The effect of human capital on FDI: A meta-regression analysis

Artane Rizvanolli, AAB-Riinvest University

Ancona, 21 May 2010

Contents Introduction: FDI and growth Rationale for MRA Sample MRA model Empirical results Conclusion and further research

Introduction: FDI and growth

FDI conventionally considered beneficial Technology and know-how transfer (?) Spillovers (?) Hence, overall productivity and growth (?)

Especially important for transition economies Need for restructuring and modernisation (at firm

and economy level) Limited domestic resources

However, are the benefits automatic?

The rationale for meta-regression analysis (MRA)

• Theory: human capital (HC) attracts FDI – Enhancement of productivity, technology adoption

and adaption• No consensus in the empirical literature– Negative, positive and insignificant results

found• Potential reasons for the diversity of results? – Wide range of specifications, HC measures,

countries– Lack of “universal” relationship between HC

and FDI: differences in motivation for FDI, sector of economic activity, etc.

The rationale for meta-regression analysis (2)

MRA as a means of Quantifying a survey of empirical literature Analysing the sensitivity of results to different

study characteristics (!) Identifying and quantifying the “genuine” effect of

HC, if present Identifying publication bias (?) Informing the specification of further research on

the HC-FDI relationship: which measures?

Sample Around 30 regression analyses identified

EconLit, SSRN, Google Scholar References in papers

Some excluded Measures not convincing/comparable No results reported Only interaction/squared terms

Preferred regressions only (?)

Sample (2) 28 studies with a total of 231 regressions

t-stats range -7.8 - 7.7, with a mean of 0.93

Structure: Developing, transition, mixed, China, developed Mostly secondary and tertiary education measures Majority(static and dynamic) panels

Model Linear regression: weighted to give

each study the same weight, clustered robust (cluster: study), dependent variables divided by SEpcc

Dependent variable: t-statistic of HC variableModerator variable

Description

Constant Provides an estimate of publication bias (bias across the whole range of results in the literature)

1/SEpcc SE of the PCC (standardised measure of association) – a precision measure; provides an estimate of the “true” effect in the literature in terms of the PCC

FDIFLOW Flow measures for FDI used

FDIREL FDI measured relative to population/GDP

HCFLOW Flow measures for FDI (enrolment, decision to invest)

Model (2)

Moderator variable

Description

LITERACY , PRIMARY, TERTIARY, SECTER, AVGYRED

HC measure: Literacy/illiteracy rate, primary education, tertiary education, secondary and tertiary combined, average yrs of education (RC: secondary education)

PANEL, DYNAMIC P., QUALITYDV

Static panel, dynamic panel, quality dependent variable model (RC: cross-section)

DEVELOPED, TRANSITION, MIXED, CHINA

Sample according to group of countries (RC: Developing countries)

HCCOST If model controls for HC cost

HCPROD If model controls for HC productivity

PUBYR Year of publication (working paper)

MEDIANYR Median year of the period covered in the study

NOEXPVAR Number of explanatory variables in the model (includes FEM dummies)

ENDOGENEITY If attempts were made to address endogeneity

Preliminary results

• Bi-variate MRA– no publication bias OR “genuine effect”

• Multi-variate MRA– Same result as above– Full model mis-specified

– Ramsey RESET test : F(3, 205) = 94.52 , Prob > F = 0.0000

– Suffers from multicollinearity

Dependent variable

Coefficient

t-statistic p-value

Con 0.60 0.99 0.33

INVSEEpcc 0.04 1.28 0.31

Preliminary results (2) Testing down: standard procedure in MRA

Improves functional form Significantly reduces multicollinearity

Some variables highly correlated with INVSEPCC (PERIOD, MEDIANYR, LABCOST, TNOEXPVAR?, EDNOGENITY?, HCSTOCK?)

Preliminary results (3)Variable Coefficient p-value

Constant -0.014 0.96

INVSEEpcc -0.002 0.96

CROSS 0.127 0.19

QUALDV 0.228 0.00

MIXED 0.091 0.01

DEVELOPED 0.152 0.05

TRANSITION 0.113 0.10

CHINA 0.143 0.00

AVGYRED 0.081 0.13

TERTIARY -0.029 0.41

LABPROD 0.043 0.27

PRIMARY -0.027 0.60

DYNAMIC 0.003 0.89

FDIREL -0.044 0.25

Conclusion and further research

Heterogeneity in HC-FDI literature can be explained to a very limited extent (!)

Appears to be no genuine effect in the literature: Models not specified correctly?

Further research: specify model in accordance with theory human capital variable: level and stock/flow

Thank you!

Questions & Comments?