2024年河南省农村教育投入与消费偏好调查统计数据集

doi:10.19788/j.issn.2096-6369.100069

Abstract

Abstract:

Educational expenditure has increasingly become an important component of rural household consumption structures, reflecting strategic choices in human capital investment and shifts in consumption perceptions. To gain an in-depth understanding of the actual situation of rural residents in terms of educational investment and consumption preferences, our research team conducted a large-scale household survey from June to July 2024 in 15 townships across five counties in Henan Province, resulting in the creation of the 2024 Henan Province Rural Education Investment and Consumption Preference Statistical Dataset. The questionnaire covered multiple dimensions, including household demographics and income, the structure of educational expenditures, online education usage, educational perceptions, and anxiety awareness. A total of 3,169 questionnaires were collected, of which 67 were excluded after checks for missing values and logical consistency, leaving 3,102 valid samples. The survey design was informed by expert interviews and literature reviews, and data collection was carried out through on-site household visits combined with random sampling, with strict quality control measures such as double-entry verification, outlier elimination, and logical consistency checks. Unlike previous studies focusing primarily on macro-level statistics, this dataset reveals at the micro level the educational expenditure behaviors, consumption preference choices, and anxiety characteristics of rural households. It provides solid empirical support for testing the applicability of the “education expenditure crowding-out effect,” examining the role of online education in promoting educational equity, and analyzing consumption differences across income groups. At the same time, the dataset offers practical references for the targeted allocation of educational resources, the assessment of rural household educational burdens, and the promotion of digital education products.

Data summary:

Items	Description
Dataset name	2024 Henan Province Rural Education Investment and Consumption Preference Survey Statistical Dataset
Discipline	Agricultural Economics and Management; Rural Sociology
Research topic	Rural educational expenditure behavior and household consumption preference structure
Data time range	June to July, 2024
Geospatial coverage	Fifteen townships under the jurisdictions of Zhongmu County, Yanjin County, Shangshui County, Lushi County, and Huaibin County, Henan Province, China
Data type and format	.xlsx
Dataset composition	The dataset includes one original primary data file and multiple processed derivative files, covering structured questionnaire data from 3102 rural households, with more than 150 variables.
Data volume	462.29 KB
Key data indicators	Household demographic information, annual income structure, composition and stage distribution of educational expenditures, frequency and expenditure levels of online education usage, education concepts and anxiety scores, consumption preference rankings, policy awareness, etc.
Data availability	CSTR:17058.11.sciencedb.agriculture.00287; https://cstr.cn/17058.11.sciencedb.agriculture.00287 DOI:10.57760/sciencedb.agriculture.00287; https://doi.org/10.57760/sciencedb.agriculture.00287

Key words: rural households, educational expenditure, consumption preference, educational perceptions, household survey, dataset

DAI JiaMin, AILIFEIRE Wufuer, ZHANG Hong. 2024 Henan Province Rural Education Investment and Consumption Preference Survey Statistical Dataset[J].Journal of Agricultural Big Data, 2025, 7(4): 551-560.

