The Economic Effects of Wildfires on Colorado County Level Personal Income:

A Synthetic Modeling Approach

(Preliminary Results)

By Dr. Robert J. Sonora1

August 27, 2018

In this White Paper I endeavor to measure the economic impacts of wildfires on small local

economies. Smaller communities, which have less diverse economies, are more susceptible to the

effects negative shocks as a relatively large percentage of their economy is in a small number of

sectors, for example, tourism, agriculture, or mineral extraction. A single negative shock to these

economies can be devastating. I employ a relatively new technique to analyzing the impacts of

a shock using four separate wildfires and the cumulative impacts of three fires over two decades

on counties in Colorado’s western slope, with particular attention paid to La Plata County. The

overall findings are that wildfires tend to have relatively short term impact on county economies.

However, it should be noted, that none of these fires studied did much damage to municipalities or

public infrastructure, which are likely to have longer lasting impacts on rural communities.

Figure 1: Number of Fires by Year (with trend)

Source: National Oceanic and Atmospheric Administration

(https://www.ncdc.noaa.gov)

Wildfire activity over the past decade or so have risen. Figures 1 – 3, show the total number of fires,

acres consumed by wildfire, and acres per fire from 2000–2017,2 the black line is the polynomial

trend. While the number of fires has trended down the total acres and acres per fire has risen

1Professor of Economics, Fort Lewis College, Durango, CO USA, sonora_t@fortlewis.edu2Data from the National Oceanic and Atmospheric Administration, https://www.ncdc.noaa.gov.

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steadily. Two of the largest fires in history, the Thomas Fire in Ventura and Santa Barbara counties

CA and the Nazko Fire in BC, Canada, consumed over 200,000 and 1 million acres respectively.

According to research done by Kitzberger et al (2017), a growing number of wildfires are likely

going to continue for the foreseeable future.3

Figure 2: Number of Acres by Year (with trend)

Source: National Oceanic and Atmospheric Administration

(https://www.ncdc.noaa.gov)

With global temperatures on the rise, Figure 4, it is likely that the number and intensity of wildfires

will continue. The temperatures shown are relative to the century average temperature, and as

can be seen the post turn of the last century has consistently above average temperatures, with

the years 2015 and 2016 being about 1 C◦. In Colorado, the effects of changing temperatures and

drought conditions, see Figure 5 are particularly acute in the Southwest corner of the state.4

This is particularly apparent if you consider Figure 6 which shows the “relative greenness” of the

US and we can see that the Southwest and Four Corners could become susceptible to an increase

in wildfires, where the drought is particularly acute, the SW corner of Colorado. Both Montezuma

and La Plata counties are experiencing an exceptional drought. A recent report on the economic

and social effects of the 2012 Drought on the state of Colorado demonstrates that the ongoing

drought could have lasting impacts on agriculture in the region.5

Table 1 data from the Insurance Information Institute (www.iii.org) shows the most expensive

fires in the United States in current and real 2016 dollars. The majority are in California, and are

3Kitzberger T, Falk DA, Westerling AL, Swetnam TW (2017) “Direct and indirect climate controls predict hetero-geneous early-mid 21st century wildfire burned area across western and boreal North America.” PLoS ONE, 12(12):e0188486. https://doi.org/10.1371/journal.pone.0188486.

4The two most acute drought conditions are: Extreme Drought (dark orange): Major crop/pasture losses;Widespread water shortages or restrictions; in the US about 5.5% of the country is in Extreme drought. ExceptionalDrought (brown): Exceptional and widespread crop/pasture losses; shortages of water creating water emergencies;1.6% of the US, source: National Integrated Drought Information System (NIDIS) drought.gov.

5Pritchett, James, Chris Goemans, and Ron Nelson (2014). “Estimating the Short and Long-term Economic andSocial Impacts of the 2012 Drought in Colorado”, white paper.

2

Figure 3: Number of Acres per Fire (with trend)

Source: National Oceanic and Atmospheric Administration

(https://www.ncdc.noaa.gov)

Figure 4: C◦ Above Century Average Temperature (with trend)

Source: National Oceanic and Atmospheric Administration

(https://www.ncdc.noaa.gov)

in less rural areas. The 1991 Oakland Hills Fire, in California, is the most expensive fire as of this

writing. The most expensive in Colorado was the 2012 Waldo Canyon Fire outside of Colorado

Springs, with a total cost of $477 million in 2016 inflation adjusted dollars. The current Carr Fire,

near Redding CA, which has burned over 115 thousand acres, is likely to be added to this list.

