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Growth accounting, supply response and factor returns in general equilibrium: The case of Indonesia
Growth Accounting, Supply Response and Factor Returns In General Equilibrium: The Case of Indonesia KUMARESAN GOVINDAN, and TERRY L. ROE
MUNISAMY
GOPINATH
This
paper draws on the envelope properties of the GDP function to identify the sources of growth and factors influencing the evolution of the agricultural, industrial and service sectors of the Indonesian economy. The non-parametric analysis suggests that level effects on growth dominate rate effects, with capital alone accounting for five-sevenths of the growth in GDP. Scale effects of trade on rate effects are also identified. The parametric results show a number of Rybczynski and Stolper-Samuelson type effects on supply and factor rental rates, and suggest strong linkages among the three sectors of the economy. Further insights into the nature of these linkages are provided by treating the service sector as predominantly a home good, and then contrasting the ‘partial’ and ‘general equilibrium’ elasticities. (JEL: 047, 053, C32).
I.
INTRODUCTION
This paper applies the envelope theorem to the Gross Domestic Product (GDP) function to derive estimates of the determinants of GDP growth for Indonesia. We draw upon general equilibrium trade theory as, for example, presented in Woodland (1982), and as empirically applied to data by Kohli (1978 and 1993), Martin and Warr (1993), Govindan (1993), among others (Gopinath and Roe, 1994). We also adapt the method developed by Diewert and Morrison (1986) to distinguish the components of GDP growth as level and rate effects (Young, 1992). Our key conceptual contributions are to demonstrate how to measure price and factor productivity components of GDP growth without empirical knowledge of the dual GDP function or its primitives, and how to estimate and use information of the dual GDP function to Kumaresan Govindan Munisamy Gopinath Roe
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JOURNAL OF ASIAN ECONOMICS 7(l), 1996
account for the linkages between the traded and services sectors of the economy on growth. The Indonesian case is particularly interesting. Following the political turbulence of the mid 196Os, the country moved slowly away from the policies of an inward oriented economy during the 1970s and then more rapidly during the 1980s.’ The 1970s are characterized by rising foreign exchange earnings from oil revenues that initially were largely used to insulate the domestic economy from the rise in world market prices of primary commodities, including rice (Rasahan, 1983). Following a modest devaluation of the currency in 197 1, rising world prices and foreign exchange earnings, the country experienced an increase in the rate of inflation and a decline in competitiveness, forcing another devaluation in 1978, and again in 1983 (March) and 1986 (September). The late 1980s are characterized by a more open economy, where agriculture is becoming a smaller component of the economy, while the industrial and the services sectors have grown (Martin and Warr, 1993). In this study, the linkages among the major sectors of the Indonesian economy are explored to obtain insights into the implications facing agriculture as it has to compete for resources made ever more dear by the growth of the other sectors. In addition, we derive estimates of the components of growth as level and rate effects (originating from inputs and technology, respectively) and their contribution to GDP growth. Our key findings are that the level of changes in capital far exceed those of technological change, changes in traded and services prices, and changes in labor and land in accounting for GDP growth since 1969. We find that the services sector has a significant effect on the “partial equilibrium” supply and factor rental elasticities of traded goods. Our econometric estimates explain why capital is the major contributor to economic growth and shows a number of Rybczynski and Stolper-Samuelson type linkages. The results also suggest that if additions could be made to human capital, the economy may experience another important source of economic growth. The conceptual and empirical framework is laid out in the next two sections, followed by a discussion of the empirical results from the growth accounting analysis and the econometric analysis.
II. Following others2 period t is defined by:
THE GDP FUNCTION
the economy’s
Gross Domestic
g’(p,v)= ma+ where, omitting the agricultural tion g’(p,v) is inputs, v = (Q, (Y, v) E rt.
{PY: (Y,v) E 7’1
Product
function
for each
(1)
t, p = (PA, pN, ps) is a price vector of net outputs Y = (ye, ye, ys) of (A), industrial (N) and services (S) sectors of the economy. The functhe maximum value of domestic output for given levels of primary vL, vK), of land (R) labor (L) and capital (K), and the technology set
Growth Accounting:
Indonesia
79
For various restrictions3 on z~, gt completely characterizes z*, g’ (p,v) is convex and linearly homogeneous in p, and concave, non-decreasing and linearly homogeneous in v. Equation 1, and its specific functional form, Equation 5, are the basis for both the non-parametric analysis of contributions to growth in GDP, and the parametric analysis of sectoral supply and factor rental rates which follows.
A.
Non-Parametric
Framework
The non-parametric analysis draws on the Quadratic Approximation Lemma of Diewert (1976). Using Equation 1 for given reference price (p) and input (v) vectors, define the period t theoretical productivity index as: Rt(p,v) = &p,v)
/gt-‘(PJ)
(2)
Rt(p,v) is the percentage increase in output (valued at reference prices) that can be produced by the period t technology. The following two cases are of special interest:
Ri
z
[g'(p"',vt-')
/gt-’ (pt-‘,vt-‘)]
R:, = [ gt(pt,vt) I$-‘(pt,vt)l
(3)
where, RLt is a Laspeyres type index which uses period t-l output prices and primary input quantities as references, while RPt is a Paasche type productivity index based on period t prices and quantities. Both indexes are interpreted as the percentage increase in GDP that has occurred solely due to improvement in technology or the organization of production between period t- 1 and t. Given a translog functional form for the GDP function, Diewert and Morrison (1986) evaluate a geometric mean of Equations 3 and 4 in a competitive profit maximizing framework. It has to be the case that: gyp*,
vt) E p* fy*E
w*. v*
(4)
In Gt = %t + Ci CY+’ In pi + C, Pmtlnvm + l/2
EiCjUe
ij
lnpi lnpj + Zm yim lnpi
IIlV,
+
l/2 Z&n Pm, lnv, lmv,
ij = A, N, S; m,n = R, L, K. with the following restrictions
on parameters to assure the properties of Equation 1:
EiCX{=l;X,P,t= +nn
(5)
l;~~j=O;CYim=O;
= O; oij = aji; Pm, = P,,
(6)
80
JOURNAL OF ASIAN ECONOMICS 7(l), 1996
Then, (RLt * Rd)”
= a/b*c
(7)
where,4
(8) lnb = (I/ ~)~=A,N,s (pity: / ptyt + p/-‘yif-’ / pf-‘y’-‘)(lnp/
/lnp/-‘)
(9)
and5 wtvt lnc = (1/2)~m=R,L,K( m
m
/wt vt+ w~‘v~‘/w’-‘v”‘)(lnv~/
lnv&‘)
(10)
Note that the right hand side of Equation 7 can be evaluated using aggregate price and quantity data. In Equation 7, ‘a’ is growth in real value of output, ‘b’ is a translog real price index, so ‘(a/b)’ is an implicit output quantity index, while ‘c’ is a primary input quantity index. Insights into individual ‘real’ price and input contributions can be obtained by disaggregating Equations 9 and 10. Output (real) price effects, PL,/ and Pp,/ for each good i (i = A, N, S) can be defined analogous to the productivity indexes in Equation 3, and a geometric mean of the two, (PL,i * P’p,i)” = bi
(11)
is derived as,6 lnbi = (1/2)(p:y:/
pfyt + pif-‘y/-l/ pf-‘yt-‘)(lnpif / Inpi”‘)
(12)
Similarly, for each input m, input quantity effects QL,k and Q&, (m = R, L, K) analogous to Equation 3 can be defined and a geometric mean of the two (QL,~’
* Qp,m’)
= Cm
(13)
is derived as lnc, = (1/2)(wAvk
/ WV + wzv&’ / w’-V’)(lnv&/
lnvtd)
(14)
Equations 8-12 and 14 comprise the key components of the non-parametric analysis. ‘bi’ and ‘Cj’ are generally referred to as level effects of growth, while ‘(a/b.c)’ is the rate efect. B.
Parametric Framework
The parametric analysis has two steps. First, the technological change index derived from aggregate price and quantity data (Equation 7) can be used to identify
Growth Accounting:
81
Indonesia
its sources. Recent contributions to endogenous growth theory suggest several scale effects on growth, in particular, the increased openness to trade as a major contributor to growth in total factor productivity (rate efsect). Following Backus, Kehoe and Kehoe (1992) the growth in total factor productivity is specified as: R’(p,v) = &(p,v) / g’-‘(p,v) = (1 + I?-‘)~
(13
where ‘p’ is a vector of variables representing the scale effect.7 Here ‘p’ is represented by exports and capital goods imports as suggested by export-led-growth theory (Melo and Robinson, 1992). Equation 15 is estimated using a log-linear approximation and a trend variable is included to maintain the stationary properties of the time series. Second, we draw upon the envelope properties of Equation 1.8 Using Equation 5, these properties imply the net output share equations,
(16) and the primary input share equations, WA VA/ G’ = S, = PA + Zixyimlnpi+ C, &,,lnv,.,
(17)
From the parameter estimates of Equations 16 and 17, the response of net supplies and rental rates of primary factors to changes in output prices and levels of primary inputs can be computed.’
III.
DATA
The Indonesian economy is divided into 3 sectors: indexed A (agriculture), N (manufacturing), and S (services), and three factors of production, indexed R (land), L (labor) and K (capital). Annual data on value added in each sector in current and constant prices for the three sectors are obtained from World Tables (STARS, World Bank, 199 1) for the period 1960- 1989. Labor force in millions of workers and (arable) land in millions of hectares are from various issues of Production Yearbook of the Food and Agricultural Organization. Capital stock in billions of local currency is from Martin and Warr (1993). The shares of factors (land, labor and capital) in GDP are derived from National Income Accounts and social accounting matrices. An index developed by Martin and War-r ( 1993) to measure the adoption of green revolution techniques is used to represent technological change in the agricultural sector. Data on exports and capital goods imports (machinery and equipment categories of total imports) are obtained from World Tables of World Bank for the same period. The share of each of the three sectors in GDP is presented in Figure 4. It shows the decline in farm sector share relative to its share in 1969 and the gain in industrial
JOURNAL OF ASIAN ECONOMICS 7( 1), 1996
82
sector, while the share of services stayed constant. Figure 5 charts the changes in the share of the three inputs in GDP relative to their respective shares in 1969. The shares of capital and land increased, however, at the expense of labor. Among inputs, capital witnessed the largest growth rate over the period 1969-1989 averaging 12.8 % per annum, while labor and land have grown at 2.10% and 0.2%, respectively.
Iv.
