Smallholder timber sale decisions on the Amazon frontier

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a v a i l a b l e a t w w w. s c i e n c e d i r e c t . c o m

w w w. e l s e v i e r. c o m / l o c a t e / e c o l e c o n

ANALYSIS

Smallholder timber sale decisions on the Amazon frontier☆ Gregory S. Amacher a,⁎, Frank D. Merry b,c , Maria S. Bowman b a

Department of Forestry, College of Natural Resources, 304D Cheatham Hall, Virginia Polytechnic Institute and State University, Blacksburg VA, 24060, USA b Woods Hole Research Center, Woods Hole MA, USA c Instituto de Pesquisa Ambiental da Amazônia, Belém, Pará, Brazil

AR TIC LE D ATA

ABSTR ACT

Article history:

We use data from a survey of 2401 households living along the Transamazon highway to

Received 5 April 2008

study timber sales decisions of smallholders settling in Amazon native forests. We develop

Received in revised form

an econometric approach, to study both the decision to harvest timber and the volume of

13 October 2008

timber sold, that corrects for limited access to loggers leading to possible selection bias,

Accepted 14 November 2008

incomplete labor markets, and differences in property rights regimes that characterize the

Available online 7 February 2009

area. We find that, irrespective of distance to markets, smallholders that have either been settled by INCRA or have access to credit are more likely to sell wood, but those with outside

Keywords:

income sources are less likely to sell. Higher timber prices decrease the likelihood of timber

Deforestation

sales. The results suggest that timber sales are viewed only as a means for smallholders to

Amazon

reduce immediate cash constraints. With some exceptions these results hold across

Household model

property rights regimes.

Smallholders

© 2008 Published by Elsevier B.V.

Timber supply

1.

Introduction

Since 1995, more than 500,000 migrant families have officially settled in forested areas of Brazil (www.incra.gov.br), of which approximately 56% live in the region described as the “Legal Amazon.” The standard settled lot is 100 ha, giving an estimated total area formally settled by smallholders of 28 million hectares. Although the law varies across Brazil, 80% of each smallholder's lot in the Transamazon must remain in forest.1 Informal settlement can add as much as 60% more area to the total settlement figure (Lima et al., 2006).

If one thinks about the future of Amazon forests, smallholders cannot be ignored. Several studies have focused on smallholder slash-andburn land clearing for crop production as one component of deforestation (Walker et al., 2002; Perz and Walker, 2002; Aldrich et al., 2006, Macqueen et al., 2005; Macqueen, 2004). This type of land clearing has been blamed for up to 20% of deforestation in the legal Amazon during the past two decades (Nepstad et al., 2004). Often, harvesting of standing forests on the lot can offer high immediate returns for impoverished new settlers. Merry et al. (2006a,b) estimate these returns to be in

☆ Funding for this study was obtained from the National Science Foundation, grant number DEB-0410315. Additional funding was provided by the Gordon and Betty Moore Foundation; NASA Large Scale Biosphere and Atmosphere Project; and United States Agency for International Development-Brazil program. Opinions and errors, however, are the authors'. ⁎ Corresponding author. Tel.: +1 540 231 5943; fax: +1 540 231 3698. E-mail address: [email protected] (G.S. Amacher). 1 In other areas outside of the Amazon biome this restriction drops to 50%. In the data discussed later, we asked smallholders whether they harvested and if so how much volume was removed from land clearing and legal deforestation areas. While deciding to harvest in any year is not illegal, deciding to harvest more than the government allows in either area is, although it is well known that this is a rule that is not enforced in the Transamazon. We proceed with this caveat.

0921-8009/$ – see front matter © 2008 Published by Elsevier B.V. doi:10.1016/j.ecolecon.2008.11.018

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some cases more than the equivalent of 15 years of smallholder agricultural production in present value terms. Given incentives smallholders should have for selling trees, it is surprising that very little is known about factors important in smallholder timber sales made to loggers. There are two ways in which wood is sold. Some smallholders sell trees through deforestation permits when clearing land for crops, as an alternative to burning, if they have access to loggers (these permits allow clearing of 3 ha and the sale of 60 m3 of logs, or approximately 12 trees, per year). Many others, however, sell wood to loggers both legally and illegally from their ‘legal reserve’ area; the legal reserve is an area comprising 80% of the settled lot for which the government legislates harvesting rates and prevents clearing for crops. This harvesting is in collusion with a logger if the smallholder has access to one or if it is possible to obtain permission to harvest from the government. However, the permitting system is now managed by state governments and requires costly management plans and formal property rights, neither of which is easy to obtain. Thus, almost all logging on smallholder legal reserve forests is illegal, and much of it is done using high grading that reduces biological diversity through removal of only the highest valued trees (Nepstad et al., 2004; Merry et al., 2006a,b). Although there is some risk to the smallholder associated with illegal logging, for the most part the government largely overlooks the illegal nature of these activities, leaving smallholders free to make decisions about wood sales. The main constraints are whether smallholders actually have access to loggers or not, and whether harvesting trees on their lots is profitable. Our purpose in this paper is to study the timber sale decisions of smallholders, both with respect to the decision to harvest and the volume of trees sold from their lots, using an expansive and unique data set for the Transamazon region of Brazil. We are not aware of any previous research that has focused on understanding the (illegal) logging by smallholders on the agricultural frontier. To describe the future economic potential for smallholders in the forest sector, their timber sale decisions must be understood. Smallholder logging in the Brazilian Amazon or for subsistence households in other areas does not fit the established timber supply literature largely developed for landowners in countries with well developed property rights. Instead, an economic household approach must be developed to understand smallholder timber sale decisions within the context of other subsistence household decisions.2 There are three new features of our approach that distinguish it from developed country timber sale studies. First, it is well documented that rural households often make decisions in labor markets that are not fully complete. This affects production decisions, including wood sales, and therefore we address this in our econometric specification. Second, smallholders may not have

