Serial Entrepreneurship in China

Layer 1: Overview

This paper studies entrepreneurship and new firm creation in China through the lens of serial entrepreneurs (SEs) — individuals who establish more than one firm — contrasting them with non-serial entrepreneurs (Non-SEs). The central question is whether serial entrepreneurs are selected on persistent productive skill or on non-skill advantages such as preferential access to finance, because the two mechanisms have opposite implications for resource allocation: skill-driven serial entrepreneurship raises aggregate productivity, while favoritism-driven serial entrepreneurship generates misallocation.\n\nThe empirical foundation is two administrative datasets for Chinese firms: the Business Registry of China (SAIC), covering the universe of all firms since 1949 with a 2015 snapshot, used for the period 1995–2015; and the Inspection Database (SAIC), providing firm-level income-statement and balance-sheet data, used for 2008–2012 due to data quality constraints. The sample focuses on individually-owned firms (with the largest shareholder being a natural person), covering roughly 17 million entrepreneurs and 20 million firms by 2015. SE firms constitute approximately one-third of all individual-owned firms throughout the period and hold nearly half of all registered capital — making serial entrepreneurship quantitatively central to the Chinese private sector. SE firms have on average about twice the registered capital of Non-SE firms (e.g., 3.22 million yuan vs. 1.91 million yuan in 1995).\n\nTo organize empirical findings the authors develop a two-period Hopenhayn (1992)-style model with collateral-constrained borrowing (k ≤ λe, where k is capital and e is equity). The model generates two competing predictions. If TFP draws across firms started by the same entrepreneur are persistent (AR(1) with autocorrelation ρ), SEs outperform Non-SEs on TFP and the second firm outperforms the first. If instead some entrepreneurs are “favored” with a less binding collateral constraint (higher λ) and persistence is low, favored entrepreneurs enter more readily, pushing SE TFP below Non-SE TFP while installing more capital conditional on TFP.\n\nEmpirically, the average evidence favors persistent skills: 1st-SE firms are 9% more productive than Non-SE firms (within 2-digit industry, province, and year) and 2nd-SE firms are 18% more productive, both significant at the 1% level. In terms of assets, 1st-SE firms are 40% larger and 2nd-SE firms are 66% larger than Non-SE firms.\n\nThis average premium, however, conceals critical heterogeneity driven by industry-switching behavior. Two-thirds of SEs (67%) start the second firm in a different 2-digit input-output industry (switchers); one-third stay in the same industry (stayers). Stayers’ 1st-SE and 2nd-SE firms are respectively 49% and 70% more productive than Non-SE firms — accounting for the entire average SE premium. Switchers’ 1st-SE and 2nd-SE firms are respectively 9% and 11% less productive than Non-SE firms. Despite their TFP deficit, switchers hold at least 7% more capital in both firm generations than stayers. TFP persistence (autocorrelation of log TFP across 1st- and 2nd-SE firms) is twice as high for stayers (0.29) as for switchers (0.14), confirming the model’s key identifying assumption that within-industry persistence exceeds cross-industry persistence. The model interprets switchers’ low-TFP/high-capital profile as the empirical signature of favored entrepreneurs.\n\nThe model further predicts that equity-constrained entrepreneurs should close the first firm when the second is substantially more productive (opportunity cost of capital). Consistently, 1st-SE firms that are shut when the 2nd starts have 32% lower TFP and 13% lower equity than those run concurrently; 2nd-SE firms operated non-concurrently have 8% higher TFP and 22% lower equity than those run alongside the first.\n\nBeyond learning, the paper documents two additional industry-choice motives for switchers. First, a diversification motive: a one-standard-deviation increase in the covariance of returns between the 1st- and 2nd-SE firm industries raises 2nd-SE TFP by 20%, consistent with entrepreneurs demanding a risk premium to enter correlated industries. Second, an input-output complementarity motive: serial entrepreneurs are significantly more likely to choose industries that are upstream-integrated (coefficient 0.46), downstream-integrated (0.47), or complementary (0.41) with the first industry (all significant at 1%), consistent with transaction-cost motives for co-owning trading partners.\n\nThe policy implication is that China’s private sector harbors both dynamism — embodied in highly productive stayer SEs driven by persistent skills — and distortion — embodied in low-productivity switcher SEs who enter and accumulate capital through preferential credit access. Since SE firms account for roughly one-third of all firms and nearly half of all capital, the aggregate productivity costs of favoritism-driven serial entrepreneurship are likely significant. Results apply to individually-owned private firms in China over 1995–2015 and may not extend to settings with more uniform financial markets or state-owned firm dynamics.

