the consumer price index as a measure of inflation in kenya

THE CONSUMER PRICE INDEX AS A MEASURE OF INFLATION IN KENYA

by

John Thinguri Mukui

Prepared for USAID Mission, Nairobi, Kenya

23 July 1990

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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.

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

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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

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measures.

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

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EXECUTIVE SUMMARY i. The consumer price index (CPI) is one of the most important measures of inflation widely used in Kenya. In particular, it is used by the Industrial Court in determining maximum wage increases and in the design of macroeconomic policies e.g. in the setting of nominal interest rates and nominal exchange rates. There is therefore a need to have accurate and timely data on CPI to avoid giving wrong signals to Government agencies, the private sector, and international organizations. The purpose of this brief paper is to evaluate the computational procedures of the Kenya’s CPI, identify possible sources of errors that may exist, and recommend possible improvements. It will be not possible to derive alternative measures of CPI, as this requires extensive primary data collection and analysis. ii. The potential sources of bias in the computation of the CPI are: (a) the appropriateness of the base weights, (b) the price quotations used, which are heavily influenced by the choice of retail outlets, and (c) the computational procedures. The market basket and the weights used are based on an expenditure survey conducted in 1974/75. Consumer patterns have changed drastically since then as a result the structural adjustment programs that the Government has undertaken in the recent past. Initially, the Central Bureau of Statistics (CBS) used prices obtained through actual purchases of some items in the consumer basket. However, due to budgetary pressures, actual purchases are not done on any meaningful scale. When rent surveys are undertaken, the rent data collected is rarely used to update the rent component of the CPI. Currently, the rate of increase of the rent index is not corrected on the basis of actual rent surveys but is assumed to be identical to the CPI excluding rent. iii. One of the weaknesses of the computation of inflation is the use of unweighted increases of CPI of the three income groups. In addition, it is also misleading to use Nairobi’s inflation rate as representative of inflation in Kenya as a whole, given the income inequalities between regions and the fact that over 80 percent of the Kenyan population is rural-based. iv. The paper has tried to highlight the potential sources of errors in the computation of the CPI, and the dangers of assuming that the Nairobi inflation rates are representative of Kenya as a whole. The delays in processing of data from various surveys means that results are sometimes released seven years after a survey is undertaken. To the extent that researchers and Government itself uses the results as pertaining to the period of release, this inevitably leads to misreading of the Kenyan economy. v. The paper recommends that: (a) a new household budget survey be undertaken to redefine base weights and income brackets; (b) empirical estimates of income distribution be used to derive weighted increases in inflation rate for Nairobi; (c) reinstate bargaining and purchasing for selected representative items in collecting retail prices to be used in the compilation of the CPI; (d) use household budget survey data to derive new estimates of internationally accepted indicators of income distribution; (e) attempt to develop a national inflation rate reflecting increases in the cost of living for the Kenyan population; and (f)

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develop an empirical basis of setting inflation targets in the structural adjustment programmes. It is important to note that the gradual slippage in the quality of retail price data, especially the rent component, is partly explained by declining budgetary allocations for these activities. vi. The paper does not give the probable inflation rate, nor does it indicate the magnitude and direction of the bias in the official CPI. Although the tendency would be to assume that official statistics are underestimated, there are no sufficient alternative data to allow one to refute or defend the official statistics. However, there is sufficient evidence to suggest that major revisions are required -- to update the base weights and to more vigorously undertake retail price data collection, especially on rent.

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INTRODUCTION 1. The consumer price index (CPI) is one of the most important measures of inflation widely used in Kenya. In particular, it is used by the Industrial Court in determining maximum wage increases as per the Government Wage Guidelines which contain predetermined escalators (for unionized employees) defined by the cost of living. The CPI, as a measure of inflation, is used in the design of macroeconomic policies. The expected correlation between the CPI and the GDP deflator is such that an underestimate of inflation as measured by the CPI means that inflation as measured by GDP deflator is also underestimated and, in consequence, the real rate of growth and per capita growth are overstated. In the recent past, Government policy of maintaining positive real interest rates concomitantly imply changing nominal interest rates with changes in the rate of inflation derived using the CPI. If the inflation rate is underestimated by, say, 2 percentage points, commercial banks’ interest rate on deposits would be set at 2 percentage points lower, which would lead to an unintended tax on savers and subsidy to investors. On the external front, inflation affects the real effective exchange rate, and the official statistics on inflation therefore influence Government decisions on changes in nominal exchange rates. There is therefore need to have accurate and timely data on CPI to avoid giving wrong signals to Government agencies, the private sector, and international organizations. 2. However, in the recent past, some observers have questioned the reliability of the CPI, partly because Price Waterhouse and Deloitte Haskins + Sells have been computing independent estimates of the Executive Cost of Living Indices (ECOLI). Since the ECOLI estimates are about 50 percent above the official inflation numbers, it is possible to increase suspicion on Government statistics. The suspicion of official statistics is also likely to extend to the GDP deflator (since they are positively correlated in the medium- to long-term), and, by implication, to the computation of real GDP and real GDP growth rates. 3. The purpose of this brief paper is to evaluate the computational procedures of Kenya’s CPI, identify possible sources of errors that may exist, and recommend possible improvements. It will not be possible to derive alternative measures of CPI, as this requires extensive primary data collection and analysis. The approach taken here is to evaluate the current methodology used in computing the CPI with a view to pinpointing the required improvements. CURRENT METHODOLOGY OF COMPUTING CPI 4. The commodity basket and weights used in the computation of the CPI for Nairobi are based on the 1974/75 Urban Household Budget Survey (UHBS) conducted in Nairobi, which updated the indices based on an earlier survey conducted in 1969. The 1974/75 UHBS classified consumption by three income brackets: lower income, KShs 0-699 per month; middle income, KShs 700-2,499; and upper income, KShs 2,500 and above. The survey was used to derive percentages of expenditure for each commodity or groups of commodities (see Tables 1, 2, 3 below). Within each section, a group of close substitutes was allocated a group

