WPS7623 Policy Research Working Paper 7623 Identifying the Economic Potential of Indian Districts Mark Roberts Social, Urban, Rural and Resilience Global Practice Group April 2016 Policy Research Working Paper 7623 Abstract Despite its rapid growth in recent decades, GDP per capita of the ability to experience a high level of productivity. The in India remains at a relatively low level by international analysis is based on a simple composite Economic Poten- standards, and the country continues to be marked by large tial Index, which is constructed from variables for which subnational disparities in levels of well-being. These large dis- robust evidence exists of their importance as determinants parities naturally lead to interest in India’s spatial landscape of of local productivity. From the analysis, a picture emerges potential for economic development. Against this backdrop, of a heterogeneous landscape of economic potential char- this paper presents the results of an analysis of underly- acterized by strong geographic clustering of districts. The ing variations in economic potential across Indian districts, paper also reveals particularly high levels of underperfor- where economic potential is defined as the extent to which mance, relative to potential, for districts in Uttar Pradesh. a district possesses factors that are important determinants This paper is a product of the Social, Urban, Rural and Resilience Global Practice Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mroberts1@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Identifying the Economic Potential of Indian Districts Mark Roberts Senior Urban Economist Keywords: India, economic potential, spatial development, productivity JEL codes: O18, O47, R11, R12 Acknowledgements The author would like to thank Parul Agarwala, Katie McWilliams, Mihir Prakash and Sangmoo Kim for their excellent research assistance and also Barjor Mehta for his invaluable guidance. Prof. Prem Pangotra, Tom Farole, Somik Lall, Martin Rama and Toby Linden also provided very useful feedback on an earlier version of this paper. Additional useful feedback was received from Peter Ellis, Augustin Maria, and Vasudha Thawakar, as well as from participants at presentations of earlier versions of the work made within the World Bank. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgement on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.     1. Introduction Despite its rapid growth in recent decades, GDP per capita in India remains at a relatively low level by international standards1 and the country continues to be marked by large sub-national disparities in levels of well-being. Illustrative of these large sub-national disparities is the fact that, according to official data, per capita income in India’s most prosperous state (Delhi) in 2011/12 was almost 7.6 times that in its least prosperous state (Bihar). These large disparities naturally lead to interest in India’s spatial landscape of potential for economic development. Is it the case, for example, that less prosperous parts of the country lack the basic ingredients that can give rise to high productivity or is it the case that, while they may possess some of these ingredients, they are failing to fully leverage them? Against the above backdrop, this paper presents the results of an analysis of underlying variations in economic potential across Indian districts, where, in this context, economic potential is defined as the extent to which a district possesses factors which are important determinants of the ability to experience a high level of productivity. The analysis is based on a composite Economic Potential Index (EPI). This index can be regarded as a simple, yet innovative, diagnostic tool which can help to improve understanding of a country’s spatial landscape of potential for rapid economic development at a granular level. More specifically, the index captures the extent to which a district possesses five crucial ingredients which have the potential to contribute to high levels of productivity: namely, market access, economic density, urbanization, skills, and local transport connectivity. These five factors represent key proximate determinants of local levels of productivity.2 While the EPI cannot, by itself, provide policymakers with direct guidance on, for example, whether they should prioritize investments in leading or lagging districts, it can, however, act as an important catalyst for stimulating discussions on both this and other important questions related to India’s spatial development. Among other things, the index provides preliminary insights into the geographic distribution of levels of economic potential across the country, the relative strengths and weaknesses of different districts with respect to the factors that enter into the calculation of the EPI, and the extent to which different districts are fulfilling their potential. The structure of the remainder of this paper is as follows. Section 2 outlines the underlying methodology used to identify the economic potential of each district, describing in detail each of the five potential determinants of local productivity that enter into the calculation of the EPI and the overall method of construction of the index. Section 3 then presents the results of the analysis. Among other things, it identifies the existence of strong spatial patterns whereby, rather than being randomly scattered, districts which exhibit similar levels of potential tend to neighbor one another. The analysis identifies several important spatially contiguous clusters of high potential districts, not to mention also several low potential clusters. Section 4 examines the relationship between a district’s economic potential as measured using the EPI and its actual observed performance. This                                                              1 Real GDP per capita, expressed in 2011 constant international dollars, grew from $2,600 to $5,238 between 2000 and 2011. 2 Although not analyzed directly in this paper, underlying the evolution of these determinants are likely to be deep- seated institutional and historical factors, including the historical availability of such factor endowments as skilled labor and natural resources and the existence of a location that has been historically favorable to trade. 