Layer 2: Deep Dive

What is the identification strategy and what are the main threats to it?

The paper does not use a natural experiment or instrumental variables for the main TFP comparisons. It relies on a structural model to interpret conditional correlations, with TFP measured relative to province-industry-year cell averages (2-digit industry, province, and year fixed effects). The theoretical identification comes from the fact that two distinct mechanisms — persistent skills and favoritism — generate opposite predictions on the joint TFP/capital relationship: skill dominance predicts higher TFP for SEs while favoritism predicts lower TFP combined with higher capital. The paper shows both signatures in data for distinct subgroups (stayers and switchers respectively), lending internal consistency. The concurrent/non-concurrent distinction provides an additional layer: the model predicts concurrency depends on equity and the TFP gap between firms, and the data confirm these predictions precisely (Table 7). The main threat is selection on unobservables: entrepreneurs who choose to start second firms may differ from non-SEs along dimensions not captured by the model, such as risk preferences, managerial talent, or social connections, and these could confound the TFP comparisons even within industry-province-year cells.

What are the main mechanisms and how are they distinguished empirically?

Two mechanisms are posited. (1) Persistent skills (ρ > 0 in an AR(1) for TFP across an entrepreneur’s firms): positive selection makes SEs more productive and the 2nd-SE more productive than the 1st-SE. (2) Favoritism/credit access heterogeneity (heterogeneous collateral multiplier λ): favored entrepreneurs enter at lower TFP thresholds, so they are over-represented among SEs but have lower TFP and more capital conditional on TFP. The mechanisms are empirically distinguished by using industry switching as a proxy for favoritism. The learning model predicts low-first-period-TFP entrepreneurs switch industry (they do better by searching elsewhere), so favored individuals, who also have low TFP, should be concentrated among switchers. The data show switchers have both lower TFP than Non-SEs and more capital — a pattern only rationalized by favoritism. Stayers exhibit high TFP consistent with persistent skills. TFP persistence (autocorrelation) is twice as high within-industry (stayers, 0.29) as across-industry (switchers, 0.14), confirming the structural assumption separating the two mechanisms.

What heterogeneity is documented across SE types?

First, stayer vs. switcher heterogeneity is the dominant finding: stayers’ 1st-SE TFP is 49% above Non-SE and 2nd-SE TFP is 70% above Non-SE; switchers’ 1st-SE TFP is 9% below Non-SE and 2nd-SE TFP is 11% below Non-SE. Switchers have more assets, equity, and registered capital than stayers despite lower TFP (at least 7% more capital). Second, concurrent vs. non-concurrent heterogeneity: 47.5% of SE firms in the 2008–2012 sample are operated concurrently. Non-concurrent 1st-SE firms have 32% lower TFP and 13% lower equity; non-concurrent 2nd-SE firms have 8% higher TFP and 22% lower equity, consistent with equity-constrained optimal capital reallocation. Third, generational heterogeneity: 2nd-SE firms are consistently larger and more productive than 1st-SE firms across all measures (TFP +18% vs. +9%; assets +66% vs. +40%), consistent with high ρ and positive selection into the second firm. Fourth, geographic stability: 72.3% of SEs locate the 2nd firm in the same prefecture as the first, suggesting local knowledge and networks matter for firm creation.

