Build or Buy? Human Capital and Corporate Diversification: Beaumont, Hebert & Lyonnet (2025)

What this is. The paper’s core results, the human capital distance measure it introduces, and the causal identification design (shift-share IV): enough to know what it found and how, without reading all 35 pages. To replicate or extend it, read the full source at the original.

TL;DR

Using exhaustive French administrative data on all firms entering new sectors from 2003 to 2014, the paper shows that firms acquire incumbents (buy) rather than develop resources organically (build) when their existing human capital is far from the target sector. The key friction is integration cost: building human capital requires not just hiring workers with the right skills but also investing organizational capacity to train, manage, and assign them to tasks. Firms with limited organizational skills face particularly high build costs and are the primary drivers of the result. Post-entry, firms that build in distant sectors underperform for at least three years; firms that buy are unaffected by the human capital distance to the entry sector, consistent with acquiring already-operational human capital that avoids integration costs.

Core results

Magnitudes and significance are as reported; \*/\*\*/\*\*\* = 10%/5%/1%. Locators point into the source PDF.

#	Result	Locator	Magnitude
R1	HC distance between a firm and the entry sector is positively associated with the probability of buying (OLS)	Table 3, col. 1, p. 1345	+0.39 pp per 1-SD increase in HC distance; ~20% of the unconditional buy probability; sig. at 5%
R2	HC Bartik instrument confirms causal effect of HC distance on buy probability	Table 4, col. 1, p. 1350	+0.26 pp per 1-SD HC Bartik; ~13% of unconditional buy probability; sig. at 5%
R3	Firms that build hire more workers in entry-sector occupations; firms that buy downsize non-key occupations	Table 5, Panel A col. 2 and Panel B col. 2, p. 1352	Build: +0.532*** in top-5 occupations per 1-SD HC distance; Buy: 0.045 (insignificant); difference sig. at 1%
R4	Build firms hire from external labor markets; buy firms do not assimilate target workforce	Table 6, col. 3, p. 1354	Build: +0.5 pp external hires per 1-SD HC distance (sig. at 5%); Buy: -4.7 pp; difference sig. at 1%
R5	The build-or-buy HC distance effect is driven by firms with low organizational skills	Table 8, p. 1357	Low top-layers: coef = 0.013***; high top-layers: coef = 0.018 (insig.); similarly for SG&A and HR presence
R6	Firms that build in distant sectors invest more in organizational skills; buy firms do not	Table 9, col. 1 and col. 3, p. 1358	Build: +6.6 pp SG&A wage share (sig. at 1%), +0.369 pp top-layers share (sig. at 1%) per 1-SD HC distance; Buy: insig.
R7	Build firms in distant sectors underperform for at least 3 years post-entry; buy firms are unaffected by HC distance	Table 11, Panel A col. 1, p. 1362	Build: coef -1.160*** on HC distance in log(Sales) (~23% lower per 1-SD); Buy: +0.037 (insig.); survival rate build -0.166***; difference in sales sig. at 1%

Overall (paper’s conclusion). Firms buy primarily when they cannot build: 98% of diversifying entries are organic (build), and buy entries are concentrated in distant sectors. Building human capital is costly because it requires organizational skills to integrate new workers, not just the right hires. Firms that buy avoid these costs and reach profitability faster in new sectors. Organizational skills are therefore non-rival: they allow firms to profitably enter multiple sectors.

Theory / model

The paper has no formal theoretical model but builds on two testable hypotheses derived from the organizational economics literature (Prescott and Visscher (1980)). The framing of human capital as a combination of occupation-specific skills follows Lazear (2009). The diversification direction literature finds firms enter sectors requiring similar resources (Neffke and Henning (2013); Hoberg and Phillips (2018); Boehm, Dhingra, and Morrow (2022); Tate and Yang (2024)). The M&A motive of acquiring human capital is developed by Ouimet and Zarutskie (2020).

Hypothesis 1 (build vs buy): If building human capital requires integrating new workers (training, managing, assigning to tasks), the cost of building increases with the level of required human capital investment, proxied by HC distance to the entry sector. Therefore, firms should be more likely to buy in distant sectors and more likely to build in close sectors.

Hypothesis 2 (organizational skills): Firms with limited organizational skills face higher integration costs, so the HC distance effect on buy probability should be stronger for low-organizational-skill firms. Moreover, firms that build in distant sectors should invest more in organizational skills to reduce integration costs.

