Assessment of the Socio-Economic Damage from Road Traffic Accidents Based on an Inter-Sectoral Damage Redistribution Matrix

Abstract

This research focuses on the challenge of measuring the socio-economic impact of road traffic accidents (RTAs) by examining how losses are redistributed across major institutional sectors, including the government, businesses, and households. Unlike traditional cost-based approaches, the analysis relies on a modified input–output framework that captures not only the direct losses but also the indirect damage flows transmitted from one sector to another. This methodology makes it possible to reveal the multiplicative propagation of losses, determine the proportion of net costs, and quantify the transfer dependencies between institutional agents. Using compiled and adapted data for the Azerbaijani economy, the study estimates the net economic damage from RTAs at 2268.17 million manats after adjusting for internal transfers. The results show that households bear more than 47% of total losses, the enterprise sector accounts for approximately 39%, and the government absorbs nearly 13%. The model also isolates an “additional damage” component, reflecting lost income, profits, and tax revenues, and demonstrates that every 1000 RTA generates a chain reaction of interlinked costs that substantially amplifies the overall effect. The findings highlight the necessity of integrating input–output analytical approaches into the practical assessment of RTA-related economic consequences, particularly in countries with limited statistical capacity and structurally diverse institutional linkages.

1. Introduction

Road traffic crashes impose a substantial public health and economic burden worldwide, causing more than one million deaths annually worldwide. International evidence suggests that the associated economic costs amount to roughly 3% of GDP in most countries and up to 5% in many low- and middle-income settings; for Azerbaijan, a recent World Bank assessment estimated the burden at about 2% of GDP (2019) [1]. In this context, we develop an inter-sectoral accounting of gross and net (irrecoverable) socioeconomic losses for Azerbaijan.

Road traffic accidents (RTAs) remain one of the most pressing social and economic challenges facing contemporary societies, exerting a complex influence on the stability of national economic systems, particularly in developing countries. Despite the apparent scale of the problem, the true cost of RTAs is often underestimated, as official statistics

Typically capture only direct material losses while overlooking the indirect and multiplicative effects that propagate across economic agents. The lack of comprehensive information on the full range of socio-economic costs and on methods for assessing them is especially evident in middle-income countries and in Azerbaijan [1]. RTA consequences extend far beyond the health sector or transport infrastructure, affecting broader socioeconomic processes—from reduced productivity and lower tax revenues to the reallocation of resources among institutional sectors, as reflected in national and international estimates of total losses [2].

In recent years, researchers have increasingly emphasized the need for a comprehensive assessment of traffic-related losses that incorporates both cost-based and structural dimensions [3]. However, most existing methodologies, including the widely used human capital approach, treat RTA damage in isolation, without accounting for inter-sectoral linkages. This omission significantly limits the applicability of results to economic policy and decision-making [4].

Alongside cost-based approaches, recent empirical studies have increasingly applied advanced valuation techniques to estimate the socio-economic costs of road traffic accidents. For example, Topcu and Coruh (2025) assess the external costs of traffic-related fatalities and injuries in Türkiye using the willingness-to-pay (WTP) method, showing that accident-related welfare losses may reach approximately 2–3% of GDP [5]. While such valuation-based studies provide important aggregate estimates of societal welfare losses, they primarily focus on total external costs and do not address how these losses are distributed across institutional sectors or how accident-related damage propagates through inter-sectoral economic linkages.

Recent empirical evidence from low- and middle-income countries further highlights the uneven distribution of RTA losses across economic agents. For example, Kpe et al. (2024), using a household-level survey in Ghana, show that direct, indirect, and intangible costs of road traffic injuries impose a substantial and often catastrophic burden on households, with uninsured families bearing significantly higher expenses [6]. While such micro-level studies provide valuable insights into the financial vulnerability of households and the social consequences of RTAs, they remain limited to a single institutional perspective and do not explain how these household-level losses are transmitted to other sectors of the economy or aggregated at the macroeconomic level.

At the macroeconomic level, national studies most commonly rely on the cost-of-illness approach to assess the overall burden of road traffic crashes and the potential benefits of safety interventions. Spencer et al. (2025) estimate that road traffic crashes cost Saudi Arabia approximately 3.3% of GDP and show that scaling up selected interventions could yield annual savings of 0.35–0.8% of GDP [7]. While highly relevant for macroeconomic calibration, such estimates primarily focus on aggregate outcomes and offer more limited insight into the institutional mechanisms through which damage is generated, propagated, and redistributed, including distinctions between gross and net losses.

