Urban Concentration and Poverty in Developing Countries

Transcription

1 Growth and Change Vol. 48 No. 3 (September 2017), pp DOI: /grow Urban Concentration and Poverty in Developing Countries KHALID SEKKAT ABSTRACT We investigate the impacts of urban concentration (share of the population living in large cities) on poverty in developing countries. We use instrumental variables to estimate a system linking urban concentration, growth and urban and rural poverty. The results show that the importance of the population living in (small) cities (less than 0.5 million inhabitants) or very large cities (beyond 5 million inhabitants) has no impact on poverty. The importance of cities of 1 to 5 million inhabitants is poverty reducing. We conclude that developing countries with a large share of the population living in very big cities could reduce poverty by deconcentrating their urbanization toward cities of between 1 and 5 million inhabitants. Introduction U rban concentration and poverty are two prominent characteristics of many developing countries. Defined as the share in total population of population living in big cities (see below), urban concentration differs from the traditional concept of urbanization 1 which refers to the share of population living in cities (irrespective of the size of the city). Urban concentration represents serious challenges for the development process and is steadily increasing in many developing countries. Following the United Nations (2012), nine out of the 30 largest urban agglomerations (according to the share in total population) in 1950 were in developing countries. In 2010, the number rises to 21 out of 30. It is expected that in 2025, 24 will be in developing countries. The 21 largest urban agglomerations in 2010 are located in 13 developing countries 2 among which 11 have a national poverty rate above 20 percent. In five cases out of 11, the national poverty rate is above the median of all developing countries for which we have data (i.e., almost half of the concerned countries). Although data problems do not allow a similar precise calculation for the poverty gap between rural and urban areas, a similar proportion seems to hold, i.e., almost half of the concerned countries has a gap higher than the median of all developing countries for which we have data. These observations suggest a possible association between urban concentration and poverty in developing countries. The purpose of this paper is to investigate the possible causal link between urban concentration and poverty in developing countries. Urban concentration is defined as the share in total population of the population living in big cities. However, the literature provides no common definition of a big city. For instance, Henderson (2003) used the primacy concept considering only the largest city of the country. Ke and Feser Khalid Sekkat is a Full Professor of Economics at the University of Brussels, Brussels, Belgium and Research Fellow of the Economic Research Forum, Cairo, Egypt. His address is: ksekkat@ulb.ac.be. He would like to thank two anonymous reviewers for very useful comments which have highly improved the paper. He is grateful to Tarek Moursi, Ragui Assad, Sultan Abou-Ali, and participants in the ERF-GDN Workshop on The Challenge of Urbanization in the ERF Region (Cairo, Egypt, December 23, 2012) for useful comments on an earlier version of this paper. The usual disclaimers apply. Submitted July 2015; revised December 2015; accepted February VC 2016 Wiley Periodicals, Inc

2 436 GROWTH AND CHANGE, SEPTEMBER 2017 (2010) and Chen and Partridge (2013) based their analysis on an administrative approach focusing on capitals of provinces, prefectures or counties. Others like Bertinelli and Strobl (2007) considered the share of the urban population living in cities of more than 750,000 inhabitants. Both the primacy and the administrative approaches are unsatisfactory because on the one hand there are often cities other than the largest ones that also account for large proportions of the population and on the other hand capitals are sometimes smaller than other cities in a country (see Bertinelli and Strobl 2007). In this paper, we adopt the approach based on population: the share of the population living in cities larger than a certain threshold. Rather than setting a threshold a priori, we will consider different levels to identify a potential one that is crucial for the relationship between urban concentration and poverty. The development of big cities has important socio-economic not only on these cities themselves but also on their neighborhoods. There are direct and indirect. Both of them can be positive or negative. In terms of positive direct, big cities can create employment opportunities for population in the surrounding areas or create new opportunities for firms to locate in a city smaller but closer to a big city in order to serve the larger market. However, big cities may (Chen and Partridge 2013) also generate direct negative. By capturing the most productive human and physical capitals, big cities may hamper the development of surrounding areas, where short term economic prospects seem less promising, and increase interregional and even intraregional inequalities (Ke and Feser 2010). Moreover, big cities may also induce higher cost of living (e.g., cost of housing), dispersion of environmental pollution and displacement of some illegal activities (to make them distant from big policy department) to the neighboring smaller cities. Following Henderson (2002) the costs of such externalities is underestimated in many developing countries. Beside the above direct, economics suggest an indirect effect. On the one hand, Krugman (1991) and others have shown that big cities may foster macroeconomic growth stemming from firms agglomeration in the same place. On the other hand, Dollar and Kraay (1992) have found that macroeconomic growth translates 1 to 1 in poor families income growth. Hence, there might be an indirect effect of urban concentrartion on povery. However, Henderson (2003) shows that there is a threshold level of urban concentration in terms of effect on growth and, hence, over or underconcentration can be very costly in terms of growth. Confirming this finding, Br ulhart and Sbergami (2009) find that urban concentration boosts GDP growth but only up to a certain level of economic development beyond which the effect may become negative. The threshold in term of per-capita income is around USD 10,000; which roughly corresponds to the current per-capita income of Mexico or Bulgaria. In sum, like the direct, the indirect of urban concentration on poverty may be positive or negative. Therefore, assessing the of urban concentration may provide very useful policy implications; especially as many countries worry about being under or over urbanized and about the size of their cities. Some of them like Korea, Thailand, and Indonesia are even pursuing a strategy of deconcentration from the core primate city into its suburbs or satellite cities (Davis and Henderson 2003). However, despite their importance for development, there is very little evidence about the possible causal of big cities on poverty in developing countries. Most of the empirical works dealing with these countries focused on the impact of urbanization (not urban concentration) on productivity, employment or per caipta income growth (see Henderson 2010). The relationship with growth sheds light on the possible indirect effect on poverty referred to above. Henderson (2003) and Au and Henderson (2006) show that urban concentration and growth are related by an inverted U relationship. Ke and Feser (2010) and Chen and Partridge (2013) found mixed from the development of big cities on smaller cities or rural counties. The few papers which have focused

