Spatial Economic Analysis最新文献

{"title":"On the geography of inequality: labour sorting in general equilibrium","authors":"Santiago Truffa, Alexis Montecinos","doi":"10.1080/17421772.2023.2271519","DOIUrl":"https://doi.org/10.1080/17421772.2023.2271519","url":null,"abstract":"ABSTRACTWe study how cities’ amenities and limited housing supply contribute to aggregate wage inequality and affect housing prices through the sorting of heterogeneous skilled workers. We develop a general equilibrium model where workers differ along a continuum of skills and compete for limited housing. Our analysis suggests that spatial sorting accounts for 7.5% of the aggregate wage dispersion, increases average housing prices by 20–40% in constrained cities, and makes the economy 1.9% more productive. In addition, we evaluate a place-based policy that aims to expand the supply of houses in 1% of constrained cities and find that it improves aggregate productivity between 0.2% and 0.4%. However, the place-based policy has the unintended consequence of aggravating aggregate wage inequality by the same magnitude.KEYWORDS: labour sortinginequalityhousingplace-based policiesJEL: D44D58F16J24R13 ACKNOWLEDGEMENTSWe are extremely grateful to Ernesto Dal Bó, William Fuchs and John Morgan for their support. We also thank Scott Baker, Victor Couture, Cecile Gaubert, Rui de Figueiredo, William Grieser, William Hardin, Enrico Moretti, Gonzalo Maturana, Steve Tadelis, Joachim Voth, Reed Walker, Zhonghua Wu and Noam Yuchtman, as well as numerous seminar and conference participants, for their helpful discussions and comments. We would also like to thank Diogo Duarte who contributed to this project on an earlier version. This paper was originally part of Santiago Truffa’s PhD dissertation titled ‘Essays in urban economics’.DISCLOSURE STATEMENTNo potential conflict of interest was reported by the authors.Notes1 In this study we will focus on wage and housing price inequality. In particular, since we are able to compute wages at the individual level, we can analyse both between- and within-city inequality. When we refer to aggregate inequality, we mean the total variance of all individual wages.2 Shapiro (Citation200Citation6), Glaeser and Gottlieb (Citation2008), Couture (Citation2015), Albouy et al. (Citation2016) and Albouy (Citation2016) have empirically shown the importance of amenities in accounting for sorting patterns. We build on this literature, and we quantify the trade-off between amenities versus restrictions on the housing supply. Related literature has explored the sorting of heterogeneous firms (Behrens et al., Citation2014; Gaubert, Citation2018; Serrato & Zidar, Citation2016) to study the welfare implications of taxes and firm incentives. We complement this literature by focusing on the worker side. Further work is required to join these two threads in the literature.3 Frameworks that divide the workforce into discrete categories are empirically sensitive since the results depend on dichotomous definitions of what type of worker qualifies for each type of category. Indeed, Baum-Snow et al. (Citation2018) show that if we change the definition of high-skilled worker to a worker with some college education, some of the results shown by Diamond (C","PeriodicalId":47008,"journal":{"name":"Spatial Economic Analysis","volume":"121 29","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135138018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"To use, or not to use the spatial Durbin model? – that is the question","authors":"Malabika Koley, Anil K. Bera","doi":"10.1080/17421772.2023.2256810","DOIUrl":"https://doi.org/10.1080/17421772.2023.2256810","url":null,"abstract":"ABSTRACT The spatial Durbin model (SDM) is one of the most widely used models in spatial econometrics. It originated as a generalisation of the spatial error model (SEM) under a non-linear parametric restriction (see Anselin (1988, pp. 110–111)). This restriction should be tested to select an appropriate model between SDM and SEM. Perhaps, due to the complexity of executing a test for a non-linear hypothesis, this restriction is rarely tested in practice, though see Burridge (1981), Mur and Angulo (2006) and LeSage and Pace (2009, p. 164). This paper considers an alternative linear hypothesis to test the suitability of the SDM. To achieve this, we first use Rao’s score (RS) testing principle and then Bera and Yoon (1993)’s methodology to robustify the original RS tests. The robust tests that require only ordinary least squares (OLS) estimation are able to identify the specific source(s) of departure(s) from the baseline linear regression model. An extensive Monte Carlo study provides evidence that our suggested tests possess excellent finite sample properties, both in terms of size and power. Our empirical illustrations, with two real data sets, attest that the tests developed in this paper could be very useful in judging the suitability of the SDM for the spatial data in hand.","PeriodicalId":47008,"journal":{"name":"Spatial