4. Expenditure and poverty

Transcription

1 Exploring Kenya s Inequality 4. Expenditure and poverty 3.1 Consumption expenditure Household welfare is highly correlated with income. Household incomes are not easy to measure, because they are not always a true reflection of actual incomes, but household consumption expenditure is usually a reliable indicator. Individual expenditures have historically been shown to be correlated with income level. Household surveys have therefore traditionally captured data on expenditures as a proxy for estimating incomes. Consumption expenditure on a determined food basket is used to estimate the poverty line. Household consumption expenditure in table 3.1 reveals inequalities both nationally and across rural and urban areas. For purposes of analyzing inequality, we divide the population into quintiles, each representing 20 percent of all the households in Kenya, from the lowest to highest expenditures. The consumption expenditure share of the fifth quintile with the highest expenditures of KSh7,200 or above is 3.6 times more than that of the first quintile with expenditures of KSh 1,440 or below. The disparities in consumption expenditure are more pronounced in urban areas with expenditures in the fifth quintile being 121 times more than the first quintile. Rural areas have minimal differences. However, when we look at the population that is in each decile across areas, there are big disparities between rural and urban areas; 44.6 percent of the total rural population is in the first quintile, while only 2.6 percent of urban population is in the first quintile. This is in comparison to the fifth decile with populations of 1.5 percent and 34.1 percent in rural and urban areas, respectively. This shows that there are large income differentials between urban and rural areas. When we separate the rural and urban areas and look at inequality within each, we see that it is not all that different as illustrated in table 3.2. In rural areas, the ratio of the top quintile to the bottom quintile is 6.4 while that of urban areas is 6.6. Inequalities in consumption expenditure are pronounced across counties (see table 3.1). Nairobi, Mombasa and Kiambu counties display significant differences with the 5th quintile spending more than the first by 691 times in Nairobi, 75 in Mombasa and 20 Kiambu. These three counties show a skewed distribution of expenditures compared to the other seven top ranked counties. In eight of the bottom 10 counties at least 50 percent of their population is in the bottom 1st quintile with expenditures of KSh 1,440 or below compared to only 0.6 percent of the population in Nairobi County. 32 A PUBLICATION OF KNBS AND SID

2 Pulling Apart or Pooling Together? Table 3.1: National and county monthly expenditure deciles distribution * by share, proportion of households, and population CONSUMPTION EXPENDITURE SHARE PER QUINTILE NUMBER OF INDIVIDUALS PER QUINTILE NUMBER OF HOUSEHOLDS PER QUINTILE 1st 2nd 3rd 4th 5th 1st 2nd 3rd 4th 5th 1st 2nd 3rd 4th 5th Name Ksh. Below 1,440 Ksh. 1,441-2,840 Ksh. 2,841-3,880 Ksh. 3,881-7,200 Ksh. Above 7,200 Ksh. Below 1,440 Ksh. 1,441-2,840 Ksh. 2,841-3,880 Ksh. 3,881-7,200 Ksh. Above 7,200 Total Ksh. Below 1,440 Ksh. 1,441-2,840 Ksh. 2,841-3,880 Ksh. 3,881-7,200 Ksh. Above 7,200 Total Kenya ,565, ,469,875 Rural ,843, ,239,316 Urban ,721, ,230,559 Top 10 counties Nairobi ,068, ,202 Mombasa , ,912 Kiambu ,601, ,106 Kisumu , ,792 Lamu , ,026 Nakuru ,562, ,448 Machakos ,084, ,791 Uasin-Gishu , ,981 Nyeri , ,439 Kajiado , ,063 Median county Kisii ,139, ,416 Bottom 10 counties Narok , ,721 33

3 Exploring Kenya s Inequality Kitui , ,933 Bomet , ,164 Makueni , ,047 Marsabit , ,536 Samburu , ,353 West Pokot , ,428 Mandera , ,602 Turkana , ,004 Wajir , ,602 *The total may not add up to 100 for some due to aggregation NB: Counties have been ranked by proportion with highest ratio between 1st and 5th quintile 34 A PUBLICATION OF KNBS AND SID

