The Gross Domestic Product (GDP) in Kenya was worth 124.50 billion US dollars in 2024, according to official data from the World Bank. The GDP value of Kenya represents 0.12 percent of the world economy. This dataset provides - Kenya GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

As of 2020, agriculture, forestry, and fishing was the industry with the highest contribution to Kenya's Gross Domestic Product (GDP), with a **** percent share, followed by transport (**** percent) and real estate (*** percent). Sectors such as trade, manufacturing, and construction contributed each approximately ***** percent to the country's GDP.

This statistic shows the share of economic sectors in the gross domestic product (GDP) in Kenya from 2013 to 2023. In 2023, the share of agriculture in Kenya's gross domestic product was 21.81 percent, industry contributed approximately 16.86 percent and the services sector contributed about 55.42 percent.

The Gross Domestic Product (GDP) in Kenya expanded 5 percent in the second quarter of 2025 over the same quarter of the previous year. This dataset provides the latest reported value for - Kenya GDP Annual Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Agriculture had the largest contribution to Kenya's economy in the second quarter of 2024. The sector added roughly **** trillion Kenyan shillings (KSh), approximately ************* U.S. dollars, to the country's Gross Domestic Product (GDP). The amount corresponded to around ** percent of the total contribution of all industries to the economy. In the same period, the Kenyan GDP at market prices was measured at roughly *** trillion KSh (**** billion U.S. dollars).

Kenya KE: GDP: % of Manufacturing: Textiles and Clothing data was reported at 13.911 % in 2013. This records an increase from the previous number of 4.359 % for 2012. Kenya KE: GDP: % of Manufacturing: Textiles and Clothing data is updated yearly, averaging 8.787 % from Dec 1963 (Median) to 2013, with 51 observations. The data reached an all-time high of 13.911 % in 2013 and a record low of 3.723 % in 2009. Kenya KE: GDP: % of Manufacturing: Textiles and Clothing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Gross Domestic Product: Share of GDP. Value added in manufacturing is the sum of gross output less the value of intermediate inputs used in production for industries classified in ISIC major division D. Textiles and clothing correspond to ISIC divisions 17-19.; ; United Nations Industrial Development Organization, International Yearbook of Industrial Statistics.; ;

Kenya KE: GDP: Growth: Gross Value Added: Industry: Manufacturing data was reported at 0.185 % in 2017. This records a decrease from the previous number of 2.689 % for 2016. Kenya KE: GDP: Growth: Gross Value Added: Industry: Manufacturing data is updated yearly, averaging 4.376 % from Dec 1965 (Median) to 2017, with 53 observations. The data reached an all-time high of 32.868 % in 1972 and a record low of -4.320 % in 1970. Kenya KE: GDP: Growth: Gross Value Added: Industry: Manufacturing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Gross Domestic Product: Annual Growth Rate. Annual growth rate for manufacturing value added based on constant local currency. Aggregates are based on constant 2010 U.S. dollars. Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3.; ; World Bank national accounts data, and OECD National Accounts data files.; Weighted average; Note: Data for OECD countries are based on ISIC, revision 4.

Kenya recorded a Government Debt to GDP of 65.50 percent of the country's Gross Domestic Product in 2024. This dataset provides - Kenya Government Debt To GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Kenya's Gross Domestic Product (GDP) grew by 4.6 percent in the second quarter of 2024. Among sectors, accommodation and food services had the strongest performance, with quarterly growth of 26.6 percent. Financial and insurance sectors followed, registering a 5.1 percent growth rate. On the other hand, the construction sector had a negative growth rate of -2.9 percent.

