Kampala parish level socio-economic index

The following data for the city of Kampala, from the 1991 Census, are included in the index.

At percentage distribution per household:

Household Cottage Industry Type Roof Material Type Carpentry Iron Sheet Metal products Tiles Leather products Asbestos Mechanical repairs Concrete Brick tile pottery Papyrus crop processing Grass embroidery crafts Banana Leaves/Fibre other Other

Housing Unit Type Floor Material Type Detached House Concrete Semi-detached Brick Tenement Muzigo Stone Flat Cement Screed Servants Quarters Rammed Earth Hut Wood Other Other

Wall Material Type Household Cottage Industry Activity Burnt Stabilised Brick No Household Activity Stone Some Household Activity Concrete Cement Blocks Unburnt Bricks Pole and Mud Wood Other

Procedure for developing the socio-economic index Entering the data. The raw census data are cut and pasted into individual work sheets (per variable) in Microsoft Excel. As in the format of the raw census data, the variable headings (for example in Roof Material Type this will be iron sheets, tiles, asbestos etc.) should be the columns (eg B, C, D, E, F etc.). The parishes should be listed in the first column A, down through rows 2 to 106.

Cleaning the data. Data are arranged by parish, within sub-parish headings. Totals are provided for each sub-parish, these are not used in developing the index and can be deleted. Any missing values, signified by a ‘-‘ in the raw census data need to be replaced with ‘0’ otherwise they will not be read by Excel. This can be done using the ‘Replace’ function under the Edit Menu. Linking pages. As mentioned above, each variable will be presented on individual worksheets. In addition, two master worksheets are needed. The first is to record the scorings for each variable category. So, for instance within the variable Roof Material Type, an average value of 0 might be given to iron sheets if they represent the norm within the city. In comparison, grass roofs may be given a score of -1 if they represent a below average standard roofing. Accordingly, concrete roofs may be given a score of +2 if they represent better than average roofing. These scores attempt to represent something of the socio- economic status of the households, through in this case the type of roof material they can afford / have access to.

The second linking page will be described later on as it relates to the final stage of developing the index.

Scoring within variables. For each variable, the following will need to be obtained: a) the cumulative score for each parish b) the total percentage for each parish c) the weighted score for each parish d) the mean weighted score for all parishes e) the standard deviation of the weighted score for all parishes f) the standardised score for each parish

The procedures described below relate mainly to obtaining statistics for the parish of Bukesa, as an example. The procedures will need to be followed for each individual parish (Excel provides a function whereby you can scroll down a column and the correct formula will be applied to each cell (parish) for you):

The cumulative score for each parish (i.e. for each line) is obtained by multiplying each of the individual scores (as found on the individual variable worksheets with the scorings for each type (as found on the Scorings worksheet (as mentioned in section 3.0 above Linking Pages).

Percentage distribution of household cottage industry by parish [From the worksheet ‘Industry’] A B C D E F G H I J K Row Carpentry Metal Leather Mechanical Brick tile Crop Embroidery other cumulative total 1 products products repairs pottery processing crafts score % 2 Central Kampala 3 Bukesa 6.06 0 2.02 12.63 2.02 7.07 53.54 16.67 -99.5 100.01 Industry Type Scorings [From ‘Scorings’ worksheet] Column A B Industry Type Row 1 2 3 Carpentry 0 4 Metal products 2 5 Leather products 2 6 Mechanical repairs 1 7 Brick tile pottery -1 8 crop processing -1 9 embroidery crafts -2 10 other 0

To obtain the cumulative score for parish Bukesa (in Sub-county Central Kampala) in this example, each score (for instance Carpentry =0) has to be multiplied by the parish value (in this case Carpentry = 6.06 %) and then all of the values are added together.