ECONOMETRIC MODEL
The econometric model is based on Equations 16 and 17, from which supply and factor rental rate elasticities with respect to output prices and primary input quantities are computed. The time dependent constant terms (a~&) in Equations 16 and 17 are replaced by (4 + aft + a;A,, @’ + Pyt + PFA,), where t denotes a trend variable ‘time’ and A, is the agricultural technological change index. Following Jorgenson (1986), we refer to these measures (of, pi”) as technical change biases” (productivity growth), although other factors, such as efficiency gains from organizational innovations, may well be captured by these parameters. For af positive, technical change is referred to as output-augmenting. For p$ positive (negative), technical change is referred to as input-using (input-saving). The parameters on A, serve to identify the own and cross effects of technological change in agricultural sector. While the agricultural and industrial sector comprise goods that are either import or export competing, the services sector comprises many goods that are not traded internationally. Hence, the price of services ps is more likely to be influenced by the other exogenous variables in the model. For this reason, a reduced form for the price of services is specified as: lnpi = 60 + Ci,A,N6ilnpi + Zm=R,L,KGmlnv~+ G,lnDt +$t + &lnA, + vt
(18)
Tests failed to reject the null hypotheses that price of services is homogeneous of degree one in traded goods prices and homogeneous of degree zero in endowments as suggested by trade theory. l1 A sequential procedure is used to estimate the system of Equations in 16, 17 and 18. At the first stage, Equation 18 is regressed on the prices of traded goods, primary factor endowments and technological change indexes to obtain the predicted values of the price of services. Then, the predicted values of price of services is substituted in the supply system (Equations 16 and 17) and the parameters are estimated using maximum likelihood estimation techniques. Since the output value shares and factor payments share sum to unity (singular covariance matrix), one equation each from output and input share equations are dropped. The right hand sides of all Equations in 16 and 17 are the same, and hence OLS on individual equation is equivalent to Seemingly Unrelated Regression Estimation. Durbin-Watson and Breush-Godfrey tests (on OLS residuals) failed to reject the null that serial correlation is absent. Tests of the restrictions imposed on Equation 5 pertaining to the homogene-
Growth Accounting:
Indonesia
83
ity and symmetry properties of Equation 1 also failed to reject the null hypothesis. The model seems to fit the data well as indicated by high R2 and t ratios.
V. A.
Non-Parametric
RESULTS
Estimates of Components
of GDP Growth
Non-parametric estimates of the contributions from prices, endowments and total factor productivity (TFP) to growth in real GDP are presented in Table 1 and Figures 1 to3. On average over the period 1969-89, growth in the level of inputs alone accounted for 85%12 of growth in real GDP, while growth in TFP accounts for only 6 percent (Table 1). This is also evident from Figure 1 where the gap between the growth rates of GDP and TFP is very large and accounted for by changes in factor endowments and relative prices. During the period 1969-1989 annual growth in real GDP averages to 7.09 percent, while TFP growth averages to 0.43 percent leaving 6.66 percent (7.090.43) attributable to endowments and prices. Hence, level efsects dominate rate effects on growth in Indonesia. As TFP is a residual, it is broadly defined to include external shocks to the economy, like oil price changes and weather conditions. Excluding outliers, such as the drought year 1982, increases the contribution of TFP to growth in real GDP from 6% to 13%. Note, however, that our primary interest is to capture the underlying ‘growth component’ of the TFP series that is devoid of such low frequency fluctuations. Following Gopinath and Roe (1995), we employ a Hodrick-Prescott filter to smoothen the series. This method involves choosin smoothed values {s,},=~’ for the series {x,},=1’ which solve the following problem: ?3 min{(l/T)C,,‘(x,
-sJ2+
(h/T)&zil[(st+l
-St--(St-st-I)]*)
(19)
Figure 1 plots the filtered TFP series on the secondary Y;utis. It is well evident from this figure that the contribution from TFP to growth in GDP has been declining. In his tale of two cities, Young (1992) points to the fact that growth in Singapore is largely a level effect,14 while Hong Kong’s TFP growth contributed significantly to TABLE 1.
Year 1969-89
GDP Growth
Components
Agr: Pr Effect
7.09
a.23
Ind. Pr Effect 0.88
of Real GDP Growth - Average (%) Sex Pr Effect -0.03
Lund Labor
Capital
TFP Growth
0.10
0.96
4.97
0.43
1969-74
9.43
-1.11
0.90
0.90
0.16
1.03
4.58
1.69
1975-79
7.09
0.24
0.85
-0.92
0.00
0.99
5.82
0.08
1983-84
6.08
-0.42
0.41
0.00
0.19
1.06
6.25
-1.40
1985-89
5.29
0.54
-0.18
0.05
0.76
3.33
a.30
1.09
JOURNAL OF ASIAN ECONOMICS 7( 1), 1996
I.1 1 IO
0.9 0.1
0.6
E
-10
FIGURE 1.
,_.
,
Growth in Real GDP and TFP
I\
.......................
.
........................
....................................................................
_.
.
...................................................................
.p. .. . . . . . . . . . . . . . . . . . . .,.....-.. a-,
lS¶
.
*. . . . . . . .._...... , 070
(
ian
,
,
im
mz
-t-
Y. . . ) I614
.._e...._...
.._............._.......e...*...em.e..mme
, cm
( mn
AGaJcuLTuw
FIGURE 2.
,
.-
,
,
ml
I¶76
*
on
’
1690
INDUSTRY
I
I661
' l9w
-e-
I
1966
.
.
..-....
I
a96
SERVKES
Price (Real) Effects on Real GDP
I
.
.-.-.........
I
I
tan
I
DIT
I
5966
I
I
Growth Accounting:
85
Indonesia
FIGURE 3.
‘_._
.___
_.__
Input Effects on Real GDP
__.__
P i i!i
FIGURE 4.
Share of Output Sectors in GDP
JOURNAL OF ASIAN ECONOMICS 7(l), 1996
86
I
--tLAmR
FIGURE 5.
-CAPITAL-E-LAW
Share of Factor Endowments
I in GDP
its GDP growth. This, he argues, is an outcome of the differences in the general level of education of the people in these two regions of Asia. The effects of changes in the prices of agriculture, industry and services on growth in GDP, holding all else constant, is reported in Figure 2 (equation 12). Since petroleum is aggregated into the industrial sector, the“spike” effects of 1974 and 1978-1980 are evidences of oil price shocks. On average over the period 1969-1989, the growth in the real price index of industrial sector goods averages 0.88 % of the 7.09 % growth in real GDP, although this contribution to growth has been declining over the period (see column 4, Table 1). The change in the index of agricultural good prices has, on average for the period, contributed to a -0.23 % decline in the growth of GDP. Note that the contributions of industrial and agricultural prices has tended to be counter-cyclical (columns 3 and 4, Table 1). The contribution to GDP growth from changes in the price index of services sector is relatively small (-0.03%) over the period. These results suggest that the terms of trade within the Indonesian economy has been in favor of the industrial goods and neutral to services sector with a strong bias against agricultural goods. l6 A decomposition of the individual effects of changes in the quantity of land, labor and capital on growth in real GDP is reported in Figure 3 (Equation 14). It is clear that capital is the largest contributor to GDP growth, averaging about 5.0 % for the 1969-89 period. However, its contribution has been declining since 1982. Labor’s contribution has remained at approximately 1 .O %, while the contribution from changes in the level of land is the least at 0.11 %. While several authors have
Growth Accounting:
Indonesia
87
identified the importance of an open economy and unrestricted capital markets to domestic capital accumulation, these indices do not permit this inference, although our econometric analysis will. The declining trend in capital’s contribution may reflect declining marginal returns, a decrease in investment following the decline in oil revenues during the latter 1980s or both.15 B.
Parametric Results
Sources of Technological Change. Table 2 presents the results from the estimation of Equation 15. Consistent with others (see endnote 8) not all of the parameters are significant. The significance of the coefficient on exports suggests that scale effects with regard to trade positively influences TFP growth in Indonesia. In spite of the relatively small contribution from TFP to growth in GDP, this result confirms the positive effects of export on growth. However, the R2 is only 26% suggesting other factors outside the model are also important to growth in TFI? Our regression results on ‘filtered TFP’ further confirm the positive association between TFP growth and exports, while the coefficients on other variables are of wrong sign and insignificant. EfSects of Exogenous Variables on the Price of Services. The estimation of reduced form Equation 18 yielded generally acceptable results with the possible exception of the effects of endowments on the price of services. The data failed to reject the null hypothesis of homogeneity of degree one in world prices at a high level of confidence, but the unrestricted coefficients of the labor and capital endowment variables have relatively large standard errors. While the endowment restriction was also accepted, albeit at a lower confidence level, some concern about the reliability of these coefficients is mentioned later in our discussion of the“tota1” general equilibrium effects of changes in endowments on the economy. ln ps = 0.6160 ln PA + 0.38401npN + 2.7185 lnvR - 2.4242111~~ (0.0491)
(0.049 1) -
(0.7820)
.2943 In vK - 0.0052 In D + 0.0479 t (0.0049)
(0.1840)
(0.0227)
R2 = 0.99 , DW = 1.84, (Std. Error) TABLE 2. Results of Regression of TFP Growth on Trade Variables Variable Constant Exports Imports
Note;
Parameter -0.05 0.06* -0.03
Time
0.002
R2
0.026
*Significant at 10%
(0.8 177) +
0.0976 In A, (0.0267) (20)
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JOURNAL
OF ASIAN ECONOMICS
7(l), 1996
The deficit (D) in the country’s balance of payments appears not, on average over the 1960-1989 period, to have had a significant effect on the price of services. Together with homogeneity in traded good prices, these results imply that changes in the relative prices of traded goods to services obtained from the Salter-type of disequilibrium models of the current account, cannot be inferred. A change in the nominal exchange rate appears not to have altered relative prices in the economy, nor have policies that gave rise to current account imbalances of the magnitudes experienced over the period. That the price of services rises due to an increase in the price of traded goods is clear; the substitution and income effect of an increase in the price of traded goods increases the demand for services while the producers of services are forced to compete for resources that are being drawn into the traded good’s sector. Hence, the price of services must rise to clear the market. The results also indicate that this effect is stronger for an increase in the border price of agricultural goods than it is for an increase in the price of industrial goods. This result reflects the fact that agriculture remains a major, albeit declining, component of the economy and that food accounts for a fairly large proportion of household expenditures (over 45 percent of total expenditures in 1992). Technological change (as captured by t and A,) and changes in land endowment also positively affect the price of services. The technological change results are consistent with the view that technological change in the other sectors of the economy have income effects that dominate technological change in the supply of services, so that the net result is to increase their price. Since the supply of services is not very responsive to changes in the endowment of land (Table 3.1), the importance of land in Equation 20 suggests that returns from this endowment, and its indirect effect on the returns to other factors, is likely an important component of income that in turn affects the demand for services. Rybczynski-type Efsects. The direct effects (holding services prices constant) of endowments on supply are shown by the elasticities presented in Table 3.1 (rows 1-3, columns 4-6). The results are generally consistent with the Rybczynski theorem,
TABLE 3.1. Elasticity of Agri. SUPPlY Agriculture Industry Services Rental Rate Land Labor Capital
Product Supply and Factor Return Elasticities w/r to the price of Ind. Serv.
w/r to the endowment of Labor Land Cap.
