Impacts of COVID-19 pandemic policies on timber markets in the Southern United States

Sonia R. Brucka,*, Rajan Parajulib, Stephanie Chizmara, and Erin O. Sillsb

a: USDA Forest Service, Southern Research Station, Forest Economics and Policy Unit, Durham, NC, USA;
b: North Carolina State University, College of Natural Resources, Department of Forestry and Environmental Resources, Raleigh, NC, USA;
*Corresponding author: E-mail: email:

Citation: Bruck SR, Parajuli R, Chizmar S, Sills EO. 2023. Impacts of COVID-19 pandemic policies on timber markets in the Southern United States. J.For.Bus.Res. 2(1): 130-167.

Received:  27 March 2023 / Accepted:  26 April 2023 / Published: 11 May 2023

Copyright: © 2023 by the authors


The global pandemic, due to SARS-CoV-2 (COVID-19), disrupted global commodity markets and individual consumption patterns. Various COVID-19-related policies were put in place by state and local governments to limit the virus outbreak, which disrupted the production and supply chains of manufacturing industries. The forest sector was not an exception. Using the Time Regression Discontinuity (T-RD) approach, we quantified the effect of various COVID-19 policies on standing timber prices in the Southern United States. We found an overall significant decrease in prices across all timber products (7%-30%) soon after COVID-19 lockdowns were implemented in early 2020. Findings from the fixed effects (FE) estimators suggest mandatory lockdowns for all individuals in certain areas of the jurisdiction had a decreasing price effect on pine pulpwood but an increasing effect on hardwood sawtimber. We expect that the findings from this study may help to set expectations for future market shocks if policies are implemented that impact the timber supply chain and consumer behavioral changes.

Keywords: COVID-19 lockdown, fixed effects, southern micromarkets, time regression discontinuity, USA


The global infection, due to SARS-CoV-2 (COVID-19), was declared a pandemic by the World Health Organization on March 11, 2020 (Cucinotta and Vanelli 2020). In the two years from March 2020 to February 2022, over 97 million people were infected, and over 1 million people died due to the pandemic in the United States (U.S.), with an estimated 62% of the avoidable deaths due occurring in the U.S. South since the beginning of recorded infections (Johns Hopkins University and Medicine 2022; Stoto et al. 2021). Many consider the beginning of the pandemic in the U.S. to be late March 2020 (Prestemon and Guo 2022; van Kooten and Schmitz 2022), because illness began to spread more widely, and consumer preferences began to change.

COVID-19-related policies were put in place by state and local governments because no pharmaceutical treatment was initially available. Thus, non-pharmaceutical interventions, such as social distancing and stay-at-home policies (also known as “shelter in place” policies or “lockdown” policies), were adopted by local governments in hopes they would prevent healthcare systems from being overwhelmed with an influx of patients sick with the virus. These early policies limited economic activity and increased behavioral changes, such as reduced travel and social contact. More individuals began working from home or not working at all, lowering economic output. This resulted in many commodity-exporting countries to experience a sharp fall in prices of commodities (Hevia and Neumeyer 2020). Furthermore, a decline in consumer spending dominated in the short-run, indicating a potential price drop of consumer goods in response to COVID-19, perhaps leading to price changes in raw materials (Balleer et al. 2020). Balleer et al. (2020) conducted an analysis on the reported impact of COVID-19 on planned price changes. They surveyed 6,000 firms in the German economy, of whom approximately 80% reported adverse effects on business productivity in direct correlation with COVID-19. Additionally, they estimated an 11-percentage point increase in the probability of a price drop in response to COVID-19, as reported by firms in 2020 (Balleer et al. 2020).

Yet, the pandemic spurred a shift in preferences among individuals, leading to increased demand for dimensional lumber and pulpwood products (i.e., finished wood products) (van Kooten and Schmitz 2022). For example, there was increased demand to manufacture personal protective equipment for public health (Hilsenroth et al. 2021). Furthermore, according to Liu and Su (2021) the pandemic resulted in a shift in housing demand from city centers to suburbs and rural housing. They suggested with the rise of remote work that there was a diminished need for living close to offices and workspaces (Liu and Su 2021). Additionally, demand for packaging and shipping materials increased in response to online shopping and the need to create at-home offices (van Kooten and Schmitz 2022; Hilsenroth et al. 2021). Nevertheless, increased demand for finished wood products did not necessarily correspond with increased prices for standing timber. 

