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Econ-612 · economics · Economics

Digital tax administration and compliance

LLM status Open unverified

Statement

Original atlas identifier: P7; entry 47. Field: Public finance and taxation. Original tractability label: Easy.

Classification: Parameterized theoretical/empirical research agenda; not a certified unsolved theorem.

Definitions and assumptions

Let $d=(d_1,\ldots,d_K)$ represent digital-administration features such as e-invoicing, pre-filling, reporting latency, and risk-score auditing. A feasible policy includes an implementation schedule and audit capacity. Firm $i$ belongs to a transaction network $\mathcal G$ and has treatment exposure $h_i(d,\mathcal G)$; outcomes can depend on neighbors' assignments. Define net revenue $R(d)$ after refunds and corrected assessments, administrative resource cost $C_A(d)$, private compliance resource cost $C_B(d)$, and expected loss $C_E(d)$ from erroneous enforcement and data breaches. The last term requires an explicitly chosen monetary valuation and event-probability model. Let $EV_i(d)$ capture private welfare changes excluding the three separately counted costs; fix nonnegative social weights $w_i$ and fiscal-value parameter $\lambda$.

Formal research question

Editorial formulation: the mathematical target below is not a theorem quoted from the references.

Identify the causal policy value \[ W(d)=\mathbb E[w_iEV_i(d)]+\lambda R(d)-C_A(d)-C_B(d)-C_E(d) \] over feature combinations satisfying budget, capacity, correction/appeal, and legal-data-use constraints. Costs and equivalent variation are expressed in a common monetary welfare unit; count each resource or loss once. Determine which randomized feature assignments identify direct effects, network spillovers, and interactions rather than merely the effect of a bundled rollout. Characterize $\varepsilon$-optimal policies and bounds for unobserved compliance burdens or rare error losses. If feature assignment is staggered rather than randomized, state counterfactual-trend, anticipation, and interference assumptions before attributing the revenue change to digitization.

Interpretation and scope

Higher assessed liability is not necessarily higher correct tax collection: include appeal reversals, payment delays, and displaced reporting in the revenue measure. “Lowest social cost” requires a service or revenue target, since doing nothing may minimize expenditure. Digital reporting can save firms time while increasing oversight; both convenience and enforcement mechanisms must be separately measured. Rare breaches cannot be estimated precisely from a short experiment, so the policy comparison needs sensitivity ranges rather than a fictitious zero cost. Supply-chain exposures make ordinary no-interference assumptions questionable; network or saturation randomization offers a more coherent design. The atlas's Easy label is retained as an original judgment, not a demonstrated tractability result for this joint welfare problem.

Source audit and status

[S1] and [S2] provide information/reporting evidence; [S3] is an OECD design framework rather than a causal evaluation. Its publication year is 2020 despite later webpage migrations. The expanded cost-inclusive feature-selection problem is editorial; the cited sources do not certify a unique current optimum.

References

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  1. Dina Pomeranz (2015). No Taxation without Information: Deterrence and Self-Enforcement in the Value Added Tax. American Economic Review 105(8), 2539--2569. DOI: 10.1257/aer.20130393. https://www.aeaweb.org/articles?id=10.1257/aer.20130393
    Locator and support limits: Abstract and experiments: Chilean VAT information and supply-chain enforcement; no comprehensive privacy-cost estimate.
  2. Joana Naritomi (2019). Consumers as Tax Auditors. American Economic Review 109(9), 3031--3072. DOI: 10.1257/aer.20160658. https://pubs.aeaweb.org/doi/10.1257/aer.20160658
    Locator and support limits: Abstract: consumer reporting incentives in Brazil; not direct evidence for every digital feature.
  3. OECD (2020). Tax Administration 3.0: The Digital Transformation of Tax Administration. Paris: OECD Publishing. DOI: 10.1787/ca274cc5-en. https://www.oecd.org/en/publications/tax-administration-3-0-the-digital-transformation-of-tax-administration_ca274cc5-en.html
    Locator and support limits: Report: institutional digital-transformation framework; descriptive/design guidance, not a causal cost-benefit estimate.

