# Does log export ban policy a good strategy to fight deforestation? Lessons from developing countries

Mouhamed Zerbo

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Mouhamed Zerbo. Does log export ban policy a good strategy to fight deforestation? Lessons from developing countries. Journal of Environmental Management, 2025, 376, pp.124474. 10.1016/j.jenvman.2025.124474. hal-04976257

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# Does log export ban policy a good strategy to fight deforestation?

## Lessons from developing countries

Mouhamed Zerbo*

### Abstract

The log export ban (LEB) policy is an environmental, trade-restrictive, and industrialization measure adopted by many forest-rich developing countries. One of the primary reasons for implementing this policy is to combat deforestation, though its effectiveness remains debated. This paper evaluates the impact of LEB adoption on deforestation in developing countries through two main channels. Firstly, LEB adoption intensifies land competition between agriculture and forestry, leading to agricultural expansion and subsequent tree cover loss. Secondly, it reduces log prices, boosting wood utilization by the wood processing industry and exerting additional pressure on forest resources. This study is the first to establish LEB policy as a significant factor in deforestation within developing countries. Analyzing data from 124 developing countries with forest resources exceeding 100 km² from 2001 to 2019, we utilize an entropy balancing approach to address the self-selection biases associated with LEB adoption. Our findings indicate that LEB adoption leads to a significant 22.3% increase in deforestation compared to non-LEB countries. These results remain robust across various tests, including alternative measures of LEB and deforestation. Additionally, addressing potential endogeneity issues does not alter the policy's impact. We further explore an alternative methodology for staggered difference-in-differences, which confirms the positive and significant effect of the policy. These findings support the conclusion that the LEB policy exacerbates deforestation, leading to two distinct recommendations: first, the removal of the LEB policy; and second, the implementation of complementary measures, such as reinforced sustainable forest management and mechanized agriculture.

JEL classification: C21, F13, F18, O13, O14, Q23.

**Keywords:** Log export ban, Deforestation, Entropy balancing, Developing countries.

*mouzerbo@gmail.com; PhD in economics; Université Clermont Auvergne, CNRS, CERDI, F-63000 Clermont-Ferrand, France.
# 1 Introduction

In many forest-rich developing countries, the industrial processing of timber is increasingly recognized by policy-
makers as a key driver of economic transformation. However, resource-based industrialization faces significant
challenges, including inadequate energy and road infrastructure, political instability, and a lack of sufficient
technical, financial, and human resources (Schulz, 2020). Additionally, domestic timber processing industries
in these countries face competition from both domestic and foreign processors who purchase raw logs locally,
often competing with processors based in developed countries.

To address this challenge, many governments in forest-rich developing countries have implemented log export bans and other restrictions on unprocessed wood.<fnref n="1" /> These measures aim to increase the domestic supply of raw logs, which often leads to lower domestic prices. While this results in reduced income for loggers and log exporters, it enhances the competitiveness of the domestic timber processing industry compared to that of foreign countries. Consequently, both domestic and foreign investors are more incentivized to invest in timber processing within the country of origin.

Furthermore, LEB can be implemented to achieve environmental objectives related to forest management.
By lowering log prices from international to domestic levels, a LEB aims to reduce the profitability of logging,
which should, in turn, decrease logging activities and promote forest conservation. Additionally, the reduction
in international demand for domestic logs and the resulting profit decline may decrease incentives for illegal
logging. As a result, some countries adopt the LEB policy as an environmental measure to combat deforestation
and forest degradation, while others implement it to achieve both industrialization and ecological goals (Kishor
et al., 2004).

This study argues that the adoption of LEB policies creates both winners and losers, which can influence
behaviors related to forest resource management. Loggers and raw log exporters are typically disadvantaged,
as the reduction in profits is redistributed to other domestic producers. As logging becomes less profitable,
there may be a shift from logging to farming, potentially leading to forest conversion. On the other hand,
the winners—particularly those in the timber processing industry—benefit from increased processing activities,
as more foreign industries are attracted to invest in the abundant domestic forest resources. This includes
traditional industries with low processing capacities that require large quantities of logs and foreign industries
with high processing capacities that also demand significant log supplies for economies of scale. As these
industries expand, the increased demand for logs puts additional pressure on forests.

The purpose of this study is to assess the impact of LEB policy adoption on deforestation. Our research
contributes both empirically and theoretically to the limited literature on the environmental effects of LEB in
four key areas.

First, while most studies on LEB policy focus on individual country cases (e.g., (Amoah et al., 2009; Fooks
et al., 2013; Kishor et al., 2004; Resosudarmo and Yusuf, 2006), we use a large panel of 124 developing countries

<footnote n="1">¹According to (Schulz, 2020), the log export ban (LEB) is the most banned policy adopted on the commodity in Africa.</footnote>
from 2001 to 2019 to provide evidence of LEB adoption's effect on deforestation.<fnref n="2" />

Second, the paper emphasizes the unexpected effect of LEB policy adoption by highlighting the agriculture and forest industry channels through which LEB increases deforestation (Damette and Delacote, 2011; Wolfersberger et al., 2015). In addition to the agricultural impacts of LEB noted by Maestad (2001), this study first empirically and theoretically highlights the evidence of the agriculture and forest industry as transmission channels and their roles in the relationship between LEB and deforestation.

Third, we contribute to the empirical literature by using impact analysis to assess the causal effect of LEB adoption on deforestation. In margin of the propensity score matching used by Marchand and Zerbo (2023) to show the impact of LEB on the forest industry and wood trade growth, our study employs an entropy balancing approach, which we find more suitable for our analysis. This paper is the first to apply this impact analysis method to examine LEB's effect on deforestation.

Finally, we enhance the literature by identifying LEB policy adoption as a significant determinant of deforestation. Previous studies on deforestation often overlooked the role of forest trade policies in tree cover loss. This paper reinforces LEB policy as a crucial factor in deforestation in developing countries, paving the way for future research to recognize LEB policy as a fundamental cause of deforestation.

This study implements an entropy balancing approach to estimate the Average Treatment Effect on the Treated (ATT) of the LEB policy. We find a positive and significant effect of the LEB policy on deforestation. This result is strong, adding other covariates progressively as main determinants in the relation between LEB and deforestation. Moreover, we check the validity of our transmission channels using the mediator variables approach. Our results show that agriculture occupation and processed wood fulfilled the transmission channel conditions. Furthermore, we check the robustness of our analysis using alternative measures of LEB, deforestation and alternative estimation methods to support our baseline identification strategy. We use instrumental variables for the binary endogenous variable as defined by Wooldridge (2015) using the share of neighbor LEB policy adoption and tariff rate on customs duty as instruments. Also, we employ the staggered differences-in-difference estimation approach following Callaway and Sant'Anna (2021). In each case, the LEB significantly increases deforestation in our analysis. In addition, we investigate the impact of the LEB policy over time, specifically in t+1, t+2, t+3, t+4 and, t+5. The significant effect of LEB on deforestation shows the short-term effect of this policy on deforestation.

The remainder of the paper is organized as follows. Section 2 explores the connection between LEB and deforestation, focusing on the role of transmission channels and illegal logging. Section 3 presents stylized facts that describe the statistical relationship between LEB and deforestation. Section 4 describes data and variables, while Section 5 outlines the identification strategy. Section 6 discusses the empirical results and examines the transmission channels. Section 7 assesses the robustness of the findings, followed by Section 8, which presents the heterogeneity analysis. The final section concludes the paper and discusses the policy implications.

<footnote n="2">²To our knowledge, only Marchand and Zerbo (2023) investigate the effect of LEB policy (on forest industrialization) with a large panel of developing countries.</footnote>
# 2 LEB and deforestation

## 2.1 The role of LEB in the deforestation process

LEB policy prohibits the export of unprocessed wood and is a measure applied in the forestry, trade, and industrial sectors for both economic and environmental reasons. Some developing countries implement LEB policies specifically to control deforestation or to promote domestic processing industries, while others adopt it for both purposes (Kishor et al., 2004). The conventional LEB is to protect and promote domestic industries by banning exports of unprocessed wood, thereby enabling these industries to acquire raw materials at lower prices than on the global market (Fooks et al., 2013).

However, there is significant debate regarding the impact of LEB on deforestation. Some studies suggest that LEB adoption has a positive effect on forest conservation. Lower log prices reduce the profitability of logging, diminishing incentives to log and thereby decreasing deforestation. Additionally, the reduced log prices could discourage illegal logging by lowering the rewards for taking high risks for minimal profit (Resosudarmo and Yusuf, 2006). For instance, Tumaneng-Diete et al. (2005) found that LEB and other trade policies contributed to forest conservation in the Philippines.

An opposite view highlights the detrimental effect of LEB adoption on deforestation. By reducing the profits from logging, LEB can cause some loggers to abandon the industry and switch to other activities. However, those who remain may try to compensate for the loss of income by increasing their logging efforts to reach previous profit levels, which can lead to an escalation in logging activities. As a result, the pressure on forests may return to pre-LEB levels. Moreover, LEB can also exacerbate deforestation through its effects on agriculture and the forest industry, as discussed in the next subsection.

## 2.2 Transmission channels

### 2.2.1 Channel of agriculture

We assume that the LEB adoption can increase deforestation through farming. López and Galinato (2005) identify agricultural expansion as a critical channel through which trade policy impacts forests. As defined by Wolfersberger et al. (2015), land-use competition primarily occurs between agriculture and forests, with agricultural expansion being the primary direct cause of deforestation in developing countries. We propose three channels to support this assumption.

First, LEB adoption aims to reduce log exports and redirect log production toward domestic industries for processing. This leads to a decrease in log prices, making logging less profitable. Consequently, land users may face a decision: retain forested land or clear it for alternative uses, such as agriculture. According to Arcand et al. (2008), the choice between forest and agricultural land use is influenced by time preferences. The shorter-term yields of agriculture, compared to the long growth periods required for forests, often make agriculture more attractive. As log prices fall, the relative value of wood decreases compared to agricultural
products, incentivizing the conversion of forests into farmland.

Second, the LEB decreases the demand addressed to the local logger for log export perspectives. So, the profit decrease generates an employment conversion from logging to nearest activities specifically farming. Moreover, the substitution between logging and farming is facilitated by the proximity between the two activities. Switching from logging to farming is easy because the two activities are technologically similar. Both are labor and land-intensive rather than capital-intensive. In developing countries, agriculture is more extensive and needs a lot of land for cultivation. As workers transition from logging to farming, the demand for agricultural land increases, intensifying competition between agricultural expansion and forest conservation. Gibbs et al. (2010) found that between 1980 and 2000, over 55% of new agricultural land was created at the expense of intact forests, with another 28% coming from disturbed forests. This deforestation process is further exacerbated when agriculture is dominated by small-scale shifting cultivators who practice fallow land rotation.

