## 2 Literature Review
### Spatial Effects on Economic Sectors and Household Income  
Understanding the effects of economic sectors on household income also requires a comprehensive understanding of spatial influence. The roles of spatial interdependencies from spillover and mobility and regional variations in spatial correlation are significant in shaping economic sectors and impacting household income. _Spatial Interdependencies from Spillover and Mobility:_ The intricate web of spatial interdependencies is evident in the way regional economies are not only influenced by local factors but also by the economic health of their neighbours through various channels such as trade, labour migration, and sectoral employment concentrations ([PERSON], 2009; [PERSON] et al., 2007; [PERSON] et al., 2010). Technological diffusion and knowledge spillover play pivotal roles in shaping regional economic landscapes. Studies have shown that these interdependencies, particularly in the context of technological spillovers and factor mobility, are crucial for understanding the dynamics of regional development and the formation of business environments ([PERSON], 2009; [PERSON], 2011; [PERSON] et al., 2010). Spatial economic policies and developments should be considered within broader, interconnected networks, recognising that changes in one region can have significant ripple effects on others ([PERSON] and [PERSON], 2007; [PERSON], 2009). _Regional Variation from Spatial Correlation:_ Due to spatial interdependencies, the spatial impact on economic growth shows notable regional variations. This variation is not only a response to different economic stimuli but also reflects the geographic and structural economic differences among regions ([PERSON], 2009; [PERSON] and [PERSON], 2003). For example, the spatial characteristics of productivity and occupational composition indices demonstrate significant variations in regional income levels ([PERSON] and [PERSON], 2007). Studies in China and Japan have highlighted how spatial factors, such as geographical location and proximity, lead to diverse growth patterns and income disparities across regions, challenging the efficacy of traditional non-spatial economic models ([PERSON], 2009; [PERSON] et al., 2012; [PERSON] and [PERSON], 2008). These spatial factors add complexity to economic interactions and growth dynamics at both regional and local levels. In Thailand, studies on spatial effects have been somewhat limited. A particular study highlighted that the concentration of multinational enterprises (MNEs) with advanced technological sophistication is predominantly in the Bangkok Metropolitan Area and a few other provinces like Chonburi, with limited spillovers to first and second-level neighbouring provinces ([PERSON] and [PERSON], 2009). Similarly, economic productivity, as measured by Gross Provincial Product (GPP), is concentrated in these regions ([PERSON], 2023). However, there exists considerable potential for economic improvement. The public and private sectors are uniquely positioned to influence Gross Provincial Product (GPP), as the key factors driving GPP have been identified as labour in the formal system and investments ([PERSON], 2023). This potential is further underscored by the observation that local governments have been relatively passive in adopting strategies aimed at attractingbusinesses ([PERSON] et al., 2023). Additionally, it is important to note that economic productivity, as measured by GPP, exhibits a spatial correlation ([PERSON], 2023). This correlation suggests a targeted approach by these sectors, particularly in areas lagging behind, could have a substantial impact on regional economic productivity. Insights from this research could inform local and national governments to implement policies that attract businesses and investments to disadvantaged provinces to spur household income. Thus, addressing the second research question becomes imperative for a comprehensive analysis of regional economic performance and its impact on households.