2

For a review of household models, see Singh et al. (1986) and Jacoby (1993). Pendleton and Howe (2002) study land clearing/ burning in Bolivia using a model similar to ours, although they do not consider timber sale decisions. Cavilglia-Harris (2004) uses similar methods of household models and selection applied to smallholders in the Amazon, but again timber sales are not examined.

unconstrained access to loggers. Access depends on several factors, such as information specific to smallholders, the overall bargaining strength of community associations, when they exist, who sometimes negotiate with loggers, and the profitability of timber harvesting on smallholder lots.3 These factors represent unobserved variables from the perspective of a researcher studying timber sales decisions, because a ‘zero’ observed for smallholder timber sales may reflect a constraint on the smallholder's choice. This type of problem is called selection, and we therefore use econometric methods that test and correct for it. Third, smallholder decisions regarding their forests can depend on the type of property rights they hold. Smallholders obtain levels of de facto title through formal settlement by INCRA (National Institute of Colonization and Agrarian Reform), informal settlement (squatting), or by purchasing previously-settled land. No option automatically brings full title but all have ranges of rights that can eventually lead to definitive title or the perception of definitive title even without formal documentation. We will therefore consider and statistically test the significance of these property regimes to smallholder timber sales. The results of our work will be important to future policy choices in Brazil. New policies currently being implemented will put smallholder forests increasingly in the path of loggers demanding wood. For example, the Brazilian government has recently allocated more than 20 million hectares to protected areas and is moving forward with plans to open over 13 million hectares for timber sale concessions in the next decade. It is likely that only large certified timber companies will be competitive bidders for these concessions. Since these firms account for a very small percentage of total timber harvested in the Amazon, smaller and medium sized loggers without large resources will continue to place pressure on smallholder lots for wood. Our results will identify the specific drivers of smallholder timber sales as a precursor for forming policies targeting these sales that balance the need for wood with logging industry pressure on native accessible forests.

2.

Smallholder decisions and timber supply

The first step is to develop the wood sale decision for a representative smallholder from the perspectives of the decision to sell and the volume of trees sold at market prices.4

3 For a discussion of smallholder settlement in new and old settlements and the roles of community associations in these capacities, see Merry et al. (2006a). 4 Smallholders are price takers and thus timber price is exogenous to their decisions. Wood sold in our data comes from either land clearing or legal reserve harvesting, because we did not have enough data on wood sold during land clearing to make it a separate category (the reason is that most smallholders had already cleared their land earlier and thus outside of our sample time frame). We will include a dummy variable in our econometric model indicating whether harvesting was undertaken in the legal reserve or not. Given that any harvesting of trees contributes to removal of native forest stocks, all timber sales in our data are a component of deforestation and degradation as trees are not replaced through reforestation, and sustainable forest harvesting is usually not done when the trees are removed.

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The model is set in a static context over one dry and wet season, which fits our data, and derives from those mentioned earlier created to study land clearing.5 For the subsistence household there are many unobserved factors associated with the seemingly simple decision to sell timber. For example, smallholders do not always have equal and open access to loggers. Even with access, their preferences for use of their forests for nontimber goods collection may play a role in their decision; indeed, other studies have suggested that some smallholders are aware of the importance of nontimber benefits on their lots to various degrees (e.g., see Sills et al., 2003). The strength of community associations, present in nearly all settlements, is also important, as these may open a path for loggers to approach smallholders within the community. Another factor is whether logging is profitable on the lots, as some smallholders are located close to improved roads, which makes logging cheaper. However, others have lots that have been previously high graded and are no longer profitable to log. All of these unobserved factors imply that zero observed harvesting for a smallholder may not be because of his actual choice, requiring an econometric approach that corrects for possible selection bias in smallholder harvesting choices. To illustrate these points as briefly as possible in a formal way, define the utility of a representative smallholder household as a positive function of nonagricultural goods purchased x, crop goods produced and consumed within the household Qc, goods derived from nontimber related forest use f(.), and − leisure time (T − L), U[ f(S0(A − A) − Ss, LN), x, Qc, T − L;Ω], where Ω is a set of household characteristics important to utility. Forest goods f(.) are defined as kilograms of nontimber forest products collected from forested areas of the smallholder lot; these are positively related to the forest stock available (first argument of f(.)) and to labor used for nontimber goods collection LN (second argument). In the first argument of f(.), S0 is cubic meters of tree volume per hectare (assumed uniform over all hectares for simplicity), so that total forest − volume present on the smallholder lot equals S0(A − A), − where A is the total number of hectares of the lot and A is the hectares for which trees are cleared for crop production. The term Ss denotes the total amount of forest volume measured in cubic meters sold by the smallholder to a logger − on his entire lot. In the language of the government, (A − A) is the legal reserve, and A is the area of land cleared under legal deforestation. Non-timber forest products collection occurs in the legal reserve area of the forest lot. The level of Ss chosen by the smallholder may be restricted by the total wood volume that is available and government laws regulating harvesting, Ss V S0 A:

ð1Þ

− It is possible for Eq. (1) to be binding, Ss = S0A, if government restrictions are ignored (these laws are rarely followed). Apart from Eq. (1), our discussion earlier suggests that smallholders face other impediments to selling wood from their lots. The 5 The extension to dynamic decisions could be made easily be considering the fact that each smallholder has a relatively stationary (old growth) stock of forest and fixed amount of land to cultivate and use over time.

