Society can be structured along different lines. Here we look at some of the most important aspects, that may provide equally some explanations to varying attitudes and behavior. Among these lines, income is the most frequent characteristic studied in structuring class affiliation. But such an approach relies on mere income strata and does not per se imply a homogeneity in behavior and attitude. Other important factors are the education of a person, but also their age.
Finally, in a country like Uganda with strong regional and ethnic identities, and limited social mobility, the origin of a person plays a role in a double sense: their geographic and ethnic origin, as well as the social position of the parents, are decisive in their trajectories. Thus, in the first part, we make a mere description of our sample and in the second part, we advance some analysis on persistent identities in addition to income.
6.1 The Composition of the Sample
Here we situate our sample in the context of national and urban statistics. We want to draw the attention once more to the fact that the sample is not statistically representative, but in the line of qualitative approaches, we believe that the observations made will still help us to draw conclusions. Albeit this is not a clear-cut separation, in the first part, which is merely descriptive, we look at income, sex, age, religion, and region. In the second, we already advance some analysis about these demographic aspects, with the intention in mind to show that there is not one shared
“middle class” identity, but rather a mix of persistent identities which can be simultaneously or subsequently evoked.
6.1.1 Income
Income is probably the most popular form of structuring class affiliation (E. O. Wright, 1979). In our analysis, we use income as well to divide the sample into the subgroups as proposed by the AfDB. In careful inquiry and with several probes we tried to determine the household income of the interviewed person as accurate as possible. Then we divided the figure by the number of people living in the household and, according to the money available to each person, we would classify the household into the subgroups of Poor, Floating Class (FC), Lower Middle Class (LMC), Upper Middle Class (UMC) and Upper Class (UC). This would yield the following income distributions:
Income per person in $
Subcategory Per day Per month
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Poor 0-2 0-60
FC 2-4 61-120
LMC 4-10 121-300
UMC 10-20 301-600
UC >20 >600
Table 5: Income by Subcategory We acknowledge several methodological caveats with the data:
The income is not weighted according to household members. It is widely known that not every household member uses the same share of the income. Fix costs such as rent, electricity, or water will not increase proportionally with every person entering a household. Hence, usually equivalence scales are used, with less value assigned to every person following the first adult. Even if this is desirable, this adjustment cannot be made to our data, since we did not inquire about the age of every household member.
Additionally, non-permanent members, such as relatives staying during the school holidays (or, inversely, during the trimester) or household helpers may not be counted in the same manner. Alber (2016a) points out that the household size is subject to variations, as it is rather the norm than the exception for middle class households to take in some relatives or foster children. As a result, economic heterogeneity even within the same household is common; a reality in stark contrast to the assumption inherent to statistical observations based on household data, which assumes an income homogeneity within the same household (Alber, 2016a, p. 188). Using household income and dividing it by household members to determine the subclass is a shortcoming we acknowledge. Thus, we bear in mind that our income stratification is a rough categorization and should only be taken as an estimate. However, in itself the sample is coherent. Nevertheless, triangulation with other characteristics, such as education, living conditions, etc. is inevitable.
The income is not converted to US $ ppp. Instead, we maintained the same conversion rate of 2500 UGX per dollar throughout the findings. This was the approximate conversion rate in 2012 and 2014. Between 2015 and 2016 the dollar has increased, leading to a conversion of approximately 3500 UGX per dollar. Hence, our data cannot be easily compared across nations and time, but in our sample consistency remains given. Ayoki (2012) has used the same conversion rate for his discussion of the Ugandan middle class, which confirms our choice as a reasonable one in the country-specific context.