0.23 a.06 a.18
-0.05 0.17 -0.08
-0.18 -0.11 0.26
0.11 0.19 0.08
0.55 0.37 0.49
0.34 0.44 0.43
0.33 0.41 0.30
0.44 0.22 0.31
0.23 0.37 0.39
-1.26 0.17 0.17
0.69 -0.46 0.34
0.57 0.29 -0.51
Growth Accounting: Indonesia
89
al~ough it is well known that the theorem does not generalize to more than the two factor-two good model.17 For an economy with more than two factors of production and two goods, the theorem essentially states that if the endowment of a factor increases, then the industry which uses that factor relatively intensively will expand relative to other industries. It is possible that when the endowment of a factor increases, the outputs of all goods increase less than propo~ionately to the increase in endowment. If one output does increase more than proportionately, some other output must fall (Woodland, 1982, p. 133). For agricultural supply, the elasticity with respect to labor endowment is larger (0.55) than that of land (0.11) and capital (0.34). For industrial supply, the elasticity with respect to capital (0.44) is larger than that of land (0.19) and labor (0.37), while for services, the elasticity with respect to labor (0.49) and capital (0.43) dominate the elasticity of land (0.08). These results support the notion that agriculture is labor intensive, the industrial sector is capital intensive while the services sector is marginally labor intensive. Further, the results suggest that the direct effects (holding services prices constant) of an increase in endowment causes all outputs to increase but, no single output increases more than proportionally to the increase in endowment. The”tota1” general equilibrium effect of endowments on supply that prevail after services prices have adjusted (equation 20) appear in Table 3.2, rows 1-3 and columns 3- 5.” Consider the case of labor; an increase in this endowment causes agricultural supply to increase almost in propo~ion to the change in labor (0.988). The result in Table 3.1 indicates that services supply increases in response to an increase in labor (0.49), and Equation 20 suggests that the services market clears at a lower services price, thereby releasing resources to agriculture (and to the industrial sector) as suggested by the coefficient (-0.18) in Table 3.1. The net result, after equilibrium is established in all markets, is the 0.988 percent response to a one percent change in labor endowment. A one percent increase in the capital endowment causes, in general equilibrium, agricultural supply to increase by 0.391 percent. In this case, services supply initially increases by 0.43 percent (Table 3.1) and its market clears at a lower price (negative
TABLE 3.2. Elasticity of
General Equilibrium
Elasticities; Services Prices Endogenous
w/r to the price of Agri. Ind.
Lund
w/r to the endowment of Labor Cap.
SUPPlY Agriculture Industry Services Rental Rate Land Labor Caoital
0.114 -0.124 -0.018
-0.114 0.124 0.018
-0.380 -0.106 0.796
0.988 0.633 -0.147
0.477 0.640 0.543
0.523 0.359 0.457
-0.624 1.177 I .220
0.127 -1.353 -0.595
0.39 1 0.473 0.352 0.497 0.176 -0.625
90
JOURNAL OF ASIAN ECONOMICS 7(l), 1996
coefficient on capital in Equation 20) causing agricultural supply to increase by 0.23 percent (Table 3.1). The net effect is the 0.391 percent increase in agricultural output. An increase in the level of land causes agricultural and services supply to expand by 0.11 and 0.08 percents, respectively (Table 3.1). However, services markets now clear at a higher price, 2.72 percent (Equation 20). This higher price pulls resources away from agriculture with the net result of a decline in agricultural supply of -0.380 percent (Table 3.2). Labor and capital are important for the production of industrial goods, while an increase in land has relatively small effects. These results suggest that land is important to the production of services, but not necessarily as a factor of production; instead, an increase in land has strong income effects that increase the demand for services with the result that the services market clears at a higher price which, of course, pulls more resources into the production of services. Supply Response. The direct (i.e., holding services prices constant) supply response of agricultural, industrial, and services sectors to their own and cross prices are represented by the elasticities presented in rows 1-3, columns 1-3, Table 3.1. All the own price supply elasticities are positive and the cross price elasticities are negative. The own price supply elasticity for agriculture is 0.23, which is close to the value obtained by Martin and Wart- (199 1). This elasticity also falls within the range of 0.10 and 0.23 obtained from the studies reviewed by Binswanger (1989) for most developing countries. The own price elasticity is larger for services (0.26) than it is for agriculture or for industrial goods (0.17). The cross price effects between agriculture and industry are smaller (-.05, -0.06, respectively) than the agricultureservices and industrial-services cross price effects. This result suggests that the agricultural and industrial sector compete more for resources employed in the services sector than they compete for resources among themselves. The ‘general equilibrium’ results of the “total” effects, of traded good prices on supply are reported in Table 3.2, rows 1-3, columns l-2 for price elasticities. In this case, the price elasticity of supply for agriculture declines to 0.114, a value that falls near the lower bound of the elasticities reported by Binswanger (1989). This result implies that an increase in the price of agricultural goods also causes the price of services to rise (Equation 20) which forces agriculture to compete for resources from the services sector, so that the indirect effect decreases the net effect of agriculture’s response to a change in its relative price. The own price elasticity of industrial good supply (0.124) also declines. An increase in its relative price forces it too to compete for resources from the services sector leading to this decline but not to the extent of agriculture. The elasticity of supply for services suggests that as the terms of trade tend to favor the industrial sector, the supply of services rises but by a fairly small magnitude (0.018). Finally, note that (both direct and total effects) the supply response is greater to a percent change in endowments of labor and capital than to own price changes. This suggests that infrastructure and human capital might be more important to generate growth in agriculture (Binswanger, 1989).
Growth Accounting:
Indonesia
91
Stolper-Samuelson-type effects. An increase in output price tends to have the largest effect on the rental rate of that factor that is used most intensively in the sector.19 These are the results obtained (Table 3.1 rows 4-6, columns l-3) as seen from the elasticities of 0.41 for wage- agriculture, 0.37 and 0.39 for wage-services and capital-services (direct effects), respectively. It is unclear why land rental rates respond more strongly to an increase in the price of industrial goods than do capital rental rates (0.44 vs 0.3 l), since the industrial sector appears relatively capital intensive. Holding the price of services constant, these results suggest that real income to labor is more responsive to an improvement in the terms of trade in agricultural than in industrial goods. The general equilibrium“tota1” effects are reported in rows 4-6, columns l-2 (Table 3.2). The most interesting result is that an increase in the price of agriculture goods has a stronger “total” effect on the rental rates of labor and capital than does an increase in the price of industrial goods. These stronger effects are of course due to the adjustments in the market for services caused by a change in traded good prices. The direct effect of a percent change in the price of agricultural goods causes wages to rise by 0.41 percent (Table 3.1). Higher cost of production coupled with increased demand for services through income effects force the market for services to clear at a higher price. The higher services price further increases the competition for resources with the net result that wages rise by about 0.64 percent (Table 3.2) for a one percent increase in the price of agricultural goods. Similar inferences can be obtained for capital rental rates. Land rental rates appear to be slightly more responsive to an increase in the price of industrial than agricultural goods (0.523 vs 0.477). This result is obtained because the direct effect of an increase in industrial good prices on land rental rates is larger than in the case of agricultural prices effects (0.44 vs 0.33, Table 2). This effect tends to out weigh the otherwise larger effect of agricultural good prices on the price of services. Together, these results suggest that an increase in the price of agricultural goods has the tendency to increase costs of production to a relatively larger degree than an increase in industrial prices. Depending on the cost structures in other countries, this result has implications to the country’s comparative advantage in world markets. Rental Rate Response to Endowments. The elasticities presented in Table 3.1 (rows 4-6, columns 4-6) show the direct effects of factor rental response to an increase in own and other factor supply. All the own factor price elasticities show negative signs as expected. All the cross factor price elasticities are positive, indicating that the factors are substitutes in production; an increase in the level of an input is associated with an increase in the price of other factors. Capital is of particular importance since land rental rates and wages appear particularly responsive to an increase in supply of this factor. However, it is the general equilibrium effects that matter, Table 3.2, rows 4-6, columns 3-5. The general equilibrium results suggest that an increase in land endowment has large positive effects on labor and capital rental rates, and a smaller negative effect
JOURNAL OF ASIAN ECONOMICS 7(l), 1996
92
on own rental rate. The relatively large positive effect of an increase in land endowment on the price of services (2.719, Equation 20) which in turn increases the price of land by 0.23 percent leads to such large cross effects. However, an increase in the endowment of labor has the opposite effect. The reason is similar; in this case the price of services falls to an increase in labor endowment by -2.424% (Equation 20) which out weighs the otherwise positive effects reported in column 5, row 4 of Table 3.1. A one percent increase in capital tends to increase land and labor rental rates and to decrease capital rates by -0.625 percent. The smaller deviation between the direct and general equilibrium effects in this situation (Table 3.1 vs Table 3.2), result from the smaller effect that capital has on the price of services. The effects of technological change on supply and facTechnological Change. tor rental rates are suggested by the last two columns of Table 4. All the parameters on both time and the index for agricultural technological change are significant except those in the capital share equation suggesting non-neutral growth in total factor productivity. The parameters on ‘time’ trend, as mentioned earlier, measure the relative rates of output augmentation and input utilization.20 Hence, the negative coefficients on the ‘time’ trend in both agricultural and industrial goods share equations suggest that ‘overall’ technical change has been services output augmenting (coefficient on the services share equation is restricted to 0.0081 as they sum to zero). On the input side, technical change has been labor-using and land and capital-saving. The coefficients on ‘A,’ (agricultural technological change index) suggest that the industrial sector may stand to gain more from faster technological change in agriculture because of their linkages.21 In addition to adding value to farm goods, industrial sector also provides intermediate inputs like fertilizers and other chemicals that are crucial to crop production in the post green revolution period. While the TABLE 4.
