# The Effectiveness of Local Conservation Ballots

Dylan Hogan, Eyal G. Frank, Josephine Gantois, Anouch Missirian

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Dylan Hogan, Eyal G. Frank, Josephine Gantois, Anouch Missirian. The Effectiveness of Local Conservation Ballots. American Economic Review Papers and Proceedings, 2025, 115, pp.403-408. 10.1257/pandp.20251073. hal-05100946

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# The Effectiveness of Local Conservation Ballots

By DYLAN HOGAN, EYAL G. FRANK, JOSÉPHINE GANTOIS, ANOUCH MISSIRIAN*

There is increasing global recognition that addressing biodiversity loss is critically needed and requires ambitious goals, but that progress towards these goals is insufficient. The Aichi Biodiversity Targets, established under the Convention on Biological Diversity in 2010 as part of a strategic plan to halt biodiversity loss by 2020, have been largely unmet (Secretariat of the Convention on Biological Diversity, 2020). Building on this, the Kunming-Montreal Global Biodiversity Framework (GBF) was adopted in 2022, and set 23 new global biodiversity targets for 2030, including ambitious land conservation goals.

Achieving these targets requires substantial investment. A recent report estimates a biodiversity financing gap of US$700 billion per year (Deutz et al., 2020) to achieve global biodiversity goals. Accordingly, the GBF includes a commitment to mobilize at least US$200 billion per year by 2030 in financial flows to implement strategies and action plans.

In this context of costly investment—particularly land conservation actions—it is important to understand whether these actions have the intended biodiversity impacts. Habitat loss is the leading cause of biodiversity erosion (Brooks, Pimm and Collar, 1997; Pimm and Raven, 2000), and thus protecting or restoring habitats is widely considered a way of halting or reversing the process. However, empirical evidence on the effectiveness of land conservation is limited. There has been long-term interest in evaluating the effectiveness of conservation actions, especially land-based actions, with a particular focus on protected areas relative to other habitat restoration interventions (Baylis et al., 2016; Langhammer et al., 2024). This literature faces a methodological challenge, as it is difficult to specify a relevant counterfactual (Ferraro and Pressey, 2015). It also faces an empirical challenge, as data on conservation

outcomes are rarely available at the spatio-temporal scales that allow relevant counterfactual analyses, with a few exceptions like forest cover. As a result, many studies estimate a priori conservation benefits of interventions using time-invariant species ranges (Ando et al., 1998; Kroetz et al., 2014), or estimate the impact of conservation on proxies (e.g. human drivers of species outcomes) (Geldmann et al., 2019).

Here we take advantage of a voting system for open space conservation projects in the United States, which gives rise to sharp discontinuities in project funding. These referenda raised more than US$87 billion for open space conservation at the state, county, and municipality levels between 1988 and 2023. We pair these with long-term and large-scale bird monitoring data to estimate the impact of public funds for land conservation projects on bird populations in the United States. Other studies have used the same voting system to estimate how much this allocation process aligns conservation funding with endangered species ranges (Kroetz et al., 2014), or estimate the impact of conservation funding on socio-economic outcomes (Lang, 2018). We uniquely estimate impacts on birds, a key conservation indicator (Fraixedas et al., 2020).

## I. Data

### LAND CONSERVATION REFERENDA

Referenda held by states and local governments are an important tool to raise public funds for open space conservation in the United States. Typically, a ballot initiative proposes to use bonds or tax increases to finance a land conservation project involving land preservation, acquisition, or management, or a broader project that involves both land conservation uses and non-conservation uses of funds (e.g. infrastructure building, historic preservation, public housing). According to the Trust for Public Land (TPL), which tracks these ballot measures through the LandVote database (The Trust for Public Land, 2024), $2.4 billion per year on average have been raised through this mechanism since 1988, for land conservation uses specifically. This compares to an annual budget of roughly $1.8 billion for the USDA's

