Usefulness of Non-Linear Input—Output Models for Economic Impact Analyses in Tourism and Recreation

Klijs, Jeroen; Peerlings, J.H.M.; Heijman, Wim

doi:10.5367/te.2014.0398

Cited by 18 publications

(17 citation statements)

References 39 publications

(44 reference statements)

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“…Because of the highly aggregated nature of national and territorial accounting data, the top-down approach cannot achieve as fine a degree of process detail as the bottom-up approach. Furthermore, any analysis based on the IO model is potentially affected by the familiar limitations of the method (Briassoulis, 1991; Dwyer et al, 2004; Klijs et al, 2015). On the other hand, since it relies on existing statistical information and standardized procedures, the IO top-down approach is transparent, easily replicable and relatively inexpensive to implement.…”

Section: Literaturementioning

confidence: 99%

Greenhouse gas emissions from tourist activities in South Tyrol

Cai

2016

Tourism Economics

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Tourism is a non-negligible source of greenhouse gas (GHG) emissions. Using South Tyrol (ST) – a small region with a tourism-intensive economy situated in the North of Italy – as a case study, this article discusses a multiregional input–output (MRIO) framework for calculating the direct and indirect emissions embodied in tourist consumption of goods and services at a subnational level. Compared to more standard single-region implementations of the input–output approach, MRIO analysis offers a more accurate depiction of the amount of emissions, that is, embodied in imports, because it acknowledges that in the modern economy supply chains often stretch across multiple borders and that the carbon intensity of production can vary widely from one location to another. Operationalizing the framework has become relatively straightforward since a number of new global MRIO databases have become available in recent years. Furthermore, the analysis could easily be extended to other environmental externalities of tourism, where the model’s capability to explicitly account for spatial spillovers might also be of interest. The modelling exercise at the heart of the article suggests that, over the course of 2010, the process of producing the goods and services consumed by tourists in ST resulted in 1092 kt CO2e of GHGs being emitted into the atmosphere. This is equivalent to average emissions of 191 kg CO2e per overnight visitor, 38 kg CO2e per night or 0.316 kg per euro of tourist expenditure. Direct emissions account for about one-fourth of the total. Almost four-fifths of total emissions appear to be the result of productive activities sited outside ST itself.

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Section: Literaturementioning

confidence: 99%

Greenhouse gas emissions from tourist activities in South Tyrol

Cai

2016

Tourism Economics

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“…Employment-based location quotients were then used to allocate those expenditures between local and imported sources. This is important due to its addressing the substitution I-O limitations, for further information on these limitations see West and Jackson (2005), Zhao and Choi (2015) and Klijs, Peerlings, and Heijman (2015). The wine industry is not included as a separate industry in the I-O tables.…”

Section: Household Expenditurementioning

confidence: 99%

Combining amenity with experience

et al. 2016

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Industry and government bodies have recommended augmentation of traditional production and marketing techniques as ways of increasing an industry's profitability. This paper values the amenity of the wine industry, a sensory experience that provides an array of opportunities both culturally to the tourist and economically to many regions across the world. Using the wine industry in the Central West region of New South Wales, Australia we use Input-Output analysis to assess the economic impacts of this industry and the amenity hidden within. Not only does the industry provide jobs and commerce supporting local prosperity, it also supplies a mixed production and consumption amenity from an agricultural product that meets the tourist's leisure desirean amenity that transcends from its origin in the vineyard to its destination at the table.

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“…The NLIO model we use is described in Klijs et al (2014) and in Appendix 2. However, we make three changes to this model to include labour productivity.…”

Section: Nlio Modelmentioning

confidence: 99%

“…The ratio of output to labour remains constant, which implies constant labour productivity. Klijs et al (2014) have shown how input substitution due to an increase in final demand leading to relative price changes can be included in an IO model, creating a non-linear input-output (NLIO) model. Substitution from and to labour will automatically lead to a change in the ratio of output to labour, that is, a labour productivity increase.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, this article provides the first application of endogenous labour productivity changes within the domain of tourism. We use an a NLIO model, instead of CGE model, because the main data source required to carry out a CGE analysis, the social accounting matrix, is rarely available on the (subnational) scale level on which economic impact analyses in tourism are often carried out (Klijs et al, 2014). Given that the production structure of the NLIO model corresponds to that of a CGE model, productivity could be introduced in the latter model in the same manner.…”

Section: Introductionmentioning

confidence: 99%

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Introducing labour productivity changes into models used for economic impact analysis in tourism

2015

Self Cite

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In tourism management, traditional input–output models are often applied to calculate economic impacts, including employment impacts. These models imply that increases in output are translated into proportional increases in labour, indicating constant labour productivity. In non-linear input–output (NLIO) models, final demand changes lead to substitution. This causes changes in labour productivity, even though one unit of labour ceteris paribus still produces the same output. Final demand changes can, however, also lead to employees working longer, harder and/or more efficiently. The goal of this article is to include this type of ‘real’ labour productivity change into an NLIO model. To do this, the authors introduce factor augmenting technical change (FATC) and a differentiation between core and peripheral labour. An NLIO model with and without FATC is used to calculate the regional economic impacts of a 10% final demand increase in tourism in the province of Zeeland in the Netherlands. Accounting for real productivity changes leads to smaller increase in the use of labour, as productivity increases allow output to be produced using fewer inputs.

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Usefulness of Non-Linear Input—Output Models for Economic Impact Analyses in Tourism and Recreation

Cited by 18 publications

References 39 publications

Greenhouse gas emissions from tourist activities in South Tyrol

Greenhouse gas emissions from tourist activities in South Tyrol

Combining amenity with experience

Introducing labour productivity changes into models used for economic impact analysis in tourism

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