Tourism as a catalyst for economic diversification in the UAE: Evidence from input–output analysis

Abstract

We examine the direct and indirect economic contribution of tourism in the United Arab Emirates (UAE) by estimating output, income, and value-added multipliers, along with backward and forward linkage indices, for 18 industries using the 2023 input–output (I–O) table. Tourism-driven sectors—such as accommodation and food services, transport, and trade—demonstrate extensive inter-industry connections, reflecting their pivotal role in propagating demand across upstream suppliers and downstream consumers. Tourism-related spending generates strong indirect effects across manufacturing, utilities, logistics, and cultural services, boosting production, income, and value creation economy-wide. For the first time in the UAE context, normalized linkage spread indices are adopted to measure the distribution of backward and forward linkages, providing nuanced insights into the depth of inter-industry interdependencies. The results indicate that tourism underpins economic diversification, reinforces structural resilience, and contributes to sustainable, service-oriented growth, highlighting its strategic importance in the evolution of the UAE’s non-oil economy.

Introduction

The tourism industry does not operate in isolation; rather, it interacts extensively with other sectors such as construction, trade, transport, finance, manufacturing, and cultural services (Ranjbar et al., 2024). These interdependencies suggest that the overall contribution of tourism extends far beyond its immediate boundaries. Therefore, a systematic framework is required to capture the full spectrum of tourism’s economic interactions within the UAE’s interconnected production network.

While we use the 2023 I–O table compiled by the Statistics Centre – Abu Dhabi (SCAD, 2025a), our analysis goes beyond existing national studies in both methodological rigor and depth. Previous efforts by SCAD and entities such as ESCWA focused on standard multipliers, linkage indices, and macroeconomic modeling to measure economic interactions. This study, for the first time in the UAE, applies normalized linkage spread indices incorporating the coefficient of variation, capturing not only the size but also the distribution of tourism’s impact. By combining linkage intensity with dispersion, it more precisely identifies “leading sectors” and informs policies for structural resilience and non-oil diversification.

The inclusion of the coefficient of variation (CV) through normalized linkage spread indices, as proposed by Lamonica and Mattioli (2015), is important because it captures how widely economic impacts are distributed across the diverse and often heterogeneous supply chains in the tourism industry. While high linkage multipliers measure the magnitude of economic stimulus, they do not show whether this stimulus is concentrated in a few sectors or spread broadly across the production network. In tourism-driven sectors such as accommodation, food services, and transport, activities connect with a wide range of upstream suppliers, including agriculture, manufacturing, utilities, logistics, and professional services. A low CV in backward linkages indicates that tourism demand flows evenly through this network rather than being confined to a few segments. Assessing the consistency of these linkages also reveals whether tourism relies on a broad base of domestic inputs or on specific, potentially vulnerable channels, which is key for understanding its contribution to structural resilience and endogenous growth in the UAE’s non-oil economy. By combining linkage intensity with dispersion, this approach enables tourism to be more rigorously classified as a “leading sector” that can drive economic diversification, moving beyond simple output measures to capture the depth and systemic integration of inter-industry relationships and providing a robust tool for multi-sectoral policy planning.

A further motivation for this study is the identification of high-growth sectors that act as catalysts of economic momentum—regardless of their relative size in output, income, or value added—producing significant spillover effects across the production network. By highlighting these drivers, we identify the segments of the non-oil economy most instrumental in fostering endogenous growth, enhancing resilience, and supporting sustainable development. The findings reveal that tourism functions not merely as a component of economic diversification but as a catalytic force linking multiple sectors. By quantifying its spillover effects, the study provides evidence to guide structural transformation, stimulate output and income growth, and enhance welfare, offering policymakers, investors, and planners actionable guidance to align tourism-led development with long-term national prosperity and sustainability.

Empirical research on the UAE economy and the ways tourism influences its development remains limited in both scope and analytical rigor. Our literature review shows that while several related studies exist, few have appeared in high-impact journals. This may reflect a tendency among leading North American and European journals to underestimate the global relevance of the UAE’s economic dynamics, assuming that region-specific issues lack international significance. Such a view overlooks the UAE’s pivotal role in global connectivity and trade: for example, even a single-day closure of its airports would disrupt a vast network of flights across multiple continents, producing significant logistical, commercial, and financial consequences worldwide. These interdependencies highlight the need for rigorous, data-driven research that examines tourism-driven economic linkages in the UAE and their broader global implications.

Our study goes beyond conventional multiplier estimation by incorporating normalized linkage and spread indices, offering a more nuanced understanding of the role of tourism-related sectors in the economy. Rather than simply measuring direct and indirect economic effects, this approach captures the depth, breadth, and distribution of tourism’s connections across industries. In doing so, it allows for an assessment of whether tourism acts not only as a source of immediate economic stimulus but also as a potential driver of broader structural change, diversification, and resilience within the UAE’s non-oil economy. Therefore, we examine the following three research questions:

(i) What are the direct and indirect economic impacts of tourism-related sectors in the UAE? Specifically, the study estimates output, income, and value-added multipliers using the 2023 input–output table to quantify how tourism-driven demand propagates across the wider economy.

(ii) How strong and widely distributed are the backward and forward inter-industry linkages of tourism-related sectors? We compute normalized backward and forward linkage indices, along with spread (dispersion) measures, to assess the depth and breadth of tourism’s integration within the domestic production network.

(iii) To what extent does tourism act as a catalyst for economic diversification and structural transformation in the UAE’s non-oil economy? By identifying key sectors with strong and well-dispersed linkages, the study examines whether tourism fosters economic resilience, enhances value creation, and supports a sustained transition toward a more diversified, service-driven growth trajectory.

Literature review

I-O analysis in tourism

As can be seen from the review of literature presented in this section, tourism-related sectors are typically disaggregated into accommodation, food and beverage services, transport, cultural and recreational activities, and supporting industries. The reviewed studies span diverse national and regional contexts, employing variations of I–O models—often integrated with Tourism Satellite Accounts (TSA) or regionalization techniques—and occasionally supplemented by primary data.