<footnote>* Hogan: School of International and Public Affairs, Columbia University (email: dth2133@columbia.edu); Frank: Harris School of Public Policy, University of Chicago, CEPR and NBER (email: eyal-frank@uchicago.edu); Gantois: Institute for Resources, Environment and Sustainability, and Food and Resource Economics Program, University of British Columbia (email: josephine.gantois@ubc.ca); Missirian: Toulouse School of Economics, INRAE, University of Toulouse Capitole (email: anouch.missirian@tse-fr.eu). We thank Guillermo Parra for excellent research assistance. All errors are ours.</footnote>
Conservation Reserve Program, and $1.1 billion for the Nature Conservancy (2022-2023 average).

Referendum occurrence is correlated with a range of ecological and socioeconomic factors: they are more likely to occur where there are more endangered species, more surface water, more open space or recent open space loss, where votes are more likely to pass, where communities are wealthier, more educated, or faster growing (Kotchen and Powers, 2006; Nelson, Uwasu and Polasky, 2007; Banzhaf, Oates and Sanchirico, 2010). Where they occur, their success is also influenced by some of these factors, as well as by ballot characteristics such as the financing mechanism (bonds are more successful than tax increases), funding rate (higher funding rates are less likely to pass at the municipality level, the reverse holds at the state and county levels), project type (vote success is greater for local farmland preservation than for non-agricultural open space) and timing of the vote (Kotchen and Powers, 2006). Finally, there are systematic patterns in referendum dynamics: Gill, Lang and Pearson-Merkowitz (2023) find that municipalities that fail an initial referendum tend to put the issue back on the ballot soon after, and that second attempts are successful about half the time, though there is no evidence of systematic strategic revisions to subsequent referenda. These features highlight the need to consider endogeneity carefully when assessing the impact of land conservation initiatives.

For each ballot measure, TPL compiles information on the jurisdiction (state, county, municipality) submitting the ballot, the vote date, the vote outcome (number of yes and no votes), the pass/fail threshold, the total and conservation funds to be raised and the respective funds approved, the proposed funding mechanism (taxes, obligation bonds, or other), and the purpose of the conservation portion of the ballot. We focus on ballots starting in 2003, since the bird outcome data is sparse prior to that year. Figure 1 shows the distribution of votes in the TPL LandVote database according to the vote margin. Overall, 77% of votes pass the threshold, and within the 5% margin, 59% of votes pass the threshold. Municipalities submit votes most frequently, followed by counties and states. The most frequent funding mechanism proposed is a tax ("pay-as-you-go") as opposed to a bond (note that 'other' funding mechanisms are usually a tax). And the conservation purpose most often includes open space, followed by recreation, parks, and farmland.

<figure><img src="image_2.png" /><figcaption>Figure 1. : Conservation Ballots by Vote Margin</figcaption></figure>

Notes: Histogram of conservations ballot measures by vote margin. Red line indicates a vote margin of zero, i.e., the vote threshold beyond which a ballot measure is approved.

## BIRDS

Citizen science platform eBird<fnref n="1" /> (Sullivan et al., 2009), launched in 2002 by the Cornell Laboratory of Ornithology and the National Audubon Society, is used to derive our metrics of bird abundance. It collects birding logs from users without imposing any sampling protocol: sampling effort thus varies across space (more prevalent in places of interest, i.e., in locations with high human population density or bird-spotting hot-spots) and time (effort increased over time, and varies seasonally). Nevertheless the platform suggests three effort-based sampling protocols to users (Sullivan et al., 2009), and we use data obtained in that manner only—leaving aside data obtained using the more flexible, less standardized option ("casual observation"). Despite concerns over bird detectability, observer skill (and hence identification reliability), and geographic bias (Sullivan et al., 2009), data collected with eBird have been particularly useful in science and conservation, and specifically particularly well-suited to study bird phenology (Hurlbert and Liang, 2012; Mayor et al., 2017), species occurrence, and abundance. Over our study period and region, 1,406 unique bird species<fnref n="2" /> were reported by eBird users.

