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We use panel data for 50 states during the 1960-2000 period to examine the deterrent effect of capital punishment, using the moratorium as a ''judicial experiment.'' We compare murder rates immediately before and after changes in states' death penalty laws, drawing on cross-state variations in the timing and duration of the moratorium. The regression analysis supplementing the beforeand-after comparisons disentangles the effect of lifting the moratorium on murder from the effect of actual executions on murder. Results suggest that capital punishment has a deterrent effect, and that executions have a distinct effect which compounds the deterrent effect of merely (re)instating the death penalty. The finding is robust across 96 regression models. (JEL C1, K1)

We use panel data for 50 states during the 1960-2000 period to examine the deterrent effect of capital punishment, using the moratorium as a ''judicial experiment.'' We compare murder rates immediately before and after changes in states' death penalty laws, drawing on cross-state variations in the timing and duration of the moratorium. The regression analysis supplementing the beforeand-after comparisons disentangles the effect of lifting the moratorium on murder from the effect of actual executions on murder. Results suggest that capital punishment has a deterrent effect, and that executions have a distinct effect which compounds the deterrent effect of merely (re)instating the death penalty. The finding is robust across 96 regression models. (JEL C1, K1)

We present evidence suggesting that the widely used regression method for testing budgetary incrementalism (Davis, Dempster, and Wildavsky, 1966a, 1966b, 1971 is not suited for U.S. budgetary data that appear to be nonstationary. The method, moreover, cannot detect a nonincremental period following (or preceding) an incremental period. We offer an alternative method that is valid even in nonstationary cases. Our method exploits both the crosssectional and time-series characteristics of the budgetary data to identify statistically the occurrence of incremental decisions (counts) and to estimate incremental cycles for various agencies. More important, the method lends itself to explanatory hypotheses testing. We formulate a set of hypotheses about how various political and economic factors may affect incremental budgeting. We test these hypotheses using the estimated counts in a Poisson regression context. Our results suggest that the Democrats' control over the political process, a switch in the party controlling the White House or Congress, and presidential election year promises (and political vulnerabilities) all cause departures from incremental budgeting. The public pressure resulting from a persistently large deficit has a similar effect. This work may contribute to our understanding of legislative choice.

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