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Identification and Estimation of Consumers' Preferences from Repeated Observations under Nonlinear Pricing
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In many economic settings, consumers face nonlinear pricing schedules where the unit price depends on the quantity purchased. Examples include public utilities such as electricity, water, and gas, which frequently employ increasing block pricing, as well as mobile phone plans, subscription services, and tax schedules with distinct marginal rates. Understanding and estimating consumer preferences under such nonlinear budget constraints is crucial for welfare analysis and policy design, yet it presents significant econometric challenges.
We begin with a model where individuals consume a good with a nonlinear price schedule. Each consumer has a utility function consisting of a common component and an idiosyncratic component representing unobserved preference heterogeneity (Ekeland, Heckman, and Nesheim (2002), -Ekeland, Heckman, and Nesheim (2004)). When making consumption choices, consumers maximize utility subject to the nonlinear price schedule, leading to a first-order condition that relates marginal utility, marginal price, and individual preference type.
Using this first-order condition, we derive a relationship between the observable distribution of consumption quantities and the unobservable distribution of preference types. With a single price schedule, this relationship does not uniquely identify the utility function and preference distribution. However, with two different price schedules, we obtain two constraining equations that can be combined to identify both components.
Our identification approach involves expressing the problem in terms of the quantile function of unobserved preference heterogeneity. By considering how percentiles of the consumption distribution under one price schedule map to percentiles under another price schedule, we derive a functional equation that the quantile function must satisfy. This equation relates the quantile function to the difference in marginal prices between the two price schedules.
From a technical perspective, our approach formulates the identification problem as an iterative functional equation. We show that the quantile function of the unobserved preference component, \(\Lambda: [0,1] \rightarrow \mathcal{E} \subset \mathbb{R}\), can be expressed as the solution of \[ \Lambda(\beta(\alpha)) - \Lambda(\alpha) = r(\alpha), \] where \(\alpha \in [0,1]\), \(\beta : [0,1] \rightarrow [0,1]\), and \(r: [0,1] \rightarrow \mathbb{R}\) are functions that are either known or easily estimated from the data. This equation is a linear functional equation in \(\Lambda\). Its solution can be obtained iteratively by noticing that the equality should hold for any arbitrary value in \((0,1)\). If \(\beta(\alpha) \rightarrow 0\) as \(\alpha \rightarrow 0\), and under additional technical conditions that we detail below, the iteration scheme converges.
We show that this functional equation has a unique solution under appropriate conditions. The solution to the functional equation can be expressed as an infinite sum, which forms the basis for our estimation approach. Once the quantile function is identified, the utility function can be identified up to a location normalization.
For estimation, we use a regularized estimator that truncates the infinite sum at a finite value. We establish the statistical properties of this estimator, analyzing both the bias due to regularization and the variance due to sampling error. We show the rate of convergence with appropriate choices of the regularization parameter.
Our work builds upon and extends several strands of literature. The econometric analysis of consumer behavior under nonlinear budget constraints dates back to the work of Hausman (1985) on labor supply under progressive taxation, and the systematic treatment of piecewise-linear budget constraints in Moffitt (1986). These early contributions relied on parametric specifications for both preferences and the distribution of unobserved heterogeneity, typically estimating the model via maximum likelihood. Blomquist and Newey (2002) relaxed some of these restrictions by developing a nonparametric estimator, but still required specific assumptions about the utility function. More recently, Matzkin (2003) and Blundell, Browning, and Crawford (2008) have advanced nonparametric approaches to demand estimation, but primarily in settings with linear budget constraints.
A separate literature has studied demand estimation and preference recovery under specific forms of nonlinear pricing. Reiss and White (2005) develop a structural model of residential electricity demand under multi-tier pricing and show that accounting for the nonlinearity of the tariff is essential for welfare analysis. In the context of taxation, Saez (2010) proposes a bunching estimator that uses the excess mass at kink points of piecewise-linear schedules to recover local elasticities. Blomquist et al. (2021) show that bunching alone cannot identify the taxable income elasticity without parametric restrictions on heterogeneity, but that variation across different budget sets can achieve identification when the distribution of preferences is unrestricted and stable. Our approach is closest in spirit to this last insight: we exploit variation across two price schedules to nonparametrically identify both the utility function and the full preference distribution, not just local elasticities. However, we require the price schedule to be continuously differentiable.
Linear functional equations have been used in other settings in economics and econometrics (see Carrasco, Florens, and Renault (2007), for a review). However, to the best of our knowledge, our analysis of a linear (or nonlinear) iterative functional equation is new. In a technical annex, we consider the properties of the linear operator which defines the inverse problem, and show that it is compact but does not have a bounded inverse. The inverse problem is therefore ill-posed, and our iterative solution regularizes the inverse by truncation (see Centorrino, Fève, and Florens (2017), Florens, Racine, and Centorrino (2018), and Centorrino, Fève, and Florens (2025) for other iterative methods in the context of ill-posed inverse problems).
We make several contributions to the literature on consumer demand estimation with nonlinear budget sets. First, we provide a novel identification strategy that relies on multiple price schedules but eschews parametric assumptions about preferences or their distribution. Second, we frame the identification problem as an iterative functional equation and develop a closed-form solution (see Kuczma, Choczewski, and Ger (1990)). Third, we propose a fully nonparametric estimator, analyze its theoretical properties, and propose a valid bootstrap procedure for finite-sample inference. Fourth, we extend our framework to accommodate endogenous prices and additional covariates, making it applicable to a wide range of empirical settings.
We extend our framework to accommodate settings where prices are potentially endogenous and need to be estimated from data. This involves an instrumental variable approach, where consumer characteristics that affect utility but not the price schedule serve as instruments. We also extend the model to incorporate additional observed heterogeneity, allowing the utility function to depend on consumer characteristics.
Our methodology provides a flexible framework for analyzing consumer behavior under nonlinear pricing that relies on minimal assumptions about functional forms. This approach is particularly valuable in contexts where imposing parametric restrictions is undesirable due to concerns about misspecification. The identification strategy leverages variation in price schedules, making it applicable in many empirical settings where different price schedules are observed (e.g., different markets, time periods, or regulatory regimes).