Economic Analysis","volume":" 35","pages":"30 - 56"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135241073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial spillovers and the effects of fiscal stimulus: evidence from pandemic-era federal aid for state and local governments","authors":"Jeffrey Clemens, John Kearns, Beatrice Lee, Stan Veuger","doi":"10.1080/17421772.2023.2264344","DOIUrl":"https://doi.org/10.1080/17421772.2023.2264344","url":null,"abstract":"ABSTRACTWe analyse whether US federal aid to state and local governments impacted economic activity through either direct or cross-state spillover effects during the COVID-19 pandemic. Deploying an instrumental-variables framework rooted in the funding advantage of states that are over-represented in Congress, we find that federal assistance had significantly less impact on state and local government employment, as well as broader measures of economic activity, than estimates from prior crisis responses would imply. The modest employment impacts we find stem largely from the direct effect of states’ own aid allocation, as opposed to spillovers across state lines. These findings point to an important role for variations in fiscal policy transmission mechanisms, namely that cross-state spillovers are less likely to be important when some of the key mechanisms for such spillovers, like robust interjurisdictional supply chains and patterns of consumption, are muted or shut down.KEYWORDS: COVID-19employmentfiscal federalismfiscal policyspatial macroeconomicsspilloversJEL: E6H5H7 ACKNOWLEDGEMENTThis article is based on the following working paper:Clemens, Jeffrey, John Kearns, Beatrice Lee, and Stan Veuger. ‘Spatial Spillovers and the Effects of Fiscal Stimulus: Evidence from Pandemic-Era Federal Aid for State and Local Governments.’ AEI Economics Working Paper 2022-14.DISCLOSURE STATEMENTNo potential conflict of interest was reported by the author(s).Notes1 These four pieces of legislation are the March 2020 Families First Coronavirus Response Act (FFCRA) and Coronavirus Aid, Relief, and Economic Security (CARES) Act, the December 2020 Response and Relief Act (RRA) of 2021 and the March 2021 American Rescue Plan Act (ARPA) of 2021.2 Nakamura and Steinsson (Citation2014), as well as Ramey (Citation2016, Citation2019) and Chodorow-Reich (Citation2020), provide frameworks for interpretation of the different estimates in these literatures.3 We use data from the CRFB’s COVID-19 Money Tracker as of August 19th, 2021.4 As in Clemens and Veuger (Citation2021), ‘[w]e obtain information on the distribution of transit funds for the RRA and ARPA from the US Federal Transit Administration (Citation2021a, Citation2021b). Data on the allocation of ARPA assistance to non-public schools come from the US Office of Elementary and Secondary Education (Citation2021). We obtain estimates of ARPA section 9817 matching increases from Chidambaram and Musumeci (Citation2021). We approximate the allocation of ARPA section 9819 federal matching funds for uncompensated care using FY2021 estimates of federal disproportionate share hospital allotments by state from the Medicaid and Chip Payment Access Commission (Citation2021).’ The Coronavirus Capital Projects Fund outlined in ARPA is distributed according to guidance from the United States Department of the Treasury (Citation2021a).5 Congressional representation per million residents is calculated as #ofRepresentativess+#ofSenato","PeriodicalId":47008,"journal":{"name":"Spatial Economic Analysis","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135368192","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic spatiotemporal ARCH models","authors":"Philipp Otto, Osman Doğan, Süleyman Taşpınar","doi":"10.1080/17421772.2023.2254817","DOIUrl":"https://doi.org/10.1080/17421772.2023.2254817","url":null,"abstract":"ABSTRACTGeo-referenced data are characterised by an inherent spatial dependence due to geographical proximity. In this paper, we introduce a dynamic spatiotemporal autoregressive conditional heteroscedasticity (ARCH) process to describe the effects of (i) the log-squared time-lagged outcome variable, the temporal effect, (ii) the spatial lag of the log-squared outcome variable, the spatial effect, and (iii) the spatiotemporal effect on the volatility of an outcome variable. We derive a generalised method of moments (GMM) estimator based on the linear and quadratic moment conditions. We show the consistency and asymptotic normality of the GMM estimator. After studying the finite-sample performance in simulations, the model is demonstrated by analysing monthly log-returns of condominium prices in Berlin from 1995 to 2015, for which we found significant volatility spillovers.Preprint: This paper is based on the preprint arXiv:2202.13856KEYWORDS: Spatial ARCHGMMvolatility clusteringvolatilityhouse price returnslocal real-estate marketJEL: C13C23P25R31 DISCLOSURE STATEMENTNo potential conflict of interest was reported by the