Additionality and Asymmetric Information in Environmental Markets: Evidence from Conservation Auctions

This paper investigates the problem of additionality — the likelihood that a conservation action is marginal to (i.e., caused by) an incentive — in the United States Department of Agriculture’s Conservation Reserve Program (CRP), one of the largest and most mature Payments for Ecosystem Services (PES) mechanisms in the world. The CRP pays landowners $1.6–$1.8 billion per year under 10-year contracts to retire cropland and plant grass mixes, trees, or wildlife habitats, using a discriminatory scoring auction in which landowners submit bids on a menu of heterogeneous contracts ranked by a scoring rule.

The central argument is that additionality represents a form of asymmetric information. Landowners possess private knowledge about their counterfactual land use (whether they would have conserved anyway), while the auction screens only on their private cost of accepting the contract. Because lower-cost landowners are lower-cost partly because they expect to conserve regardless of the CRP, cost and additionality are positively correlated — generating adverse selection: the least costly participants to purchase are the least socially valuable. The status quo scoring rule implicitly assumes all landowners are fully additional (tau = 1), an assumption the paper tests and rejects.

The authors construct a dataset linking confidential administrative CRP bid data across seven auctions from 2009 to 2021 to satellite-derived land use classifications from the Cropland Data Layer (30m resolution) and USDA administrative land use reports. They exploit a regression discontinuity (RD) in contract awards around the winning score threshold to estimate the causal effect of CRP contracts on land use at the margin. The first-stage is close to one. The key finding is that CRP contracts reduce cropping by approximately eight percentage points at the margin, but the 100%-additional benchmark predicts a reduction of roughly 33 percentage points (matching the share of land covered by a contract at the margin). Therefore, only approximately one quarter (22–29%) of marginal auction winners are additional — meaning three-quarters would have conserved without the CRP contract.

To test for adverse selection, the authors use the 82% of rejected bidders in the 2016 auction (the most restrictive) for whom counterfactual land use is observed, constructing a landowner-specific additionality measure. They document a systematic positive correlation between bid rental rates (reflecting higher costs) and additionality, which persists conditional on rich observable characteristics including prior land use interacted with soil productivity. Contract choice further reveals additionality: tree-related contract bidders exhibit substantially lower additionality than base grassland contract bidders.

To quantify welfare implications, the authors develop and estimate a joint structural model of bidding and additionality. Costs are inferred via revealed preferences in optimal bidding (following the empirical auctions literature), and additionality is estimated as a conditional expectation function of observable characteristics and unobserved costs, matched to observed land use among rejected bidders via Method of Simulated Moments. Social benefits are taken from the CRP literature and USDA revealed preferences.

Key welfare findings: (1) Despite widespread non-additionality and adverse selection, a hypothetical uniform-price market for the base conservation contract generates social welfare gains of $14.37 per acre-year at the socially-optimal price. Setting price equal to the full social benefit B — ignoring counterfactual land use — causes welfare losses of $12.68 per acre-year, nearly eliminating the gains. (2) The status quo auction generates social welfare gains of approximately $120 million per auction relative to no market, but implements only 12% of the gains achievable under the efficient allocation. (3) Simple modifications to the scoring rule that incorporate expected additionality — via uniform adjustments and market-size reductions — close 37% of the gap between the status quo and the efficient allocation, increasing social welfare by over $300 million per auction. Nearly all gains arise from incorporating additionality into the scoring rule. These modifications are described as implementable by the USDA in practice.

Q: What is additionality, and why does it matter for conservation markets? A: Additionality is defined as the expected impact of contracting on a landowner’s conservation action — i.e., the probability that a landowner would not have conserved absent the incentive. Social surplus depends on both a landowner’s cost of accepting a contract and her additionality, but market mechanisms screen only on cost. When the lowest-cost participants are the least additional, standard procurement mechanisms fail to implement the efficient allocation, undermining the environmental and fiscal effectiveness of conservation programs.

Q: What is the rate of additionality at the margin of CRP contract awards? A: Approximately one quarter (22–29% depending on specification) of marginal auction winners are additional. The RD design shows contracts reduce cropping by about eight percentage points at the margin, compared to the 100%-additional benchmark of approximately 33 percentage points (the share of land covered by the contract at the margin). This implies three-quarters of marginal winners would have conserved without a CRP contract.

Q: What is the empirical evidence for adverse selection? A: Among rejected bidders in the 2016 auction — where additionality is directly observed for 82% of bidders — there is a systematic positive correlation between bid rental rates (reflecting higher costs of accepting the contract) and additionality. This correlation persists conditional on rich observable characteristics, including prior land use interacted with soil productivity estimates. Contract choice also reveals additionality: bidders selecting tree-related contracts have substantially lower additionality than those choosing base grassland contracts.

Q: How does soil productivity relate to additionality? A: USDA-constructed soil productivity estimates, which approximate the earning potential of a parcel, are predictive of additionality in practice, consistent with theory. Higher soil productivity is associated with lower additionality — landowners with less productive land are more likely to conserve regardless of the CRP. Soil productivity is not currently incorporated into the CRP scoring rule to rank bidders.

Q: How is the RD design validated? A: The histogram of normalized score distributions shows no bunching at the winning threshold, validating that bidders do not know the exact ex-post threshold realization. Pre-period RD coefficients are indistinguishable from zero in both the remote sensing and administrative land use data. The first stage (share of bidders with a CRP contract just above the threshold) is close to one. Treatment effect magnitudes are stable over the 10-year contract period with no evidence of attenuation, and there are no spillovers to non-bid fields.