4 Pulling Apart or Pooling Together? Table 3.2: Average rural and urban consumption expenditure deciles RURAL HOUSEHOLDS Quintile Expenditure share % of Households % of Population Ksh Ksh Ksh Ksh Ksh Total/Number 100 5,239,316 25,843,775 URBAN HOUSEHOLDS Quintile Expenditure share % Households % of Population Ksh Ksh Ksh Ksh Ksh Total/Number 100 3,230,559 11,721, Poverty line Poverty in this report is measured using estimated consumption expenditures. The poverty line is a threshold below which people are deemed poor. In 2005/06, the poverty line was estimated at KSh 1,562 and KSh 2,913 per adult equivalent per month for rural and urban households respectively. Nationally, 45.2 percent of the population lives below the poverty line (2009 estimates), down from 46 percent in 2005/06. At county level, there are significant inequalities in poverty. The proportion of individuals below the poverty line in Turkana (87.5 percent), Mandera (85.8 percent) and Wajir (84.2 percent) is four times that of Nairobi, which has the lowest poverty at 21.8 percent (see table 3.3), and almost double that of Laikipia (47.9 percent), the median county. The national inequalities in poverty across counties are depicted in figure 3.1, where the incidence of poverty is higher in the northern and coastal parts of the country and significantly lower in others especially in Nairobi and the central parts of the country. These differences are more pronounced at constituency level (see table 3.3) whereby the proportion of the population below the poverty line in the poorest constituency (Turkana East) with 93.1 percent of its population living below the poverty line is nine times greater than the least poor constituency (Embakasi West) where only 10.3 percent of the population live below the poverty line. In total, 25 counties and 148 constituencies have more than 45.2% of their populations below the national poverty line. 35

5 Exploring Kenya s Inequality Figure 3.1: Proportion of population below poverty line (headcount index) by county 36 A PUBLICATION OF KNBS AND SID

6 Pulling Apart or Pooling Together? Table 3.3: Counties ranking by poverty indicators Name Headcount index: percentage of individuals below poverty line Standard error of headcount index County Poverty gap as percent of poverty line Standard error of poverty gap County Severity of poverty as percent of poverty line Standard error of severity of poverty Kenya Rural Urban Top 10 counties Turkana Tana River Tana River Mandera Kwale Kwale Wajir Mandera Kilifi Marsabit Wajir Wajir Tana River Kilifi Mandera Samburu Turkana Turkana Kwale Marsabit Lamu West Pokot Isiolo Marsabit Isiolo Garissa Garissa Taita- Makueni Samburu Taveta Median county Laikipia Laikipia Baringo Bottom 10 counties Mombasa Siaya Kiambu Uasin-Gishu Nandi Nyeri Trans- Nakuru Narok Nzoia Murang a Nyeri Nandi Lamu Nakuru Kirinyaga Meru Meru Narok Uasin- Nyeri Gishu Meru Kirinyaga Kiambu Nakuru Uasin- Kiambu Kirinyaga Gishu Nairobi Nairobi Nairobi

7 Exploring Kenya s Inequality Table 3.4: Constituencies ranking by poverty indicators Constituency Headcount index: percentage of individuals below poverty line Standard error of headcount index Constituency Poverty gap as percent of poverty line Standard error of poverty gap Constituency Severity of poverty as percent of poverty line Standard error of severity of poverty Top 10 constituencies Turkana East Magarini Kinango Turkana North Kinango Magarini Mandera South Ganze Lunga Lunga Loima Lunga Lunga Ganze Eldas Garsen Bura Turkana South Bura Garsen Mandera North Galole Galole Wajir North Mandera South Matuga Banissa Wajir North Malindi Mandera West Tarbaj Kilifi North Median constituency Kisumu West Kitui Central Kitui Rural Bottom 10 constituencies Kisumu Central Kapseret Kapseret Githunguri Kajiado North Langata Starehe Starehe Nakuru Town East Starehe Nakuru Town West Kamukunji Embakasi East Nakuru Town East Nakuru Town West Embakasi East Kamukunji Embakasi East Kamukunji Kajiado North Kajiado North Makadara Makadara Makadara Embakasi Central Embakasi West Embakasi Central Embakasi West Embakasi Central Embakasi West A PUBLICATION OF KNBS AND SID

