Raising the bar (10)

Spatial Economic Analysis ISSN: 1742-1772 (Print) 1742-1780 (Online) Journal homepage: https://www.tandfonline.com/loi/rsea20 Raising the bar (10) Paul Elhorst, Maria Abreu, Pedro Amaral, Arnab Bhattacharjee, Luisa Corrado, Justin Doran, Franz Fuerst, Julie Le Gallo, Philip McCann, Vassilis Monastiriotis, Francesco Quatraro & Jihai Yu To cite this article: Paul Elhorst, Maria Abreu, Pedro Amaral, Arnab Bhattacharjee, Luisa Corrado, Justin Doran, Franz Fuerst, Julie Le Gallo, Philip McCann, Vassilis Monastiriotis, Francesco Quatraro & Jihai Yu (2019) Raising the bar (10), Spatial Economic Analysis, 14:1, 1-4, DOI: 10.1080/17421772.2019.1553658 To link to this article: https://doi.org/10.1080/17421772.2019.1553658 Published online: 18 Jan 2019. Submit your article to this journal Article views: 441 View related articles View Crossmark data Citing articles: 2 View citing articles Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=rsea20 SPATIAL ECONOMIC ANALYSIS 2019, VOL. 14, NO. 1, 1–4 https://doi.org/10.1080/17421772.2019.1553658 EDITORIAL Raising the bar (10) Paul Elhorst, Maria Abreu, Pedro Amaral, Arnab Bhattacharjee, Luisa Corrado, Justin Doran, Franz Fuerst, Julie Le Gallo, Philip McCann, Vassilis Monastiriotis, Francesco Quatraro and Jihai Yu ABSTRACT This editorial summarizes the papers published in issue 14(1) so as to raise the bar in applied spatial economic research and highlight new trends. The first paper applies the Shapley-based decomposition approach to determine the impact of firm-, linkage- and location-specific factors to the survival probability of enterprises. The second paper applies Bayesian comparison methods to identify simultaneously the most likely spatial econometric model and spatial weight matrix explaining new business creation. The third paper compares the performance of continuous and discrete approaches to explain subjective well-being across space. The fourth paper applies a multiple imputation approach to determine regional purchasing power parities at the NUTS-3 level using data available at the NUTS-2 level. Finally, the last paper constructs a regional input–output table for Japan from its national counterpart using and comparing the performance of four non-survey techniques. KEYWORDS survival, well-being, purchasing power, input–output, spatial econometrics JEL C21, C67, I31, O18, M13 Spatial Economic Analysis is a pioneering journal dedicated to the development of theory and methods in spatial economic analysis. This issue contains five papers contributing to these developments. All are methodological in nature, illustrate their innovative findings by focusing on an empirical application and discuss the implications of their findings from a policy point of view and/or the perspective of further research. The first paper by Sohns and Revilla Diez (2018, in this issue), explains the survival probability of 309 micro-enterprises in three rural Vietnamese provinces over the period 2010–13, using a three-level mixed-effects parametric model. In addition, a distinction is made between opportunity-driven (n = 174) and necessity-driven (n = 135) enterprises. The first group is willing to hire non-family employees and to invest more if they observe or are challenged by new opportunities in the market. The second group is more reserved since the main focus is to guarantee a sufficient level of income. The authors attempt to determine the impact of enterprisespecific factors (first level), production and consumption linkage-related factors (second level) and location-specific factors (third level). The latter factors consist of market institutional variables, including state- versus non-state-owned firms, pro- and anti-cyclical external effects, proximity of customers and markets, and access to financial services. To determine the impact CONTACT (Corresponding author) j.p.elhorst@rug.nl © 2018 Regional Studies Association 2 Paul Elhorst et al. of each set of factors, the authors employ the Shapley-based decomposition approach applied to R 2. The enterprise-specific factors appear to be the most important: their contribution to the survival probability amounts to 64.3% of the opportunity-driven and to 64.5% of the necessity-driven enterprises. This is followed by, respectively, 23.0% and 15.4% for the linkage-related factors and 12.6% and 20.1% of the location-specific factors. Based on these numbers, the authors make several policy recommendations to foster the survival and growth of micro-enterprises. The second paper by Credit (2018, in this issue) does not deal with business survival but with the related topic of business creation. It studies the relationship between rail transit proximity and the creation of new high-technology businesses and finds that transit proximity has a significant positive impact, given that the region has a relatively mature and extensive transit system, such as those in Boston and Philadelphia. The paper also finds that the exposure variable area provides the most consistent and stable foundation for calculating the expected rates of new business