Third, the LEB policy can reduce the costs associated with converting forests into farmland. For small-scale shifting cultivators, forest accessibility is a significant barrier to expansion (Maestad, 2001). LEB policies are often implemented alongside the development of infrastructure, such as roads, to support the timber industry. As a result, when LEB reforms are active, forests become more accessible for agricultural conversion due to the existing infrastructure network (Aggarwal, 2018; Kleinschroth et al., 2019).

### 2.2.2 Channel of forest industry

LEB policy exacerbates deforestation by influencing timber harvesting and pricing through the forest industry (Damette and Delacote, 2011). The protectionism associated with the LEB policy enables the forest industry to increase log supply in the domestic market by maintaining a large availability of logs without considering international market dynamics. As a result, the LEB policy significantly lowers domestic log prices compared to international prices. For industries focused solely on processing rather than logging, this reduction in timber prices makes log supply more affordable and creates a level of exclusivity around forest resources for industries operating in adopting countries. Consequently, the LEB policy leads to the emergence of two types of firms in adopting countries: traditional and foreign. More precisely, we can identify two channels through which the forest industry could affect deforestation.

First, the policy favors the establishment of traditional industries that are already present but rely on outdated processing technology, which requires a large quantity of logs for wood processing. The LEB often discourages sustainable logging practices and reduces incentives to adopt modern technologies that could increase wood recovery ratios in timber processing (Kishor et al., 2004). To compensate for their technological shortcomings, these industries increase their consumption of cheap logs, thereby intensifying pressure on forests.

Second, the adoption of the policy attracts significant investment due to resource proximity and low log prices. Foreign investments, in particular, capitalize on forest resources with more adaptable processing technologies. Foreign firms tend to be more wood-intensive, with high processing capacities that enable them to gradually
increase their output. To optimize production, these firms may resort to illegal logging to meet their input demands, further escalating pressure on forest resources. For foreign-owned firms, logging activities become more intensive, driven by advanced wood extraction technologies. As a result, forest depletion accelerates significantly over a shorter period. These firms expand wood extraction in response to their processing needs, leading to a substantial increase in timber harvesting for industrial processing.

## 2.3 The potential role of illegal logging

It is important to note that illegal logging may influence the relationship between LEB policies and deforestation, even though the adoption of such policies is justified in some countries by illegal logging, like Guinea-Bissau. Two arguments could intensify the relationship between LEB and deforestation through illegal logging.

Firstly, the expansion of wood processing capacity is a significant factor. For example, Indonesia's adoption of the LEB policy in the early 1980s contributed to increased deforestation. The policy followed restrictive trade measures implemented in 1975 and led to Indonesia rising from 41% of the world's tropical timber exports in 1979 to about 50% of global plywood exports by 1989 (Karsenty and Piketty, 1996). However, during this period, the material yield rate remained around 50%, which was lower than in other Asian countries.<fnref n="3" /> With a processing capacity of 26.7 million m<fnref n="3" /> of wood, Indonesia's demand for logs was about 54.5 million m<fnref n="3" />, while official figures indicated that only 26 million m<fnref n="3" /> were legally harvested. This gap was filled by illegal logging.

Secondly, the rise of illegal log exports is another critical factor. Following the adoption of the LEB policy, log exports were banned. In response, loggers and exporters, facing restrictions in the international log market, could adapt their strategies to continue operations. In many forest-rich developing countries, porous borders enable smuggling, which affects how agents manage forests. Coastal countries with stringent controls rely heavily on harbors for log exports, but in neighboring countries without similar policies (as shown in Figure A1), alternative export networks emerge where law enforcement is weak. Thus, the adoption of LEB policies can exacerbate illegal logging and illicit log exports, further intensifying pressure on forest resources.

# 3 Stylized facts

This study aims to assess the impact of LEB policy adoption on deforestation. We seek to highlight the causal relationship between LEB adoption and deforestation in the countries that have implemented this policy. To do so, we examined a sample of countries that applied the LEB policy during the period of our analysis. Figure 1 illustrates the growth in tree cover loss five years before and five years after the policy's adoption. Although most countries are clustered around the diagonal (45° line) and close to zero, several countries are positioned above the diagonal. This indicates that, in these cases, deforestation levels were higher five years after the implementation of the LEB policy compared to the five years preceding it. Approximately four-fifths of the

<footnote n="3">³The material yield rate is the ratio between the volume of wood produced after processing and the volume of raw wood entering the plant.</footnote>
<figure><img src="image_2.png" /><figcaption>Figure 1: Tree Cover Loss 5 years before and after Log Export Ban adoption</figcaption></figure>

countries that adopted the LEB policy experienced an increase in deforestation following its introduction. This figure provides an initial overview of the effect of LEB policy adoption on deforestation.

Next, we utilize the transmission channels identified earlier to statistically assess the role of LEB policies. As outlined in Section 2.2.1, agricultural land expansion is often the primary driver of deforestation in developing and emerging countries. In these regions, a significant portion of the population is employed in the primary sector, particularly in agriculture, which is often characterized by extensive and shifting cultivation practices. The correlation between deforestation and agricultural land occupation is depicted in Figure 2. To analyze the impact of LEB policy adoption, we divided our sample into adopters and non-adopters. In non-adopter countries, the correlation between deforestation and agricultural land occupation is quite low. However, in countries that have adopted LEB policies, this correlation is significantly higher. Figure 2 provides an overview of how policy adoption influences the relationship between deforestation and agricultural land use. It is important to note that we may encounter selection bias, as the level of deforestation could influence whether a country adopts the LEB policy. To address this, we restrict our analysis to countries that have implemented LEB policies, comparing the relationship between deforestation and agricultural land occupation before and after policy adoption. Figure 3 shows evidence that this relationship intensifies following policy adoption. The stronger correlation after policy adoption confirms that LEB policies amplify the impact of agricultural land expansion on deforestation.

The second channel in our analysis involves using forest industry production as a proxy to measure the industry's impact on deforestation. We apply the same approach as with the agriculture channel to examine the correlation between deforestation and processed wood production. Figure 4 illustrates this correlation for both LEB adopter and non-adopter countries. The correlation is notably higher for LEB adopters, providing evidence of the policy's effect. To delve deeper, we focus exclusively on LEB adopters and analyze the correlation between tree cover loss and processed wood production before and after policy adoption. Figure 5 demonstrates that this correlation intensifies post-adoption, as the policy attracts numerous investors to forested areas for processing purposes. Consequently, the growth of the forest industry leads to increased forest extraction.
<figure><img src="image_3.png" /><figcaption>Figure 3: Tree Cover Loss and Agriculture Occupation before and after LEB adoption</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Figure 4: Tree Cover Loss and Processed Wood by LEB</figcaption></figure>

<figure><img src="image_5.png" /></figure>
<figure><img src="image_6.png" /><figcaption>Figure 5: Tree Cover Loss and Processed wood before and after LEB adoption</figcaption></figure>

# 4 Data and variables

## 4.1 Deforestation data

The deforestation data or annual loss is high-resolution data from Hansen et al. (2013). It provides a 1-arc-second grid of land cover estimate representing the pixel size share with vegetation more than 5 m tall in 2000. This resolution is estimated each year from 2001 to 2012 for forest loss and gain. Forest loss is a stand-replacement disturbance or removing a tree cover canopy at the Landsat pixel scale. Inversely, forest gain has defined the establishment of tree canopy from a non-forest state. From 2013 to 2019, the data only focused on forest loss based on the forest stand 2000. There is a lack of data updates concerning forest gain after 2012. Also, the forest gain data is available for 2001-2012 without information data by year. Thus, we cannot merge the forest gain data with the forest loss data to get the net deforestation. Therefore, our study uses only the tree cover loss data from 2001 to 2019 to measure deforestation.

As mentioned by Leblois et al. (2017), we begin by averaging the tree cover of all pixels i of 1 arc-second (AverageCoord) in the year 2000 for each country j (with N pixels inside its borders). Data is computed from satellite imagery for only grid cells i with tree canopy greater than 20%.<fnref n="4" />

$$T_{cover_j} = \frac{1}{N} \sum_{i=1}^{N} T_{cover_i}^{1n}, \quad \text{if } (T_{cover_i}^{1n} < .20 \ \& \ \text{AverageCoord}(T_{cover_i}^{1n}) \in \text{Country}_j \quad (1)$$

After computing $T_{cover_j}$, we average the cover of the tree for every 30 arc-second pixels, i.e. about 900 m at the equator, reducing the resolution to meet computing time constraints.<fnref n="5" /> As 1 arc-second pixel means 30 meters at the equator, then 30 arc-second pixel signify 900 meters at the equator. Here we would reduce the time-consuming computation pixel by pixel by aggregating our analysis into 30 pixels (1 pixel=30mx30m)

<footnote n="4">⁴20% canopy cover threshold balances ecological significance, detection accuracy, and international reporting standards in deforestation studies.</footnote>

<footnote n="5">⁵http://earthenginepartnersSpot.com/science-2013-global-forest/downloadv1.2.html</footnote>
which means 30x30x30m = 27.000m². Also, we multiply the percentage of tree cover with the annual weighted average of deforestation for 1 arc second (Defor<sub>j</sub><sup>30n</sup>).

$$Defor_{j}^{30n} = \frac{1}{N} \sum_{i=1}^{N} Tcover_{i}^{1n} \times Defor_{i}^{1n}, \quad \text{if } Pix_{i}^{1n} \in Pix_{j}^{30n} \quad (2)$$

Then, we use the resolution grids of 30 arc-seconds, and we multiply the deforestation ratios and the tree cover by the surface area of each cell in square km, and we add all the cells that have their barycenters inside a given country's border for a given year y.

$$Defor_{y,j} = \sum_{i=1}^{N} Defor_{i}^{30n} \times Area_{i}^{30n}, \quad AverageCoord(Tcover_{i}^{30n}) \in Country_{j} \quad (3)$$

Despite criticism of these data because of the distinct lack between tropical forests from the plantation and even herbaceous, we use this dataset. Hansen et al. (2013) data are often chosen because they are comprehensive, high-resolution and widely regarded as a benchmark for monitoring global forest change. These high-resolution global data provide detailed and accurate measurements of forest loss and gain worldwide, making them suitable for granular analysis. They started in 2000, with consistent methodologies. This consistency enables a robust longitudinal analysis of deforestation trends, which is essential to track changes over time. The data set integrates well with other geospatial datasets, facilitating multidisciplinary studies that link deforestation to socioeconomic, agricultural, or biodiversity outcomes. Our study considers only developing countries to reduce the potential bias because intensive forest harvesting (with short rotation periods) and/or harvest plantations are scarce (Leblois et al., 2017).

