1960–65: Political chaos and economic disruption

This period witnessed a decline in output because of disruption in the transport network and the departure of many foreign entrepreneurs following political turmoil, civil strife, and the failed secession of the Katanga Province. Real GDP declined by about 4 percent between 1960 and 1965.

1966–74: Stability and growth

This period was characterized by increased involvement of the state in the productive sectors of the economy. Thanks to La Politique des Grands Travaux,³ public investment quadrupled. In 1971, the first Mobutu plan (Plan Décennal 1971–80) was launched, which aimed to raise real GDP growth to about 7 percent per year. Against this backdrop, in 1973–74 the government took steps toward the nationalization of all small, medium-sized, and large foreign enterprises.

Increasing state control of the economy was accompanied by an impressive economic expansion, with real GDP growing at an average annual rate of 5.1 percent during 1966–74. However, following the adverse terms of trade shocks caused by both a reversal in copper prices and the oil crisis of 1973, the centralized economy, unable to adjust, soon revealed its severe limitations.

1975–82: Economic recession and debt crisis

The ill-advised economic policies and public investments of the early 1970s precipitated a debt crisis with a damaging impact on economic activity. In 1975, the country stopped servicing its debt and requested an IMF-supported program for the first time to help extricate the DRC from its economic crisis. Because of the overall downturn, the public investment program was grounded, capital invested in “white elephants” was lost, and the maintenance of infrastructure and productive capital was neglected or postponed indefinitely. As a result, economic activity experienced a severe decline, compounded by the invasions of the Shaba Province (the heart of mining activities) in 1977 and 1978. Altogether, real GDP fell by 12 percent.

1983–89: Adjustment supported by the IMF and stop-and-go policies

To improve the economic and financial situation, and eliminate the significant distortions that had grown in the preceding period, the government started to implement in September 1983 a strong stabilization and liberalization program. This strategy had a positive impact as real GDP, which had declined by 2.2 percent in 1982, recovered with an average annual growth rate of 2.6 percent during the period, 1984—86.

In 1987, with the support of the IMF and the World Bank, the government launched a structural adjustment program aimed at establishing the basis for long-term economic growth and a sustainable external financial position. The program also benefited from improved terms of trade, mostly reflecting a strong upturn in copper prices beginning in early 1987. However, with the more favorable external environment, the government all but ceased its adjustment efforts. As a result, the country’s financial performance deteriorated markedly. Annual real GDP growth decelerated to 0.5 percent on average during the period 1987–89.

1990–2000: Hyperinflation and collapse of the economic and political system

In the midst of failed attempts at political liberalization, control over economic policies was lost, and the country fell into the grip of an unprecedented circle of hyperinflation, currency depreciation, increasing dollarization and financial disintermediation, declining savings, deteriorating economic infrastructure, and broad-based output decline. The alarming economic and social situation was compounded by the full-fledged war that broke out on August 2, 1998.

In this context, a large part of the country’s capital stock was destroyed, and investment was discouraged. As a result, real GDP contracted cumulatively by some 43 percent during the decade, and per capita real GDP plummeted from US$224 in 1990 to US$85 (23 cents a day) in 2000. Over the same period, consumer prices rose at an annual average rate of 684 percent. Government revenue fell by 80 percent, and external debt rose to about 300 percent of GDP (or almost US$13 billion).

Agriculture

Combined with forestry, animal husbandry, and fishing, agriculture provides direct employment to more than 75 percent of the labor force and accounts on average for about 45 percent of real GDP. Agriculture has great potential as a source of economic growth, export diversification, and gainful employment. Nevertheless, agricultural output has not recorded substantial growth (Figure 3), and its contribution to exports declined continuously from about 40 percent of exports in 1960 to less than 10 percent in 2000, with some traditional export products virtually not being grown any more.⁴ With regard to food crops, Maton, Schoors, and Van Bauwel, (1998) have shown that food surplus for each person increased between 1965 and 1974; thereafter, a steep downward trend began, partly owing to the Zaireanisation⁵ process, which has undermined productivity growth.

Democratic Republic of the Congo: Agricultural Real Value Added per Worker (Index, 1960 = 100)

Citation: IMF Working Papers 2004, 114; 10.5089/9781451853827.001.A001

Sources: Congolese authorities; and IMF staff estimates.

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Democratic Republic of the Congo: Agricultural Real Value Added per Worker (Index, 1960 = 100)

Citation: IMF Working Papers 2004, 114; 10.5089/9781451853827.001.A001

Sources: Congolese authorities; and IMF staff estimates.

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Democratic Republic of the Congo: Agricultural Real Value Added per Worker (Index, 1960 = 100)

Citation: IMF Working Papers 2004, 114; 10.5089/9781451853827.001.A001

Sources: Congolese authorities; and IMF staff estimates.

