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In , the manufacturing sector contributed Note that apart from the agriculture based sub-sectors offering a higher contribution to value added than all other manufacturing sub-sectors, these sub-sectors also absorb the largest share about 13 per cent of manufacturing contribution to overall formal employment. Firms are classified as follows: micro-establishments with fewer than five employees; small establishments from 5—20 employees ; medium establishments 21—50 employees ; and large establishments over 50 employees. As shown in Table 4. The share has remained high in both medium and large enterprises over the years.

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Distribution in the sub-sectors indicates that FBT had the highest percentage share of the total medium and large p. Concentration of the number of establishments in large businesses declined over time. In particular, the percentage share of plants employing over 50 workers reduced at a decreasing rate up to In contrast, the percentage share of small plants employing 11 to 50 workers slowly increased up to while the percentage share of micro-plants fluctuated over the study period.

Consequently, distribution of employment by number of establishment size indicates that the role of micro- and small enterprises MSEs in employment is increasingly important, while the role of medium and large business in employment weakened during —9.


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Small-size enterprises maintain the largest share in number of establishments over time even when employment is declining. Interestingly, a decline in the number of employees in MSEs is accompanied by an increase in the number of establishments indicating new firm creation rather than increases in sizes in these categories. This is in contrast to large enterprises where a decline in the number of employees is reflected in a reduction in the number of establishments, while an increase in the number of employees is not reflected in the number of establishments, indicating that large enterprises absorb employees into their existing enterprises.

In Kenya, there are two ethnically distinct groups of businesses, namely, those owned by Kenyans of Asian largely Indian origin and others owned by Kenyans of African origin. While the former group constitutes a small minority their presence in trade and manufacturing is substantial Himbara There are possibly extensive flows of information among Kenyan—Asian entrepreneurs. For example, the formal or organized sector is relatively small with correspondingly few players, most of whom are Kenyan Asians. In addition, for various political and historical reasons, this immigrant entrepreneurial community is socially embedded.

Industrializing Malaysia : Policy, Performance, Prospects

For example, its members tend to live in clusters of close proximity, participate vigorously in social clubs, and have numerous community activities both within and outside these clubs. Currently, multinationals and parastatals dominate the large industries while Kenyans of African origin dominate the micro- and small establishments. Most of the micro- and small-scale enterprises are owned by Kenyans of African origin Table 4. The share of African owned businesses falls sharply as those of Indian origin and other Asian origin ownership increase up the enterprise size scale.

As a consequence, Asians own a majority of the medium and large-scale enterprises, a finding consistent with Ikiara et al. Europeans and entrepreneurs from the Middle East own a small proportion only of all size categories compared with other entrepreneurs. The majority of micro-establishments were owned by sole proprietors, with ownership declining with size category. Private limited companies held the highest percentage share in small enterprises.

About 94 per cent of African owned firms were sole proprietorships compared to 5. The industrialization process in Kenya was, in the early post-independence period, clustered around three sub-sectors namely textile, food processing, and metal industries. Whereas the market liberalization policies from the s led to the collapse of the textile industry, food processing and metal industries have withstood the vagaries of a changing macro- and policy environment and developed over time, albeit slowly. The growth in food processing and metal industries has largely been driven by expanding local and regional markets.

However, growth in these sub-sectors was at its lowest ebb during — as the cost of doing business worsened during the period, occasioned by the near-collapse of infrastructure, declining local demand, and lack of clear policy direction. Failure to create backward linkages to fully exploit the AGOA initiative, particularly in reviving the cotton industry, delayed the envisioned growth in the textile industry.

Increasing input costs and uncoordinated cotton marketing have been a disincentive to cotton farmers. Similarly, local textile products are not sufficiently competitive to penetrate the local market which has largely become the domain of textile imports. Further, enhanced discourse between the government and the private sector, particularly through the Kenya Association of Manufactures, has been catalytic in raising government interest in addressing problems facing manufacturers. Indeed, there has been an increased trend for government to involve the private sector in the budget process, as well as in institutions responsible for prioritizing critical areas where the government should invest to improve the business environment.

Beyond textile, food processing, and metal industries, Kenya has diversified her manufacturing activities. Refining of petroleum products, rubber and plastics manufacturing, and paper and printing have increasingly contributed to manufacturing GDP. Similarly, manufacturing of construction products, particularly cement, have had phenomenal growth fuelled by increased demand in the real estate industry. This growth is expected to continue, given the many infrastructure projects planned under Vision , with general growth in the real estate and construction sectors.

The oil and steel industry and other industries that feed on iron ore petroleum products such as plastics are poised to grow phenomenally given recent discoveries in Kenya of fossil resources and iron ore. Given the increasing openness of the economies of many countries, discussions on how competitive is a country are of special importance because a globalized market environment demands that products compete locally with imports while at the same time trying to have a competitive edge in world markets. A country or a firm can be either price- or quality-competitive in specific products.

Productivity is important for competitiveness. Firms that are not productive have a poor chance of competing for domestic and export markets. This is particularly so considering recent developments in which countries have opened their economies by dismantling trade barriers and enacting policies for promoting trade. Not only does productivity performance have a bearing on competitiveness, it also has a bearing on profits and wages, and ultimately poverty reduction and overall welfare.

