# Agricultural Exports, Market Power, and Deforestation

Clément Nedoncelle, Philippe Delacote, Léa Crepin

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Clément Nedoncelle, Philippe Delacote, Léa Crepin. Agricultural Exports, Market Power, and Deforestation. 2024. hal-04934081

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Agricultural Exports, Market Power, and
Deforestation

Clément Nedoncelle*¹, Philippe Delacote²,³, and Léa Crepin³,⁴

¹Université Paris-Saclay, INRAE, AgroParisTech, Paris-Saclay Applied Economics, 91120,
Palaiseau, France

²INRAE, BETA

³Climate Economics Chair

⁴Paris School of Economics

February 7, 2025

**Abstract**

This paper explores how market structure shapes agricultural production and environmental outcomes, particularly deforestation. We focus on the soybean export sector in Brazil—a major driver of deforestation. We show that large exporters, operating within an oligopolistic market, exert market power by purchasing soybeans at lower prices ( markdown) rather than raising prices for foreign consumers. Providing both theoretical and empirical evidence, our results suggest that this markdown behavior reduces soybean production and deforestation. Using data from 2004-2017, we find that higher market concentration, as measured by the Herfindahl-Hirschman Index (HHI), is associated with lower soybean production and deforestation. Our findings highlight the importance of trade-related supply chains in understanding deforestation dynamics.

**Keywords:** Market power, markdown, exporters, deforestation

**JEL codes:** Q17, F18, Q18, Q23.

<footnote>*Corresponding author: clement.nedoncelle@inrae.fr</footnote>
# 1 Introduction

Deforestation constitutes a pressing global concern, largely driven by the expansion of agricultural activities (Pendrill et al., 2019a; Macedo et al., 2012; Pendrill et al., 2022). While the drivers of this expansion have been extensively studied (Balboni et al., 2023), the role of agribusiness value chain dynamics and commodity market structures in shaping agricultural expansion and deforestation has received limited attention.

Commodity exporters are generally multinational, multi-commodity exporters, serving many foreign markets. Yet, these exporters are not producing (i.e. growing) the commodity: they rather buy the commodity from independent and individual (or sometimes collective) producers. The relationship between commodities producers and exporters is central to agricultural production choices and export decisions. Besides, one feature of these supply chains is the dominance of a few but large exporters (Clapp, 2015, 2014). Commodity markets hence exhibit a peculiar market structure, close to an oligopoly. This market structure is likely to affect the relationship between multinational exporters and individual producers, and therefore production and land-use decisions.

In this paper, we evaluate – both empirically and theoretically– the consequences of this market structure on commodity production, exports and its impact on deforestation. To address this question, we focus on the soybean sector in Brazil, for two main reasons. First, soybean production has been a major driver of agricultural expansion and deforestation in the tropics, especially in Brazil. Second, soybean export market exhibits the market structure as depicted above: few large exporters and a competitive fringe of exporters. Focusing on this sector allows us to identify the potential impact of the market structure on production levels and conversion of forests into agricultural land.

Our main hypothesis is that the market structure of soybean exports grants market power to the largest soybean exporters. Because soybean is a homogeneous commodity, it is hardly differentiated and exporters cannot exert their market power on foreign consumers (by selling soybean at higher prices). On the contrary, we hypothesize that these exporters exert their power on soybean producers, by buying soybeans at lower prices. The largest firms may thus use their power to * markdown* instead of to * markup*. This paper evaluates the consequences of this markdown behavior, in theory and based on empirical data, focusing on soybean production and exports in Brazil between 2004 and 2017. Our dataset allows us to identify the export flows of soybean at the exporter-source municipality-destination market and year level. This information allows us to map soybean exports and firm behavior with forest cover data at the municipality level.

Our analysis is threefold, using the heterogeneous presence of the large exporters between municipalities. First, we provide empirical evidence suggesting that large exporters use their market power to reduce the prices paid to soybean producers. By comparing municipal trajectories over time, we find that the presence of large exporters is associated with lower agricultural revenues per unit of soybean produced. Specifically, con-
ditional on total soybean production, municipalities with large exporters exhibit lower agricultural revenues, which indirectly points to markdown behavior, as we cannot directly observe the prices paid by exporters to producers. In contrast, we find no evidence that large exporters increase the price of soybeans when selling to foreign markets. When comparing the unit values of soybeans sold to the same foreign destination on the same year, we find no significant difference based on exporter size. These findings support the notion that the largest firms engage in markdown, rather than markup, behavior.

Second, we theoretically explore the consequences of markdown behavior by large soybean exporters. We develop a model of soybean exports, production, and deforestation, featuring a market structure where a few large firms, that we call the oligopsony<fnref n="1" />, buy soybean from local markets, while a competitive fringe of smaller firms also participates. The model predicts that the level of soybean production in a given municipality is negatively related to the presence of the oligopsony and hence to the magnitude of the markdown. Specifically, in regions where the exporters are highly concentrated, resulting in larger markdown, less soybean is produced. Consequently, these areas are expected to experience lower levels of deforestation, as the reduced demand for land to cultivate soybeans lessens the pressure on forests.

Third, we provide robust empirical support for our theoretical predictions. Our analysis shows that higher concentration within municipalities, as measured by the Herfindahl-Hirschman Index (HHI), imply smaller soybean production. This result holds across various specifications and is notably robust when dealing with potential endogeneity concerns, particularly those arising from the nonrandom presence of large exporters in certain municipalities. In this regard, we use an instrumental variable (IV-2SLS) strategy to address endogeneity issues. Specifically, we instrument the local concentration (HHI) with the concentration in surrounding municipalities, ensuring that the identified relationship between concentration and soybean production is causal. The estimated elasticity suggests that a 10% increase in market concentration leads to an approximately 2% reduction in soybean production, highlighting the significant negative impact of oligopolistic structures on agricultural output. Considering deforestation outcomes, we find that higher concentration in the export market is associated smaller deforestation. This negative relationship is particularly strong when controlling for conservation policies, such as Brazil's PPCDAm priority list, which targets municipalities most vulnerable to deforestation. Importantly, we show that this effect operates primarily through changes in soybean production: concentration reduces soybean output, which in turn decreases the incentives for land expansion and deforestation.

<footnote n="1">¹Regarding terminology, in the first part of the paper, we describe large firms as oligopolists based on their dominant role in soybeans exports, where their size gives them a competitive advantage over smaller exporters. However, our analysis shows that they do not exert significant market power in the export market. We hence shift our focus to the input side, where these same firms appear to influence the purchase of soybeans from farmers. Given their size relative to suppliers, we characterize them as oligopsonists, as they seem to exert market power when sourcing crops from producers.</footnote>
Overall, our paper highlights the importance of considering how market structure shapes not only agricultural but also environmental outcomes. First, by focusing on the soybean export sector in Brazil, we demonstrate that oligopolistic market structures, where large exporters exert market power through markdown behavior, can have significant implications for both agricultural production and deforestation. Second, our findings underscore the need to broaden the focus of deforestation analysis beyond local producers. Specifically, examining trade supply chains — where multinational exporters play a central role — can provide a different understanding of the drivers of deforestation. In this regard, the concept of "imported deforestation" emerges as a key consideration. By analyzing the market dynamics of export-driven supply chains, we reveal how the actions of exporters can contribute to land-use changes in producer countries.

**Related Literature.** Our analysis is related to three strands of the existing literature. First, it is related to the literature evaluating the determinants of deforestation (Geist and Lambin, 2002; Balboni et al., 2023). This literature emphasized the role of agricultural expansion in the deforestation process (Curtis et al., 2018; Pendrill et al., 2019a; Macedo et al., 2012; Song et al., 2021; Pendrill et al., 2022). Beyond production, the role of international trade in commodities and market access is also largely investigated (Pendrill et al., 2019b; Abman and Lundberg, 2024). These studies complement existing literature focusing on the role of international trade (beyond agri-food trade) on deforestation (Leblois et al., 2017; Carreira et al., 2024; Ferreira, 2004; Abman and Lundberg, 2020). With respect to this literature, our paper settles apart by quantifying the role of large exporters in the agricultural expansion, and by investigating the importance of the market structure in this production-expansion relationship.

Second, our paper builds upon contributions focusing on the role of market power in agriculture. The importance of market power in the agri-food production has been largely studied, in various setups and both in theory and empirics (Sexton, 2013; Bonanno et al., 2018; Mérel and Sexton, 2017; Sheldon, 2017; Courtois and Subervie, 2015; Jensen, 2010; Mérel et al., 2009), Recently, many papers have investigated the importance of competition in agricultural markets and its effect on farmers’ incomes. Chatterjee (2023) shows that competition has an influence on price paid to farmers: favoring competition would increase both output and farmers’ revenues. Dhingra and Tenreyro (2020) finds that the shift to the agribusiness model reduced farmer incomes, particularly for those selling primarily to large agribusinesses. Bergquist and Dinerstein (2020) evaluates the effect of the imperfect competition among intermediaries in agri-food trade in Kenya and suggests that traders capture a large portion of the total surplus. Zavala (2022) explores how exporters exert monopsony power within agricultural value chains in Ecuador, leading to farmers receiving only a small share of export revenues. Domínguez-Iino (2022) examines how environmental policies diffuse through supply chains when direct regulation at the source of externalities is impractical. Finally, in a different context, Avignon and Guigue (2023) document that markups and markdowns can be estimated
separately: applied to the French dairy market, firms exert buyer power when purchasing raw milk, and seller power when marketing dairy products.