The remainder of the paper is organized as follows. Section (ref) introduces the model and Section (ref) establishes the identification results using two price schedules. Section (ref) develops the estimation procedure and establishes its statistical properties. Section (ref) presents a bootstrap approach for inference, Section (ref) extends the framework to incorporate additional observed heterogeneity, and Section (ref) addresses the case of endogenous prices. Section (ref) contains a study of the small-sample properties of our estimator. Finally, Section (ref) presents an empirical application to unaddressed advertising in a European country, where, among other things, we discuss counterfactual elasticities to nonlinear prices.
We have individuals who consume a quantity \(Q\) of a good for a price \(P(Q)\), which is not necessarily linear in \(Q\). Every individual has a utility function that can be written as \(u(Q) + Q \varepsilon\), where \(\varepsilon\) is an unobserved individual type generated by a probability distribution on \(\mathbb{R}\), such that \(\varepsilon \sim F_\varepsilon\).
We impose the following regularity conditions.
Every individual solves the following maximization problem \[\max_{Q} u(Q) + Q \varepsilon - \tau( P(Q)- B),\] where \(\tau > 0\) is a Lagrange multiplier and \(B\) is the total budget available to purchase the good. As \(B\) is not observed, we assume that the constraint is always binding, and therefore \(\tau\) is strictly positive. For a given \(\varepsilon\), the optimal quantity demanded, \(Q^\ast\), satisfies the following first order condition
Under the conditions in Assumption (ref)(i), the maximization problem in Equation (ref) has a unique solution. Assumptions (ref)(ii)-(iii) are regularity conditions on the support and cdf of \(\varepsilon\).
We let \(\varphi(Q) = u^\prime(Q) - \tau P^\prime(Q)\) which, from Assumption (ref), is a strictly decreasing function defined on \(\mathbb{R}^{+}\).
The price schedule \(P(Q)\) is supposed to be known. Hence, the unknown parameters of this model are the utility function \(u(Q)\), the distribution of types \(F_\varepsilon\), and the Lagrange multiplier \(\tau\).
We observe the quantity consumed by each individual whose cdf \(G(q) = \Pr(Q\leq q)\), with compact support \(\left[ \underset{\bar{}}{q}, \bar{q} \right]\). This cdf can have a positive mass at \(0\), if a positive mass of individuals does not consume the good.
The objective of this work is to estimate \(u(Q)\), \(F_\varepsilon\), and \(\tau\), when \((G,P)\) are known or can be identified from the data. This model is identified when we have multiple observations available and under further technical conditions.
For any \(G\), if \(u^\prime(Q)\) is shifted by a constant \(c\), and \(\varepsilon_c = \varepsilon - c\), then the solution of Equation (ref) is not modified. To get rid of this identification problem, we make the following normalization assumption.
The fundamental relation between observables and parameters of the model is then given by
\[ \Pr(Q \leq q) = \Pr(\varphi(Q) \geq \varphi(q)) = \Pr(-\varepsilon \geq \varphi(q)) =\Pr(\varepsilon \leq -\varphi(q)), \]
which implies
We cannot uniquely identify the triplet \((u,F_\varepsilon,\tau)\) from Equation (ref). One could impose a variety of restrictions based on the knowledge of either \(F_\varepsilon\) or \(u\), or other parametric restriction to aid identification. Here, we follow a purely nonparametric approach.
First, we may have an issue of selection at \(0\), as individuals who do not consume are usually not observed. In that scenario, the cdf of \(\varepsilon\) captures the distribution of types conditional on consuming a positive quantity of the good. We can potentially have two cases:
This discussion shows that in both cases we can normalize \(\varphi(0) = 0\), and the object of interest is the distribution of \(\varepsilon\) conditional on \(Q>0\). The cdfs \(G\) and \(F_\varepsilon\) should always be taken conditionally on \(Q > 0\), but, for simplicity, we omit this in the following.
Let us consider the model defined by Equation (ref), with the additional normalization \(\varphi(0) = 0\). We are going to assume that we have two repeated cross-sections generated by different price schedules, \(P_1(Q)\) and \(P_2(Q)\), and distribution of quantities \(G_1(q)\) and \(G_2(q)\), while \(F_\varepsilon\) and \(u\) are the same across the two samples. We assume the following.
Assumption (ref) requires the marginal utility to be equal to the derivative of the budget constraints at \(0\) for both price schedules. This is a boundary condition, consistent with the normalization \(\varphi_1(0) = \varphi_2(0) = 0\). When \(\varepsilon = 0\), then \(0\) is the optimal solution to the agent's maximization problem. While, for \(\varepsilon > 0\), we must observe \(Q^\ast > 0\).
We also require the following lemma.
By Lemma (ref), \(\mathcal{Q} = \mathcal{Q}_1 \cap \mathcal{Q}_2 = \left[ 0 , \min(\bar{q}_1, \bar{q}_2) \right]\), and we assume wlog that \(\bar{q}_1 \geq \bar{q}_2\). Finally, we impose the following.
Assumption (ref) imposes that repeated observations are informative as long as there is an overlap between the quantities consumed, and identification is possible only in the intersection between the two supports. If that intersection is empty, or if \(G_1 \overset{a.s.}{=} G_2\), then we revert to the identification issue with only one sample. If \(G_1 \overset{a.s.}{=} G_2\) only on a strict subset of \(\mathcal{Q}\), then one may be able to partially identify the objects of interest, but we do explicitly treat this case in the present paper.
We thus have two identifying restrictions
where \(\Lambda = F^{-1}_\varepsilon\) is the quantile function of \(\varepsilon\). Note that by Assumption (ref) and the normalization \(\varphi_j(0) = 0\), we have \(\Lambda(G_j(0)) = \Lambda(0) = 0\).