Fletcher (1989) pioneered tourism impact analysis using the Leontief inverse across multiple countries, including Turkey, the United Kingdom, Bermuda, and Western Samoa. Tourism sectors encompassed hotels, restaurants, transport, and travel agencies. Income multipliers ranged from 0.39 (Western Samoa) to 1.96 (Turkey), reflecting stronger intersectoral linkages in larger, diversified economies. Backward linkages were consistently strong, while forward linkages were weak, and no CV or linkage spread measures were applied. Similarly, Archer (1995) employed customized I–O tables for Bermuda, disaggregating seven accommodation types alongside restaurants, bars, transport, and attractions. Income multipliers increased from 1.095 in 1985 to 1.257 in 1992, with accommodation and food services showing high backward linkages and weak forward linkages.

Cai et al. (2006) introduced explicit forward and backward linkage analysis for Hawaii using Leontief multipliers. Hotels (BL = 1.412; FL = 1.004) and food services (BL = 1.447; FL = 1.045) exhibited strong backward linkages but weak forward linkages, while several supporting industries (e.g., advertising) recorded FL > 2. Drakakis et al. (2021) applied a hybrid I–O and ad hoc model in Messinia, Greece, finding golf with the highest income multiplier (∼0.39) and scuba diving with the largest employment effect per €1,000 expenditure. Backward linkages dominated, and forward linkages remained weak.

Teigeiro and Díaz (2014) extended the I-O analysis to 40 countries using OECD harmonized tables, focusing on Hotels and Restaurants. Backward multipliers ranged from 1.276 (Luxembourg) to 2.408 (China), underscoring the influence of economic size and complexity. Forward linkages were not reported, and CV was not applied. Munjal (2013) integrated India’s TSA with national I–O tables to create tourism as a distinct industry, reporting an output multiplier of 2.141 and substantial forward linkages (1.705), indicating higher sensitivity to changes in other sectors.

Khanal et al. (2014) employed multiple linkage measures in Lao PDR, including Hirschman–Rasmussen indices, revealing strong backward linkages (1.09–1.24) and moderate forward linkages. Hor (2021), using a SAM-based framework for Cambodia, documented a gradual strengthening in tourism’s forward linkages from 0.91 to 1.05 between 2005 and 2015, while backward linkages remained broadly stable at approximately 1.00, suggesting a slow reorientation of tourism toward upstream integration.

Figini and Patuelli (2022) combined EU I–O tables with TSA to estimate direct and indirect impacts across member states. Multipliers varied significantly (0.58–1.26), with stronger backward linkages in more integrated economies. Lee et al. (2024) assessed Smart Tourism Platform investment in Seoul using a demand-driven I–O approach, reporting output, income, and value-added multipliers of 1.302, 0.453, and 0.969, respectively. Lamonica and Mattioli (2015) were the only study that applied CV to evaluate linkage dispersion across 35 sectors in 11 major economies. China emerged as a key sector (BL = 1.21; FL = 1.20), while Germany and Spain were classified as “island sectors” with weak integration.

Kronenberg et al. (2018) applied a multi-period regional I–O model to Jämtland, Sweden, reporting output multipliers of 1.083–1.309 and notable growth in tourism’s contribution to output and employment. Tohmo (2018) estimated multipliers for Central Finland, with hotels/restaurants at 1.2604 and transport at 1.3085. Wood and Meng (2020) analyzed the 2018 Pyeongchang Winter Olympics, finding strong backward linkages in trade, restaurants, and hotels. Vayá et al. (2024) assessed the COVID-19 shock in Spain, revealing severe multiplier losses, with turnover and GDP reductions linked to lost tourist expenditure.

Drawing on the reviewed studies, Table 1 presents the tourism-related sectors and key findings in the I-O literature, offering a basis for identifying the relevant sectors for the UAE in the present study. The evidence shows that I–O analysis effectively captures tourism’s economic effects across national, regional, and sectoral contexts. Although multiplier values and linkage structures vary by country, sector, and modelling approach, backward linkages consistently exceed forward linkages, reflecting tourism’s dependence on domestic inputs.

Table 1.

Tourism-related sectors and key results reported in the input–output literature.

Study	Country/Region	Tourism sectors considered	Multipliers	Backward/Forward linkages
Fletcher (1989)	Turkey, UK, Bermuda, Western Samoa	Hotels, restaurants, bars, transport, entertainment, travel agencies	Income multipliers: Turkey 1.96, UK 1.73, Bermuda 1.09, Western Samoa 0.39	Strong backward, weak forward
Archer (1995)	Bermuda	Accommodation (7 types), restaurants/cafés, bars/nightclubs, transport, livery, tourist attractions	Income multipliers 1985: 1.095; 1987: 1.168; 1992: 1.257	Strong backward for accommodation/food, weak forward
Robles et al. (2014)	40 countries	Hotels & restaurants	Backward multipliers: Luxembourg 1.276 – China 2.408; import multipliers: 0.04–0.35	Backward multipliers high in larger economies, forward not discussed
Cai et al. (2006)	Hawaii, USA	Accommodation, transport, recreation, retail, supporting services	BL: hotel 1.412, food 1.447; FL: hotel 1.004, food 1.045	Strong backward, weak forward (some industries FL > 2.0)
Drakakis et al. (2021)	Greece (Messinia)	Accommodation, catering, retail, sport/outdoor, spa services, other tourism	Income multipliers: Golf ≈0.39; employment highest for scuba diving	Backward higher for sports tourists
Figini and Patuelli (2022)	EU countries	Accommodation, F&B, passenger transport, travel agencies, cultural/recreation, other connected products	Total expenditure multipliers: Estonia 0.58 – Czech Rep 1.26	Strong backward linkages in integrated economies
Khanal et al. (2014)	Lao PDR	Tourism, food & beverage, wholesale/retail, agriculture & livestock	Backward 1.09–1.24; total linkages 2.06–2.21	Strong backward, average forward
Lamonica and Mattioli (2015)	11 major economies	Hotel & restaurant services	Backward <1 for most (except China 1.21); forward <1 (China 1.20)	Backward generally higher than forward
Lee et al. (2024)	Seoul, South Korea	IT services, transportation, wholesale/retail, broadcasting/publishing, cultural/tour services, sports/recreation	Output 1.302; income 0.453; value-added 0.969; employment ≈14–15 jobs per 1bn KRW	Inter-industry effects computed
Munjal (2013)	India	Accommodation, F&B, transport, travel agencies, recreational, processed food, textiles, gems/jewelry, other consumer goods	Output multiplier 2.1411; forward linkage 1.7046	Backward weaker than forward in some sectors
Hor (2021)	Cambodia	Accommodation & food, arts/entertainment/recreation, business services (travel agencies etc.)	BL ≈1.00–1.04; FL 0.91–1.05	Tourism shifted to forward-oriented over time
Kronenberg et al. (2018)	Sweden (Jämtland)	Accommodation & food, wholesale/retail, land/air transport, travel agencies, creative arts, sporting/recreation	Output multipliers 1.083–1.309	Fluctuating annually, backward linkages highest in air transport
Tohmo (2018)	Central Finland	Accommodation & food, transport, wholesale/retail, cultural, sports/recreation	Production multipliers: Hotels/restaurants 1.2604, transport 1.3085	Strong inter-industry effects
Ünlüönen et al. (2011)	Turkey	Accommodation, F&B, transport, travel agencies, car rental, communication, cultural/entertainment, sports, retail	Total leakage 38.6%; multipliers reduced accordingly	Backward vs forward implied
Vayá et al. (2024)	Spain	Accommodation & food, transport, travel agencies, retail, rental/real estate, cultural/entertainment, sports/recreation	Turnover lost €3628 per €1000 tourist spending; GDP €1883 per €1000	Regional multipliers vary
Wood and Meng (2020)	South Korea	Trade, accommodation, restaurants, transport, cultural/sports/entertainment, plus non-tourism sectors	Output ₩1.88 T; income ₩324B; value-added ₩744B; employment 19,097	Strong backward linkages in trade, restaurants, hotels

