1. The effect of human capital on FDI: A meta-regression analysis Artane Rizvanolli, AAB-Riinvest University Ancona, 21 May 2010

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

3. 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?

4. 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.

5. 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?

6. 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 (?)

7. 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

8. 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 variable Moderator variableDescription ConstantProvides an estimate of publication bias (bias across the whole range of results in the literature) 1/SEpccSE 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 FDIFLOWFlow measures for FDI used FDIRELFDI measured relative to population/GDP HCFLOWFlow measures for FDI (enrolment, decision to invest)

9. Model (2) Moderator variableDescription 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) HCCOSTIf model controls for HC cost HCPRODIf model controls for HC productivity PUBYRYear of publication (working paper) MEDIANYRMedian year of the period covered in the study NOEXPVARNumber of explanatory variables in the model (includes FEM dummies) ENDOGENEITYIf attempts were made to address endogeneity

10. 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 Coefficientt-statisticp-value Con0.600.990.33 INVSEEpcc0.041.280.31

11. 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?)

12. Preliminary results (3) VariableCoefficientp-value Constant-0.0140.96 INVSEEpcc-0.0020.96 CROSS0.1270.19 QUALDV0.2280.00 MIXED0.0910.01 DEVELOPED0.1520.05 TRANSITION0.1130.10 CHINA0.1430.00 AVGYRED0.0810.13 TERTIARY-0.0290.41 LABPROD0.0430.27 PRIMARY-0.0270.60 DYNAMIC0.0030.89 FDIREL-0.0440.25

13. 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

14. Thank you! Questions & Comments?