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FDI and Wages:
Evidence from Linked Employer-Employee Data in Hungary, 1986-2005
John S. Earle Upjohn Institute for Employment ResearchCentral European UniversityÁlmos Telegdy
Central European University
Institute of Economics of the Hungarian Academy of Sciences
Gábor Antal
Central European University
Institute of Economics of the Hungarian Academy of Sciences
Draft: September 2009

Abstract
This paper analyzes the effects of foreign ownership on wage structure and employment
composition using a linked employer-employee panel in Hungary. The data set follows workers
and firms from 1986, when any foreign presence in corporations was prohibited, until 2005, theyear after EU accession. Hungarian policy encouraged and frequently even subsidized foreign
entry, and it has experienced an unusual volume of foreign investment, so our data contain a large
number of foreign-owned firms and more foreign acquisitions and divestitures than in previous
studies. To control for ownership selection, we employ fixed effects for firms, detailed worker
groups, and individuals (where workers can be linked inside firms), and to focus on acquisitions
we construct matched samples using multiple years of pre-acquisition data. Point estimates arehighly sensitive to specification, but in all cases the results point to positive effects of foreign
ownership on wages of all worker types. The only significantly larger foreign differential
concerns workers with university education. We consider possible explanations for the findings,
including issues of selection and measurement, and possibility of higher productivity and rentsharing. Preliminary results suggest that the higher wages paid by foreign-owned firms are fully
accounted for by higher productivity.



We are grateful to János Köllő for advice and cooperation on the Wage Survey data, to Anna Lovász and Mariann
Rigó for conscientious assistance in data preparation, and to Juan Manuel Puerta and other participants at apresentation at CEU Budapest. We also thank the Hungarian National Bank for cooperation and data support. Allerrors are our own.

1. Introduction
Economic policy in many countries seems to display a certain ambivalence towards
inward foreign direct investment (FDI). On the one hand, FDI is valued as a source of finance,technologies, and markets, and governments frequently compete for the favor of foreign investors,
awarding them various subsidies, preferences, and tax abatements. On the other hand, mostcountries completely prohibit FDI in so-called “strategic” sectors – for instance, airlines and(until recently) banking in the US – and they often impose additional regulatory burdens anduncertainties that add to the inherently higher costs of sending capital and exerting control acrossnational boundaries. In adjusting these policies towards FDI, a major issue concerns the effects
of foreign acquisitions on workers, in particular on the wages of employees in the acquiredcompanies.
This paper addresses the impact of FDI on the level and structure of wages in Hungary, an
economy that rapidly reformed and liberalized inward investment during the 1990s. The data weanalyze begin during the last years of central planning when foreign involvement was completely
banned, they continue through the adoption of a very liberal regime in which, despite significant
opposition from the population, the government awarded special treatment to many foreign
investors, and they end in 2005, just after accession to the European Union. The result ofliberalization was ownership transfer from domestic to foreign owners that took place not only
quickly but also broadly across nearly all sectors. At the same time, the tightly controlled wagesof the centrally planned systems were abruptly liberalized, permitting organizations to set theirown wages and to increase skill differentials, which had tended to be compressed under socialism(e.g., Kornai, 1990).
Our analysis of the wage effects of FDI in Hungary builds on a considerable body ofprevious research. Early studies tended to have small samples and short time series, and theysometimes lacked a comparison group. An important and widely recognized problem is the needto account for ownership selection effects. If firms are not randomly selected into ownershiptypes with respect to their wage policies (for instance due to “cherry picking”), and the researcher
does not take this into account, the estimated causal effect will be biased. Indeed, some recent
studies of firm-level data attack this problem directly: Conyon et al. (2002) employ firm fixed
effects; Brown et al. (forthcoming) employ matching, firm fixed effects, and random trend
methods; and Girma and Görg (2007) combine matching with difference-in-differences. These

1

studies, and most others using firm-level data, tend to find a wage premium in foreign firms,
although identification with fixed effects in panel data frequently relies upon small numbers ofownership switchers. 1 Moreover, a further problem with analyses based on firm-level data is thatthey typically contain little or no data on individual worker wages and characteristics. If worker
skills change simultaneously with a foreign acquisition, then an average wage differential may
represent a change in employee composition rather than a change in employee compensation. 2
As an alternative to firm-level data, several studies employ individual data that contain
information on ownership of employers. Kertesi and Kollo (2002), for example, compare thereturns to education between foreign and domestic firms in Hungary, and find a foreign wage
premium.

While the ability to control for measures of skills and other variables is a big

advantage, a potential problem with these studies is possibly inaccurate measures of ownership,
which are reported by household respondents who may not be fully informed about the progressof acquisitions. More importantly, worker-level data generally do not permit controls for firm
selection into ownership type, nor do they usually include much employer information, which canbe useful for disentangling the possible mechanisms underlying an FDI-wage correlation. 3
The advantages of both firm- and worker-level data can be exploited only if one combines
the two data types and uses linked employer-employee data (LEED), and recently there have been
several such studies. Huttunen (2007) uses LEED for Finland, and finds positive foreign wage
effects, which decreases with skill. Almeida (2007) finds that foreign ownership in Portugal has
a modest positive effects on the wages of workers, and the effect is higher for the skilled group.
Martins (2004) uses the same data source, but finds a negative effect. Andrews et al. (2007) findsmall positive, sometimes insignificant effects in Germany. Heyman et al. (2006, 2007) find verysmall, sometimes negative average wage effects in Sweden, but this masks positive effects for thehigh skilled, and negative effects for the low skilled workers. The only study of a transitionalcountry is Earle and Telegdy (2008) for Hungary, who however focus only on ownership change
1

A related area analyzes effects of all types of ownership change on wages: e.g., Lichtenberg and Siegel (1990) on
LBOs, Gokhale et al. (1995), on hostile takeovers, McGuckin and Nguyen (2001) and Siegel et al. (2009) on mergers
and acquisitions. Our data, however, contain information only on transitions between domestic and foreign
ownership types, not on changes of ownership within these groups.
2
Other firm-level studies include Feliciano and Lipsey (1999) on the US; Aitken, Harrison, and Lipsey (1996) on
Mexico, Venezuela, and the US; Lipsey and Sjöholm (2004) on Indonesia; and Almeida (2007) on Portugal; thelatter two studies include controls for the composition of workforce at the firm level.
3
A possible identification approach in analyzing the effects of private-public wage differentials is to examine wagechanges of workers who switch sectors. The approach here is instead to examine firms that switch sectors. For asurvey of studies analyzing wage differences between the public and private sector, see Gregory and Borland (1999).

