wealth. Let r1 be the unique value of r that solves the first-order condition given in Equation (3). It is straightforward that the existence of a unique solution is guaranteed by the concavity of W in both its arguments. Now consider the case where the positive relationship between land and household size is abolished. This implies that k<sup>′</sup> (r) = 0 and the first-order condition reduces to<sup><u>dW</u></sup> dr<sup>= c′(r).</sup> Assuming that r2 is the unique value of r that solves the modified first-order condition, the convexity of the cost function ensures that r1 will be greater than r2, which, in turn, implies that disentangling access to land from children will lead to a reduction in fertility.

# 3 Data and empirical strategy

## 3.1 Data and fertility measures

The empirical analysis in this paper employs panel data from the Ethiopian 1994 and 2007 Population and Housing Censuses. Census waves were collected by the Central Statistical Agency of Ethiopia with the technical and financial support of various international partners, such as the United Nations Fund for Population Activities (UNFPA), the United Nations Development Fund (UNDP), and the Department for International Development (DFID). They contain data on the demographic, economic, and social characteristics of all persons and their housing in Ethiopia. In each wave, 20 percent of the census data was collected with a long questionnaire, roughly 10 percent of which were accessible to us. It is a random sample of the full census.

We matched the Amhara sub-sample of the census data with administrative data on the 1997 land reform in the Amhara region from the Environmental Protection, Land Administration and Use Authority. The latter allows us to draw on within-Amhara variation in the empirical analysis. Data from the Population and Housing Census is aggregated at the district level, i.e., the administrative level at which the implementation of the reform varies. We used three samples for the empirical analysis: a rural sample, an urban sample, and a full sample, including all data from the rural and urban samples.<sup>14</sup> As a consequence of changes in the administrative boundaries (typically as a result of splits), there are more districts in 2007 than in 1994. Thus the data across census waves were spatially matched if a 1997 district contains the centroid of a 2007 district. We cross-checked the spatial match by comparing whether the district names in the two census waves match. In addition, we also cross-validated the administrative land reform data with independent accounts wherever possible, i.e., there are ethnographic accounts from research operating on the ground during or shortly after the reform (see, for instance, Abate, 1997a, and Abate, 1997b, for the zone of South Wollo; Ege, 1997, for North Shoa; Gelaye, 1999,

> 14 The rural sample was generated by aggregating data on households living in the enumeration areas classified as rural by the Population and Housing Census. The urban sample was generated by aggregating data on households living in the enumeration areas classified as urban by the Population and Housing Census. The full sample aggregates all the available data.

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