Abstract
Like most Sub-Saharan African countries, Kenya’s economic growth appears to have been primarily driven by factor accumulation. The Selected Issues paper and Statistical Appendix for Kenya examines economic developments and policies. During the last two decades, Kenya has been plagued by pervasive problems of internal conflicts, constitutional crises, and corruption scandals. The governance agenda focuses on several reforms, including upgrading the public budget and financial management systems, strengthening the anticorruption institutions, and improving the judicial framework.
II. Price Dynamics in Kenya During 1995–20041
A. Introduction
1. Kenya’s inflation has recently risen sharply. Annual overall inflation, as measured by the twelve-month percent change in the consumer price index (CPI), rose from 8 percent in September 2003 to 19 percent in September 2004. Underlying inflation (overall inflation excluding food and energy prices) reached 7 percent from 3 percent a year earlier, exceeding the Central Bank of Kenya (CBK)’s target of 5 percent.
2. This chapter examines key determinants of Kenya’s price dynamics during the past decade. A simple econometric model suggests that key determinants of inflation during 1995–2004 have included broad money growth, food crop output, movements of the nominal effective exchange rate (NEER), and the international commodity prices of fuel and energy. Against this background, the recent episode of high inflation appears to have been triggered by the excessively loose monetary conditions, a poor harvest, high energy prices, and a weakening Kenyan shilling.
B. Background
3. The recent episode of high inflation is the most severe since 1995. After peaking at 61 percent in early 1994, inflation declined substantially in 1995 to around 1.6 percent (Figure II.1). Between 1995–2003, Kenya’s inflation averaged around 7 percent per annum, and has been lower than the averages for Sub-Saharan Africa as well as other developing countries (Table II.1). However, inflation has recently accelerated substantially, reaching a historic high since 1995 in September.
Kenya: Inflation, 1995-2004
(12-month percent change)
Citation: IMF Staff Country Reports 2009, 192; 10.5089/9781451821253.002.A002
Kenya: A Comparison on Inflation with Other Countries, 1995-2003
Kenya: A Comparison on Inflation with Other Countries, 1995-2003
| 1995-1999, average | 2000 | 2001 | 2002 | 2003 | 1995-2003, average | |
|---|---|---|---|---|---|---|
| Kenya | 7.0 | 10.0 | 5.8 | 2.0 | 9.8 | 6.9 |
| Tanzania | 16.4 | 6.2 | 5.2 | 4.6 | 4.5 | 11.9 |
| Uganda | 5.4 | 4.5 | –2.0 | 5.7 | 5.1 | 4.6 |
| Sub-Sahara Africa | 22.7 | 16.9 | 15.0 | 12.1 | 12.9 | 19.3 |
| Least Developed Countries | 30.0 | 18.2 | 15.3 | 11.9 | 11.8 | 23.7 |
| Developing Countries | 17.3 | 7.3 | 6.8 | 6.0 | 6.1 | 13.0 |
| World | 8.0 | 4.3 | 4.1 | 3.5 | 3.7 | 6.3 |
Kenya: A Comparison on Inflation with Other Countries, 1995-2003
| 1995-1999, average | 2000 | 2001 | 2002 | 2003 | 1995-2003, average | |
|---|---|---|---|---|---|---|
| Kenya | 7.0 | 10.0 | 5.8 | 2.0 | 9.8 | 6.9 |
| Tanzania | 16.4 | 6.2 | 5.2 | 4.6 | 4.5 | 11.9 |
| Uganda | 5.4 | 4.5 | –2.0 | 5.7 | 5.1 | 4.6 |
| Sub-Sahara Africa | 22.7 | 16.9 | 15.0 | 12.1 | 12.9 | 19.3 |
| Least Developed Countries | 30.0 | 18.2 | 15.3 | 11.9 | 11.8 | 23.7 |
| Developing Countries | 17.3 | 7.3 | 6.8 | 6.0 | 6.1 | 13.0 |
| World | 8.0 | 4.3 | 4.1 | 3.5 | 3.7 | 6.3 |
4. During the past decade, fluctuations in food prices accounted for the bulk of the movements in the CPI (Table II.2). With food and nonalcoholic beverages carrying more than 50 percent of the weight in the CPI basket, food supply conditions play a significant role in Kenya’s price developments. In this connection, more than 60–70 percent of the increases in the CPI during recent months have been attributed to rises in food prices. Underlying inflation, which excludes food, has also risen substantially in recent months. 2
Kenya: Contributions to Overall Inflation by Components, 1995-2003
Kenya: Contributions to Overall Inflation by Components, 1995-2003
| 95-03 | 2004 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | ||
| Food & Nonalcoholic Beverages | 3.9 | 7.9 | 7.8 | 6.1 | 5.5 | 2.7 | 3.4 | 5.7 | 12.0 | 14.6 |
| Alcohol & Tobacco | 0.2 | 0.1 | 0.1 | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.1 | 0.1 |
| Clothing & Footwear | 0.3 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.3 | 0.3 | 0.3 | 0.4 |
| Housing | 0.7 | 0.4 | 0.3 | 0.2 | 0.2 | 0.2 | 0.2 | 0.3 | 0.4 | 0.5 |
| Fuel & Power | 0.5 | 0.0 | 0.1 | 0.0 | 0.1 | 0.2 | 0.4 | 0.5 | 1.2 | 1.5 |
| Household Goods & Services | 0.2 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.2 | 0.2 | 0.2 |
| Medical Goods & Services | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.0 | 0.0 | 0.1 | 0.1 | 0.1 |
