Fdi and wages evidence from leed in hungary
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GÁBOR ANTAL Central European University Institute of Economics - HAS JOHN S. EARLE Central European University W.E. Upjohn Institute ÁLMOS TELEGDY Central European University Institute of Economics - HAS EACES Workshop April 8, 2010 CEU, Budapest September 24, 2009.

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Fdi and wages evidence from leed in hungary

GÁBOR ANTAL

Central European University

Institute of Economics - HAS

JOHN S. EARLE

Central European University

W.E. Upjohn Institute

ÁLMOS TELEGDY

Central European University

Institute of Economics - HAS

EACES Workshop

April 8, 2010

CEU, Budapest

September 24, 2009

FDI and Wages:Evidence from LEED in Hungary


Motivation employer w age e ffects
Motivation: Employer Wage Effects

Employer effects on wages (Abowd et al., 1999; Haltiwanger et al. 2007)

Questions:

What firm characteristics associated with high/low wage?

Neutral or biased across types of workers?

What explains?

selection

measurement

unmeasured heterogeneity

wage policy

productivity/rents


Motivation fdi
Motivation: FDI

  • Ownership: distinguished characteristic of employer (residual rights)

  • Policy ambivalence towards FDI

    + Source of finance, technologies, markets and new jobs

    - Prohibited in strategic sectors, regulatory burdens

  • Major issue in shaping policies towards FDI: Worker outcomes in foreign-owned enterprises


Why i s hungary d ifferent
Why Is Hungary Different?

  • During the 90’s liberalization of factormarkets, large FDI inflow

    • Supportive policy, taxabatements/subsidiesforforeignfirms

  • Foreign owners likely to be very different from domestic owners

    • Capacity for improvement (technology, know-how, knowledge of market economy, access to financing)

    • Gaps in the industrial structure

    • Low wage country


Contribution
Contribution

  • LEED for Hungary

  • Many ownership switches: 905

    • 594 acquisitions

    • 311 divestments

  • Long time series (20 years: 1986 - 2005)

    • Mean of pre-treatment years: 3.2

    • Mean of post-treatment years: 5.7

  • Effects on wage structure

  • Examine explanations for foreign wage premium


Data i
Data I

  • Employee information: Hungarian Wage Survey

    • Includes all firms with >20 employees plus random sample of small (11-20 employees in 1996-99, 5-20 in 2000-05)

    • Workers sampled randomly based on birth date (5th and 15th for production workers, also 25th for nonproduction)

    • All workers in small firms (<20 employees in 1996-2001, <50 since 2002)

  • Employer information: Hungarian Tax Authority Data

    • All legal entities using double-entry bookkeeping

    • Total employment = all employees in Hungary


Data ii
Data II

  • Data weighted to represent corporate sector

    • Worker weights within firm

    • Firm weights

  • Sample size

    • 2,331,566 worker-years

    • 29,169 enterprises

  • Firms are linked over time

  • Majority of workers linked within firm


Sample s election

Sample of firms

Only the corporate sector

Only industries where any ownership change involving foreign investors

Only firms with switches ≤ 2 (14 firms dropped)

Worker sample

Full time workers

Age 15 -74

Sample Selection


Definition of foreign o wnership and e arnings
Definition of Foreign Ownership andEarnings

Foreign ownership

> 50 percent of the firm’s shares owned by foreign owners (same results with >10 percent)

Distinguishing acquisitions (594), divestments (311) and greenfield investments (2,140)

Earnings

Monthly base salary

Overtime

Regular bonuses and premia, commissions, allowances

Extraordinary bonuses based on previous year’s records


Evolution of o wnership and earnings
Evolution of Ownership and Earnings


Composition of w orkforce by o wnership
Composition of Workforce by Ownership


Firm c haracteristics by o wnership i
Firm Characteristics by Ownership I


Estimation
Estimation

lnwijt =  + Xitβ + δFOREIGNjt-1+ ΣγjREGIONj + ΣλtYEARt + uijt

i = workers

j = firms

t = time


Specifications i
Specifications I

Controls (Xit):

No additional controls

Gender, education category, potential experience

+ interactions

+ manager, new hire dummies

Dynamics: Ownership interacted with event time


Specifications ii
Specifications II

  • Error term (uijt):

    • OLS

    • Firm fixed effects (FE) ~29,000

    • FE combined with narrowly defined worker groups (GFE) ~400,000

  • NN PS matching (e, lp, w, expshare 1 and 2 years before acqusition; quadratic polynom.)

    • 325 acqd, 279 control firms; 330,510 obs.

    • PS: normalize around acquisition year, weight controls

    • Exact matching on 2-digit industry and year

    • OLS, FE, GFE

    • Good covariate balance





What m ight e xplain h igher w ages with fdi
What Might Explain Higher Wages with FDI?

  • Observed foreign wage difference could be related to:

    • Selection

      • At firm and worker level before treatment

      • Change in workforce composition after treatment (observed and unobserved)

      • Attrition correlated with ownership and wages

    • Measurement error, differences in job attributes

      • Working conditions (hours, job security)

      • Undeclared wages and employment

      • Structure of compensation (fringe benefits, incentive pay...)