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volume of merchantable trees may be too small on lots previously high graded, the political stability of the area may not be conducive to a stable forest industry base, or smallholders simply may not have any access to forest industry. All of these unobserved factors are best reflected in an additional simple non-negativity condition for harvesting, i.e., Ss z 0:

ð2Þ

If Eq. (2) is binding, so that Ss = 0, then Eq. (1) is not binding − and holds as an inequality (Ss b S0A ). This is a case where either access to a logger does not exist, or the smallholder learns that harvesting is not profitable for reasons noted once a logger is contacted.6 Thus, observing a zero for harvesting in the data may not indicate a smallholder choice per se. However, if the smallholder has access to a logger and logging is profitable, then Eq. (2) is not binding, Ss N 0, but Eq. (1) may or may not be binding depending on smallholder preferences as we will see below. In any case, when harvesting occurs and Ss N 0, all trees are harvested by a logger and not the smallholder given the capital intensive nature of hauling wood away to the market (no smallholder labor is devoted to harvesting). Furthermore, when smallholders do not sell wood from land clearing, the trees are simply burned prior to planting of crops in the classic slash and burn cycle discussed in Pendleton and Howe (2002), Pattanayak and Sills (2001), and Bowman et al. (2008). The strongest evidence of logger access problems in smallholder settlements comes from realizing that the smallholder would always be better off selling trees when clearing land for crops, since the alternative is burning the trees and losing all potential rents. That said, in our data, only about 20–30% actually sell wood at all even though all reported clearing land in the past year. This clearly suggests that the unobserved reasons discussed above may be a reason for observing zero wood sales. On land that is cleared, the smallholder produces crops according to a standard production function given in the literature, Q = Q(A, LA, KA), where Q is total kg of crops grown, LA is household crop production labor time, and KA is household capital used for crops. The smallholder may sell Q − Qc in the market. Total labor time is constrained by a simple labor time constraint, L = LA + LN = T − l. Labor markets are incomplete in our sampling region as they are in many parts of the Amazon, which implies in the strictest sense that there is little off farm labor or hiring opportunities.7 Assuming this and that there is no borrowing (common among households in our 6 From the perspective of our estimation, a reviewer pointed out that this could also follow because the timing of a sale made did not match our sampling period. Even with this (unobserved) reason, again (2) would be binding in the data and selection would potentially be present. 7 Others have found and suggested this numerous times with the rural poor, and it is especially the case in smallholder settlements within the Amazon (Pendleton and Howe, 2002; Pattanayak and Sills, 2001; Bowman et al., 2008). Smallholders have difficulty obtaining off farm work and freely hiring labor outside of the family unit. These observations are best described by constraints on off farm labor and hired labor time. This also implies that hired labor is not present in the labor time constraint.

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sample), the household faces the following income constraint, I = {Pf(Ss) + R + PA(Q − Qc) + wZ − x − rKA} ≥ 0, where Pf is the market price of wood sold when sales are positive, the price of x is set equal to one for notation, R is exogenous income not related to production, PA is the crop price, and r is the rental rate for capital. The smallholder optimization problem follows from making all labor, consumption, and timber sales decisions by maximizing utility subject to Eqs. (1) and (2), the crop production function, and the income constraint. All of the necessary conditions of this problem do not need to be examined in order to focus on the timber sales decision. Letting γ be the Lagrange multiplier for Eq. (1), η the multiplier for Eq. (2), λ the multiplier for the income constraint, and μ the multiplier for the labor time constraint L, the following four simple necessary conditions can be derived to completely explain the decision to harvest timber,8 Ss z 0fUf fSs + g  g + kPf V 0;

ð3Þ

LN z 0fUf fLN  l V 0

ð4Þ

  g Ss  S0 A = 0;

ð5Þ

l½L = 0

g½Ss  = 0

ð6Þ

where subscripts of U(.) and f(.) indicate partial derivatives. Two points can be made immediately. First, assuming that the income constraint is binding, we would also have λI =0, with λN 0 defined as the marginal utility of smallholder income. Second, μ in Eq. (6) is the opportunity cost of household time from the labor time constraint. It determines the amount of nontimber labor employed from Eq. (4), so that LN is chosen to balance marginal value of nontimber product collection with the cost of labor time. This labor time decision enters into the first term in Eq. (3) through the utility function, implying that the timber sale decision of the smallholder depends on the opportunity cost of time. This connection also implies the model is nonseparable, in that production decisions (timber sales) are not made independently of consumption and labor decisions. Nonseparability means that the timber sale decision depends on all production, preference, and opportunity cost of time variables. It also means that the formal comparative statics of labor and timber sale decisions are ambiguous, as noted by Singh et al. (1986) and Bardhan and Barrett, (1999) for general household models. Eq. (3) shows that the household sells wood to balance the effects of harvesting on nontimber forest products collection and utility (Uf(.)fSs) with the marginal benefits from harvesting to income (λPf). If the smallholder does not harvest, so that Eq. (2) is binding, then Eq. (1) is not binding. This implies that η N 0 but γ = 0 in Eq. (3), in which case we must have Uf(.)fSs N λPf for the first order condition to hold. There are two cases for which this is possible. First, the smallholder could value nontimber

