Regression results

Table 3.1 shows OLS regression results with time fixed effects on basic numeracy (lagged by one decade) for the cross-section of African countries between the 1880s and

enrolment ratio on the numeracy level. Enrolment ratio alone has an explanatory power of around 34 percent. Colonizer’s identity shows ambiguous results. This is likely due to the high correlation between colonizer’s identity and enrolment ratio (Table A.3). As mentioned above, countries colonized by the British had higher primary school enrolment ratios than countries colonized by other colonial powers. Comparing model 2 and model 3, the indicated inverse relationship between ABCC level and colonizer’s identity in the second model runs apparently via the enrolment ratio. Hence, colonizer’s identity is excluded from the remaining models.

The data indicate that the presence of European settlers had a strong positive and significant influence on the numeracy level: settler colonies score over 20 ABCC points higher in numeracy than peasant colonies. Angeles’ (2007) definition of a settler colony is implemented here. Angeles distinguishes between ‘New Europes’ (more than 50 percent European settlers), settler colonies (between one and 30 percent) and peasant colonies (less than one percent). New Europes include Australia, Canada, New Zealand and the United States. African countries fall only in either the category of settler or peasant colonies. For this study the settler ratios as cited by Acemoglu et al. (2001) are used.

As the availability of historical data on infrastructure in colonial Africa is very limited for most countries, the numbers of open railway lines are used in these regressions as a proxy for the state of infrastructure. However, the coefficients in the different models do not point in a clear direction and offer no further insight into the causal relationship.

Table 3.1: OLS regression results (with time fixed effects), dependent variable: basic numeracy (ABCC index) 1880 - 1940 in Africa

(1) (2) (3) (4) (5) (6) (7) (8) VARIABLES ABCC ABCC ABCC ABCC ABCC ABCC ABCC ABCC

Enrolment ratio (log) 10.18*** 8.588*** 6.029*** -0.119 4.818*** 4.742*** 4.832***

(1.438) (2.572) (1.489) (1.905) (1.339) (1.441) (1.568)

UK colony -11.56** 3.159

(5.237) (4.037)

Settler colony 20.45*** 27.93*** 31.47***

(5.295) (4.576) (5.603) Railway per qkm (log) 1.643 -0.317

(1.426) (1.313)

Islam -0.143* -0.221*** -0.260*** -0.242*** -0.257***

(0.0776) (0.0538) (0.0650) (0.0608) (0.0615) Malaria -0.209** 0.0682 -0.141** -0.162 -0.124 (0.0846) (0.0665) (0.0682) (0.0982) (0.139) Population density (log) -2.344 2.513*

(1.418) (1.350)

Ethnic fractionalization -21.01 -21.20 -19.59

(13.53) (14.85) (23.85)

Pre-colonial -9.595**

settlement pattern (4.264)

Pre-colonial state 0.994

stucture (4.873)

Constant 36.18* 46.00*** 26.77 68.16*** 43.69*** 82.68*** 113.0*** 58.60 (20.24) (16.79) (19.29) (7.858) (6.935) (11.58) (29.04) (41.48) Observations 73 54 69 65 65 65 57 57 R-squared (adj.) 0.344 0.584 0.469 0.432 0.690 0.444 0.430 0.400

Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1. The coefficients for time fixed effects are not reported in this table.

See Appendix for variable definitions and sources, the summary statistic of the variables used are displayed in Table A.4.

Dependent variable: ABCC index, lagged by one birth decade.

Models 4 through 8 include two variables measuring local conditions that the colonial powers faced in Africa: the percentage of the population that was Muslim and the percentage of the population living under the threat of malaria. The prevalence of Islam shows a consistently negative impact on numeracy. As the variable ‘Islam’ is defined in percentage points, a country with 100 percent Muslim population has (depending on the model specification) on average 14 to 25 ABCC points less than a country without a Muslim population. This is a very strong influence.

A glance at the relationship between the share of Muslims in the population and

North African countries - Algeria and Tunisia, both former French colonies - with populations nearly 100 percent Muslim are stand outs, showing almost full numeracy.