As determined by recent studies (Ajanaku and Collins, 2021; Bauhoff and Busch, 2020; Morpurgo et al., 2023), our deforestation indicator is the annual decrease in tree cover ($Tcover_{t-1} - Tcover_{t}$). Also, our sample is focused on countries with a forest area greater than 10,000 hectares to eliminate countries with low potential in forest resources.

## 4.2 Main interest variables

Our analysis covers 124 developing countries that have forest resources of more than 10,000 hectares to exclude countries that have no potential forest resource from 2001 to 2019. As defined above, this study assesses the effect of the LEB policy adoption on deforestation. Our interest variable LEB comes from the FPER database that identifies each restriction applied on the log export in all adopter countries and is used by Marchand and Zerbo (2023).<fnref n="6" /> LEB is a dummy variable taking 1 for countries that apply restriction on the log export and 0 otherwise. Thus, 53 LEB adopters and 71 non-LEB adopters (see Map A1 in Appendix). For each country and each year, the LEB treatment variable is defined as 1 if the country is under a LEB policy and 0 if not. As described in the law, this ban is applied to log and raw wood or unprocessed. The ban might cover some

<footnote n="6">⁶The FPER database is accessible via the following link: https://www.forest-trends.org/known-forest-product-export-restrictions/</footnote>
species or all species. For some species, the ban covers the major species produced and exported by the country. Furthermore, the export quota promotes, protects, and develops the domestic processing sector like the LEB. So, the quotas on export might have the proximate effect of an export ban. In our study, each quota export adopter is already an LEB adopter except DRC, which does not clearly define the beginning and application of the policy. Thus, we consider DRC as non-treated in our study.

As explained in Section 2.2, we highlight two variables that act as transmission channels of the effect of LEB policy on deforestation: agriculture and the timber industry.

Regarding farming data, we use yearly data on land use for food production that comes from Earth Data.<fnref n="7" /> This database maps the global land cover at a 500-meter spatial resolution from 2001 to the present annually for six different land covers (Sulla-Menashe and Friedl, 2018). The agriculture occupation represents the sum of grassland and cropland collected by localization data in pixels and converted in kilometers focusing on the country area. Agriculture occupation is used to capture the agricultural land expansion on deforestation. Agriculture represents the primary sector in many developing countries. The major practice in the country is shifting cultivation or agribusiness that needs a lot of space for constant production. Shifting cultivation has been attributed to causing large-scale deforestation in tropical forest-agriculture frontiers in many studies (Angelsen, 1995; Mukul and Herbohn, 2016). Thus, this practice forces farmers to migrate regularly to forest areas to find new land. Also, agricultural land expansion is the main strategy to increase agriculture income and production. Family agriculture migrates for subsistence and the big farmers convert the forestland frequently for crop production on a high scale (Culas, 2007).

Regarding wood industry data, we use processed wood data from ITTO, the main source of tropical timber trade and production.<fnref n="8" /> This variable combines sawn wood, veneer and plywood production representing the processed wood. The main input in the forest industry is wood. Following the low material yield of developing countries in wood processing, processed wood can be used as a proxy to measure the forest industry's pressure on forest resources.

## 4.3 Others control variables

The set of other control variables is from WDI: GDP per capita, population density, population growth, international trade, exchange rate and mineral rent (see statistics in Table A1 and variable definitions in Table A2).<fnref n="9" /> GDP per capita is used to capture the effect of national wealth on forest loss. Focused on the Environmental Kuznets Curve (EKC) hypothesis, GDP per capita and GDP per capita squared should positively and negatively affect the tree cover loss. So, deforestation increases with the income for the low level of income and achieves a threshold where the link becomes negative. So, at the beginning of development, countries applied some pressure on forests for logging, firewood, and agricultural goods. This pressure decreases when countries

<footnote n="7">⁷Data accessible at: https://lpdaac.usgs.gov/products/mcd12q1v006/</footnote>

<footnote n="8">⁸Data accessible on https://www.itto.int/biennial-review/</footnote>

<footnote n="9">⁹Data accessible on https://databank.worldbank.org/source/world-development-indicators</footnote>
achieve some level of development where the cooking energy is modern and the secondary and tertiary sectors are well developed, reducing the logging activities and farming in rural areas (Culas, 2007). However, other empirical studies show no evidence of the environmental EKC for deforestation (Choumert et al., 2013).

Furthermore, international trade affects the dynamics of deforestation. Countries with comparative advantage in forest resources are generally timber exporters. They answer the demand of the international market by cutting down forests. Also, the cropland expansion and cattle activities for agricultural goods export increase deforestation (Faria and Almeida, 2016). Moreover, the exchange rate indicates wood prices in international markets, specifically for log exporters. Thus, the wood price guides the model choice of forest exploitation by the local community and forest industry. For instance, Arcand et al. (2008) show that a depreciation in the real exchange rate exacerbates deforestation in developing countries. In addition, the mineral rent is used as an indicator to assess the effect of mining extraction on deforestation (Kinda and Thiombiano, 2021). The extractive industries are invasive in the natural resources richest developing countries, occupying more high areas for operation.

Population density is most widely used in deforestation literature (Bhattarai and Hammig, 2001; Cropper and Griffiths, 1994; Culas, 2007). It is followed by population growth, also mentioned as a major factor of deforestation. Government effectiveness is considered for the institutional channel. Ferreira and Vincent (2010) highlight a nonmonotonic impact of governance on timber harvesting. Institutional structure, such as property rights, quality of governance and corruption, significantly affect tropical deforestation (Araujo et al., 2009; Bhattarai and Hammig, 2001; Culas, 2007).

In addition, we consider the Forest Stewardship Council (FSC) certification initiated in 1993 and covered more than 200 million hectares. This variable is the proportion of the forest area under FSC certification in the forest considered by Damette and Delacote (2011) as a covariate to explain deforestation. Tritsch et al. (2020) using Hansen et al. (2013) dataset, highlight the role of FSC certification in the deforestation process in the Congo Basin. It represents the share of the total forest area under FSC certification.

Finally, we also use the temperature shock data available on FAO Statistics and disseminate statistics of mean surface temperature change by country.<fnref n="10" />

The temperature shocks capture the effect of floods, forest fires, or droughts considered proximate causes of deforestation (da Silva et al., 2023; Geist and Lambin, 2002; Vaglietti et al., 2022).

# 5 Identification strategy

This study aims to analyze whether the LEB has a detrimental effect on deforestation in adopter countries. Thus, our analysis considers treated countries that apply log export restrictions. The reason for LEB adoption differs between countries. Deforestation fighting and forest industry promotion are the main objectives of the reform. Since most countries apply LEB to fight deforestation and low value-added forest manufacturing

<footnote n="10">¹⁰Data accessible on: https://www.fao.org/faostat/en/home</footnote>
industries, one may have selection because LEB adoption may be correlated with unobservables, which may also affect the overall deforestation level. Thus, LEB policy will face the selection bias problem. To circumvent this issue and identify the effect of the LEB policy, we use the matching method, namely entropy balancing, developed by Hainmueller (2012). This impact assessment strategy is used in economic literature by Neuenkirch and Neumeier (2016) to measure the effect of U.S. sanctions on poverty and Apeti (2023) to evaluate the impact of mobile money adoption on household consumption volatility. In this study, our potential outcome is annual deforestation. Thus, countries under the LEB policy are the treatment group and countries without the LEB are the control group. Then, the average treatment effect on treated (ATT) based on matching is defined as follows:

$$ATT = E[Y_{i1}|T=1] - E[Y_{i0}|T=0], \qquad (4)$$

where deforestation is noted Y⁰ for the outcome not located in the country under the LEB (T=0) and Y¹ for a country under treatment (T=1). E[Yᵢ₁|T=1] is the deforestation level during the LEB period and E[Yᵢ₀|T=0] is the counterfactual result for countries under LEB, i.e. the annual deforestation level for LEB adopter countries if they had not. The problem is that E[Yᵢ₀|T=0] is not observable related to the non-random nature of LEB policy adoption. If identifiable, the ATT could be easily defined by comparing the tree cover loss in LEB adopters with the non-LEB adoption situation. Thus, we need a good proxy to identify the ATT. So, we match treatment units with control units based on the pre-treatment country-level covariates that are as close as possible and correlated with both LEB adoption and annual deforestation. Under the condition, the treatment and control groups are pretty close, and the difference in deforestation is imputable to LEB adoption. Focused on this condition, we can rewrite Equation 4 as follows:

$$ATT = E[Y_{i1}|T=1, X=\chi] - E[Y_{i0}|T=0, X=\chi], \qquad (5)$$

where χ is a vector of pre-treatment characteristics correlated with treatment assignment and potentially correlated with annual deforestation. E[Yᵢ₁|T=1, X=χ] is expected to be the best counterfactual for treatment units. Using entropy balancing to estimate the ATT involves two steps. The first step is to compute the weights for the non-treated group allowing the average of pre-treatment variables in the control group not to be statistically different from the average in the treated group. In this step, we want to ensure that the control group and treated units, on average, are as similar as possible. The second step uses the weight from the first stage in regression with the treatment indicator as an explanatory variable to counteract the eventual influence of pre-treatment differences on the treatment effect. In this regression, deforestation is the dependent variable to assess the effect of the LEB on annual tree cover loss. Furthermore, we control for time and country-specific effects in addition to entropy-balancing covariates, as in the randomized experiment, to increase estimation efficiency and to control for potential unobserved heterogeneity across countries and bias linked to changes over
time, independent of treatment. This approach allows the weighted least squared (WLS) to be used in empirical estimation.

Entropy balancing allows us to estimate the effect of a LEB by comparing LEB and non-LEB countries that are as close as possible to observable characteristics while controlling for time and country-specific effects. By doing both a matching and regression approach, this method offers some advantages compared to other impact analysis methods, such as propensity score matching (PSM) or difference-in-differences (Neuenkirch and Neumeier, 2016). Entropy balancing is non-parametric because it requires no empirical model for either the outcome variable or selection into treatment needs to be specified. Hence, potential types of misspecification like those, for instance, regarding the functional form of the empirical model, which likely leads to biased estimates, are ruled out. Moreover, compared to regression-based analyses, treatment effects estimates based on entropy balancing do not suffer from multicollinearity, as the reweighting scheme orthogonalized the covariates concerning the treatment indicator. Also, entropy balancing ensures a high covariates balance between the units treated and the counterfactual group, even in small samples. Furthermore, in traditional matching methods such as nearest neighbour matching or propensity score matching, each unit subject to treatment, in the simplest case, is matched with the one unit non-subject to treatment that is closest in terms of a metric balancing score. Accordingly, the non-treated group comprises only a subset of the units not subject to treatment (Hainmueller, 2012; Diamond and Sekhon, 2013). Thus, with the conventional matching approach, each unit non-subject to treatment either receives a weight equal to 0 if it does not represent the best match for a treated unit or 1 if it is the best match for one treated unit. However, when the number of untreated units is limited and the number of pre-treatment characteristics is large, this procedure does not guarantee a sufficient balance of pre-treatment characteristics across the treatment and control groups. This is a severe issue, as a low covariate balance may lead to estimation bias. However, with entropy balancing, the vector of weights attributed to the units not assigned to treatment can contain any non-negative values. Thus, a synthetic control group is designed that represents a virtual image of the treatment group. Entropy balancing can be explicit as a generalization of conventional matching approaches.

