From poverty to prosperity: assessing of sustainable poverty reduction effect of "welfare-to-work" in Chinese counties

China eradicated absolute poverty by 2021, shifting the policy focus from targeted alleviation of absolute poverty to managing relative and multidimensional poverty in less developed areas. The welfare-to-work policy—rooted in China’s long-standing practice of employment-based relief—has evolved into a multifunctional tool that combines poverty alleviation with social welfare and regional development. It aims to stabilize employment, raise incomes, build infrastructure, and enhance resilience to shocks (e.g., pandemics, disasters). However, implementation outcomes vary by region due to differences in development levels, geography, and labor skills, raising concerns about sustainability and the risk of poverty relapse amid external shocks. This study asks: What is the sustainable poverty reduction impact of welfare-to-work? Is there heterogeneity across districts/counties? Through what mechanisms does it operate? Using county-level data (2000–2019), the paper constructs a multidimensional relative poverty evaluation system and applies DID to estimate effects, explores mechanisms via panel regressions, and quantifies contributions with Shapley values.

The paper traces the evolution of China’s welfare-to-work (work-relief) policy across four stages: (1) 1949–1983 disaster relief and production recovery; (2) 1984–1999 early poverty alleviation integrating labor with in-kind compensation, later supported by treasury bonds; (3) 2000–2020 special-purpose poverty alleviation with refined management, expanded to rural infrastructure and asset-income models; (4) 2021–present comprehensive stage emphasizing employment, income, key projects, and diversified relief modes. Theoretical framing integrates poverty cycle theory (capital scarcity traps), human capital theory (skills as growth driver), sustainable livelihoods and Sen’s capability approach (expanding substantive freedoms and resilience). Welfare-to-work is conceptualized as public investment in infrastructure that provides employment and income, produces public goods, potentially augments skills, and strengthens social cohesion. From these, the study posits hypotheses: H1: welfare-to-work actively promotes sustainable poverty alleviation. H2a: effects operate via infrastructure construction. H2b: effects operate via fiscal intervention. H2c: effects operate via financial instruments. The review also notes mixed findings in the literature on the effectiveness of fiscal transfers and finance for poverty reduction and motivates quantifying mechanism contributions and heterogeneity.

Design: Treats the 2006 rollout of the National Administrative Measures for welfare-to-work as a quasi-natural experiment. Defines treat=1 for counties implementing welfare-to-work since 2006; control group are counties without implementation (excluding developed eastern coastal areas). Time=0 before 2006; time=1 after 2006. Main model: two-way fixed effects DID: CMDI = β0 + β1*(treat×time) + γX + νi + τt + εit, where CMDI is the County Multidimensional Development Index. Robustness: PSM-DID to address selection/endogeneity; placebo tests with random treatment assignment; alternative CMDI measures using A-F and FGT methods. Dynamic effects: event-study specification with leads/lags to test parallel trends and persistence. Mechanism tests: Interacts DID with mechanism variables (infrastructure construction, fiscal intervention, financial instruments—loan balance and savings) to assess channels and regional heterogeneity (central, northwest, southwest). Data: County-level panel (2000–2019), 1,687 county units; 456 treatment counties; 1,231 controls. Data from county statistical yearbooks and the China Regional Database; variables CPI-adjusted (2020 base) and log-transformed where applicable. Variables: - Outcome CMDI constructed from five dimensions (agricultural production efficiency; employment opportunities; per capita GDP; rural per capita disposable income; fixed asset investment level). Weights via entropy method; index aggregated via polygon area method to capture balance and sustainability across dimensions. - Controls: population density; industrial scale (log of large-scale industrial output); social consumption power (retail sales per registered population); urban–rural structure (rural households share); information level (telephone users); education level (primary/secondary students/population). Mechanism measures: infrastructure (log capital construction output), fiscal intervention (log general public budget expenditure), finance (log loan balance; savings per capita). Estimation includes county and year fixed effects with robust standard errors. Shapley value decomposition quantifies contributions of mechanisms to outcomes across regions.