Our paper differentiates from this literature by evaluating the effect of market structure on soybean production (which has not been documented) and on deforestation. Doing so allows us to relate economic behavior with climate issues.

Finally, because we highlight the role of market structure on exports, our paper is also connected to the literature relating large firms and oligopoly in international trade. On the one hand, we build upon literature highlighting the importance and the consequences of oligopoly in trade models: (Head and Spencer, 2017; Parenti, 2018). On the other hand, our results are related to the recent evidence regarding the emergence of market power – and the existence of markup and markdown in trade (Gaigné and Gouel, 2022; De Loecker et al., 2020). With respect to this set of papers, our article assesses the consequence of this market structure for agri-food trade and for environmental and climate-related outcomes such as deforestation.

The rest of the paper is organized as follows. The next section details the data sources we used in the analysis. Section 3 shows that soybean exports are realized by large agribusiness firms and that these firms exert their market power on farmers rather than on the final consumers. Section 4 provides a theoretical model of soybean exports, production and deforestation, with a market structure made of a few oligopolist exporters and a competitive fringe. This theoretical section delivers testable predictions, which are empirically supported in Section 5. Section 6 concludes.

## 2 Data

Data cover the universe of soybean production and exports from 2004 to 2017 in Brazil.

**Exporters characteristics and market structure.** We first rely on the TRASE database (Lathuillière et al., 2022) to identify firm-level export transactions. The raw data provides export values (FOB) and quantities (in tonnes) at the exporter (f), source municipality (m) in Brazil, year (t), and destination market (d) (country) level. Using export values ($x_{fmdt}$) and export quantities ($q_{fmdt}$), we can compute the "transaction price" ($p_{fcdt}^{up}$), defined as the unit value of the export transaction. The data is initially aggregated at the exporter-municipality-year level for the first part of our analysis and then aggregated at the municipality-year level for the latter part of the analysis.

The TRASE database allows us to identify exporters’ characteristics (such as total firm-level exports and other relevant proxies) as well as the market structure of soybean production in Brazil. In particular, we can identify the sourcing strategies of exporters across municipalities. By municipality of soybean production,
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we can identify the presence of exporters, which will be used in the second part of the analysis to measure
the concentration of exporters at the local level.

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The TRASE database also contains information about firms' commitments to sustainable soybean produc-
tion. Some firms engaged in the soy moratorium—a commitment by grain traders not to purchase soybean
grown on recently deforested land—(Heilmayr et al., 2020), while others took firm-specific actions in the same
direction. We group these two types of commitments into a single indicator signaling the firm's commitment
to sustainable practices.

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**Municipality characteristics.** We also collected municipality-specific time-varying variables that could affect soybean production and deforestation. Among these, we gathered data on municipal GDP, population, and theoretical soybean yields from the Brazilian National Institute of Geography and Statistics (IBGE). Additionally, we identified municipalities that have been blacklisted under the PPCDAm, one of Brazil's key conservation measures. This priority list highlights municipalities that are most vulnerable to deforestation in the Brazilian Legal Amazon, enabling targeted efforts to prevent and control deforestation.

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**Deforestation Patterns.** To measure deforestation, we rely on the MapBiomas database (Souza and Azevedo, 2017), aggregated at the municipality-year level. Since much of the soybean expansion takes place in the Cerrado (a tropical savanna ecosystem that is not classified as forest), we broaden our analysis to consider the broad effects of conversion of natural vegetation. This ecosystem, with lower legal protection than the Brazilian Amazon, is currently experiencing high conversion rates—five times higher than deforestation rates in the Amazon before the soy moratorium—where soybean production plays a direct and significant role (Rausch et al., 2019). Our primary measure of deforestation is the loss of natural vegetation, focusing solely on primary vegetation (vegetation that persisted from the start of the series). For robustness, we will also consider total deforestation data from MapBiomas, which includes the loss of secondary deforestation, and additionally use deforestation data from PRODES.

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3 Large Firms and Their Market Power

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This section shows that soybean exports are realized by large agribusiness firms. While this is not a new
observation, we document that the presence of these oligopolists shapes the market structure, creating a
situation with an oligopoly and a competitive fringe in almost all municipalities. This section also provides
evidence of market power exerted by these oligopolists. In particular, we show that oligopolists exert their
market power on farmers rather than on final consumers. In other words, we provide evidence of markdown
rather than markup.
<figure><img src="image_2.png" /><figcaption>Figure 1: Distribution of exporters</figcaption></figure>

## 3.1 Oligopolists

The soybean export market is characterized by an oligopolistic market structure. Figure 1 plots the export share (in terms of quantities) by exporter over the entire period, focusing on the top 30 exporters (representing 87% of total exports in the sample). Each bar represents the average exporter-specific share of total exports, ranked in decreasing order by exporter. Top exporters play a massive role in total exports, with a single firm contributing nearly 20% of exports during the period, and the top 4 firms accounting for over 50%. This suggests that the market structure is close to an oligopoly, composed of a few very large oligopolists and a competitive fringe made up of smaller exporters.

## 3.2 How Large Are These Oligopolists?

Figure 2 shows the yearly share of exports held by the top 4 oligopolists in our sample. On average, their collective exports account for 55% of total exports. Over time, however, their share appears to be decreasing. The same conclusions hold when considering alternative measures of the oligopoly, such as the top 5 or top 6 firms, or even just the top firm (Bunge).

How large are these firms within municipalities? Figure 3 plots the (log) total exports by municipality (in red) ($\sum_{f \in m} \text{SoyExports}_{\text{fmt}}$) and aggregate exports by these top 4 firms only ($\sum_{f \in m} \text{SoyExports}_{\text{fmt}} \times 1_{[\text{TOP4}]}$) (in black dots). Whereas the distribution is highly skewed in the cross-section of municipalities, oligopolists
<figure><img src="image_3.png" /><figcaption>Figure 2: Share of exports by oligopolists</figcaption></figure>

appear to be major players within each municipality. We infer from this graph that oligopolists are present and important firms in almost all municipalities.

## 3.3 No Evidence of Markups

How does this market power manifest? On one hand, firms with market power might charge higher prices to final consumers. Controlling for production and shipping costs, larger prices for consumers would lead to greater profits for these firms. This potential markup (over costs) could reflect market power over (foreign) consumers.

In our sample, markups can be inferred from transaction price differentials across firms serving the same destination market. In other words, oligopolists would always charge different transaction prices than competitors in the competitive fringe. We provide two pieces of evidence against this hypothesis.

First, Figure 4 plots for each municipality the minimum and maximum transaction unit values $uv_{fmdt}$ (black bars) as well as the average transaction unit value for the oligopolists (red points). Oligopolists’ transaction prices do not differ from the competitive fringe’s prices. On the whole, within municipalities, oligopolists do not charge substantially different prices than their smaller competitors.

Second, Table 1 formally tests whether transaction prices (at the micro-level) differ statistically between oligopolists and the competitive fringe. We estimate the following regression:

$$uv_{fmdt} = \alpha + \beta_1[\text{TOP4},f] + \lambda_{mdt} + \varepsilon_{fmdt}$$
<figure><img src="image_4.png" /><figcaption>Figure 3: Share of exports by oligopolists across municipalities</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>Figure 4: Price differentials within municipalities.</figcaption></figure>
<table><caption>Table 1: Price differential across firms</caption><thead><tr><th></th><th colspan="5">Dep Variable: Log Unit Value</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Top-4 Firm Indicator (1<sub>[TOP4],f</sub>)</td><td>-0.004<br>(0.007)</td><td></td><td></td><td></td><td>-0.006<br>(0.007)</td></tr><tr><td>Top-5 Firm Indicator (1<sub>[TOP5],f</sub>)</td><td></td><td>0.003<br>(0.007)</td><td></td><td></td><td></td></tr><tr><td>Top-6 Firm Indicator (1<sub>[TOP6],f</sub>)</td><td></td><td></td><td>-0.002<br>(0.007)</td><td></td><td></td></tr><tr><td>Top-1 Firm Indicator (1<sub>[TOP1],f</sub>)</td><td></td><td></td><td></td><td>0.001<br>(0.009)</td><td>0.005<br>(0.009)</td></tr><tr><td>Observations</td><td>34031</td><td>34031</td><td>34031</td><td>34031</td><td>34031</td></tr><tr><td>R<sup>2</sup></td><td>0.674</td><td>0.674</td><td>0.674</td><td>0.674</td><td>0.674</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. The dependent variable is the log transaction price ($uv_{fmdt}$), which represents the observed price of soybean transactions at the micro-level. The indicator variable $1_{[TOP4],f}$ (denoted as $D_{top4}$) equals 1 if firm $f$ is one of the top 4 oligopolists, and 0 otherwise. OLS estimations. Standard errors are clustered at the municipality-country-year level ($mt$) to account for potential correlations in transaction prices within these groups. All estimations include municipality-destination country-year fixed effects.

where $uv_{fmdt}$ is the observed transaction price, $1_{[TOP4],f}$ is an indicator variable capturing whether firm $f$ is an oligopolist, and $\lambda_{mdt}$ is a set of municipality-destination country-year fixed effects. This specification is crucial because it implies that price differentials are identified across firms serving the same destination country, from the same municipality, and in the same year. Table 1 shows the OLS results with different sets of oligopolists: top-4 firms (col.1), top-5 firms (col.2), top-6 firms (col.3), and the top firm (Bunge) (cols. 4 and 5). Standard errors are clustered at the municipality-country-year level ($mt$) to account for potential correlations in transaction prices within these groups. None of the coefficients are significant, implying that oligopolists do not exhibit different transaction prices compared to other exporters. On average, exporters sell at the same price (independently of their size) on a given destination market.

