Integration of Disaggregated Elasticity Estimates
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1 Integration of Disaggregated Elasticity Estimates into the Sub-Regional Timber Supply (SRTS) Model R.C. Abt (NC State University), R.H. Beach (RTI International), Jui-Chen Yang (RTI International), F.W. Cubbage (NC State University) Forest Sector Modeling Conference November 19, 2008 Seattle, Washington RTI International is a trade name of Research Triangle Institute
2 Objectives Estimate disaggregated harvest elasticities based on more comprehensive and representative data. Run SRTS using default and empirical harvest elasticities. Examine effects of disaggregated empirical elasticities on forest management decisions for forest outputs and inventories. 2
3 Overview of Multi-Product SRTS Model Demand (by product product demands assumed independent) Qd = ƒ(price, Demand Shifters) Supply (by product, region, owner) Qs = ƒ(price, Inventory t-1 ) Constant elasticity functional form Demand Assumptions Can Be Harvest Driven Solve for price consistent with exogenous harvest level Short run basin level studies Demand Driven Solve for price/harvest consistent with exogenous demand Medium run regional analysis Price Driven Long-run harvest at constant real prices 3
4 Overview of MP-SRTS Model (continued) Inventory, growth, removals and acres are tracked by: Region: FIA survey units Owner: Forest Industry/Corporate, Non-Corporate/NIPF (private owners only) Species group: Pine, Soft Hardwood, Hard Hardwood Management type: Planted Pine, Natural Pine, Mixed Oak- Pine, Upland Hardwood, Lowland Hardwood Age class: 5 year cohorts 4
5 Multi-Product Equilibrium in SRTS Demand Price or Harvest Projection by Product Demand Elasticities by Product Equilibrium Price by Product Supply/Inv Shifts by Product- Owner-Unit Supply Price & Inventory Elasticities by Product-Owner-Unit Inventory Module by product Inv t+1 =Inv t +Grw - Rem Removals by Product-Owner-Unit Goal Program Removals by mgt. type-age class 5
6 Data Plot- and tree-level data from Forest Inventory and Analysis (FIA) program of Forest Service AL, AR, FL, GA, LA, MS NC, OK, SC, TN, VA, TX-East Timber prices from Timber Mart South (TMS) from Norris Foundation Stumpage and delivered timber prices by state, TMS regions & product type (e.g., pulpwood & sawtimber in softwoods & hardwoods) TMS regions vs. FIA units (Prestemon and Pye, 2000) Short- and long-term interest rates from Federal Reserve County-level per capital incomes from Census Climate data from U.S. Southern Global Change Center Temperature and precipitationp 6
7 Harvest Probit Regressions SW pulpwood SW sawtimber HW pulpwood HW sawtimber # of obs Product price + *** + *** + + *** Being NIPF + + ** - *** - *** Temperature - *** - *** + ** + *** Precipitation * - Easy access + *** + *** + *** + *** County level per capita - *** - *** - *** - *** income Mgt type = Plt Pine - *** - *** + *** + *** Mgt type = Nat Pine - *** - *** + *** + *** Mgt type = Mix Pine *** + *** Mgt type = Upland Hwd + *** + *** + *** + *** % inventory is softwood + *** + *** - *** - *** 7
8 Summary of Default and Imputed Elasticities Disclaimer 8
9 Summary of Default and Imputed Elasticities Combined SW (min max) Combined HW (min max) Default elasticity set at 0.30 for all owners and all regions State Corp. NIPF Corp. NIPF BEFORE AL AR FL Disaggregated elasticities estimated by FIA unit, ownership, and product type Followed Prestemon and Wear (2000) 51 FIA survey units 2 owner types 2 product types (SW & HW) AFTER GA LA MS NC OK SC TN TX VA Avg