Against this background, the use of an inter-sectoral damage redistribution matrix provides a means not only to quantify direct and indirect losses, but also to explicitly trace the internal relationships among institutional sectors that govern damage transmission. A substantial methodological foundation already exists in disaster-impact and economic-disruption studies, where cross-sector effects are traced using static and adaptive variants of input–output (I–O) models [8]. At the same time, the presence of transfers often creates the illusion of recovery, although many of these flows represent nothing more than a redistribution of resources without any real reduction in overall socio-economic costs [1].

In line with the above-mentioned problems, this study pursues the following research objectives:

• To identify the channels through which RTA-related damage is transmitted across institutional sectors;
• To determine the coefficients of intermediate losses and construct the inter-sectoral damage redistribution matrix;
• To estimate the total and net economic damage;
• To analyze the structure of foregone income and evaluate the multiplicative effect generated by inter-sectoral propagation of losses.

2. Materials and Methods

Methodologically, this study relies on a reduced balance matrix covering the three core institutional sectors, complemented by a Leontief-style input–output (I–O) framework to trace and aggregate the costs associated with road traffic accidents (RTAs). Such a structure makes it possible to consider RTAs as an economic phenomenon embedded within the system of interdependencies among the government, enterprises, and households.

Recent methodological advances have increasingly applied refined input–output frameworks to assess the economic impacts of external shocks and disaster events, capturing both direct and indirect losses across interconnected sectors [9]. Such studies underscore the suitability of structural I–O approaches for tracing cascading effects through production networks. It should be noted that the process of damage generation and transmission associated with road traffic accidents—including inter-institutional redistribution of losses, foregone income, and the formation of final and total damage—is conceptually adapted to the logic of the input–output framework. Specifically, the indicators of the inter-institutional damage balance are mapped onto the corresponding elements of the conventional input–output table used to describe the production of goods and services.

Within this adapted framework, intermediate inputs are interpreted as transferred damage, intermediate output as received damage, value added as foregone income, final demand as final damage, and gross output as total economic damage. From this perspective, the inter-institutional balance of damage generation and distribution can be referred to as an “event–damage” table, while the corresponding analytical framework may be termed an “event–damage” model.

Although the mathematical notation and formal structure of the proposed event–damage model remain consistent with those of the classical input–output framework, its economic interpretation is fundamentally different. This reinterpretation constitutes a key applied innovation of the study, as it enables the input–output apparatus to be operationalized for road traffic accident damage accounting and allows for a structured separation of gross and irrecoverable losses across institutional sectors.

The usefulness of this approach becomes especially apparent when comparing the input–output method for assessing intermediate, indirect, and total losses with classical cost-based methodologies. Although the concept of interconnected economic flows had been discussed as early as the eighteenth century, it was Wassily Leontief who formalized and empirically validated the I–O model, giving it a complete analytical form [10]. Initially applied to study structural changes arising from fluctuations in aggregate demand or changes in sectoral output, the I–O approach has since been extended to estimate the multiplicative effects of non-economic disturbances, including losses from accidents, disasters, and other non-productive shocks. The input–output model enables the determination of direct, indirect, and income-induced changes in sectoral output triggered by a disturbance in one or more sectors.

Although, as noted by Wijnen, W. et al. (2016) [11], the method has certain limitations—such as the assumption of linear relationships and the need for accurate data for each cell of the matrix—it remains one of the most effective analytical instruments for examining the socio-economic consequences of RTAs. One important advantage of this approach is its ability to assess sectoral vulnerability to RTAs in two ways: first, by quantifying the indirect damage transmitted across sectors; and second, by capturing the dual nature of direct losses—those incurred directly from another sector and those arising because of losses propagated to other sectors.

It is important to emphasize that financial transfers are not included in the calculation of total socio-economic costs, as they represent redistribution rather than an actual loss. Transfers for one agent but benefits for another; therefore, at the level of the national economy, such operations do not generate new losses and cannot be considered part of net damage [11]. Nevertheless, to accurately reflect all inter-sectoral linkages, these transfers must be included in the model, with the understanding that they will later be deducted from the aggregate loss to obtain the measure of net economic damage.

The socio-economic costs of RTAs represent the aggregate value of crash consequences and encompass all categories of direct and indirect effects. Their structure and magnitude depend not only on crash severity but also on the unit costs assigned to damage components under country-specific methodological standards, which largely explains the substantial variation in estimates across studies. To make the uneven distribution of the burden explicit, we employ an inter-sectoral input–output accounting. This framework clearly identifies who ultimately bears which costs and through which mechanisms in a middle-income setting; and because it relies on official statistics and standard unit-cost templates, a data-light variant (reduced category set; truncated multipliers) remains applicable in low-income countries as well, preserving the key distributional insights where statistics are limited.

