Chapter

Chapter 7 Exporting and Efficiency in African Manufacturing

Author(s):
International Monetary Fund
Published Date:
August 2001
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The many cross-country studies of the determinants of growth in Africa undertaken in the last few years typically conclude that the inward orientation of African countries has been a major obstacle to growth (see, for example, the survey in Collier and Gunning, 1999)1. A variety of mechanisms to increase openness and thereby growth have been proposed. To compete against international producers, domestic firms must adopt newer and more efficient technology or use the same technology with less x-inefficiency in order to reduce costs (Nishimizu and Robinson, 1984). Higher volumes of trade increase international technical knowledge transfer (Grossman and Helpman, 1991). If domestic firms have different degrees of inefficiency, the exit of the less efficient ones results in lower average costs and higher productivity. The firms that remain are forced to adjust in two ways: by expanding their scale of production and exploiting economies of scale, and by reducing their technical inefficiencies2. Both these adjustments will decrease average industry costs and raise productivity (Krugman, 1984; Roberts and Tybout, 1991). One may argue that the primary sources of development are learning and knowledge accumulation and, since international trade is one of the most important channels through which knowledge gets transferred, the degree of integration in the world trading system becomes a crucial determinant of growth prospects.

This chapter investigates the extent to which sub-Saharan African manufacturing firms that export are more efficient than those that do not. By observing firms over time, one can find out whether exporting firms increase their efficiency relative to non-exporting firms. Causality is difficult to establish, however. Firms with good management may have both a high level of efficiency and so be more likely to export, and faster growth in efficiency, giving rise to a spurious association between exporting and efficiency gains. Some recent analyses control for this by explaining the change in efficiency in terms of various firm characteristics, including initial levels of efficiency, and allowing for fixed firm effects (see Bernard and Jensen, 1995, on the US economy; Clerides, Lach and Tybout, 1998, on Mexico, Colombia and Morocco; and Kraay, 1997, on China).

This study looks at four sub-Saharan African countries: Cameroon, Ghana, Kenya and Zimbabwe. AH are of similarly modest size, with GNP averaging only $7.7 billion as of 1996. Africa has had the highest level of trade restrictions (Dollar, 1992; Sachs and Warner, 1997), and the four economies conformed to this pattern, with consequently low levels of competition. They have all been technologically backward, for example, with low levels of human capital endowment. Thus, it is useful to explore whether the pattern of higher efficiency associated with exporting exists in these countries as in countries in other regions.

Establishing the link between exports and firm-level efficiency requires firm-level microeconomic data on factor use and output. To date, most studies of the relationship have used industry- or sector-level data (Ghani and Jayarah, 1995), with very few exceptions, especially for Africa. Haddad (1993) finds a positive association between productivity and exports at the firm level: firms closest to maximum efficiency tend to have high export shares. Harrison (1994) shows that ignoring the effects of liberalisation has led researchers to mis-measure the effect of trade reform on productivity.

This chapter extends these papers by using comparable panel sample surveys of four sub-sectors of manufacturing covering 1992–95 in the four countries, to ask whether both the degree of efficiency and its rate of growth associate with exporting. It constructs measures of firm-level efficiency using stochastic production-frontier models to show the relationship between exporting and firm efficiency. There is no attempt to test for causality; that is a subject for future research.

The Manufacturing Sector in Four Sub-Saharan African Countries

The data were obtained during 1991-95 as part of the Regional Programme on Enterprise Development co-ordinated by the World Bank. In each country, surveys gathered information from a panel of manufacturing firms over three years on a variety of issues, including outputs and resource use. They covered 1992-94 for Kenya, 1991-93 for Ghana, 1992-94 for Zimbabwe and 1992/93-94/95 for Cameroon.

All the countries faced macroeconomic problems that had a significant impact on manufacturing performance. They all had import-substitution development policies from independence through the late 1970s. All introduced structural adjustment programmes in the 1980s, with the support of the World Bank and other aid organisations, and with an emphasis on macroeconomic reforms, trade liberalisation and privatisation.