forest products more than the marginal utility of harvest income received from selling trees so that the left hand side of the inequality Uf(.)fSs N λPf is large, or, second, the smallholder may not be able to sell either because his land was previously high graded or there is little access to loggers, both of which would result in a low or zero timber price for the smallholder's trees and lower the right hand side of the inequality. Because we do not observe which case exists, it is possible that timber sales are zero even if the smallholder would sell wood when access to loggers and markets made it possible. We will show how this is important to the econometric specification in the next section.9 In sum, the wood sales decision for a smallholder depends directly on preference factors, opportunity costs of time, household demographics, and indirectly on logging access. The smallholder is more likely to harvest if the opportunity cost of labor time μ is high so that nontimber labor is low, lost utility from nontimber goods collection is small, or marginal utility of income λ is high (i.e., income is low). The forest stock is also indirectly important through the constraint multipliers, particularly η and γ from Eqs. (1) and (2). The quantity of wood sold by an individual smallholder comes from an interior solution to the first order condition (3) and has the following reduced stochastic form,
Ss = Ss l; X; I; A; r; Pf ; PA ; es if Ss N 0

where εs is an error term. Nonseparability implies that household characteristics through utility Ω, income, and the opportunity cost of time μ are potentially important to this decision as well. The price of crops PA can work in opposite directions in determining the level of wood sold, through increasing land clearing and possibly sales of timber from this activity, but negatively through reducing land set aside for the legal reserve. A higher opportunity cost of time in household production may decrease timber supply as smallholder utility, or income, is met more easily through agricultural production. Household characteristics such as time on lot and household size could be important through the utility function. Smallholders also face some risk they will be evicted, although this risk is reduced by lot tenure or holding formal title, the latter of which is costly to obtain (Alston et al., 2000). This type of uncertainty can be introduced by considering the property rights regimes under which smallholders hold their lots. Squatting may lead to the greatest timber sales given that these smallholders run the highest risk of eviction. Although holding formal title may reduce incentives to harvest for quick returns, it may increase incentives to harvest because smallholders with better property rights present a reduced risk to the logger (these households may have greater logger access). The effect of timber price Pf is unknown a priori. A higher price means higher income and lower marginal utility of income, implying lower harvesting because income needs 9

8

The complete problem is: MAXSs,LA,LN,x,Qc{EU(f(.),x,Qc,T – L;Ω) + − λI + μL + γ[Ss – S0A]η(Ss)} The necessary conditions are obtained by first substituting the production function into the income constraint, and substituting this constraint and the labor time constraint into the utility function before differentiating.

ð7Þ

We can also see how a smallholder who ignores the government harvesting restriction will behave. In this case, (2) is not binding and γ N 0 but η = 0 since harvesting is positive. This will be the case when prices are high or marginal utility of income is high (the smallholder is poorer). It can also happen if the loss in nontimber forest goods from harvesting is not highly valued, so that Uf(.)fSs b λPf.

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can be met by selling lower volumes. Higher timber prices, however, increase the incentive to harvest at all and forego nontimber products benefits when the smallholder has access to loggers. Finally, higher exogenous non-production income can have differing effects through either changes in the marginal utility of income (see Eq. (3)) or through improved information wealthier households may have concerning timber market functioning or access to markets.

3.

Data and econometric approach

We now describe an estimation approach that follows from nonseparability and corrects for possible selection due to unobservability of timber sale factors discussed in the last section.10 An approach that is fairly standard for this problem and corrects for selection in estimation of how much wood a smallholder sells is a two stage Tobit type II model (Madalla 1983; Green 2002). The Tobit type II model requires a first stage estimation of a Probit model and then estimation of a selection Tobit function for the level of timber harvests for all smallholders. Following Madalla (1983), assume there is a vector of unobserved functions for the choice to sell wood or not given by Δj for each smallholder j in the sample. These are called selection functions and are assumed to depend on a vector of variables, vj, identified in Eq. (7), and an error term τj that includes unobserved reasons for not harvesting, Dj = hvj + sj :

ð8Þ

For purposes of estimation, Δj is an unobserved latent variable, and so actual harvesting observed must be used as a proxy. Letting cj = (0,1) now denote a dummy variable concerning actual harvesting by smallholder j, the first stage Probit model is based on the following decision for all j smallholders, cj = 1 if Ss N 0:

ð9Þ

That is, if the smallholder decides and is able to sell wood, then cj = 1 and Ss N 0. A regression for the quantity of wood sold Ss observed for the smallholder is estimated by applying the second stage selection Tobit function to Eq. (7). This Type II Tobit model relies on the presumption that the errors in the decisions of Eqs. (7) and (8) are potentially correlated, or E(εsτj) ≠ 0 for all smallholders in our sample; this is true if the unobserved factors discussed in the last section influence smallholders' observed choices of selling wood or not. If selection is not present, then these errors are not correlated, and the decision (9) and the level of wood sold (7) can be estimated separately using simple Probit and non-selection based Tobit models. In our regressions, we will test for selection using methods for the type II Tobit model discussed in Madalla (1983). Regardless of whether selection is deemed to be present or not, there is some potential endogeneity in the regressions.