Methodological approaches to assessing the socio-economic costs of RTAs differ significantly across countries. High-income economies predominantly use the willingness-to-pay (WTP) approach, which captures intangible losses, whereas middle- and low-income countries tend to rely on the human capital approach. Notably, only about 5% of existing studies, as highlighted by Azmi and Ram (2024), focus on low-income countries, indicating a substantial gap in the empirical evidence [12]. Given data constraints and the practical difficulties of implementing the WTP method, hybrid approaches that combine both valuation-based and productivity-based metrics are particularly valuable.

According to Wijnen et al. (2019) and Bougna et al. (2022), the applicability of these methods depends on country-specific characteristics, including population density, road safety levels, and socio-economic structures [13,14]. Without considering such factors, studies relying solely on the human capital approach systematically underestimate total RTA costs, often by as much as half compared with WTP-based estimates [15].

It has been noted that the effectiveness of data analysis depends not solely on the choice of method but on the clarity of the research objective—understanding which patterns and interdependencies the analysis seeks to uncover [16]. This consideration is particularly relevant for economic studies of RTAs, where the balance between methodological precision and interpretability is crucial.

Dimitriou and Poufinas (2016) [17] apply insurance-based methods to estimate the “pure premium”—the reserve needed to compensate victims and their families for lost income. Although this measure reflects the present value of future earnings, it does not incorporate institutional losses borne by the government and enterprises [17].

Pukalskas et al. (2015) define the distinction between direct and indirect costs, where direct costs include those that can be immediately quantified (such as health-related damage, property losses, environmental harm, and investigation expenses), while indirect costs refer to those that cannot be assessed directly but have delayed negative effects, typically affecting productivity of labor and transportation [18]. Msallam (2019) and Sugiyanto and Santi (2017) employ regression and mathematical models to establish relationships between accident types, driver behavior, road infrastructure, and weather conditions [19,20]. However, as Ghadi et al. (2018) note, many such models do not incorporate indirect and environmental consequences, resulting in systematic underestimation of total RTA costs [15].

Two key assumptions underpinning the input–output analysis of RTA damage are, first, the existence of fixed linear relationships between costs and outputs, and second, a single-level valuation of consequences. The primary objective of the method is to identify the structure and magnitude of mutual influences among sectors, which, in the context of this study, makes it possible to trace how losses incurred by one agent (e.g., households) are transmitted to others (the government and enterprises).

Thus, the input–output technique—widely applied in other studies of structural interdependencies in the economy [21]—combined with the social balance approach, provides a means not only to estimate total losses but also to decompose them across institutional sectors, including the government, enterprises, and households. This forms the analytical foundation of the present study.

Overall, the review confirms the necessity of a comprehensive approach to assessing the consequences of RTAs, one that accounts for both direct and indirect costs as well as the redistribution of losses among government, enterprises, and households.

The inter-sectoral damage redistribution matrix reflects the system of relationships among the key institutional sectors—the government, enterprises, and households—demonstrating that the total amount of damage received is equivalent to the total amount of damage inflicted by all agents. In the context of road traffic accidents, this implies that the loss incurred by one sector inevitably becomes an expenditure for another, generating a chain reaction of economic consequences. For example, losses faced by enterprises due to disruptions in logistics chains reduce government tax revenues and eventually influence household income levels.

Loss categories are quantified using pre-specified deterministic rules, assigned to model channels (intermediate resource use, final unrealized benefits, and monetary transfers). The inter-sectoral damage-redistribution matrix thereby formalizes the relationships among government, enterprises, and households and ensures internal accounting consistency that gross damage inflicted equals the aggregate damage received across agents. In the first-quadrant rows, the propagated (intermediate) damage $x_{ij}$ records losses transmitted from agent $i$, alongside each sector’s foregone economic and social benefits and the total inflicted damage; in the columns, the matrix shows the damage received by agent $j$, the corresponding foregone value, and the total losses borne due to RTAs.

Mathematically, this relationship is described by the following system of equations:

$$X=AX+Y$$

(1)

In this system of equations, the vector $X_i$ represents the total damage received by the $i$-th agent; the matrix $A$ denotes the matrix of direct damage coefficients; and $Y_i$ corresponds to the final damage, that is, the potentially unrealized future economic value—including foregone tax revenues, profits, and incomes—resulting from RTAs. The matrix $A$ consists of technological coefficients, that is, the direct damage coefficients $a_{ij}$. These coefficients are calculated as follows:

$$a_{ij}= x_{ij}{/X}_j,$$

(2)

where $x_{ij}$ is the amount of damage transmitted from agent $j$ to agent $i$ as intermediate damage, and $X_j$ is the gross damage incurred by agent $j$.