Taking the difference between these two equations, we obtain
Our goal is to identify the function \(\Lambda\), and the parameters \((\tau_1,\tau_2)\), from Equation (ref) when the price schedules are known or can be identified from the data, and for given \(\lbrace G_j,j = 1,2 \rbrace\).
We first discuss the separate identification of \((\tau_1,\tau_2)\). Equation (ref) can only identify these parameters up to scale. For instance, if we define \(\tilde{\tau}_1 = c \tau_1\), for any constant \(c>0\), then Equation (ref) is equivalent to \[ c\Lambda (G_2(q)) - c\Lambda (G_1(q)) = c\tau_2 P^\prime_2(q) - \tilde{\tau}_1 P^\prime_1(q). \]
A convenient normalization is to impose \(\tau_2 = 1\). \(\tau_1\) can then be identified as follows. Assume there exists \(\tilde{q}\), such that \(G_2(\tilde{q}) = G_1(\tilde{q})\). Then, assuming that both price derivatives exist at the point \(\tilde{q}\), we must have
\[ \tau_1 = \frac{P_2^\prime (\tilde{q})}{P_1^\prime (\tilde{q})}. \]
If either cdf is stochastically dominated by the other one on \(\mathcal{Q}\), then \(\tilde{q}\) is a point at the boundary of the support. If the two cdfs intersect in the interior of \(\mathcal{Q}\), we can use the value of the price derivative at the crossing point to identify and estimate \(\tau_1\).
All these quantities can be identified from the data, and therefore \(\tau_1\) is identified under the additional condition that the first derivative of the price function is finite and not equal to zero at the chosen crossing point, \(\tilde{q}\). When the price schedule is known, and because of Assumption (ref), then \(\tau_1 = \frac{P_2^\prime (0)}{P_1^\prime (0)}\), which implies that \(\tau_1\) is known when the two price functions are known. If the price functions are unknown but can be estimated at \(\sqrt{n}\) rate, we can construct a \(\sqrt{n}\)-consistent estimator of \(\tau_1\) based on the estimation of \(G_1\) and \(G_2\). As this is relatively straightforward, we consider for simplicity the case where \(\tau_1 = \tau_2 = 1\), and we show the properties of this estimation approach in simulations.
Let us now turn to the identification of \(\Lambda\). For any \(\alpha \in [0,1]\), let \(\beta(\alpha) = G_2 \left( G_1^{-1}(\alpha)\right)\), where \(\beta: [0,1] \rightarrow [0,1]\), with \(\beta(0) = 0\). This function is strictly monotone by Lemma (ref). Therefore, we can rewrite equation Equation (ref) as
where \(r(\alpha) = P^\prime_2(G_1^{-1}(\alpha)) - P^\prime_1(G_1^{-1}(\alpha))\). Letting \[ (T\Lambda)(\alpha) = \Lambda (\beta(\alpha)) - \Lambda (\alpha), \] Equation (ref) becomes
where \(T\) is an operator. The functional equation in Equation (ref) is a linear equation (see Kuczma, Choczewski, and Ger (1990), Ch. 2). Structural identifying restrictions in several econometric models can be cast as linear functional equations (for instance, in the case of nonparametric instrumental regressions, see, e.g. Newey and Powell (2003), Carrasco, Florens, and Renault (2007), Darolles et al. (2011)). However, to the best of our knowledge, results about identification and estimation of Equation (ref) are new in econometrics. We are going to consider Equation (ref) as a mapping from \(\mathcal{L}\) to \(\mathcal{L}\), where \(\mathcal{L}= \mathcal{C}_{[0,1]}\), the class of continuous uniformly bounded functions in \([0,1]\).
The operator \(T\) is linear and, to study identification of \(\Lambda\) in \(\mathcal{L}\), we consider its null space. We first prove the following.
The proof of this Corollary follows from the proof of Proposition (ref), with the additional constraint that, when \(\Lambda\) is constant, it is also equal to zero almost everywhere on \([0,1]\). This implies that \(T\) is injective and that \(\Lambda\) is identified. The results of Proposition (ref), and Corollary (ref) strongly depend on the continuity of \(\Lambda\) over the support of \(\varepsilon\). In particular, we rule out the existence of gaps in the distribution of \(\varepsilon\), i.e., sets where the cdf of \(\varepsilon\) is constant.
When \(\Lambda\) is identified, then the function \(u\) is identified up to location as
Let us focus on the simple case in which \(\mathcal{Q} = \mathcal{Q}_1 = \mathcal{Q}_2\), and \(G_1(q) > G_2(q)\), which implies that \(\beta(\alpha) < \alpha\). The latter is a stochastic dominance assumption that can be tested (see, e.g., Linton, Maasoumi, and Whang (2005)). In the case in which \(\beta(\alpha) > \alpha\), we can simply reverse the roles of \(G_1\) and \(G_2\). When the functions \(G_1\) and \(G_2\) cross in the interval \((0,1)\), one can consider estimation between the crossing points. From the practical perspective, this case does not modify the estimation strategy, and we illustrate it using simulations in Section (ref). When \(\beta(\alpha) < \alpha\), with \(\beta\) continuous, and \(\beta(0) = 0\), \(\beta^{(k)}(\alpha)\) converges to \(0\) as \(k \rightarrow \infty\).
Having established in Section (ref) that Equation (ref) has a unique solution under our assumptions, let us consider the closed-form expression of that solution. First, notice that for \(\beta(\alpha) \in (0,1)\), one needs to have that
Taking the sum between Equation (ref) and Equation (ref), we have that
More generally, for any \(k \geq 1\), we must have
Iterating the reasoning in Equation (ref), we have that a general solution to Equation (ref) can be written as
(see Kuczma, Choczewski, and Ger (1990), Th. 2.3.5, p. 65), and this solution exists if the sequence on the right-hand-side is convergent for all \(\alpha \in [0,1)\).
The following two assumptions provide sufficient conditions for Equation (ref) to converge.
Assumption (ref) is a Hölder continuity condition of the function \(r\) around \(0\).