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weight on the basis of the proportion of total expenditure spent on the group. The base period used is the first half of 1975, as base periods of shorter durations (say, one month) can be misleading, especially if the month selected was abnormal. 5. As detailed in Consumer Price Indices, Nairobi (Kenya, Central Bureau of Statistics, 1977), prices for items in the lower income group are collected during the first four days of each month, while those for the upper and middle income are collected during the week following the fifteenth of each month. The prices are collected by CBS enumerators through actual bargaining and an estimated 50 percent of all goods included in the computation of the CPI for the lower income group are actually purchased, although the percentage purchased is lower for the other two income groups. 6. The collection of rent data is supposed to be done every six months. During the intervening five months, it is assumed that the price relative of rents increases at the same rate as the consumer price index for all sections excluding rent. The calculations are supposed to be revised when the actual rent survey is undertaken. POTENTIAL SOURCES OF BIAS 7. The potential sources of bias in the computation of the CPI are: (a) the appropriateness of the base weights, (b) the price quotations used, which are heavily influenced by the choice of retail outlets, and (c) the computational procedures. However, although the income brackets used to define the income groups are very low and outdated, they would not lead to a bias in the official CPI unless they were inappropriate when the Urban Household Budget Survey was undertaken (1974/75). The bias resulting from unrealistic income brackets would manifest itself through inappropriate base weights and selection of retail outlets used in collecting retail prices. 8. The market basket and the weights used are based on an expenditure survey conducted in 1974/75. Consumer patterns have changed drastically since then. First, it is obvious that the weights allocated to education have a large downward bias. The weight is 2.80 percent for the lower income group, 2.97 percent for the middle income group, and 2.50 percent for the upper income group. During the intervening period since 1975, there have been a lot of changes in the education sector, principally the increased provision of expensive education services by the private sector, which is not adequately captured in the computation of the CPI. 9. It is, however, important to distinguish between education-related expenditures and what the CPI compilation includes. In CBS, education is taken to refer to school fees only. The implication of cost sharing in education is to pass the burden of school books, pencils and school uniforms (which are no longer price controlled) from Government to parents. This has the effect of raising the share of, say, stationery (which falls under “miscellaneous goods and services”). The underestimation of education costs therefore extends to the disproportionately low weights assigned to other classes of goods where the items legitimately come under. 10. The Government has started revising the market basket, the weights, the base period

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and the income brackets, based on the 1982/83 Urban Household Budget Survey. The results of the 1982/83 survey are not likely to be drastically different from those of the 1974/75 survey. Indeed, the consumption patterns in 1982/83 are likely to correspond more with those of 1974/75 than those of 1990. The Kenya Government has been undertaking structural adjustment measures, mainly in the liberalization of interest rates, exchange rates and the trade regime. The flexible exchange rate policy that Kenya has pursued in the recent past imply that the Shilling cost of imports has increased dramatically, thereby raising the prices of the market basket of the upper income group who are expected to spend a relatively larger share on imported consumer goods. To the extent that some luxury consumer goods have low demand elasticities, they would command a bigger share of expenditure of the upper income bracket. The reform of the system of incentives of the agricultural sector is also likely to raise the share of food in total consumption among the lower and the middle income groups, especially for items with low demand elasticities. 11. The choice of retail outlets and the process of collecting retail prices are important determinants of the accuracy of consumer price indices. Initially, the CBS used prices obtained through actual purchases of some items in the consumer basket, which are then donated to Kenyatta National Hospital. However, due to budgetary pressures, it is no longer possible to engage in actual purchases on any meaningful scale. There are many instances where the prices entering the CPI are based on prices on display in the shops, especially in the supermarkets. While the prices of manufactured consumer goods are easy to collect by simply recording the prices displayed, it is not appropriate to collect the prices of perishables (e.g. vegetables and fruits) in the same manner. This is mainly because prices are recorded in reference to the weight of the commodity, hence the need to purchase and weigh. In addition, in the process of bargaining, a household normally pays less than the tag price. Ideally, price quotations should be collected from households as price data from retail outlets is only a proxy of prices at which consumers purchase the items. 12. When rent surveys are undertaken, the rent data collected is rarely used to update the rent component of CPI. When rent data used to be collected every six months, the monthly increases in the rent index between the surveys was assumed to be identical to the weighted increases in all other components of the CPI excluding rent, but the rent index was later revised based on data from the actual rent survey. Currently, the rate of increase of the rent index is not corrected on the basis of actual rent surveys but is assumed to be identical to the CPI excluding rent. As shown in Table 5, the increases in the rent index has been almost identical to the increase in the weighted average CPI excluding rent from 1978 onwards, which confirms that rent data is not used in the derivation of the CPI. Given budgetary limitations, the approach has ostensibly been rationalized on the grounds that landlords may correctly observe increases in prices of their consumption baskets and effortlessly and fully compensate themselves by increasing rent without any leads or lags. It is difficult to justify this approach on theoretical or empirical grounds. 13. The computational procedure of arriving at CPI for each income group is by use of Laspeyres base-period quantity weights. The base weights remain constant over time, and CPI is therefore easy to calculate as only prices are variable. As we have seen, the weights become outdated. The inflation rate for each income group is obtained as the percentage increase of the arithmetic mean of monthly observations of one year over that of the previous

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year. The aggregate inflation rate is the unweighted average of inflation rates of the three income groups. 14. One of the weaknesses of the computation of inflation rate is the use of unweighted increases of CPI of the three income groups. Studies on income distribution, e.g. the works of Bigsten (1987) and Vandemoortele (1987), indicate that incomes are skewed. If the actual proportion of low income group is, say, above 70 percent, the official CPI for low income group would approximate that of the entire Nairobi population (disregarding other possible sources of distortions in the CPI, e.g. the prices observed and weights of various expenditure items and categories). In addition, it is also misleading to use Nairobi’s inflation rate as representative of inflation in Kenya as a whole. Given the income inequalities between individuals and regions, and the fact that over 80 percent of the Kenyan population is rural-based, the Nairobi rate is not truly representative of the Kenyan population. Prices in rural areas usually differ from urban areas, mainly due to transport costs and the sources of the items (spatial distance to source and whether expenditure occurs in form of cash or kind). Rural expenditure patterns are also different from those in urban areas, as demonstrated by the results of the Rural Household Budget Survey 1981/82 (Economic Survey 1988). 15. It is difficult to evaluate impact of the outdated weighting system on CPI, mainly because this entails collecting or imputing independent weights. However, if we disregard possibilities of distortions in the retail prices used (or that any distortions are consistent and in the same direction), then the distortion that may arise from the weights will depend on the relative increases in the prices of different expenditure categories. If indices for all expenditure groups increase at the same rate at all points in time, then the base weights don’t matter. 16. The last lines of Tables 1b, 2b and 3b compare increases in price indices for major expenditure categories for the three income groups for the period 1975-1990. The exercise is for illustration purposes only since, as we have seen, the rent index is not independently derived from actual rent surveys. For the lower income group, the compound annual increase for the period 1975-1990 was 11.6 percent, while the highest annual increase was recorded for “miscellaneous goods and services” (14.9 percent) and the lowest was “recreation, entertainment and education” (6.2 percent). This means that, the actual inflation rate for the low income group lies between 6.2 percent (assuming all expenditure was on “recreation, entertainment and education”) and 14.9 percent (assuming all expenditure was on “miscellaneous goods and services”). A similar exercise is done for the other income groups. Likewise, the actual annual increase in middle income CPI lies between 8.2 percent (miscellaneous goods and services) and 20.1 percent (transport and communications), and between 9.3 percent (clothing and footwear) and 13.9 percent (beverages and tobacco) for the upper income group. Although the figures are based on the very restrictive assumption that the prices used in compiling the indices are correct, they do demonstrate that a bias in weighting system alone will give a lower margin of error on the overall CPI as the expenditure categories recording the highest and lowest price increases command very small proportions of total expenditure. 17. Table 6 shows the simple average index of consumer prices for the lower, middle and upper income groups. The compound growth rate during the period 1975/1990 was 11.84