Thus, the coefficient $a_{ij}$ measures the share of losses transmitted from one participant to another and reflects the extent to which the losses of one sector become the costs of another. This may include not only financial compensations but also broader economic interdependencies, such as increased social protection expenditures, reduced labor productivity, and other systemic effects.

Indirect damage is interpreted as the portion of losses transmitted from one agent to another, which is not captured by traditional cost-assessment methods. Indirect damage may include:

• A compensatory (positive) effect:

An increase in the number of RTAs leads to higher insurance payouts, which in turn contribute to rising insurance premiums for all participants—both individuals and enterprises.

• An aggravating (negative) effect:

Damage to infrastructure or disruptions to economic activity (e.g., road closures, damaged enterprises) may lead to breakdowns in supply chains and interruptions in production processes.

To determine the multiplicative effect of RTA-related damage, the matrix $(E-A)$ was inverted. However, when its determinant $\mid E-A\mid$ approaches zero, direct numerical inversion becomes unstable. In such cases, the inverse matrix is expressed as a convergent power series:

$${(E-A)}^{-1}=1+A+A^2+A^3+\cdots +A^{k }+\cdots ,$$

(3)

where $A^k$ represents the indirect damage of order $k-1$. Since the direct damage coefficients $a_{ij}$ are less than one, the coefficients of indirect damage in the matrices $A^k$ decrease rapidly as $k$ increases. Therefore, in practical applications, the inverse matrix is often approximated by truncating the series at a finite value of $k$, and typically $k=2$ is sufficient.

By multiplying the inverted matrix $(E-A{)}^{-1}$ by the vector of unrealized economic benefits (future losses) $Y_i$, one can determine the distribution of indirect losses across sectors and compute the gross damage:

$${∆X=(E-A)}^{-1}∆Y,$$

(4)

where $\mathsf{\Delta }X$ is the increment (total effect) of damage, and $\mathsf{\Delta }Y$ represents the increment of the initial loss associated with unrealized future benefits.

Once $(E-A{)}^{-1}$ is obtained, it becomes possible to interpret how primary damage triggers a chain reaction of additional losses across all sectors—that is, the multiplicative effect. For example, a reduction in enterprise-level damage resulting from a decrease of 1000 RTAs may translate into lower government losses through increased tax revenues and higher household incomes. We treat the “−1000 RTAs” case as a discrete, policy-relevant benchmark calibrated to the 2023 composition; given linear homogeneity, results scale proportionally with the perturbation size (e.g., per 100 crashes or per 1%).

The concept of final economic damage—understood here as unrealized tax revenues, profits, and incomes—is treated as an aggregated indicator reflecting the total volume of future expenditures caused by RTAs. In economic terms, it is analogous to the notion of final output in classical input–output models, but with a fundamentally different interpretation: instead of quantifying the creation of value added, it measures the destruction of existing economic value and the loss of potential gains that could have been realized under normal system functioning. Thus, the indicator captures the combined effect of reduced productive and social capacity resulting from RTAs, expressed in the form of forgone income, tax revenues, productivity, and other types of net losses. From a policy perspective, this interpretation of final economic damage can be applied to optimize resource allocation and evaluate the effectiveness of road safety programs.

In economics, final output refers to the portion of gross output that generates new value. In the damage framework, final damage similarly refers only to the portion of total losses that is not redistributed among the main institutional sectors and constitutes new, non-transferable loss.

To analyze the impact of the unrealized benefits of sector $i$ on the total amount of economic damage, the inter-sectoral damage redistribution equation is modified accordingly: the multiplier reflects the full aggregate effect of final damage as it spreads through all economic and social linkages among agents.

To obtain numerical estimates of intermediate direct losses and losses from unrealized benefits, the authors applied existing methodologies for calculating socio-economic damage and classified them according to the types of losses they include:

Government-sector damage

• Human capital valuation method, which estimates government losses resulting from reduced productivity due to temporary or permanent disability of individuals injured in RTAs (foregone benefits—$V_i$).
• Law-enforcement cost method, which includes expenditures on policing, investigations, courts, and incarceration associated with RTAs (direct intermediate damage—$X_{ij}$).
• Method of direct government expenditures, such as emergency response services, medical care, public insurance programs, road infrastructure expenditures, and rehabilitation programs (direct intermediate damage—$X_{ij}$).

Enterprise-sector damage

• Insurance-based valuation, which includes compensation paid by insurance companies for property damage as well as payouts in cases of death or disability of household members (direct intermediate damage—$X_{ij}$).
• Loss-of-profit method, which includes foregone earnings and productivity losses (foregone profit—$V_i$).

