Closing the HolesExemptions, Border Compliance and Disaster Risk in Tonga

Abstract

Tonga has a complete modern tax system: consumption tax, income tax, excise and customs duty. Its revenue problem is erosion, not absence. The IMF's August 2025 mission advised boosting domestic revenue by removing tax-inefficient exemptions without increasing statutory rates, and by improving administration.^[1] Tonga is at high risk of debt distress, and its FY2025 surplus of 5.6% of GDP turns into a projected 7.7% deficit for FY2026 once uncommitted grants are excluded. This paper specifies three models: a tax-expenditure model that prices each exemption and ranks it by how much of its benefit reaches poorer households; a label-free customs valuation model, since most tax is collected at the wharf; and a disaster revenue-buffer model that sets a contribution rule from the simulated distribution of revenue losses. An illustrative run of the buffer model finds a mean annual disaster revenue loss of 2.7% of revenue but a 1-in-20-year loss of 15.6%, which a rule-based buffer of about 16% of annual revenue would cover.

The paper then sets out the solution in deployable form: the policy measures, the data science and AI models behind each, and an open-source software architecture that plugs into the customs and revenue systems Tonga already runs.

Introduction

Tonga collects more tax relative to GDP than most Pacific neighbours, yet remains dependent on grants. Three structural facts explain this. Most taxable consumption is imported and financed by remittances, so revenue rises and falls with transfers from Tongans abroad. Labour mobility schemes move wage earners offshore, thinning the PAYE base. And every disaster, including the January 2022 Hunga Tonga-Hunga Ha'apai eruption and tsunami, produces both a collection shock and a new layer of exemptions that rarely expire.

The research question follows the IMF's constraint. If rates cannot rise, how much revenue can be recovered from the existing base, from which exemptions and which compliance channels, at what cost to poorer households, and how should part of it be set aside so that the next disaster does not force new borrowing?

The organising identity is the split of the revenue gap for tax k into a policy gap and a compliance gap:

R^{pot}_{k} - R_{k} = \underbrace{\left(R^{pot}_{k} - R^{law}_{k}\right)}_{\text{policy gap: exemptions}} + \underbrace{\left(R^{law}_{k} - R_{k}\right)}_{\text{compliance gap: border and inland}}

Current tax issues

2.1 Grant-masked deficits and debt

The FY2025 overall surplus of 5.6% of GDP was largely driven by grants. Counting only firmly committed grants while reconstruction spending stays high, the IMF projects a deficit of 7.7% of GDP in FY2026. Tonga is assessed at high risk of debt distress.^[1] Debt service reached 3.5% of GDP in FY2024 and stays above 3% until FY2027. The IMF asks the government to avoid new non-concessional borrowing, which leaves domestic revenue and grants as the only financing.

2.2 Exemptions

Consumption tax and duty concessions have accumulated through disaster relief, reconstruction, development projects, diplomatic and church privileges, and sector incentives for tourism, agriculture and fisheries. Each is defensible alone. Together they break the input-credit chain, create routes for diverting exempt goods into taxed markets, and require officer discretion at the counter. I found no regularly published tax-expenditure statement, so the cost is unmeasured. That is the first gap this paper addresses.

2.3 Border dependence

Most consumption tax and excise is collected on imports at Nuku'alofa. The IMF credited stronger consumption and income tax receipts in FY2024 to remittances and construction. The main evasion channels are therefore under-valuation, misclassification into lower-duty or exempt codes, and abuse of exemption certificates. The inland taxpayer population is small, so audit coverage inland yields less than targeting at the wharf.

2.4 A shrinking wage base

Seasonal and longer-term labour schemes in Australia and New Zealand take working-age Tongans offshore. Their income is taxed abroad or not at all, and returns as remittances that are taxed only when spent. The tax mix drifts toward the border whether or not policy intends it, which raises the stakes on customs integrity.

2.5 Disaster shocks

Tonga ranks among the most disaster-exposed countries in the world. The 2022 eruption halted commerce and collection for weeks. Tonga has led support for the Pacific Resilience Facility, and a Disaster Risk Management bill is in train, but no fiscal rule links good-year revenue to a national buffer.

2.6 Governance

The IMF flags governance and corruption vulnerabilities and notes the 2024 establishment of the Anti-Corruption Commission Office. Discretionary exemptions and manual valuation are the two points in the tax system where those vulnerabilities bite. Models that replace discretion with a published score reduce that exposure.

Models

3.1 Model A: pricing and ranking exemptions

Model A answers the IMF's instruction directly: which exemptions to remove at unchanged rates. It starts from the theoretical consumption tax liability across commodities i:

VTTL = \sum_{i} \tau_i \left[ HC_{i} + GC_{i}\,\phi^{G}_i + \sum_{s} IC_{i,s}\left(1-\rho_s\right) + GFCF_{i}\left(1-\rho^{K}_i\right) \right] - \theta

HC is household consumption, GC government consumption with taxable share phi, IC intermediate consumption by sector s with input-credit recovery rho (zero for exempt sectors), GFCF investment, and theta the revenue excluded by the registration threshold. In Tonga, where most taxable goods are imported, VTTL can be built from customs entries by HS code plus the Household Income and Expenditure Survey, without a supply-use table.