Parameter
Estimates
of the Indonesian
GDP Share Equations
GDP Shares
IMPA)
Ag.
0.31 I5
Ind.
4). I I69
169
1nfP.J
IN vd
In(q,)
In(vK)
-0.1946
-0.0032
(0.0 179)
(0.0387
(0.0 I84
(0.0248
(0.0 I9 I )
0.2478
-0. I309
0.0 I85
-0.0301
0.0 I I6
(O.OI 79
(0.0229)
(0.0263
(0.0084)
-0. I946
-0. I.709
0.3255
(0.0387
(0.0263)
(0.04 17)
(0.0277)
(0.029 I )
Ser.
In(PN) 4.1
-0.0153
0.0254
-0.0222
(0.0 147)
(0.0 I 30) 0.0 IO6
0.008 I
(0.0277)
(0.0035)
-0.0032
0.0 I85
-0.0 I53
-0.045 I
0.0253
0.0 I98
(0.0 184)
(0.0084)
(0.0277)
(0.0823)
(O.IO46)
(0.05 16)
0.0254
-0.030 I
(0.0248) Cap.
-0.0222
Rent
(0.0 I9 I )
0.0047
0.0253
0.0300
(O.OI 47)
(0.0354)
(O.Io46)
(0. IO 16)
0.0 I I6
0.0 IO6
(0.0130) (0.0277)
0.0 I98 (0.05 16)
(0.0023)
O.(K)47
Rent Wages
(0.0025) -0.0028
(0.0354)
Land
-0.0553
-0.0553 (0.0278)
‘A’
lime
-0.0053
-0.0029
0.0 I26 (0.0074) 0.03 I4
A=Agricultural
0.97
(0.0075) -0.0440
0.95
(0.005 I ) O.(HK3I
(0.00 17)
(0.0039)
0.0045
-0.0 IO7
(0.0278)
(0.0032)
(0.0059)
0.0355
-0.00 I6
0.0076
(0.0268)
(0.0030)
(0.005 I )
NIJIL’.Y: (Figurcx
in the parcnthcscs xc standard errors) (Ag.=Agriculturc; Ind.=lndustry; Cap.=CapiM;
R2 0.99
Technological Change)
0.94 0.94 0.93
Growth Accounting:
Indonesia
93
‘overall’ technical change has been labor-using, agricultural sector experienced a bias towards the use biological and chemical inputs and hence, the negative coefficient of ‘A,’ on labor share is not surprising. The coefficient of ‘A,’ in the capital share equation is positive, but insignificant. Technological change in agriculture has also tended to increase land rental rates while forcing the services sector to compete more for resources, hence the negative co-efficient (-0.044) in the services share equations.
VI.
SUMMARY REMARKS
This paper draws upon trade theory to provide insights into the sources of growth and the evolution of agricultural, industrial and services sectors of the Indonesian economy. Our non-parametric analysis suggests that level effects on growth dominate rate effects, with growth in capital input alone accounting for five-sevenths of the growth in GDP. In addition, terms of trade within the economy has a strong bias towards industry and against agriculture. Consistent with the findings of Levine and Renelt (1992), benefits of an open economy are reflected in the scale effects from trade on TFP growth. However, declining trends in the contributions from capital and TFP to growth in GDP is of serious concern. This suggests that gains from the accumulation of physical capital and trade reform appear to be exhausted, leaving potential sources of TFP growth like human capital, research and development and learning-by doing as major sources of future growth. The envelope properties of the postulated gross domestic product function are then exploited to derive the supply and factor rental elasticities from the estimation of the coefficients in share equations for both the direct and general equilibrium effects of changes in exogenous variables. However, the general equilibrium results are sensitive to our estimates of the effects of endowments on the market clearing price of services. A number of Rybczynski and Stolper-Samuelson-type effects are obtained. The own elasticity of agricultural supply is consistent with most other studies, however, output response to factor endowment changes are larger than price responses. Agricultural supply response to an increase in the endowment of labor is relatively greater than its response to an increase in the endowments of either capital or land. In addition, an increase in the border price of agricultural goods tends to have relatively large effects on the rental rates for labor and capital thus contributing significantly to an ‘overall’ increase in the cost of production. The market for services tends to be more closely linked to the agricultural sector than to the industrial sector. Technological change appears to augment the production of services and is biased against agricultural goods more than industrial goods while also increasing the market clearing price for services. On the input side, evidence suggests that technological change (at the aggregate level) has been labor-using. Technological change within the agricultural sector appears to be more beneficial to the industrial sector suggesting strong linkages between the two sectors. As the world markets are
94
JOURNAL
OF ASIAN ECONOMICS
7( 1), 1996
becoming increasingly integrated, elimination of the existing trade protection and market distortions both for agriculture and the non-agricultural sectors is required for efficient reallocation of resources. This, in turn, will make Indonesia more competitive in world markets. ACKNOWLEDGMENTS: Funding was provided by a NRI grant to USDAIERSICAD-University of Minnesota. We thank the anonymous referees for their suggestions, any remaining errors are the responsibility of the authors.
NOTES 1. See Papageorgiou, Michaely, and Choksi (1991). 2. See Woodland (1982) and Diewert (1974). 3. See Diewert (1974, p. 134). 4. Like Equation 2 both Laspeyres and Paasche indexes are not observable unless the parameters of the GDP function are known. However a geometric mean can be computed without knowledge of these parameters, hence the term non-parametrics. 5. p’ and pfml are real prices and so ‘a’ is growth in real value of output. We derive the real prices by deflating the sectoral price indices by a GDP deflator, in principle, discounting them for average price increases in the economy. 6. For instance, ‘bi’ is interpreted as the change in GDP (between periods t and r-1) attributable to change in real price of ‘i’ th good from p’-’ to pt’ holding other prices, all inputs and technology constant. 7. Backus, Kehoe and Kehoe (1992) find little evidence of a relation between growth rate of GDP per capita and scale variables implied by theories of learning by doing and investment in human capital. However, they find a significant relation between growth rate of GDP per capita and indexes of intra-industry trade. Gopinath, Kennedy and Roe (1995) report that increased share of Mexico in trade among NAITA countries has contributed significantly to its TFP growth. 8. See Kohli (1993). 9. See Takayama (1985, pp. 147-149 for the derivation of supply and factor rental rate elasticities. 10. These coefftcients measure the relative rates of output augmentation (i) and input utilization (m) and hence, sum to zero respectively. Il. See Woodland (1982, Chapter 8). 12. (6.55/7.09)*100. 13. The averages of the two series will remain the same. See Gopinath and Roe (1995) for more details. 14. Capital, again, contributed to as much as 83% of growth in Singapore’s GDP 15. In addition, this result suggests a transitional (dynamic) path towards long-run growth from a period of relatively high trade distortions. 16. In the case of the U.S., Gopinath and Roe (1994) report that the terms of trade has been biased towards services sector and against farm and industrial sectors. 17. The parameter estimates reported in Equation 20 are crucial to the following discussion. 18. See Woodland (1982, p. 235) for conditions under which the theorem can be generalized. 19. See Woodland (1982) for this generalized version of the Stolper-Samuelson Theorem. 20. Jorgenson ( 1986). 21. Roe and Gopinath (1995) report a similar result on the linkages of the U.S. farm sector. TFP growth in the U.S. farm sector contributes to real GDP growth in the value added sector through level effects, while rate effects in the value added sector mitigate negative relative price effect on real GDP growth in farm sector.
Growth Accounting:
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95
REFERENCES Backus, D.K., Kehoe PJ. and Kehoe, T.J. 1992. In Search of Scale Effects in Trade and Growth. Journal of Economic Theory: 377-399. Binswanger, Hans. 1989. The Policy Response of Agriculture. Proceedings of the World Bank Annual Conference on Development Economics, 1989. Bowden, R.J. and Turkington, D.A. 1984. Znstrumental Variables. Cambridge: Cambridge University Press. Diewert, W.E. 1974. Applications of Duality Theory. In M.D. Intriligator, and D.A.Kendrick Frontiers of Quantitative Economics, Vol. 2. Amsterdam: North-Holland. 1976. Exact and Superlative Index Numbers. Journal of Econometrics: 115145. -. Diewert, W.E. and Morrison, C.J. 1986. Adjusting Output and Productivity Terms of Trade. The Economic Journal, 96: 659-679.
(eds),
Indexes for Changes in
Gopinath, M. and Roe, T.L. 1994. Sources of Growth in U.S. GDP and Economy-wide Linkages to the Farm Sector. Working Paper, Department of Applied Economics, University of Minnesota. Gopinath, M., Kennedy, P.L. and Roe, T.L. 1995. Trade, America: An Empirical Analysis. North American (forthcoming). Gopinath, M. and Roe, T.L. 1995. Sources of Sectoral Growth U.S. Agriculture. Economic Development Center Bulletin
Growth and Welfare Linkages Journal of Economics and
in North Finance,
in an Economywide Context: The Case of 95-7, University of Minnesota.
Govindan, K. 1993. Technological Change and Trade Patterns: An Econometric Analysis of the Indonesian Economy. Unpublished Ph.D. Dissertation, University of Minnesota, St. Paul. Griliches, Z. 1993. Productivity, R&D and the Data Constraint. American Economic Review,4: l-23. Jorgenson, D.W. 1986. Econometric Methods for Modeling Producer Behavior. In Z. Griliches, and M.D.Intriligator (eds.), Handbook of Econometrics, Vol. 3. Amsterdam: North-Holland. Kohli, U. 1978. A GNP Function and the Derived Demand for Imports and Supply of Exports. Canadian Journal of Economics, 1l(2): 167-183. 1993. GNP Growth Accounting in the Open Economy: Parametric and Non-Parametric Estimates for Switzerland. Swiss Journal of Economics and Sfatistics, 129: 601-615. Krueger, O.A. 1983. Exchange-Rate Determination. Cambridge: Cambridge University Press. Levine, R. and Renelt, D. 1992. A Sensitivity Economic Review, 82: 942-963.
Analysis
of Cross-Country
Regressions.
American
A Supply-Side
Analysis
Melo, Jaime De and Robinson, S. 1992. Productivity and Externalities: Models of Export-led Journal of International Trade and Economic Development, l( 1): 41-68.
Growth.