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Overall, agricultural development has been constrained by several factors. These include the deterioration of the network of rural feeder roads; the dislocation caused by the Zaireanisation measures of 1973–74; inadequate credit for small-scale producers; lack of foreign exchange for essential imports; insufficient storage and other marketing facilities; and the uncertainties created by the government’s pricing policies.

Mining sector

The DRC is extremely rich in mineral resources, and its mining potential remains largely untapped. Its mineral resources include copper, cobalt, diamonds, gold, zinc, uranium, tin, silver, coal, manganese, tungsten, cadmium, and crude oil. Most mining is carried out by the largest state-owned company, the Générale des Carrières et des Mines du Congo (GECAMINES), which accounts for over 90 percent of total copper production, and the entire cobalt and zinc output. In the diamond sector, while the Société Minière de Bakwanga (MIBA), partly owned by the government, is responsible for the industrial mining of diamonds, individual prospectors account for some 60 percent of total diamond production.

Beginning in the mid-1980s, the mining industry of the DRC entered a phase of steep decline (Figure 4):

Democratic Republic of the Congo: Mining Real Value Added (Index, 1960 = 100)

Citation: IMF Working Papers 2004, 114; 10.5089/9781451853827.001.A001

Sources: Congolese Authorities; and IMF staff estimates.

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Democratic Republic of the Congo: Mining Real Value Added (Index, 1960 = 100)

Citation: IMF Working Papers 2004, 114; 10.5089/9781451853827.001.A001

Sources: Congolese Authorities; and IMF staff estimates.

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Democratic Republic of the Congo: Mining Real Value Added (Index, 1960 = 100)

Citation: IMF Working Papers 2004, 114; 10.5089/9781451853827.001.A001

Sources: Congolese Authorities; and IMF staff estimates.

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• By the late 1990s, copper production by GECAMINES had declined to 5 percent of the peak mid-1980s output level of more than 500,000 tons, while cobalt production had fallen by 70 percent from pre-conflict levels.

• Production of zinc has virtually ceased, compared with a capacity of 200,000 tons.

• Gold production has practically come to a halt, compared with a capacity of 6 tons per year.

• Manganese production was discontinued at the Kisenge Mining Enterprise (EMK-MN), where capacity was 360,000 tons per year during the early 1980s.

• With the sharp fall in GECAMINES’s output, diamonds became the single largest source of export earnings for the country. Because of frequent changes in marketing policies (including nationalization and the banning of foreigners from diamond-producing areas), large amounts of diamonds were exported through the parallel market. A monopoly to export artisanal diamonds granted in 2000 to a foreign company was rescinded in early 2001.

Reflecting its steep output slump, the mining sector’s contributions to GDP and export earnings have been declining continuously. In the mid-1980s, mining accounted for almost one-fourth of real GDP and provided over 70 percent of export receipts; in 2000, while the mining sector remained the main source of export earnings (owing to diamond exports), it accounted for only about 6 percent of real GDP.

The mining sector has been facing a number of problems that have constrained its development. These include (a) a legal and regulatory framework not conducive to the development of the private sector; (b) serious transportation problems; and (c) chronic lack of investment.

Transport sector

When the DRC gained its independence in 1960, it inherited a comprehensive transport system, including strategically interconnecting roads, rivers, and railways. The transport sector accounted on average for about 12 percent of real GDP in the 40-year period, 1960–2000. Given the large size of the country, its limited access to the sea, and the remoteness of its mineral deposits, the transport network is of vital importance to present and future economic activity. However, the sector’s performance remains less than satisfactory (Figure 5), and difficulties in transportation constitute a major obstacle to the realization of the DRC’s immense agroindustrial and mining potential.

Democratic Republic of the Congo: Transport Real Value Added (Index, 1960 = 100)

Citation: IMF Working Papers 2004, 114; 10.5089/9781451853827.001.A001

Sources: Congolese authorities; and IMF staff estimates.

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Democratic Republic of the Congo: Transport Real Value Added (Index, 1960 = 100)

Citation: IMF Working Papers 2004, 114; 10.5089/9781451853827.001.A001

Sources: Congolese authorities; and IMF staff estimates.

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Democratic Republic of the Congo: Transport Real Value Added (Index, 1960 = 100)

Citation: IMF Working Papers 2004, 114; 10.5089/9781451853827.001.A001

Sources: Congolese authorities; and IMF staff estimates.

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Three public agencies—the Société Nationale des Chemins de Fer du Congo (SNCC), the Office National des Transports (ONATRA), and the Office des Routes—play a critical role because they are responsible for operating rail and river transport, and for building and maintaining the main highway network, respectively.

