What Is the Cost of Privatization for Workers?: Olsson & Tag (2025)

What this is. The paper’s core results, identification strategy, and estimating equations extracted from the source: enough to know what privatization costs workers and why, without reading all 45 pages. To replicate or extend it, read the full source at the original.

TL;DR

Using Swedish administrative data from 1990 to 2017, the paper traces workers and firms through 553 privatization events involving 70,079 incumbent workers. Privatization raises unemployment by 12% and cuts wages by 5-9%, with losses persisting up to eight years. The Swedish social safety net cushions roughly half the income loss through increased transfers (unemployment benefits and activity support). At the firm level, employment falls 16% and the job destruction rate rises 11 percentage points, while productivity increases 35.7% and profitability improves by 2.1 percentage points. Productivity gains occur only when the CEO is replaced, consistent with governance changes breaching implicit labor contracts and enabling workforce reallocation. Rough cost-benefit calculations show productivity gains from privatization outweigh worker income losses by a factor of two to six.

Core results

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

#	Result	Locator	Magnitude
R1	Workers experience persistent wage declines after privatization; wages fall 5.8% short-run, 9.3% medium-run, and 8.4% long-run	Table II, p. 2124	Avg. effect -7.9% (t = -2.96); short run -5.8% (t = -3.47); medium run -9.3% (t = -4.13); long run -8.4% (t = -2.23)
R2	Unemployment incidence rises persistently by ~12-14% relative to pre-privatization levels	Table II, p. 2124	Full period +1.3 pp (t = 5.16), +12.4% vs baseline; short run +1.1 pp (10.5%); medium run +1.2 pp (11.4%); long run +1.5 pp (14.3%)
R3	Government transfers offset roughly half the wage income loss; total income drops only 3.5% despite a 7.9% wage cut	Table II, p. 2124	Transfers up 11.9% (t = 5.82); total income effect -3.5% (t = -1.52) full period; -2.9% to -4.3% in sub-periods
R4	Firm productivity rises 35.7% (or 11.5% using log specification); gains driven by the top 75th-90th percentiles	Table V, p. 2134; Table IA.VI	Full period avg. effect +109.5 KSEK (t = 2.77); log-productivity +11.5% (t = 2.03)
R5	Firm employment falls 16.3% through higher job destruction, with total payroll down 12.2%	Table V, p. 2134	Employee count -16.3% (t = -2.82); job destruction rate +10.9 pp (t = 4.92); job creation rate unchanged (t = -1.40); payroll -12.2% (t = -2.15)
R6	Firm profitability (ROA) improves by 2.1 percentage points from a near-zero baseline	Table V, p. 2134	ROA +2.1 pp (t = 1.82), +321.3% from mean of 0.007
R7	Business ownership among former SOE workers doubles, rising ~97-132% over eight years; effect driven by limited liability company formation, not self-employment	Table III, p. 2127	Business owner rate +6.3 bps (96.8%, t = 3.61) full period; +20 bps long run (131.7%, t = 3.52)
R8	Productivity gains occur only when the CEO is replaced; firms where the CEO stays show no productivity improvement	Table VI, p. 2138	Productivity: CEO leaves +31.4% (t = 2.00); CEO stays +12.8% (t = 0.81); CEO replacement also associated with greater employment and payroll reductions (Table IA.VII)

Overall (paper’s conclusion). Privatization leads to a reallocation of human capital that contributes to both an increase in firm-level productivity and losses in income for workers. The productivity gains from privatization exceed the associated worker costs (pre-government transfers) by a factor of two to six. Government transfers total 10-30% of the per-worker productivity gains, implying workers could receive full compensation with a residual surplus remaining for distribution between the new firm owners and the government.

Theory / model

The paper has no formal structural model. The identification logic rests on an event study difference-in-differences framework under a parallel trends assumption.

Key hypotheses tested. Privatization has ex ante ambiguous effects on workers. On one hand, better governance raises labor demand, increasing wages and reducing unemployment, consistent with the firm-level survey evidence in Megginson and Netter (2001). On the other hand, new private owners face a profit motive to replace workers who enjoyed state protection, and the ownership change may breach implicit contracts between managers and workers (Shleifer and Summers (1988)), making labor cost reductions more likely. Prior work documents wage declines in Ukraine (Brown, Earle, and Vakhitov (2006)) and Brazil (Arnold (2022)), while Bastos, Monteiro, and Straume (2014) find wage increases in Portugal due to different wage-setting institutions. The productivity gain mechanism is tested by examining whether productivity increases only when the CEO is replaced, consistent with the governance-and-implicit-contract channel. The matching-plus-stacked-DiD approach extends the design used in Olsson and Tag (2017) on private equity buyouts.

