Constraints on Manufacturing Growth in Kenya1 by Arne Bigsten, Karl Lundvall, and Måns Söderbom Department of Economics Göteborg University Box 640 SE 405 30 Göteborg Sweden2
Paper prepared for a Workshop on “Firm Analysis and Competitiveness Surveys” at the World Bank, Washington DC, December 14-15, 1998
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This report is based on Bigsten and Kimuyu (1998), which includes contributions by Renato Aguilar, GK Ikiara, Mutsembi Manundu, Wafula Masai, Damiano Kulundu, Anders Isaksson, Clas Wihlborg, Måns Söderbom, Karl Lundvall, Walter Ochoro, Lennart Hjalmarsson, Mats Granér, Jörgen Levin, Njuguna Ndungu, and Steve Kayizzi-Mugerwa. 2 email:
[email protected],
[email protected],
[email protected]
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1. Introduction Kenya experienced relatively high growth rates up until the first oil crisis in 1973. During the sixties and early 1970s there was an investment boom in Kenya. Between 1964 and 1971 gross fixed capital formation grew by 17.2% per year in constant prices, while the growth rate was only 0.1% per year between 1971 and 1993. From 1994, however, there has been some recovery. There was a rapid build-up of manufacturing capital in the 1960s, much in relatively recent machinery and equipment. Eventually excess capacity was built up, and since several of the industries were meant to serve the wider East African market they suffered when the community collapsed. Given the excess capacity there was less need for new investment. The investment boom of the 1960s was largely built on MNCs, but given that the wider East African market was no longer available from the Kenyan base and given that Kenya was not perceived as a location from which MNCs could penetrate the world market, MNC investment slowed down dramatically in the mid-1970s. The global recession in the early 1980s plus the coup attempt in 1982 further reduced incentives to invest. Several MNCs actually withdrew or sold off their investments in Kenya in the late 1980s. During the coup attempt there was looting of Asian property, which further increased their sense of insecurity. Structural adjustment efforts from the early 1980s have gradually changed the economic environment, but so far the response of the manufacturing sector has been rather poor. Manufacturing growth depends on investment and productivity change. This paper will use micro data to investigate their determinants and interaction. It is worth asking whether the reforms and inducements introduced are sufficient to make Kenya competitive outside of agro-processing, horticulture and tourism. Has an environment that is sufficiently efficient and attractive to investors been put in place? Kenya does need both higher levels of manufacturing investment and productivity improvements to achieve high growth in the sector. This paper presents some results on these issues from a comprehensive report on the Kenyan RPED study (Bigsten, Kimuyu, 1998). Section 2 briefly describes the data. Section 3 presents an analysis of the determinants of investment in Kenyan manufacturing, while Section 4 reports results on the determinants of productivity and efficiency. The conclusions from the analysis are summarised in Section 5. 2. The Data
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The RPED survey of Kenyan manufacturing firms was undertaken at three points in time. The first round of interviews was done in February-March 1993, the second in May-June 1994 and the third in August-September 1995. Much of the data gathered refers to the most recent financial year of the firms, which means that such data refers to the period 1992-1994. When firms were asked about their current situation, their replies refer to the actual time of the interview. The towns covered were the four major towns of Nairobi, Mombasa, Nakuru, and Eldoret. Our survey did not cover the whole of manufacturing, but concentrated on four sub-sectors: food processing, textiles and garments, wood working, and metal working. However, since these four sub-sectors comprise about 73% of formal manufacturing employment, we still provide a reasonably comprehensive picture of the manufacturing sector in Kenya. In terms of employment the food sector is the largest of the four. It provides 32% 3
This section is based on Aguilar and Bigsten (1998),
2
of the employment in our four subsectors, compared to 26% for textiles and garments, 25% for metal, and 17% for wood. If we, instead, look at employment by firm size, we see that 20% is in the category 1-5 employees, 17% in 6-20, 7% in 21-75, 19% in 76-500, while as much as 36% is in the 500+ category. All the four sectors chosen for analysis emerged early in the country’s industrialisation process. In 1961 they produced 68.4% of manufacturing value added in Kenya, and the share was still high in 1990, when it was 65.6%. In terms of productivity (value added/employment) the food sector lies considerably above the manufacturing average, while the other three lie well below. Particularly the wood working sector is characterised by a lower productivity, although the gap vis-a-vis the rest has decreased over the last three decades. In 1990 the labour productivity index in food processing was 127 (manufacturing average = 100), while it was 86 in fabricated metal, 57 in textiles and garments, and 45 in the woodworking sector. The gap among the first three sectors was rather stable over the period 1961-1990, while the index of woodworking moved up from 18 in 1961 to 45 in 1990. The primary sample consisted of a total of 200 firms in manufacturing, 50 each in food processing (ISIC 31), textiles and garments (32), woodworking (33), and metalworking (38). The sample included both the formal and the informal sectors with 75% of the interviews in formal firms and 25% in informal ones. The formal firms were chosen from the Central Bureau of