3 URBAN CONCENTRATION AND POVERTY 437 specifically on poverty are Cali and Menon (2012), Christiaensen and Todo (2014), and Ferre, Ferreira, and Lanjouw (2012). Cali and Menon (2012) focused on India and establish a substantial and systematic poverty-reducing effect of urbanization in the surrounding rural areas. Christiaensen and Todo (2014) showed that in 50 developing countries the move from agriculture to rural nonfarm activities and to secondary cities induces faster poverty reduction than agglomeration in mega cities. Finally, Ferre, Ferreira, and Lanjouw (2012) observe an association, not a causality, between urban concentration and poverty in eight developing countries. Using a panel of developing countries (see Appendix A) and instrumental variables (IV), our analysis seeks to fill the gap in the literature by testing the existence of a possible causal link running from concentration in big cities to poverty. Note that a reverse causality running from poverty to concentration in big cities might exist. For instance urban concentration might increase if poverty pushes rural people or those living in smal cities to move to larger cities. While this an intresting issue, this paper does not deal with it. In addition to the novelty of the question it addresses, the paper offers three additional contributions. First, it considers different thresholds for the definition of a big city rather than setting a given one a priori. The objective is to identify a potential threshold that is crucial for the relationship between urban concentration and poverty. Second, the paper examines the impacts on the rural-urban poverty gap because regional polarization might pose important problems in terms of social cohesion, political stability, and even growth. Finally, the literature suggests that urban concentration has, at least, two on poverty: direct and indirect which may each be positive or negative (see above). Disentangling the relative importance of these can be very useful for policy purposes. For instance, if the potentially negative direct impact of urban concentration is more important than its potentially positive indirect impact, then a policy of urban deconcentration would be recommended to reduce poverty. In the opposite situation, policies fostering growth (even through more urban concentration) could be recommended. The analysis is conducted on a sample of 40 developing countries over the period Taking non-overlapping 5-year averages of the variables, it estimates a three equation system. One equation considers the direct impact of urban concentration on rural poverty. A second equation does the same for urban poverty. Both equations allow macroeconomic growth to affect poverty. The third equation explains macroeconomic growth in terms of urban concentration in addition to traditional variables. This allows for recovering the indirect effect. The results of the analysis support the need for urban deconcentration, to a certain degree, in developing countries but warn against the resulting regional imbalance. The rest of the paper is organized as follows. The next section discusses the relation to the literature. The third section is devoted to a descriptive analysis. The fourth and the fifth sections present the methodology and the econometric analysis. The sixth concludes. Relation to the Literature This section presents some major findings of the literature in order to situate our analysis. The section does not aim at offering a survey of the literature but at highlighting the paper s contribution. A recent survey of the literature pertaining to developing countries is Henderson (2010). As explained in the Section Introduction, urban concentration has direct and indirect impacts on poverty. The direct impacts can play through employment. The proximity to big cities may offer the neighboring workforce interesting work opportunities which, if grasped, may generate other jobs in the origin area such as consumer services and retail trade needed by the growing moving population

