Incremental Capital Output Ratio as Measure of Productivity of Investment: Theory and a Kenyan...

INCREMENTAL CAPITAL-OUTPUT RATIO AS MEASURE OF PRODUCTIVITY OF

INVESTMENT: THEORY AND A KENYAN EXAMPLE

by

John Thinguri Mukui

Report Prepared for USAID, Nairobi, Kenya

5 October 1990

i

STATEMENT OF WORK

PURPOSE AND OBJECTIVES

The purpose of the consultancy is to assist USAID/Kenya to develop a database for use in monitoring and evaluation of program impact at the goal, sub-goal and purpose levels of the Mission’s development assistance program. The objectives of this consultancy are to review, interpret and analyze data, as well as develop a database on income distribution, consumer price index (CPI), agricultural productivity, and the level and productivity of private investment. Much of this program performance information will also be useful to the Government of Kenya and private sector groups. In order to assess the goal of sustained broad-based economic growth and sub-goals of increased production and incomes, indicators of income have an important role to play. While major indicators of income e.g. Gross Domestic Product (GDP), Gross National Product (GNP), GDP per capita etc are easily available, information on income distribution is scarce and not easily accessible. Yet measures of income distribution are necessary in assessing the broad-based characteristics of economic growth. USAID/Kenya is interested in real growth. An important component of real growth is the rate of inflation. A number of observers are of the opinion that the rate of inflation in Kenya is higher than the official estimates. An understanding of what the rate of inflation is in Kenya will shed light on real growth of the Kenyan economy and on price stability. In order to measure purpose-level program impact and for decision-making, USAID/Kenya would like to get a good handle on indicators of productivity of investment (especially private investment), capital and labor, as well as productivity of agriculture (especially smallholder agricultural productivity). Productivity is one the major determinants of the standard of living since increases in productivity may result in higher real income and promote price stability. The measurement of productivity is also an important element in the evaluation of the relative efficiency of factor utilization.

SCOPE OF WORK The following information and analysis shall be provided under this consultancy:

i Review, interpret, and analyze data on income distribution from published and unpublished sources. Develop a database on income distribution including the following indicators of income distribution: Gini coefficients, total income distribution, land Gini coefficients, regional income distribution, and factorial income distribution. Discuss the status of the Social Dimensions of Adjustment (SDA) project.

ii Analyze the Government’s computed consumer price index (CPI). Provide revised CPI

based on appropriate commodity basket, weights and income groups. Based on this alternative CPI, develop a series of CPI for the period 1980-1989.

iii Identify and compute appropriate indicators for productivity of investment (especially

ii

private investment), capital and labor. Analyze trends in productivity during the period 1975-1989.

iv Identify and compute appropriate indicators for agricultural productivity (with special

emphasis on smallholder productivity). Develop a series of trends in agricultural productivity during the period 1975-1989.

v Describe and discuss the difficulties in assembling the various data, as well as the

adequacy of the data. Discuss in depth the reliability and validity of various data. Discuss the strengths and drawbacks of various indicators.

REPORTS AND DELIVERABLES

The consultant shall produce a comprehensive database for the USAID/Kenya Mission. The report shall include a series of tables for all indictors identified above. It shall also include an analysis of these data, an assessment of their reliability and validity, and identification of underlying assumptions, as well as recommendations for collecting and updating the information. The consultant should discuss the usefulness of each factor of production by sector (labor, land and capital), focusing mainly on agriculture and industry, in an attempt to justify the choice of sector-specific measures of productivity.

SPECIFIC TERMS OF REFERENCE 1. INDICATORS OF AGRICULTURAL PRODUCTIVITY

i Identify and compute appropriate indicators for agricultural productivity (with special emphasis on smallholder productivity). Develop a series of trends in agricultural productivity during 1975-1989.

ii Describe and discuss the difficulties in assembling the various sources of data, as well

as the adequacy of the data. Discuss in depth the reliability and validity of various data. Discuss the strengths and drawbacks of various indicators.

iii Carry out a detailed analysis of trends in maize productivity increases especially for

smallholders. Use the physical indicator of yield per hectare as measure of productivity.

iv Undertake a detailed analysis of trends in sorghum/millet productivity increases. Use

the physical indicator of yield per hectare as measure of productivity.

v Carry out a detailed analysis of trends in wheat productivity increases. Discuss both smallholder and large-scale farms’ wheat productivity. Use the physical indicator of yield per hectare as measure of productivity.

vi Assess the productivity gap, that is, yield gaps in the above basic food grains.

vii. Analyze factors underlying yield gaps.

iii

2. MEASURING PRODUCTIVITY OF INVESTMENT AND LEVELS OF INVESTMENT Objective The objective of this consultancy is to investigate the productivity of investment in Kenya for the economy as a whole and for specific sectors including manufacturing, agriculture and construction. In addition, the consultancy will include the analysis of government, parastatals and private sector investment in each sector and the economy as a whole with a view to defining purely private investment. This will be accomplished by analyzing existing data, discussions with Government officials, and developing alternative indicators for productivity of investment including the incremental capital-output ratio. Background One of the primary objectives of the USAID Mission in Kenya is to raise the level and productivity of private investment in order to put the economy on a sustainable broad-based growth path. Presumably, therefore, increased productivity of private investment, or an accepted proxy, must be an objective target in the Mission’s strategy. After some initial research, it does not appear so easy to provide reliable quantified benchmarks to measure that objective. Sessional Paper No. 1 of 1986 on Economic Management for Renewed Growth states that Kenya “has required nearly six units of new capital to produce one new unit of output” in the past decade. According to the GOK-published data on the economy as a whole, Kenya’s ICORs compare favorably with those of other countries and Kenya’s ICOR has declined over the past several years. The interpretation of these results is difficult given controls on interest rates, exchange rates and many prices. In addition, the relative amount of excess capacity could affect the ICOR if growth in the past five years has come from existing capacity. Another issue with the productivity of investment is the contribution of Government, parastatals and the private sector. It would be interesting to analyze each of these actors in detail. As USAID/Kenya analyzes different program options, it would also be interesting to look at the productivity of investment in different sectors like manufacturing, horticulture, and construction. Our understanding of the economy is not as good as might be. More research is required before USAID or the Government can rely on the ICOR or other measures of productivity of investment to make investment policy decisions. Deliverables under the contract The completed report will include the following information and analysis:

i Discussion of different measures of the productivity of investment including the incremental capital-output ratio (ICOR) and at least two additional alternative measures.

ii Discussion of data sources on investment broken down by sector and economic agent

iv

(government, parastatals or private sector) for Kenya.

iii Calculation of the incremental capital-output ratio and two alternative measures of productivity of capital for the economy as a whole and for each of the productive sectors for the period 1970-1988.

iv Calculation of the ICOR and two additional measures of productivity of capital for

each sector of the economy by economic agent (government, parastatal and private sector) for the period 1970-1988. This calculation will be dependent on data availability.

v An analysis of the quality of data provided, explaining the results of the ICOR

calculations, trends in the results, anomalies in the calculations, how the results compare to other African countries, and recommended areas for further research.

vi Discussion of problems in using ICOR and other measures in calculating productivity

of investment.

vii Discussion of capacity utilization in the Kenya economy and estimates of annual capacity utilization for the period 1970-1988.

PHASING OF THE WORK

The first study undertaken was on the CPI, followed by preliminary analysis of income distribution, incremental capital-output ratio, and finally agricultural productivity for selected food crops. Due to time constraint based on the contract, the scope of work was scaled down substantially. For example, the study on the consumer price index no longer required development of alternative CPI for the period 1980-1989. The study on agricultural productivity excluded the issue of assessing productivity (yield) gaps for each crop and factors underlying yield gaps, since yield gaps were considered region-specific based on agricultural potential. In the case of productivity of capital investment, the issues excluded were analysis of contribution of the governmental sector since there was an existing study conducted by the Long Range Planning Division of the Ministry of Planning and National Development; use of at least two additional measures of productivity (apart from the capital-output ratio); and estimates of annual capacity utilization. The USAID/Kenya Mission decided to support the Central Bureau of Statistics to update the CPI through financial support and technical assistance, while more sophisticated analysis on productivity of capital investment was to be undertaken later based on preliminary findings on the capital-output ratio.

1

INCREMENTAL CAPITAL OUTPUT RATIO AS MEASURE OF PRODUCTIVITY OF


EXECUTIVE SUMMARY i. The main factors of production are labour and capital. While there are divergent views on what are the binding constraints on growth, it is necessary to study the trend in returns on capital as efficiency in capital utilization has implications on growth, the competitiveness of Kenya’s exports, and on the imports required to finance capital accumulation. One of the popular measures of return on investment at the macro-level is the capital-output ratio, which measures the relationship between capital and output. However, since data on capital stock is usually unavailable or unreliable, the more common measure is the incremental capital-output ratio (ICOR) because it only requires data on fixed investment and GDP, which is available from the national accounts. ii. The Ministry of Planning and National Development (MPND) computes ICORs as part of the routine macro-modelling work. The MPND uses three-year moving totals in place of single year values of investment and output. One of the appeals of MPND approach is the use of depreciation estimates. The MPND uses two sectoral depreciation rates (share of current income devoted to replacement) of 0.02 or 0.05, based on whether the share of machinery and other equipment in total sector’s capital formation is less or more than 70 percent. Investment, as given in the Statistical Abstract, is in gross terms, while net investment is gross investment minus depreciation. Our analysis will relate to fixed investment. It will not cover inventory accumulation, as the latter is not necessarily related to growth in output, but is also influenced by other factors e.g. import controls and expectations about trends in exchange rates, interest rates and inflation. iii. During the period 1972-76, gross fixed capital formation at constant prices is generally characterized by a declining trend but grew during 1977-78 due to the coffee boom, which led to a surge in investment other than in the services sector. The price increase was not taxed, and coffee farmers converted an estimated 60 percent of the windfall into savings. The 1979 oil crisis and the 1980 drought reversed the upward trend, followed by some recovery in 1981, and a further reversal in 1982. In the period 1981-85, fixed capital formation registered a continuous decline but started picking up after 1986. iv. Kenya registered declining trend in GDP growth rates in early 1970s. However, the 1976-77 coffee boom reversed the trend and GDP growth rates rose tremendously. After 1977, GDP growth rates took a declining trend as a result of the 1979 oil price shock and the 1980 drought. In 1982, GDP growth plunged to 3.4 percent and continued declining to its lowest level in 1984 when the country experienced a severe drought. Since 1985, the economy has managed to grow at an annual rate of 5 percent. In the second half of the 1980s, the high GDP growth rates can be attributed to increased utilization of capacity created in the earlier years as fixed investment at constant prices was lower in 1980s compared to the late 1970s.

2

v. We shall not interpret ICORs for “producers of Government services” due to conceptual and data problems. In national accounts terminology, GDP accruing to “producers of Government services” excludes all enterprise activities of the government sector e.g. building and construction and forestry; and consumption of fixed capital, other than an imputed amount in respect of vehicles. What is usually termed as “contribution of public sector to capital formation” lumps up both parastatals and Government capital formation on “nongovernment” activities. Due care should therefore be taken in computing ICORs for “producers of government services” as this is almost identical to the ratio of investment to increase in the wage bill. However, GDP generated by government expenditure on “nongovernment” economic activities is derived in the same way as private and parastatal sector GDP. In addition, since the implicit GDP and fixed investment deflators are not identical, we have used constant price GDP and investment data. vi. In using the ICOR to determine the trend in return to investment, the length of period to be considered is important. During the period 1972-89, the range of gross ICOR for total GDP declines (and ICORs appear more stable) the longer the lag period considered. A second important factor is the base output. For example, due to slowdown in growth in 1984 due to drought, ICORs using 1984 as base year will show a decline in ICORs, and thus give a misleading indication of increase in the productivity of investment. ICORs for the whole economy show a declining trend over time and are lower and more stable in the 1980s than in 1970s. The net ICOR, with three-year moving average and assuming a weighted depreciation rate of 0.030427674, declined from 4.98 in 1975 to 2.55 in 1989. The downward trend may be a result of gradual increase in utilization of productive capacity built up in the second half of the 1970s following the coffee boom and the breakup of the East African Community. After 1986, net ICORs (with three-year moving average) declined, but have stabilized around a value of 2.5. The recent stabilization of ICOR indicates that this source of growth might be running out of steam, and there is need for increased capital formation in the economy. vii. Some of the sectoral net ICORs (using three-year moving average) exhibit a declining trend, especially for the major national accounts sectors such as manufacturing, trade, and “other services”; while other sectors show considerable fluctuations such as mining and quarrying, building and construction, and electricity and water. The estimated values for some sectors are unusually large (positive and negative) for some periods which in general is a consequence of decline or stagnation in sectoral GDP in the period. For the agricultural sector, the net ICOR increased to a peak of 2.02 in 1980 due to unfavourable weather conditions; declined to 0.85 in 1983 due to improvement in weather conditions; increased in 1984 and 1985 due to drought; but declined again after 1986 as the favourable weather conditions and structural adjustment began to impact on producers. The ICOR as a measure of trends in return on capital in the agricultural sector is somewhat deceptive, mainly because agricultural output is also influenced by other factors including weather, quality of crop and animal husbandry, agricultural producer prices, and other Government policies. viii. For manufacturing sector, the ICORs were high in the mid-1970s probably reflecting low capacity utilization but fell briefly during the coffee boom of 1976-77 due to foreign exchange availability. After 1981, net ICOR has shown a declining trend, having fallen from 3.46 in 1981 to 2.00 in 1989. The observed declined in the ICOR in the 1980s is