Since 1985, the financial situation of both ONATRA and SNCC has deteriorated sharply. Moreover, the services that they provide on the Voie Nationale, which combines the rail and water route from the Shaba mining area to the port of Matadi, have progressively declined. The poor performance of these two key agencies stems from a number of factors: the delay in adjusting tariffs in a highly inflationary environment; a decrease, or at best, stagnation in traffic; high operating costs; and chronic lack of maintenance.

In the late 1990s, the civil war took a toll on the transport sector and infrastructure collapsed. As a result, farmers have great difficulty in selling any surplus, while food prices in urban centers are high. Interregional connections are often limited to minimal air transport; as a result the country has essentially broken down into a set of economic enclaves.

Democratic Republic of the Congo: Growth Accounting, 1960–2000

Citation: IMF Working Papers 2004, 114; 10.5089/9781451853827.001.A001

Sources: Congolese authorities; and IMF staff estimates.

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Democratic Republic of the Congo: Growth Accounting, 1960–2000

Citation: IMF Working Papers 2004, 114; 10.5089/9781451853827.001.A001

Sources: Congolese authorities; and IMF staff estimates.

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Democratic Republic of the Congo: Growth Accounting, 1960–2000

Citation: IMF Working Papers 2004, 114; 10.5089/9781451853827.001.A001

Sources: Congolese authorities; and IMF staff estimates.

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Measurement of variables

Output. Output is measured by gross domestic product (GDP) at constant prices as published by the Central Bank of Congo. The sectoral output measures are given by the sectoral value added.

Labor inputs. Because employment series are not available, labor inputs are estimated by data on the economically active population (labor force) published by the International Labor Organization.⁷ No adjustments for labor quality were introduced, due to lack of information on educational attainment,

Physical capital. As is the case in most developing countries, capital stock series are not readily available. Following a number of past studies,⁸ we base the measure of capital on the perpetual inventory methodology. Having taken this route, two issues need to be dealt with: the initial capital stock and the rate of depreciation. We assume an initial capital-output ratio of 1.5 (a value of 1 was chosen for the agricultural sector) and the depreciation rate was set at 15 percent.⁹ Based on these assumptions, the capital stock dynamics is as follows:¹⁰

where I_t is gross investment and δ the depreciation rate.

Unit root test

Before turning to the tests for cointegration, one must determine the order of integration of the variables. Using the augmented Dickey-Fuller (ADF) test, the unit-root hypothesis is tested in the level of variables as well as in their first differences (Table 1 and 2). The null hypothesis is the presence of unit-root. The lag length in the ADF regression is selected striking a balance between the lag length chosen by the Akaike information criterion (AIC) and the t-test of the lags. Tables 1 and 2 show the unit root test results on the variables entering the overall and sectoral production functions. As can be seen, the null hypothesis that the level variables contain a unit root cannot be rejected at 5 percent or less.¹¹ All the variables were tested to be stationary in first differences. In light of these results, we conclude that all variables are integrated of the order of 1.

Table 1:

Testing for Unit Roots (ADF Test with Constant)

(Augmented Dickey-Fuller ADF(s) Test: ${\Delta }y=\mathit{\mu }+(\mathit{\beta }-1)y_{t-1}+{\displaystyle \sum _i^s\gamma \Delta y_{t-1}+{\epsilon }_t)}$

Note: The asterisks, * and **, indicate significance at the 5 percent and 1 percent leves respectively. The critical values are −2.94 at the 5 percent significance level and -3.62 at the 1 percent significance level. The sample period is 1960–2000. Variables are as follows: output per worker (y) and physical capital per worker (k). The subscript “a” stands for agricultural sector, “m” for mining sector, and “r” for transport.

Table 1:

Testing for Unit Roots (ADF Test with Constant)

(Augmented Dickey-Fuller ADF(s) Test: ${\Delta }y=\mathit{\mu }+(\mathit{\beta }-1)y_{t-1}+{\displaystyle \sum _i^s\gamma \Delta y_{t-1}+{\epsilon }_t)}$

Variable	Level		Difference
	ADF Statistic	Lag length	ADF Statistic	Lag length
Y	0.967	1	-2.379	0
K	-0.505	2	-1.705	1
ya	-0.683	2	-3.947**	0
Ka	-2884	0	-4.498**	0
ym	-3376*	0	-6.093**	0
Km	-2.504	0	-6.824**	0
yr	-1.307	0	-5.655**	1
kr	-1.839	0	-4.707**	0

Note: The asterisks, * and **, indicate significance at the 5 percent and 1 percent leves respectively. The critical values are −2.94 at the 5 percent significance level and -3.62 at the 1 percent significance level. The sample period is 1960–2000. Variables are as follows: output per worker (y) and physical capital per worker (k). The subscript “a” stands for agricultural sector, “m” for mining sector, and “r” for transport.