10

We focus our estimation on the timber sale decisions from the utility maximization problem of the smallholder. Note that the other necessary conditions need not be estimated since the econometric procedures we use follow the 2sls formulation common in the household econometric literature.

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The opportunity cost of time is an unobserved and endogenous variable, as is well known in non-separable household models. This will be instrumented in the conventional manner using a predicted opportunity cost of time that comes from the value marginal product of household labor ˆ LA, where Q ˆ LA is the marginal in crop production, û = PAQ product of the estimated production function with respect to crop labor time. We use û in place of the opportunity cost of time μ in the estimation.11 Household production income from sales of crops and timber is also potentially endogenous to timber sale decisions (i.e., the error in these income variables can be correlated with the error in the timber sale decision). This can be instrumented in the usual way by using exogenous income constructed from remittances and other income not related to production. Finally, we will include a dummy variable for whether the household has harvested from their legal deforestation area or not. Since this dummy variable represents a choice that may have an error correlated with the timber sale decision dependent variable errors, we will proxy it using a first stage prediction based on a regression of the dummy variable on all exogenous variables.

3.1.

Descriptive statistics

Recall-based interviews of 2401 randomly selected households within randomly selected village settlements were conducted in the Transamazon region between June and December of 2003 along a 900 km stretch of the highway in the municipalities of Brazil Novo, Placas, Pacajá, Medicilândia, Uruará, Itupiranga, and Novo Repartimento (Fig. 1). The survey instrument was designed by the authors and implemented by a team of ten enumerators trained by the both the authors through the Instituto de Pesquisa Ambiental da Amazônia (IPAM). The authors and IPAM field technicians accompanied the enumerators in the field. The sampling approach improved upon ‘first opportunity’ approaches used in existing surveys of the region (Perz, 2004, 2005; Walker et al., 2002; Perz and Walker 2002). Further, the extensive and expansive data include sufficient spatial distribution to maximize variation in variables important to the model. The descriptive statistics for some key variables (time on lot, household size, income) collected are similar to smaller scale studies not focusing on timber sale behavior undertaken in the region, such as

11

For detailed justifications of this opportunity cost estimation procedure, see Jacoby (1993), Amacher et al. (1996), and Bardhan and Barrett (1999). Although we will not present it here, the production functionwas estimated as a precursor to estimating opportunity cost of time. The production function regression was based on a Cobb Douglas specification corrected for heteroskedasticity using White's method. This yielded an overall F statistic of 25.38 with 1229 degrees of freedom (significant at b 0.001). The dependent variable for this regression was value of agricultural products produced by the household, while significant (p b 0.01) independent variables were family labor time (+), capital use (+), years on the lot (+), planted area (+), hired labor if available in wet and dry seasons (+), and various market distance and access measures. The estimated coefficient for the labor time variable, used to estimate opportunity cost of time, was highly significant with a significance probability equal to 0.0014. Further details are available from the authors upon request.

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Fig. 1 – Location of study region along the TransAmazon highway Source: Paul Lefebvre, Woods Hole Research Center.

Merry et al. (2006a), Walker et al. (2002), and Bowman et al. (2008). Table 1 shows descriptive statistics of the sample. The mean estimated lot value for the whole sample was R$41,927 or approximately US$14,000. There are three distinct smallholder groups represented in the data: individuals who had bought their lots (58% of sample); individuals formally settled by INCRA (27% of sample); and individuals who settled informally (16% of sample). Those who claim to hold definitive title come from all groups.12 Although not shown in the table, of the 27% of the sample who claim to hold definite title, 56% of them had bought their lots, 32% were formally settled and 10% had settled informally. The mean distance from a household to a city in the sample was 63 km, and there was 14 km of all weather dirt highway accessible to the average smallholder. Our results show that the formal settlers had access to the best quality roads (judged solely by the highest percentage of distance with gravel). The mean years in residence on the lot for the whole sample was 10.2. Individuals who were formally settled have been on the lots significantly longer (p ≤ 0.05) than both informal settlers and those who had purchased their lots. Forest area averaged approximately 51 hectares. Roughly 27 percent of the individuals sold wood from either legal deforestation or legal reserve areas. The average number of trees sold was 20 with an average price of 47 Reais per tree — dividing by 5 m3 per tree give a stumpage price of almost 10 Reais per cubic meter. Log buyers, when available, fell into two categories: single truck operations (toreiros), which were strictly small-scale illegal operators, and loggers from local

mills. The vast majority (more than 75 percent) were associated with a local mill.

4.

Estimation results are presented in Tables 2 and 3. Table 2 shows the type II Tobit regressions and first stage Probit regression for groups in which selection bias was statistically significant, and Table 3 shows simple probit and Tobit models for groups in which selection was insignificant. The numbers of observations shown in the tables are those that remained after missing data were dropped for all variables in each regression. Selection was significant at the 0.001 probability level for the full sample and for the sub-group of smallholders who had purchased their lots. At the bottom of each table, we also present Likelihood ratio test statistics and their significance probabilities for rejection of the null hypotheses that the regressions for each property rights regime (i.e., the estimated coefficients) are not significantly different than the regression estimates for the full sample regression. Referring to these results, we see that there are significant differences for each group, therefore supporting our different property rights regime regressions. Corresponding variables from the theory are shown in parentheses in the first column of each table.13 In both tables, the dependent variable for the Probit regressions equals one if the smallholder sold wood and zero otherwise. The dependent variable for the Tobit models is

13

12

Households were asked in the survey whether they held definitive title, but the documentation could not be checked. Therefore the results represent households that ‘claim’ definitive title.

