Abstract
This paper aims to contribute to the literature on rural-to-urban migration in developing economies by shedding more light on the hitherto little-investigated linkage between the rural sector and the process of urban development. To this end, we combine existing spatial rural-to-urban migration frameworks and extend them by including some key assumptions of the urban development theory. Specifically, we construct a dynamic rural-to-urban migration model in which the links between the urban and rural areas are modeled using the spatial variables of rural land rents and an urban productivity spillover, in order to investigate the contribution of the rural labor supply to urban agglomeration and city growth. The results of our model indicate that rural land rents affect the optimal level of urban agglomeration in scenarios of both unplanned and planned migration, and that the city planner’s solution of optimal agglomeration may not necessarily be inferior to the market solution. In addition, we find that due to their impact on rural workers’ incomes, the spatial variables exert an influence on the steady-state level of growth to which the urban economy can converge. Our study bears policy implications, including the need for city planners to consider spatial variables in order to achieve the desired trade-off between city growth and urban agglomeration.
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Notes
For a comprehensive literature survey, see Lall et al. (2006).
In addition, classical new economic geography (NEG) models, which have been used extensively in the literature to characterize the level of concentration of economic activities and agglomeration dynamics in urban areas (see vom Berge 2013; Krugman 1991; Fujita et al. 1999), have the limitation of assuming an immobile rural labor supply.
The hypothesis of a gap in the expected level of income between urban and rural dwellers triggering migration was formalized initially in the well-known Harris–Todaro (HT) model (Harris and Todaro 1970). In contrast to the HT model, Brueckner’s (1990) simplified model does not consider urban unemployment.
In these and subsequent theoretical contributions, different sets of distance-dependent spatial variables (such as commuting costs, land rents, house size, etc.) are utilized to formulate a rigorous economic explanation for a variety of observed regularities in the spatial structures of real-world cities (Brueckner 1987). For a detailed literature review, see Henderson (2004).
As highlighted by Brueckner and Lall (2015), a dynamic, rather than a static approach would instead allow to overcome the limitation of a stable population split between a rural and an urban area. In fact, this assumption does not reflect the reality in developing countries, where rural-to-urban migration represents an ongoing process leading to different scenarios of urban growth.
For simplicity, physical capital is not included in the model. Also, in line with Brueckner (1990), we ignore the possibility of unemployment.
The exogenous productivity factor includes, for instance, the quality of the infrastructures, the efficiency and quality of public utilities, etc.
Even if rural workers are house owners, their own occupation of the dwelling represents an opportunity cost based on the potential rent lost by not renting out their house to other workers.
Here, we use the terms income, salary, and earning interchangeably.
As highlighted by Jixiang et al. (2015), due to a gap in the urban–rural education system, the accumulation of human capital by rural migrants in the city might be lower than that achieved by native urban workers. For simplicity, in the present analysis, we do not consider the role of substitutability of human capital between rural and urban workers.
The issue of congestion mainly affects the urban area, since it is the urban area that is likely to suffer from space constraints; conversely, rural areas are either not (or are remarkably less) affected by (severe) issues of congestion due to overpopulation (Haggblade et al. 2007). For this reason, we model congestion externalities only for the urban area.
In the model, the cost of human capital acquisition is considered constant. We acknowledge that this simplification represents a limitation, since in reality the cost of investing in human capital will differ for different time periods, i.e., the cost borne by the individual worker of investing in education over a limited number of years is generally lower than the cost of investing in education over a higher number of years (Becker 1962).
We further assume a one-to-one response in relation to the amount of urban population with respect to changes in congestion costs.
In the literature, the migration costs incurred by rural migrants refer mainly to the fixed costs associated with mobility (Miyao and Shapiro 1979; Laing et al. 2005; Lee 2015). Our theoretical assumption of costless migration can be justified by recent empirical findings, which demonstrate that the mobility costs in developing economies have reduced drastically over time (see, e.g., Imbert and Papp 2020; Liu and Meng 2019), mainly due to transport improvements. At the same time, these works point out that non-monetary forms of migration costs (such as psychological costs) have been increasing.