3

probably due to increased utilization of idle capacity created in the earlier years. Since manufacturing output has consistently increased during the period 1972-1989 while investment has on average declined, the decline in ICORs reflects actual improvements in efficiency of investment and increased capacity utilization. The annual variations on a trend are more likely due to fluctuations in availability of imported inputs brought about by import controls and foreign exchange shortages. The building and construction sector is characterized by unusually large or negative ICORs, which implies stagnation and are signs of serious problems in the sector. The estimated net ICORs for the “trade, restaurants and hotels” registered a significant downward trend, implying that capacity utilization (e.g. hotel occupancy) has been improving. The “transport, storage and communications” sector exhibited a declining trend in estimated ICORs until 1984, but climbed from 3.8 in 1984 to 7.0 in 1989. ix. The data on net ICORs are assumed to measure the impact of flow of capital services to the flow of output. This is expected to minimize errors of the capital-output ratio as a stock-flow concept. However, the net ICOR does not completely remove such bias because (a) it does not take into account variations in capacity utilization, and (b) the difficulties of arriving at reasonable indicators of depreciation rates. More reliable estimates of depreciation rates should be made on the basis of capital formation by type of asset or by updating the Input-Output Tables. x. At best, data on ICORs can be used to identify the trouble sectors but cannot be directly used to specify policy choices. A consistent increase in a sectoral ICOR is an indication that more disaggregated analysis (probably at firm-level) is required to determine the causes of increasing inefficiency. In addition, ICOR is not a suitable indicator of return on assets by ownership (private and parastatal) due to (a) the fact that GDP data in Kenya is not disaggregated by ownership, and (b) data on parastatals would not include joint ventures and would only include firms incorporated through Acts of Parliament. In addition, the problems plaguing the parastatal sector are well documented and need not be supported or counteracted by macroeconomic indicators such as the ICOR.

4

INCREMENTAL CAPITAL OUTPUT RATIO AS MEASURE OF PRODUCTIVITY OF


1. INTRODUCTION 1. The main factors of production are labour and capital. Early theories of growth concentrated on the role of capital accumulation in economic development, assuming that developing countries were capital-scarce, labour-surplus, economies. While there are divergent views on what are the binding constraints on growth, we will proceed on the assumption that the availability of investment -- from both domestic and foreign savings -- determines a (but not the only) limit to growth of output. Furthermore, since a big portion of capital is imported, the foreign exchange constraint (due to a structural external account deficit) is almost translated to a capital constraint. It is necessary to study the trend in returns on capital as efficiency in capital utilization has implications on growth, the competitiveness of Kenya’s exports, and on the imports required to finance capital accumulation1. At least in the Kenya case, the GDP deflator is on average lower than the fixed investment deflator, and the cost of capital therefore has a disproportionately large influence on costs of production and consumer prices. 2. There are two main approaches to the analysis of returns on investment at the sectoral and aggregate levels. The first --capital-output ratio -- assumes fixed technical or behavioural coefficients relating capital stock and output. This has its roots in the Harrod-Domar growth model, which relates rate of growth of output with propensity to save and the capital-output ratio. The second approach -- sources of growth or total factor productivity growth -- breaks observed economic growth into components associated with changes in factor inputs and a residual representing technical progress and other elements, and normally uses an aggregate Cobb-Douglas production function of the form:

Y = ALαKβ Where Y is output, L is labour, K is capital, α and β are output elasticity coefficients for labour and capital respectively, and A is a measure of Hicks-neutral technical change (i.e. the ratio of the factor marginal products remain unchanged for a given capital-labour ratio) and is an increasing function of time. A quick but approximate method of obtaining an ICOR is the ratio of average annual share of investment in GDP to average annual growth rate of GDP. It is important to state at the outset that the main criticism of the Harrod-Domar growth model also applies to the capital-output ratio, i.e. the crucial assumption that production takes place under conditions of fixed factor proportions in the medium term.

1 The paper makes reference to materials directly relevant to computation of capital-output ratios, but does

not dwell on general macroeconomic modeling in Kenya, for example, (a) the development and applications of the Kenya Simulation Model (KENSIM) described in Slater and Walsham (1975), Slater, Walsham and Shah (1977), and Masakhalia, Shah, Slater and Walsham (1977); and (b) and other sectoral and macroeconomic planning models of the Kenyan economy (see, Karani and Howe, 1965; Hodd, 1974; Whitacre, 1975; and World Bank, 1983).

5

3. One of the popular measures of return on investment at the macro-level is the capital-output ratio, which measures the relationship between capital and output. However, since data on capital stock is usually unavailable or unreliable, the more common measure is the incremental capital output ratio (ICOR). Its popular appeal is not based on its conceptual or empirical usefulness but more because it only requires data on fixed investment and GDP, which is available from national accounts data. 4. Several studies have also proposed that there is a strong relationship between economic growth and rates of investment in machinery and equipment (Hill, 1964), and the composition of capital formation therefore matters2. As shown in Table 5, fixed investment consists of Residential Buildings, Non-Residential Buildings, Construction and Works, Land Improvement and Plantation Development, Transport Equipment, Machinery and other Equipment, and Breeding Stock and Dairy Cattle. The different components of fixed investment may have different capital-output ratios, which may not be identified in an aggregate relationship between total investment and GDP growth in a sector. In addition, some of the growth in GDP could be explained by the changing composition of fixed investment over time, and the extent to which various components of capital formation complement each other to generate long run economic growth.

2. LITERATURE REVIEW 5. Most of the literature on ICORs centres on methods of derivation and its adequacy (or otherwise) as a measure of trends in return on investment at the sectoral and national levels. A common concern in the literature is the span of period to be covered in the computation of ICORs. A quick, back of the envelope, method of obtaining an ICOR is the ratio of average annual share of investment in GDP to average annual growth rate of GDP. A simple approach is to calculate the ICORs for sectors or the economy as a whole using the formula:

ICORt = INVt-1/(GDPt - GDPt-1) 6. There are various problems with this formulation for the ICOR. First, ideally, we should calculate net ICOR using information on net investment -- gross investment minus depreciation -- but estimates of depreciation are usually not reliable. Put in another way, the gross ICOR does not capture differences in durability of investment, while net ICOR does (with durable investments having low depreciation rates and nondurable investments having high depreciation rates). Second, the lag between investment and production is assumed to be one year, thereby giving big annual fluctuations in ICORs. The annual fluctuations could also be attributed to changes in weather (especially for an economy such as Kenya’s which is basically agricultural), changes in rates of capacity utilization, variations in factors other than capital, and differences in construction periods (which is likely to vary 2 Hill (1964) found that “… the relation between growth and one kind of investment cannot be the same

as that between growth and another kind ... In so far as any general association exists between growth and investment, it is largely due to investment in machinery and equipment. This is especially the case for growth in GNP per person employed, where all of the correlations, excepting that with machinery and equipment, are quite trivial” (cited in Morgan, 1966; and Aseto, 1977).

6

within and between sectors). The lag problem can be minimized by measuring ICOR over a period. 7. Chenery and Eckstein (1967; 1970), in a study of Latin America, starts from the premise that ICOR is expected to be lower at higher rates of growth. This is because (a) during times of rapid growth, capacity is likely to be utilized faster than it is created, while during slow growth it is often created faster than it is utilized; and (b) when higher rates of growth are based on greater investment, the part of gross investment used to replace old equipment and to construct social overhead facilities usually represents a smaller share of the total 3. In addition, Chenery and Eckstein (1970) say that annual ICORs cannot always be used as units of observation in a time-series as year-to-year movements reflect the effects of changes in degree of utilization of capital as well as in its amount. Chenery and Eckstein (1970) say that “those ratios are meaningful only when growth of GNP has been constrained by the growth of capital stock. The annual series, however, are marked by cycles, during which abnormally high (or negative) values for the ratio indicating the creation of unutilized capacity, are followed by abnormally low values indicating that capacity is being utilized faster than it is being created.” 8. Chenery and Eckstein define the conventional gross ICOR (Kt) as result of the regression equation: Kt = K1 + Z/Rt+1, where K1 is a constant capital-output ratio for directly productive net investment (with one year lag), Z is a constant share of income devoted to replacement plus social overhead investment, and Rt+1 is following year’s growth rate of GDP. This method of arriving at capital-output ratios is taken from Strout (1965). In the model, fixed investment (INVEST) is the sum of net investment (INVNET) and depreciation, which is assumed to bear a fixed relationship to the value added in a sector, and is given as z*GDP. Dividing both sides by change in GDP, we get gross ICOR as the sum of net ICOR and z/r, where r is the sectoral rate of growth4. Kt is a decreasing function of Rt+1 because a high growth rate implies that the depreciation component will constitute a smaller fraction of gross investment. 9. In estimating capital requirements, Chenery and Eckstein reduced the estimated value of z (the responsiveness of Kt to the growth rate) by one half, and constrained K1 to be no less than two (as a capacity benchmark), which was not appreciably lower than the lowest average incremental ratios found in the study. However, it would have been more appropriate to establish a fixed capital-output ratio for each industry classification for a benchmark year which independent evidence indicates was a period when capacity was virtually fully utilized, rather than for the entire economy (Creamer, 1962; cited in Brown and Conrad, 1967). Basically, Chenery and Eckstein decomposed the gross ICOR (Kt) into two parts: one showing the effect of new investment on output (K1) and the other showing the investment needed to replace worn-out capital. In the Kenya case, it is not necessary to derive Z since we have estimates of depreciation from the composition of gross fixed capital formation.

3 See, Hill, 1964; Leibenstein, 1966; and Strout, 1965. Earlier studies had also established a positive

association between ICOR and level of per capita income (Kuznets, 1960) and an inverse relationship between ICOR and growth rate of GDP (Ohkawa and Rosovsky, 1962; and Leibenstein, 1966). 4 Shourie (1970; 1972) cautions about putting too much faith in such a functional relationship where the

ratios have the same denominator, as each ratio in the equation is growth rate of GDP plus something else. See also, Kuh and Meyer (1955) and Madansky (1964).

7

10. One of the early uses of ICORs in evaluating trends in return on investment in Kenya is by the World Bank (1969; 1975). The World Bank starts from the Chenery-Eckstein (1967; 1970) approach, but instead of fixing K1, the value of Z is derived from the implied depreciation rates in the Input-Output Tables for Kenya, 1967 (Kenya, 1972). The World Bank obtained K1 as K-(Z/R) and then run a regression of K1 on time to test whether productivity of investment was increasing or declining. The World Bank (1975) observed that the incremental capital output ratio showed that efficiency of resource use in Kenya was high by international standards, but was on an upward trend due to shift in the structure of investment and growth towards those sectors in which ICOR was either high or rising, and increase in ICORs within a number of important sectors. 11. Tobin (1972) estimated capital-output ratios for fourteen sectors of the Kenyan economy for the period 1964-71. He objected to the use of gross ICORs on the grounds that “no allowance is made for depreciation, and the implication is that no gross investment is needed if output is not growing”. Tobin also objected to the customary computation of the ICOR (as ratio of gross investment over a span of one year or more to the increment in GDP over the same period) as the change in ICOR from year to year does not imply genuine change in the parameter we intend to measure, and for this reason preferred a regression procedure. Using observed data on sectoral capital formation and sectoral GDP, Tobin derived estimates of capital stock and ICORs under various assumptions about depreciation rates. 12. Tobin’s methodology circumvents the problem arising from the fact that capital formation data is in gross terms (including depreciation), and that estimates of capital stock (based on Powell, 1973) and depreciation rates were not available then (1972). However, a regression approach assumes that ICORs are constant over the period, and therefore does not capture the annual fluctuations in ICORs. Tobin makes the important observation that projections of gross investment are insensitive to the assumptions on the depreciation rates. As Tobin puts it, “when the estimates thus obtained are used to calculate the gross investment required for a given sectoral GDP and its rate of growth, the low-depreciation estimate says there is a large net investment requirement but not much replacement investment, while the high depreciation estimate says little net investment is required but a large amount of replacement”. This implies that, while net ICORs are useful in estimating trends in return on capital at the aggregate level, in projecting capital requirements over a Plan period, it does not matter whether gross or net ICORs are used or what the rate of depreciation is assumed to be. 13. Paul Streeten (1976) makes wide-ranging criticisms of the capital-output ratio, and questions the concept on both empirical and logical grounds. He says that this approach introduces a bias that capital is the only, or the main, source of development in developing countries. In addition, he asks valid questions about the validity of the concept, but unfortunately we will not be able to dwell on them here. He concludes that “if we possessed all the required microeconomic information, the aggregate ratio would be unnecessary. The capital/output model is thus either useless or unnecessary”. 14. Lancaster (1981) argues that using three-year moving totals in place of single-year values gives a consistent estimate of the ICOR for any given period over which it may be