Figures/Tables 5

Table 1

Table 2

Table 3

Fig. 1

Fig. 2

References 15

[1]	刘晓楠. 新时代我国农村教育的实践成效与改革进路. 教育理论与实践, 2023, 43(19): 20-25.
	LIU X N. The practical effect and reform approach of Chinese rural education in the new era. Theory and Practice of Education, 2023, 43(19):20-25.
[2]	鄢姣, 许敏波. 中国农村家庭教育支出的变化特征及驱动因素. 北京师范大学学报 (社会科学版), 2023(5): 44-52.
	YAN J, XU M B. The changing characteristics and driving factors of rural household education expenditure in China. Journal of Beijing Normal University (Social Sciences), 2023(5): 44-52.
[3]	任杲, 宋迎昌. 中国居民消费结构变迁研究——基于城镇居民与农村居民的统计数据. 兰州学刊, 2021(3): 33-45.
	REN G, SONG Y Y. Research on the changes of the consumption structure of Chinese residents——based on the statistical data of urban residents and rural residents. Lanzhou Academic Journal, 2021(3): 33-45.
[4]	BECKER G S. Investment in human capital: A theoretical analysis. Journal of Political Economy, 1962, 70(5, Part 2): 9-49. doi: 10.1086/258724
[5]	MCMAHON W W. PSACHAROPOULOS G. Consumption and Other Benefits of Education// Economics of Education[M]. Pergamon Press, 1987: 129-133.
[6]	AGHION P, BOUSTAN L, HOXBY C, et al. The causal impact of education on economic growth: evidence from the US. Brookings Papers on Economic Activity, 2009, 1(1): 1-73.
[7]	龙斧, 梁晓青. 代际消费不平等: 阶层化视角下子女教育支出对家庭消费的挤出效应. 南方人口, 2019, 34(4): 26-36.
	LONG F, LIANG X Q. Intergenerational consumption inequality: education expenditure for children and its crowdingout effect on household consumption from the perspective of social hierarchy. South China Population, 2019, 34(4): 26-36.
[8]	赵颖, 王亚丽. 物价指数变动的城镇居民收入超分配效应. 经济学家, 2013, 12(1): 48-57.
	ZHAO Y, WANG Y L. The over-distribution effect of urban residents income under changes in the price index. Economist, 2013, 12(1): 48-57.
[9]	张学敏, 何酉宁. 受教育程度对居民消费影响研究. 教育与经济, 2006(3): 1-5.
	ZHANG X M, HE Y N. Research on the influence of education degree to inhabitant's consumption. Education & Economy, 2006(3): 1-5.
[10]	闵维方. 教育促进经济增长的作用机制研究. 北京大学教育评论, 2017, 15(3): 123-136.
	MIN W F. Research on the mechanism of how education promotes economic growth. Peking University Education Review, 2017, 15(3): 123-136.
[11]	孔祥利, 周晓峰. 城镇化率区域差异对农村居民消费结构的影响. 西北大学学报 (哲学社会科学版), 2021, 51(3): 54-68.
	KONG X L, ZHOU X F. Effect of the regional differences of urbanization on the consumption structure of rural citizens. Journal of Northwest University (philosophy and social sciences edition), 2021, 51(3): 54-68.
[12]	马红梅, 邓钟毓. 优质均衡视域下农村教育财政投入与人力资本质量——基于“新机制”改革的自然实验证据. 宏观质量研究, 2025, 13(2): 108-118.
	MA H M, DENG Z Y. Financial education investment for rural China and its impact on human capital: evidence from "New Mechanism" reform as a natural experiment. Journal of Macro-quality Research, 2025, 13(2): 108-118.
[13]	王士鹞. 乡村振兴背景下农村教育的问题与出路——以河南省鹤壁市为例. 农业科技与信息, 2023(6): 193-196.
	WANG S Y. Problems and solutions of rural education under the background of rural revitalization——A case study of Hebi City. Henan Province. Agricultural Science-Technology and Information, 2023(6): 193-196.
[14]	SPIELBERGER C D, GONZALEZ-REIGOSA F, MARTINEZ- URRUTIA A, et al. The state-trait anxiety inventory. Revista Interamericana de Psicología / Interamerican Journal of Psychology, 1971, 5:3-4.
[15]	王极盛, 邱炳武, 赫尔实. 中学生焦虑量表的编制及其标准化. 社会心理科学, 1997(3): 4-6.
	WANG J S, QIU B W, HAO E S. Development and standardization of an anxiety scale for middle school students. Science of Social Psychology, 1997(3): 4-6

县（市/区）	乡镇	样本数（份）
中牟县	雁鸣湖镇	183
	韩寺镇	209
	黄店镇	232
卢氏县	官道口镇	300
	杜关镇	235
	汤河乡	180
商水县	平店乡	223
	胡吉镇	171
	袁老乡	218
延津县	榆林乡	286
	胙城乡	264
	魏邱乡	196
淮滨县	台头乡	157
	谷堆乡	55
	马集镇	193
合计	15	3102

问卷模块	核心变量示例	变量类型	预期用途
人口与经济信息	家庭人口数、在读学生数、父母学历、家庭收入	定类、定量	样本特征描述
教育支出结构	教育总支出、支出占比、主要支出项目	定类、定量、多选	教育投入分析
线上教育使用	线上支出、是否购买APP、主要资源类型	定类、多选	数字教育研究
教育观念与焦虑	最关注因素、素质教育态度、焦虑程度	定类、有序定类	观念与心理分析

变量类别	变量名称	定义与测量口径	变量类型	选项/分级说明
教育支出	教育总支出	2024年度家庭教育总支出（不含补贴抵扣）	定量型	单位：元
	支出占比	教育总支出与家庭年收入之比	有序定类	<10%, [10-20)%, [20-30)%, [30-50)%, >50%
	主要支出项目	区分校内与校外支出	定类/多选	学费、校外辅导、兴趣班、书籍资料、线上课程、生活费用
教育焦虑与消费偏好	焦虑程度	家长教育焦虑水平	有序定类	无、轻度、中度、较强、极度
教育焦虑与消费偏好	消费偏好排序	家长在教育选择中的优先排序	有序定类	教学质量、价格、课程灵活性、口碑、距离便利度
线上教育	使用频率	每周线上教育使用次数	有序定类	0，1－2 次，3－4 次，≥5 次
线上教育	线上支出	硬件设备支出与课程购买支出	定量型	单位：元