Integrated refinement: ECON-089

Digital tax data quality and revenue administration

This refinement adopts the additional source conventions (audited:conventions), subject to its local restrictions. Record coverage and quality versus real activity. Under digital reform $z$, let true activity be $Y_i(z)$, its record $Z_i(z)$, and usable linkage indicator $M_i(z)$. Use an independently specified audit selection/error model. Alongside net revenue $\tau_R=\mathbb E[R(1)-R(0)]-\Delta C_{\rm admin}$, identify coverage changes and a fixed-cohort quality contrast such as \[ \tau_Q=\mathbb E[(Z_i(1)-Y_i(1))^2-(Z_i(0)-Y_i(0))^2]. \] Require both records by design or declare a missing-record loss; do not silently condition on post-reform linkage. Use common measurement units and finite moments. Separate improved recording from real-activity, selection and enforcement changes, including transaction-network spillovers and incorrect assessments. An audit is a validation measurement with its own errors rather than an assumed perfect truth label.

Supplied source audit for ECON-089

Local [S1], [S2], etc. in this audit and the references immediately below belong to ECON-089, independently of the original entry's reference numbering. [S1] and [S2] support digitization, registry quality and revenue administration. This fixed-cohort/policy-dependent truth formulation is editorial and corrects the original estimand.

References for integrated refinement ECON-089

[S1] Anders Jensen, Anne Brockmeyer, and Lucie Gadenne (2024). Taxation and Development. VoxDevLit 12(1), September 2024.

Verified locator / source role: Primary publisher page checked for review identity, authors, version and background. The specific published agenda is corroborated in [S2]; the exact formal target is editorial.

https://voxdev.org/voxdevlit/taxation

[S2] Oliver Hanney / VoxDev (living compilation). Evidence gaps: Key unanswered questions in development economics. Accessed 8 October 2026. The page currently covers 20 topics; the "16-topics URL is a historical slug.

Verified locator / source role: Topic heading: Taxation and Development. Supports the broad research agenda; this is not a verbatim quotation or an attribution of the displayed mathematical target.

https://voxdev.org/topic/evidence-gaps-key-unanswered-questions-across-16-topics-development-economics

Common conventions: Source mathematical conventions

Unless an entry states otherwise, agent and firm sets are finite. State and action spaces are measurable, strategies and outcome maps are measurable, probability laws are specified, and expectations exist for the targets under discussion. Infinite-horizon discount factors lie in $(0,1)$ and discounted objectives are finite. Norms, tolerances and complexity input sizes must be specified for computational comparisons. Constants or primitives that appear locally are inputs, not empirical facts asserted by this catalogue.

Identification. A statistical model $m\in\mathcal M$ implies an observable law $P_m$ and target $\theta(m)$. At law $P$, its identified set is \[ \Theta(P;\mathcal M)=\{\theta(m):m\in\mathcal M,\ P_m=P\}. \] Point identification holds when this set is a singleton, or equivalently when $P_{m_1}=P_{m_2}$ implies $\theta(m_1)=\theta(m_2)$ for all compatible models. Sharp bounds mean bounds attained, or approached when the boundary is not attained, by compatible models. Writing this set is a definition, not a solution: the substantive task is to establish informative restrictions and derive or estimate the set. An unrestricted-confounding model may give only trivial bounds.

Inference. Identification, estimability and valid uncertainty quantification are separate questions. Fix $\alpha\in(0,1)$. A confidence procedure $C_n$ over a declared law class $\mathcal P$ has asymptotic uniform coverage at level $1-\alpha$ when \[ \liminf_{n\to\infty}\inf_{P\in\mathcal P}\Pr_P\{\theta(P)\in C_n\}\geq1-\alpha. \] For set-identified targets the coverage event must be defined separately, for example coverage of the full identified set. If an entry uses identification-indexed models instead of uniquely indexed laws, the same coverage convention is applied over those models. Pointwise limits do not establish uniform validity, and asymptotic validity does not guarantee finite-sample precision. Sample sizes, dependence structures and weak-identification sequences are part of each application.

Counterfactuals. $Y_i(a)$ is a potential outcome under a specified intervention, including its timing, implementation and versions. It is not $Y_i$ conditional on voluntarily choosing $a$. A full intervention vector permits interference; shorthand scalar treatment notation does not silently impose no interference. Assignment, selection, attrition, anticipation and measurement must be specified. A claim about an instrument's local effect is not automatically a population policy effect.

Economic equilibrium. A counterfactual model includes best responses, constraints, feasibility, market clearing, state transitions and expectations consistent with the stated information. When several equilibria exist, either a declared selector is an input or the target is set-valued. Comparisons across policy regimes must use the stated selection convention. Reduced-form relationships are not presumed invariant after an equilibrium-changing intervention.