4 438 GROWTH AND CHANGE, SEPTEMBER 2017 (Calı and Menon 2012). However, movement of workers from rural to urban areas may induce competition between incumbent workers and migrants on the labor market. Depending on various factors, such a competition might increase or decrease incumbents wages (Card 2009; Ottaviano and Peri 2012) which might, in turn, affect poverty. Another effect stems from the price of rural land which may increase with the growth of the neighboring cities. The impact of such price increase on poverty depends on its distribution across the rural population. It can benefit a few landowners at the expense of landless population dependent on land renting or waged agricultural work (Calı and Menon 2012). Finally, urban concentration might induce congestion and environmental degradation which reduce productivity and increase poverty (Glaeser, Kahn, and Rappaport 2008). For example, Japanese vehicle manufacturers in Thailand consider that Bangkok traffic congestion increases their costs by raising the amount of parts stock they need to hold (Straub 2011). Consumption is a channel through which urban concentration may indirectly poverty. Development of big cities may generate an increase in the demand for rural goods. Remittances sent to rural households of origin by rural-urban migrants are another potentially important channel through which urban concentration affects rural poverty. Moreover, the growth effect of urban concentration may be associated with larger remittance flows to the rural place of origin. Finally the growth of big cities can be associated either with lower consumer prices thanks to increased competition among a larger number of producers in the growing urban area or higher consumer prices due to higher demand for some goods. In what follows, we will first present the empirical evidence pertaining to growth and urban concentration and then we turn to the evidence concerning poverty. Major contributions in the field of growth and urban concentration are Henderson (2003) and Au and Henderson (2006). The former used a panel data of more than 80 developed and developing countries over in 5-year intervals to estimate the of urbanization and urban concentration on productivity growth. Urbanization is defined in terms of importance of urban population while urban concentration is defined in terms of primacy (importance of the population living in the largest city). The paper shows that productivity growth is not strongly affected by urbanization per se, but it is strongly affected by the degree of urban concentration. Concentration, affects growth following an inverted U relationship: growth increases up to a certain threshold of concentration and then starts decreasing. The threshold varies with the level of development and country size. Hence, over or under-concentration can be very costly in terms of productivity growth. Bertinelli and Strobl (2007) examined the effect of urban concentration (the share of the urban population living in cities larger than 750,000 inhabitants) and urbanization on economic growth in a sample of 39 developing countries for the years Although, focusing on developing countries only, the authors found similar results as Henderson (2003): Urbanization has no straightforward effect on economic growth while urban concentration is linked to growth through an inverted U relationship. Au and Henderson (2006) investigated the relationship between the size of 205 Chinese cities and income per worker over the period They found similar inverted U relationship. Urban concentration induces higher real incomes per worker which first increases with city size and then decline very slowly. The authors used their results to show that a large fraction of cities in China are undersized resulting in large income losses. Ke and Feser (2010) test whether there are growth and employment spillovers from cities to smaller counties in the Greater Central China region. They found mixed from prefecture and higher-level cities to comparable or lower-level cities and counties. Positive on gross domestic product and employment growth are also found between county-level cities. However, county-level cities exert negative on rural counties. Chen and Partridge (2013) extended the work of Ke and Feser by considering the entire country. They found

5 URBAN CONCENTRATION AND POVERTY 439 FIGURE 1. IMPORTANCE OF CITIES BY CLASS OF SIZE AROUND THE WORLD. Source: UNO (2012) (percent of total population, Average ) heterogeneous. The greater market potential of the provincial capitals has the highest effect on per-capita GDP growth in the nearest smaller counties. In contrast, the market potential of megacities is inversely associated with per-capita GDP growth in the surrounding areas. Three recent papers examined the impacts of urban concentration on poverty. Cali and Menon (2012) used a large sample of Indian districts between 1983 and They found that the size of urban population of a given district is associated with lower poverty in the surrounding rural areas. The results obtained through an instrumental variable estimation suggest that this effect is causal and is attributable to the positive spillovers of urbanization on the rural economy instead of the movement of the rural poor to urban areas. Christiaensen and Todo (2014) focused on agriculture. Using cross-country panel data for developing countries over the period , they revealed that migration out of agriculture into the rural nonfarm economy and secondary towns yields faster poverty reduction than agglomeration in mega cities. Finally, Ferre, Ferreira, and Lanjouw (2012) showed in eight developing countries that poverty is more widespread and deeper in very small and small towns than in large or very large cities. However, their analysis does not allow the establishment of a causal link between urban concentration and poverty. Descriptive Analysis Following different authors, urban concentration and poverty are nowadays increasing. Where urban concentration is still low, it is expected to increase. Sub-Saharan Africa (SSA) is seen by the United Nations as the fastest urbanizing region of the world and will be predominantly urban by 2030 (Dudwick et al., 2011). In parallel, the poverty of people living in cities seems worsening. Ravallion, Chen, and Sangraula (2007) showed that, between 1993 and 2002, rural poverty worldwide was higher than urban poverty. However the number of rural poor decreased while the number of urban poor increased. Figure 1 presents the importance of small and big cities in five regions of the world: Asia, Latin America and the Caribbean (LAC), Middle East and North Africa (MENA), sub-saharan Africa (SSA) and transitions economies. We consider four classes of city size: Cities with a number of inhabitants between 0.5 to 1 million, 1 and 5 million, 5 and 10 million and over 10 million. In all

6 440 GROWTH AND CHANGE, SEPTEMBER 2017 FIGURE 2. RURAL AND URBAN POVERTY AROUND THE WORLD. (Average ) regions, cities of 1 and 5 million inhabitants represent the largest share of the total population followed by cities of 0.5 to 1 million inhabitants. Except in Asia, cities of more than 10 million inhabitants represent the lowest share of the total population. The importance of cities of more than 5 million inhabitants is negligible in SSA and transition economies. The distribution of the population across city sizes is the least dispersed in Asia. Figure 2 shows that poverty of people living in rural areas seems high. Here we focus on poverty as measured by the Poverty headcount ratio at urban poverty line (in percent of urban population) and the similar for rural areas published in the World Development Indicators (WDI) of the World Bank. The Figure shows that rural poverty is the highest in LAC where the gap between rural and urban poverty is also the largest. SSA ranks second in terms of rural poverty. It also exhibits a large gap between rural and urban areas. The MENA exhibits much lower poverty levels and a lower gap between rural and urban areas. The Methodology and the Data The methodology. The discussion in the previous sections suggests that urban concentration might affect poverty directly and indirectly. The latter effect operates through the relationships concentration-macroeconomic growth and macroeconomic growth-poverty. Both the direct and the indirect might be positive or negative. The proposed methodology investigates the direct and indirect impacts of urban concentration on poverty. To investigate the impacts of the two variables on the rural-urban poverty gap, the analysis distinguishes the impacts on the two areas. We propose the following three equations: P r i;t5a 0 1a 1 y i;t 1a 2 Concentration i;t 1 m i;t (1) P u i;t5g 0 1g 1 y i;t 1g 2 Concentration i;t 1l i;t (2) ln y i;t 2ln yi;t21 5b0 1b 1 ln y i;t21 1b2 ln S Ki;t 1b 3 ln SH i;t 1b4 ln d1g 1n i;t 1 b5 Concentration i;t 1e i;t (3) where P rp is poverty headcount ratio at rural poverty line (in percent of rural population) P up is poverty headcount ratio at urban poverty line (percent of urban population)