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percent, with the highest increase recorded for transport and communication (15.66 percent) and fuel and power (13.39 percent). The lowest were recorded for recreation, entertainment and education (8.60 percent) and clothing and footwear (10.16 percent), while the growth rate for food was about one percentage point lower than the overall average at 10.90 percent. This means that, the actual inflation rate based on the simple average lies between 8.60 percent (assuming all expenditure was on recreation, entertainment and education) and 15.66 percent (assuming all expenditure was on transport and communication). The weight of recreation, entertainment and education in the consumer baskets was 4.1 percent for the lower income group, 4.5 percent for the middle income group and 5.6 percent for the upper income group, giving a simple average of 4.73 percent. The weight of transport and communication in the consumer baskets was 3.8 percent for the lower income group, 7.9 percent for the middle income group and 14.3 percent for the upper income group, giving a simple average of 8.67 percent. 18. Strictly speaking, outdated income brackets used to define income classes do not directly distort CPI unless the base weights were inappropriate when the reference Household Budget Survey was undertaken. However, when a new Household Budget Survey is undertaken, it would be necessary to use definitions of income classes comparable with those of the 1974/75 Survey. An attempt is made here to construct new income classes (see Table 7 below). The approach is to inflate the cut-off points of income groupings used in 1974/75 by the increases in CPI. We assume that a revised cut-off point between lower and middle income groups is the 1975 cut-off point multiplied by the increase in middle income CPI. The revised cut-off point for the middle and upper income is the old cut-off point multiplied by the increase in upper income CPI. Disregarding any computational problems in the derivation of the official CPI, the revised ranges are expected to ensure that various income classes command the same standards of living as in 1974/75 (earning real incomes as defined then). 19. It might not be politically expedient for the Government to put higher cut-off points in defining income classes. However, if the revised income ranges used to define income classes do not adequately reflect the changes in consumer prices since 1975, it will be misleading to compare the differences in consumption patterns between 1975 and the date of the new household budget survey. If the cut-off point between the lower and the middle income groups is lower than indicated in Table 7, a portion of lower income earners will be included in the revised middle income group. Similarly, a relatively low cut-off point between middle and upper income groups implies statistically transferring a portion of middle income earners to a revised upper income group. Thus, only a current-price updating of the income ranges will give consumption patterns that can be legitimately compared with those obtained in the 1974/75 Urban Household Budget Survey. In addition, the definition of income classes will make it impossible to splice current and future consumer price index series by income groups. OTHER PARALLEL SOURCES OF PRICES AND WEIGHTS 20. In a response to a paper presented by the Ministry of Labour on employment creation in Kenya (December 1987), the Secretary-General of the Central Organization of Trade Unions (COTU) criticized the Government’s wage guidelines and minimum wage policy

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which, he argued, had led to a gradual decline in the purchasing power of workers. The paper, however, did not criticize the conceptual or empirical basis of the computation of the CPI, and even used the official statistics as a basis for its argument. The approach taken by COTU was to present a nutritional least-cost balanced diet for a typical low-income household living in Nairobi in 1988, plus other necessary expenditures, to derive the required floor wage increases to achieve a minimum standard of living. The argument by COTU therefore relates more to income distribution issues and the changing economic fortunes rather than faulting the derivation of the official CPI. From the COTU survey, it is apparent that urban workers are slouching towards the lower income group due to decline in real wages. 21. Deloitte Haskins + Sells (DH+S), management consultants, have been computing the Executive Cost of Living Index (ECOLI) since January 1989, specifically to reflect the spending patterns of executives rather than the average upper income consumers represented in the official statistics. The index is published in two forms: with and without housing. This is because rent or house prices move unevenly, and it is possible for similar houses in the same neighbourhood to command very different rents. The “without-housing” expenditure groups include domestic workers, medical insurance and consultations, clubs and sports, restaurants/theatre, overseas travel, and safaris in Kenya. Price Waterhouse has also been computing an ECOLI for Nairobi using weights derived from a survey conducted during November 1988. The Survey covered executives earning a take-home-pay in 1988 of about KShs 75,000/= per month. 22. DH+S index shows an inflation rate of 14.1 percent in 1989 compared with 10.8 percent for the upper income group given in the official statistics. According to Price Waterhouse, the annualized inflation rate for executives was 18.5 percent for the period November 1988-December 1989 and 19.8 percent for the period November 1988-March 1990, indicating a faster rise in the index in the first quarter of 1990. The difference between the official statistics and DH+S and Price Waterhouse inflation rates can be due to measurement errors in either or both, or may truly reflect differences in inflation facing different income groups in Nairobi. 23. Given the potential sources of bias in the official CPI, it is likely that a portion of the difference is accounted for by measurement errors in the official CPI. Officially, the Government views the discrepancy as reflecting actual rates of increase of prices of the consumption baskets of executives, due to their higher propensity to spend a relatively larger share on imported consumer goods. In addition, the retail prices of beers and soft drinks were decontrolled for the upper class hotels, but are controlled for low class hotels. This could cause a once-and-for-all rate of increase of prices in this category, but is unlikely to account for any increases thereafter. Executives also allocate a smaller share of their budget on price-controlled items. For the upper income bracket, price controlled items had a total weight of about 12.3 percent in 1987; middle income 22.0 percent, and lower income 19.2 percent. Depending on the extent to which price controls depress prices below market levels and the degree of enforcement by Government (or observance by producers and middlemen), a portion of the discrepancy probably reflects the extent of inflationary pressures contained by price controls.