Optimization and welfare. Optimization is over the stated feasible set. If attainment is not established, $\sup$ or $\inf$ and $\varepsilon$-optimal choices replace an asserted maximizer or minimizer. For example, with value $V=\sup_{a\in\mathcal A}W(a)<\infty$, an $\varepsilon$-optimal set is $\{a\in\mathcal A:W(a)\geq V-\varepsilon\}$ for $\varepsilon>0$. Benefits, costs, risk and health or environmental outcomes can be aggregated only using declared units and conversion weights. Interpersonal utility weights, the treatment of fiscal transfers and foreign profits, discounting, and the behavioral welfare criterion are explicit inputs. A comparative-static derivative additionally requires existence and appropriate differentiability of the selected counterfactual.

Sources. Local labels [S1], [S2], and so forth restart in every question. Locators identify the relevant abstract, model, section or result at the level actually checked; a bibliographic or abstract-level verification is not represented as inspection of an entire proof. Working papers are distinguished from later publications and changing versions. Source summaries are paraphrased. All URL checks and editorial formulations refer to the audit date stated above.

Common conventions: Additional-source status and mathematical conventions

Of the 100 supplied ECON entries, 65 distinct problems appear here; 32 close matches refine earlier entries, and three duplicate questions map to existing entries. Appendix app:audited-crosswalk lists every destination. The attachment's P/R/S/U distinctions and source audits are retained. Its precise mathematical formalizations are editorial unless the individual audit says otherwise. Candidate subsidy targets ECON-004--006 retain their U flags; the resolved approval-core question ECON-007 updates OP-0202 above. The following supplied conventions govern both this part and the attributed ECON refinements elsewhere in the catalogue, subject to local restrictions.

Parameterized questions. A problem family lists inputs that must be fixed before an instance is evaluated: population, horizon, policies, information, data law, model class and welfare weights. These are required choices, not quantities the citations are assumed to specify. Undefined applications should not be treated as identified estimands.

Indices and integrability. Sets of agents, firms and regions are finite unless a population measure is explicitly used. $i,j$ index units, $r$ regions, $t$ dates and $h$ horizons. $\mathbb E$ and $\Pr$ refer to the declared population and law; $\mathbf1\{A\}$ is an indicator. Every displayed expectation and discounted sum needs existence in the stated domain. Infinite horizons use a discount in $(0,1)$ and a convergence condition; finite horizons may use discount one.

Optimization and existence. A displayed $\max$, $\min$, or $\arg\max$ is conditional on attainment. For finite feasible menus this is immediate; general spaces need continuity and compactness/coercivity or another existence argument. Without attainment, the target is the supremum/infimum and arbitrarily near-optimal feasible choices. $\inf\varnothing=+\infty$. Empty feasibility and an unidentified target are different issues.

Potential outcomes. $Y_i(z)$ is the outcome under a specified intervention, not the observed conditional mean among people choosing $z$. In binary comparisons $\Delta Y_i=Y_i(1)-Y_i(0)$. Specify assignment, treatment versions, compliance, information and observation horizon. Interference is allowed under a full policy vector; compressed exposure notation needs a justified mapping. No interference, consistency and overlap are substantive assumptions, not automatic consequences of notation.

Populations and observation. Fix baseline eligibility and population weights. Conditioning on post-policy employment, survival, relocation or program uptake can change the population being compared. Death is not ordinary loss to follow-up; outcomes truncated by death need a composite convention, joint survival target, or explicitly defined survivor stratum. Fertility-changing interventions need a population functional rather than an unexplained fixed descendant cohort. Measurement and missingness models must be supplied.

Identification. A structural model $m\in\mathcal M$ implies observable law $P_m$ and target $\theta(m)$. Identification means \[ P_{m_1}=P_{m_2}\ \Longrightarrow\ \theta(m_1)=\theta(m_2) \quad\text{for all }m_1,m_2\in\mathcal M . \] Without identification, the target is \[ \Theta(P;\mathcal M)=\{\theta(m):m\in\mathcal M,\ P_m=P\}. \] Writing $\theta(P)$ before establishing identification can hide incompatible latent models. Confidence sets additionally need a sampling law, confidence level, asymptotic sequence and uniformity class. Point identification, coverage and informative precision are separate properties.