Household-sector damage

• Loss-of-income method, which accounts for reduced earnings, productivity losses, and permanent income losses resulting from disability or death (foregone income—$V_i$).
• Direct cost method, which includes medical expenses, funeral costs, property damage, and related expenditures (direct intermediate damage—$X_{ij}$).

The gross damage $X$ comprises both direct and indirect (resource-based and transfer-based) losses incurred by all sectors, regardless of whether they are compensated within the economy. To avoid double-counting when estimating the aggregate losses from RTAs, the authors apply an adjustment based on the amount of inter-sectoral transfers. These transfers represent financial flows redistributed among the government, enterprises, and households, and do not reduce the total wealth of the national economy. Some portion of these losses is reimbursed through insurance payouts, compensations, fines, and other financial transfers. To eliminate repeated accounting of these flows, the following relation is used:

$$U=X-Tr,$$

(5)

where

$U$—net losses for the national economy;

$X$—gross damage;

$Tr$—the value of internal (intermediate) transfers among sectors.

In the context of the present study, the productivity conditions remain valid for the modified “impact–damage” model, where the matrix $A$ represents the coefficients of damage transmission among economic agents. Here, the productivity of the matrix signifies that the system of inter-agent interactions remains stable: any final damage $Y$ generates a gross damage $X$, the values of which are finite and non-negative. Accordingly, the condition $\mid A\mid <1$ guarantees the stability of the model and ensures the possibility of correctly computing the multiplicative effects of damage.

3. Results

The World Bank Group (2021) estimated the total socio-economic cost of RTAs in Azerbaijan for the year 2019, while noting significant issues related to the availability of official statistical data. Building on the methodology and assumptions presented in that report, the present study incorporates actual data on the number of severely injured people and the number of RTA cases (except for atmospheric pollution damage, i.e., environmental losses), adjusted using national statistics on RTA-related mortality. The analysis also accounts for accidents involving vehicle damage only, the true number of which substantially exceeds the figures published by the State Statistical Committee of the Republic of Azerbaijan for 2023 [22].

The inter-sectoral damage redistribution matrix, which underlies the analytical framework, is reported in

Table 1. Inter-sectoral damage redistribution matrix (“event–damage” table).

Propagated Damage	Received Damage			Final Damage (Unrealized Taxes, Profits, Incomes) (Y)	Total Damage (X)
	1	…	n
1	$x_{11}$	…	$x_{n1}$	$y_1$	$x_1$
…	…	I	…	II	…
n	$x_{n1}$	…	$x_{nn}$	$y_n$	$x_n$
Lost Income (Foregone Value) (V)	$V_1$	III	$V_n$	${\displaystyle \sum _{i=1}^n}{Y}_i={\displaystyle \sum _{i=1}^n}{V}_i$
Gross (Total) Damage (X)	$x_1$	…	$x_n$	1	${\displaystyle \sum _{i=1}^n}{x}_i={\displaystyle \sum _{j=1}^n}{x}_j$

Roman numerals I–III denote the quadrants of the extended damage matrix: I—inter-sectoral transfers; II—final damage (Y); III—foregone value added, indices $i,j\in \left\{1,\dots ,n\right\}$ index agents/sectors; rows correspond to originating (inflicting) agents and columns to receiving agents. $x_{ij}\ge 0$ denotes propagated (intermediate) damage transmitted from agent $i$ to agent $j$ through the resource-use channel. Row totals $x_i={\sum }_j{x}_{ij}$ equal the gross (total) damage inflicted by agent $i$; column totals $x_j={\sum }_i{x}_{ij}$ equal the gross (total) damage received by agent $j$. $Y_i$ denotes final (unrealized) benefits by sector—foregone taxes, profits, and household incomes; $V_i$ denotes lost income (foregone income). The accounting identity ${\sum }_{i=1}^n{Y}_i={\sum }_{i=1}^n{V}_i$ holds. $X_i$ is gross damage by sector.

. In addition to refining the definition of RTAs, we adopt internationally recognized approaches to quantify socio-economic losses and use them to compute specific damage categories.

Table 2. Damage incurred by the government.