The paper defines the HC distance between firm $g$ and sector $n$ as one minus the cosine similarity between the firm’s occupation wage-share vector and the incumbent representative firm’s occupation wage-share vector (pp. 1340-1341):

$$\text{HC distance}_{g,n} = 1 - \frac{\sum_i s_{g,i} \cdot s_{n,i}}{\sqrt{\sum_i s_{g,i}^2} \sqrt{\sum_i s_{n,i}^2}}, \tag{--}$$

where $s_{g,i}$ is the share of firm $g$‘s wage bill going to workers in occupation $i$, and $s_{n,i}$ is the share of the sector $n$ wage bill in occupation $i$, computed by consolidating all single-sector firms in the sector. HC distance ranges from 0 (identical workforce composition) to 1 (no overlapping occupations). The paper uses 414 four-digit PCS-ESE occupation codes from the French matched employer-employee data set (DADS, p. 1340).

Method

The primary empirical tool is an OLS linear probability model of the build-or-buy decision, with the shift-share IV (HC Bartik) used to establish causality. The paper exploits the richness of the French employer-employee data to construct firm-level occupation vectors unavailable in standard M&A databases.

OLS baseline (eq. 1, p. 1344). The main specification compares firms in the same sector of origin that enter the same new sector in the same year:

$$\mathbf{1}(\text{Buy})_{g,n,t} = \lambda_{n,o,t} + \delta \cdot \text{HC distance}_{g,n,t-1} + \beta X_{g,n,t-1} + \varepsilon_{g,n,t}, \tag{1}$$

where $\mathbf{1}(\text{Buy})_{g,n,t}$ is one if firm $g$ acquires an incumbent in sector $n$ at year $t$ and zero if it builds. The fixed effects $\lambda_{n,o,t}$ are sector of origin $o$ times entry sector $n$ times year $t$ fixed effects, absorbing all sector-pair-year factors (synergies, barriers to entry, demand shocks). Controls $X_{g,n,t-1}$ include firm size, value added, number of occupations, cash holdings, tangible assets, and total wages, all scaled by workers. Standard errors are double-clustered at the sector of origin and sector of entry.

Shift-share instrument (HC Bartik) (eq. 2-3, p. 1348). To address omitted time-varying firm factors, the paper constructs an instrument that captures variation in HC distance due to changes in incumbent firms’ human capital, holding firms’ workforce composition fixed at their 2003 values:

$$\text{HC Bartik}_{g,n} = \sum_i \hat{s}_{g,i,03} \cdot \Delta \hat{s}_{n,i,03,11}, \tag{2}$$

where $\Delta \hat{s}_{n,i,03,11} = \hat{s}_{n,i,11} - \hat{s}_{n,i,03}$ is the change in the normalized occupation share of sector $n$ from 2003 to 2011. This is equivalently:

$$\text{HC Bartik}_{g,n} = -(\text{HC distance}^{03}_{g,n,11} - \text{HC distance}^{03}_{g,n,03}), \tag{3}$$

so a high HC Bartik value means sector $n$ has become less distant from firm $g$ over 2003-2011. Identification follows Borusyak, Hull, and Jaravel (2022): the shocks (occupation-level workforce changes $\Delta \hat{s}_{n,i,03,11}$) are the source of variation and the shares ($\hat{s}_{g,i,03}$) are the weights. The identification differs from the exposure-share approach of Goldsmith-Pinkham, Sorkin, and Swift (2020). The identification assumption is that long-term changes in incumbents’ workforce composition are uncorrelated with firm-level unobservables. The paper validates this assumption by regressing shocks on potential observable confounders following Xu (2022).

Event study around entry (eq. 5-6, pp. 1351, 1361). For employment and performance outcomes, the paper estimates dynamic regressions from $t-4$ to $t+3$ around entry, separately for firms above and below the HC distance median, plotting coefficient paths to show the timing and persistence of effects. Specifications include sector-of-origin, sector-of-entry, and year fixed effects; standard errors are double-clustered.

Empirical specifications

Main build-or-buy regression (R1, R2). The OLS specification (eq. 1) is estimated on 61,228 firm-sector-year observations from 2005 to 2014 (firms with at least 20 workers and at least 1% of entry-year sales in the new sector). Table 3 reports four specifications: baseline sector-triplet FE (col. 1), adding size-quartile FE (col. 2), adding main-sector FE to account for synergies (col. 3), and adding firm FE to exploit within-firm variation across sectors (col. 4). The HC distance coefficient is positive and significant at 5% across all four. The IV specification (Table 4) is estimated on the 2011-2014 subsample (after the 2003-2011 shock period); Column 1 uses sector-of-origin by entry FE; column 2 adds sector-of-origin by entry by year FE.