The cost of exemption j is found by re-running VTTL with j switched off, allowing demand to respond through own-price elasticity epsilon and netting off the input credits that become claimable:

TE_j = \tau \cdot B_j \cdot \left(1 + \varepsilon_j \cdot \frac{\tau}{1+\tau}\right) - \Delta\rho_j

Each exemption then gets a targeting ratio: the share of its benefit that reaches the bottom 40% of households, using survey spending x and weights w:

\eta_j = \frac{\sum_{h \in B40} w_h\, x_{h,j}}{\sum_{h} w_h\, x_{h,j}}

A leakage term is added for exemptions granted by certificate, estimated as the share of exempt imports by certificate holders that exceeds a plausible own-use benchmark U:

\ell_j = \frac{\sum_{c} \max\!\left(0,\; M^{exempt}_{c,j} - U_{c,j}\right)}{\sum_c M^{exempt}_{c,j}}

The decision rule removes exemption j when its targeting is poor or its leakage high, and returns part of the revenue to poor households as a transfer:

\text{remove } j \;\; \text{if} \;\; \eta_j < \eta^{*} \;\text{or}\; \ell_j > \ell^{*}; \qquad \Gamma = \sum_{j \in removed} TE_j \cdot \eta_j; \qquad \Delta R^{net} = \sum_{j \in removed} TE_j \left(1 - \eta_j\right)

Because eta is below 1 for every exemption, net revenue is positive for every removal, and the bottom 40% are held harmless in aggregate.

3.2 Model B: label-free customs valuation

Model B scores every import entry for under-valuation and misclassification without needing past seizure data. For entry e of HS code g from origin o in month t, the declared unit value u is compared with a robust reference built from three independent sources: the peer distribution of other importers, mirror export data from the partner country, and published international prices where they exist:

z^{peer}_e = \frac{\ln \tilde{u}_{g,o,t} - \ln u_e}{1.4826 \cdot MAD_{g,o,t}}, \qquad z^{mirror}_e = \frac{\ln u^{X}_{g,o,t} - \ln u_e}{\sigma^{X}_g}

The tilde denotes the median and MAD the median absolute deviation, so a cluster of under-valuers cannot pull the benchmark down. Physical constraints give hard flags: declared weight must be consistent with container count and shipping manifest; declared quantity of vehicles must equal registrations. Misclassification is scored by the duty saved if the entry moved to its nearest plausible neighbour code g-prime, weighted by the similarity of the goods description d:

m_e = \max_{g'} \left[ \left(\tau_{g'} - \tau_g\right) \cdot \mathrm{sim}\!\left(d_e, g'\right) \right]

The entry score and the importer score, which accumulates over the importer's entries, are:

A_e = \omega_1 \max(0, z^{peer}_e) + \omega_2 \max(0, z^{mirror}_e) + \omega_3 m_e + \delta H_e, \qquad A_i = \frac{\sum_{e \in i} V_e A_e}{\sum_{e \in i} V_e}

Expected revenue at stake converts the score to pa'anga, and determines whether the response is inspection at the wharf or post-clearance audit of the importer:

\mathbb{E}[Y_e] = \left(\tau^{duty}_g + \tau^{excise}_g + \tau^{CT}\left(1 + \tau^{duty}_g + \tau^{excise}_g\right)\right) \cdot q_e \cdot \max\!\left(0, \tilde{u}_{g,o,t} - u_e\right)

3.3 Model C: disaster revenue buffer

Model C sets how much revenue to save in normal years. Disaster events arrive as a Poisson process with rate lambda. Each event destroys a share X of annual revenue, with X lognormal and capped. Annual revenue loss L is the compound sum:

N \sim \mathrm{Poisson}(\lambda), \qquad X_n \sim \mathrm{LogNormal}(\mu_X, \sigma_X^2), \qquad L = \min\!\left(\sum_{n=1}^{N} X_n,\; L^{max}\right)

The buffer target is the loss quantile at confidence q, net of expected insurance and facility payouts I:

B^{*} = VaR_q(L) \cdot \bar{R} - \mathbb{E}\left[I \mid L \ge VaR_q\right]

The contribution rule saves a fixed share c of revenue plus a share of any revenue surprise above forecast, until the buffer is full, and permits drawdown only on a declared disaster:

B_{t+1} = B_t (1 + r) + \mathbb{1}\!\left[B_t < B^{*}\right]\left( c R_t + \psi \max\!\left(0, R_t - \hat{R}_t\right) \right) - \mathbb{1}\!\left[\text{disaster}_t\right] \min\!\left(B_t, L_t R_t\right)

The rule links Models A and B to Model C: the first use of revenue recovered from exemptions and customs is filling the buffer, which is a use the IMF's call to rebuild buffers supports and which does not raise recurrent spending.