What robustness checks and data restrictions are applied?

The paper trims the top and bottom 1% of assets and TFP before computing relative TFP. It excludes the 2007–2008 period from return-to-capital calculations (financial crisis concern). It excludes post-2014 registry data because of a registry reform that inflated new registrations and depressed measured exit. It confirms the covariance-TFP diversification result holds when including SE firms not run concurrently. It excludes entrepreneurs who established more than 20 firms (542 individuals, 188,266 firms) to avoid chain-store effects. The paper does not report instrumental-variable estimates, placebo tests, or alternative TFP measures as formal robustness exercises.

How does this paper relate to and differ from closely related prior work?

Prior work on serial entrepreneurship (Holmes and Schmitz 1990, 1995; Lafontaine and Shaw 2016 for US; Rocha et al. 2015 for Portugal; Shaw and Sørensen 2019, 2022 for Denmark; Felix et al. 2021) uniformly finds SEs are more productive or larger than Non-SEs and attributes this to ability or learning. This paper confirms the average finding but is the first to demonstrate that the premium fully disappears and reverses for industry switchers, and to link this reversal to capital market distortions and favoritism rather than skill. The use of a comprehensive universe of firms (not manufacturing-only or survey-based samples) distinguishes it empirically. The misallocation literature (Hsieh and Klenow 2009; Buera, Kaboski, Shin 2011; Midrigan and Xu 2014; Moll 2014) analyzes distortions across all firms but does not analyze serial entrepreneurship. Song, Storesletten and Zilibotti (2011) and Hsieh and Song (2015) focus on state vs. private sector differences; this paper shows distortions exist within the private sector among individual-owned firms. Contemporaneous work by Shaw and Sørensen (2022) on Denmark documents similar properties of SE firms to the Chinese average findings.

What are the model’s key structural propositions?

Proposition 1: entrepreneurs enter iff TFP z ≥ z*(e), where the entry threshold is decreasing in equity e. Proposition 2: without financial frictions and with ρ > 0, 1st-SE and 2nd-SE firms have higher expected TFP than Non-SE, and 2nd-SE > 1st-SE for sufficiently large ρ. Proposition 3: with frictions, the 2nd-period entry threshold Z(z1, e) is increasing in z1 (opportunity cost of first firm’s capital) and decreasing in e. Proposition 4: with frictions and Assumption 1 (equity monotone in TFP) and sufficiently large ρ, SE firms are more productive than Non-SE. Proposition 5: with ρ = 0 and heterogeneous λ, favored entrepreneurs are over-represented among SEs, which then have lower average TFP but more capital conditional on TFP. Proposition 6: concurrent operation is increasing in equity and decreasing in |z2 − z1|. Proposition 7: entrepreneurs stay in the same industry iff 1st-firm TFP exceeds the unconditional mean; stayers have higher TFP than switchers for both SE firms. Proposition 8: with a risk diversification motive, the probability of choosing industry s’ for the 2nd firm is decreasing in Cov(δs’, δs); conditional on choosing s’, 2nd-SE TFP is increasing in Cov(δs’, δs).

What are the diversification and input-output linkage findings?

For diversification, the authors construct an industry-level return-on-assets covariance matrix using 2010–2012 Inspection Data (excluding the financial crisis year). A one-standard-deviation increase in the covariance of returns between 1st and 2nd SE firm industries increases 2nd-SE TFP by 20% (significant at 1%), meaning entrepreneurs require a TFP risk premium to enter a correlated industry. In the excess-probability regression for industry choice, the covariance has a coefficient of -0.11 (significant at 1%), confirming switchers prefer industries negatively correlated with their first industry. For linkages, using 2007 Chinese Input-Output tables and Fan-Lang (2000) methodology, the authors find excess probability of industry choice is significantly higher for downstream-integrated industries (0.47), upstream-integrated industries (0.46), and complementary industries (0.41), all at the 1% level in a joint regression. These results hold controlling for 1st-SE industry fixed effects and year of establishment.

