Land Value Taxation: A Spatially Explicit Economic Experiment with Endogenous Institutions

The choice problem involves revenue, operating and maintenance costs, and taxes. All participants derived homogeneous revenue at location i from each brick placement, or R_ijt = 200B_ijt^1.6/i expressed in experimental dollars. All else equal, bricks generate more revenue when bricks are closer to the CBD. Also, marginal revenue increases with each additional brick at a given location. The operating and maintenance (O&M) costs arising from brick placement accrue in the period of placement and in each successive period: C_ijt = (a₁B_ijt + a₂B_ijt²)/i. The parameters a₁ and a₂ are the sole source of induced heterogeneity in the experiment, with five types (see Table 1). For all parameters, costs increase at an increasing rate with bricks at any one location. Property taxes (introduced below) change participants’ location choices and earnings.

The parameterization of five types reflects modeling decisions by the researchers, who sought predicted treatment effects on efficiency, spatial development patterns, and voting behavior. However, the parameters hold several connections to real-world landowners. First, because of revenue homogeneity, a given building will earn the same market price regardless of which type builds. Also, given that each type gets one brick each period, then the types have the same endowment and production technology associated with building; one could think of very similar “box” buildings where it is easy to scale them to have small or large footprints and short or tall height. The formulation of production as a “brick” further simplifies the output of land use to be a homogeneous production unit. Second, heterogeneous costs mean some types have a comparative advantage in O&M; these are ordered by this skill from best (Type1) to worst (Type5). Type1 has the best human capital or technology for O&M such that they can service any building at any given location at a lower cost than the other types. These simplifications try to capture real-world settings in which relatively similar, scalable commercial or residential buildings—such as office buildings, apartment buildings, warehouses, etc.—could be placed at various distances from the CBD. More “bricks” at any location could be thought of as more intensity or vertical development.

Type1 is labeled a “LVT lover” because this type has the best O&M technology for higher buildings and therefore will be more profitable from more intensity at any location relative to the other types. They “love” LVT in that this tax does not penalize the intensive building pattern they prefer. In contrast, Type4 is a “UPT lover” because their technology is not skilled like Type1 at O&M. Type4 profits more when land and improvements are taxed at the same rate because they tend to have lower relative costs when there is little intensity at a location. The real-world landowner of Type4 would have lower-than-average improvements, such as a one-story office park, while Type1 would have higher intensity. Type2 and Type3 are the intermediate cases. Type2 is a marginal “LVT lover” who benefits slightly from LVT, while Type3 is a marginal “UPT lover” who benefits slightly from UPT. The behavior of these two marginal types will be important in the analysis because they have the lowest opportunity cost for an earnings-irrational tax institution. Type5 is interesting because its O&M technology is so poor that it is indifferent between LVT and UPT in terms of location choices, though it earns more under UPT. This type may relate to low-intensity commercial land uses that have large parking lots. Their O&M costs increase too rapidly if they add any density, so their profitability comes from having minimal possible improvements. They have the largest earnings preference for UPT because the tax rate on land is lower than LVT and they, in effect, can free ride on the public goods funded through the tax paid by their neighboring owners who have more improvements. Although the Type1 “LVT lovers” make the most efficient use of land, their comparative advantage in O&M of bricks is unique and cannot simply be replicated by the other types. So, the social planner’s problem is to devise a system of tax incentives that maximizes the net social product of the five types in the city. As explained below, the distribution of types varies so that some sessions have only one Type1, while others have two or three.

Following Banzhaf and Lavery (2010) and Duke and Gao (2018), this paper models the total property value (PV) as the sum of the land value (LV) and improvement value (IV), i.e., PV = LV + IV. For simplicity, IV is modeled as the revenue from bricks at each location. Land values on one road evolve independently² of decisions on other roads. Land value at any location i is only affected by j’s own choices:

In this simple formulation, there are land-value spillovers, but they are internalized; this approach establishes baseline results for future studies in which the spillovers are not internalized. Land values are only positive if: (1) there is a brick at a location; and/or (2) there is a brick at an “inwards” location i-1 or an “outwards” location i + 1. The CBD always has six bricks, so location 1 will always have a positive land value. The agricultural area has zero bricks. There is no negative capitalization externality in this model because j’s own choices of brick location determine land value and, thus, taxes. Although the CBD effect on location i = 1 is external to participant j, it (1) is not an externality that arises from participant -j’s choices; (2) affects all participants in the same way; and (3) simply puts a baseline, nonzero land value on location 1. Until an endogenous voting process is introduced, the optimal choices are known with certainty.³