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There is also the risk of misreporting income. Since some income types are not regular and hard to foresee, such as earnings from harvest or unexpected money earned with brokerage and consultancy, respondents would only give these figures after several probes and sometimes by giving rough estimates. Similarly, because many couples have separated bank accounts (see chapter 8.5), not everyone was aware of the earnings of their spouse. Finally, some respondents might have deliberately chosen not to tell their actual income, because they judged the question inappropriate or they might have forgotten to mention some of their side incomes. Very telling in this matter was the discussion that unfolded with Andrew, who was very unsatisfied with his income: “I don’t earn quite a lot of money to sustain more than a family of three. […] I earn one million [$400]
per month. I fuel my car about more than half of that money, pay bills for water and electricity. […] A man with two degrees should earn more than that.” (Andrew) It turned out, upon probing, that he had several side incomes, rental units, his wife runs a well-performing shop and he is doing some consultancy work that their actual monthly income added up to $2240. In this case, his various side incomes were finally mentioned, but it is likely that in other cases, despite careful probing, these incomes went undetected.
We used disposable income as a reference, that is income after tax but before expenditure.
Some authors suggest considering daily consumption, rather than income (Ncube &
Lufumpa, 2015, p. 11). The main reason is that consumption expenditure is considered more reliable, even though they admit to the possibility of a measurement error since the difference between consumption and income increase with rising income (Ncube &
Lufumpa, 2015, p. 11). Unfortunately, we are not able to use that approach because expenditure has not been inquired as consistently as income. We believe, however, that our data in itself is reliable because the same income inquiry has been used throughout the entire research.
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Upon these calculations, our sample was repartitioned among the different subgroups, as follows:
First, it has to be noted that the spread of the income classes does not reflect the income repartition of the Ugandan population as a whole and it is not intended to do so either.
For example, the World Bank states that 35% of the Ugandan population live below the poverty line. In urban areas that rate drops to 9% and thus reflects more our findings (World Bank, 2016a).
This reveals an essential bias of our work that will also be reflected in most of the other findings.
By limiting the research to Kampala, the most prosperous region of the country (Uganda Bureau of Statistics, 2014a, p. 99), the distribution will necessarily be skewed towards the wealthier fractions of society. At the same time, increased costs of living somewhat relativize this distortion.
As argued before (see p. 88), we do believe that this environment is most conducive to research trajectories of emergence, as life chances and opportunities for choice are greater than in rural regions. Some even argue that “Africa rising” is not a phenomenon of the continent, neither of nations, but one of the 67 cities with over one million inhabitants (Quénot-Suarez, 2012, p. 39). If we take such an argumentation seriously, we also have to re-adapt national statistics to urban contexts for our purposes. Besides, it is not our intention to measure the Ugandan middle income groups but to describe their features and main traits.
Hence, our data displays a relative overrepresentation of the lower middle to upper class and underrepresentation of the poor. The LMC is the most represented among the sample. At the same time, even if represented by only a few cases, UMC and UC are overrepresented in comparison to their frequency in the Kampala population. Besides, impoverished people have been targeted only
15
18
29
11
14
22
0 5 10 15 20 25 30 35
Poor FC LMC UMC UC n/a
Figure 7: Income Classes
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punctually. However, outliers on both ends (UC and poor) are allowed to appear in the data as well, to learn from the delimitations that exist between the different subgroups.
Having established the subgroups that will be referred to throughout the analysis, we like to recall a crucial factor before proceeding: these income groups are a mere means to an end, they do not in itself constitute classes. We use the terms floating, lower and upper middle class because this has been done so otherwise (Ncube et al., 2011) and we believe that these subdivisions may provide some valuable insight. However, definitions of middle class based on income remain merely descriptive, they have neither the potential to uncover underlying social dynamics nor to explain common identities or collective social action (E. O. Wright, 1979). Hence, income groups can structure the analysis, but to deduce class cohesiveness they are not sufficient.
6.1.2 Age
Similar to income, age is another essential description for analysis. For one, income and age are often linked, as usually income progresses with advanced age, until to the state of retirement. But the commonly shared experience of one birth cohort will also have an effect on the mindset and behaviors of people from the same generation (Ryder, 1965, p. 846).