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Table 1:

Testing for Unit Roots (ADF Test with Constant)

(Augmented Dickey-Fuller ADF(s) Test: ${\Delta }y=\mathit{\mu }+(\mathit{\beta }-1)y_{t-1}+{\displaystyle \sum _i^s\gamma \Delta y_{t-1}+{\epsilon }_t)}$

Variable	Level		Difference
	ADF Statistic	Lag length	ADF Statistic	Lag length
Y	0.967	1	-2.379	0
K	-0.505	2	-1.705	1
ya	-0.683	2	-3.947**	0
Ka	-2884	0	-4.498**	0
ym	-3376*	0	-6.093**	0
Km	-2.504	0	-6.824**	0
yr	-1.307	0	-5.655**	1
kr	-1.839	0	-4.707**	0

Note: The asterisks, * and **, indicate significance at the 5 percent and 1 percent leves respectively. The critical values are −2.94 at the 5 percent significance level and -3.62 at the 1 percent significance level. The sample period is 1960–2000. Variables are as follows: output per worker (y) and physical capital per worker (k). The subscript “a” stands for agricultural sector, “m” for mining sector, and “r” for transport.

Table 2.

Testing for Unit Roots (ADF Test with Linear Trend)

(Augmented Dickey-Fuller ADF(s) Test: $\mathit{\Delta }y=\mathit{\mu }+\mathit{\alpha }T+(\mathit{\beta }-1)y_{t-1}+{\displaystyle \sum _i^s\mathit{\gamma }{\Delta }{y}_{t-1}+{\mathit{\epsilon }}_t)}$

Note: The asterisks, * and **, indicate significance at the 5 percent and 1 percent levels respectively. The critical values are −2.94 at the 5 percent significance level and -3.62 at 1 percent significance level. The sample period is 1960–2000. Variables are as follows: output per worker (y) and physical capital per worker (k). The subscript “a” stands for agricultural sector, “m” for mining sector, and “r” for transport.

Table 2.

Testing for Unit Roots (ADF Test with Linear Trend)

(Augmented Dickey-Fuller ADF(s) Test: $\mathit{\Delta }y=\mathit{\mu }+\mathit{\alpha }T+(\mathit{\beta }-1)y_{t-1}+{\displaystyle \sum _i^s\mathit{\gamma }{\Delta }{y}_{t-1}+{\mathit{\epsilon }}_t)}$

Variable	Level		Difference
	ADF Statistic	Lag length	ADF Statistic	Lag length
Y	-1.593	1	-3.760**	4
K	-0.465	1	-3.945**	0
ya	-0.318	2	-3.853**	1
ka	-2.386	0	-4.415**	0
ym	-2.187	0	-7.227**	0
km	-1.236	0	-5.799**	2
yr	-3.472*	1	-5.614**	1
kr	-1.779	0	-4.672**	0

Note: The asterisks, * and **, indicate significance at the 5 percent and 1 percent levels respectively. The critical values are −2.94 at the 5 percent significance level and -3.62 at 1 percent significance level. The sample period is 1960–2000. Variables are as follows: output per worker (y) and physical capital per worker (k). The subscript “a” stands for agricultural sector, “m” for mining sector, and “r” for transport.

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Table 2.

Testing for Unit Roots (ADF Test with Linear Trend)

(Augmented Dickey-Fuller ADF(s) Test: $\mathit{\Delta }y=\mathit{\mu }+\mathit{\alpha }T+(\mathit{\beta }-1)y_{t-1}+{\displaystyle \sum _i^s\mathit{\gamma }{\Delta }{y}_{t-1}+{\mathit{\epsilon }}_t)}$

Variable	Level		Difference
	ADF Statistic	Lag length	ADF Statistic	Lag length
Y	-1.593	1	-3.760**	4
K	-0.465	1	-3.945**	0
ya	-0.318	2	-3.853**	1
ka	-2.386	0	-4.415**	0
ym	-2.187	0	-7.227**	0
km	-1.236	0	-5.799**	2
yr	-3.472*	1	-5.614**	1
kr	-1.779	0	-4.672**	0

Note: The asterisks, * and **, indicate significance at the 5 percent and 1 percent levels respectively. The critical values are −2.94 at the 5 percent significance level and -3.62 at 1 percent significance level. The sample period is 1960–2000. Variables are as follows: output per worker (y) and physical capital per worker (k). The subscript “a” stands for agricultural sector, “m” for mining sector, and “r” for transport.

Cointegration results

The presence of a unit-root justifies the estimation of the production function within a cointegration framework (Tables 3–6). The Johansen’s cointegration procedure starts with the determination of the length of the VAR version of equation (2). Since the cointegration test critically depends on the choice of the lag length, we base the lag selection on the likelihood ratio test of model reduction, moving from eight to four lags.¹² For the VAR estimated, different misspecification tests are also reported. Overall, no serious misspecification was detected, apart from the rejection of normality in one case. However, as pointed out by Gonsalo (1994) and Hubrich (1999), the Johansen procedure is not sensitive to non-normality errors.