Martin, W. and Warr, P 1993. Explaining the Relative Decline of Agriculture: for Indonesia. The World Bank Economic Review, 7(3): 381-401.
Papageorgiou, D., Michaely, M. and Choksi, A.M. (eds.). 1991. Liberalizing Foreign Trade-Zndonesia, Pakistan and Sri Lanka, Vol. 5. Basil: Blackwell. Rasahan, CA. 1983. Government Intervention in Food Grain Markets: An Econometric Indonesian Economy. Unpublished Ph.D Thesis, University of Minnesota.
Study of the
Roe, T.L and Gopinath, M. 1995. Private vs. Public Incentives to Market Development Investments: Is There a Role for Public Policy? In D. Padberg (ed.), The Role of Public Policy in Foreign and Domestic Market Development. College Station, TX: Texas A&M University Press. Takayama, A. 1985. Mathematical Economics. Cambridge: Cambridge University Press. Woodland, A.D. 1982. International Trade and Resource Allocation. Amsterdam: North-Holland. Young, A. 1992. A Tale of Two Cities: Factor Accumulation and Technical Change in Hong Kong and Singapore. Working paper,. Massachusetts Institute of Technology.
MUNISAMY
GOPINATH
This
paper draws on the envelope properties of the GDP function to identify the sources of growth and factors influencing the evolution of the agricultural, industrial and service sectors of the Indonesian economy. The non-parametric analysis suggests that level effects on growth dominate rate effects, with capital alone accounting for five-sevenths of the growth in GDP. Scale effects of trade on rate effects are also identified. The parametric results show a number of Rybczynski and Stolper-Samuelson type effects on supply and factor rental rates, and suggest strong linkages among the three sectors of the economy. Further insights into the nature of these linkages are provided by treating the service sector as predominantly a home good, and then contrasting the ‘partial’ and ‘general equilibrium’ elasticities. (JEL: 047, 053, C32).
I.
INTRODUCTION
This paper applies the envelope theorem to the Gross Domestic Product (GDP) function to derive estimates of the determinants of GDP growth for Indonesia. We draw upon general equilibrium trade theory as, for example, presented in Woodland (1982), and as empirically applied to data by Kohli (1978 and 1993), Martin and Warr (1993), Govindan (1993), among others (Gopinath and Roe, 1994). We also adapt the method developed by Diewert and Morrison (1986) to distinguish the components of GDP growth as level and rate effects (Young, 1992). Our key conceptual contributions are to demonstrate how to measure price and factor productivity components of GDP growth without empirical knowledge of the dual GDP function or its primitives, and how to estimate and use information of the dual GDP function to Kumaresan Govindan Munisamy Gopinath Roe
l
Univcrsily
Inlcmalional
l l
Univcrsily
of Minnwxa.
Food Policy
Dcpanmcm
Journal of Asian Economics, Vol. 7, No. ISSN:
Rcscarch Inslilulc.
of Minnesota. of Applied
I, I9Yh
12(W) Scvcnlccnth
Dcpartmcnl
of Applied
Economics.
SI. Paul. MN
pp. 77-9.5
Slrccl. NW, Washington,
Economics,
St. Paul.
77
DC 20036-3(Wb. SSII~X.
Terry
I,.
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1049-(X)78
MN
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19% by JAI Press Inc.
All rights of rcproduclion
in any form rcscrvcd.
78
JOURNAL OF ASIAN ECONOMICS 7(l), 1996
account for the linkages between the traded and services sectors of the economy on growth. The Indonesian case is particularly interesting. Following the political turbulence of the mid 196Os, the country moved slowly away from the policies of an inward oriented economy during the 1970s and then more rapidly during the 1980s.’ The 1970s are characterized by rising foreign exchange earnings from oil revenues that initially were largely used to insulate the domestic economy from the rise in world market prices of primary commodities, including rice (Rasahan, 1983). Following a modest devaluation of the currency in 197 1, rising world prices and foreign exchange earnings, the country experienced an increase in the rate of inflation and a decline in competitiveness, forcing another devaluation in 1978, and again in 1983 (March) and 1986 (September). The late 1980s are characterized by a more open economy, where agriculture is becoming a smaller component of the economy, while the industrial and the services sectors have grown (Martin and Warr, 1993). In this study, the linkages among the major sectors of the Indonesian economy are explored to obtain insights into the implications facing agriculture as it has to compete for resources made ever more dear by the growth of the other sectors. In addition, we derive estimates of the components of growth as level and rate effects (originating from inputs and technology, respectively) and their contribution to GDP growth. Our key findings are that the level of changes in capital far exceed those of technological change, changes in traded and services prices, and changes in labor and land in accounting for GDP growth since 1969. We find that the services sector has a significant effect on the “partial equilibrium” supply and factor rental elasticities of traded goods. Our econometric estimates explain why capital is the major contributor to economic growth and shows a number of Rybczynski and Stolper-Samuelson type linkages. The results also suggest that if additions could be made to human capital, the economy may experience another important source of economic growth. The conceptual and empirical framework is laid out in the next two sections, followed by a discussion of the empirical results from the growth accounting analysis and the econometric analysis.
II. Following others2 period t is defined by:
THE GDP FUNCTION
the economy’s
Gross Domestic
g’(p,v)= ma+ where, omitting the agricultural tion g’(p,v) is inputs, v = (Q, (Y, v) E rt.
{PY: (Y,v) E 7’1
Product
function
for each
(1)
t, p = (PA, pN, ps) is a price vector of net outputs Y = (ye, ye, ys) of (A), industrial (N) and services (S) sectors of the economy. The functhe maximum value of domestic output for given levels of primary vL, vK), of land (R) labor (L) and capital (K), and the technology set
Growth Accounting:
Indonesia
79
For various restrictions3 on z~, gt completely characterizes z*, g’ (p,v) is convex and linearly homogeneous in p, and concave, non-decreasing and linearly homogeneous in v. Equation 1, and its specific functional form, Equation 5, are the basis for both the non-parametric analysis of contributions to growth in GDP, and the parametric analysis of sectoral supply and factor rental rates which follows.
A.
Non-Parametric
Framework
The non-parametric analysis draws on the Quadratic Approximation Lemma of Diewert (1976). Using Equation 1 for given reference price (p) and input (v) vectors, define the period t theoretical productivity index as: Rt(p,v) = &p,v)
/gt-‘(PJ)
(2)
Rt(p,v) is the percentage increase in output (valued at reference prices) that can be produced by the period t technology. The following two cases are of special interest:
Ri
z
[g'(p"',vt-')
/gt-’ (pt-‘,vt-‘)]
R:, = [ gt(pt,vt) I$-‘(pt,vt)l
(3)
where, RLt is a Laspeyres type index which uses period t-l output prices and primary input quantities as references, while RPt is a Paasche type productivity index based on period t prices and quantities. Both indexes are interpreted as the percentage increase in GDP that has occurred solely due to improvement in technology or the organization of production between period t- 1 and t. Given a translog functional form for the GDP function, Diewert and Morrison (1986) evaluate a geometric mean of Equations 3 and 4 in a competitive profit maximizing framework. It has to be the case that: gyp*,
vt) E p* fy*E
w*. v*
(4)
In Gt = %t + Ci CY+’ In pi + C, Pmtlnvm + l/2
EiCjUe
ij
lnpi lnpj + Zm yim lnpi
IIlV,
+
l/2 Z&n Pm, lnv, lmv,
ij = A, N, S; m,n = R, L, K. with the following restrictions
on parameters to assure the properties of Equation 1:
EiCX{=l;X,P,t= +nn
(5)
l;~~j=O;CYim=O;
= O; oij = aji; Pm, = P,,
(6)
80
JOURNAL OF ASIAN ECONOMICS 7(l), 1996
Then, (RLt * Rd)”
= a/b*c
(7)
where,4
(8) lnb = (I/ ~)~=A,N,s (pity: / ptyt + p/-‘yif-’ / pf-‘y’-‘)(lnp/
/lnp/-‘)
(9)
and5 wtvt lnc = (1/2)~m=R,L,K( m
m
/wt vt+ w~‘v~‘/w’-‘v”‘)(lnv~/
lnv&‘)
(10)
Note that the right hand side of Equation 7 can be evaluated using aggregate price and quantity data. In Equation 7, ‘a’ is growth in real value of output, ‘b’ is a translog real price index, so ‘(a/b)’ is an implicit output quantity index, while ‘c’ is a primary input quantity index. Insights into individual ‘real’ price and input contributions can be obtained by disaggregating Equations 9 and 10. Output (real) price effects, PL,/ and Pp,/ for each good i (i = A, N, S) can be defined analogous to the productivity indexes in Equation 3, and a geometric mean of the two, (PL,i * P’p,i)” = bi
(11)
is derived as,6 lnbi = (1/2)(p:y:/
pfyt + pif-‘y/-l/ pf-‘yt-‘)(lnpif / Inpi”‘)
(12)
Similarly, for each input m, input quantity effects QL,k and Q&, (m = R, L, K) analogous to Equation 3 can be defined and a geometric mean of the two (QL,~’
* Qp,m’)
= Cm
(13)
is derived as lnc, = (1/2)(wAvk
/ WV + wzv&’ / w’-V’)(lnv&/
lnvtd)
(14)
Equations 8-12 and 14 comprise the key components of the non-parametric analysis. ‘bi’ and ‘Cj’ are generally referred to as level effects of growth, while ‘(a/b.c)’ is the rate efect. B.
Parametric Framework
The parametric analysis has two steps. First, the technological change index derived from aggregate price and quantity data (Equation 7) can be used to identify
Growth Accounting:
81
Indonesia
its sources. Recent contributions to endogenous growth theory suggest several scale effects on growth, in particular, the increased openness to trade as a major contributor to growth in total factor productivity (rate efsect). Following Backus, Kehoe and Kehoe (1992) the growth in total factor productivity is specified as: R’(p,v) = &(p,v) / g’-‘(p,v) = (1 + I?-‘)~
(13
where ‘p’ is a vector of variables representing the scale effect.7 Here ‘p’ is represented by exports and capital goods imports as suggested by export-led-growth theory (Melo and Robinson, 1992). Equation 15 is estimated using a log-linear approximation and a trend variable is included to maintain the stationary properties of the time series. Second, we draw upon the envelope properties of Equation 1.8 Using Equation 5, these properties imply the net output share equations,
(16) and the primary input share equations, WA VA/ G’ = S, = PA + Zixyimlnpi+ C, &,,lnv,.,
(17)
From the parameter estimates of Equations 16 and 17, the response of net supplies and rental rates of primary factors to changes in output prices and levels of primary inputs can be computed.’