2

through privatization in which they show a foreign ownership effect on wages of 7-12 percent.
Finally, Brazil is the single developing country where the effect of foreign ownership on wages
was studied with LEED. Martins and Esteves (2008) find no effect of foreign ownership on
wages.
All of these studies pose selection as the central issue in studying the wage effects offoreign ownership, and most also provide empirical evidence that selection does matter. To
address this issue, many studies use panel data to carry out difference-in-differences calculations,and some employ propensity score matching techniques to select a control group with similar
characteristics. These methods, however, are very demanding on the data, requiring a large
number of treatments and long time series. Most studies, however, face problems of relatively
few ownership changes, short time series, or both, so that the ownership effect may be identified
only on a relatively small sample of firms observed in only briefly before and after the treatment. 4
Our paper builds on and contributes to this literature in a number of ways. Our datacontain over 900 changes in foreign ownership, mostly acquisitions, and the time series extends
over a 20-year period. Our panel data contain detailed information on firm-level financial andperformance measures, including firm-level wages, which we use to condition the selection ofmatched control firms over multiple years. By contrast, most studies use firm characteristics onlyin the year before acquisition to select matched controls, which ignores any dynamics, such as
possible wage growth or improving productivity, over the pre-acquisition period. Our methods
combine matching with panel regression methods, using the full longitudinal structure of the datarather than selecting individual pre- and post-acquisition years for comparison. This approachhas the additional benefit of permitting us to estimate dynamics of the acquisition effects bothprior to acquisition, which are useful for “pre-program” specification tests, and after acquisition,
which are useful for assessing the short-term versus permanence of the effects.
While we follow much of the literature in controlling for firm fixed effects in many
specifications, we also exploit the detailed information on worker characteristics to constructmore detailed fixed effects at the firm-worker-group level, based on cells defined by gender,
educational category, number of years of experience, and county in each firm (which produces
4

The length of the panel is 5 years or less in most of these studies, and the number of ownership switches is typicallybetween 100 and 300. Almeida (2007) has 1,193 switches but about 20 percent have no pre-switch observations, 30
percent have no post-observations, 50 percent have at most one pre- and 50 percent have at most one post-3

about 400,000 cells with at least two worker observations in a total sample size of 2.4 million).
Moreover, we have linked individual workers over time within firms, which permits us to use
worker fixed effect to identify the impact of foreign acquisitions on incumbent workers at thetime of takeover.
Using these methods, we find consistent evidence of a positive impact of FDI, albeit one
that varies considerably across specifications. We then consider a variety of explanations for thisfinding, including measurement and selection issues and mechanisms working through enhanced
productivity, rent-sharing, and personnel policies.

For addressing these issues, we rely on

detailed information on both the worker side, where we can estimate the impact of FDI on
workers by skill and other characteristics, and the firm side, where we can examine changes infirm performance.
The next section describes the construction of our database. In Section 3 we briefly
explain the evolution of the ownership structure during the period studied and provide summary
statistics for wages and worker characteristics. Section 4 describes the estimation procedure, andSection 5 presents the results. Section 6 concludes with a summary and suggested directions forfurther research.

2. Data Sources and Sample Selection
We study a linked employer-employee dataset that relies on two data sources. The
Hungarian Wage Survey, hosted by the National Employment Office, gathered information on
workers’ earnings and characteristics every three years between 1986 and 1992, and on an annual
basis since then. The last round we use in this analysis is 2005 so we have information on firms
and their employees starting as early as 1986, well before the transition started, until 2005, one
year after the country’s accession to the European Union. In 1986 and 1989 the survey covered
all firms, then in 1992-1995 each firm with more than 20 employees. From 1996, a random
sample of smaller firms was added, including employers with 11-20 employees between 1996
and 1999, and those with 5-20 employees for the 2000-2005 period.
In 1986 and 1989, workers were selected from narrowly defined occupational andearnings groups within firms, using a systematic random design with a fixed interval of selection.
observation. Andrews et al. (2006) also have a large number of foreign takeovers and divestments, but the data set

4

High-ranking managers were exempt from this rule and were surveyed comprehensively. In 1992
the sample design changed and was based on the day of birth of workers. Production workers
were selected if born on the 5th or 15th of any month, while non-production workers were chosen
if born on the 5th, 15th, or 25th of any month. Therefore, even though the target group of thesurvey was the population of firms above 20 employees, if a firm did not have an employee bornon the given days in a particular year, the firm is missing from the data.
Starting with 1996, the survey scheme of workers was maintained for medium and large
firms, but all employees’ information were required from smaller companies. The size threshold
below which all workers were sampled was 20 employees between 1996 and 2001, and 50 since2002.
For most firms, this selection procedure oversamples non-production workers, providing arandom sample of workers of about 6.6 percent of production workers and 10 percent of nonproduction workers. We use information on the numbers of production and non-production
workers in the firm to weight the within-firm samples and adjust for the oversampling of nonproduction workers. In addition to this problem, a firm also drops out from the sample if none ofits employees were born on the relevant dates, and the probability of being included therefore
increases with employment size of the firm.5 To overcome this problem, we also construct a firm
weight which varies by firm size and adjusts the sample to the total number of employees in therelevant sectors of the Hungarian economy.
The Wage Survey data include some firm characteristics, which we use to link the worker
data to firm-level data. These data contain each double-entry bookkeeping firm between 1992
and 2005, and they include the balance sheet and income statement, the proportion of sharecapital held by different types of owners, and some basic variables, such as employment, locationand industrial branch of the firm. For the two early years – 1986 and 1989 – these universal data
do not exist, and for these years we used firm information provided with the worker sample.
We cleaned firm ownership data extensively, checking for miscoding and dubious
changes. We also cleaned the longitudinal linkages of the data extensively. For this procedure
we used a dataset by the Hungarian Statistical Office that provides information on reregistration
and boundary changes, which result in spurious entry and exit of firms from our data. As thiscontains information for only two years.

