Heritage Tourism and Economic Development:

An Input-Output Analysis for Minas Gerais, Brazil

Arthur Alvarenga, Cláudio Seibert, Elvira Medeiros, Filipe Reis, and Fernando Perobelli

2023-07-30

Aknowledgements

Motivation

  • What role does tourism plays in a developing economy?
  • Minas Gerais, Brazil:
    • Highly dependent on the mining industry
    • Rich cultural and architectonic heritage
  • How related is it to the rest of the economy?
  • What about regional disparities?

We have a lot of questions, but not so many answers.

Minas Gerais: an Overview

  • Population: 20.5 million (2022)
    • #2 in Brazil
  • HDI: 0.774 (2021)
    • #4 in Brazil
  • GDP: US$ 177 billion (2022)
    • 9.3% of national output

Minas Gerais is a snapshot of Brazil: It reproduces national patterns of income and quality of life disparities

Minas Gerais: an Overview

  • Trade (B08), Real Estate (B14), Manufacturing (B04), and Agriculture (B01) account for 46% of the Value Added
  • Services (B05-B07, B09-B20): 56% of VA

Minas Gerais: an Overview

Summary Statistics

Code Region Population % MG GVA (R$M) % MG HDI-M
R01 Belo Horizonte 2,315,560 11.27% 71,892 12.69% 0.810
R02 Barão de Cocais 30,778 0.15% 723 0.13% 0.722
R03 Caeté 38,776 0.19% 575 0.10% 0.728
R04 Catas Altas 5,476 0.03% 501 0.09% 0.684
R05 Congonhas 52,890 0.26% 2,281 0.40% 0.753
R06 Diamantina 47,702 0.23% 686 0.12% 0.716
R07 Mariana 61,387 0.30% 3,832 0.68% 0.742
R08 Ouro Preto 74,824 0.36% 5,978 1.05% 0.741
R09 Sabará 129,372 0.63% 2,947 0.52% 0.731
R10 Santa Bárbara 30,466 0.15% 1,159 0.20% 0.707
R11 São João del Rei 90,225 0.44% 1,951 0.34% 0.758
R12 Serro 21,952 0.11% 192 0.03% 0.656
R13 Tiradentes 7,744 0.04% 118 0.02% 0.740
R14 Rest of MG 17,679,167 85.31% 473,863 83.62% 0.667

The Baroque Heritage

  • Gold run, 17th and 18th centuries
  • Portuguese-inspired with local features
  • Sacred art, buildings, landmarks

Three Unesco World Cultural Heritage sites: Diamantina, Ouro Preto, and the Congonhas Sanctuary

The Baroque Heritage

Methodology

First challenge: lack of regional data

  • Framework: Interregional Input-Output Setting (IIOAS)
    • Haddad, Gonçalves Júnior, and Nascimento (2017)
    • Homogenous production and utility functions across regions
    • Consistent with national aggregates
    • 15 regions, 20 sectors
  • Trade matrices between regions
    • Supply: \(TOTSUP := DOMSUP + EXP\)
    • Exports data (\(EXP\)) known for each region and sector
    • Domestic supply (\(DOMSUP\)): labor microdata as proxy

Methodology

Linkages

  • Within regions (\(intra\))
  • Key sector: \(FL > 1\) and \(BL > 1\)
  • E.g.: Backward linakges for region 1
    • \(FL\)s are likewise, but on rows instead of columns

\[ \begin{aligned} BL_A^{intra, 1} = \frac{2^{-1} (l_{AA}^{11} + l_{BA}^{11})}{2^{-2} \sum_{j=1}^{15} \sum_{k=1}^{15} l_{jk}^{11}} \end{aligned} \]

Hypothetical Extraction

  • Total extraction (backward and forward)
  • Intermediate consumption and final demand
    • Simulates sudden vanishing of the extracted industry
    • Otherwise: assumes agents would substitute with imports

Methodology

Second challenge: tourism is a cross-sector industry

  • Common approach: tourism satellite account (TSA)
    • Not available for the state
  • Our approach:
    • Estimate tourism shares in each sector and region
    • Tourism Characteristic Activities (TCA)s, as defined by the state government
    • Sensitivity analysis + state survey
    • Apply shares as extraction coefficient

Example: if 10% of a sector’s output in a city is tourism-related, then 10% of the sector is extracted.

