Abstract:Purpose
The purpose of this study is to account for a recent non-mainstream econometric approach using microdata and how it can inform research in business administration. More specifically, the paper draws from the applied microeconometric literature stances in favor of fitting Poisson regression with robust standard errors rather than the OLS linear regression of a log-transformed dependent variable. In addition, the authors point to the appropriate Stata coding and take into account the possibility of faili… Show more
“…In the calibration, we refer to ~ A i = A i Z T,i β as the tourism attractiveness shifter in region i, which captures both a productivity and an amenity shifter. It follows that tourism trade shares can be written as (7) λ…”
Section: A Model Setupmentioning
confidence: 99%
“…Step: Calibration of Regional Fundamentals.-Using information on ( w n , L M,n , L S,n , L T,n , G n ) with parameters ( ν j j′ , α MT , α G , β, θ, ρ ) , we calibrate the baseline equilibrium according to equations (9), (10), (7), (8), (13), (14), and (15). Following Redding (2016), we invert the calibrated model to recover the unique tourism and manufacturing shifters ˜ A n and M n (up to scale) that are consistent with the data.…”
Section: Firstmentioning
confidence: 99%
“…Methodologically, the paper follows a recent but growing literature that uses quantitative spatial equilibrium models to analyze the welfare consequences of aggregate or local shocks, taking into account the frictions to trade and mobility between regions within countries (e.g., Redding 2016;Caliendo et al 2018;Monte, Redding, and Rossi-Hansberg 2018;Bryan and Morten 2015;Caliendo, Dvorkin, and Parro 2015;Fajgelbaum et al 2019;Adao, Arkolakis, and Esposito 2017;and Galle, Rodríguez-Clare, and Yi 2014). 7 We build on the framework developed by Allen and Arkolakis (2014) and Redding (2016) and extend the model and methodology in several dimensions to study the role of within-and cross-sector agglomeration externalities, a novel dimension in this class of quantitative frameworks. We combine the structure of the model with observed empirical moments to identify the strength of the agglomeration forces, close to the approach followed in a one-sector model in Ahlfeldt et al (2015).…”
Tourism is a fast-growing services sector in developing countries. This paper combines a rich collection of Mexican microdata with a quantitative spatial equilibrium model and a new empirical strategy to study the long-term economic consequences of tourism both locally and in the aggregate. We find that tourism causes large and significant local economic gains relative to less touristic regions that are in part driven by significant positive spillovers on manufacturing. In the aggregate, however, these local spillovers are largely offset by reductions in agglomeration economies among less touristic regions, so that the national gains from trade in tourism are mainly driven by a classical market integration effect. (JEL L60, L83, O14, O18, R11, Z31, Z32)A conventional view in the literature on economic growth and development is that the production of traded goods is subject to dynamic productivity improvements, whereas the services sector is perceived to be more stagnant. 1 In line with this view, the locus of agglomeration economies is generally assumed to be the manufacturing sector, rather than services. This asymmetry has important implications for the growth strategies of developing countries, and whether they should prioritize the development of traded goods producing sectors. At the same time, there is relatively little empirical evidence on the economic consequences of the development of the services sector in developing countries, and whether the reallocation of factors of production into services can give rise to adverse long-term effects both locally and in the aggregate. 2 This paper sets out to study the economic consequences of tourism, a fast-growing services sector in developing countries. Tourism involves the export of otherwise non-traded local services by temporarily moving consumers across space, rather than 1 This view is in the tradition of Baumol (1967). See Herrendorf, Rogerson, and Valentinyi (2014) for a review of the recent literature, and McMillan and Rodrik (2011) for an analysis in the context of developing countries. 2 See, for example, Copeland (1991) for an early theoretical discussion of tourism as a potential "Dutch disease."