Productivity growth permits sustained economic expansion, a greater demand for labour, and increased real wages. Two broad categories of factors determine competitiveness Urata ; Onjala One includes factors defining the operating environment and is therefore exogenous to the firm. Competitive pressure, indicated by market concentration and openness of an economy and buttressed by trade policies is p. In an acutely competitive environment, firms either match up regarding efficient use of factors of production or are forced out of the market purely as a response to external forces.

The other category includes those associated with the internal capability of firms. Such factors include managerial talent, reward systems, value—cost ratios, and technological fitness. An analysis of firm-level productivity is important in understanding the extent of preparedness by Kenyan manufacturers to compete in external markets. Parametric measures of productivity are sensitive to the models used. For that reason, results obtained from this level of our analysis need to be viewed as indicative only. In order to generate estimates on output per worker and firm-level total factor productivity, we use the WB datasets on manufacturing.

These datasets provide information on the value of sales, used in this report to proxy for output; expenditure on equipment and buildings, used to proxy for capital; wage bills, used to represent labour input; and the cost of raw materials. For growth in total factor productivity in manufacturing, we need time series information on aggregate manufacturing output, capital, and labour. Official statistical reports such as the Statistical Abstract and Economic Survey publish sufficiently disaggregated data on these variables. We were able to build a — series from these sources from which we applied a lag operator to generate the necessary changes in the variables in our estimation.

While it is quite easy to obtain direct information on the value of manufacturing output and manufacturing wage bills, capital is always difficult to measure. This is because it is the value of the flow of capital services rather than the actual capital stock that is desirable for purposes of modelling productivity.

However, such a flow is difficult to track through time since it contains components such as dividends and interest charges that are not usually recorded on an annual basis. To circumvent this problem, the practice is normally to assume that capital services are proportional to the stock of capital. In addition, we use fixed capital formation in the manufacturing sector as a proxy for the flow of capital services. Because of these data problems, our estimated productivity measures must be treated as initial approximations. Official statistics on value added and employment, only available for firms with more than 50 employees, reveal that leather and footwear, industrial p.

Firm-level data obtained from a WB survey in indicate that productivity in the food sector is greater than that for garments and other sectors. The food sub-sector appears an outlier from a manufacturing sector labour productivity perspective. Overall, labour productivity is greater for medium and large firms than for small firms, and higher in privately owned firms than in firms under different forms of ownership. Industrial policies in Kenya have been inconsistent over time. Even when policy statements are potentially efficacious, the stated policies have not always been diligently implemented.

For example there are many useful policies developed in the recent past which have not yet been implemented. Failure to implement has often led to loss of industrial development opportunities. For example, the NIP, which makes significant proposals, is yet to be implemented. While other countries have used the less technologically complicated textile sector to kick off rapid industrialization, Kenya allowed marketing boards to destroy the cotton value chain beginning with the destruction of cotton growing and ginning.

Major joint venture investments in cotton mills were unable to survive the lack of cotton and opportunistic management. State involvement in other agro-processing industries such as the sugar sector and dairy and meat processing combined with the excesses of the cooperative movement to undermine what would have been huge industrial operations. Many of the manufacturing enterprises are either micro- or small establishments. Studies have shown that firms in this size category face particular problems.

First, they are under-capitalized and face very poor transformation prospects Lundvall et al. Second, they have more limited access to financial services Isaksson and Wihlborg Most of their start-up cost and upgrading is funded through borrowing from family and friends Green et al. As a result, they invest very little if at all, p.

MSEs are less productive and less able to participate in external markets: studies have established a strong positive association between size and propensity to export Graner and Isaksson What all this means is that the dominance of small firms in Kenyan industry gets in the way of industrial development. Generally, Kenyan manufacturing has suffered poor productivity growth.

There are numerous reasons for this outcome. Very little investment takes place, either at firm or national level. The public sector emphasizes academic rather than technical education which tends to have greater impact on overall productivity. Kenya does not have an adequate supply of infrastructure and many firms are forced to self-provide for water, power, and security. Also, firms are not always able to focus on their core business because of failures in the market for such complementary services as transport. Most firms end up spending their resources on providing services which can be supplied more cost effectively through outsourcing.

There are no mechanisms for linking industry with institutions of higher learning. The result is that there is neither obvious demand for nor beneficial application of results arising from research carried out by such institutions. Asanuma, Shinji et al. Bigsten, A. Bigsten and P. Kimuyu eds Structure and Performance of Manufacturing in Kenya.

New York: Palgrave, 7— Bigsten A. Adams, P.

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Collier, Ndungu and N. Oxford: Oxford University Press. Chirwa, E. African Development Review 12 1 : 89— Coughlin, P. Coughlin and G. Nairobi: Heinemann, ch. Foroutan, F. World Bank Policy Research Paper Washington, DC: World Bank.


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Graner, M. The Developing Economies 47 3 : — Green, C. Hirschey, K. John, and A. Makhija eds Issues in Corporate Governance and Finance vol. Amsterdam: Elsevier, — Himbara, D. World Development 3. Ikiara, G. New York, NY: Palgrave, — Geneva: ILO. Isaksson, A. New York, NY: Palgrave, 93— Nairobi: KAM. Sessional Paper No. Narobi: Government Printer. National Development Plan — Nairobi: Government Printer. Statistical Abstract. Economic Survey. Kimuyu, P. Kinyanjui, B. Lundvall, K. Onjala, Joseph Nairobi: African Economic Research Consortium. Republic of Kenya New York: Palgrave, — Swamy, G.

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