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24. It is also likely that the prices of a relatively large proportion of the executives’ consumption basket are affected by excessive profit margins. Thus, a house renting for KShs 5,000 per month in a middle class housing estate would fetch over KShs 15,000 in a high class location. The rents in high class housing areas are also not likely to increase at a consistent rate due to lease arrangements, hence the need for caution in using short-term movements to interpret long-term trends. It is also possible that the relatively high inflation rate for executives is a statistical illusion. Inflation is the rate of increase in the general price level. For example, when beer was decontrolled for the high class hotels, the effect was to increase the price of beer from, say, KShs 10 to 30, an increase of 200 percent! If the price increased by a further KShs 20 in the current year, this would be an increase in its CPI of 66 percent, although the absolute price increase in the two consecutive years would be the same. We should therefore expect a levelling off of the inflation (as a rate of increase in CPI) for executives, due to high upward adjustment in the base prices immediately after decontrol of prices. 25. In addition, the prices faced by executives are affected by movements in the nominal value of the Kenyan Shilling (which depreciated by 24.4 percent against the US Dollar and 20.7 percent against the British Pound between December 1988 and June 1990), and their expected inflation rate would therefore be above the actual inflation rate since the nominal depreciation of the Kenyan Shilling enters their determination of inflation. AN ALTERNATIVE MEASURE OF INFLATION: THE IMPLICIT GDP DEFLATOR 26. The implicit GDP deflator is a measure of the price level of all final goods and services entering the GDP relative to the cost of the same goods and services in a particular base year. Details on its method of derivation is given in CBS publications (see Sources and Methods used for the National Accounts of Kenya, December 1977) while Nagda (1985) gives a detailed critique of the GDP deflator, including the potential weaknesses and bias. The GDP deflator uses current levels of production of all goods as the weights, while the CPI uses the household representative basket and quantities as the weights. 27. The GDP deflator is likely to have more weaknesses than CPI, mainly because the GDP deflator is not computed on the basis of actual price quotations but is a derived deflator. The reliability of the GDP deflator depends on the accuracy of GDP estimates and sectoral deflators used. Sectoral estimates of GDP and GDP deflators are likely to be less reliable than those of total GDP, as some errors might even out in the process of aggregation. In comparing sectoral deflators from year to year, some consistent biases in GDP and GDP deflators are likely to result in reduced bias in the rate of change of the deflator as a measure of inflation. On the whole, due to the cumbersome and indirect process of deriving the GDP deflator, the CPI, properly measured, is a better measure of consumer inflation. 28. During the period 1980-90, the year-on-year GDP deflator has been lower than consumer inflation, except in 1984 and 1986. One would expect the two measures of inflation to approximate each other in the long-term, but annual changes do not have to be identical. For example, when the world price of coffee goes up, the impact on the GDP deflator is more direct and immediate. Since exports are valued at international prices changes in world prices have an influence on GDP deflator largely unrelated to (but which

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can initiate) domestic price movements. Although the two measures are largely determined by the same economic forces, there may be lags in adjustment due to structural rigidity and administrative fiat, e.g. wage guidelines and price controls. If the domestic prices of imported capital equipment and intermediate inputs increase (through high international prices and/or depreciation of the Kenya Shilling), this will raise production costs, but the extent and the lag of the impact on CPI will partly depend on whether the output is subject to price controls. Due to the wage guidelines, increases in CPI are not fully compensated for through increased wages (production costs). The cost of imported consumer goods has an immediate impact on the CPI, but can work its way to the GDP deflator if it affects wage awards.

A Comparison of GDP and Capital Formation Deflators, and the CPI (Annual Change, %) Year 1973 1974 1975 1976 1977 1978 1979 1980 1981 GDP Deflator 9.2 15.7 17.5 15.8 18.5 1.1 5.7 8.7 9.2 GFCF Deflator 11.3 27.6 16.4 21.5 11.1 11.8 13.8 12.4 11.4 CPI 9.6 15.2 15.6 10.4 12.8 12.5 8.4 12.9 12.6 Year 1982 1983 1984 1985 1986 1987 1988 1989 1990 GDP Deflator 10.9 10.6 11.1 8.6 9.5 5.3 9.1 8.5 8.8 GFCF Deflator 16.4 24.6 9.4 8.2 17.1 5.3 8.8 10.3 22.3 CPI 22.0 14.5 9.1 10.7 5.6 7.1 10.7 10.6 12.6

29. However, the degree of correlation of the two measures of inflation is expected to be high in the long-term mainly because leads and lags eventually even out. If the GDP deflator is consistently higher than the CPI, it implies that producers do not fully pass on increased costs of production to consumers. This would be unlikely in the Kenya case since the Government is gradually decontrolling prices, and the degree of enforcement of controls still in place is diminishing. However, the CPI can be consistently higher than the GDP deflator if prices of imported consumer goods are generally higher than for locally produced items in the consumption basket, on account of changes in international prices and/or exchange rate movements. During the period 1972-89, the implicit GDP deflator increased by about 420 percent, gross fixed capital formation (GFCF) deflator by about 840 percent, and the simple average CPI by about 560 percent. It would be useful to study the factorial income distribution implications of the differences in CPI and the GDP deflator as measures of inflation. SOME NECESSARY DIGRESSIONS 30. It is useful to consider three other basic issues that relate to CPI. First is the possible discrepancy between the popular perception of weights of various goods and services in total consumption and what a household expenditure survey is likely to show. Second, is whether the public observes consumer price increases or the net impact of income redistribution through the entire price system, including wages, interest rates and prices of inputs (e.g. for the agricultural sector). Third, is the trade-off between inflation and structural adjustment, and the realism of inflation targets in the structural adjustment operations. 31. There is a popular perception that rents and school fees, for example, take much higher weights than the official statistics reveal. In the case of rent, the perception is mainly because rent is paid on a monthly basis while other items (e.g. food) are purchased on a daily

