Empirical Spatial Econometrics: Applications to China’s Economy

Spatial econometrics is a branch of econometrics that began to emerge in the 1970s and 1980s. The spatial correlation study in the economic field can be dated back to Cliff and Ord (1973), but until the late 1970s, Paelinck and Klaassen (1979) coined the term spatial econometrics for the first time and outlined the five main characteristics of spatial econometrics: (1) the role of spatial interdependence in spatial models; (2) the asymmetry in spatial relations; (3) the importance of explanatory factors located in other spaces; (4) the differentiation between ex-post and ex-ante interaction; and (5) the explicit modeling of space (Paelinck and Klaassen, 1979, 5−11). On the basis of Paelinck and Klaassen (1979), Anselin (1988) provides a new definition and richer contents on spatial econometrics and defines spatial econometrics as “the collection of techniques that deal with the peculiarities caused by space in the statistical analysis of regional science models.” According to Anselin, “Spatial econometrics is a subfield of econometrics that deals with spatial interaction (spatial autocorrelation) and spatial structure (spatial heterogeneity) in regression models for cross-sectional and panel data” (Baltagi 2001, 310). The main contents of spatial econometrics are to consider the spatial effects of economic variables in the econometric models and to carry out a series of model specifications, estimations, testing, and predictions.

As Arbia (2016) pointed out, spatial econometrics is a rapidly evolving discipline. In the past thirty years, spatial econometrics has been advanced quickly through the endeavors of an exploding number of spatial econometricians such as Luc Anselin, Giuseppe Arbia, Badi Baltagi, Anil Bera, Paul Elhorst, Bernard Fingleton, Raymond Florax, Arthur Getis, Mudit Kapoor, Solmaria Halleck Vega, Harry Kelejian, Donald Lacombe, Lung-Fei Lee, James LeSage, Jean Paelinck, Michael Pfaffermayr, Gianfranco Piras, Ingmar Prucha, Sergio Rey, Jihai Yu, etc. Empirical spatial econometrics span a wide range of fields, such as regional economics, public finance, international trade, labor economics, industrial organization, political sciences, agricultural economics, health economics, urban planning, social sciences, economic development, innovation diffusion, environmental studies, resources and energy economics, transportation, and real estate analyses. The list of applied disciplines is in fact a lot longer and likely to further increase in the future (Arbia, 2016).

Motivated by the rapidly expanding applications of spatial econometric techniques in so many diverse scientific fields and also the widespread interests in the subject, as can be seen from some special issues on spatial econometrics in journals like Geographical Analysis (2004), Papers in Regional Sciences (2008), Regional Science and Urban Economics (2010), Economic Modelling (2012), International Regional Science Review (1997 and 2014), Review of Regional Studies (2014), Econometrics (2016), and Annals of Regional Science (coming soon), I am delighted to organize a special topic titled “Empirical Spatial Econometrics: Applications to China’s Economy” with the invitation from the Editor-in-Chief, Ali M. Kutan.

The theme of this symposium focuses on China, as it serves as an excellent laboratory for the spatial study of emerging markets due to its largest developing economy, fast economic growth, largest population size, vast regional disparities, increasing role in the global community, and especially, uniqueness to other countries in various aspects which, with no doubt, include mechanisms of spatial interactions in the spatial econometrics framework. For instance, Yu et al. (2013) found that a provincial government will decrease its own health spending as a response to the rise of health spending of its neighboring provinces. This result supports the expenditure externality hypothesis, or alternatively rules out the yardstick competition or fiscal competition hypothesis, which is in line with our expectation if we notice that (1) yardstick competition hypothesis cannot be applied in China where it does not have an effective voting system established (as might be seen in the developed nations); (2) China is a country with a unified tax system where the tax rate is identical across all jurisdictions (local governments are unable to alter the tax base, but instead take the tax set by the central government, implying that local governments are not expected to engage in tax competition); and (3) a race to the bottom in terms of local health expenditures will not occur (as might be seen in the developed nations), as people are not mobile without costs due to China’s Hukou (or household registration) system. Following our call for papers, we received many articles and decided to publish only those that were successfully passed a rigorous peer-review process. At the end of this process, 13 articles, all empirical applications, were selected which addressed empirically various hot topics in present-day China using different spatial econometric techniques. Although only a limited number of papers can be published in this symposium, the selected papers, two of which refer to cross-sectional spatial models and 11 to spatial panel data models, shall provide a good snapshot of the on-going, cutting-edge spatial research on China.

This special issue opens up with two papers of local governmental interactions. The first paper, by Lei, Chen, Jia, and Liu, proposes a spatial simultaneous equations approach to model and estimate the spatial interactions of multiple spending categories across Chinese local governments. Using a panel data set of counties (a smaller administrative unit than city in China), they find mainly that a positive endogenous peer effect presents for capital construction among Chinese local governments, capital construction expenditure and administrative expenditure are complements rather than substitutes, administrative expenditure crowds out social welfare spending, and lastly, there is no fiscal mimicking of social welfare expenditure, a result consistent with Yu et al. (2013) on examining strategic behaviors of public health expenditure across local governments. The second paper by Yu, Zheng, and Zhang, examines the role of local governments that played on the formation of enterprise zones using China’s city-level data and a spatial autoregressive modeling approach with three different types of spatial weighting matrices accounted for, respectively. They find that enterprise zones are slightly related to the economic growth, job creation, or poverty reduction, which deviates from the governments’ initial intention that they expect to promote the local economic growth via building enterprise zones. Empirical analysis indicates that Chinese local governments act strategically when considering establishing their own enterprise zones, and more importantly, the mechanism of spatial interaction is due to yardstick competition created across China’s local governments. Overall, this study seems to imply that yardstick competition originates from local governments’ “card-playing” mimicking behavior, regardless of whether the enterprise zone to be built will indeed boost local economic growth, job growth, or poverty reduction, or not.