Statistics’ master file of registered firms. This is in principle continuously updated, but it did have numerous problems such as the inclusion of firms, which either had never started or which were defunct. Still, it was the best source available. We defined five sizestrata for each of the four industrial sectors included in the study. These strata include firms from 1 to 5 employees, from 6 to 20, from 21 to 75, from 76 to 500, and 501 or more employees. Thus, a total of 20 strata were defined for the formal sector. Firms were randomly chosen within each stratum according to their distribution among the four cities covered by the study. The selection process slightly over-represented employment in the larger formal firms. Apart from the stratification by size, the selection was itself random across the four cities included in the study. Out of our primary sample of 150 formal firms 12 firms declined to be interviewed. A few other firms were not found, had closed or switched into other activity. These firms that could not be interviewed were replaced by firms with the same characteristics from our reserve list. To construct a sample frame for the informal sector we undertook a primary listing of firms engaged in the four sectors in the four cities. The listing for Nakuru and Eldoret was close to complete, while for Nairobi and Mombasa we only covered some sections of the towns. On the basis of this listing, we estimated the distribution of firms across the four subsectors, and found this to be 30% textile, 28 % metalworking, 30 % woodworking and 12 % food processing. If we had tried to represent the employment in the informal sector as well as formal employment, we would have had to choose most of our sample from this sector. However, since the degree of variation in the informal sector is much smaller than in the formal one we decided to include a relatively small number of informal firms. The primary sample of 50 firms was then chosen in the given proportions. With regard to the informal sample we assumed that the best predictor of its distribution among the cities was the population. We therefore selected 65 % of the informal firms in Nairobi, 27% in Mombasa, 8% in Nakuru and 5% in Eldoret. In the case of the informal sector, no stratification according to size was necessary. Thus, this subsample includes only four strata; one for each industrial branch.
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We also added the firms from the pilot survey to the first round sample to beef up the sample. A total of 276 firms were interviewed at least once. We interviewed 224 firms in 1994, 216 firms in 1994, and 218 firms in 1995. The sample had a significant rate of attrition, although smaller than expected. Of the 224 firms interviewed in 1993, 29 firms were lost in the second wave and a further 26 were lost in the 1995 wave. Since we have a stratified sample, particularly between formal and informal firms, we have constructed weights that make it possible to make representative employment oriented as well as firm oriented inferences. The employment weights, which also may be taken as proxies for output weights, are weights, which give each employee in a firm the same probability of being included in the sample. The firm weights are weights giving each firm the same probability to be sampled. We have weights for the whole sample as well as for the formal and informal categories separately. These weights are useful for industry-wide inferences as well as for inferences about the informal and formal sectors respectively. 3. Investment Behaviour4 3.1. Introduction It has long been argued that investments in less developed economies are constrained by financial markets being segmented, resulting in inefficient financial intermediation and misallocation of resources (McKinnon, 1973). This has been supported by the empirical results reported by King and Levine (1993) and Easterly and Levine (1995),, which indicate that African growth has been hampered by poorly developed financial markets. In the case of Kenya, consecutive financial crises in the late 1980s triggered significant reforms of the financial system in 1989, aimed at enhancing prudential controls and ensuring a more efficient, market-oriented, allocation of credit. Although some important steps, such as the liberalisation of interest rates, were taken in the early 1990s, the poor investment record of the economy largely persisted. This section aims to shed light on whether factors of external finance have influenced the investment behaviour of Kenyan manufacturing firms. We confine the analysis to investment in equipment, since investments in land and buildings are rare and different in nature compared to investments in equipment. One remarkable feature of the data is the predominance of zero investments, even among relatively large firms. We explicitly take this into account in Section 3.2, where we outline the theoretical framework for investment behaviour. This is based on recent theoretical contributions explaining how firms from time to time may find it optimal to refrain from investing if there are non-convexities in the adjustment cost function. We derive an investment equation under the assumption of perfect financial markets, and then discuss how financial imperfections would affect the model. In Section 3.3 we present descriptive statistics concerning firms’ investments in general, and the role of external finance in particular. We discuss the empirical formulation of the theoretical investment equation, and present the regression results, in Section 3.4. We provide conclusions from the investment analysis in Section 3.5. 3.2. Theoretical Background In this section we summarise the theoretical investment model, a neo-classical model under perfect financial markets, which is used to analyse the mechanics discussed in the 4
This section is based on Söderbom (1998).