5dataset is not comprehensive, we also tried to find false entries and exits by looking for matchesof exits among the entries on the basis of headquarter settlement, industry, sales, and employment.
While we know that spurious entry and exit is still a potential source of bias in our data, we
believe we decreased the magnitude of this problem to a large extent. Finally, since our data span
two decades during which radical change took place in the Hungarian political and economic
system, we paid a great deal of attention to harmonizing variable definitions and classifications
over time. 6
From the resulting dataset we selected those firms which were above the respective size
threshold of sample inclusion in a particular year. Companies that went through more than twochanges in majority ownership during their presence in the dataset even after extensive cleaning
were dropped from the data (14 companies). We restrict our attention to full-time employees
only, and workers below the age of 15 and above the age of 74 are excluded from the analysis.
Our final selection rule is to drop firms from those two-digit industries where there were
no foreign acquisitions or divestments. 7 After further minor decreases due to missing values, theresulting data is a large firm panel of 30,038 firms, linked with a within-firm random sample of
2.4 million workers. Table 1 shows the number of workers with full information on individual
characteristics, the number of firms with information on ownership and location as well as thetotal employment of the relevant corporate sector. Except for the first two observed years, ineach year the number of workers in our sample is between 79 and 131 thousand, making arandom sample of 3,674 firms in 1986, 7,535 in 1997, and 6,411 in 2005. Our sample represents
a total employment of over 3.5 million in the first year of the analysis, and around 2 million in thelast year.
3. Estimation Procedures
We follow the broader literature on the effects of ownership in estimating reduced form
equations, while trying to account for potential problems of heterogeneity and simultaneity bias
(Djankov and Murrell, 2002; Megginson and Netter, 2001). We are able to exploit the rich set ofworker characteristics as well as the longitudinal structure of the data in order to estimate panel

5

For example, a firm with 20 production workers will have a probability of 0.11 to be excluded from the sample,
while for a similar firm with 100 employees this probability is only 0.012.
6
We thank the help of the Databank from the Institute of Economics of the Hungarian Academy of Sciences.
7
The following 2-digit NACE industries were excluded from the analysis: 5, 12, 13, 30, 41, 62, 67, 80, 85, 91, 93.

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regressions with several types of fixed effects and to construct matched samples that include a setof control firms similar to those acquired by foreigners. We combine matching and regressions ina number of specifications, and to focus on the impact of FDI on incumbent workers we employ
worker fixed effects in a subsample where workers can be linked.
Our first estimating equation describes the foreign differential controlling only for region
and year:lnwijt =  + δfFOREIGNjt-1+ ΣγjREGIONj + ΣλtYEARt + uijt,(1)

where i indexes workers, j indexes firms and t indexes time. Lnwi is the natural logarithm of thewage, and we control in each specification for 20 year effects (YEAR) and 6 regional effects(REGION) – the latter defined at the plant level. 8

FOREIGN indicates whether the firm iscontrolled by foreign owners. As ownership is measured at the end of each year, our ownershipvariable is lagged. In a set of specifications we disaggregate FOREIGN into acquisitions anddivestments to allow for non-symmetric treatment of ownership switches involving foreign
presence. Observations are weighted, as described in Section 2, so that results are representativeof Hungarian tax-paying, double-bookkeeping entities.
Next, we augment this simple equation (1) with standard human capital variables and agender dummy, to account for the composition of employment (which may be correlated withownership):lnwijt =  + Xitβ + δfFOREIGNjt-1+ ΣγREGIONj + ΣλYEARt + uijt.

(2)

Xi is a vector of individual characteristics including three educational dummies (VOCATIONAL,
HIGH SCHOOL, and UNIVERSITY, the omitted category is 8 or less years of schooling),(potential) EXPERIENCE and its square, and a dummy variable indicating whether the worker isfemale or not (FEMALE). As education and experience may be correlated, and gender may
influence both, in a third specification we add to equation (2) a full set of interactions among
these variables.
In the fourth specification, we add a dummy variable indicating that the employee ismanager and whether the worker is newly hired in the past year, to control more precisely for the8

An alternative control for non-random shocks would be to interact region and year in order to permit these shocks
to vary over both dimensions simultaneously. In our data, this would imply adding 380 dummies instead of 19,
which would make some of our computations very time consuming. Nevertheless, we ran most of our specifications
including the full region-year interactions, and the results were little changed.

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composition of the workforce. Finally, we also control for the industrial composition of firms toaccount for industrial wage differentials.
We fit this equation under alternative assumptions about the error term, uijt, first assumingthat it is uncorrelated with each right-hand-side variable.

We use this simple OLS as abenchmark for examining other specifications that can more seriously claim to estimate causal
effects, under alternative assumptions about firm and worker heterogeneity. 9
A serious problem that contaminates studies of ownership change is non-random selectionof firms into ownership type. The owners of the acquiring firms are likely to select targets thathave better growth prospects or a more skilled workforce, for example. If this aspect of the firm
is not observed for the researcher, the estimated effect of ownership on wages will be biased ifthey are estimated by OLS.
We try to account for selection bias in several ways. First, we add firm fixed effects to
the regression to control for all unobserved time invariant effects at the firm level. The datacontain sufficient within-firm variation in ownership to allow the foreign effect to be identified:
Table 2 shows numbers of firm-year observations for acquisitions and divestments as well as foralways foreign and always domestic firms in the regression sample.
Unobserved heterogeneity may vary not only at the firm level but also within groups ofworkers in the same firm, in another specification we interact the firm fixed effect with narrowly
defined groups of workers. We construct groups of workers defined by gender, four education
categories, and years of experience. We also distinguish workers by county (which is defined at
the plant level) and the resulting grouping is interacted with firm identifiers. In this specification
therefore we allow a different intercept for each education-gender-experience-county groupwithin each firm.
The universe of domestically owned firms may be very different from those which wereacquired by foreign investors. To construct a control group that is similar to treated firms before
the treatment took place, we do propensity score matching of firms before running the regressions.
First, we restrict the sample to those treated firms that exist at least two years before and two after
the treatment, and we drop firms that are always foreign-owned, as they are obviously not at risk
9

We report all standard errors permitting general within-firm correlation of residuals using Arellano’s (1987)
clustering method. The standard errors of all our test statistics are robust to both serial correlation andheteroskedasticity. See Kézdi (2003) for a detailed analysis of autocorrelation and the robust cluster estimator inpanel data models.