8

considered as constant. The Ministry of Planning and National Development (MPND) computes ICORs as part of the routine macro-modelling work. In calculating ICORs at the sectoral and aggregate levels, the MPND uses three-year moving totals in place of single year values of investment and output. Theoretically, it is more ideal to derive the aggregate ICOR by weighting sectoral ICORs with the sector’s share in increases in output. However, in doing an international comparison of ICORs, it is more appealing to use the MPND approach to avoid the tedious work of analyzing sectoral data for a large number of countries. Another appeal of MPND approach is the use of depreciation estimates. 15. The MPND uses two alternative sectoral depreciation rates (share of current income devoted to replacement), 0.02 or 0.05, based on whether the share of machinery in total sector’s capital formation is less or more than 70 percent, using data on fixed investment by type of asset which is published in the annual Statistical Abstract. As shown in Table 5, Transport, Storage and Communication had the highest share of 15.71 percent of total fixed investment, followed by Manufacturing at 11.90 percent and Agriculture at 6.96 percent. The share of Machinery and Other Equipment in gross fixed capital formation in 1989 was 99.5 percent in Mining and Quarrying, 90.8 percent in Building and Construction, 78.4 percent in Manufacturing, 67.3 percent in Trade, Restaurants and Hotels, and 47.7 percent in Transport, Storage and Communication. The share of transport equipment was high in Trade, Restaurants and Hotels at 13.2 percent and 45.2 percent in Transport, Storage and Communication, and the combined shares of “Machinery and Other Equipment” and “Transport Equipment” were therefore 80.5 percent and 92.9 percent, respectively.

3. EMPIRICAL ESTIMATES OF ICOR 3.1 Data on Capital Formation and Output 16. Investment, as given in the Statistical Abstract, is the sum of new capital assets in the form of residential buildings, non-residential buildings, construction and works, land improvement and plantation development, transport equipment, machinery and other equipment, and breeding stock and dairy cattle. Investment so defined is in gross terms, while net investment is gross investment minus depreciation. Our analysis will relate to fixed investment. It will not cover changes in stocks (inventory accumulation), which is the other component of gross investment. Inventory accumulation is not necessarily related to growth in output, but is also influenced by other factors e.g. import controls (building up stocks when the controls are relaxed and spending when the controls are tight), and expectations about trends in exchange rates, interest rates and inflation. 17. During the period 1972-76, gross fixed capital formation at constant 1982 prices is generally characterized by a declining trend. However, this trend was interrupted briefly in 1977 and 1978 when capital formation grew by 20.9 percent and 17.6 percent, respectively. The high growth rate in the two years emanated from the coffee boom, which led to a surge in investment in agriculture, manufacturing, transport/storage and communication, building and construction, ownership of dwellings, and Government services. The price increase from the coffee boom was passed on to the farmers as it was not taxed, and farmers converted an estimated 60 percent of the windfall into savings (Bevan, Collier and

9

Gunning, 1987; see also, Killick, 1981, Davis, 1983, and Balassa, 1988). However, real investment in the services sector remained below the 1975 level throughout the boom period. The 1979 oil crisis and the 1980 drought reversed the upward trend, followed by some recovery in 1981, and a further reversal in 1982 due to political disturbances. In the period 1981-85, fixed capital formation registered a continuous decline but started picking up after 1986 due to recovery from drought, the 1986 mini-boom in coffee prices, and the fact that imports of capital goods are eligible under donor-financed structural adjustment loans. 18. Kenya registered declining trend in GDP growth rates in early 1970s. However, the 1976-77 coffee boom reversed the trend and GDP growth rates rose tremendously, registering a high 8.1 percent in 1977. The rising trend in GDP growth rates was not sustained for long: after 1977, GDP growth rates took a declining trend as a result of the 1979 oil price shock and the 1980 drought. In 1981, the economy registered a growth rate of 6.0 percent, but was adversely affected by the political disturbances of 1982 when GDP growth plunged to 3.4 percent and continued declining to the lowest level (0.74 percent) in 1984 when the country experienced severe drought. Since 1985, the economy has managed to grow at an annual rate of 5 percent, mainly due to Government’s economic reform measures, good weather, and availability of foreign resources. The data shows that periods of high capital formation in 1970s and the early 1980s were followed by high GDP growth rates. However, in the second half of the 1980s, the high GDP growth rates cannot be explained by capital formation as fixed investment at constant prices was lower in 1980s compared to the late 1970s. Therefore, the high GDP growth rates in the late 1980s can be attributed to increased utilization of capacity created in the earlier years. 19. We shall not interpret ICORs for “producers of Government services” due to conceptual and data problems. In national accounts terminology, “Government is defined to cover units performing government functions, that is, the implementation of public policy through the provision of primarily non-market services and the transfer of income, supported mainly by compulsory levies on other sectors” (Host-Madsen, 1979, p. 72). In Kenya’s national accounts, the Government sector includes public administration, defence, education, health, agricultural services and “other services”. GDP accruing to “producers of Government services” excludes:

All enterprise activities of the government sector e.g. building and construction and forestry (see, Kenya, 1977; and FitzPatrick, 1989, for details); and

Consumption of fixed capital, other than an imputed amount in respect of vehicles (calculated as average value of the replacement of vehicles during the previous three years).

20. In short, Government GDP includes only labour costs and some adjustments to cover depreciation of vehicles, but excludes all activities where the Government behaves in a private sector manner -- which is included in the relevant nongovernment national accounts sectors. In the Government sector, labour costs and GDP are almost identical, the only difference being its operating surplus (depreciation of vehicles). The seemingly obvious response to the problem would be to focus on GDP excluding “producers of government services”, but this still retains the government expenditures which are “nongovernment” in nature. What is usually termed as “contribution of public sector to

10

capital formation” also lumps up parastatals and Government capital formation on “nongovernment” activities. It is therefore difficult to obtain fixed investment, depreciation, labour costs and net operating surplus for private, Government and parastatal sectors separately. Indeed, due care should be taken in computing macroeconomic ratios using Government GDP (specifically “producers of government services”). For example, capital-output ratio of “producers of government services” is almost identical to the ratio of investment to change in the wage bill. However, GDP generated by government expenditure on “nongovernment” economic activities is derived in the same way as private and parastatal sector GDP. 3.2 Methodology 21. The approach used in the computation of gross ICORS is to assume one-year, three-year and five-year lags (gestation periods between investment and output) separately. A similar exercise is undertaken assuming depreciation rates (share of current income devoted to replacement) given in the MPND manual to arrive at sectoral net ICORs. The net ICOR is computed as a ratio of gross fixed capital formation to Net Domestic Product (NDP) in the following year, since NDP equals GDP minus depreciation of capital. 22. The depreciation rates used in computation of net sectoral ICORs are 0.02 for Agriculture; Forestry; Electricity and Water; Finance, Real Estate and Business Services; Ownership of Dwellings; Other Services; and Producers of Government Services, and 0.05 for Mining and Quarrying; Manufacturing; Building and Construction; Trade, Restaurants and Hotels; and Transport, Storage and Communication. The sectoral depreciation rates were used to derive weighted depreciation rate for total monetary GDP at constant prices for each year, with weights as sector shares in GDP. The computed aggregate depreciation rate showed minimal annual variations, and net ICOR for total monetary GDP was therefore computed using the average over the period 1972-1989 of 0.030427674. The estimates of share of depreciation in gross value-added are lower than those obtained by the World Bank (1975) using Input-Output Tables for Kenya 1967 (Kenya, 1972), which were 0.0711 for total monetary GDP, 0.1116 for mining and quarrying, 0.0994 for manufacturing, 0.0520 for building and construction, and 0.1543 for transport, storage and communication. 23. When the depreciation is assumed to be zero, ICOR derived using moving averages is equivalent to totalling investment between the base year and the final year and using the difference in GDP between the base and the final year (with one year lag). The use of simple moving average encounters several questions. The first problem is assigning the same weight to each year’s investment and GDP without much regard to its justification from empirical work or economic theory. A more realistic functional form is to assume that incremental output in the current year is related to capital formation carried during previous years (e.g. three) excluding the current year, such that INVt = v [a1GDPt+1 + a2GDPt+2 + a3GDPt+3]; where v is the capital-output ratio, a1 +a2 + a3 = 1, and a1 > a2 > a3. However, this formulation would require empirical estimates of a1, a2 and a3. 24. The second problem is the reference year to assign the moving average. The two common methods are to centre it or place it at the end of the period. However, we have assigned the moving average to the end of period, although one is able to interpret the results as referring to centre of period by changing the reference year in interpreting the

11

results. In addition, since the implicit GDP and fixed investment deflators are not identical, we have used constant price GDP and fixed investment data. 3.3 Findings 25. Unlike other macroeconomic ratios (e.g. savings ratio), ICOR is a ratio of stock (capital) to flow (income) and therefore has a time dimension. In using ICOR to determine trend in return on investment, it is important to consider the length of period. During the period 1972-89, the range of gross ICOR for total GDP is 3.23 (in 1986) to 25.03 (1984) assuming one year lag, 3.57 (1987) to 9.93 (1975) assuming three-year moving average, and 3.70 (1989) to 7.58 (1984) assuming five-year moving average. Therefore, the range declines (and ICORs appear more stable) the longer the lag period considered. A second important factor is the base output. For example, due to slowdown in growth in 1984 due to drought, ICORs using 1984 as the base year will show a decline in ICORs, and thus give a misleading indication of increase in the productivity of investment. The decline in ICORs due to the 1984 drought will be reflected in 1985 for one-year lag, 1987 for three-year lag, and 1989 for five-year lag. ICORs with 1979 and 1984 (when there was drought though with differing impact on output) as base years will be biased downwards. 26. The ICORs for the entire economy tend to be more stable the longer the lag period considered and the higher the depreciation rate, i.e. the net ICORs are more stable than gross ICORs. ICORs for the whole economy show a declining trend over time and are lower and more stable in the 1980s than in 1970s. The net ICOR, with three-year moving average and assuming a depreciation rate of 0.05, declined from 3.81 in 1975 to 2.05 in 1989, compared with 4.98 in 1975 to 2.55 in 1989 using the weighted average depreciation rate. The downward trend may be a result of gradual increase in utilization of productive capacity built up in second half of the 1970s following the coffee boom and the breakup of the East African Community. After 1986, net ICORs (with three-year moving average) declined, but have stabilized around a value of 2.5. The decline in net ICOR in the second half of the 1980s could be attributed to increased efficiency in the use of installed capacity due to economic reforms. The recent stabilization of the ICOR indicates that this source of growth might be running out of steam, and there is need for increased capital formation in the economy. 27. Some of the sectoral net ICORs (using three-year moving average) exhibit a declining trend, especially for the major national accounts sectors such as manufacturing, trade, and “other services”; while other sectors show considerable fluctuations such as mining and quarrying, building and construction, and electricity and water. The estimated values for some sectors are unusually large (positive and negative) for some periods which in general is a consequence of decline or stagnation in sectoral GDP in the period. 28. For the agricultural sector, the net ICOR (using three-year moving average) increased to a peak of 2.02 in 1980 due to unfavourable weather conditions (which adversely affected output) and increased investment during 1977-78 due to the coffee boom. The net ICOR declined to 0.85 in 1983 due to improvement in weather conditions, increased in 1984 and 1985 due to drought, but declined again after 1986 as the favourable weather conditions and structural adjustment (mainly improved producer prices) began to impact on producers. The ICOR as a measure of trends in return on capital in the