Mediation. Total effects of randomized assignment are distinct from cross-world natural indirect effects. Belief/effort-channel effects require a meaningful mediator intervention and additional measurement, exclusion or mediation assumptions. Randomization of the initial policy alone does not supply those assumptions. Report bounds or interventional alternatives when the stronger target is unsupported.

Equilibrium. A counterfactual $W(z)$, $p(z)$, or $\mu_t^z$ requires individual optimization, feasibility, government/resource budgets, market clearing and state-transition laws. State equilibrium existence and selection, or report ranges over compatible equilibria. Initial-state integration includes subsequent endogenous transitions; current-state integration must use the policy-dependent distribution. Reduced-form invariance after policy is not assumed.

Welfare accounts. Scores, utility, health, dollars and ecological quantities require explicit conversion before addition. Fees, taxes, subsidies, wages and extortion payments are transfers between parties; distributional weights can make them welfare relevant, but their face value is not automatically a resource loss. State ownership, geographic boundaries, foreign-income weights and fiscal finance. Do not add profits to household welfare already including dividends, or count land capitalization and the underlying benefit twice. A benefit-cost expression must distinguish gross benefits from net welfare and subtract every real cost exactly once.

Derivatives and risk. Log derivatives require positive arguments; local responses require admissible perturbations and specified equilibrium branches. Differentiation under expectations needs regularity/domination, and boundaries may allow only one-sided derivatives. Risk uses a specified law; ambiguity uses a declared nonempty law set on a common history space. Adaptive policies must be nonanticipative. A robust criterion is an editorial choice unless attributed explicitly.

Scope overlaps. Allocation, collective choice, contracts, household bargaining and coordinated policy overlap economics with optimization and game theory. They are retained because they appeared in the original catalogue; no claim of a strictly game-theory-free selection is made.

Statement licence: CC-BY-4.0 · Source file

Contributions 1

Econ-612-A1 · Exploration · LLM-generated · gpt 6 astra ultra ·

GPT 6 Astra Ultra attempt on Econ-612

Digital tax features: network experiments and validation without perfect audits

Catalogue problem: H26-608. Status: Unresolved. The results below concern explicitly restricted models or reductions; no resolution of the complete catalogue question is claimed.

Question and scope

The catalogue asks for the welfare value of digital tax features, accounting for transaction-network spillovers, corrected collections, private burdens and enforcement errors. Its integrated refinement also asks whether records become more accurate relative to policy-dependent real activity. Pomeranz [pomeranz] provides experimental evidence on information and supply-chain enforcement; this does not identify every feature or privacy cost. We derive a factorial exposure design, a two-audit correction for record error and explicit limits on what bundled rollout and rare-event observations establish.

A fixed-cohort network design

There are $N$ baseline firms on a fixed undirected simple graph of maximum degree $D$. The two features are e-invoicing $E_i$ and prefilling $P_i$, independently randomized with probabilities $p_E,p_P\in(0,1)$ across firms and features. Define peer counts \[ H_i=\left(\sum_{j\sim i}E_j,\sum_{j\sim i}P_j\right),\qquad X_i=(E_i,P_i,H_i). \] Assume an exposure mapping: each firm's potential endpoint is a fixed function of $X_i$ rather than the rest of the assignment. This excludes unrecorded higher-order exposure and endogenous network rewiring. All outcomes refer to the same horizon and initial firm cohort, including closures by a declared outcome convention.

For exposure $x=(e,p,h_E,h_P)$, its assignment probability is \begin{align*} \pi_i(x)={}&p_E^e(1-p_E)^{1-e}p_P^p(1-p_P)^{1-p}\\ &\times {d_i\choose h_E}p_E^{h_E}(1-p_E)^{d_i-h_E} {d_i\choose h_P}p_P^{h_P}(1-p_P)^{d_i-h_P}, \end{align*} where $d_i$ is degree. Restrict a target population to firms for which the specified counts are feasible, so every relevant $\pi_i(x)>0$. Relabel its size as $N$ if necessary. Exposure comparisons average over this same population; one must not compare different degree-selected populations without declaring it.

Let $Y_i(x)$ be an endpoint, such as collected net tax after reversals and refunds, or a measured compliance burden. A validation procedure may observe a noisy variable $\widetilde Y_i$ with conditional mean $Y_i(x)$. It is performed with known probability $q_i(x)\geq q_*>0$, using an independent local audit coin $A_i$. Assume $|\widetilde Y_i|\leq B$. For administrative outcomes observed on every firm, take $A_i=q_i=1$ and no measurement noise.