8

of being acquired. When we match, we also use only those domestic firms that satisfy thiscriterion for a given treatment year. To obtain the propensity score, we run a pooled probit on all
years included in the data, with the dependent variable equal to one if the firm was acquired inthat year by a foreign investor and zero otherwise. We prefer this to estimating separate probits
each year because the relatively small number of treated firms make the estimation of thepropensity score less robust than in the case of a pooled regression. In order to account for thedifferential proportions of treated firms across years, we construct the ratio of treated firms in aparticular year to the total number of treated firms, and we use this proportion to weight theuntreated firms in the probit regression (the treated firms receive a weight of one). For example,
if there were relatively few treatments in a year, all observations from that year will contribute to
the propensity score to a lesser extent than those from a year in which many treatments took
place. 10

The independent variables include the lagged and twice lagged values of firm

characteristics, among them the dependent variable, to match not only on observable, but also on
unobservable characteristics. To allow for a flexible relation between the probability of being
acquired and the firm characteristics pre-treatment, we include all continuous variable in levels
and squared as well. More specifically, the set of variables used for matching is the following:
log employment, log average wage (total personal expenditures/employment), log sales, log
capital intensity (tangible assets/employment), and the first lag of log export share (export
sales/total sales). 11
Having obtained the propensity score, we match on nearest neighbors with replacement,
matching exactly by year and industry. We pair each treated firm with a domestic firm that will
never become foreign with the smallest distance measured by the propensity score. We allow thisfirm to become a control in the second stage if the difference between the two propensity scoresis smaller than 10 percent of the treated firm’s propensity score. If there are multiple firms inthat range, we take the closest one and all domestic firms whose propensity score is less than 10
percent different from the closest firm’s. One firm may become a control several times either
because it is chosen for several treated firms, or because it is chosen in different years. [State

10

This method is similar to that used by Chari et al. (2009), but they drop the sample proportionally to the proportionof treated firms in each year. Instead of dropping, we rather keep all the potentially treated firms in the sample andreweight them.
11
In order not to lose firms because one variable is missing for a year, we dummy out the missing values.

9numbers of duplicate controls, number of extra controls, number of non-matched treated firms,
etc.]

4. Ownership Evolution, Variable Definitions, and Summary Statistics
Hungary got off to an early start in corporate control changes with gradualdecentralization and increased autonomy provided to state-owned enterprises during the late1980s. The first foreign acquisition took place already in 1989, when General Electric bought the
majority of shares in the lighting company Tungsram.

In the early 1990s, not only wereconstraints on foreign investment drastically eased, but tax and other preferences for foreign
investors were also provided. By the mid-1990s, Hungary had the highest value of foreign direct
investment per capita among the post-socialist countries (King and Varadi, 2002).
Our database provides the ownership shares of domestic and foreign owners at the end ofeach year (the reporting date). We define a firm to be foreign controlled if a majority of its sharesare in foreign hands [because foreigners rarely take minority stakes, this classification is little
altered by other thresholds – e.g., 10 percent]. The evolution of the ownership structure among
the firms in our sample is presented in Figure 1, clearly reflecting the early start and the heavy
presence of foreign ownership in Hungarian corporate ownership, at least compared to otherformer socialist countries. Already in 1992, the first post-transition year observed in these data,
foreigners owned about 5 percent of all firms in our data, and their holdings increased to above
ten percent by the next year. By 2005, the last year in our data, 20 percent of the companies inour sample was foreign-owned. These firms tend to be larger than the average Hungarian firm,
so the proportion of employees working for foreign owners was about twice as high, about 40
percent.
The wage variable we focus on consists of the sum of the base wage, overtime pay,regular payments other than the base wage (such as language and managerial allowances), and1/12th of the previous year’s irregular payments (such as end-of-year bonuses). If the worker was
hired during the previous year, we divide this wage component by the number of months theworker spent with the company that year. Figure 1 also shows the U-shaped evolution of theaverage real wage over this period: an initial decline of around 30 percent and subsequent rise of10

about 40 percent. 12 The steady, substantial growth in the Hungarian real wage since 1997 isunusual among the transition economies. The reliability of the real wage measure is of course
strongly influenced by the quality of the deflator (in this case, the CPI), and the large changes inquality and availability of goods suggest caution should be exercised when interpreting thesefigures. When we estimate wage differences by ownership, however, we control for time effects,
so our comparisons are not influenced by these measurement problems.
Table 3 presents the average wage and characteristics of workers by the type of owner for
the whole database. Workers employed in domestic firms receive lower wages than thoseworking in foreign-owned companies, and the difference is quite large: about 53 percent. But the
average characteristics of workers also differ substantially by ownership type. The proportion
female is larger in foreign firms by 6.5 percentage points.

Workers with only elementary

education are 10 percentage points less common, and those with university studies are 7
percentage points much more prevalent in foreign firms. The average worker in domestic firms
has 22.6 years of potential work experience (age-imputed years of schooling-6), larger by almost
three years than the potential experience of the typical employee of a foreign company. More
workers at foreign firms have been newly hired in the previous year: 15.2 percent in foreignowned and 10.8 percent in domestic. But somewhat fewer workers at foreign firms are managers.
To sum up, workers in foreign companies tend to be more educated, female, and less experienced,
and they are less likely to be managers, more likely to be new hires, and have higher wages thanin state-owned companies. Of course, these are unconditional means that take no account of any
other characteristics of foreign and domestic companies, but they are suggestive of the underlying
heterogeneity in the population.
Some firm characteristics by ownership type, again unconditional means over the entire
sample, are displayed in Table 4. Measured by the value of tangible assets (in 2005 HUF),
foreign firms are 4 times larger than domestic firms on average, and their employment (measured
by the yearly average number of employees) is twice as large; together these imply capital-labor
intensity again twice as large. Labor productivity (measured by the ratio of real value of sales tothe average number of employees) is three times as high, and the probability of exporting is fourtimes. The industrial composition of foreign and domestic firms also differ. Relative to domestic

12

To maintain comparability over time, the evolution of the average real wage is estimated from the year effects in aln(real wage) equation that controls for firm fixed effects.

11firms, foreign-owned firms predominate in industry, 43 compared to 26 percent, and finance,
insurance and real estate (FIRE), 8 compared to 4 percent. They are less prevalent in agriculture,
construction and other services. In summary, foreign-owned firms are larger, more capitalintensive, more productive, and more export-oriented than domestic firms, and they areconcentrated in the industrial and FIRE sectors.
By contrast with these stark differences in domestic and foreign firms in the full sample
are the tests of covariate balance in the matched samples of acquisitions, shown in Table 5. For
each variable in both the year prior to acquisition and two years prior to acquisition, thenormalized difference between the acquired firms and the controls is small and statisticallyinsignificant.