## 3.4 Indirect Evidence of Markdown

On the other hand, oligopolists may use their market power as a markdown, to buy soybeans at lower prices.

As a result, farmers would obtain lower revenues per unit of output. Unfortunately, we do not have access
to the prices paid by exporters to farmers or cooperatives. However, we can obtain indirect evidence of markdown using municipality-level data.

The reasoning is as follows: if markdown is occurring, conditional on soybean production, the larger the presence of oligopolists, the lower the agricultural GDP (i.e., the total revenues of soybean farmers) should be. Although agricultural GDP includes a broader set of agricultural activities beyond soybean production, previous analyses suggest a strong correlation between agricultural GDP and soybean farmers’ revenues (Pellegrina, 2022).

To test this hypothesis, we estimate the following equation at the municipality-year level:

$$ \log \text{AgriGDP}_{mt} = \alpha + \beta Q_{mt} + \gamma \text{ShareOligopolists}_{mt} + \eta(Q_{mt} \times \text{ShareOligopolists}_{mt}) \\ + \zeta \text{ShareCommitted}_{mt} + \theta(Q_{mt} \times \text{ShareCommitted}_{mt}) + \lambda_m + \lambda_t + \varepsilon_{mt} $$

where the presence of oligopolists is measured by the output share of the top 4 firms:

$$ \text{ShareOligopolists}_{mt} = \frac{\sum_{f \in m} \text{SoyProd}_{fmt} \times 1_{[\text{TOP4}],f}}{\sum_{f \in m} \text{SoyProd}_{ffmt}} $$

By including an interaction term between soybean production and the presence of oligopolists, this specification allows the marginal effect of soybean production on agricultural GDP to vary across municipalities, depending on the presence of oligopolists.

We also control for the importance of committed firms to account for the potential impact of voluntary sustainability commitments on agricultural GDP, with

$$ \text{ShareCommitted}_{mt} = \frac{\sum_{f \in m} \text{SoyProd}_{ffmt} \times 1_{[\text{Committed}],f}}{\sum_{f \in m} \text{SoyProd}_{ffmt}} $$

where $1_{[\text{Committed}],f}$ indicates whether firm f committed or not. Indeed, these commitments may mitigate some effects of markdown by encouraging more equitable pricing for soy.

Table 2 presents the OLS results. Each column uses a different definition of oligopolist firms, but in all cases, the interactions between the share of oligopolists and soybean production are negative. The larger the presence of oligopolists, the lower the marginal positive impact of soybean production on agricultural GDP, supporting our hypothesis. We also estimate that firms committed to zero deforestation, in contrast, increase agricultural GDP, as expected. Firms committed to zero deforestation likely invest in sustainable practices and foster long-term relationships with producers, potentially enhancing productivity and revenue stability. These practices may contribute to higher agricultural GDP by creating value-added and supporting premium pricing opportunities.

Overall, the evidence highlights that while oligopolists dominate export markets, their market power is primarily exerted upstream, affecting farmers through markdowns, rather than consumers through markups.
<table><caption>Table 2: Indirect evidence of markdown</caption><thead><tr><th></th><th colspan="3">Dep Variable: Log Agri. GDP</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>Soy Prod.</td><td>0.026***<br>(0.003)</td><td>0.026***<br>(0.003)</td><td>0.025***<br>(0.003)</td></tr><tr><td>Oligopolist Share (top4)</td><td>0.074***<br>(0.026)</td><td></td><td></td></tr><tr><td>Soy Prod. × Oligopolist Share (top4)</td><td>-0.008***<br>(0.003)</td><td></td><td></td></tr><tr><td>Oligopolist Share (top5)</td><td></td><td>0.071***<br>(0.026)</td><td></td></tr><tr><td>Soy Prod. × Oligopolist Share (top5)</td><td></td><td>-0.008***<br>(0.003)</td><td></td></tr><tr><td>Oligopolist Share (top6)</td><td></td><td></td><td>0.068***<br>(0.025)</td></tr><tr><td>Soy Prod. × Oligopolist Share (top6)</td><td></td><td></td><td>-0.006**<br>(0.003)</td></tr><tr><td>Committed Firms Share</td><td>-0.218***<br>(0.042)</td><td>-0.218***<br>(0.042)</td><td>-0.218***<br>(0.042)</td></tr><tr><td>Soy Prod. × Committed Firms Share</td><td>0.025***<br>(0.004)</td><td>0.025***<br>(0.004)</td><td>0.025***<br>(0.004)</td></tr><tr><td>Observations</td><td>17096</td><td>17096</td><td>17096</td></tr><tr><td>R<sup>2</sup></td><td>0.950</td><td>0.950</td><td>0.950</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. The dependent variable is the log agricultural GDP per municipality-year. OLS estimations. Standard errors are clustered at the municipality level (m). All estimations include municipality and year fixed effects.
## 4 A simple model of soybean export, oligopsony and deforestation

In line with the evidence above, we consider the following soybean supply chain: an asymmetric oligopsony of N exporters buy soybean in J local markets, and sell it on international markets.

### 4.1 Oligopsony, markdown and soybean production

Exporters are assumed to be price takers on international markets, selling soybean at price $\tilde{P}$; while they have some level of market power and compete à la Cournot upstream. The inverse supply function of local market j is: $P_j(Q_i)$; with $Q_j$, the total quantity of soybean purchased in market j, which can be written: $Q_j = q_{ij} + Q_{-ij}$; with $q_{ij}$ the quantity of soybean purchased by firm i on market j and $Q_{-ij}$ the quantity purchased by all other firms on market j. Hence the price elasticity of supply in market j can be written: $\epsilon_j = \frac{\partial Q_j}{\partial P_j} \frac{P_j}{Q_j} = \frac{P(Q_j)}{P'(Q_j)Q_j}$; and the market share of firm i in market j is: $s_{ij} = \frac{q_{ij}}{Q_j}$.

Firm i experiences heterogeneous costs from purchasing from market j: $C_{ij}(q_{ij})$. We assume standard properties for the (increasing and concave) inverse supply function and the (increasing and convex) cost function.²

Firm i's profit thus takes the form:

$$ \Pi_i(q_{ij}) = \sum_j (\tilde{P}q_{ij} - P_j(q_{ij} + Q_{-ij})q_{ij} - C_{ij}(q_{ij})) \quad (1) $$

The first-order condition on $q_{ij}$ gives the Cournot-Nash equilibrium:

$$ \tilde{P} = P_j(q_{ij} + Q_{-ij}) + P'_j(q_{ij} + Q_{-ij})q_{ij} + C'_{ij}(q_{ij}) \quad (2) $$

where $P'_j(Q_j)q_{ij} + C'_{ij}$ represents the markdown related to the oligopsony market power. This markdown is heterogeneous across firms and local markets, as transaction costs are heterogeneous.

In our case of interest, one can expect that major firms have low transaction costs (and large markdown), while a fringe of minor firms have high transaction costs (hence small markdown).

Equation (2) can be written:

$$ v_{ij} = \frac{\tilde{P} - P_j(Q_j) - C'_{ij}(q_{ij})}{P_j(Q_j)} = \frac{P'_j(Q_j)Q_j}{P_j(Q_j)} \frac{q_{ij}}{Q_j} = \frac{s_{ij}}{\epsilon_j} \quad (3) $$

Where the markdown $v_{ij}$ of firm i on market j depends on the elasticity of supply in market j and the market share of firm i on that market.

$$ ^2P'_j(Q_j) = \frac{\partial P_j(Q_j)}{\partial Q_j} > 0; \quad P''_j(Q_j) = \frac{\partial^2 P_j(Q_j)}{\partial Q_j^2} < 0; \quad C'_{ij}(q_{ij}) = \frac{\partial C_{ij}(q_{ij})}{\partial q_{ij}} > 0; \quad C''_{ij}(q_{ij}) = \frac{\partial^2 C_{ij}(q_{ij})}{\partial q_{ij}^2} > 0 $$
The Herfindahl-Hirschman index of market j can be written: $HHI_j = \sum_{i=1}^{N} s_{ij}^2$. With some manipulations, we get that the average markdown in market j is equal to the Herfindahl-Hirschman index divided by the elasticity of supply in that market:

$$ \frac{HHI_j}{\epsilon_j} = \sum_{i=1}^{N} s_{ij}v_{ij} \qquad (4) $$

**Proposition 1:** the local soybean price on market j is negatively related to the concentration of the oligopsony. Consequently soybean production is smaller in municipality m if the oligopsony is more concentrated in that municipality.

Hence we write: $: Q_j^*(HHI_j)$, with $\frac{\partial Q_j^*(HHI_j)}{\partial HHI_j} < 0$.

