variation in educational laws, the authors conclude that keeping girls in school until age 16 reduced the probability of having a child before 20 by 4.7% in the United States and 3.5% in Norway5<sup>6</sup> . Moreover, the authors conclude that the effect of education goes beyond a mere incarceration effect, suggesting that human capital accumulation due to extended education may impact teenage fertility choices: if the effects of the compulsory schooling laws occur solely due to the incarceration effect, then the laws should have no impact on behavior at ages above which the laws induce schooling. The authors estimate Probit models of the probability of having a first birth at age x, x in [15, 20], conditional on having no birth prior to that. Since the laws impact the probability of first birth at ages above which they bind, there is strong evidence that the incarceration effect is not the only effect.

**_The present study continues in the same vein as the above-mentioned papers, yet is different for two main reasons._** First, we use data from a developing country: Kenya, where almost half of the population is below the age of 15, where teenage fertility rates (96‰) are known to be twice those of the United States (50‰), and almost ten times those of Norway (10‰)<sup>7</sup> , and where the proportion of girls who become mothers before age 19 reaches 39.4% in 2003. The question of teenage fertility is there of very real consequences on demographic changes and the accumulation of human capital. Second, our data is extremely recent (the youngest cohorts that we take into account were born in 1980). This contrasts with Black et al. (2004) who use cohorts from 19101960 in the United States and 1947-1958 in Norway and Leon (2004) who uses cohorts from 1900-1950.

# 3. Data

## 3.1 The Kenyan Demographic and Health Surveys

**_All data used in this study come from the different rounds of Kenyan Demographic and Health Surveys (DHS)._** DHS are nationally representative household surveys with large sample sizes (usually between 5,000 and 30,000 households). DHS questionnaires provide data for a wide range of monitoring and impact evaluation indicators in the areas of population, health, and nutrition. To date, there exist 4 Kenyan DHS implemented in 1989, 1993, 1998 and 2003. The Kenyan DHS adopted a two-stage stratified sampling approach that selected households located within each primary sampling unit (PSU) or sampling clusters. The locations of the clusters geographically are identifiable within the district and province. In addition to geographical stratification of data by cluster and household, it is also possible to stratify these data by ethnicity. The fertility data were collected by asking all women of reproductive age (15-49 years) to provide complete birth histories of all children they had given birth to. Education information is collected simultaneously, and reports the number of years of education of each woman, the highest educational level attended, and completed.

**_We use data from all rounds of the Kenyan DHS, yielding information on 18,777 women aged 20-49 when the interview took place._** For our specific analysis, we use 4 rounds of data, including only women who are at least 20 years old when the interview took place (to have complete teenage fertility history), and were born after 1955 and before 1980<sup>8</sup> . Because we use different rounds, and because women had different recall time, we included controls for both in

> 6 The average proportion of girls in both samples who had their first pregnancy before the age of 20 is 17% in both samples (US and Norway).

> 7 Sources: all statistics come from HNPStats (2006).

> 8 Older (born before 1955) and younger (born after 1980) cohorts had too few observations and where thus removed from the analysis.

8