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