Theorem (Exposure identification with a network variance bound): The estimator \[ \widehat\mu(x)=\frac1N\sum_i \frac{{\mathbf 1}\{X_i=x\}A_i\widetilde Y_i}{\pi_i(x)q_i(x)} \] is unbiased for $\mu(x)=N^{-1}\sum_iY_i(x)$. Suppose audit noises and audit coins are generated independently across firms and independently of feature assignments, conditional on the firm's exposure and fixed potential quantities. If $\pi_i(x)\geq\pi_*>0$, then \[ {\operatorname{Var}}\{\widehat\mu(x)\} \leq\frac{(D^2+1)B^2}{N\pi_*q_*}. \] Consequently the factorial interaction at fixed peer counts, \[ \iota(h)=\mu(1,1,h)-\mu(1,0,h)-\mu(0,1,h)+\mu(0,0,h), \] has an unbiased estimator with variance at most $16(D^2+1)B^2/(N\pi_*q_*)$ when the same lower bounds hold for all four exposures.
Proof: Condition first on the full feature assignment. Independent audit sampling and conditional unbiasedness make the conditional expectation of $A_i\widetilde Y_i/q_i(x)$ equal to $Y_i(x)$ whenever $X_i=x$. Averaging over assignment then cancels $\pi_i(x)$, proving unbiasedness.

Write $T_i$ for the summand before division by $N$. Its second moment is at most $B^2/(\pi_iq_i)$, because it is nonzero with probability $\pi_iq_i$ and then has absolute value at most $B/(\pi_iq_i)$. If the closed graph neighborhoods of $i,j$ are disjoint, the summands depend on disjoint independent assignment and validation variables and are independent. Each firm has at most $D+D(D-1)=D^2$ other firms within distance two. For possibly dependent pairs, Cauchy--Schwarz bounds covariance in absolute value by $B^2/(\pi_*q_*)$. There are at most $N(D^2+1)$ ordered pairs including diagonals that need be retained in the variance sum. Dividing by $N^2$ gives the result. Finally, the standard deviation of a sum is at most the sum of the standard deviations, by Cauchy--Schwarz in $L^2$; summing four bounds and squaring gives the factor sixteen. ∎

The positivity constants can be very small for extreme exposure counts, so formal identification does not ensure useful precision. A cluster or saturation design may improve support for policy-relevant exposures, but its probabilities and covariance structure must be recalculated rather than importing the formula above.

Record accuracy when audits have errors

For a fixed exposure let true activity be $T_i$, administrative record $Z_i$ and usable-linkage indicator $M_i\in\{0,1\}$. A missing record is assigned a predeclared loss $L_{\rm miss}\geq0$. The fixed-cohort accuracy endpoint is \[ L_i=M_i(Z_i-T_i)^2+(1-M_i)L_{\rm miss}. \] This definition avoids conditioning on a post-policy linkage outcome. True activity is allowed to change with policy.

Two validation measurements satisfy $V_{ir}=T_i+\varepsilon_{ir}$, $r=1,2$. Conditional on exposure and $(T_i,Z_i,M_i)$, assume each error has mean zero and their cross product has mean zero. Conditional independence is sufficient but stronger than necessary.

Proposition (Two noisy audits identify squared record loss): Under these conditions, \[ \widetilde L_i=M_i(Z_i-V_{i1})(Z_i-V_{i2}) +(1-M_i)L_{\rm miss} \] has conditional expectation $L_i$. Therefore Theorem Theorem (Exposure identification with a network variance bound) identifies policy-specific mean record loss and its exposure contrasts from independently sampled paired audits. If $|Z_i|,|T_i|,|V_{ir}|\leq K$, one may use $B=\max\{4K^2,L_{\rm miss}\}$ in that theorem. The validation variable itself can be negative even though its target is nonnegative.
Proof: Expand the observed cross product as \[ (Z_i-T_i)^2-(Z_i-T_i)(\varepsilon_{i1}+\varepsilon_{i2}) +\varepsilon_{i1}\varepsilon_{i2}. \] Conditional mean-zero errors and zero conditional cross moment remove the last three contributions. The missing-record term is already observed and fixed by convention. The absolute bound follows because each record--audit difference is at most $2K$. ∎
Proposition (One mean-unbiased audit is insufficient): If the audit-error variance is unknown and may change with policy, the joint observed law of a record and one conditionally mean-unbiased audit does not generally identify a policy contrast in squared record accuracy.
Proof: Take two randomized policy regimes, with every record linked and equal to zero. Under each regime the observed audit is an independent equiprobable $+1$ or $-1$. In model A, true activity is zero under both regimes and audit error is this sign, so both squared record losses are zero. In model B, keep the same construction under regime zero, but under regime one let true activity itself be the equiprobable sign and make the audit exact. Both models satisfy conditional audit mean-unbiasedness and generate the same observed record--audit law in both randomized arms. Their accuracy contrasts are zero and one. The second audit in Proposition Proposition (Two noisy audits identify squared record loss) distinguishes the models through cross-measurement dependence. ∎

