What Change Caused People To Migrate To Cities From Rural Areas?

Abstract

Migration is often seen as an adaptive human response to adverse socio-ecology conditions, such as water scarcity. A rigorous assessment of the causes of migration, however, requires reliable data on the migration in question and related variables, such equally, unemployment, which is ofttimes missing. This study explores the causes of one such type of migration, from rural to urban areas, in the Jiangsu province of Mainland china. A migration model is developed to fill a gap in the understanding of how rural to urban migration responds to variations in inputs to agricultural output including h2o availability and labor and how rural population forms expectations of better livelihood in urban areas. Rural to urban migration is estimated at provincial calibration for menstruum 1985–2013 and is found to exist significantly linked with rural unemployment. Further, migration reacts to a change in rural unemployment after two–4 years with 1% increase in rural unemployment, on average, leading to migration of xvi,000 additional people. This implies that rural population takes a couple of years to internalize a shock in employment opportunities before migrating to cities. The analysis finds neither any show of migrants beingness pulled by improve income prospects to urban areas nor existence pushed out of rural areas by water scarcity. Corroborated by rural–urban migration in China migration survey data for 2008 and 2009, this ways that local governments have 2–four years of lead time after an unemployment shock, not necessarily linked to h2o scarcity, in rural areas to prepare for the migration wave in urban areas. This original analysis of migration over a 30-twelvemonth period and finding its articulate link with unemployment, and not with better income in urban areas or poor rainfall, thus provides conclusive testify in back up of policy interventions that focus on generating employment opportunities in rural areas to reduce migration period to urban areas.

Introduction

Better understanding of linkages betwixt water scarcity and migration is needed in the fence of whether migration is an adaptive strategy or a nonbeneficial struggle of humans under climate variability and change (Lilleør and Van den Broeck, 2011; Tacoli, 2009; Yan and Shi, 2017). The consequences of migration are two-sided. On ane hand it helps migrants to observe more than suitable livelihood, on the other hand information technology causes overpopulation and excessive competition in large cities, and even conflicts between migrants and prior residents over public goods such as water, sanitation, and hygiene (Li, 2010, 2005). Even if migrants are able to earn and eat more in urban areas, their subjective well-being might be worse due to weak or nonexistent social networks (Chen et al., 2019; Knight and Gunatilaka, 2010; Akay et al., 2012). The continuous migration from less-developed rural areas to cities causes an "Empty Nest" trouble, which occurs when immature adults leave their families in rural areas, leaving backside the very old (Chen, 2009; Yao, 2006), the very immature (Duan and Zhou, 2005; Zhou and Duan, 2006), and the socially vulnerable.

Urbanization patterns in developing countries, such as rural–urban migration in sub-Saharan Africa, appear to take been influenced by climate variability (Barrios et al., 2006; McLeman and Hunter, 2010). Yu et al. (2011) revealed a similar blueprint in southwest People's republic of china, where a pregnant rural to urban migration occurred apparently in response to an extreme drought from August to September 2010. Environmental changes are therefore often seen as one of the drivers of migration (Blackness et al., 2011), but other drivers such every bit economic production and employment are equally linked with poverty and migration (Pande et al., 2014; Wang and Luo, 2014), rendering the relationship between ecology extremes, such as flood and droughts, and migration unclear (Gray and Mueller, 2012). Economic opportunities have been recognized as key drivers to nonpermanent migration (Wang, 2017), irrespective of the scale of migration. Links between employment opportunities and rural–urban migration at national scale have been reported in countries such equally China, Senegal, People's republic of bangladesh and Bharat (Goldsmith et al., 2004; Zhang and Shunfeng, 2003; Zhao, 1999; Munshi and Rosenzweig, 2016). For case, both skilled and nonskilled labor tend to migrate from interior regions of China to its coast. Still, the migration of skilled labor appears to be less influenced by the regional unemployment charge per unit and concentration of foreign investment and more than by regional wage disparity (Liu and Shen, 2017). Thomas (1973) even found linkages between out-migration and economic growth of U.k. with cross-Atlantic economies.

Unemployment influences regional labor migration because an unemployed worker is more than likely to move with regional unemployment differentials encouraging such mobility (Pissarides and Wadsworth, 1989). Even though rural–urban migration might negatively influence urban employment (Chen, 2007), rural migrants are often driven by their expectation of improved employment or earnings and desire to urbanize (Glomm, 1992; Mabogunje, 1970; Todaro, 1969). Such aspirations of better lives in urban areas are based on household level decisions to either maximize expected income or minimize risk that the household is exposed to by diversifying the portfolio of income-generating activities (Chen et al., 2019; Akay et al., 2012; Massey et al., 1993). Such decisions to drift are based on experienced utility, but are oftentimes limited by substantial social and economic barriers (Chen at al., 2019; Bryan et al., 2014). Every bit a result, information technology is oft the individuals from households that are at above-boilerplate incomes that migrate (Knight and Gunatilaka, 2010). The event of natural disasters such as droughts and flooding tin can therefore be cryptic (Gray and Mueller, 2012; Chen et al., 2017). On 1 hand, it can reduce migration by removing resources necessary for migration to overcome setup costs or increasing labor demand in originating areas, while in some other cases it may reduce all possible income-generating possibilities, pushing migrants in masses out of afflicted areas (Chen et al., 2017). Such "push" factors dissimilarity the "pull" factors of large city dreams or explicit solicitation campaigns past employers in urban areas (Massey et al., 1993).