Category of Government Damage	Source of Damage	Amount (Million AZN)
Loss of income tax revenues	Households	3.19
Loss of social fund contributions (22%) due to death, temporary disability, or permanent incapacity of an employee	Enterprises	5.02
Loss of social fund contributions (3%) due to death, temporary disability, or permanent incapacity of a taxpayer	Households	0.70
Medical expenses covered by state insurance	Government	61.95
Medical expenses for services not provided by public hospitals (covered by enterprises)	Enterprises	12.56
Share of environmental damage costs	Government	7.60
Cost of road congestion	Government	80.10
Costs of court proceedings and incarceration	Government	1.03
Damage related to the restoration of road infrastructure and facilities	Government	0.75
Losses arising from funeral-related payments	Government	3.51
Loss of profit tax revenues (until replacement of personnel) due to death, temporary disability, or permanent incapacity of an employee	Enterprises	10.10
Losses due to employee payments during on-site accident investigations	Government	2.17
Investment losses	Enterprises	6.72
Loss of savings	Households	2.24
Losses from disability and survivor benefits	Households	3.20
Total		200.84

Authors’ classification of categories and damage directions; calculations based on statistical data from the State Statistical Committee of the Republic of Azerbaijan using the World Bank methodology for estimating the socio-economic costs of road traffic accidents in Azerbaijan [1,22].

,

Table 3. Types of losses incurred by enterprises.

Category of Enterprise Damage	Source of Damage	Amount (Million AZN)
Restoration of damaged commercial road infrastructure and facilities	Government	0.75
Environmental damage costs	Government	4.75
Temporary unavailability of vehicles	Enterprises	169.05
Damage equivalent to congestion costs caused by RTAs	Enterprises	186.90
Costs of training replacement personnel	Enterprises	1.90
Losses due to cargo damage in RTAs	Enterprises	10.99
Mandatory insurance payouts (damage to other vehicles, etc.)	Households	98.20
Life and health insurance payments	Households	0.55
Compensation equal to three months’ salary	Households	1.90
Loss or repair costs of corporate vehicles	Enterprises	124.00
Total		598.99

Authors’ classification of categories and damage directions; calculations based on statistical data from the State Statistical Committee of the Republic of Azerbaijan using the World Bank methodology for estimating the socio-economic costs of road traffic accidents in Azerbaijan [1,22].

and

Table 4. Damage incurred by households.

№	Category of Household Damage	Source of Damage	Amount (Million AZN)
1	Judicial and administrative expenses (the compensation process is complex and requires personal court appeals and hiring a private attorney)	Government	5.20
2	Fines for traffic violations that led to the accident	Government	8.23
3	Environmental damage costs	Government	6.65
4	Towing service fees	Enterprises	1.26
5	Losses due to job search following temporary incapacity	Enterprises	5.03
6	Memorial ceremony expenses (excluding funeral benefits)	Enterprises	3.09
7	Property losses (personal vehicle) or repair costs	Government	568.80
8	Non-market losses (caregiving time and purchased resources)	Households	29.30
9	Home-based care (rehabilitation, medications) not covered by public-health insurance	Households	0.39
10	Temporary unavailability of personal vehicles	Households	108.19
11	Congestion-related costs caused by RTAs	Enterprises	267.00
	Total		1003.14

Authors’ classification of categories and damage directions; calculations based on statistical data from the State Statistical Committee of the Republic of Azerbaijan using the World Bank methodology for estimating the socio-economic costs of road traffic accidents in Azerbaijan [1,22].

report the resulting aggregates for the three institutional agents—government, enterprises, and households. These categories are mapped to the corresponding cells of the first quadrant of the redistribution matrix (Table 1) such that aggregation proceeds by columns: each column $j$ gives the total damage received by sector $j$ from all counterpart sectors $i$, (i.e., first-quadrant totals ${\sum }_i{x}_{ij}$). In subsequent tables, transfers are presented in italics for clarity and excluded when reporting net damage.

As shown in Table 2, the largest components of government-sector damage are medical expenditures (61.95 million AZN) and road congestion costs (80.1 million AZN), indicating the substantial burden associated with emergency response, treatment of victims, and reduced transport efficiency. A significant share of losses is also linked to tax shortfalls and social fund contributions (approximately 9 million AZN in total), which underscores the dependence of fiscal revenues on the productive and labor capacity of affected individuals. Overall, the government’s gross damage is estimated at 200.84 million AZN.

Table 3 shows that enterprises incurred substantial losses amounting to 598.99 million AZN. The largest components include expenditures associated with the temporary unavailability of vehicles (169.05 million AZN) and congestion-related costs (186.9 million AZN), both of which represent direct constraints on productive activity. Significant contributions also come from property losses (124 million AZN) and insurance obligations to households (approximately 100 million AZN). Overall, enterprise-sector damage is predominantly material and production-related in nature, directly influencing the dynamics of gross value added.