Results

Crop prices could not be included in the regressions because, in our sample, there is little variation of these prices, and in many cases households did not sell what they produced and did not indicate an observation for crop prices. When wood is harvested, it is not for own consumption.

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Table 1 – Descriptive statistics for households and timber sales Full Sample n = 2401 Variable Household size (number per hh) Estimated lot value (smallholder assessment) Lot size Years on the lot Distance to nearest city House value (smallholder assessment) Area in pasture Area in crops Crop production (value of all crops grown) Received credit Area in forest Sold wood Sold from legal reserve Sold from land clearing Number of trees sold Tree price Definite title holder

Unit People $R Ha Years Km $R Ha Ha $R % Ha % % % Trees $R %

Formally Settled n = 629

Informal Settlement n = 395

Mean (St. dev.)

Mean

(St. dev.)

Mean

(St.dev.)

Mean

(St.dev.)

5 41,927 83 10 63 3110 21 3 3191 27 51 27 19 6 20 47 27

5 42,286 85 9 59 3048 24 3 3107 25 51 23 15 6 19 50 26

(2) (46,335) (47) (7) (48) (3880) (25) (4) (10,903)

5 46,170 79 14 71 3644 20 3 4137 39 47 41 29 8 19 44 35

(3) (49,971) (44) (9) (54) (5536) (18) (3) (13,104)

5 30,915 8 9 68 2298 16 3 1721 15 57 20 15 4 25 48 12

(3) (31,982) (41) (8) (54) (2909) (21) (3) (5210)

(45,988) (45) (8) (51) (4312) (22) (4) (10,992) (37)

(33) (38)

the volume of wood sold by the smallholder for all smallholders in the sample.

4.1.

Bought Lot n = 1377

Full sample results

In the full sample, four variables are significant in the decision to sell timber: credit, exogenous income, forest area, timber price, and formal settlement. Of these, variables that increase the probability of choosing to sell timber are receiving credit, forest area, and being formally settled. Receiving credit is a good proxy for individuals who have more information and access to markets, having gone through a documentation process, or are members of community associations. Improved documentation and better contact with markets would allow easier access to deforestation permits, possibly leading to a better understanding of their timber resource. Individuals with larger forest area are more likely to sell because they may have less production income from crops, or they may stand to gain sufficiently from forest sales to offset time allocated to crops. Being formally settled may have the same effect as having access to credit, in that these individuals have a temporary title (protocolo) and better documentation than others, even compared to those who bought their lots. Variables that reduce the incentive to sell wood are the level of exogenous income and, consistent with our utility discussion in Section 2, timber price. Additional income from outside sources, such as exogenous income, has the effect of meeting some of the household income requirements that would otherwise be met by timber sales or use of (unconstrained) labor supply. Many households without high wealth might be willing to engage in illegal harvesting to meet income shortfalls when access to loggers exists. The dampening effect of increasing timber prices on the decision to sell timber may also be a reflection of risk in household decision making. As timber prices increase, households can sell fewer trees to meet income needs, thereby reducing exposure to IBAMA and other agencies charged with enforcement of illegal harvesting.

(38)

(29) (40)

(34)

(20) (34)

(36)

(63) (46)

It is interesting to point out some of the variables found to be insignificant in determining whether or not a smallholder engages in timber sales. These include distance to the nearest city and number of years on the lot. The latter indicates that sales could occur by smallholders at any time after settlement. Having definitive title also does not appear in our data to affect the decision to sell timber. Even households with a temporary title are required to develop a management plan or apply for a formal deforestation permit. Our result demonstrates that the logic of timber sales by a smallholder is not constrained by these bureaucratic formalities. Our results for the Tobit type II model are consistent with the Probit results, with only a few variables losing significance in the Tobit regressions. Wealthier families, represented by value of capital items, sell more timber volume. This makes sense, as these households are likely to have better information and access to markets. Households that have a higher opportunity cost of time also sell greater volume as expected from the theory; these households are likely better farmers, and they may have more information about the market values of logs. Further, when they do decide to sell from trees from land clearing, they are more likely to sell more as a higher opportunity cost of time creates incentives to clear more land to make way for crop production. Having definitive title to the land increases the volume of sales, probably because smallholders here are more in tune with markets and thus the rents from harvesting. Once again, interesting results in the decision of how much to sell concern we find to be not significant. Many variables have no effect, indicating that when access to a logger exists, the determination of harvest volume may lie with the logger and not with the smallholder. This may also be a clear indication of high grading, where loggers selectively take only the best trees.

4.2.

Timber sales decisions by settlement regime

In Tables 2 and 3, the likelihood ratio tests indicate that separate regressions by property rights regimes are warranted.

