How does land titling affect agricultural productivity: a differences-in-differences study based on China Household Finance Survey (CHFS) data

doi:10.3969/j.issn.1004-1524.2023.02.23

Acta Agriculturae Zhejiangensis ›› 2023, Vol. 35 ›› Issue (2): 455-467.DOI: 10.3969/j.issn.1004-1524.2023.02.23

• Agricultural Economy and Development • Previous Articles Next Articles

How does land titling affect agricultural productivity: a differences-in-differences study based on China Household Finance Survey (CHFS) data

WANG Pingping()

National School of Development, Peking University, Beijing 100871, China

Received:2020-04-15 Online:2023-02-25 Published:2023-03-14

Abstract

Abstract:

Clearly defined land property rights are important for agricultural productivity growth, and the new round of progressive land titling reforms in China provides a good exogenous natural experiment to explore the relationship between the two. Based on the two rounds of tracking panel data from the China Household Finance Survey (CHFS) in 2013 and 2015, a continuous difference-in-differences (Staggered DID) model was constructed by utilizing the progress differences of land titling between villages to test the effect of land titling on agricultural productivity and the mechanism of action. It was revealed that the land titling significantly increased agricultural productivity of farm households, and the result was significantly robust after undergoing a series of tests including corrections for sample selection bias, dealing with endogeneity problems, and replacing productivity measurement indexes. Further analysis showed that the effect of land titling on agricultural productivity was heterogeneous across farmers with different land sizes, regions and productivity levels, and for farmers with larger land sizes, higher agricultural productivity, or in the east regions, the productivity growth effect caused by titling was significantly greater. The mechanism test showed that the productivity growth effect of land titling was mainly achieved through promoting land transfer among farmers and increasing productivity-oriented investment in agriculture, while the mechanism of promoting agricultural productivity by increasing credit availability was not effective.

Key words: property rights, land titling, agricultural productivity

CLC Number:

S-9
F325

WANG Pingping. How does land titling affect agricultural productivity: a differences-in-differences study based on China Household Finance Survey (CHFS) data[J]. Acta Agriculturae Zhejiangensis, 2023, 35(2): 455-467.

Figures/Tables 8

Table 1 Descriptive statistics of variables

变量符号 Variable symbol	平均值 Mean	标准差 Standard deviation
E	6.930	1.346
T	0.132	0.222
X₁	0.528	0.152
X₂	3.688	0.295
$X 22$	13.687	2.188
X₃	0.700	0.229
X₄	0.713	0.272
X₅	0.084	0.278
X₆	0.140	0.347

Table 1 Descriptive statistics of variables

变量符号 Variable symbol	平均值 Mean	标准差 Standard deviation
E	6.930	1.346
T	0.132	0.222
X₁	0.528	0.152
X₂	3.688	0.295
$X 22$	13.687	2.188
X₃	0.700	0.229
X₄	0.713	0.272
X₅	0.084	0.278
X₆	0.140	0.347

Table 2 Comparison of households without land titling and households with land titling

变量 Variable	2013		2015
变量 Variable	未确权户 Household without land titling	确权户 Household with land titling	未确权户 Household without land titling	确权户 Household with land titling
E	6.898	6.844	6.948	7.063
X₁	0.532	0.525	0.528	0.524
X₂	3.670	3.692^**	3.697	3.724^**
X₃	0.699	0.722^**	0.693	0.714^**
X₄	0.726	0.725	0.699	0.703
X₅	0.036	0.036	0.126	0.141
X₆	0.226	0.218	0.061	0.053

Table 3 Benchmark regression results

指标 Index	模型1 Model 1	模型2 Model 2	模型3 Model 3
T	0.683^*** (0.249)	0.691^*** (0.249)	0.618^*** (0.236)
R²	0.318	0.323	0.692
n	4753	4753	2912

Table 4 Regression results based on propensity score matching (PSM) and instrumental variables

指标 Index	PSM+DID		模型6 Model 6
指标 Index	模型4 Model 4	模型5 Model 5	一阶段 Stage 1	二阶段 Stage 2
T	0.526^** (0.251)	0.526^** (0.252)	—	0.573^** (0.235)
C₁×P	—	—	-4.631^*** (0.206)	—
R²	0.319	0.322	0.010	0.010
一阶段F统计量	—	—	507.592	—
F statistics in stage 1
n	3 683	3 683	4 753	4 753

Table 5 Results of robustness test

指标 Index	模型7 Model 7	模型8 Model 8	模型9 Model 9	模型10 Model 10	模型11 Model 11
T	0.717^*** (0.253)	0.071^*** (0.023)	0.390^** (0.174)	0.390^*** (0.135)	0.679^** (0.265)
R²	0.329	0.106	0.340	0.323	0.328
n	5 359	5 421	5 421	4 753	4 205

Fig.1 Result of placebo test

Table 6 Heterogeneity analysis results

指标 Index	规模水平Scale		地区Region			生产率水平Agricultural productivity
指标 Index	小规模 Small-scale	大规模 Large-scale	东部 Eastern	中部 Central	西部 Western	q25	q50	q75
T	0.717^** (0.286)	1.529^*** (0.526)	1.391^*** (0.435)	0.394 (0.319)	0.350 (0.494)	0.337 (0.256)	0.383 (0.236)	0.618^** (0.310)
R²	0.319	0.486	0.323	0.270	0.374	0.256	0.259	0.265
n	4 053	622	1 359	2 084	1 309	4 753	4 753	4 753

Table 7 Test results of impact mechanism

指标 Index	机制1 Mechanism 1		机制2 Mechanism 2		机制3 Mechanism 3
指标 Index	R	E	I	E	C_r	E
T	0.196^** (0.078)	0.347^*** (0.053)	0.166^*** (0.057)	0.381^*** (0.050)	-0.002 (0.058)	0.691^*** (0.250)
R	—	0.643^*** (0.248)	—	—	—	—
I	—	—	—	0.712^*** (0.248)	—	—
C_r	—	—	—	—	—	-0.008 (0.055)
R²	0.245	0.333	0.311	0.337	0.323	0.323
n	4 745	4 745	4 753	4 753	4 753	4 753

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How does land titling affect agricultural productivity: a differences-in-differences study based on China Household Finance Survey (CHFS) data

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