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The use of self-reported identities does, however introduce the risk of response bias. Respondents in
societies where ethnic labels mark people for discrimination or where expressing one’s identity in ethnic
terms is frowned upon could potentially be less likely to self-identify in ethnic terms. While these
concerns cannot be ruled out, they are dampened by the structure of the Afrobarometer survey, which is
conducted confidentially and in private by enumerators who are not affiliated with the government. Also,
the Afrobarometer survey is not primarily about ethnicity – the question about how respondents identify
themselves was just one of more than 175 questions asked in the questionnaire – so respondents are likely
to have treated the ethnic identification question as a background question rather than as the central issue
about which the survey was designed to elicit information. Given these factors, we expect that
respondents were likely to have been less guarded in their responses about their ethnic identities than
might otherwise have been the case.
The Salience of Ethnic Identities
Table 2 reports the frequency distribution of responses to the “which specific group do you feel you
belong to first and foremost” question for all respondents taken together, and then broken down for each
of the nine countries in the sample. Contrary to the stereotype that Africans are intrinsically “ethnic”
people above all else, fewer than a third of the respondents in the nine countries identify themselves first
and foremost in ethnic terms. Indeed, ethnic responses are not even the most frequent response type:
class/occupation is the most common answer with 40 percent of responses. In addition, responses vary
tremendously across countries: whereas 92 percent of respondents in Botswana identify themselves “first
and foremost” in ethnic terms, only 3 percent of respondents do so in Tanzania.
This diversity of
responses suggests that the national context matters for the salience of ethnicity – a point to which we
shall return shortly.
Confidence in the validity of these findings is bolstered by the consistency of the
patterns with what we know about the history and politics of the countries in the sample. Race is most
salient in countries with large white settler populations and recent histories of racial discrimination (South
Africa, Zimbabwe, and Namibia). Class is most salient in the traditionally socialist countries of Tanzania
and Zambia (and, less predictably, also in Uganda).
Table 2: Respondent Self-Identifications
“
…which specific group do you feel you belong to first and foremost?
” (from Afrobarometer)
Ethnic
Race
Religion
Class/
Occupation
Other
Obs.
All
Respondents
0.31
0.07 0.14 0.40 0.08 14414
Botswana
0.92
0.03 0.02 0.03 0.01 1196
Malawi
0.59
0.02 0.11 0.23 0.05 1132
Namibia
0.38
0.15 0.12 0.33 0.01 838
Nigeria
0.47
0.00 0.21 0.29 0.03 3516
South Africa
0.17
0.34 0.19 0.16 0.15 2152
Tanzania
0.03
0.00 0.05 0.79 0.12 2151
Uganda
0.12
0.00 0.09 0.66 0.12 1945
Zambia
0.07
0.04 0.32 0.54 0.02 905
Zimbabwe
0.37
0.15 0.08 0.33 0.07 930
Notes: The “other” category includes gender, region, and other responses. The rows may not sum
to 100 percent because of rounding errors.
4
In Botswana, where approximately 80 percent of the country’s population is Setswana, ethnic responses were in
terms of sub-tribes (i.e., Mongwato, Mokweme, Mokgatla, and so forth).
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Moreover, these findings are robust to alternative interpretations of what constitutes an “ethnic” response.
Using our broader coding of ethnicity, the share of ethnic responses rises to 45 percent, largely because of
increases in the number of responses coded as “ethnic” in Nigeria, South Africa, Namibia, and Zimbabwe
(see Appendix, Table A1). But even this figure falls short of what popular perceptions about the
centrality of ethnicity in Africa might have led us to expect. Even with this broad measure, only two
countries have ethnic responses over 60 percent (Botswana and Nigeria). Cross-country variation in the
salience of ethnic identification also remains high.
If Africans are not uniformly “ethnic” people, what makes some Africans more likely to identify in ethnic
terms? Although popularly viewed as vestiges of pre-modern society that will wither away in the face of
political and economic development, ethnic identities have long been recognized to be products of
modernization (Deutsch 1961; Anderson, von der Mehden and Young 1967; Lloyd 1967; Melson and
Wolpe 1970; Gulliver 1971; Bates 1983).
