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Spatial Diffusion of Social Policy in China: Spatial Convergence and Neighborhood Interaction of Vocational Education

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Abstract

There has been a vast amount of discussion about the positive and negative regional effect on policy diffusion. During this debate, the role of neighborhood structure is ignored and the linear assumption is still prevailing in this field. By analyzing the spatial convergence of local vocational education development with data of 31 provinces from 1995 to 2008 in China, we explore the effects of neighborhood interactions on policy diffusion, paying specific attention to the dynamical role of neighborhood structures in policy diffusion. The empirical results clearly indicate that the development of local vocational education systems in China is spatially autocorrelated to the neighboring provinces. Local vocational education systems converge more slowly if a spatially lagged dependent variable is introduced, while they converge faster if a spatially error variable is introduced. The policy transition between neighbors considering their local spatial context is analyzed with Spatial Markov Chain and a fundamental nonlinear connection between neighborhood structure and policy transition is unveiled. Using spatial econometric models, we also find that the socio-spatial diffusion patterns with the social factors such as consumption, urban/rural distribution and occupation serve as barriers to and amplifiers of policy diffusion. These results not only resonate with conventional linear wisdom on policy diffusion but also offer a new nonlinear perspective on socio-spatial patterns of policy diffusion that are clearly embedded within the local neighborhood structures.

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Notes

  1. Research on policy transfer has shown that in many instances a transfer took place in a region because of particular political circumstances (e.g., a government trying to implement something new, a personal relationship between politicians, a broader regional policy agenda tied to certain funding mechanisms). All those factors are classified as institutional factors. Particular individuals in power positions have often played a key role when it came to implementing new or different vocational education structures and supporting the expansion of vocational education. They are the individual level factors. There is no doubt that policy diffusion is determined by institutional factors, individual level factors and neighborhood interactions at least. Here at least the roles of neighborhood interaction on local vocational education development have been detected.

  2. There is conditional convergence besides absolute convergence. However, here it’s enough to just test absolute convergence including σ-convergence and β-convergence because the exploration of the convergence in vocational education brings insight into the neighborhood interaction and policy diffusion.

  3. The relationship between convergence speed and spatial neighborhood structure has been verified by previous studies (Gu 2012a, 2013).

  4. It is emphasized that the regional effect is positive by some policy scholars such as Berry and Berry (1990), Mooney and Lee (1995) and Ghosh(2010). However, the negative effect is also detected (De Groot 2010; Easterly and Levine 1998; Ades and Chua 1997). Spatial Markov Chain analysis is used here like a high power microscope to look inside of the neighborhood structures, identify the various parts of the neighbors and their spatial interactions to predict the development trend. It’s the first time to use this tool to investigate policy diffusion and the results are amazing. According to the results, both the positive and negative regional effects are captured and neighborhood structure is the determining factor.

  5. Within this regionally based policy diffusion framework, policy scholars have emphasized that there is a regional effect on policy diffusion. Some scholars insist that the regional effect is positive, whereas others contend that there is a negative regional effect. Those two contrasting views have the same premise that the regional effect on policy diffusion is liner. However, the socio-spatial system in vocational education reflects a complex phenomenon characterized by its nonlinearity and inherent complexity and it cannot be understood by studying parts in isolation and in linear perspective. Our empirical results show that the very essence of the system lies in the spatial interaction between neighbors and the overall behavior that emerges from the neighborhood interactions due to different neighborhood structures.

  6. Spatial regression models(spatial lag model and spatial error model) include relationships between variables and their neighboring values, which allows us to examine the impact that one policy observation has on other proximate policy observations. When the value of a policy indicator observed in one location depends on the values observed at neighboring locations, there is a spatial dependence. And spatial data may show spatial dependence in the variables (spatial lag model) and error terms (spatial error model). So, as Rincke (2007) and Gu(2012c) did, we use both spatial lag model and spatial error model here to simulate and forecast different potential policy diffusion mechanisms. Both models can be used to investigate policy diffusion, however, in this paper spatial lag model is better than spatial error model due to those statistic tests such as AIC, Lagrange Multiplier (lag), Robust LM (lag), Lagrange Multiplier (error) and Robust LM (error) in Table 7.

  7. The question of what is the optimal neighborhood size is largely dependent on the relative strength of neighbors and is particularly important when one seeks to examine the dynamic equilibrium of a spatial game. As Gu (2013)’s research shows that the choice of the optimal neighborhood size typically is influenced not solely by the quality of the various competing strategies, but by the effect of the frequency with which those various competing strategies are found in the population. As a result, the optimal neighborhood size is not invariable when the competition pattern changes.

  8. The dual structure theory of policy diffusion is tested and elaborated by us in another research that will be published soon.

  9. In spatial neighborhood studies, building notions of space into analytical procedures may yield more comprehensive information than heretofore has been gathered on the spatial distribution of individual person. The comparisons on individual level and provincial level share the same characteristic of spatial interaction and spatial neighbors play important roles when the comparisons happen.

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  1. Institute of Social Science Survey, Peking University, Room 445, No. 5 Science Building, Beijing, 10087, China

    Jiafeng Gu

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Gu, J. Spatial Diffusion of Social Policy in China: Spatial Convergence and Neighborhood Interaction of Vocational Education. Appl. Spatial Analysis 9, 503–527 (2016). https://doi.org/10.1007/s12061-015-9161-3

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