Identification strategy. The primary concern is selection bias: SOEs that are privatized may differ systematically from those that remain public. The paper addresses this via:

1. A matched comparison group: each treated worker (or firm) is matched one year before privatization to a similar worker (or firm) that remains state-owned, on age, gender, industry, region, and wage (for workers) or industry, region, size, and ROA sign (for firms).
2. A stacked difference-in-differences design (de Chaisemartin and D’Haultfoeuille (2020), Goodman-Bacon (2021)) to handle heterogeneous treatment effects arising from staggered privatization timing across 1997-2017.
3. Pre-trend testing: event-time plots confirm parallel trends in all outcomes for three years before privatization (Figures 3, 4, 5).

A residual concern is anticipation bias (workers sorting out of SOEs before privatization). The paper addresses this by matching one year before the event, and confirms robustness when matching three years earlier (Table IA.VIII).

Method

The core estimator is a stacked difference-in-differences regression run in event time, combined with cell matching without replacement.

Matching. For workers, each of the 70,079 treated workers is matched to one control worker from the 3.3 million non-privatized SOE worker pool on the Cartesian product of age quartile, gender, industry (four broad NACE groups), NUTS1 region, and wage quartile. The match is one-to-one without replacement, yielding 63,231 pairs.

Worker-level estimating equation. Let $Y_{i,f,k,t}$ be an outcome for worker $i$ at firm $f$ in event year $k$ and calendar year $t$, where $k = 0$ is the privatization year. The stacked DiD model (equation 1, p. 2121) is:

$$Y_{i,f,k,t} = \alpha + \pi \, After_k + \gamma D_i + \beta \, After_k \times D_i + \omega_t + X_i + X_f + \varepsilon_{i,f,k,t} \tag{1}$$

where $After_k = 1$ for $k \geq 0$; $D_i = 1$ for workers in a privatized SOE (treated) and 0 for matched controls; $\omega_t$ is calendar year fixed effects; $X_i$ includes individual controls (age, gender, immigrant status, labor market experience, tenure, education, municipality, industry, calendar year, and privatization year fixed effects); $X_f$ includes firm age, industry, and region fixed effects. $\beta$ is the average intention-to-treat effect.

Dynamic specification. To trace the time profile, $After_k$ is replaced by event-time dummies $\tau_k$ for $k = -3$ to $k = +8$ (equation 2, p. 2121):

$$Y_{i,f,k,t} = \alpha + \tau_k + \gamma D_i + \sum_{k=-3}^{k=8} \beta_k \, \tau_k \times D_i + \omega_t + X_i + X_f + \varepsilon_{i,f,k,t} \tag{2}$$

where $k - 0$ is the reference period, so $\beta_k$ is the average intention-to-treat effect at event time $k$.

Firm-level equation. The firm-level analog (equation 3, p. 2122) replaces worker subscripts with firm subscripts; $X_f$ includes firm age, industry fixed effects, region fixed effects; standard errors are clustered at the firm level:

$$Y_{f,t} = \alpha + \pi \, After_k + \gamma D_f + \beta \, After_k \times D_f + \omega_t + X_f + \varepsilon_{f,t} \tag{3}$$

Employment growth rates. Following Davis, Haltiwanger, and Schuh (1998), job flows are computed as (equations 4-6, p. 2133):

$$g_{f,t} = \frac{E_{f,t} - E_{f,t-1}}{0.5 \times (E_{f,t} + E_{f,t-1})}, \quad JDR_{f,t} = |min\{g_{f,t}, 0\}|, \quad JCR_{f,t} = max\{g_{f,t}, 0\} \tag{4-6}$$

bounded between -2 (exits) and 2 (entries).

Standard errors are clustered at the municipality level for worker-level regressions and at the firm level for firm-level regressions.

Empirical specifications

All worker-level results (R1-R3, R7) use equation (1) / (2) on 1,414,270 worker-year observations from 63,231 matched pairs (p. 2124, Table II). Firm-level results (R4-R6, R8) use equation (3) on 4,804 firm-year observations from 368 privatized firms matched to 368 control firms (p. 2134, Table V).

Wage income (R1). Dependent variable is the log of annual gross salary income via the inverse hyperbolic sine transformation. The pre-privatization mean is 271,080 SEK (~27,108 USD). The full-period DiD coefficient is -0.079 (t = -2.96), -7.9%. Short-run coefficients: -0.058 (t = -3.47, -5.8%), medium-run: -0.093 (t = -4.13, -9.3%), long-run: -0.084 (t = -2.23, -8.4%). The mechanical unemployment channel accounts for only 16.4% of the observed wage cut.