Assumption (ref) implies that, \[ \Lambda (\beta^{(k)}(\alpha)) \rightarrow 0, \text{ as } k \rightarrow \infty. \] Assumptions (ref) and (ref) together imply that \[ \vert r(\beta^{(k)}(\alpha)) \vert \leq C \beta^{(k)}(\alpha)^\kappa < C \theta^{k \kappa} \alpha^{\kappa}, \] which implies that Equation (ref) converges for \(\alpha \in [0,1)\).
In practice, we regularize the solution in Equation (ref) by choosing a finite number of iterations, \(N\). That is,
The approximation bias can be characterized by the regularity of \(\Lambda\), which itself depends on the regularity of \(r\) given in Assumption (ref). We thus have the following.
Under our Assumptions, we show that the bias decreases exponentially with the regularization parameters \(N\), which differentiate our result from the existing literature on ill-posed inverse problem (Newey and Powell (2003),Darolles et al. (2011)).
We have iid observations from samples \(1\) and \(2\), denoted \(\lbrace \left(P_{ji},Q_{ji}\right),i = 1,\dots,n_j\rbrace\) for \(j = 1,2\). For simplicity, and without loss of generality, we assume that \(n_1 = n_2 = n\). As the support of \(Q_1\) and \(Q_2\) is assumed to be compact, their cdfs, \((G_1,G_2)\), and quantile functions, \((G^{-1}_1,G_2^{-1})\), can be estimated at a parametric rate (Li and Racine (2008)). Regarding the price function, we first focus on the case in which the price schedule (and hence its derivative) are known to both the decision maker and the econometrician, and we defer the discussion about their estimation in Section (ref).
We consider the following nonparametric estimators
where \(\bar{K}\) is the cdf of a nonparametric kernel function \(K\) and \(h_{j}\) is a bandwidth parameter for \(j = 1,2\). Let \(\alpha \in (0,1)\) be given. We consider here a kernel estimation of the cdfs, as we can leverage known results about their uniform in-bandwidth consistency. However, one could decide to use sieve estimators of the same quantities. Then
where \(P^{\prime}\) is the known derivative of the price function. We take the kernel smoothed estimator, as we can immediately apply existing results to prove its uniform consistency. However, we could have as well used sieve estimators for the same purpose (Tsybakov (2008)).
Our regularized estimator of the solution \(\Lambda^\ast\) is then given by
When using the empirical cdf and the empirical quantile function for estimation, standard results from empirical process theory would imply the uniform convergence of \(\hat{G}_{h,j}(q)\) and \(\hat{G}^{-1}_{h,j}(\alpha)\), for \(j = 1,2\) over the class \(\mathcal{L}\). However, as we use a smooth version of the empirical cdf and quantile function, the properties of our estimator depend on the choice of the bandwidth parameter. We have the following Proposition.
Proposition (ref) gives a version of the Glivenko-Cantelly and Donsker theorems when the cdf is estimated using a smooth kernel function. This result is uniform in the bandwidth parameter (see, e.g., Van der Vaart (1998) p. 266 for a textbook reference). By the law of iterated logarithms, the rate of convergence for the supremum distance is \(n^{-1/2} \left( \log \log n \right)^{1/2}\) uniformly on \(h\) (see Mason and Swanepoel (2013)).
The functions \(\beta^{(k)} \in \mathcal{L}\) and thus they also form a Glivenko-Cantelli and Donsker class of functions for all \(k = 1,2,\dots\). We have the following result.
Having established the properties of the bias term in the previous section and using the result in Proposition (ref), we consider the properties of the term \(\hat{\Lambda}_h^{\ast,N}(\alpha) - \Lambda^{\ast,N}(\alpha)\), which we loosely refer to as the variance term. We have the following result.
The variance term thus diverges with \(N^2\) for any given \(n\). However, the bias term has an exponential decay as \(N\) grows. In practice, one could balance these terms so that the estimator would have an asymptotic bias. Another approach is to choose \(N\) to diverge more slowly with \(n\) in a way that the asymptotic distribution is dominated by the variance and we should not be too concerned with the bias when conducting inference.
Directly from the results in the previous section, we have the following (see Thas (2009), Theorem 7.6, and Beare and Moon (2015)).
From the results of Proposition (ref) and Lemma (ref), we have the following result about the weak convergence of \(\hat{\Lambda}_h^{\ast,N}\) to \(\Lambda^{\ast,N}\).
Theorem (ref) follows directly from Lemma (ref) and the proof is straightforward.
Finally the estimator of \(u\) is given by
so that it inherits the properties of \(\hat{\Lambda}^{\ast,N}\), except that the weak convergence should be expressed with respect to the Brownian Bridge \(\mathbb{B}_{G_2}\) with covariance function equal to \[ E \left[ \mathbb{B}_{G_2}(t) \mathbb{B}_{G_2}(s) \right] = G_2 \left( t \wedge s \right) - G_2(t) G_2(s). \]
The result is formally stated in the following theorem.
The results of Theorem (ref) and Theorem (ref) apply for a fixed regularization parameter \(N\). However, to get a consistent estimator of \(\Lambda\) and \(u\), we must allow \(N\) to diverge with the sample size at an appropriate rate. The following result combines the bias and variance bounds to give a convergence rate for the regularized estimator.
The estimator thus achieves a near-parametric rate, with the \(\log(n)\) factor reflecting the cost of regularization. The choice of \(c\) controls the bias-variance tradeoff: larger \(c\) reduces the bias faster but inflates the variance. A formal data-driven procedure for selecting \(N\), remains an open question.
Theorem (ref) and Theorem (ref) are difficult to use directly for inferential procedures. It is therefore advisable to use the bootstrap to construct asymptotically valid confidence intervals.
