1. Do Countries Catch-up with the Technological Frontier? Antonio Álvarez University of Oviedo Alejandro Fernández CEMFI

2. Technological Catch-up Technological Catch-up: backward countries move towards the technological frontier This is due to a process of technological diffusion which depends on the ability to assimilate and apply new knowledge Trade Openness Foreign Direct Investment (FDI) Human capital

3. Objective of the paper Two main questions  Is the technological gap between rich and poor countries closing?  What factors contribute to catch-up? In particular, study the role of institutional variables (social infrastructure)

4. Empirical Models of Technological Catch-up

5. Stochastic Frontiers and Catch-up Our approach is based on the Stochastic Frontier Model  U (inefficiency) is the distance to the frontier It is specified as a non-negative random term It is assumed to follow a half-normal distribution  The change in U can be interpreted as a measure of catching-up with the technological frontier

6. Explaining Catch-up The general form of a model that incorporates the factors that affect catching up is: Three general alternatives for U it (Z it ):  Allow the exogenous variables Z to affect the mean, the variance or both the mean and the variance of the pretruncated distribution of U

7. Empirical models Heterogeneity in the mean  Battese and Coelli (1995) Heterogeneity in the variance  Caudill, Ford and Gropper (1995) Heterogeneity in the mean and the variance  Alvarez, Amsler, Orea and Schmidt (2006)

8. Empirical Application

9. Data Balanced panel data set 78 countries  Excluded: Eastern European countries, very small countries 26 years (1975-2000) Sources:  Penn World Table 6.2  Other

10. Variables (I) Dependent variable:  Gross Domestic Product Inputs:  K: Stock of private capital  L: Labor Control variables:  Z: Percentage of Rural Population  Country-group dummies

11. Variables (II)  Leading Country: USA.  Industrialized Countries: Canada, Australia, New Zealand, Japan, Israel.  Northern Europe: Denmark, Norway, Sweden, Finland, UK, Ireland, Germany, Belgium, Netherlands, Austria, Switzerland, France, Italy.  Southern Europe: Portugal, Spain, Greece.  Latin America: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Rep., Ecuador, El Salvador, Guatemala, Honduras, Nicaragua, Panama, Paraguay, Peru, Uruguay.  Asia: China, Hong Kong, India, Indonesia, Jordan, Korea, Malaysia, Nepal, Pakistan, Papua New Guinea, Philippines, Sri Lanka, Syria, Taiwan, Thailand, Turkey.  Northern Africa: Benin, Central African Rep., Congo, Ivory Coast, Ghana, Mali, Niger, Senegal, Sierra Leone, Togo, Tunisia.  Southern Africa: Cameroon, Congo, Kenya, Malawi, Mozambique, Rwanda, South Africa, Tanzania, Uganda, Zambia, Zimbabwe.  Oil Exporting: Algeria, Mexico, Venezuela, Iran.

12. Variables (III) Efficiency determinants (Z it )  Economic variables Human Capital (H) Average years of schooling of the population 15+ Source: Barro and Lee (2000) Trade Openness Exports plus imports over GDP Source: PWT 6.2

13. Variables (IV) Efficiency determinants  Institutional variables Political Rights and Civil Liberties Source: Freedom House Dummy of Conflict from Uppsala Source:Uppsala Conflict Data Program  Geographic variables from CID: Latitude Distance to equator Source:Center for International Development Dummy of access to sea coast Source: Center for International Development

14. Empirical Specification Translog production frontier Neutral technical change Estimation by Maximum Likelihood  LIMDEP 9

15. Estimation – Production frontier Battese and CoelliCaudill et al.Alvarez et al. VariablePar. Coef. Constant 00 18.48 18.4718.49 lnL 11 0.5750.574 lnK 22 0.3810.394 0.388 lnL * lnL  11 -0.096-0.103 -0.099 lnK * lnK  22 -0.042-0.056 -0.045 lnL* lnK  12 0.0660.076 0.066 Trend tt 0.004 Observations2028 Log-likelihood-16.92-52.1122.67

16. Estimation – Control variables Battese and CoelliCaudill et al.Alvarez et al. Variable Coef. Rural Population-0.063 -0.050-0.063 USA0.5180.517 0.570 Industrial Count.0.2470.238 0.275 Northern Europe0.2340.230 0.248 Southern Europe0.1900.177 0.183 Latin America0.1430.137 0.147 Asia0.1410.088 0.132 Southern Africa0.1150.3610.054 Oil Countries0.1780.2450.161

17. Estimation – Inefficiency term Battese and CoelliCaudill et al.Alvarez et al. Variable Coef. Coef. Mean Constant 2.3402.2442.44 Human Capital -0.167-0.198-0.999 Trade Openness 0.003-0.0040.002 Political Rights -0.126-.5880.295 Civil Liberties -1.213-1.111-0.558 Conflict -0.256-0.5110.338 Latitude -0.031-0.033-0.169 Coast-0.570-0.640-0.322 σ u / σ v 4.794.164.00 σuσu 0.540.530.44 σvσv 0.11

18. Results On average there has been no catch-up effect  Only 40 countries got closer to the frontier Best performers:  China, Malawi, Zimbabwe, South Africa, Paraguay Worst performers  Turkey, Nepal, Ecuador, Philippines

19. Conclusions Modelling  The frontier is robust to the specification of the inefficiency term Results  Human Capital and Institutional variables play a significant role in the process of catching-up