So the cumulative score = (by column and row)

[B3 (Industry worksheet ) x B3 (Scorings worksheet)] + [C3 (Industry worksheet ) x B4 (Scorings worksheet)] + [D3 (Industry worksheet ) x B5 (Scorings worksheet)] + [E3 (Industry worksheet ) x B6 (Scorings worksheet)] + [F3 (Industry worksheet ) x B7 (Scorings worksheet)] + [G3 (Industry worksheet ) x B8 (Scorings worksheet)] + [H3 (Industry worksheet ) x B9 (Scorings worksheet)] + [I3 (Industry worksheet ) x B10 (Scorings worksheet)]

Using the figures:

Cumulative score = (6.06 x 0)+ (0 x 2) + (2.02 x 2) + (12.63 x 1) + (2.02 x -1 ) + (7.07 x -1) + (53.54 x -2 ) + (16.67 x 0) = -99.5

The total percentage for each parish is simply obtained by adding together all of the values given for each type for each parish. The values are percentages so adding them together provides the total percentage. The total percentage should in each case add up to around 100. In the example above the total percentage is obtained as follows:

Total percent = B3 + C4 + D4+ E4 + F4 + G4 + H4 + I4 (from the Industry worksheet)

Total percent = 100.01 Percentage distribution of household cottage industry by parish [From the worksheet ‘Industry’] Column A ------ J K L M row cumulative Total weighted standardised 1 score %age score score 2 Central Kampala 3 Bukesa -99.5 100.01 -0.9949 0.533624

The weighted score for each parish simply turns the values into proportions so that we can work with values between 0 and 1 for the index. These are obtained by dividing the cumulative score by the total percentage.

So for the above example, the weighted score is obtained as follows;

Weighted score = J3 / K3 Weighted score = -0.9949

The mean (average) weighted score for all parishes is obtained by adding the cumulative scores for all of the parishes and dividing this by the total number of parishes.

In this example, the mean weighted score is obtained by adding all of the weighted scores in column L and dividing them by the number of parishes. In this case the table above does not contain the values for all of the parishes, instead it only shows the values for Bukesa parish. Given the remaining values, the mean weighted score is -1.08616.

The standard deviation of the weighted scores for all parishes is a measure of how much these scores vary from the mean of the scores. This is a measure of how variable the numbers are.

It can be obtained by the square root of the variance of all of the weighted scores. Excel has a function which can obtain this figure for you. Again, the tables above only contain data for Bukesa parish. In order to work out the standard deviation we need to have the weighted scores for each of the parishes. Given the remaining values, the standard deviation is 0.17101.

You can now calculate the standardised score for each parish. Standardising the scores is a way of making each of the scores comparable because it effectively puts them on the same scale. This means that for each variable (e.g. Household Cottage Industry Type or Roof Material Type) a standardised score of +1 for Household Industry has the same value as a score of +1 for Roof Material Type.

Standardised scores are obtained by taking away the mean value from the weighted score and dividing it by the standard deviation.

For the example of Bukesa, this would be calculated: Standardised score = -0.9949 - -1.08616 / 0.17101 Standardised score = 0.5336 Scoring between variables. The standardised scores for each variable can now be entered on to one final linking sheet which incorporates all of these final values for each parish.

Weightings for each variable are also entered onto this overall worksheet. These will reflect the relevant importance that each variable has within the index. So, for instance if household size is thought to be a stronger indicator of socio-economic status than the wall material used for the house, then household size might be accorded a weight of 3, whilst wall material might have a weight of one.

The standardised scores for each parish are multiplied by the respective variable weights, thus affecting the final index score for each parish.

As with the individual variable worksheets, this overall sheet should be arranged with variables by column and parishes by row. The standardised scores can then be recorded for each as follows;

Overall worksheet showing final scores

A B C D E F G Industry Roof type Household Floor Wall Household Final Type Size material material activity score Row 2.0 1.0 3.0 0.0 1.0 1.0 1 2 Central Kampala 3.3 Bukesa 0.53 -0.42 -0.20 0.11 0.13 0.34 0.51

In the table above, we have six variables for the parish of Bukesa. In order to obtain the final score, we multiply each parish standardised score by the variable weights.

So in the example above, the final score for Bukesa would be calculated as follows:

Final score = (B3 x B1) + (C3 x C1) + (D3 x D1) + (E3 xE1) + (F3 x F1) + (G3 x G1)

Final score = 0.51