7 URBAN CONCENTRATION AND POVERTY 441 TABLE 1. ESTIMATION RESULTS: IMPACT OF BIG CITIES Share of the total population living in cities of 0.5 to 1 Million inhabitants 1 to 5 Million inhabitants GMM GMM Dependent variable: Urban poverty National per capita income (3.179) *** (2.627) *** (2.265) ** (2.763) *** (2.795) *** (1.960) ** Urban concentration (2.679) *** (0.760) (0.928) (0.152) (1.085) (1.675) * Number of observations Adjusted R F-test (Null hypothesis: no fixed ); P-value Hausman (Null hypothesis: random ) test; P-value Correlation between the covariate and fixed First stage F-statistics Test of overidentifying restrictions; P-value Dependent variable: Rural poverty National per capita income (3.934) *** (2.375) ** (2.884) *** (3.795) *** (2.688) *** (2.968) *** Urban concentration (2.093) ** (0.163) (0.176) (1.239) (1.376) (0.033)

8 442 GROWTH AND CHANGE, SEPTEMBER 2017 TABLE 1. (CONTINUED) Share of the total population living in cities of 0.5 to 1 Million inhabitants 1 to 5 Million inhabitants GMM GMM Number of observations Adjusted R F-test (Null hypothesis: no fixed ); P-value Hausman (Null hypothesis: random ) test; P-value Correlation between the covariate and fixed First stage F-statistics Test of overidentifying restrictions; P-value Dependent variable: Growth Log of Initial per capita income (2.429)** (3.595) *** (2.162)** (2.528) ** (4.628) *** (2.203) **

9 URBAN CONCENTRATION AND POVERTY 443 TABLE 1. (CONTINUED) Share of the total population living in cities of 0.5 to 1 Million inhabitants 1 to 5 Million inhabitants GMM GMM Log of Investment ratio (3.622) *** (4.502) *** (3.021) *** (3.696) *** (4.598) *** (2.970) *** Log of Human Capital (2.749) *** (2.156) ** (3.72) *** (2.457) ** (2.073) ** (3.403) *** Log of (d 1 g* 1 n it ) (4.493) *** (4.933) *** (0.771) (4.460) *** (4.783) *** (0.13) Urban concentration (2.050) ** (2.678) *** (1.916) * (3.461) *** (2.842) *** (2.207) ** Number of observations Adjusted R F-test (Null hypothesis: no fixed ); P-value Hausman (Null hypothesis: random ) test; P-value Correlation between the covariate and fixed First stage F-statistics

10 444 GROWTH AND CHANGE, SEPTEMBER 2017 TABLE 1. (CONTINUED) Share of the total population living in cities of 0.5 to 1 Million inhabitants 1 to 5 Million inhabitants GMM GMM Test of overidentifying restrictions; P-value Share of the total population living in cities of 5 to 10 Million inhabitants Over 10 Million inhabitants GMM GMM Dependent variable: Urban poverty National per capita income (2.246) ** (2.521) ** (2.521) ** (2.364) ** (2.523) ** (2.546) ** Urban concentration (0.891) (0.192) (0.537) (12.457)*** (0.278) (0.054) Number of observations

11 URBAN CONCENTRATION AND POVERTY 445 TABLE 1. (CONTINUED) Share of the total population living in cities of 5 to 10 Million inhabitants Over 10 Million inhabitants GMM GMM Adjusted R F-test (Null hypothesis: no fixed ); P-value Hausman (Null hypothesis: random ) test; P-value Correlation between the covariate and fixed First stage F-statistics Test of overidentifying restrictions; P-value Dependent variable: Rural poverty National per capita income (2.515) ** (2.146) *** (2.680) *** (2.711) *** (2.345) ** (2.860) *** Urban concentration (3.038) *** (0.642) (0.546) (6.712) *** (0.468) (0.474) Number of observations Adjusted R F-test (Null hypothesis: no fixed ); P-value Hausman (Null hypothesis: random ) test; P-value Correlation between the covariate and fixed First stage F-statistics Test of overidentifying restrictions; P-value Dependent variable: Growth Log of Initial per capita income (2.313) ** (3.901) *** (1.564) (2.391) ** (3.991) *** (1.661) *

12 446 GROWTH AND CHANGE, SEPTEMBER 2017 TABLE 1. (CONTINUED) Share of the total population living in cities of 5 to 10 Million inhabitants Over 10 Million inhabitants GMM GMM Log of Investment ratio (3.938) *** (4.364) *** (3.033) *** (3.923) *** (4.444) *** (2.568) ** Log of Human Capital (2.367) ** (2.081) ** (2.894) *** (2.330) ** (1.992) * (1.305) Log of (d 1 g* 1 nit) (4.840) *** (5.202) *** (1.705) * (4.807) *** (5.188) *** (0.007) Urban concentration (1.297) (0.331) (0.201) (1.251) (0.84) (0.922) Number of observations Adjusted R F-test (Null hypothesis: no fixed ); P-value Hausman (Null hypothesis: random ) test; P-value Correlation between the covariate and fixed First stage F-statistics Test of overidentifying restrictions; P-value Standard errors are heteroscedastic-consistent. Absolute t statistics are in parentheses. *** 5 significant at 1%, ** 5 significant at 5%, * 5 significant at 10%.

