京津冀地区乡村振兴地域模式研究——基于乡村非农就业与农民增收的空间效应

doi:10.11821/dlyj020181151

摘要/Abstract

摘要：

探讨乡村非农就业与农民增收、农业农村发展之间的辩证关系,构建乡村振兴地域模式,对有序推进城镇化,实现农民生活富裕与乡村振兴具有重要意义。将京津冀地区划分为京津近郊区（I）、冀东北地区（II）、冀中平原区（III）、冀西太行山区（IV）、坝上地区（V）五类地域类型,采用2000—2016年京津冀县域面板数据,应用空间回归模型测算了京津冀地区乡村非农就业对农民增收的空间效应,提炼了京津冀地区乡村振兴地域模式。结果表明：① 京津冀地区及五类地域类型区乡村就业非农化率与农民收入持续增长,2000—2016年,京津冀地区农村居民人均纯收入由2486元增至11920元,乡村就业非农化率由38.9%增至57.5%,五类地域类型区乡村就业非农化率排序依次为：I>III>II>IV>V,农村居民人均纯收入排序依次为：I>II>III>IV>V;② 京津冀地区乡村就业非农化率每提高1个单位,农民收入增长9.215个单位,I、II、III、V区就业非农化对农民收入的弹性系数分别为29.986、-23.605、-10.388、9.951,IV区两者之间关系不显著;③ 依据不同地域类型乡村非农就业与农民增收的特征、机理与未来发展趋势,提炼了京津冀地区乡村振兴的四种地域模式,即就业平稳型+农工商主导、兼业型+现代农业主导、就业输出型+复合型特色农业主导、就业输出型+生态农旅主导。

关键词: 乡村非农就业, 农民增收, 空间效应, 地域模式, 乡村振兴, 京津冀地区

Abstract:

Illustrating the dialectical relationship between rural off- farm employment, farmers' income growth and agricultural and rural development, and constructing regional modes of rural revitalization are of great significance for orderly promoting urbanization, and realizing the prosperity of farmers’ living and rural revitalization. This article first divides the counties of Beijing- Tianjin- Hebei Region into five types of region: Beijing and Tianjin suburban region (I), Northeast Hebei region (II), Central Hebei Plain region (III), Taihang Mountains region (IV) and Bashang region (V). Then it measures the spatial effect of rural off-farm employment on farmers' income growth by using panel data of the counties of Beijing-Tianjin-Hebei Region and Spatial Regression Model. Finally, this paper refines the regional modes of rural revitalization in the study region. The results show that: (1) from 2000 to 2016, the rural per capita net income increased from 2486 yuan to 11920 yuan, while the rate of rural off-farm employment rose from 38.9% to 57.5%; the order of the rate of rural off- farm employment in five types of region was I>III>II>IV>V, and the order of the rural per capita net income was I>II>III>IV>V; (2) the rural off-farm employment effectively promotes the farmers' income growth; the process of rural off-farm employment leads to the change of farmers' income structure showing a trend of shift from one to another: the stronger the role of off- farm employment income in farmers' income growth, the weaker the role of agricultural income in farmers' income growth; every unit increase of the rate of rural off-farm employment leads to 9.215 units increase of farmers’ income; the elasticity coefficients of the rate of rural off-farm employment to farmers’ income are 29.986, -23.605, -10.388, and 9.951 in I, II, III, and V region, respectively, and there is no significant relationship between them in IV region; (3) according to the characteristic, mechanism and future development trend of rural off-farm employment and farmers' income growth in different regional types, four regional modes of rural revitalization in this region are refined, namely, stable employment + agriculture - commerce- industry dominated type, concurrent operation + modern agriculture dominated type, employment export + compound featured agriculture dominated type, employment export + compound ecological agriculture - tourism dominated type. Finally, this article puts forward the guarantee mechanism of rural off-farm employment and talent cultivation and industrial integration in order to promote the positive interaction between rural off-farm employment and farmers' income growth.

Key words: rural off-farm employment, farmers' income growth, spatial effect, regional mode, rural revitalization, Beijing-Tianjin-Hebei Region

方方, 何仁伟, 李立娜. 京津冀地区乡村振兴地域模式研究——基于乡村非农就业与农民增收的空间效应[J]. 地理研究, 2019, 38(3): 699-712.

