publication . Doctoral thesis . 2016

Tourism, income, and jobs: Improving the measurement of regional economic impacts of tourism

Klijs, J.;
Open Access English
  • Published: 01 Jan 2016
  • Publisher: NRIT
Abstract
<p style="margin-left: 28pt;"><strong>Summary</strong></p> <p>Tourism can have a broad range of impacts, including impact on the economy, on the natural and built environment, on the local population, and on visitors themselves. This PhD thesis discussed the measurement of regional economic impacts of tourism, including impacts on output, value added, and employment caused by visitor expenditure. The focus was on the choice between models that can be used to calculate these regional economic impacts and the data requirements, usage, and further development of one specific model; the Input-Output (I-O) model.</p> <p>The starting point of an I-O model is final dem...
Subjects
free text keywords: income, models, inpu-output model, impact van toerisme, regional economic impacts, Economie (algemeen), economic impact, tourism impact, toerisme, tourism, employment, economische impact, visitor impact, regionale economie, impact van bezoekers, werkgelegenheid, inkomen, modellen, Economics (General), regional economics
Download fromView all 2 versions
Wageningen Yield
Doctoral thesis . 2016
Provider: NARCIS
218 references, page 1 of 15

6. Labour productivity in a Non-linear I-O model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 6.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 6.2 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 6.2.1 Core and peripheral labour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 6.2.2 Labour productivity in tourism. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 6.3 Labour productivity in a Non-linear I-O model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.4 Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 6.5 Scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 6.6 Results and discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 6.7 Data demand and complexity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 6.8 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

1. The I-O model is relatively simple. Computations can be done in standard software such as MS Excel. The calculations and the outcomes can be explained to non-experts, including most clients. Nonetheless, they might struggle to understand consequences of underlying assumptions (e.g. Dwyer et al., 2004; Horváth & Frechtling, 1999; Zhang, 2002).

2. I-O models are well known. The advantages, disadvantages, structure, and usage are extensively discussed in many publications (e.g. Dwyer et al., 2004; Horváth & Frechtling, 1999; Schaffer, 1999). The extensive usage of I-O models implies that new applications can potentially be compared to applications in other regions, at other times, or for other final demand (changes) (Archer, 1995; Fletcher, 1989).

3. Data demands of I-O models are relatively modest. They require an I-O table, data on final demand per industry and, for calculation of employment impacts, ratios between employment and output per industry. When an I-O table is not available at the appropriate spatial scale methods are available to create such a table2. For some EIAs data on final demand can be derived from a Tourism Satellite Account3.

4. The level of detail of outcomes is relatively high. I-O models show impacts on output, value added, income, and employment, per industry. This can lead to valuable insights for clients and the organizations they want to inform or convince (e.g. Horváth & Frechtling, 1999; Loveridge, 2004; West & Gamage, 2001).

5. An I-O model offers flexibility. It can be used for • significance analysis, to calculate the indirect economic impacts related to (a part of) final demand (e.g. Liu et al. 2013; Martínez-Roget et al., 2013; Murillo et al., 2013); • impact analyses, to calculate the indirect impact of a change of final demand (Barajas et al., 2014; Huang et al., 2014; Warnick et al., 2015); • and/or linkage analysis, to calculate the strength of relationships between industries (e.g. Cai et al., 2006; Khanal et al., 2014; Robles Teigeiro & Díaz, 2014; Soulie & Valle, 2014).

4 I-O models with endogenous consumptions: Include relationships between income earned by households and their consumption (e.g. Bracalente et al., 2011; Polo & Valle, 2008)

5 Bi- or multiregional I-O models: Include relationships between the demand by industries in one region for imports from other regions and the output produced by industries in these other regions. Regional spillovers are taken into consideration (e.g. Freeman & Sultan, 1997; Manente, 1999; Soulie & Valle, 2014)

6 Econometrically extended I-O models: Relationships, that are assumed fixed or absent in the I-O model, are econometrically estimated (Bonn & Harrington, 2008; Israilevich & Hewings, 1996; Loveridge, 2004; Oosterhaven & Polenske, 2009; Rey, 2000; West, 1995). Can include a spatial (Batey & Rose, 1990; Loveridge, 2004) or temporal dimension (Loveridge, 2004; West, 1993, 1995).

7 I-O models with environmental impacts: By establishing ratios between output and environmental impacts (e.g. CO2 emissions), for each relevant industry, economic impacts calculated by the I-O model can be translated into environmental impacts (e.g. Collins et al., 2012; Jones, 2008; Sun & Pratt, 2014)

8 Advantages and disadvantages of three of the multipliers models (Export Base, Keynesian and Ad Hoc) are discussed in more detail in chapter 2

9 Var and Liu combine Ad Hoc and I-O Models (e.g. Liu et al., 1984; Liu & Var 1982; Var & Quayson1985).

10 CGE models and their advantages and disadvantages are discussed in more detail in chapter 2.

11 As discussed in chapter 5 different types of NLIO models are possible. Here we refer to NLIO modes in which the Leontief production function has been replaced by alternative production functions, resulting in price induced input substitution. The NLIO is discussed in much greater detail in chapter 6 and 7.