# 6 Empirical results

## 6.1 Descriptive statistics

Table 1 shows the comparison of pre-reweighting means of each covariate between countries under LEB policy (column (1)) and the potential synthetic control group (column (2)). Column (3) reports the t-test difference of each covariate in means between the two groups. The p-value in the last column shows the significant difference between our two groups except for population density, trade openness and government effectiveness. Thus, such differences could bias the real treatment effect caused by a potential selection problem. The descriptive findings show why choosing an appropriate synthetic control group by matching approach is important before assessing
the treatment effect.

<table><caption>Table 1: Descriptive statistics</caption><thead><tr><th rowspan="2"></th><th>(1)</th><th>(2)</th><th colspan="2">(3)=(2)-(1)</th></tr><tr><th>LEB</th><th>No LEB</th><th>Difference</th><th>p-Val</th></tr></thead><tbody><tr><td>GDP Per Capita log<sub>t-1</sub></td><td>7.74</td><td>7.84</td><td>0.10</td><td>0.043</td></tr><tr><td>Agriculture occupation log<sub>t-1</sub></td><td>6.08</td><td>4.95</td><td>-1.13</td><td>0.000</td></tr><tr><td>Processed wood log<sub>t-1</sub></td><td>12.83</td><td>9.16</td><td>-3.67</td><td>0.000</td></tr><tr><td>FSC share<sub>t-1</sub></td><td>2.63</td><td>1.74</td><td>-0.89</td><td>0.009</td></tr><tr><td>Population density log<sub>t-1</sub></td><td>4.01</td><td>4.03</td><td>0.02</td><td>0.667</td></tr><tr><td>Population growth<sub>t-1</sub></td><td>1.89</td><td>1.43</td><td>-0.46</td><td>0.000</td></tr><tr><td>International trade<sub>t-1</sub></td><td>77.20</td><td>78.31</td><td>1.11</td><td>0.562</td></tr><tr><td>Exchange trade log<sub>t-1</sub></td><td>4.78</td><td>3.65</td><td>-1.13</td><td>0.000</td></tr><tr><td>Temperature shock<sub>t-1</sub></td><td>0.96</td><td>1.00</td><td>0.04</td><td>0.128</td></tr><tr><td>Government effectiveness<sub>t-1</sub></td><td>-0.50</td><td>-0.48</td><td>0.02</td><td>0.582</td></tr><tr><td>Mineral rent<sub>t-1</sub></td><td>1.28</td><td>0.99</td><td>-0.29</td><td>0.035</td></tr><tr><td>Observations</td><td>520</td><td>950</td><td></td><td></td></tr></tbody></table>

In Table 2, we compute a synthetic control group as defined in Section 5 to reweight the control units (column (4)). This strategy allows us to construct a perfect synthetic group similar to the treated units. Column (5) shows the difference in means after reweighting. The p-value and the t-test show no statistically significant difference between all covariates. Thus, statistical evidence shows the efficacy of entropy balancing to make a perfect synthetic control group related to the treatment group.

<table><caption>Table 2: Covariate balancing</caption><thead><tr><th rowspan="2"></th><th>(1)</th><th>(4)</th><th colspan="2">(5)=(4)-(1)</th></tr><tr><th>LEB</th><th>Control</th><th>Difference</th><th>p-Val</th></tr></thead><tbody><tr><td>GDP Per Capita log<sub>t-1</sub></td><td>7.74</td><td>7.74</td><td>0.00</td><td>1.00</td></tr><tr><td>Agriculture occupation log<sub>t-1</sub></td><td>6.08</td><td>6.08</td><td>0.01</td><td>1.00</td></tr><tr><td>Processed wood log<sub>t-1</sub></td><td>12.83</td><td>12.83</td><td>0.02</td><td>1.00</td></tr><tr><td>FSC share<sub>t-1</sub></td><td>2.63</td><td>2.63</td><td>0.03</td><td>1.00</td></tr><tr><td>Population density log<sub>t-1</sub></td><td>4.01</td><td>4.01</td><td>0.04</td><td>1.00</td></tr><tr><td>Population growth<sub>t-1</sub></td><td>1.89</td><td>1.89</td><td>0.05</td><td>1.00</td></tr><tr><td>International trade<sub>t-1</sub></td><td>77.20</td><td>77.20</td><td>0.06</td><td>1.00</td></tr><tr><td>Exchange trade log<sub>t-1</sub></td><td>4.78</td><td>4.78</td><td>0.07</td><td>1.00</td></tr><tr><td>Temperature shock<sub>t-1</sub></td><td>0.96</td><td>0.96</td><td>0.08</td><td>1.00</td></tr><tr><td>Government effectiveness<sub>t-1</sub></td><td>-0.50</td><td>-0.50</td><td>0.09</td><td>1.00</td></tr><tr><td>Mine rent<sub>t-1</sub></td><td>1.28</td><td>1.28</td><td>0.11</td><td>1.00</td></tr><tr><td>Reweighted Observations</td><td>520</td><td>520</td><td></td><td></td></tr></tbody></table>

## 6.2 Baseline results
<table><caption>Table 3: Log export ban and deforestation</caption><thead><tr><th>Log Deforestation</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th><th>(11)</th><th>(12)</th><th>(13)</th><th>(14)</th></tr></thead><tbody><tr><td>LEB</td><td>0.350***<br>(0.057)</td><td>0.293***<br>(0.057)</td><td>0.245***<br>(0.057)</td><td>0.222***<br>(0.055)</td><td>0.222***<br>(0.053)</td><td>0.228***<br>(0.066)</td><td>0.242***<br>(0.064)</td><td>0.296***<br>(0.068)</td><td>0.279***<br>(0.071)</td><td>0.307***<br>(0.075)</td><td>0.308***<br>(0.079)</td><td>0.320***<br>(0.077)</td><td>0.339***<br>(0.082)</td><td>0.223***<br>(0.083)</td></tr><tr><td>Log GDP Per Capita<sub>t-1</sub></td><td></td><td>-0.065<br>(0.099)</td><td>4.507***<br>(0.532)</td><td>3.861***<br>(0.539)</td><td>2.659***<br>(0.638)</td><td>3.677***<br>(0.967)</td><td>2.622***<br>(0.932)</td><td>1.470<br>(1.153)</td><td>2.555*<br>(1.305)</td><td>3.012**<br>(1.338)</td><td>3.004**<br>(1.342)</td><td>3.026**<br>(1.335)</td><td>3.081**<br>(1.415)</td><td>2.945**<br>(1.420)</td></tr><tr><td>Log GDP Per Capita squared<sub>t-1</sub></td><td></td><td></td><td>-0.306***<br>(0.036)</td><td>-0.262***<br>(0.037)</td><td>-0.158***<br>(0.045)</td><td>-0.212***<br>(0.068)</td><td>-0.150**<br>(0.064)</td><td>-0.062<br>(0.078)</td><td>-0.128<br>(0.088)</td><td>-0.161*<br>(0.089)</td><td>-0.154*<br>(0.087)</td><td>-0.160*<br>(0.086)</td><td>-0.177*<br>(0.091)</td><td>-0.168*<br>(0.091)</td></tr><tr><td>Log Agriculture occupation<sub>t-1</sub></td><td></td><td></td><td></td><td>1.798***<br>(0.217)</td><td>1.637***<br>(0.241)</td><td>1.802***<br>(0.306)</td><td>1.610***<br>(0.329)</td><td>1.851***<br>(0.404)</td><td>1.734***<br>(0.422)</td><td>1.728***<br>(0.408)</td><td>1.695***<br>(0.464)</td><td>1.670***<br>(0.501)</td><td>1.705***<br>(0.526)</td><td>1.570***<br>(0.523)</td></tr><tr><td>Log Processed wood<sub>t-1</sub></td><td></td><td></td><td></td><td></td><td>0.195***<br>(0.039)</td><td>0.261***<br>(0.047)</td><td>0.314***<br>(0.065)</td><td>0.372***<br>(0.073)</td><td>0.381***<br>(0.078)</td><td>0.378***<br>(0.075)</td><td>0.376***<br>(0.091)</td><td>0.375***<br>(0.078)</td><td>0.363***<br>(0.080)</td><td>0.353***<br>(0.079)</td></tr><tr><td>FSC share<sub>t-1</sub></td><td></td><td></td><td></td><td></td><td></td><td>0.004<br>(0.002)</td><td>0.005**<br>(0.003)</td><td>0.008**<br>(0.003)</td><td>0.008**<br>(0.003)</td><td>0.009***<br>(0.003)</td><td>0.008**<br>(0.004)</td><td>0.010***<br>(0.004)</td><td>0.009**<br>(0.004)</td><td>0.008**<br>(0.004)</td></tr><tr><td>Log population density<sub>t-1</sub></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.554<br>(0.479)</td><td>0.718<br>(0.452)</td><td>0.866*<br>(0.475)</td><td>0.825*<br>(0.481)</td><td>1.052**<br>(0.503)</td><td>1.082**<br>(0.481)</td><td>1.240**<br>(0.519)</td><td>1.022*<br>(0.538)</td></tr><tr><td>Population growth<sub>t-1</sub></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.146**<br>(0.060)</td><td>0.096<br>(0.060)</td><td>0.086<br>(0.055)</td><td>0.089<br>(0.060)</td><td>0.110*<br>(0.060)</td><td>0.101*<br>(0.061)</td><td>0.107*<br>(0.062)</td></tr><tr><td>International Trade<sub>t-1</sub></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.000<br>(0.001)</td><td>0.000<br>(0.001)</td><td>0.001<br>(0.001)</td><td>0.000<br>(0.001)</td><td>0.000<br>(0.001)</td><td>0.001<br>(0.001)</td></tr><tr><td>Log Exchange rate<sub>t-1</sub></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.105*<br>(0.056)</td><td>-0.100*<br>(0.058)</td><td>-0.092<br>(0.060)</td><td>-0.101<br>(0.064)</td><td>-0.137*<br>(0.073)</td></tr><tr><td>Temperature shock<sub>t-1</sub></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.175<br>(0.155)</td><td>0.102<br>(0.148)</td><td>0.105<br>(0.149)</td><td>0.099<br>(0.149)</td></tr><tr><td>Mineral rent<sub>t-1</sub></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.046**<br>(0.021)</td><td>-0.038*<br>(0.021)</td><td>-0.038*<br>(0.021)</td></tr><tr><td>Government effectiveness<sub>t-1</sub></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.316<br>(0.193)</td><td>0.417**<br>(0.212)</td></tr><tr><td>LEBxGovernment effectiveness<sub>t-1</sub></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>-0.286**<br>(0.125)</td></tr><tr><td>Constant</td><td>0.351**<br>(0.149)</td><td>0.397<br>(0.618)</td><td>-16.365***<br>(1.984)</td><td>-26.356***<br>(2.408)</td><td>-24.330***<br>(2.652)</td><td>-30.552***<br>(4.036)</td><td>-27.866***<br>(3.963)</td><td>-27.408***<br>(5.000)</td><td>-24.106***<br>(4.943)</td><td>-24.997***<br>(5.202)</td><td>-26.439***<br>(5.436)</td><td>-26.227***<br>(5.497)</td><td>-26.492***<br>(5.853)</td><td>-24.174***<br>(5.948)</td></tr><tr><td>Observation</td><td>2353</td><td>2134</td><td>2134</td><td>2134</td><td>2109</td><td>1831</td><td>1831</td><td>1831</td><td>1691</td><td>1568</td><td>1548</td><td>1541</td><td>1470</td><td>1470</td></tr><tr><td>Number of countries</td><td>124</td><td>120</td><td>120</td><td>120</td><td>120</td><td>120</td><td>120</td><td>120</td><td>114</td><td>112</td><td>111</td><td>110</td><td>109</td><td>109</td></tr><tr><td>Country fixed effect</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effect</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>R-squared</td><td>0.969</td><td>0.973</td><td>0.973</td><td>0.970</td><td>0.962</td><td>0.962</td><td>0.964</td><td>0.963</td><td>0.961</td><td>0.961</td><td>0.958</td><td>0.959</td><td>0.959</td><td>0.960</td></tr></tbody></table>