The example is in common centered measurement units; adding a sufficiently large common constant to truth, record and audit makes all levels nonnegative without changing the loss or argument. Correlated audit errors would contribute their conditional covariance to the paired-audit estimator and require a separate correction or sensitivity bound.

Two further design limits

Proposition (A bundle does not identify a feature interaction): A design that assigns only $(E_i,P_i)=(0,0)$ or $(1,1)$ cannot, without extra restrictions, identify the two-feature interaction even in the absence of network interference.
Proof: Let every unit's endpoint be bounded in $[0,1]$, with potential values $Y(0,0)=0$ and $Y(1,1)=1$. Model A sets both unobserved discordant-arm outcomes to zero, giving interaction one. Model B sets both to one, giving interaction minus one. All observed potential outcomes under the bundled design agree exactly. No assignment sample size distinguishes the models. ∎
Proposition (Zero observed breaches and bounded severity): In $n\geq1$ independent audit-period observations with common breach probability $p$, observing no breaches gives a one-sided $(1-\alpha)$ upper confidence bound \[ p\leq u_n:=1-\alpha^{1/n},\qquad 0<\alpha<1. \] More precisely, the rule reporting this bound on zero events and the bound one otherwise has coverage at least $1-\alpha$ for every $p$. If breach severity is bounded by a known $S_{\max}$, expected breach loss is at most $S_{\max}u_n$ on the covered event. Without a finite severity restriction, zero events cannot imply a finite uniform upper bound on expected loss.
Proof: If $p>u_n$, the only way the stated rule fails is to observe zero events, whose probability is $(1-p)^n<\alpha$. For $p\leq u_n$ it cannot fail. Expected loss is at most $S_{\max}p$. Without a severity bound, fix any sufficiently small positive $p$ so zero events remain likely and let loss conditional on a breach tend to infinity; the expected loss becomes arbitrarily large while the zero-event probability is unchanged. ∎

Welfare interpretation and remaining gaps

Use the identified corrected revenue, private welfare and measured cost endpoints in the catalogue's declared monetary objective. Transfer payments, private equivalent variation and separately valued errors must have disjoint accounting roles. If a finite policy menu has simultaneous welfare intervals $[L_d,U_d]$, choosing a feasible policy maximizing $L_d$ has regret at most $\max_d(U_d-L_d)$ relative to an optimal policy included in the same feasible set: its lower bound is at least the optimum's lower bound. Unmeasured burden or breach losses must widen these intervals, not be set to zero.

The results depend on a valid fixed network exposure map, known assignment probabilities, independent validation noise and externally chosen loss valuations. They do not identify the consequences of nationwide network rewiring, nonrandom staggered adoption, endogenous audit targeting without known propensities, or unbounded privacy harm. Those issues and the empirical feature-response surfaces remain unresolved. The contribution here is an explicit design-and-validation calculation rather than an asserted universal benefit of digitization.

Completion Estimate

61%

This is a post hoc, heuristic estimate for the full catalogue target. The attempt proves network-exposure identification and variance bounds, corrects record loss with paired noisy audits, and derives bundle and rare-breach identification limits. The complete digital-tax welfare choice still needs measured feature-response surfaces and valuations, valid exposure and audit models, and treatment of endogenous rollout, network rewiring, private burdens and privacy severity.

References

  • [pomeranz] D. Pomeranz (2015), "No Taxation without Information: Deterrence and Self-Enforcement in the Value Added Tax," American Economic Review 105(8), 2539--2569. https://doi.org/10.1257/aer.20130393. Primary publisher abstract verifies the information and supply-chain experimental motivation; no paired-audit theorem or comprehensive privacy valuation is attributed to it.

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