Two tax treatments are used. LVT assesses each location on land value alone: Tax_ijt^LVT = τ_LVTLV_ijt. UPT taxes at the same rate for land value and for improvements, which are simplified as the revenue from bricks at each location: Tax_ijt^UPT = τ_UPT(LV_ijt + R_ijt). The tax rates were set as τ_LVT = 0.90 and τ_UPT = 0.20. The model predicts LVT generates more revenue because it is parameterized to incentivize denser, higher-value building patterns. All references to the tax plans in the experiment used neutral language whereby UPT was “Tax Plan 1” and LVT was “Tax Plan 2.” Table 1 shows the set of five types with heterogeneity only in the cost functions and a fixed payment. Paid at the end of a round, the fixed payment balanced the expected earnings among the types when earnings-rational behavior is pursued. Although the instructions explained that participants would receive the fixed payments, the payment levels ought not affect behavior because they do not affect marginal earnings from decisions. The five types were labeled by color within the experiment so that the participants would not infer any ordering.

Participant j’s problem is to decide the location i for one brick in each of four periods t in a round; each round restarts the decision problem. Let the choice variable L_jt ϵ {1,2,3,4} be an indicator of which one of the four locations was selected. L_jt determines the increment of bricks on location i as X_ijt such that X_ijt = 1 if i = L_jt and 0 otherwise. The optimization problem for each participant j is to maximize earnings, which is a summation of revenue, cost, and tax: \(\begin{array}{cc}{\mathrm{max}}_{{L}_{jt}}{\sum }_{t=1}^{4}\sum_{i=1}^{4}& [{R}_{ijt}-\end{array}{C}_{ijt}-{Tax}_{ijt}]\), subject to the constraint of a single brick available in each period. When parameterized, this problem is:

The first constraint shows how bricks increase based on those placed in the previous period. The second constraint indicates that there is only one brick available in a period and that it will be added to the location of the participants’ choice. The final constraint indicates that each round starts with no bricks at any location.

Although this decision problem is relatively easy to describe to participants, it is too complex to solve by hand in the lab. There are 4⁴ = 256 possible brick placement choices in the four-period round. As such, the research team provided participants with a printout of the earnings from each of 256 possible choices; a different printout was provided for each type. This table had the top five earning decisions for LVT and for UPT highlighted, so participants could quickly identify high-earning choices.

The marginal earnings derived by a participant in a round are indicated by the variable, \({IndividualEarnings}_{j}={\sum }_{t=1}^{4}{\sum }_{i=1}^{4}[200{B}_{ijt}{}^{1.6}/i-\left({a}_{1}{B}_{ijt}+{a}_{2}{B}_{ijt}{}^{2}\right)/i-{Tax}_{ijt}]\). A related variable, \(AggregatedIndividualEarnings={\sum }_{j=1}^{5}{IndividualEarnings}_{j}\), adds up the city earnings over one round. The earnings variables measure private efficiency for the participants, capturing all wealth other than the tax revenue created by decisions in a city in a round. The social efficiency measure captures all wealth created by the participants choices, i.e., all five participants’ AggregatedIndividualEarnings plus the tax revenue: \(NetSocietalBenefit ={\sum }_{j=1}^{5}{\sum }_{t=1}^{4}{\sum }_{i=1}^{4}[200{B}_{ijt}{}^{1.6}/i-\left({a}_{1}{B}_{ijt}+{a}_{2}{B}_{ijt}{}^{2}\right)/i]\).

Four location choices lead to a spatial distribution of building patterns. Many indices of sprawl exist, but this research developed a simple measure based on the metric used in gravity models of trade. City compactness measures the spatial density in terms of distance from the existing CBD:

Comp_j = 6*(B_1jt/1 + B_2jt/2 + B_3jt/3 + B_4jt/4), evaluated at t = 4.

On any road, Comp ranges from 24, when all bricks are in location 1, to 6 when all bricks are in location 4. The higher the value of Comp, the more compact is the participant’s “road.” The five-person “city” also can be represented by a simple average of the individual road compactness values: CityComp = \(\sum_{j=1}^{5}{Comp}_{j}/5\). Although the model is stylistic, it captures some of the main forces from the existing literature on intensification and sprawl, it develops predictions that correspond to prior findings, and the decision problem is presented in a manner that is understandable to participants.