Only Ghana saw a substantial recovery in real GDP from the mid-1980s. Between 1983 and 1991, it liberalised its exchange rate, so that at the start of the survey period in 1992 the premium on foreign exchange had been eliminated. Financial-sector reforms in the late 1980s had removed a significant part of the non-performing loans from the banking system and liberalised interest rates. Growth slowed somewhat during the survey period, but Ghana still had the highest trend growth in real per capita income between 1983 and 1992 in the sample, at 1.5 per cent per year.

Between 1983 and 1992, real per capita GDP in Kenya grew by 0.7 per cent, but the withdrawal of donor support in 1991 began a serious economic crisis and a fall in per capita GDP. Political turmoil and ethnic clashes in the run-up to the 1992 election had serious economic repercussions. Uncertainty about government policies and a shortage of foreign exchange held back growth, which fell to 0.5 per cent in 1992 and 0.3 per cent in 1993. The manufacturing sector grew by only 1 per cent and 1.8 per cent in those years. Some recovery emerged in 1994 as macroeconomic efforts began to bear fruit and the reforms were broadened to include structural and institutional improvements. GDP grew by 3 per cent, but the manufacturing sector still managed only 1.9 per cent growth.

Per capita incomes declined in Zimbabwe by 0.2 per cent and in Cameroon by 3.3 per cent between 1983 and 1992. Both countries were thus hard pressed to undertake reforms in the 1990s. Zimbabwe finally adopted a structural adjustment programme in 1991. Policy changes focused initially on dismantling the highly restrictive system of import and foreign exchange controls. This included liberalisation of the foreign exchange market, which eliminated most of the parallel-market premium. Imports were shifted gradually to the Open General Import Licence list, where foreign exchange rationing did not apply. By the time of the first survey in 1993, these reforms had essentially eliminated Zimbabwe’s trade and foreign-exchange problems. A very serious drought in 1991/92, however, had strong repercussions on the manufacturing sector until 1993, after which competition increased from both new domestic firms and imports, and the combination of financial liberalisation and large fiscal deficits led to very high real interest rates. They approached 15 per cent in 1994.

Long regarded as an example of success in sub-Saharan Africa, Cameroon suffered a series of external shocks in 1986 that revealed severe structural weaknesses. Its terms of trade worsened by 50 per cent between 1986 and 1994, as prices of its main exports fell while the nominal exchange rate remained fixed and domestic distortions persisted. Per capita income plunged by almost half. The government initially resisted adjustments; it continued investment programmes and maintained public-sector salaries, financed itself by borrowing and building up arrears to the private sector. In 1988, it accepted an IMF-sponsored stabilisation programme, followed by a structural adjustment programme in 1989. Due to the CFA zone’s fixed nominal exchange rate vis-à-vis the French franc, the government had to use other adjustment instruments. It undertook some reforms, such as price deregulation, financial reforms and tariff reductions, but incomes continued to fall and exports stagnated. The inward orientation of industry, widespread public-sector control of economic activities and the overvalued exchange rate made it hard for exporters to make a breakthrough. In 1994, the CFA franc was finally devalued by 50 per cent against the French franc, and trade and indirect tax reforms were undertaken. Per capita incomes then rose for the first time since 1986. Large manufacturing firms, particularly exporters, increased production after the devaluation, but among smaller and informal firms production continued to decline.

Only Kenya saw no real devaluation between 1990 and 1994. Zimbabwe’s was about 5 per cent, while both Cameroon’s and Ghana’s were close to 10 per cent. All four countries had relatively extensive reforms under way, although much still remained to accomplish before stable, growth-enhancing environments emerged.

Efficiency-Frontier Models

To what extent did export activity make it possible for firms to achieve higher efficiency under these turbulent economic circumstances? The econometric estimates of technical efficiency in this section come from stochastic efficiency-frontier models that estimate production-function frontiers and derive technical efficiencies using fixed-and random-effects techniques and a time-variant productivity approach. The data cover a balanced panel of firms for which observations exist for all the years, because the reliability of the measure of technical efficiency depends crucially on the length of time covered by the panel.

