Literature notes — grounding the causal diagram in research

Purpose: justify every node/arrow in our DAG with published evidence (not intuition), per the course requirement that DAGs come from “theory and domain knowledge to think through the data-generating process” (Class 1, slide 38). The dataset is the World Management Survey (WMS), so the WMS research programme is the directly relevant literature.

Core sources (World Management Survey programme)

  • Bloom, N. & Van Reenen, J. (2007). “Measuring and Explaining Management Practices Across Firms and Countries.” Quarterly Journal of Economics 122(4). — the foundational paper; defines the 18-item management score and its determinants.
  • Bloom, Genakos, Sadun & Van Reenen (2012). “Management Practices Across Firms and Countries.” Academy of Management Perspectives. — accessible summary of determinants.
  • Scur, Sadun, Van Reenen, Lemos & Bloom (2021). “The World Management Survey at 18: lessons and the way forward.” NBER WP 28524 / IZA DP 14146.
  • Lemos & Scur; Bloom et al. on family/dynastic ownership and primogeniture.

Finding 1 — the treatment effect itself (founder/family ownership → management)

  • Founder-owned and family-owned-and-family-run firms are consistently among the worst managed. Family firms that bring in an external (non-family) CEO score as well as dispersed-shareholder / PE firms.
  • Mechanism (theory for the arrow D→Y): reluctance to delegate/professionalise; CEO selection by primogeniture (eldest-son succession) rather than talent. This is a governance/incentive channel, NOT merely a size or country artefact.
  • Implication: there is a strong, theoretically-motivated direct effect of founder/ family ownership on management quality — exactly the estimand the VC fund wants.

Finding 2 — determinants of management quality (causes of the outcome Y)

Per WMS papers, management score is higher with: - Stronger product-market competition (compet_* dummies). - Larger firm size (employees). - Multinational status (MNEs are better managed everywhere they operate). - Higher human capital / workforce skills & education (degree_nm). - Country factors (GDP/capita, labour-market flexibility) and industry/skill-intensity.

Finding 3 — implications for variable ROLES in our DAG (to be FINALISED after Week 2 slides)

The literature tells us these variables cause Y. The open question for each is whether it also causes the treatment (→ confounder, control) or is caused by the treatment (→ mediator, do NOT control for the total effect). Working hypotheses, with reasoning:

  • country → CONFOUNDER. Family-ownership prevalence varies strongly by country (institutions, inheritance norms, capital-market development) AND country drives management (GDP/capita, regulation). Common cause of D and Y. Handle as fixed effects (Class 1 slides 31–32; nested data).
  • industry → CONFOUNDER (similar logic): some sectors are structurally more family-dominated, and sector drives management/competition. Fixed effects.
  • firmage → CONFOUNDER. Pre-determined; older firms are likelier to have transitioned ownership away from the founder AND age relates to management maturity. (Watch reverse story: better mgmt → survival → age; treat as pre-treatment control.)
  • **competition (compet_*)** → CONFOUNDER / cause-of-Y. Market structure is largely exogenous to a single firm’s ownership; strongly drives management. Safe to control (closes a backdoor and/or adds precision).
  • employees (size) → CONTESTED (likely MEDIATOR). Family/founder firms tend to stay smaller (capital access, control retention) → D affects size; size strongly drives management. So size plausibly sits on D→size→Y. Controlling it estimates the DIRECT effect and removes a real mechanism. Decision: primary spec = total effect (exclude), secondary spec = include to quantify the size channel. TEST empirically (regress size on D).
  • multinational → CONTESTED (likely MEDIATOR). Family firms internationalise less → D affects multinational; multinational drives management. Same treatment as size.
  • degree_nm (workforce education) → CONTESTED. Could be a mediator (ownership shapes hiring) or a confounder/cause-of-Y (local labour supply). Weakest prior; test empirically.

Method implication (consistent with course)

The WMS papers themselves estimate management score via OLS with controls for country, industry, size, etc. — i.e. regression adjustment guided by which factors are confounders. This supports our Week 1–2 + Week 4 plan: causal-diagram-guided control set, then regression adjustment and propensity-score matching for robustness. Our methodological contribution beyond a naive regression is the explicit confounder/mediator separation and the matching/weighting robustness checks.

Still to do before finalising the DAG

  1. Read Week 2 slides (the dedicated causal-diagram week) for the precise course rules on confounder / mediator / collider and the control criteria.
  2. Optionally fetch Bloom et al. (2012) PDF to confirm how they treat size/MNE/competition as controls vs mechanisms.