Rural Development Policies and Sustainable Land Use in the ...

88 CHAPTER 5
and wage labor. Nevertheless, simple analysis
of descriptive statistics (results not reported)
shows that the value of production
per manzana is higher on perennial than
annual plots, owing to the higher prices of
perennial crops.
Predicted Impacts of
Changes in Selected
Explanatory Variables
To better assess the magnitude of impacts
(as opposed to just their direction and statistical
significance) of particular factors on
livelihood strategies, land management practices,
input use, productivity, and household
income, we present results of simulations
of these impacts based upon the regression
results presented earlier. In the simulations,
we calculate the direct effect of changes
in particular explanatory variables upon the
dependent variables by altering the value
of the explanatory variable (e.g., increasing
population density by 1 percent) for each
observation and predicting new values of
the dependent variable based on the regression
coefficients. For many dependent
variables, the impacts of a change in a particular
explanatory variable may come via
multiple “channels.” For example, an increase
in road density can affect the value
of crop yield by affecting households’ choice
of livelihood strategy, land management
practices, labor, and purchased input use, as
well as by possibly affecting local prices
and hence the value of yields directly, independently
of quantitative changes in yields.
These indirect effects are estimated by predicting
the effects of the change in the explanatory
variable on all of these intermediate
dependent variables, and then using
these values to predict the value of crop
yield. Combining the effects of these indirect
and direct effects results in an estimate
of the total impact of the change, which may
be helpful to policymakers and others interested
in our results. 60
For our simulations, we focus on the impacts
of changes in several policy-relevant
variables that are found to have statistically
significant impacts on at least one of our
main response or outcome variables (livelihood
strategy, land management, crop productivity,
income): population density, road
density, market access, amount of land
owned, value of machinery and equipment
owned, value of livestock owned, and
median level of schooling of household
members. For all of the simulations, we estimated
the percent change in the dependent
variables associated with a 1 percent increase
in the explanatory variable. 61 Our simulation
estimates for these variables thus represent
response or impact elasticities. The
results of the simulations are presented in
Table 5.11. We discuss these by type of explanatory
factor, focusing on results that are
based on statistically significant regression
coefficients and that are relatively large in
quantitative terms.
Land Owned
Increased land ownership is predicted to
increase pursuit of livelihood strategies involving
livestock (livestock production and
basic grains/livestock/farmworker), and reduce
pursuit of the basic grains/farmworker
strategy. Greater land ownership is predicted
to reduce perennial crop yields significantly
(elasticity = –0.21). This result suggests
that increased allocation of land to smaller
farms could result in increased average crop
60
See Nkonya et al. (2004) for a detailed explanation of the simulation approach.
61
When the dependent variable took on discrete values such as the livelihood strategy or use of land management
practices or purchased inputs, the simulation results predict the percentage change in the probability of one
of these discrete values occurring. For example, if the probability of a particular livelihood strategy increased
from 0.1 to 0.15, that represents a 50 percent increase in probability. For the market access variable which is
measured in terms of travel time to the nearest market, we simulated a reduction.