Table 3.

Cointegrating Tests Results

Note: The estimation period is 1968–2000 and the asterisks, * and **, denote rejection at the 5 percent and1 percent levels, respectively.

Table 3.

Cointegrating Tests Results

	Overall Function		Agricultural Sector		Mining Sector		Transport Sector
Eigenvalue	0.492	0.186	0.344	0.255	0.608	0.261	0.574	0.114
Null hypothesis on rank = r	r = 0	r ≤ l	r = 0	r≤1	r = 0	r ≤ l	r = 0	r ≤ l
λtrace	29.22*	6.81	23.65	9.71	40.88**	9.99	32.22**	3.99
λmax	22.41*	6.81	13.94	9.71	30.90**	9.99	28.24**	3.99

Note: The estimation period is 1968–2000 and the asterisks, * and **, denote rejection at the 5 percent and1 percent levels, respectively.

View Table

Table 3.

Cointegrating Tests Results

	Overall Function		Agricultural Sector		Mining Sector		Transport Sector
Eigenvalue	0.492	0.186	0.344	0.255	0.608	0.261	0.574	0.114
Null hypothesis on rank = r	r = 0	r ≤ l	r = 0	r≤1	r = 0	r ≤ l	r = 0	r ≤ l
λtrace	29.22*	6.81	23.65	9.71	40.88**	9.99	32.22**	3.99
λmax	22.41*	6.81	13.94	9.71	30.90**	9.99	28.24**	3.99

Note: The estimation period is 1968–2000 and the asterisks, * and **, denote rejection at the 5 percent and1 percent levels, respectively.

Table 4.

Cointegrating Vectors and Adjustment Coefficients

Note: Since the capital coefficients are reported as an element of the cointegrating vector, their signs are negative.

Table 4.

Cointegrating Vectors and Adjustment Coefficients

		Y	K	Constant	Trend
Cointegrating vectors
	Aggregate function	1	-0.34	2.43	0.03
	Mining sector	1	-1.51	-3.47	-0.06
	Transport sector	1	-0.33	0.65	0.05
Adjustment coefficients
	Aggregate function	-0.29	-0.11
	Mining sector	0.09	0.16
	Transport sector	-1	-0.60

Note: Since the capital coefficients are reported as an element of the cointegrating vector, their signs are negative.

View Table

Table 4.

Cointegrating Vectors and Adjustment Coefficients

		Y	K	Constant	Trend
Cointegrating vectors
	Aggregate function	1	-0.34	2.43	0.03
	Mining sector	1	-1.51	-3.47	-0.06
	Transport sector	1	-0.33	0.65	0.05
Adjustment coefficients
	Aggregate function	-0.29	-0.11
	Mining sector	0.09	0.16
	Transport sector	-1	-0.60

Note: Since the capital coefficients are reported as an element of the cointegrating vector, their signs are negative.

Table 5.

Properties of Cointegration VAR’s Residuals

Note: The normality property is tested with χ². The asterisks, * and **, denote rejection at the 5 percent and1 percent levels, respectively. There are not enough observations for a heteroscedasticity test for the miningsector.

Table 5.

Properties of Cointegration VAR’s Residuals

Diagnostic Tests	Aggregate Function		Mining Function		Transport Function
	F-test value	p-value	F-test value	p-value	F-test value	p-value
AR	1.710	0.129	0.892	0.532	0.685	0.700
Normality	9.386	0.052	13.862	0.007**	1.311	0.859
ARCH	0.018	0.894	7.447	0.012*	0.249	0.624
Heteroscedasticity	0.318	0.989	0.339	0.985	---	----

Note: The normality property is tested with χ². The asterisks, * and **, denote rejection at the 5 percent and1 percent levels, respectively. There are not enough observations for a heteroscedasticity test for the miningsector.

View Table

Table 5.

Properties of Cointegration VAR’s Residuals

Diagnostic Tests	Aggregate Function		Mining Function		Transport Function
	F-test value	p-value	F-test value	p-value	F-test value	p-value
AR	1.710	0.129	0.892	0.532	0.685	0.700
Normality	9.386	0.052	13.862	0.007**	1.311	0.859
ARCH	0.018	0.894	7.447	0.012*	0.249	0.624
Heteroscedasticity	0.318	0.989	0.339	0.985	---	----

Note: The normality property is tested with χ². The asterisks, * and **, denote rejection at the 5 percent and1 percent levels, respectively. There are not enough observations for a heteroscedasticity test for the miningsector.

Table 6.