III.
DATA
The Indonesian economy is divided into 3 sectors: indexed A (agriculture), N (manufacturing), and S (services), and three factors of production, indexed R (land), L (labor) and K (capital). Annual data on value added in each sector in current and constant prices for the three sectors are obtained from World Tables (STARS, World Bank, 199 1) for the period 1960- 1989. Labor force in millions of workers and (arable) land in millions of hectares are from various issues of Production Yearbook of the Food and Agricultural Organization. Capital stock in billions of local currency is from Martin and Warr (1993). The shares of factors (land, labor and capital) in GDP are derived from National Income Accounts and social accounting matrices. An index developed by Martin and War-r ( 1993) to measure the adoption of green revolution techniques is used to represent technological change in the agricultural sector. Data on exports and capital goods imports (machinery and equipment categories of total imports) are obtained from World Tables of World Bank for the same period. The share of each of the three sectors in GDP is presented in Figure 4. It shows the decline in farm sector share relative to its share in 1969 and the gain in industrial
JOURNAL OF ASIAN ECONOMICS 7( 1), 1996
82
sector, while the share of services stayed constant. Figure 5 charts the changes in the share of the three inputs in GDP relative to their respective shares in 1969. The shares of capital and land increased, however, at the expense of labor. Among inputs, capital witnessed the largest growth rate over the period 1969-1989 averaging 12.8 % per annum, while labor and land have grown at 2.10% and 0.2%, respectively.
Iv.
ECONOMETRIC MODEL
The econometric model is based on Equations 16 and 17, from which supply and factor rental rate elasticities with respect to output prices and primary input quantities are computed. The time dependent constant terms (a~&) in Equations 16 and 17 are replaced by (4 + aft + a;A,, @’ + Pyt + PFA,), where t denotes a trend variable ‘time’ and A, is the agricultural technological change index. Following Jorgenson (1986), we refer to these measures (of, pi”) as technical change biases” (productivity growth), although other factors, such as efficiency gains from organizational innovations, may well be captured by these parameters. For af positive, technical change is referred to as output-augmenting. For p$ positive (negative), technical change is referred to as input-using (input-saving). The parameters on A, serve to identify the own and cross effects of technological change in agricultural sector. While the agricultural and industrial sector comprise goods that are either import or export competing, the services sector comprises many goods that are not traded internationally. Hence, the price of services ps is more likely to be influenced by the other exogenous variables in the model. For this reason, a reduced form for the price of services is specified as: lnpi = 60 + Ci,A,N6ilnpi + Zm=R,L,KGmlnv~+ G,lnDt +$t + &lnA, + vt
(18)
Tests failed to reject the null hypotheses that price of services is homogeneous of degree one in traded goods prices and homogeneous of degree zero in endowments as suggested by trade theory. l1 A sequential procedure is used to estimate the system of Equations in 16, 17 and 18. At the first stage, Equation 18 is regressed on the prices of traded goods, primary factor endowments and technological change indexes to obtain the predicted values of the price of services. Then, the predicted values of price of services is substituted in the supply system (Equations 16 and 17) and the parameters are estimated using maximum likelihood estimation techniques. Since the output value shares and factor payments share sum to unity (singular covariance matrix), one equation each from output and input share equations are dropped. The right hand sides of all Equations in 16 and 17 are the same, and hence OLS on individual equation is equivalent to Seemingly Unrelated Regression Estimation. Durbin-Watson and Breush-Godfrey tests (on OLS residuals) failed to reject the null that serial correlation is absent. Tests of the restrictions imposed on Equation 5 pertaining to the homogene-
Growth Accounting:
Indonesia
83
ity and symmetry properties of Equation 1 also failed to reject the null hypothesis. The model seems to fit the data well as indicated by high R2 and t ratios.
V. A.
Non-Parametric
RESULTS
Estimates of Components
of GDP Growth
Non-parametric estimates of the contributions from prices, endowments and total factor productivity (TFP) to growth in real GDP are presented in Table 1 and Figures 1 to3. On average over the period 1969-89, growth in the level of inputs alone accounted for 85%12 of growth in real GDP, while growth in TFP accounts for only 6 percent (Table 1). This is also evident from Figure 1 where the gap between the growth rates of GDP and TFP is very large and accounted for by changes in factor endowments and relative prices. During the period 1969-1989 annual growth in real GDP averages to 7.09 percent, while TFP growth averages to 0.43 percent leaving 6.66 percent (7.090.43) attributable to endowments and prices. Hence, level efsects dominate rate effects on growth in Indonesia. As TFP is a residual, it is broadly defined to include external shocks to the economy, like oil price changes and weather conditions. Excluding outliers, such as the drought year 1982, increases the contribution of TFP to growth in real GDP from 6% to 13%. Note, however, that our primary interest is to capture the underlying ‘growth component’ of the TFP series that is devoid of such low frequency fluctuations. Following Gopinath and Roe (1995), we employ a Hodrick-Prescott filter to smoothen the series. This method involves choosin smoothed values {s,},=~’ for the series {x,},=1’ which solve the following problem: ?3 min{(l/T)C,,‘(x,
-sJ2+
(h/T)&zil[(st+l
-St--(St-st-I)]*)
(19)
Figure 1 plots the filtered TFP series on the secondary Y;utis. It is well evident from this figure that the contribution from TFP to growth in GDP has been declining. In his tale of two cities, Young (1992) points to the fact that growth in Singapore is largely a level effect,14 while Hong Kong’s TFP growth contributed significantly to TABLE 1.
Year 1969-89
GDP Growth
Components
Agr: Pr Effect
7.09
a.23
Ind. Pr Effect 0.88
of Real GDP Growth - Average (%) Sex Pr Effect -0.03
Lund Labor
Capital
TFP Growth
0.10
0.96
4.97
0.43
1969-74
9.43
-1.11
0.90
0.90
0.16
1.03
4.58
1.69
1975-79
7.09
0.24
0.85
-0.92
0.00
0.99
5.82
0.08
1983-84
6.08
-0.42
0.41
0.00
0.19
1.06
6.25
-1.40
1985-89
5.29
0.54
-0.18
0.05
0.76
3.33
a.30
1.09
JOURNAL OF ASIAN ECONOMICS 7( 1), 1996
I.1 1 IO
0.9 0.1
0.6
E
-10
FIGURE 1.
,_.
,
Growth in Real GDP and TFP
I\
.......................
.
........................
....................................................................
_.
.
...................................................................
.p. .. . . . . . . . . . . . . . . . . . . .,.....-.. a-,
lS¶
.
*. . . . . . . .._...... , 070
(
ian
,
,
im
mz
-t-
Y. . . ) I614
.._e...._...
.._............._.......e...*...em.e..mme
, cm
( mn
AGaJcuLTuw
FIGURE 2.
,
.-
,
,
ml
I¶76
*
on
’
1690
INDUSTRY
I
I661
' l9w
-e-
I
1966
.
.
..-....
I
a96
SERVKES
Price (Real) Effects on Real GDP
I
.
.-.-.........
I
I
tan
I
DIT
I
5966
I
I
Growth Accounting:
85
Indonesia
FIGURE 3.
‘_._
.___
_.__
Input Effects on Real GDP
__.__
P i i!i
FIGURE 4.
Share of Output Sectors in GDP
JOURNAL OF ASIAN ECONOMICS 7(l), 1996
86
I
--tLAmR
FIGURE 5.
-CAPITAL-E-LAW
Share of Factor Endowments
I in GDP
its GDP growth. This, he argues, is an outcome of the differences in the general level of education of the people in these two regions of Asia. The effects of changes in the prices of agriculture, industry and services on growth in GDP, holding all else constant, is reported in Figure 2 (equation 12). Since petroleum is aggregated into the industrial sector, the“spike” effects of 1974 and 1978-1980 are evidences of oil price shocks. On average over the period 1969-1989, the growth in the real price index of industrial sector goods averages 0.88 % of the 7.09 % growth in real GDP, although this contribution to growth has been declining over the period (see column 4, Table 1). The change in the index of agricultural good prices has, on average for the period, contributed to a -0.23 % decline in the growth of GDP. Note that the contributions of industrial and agricultural prices has tended to be counter-cyclical (columns 3 and 4, Table 1). The contribution to GDP growth from changes in the price index of services sector is relatively small (-0.03%) over the period. These results suggest that the terms of trade within the Indonesian economy has been in favor of the industrial goods and neutral to services sector with a strong bias against agricultural goods. l6 A decomposition of the individual effects of changes in the quantity of land, labor and capital on growth in real GDP is reported in Figure 3 (Equation 14). It is clear that capital is the largest contributor to GDP growth, averaging about 5.0 % for the 1969-89 period. However, its contribution has been declining since 1982. Labor’s contribution has remained at approximately 1 .O %, while the contribution from changes in the level of land is the least at 0.11 %. While several authors have
Growth Accounting:
Indonesia
87
identified the importance of an open economy and unrestricted capital markets to domestic capital accumulation, these indices do not permit this inference, although our econometric analysis will. The declining trend in capital’s contribution may reflect declining marginal returns, a decrease in investment following the decline in oil revenues during the latter 1980s or both.15 B.