2024 Henan Province Rural Education Investment and Consumption Preference Survey Statistical Dataset

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 5

References 15

Related Articles 15

Metrics

Comments

Recommended 0

[1]	WANG LiLi, SHE YanFeng, ZHAO HaiMing, LI FuQiang, CUI SongQi, GUO XiaoRan, LI E, SUN HuiZhong, LI PengFei, REN WeiKe, TIAN ChunLi, YAN MingHua. Detection Dataset of Multiple Taq-Man Real-time PCR for Fowl Aviadenoviruses [J]. Journal of Agricultural Big Data, 2025, 7(4): 532-542.
[2]	WANG Zhen, QIN Feng, QIAO Xi, HUANG Cong, LIU Bo, WAN FangHao, WANG Chen JiaoZi, HUANG YiQi. Agricultural Pest and Disease Information Retrieval Dataset [J]. Journal of Agricultural Big Data, 2025, 7(3): 379-392.
[3]	QI ShuShu, LIU XiaoChen. Toxic Effects and Cumulative Characteristics of Dibutyl Phthalate and Monobutyl Phthalate on Spinach Datasets [J]. Journal of Agricultural Big Data, 2025, 7(1): 132-140.
[4]	QIN JiaLe, YUAN JiangHao, SONG GuoZhu, YAO HongXun, GUO LeiFeng, WANG XiaoLi. Image Dataset of Wheat, Corn, and Rice Seedlings in Heilongjiang Province in 2022 [J]. Journal of Agricultural Big Data, 2024, 6(4): 558-563.
[5]	DING DaWei, YONG BeiBei, REN Wen, XIE Kun, ZHAO YongJian, WANG GuangShuai, CHEN JinPing, WANG MingHui. The Dataset for Crop Phenology of Winter Wheat and Summer Maize in The Alluvium Plain of the Old Yellow River From 2008 to 2022 [J]. Journal of Agricultural Big Data, 2024, 6(4): 552-557.
[6]	LI JiaLe, ZHANG JianHua, WANG Jian, ZHOU GuoMin. Metrological Analysis of Data-driven Deep Learning Methods for Agriculture [J]. Journal of Agricultural Big Data, 2024, 6(3): 400-411.
[7]	CHEN Lei, ZHOU Na, ZHU PengXuan, YUAN Yuan. A Dataset for Constructing Agricultural Knowledge Graph [J]. Journal of Agricultural Big Data, 2024, 6(1): 1-8.
[8]	JIA Cheng, YI HongMei. Analysis of China's Rural E-commerce Research Dataset [J]. Journal of Agricultural Big Data, 2023, 5(4): 95-102.
[9]	ZHAO YaQin, SHEN SiLi, ZHANG Jing, CHEN Lin. Dataset of Survey on Cultivation Production in Otog Front Banner, Inner Mongolia Autonomous Region, 2022 [J]. Journal of Agricultural Big Data, 2023, 5(4): 144-151.
[10]	ZHEN ShiHang, LIU Qi, LUO Rui, TIAN SiYu, SHEN Shuang, REN YanJun. Food Consumption Behavior Analysis Dataset Based on Eye Tracking: A Case Study on the Functional Foods [J]. Journal of Agricultural Big Data, 2023, 5(4): 103-109.
[11]	WEI TongYang, XU Ke, XU Lei. Dataset for Analyzing the Horizontal Transmission Mechanism of Domestic Financial Markets to Agricultural Commodity Prices, 2017-2021 [J]. Journal of Agricultural Big Data, 2023, 5(3): 19-25.
[12]	WANG Kexiao, ZHOU Rui, LI Bo. Biochemistry Indexes and Reflectance Spectra Datasets of Rape Pod Pericarp at Different Maturities [J]. Journal of Agricultural Big Data, 2023, 5(1): 29-33.
[13]	Ziwei Song, Yuan Zheng, Baoxin Zhang. A Dataset on Species and Population Dynamics of Phytoseiid Mites on Citrus in Guangdong Province from 2019 to 2021 [J]. Journal of Agricultural Big Data, 2022, 4(4): 74-79.
[14]	Lixue Kuang, Guofeng Xu, Zhijun Chen, Yinping Li, Yang Cheng, Youming Shen. Quality Datasets of ‘Fuji’ Apples Cultivated in China in 2017 [J]. Journal of Agricultural Big Data, 2022, 4(3): 49-53.
[15]	Kai Xu, Shaoyu Zhang. TPA Test Parameter Setting and Dataset Acquisition of Zaosu Pear in Xingcheng，Liaoning in 2020 [J]. Journal of Agricultural Big Data, 2022, 4(3): 41-48.