11

basis. It is possible for goods with low frequency of payments (or purchases) to appear to have higher weights than those that a household budget survey might reveal. Although the weights assigned to education in the official statistics are lower than expected, education-related expenditures (e.g. school books) give the misleading impression that education (defined in the CPI consumer baskets as school fees) may be taking a larger share than it actually does. This demonstrates that casual empiricism is not a substitute for detailed household budget surveys, accompanied by rigorous analysis of data and timely release of results. The Government should therefore diffuse the criticisms of its basis of computing CPI and inflation in general by engaging in a transparent and fresh analysis of the issues. 32. The second issue is whether consumers observe changes in consumer prices per se, or the total impact of all the prices they face. The index of real wages (1982=100) stood at 95 in 1990, while the agricultural sector terms of trade was 95 in 1990. Urban workers were therefore, on average, worse off in 1990 than they were in 1982. The agricultural sector terms of trade was also lower than it was in 1982, mainly due to the fact that the index of purchased consumer goods increased faster than that of value added (the net impact of agricultural output prices and purchased inputs). It would be useful to ask who the gainers in the economy are, given that both urban workers and the rural sector are net losers, but the issue is more in the domain of changing patterns of income distribution than the CPI qua CPI. The relevant question is: If increases in nominal wages and nominal agricultural value added were keeping pace with increases in the cost of living, would the urban workers and agriculturalists be under the illusion that the CPI is lower than it actually is? Is there a divergence between the subjective and the objective consumer price indices? 33. Third, as part of the structural adjustment programmes (SAP), the Government sets inflation targets for the coming three years. However, the macroeconomic adjustments postulated in the SAP (e.g. flexible exchange rate policy, improving the system of agricultural incentives through minimum producer price supports, liberalization of interest rates, and gradual price decontrol) might be inflationary in the short-term. The impact of price decontrol is moderate, as demonstrated by the decontrol of meat prices in early 1987, and only leads to a once-and-for-all jump in its consumer price index. Due to import dependence, especially of the industrial sector, depreciation of the Kenya Shilling also spills over into domestic inflation. The upward revisions in floor agricultural producer prices results in higher food production in the medium- to long-term, which therefore eventually reduces pressure on food prices. One can not doubt the long-run beneficial impact of SAPs, including an eventual reduction in inflationary pressures. But the short-term trade-off between inflation and structural adjustment need to be taken into account in the setting of inflation targets at the design stage of the SAPs. CONCLUSION AND RECOMMENDATIONS 34. The paper has tried to highlight the potential sources of errors in the computation of the CPI, and the dangers of assuming that the Nairobi inflation rates are representative of Kenya as a whole. The base weights used were derived from an urban household budget survey conducted during 1974/75, and therefore does not reflect changes in consumption patterns since then. It is important that new surveys are undertaken to provide revised set of weights for computation of consumer price indices, defining income classes, estimating levels

12

and distribution of incomes, and computing demand elasticities for goods and services. The latter will also be useful in the design of cost-sharing policies in various sectors e.g. education and health. A supplementary survey could be undertaken to determine suitable retail outlets (where various items are sourced) for price collection for each income group. 35. Collection of retail price data is also an issue that the Government needs to address. This will entail increased budgetary resources for the Central Bureau of Statistics to engage in actual purchases of a proportion of items while collecting retail price data, and to undertake regular and meaningful rent surveys, say, every six months. 36. The delays in processing of data from various surveys means that results are sometimes released seven years after a survey is undertaken. To the extent that researchers and Government itself use the results as pertaining to the period of release, this inevitably leads to misreading of the Kenyan economy. It will be useful for the Government to seek donor finance for occasional surveys to ensure timely release of results and to reduce the financial burden on CBS. 37. To reduce the bias in the official CPI, it is recommended that:

a) A new household budget survey be undertaken to redefine base weights and income brackets.

b) Use empirical estimates of income distribution to derive weighted increases in inflation rate in Nairobi for all income classes.

c) Reinstate bargaining and purchasing for selected items in collecting retail prices to be used in the compilation of the CPI.

d) Undertake semi-annual rent surveys to collect rent data. e) Undertake an empirical study of the process of deriving the sectoral and GDP

deflators; and determine the implications of differences between the GDP deflator and the CPI on income re-distribution i.e. between labour/consumers and capital/producers. This can be done as part of monitoring and evaluation of the impact of structural adjustment (price decontrol, cost-sharing policies, etc.) on income distribution.

f) Use household budget survey data to derive new estimates of internationally accepted indicators of income distribution. It should be remembered that the data used by the United Nations and the World Bank are based on 1976 estimates, and have been dropped in the World Development Report 1990 for being out of date.

g) Attempt to develop a national inflation rate reflecting increases in the cost of living for the Kenyan population.

h) Base inflation targets in the structural adjustment programmes on empirical work or pragmatic considerations.

i) Given the budgetary implications of implementing the remedial actions outlined above, CBS should seek donor financing to complement its own resources. It is important to note that the gradual slippage in the quality of retail price data, especially the rent component, is partly explained by declining budgetary allocations for these activities.

13

REFERENCES

Bigsten, Arne, Income Distribution and Growth in a Dual Economy: Kenya 1914-76, Memorandum No. 101, Department of Economics, Gothenburg University, 1987 Kenya, Central Bureau of Statistics, New Lower and Middle Income Cost of Living Indices, 1971: A Description of the Method of Compilation, January 1972 Kenya, Central Bureau of Statistics, Consumer Price Indices, Nairobi, March 1977 Kenya, Central Bureau of Statistics, Wholesale Price Index Numbers (Mainly related to Kenya Manufactured Products), Kenya Statistical Digest, 20(3), September 1981 Kenya, Central Bureau of Statistics, “Rent Surveys”, Kenya Statistical Digest, 21(1), March 1982 Kenya, Central Bureau of Statistics, Economic Survey 1988 (Chapter 3: Rural Household Budget Survey 1981/82), Government Printer, Nairobi, 1988 Kenya, Central Bureau of Statistics, Kenya Symposium on Statistical Surveys: Integration of Population Data into National Development, September 1988 Nagda, S.M., “An Exploration towards an Inflation Index for Kenya”, M.A. Research Paper, Department of Economics, University of Nairobi, June 1985 Nagda, S.M., “An Exploration towards an Inflation Index for Kenya”, in: Kenya Symposium on Statistical Surveys, Central Bureau of Statistics, Ministry of Planning and National Development, 1988 Nagda, S.M., “Consumer Price Index”, in: Proceedings of the Symposium of Producers and Users of Statistics, Central Bureau of Statistics, Ministry of Planning and National Development, 1990 Vandemoortele, J., Social Accounting Matrix: A Tool for Socio-Economic Planning and Analysis, World Employment Programme, International Labour Office, Geneva, 1987 World Bank, World Development Report 1990, Oxford University Press, 1990

14

STATISTICAL ANNEX

Table 1a: Nairobi Consumer Price Indices: Lower Income Table 1b: Average Rates of Change by Commodity Groups: Lower Income (%) Table 2a: Nairobi Consumer Price Indices: Middle Income Table 2b: Average Rates of Change by Commodity Groups: Middle Income (%) Table 3a: Nairobi Consumer Price Indices: Upper Income Table 3b: Average Rates of Change by Commodity Groups: Upper Income (%) Table 4: Percentage Rates of Change of the Consumer Price Index (December to December) Table 5: Rates of Change of Rent, and CPI Excluding Rent Table 6: Simple Average Index of Consumer Prices Table 7: New Definitions of Income Classes (KShs per month)

15

Table 1a: Nairobi Consumer Price Indices: Lower Income (Base: Jan/June 1975 = 100)