Synthetic Modeling

Most empirical studies, especially in medical trials or longitudinal surveys, are able to rely on a

“control” group as compared to the “treated” group to make meaningful recommendations. How-

ever, in most social science studies, such comparisons are difficult to make other than comparing,

for example, the effects of minimum wage laws in one state versus another state. However, re-

3

Figure 5: Drought Conditions: Colorado 2018

Source: NIDIS (www.drought.gov/)

Figure 6: “Relative Greenness”: 6-7/2018

Source: National Interagency Coordination Center, USGS

(https://www.predictiveservices.nifc.gov/)

cently a methodology was developed by Abadie et al (2010)6 to use other similar data to produce

a “synthetic” control which we can then use to compare to what actually happened. Thus, we

can compare a variable which has been “treated” by some exogenous factor – like a fire – to the

synthetic control. The synthetic variable is constructed from other “like” variables. Keeping with

the medical analogy, the synthetic variable is the placebo control group which is then compared to

the sample of those that receive the treatment, or new medical remedy.

In this paper, I estimate the effects of wildfires on countywide real total personal income, in thou-

6Alberto Abadie, Alexis Diamond & Jens Hainmueller (2010) “Synthetic Control Methods for Comparative CaseStudies: Estimating the Effect of California’s Tobacco Control Program”, Journal of the American Statistical Asso-ciation, 105, 493-505.

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Table 1: Most Costly Wildfires in the United States

Estimated insured loss ($mil.)Rank Date Name, Location Current dollars In 2016 dollars

1 Oct. 20-21, 1991 Oakland Hills Fire, CA $1,700 $2,7462 Oct. 21-24, 2007 Witch Fire, CA 1,300 1,4883 Oct. 25-Nov. 4, 2003 Cedar Fire, CA 1,060 1,3624 Oct. 25-Nov. 3, 2003 Old Fire, CA 975 1,2535 Nov. 28-30, 2016 Great Smoky Mountains

Fire, TN938 938

6 Sep. 12-14, 2015 Valley Fire, CA 921 9337 Nov. 2-3, 1993 Topanga Fire, CA 375 5788 Sep. 4-9, 2011 Bastrop County Complex

Fire, TX530 572

9 Oct. 27-28, 1993 Laguna Canyon Fire, CA 350 54010 Jun. 24-28, 2012 Waldo Canyon Fire, CO 450 477

Source: Insurance Information Institute (www.iii.org/)

sands of dollars, which is used as proxy for county level output.7 The “treatment” is wildfires.

Synthetic income for each county is constructed from the other counties and two controls variables,

employment, government employment, and, to correct for recessions, the log change in transfer

payments – during recessions, transfer payment growth increases, and vice-versa for expansions.

All data is inflation adjusted, where pertinent, using the price of consumer expenditures. All the

data is annual and from the Bureau of Economic Analysis (BEA) for the period 1969 – 2016 for 63

counties, Bloomfield county has been “rolled into” Boulder county by the BEA and is not part of

the analysis.

Adding more control variables would attractive. This study could be considered part of the eco-

nomic growth literature, and the list of controls would include: education, productivity, investment,

etc. However, most of this data is either not available, or only available for a short period of time.

For example, the percentage of BA degree holders at county level is only available from 2010-16.

The basic framework is as follows: consider two alternative “realities”, Yi is the realized outcome,

in this case total real personal income, of an external treatment and Y Si is the same outcome which

was not treated, i.e. the synthetic variable.8 The relationship between these two possible outcomes

is given by:

Yct = Y Sct + αctDct (1)

where c = 1, . . . , C indexes counties, the total number of which is denoted C, or 63; t represents

7Unfortunately, no data for county output is currently available. However, the Bureau of Economic Analysisrecently announced plans to construct GCP (gross county product) data.

8Interested readers are encouraged to have a look at the original reference, Abadie et al (2010) in Footnote 6above.