13 URBAN CONCENTRATION AND POVERTY 447 y is average national real per capita income Concentration is urban concentration S K is the rate of savings in physical capital, S H is the rate of saving in human capital, g* is the rate of exogenous technical progress, n is the population growth rate, d is the depreciation rate of physical capital m, l, e are error terms indices i and t refer to the country and time respectively. Equations (1 3) are standards in the literature (e.g., Barro 1991; Dollar and Kraay 1992; Mankiw, Romer, and Weil 1992). The only novelty here is to take urban concentration into account. Since one objective of the paper is to draw useful policy recommendations, we need to disentangle the direct and indirect of urban concentration on poverty. Hence, we must estimate all three equations instead of one reduced form linking poverty and urban concentration. Equations (1 and 2) are direct extensions of Dollar and Kraay (1992). The parameters a 1 and g 1 measure the sensivity of poverty, in rural and urban areas respectively, with respect to national income. Equation (3) is in the tradition of Barro (1991) and Mankiw, Romer, and Weil (1992). The lagged per capita income y i, t-1 captures the possible conditional convergence of income. The variable S K is the investment in physical capital which is expected to have a positive impact on the growth rate. The variable S H is the rate of saving in human capital which should have a positive impact on growth. The parameters a 2 and g 2 give the direct of concentration on rural and urban poverty respectively. The indirect effect on rural and urban poverty is assessed combining respectively the coefficients a 1 with the coefficient b 5 and g 1 with b 5. The data. The sample used to estimate the three equations is determined by the availability of the data. We tried to use the largest available sample of developing countries and the largest possible time coverage. We end up with 40 countries over the period The rate of savings in physical capital and the rate of saving in human capital are measured by the ratio of investment to GDP and the secondary school enrolment ratio respectively. Following Mankiw, Romer, and Weil (1992), we assume that g*1d is equal to All these variables are readily available from the WDI. The definition and the descriptive statistics of the variables are presented in Appendices B, C and D. As explained in Section Introduction, urban concentration is defined as the share in total population of the population living in big cities. Here, we define big cities as the ones having a population larger than a certain threshold. Rather than setting a given threshold a priori, we will consider different levels. Four classes of cities are considered: from 0.5 to 1 million inhabitants, between 1 and 5 million inhabitants, from 5 to 10 million inhabitants and over 10 million inhabitants. Data come from UNO (212). Econometric issues. Traditional estimation of the equations consisted in running a simple OLS on the time average of the variables for each country; i.e. cross-section data. However, Islam (1995) argued that such approach does not allow controlling for countries unobserved. He advocated, and implemented, a panel data approach to deal with this issue. The panel data framework takes of the differences across countries, in the form of country-fixed, into account. A F-test allows for cheking whether the use of fixed is well founded. A common alternative to the fixed model is the random model. The choice between the two is generally based on the Hausman test. However, as shown by Clark and Linzer (2015) the Hausman test is of weak power when the number of periods is small and the number of country is high as it is the case here. These authors suggested basing the decision on the correlation between the independent variable of interest and the fixed. They showed that if the correlation is less

14 448 GROWTH AND CHANGE, SEPTEMBER 2017 TABLE 2. IMPACT OF BIG AND SMALL CITIES GMM GMM Dependent variable: Urban poverty National per capita income (2.339)** (2.346) ** (2.113) ** (2.342) ** (2.595) *** (2.342) *** Share of the total population living in cities of less than 0.5 inhabitants (1.017) (0.445) (0.918) (0.356) (0.595) (1.415) 0.5 to 1 Million inhabitants (2.248) ** (0.280) (1.367) 1 to 5 Million inhabitants (1.875) * (0.673) (2.508) ** Number of observations Adjusted R F-test (Null hypothesis: no fixed ); P-value Hausman (Null hypothesis: random ) test; P-value Correlation between the covariate and fixed First stage F-statistics Test of overidentifying restrictions; P-value Dependent variable: Rural poverty National per capita income (2.870) *** (2.266) ** (3.064) *** (4.135) *** (2.792) *** (3.029) *** Share of the total population living in cities of less than 0.5 inhabitants (0.961) (0.589) (1.281) (0.498) (0.744) (0.984)

15 URBAN CONCENTRATION AND POVERTY 449 TABLE 2. (CONTINUED) GMM GMM 0.5 to 1 Million inhabitants (1.112) (0.994) (0.214) 1 to 5 Million inhabitants (0.254) (1.761) * (0.353) Number of observations Adjusted R F-test (Null hypothesis: no fixed ); P-value Hausman (Null hypothesis: random ) test; P-value Correlation between the covariate and fixed First stage F-statistics Test of overidentifying restrictions; P-value Dependent variable: Growth Log of Initial per capita income (2.402) ** (3.088) *** (1.550) (2.619) *** (3.288) *** (2.158) ** Log of Investment ratio (3.713) *** (4.332) *** (3.177) *** (3.503) *** (4.535) *** (3.619) *** Log of Human Capital (2.491) ** (2.082) ** (3.021) *** (1.844) * (2.144) ** (2.446) ** Log of (d 1 g* 1 nit) (4.723) *** (5.126) *** (2.058) ** (4.371) *** (4.736) *** (0.195) Share of the total population living in cities of less than 0.5 inhabitants (0.872) (0.134) (0.614) (1.42) (0.339) (0.884)

