The profitability implications of supplier concentration during economic recession and restoration: the moderating role of supply localization

The study examines how supplier concentration affects firm profitability across distinct phases of the economic cycle—recession and restoration—and whether supply localization (a higher proportion of intra-provincial suppliers) moderates these effects. Motivated by recurring economic contractions and rebounds that reshape demand and supply conditions, the paper leverages Resource Dependence Theory to argue that power dynamics between firms and key suppliers shift with changing resource scarcities. The core questions are: (1) Does supplier concentration help or hurt profitability during recession versus restoration? (2) Does supply localization strengthen or weaken these effects? The authors hypothesize that concentration is beneficial in recessions (when downstream demand power rises) and harmful in restorations (when upstream supply power dominates), with localization amplifying both stage-specific effects. This inquiry is important for aligning supply chain relationship strategies with volatile external environments to enhance resilience and profitability.

Grounded in Resource Dependence Theory (RDT), the paper reviews how control over critical resources drives interorganizational power and dependence. Supplier concentration increases a buyer’s dependence on few upstream partners, potentially creating power imbalances that can invite opportunism and unfavorable terms. However, concentration can also foster closer collaboration and coordination. The review situates these conflicting views within economic cycles: during recessions, demand collapses shift bargaining power toward downstream firms, mitigating imbalances; during restorations, supply constraints elevate supplier power, intensifying imbalances. The authors also synthesize research on geographic proximity between buyers and suppliers, which may improve feedback, reduce transportation costs, enable agglomeration benefits, and strengthen social ties—but reverse localization can provide resource diversity and new market knowledge. From these perspectives, the authors derive four hypotheses: H1 (recession: supplier concentration positively associates with profitability), H2 (restoration: supplier concentration negatively associates with profitability), H3 (recession: supply localization strengthens the positive concentration–profitability link), and H4 (restoration: supply localization strengthens the negative concentration–profitability link).

Data: The sample comprises Chinese A-share firms listed on the Shanghai and Shenzhen stock exchanges. From 4828 firms, the authors exclude 125 financial firms and 1384 with missing or incomplete supplier disclosures, yielding 3319 firms. Supplier information (top five suppliers per CSRC voluntary disclosure) is compiled using CSMAR, Wind, and Tianyancha, with manual completion of missing entries. Supplier locations are geocoded (latitude/longitude) via Python, and provinces/cities are identified; ArcGIS maps depict supplier networks for 2020 and 2021. Macroeconomic indicators (NBS of China, ILO unemployment) justify classifying 2020 as recession and 2021 as restoration. Variables: Dependent variable is Change in ROA, computed as ROA_it − ROA_i(t−1), where ROA is net income divided by total assets. Robustness checks use ROA, ROE, and Change in ROE. Key independent variable is supplier concentration (SC), a Herfindahl–Hirschman Index over the top five suppliers based on purchase shares; robustness replaces the denominator with total purchases. Moderator is supply localization (SL), the proportion of purchases with intra-provincial suppliers among the top five; robustness replaces the denominator with total purchases. Controls include Size (log employees), Age (years since establishment), LEV (liabilities/assets), ATI (assets/sales), FIX (fixed assets/total assets), SG (sales growth), Property (SOE dummy), and Multi (multi-industry dummy). Empirical strategy: OLS regressions with industry and province fixed effects estimate the relationships separately for recession (2020) and restoration (2021). The main model includes SC, SL, and SC×SL (mean-centered interaction), plus controls and fixed effects. To address endogeneity (e.g., omitted variables), a 2SLS approach instruments SC using (i) Peer SC (industry-year average supplier concentration excluding the focal firm) and (ii) Local SC (province-year average supplier concentration excluding firms in the same industry). Instrument validity is assessed via Sargan–Hansen tests (exogeneity), Anderson canonical correlation tests (under-identification), and Cragg–Donald Wald F (weak identification). Robustness: The authors test alternative dependent variables, alternative definitions of SC and SL, alternative model specifications (removing controls, industry FE, province FE), and conduct leave-one-industry-out analyses.