Rural-to-urban migration is a well-observed phenomenon in Prc. For example, Zhao (1999) found that migration decisions in China are based on economical factors (shortage of farmland and rural taxation), although a lack of stable returns from employment in urban areas has slowed down permanent migration (Zhao, 1999). Zhang and Shunfeng (2003) reported similar links betwixt rural–urban migration in China (RUMiC) during 1978–1999 and attributed it to economic growth, especially in highly urbanized coastal areas (Zhang and Shunfeng, 2003). This newspaper investigates rural–urban migration in one such case, and inquires whether it is driven by water availability, rural unemployment, or urban employment at college wages. Jiangsu is a province with a stiff economy in urban areas and one of the highest population densities. Its rural population continues to shrink as urban areas expand (Bureau of Statistics of Jiangsu, 1987–2016; Xu, 2009; Liu et al., 2010). For instance, Liu et al. (2010) reported a meaning 21.6% (>ten million) of the population with rural registration that emigrated from rural areas in Jiangsu in 2005 and 2006, generally originated from northern Jiangsu and relocated to its more urbanized southern parts (Chen, 2007; Huang, 2006; Wang, 2017; Zhang and Huang, 2009).

Understanding the drivers of migration demands its analysis over an extended catamenia. This is challenging in absence of migration-related household surveys such as RUMiC 2008 and 2009 (China Plant For Income Distribution, Beijing Normal University, 2019). Section "Methodology" therefore outset presents methodologies to gauge a time series of rural to urban migration in Jiangsu, rural labor need and the rural–urban unemployment rate gradient from 1985 to 2015. Information technology then interprets migration in terms of unemployment and income. Section "Rural–urban net migration" presents the data analysis, which includes the estimated migration time series and assay of other data sets used such as the time serial of inputs to agricultural output in Jiangsu province. Section "Modeling results" presents the results that are further discussed in the adjacent section. The final section summarizes the conclusions.

Methodology

Study area

The Jiangsu province is relatively developed with a potent economy and high population density within the economic belt of the Yangzi River. Located in a subtropical and humid area with more than yard mm of precipitation per year, the Jiangsu province has abundant water resource and a complex hydrological arrangement, represented by the river network of the Taihu Lake system in the downstream area of the Yangzi River Basin (Fig. ane). Jiangsu is a producer of crops such as rice and wheat, which occupy 58.4% of the total plant area in 2016. Agriculture production is closely linked to water availability and is influenced by climatic factors. Mechanization and technology uptake further contribute to agriculture output. In 2016, the full power of agricultural equipment reached 4.906 × ten⁷ KW, bringing the mechanization level to 82%. Preferential policies such as agronomical back up and protection subsidies have too supported farmers. These government initiatives have produced rapid development and industrialization of agronomics in Jiangsu. At the aforementioned fourth dimension, Jiangsu is undergoing an industrial revolution. The proportion of agronomics output is beingness gradually reduced by mod secondary and 3rd industries. Nonagricultural sectors add to the income sources of rural families. Agricultural mechanization has reduced labor demand, while farm workers have go employed in modern industries (Fig. 2).

Fig. 1

Jiangsu province. Also shown are locations of the 2 precipitation stations used to approximate precipitation anomalies (PRE Point—Northern Jiangsu, PRE Point—Southern Jiangsu)

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Fig. 2

The framework of rural–urban migration assay. Boxes with curved corners indicate input data. Estimated unemployment rate and rural to urban migration are analyzed

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Methodology

The interpretation and simulation of rural–urban cyberspace migration is inspired by the Todaro model (Todaro, 1969), which was originally developed to understand labor transfer between traditional (e.g., in rural areas) and modern (e.grand., in urban areas) sectors of an economy. The Todaro model interprets the transfer of labor every bit an increasing function of expected difference between the traditional and modern sector incomes, which we bear witness tin be dominated by the difference in unemployment rates. Unemployment rates in urban areas are obtained from the data of the Statistical Agency of Jiangsu (Bureau of Statistics of Jiangsu, 1987–2016) and used since the labor markets in urban areas are more than formally monitored and managed by the government. Past contrast, the unemployment charge per unit in rural areas cannot be directly measured because of the nature of agronomical employment in People's republic of china. There is no clear boundary between the "employed" and "unemployed" farmers because every person in a household can work on household farms in some form. Therefore, we first estimate the rural agricultural labor need using the Cobb–Douglas production function, and so represent the unemployment rate in rural agricultural sectors every bit the ratio of surplus labor to employable (or available) labor supply (following Roobavannan et al., 2017a, b). In this way, underemployed individuals that are non efficiently employed in rural areas are also counted as unemployed.

Interpretation of net rural–urban migration

The rural–urban net migration data series has been estimated based on the population dataset from the Statistical Yearbook of Jiangsu (1987–2016) (Agency of Statistics of Jiangsu, 1987–2016), that includes total population, rural population, and total natural population growth.

If we gear up the full population of the province at time pace t as \({\mathrm{\Phi }}_T(t)\), rural population as \({\mathrm{\Phi }}_R(t)\), natural total population growth (the departure between full birth and death rate) as \({\mathrm{\Delta \Phi }}_{NT}(t)\) and then urban population \({\mathrm{\Phi }}_U\left( t \correct)\) is obtained by

$${\mathrm{\Phi }}_U\left( t \right) = {\mathrm{\Phi }}_T\left( t \right) - {\mathrm{\Phi }}_R(t).$$

(1)

We focus on analyzing the source of urban population net growth, which is calculated as

$${\mathrm{\Delta \Phi }}_U\left( t \right) = {\mathrm{\Phi }}_U\left( t \right) - {\mathrm{\Phi }}_U(t - one).$$

(2)