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Table 2 – Determinants of timber supply in groups with significant selection Independent variable1

Full sample n = 1187 Stage I Probit

Constant

Stage II Tobit n = 252

0.386 (0.535)a 0.151 (0.102) 0.030 (0.084) 0.005 (0.032) 0.197⁎ (0.119) − 0.031⁎⁎⁎ (0.011) − 0.052 (0.058) 0.125⁎⁎ (0.053) 0.011 (0.048) 0.091 (0.089) − 0.526⁎⁎⁎ (0.085)

1.415 (1.300)a Number of years on lot −0.248 (Ω) (0.158) Household size −0.031 (Ω) (0.118) Value of capital items ($R) 0.076⁎ (r) (0.044) Received credit (0,1) −0.194 (Ω) (0.155) Exogenous income ($R) −0.008 (I) (0.016) Distance to nearest city (km) 0.0463 (Ω) (0.082) Forest area (ha) 0.089 − (A− A) (0.106) Crop/pasture area (ha) 0.013 (A) (0.070) Opportunity cost of time ($R) 0.213⁎ (μ) (0.117) Timber price ($R/tree) −0.209 (0.297) (Pf) Probability household sold trees from 1.315 legal reserve (predicted) (1.768) Household claims definite title (0,1) − 0.152 0.367⁎⁎ (0.108) (0.153) Household formally settled (0,1) 0.266⁎⁎ 0.188 ( Ω) (0.109) (0.192) Household bought lot (0,1) − 0.059 −0.0163 ( Ω) (0.104) (0.154) % correctly predicted= 79 Log −L = −330.095 Log − L = − 589.6367 Rest. Log −L = −366.374 Rest. Log − L = − 654.2702 F[15, 236] = 3.92, p = .0000 LR Test statistic (significance)

Bought lot n = 705 Stage I Probit

Stage II Tobit n = 125

0.617 (0.713)a 0.081 (0.143) − 0.003 (0.117) − 0.003 (0.045) 0.200 (0.161) − 0.019 (0.014) − 0.112 (0.083) 0.118⁎ (0.068) − 0.049 (0.069) 0.202 (0.133) − 0.549⁎⁎⁎ (0.112)

1.804 (1.512)a − 0.503⁎⁎ (0.256) − 0.135 (0.181) 0.172⁎⁎ (0.068) − 0.204 (0.237) − 0.011 (0.023) − 0.153 (0.125) 0.018 (0.135) − 0.055 (0.115) 0.366⁎ (0.201) − 0.122 (0.338) 1.514 (2.020)

% correctly predicted = 81 Log − L = − 323.630 Rest. Log − L = − 352.784 532.02 (b 0.001)

Log − L = −158.983 Rest. Log − L = − 182.423 F[12, 112] = 2.83 p = .0020 295.34(b 0.001)

a

Asymptotically robust standard errors of coefficients presented in parentheses. Dependent variable (probit): Household sold wood (0,1). Dependent variable (Tobit): volume trees sold by the household. All independent variables in log form. ⁎⁎⁎b 0.01, ⁎⁎b 0.05, ⁎b 0.10. 1 Corresponding variable in the theory indicated in parentheses in the first column.

For smallholders who bought their lots, we see similar results to the full sample, reflecting the percentage of people who bought lots in the sample. The main difference between the full sample and the subset of individuals who bought their lots is that the longer the buyers stay on the lot, the less volume they will sell. This is due to the fact that they may have previously sold and their forests are no longer profitable for logging, which would be consistent with the reason that selection is found to be significant for this group. For smallholders who were settled formally we see that exogenous income has a significant dampening effect on the decision to sell timber. This is again possibly due to the fact that, in cash constrained production systems such as frontier Amazon agriculture, a minimum income is needed to begin agricultural production; once that income is met, there is less need to engage in the illegal activity of harvesting trees. Similar to the other property regime groups, formal settlers are more likely to sell timber if they have more forest but less likely to sell as the timber price increases. For formal settlers,

the only significant variable determining the volume of sales is whether the smallholder sold from their legal reserve. Again, this shows the important sales volume effect of accessing the forest area beyond the deforestation limits, whether legally or illegally. Finally, for smallholders who had settled informally, none of the variables in the regression were significant in the decision to sell. However, the volume of sales decreased the longer the smallholder had resided on the lot. This result again supports the role of timber income in relieving cash constraints present early on in smallholder settlements of this region. The further away an informal settler is from a city, the greater the volume he or she will sell. These individuals are squatters and have less access to credit, other sources of income, and markets, but they also are harder to monitor, and thus is it more difficult to control illegal sales the more distant the lot is from centrally-located local governments. Forest area has a negative impact on sales volume for informal settlers. But, this is the only group that increases sales with an

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Table 3 – Determinants of household timber supply in groups without significant selection Independent variable1

Constant Number of years on lot (β) Household size (Ω) Value of capital items ($R) (r) Received credit (0,1) (Ω) Exogenous income to the household ($R) (I) Distance to nearest city (km) (Ω) Forest area (ha) − (A− A) Pasture area (ha) (A) Opportunity cost of time ($R) (μ) Timber price ($R/tree) (Pf) Probability household sold trees from legal reserve (predicted)

LR Test Statistic (Significance)

Formally settled n = 367

Informally settled n = 196

Probit

Tobit

Probit

Tobit

1.358 (0.895)a 0.038 (0.170) 0.067 (0.139) 0.014 (0.050) 0.165 (0.183) − 0.062⁎⁎⁎ (0.018) − 0.081 (0.090) 0.286⁎⁎⁎ (0.097) 0.027 (0.079) − 0.025 (0.128) − 0.670⁎⁎⁎ (0.147)

