Linear Special Case

We conclude this section with a discussion of the special case in which 𝑞 is known to be a linear function of 𝑝̅ and 𝜏 (for fixed 𝑝^𝑁𝑇). We focus on this example both because of how frequently economists estimate linear models and because of its relationship to the second

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order approximation of deadweight loss. As demonstrated in section 2.3, one can express a second order approximation to deadweight loss as a function of derivatives. In the case where the choice function is linear in regressors 𝑝̅ & 𝜏, the second order approximation is an exact calculation of deadweight loss, and our results from the previous subsections apply.

Formally, each preference type 𝜃_𝑖 takes the form 𝜃_𝑖 = (𝛽_𝑖, 𝜖_𝑖) ∈ ℝ².²⁹ To maintain linearity in regressors, we also assume that tax salience 𝑚 is constant with respect to 𝜏. The choice function 𝑞 then takes the form:

𝑞_𝑖 = 𝛼 + 𝛽_𝑖𝑝_𝑖^𝑠+ 𝜖_𝑖 = 𝛼 + 𝛽_𝑖[𝑝̅ + 𝑚_𝑖 𝜏_𝑖] + 𝜖_𝑖

We are suppressing tie-breaking parameter 𝜁 because 𝜃_𝑖, 𝑝̅_𝑖, 𝑚_𝑖, and 𝜏𝑖 always uniquely determine consumption. We have parameter 𝛼 so that we can assume without loss of generality that 𝔼[𝜖] = 0. Defining 𝛽̃_𝑖 ≡ 𝑚_𝑖𝛽_𝑖 yields:

𝑞_𝑖 = 𝛼 + 𝛽_𝑖𝑝̅ + 𝛽̃_𝑖𝜏_𝑖+ 𝜖_𝑖 with corresponding deadweight loss per agent from equation (3):

𝑑𝑤𝑙_𝑖 = ∫ [𝛼 + 𝛽_𝑖𝑝 + 𝜖_𝑖]𝑑𝑝

We assume that the joint distribution of parameters remains unaffected by the specific values of 𝑝̅ and 𝜏. The econometrician observes for various values of regressors:

29 Agents have quasi-linear utility 𝑢𝑖=

𝑞𝑖2

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𝔼[𝑞|𝑝̅, 𝜏] ≡ ∫ [𝛼 + 𝛽_𝑖𝑝̅ + 𝛽̃_𝑖𝜏 + 𝜖_𝑖]𝑑𝐹_𝛽,𝛽^∗_̃,𝜖(𝛽_𝑖, 𝛽̃_𝑖, 𝜖)

𝛽_𝑖,𝛽̃_𝑖,𝜖_𝑖

= 𝛼 + 𝔼[𝛽]𝑝̅ + 𝔼[𝛽̃]𝜏 (7)

where 𝐹_𝛽,𝛽^∗_̃,𝜖 is the true distribution of (𝛽, 𝑚𝛽, 𝜖). The challenge is to use the observed values of triplets (𝑝̅, 𝜏, 𝔼[𝑞|𝑝̅, 𝜏]) to infer aggregate deadweight loss, which is equivalent to its second order approximation around 𝜏 = 0:

𝐷𝑊𝐿 = −1

2∫ 𝑚_𝑖²𝛽_𝑖𝑑𝐹_𝛽,𝑚(𝛽_𝑖, 𝑚_𝑖)

𝛽𝑖,𝑚𝑖

𝜏² = −1

2𝔼[𝑚²𝛽]𝜏²= −1

2𝔼[𝑚 𝛽̃]𝜏²

The only restriction that the econometrician imposes on the distribution of tax salience 𝑚 is that the support of tax salience is contained within the interval [0, 𝑚̅ ].³⁰ The econometrician can also use the CLD as in lemma 1, so that ℙ[𝛽 ≤ 0] = 1. In fact, we can permit the

econometrician to know the entire distribution of 𝜃 = (𝛽, 𝜖). It will not affect our results.

First, we can find a homogeneous perceived price that rationalizes the data for any 𝜏. A linear regression of aggregate demand on sticker prices and taxes may permit identification of 𝛽̂ ≡ 𝔼[𝛽] and 𝛽̃̂ ≡ 𝔼[𝛽̃], respectively.³¹ We define a measure of central tendency of tax salience:³²

𝑚̂ ≡𝛽̃̂

𝛽̂

Then the homogeneous perceived price that rationalizes the data is 𝑝̂^𝑠 = 𝑝̅ + 𝑚̂ 𝜏. To see this, note that assuming all agents have tax salience 𝑚_𝑖 = 𝑚̂ yields aggregate demand as in equation (7):

30 We assume that 𝑚̅ is sufficiently small so that 𝑞_𝑖≥ 0 with probability one, ruling out instances of negative consumption.

31 Such identification requires exogenous & non-collinear variation in sticker prices & taxes. If the econometrician cannot identify these terms, so much the worse for identifying aggregate deadweight loss.