As we are working with empirical processes, we follow the approach in Kosorok (2008) and use a version of the multiplier bootstrap to perform asymptotically valid inference. Let \(\xi = \lbrace \xi_1, \xi_2,\dots \rbrace\) be an infinite sequence of non-negative iid random variables with mean \(1\) and variance \(1\) and such that \[ \int_0^\infty \sqrt{P\left( \vert \xi \vert > x \right)} dx < \infty. \] (where the latter condition is satisfied whenever the \(2 + \epsilon\) moment of \(\xi\) exists, for any \(\epsilon > 0\), see Kosorok (2008), p. 20). We can, for instance, draw the random variable \(\xi\) from a standard exponential distribution. Then we can replace our estimators of \(G_1\) and \(G_2\) (and their corresponding quantiles), with the following bootstrap estimate
for \(j = 1,2\), where \(\bar{\xi}\) is the sample mean of \(\lbrace \xi_1,\dots,\xi_n \rbrace\). Under the same conditions as for Theorem (ref) and Theorem (ref), this version of the multiplier bootstrap converges (see Kosorok (2008), Theorems 2.6 and 2.7, p. 20).
The utility function and the distribution of unobserved heterogeneity can also depend on additional observable characteristics of the agent, \(\lbrace X_j \in \mathbb{R}^p, j = 1,2 \rbrace\) that we wish to include in the analysis.
We impose the following additional assumption.
Assumption (ref) imposes that the \(X\) has identical support across the two samples (have the same elements of null measure), so that we can condition on the same values of \(X\) in both samples. One could potentially relax this Assumption by letting \(\mathcal{X} = \mathcal{X}_1 \cap \mathcal{X}_2\) to be non-empty. Nonetheless, the identification argument below applies verbatim conditional on all \(x \in \mathcal{X}\).
For \(j = \lbrace 1,2\rbrace\), the individual maximization problem becomes \[ \max_Q u(Q,X) + Q\varepsilon - P(Q), \] and the first order condition writes
where \(u^\prime_Q\) denotes the first partial derivative of \(u\) wrt \(Q\). The conditions for existence and uniqueness of the solution to Equation (ref) are the same as above. However, the optimal quantity is not only determined by \(\varepsilon\) and the utility function, but also depends on \(X\).
In the case of two observations, we have \[
\] which, because of Assumption (ref), implies
Assuming that \(G_{j\vert X}\) is strictly increasing in \(q\), for \(j = \lbrace 1,2 \rbrace\), \(G_{2 \vert X} (q \vert X = x ) > G_{1 \vert X} (q \vert X = x )\), for all \(x \in \mathcal{X}\), and letting \(q = G^{-1}_{2 \vert X}(\alpha \vert X = x)\), \(\beta(\alpha,x) = G_{1 \vert X} \left( G^{-1}_{2 \vert X}(\alpha \vert X = x) \vert X = x \right)\) and \[ r(\alpha,x) = P^\prime_{2}(G^{-1}_{2 \vert X}(\alpha \vert X = x)) - P^\prime_{1}(G^{-1}_{2 \vert X}(\alpha \vert X = x)), \] we can write \[ \Lambda_{\varepsilon \vert X} \left(\alpha,x\right) = -\sum_{k = 0}^\infty r(\beta^{(k)}(\alpha,x),x). \]
When \(X\) is a discrete random variable, we can stratify the sample wrt the distribution of \(X\), and estimate the utility function and the quantile function of \(\varepsilon\) conditional on \(X\). The asymptotic theory we studied in the previous section can then be applied after conditioning on \(X\).
When \(X\) is continuous, however, we need to smooth wrt its distribution, which does not modify the estimation technique, but requires a different approach to the asymptotic properties. We defer the study of this case to further research.
For each period of observation, our model is specified by the triplet \((X,Q,P)\) and by a functional relationship between \(P\) and \(Q\), the inverse demand function. We initially assume that the latter is known to both the consumer and the econometrician. However, in many situations, the inverse demand function is unknown to the econometrician.
We can then model price and quantity as jointly determined conditional on \(X\). The observed value of \((Q,P)\) can result, for instance, from a negotiation between producer and consumer. We consider that the price fixed by the producer does not depend on \(X\), but it is solely determined by the quantity demanded. In particular, we make the following assumption.
Assumption (ref) stipulates that the observed price is determined by Equation (ref), and it is a function of the quantity consumed, and of an unobservable component \(\eta\), which is mean independent of \(X\). This random component can have several interpretations. It could be related to unobservable taste of the decision maker but also to their bargaining power. For instance, agents who purchase higher quantities may be able to negotiate a lower price per unit. \(\eta\) is centered wlog. If \(\eta\) is not centered, the slope of the inverse demand curve can be adjusted by \(E[\eta]\).
The equilibrium quantity is then determined by the following maximization problem \[ \max_Q u(Q,X) + Q\varepsilon - \Psi_{\theta}(Q) - Q \eta, \] whose first order condition writes
This condition implies that the equilibrium quantity depends on \(X\) both through the utility function and through the conditional distribution of \(\varepsilon\) given \(X\).
If the parameters \(\theta\) were known, then \(\eta\) can also be taken as known. In this case, thanks to Assumption (ref), one could perform estimation conditional on \(X\), as explained in the previous section.
In the case of two observations, we have \[
\] which implies
with \(\zeta = \varepsilon - \eta\), and \(\Lambda_{\zeta \vert X}\), the conditional quantile function of the composite error \(\zeta\) conditional on \(X\). Similar identification conditions as the one outlined above would hold conditionally on \(X\). The quantile function of \(\zeta\) conditional on \(X\) and the utility function are estimated exactly as before.
As discussed in Section (ref), the object we identify is the distribution of \(\zeta\) conditional on \(X\) and \(Q > 0\). This is because we are only able to observe those consumers who choose a positive quantity. While we do not explicitly condition on \(Q >0\) below, this is an important detail that we would like the reader to keep in mind. Our identification of the distribution of \(\varepsilon\) given \(X\) and \(Q > 0\) is based on the estimation of the error term \(\zeta\), as follows.
From Equation (ref), and for known \(\theta\) and \(\eta\), the structural residuals can be obtained as
and thus we have \(\hat{\varepsilon}_i(\theta) = \hat{\zeta}_i(\theta) + \eta_i\), and the conditional cdf is obtained using a simple nonparametric approach (Li and Racine (2008)).