2    allows for the identification of, in particular, high potential districts which possess significant “untapped” potential. Finally, Section 5 concludes by summarizing the paper’s main findings. 2. Methodology 2.1. Determinants of District Economic Potential There are numerous factors that can potentially influence productivity at the sub-national level thereby making the task of assessing underlying variations in economic potential across India’s districts seem like a daunting task. Over the last two decades, however, a large academic literature has developed which has sought to statistically test, and, in some cases, establish the causal importance of, a wide variety of potential determinants of local productivity levels. A review of this literature shows that relatively few of these factors are consistently robust across both different countries and time-periods. The Economic Potential Index (EPI) on which the analysis of this paper is based, therefore, draws on this literature for its construction. In particular, the EPI is a composite index which assesses a district’s potential to develop a high level of productivity based on the extent to which it possesses the following five factors:  Market access: captures proximity of firms to large domestic consumer markets, which facilitates lower costs of trade and increases profits; also captures better access of firms to suppliers of intermediate inputs;  Economic density: measures the potential which exists for both firms and workers to benefit from the various sources of agglomeration economies associated with such density;  Rate of urbanization: complementary measure of density and, therefore, of a district’s potential to benefit from agglomeration economies; urbanization also tends to be associated with the production of modern, as opposed to traditional, goods and services which have the potential to drive productivity through trade with both other districts and the rest of the world; modern goods and services include, for example, modern manufacturing and tradable service activities such as financial services;  Availability of human capital: human capital has a direct positive impact on the productive potential of a district’s firms and the earnings potential of its workers; an abundant availability of human capital can also bring important indirect benefits for productivity through facilitating spillovers of knowledge between workers and improving adaptability to long-term structural shifts in the wider macro-economy;  Local transport connectivity: captures the ability of urban markets to service their hinterlands through reduced costs of transportation of goods outwards and reduced cost of transportation of skills inwards towards urban areas. There exists adequate empirical evidence, covering a variety of countries and time-periods, on the importance of each of the above factors to warrant their inclusion in the construction of the EPI. This is especially the case for the first four factors. The international evidence on the importance of the fifth factor – local transport connectivity – is a little more mixed. Nevertheless, there is 3    strong suggestive evidence of the importance of this factor in the Indian context which merits its inclusion. As such, there is a firm basis for believing that if an Indian district is well-positioned with respect to the above five factors, it possesses some of the most essential pre-conditions for the achievement of high levels of productivity, even if other policy and/or non-policy factors – which may be somewhat unique to the district and/or the state in which it is located – currently constrain the full realization of that potential. Table 1 expands on the rationale for the selection of the five above-mentioned factors, the indicators that are used to measure these factors, and the sources of data. Annex 1 presents a more detailed technical discussion, which includes references to the relevant academic literature, of the rationale underlying the selection of the factors. It will be noted that, for the human capital component of the EPI, the choice of indicator is not theoretically ideal. Hence, while, ideally, the indicator would be a measure of the stock of education and skills embodied within a district’s workforce, the indicator used is a district’s literacy rate, as taken from the Census of India, 2011. Similarly, while the GDP based specification of market access used in the construction of the EPI does correspond to that which has been most frequently used in the relevant literature, there are alternative specifications of market access – based, for example, instead on population – that also appear in the literature. Given this, Annex 2 examines the robustness of the EPI results to the exact choice of indicators for both the human capital and market access components of the index. Overall, the analysis presented in this annex demonstrates that the basic EPI results are extremely robust to the exact choice of indicators. Table 1: The Five Components of the Economic Potential Index (EPI) Component Rationale Indicator Source of data Market access Better access to areas of buoyant Measure of market Geographic Information System economic activity: (i) stimulates access constructed (GIS) analysis of the Indian road demand for locally produced using district GDP network tradable products; (ii) provides levels and travel better access to intermediate times through the Most recently available (2005) inputs; and (iii) stimulates Indian road network district GDP data from the Planning beneficial spillovers from those Commission, GoI areas Economic Provides for greater potential GDP per km2 of land Most recently available (2005) density agglomeration economies area district GDP data – Planning emanating from the existence of: Commission, GoI (i) a large local pool of workers; (ii) a wide variety of local Night-time light intensity data used supplier firms and intermediate to help generate missing values: inputs; and (iii) spillovers of satellite data accessed from National knowledge