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time, and D is an indicator variable equal to one post-treatment,

Di =

{1 if i = c and t ≥ T0,

0 otherwise.

c is the county in question, e.g. La Plata, T0 is the date of the treatment, that is the year the fire

occurs. Y Si is constructed from 63 of 64 Colorado counties, including the county being analyzed,

the analysis does not include Bloomfield, see above. Essentially, Y Si is the weighted average of the

63 counties, that is: Y Sc =

∑i∈C ωiYi,

∑ω = 1, where ωi is the weight associated with county

i. Note, not all of the counties need be included in the calculation and some county weights will

be larger than others. The algorithm then chooses the “best” synthetic control from thousands of

candidate synthetics using the root mean squared prediction (RMSP) error statistic.

The term αiDi is the degree the actual data deviates from the synthetic control variable, with D

representing the treatment. Thus, if a fire is a negative shock, α < 0 and observed income is lower

than synthetic income. Note too, α need not be the same for each period. In this analysis, α is a

one time estimate, however, below we relax this restriction to allow for multiple events. It is the

relationship between Yi?≷ Y S

i that we are interested in.

There is an advantage to this approach over the standard static “multiplier” methodology. The

majority of impact studies investigate how an “event” impacts, say, an economy over the course of

a year. The basic premise of these studies is that a shock reverberates through an economy via the

direct and indirect effects, the so-called multiplier. Thus the gross impact on economy is equal to

direct effects + indirect effects. Thus, the total impact on GDP is

∆GDP = (1 + z) · shock, 0 ≤ z < 1

where z is the multiplier. In this scenario, the multiplier effect is the same whether the shock is

positive or negative.9 This method has several issues. First, economic impact studies can be highly

politicized leading to a significant moral hazard leading to using multiplier which exaggerate the

economic impact. Second, many analyses misuse the multipliers themselves, believing that a short

term event, e.g. the Olympics, have the same impact as a new business which would increase output

for the course of a year rather than just a few weeks or months. Similarly, many analyses implicitly

assume that a shock is new spending, not redirected spending, thereby attributing all the impact

to a single economic event. Fourth, the analysis assumes that z is symmetric irrespective of the

sign of the shock, i.e. positive or negative. However, there is no reason to assume that z+ = z−, a

negative shock may cause a deeper decline than the expansionary impacts of a positive shock, or

vice-versa. Finally, this method ignores the dynamic impacts of a shock. For example, the Olympics

9Some have argued that z rather than be strictly non-negative, can also take a negative value, thus becoming a“divider”.

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might in fact attract many tourists, boosting spending in the host country, but investment in tourist

infrastructure may “crowd-out” new capital spending, reducing productivity over subsequent years.

Or the country may go into debt and be unable to increase other public investments with the same

outcome. Economists have made efforts to fix several of these latter three issues by using other

econometric techniques, but a discussion is beyond the scope of this paper.

Synthetic analysis has been used in a variety of contexts to measure the economic impact of policy

decisions. Recently Springford (2018) used this technique to estimate the losses of the Brexit vote

to the UK economy.10 He found that as a result of the Brexit vote, UK real GDP is about 2.1%

smaller compared to a synthetic UK economy. His result compares favorably to another paper

in VOX on the effects of Brexit which, as of November, 2017, the negative impacts were roughly

–1.3%.11 The differences in these estimates can be attributed to timing, the Springford analysis

was seven months later, and variables added.

Recent Western Colorado Wildfires

In this section we analyze the economic effects of regional four wild fires from 1994 – 2008 using

synthetic methods. The fires are: the Black Ridge Fire in La Plata County (LPC, 1994); the

Missionary Ridge Fire (LPC, 2002); the Bircher Fire (Montezuma, 2000); and the Spring Creek

Complex Fire (Garfield, 2002). There were numerous other fires over the period, but these were

chosen in particular because they are: (i) generally constrained to a single county, unlike, e.g. the

Hayman Fire (2002) which was in Clear Creek, Teller, Park, Jefferson, Douglas and El Paso counties;

(ii) relatively near populated areas (except Moffat, Co.); and (iii) they are in the Western Slope, and

near La Plata Co., where the 416 and Burro Fire Complex have recently been extinguished. Ideally,

in synthetic modeling, you would not include two counties that are undergoing the same treatment

at the same time, e.g. La Plata and Garfield counties in 2002, but they they are sufficiently far

away so as to not likely create much problems.