## 4.2 Deforestation implications of soybean trade relationships

The representative farmer in market j allocates her production factors to minimize costs and achieve the level of production $Q_j^*(H_j)$. Production factors are land L (related to deforestation in municipality) and other production factors W. The soybean production function takes the form $Y_j(L, W)$:

$$ \min_{L,W} \quad C_j(L, W) = c_{jL}(L) + c_{jW}(W) \qquad (5) $$

s.t

$$ Y_j(L, W) = Q_j^*(HHI_j) $$

with $c_{jL}(L)$ and $c_{jW}(W)$ the convex costs of access to land and other production factors, respectively.<fnref n="3" /> Standard properties are assumed for the (increasing and convex) costs and (increasing and concave) production function.<fnref n="4" />

First-order conditions bring:

$$ \frac{Y_{jL}(L^*, W^*)'}{Y_{jW}(L^*, W^*)'} = \frac{c'_{jL}(L^*)}{c'_{jW}(W^*)} \qquad (6) $$

$$ Y_j(L, W) = Q_j^*(HHI_j) \qquad (7) $$

In a standard manner, equation (6) describes the optimal factor allocation as the equalization between the marginal rate of technical substitutions and the marginal costs ratio. Equations (6) and (7) show that the land allocated to soybean increases with the targeted level of output as long as the marginal productivity of land $Y'_{jL}$ is positive. This increase in L is smaller when the marginal cost of access to land $c'_{jL}(L)$ increases, when the marginal cost of other factors $c'_{jW}(W)$ decreases, and when the marginal productivity of other factors $Y'_{jW}$ increases (as long as production factors L and W are substitutes in the production function).

<footnote n="3">³Cost functions include the opportunity costs from other land and production factors use.</footnote>

<footnote n="4">⁴$c'_{jL}(L) > 0, c'_{jW}(W) > 0, c''_{jL}(L) > 0, c''_{jW}(W) > 0, Y'_{jL}(L, W) > 0, Y'_{jW}(L, W) > 0, Y''_{jL}(L, W) < 0, Y''_{jW}(L, W) < 0$.</footnote>
Since the amount of soybean produced is lower in markets with higher concentration and higher level of markdown (proposition 1), lower levels of deforestation are expected in those markets.

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**Proposition 2:** the level of soybean production in market j is negatively related to the concentration of the oligopsony. This leads to a decrease in land used for soybean production and deforestation in municipality m. This decrease is higher when access to land is more costly, when other production factors are less costly, and when other factors are more productive (and substitutes in the production function).

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We expect: $\frac{\partial L_j^*}{\partial HHI_j} < 0$.

## 5 Impact of concentration on soybean production and deforestation

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In this section, we test those theoretical predictions regarding the relationship between firm concentration and soybean production, as well as its effect on deforestation. Specifically, we examine: (i) whether soybean production decreases with higher concentration of exporters within a municipality, and (ii) whether deforestation follows a similar pattern.

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### 5.1 General Empirical Strategy

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**Setting** We focus on municipality-year observations where soybeans have been produced and exported. To estimate the impact of firm concentration on soybean production, we estimate the following model at the municipality-year level:

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$$ \text{SoyProduction}_{mt} = \alpha + \gamma_{ols} \text{HHI}_{mt} + \beta \text{Controls}_{mt} + \lambda_m + \lambda_t + \varepsilon_{mt} $$

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In this specification, the dependent variable, SoyProduction\_mt, is the log of soybean production (in tonnes) in municipality m during year t.

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**Concentration and pertinent market.** The key explanatory variable is HHI\_mt, the Herfindahl-Hirschman index at the municipality-year level. This index measures the level of competition in the soybean export market for municipality m at time t. Defining the relevant "market" for the competition analysis is crucial. In the benchmark specification, we define the market for municipality m as the set of contiguous municipalities, including municipality m itself. Specifically:
$$C(m, m') = \begin{cases} 1, & \text{if municipalities } m \text{ and } m' \text{ are contiguous,} \\ 0, & \text{otherwise.} \end{cases}$$

The market related to municipality $m$ is denoted by $\mathcal{P}(m)$ – as the empirical counterpart of market $j$ (in the previous sections) – and is defined as:

$$j(m) = \mathcal{P}(m) \equiv \{m, m' \in \mathcal{C} \mid C(m, m') = 1\}.$$

On average, a municipality has 7 contiguous neighbors (see Figure A.1).

The Herfindahl-Hirschman index for municipality $m$ in year $t$ is then calculated as the sum of squared market shares of firms in $\mathcal{P}(m)$:

$$\text{HHI}_{m,t} = \sum_{i=1}^{N_{\mathcal{P}(m)}} \left( \frac{x_{i, \mathcal{P}(m),t}}{x_{\mathcal{P}(m),t}} \right)^2$$

where $x_{i, \mathcal{P}(m),t}$ represents the purchases of firm $i$ in the market $\mathcal{P}(m)$, and $x_{\mathcal{P}(m),t}$ is the total purchases in the market$^5$. Following our theoretical predictions, we expect $\gamma$ to be negative: higher concentration should reduce soybean production.

We also include a set of control variables, such as municipality characteristics, soybean yields, and measures of land that may affect soybean production. Municipality fixed effects ($\lambda_m$) account for all time-invariant characteristics of municipalities, such as location, distance to markets, and typical weather conditions. Year fixed effects ($\lambda_t$) capture common shocks affecting all municipalities, such as fluctuations in global soybean prices and trends in production. Finally, we cluster the standard errors at the municipality level to account for potential correlation in unobserved factors across municipalities.

**Endogeneity Issues and excluded IV** Standard OLS estimations may be biased due to endogeneity concerns. First, the level of soybean production could influence the presence of oligopolistic firms, leading to reverse causality. Additionally, other factors could drive both soybean production and the presence of large firms. More generally, the location of large firms across municipalities is unlikely to be random. Second, the OLS specification may omit critical variables that affect both the entry and presence of firms in a municipality, as well as soybean production and its expansion. Consequently, the benchmark OLS estimate may suffer from bias.

<footnote>$^5$By construction, each municipality $m$ belongs to a unique market $j(m)$. Since the Herfindahl-Hirschman Index (HHI) is computed at the market level, we have the equivalence $HHI_{j(m)} = HHI_m$. Therefore, for notational simplicity, we denote the HHI directly as $HHI_m$, with the understanding that it refers to the market-level competition corresponding to municipality $m$.</footnote>
To address this endogeneity issue and establish a plausible causal link from concentration to soybean production and deforestation, we prefer IV-2SLS estimation, where the municipality-specific Herfindahl-Hirschman Index (HHI) is instrumented using the surrounding municipalities' HHI. While the market for municipality $m$ is defined as the set of contiguous municipalities, we define the surroundings as the set of non-contiguous municipalities that are within a 50 km radius of $m^6$, excluding the municipality itself. This set, denoted $S(m)$, is defined as follows:

$$S(m) = \{m' \in C \mid C(m, m') = 0, \text{dist}(m, m') \le 50 \text{ km}\}.$$

Figure A.2 schematically describes the relationship between a municipality, its pertinent market and the surrounding market.

Using this set, the excluded instrument is the HHI calculated for municipalities within this 50 km radius, excluding contiguous municipalities. On average, a municipality matches with around 40 non-contiguous municipalities (see Figure A.3). Formally, for a given municipality $m$, we compute the Herfindahl-Hirschman index over the set $S(m)$ as follows:

$$HHI_{m,t}^{Surr.} = \sum_{i=1}^{N_{S(m)}} \left( \frac{x_{i,S(m),t}}{x_{S(m),t}} \right)^2,$$

where $x_{i,S(m),t}$ represents the sales of firm $i$ in the surrounding municipalities.

In the IV-2SLS estimation, we use a two-stage procedure. In the first stage, we estimate how local concentration (HHI) is determined by the surrounding municipalities' concentration, conditional on controls:

$$HHI_{mt} = \zeta_0 + \zeta_1 HHI_{m,t}^{Surr.} + \beta Controls_{mt} + \lambda_m + \lambda_t + \varepsilon_{mt}.$$

In the second stage, we use the predicted concentration ($\widehat{HHI}_{mt}$) from the first stage as the main explanatory variable:

$$SoyProduction_{mt} = \alpha + \gamma_{IV} \widehat{HHI}_{mt} + \beta Controls_{mt} + \lambda_m + \lambda_t + \varepsilon_{mt}.$$

This two-stage procedure allows us to address the endogeneity issue and obtain a consistent estimate of the causal effect of concentration on soybean production.

**Validity of the IV Strategy** We provide several statistics supporting the choice of the excluded instrument and the related causal inference from concentration to soybean production and deforestation.

<footnote n="6">⁶All distances are measures from municipality centroids.</footnote>
The validity of this IV strategy hinges on two key assumptions. First, there must be a correlation between the surrounding concentration and the local concentration. This correlation is assessed in the first-stage estimation. The first-stage results, presented in Table B.1, show a strong correlation between the two measures of concentration, conditional on controls. Additionally, the first-stage F-statistics exceed the standard thresholds (shown in the second-stage tables). This indicates that the surrounding concentration is a strong predictor of local concentration and thus is not a weak instrument.

Second, the surrounding concentration should affect soybean production and other related outcomes in municipality *m* solely through its impact on local concentration (exclusion restriction). Although this assumption is not directly testable, it is plausible because the excluded instrument is an external counterpart to the potentially endogenous variable. To further support this assumption, Table B.2 shows that the excluded instrument is uncorrelated with any observable characteristics at the municipality-year level. In particular, the surrounding concentration behaves like a random variable when compared to municipality characteristics.

Spatial spillovers could, in theory, undermine the exogeneity of the instrument. Spatial instruments, by their nature, can introduce spillovers in general equilibrium. However, by excluding contiguous municipalities and focusing on municipality-level outcomes, we believe the risk of significant spatial spillovers is minimal.

For example, a change in concentration within a 50 km radius around municipality *m* is unlikely to directly affect land use or production in *m*. This is because the buffer zone—comprising the contiguous municipalities excluded from the second-stage analysis—helps ensure that spillovers do not directly impact the outcomes of interest.