We assume that the natural population growth rate is the aforementioned in urban and rural areas. For instance, the difference between rural and urban areas in birth charge per unit is of the order of no more than than 2.49‰ for years 1985–1990 (Jiangsu Provincial Local Relate Compilation Commission, 1999). Notation that is based on the reported urban-canton birth rate difference, which Jiangsu Provincial Local Chronicle Compilation Committee (1999) has suggested to be a good approximation of urban–rural nascency rate difference. Given that the rural mortality charge per unit is unremarkably higher than that of urban areas, the gap between rural and urban natural growth rate would exist even smaller. As well, referring to a population simulation by Shen and Spence (1996) and our sensitivity assay of the model, the gap between rural and urban natural growth rates in Jiangsu from 1980s to 2010s should be relatively stable and <three‰. Therefore we let natural growth rate in urban areas, \(\frac{{{\mathrm{\Delta \Phi }}_{NU}\left( t \right)}}{{{\mathrm{\Phi }}_U\left( t \right)}}\), to exist equal to the natural growth charge per unit of the province, \(\frac{{{\mathrm{\Delta \Phi }}_{NT}\left( t \right)}}{{{\mathrm{\Phi }}_T\left( t \right)}}\). The natural growth charge per unit for urban areas, \({\mathrm{\Delta \Phi }}_{NU}\left( t \right)\), is obtained as

$${\mathrm{\Delta \Phi }}_{NU}\left( t \right) = {\mathrm{\Delta \Phi }}_{NT}\left( t \correct)\frac{{{\mathrm{\Phi }}_U\left( t \right)}}{{{\mathrm{\Phi }}_T\left( t \right)}}.$$

(iii)

With the full population net growth and natural population growth, we can summate the "un-natural" population growth in urban areas, \({\mathrm{\Delta \Phi }}_{UNU}\left( t \right)\), which is migration from rural areas and from other regions outside the province.

$${\mathrm{\Delta \Phi }}_{UNU}\left( t \right) = {\mathrm{\Delta \Phi }}_U\left( t \correct) - {\mathrm{\Delta \Phi }}_{NU}\left( t \right)$$

(iv)

We also assume that cyberspace migration from outside of the province (foreign migration), μ _F (t), moves toward urban areas of Jiangsu. The supposition is based on the observation that about fourscore% of the foreign migrants moved to urban areas within Jiangsu province during 1985–1990 (Jiangsu Provincial Local Chronicle Compilation Committee, 1999). The main trend in rural Jiangsu is emigration (Liu et al., 2010). Thus, the destination of migrants from outside of the province can safely exist assumed to be urban areas.

Since the unnatural growth rate of urban areas is the sum of strange migration, μ _F (t), from outside Jiangsu and rural–urban migration within Jiangsu, μ _RU (t), the rural–urban net migration can be given every bit

$$\mu _{RU}\left( t \right) = {\mathrm{\Delta \Phi }}_{UNU}\left( t \right) - \mu _F(t)$$

(5a)

Here foreign migration rate, μ _F (t), because it comes from outside Jiangsu, is given past

$$\mu _F\left( t \right) = {\mathrm{\Delta \Phi }}_T\left( t \right) - {\mathrm{\Delta \Phi }}_{NT}(t)$$

(5b)

and

$${\mathrm{\Delta \Phi }}_T\left( t \right) = {\mathrm{\Phi }}_T\left( t \right) - {\mathrm{\Phi }}_T\left( {t - i} \correct).$$

(5c)

Labor demand

The demand for labor in both agricultural and nonagricultural sectors within rural areas of Jiangsu determines the employment rate. This section discusses how labor demand is estimated.

Cobb–Douglas production function

The Cobb–Douglas production function has been applied to describe the evolution of labor demand in rural agricultural and nonagricultural sectors, which are driven by diverse factors of production. The Cobb–Douglas production function links the output of an industry with unlike inputs, which are commonly categorized into labor and capital. The original equation of Cobb–Douglas production (Cobb and Douglas, 1928) is shown equally below

$$Y_j = A_jK_j^\alpha L_j^\beta.$$

(6a)

Where Y _j is the total output of industry j; A _j is the total gene productivity of industry j; M _j is capital input of manufacture j; Fifty _j is labor input of industry j. As well, α and β are output elasticities of capital and labor, which are in the form of exponential weights such that α +β = 1.

To incorporate more than driving factors (inputs), we use a more generalized grade of the Cobb–Douglas production function, in the form of Eq. (6b), with northward driving factors \(F_{j1},...,F_{ji},...,F_{jn}\) (including labor) and associated elasticities \(\blastoff _1,...,\alpha _i,...,\alpha _n\).

$$Y_j = A_j\mathop {\prod }\limits_{i = 1}^n F_{ji}^{\alpha _i}$$

(6b)

For α _i , we have

\(\mathop {\sum }\limits_{i = i}^n \alpha _i = ane\)

To distinguish the variable "Labor" from other factors, we prepare "Labor" F _jn every bit L _j with α _n , then Eq. (6b) turns into the form of

$$Y_j = A_j\mathop {\prod }\limits_{i = one}^{due north - 1} F_{ji}^{\alpha _i}L_j^{\alpha _n}.$$

(6c)

With

$$\alpha _n = 1 - \mathop {\sum }\limits_{i = ane}^{n - 1} \blastoff _i.$$

Labor wage is set equal to the marginal value of labor, and then assumed to be equal to income per unit of measurement labor.

$$w_j = \frac{{\partial Y_j}}{{\partial L_j}} = \frac{{M_j}}{{L_j}}$$

(7a)

The incomes, M _j , of both agricultural and nonagricultural sectors are associated with corresponding outputs that they generate using labor wage every bit an intermediary. We therefore assume that labor is allocated efficiently to agronomical and nonagricultural production. To obtain the equation for estimating labor demand L _j , we first calculate the growth rate of labor wage west _j .

$$w_j = \frac{{\partial Y_j}}{{\fractional L_j}} = (ane - \mathop {\sum }\limits_{i = one}^{north - 1} \alpha _i)\frac{{A_j\mathop {\prod }\nolimits_{i = 1}^{due north - 1} F_{ji}^{\blastoff _i}}}{{L_j^{\mathop {\sum }\nolimits_{i = 1}^{due north - 1} \blastoff _i}}}$$