“…In the calibration, we refer to ~ A i = A i Z T,i β as the tourism attractiveness shifter in region i, which captures both a productivity and an amenity shifter. It follows that tourism trade shares can be written as (7) λ…”
Section: A Model Setupmentioning
confidence: 99%
“…Step: Calibration of Regional Fundamentals.-Using information on ( w n , L M,n , L S,n , L T,n , G n ) with parameters ( ν j j′ , α MT , α G , β, θ, ρ ) , we calibrate the baseline equilibrium according to equations (9), (10), (7), (8), (13), (14), and (15). Following Redding (2016), we invert the calibrated model to recover the unique tourism and manufacturing shifters ˜ A n and M n (up to scale) that are consistent with the data.…”
Section: Firstmentioning
confidence: 99%
“…Methodologically, the paper follows a recent but growing literature that uses quantitative spatial equilibrium models to analyze the welfare consequences of aggregate or local shocks, taking into account the frictions to trade and mobility between regions within countries (e.g., Redding 2016;Caliendo et al 2018;Monte, Redding, and Rossi-Hansberg 2018;Bryan and Morten 2015;Caliendo, Dvorkin, and Parro 2015;Fajgelbaum et al 2019;Adao, Arkolakis, and Esposito 2017;and Galle, Rodríguez-Clare, and Yi 2014). 7 We build on the framework developed by Allen and Arkolakis (2014) and Redding (2016) and extend the model and methodology in several dimensions to study the role of within-and cross-sector agglomeration externalities, a novel dimension in this class of quantitative frameworks. We combine the structure of the model with observed empirical moments to identify the strength of the agglomeration forces, close to the approach followed in a one-sector model in Ahlfeldt et al (2015).…”
Tourism is a fast-growing services sector in developing countries. This paper combines a rich collection of Mexican microdata with a quantitative spatial equilibrium model and a new empirical strategy to study the long-term economic consequences of tourism both locally and in the aggregate. We find that tourism causes large and significant local economic gains relative to less touristic regions that are in part driven by significant positive spillovers on manufacturing. In the aggregate, however, these local spillovers are largely offset by reductions in agglomeration economies among less touristic regions, so that the national gains from trade in tourism are mainly driven by a classical market integration effect. (JEL L60, L83, O14, O18, R11, Z31, Z32)A conventional view in the literature on economic growth and development is that the production of traded goods is subject to dynamic productivity improvements, whereas the services sector is perceived to be more stagnant. 1 In line with this view, the locus of agglomeration economies is generally assumed to be the manufacturing sector, rather than services. This asymmetry has important implications for the growth strategies of developing countries, and whether they should prioritize the development of traded goods producing sectors. At the same time, there is relatively little empirical evidence on the economic consequences of the development of the services sector in developing countries, and whether the reallocation of factors of production into services can give rise to adverse long-term effects both locally and in the aggregate. 2 This paper sets out to study the economic consequences of tourism, a fast-growing services sector in developing countries. Tourism involves the export of otherwise non-traded local services by temporarily moving consumers across space, rather than 1 This view is in the tradition of Baumol (1967). See Herrendorf, Rogerson, and Valentinyi (2014) for a review of the recent literature, and McMillan and Rodrik (2011) for an analysis in the context of developing countries. 2 See, for example, Copeland (1991) for an early theoretical discussion of tourism as a potential "Dutch disease."
“…Silva and Teneyro (18,19) proposed the PPML estimation method to solve the above two problems and exemplified the gravity model used in international trade research. Note that the PPML estimator is scale-invariant since we have used the logtransformed dependent variable (20). Besides, the PPML method can solve a possible endogeneity bias.…”
Section: Empirical Model and Variable Selection Model Settingmentioning
The slow-down of the Chinese economy and the depression in the global economy during the COVID-19 show that governments should provide stimulus packages. These policies should be inclusive in terms of financial gains. Using the panel data of 30 regions in China from 2006 to 2016, this paper uses the Poisson Pseudo-Maximum Likelihood (PPML) estimator to analyze the impact of inclusive finance on public health. The results show that inclusive finance has a significant positive effect on public health. The performance of the eastern region is significantly better than that of the central and western regions. When we consider the combined effect of environmental regulation, the improvement effect of inclusive finance on public health is still significant, and the coefficient increases in the eastern region. Similarly, there is also a significant improvement effect in the central and western regions. Our findings reveal that environmental regulation promotes the beneficial effect of inclusive finance. Therefore, it is important to improve the inclusive financial development mechanism and enhance environmental regulation intensity for solving public health issues. Lessons related to the COVID-19 pandemic are also discussed.
“…The fifth quantitative article was written by Motta (2019), who argues that researchers in different fields within business administration often estimate models with a log-transformed dependent variable. Failure to account for adjustments for heteroskedasticity and normality of residuals may lead to biased estimates of the conditional mean and the slope on its original scale.…”
Section: The Articles Of This Special Issuementioning
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