activity compared with other variables, such as population and existing business activities. Finally, the paper argues for the crucial role of spatial dependence when studying the impact of transit proximity on the creation of new high-technology business. To investigate this role, the author applies the most advanced techniques currently available. In the latest version of the Encyclopedia of GIS [Geographical Information Systems], Elhorst (2017) points out that revision is needed to the way of thinking about, and the model selection strategies that are used in, most empirical studies to determine the structure of spatial processes, and identifies two promising new approaches. The first, developed by LeSage (2014, 2015), is based on Bayesian comparison methods; and the second, developed by Halleck Vega and Elhorst (2015), is based on taking the spatial lag of X (SLX) model as a point of departure. The Bayesian comparison method of Credit (2018, in this issue) is used to test whether the SLX model needs to be extended to a spatial Durbin model (SDM) with a spatial lag in the dependent variable or to a spatial Durbin error model (SDEM) with a spatial lag in the error term. The first model implies that spillover effects are global and the second that they are local. The first occurs when a change in one of the explanatory variables at any location is transmitted to all other locations, even if two locations are unconnected according to the spatial weight matrix describing the spatial arrangement between the units in the sample. By contrast, local spillovers occur at other locations only if two locations are connected to each other according to the spatial weight matrix. Generally, global spillovers are more difficult to justify than local spillovers. Nevertheless, Credit does find evidence in favour of this type of spillovers, which he explains by the specific nature of knowledge transfers, information exchange and other agglomeration factors. This result is achieved by comparing 54 possible model specifications: 18 weights matrices, ranging from three to 20 nearest neighbours, and three model specifications: SLX, SDM and SDEM. This contribution is one of the few examples that successfully identifies the most likely candidate for both the spatial econometric type of model and the spatial weight matrix. Previous examples appeared in Spatial Economic Analysis by Rios, Pascual, and Cabases (2017) and in Regional Studies by Da Silva, Elhorst, and Neto (2017). The third paper by Sarrias (2018, in this issue) endeavours to provide enhanced methodologies to examine subjective well-being (SWB), measured by a binary indicator, and how the relationship between this indicator and individual characteristics vary over space. Two reasons for this spatial variation are statistical in nature: sampling variation and variables omitted from the model that follow a spatial non-stationary process, but the third and most relevant reason is that people’s preferences for some attributes are intrinsically different across space. Ignoring spatial heterogeneity in consumer preferences and compensation schemes is an acknowledged weakness of many studies, and this paper attempts to address that concern. The author compares two main specifications: (1) a random parameter specification where estimates associated with each covariate are allowed to vary across municipalities according to a normal distribution – this method has SPATIAL ECONOMIC ANALYSIS Raising the bar (10) 3 similarities with the random coefficient model originally developed by Swamy (1970), and extended with cross-sectional dependence by Pesaran (2006); and (2) a latent class specification with a prespecified number of groups, which provides a discrete alternative to parameter heterogeneity. Most of the discussion in the paper focuses on which approach, continuous versus discrete, is better suited to quantify compensating variation for a number of local amenities. The analysis is based on a micro-economic data set of 16,008 individuals between 15 and 64 years of age living in 324 different communes across Chile. The reviewers of this paper especially liked its positioning in a policy context, that is, the paper explains how policies to compensate for welfare changes as a result of, for example, environmental changes may not compensate appropriately if an averaging approach is taken to such relationships. Spatial heterogeneity is also the topic of the fourth paper by Rokicki and Hewings (2018, in this issue). It constructs regional prices for Poland at NUTS-2 and NUTS-3 levels. Unique raw price data for 300 goods and services are used to calculate annual regional purchasing power parity (PPP) deflators for 16 NUTS-2 regions over the 2000–12 period, following previous approaches developed by EUROSTAT and the Organisation for Economic Co-operation and Development (OECD). Based on these indices, similar deflators are estimated for the 66 NUTS-3 regions by a multiple imputation approach: a Bayesian Monte Carlo technique. Regions with the highest prices appear to be located in and around large