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

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Presentation on theme: "0 Productivity Growth and Convergence in Vietnamese Agriculture Wendi Sun Rockland Trust Company March 25 th, 2015."— Presentation transcript:

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 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  Red River Delta starts at  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 Delta N. Midlands C. Coast C. Highlands S. East Mekong River Delta Country

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*** Intercept0.0398*** 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 Estimated CoefficientsStandard Error s= *** s= *** s= s= 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%


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