Going Public and the Internal Organization of the Firm: Bias, Lochner, Obernberger & Sevilir (2026)

What this is. The paper’s core results, datasets, theory, and estimating equations: enough to know what it found and how without reading all 47 pages. To replicate or extend it, read the original: DOI 10.1111/jofi.70012. The CC BY 4.0 licence permits mirroring; this batch does not host the PDF.

TL;DR

Using administrative employment data matched to 312 German IPOs (1986–2015), the paper tracks how firms change their internal hierarchies across an eight-year window (five years pre-IPO to two years post-IPO) relative to matched private-firm controls. IPO firms become more hierarchical in preparation for listing: they add management layers (especially a new middle management layer), narrow control spans, shift employment toward managerial and administrative functions, hire finance, legal, and public-firm experts, and standardize job profiles to align with industry job ladders. Approximately 40% of the hierarchical change is not explained by firm growth. Firms with greater human capital risk undergo the largest hierarchical changes, consistent with the theory that going public requires reducing dependence on key individuals. Firms that withdraw their IPOs reverse these changes; private-equity-backed firms show no analogous hierarchical transformation.

Core results

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

#	Result	Locator	Magnitude
R1	IPO firms become more hierarchical starting two years pre-IPO, with changes concentrated in the anticipation and IPO year	Table III, p. 479; Figure 2, p. 478	DiD coef on layers: +0.119*** at t-2, +0.202*** at t-1, +0.388*** at t (all vs. matched private controls; t-stats 3.1–7.3); 11.8% more hierarchical by IPO year (0.388/3.28 mean layers)
R2	About 60% of hierarchical changes cannot be explained by firm growth; the residual reflects going public per se	Table III col. (1) vs. (2), p. 479	Without growth controls: +0.394 at t+2; with controls: +0.244 (7.4% more hierarchical); growth explains ~40% of the raw change
R3	Middle and top management (layers 3 and 4) grow disproportionately; control spans narrow significantly	Table IV, p. 482	Layer 3 employment +23.9% (p=0.002); layer 4 employment +18.9% (p=0.009) relative to controls; control span of layer 3 falls 23.3% (p=0.003), layer 4 falls 17.0% (p=0.015)
R4	Production share falls; administration and management shares rise in IPO firms	Table V, p. 484	Production share: -4.971pp*** at t+2 (10.3% relative decline vs. 48.1% mean share); administration: +2.708pp*** (p=0.003); management: +1.986pp*** (p=0.001); management share rises 31.5% relative to its t-3 base
R5	Finance, accounting, legal, and public-firm expert shares all rise around the IPO	Table V cols. (4)–(6), p. 484	Finance/accounting: +1.376pp** at t+2 (53.1% relative increase, p=0.018); legal experts: +0.254pp*** (158.8% relative); public-firm experts: +2.779pp** at t+2, peak +7.570pp*** at IPO year
R6	Firms with higher human capital risk experience larger hierarchical changes, consistent with Rajan (2012)	Table VI Panel A, p. 486	Triple-DiD: human-capital-intensive industry: interaction 0.202* (p=0.054); highly skilled labor force: 0.261*** (p=0.009); R&D-intensive industry: 0.207* (p=0.062) in post-IPO period
R7	IPO firms add job roles that align with industry-specific job ladders (standardization); hierarchical changes correlate with more formalized internal processes	Table VII, p. 491; Figure 4, p. 489	Promotion levels DiD: +0.196*** to +0.423*** across t+1/t+2 (3.8–8.3% increase relative to mean of 5.11; col. (1) without controls); with controls col. (2): +0.196*** at t+2, +0.256*** at t+1; more layers linked to written HR plans, job descriptions, performance reviews (all p<0.10)
R8	Withdrawn IPO firms build layers before withdrawal then reverse them; PE-backed firms show no comparable hierarchical change beyond scale	Table VIII, p. 492; Table IX, p. 494	Withdrawn firms: +0.183 at t-1 (p=0.123), -0.089 at t+2 (p=0.609); difference significant (F-stat 3.699*). PE firms: all DiD estimates statistically indistinguishable from zero except borderline at t+2 (0.254*, p~0.10) and fully explained by scale

Overall (paper’s conclusion). Firms reorganize to reduce dependence on key individuals’ human capital when transitioning to public markets. Most organizational changes precede the listing date, occur in the two years before the IPO, and are not fully explained by growth. The internal organization of a firm is linked to its financing choices.