Note: robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1. The dependent variable is Log Deforestation. Estimator: WLS with synthetic control variables approach
Based on the synthetic controls in Table 2, we use the WLS method to estimate the effect of LEB on deforestation (ATT) in developing countries. The results in Table 3 present a set of matching covariates used in the first stage to build the synthetic group. We put progressively matching covariates in the second stage to see the LEB adoption effect on deforestation for each covariate controlled. We note that including the matching covariates in the second stage of entropy balancing increases matching quality (as in randomized experiments). Columns (1)-(14), including country and time-fixed effects, show that the adoption of LEB policy significantly increases (at 1%) deforestation in LEB countries compared to non-LEB countries. The magnitude of this effect is between 22.2% in columns (4) and (5) and 35% in column (1), showing that our result is robust given the relative stability of the coefficients across all specifications. Focusing on column (14) where we use the interaction between LEB policy and Government effectiveness, the full matching covariates used in the first stage and shown in Table 2, we can estimate the average effect at 22.3% deforestation increase after the policy adoption. However, we can highlight that fostering law enforcement increases the government's effectiveness in policy adoption and significantly reduces the effect of LEB policy on deforestation as shown in column (14). This specification is the baseline for further estimation.

## 6.3 Channels

As defined above, LEB adoption increases deforestation in developing countries. This section aims at highlighting the mechanisms underlying our results. Thus, we test the relevance of our two potential transmission channels, agriculture occupation and processed wood quantity, as described in Section 2.2. We use the mediator variables approach that matches our analysis as defined by Baron and Kenny (1986). Then, we test the linkages of the mediational model. Before testing our channels, we first assess their relevance to the mediator variable using a simple country and year-fixed effects regression. This strategy aims to check if LEB adoption affects our two mediator variables defined as the transmission channels. Second, we assess the effect of LEB on deforestation without controlling for mediator variables. Third, we estimate the effect of LEB adoption on deforestation control for agricultural occupation and processed wood quantity as mediator variables. We use the synthetic control variable approach and the WLS estimator in each step.

Table 4 shows the result where the coefficient of each regression establishes the mediation. Columns (1) and (2) show that LEB is associated with a positive effect on agriculture occupation and processed wood. This result allows for meeting the first condition of the significant effect of LEB on mediator variables. In column (3), we assess the second condition of the effect of LEB on deforestation without mediator variables. Last, controlling respectively for agriculture occupation, processed wood quantity, and both variables, the coefficient of the effect of LEB on deforestation must be less in columns (4), (5), and (6) than the coefficient in columns (3). The last condition is fulfilled. Overall, we can state that our potential transmission channels meet the mediator's variables conditions to support our explanation given in Section 2.2 and Section 3.
<table><caption>Table 4: Transmission channels analysis</caption><thead><tr><th>Dependent variable</th><th>(1)<br>Log Agriculture<br>occupation</th><th>(2)<br>Log Processed<br>wood</th><th>(3)<br>Log deforestation</th><th>(4)<br>Log deforestation</th><th>(5)<br>Log deforestation</th><th>(6)<br>Log deforestation</th></tr></thead><tbody><tr><td>LEB</td><td>0.030***<br>(0.008)</td><td>0.231***<br>(0.066)</td><td>0.350***<br>(0.057)</td><td>0.205***<br>(0.052)<br>2.294***<br>(0.215)</td><td>0.251***<br>(0.050)</td><td>0.192***<br>(0.048)<br>2.051***<br>(0.213)</td></tr><tr><td>Log Agriculture occupation</td><td></td><td></td><td></td><td></td><td></td><td>0.157***<br>(0.030)</td></tr><tr><td>Log Processed wood</td><td></td><td></td><td></td><td></td><td></td><td>0.155***<br>(0.029)</td></tr><tr><td>Constant</td><td>6.880***<br>(0.009)</td><td>12.722***<br>(0.120)</td><td>0.351**<br>(0.149)</td><td>-15.682***<br>(1.482)</td><td>-1.994***<br>(0.412)</td><td>-16.070***<br>(1.506)</td></tr><tr><td>Observation</td><td>2353</td><td>2321</td><td>2353</td><td>2353</td><td>2321</td><td>2321</td></tr><tr><td>Number of countries</td><td>124</td><td>124</td><td>124</td><td>124</td><td>124</td><td>124</td></tr><tr><td>Country fixed effect</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effect</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Covariates</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td><td>No</td></tr><tr><td>R-squared</td><td>0.999</td><td>0.962</td><td>0.969</td><td>0.971</td><td>0.967</td><td>0.969</td></tr></tbody></table>

Note: robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1. Estimator: WLS with synthetic control variables approach

# 7 Robustness check

## 7.1 Alternative LEB measure

The LEB adoption is not homogeneous in all adopter countries. Other forest policies, like the sawn wood export, sometimes follow LEB policy. This policy could affect too much the effect of the LEB policy on deforestation. First, we exclude all sawn wood export ban adopters. Excluding sawn wood export ban countries (column (2)) increases the LEB effect related to our baseline result (Table 5). The policy adoption effect is less significant in countries that apply both LEB and sawn wood export bans because their wood processing perspective is lower than in other countries. The processing industry channel is less significant. The exclusion of the group of countries banning the sawn wood export increases the impact of LEB policy adoption on deforestation.

## 7.2 Alternative estimation methods

### 7.2.1 Instrumental variable approach

In our analysis, two potential endogeneity biases are defined as the correlation between the error term and the dependent variable. First, the inverse causality may emerge because some countries apply LEB to combat the over-exploitation generating forest degradation and deforestation. Thus, the deforestation level could raise the probability of LEB adoption. Second, potential omitted explanatory variables may explain the deforestation level. This omitted variable would be in the error term, correlated to our outcome. If the endogeneity has an essential effect on the OLS estimation, we will see an overestimation of the LEB policy coefficient value. To resolve the endogeneity issue, we apply the instrumental variables approach as Wooldridge (2015) recommended for explanatory endogenous binary variables. Finding a good instrument for a LEB policy is not a simple task. The instrument must be correlated to the suspected endogenous variable without impacting the outcomes through another channel. Thus, the first step is the regression of the instrument variable and the set of control variables on LEB policy. We will use the probit approach (Equation 6) to estimate the probability of LEB adoption because the policy is a dummy variable. This step allows the elimination of endogeneity bias from LEB policy.
<table><caption>Table 5: LEB redefinition</caption><thead><tr><th>Log Deforestation</th><th>(1)<br>LEB</th><th>(2)<br>LEB Excluding<br>Sawnwood Ban adopter</th></tr></thead><tbody><tr><td>LEB</td><td>0.223***<br>(0.083)</td><td>0.367**<br>(0.171)</td></tr><tr><td>Log GDP Per Capita<sub>t-1</sub></td><td>2.945**<br>(1.420)</td><td>-0.910<br>(1.827)</td></tr><tr><td>Log GDP Per Capita squared<sub>t-1</sub></td><td>-0.168*<br>(0.091)</td><td>0.108<br>(0.129)</td></tr><tr><td>Log Agriculture occupation<sub>t-1</sub></td><td>1.570***<br>(0.523)</td><td>1.819**<br>(0.707)</td></tr><tr><td>Log Processed wood<sub>t-1</sub></td><td>0.353***<br>(0.079)</td><td>0.421***<br>(0.112)</td></tr><tr><td>FSC share<sub>t-1</sub></td><td>0.008**<br>(0.004)</td><td>0.005<br>(0.007)</td></tr><tr><td>Log population density<sub>t-1</sub></td><td>1.022*<br>(0.538)</td><td>1.061<br>(0.793)</td></tr><tr><td>Population growth<sub>t-1</sub></td><td>0.107*<br>(0.062)</td><td>0.073<br>(0.085)</td></tr><tr><td>International Trade<sub>t-1</sub></td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td></tr><tr><td>Log Exchange rate<sub>t-1</sub></td><td>-0.137*<br>(0.073)</td><td>-0.293***<br>(0.112)</td></tr><tr><td>Temperature shock<sub>t-1</sub></td><td>0.099<br>(0.149)</td><td>0.338<br>(0.212)</td></tr><tr><td>Mineral rent<sub>t-1</sub></td><td>-0.038*<br>(0.021)</td><td>-0.014<br>(0.023)</td></tr><tr><td>Government effectiveness<sub>t-1</sub></td><td>0.417**<br>(0.212)</td><td>0.480*<br>(0.291)</td></tr><tr><td>LEBxGovernment effectiveness<sub>t-1</sub></td><td>-0.286**<br>(0.125)</td><td>-0.405**<br>(0.175)</td></tr><tr><td>Constant</td><td>-24.174***<br>(5.948)</td><td>-12.289*<br>(7.209)</td></tr><tr><td>Observation</td><td>1470</td><td>1245</td></tr><tr><td>Number of country</td><td>109</td><td>92</td></tr><tr><td>Country fixed effect</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effect</td><td>Yes</td><td>Yes</td></tr><tr><td>Rsquared</td><td>0.960</td><td>0.951</td></tr></tbody></table>