Significance Test of Cointegration Vectors

Note: The estimation period is 1968–2000. The asterisks, * and **, denote rejection at the 5 percent and 1 percent levels, respectively.

Table 6.

Significance Test of Cointegration Vectors

	Cointegration Vectors	χ2(1)	p-Value for the Test Statistic
Aggregate production function
	Y	9.222	0.002**
	K	6.015	0.014*
	Trend	15.380	0.000 **
	Constant	8.762	0.003**
Transport production function
	Yr	21.067	0.000 **
	Kr	16.487	0.000 **
	Trend	16.675	0.000 **
	Constant	16.199	0.001**
Mining production function
	ym	1.283	0.257
	km	6.513	0.010*
	Trend	1.360	0.243
	Constant	1.4972	0.221

Note: The estimation period is 1968–2000. The asterisks, * and **, denote rejection at the 5 percent and 1 percent levels, respectively.

View Table

Table 6.

Significance Test of Cointegration Vectors

	Cointegration Vectors	χ2(1)	p-Value for the Test Statistic
Aggregate production function
	Y	9.222	0.002**
	K	6.015	0.014*
	Trend	15.380	0.000 **
	Constant	8.762	0.003**
Transport production function
	Yr	21.067	0.000 **
	Kr	16.487	0.000 **
	Trend	16.675	0.000 **
	Constant	16.199	0.001**
Mining production function
	ym	1.283	0.257
	km	6.513	0.010*
	Trend	1.360	0.243
	Constant	1.4972	0.221

Note: The estimation period is 1968–2000. The asterisks, * and **, denote rejection at the 5 percent and 1 percent levels, respectively.

Results for testing the number of cointegrating vectors are reported, with both the trace and maximum eigenvalue statistics. In the trace test, the null hypothesis is that the number of cointegating vectors is less than or equal to r (r = 0, 1); while in the maximum eigenvalue test, the alternative for r = 0 is r = 1. For the agricultural sector, we found no cointegrating vector (precluding any long-run relationship), while in all other cases, both tests reject the null hypothesis of no cointegration vector at 5 percent or less in favor of one cointegating vector.¹³ The results yield the following long-run production functions:

• Overall production function

• Transport sector production function

As expected, the overall production function and the transport production function are characterized by decreasing returns to scale. Capital per worker is found to be a key determinant of long-term output per worker. The estimated coefficients for capital (0.34 for the overall function and 0.33 for the transport sector) have the right sign and are in line with the share of this factor in GDP (about 0.35 percent in developing countries). They are also consistent with the values (0.30 to 0.40) used in most growth studies. By comparison, Bosworth, Collins, and Chen (1995) obtained a coefficient of 0.4 on the capital term in the growth regression for developing countries, while Sacerdoti, Brunschwig, and Tang (1998) estimate the coefficient on physical capital at about 0.35 for West African countries.

The deterministic component of TFP growth is found to be negative (−3 percent for the overall function and −5 percent for the transport sector). This result shows, to some extent, how inappropriate economic policies implemented during the 40 years from 1960 to 2000 have negatively affected TFP. By comparison, Fischer (1993) estimates that productivity growth from 1961 to 1988 is about −5 percent a year for Haiti and Madagascar.

Factor sources of growth

Table 7 and Figure 1 summarize the decomposition of the overall and sectoral outputs per worker into TFP growth and the contribution of physical capital per worker (defined as its share in output per worker multiplied by its growth rate). At the macroeconomic level, annual output per worker posted a negative average annual growth rate of -3.3 percent during 1960–2000. Negative TFP growth contributed to 60 percent of this decline, while the decline in physical capital per worker accounted for 40 percent.

Table 7.

Democratic Republic of the Congo: Sources of Growth by Factor Accumulation and Sectors, 1960–2000

(Annual percentage change)

Table 7.

Democratic Republic of the Congo: Sources of Growth by Factor Accumulation and Sectors, 1960–2000

(Annual percentage change)

Period		Output per Worker		Contribution of:
				Physical Capital	Total Factor Productivity
1960–2000			-3.3	-1.3	-2.0
1960–65			-4.4	-3.7	-0.7
1966–74			2.9	1.6	1.3
1975–82			-3.8	1.0	-4.8
1983–89			-1.1	-1.9	0.8
1990–2000			-8.8	-3.9	-4.9
Period	Output	Contribution of:
		Agriculture	Mining	Transportation	Other
1960–2000	-0.3	0.2	-0.4	-0.4	0.3
1960–65	0.5	-2.0	-0.7	-1.3	4.5
1966–74	5.1	0.9	0.2	0.7	3.3
1975–82	-1.6	0.6	-0.2	-0.9	-1.1
1983–89	1.9	1.5	0.1	0.0	0.3
1990–2000	-5.5	-0.4	-1.0	-0.8	-3.3

View Table

Table 7.