Source: Authors’ compilations.

The literature on tourism-related I–O analysis reveals a methodological gap concerning the distributional breadth of inter-sectoral connections. While many studies have quantified the magnitude of direct and indirect economic contributions using multipliers and linkage indices, they often overlook whether these effects are evenly distributed across industries or concentrated in a few sectors. With the exception of Lamonica and Mattioli (2015), prior research has generally omitted the use of normalized linkage spread indices, which incorporate the coefficient of variation, to evaluate the depth and consistency of inter-industry dependencies. UAE-focused research has typically concentrated on broad macroeconomic modeling, social accounting frameworks, or pandemic-related stimulus effects, leaving a critical gap in detailed, data-driven analyses of how tourism functions as a driver of economic diversification through multi-sectoral linkages. By addressing these deficiencies, the present study moves beyond chronological or descriptive summaries to provide nuanced insights into structural resilience and non-oil growth.

While the reviewed studies provide valuable insights into tourism’s economic impacts across diverse countries, their implications for an oil-based, service-oriented economy like the UAE remain unclear. Unlike the more diversified economies examined, the UAE’s non-oil sector is heavily service-driven, and tourism interacts with a relatively concentrated set of domestic and regional supply chains. Understanding how tourism’s backward and forward linkages operate in this context is therefore crucial for assessing its potential to support economic diversification, structural transformation, and resilience in a predominantly oil-dependent economy. This motivates the present study’s focus on quantifying both the magnitude and distribution of tourism-related inter-industry effects in the UAE.

I-O studies in the UAE

One of the earliest I–O studies is the 37-sector table developed by Green (2011), which integrates national accounts and trade data and was later incorporated into the GTAP 8 (Global Trade Analysis Project) database. This table provided an important foundation for modeling the UAE economy, enabling computable general equilibrium (CGE) simulations and international trade analysis. In parallel, Arabian Business and Economic Consultants (ABEC, 2025) developed detailed I-O tables for the UAE and other Gulf Cooperation Council (GCC) countries. Constructed from official statistics and industry-level data, these tables facilitate the computation of key economic multipliers—such as output, value-added, and employment effects—and allow for the assessment of sector-specific policies, investments, and inter-industry ripple effects.

The United Nations Economic and Social Commission for Western Asia (ESCWA), together with the United Arab Emirates (UAE) Ministry of Economy, developed Social Accounting Matrices (SAMs) for Abu Dhabi and Dubai in 2023 (ESCWA, 2023). These SAMs support computable general equilibrium (CGE) modeling to simulate fiscal, trade, and investment policies, allowing policymakers to assess macroeconomic and distributional impacts for sustainable and equitable growth.

The practical application of these frameworks is exemplified by Yasmin et al. (2023), who employed I-O modeling to assess the effectiveness of government stimulus packages during the COVID-19 pandemic. Their analysis indicated that the stimulus packages increased aggregate sectoral outputs, projecting an annual GDP growth of 2.9% over 4 years, while also mitigating income disparities, thereby illustrating the efficacy of targeted policy interventions in the UAE (Yasmin et al., 2023).

Beyond macroeconomic and policy evaluation, I-O analysis has been extended to environmental and urban development studies (Rauf et al., 2022). In the urban planning context, Noori et al. (2021) developed an I-O model for Dubai’s smart city initiatives, enabling policymakers to assess economic and environmental trade-offs associated with smart infrastructure investments. These studies demonstrate the adaptability of I-O frameworks in addressing diverse research questions, from environmental sustainability to strategic urban planning. While previous UAE-based studies have assessed economic impacts through traditional multipliers, they often overlook the breadth of these effects across various industries. This study addresses that gap by introducing normalized linkage spread indices—a first in the UAE context—to measure the systemic dispersion and depth of tourism’s contribution to the non-oil economy.

Method

Output, income, and value-added multipliers quantify the total economy-wide impact of a one-unit increase in final demand for a specific sector. The output multiplier reflects the overall rise in gross output across all sectors, the income multiplier captures the total income generated through factor payments such as wages and operating surplus, and the value-added multiplier measures the total value created across the production network resulting from the initial demand shock (Miller and Blair, 2009). The output (equation (1)), income (equation (2)) and value added (equation (3)) multipliers are computed using the Leontief inverse matrix, i.e. ${\left(\mathbf{I}-{\mathbf{A}}_d\right)}^{-1}$ , as follows:

$m_j^{Output}={\displaystyle \sum }_{i=1}^n{l}_{ij}$

(1)

$m_j^{Income}={\displaystyle \sum }_{i=1}^n{w}_i{l}_{ij}$

(2)

$m_j^{VA}={\displaystyle \sum }_{i=1}^n{v}_i{l}_{ij}$

(3)

In the above equations l _ij denotes the element of the Leontief inverse, w _i is income coefficient (income per unit of output in sector i), and v _i is value-added per unit of output (i.e. value added in sector i divided by output in sector i). Put it simply, the output multipliers correspond to the column sums of the Leontief inverse matrix, while the income and value-added multipliers are obtained by weighting the columns of the Leontief inverse by the respective income and value-added coefficients (Miller and Blair, 2009).

