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<br /> <br />the model. The outputs-which are <br />usually in the forIn of probability distri- <br />butions-show.the range of potential <br />risks and rewards of decisions based on <br />the theoreticalluodel under study. <br />In conducting a Monte Carlo simula- <br />tion for the fiscal impacts of TIF, I created <br />a deterministic model of the 'TIF'decision- <br />making process. I then replaced uncertain <br />variables in the model-the growth rate <br />of non-TIF properties, the effect of TIF on <br />non-TIF properties, the post-TIP growth <br />rate of TIF properties, and the elasticity of <br />expenditures with respect to changes in <br /> <br />4 CURA REPORTER <br /> <br />market value-with random variables <br />drawn from a specified distribution. The <br />uncertain variables and the sources from <br />which the random variables were derived <br />are listed in Table 2. <br />Along with the random variables, <br />certain parameters were established <br />wi thin the luodel. Parameters are vari- <br />abies whose values are more certain or <br />more controllable than the uncertain <br />variables. For example, if one were <br />simulating traffic flow on a freeway, one <br />would set as a parameter the number of <br />lanes on the freeway. One can vary <br /> <br />parameters, but usually this is done only <br />to analyze policy changes. Initially, the <br />following parameters were set: initial <br />investment into the TIP project, effec- <br />tive tax rate, operating subsidy, pre- TIF <br />growth rate of TIF properties, and <br />discount rate. The initial investment <br />parameter was set at 500/0 of the TIF <br />market value. In other words, we <br />assume that a $30 million TIF project <br />will generate a $15 million initial <br />market value investment. The city effec- <br />tive tax rate was set at 0.919'6, calculated <br />from City of Minneapolis financial <br />