller than a 1% increase in income. In the theoretical model, the impact of the external terms of trade on investment can be positive or negative; in the estimated model, it is negative, with an elasticity o f approximately - 0.57. It appears that the direct effect of the terms of

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Table 4 Conditional model of ln(1) ( 1 9 7 1 - 1 9 8 8 ) Variable

Coeff.

SE

t-value

HCSE

Ptl. R ~

Ins.

In(Y) ln(Y)_~ ln(Y).q0a In( Y)_ t ' ~ B In(T) ln(T).~ B In(P x ) l n ( P x ) 1.qbc l n ( Q ) . [ l - qbA ] A~ c Constant C. Nigeria C. Zimbabwe C. C6te d'Ivoire C. Cameroon C. Mauritius Trend T.Nigeria T. Zimbabwe T. C6te d'lvoire T. Cameroon T. Mauritius

1.45110 1.01090 1.86020 2.58310 0.07690 1.08230 -0.56971 -2.78660 -0.85533 - 0.72779 0.57994 - 0.26369 -0.10344 0.44239 0.63508 0.09271 - 0.07587 0.00431 - 0.01824 -0.00269 -0.01722 -0.08358

0.19102 0.15793 0.92040 1.06400 0.03097 0.32054 0.11462 0.55759 0.13184 0.11273 0.06688 0.07380 0.09125 0.05761 0.06343 0.04938 0.00990 0.00818 0.01232 0.01497 0.00988 0.01103

7.597 6.401 2.021 2.428 2.483 3.377 -4.971 -4.998 -6.488 - 6.456 8.672 - 3.573 - 1.134 7.680 10.013 1.878 - 7.666 0.527 - 1.480 -0.180 - 1.743 -7.579

0.23960 0.25342 0.62954 0.70476 0.03131 0.20890 0.12214 0.51560 0.12284 0.07790 0.05850 0.06965 0.09158 0.05734 0.05909 0.04051 0.00812 0.00781 0.00954 0.01512 0.00930 0.01017

0.4016 0.3227 0.0453 0.0641 0.0669 0.1171 0.2232 0.2251 0.3286 0.3265 0.4665 0.1293 0.0147 0.4068 0.5383 0.0394 0.4060 0.0032 0.0248 0.0004 0.0341 0.4005

0.06 0.07 0.01 0.03 0.12 0.01 0.16 0.04 0.08 0.10 0.04 0.08 0.06 0.08 0.04 0.06 0.05 0.08 0.12 0.10 0.13 0.04

~2 = 0.9521 R 2 = 0.9403 tr = 0.1205 RSS = 1.2479 Joint significance: F(21, 86) = 81.406 [0.0000] Hansen variance 0.4635 instability test: Joint instability 3.1243 test: RESET test: F(1, 85) = 0.0247 [0.8756] Schwartz criterion = - 3 . 5 0 6 9 LM error: autoF ( I , 85) = 0.0555 [0.8143] correlation test: LM heteroscedasF(36, 49) = 0.5419 [0.9714] ticity test: LM A R C H test: F(6, 74) = 0.3020 [0.9340] Exogeneity t-tests Weak ln(P x) In( Y ) In(T) In(Q)

0.637 1.367 0.093

Super -0.817 - 0.775 - 1.854" - 2.367 *

HCSE, heteroscedasticity-consistent standard error. Ins., Hansen parameter instability statistic. * Significant at the 5% level.

