Financial constraints and firms’ markup: evidence from China

China’s transition from a planned to a market economy has left persistent financial frictions due to state influence over the financial system and non-market allocation of credit. Many firms, particularly private and small and medium-sized enterprises, face high external financing costs and credit discrimination, which may depress investment, innovation, exporting, and productivity. Markup—the gap between price and marginal cost—is a key indicator of firm market power and profitability and relates to welfare in international trade. This study asks how financial constraints affect firms’ markups and whether such constraints restrain their increase. Using Chinese manufacturing firms’ microdata (1999–2007), the paper estimates firm-level markups and examines both the magnitude and mechanisms of the effect of financing constraints, with implications for China’s ongoing financial reforms and industrial upgrading.

Two strands are most related. First, on financing constraints and firm performance: prior work documents that financing frictions reduce productivity (e.g., Bulgaria, Estonia, Italy, India, Canada, Vietnam), R&D and innovation (US, France, Italy), and exporting (theory and evidence across China and multiple countries). Financing constraints also affect product quality and outward FDI. Second, on determinants of markups: studies link higher productivity and exporting to higher markups; other determinants include FDI, minimum wages (via innovation and TFP), import competition, trade liberalization, and exchange rate shocks. A few papers consider interactions of financial constraints with other factors (e.g., economic policy uncertainty, intangible investment) on markups, but a systematic, direct examination of financial constraints’ standalone impact and mechanisms on markups is limited. This study fills that gap by integrating constraints and markups in one framework, testing mechanisms (production efficiency and market pricing), and exploring heterogeneity across firm types.

Data: Annual Survey of Industrial Firms (ASIF) from China’s National Bureau of Statistics, covering all SOEs and above-scale firms (sales > RMB 5 million). Manufacturing firms, 1999–2007. Firms with missing key variables, fewer than 8 employees, or accounting inconsistencies are excluded. Industries are defined at two-digit codes.

Markup estimation: Following De Loecker and Warzynski (2012), markup is price over marginal cost. Because labor is not a fully variable input (especially for SOEs) and capital is dynamic, the output elasticity of intermediate inputs is used. A translog production function is estimated separately by industry using the ACF (Ackerberg-Caves-Frazer, 2015) semiparametric method to address input endogeneity. From the estimated translog, the output elasticity with respect to intermediates is derived and combined with the expenditure share to compute firm-level markups. As robustness, an accounting-based markup measure (using value added, wage costs, and intermediate input costs) is also constructed (Domowitz et al., 1986).

Financial constraints measurement: Primary measure is the SA index (Hadlock and Pierce, 2010), constructed from firm size and age; the absolute value is used so that higher values indicate tighter constraints. Alternative measures for robustness include leverage (current liabilities/current assets), interest payment ratio (interest expense/total debt), debt ratio (total liabilities/total assets), accounts receivable ratio (accounts receivable/current assets), and liquidity ((current assets − current liabilities)/total assets).

Baseline model: ln(markup_ijkt) = β·CC_ijkt + γX_ijkt + firm, province, industry, and year fixed effects + ε_ijkt. Controls: size (log sales), capital–labor intensity (log capital/labor), average wage (log total wages/employment), firm age (log years since founding), government subsidies (subsidies/sales), and industry concentration HHI.

Endogeneity: Addressed with firm, industry, province, and year fixed effects; IV approaches using (i) industry–province means of constraints and (ii) lagged constraints; both in levels and first-differences.

Further robustness: Balanced-panel subsample; accounting-method markup; excluding pre-WTO years by focusing on 2002–2007; dynamic panel estimation via system GMM including lagged markup; quantile regressions; alternative constraint measures.

Mechanism analysis: Two channels—production efficiency and market pricing. Productivity (TFP) is estimated via ACF and used to test whether constraints reduce TFP (raising marginal cost) and whether, conditional on TFP, constraints still depress markups (interpreted as reduced market pricing power). Heterogeneity is examined across industry types (labor-, capital-, technology-intensive), firm size (split by median sales), ownership (SOE, private, foreign-funded), and export status (exporters vs non-exporters).