10 Overview of Softwood Price, Inventory, and Removals, BEFORE AFTER Pine Pulpwood p no demand inc. Pine Pulpwood no demand inc Price Inventory Removals = = Price Inventory Removals = = Pine Sawtimber no demand inc. Pine Sawtimber no demand inc Price Inventory Removals = Price Inventory Removals =100 10
11 Aggregate Percentage Changes in Price, Inventory, and Removals, (%) Price Inventory 15 Removals SW-PULP SW-SAW HW-PULP HW-SAW 11
12 Overview of Hardwood Price, Inventory, and Removals, BEFORE Hardwood Pulpwood AFTER Hardwood Pulpwood no demand inc. no demand inc Price Inventory Removals = = Price Inventory Removals = = Hardwood Sawtimber no demand inc. Hardwood Sawtimber no demand inc Price Inventory Removals = Price Inventory Removals =100 12
13 Aggregate Percentage Changes in Price, Inventory, and Removals, (%) Price Inventory 15 Removals SW-PULP SW-SAW HW-PULP HW-SAW 13
14 Starting Inventory & Removals: SW-Pulp 14
15 Percent Changes in Softwood Pulpwood Removal, 2025 BEFORE AFTER 15
16 Changes in Industry/Corporate SW Pulpwood Removals, AL-SW_S AL-SW_N AL-SE AL-WCtrl AL-NCtrl AL-North AR-SDelt AR-NDel AR-SW AR- AR-Ozark FL-NE FL-NW FL-Ctrl FL-South GA-SE GA-SW GA-Ctrl GA-NCtrl GA-North LA-NDelt LL LA-SDelt LA-SW LA-SE LA-NW MS-Delta MS-North MS-Ctrl MS-South MS-SW NC-SCP NC-NCP NC-Pdm NC-Mtn OK-SE OK-NE SC-SCP SC-NCP SC-Pdm TN-West TN-WCtrl TN-Ctrl TN-Plat TN-East TX-SE TX-NE VA-CP VA- VA-NPdm VA-NMtn VV VA-SMtn (Million Tons)
17 Changes in NIPF SW Pulpwood Removals, AL-SW_S AL-SW_N AL-SE AL-WCtrl AL-NCtrl AL-North AR-SDelt AR-NDel AR-SW AR- AR-Ozark FL-NE FL-NW FL-Ctrl FL-South GA-SE GA-SW GA-Ctrl GA-NCtrl GA-North LA-NDelt LL LA-SDelt LA-SW LA-SE LA-NW MS-Delta MS-North MS-Ctrl MS-South MS-SW NC-SCP NC-NCP NC-Pdm NC-Mtn OK-SE OK-NE SC-SCP SC-NCP SC-Pdm TN-West TN-WCtrl TN-Ctrl TN-Plat TN-East TX-SE TX-NE VA-CP VA- VA-NPdm VA-NMtn VV VA-SMtn (Million Tons)
18 Percent Changes in Softwood Pulpwood Inventory, 2025 BEFORE AFTER 18
19 Changes in Industry/Corporate SW Pulpwood Inventory 2025 SW Pulpwood Inventory, W_S W_N L-SE WCtrl NCtrl North SDelt NDel -SW ARzark L-NE -NW -Ctrl outh A-SE A-SW A-Ctrl NCtrl North NDelt SDelt A-SW A-SE -NW Delta North -Ctrl outh -SW SCP NCP Pdm -Mtn K-SE K-NE SCP NCP Pdm West WCtrl -Ctrl -Plat East X-SE X-NE A-CP VA- Pdm NMtn SMtn ion Tons) -1 AL-SW AL-SW AL AL-W AL-N AL-N AR-S AR-N AR AR-Oz FL FL- FL FL-S GA GA GA GA-N GA-N LA-N LA-S LA LA LA- MS-D MS-N MS MS-S MS NC-S NC-N NC-P NC- OK OK SC-S SC-N SC-P TN-W TN-W TN TN- TN-E TX TX VA VA-NP VA-N VA-S (Mill
20 Changes in NIPF SW Pulpwood Inventory 2025 SW Pulpwood Inventory, W_S W_N L-SE WCtrl NCtrl North SDelt NDel -SW ARzark L-NE -NW -Ctrl outh A-SE A-SW A-Ctrl NCtrl North NDelt SDelt A-SW A-SE -NW Delta North -Ctrl outh -SW SCP NCP Pdm -Mtn K-SE K-NE SCP NCP Pdm West WCtrl -Ctrl -Plat East X-SE X-NE A-CP VA- Pdm NMtn SMtn ion Tons) -1 AL-SW AL-SW AL AL-W AL-N AL-N AR-S AR-N AR AR-Oz FL FL- FL FL-S GA GA GA GA-N GA-N LA-N LA-S LA LA LA- MS-D MS-N MS MS-S MS NC-S NC-N NC-P NC- OK OK SC-S SC-N SC-P TN-W TN-W TN TN- TN-E TX TX VA VA-NP VA-N VA-S (Mill
21 Starting Inventory & Removals: SW-Saw 21
22 Percent Changes in Softwood Sawtimber Removal, 2025 BEFORE AFTER 22
23 Percent Changes in Softwood Sawtimber Inventory, 2025 BEFORE AFTER 23
24 Starting Inventory & Removals: HW-Pulp 24
25 Percent Changes in Hardwood Pulpwood Removal, 2025 BEFORE AFTER 25
26 Percent Changes in Hardwood Pulpwood Inventory, 2025 BEFORE AFTER 26
27 Starting Inventory & Removals: HW-Saw 27
28 Percent Changes in Hardwood Sawtimber Removal, 2025 BEFORE AFTER 28
29 Percent Changes in Hardwood Sawtimber Inventory, 2025 BEFORE AFTER 29