Thus far this year, the cumulative number of fires is about normal, but the number of acres burned

is above normal, though much of this is due to the growth of fires in March, which is the fourth

highest on record for March, Figure 7.12

Meanwhile, in addition to the two fires north of Durango, two other fires are currently burning

in Colorado, the Spring Creek Fire, near Ft. Garland, and Weston Pass Fire, around Fairplay.

Collectively the number of acres, to date, of the three 2018 fires is: 416/Burro Fire (55,000 acres);

the Spring Creek Fire (106,000 acres) and the Weston Pass Fire (13,000 acres). To compound

10John Springford, “Whats the cost of Brexit so far?”, Insight, Centre for European Reform, 23 June 2018.11Benjamin Born, Gernot Muller, Moritz Schularick, Petr Sedlacek (2017), “£300 million a week: The output cost

of the Brexit vote”, VOX: CEPR Portal, 28 November 2017.12NOAA, https://www.ncdc.noaa.gov/sotc/fire/201803

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Figure 7: Cumulative Number of Fires and Acres Burned: 2018

Source: National Interagency Coordination Center (https://www.predictiveservices.nifc.gov/)

the costs of fires are the possibility that the Monsoon season could lead to flooding from the

de-forestation effects from the various fires.13

All background information on the fires analyzed is from Wikipedia page “List of Colorado wild-

fires”.14 With the exception of the Bircher Fire, this type of analysis restricts the impact to the

county where the fire physically was. Because there is no doubt the impact was probably felt in

adjoining counties as well, for example, the Bircher Fire in Mesa Verde likely had an impact on La

Plata and Montezuma, CO as well as San Juan, NM.

13Kieran Nicholson, “Colorado wildfire update: Monsoon moisture calms flames, raises threatsof flash floods”, The Denver Post, July 12, 2018 (https://www.denverpost.com/2018/07/12/colorado-wildfire-update-flash-flood-threats/).

14https://en.wikipedia.org/wiki/List_of_Colorado_wildfires

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LPC 1994 Black Ridge Fire

The Black Ridge Fire, south of Durango, burned roughly 13,234 acres in 1994, and at the time was

the third largest wildfire in Colorado. Figure 8 has two graphs, which will be presented for each

fire, panel (a) shows the treated income in bold and the synthetic control as dashed, in thousands of

dollars. The vertical line is represents the year the fire occurred, that is, the date of the treatment.

The synthetic control is the level of income that would existed had the fire not happened. The

second panel, (b), shows the percentage difference between the treated data and the synthetic

control, 100 · (Yct/YSct − 1), ± five years. If this is positive, then treated income is above synthetic

income, and vice versa. In general, we might expect the data that has been treated by the fire to

be lower post, or during, the fire.

Figure 8: Treated and Synthetic Real Personal Income: LPC 1994 Fire

(a) Personal Income (b) % Difference

Data Source: Bureau of Economic Analysis (www.bea.gov)

If we look at panel (a) we can see that fire had a slight positive impact on income, or, perhaps more

likely, no real impact on county income. This fire probably had little impact on the underlying

economy, not effecting tourism too much nor other industries at the time. While transportation

from the south of Durango may have been encumbered by fire fighting infrastructure, it had no

real impact on the economy, and may have temporarily boosted activity.

The percentage difference, in panel (b), we see that the treated income rose to about 5% of the

synthetic income at the time of the fire, and stayed above 0% through 1998, or so. The decline in

later income is likely due to other factors affecting the economy in the late 1990s. It’s also worth

noting, that a recession was around the corner, in 2001 and the US economy was beginning to cool

in 2000.

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LPC 2002 Missionary Ridge Fire

The Missionary Ridge fire was substantially larger than the Black Ridge fire, consuming roughly

72,000 acres north of Durango. It started June 9, 2002 and cost $40 million and the death of one

firefighter. 46 houses and cabins were destroyed. This fire is probably the closest to the current

419-Burro fire as it was relatively near the town of Durango and will likely have a considerable

effect on tourist activity in La Plata county, closing down both Purgatory and the Durango &

Silverton Train for a time. Results of the test are in Figure 9.