• Baseline effect: Financial constraints significantly reduce firm markups. With full controls and fixed effects, SA coefficient ≈ −0.0011 (p < 0.01), implying a 1% increase in SA reduces markup by approximately 0.11%. In balanced panels, coefficients range −0.0006 to −0.0007 (p < 0.05).
• Control variables: Size and average wage are positively associated with markups; firm age is negatively associated; factor intensity, subsidies, and HHI are generally insignificant.
• IV and endogeneity checks: Using industry–province mean SA as IV yields significant negative effects (e.g., −0.0046 to −0.0032). First-difference IV estimates remain negative (≈ −0.0035 to −0.0027). Using lagged SA as IV also gives significantly negative coefficients (≈ −0.23 to −0.20), and identification tests (Kleibergen-Paap LM/Wald, Sargan/Hansen) support instrument relevance/exogeneity.
• Alternative markup measure: With accounting-based markups, the SA effect is negative and significant (≈ −0.0011 to −0.0005).
• Post-WTO subsample (2002–2007): SA remains negative (≈ −0.0110*** to −0.0096), indicating robustness to trade-liberalization-era dynamics.
• Dynamic panel (system GMM): Lagged ln(markup) is strongly positive (≈ 0.77–0.80), confirming persistence; SA remains negative (≈ −0.0019 to −0.0020, p < 0.05).
• Alternative constraint measures: Higher leverage, interest payment ratio, debt ratio, and accounts receivable ratio are associated with lower markups; higher liquidity is associated with higher markups (Liquidity coefficient ≈ +0.0246***).
• Quantile regression: Negative effects across most of the markup distribution, stronger in the middle: p50 ≈ −0.0015***; p25 ≈ −0.0007*; p75 ≈ −0.0014***; p90 ≈ −0.0010**; p10 small and not significant.
• Mechanisms: Constraints reduce TFP (e.g., SA → TFP ≈ −0.0005**), and TFP strongly raises markups (TFP → markup ≈ 0.897–0.901). Conditioning on TFP, SA remains negative for markup (e.g., ≈ −0.0007***), consistent with a market-pricing channel. IV-differenced results reinforce both channels (e.g., SA → TFP ≈ −0.0030**; SA → markup|TFP ≈ −0.0009*).
• Heterogeneity: • By industry: Labor-intensive (−0.0044***), technology-intensive (−0.0015***), capital-intensive (−0.0006***); effects are strongest in labor-intensive sectors. • By size: Small firms show significant reductions (−0.0011***); large firms’ effect is small and not significant (−0.0003). • By ownership: Private (−0.0025***), foreign-funded (−0.0015**), SOEs not significant (−0.0001). • By export status: Exporters (−0.0047*), non-exporters (−0.0010***), with larger negative effects for exporters.
• Descriptive range of markups: Across industries, estimated markups largely fall between 1 and 2; overall mean ≈ 1.388 (SD ≈ 0.248).

The findings directly answer the research question: financial constraints depress firms’ markups. They do so via two complementary channels: by lowering productivity (raising marginal costs) and by weakening market pricing power (lowering prices conditional on costs). This aligns with prior evidence that constraints reduce R&D, innovation, and exporting, thereby limiting efficiency gains and market positioning. The stronger impacts in labor-intensive sectors, small firms, and private/exporting firms reflect differential exposure to financial frictions and limited access to low-cost credit in China’s state-dominated financial system. These results underscore the importance of financial development and neutral credit allocation for enhancing firm market power and profitability, with broader implications for industrial upgrading and welfare in trade.

Using firm-level data for Chinese manufacturing (1999–2007) and production-function-based markups, the paper shows that financial constraints significantly reduce markups. Robustness is confirmed through multiple tests (IV, alternative markup and constraint measures, dynamic and quantile models, and policy-period subsamples). Mechanism analysis indicates constraints lower markups by reducing TFP and by diminishing market pricing power. Heterogeneity analyses reveal stronger adverse effects in labor-intensive industries, small firms, private and foreign-funded firms, and exporters, with SOEs relatively insulated. Policy implications include deepening financial reforms, improving legal and institutional frameworks, expanding credit supply (including private financial institutions), and eliminating credit discrimination against private and small firms to enhance competitiveness and profitability. Future work should use more recent data to assess whether these relationships persist amid ongoing financial reforms.

The analysis relies on ASIF manufacturing data from 1999–2007; lack of more recent firm-level data limits timeliness and generalizability to the current financial environment. While extensive robustness and IV strategies are used, residual concerns about measurement and evolving institutional contexts remain; future research with updated data would yield more current policy insights.