Using the Leontief inverse ${\left(\mathbf{I}-{\mathbf{A}}_d\right)}^{-1}$ , total backward linkage for sector j is obtained by $B{L}_j={\displaystyle \sum }_{i=1}^n{l}_{ij}$ . Following West (1993) and Miller and Blair (2009), the normalised backward linkage (BL) can be written as follows:

${\overline{BL}}_{\mathrm{j}}=\frac{{\displaystyle \sum }_{i=1}^n{l}_{ij}}{\frac{1}{n}{\displaystyle \sum }_{j=1}^n{\displaystyle \sum }_{i=1}^n{l}_{ij}}$

(4)

We computed forward linkages based on the output distribution structure of the economy, captured by the row-normalised coefficients (B matrix), defined as b _ij =z _ij /X _i . Here, each row represents how the output of sector i is distributed across all purchasing sectors j, reflecting supply-side interdependencies. After constructing the B matrix, we calculated forward linkages using the elements ( $b_{ij}^{*}$ ) of the Ghosh inverse matrix ${\left(\mathbf{I}-\mathbf{B}\right)}^{-1}$ (Bulmer-Thomas, 1982; Kweka et al., 2003). The normalised forward linkage is thus computed as follows:

${\overline{FL}}_{\mathrm{j}}=\frac{{\displaystyle \sum }_{i=1}^n{b}_{ij}^{*}}{\frac{1}{n}{\displaystyle \sum }_{j=1}^n{\displaystyle \sum }_{i=1}^n{b}_{ij}^{*}}$

(5)

Where $b_{ij}^{*}$ denotes the total (direct and indirect) increase in the output of using sectors as a result of a unit increase in the output of the supplying sector. If the normalized forward linkage index is greater than one, sector j supplies many other sectors and helps spread growth across the economy. In economic terms, a high forward linkage sector provides critical intermediate goods or services, so expanding its output facilitates production in multiple downstream industries.

However, to identify key sectors in an economy, it is not sufficient to rely solely on the magnitude of backward and forward linkage indices. While sectors with normalized linkage indices greater than unity are considered influential, this criterion does not account for the distributional breadth of inter-sectoral connections. A sector may exhibit high linkages but be connected to only a few industries, limiting its systemic impact. Therefore, spread indices—which incorporate the coefficient of variation—are essential for evaluating the evenness of these linkages. To examine the dispersion of linkages, we first need to compute the coefficients of variation (CV) for backward and forward linkages, defined as the standard deviation divided by the mean (σ/μ), as follows:

$C{V}_j^B=\frac{{\sigma }_j}{{\overline{l}}_{.j}}=\frac{\sqrt{\frac{1}{n}{\displaystyle \sum }_{i=1}^n{\left(l_{ij}-{\overline{l}}_{.j}\right)}^2}}{{\overline{l}}_{.j}},C{V}_j^F=\frac{{\sigma }_j^{*}}{{\overline{b}}_{.j}^{*}}=\frac{\sqrt{\frac{1}{n}{\displaystyle \sum }_{i=1}^n{\left(b_{ij}^{*}-{\overline{b}}_{.j}^{*}\right)}^2}}{{\overline{b}}_{.j}^{*}}$

(6)

Then, the normalized backward and forward spread indices are calculated by dividing each coefficient of variation by its respective average coefficient of variation. Backward and forward linkage indices provide a systematic way to identify key sectors in an economy. A sector with a high normalized backward linkage (power of dispersion) generates above-average total production effects throughout the economy when its final demand increases, reflecting strong interindustry dependence on inputs from other sectors. A sector with a high normalized forward linkage (sensitivity of dispersion) supplies inputs extensively to other industries and is therefore highly responsive to changes in overall economic activity. However, the magnitude of these linkages alone does not fully capture structural importance. The associated spread indices, calculated using the coefficient of variation, indicate whether the sector’s interindustry effects are broadly distributed across many sectors or concentrated in only a few. A sector with a linkage index greater than one and a relatively low dispersion (i.e., effects spread evenly across sectors) is typically regarded as a key sector, since it both exerts strong multiplier effects and transmits them widely through the production network. By jointly examining linkage strength and dispersion, the approach identifies sectors that play a strategic role in economic structure and development.

Data, analysis and findings

Data

The 2023 I-O table, compiled by the Statistics Centre – Abu Dhabi (SCAD, 2025a), is used to analyze sectoral multipliers, inter-industry linkages, and spread indices. In the SCAD tables, imports are recorded in the primary inputs quadrant and treated as non-competitive, so domestic intermediate use in Quadrant 1 reflects only domestically produced inputs. The resulting matrix captures the net production relationships among domestic industries. Although the SCAD provides a 59-sector table that allows for more detailed, sector-specific analysis, this study uses the aggregated 18-sector version, which closely follows standard industry classification codes. This approach still allows a clear distinction between tourism-related and unrelated sectors, while recognizing that some intra-sectoral differences are not captured.

Table 2 shows the descriptive statistics for the percentage share of each sector in the UAE’s non-oil GDP from 2014Q1 to 2025Q2. Excluding the oil and gas sector (mining), the top five sectors in terms of mean contribution are Construction (16.4%), Manufacturing (15.4%), Financial and Insurance Services (12.5%), Administration (10.8%), and Trade (9.6%). These industries constitute the backbone of the UAE’s diversified non-oil economy, reflecting the country’s strong emphasis on infrastructure development, industrial upgrading, and financial market deepening. Sustained investment in large-scale construction projects—particularly in real estate, logistics hubs, and transport infrastructure—alongside the expansion of financial and administrative services, has underpinned the stability and dominance of these sectors over the past decade.

Table 2.

UAE non-oil GDP by sector (%), 2014Q1–2025Q2.