Time series data for the statistic Real_GDP_Per_Capita_Constant_2015_USD and country Kenya. Indicator Definition:GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 U.S. dollars.The statistic "Real GDP Per Capita Constant 2015 USD" stands at 1,853.09 United States Dollars as of 12/31/2024, the highest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.47 percent compared to the value the year prior.The 1 year change in percent is 2.47.The 3 year change in percent is 9.08.The 5 year change in percent is 12.60.The 10 year change in percent is 27.75.The Serie's long term average value is 1,229.33 United States Dollars. It's latest available value, on 12/31/2024, is 50.74 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1961, to it's latest available value, on 12/31/2024, is +163.02%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

Kenya KE: GDP: % of Manufacturing: Food, Beverages and Tobacco data was reported at 39.744 % in 2013. This records an increase from the previous number of 32.870 % for 2012. Kenya KE: GDP: % of Manufacturing: Food, Beverages and Tobacco data is updated yearly, averaging 36.205 % from Dec 1963 (Median) to 2013, with 51 observations. The data reached an all-time high of 48.386 % in 1996 and a record low of 28.525 % in 2008. Kenya KE: GDP: % of Manufacturing: Food, Beverages and Tobacco data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Gross Domestic Product: Share of GDP. Value added in manufacturing is the sum of gross output less the value of intermediate inputs used in production for industries classified in ISIC major division D. Food, beverages, and tobacco correspond to ISIC divisions 15 and 16.; ; United Nations Industrial Development Organization, International Yearbook of Industrial Statistics.; ;

Time series data for the statistic GDP (constant 2015 US$) and country Kenya. Indicator Definition:GDP at purchaser's prices is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in constant 2015 prices, expressed in U.S. dollars. Dollar figures for GDP are converted from domestic currencies using 2015 official exchange rates. For a few countries where the official exchange rate does not reflect the rate effectively applied to actual foreign exchange transactions, an alternative conversion factor is used.The indicator "GDP (constant 2015 US$)" stands at 104.58 Billion usd as of 12/31/2024, the highest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 4.50 percent compared to the value the year prior.The 1 year change in percent is 4.50.The 3 year change in percent is 15.66.The 5 year change in percent is 24.10.The 10 year change in percent is 56.55.The Serie's long term average value is 36.74 Billion usd. It's latest available value, on 12/31/2024, is 184.64 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1961, to it's latest available value, on 12/31/2024, is +1,758.24%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

As of April 2025, South Africa's GDP was estimated at over 410 billion U.S. dollars, the highest in Africa. Egypt followed, with a GDP worth around 347 billion U.S. dollars, and ranked as the second-highest on the continent. Algeria ranked third, with nearly 269 billion U.S. dollars. These African economies are among some of the fastest-growing economies worldwide. Dependency on oil For some African countries, the oil industry represents an enormous source of income. In Nigeria, oil generates over five percent of the country’s GDP in the third quarter of 2023. However, economies such as the Libyan, Algerian, or Angolan are even much more dependent on the oil sector. In Libya, for instance, oil rents account for over 40 percent of the GDP. Indeed, Libya is one of the economies most dependent on oil worldwide. Similarly, oil represents for some of Africa’s largest economies a substantial source of export value. The giants do not make the ranking Most of Africa’s largest economies do not appear in the leading ten African countries for GDP per capita. The GDP per capita is calculated by dividing a country’s GDP by its population. Therefore, a populated country with a low total GDP will have a low GDP per capita, while a small rich nation has a high GDP per capita. For instance, South Africa has Africa’s highest GDP, but also counts the sixth-largest population, so wealth has to be divided into its big population. The GDP per capita also indicates how a country’s wealth reaches each of its citizens. In Africa, Seychelles has the greatest GDP per capita.