Parametric Results
Sources of Technological Change. Table 2 presents the results from the estimation of Equation 15. Consistent with others (see endnote 8) not all of the parameters are significant. The significance of the coefficient on exports suggests that scale effects with regard to trade positively influences TFP growth in Indonesia. In spite of the relatively small contribution from TFP to growth in GDP, this result confirms the positive effects of export on growth. However, the R2 is only 26% suggesting other factors outside the model are also important to growth in TFI? Our regression results on ‘filtered TFP’ further confirm the positive association between TFP growth and exports, while the coefficients on other variables are of wrong sign and insignificant. EfSects of Exogenous Variables on the Price of Services. The estimation of reduced form Equation 18 yielded generally acceptable results with the possible exception of the effects of endowments on the price of services. The data failed to reject the null hypothesis of homogeneity of degree one in world prices at a high level of confidence, but the unrestricted coefficients of the labor and capital endowment variables have relatively large standard errors. While the endowment restriction was also accepted, albeit at a lower confidence level, some concern about the reliability of these coefficients is mentioned later in our discussion of the“tota1” general equilibrium effects of changes in endowments on the economy. ln ps = 0.6160 ln PA + 0.38401npN + 2.7185 lnvR - 2.4242111~~ (0.0491)
(0.049 1) -
(0.7820)
.2943 In vK - 0.0052 In D + 0.0479 t (0.0049)
(0.1840)
(0.0227)
R2 = 0.99 , DW = 1.84, (Std. Error) TABLE 2. Results of Regression of TFP Growth on Trade Variables Variable Constant Exports Imports
Note;
Parameter -0.05 0.06* -0.03
Time
0.002
R2
0.026
*Significant at 10%
(0.8 177) +
0.0976 In A, (0.0267) (20)
88
JOURNAL
OF ASIAN ECONOMICS
7(l), 1996
The deficit (D) in the country’s balance of payments appears not, on average over the 1960-1989 period, to have had a significant effect on the price of services. Together with homogeneity in traded good prices, these results imply that changes in the relative prices of traded goods to services obtained from the Salter-type of disequilibrium models of the current account, cannot be inferred. A change in the nominal exchange rate appears not to have altered relative prices in the economy, nor have policies that gave rise to current account imbalances of the magnitudes experienced over the period. That the price of services rises due to an increase in the price of traded goods is clear; the substitution and income effect of an increase in the price of traded goods increases the demand for services while the producers of services are forced to compete for resources that are being drawn into the traded good’s sector. Hence, the price of services must rise to clear the market. The results also indicate that this effect is stronger for an increase in the border price of agricultural goods than it is for an increase in the price of industrial goods. This result reflects the fact that agriculture remains a major, albeit declining, component of the economy and that food accounts for a fairly large proportion of household expenditures (over 45 percent of total expenditures in 1992). Technological change (as captured by t and A,) and changes in land endowment also positively affect the price of services. The technological change results are consistent with the view that technological change in the other sectors of the economy have income effects that dominate technological change in the supply of services, so that the net result is to increase their price. Since the supply of services is not very responsive to changes in the endowment of land (Table 3.1), the importance of land in Equation 20 suggests that returns from this endowment, and its indirect effect on the returns to other factors, is likely an important component of income that in turn affects the demand for services. Rybczynski-type Efsects. The direct effects (holding services prices constant) of endowments on supply are shown by the elasticities presented in Table 3.1 (rows 1-3, columns 4-6). The results are generally consistent with the Rybczynski theorem,
TABLE 3.1. Elasticity of Agri. SUPPlY Agriculture Industry Services Rental Rate Land Labor Capital
Product Supply and Factor Return Elasticities w/r to the price of Ind. Serv.
w/r to the endowment of Labor Land Cap.
0.23 a.06 a.18
-0.05 0.17 -0.08
-0.18 -0.11 0.26
0.11 0.19 0.08
0.55 0.37 0.49
0.34 0.44 0.43
0.33 0.41 0.30
0.44 0.22 0.31
0.23 0.37 0.39
-1.26 0.17 0.17
0.69 -0.46 0.34
0.57 0.29 -0.51
Growth Accounting: Indonesia
89
al~ough it is well known that the theorem does not generalize to more than the two factor-two good model.17 For an economy with more than two factors of production and two goods, the theorem essentially states that if the endowment of a factor increases, then the industry which uses that factor relatively intensively will expand relative to other industries. It is possible that when the endowment of a factor increases, the outputs of all goods increase less than propo~ionately to the increase in endowment. If one output does increase more than proportionately, some other output must fall (Woodland, 1982, p. 133). For agricultural supply, the elasticity with respect to labor endowment is larger (0.55) than that of land (0.11) and capital (0.34). For industrial supply, the elasticity with respect to capital (0.44) is larger than that of land (0.19) and labor (0.37), while for services, the elasticity with respect to labor (0.49) and capital (0.43) dominate the elasticity of land (0.08). These results support the notion that agriculture is labor intensive, the industrial sector is capital intensive while the services sector is marginally labor intensive. Further, the results suggest that the direct effects (holding services prices constant) of an increase in endowment causes all outputs to increase but, no single output increases more than proportionally to the increase in endowment. The”tota1” general equilibrium effect of endowments on supply that prevail after services prices have adjusted (equation 20) appear in Table 3.2, rows 1-3 and columns 3- 5.” Consider the case of labor; an increase in this endowment causes agricultural supply to increase almost in propo~ion to the change in labor (0.988). The result in Table 3.1 indicates that services supply increases in response to an increase in labor (0.49), and Equation 20 suggests that the services market clears at a lower services price, thereby releasing resources to agriculture (and to the industrial sector) as suggested by the coefficient (-0.18) in Table 3.1. The net result, after equilibrium is established in all markets, is the 0.988 percent response to a one percent change in labor endowment. A one percent increase in the capital endowment causes, in general equilibrium, agricultural supply to increase by 0.391 percent. In this case, services supply initially increases by 0.43 percent (Table 3.1) and its market clears at a lower price (negative
TABLE 3.2. Elasticity of
General Equilibrium
Elasticities; Services Prices Endogenous
w/r to the price of Agri. Ind.
Lund
w/r to the endowment of Labor Cap.
SUPPlY Agriculture Industry Services Rental Rate Land Labor Caoital
0.114 -0.124 -0.018
-0.114 0.124 0.018
-0.380 -0.106 0.796
0.988 0.633 -0.147
0.477 0.640 0.543
0.523 0.359 0.457
-0.624 1.177 I .220
0.127 -1.353 -0.595
0.39 1 0.473 0.352 0.497 0.176 -0.625
90
JOURNAL OF ASIAN ECONOMICS 7(l), 1996
coefficient on capital in Equation 20) causing agricultural supply to increase by 0.23 percent (Table 3.1). The net effect is the 0.391 percent increase in agricultural output. An increase in the level of land causes agricultural and services supply to expand by 0.11 and 0.08 percents, respectively (Table 3.1). However, services markets now clear at a higher price, 2.72 percent (Equation 20). This higher price pulls resources away from agriculture with the net result of a decline in agricultural supply of -0.380 percent (Table 3.2). Labor and capital are important for the production of industrial goods, while an increase in land has relatively small effects. These results suggest that land is important to the production of services, but not necessarily as a factor of production; instead, an increase in land has strong income effects that increase the demand for services with the result that the services market clears at a higher price which, of course, pulls more resources into the production of services. Supply Response. The direct (i.e., holding services prices constant) supply response of agricultural, industrial, and services sectors to their own and cross prices are represented by the elasticities presented in rows 1-3, columns 1-3, Table 3.1. All the own price supply elasticities are positive and the cross price elasticities are negative. The own price supply elasticity for agriculture is 0.23, which is close to the value obtained by Martin and Wart- (199 1). This elasticity also falls within the range of 0.10 and 0.23 obtained from the studies reviewed by Binswanger (1989) for most developing countries. The own price elasticity is larger for services (0.26) than it is for agriculture or for industrial goods (0.17). The cross price effects between agriculture and industry are smaller (-.05, -0.06, respectively) than the agricultureservices and industrial-services cross price effects. This result suggests that the agricultural and industrial sector compete more for resources employed in the services sector than they compete for resources among themselves. The ‘general equilibrium’ results of the “total” effects, of traded good prices on supply are reported in Table 3.2, rows 1-3, columns l-2 for price elasticities. In this case, the price elasticity of supply for agriculture declines to 0.114, a value that falls near the lower bound of the elasticities reported by Binswanger (1989). This result implies that an increase in the price of agricultural goods also causes the price of services to rise (Equation 20) which forces agriculture to compete for resources from the services sector, so that the indirect effect decreases the net effect of agriculture’s response to a change in its relative price. The own price elasticity of industrial good supply (0.124) also declines. An increase in its relative price forces it too to compete for resources from the services sector leading to this decline but not to the extent of agriculture. The elasticity of supply for services suggests that as the terms of trade tend to favor the industrial sector, the supply of services rises but by a fairly small magnitude (0.018). Finally, note that (both direct and total effects) the supply response is greater to a percent change in endowments of labor and capital than to own price changes. This suggests that infrastructure and human capital might be more important to generate growth in agriculture (Binswanger, 1989).
Growth Accounting:
Indonesia
91
Stolper-Samuelson-type effects. An increase in output price tends to have the largest effect on the rental rate of that factor that is used most intensively in the sector.19 These are the results obtained (Table 3.1 rows 4-6, columns l-3) as seen from the elasticities of 0.41 for wage- agriculture, 0.37 and 0.39 for wage-services and capital-services (direct effects), respectively. It is unclear why land rental rates respond more strongly to an increase in the price of industrial goods than do capital rental rates (0.44 vs 0.3 l), since the industrial sector appears relatively capital intensive. Holding the price of services constant, these results suggest that real income to labor is more responsive to an improvement in the terms of trade in agricultural than in industrial goods. The general equilibrium“tota1” effects are reported in rows 4-6, columns l-2 (Table 3.2). The most interesting result is that an increase in the price of agriculture goods has a stronger “total” effect on the rental rates of labor and capital than does an increase in the price of industrial goods. These stronger effects are of course due to the adjustments in the market for services caused by a change in traded good prices. The direct effect of a percent change in the price of agricultural goods causes wages to rise by 0.41 percent (Table 3.1). Higher cost of production coupled with increased demand for services through income effects force the market for services to clear at a higher price. The higher services price further increases the competition for resources with the net result that wages rise by about 0.64 percent (Table 3.2) for a one percent increase in the price of agricultural goods. Similar inferences can be obtained for capital rental rates. Land rental rates appear to be slightly more responsive to an increase in the price of industrial than agricultural goods (0.523 vs 0.477). This result is obtained because the direct effect of an increase in industrial good prices on land rental rates is larger than in the case of agricultural prices effects (0.44 vs 0.33, Table 2). This effect tends to out weigh the otherwise larger effect of agricultural good prices on the price of services. Together, these results suggest that an increase in the price of agricultural goods has the tendency to increase costs of production to a relatively larger degree than an increase in industrial prices. Depending on the cost structures in other countries, this result has implications to the country’s comparative advantage in world markets. Rental Rate Response to Endowments. The elasticities presented in Table 3.1 (rows 4-6, columns 4-6) show the direct effects of factor rental response to an increase in own and other factor supply. All the own factor price elasticities show negative signs as expected. All the cross factor price elasticities are positive, indicating that the factors are substitutes in production; an increase in the level of an input is associated with an increase in the price of other factors. Capital is of particular importance since land rental rates and wages appear particularly responsive to an increase in supply of this factor. However, it is the general equilibrium effects that matter, Table 3.2, rows 4-6, columns 3-5. The general equilibrium results suggest that an increase in land endowment has large positive effects on labor and capital rental rates, and a smaller negative effect
JOURNAL OF ASIAN ECONOMICS 7(l), 1996
92
on own rental rate. The relatively large positive effect of an increase in land endowment on the price of services (2.719, Equation 20) which in turn increases the price of land by 0.23 percent leads to such large cross effects. However, an increase in the endowment of labor has the opposite effect. The reason is similar; in this case the price of services falls to an increase in labor endowment by -2.424% (Equation 20) which out weighs the otherwise positive effects reported in column 5, row 4 of Table 3.1. A one percent increase in capital tends to increase land and labor rental rates and to decrease capital rates by -0.625 percent. The smaller deviation between the direct and general equilibrium effects in this situation (Table 3.1 vs Table 3.2), result from the smaller effect that capital has on the price of services. The effects of technological change on supply and facTechnological Change. tor rental rates are suggested by the last two columns of Table 4. All the parameters on both time and the index for agricultural technological change are significant except those in the capital share equation suggesting non-neutral growth in total factor productivity. The parameters on ‘time’ trend, as mentioned earlier, measure the relative rates of output augmentation and input utilization.20 Hence, the negative coefficients on the ‘time’ trend in both agricultural and industrial goods share equations suggest that ‘overall’ technical change has been services output augmenting (coefficient on the services share equation is restricted to 0.0081 as they sum to zero). On the input side, technical change has been labor-using and land and capital-saving. The coefficients on ‘A,’ (agricultural technological change index) suggest that the industrial sector may stand to gain more from faster technological change in agriculture because of their linkages.21 In addition to adding value to farm goods, industrial sector also provides intermediate inputs like fertilizers and other chemicals that are crucial to crop production in the post green revolution period. While the TABLE 4.