Our sample shows a strong representation of the 26-35-year-olds, the category accounts for more than half of our respondents. Minors have not been surveyed at all, and even those up to the age of 25 are only a few because many of them are still schooling. The absence of regular employment and salary would distort the sample because parental support could overcome the lack of income and thus not make them poor per se. Some students were surveyed, but we tried to limit these
14
52
32
6
2 0
10 20 30 40 50 60
<26 26-35 36-45 46-60 >60
Frequency
Figure 8: Age Groups
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cases. This does imply, however, an underrepresentation of those below the age of 26 who are not schooling, but already working or in search of employment. Second, older adults, above the age of 60, but also in the group of 46-60 are few for two reasons. One, in comparison with the rest of Uganda, there are only few people above the age of 60. One reason might be that elderly would often become less visible, once retired. It is common practice for them to return to their home village, or they remain more in private settings and thus making it harder to get in contact with them.
Trajectories of economic emergence are not solely linked with income and income opportunities, but also with age. Depending on the age of someone, their living conditions, job, education, family status, even leisure time will very likely change and depending on the potentials for earning income will vary according to age, as the figure below shows.
Figure 9: Age and Income
Each dot stands for one respondent. The spread of the data is high, as all income categories and ages are covered. However, the trend line (shown as solid line in the figure) has the shape of a flat, inverse U-curve because in general income starts low and raises with an advanced career and increasing household size (because fixed costs are lowered), and then declines with advanced age due to retirement. Indeed, we find that the highest income on average ($365 per person per month) can be found in the group of the 36-45-year-olds.
0 10 20 30 40 50 60 70 80
0 200 400 600 800 1000 1200 1400 1600 1800
Income per Person in US $
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6.1.3 Gender
Our data reveal a bias towards an overrepresentation of men, who constituted two-thirds of the interviewees.
We assume that this is most likely because more men than women are in the workforce. This assumption is supported by the data which indicates that men have been three times more likely as women to indicate that they are the only person with an income in the family. Since we concentrated most of our efforts on the working population, less visible members of society, such as housewives (or, as mentioned before, elderly) are less represented.
This underrepresentation is regrettable since women face different challenges than men and the position they occupy in society equally structures their perceptions and behaviors. Their income position depends a lot on their marital status, with female-headed households being more prone to poverty than male-headed ones (Uganda Bureau of Statistics, 2014a). They have less access to politics (Ferree Marx & McClurg Mueller, 2004; Tripp, 2000) and are more prone to be victims of violence (I. C. F. International & Uganda Bureau of Statistics, 2012). However, the reliance on their identity as women also has the potential to transcend ethnic or religious barriers because of shared interests common to all women (Daniel, 2016; Tripp, 2000). Thus, in the analysis, we might expect to see behavior that can also be explained by gender difference, and we will include this factor when opportune to do so.
6.1.4 Household Composition
On average, interviewees indicated to live in households composed of 5.11 people. We see that this number is declining with rising income, with poor households hosting on average 7.57 people and the wealthiest households 3.5.
Regarding the number of children, we witness a similar trend with rising income: the number of children declines, from 2.4 among the poor to 1.84 among the UC. Hence, we might conclude with an observation also made elsewhere that the middle income groups tend to have lesser children and live in smaller households together. As mentioned above, however, the complex realities in
N=107 male female
Figure 10: Distribution by Gender
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terms of household composition and income stratification even within the same household need to be kept in mind (Alber, 2016a).
6.1.5 Occupation
Occupation has in some cases been the sole defining factor of a middle class (Mills, 1953), sometimes it has been an essential feature besides economic factors (Weber, 2002). In the context of the middle class in the developing world, the focus on profession had been determined by the access to a stable, predictable salary. In Uganda, 47.8% of the working population is employed, and only 47.4% of those have a paid employment. Hence, more than half of them are self-employed (Uganda Bureau of Statistics, 2014a, p. 47). Thus, salaried employment touches only about 25%
of the Ugandan working population. In our sample, roughly two-thirds were employed and one-third self-employed. As shall be seen in the discussion on the diversification of income (7.2 Diversifying Income), the salaried job is not necessarily the only one nor the one generating the most income, but frequently it is the one that the respondents used to define themselves.