Overall, this threefold evidence supports the validity of our benchmark IV and provides causal evidence for the relationship between concentration and soybean production. Nevertheless, we check the sensitivity of our estimates to the choice of the exact instrument.

## 5.2 *Effect of concentration on soybean production*

Table 4 shows the IV-2SLS results, where we instrument the "local" HHI by the surrounding HHI.

In all specifications, we find a negative effect of concentration (HHI) on soybean production, in line with our theoretical predictions: concentration is associated with lower soybean production. Across specifications that differ along the set of controls included, the elasticity of soybean production to concentration lies around -0.2. A 10% increase in concentration decreases soybean production by around 2%.

Other determinants of soybean production have the expected signs. Soybean yields is a strong predictor of soybean production, without any surprise.

Specifications further examine whether the differential trajectories of soybean production across municipalities is influenced by commitments to deforestation-free soybean production made by large firms. While it is possible that these firms exert pressure on farmers (as we hypothesize), they may also commit to reducing
<table><caption>Table 3: Benchmark effect of concentration on soybean production - IV-2LSLS results</caption><thead><tr><th></th><th colspan="3">Dep Variable: Soy Prod</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>HHI</td><td>-0.188**<br>(0.090)</td><td>-0.233***<br>(0.088)</td><td>-0.223**<br>(0.087)</td></tr><tr><td>Soy Yields</td><td></td><td>0.920***<br>(0.054)</td><td>0.925***<br>(0.054)</td></tr><tr><td>GDP per cap.</td><td></td><td>0.188*<br>(0.102)</td><td>0.179*<br>(0.102)</td></tr><tr><td>Committed Exports Share</td><td></td><td>0.000<br>(0.055)</td><td>-0.008<br>(0.055)</td></tr><tr><td>PPCDAM (1/0)</td><td></td><td></td><td>0.926*<br>(0.493)</td></tr><tr><td>Observations</td><td>4679</td><td>4679</td><td>4679</td></tr><tr><td>R<sup>2</sup></td><td>-0.001</td><td>0.108</td><td>0.111</td></tr><tr><td>Cragg-Donald Wald F statistic</td><td>499.973</td><td>503.801</td><td>501.340</td></tr><tr><td>Kleibergen-Paap rk Wald F statistic</td><td>1091.987</td><td>1076.566</td><td>1067.345</td></tr><tr><td>Municipality fixed effects</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Year fixed effects</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. IV-2SLS estimations, in which concentration (HHI) is instrumented. The dependent variable is the log soybean production by municipality-year. HHI is defined over the pertinent market, comprising the municipalities and the contiguous municipalities. Excluded instrument is the HHI in the surrounding area (non-contiguous, less than 50 kms.). Standard Errors (in parentheses) are clustered at the municipality level. All estimations include municipality and year fixed effects. All variables in logs, except binary variables. See text for details.
their environmental footprint. Our analysis does not support this hypothesis, showing that the share of ex-ports from committed firms in each municipality does not significantly alter the observed production decline. Results tend to show that corporate sustainability commitments exist but are not large enough to explain the different patterns of soybean production across municipalities, once accounting for concentration.

Additionally, large firms may affect production through changes in land-use policies. They could leverage their influence to shape conservation policies, potentially resulting in less stringent production constraints. To address this, our main estimations include an indicator variable for municipalities blacklisted under the PPCDAm, one of the key conservation measures in Brazil. This priority list identifies municipalities most vulnerable to deforestation in the Brazilian Legal Amazon, enabling targeted efforts to prevent and control deforestation<fnref n="7" />. We find that this conservation measure increased soybean production, in line with recent evidence (Crepin, 2024). Controlling for this, our main conclusion is however unaffected: concentration is associated with lower soybean production.

Overall, we find conclusive evidence in favor of the causal effect of oligopoly on reduced soybean production, in line with our prediction.

## 5.3 Effect of concentration on deforestation

Our predictions insights suggest that local concentration impacts deforestation. We hence replicate our analysis focusing on the effect of concentration on deforestation. We use deforestation data from MapBiomas, and estimate the following specification:

$$Deforestation_{mt} = \alpha + \gamma HHI_{mt} + \beta Controls_{mt} + \lambda_m + \lambda_t + \varepsilon_{mt} \quad (8)$$

using the same IV-2SLS strategy as above. Indeed, the same reasons that lead concentration to be endogenous to soybean production yield endogeneity concerns regarding concentration and deforestation. The major reason is that deforestation is primarily driven by agricultural expansion, in particular for soybean production (Pendrill et al., 2022). As a result, the non-random presence of exporters cannot be unrelated to agricultural expansion possibilities and hence deforestation. As presented before, the specification includes municipality-specific and year-specific fixed effects, and the set of controls includes many deforestation determinants.

The results of the IV-2SLS estimation, presented in Table 4, show several key findings. First, we observe a negative and statistically significant effect of concentration on deforestation. This outcome is consistent with previous evidence on the effect of concentration on soybean production. We estimate similar marginal effects of concentration. Higher concentration among exporters leads to reduced incentives for land expansion.

<footnote n="7">⁷Measures for these municipalities include enhanced environmental monitoring, restricted access to deforestation permits, limited credits and market opportunities, and embargoes on illegally cleared areas. Initially, the PPCDAM designated 36 municipalities, which was later expanded to 43 in 2009 and 48 in 2011, before stabilizing at 46 municipalities since 2012.</footnote>
<table><caption>Table 4: Benchmark effect of concentration on deforestation - IV-2LSLS results</caption><thead><tr><th></th><th colspan="4">Dep Variable: Deforestation</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>HHI</td><td>-0.156**<br>(0.075)</td><td>-0.206**<br>(0.083)</td><td>-0.208**<br>(0.085)</td><td>-0.104<br>(0.098)</td></tr><tr><td>Committed Exports Share</td><td></td><td>0.080*<br>(0.042)</td><td>0.072*<br>(0.038)</td><td>0.057<br>(0.040)</td></tr><tr><td>PPCDAM (1/0)</td><td></td><td>-0.914***<br>(0.226)</td><td>-0.650**<br>(0.287)</td><td>-0.650**<br>(0.292)</td></tr><tr><td>Deforestation (t-1)</td><td></td><td></td><td>0.171***<br>(0.027)</td><td>0.169***<br>(0.029)</td></tr><tr><td>Log Soy Prod.</td><td></td><td></td><td></td><td>-0.003<br>(0.010)</td></tr><tr><td>Observations</td><td>4617</td><td>4567</td><td>3824</td><td>2982</td></tr><tr><td>R<sup>2</sup></td><td>-0.010</td><td>-0.015</td><td>0.017</td><td>0.031</td></tr><tr><td>Cragg-Donald Wald F statistic</td><td>451.371</td><td>329.944</td><td>261.593</td><td>211.417</td></tr><tr><td>Kleibergen-Paap rk Wald F statistic</td><td>931.336</td><td>621.105</td><td>504.046</td><td>419.519</td></tr><tr><td>Municipality fixed effects</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Year fixed effects</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. IV-2LSLS estimations, in which concentration (HHI) is instrumented. The dependent variable is the change in primary forest cover (MapBiomas). HHI is defined over the pertinent market, comprising the municipalities and the contiguous municipalities. Excluded instrument is the HHI in the surrounding area (non-contiguous, less than 50 kms.) Standard Errors (in parentheses) are clustered at the municipality level. All estimations include municipality and year fixed effects. All variables in logs, except binary variables. See text for details.
Second, coefficients suggest a strong negative impact of conservation policies on deforestation, underlining the effectiveness of these policies in curbing land conversion. These results are robust across specifications, reinforcing the notion that regulatory measures play a role in controlling the environmental impact of agricultural activities. Moreover, while lagged deforestation is included as a control in the specification, it does not alter the overall pattern identified in the main effect. The persistence of past deforestation is supported in our estimations, but it does not fundamentally change the direction and magnitude of the relationship between concentration and deforestation.

Third, the primary mechanism through which concentration affects deforestation appears to be changes in soybean production. Indeed, when controlling for soybean production (col. 4), the relationship between concentration and deforestation becomes insignificant. As expected from the theoretical framework, concentration reduces soybean production and hence the incentive for expansion into new lands by soybean producers, leading to lower rates of deforestation.

## 5.4 Robustness checks.

We performed several sensitivity checks to validate our results.

**Alternative excluded IV** We replicate the main analysis using a 100 km buffer instead of 50 km for the surrounding market. Table B.3 confirms the validity of the main results, with similar point estimates, standard errors, and coefficient effects.

**Potential violation of the exclusion restriction** To address concerns that surrounding market concentration may affect deforestation beyond local market concentration, we include biome-state-year and municipality fixed effects. Table B.4 shows consistent results with an estimated coefficient of -0.3, confirming robustness and supporting causal interpretation.

**Changes in Pertinent Market Definition** We test alternative pertinent market definitions: (i) a 25 km radius, where the surrounding market is 25-50 km away, leading to larger estimated impacts with slightly lower precision (Table B.5), and (ii) a 50 km radius, which further increases coefficient magnitudes and improves precision (Table B.6). These results show that broader market definitions yield stronger effects, indicating our benchmark estimates are conservative.