Kleijweg, A., & Thurik, R. (1988). Determinants of aggregate employment: an example of the food retail and the hotel and catering sectors. Service Industries Journal, 8(1), 91-100. [OpenAIRE]

218 references, page 1 of 15
Abstract
<p style="margin-left: 28pt;"><strong>Summary</strong></p> <p>Tourism can have a broad range of impacts, including impact on the economy, on the natural and built environment, on the local population, and on visitors themselves. This PhD thesis discussed the measurement of regional economic impacts of tourism, including impacts on output, value added, and employment caused by visitor expenditure. The focus was on the choice between models that can be used to calculate these regional economic impacts and the data requirements, usage, and further development of one specific model; the Input-Output (I-O) model.</p> <p>The starting point of an I-O model is final dem...
Subjects
free text keywords: income, models, inpu-output model, impact van toerisme, regional economic impacts, Economie (algemeen), economic impact, tourism impact, toerisme, tourism, employment, economische impact, visitor impact, regionale economie, impact van bezoekers, werkgelegenheid, inkomen, modellen, Economics (General), regional economics
Download fromView all 2 versions
Wageningen Yield
Doctoral thesis . 2016
Provider: NARCIS
218 references, page 1 of 15

6. Labour productivity in a Non-linear I-O model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 6.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 6.2 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 6.2.1 Core and peripheral labour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 6.2.2 Labour productivity in tourism. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 6.3 Labour productivity in a Non-linear I-O model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 6.4 Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 6.5 Scenario. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 6.6 Results and discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 6.7 Data demand and complexity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 6.8 Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133

1. The I-O model is relatively simple. Computations can be done in standard software such as MS Excel. The calculations and the outcomes can be explained to non-experts, including most clients. Nonetheless, they might struggle to understand consequences of underlying assumptions (e.g. Dwyer et al., 2004; Horváth & Frechtling, 1999; Zhang, 2002).

2. I-O models are well known. The advantages, disadvantages, structure, and usage are extensively discussed in many publications (e.g. Dwyer et al., 2004; Horváth & Frechtling, 1999; Schaffer, 1999). The extensive usage of I-O models implies that new applications can potentially be compared to applications in other regions, at other times, or for other final demand (changes) (Archer, 1995; Fletcher, 1989).

3. Data demands of I-O models are relatively modest. They require an I-O table, data on final demand per industry and, for calculation of employment impacts, ratios between employment and output per industry. When an I-O table is not available at the appropriate spatial scale methods are available to create such a table2. For some EIAs data on final demand can be derived from a Tourism Satellite Account3.

4. The level of detail of outcomes is relatively high. I-O models show impacts on output, value added, income, and employment, per industry. This can lead to valuable insights for clients and the organizations they want to inform or convince (e.g. Horváth & Frechtling, 1999; Loveridge, 2004; West & Gamage, 2001).

5. An I-O model offers flexibility. It can be used for • significance analysis, to calculate the indirect economic impacts related to (a part of) final demand (e.g. Liu et al. 2013; Martínez-Roget et al., 2013; Murillo et al., 2013); • impact analyses, to calculate the indirect impact of a change of final demand (Barajas et al., 2014; Huang et al., 2014; Warnick et al., 2015); • and/or linkage analysis, to calculate the strength of relationships between industries (e.g. Cai et al., 2006; Khanal et al., 2014; Robles Teigeiro & Díaz, 2014; Soulie & Valle, 2014).

4 I-O models with endogenous consumptions: Include relationships between income earned by households and their consumption (e.g. Bracalente et al., 2011; Polo & Valle, 2008)

5 Bi- or multiregional I-O models: Include relationships between the demand by industries in one region for imports from other regions and the output produced by industries in these other regions. Regional spillovers are taken into consideration (e.g. Freeman & Sultan, 1997; Manente, 1999; Soulie & Valle, 2014)

6 Econometrically extended I-O models: Relationships, that are assumed fixed or absent in the I-O model, are econometrically estimated (Bonn & Harrington, 2008; Israilevich & Hewings, 1996; Loveridge, 2004; Oosterhaven & Polenske, 2009; Rey, 2000; West, 1995). Can include a spatial (Batey & Rose, 1990; Loveridge, 2004) or temporal dimension (Loveridge, 2004; West, 1993, 1995).

7 I-O models with environmental impacts: By establishing ratios between output and environmental impacts (e.g. CO2 emissions), for each relevant industry, economic impacts calculated by the I-O model can be translated into environmental impacts (e.g. Collins et al., 2012; Jones, 2008; Sun & Pratt, 2014)

8 Advantages and disadvantages of three of the multipliers models (Export Base, Keynesian and Ad Hoc) are discussed in more detail in chapter 2

9 Var and Liu combine Ad Hoc and I-O Models (e.g. Liu et al., 1984; Liu & Var 1982; Var & Quayson1985).

10 CGE models and their advantages and disadvantages are discussed in more detail in chapter 2.

11 As discussed in chapter 5 different types of NLIO models are possible. Here we refer to NLIO modes in which the Leontief production function has been replaced by alternative production functions, resulting in price induced input substitution. The NLIO is discussed in much greater detail in chapter 6 and 7.

Kleijweg, A., & Thurik, R. (1988). Determinants of aggregate employment: an example of the food retail and the hotel and catering sectors. Service Industries Journal, 8(1), 91-100. [OpenAIRE]

218 references, page 1 of 15
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publication . Doctoral thesis . 2016

Tourism, income, and jobs: Improving the measurement of regional economic impacts of tourism

Klijs, J.;