Industry	Mean	Rank	Max.	Min.	Std.Dev.	Rank	CV	Rank
Agriculture	1.2	15	1.8	0.9	0.2	16	15.4	8
Mining	96.4	1	112.0	76.0	9.3	1	9.6	11
Manufacturing	15.4	3	18.9	10.9	2.1	2	13.4	9
Utilities	3.4	11	5.7	2.2	0.7	9	19.7	3
Construction	16.4	2	18.3	14.7	0.9	6	5.4	17
Trade	9.6	6	10.8	7.5	0.7	7	7.4	16
Transport	4.6	10	7.9	2.2	1.3	3	27.6	1
Accommodation and food	1.8	14	2.3	1.3	0.3	14	15.4	7
Information and communication	4.8	9	6.0	4.1	0.5	12	9.5	12
Financial and insurance	12.5	4	14.2	10.4	1.0	4	8.0	15
Real estate	7.1	7	8.8	5.9	0.7	8	10.1	10
Professional, scientific and technical activities	5.4	8	6.6	4.6	0.5	11	8.7	14
Administration	10.8	5	12.9	9.1	1.0	5	9.2	13
Education	3.0	12	4.0	2.3	0.5	10	17.1	4
Health and social work	2.3	13	2.9	1.7	0.4	13	16.0	5
Arts, entertainment and recreation	0.4	17	0.5	0.3	0.1	17	15.7	6
Household services	1.1	16	1.4	0.7	0.2	15	22.8	2

Source: The authors’ calculation based on the 2023 input-output table (SCAD, 2025a).

The bottom five sectors—Arts, Entertainment and Recreation (0.4%), Household Services (1.1%), Agriculture (1.2%), Accommodation and Food (1.8%), and Health and Social Work (2.3%)—account for a relatively small share of total non-oil GDP. However, despite their modest size, several of these sectors—particularly Manufacturing, Construction, Transport, Financial and Insurance Services, Health and Social Work, and Arts, Entertainment and Recreation—have been on an upward trajectory since the post-pandemic recovery in 2021. This growth can be attributed to several reinforcing factors: the rapid rebound in tourism and travel following the easing of COVID-19 restrictions; continued population growth driving higher demand for health, education, and household services; and increased government expenditure on cultural, recreational, and knowledge-based industries as part of the UAE’s Vision 2031 strategy to foster innovation, creativity, and social well-being. The expansion of the transport and logistics sector also reflects the UAE’s growing role as a global trade and transit hub connecting Asia, Europe, and Africa.

According to Table 2, Construction and Trade record the lowest coefficients of variation (CVs)—5.4% and 7.4%, respectively—signifying stable and sustained contributions over time. Manufacturing and Financial and Insurance Services also show moderate variability, reflecting their resilience across business cycles and policy continuity. By contrast, Transport displays the highest relative variability (CV = 27.6%), followed by Household Services (22.8%) and Utilities (19.7%), indicating greater exposure to cyclical shifts in demand, seasonal fluctuations, and external economic shocks.

Results and discussion

Table 3 reports the estimated output, income, and value-added multipliers for 18 aggregated industries in the UAE economy. These multipliers quantify the extent to which an increase in final demand for a given sector stimulates production, generates domestic income, and creates value added across the broader economy. The results reveal substantial variation across sectors, with tourism-related industries consistently ranking among the top contributors to output, income, and value added, highlighting their central role in the UAE’s service-oriented economic structure.

Table 3.

Output, income and value-added multipliers for the UAE in 2023.

Industry	Output multiplier	Output multiplier (induced)	Income multiplier	Value added multiplier
Agriculture	1.7	2.31	0.41	1.26
Mining	1.14	1.26	0.08	1.04
Manufacturing	1.89	2.24	0.24	1.05
Utilities	1.72	2.17	0.31	1.23
Construction	1.63	2.16	0.36	1.13
Trade	1.34	2.05	0.48	1.36
Transport	1.62	2.28	0.44	1.25
Accommodation and food	1.66	2.51	0.57	1.35
Information and communication	1.39	1.93	0.36	1.19
Financial and insurance	1.31	1.90	0.4	1.31
Real estate	1.36	1.67	0.21	1.14
Professional, scientific and technical activities	1.38	2.62	0.83	1.67
Administrative services	1.34	2.53	0.8	1.63
Public administration	1.39	2.8	0.95	1.74
Education	1.39	2.78	0.93	1.75
Health and social work	1.59	2.8	0.81	1.58
Arts, entertainment and recreation	1.34	2.1	0.51	1.34
Household services	1	3.17	1.46	2.32

Source: The authors’ calculation based on the 2023 input-output table (SCAD, 2025a).

The three highest output multipliers are recorded for Household services (3.17), Public administration (2.80), and Health and social work (2.80), reflecting extensive inter-industry linkages and broad spillover effects across the economy. Tourism-related sectors such as Accommodation and food (2.51), Transport (2.28), and Trade (2.05) also exhibit strong induced output multipliers, illustrating that expansions in tourism-related demand trigger substantial production responses in interconnected sectors, including retail, logistics, communications, and professional services. These sectors act as key transmission channels, propagating demand increases throughout the production network. In contrast, Mining (oil and gas) (1.26), Real estate (1.67), and Agriculture (1.7, direct) display the smallest output multipliers, reflecting their capital-intensive nature and limited integration with other domestic industries. Overall, the output multipliers highlight tourism and its allied services as major drivers of aggregate production in the UAE.

The highest income multipliers are observed in Household services (1.46), Public administration (0.95), and Education (0.93), indicating their strong ability to generate domestic income per unit of final demand. Among tourism-linked industries, Accommodation and food (0.57), Arts, entertainment, and recreation (0.51), Trade (0.48), and Transport (0.44) demonstrate significant income-generating capacity. These sectors circulate earnings through multiple domestic channels, including supporting service providers and suppliers. Conversely, Mining (oil and gas) (0.08), Manufacturing (0.24), and Real estate (0.21) record the lowest income multipliers, reflecting high import dependency and limited domestic circulation of income. The income multipliers thus emphasize that tourism and its connected service sectors are pivotal in channeling final demand into domestic income growth.

The highest value-added multipliers are led by Household services (2.32), followed by Education (1.75) and Public administration (1.74), demonstrating exceptional capacity to retain output as domestic value. Tourism-related sectors such as Trade (1.36), Accommodation and food (1.35), Arts, entertainment, and recreation (1.34), and Transport (1.25) also display substantial value-added multipliers, reflecting the domestic retention of value from tourism expenditure and the strong interconnections with other sectors, including logistics and professional services. In contrast, the lowest value-added multipliers are recorded for Mining (oil and gas) (1.04), Manufacturing (1.05), and Construction (1.13), indicating limited domestic value creation due to higher reliance on imported inputs. Overall, the value-added multipliers confirm that tourism and associated services, together with key social and household sectors, are central to sustaining domestic value creation and promoting broad-based, inclusive growth in the UAE.