Theory / model

The paper has no structural model and no formal estimation of structural parameters. It derives testable predictions from two bodies of organizational- economics theory and tests them empirically.

Bolton and Dewatripont (1994) / Garicano (2000): firms as communication networks (pp. 464–465). In Bolton and Dewatripont (1994), firms are information-processing networks that balance specialization and communication costs. In Garicano (2000), employees in lower layers handle routine problems while complex tasks escalate to specialized problem-solvers in upper layers. Going public raises operational complexity (regulations, disclosure requirements, investor scrutiny), making problems less predictable and increasing the information load. The model predicts:

• BG.1: IPO firms increase the number of hierarchical layers.
• BG.2: IPO firms allocate more of the workforce to upper layers (more top-heavy), narrowing control spans.
• BG.3: IPO firms increase the proportion of specialized-role employees (finance, accounting, legal experts).
• BG.4: Hierarchical changes are greater in firms facing stricter listing standards (higher regulatory complexity).

Rajan (2012): standardization to reduce key-person dependence (pp. 465–466). Early-stage firms rely on founders and key employees whose human capital creates bargaining power and a risk to outside shareholders. Going public requires transferring control to professional managers and creating conventional job profiles that exist across firms in the same industry and can be staffed with external recruits. The predictions are:

• R.1: IPO firms expand management capacity (top-heavy hierarchy, replaceable managers instead of founders/early employees).
• R.2: IPO firms grow administrative functions (personnel management, etc.).
• R.3: Hierarchical changes are more pronounced in firms with more valuable human capital (harder to replace).
• R.4: IPO firms align their hierarchies with industry-specific job ladders (jobs that exist at other firms in the same industry).

The two frameworks are treated as complementary (p. 467): both predict more hierarchy; they differ in mechanism (complexity vs. key-person dependence). The paper’s goal is not to pit them against each other but to measure organizational changes and assess which channel explains more variation.

Identification logic (pp. 475–476). The design is a stacked difference-in-differences with matched never-treated private controls. The identifying assumptions are (i) no treatment anticipation more than two years before the IPO (preparation for a German IPO typically takes 12–24 months, so $t{-}3$ and earlier are assumed clean); and (ii) conditional parallel trends: matched controls would have followed the same hierarchy trajectory as IPO firms in the absence of the IPO. Parallel trends cannot be tested directly but is supported by near-zero, statistically insignificant pre-IPO period coefficients at $t{-}5$ and $t{-}4$ (Table III).

Method

The estimator is a stacked difference-in-differences (DiD) that builds on difference-in-differences and matching as its technique primitives (pp. 475–476). It is applied-method, not method-proposing; the stacked DiD design follows Gormley and Matsa (2011) and Cengiz et al. (2019).

Hierarchy measurement. Three complementary measures of a firm’s hierarchical structure are constructed from German administrative occupation codes (KldB1988):

1. layers: the number of hierarchical levels a firm has, where each occupation is mapped to one of four layers (layer 1 = production/blue-collar; layer 2 = supervisors/experts; layer 3 = middle management; layer 4 = top management/directors). Follows Caliendo, Monte, and Rossi-Hansberg (2015) and Gumpert, Steimer, and Antoni (2021).