8 Pulling Apart or Pooling Together? 3.3 Poverty gap The poverty gap provides an indicator of the depth of poverty. It is therefore an important indicator for fiscal decentralisation because it can be used to estimate the resources required in order to bring the population above the poverty line. The poverty gap is expressed as the mean of the ratio of expenditure shortfalls to the poverty over all the households in a given area expressed as a percentage. Poverty gap as a percentage of the poverty line in Kenya is at 12.2 percent. The poverty gap is lowest in counties around the centre of the country and parts of the southeast (see figure 3.2). It is highest in Tana River, Kwale and Mandera Counties at 46.1 percent, 41.8 percent and 32.4 percent, respectively (see table 3.3). These huge disparities in the poverty gap illustrate the variation in the extent of county requirements to pull the population out of poverty. 39

9 Exploring Kenya s Inequality Figure 3.2: Poverty gap as a percentage of poverty line 40 A PUBLICATION OF KNBS AND SID

10 Pulling Apart or Pooling Together? 3.4 Gini coefficient The Gini coefficient is a commonly used measure of inequality in incomes among populations. It varies between 0 reflecting complete equality and 1 indicating complete inequality. The Gini coefficient is based on the Lorenz curve which compares income across the entire population of an area. It is a useful measure because it incorporates all of the information available from a particular area. The Gini coefficient of inequality, using consumption expenditures per capita, is presented in table 3.5. The national Gini coefficient is estimated at This reflects a high level of inequality. When we look at the Gini coefficient within rural areas, it is In urban areas, it is Thus while inequality in urban areas appear similar to that of rural areas, rural areas have a disproportionately higher population at 68.8 percent (compared to 31.2 percent in urban areas) but control only 45.4 percent of the consumption expenditure. Once we bring them together, however, the inequality levels jump. The leap in national Gini coefficient is due to income gaps between rural and urban areas. A decrease in the income gap between urban and rural areas is therefore a necessary condition to reduce the national Gini coefficient. This is highlighted in the consumption expenditure patterns of rural and urban areas that reflect the prevailing income gaps between the populations in the two areas. As illustrated in table 3.5, household expenditure in Kenya averages KSh 3,440 per adult equivalent 1. In rural areas, household expenditure per adult equilvalent per month is KSh 2,270 and in urban areas it is KSh 6,010. The Lorenz curve in figure 3.3 compares inequality between rural and urban areas. Rural areas show slightly higher levels of inequality than urban areas in counties. This can be explained by the weight of counties with high differences in inequality between their urban and rural areas. Figure 3.3: The Lorenz Curve Counties with the highest income inequalities are Tana River, Kwale and Kilifi with Gini coefficients of 0.617, and respectively (Table 3.5). These are situated along the coastal area of the country as seen in figure 3.4. The most equal counties are Turkana, Narok, and West Pokot with Gini coefficients of 0.283, 0.315, and 0.318, respectively. This data shows that high poverty is not equivalent to high inequality, as the most equal counties, such as Turkana, are also among the poorest. 1 This is basically the idea that every person needs different levels of consumption because of their age, gender, height, weight etc and therefore we take this into acount to create an adult equivalent based on the average needs of the different populations 41

11 Exploring Kenya s Inequality Figure 3.4: Gini coefficient by county 42 A PUBLICATION OF KNBS AND SID

12 Pulling Apart or Pooling Together? Table 3.5: Gini coefficient: national, counties and constituencies NATIONAL Name Pop. Share Mean Consump. Share Gini Kenya 1 3, Rural , Urban , COUNTIES CONSTITUENCIES County Pop. Share Mean Consump. Share Gini Constituency Pop. Share Mean Top 10 counties Top 10 constituencies Consump. Share Gini Tana River , Teso South , Kwale , Galole , Kilifi , Bura , Lamu , Garsen , Migori , Magarini , Busia , Kinango , Taita-Taveta , Kilifi North , Garissa , Lunga Lunga , Isiolo , Malindi , Bungoma , Kaloleni , Median County Median constituency Kericho , Samburu West , Bottom 10 counties Bottom 10 constituencies Nandi , Emurua Dikirr , Nairobi , Laisamis ,

13 Exploring Kenya s Inequality Bomet , Kacheliba , Kiambu , Banissa , Samburu , Tiaty , Mandera , North Horr , Wajir , Turkana South , West Pokot , Loima , Narok , Turkana North , Turkana , Turkana East , The analysis from this chapter indicates that income inequality is somewhat higher among rural households than urban households. The reduction in national income inequality can be achieved through addressing the significant income differentials between urban and rural areas. At county level, the most unequal counties are not the poorest, but they do tend to be unequal in both rural and urban areas. This supports the notion of a set of transfers from national government that are redistributive and that target inequalities across and within counties. 44 A PUBLICATION OF KNBS AND SID