What m ight e xplain h igher w ages with fdi1
What Might Explain Higher Wages with FDI?

  • Observed foreign wage difference could be related to

    • Productivity and rents

      • Restructuring

      • Technological advantage, technology-skill complementarity

      • On-the-job training

      • Efficiency wages

      • Export status

      • Rent sharing, unions


Productivity and wages estimation
Productivity and Wages: Estimation

  • SUR modell: 2 equations, demeaned at the firm level

    lnoutputj = 0 + 1lnKj +2lnMj+3lnempj +

    δ1lnempjFOjt-1+ ΣλktINDkYEARt+ ujt

    lnwbillj = β0 + β1lnempj +δ2lnempjFOjt-1+ΣλktINDkYEARt+ vjt

  • Hypothesis: MPFO/MPDO = WFO/WDO

    that is: (3 + δ1)/ 3 = (β1 + δ2)/ β1


Productivity and wages results and tests
Productivity and Wages: Results and Tests

MPFO/MPDO = WFO/WDO

General foreign effect: 8.9% > 6.5%

Acquisition effect: 12.4% > 7.9%


Further productivity evidence catch up
Further Productivity Evidence: “Catch-Up”

Why is the wage effect of FDI so large in Hungary?

Distance from the frontier and the transition

Divide period into early (<1999) and late (>1998)

Larger effects earlier

Divide FDI acquisition targets into state and private

Larger effects for state-owned targets

=> Part of large effect in Hungary may be catch-up. FDI to developed countries may have little effect.


Composition of workforce i
Composition of Workforce I

  • Foreign effect for incumbent workers


Composition of workforce ii
Composition of Workforce II

  • Stock of university graduates and young workers increases after acquisition

    LPMs with individual characteristics on LHS, acquisition dummy on RHS; FE estimation

  • More hiring after acquisition (mostly one year after), in favor of young high-skilled

    LPMs with new hire dummy on LHS, acquisition dummy interacted with individual characteristics on RHS; FE estimation

  • Separation rates: to be done


Composition of firms
Composition of Firms

  • Acquisitions weakly correlated with wages and firm exit

    Probit with firm-level exit on LHS, acquisition dummy interacted with log wagebill on RHS


Foreign a cquisitions and w age s tructure
Foreign Acquisitions and Wage Structure


Measurement i
Measurement I

  • Hypothesis: Higher working hours at acquired firms

  • Monthly paid hours for 1999-2005

  • Tests:

    • Monthly vs hourly earnings

      • Same effect

    • Hours as a dependent variable

      • No foreign effect

    • Hours as a covariate

      • Leaves foreign effect unchanged

  • Caveat: Overtime probably mismeasured for non-production workers, and hard to test for production separately, since no wage effect


Measurement ii
Measurement II

  • Hypothesis: Domestic firms are more likely to underreport wages

    • Aux. hypotheses: Probability of cheating is lower in big enterprises and in industries with a low cheating index (Elek and Szabó 2008)

  • Tests:

    • LPM for 1[w < minw + 3%]

      • Negative foreign effect (not high enough to explain total wage difference)

    • Foreign interacted with size

      • Zero/positive effect (reject hypothesis)

    • Foreign interacted with industry cheating index

      • Zero/negative correlation (reject hypothesis)


Conclusions
Conclusions

OLS: foreign wage premium is 36 percent

FE, GFE, matching premium is 9–17 percent

Divestment effect is 40-50% of acquisition effect

All worker types benefit; high educated the most

5% premium for incumbent workers, composition change in favor of young high-skilled

Results not driven by measurement error

Productivity best candidate for explaining the gap


Previous s tudies i
Previous Studies I

  • Firm-level data:

    Positive, sometimes large foreign wage premium

  • Controls for employment composition or LEED:

    Smaller effects, sometimes insignificant

  • The premium varies by skill group

  • Treatment of selection bias is important


Previous s tudies ii
Previous Studies II

Many datasets are not real LEED, but firm-level data with information on composition

Short time series (usually ≤ 5 years)

Matching only on immediate pre-acquisition year

Few ownership changes with enough pre- and post treatment observations

Most studies from developed countries exposed to FDI for a long time

Wage structure: mostly skilled-unskilled


Firm c haracteristics by o wnership ii
Firm Characteristics by Ownership II



Foreign w age p remium ols
Foreign Wage Premium: OLS


Foreign w age p remium a lternative s pecifications
Foreign Wage Premium: Alternative Specifications




Dynamics matching and ols
Dynamics: Matching and OLS


Dynamics matching and fe
Dynamics: Matching and FE




Productivity and wages i
Productivity and Wages I

  • If productivity increases, wages may rise as well, and differentials may come closer to relative MPs

  • SUR models: productivity and wage equations, error terms allowed to be correlated

  • SUR model I: labor productivity and average wages

    • RHS: ACQ, ind-year interactions

  • SUR model II: TFP and wagebill

    • RHS TFP: lnK, lnM, lnL, ACQ*lnL, ind-year interactions

    • H=university-educated; L=less than university



Relative p roductivity and w ages
RelativeProductivity and Wages


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