12

agricultural sector is somewhat deceptive. During the 1970s, agricultural investment increased, and the trend was then reversed such that the 1989 investment is lower than that of 1972. Other things being equal, ICOR in the agricultural sector has increased when investment increases and declined when investment declines. This confirms the fact that agricultural output is also influenced by a host of other factors including weather, quality of crop and animal husbandry, agricultural producer prices (including export prices for the export crops), and other Government policies. 29. For the manufacturing sector, the net ICORs (using three-year moving average) were high in the mid-1970s, probably reflecting low capacity utilization due to shortage of imported intermediate inputs as a result of foreign exchange shortages brought about by the 1973 oil price shock. However, ICORs fell briefly during the coffee boom of 1976-77 due to foreign exchange availability, but exhibit a rising trend for the period 1979-81. The rising trend during this period could be due to the 1979 oil price shock and the excess capacity created by the breakup of the East African Community and the increased capital formation following the coffee boom. After 1981, net ICOR has shown a declining trend, having fallen from 3.46 in 1981 to 2.00 in 1989. The observed declined in the ICOR in the 1980s is probably due changing substitution between factors of production away from capital intensity (as indicated by low capital formation) and to increased utilization of idle capacity created in the earlier years. Since manufacturing output has consistently increased during the period 1972-1989 while investment has on average declined, the trend in ICORs values reflects actual improvements in efficiency of investment and in capacity utilization. The annual variations on a trend are more likely due to fluctuations in availability of imported inputs brought about by import controls and foreign exchange shortages. The import dependence of the manufacturing sector, including the fact the sector is a net user of foreign exchange, implies that the performance of the sector will continue to depend on movements in the external account and on the success of trade liberalization. 30. The building and construction sector is characterized by annual fluctuations in both output and investment, which gives unstable ICORs. When output growth is negative, ICORs are also negative. While the unusually large or negative ICORs of course implies stagnation and are signs of serious problems in the sector, the estimated ICORs should be interpreted with caution. Negative ICORs are theoretically meaningless: does a negative ICOR imply that lower investment would have resulted in higher output? 31. The estimated net ICORs (with three-year moving average) for the “trade, restaurants and hotels” registered a significant downward trend, but the trend was interrupted during the period 1982-83. Although net ICOR has been decreasing, the sector has also been characterized by a marked decline in investment, implying that capacity utilization (e.g. hotel occupancy) has been improving. The transport and communications sector exhibited a declining trend in estimated net ICORs until 1984, but climbed from 3.76 in 1984 to 7.01 in 1989. The net ICORs for the sector are relatively higher than those of other sectors like agriculture, trade and manufacturing, probably because the sector is generally capital-intensive. The period 1985-89 was also characterized by high capital formation due to increased imports for communications facilities. 32. Disregarding the 1984 drought year, gross ICOR (with three-year moving average) for the whole economy has been more stable and lower in the 1980s than in the 1970s.

13

Aggregate gross ICORs declined during the 1970s, and had a peak of 9.76 in 1984 mainly due to drought which affected agricultural production and supply of raw materials to agricultural processing industries. However, ICOR declined in the second half of the 1980s to around 3.7 due to recovery in agriculture; and increased productivity of manufacturing and trade, restaurants and hotels. 33. The net ICOR, with one-year lag and assuming a weighted depreciation rate of 0.030427674, declined from 5.07 in 1973 to 2.49 in 1989. After 1985, net ICORs (with one-year lag) declined, but have stabilized around a value of 2.3. The net ICOR, with three-year moving average and assuming a weighted depreciation rate, declined from 4.98 in 1975 to 2.55 in 1989. After 1986, net ICORs (with three-year moving average) declined, but have stabilized around a value of 2.5. The net ICOR, with five-year moving average and assuming a weighted depreciation rate, declined from 4.06 in 1977 to 2.12 in 1989. After 1986, net ICORs (with five-year moving average) declined, but have stabilized around a value of 2.4. The interpretation of net ICOR for total monetary GDP should be based on the weighted average depreciation rate, and the depreciation rates of 0.02 and 0.05 are presented only to show the sensitivity of computed net ICORs on assumptions of the depreciation rate. 34. It is however important to note that the computed ICOR is affected by the poor economic performance in 1984 when the economy grew by 0.74 percent in real terms, and the particular years affected depend on the assumed gestation period (lag) between investment and output. The low growth in output in 1984 was also accompanied by relatively high growth rate in gross fixed capital formation of 3.05 percent in real terms.

4. SUMMARY AND CONCLUSION 35. The data on net ICORs are assumed to measure the impact of flow of capital services to the flow of output. This is expected to minimize errors of the capital-output ratio as a stock-flow concept. However, the net ICOR does not completely remove such bias because (a) it does not take into account variations in capacity utilization, and (b) the difficulties of arriving at reasonable indicators of depreciation rates. The cut-off point assumed in the MPND estimates (i.e. assuming values of either 0.02 or 0.05) might not reflect actual depreciation rates. More reliable estimates of depreciation rates should be made on the basis of capital formation by type of asset, which is available from the annual Statistical Abstract. Another source would be an updated Input-Output Tables, which the Ministry of Planning and National Development is in the process of revising. 36. As expected, net ICORs are lower than gross ICORs because it nets out the proportion of output devoted to replacement investment. The trend depicted by net ICORs is largely similar to that of gross ICORs, although one should interpret inter-sectoral variations in net ICORs with caution as they are sensitive to the assumptions on the depreciation rates. 37. It is important to consider the impact of the patterns of growth on ICORs. This is, of course, simplistic as it disregards interdependence of sectors through forward and backward

14

linkages, which are best captured in an input-output analysis. The input-output tables also assume fixed input coefficients and do not therefore take into account structural change that is likely to result from the economic reforms that the Government is undertaking. During the 1980s, ICORs have declined in agriculture, manufacturing, trade, finance/real estate, and remained somewhat stable in other sectors. This can be interpreted as reflecting improvements in efficiency as a result of economic reforms e.g. trade liberalization, flexible exchange rate management, attractive agricultural producer prices, and the maintenance of positive real interest rates. The return on capital (disregarding the impact of other factors and changes in the input mix) is higher in agriculture than in manufacturing. This implies that it is necessary to improve efficiency in manufacturing even further and reduce import dependence through aggressive export promotion and change in the input mix. 38. At best, data on ICORs can be used to identify the trouble sectors but cannot be directly used to specify policy choices. A consistent increase in a sectoral ICOR is an indication that more disaggregated analysis (probably at firm-level) is required to determine the causes of increasing inefficiency and arrive at specific programs of action. In addition, ICOR is not a suitable indicator of return on assets by ownership (private and parastatal) due to (a) the fact that GDP data in Kenya is not disaggregated by ownership, and (b) such data would exclude joint ventures and would only include firms incorporated through Acts of Parliament. In addition, the problems plaguing the parastatal sector are well documented and need not be supported or counteracted by macroeconomic indicators such as the ICOR.

REFERENCES Abramovitz, Moses, “Resource and Output Trends in the United States since 1870”, American Economic Review, 46(2), May 1956 Aseto, Oyugi, “Capital Growth and Development Policy: The Kenya Experience”, Working Paper No. 311, Institute for Development Studies, University of Nairobi, June 1977 Ayako, A.B., “Productivity of Private Investment in Kenya, 1970-1988”, Consultant report for USAID, Nairobi, June 1990 Balassa, Bela, “Temporary Windfalls and Compensation Arrangements”, Policy, Planning and Research Working Paper No. 28, World Bank, June 1988 Bevan, D.L., P. Collier, and J.W. Gunning, “Consequences of a Commodity Boom in a Controlled Economy: Accumulation and redistribution in Kenya, 1975-83”, World Bank Economic Review, 1(3), 1987 Bhatt, V.V., “Aggregate Capital-Output Ratio: Some Conceptual Issues”, Indian Economic Journal, 10(4), April 1963 Bigsten, Arne, Regional Inequality and Development: A Case Study of Kenya, University of Gothenburg, 1978

15

Brown, Murray, and Alfred H. Conrad, “The Influence of Research and Education on CES Production Relations”, in: Murray Brown (editor), The Theory and Empirical Analysis of Production, National Bureau of Economic Research, Columbia University Press, 1967 Chakrabarti, S.K., “Estimating Sectoral Capital-Output Ratios: A Comment”, Ministry of Economic Planning and Development, Nairobi, 1981 Chenery, Hollis B., and Peter Eckstein, “Development Alternatives for Latin America”, Economic Development Report No. 29, Center for International Affairs, Harvard University, Cambridge, Massachusetts, April 1967 Chenery, Hollis B., and Peter Eckstein, “Development Alternatives for Latin America”, Journal of Political Economy, 78(4), July/August 1970 Creamer, Daniel, in: United States Congress, Joint Economic Committee, Measures of Productive Capacity, Hearings before the Sub-committee on Economic Statistics, Washington, D.C., 1962 Davis, Jeffrey M., “The economic effects of windfall gains in export earnings, 1975-78”, World Development, 11(2), February 1983 Elliott, James, Sung Y. Kwack, and George S. Tavlas, “An Econometric Model of the Kenyan Economy”, Economic Modelling, 3(1), January 1986 Faaland, Just, and Hans-Erik Dahl, “The economy of Kenya: An econometric study of structural relationships 1956-1965 with projections of trade and resource gaps for 1970 and 1975”, Chr. Michelsen Institute, 1967 [included in: Trade Prospects and Capital Needs of Developing Countries, Study prepared by the United Nations Conference on Trade and Development, New York, 1968] FitzPatrick, L.W., and W.R. Spence, “Analysis of Kenyan Government Expenditure Data, 1969-85: For purposes of constructing production functions”, Technical Paper No. 89-06, Long Range Planning Unit, Ministry of Planning and National Development, June 1989 Hill, T.P., “Growth and Investment according to International Comparisons”, Economic Journal, 74(294), June 1964 Hodd, Mike, “A Design for an Econometric Model of the Kenyan Economy”, Working Paper No. 171, Institute for Development Studies, University of Nairobi, July 1974 Hogan, N.P., “The Limitations of Capital Coefficients: A Comment”, American Economic Review, 49(1), March 1959 Host-Madsen, Poul, Macroeconomic Accounts: An Overview, Pamphlet Series No. 29, International Monetary Fund, Washington, D.C., 1979 Karani, H., and C. Howe, “A. Statistical Projection Model for the Kenya Economy”, Discussion Paper No. 1, Institute for Development Studies, University of Nairobi, 1965

16

Kenya, Central Bureau of Statistics, Input/ Output Tables for Kenya, 1967, December 1972 Kenya, Central Bureau of Statistics, Sources and Methods Used for the National Accounts of Kenya, December 1977 Kenya, Ministry of Planning and National Development, “Macroeconomic Policy Model for Kenya: An Explanatory Manual”, August 1987 Killick, Tony, “The IMF and Economic Management in Kenya”, ODI Working Paper No. 4, Overseas Development Institute, July 1981 Klein, L.R., and R.F. Kosobud, “Some Econometrics of Growth: Great Ratios of Economics”, Quarterly Journal of Economics, 75(2), May 1961 Kuh, Edwin, and John R. Meyer, “Correlation and regression estimates when the data are ratios”, Econometrica, 23(4), October 1955 Kuznets, S., “Quantitative Aspects of the Economic Growth of Nations: V. Capital Formation Proportions: International Comparisons for Recent Years”, Economic Development and Cultural Change, 8(4), Part 2, July 1960 Lancaster, T., “Improving Sectoral ICOR Estimates”, Ministry of Economic Planning and Development, Nairobi, Kenya, 1981 Leibenstein, H., “Incremental capital-Output Ratios and growth Rates in the Short-Run”, Review of Economics and Statistics, 48(1), February 1966 Lluch, Constantino, “ICORS, Savings Rates, and Determinants of Public Expenditure in Developing Countries”, in: Deepak Lal and Martin Wolf (eds.), Stagflation, Savings and the State, Oxford University Press, World Bank, 1986 Madansky, Albert, “Spurious correlation due to deflating variables”, Econometrica, 32(4), October 1964 Masakhalia, Y.F.O., M.M. Shah, C.S. Slater, and G. Walsham, “A Simulation Model of the Kenya National Economy and its use as a Guide to Economic Policy”, Discussion Paper No. 246, Institute for Development Studies, University of Nairobi, July 1977 Meier, G.M., Leading Issues in Economic Development, Third edition, Oxford University Press, New York, 1976 Morgan, Theodore, “Investment versus Economic Growth”, Research Paper No. 11, AID/University of Wisconsin Research Project on “Economic Interdependence in Southeast Asia”, August 1966 Ndulu, B.J., “Investment, Output Growth and Capacity Utilization in an African Economy: The case of manufacturing sector in Tanzania”, Eastern Africa Economic Review, 2(1), June