32 If 𝛽̂ = 0, then let 𝑚̂ = 0.

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Thus, the agent cannot rule out all agents perceiving the same price 𝑝̂^𝑠, and so cannot rule out 𝑚_𝑖 = 𝑚̂ ∀𝑖. For tax 𝜏, this would yield deadweight loss, which by Theorem 1 would be a lower bound:

𝐷𝑊𝐿_𝑙𝑜𝑤 = −1 2𝑚̂ 𝛽̃̂𝜏²

Alternatively, the econometrician cannot rule out the perceived tax 𝜏^𝑠 having support in {0, 𝑚̅ 𝜏}. To see this, consider ℙ(𝑝^𝑠 = 𝑝̅ + 𝑚̅ 𝜏) =^𝑚̂

𝑚̅ and ℙ(𝑝^𝑠 = 𝑝̅) = 1 −^𝑚̂

𝑚̅ independently of other parameters and regressors.³³ This will rationalize aggregate demand:

∫ [𝛼 + 𝛽_𝑖𝑝̅ +𝑚̂

𝑚̅ 𝛽_𝑖𝑚̅ 𝜏_𝑖+ 𝜖_𝑖] 𝑑𝐹_𝛽,𝜖(𝛽_𝑖, 𝜖_𝑖)

𝛽𝑖,𝜖𝑖

= 𝛼 + 𝔼[𝛽]𝑝̅ + 𝑚̂ 𝔼[𝛽]𝜏

This yields deadweight loss for tax 𝜏:

𝐷𝑊𝐿_{ℎ𝑖𝑔ℎ} = −1

For instance, if 𝑚̅ = 1, then the value of deadweight loss under a homogeneous perceived price is fraction 𝑚̂ of the above calculation of deadweight loss.

Proceeding from Theorem 2 in the previous subsection, we noted that there is a specific distribution of perceived prices on {𝑝̅, 𝑝̅ + 𝑚̅ 𝜏} that maximizes deadweight loss. We describe that distribution in Theorem 3, noting that it involves assigning high or low perceived prices

33 In the true distribution, it must be that 𝑚̂ ∈ [0, 𝑚̅ ]. Alternatively, one could check whether 𝑚̂ ∈ [0, 𝑚̅] as a weak test of the null hypothesis that the tax salience is bounded within that interval.

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based on the ratio of per-person deadweight loss to the change in consumption for that individual. But in this context:

𝑑𝑤𝑙_𝑖

𝑞_𝑖(𝑝̅) − 𝑞_𝑖(𝑝^𝑠) =𝜏^𝑠 2

Thus, the distribution of tax salience independent of all other parameters and regressors in which ℙ(𝑚 = 𝑚̅ ) =^𝑚̂

𝑚̅ and ℙ(𝑚 = 0) = 1 −^𝑚_𝑚^̂_̅ maximizes deadweight loss. More generally, the econometrician cannot rule out this maximal value of deadweight loss so long as they cannot rule out the possibility of some distribution 𝐹 with 𝐹_𝛽 = 𝐹_𝛽^∗ such that 𝑠𝑢𝑝𝑝(𝑚) ∈ {0, 𝑚̅ } with:

ℙ_𝐹(𝑚 = 𝑚̅ )𝔼_𝐹[𝛽̃|𝑚 = 𝑚̅ ] = 𝑚̂ 𝛽̂ = 𝛽̃̂

Mathematically, one can check that such a distribution rationalizes the data and yields the maximal value of deadweight loss:

𝛼 + 𝔼[𝛽]𝑝̅ + 𝔼_𝐹[𝛽̃]𝜏 = 𝛼 + 𝛽̂𝑝̅ + ℙ_𝐹(𝑚 = 𝑚̅ )𝔼_𝐹[𝛽̃|𝑚 = 𝑚̅ ] 𝜏 = 𝛼 + 𝛽̂𝑝̅ + 𝛽̃̂𝜏

−1

2𝔼_𝐹[𝑚²𝛽] = −1

2ℙ_𝐹(𝑚 = 𝑚̅ )𝑚̅ 𝔼_𝐹[𝛽̃|𝑚 = 𝑚̅ ] 𝜏² = −1

2𝑚̅ 𝛽̃̂𝜏²= 𝐷𝑊𝐿_{ℎ𝑖𝑔ℎ} More intuitively, once one knows 𝛽̂ and 𝛽̃̂, one can rationalize the aggregate data. Since the ratio of deadweight loss to the change in quantity is constant, the relationship between tax salience and preference doesn’t matter upon attaining the observed aggregate demand.

Finally, consider a distribution with 𝑚 ⊥ (𝛽, 𝜖) with 𝑠𝑢𝑝𝑝(𝑚) ⊆ {0, 𝑚̂ , 𝑚̅ }, ℙ(𝑚 = 𝑚̂ ) = 𝜆 and ℙ(𝑚 = 𝑚̂ |𝑚 ≠ 𝑚̂ ) =^𝑚̂

𝑚̅. Varying 𝜆 from zero to one yields:

𝐷𝑊𝐿 ∈ [−1

2𝑚̂ 𝛽̃̂𝜏², −1

2𝑚̅ 𝛽̃̂𝜏²]

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We can conclude from this result that one cannot even identify a second order approximation of deadweight loss with aggregate data alone.³⁴ Imposing structure on preferences to facilitate identification of 𝐹_𝜃^∗ still only permits interval identification. Nonetheless, we can use aggregate data to obtain bounds, or at least 𝑚̂ , which gives us a sense of the uncertainty over the possible values of deadweight loss.

We showcase such an application in section B of the appendix, based on data on aggregate beer consumption from CLK (2009). We replicate the regressions they ran to estimate tax salience, but using a linear (rather than log-log) specification to match this subsection. Taking the ratio of averages across regressions of measures of aggregate demand responses to sales and excise tax variation, we estimate 𝑚̂ ≈ 0.27. This estimate suggests that for 𝑚̅ ≥ 1, the upper bound of deadweight loss is more than four times the lower bound.