**Alternative Measures of Deforestation** Using deforestation data from MapBiomas (cols. 1-4) and PRODES (cols. 5-8) in Table B.7, we find similar results: concentration reduces deforestation, primarily through soybean production shifts. Estimates remain close to the benchmark, with varying precision but unchanged conclusions.
**Clustering of standard errors** We test clustering at broader geographic levels (state-year and biome-year) instead of municipalities. Table B.8 shows stable standard errors and confidence intervals, reinforcing the reliability of our inference.

**OLS estimations** Table B.9 presents OLS results, showing that (i) soybean production elasticities are about half the size of IV estimates, and (ii) the effect of concentration on deforestation is not significant. This aligns with downward bias due to endogeneity and measurement error, explaining the discrepancy between OLS and IV estimates.

**Placebo** Table B.10 finally shows placebo tests on beef (cols. 1-2) and corn (cols. 3-4) production. Most results are non-significant, confirming that soybean concentration does not capture broader economic trends. However, a small positive effect on corn production may suggest substitution from soybean.

# 6 Conclusion

Local market structures and the market power of major exporters is likely to have strong effects on local commodity suppliers and their environmental impacts. Applied to Brazilian soybean, our paper provides evidence of those impacts on soybean production and deforestation.

Several contributions have to be noted. First, we provide evidence suggesting that firms use their market power to buy soybean at lower prices. Indeed, by comparing municipalities’ trajectory over time, we find that the presence of large firms is associated to lower agricultural revenues per unit of soybean produced. We find that conditional on total soybean production, the presence of the oligopolists in the municipality is associated with lower agricultural revenues. On the contrary, we do not find evidence that large exporters sell soybean at larger price to foreign consumers.

Second, we theoretically assess the consequences of this markdown behavior by the largest exporters; with a simple theoretical model of soybean exports, production and deforestation, with a market structure made of a few oligopsony firms and a competitive fringe. The model predicts that the amount of soybean produced in a given local market is decreasing in the level of markdown and the concentration of the oligopsony. It follows that in areas where the markdown is important because of a high concentration of the oligopsony, lower levels of deforestation can be expected.

Third, theoretical predictions are empirically confirmed. To address potential endogeneity concerns, we employed a robust IV-2SLS strategy. By instrumenting local concentration (HHI) with the concentration of surrounding municipalities, we effectively identified the causal effects of oligopsony on soybean production and deforestation, accounting for the non-random presence of exporters. The validity of this approach was
supported by strong first-stage results, high F-statistics, and the exclusion restriction being plausible under our assumptions.

Our findings demonstrate that higher levels of concentration among large exporters within a municipality-year have significant effects on both soybean production and deforestation. Specifically, we estimate that larger concentration is associated with a reduction in soybean production, with an elasticity of approximately -0.2, indicating that a 10% increase in concentration leads to a 2% decline in soybean output. Furthermore, we observe a parallel reduction in deforestation, suggesting that concentration mitigates the incentive for land expansion related to soybean cultivation. This relationship is mediated by changes in production behavior, as evidenced by the diminished significance of concentration's direct effect on deforestation when controlling for soybean production. Importantly, our results hold across various specifications and robustness checks, including alternative definitions of the surrounding market for the excluded instrument.

Our analysis provides evidence that market concentration among exporters influences soybean production and deforestation. These effects highlight the broader implications of agricultural market structures for environmental and economic outcomes. On the economic side, our findings suggest that market power exerts downward pressure on farmers' revenues, potentially limiting their profitability and growth. However, it is worth considering that market concentration may also bring potential benefits, such as contractual arrangements that could offer farmers more stability and access to credit—similar to the findings in (Abman and Lundberg, 2024) regarding market access and contracting programs. On the environmental side, the presence of market power appears to have a mitigating effect on deforestation, highlighting a broader, generalizable result: the economic losses induced by market failures such as oligopsony may, in part, counterbalance the environmental costs associated with externalities like deforestation. This underscores the need to further explore the welfare trade-offs between agricultural development and forest conservation.
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# Appendix
<figure><img src="image_6.png" /><figcaption>Figure A.1: Distribution of the number of contiguous municipalities</figcaption></figure>

565 A Data details

566 B Further Results
<figure><img src="image_7.png" /><figcaption>Figure A.2: Description of the benchmark IV strategy</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Figure A.3: Distribution of the number of non-contiguous municipalities, within a 50 kms radius</figcaption></figure>
<table><caption>Table B.1: Benchmark first-stage results</caption><thead><tr><th></th><th colspan="3">Dep. Variable: HHI<sub>mt</sub></th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th></tr></thead><tbody><tr><td>HHI<sup>Surr.</sup><sub>m,t</sub></td><td>0.481***<br>(0.022)</td><td>0.479***<br>(0.021)</td><td>0.477***<br>(0.021)</td></tr><tr><td>Soy Yields</td><td></td><td>0.016<br>(0.020)</td><td>0.014<br>(0.020)</td></tr><tr><td>GDP per cap.</td><td></td><td>-0.079**<br>(0.033)</td><td>-0.075**<br>(0.033)</td></tr><tr><td>Committed Exports Share</td><td></td><td>-0.027<br>(0.023)</td><td>-0.023<br>(0.024)</td></tr><tr><td>PPCDAM (1/0)</td><td></td><td></td><td>-0.359***<br>(0.041)</td></tr><tr><td>Observations</td><td>4679</td><td>4679</td><td>4679</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. first-stage estimation. Dependent variable: HHI<sub>mt</sub>. Standard Errors (in parentheses) are clustered at the municipality level. All estimations include municipality and year fixed effects. All variables in logs, except binary variables. See text for details.