Note: robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.

$$E[LEB|Z_i, X_i] = \Pr[LEB = 1|Z_i, X_i] \qquad (6)$$

In the second stage, we use the prediction of LEB policy adoption probability as the interest variable and the set of control variables where deforestation is the outcome as follows:

$$log(TCL_{i,t}) = \beta_0 + \beta_1 LEB_{i,t} + \gamma X_{i,t-1} + \eta_i + \theta t + \epsilon_{i,t} \qquad (7)$$

We use two instruments for the instrumentation of the LEB policy variable. The first instrument is the neighbourhood effect of LEB policy adoption. The country's policy choice and market orientation correlate with neighbouring countries' share following these policies (Buera et al., 2011; Persson and Tabellini, 2009). Also, countries in an economic regional area where the policy is harmonised are attempting to apply the policy already used by most countries. In these regions, countries try to converge toward the same economic and environmental policy. Thus, LEB policy adoption in the neighbour increases the probability of policy adoption in non-adopter countries. In the deforestation analysis, the policy adopted by the neighbour cannot directly affect deforestation in non-adopter countries. The neighbourhood effect is from the FPER database as our interest variable and computed as the proportion of neighbours of the country that implemented LEB in period t-1. It is generally said that demand for wood is redirected towards other countries after LEB adoption, but
not necessarily neighbouring countries because this demand depends on the value, type and use of the wood.
We, therefore, control for this potential loss, which is not necessarily apparent in the case of our study. Thus,
we control for the neighbouring country's deforestation in our estimation.

The second instrument is the tariff rate weighted mean applied on primary products from WDI. This variable
shows each country's international trade dependence level regarding the tariff rate. To avoid the reciprocity in
multilateral trade that can affect the international trade level, many countries have fewer incentives to apply
some strict prohibitions on exports and imports (Bagwell and Staiger, 2001). Also, high levels of tariff rates
show how much countries are based on revenue from customs cordons. Thus, countries with higher tariff rates
are less motivated to apply LEB policies that include renouncing tax revenue from log export.

Table 6 presents the results.

<table><caption>Table 6: LEB and deforestation: IV approach</caption><thead><tr><th rowspan="3"></th><th colspan="1">Entropy (WLS)</th><th colspan="2">Instrumental Variable</th></tr><tr><th rowspan="2">Log Deforestation<br>(Km²)</th><th colspan="1">First stage</th><th colspan="1">Second stage</th></tr><tr><th>LEB</th><th>Log Deforestation<br>(Km²)</th></tr></thead><tbody><tr><td>LEB</td><td>0.291***<br>(0.083)</td><td></td><td>0.415***<br>(0.159)</td></tr><tr><td>Log GDP Per Capita</td><td>2.288*<br>(1.309)</td><td>11.067***<br>(1.068)</td><td>4.749***<br>(1.147)</td></tr><tr><td>Log GDP Per Capita squared</td><td>-0.120<br>(0.088)</td><td>-0.742***<br>(0.070)</td><td>-0.319***<br>(0.079)</td></tr><tr><td>Log Agriculture occupation</td><td>2.116***<br>(0.467)</td><td>-0.034<br>(0.029)</td><td>1.684***<br>(0.466)</td></tr><tr><td>Log Processed wood</td><td>0.370***<br>(0.092)</td><td>0.338***<br>(0.036)</td><td>0.118***<br>(0.038)</td></tr><tr><td>FSC share</td><td>0.003<br>(0.004)</td><td>0.028***<br>(0.008)</td><td>0.004<br>(0.004)</td></tr><tr><td>Log population density</td><td>0.039<br>(0.586)</td><td>-0.160**<br>(0.067)</td><td>-0.201<br>(0.472)</td></tr><tr><td>Population growth</td><td>0.064<br>(0.059)</td><td>0.752***<br>(0.084)</td><td>-0.007<br>(0.053)</td></tr><tr><td>Temperature shock</td><td>0.144<br>(0.156)</td><td>0.288*<br>(0.162)</td><td>-0.029<br>(0.055)</td></tr><tr><td>Mineral rent</td><td>-0.031*<br>(0.017)</td><td>-0.040**<br>(0.019)</td><td>0.002<br>(0.011)</td></tr><tr><td>Government effectiveness</td><td>0.334<br>(0.207)</td><td>0.447***<br>(0.154)</td><td>-0.251**<br>(0.125)</td></tr><tr><td>Log Neighbors deforestation</td><td>0.282***<br>(0.075)</td><td>0.048<br>(0.035)</td><td>0.189***<br>(0.047)</td></tr><tr><td colspan="4"><strong>Instruments</strong></td></tr><tr><td>Neighbor</td><td></td><td>2.719***<br>(0.215)</td><td></td></tr><tr><td>Tariff rate weighted</td><td></td><td>-0.079***<br>(0.016)</td><td></td></tr><tr><td>Constant</td><td>-33.609***<br>(6.237)</td><td>-46.925***<br>(4.221)</td><td>-26.077***<br>(4.879)</td></tr><tr><td>Observations</td><td>1357</td><td>979</td><td>979</td></tr><tr><td>Number of countries</td><td>94</td><td>85</td><td>85</td></tr><tr><td>Country fixed effect</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Year fixed effect</td><td>Yes</td><td>Yes</td><td>Yes</td></tr></tbody></table>

Note: robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1. We control for neighbor deforestation in this estimation and we drop International Trade and exchange rate that can create multicollinearity with our second IV (tariff rate weighted).

The following analysis is focused on column (14) of Table 3 where all control variables are used. To address
the endogenous problem defined above, we use those two instruments to resolve the inverse causality and the
omission variable problem. Table 6 shows the entropy balancing and 2SLS describing the first (Equation 6) and
second stage (Equation 7). In the first stage focusing on Equation 6, we use the probit model to see the effect
of our instruments on LEB policy. The first stage shows the positive and significant effects of the neighbour LEB adoption on the probability of LEB policy adoption, while the tariff rate weighted has a negative and significant effect at 1%. Thus, the instruments are valid for the first stage. In the second stage, we extract the prediction of LEB from the probit model and add it to the baseline OLS model. We then focus on the LEB prediction coefficient. The results show that the LEB policy coefficient is significant at 5%, with an increase in its magnitude (from 0.291 to 0.415). However, the classical instrumental variable approach ignores the binary nature of the variable and applies a linear regression to it in the first stage of instrumentation. This linear regression, instead of a probit or logit regression, can lead to fallacious regressions. Then, Wooldridge proposes an estimator that considers the variable's character, which is supposed to be endogenous. However, we use the Stata software for our estimates, and unfortunately, the command proposed by Wooldridge does not allow us to carry out tests on the instruments. It focuses solely on significance. Therefore, in the Table A4, we propose a table using the classic instrumental variables, which highlights all the tests concerning the instruments. The Kleibergen statistics are higher than the Stock-Yogo statistics, so the instruments are robust. The non-significance of the Hansen statistic shows that the instruments are exogenous. Statistically, based on these two conditions, we can conclude that the instruments are relevant.

### 7.2.2 Staggered Difference-in-Differences

Then, we assess the effect of LEB adoption on the tree cover loss using the staggered Difference-in-differences (DiD) with variation in the treatment (difference-in-differences with multiple periods and variation in treatment timing) following Callaway and Sant'Anna (2021). In this approach, when one unit is treated, it remains treated in the next periods. We divide our sample into treatment and control groups. For each year y in the time series observation, countries that adopted LEB in that year comprise the treatment group ($C_i = y$), while observations from countries before the year of policy adoption are included in the respective control group until their treatment. We apply the average treatment effect of units who are members of a specific group g at a particular period t conditional on pre-treatment covariates X as follows:

$$ATT(g, t) = E[Y_t(g) - Y_t(0) | X, G_g = 1] \quad (8)$$

Where $G_g$ is a binary variable that is equal to one if a unit is first treated in period g. $Y_t$ represents the deforestation in a given reporting period t.

Then, we aggregate these results to produce a single set of estimation results for the treatment year. It consists of calculating the average effect on the treated ATT for event times e which represents the time difference between the observation year t and the first year of treatment g ($e = t - g$). Callaway and Sant'Anna (2021) suggest the weights from the group-dependent $w(g, t)$ as follows
$$ATT_{es}(e) = \sum_{g \in G} w(g, t) * ATT(g, t). \qquad (9)$$

Table 7 shows the post-estimation impact of LEB policy adoption on deforestation following simple weighted average, group average, and event study aggregations models.

<table><caption>Table 7: Staggered Difference-in-Differences</caption><thead><tr><th></th><th>Simple weighted<br>Average</th><th>Simple weighted<br>Average</th><th>Group<br>Average</th><th>Event<br>Study</th></tr></thead><tbody><tr><td>Post-adoption impact</td><td>0.444***<br>(0.129)</td><td>0.445**<br>(0.202)</td><td>0.444***<br>(0.163)</td><td>0.543**<br>(0.214)</td></tr><tr><td>Covariates</td><td>No</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>2,030</td><td>1,323</td><td>1,323</td><td>1,323</td></tr></tbody></table>

Note: robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.

# 8 Heterogeneity

Furthermore, we check the time effect of LEB policy adoption. We test whether LEB adoption has a temporary or persistent effect on deforestation. Many policies have effects on policy adoption only in the beginning period. The effect disappears rapidly over time. Figure 6 reports the policy adoption effect after one, two, three, four and five years respectively (full estimation results are in Table A3 in Appendix). In each case, LEB is positive and significant showing that LEB adoption increases deforestation. Thus, the effect of LEB adoption is not persistent. The effect disappears after two years, showing the short-term effect of this policy adoption on deforestation.