Methodology

Sensitivity analysis

Region B09 B10 B11 B14 B15 B19
R01 Belo Horizonte - - - - - -
R02 Barão de Cocais 25% 98% 25% 1.25% 0.25% 25%
R03 Caeté 25% 100% 25% 1.25% 0.25% 50%
R04 Catas Altas 25% 98% 25% 1.25% 0.25% 50%
R05 Congonhas 25% 96% 25% 1.25% 0.25% 50%
R06 Diamantina 50% 92% 75% 5.00% 1.00% 100%
R07 Mariana 50% 96% 50% 5.00% 1.00% 100%
R08 Ouro Preto 50% 100% 75% 5.00% 1.00% 100%
R09 Sabará 13% 98% 25% 1.25% 0.25% 25%
R10 Santa Bárbara 25% 98% 50% 2.50% 0.50% 50%
R11 São João del Rei 25% 100% 25% 5.00% 1.00% 100%
R12 Serro 50% 98% 50% 5.00% 1.00% 100%
R13 Tiradentes 75% 100% 100% 5.00% 1.00% 100%
R14 Rest of MG - - - - - -
R15 Rest of Brazil - - - - - -
  • Accommodation (B10): data from state survey
  • Transportation (B09), Food Services (B11), and Arts, Culture, Sports, and Recreation (B19): relative to city size and its importance as touristic destination
  • Real Estate (B14) and Administrative Services (B15): minor role, smaller coefficients

Data

Results

Linkages

  • Except for Transportation, no TCA is a key sector
  • Most key sectors are services
    • Especially Construction, Wholesale and Retail Trade, Transportation, and Finance
  • Extractive Industries
    • Biggest part of Value Added in most cities
    • Key sector only in Sabará
    • Mostly export commodities, with little local processing

Results

Table 1: Linkages
Code Region B02 B03 B04 B05 B06 B07 B08 B09 B12 B13 B14 B15
R01 Belo Horizonte Key Key Key Key Key
R02 B. Cocais Key Key Key Key Key
R03 Caeté Key Key Key Key
R04 Catas Altas Key Key Key
R05 Congonhas Key Key Key
R06 Diamantina Key Key Key Key Key
R07 Mariana Key Key Key
R08 Ouro Preto Key Key Key Key
R09 Sabará Key Key Key Key Key
R10 Santa Bárbara Key Key Key Key
R11 São João del Rei Key Key Key Key Key
R12 Serro Key Key Key
R13 Tiradentes Key Key Key Key
R14 Rest of MG Key Key Key Key Key
R15 Rest of BR Key Key Key Key Key Key Key Key Key Key Key

Results

Hypothetical Extraction

Simultaneous extraction

  • Negligible impacts outside the Baroque Circuit
  • Lowest: Catas Altas, \(< 1\%\)
  • Highest: Tiradentes, \(65\%\)
  • Average: \(10\%\)
  • Median: \(6\%\)

Local extractions

  • Smaller than in the simultaneous scenario
    • Modest regional dependency
    • Biggest difference in Tiradentes (\(33\%\)), average \(17\%\)
  • Almost no intraregional impacts
    • Notable exceptions: Ouro Preto & Mariana and Tiradentes & São João del Rei