(7b)

$$\frac{\dot{w}_{j}}{w_{j}} = \frac{\dot{A}_{j}}{A_{j}} + \sum\limits_{i = 1}^{n - 1} \alpha _i\frac{\dot{F}_{ji}}{F_{ji}} - \frac{\dot{L}_{j}}{L_{j}}\sum\limits_{i = 1}^{n - 1} \alpha _i$$

(8a)

And so, w _j is replaced by \(\frac{{M_j}}{{L_j}}\) in Eq. (8a) to obtain an expression for labor demand growth charge per unit \(\frac{{\dot L_j}}{{Lj}} \cdot\).

$$\frac{{\dot M_j}}{{M_j}} - \frac{{\dot L_j}}{{L_j}} = \frac{{\dot A_j}}{{A_j}} + \sum\limits_{i = 1}^{n - ane} \alpha _i\frac{{\dot F_{ji}}}{{F_{ji}}} - \frac{{\dot L_j}}{{L_j}}\sum\limits_{i = 1}^{n - 1} \alpha _i$$

(8b)

$$\frac{{\dot L_j}}{{L_j}} = \frac{{\frac{{\dot A_j}}{{A_j}} + \sum\nolimits_{i = 1}^{north - one} \alpha _i\frac{{\dot F_{ji}}}{{F_{ji}}} - \frac{{\dot M_j}}{{M_j}}}}{\sum\nolimits_{i = ane}^{n - 1} \blastoff _i} \cdot \left(1 - \frac{1}{\sum \nolimits_{i = i}^{north - 1} \blastoff _i} \right)^{ - 1} = \frac{{\frac{{\dot A_j}}{{A_j}} + \mathop {\sum }\nolimits_{i = ane}^{n - 1} \blastoff _i\frac{{\dot F_{ji}}}{{F_{ji}}} - \frac{{\dot M_j}}{{M_j}}}}{\sum\nolimits_{i = 1}^{n - ane} \alpha _i - 1}$$

(8c)

With the growth rate of labor demand and an initial value of L ₀, a complete time series of labor demand for manufacture j can be obtained recursively as shown below

$$L_j\left( t \right) = L_j(t - ane)\left( {\frac{{\dot L_j}}{{L_j}}(t) + 1} \correct)$$

(ix)

Factors of production

Rural agricultural production

We gear up six factors of production (including labor) as inputs to farm production in Jiangsu Province, Cathay. These are labor, institute area, water availability (hither represented past precipitation), fertilizer, machinery, and pesticide. The shares of agricultural inputs (or elasticities) are based on Sunday et al. (2008) and shown in Table ane. Amid the factors selected by Sun et al. (2008), irrigated area was chosen to represent the influence of water availability during agronomical production. Nosotros use almanac precipitation every bit the proxy of water input, which then includes not but irrigated but besides rainfed agriculture. The precipitation influences the estimated agronomical labor demand since water is one important input to agricultural output.

Table 1 Elasticities of factors of agricultural output

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Rural not-agricultural production

For the nonagricultural sector, ii driving factors (inputs) of output, which are labor and gross capital formation (represents the capital input for nonagricultural production) (National Agency of Statistics of China, 2016), are used to model the temporal evolution of labor demand of the rural nonagricultural sector. A two-factor version of the Cobb–Douglas production role in Eq. (8c) is used. Information sources related to rural nonagricultural product are listed in Table S1.

Unemployment driven net migration

Similar to Todaro (1969), nosotros assume that transfer of labor from rural to urban sectors, τ _U(t), per unit labor supply, Southward _U (t), can be linked to expectations of future net income that people course in rural and urban areas (5 _U (t) and Five _R (t), respectively). The hypothesized relationship is expressed via the equation below

$$\frac{{\tau _U}}{{S_U}}\left( t \correct) = F\left( {\frac{{V_U\left( t \right) - V_R(t)}}{{V_R(t)}}} \right)$$

(10a)

Where F[.] is an increasing function with F>0. V _U (t) is the discounted present value of the expected urban real income stream over time t', \(\bar Y_U(t\prime number )\), from the present (at time t) to a distant future. V _R (t) is the discounted present value of the expected rural real income stream \(\bar Y_R(t\prime )\). Equation (10a) suggests that transfer of labor from rural to urban sectors increases as the deviation between expected income streams of urban and rural sector increases relative to rural income.

We hither note that a decision to migrate is costly for rural labor yet information technology is motivated past their expectation of future income. The latter is known to be based on subjective probabilities, which often depends on turning points of income in the past (Fisher, 1962; Schmalensee, 1976). This ways that expectations of future income streams, which rural labor form, may rely on some significant income changes in the by. Nosotros assume that a population forms its discounted expectation of hereafter income stream based on average income across population that people observe \(\begin{array}{ccccc}\\ & V_U\left( t \right) = \bar Y_U\left( {t - \ell } \right) = [1 - U_U\left( {t - \ell } \right)]Y_U\left( {t - \ell } \right)\cr \\ & V_R\left( t \right) = \bar Y_R\left( {t - \ell } \right) = [1 - U_R\left( {t - \ell } \right)]Y_R\left( {t - \ell } \right).\\ \cease{array}\) years in the past. Given that the average income in the past is income realized conditioned on being employed, nosotros take

$$\brainstorm{array}{ccccc}\\ & V_U\left( t \correct) = \bar Y_U\left( {t - \ell } \correct) = [1 - U_U\left( {t - \ell } \right)]Y_U\left( {t - \ell } \right)\cr \\ & V_R\left( t \right) = \bar Y_R\left( {t - \ell } \right) = [i - U_R\left( {t - \ell } \correct)]Y_R\left( {t - \ell } \right).\\ \end{array}$$

Replacing the in a higher place in Eq. (10a), nosotros obtain

$$\frac{{\tau _U}}{{S_U}}\left( t \right) = F\left( {\frac{{\left[ {1 - U_U\left( {t - \ell } \correct)} \right]Y_U\left( {t - \ell } \right)}}{{\left[ {1 - U_R\left( {t - \ell } \correct)} \right]Y_R\left( {t - \ell } \right).}} - i} \right)$$

(10b)

Here, S _U (t) is the existing size of the urban labor strength, which is calculated as a product of labor participation rate and population in urban areas; τ _U (t) represents net rural–urban net migration μ _RU ; U _U (t), U _R (t) are unemployment rates in the urban area and the rural surface area, respectively. Y _u (t) is cyberspace urban real income. Y _R (t) is net rural real income. F(·) is an increasing role with F′>0. From here on we denote \(\ell\) years lagged variables by superscript \(\ell\). For example, \(U_U\left( {t - \ell } \right)\) is denoted by \(U_U^\ell\).