Fang FANG, Renwei HE, Lina LI. Research of the regional mode of rural revitalization in Beijing-Tianjin-Hebei Region: Based on the spatial effect between rural off-farm employment and farmers' income growth[J]. GEOGRAPHICAL RESEARCH, 2019, 38(3): 699-712.

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表4

京津冀地区乡村振兴地域模式"

类型	适宜区域	现状特征	问题剖析	模式内涵	实施路径
就业平稳型+农工商主导	适于城乡融合发展程度较高、非农就业相对稳定的京津近郊农区（I）	2016年,经济发展水平较高,人均GDP 55019元,二三产业比重为95.3%,就业非农化率为68.5%,农民人均纯收入较高,达到18395元,非农就业转移对农民增收的带动作用较强	职业农民与现代农业经营管理人才缺失、农业组织化与管理水平有待提高、农业增收效应有限	通过培育农业产业化龙头企业、专业合作社等新型主体,推进非农就业有序转移与就业类型进一步分化,采取市场化配置农村资源要素,构建农业资本化带动农民增收的长效机制	① 充分发挥大都市区对乡村振兴的辐射带动作用,培育农业产业化龙头企业、专业合作社等新型主体,吸纳农业技术人才与农业产业化经营管理人才,在推进农民非农平稳就业的同时,吸引部分高素质非农就业劳动力回流;② 明确服务京津都市区农产品供给的功能定位,优化现代农业生产格局;③ 构建农业资本化带动农民增收的长效机制,推进农产品生产的标准化与品牌化,推进农村一二三产业融合发展
兼业型+现代农业主导	适于农业资源条件较好、以农业为主导的冀东北地区（II）和冀中平原地区（III）	2016年,人均GDP达到36891元,就业非农化率较高,为58.8%,农民人均纯收入为11960元,乡村非农就业不利于农民收入增长	受非农就业机会成本的影响,以异地、远距离、兼业等方式实现非农就业,农业资源禀赋潜力有待挖掘	通过就近城镇化推进乡村劳动力有序转移,创新农业产业化经营模式,培育传统农区农村转型的新动能,构建集约高效、规模经营的现代农业生产格局	① 通过扶持中小城市、中心城镇,发挥其辐射带动作用,培育村镇经济,提升农民兼业化水平与就地非农化;② 开展农村高标准农田整治,创新农业产业化经营模式,推进农地适度规模经营,培育专业户、新型职业农民,以及“公司+农户”、“合作社+专业户”等经营模式;③ 创新传统农产品加工业、农业规模化经营与市场化运营,尝试推广“互联网+现代农业”等农业新业态,促进城乡融合发展
就业输出型+复合型特色农业主导	适于农村自我发展能力相对较弱、生态环境相对脆弱的冀西太行山农区（IV）	2016年,经济发展处于中等水平,人均GDP为31732元,就业非农化率与农民人均纯收入相对较低,分别为44.3%和9039元,非农就业对农民增收的带动作用不显著	水源保护与生态治理的功能定位限制了农业生产,农民非农就业比重较低,农民收入结构单一,农民生活相对贫困	进一步推进乡村非农就业转移,培育专业农户、农民合作社等新型农业经营主体,以土地整治带动特色产业发展实现农民增收	① 持续推进乡村非农就业转移,通过农业与二三产业融合发展,拓宽非农就业渠道,构建非农务工带动农民持续稳定增收的长效机制,鼓励农民在城镇就近就业、就近居住;② 开展农田整治工程,整合山区特色资源,提高机械化经营与农地流转规模,推进特色农业生产、技术、加工、销售一体化;③ 鼓励农民自主创业,探索农业与旅游、教育、文化产业融合路径
就业输出型+生态农旅主导	适于农业资源禀赋较差、生态环境脆弱的坝上地区（V）	2016年,经济发展水平相对较低,人均GDP为22558元,就业非农化率与农民人均纯收入均较低,分别为33.0%和7659元,农民生活相对贫困,非农就业对农民增收作用较强	生态修复任务艰巨,在一定程度上限制了农业生产;发展旱作节水农业带动了农业增产,但对农民增收的贡献有限	持续推进乡村非农就业转移,通过政府扶持,生态保护与农业生产的融合,促进农业生态化发展	① 加强政府财政转移支付与扶持力度,探索生态脆弱区生态扶贫模式与农户生计多样化模式,持续推进乡村非农就业转移,在禁止开发活动的农区,通过异地搬迁实现脱贫;② 挖掘农业对生态保护的多功能属性,加强生态保护与农业生产的融合,探索坝上生态旅游与休闲农业一体化模式