Hypothetical Extraction

Region Global R02 R03 R04 R05 R06 R07 R08 R09 R10 R11 R12 R13 Diff.
Belo Horizonte -0.33 0.00 -0.01 0.00 -0.01 0.00 -0.01 -0.02 -0.04 0.00 0.00 0.00 0.00 0
B. Cocais -3.97 -3.02 -0.01 0.00 0.00 0.00 -0.02 -0.02 -0.01 -0.15 0.00 0.00 0.00 -23
Caeté -6.62 0.00 -5.75 0.00 0.00 0.00 0.00 -0.01 -0.01 0.00 0.00 0.00 0.00 -13
Catas Altas -0.75 0.00 0.00 -0.65 0.00 0.00 0.00 -0.01 0.00 -0.01 0.00 0.00 0.00 -13
Congonhas -2.05 0.00 0.00 0.00 -1.93 0.00 0.00 -0.01 0.00 0.00 0.00 0.00 0.00 -6
Diamantina -6.50 0.00 0.00 0.00 0.00 -4.42 0.00 0.00 0.00 0.00 0.00 0.00 0.00 -31
Mariana -8.41 0.00 0.00 0.00 0.00 0.00 -5.25 -0.15 0.00 0.00 0.00 0.00 0.00 -37
Ouro Preto -6.37 0.00 0.00 0.00 0.00 0.00 -0.13 -4.08 0.00 0.00 0.00 0.00 0.00 -35
Sabará -4.82 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 -2.96 0.00 0.00 0.00 0.00 -38
Santa Bárbara -3.29 -0.01 0.00 0.00 0.00 0.00 -0.01 -0.01 0.00 -2.50 0.00 0.00 0.00 -24
S. João del Rei -8.00 0.00 0.00 0.00 -0.01 0.00 -0.01 -0.01 0.00 0.00 -3.43 0.00 -0.21 -57
Serro -9.22 0.00 0.00 0.00 0.00 -0.01 0.00 0.00 0.00 0.00 0.00 -5.52 0.00 -40
Tiradentes -64.67 0.00 0.00 0.00 0.00 0.00 0.00 -0.01 0.00 0.00 -0.17 0.00 -43.34 -32
Rest of MG -0.02 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0
Rest of BR -0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0

Note: values as percentage of Gross Value of Production (GVP)

Why Are Results so Different?

Sabará, Ouro Preto, and Tiradentes: a case study

Region Population Value Added (R$ M) Extractive Industries (%) Trade (%) TCAs (%) Other Services (%) Others (%)
Sabará 135421 2690.69 33.45 12.28 19.26 24.87 10.14
Ouro Preto 73994 6630.11 46.56 6.23 8.50 34.47 4.23
Tiradentes 7886 152.05 1.84 17.11 46.85 22.03 12.17
  • Sabará
    • Mining industry and part of the capital’s metropolitan area
    • Not a well-known touristic destination
  • Ouro Preto
    • Mining industry, large university, regional center
    • UNESCO World Heritage Site, famous touristic destination
  • Tiradentes
    • Small town
    • Highly dependent on tourism activity

Final Remarks

  • IIOAS framework as an estimate for regional figures
    • Reproduces regional disparities and each city’s main industries adequately
  • Determining Tourism Characteristic Activities
    • Room to improve the sensitivity analysis
  • Tourism seems disconnected from other industries
  • While some cities are consolidated touristic destinations, others have potential to develop their tourism industry
  • Investment in accessibility (road paving, regional airports) have potential to increase tourism traffic

Thanks!

Arthur Bazolli Alvarenga

arthur.bazolli@estudante.ufjf.br

baarthur.github.io

@baarthur0

@baarthur


Cláudio Eurico Seibert

claudioseibertf@gmail.com

Elvira Helena Oliveira de Medeiros

helena.elvira@estudante.ufjf.br


Filipe Santiago dos Reis

filipe.santiago@estudante.ufjf.br


Fernando Salgueiro Perobelli

fernando.perobelli@ufjf.br

References

Brazil. 2022a. “Comex Stat - Sobre.” Comex Stat. http://comexstat.mdic.gov.br/pt/sobre.
———. 2022b. “Microdados RAIS e Caged.” PDET - Programa de Disseminação das Estatísticas do Trabalho. http://pdet.mte.gov.br/microdados-rais-e-caged.
Guilhoto, Joaquim José Martins. 2021. “Sistema de Matrizes de Insumo-Produto para o Brasil 2018 - 68 setores.” Nereus.
Haddad, Eduardo Amaral, Carlos Alberto Gonçalves Júnior, and Thiago Oliveira Nascimento. 2017. “Matriz Interestadual de Insumo-Produto Para o Brasil: Uma Aplicação Do Método IIOAS.” Revista Brasileira de Estudos Regionais e Urbanos 11 (4): 424–46.