author(s).Notes1 Note that the matrix equation ABC=D, where D, A, B, and C are suitable matrices, can be expressed as vec(D)=(C′⊗A)vec(B), where vec(B) denotes the vectorisation of the matrix B (Abadir & Magnus, Citation2005, p. 282). This property can be applied to (U1∗,U2∗,…,UT−1∗)=(U1,U2,…,UT)FT,T−1 by setting D=(U1∗,U2∗,…,UT−1∗), C=FT,T−1, B=(U1,U2,…,UT) and A=In.2 In applying Lemma 1 in the Appendix in the supplemental data online, we use the fact that tr(A′B)=vec′(A)vec(B)=vec′(B)vec(A), where A and B are any two N×N matrices.3 The explicit forms of D1N and D2N are given in Section C of the Appendix.4 Note that when t=1, we may simply use H1=c1((In−1T−1∑h=1T−1Ah)Y0∗−1T−1∑r=1T−1(∑h=0T−r−1Ah)S−1(Xrβ0+αr,01n)).5 Note that when T is large, μ~0=(μ0+μϵ1n) can be estimated by μ~ˆN=1T∑t=1T(ϑˆt−1n1n′ϑˆt1n).","PeriodicalId":47008,"journal":{"name":"Spatial Economic Analysis","volume":"166 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135569233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Peer effect, political competition and eco-efficiency: evidence from city-level data in China","authors":"Xudong Chen, Bihong Huang, Yantuan Yu","doi":"10.1080/17421772.2023.2261464","DOIUrl":"https://doi.org/10.1080/17421772.2023.2261464","url":null,"abstract":"ABSTRACTThis study examines the impacts of political competition on eco-efficiency. We first develop a theoretical model in which local government officials compete against each other to maximise their own political score. We find that after an initial stage of decline, eco-efficiency eventually turns upwards, once environmental performance becomes a meaningful component of local government officials’ annual assessment. Eco-efficiency also exhibits a pattern of convergence. Lastly, the level of political competition is found to be negatively correlated with eco-efficiency. For the empirical analysis, we use a data envelopment analysis (DEA) model to compute the eco-efficiency level for 191 Chinese cities from 2003 to 2015. Our empirical evidence presents a ‘U’-shape pattern in the trend of eco-efficiency and identifies two peer effects that work in opposite directions: the incentivising effect arising from higher performing neighbours, and the disincentivising effect when a city outperforms its competitors. Both peer effects lead to convergence in eco-efficiency, and our spatial econometric modeling analysis suggests that the net peer effect is significantly positive. We also find evidence of political competition reducing eco-efficiency, as predicted in the theoretical model. Our findings are robust to alternative measures of eco-efficiency.KEYWORDS: peer effectpolitical competitioneco-efficiencyspatial analysisChinaJEL: C61C67Q56R15 DISCLOSURE STATEMENTNo potential conflict of interest was reported by the authors.Notes1 This body of research, known as the environmental Kuznets curve (EKC) literature, has been enormously influential. The work by Grossman and Krueger (Citation1995) is widely regarded as one of the earliest attempts at EKC hypotheses. For an extensive overview of theoretical studies and empirical evidence regarding EKC, see Kaika and Zervas (Citation2013).2 The term ‘eco-efficiency’ is a concept and philosophy geared toward sustainability, combining ecological and economic efficiency.3 The pollution haven hypothesis was first developed by Pethig (Citation1976) and McGuire (Citation1982), and later improved by Copeland and Taylor (Citation1994) and Levinson and Taylor (Citation2008), among others.4 For example, in its National 10th Five-Year Plan (2001–05), released in 2001, the central government for the first time added environmental protection and pollution reduction to its list of ‘national strategic goals’, and set a target to reduce pollutant discharges by 10% by the end of 2005. Under the new regulation framework, each province was assigned a specific target, and the provincial government officials were to be evaluated on, among other things, how well these targets were met. However, little improvement in environmental quality has been observed in China based on data between 1998 and 2008, because the pollution mandates imposed by the central government have triggered strategic polluting responses from the provinces (Cai et ","PeriodicalId":47008,"journal":{"name":"Spatial Economic Analysis","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135142197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial board 2023","authors":"","doi":"10.1080/17421772.2023.2258700","DOIUrl":"https://doi.org/10.1080/17421772.2023.2258700","url":null,"abstract":"Published in Spatial Economic Analysis (Vol. 18, No. 4, 2023)","PeriodicalId":47008,"journal":{"name":"Spatial Economic Analysis","volume":"55 6","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138519183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Raising the bar (final)","authors":"Paul Elhorst, Ugo Fratesi, Maria Abreu, Pedro Amaral, Steven Bond-Smith, Coro Chasco, Luisa Corrado, Jan Ditzen, Daniel Felsenstein, Franz