Algorithms and illustrative results

4.1 Algorithm 1: exemption ranking

INPUT  customs entries with exemption codes, HIES microdata, CT returns by sector,
       elasticities eps[j], thresholds eta_star, ell_star
BUILD  VTTL_base from imports by HS code + domestic taxable supply
FOR each exemption j:
  VTTL_j   <- rebuild with exemption j removed, demand adjusted by eps[j]
  TE[j]    <- VTTL_j - VTTL_base - new input credits
  eta[j]   <- bottom-40% share of spending on j from HIES
  ell[j]   <- share of certificate imports above own-use benchmark
  score[j] <- TE[j] * (1 - eta[j]) * (1 + ell[j])
SORT exemptions by score descending
SELECT j where eta[j] < eta_star OR ell[j] > ell_star
COMPUTE transfer Gamma = sum TE[j]*eta[j]; net gain = sum TE[j]*(1 - eta[j])
PUBLISH annual tax-expenditure statement with TE, eta, ell per exemption

4.2 Algorithm 2: customs entry scoring

FOR each (HS code g, origin o, rolling 12-month window):
  u_med, MAD <- robust centre and spread of declared unit values
  u_mirror   <- partner-country export unit value where available
FOR each new entry e at lodgement:
  z_peer, z_mirror <- standardised shortfalls (positive side only)
  m_e   <- duty saved under nearest plausible code * description similarity
  H_e   <- 1 if weight, container or registration identities are breached
  A_e   <- w1*z_peer + w2*z_mirror + w3*m_e + delta*H_e
  EY_e  <- full tax rate stack * quantity * max(0, u_med - u_e)
  IF H_e = 1 OR EY_e > inspect_threshold: hold for documentary or physical check
  ELSE release, and add A_e to importer running score A_i
MONTHLY: rank importers by value-weighted A_i -> post-clearance audit list
AFTER each finding: update weights w by Bayesian reweighting

4.3 Illustrative results: disaster buffer

Inputs are assumed, not estimated: event rate 0.3 a year, median loss per event 6% of annual revenue, log standard deviation 0.9, annual loss capped at 80%. The run uses 200,000 simulated years.

Budgeting for the mean loss leaves the government short in exactly the years that matter. A buffer sized to the 1-in-20-year loss is about 16% of annual revenue. A contribution of 3% of revenue a year fills it in about five years; 2% takes about eight. Losses beyond the 95th percentile are better transferred to parametric insurance and the Pacific Resilience Facility than self-insured.

4.4 Illustrative arithmetic: exemptions

No Tongan tax-expenditure estimate is public, so this is arithmetic, not a finding. If exemptions cost 3% of GDP, a range typical for small states, and the exemptions failing the targeting and leakage tests account for half of that with an average eta of 0.25, then removal yields 1.5% of GDP gross, 0.4% returns to poorer households as transfers, and 1.1% of GDP is net new revenue with no rate increase. That is about one-seventh of the projected FY2026 deficit.

Solutions, software, data science and AI models

Tonga's solution is mostly at the border and in the exemption register, so the software centres on customs entries, exemption certificates and a published tax-expenditure statement. Each issue maps to a policy measure, a model and a component.

5.1 Issue-to-solution map

5.2 Model catalogue

5.3 Reference architecture

flowchart TD
  A[Sources<br/>customs entries, CT and PAYE returns, exemption certificates, HIES, remittance data] --> B[Ingestion and data quality]
  B --> C[Warehouse<br/>bitemporal, single taxpayer view]
  C --> D[Entry scoring service<br/>unit value, HS classifier, hard flags]
  D --> E[Customs selectivity<br/>release, document check, inspect]
  C --> F[Importer risk and certificate leakage]
  F --> G[Post-clearance audit cases]
  G --> H[Findings]
  H --> D
  C --> I[Policy lab<br/>tax expenditures, forecasts, buffer rule]
  I --> J[Published TE statement and dashboards]

The entry scoring service is the only component that must respond in real time; everything else runs nightly or monthly.

5.4 Software stack

5.5 Governance of AI use

5.6 Team and indicative effort

A joint customs and revenue analytics cell of five is enough: one lead, one data engineer, two analysts and one customs valuation specialist. External build support of about 12 months is realistic, and regional pooling with other Pacific administrations would cut cost, since unit-value benchmarks improve when several small islands share reference prices. Cost and staffing figures are my estimates.

Reform sequence

Limitations

References & keywords

• IMF — Tonga 2025 Article IV mission concluding statement ↗
  August 25, 2025 · Fiscal balance, debt risk and exemptions
• IMF — Tonga 2025 Article IV consultation ↗
  Country Report 25/293 · Subsequent staff report and debt analysis
• IMF — Tonga 2024 Article IV consultation ↗
  Country Report 24/326 · Prior fiscal and external context
• Keen — The Anatomy of the VAT ↗
  IMF Working Paper 13/111 · VAT performance framework
• Hutton — Revenue Administration Gap Analysis Program ↗
  IMF Technical Notes and Manuals 17/04 · VAT gap methodology
• WTO — Customs Valuation Agreement ↗
  Primary legal text on customs valuation methods and safeguards
• World Bank — Catastrophe Risk Financing in Developing Countries ↗
  Cummins and Mahul · Disaster financing principles