2. refined layers (log): sublayers within each of the four layers, formed by clustering employees by wages within each layer (Bonhomme, Lamadon, and Manresa (2022)). Captures within-layer heterogeneity. Mean of 5.89 for IPO firms and 5.68 for controls at $t{-}3$ (Table II, p. 474).

3. promotion levels: industry-specific job-ladder positions derived from within-firm occupational transitions associated with wage increases, building on Huitfeldt et al. (2023). Captures the alignment of a firm’s hierarchy with the industry job ladder.

Sample construction (pp. 472–473). Starting from 888 German IPOs (1984–2016, compiled from SDC, Deutsche Boerse website, Bloomberg, and a manual dataset from Christoph Kaserer at TU Munich), the paper links each IPO to employment data via Orbis (BvD) firm identifiers and the ADIAB linking table to the Betriebs-Historik-Panel. The matching algorithm has two steps: (1) match each IPO to up to 20 private firms in the same industry with the most similar size, age, employment growth, and mean wage three years before the IPO; then (2) restrict to controls with the same layer structure as the IPO firm at $t{-}3$. This yields 312 matched pairs (312 IPO firms and 312 matched private firms, 4,992 firm-years in the main panel).

Empirical specifications

The main specification is the dynamic stacked DiD (Equation 1, p. 476):

$$y_{f,t,c} = \alpha + \sum_{k=-5}^{-4} \beta_k \cdot \mathbf{1}(\text{IPO})_f \cdot \mathbf{1}(t{+}k)_{f,t} + \sum_{k=-2}^{0} \beta_k \cdot \mathbf{1}(\text{IPO})_f \cdot \mathbf{1}(t{+}k)_{f,t} + \sum_{k=1}^{2} \beta_k \cdot \mathbf{1}(\text{IPO})_f \cdot \mathbf{1}(t{+}k)_{f,t} + \phi_f + \psi_{t,c} + \Pi \cdot X_{f,t} + \epsilon_{f,t}$$

• $f$ = firm, $t$ = year, $c$ = cohort of IPO firms going public in the focal year.
• $\mathbf{1}(\text{IPO})_f$ is an indicator for the IPO (treated) firm.
• $\mathbf{1}(t{+}k)_{f,t}$ is an indicator for calendar year $t$ being $k$ years relative to the firm’s IPO year.
• The omitted reference period is $t{-}3$ (the matching year).
• $\phi_f$ = firm fixed effects (equivalent to cohort-by-firm fixed effects).
• $\psi_{t,c}$ = cohort-by-year fixed effects.
• $X_{f,t}$ = controls: log number of layer-1 employees and log number of establishments.
• Standard errors are clustered at the firm level.

The dependent variable $y_{f,t,c}$ varies by table:

• Table III: layers (cols. 1-2) and $\log(\text{refined layers})$ (cols. 3-4), the extensive margin of hierarchy. Columns (2) and (4) add firm-growth controls.
• Table IV: $\log(\text{employment in layer } L)$ for layers 2, 3, 4 and $\log(\text{control span of layer } L)$ for layers 2, 3, 4, the intensive margin.
• Table V: employment share in production/service, administration, management, finance/accounting, legal experts, and public-firm experts.
• Table VII: promotion levels (industry-specific job-ladder alignment).
• Tables VIII and IX: the same specification estimated on withdrawn-IPO and PE-investment samples respectively.

Triple-DiD specification for heterogeneity (Table VI, p. 486). To test whether human capital risk or regulatory complexity moderates the main effect, the paper adds a triple interaction:

$$y_{f,t,c} = \alpha + \sum_{\text{period}} \beta_k \cdot \mathbf{1}(\text{IPO})_f \cdot \mathbf{1}(\text{period})_{f,t} + \sum_{\text{period}} \gamma_k \cdot \mathbf{1}(\text{IPO})_f \cdot \mathbf{1}(\text{period})_{f,t} \cdot \mathbf{1}(\text{Split})_f + \delta \cdot \mathbf{1}(\text{Split})_f \cdot \mathbf{1}(\text{period})_{f,t} + \phi_f + \psi_{t,c} + \Pi \cdot X_{f,t} + \epsilon_{f,t}$$

• $\mathbf{1}(\text{Split})_f$ is an indicator for higher human capital risk (human-capital-intensive industry, highly skilled labor force, R&D-intensive industry) or for higher IPO proceeds or listing in a more regulated market segment.
• The coefficient of interest is $\gamma_k$ in the post-IPO period.