Earnings-rational or privately optimal behavior is an ordered set of location decisions for the four periods: {L_j1, L_j2, L_j3, L_j4} for any participant j—all of which can be expressed as four location decisions i for simplicity. As Table 1 shows, Type1 ought to have the most compact (Comp = 18) investment under LVT, i.e., {1,1,2,2}, followed by types 2–4, i.e., {1,1,2,3} or {1,1,3,2}, Comp = 17. All types ought to pursue the same building pattern under UPT, i.e., {1,2,3,4} with Comp = 12.5. Type 5 has no spatial treatment effect in that the optimal choice for LVT and UPT is {1,2,3,4}. IndividualEarnings_j vary considerably more than Comp_j because of the type parameters. As shown in Table 1, Type1 and Type2 earn more from their optimal choices when taxed with LVT, while the other three types prefer UPT. Table 1 presents the induced earnings advantage under LVT for each type.

The researchers examine how the heterogeneity of types affects the earnings, compactness, and voting outcomes from the tax institutions. TypeDistA had one of each type (1–5). This distribution predicted a 2:3 ratio of participants who “win” from LVT, so UPT should win in an earnings-rational vote. Nevertheless, AggregatedIndividualEarnings and CityComp are higher in LVT. So, LVT is potentially Pareto efficient among the five participants, but will not be selected by earnings-rational voting. This 2:3 contrast between what is individually and socially optimal replicates Duke and Gao’s (2018) experiment, which sought to capture the principal U.S. experience with LVT. Specifically, LVT is thought to make society wealthier but tends to be voted down by communities. The current model extends prior work, in part, with two new voting distributions (3:2 and 4:1). TypeDistB had two Type1 participants and no Type5, so earnings-rational voting is 3:2 for LVT. TypeDistC had three Type1 participants and no Type4 or Type5, so earnings-rational voting is 4:1 for LVT. Type distributions did not vary within sessions to avoid participant confusion. Despite these varying predicted voting patterns, all three distributions led to a predicted outcome where LVT was best for the participants as a group in terms of aggregated cash earnings (+ 64.63, + 233.63, and + 394.89, respectively).

When one both considers participant earnings and the tax revenue, NetSocietalBenefit, the benefit advantage of LVT is even greater than it is in terms of AggregatedIndividualEarnings. As Table 1 shows, for Type1, Type2, Type3, and Type4, NetSocietalBenefit under LVT is greater than under UPT when participants make privately efficient decisions in response to the tax institution even when the institution is not the preferred institution of Type3 and Type4. The NetSocietalBenefit for Type5 is the same under LVT and UPT. Thus, NetSocietalBenefit will be positive and increasingly large as the experiment shifts from TypeDistA to TypeDistB to TypeDistC. In sum, earnings-rational participant voting and brick-placement choices will lead to social efficiency in TypeDistB and TypeDistC but not TypeDistA and regardless of type distributions LVT leads to higher NetSocietalBenefit. Although AggregatedIndividualEarnings is an index of the value of production to participants, the choices also generate tax revenue that is not returned to the participants and thus NetSocietalBenefit is a better metric of social efficiency. This claim relies on an assumption about how tax revenue might be used to generate public goods: The value of the supplied public goods minus the government costs of collecting, redistributing, and spending tax revenue is greater than the tax revenue collected. Otherwise, resources are destroyed through taxation and the social efficiency calculation becomes more complicated because the amount of tax collected can affect our efficiency conclusions.

The experiment makes the tax institution endogenous in two of four rounds by allowing participants to select their preferred tax institution by a majority vote. A voting round begins with either LVT or UPT. Participants vote in the first period whether to switch to the other tax institution before they make their location choice. If a majority of 3, 4, or 5 voted to switch, then the present period and the remaining periods of the round will have the new tax institution. If 0, 1, or 2 voted to switch, then one more period of the initial tax is played and another vote is conducted in the following period. The starting tax institution was alternated for each round. Three variables derive from the voting treatments. First, Vote is an indicator of whether the round was played with a voting treatment; when Vote = 0 the tax is exogenous. Only rounds 1 and 2 of any session have Vote = 0; this decision was made to avoid participant confusion with the treatments, but it will be collinear with standard learning measures (period number) in regressions because votes never occur in period 1 or 2. Second, LVTStart indicates whether the initial period started in LVT. In the session’s data, two rounds will start in LVT and two in UPT. This variable enables statistical tests of anchoring. Third, LVTPeriods \(\in\) {0,1,2,3,4} measures the number of periods in a round under LVT. In voting rounds the tax institution can switch, so one must track how much of the round was under LVT.