<figure><img src="image_7.png" /><figcaption>Figure 6: The effect of LEB on deforestation over time</figcaption></figure>
# 9 Conclusion

This study aims to assess the effect of LEB policy adoption on deforestation. We focus our analysis on a large panel of 124 developing countries with more than 100 Km² of forest resource from 2001 to 2019. We use entropy balancing to evaluate this effect. We find that the policy adoption increases tree cover loss by around 22.3% in LEB country adopters compared to non-adopters. Our finding is robust to various tests, including alternative LEB measures, and alternative estimation methods. We redefine the identification of countries that have adopted the LEB policy by excluding those that have adopted sawnwwod export ban simultaneously. We also use (1) an instrumental variable strategy with two instruments of LEB policy to resolve endogenous problems of LEB policy and (2) a staggered differences-in-difference estimation as identification strategies for impact analysis. After using these approaches, the LEB effect is still positive and significant at 1%. Moreover, we implement a transmission channel analysis that reveals that LEB policy impacts deforestation through agricultural land use and processed wood production. Then, we check the overtime effect of LEB adoption for 5 years after the policy adoption. Overall, LEB adoption is still positive and significant but has not had a persistent effect on deforestation. This study contributes to the literature on deforestation in developing countries by highlighting the LEB role.

Overall, LEB policy adoption increases deforestation in adopter countries compared to non-adopters. The LEB policy has been applied to promote the dynamics of wood markets and the growth of the timber industry, and forest conservation. Thus, our findings in this paper on the LEB policy on deforestation can't show if the overall effect of LEB policy creates net economic and environmental loss or gain for adopters countries. Focused on the environmental side, the policy adoption is damageable for country adopters. The next discussion section will allow to guide policymakers concerning LEB.

This study on deforestation builds on the findings of Marchand and Zerbo (2023), who explore the impact of the Log Export Ban (LEB) policy on the forest industry in developing countries. Their research demonstrates that LEB policies enhance forest industry output and promote processed wood exports, emphasizing the policy's economic advantages through industrial expansion and trade growth like Amoah et al. (2009) in Ghana. However, the findings of this study highlight a critical policy dilemma: balancing economic development with environmental sustainability.

Based on these insights, policymakers face two main options when considering LEB policy adoption. The first is to eliminate the policy to reduce deforestation and encourage forest conservation. This approach would alleviate pressure on forest resources, preserving tree cover, biodiversity, and essential ecosystem services such as carbon sequestration and climate regulation. It would also support global environmental goals and international climate commitments. For (Kishor et al., 2004) LEB removal can benefit Costa Rica in terms of economic and environmental gain. However, as Marchand and Zerbo (2023) note, this conservation-oriented strategy would reduce forest industry output, lower export revenues, and cause potential job losses in the wood-processing sector, thus prioritizing environmental protection at the expense of economic and industrial development.
The second option is to retain the LEB policy while mitigating its environmental downsides through targeted reforms. This approach would involve policy interventions aimed at the three main channels through which LEB policies contribute to deforestation: agricultural expansion, forest industry development, and illegal logging. Addressing these channels could reduce deforestation while maintaining the policy's economic advantages.

In terms of agricultural expansion, Maestad (2001) highlights trade restriction policy indirectly encourages forest-to-agriculture land conversion due to increased economic returns from farming. To counter this, governments should promote mechanized, intensive, and sustainable farming practices through specialized training for rural communities. This would increase agricultural productivity on existing farmland, reducing the need to clear additional forests. Supporting agroforestry practices could further integrate tree planting with agricultural crops, enhancing environmental resilience while diversifying farmers' incomes.

Within the forest industry, the LEB policy drops the log price and makes it cheap (Amsberg, 1998), and create an exclusivity around forest resources. Then, this context spurs the development of wood-processing industries, increasing demand for logs and driving deforestation. Policymakers should enforce stricter wood extraction regulations through forest concession monitoring, and sustainable harvesting guidelines. Requiring forest industry firms to adopt sustainable management practices such as reduced-impact logging and reforestation could further minimize environmental damage. Supporting technological upgrades in the wood-processing sector would also promote high-efficiency, low-waste production, reducing the industry's environmental footprint. Technological advancements play a pivotal role in balancing economic and environmental priorities. Investments in high-efficiency wood-processing technologies could reduce raw material consumption, decrease waste, and boost industrial competitiveness. Establishing a robust legal framework supported by clear enforcement mechanisms would further strengthen forest governance and deter environmentally harmful practices.

Illegal logging poses another significant challenge, undermining regulatory efforts and accelerating deforestation. Introducing national wood certification systems would enhance traceability and accountability in the timber supply chain. Strengthening property rights would empower local communities to manage forests sustainably, while anti-corruption measures would ensure transparency and compliance with forest management regulations.

In conclusion, this study, in line with the findings of Marchand and Zerbo (2023), underscores the complexities of balancing economic growth and environmental sustainability through LEB policy. While eliminating the policy could maximize environmental gains, it would hinder industrial development and trade expansion. Conversely, maintaining the policy without reforms would further degrade forest ecosystems. Therefore, a comprehensive strategy involving targeted policy reforms, sustainable land use practices, technological investments, and stronger governance offers a promising path toward achieving both environmental sustainability and economic development. This integrated approach enables policymakers to align forest sector growth with long-term ecological resilience, promoting a sustainable and prosperous future for developing countries.
## Acknowledgements

This work was supported by the Agence Nationale de la Recherche of the French government through the program "Investissements d'avenir" ANR-10-LABX-14-01".

## Competing Interests

I declare that there is no conflict of interest concerning financial and personal relationships with other people or organisations that could inappropriately influence the results of this study.
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# Appendix

<figure><img src="image_8.png" /><figcaption>Figure A1: Sample used in this study: adopters and non-adopters LEB</figcaption></figure>

<table><caption>Table A1: Descriptives statistics</caption><thead><tr><th>Variable</th><th>Observations</th><th>Mean</th><th>Std. Dev.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td>Deforestation (Km²)</td><td>2,486</td><td>1,957.804</td><td>7,601.418</td><td>0</td><td>107,488.68</td></tr><tr><td>LEB (0-1)</td><td>2,486</td><td>.296</td><td>.457</td><td>0</td><td>1</td></tr><tr><td>GDP Per Capita (constant 2015 US $)</td><td>2,370</td><td>3,520.746</td><td>2,932.162</td><td>258.629</td><td>14,200.27</td></tr><tr><td>Agriculture occupation (Km²)</td><td>2,486</td><td>2,309.77</td><td>5,561.238</td><td>0</td><td>31,222.973</td></tr><tr><td>Processed wood (m³)</td><td>2,452</td><td>1,230,519.1</td><td>4,511,527.2</td><td>0</td><td>47,394,000</td></tr><tr><td>FSC share (% of forest area)</td><td>2,154</td><td>1.658</td><td>6.443</td><td>0</td><td>81.949</td></tr><tr><td>Population density (resident/Km²)</td><td>2,476</td><td>125.65</td><td>183.437</td><td>1.557</td><td>1,769.857</td></tr><tr><td>Population growth (%)</td><td>2,478</td><td>1.586</td><td>1.218</td><td>-4.533</td><td>6.559</td></tr><tr><td>International Trade (% of GDP)</td><td>2,201</td><td>79.099</td><td>37.984</td><td>.167</td><td>347.997</td></tr><tr><td>Exchange rate (LCU per $)</td><td>2,235</td><td>942.284</td><td>3,355.308</td><td>.055</td><td>42,000</td></tr><tr><td>Temperature shock (°C)</td><td>2,387</td><td>.972</td><td>.468</td><td>-.499</td><td>2.767</td></tr><tr><td>Government effectiveness (-2.5 - 2.5)</td><td>2,336</td><td>-.553</td><td>.622</td><td>-2.475</td><td>1.267</td></tr><tr><td>Mineral rent (% of GDP)</td><td>2,393</td><td>1.026</td><td>2.607</td><td>0</td><td>25.163</td></tr><tr><td>Neighbor deforestation (Km²)</td><td>1,935</td><td>3,929,429.1</td><td>10,893,639</td><td>0</td><td>1.288e+08</td></tr><tr><td>Neighbor (0-1)</td><td>2,277</td><td>.303</td><td>.345</td><td>0</td><td>1</td></tr><tr><td>Tariff rate weighted (%)</td><td>1,701</td><td>7.891</td><td>6.423</td><td>0</td><td>63.69</td></tr></tbody></table>
<table><caption>Table A2: Variables definition</caption><thead><tr><th>Variables</th><th>Description</th><th>Source</th></tr></thead><tbody><tr><td>Deforestation (Km²)</td><td>Annual forest cover loss with vegetation taller than 5 m in 2020.</td><td>Hansen et al. (2013)</td></tr><tr><td>Log Export Ban</td><td>Log export ban is the dummy variable taking 1 if the country applies a ban on log export and 0 if not.</td><td>FPER*</td></tr><tr><td>GDP Per Capita (constant 2015 US $)</td><td>GDP per capita is gross domestic product divided by midyear population. GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for the depreciation of fabricated assets or for the depletion and degradation of natural resources. 2015 U.S. dollars.</td><td>WDI**</td></tr><tr><td>Agriculture occupation (Km²)</td><td>The sum of grasslands, croplands, and cropland/natural vegetation mosaic (Km²).</td><td>Earth Data</td></tr><tr><td>Processed wood (m³)</td><td>The sum of the Volume of Sawnwood production, Veneer production, and Plywood production (m³).</td><td>ITTO***</td></tr><tr><td>FSC share (% of forest area)</td><td>The total forest area under the Forest Stewardship Council (FSC) certification in percentage of total forest area</td><td>FSC****</td></tr><tr><td>Population density (resident/Km²)</td><td>Population density is midyear population divided by land area in square kilometers. The population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship-except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding areas under inland water bodies, national claims to continental shelves, and exclusive economic zones. In most cases, the definition of inland water bodies includes major rivers and lakes.</td><td>WDI</td></tr><tr><td>Population growth (%)</td><td>The annual population growth rate for year t is the exponential rate of growth of the midyear population from year t-1 to t, expressed as a percentage. The population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship.</td><td>WDI</td></tr><tr><td>International trade (% of GDP)</td><td>Trade is the sum of exports and imports of goods and services measured as a share of gross domestic product</td><td>WDI</td></tr><tr><td>Exchange trade (LCU per $)</td><td>Official exchange rate refers to the exchange rate determined by national authorities or to the rate determined in the legally sanctioned exchange market. It is calculated as an annual average based on monthly averages (local currency units relative to the U.S. dollar).</td><td>WDI</td></tr><tr><td>Temperature shock (°C)</td><td>The FAOSTAT Temperature change on land domain disseminates statistics of mean surface temperature change by country, with annual updates. The current dissemination covers the period 1961-2022. Statistics are available for monthly, seasonal, and annual mean temperature anomalies, i.e., temperature change concerning to a baseline climatology, corresponding to the period 1951-1980. The standard deviation of the temperature change of the baseline methodology is also available. Data are based on the publicly available GISTEMP data, the Global Surface Temperature Change data distributed by the National Aeronautics and Space Administration Goddard Institute for Space Studies (NASA-GISS).</td><td>FAOSTAT</td></tr><tr><td>Government effectiveness (-2.5 - 2.5)</td><td>Government Effectiveness captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies. Estimate gives the country's score on the aggregate indicator, in units of standard normal distribution, i.e. ranging from approximately -2.5 to 2.5.</td><td>WGI*****</td></tr><tr><td>Mineral rent (% of GDP)</td><td>Mineral rents are the difference between the value of production for a stock of minerals at world prices and their total costs of production. Minerals included in the calculation are tin, gold, lead, zinc, iron, copper, nickel, silver, bauxite, and phosphate.</td><td>WDI</td></tr><tr><td>Neighbors' deforestation (Km²)</td><td>The mean annual forest cover loss with vegetation taller than 5 m in 2020 in neighboring countries.</td><td>Hansen et al. (2013)</td></tr><tr><td>Neighbor (0-1)</td><td>Share of neighborhood countries applying log export ban.</td><td>FPER</td></tr><tr><td>Tariff rate weighted (%)</td><td>Weighted mean applied tariff is the average of effectively applied rates weighted by the product import shares corresponding to each partner country. Primary products are commodities classified in SITC revision 3 sections 0-4 plus division 68 (nonferrous metals).</td><td>WDI</td></tr></tbody></table>