Formalization cross-section (Figure 4, p. 489, footnote p. 490). Using the IAB Establishment Panel survey, the paper regresses 10 standardization/ reorganization indicators on number-of-layers dummies, controlling for establishment and year fixed effects:

$$\mathbf{1}(\text{standardization/reorganization})_{j,t} = \alpha + \beta_1 \cdot \mathbf{1}(\text{firm has two layers})_{j,t} + \beta_2 \cdot \mathbf{1}(\text{firm has three layers})_{j,t} + \beta_3 \cdot \mathbf{1}(\text{firm has four layers})_{j,t} + \phi_j + \psi_t + \epsilon_{j,t}$$

• $j$ = establishment.
• $\phi_j$ = establishment fixed effects.
• $\psi_t$ = year fixed effects.
• The omitted category is firms with one layer.
• Vertical bars in Figure 4 are 95% confidence intervals from heteroskedasticity-consistent standard errors clustered at the establishment level.

Datasets used

Dataset	Role in paper	Wiki page
Integrated Employment Biographies (IEB), German Institute for Employment Research (IAB)	Administrative employee-level data: occupational codes, wages, layering, functions, tenure; main source for all hierarchy measures	No page yet
Orbis (Bureau van Dijk)	Firm identifier linking IPO list to employment data; firm characteristics	Orbis (BvD) (licensed)
Thomson Reuters Securities Data Corporation (SDC)	German IPO list (888 IPOs, 1984–2016)	No page yet
Bloomberg database	IPO list compilation (supplementary to SDC)	Bloomberg (licensed)
Deutsche Boerse AG website	IPO list compilation	No page yet
Manually collected data (Christoph Kaserer, TU Munich)	German IPO identifiers	No page yet
IAB Establishment Panel	Representative survey of German establishments; used to test whether hierarchical changes correlate with formalization of internal processes	No page yet
Bureau van Dijk Orbis / VentureSource	PE growth investment sample (71 firms)	Orbis (BvD) (licensed)

Sample: 312 IPO firms and 312 matched private-firm controls; 4,992 firm-years in the main panel (eight-year window, $t{-}5$ to $t{+}2$). IPOs span 1986–2015; primarily Manufacturing (34.9%), Information and Communication (21.8%), Wholesale and Retail Trade (15.3%), and Professional, Scientific, and Technical Activities (14.4%).

When to read the full paper

Read the original if you are: studying organizational economics of IPOs or corporate governance; extending the hierarchy-measurement methodology (layers, refined layers, promotion levels); using the German IPO sample or IAB/IEB data; examining the standardization-via-job-ladders mechanism; or auditing a specific coefficient. The locators above point to the exact tables. For “what did this paper find,” the table above is the intended default.

Attribution and rights

Source: peer-reviewed, The Journal of Finance 81(1), February 2026. This distillation was extracted by an LLM on 2026-05-31 and augmented on 2026-06-01; it is not human-verified or independently reproduced. The article is published under CC BY 4.0, which permits mirroring; the PDF is not hosted in this batch.

Attribution (CC BY 4.0). Bias, Daniel, Benjamin Lochner, Stefan Obernberger, and Merih Sevilir. “Going Public and the Internal Organization of the Firm.” The Journal of Finance 81, no. 1 (February 2026): 459–505. DOI: 10.1111/jofi.70012. © 2025 The Author(s). Licensed under CC BY 4.0. Open access funding enabled and organized by Projekt DEAL. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.