The literature exploring COVID-19-related price effects on stumpage values have looked at price trends at varying points in time. For example, the pandemic led to an increase in the price of Southern softwood composite lumber, from $333 per thousand board feet (mbf) in April 2020 to $900 mbf in April 2021, a 170% increase from 2020-2021 (Fastmarkets RISI 2021). van Kooten and Schmitz (2022) and Prestemon and Guo (2022) explored impacts of COVID-19 on the forestry sector from 2020 to 2021. They attributed price changes partly to labor shortages that resulted from policies mandating citizens to stay home to prevent the spread of the virus and hesitancy to return to social settings. Labor shortages were also impacted by early retirement (Faria-e-Castro 2021) and limited immigration (U.S. Department of State 2020), in addition to COVID-19 illness.

On the other hand, according to Zhang and Stottlemyer (2021), average sawtimber prices were lower in 2020 as compared to the two previous years. For example, Southern softwood composite lumber was $476 mbf in April 2018 or 30% lower compared to 2020, and $402 mbf in April 2019 or 17% lower compared to 2020 (Fastmarkets RISI 2021). They argue that the combination of public health regulations and recommendations, such as social distancing, lockdowns, and quarantines, resulted in a slowdown of production activity for a period soon after COVID-19 became a concern (Hevia and Neumeyer 2020).

The U.S. South has approximately 245 million acres of forestland (1 acre = 0.405 ha), covering about 46% of the total land use. Of these, 87% are privately owned, including 147 million acres (60%) private non-corporate and 65 million acres (27%) private corporate (Oswalt et al. 2019). The South produces approximately 60% of the Nation’s timber products, almost entirely from private forests, as well as produces a significant amount of wood products for the U.S. and global market, generating over $130 billion of gross output annually (Dahal et al. 2015).

In the early days of the COVID-19 pandemic, U.S. case-to-death ratio was relatively high compared to the later stages of the pandemic. For example, on May 3, 2020, there were approximately 200,000 documented U.S. COVID cases, with over 13,000 COVID-related deaths (6.5% mortality rate) (Johns Hopkins University and Medicine 2022). We compare this to a later wave in the pandemic, January 10, 2021, with a recorded 1.74 million cases and 23,000 COVID-related deaths (1.3% mortality rate)[1]. Even though the case count was relatively low across the Nation, including the U.S. South early in the pandemic (Figure 1 and Table 1), the high mortality rate and overwhelmed healthcare systems encouraged adoption of strict policies to prevent further spread of the virus.

Figure 1. COVID-19 cases in the Southern U.S. from February 1, 2020, to December 31, 2020. Data via the Centers for Disease Control and Prevention (CDC 2021).

Table 1. COVID-19 cases in each Southern U.S. State by month from March to June 2020. By June 2020, Mississippi had the highest amount of reported cases as a percent of total State population. Total State population is via the United States Census (


Mar-20 cases

Percent of population

Apr-20 cases

Percent of population

May-20 cases

Percent of population

Jun-20 cases

Percent of population

Total state population (2022)




























































































































Note: MS= Mississippi, AR= Arkansas, LA= Lousiana, AL= Alabama, SC= South Carolina, TN= Tennessee, VA= Virginia, NC= North Carolina, GA= Georgia, FL= Florida, TX= Texas 

COVID-19-related policy decisions were largely left up to state and local governments. Policies varied over time and across counties and cities, reflecting changing and highly local perceptions of the COVID-19 caseload, the status of healthcare systems, and later on, emerging new variants and vaccination rates.