Regarding the type of job executed, limited conclusions could be made. While it is sometimes said that civil servants would constitute an important part of the middle class, they did not figure prominently among our sample. Most frequently administration was mentioned, allowing at least for the conclusion that indeed most of the respondents were white-collar workers, working in the service sector. This raises questions about their dependency on the state for employment: since they are not predominantly employed by the state, they may feel less obliged towards it (Darbon
& Dedieu, 2014, p. 298). Secondly, since they are not deriving their primary income from property, they might be less fearful to protest (Oberschall, 1973, p. 164). Such an analysis quickly reaches its limits, however, if one considers the multiplication of class positions through the
0 1 2 3 4 5 6 7 8
Poor FC LMC UMC UC
People per household
N=81
Figure 11: Average Number of People per Household
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diversification of income, making it the rule rather than the exception to be an employer and employed at the same time.
6.1.6 Region
Uganda has about 60 different ethnic groups, whereby the six major groups (in descending order:
Baganda, Banyankole, Basoga, Bakiga, Iteso, and Langi) constitute more than half of the population (Uganda Bureau of Statistics, 2016, p. 20).
0 10 20 30 40 50 60 70
N=107
Figure 12: Occupation of the Respondents
0 5 10 15 20 25 30 35 40 45 50
Central West North East n/a
N=109
Figure 13: Region of Origin of the Respondents
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Among the respondents in our sample, Baganda and Banyankole are over-represented, with other ethnicities being less frequent. Due to the ethnic diversity, we decided to distinguish between regions of origin, rather than ethnicity itself, which could have easily led to oversampling. Where it seems viable for understanding, ethnic groups may explicitly be referred to.
The high incidence of people from the Central region, mainly Baganda, is not particular surprising, as Kampala, the site of the fieldwork, is located there, thus making their presence more likely.
People from the West are mainly Banyankole, but not exclusively. However, since they are the second biggest ethnic group in Uganda, it is not surprising that Westerners also represent the second largest group in our sample. Inversely, the North is only sparsely populated, and by far the most impoverished region, thus we might suspect that they are less present in Kampala.
Map 1: Ethno-regional Cleavages in Uganda (Lindemann 2011, p. 394)
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When coupling region of origin with income, we see only little variation of the median, as shown in figure 14 except for the East. The earnings of the upper half of the respondents from each region differ significantly though. Here, contrary to the perceptions shared by some that people from the West are advantaged, Ugandans from the Center are earning better than any other group. It seems then that the distribution better reflects the more historical advantages that have grown since colonial times rather than more recent, political changes. Interestingly, it may hint at an often ascertained claim, that the middle class accumulated its wealth independently from the governing elite. If the government favors groups with ethno-regional proximity, yet here the best earning individuals are from the Central and the East, we may deduce that they reached their achievements without direct intervention from governing elites. The most destitute respondents come from the Center as well. This is most likely due to the fact that poor people from other regions probably lack the means to move to the capital, whereas poor people from Central region are already there. The boxplot of the North shows little variation and is indicating rather low incomes. Thus, it confirms the marginalized position of people from the North, as had been
When coupling region of origin with income, we see only little variation of the median, as shown in figure 14 except for the East. The earnings of the upper half of the respondents from each region differ significantly though. Here, contrary to the perceptions shared by some that people from the West are advantaged, Ugandans from the Center are earning better than any other group. It seems then that the distribution better reflects the more historical advantages that have grown since colonial times rather than more recent, political changes. Interestingly, it may hint at an often ascertained claim, that the middle class accumulated its wealth independently from the governing elite. If the government favors groups with ethno-regional proximity, yet here the best earning individuals are from the Central and the East, we may deduce that they reached their achievements without direct intervention from governing elites. The most destitute respondents come from the Center as well. This is most likely due to the fact that poor people from other regions probably lack the means to move to the capital, whereas poor people from Central region are already there. The boxplot of the North shows little variation and is indicating rather low incomes. Thus, it confirms the marginalized position of people from the North, as had been