Figure 9: Treated and Synthetic Real Personal Income: LPC Missionary Ridge (2002)

(a) Personal Income (b) % Difference

Data Source: Bureau of Economic Analysis (www.bea.gov)

In contrast to the Black Ridge Fire, the Missionary Ridge Fire did have a significant impact on

the local economy. Looking at both panels we see that treated income was about 8% below the

synthetic control or roughly $250 million. However, the impact of this fire was corrected relatively

quickly, by 2004, La Plata income was above synthetic income. There was another short reduction

in 2005, but it was not significant

Table 2 shows the counties and weights used to construct the synthetic La Plata county, note, at

three decimal places, these may not sum to 1.00 because of rounding. While some these counties

may seem “un-similar” to La Plata, though the counties with largest weights are in southern and

western Colorado, it was this combination of counties which minimized the RMSP statistic and

represent the best fitting synthetic control.

Montezuma, Co 2000 Bircher Fire

This fire was in Mesa Verde national park and burned almost 24,000 acres in Montezuma county

(MON). Given that a fire in one the regions most visited tourist attraction is likely to have a

10

Table 2: Counties and weightsCounty Weight

Alamosa 0.228Conejos 0.215Douglas 0.045El Paso 0.017Gunnison 0.003Larimer 0.027Mesa 0.058Moffat 0.299Saguache 0.103Weld 0.004

Sum 1.000

regional effect, we next consider the effects of the Mesa Verde fire on La Plata county by treating

LPC rather than Montezuma. Figure 10 shows the impact of the fire on Montezuma while La Plata

is in 11. For Montezuma county the fire’s impact is primarily in slowing the local economy down

from what it could have been. The figure shows that the treated economy was about 2% of the

synthetic economy, but the year of the fire, it fell to just below 0% of the synthetic. However, the

following two years the treatment stayed above the synthetic control. Again, some of this could be

explained by the lead up to the 2001 recession.

Figure 10: Treated and Synthetic Real Personal Income: MON Bircher (2000) Fire

(a) Personal Income (b) % Difference

Data Source: Bureau of Economic Analysis (www.bea.gov)

Interestingly, but perhaps not surprisingly, the impact on La Plata was significantly larger, though

we can see that the difference between the treated and synthetic La Plata were already in by 1998.

The fire hastens and deepens the decline, so that in the year following the fire, income fell to

10% below the synthetic levels. It should be added, however, that the synthetic level of income

11

Figure 11: Treated and Synthetic Real Personal Income: Bircher Fire, Effect on LPC

(a) Personal Income (b) % Difference

Data Source: Bureau of Economic Analysis (www.bea.gov)

is constructed from the weighted average of other Colorado states which we also affected by the

2001 recession. Because the data includes the effects of the recession in the synthetic control,

the deviation of the treated series is net of the recessionary effects. The synthetic La Plata was

generated from the counties and weights in Table 3. Mineral county, to the Northeast, contributed

about two-thirds to the synthetic La Plata. Notice, the weights are not necessarily the same, as

the nature and timing of the shock differ across the two episodes.

If we consider Figures 9 and 11 side-by-side, it is worth making a couple of observations. First, the

impact of the 2000 fire in Montezuma had a larger impact on the La Plata than the 2002 LPC fire.

Secondly, the 2002 fire occurred withing two years of the 2000 fire and LPC was likely still feeling

the aftershocks of the 2000 fire when the Missionary ridge fire began.

Table 3: Counties and weightsCounty Weight

Douglas 0.064El Paso 0.024Lincoln 0.067Mineral 0.625Washington 0.219

Sum 1.000

Garfield, Co 2002 Spring Creek Complex Fire

The 2002 Spring Creek Complex fire – not to be confused with the Spring Creek Fire currently

burning near in the La Veta pass area – burned 13,490 acres north of Glenwood Springs in Garfield

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Figure 12: Treated and Synthetic Real Personal Income: GAR Spring Creek (2002)

(a) Personal Income (b) % Difference

Data Source: Bureau of Economic Analysis (www.bea.gov)

county (GAR), the impacts are in Figure 12. This fire had a dramatic impact on the county, while

during the year of the fire the difference between the treated and synthetic counties are the same,

in the two years following the fire, the impact was significant. By 2005 the impact of the fire was

about –10% as compared to the synthetic control. By 2007, the impacts we essentially negated as

county income economy returned to control levels.

