## 5 Discussion and Conclusions  
### Discussion of the Findings  
In the analysis of RQ1, \"what are the effects of changes in various economic sectors on average household income?\", the study leverages the data from official sources to develop predictive models. Specifically, it employs Spatial Lag Models (SLM) and their temporally adjusted variants (SLM\({}_{\text{t-1}}\)) to delve into the effects of sectoral Gross Provincial Product (GPP) on household income. The findings indicate a potential simultaneity issue, suggesting that sectoral productivity (GPP) and average household income may be mutually reinforcing, rather than exhibiting a straightforward causal relationship. To mitigate the simultaneity issue, a 2 SLS estimator with fixed effects was employed. The analysis revealed that five sectors--agriculture, real estate, professional services, support services, and leisure--demonstrate a significant association with average household income in either or both models. Conversely, health-related sectors and other services show a negative association with average household income. These findings contribute to an expanded understanding of sectoral effects on income. In the context of Thailand, the growth of key economic sectors is often seen as indicative of increased household income. Agriculture, for instance, employs about 30 percent of the labor force. However, this sector is characterised by relatively low income and productivity.11 Therefore, improvements in agricultural productivity are strongly associated with an increase in average household income. Additionally, the sectors of professional, scientific, and technical activities, along with administrative and support activities, can be viewed as integral to enhancing 'business capacity' within a province. These sectors contribute significantly to innovation, competitive advantage, and management efficiency, all of which are closely linked to average household income. Footnote 11: [[https://www.springer.com/00001](https://www.springer.com/00001)]([https://www.springer.com/00001](https://www.springer.com/00001))  
an empirical foundation for strategies aimed at fostering household income growth and highlight the importance of sector-specific policies. In response to RQ2, \"what are the roles and characteristics of spatial dependencies on the effects of economic sectors and the dynamics of household income?\", the application of SLM and subsequent analysis have been instrumental. The findings demonstrate a pronounced spatial autoregression, suggesting that the household income of one province can be significantly influenced by the income levels in nearby provinces. This aligns with existing literature that emphasises spatial correlation ([PERSON], 2009; [PERSON] et al., 2007). The study also conducted global Moran's I and LISA analyses on household income, growth, and GPP per capita across five key sectors. These analyses yield a two-part answer to RQ2. First, there is an apparent spatial autoregressive effect on household income, with a cluster of high-income provinces centred around the Bangkok Metropolitan region and the Eastern region, confirming a previous study ([PERSON] and [PERSON], 2009). This reflects agglomeration principles outlined in the literature, including backward linkages, innovative activity, and skilled labour pooling, which foster economic activity concentration in specific regions ([PERSON] et al., 2009). Second, sectors such as agriculture, real estate, professional and support services, and leisure, identified as potential income drivers, are predominantly clustered in this same high-income region. While existing literature discusses the convergence hypothesis, where lower-income regions gradually align with higher-income counterparts ([PERSON] and [PERSON], 2016; [PERSON] et al., 2010), this study reveals a contrasting scenario in Thailand's economic landscape. The spatial concentration of key sectors around affluent areas like Bangkok and the Eastern region signals enduring spatial inequality.