Indirect Coal Impacts

Much of this is due to the low RPC for coal in WY - just 0.288. Although the electric power industry in WY consumed 26,102 short tons of coal, did all of that coal necessarily come from within WY? If you think/know so, then you can adjust the RPC for coal for the Electric Power Generation sector accordingly: Go to Customize > Tradeflows > Industry/Institution RPC tab, select Commodity 3021 from the drop-down menu and change Sector 31's RPC. Also, Sector 31 includes all types of electicity generation, not just from coal, so you may want to zero-out the Coefficient for Commodity 3020 (Oil and natural gas) and add it to Commodity 3021's Coefficient via Customize > Industry Production > Sector 31.

Jenny, Thank you for the response - I think I've found the area that I need more knowledge on IMPLAN. You said "did all of that coal necessarily come from within WY?" I understand how this is relevant, and I think I'm misinterpreting it. IMPLAN's glossary for indirect states: "The impact of local industries buying goods and services from other local industries. The cycle of spending works its way backward through the supply chain until all money leaks from the local economy, either through imports or by payments to value added. The impacts are calculated by applying Direct Effects to the Type I Multipliers." -So, in my coal example, this means that for the electric power industry in Wyoming, "mining coal" will only be counted as an indirect effect if the coal is mined and used for electric power in Wyoming? -I assume coal mined and shipped to other states will obviously not be counted as an indirect effect in Wyoming. -Will coal mined in Montana and shipped to Wyoming count as an indirect effect to Wyoming - with the logic that the output dollars of Wyoming's electric power industry went to pay for the mining workers in Montana? ------- After I posted earlier, I looked at all the indirect job effects of the coal mining industry on the electric power industry - state by state - and found that IMPLAN estimates that there are approximately 5,500 indirect jobs supported by the electric power industry. When I run a national model, the number of indirect coal mining jobs is over 58,000. Thank you, as always, for your immense insight and prompt replies while I learn this new program.

To your questions: Not necessarily - the electric power sector's input purchases are only the first round of indirect effect. Even if the coal purchased by the electric power sector is not mined in Wyoming, other sectors indirectly affected by the electric power sector (i.e., its suppliers and the suppliers' suppliers, and so forth) may purchase coal mined in Wyoming, so there still may be some indirect effect on the Wyoming coal sector. Correct - coal mined and shipped to other states will obviously not be counted as an indirect effect in Wyoming - those shipments are unrelated to Wyoming's electric power sector. No - coal mined in Montana and shipped to Wyoming will not count as an indirect effect to Wyoming. To capture this, you would want to run the impact as an MRIO with a Montana model linked to the Wyoming model. This is b/c the individual state runs do not include the spillover effects to other states in the nation - you would need to run each state as an MRIO linked to the rest of the U.S. to capture those feedback effects that are captured in the U.S. model.

Jenny, Thank you for your response. We are lucky enough in our industry to have "fuel receipts" from the power plants from the Energy Information Administration. We looked at all of Wyoming's coal receipts and found that Wyoming's power industry consumes 100% of its coal from Wyoming mines. With this knowledge, we customized the "trade flows" Industry/Institution RPC for commodity 3021 (coal mining) to 1.0 (changing from the previous 0.298) for sectors 31, and 431 (electric and state/local electric), signifying that 100% of commodity demand is consumed within Wyoming and 0% is imported. After reconstructing the model (options > construct > multipliers), and running the analysis, IMPLAN reported 93 indirect jobs, or approximately 1% of the 7,546 mining jobs in the state. ----- In my first post, I estimated that 6% of all coal mined in Wyoming was used by the electric power industry in Wyoming, and with the coal receipts, I know that 100% of that 6% is local to Wyoming (not imported). Do you have any suggestion of more customization? Maybe the commodity demand number from IMPLAN's defaults is lower than the data at EIA, which I believe to be more accurate on industry specific data. Thank you

Your suggestion gets at the second issue mentioned in post #12738, whereby Sector 31 includes all types of electicity generation, not just from coal. Did you zero-out the Coefficient for Commodity 3020 and add that value to Commodity 3021's Coefficient?

I modified the coefficients, adding oil/natural gas to coal's coefficients, zeroing out the oil/natural gas coefficient, rebuilt the model (construct > multipliers), and ran my scenario again. The result was decrease to about 50 coal mining jobs. For project details: we are looking at the entire power industry, including all fuels (oil/natural gas/coal/nuclear etc). My intuition from looking at the coal data available is that the coal employment (and supplementary IMPLAN reported statistics) should be higher than IMPLAN is modeling while looking at the entire electric power industry. Would zeroing out the oil/ natural gas have decreased the effects of that part of the electric power sector? If so, I would still like to capture the economic impact of oil/natural gas as well as an impact on coal that I believe to be more significant than what is currently being reported by the model. Any further suggestions would be greatly appreciated.