5. The Effect of Foreign Ownership on Average Wages
We first discuss the results of the benchmark estimation, the pooled OLS controlling only
for region and year effects in addition to the variables shown. The first column of Table 6
includes no extra controls and shows that the wages of workers employed in foreign firms is 37.1
percent higher than in domestic firms. Controlling for worker gender, education and potential
experience does not change the estimated coefficient at all. The inclusion of interaction terms
between worker characteristics also leaves the estimated wage coefficient unchanged(interactions are demeaned, so the main effects are measured for the average worker). When we
add controls for workers in managerial position and new hires, the foreign wage effect increases
by one percentage point. Finally, the inclusion of two-digit industry effects reduces the foreign
wage effect by only 2 percentage points.
It is notable that the estimated wage effects of worker characteristics are always highly
statistically significant and are in the conventional range. The gender wage gap is 0.17 – 0.22.
Compared to workers with elementary education, the wage premium associated with vocational
studies is measured to be 0.9 – 0.12, high school premium 0.26 – 0.38, and the wage difference
associated with a university degree 0.73 – 0.95, depending on the controls used. The first year of
potential experience increases wages by 2 – 2.5 percent on average, and the starting wage of
employees is 0.9 – 0.11 less than average wages of workers with more than one year of job tenure.
Managers earn 0.35 – 0.40 on average more than ordinary employees.

12In Table 7, we show the estimated foreign wage premium for specifications (1) – (4) from
Table 6, with further attention to the possibility os selection bias in the selection of target firms
by future foreign owners. When we add firm fixed effects (FE) to the OLS specification, theforeign wage effect drops by more than half to 15 – 17 percent, showing that selection on fixed
firm heterogeneity is an important phenomenon in foreign acquisitions and divestments. The
inclusion of worker group effects interacted with firm effects (GFE) results in a further decline ofabout 5 percentage points. When we combine matching with linear estimation methods theresults further decrease, but nonetheless they remain positive, sizable, and statistically highly
significant. The estimated coefficients are around 9 – 10 percent, regardless of the estimation
method applied in the second stage (OLS, FE, or GFE). 13
The analysis so far does not distinguish greenfield FDI from acquisitions and divestitures,
although only the latter switches identify the estimated effects in specifications with firm FE (orfirm-worker GFE). The implicit assumption is that the foreign wage effect is symmetric in both
directions, but an interesting question is whether this assumption is correct. To allow the foreign
effect to vary by the type of ownership change, we disaggregate the foreign ownership variable
into an acquisition and a divestment dummy (both being equal to one when the firm is owned byforeigners). The results, presented in Table 8, show very different effects of foreign acquisitions
and divestments on the wages of workers. The effects of acquisitions are very similar to the main
effect presented in the previous table, while we do not find much evidence that divestments haveany effect on wages, as the estimated coefficients are small, insignificant, positive in some, andnegative in other specifications. The sole exception is the GFE specification, where the estimatedcoefficient is 0.055 and significant at the five-percent level. But this effect disappears whencontrols for new hire and manager are included, and when GFE is combined with matching.
To summarize our results, we find that foreign owners increase wages in acquired firms,
but divestment does not appear to result in a wage decline. The foreign acquisition effect does
not vary much with the set of controls, but it does with the selection method applied. When we
use the matched sample, the results do not vary much across regression methods, and the13

To test whether the matched sample is balanced we compute normalized differences in means of the matchingvariables between treated firms and their assigned pairs. All differences are small, less than 0.1 standard deviations.
Also, standard t-tests accept the hypothesis of equal sample means at any conventional level.

13

magnitude of the coefficient is around 8 – 10 percent. This estimated effect is larger than foundin other studies.
Next we analyze dynamics of the foreign acquisition effect. Specifications where we
permit the FDI effect to vary by event (acquisition) time are presented in Figures 2 and 3. The
OLS dynamics show that the estimated foreign effect is large even before the acquisition
happened and it is constantly increasing. The addition of FEs decreases all coefficients inmagnitude, and the pre-treatment difference between treated and non-treated firms four years
before acquisition becomes statistically insignificant. The inclusion of GFE further reduces thecoefficients and the pre-acquisition effects all become insignificant except one year before
treatment, while the increasing post-acquisition profile is unchanged. Matching on pre-treatment
firm characteristics results in a total loss of significance in the estimated pre-acquisition effects
regardless of the regression method, while the post-acquisition profile remains positive andupward-trending. Therefore, the dynamics provide further evidence that selection is an important
issue in foreign acquisitions, and that matching combined with regression help control forunobserved heterogeneity. The foreign acquisition effect is estimated to be positive and steadily
increasing, even after 4 years.
Another specification adds fixed firm-worker effects, dummy variables for individual
workers who we can track within firms. The foreign effects in this specification are identified
from workers who are observed at a particular employer both before and after an ownershipchange, and thus they refer to the wage effect of foreign acquisitions (and divestments) on
incumbents. Results are displayed in Table 9. Even for incumbents under this very demanding
specification, and regardless of whether we control for other individual and job characteristics,
the estimated effects of acquisitions are positive and statistically significant. And again theeffects of foreign ownership in firms that will be divested are smaller and statisticallyinsignificant.
Finally, we investigate whether the foreign premium varies across different types ofworkers. Table 10 contains the results from adding interactions of the foreign acquisition todummy to specification (4) in Table 7. The results imply that all types of workers – defined bygender, education, experience, new hire status, and managerial or nonmanagerial occupation –receive higher wages under foreign ownership. FDI is estimated to raise the college premium in14

all specifications, and the high school and managerial differentials in some specifications, butthere is little difference associated with new hire status or gender.