Therefore, the magnitudes of output, income, and value-added multipliers in Table 2 indicate that tourism and its linked service industries are among the most effective channels for propagating final demand throughout the UAE economy, driving widespread production, generating domestic income, and retaining a significant share of value added. These results highlight the strategic importance of tourism not only as a source of direct economic activity but also as a catalyst for cross-sectoral growth and long-term economic development.

Table 4 shows the normalised backward and forward linkage and spread indices for 18 aggregated sectors in the UAE. A backward linkage greater than 1 indicates that a sector purchases above-average inputs from other domestic industries, while a backward spread index above 1 shows that these purchases are concentrated in a few suppliers rather than evenly distributed. In the UAE, sectors such as Manufacturing (1.30, 0.91), Utilities (1.18, 0.95), Agriculture (1.17, 0.86), Construction (1.12, 0.92), Transport (1.11, 0.87), and Accommodation and food (1.14, 0.78) demonstrate this pattern, reflecting strong upstream dependence on specific suppliers. Manufacturing’s high backward linkage stems from intensive input requirements from related industries, while household services show the lowest backward linkage (0.69) due to minimal reliance on intermediate inputs.

Table 4.

Normalized backward and forward linkages and spread indices, the UAE, 2023.

Industry	Backward linkage	Backward spread	Forward linkage	Forward spread	Average linkage	Average spread
Agriculture	1.17	0.86	0.97	1.03	1.07	0.94
Mining	0.79	1.21	0.99	0.96	0.89	1.09
Manufacturing	1.30	0.91	1.07	1.04	1.18	0.98
Utilities	1.18	0.95	1.16	0.91	1.17	0.93
Construction	1.12	0.92	0.82	1.23	0.97	1.07
Trade	0.92	0.99	1.20	0.76	1.06	0.87
Transport	1.11	0.87	1.07	0.88	1.09	0.87
Accommodation and food	1.14	0.78	0.77	1.16	0.96	0.97
Information and communication	0.95	1.05	1.04	0.93	1.00	0.99
Financial and insurance	0.90	1.17	1.38	0.72	1.14	0.95
Real estate	0.94	0.97	0.85	1.05	0.89	1.01
Professional, scientific and technical activities	0.95	1.10	1.43	0.71	1.19	0.90
Administrative services	0.92	1.01	1.23	0.73	1.08	0.87
Public administration	0.96	0.94	0.70	1.28	0.83	1.11
Education	0.96	0.97	0.79	1.16	0.87	1.06
Health and social work	1.10	1.00	0.97	1.12	1.03	1.06
Arts, entertainment and recreation	0.92	0.97	0.88	1.02	0.90	0.99
Household services	0.69	1.33	0.68	1.31	0.68	1.32
Average	1.00	1.00	1.00	1.00	1.00	1.00

Source: The authors’ calculation based on the 2023 input-output table (SCAD, 2025a). We have defined the key sectors (shown in bold) as those where both the average backward and forward linkage indices exceed one, while the average backward and forward spread indices remain below one.

Similarly, forward linkages above 1 indicate strong downstream use by other sectors, whereas a forward spread index below 1 points to effects that are evenly spread rather than heavily concentrated. Sectors meeting this condition include Professional, scientific and technical activities (1.43, 0.71), Financial and insurance (1.38, 0.72), Trade (1.20, 0.76), Administrative services (1.23, 0.73), and Utilities (1.16, 0.91). Professional and technical activities provide critical inputs to select sectors and can act as a catalyst for AI adoption, further amplifying productivity and innovation. Financial services similarly support specific industries with essential capital, while household services (0.68) have the lowest forward linkage as their outputs primarily serve final consumers rather than other industries.

As discussed earlier, backward and forward linkages capture the interconnections between sectors in an economy. A backward linkage reflects the extent to which a sector depends on inputs from other sectors, while a forward linkage indicates how much a sector provides inputs to other sectors. Spread indices complement these measures by showing how widely a sector’s linkages are distributed across the economy: a spread greater than unity suggests that linkages are concentrated in a few sectors, whereas a spread below unity indicates a more even distribution. The last two columns of Table 4 present the averages of both backward and forward linkages along with the corresponding spread indices.

Applying the joint criteria—where both average backward and forward linkage indices exceed one while the spread indices remain below one—Professional, Scientific, and Technical Activities (average linkage = 1.19; average spread = 0.90), Manufacturing (1.18; 0.98), Utilities (1.17; 0.93), Financial and Insurance (1.14; 0.95), and Transport (1.09; 0.87) emerge as the key sectors. These sectors exhibit both strong and well-dispersed intersectoral linkages, indicating that their expansion can trigger significant indirect effects throughout the economy. Manufacturing and utilities serve as critical input providers through materials and energy, while transport and professional services function as essential facilitators of production, trade, and innovation across industries.

Tourism-related sectors—Trade, Transport, and Accommodation and Food Services—also display substantial linkage values, highlighting their integrative role in the UAE economy. Among them, transport demonstrates the highest combined linkage (average linkage = 1.09) with low dispersion (average spread = 0.87), reflecting its dual role as a major consumer of intermediate inputs such as fuel, maintenance, and logistics services, and as a key supplier enabling mobility and trade. Trade also exhibits above-average linkage (1.06) with low spread (0.87), emphasizing its importance in connecting domestic production with final demand and other industries. Although Accommodation and Food Services show a slightly lower average backward-forward linkage (0.96), their backward linkage (1.14) remains strong, highlighting the sector’s reliance on a diverse range of domestic suppliers in food processing, utilities, and transport. All in all, these results indicate that tourism-oriented activities not only contribute directly to output but also generate widespread indirect effects across both upstream and downstream industries.