However, \(\theta\) and \(f_{\eta\vert X}\) are unknown and need to be estimated. A major hurdle is that the quantity chosen by the decision maker (and observed by the econometrician) is a function of the composite shock \(\zeta\). In Equation (ref), quantity is therefore an endogenous variable.
The endogeneity issue can be solved by using \(X\) as an instrumental variable (IV) in Equation (ref). In particular, we have that \[ E\left[ \frac{P_j}{Q_j} \vert X_j = x \right] = E \left[ \frac{\Psi_{k,j}(Q_j)}{Q_j} \vert X_j = x \right] \theta_j + E\left[ \eta_j \vert X = x \right] = E \left[ \frac{\Psi_{\theta_j}(Q_j)}{Q_j} \vert X_j = x \right], \] where the last equality follows from Assumption (ref), which specifies an exogeneity condition for \(X\). Moreover, as \(X\) enters the utility function, it also determines the chosen quantity, and it is therefore a relevant IV. Estimation of the inverse demand function using a parametric IV model allows us to obtain an estimator of the density of \(\eta\) conditional on \(X\). Depending on the specification of \(\Psi_{\theta}(q)\), we could use several approaches for estimation. If the function \(\Psi_{\theta_j}(q)\) is linear in parameters, we can directly use an instrumental variable approach (see Example (ref) below). If, instead, \(\Psi_{\theta_j}(q)\) is nonlinear in parameters, one can use a nonlinear IV model that can be estimated by the method of moments (see Example (ref) below).
One can then construct the residuals \[ \hat\eta_j = \frac{P_j}{Q_j} - \frac{\Psi_{\hat\theta_j}(Q_j)}{Q_j}, \text{ for } j = 1,2. \]
The estimator of the difference in the derivative of the price functions at a point \(q = G_1^{-1} (\alpha)\) is then given by
Finally,
where \(j\) could be either \(1\) or \(2\) (depending on the sample we use to estimate the utility function).
We consider the following data-generating process. \(\varepsilon \sim F_\varepsilon\), with density equal to \(f_\varepsilon(\varepsilon) = 1.5 \sqrt{\varepsilon}\) supported on \([0,1]\). The utility function is given by \(u(Q) = 2Q - Q^2\). We have two simulation designs, depending on the specification of the price function.
with \(\tau_1 = 2\), and \(\tau_2 = 1\), in a way that the equilibrium quantities \(Q^\ast_1 = \sqrt{\varepsilon}\) and \(Q^\ast_2 = \varepsilon\), respectively. In this case, \(G_1\) stochastically dominates \(G_2\) over \(\mathcal{Q}\).
with \(\tau_1 = 2\), and \(\tau_2 = 1\). The equilibrium quantity \(Q^\ast_1\) is obtained by solving \(u'(Q^\ast_1) = \tau_1 P'_1(Q^\ast_1) - \varepsilon\) numerically, while \(Q^\ast_2 = \varepsilon\) as in Design 1. In this case, \(G_1\) and \(G_2\) intersect at exactly one point in \(\mathcal{Q}\).
The number of observations per sample is set to \(n = n_1 = n_2 = \lbrace 500, 1000, 2500 \rbrace\). We consider the case in which the price functions are known, and we wish to estimate the utility function and the quantile function of \(\varepsilon\) from data. We also include in the Appendix a set of simulations for the case when the price function is estimated following the approach in Section (ref).
To generate the data on quantity, we simulate \(2n\) observations from the distribution of \(\varepsilon\), and then we fix the quantities \(Q^\ast_1\) and \(Q^\ast_2\) according to their equilibrium values. \(G_1\) and \(G_2\) are estimated using a Gaussian kernel, with bandwidths chosen by least-squares cross-validation. The estimated quantile functions \(\hat{G}_{h,j}^{-1}(\alpha) = \min_q \lbrace \hat{G}_{h,j}(q) \geq \alpha \rbrace\).
For Design 1, our starting value is given by \(r(\alpha) = \tau_1 P^\prime_1(\hat{Q}_\alpha) - P^\prime_2(\hat{Q}_\alpha)\), for a grid of percentiles \(\hat{Q}_\alpha\) taken from the empirical distribution of \(Q^\ast_1\). At step 1, we estimate \(\beta(\alpha)\) as \[ \hat{\beta}^{(1)}_h (\alpha) = \left( \hat{G}_{h,2} \right)^{-1} \left( \hat{G}_{h,1} (\hat{Q}_\alpha) \right), \] and then compute \[ \hat{\Lambda}^{\ast,1}(\alpha) = -r(\alpha) - r\left( \hat{\beta}^{(1)}_h (\alpha) \right). \] We then iterate the approach for \(k = 2,\dots,N\), in a way that \[ \hat{\beta}^{(k)}_h (\alpha) = \left( \hat{G}_{h,2} \right)^{-1} \left( \hat{G}_{h,1} (\hat{\beta}^{(k-1)}_h (\alpha) ) \right), \] and \[ \hat{\Lambda}^{\ast,k}(\alpha) = -r(\alpha) - \sum_{j = 1}^{k} r\left( \hat{\beta}^{(j)}_h (\alpha) \right). \]
To choose the number of iterations \(N\), we proceed as follows. As \(G_1\) stochastically dominates \(G_2\) over \(\mathcal{Q}\), we have that \(0\) is an attractive point for \(\hat{\beta}^{(k)}_h (\alpha)\) as \(k\) increases. Hence, at each step, we compute the variance of \(\hat{\beta}^{(k)}_h (\alpha)\) and we stop when its variance is smaller than a given tolerance level, which we take to be equal to \(10^{-10}\). We also cap the number of iterations at \(5 \log(n)\) to control the approximation error.
Finally, the utility function is estimated as \[ \hat{u}^N_h(Q) = P_2(Q) - \int_0^Q \hat{\Lambda}^{\ast,N}\left( \hat{G}_{h,2}(q) \right) dq. \]
For Design 2, we proceed in a similar way, except that we now have to split \(\mathcal{Q}\) into two subsets, one in which \(G_1\) stochastically dominates \(G_2\) and another one in which the opposite is true. We choose the splitting point based on the difference between the price derivatives, and conduct separate estimation of \(\Lambda\) and \(u\) over these two nonoverlapping subsets.