<footnote>*Forest Product Export Restriction; **World Development Indicators; ***International Tropical Timber Organization; ****Forest Stewardship Council *****Worldwide Governance Indicators</footnote>
<table><caption>Table A3: LEB effect over time</caption><thead><tr><th>Log Deforestation</th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>LEB</td><td>0.223***<br>(0.083)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>First year after adoption (t+1)</td><td></td><td>0.163**<br>(0.077)</td><td></td><td></td><td></td><td></td></tr><tr><td>Second year after adoption (t+2)</td><td></td><td></td><td>0.153*<br>(0.085)</td><td></td><td></td><td></td></tr><tr><td>Third year after adoption (t+3)</td><td></td><td></td><td></td><td>0.038<br>(0.094)</td><td></td><td></td></tr><tr><td>Fourth year after adoption (t+4)</td><td></td><td></td><td></td><td></td><td>0.050<br>(0.095)</td><td></td></tr><tr><td>Five year after adoption (t+5)</td><td></td><td></td><td></td><td></td><td></td><td>0.032<br>(0.105)</td></tr><tr><td>Log GDP Per Capita<sub>t-1</sub></td><td>2.945**<br>(1.420)</td><td>4.779***<br>(1.527)</td><td>4.893***<br>(1.602)</td><td>4.983***<br>(1.753)</td><td>6.204***<br>(1.825)</td><td>4.614**<br>(1.960)</td></tr><tr><td>Log GDP Per Capita squared<sub>t-1</sub></td><td>-0.168*<br>(0.091)</td><td>-0.313***<br>(0.096)</td><td>-0.318***<br>(0.101)</td><td>-0.329***<br>(0.109)</td><td>-0.414***<br>(0.118)</td><td>-0.328***<br>(0.127)</td></tr><tr><td>Log Agriculture occupation<sub>t-1</sub></td><td>1.570***<br>(0.523)</td><td>0.974**<br>(0.490)</td><td>0.214<br>(0.495)</td><td>-0.603<br>(0.472)</td><td>-0.521<br>(0.639)</td><td>0.144<br>(0.764)</td></tr><tr><td>Log Processed wood<sub>t-1</sub></td><td>0.353***<br>(0.079)</td><td>0.195**<br>(0.087)</td><td>0.118<br>(0.090)</td><td>0.040<br>(0.089)</td><td>0.019<br>(0.077)</td><td>0.153**<br>(0.075)</td></tr><tr><td>FSC share<sub>t-1</sub></td><td>0.008**<br>(0.004)</td><td>0.003<br>(0.004)</td><td>0.001<br>(0.004)</td><td>-0.003<br>(0.004)</td><td>0.002<br>(0.005)</td><td>-0.002<br>(0.005)</td></tr><tr><td>Log population density<sub>t-1</sub></td><td>1.022*<br>(0.538)</td><td>1.087*<br>(0.599)</td><td>1.225*<br>(0.736)</td><td>1.829**<br>(0.794)</td><td>1.517**<br>(0.705)</td><td>1.410**<br>(0.716)</td></tr><tr><td>Population growth<sub>t-1</sub></td><td>0.107*<br>(0.062)</td><td>0.038<br>(0.066)</td><td>-0.000<br>(0.074)</td><td>-0.084<br>(0.076)</td><td>0.053<br>(0.076)</td><td>0.150*<br>(0.080)</td></tr><tr><td>International Trade<sub>t-1</sub></td><td>0.001<br>(0.001)</td><td>0.001<br>(0.001)</td><td>-0.002<br>(0.002)</td><td>-0.002<br>(0.001)</td><td>-0.002*<br>(0.001)</td><td>0.000<br>(0.001)</td></tr><tr><td>Log Exchange rate<sub>t-1</sub></td><td>-0.137*<br>(0.073)</td><td>-0.207**<br>(0.097)</td><td>-0.165*<br>(0.092)</td><td>-0.210**<br>(0.085)</td><td>-0.213***<br>(0.067)</td><td>-0.130**<br>(0.063)</td></tr><tr><td>Temperature shock<sub>t-1</sub></td><td>0.099<br>(0.149)</td><td>0.026<br>(0.114)</td><td>0.112<br>(0.138)</td><td>0.170<br>(0.157)</td><td>-0.204<br>(0.124)</td><td>-0.165<br>(0.119)</td></tr><tr><td>Mineral rent<sub>t-1</sub></td><td>-0.038*<br>(0.021)</td><td>0.010<br>(0.022)</td><td>-0.008<br>(0.027)</td><td>-0.016<br>(0.030)</td><td>-0.038<br>(0.026)</td><td>-0.065*<br>(0.034)</td></tr><tr><td>Government effectiveness<sub>t-1</sub></td><td>0.417**<br>(0.212)</td><td>0.847***<br>(0.252)</td><td>0.619**<br>(0.266)</td><td>0.876***<br>(0.256)</td><td>0.892***<br>(0.241)</td><td>0.695***<br>(0.262)</td></tr><tr><td>LEBxGovernment effectiveness<sub>t-1</sub></td><td>-0.286**<br>(0.125)</td><td>-0.283**<br>(0.126)</td><td>-0.294**<br>(0.138)</td><td>-0.426***<br>(0.138)</td><td>-0.309*<br>(0.165)</td><td>-0.341**<br>(0.169)</td></tr><tr><td>Constant</td><td>-24.174***<br>(5.948)</td><td>-25.210***<br>(6.225)</td><td>-22.876***<br>(6.681)</td><td>-21.585***<br>(6.947)</td><td>-24.176***<br>(7.488)</td><td>-21.088**<br>(8.203)</td></tr><tr><td>Observations</td><td>1470</td><td>1383</td><td>1298</td><td>1214</td><td>1130</td><td>1045</td></tr><tr><td>Number of countries</td><td>109</td><td>108</td><td>108</td><td>107</td><td>107</td><td>107</td></tr><tr><td>Country fixed effect</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Year fixed effect</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Rsquared</td><td>0.960</td><td>0.962</td><td>0.960</td><td>0.962</td><td>0.968</td><td>0.970</td></tr></tbody></table>

Note: robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1. The first column is the baseline result (Column (13) of Table 3). The second column constructs a new treatment variable by dropping all observations following the LEB initiation year. Columns [2–6] consider only observations for 1, 2, 3, 4 and 5 years after the LEB adoption year, respectively.
<table><caption>Table A4: LEB and deforestation: IV approach</caption><thead><tr><th rowspan="2"></th><th colspan="2">Instrumental Variable</th></tr><tr><th>First stage</th><th>Second stage</th></tr><tr><th></th><th>LEB</th><th>Log Deforestation (Km2)</th></tr></thead><tbody><tr><td>LEB</td><td></td><td>3.586***<br>(0.479)</td></tr><tr><td>Neighbor</td><td>0.390***<br>(0.038)</td><td></td></tr><tr><td>Tariff rate weighted</td><td>-0.009***<br>(0.002)</td><td></td></tr><tr><td>Log GDP Per Capita</td><td>10.175***<br>(0.175)</td><td>-1.926*<br>(1.122)</td></tr><tr><td>Log GDP Per Capita squared</td><td>-0.081***<br>(0.012)</td><td>0.177**<br>(0.073)</td></tr><tr><td>Log Agriculture occupation</td><td>0.002<br>(0.006)</td><td>0.193***<br>(0.029)</td></tr><tr><td>Log Processed wood</td><td>0.044***<br>(0.004)</td><td>0.382***<br>(0.030)</td></tr><tr><td>FSC share</td><td>0.002**<br>(0.001)</td><td>-0.017***<br>(0.005)</td></tr><tr><td>Log population density</td><td>0.001<br>(0.011)</td><td>-0.187***<br>(0.061)</td></tr><tr><td>Population growth</td><td>0.095***<br>(0.013)</td><td>0.103<br>(0.083)</td></tr><tr><td>Temperature shock</td><td>0.016<br>(0.024)</td><td>-0.038<br>(0.154)</td></tr><tr><td>Mineral rent</td><td>-0.008***<br>(0.002)</td><td>-0.068***<br>(0.023)</td></tr><tr><td>Government effectiveness</td><td>0.388***<br>(0.029)</td><td>-2.160***<br>(0.263)</td></tr><tr><td>LEBxGovernment effectiveness</td><td>-0.669***<br>(0.032)</td><td>2.296***<br>(0.428)</td></tr><tr><td>Log Neighbors deforestation</td><td>0.016***<br>(0.004)</td><td>0.204***<br>(0.033)</td></tr><tr><td>Constant</td><td>-4.734***<br>(0.654)</td><td>-0.286<br>(4.537)</td></tr><tr><td>Observations</td><td>1037</td><td>1037</td></tr><tr><td>Number of countries</td><td>85</td><td>85</td></tr><tr><td>Kleibergen-Paap rk Wald F statistic</td><td></td><td>70.373</td></tr><tr><td>Cragg-Donald Wald F statistic</td><td></td><td>76.314</td></tr><tr><td>Hansen statistic</td><td></td><td>0.073</td></tr><tr><td>Rsquared</td><td></td><td>0.609</td></tr></tbody></table>

Note: robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.

We control for neighbor deforestation in this estimation and we drop International Trade and exchange rate that can create multicollinearity with our second IV (tariff rate weighted).