Nosotros update Eq. (10b) by replacing τ _U (t) past μ _RU , replacing the normalizing variable S _U (t) of the left hand side with labor supply in rural area S _R (t) and replacing the normalizing variable of the argument of F(·) with \(\left( {i - U_U^\ell } \right)Y_U^\ell\), which ways that the rates are made relative to rural variables

$$\frac{{\mu _{RU}}}{{S_R}} = F\left( {\frac{{\left( {1 - U_U^\ell } \right)Y_U^\ell - \left( {1 - U_R^\ell } \right)Y_R^\ell }}{{\left( {1 - U_U^\ell } \right)Y_U^\ell }}} \right)$$

(11a)

By multiplying S _R on both sides, we have

$$\mu _{RU} = \hat F\left( {\frac{{\left( {1 - U_U^\ell } \right)Y_U^\ell - \left( {1 - U_R^\ell } \right)Y_R^\ell }}{{\left( {one - U_U^\ell } \right)Y_U^\ell }}} \right) = \hat F\left( {\eta ^\ell } \right),$$

(11b)

where superscript \(\ell\) stand for a lag of \(\ell\) years and \(\hat F\left( \cdot \right)\) is another increasing function since Due south _R >0.

Under the assumptions that, (1) the unemployment rate U _U is much less than U _R (U _U <<U _R ) and shut to 0; and (2) the migration prospect of rural population is non sensitive to income difference betwixt rural and urban areas; Eq. (11b) transforms to

$$\mu _{RU} = \tilde F\left( {U_R^\ell - U_U^\ell } \correct)$$

(12)

Where \(\tilde F\left( \cdot \right)\) is another increasing function. We here note that Eq. (12) essentially assumes that migrants are being "pushed" to urban areas.

Data serial analysis

We obtained hydro-meteorological fourth dimension series from the Climate Inquiry Unit (CRU, 1985–2015; Harris et al., 2014) and social data series from Agency of Statistics, Jiangsu (1987–2016). Nosotros focus on the menses from 1985 to 2013, which covers the period of Chinese economical reforms and where data was bachelor. A detailed table for data resources is given in the Supplementary information A1.

Rural–urban net migration

In China, a population census (The Country Quango of The People'south Republic of Prc, 2010) is taken every x years roofing the unabridged country, during which data of population and migration are directly obtained by surveys. Meanwhile, every v years, sampling surveys (The Land Council of The People's Republic of China, 2010) are carried out in guild to estimate population-related data such equally total and rural population. For the years in-betwixt, these population time serial can exist obtained from estimates fabricated by the Bureaus of Statistics of every province and every city. Here, cities and towns are classified as urban areas.

The official migration data for the Jiangsu province is "Migrating Population", which is available every 10 years. "Migrating Population" counts the number of people whose places of residence are dissimilar from where they had been registered. Notwithstanding, the "Migrating Population" but informs how many people accept migrated and does non specify the origin or destination of migrants. In this written report, nosotros estimated a cyberspace migration time serial of population flows from rural to urban areas using the time serial of rural, urban and total population and natural population growth rate (run across Eqs. (1)–(5c)). The estimated net migration from rural areas toward cities, μ _RU , is shown in Fig. three.

Fig. 3

The calculated time series of rural–urban net migration, µRU, based on Eqs. (1)–(5c)

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Effigy 3 shows the net migration flow from rural to urban areas, which corroborates the narratives based on the five-yearly aggregate data where available. For example, internet migration of people within the province from rural to urban areas during the 1986–1990 flow amounted to 650,000 (Jiangsu Provincial Local Chronicle Compilation Committee, 1999). This is close to the estimated net migration of around 64,700 (sum of migration values during this menstruum). The first peak of rural–urban cyberspace migration occurred in the 1990s, overlapping with the catamenia of large migration, followed by a reject in migration rates in the mid 2000s. The second peak occurred in the period from 2009 to 2014, which shows another boom in rural to urban migration.

Factors of product in rural agriculture

Sunday et al. (2008) selected half dozen factors, which included labor, planted expanse, irrigated surface area, fertilizer use, machinery power, and pesticide utilise, equally the factors of rural agricultural production, and hence equally the drivers of labor demand in the Cobb–Douglas product part, of the Nanjing province of China. The area of effective irrigation is already included in full planted expanse, and water availability may be represented by precipitation. Precipitation has a more meaning bear on on regional and temporal ingather growth modify inside Jiangsu province than those of calorie-free and temperature (Zhang et al., 2011). Therefore, we cull precipitation rather than irrigated area as one of the factors of agricultural product.

The standardized fourth dimension serial of all factors of agricultural product are shown in Fig. 4.

