individual and community characteristics of a woman when she was a teenager. This is why _Xi_ and _Gi_ only include women time-invariant characteristics.

# 4.3 Findings

Table 5 shows the results of equation (1) for different subsamples (whole population, at least primary completed, at least secondary completed<sup>14</sup> ).

The first important result to take from that table is that the reform has a robust positive impact on the number of years spent in school for girls completing at least primary school: the first-stage of the I.V. model is always highly significant and positive (0.3 for teenagers with at least a completed primary level of education and 0.6 for those with at least a completed secondary level of education). Looking at the O.L.S. estimates, we see (columns 1, 4 and 7) that education is negatively correlated with the probability of giving birth before age 19, confirming the results from the literature review.

Turning to I.V. models, the impact of education on early fertility is not significant if we take the whole sample (columns 2 and 3): this may be explained by the number of women who did not complete at least the KCPE (6,759 observations) and who were not affected by the curriculum change. However, the results become negative and significant if we focus on the sub-sample of teenagers with at least the KCPE (columns 5 and 6) for whom one more year of schooling entails a reduction of 18.8 percentage points in the probability of teenage childbearing (column 6). This figure remains very similar if we shrink the sample to the population of teenagers having completed secondary school (19.3pp). Since girls with at least a primary (secondary) school degree had a 64.6% (62.2%) probability of teenage childbearing before the Reform implementation, one more year of school led to an average reduction of 29.1% (31%). These results are considerably higher than those obtained by Black et al. (2004), who find that an increase in one more year of education entails a 5% reduction of teenage fertility in Norway and the US.

Ethnic affiliation seems to play an important role (this result will be confirmed in all subsequent specifications). One of the drawbacks of DHS data is that it does not contain information on income or consumption. K-DHS-03 does have a wealth index, but as it refers to present wealth, it does not give information about wealth status of women when they were teenagers. Since we do not have either information on a woman’s siblings, we cannot have household fixed effects. However, we can argue that ethnic affiliation gives information about the degree of economic development of the place of residence when a teenager. Among ethnic tribes, the probability of having a first birth as a teenager is the following: (i) Masai are the less likely, then come (ii) Kamba and Kikuyu, (iii) Embu and Kalenjin, (iv) Luo and Kisii and finally (v) Luhya<sup>15</sup> . Kamba and Kikuyu leave in the Central region or Nairobi, which are from the least poor areas (headcount ratios of less than 40%<sup>16</sup> ) Embu and Kalenjin are from the Rift Valley, where poverty rates average 47%. Luo and Kisii live in Nyanza, a province that has poverty rates ranging from 60 to 70%. Luhya reside in the Western province known to be a poverty hotspot: districts are uniformly desperately poor and no location has a poverty rate below 63%. Women originating from less poor regions have lower probabilities of early pregnancies. However, being born and raised in the countryside or in an urban area does not seem to play a significant role in explaining early

> 14 The last group is a subset of the second which is a subset of the first.

> 15 The reference group is the Somali ethnic group.

> 16 All poverty rates are taken from Ndeng’e et al. (2003)’s estimations from the 1999 Kenya Census.

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