Data were collected at the University of Delaware’s Center for Experimental and Applied Economics. The z-Tree software was used (Fischbacher, 2007) with 10 tablet computers linked to an administrator computer. Student participants were largely undergraduate business and economics majors, though others were recruited when sessions did not fill. The University of Delaware Institutional Review Board approved the protocol. Participants completed informed consent, read paper instructions, watched an instructional presentation, asked questions, and then were trained in two unpaid practice rounds. The practice rounds helped participants learn how the interface works and the basic aspects of the experiment. The first practice round had no taxation, so participants simply placed four bricks. The second practice round then introduced taxation with a different set of tax rates.

Participants completed eight rounds of four periods each—but only the first four rounds without information treatments are reported herein.⁵ New instructions were distributed with each treatment. Only one round was randomly selected for payment in order to magnify the incentives from each decision. A session took 1.5–2.0 h to complete. Table A2 presents the average participant earnings by type. The exchange rate for experimental dollars was 17: $1. To prevent bankruptcy in the experiment, the administrator gave a participation incentive of $5 to all who completed the experiment. A post-experiment survey (see appendix) showed that respondents tended to make earnings-rational decisions and focus on the designed aspects of the experiment (see Sauter, et al., 2016 for an experiment-survey of participants’ views when the experiment aligns with their profession).

Model predictions motivate hypotheses that LVT generates more participant earnings, more tax revenue, more NetSocietalBenefit, and a more compact city than UPT (Table 2). Wealth creation is measured with tax revenue, AggregatedIndividualEarnings, and NetSocietalBenefit, but we devote most of our analysis to AggregatedIndividualEarnings as our main efficiency performance metric; this is because the participants made decisions to earn money and thus maximize private efficiency rather than NetSocietalBenefit. A linear regression of AggregatedIndividualEarnings with 72 rounds as the units of analysis can be largely explained by controls on the induced values; TypeDistA and TypeDistB ought to have negative coefficients because they earn less in expectation than the regression’s reserve category TypeDistC. Round is an integer between 1 and 4 indicating the round number in the session where AggregatedIndividualEarnings was generated, and this effect can be also examined with indicators on round numbers 1 to 4. This controls for experience with the setting. The tax treatments require more complex controls because, in the voting treatments, the tax institution in periods 1–4 of any round can switch. LVTStart checks for anchoring and should not affect earnings. LVTPeriods should increase with earnings because LVT generates higher earnings in the parameterized model.

Another treatment variable is Vote, which indicates whether the round had a voting option. As mentioned, round measures and Vote are collinear and thus difficult to separate in the discussion. Voting should lead to higher AggregatedIndividualEarnings in TypeDistB and TypeDistC because participants can select the preferred tax. TypeDistB and TypeDistC ought to prefer LVT (3:2 and 4:1), which makes the earnings higher. TypeDistA prefers UPT, but will vote for LVT if one voter goes against earnings rationality. Alternatively, it could be that participants in all type distributions fail to select LVT even when it tends to lead to higher AggregatedIndividualEarnings, because of the aforementioned political objections to LVT. For example, some might reject LVT in this experiment because the tax rate is very high—in this case it is 90 percent versus only 20 percent for UPT. The same variables to explain the AggregatedIndividualEarnings regression can be used to explain CityComp. The logic associated with the hypotheses is similar because CityComp like AggregatedIndividualEarnings was parameterized to increase with LVT for all type distributions.

First, LVT consistently generated more tax revenue, which aligns with model predictions. Table 2 presents the predictions and experimental results on average tax revenue collected. In 42 of 72 periods, average tax revenue was statistically indistinguishable from the prediction. In 16 periods, TypeDistA averaged slightly more revenue than predicted under UPT, while 14 periods under LVT in TypeDistB averaged slightly less revenue than predicted. The instances where statistical differences were not found were likely affected by a small N, or number of rounds in which the earnings-irrational tax was endogenously selected or exogenously imposed. The point estimates suggest that UPT tends to generate more revenue than predicted. Conversely, in LVT rounds there is less tax revenue than predicted. That said, the LVT differences are not substantively large. The most important result is that LVT consistently generated far more tax revenue than UPT, as predicted. The predicted UPT tax is 1,708 while LVT averages 2,082, so the predicted tax-revenue advantage for LVT is 22 percent. Although the experiment advantage was slightly less (UPT weighted average 1,759 and LVT averages 2,078), the absolute advantage for LVT of 18 percent (or 319 units) is large.

