Place the ‘Candy’ and ‘ Crush ’ it: Entry Determinants of the Software and Video game firms in Barcelona Carles Méndez-Ortega – carles.mendez@urv.cat Universitat Rovira i Virgili (CREIP-QURE) Seminario ECONRES March 29th, 2019 Facultad de Ciencias Económicas y Empresariales (UAM)
Outline: 1. Introduction 2. Data and Area of Study 3. Methodology 4. Results 5. Robustness Analysis 6. Conclusion Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 2/37
Introduction (I): Definitions Creative ICT industry Industry Software • • Exponential growth Creative components and Video • Technical procedures • Abstact skills game • Business features Industry 5.4 % of the World GDP in 2010. (Dutta and Mia, 2010) Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 3/37
Introduction (II): Motivation • The Computer revolution has caused the appearance and rise of high-tech industries, which are considered key drivers of economic growth in developed countries due to their capacity in knowledge-generation, creativity and innovation. (Berger and Frey, 2016). • Concretely, the impact of Software and Video games is huge and growing over time. • European Union (2014) • 900 billion euros (7.9% of EU28 GDP). • 11.6 million jobs (5.3% of EU28 jobs). • The average wage per worker is 34% higher than the EU wage average and 80% higher than the service sector average). Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 4/37
Motivation (III): Previous Work • Méndez-Ortega, Carles and Arauzo-Carod, JM. (2017): • Location patterns of the Software and Video game Industry in Barcelona at micropolitan level: • Located in urban areas. • Polycentric location: 22@ Urban project Eixample district • Tended to be collocated with some others specific creative sectors. • VFI, ADV, RTV. • Young and small firms more concentrated than big and old ones. Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 5/37
Introduction (IV): Aim and Contribution The aim of this paper is to determine which reasons lead Software and Video games firms to locate in certain areas of Barcelona. • Despite being an industry located in urban areas, most of previous empirical research in location determinants of high-tech firms has been done at country and/or regional level. For this reason: This paper contributes to the literature filling the lack of empirical studies that analyze location determinants of SVE industry at urban level, dealing with factors that either had not been taken into account, or had not been analyzed together at this scale. Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 6/37
Introduction (V): Why Barcelona? • The province of Barcelona (NUTS3) accounts more than the 80% of the Software and Video game firms (SVE) of Catalonia. • The city of Barcelona accounts more than the 60% of SVE firms in Catalonia. • Urban renewal projects (e.g. 22@), university degrees and cultural environment among others, have made Barcelona one of the most attractive cities for these firms in Europe and worldwide. SVE firms ’ entries in Barcelona between 2011 and 2013 Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 7/37
Introduction (VI): Theoretical Model and Literature Review • The cost for a firm selecting a location has been represented in the literature with the following function (Brülhart et al., 2012; Gómez-Antonio and Sweeney, 2018): 𝐷 = 𝐺(𝐵𝐹, 𝐻, 𝐼𝐷, 𝑢, 𝑀𝑄, 𝑇 • AE: Agglomeration Economies. • G: Public goods ( e.g. Transport services, Wi-Fi Public services, Public centers and Urban renewal areas by public iniciative). • HC: Human Capital or Labour. • t: Taxes (are constant inside the city). • LP: Land Price. • S: Other site characteristics (e.g. Technological parks, Universities, Creative diversity, Crime, among others). Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 8/37
Introduction (VII): Theoretical Model and Literature Review Studies LAE HTA CCD HC LPT C Abramovsky and Simpson (2011) X X X Acosta et al. (2011) X X X X Audretsch and Lehmann (2005) X X X Audretsch and Keilbach (2004) X X Chatman and Noland (2011) X X X Marra et al. (2017) X Florida and Mellander (2016) X X X X Florida and Mellander (2009) X X Goetz and Rupasingha (2002) X X X X Hackler (2003) X X X X Kinne and Resch (2017) X X X X X Li and Zhu (2017) X X X Li et al. (2016) X X X Méndez-Ortega and Arauzo-Carod (2018) X X X Viladecans-Marsal and Arauzo-Carod (2012) X X X Wang et al. (2017) X X X Wood and Dovey (2015) X X X X Woodward et al. (2006) X X X X Zandiatashbar and Hamidi (2018) X X X X Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 9/37 Méndez-Ortega, Carles REAL SEMINARS – FALL 2018