Food Beverages/

Tobacco

Clothing/

Footwear

Household

operation

Rent Fuel/

power

Health/

Personal

care

Transport/

communications

Recreation/

entertainment/education

Misc Average

for all

groups

Weight 0.410 0.044 0.099 0.061 0.229 0.037 0.016 0.038 0.041 0.025 1.000

End

Year

1975 106.6 109.9 117.7 102.0 114.0 113.3 99.7 119.0 100.0 107.4 109.6

1976 111.5 129.7 120.9 112.5 124.3 128.3 102.8 138.0 108.7 127.4 118.0

1977 130.7 142.2 144.2 153.8 164.0 137.3 108.9 155.1 118.0 164.5 142.7

1978 147.8 157.4 189.5 171.6 187.7 139.7 116.4 160.9 118.9 168.3 162.0

1979 159.5 173.1 210.0 197.1 205.1 183.2 121.1 165.8 121.9 173.5 177.1

1980 185.3 181.9 240.3 206.6 232.0 198.0 128.1 200.8 123.8 191.0 200.4

1981 219.7 206.0 278.3 237.6 277.6 301.9 162.5 240.6 138.3 224.1 238.9

1982 241.6 267.4 308.4 275.2 314.6 393.0 209.5 282.9 140.1 249.1 270.8

1983 262.3 278.3 377.4 316.8 346.0 394.4 217.8 284.9 148.8 290.9 298.0

1984 303.4 300.0 391.0 351.3 383.6 406.5 259.0 292.5 165.2 315.9 330.5

1985 344.0 321.6 416.9 358.9 423.3 429.4 288.5 326.1 173.2 375.3 364.7

1986 352.5 347.8 429.3 374.6 440.2 454.6 285.4 327.0 192.7 460.1 379.2

1987 369.4 383.3 454.4 413.8 464.9 497.4 299.9 348.6 195.5 488.3 401.7

1988 409.9 419.6 493.0 428.0 511.3 575.7 335.8 360.8 222.6 495.3 440.4

1989 436.1 463.6 519.2 472.8 560.8 753.8 370.4 446.9 223.0 614.5 483.2

1990 501.9 603.0 574.8 557.1 659.3 839.6 442.2 628.2 246.5 866.0 567.9

Source: Central Bureau of Statistics

Table 1b: Average Rates of Change by Commodity Groups: Lower Income (%)

Food Beverages/

Tobacco

Clothing/

Footwear

Household

operation

Rent Fuel/

power

Health/

Personal

care

Transport/

commu.

Recreation/


Misc Average

for all

groups

Weight 0.410 0.044 0.099 0.061 0.229 0.037 0.016 0.038 0.041 0.025 1.000

1976 4.6 18.0 2.7 10.3 9.0 13.2 3.1 16.0 8.7 18.6 7.6

1977 17.2 9.6 19.3 36.7 31.9 7.0 5.9 12.4 8.6 29.1 21.0

1978 13.1 10.7 31.4 11.6 14.5 1.7 6.9 3.7 0.8 2.3 13.5

1979 7.9 10.0 10.8 14.9 9.3 31.1 4.0 3.0 2.5 3.1 9.4

1980 16.2 5.1 14.4 4.8 13.1 8.1 5.8 21.1 1.6 10.1 13.1

1981 18.6 13.2 15.8 15.0 19.7 52.5 26.9 19.8 11.7 17.3 19.3

1982 10.0 29.8 10.8 15.8 13.3 30.2 28.9 17.6 1.3 11.2 13.3

1983 8.6 4.1 22.4 15.1 10.0 0.4 4.0 0.7 6.2 16.8 10.0

1984 15.7 7.8 3.6 10.9 10.9 3.1 18.9 2.7 11.0 8.6 10.9

1985 13.4 7.2 6.6 2.2 10.3 5.6 11.4 11.5 4.8 18.8 10.3

1986 2.5 8.1 3.0 4.4 4.0 5.9 -1.1 0.3 11.3 22.6 4.0

1987 4.8 10.2 5.8 10.5 5.6 9.4 5.1 6.6 1.5 6.1 5.9

1988 11.0 9.5 8.5 3.4 10.0 15.7 12.0 3.5 13.9 1.4 9.6

1989 6.4 10.5 5.3 10.5 9.7 30.9 10.3 23.9 0.2 24.1 9.7

1990 15.1 30.1 10.7 17.8 17.6 11.4 19.4 40.6 10.5 40.9 17.5

Compound

annual rate

1975/90

(%)

10.9 12.0 11.2 12.0 12.4 14.3 10.4 11.7 6.2 14.9 11.6

Source of basic data: Central Bureau of Statistics

16

Table 2a: Nairobi Consumer Price Indices: Middle Income (Base: Jan/June 1975 = 100)

Food Beverages/

Tobacco

Clothing/

Footwear

Household

operation

Rent Fuel/

power

Health/

Personal

care

Transport/

communications

Recreation/


Misc Average

for all

groups

Weight 0.356 0.044 0.119 0.081 0.160 0.044 0.024 0.079 0.045 0.048 1.000

End

Year

1975 104.8 101.5 103.4 102.9 111.1 109.3 102.3 103.0 100.0 112.3 105.5

1976 110.8 111.8 113.4 124.6 120.4 131.3 114.7 117.5 103.0 109.3 114.9

1977 127.1 124.3 128.3 146.4 146.3 135.2 138.3 121.3 106.2 114.0 130.4

1978 141.1 138.2 140.9 153.4 158.7 136.1 144.1 123.0 117.1 115.9 140.9

1979 154.8 156.8 144.8 166.8 175.3 163.5 152.0 165.2 118.1 121.4 155.8

1980 178.0 164.2 160.1 184.0 195.2 175.8 171.0 178.4 126.9 122.9 173.3

1981 211.0 185.8 187.7 229.9 243.5 231.2 243.0 276.7 168.9 157.4 216.2

1982 228.6 246.1 219.9 275.0 288.3 363.9 266.8 345.5 184.3 231.7 255.9

1983 252.6 255.8 244.7 314.6 317.3 374.3 303.4 384.4 204.8 243.3 281.6

1984 287.8 273.4 269.0 335.7 352.1 385.5 324.1 450.9 220.8 255.7 312.6

1985 313.6 291.2 282.5 391.6 392.3 433.3 346.1 575.5 224.4 265.9 348.3

1986 313.5 312.1 303.2 401.6 415.2 444.1 344.1 704.0 234.4 300.1 368.7

1987 336.5 340.2 320.3 449.6 457.0 527.5 380.5 816.0 271.0 300.1 405.8

1988 379.6 371.0 352.0 506.3 509.7 572.7 420.3 913.0 308.2 322.6 452.7

1989 406.9 406.9 373.6 533.9 569.0 657.3 464.6 1159.4 356.4 343.0 505.7

1990 465.3 510.2 419.1 580.7 692.0 684.7 528.0 1615.0 375.7 365.1 600.5


Table 2b: Average Rates of Change by Commodity Groups: Middle Income (%)

Food Beverages/

Tobacco

Clothing/

Footwear

Household

operation

Rent Fuel/

power

Health/

Personal

care

Transport/

commu.