Unemployment incidence (R2). Dependent variable is a binary indicator equal to one if the worker was unemployed at any time during the year. The full-period effect is +0.013 (t = 5.16, +1.3 pp, +12.4% vs baseline mean of 0.105). Short-run: +0.011 pp (t = 4.02); medium-run: +0.012 pp (t = 4.27); long-run: +0.015 pp (t = 5.27).

Government transfers (R3). Dependent variable is the log of annual gross government transfers (unemployment benefits, activity support, social benefits) via inverse hyperbolic sine. Full-period effect: +0.119 (t = 5.82, +11.9%). Total income (wages plus transfers): -0.035 (t = -1.52, -3.5%), roughly half the wage loss. Transfers break down as: unemployment benefits +11.1% (t = 7.45), activity support payments +4.3% (t = 6.69), social benefit payouts essentially unchanged (-0.02%, t = 0.23) (Table IA.III).

Firm productivity and employment (R4-R6). Productivity (value-added per employee) full-period effect: +109.5 KSEK (t = 2.77, +35.7%), driven by the top 75th-90th percentile; log-productivity: +11.5% (t = 2.03). Employment: -16.3% (t = -2.82). Payroll: -12.2% (t = -2.15). ROA: +2.1 pp (t = 1.82). All effects are present in both short and medium run.

CEO channel (R8). The sample is split into privatizations where the CEO remains (column 5, Table VI, N = 2,433 observations) and where the CEO departs (column 6, N = 2,278). Productivity effect: CEO stays +12.8% (t = 0.81, insignificant); CEO leaves +31.4% (t = 2.00, significant). The same split is applied to employment and payroll (Table IA.VII).

Heterogeneity (Section V, Tables VII-IX). Partial privatizations (28,694 treated workers) show larger effects: wages -16.5% (t = -4.11), unemployment +2.5 pp (42%, t = 5.77). Foreign buyers lead to unemployment increases of +3.2 pp (32%, t = 8.59) versus +0.5 pp for domestic buyers; total income falls -16.6% with foreign buyers (t = -6.07) versus no change for domestic. MBO privatizations show no statistically significant adverse effects. Triple-difference regressions find no differential effects across industries, high-unemployment regions, or recession years (Table IX).

Datasets used

Dataset	Role in paper	Wiki page
LISA database (Statistics Sweden, longitudinal integration database for health insurance and labor market studies)	Individual-level annual data on wages, unemployment, transfers, family structure, education, wealth for all Swedish residents aged 15+, 1990-2017	no page yet
Structural Business Statistics (FEK) database (Statistics Sweden)	Firm-level data on employees, payroll, productivity (value-added per employee), ROA, investment ratio, leverage, 1997-2017	no page yet
Wealth Register (Statistics Sweden)	Individual total wealth, risky assets, cash, and debt, 1999-2007; used for household finance outcomes	no page yet
Swedish military draft cognitive/noncognitive skill scores	Quality-of-hire measure for male employees; stanine scales for cognitive and noncognitive ability at enlistment	no page yet

Sample: Sweden, 1990-2017 (individual data) and 1997-2017 (firm data). 553 privatization events identified between 1997 and 2017. 63,231 matched worker pairs (70,079 treated workers, matched from 3.3 million non-privatized SOE workers). 368 matched privatized firms (368 matched control firms).

When to read the full paper

Read the original if you are: (i) designing privatization policy and want the cost-benefit arithmetic (Table IA.XVI) and the policy intervention discussion (Section V.D); (ii) studying the role of CEO governance changes in driving firm performance post-ownership-change (Table VI and Table IA.VII); (iii) replicating with the stacked DiD design, which requires the event-specific data sets and the matched control construction; (iv) extending the analysis to household finance and entrepreneurship outcomes (Tables III-IV and Section III.B-B.4); or (v) assessing heterogeneity by privatization type, buyer nationality, or macroeconomic conditions (Tables VII-IX).

Attribution and rights

Source: peer-reviewed, The Journal of Finance 80(4), August 2025. This distillation was extracted by an LLM on 2026-06-05 and is not human-verified or independently reproduced. The CC BY-NC 4.0 licence permits non-commercial reuse with attribution; the verbatim PDF is not hosted in this batch.

Attribution (CC BY-NC 4.0). Olsson, Martin, and Joacim Tag. “What Is the Cost of Privatization for Workers?” The Journal of Finance 80, no. 4 (August 2025): 2107-2151. DOI: 10.1111/jofi.13462. (c) 2025 The Author(s). Licensed under CC BY-NC 4.0. This page is an adaptation by the Institute for Automated Research: core results extracted and re-expressed; changes were made.