Introduction (VIII): Hypotheses Hypothesis 1: The impact of high-tech amenities, Cultural and creative diversity and Human Capital will have a positive impact on the SVE firms’ entries while this impact will be different across type of entries (i.e. Creative and All entries). The impact of High-tech amenities and Human Capital will be higher for SVE firms’ entries than for Creative and All firms’ entries. Hypothesis 2: The impact of Agglomeration economies, High-tech amenities, Human Capital, Creative and Cultural Diversity and Crime on the SVE firms’ entries will go beyond neighborhood borders. ECONRES SEMINARS (UAM) 1/21 Méndez-Ortega, Carles Méndez-Ortega, Carles 10/37
Data (I) • The Data used in this paper come from different sources: • Data about firms comes from SABI ( Sistema de Análisis de Balances Ibéricos ). • Data about amenities and social characteristics comes from Barcelona Statistical Service ( Open Data Barcelona ). • Some variables by Own elaboration (Scientific parks, entropy index, Co-working spaces, among others). • Area Study: City of Barcelona, at Neighborhood level (73). • Period of Study: Stock of firms and amenities (2010) and entry of firms (2011-2013). Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 11/37 Méndez-Ortega, Carles REAL SEMINARS – FALL 2018
Data (II): Own created variables - Agglomeration ( Aggl_10 ): Stock of Video and Film, Graphic arts and Radio and TV firms (Méndez-Ortega and Arauzo-Carod, 2017, 2018). - Entropy index ( Entro ): Indicator of inequality (Theil, 1972). It takes values between 0 and 1 and is typically used to detect whether a spatial unit (i.e., neighbourhood) is homogenous or diverse. - Measures the Creative diversity: 17 Creative sectors (Boix and Lazzeretti, 2012) Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 12/37 Méndez-Ortega, Carles REAL SEMINARS – FALL 2018
Data(III): Descriptive analysis Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 13/37
Methodology (I): Empirical Model The empirical model is the following*: 𝐺𝑗𝑠𝑛 𝑓𝑜𝑢𝑠𝑗𝑓𝑡 𝑗(2011−2013 = 𝛾 0 + 𝛾 1𝑜 𝐵𝐹 𝑗𝑜 + 𝛾 2𝑙 𝐼𝑈𝐵 𝑗𝑙 + 𝛾 3𝑘 𝐷𝐷𝐸 𝑗𝑘 + 𝛾 4ℎ 𝐼𝐷 𝑗ℎ + 𝛾 5 𝐷𝑠𝑗𝑛𝑓 𝑗 + 𝜈 𝑗 Count data model (CDM): - Software and video games firms: Poisson Model. - Creative and All fims: Negative Binomial Model. (*) Taxes are constant in the city and land price effect is captured by other variables, as population density or agglomeration economies. Therefore, land price and taxes are not included in the empirical specification (Figueiredo et al., 2002). Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 14/37
Methodology (III): Spatial Aproach (SVE firms) • SLX Model • P-SAR model Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 15/37 Méndez-Ortega, Carles REAL SEMINARS – FALL 2018
Methodology (IV): SLX Model SLX: Spatial lag of the covariates. 𝑧 = 𝑌𝛾 + 𝑋𝑌𝜄 + 𝜁 • W : First Order Queen contiguity. • Variable Selection: • Correlation X vs WX • Significance in the Aspatial Model • Moran’s I (Moran 1948) • LISA (Anselin 1995) Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 16/37
Methodology (III): SLX Model Variable Correlation with WX Moran I Sig. As patial SLX Model SVE_10 0.574* 0.406 Yes Yes cowork2 0.680* 0.538 No No wifi 0.618* 0.447 Yes No ctp 0.134 0.060 Yes Yes dis t_22 0.472* 0.343 Yes Yes ent_f 0.739* 0.566 Yes No markets 0.089* 0.046 Yes Yes cc -0.020 -0.011 No No dis t_centre - - No Pol_rat 0.668* 0.478 Yes Yes uni 0.291* 0.143 Yes Yes edu_2010 0.841* 0.681 Yes No popd_2010 0.385* 0.240 Yes No Source: Author. Note: Sig. Aspatial indicates whether this variable was significant in the aspatial model. � Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 17/37
Methodology (III): SLX Model Sve_10 CTP Dis t_22 Markets Pol_rat Uni Source: Author’s calculations Source: Author. Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 18/37
Methodology (III): P-SAR Developed by Lambert et al., (2010) • The function (𝑧 𝑘 represents the logged-transformed values approximating neighboring counts (Burbidge et al., 1988). Log-likelihood function of the first-stage estimator is: 𝑜 𝑚𝑜𝑀 1 = 𝑔 1 𝑋 · 𝑧 𝑘 𝑅 𝑗 ; 𝜀 𝑗=1 With the instruments: 𝑅 = [𝑌, 𝑋𝑌, 𝑋𝑋𝑌] Méndez-Ortega, Carles ECONRES SEMINARS (UAM) 19/37
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