17

1986 Ohkawa, K., and H. Rosovsky, “Economic Fluctuations in Prewar Japan: A Preliminary Analysis of Cycles and Long Swings”, Hitotsubashi Journal of Economics, 3(1), October 1962 Pesek, Boris P., “Kuznets’ Incremental Capital-Output Ratios”, Economic Development and Cultural Change, 12(1), October 1963 Powell, Raymond P., “The Stock of Fixed Capital in Kenya in the Monetary Economy”, Occasional Paper No. 9, Institute for Development Studies, University of Nairobi, 1973 Ritter, A.R.M, “Productivity Change in the Non-Agricultural Economy of Kenya, 1964-1987”, Technical Paper 88-06, Long Range Planning Unit, Ministry of Planning and National Development, Nairobi, Kenya, July 1988 Sen, Pankaj Kumar, “Use of the Capital-Output Ratio in Economic Planning”, Indian Economic Review, 5(1), February 1960 Shaaeldin, Elfaith, “Sources of Industrial Growth in Kenya, Tanzania, Zambia and Zimbabwe”, Eastern Africa Economic Review, 4(2), December 1988 Shourie, Arun, “The Relevance of Econometric Models for Medium- and Longer-Term Forecasts and Policy Prescription”, Economics Department Working Paper No. 75, Quantitative Techniques and Analysis Division, World Bank, May 6, 1970 Shourie, Arun, “The Use of Macro-Economic Regression Models of Developing Countries for Forecasts and Policy Prescription: Some Reflections on Current Practice”, Oxford Economic Papers, 24(1), March 1972 Slater, C.S., and G. Walsham, “A Systems Simulation Model of the Kenyan Economy”, Omega: The International Journal of Management Science, 3(5), October 1975 Slater, C.S, G. Walsham, and M.M. Shah, KENSIM: A Systems Simulation of the Developing Kenyan economy, 1970-1978, Westview Press, Boulder, Colorado, 1977 Streeten, Paul, The Frontiers of Development Studies [Chapter 6: A Critique of the ‘Capital/Output Ratio’ and its Application to Development Planning], John Wiley and Sons, New York, 1976 Strout, A.M., “Savings, Imports and Capital Productivity in Developing Countries”, First World Congress of the Econometric Society, Rome, September 1965 Tobin, James, “Estimates of Sectoral Capital/Output Ratios for Kenya”, Discussion Paper No. 171, Institute for Development Studies, University of Nairobi, 1972 Whitacre, Robert James, “Sectoral Planning in Kenya: A Proposed Macroeconomic Model”, Working Paper No. 216, Institute for Development Studies, University of Nairobi, June

18

1975 World Bank, Economic Development Prospects in Kenya, Part 1: Main Report, Eastern Africa Department, October 22, 1969 World Bank, Kenya into the Second Decade, Johns Hopkins University Press, 1975 World Bank, Kenya: Growth and Structural Change, August 1983 (Annex IV: Income Distribution and Growth: A Simulation Model for Kenya) Yotopoulos, P.A., and J.B. Nugent, Economics of Development: Empirical investigations, Harper & Row, New York, 1976

19

NOTES ON COMPUTATION OF INCREMENTAL CAPITAL-OUTPUT RATIO

Gross sectoral ICOR with one year lag is derived thus: ICORt = It-1/(Yt-Yt-1) **** Net sectoral ICOR (with one year lag) is derived thus: ICORt = It-1/(Yt-(1-d)Yt-1) *** Gross sectoral ICORs (with three year moving average): ICORt = Σ(It-1, It-3)/(Σ(Yt,Yt-2) - Σ(1-d)(Yt-1,Yt-3)) but assuming that d (depreciation) = 0 *** Net sectoral ICORs (with three year moving average): ICORt = Σ(It-1, It-3)/(Σ(Yt,Yt-2) - Σ(1-d)(Yt-1,Yt-3)) *** Gross sectoral ICORs (with five year moving average): ICORt = Σ(It-1, It-6)/(Σ(Yt,Yt-5) - Σ(1-d)(Yt-1,Yt-6)) but assuming that d (depreciation) = 0 *** Net sectoral ICORs (with five year moving average): ICORt = Σ(It-1, It-6)/(Σ(Yt,Yt-5 - Σ(1-d)(Yt-1,Yt-6))

20

STATISTICAL ANNEX

Table 1: Gross Fixed Capital Formation by Industry at Current Prices Table 2: Gross Fixed Capital Formation by Industry at Constant 1982 Prices Table 3: Gross Fixed Capital Formation: Growth Rates (%) Table 4: Gross Fixed Capital Formation Deflators by Industry Table 5: Capital Formation: Analysis by Industry and Type of Asset, 1989 Table 6: Gross Domestic Product at Current prices Table 7: Gross Domestic Product at Constant 1982 Prices Table 8: Gross Domestic Product at Constant 1982 Prices: Growth Rates (%) Table 9: Implicit Gross Domestic Product Deflators Table 10: Gross Sectoral ICORs: One-Year Lag Table 11: Gross Sectoral ICORs: Three-Year Moving average Table 12: Gross Sectoral ICORs: Five-Year Moving average Table 13: Net Sectoral ICORs: One-Year Lag Table 14: Net Sectoral ICORs: Three-Year Moving average Table 15: Net Sectoral ICORs: Five-Year Moving average

21

Table 1: Gross Fixed Capital Formation by Industry (millions of Kenyan pounds at current prices)

1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

Traditional Economy

Ownership of Dwellings 10.65 12.74 15.17 20.12 23.01 27.51 33.17 39.65 43.64 50.52 54.18 66.42 77.75 76.75 81.89 89.51 97.62 109.49

Monetary Economy

Enterprises & Non-Profit Institutions

Agriculture 13.26 12.57 20.17 22.54 24.94 42.93 51.13 42.05 47.16 54.85 51.21 53.80 58.46 75.70 89.64 106.36 113.52 102.8

Forestry 0.26 0.25 0.23 0.15 0.34 0.68 0.73 0.69 1.03 0.75 0.68 0.24 0.50 0.60 0.38 0.31 1.66 1.08

Fishing 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Mining & Quarrying 1.70 1.46 3.60 1.53 1.62 1.94 4.95 4.10 5.02 4.85 4.05 5.09 7.12 4.88 7.03 12.74 10.21 9.42

Manufacturing 28.19 31.84 29.86 31.23 45.96 63.27 83.71 88.52 76.91 90.31 67.03 111.70 95.33 101.76 161.34 171.83 218.74 249.91

Electricity & Water 8.51 12.17 10.03 17.19 33.15 33.72 40.20 31.99 41.26 65.47 75.22 57.19 37.00 43.30 48.55 62.20 81.70 124.51

Building & Construction 8.50 9.14 7.00 7.81 9.72 15.50 32.25 25.68 33.41 32.90 28.86 59.35 68.25 31.47 50.31 70.19 70.46 65.2

Trade, Restaurants & Hotels 8.19 8.86 10.19 14.47 20.61 21.42 20.24 17.30 28.29 19.69 21.78 26.44 24.75 34.51 24.86 24.88 36.35 33.55

Transport, Storage & Comm. 22.47 28.03 35.46 50.39 50.41 79.41 110.50 101.65 102.80 113.48 101.50 110.14 149.94 164.19 289.04 306.66 269.28 349.24

Finance, Real Estate & Bus. 2.12 2.04 4.01 4.76 3.46 4.42 7.57 8.25 10.19 23.68 9.42 16.68 18.42 19.15 13.46 21.60 38.40 47.07


Other Services 7.60 8.92 8.52 7.16 11.63 17.25 20.33 31.76 41.25 53.56 55.29 60.72 59.51 80.02 75.89 85.80 105.98 145.65

TOTAL 120.02 131.44 148.89 180.32 218.96 303.66 410.27 407.09 450.25 529.66 487.38 549.02 576.29 611.28 851.36 967.12 1062.15 1252.86

Producers of Government Services

Public Administration 4.84 4.87 4.90 5.35 4.00 7.95 14.66 14.36 20.06 17.75 14.43 17.06 29.61 34.52 69.62 77.30 56.98 128.19

Defence 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.02 0.05 0.02

Education 3.59 3.88 4.12 4.89 5.10 7.23 8.87 11.75 20.16 21.60 20.91 14.75 16.48 24.81 31.00 31.30 87.41 54.24

Health 2.94 2.84 3.30 4.35 8.19 8.33 10.91 14.06 12.57 8.78 8.78 8.28 9.12 10.65 12.89 12.60 21.48 12.38

Agricultural Services 1.09 1.63 2.96 5.71 8.66 9.00 8.64 8.96 13.08 12.31 12.50 10.55 6.43 10.40 15.92 13.43 19.50 21.38

Other Services 20.64 22.21 23.45 21.15 23.69 26.46 30.07 47.73 61.28 84.08 70.05 51.39 91.47 111.98 90.52 95.46 177.21 133.47

TOTAL 33.10 35.43 38.73 41.45 49.64 58.97 73.15 96.86 127.15 144.52 126.67 102.03 153.11 192.36 219.95 230.11 362.63 349.68

Traditional Economy 10.65 12.74 15.17 20.12 23.01 27.51 33.17 39.65 43.64 50.52 54.18 66.42 77.75 76.75 81.89 89.51 97.62 109.49

Monetary Economy 153.12 166.87 187.62 221.77 268.60 362.63 483.42 503.95 577.40 674.18 614.05 651.05 729.40 803.64 1071.31 1197.23 1424.78 1611.54

GRAND TOTAL 163.77 179.61 202.79 241.89 291.61 390.14 516.59 543.60 621.04 724.70 668.23 717.47 807.15 880.39 1153.20 1286.74 1522.40 1721.03

Memorandum items

TOTAL GDP 663.53 754.97 900.34 1087.82 1313.56 1683.76 1833.45 2033.19 2298.41 2659.49 3033.05 3455.35 3851.78 4418.59 5114.96 5612.51 6391.11 7330.50

Ratio of fixed investment to GDP (%) 24.68 23.79 22.52 22.24 22.20 23.17 28.18 26.74 27.02 27.25 22.03 20.76 20.96 19.92 22.55 22.93 23.82 23.48

22

Table 2: Gross Fixed Capital Formation by Industry (millions of Kenyan pounds at 1982 prices)

INDUSTRY 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

Traditional Economy


Monetary Economy


Agriculture 50.57 46.34 59.73 59.84 51.35 81.96 85.85 61.62 61.86 64.10 51.21 43.82 40.35 50.84 53.79 58.87 60.09 47.71

Forestry 1.00 0.88 0.64 0.33 0.64 1.15 1.09 0.89 1.22 0.83 0.68 0.20 0.38 0.40 0.23 0.18 0.78 0.50

Fishing 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00


Manufacturing 138.83 138.37 95.48 92.62 103.14 125.82 147.83 137.66 110.08 109.66 67.03 81.01 65.65 65.18 83.87 89.64 112.53 108.59







Other Services 34.16 35.80 26.06 17.73 24.39 32.09 33.89 49.42 55.28 63.57 55.29 47.31 43.87 55.10 45.49 49.55 57.84 69.26

TOTAL 513.37 509.00 448.96 462.95 456.51 565.92 680.20 597.00 592.69 620.68 487.38 428.60 413.88 408.63 473.27 518.64 522.79 562.35



Defence 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.02 0.08

Education 15.48 14.84 11.46 11.99 10.29 13.68 14.88 17.17 26.38 25.00 20.91 12.39 12.70 17.42 19.38 18.28 47.92 26.33

Health 11.49 10.17 8.99 10.33 13.79 15.07 13.85 16.01 17.74 14.51 8.78 7.09 7.41 7.88 8.57 7.51 11.63 5.76


Other Services 72.79 66.21 58.24 45.24 44.85 45.79 45.91 64.00 74.59 95.14 70.05 44.73 67.37 75.90 56.41 55.64 93.89 64.33

TOTAL 123.04 113.56 99.70 92.70 92.99 104.83 112.43 130.62 160.18 169.25 126.67 88.02 114.66 132.14 138.13 133.85 192.93 165.13



GRAND TOTAL 676.73 666.18 590.46 605.21 598.16 723.05 850.22 787.51 807.28 847.80 668.23 575.97 593.55 597.16 668.07 707.95 769.29 781.41

23

Table 3: Gross Fixed Capital Formation: Growth Rates (%)

INDUSTRY 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

Traditional Economy

Ownership of Dwellings 8.18 -4.17 18.56 -1.82 7.48 10.11 3.99 -9.15 6.36 -6.38 9.54 9.54 -13.26 0.50 -5.47 0.00 0.67

Monetary Economy


Agriculture -8.36 28.90 0.18 -14.19 59.61 4.75 -28.22 0.39 3.62 -20.11 -14.43 -7.92 26.00 5.80 9.44 2.07 -20.60

Forestry -12.00 -27.27 -48.44 93.94 79.69 -5.22 -18.35 37.08 -31.97 -18.07 -70.59 90.00 5.26 -42.50 -21.74 333.33 -35.90

Fishing

Mining & Quarrying -27.46 103.13 -58.97 -22.08 2.67 132.29 -26.35 14.92 -20.26 -32.72 -11.60 36.31 -36.27 17.04 84.89 -19.17 -24.26

Manufacturing -0.33 -31.00 -3.00 11.36 21.99 17.49 -6.88 -20.03 -0.38 -38.87 20.86 -18.96 -0.72 28.67 6.88 25.54 -3.50

Electricity & Water 18.22 -30.87 42.90 74.30 -10.91 8.81 -30.86 16.84 52.39 -1.04 -34.15 -45.47 7.55 3.03 19.21 21.64 38.46