<table><caption>Table B.2: Absence of correlation between the excluded IV and a set of municipality characteristics</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr><tr><th></th><th>Def (t)</th><th>Def (t-1)</th><th>Soy Prod (t)</th><th>Soy Prod (t-1)</th><th>Agri. GDP</th><th>PPCDAm</th></tr></thead><tbody><tr><td>HHI<sup>Surr.</sup><sub>m,t</sub></td><td>-0.048<br>(0.059)</td><td>2.941<br>(3.159)</td><td>0.108<br>(0.097)</td><td>-0.046<br>(0.128)</td><td>-0.086<br>(0.040)</td><td>-0.006<br>(0.008)</td></tr><tr><td>Observations</td><td>4617</td><td>3933</td><td>3601</td><td>3058</td><td>4669</td><td>4669</td></tr><tr><td>R<sup>2</sup></td><td>0.944</td><td>0.800</td><td>0.762</td><td>0.739</td><td>0.947</td><td>0.842</td></tr><tr><td>F-statistic</td><td>0.659</td><td>0.867</td><td>1.244</td><td>0.127</td><td>4.749</td><td>0.573</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. Standard Errors (in parentheses) are clustered at the municipality level. All estimations include municipality and year fixed effects. All variables in logs. See text for details.
<table><caption>Table B.3: Robustness: Alternative IV</caption><thead><tr><th rowspan="2"></th><th colspan="3">Soy Prod</th><th colspan="3">Deforestation</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>HHI</td><td>-0.205**<br>(0.092)</td><td>-0.191**<br>(0.092)</td><td>-0.189**<br>(0.092)</td><td>-0.199*<br>(0.113)</td><td>-0.321**<br>(0.145)</td><td>-0.158<br>(0.156)</td></tr><tr><td>Soy Yields</td><td>0.956***<br>(0.054)</td><td>0.932***<br>(0.057)</td><td>0.934***<br>(0.057)</td><td></td><td></td><td></td></tr><tr><td>GDP per cap.</td><td></td><td>0.163<br>(0.101)</td><td>0.156<br>(0.101)</td><td></td><td></td><td></td></tr><tr><td>Committed Exports Share</td><td></td><td>-0.027<br>(0.054)</td><td>-0.029<br>(0.054)</td><td></td><td>0.083**<br>(0.039)</td><td>0.067<br>(0.042)</td></tr><tr><td>PPCDAM (1/0)</td><td></td><td></td><td>0.697<br>(0.604)</td><td></td><td>-0.655**<br>(0.296)</td><td>-0.645**<br>(0.301)</td></tr><tr><td>Deforestation (t-1)</td><td></td><td></td><td></td><td>0.185***<br>(0.025)</td><td>0.168***<br>(0.026)</td><td>0.171***<br>(0.028)</td></tr><tr><td>Log Soy Prod.</td><td></td><td></td><td></td><td></td><td></td><td>-0.004<br>(0.010)</td></tr><tr><td>Observations</td><td>4726</td><td>4726</td><td>4726</td><td>4041</td><td>3994</td><td>3079</td></tr><tr><td>Cragg-Donald Wald F statistic</td><td>233.924</td><td>233.383</td><td>233.359</td><td>154.787</td><td>89.195</td><td>72.905</td></tr><tr><td>Kleibergen-Paap rk Wald F statistic</td><td>477.570</td><td>471.565</td><td>471.229</td><td>318.329</td><td>161.157</td><td>145.246</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. IV-2LSLS estimations, in which concentration (HHI) is instrumented. The dependent variable is the log soybean production (cols. 1 to 3) by municipality-year and the change in primary forest cover (cols.4 to 6). HHI is defined over the pertinent market, comprising the municipalities and the contiguous municipalities. Excluded instrument is the HHI in the surrounding area (non-contiguous, less than 100 kms.). Standard Errors (in parentheses) are clustered at the municipality level. All estimations include municipality and year fixed effects. All variables in logs, except binary variables. See text for details.
<table><caption>Table B.4: Robustness: exclusion restriction and further fixed effects</caption><thead><tr><th></th><th colspan="6">Dep Variable: Soy Prod</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>HHI</td><td>-0.289**<br>(0.129)</td><td>-0.287**<br>(0.123)</td><td>-0.287**<br>(0.123)</td><td>-0.201**<br>(0.082)</td><td>-0.209**<br>(0.082)</td><td>-0.104<br>(0.076)</td></tr><tr><td>Soy Yields</td><td></td><td>0.925***<br>(0.074)</td><td>0.925***<br>(0.074)</td><td></td><td></td><td></td></tr><tr><td>GDP per cap.</td><td></td><td>0.191*<br>(0.104)</td><td>0.194*<br>(0.104)</td><td></td><td></td><td></td></tr><tr><td>Committed Exports Share</td><td></td><td>-0.042<br>(0.056)</td><td>-0.041<br>(0.056)</td><td></td><td>0.080**<br>(0.038)</td><td>0.071*<br>(0.038)</td></tr><tr><td>PPCDAM (1/0)</td><td></td><td></td><td>-0.329<br>(0.352)</td><td></td><td>-0.652**<br>(0.297)</td><td>-0.587**<br>(0.275)</td></tr><tr><td>Deforestation (t-1)</td><td></td><td></td><td></td><td>0.170***<br>(0.026)</td><td>0.168***<br>(0.026)</td><td>0.186***<br>(0.028)</td></tr><tr><td>Log Soy Prod.</td><td></td><td></td><td></td><td></td><td></td><td>-0.001<br>(0.009)</td></tr><tr><td>Observations</td><td>4677</td><td>4677</td><td>4677</td><td>3994</td><td>3994</td><td>3118</td></tr><tr><td>Cragg-Donald Wald F statistic</td><td>331.749</td><td>331.553</td><td>331.631</td><td>273.310</td><td>277.123</td><td>302.151</td></tr><tr><td>Kleibergen-Paap rk Wald F statistic</td><td>577.242</td><td>573.409</td><td>573.110</td><td>518.613</td><td>517.827</td><td>631.776</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. IV-2LSLS estimations, in which concentration (HHI) is instrumented. The dependent variable is the log soybean production (cols. 1 to 3) by municipality-year and the change in primary forest cover (cols.4 to 6). HHI is defined over the pertinent market, comprising the municipalities and the contiguous municipalities. Excluded instrument is the HHI in the surrounding area (non-contiguous, less than 50 kms.). Standard Errors (in parentheses) are clustered at the municipality level. All estimations include municipality and state-biome-year fixed effects. All variables in logs, except binary variables. See text for details.
<table><caption>Table B.5: Robustness: Pertinent Market: <25 km</caption><thead><tr><th></th><th colspan="3">Soy Prod</th><th colspan="3">Deforestation</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>HHI (25 km radius)</td><td>-0.467**<br>(0.163)</td><td>-0.540**<br>(0.220)</td><td>-0.538**<br>(0.221)</td><td>-0.584*<br>(0.316)</td><td>-0.635**<br>(0.322)</td><td>-0.468<br>(0.391)</td></tr><tr><td>Soy Yields</td><td></td><td>0.928***<br>(0.052)</td><td>0.929***<br>(0.052)</td><td></td><td></td><td></td></tr><tr><td>GDP per cap.</td><td></td><td>0.122<br>(0.100)</td><td>0.120<br>(0.101)</td><td></td><td></td><td></td></tr><tr><td>Committed Exports Share</td><td></td><td>-0.026<br>(0.057)</td><td>-0.026<br>(0.057)</td><td></td><td>0.110***<br>(0.042)</td><td>0.094*<br>(0.049)</td></tr><tr><td>PPCDAM (1/0)</td><td></td><td></td><td>0.138<br>(0.127)</td><td></td><td>-1.025***<br>(0.359)</td><td>-0.951***<br>(0.348)</td></tr><tr><td>Deforestation (t-1)</td><td></td><td></td><td></td><td>0.152***<br>(0.026)</td><td>0.148***<br>(0.027)</td><td>0.163***<br>(0.029)</td></tr><tr><td>Log Soy Prod.</td><td></td><td></td><td></td><td></td><td></td><td>-0.006<br>(0.010)</td></tr><tr><td>Observations</td><td>4782</td><td>4782</td><td>4782</td><td>4030</td><td>4030</td><td>2983</td></tr><tr><td>Cragg-Donald Wald F statistic</td><td>48.319</td><td>109.974</td><td>109.442</td><td>25.179</td><td>24.881</td><td>14.142</td></tr><tr><td>Kleibergen-Paap rk Wald F statistic</td><td>177.992</td><td>176.049</td><td>174.357</td><td>40.922</td><td>39.935</td><td>24.280</td></tr></tbody></table>