6. Possible Explanations (preliminary)
All the evidence suggests that foreign ownership is associated with higher wages, evenwhen we control for various forms of ownership selection and firm and worker heterogeneity.
Why should foreign-owned firms pay more? Several possible explanations exist, including themeasurement of wages and other job attributes, selection at the firm and worker level,
productivity changes, and rent sharing. In this section we discuss each of these issues and try totest them empirically.
To start with the measurement of wages, one possible critique is that domestic firms are
more likely to report lower wages to avoid taxes. The tax burden on work in Hungary is high andtax avoidance is widely considered rife. Our results may be contaminated by underreporting ofwages if the proportion of this is different across ownership types. To check whether domestic
firms are indeed more likely to avoid taxes than foreign-owned enterprises, we carry out several
exercises, shown in Table 11. First, it is likely that underreporting is inversely proportional to thesize of the company; it is harder to imagine that in a large corporation workers are paid under thetable than in small firms, where managers and workers are working closely together and can trust
more that this practice will not be reported to the tax authority. If our results are driven by theunderreporting of domestic companies, the foreign wage premium should increase with firm size.
We interact employment size with the foreign dummy to test for this possibility, and receive apositive, significant coefficient on this interaction. Second, we interact the foreign dummy with acheating index which is defined at the industry level and shows the likeliness of cheating (Elek etal., 2008). Our results show that in industries where underreporting is less likely, the foreign
wage difference is larger than in cheating industries. Both of these set of results seem to reject
the hypothesis of domestic firms being less honest in terms of reporting true earnings, although
they are also consistent with other differences across size and industry categories in how foreign
firms operate.
As a third check, we apply specification (2), but replace wages with a dependent variableindicating whether the worker was paid very close to the minimum wage that year (defined as

15

being paid less than 3 percent more than the minimum wage). We find that a lower proportion ofworkers were paid the minimum wage in foreign-owned companies, and the estimated coefficient
is significantly different from zero. This result may suggest more misreporting in domestic firms,
but the magnitude of the coefficient is rather small, 2-5 percent. As only about 10 percent ofworkers receive the minimum wage in our sample, this wage differential cannot explain the 9 to
15 percentage points foreign wage premium. 14
Another potential measurement problem is related to the wage variable itself.

As

common in many European studies, we use monthly, instead of hourly wage. If hours changed asa result of the ownership change, it is possible that workers get the same wage and the increased
wages are explained by the longer hours the new foreign owner requires them to work. We haveinformation on hours for a limited set of years (between 1999 and 2005), and we perform several
checks on these years. First we put hours as a dependent variable and find an insignificant
foreign coefficient. Second, we compute an hourly wage by dividing the monthly wage by the
number of monthly working hours, and use this as the dependent variable. Third, we use theoriginal specification, but we control for hours. The results do not change.
Another major issue concerns the composition of firms and workers. Although our resultsin the previous section control for observable characteristics of workers as well as selection offirms by unobservables, differential firm exit by ownership and unobserved worker heterogeneity
may contaminate our results. To check – at least indirectly – for the effect of firm exit, we run
exit regressions to check whether the exit rate differs by firm ownership [not done yet). To
control for worker unobserved effects, we link those workers across years that do not change theirworkplace, and control for these firm-worker fixed effects in the wage regression. These controls
reduce the estimated coefficient of foreign ownership, but it nevertheless remains significant andas large as 5 percent (see Table 9). It should be noted that this specification estimates the wage
effect only for those workers who were employed at the firm before and after the foreignacquisition took place and does not take into account the effect on new hires.
We also investigate how the composition of workers changed after the foreigners took
over the company.

We run several linear probability models with the dependent variableindicating the worker’s gender, her highest degree of education, her category of potential
experience and whether she is in a managerial position. By adding firm fixed effects to the14

This result can also be interpreted as another piece of evidence for the foreign wage premium.

16

regression, we can check how these worker characteristics changed when the company was takenover by a foreign owner. We find only small foreign effects, except for experience, where wefind that the foreign workforce gets younger. When we do the same for the new hire variable, wefind that younger workers are more likely to be hired by foreigners.
Another possible cause of wage increases is the increased productivity of the firm. To test
this, we run a seemingly unrelated regression model at the firm level, where the dependent
variables are labor productivity and average compensation. By comparing the magnitudes andtesting the difference of the two estimated coefficients of the foreign dummy, we can draw
conclusions about the similarity of the productivity and wage effects. Table 12 contains theresults, which show rather similar effects of 0.12 and 0.10 on labor productivity and wages,
respectively. The formal test rejects equality, but that is not unexpected with such a large sample.
The correlation of the residuals from the two equations is quite high (0.47), although again theformal test of equality (Breusch-Pagan) rejects. Specification (2) shows that the productivity
effect of acquisitions exceeds the wage effect, while the reverse is true for the foreign effectassociated with divestments: after divestment, the new domestic owners tend to reduce wages
but maintain productivity (or even increase it, according to the point estimate).
An extended version of this approach examines the foreign impact on the skill differential.
In the spirit of Hellerstein, Neumark, and Troske (1999), we include capital, materials, andskilled and unskilled labor (where skill is defined as university-educated) in both the productivity
and wage equations and we interact the foreign variable with the two types of labor. The results
are shown in Table 13. The main foreign effects are larger than in Table 12: 0.17 in the
production function and 0.13 in the wage equation. The coefficients on high and low skill groups
are not very similar for domestic firms but much more so for foreign. This provides somesuggestive evidence that foreign owners award skilled workers more highly because such workers
are in fact more productive at foreign firms.
One possibility suggested in previous research is that foreign owners provide more on the
job training to their employees than domestic owners, and the increased productivity istransformed into higher wages. [to be added].
Finally, rent sharing may also be of different magnitude among domestic and foreignowned firms. To test this, we add a union dummy to the regressions. The results do not change.

177. Conclusions[To be added]18

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Gregory, Robert G., and Jeff Borland, “Recent Developments in Public Sector Labor Markets.”
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21

Tables and Figures

Figure 1: Evolution of Foreign Ownership and Average Real Earnings
120
100
80
60
40
20
0
1986

1989

1992

1994

1996

1998

2000

2002

2004

Year
Percent foreign firms
Average real wage (1986=100)

Percent foreign employment

Notes: Number of observations for the ownership variables: 29,176. Percent foreign firms = percent offirms majority foreign owned. Percent foreign employment = percentage of employees employed byforeign owned firms. The evolution of average real earnings is presented as estimated year effects from a
regression including firm fixed effects to control for sample changes (dependent variable = log real earnings,
normalized at 100 in 1986).