We assume that rural workers relocating to the urban area do not consider congestion costs.
In order for \(\bar{\bar{\delta _{t}^{u}}}\) to be positive, the condition where \(\bar{\varPsi } \omega > \kappa ^{u} \pi _t \tilde{s_t}^\gamma - \phi \tilde{s_t} - \kappa ^r \pi _t + \hat{r}\) must always hold.
This condition likely verifies when the exogenous productivity factor in the rural area increases, for instance because of an improvement in infrastructure quality. This has indeed been shown to be crucial for increasing productivity levels in rural areas (see Duvivier 2013).
As specified above, in our model we do not consider migration costs. However, the inclusion of a constant term to capture the mobility costs associated with migration in the wage equation for the urban income would not modify the results for the planned and unplanned urban congestion scenarios.
The average level of human capital in the whole economy depends solely on the average level of urban human capital, since in the rural area we postulated no human capital. Indeed, denoting \(\bar{s}_{t}\) as the average level of human capital in the whole economy, we obtain: \(\bar{s}_{t}\) = \((1-\delta _t)0 + \delta _t s_t\). Thus, \(\bar{s}_{t}\) = \(\delta _t s_t\). For simplicity, we assume there is no depreciation.
A more sophisticated spatial framework could model different levels of agglomeration within the city in function of the proximity to the CBD (see, e.g., Duranton and Puga 2004). However, this is beyond the scope of this paper.
Specifically, the degree of urban agglomeration affects the growth process both directly and indirectly. Directly as it appears in Eq. (7), and indirectly because in affecting the level of technology, it automatically influences the optimal level of human capital chosen by urban workers (as the latter depends on \(\pi _t\)), which ultimately will affect the final equation of motion. It is possible to show this by plugging \((\delta _t s_t)^\rho \) into expression (4), to obtain: \(s_t = \left[ \frac{\gamma \kappa ^{u} (\delta _{t-1} s_{t-1})^\rho }{\phi }\right] ^{1/(1-\gamma )}\), which in turn will have an impact on the final equation of motion in expression (7). Of course, an essential condition for generating growth is that the levels of human capital and degree of urban agglomeration at time \(t-1\) are higher than the current level of technology at time t (i.e., (\(\delta _{t-1} s_{t-1})^\rho >\pi _t\)).
Since \(\delta _t=0\), in the expression for the rural income the value for rural land rents and the spillover effect (which both depend on the distance from the urban area) will be zero.
\(\pi _{t+1}^p\) will be concave iff \((1-\gamma )/\gamma <1\); conversely, for certain low levels of \(\gamma \), it may be that \((1-\gamma )/\gamma >1\), and therefore, the same function of motion will end up being convex. In that case however, nothing would change if the function \(\pi _{t+1}^f\) is concave and crosses the function \(\pi _{t+1}^p\) below the \(45^\circ \) line.
The initial level of technology could also start above \(\tilde{\pi _2}\), but in such a case, a downturn in investments in human capital (and in the urbanization rate) eventually would restore the equilibrium to the steady-state level of \(\tilde{\pi _2}\).
A more sophisticated modeling of the speed of convergence of rural-to-urban migration and agglomeration toward the steady-state level is beyond the scope of this paper. However, the empirical evidence demonstrates that the most common patterns of convergence in developing economies are gradual, rather than being characterized by episodes of fast mass relocation. As Lee (2015) suggests, the emergence of such patterns may be attributable to frictions related to the fixed costs incurred by rural migrants (such as information and search costs) when relocating to the city.
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Acknowledgements
The author is indebted to Gaetano Alfredo Minerva and Antonio Minniti for their useful comments and suggestions. This paper benefited also from comments from Jackie Krafft, Michele Pezzoni, Xin Meng, Robert Gregory and Sen Xue, and comments from participants in the Research School of Economics of the Australian National University (ANU) seminars. Finally, the author thanks two anonymous referees and the editor for useful remarks, which greatly improved the paper. This work was supported by the French government through the UCAJEDI Investments in the Future project managed by the National Research Agency (ANR), reference number ANR-15-IDEX-01.