16 450 GROWTH AND CHANGE, SEPTEMBER 2017 TABLE 2. (CONTINUED) GMM GMM 0.5 to 1 Million inhabitants (1.344) (1.251) (0.172) 1 to 5 Million inhabitants (2.822) *** (1.645) * (2.106) ** Number of observations Adjusted R F-test (Null hypothesis: no fixed ); P-value Hausman (Null hypothesis: random ) test; P-value Correlation between the covariate and fixed First stage F-statistics Test of overidentifying restrictions; P-value Standard errors are heteroscedastic-consistent. Absolute t statistics are in parentheses. *** 5 significant at 1%, ** 5 significant at 5%, * 5 significant at 10%. GMM: Same as GMM-3 in Table 1.

17 URBAN CONCENTRATION AND POVERTY 451 than approximately 0.3 to 0.5, the random estimator outperforms the fixed estimator (Clark and Linzer 2015: page 28 and Table 2). In the following, we will use both fixed and random. Estimation using these methods may, however, result in inconsistent parameter estimates because of possible correlation between the error term and some explanatory variables. To address this problem, we use the GMM estimation method (Arellano and Bond, 1991) and explicitly consider countries fixed. The method uses lagged values of the regressors and of the endogenous variables as instruments. However, the instruments should be highly correlated with the variables to be instrumented (i.e., be strong) and uncorrelated with the disturbances of the equation of interest (i.e., be valid). To gauge that the instruments are strong we use the Staiger and Stock (1997) s rule : the F-statistic of the regression of the variable to be instrumented on the instruments should be above 10. To assess the validity of the instruments we use the test of overidentifying restrictions (see Murray 2006). Results and Discussion The equations are in general augmented with additional explanatory variables. The choice of such variables is not easy. For instance, Durlauf, Johnson, and Temple (2005) concluded that the number of regressors that can be potentially added to a growth regression approaches the number of countries available in the broadest samples. This plethora of potential regressors illustrates one of the fundamental problems with empirical growth research, namely, the absence of any consensus on which growth determinants should be included in a regression. A number of economists suggest that one focuses on a core set of explanatory variables that have been shown to be consistently associated with the studied phenomenon and evaluate the importance of the variable of interest (here urban concentration) conditional on the inclusion of the core set (Woo 2009). It remains that the equations do not directly control for a number of factors among which those linked to the political economy have been found to play an important role. For instance, Davis and Henderson (2003) found that urban concentration is affected significantly by democratization, federalism, and whether a country was a planned economy. However, analyzing the impacts of all these factors is beyond the scope of this paper and might even induce confusion. The econometric approaches adopted (fixed and random and GMM with fixed ) aim at limiting the inconvenience of not explicitly incorporating all these variables. Table 1 presents the results. All variables are taken as non-overlapping 5-year averages. This should give a sample size of 240 (40*6) observations. However, due to a number of missing observations, especially with poverty indicators, the sample size was reduced to between 83 and 185 observations depending on the estimated equation. The direct and indirect. The table reports the results of the estimation using each of the four definitions of a big city presented above separately. In each case, the first column gives the results of the fixed, the second gives the results of random and the third gives the GMM results with one lag of the variables as instruments. The three first columns in Table 1 give the results if a big city is defined as having between 0.5 and 1 million inhabitants. The relevant F-test statistic rejects the null hypothesis of no fixed. The Hausman tests leads to different conclusions across equations while the correlation tests always imply that the random should be preferred to the fixed. Regarding the GMM estimates, the first stage F-statistic shows that the instruments are strong while the tests of overidentifying restrictions implies that the instruments are valid. Hence, given the above discussion, we will focus on the random and the GMM results. The pattern of significance is highly similar across the

