For the sake of simplicity, let’s examine how to model a closure. While simulating the entry of a new firm into the model region is generally analogous, modeling new firms can be tougher since you may not have accurate projections of the firm’s output or employment.
You can elect to reduce either employment or sales for the firm’s industry by the amount which the firm represents. However, be sure that you don’t reduce both or you will double-count the firm closure’s direct effects, as lowering one automatically lowers the other in accordance with industry productivity. Since employment information tends to be more available and reliable than sales data (which is often suppressed or inflated), the best approach is to reduce employment by the number of jobs in the firm. Also, be sure not to remove more employment or output than exist in the baseline, or your results will be unreliable.
The only question is whether to use “firm employment” or “industry employment” as your policy variable. If the company is the only firm of its industry type in the model region, using “industry employment” is preferable. But if there are other firms of its type in the region, you should use “firm employment” since other local firms can pick up a portion of the slack. Please see the FAQ about industry vs. firm for more discussion of this issue.
Inputting a negative shock into an employment policy variable reduces a certain amount of output from that industry, which then translates into two effects that the model captures automatically. First, the shock reduces demand for the industry’s intermediate inputs, with the sectoral breakdown of these reductions based on the industry’s column in the input-output (IO) matrix. Second, industries that used the firm’s output as intermediate inputs satisfy their demand from other sources (other regions or imports). That includes the “diagonal” of the IO, where the firms demanding these inputs are in the same industry as the firm being closed. Again, this effect is automatic and endogenous, so if you then change firm sales or imports, you double-count the effect.
However, this example assumes that the closing firm is a “typical” firm within its industry. If you have information suggesting that its IO parameters would be different than the average firm in the industry, you could apply adjustments to intermediate demand based on the composition of the firm’s actual intermediate inputs. If these adjustments cause a net change in the aggregate input demand, you must balance the input vector by inputting an equal and opposite change into the policy variable “value-added with no effect on sales or employment”. But unless you have information describing how the closing firm’s input composition is different from the average, you could probably ignore this step.
You can perform similar adjustments to the compensation if the company’s average wage exceeds its industry’s average, as reflected in the baseline control. To check this, find the Compensation Rate for the relevant industry in the results of your baseline, and compare it with the average rate that prevailed in the closing firm. Suppose the firm’s average compensation was $1,000 higher than the industry average. When the model takes out employment, it does not remove enough compensation from the affected region. You must input a negative shock to the Compensation (amount) variable, equaling the disparity in the per-person rate ($1,000 in our example) multiplied by the total employees in the firm. This adjustment enables you to fully capture the true income impacts of the firm closure.
The BizDev Blueprint allows you to customize industry data such as intermediate inputs, productivity and compensation, in order to more closely match a specific firm being modeled.