Parameter
Estimates
of the Indonesian
GDP Share Equations
GDP Shares
IMPA)
Ag.
0.31 I5
Ind.
4). I I69
169
1nfP.J
IN vd
In(q,)
In(vK)
-0.1946
-0.0032
(0.0 179)
(0.0387
(0.0 I84
(0.0248
(0.0 I9 I )
0.2478
-0. I309
0.0 I85
-0.0301
0.0 I I6
(O.OI 79
(0.0229)
(0.0263
(0.0084)
-0. I946
-0. I.709
0.3255
(0.0387
(0.0263)
(0.04 17)
(0.0277)
(0.029 I )
Ser.
In(PN) 4.1
-0.0153
0.0254
-0.0222
(0.0 147)
(0.0 I 30) 0.0 IO6
0.008 I
(0.0277)
(0.0035)
-0.0032
0.0 I85
-0.0 I53
-0.045 I
0.0253
0.0 I98
(0.0 184)
(0.0084)
(0.0277)
(0.0823)
(O.IO46)
(0.05 16)
0.0254
-0.030 I
(0.0248) Cap.
-0.0222
Rent
(0.0 I9 I )
0.0047
0.0253
0.0300
(O.OI 47)
(0.0354)
(O.Io46)
(0. IO 16)
0.0 I I6
0.0 IO6
(0.0130) (0.0277)
0.0 I98 (0.05 16)
(0.0023)
O.(K)47
Rent Wages
(0.0025) -0.0028
(0.0354)
Land
-0.0553
-0.0553 (0.0278)
‘A’
lime
-0.0053
-0.0029
0.0 I26 (0.0074) 0.03 I4
A=Agricultural
0.97
(0.0075) -0.0440
0.95
(0.005 I ) O.(HK3I
(0.00 17)
(0.0039)
0.0045
-0.0 IO7
(0.0278)
(0.0032)
(0.0059)
0.0355
-0.00 I6
0.0076
(0.0268)
(0.0030)
(0.005 I )
NIJIL’.Y: (Figurcx
in the parcnthcscs xc standard errors) (Ag.=Agriculturc; Ind.=lndustry; Cap.=CapiM;
R2 0.99
Technological Change)
0.94 0.94 0.93
Growth Accounting:
Indonesia
93
‘overall’ technical change has been labor-using, agricultural sector experienced a bias towards the use biological and chemical inputs and hence, the negative coefficient of ‘A,’ on labor share is not surprising. The coefficient of ‘A,’ in the capital share equation is positive, but insignificant. Technological change in agriculture has also tended to increase land rental rates while forcing the services sector to compete more for resources, hence the negative co-efficient (-0.044) in the services share equations.
VI.
SUMMARY REMARKS
This paper draws upon trade theory to provide insights into the sources of growth and the evolution of agricultural, industrial and services sectors of the Indonesian economy. Our non-parametric analysis suggests that level effects on growth dominate rate effects, with growth in capital input alone accounting for five-sevenths of the growth in GDP. In addition, terms of trade within the economy has a strong bias towards industry and against agriculture. Consistent with the findings of Levine and Renelt (1992), benefits of an open economy are reflected in the scale effects from trade on TFP growth. However, declining trends in the contributions from capital and TFP to growth in GDP is of serious concern. This suggests that gains from the accumulation of physical capital and trade reform appear to be exhausted, leaving potential sources of TFP growth like human capital, research and development and learning-by doing as major sources of future growth. The envelope properties of the postulated gross domestic product function are then exploited to derive the supply and factor rental elasticities from the estimation of the coefficients in share equations for both the direct and general equilibrium effects of changes in exogenous variables. However, the general equilibrium results are sensitive to our estimates of the effects of endowments on the market clearing price of services. A number of Rybczynski and Stolper-Samuelson-type effects are obtained. The own elasticity of agricultural supply is consistent with most other studies, however, output response to factor endowment changes are larger than price responses. Agricultural supply response to an increase in the endowment of labor is relatively greater than its response to an increase in the endowments of either capital or land. In addition, an increase in the border price of agricultural goods tends to have relatively large effects on the rental rates for labor and capital thus contributing significantly to an ‘overall’ increase in the cost of production. The market for services tends to be more closely linked to the agricultural sector than to the industrial sector. Technological change appears to augment the production of services and is biased against agricultural goods more than industrial goods while also increasing the market clearing price for services. On the input side, evidence suggests that technological change (at the aggregate level) has been labor-using. Technological change within the agricultural sector appears to be more beneficial to the industrial sector suggesting strong linkages between the two sectors. As the world markets are
94
JOURNAL
OF ASIAN ECONOMICS
7( 1), 1996
becoming increasingly integrated, elimination of the existing trade protection and market distortions both for agriculture and the non-agricultural sectors is required for efficient reallocation of resources. This, in turn, will make Indonesia more competitive in world markets. ACKNOWLEDGMENTS: Funding was provided by a NRI grant to USDAIERSICAD-University of Minnesota. We thank the anonymous referees for their suggestions, any remaining errors are the responsibility of the authors.
NOTES 1. See Papageorgiou, Michaely, and Choksi (1991). 2. See Woodland (1982) and Diewert (1974). 3. See Diewert (1974, p. 134). 4. Like Equation 2 both Laspeyres and Paasche indexes are not observable unless the parameters of the GDP function are known. However a geometric mean can be computed without knowledge of these parameters, hence the term non-parametrics. 5. p’ and pfml are real prices and so ‘a’ is growth in real value of output. We derive the real prices by deflating the sectoral price indices by a GDP deflator, in principle, discounting them for average price increases in the economy. 6. For instance, ‘bi’ is interpreted as the change in GDP (between periods t and r-1) attributable to change in real price of ‘i’ th good from p’-’ to pt’ holding other prices, all inputs and technology constant. 7. Backus, Kehoe and Kehoe (1992) find little evidence of a relation between growth rate of GDP per capita and scale variables implied by theories of learning by doing and investment in human capital. However, they find a significant relation between growth rate of GDP per capita and indexes of intra-industry trade. Gopinath, Kennedy and Roe (1995) report that increased share of Mexico in trade among NAITA countries has contributed significantly to its TFP growth. 8. See Kohli (1993). 9. See Takayama (1985, pp. 147-149 for the derivation of supply and factor rental rate elasticities. 10. These coefftcients measure the relative rates of output augmentation (i) and input utilization (m) and hence, sum to zero respectively. Il. See Woodland (1982, Chapter 8). 12. (6.55/7.09)*100. 13. The averages of the two series will remain the same. See Gopinath and Roe (1995) for more details. 14. Capital, again, contributed to as much as 83% of growth in Singapore’s GDP 15. In addition, this result suggests a transitional (dynamic) path towards long-run growth from a period of relatively high trade distortions. 16. In the case of the U.S., Gopinath and Roe (1994) report that the terms of trade has been biased towards services sector and against farm and industrial sectors. 17. The parameter estimates reported in Equation 20 are crucial to the following discussion. 18. See Woodland (1982, p. 235) for conditions under which the theorem can be generalized. 19. See Woodland (1982) for this generalized version of the Stolper-Samuelson Theorem. 20. Jorgenson ( 1986). 21. Roe and Gopinath (1995) report a similar result on the linkages of the U.S. farm sector. TFP growth in the U.S. farm sector contributes to real GDP growth in the value added sector through level effects, while rate effects in the value added sector mitigate negative relative price effect on real GDP growth in farm sector.
Growth Accounting:
Indonesia
95
REFERENCES Backus, D.K., Kehoe PJ. and Kehoe, T.J. 1992. In Search of Scale Effects in Trade and Growth. Journal of Economic Theory: 377-399. Binswanger, Hans. 1989. The Policy Response of Agriculture. Proceedings of the World Bank Annual Conference on Development Economics, 1989. Bowden, R.J. and Turkington, D.A. 1984. Znstrumental Variables. Cambridge: Cambridge University Press. Diewert, W.E. 1974. Applications of Duality Theory. In M.D. Intriligator, and D.A.Kendrick Frontiers of Quantitative Economics, Vol. 2. Amsterdam: North-Holland. 1976. Exact and Superlative Index Numbers. Journal of Econometrics: 115145. -. Diewert, W.E. and Morrison, C.J. 1986. Adjusting Output and Productivity Terms of Trade. The Economic Journal, 96: 659-679.
(eds),
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Gopinath, M. and Roe, T.L. 1994. Sources of Growth in U.S. GDP and Economy-wide Linkages to the Farm Sector. Working Paper, Department of Applied Economics, University of Minnesota. Gopinath, M., Kennedy, P.L. and Roe, T.L. 1995. Trade, America: An Empirical Analysis. North American (forthcoming). Gopinath, M. and Roe, T.L. 1995. Sources of Sectoral Growth U.S. Agriculture. Economic Development Center Bulletin
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