Building & Construction -0.90 -39.77 -4.86 -3.95 37.34 85.72 -28.59 18.05 -16.58 -28.14 44.91 12.15 -56.74 33.12 37.69 -0.32 -23.36

Trade, Restaurants & Hotels 1.47 -6.60 19.51 9.46 -11.59 -15.62 -23.68 46.99 29.16 -57.32 -1.56 -16.60 35.46 -41.25 -6.11 42.07 -22.02

Transport, Storage & Comm. 15.27 1.72 12.66 -16.50 44.74 22.48 -21.07 -4.90 -20.13 -0.60 -17.96 26.46 1.23 35.86 4.32 -20.82 21.48

Finance, Real Estate & Bus. -8.10 34.46 1.93 -38.58 15.70 50.70 -3.31 0.88 111.13 -64.79 52.02 -0.84 -8.10 -41.92 56.46 74.87 7.86

Ownership of Dwellings -23.64 -2.01 4.17 -36.59 21.19 47.76 27.97 -5.80 2.26 -10.07 -41.53 12.20 -14.05 53.64 2.92 -28.22 32.14

Other Services 4.80 -27.21 -31.96 37.56 31.57 5.61 45.82 11.86 15.00 -13.03 -14.43 -7.27 25.60 -17.44 8.93 16.73 19.74

TOTAL -0.85 -11.80 3.12 -1.39 23.97 20.19 -12.23 -0.72 4.72 -21.48 -12.06 -3.43 -1.27 15.82 9.59 0.80 7.57


Public Administration -10.91 -21.81 -4.86 -36.82 80.91 65.48 -12.75 20.86 -18.34 -29.23 1.46 52.73 6.44 82.52 2.35 -33.65 99.49

Defence

Education -4.13 -22.78 4.62 -14.18 32.94 8.77 15.39 53.64 -5.23 -16.36 -40.75 2.50 37.17 11.25 -5.68 162.14 -45.05

Health -11.49 -11.60 14.91 33.49 9.28 -8.10 15.60 10.81 -18.21 -39.49 -19.25 4.51 6.34 8.76 -12.37 54.86 -50.47

Agricultural Services 25.51 42.47 60.76 27.97 -1.05 -11.70 -9.43 29.11 -13.88 -12.03 -26.64 -47.44 48.13 44.68 -23.04 25.41 -1.91

Other Services -9.04 -12.04 -22.32 -0.86 2.10 0.26 39.40 16.55 27.55 -26.37 -36.15 50.61 12.66 -25.68 -1.37 68.75 -31.48

TOTAL -7.70 -12.21 -7.02 0.31 12.73 7.25 16.18 22.63 5.66 -25.16 -30.51 30.27 15.25 4.53 -3.10 44.14 -14.41

Traditional Economy 8.18 -4.17 18.56 -1.82 7.48 10.11 3.99 -9.15 6.36 -6.38 9.54 9.54 -13.26 0.50 -2.14 -3.41 0.67

Monetary Economy -2.18 -11.87 1.27 -1.11 22.07 18.17 -8.20 3.47 4.92 -22.27 -15.87 2.31 2.31 13.06 6.72 9.69 1.64

GRAND TOTAL -1.56 -11.37 2.50 -1.16 20.88 17.59 -7.38 2.51 5.02 -21.18 -13.81 3.05 0.61 11.87 5.97 8.66 1.58

24

Table 4: Gross Fixed Capital Formation Deflators by Industry (1982 = 1.0)

INDUSTRY 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

Traditional Economy


Monetary Economy


Agriculture 0.262 0.271 0.338 0.377 0.486 0.524 0.596 0.682 0.762 0.856 1.000 1.228 1.449 1.489 1.666 1.807 1.889 2.155

Forestry 0.260 0.284 0.359 0.455 0.531 0.591 0.670 0.775 0.844 0.904 1.000 1.200 1.316 1.500 1.652 1.722 2.128 2.160

Fishing na na na na na na na na na na na na na na na na na na


Manufacturing 0.203 0.230 0.313 0.337 0.446 0.503 0.566 0.643 0.699 0.824 1.000 1.379 1.452 1.561 1.924 1.917 1.944 2.301







Other Services 0.222 0.249 0.327 0.404 0.477 0.538 0.600 0.643 0.746 0.843 1.000 1.283 1.357 1.452 1.668 1.732 1.832 2.103

TOTAL 0.234 0.258 0.332 0.390 0.480 0.537 0.603 0.682 0.760 0.853 1.000 1.281 1.392 1.496 1.799 1.865 2.032 2.244



Defence na na na na na na na na na na na na na na na 2.000 2.500 0.250

Education 0.232 0.261 0.360 0.408 0.496 0.529 0.596 0.684 0.764 0.864 1.000 1.190 1.298 1.424 1.600 1.712 1.824 2.060

Health 0.256 0.279 0.367 0.421 0.594 0.553 0.788 0.878 0.709 0.605 1.000 1.168 1.231 1.352 1.504 1.678 1.847 2.149


Other Services 0.284 0.335 0.403 0.468 0.528 0.578 0.655 0.746 0.822 0.884 1.000 1.149 1.358 1.475 1.605 1.716 1.887 2.075

TOTAL 0.269 0.312 0.388 0.447 0.534 0.563 0.651 0.742 0.794 0.854 1.000 1.159 1.335 1.456 1.592 1.719 1.880 2.118



GRAND TOTAL 0.242 0.270 0.343 0.400 0.488 0.540 0.608 0.690 0.769 0.855 1.000 1.246 1.360 1.474 1.726 1.818 1.979 2.202

25

Table 5: Capital Formation: Analysis by Industry and Type of Asset, 1989 (millions of Kenyan pounds)

SECTOR Residential

Buildings

Non-Residential

Buildings

Construction and

Works

Land Improvement and Plantation

Development

Transport

Equipment

Machinery and Other

Equipment

Breeding Stock and

Dairy Cattle

Total

Traditional Economy

Ownership of Dwellings 109.49 109.49

Monetary Economy

Enterprises & Non-Profit Institutions:

Agriculture 4.97 12.90 12.48 10.68 71.93 5.42 118.38

Forestry 0.20 0.51 0.26 0.12 1.09

Fishing

Mining & Quarrying 0.05 10.11 10.16

Manufacturing 7.71 14.17 32.99 198.99 253.86

Building & Construction 1.89 0.61 5.31 77.42 85.23

Electricity & Water 11.93 98.47 4.00 6.72 121.12

Trade, Restaurants & Hotels 2.90 2.17 3.44 17.50 26.01

Transport, Storage & Comm. 15.62 4.18 127.00 134.00 280.80

Finance, Real Estate & Bus. 29.13 0.02 7.00 18.35 54.50

Ownership of Dwellings 122.43 1.55 123.98

Other Services 81.36 0.10 0.37 42.46 124.29

SUB-TOTAL 122.43 155.71 133.13 12.48 191.10 579.15 5.42 1199.42

Government Services 93.30 150.77 43.26 62.48 349.81

Total Monetary Economy 122.43 249.01 283.90 12.48 234.36 641.63 5.42 1549.23

TOTAL GDP 231.92 249.01 283.90 12.48 234.36 641.63 5.42 1658.72

PERCENT

Traditional Economy

Ownership of Dwellings 100.00 0.00 0.00 0.00 0.00 0.00 0.00 100.00

Monetary Economy


Agriculture 0.00 4.20 10.90 10.54 9.02 60.76 4.58 100.00

Forestry 0.00 18.35 46.79 0.00 23.85 11.01 0.00 100.00

Fishing

Mining & Quarrying 0.00 0.00 0.00 0.00 0.49 99.51 0.00 100.00

Manufacturing 0.00 3.04 5.58 0.00 13.00 78.39 0.00 100.00

Building & Construction 0.00 2.22 0.72 0.00 6.23 90.84 0.00 100.00

Electricity & Water 0.00 9.85 81.30 0.00 3.30 5.55 0.00 100.00

Trade, Restaurants & Hotels 0.00 11.15 8.34 0.00 13.23 67.28 0.00 100.00

Transport, Storage & Comm. 0.00 5.56 1.49 0.00 45.23 47.72 0.00 100.00

Finance, Real Estate & Bus. 0.00 53.45 0.04 0.00 12.84 33.67 0.00 100.00

Ownership of Dwellings 98.75 0.00 0.00 0.00 0.00 1.25 0.00 100.00

Other Services 0.00 65.46 0.08 0.00 0.30 34.16 0.00 100.00

SUB-TOTAL 10.21 12.98 11.10 1.04 15.93 48.29 0.45 100.00

Government Services 0.00 26.67 43.10 0.00 12.37 17.86 0.00 100.00

Total Monetary Economy 7.90 16.07 18.33 0.81 15.13 41.42 0.35 100.00

TOTAL GDP 13.98 15.01 17.12 0.75 14.13 38.68 0.33 100.00

26

Table 6: Gross Domestic Product at Current Prices (millions of Kenyan pounds)

SECTOR 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

TRADITIONAL ECONOMY

Forestry 4.83 5.21 5.57 7.30 9.40 10.77 13.30 15.21 16.66 19.22 21.71 25.28 28.36 33.58 37.26 43.68 52.60 59.27

Fishing 0.15 0.16 0.18 0.20 0.29 0.29 0.43 0.46 0.55 0.82 1.03 1.11 1.25 1.46 1.80 1.85 2.11 2.85


Water Collection 5.00 5.16 5.28 6.78 8.90 11.11 12.66 14.04 15.10 17.12 19.29 21.77 24.89 28.09 31.57 35.78 40.73 47.06


SUB-TOTAL 33.74 39.20 44.63 59.02 69.81 83.84 100.55 118.96 131.67 150.77 164.58 199.39 216.49 246.93 263.81 297.38 341.90 389.03

MONETARY ECONOMY


Agriculture 190.45 220.22 261.46 357.19 480.01 689.02 653.52 671.17 711.87 819.06 938.46 1126.53 1244.34 1357.17 1598.05 1669.26 1902.69 2088.39

Forestry 3.55 4.27 5.42 5.93 6.24 6.89 8.25 12.88 15.66 18.59 32.81 25.84 27.99 32.39 37.91 49.57 61.60 93.42

Fishing 1.26 1.34 1.45 1.65 2.36 2.33 3.52 3.70 4.37 6.56 8.33 9.02 10.34 12.09 15.09 17.50 20.09 27.39


Manufacturing 77.93 95.62 119.42 127.00 144.18 179.94 219.32 249.84 295.14 328.16 372.32 408.26 460.96 518.40 608.23 652.47 752.96 855.36







Other Services 21.38 25.47 27.31 30.69 35.52 40.51 46.64 52.08 64.99 73.86 82.45 95.21 107.27 129.58 153.72 181.66 197.92 228.00

Imputed Bank Service Chg. -12.94 -16.99 -20.65 -22.60 -26.55 -37.40 -47.70 -56.10 -62.86 -71.21 -87.29 -114.51 -120.18 -130.64 -150.24 -172.98 -245.95 -281.62

SUB-TOTAL 514.11 589.06 711.53 857.87 1048.12 1371.08 1465.18 1605.81 1810.94 2089.17 2394.37 2745.00 3068.19 3503.54 4031.74 4420.63 5047.98 5766.14

PRIVATE HOUSEHOLDS 5.12 6.12 7.27 8.86 10.93 13.44 17.06 19.16 23.34 28.62 32.75 35.71 44.88 51.78 62.96 71.78 83.94 97.49

GOVERNMENT SERVICES 110.56 120.59 136.91 162.07 184.70 215.40 250.66 289.26 332.46 390.93 441.35 475.25 522.22 616.34 756.45 822.72 917.29 1077.84

TOTAL MONETARY ECONOMY 629.79 715.77 855.71 1028.80 1243.75 1599.92 1732.90 1914.23 2166.74 2508.72 2868.47 3255.96 3635.29 4171.66 4851.15 5315.13 6049.21 6941.47

TOTAL GDP 663.53 754.97 900.34 1087.82 1313.56 1683.76 1833.45 2033.19 2298.41 2659.49 3033.05 3455.35 3851.78 4418.59 5114.96 5612.51 6391.11 7330.50

27

Table 7: Gross Domestic Product at Constant 1982 Prices (millions of Kenyan pounds)

SECTOR 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

TRADITIONAL ECONOMY

Forestry 15.68 16.20 16.73 17.32 17.88 18.78 19.09 19.61 20.31 21.01 21.71 22.48 23.21 26.86 27.72 28.62 29.53 30.46

Fishing 0.48 0.41 0.37 0.39 0.45 0.61 0.70 0.72 0.74 0.82 1.03 1.19 1.29 1.66 1.45 1.50 1.53 1.54