Recreation/


Misc Average

for all

groups

Weight 0.410 0.044 0.099 0.061 0.229 0.037 0.016 0.038 0.041 0.025 1.000

1976 5.7 10.1 9.7 21.1 8.4 20.1 12.1 14.1 3.0 -2.7 8.9

1977 14.7 11.2 13.1 17.5 21.5 3.0 20.6 3.2 3.1 4.3 13.4

1978 11.0 11.2 9.8 4.8 8.5 0.7 4.2 1.4 10.3 1.7 8.1

1979 9.7 13.5 2.8 8.7 10.5 20.1 5.5 34.3 0.9 4.7 10.6

1980 15.0 4.7 10.6 10.3 11.4 7.5 12.5 8.0 7.5 1.2 11.2

1981 18.5 13.2 17.2 24.9 24.7 31.5 42.1 55.1 33.1 28.1 24.8

1982 8.3 32.5 17.2 19.6 18.4 57.4 9.8 24.9 9.1 47.2 18.3

1983 10.5 3.9 11.3 14.4 10.1 2.9 13.7 11.3 11.1 5.0 10.0

1984 13.9 6.9 9.9 6.7 11.0 3.0 6.8 17.3 7.8 5.1 11.0

1985 9.0 6.5 5.0 16.7 11.4 12.4 6.8 27.6 1.6 4.0 11.4

1986 0.0 7.2 7.3 2.6 5.8 2.5 -0.6 22.3 4.5 12.9 5.9

1987 7.3 9.0 5.6 12.0 10.1 18.8 10.6 15.9 15.6 0.0 10.1

1988 12.8 9.1 9.9 12.6 11.5 8.6 10.5 11.9 13.7 7.5 11.5

1989 7.2 9.7 6.1 5.5 11.6 14.8 10.5 27.0 15.6 6.3 11.7

1990 14.4 25.4 12.2 8.8 21.6 4.2 13.6 39.3 5.4 6.4 18.8

Compound

annual rate

1975/90

(%)

10.4 11.4 9.8 12.2 13.0 13.0 11.6 20.1 9.2 8.2 12.3


17

Table 3a: Nairobi Consumer Price Indices: Upper Income (Base: Jan/June 1975 = 100)

Food Beverages/

Tobacco

Clothing/

Footwear

Household

operation

Rent Fuel/

power

Health/

Personal

care

Transport/

communications

Recreation/


Misc Average

for all

groups

Weight 0.250 0.023 0.046 0.085 0.271 0.048 0.029 0.143 0.056 0.049 1.000

End

Year

1975 109.7 107.3 106.5 103.4 109.3 105.1 110.1 112.6 100.0 112.0 108.6

1976 119.2 116.7 116.2 113.6 115.6 123.0 117.0 126.2 114.5 105.7 117.7

1977 139.8 130.4 125.0 137.0 135.1 123.7 147.8 134.9 125.0 128.3 134.8

1978 149.9 145.9 144.0 143.9 145.7 124.7 161.9 148.5 143.5 130.0 145.5

1979 162.0 163.5 149.6 174.1 164.1 145.7 184.1 180.5 145.7 140.7 163.6

1980 185.1 173.7 168.4 181.8 186.1 175.4 210.1 205.7 165.8 169.5 185.4

1981 214.1 203.7 190.0 210.9 221.1 201.6 313.9 250.9 191.2 210.0 220.5

1982 231.6 275.3 212.9 272.3 259.4 311.5 372.0 288.3 205.1 264.7 258.9

1983 263.9 302.3 254.4 294.2 286.1 341.2 412.9 307.0 224.3 278.2 285.6

1984 295.9 318.1 268.7 299.1 307.4 344.4 423.4 327.2 236.7 320.1 306.9

1985 324.7 342.8 285.7 318.7 333.7 400.6 466.0 348.7 241.8 354.7 333.3

1986 331.7 355.7 320.4 333.4 347.0 401.5 477.8 363.6 281.2 359.5 346.7

1987 358.6 390.9 331.9 356.2 382.8 489.3 531.7 407.8 328.4 379.2 382.1

1988 395.8 417.6 348.6 379.8 429.0 575.1 609.8 497.5 351.8 397.0 428.7

1989 435.1 570.9 366.6 399.8 475.4 590.3 661.0 571.9 396.0 447.3 475.0

1990 548.8 751.0 404.6 449.2 565.9 633.6 701.5 724.8 412.5 477.7 565.5


Table 3b: Average Rates of Change by Commodity Groups: Upper Income (%)

Food Beverages/

Tobacco

Clothing/

Footwear

Household

operation

Rent Fuel/

power

Health/

Personal

care

Transport/

commu.

Recreation/


Misc Average

for all

groups

Weight 0.410 0.044 0.099 0.061 0.229 0.037 0.016 0.038 0.041 0.025 1.000

1976 8.7 8.8 9.1 9.9 5.8 17.0 6.3 12.1 14.5 -5.6 8.4

1977 17.3 11.7 7.6 20.6 16.9 0.6 26.3 6.9 9.2 21.4 14.4

1978 7.2 11.9 15.2 5.0 7.8 0.8 9.5 10.1 14.8 1.3 8.0

1979 8.1 12.1 3.9 21.0 12.6 16.8 13.7 21.5 1.5 8.2 12.5

1980 14.3 6.2 12.6 4.4 13.4 20.4 14.1 14.0 13.8 20.5 13.3

1981 15.7 17.3 12.8 16.0 18.8 14.9 49.4 22.0 15.3 23.9 18.9

1982 8.2 35.1 12.1 29.1 17.3 54.5 18.5 14.9 7.3 26.0 17.4

1983 13.9 9.8 19.5 8.0 10.3 9.5 11.0 6.5 9.4 5.1 10.3

1984 12.1 5.2 5.6 1.7 7.4 0.9 2.5 6.6 5.5 15.1 7.5

1985 9.7 7.8 6.3 6.6 8.6 16.3 10.1 6.6 2.2 10.8 8.6

1986 2.2 3.8 12.1 4.6 4.0 0.2 2.5 4.3 16.3 1.4 4.0

1987 8.1 9.9 3.6 6.8 10.3 21.9 11.3 12.2 16.8 5.5 10.2

1988 10.4 6.8 5.0 6.6 12.1 17.5 14.7 22.0 7.1 4.7 12.2

1989 9.9 36.7 5.2 5.3 10.8 2.6 8.4 15.0 12.6 12.7 10.8

1990 26.1 31.5 10.4 12.4 19.0 7.3 6.1 26.7 4.2 6.8 19.1

Compound

annual rate

1975/90

(%)