18 452 GROWTH AND CHANGE, SEPTEMBER 2017 two estimation methods. The direct of urban concentration (corresponding to the coefficients a 2 and g 2 in equations 1 and 2) are non-significant irrespective of the estimation method and the considered area (urban or rural). The indirect effect is assessed combining the coefficients of income in the poverty equation (corresponding to the coefficients a 1 and g 1 in equations 1 and 2) and the coefficient of urban concentration in the growth equation (corresponding to the coefficient b 5 in equation [3]). Both estimation methods show that the coefficients of income in the poverty equations are negative and significant implying that growth reduces poverty in urban and rural areas. The magnitude of the effect is similar across areas but lower when the random method is used rather than the GMM. The coefficient of urban concentration in the growth equation is significantly negative irrespective of the estimation method showing that urban concentration is associated with lower growth. Hence, the indirect effect is poverty increasing. Since there is no direct effect of urban concentration, this last result implies that the importance in total population of the population living in cities of 0.5 to 1 million inhabitants is poverty increasing in both areas. Turning to cities having between 1 and 5 million inhabitants, the F-test for fixed and the correlation tests imply the focus on the random results. The Hausman test leads to different conclusions across equations. With respect to the GMM estimates, the first stage F-statistic shows that the instruments are strong while the test of overidentifying restrictions implies that the instruments are valid. Hence, we will focus on the random and the GMM results. Again, the pattern of significance is highly similar across the two estimation methods. The main difference is that the coefficeint of urban concentration is significantly (at 10 percent) negative in the urban poverty equation with the GMM but not with the random method. The direct effect of urban concentration is potentially poverty reducing in urban areas but not in rurals. As before, the indirect effect is the results of the combination of coefficients a 1, g 1 and b 5. The results show that a 1 and g 1 are both negative and significant irrespective of the estimation method meaning that that growth reduces poverty in urban and rural areas. The magnitude of the effect is broadly similar across areas and estimation methods. Moreover, the coefficient of b 5 is significantly positive with both estimation methods implying that urban concentration is associated with higher growth. Hence, the indirect effect is poverty decreasing. Combining the direct and indirect, the importance in total population of the population living in cities of 1 to 5 million inhabitants appears as poverty reducing in urban and rural areas. The results using the definition of urban concentration as the the importance in total population of the population living in cities of 5 to 10 million inhabitants are broadly similar to those with a definition considering the importance in total population of the population living in cities of more than 10 million inhabitants. The F-test for fixed and the correlation tests imply the focus on the random. The GMM results appear strong and valid. Neither a direct nor an indirect effect of urban concentration on poverty emerges irrespective of the areas and the estimation method. The coefficient a 2 and g 2 are always insignificant (there is no direct efect) and while the coefficients a 1 and g 1 are always negative and signficant, b 5 is never significant (there is no indirect effect). Hence, these two sets of results imply different conclusions from the ones based on the two other definitions of urban concentration. The importance in total population of the population living in cities of 5 to 10 million inhabitants or in cities of more than 10 million inhabitants has no impact on poverty. The above discussion yielded information about the impact of different degrees of urban concentration on poverty in rural and urban areas. It is, however, not sufficient to state that these are specific to urban concentration and not linked to the the whole urbanization process. To address this concern, we rerun a similar estimation to the above, but using the importance in total population of the population living in small cities as explanatory variable. Small cities are defined as having less than 0.5 million inhabitants.

19 URBAN CONCENTRATION AND POVERTY 453 The results are reported in Table 2. For robustness check, we present two sets of estimates. The first three columns give the results with the importance of the 0.5 million inhabitants cities only. The second set of regressions present the results with the importance of the cities with less than 0.5, between 0.5 to 1 and between 1 to 5 million inhabitants introduced simultaneoulsy as explanatory variables. We consider the last two classes of cities because these are the only ones which seem to affect poverty following Table 1. Starting with the regression focusing on small cities only (less than 0.5 million inhabitants), the results show that relevant tests recommend the random model instead of the fixed. The first stage F-statistic and the tests of overidentifying restrictions imply that the GMM estimates are strong and valid. The coefficient pertaining to small cities in poverty regressions are never significant rejecting the existence of any direct effect on poverty in rural or urban areas. Since the coefficient of small cities in the growth regression is also always insignficant, there can be no indirect effect on poverty. Hence, the importance of the population living in cities less of than 0.5 million inhabitants has no impact on poverty. The results in the last three columns of Table 2 further check for the robustness of the conclusion regarding cities of less than 0.5 million inhabitants. The random model should be preferred to the fixed and the GMM estimates are strong and valid. Neither the coefficients of small cities in poverty regressions nor the corresponding coefficient in the growth regression are significant. The finding that the imporatnce of the population living in cities less than 0.5 million inhabitants has no impact on poverty is confirmed. The results in the last three columns of Table 2 also broadly confirm the findings concerning the impact of big cities in Table 1. As far as cities of between 0.5 and 1 million inhabitants are concerned, the main difference is that the coefficient of urban concentration in the growth equation is non significant irrespective of the method while it was always significantly negative in Table 1. Hence, there is no indirect effect. As in Table 1, the direct of urban concentration are non significant irrespective of the estimation method and the area considered (urban or rural). This last result implies that the importance in total population of the population living in cities of 0.5 to 1 million inhabitants has no effect on poverty. Turning to cities having between 1 and 5 million inhabitants, the main differences concern the random results. The results differ across specifications ( in Tables 1 and 2). In contrast, with the GMM method the pattern of significance is similar across specifications (Those in Tables 1 and 2). This shows that the results is consistent for cities of between 1 and 5 million inhabitants when the GMM method is employed. The GMM results in Table 2 show that in the urban poverty equation the coefficient of urban concentration is significantly negative at a higher level of significance than in Table 1. More importantly, the coefficients of income in the poverty equations are negative and signficant. The magnitude of the effect is broadly similar across areas. Moreover, the coefficient of urban concentration is significantly positive implying that urban concentration is associated with higher growth. Hence, the indirect effect is poverty decreasing. Combining the direct and indirect, the importance in total population of the population living in cities of 1 to 5 million inhabitants appears as poverty reducing in urban and rural areas. So far we have obtained some consistent results across estimation method and specification: The importance of the population living in cities of less than 0.5 million inhabitants has no impact on poverty. The importance in total population of the population living in cities of 5 10 million inhabitants or in the cities of more than 10 million inhabitants has no impact on poverty. The importance in total population of the population living in cities of 1 to 5 million inhabitants is poverty reducing. The impact of cities of million inhabitants depends on the specification.