<footnote n="1">¹eBird (2021), ebird.org.</footnote>

<footnote n="2">²And a total of 2,817 distinct taxonomic entities, i.e., species properly speaking, but also "Spuh"s (sp.: genus or identification at broad level when species cannot be determined) and "Slash"es (identification to the species pair), subspecies, hybrids, forms.</footnote>
## II. Model

### ESTIMATING BIRD ABUNDANCE

As mentioned in the previous section, eBird does not standardize sampling effort among its user base. Thus, the raw observations in the eBird dataset reflect both the abundance of birds in a given location as well as other determinants of the probability that a bird is observed by an eBird user, such as the preferences of the observers and the detectability of birds at the time of observation (e.g., season or time of day). In the context of ballot measures for conservation projects, failing to account for sampling effort would make it impossible to distinguish the impact on bird populations from impacts on the decisions and preferences of eBird users. To this end, we adapt an abundance model from Liang et al. (2020) to estimate the relative abundance of birds conditional on several proxies for observer effort and bird detectability available in the eBird database.

$$ (1) \qquad \begin{aligned} \log(\text{\#birds}_{iohdmt}) = & \beta_0 \text{hours}_{iohdmt} + \\ & \beta_1 \text{\#observers}_{iohdmt} + \\ & \theta_h + \alpha_{sm} + \Gamma_{it} + \varepsilon_{iohdmt} \end{aligned} $$

In particular, we regress the log of the total count of birds observed in county *i* during trip *o* and in year *t*, month *m*, day *d*, and hour *h* on the number of hours spent observing birds during that trip and the number of observers in the group. These variables are proxies for the effort devoted to birding by each group of eBird users. To account for differences in the detectability of birds across time and space, we include hour-of-day fixed effects $\theta_h$, which control for bird activity and the ability to observe birds at different times during the day, and state-by-month fixed effects $\alpha_{sm}$, which control for state-specific seasonality in bird detectability and abundance. $\Gamma_{it}$, which is a county-by-year fixed effect, is the object of interest from this regression as it represents the relative abundance of birds conditional on the proxies for preferences and detectability. Finally, we normalize estimates of $\Gamma_{it}$ from Equation 1 to mean 0 and standard deviation 1 (i.e., z-score) to construct our outcome of interest.

### REGRESSION DISCONTINUITY DESIGN

As funding from the ballot measures we study are not randomly assigned to jurisdictions, a simple comparison of relative bird abundance in a county that ap-

proves a measure to one that rejects it would be con- founded by other determinants of bird populations that correlate with the probability that a ballot measure is approved. In order to isolate the causal effect of the funding, we implement a vote share regression discontinuity design (RDD), in which ballot measures that *narrowly* pass and fail are used to construct treatment and control groups. The intuition behind this approach is that very close elections are dictated by chance and thus are less vulnerable to omitted variables bias than comparisons across the full sample. The approach is valid if none of the potentially confounding characteristics of jurisdictions that approve or reject conservation funding vary continuously across the vote threshold.

We estimate the following RDD specification:

$$ (2) \qquad y_{it}^{[t+\tau_1, t+\tau_2]} = \theta b_{it} + f(v_{it}) + u_{it} $$

where $y_{it}^{[t+\tau_1, t+\tau_2]}$ is the abundance of birds in jurisdiction *i* averaged between $\tau_1$ and $\tau_2$ years relative to the vote in year *t*. Since additional conservation funding is likely to impact bird abundance with substantial delay, we estimate Equation 2 for several groups of years up to a decade after the vote. Average abundance is regressed on a binary variable $b_{it}$ that indicates whether the vote share $v_{it}$ exceeds the vote threshold $v^*$ (in most cases, the vote threshold is 50 percent, but it can be 60 or 66 percent) and a flexible function *f* of the vote share. In practice, we estimate *f* non-parametrically using local linear regression with a triangular kernel and a data-driven mean-square-error-optimal bandwidth (Calonico, Cattaneo and Farrell, 2020).<fnref n="3" /> $u_{it}$ is a random error term. The parameter of interest in Equation 2 is $\theta$, which represents the causal effect of conservation ballot approval on the relative abundance of birds.