*Note:* * p < 0.1, ** p < 0.05, *** p < 0.01. IV-2LSLS estimations, where concentration (HHI) is instrumented. The dependent variable is the log soybean production (cols. 1 to 3) by municipality-year and the change in primary forest cover (cols.4 to 6). A 25 km radius around the city is considered as the pertinent market. The excluded IV is measured on the surrounding market (municipalities between 25 and 50 km). Standard errors (in parentheses) are clustered at the municipality level. All estimations include municipality and year fixed effects. All variables are in logs, except binary variables. See text for details.
<table><caption>Table B.6: Robustness: Pertinent Market: <50 km</caption><thead><tr><th rowspan="2"></th><th colspan="3">Soy Prod</th><th colspan="3">Deforestation</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>HHI (50 km radius)</td><td>-0.737***<br>(0.255)</td><td>-0.733***<br>(0.254)</td><td>-0.706***<br>(0.258)</td><td>-0.378*<br>(0.204)</td><td>-0.403**<br>(0.176)</td><td>-0.348*<br>(0.181)</td></tr><tr><td>Soy Yields</td><td>0.972***<br>(0.051)</td><td>0.957***<br>(0.055)</td><td>0.960***<br>(0.054)</td><td></td><td></td><td></td></tr><tr><td>GDP per cap.</td><td></td><td>0.104<br>(0.098)</td><td>0.100<br>(0.098)</td><td></td><td></td><td></td></tr><tr><td>Committed Exports Share</td><td></td><td>0.030<br>(0.059)</td><td>0.023<br>(0.059)</td><td></td><td>0.124***<br>(0.042)</td><td>0.095**<br>(0.045)</td></tr><tr><td>PPCDAM (1/0)</td><td></td><td></td><td>0.662<br>(0.473)</td><td></td><td>-0.715**<br>(0.344)</td><td>-0.681**<br>(0.333)</td></tr><tr><td>Deforestation (t-1)</td><td></td><td></td><td></td><td></td><td>0.145***<br>(0.027)</td><td>0.167***<br>(0.029)</td></tr><tr><td>Log Soy Prod.</td><td></td><td></td><td></td><td></td><td></td><td>0.001<br>(0.010)</td></tr><tr><td>Observations</td><td>4979</td><td>4979</td><td>4979</td><td>4868</td><td>4179</td><td>3063</td></tr><tr><td>Cragg-Donald Wald F statistic</td><td>93.318</td><td>96.662</td><td>92.271</td><td>60.661</td><td>69.770</td><td>56.414</td></tr><tr><td>Kleibergen-Paap rk Wald F statistic</td><td>190.413</td><td>191.789</td><td>185.436</td><td>126.163</td><td>136.906</td><td>118.660</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. IV-2LSLS estimations, where concentration (HHI) is instrumented. The dependent variable is the log soybean production (cols. 1 to 3) by municipality-year and the change in primary forest cover (cols.4 to 6). A 50 km radius around the city is considered as the pertinent market. The excluded IV is measured on the surrounding market (municipalities between 50 and 100 km). Standard errors (in parentheses) are clustered at the municipality level. All estimations include municipality and year fixed effects. All variables are in logs, except binary variables. See text for details.
<table><caption>Table B.7: Robustness: Alternative Measures of Deforestation</caption><thead><tr><th></th><th colspan="8">Dep Variable: Deforestation</th></tr><tr><th></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></tr></thead><tbody><tr><td>HHI</td><td>-0.141**<br>(0.057)</td><td>-0.172***<br>(0.066)</td><td>-0.179***<br>(0.068)</td><td>-0.116<br>(0.076)</td><td>-0.247***<br>(0.090)</td><td>-0.211**<br>(0.090)</td><td>-0.241***<br>(0.087)</td><td>-0.139<br>(0.094)</td></tr><tr><td>Committed Exports Share</td><td></td><td>0.053<br>(0.034)</td><td>0.056*<br>(0.032)</td><td>0.057*<br>(0.034)</td><td></td><td>-0.010<br>(0.056)</td><td>0.008<br>(0.057)</td><td>-0.009<br>(0.063)</td></tr><tr><td>PPCDAM (1/0)</td><td></td><td>-0.750***<br>(0.155)</td><td>-0.489**<br>(0.221)</td><td>-0.485**<br>(0.227)</td><td></td><td>-0.853**<br>(0.343)</td><td>-0.661***<br>(0.232)</td><td>-0.672***<br>(0.222)</td></tr><tr><td>Log Soy Prod.</td><td></td><td></td><td></td><td>-0.003<br>(0.008)</td><td></td><td></td><td></td><td>0.003<br>(0.013)</td></tr><tr><td>Observations</td><td>4673</td><td>4624</td><td>3885</td><td>3039</td><td>3368</td><td>3294</td><td>2614</td><td>2034</td></tr><tr><td>Cragg-Donald Wald F statistic</td><td>457.301</td><td>340.026</td><td>272.718</td><td>221.021</td><td>383.673</td><td>271.302</td><td>243.507</td><td>192.798</td></tr><tr><td>Kleibergen-Paap Wald Fstat.</td><td>945.844</td><td>639.261</td><td>517.190</td><td>434.827</td><td>887.219</td><td>563.227</td><td>484.318</td><td>422.850</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. IV-2LSLS estimations, in which concentration (HHI) is instrumented. Dependent variable is the change in primary forest cover (cols. 1 to 4) and the change in forest cover from PRODES (cols. 5 to 8). Standard Errors (in parentheses) are clustered at the municipality level. All estimations include municipality and year fixed effects. All variables in logs, except binary variables. See text for details.
<table><caption>Table B.8: Robustness: Alternative Clustering Levels</caption><thead><tr><th></th><th colspan="3">soybean Prod</th><th colspan="3">Deforestation</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td colspan="7"><strong>Panel A: Clustering at the sate-year level</strong></td></tr><tr><td>HHI</td><td>-0.254***<br>(0.071)</td><td>-0.240***<br>(0.072)</td><td>-0.230***<br>(0.072)</td><td>-0.154*<br>(0.087)</td><td>-0.213**<br>(0.095)</td><td>-0.158*<br>(0.092)</td></tr><tr><td>soybean Yields</td><td>0.941***<br>(0.061)</td><td>0.911***<br>(0.063)</td><td>0.918***<br>(0.062)</td><td></td><td></td><td></td></tr><tr><td>GDP per cap.</td><td></td><td>0.188***<br>(0.058)</td><td>0.178***<br>(0.057)</td><td></td><td></td><td></td></tr><tr><td>Committed Exports Share</td><td></td><td>-0.004<br>(0.056)</td><td>-0.013<br>(0.053)</td><td></td><td>0.076**<br>(0.037)</td><td>0.075**<br>(0.032)</td></tr><tr><td>PPCDAM (1/0)</td><td></td><td></td><td>0.942***<br>(0.209)</td><td></td><td>-0.852***<br>(0.272)</td><td>-0.843***<br>(0.275)</td></tr><tr><td>Log soybean Prod.</td><td></td><td></td><td></td><td></td><td></td><td>-0.001<br>(0.009)</td></tr><tr><td>Observations</td><td>4736</td><td>4736</td><td>4736</td><td>4673</td><td>4624</td><td>3575</td></tr><tr><td>R<sup>2</sup></td><td>0.103</td><td>0.106</td><td>0.109</td><td>-0.010</td><td>-0.016</td><td>-0.005</td></tr><tr><td>Cragg-Donald Wald F statistic</td><td>356.548</td><td>353.047</td><td>350.535</td><td>302.201</td><td>270.515</td><td>283.389</td></tr><tr><td>Kleibergen-Paap rk Wald F statistic</td><td>1004.660</td><td>991.268</td><td>983.263</td><td>868.390</td><td>613.644</td><td>524.729</td></tr><tr><td colspan="7"><strong>Panel B: Clustering at the biome-year level</strong></td></tr><tr><td>HHI</td><td>-0.254***<br>(0.084)</td><td>-0.240***<br>(0.086)</td><td>-0.230***<br>(0.086)</td><td>-0.154*<br>(0.084)</td><td>-0.213**<br>(0.091)</td><td>-0.158*<br>(0.086)</td></tr><tr><td>soybean Yields</td><td>0.941***<br>(0.059)</td><td>0.911***<br>(0.062)</td><td>0.918***<br>(0.062)</td><td></td><td></td><td></td></tr><tr><td>GDP per cap.</td><td></td><td>0.188***<br>(0.071)</td><td>0.178**<br>(0.071)</td><td></td><td></td><td></td></tr><tr><td>Committed Exports Share</td><td></td><td>-0.004<br>(0.059)</td><td>-0.013<br>(0.058)</td><td></td><td>0.076*<br>(0.042)</td><td>0.075*<br>(0.041)</td></tr><tr><td>PPCDAM (1/0)</td><td></td><td></td><td>0.942***<br>(0.264)</td><td></td><td>-0.852***<br>(0.260)</td><td>-0.843***<br>(0.262)</td></tr><tr><td>Log soybean Prod.</td><td></td><td></td><td></td><td></td><td></td><td>-0.001<br>(0.011)</td></tr><tr><td>Observations</td><td>4736</td><td>4736</td><td>4736</td><td>4673</td><td>4624</td><td>3575</td></tr><tr><td>R<sup>2</sup></td><td>0.103</td><td>0.106</td><td>0.109</td><td>-0.010</td><td>-0.016</td><td>-0.005</td></tr><tr><td>Cragg-Donald Wald F statistic</td><td>544.004</td><td>538.746</td><td>539.592</td><td>558.451</td><td>390.490</td><td>376.697</td></tr><tr><td>Kleibergen-Paap rk Wald F statistic</td><td>1004.660</td><td>991.268</td><td>983.263</td><td>857.735</td><td>613.644</td><td>524.729</td></tr></tbody></table>

<footnote>Notes: * p < 0.1, ** p < 0.05, *** p < 0.01. IV-2SLS estimations, where concentration (HHI) is instrumented. The dependent variable is the log soybean production (cols. 1 to 3) by municipality-year and the change in primary forest cover (cols.4 to 6). HHI is defined over the pertinent market, comprising the municipalities and the contiguous municipalities. Excluded instrument is the HHI in the surrounding area (non-contiguous, less than 50 kms.). Standard errors (in parentheses) are clustered as indicated. All estimations include municipality and year fixed effects. All variables are in logs, except binary variables. See text for further details.</footnote>
<table><caption>Table B.9: Robustness: OLS estimation results</caption><thead><tr><th rowspan="2"></th><th colspan="3">Soy Prod</th><th colspan="3">Deforestation</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>HHI</td><td>-0.095**<br>(0.037)</td><td>-0.088**<br>(0.036)</td><td>-0.082**<br>(0.036)</td><td>0.019<br>(0.031)</td><td>0.017<br>(0.031)</td><td>-0.009<br>(0.038)</td></tr><tr><td>Soy Yields</td><td>0.937***<br>(0.049)</td><td>0.904***<br>(0.054)</td><td>0.912***<br>(0.054)</td><td></td><td></td><td></td></tr><tr><td>GDP per cap.</td><td></td><td>0.209**<br>(0.102)</td><td>0.197*<br>(0.102)</td><td></td><td></td><td></td></tr><tr><td>Committed Exports Share</td><td></td><td>-0.004<br>(0.054)</td><td>-0.014<br>(0.054)</td><td></td><td>0.091**<br>(0.041)</td><td>0.085**<br>(0.042)</td></tr><tr><td>PPCDAM (1/0)</td><td></td><td></td><td>1.021**<br>(0.488)</td><td></td><td>-0.722***<br>(0.236)</td><td>-0.669**<br>(0.283)</td></tr><tr><td>Log Soy Prod.</td><td></td><td></td><td></td><td></td><td></td><td>0.001<br>(0.010)</td></tr><tr><td>Observations</td><td>4736</td><td>4736</td><td>4736</td><td>4673</td><td>4673</td><td>3616</td></tr><tr><td>R<sup>2</sup></td><td>0.899</td><td>0.900</td><td>0.900</td><td>0.947</td><td>0.947</td><td>0.953</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. OLS estimations, in which concentration (HHI) is not instrumented. The dependent variable is the log soybean production (cols. 1 to 3) by municipality-year and the change in primary forest cover (cols. 4 to 6). Standard Errors (in parentheses) are clustered. All estimations include municipality and year fixed effects. All variables in logs, except binary variables. See text for details.
<table><caption>Table B.10: Robustness: placebo tests</caption><thead><tr><th></th><th colspan="2">Beef Prod.</th><th colspan="2">Corn Prod.</th></tr><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th></tr></thead><tbody><tr><td>HHI</td><td>-0.068<br>(0.071)</td><td>-0.071<br>(0.071)</td><td>0.356<br>(0.227)</td><td>0.368*<br>(0.223)</td></tr><tr><td>Soy Yields</td><td>-0.039<br>(0.032)</td><td>-0.031<br>(0.032)</td><td>0.194<br>(0.214)</td><td>0.117<br>(0.215)</td></tr><tr><td>GDP per cap.</td><td></td><td>-0.065<br>(0.059)</td><td></td><td>0.725***<br>(0.248)</td></tr><tr><td>Committed Exports Share</td><td></td><td>-0.003<br>(0.031)</td><td></td><td>-0.004<br>(0.060)</td></tr><tr><td>Observations</td><td>2528</td><td>2528</td><td>1152</td><td>1152</td></tr><tr><td>Cragg-Donald Wald F statistic</td><td>232.145</td><td>235.515</td><td>57.683</td><td>59.324</td></tr><tr><td>Kleibergen-Paap rk Wald F statistic</td><td>384.002</td><td>380.569</td><td>84.607</td><td>85.842</td></tr></tbody></table>

Note: * p < 0.1, ** p < 0.05, *** p < 0.01. IV-2SLS estimations, where concentration (HHI) is instrumented. The dependent variable is the log beef production (cols. 1 and 2) by municipality-year and the log corn production (cols. 3 and 4). HHI is defined over the pertinent market, comprising the municipalities and the contiguous municipalities. Excluded instrument is the HHI in the surrounding area (non-contiguous, less than 50 kms.). Standard errors (in parentheses) are clustered at the municipality level. All estimations include municipality and year fixed effects. All variables are in logs, except binary variables. See text for further details.