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Appendices
Appendix 1
Dynamics of \(\pi _{t+1}^p\) and \(\pi _{t+1}^f\) functions
The dynamic evolution of the technological level of the economy will depend on the shape of the function \(\pi _{t+1}\). Specifically:
where:
and:
From this, \(\pi _{t+1}^p < 0\) when the following conditions are met: \(\pi _t< \frac{\phi }{\kappa ^{u} \varTheta ^{(\gamma -1)/(1-\gamma )}} \qquad \wedge \qquad \pi _t < \frac{2\hat{r} + \bar{\varPsi } \omega }{2 \kappa ^r}\nonumber \), or \(\pi _t < \frac{\phi }{\kappa ^{u} \varTheta ^{(\gamma -1)/(1-\gamma )}} \qquad \wedge \qquad \pi _t > \frac{2\hat{r} + \bar{\varPsi } \omega }{2 \kappa ^r}\), with \(\varTheta ^{2/(1-\gamma )} \left( \frac{-2\phi }{\bar{\varPsi }(2 q + \omega )} + \frac{2 \kappa ^{u} \pi _t \varTheta ^{(\gamma -1)/(1-\gamma )}}{\bar{\varPsi }(2 q + \omega )}\right) > \varTheta ^{1/(1-\gamma )} \frac{2\hat{r} + \bar{\varPsi } \omega - 2 \kappa ^r \pi _t}{\bar{\varPsi }(2 q + \omega )}\). The same conditions are always satisfied when \(\pi _t > \frac{\phi }{\kappa ^{u} \varTheta ^{(\gamma -1)/(1-\gamma )}} \qquad \wedge \qquad \pi _t < \frac{2\hat{r} + \bar{\varPsi } \omega }{2 \kappa ^r}\nonumber \), with \(\varTheta ^{2/(1-\gamma )} \left( \frac{-2\phi }{\bar{\varPsi }(2 q + \omega )} + \frac{2 \kappa ^{u} \pi _t \varTheta ^{(\gamma -1)/(1-\gamma )}}{\bar{\varPsi }(2 q + \omega )}\right) < \varTheta ^{1/(1-\gamma )} \frac{2\hat{r} + \bar{\varPsi }\omega - 2 \kappa ^r \pi _t}{\bar{\varPsi }(2 q + \omega )}\). Otherwise, the function \(\pi _{t+1}^p\) is strictly positive.
In differentiating \(\pi _{t+1}^p\) with respect to \(\pi _t\), we obtain:
In the relevant range where \(\pi _{t+1}^p\) and \(\delta _t\) are both positive, the first expression in brackets of \(\frac{\partial {\pi _{t+1}^p}}{\partial {\pi _t}}\) is positive. The second expression in brackets of \(\frac{\partial {\pi _{t+1}^p}}{\partial {\pi _t}}\) is positive as long as \(\varTheta > (\frac{\kappa ^r}{\kappa ^{u}})^{\frac{1-\gamma }{\gamma }}\), which can be rewritten as \(\pi _t > \frac{\phi }{\gamma } {\kappa ^r}^\frac{1-\gamma }{\gamma } (\frac{1}{\kappa ^{u}})^\frac{1}{\gamma }\), which is always the case in the relevant range in question. The function reaches its minimum at \(\pi _t = \frac{\phi }{\gamma } {\kappa ^r}^\frac{1-\gamma }{\gamma } (\frac{1}{\kappa ^{u}})^\frac{1}{\gamma }\) and is monotonically increasing thereafter.