Figure 1 illustrates the time plot of the UAE’s total real GDP and real non-oil GDP from the first quarter of 2014 to the second quarter of 2025. Over this nearly 12-year period, both indicators show a clear upward trend, though with differing growth dynamics. Total real GDP increased from 239.9 billion AED in 2014Q1 to 306.3 billion AED in 2025Q2, representing a cumulative growth of 27.7%. In contrast, real non-oil GDP grew at a significantly faster pace, rising from 113.2 billion AED to 174.1 billion AED, a substantial increase of 53.8%. On average, non-oil GDP accounted for approximately 51.0% of total real GDP during this period, rising from 47.2% in 2014Q1 to 56.8% in 2025Q2 (see Table 5). This sustained increase reflects the effectiveness of long-term policy initiatives aimed at reducing hydrocarbon dependence and fostering sustainable expansion through targeted investment in manufacturing, tourism, trade, and advanced services.

Figure 1.

Total real GDP versus real non-oil GDP (2014Q1-2025Q2). Source: The authors’ calculation using quarterly real GDP at 2014 prices obtained from SCAD (2025b).

Table 5.

Percentage share of UAE economic sectors in total real GDP (2014–2025).

Year	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	2025*
Agriculture	0.58	0.53	0.52	0.58	0.60	0.65	0.69	0.83	0.67	0.66	0.65	0.70
Mining	50.39	51.08	49.98	48.31	50.11	49.28	51.31	49.57	49.54	46.55	45.32	43.49
Manufacturing	5.42	6.66	7.38	7.44	7.62	8.32	7.01	8.16	8.68	9.57	9.47	9.81
Utilities	2.49	1.15	1.31	1.61	1.65	1.89	1.74	1.77	1.81	1.85	1.84	2.03
Construction	8.75	8.78	8.25	8.36	8.36	8.09	7.82	7.90	7.75	8.50	9.11	9.62
Trade	3.89	4.86	4.69	4.84	4.75	4.75	4.94	5.17	5.25	5.41	5.33	5.36
Transport	3.78	2.88	2.60	2.42	2.34	2.31	1.38	1.65	1.86	2.09	2.35	2.35
Accommodation and food	0.95	0.98	1.00	1.13	1.02	1.07	0.78	0.79	0.81	0.83	0.84	0.83
Information and communication	2.16	2.11	2.15	2.42	2.27	2.27	2.69	2.72	2.66	2.67	2.74	2.82
Financial and insurance	6.42	6.69	6.93	7.07	6.32	6.57	5.70	5.72	5.66	6.19	6.60	6.92
Real estate	3.77	3.56	3.79	4.28	3.79	3.86	3.22	3.10	3.28	3.53	3.54	3.67
Professional, scientific and technical activities	2.99	2.90	2.81	3.05	2.84	2.41	2.65	2.53	2.64	2.73	2.79	2.96
Public administration and defence	5.37	4.95	5.79	5.59	5.27	5.18	6.16	5.95	5.36	5.33	5.35	5.35
Education	1.20	1.18	1.25	1.30	1.40	1.56	1.90	1.88	1.75	1.76	1.73	1.70
Health and social work	1.17	1.10	0.91	0.93	0.98	1.05	1.16	1.42	1.42	1.44	1.45	1.49
Arts, entertainment and recreation	0.24	0.23	0.23	0.27	0.21	0.17	0.16	0.17	0.18	0.22	0.22	0.24
Household services	0.42	0.38	0.39	0.42	0.47	0.58	0.68	0.69	0.69	0.68	0.67	0.69

Note. * preliminary estimated based on the first two quarters of 2025.

Source: The authors’ calculation using quarterly real GDP at 2014 prices obtained from SCAD (2025b).

Having identified the key sectors, the next step is to examine how their relative contributions have evolved over time. Table 5 therefore presents the temporal trajectory of sectoral shares in the UAE’s real GDP from 2014 to 2025, highlighting the country’s gradual progression toward a more diversified and resilient non-oil economy. Between 2014 and 2025, the structure of the UAE economy reveals both continuity and rebalancing across major sectors. The three largest contributors to real GDP remain Mining and quarrying (i.e. oil and gas), Manufacturing, and Construction, although their relative weights have changed considerably. Mining and quarrying, encompassing crude oil and natural gas, continues to dominate but its share declines from 50.4% in 2014 to 43.5% in 2025, signalling a deliberate and successful effort to moderate hydrocarbon reliance. In contrast, Manufacturing emerges as a key growth driver, expanding from 5.4% to 9.8% over the same period—an almost twofold increase—reflecting the outcomes of industrial diversification, technology-driven productivity gains, and deeper integration into regional and global production networks. Construction also consolidates its strong position, rising from 8.8% to 9.6%, underpinned by large-scale infrastructure projects, urbanisation, and the expansion of mixed-use developments.

At the lower end of the output spectrum, Agriculture, Arts, entertainment and recreation, and Household services each contribute less than 1% of GDP throughout the period. Nonetheless, Agriculture and Household services record gradual increases—from 0.58% to 0.70% and from 0.42% to 0.69%, respectively—reflecting initiatives to enhance food security, rural productivity, and the provision of household-based services.

Several other sectors exhibit sustained and broad-based growth. Trade expands from 3.9% to 5.4%, driven by continued progress in logistics, re-export capacity, and regional trade integration. Information and communication increases from 2.2% to 2.8%, supported by investments in digital infrastructure, smart city projects, and innovation ecosystems. Health and social work grows from 1.2% to 1.5%, consistent with rising healthcare demand and population growth, while Financial and insurance activities expand from 6.4% to 6.9%, underscoring the UAE’s emergence as a regional financial centre. Conversely, Public administration and defence shows a marginal decline from 5.4% to 5.35%, reflecting fiscal rationalisation and a gradual shift toward private-sector-led growth.

The COVID-19 pandemic (2020–2021) marks a temporary disruption in this structural trajectory. Transport and Accommodation and food services—two sectors highly dependent on international tourism—experienced sharp contractions, with Transport’s share falling from 2.3% in 2019 to 1.4% in 2020, and Accommodation and food services from 1.07% to 0.78%. Although both sectors have recovered steadily—reaching 2.35% and 0.83% of GDP, respectively, in 2025—they remain slightly below their pre-pandemic levels. In contrast, Health, Education, and Information and communication strengthened during and after the pandemic, with Health and social work increasing from 1.05% in 2019 to 1.49% in 2025, and Information and communication from 2.27% to 2.82%, reflecting adaptive structural resilience and accelerated digital transformation.