agglomerations (especially Warsaw) and adjacent to the border with Germany. Lower prices are found in the central and eastern parts of the country in which the agricultural sector plays a dominant role. Over the period 2000–11, regional price levels do not show a clear tendency to convergence, although when employing their data imputed at the NUTS-3 level the authors find that price disparities increased in the first years following European Union accession in 2004. When using data at the NUTS-2 level, they are unable to find this pattern. Given the lack of information on regional price levels within European Union countries, the paper offers a number of interesting policy implications. The main one is that the allocation of Structural Funding in the European Union based on per capita income levels might be biased, as the purchasing power might differ across regions much more than has been accounted for. Notably, rural regions might be overvalued. The last paper by Fujimoto (2018, in this issue) is part of a series of contributions to Spatial Economic Analysis on input–output models, including, for example, those by Hermannsson (2016), Hermannsson, Lecca, and Swales (2017), and Oosterhaven and Többen (2017). This paper constructs a regional input–output table for Japan from its national counterpart using and comparing the performance of four non-survey techniques, each based on different assumptions regarding cross-hauling to estimate export and imports. The cross-hauling-adjusted regionalization method developed by Többen and Kronenberg (2015), and modified by Fujimoto (2015) in a previous study published in Japanese, comes out as the best. Hopefully, all five methodological contributions to the literature will reach a broad audience. REFERENCES Credit, K. (2018). Transitive properties: A spatial econometric analysis of new business creation around transit. Spatial Economic Analysis, 1–27. doi:10.1080/17421772.2019.1523548 Da Silva, D. F. C., Elhorst, J. P., & Neto, R. D. M. S. (2017). Urban and rural population growth in a spatial panel of municipalities. Regional Studies, 51(6), 894–908. doi:10.1080/00343404.2016.1144922 Elhorst, J. P. (2017). Spatial panel data analysis. In S. Shekhar, H. Xiong, & X. Zhou (Eds.), Encyclopedia of GIS, 2nd ed. (pp. 2050–2058). Cham: Springer. Fujimoto, T. (2015). Quantitative analysis of the regional income determinant factors in the remote island economy: Generation and application of regional input–output table. Journal of Rural Economics, 86(4), 257–272. [in Japanese] SPATIAL ECONOMIC ANALYSIS 4 Paul Elhorst et al. Fujimoto, T. (2018). Appropriate assumption on cross-hauling national input–output table regionalization. Spatial Economic Analysis, 1–23. doi:10.1080/17421772.2018.1506151 Halleck Vega, S., & Elhorst, J. P. (2015). The SLX model. Journal of Regional Science, 55(3), 339–363. doi:10. 1111/jors.12188 Hermannsson, K. (2016). Beyond intermediates: The role of consumption and commuting in the construction of local input–output tables. Spatial Economic Analysis, 11(3), 315–339. doi:10.1080/17421772.2016.1177194 Hermannsson, K., Lecca, P., & Swales, J. K. (2017). How much does a single graduation cohort from further education colleges contribute to an open regional economy? Spatial Economic Analysis, 12(4), 429–451. doi:10. 1080/17421772.2017.1316417 LeSage, J. P. (2014). Spatial econometric panel data model specification: A Bayesian approach. Spatial Statistics, 9 (2), 122–145. doi:10.1016/j.spasta.2014.02.002 LeSage, J. P. (2015). Software for Bayesian cross section and panel spatial model comparison. Journal of Geographical Systems, 17(4), 297–310. doi:10.1007/s10109-015-0217-3 Oosterhaven, J., & Többen, J. (2017). Wider economic impacts of heavy flooding in Germany: A non-linear programming approach. Spatial Economic Analysis, 12(4), 404–428. doi:10.1080/17421772.2017.1300680 Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967–1012. doi:10.1111/j.1468-0262.2006.00692.x Rios, V., Pascual, P., & Cabases, F. (2017). What drives local government spending in Spain? A dynamic spatial panel approach. Spatial Economic Analysis, 12(2–3), 230–250. doi:10.1080/17421772.2017.1282166 Rokicki, B., & Hewings, G. J. D. (2018). Regional price deflators in Poland: Evidence from NUTS-2 and NUTS3 regions. Spatial Economic Analysis, 1–18. doi:10.1080/17421772.2018.1503705 Sarrias, M. (2018). Do monetary subjective well-being evaluations vary across space? Comparing continuous and discrete spatial heterogeneity. Spatial Economic Analysis, 1–35. doi:10.1080/17421772.2018.1485968 Sohns, F., & Revilla Diez, J. (2018). Explaining micro-enterprise survival in rural Vietnam: A multilevel analysis. Spatial Economic Analysis, 1–21. doi:10.1080/17421772.2019.1535184 Swamy, P. A. V. B. (1970). Efficient inference in a random coefficient regression model. Econometrica, 38(2), 311– 323. doi:10.2307/1913012 Többen, J., & Kronenberg, T. (2015). Construction of multi-regional input–output tables using the Charm method. Economic Systems Research, 27(4), 487–507. doi:10.1080/09535314.2015.1091765 SPATIAL ECONOMIC ANALYSIS