China's economic growth, while significant, has been accompanied by insufficient domestic consumption, with household consumption accounting for less than 40% of GDP in the past 15 years—a concerning figure compared to developed nations. While investment and exports have historically driven growth, their diminishing marginal effects and vulnerabilities to external shocks (like the COVID-19 pandemic and global economic uncertainty) highlight the need to boost domestic consumption. Public services, as a key component of government expenditure, are posited as a crucial lever to increase residents' disposable income and stimulate consumption. Keynesian economics supports expansionary fiscal policies during periods of insufficient demand, advocating for increased government expenditure to boost effective social demand. This study investigates the relationship between public service provision and consumption in China, considering both income and substitution effects, and focusing on the disparities between urban and rural areas.

Existing literature in developed economies demonstrates a relatively small impact of public services on consumption, given the already high levels of both. This study contributes by providing evidence from a developing economy (China) with low public service and consumption levels, enriching the understanding of this relationship in such contexts. Furthermore, it specifically analyzes the impact on the urban-rural consumption gap, a critical issue in China's dual economic structure. The study draws on theories of preventive savings (explaining high savings and low consumption due to future uncertainty) and Roemer's equal opportunity theory (highlighting the role of public services in reducing inequality and fostering consumption).

The study utilizes panel data from 31 Chinese provinces (2014-2019), sourced from the China Statistical Yearbook (2015-2020). The methodology involves two key steps: 1) **Public Service Level Evaluation:** The entropy weight TOPSIS method is employed to evaluate the public service level across provinces. This method involves several steps: a) same trend processing of variables using the reciprocal method; b) computation of the standardized matrix; c) calculation of information entropy and information utility; d) computation of weights for each sub-dimension; e) creation of a weighted decision matrix; f) determination of ideal and negative ideal solutions; g) calculation of separation distances; and h) measurement of closeness to the optimal solution. 2) **Impact Analysis:** Ordinary Least Squares (OLS) regression is used to analyze the impact of public service levels (the output of the TOPSIS method) on residents' consumption. Separate regressions are performed for overall consumption, urban consumption, rural consumption, and the urban-rural consumption gap. Control variables include industrial structure, urbanization level, and per capita GDP. The equations used are: HCit = α0 + α1PSit + CONTROLit + εit (1) UCit = α0 + α1PSit + CONTROLit + εit (2) RCit = α0 + α1PSit + CONTROLit + εit (3) URGit = α0 + α1PSit + CONTROLit + εit (4) where HC represents household consumption, UC urban consumption, RC rural consumption, URG the urban-rural consumption gap, PS public services, and CONTROL control variables. The weights of the public service indicators are determined using an average of yearly entropy method calculations (2014-2019).

The study reveals a significant positive relationship between public service levels and residents' consumption. The average public service level across the 31 provinces was 0.1637, indicating significant regional disparities (the highest scoring province was 34.39 times higher than the lowest). The OLS regression results demonstrate: * **Overall Consumption:** A 1% increase in public service level leads to a 0.0947% increase in overall consumption (significant at 1%). * **Urban Consumption:** A 1% increase in public service level leads to a 0.1348% increase in urban consumption (significant at 1%). * **Rural Consumption:** A 1% increase in public service level leads to a 0.0445% increase in rural consumption (significant at 5%). * **Urban-Rural Consumption Gap:** A 1% increase in public service level leads to a 0.1340% reduction in the urban-rural consumption gap (significant at 1%). These findings suggest a stronger impact of public services on urban consumption compared to rural consumption. The income effect of public services outweighs the substitution effect. Control variables show that industrial structure generally inhibits consumption, while urbanization and economic growth positively affect consumption and reduce the urban-rural consumption gap.

The study's findings support the hypothesis that improved public service provision can significantly boost consumption in China. The stronger effect observed in urban areas likely reflects higher income levels and greater capacity to benefit from improved services. The positive effect on narrowing the urban-rural consumption gap is a crucial outcome, indicating that investing in public services can contribute to greater equity. The impact of control variables aligns with existing literature, with industrial structure having a complex, context-dependent effect. The results highlight the importance of both increasing expenditure on public services and optimizing their allocation to address regional imbalances and promote more inclusive growth.

This study demonstrates the significant role of basic public services in driving residents' consumption in China, particularly in narrowing the urban-rural consumption gap. Increasing investment in public services and ensuring equitable distribution are crucial policy measures to stimulate consumption and support sustainable economic growth. Future research could focus on disaggregating public services to analyze the specific impact of different types of services and investigate the long-term impacts of public service investments on consumption patterns.

The study relies on aggregate data, which may obscure variations at the individual or household level. The cross-sectional nature of the data also limits the ability to establish causal relationships definitively. Further research using more granular data and employing advanced econometric techniques could address these limitations and enhance the robustness of the findings.