Rather than causing ethnic identifications to “disappear into
museums” (Davidson 1992: 100), 40 years of scholarship has shown that urbanization, industrialization,
education, political mobilization, and competition for jobs
deepen
ethnic identities rather than weaken
them, as individuals exploit their ethnic group memberships as tools for political, economic, and social
advancement. The implication is that we should find the greatest frequency of ethnic responses among
urban, educated respondents who are competing for scarce jobs and maximally exposed to political
mobilization.
We test these expectations using the Afrobarometer dataset in two stages. First, we address the
individual-level determinants of ethnic identification. Then we turn to the country-level factors that
predispose individuals to identify themselves in ethnic terms. Descriptive statistics for the data used in
these analyses are reported in Table 3.
Individual-Level Sources of Ethnic Identification
The key individual-level determinants of ethnic identification that we investigate are gender, age,
education level, occupation, urban/rural location, and media exposure (as proxied by how often the
respondent gets his or her news from the radio or from newspapers – Table 4, column 1).
Given the high
degree of variation in the dependent variable across countries, we employ country fixed effects in column
1, and extensive country controls later. We also cluster regression disturbance terms at the country level
in all specifications in Table 4.
Gender has no effect, and the effect of age is weak. The point estimate on the age variable implies that an
increase in twenty years (roughly half the average life expectancy in the countries in the sample) would
increase the likelihood that a respondent identifies him or herself in ethnic terms by just over 3 percent.
Education, however, has a strong effect, even when controlling for other variables like occupation.
Compared to people with at least some primary education (the omitted category), respondents with no
formal education are nearly 11 percent less likely to say that they feel they belong to an ethnic group first
and foremost. Even a small amount of formal education appears to increase the likelihood that a
5
During the 1950s and 1960s “it was accepted that…parochial ethnic loyalties were mere cultural ghosts lingering
on into the present, weakened anomalies from a fast receding past…[that] were destined to disappear in the face of
the social, economic and political changes that were everywhere at work” (Vail 1989: 1). Notwithstanding Vail’s
claim that “people from all sectors of the political spectrum believed in this vision,” it is almost impossible to find
examples of serious scholars who actually predicted that ethnic identities would evaporate in the face of
modernization.
6
Questions about respondents’ household incomes are only included in five of the round 1 Afrobarometer surveys
(Ghana, Mali, Nigeria, Tanzania, and Uganda), so the effect of wealth can only be explored at the country level,
which we do in the next section. Note that, in contrast to other Afrobarometer publications, we do not weight our
individual-level analyses by country size.
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respondent will identify him or herself in ethnic terms. Beyond primary schooling, however, additional
education has little impact on the likelihood an individual self-identifies in ethnic terms.
Table 3: Descriptive Statistics (for Afrobarometer Analysis)
Variable Mean Std.
dev. Obs.
Panel A: Individual-level characteristics
Female 0.50
0.50
14414
Age (years)
35.0
13.5
14414
No formal education
0.13
0.34
14414
Some primary education
0.15
0.36
14414
Completed primary education
0.19
0.39
14414
Some secondary education
0.22
0.41
14414
Completed secondary education
0.18
0.39
14414
At least some post-secondary education
0.12
0.32
14414
Occupation: Farming or fishing
0.24
0.43
14414
Occupation: White collar, teacher, or government employee 0.16
0.36
14414
Occupation: Blue collar or miner
0.13
0.34
14414
Occupation: Student
0.08
0.28
14414
Occupation: Business, shop keeper, or petty trader
0.16
0.37
14414
Occupation: Other (e.g., unemployed, housewife, don’t know)
0.22
0.41
14414
Lives in rural area
0.57
0.50
14414
Gets news from radio daily
0.57
0.49
14414
Gets news from newspaper at least weekly
0.31
0.46
14414
Panel B: Country-level characteristics
Log per capita income (2000, 2000 US$ –
source: World Bank 2003
) 6.2
1.0
14414
Ethnic fractionalization – Fearon (
source: Fearon 2003
) 0.79
0.18
14414
Ethnic fractionalization – Alesina (
source: Alesina et al. 2003
) 0.74
0.15
14414
Ethno-linguistic fractionalization (
source: Easterly and Levine 1997
)
0.81 0.14 14414
Size of largest ethnic group (
source: Morrison et al. 1989
) 0.35
0.20
14414
Politically relevant ethnic groups score (
source: Posner 2004b
) 0.55
0.16
14414
Proximity to closest next or previous election, in months (
source: authors)
8.3 5.5 14414
Average political rights, 1-7 (last 10 years, 1 is best –
source: Freedom
House
)
4.4 1.5 14414
Notes: The education categories are mutually exclusive. The occupation categories are mutually exclusive.