Main OLS results (Table 3): Recession (2020): SC is positively associated with Change in ROA (β = 2.612, t = 2.16), supporting H1. SL’s main effect is negative (β = −1.098, t = −1.93). The interaction SC×SL is positive and significant (β = 5.301, t = 2.30), indicating that localization amplifies the beneficial effect of concentration during recession (H3). Restoration (2021): SC is negatively associated with Change in ROA (β = −1.963, t = −1.79); in the full model it is more negative (β = −3.450, t = −2.36), supporting H2. SC×SL is negative and significant (β = −14.368, t = −3.09), showing localization intensifies the detrimental effect of concentration during restoration (H4). Interaction plots (Figs. 5–6) visualize these moderated relationships: positive slopes in recession with a steeper slope for high SL; negative slopes in restoration with a steeper decline for high SL. Endogeneity checks (2SLS, Table 4): Instruments Peer SC and Local SC are strongly related to SC (first-stage tests significant: Anderson and Cragg–Donald). Sargan–Hansen tests indicate exogeneity (p = 0.741 recession; p = 0.916 restoration). Second-stage coefficients for instrumental SC confirm the main findings: positive in recession (β = 7.572, t = 2.21) and negative in restoration (β = −5.824, t = −2.06). Robustness (Tables 5–7): Using alternative profitability metrics (ROA, ROE, Change in ROE), alternative definitions of SC and SL, and alternative model specifications (removing controls or fixed effects) yields consistent signs and significance for the key interaction (SC×SL) in both stages. Leave-one-industry-out tests (untabulated) show results are not driven by any single GICS sector. Descriptives and diagnostics: Low correlations and VIFs (avg VIF 1.19; max 1.31) indicate limited multicollinearity.

Findings align with Resource Dependence Theory’s prediction that economic conditions reconfigure interfirm power. In recessions, collapsing demand shifts bargaining power downstream, easing dependence-related disadvantages and enabling concentrated supplier relationships to deliver coordination, trust, and operational flexibility benefits—especially when partners are co-located, which reduces logistics frictions and facilitates rapid, rich information exchange. During restoration, resurgent demand and tighter supplies shift power upstream; concentrated suppliers can raise prices, ration capacity, and impose constraints. Localization then magnifies dependence, limiting access to diverse inputs and constraining firms’ adaptive capacity, which harms profitability. The results reconcile mixed prior findings on supplier concentration by showing stage-contingent effects and demonstrate that localization is a double-edged moderator, strengthening whichever directional effect dominates in the prevailing macro context. Managerially, firms should dynamically align supplier concentration and geographic scope with cycle phase: consolidate and localize in downturns; diversify and de-localize in recoveries, while investing in capabilities that rebalance power with key suppliers.

This study shows that the profitability effects of supplier concentration are asymmetric across economic stages and are significantly moderated by supply localization. Using a large sample of Chinese listed firms during 2020–2021, the authors find that concentration improves profitability during recession, especially with more intra-provincial sourcing, but harms profitability during restoration, with localization intensifying this harm. Contributions include clarifying stage-contingent consequences of supplier concentration, identifying the amplifying role of localization across stages, and extending business cycle management insights to buyer–supplier relationship design. Managerial implications suggest concentrating and localizing with reliable core suppliers in downturns to reduce costs and ensure supply, then diversifying and geographically expanding sources in upswings to access capacity and resources. Future research should measure power dynamics more directly, examine additional contingencies (e.g., cost structures, resilience, network characteristics), and test generalizability across industries and regions (including developed vs. developing contexts and manufacturing, services, and agriculture).

Power dynamics are theorized but not directly measured; future work should quantify interfirm power shifts across cycles. Additional contingencies—such as cost structures, financial/operational resilience, and network attributes—may shape the effects and warrant investigation. Generalizability beyond the Chinese context and across sectors is untested; comparative studies across industries (manufacturing, services, agriculture) and across developed and developing economies are needed.