To provide a sense of the variability in our estimator, we provide the result for 20 random samples of size \(n = 1000\) for Design 1 and 2 in Figure (ref) and Figure (ref), respectively.
Bootstrap confidence intervals are obtained using the strategy outlined in Section (ref). Results for one randomly selected sample with \(n = 1000\) are given in Figure (ref) and Figure (ref). We also visually compare the Monte-Carlo pointwise confidence intervals with their bootstrap counterpart to assess their accuracy, and coverage rates. The bootstrap CIs are slightly conservative in this setting.
Table (ref) gives the average maximum absolute error of the estimator for both simulation designs and all sample sizes. As expected the error of our estimator decreases as the sample size increases.
Finally, and only for Design 1, we compare our iterative estimator of the quantile function of heterogenous types, with an estimator which is based on the first derivative of \(\lambda\) obtained by Tikhonov-regularization. That is \[ \hat{\Lambda}_{Tik}(\alpha) = \int_{0}^\alpha \hat{\lambda}_{Tik}(t) dt = \int_{0}^\alpha \left[ \left( \rho I + \hat{K}^\ast \hat{K} \right)^{-1} \hat{K}^\ast r \right](t) dt, \] where \(\hat{K}\) is a finite approximation to the integral operator, \(\hat{K}^\ast\) its adjoint, and \(\rho\) a regularization parameter that we choose proportional to the inverse of the number of iterations for each simulated DGP.
The results reported in Table (ref) confirm that this estimator also has good properties in finite sample. In the specific case of our simulation study, the Tikhonov-regularized estimator has smaller mean absolute error than our iterative estimator. We conjecture that this difference is due to the fact that the Tikhonov estimator takes additional advantage of the differentiability of the quantile function, which is neither required nor used by the iterative estimator.
We obtained data from a European mail carrier, which contain information about unaddressed advertising (or admail). Unaddressed advertising is a form of direct mail where marketing materials are distributed to a specific geographic area without the need to be addressed to individual residences or businesses. This method aims to reach a broad audience within a particular area, increasing brand visibility and potentially generating higher response rates compared to targeted mail.
Negotiations about the quantity and unit price of unaddressed mail are conducted between the mail carrier and individual businesses. The contract can include one or more geographical areas. We have information about the nature of the business (a broadly defined sector of operation), the total price paid and the quantity negotiated. If a single contract involves multiple geographical areas, we have information about price/quantity for each geographical area, and we consider these as separate contracts for simplicity.
The data are a repeated cross-section that span 6 years of observations (from 2008 to 2013). Observations from the first and last year are incomplete, and we therefore use data from 2009 and 2012 for our analysis. We refer to 2009 as \(t = 1\), and 2012 as \(t = 2\) henceforth.
We remove from the sample all the sectors that cannot be found in both years (as we cannot apply our proposed framework in that case), and we also remove sectors with fewer than 400 observations in total between the two periods. Also, we remove sectors with outliers (contracts with very small or extremely large quantities, based on their interquartile range). We end up with 15341 observations at \(t = 1\), and 17875 observations at \(t = 2\).
Table (ref) reports summary statistics for the four chosen sectors: breakdown services, car dealers, electronics shops, and health aid products. For each sector and year, we report the number of contracts, the mean and standard deviation of quantity (in thousands of items delivered) and of unit price (in pence per item).
The price schedule is unobserved, and we estimate it assuming that the utility function and the consumer types can be sector-specific. That is, we assume that each firm solves
\[ \max_{q \in \mathcal{Q}} u(Q,X) - \Psi_{\theta_j} (Q) + Q \zeta \]
where \(\zeta = \varepsilon - \eta\) is the composite unobservable defined in Section (ref), with \(\varepsilon\) capturing unobserved preference heterogeneity and \(\eta\) the price disturbance. We estimate the price function, recover the structural residuals, and apply the iterative procedure described in Section (ref) and Section (ref) to obtain estimates of the utility function and the distribution of unobserved types for each sector. Results are displayed in Figure (ref).
Once the utility function \(u(Q; X)\) and the price schedules \(P(Q)\) have been estimated, one of the potential objects of interest are demand elasticities. Under nonlinear pricing, the notion of a price elasticity of demand is not uniquely defined: since the price schedule is a function rather than a scalar, different counterfactual experiments yield conceptually distinct elasticity measures (Wilson 1993; Tirole 1988). We consider two natural counterfactual experiments, based on transformations of the price schedule, and derive the corresponding elasticities using the implicit function theorem applied to the first-order condition.
The interior optimum is determined by the first-order condition
We modify the price schedule \(P\) using a one-parameter family \(\Pi_\sigma(P)\), indexed by \(\sigma \in \mathbb{R}\), such that \(\Pi_{\sigma_0}(P) = P\) for \(\sigma_0\) given. The elasticity of demand with respect to \(\sigma\) is defined as
Under the modified schedule, the first-order condition becomes \(u'(Q;\,x) - \Pi_\sigma\left(P(Q)\right)^\prime + \varepsilon = 0\), where \(\Pi_\sigma\left(P(Q)\right)^\prime\) is the first derivative of the composite function \(\Pi_\sigma(P(Q))\) wrt \(Q\). Total differentiation of Equation (ref) with respect to \(\sigma\) at fixed \(\varepsilon\) yields \[ \bigl[u''(Q;\,x) - \Pi_\sigma\left(P(Q)\right)^{\prime \prime}\bigr]\, dQ \;=\; \frac{\partial}{\partial \sigma}\, \Pi_\sigma\left(P(Q)\right)^\prime\, d\sigma, \] so that
At the point, \(\sigma = \sigma_0\), \(\Pi_\sigma\left(P(Q)\right)^\prime = P^\prime (Q)\), and \(\Pi_\sigma\left(P(Q)\right)^{\prime \prime} = P^{\prime \prime} (Q)\). This yields
Under the conditions in Assumption (ref), \(u''(Q,x)-P''(Q)<0\). For any price-increasing perturbation, the partial derivative \(\frac{\partial}{\partial \sigma}\,\Pi_\sigma\left(P(Q)\right)^\prime\) is positive, and the elasticity is therefore negative, confirming that demand is downward-sloping in the price parameter.