SUB-TOTAL 118.10 122.32 125.67 129.64 133.50 139.96 144.28 149.39 155.79 159.59 164.58 170.35 178.40 202.43 201.70 208.82 214.75 223.35

MONETARY ECONOMY


Agriculture 640.01 668.15 666.56 697.46 722.98 791.81 840.60 838.00 845.87 897.26 938.46 979.07 941.05 975.59 1023.39 1062.57 1109.26 1152.51

Forestry 11.40 13.26 15.12 13.85 14.69 16.10 17.18 20.88 21.37 21.97 32.81 23.67 24.84 26.75 29.37 33.64 38.14 40.62

Fishing 3.94 3.20 2.87 3.05 3.47 4.78 5.48 5.75 6.18 6.40 8.33 8.85 8.05 9.43 9.59 10.93 12.27 12.83


Manufacturing 165.68 189.51 200.68 208.71 237.91 275.89 310.51 333.97 351.47 364.13 372.32 389.07 405.84 424.07 448.67 474.34 502.80 532.47







Other Services 49.63 50.71 53.20 54.17 55.81 59.55 58.72 68.20 74.97 78.03 82.45 86.26 94.20 99.10 104.05 111.74 119.72 127.86

Imputed Bank Service Chg. -34.42 -41.76 -43.75 -43.93 -44.77 -53.19 -67.14 -81.14 -78.41 -93.36 -87.29 -104.10 -99.40 -102.97 -105.94 -113.43 -121.81 -129.12

SUB-TOTAL 1517.66 1572.57 1610.44 1639.07 1709.41 1862.12 2014.20 2105.70 2180.28 2318.06 2394.37 2459.85 2459.29 2573.79 2723.76 2856.78 3002.44 3145.84




TOTAL GDP 1891.74 1967.60 2028.17 2086.53 2178.44 2355.36 2535.95 2661.54 2768.24 2933.54 3033.05 3124.97 3147.98 3313.28 3498.19 3668.44 3858.65 4049.95

28

Table 8: Gross Domestic Product at Constant 1982 Prices: Growth Rates (%)

SECTOR 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

TRADITIONAL ECONOMY

Forestry 3.32 3.27 3.53 3.23 5.03 1.65 2.72 3.57 3.45 3.33 3.55 3.25 15.73 3.20 3.25 3.18 3.15

Fishing -14.58 -9.76 5.41 15.38 35.56 14.75 2.86 2.78 10.81 25.61 15.53 8.40 28.68 -12.65 3.45 2.00 0.65

Building & Construction 3.77 1.09 2.06 1.69 4.98 2.67 4.25 5.91 -0.06 1.96 3.31 6.78 30.72 -7.46 3.55 0.87 4.78

Water Collection 1.56 1.48 1.82 1.31 5.70 1.61 0.77 1.63 1.55 1.53 1.50 3.88 1.57 2.27 2.79 3.87 3.90

Ownership of Dwellings 4.34 4.32 4.25 4.24 4.19 4.15 4.11 4.14 4.06 4.03 3.98 3.96 3.90 4.14 3.79 3.99 3.79

SUB-TOTAL 3.57 2.74 3.16 2.98 4.84 3.09 3.54 4.28 2.44 3.13 3.51 4.73 13.47 -0.36 3.53 2.84 4.00

MONETARY ECONOMY


Agriculture 4.40 -0.24 4.64 3.66 9.52 6.16 -0.31 0.94 6.07 4.59 4.33 -3.88 3.67 4.90 3.83 4.39 3.90

Forestry 16.32 14.03 -8.40 6.06 9.60 6.71 21.54 2.35 2.81 49.34 -27.86 4.94 7.69 9.79 14.54 13.38 6.50

Fishing -18.78 -10.31 6.27 13.77 37.75 14.64 4.93 7.48 3.56 30.16 6.24 -9.04 17.14 1.70 13.97 12.26 4.56

Mining & Quarrying 6.80 8.97 -5.98 -9.18 2.95 15.26 4.19 10.06 -37.71 21.28 1.21 10.76 9.45 3.58 8.57 11.29 4.63

Manufacturing 14.38 5.89 4.00 13.99 15.96 12.55 7.56 5.24 3.60 2.25 4.50 4.31 4.49 5.80 5.72 6.00 5.90

Building & Construction -0.29 -9.65 -3.71 -4.75 9.17 12.58 7.58 7.03 7.99 -0.67 -15.67 -7.70 2.22 3.69 4.12 4.29 5.40

Electricity & Water 6.73 9.89 11.24 12.39 5.99 17.44 11.42 -2.07 15.34 -3.50 5.69 4.55 10.76 7.54 7.66 8.51 8.39

Trade, Restaurants & Hotels -1.84 6.20 -6.48 -0.26 7.41 11.46 4.21 4.96 1.30 -4.91 2.80 5.50 6.80 9.79 5.78 5.75 4.40

Transport, Storage & Comm. 7.83 2.12 -2.29 7.03 8.20 7.01 6.14 5.66 1.92 16.64 13.88 0.39 2.10 4.30 4.40 4.06 3.01

Finance, Real Estate & Bus. 1.23 16.54 5.88 6.77 4.71 18.66 21.63 -0.52 30.78 -5.24 7.77 -1.57 9.89 6.75 5.17 6.10 7.50


Other Services 2.17 4.90 1.83 3.04 6.69 -1.39 16.15 9.91 4.09 5.66 4.62 9.20 5.20 4.99 7.39 7.14 6.80

SUB-TOTAL 3.62 2.41 1.78 4.29 8.93 8.17 4.54 3.54 6.32 3.29 2.73 -0.02 4.66 5.83 4.88 5.10 4.78

PRIVATE HOUSEHOLDS 11.43 13.43 15.05 14.94 9.95 15.50 17.83 17.75 8.33 6.71 6.50 6.54 7.10 10.55 10.70 13.53 12.77

GOVERNMENT SERVICES 6.34 6.84 8.55 5.14 5.06 6.39 7.10 5.61 5.29 3.80 4.20 2.88 5.10 6.33 4.80 5.78 5.50

TOTAL MONETARY ECONOMY 4.04 3.10 2.86 4.50 8.34 7.96 5.04 3.99 6.18 3.41 3.00 0.51 4.76 5.97 4.95 5.33 5.01

TOTAL GDP 4.01 3.08 2.88 4.41 8.12 7.67 4.95 4.01 5.97 3.39 3.03 0.74 5.25 5.58 4.87 5.19 4.96 TOTAL GDP

29

Table 9: Implicit Gross Domestic Product Deflators (1982=1.00)

SECTOR 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

TRADITIONAL ECONOMY

Forestry 0.308 0.322 0.333 0.421 0.526 0.573 0.697 0.776 0.820 0.915 1.000 1.125 1.222 1.250 1.344 1.526 1.781 1.946

Fishing 0.313 0.390 0.486 0.513 0.644 0.475 0.614 0.639 0.743 1.000 1.000 0.933 0.969 0.880 1.241 1.233 1.379 1.851




SUB-TOTAL 0.286 0.320 0.355 0.455 0.523 0.599 0.697 0.796 0.845 0.945 1.000 1.170 1.214 1.220 1.308 1.424 1.592 1.742

MONETARY ECONOMY


Agriculture 0.298 0.330 0.392 0.512 0.664 0.870 0.777 0.801 0.842 0.913 1.000 1.151 1.322 1.391 1.562 1.571 1.715 1.812

Forestry 0.311 0.322 0.358 0.428 0.425 0.428 0.480 0.617 0.733 0.846 1.000 1.092 1.127 1.211 1.291 1.474 1.615 2.300

Fishing 0.320 0.419 0.505 0.541 0.680 0.487 0.642 0.643 0.707 1.025 1.000 1.019 1.284 1.282 1.574 1.601 1.637 2.135


Manufacturing 0.470 0.505 0.595 0.608 0.606 0.652 0.706 0.748 0.840 0.901 1.000 1.049 1.136 1.222 1.356 1.376 1.498 1.606







Other Services 0.431 0.502 0.513 0.567 0.636 0.680 0.794 0.764 0.867 0.947 1.000 1.104 1.139 1.308 1.477 1.626 1.653 1.783

Imputed Bank Service Chg. 0.376 0.407 0.472 0.514 0.593 0.703 0.710 0.691 0.802 0.763 1.000 1.100 1.209 1.269 1.418 1.525 2.019 2.181

SUB-TOTAL 0.339 0.375 0.442 0.523 0.613 0.736 0.727 0.763 0.831 0.901 1.000 1.116 1.248 1.361 1.480 1.547 1.681 1.833




TOTAL GDP 0.351 0.384 0.444 0.521 0.603 0.715 0.723 0.764 0.830 0.907 1.000 1.106 1.224 1.334 1.462 1.530 1.656 1.810

30

Table 10: GROSS SECTORAL ICORS (WITH ONE YEAR LAG)

SECTOR 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

MONETARY ECONOMY


Agriculture 1.80 -29.09 1.93 2.34 0.75 1.68 -33.06 7.83 1.20 1.56 1.26 -1.15 1.17 1.06 1.37 1.26 1.39

Forestry 0.54 0.47 -0.50 0.39 0.45 1.06 0.29 1.82 2.03 0.08 -0.07 0.17 0.20 0.15 0.05 0.04 0.31

Mining & Quarrying 18.05 9.29 -26.00 -7.38 19.68 3.80 27.88 8.21 -2.29 5.19 50.63 4.97 6.97 10.72 5.06 6.53 11.57

Manufacturing 5.83 12.39 11.89 3.17 2.72 3.63 6.30 7.87 8.70 13.39 4.00 4.83 3.60 2.65 3.27 3.15 3.79

Building & Construction -132.42 -3.90 -6.77 -5.23 2.73 2.50 6.86 4.90 4.76 -44.13 -1.36 -4.74 19.96 5.09 5.85 7.44 5.64

Electricity & Water 43.46 32.73 18.13 21.12 67.76 19.57 27.69 -94.87 15.25 -88.38 55.72 43.45 9.58 13.26 12.52 12.48 14.18

Trade, Restaurants & Hotels -8.30 2.54 -2.14 -67.39 2.62 1.39 2.87 1.79 9.54 -3.22 2.54 1.24 0.79 0.70 0.63 0.56 0.99

Transport, Storage & Comm. 11.18 44.03 -40.69 15.28 10.22 15.98 20.90 16.85 44.70 4.05 4.13 106.76 24.78 12.00 15.28 16.57 16.99

Finance, Real Estate & Bus. 9.26 0.63 2.03 1.70 1.41 0.39 0.43 -14.11 0.24 -2.3 10.58 -4.05 0.65 0.79 0.56 0.71 0.95


Other Services 31.67 14.40 26.77 10.78 6.53 -38.72 3.57 7.31 18.04 14.38 14.51 5.96 8.95 11.13 5.92 6.21 7.11

SUB-TOTAL 9.35 13.44 15.68 6.58 2.99 3.72 7.43 8.01 4.30 8.13 7.44 -765.36 3.61 2.72 3.56 3.56 3.65

Producers of Government Services 7.87 6.34 4.17 5.93 5.76 4.89 4.44 6.09 7.50 10.48 6.83 6.65 4.75 4.20 5.44 4.18 5.98

TOTAL GDP 8.92 11.00 10.12 6.58 3.38 4.00 6.77 7.38 4.88 8.52 7.27 25.03 3.59 3.23 3.92 3.72 4.02

Note: Derived with one year lag, thus ICORt= It-1/(Yt –Yt-1)

31

Table 11: GROSS SECTORAL ICORS (WITH THREE YEAR MOVING AVERAGE)

SECTOR 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

MONETARY ECONOMY


Agriculture 2.73 3.03 1.36 1.35 1.91 4.24 3.69 1.87 1.33 3.63 3.65 3.05 1.19 1.22 1.34

Forestry 1.03 1.29 1.64 0.64 0.47 0.59 0.67 0.25 1.19 0.60 -0.21 0.17 0.11 0.07 0.11

Mining & Quarrying 41.64 -46.38 -22.24 22.51 10.86 9.08 -10.57 -15.03 -8.55 6.96 8.34 6.77 6.80 6.61 7.12

Manufacturing 8.66 6.75 3.87 3.16 3.92 5.44 7.38 9.32 7.63 6.18 4.13 3.55 3.13 3.03 3.41

Building & Construction -7.40 -4.78 585.00 4.77 3.83 4.45 5.45 7.36 -9.71 -3.57 -4.24 -43.96 8.59 6.21 6.26

Electricity & Water 28.79 23.21 30.83 28.18 30.01 34.44 30.56 86.01 53.49 123.17 28.58 17.17 11.62 12.72 13.12

Trade, Restaurants & Hotels -17.96 -50.58 269.00 2.79 2.06 1.81 3.23 35.16 -36.00 9.35 1.26 0.85 0.70 0.64 0.71