14 3.5 Expenditure and poverty annex tables

15 Exploring Kenya s Inequality Annex Table 2: Consumption expenditure quintiles by number of households and number of individuals at county level Consumption expenditure quintiles by county and number of households Consumption expenditure quintiles by county and number of individuals Quintiles Quintiles County 1st 2nd 3rd 4th 5th Ksh. Below 1,440 Ksh. 1,441-2,840 Ksh. 2,841-3,880 Ksh. 3,881-7,200 Ksh. Above 7,200 Total Number of Households County 1st 2nd 3rd 4th 5th Ksh. Below 1,440 Ksh. 1,441-2,840 Ksh. 2,841-3,880 Ksh. 3,881-7,200 Ksh. Above 7,200 Total Number of Individuals Kenya ,469,875 Kenya ,565,589 Rural ,239,316 Rural ,843,775 Urban ,230,559 Urban ,721,814 Nairobi ,202 Nairobi ,068,835 Nyandarua ,685 Nyandarua ,436 Nyeri ,439 Nyeri ,735 Kirinyaga ,601 Kirinyaga ,585 Muranga ,171 Muranga ,286 Kiambu ,106 Kiambu ,601,101 Mombasa ,912 Mombasa ,681 Kwale ,288 Kwale ,118 Kilifi ,439 Kilifi ,089,085 Tana River ,201 Tana River ,372 Lamu ,026 Lamu ,646 Taita-Taveta ,274 Taita-Taveta ,662 Marsabit ,536 Marsabit ,272 Isiolo ,167 Isiolo ,418 Meru ,776 Meru ,337, A PUBLICATION OF KNBS AND SID

16 Pulling Apart or Pooling Together? Tharaka Nithi ,404 Tharaka Nithi ,717 Embu ,629 Embu ,047 Kitui ,933 Kitui ,563 Machakos ,791 Machakos ,084,129 Makueni ,047 Makueni ,416 Garissa ,773 Garissa ,868 Wajir ,602 Wajir ,830 Mandera ,602 Mandera ,605 Siaya ,367 Siaya ,230 Kisumu ,792 Kisumu ,587 Homa Bay ,139 Homa Bay ,315 Migori ,410 Migori ,745 Kisii ,416 Kisii ,139,552 Nyamira ,704 Nyamira ,491 Turkana ,004 Turkana ,346 West Pokot ,428 West Pokot ,464 Samburu ,353 Samburu ,978 Trans-Nzoia ,549 Trans-Nzoia ,257 Baringo ,094 Baringo ,107 Uasin-Gishu ,981 Elgeyo- Marakwet ,717 Uasin- Gishu ,712 Elgeyo- Marakwet ,115 Nandi ,418 Nandi ,556 Laikipia ,996 Laikipia ,382 47

17 Exploring Kenya s Inequality Nakuru ,448 Nakuru ,562,625 Narok ,721 Narok ,624 Kajiado ,063 Kajiado ,955 Kericho ,087 Kericho ,942 Bomet ,164 Bomet ,873 Kakamega ,619 Kakamega ,644,328 Vihiga ,398 Vihiga ,676 Bungoma ,546 Bungoma ,359,983 Busia ,857 Busia , A PUBLICATION OF KNBS AND SID

18 Pulling Apart or Pooling Together? Annex Table 3: Consumption expenditure shares by quintiles at county level Consumption Expenditure Shares by Quintiles Quintiles County 1st 2nd 3rd 4th 5th Ksh. Below 1,440 Ksh. 1,441-2,840 Ksh. 2,841-3,880 Ksh. 3,881-7,200 Ksh. Above 7,200 Kenya Rural Urban Total Number of Households 8,469,875 5,239,316 3,230, Nairobi , Nyandarua , Nyeri , Kirinyaga , Muranga , Kiambu , Mombasa , Kwale , Kilifi , Tana River , Lamu , Taita-Taveta , Marsabit , Isiolo , Meru , Tharaka Nithi , Embu , Kitui , Machakos , Makueni , Garissa , Wajir , Mandera , Siaya , Kisumu ,792 49