The next four papers in the issue focus on different types of investment at the national, provincial, city, and firm levels, respectively. The paper by Zhou, Liu, Pan, Yang, Wen, and Xia investigates qualitatively and quantitatively the effect of tourism-building investments on tourism revenues in China. The empirical results of this paper show that the development of China’s tourism industry has both significant geographical clustering effect and positive spillover effect. While the paper by Wang, Wei, Deng, and Yu examines whether and how fiscal decentralization as well as degree of environmental regulation stringency affects foreign direct investment (FDI) inflows in China using a spatial Durbin model (SDM) approach, the two papers by Sun and Shao and Wu, Song, and Deng respectively target outward FDIs (OFDIs). Specifically, the former explores how the distribution of Chinese OFDIs across different host countries are affected by various traditional determinants (market size, natural resource endowment, openness, political risk, etc.), and more importantly by economic cooperation between the host country and home country (a factor ignored in the existing literature); the latter uses the Chinese industrial enterprises and foreign investment enterprises database (2003–2007) to examine the influence of OFDIs, together with institutional environment, on total factor productivity (TFP) at both the firm and provincial levels, via using a SDM approach with three different types of spatial weight matrices (geographic distance-, economic distance-, and human capital distance–based weighting matrix) accounted for, respectively.

The third set of papers focuses on applying spatial econometrics to China’s financial and banking issues. The paper by Zhang, Guo, Xiao, and Wang applies spatial panel data models to investigate the spatial spillover effects of non-performing loans for commercial banks for 31 provinces of China during the 2005–2014 period. After a set of model specification tests (panel unit root tests, Moran’s test for spatial dependence, and likelihood-ratio tests for various spatial models), they find that the spatial spillover effect (or indirect effect of an explanatory variable of interest) plays a significant role in regional non-performing loans for commercial banks. Similarly, using the provincial level data covering the same period as the first one, the paper by Yu, Li, and Huang aims to study how regional financial development (measured by per capita GDP of regional financial industry) in China is affected by various financial functions, i.e., financial depth measured by financial institutions’ loans to GDP ratio, financial access measured by the number of bank branches for every million people in each province, and financial efficiency measured by financial institutions’ loans divided by its deposits. Applying three spatial panel models of different spatial weight matrices (contiguity, geographic distance, or economy-spatial distance based matrix), the authors show that two financial function variables (i.e., financial access and financial efficiency) have significantly positive direct effects and indirect effects (or spatial spillover effects) on promoting the local financial development, while these effects are not obvious for financial depth.

The last set of papers can be broadly classified in the context of economic development. The first paper focuses on economic growth, the second on employment, the third on innovation, and the last two papers on green development. Particularly, in the first paper in this set, Sun, Chen, and Hewings develop a spatially extended neoclassical Solow growth model to examine several spatial characteristics of regional economic growth using China’s city-level panel data such as spatial heterogeneity or convergence in regional economic growth, and spatial spillover effects on neighbors. The second paper, by Wang and Tian, analyzes how the spatial and sectoral patterns of employment growth have changed between 2000 and 2010 by estimating aggregate and sectoral employment growth equations using county-level employment data. The main result of this empirical study is that significant β convergence effects are found in all sectors. The third paper, by Song and Zhang, assesses whether and how the effects of spatial spillovers contribute to regional innovation growth in China using provincial-level panel data and the spatial Durbin model. The author states that the study contributes to the literature by providing the following empirical evidence: (1) innovation in China is spatially interdependent; (2) the innovation output and R&D input are sources of spillover effects that have substantially promoted innovation within and surrounding a given region; (3) the impact of absorptive capacity (measured by average schooling years) and agglomeration economies (measured by urban employment density) on innovation is spatially localized, and FDI is a negative driver of innovation; and (4) different types of patenting innovation show different growth patterns. The last two papers, written respectively by Tao, Zhang, Hu, and Duncan and Liu, Tao, and Zhang, focus on the development of green economy. Both papers introduce the global Malmquist–Luenberger productivity index to measure green TFP growth, except that the index is calculated at the city level in the former paper and at the provincial level in the latter. In addition, both papers analyze spatial spillovers and determinants of green TFP growth, though each from somewhat different angles. For instance, although environmental regulation is accounted for in both papers as one determinant of green TFP, the latter pays more attention to the role of the term limits of public officials.

Acknowledgments

I would like to thank Ali M. Kutan, the Editor-in-Chief of Emerging Markets Finance & Trade, for his close and professional guidance, as well as all authors and reviewers for the contributions of their valuable comments to the academic quality of this symposium.

Funding

I acknowledge the financial support provided by the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (13XNJ017).

Additional information

Funding

I acknowledge the financial support provided by the Fundamental Research Funds for the Central Universities and the Research Funds of Renmin University of China (13XNJ017).