Democratic Republic of the Congo: Sources of Growth by Factor Accumulation and Sectors, 1960–2000

(Annual percentage change)

Period		Output per Worker		Contribution of:
				Physical Capital	Total Factor Productivity
1960–2000			-3.3	-1.3	-2.0
1960–65			-4.4	-3.7	-0.7
1966–74			2.9	1.6	1.3
1975–82			-3.8	1.0	-4.8
1983–89			-1.1	-1.9	0.8
1990–2000			-8.8	-3.9	-4.9
Period	Output	Contribution of:
		Agriculture	Mining	Transportation	Other
1960–2000	-0.3	0.2	-0.4	-0.4	0.3
1960–65	0.5	-2.0	-0.7	-1.3	4.5
1966–74	5.1	0.9	0.2	0.7	3.3
1975–82	-1.6	0.6	-0.2	-0.9	-1.1
1983–89	1.9	1.5	0.1	0.0	0.3
1990–2000	-5.5	-0.4	-1.0	-0.8	-3.3

At the sectoral level, in the agricultural sector, which experienced zero average annual TFP growth during 1960–2000, negative physical capital growth explained the negative growth of output per worker of 1.7 percent over this period. In the transport sector, TFP declines accounted for 92 percent of the negative growth rate of -6 percent of output per worker during the 40 years between 1960 and 2000. The mining sector recorded some TFP growth gains, but mining output per worker fell by an average 4.1 percent per year, owing to the rapid decline in physical capital per worker.

Sectoral contributions to overall growth

The bottom panel of Table 7 reports the sectoral contributions to GDP. The results indicate that the mining and transport sectors account for the negative real GDP growth of 0.3 percent per year during 1960–2000. Reflecting the negative trend in their outputs, the mining and transport shares in GDP have been completely eroded. The mining sector’s share in GDP fell from 20 percent in 1960 to just 6 percent in 2000, while the transport sector’s share declined from 18.5 percent in 1960 to a low of 3.7 percent in 2000.

Analysis across subperiods

The decomposition of sources of growth across the five subperiods (identified in the background subsection) reveals interesting patterns in the DRC’s growth experience (Table 8). The subperiod, 1966–74, is the only one to experience a positive average growth rate, 2.9 percent, with physical capital per worker and TFP contributing equally. During the subperiod, 1975–82, notwithstanding the positive contribution of physical capital per worker of 1 percentage point (the second highest of the five subperiods), output per worker still declined by 3.8 percent, reflecting the sharp drop in TFP. The reason is that a series of so-called prestigious projects (mainly white elephants) implemented during this subperiod had damaging impacts on TFP. As can be seen in Table 8, physical capital per worker in the key sectors of mining and transport sharply fell at the same time, as these white elephants pulled away resources from both sectors. Finally, the last subperiod (1990–2000) witnesses the largest decline in output per worker and in both physical capital and TFP, reflecting the damaging effects of hyperinflation, as well as of armed conflicts (since 1998).

Table 8.

Democratic Republic of the Congo: Sources of Growth in Mining and Transport, 1960–2000

(Annual percentage change)

Table 8.

Democratic Republic of the Congo: Sources of Growth in Mining and Transport, 1960–2000

(Annual percentage change)

	Sectors and Period	Output per Worker	Contribution of:
			Physical Capital	Factor Productivity
Agriculture
	1960–2000	-1.7	-1.7	0.0
	1960–65	-9.6	-5.3	-4.3
	1966–74	0.9	-2.8	3.7
	1975–82	-0.1	-1.6	1.5
	1983–89	0.8	1.1	-0.3
	1990–2000	-2.9	-1.0	-1.9
Mining
	1960–2000	-4.1	-6.8	2.7
	1960–65	-6.0	-0.1	-5.9
	1966–74	-18.7	-15.9	-2.8
	1975–82	-2.2	-9.7	7.5
	1983–89	0.7	-5.3	6.0
	1990–2000	4.2	-1.1	5.3
Transportation
	1960–2000	-6.2	-0.5	-5.7
	1960–65	-5.0	-0.6	-4.4
	1966–74	-2.1	-1.7	-0.4
	1975–82	-13.2	-4.7	-8.5
	1983–89	3.0	1.2	1.8
	1990–2000	-11.0	2.4	-13.4

View Table

Table 8.