The reason the impact to coal decreased this second time is that when you rebuilt the multipliers after making the changes to the production function, it over-wrote the changes you made to the RPCs. The order as listed in the Customize menu must be followed - in other words, if you were to customize all 4 menu items you would need to: 1. Customize Study Area Data, then reconstruct multipliers 2. Customize Industry Production, then reconstruct multipliers 3. Customize Commodity Production, then reconstruct multipliers 4. Customize Trade Flows, then reconstruct multipliers You are doing steps 2 and 4 but you did step 4 before you did step 2, so when you did step 2 your changes from step 4 were wiped out. So now you need to do step 4 again.

I have started a model for Wyoming from scratch and performed the operations in the order you specified, and IMPLAN reported 144 indirect mining jobs, just under 2% of the 7,546 mining jobs in the state. Are there any other factors that could be affecting a low employment report? If we have better data on local commodity demand, is there a way to incorporate that into the model? With the zeroed out coefficient for oil/natural gas, are we still computing a diverse electric power industry, or has changing the coefficient made the assumption that all commodity being used by the electric power industry comes from coal and excludes oil/natural gas? Thank you

With the zeroed out coefficient for oil/natural gas, you are no longer modeling a diverse electric power industry - changing the coefficient has indeed made the assumption that all commodity being used by the electric power industry comes from coal and excludes oil/natural gas. Note: You will also want to make this change for Sector 431 if you haven't already. Are you running impacts to both sectors 31 and 431? How are you setting up the impacts?

Activity is an industry change, value 1.0 3 events in sectors 31, 428, and 431 (electric utilities, federal electric, local electric) industry sales = output = EIA sales revenue data (which is relatively close for Wyoming, but differed more for other states) reported independently for each industry Then run the analysis. Per your suggestion, the study area has been customized with EIA data, and trade flows coefficients for 31, 428, and 431 have been set to 1 to reflect 100% local use of coal commodity demand.

Looks good. Before setting the RPCs for commodity 3021 to 1.00 for sectors 31, 428, and 431, did you customize the industry production for each of those 3 sectors so that they only used coal instead of a mix of coal and gas? The only thing left to do after that would be to increase the coefficient for coal further still (via Customize > Industry Production), which would decrease the coefficients for other goods and services.

"did you customize the industry production for each of those 3 sectors so that they only used coal instead of a mix of coal and gas?" I did when we were playing with Wyoming, but for our final product, I will not - the customize: industry production will be kept at defaults. The topic of trade flows: We have gathered the coefficients for many of the coal producing states (through the in-state coal receipts) to determine the ratio of local use to export for "trade flows" customization. I have run into a few problems in updating these "trade flows" coefficients: 1. there are states that when I input the coefficient (0.27464 for example in Louisiana), IMPLAN displays an error reading "Edit Error - Your change will exceed available supply and has been cancelled." How is this possible if my coefficient is less than 1? If I believe that coefficient to be true, what must be done to modify the model? 2. There are states, such as Montana, that use coal in the electric power industry in-state and we have computed a coefficient by looking at actual coal receipts by power plants in that state. However, in Customize > Tradeflows > Industry/Institution RPC tab > Commodity 3021 Coal Mining > Sector 31 does not show up at all. Can you provide any insight into why this would be? Is this representing that the default IMPLAN data (or EIA customized study area data) does not think there is any domestic use of the "coal mining" commodity by the electric power sector (31) in Montana?

1. This is possible b/c sector 31 is not the only demander of coal. If you look at Social Accounts > Balance Sheets tab > View By: Commodity Balance Sheet > Industry Demand tab you will see that total industry demand for locally-produced coal is $115,235,904. If you go to the Institutional Demand tab you will see that total institutional demand for locally-produced coal is $384,803,904. Together this is $500,039,808. If you then go to the Industry-Institutional Production tab, you will see that total local production of cal is $500,039,808. All the local coal is already being consumed locally. So you would need to make another industry or institution import more if you want to make sector 31 import less. Or you could increase Louisiana's coal production. 2. The Customize > Trade Flows > Industry/Institution tab of Montana model shows sector 31 as demanding commodity 3021. Have you tried clicking on the "Sector" column heading to sort by sector?

1. I see the issue now, thank you. 2. See attached screenshot from Montana. The view is sorted by sector number. The only customization on this model is changing the study area data to EIA revenue numbers.