Therefore, it cannot be said that a high prevalence of Islam in a country necessarily leads to lower numeracy levels. With the distinction between settler and non-settler colonies, the graph shows clearly that the variation among settler colonies is much smaller than among non-settler colonies and that all settler colonies have numeracy levels higher than 80 ABCC points around 1910/1920. But even if we control for settler colonies as in model 5, the coefficient of Islam remains negative and significant. However, in this case, enrolment ratio turns negative. This is probably due to multicollinearity as enrolment ratio and settler colony are highly correlated. Similarly, the prevalence of malaria turns out to have a strong negative influence on ABCC: A country totally exposed to malaria has on average between 12 and 20 fewer ABCC points (depending on the model) than a country without a disease environment in which malaria prevails. The negative influence of Islam and malaria is therefore consistent with the suggestions discussed in section 4.

Figure 3.7: Relationship between the share of Muslim population and the ABCC index early 20^th century

020406080100ABCC early 20th century

0 20 40 60 80 100

share of Muslim population peasant colonies settler colonies

Country codes: bf=Burkina Faso, bi=Burundi, bj=Benin, bw=Botswana, cf=Central African Republic,

cm=Cameroon, cv=Cape Verde, dz=Algeria, eg=Egypt, et=Ethiopia, gh=Ghana, gm=Gambia, gw=Guinea-Bissau, ke=Kenya, km=Comoros, lr=Liberia, ma=Morocco, mg=Madagascar, mu=Mauritius, mw=Malawi,

mz=Mozambique, na=Namibia, ng=Nigeria, re=Reunion, sc=Seychelles, st=Sao Tome and Principe, sz=Swaziland,

Population density data for early 20^th-century Africa are collected from Hübners Geographisch-Statistische Tabellen (1932), a compilation of national statistical reports worldwide, including protectorates and colonies. As with the infrastructure variable, the coefficients of this variable do not point to an influence on basic numeracy.²⁷

Models 6 through 8 test for the added effects of ethnic fractionalization. In this study fractionalization data from Alesina et al. (2003) are used. As assumed, the influence of fractionalization on numeracy turns out to be negative (albeit insignificant): the higher the fractionalization the lower the ABCC level. The coefficients in these models indicate that a country with high ethnic fractionalization scores approximately 20 ABCC points lower than a country with no fractionalization.

Models 7 and 8 include variables for the pre-colonial state and settlement structure. The variable measuring the settlement pattern of the indigenous population ranges between 1 for nomadic patterns and 5 for complex settlements. The variable measuring the pre-colonial political structure ranges from 1 for no political authority beyond the local community to 5 for large states (see Appendix for a precise variable description). Both yield no additional explanatory power to the model, nor do they show a clear influence on numeracy. As discussed in the previous section, the hypothesis is that the more complex a society, the higher the numeracy level. Model 8 bears out this expectation, but the settlement variable in model 7 shows the opposite of what was expected. The other coefficients are unaffected. Even if this regression model does not give deep insight into the explanation of numeracy, a look at the correlation matrix (Table A.3) reveals these characteristics may still play a role: as the indigenous settlement pattern can be interpreted as a kind of urbanization variable, it is not surprising to see that it is correlated with population density and school enrolment rates. Formal education is

more likely in permanent settlements than nomadic settlement structures. However, numeracy cannot be explained upon these data. Similar relationships are observable for the pre-colonial state structure. As discussed in the literature review, the correlation matrix reveals that a more complex state structure is linked to a less heterogenic population. In addition, Europeans tended to prefer to settle in areas with pre-existing state structures.

To conclude, the results of the regression analyses indicate that enrolment ratio is the main determinant of basic numeracy. Additionally, they confirm that the ABCC index proxies an output factor of human capital. It also became clear that numeracy levels in Africa during the period under consideration were highly dependent on European settlement policies. Colonies with European settlers had significantly higher numeracy levels than non-settler colonies. Unfortunately, the data at hand cannot disentangle the exact mechanism behind this finding. It seems however, that the effect can be attributed to variations in schooling policies. Additionally, local circumstances such as the prevalence of malaria and Islam, seem to have had some influence on numeracy. The ethnic heterogeneity and state structure of a pre-colonial society also did not have an impact on numeracy directly, but they seem to have had an effect on the settlement patterns of the Europeans. This is in line with the argument that colonizers established institutions consistent with the existing distribution of political power (Gallego 2010).

The results point to the presence of a strong influence of the settlement pattern of the Europeans on the educational situation in African countries. European settlers brought themselves and their institutions and school systems, but their presence likely generated additional spill-over effects. This seems to have had a significant and long-lasting effect on education in Africa.