Since the pioneering work of Farrell (1957), further developed by Aigner and Chu (1968), firm-level efficiency has often been measured using the efficiency-frontier approach. Given variations in plant technology, the concept estimates actual deviations from an efficient isoquant instead of an average production function. With the frontier-production technique, the expression y = f(x, t) represents the maximum output achievable with the vector of inputs x at time t. The observed production of firm i will fall short of the frontier by some amount ui = f(xi, i) - yi. If the production function f(.) can be estimated, then a set of specific efficiency indexes ui can be obtained.

Several techniques have been proposed to estimate f(.) (see surveys by Bauer, 1990; Green, 1993). Following Schmidt and Sickles (1984) and Green (1993), the panel data extension of the frontier model can be written as:

where yit is the observed value added of the ith firm (i = 1, ….., N) at time t, xijt is a vector of the amount of the jth inputs (j = 1,… j) employed in firm i at time t (t = 1,…., T), and β0 and βj represent a vector of technology parameters to be estimated. The compound disturbance is composed of two terms. The first, vit, is a random disturbance assumed to be distributed identically and independently across plants with identical zero mean and constant variance. It represents factors such as luck, weather conditions and unpredicted variation in inputs. The second, uit, is a firm-specific effect that reflects firm efficiency and management skills. The distribution of uit is one-sided, reflecting that output must lie on or below the frontier, and it is assumed to be independently and identically distributed across plants, with mean μ and variance σ2.

The stochastic production frontier recognises that deviation from the production frontier might not be entirely under the control of the firm. Contrary to deterministic models, in which events like bad weather or a high number of random equipment failures might appear to constitute inefficiency and translate into measures of increased inefficiency, the stochastic-frontier model allows for such random events (Green, 1993). Also in contrast to deterministic models, the stochastic nature of the model allows some observations to lie above the efficiency frontier, making the estimates less vulnerable to outliers.

Assuming a standard log-linear (Cobb-Douglas) production function and taking logs produces the stochastic production-frontier model in the form proposed by Lovell, Defourny and N’Gbo (1992):

where K represents the replacement value of equipment and L the number of employees in firm i in period t. The error term vit is assumed to be independently and identically distributed as N(0,σ2), independent of the disturbance component uit, which is assumed to be independently and identically distributed as the non-positive part of a N(0, σ2) distribution truncated at zero. Both v and u are assumed to be distributed independently of the exogenous variables in the model.

Following Aigner, Lovell and Schmidt (1977), Jondrow et al (1982), and Battese and Coelli (1992), an estimate of the efficiency measure of the ith firm at the rth time period is given by:

Assuming that firm-level inefficiency, uit, is constant over time, one can estimate the model using either a fixed-effects or a random-effects approach.

With constant firm effects over time, the model can be estimated using a within estimator or least-squares-dummy-variable (LSDV) estimator (see Schmidt and Sickles, 1984). When verifying the assumption of independence between the inefficiency parameter and input levels, a random-effects model is generally preferable (Green, 1993). In such cases, firm effects are treated as random variables and estimated using the variance-components or generalised-least-squares (GLS) approach. The choice between them could be made using the Hausman test (Hausman, 1978). Relaxing the assumption that firm-specific technical efficiency is time-invariant and allowing productivity to change over time, one can identify time paths for firms’ technical efficiency (see Cornwell, Schmidt and Sickles, 1990).

Empirical Results

Estimation of Technical Efficiencies

To derive firm-level inefficiency indexes, a simple production function with capital and labour was estimated separately for each manufacturing sector in the four countries, using the fixed-effects and random-effects approaches. The estimates of the random-effect estimators (GLS) were chosen, because the hypothesis of non-correlation between the inefficiency term and inputs could not be rejected using a Hausman test in nine of the 16 sectors. In the sectors where the hypothesis was rejected, the differences between LSDV and GLS estimates were not significant. The estimation results have a reasonable fit.