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introduction. Following standard theory, the model is based on the theory of adjustment costs. Consider a price taking firm with a constant returns production function
Y (s ) = A(s )F {K (s ), L(s )}, where Y represents output, K denotes capital, L is employment, A represents total factor productivity, and s is time. In the absence of taxes, the firm’s profit at time s is given by
{
}
Π (s ) = p(s )A(s )F {K (s ), L(s )}− w(s )L(s ) − G I (s ), p I (s ) , where p is the output price, w is the wage, I is investment, and G is an amalgam of the purchase price of capital, p I , and adjustment costs. At any point in time the firm’s objective is to maximise the sum of expected discounted profits, ∞
(3.1)
max Et ∫ e −r Π (s )ds , Ls , I s
t
subject to the capital evolution constraint (3.2)
K& (s ) = I (s ) − δK (s ),
where r is the discount rate (assumed to be constant), t is the current time period, and δ is the geometric depreciation rate of capital. We assume a fairly flexible form for the adjustment cost function, allowing for non-convexities:
(3.3)
b I (t ) G I (t ), p I (t ) = B1 1 1 + b1 S
(
)
1+ b1 b1
[p (t )]{ I
1{I (t )> 0 }b2 +1{I (t )< 0 }b3 }
∆t + 1{I (t )≠ 0} ⋅ S ⋅ Z∆t
where B1 and b1 are strictly positive parameters, 1{A} is an indicator function equal to one if the event A is true and zero otherwise, b2 and b3 are parameters allowing for a price wedge between the purchase price and the selling price of capital goods, ∆t is a (short) interval of time, S represents the size of the firm (which is treated as constant, for simplicity), and Z is a ‘flow’ fixed adjustment cost. The presence of fixed costs generates infrequent investments, since the firm benefits from concentrating its investment activities to a few short periods rather than incurring the fixed costs in every period. This incentive to make very rapid and concentrated capital adjustments is balanced by the first part of (3.3), which, due to convexity, implies that large investments would be extremely costly. The firm will invest if and only if there exists at least one investment level for which ∞
(3.4)
(
)
I t ⋅ Et ∫ exp{− (r + δ )}Π′K (s )ds − G I t , ptI > 0 t
holds. Given that it is optimal to invest, the optimal level will satisfy the first-order condition
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(3.5)
I (t ) B1 S
(1 b1 )
[p (t )] I
b2
∞
= Et ∫ exp{− (r + δ )}Π′K (s )ds t
for I(t)>0.5 In this analysis we focus on (3.5), determining the optimal level. We assume a Cobb-Douglas production function and utilise first order conditions for labour demand, and that expected future variables appearing in (3.5) can be written as E t X (t + s ) = X (t )e Et (g Xt )s , where Et(gXt) is the expected growth rate of X={p, A, w}. Equation (3.5) can then be written in log linear form as (3.6)
ln I (t ) = α 0 − α 1 ln p I (t ) + α 2 ln p (t ) + α 3 ln A(t ) − α 4 ln w(t ) + α 5 Et g tp + α 6 Et g tA − α 7 Et g tw − α 8 rt ,
where α 0 − α 8 are (positive) composites of the structural parameters.6 One potentially strong assumption under which (3.6) has been derived is that economically viable investment projects can always be funded, and that the cost of funds is independent of the source of financing. This makes investments independent of liquidity. However, whenever there exist financial market imperfections, caused by, say, informational asymmetries between the firm’s managers and potential lenders, the use of internal funds will be less costly than external financing, and changes in the debt-equity relation may therefore affect investments. Since the firm’s cash flow is a natural proxy for changes in internal funds, one common strategy to test the null of no financial imperfections is to examine whether investment responds to cash-flow movements, conditional on the fundamental variables appearing on the right-hand side of (3.6). Hence, if this is the case, policy measures mitigating the imperfections will be effective in increasing investments. 3.3 Descriptive Analysis of Investments and Financial Issues We begin the empirical analysis by examining descriptive statistics on firms’ investments, with a particular focus on financing. We have discarded the smallest firms in the data-set, because the economic theory above is unlikely to provide a good approximation for the decision rules of too small firms. We have also deleted observations which, due to missing data, cannot be used in the regression analysis below. This has left a sample of 450 observations on 216 firms. We start by exploring the distribution of investment rates (i.e. the investment to capital ratio). Empirical studies of investments typically report this distribution to be highly skewed to the right. As can be seen in Figure 3.1, there is a large frequency of zero investments, and it is extremely unusual for these firms to sell off equipment, as indicated by the low frequency of negative investment rates. Moreover, not only do half of the firms refrain from investing during a year, but the vast majority of the firms make no or modest investments (approximately 75 percent of the firms have investment rates less than 0.1). Finally, note that a large share of total investment expenditure is accounted for by a very small fraction of the firms, as indicated by the “share of aggregate” bar.
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Optimum is guaranteed by the strict convexity of the adjustment cost function for all non-zero investments. Note that the log of the expectation is approximated by the expectation of the log. See Denny and Nickell (1992).
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Figure 3.1. Frequency Distribution of Net Investment Rates and Share of Total 0.6 0.5 0.4
Formal (n=370)
0.3
Informal (n=80) Formal, Share of Total Gross Inv
0.2 0.1
.9
