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 French administrative data on firms entering new sectors from 2005 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 show no significant association in entry sales or survival, although their sales-growth estimate is weakly negative, 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 supports a 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%. Since HC Bartik is the negative change in HC distance, the implied distance effect on buying is positive
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: top-5-occupation growth coefficient +0.532*** (SE 0.034); Buy: +0.045 (SE 0.094, insignificant); Buy-Build difference -0.487*** (SE 0.099)
R4	Build firms add workers from external labor markets; buy firms shed external workers	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, cols. 1 and 3, p. 1359	Build: +6.6 pp SG&A wage share (sig. at 1%); top-layers share-growth coefficient 0.369*** (SE 0.107); Buy: insig.
R7	Build firms in distant sectors underperform for at least 3 years post-entry; buy firms show no entry-sales or survival association, though sales growth is weakly negative	Table 11, cols. 1-3, p. 1362	Build: coef -1.160*** on HC distance in log(Sales) (~23% lower per 1-SD); Buy: entry sales +0.037 and survival -0.079 (insig.), but sales growth -0.203*; entry-sales build-buy difference sig. at 1%
R8	The buy response to HC distance appears when key occupations are scarce in the local labor market	Table 7, Panel A, col. 3, p. 1356	HC distance coefficient 0.023** in tightest LLM tercile (SE 0.011); 0.001 and -0.013 in terciles 1 and 2, both insignificant; N = 3,825, 3,673, 3,815
R9	The buy response is concentrated among firms with less flexible hiring due to permanent contracts	Table 7, Panel B, col. 3, p. 1356	HC distance coefficient 0.024* in highest permanent-contract tercile (SE 0.012); 0.008 and 0.005 in lower terciles, insignificant; N = 14,260, 13,429, 13,802
R10	The shift-share result is concentrated in entry sectors with larger increases in IT labor shares	Table 4, cols. 4-5, p. 1350	HC Bartik coefficient: -0.008 (SE 0.006, insignificant) in below-median IT-share-change sectors; -0.004** (SE 0.002) in above-median sectors; N = 8,289 and 8,058
R11	Initial HC distance does not explain the IV result, supporting the role of changes in incumbents’ workforce composition	Table 4, col. 3, p. 1350	HC Bartik coefficient -0.006** (SE 0.003); 2003 HC distance coefficient -0.000 (SE 0.002, insignificant); N = 16,347
R12	HC distance matters more for smaller firms’ buy decisions	Table 10, col. 1, p. 1360; text p. 1359	Per 1-SD HC distance, buy probability rises 0.56 pp in the smallest employment tercile, 0.26 pp in the middle, and 0.18 pp in the largest; interaction for largest tercile -0.019* (SE 0.010)
R13	Organizational skills attenuate the negative sales effect of distance for build entries, while buy-entry sales show no such moderation	Table 12, cols. 1-6, p. 1364; text p. 1363	Build interaction coefficients: HC distance x high SG&A 0.245** (SE 0.121), x high top layers 0.213** (SE 0.099), x HR workers 0.385*** (SE 0.081); corresponding buy interactions -2.094, -0.644, 1.152, all insignificant
R14	Most diversification entries are organic, while buy entries account for a larger share when weighted by sales	Table 1, Panel A, p. 1341	127,185 build and 2,415 buy entries (2005-2014); buy entries are 1.86% equally weighted and 15.68% of entry sales
R15	Firms that enter distant sectors build by expanding key occupations while reducing employment in other occupations	Table 5, cols. 2-3, p. 1352	Other-occupation growth coefficients: -0.511*** for build (SE 0.035), -0.212* for buy (SE 0.121); top-5 occupation growth coefficients are +0.532*** for build and +0.045 insignificant for buy (R3)
R16	Firms reduce their HC distance to the new sector after entry, for both build and buy entries	Table 5, col. 4, p. 1352	HC-distance-change coefficients: -0.254*** for build (SE 0.021), -0.168** for buy (SE 0.073)
R17	Internal transfers do not rise significantly with HC distance, consistent with limited use of internal labor markets	Table 6, col. 2, p. 1354	Internal-flow coefficient: -0.003 (SE 0.003) for build and 0.069 (SE 0.051) for buy, both insignificant
R18	Distant build entries also show weaker three-year sales growth, survival, and firm-level performance	Table 11, cols. 2-5, p. 1362	Build coefficients: sales growth -0.217*** (SE 0.034), survival -0.166*** (SE 0.023), operating income per worker -0.062** (SE 0.028), ROA -0.064** (SE 0.030); buy coefficients -0.203* (SE 0.110), -0.079 (SE 0.084), -0.508 (SE 0.558), -0.605 (SE 0.608)

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).

Shock-balance regression (eq. 4, p. 1349). The authors regress sector-occupation shocks on weighted average characteristics of firms entering the sector after 2011:

$$\Delta \hat{s}_{n,i,03,11} = \alpha + \beta \bar{X}_{n,i,03,11} + \varepsilon_{n,i}, \tag{4}$$

Here firms are weighted by occupation i’s wage-bill share in 2003; the average characteristics are standardized and winsorized at 1%. The balance regression is in Internet Appendix Table B7, over occupation-sector shocks from 2003 to 2011. The main text reports that shocks are balanced across the observable characteristics, but does not state its standard-error procedure.

