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Gini coefficient

The Gini coefficient is a commonly used measure that 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 0.445. This reflects a high level of inequality. When we look at the Gini coefficient within rural areas, it is 0.361. In urban areas, it is 0.368. 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.

The Lorenz curve in Figure 3.3 compares inequality between rural and urban areas between counties. 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

 The Lorenz Curve

 

Counties with the highest inequalities are Tana River, Kwale and Kilifi with Gini coefficients of 0.617, 0.597 and 0.565 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.

 

 

Figure 3.4: Gini coefficient by county

Gini coefficient by county
 

 

 

Table 3.5: Gini coefficient: national, counties and constituencies

NATIONAL

 

Name

Pop. Share

Mean

Consump. Share

Gini

Kenya

1

         3,440

1

         0.445

Rural

         0.688

         2,270

         0.454

         0.361

Urban

         0.312

         6,010

         0.546

         0.368

COUNTIES

CONSTITUENCIES

County

Pop. Share

Mean

Consump. Share

Gini

Constituency

Pop. Share

Mean

Consump. Share

Gini

Top 10 counties

Top 10 constituencies

Tana River

         0.006

         2,010

         0.004

         0.617

 Teso South

         0.004

         4,300

       0.0045

         0.638

Kwale

         0.017

         2,060

         0.010

         0.597

 Galole

         0.002

         2,280

       0.0011

         0.622

Kilifi

         0.029

         2,870

         0.024

         0.565

 Bura

         0.002

         2,040

       0.0013

         0.616

Lamu

         0.003

         4,190

         0.003

         0.471

 Garsen

         0.003

         1,810

       0.0014

         0.608

Migori

         0.024

         3,450

         0.024

         0.464

 Magarini

         0.005

         1,450

       0.0019

         0.608

Busia

         0.020

         2,560

         0.015

         0.459

 Kinango

         0.006

         1,210

       0.0019

         0.575

Taita-Taveta

         0.007

         2,850

         0.006

         0.437

 Kilifi North

         0.005

         3,250

       0.0051

         0.550

Garissa

         0.011

         2,640

         0.009

         0.436

 Lunga Lunga

         0.004

         1,270

       0.0015

         0.544

Isiolo

         0.005

         3,030

         0.004

         0.431

 Malindi

         0.004

         4,510

       0.0056

         0.540

Bungoma

         0.036

         3,020

         0.032

         0.430

 Kaloleni

         0.004

         2,750

       0.0033

         0.539

Median County

Median constituency

Kericho

         0.020

         3,300

         0.019

         0.378

 Samburu West

         0.002

         2,010

       0.0013

         0.344

Bottom 10 counties

Bottom 10 constituencies

Nandi

         0.020

         2,820

         0.016

         0.343

 Emurua Dikirr

         0.003

         2,040

       0.0015

         0.263

Nairobi

         0.082

         7,230

         0.172

         0.341

 Laisamis

         0.002

         1,430

       0.0007

         0.252

Bomet

         0.019

         2,390

         0.013

         0.338

 Kacheliba

         0.004

         1,410

       0.0014

         0.246

Kiambu

         0.043

         5,050

         0.063

         0.335

 Banissa

         0.004

         1,140

       0.0014

         0.241

Samburu

         0.006

         1,920

         0.003

         0.332

 Tiaty

         0.004

         1,610

       0.0017

         0.234

Mandera

         0.025

         1,400

         0.010

         0.332

 North Horr

         0.002

         1,330

       0.0008

         0.214

Wajir

         0.014

         1,320

         0.005

         0.321

 Turkana South

         0.004

         1,210

       0.0013

         0.211

West Pokot

         0.013

         1,900

         0.007

         0.318

 Loima

         0.003

         1,110

       0.0010

         0.185

Narok

         0.022

         2,510

         0.016

         0.315

 Turkana North

         0.004

         1,150

       0.0013

         0.173

Turkana

         0.021

         1,380

         0.009

         0.283

 Turkana East

         0.002

         1,150

       0.0008

         0.169

 

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. Whereas counties are mandated to collect some revenues locally, taxation of the rich to reduce the poverty gap in the affected counties will remain a big challenge. This supports the notion of a set of transfers from national government that are redistributive and that target inequalities across and within counties.

 

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