30 Conclusions and Next Steps Disaggregated harvest elasticities do have effects on forest management decisions for outputs and inventories. Disaggregated elasticities estimated from comprehensive and representative data may significantly improve performance of SRTS in the long run. As to our next steps Prepare paper for submission and consideration for the special issue of Forest Policy and Economics Re-estimate estimate elasticities after incorporating the latest available FIA plot and tree data Estimate regeneration and timber stand improvement (TSI) elasticities, incorporate them into SRTS, and evaluate the results 30
31 Overview of MP-SRTS Model (continued) Demand Assumptions Can Be Harvest Driven Solve for price consistent with exogenous harvest level Short run basin level studies Demand Driven Solve for price/harvest consistent with exogenous demand Medium run regional analysis Pi Price Di Driven Long-run harvest at constant real prices Harvest starts with FIA harvest distribution change modeled as a function of stumpage price and inventory (by user-defined product) 31
32 Overview of MP-SRTS Model (continued) Demand (by product product demands assumed independent) Qd = ƒ(price, Demand Shifters) Supply (by product, region, owner) Qs = ƒ(price, Inventory t-1 ) Constant elasticity functional form Model Solution Given the demand for this scenario (by year and product) Given the supply from changing inventory (by region, owner, year and product) Find the price - harvest level and harvest location (region/owner) that clears the market 32
33 SRTS Market By Product Demand is modeled at the aggregate level, i.e. whatever regions are included in the model are assumed to face the same demand curve Inventory is modeled to shift supply by product Binary search is used to find the market clearing price given the location of the aggregate demand curve and the location of the sub-basin/owner supply curves Supply curves are shifted by the previous change in inventory When the market clearing price is found, harvest levels for that price are calculated by sub-region/owner 33
34 Data Plot- and tree-level data from Forest Inventory and Analysis (FIA) program of Forest Service AL (1982 & 1990), AR (1988 & 1995), FL (1987 & 1995), GA (1989 & 1997), LA (1984 & 1991), MS (1987 & 1994), NC (1984 & 1990), OK (1986 & 1993), SC (1986 & 1993), TN (1989 & 1999), TX-East (1986 & 1992), VA (1984 & 1992) Timber prices from Timber Mart South (TMS) from Norris Foundation Stumpage and delivered d timber prices by state, TMS regions & product type (e.g., pulpwood & sawtimber in softwoods & hardwoods) TMS regions vs. FIA units (Prestemon and Pye, 2000) Short- and long-term interest rates from Federal Reserve County-level per capital incomes from Census Climate data from U.S. Southern Global Change Center Temperature and precipitation 34
35 Conclusions and Next Steps Disaggregated g harvest elasticities do have effects on forest management decisions for outputs and inventories. Disaggregated elasticities estimated from comprehensive and representative data may significantly improve performance of SRTS in the long run. As to our next steps Prepare paper for submission and consideration for the special issue of Forest Policy and Economics Re-estimate elasticities after incorporating the latest, available FIA plot and tree data AL (2000, 2004), AR (2005), FL (2005), GA (2004), LA (2005), MS (no recent data), NC (2002), OK (no recent data), SC (2001, 2005), TN (2004), TX-East (2005), VA (2003) Estimate regeneration and timber stand improvement (TSI) elasticities, incorporate them into SRTS, and evaluate the results 35
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