Analysis of grant program in healthcare sector


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    Please see responses to the questions below:

    1. I don’t have this specific information, but it is generally assumed that these doctors are full-time employees.  Should I be using the headcount to FTE conversion and actually adding more than a single employee to the sector to reflect a full-time hire?

    Employment data in IMPLAN follows the same definition as BEA REA and BLS CEW data, which is FT/PT annual average. Thus, it adjusts for seasonality but does not indicate the number of hours worked per day. Since the user knows (or at least appears willing to assume) that the doctors are full time, then we would recommend using the FTE conversion to increase the employment input value.

    a. That said, it would be better to start with Employee Compensation instead of employment (more on that below) and then adjust the employment value to the known employment (though it would technically be more correct to still make the FTE adjustment so that employment units are consistent).


    2. Because the output per worker is likely higher for a doctor than the average for all employees in the hospital industry, is there a better way to approximate the doctor’s actual impact on “sales” in the hospital without bringing in additional outside information? I know that editing the labor income only affects the induced effect, and I assume I am understating the true production impact of each doctor.  I don’t have information on the “fully-loaded wages” or total compensation package for each doctor, so I’m replacing the labor income value with only a salary figure, so I’m already probably understating the induced impact.  At some point, I hope to put some numbers together on the doctors’ productivity, but do not currently have those figures.

    If Industry Sales is unknown, either employment, employee compensation, or proprietor income can be used to estimate Industry Sales (the first input used will determine Industry Sales and all other values are then estimated based on Industry Sales). The user's situation is a perfect example of why we recommend starting with employee compensation instead of employment in cases in which both are known. In general, higher compensation is related to higher output generation potential (as the customer points out, a doctor is likely to have a higher per worker output than the average employee and is also likely to be paid more).


    As to the issue with only having a wage and salary value instead of an employee compensation number, the FTE conversion sheet also includes directions for converting Wage and Salary to EC. We recommend converting known Wage and Salary to EC and then using the EC value as the first input.

    3. In aggregating up to get the state impact, would it be reasonably accurate to sum up the individual county impacts? Because the impacts are small, using multi-regional models does not significantly change my individual results for each doctor.  I figured that going through each county and doing separate MRIOs might not be worth the trouble, but it’s certainly something I could do.

    If the goal is to see the impact at both the county and state level, we recommend MRIO. Adding up individual county impacts simply sums county level impacts and ignores ripple effects that would occur throughout the state but outside those few specific counties. Even if the individual doctor impacts are not likely to be large, the MRIO approach does tell a more complete story.
    a. Note: If you do utilize MRIO, you will need a separate set of models for each MRIO analysis. Please see this article.

    4. I am particularly interested in the state and local tax impact of this program.  Is there anything that I am doing, especially with respect to aggregating county impacts, that would skew the tax results incorrectly? I’d prefer to give a conservative estimate of the impact, but would like it to be as accurate as possible.

    If tax results are of specific concern, we do recommend taking a close look at the Direct Tax Results for TOPI.


    Notes on TOPI taxes

    · Note that our source data on total TOPI taxes by state and by industry are collected at a more aggregate level (approximately 90 sectors) than the sectoring scheme of IMPLAN. We distribute TOPI by state and industry to detailed industries using national data and other proxy data.

    · Total TOPI taxes are industry- and place-specific, but the allocation of TOPI among its components, e.g., sales tax and property tax, is only place-specific.

    · For these reasons, we recommend constructing your own estimates of direct taxes whenever possible and using the model estimates for indirect and induced tax impacts.


    You can find more information at the link below.

    Please let me know if you have any additional questions!

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