Reported figures are unweighted by country size.
The salience of ethnicity also varies strongly with occupation. Compared to farmers and fishermen (the
omitted category), blue collar workers/miners, students, business people, and the unemployed are
significantly more likely to identity themselves in ethnic terms. The effect is strongest for students, who
are more than 12 percent more likely to identify themselves in ethnic terms than farmers or fishermen.
One interpretation of this pattern is that strong ethnic identification among students stems from the
competition that they know they will face with their fellow graduates for scarce jobs, and the role that
ethnic ties may play in securing employment.
We also find that respondents living in rural areas tend to be less likely than urban residents to privilege
their ethnic group memberships, although this result is only statistically significant at traditional
confidence levels in one of the specifications.
7
The finding for students is robust to the exclusion of the education indicator variables.
8
The urban variable remains insignificant if we drop the occupation indicator variables, so the finding is not an
artifact of colinearity between occupation and urbanization. This is probably because of heterogeneity of individual
types in the rural domain.
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Table 4: Sources of Ethnic Identity: Individual and Country Characteristics
Dependent variable: Ethnic identity
“describes respondent best”
(1)
(2)
(3)
Female
-0.010
(0.018)
0.001
(0.015)
0.002
(0.012)
Age (years)
0.0016
*
(0.0008)
0.0008
(0.0009)
0.006
(0.006)
No formal education
-0.106
***
(0.035)
-0.038
(0.044)
-0.017
(0.030)
Completed primary education
-0.002
(0.014)
-0.068
(0.042)
-0.053
(0.038)
Some secondary education
0.005
(0.028)
-0.014
(0.026)
-0.024
(0.017)
Completed secondary education
0.012
(0.035)
0.039
(0.043)
0.024
(0.042)
At least some post-secondary education
-0.026
(0.043)
0.009
(0.049)
-0.042
(0.059)
Occupation: White collar, teacher, or government employee
0.015
(0.024)
0.066
***
(0.022)
0.054
(0.037)
Occupation: Blue collar or miner
0.103
***
(0.018)
0.203
***
(0.025)
0.184
***
(0.040)
Occupation: Student
0.123
***
(0.022)
0.246
***
(0.032)
0.245
***
(0.042)
Occupation: Business, shop keeper, or petty trader
0.062
***
(0.014)
0.136
***
(0.025)
0.120
***
(0.039)
Occupation: Other (e.g., unemployed, housewife, don’t know)
0.117
***
(0.020)
0.164
***
(0.022)
0.147
***
(0.039)
Lives in rural area
-0.035
(0.039)
-0.118
***
(0.047)
-0.079
(0.055)
Gets news from radio daily
-0.011
(0.011)
-0.041
**
(0.017)
-0.049
***
(0.018)
Gets news from newspaper at least weekly
-0.047
**
(0.019)
-0.097
***
(0.020)
-0.085
***
(0.033)
Log per capita income (in 2000, 2000 US$)
-0.119
**
(0.057)
-0.158
***
(0.051)
Ethnic fractionalization – Fearon measure
-0.74
***
(0.28)
-0.77
***
(0.24)
Proximity to closest next or previous election, in months
-0.023
***
(0.008)
-0.024
***
(0.007)
Average political rights, 1-7 (1 is best) – Freedom House
-0.078
(0.052)
-0.120
**
(0.055)
Country fixed effects
Yes
No
No
Country population weights
No
No
Yes
Observations (respondents)
14414
14414
14414
Notes: Probit estimation, with marginal coefficient estimates (at mean values for the explanatory variables). Huber
robust standard errors in parentheses. Significantly different than zero at 90 percent (*), 95 percent (**), 99 percent
(***) confidence. Regression disturbance terms are clustered at the country level. The omitted education category is
“Some primary education”. The omitted occupation category is “Occupation: farming or fishing”. The F-test on
the hypothesis that all of the country fixed effects (in regression 1) equal zero has p-value<0.001. Regression 3
weights each observation by 1 / (Number of Afrobarometer observations for the country in which the respondent is
located), thus effectively weighting each country equally.