11.3 13.9 9.3 10.3 11.6 12.7 13.1 13.2 9.9 10.2 11.6


18

Table 4: Percentage Rates of Change of the Consumer Price Index (December to December)

Upper Income Middle Income Lower Income Simple Average

1975

1976 8.4 8.9 7.6 8.3

1977 14.4 13.4 21.0 16.3

1978 8.0 8.1 13.5 9.8

1979 12.5 10.6 9.4 10.8

1980 13.3 11.2 13.1 12.6

1981 18.9 24.8 19.3 21.0

1982 17.4 18.3 13.3 16.4

1983 10.3 10.0 10.0 10.1

1984 7.5 11.0 10.9 9.8

1985 8.6 11.4 10.3 10.1

1986 4.0 5.9 4.0 4.6

1987 10.2 10.1 5.9 8.7

1988 12.2 11.5 9.6 11.1

1989 10.8 11.7 9.7 10.7

1990 19.1 18.8 17.5 18.5

Table 5: Rates of Change of Rent, and CPI Excluding Rent (Annual Change, %)

LOWER INCOME MIDDLE INCOME UPPER INCOME

(i) (ii) (i-ii) (i) (ii) (i-ii) (i) (ii) (i-ii) Excl. Rent Rent Difference Excl. Rent Rent Difference Excl. Rent Rent Difference

1976 7.2 9.0 -1.8 9.1 8.4 0.7 9.4 5.8 3.6

1977 17.5 31.9 -14.5 11.8 21.5 -9.7 13.6 16.9 -3.3

1978 13.1 14.5 -1.3 8.0 8.5 -0.5 8.0 7.8 0.2

1979 9.4 9.3 0.1 10.6 10.5 0.2 12.4 12.6 -0.2

1980 13.1 13.1 0.0 11.2 11.4 -0.2 13.3 13.4 -0.1

1981 19.1 19.7 -0.5 24.8 24.7 0.0 18.9 18.8 0.1

1982 13.3 13.3 0.0 18.3 18.4 -0.1 17.5 17.3 0.2

1983 10.1 10.0 0.1 10.0 10.1 0.0 10.3 10.3 0.0

1984 10.9 10.9 0.1 11.0 11.0 0.1 7.5 7.4 0.0

1985 10.3 10.3 0.0 11.4 11.4 0.0 8.6 8.6 0.0

1986 4.0 4.0 0.0 5.9 5.8 0.1 4.1 4.0 0.1

1987 6.0 5.6 0.4 10.1 10.1 0.0 10.2 10.3 -0.1

1988 9.5 10.0 -0.5 11.5 11.5 0.0 12.2 12.1 0.2

1989 9.7 9.7 0.0 11.7 11.6 0.1 10.8 10.8 0.0

1990 17.5 17.6 0.0 18.1 21.6 -3.5 19.1 19.0 0.0

19

Table 6: Simple Average Index of Consumer Prices (Base: Jan/June 1975 = 100)

Food Beverages/

tobacco

Clothing/

footwear

Hhd

operation

Rent Fuel/

power

Health/

personal

care

Transport/

Commu.

Recreation/

entertain./

education

Misc Average

for all

groups

Weight 33.87 3.70 8.80 7.57 22.00 4.30 2.30 8.67 4.73 4.07 100.00

1975 107.03 106.23 109.20 102.77 111.47 109.23 104.03 111.53 100.00 110.57 107.91

1976 113.83 119.40 116.83 116.90 120.10 127.53 111.50 127.23 108.73 114.13 116.88

1977 132.53 132.30 132.50 145.73 148.47 132.07 131.67 137.10 116.40 135.60 135.94

1978 146.27 147.17 158.13 156.30 164.03 133.50 140.80 144.13 126.50 138.07 149.45

1979 158.77 164.47 168.13 179.33 181.50 164.13 152.40 170.50 128.57 145.20 165.53

1980 182.80 173.27 189.60 190.80 204.43 183.07 169.73 194.97 138.83 161.13 186.37

1981 214.93 198.50 218.67 226.13 247.40 244.90 239.80 256.07 166.13 197.17 225.21

1982 233.93 262.93 247.07 274.17 287.43 356.13 282.77 305.57 176.50 248.50 261.87

1983 259.60 278.80 292.17 308.53 316.47 369.97 311.37 325.43 192.63 270.80 288.39

1984 295.70 297.17 309.57 328.70 347.70 378.80 335.50 356.87 207.57 297.23 316.69

1985 327.43 318.53 328.37 356.40 383.10 421.10 366.87 416.77 213.13 331.97 348.73

1986 332.57 338.53 350.97 369.87 400.80 433.40 369.10 464.87 236.10 373.23 364.89

1987 354.83 371.47 368.87 406.53 434.90 504.73 404.03 524.13 264.97 389.20 396.54

1988 395.10 402.73 397.87 438.03 483.33 574.50 455.30 590.43 294.20 404.97 440.60

1989 426.03 480.47 419.80 468.83 535.07 667.13 498.67 726.07 325.13 468.27 487.93

1990 505.33 621.40 466.17 529.00 639.07 719.30 557.23 989.33 344.90 569.60 578.01

Compound

annual rate

1975/90

(%)

10.90 12.50 10.16 11.54 12.35 13.39 11.84 15.66 8.60 11.55 11.84

Source: Tables 1(a), 2(a) and 3(a)

Table 7: New Definitions of Income Classes (KShs per month)

Maximum Lower Minimum Middle Maximum Middle Minimum Upper

1975 738 738 2,716 2,716

1976 804 804 2,944 2,944

1977 912 912 3,369 3,369

1978 986 986 3,637 3,637

1979 1,091 1,091 4,090 4,090

1980 1,213 1,213 4,636 4,636

1981 1,514 1,514 5,511 5,511

1982 1,791 1,791 6,472 6,472

1983 1,971 1,971 7,140 7,140

1984 2,188 2,188 7,673 7,673

1985 2,438 2,438 8,331 8,331

1986 2,581 2,581 8,668 8,668

1987 2,841 2,841 9,553 9,553

1988 3,169 3,169 10,718 10,718

1989 3,540 3,540 11,874 11,874

1990 4,204 4,204 14,138 14,138