- Main effect: Welfare-to-work significantly reduces relative poverty and improves multidimensional development. The DID estimate on CMDI is 0.161 (about 16.1%), robust to controls, fixed effects, PSM-DID, placebo tests, and alternative poverty metrics (A-F and FGT). Dynamic/event-study estimates show no pre-trend and a growing, sustained positive effect post-2006. - Component outcomes (Table 4): Welfare-to-work increases county GDP per capita by 11.1%, rural per capita disposable income by 1.3%, fixed asset investment by 14.0%, cultivated land quality by 73.7%, and rural employment opportunities by 0.7% (all significant; employment effect smallest). - Mechanisms: Infrastructure construction, fiscal intervention, and financial tools significantly mediate poverty reduction. Infrastructure is the dominant channel overall. Fiscal intervention increases rural incomes and fixed investment but may dampen measured GDP in some specifications. Financial tools promote economic and income growth overall. - Heterogeneity by county development: Quantile DID shows larger effects at higher CMDI quantiles (10%: 0.092; 90%: 0.403), indicating stronger policy impact in more developed counties. - Regional heterogeneity: Welfare-to-work significantly reduces poverty in central (coef. 0.172) and northwest (0.157) regions; effect is small and insignificant in the southwest (0.035). Mechanism heterogeneity: financial tools are positive in the northwest, significantly negative in the southwest, and insignificant in the central region; infrastructure and fiscal intervention are positive across regions. - Contribution analysis (Shapley): Infrastructure contributes the largest share to improvements in GDP per capita, rural incomes, and employment opportunities overall (e.g., 58.31%, 51.96%, and 57.33% respectively). Fiscal intervention contributes most to fixed asset investment (45.29%) and cultivated land quality (66.23%). Financial tools contribute substantially to GDP and rural income (≈40–47% overall) and cultivated land quality in the northwest (≈67.6%), but their contribution to fixed investment and employment is relatively lower and varies by region.

The findings confirm that welfare-to-work is an effective, sustainable poverty reduction tool that operates primarily through infrastructure-led improvements, supported by fiscal spending and financial mechanisms. By raising local economic capacity, living standards, and resilience, the policy addresses relative and multidimensional poverty in line with sustainable development principles. The modest employment effect reflects a long transmission chain where employment gains are a by-product of growth-oriented infrastructure investments and policy support, consistent with prior theory on job guarantee and work-relief programs. Heterogeneity results underscore that higher baseline development amplifies impacts—counties with stronger economic and institutional capacity better convert infrastructure and financial inputs into multidimensional improvements. Regional differences, especially the negative effect of financial tools in the southwest, highlight constraints such as weaker financial infrastructure, access barriers, and mismatches between financial supply and local absorptive capacity. These insights inform targeted policy design: prioritize infrastructure in lagging areas, calibrate fiscal support to public demand, and improve financial inclusion and service delivery—especially in the southwest—to avoid crowding out and ensure financial resources reach low-income groups.

This study shows that China’s welfare-to-work policy significantly and sustainably reduces relative poverty at the county level, with a benchmark effect of about 16.1% on CMDI and substantial gains in GDP per capita, rural incomes, fixed investment, cultivated land quality, and employment opportunities. Infrastructure construction is the primary driver, complemented by fiscal intervention and financial instruments. Effects are stronger in better-developed counties and vary by region: central and northwest regions benefit significantly, while the southwest lags, partly due to financial access constraints. Policy suggestions include: adopting differentiated, locally-adapted welfare-to-work strategies emphasizing suitable infrastructure and characteristic industries; strengthening financial service infrastructure and inclusion (notably in southwest and central regions) and aligning finance with infrastructure to amplify impacts; and increasing, better-targeted fiscal investment to improve supply efficiency and the effectiveness of poverty reduction while leveraging social participation and third distribution mechanisms. The paper focuses on empirical assessment and policy recommendations; explicit future research directions are not specified.