19 Exploring Kenya s Inequality 603 Homa Bay , Migori , Kisii , Nyamira , Turkana , West Pokot , Samburu , Trans-Nzoia , Baringo , Uasin-Gishu ,981 Elgeyo- 707 Marakwet , Nandi , Laikipia , Nakuru , Narok , Kajiado , Kericho , Bomet , Kakamega , Vihiga , Bungoma , Busia , A PUBLICATION OF KNBS AND SID

20 Pulling Apart or Pooling Together? Annex Table 4: Poverty index by county and constituency County/constituency Headcount index: percent of Individuals below poverty line Standard error of headcount index Poverty gap as percent of poverty line Standard error of poverty gap Severity of poverty as percent of poverty line Standard error of severity of poverty Total population Kenya ,565,589 Rural ,843,775 Urban ,721,814 Nairobi ,068,835 Westlands ,913 Dagoreti North ,588 Dagoreti South ,066 Langata ,323 Kibra ,293 Roysambu ,409 Kasarani ,019 Ruaraka ,089 Embakasi South ,641 Embakasi North ,773 Embakasi Central ,289 Embakasi East ,751 Embakasi West ,232 Makadara ,295 Kamukunji ,039 Starehe ,214 Mathare ,901 Nyandarua ,436 Kinangop ,714 Kipipiri ,665 Ol Kalou ,214 Ol Jorok ,415 Ndaragwa ,428 Nyeri ,735 Tetu ,992 51

21 Exploring Kenya s Inequality Kieni ,965 Mathira ,094 Othaya ,621 Mukurwe-Ini ,501 Nyeri Town ,562 Kirinyaga ,585 Mwea ,230 Gichugu ,696 Ndia ,727 Kirinyaga Central ,932 Muranga ,286 Kangema ,333 Mathioya ,877 Kiharu ,234 Kigumo ,311 Maragwa ,619 Kandara ,048 Gatanga ,864 Kiambu ,601,101 Gatundu South ,830 Gatundu North ,936 Juja ,138 Thika Town ,775 Ruiru ,242 Githunguri ,987 Kiambu ,153 Kiambaa ,386 Kabete ,303 Kikuyu ,562 Limuru ,408 Lari ,381 Mombasa ,681 Changamwe , A PUBLICATION OF KNBS AND SID

22 Pulling Apart or Pooling Together? Jomvu ,640 Kisauni ,866 Nyali ,320 Likoni ,189 Mvita ,467 Kwale ,118 Msabweni ,469 Lunga Lunga ,665 Matuga ,230 Kinango ,754 Kilifi ,089,085 Kilifi North ,568 Kilifi South ,588 Kaloleni ,062 Rabai ,292 Ganze ,961 Malindi ,295 Magarini ,319 Tana River ,372 Garsen ,149 Galole ,348 Bura ,875 Lamu ,646 Lamu East ,531 Lamu West ,115 Taita-Taveta ,662 Taveta ,939 Wundanyi ,991 Mwatate ,127 Voi ,605 Marsabit ,272 Moyale ,811 North Horr ,669 53

23 Exploring Kenya s Inequality Saku ,854 Laisamis ,938 Isiolo ,418 Isiolo North ,724 Isiolo South ,694 Meru ,337,045 Igembe South ,204 Igembe Central ,762 Igembe North ,984 Tigania West ,059 Tigania East ,459 North Imenti ,250 Buuri ,356 Central Imenti ,222 South Imenti ,749 Tharaka Nithi ,717 Maara ,662 Chuka/Igambangombe ,473 Tharaka ,582 Embu ,047 Manyatta ,380 Runyenjes ,327 Mbeere South ,486 Mbeere North ,854 Kitui ,563 Mwingi North ,852 Mwingi West ,556 Mwingi Central ,960 Kitui West ,196 Kitui Rural ,183 Kitui Central ,077 Kitui East ,304 Kitui South , A PUBLICATION OF KNBS AND SID

24 Pulling Apart or Pooling Together? Machakos ,084,129 Masinga ,222 Yatta ,039 Kangundo ,770 Matungulu ,588 Kathiani ,868 Mavoko ,112 Machakos Town ,848 Mwala ,682 Makueni ,416 Mbooni ,383 Kilome ,432 Kaiti ,478 Makueni ,129 Kibwezi West ,580 Kibwezi East ,414 Garissa ,868 Garissa Township ,383 Balambala ,996 Lagdera ,629 Dadaab ,837 Fafi ,430 Ijara ,593 Wajir ,830 Wajir North ,447 Wajir East ,338 Tarbaj ,395 Wajir West ,427 Eldas ,905 Wajir South ,318 Mandera ,605 Mandera West ,711 Banissa ,609 55

