where the output demand from Nairobi played an important role (Tiffen and Mortimore 1992). Moreover the opportunities of earning income in Nairobi provided resources for investment in Machakos district. Clearly urban centers present both market and trade opportunities which is important in interpreting the results in this paper. Finally Turner and Fisher-Kovalski (2010), in a tribute to Boserup’s 100th birthday, find that the Boserup framework has held up well.

Heady and Jayne (2014) in a study using FAO data over the past decades from their regular reporting and periodic agricultural censuses elaborated on their findings in the following three points:

1. “Farms sizes are typically declining in countries believed to have had more binding initial land constraints, whereas farm sizes in less land constrained countries have mostly stayed constant.”

2. “Per hectare measures of input and output trends are mostly consistent with the Boserup hypothesis. Specifically, we typically observe more intensive production patterns in more land constrained countries. Cattle/oxen, fertilizers, cropping intensity and yields all appear to increase with land constraints, but irrigation, mechanization and other forms of capital appear low and fairly unresponsive to land pressures in the vast majority of African countries.” And 3. “Gross output value per hectare appears highly responsive to land constraints. However, since net output per hectare is the more [relevant] welfare indicator, it is difficult to make strong welfare inferences form this fact.” (All from Heady and Jayne, forthcoming, p...).

Their findings therefore are consistent with the BR framework, except that capital investments have been lagging behind what population pressure would suggest. Therefore their findings provide only mixed support for the BR framework.

# **3. Analytical Framework**

We model the current status of the farming system within a cross section framework, and show that we will have to analyze the changes in the farming system with panel data over time. This is because population growth has been higher and infrastructure more developed in areas of high AEP and UG. AEP and UG also influence the farming system directly. It is therefore not possible to instrument population density and infrastructure with AEP and UG and then use the predicted value in a second stage to estimate the causal effect of population density and infrastructure on measures of agricultural intensification. Instead we can use only variables that are strictly exogenous to what we want to explain: AEP, UG of the nearest large city. The coefficients of AEP and UG of these reduced form regressions reflect the total impact of the exogenous variables on the endogenous farming systems characteristics, both via their direct impacts on the farming system, as well as via effects that operate via population density and road infrastructure. Let index stand for household i , for the Enumeration Area (EA) and k for the k h dependent population density, infrastructure or farming system characteristic. The cross section regression then will take the form ii zz HH H = f(AEP , UG ) (1) H stands for the dependent variable iik kk kk , (see below) iik kk= 1, ... KK 6