As shown in Table 4, household-sector losses are the largest among the three sectors, amounting to 1003.14 million AZN. The main components—personal property losses (568.8 million AZN) and congestion-related costs (267 million AZN)—reflect the direct impact of RTAs on household welfare. Non-market losses, including caregiving time and lost personal time (29.3 million AZN), also represent a significant share, highlighting the hidden social consequences of accidents. Overall, the structure of household losses demonstrates the predominance of individual and social burdens over institutional ones, which increases vulnerability and reduces overall economic activity.

The gross economic damage from road traffic accidents across the three institutional sectors, including foregone income (lost future benefits), is estimated at approximately 2.4 billion AZN. This figure captures not only the direct and intermediate losses recorded in monetary terms but also the future economic effects associated with the erosion of human capital, reduced labor productivity, and the disruption of production and social linkages.

To avoid double-counting, intra-sectoral and inter-sectoral transfers, which represent the redistribution of financial resources within the economy, were subtracted from the gross economic damage.

Based on the identified transfers and after adjusting for the total transfer amount (

Table 5. Inter-sectoral transfers.

Inter-Sectoral Transfers (Million AZN)
Government	Enterprises	Medical insurance expenses for services not provided by public hospitals	12.56
	Households	Fines for traffic violations that caused the accident	8.23
Enterprises	Households	Memorial ceremony expenses (excluding funeral benefits)	3.09
	Households	Towing service fees	1.26
Households	Government	Disability and survivor benefit contributions	3.20
	Government	Payments for ritual services	3.51
	Enterprises	Compensation equal to three months’ salary	1.90
	Enterprises	Mandatory insurance payouts (damage to other vehicles, etc.)	98.20
	Enterprises	Life and health insurance payments	0.55
Total			132.50

Authors’ classification of categories and damage directions; calculations based on statistical data from the State Statistical Committee of the Republic of Azerbaijan using the World Bank methodology for estimating the socio-economic costs of road traffic accidents in Azerbaijan [1,22].

), the net economic damage—representing the net losses for the national economy—totaled $U=X-132.5=2268.17 \mathrm{million} \mathrm{AZN}$ (USD 1334.22 million, at the 2023 annual-average rate 1 USD = 1.70 AZN, Central Bank of Azerbaijan).

This adjustment makes it possible to more accurately determine the actual economic losses from RTAs by excluding funds redistributed within the system and isolating only those losses that are not compensated by any sector.

According to the developed inter-sectoral damage matrix, the gross economic damage from road traffic accidents (RTAs) in the Azerbaijani economy amounted to 2400.67 million AZN. Of this total:

• Government sector—310.24 million AZN (≈12.9% of gross damage);
• Enterprise sector—943.29 million AZN (≈39.3%);
• Households—1147.14 million AZN (≈47.8%).

Thus, the primary financial burden of RTAs falls on households, which absorb almost half of all losses. This reflects their vulnerability to the direct consequences of accidents, including income loss, property damage, medical expenses, and reduced quality of life.

The inter-sectoral structure (intermediate flows) reveals relatively limited redistribution of losses involving the government sector:

• Government → Enterprises (5.5 million AZN) and Government → Households (20.08 million AZN)—mainly medical, insurance, and social payments;
• Enterprises → Government (34.4 million AZN)—declines in tax revenues due to the death or disability of employees;
• Enterprises → Households (9.38 million AZN)—insurance and compensation payments;
• Households → Government (12.84 million AZN)—administrative fines, court expenses, and official fees;
• Households → Enterprises (100.65 million AZN)—repair costs and services related to vehicle or property damage.

The largest internal flows occur within the household sector (973.68 million AZN), indicating a high share of self-financed expenses such as vehicle repairs, treatment costs, and temporary loss of income.

Foregone benefits—including lost income, profits, and tax revenues—amounted to 597.7 million AZN, or 24.9% of the total losses. These include:

• Government—109.4 million AZN (lost taxes and public revenues);
• Enterprises—344.3 million AZN (lost profits, reduced productivity);
• Households—144 million AZN (lost personal income and wages).

Nearly 60% of all foregone benefits fall on enterprises, reflecting the substantial loss of human capital and productive capacity.

The indicator of unrealized economic benefits was estimated by calibrating the model under the assumption of no missing financial flows and strict balance. This indicator reflects the uncompensated share of damage after all inter-sectoral reallocations. Across sectors:

• Government—127.55 million AZN;
• Enterprises—406.67 million AZN;
• Households—63.48 million AZN.

The relatively small figure for households (≈44% of their foregone benefits, i.e., V > Y) indicates a high degree of compensation from the government and enterprises through social payments, medical services, and insurance coverage.

In contrast, enterprises and the government exhibit unrealized benefits exceeding their foregone income (Y > V), reflecting the presence of uncompensated losses in the form of lost profits and tax revenues and revealing a cumulative financial-loss effect: the government faces long-term expenditure burdens, while businesses lose productive capacity. It must also be noted that, for simplification and due to a lack of disability-related data, the model does not account for cases in which disabled individuals are permanently removed from the labor force.