The second derivative \(u''(Q,x)\) is recovered using the relationship between the conditional density of observed quantities and the quantile function of the unobserved heterogeneity. Specifically, differentiating Equation (ref) twice with respect to \(q\) yields \[ u''(Q,x) \;=\; P''(Q) \;-\; \frac{g_{Q \vert X}(Q \mid X=x)}{f_{\varepsilon \vert X}\!\bigl(\Lambda(G_{Q\vert X}(Q \mid X=x))\mid X=x\bigr)}, \] where \(g_{Q \vert X}(\cdot \mid X)\) is the conditional density of \(Q\), \(f_{\varepsilon \vert X}(\cdot \mid X)\) is the conditional pdf of \(\varepsilon\), and \(\Lambda(G_{Q\vert X}(Q \mid X=x))\) is the quantile function of \(\varepsilon\) conditional on sector \(X\).
Level change. Consider the family \(\Pi_\sigma(P)(Q) = \sigma \, P(Q)\), with reference value \(\sigma_0 = 1\). This rescales the schedule uniformly, capturing a change in the overall level of prices. We have \(\Pi_\sigma(P)'(Q) = \sigma\, P'(Q)\), so \(\frac{\partial}{\partial \sigma}\, \Pi_\sigma(P(Q))^\prime = P'(Q)\). Evaluating Equation (ref) at \(\sigma_0 = 1\) gives
Curvature change. Consider the family \(\Pi_\sigma(P)(Q) = P(Q)^\sigma\), with reference value \(\sigma_0 = 1\). This applies a power-law transformation, altering the curvature of the schedule and the rebate structure faced by high-volume buyers. We have \[ \Pi_\sigma(P(Q))^\prime \;=\; \sigma\, P(Q)^{\sigma - 1}\, P'(Q), \] and therefore \[ \frac{\partial}{\partial \sigma}\,\Pi_\sigma(P(Q))^\prime \bigg|_{\sigma = 1} \;=\; P'(Q)\,\bigl(1 + \ln P(Q)\bigr). \] Hence. evaluating Equation (ref) at \(\sigma_0 = 1\) gives
Figure (ref) and Figure (ref) show the baseline estimates (\(\sigma = 1\)) together with 90% confidence bands. We find that the two counterfactuals lead to qualitatively similar conclusions, with low-volume buyers having higher elasticity than high-volume ones. High-volume buyers may be purchasing the service independently of the total price, especially if the mail carrier operates as a monopolist in the postal market, either because of lack of competition, or because competitors are unable to handle high-volume requests. On the contrary, low-volume buyers may decide to pursue other advertising channels if the service is over-priced.
This paper develops a nonparametric framework for identifying and estimating consumer preferences from repeated cross-sections when consumers face nonlinear pricing schedules. The key insight is that variation across different price regimes generates a pair of identifying restrictions whose difference yields a linear iterative functional equation in the quantile function of unobserved preference heterogeneity. We show that this equation has a unique solution under mild regularity conditions, derive a closed-form representation as a convergent infinite series, and provide tools for estimation and inference.
From a methodological standpoint, our approach contributes a new regularization strategy for a class of ill-posed inverse problems defined by iterative functional equations. We show that the linear operator defining the problem is compact but does not admit a bounded inverse, so that the inverse problem is ill-posed. Our iterative truncation scheme regularizes the inverse by retaining only finitely many terms of the series solution. Unlike spectral or Tikhonov regularization, which require choosing a continuous tuning parameter and inverting an operator, the iterative approach yields a closed-form expression. The resulting estimator achieves a near-parametric convergence rate.
The empirical application to unaddressed advertising data from a European mail carrier illustrates how the framework can be used in practice. By estimating sector-specific utility functions and preference distributions, we recover demand elasticities under two types of counterfactual price experiments: uniform level shifts and curvature changes. The estimated elasticities reveal that low-volume buyers are substantially more price-sensitive than high-volume ones, a pattern consistent across sectors and robust to the type of counterfactual considered. This finding is consistent with market power on the part of the mail carrier over large-scale clients, who may lack viable alternatives for high-volume distribution, while smaller buyers can substitute toward other advertising channels. These estimates could inform the design of nonlinear tariffs and the evaluation of regulatory interventions in postal markets.
Several limitations of the current framework should be acknowledged. First, our identification strategy requires at least two distinct price schedules observed on comparable populations; when only a single schedule is available, the utility function and preference distribution are not separately identified. Second, we assume that the distribution of unobserved preference heterogeneity is stable across the two samples. If preferences shift between periods, for instance, due to aggregate demand shocks, the estimator is inconsistent. Third, while we extend the framework to discrete covariates, the case of continuously distributed observed heterogeneity requires nonparametric smoothing in \(X\) and a modified asymptotic theory that we do not develop here. Fourth, the choice of the regularization parameter \(N\) is currently guided by a heuristic stopping rule; a formal, data-driven selection procedure remains an open problem. Finally, the empirical application relies on a parametric specification for the price function at the first stage, which reintroduces functional form assumptions even though the utility function is estimated nonparametrically. Extending the framework to allow for fully nonparametric price estimation is a natural next step.
These limitations suggest several directions for future research. A formal analysis of the optimal choice of \(N\), possibly via cross-validation or information criteria adapted to the iterative structure, would be valuable. Extending the identification and estimation theory to settings with more than two price schedules could yield efficiency gains and enable over-identification tests of the model's restrictions. The framework could also be extended to other settings where repeated observations under different policy regimes are used to recover structural objects --- such as labor supply under changing tax schedules or insurance demand under varying contract menus.
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