The production functions were then used to estimate the efficiency index. To distinguish the efficiency levels of exporters from those of non-exporters, the analysis divided the firms into two categories, initial exporters and non-exporters, then asked, “Are (say) non-exporting firms generally farther from the frontier than firms that export initially?“

Table 7.1 presents average efficiency in the four countries during the survey period, for initial exporters and non-exporters in each sector. Low average technical-efficiency levels in some sectors might indicate unexploited opportunities for productivity improvements through learning. These results are consistent with observed significant average inefficiency in the African manufacturing sector (Nishimizu and Page, 1982; Pack, 1988). In all countries, exporters exhibit higher average efficiency than non-exporters.

Table 7.1.Efficiency Levels by Category of Initial Exporter: Panel(Random Effects)
FoodWoodTextilesMetalsAll
CountrynMeannMeannMeannMeannMean
Cameroon
Initial exporters559.5459.11100.0633.21652.1
Initial non-exporters1330.4737.1458.21017.93431.4
All1838.51145.1566.61623.75038.0
P value180.0001110.000150.0022160.0001500.0001
Ghana
Initial exporters0---563.70---212.6749.1
Initial non-exporters2516.81728.32432.92022.48624.9
All2516.82236.42432.92221.59326.7
P value250.0008220.0001240.001220.0002930.0001
Kenya
Initial exporters344.4660.4510.8718.82132.4
Initial non-exporters810.61626.01221.6138.24917.7
All1119.82235.41718.42011.97022.1
P value110.0245220.0001170.0032200.0205700.0001
Zimbabwe
Initial exporters1144.5554.02132.51343.45040.1
Initial non-exporters1418.21055.21534.7530.04433.6
All2529.81554.83633.41839.79437.1
P value250.0001150.0001360.0001180.0001940.0001
Note: The P-value tests the null hypothesis that the means for exporters and non-exporters are equal.
Note: The P-value tests the null hypothesis that the means for exporters and non-exporters are equal.

Table 7.2 presents firm-level efficiency indexes for each year of the survey, derived by repeating the estimates with time-variant efficiency parameters for each country. With random-effects average estimates for the period, exporters exhibit higher yearly average efficiency than non-exporters in all countries. These results are consistent with those of Kraay (1997). Using Chinese panel data, he finds that exporting firms tend to be larger and enjoy higher productivity and lower unit costs than non-exporting firms. These observations of greater efficiency among exporters as opposed to non-exporters may, however, simply reflect a selection effect, as the most efficient producers are the most likely to export (Roberts and Tybout, 1997). Whether that is the case for these data remains an issue to be explored.

Table 7.2.Efficiency Levels by Category of Initial Exporter(Time-Variant Productivity Model)
CountrynMean for Survey Year Indicated
Cameroon199319941995
Initial exporters1639.947.552.7
Initial non-exporters3433.726.823.9
All5035.733.433.1
Ghana199119921993
Initial exporters732.142.147.3
Initial non-exporters8624.123.421.1
All9324.724.823.0
Kenya199219931994
Initial exporters2123.620.032.0
Initial non-exporters4918.27.020.2
All7019.810.923.8
Zimbabwe199219931994
Initial exporters5028.940.737.5
Initial non-exporters4433.232.935.1
All9430.637.136.4

The Relationship Between Exports and Technical Efficiencies

To test more formally whether exporting firms are more efficient and whether they have higher rates of efficiency growth one can estimate the following equation:

where X is a vector of exogenous variables that include firm characteristics and competitive conditions. Table 7.3 presents the results. Regression (a) is an OLS estimate of the efficiency level for the three-year period that simply includes the initial exporting status of the firm. Initial exporters tended to exhibit significantly higher efficiency levels than other firms. These results are consistent with those of Roberts and Tybout (1997), who found that exporting firms were more efficient than non-exporters. To control for self-selection of the efficient firms as exporters, regression (b), a GLS estimate of efficiency levels in years two and three, includes the efficiency for the first period. It assumes no serial dependence in eit—i.e. that E(eit. eis) = 0 for all s, t— and that although firm performance and exports are jointly determined, exports are predetermined with respect to eit. The results show that even with control for initial efficiency levels, initial exporting raises efficiency in the two subsequent years. The effects are quite substantial: initial exporters show 13 per cent higher efficiency during the next two years.