What are the policy implications and their scope conditions?

The paper implies that China’s private sector suffers from a specific type of misallocation: entrepreneurs with preferential credit access (favored individuals, proxied by industry switchers) establish and expand firms despite lower productivity, crowding out more productive entrepreneurs. Reducing distortions in credit access — leveling the collateral constraint across entrepreneurs — would shift resources toward skill-driven serial entrepreneurs (stayers) and raise aggregate productivity. The scale of the problem is meaningful: SE firms hold roughly half of all capital in the individual-owner sector. Scope conditions: these findings apply to individually-owned private firms in China during 1995–2015, a period characterized by rapid private-sector growth, underdeveloped financial markets, and significant political-economic favoritism. The results abstract from cross-regional and cross-industry variation in financial frictions; if such variation matters (as Brandt, Kambourov and Storesletten 2023 suggest), the aggregate distortion estimates could differ. The paper does not quantify the aggregate TFP losses from misallocation in a counterfactual exercise.

What data limitations and caveats apply?

The Inspection Data lack employment information, so the authors impute labor input from the labor first-order condition under competitive wages within province-industry-year cells — a valid proxy only if factor market prices are equalized within cells. Revenue is used as a proxy for value added, valid only if intermediate input shares are constant within industry-province-year cells. The registry snapshot is from end-2015, so ownership history must be inferred; the authors note that for over 80% of individual-owned firms the founding owner coincides with the exit-period or current owner. Post-2014 data are excluded due to registry reform contamination. The analysis excludes entrepreneurs who established more than 20 firms (542 individuals, 188,266 firms) to avoid chain-store effects. The analysis excludes SEs who start a 2nd firm through an enterprise they control (expanding the definition would add 300,400 such cases). Concurrent/non-concurrent classification uses the Inspection Data’s 2008–2012 window, which may misclassify some firms. The TFP measure is relative within province-industry-year cells, so cross-cell TFP comparisons are not made.

Key Concepts

Serial entrepreneur (SE): In this paper, an individual investor who is or has been the largest shareholder in at least two separate firms over the observation period, not necessarily concurrently; 1st-SE refers to the entrepreneur’s first firm and 2nd-SE to all subsequent firms.

Non-serial entrepreneur (Non-SE): An individual investor who is or was the largest shareholder in exactly one firm over the entire observation window; the benchmark category for TFP and size comparisons.

Stayer: A serial entrepreneur whose 2nd-SE firm is in the same 2-digit input-output industry as the 1st-SE firm; interpreted in the model as evidence of high industry-specific comparative advantage and high TFP persistence.

Switcher: A serial entrepreneur whose 2nd-SE firm is in a different 2-digit input-output industry from the 1st-SE firm; interpreted as evidence of either low first-period TFP (learning/Jovanovic motive) or preferential credit access (favoritism motive); empirically identified by lower TFP than Non-SEs combined with more capital.

Favored entrepreneur: In the model, an entrepreneur with a less binding collateral constraint (higher λ), representing individuals with preferential access to bank credit or other non-skill advantages; they enter at lower TFP thresholds, are over-represented among SEs, and display the signature pattern of lower TFP combined with more capital conditional on TFP.

Collateral constraint: A borrowing limit of the form k ≤ λe, where k is installed capital, e is equity, and λ ≥ 1 is the collateral multiplier; the central financial friction in the model, generating the observed co-movement between TFP, assets, and debt-equity ratios in the data.

Concurrent vs. non-concurrent SE operation: Whether the entrepreneur’s 1st and 2nd firms are both operating simultaneously (concurrent) or the 1st firm is closed before or when the 2nd begins (non-concurrent); the model predicts non-concurrent operation is optimal when equity is scarce and the TFP gap between firms is large, rationalizing the observed pattern that non-concurrent 2nd-SE firms have higher TFP and lower equity.