Land tenure inequality, harvests, and rural conflict ... - e-Archivo

(Plümper, Troeger, Manow, 2005). For this reason, I display specifications excluding
and including random effects.
True state-dependence means that successive draws might be correlated with
one another. A typical solution is using lagged values of the dependent variable or
more complex models with lagged values of the latent dependent variable behind the
0, 1 realizations (Beck, Epstein, Jackman, O’Halloran, 2002: 2). However, it would
still be necessary to model the first cross-section and then assume an autocorrelated
structure between the observed event and past events (Heckman, 1981; Stewart,
2007). I follow in this case the standard practice to deal with serial correlation in
time-series cross-section data, using the lagged dependent variable as a regressor plus
unit and period dummies (Beck and Katz, 1995; Beck, 2001). Because this
“standard” has been subject to various criticisms, especially if the lags and time
effects soak up interesting time series variation (Plümper, Troeger, Manow, 2005), I
display specifications with and without lagged dependent variables.
In the case at hand, morever, it is perhaps ill-advised to model the first crosssection
with instrumental variables. Between April 1931 and June 1934, I am
observing conflicts in a period sandwiched between two periods in wich peasant
collective action was severely repressed. Therefore, the problem of left censoring
typical in continuous surveys, when the observation of an ongoing dynamic system
starts at one arbitrary point in time, is not a problem here as I collect data on
behaviour in a new political regime in which unions and strikes are legal. In my case,
unobserved time-invariant variables are going to affect initial probabilities of state
realizations, but random-effects will capture those effects.
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