The Cumulative Effects of 1994, 2000, and 2002 Fires in La Plata County

Given that a succession of shocks can have a cumulative impact on an economy, we next consider

the cumulative effects of a series of wildfires on La Plata county. Mathematically, the α in equation

(1) is now a vector. Results are in Figure 13, the vertical lines represent the dates of the three fires,

1994 (red), 2000 (green), and 2002 (blue). As before, panel (a) shows personal income, however in

panel (b) rather than show the percentage difference, shows the difference between the treated and

synthetic economies, Y − Y S . As can be seen, the 1994 has no real impact on the local economy,

but there is a slight deviation in the year before the 2000 fire. Again, the 2000 fire caused the

treated economy to fall below the synthetic economy, with the 2002 fire causing the largest decline

in personal income, about $250 thousand below the control economy. But after the Missionary

Ridge fire there was a rapid, about two years, recovery of the treated personal income relative

to the synthetic control. Part of this is likely due to two factors, first, the expansion of mineral

extraction – the number of wells in La Plata peaked in 2010 – reacting to rising natural gas prices,

and an accelerating real estate market – inflation adjusted real estate price peaked in 2008.

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Figure 13: Treated and Synthetic Real Personal Income: LPC Cumulative

(a) Personal Income (b) Difference

Data Source: Bureau of Economic Analysis (www.bea.gov)

The financial crisis undermined both the treated and synthetic economies, but, interestingly the

treated economy recovered relatively quickly and by 2010 the treated economy outpaced the syn-

thetic economy. The two economies look to be converging as of 2017.

Summary

Often disasters, while damaging, are thought to be a benefit to an economy over time. If buildings,

roads, etc. are damaged then investment in the area is often construed as benefit to the economy

promoting growth. We have seen, in the majority of the fires, income generally rebounded withing

five years to previous levels. However, it should be noted that income would likely have been

higher without the shock. Resources are spent on re-building rather than purely new investment.

This is often referred to as the broken window fallacy. On the other hand, it could be viewed

as re-newal, that is older capital will depreciate and injecting new investment to replace dated

technology provides a boost to the economy. What this analysis demonstrates is that in most cases

local economies did recover.

However, in the two previous fires in La Plata not much damage was done to existing infrastructure

and/or capital. What was lost was income from tourists and, perhaps, natural resource extraction.

The loss of tourist revenues would have partially offset by increased demand for goods and services

by non-resident fire fighting personnel, but, I suspect this effect would be minimal.

Taking stock of the current fire in La Plata, relatively few structures have been damaged. The

local economy was disrupted by evacuation calls, but, as before, the largest impact will be the loss

of tourist revenues, which we should know by the end of the year. Evidence from previous fires

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suggest the losses to the economy will be short lived and relatively small. The fire best suited for

comparison is the 2002 Missionary Fire, and while the income was about 7% below the treated

data in 2002, by 2004 the gap had closed. It is also worth noting that the local economy is more

diverse today than it was in 2002 and, therefore, more resilient to negative shocks.

However, a couple of differences should be discussed. The 416/Burro fire directly effected a number

of large tourist attractions for the Durango area, which were not impacted by the other two fires.

As of this writing, we will not be able to full capture the impacts of damage of the 416 fire to the

economy for a year or so. As a topic for future analysis, is to use inflation adjusted tax data in the

model to measure the impacts of natural disaster on the tourism industry in particular. Viewed

through this lens, one could argue that La Plata county dodged an economic bullet in the 1994 and

2002 fires, and may be less fortunate with the 2018 wildfire.

Another negative factor which is not captured by this model, as the fire is an event in a given

month or year, is the effects of floods and mudslides that occur in the years following the fires.

Though the Missionary Ridge fire was sixteen years ago, the annual monsoon season, generally in

July-August of every year, still trigger mudslides in the Animas Valley north of Durango, damaging

both private structures and public infrastructure.

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