As shown in Tables 4 and 5, tourism-related activities—including Transport, Accommodation and food services, Trade, and Arts, entertainment and recreation—play a disproportionately influential role in driving inter-industry linkages across the UAE economy. While their direct GDP contributions remain modest, their strong backward and forward linkages (Table 4) magnify their overall impact on output, employment, and value-chain integration. Transport, accounting for 2.35% of GDP in 2025, is among the most interconnected sectors, with an average linkage index of 1.09 and an average spread of 0.87. This configuration indicates robust upstream connections—through demand for fuel, logistics, and maintenance services—and downstream effects that enhance mobility, trade, and tourism. Trade, representing 5.36% of GDP, also exhibits strong interindustry relationships (average linkage = 1.06; spread = 0.87), reinforcing its role as a critical facilitator of domestic and international economic flows.

Accommodation and food services, contributing 0.83% of GDP in 2025, exhibit a pronounced backward linkage (1.14), reflecting reliance on inputs from agriculture, food manufacturing, utilities, and transport. Similarly, Arts, entertainment and recreation, though contributing only 0.24% of GDP, generate significant multiplier effects by complementing the hospitality, cultural, and leisure ecosystem, thereby strengthening the UAE’s global tourism appeal.

Therefore, the tourism-related sectors form a highly integrated production network that extends far beyond their immediate GDP shares. Their operations stimulate demand across multiple upstream industries—particularly manufacturing, agriculture, and utilities—while generating downstream spillovers in retail, logistics, and business services. This interconnectedness positions tourism as both a catalyst and a conduit of diversification, reinforcing synergies between traditional and emerging sectors. The steady post-2021 recovery of these activities, combined with their strong backward and forward linkages, highlights their essential role in sustaining non-oil growth. Continued investment in tourism infrastructure, air transport connectivity, and cultural development is therefore pivotal to deepening these linkages, enhancing resilience, and consolidating the UAE’s transformation into a globally competitive, service-oriented economy. ¹

The findings align closely with the broader tourism I–O literature while also extending it in important ways. Consistent with early multiplier studies such as Fletcher (1989) and Archer (1995), tourism-related sectors in the UAE exhibit strong backward linkages and comparatively weaker forward linkages, confirming tourism’s upstream dependence on domestic suppliers. Similar to Cai et al. (2006) and Khanal et al. (2014), transport, accommodation and food, and trade emerge as key transmission channels through which demand shocks propagate across the production network. However, unlike cross-country analyses such as Teigeiro and Díaz (2014), which focus primarily on output multipliers, our results incorporate normalized linkage and spread indices, thereby addressing the methodological gap highlighted by Lamonica and Mattioli (2015). The evidence that tourism-related sectors in the UAE combine above-average linkages with relatively low dispersion reinforces the view advanced by Figini and Patuelli (2022) that more diversified and service-integrated economies exhibit stronger and more broadly distributed multiplier effects. Moreover, the rising share of non-oil GDP and the post-pandemic resilience of tourism-linked activities parallel the structural evolution documented by Hor (2021) and Kronenberg et al. (2018), highlighting tourism’s dynamic role in economic transformation. Overall, the UAE results confirm the established pattern of tourism’s strong upstream integration while demonstrating—through combined multiplier and dispersion analysis—its strategic contribution to economic diversification in a hydrocarbon-dependent economy.

Policy implications

The findings of this study have important implications for policymakers. Tourism in the UAE is not simply a narrowly defined sector; it functions as a strategic driver of economic diversification, generating significant upstream and downstream effects throughout the national production system. The linkage patterns presented in Tables 3–4 show that tourism-related demand stimulates a broad range of domestic industries, reinforcing its role as a catalyst for structural transformation. For example, hotels, restaurants, and event venues source inputs from agriculture, food and beverage manufacturing, packaging, warehousing, utilities, and transport, demonstrating tourism’s ability to drive upstream production. Energy-intensive aviation and maritime activities further link tourism to refined petroleum products, mechanical and electrical equipment manufacturing, and ongoing maintenance services. At the same time, conferences, exhibitions, and creative events create strong connections with publishing, broadcasting, advertising, printing, textiles, and furniture manufacturing, reflecting tourism’s integration with information, communication, and cultural industries.

Tourism stimulates both upstream and downstream inter-industry activity, as shown in Tables 3–5. Upstream, it generates demand for manufacturing and service inputs, including food processing, utilities, transport, logistics, petroleum products, spare parts, maintenance, telecommunications, pharmaceuticals, and medical devices. Downstream, it supports retail, hospitality, entertainment, education, and health services, including international student mobility and medical tourism. Postal, courier, and warehousing services enhance logistics efficiency and strengthen trade–tourism linkages. Furthermore, cultural and creative industries, alongside finance, real estate, and professional services, support tourism-related investment and infrastructure, connecting the sector to construction, utilities, and public administration while elevating the UAE’s global cultural profile.

Based on these insights, policymakers can enhance tourism’s contribution to broad-based economic growth by targeting high-impact upstream inputs and reducing import leakage in food and entertainment services to retain more domestic value. Strategic investments in transport, professional services, and cultural industries can propagate tourism-driven demand across multiple sectors. Encouraging local sourcing and domestic supply chains can strengthen high-multiplier tourism segments, consolidating the sector’s contribution to GDP, employment, and non-oil economic diversification. The increasing share of tourism-related sectors in GDP from 2014 to 2025 underscores its growing economic significance and its pivotal role in the UAE’s transition toward a resilient, service-oriented growth model.

Conclusion

This study substantiates that tourism plays a central role in the UAE’s structural diversification by linking traditional and emerging sectors across the economy. Using the 2023 input–output table and a domestic Leontief framework, we show that tourism-related sectors—particularly Accommodation and food services, Transport, and Trade—generate substantial direct and indirect effects, stimulating upstream industries such as agriculture, food processing, utilities, and logistics, while creating downstream demand in retail, hospitality, entertainment, education, and health services. Cultural, creative, and professional services further amplify these effects by supporting investment, infrastructure, and innovation. Multipliers and normalized backward and forward linkage indices confirm that tourism facilitates well-dispersed economic impacts, retains domestic value, and channels income across multiple industries. The rising GDP shares of key tourism-linked sectors from 2014 to 2025 highlight its growing weight in the non-oil economy. Therefore, these findings indicate that tourism acts as a catalyst for structural transformation, promoting diversification, enhancing resilience, and supporting the UAE’s transition toward a service-oriented, knowledge-based economy.