For India’s Rural Poor, Growing Towns Matter More Than Growing Cities
Introduction
There appears to be a broad consensus among development economists that agricultural growth, and rural development more broadly, is good for rural poverty reduction (although this was not always widely accepted).1 Models of the development process have also attached importance to the scope for rural poverty reduction through urban economic growth, and some observers have seen this as the more important channel for rural poverty reduction.2 Urban economic growth is expected to contribute to reducing rural poverty through two main channels:
- (i)
Labor absorption: an expanding urban economy will benefit the rural poor by either absorbing surplus rural labor, as in the classic Lewis (1954) model, or by tightening rural labor markets (leading to higher wage rates).
- (ii)
Backward linkages: growth in the urban economy increases public or private resources that benefit the rural poor; for example, greater urban demand for rural products may increase rural incomes or labor-augmenting technical progress in urban areas may increase the remittances sent back to rural families.
The strength of these channels has been an important issue for setting development priorities for India, as elsewhere. The evidence suggests that India’s urban economic growth in the post-Independence period up to around 1990 did rather little to reduce rural poverty, although urban growth had reduced urban poverty, and rural poverty was primarily driven by rural growth (Ravallion & Datt, 1996). Since economic reforms began in earnest in India in the early 1990s, there has been considerable progress in reducing poverty, with trend rates of decline that are higher than in the pre-reform era (Datt, Ravallion, & Murgai, 2016). The indications are that urban economic growth since the early 1990s has been more poverty reducing, and that this has come with larger gains to India’s rural poor (Datt and Ravallion, 2011, Datt et al., 2016).
Lanjouw and Murgai (2014) conjecture that the link from urban development to rural poverty reduction is stronger if urban economic growth stems from India’s secondary towns rather than from the big cities. The secondary towns may be more tightly connected to the surrounding rural hinterland than are the cities, so growth in small towns may have more effect on rural poverty. Yet it is the big cities, defined as those with population above one million, that have the lowest poverty rates and that appear to be growing faster than the smaller statutory towns, with the share of the urban population in the big cities rising from 38% in 2001 to 42% in 2011 (Tripathi, 2013). Higher wage rates in larger cities will to some degree spill over to the towns and rural areas both through labor market adjustment and because they may generate larger trade and remittance flows. Thus, it is theoretically ambiguous whether larger cities generate larger gains to the rural poor.
The Lanjouw and Murgai hypothesis that India may have experienced faster poverty reduction if smaller towns had grown as fast as the cities is consistent with evidence from other countries on the relationship between poverty and city size (Ferré, Ferreira, & Lanjouw, 2012). However, it is difficult to test this hypothesis for India, or more generally to relate variation in growth of different types of cities to variation in rural poverty reduction. One difficulty arises because it is only once every ten years that city growth (in terms of population rather than economic output) is measured in India, using the census. A lot of the variation in rural poverty reduction occurs within a ten-year censual period and so would be missed by studies that rely on the census data to measure urban growth. Another difficulty is the absence of timely and spatially detailed (e.g., at city level) economic statistics.
This study tests the hypothesis that it is the growth in India’s secondary towns, rather than the big cities, that matters most for rural poverty reduction. Recognizing the lack of spatially disaggregated production data, we use night lights data to indicate urban economic growth, following Henderson, Storeygard, and Weil (2011). We distinguish between growth on the extensive and intensive margins, and between the growth of cities and of secondary towns. These new measures of urban economic activity using night lights data are econometrically related to sub-national poverty estimates that are formed at a finer spatial resolution than in the existing literature. Specifically, we use a division of India into 59 National Sample Survey (NSS) regions that are more finely grained than the usual division into states and union territories. Our study covers four observations for each of these regions between 1993/94 and 2011/12, based on NSS “thick” rounds (with larger sample sizes such that the survey is representative at the NSS regional level). We also account for the spatial autocorrelation that is increasingly apparent in patterns of rural poverty in India.
The following section provides a simple theoretical model of a three-sector labor market in which one of the sectors—the “big city”—has a labor market distortion, but wages are flexible in the other two sectors, the secondary towns and the rural hinterland, with workers free to move between the two. For this model, we derive conditions under which a given proportionate gain in output of the big cities has less impact on the rural wage rate than does growth in output of the secondary towns. However, this is only one possible outcome. Even in this simple model, city growth could more effectively “trickle down” to the rural poor. It is an empirical question as to which type of urban growth is better for the rural poor.
Section 3 describes our data for addressing that question, in which we have formed a regional panel data set, combining results from household surveys with data on the extent of nightlight. Section 4 explains our econometric model, which is calibrated to the panel data. Alternative models are described and are shown to be testable restrictions on our preferred (encompassing) specification. Our results are then presented in Section 5, which provide strong support for the hypothesis that economic growth in secondary towns has more impact on rural poverty than does growth of the big cities. Section 6 concludes.
Section snippets
A simple theoretical model
The purpose of the following model is to illustrate one source of urban–rural linkage, namely through the labor market, for which urban economic growth emanating from cities brings different gains to the rural poor than growth in towns. We suppose that the urban economy comprises two sectors, a town and a city. These are, of course, spatially separated, and there is also a rural hinterland. (In our empirical work we will use regional observations, with inter-regional spillover effects, but we
Poverty data
Our poverty data are based on estimates of real household consumption that are measured in four “thick” rounds of household surveys conducted by the National Sample Survey Organization (NSSO). These “thick” rounds each have a sample size of over 100,000 households as opposed to the more frequent “thin” rounds. We use data from 1993/94 (50th round), 2004/05 (61st round), 2009/10 (66th round), and 2011/12 (68th round). The NSSO surveys began in the early 1950 s and in terms of international norms
The econometric model
Spatial autocorrelation in levels of, and changes in, poverty was noted in 3(a) 3(), and our econometric modeling recognizes this by using the spatial panel estimator of Belotti, Hughes, and Mortari (2017). The regional N-vector of poverty measures for date t (=1,…,T) is denoted and the matrix of explanatory variables is which includes our measures of night lights. Our spatial Durbin model (SDM) can be written as:
Effects of unmasked regional lights on rural poverty
We start by examining whether night lights have any effect at all, before we compare effects of lights coming from big cities with those from smaller towns. We refer to these results as “unmasked” since we consider the entire area of each NSS region without first masking the pixels lit by the big cities.
Table 3 reports the estimates of the SDM for the headcount poverty index, H and the poverty gap index, PG for four different ways of using the night lights data. In columns (1) and (5), the
Conclusions
The scope for escaping rural poverty through urban economic growth has been a longstanding development issue, going back to the classic model of Lewis (1954). We have revisited this issue, focusing specifically on the question of whether cities or towns are better generators of rural-poverty reducing growth in India, using data on 59 regions observed four times from 1993/94 to 2011/12. The rural headcount poverty rate fell by half in this period and the poverty gap index fell by two-thirds. The
Acknowledgments
We are grateful to two anonymous referees and participants in the Secondary Towns, Jobs and Poverty Reduction Conference at the World Bank for their helpful comments, and to Geua Boe-Gibson for the preparation of the maps.
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