22

Figure 2: Dynamics of the Foreign Acquisition Effect
Full Sample Results
.6

.4

.2

0
-5-

-4

-3

-2

-1

0

1

OLS

2

3

4

5+

2

3

4

5+

2

3

4

5+

CI

.4

.3

.2

.1

0
-5-

-4

-3

-2

-1

0

1

FE

CI

.3

.2

.1

0

-5-

-4

-3

-2

-1

0
GFE

23

1
CI

Figure 3: Dynamics of the Foreign Acquisition EffectMatched Sample Results
.3

.2

.1

0

-.1
-5-

-4

-3

-2

-1

0

1

Matching with OLS

2

3

4

5+

2

3

4

5+

2

3

4

5+

CI

.3

.2

.1

0

-.1
-5-

-4

-3

-2

-1

0

1

Matching with FE

CI

.3

.2

.1

0

-.1
-5-

-4

-3

-2

-1

0
Matching with GFE

24

1

CI

Table 1: Sample Size by Year
Unweighted

Weighted

Year

Workers
Observed

Firms
Observed

Total
Employment

1986
1989
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005

527.6
355.6
76.6
80.8
98.2
100.4
97.2
96.7
97.7
99.2
116.8
118.3
116.8
115.0
125.5
109.3

3,596
3,956
5,107
5,808
7,568
7,720
7,309
7,318
7,239
7,802
9,984
10,641
7,446
7,155
7,923
6,103

3,580.9
3,530.0
1,912.8
1,732.8
1,697.0
1,635.3
1,625.0
1,633.8
1,673.9
1,671.0
1,820.3
1,835.9
1,856.0
1,951.5
1,961.4
1,983.0

Note: Workers observed = thousands of workers in the sample with information on
earnings, education, experience, and gender. Firms observed = number of firms withinformation on ownership and location, and with at least one worker in the given yearwith information on earnings, education, experience, and gender. Total employment= total employment in thousands, as represented by the sum of weights in the sample(approximating total employment in the corporate sector).

25

Table 2: Number of Observations in Regressions by Ownership type
Number of Firms

Number of Firm-Years
Total

Average

Always Foreign

2,830

11,713

4.14

Always Domestic

25,534

94,349

3.70

Total Before

Total After

Average Before

Average After

Foreign Acquisition

592

1,920

3,384

3.25

5.72

Foreign Divestment

311

501

1,555

1.61

5.00

26

Table 3: Individual Characteristics by
Ownership TypeEarnings
FemaleEducation
Elementary
Vocational
High school
University
Experience
New Hire
ManagerN

Domestic

Foreign

115.9(101.3)177.5(188.7)

38.5

45.0

29.1
32.8
29.4
8.7
22.6
(11.0)

19.4
31.9
33.0
15.7
19.8
(10.9)

10.8
10.2

15.2
8.5

2,019,957

311,649

Notes: Weighted results. Earnings measured in thousands of 2005 HUF,
deflated by CPI. Female, education, new hire and manager measured aspercentages of total workforce. Standard deviations in parentheses.

27

Table 4: Firm Characteristics by Ownership Type
Domestic

Foreign

Tangible Assets

284(7,564.0)

1,152(11,262.0)

Employment

54
(574)

111
(443)

Labor Productivity

20.2(213.8)63.4(274.2)0.12
0.40(0.30)

0.48
0.60(0.34)

8.4
25.7
10.6
28.7
4.2
9.2
13.0

1.9
43.4
2.2
27.0
8.1
8.8
8.5

97,147

15,524

Exporter
Export Share
Industry
Agriculture
Industry
Construction
Trade
FIRE
Business Services
Other Services
N

Notes: Tangible assets and labor productivity measured in millions of2005 HUF. Standard deviations in parentheses. Export share definedas the ratio of export sales to total sales. Industrial distribution
measured as percentages within ownership type. A firm is considered
to be an exporter if its export share is larger than 5% in a given year.

28

Table 5: Tests of Covariate Balance
Normalized Difference
Treated-Controls

Probability of Rejecting
Inequality of Means

0.042
0.058
0.068
0.016
0.003

0.473
0.317
0.247
0.787
0.954

0.036
0.053
0.058
-0.060

0.520
0.362
0.312
0.291

One Year Before Acquisition
Average Earnings
Sales
Employment
Capital
Export ShareTwo Years Before Acquisition
Average Earnings
Sales
Employment
Capital29

Table 6: The Effect of Foreign Ownership on Wages
OLS Estimation

Foreign

(1)

(2)

(3)

(4)

(5)

0.371**

0.371**

0.373**

0.385**

0.354**(0.023)(0.017)(0.017)(0.016)(0.011)-0.218**(0.007)0.118**(0.004)0.378**(0.009)0.951**(0.015)0.025**(0.001)-0.037**(0.001)-0.218**(0.007)0.103**(0.005)0.372**(0.009)0.944**(0.014)0.025**(0.001)-0.036**(0.001)-0.202**(0.007)
0.092**(0.005)0.312**(0.009)0.792**(0.015)0.021**(0.001)-0.031**(0.001)-0.171**(0.004)0.104**(0.004)0.263**(0.006)
0.731**(0.009)0.019**(0.001)-0.027**(0.001)-0.114**(0.007)0.353**-0.093**(0.004)0.399**(0.008)(0.008)

Individual Characteristics
FemaleVocational
High school
University
Experience
Experience2* 100
Job Characteristics
New Hire
ManagerInteractions between gender,
education and experience
Industry effects
2

R

No

No

Yes

Yes

Yes

No

No

No

No

Yes

0.124

0.369

0.372

0.401

0.460

Notes: N = 2,331,606 for specifications (1)-(4) and N = 2,331,605 for specification (5). Dependent variable = ln(real grossearnings). Foreign = 1 if the firm is majority foreign owned in t-1. All equations include year and region effects. Standard
errors (corrected for firm clustering) are shown in parentheses. ** = significant at 0.01; * = significant at 0.05.