Fig. four

Standardized factors of product: precipitation (a 1985–2015), planted area, fertilizer, machinery and pesticide utilize on agriculture production in rural areas (b 1985–2013). The time series are standardized by subtracting the mean and dividing the time series by its standard divergence

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Figure 4a shows that the standardized precipitation in the northern and southern regions prove like patterns (atmospheric precipitation in southern and northern Jiangsu are similar with each other with R = 0.56, p-value < 0.001. Southern-Jiangsu precipitation has been finally chosen as model input since it provides a ameliorate model performance). Long-term fluctuations occur over periods of 5–x years. Effigy 4b shows other inputs to agronomical production. First, a decreasing trend of planted area can be observed. This indicates either shrinking or more land intensive rural agricultural output in Jiangsu. Fertilizer utilize increased from ~1.v meg tons in 1985 to ~three.3 million tons in 1998 and remained virtually constant over the residuum of the menstruation, indicating that fertilizer use in agricultural production in Jiangsu province has reached a steady state. Machinery power rose gradually and steadily over the years, implying a steadily increasing reliance on agronomical mechanization. The use of pesticides has been declining since 2005. Figure 4a, b therefore suggest that agriculture output, and thus agricultural labor demand, remains susceptible to changes in rainfall in spite of heavy mechanization in recent years.

Every bit described by the Cobb–Douglas product function \(Y = A\mathop {\prod }\nolimits_{i = 1}^n F_i^{\alpha _i}\) with F _i equally an input and α _i equally its elasticity, the farm production depends only on its factors and how elastic agricultural production is in relation to these factors (Cobb and Douglas, 1928). Table 1 shows the elasticities corresponding to each of the factors of rural agronomical productivity, every bit provided past Sun et al. (2008). These elasticities were first estimated by Fan and Zhang (2002) for eight factors. Considering the data availability, Sun et al. (2008) further selected six of the 9 factors, which covered ~80% of the factors that contributed to agricultural product.

Amidst the factors selected by Fan and Zhang (2002), irrigated surface area was chosen to represent the influence of water availability during the process of crop tillage. We replaced irrigated expanse with precipitation (Fig. 5).

Fig. v

Urban unemployment rate of Jiangsu Province, Mainland china: a steady increase until early 2000s can be observed where after information technology has stabilized

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Urban unemployment charge per unit

The time series of urban unemployment rate, as shown in Fig. v, was obtained from the official dataset of the Jiangsu province (Bureau of Statistics of Jiangsu, 1987–2016). The range of fluctuations in urban unemployment rate is much narrower than the modeled unemployment rate in rural areas since the labor market place in urban areas is highly ordered. The boundary betwixt employed and unemployed is clear in urban areas based on the registered population seeking employment.

Income Time Series

Effigy 6 shows that rural income per capita, which remained steady betwixt 1985 and 1994, began to ascent slowly in 1995. This trend accelerated effectually 2005. All the same, the acceleration in urban income per capita was much earlier and much faster. A deviation between rural and urban income is axiomatic. This can also exist seen from the rural–urban income ratio that began to decline in late 1990s.

Fig. half dozen

(1) Rural and urban income per capita time series, (ii) rural agricultural income as a proportion of average rural income (in red), and (3) rural to urban income per capita ratio Y_R/Y_U (in yellow). Agricultural income relative to nonagricultural and urban income has been declining over time

Total size image

Figure half dozen also shows the ratio of rural agricultural and nonagricultural income of Jiangsu (Bureau of Statistics of Jiangsu, 1987–2016). The difference betwixt these two sources of income in rural areas is much sharper. As shown in Fig. 6, the proportion of income from the agricultural sector in rural areas has decreased over time, which indicates the emergence of modernistic secondary and 3rd sectors such every bit manufacturing and services.

Modeling results

Labor demand and unemployment

Labor demand in rural agricultural and nonagricultural sectors

The calculated labor demands of the rural agricultural and nonagricultural sectors in the Jiangsu province are shown in Fig. 7 against surveyed (observed) data series from the Bureau of Statistics of Jiangsu (1987–2016). The comparing therefore assumes that the labor demand for efficient agronomical production is the same equally labor employed, which may non always exist the same. The unemployment calculated thereof considers all the labor that is non efficiently employed past agriculture. It therefore includes under-employed people every bit well. For example, more family members may on-record be employed in the family unit plot, which may require less workers for efficient production. Also, the rural labor force recorded may include people who work at other locations, only accept not all the same changed their household registrations (Jiangsu, 1987–2016).

Fig. seven

Simulated labor demand in rural agricultural and nonagricultural sectors when compared to reported statistics. Underemployment in the agronomical sector is evident

Full size image

Figure 7 shows that the rural agricultural labor kept decreasing while the number of nonagricultural laborers who worked in secondary and tertiary sectors rose over time. Since the rural labor supply remained relatively constant (see Fig. 8), this ways that labor from traditional agronomical product either transferred to modern industries or emigrated. The estimated labor need in the agronomical sector witnessed a sharp decline from 1997 to 2003, which was driven by a drib in agricultural income in the same menses of fourth dimension (Bureau of Statistics of Jiangsu, 1987–2016). According to the official report of Agency of Statistics of Jiangsu in 2009 (Bureau of Statistics of Jiangsu, 2008), there was an increment in the price of agricultural inputs, with the prices of agricultural output remaining constant, thereby reducing net income from agricultural production during the catamenia. This pass up of agricultural net income led to a reduction of labor need estimated past the Cobb–Douglas production function (Eq. (8c)).

Fig. 8

Calculated rural total labor demand, rural labor supply and rural unemployment

Full size epitome

The observed data series, obtained from official surveys of the Bureau of Statistics of Jiangsu (1987–2016), shows a similar pattern to the Cobb–Douglas-estimated labor demand, only differences are however evident. The number of rural agricultural laborers is stable over time, and the drib in agricultural income from 1997 to 2003 seems to not influence the observed amount of labor as much. The labor need calculated past the Cobb–Douglas product function is labor needed for efficient product. However, more than and more than labor in rural areas obtain their incomes from different types of employment and thus are generally underemployed, especially during lean years in agriculture. In official surveys, people who spend the largest proportion of their time in agronomics or obtain the largest portion of their income from agronomics are categorized as farmers. This ways that virtually of the rural agricultural labor is not efficiently employed, peculiarly in contempo years that has witnessed expanding manufacturing and service sectors and dwindling returns from the agricultural sector. This explains why estimated rural nonagricultural demand follows the observed data more closely than rural agricultural labor demand, especially post 1995.

