Hazards of Expropriation: Tenure Insecurity and Investment in Rural China
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Citations
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Land policies for growth and poverty reduction
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References
Constitutions and Commitment: The Evolution of Institutions Governing Public Choice in Seventeenth-Century England
A method for minimizing the impact of distributional assumptions in econometric models for duration data
Property Rights and Investment Incentives: Theory and Evidence from Ghana
Least absolute deviations estimation for the censored regression model
Why don't poor countries catch up? a cross-national test of an institutional explanation
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Frequently Asked Questions (10)
Q2. What is the way to get an upper bound on the coefficient bias in this scenario?
The authors can get an upper bound on the coefficient bias in this scenario by assuming (1) current and lagged organic fertilizer use are the only variables in the production function regression, (2) they have equal effects on yields, (3) they have equal variances, and (4) they are perfectly negativelyA more fundamental assumption underlying their efficiency calculations is that all the benefit from reduced expropriation risk comes through an increase in organic fertilizer use.
Q3. What is the reason for the inclusion of crop controls in the fertilizer regressions?
Since the marginal product of a given fertilizer may differ across crops, it is tempting to include crop controls in the fertilizer regressions.
Q4. What is the likely explanation for the low incidence of crop rotation in their sample?
A likely explanation for this result, and for the low incidence of crop rotation in their sample, is that farmers find it cheaper to use organic fertilizer for rebuilding soil structure than to rotate their crops.
Q5. What is the hypothesis of no village random effects rejected across specifications?
The hypothesis of no village random effects is strongly rejected across specifications and, as expected, allowing for village random effects dramatically raises standard errors in some cases.
Q6. how many plots did the farmer know when the contract would expire?
For only 32 percent of the plots (303 of the 961 collectively-held plots--there are 16 missing values), did the farmer reply that he knew when the contract would (had) expire(d).
Q7. What is the argument that farmers should not invest more on these more secure plots?
If so, farmers should not only invest more on these more secure plots, ceteris paribus, they should also be less responsive to current expropriation risk.
Q8. What is the reason for the low estimate of the marginal product of organic fertilizer?
Regarding the latter, one concern might be that, because of attenuation bias due to measurement error in organic fertilizer use, their estimate of the marginal product of organic fertilizer from Table 6 is too low.
Q9. how many plots are left with no information about their contract expiration date?
The authors are left then with 164 plots (54 percent of the 303 plots) for which farmers claim to know that their contract expires some time after 1995.
Q10. How is the correlation coefficient between the estimates and the frequency of reallocations?
the correlation coefficient between the estimates, vζ̂ , and the frequency of village-wide reallocations is 0.37, which is significant at the five percent level.