Fuerst, Vassilis Monastiriotis, Francesco Quatraro, Dimitrios Tsiotas, Jihai Yu","doi":"10.1080/17421772.2023.2252691","DOIUrl":"https://doi.org/10.1080/17421772.2023.2252691","url":null,"abstract":"This editorial summarises the papers in issue 18(4) (2023). The first paper investigates attitudes towards civic engagement in relation to living closer to individuals with the same social status. The second paper develops a Bayesian estimator of a dynamic multivariate spatial ordered probit (DMSOP) model. The third paper examines the impact of drug-related activities on violent crime. The fourth paper web-scrapes data from individual firms to provide a better understanding of the determinants of innovation. The fifth paper tests the forecasting performance in post-crises years of spatial dynamic panel data (SDPD) models reformulated in first-differences. The sixth paper applies a count-data econometric model to explain early-stage (GE) business creation. The seventh paper examines patient migration flows among cantons and hospitals using a gravity model extended with spatial lags and a hospital efficiency score as an explanatory variable. The eighth paper studies whether the decision to migrate to pursue a tertiary education negatively affects student achievement at the university level as migration distance increases.","PeriodicalId":47008,"journal":{"name":"Spatial Economic Analysis","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135537774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial GARCH models for unknown spatial locations – an application to financial stock returns","authors":"Markus J. Fülle, Philipp Otto","doi":"10.1080/17421772.2023.2237067","DOIUrl":"https://doi.org/10.1080/17421772.2023.2237067","url":null,"abstract":"ABSTRACT Spatial GARCH models, like all other spatial econometric models, require the definition of a suitable weight matrix. This matrix implies a certain structure for spatial interactions. GARCH-type models are often applied to financial data because the conditional variance, which can be translated as financial risks, is easy to interpret. However, when it comes to instantaneous/spatial interactions, the proximity between observations has to be determined. Thus, we introduce an estimation procedure for spatial GARCH models under unknown locations employing the proximity in a covariate space. We use one-year stock returns of companies listed in the Dow Jones Global Titans 50 index as an empirical illustration. Financial stability is most relevant for determining similar firms concerning stock return volatility.","PeriodicalId":47008,"journal":{"name":"Spatial Economic Analysis","volume":" ","pages":"92 - 105"},"PeriodicalIF":2.3,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48614093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing modelling performance and evaluating differences of feature importance on defined geographical appraisal zones for mass real estate appraisal","authors":"A. C. Aydinoglu, S. Sisman","doi":"10.1080/17421772.2023.2242897","DOIUrl":"https://doi.org/10.1080/17421772.2023.2242897","url":null,"abstract":"","PeriodicalId":47008,"journal":{"name":"Spatial Economic Analysis","volume":"1 1","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60064147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unequal response to mobility restrictions: evidence from COVID-19 lockdown in the city of Bogotá","authors":"David Castells‐Quintana, Paula Herrera-Idárraga, L. Quintero, Guillermo Sinisterra","doi":"10.1080/17421772.2023.2235377","DOIUrl":"https://doi.org/10.1080/17421772.2023.2235377","url":null,"abstract":"This paper examines the efficacy of government-mandated mobility restrictions on curbing urban mobility, paying special attention to spatial heterogeneity in lockdown compliance. In particular, it explores the role of cash subsidies disbursed during lockdown as well as socio-economic differences across neighbourhoods to explain their unequal response to mobility restrictions. To do so, it relies on novel data showing changes in movement at highly disaggregated spatial levels in Bogotá, before and during the first wave of the COVID-19 pandemic, matched with data on socio-economic characteristics and non-pharmaceutical interventions implemented in the period of analysis. Findings indicate that the general lockdown imposed in the city significantly reduced mobility (by about 41 percentage points). In terms of the unequal response across locations, the findings indicate that low-income areas with higher population density, informality and overcrowding reacted less to mobility restrictions. In this regard, despite government efforts, the findings indicate that cash subsidies were not sufficient to make compliance easier in low-income neighbourhoods.","PeriodicalId":47008,"journal":{"name":"Spatial Economic Analysis","volume":" ","pages":""},"PeriodicalIF":2.3,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45190100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}