Rural unemployment

With the labor demand calculated using the Cobb–Douglas product office, nosotros obtain the overall labor demand and unemployment for rural areas.

The full labor supply in rural areas of Jiangsu remained steady over the period between 1985 and 2013. Starting from 30 million people, labor supply rose slightly during the first decade and and then gradually declined back to 30 1000000 people in the 2010s.

Total rural labor need, calculated by Cobb–Douglas production functions, however, underwent periodic fluctuations. It declined from the end of 1980s reaching its first minimum in 1993, then rose up to a peak in 1999. In the 2000s the labor demand declined over again from ~25 million to ~20 million people in 2007. After 2007, the total labor demand again rose. The shaded area represents the gap between labor supply and labor demand in the rural areas of Jiangsu province and represents rural unemployment (including underemployed people not efficiently used in the agronomical sector). Nonetheless, inherent uncertainties in estimated full labor supply and need may mean certain point estimates, such as close to zero unemployment in 2013, demand closer scrutiny.

Rural–urban migration model

A linear office is practical to reveal the relationship, \(\tilde F[.]\) in Eq. (12), between internet migration μ _RU and the unemployment gradient U _R -U _U .

Figure 9a shows that a pregnant positive correlation exists betwixt cyberspace migration and unemployment difference when the rural–urban unemployment gradient lags rural to urban internet migration by ii–iv years. This ways that worsening rural unemployment status takes two–4 years to touch on migration flows. Another interpretation is that rural unemployed have between 2–4 years to make up one's mind and move to urban areas.

Fig. nine

a Correlation coefficients between rural–urban internet migration and rural–urban unemployment gradient at unlike lags; b unemployment-driven rural to urban net migration simulated with 2 years lag. Shaded area shows 99% confidence interval. The observed migration data in b is the time serial shown in Fig. iii

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Figure 9b shows the rural–urban net migration faux by Eq. (12) shown in the red color, together with rural–urban internet migration calculated from data series obtained from the Statistical Yearbook of Jiangsu (Agency of Statistics of Jiangsu, 1987–2016) (based on Fig. 4). It shows a significant agreement betwixt the observed and imitation fourth dimension series, with the migration model explaining nigh 65% of the variance in the observed time series (see Fig. 10, R = 0.65). Equally Fig. 10 shows, the fake fourth dimension series of migration is based on a statistically significant relationship (p-value < 0.001) between rural–urban migration and the unemployment slope. Its slope suggests that a one% increase in rural unemployment tin can lead to the migration of an additional 16,000 people, on boilerplate, from rural to urban areas.

Fig. 10

Rural–urban net migration (1987–2015) as a linear function of rural–urban unemployment gradient (1985–2013) as hypothesized by Eq. 12. a the calibrated linear office in red with RMSE of 17.60 (104), R = 0.6504 and p-value < 0.01. b Time serial of residuals betwixt the observed and false migration fourth dimension serial and c the scatter plot of observed vs imitation migration

Full size prototype

With a White Statistics of 0.199, which is less than \(\chi _{0.05}^2\) = 5.99 (degree of freedom is 2), the time series of residuals in the regression analysis is recognized as homoscedastic (Li and Pan, 2010). The autocorrelation coefficients of residuals with lag of ane and ii years are 0.31 (p-value = 0.11) and −0.17 (p-value = 0.39), respectively, which indicates that the residuals are non autocorrelated.

Discussion

Here we compare the linear migration model of Eq. (12) with six other linear models of rural–urban migration.

Correlations of migration with atmospheric precipitation from 1985 to 2015 at Northern and Southern Jiangsu stations (shown in Fig. ane) are −0.xix (p-value = 0.31) and −0.15 (p-value = 0.43), respectively. This suggests that at that place is no clear direct point of precipitation in migration fourth dimension series at the provincial scale. Other studies in countries such as People's republic of bangladesh, Burkina Faso, and El Salvadore accept constitute that even environmental shocks such as droughts, flooding, and fifty-fifty earthquakes do not necessarily trigger migration (Gray and Mueller, 2012; Chen et al., 2017).

However, a statistically significant relationship betwixt migration and lagged rural unemployment suggests that climate variability may influence migration through unemployment alongside the influence of other inputs to rural production activities. Unemployment here is calculated as the percentage of labor supply that is not efficiently employed. It thus as well includes the underemployed, unlike the official statistics. The 2d column (second candidate model) of Table 2 shows a linear regression with but the unemployment rate in rural area and the relationship (slope and significance level) suggests that migrants are not being "pulled" to urban areas, but instead are being "pushed" out of rural areas due to unemployment. Such observation is also corroborated by RUMiC 2007 and 2008 survey data for Jiangsu province (see Table S2 in supplementary materials), where in more than than threescore% of the respondents answered "push button" related factors in response to "Why did you leave your rural hometown?" A dominant response had been "No hereafter in hometown, practice not like rural life-style", which is linked to unemployment or underemployment in rural areas (22% in RUMiC 2008; 27% in RUMiC 2009). This was in spite of low levels of migration in 2007 and 2008 (Fig. three). In social club to further test if low income in rural areas plays a role, model 3 of Table 2 shows the regression of expected income in rural areas with migration. A poorer fit and lower R suggests that that model was a poorer interpreter of migration statistics. Table ii, models 4 and v also evidence linear regression of μ _RU with \(\eta ^\ell\) (i.e., relative gradient in expected income) and unemployment in urban areas, respectively. Both are insignificant at the 99% confidence level suggesting that "pull" factors are not detectable at the Jiangsu province calibration using the time series over the selected menstruation. Model 6 of Table 2 further shows that neither urban to rural gradient in expected income nor income volatility in rural area appear to influence rural to urban migration.