Since COVID-19 is relatively novel, there are only few studies to draw from that specifically use regression discontinuity design (RDD) or time regression discontinuity design (T-RD) methods to assess COVID-19-related commodity price impacts, and none that we are aware of that assess timber prices. Diop and Vedrine (2020) and Cuaresma and Heger (2019) use RDD methods to assess forestry-related policies in Africa, and Guan and Zhang (2022) use RDD methods to explore logging bans in China. Typically, RDD is used when there is a clear policy or spatial break (as compared to a time break). There are a few studies that we present here, which consider food-crop price changes, virus spread, and demand for healthcare using T-RD methods during COVID-19 (Ruan et al. 2021; Liu et al. 2021).

Studies that have assessed the causal effects of lockdown policies on COVID-19 spread and demand for new health services have focused primarily on different behavior changes in the health sector. Bakolis et al. (2021) investigated changes in daily mental health service use and mortality using T-RD methods, finding causal evidence that lockdown policies reduced inpatient admissions for mental health services compared to the pre-lockdown period. Similarly, Aiken et al. (2020) found that European countries, including Northern Ireland and Portugal, showed significant increase in self-managed telemedicine as compared to the pre-COVID period. Furthermore, Liu et al. (2021) evaluated COVID-19 policy effectiveness in controlling the spread of infection in China, Germany, Austria, and the United States using T-RD methods, and were one of the first to take into consideration the effect through time on behavioral response. They found that policies implemented later in the pandemic exerted a weaker effect on controlling COVID-19 case counts (Liu et al. 2021). For this article, we drew on the study design of Ruan et al. (2021), which used T-RD to causally assess changes in the level of Chinese cabbage prices in response to the lockdown policies in China. Ruan et al. (2021), did not explore the effects of varying policies over different regions in China, which we aim to address in this study.

This study, to our knowledge, is the first to explore roundwood prices in the southern U.S. in relation to various COVID-19 policies using causal analysis methods. Furthermore, other researchers have explored the effects of COVID-19 policies on prices of goods and behavior changes, but typically they have largely ignored how policies varied over space and through time (Ruan et al. 2021; Bakolis et al. 2021; Liu et al. 2021; Aiken et al. 2020). The purpose is to explore the causal effects of the COVID-19 lockdown on roundwood prices in the U.S. South using T-RD methods to assess timber price changes, as well as to explore price effects of varying COVID-19 policies across Southern counties using a fixed effect regression.

The rest of the article is organized as follows. Next we describe the methods used for analyses. The data was collected from publicly available sources, which we outline in the section after methods. Results from the T-RD estimation are then presented, followed by associated sensitivity analyses. We then describe the fixed effects estimation findings, which emphasize the different COVID-19-related policies put in place across micromarkets. We conclude with a discussion of limitations and implications of model results.


Detailed discussions of appropriate use of regression discontinuity design (RDD) as a method of causal inference have been published in Lee and Lemieux (2010) and Hahn, Todd, and Van der Klaauw (2001). Via Hahn, Todd, and Van der Klaauw (2001), the goal of the RDD method is to determine the effect of a binary treatment xi on an outcome yi. Each individual either receives or does not receive a treatment (observed by what is known as the “running variable”), and no individual is observed in both states at the same time (Hahn et al. 2001). Let y1i denote the outcome with treatment and y0i denote the absence of treatment. Additionally, let xi = 1 if treatment is received and xi = 0 if the treatment is not received. The model for the observed outcome can be written as: 

yi=∝i+xii)          (1)

Where: ∝i ≡ y0i, and, βi ≡ y1i - y0i.

There are two main types of RDD, the sharp and fuzzy design (Hahn et al. 2001). With the sharp design, treatment xi is deterministic [zi,xi = f(zi)], where zi takes on a continuum of values, and the point z0, where the function f(z) is discontinuous is assumed to be known. With fuzzy design, the conditional probability is known to be discontinuous at z0 and the treatment assignment is not a deterministic function of zi. We use a sharp time regression discontinuity design (T-RD), because there is a clear cutoff period, to identify the causal effects of lockdown policies on roundwood prices in the southern U.S.