Q: What do the social welfare calculations show for a uniform-price market? A: Despite widespread non-additionality and adverse selection, a hypothetical uniform-price market for the base conservation contract generates social welfare gains of $14.37 per acre-year at the socially-optimal uniform price. However, setting price equal to the full social benefit B — as the status quo implicitly does by assuming tau = 1 — causes welfare losses of $12.68 per acre-year, nearly eliminating all gains.

Q: How does the status quo auction perform relative to the efficient benchmark? A: The status quo auction generates social welfare gains of approximately $120 million per auction relative to no market. The efficient allocation, which awards contracts based on both landowner costs and expected social benefits (incorporating additionality), would be substantially larger. The status quo implements only 12% of the social welfare gains achievable under the efficient allocation.

Q: Can the efficient allocation be implemented by any mechanism? A: Not necessarily. Implementing the efficient allocation requires that the expected net social surplus function B·tau(c) - c be monotonically decreasing in cost, so that a standard incentive-compatible auction can rank bidders appropriately. If lower-cost landowners are sufficiently less additional that the allocation rule is non-monotone in cost, no incentive-compatible mechanism can implement the efficient allocation (per Myerson 1981). Empirically, the authors find that for the base contract the efficient allocation is in the implementable case (similar to their Figure 1a), but implementing it exactly via an incentive-compatible auction remains complex.

Q: What alternative auction designs are proposed, and how much do they improve welfare? A: The authors propose alternative scoring rules that incorporate expected additionality — through uniform adjustments to the scoring rule, reductions in market size, and differentiation among heterogeneously additional landowners based on observables such as soil productivity and contract choice. These simple modifications close 37% of the gap between the status quo and the efficient allocation, increasing social welfare by over $300 million per auction. Nearly all gains come from incorporating additionality into the scoring rule, with a large share accruing through simple uniform adjustments.

Q: How is the structural model of bidding estimated? A: Estimation proceeds in three steps. First, beliefs about the winning score threshold distribution are estimated by simulating auctions via resampling (following Hortacsu 2000). Second, landowner costs are estimated via Maximum Simulated Likelihood using revealed preference inequalities from optimal bidding in the scoring auction. Third, the additionality conditional expectation function is estimated via Method of Simulated Moments, matching observed additionality levels, its distribution across rejected bidders, its covariance with scores, and its distribution by contract choice.

Q: What sources of scoring rule variation identify the model? A: Three sources are used. A mid-mechanism policy change in the 2021 auction added carbon sequestration payments differentially across contracts, providing two bids from the same bidders under different scoring rules. A policy change around 2011 shifted Wildlife Priority Zone (WPZ) bonus points to be contract-specific. Air Quality Zone (AQZ) status shifts the level of the score. These sources provide variation in relative payments across contracts, though the authors note the variation is modest and rely also on parametric extrapolation.

Q: What assumptions are required for identification and how robust are results? A: Key assumptions include perfect compliance (validated by inspection of over 1,000 aerial photographs), no spillovers to non-bid fields (validated in Table 2), and stability of the additionality function tau(z,c,kappa) across auction years. The authors assess robustness to alternative functional forms of tau, conduct a non-parametric inversion exercise across cost quantiles, and construct alternative scoring rules using cross-auction and cross-tract variation to probe the stability assumption. Model-implied additionality at the RD margin (23%) closely matches the empirical RD estimate.

Q: Are the adverse selection and additionality findings specific to the 2016 auction? A: The 2016 auction provides the most complete view because bid fields are observed and 82% of bidders are rejected. But cross-auction evidence replicates the core patterns. RD estimates exploiting threshold variation across auctions show additionality ranging from 10–20% among lower bidders to 40–50% among higher bidders across auctions, consistent with adverse selection. Tree-contract null RD effects replicate across all auctions. Cross-tract cropping rates show similar observable heterogeneity across auctions.

Q: What is the social welfare impact of the market for conservation existing at all? A: Theoretically ambiguous because non-additional landowners may receive transfers without generating social value, and adverse selection may tilt the market toward low-additionality participants. Empirically, despite these concerns, there exist positive social welfare gains of $14.37 per acre-year at the socially-optimal uniform price for the base contract, indicating that conservation markets of this type can improve welfare even in the presence of substantial non-additionality and adverse selection.

Additionality: The expected impact of contracting on a landowner’s conservation action — formally, tau(c) = E[1 - a_i0 | c = c_i], the probability that a landowner would not have conserved absent the incentive. A landowner is additional if she would have cropped without the CRP contract; the social benefit of contracting depends only on this incremental conservation impact.

Adverse Selection: The positive correlation between landowner cost of accepting a contract and additionality. Because landowners with low costs are low-cost partly because they expected to conserve regardless of the program, lower-cost participants are less socially valuable. This upward-sloping contract value curve mirrors adverse selection in insurance markets as modeled by Einav, Finkelstein, and Cullen (2010).

Contract Value Curve: The function B·tau(F^{-1}_C(q)) plotting the expected social value of contracting at each quantile q of the cost distribution. It lies below the social benefit B due to non-additionality and slopes upward due to adverse selection. The vertical distance between the contract value and marginal cost curves equals expected social surplus B·tau(c) - c.

Efficient Allocation: The allocation that maximizes expected social surplus B·tau(c) - c by awarding contracts to landowners for whom this quantity is positive. Implementing this allocation via an incentive-compatible mechanism requires that B·tau(c) - c be monotonically decreasing in cost; if not, no standard mechanism can achieve it.

Scoring Rule: The known function s(b_i, z^s_i) that converts a landowner’s multi-dimensional bid (rental rate and contract choice) and observed characteristics into a score, determining contract awards. The status quo scoring rule implicitly assumes full additionality (tau = 1), ranking bidders as if all conservation actions are marginal to the incentive.

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