Table 7.3.Determinants of Technical Efficiency(Regressions)
Variable(a) Random-Effect Efficiency Level, OLS(b) Time-Variant Efficiency Level, GLS
Constant0.17** (4.05)0.07* (1.87)
Initial exporter0.13** (3.61)0.13** (4.29)
Initial efficiency0.38** (8.57)
Cameroon0.08** (1.99)0.03 (0.94)
Kenya-0.09** (-2.27)-0.09** (-2.70)
Zimbabwe0.05 (1.42)0.05 (1.60)
Micro-0.005(-0.11)0.04(1.10)
Medium0.02 (0.46)0.01 (0.51)
Large-0.05 (-1.05)-0.02 (-0.46)
Wood0.19** (4.83)0.07** (2.22)
Textiles0.08** (2.21)0.09** (2.84)
Metals0.03 (-0.85)0.06* (1.72)
Capital city0.02 (0.69)0.01 (0.50)
Foreign owned0.13** (3.52)0.02 (0.49)
Publicly owned-0.04 (-0.70)-0.03 (-0.58)
Number of observations306606
fl20.240.23
Notes:OLS = ordinary least squares; GLS = generalised least squares. T-statistics are in parentheses. For statistical significance, * indicates significant at the 10 per cent level, ** at the 5 per cent level. Dummy variables: value of one if as specified below, value of zero otherwise.
Notes:OLS = ordinary least squares; GLS = generalised least squares. T-statistics are in parentheses. For statistical significance, * indicates significant at the 10 per cent level, ** at the 5 per cent level. Dummy variables: value of one if as specified below, value of zero otherwise.
VariableValue of one ifVariableValue of one if
CountryCameroonWoodIn wood sector
CountryZimbabweTextilesIn textiles sector
CountryKenyaMetalsIn metals sector
Micro1 < employment < 4MachinesIn machinery sector
Medium30 < employment < 99Capital cityIn capital city
LargeEmployment = 100 orForeign ownedForeign owned
more
Publicly ownedPublic ownership

Conclusion

This chapter has examined exports and firm-level efficiency in four small African countries, showing the link between efficiency and exporting. The analysis here, however, cannot answer questions about how much higher efficiency enables firms to enter the export market or whether exporting generates a gain in efficiency. Both require further work.

Nevertheless, exporters do increase their efficiency quite rapidly while non-exporting firms do not. This certainly has important policy implications. A strategy of openness and export orientation obviously will have more beneficial efficiency and productivity effects than an inward-oriented strategy. Policies that open the economy should be pursued. The countries studied in this chapter were in the midst of a policy reform process at the time of the surveys, but it was far from complete and policy distortions remained. Despite both this and stagnation in the world economy, firms that ventured into the export market managed to improve their technical efficiency very significantly—a strong indication that export orientation is the appropriate route for African economies. A good strategy for export promotion is a good strategy for growth.

Whole ranges of domestic constraints need to be removed for the beneficial effects of openness to be realised, however. An environment where exporters can thrive requires not only appropriate trade and exchange-rate policies but also readily available human capital and infrastructure that keep transaction costs down. Governments must pursue stable, consistent, credible economic policies and apply them in a non-biased way. Entrepreneurs need economic security and the ability to enforce contracts. Increased trade will build a constituency supporting these types of reforms, which at the same time support trade. Over time, a virtuous circle may develop to reduce the risk of governmental backtracking. Once this process is secured, one can believe that more and more African manufacturers will become able to approach the international best-practice frontier.

Notes

This chapter draws on work undertaken as part of the Regional Programme on Enterprise Development, organised by the World Bank and funded by the Belgian, British, Canadian, Dutch, French and Swedish governments. Support of the British, Dutch, French and Swedish governments for workshops of the group is gratefully acknowledged. The use of the data and the responsibility for the views expressed are those of the authors. The authors form the Industrial Surveys in Africa Group, which uses multi-country data sets to analyse the microeconomics of industrial performance in Africa.

If economies of scale exist in previously protected sectors, the same policies that favour scale efficiency in the export sector may reduce scale efficiency in those firms competing with imports, as these producers typically contract or exit when trade liberalisation increases import penetration in the domestic market. See Krugman (1987); Rodrik (1988, 1991).

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