Table 2 Parameters and statistics of linear candidate models

Full size table

In response to the question "Accept you been back to your abode village for longer than 3 months after migrating to the city?" 86.9 and 91.9% of rural to urban migrants take not been back to their dwelling hamlet for longer than 3 months in 2007 and 2008, respectively, which means near migrants in Jiangsu are not temporary migrants (Table S3 in supplementary materials). Furthermore, in response to the question "If you were all the same in your home village, how much do you approximate y'all could earn every calendar month?", a median answer of ~800 CNY/month was given (Table S4 in supplementary materials). According to the statistical yearbook of the Jiangsu province, achieving a monthly total income of 800 CNY per calendar month, i.eastward., 9600 CNY per year, is higher up the average income level of rural population (8119.five CNY in 2007 and 9092.viii CNY in 2008). This indicates that the migrants in Jiangsu are not the poorest in rural areas.

The statistically meaning relationships between migration and unemployment (and not rural or urban income) corroborates with RUMiC information that migration inside the Jiangsu province is likely to be occurring due to poorer employment opportunities in rural areas in spite of the possibility of earning above average income. Given that the decision to drift is difficult and has costs associated with it, several studies take establish that migrants tend not to be poorest and are frequently driven by under/unemployment (Chen et al., 2019; Massey et al., 1993). This likely explains the lack of sensitivity of migration to the income gradient over time.

We generated a migration fourth dimension series for Jiangsu province based on diverse socio-economic data sets for the beginning time. This immune united states to not just investigate its sensitivity to unemployment or income gradients, just also to whatsoever changes in income over lagged time periods (equally a measure of volatility). Statistically significant correlation betwixt lagged unemployment and migration suggests that a window of ii–4 years subsequently experiencing unemployment is crucial. This is based on the notion that humans form expectations based on past events (encounter Eq. (10b)). Thus, high unemployment is plenty to trigger a farmer to decide to migrate in the next 2–4 years, irrespective of whether he or she will earn ameliorate or worse living in urban areas. This suggests that households announced to wait for two–4 years, possibly reflecting on the social costs of migration before migrating. Farther, such decisions do not appear to be sensitive to changes in rural income over lagged periods (Table two) further indicating that rural–urban migration in Jiangsu provide is pushed by unemployment in rural areas, often undertaken past relatively rich who appear sensitive neither to maximizing their household income nor minimizing income gamble.

Rural to urban migration in the Jiangsu province is a transfer of labor from more often than not chief employment in rural areas to secondary and tertiary employment in urban areas. Figure x shows that a ane% contraction in rural labor demand can result, on average, in sixteen,000 more people migrating from rural to urban areas at Jiangsu provincial scale. Given that migrants appear to experience ii–4 years of unemployment earlier migrating, provincial or local governments have some lead time to prepare for a migrant wave. A shock in rural employment may announced due to shocks in input prices of fertilizers or machinery, of agronomical outputs or farthermost drought conditions. Such shocks thus may be considered as predictive triggers of above normal rural to urban migration.

Conclusions

This paper interpreted rural–urban migration in the Jiangsu province of China for the final thirty years. The cyberspace migration period from rural to urban areas was analyzed together with rural and urban unemployment rates and incomes in order to unravel the underlying mechanisms.

The labor demands in rural agricultural and nonagricultural sectors were estimated based on Cobb–Douglas production functions. Simulations showed that rural agricultural labor demand declined chop-chop around 1995, every bit a outcome of rising input prices. Its comparison with the official surveyed data of rural labor indicated that many in rural areas whom officially registered employed in the agricultural sector were underemployed. Such rural underemployment was therefore implicitly considered in the estimated rural unemployment when interpreting rural to urban migration.

We constitute that migration from rural to urban areas was sensitive to rural unemployment rate lagged by 2–4 years. However, our analysis was unable to detect any significant correlation between migration and atmospheric precipitation anomalies, urban employment or income (neither rural nor urban), or volatility in rural income. Likewise corroborated by RUMiC 2007 and 2008 surveys, this thus suggests that rural to urban migration in Jiangsu is a result of rural unemployment (and underemployment) that is pushing rural residents to urban areas. Economic or employment shocks that atomic number 82 to unemployment may therefore be considered equally predictive triggers of above normal rural to urban migration since a 1% increase in such unemployment tin button, on average, 16,000 people from rural to urban areas within 2–4 years.

Data availability

The datasets analyzed in the report are listed in the supporting information. The datasets generated in the current report and plotted in the figures are available in the Dataverse repository: https://doi.org/ten.7910/DVN/BJWIOB

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Acknowledgements

Thanks are due to Nathan Hatch for his comments on the paper. The authors are solely responsible for any errors or omissions that may remain.

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Affiliations

1. Department of Water Management, Delft Academy of Technology, 2628CN, Delft, Netherlands

   Haoyang Lyu & Saket Pande

2. College of Hydrology and Water Resource, Hohai University, Nanjing, Mainland china

   Haoyang Lyu & Zengchuan Dong

3. School of Civil and Environmental Engineering science, Faculty of Engineering science and Information Applied science, Academy of Technology Sydney, PO BOX 123 Broadway, Sydney, Australia

   Mahendran Roobavannan & Jaya Kandasamy

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Correspondence to Saket Pande.

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Lyu, H., Dong, Z., Roobavannan, One thousand. et al. Rural unemployment pushes migrants to urban areas in Jiangsu Province, Prc. Palgrave Commun 5, 92 (2019). https://doi.org/ten.1057/s41599-019-0302-1

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• Received: 18 February 2019

• Accepted: 16 July 2019

• Published: thirteen August 2019

• DOI : https://doi.org/10.1057/s41599-019-0302-1

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