25 Exploring Kenya s Inequality Mandera North ,635 Mandera South ,536 Mandera East ,555 Lafey ,559 Siaya ,230 Ugenya ,980 Ugunja ,306 Alego Usonga ,211 Gem ,204 Bondo ,268 Rarieda ,261 Kisumu ,587 Kisumu East ,363 Kisumu West ,811 Kisumu Central ,499 Seme ,239 Nyando ,376 Muhoroni ,565 Nyakach ,734 Homa Bay ,315 Kasipul ,663 Kabondo Kasipul ,002 Karachuonyo ,829 Rangwe ,607 Homa Bay Town ,356 Ndhiwa ,599 Suba North ,190 Suba South ,069 Migori ,745 Rongo ,490 Awendo ,730 Suna East ,732 Suna West , A PUBLICATION OF KNBS AND SID

26 Pulling Apart or Pooling Together? Uriri ,448 Nyatike ,209 Kuria West ,897 Kuria East ,480 Kisii ,139,552 Bonchari ,460 South Mugirango ,361 Bomachoge Borabu ,564 Bobasi ,656 Bomachoge Chache ,252 Nyaribari Masaba ,782 Nyaribari Chache ,481 Kitutu Chache North ,538 Kitutu Chache South ,458 Nyamira ,491 Kitutu Masaba ,745 West Mugirango ,204 North Mugirango ,236 Borabu ,306 Turkana ,346 Turkana North ,932 Turkana West ,673 Turkana Central ,461 Loima ,402 Turkana South ,680 Turkana East ,198 West Pokot ,464 Kapenguria ,658 Sigor ,493 Kacheliba ,859 Pokot South ,454 Samburu ,978 Samburu West ,911 57

27 Exploring Kenya s Inequality Samburu North ,945 Samburu East ,122 Trans-Nzoia ,257 Kwanza ,986 Endebess ,875 Saboti ,570 Kiminini ,823 Cherangany ,003 Baringo ,107 Tiaty ,984 Baringo North ,213 Baringo Central ,503 Baringo South ,214 Mogotio ,096 Eldama Ravine ,097 Uasin-Gishu ,712 Soy ,767 Turbo ,201 Moiben ,556 Ainabkoi ,160 Kapseret ,046 Kesses ,982 Elgeyo Marakwet ,115 Marakwet East ,431 Marakwet West ,694 Keiyo North ,477 Keiyo South ,513 Nandi ,556 Tinderet ,951 Aldai ,407 Nandi Hills ,377 Chesumei ,047 Emgwen , A PUBLICATION OF KNBS AND SID

28 Pulling Apart or Pooling Together? Mosop ,284 Laikipia ,382 Laikipia West ,338 Laikipia East ,267 Laikipia North ,777 Nakuru ,562,625 Molo ,525 Njoro ,494 Naivasha ,795 Gilgil ,308 Kuresoi South ,356 Kuresoi North ,374 Subukia ,188 Rongai ,786 Bahati ,256 Nakuru Town West ,466 Nakuru Town East ,077 Narok ,624 Kilgoris ,157 Emurua Dikirr ,730 Narok North ,556 Narok East ,333 Narok South ,764 Narok West ,084 Kajiado ,955 Kajiado North ,354 Kajiado Central ,631 Kajiado East ,910 Kajiado West ,339 Kajiado South ,721 Kericho ,942 Kipkelion East ,708 Kipkelion West ,028 59

29 Exploring Kenya s Inequality Ainamoi ,740 Bureti ,830 Belgut ,816 Sigowet/Soin ,820 Bomet ,873 Sotik ,420 Chepalungu ,714 Bomet East ,826 Bomet Central ,120 Konoin ,793 Kakamega ,644,328 Lugari ,233 Likuyani ,362 Malava ,902 Lurambi ,769 Navakholo ,838 Mumias West ,838 Mumias East ,298 Matungu ,875 Butere ,066 Khwisero ,061 Shinyalu ,497 Ikolomani ,589 Vihiga ,676 Vihiga ,538 Sabatia ,430 Hamisi ,314 Luanda ,438 Emuhaya ,956 Bungoma ,359,983 Mt Elgon ,087 Sirisia ,983 Kabuchai , A PUBLICATION OF KNBS AND SID