Overall, the distribution of damage across sectors demonstrates a shift in financial burden towards the institutional sectors—government and enterprises—while households receive partial compensation for their direct losses.

Simulation results indicate that reducing the number of RTAs by 1000 cases—equivalent to a 2.40 million AZN reduction in final damage—leads to a 9.80 million AZN decrease in annual economic losses (all figures in 2023 base-year prices; AZN → USD at 1.70 AZN/USD), broken down as follows:

• Government—1.27 million AZN;
• Enterprises—3.85 million AZN;
• Households—4.68 million AZN.

This distribution reflects the differing roles of institutional sectors in the transmission of accident-related losses within the inter-sectoral framework.

4. Discussion

The results indicate that households experience the largest share of direct damage, although part of these losses is compensated through social and insurance mechanisms, while enterprises bear substantial losses in terms of foregone profits and workforce replacement costs. Within this framework, an equivalent reduction in road traffic accidents generates different reductions in gross economic damage depending on the institutional sector in which the reduction occurs. These differences reflect the distinct inter-sectoral transmission channels through which accident-related losses propagate across the economy.

The “−1000 accidents” scenario should therefore be interpreted as an illustrative, policy-oriented benchmark derived from the linear structure of the model rather than as a forecast. Within this linear framework, the relative proportions of the resulting multiplicative effects remain stable in the short run, as they are structurally determined by the inter-sectoral coefficient matrix underlying the accounting framework.

Accordingly, the scenario provides a transparent basis for comparing sectoral intervention priorities, while substantial changes in the magnitude or distribution of multiplicative effects would require long-term structural transformations of the road transport system and the broader economic structure.

The results of the present study provide an empirical validation of the GDP-based benchmarks discussed in the Introduction. Specifically, the estimated gross economic damage from road traffic accidents in Azerbaijan amounts to approximately AZN 2.4 billion, which corresponds to about 1.95% of GDP in 2023. This figure is fully consistent with international evidence reporting road traffic accident costs in the range of 2–5% of GDP for many low- and middle-income countries and closely aligns with the World Bank’s earlier assessment for Azerbaijan.

Although the government accounts for a smaller share of gross damage (13%), it serves as a key compensating agent, redistributing resources across sectors. The structure of losses demonstrates a systemic multiplicative effect: each unit of direct loss generates a cascade of secondary costs, causing gross economic damage to exceed the sum of individual losses. This pattern for Azerbaijan aligns with general trends observed in middle-income countries, where households account for more than 45% of total losses, and the overall multiplicative effect leads to a threefold amplification of aggregate damage.

The approach applied in this study, which captures secondary redistribution effects between the government, enterprises, and households, provides a foundation for designing balanced public policies in the areas of road safety, prevention, and compensation mechanisms. The framework can incorporate a wide range of actors, highlighting the potential for a more detailed structure that includes private insurance companies, various ministries, and healthcare institutions. This would make it possible to account for interactions among stakeholders at both local and regional levels and to direct efforts toward protecting vulnerable road users.

These policy-relevant insights are directly derived from the quantified results of the model, including the distribution of loss shares across institutional sectors, the distinction between gross and irrecoverable damage, and the estimated multiplicative effects. Accordingly, the policy implications discussed in this study are strictly grounded in the model’s outputs and do not extend beyond its explanatory scope.

Beyond economic and social losses, road accidents cause profound grief and suffering for victims, their families, and society at large. The estimates presented in this study pertain only to the socio-economic cost of RTAs. A comprehensive assessment of total damage would additionally require a dedicated investigation into moral and psychological harm.

5. Conclusions

Future research could therefore extend the model by incorporating temporal dynamics (panel data across multiple years) and integrating moral-damage components into the evaluation framework. The findings of this study confirm the importance of accounting for inter-sectoral linkages and multiplicative effects when assessing the socio-economic consequences of RTAs.

In applied terms, this study operationalizes the established input–output framework for road traffic accident damage accounting by adapting it to an institutional-sector perspective. Its main contribution lies in the construction of a reproducible event–damage accounting structure that enables a transparent separation of gross and irrecoverable losses across government, enterprises, and households.

The proposed framework offers a robust basis for improving the design and targeting of road-safety policies, as it is grounded in the quantified inter-sectoral distribution of losses and the estimated multiplicative effects revealed by the model.

Future research could therefore extend the model by incorporating temporal dynamics (panel data across multiple years) and integrating moral-damage components into the evaluation framework.