1. 0 Productivity Growth and Convergence in Vietnamese Agriculture Wendi Sun Rockland Trust Company March 25 th, 2015

2. Introduction “Given limited resources, productivity growth is the only way to sustain and increase standards of living.” Questions:  Has TFP converged or diverged for different regions in Vietnam?  How should limited resources be allocated among the important drivers of productivity growth to best improve TFP?  How do investments in health affect productivity growth both in the short-run and in the long-run? 1

3. Introduction  If TFP converges to a common level without intervention, there is little need for explicit policies to promote catch up.  Otherwise, if TFP is divergent, then explicit policies will be needed to prevent further lagging of TFP and standard of living.  Tests three productivity convergence hypotheses by using panel data.  Examine the impacts of major drivers of productivity growth in Vietnamese agriculture. 2

4. Methodology – Theoretical Model σ-convergence: Following Sala-i-Martin (1996), we can estimate the basic model as, where G is TFP; is across-regions variance of the logarithm of TFP in period t; are parameters; is a zero-mean random disturbance term. A significantly negative coefficient associated with the time variable t, implies σ- convergence. 3

5. Methodology – Theoretical Model Absolute β-convergence : Following Fung (2005), we can estimate the basic model as, where is TFP growth in region i between the initial and final periods; are TFP in the initial and final periods, respectively, for region i; are parameters; is a random disturbance term. A significantly negative coefficient associated with, implies absolute β- convergence. 4

6. Methodology – Theoretical Model Conditional β-convergence : based on the following dynamic growth model: where is region i’s TFP at time t; X is a vector of hypothesized determinants of TFP; are parameters; is a random disturbance term. By subtracting from both sides of the above equation, we obtain the ECM: A significantly estimate of with a value less than 0 and greater than -2, implies conditional β-convergence. 5

7. Data 6  The index of TFP for each of the regions for the period 1990-2006 were computed by Bao Dinh Ho (2012);  The comprehensive inventory of agriculture output and input quantities was provided by GSO;  The ratio of output to an index of land, capital, labor, and materials inputs was given by GSO and VHLSS;  Average farm size, farm asset data, farm numbers were provided by MARD and GSO;  Human capital index was constructed by gender, age, education, and employment types.

8. Result 1: Data summary for average TFP level and TFP growth in 1990-2006 7  Red River Delta starts at 1.792.  Central Highland, South East, and Mekong River Delta started at much lower levels.  North Midlands and Central Coast had not only lower levels of agriculture TFP but also negative growth rates. TFP level 1990TFP growth (%) Red River Delta1.7921.38 N. Midlands1.453-3.05 C. Coast1.531-2.1 C. Highlands1.6462.3 S. East1.4153.8 Mekong River Delta1.6953.37 Country1.5880.3

9. Result 2: Test for the TFP σ-convergence 8 σ-convergence:  The hypothesis (that the dispersion of TFP across states diminishes over time) is rejected.  The agricultural sector in Vietnam is not σ-convergence.  Explanation: σ-convergence is sensitive to temporary shocks. Dependent Variable: Variance of the ln of agricultural TFP Independent Variable:Estimated CoefficientsStandard Error time t0.0253***0.0013 0.000 Intercept0.0398***0.0133 0.009 Significance level: *:10%, **:5%, ***: 1%.

10. Result 3-1: Test for the TFP β-convergence 9 Absolute β-convergence:  The estimated result shows no evidence for the absolute β-convergence. Conditional β-convergence:  Indicates evidence for agriculture TFP convergence in Vietnam.  The intercept of five regions are statistically significant.

11. Result 3-2: Test for the TFP β-convergence 10 Absolute β-convergence:  Use different time lags between the initial and final time periods to reduce effects of random noise.  The estimated coefficients are positive for all four cases.  Again the estimated results show no evidence for the absolute β-convergence hypothesis in Vietnamese agricultural sectors in 1990-2006. Estimated CoefficientsStandard Error s=150.0549***0.0092 0.000 s=100.3251***0.0841 0.000 s=50.24920.1911 0.198 s=30.07290.2140 0.735 Significance level: *:10%, **:5%, ***: 1%.

12. Result 4: Levin-Lin-Chu test for panel time unit root test 11  The null hypothesis is rejected at 1% significance level.  The alternative hypothesis is accepted.  The catch up term is statistically significant.  TFP convergence occurred among provinces in Vietnamese agriculture.

13. Result 5: Levin-Lin-Chu test at regional levels 12  The null hypothesis is rejected for all six regions.  The alternative hypothesis is accepted.  Long-run TFP convergence occurred in all regions of Vietnam.

14. Conclusion 13 Cross-sectional tests:  Evidence against σ-convergence  No evidence for absolute β-convergence  Stronger evidence for conditional β-convergence  Provinces within a given region are converging at an average annual speed of 3.8%