20 454 GROWTH AND CHANGE, SEPTEMBER 2017 TABLE 3. NET IMPACTS OF URBAN CONCENTRATION Urban poverty Equation 7 Rural poverty Equation 6 Rural-Urban gap (2) 2 (1) Net impact of urban concentration at Minimum (2.530)** (0.439) (2.275)** Median (2.540)** (0.460) (2.270)** Maxmimum (2.548)** (0.477) (2.266)** Absolute t statistics are in parentheses. *** 5 significant at 1%, ** 5 significant at 5%, * 5 significant at 10%. The urban rural poverty gap. The sign and magnitude of the impacts identified above differ between rural and urban areas, which might reduce or widen the poverty gap between the two areas. This is an important issue often subject to debate when dealing with urban concentration and poverty. Metropolitan areas (e.g., Delhi, Cairo, and S~ao Paulo) and coastal regions (e.g., Rio de Janeiro, Karachi and Lagos) are often considered as capturing most of the gains from growth. Peripheral geographic areas, particularly rural, seem to lag behind (Ades and Glaeser 1995; Davis and Henderson 2003). The resulting regional polarization might pose important problems in terms of social cohesion, political stability and even growth. In order to assess the net effect of urban concentration on rural and urban poverty, we solve the system of equations (1 3) to obtain the following reduced forms: P r i;t5a 0 1a 1 e X it 1b 5 Concentration i;t 1a 2 Concentration i;t (4) P u i;t5g 0 1g 1 e X it 1b 5 Concentration i;t 1g 2 Concentration i;t (5) where: X it 5ln y i;t21 1b0 1b 1 ln y i;t21 1b2 ln S Ki;t 1b3 ln SH i;t 1b4 ln d1g 1n i;t The net impacts of urban concentration are given by the first derivatives of (4) and (5) with respect to r Concentration i;t 5a 0 1a 1 b 5 e X it 1b 5 Concentration i;t 1a 2 u Concentration i;t 5g 0 1g 1 b 5 e X it 1b 5 Concentration i;t 1g 2 (7) Equations (6 and 7) show that the impacts of urban concentration on rural and urban poverty depend on the traditionnal determinants of the national income and on the level of concentration. Table 3 provides the levels of equations (6 and 7) together with their t-statistics using the GMM results in Table 2. The measure of concentration is the share of population living in cities of 1 5 million

21 URBAN CONCENTRATION AND POVERTY 455 inhabitants, which gives the most consistent results across specifications. Since the computed levels depend on the level of urban concentration, Table 3 gives them at the minimum, median and maximum of the observed levels of urban concentration in our sample. To simplify exposition the other detreminants of income are taken at their average levels. Table 3 shows that while the net impact of urban concentration is a significant decrease in urban poverty, there is no impact on rural poverty. An increase in the indicator of urban concentration by 1 precentage point reduces the share of the poor in urban area by 0.8 percentage point. This impact does not change significantly as the share in total population of the population living in cities of 1 to 5 million inhabitants increase. While the theoretical analysis suggests a nonlinear relationship the emperical analysis shows a quasi linear relation over our interval of observation: Moving from the minimum to the maximum of the indicator of urban concentration does not change the net impact. As a result, the gap between rural and urban poverty is widening. An increase in the indicator of urban concentration by 1 percentage point increases the gap between rural and urban poverty by 0.65 percentage point. Conclusion The paper focuses on a poorly investigated issue, although highly challenging, for developing countries which is the nexus between urban concentration and poverty. To this end, it presents the estimation results of a system of three equations. One equation distinguishes the impacts of macroeconomic growth and urban concentration on rural poverty. A second equation considers the same for urban poverty. The third equation explains macroeconomic growth in terms of urban concentration and a set of common variables used in growth analysis. Urban concentration is defined as the share of population living in cities larger than a certain threshold. Rather than setting a threshold a priori, we consider four classes of cities: from 0.5 to 1 million inhabitants, between 1 and 5 million inhabitants, from 5 to 10 million inhabitants and over 10 million inhabitants. To be sure that the identified are attributable to urban concentration and not to urbanization in general, we rerun similar estimations but using as explanatory variable the share of small cities. That is the share of population living in less than 0.5 million inhabitants cities. The estimation gives a number of consistent results across estimation methods and specifications: The share of population living in (small) cities of less than 0.5 million inhabitants has no impact on poverty. The share of population living in (big) cities of 5 10 million inhabitants or in the cities of more than 10 million inhabitants has no impact on poverty. The share of population living in cities of 1 to 5 million inhabitants is poverty reducing. The above results echo the findings regarding the relationship between urban concentration and growth. First, the identified on poverty are specific to urban concentration. Second, the growth related literature show an inverted U relationship between concentration and growth while we find a similar (although non-inverted) U relationship with poverty. The share of population living in cities of 1 5 million inhabitants has direct and indirect impacts on poverty. The indirect impacts come from the impacts of urban concentration on macroeconomic growth. Combining the direct and indirect, these cities appears as poverty reduding in urban but not in rural areas. An increase in the indicator of urban concentration by 1 percentage point reduces the share of the poor in urban area by 0.8 percentage point. The impact of a similar change is non significant in rural area. As a result, the gap between rural and urban poverty is widening. An increase in the indicator of urban concentration by 1 percentage point increases the gap between rural and urban poverty by 0.65 percentage point.