Transport, Storage & Comm. 38.31 43.50 21.72 13.66 15.44 17.87 22.78 10.10 6.29 5.67 9.80 21.22 15.78 14.65 16.21

Finance, Real Estate & Bus. 1.44 1.21 1.72 0.82 0.50 0.67 0.47 1.31 0.86 43.53 1.09 1.19 0.67 0.69 0.77

Ownership of Dwellings 14.81 13.60 9.97 10.17 7.18 8.59 6.84 8.07 10.41 29.23 63.32 15.16 8.55 7.68 7.20

Other Services 21.16 15.59 10.73 16.31 7.29 7.49 7.18 11.81 15.42 10.28 8.80 8.22 8.24 7.28 6.42

SUB-TOTAL 12.12 10.38 5.44 3.96 4.30 5.79 6.15 6.27 6.08 10.88 7.41 4.74 3.26 3.27 3.59

Producers of Government Services 5.85 5.32 5.12 5.46 4.93 5.10 5.92 7.80 8.14 8.01 5.89 4.86 4.75 4.55 5.18

TOTAL GDP 9.93 8.83 5.48 4.29 4.49 5.72 6.15 6.57 6.51 9.76 6.56 4.73 3.57 3.62 3.89

Note: Derived with three year moving average, thus ICORt= Σ(It-1, It-3)/(Σ(Yt,Yt-2) – Σ(Yt-1,Yt-3))

32

Table 12: GROSS SECTORAL ICORS (WITH FIVE YEAR MOVING AVERAGE)

SECTOR 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989

MONETARY ECONOMY


Agriculture 1.76 1.74 1.98 2.30 1.97 2.42 2.34 2.74 2.01 1.98 1.93 1.90 1.25

Forestry 0.74 0.93 0.67 0.55 0.69 0.31 0.73 0.96 0.62 0.34 2.28 0.10 0.12

Mining & Quarrying 226.27 41.44 78.57 16.69 -31.24 -3290.00 -35.22 -51.43 -40.75 7.34 7.67 6.34 7.41

Manufacturing 5.16 4.59 4.24 4.25 4.95 6.54 7.28 7.03 5.97 4.60 3.56 3.39 3.29

Building & Construction -14.32 71.17 7.62 5.33 4.21 5.69 46.95 -15.89 -11.10 -7.22 -8.61 24.26 7.48

Electricity & Water 32.73 27.07 26.52 35.56 30.90 40.49 55.15 65.92 35.97 38.68 21.31 15.02 12.39

Trade, Restaurants & Hotels 18.32 4.53 4.77 2.60 2.40 4.27 7.21 5.49 4.12 2.02 0.94 0.75 0.72

Transport, Storage & Comm. 20.45 21.76 19.91 15.68 18.00 12.73 9.25 8.94 9.01 7.83 11.29 18.18 16.18

Finance, Real Estate & Bus. 1.49 0.90 0.75 0.84 0.50 0.80 0.86 1.45 1.03 1.96 0.90 0.94 0.74

Ownership of Dwellings 13.14 10.81 8.78 9.19 7.01 7.90 9.54 11.80 13.03 18.61 14.88 10.61 8.02

Other Services 13.93 16.99 8.94 7.57 8.78 10.23 9.35 10.42 10.99 10.19 8.43 7.21 7.48

SUB-TOTAL 6.94 5.53 5.28 5.10 4.75 5.74 6.68 7.71 6.46 5.82 4.78 4.13 3.40

Producers of Government Services 5.85 5.30 4.90 5.34 5.68 6.40 6.80 7.44 7.05 6.09 5.32 4.81 4.90

TOTAL GDP 6.77 5.60 5.32 5.23 4.99 5.93 6.72 7.58 6.41 5.81 4.88 4.28 3.70

Note: Derived with five year moving average, thus ICORt= Σ(It-1, It-6)/(Σ(Yt,Yt-5) - Σ(Yt-1,Yt-6))

33

Table 13: NET SECTORAL ICORS (WITH ONE YEAR LAG)

SECTOR 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 D

MONETARY ECONOMY


Agriculture 1.23 3.94 1.35 1.52 0.62 1.27 6.04 2.50 0.91 1.08 0.86 -2.38 0.76 0.76 0.90 0.87 0.92 0.02

Forestry 0.48 0.41 -0.66 0.30 0.38 0.82 0.27 0.98 1.19 0.07 -0.08 0.12 0.16 0.13 0.05 0.03 0.24 0.02

Mining & Quarrying 10.40 5.97 -159.18 -16.22 7.31 2.86 12.72 5.49 -2.64 4.20 9.87 3.39 4.56 4.47 3.19 4.53 5.57 0.05

Manufacturing 4.32 6.70 5.29 2.34 2.07 2.60 3.79 4.03 3.64 4.15 1.90 2.24 1.70 1.42 1.74 1.72 2.05 0.05

Building & Construction 8.04 -8.10 19.48 98.56 1.77 1.79 4.13 2.86 2.93 6.78 -1.99 -13.52 6.14 2.16 2.64 3.43 2.93 0.05

Electricity & Water 33.50 27.23 15.39 18.19 50.80 17.55 23.56 -2884.46 13.49 -206.32 41.23 30.18 8.08 10.49 9.93 10.10 11.45 0.02

Trade, Restaurants & Hotels 4.81 1.41 -9.33 3.73 1.56 0.97 1.31 0.89 1.97 184.89 0.91 0.65 0.46 0.46 0.34 0.30 0.46 0.05


Finance, Real Estate & Bus. 3.53 0.56 1.52 1.31 0.99 0.35 0.39 5.00 0.23 -3.73 0.46 14.60 0.54 0.61 0.40 0.53 0.75 0.02


Other Services 16.49 10.23 12.79 6.50 5.03 88.58 3.18 6.08 12.11 10.63 10.13 4.89 6.47 7.95 4.66 4.85 5.49 0.02

Producers of Government Services 5.99 4.91 3.38 4.27 4.13 3.72 3.46 4.49 5.44 6.87 4.63 3.92 3.41 3.19 3.84 3.10 4.39 0.02

TOTAL GDP (d=0.02) 5.95 6.67 5.97 4.53 2.71 3.18 4.82 4.92 3.66 5.36 4.38 6.74 2.60 2.38 2.78 2.69 2.87 0.02

TOTAL GDP (d=0.05) 3.97 4.19 3.70 3.08 2.09 2.42 3.37 3.28 2.66 3.44 2.74 3.21 1.84 1.70 1.94 1.89 2.00 0.05

Total GDP (weighted depreciation rate) 5.07 5.53 4.92 3.89 2.46 2.87 4.19 4.20 3.24 4.49 3.63 4.88 2.27 2.09 2.41 2.35 2.49 0.0304

Note: Derived with one year lag, thus ICORt= It-1/(Yt-(1-d)Yt-1)

34

Table 14: NET SECTORAL ICORS (THREE-YEAR MOVING AVERAGE)

SECTOR 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 D

MONETARY ECONOMY


Agriculture 1.71 1.79 1.16 1.17 1.42 2.02 1.75 1.16 0.85 1.35 1.43 1.42 0.91 0.89 0.86 0.02

Forestry 0.57 0.71 1.14 0.68 0.44 0.50 0.49 0.21 0.45 0.28 -0.22 0.14 0.08 0.09 0.11 0.02

Mining & Quarrying 13.45 33.96 87.18 10.60 7.57 7.11 -20.90 -75.78 -13.39 4.32 4.56 4.12 4.64 4.76 4.52 0.05

Manufacturing 4.61 3.72 2.99 2.73 3.00 3.25 3.46 3.16 2.80 2.18 1.92 1.78 1.80 1.96 2.00 0.05

Building & Construction 66.04 -19.90 5.40 3.66 2.95 3.23 2.90 3.20 14.05 -10.09 -10.96 6.76 3.10 3.33 3.05 0.05

Electricity & Water 25.07 25.02 31.88 28.05 22.68 26.43 26.75 60.70 38.97 49.00 15.51 11.09 10.37 11.70 13.46 0.02

Trade, Restaurants & Hotels 3.74 3.60 3.49 1.47 1.04 1.02 1.52 2.22 2.13 1.07 0.66 0.45 0.39 0.34 0.37 0.05

Transport, Storage & Comm. 13.58 13.33 11.30 9.45 9.90 9.68 9.08 5.70 3.75 3.76 5.03 8.03 7.48 6.95 7.01 0.05

Finance, Real Estate & Bus. 1.24 0.96 1.14 0.71 0.53 0.64 0.57 0.96 0.73 2.65 0.87 0.71 0.49 0.64 0.80 0.02

Ownership of Dwellings 8.44 7.39 6.02 6.67 6.68 7.23 5.68 5.76 5.92 9.10 9.44 7.57 5.76 5.15 4.73 0.02

Other Services 10.46 8.25 7.72 11.38 7.27 7.24 7.21 9.33 10.38 6.94 6.68 6.18 6.39 5.68 5.79 0.02

Producers of Government Services 4.18 3.84 3.94 4.29 4.18 4.49 5.05 5.48 4.71 4.42 4.01 3.95 3.64 3.86 4.00 0.02

TOTAL GDP (d=0.02) 5.96 5.40 4.25 3.73 3.78 4.34 4.39 4.31 3.94 4.64 3.79 3.29 2.74 2.84 2.92 0.02

TOTAL GDP (d=0.05) 3.81 3.48 3.00 2.78 2.82 3.09 3.07 2.94 2.64 2.75 2.37 2.18 1.94 2.01 2.05 0.05

Total GDP (weighted depreciation rate) 4.98 4.53 3.71 3.34 3.38 3.80 3.82 3.71 3.36 3.74 3.14 2.80 2.40 2.48 2.55 0.0304

Note: Derived with three year moving average, thus ICORt= Σ(It-1, It-3)/(Σ(Yt,Yt-2) - Σ(1-d)(Yt-1,Yt-3))

35

Table 15: NET SECTORAL ICORS (WITH FIVE YEAR MOVING AVERAGE)

SECTOR 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 Depreciation

MONETARY ECONOMY


Agriculture 1.22 1.23 1.38 1.51 1.34 1.54 1.44 1.46 1.18 1.13 1.08 1.08 0.84 0.02

Forestry 0.58 0.68 0.53 0.45 0.55 0.28 0.54 0.60 0.42 0.25 0.53 0.08 0.10 0.02

Mining & Quarrying 18.14 12.19 15.11 8.07 34.44 18.18 37.65 22.53 23.59 4.64 4.41 4.03 4.43 0.05

Manufacturing 3.55 3.14 2.90 2.88 3.10 3.54 3.46 3.11 2.60 2.13 1.78 1.75 1.74 0.05

Building & Construction 10.66 5.38 3.50 3.00 2.68 3.21 5.98 10.50 16.56 33.13 17.05 4.95 3.32 0.05

Electricity & Water 26.89 23.07 22.67 28.72 25.47 31.32 39.38 43.27 27.39 27.88 16.73 11.97 10.03 0.02

Trade, Restaurants & Hotels 2.77 1.91 1.89 1.37 1.27 1.62 1.72 1.49 1.30 0.91 0.52 0.43 0.40 0.05

Transport, Storage & Comm. 9.74 10.32 10.26 9.01 9.52 7.67 5.98 5.36 5.11 4.51 5.52 6.95 6.75 0.05

Finance, Real Estate & Bus. 1.16 0.75 0.65 0.70 0.44 0.68 0.71 1.05 0.80 1.25 0.67 0.68 0.58 0.02

Ownership of Dwellings 8.16 6.84 5.98 6.29 5.22 5.82 6.71 7.46 7.49 8.35 7.20 5.90 4.98 0.02

Other Services 9.10 10.09 6.50 5.89 6.83 7.89 7.40 8.02 8.18 7.61 6.40 5.56 5.69 0.02

Producers of Government Services 4.44 4.03 3.74 3.98 4.24 4.71 4.89 5.07 4.79 4.22 3.73 3.44 3.59 0.02

TOTAL GDP (d=0.02) 4.73 4.01 3.89 3.80 3.59 4.12 4.47 4.68 4.04 3.63 3.09 2.79 2.44 0.02

TOTAL GDP (d=0.05) 3.20 2.84 2.79 2.74 2.62 2.89 2.99 2.94 2.58 2.32 2.02 1.88 1.71 0.05

Total GDP (weighted depreciation rate) 4.06 3.51 3.42 3.35 3.18 3.59 3.81 3.88 3.38 3.04 2.61 2.39 2.12 0.0304

Note: Derived with five year moving average, thus ICORt= Σ(It-1, It-6)/(Σ(Yt,Yt-5 - Σ(1-d)(Yt-1,Yt-6))

Date post:	17-Jan-2016
Category:	Documents
Upload:	jtmukui2000
View:	57 times
Download:	0 times

Incremental Capital Output Ratio as Measure of Productivity of Investment: Theory and a Kenyan...

Documents