30

Table 7: The Effect of Foreign Ownership on Wages
Estimations with Correction for Selection Bias

Firm FE

Foreign
R2

Group Effects

Foreign
R2

Matching and OLS Foreign
R2
Matching and FE

Foreign
R2

Matching and GE

Foreign
R2

Individual
Characteristics
Job Characteristics

(1)

(2)

(3)

(4)

0.172**(0.018)0.066

0.147**(0.016)0.331

0.147**(0.016)0.334

0.153**(0.015)0.401

0.124**(0.018)0.071

0.130**(0.017)
0.157

0.085
(0.045)0.087

0.103**(0.031)0.416

0.104**(0.031)0.420

0.102**(0.032)0.468

0.106**(0.021)
0.083

0.098**(0.018)0.400

0.098**(0.018)0.406

0.096**(0.019)0.481

0.093**(0.018)0.068

0.092**(0.019)
0.181

No

Yes

Yes

Yes

No

No

No

Yes

Notes: See Table 6. The structure of covariates is the same as in Table 6, except for group effects, where
specifications (2) and (3) are missing. For the matched sample, N=245,382 for specifications (1)-(4). Group effects
control for unobserved heterogeneity by grouping workers within the same employer along the lines of gender,
education, experience and county. Within R2 reported for firm and group effects.

31Table 8: The Effect of Foreign Acquisition and Divestment on Wages

OLS

Acquisition
Divestment

Firm FE

R2
Acquisition
Divestment

Group Effects

R2
Acquisition
Divestment

Matching with OLS

R2Acquisition
Divestment

Matching with FE

R2
Acquisition
Divestment

Matching with GE

R2Acquisition
Divestment
R2

Ind. Characteristics
Job Characteristics

(1)

(2)

(3)

(4)

0.360**(0.033)-0.022(0.043)0.102
0.172**(0.021)
0.025(0.028)0.066
0.113**(0.020)0.055*(0.028)0.071
0.096(0.049)-0.050(0.057)0.087
0.103**(0.024)0.010(0.025)0.083
0.084**(0.020)0.035(0.024)
0.069
No
No

0.337**(0.020)-0.013(0.028)0.345
0.146**(0.018)0.024(0.028)0.331

0.337**(0.020)-0.013(0.028)0.347
0.147**(0.018)0.023(0.028)0.334

0.113**(0.034)-0.041(0.035)0.417
0.098**(0.020)-0.001(0.025)0.401

0.113**(0.034)-0.042(0.035)0.421
0.099**(0.020)-0.000(0.025)
0.406

Yes
No

Yes
No

0.343**(0.021)-0.012(0.028)0.374
0.154**(0.017)0.019(0.029)0.401
0.120**(0.019)0.052(0.029)0.157
0.109**(0.035)-0.033(0.035)0.468
0.095**(0.021)
0.004(0.025)0.481
0.081**(0.022)0.038(0.025)
0.181
Yes
Yes

Notes: See Table 6. Foreign acquisition: the firm was either majority state or majority domestic private in t-2 andmajority foreign in t-1. Divestment: the firm was majority foreign in t-2 and majority domestic private in t-1.

32

Table 9: Foreign Wage Effect for Incumbent Workers

Firm-Worker Effects

Acquisition
Divestment

Matching with
Firm-Worker Effects

R2
Acquisition
Divestment
R2

Individual Characteristics
Job Characteristics

33

(1)

(4)

0.049**(0.017)0.017(0.024)0.072
0.051*(0.024)0.020(0.044)
0.088
No
No

0.049**(0.017)0.017(0.025)0.072
0.051*(0.024)0.020(0.044)
0.089
Yes
Yes

Table 10: Effects of Foreign Acquisition on the Wage Structure

FemaleVocational
High school
University
Experience
Experience2 * 100
New Hire
Manager

Fixed Effects

Matching (FE)Group Effects

Matching (GFE)

0.009(0.009)0.014(0.011)0.039**(0.012)0.166**(0.026)-0.001(0.001)-0.001(0.002)-0.009(0.008)0.079**(0.020)0.008(0.014)0.010(0.016)
0.063**(0.015)0.107**(0.025)-0.006**(0.002)0.010**(0.003)0.030*(0.013)-0.001(0.031)

0.003(0.018)-0.016(0.014)0.014(0.016)
0.095**(0.025)-0.001(0.002)-0.003(0.004)0.014(0.011)0.088*(0.037)-0.005(0.016)0.005(0.016)
0.054**(0.017)0.113**(0.025)-0.001(0.002)-0.002(0.004)0.036*(0.014)-0.000(0.037)

Note: The table shows coefficients and standard errors from adding interactions of the foreignacquisition dummy with the individual characteristics shown. The other variables in the
regressions correspond to those in specification (4) in Table 6.

34

Table 11: Wage Underreporting and Ownership
GFE

Matching with
GFE

0.098**(0.034)0.021(0.033)-0.057(0.048)0.137**(0.051)

0.066*(0.026)
0.057*(0.027)0.048(0.038)0.035(0.035)-0.032**(0.011)-0.023(0.015)Size Interactions
Foreign
Foreign * Above 50
Interactions with Cheating Industry
Foreign
Foreign * Non-Cheating Industry
Proportion of Workers at Minimum Wage
Foreign35

Table 12: Productivity and Wage Effects—Joint SUR Estimation I(1)
Average Compensation

Foreign

0.096**(0.004)

Acquisition

0.099**(0.005)0.031**(0.006)

Divestment
Labor Productivity

Foreign

0.118**(0.007)

Acquisition
Divestment
P ( βcomp = βlp)
Corr ( ucomp, ulp)
Breusch-Pagan test

0.000
0.472
0.000

36

(2)

0.166**(0.009)-0.015(0.010)
0.000
0.472
0.000

Table 13: Productivity and Wage Effects—Joint SUR Estimation II
OutputForeign
Low Skill
High Skill
Foreign * Low Skill
Foreign * High Skill

Wage

Foreign
Low Skill
High Skill
Foreign * Low Skill
Foreign * High Skill

0.166**(0.038)-0.139**(0.028)0.022**(0.005)-0.010(0.044)0.040**(0.014)0.127**(0.031)0.024(0.023)0.024**(0.004)-0.062(0.036)0.028**(0.011)

Corr ( uout, uwbill)
Chi2 (Breusch-Pagan) Test of independence

0.386
3836.672(0.000)2
Chi (Output ratio=Wage ratio) in domestic firms
1.74(0.189)0.59
Chi2 (Output ratio=Wage ratio) in foreign firms(0.442)2
Chi (Output ratio=Wage ratio) in foreign relative
0.04
to domestic firms(0.843)
Note: The table shows the results of SUR estimation of production
and wage functions controlling for capital, materials, and industry
and year dummies. “Output ratio” and “wage ratio” refer to theratio of coefficients of high versus low skill employment in the
output and wage equations, respectively.

37