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Taken together, the findings are broadly consistent with the hypothesis that “modern” individuals –
individuals who are educated, working in non-traditional occupations, and living in urban areas – are
more likely to identify in ethnic terms. One important caveat comes when we look at media exposure.
Exposure to news through newspapers and radio seems to dampen ethnic salience, whereas the
modernization scholarship cited earlier would lead us to expect the opposite relationship. It may be that
media exposure is too coarse a measure of “modernization.” An alternative hypothesis is that the
relationship between modernity and ethnic salience is curvilinear: both those least exposed to modern
currents and pressures and those most exposed to them (for example, those who never read the newspaper
and who do so every day) are least likely to view their as fate bound up with their ethnic affiliation, and
thus are less prone to identify themselves in ethnic terms. The pattern of coefficient estimates on the
employment indicators is broadly consistent with this view, as white-collar employees, teachers, and
government workers are somewhat less likely than lower status blue collar employees, shopkeepers,
traders, and students to identify in ethnic terms.
Country-level Sources of Ethnic Identification
Having considered individual-level sources of ethnic identification, we turn to the country-level. The
country-level variables we introduce both complement and complicate the modernization story we have
explored thus far. First, and most provocatively, we find a challenge to the assumption that higher
degrees of ethnic fractionalization lead to greater ethnic salience. Second, we find strong support for the
modernization thesis about the relationship between political mobilization and the salience of ethnicity:
both the proximity of national elections and the degree of political rights enjoyed by a country’s citizens
affect the likelihood that respondents will identify themselves in ethnic terms. The key limitation of these
results is the inclusion of only nine countries in the analysis, but data limitations make this impossible to
overcome (until further rounds of Afrobarometer data are made publicly available).
We present two different country-level specifications. In columns 2 and 3 of Table 4, we replace the
country fixed effects with measures of the country’s per capita income, degree of ethnic fractionalization,
the number of months since the previous (or before the next) election, and a measure of the country’s
average level of political rights over the past ten years. Column 2 presents the model in unweighted form,
while column 3 weights each observation by 1/(number of observations from that country). The main
effect of including the country population weights is to reduce the influence of Nigeria, which accounts
for 25 percent of the total respondents in our analysis. Since we are concerned here with country-level
effects, the regression that includes the country weights is the preferred specification.
We first investigate the impact of per capita income on the salience of ethnicity. While we find the log of
per capita income to be negatively related to ethnic salience, the substantive effect is small: the coefficient
estimate suggests that a country’s per capita income would have to increase ten-fold in order to generate a
16 percent drop in the share of the population identifying itself in ethnic terms.
A more substantively and theoretically important finding is that ethnic fractionalization is negatively
related to the salience of ethnicity in the countries in question.
In the large literature that employs
indices of ethnic fractionalization to account for outcomes such as civil war (Collier 2001; Elbadawi and
Sambanis 2002; Reyna-Querol 2002), economic growth (Easterly and Levine 1997; Collier and Gunning
1999; Alesina,
et al
. 2003), and the quality of governance (Mauro 1995; La Porta,
et al.
1999), ethnic
diversity is frequently used as a proxy for the salience of ethnic identity per se. Our results suggest that
the assumption underlying this approach has it exactly backwards. It turns out that the more diverse a
country is, the
less
salient ethnicity is for its citizens (Table 4, columns 2 and 3). For a sense of the
magnitude of this relationship, an increase in ethno-linguistic fractionalization of 0.18, or one standard
deviation in our sample, is associated with a reduction in expressed ethnic identification of 14 percentage
9
We use Fearon’s (2003) measure, which we feel to be the most reliable.
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