Is this a 2010 MT model and did you build it with the default Trade Flow Model RPCs (as opposed to Econometric RPCs)? I am seeing lower RPCs and as I said before, I see sector 31 in the list of sectors purchasing coal. If you'd like you can zip up the model and send it to support@implan.com and we'll take a look at it.

Thank you for sending your model in. When you reduced the output of sector 31, you reduced it to a level that was below value-added and you got an error message stating that "Value added cannot be greater than output". If you ignore this message (i.e., if you click OK and continue on without readjustment), the software automatically reduces the Intermediate Expenditures to a negative number to compensate (i.e., to ensure that Intermediate Expenditures + Value Added = Output). Thus, sector 31 is no longer making any purchases at all. This is why you don't see it purchasing coal and this is why your RPC for coal increased (since sector 31 is no longer buying coal, there is more local coal supply that other industries and institutions can purchase). We suggest decreasing each of the value added components by the same proportion that you decreased output.

Thank you for the input on the value added changes. Can you advise me on how to increase the demand by our industry for coal in a certain region. Back in my Louisiana example, you suggested "...you would need to make another industry or institution import more if you want to make sector 31 import less. Or you could increase Louisiana's coal production." Can I modify how much of each commodity is produced? Can I modify each sector's use of coal in that region? These are areas in which I may have EIA data for that would help solve a problem in West Virginia, who is still showing only about 1% of mining jobs in the state, despite using almost 60% of the coal commodity for that state.

You cannot increase the demand any higher than the existing local supply. If you think IMPLAN's estimate of Output is too low, then customize the Study Area Data to increase Output. If you are okay with the output value for coal but you want sector 31 to purchase more of its coal locally, then you need to decrease the RPC for all the other users of coal: Go to Customize > Trade Flows > Trade Model tab and decrease the Local Use Ratio (RSC) to some smaller percentage (how much smaller depends on how much more local coal you want sector 31 to purchase - you can use an equation or do it by trial and error). This will decrease the RPC for all users of coal. You can then go back to Go to Customize > Trade Flows > Industry/Institution tab and increase the RPC for coal for sector 31.

Jenny, Thank you for the instructions - unfortunately, the problem persists. For West Virginia, we have tracked down output for coal from the EIA. It was close to IMPLAN's study area data, but slightly higher. The maximum amount of jobs I can get by customizing what you've advised is only 1087, approximately 5% of the state's 22,166 coal mining jobs - and that is assuming (as I understand the model) that sector 31 and 431 use coal exclusively and use only locally supplied coal - two assumptions we do not believe to be the case. We believe, by investigating state local coal receipts by power plants, that WV uses approximately 40% of West Virginia mined coal in West Virginia for electric power generation in sectors 31 and 431, and we also have EIA data that suggests that 88% of electric generated in the state came from coal. Here's what has been customized: Study area data > Sectors 31 and 431 (there is no 428 in WV) output, I have also increased the value added components by the same ratio. Study area data > sector 21 (coal) output, and appropriate value added adjustment as well. I then constructed my multipliers, then customized: Industry production for sector 31 and 431, which I maximized to see the limit at their respective "total absorption values" of approximately 0.14 and 0.58 respectively. I customized trade flows in two different examples, leading to 1030.8 indirect mining jobs and 1087 mining jobs. First: I constructed the multipliers again, then customized: Trade Model tab > commodity 3021 to be $0=0% RSC I constructed the multipliers again, then customized: Industry/Institution tab > commodity 3021 > adjusted sector 31 and 431 to "1.0", all other industries showed 0%. After constructing my multipliers again, I ran my model and IMPLAN reported 1030.8 indirect mining jobs supporting 4,357 direct electric power jobs. Second: I constructed the multipliers again, then customized: Trade Model tab > commodity 3021 to be $774,960,900 = 14.652% RSC I constructed the multipliers again, then customized: Industry/Institution tab > commodity 3021 > adjusted sector 31 and 431 to "1.0", all other industries showed ~95%. After constructing my multipliers again, I ran my model and IMPLAN reported 1087.1 indirect mining jobs. The examples described use unnecessary assumptions and still report a number we believe to be not an accurate representation of the impact the electric power industry has on the WV coal mining industry. By intuition, if the WV power sector purchases 40% of the coal mined in WV, the employment supported by the electric power industry must be more than 5% of all mining jobs in the state. Can you provide any advice on further modifying the model? If you need any clarification on my procedure or data, please let me know and I'll be happy to provide detailed answers.