Event-study specifications (eq. 5-6, pp. 1351, 1361). Equation 5 estimates subsidiary employment changes around entry, with event time from -4 to +3 and interactions among build status, high HC distance, and event-time indicators:

$$\Delta \text{Employment}_{ft} = \alpha_o + \beta_n + \gamma_f + \delta_{t_0} + \varepsilon_{ft} + \sum_k \left(\mu_k \mathbf{1}(\text{Build})_{gn} + \nu_k \mathbf{1}(\text{High Distance})_{gn} + \rho_k \mathbf{1}(\text{Build})_{gn} \mathbf{1}(\text{High Distance})_{gn}\right)\mathbf{1}_k, \tag{5}$$

Equation 6 replaces the employment outcome with log sales in the sector of entry and uses the same build, high-distance, and event-time interactions:

$$\log(\text{Sales})_{gnt} = \alpha_o + \beta_n + \delta_{t_0} + \varepsilon_{gnt} + \sum_k \left(\mu_k \mathbf{1}(\text{Build})_{gn} + \nu_k \mathbf{1}(\text{High Distance})_{gn} + \rho_k \mathbf{1}(\text{Build})_{gn} \mathbf{1}(\text{High Distance})_{gn}\right)\mathbf{1}_k, \tag{6}$$

For both event studies, high distance is above the median within origin-entry-year triplets. Equation 5 includes origin-sector, entry-sector, subsidiary, and entry-year fixed effects; equation 6 includes origin-sector, entry-sector, and entry-year fixed effects. The plotted OLS specifications use standard errors double-clustered by subsidiary and time-to-entry for equation 5, and use the regression described in the figure note for equation 6; the figure note does not state its standard-error clustering.

The subsidiary-level entry choice is also estimated in an unnumbered specification (footnote 19, p. 1347), on build entries:

$$\mathbf{1}(\text{Build})_{f,n,t} = \lambda_{g,n,t} + \beta \, \text{HC distance}_{f,n,t-1} + \gamma X_{f,n,t-1} + \varepsilon_{f,n,t}.$$

It includes business-group by entry-sector by year fixed effects to compare subsidiaries within a group; the footnote does not report the standard-error treatment or subsidiary sample size.

Empirical specifications

Main build-or-buy regression (R1, R2, R10, R11). 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: sector-of-origin by entry-sector by year fixed effects (col. 1); origin-sector by size-quartile by entry-sector fixed effects plus year fixed effects (col. 2); main and second origin-sector by entry-sector fixed effects plus year fixed effects (col. 3); and origin-sector by entry-sector, year, and firm fixed effects (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 fixed effects and year fixed effects, while column 2 uses sector-of-origin by entry by year fixed effects.

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 (R8, R9). 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, R13). 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.

Firm-size heterogeneity (R12). Table 10 interacts HC distance with size terciles and indicators for prior diversification and public listing. The specifications retain the Table 3 controls, sector-origin by entry-year fixed effects, and double-clustered standard errors at origin and entry sectors; the table sample is 2005-2014.

Post-entry labor and organizational outcomes (R3-R6, R15-R17). Tables 5, 6, and 9 estimate separate build and buy subsamples using the common outcome specification:

$$Y_{f,g,n,t} = \alpha_o + \beta_n + \delta_t + \theta \, \text{HC distance}_{g,n,t-1} + \Gamma X_{g,n,t-1} + \varepsilon_{f,g,n,t}.$$

The outcome is employment growth, growth in top-five or other occupations, change in subsidiary HC distance, internal or external labor flows, or change in organizational-skill measures as specified in each table. Tables 5-6 use firms surviving three years after entry for the employment-flow outcomes; Table 9 uses entry observations from 2005-2014. These regressions include sector-of-origin, sector-of-entry, and entry-year fixed effects, controls for firm characteristics, and standard errors double-clustered by origin and entry sectors.

Firm-size heterogeneity (R12). Table 10 interacts HC distance with size terciles and indicators for prior diversification and public listing. For the employment-size split, the specification is:

$$\mathbf{1}(\text{Buy})_{g,n,t} = \lambda_{n,o,t} + \delta \, \text{HC distance}_{g,n,t-1} + \sum_{q=2}^{3} \eta_q \, \mathbf{1}(\text{Size tercile}=q) + \sum_{q=2}^{3} \kappa_q \, \text{HC distance}_{g,n,t-1} \, \mathbf{1}(\text{Size tercile}=q) + \beta X_{g,n,t-1} + \varepsilon_{g,n,t}.$$

The table sample is 2005-2014; it uses controls, sector-of-origin by entry-year fixed effects, and double-clustered standard errors at origin and entry sectors.

Post-entry performance (R7, R13, R18). Table 11 estimates the common outcome regression above for log sales at entry, three-year sales growth, survival, operating income per worker, and ROA, separately for build and buy entries. The sample is entries from 2005-2014 with at least 1% of sales in the new sector; specifications include controls and sector-of-origin, sector-of-entry, and entry-year fixed effects, with errors double-clustered by origin and entry sectors. Table 12 adds organizational-skill interactions to entry sales:

$$\log(\text{Sales})_{g,n,t+1} = \alpha_o + \beta_n + \delta_t + \theta \, \text{HC distance}_{g,n,t-1} + \phi \, \text{Skill}_{g,t-1} + \psi \, \text{HC distance}_{g,n,t-1} \, \text{Skill}_{g,t-1} + \Gamma X_{g,n,t-1} + \varepsilon_{g,n,t+1}.$$

The interaction is estimated separately for SG&A, top-layers, and HR measures and for build and buy samples, using controls, sector-of-origin, sector-of-entry, and entry-year fixed effects and double-clustered standard errors. The build interaction coefficients are 0.245**, 0.213**, and 0.385***, respectively (Table 12, p. 1364).

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 regression observations from 2005 to 2014; Table 1 entry totals cover 127,185 build and 2,415 buy entries. 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.