|||**Percenta**|**ge of the p**|**opulatio**|**n (cells) wit**|<br>**h access**|<br>**to x% of jo**|**bs (rows)**|**in each ur**|**ban area**||
|---|---|---|---|---|---|---|---|---|---|---|---|
|||||||**Cape**|**Dar es**|||||
||**Nairobi**|**Lusaka**|**Bamako**|**Kigali**|**Kampala**|<br>**Town**|<br>**Salaam**|**Dakar**|**Harare**|**Douala**|**Conakry**|
|**>0%**|100%|100%|100%|100%|100%|100%|100%|100%|100%|100%|100%|
|**>5%**|85%|85%|89%|84%|86%|39%|62%|97%|85%|88%|82%|
|**>10%**|78%|76%|81%|77%|80%|25%|42%|95%|71%|86%|78%|
|**>15%**|71%|69%|75%|63%|74%|16%|30%|93%|56%|82%|73%|
|**>20%**|64%|62%|69%|39%|68%|9%|20%|90%|43%|76%|65%|
|**>25%**|58%|56%|62%|0%|61%|4%|14%|86%|32%|72%|57%|
|**>30%**|51%|51%|58%|0%|55%|2%|10%|82%|24%|67%|49%|
|**>35%**|43%|46%|49%|0%|48%|0%|7%|79%|18%|63%|44%|
|**>40%**|35%|41%|39%|0%|40%|0%|5%|77%|14%|57%|37%|
|**>45%**|22%|37%|29%|0%|31%|0%|3%|70%|10%|52%|29%|
|**>50%**|11%|32%|27%|0%|23%|0%|2%|56%|6%|43%|20%|
|**>55%**|4%|26%|21%|0%|12%|0%|1%|39%|4%|31%|8%|
|**>60%**|1%|21%|13%|0%|6%|0%|0%|17%|2%|15%|1%|
|**>65%**|0%|15%|7%|0%|2%|0%|0%|4%|1%|4%|0%|
|**>70%**|0%|10%|6%|0%|0%|0%|0%|0%|1%|0%|0%|
|**>75%**|0%|5%|4%|0%|0%|0%|0%|0%|0%|0%|0%|
|**>80%**|0%|2%|3%|0%|0%|0%|0%|0%|0%|0%|0%|
|**>85%**|0%|0%|2%|0%|0%|0%|0%|0%|0%|0%|0%|
|**>90%**|0%|0%|2%|0%|0%|0%|0%|0%|0%|0%|0%|
|**>95%**|0%|0%|1%|0%|0%|0%|0%|0%|0%|0%|0%|
|**100%**|0%|0%|0%|0%|0|0%|0%|0%|0%|0%|0%|

TABLE 4: PERCENTAGE OF THE POPULATION (COLOR/CELL) WITH ACCESS TO X% OF OPPORTUNITIES (LEFT COLUMN/ROWS) Table 3 and Table 4 provide some information on how equally distributed are the access levels to employment opportunities. Cape Town’s best accessibility from a single point, for example, is 42%. This is higher than Kigali, where it is 23% (Table 3). However, in Cape Town the higher levels of access are available to only a small proportion of the population and the average is 7%, while in Kigali the average is 15% as a relatively larger proportion of the population is located within higher-access areas.

Another, maybe more common or intuitive, way of understanding how (un)equally distributed levels of accessibility are, is through the use of Lorenz curves and Gini coefficients of accessibility. Section 3.1 provided some details as to how Lorenz curves for accessibility relate to the more common metric for income inequality. Lorenz curves and Gini coefficients have the advantage of focusing solely on how equally distributed accessibility levels are, irrespective of the absolute accessibility levels in the city.

Similar to the way income inequality is often captured, we compute Lorenz curves and Gini coefficients to represent how equally accessibility is distributed for each urban area in our sample. When looking at income inequality, it is common to sum the total incomes of a country and explore what proportion of that total “income pot” goes to a given share of the country’s population. Graphically, the x-axis represents the increasing population share, ordered from poorest to richest, and the y-axis the increasing share of the total “income pot”. When there is perfect equality in a given economy, the Lorenz curve drawn from joining the pairs of population and “income pot” shares, is the 45% degree line. It means that x% of the population has access to exactly x% of the total wealth in the economy, with x being exactly the average.

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