− 6.610 (6.020)a − 0.616 (0.605) 0.289 (0.377) − 0.092 (0.132) 0.471 (0.478) − 0.051 (0.063) − 0.136 (0.275) 0.401 (0.376) 0.145 (0.212) − 0.266 (0.350) 0.923 (1.550) 15.311⁎

− 0.639 (1.402)a 0.063 (0.304) − 0.151 (0.224) − 0.063 (0.087) 0.033 (0.369) − 0.041 (0.029) 0.233 (0.157) 0.048 (0.136) − 0.075 (0.125) 0.308 (0.285) − 0.391 (0.241)

−26.564⁎⁎⁎ (8.469)a −1.741⁎ (0.909) −1.020 (0.677) 0.092 (0.267) −1.245 (1.186) 0.020 (0.094) 1.375⁎⁎⁎ (0.529) −0.970⁎ (0.512) −0.171 (0.397) 0.540 (0.867) 4.634⁎⁎ (1.932) 42.217⁎⁎⁎

%correctly predicted = 72 Log − L = −213.32

(8.703) Log − L = − 403.915 LM Test [df] for

% correctly predicted = 84 Log − L = − 83.05

Rest. Log − L = − 238.56 752.64(b 0.001)

Tobit = 20.013[ 12] 147.64 (b 0.001)

Rest. Log − L = − 90.42 1013.18(b 0.001)

(12.174) Log −L = −117.069 LM Test [df] for Tobit = 9.727[12] 426.04 (b0.001)

a

Asymptotically robust standard errors of coefficients presented in parentheses. Dependent variable (probit): Household sold wood (0,1). Dependent variable (Tobit): Number of trees sold by the household. All independent variables in log form. ⁎⁎⁎b 0.01, ⁎⁎b 0.05, ⁎b 0.10. 1 Corresponding variable in the theory indicated in parentheses in the first column.

increase in prices, perhaps indicative of greater cash present because agricultural production is not yet well established. Finally, again we see that selling from the legal reserve increases sales volumes.

5.

Conclusions

Timber is an underutilized resource in the Amazon for migrant smallholders. The sustainable management of remaining forests by these households is therefore an important policy objective on the frontier. When logging is done poorly here, as is almost always the case, it degrades the forest while only providing temporary respite for smallholder poverty. The incentives for smallholders to harvest trees on their lots are therefore important to consider. In this article, we determine the driving factors of smallholder harvesting, going beyond previous smallholder work that has focused exclusively on land clearing for crop production. Our contribution is three-fold, first, we accommodate unobservability in the decision to harvest for various reasons through an econometric approach that corrects for potential selection bias. Second, we allow for incomplete labor

markets that are known to characterize the region and which have impacts on the need for income. Finally, we examine and test for differences in timber sale decisions across property rights regimes that characterize smallholder migrant settlements. Our results identify important factors of timber sale behavior that can be the target of policies increasingly needed as pressures mount on remaining native smallholder forests from small and medium sized forest industry firms that will likely not be part of Brazil's new concession program. Our first look at timber sale propensity and the volumes sold by smallholders is revealing. In particular, the results suggest that credit, forest area, and formal settlement all increase timber sale propensities, while exogenous income and timber price decrease the likelihood of sales. Of these, access to credit, a larger forest area, and being formally settled by the Brazilian land management agency INCRA lead to more timber sales in smallholder settlements. To receive credit, individuals must have some formal recognition which will lower the transaction costs of “legalizing” timber sales, as would formal settlement which generates a temporary title and quasi-legal status for the smallholder. The receipt of exogenous income will decrease timber sales, because settlers are relieved of the cash constraints that our theoretical model

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and empirical estimation show as important to timber sales. The timber price effect follows from the fact that the smallholder may have predetermined revenue targets that timber sales are intended to help meet. The volume a smallholder chooses to sell depends on some interesting factors. Our results suggest that wealthier, better organized households and those who hold definitive title tend to sell more. Definitive title is one indication that smallholders understand markets, and this may translate into harvesting timber, suggesting that more work is needed to study the importance of the myriad of property rights regimes present on the frontier. We also find that selling from the legal reserve does not appreciably affect sales volume. Given that our approach corrects for selection when it is present, this implies that smallholder lots may have been high graded in the past, and this has reduced profitability of harvesting in the legal reserve. This result is fully expected and confirms that smallholders cannot capture the full rents from their forests. The results collectively suggest that Brazil's move to refocus timber sales on concessions allocated within national public forests will miss an important source of timber that could support equitable development on forest frontiers in the Amazon. Smallholders do indeed sell trees when they have access to loggers. Without adequate investment and a dedicated policy targeting them, the large forest estate controlled by these households will likely remain ignored as a potential sustainable source of sustainable timber and income for migrant settlers. Moreover, in smallholder forest estates, the continuing government interest in formalizing property rights may actually encourage greater harvesting of remaining forest stocks. Programs to inform smallholders of the volume and value of their forest as a long term investment must be established to counter the continued exploitation of cash-poor smallholders by loggers. Doing so will allow smallholders to extract a fair share of rents from loggers, which ultimately will help them view forests as a sustainable long term resource.

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