Employment decomposition (R3, R4). Table 5 regresses 3-year employment growth ($t-1$ to $t+3$) and its decomposition into top-5-occupation growth and other-occupation growth on HC distance, separately for build (Panel A) and buy (Panel B) entries surviving 3 years post-entry. Table 6 further decomposes total employment growth into internal flows (from other subsidiaries) and external flows (from outside the firm), revealing that builds rely exclusively on external hiring. All specifications include sector-of-origin, sector-of-entry, and year FE; errors double-clustered.

Labor market tightness and contract type (channels for R1). Table 7 splits the main regression by local labor market (LLM) tightness tercile (Panel A, 2010-2014 subsample) and by fraction of permanent contracts tercile (Panel B). The HC distance effect on buy probability is significant only in the tightest LLM tercile (coef = 0.023**) and in the highest permanent-contract tercile (coef = 0.024*), consistent with higher build costs when hiring is harder.

Organizational skills heterogeneity (R5, R6). Table 8 splits the sample by three proxies for organizational skills: (i) fraction of top-layer managers in the wage bill (Caliendo, Monte, and Rossi-Hansberg (2015)), (ii) SG&A-related occupations wage share, and (iii) presence of any HR worker. The HC distance effect is significant only for the lowest-skill tercile in all three measures. Table 9 regresses the change in SG&A wage share, top-layers share, and HR adoption between $t-1$ and $t$ on HC distance, separately for builds (Panel A) and buys (Panel B), finding investment only among build entries.

Post-entry performance (R7). Table 11 regresses log(Sales), sales growth rate, survival indicator, change in operating income per worker, and return on assets at $t+3$ on HC distance for build entries (Panel A) and buy entries (Panel B). Table 12 adds an interaction between HC distance and organizational skill indicators for build entries, finding that high-organizational- skill firms suffer 20% lower sales vs 30% for low-skill firms in distant sectors (coefficient on interaction HC distance x High SG&A = 0.245**, Table 12 col. 1, p. 1363).

Datasets used

Dataset	Role in paper	Wiki page
French matched employer-employee data (DADS - Declarations Annuelles des Donnees Sociales)	Firm-level workforce composition by 4-digit occupation code (414 codes); used to compute HC distance	No page yet
French ownership links data (LIFI - Enquete sur les Liaisons financieres entre societes)	Identifies business group structure and subsidiaries; links firms to M&A targets	No page yet
French tax files (BIC - Benefices Industriels et Commerciaux)	Balance sheets and income statements at subsidiary level; firm-level controls	No page yet
French sales breakdown data (EAE/VAC - Enquete Annuelle des Entreprises / Ventilation des Ventes par Activite)	Identifies firm entries into new sectors (sales by sector, 3-digit SIC); sample period 2003-2014	No page yet
SDC Platinum (M&A deals)	M&A transaction data (acquirer, target, deal date, ownership stakes) for buy entries 2003-2014	SDC Platinum (licensed)
Bureau van Dijk Zephyr (M&A deals)	Supplementary M&A data matched with French administrative records; 7,165 M&A deals, 4,139 acquirers	No page yet
Pole emploi (French unemployment agency) occupational tightness	Local labor market (LLM) tightness measure by occupation; 348 LLMs, used for Table 7 robustness	No page yet

Sample: 61,228 firm-sector-year entries from 2005 to 2014 (127,185 build; 2,415 buy). Administrative data covers all French firms with at least 20 workers from 2003 to 2014.

When to read the full paper

Use the original if you are: studying the determinants of the build-versus-buy choice in diversification; measuring human capital distance between firms and sectors; replicating the shift-share IV (HC Bartik) design for workforce composition shocks; understanding how organizational skills affect labor integration costs in M&As; or extending the analysis to non-French settings. The locators above point to the exact tables.

Attribution and rights

Source: peer-reviewed, The Review of Financial Studies 38(5), 2025. This distillation was extracted by an LLM on 2026-06-06 and is not human-verified or independently reproduced. The CC BY 4.0 licence permits mirroring; the verbatim PDF is not hosted in this batch.

Attribution (CC BY 4.0). Beaumont, Paul, Camille Hebert, and Victor Lyonnet. “Build or Buy? Human Capital and Corporate Diversification.” The Review of Financial Studies 38, no. 5 (2025): 1333-1367. DOI: 10.1093/rfs/hhaf004. (C) 2025 The Author(s). Licensed under CC BY 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.