This dataset provides values for GDP reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Kenya KE: GDP: % of Manufacturing: Other Manufacturing data was reported at 33.936 % in 2013. This records a decrease from the previous number of 54.494 % for 2012. Kenya KE: GDP: % of Manufacturing: Other Manufacturing data is updated yearly, averaging 37.740 % from Dec 1963 (Median) to 2013, with 51 observations. The data reached an all-time high of 59.203 % in 2008 and a record low of 30.524 % in 1996. Kenya KE: GDP: % of Manufacturing: Other Manufacturing data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank: Gross Domestic Product: Share of GDP. Value added in manufacturing is the sum of gross output less the value of intermediate inputs used in production for industries classified in ISIC major division D. Other manufacturing, a residual, covers wood and related products (ISIC division 20), paper and related products (ISIC divisions 21 and 22), petroleum and related products (ISIC division 23), basic metals and mineral products (ISIC division27), fabricated metal products and professional goods (ISIC division 28), and other industries (ISIC divisions 25, 26, 31, 33, 36, and 37). Includes unallocated data. When data for textiles, machinery, or chemicals are shown as not available, they are included in other manufacturing.; ; United Nations Industrial Development Organization, International Yearbook of Industrial Statistics.; ;

Kenya KE: GDP: % of Manufacturing: Machinery and Transport Equipment data was reported at 5.542 % in 2013. This records an increase from the previous number of 2.559 % for 2012. Kenya KE: GDP: % of Manufacturing: Machinery and Transport Equipment data is updated yearly, averaging 5.542 % from Dec 1963 (Median) to 2013, with 51 observations. The data reached an all-time high of 14.267 % in 1964 and a record low of 2.131 % in 2005. Kenya KE: GDP: % of Manufacturing: Machinery and Transport Equipment data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank: Gross Domestic Product: Share of GDP. Value added in manufacturing is the sum of gross output less the value of intermediate inputs used in production for industries classified in ISIC major division D. Machinery and transport equipment correspond to ISIC divisions 29, 30, 32, 34, and 35.; ; United Nations Industrial Development Organization, International Yearbook of Industrial Statistics.; ;

Abstract

In 1999, the International Center for Economic Growth (ICEG) organised a national baseline survey of micro and small enterprises in Kenya, in collaboration with the Central Bureau of Statistics (CBS) and K-Rep Holdings Limited. The survey was conducted from March 1999 through October 1999. The primary objectives of the survey were two-fold: First, to update and expand on the information generated in the 1993 and 1995 surveys. And second, to improve the reliability of estimates on the MSE sectors contribution to the Kenyan economy in terms of employment incomes, and gross domestic product.

The first specific objective of the study was to measure the size and magnitude of the sector by estimating the total number of micro and small enterprises in the country. Estimates of the overall magnitude of the MSE sector become critical in analyzing the structure of the MSE sector in Kenya in order to understand the various distribution aspects of type of activity, rural-urban distribution, enterprise size and gender composition. This information is important for the appropriate design of policy instruments as well as in targeting various support interventions for the sector.

In addition, the survey assesses the contribution of the sector to income and analyses production dynamics through an estimation of wages, entrepreneurs income value added and accounts by activity size, gender distribution etc. This assessment is particularly useful considering the prominent role attributed to the sector in terms of income generation for the poor (poverty alleviation). The measurement of value added should establish the extent to which the sector generates profits for re-investment, while an estimation of wages informs about the cost of labour, and by implication, the sector's competitiveness.

The 1999 survey also assesses the overall size and contribution of the MSE sector to the national economy by conducting a macroeconomic estimation of the total labour force and contribution to GDP. The survey analyses issues of entrepreneurship and business characteristics in the context of demand and supply of business support services including credit, infrastructure (water, electricity, roads and telephone), training, and technology Finally, the 1999 survey assesses business constraints, business entry and closures and conclusions.

Geographic coverage

The survey covered all the districts in Kenya. The data representativeness are at the following levels -National -Urban/Rural -Provincial -District

Analysis unit

Households Indviduals within Households Community

Kind of data

Sample survey data [ssd]

Sampling procedure

The usual sampling procedure m Kenya consists of a randomized selection of clusters corresponding to enumeration areas (or a division of them) within the master sample with a probability equivalent to the size m number of households in the selected clusters all households are interviewed The sample for the 1999 survey was based on the National Sample Survey and Evaluation Programme (NASSEP) III sampling frame of the Central Bureau of Statistics developed from the 1989 Population and Housing Census The NASSEP III sampling frame is a two-stage stratified cluster sample design with individual districts forming the strata.