Democratic Republic of the Congo: Sources of Growth in Mining and Transport, 1960–2000

(Annual percentage change)

	Sectors and Period	Output per Worker	Contribution of:
			Physical Capital	Factor Productivity
Agriculture
	1960–2000	-1.7	-1.7	0.0
	1960–65	-9.6	-5.3	-4.3
	1966–74	0.9	-2.8	3.7
	1975–82	-0.1	-1.6	1.5
	1983–89	0.8	1.1	-0.3
	1990–2000	-2.9	-1.0	-1.9
Mining
	1960–2000	-4.1	-6.8	2.7
	1960–65	-6.0	-0.1	-5.9
	1966–74	-18.7	-15.9	-2.8
	1975–82	-2.2	-9.7	7.5
	1983–89	0.7	-5.3	6.0
	1990–2000	4.2	-1.1	5.3
Transportation
	1960–2000	-6.2	-0.5	-5.7
	1960–65	-5.0	-0.6	-4.4
	1966–74	-2.1	-1.7	-0.4
	1975–82	-13.2	-4.7	-8.5
	1983–89	3.0	1.2	1.8
	1990–2000	-11.0	2.4	-13.4

Assessing the credibility of the program’s medium-term growth prospects

With the estimated production function derived in Section III, growth forecasts are based on projections of labor force, capital stock (based on investment and the perpetual inventory methodology), and productivity growth rates (reflecting ongoing economic reforms).

Using this projection methodology¹⁹ and assuming that the labor force will grow at the population growth rate, that is, 3 percent per year, the study finds that a “Solow residual”²⁰ growth rate of 2.5 percent implicitly underpins the program’s real GDP average growth rate of about 5 percent²¹ and the average investment-to-GDP ratio of 16 percent from 2002–05. The whole Solow residual should not be ascribed to TFP growth, because it incorporates a “catch-up” factor that is typical for a post-conflict country.²² Unfortunately, it is not easy to estimate the latter. One indirect estimation would be to estimate the TFP growth in a normal period and argue that one would expect at least this TFP growth to be achieved in the forecast period, mainly because of policy implementation. Using this procedure, we estimate TFP growth at 1.3 percent, which implies a catch-up factor of 1.2 percent. While the 1.3 percent TFP growth is higher than those experienced in industrial, Latin American, and African countries, it is well below East Asian TFP growth rates (Table 10). In sum, if the actual investment rate were to fall below 16 percent of GDP over 2002–05, the average economic growth rate of 5 percent would be difficult to achieve, because it would imply unrealistic TFP growth rates.

Table 10.

Sources of Growth in Selected Countries and Regions, 1986–92

Source: Bosworth, Collins, and Chen (1995).

Table 10.

Sources of Growth in Selected Countries and Regions, 1986–92

			Contribution of:
	Regions/Countries	Output per Worker	Physical Capital	Factor Productivity
Regions
	Industrial countries	1.5	0.7	0.8
	Africa	-0.4	-0.5	0.1
	Latin America	-0.6	0.0	-0.6
	East Asia (excluding China)	5.1	2.6	2.5
	South Asia	2.9	1.2	1.7
East Asian countries
	China	6.2	3.1	3.1
	Korea	6.6	3.9	2.7
	Malaysia	5.4	1.9	3.5
	Singapore	7.4	2.6	4.6
	Thailand	8.3	3.2	4.8
	Taiwan Province of China	5.9	2.8	3.1

Source: Bosworth, Collins, and Chen (1995).

View Table

Table 10.

Sources of Growth in Selected Countries and Regions, 1986–92

			Contribution of:
	Regions/Countries	Output per Worker	Physical Capital	Factor Productivity
Regions
	Industrial countries	1.5	0.7	0.8
	Africa	-0.4	-0.5	0.1
	Latin America	-0.6	0.0	-0.6
	East Asia (excluding China)	5.1	2.6	2.5
	South Asia	2.9	1.2	1.7
East Asian countries
	China	6.2	3.1	3.1
	Korea	6.6	3.9	2.7
	Malaysia	5.4	1.9	3.5
	Singapore	7.4	2.6	4.6
	Thailand	8.3	3.2	4.8
	Taiwan Province of China	5.9	2.8	3.1

Source: Bosworth, Collins, and Chen (1995).

Assessing the time required to recoup the lost ground

As indicated in Section II, output per capita and real GDP have followed a steep decline, especially since 1990. Real GDP had risen to 150 percent of its 1960 level by 1990, only to decline to about 80 percent of its 1960 level by 2001. At the same time, with 3 percent annual growth of the population, real GDP per capita fell to 60 percent of its 1960 level in 1990, and further to 25 percent of its 1960 level in 2001.

An interesting question is how many years, beginning in 2002, would it take both aggregates to reach their 1960 and 1990 levels, if the projected 5 percent real GDP growth were to continue beyond 2005 for several decades. We calculate that it would take four years for real GDP to reach its 1960 level and 13 years to return to its 1990 level. Assuming that the population continues to grow at an annual rate of 3 percent, reaching the 1960 real GDP per capita level would take 70 years, while the 1990 level would be attained in 45 years. These time frames highlight the DRC’s disappointing economic performance in the 40 years from 1960 to 2000.