## III. Results

The estimates of the effect of conservation ballot approval on the relative abundance of birds from Equation 2 are shown in Table 1. We estimate impacts for several groups of years relative to the year of the vote, including a placebo test for differences in abundance in the five-year window prior to when the vote was held. In particular, the first row shows a conventional estimation of Equation 2. The second and third rows show bias-corrected and robust standard error exten-

<footnote n="3">³Equation 2 with a non-parametric specification of *f* was estimated using the rdrobust package.</footnote>
sions of the baseline RDD estimates as described in Calonico, Cattaneo and Farrell (2020). We report robust standard errors, but the precision of the estimates remains similar if we cluster by state or by year (not reported here). Across the three implementations, we find that relative bird abundance in the 5 years prior to the vote and up to 6 years after the vote are unaffected by conservation ballot approval, with negative coefficient estimates and large standard errors. However, between 7 and 10 years after ballot approval, the relative abundance of birds is between 0.152 and 0.165 standard deviations greater in treated jurisdictions. The magnitude of these effects and their precision allows us to reject the null hypothesis of no effect, on average, of the ballots on bird abundance at the 5 percent (conventional or bias-corrected) or 10 percent (robust) significance levels. Our results therefore suggest that bird abundance significantly and meaningfully responds to ballot approval, but that the effects take up to a decade to materialize.

## IV. Conclusion

Recent reports suggest that substantial global funding toward land-based conservation is required to slow the current rate of biodiversity loss, yet there is little empirical evidence on the impact of existing funding on key ecosystem indicators. In this study, we link data on county and municipal ballot measures for conservation projects with high resolution data on bird abundance. We exploit sharp discontinuities in the vote margin to estimate the causal effect of ballot measure approval on relative bird abundance. Our results suggest that conservation ballot measures have a positive impact on the relative abundance of birds that accrue over 7-10 years after ballot approval. Additional research is needed, however, to fully examine the dynamics of bird abundance following successful conservation ballots.

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<table><caption>Table 1—Effect of Conservation Ballot Measures on Relative Bird Abundance</caption><thead><tr><th rowspan="2"></th><th colspan="4">Years since vote</th></tr><tr><th>[-5, -1]</th><th>[0, 2]</th><th>[3, 6]</th><th>[7, 10]</th></tr></thead><tbody><tr><td>Conventional</td><td>-0.023<br>(0.196)</td><td>-0.195<br>(0.168)</td><td>-0.070<br>(0.093)</td><td>0.152<br>(0.071)</td></tr><tr><td>Bias-Corrected</td><td>-0.029<br>(0.196)</td><td>-0.204<br>(0.168)</td><td>-0.101<br>(0.093)</td><td>0.165<br>(0.071)</td></tr><tr><td>Robust</td><td>-0.029<br>(0.231)</td><td>-0.204<br>(0.202)</td><td>-0.101<br>(0.106)</td><td>0.165<br>(0.084)</td></tr><tr><td>N</td><td>883</td><td>960</td><td>901</td><td>772</td></tr><tr><td>Bandwidth (Conventional)</td><td>8.2</td><td>7.5</td><td>8</td><td>7.3</td></tr><tr><td>Bandwidth (Bias-Corrected &amp; Robust)</td><td>12.8</td><td>11.5</td><td>15.1</td><td>12.3</td></tr></tbody></table>

Notes: Estimation results for the static RDD specification in Equation (2) using a triangular kernel. Robust standard errors are reported in parentheses. Each cell reports estimation from a separately estimated regression using years before or after the conservation funding ballot. The optimal bandwidth is chosen using the MSERD algorithm. We report the chosen bandwidths for the conventional, as well as the bias-corrected and robust approaches.

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