In the creation of the NASSEP I11 sampling frame the first stage of sampling involved selection of enumeration areas (EAs) from the 1989 population census within the stratum forming the primary sampling units (PSUs) This master sample corresponds to the task of one single enumerator during the population census For sampling purposes the EAs are split into several clusters of approximately 100 households The master sample is made of 1,300 clusters and the 146 selected clusters for the 1999 National MSE Baseline Survey represent 11 2% of the master sample.

While planning for the sample selection for the 1999 survey consideration was given to combining the features of the previous two surveys (see Annex V) with provisions for possible modification to formulate a sampling scheme that would provide accurate estimates of the characteristics of the MSEs in the country. From the objectives of this survey it was expected that the clusters covered in the 1993 MSE survey would be included (for follow up purposes) as well as the industrial and commercial areas of the major towns for a more appropriate coverage of small and medium enterprises However it was finally decided not to follow these orientations because sample selection would not then meet the statistical requirements of randomization it was then decided to do a fresh random sample to avoid problems of coherence aggregation at national level and respondent fatigue.

Usually the selection of clusters (or EAs) is based on a preliminary stratification to distinguish the several strata m the country The need for stratification arises from the &verse economic and demographic characteristics in the various parts of the country The grouping of identical units into one stratum results in a homogeneous set, the strata differing from each other as much as possible This results in increased precision of the estimates of the characteristics of the population as the variance is substantially reduced.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

The 1999 survey questionnaire collected information on revenue, value added and income by reconstituting simplified accounts for the enterprise, in conformity with the System of National Accounts (SNA). Recording expenditures in parallel with revenues and income opens the door to the possibility for cross-checking of responses in the field as well as once the questionnaire is being supervised or at data entry where purchases of raw materials or goods cannot exceed the revenues unless stocks at end of year are much higher than at start. Furthermore extreme values for revenues and incomes were thoroughly examined during data cleaning and appropriately corrected for by returning to the questionnaire and confronting the responses to other information given by the respondent (in particular responses to total sales net income and normal returns in section 7 of the questionnaire giving room to comparisons between indirect and direct responses which proved to be under-estimated by a factor 2 in Tunisian surveys for example) In addition, the reference to standard deviation and median values has been made as often as possible in the report.

This dataset provides values for GDP PER CAPITA reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Kenya Agriculture

Agriculture is the engine of economic growth in Kenya. About 75% of Kenyans earn all or part of their income from this sector. Agriculture accounts for 33% of the nation's gross domestic product (GDP). Despite continuous population growth, agricultural productivity has stagnated in recent years. Only 20% of Kenyan land is suitable for farming and that land is not utilized efficiently. Recurrent crises such as drought add to agricultural challenges. In response to these challenges, USAID is increasing productivity for smallholder farmers.

Summary

The dataset contains a summary of the distribution of households practicing agriculture by county and sub-county level. Kenya currently has 47 counties (states) which contain several sub-counties. The dataset is based on the previous census enumeration done in 2019.

Content

The first row contains the total numbers for the whole country. i.e the row with "kenya" entry under County column. The dataset has 42 columns representing the county and corresponding number of household involved in production of that crop.

Acknowledgements

Africa's Largest Volunteer Driven Open Data Platform OpenAFRICA aims to be the largest independent repository of open data on the African continent. Also the open data portal in Kenya http://www.opendata.go.ke/ gave out quite some insight

Inspiration

What questions do you want to see answered?

• Showcase the impact of data science in agricultural resource planning
• Explolatory Data Analysis of the current crop production distribution
• Formulaion of strategic food reserve policies based on field data Any other purpose of course

Agricultural activities have been the industry with the highest contribution to the Gross Domestic Product (GDP) in Kenya since 2016. In 2020, agriculture, forestry, and fishing added **** percent to the total GDP. The share has overall increased during the period observed, meaning a higher economic dependency on the agricultural industry.