134

D. Fielding~Journalof DevelopmentEconomics 52 (1997)121-137

trade, which is to encourage higher investment, is offset by the indirect effects, such as the increased outflow of resources entailed by an improved current account position. This result contradicts Bleaney and Greenaway (1993), where the terms of trade have a positive effect on investment. One interpretation of their coefficient is that lower import prices correspond to lower capital goods prices. However, our paper controls for capital goods prices (the coefficient on In(Q) is around - 0 . 8 5 before reform). A number of the coefficients on the q~j terms are significant, indicating that the various types of economic reform do alter the structure of the investment function. There is a negative coefficient on ~ c ' l n ( P x ) t - l , indicating that trade reform increases the magnitude of the impact of changes in the terms of trade on investment (in the theoretical model, the effect of reform on this elasticity is ambiguous). After reform, the elasticity falls to - 2 . 7 9 . In addition, trade reform appears to shift down the intercept of the investment function (the coefficient on qbc is negative), although the coefficient on lagged qOc is positive and in the long run the effect is insignificant (in Table 4, the zero long-run impact restriction has been imposed, and a coefficient for A~ c reported), lZ Overall, the impact of trade reform on investment is likely to be strongly negative. By contrast, the impact of SALs is to reduce the magnitude of the elasticity of investment with respect to capital goods prices, which falls to zero. As a result, investment will be higher than otherwise. With more data on the content of individual SALs, it might be possible to ascertain which component of the SAL and accompanying macroeconomic reform was responsible for this effect. Terms in @B, the microeconomic reform dummy, are also significant. The impact of this kind of reform appears to be to increase the income elasticity of investment, with substantial increases in both d l n ( I p ) / d l n ( Y ) and dln(Ip)/dln(T). One explanation for this is a rise in I~ in response to increased industrial efficiency (the efficiency gain is greater at lower levels of investment). For a given S'r/S'r, this will increase d l p / d Y . However, this does not explain the relatively large impact of reform on the ln(T) t coefficient. Whilst reform doubles the elasticity of investment with respect to factor income, the coefficient on non-factor income rises from 0.075 to 1.15. One explanation is that the foreign funds associated with a reform package are allocated more efficiently than those not associated with reform. The exogeneity tests reported in Table 4 indicate that the hypothesis that all the explanatory variables are at least weakly exogenous cannot be rejected. However, ln(T) t and In(Q) t appear not to be super-exogenous. Although this does not

12The temporaryshift in the intercept of the investmentfunction may reflect adjustmentcosts in the wake of the removalof quantitative restrictions. Note that trade reform does have a permanent effect on investmentthrough the reduction of the value of the coefficienton Px.

D. Fielding/Journal of Development Economics 52 (1997) 121-137

135

jeopardise the validity of inferences made on the estimated model, is should be borne in mind that the parameters on these variables are not invariant.

4. Conclusion It has been possible to construct a robust model of aggregate investment using macroeconornic data for the 1970s and 80s from six African economies. Whilst investment responds to income and capital goods prices in the expected way, the impact of the terms of trade is not that which is usually assumed: an improvement of the terms of trade tends to reduce investment. However, this result is quite consistent with a general equilibrium model, in which the positive direct effects of an improvement in the terms of trade can be offset by negative indirect effects. A large part of investment in developing countries involves the purchase of non-traded capital goods. An increase in the relative price of exports is likely to attract resources away from non-traded capital goods production, inducing a supply contraction which outweighs any outward shift in investment demand. Correspondingly, trade reform appears to be accompanied by a fall in investment (the most important route by which this happens is that reform increases the sensitivity of investment to the external terms of trade). Whilst other aspects of economic reform do seem to stimulate investment activity (in particular those reforms directed towards improving the performance of particular industries, as indicated by the positive coefficient on ~B in the regression model) orthodox trade policy should not be seen as a way of stimulating investment. This is not to say that orthodox trade policy is necessarily undesirable: it may be an essential component of government policy, if only because it is a precondition for foreign aid. However, there may well be a tradeoff between the level of aggregate investment and the achievement of trade policy goals. 13

Appendix A. Variable definitions A.1. The theoretical model

Ip: QM:

real gross private investment capital import prices (as a fraction of consumer import prices)

13A final caveat: the explanatory variables used in this study explain only the variation in investment across a small sample of countries; they do not constitute a comprehensive list of all the factors determining investment in LDCs. For example, it has been noted that investment performance in the CFA zone is better than elsewhere in Africa, a phenomenon explained by factors such as exchange rate stability and financial openness which do not vary all that much across our sample of countries. See Fielding (1994).

D. Fielding~Journal of Development Economics 52 (1997) 121-137

136 QN:

Q: Y: T: r: t: v: Px: PN: S: f: F: G:

non-traded capital good prices (ditto) aggregate capital goods price index real factor income real non-factor income the real interest rate the domestic consumer good import tariff rate the export tax rate the terms of trade the real exchange rate real private sector saving the savings deflator as a fraction of consumer import prices foreign capital inflows net government borrowing

A.2. The econometric model

real industrial gross fixed capital formation in domestic prices * the gross fixed capital formation deflator as a fraction of the GDP deflator * Y: real gross domestic income in domestic prices * T: real net overseas development assistance from all donors in domestic consumer prices * the terms of trade* Px: dummy variable for the introduction of a structural adjustment programme dummy variable for the introduction of other adjustment programme dummy variable for the introduction of a trade reform ~c: intercept dummy for country X C.X: T.X: trend dummy for country X All variables marked * are scaled so as to equal unity in 1987.

Ip:

Q:

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