表4

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	指标（变量,单位）	计算公式	平均值	标准差	最小值	最大值
被解释变量	农村居民人均纯收入(PI,元)	来自统计年鉴	6450	4118	846	24649
解释变量	乡村就业非农化率(LT,%)	(乡村从业人员-农林牧渔从业人员)/乡村从业人员	47.1	16.6	11.8	95.9
控制变量	人均GDP(CG,元)	地区生产总值/地区总人口	21069	18351	1888	148056
	人均农林牧渔总产值(PTA,元)	农林牧渔总产值/农林牧渔从业人员	31192	25572	2605	201964
	人均耕地面积(PAA,亩)	耕地面积/乡村人口	3.00	1.00	1.20	9.63
	经济作物比重(PEC,%)	1-粮食作物播种面积/农作物总播种面积	27.0	12.9	2.8	84.1
	复种指数(MCI,%)	农作物总播种面积/耕地面积	139.3	32.3	35.6	242.1

	SLM		SEM
	FE	RE	FE	RE
常数项	—	-822.093*	—	5920.025***
		(-1.71)		(5.76) ***
LT	9.215***	11.041***	15.479***	17.056
	(2.98)	(3.49)	(5.01)	(5.46) ***
Ln(CG)	742.329***	711.141***	579.326***	722.248***
	(11.07)	(10.03)	(6.07)	(6.17)
Ln(PTA)	-341.587***	-295.167***	-605.097***	-501.985***
	(-4.53)	(-3.93)	(-6.05)	(-5.81)
Ln(PAA)	-1809.732***	-1559.153***	-1726.046***	-1675.119***
	(-13.91)	(-12.97)	(-13.90)	(-14.52)
Ln(PEC)	-711.082***	-588.614***	-770.162***	-649.013***
	(-9.37)	(-8.01)	(-9.16)	(-7.55)
Ln(MCI)	-11.361***	-10.548***	-5.101***	-5.125***
	(-8.06)	(-7.44)	(-3.70)	(-3.72)
ρ	0.869***	0.866***	—	—
λ	—	—	0.951***	0.946***
Adj.R²	0.7816	0.7804	0.3066	0.5015
Log lihood	-20375	-20545	-20441	-20663
Hausman Test	66.01***		93.83***
时间	2000—2016年	2000—2016年	2000—2016年	2000—2016年
样本数	2567	2567	2567	2567

	I	II	III	IV	V
LT	29.986***	-23.605*	-10.388**	-1.109	9.951**
	(3.61)	(-1.89)	(-2.59)	(-0.11)	(2.08)
Ln(CG)	1714.570***	656.854***	583.331***	395.590**	1249.278***
	(7.76)	(3.04)	(5.64)	(2.30)	(8.66)
Ln(PTA)	-316.118	746.308**	216.327*	440.751**	-191.660
	(-1.02)	(2.39)	(1.85)	(2.49)	(-1.43)
Ln(PAA)	-1947.327***	-2247.918***	-1645.835***	-176.911	279.818
	(-9.20)	(-5.32)	(-5.86)	(-0.44)	(1.24)
Ln(PEC)	-495.687***	-1699.062***	-343.814***	47.824	-175.657*
	(-3.31)	(-6.16)	(-3.89)	(0.23)	(-1.87)
Ln(MCI)	-6.319**	-16.373***	-20.489***	-0.781	1.289
	(-2.40)	(-3.64)	(-7.69)	(-0.18)	(0.65)
ρ	0.927***	0.793***	0.850***	0.740***	0.933***
Adj.R²	0.8698	0.7916	0.8623	0.7073	0.8213
Log lihood	-2660	-2366	-10080	-4050	-836
模型	SEM	SLM	SLM	SLM	SEM
时间	2000—2016年	2000—2016年	2000—2016年	2000—2016年	2000—2016年
样本数	391	289	1275	493	119