The evolution of the economy will eventually depend on the curvature of the \(\pi _{t+1}\) function. In particular, the number of times that this function intersects the \(45^\circ \) line (where \(\pi _{t+1}^p = \pi _t\)) will be a crucial factor for the dynamic path of the economy. Subsequently, in taking the second derivative of \(\pi _{t+1}^p\), we get:
To simplify the calculations, label \(\varTheta ^{2/(1-\gamma )} \left( \frac{-2\phi }{\bar{\varPsi }(2 q + \omega )} + \frac{2 \kappa ^{u} \pi _t \varTheta ^{(\gamma -1)/(1-\gamma )}}{\bar{\varPsi }(2 q + \omega )}\right) = \varPhi \) and \(\varTheta ^{1/(1-\gamma )} \times \frac{2\hat{r} + \bar{\varPsi } \omega - 2 \kappa ^r \pi _t}{\bar{\varPsi }(2 q + \omega )} = \varOmega \).
The function \(\pi _{t+1}^p\) will be convex when \(\frac{\partial ^2{\pi _{t+1}^p}}{\partial {\pi _t}^2}\) is positive, and this happens when both \(\varPhi <0\), \(\varOmega <0\). Or when \(\varPhi >0\) and \(\varOmega <0\), with \( |\varPhi |< |\varOmega |\). Or \(\varPhi <0\) and \(\varOmega >0\), with \(|\varPhi |> |\varOmega |\). In these three cases, the slope of the monotonically increasing function of motion will be increasing for \(\forall {\pi _t}\) and therefore there will always be a single intersection point where \(\pi _{t+1}^p = \pi _t\).
Conversely, \(\pi _{t+1}^p\) will be concave when \(\frac{\partial ^2{\pi _{t+1}^p}}{\partial {\pi _t}^2}\) is negative, and this happens when both \(\varPhi >0\), \(\varOmega >0\). Or when \(\varPhi >0\) and \(\varOmega <0\), with \(|\varPhi |> |\varOmega |\). Or \(\varPhi <0\) and \(\varOmega >0\), with \(|\varPhi |< |\varOmega |\). In these cases, the slope of the monotonically increasing function of motion will be decreasing and therefore there could be zero, one or two values of \(\pi _t\) to allow tangency points (i.e., when \(\pi _{t+1}^p = \pi _t\)); depending on the values of the parameters, it can hence be the case that for \(\forall {\pi _t}\), \(\pi _{t+1}^p < \pi _t\). In this case, there will be no tangency points. On the other hand, if the condition \(\pi _{t+1}^p = \pi _t\) is verified, we have one single crossing point. Finally, if \(\pi _{t+1}^p > \pi _t\), there will be two tangency points.
For the study of the \(\pi _{t+1}^f\) function, the calculations are relatively easier. In differentiating \(\pi _{t+1}^f\), we get:
The function is hence monotonically increasing. Taking the second derivative, we obtain:
Therefore, \(\pi _{t+1}^f\) will be monotonically increasing and globally concave if \(\gamma + \rho < 1\), and monotonically increasing but globally convex if \(\gamma + \rho > 1\).
Appendix 2
\(\partial {\pi _{t+1}^p}/\partial {\pi _t} > \partial {\pi _{t+1}^f}/\partial {\pi _t}\).
Proof
Consider the point where \(\delta _t = 1\), and \(\pi _{t+1}^p = \pi _{t+1}^f\). Recalling that:
and
at the intersection of the two functions we have that:
Assume now that \(\frac{\partial {\pi _{t+1}^p}}{\partial {\pi _t}} > \frac{\partial {\pi _{t+1}^f}}{\partial {\pi _t}}\). This entails that:
Simplifying, we obtain:
which leads to:
or:
Since \(y_{t}^{r} \ge 0\) and \(0 < \delta _t \le 1\), given expression (2), the above inequality is always true. \(\square \)
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Baudino, M. Rural-to-urban migration in developing economies: characterizing the role of the rural labor supply in the process of urban agglomeration and city growth. Ann Reg Sci 66, 533–556 (2021). https://doi.org/10.1007/s00168-020-01028-9
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DOI: https://doi.org/10.1007/s00168-020-01028-9