1. Human Capital, Industrial Growth and Resource Curse by Elena Suslova (NES’06) and Natalya Volchkova (CEFIR at NES)

2. Motivation: cracking the “black box” of “resource curse” High oil prices → “resource curse” Is there a charm against the curse? Literature – provide us with a number of speculations on the origin and propagation of the curse. Little of empirical backup. Test various channels of “resource curse” transmission Policy application – case of Russia

3. Literature: are natural resources a blessing or a curse?  Three channels of negative effects transmission  Dutch Disease  Sachs&Warner’95, 97, 99: cross country studies revealed negative relation between natural resource abundance and growth rates  Spatafora&Warner’95, Hutchison’94: time series analysis does not confirm diagnosis  Political Economy  Auty’01, Paldam&Svendse’00: huge rents provoke rent- seeking, corruption, postponement of reforms, competitive industrialization  Human Capital Development  Gylfason’01, Leamer et al.’99: resource intense sectors absorb national savings while creating only a few eminently qualified jobs, thus preventing the development of innovative industries

4. Research question: Is there a human capital underdevelopment channel within the “black box” of “resource curse”?

5. How to crack the box? Cross-country growth studies: –omitted variables problem –endogeneity issues –failure to distinguished among possible mechanisms of transmission Difference in differences - cross-country and cross-industry growth - study a la Rajan&Zingales’98: is there a growth disadvantage of industries that are more human capital intensive in the resource abundant economies? –fixed country and industry effects –mostly exogenous explanatory variables –model the transmission mechanism

6. Model: Leamer et al’99 Assumptions –Growth mechanism: capital accumulation– both physical and human –Open economy: production pattern is determined by comparative advantage a la Hecksher-Ohlin model Compare the implied dynamics of human capital accumulation between two countries: rich in natural resources vs. poor in natural resources

7. Physical capital accumulation Resource abundant economy Resource poor economy Higher (more skilled) human capital is required Capital substitution makes labor cheaper Capital accumulation makes labor more expensive

8. Model prediction: Weaker private incentives to invest in development of higher human capital in resource rich economies compared to resource poor economies Resource rich economy needs to overcome coordination problem with respect to development of higher human capital in order to switch to next product mix Warning: the story is not about the lower volume of human capital but about the deficit of marginal skilled human capital in resource rich economies

9. Hypotheses: the higher is demand of industrial sector for high skilled labor the slowly it grows relative to industries less dependent on high skilled labor in resource rich economies compared to resource poor economies we could not expect that there is the differentiated effect of resource abundance on industries based on their demand for average or lower skilled labor.

10. Data: industrial sectors’ demand for human capital Abowd et al. ( 2003) estimate the human capital index for each of 68 millions of U.S. workers (which covers 45% of U.S. labor force) that were surveyed in 1992 within Longitudinal Employer - Household Dynamics (LEHD Program’s individual, employer, and employment history databases). Then each individual human capital index was placed into the industry where the firm she employed in belongs to. This allows constructing the comparable distribution of the level of human capital within U.S. industries.

11. Distribution of human capital within U.S. industries

12. Measures of various levels of human capital demand

13. Data: other industrial characteristics Average annual real growth rates of manufacturing sector in 1980-1990 –Nominal value added data from UNIDO (United Nation Industrial Development Organization) database for 3-digit ISIC codes (Rev.2) –GDP deflator obtained from WDI (World Development Indicators) database. Share of sector in total manufacturing value added in 1980 from UNIDO database.

14. Data: country level raw hydrocarbon production of the economy as a share of country’s GDP

15. Example Norway: Machinery grew at a 4 percent lower annual real rate than Metallurgy Belgium: Machinery grew at 2 percent higher rate than Metallurgy

16. Estimated equation Growth i,k =Const +Σ k β k *Dummy for country k + + Σ i δ i *Dummy for industry i + +γ*Share of industry i in IVA 1980 + +λ h *(Demand of industry i for HC h * *Resource abund.of country k ) +ε i,k

17. Estimation results Higher level of HC

18. Results If we rank industries based on the demand for very sophisticated labor (top deciles of human capital distribution) then we observe the significant systematical losses in growth rates of industries with higher demand relative to those with lower demand in countries rich in natural resources compared to resource poor countries. The estimated losses become smaller and insignificant as we use ranking of industries based on the demand for less sophisticated labor.

19. Interpretation of results Natural resource abundance which is an exogenous characteristic of the country serves as an impediment for manufacturing sectors that depend on sophisticated human capital. There is no systematic effect of natural resource abundance on the growth of industrial sectors when we differentiate industries based on their average human capital level demands. This implies that one of the links between natural resource abundance and industrial growth might be through an underdevelopment of very skilled human capital.

20. Policy application One of the possible charms against the resource curse: investment in education Leamer at al: “If the model is somehow backed up with hard evidence, the policy advice is very clear: Governments in countries that are in a stage of “old product mix” but close to the stage of “new product mix” should be making major improvements in their educational systems, in particular eliminating the dumbbell educational systems that were economically efficient in old product mix but inappropriate in new one.”

21. Relevant to Russia? Managerial survey: –Tough deficit of middle level managers –Most of top managers are foreigners Business development evaluations: lack of skilled labor always mentioned as an important impediment for business At the same time: –90’s: most of “Institutes” changed the titles to “Universities”. –General negligence to professional schools – technical schools. –May be it is time to revert some “Universities” to “Professional schools”