Time Regression Discontinuity Design (T-RD) or Regression Discontinuity Design in Time (RDiT) is an effective method to assess causal policy impacts. From this point forward, we will refer to this method as T-RD. The main difference in interpretation between the standard RDD and T-RD is that the running variable is time itself (Hausman and Rapson 2018). Our cutoff point is based on the first date of known COVID-19 cases in the United States, beginning in March of 2020. Additionally, 100% of COVID-19 induced mandatory stay-at-home lockdowns in the southern states were imposed between April and June 2020. Other policy measures, such as advisory or recommendations to remain at home, were implemented only after March-April 2020. Therefore, we chose May-June as the time cutoff to allow for the lag between behavioral changes and COVID-19 policies in late March.

Similar to Calonico et al. (2014), Heckman and Vytlacil (2007), and Imbens and Wooldridge (2009), we adopt the potential-outcomes framework to identify causal effects of COVID-19 prevalence on timber product prices across the southern U.S. states. For each county i the scalar random variable Yi denotes price of timber products. The scalar regressor Xi is the “running variable,” which determines treatment assignment based on a known cutoff point x̄, defined as the beginning of widespread COVID-19 policy prevalence in the Sothern United States, which we determine to be May-June 2020. Let [{Yi(0),Yi(1),Xi}': i=1,2,...,n] be a random sample from the population {Y(0), Y(1), X}', where Y(1), and Y(0) denote the potential timber prices with and without treatment. In our study, treatment is defined as a county in the southern U.S. being subject to a COVID-19 lockdown policy, which only occurs after spring 2020. Treatment assignment is determined when county i is assigned to the treatment condition (that is, if Xi x̄) and is assigned to the control condition if Xi < x̄.

Therefore, the observed outcome is:


Thus, the T-RD design of the estimation strategy is:

ln(Yi) = β0 + β1 * 1(Xi ≥ x̄) + φ * f(Xi) + χ * 1(Xi ≥ x̄) * f(Xi) + εi          (3)

where ln(Yi) is the natural logarithm of each of the roundwood prices in each county (i) in each bi-monthly time period (Xi). The dummy 1(Xi x̄) equals one if time is post period 9 (May-June 2020) and zero if before period 9. Furthermore, f(Xi) contains the polynomial time trend to flexibly control for time series variation in wood products prices, which would have occurred in the absence of the lockdown. The estimated effect of lockdown policies implemented at time x̄ is represented by the estimated value of β1. Equation (1) is estimated for each industrial roundwood product, including pine pulpwood, pine chip-n-saw, pine sawtimber, hardwood pulpwood, and hardwood sawtimber.

Additionally, with RD design, the direction of the slope of the data during the period before and after the cutoff period is typically constant. If there is a change in the slope at the cut-point, this is considered a Kink RD. The primary differences in Kink RD estimation are that the derivative of the regression function is set to one, and the kernel function is set to uniform (Calonico et al. 2014). The kernel function is used to construct the local-polynomial estimator.

Equation 3 assumes that all counties are impacted by the same type of lockdown policy in the same time period x̄. However, each county was subject to multiple policies over time (see Figure 3 for a description of the alternative policy types through time during 2020). To examine the differential impacts of alternative policy effects, we estimate a series of fixed effects regressions for each roundwood type. We aggregated up from the county-level to micromarket averages across variables:

ln(Yi) = X'itβ + ∝i + εit , i = 1,...,n, t = 1,...,T          (4)

where ln(Yi) is the natural logarithm of each of the roundwood prices in each micromarket (i) in each bi-monthly time period (Xi). The fixed effects approach takes ∝i to be an unobserved group-specific constant term in the regression (Greene 2018), for example, we assume forest cover and economic activity from the forestry sector within micromarket county are fixed. Parameters we are most interested in exploring are the effects of different policies as a comparison of findings to the T-RD estimator.