Second, AggregatedIndividualEarnings also are higher under LVT. The second panel of Table 2 compares the predicted AggregatedIndividualEarnings to the experiment results. Rounds under LVT tended to have higher average AggregatedIndividualEarnings than UPT. Next, Table 2 shows two treatments where AggregatedIndividualEarnings were statistically indistinguishable from predictions. In 7 of 24 rounds of TypeDistA the participants played all four rounds in LVT, earning an average of 863 relative to 910 as predicted. Although this point estimate suggests the earnings were lower, there was a low N and no statistical difference was found. The other statistically efficient treatment was TypeDistC for UPT was also a low-N treatment. Among the remaining four treatment combinations of UPT and LVT, the resulting average earnings were statistically lower than the prediction. In sum, for TypeDistA, the average round earnings were lower than expected under UPT but not LVT. In TypeDistB, the average round earnings were lower in both UPT and LVT. In TypeDistC, the average round earnings were lower than expected under LVT but not UPT. The key points therefore are that observed earnings tended to be lower than predicted and that there is no evidence that participant earnings were less in LVT versus UPT treatments.

The collective efficiency results show that under the modeled conditions LVT is more efficient than UPT. The statistical results for NetSocietalBenefit—i.e., once the tax revenue is included—are slightly different for AggregatedIndividualEarnings (third panel of Table 2). For 16 of 24 rounds that TypeDistA played entirely in UPT, NetSocietalBenefit produced higher than expected wealth; the slightly higher tax revenue in these rounds more than compensated for the lower participant earnings. Under LVT and the TypeDistA distribution, however, average NetSocietalBenefit was indistinguishable from the overall predicted benefit, but the average NetSocietalBenefit was still 12.7% higher in LVT than UPT. NetSocietalBenefit under TypeDistB and TypeDistC was different than under TypeDistA. Specifically, UPT rounds resulted in an average NetSocietalBenefit that was statistically the same as prediction, while LVT rounds had average NetSocietalBenefit that was statistically less than prediction. The reason for this is that both the average tax revenue and the average participant earnings under LVT tended to be lower than predicted. Under UPT, there was little statistical difference from prediction found on these same two measures, but the point estimate averages were countervailing—i.e., tax revenue had a higher point estimate while participant earnings had a lower point estimate than predicted for UPT. Despite these deviations from predictions, average NetSocietalBenefit was substantially higher under LVT than UPT (+ 21.1% and + 26.4% for TypeDistB and TypeDistC, respectively).

Third, Table 3 offers a controlled econometric test explaining the drivers of AggregatedIndividualEarnings (Models 1–2); this is a behavioral model derived from the experiment, so we focus on the earning accruing from the participant decisions. We present two models as a robustness check because of collinearity of Vote with the round measures; however, we focus explanations on model 1, where only Vote is used to capture the effect.⁶ All models have high explanatory power. Starting with LVT does not alter earnings, so there is no anchoring effect. LVT leads to higher earnings and thus efficiency, as predicted. LVTPeriods has a positive impact, so earnings are higher with more periods under LVT. The net effect of LVT is strongly positive; for instance, four periods of LVT would increase earnings by approximately 220 (at point estimates). The impact of the control variables, capturing heterogeneity and other non-treatment impacts, was as expected. The main effects of TypeDistA and TypeDistB were induced in the model to earn less than TypeDistC, the reserve category, and the results show this. The coefficients magnitudes are roughly similar to the induced value structure.

The coefficients on Vote suggest that voting increases earnings—probably because it allows a group to select its preferred tax. For TypeDistC, one clearly expects the option to vote would increase private earnings because this distribution has three “LVT lovers,” who can control the majority outcome, and one marginal “LVT lover.” Earnings are higher under LVT for all type distributions, but TypeDistC earns considerably more under LVT than the other distributions. Having this voting advantage, 4:1, allows TypeDistC to increase earnings by approximately 125 when allowed to vote. The result on TypeDistB and voting show that these combined effects tended to produce no extra earnings (b^Vote and b^{TypeDistB*Vote} roughly cancel each other out). As discussed above, TypeDistB should have preferred LVT 3:2 and should have earned more under LVT. However, this distribution was the most likely to select the earnings-irrational tax (Table 2 shows the city votes for UPT 4 of 12 times). TypeDistA tended to earn more when allowed to vote. This was surprising because TypeDistA was induced to vote for UPT, which would have lower AggregatedIndividualEarnings. Table 2 showed this group tended to vote too often for LVT: 1 time when LVTPeriods = 1 and 1 time when LVTPeriods = 4. This may explain why b^{TypeDistA*Vote} did not match the hypothesis of a negative effect on earnings.