| Transport & Communication | 0.4 | 0.2 | 1.1 | 1.3 | 1.3 | 1.1 | 1.1 | 1.2 | 1.3 | 1.3 |
| Recreation & Education | 0.4 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.3 | 0.3 |
| Personal Goods & Services | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.1 |
| Total | 6.9 | 9.1 | 9.9 | 8.3 | 7.6 | 4.7 | 5.9 | 8.5 | 15.8 | 19.0 |
Kenya: Contributions to Overall Inflation by Components, 1995-2003
| 95-03 | 2004 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | ||
| Food & Nonalcoholic Beverages | 3.9 | 7.9 | 7.8 | 6.1 | 5.5 | 2.7 | 3.4 | 5.7 | 12.0 | 14.6 |
| Alcohol & Tobacco | 0.2 | 0.1 | 0.1 | 0.1 | 0.0 | 0.0 | 0.0 | 0.1 | 0.1 | 0.1 |
| Clothing & Footwear | 0.3 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.3 | 0.3 | 0.3 | 0.4 |
| Housing | 0.7 | 0.4 | 0.3 | 0.2 | 0.2 | 0.2 | 0.2 | 0.3 | 0.4 | 0.5 |
| Fuel & Power | 0.5 | 0.0 | 0.1 | 0.0 | 0.1 | 0.2 | 0.4 | 0.5 | 1.2 | 1.5 |
| Household Goods & Services | 0.2 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.2 | 0.2 | 0.2 |
| Medical Goods & Services | 0.1 | 0.1 | 0.1 | 0.1 | 0.1 | 0.0 | 0.0 | 0.1 | 0.1 | 0.1 |
| Transport & Communication | 0.4 | 0.2 | 1.1 | 1.3 | 1.3 | 1.1 | 1.1 | 1.2 | 1.3 | 1.3 |
| Recreation & Education | 0.4 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.2 | 0.3 | 0.3 |
| Personal Goods & Services | 0.1 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.1 | 0.1 |
| Total | 6.9 | 9.1 | 9.9 | 8.3 | 7.6 | 4.7 | 5.9 | 8.5 | 15.8 | 19.0 |
C. Key Determinants of Inflation
Intuitive Reasoning
5. Both demand and supply factors appear to have contributed to the recent spike in inflation (Figure II.2). These factors include:
Low food crop output—The drought experienced in parts of the country during May-September is estimated to have taken a heavy toll on food crop output. The production of maize production, the most important commodity in the diet of the Kenyans, is estimated to have decreased by 14 percent in 2004. 3
High energy prices—While fuel and energy directly accounts for less than 5 percent in the CPI basket, the effects of the surge in world energy prices could have been considerable as the indirect effects, such as the increased cost of transporting food, have been significant.
Weakening Kenyan Shilling—The Kenya shilling has depreciated in nominal effective terms by around 10 percent in the year to September 2004, thereby putting upward pressure on import prices and inflation.
Excessively loose monetary conditions—The loosening of monetary policy since July 2003 has resulted in sharply negative real interest rates and a 15 percent expansion in broad money (M3X) in the twelve months preceding September 2004. 4
Kenya: Potential Factors Underpinning Inflation, 1995-2004
Citation: IMF Staff Country Reports 2009, 192; 10.5089/9781451821253.002.A002
Source: Central Bureau of Statistics, Central Bank of Kenya, and staff estimatesD. Econometric Analysis
6. A simple econometric model has been devised to capture the potential impact of these factors on price dynamics in Kenya. Specifically, the following single reduced-form equation was estimated using quarterly data for the period 1995–2004:5
inf=f(lagged inf, lagged maize, lagged energy, lagged neer, lagged money)
where inf is CPI inflation; maize is the annual percent change of the gross production of maize;6 energy is the annual percent change of the world energy commodity price index; neer is the annual percent change of Kenya’s nominal effective exchange rate; money is the annual percent change of broad money (M3X). 7
7. The model is estimated using ordinary least squares. First, a general model encompassing all potential factors that may have affected inflation and their lags was estimated. Second, variables and lags that were not statistically significant were eliminated sequentially. 8
8. The results for the final specification are presented in Table II.3. The results suggest that the growth rate of broad money, the world commodity price index of fuel and energy, the nominal effective exchange rate, and the gross production of maize are important determinants of inflation. Specifically, the results suggest that:
A one percentage point increase in the growth of broad money has been associated with an increase in inflation by 0.34 percentage points with a half-year lag;
A one percentage point decrease in the growth of gross production of maize has been associated with an increase in inflation by 0.03 percentage points with a half-year lag;
A one percentage point increase in the world commodity price index of fuel and energy has been associated with an increase in inflation by 0.05 percentage point with a quarter lag; and
A one percentage point depreciation of Kenya’s nominal effective exchange rate has been associated with an increase in inflation by 0.06 percentage with a one-year lag.