We created a time-varying panel dataset for all U.S. South counties included in the Forest2Market micromarket dataset (N=11,004 counties) (Forest2Market 2021). We first gathered Forest2Market bi-monthly micromarket price data. Micromarkets are broken into 39 regions across the South, encompassing eleven states, including: Alabama, Arkansas, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee, Virginia, Florida, and eastern Texas. Micromarket prices were matched to each county for twelve bi-monthly periods from 2019 to 2020 for T-RD estimation[2]. We then aggregated all variables up to the micromarket level by taking averages of co-variates for the fixed effects regressions to correct for autocorrelation between counties. We collected open-access data from the Centers for Disease Control and Prevention (CDC 2021), including COVID-19 policies and when they occurred and COVID-case counts by county. We characterized policies as follows according to the CDC labelling (See Table 2):

Table 2. Policy orders as described by the Centers for Disease Control and Prevention (CDC 2021). Policy 4 did not occur in any of the counties we observed in our dataset in the Southern United States.

Policy 0

No policy or order to stay home

Policy 1

Mandatory for all individuals

Policy 2

Mandatory only for all individuals in certain areas of the jurisdiction

Policy 3

Mandatory only for at-risk individuals in the jurisdiction

Policy 4

Mandatory only for at-risk individuals in certain areas of the jurisdiction

Policy 5

Advisory or recommendation to stay at home

Policy 1 occurred only in period 8, March-April (n=790 counties) and period 9, May-June (n=387 counties) 2020 in some counties across the U.S. South. Policy 1 occurred in 100% of counties in Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Tennessee and Virginia in Period 8. Policy 1 also occurred in period 9 in 100% of counties in Florida, Louisiana, North Carolina, South Carolina and Virginia. Yet, Policy 1 never occurred in Texas or Arkansas in either period, with counties that we can observe. Furthermore, multiple policies occurred within the same period. This is likely, because a policy lapsed and then was reinstated in the same bi-monthly time period, or a policy lapsed and a new policy was put in place. For example, 49% of counties had both Policy 1 and Policy 5 from March to April 2020. Georgia was the only state to put Policy 3 in place, which occurred in the same bi-monthly time period as Policy 1 for all periods. Policy 4 was never found in any county in the U.S. South but did occur in other States across the U.S.

Some scholars who have conducted analyses on COVID-19 policies have assumed a lag between policy implementation and societal behavioral changes (Liu et al. 2021). Others have conducted sensitivity analyses between COVID-19 policy implementation and changes in commodity prices (Ruan et al. 2021). We, therefore, checked price changes in period 8 and period 9. A similar causal effect can be observed in both period 8 and 9, however, some products have an outlier, where the price increased during period 8, and then sharply dropped during period 9 (Appendix A). In addition, our data source collects infrequent information (bi-monthly). We, therefore, chose to conduct analysis at period 9, which we believe better represents the effect of the COVID-19 policies and behavioral changes captured in the market.

Furthermore, to measure the differential impacts of each policy type on timber prices, we collected information about behavioral changes from COVID-19 policies, as well as other supply and demand determinants (Guerrieri 2022; Schemer et al. 2022; Cot et al. 2021). We collected an indicator of travel out of the home via Apple Inc., which took an aggregate, county level time varying measure of driving requests via Apple Maps[3]. Apple Inc. created a daily baseline of average number of travel requests prior to the Pandemic. They then tracked requests above and below baseline starting January 1, 2020. We aggregated this information to bi-monthly indicators of movement above and below baseline (Apple Inc.). We acknowledged this information might have measurement error. For example, an individual could request driving directions and not make the trip, or individuals may not need directions and make a trip without requesting directions. Furthermore, this data does not measure individuals who do not have an iPhone. However, we observed a sharp decrease in the number of map requests below baseline starting March 2020 and then an increase back to baseline by July 2020, reflecting the potential impacts of policy changes. Additionally, there were approximately 113.5 million iPhone users in the U.S. in 2020 (Ruby 2022), or approximately 34% of the total U.S. population, indicating the technology is widely used. Globally, researchers have used the Apple mobility data to explore COVID-19 impacts (Venter et al. 2020; Kurita et al. 2021; Nouvellet et al. 2021; Jing et al. 2021; Hu 2021). For example, Cot et al. (2021), were able to identify the period of social distancing via Apple mobility data independently of political decisions.