The experiment results on spatial impacts collectively show that LVT leads to less sprawl than does UPT, matching the model prediction. The fourth panel of Table 2 compares the experiment results to predictions on the average compactness of the cities, by round. The model predicted that LVT would have cities 3–4 units more compact than the same cities under UPT; however, the experiment results either matched this prediction or exceeded it. Table 2 also shows that in all UPT treatment averages, the city averaged less compactness than predicted. Under LVT, it was less compact in two of three treatments. Together these results suggest a slightly greater tendency for UPT, relative to LVT, to cause sprawl. Comparing the observed outcomes for each type distribution in Table 2 shows that the average point-estimate difference of 3.3–4.7 in Comp from the tax treatments was roughly the same magnitude as predicted. In TypeDistC, LVT produced a possibly greater Comp advantage than expected (TypeDistC) because the average Comp under LVT was statistically indistinguishable from the prediction, while the average Comp under UPT was statistically less than the prediction. As with the efficiency results, these observed differences in behavior from the type distributions likely reflect differences in the magnitude of incentives; TypeDistC has three Type1 participants and two marginal types (2 and 3), so a majority have the clearest incentives about where to build and what tax plan to select. In contrast, TypeDistB has only two Type1 participants and TypeDistA has only one Type1. Collectively, the TypeDistA and TypeDistB sessions are more likely to face uncertainty in their decision-making and, thus, one expects the resulting average compactness to be less in line with predictions than TypeDistC sessions. A more complete causal explanation of these differences is shown in the regression results that explain CityComp.

Table 3 presents two regressions that explain CityComp. The collinearity of Round and Vote is the same as in the AggregatedIndividualEarnings regressions. Thus, the following part explains CityComp Model 1. As with AggregatedIndividualEarnings, the coefficients on the type distribution controls tend to be significant, but the unexpected result is TypeDistB in the second model. A reason why the TypeDistB coefficient lacks significance may be that (as in Table 2) a large number (N = 10) of 24 rounds were played in UPT, with 4 selected by a city against the majority interest. Table 3 shows that LVT leads to more compactness because an additional LVT period (LVTPeriods) leads to 1 point of Comp. The prediction (from Table 2) was that, on average, LVT would lead TypeDistA to 3 more CityComp points, TypeDistB to 3.9 more CityComp points, and TypeDistC to 4.1 more CityComp points. The coefficients show a similar treatment effect, where 4 LVT periods lead to roughly 3.3 Comp points for TypeDistA (4b^LVTPeriods + b^TypeDistA) and about 4 for TypeDistB (4b^LVTPeriods + b^TypeDistB) and TypeDistC (4b^LVTPeriods).

The collective behavioral results (Tables 2 and 3) on compactness suggest that participants were either making decisions that resulted in as much or less compactness than predicted; there was no evidence of behavior leading systematically and substantively to more compactness than predicted. There were several situations most likely to produce predicted compactness. First, when the land characteristics have more “LVT lover” types. These settings would be more likely to observe compact building patterns, and this tendency would be higher under LVT. Second, LVT tended to produce more compactness than UPT at levels predicted or at levels slightly exceeding predictions. This provides some experimental evidence to the longstanding claim that LVT does indeed reduce sprawl, and the behavioral evidence suggests that the impact may even be larger than expected.

Table 4 presents the group voting data averaged by treatment and the predicted votes (2 votes for TypeDistA, 3 votes for TypeDistB, and 4 votes for TypeDistC). Votes for LVT were statistically indistinguishable from predictions for all three type distributions. Collectively, the voting results show that participants tend to favor LVT only when the underlying incentive structure has a majority of LVT lovers (TypeDistB and TypeDistC). In other words, LVT was preordained to be the most privately efficient institution for the participants, and yet the results suggest that groups only select LVT when the majority of participants win from LVT. There is no evidence of a universal objection to LVT.