30 Pulling Apart or Pooling Together? Bumula ,307 Kanduyi ,906 Webuye East ,869 Webuye West ,710 Kimilili ,879 Tongaren ,152 Busia ,294 Teso North ,953 Teso South ,790 Nambale ,959 Matayos ,867 Butula ,921 Funyula ,272 Budalangi ,532 61

31 Exploring Kenya s Inequality Annex Table 5: Gini coefficient by county and constituency County/constituency/wards Population share Mean Consumption share Gini Kenya 1 3, Rural , Urban , Nairobi County , Westlands Constituency , Dagoreti North Constituency , Dagoreti South Constituency , Langata Constituency , Kibra Constituency , Roysambu Constituency , Kasarani Constituency , Ruaraka Constituency , Embakasi South Constituency , Embakasi North Constituency , Embakasi Central Constituency , Embakasi East Constituency , Embakasi West Constituency , Makadara Constituency , Kamukunji Constituency , Starehe Constituency , Mathare Constituency , Nyandarua County , Kinangop Constituency , Kipipiri Constituency , Ol Kalou Constituency , Ol Jorok Constituency , Ndaragwa Constituency , Nyeri County , Tetu Constituency , Kieni Constituency , Mathira Constituency , Othaya Constituency , Mukurwe-Ini Constituency , Nyeri Town Constituency , Kirinyaga County , Mwea Constituency , Gichugu Constituency , Ndia Constituency , Kirinyaga Central Constituency , Muranga County , Kangema Constituency , Mathioya Constituency , Kiharu Constituency , Kigumo Constituency , Maragwa Constituency , Kandara Constituency , A PUBLICATION OF KNBS AND SID

32 Pulling Apart or Pooling Together? Gatanga Constituency , Kiambu County , Gatundu South Con stituency , Gatundu North Constituency , Juja Constituency , Thika Town Constituency , Ruiru Constituency , Githunguri Constituency , Kiambu Constituency , Kiambaa Constituency , Kabete Constituency , Kikuyu Constituency , Limuru Constituency , Lari Constituency , Mombasa County , Changamwe Constituency , Jomvu Constituency , Kisauni Constituency , Nyali Constituency , Likoni Constituency , Mvita Constituency , Kwale County , Msabweni Constituency , Lunga Lunga Constituency , Matuga Constituency , Kinango Constituency , Kilifi County , Kilifi North Constituency , Kilifi South Constituency , Kaloleni Constituency , Rabai Constituency , Ganze Constituency , Malindi Constituency , Magarini Constituency , Tana River County , Garsen Constituency , Galole Constituency , Bura Constituency , Lamu County , Lamu East Constituency , Lamu West Constituency , Taita-Taveta County , Taveta Constituency , Wundanyi Constituency , Mwatate Constituency , Voi Constituency , Marsabit County , Moyale Constituency ,

33 Exploring Kenya s Inequality North Horr Constituency , Saku Constituency , Laisamis Constituency , Isiolo County , Isiolo North Constituency , Isiolo South Constituency , Meru County , Igembe South Constituency , Igembe Central Constituency , Igembe North Constituency , Tigania West Constituency , Tigania East Constituency , North Imenti Constituency , Buuri Constituency , Central Imenti Constituency , South Imenti Constituency , Tharaka Nithi County , Maara Constituency , Chuka/Igambangombe Constituency , Tharaka Constituency , Embu County , Manyatta Constituency , Runyenjes Constituency , Mbeere South Constituency , Mbeere North Constituency , Kitui County , Mwingi North Constituency , Mwingi West Constituency , Mwingi Central Constituency , Kitui West Constituency , Kitui Rural Constituency , Kitui Central Constituency , Kitui East Constituency , Kitui South Constituency , Machakos County , Masinga Constituency , Yatta Constituency , Kangundo Constituency , Matungulu Constituency , Kathiani Constituency , Mavoko Constituency , Machakos Town Constituency , Mwala Constituency , Makueni County , Mbooni Constituency , Kilome Constituency , Kaiti Constituency , Makueni Constituency , A PUBLICATION OF KNBS AND SID