Moreover, we included the percentage of the county population with COVID-19. However, some measurement error exists within this variable. We obtained daily measurements of COVID-19 cases for each county from the CDC. Some counties miscounted number of cases, by reporting more cases than existed. The county would then reduce the number of cases at a later date to reflect the true number of COVID-19 cases in the population. This rough measurement is generally acceptable since we aggregated to bi-monthly periods (absorbing daily miscounts). Furthermore, we were only able to obtain a yearly county population number from the 2020 Census. Therefore, the county population is constant across bi-monthly COVID-19 case averages. However, there is likely not a significantly large death or migration rate at the county level. Again, we averaged county-level information to the micromarket level for FE regressions. Finally, we included average precipitation levels via the National Oceanic and Atmospheric Administration (NOAA 2022), to control for weather-related logging factors (Greene et al. 2004).

Summary statistics of micromarket-level data are presented in Table 3. The average price between 2019 and 2020 for pine sawtimber was $23/ton, pine pulpwood was $7/ton, and pine chip-n-saw was $14/ton. The advisory or recommendation policy was put in place for the longest number of days across bi-monthly periods (average of 18 days), followed by no policy (average of 14 days), lockdown policy (average of 4 days). The policies which dictate only certain individuals within a jurisdiction were only applied in Georgia (Policy 2 and Policy 3). Mandatory for at risk individuals in a jurisdiction was applied an average of 5 days within a bi-monthly period, and mandatory only for all individuals in certain areas of the jurisdiction was applied on average less than one day within a bi-monthly period.

The percent of the population with COVID-19 in 2020 was still relatively low, with an average of 0.01% of a county infected with the disease (from March-December 2020). Furthermore, to assess movement as a potential indicator of economic activity, we include mobility data collected by Apple Inc., which calculated a baseline level of Apple Map requests (baseline = 100). An indicator above 100 is above-average map requests. The average number of map requests was above baseline (about 118), with a minimum of 74 and a maximum of 257. Below-average map requests occur most often right after the lockdown policies were implemented. Additionally, we include the average precipitation by county (average of 10 inches). 

Table 3. Summary statistics with aggregated averages to the micromarket level. 

Variable Name



Std. dev.





Std. dev.



Hardwood pulpwood

natural log price per ton





price per ton





Pine sawtimber

natural log price per ton





price per ton





Pine pulpwood

natural log price per ton





price per ton





Pine chip-n-saw

natural log price per ton





price per ton





Hardwood sawtimber

natural log price per ton





price per ton





No policy

policy days post-period 6






Mandatory for all (lockdown)

policy days post-period 6






Mandatory only for all individuals in certain areas of the jurisdiction

policy days post-period 6






Mandatory only for at-risk individuals in the jurisdiction

policy days post-period 6






Advisory or recommendation

policy days post-period 6






Percent of population with COVID-19

percent of micromarket population test positive for COVID-19







average micromarket average precipitation by bi-monthly period






Apple Inc. Mobility

request for apple maps above or below baseline level of requests






Periods (1-12)

bi-monthly period






Observations (N) = 468


Observations post-period 6 (after COVID-19 policies) (n) = 263



Figure 2 and 3 present graphical analyses of the different roundwood products. Figure 3 depicts the discontinuous jump in period 9 of mean prices for each timber product type in the southern U.S. We carefully observed the slope of mean price before and after the cutoff period. We can clearly see that hardwood sawtimber, hardwood pulpwood, and pine pulpwood could be categorized as a Kink RD, while pine sawtimber and pine chip-n-saw have a typical RD design.

Figure 2. Mean prices of roundwood products in the U.S. South with cutoff at period 9 (May-June 2020).

Figure 2 assesses the fit of the regression function. Using rdplot in STATA (Calonico et al. 2015), a command that implements different bins to approximate the underlying regression function (IMSE-optimal selectors), we detect a discontinuity at the cutoff through the visual representation of the mean variability of the price data for each product type (Calonico et al. 2015). The most common RD plot is an evenly-spaced binning of the data (Calonico et al. 2015). Table 4 presents the IMSE optimal evenly spaced bin lengths and observations to the left and right of the cutoff.