Kenya: OLS Estimates of a Reduced-Form Inflation Equation, 1995-2004
Kenya: OLS Estimates of a Reduced-Form Inflation Equation, 1995-2004
| constant | money | energy | maize | NEER | |
|---|---|---|---|---|---|
| Overall Inflation | 4.52* | 0.34* | 0.05* | –0.03* | –0.06* |
| (7.01) | (5.55) | (2.46) | (3.61) | (2.47) | |
| Number of quarters lagged | … | 2 | 1 | 2 | 4 |
| Number of observations: 39 | |||||
| R-squared: 0.66 | |||||
| Prob(F-statistic): 0.00 |
Kenya: OLS Estimates of a Reduced-Form Inflation Equation, 1995-2004
| constant | money | energy | maize | NEER | |
|---|---|---|---|---|---|
| Overall Inflation | 4.52* | 0.34* | 0.05* | –0.03* | –0.06* |
| (7.01) | (5.55) | (2.46) | (3.61) | (2.47) | |
| Number of quarters lagged | … | 2 | 1 | 2 | 4 |
| Number of observations: 39 | |||||
| R-squared: 0.66 | |||||
| Prob(F-statistic): 0.00 |
9. Movements of explanatory variables, coupled with coefficient estimates presented in Figure II.3 suggests that broad money growth is a key factor underpinning inflation. The 18 percent growth in broad money in 1996 appears to have been the main cause of the 15 percent inflation in early 1997. Furthermore, disciplined management of monetary aggregates was partly responsible for the low inflation during 2001–02. Likewise, the excessively loose monetary policy since the second half of 2003 has contributed to the recent inflation.
Kenya: Impact of the Regression Variables on Inflation, 1996-2004
Citation: IMF Staff Country Reports 2009, 192; 10.5089/9781451821253.002.A002
Source: Staff estimates.Note: The figures show the impact of the variables on inflation. The impact is measured by the product of the coefficient estimates of the factors and their magnitude.10. Factors other than money have also played an important role in determining inflation during the past decade. In particular, the double-digit inflation experienced in 2000 occurred during a period of relatively moderate growth in money; low crop production, a weakening currency, and a high growth rate of energy prices appears to have been the main contributing factors to high inflation during the period. As regards recent inflation, a poor harvest, high energy prices, and a weakening Kenya shilling were also main contributing factors.
E. Policy Implications
11. Looking ahead, the overriding objective of monetary policy should be to maintain price stability. While some of the factors contributing to high inflation, such as the high energy prices and the poor harvest of food crops, were beyond the control of the CBK, the CBK could have mitigated inflation by adopting less expansionary monetary policy. Against this background, with a view to curbing inflation, the monetary program under the Fund’s Poverty Reduction and Growth Facility envisages a cut in broad money growth to 7.5 percent during 2004/05 from 13 percent in the previous fiscal year. 9
This chapter was prepared by Kevin C. Cheng (AFR).
Three concepts of underlying inflation are currently used for Kenya. The Central Bureau of Statistics of Kenya compiles a measure that excludes only food and nonalcoholic beverages (FNB). In addition to excluding FNB, the underlying inflation used by the Central Bank of Kenya to guide monetary policy, excludes fuel and power (FP) as well as transport and communication (TC). The underlying inflation presented in the Fund’s Staff Reports excludes FNB as well as FP while including TC.
Apart from its significance in the Kenyan diet, maize production is also a good proxy for crop production generally, because adverse weather with a significant impact on maize production usually also affects the output of other food crops, such as wheat and beans.
In this paper, the growth rate of M3X in terms of the current exchange rate is used. The Staff Report presents money growth rates in terms of a constant program exchange rate.
The sample is truncated in 1995 because prior data cover a period of extremely high inflation peaking at over 60 percent in early 1994. Extending the data to an earlier period could potentially distort the estimates for the later period, which is the focus of this paper.
Maize is included in the regression owing to its significance in the Kenyan diets. This variable is preferred to a more aggregate measure, such as the total agricultural production, which includes a sizable amount of commodities meant for exports, such as tea, coffee, and horticulture that do not carry significant weights in the CPI basket.
For each variable included in the regression, an Augmented Dickey-Fuller test rejects the hypothesis of a unit root.
Fiscal variables have also been used initially, but were later dropped because of their statistical insignificance.
Growth rate is in terms of a constant program exchange rate.