Looking at WY output for Power and Distribution (sector 31) I see an output value of 2.2 billion. If the Power sector produces 40% of the coal produced in WV, then it would need to buy 2.68 billion dollars worth of coal (.4 * 6.7 billion[coal production]). This means the to produce 2.2 billion worth of power, the electric sector buys 2.68 billion worth of coal, which leads to an impossible absorption coefficient of 1.22. We've either vastly underestimated our production of electric power in WV or the estimate of 40% of WV coal production sold to WV electric power production is high. The low indirect coal mining employment number only reflects the indirect coal mining output impact divided by the coal mining output per worker. The EIA, does estimate power sales to states but it is not the same as power production by states.

Doug, Thank you for your response, it was very insightful. You are absolutely correct about the 40% number, and I had mixed up my data. The 40% number is incorrect. To make things easier, I'll articulate all my customization here to prevent the same error. For WV output: Sector 31: $2,379,284,000 Sector 431: $6,294,000 Sector 21: $9,474,865,400 Sector 21 employment: 22,165.5 (IMPLAN default) Sector 21 output/worker: $427,460.04 WV power (31 & 431) purchases $1,342,000,000 of coal (21) from WV, or about 14% of all coal output. To find the target indirect employment, I used the formula you articulated. If I understood you correctly, "indirect coal mining employment" = "indirect coal mining output impact divided by the coal mining output per worker" indirect coal mining employment = $1,342,000,000/($9,474,865,400/22,165.5) = $1,342,000,000/$427,460.04 indirect coal mining employment = ~3,140 jobs If these calculations appear sound, can you help me impute these into the model?

Those calculations do appear sound, as that indirect employment (3,140) is about 14% of current coal employment, in line with your estimates that the power sector purchases about 14% of local coal output. A few thoughts: 1. When editing the output of sectors 31, 431, and 21, did you uncheck the Lock box before clicking Update? If not, the incre1342ase in output will go to value-added components rather than input purchases (i.e., the change will have zero effect on coal purchases). 2. The national BEA Benchmark I-O model shows sector 31 having an absorption coefficient for commodity 3021 of only 0.0316, which is even lower than the coefficient in WV, so I don't believe there is a mistake in WV's production function for sector 31, as far as matching to BEA goes. However, I did notice that sector 431's coefficient for coal is much higher (0.1808), as is its coefficient for oil and natural gas (0.5262), so it appears that the private power generation sector is much more diverse. To guarantee an output effect on the coal sector of $1,342,000,000, we recommend an analysis-by-parts approach, where we model the coal and electricity impact separately (you may want to start from scratch with a clean WV model): 1. Build a new WV model 2. Go to Customize > Industry Production and zero out sector 31 and 431's coefficients for commodity 3020 and 3021. Click Balance and Save, then go to Options > Construct > Industry Accounts. This ensures that the power sector purchase no coal (we will model the coal purchases separately in a later step) and no oil and natural gas (which I believe you've stated is the case? If not, do not do this step for commodity 3020). 3. Go to Customize > Trade Flows > Trade Model tab and set the RSC for commodity 3021 to 0. This ensures no feedback effects to the coal sector. Click Save, the Options > Construct > Multipliers. 4. Go to the Setup Activities screen and create an Industry Change Activity for sector 21, setting the Industry Sales value to $1,342,000,000. Edit the Employment figure to reflect your known employment figure (3,140), then run the impact. The total output effect to the coal sector will be $1,342,000,000 and the total employment effect in the coal sector will be 3,140 5. Finally, return to the Setup Activities screen and run your scenarios on the power sectors. Since we've set these sectors' coefficients for coal to 0 and the RSC for coal to 0, there will be no double-counting to the coal sector.

A couple of other thoughts on this. The main problem we are seeing here is that the BEA's production function that we use for our model is for both power generation and distribution with a mix of all different types of power production such as natural gas, hydroelectric, nuclear etc. The coal purchases of your specific coal fired plant simply is not captured with a production function with such a mix of spending. The method Jenny outlined is going to give you the best estimate baring actually creating a unique production function for your coal fired plant.

Thank you Jenny and Scott, analysis by parts will work for our application. As a question of IMPLAN scenario results interpretation: -I will run a scenario with only the COAL activity/events to get the jobs, wages, and output. -I will run a separate scenario with the ELECTRIC power industry activity/events. -The results will be combined on my published report, using the "direct" impacts reported in the COAL scenario added to the "indirect" impacts of the ELECTRIC power scenario. Is this an accurate interpretation of the analysis by parts? Will the indirect and induced effects of the COAL scenario contribute to the indirect and induced effects of the ELECTRIC power scenario?

Great questions. That is correct - you want to add the "direct" effects from the coal activity to the indirect effects of the electric power scenario. You also want to add the indirect and induced from the coal scenario to the indirect and induced, respectively, of the electric power scenario.