between firms and Oceanic and Atmospheric workers which are facilitated by Association (NOAA) - geographic proximity http://ngdc.noaa.gov/eog/dmsp.html Level of Together with economic density, % of population Census of India, 2011 urbanization affects a district’s potential living in urban areas, ability to benefit from 2011 agglomeration economies; and the potential propensity to engage in the production of modern tradable goods and services 4    Component Rationale Indicator Source of data Human capital Has a direct positive impact on % of population Census of India, 2011 production, as well as potential which is literate, indirect impacts through 2011 facilitating knowledge spillovers and improving adaptability to long-term underlying structural changes in the macro-economy Local transport Better local connectivity reduces Density of primary Based on GIS data used for the connectivity costs of transporting goods and secondary roads construction of the Market Access within the district and contributes – i.e. length of roads indicator as above to reduced potential commute per 100 km2 of land times area It is important to note that the five factors that enter into the EPI represent what may be regarded as the proximate determinants of district economic potential. This is because underlying the long- run evolution of these factors there are likely to be deep-seated institutional and historical factors, including the historical availability of such factor endowments as skilled labor and natural resources and the existence of a location that has been historically favorable to trade. With respect to institutional factors, for example, empirical evidence shows that historical land tenure systems continue to influence, amongst other things, levels of public investment and educational outcomes across districts in contemporary India,3 thereby indirectly shaping a district’s EPI score.  2.2. Construction and Interpretation of the Economic Potential Index   The EPI was constructed by first converting each district’s indicator level for each of the five factors into units which are comparable across the indicators. The simple average of the scores across the five indicators was then taken. This average was then re-scaled so as to give an easy to interpret final index (Annex 1 provides a more detailed methodological discussion).4 On the final index, a district will achieve an EPI score of 50 if its indicator levels on each of the five key proximate determinants of potential are all exactly equal to the district average. Meanwhile, an EPI score greater than 50 reflects an above average level of potential, while a score of less than 50 indicates a level of potential which is below average. Based on their EPI scores, districts can also be categorized into different bands of potential, which range from ‘very high’ to ‘very low’ potential (see Table 2).5                                                              3 See Banerjee and Iyer (2005). 4 Given a lack of compelling evidence on the appropriate weights to attach to each of the five indicators, it was felt best to adopt the assumption of equal weights by taking the simple average. 5 These bands of potential are based on the average number of standard deviations across the five EPI indicators by which a district’s score deviates from the mean. The ‘very high’ (‘very low’) band of potential, therefore, corresponds to districts which, on average across the five indicators, have scores which exceed (fall short of) the mean by one standard deviation or more, whilst the ‘high’ (‘low’) bands correspond to scores which, on average, exceed (fall short of) the mean by 0.5 – 1 standard deviations. Finally, the ‘medium’ potential category corresponds to scores which, on average, fall within 0.5 standard deviations of the mean. The fact that the ‘very high’ and ‘very low’ categories are separated by two standard deviations provides confidence that the districts falling into these categories exhibit statistically meaningful differences in economic potential. 5    Table 2: Categorization of district potential Category Basis of Categorization Number of Districts (% of Districts) Very high EPI  68.8 50 (8.5) High EPI  59.4 84 (14.2) Medium 59.4 > EPI > 40.6 328 (55.5) Low EPI  40.6 91 (15.4 Very low EPI  31.2 38 (6.4) Total 591 As Table 2 indicates, although, based on current administrative boundaries, India has 676 districts, the EPI results are only reported for a total of 591 districts. This is primarily because limitations with the data, particularly the GDP data which relates to the year 2005, made it difficult to construct the index based on current administrative boundaries. Rather, the index had to be constructed based on the matching of data to circa 2007/8 district boundaries.6, 7 In reporting the results of the EPI in the following section, we refrain from providing the exact EPI scores and rankings of districts, instead choosing only to report the band of potential to which each district belongs. This is in order to avoid over-interpretation of the detailed scores and rankings. In particular, given the inherent difficulties in measuring economic potential and the fact that the indicators used for each of the five components of the EPI may be subject to some degree of measurement error, it is preferable to assess districts according to their broad categories of potential rather than their detailed EPI scores. It is also important to keep the following two points in mind when interpreting the results in the next section:  EPI levels capture potential and not performance of districts: the EPI aims to capture potential rather than actual performance. As such, although, in general, we expect performance as measured by, say, GDP per capita, to be positively correlated with potential,8 it is possible for similar EPI scores to translate into different levels of performance. Thus, for example, two districts which share similar EPI scores may, nevertheless, exhibit very different levels of GDP per capita depending on how successful they are in leveraging their potential. Differences across districts in how potential translates into performance are analyzed in greater detail in Section 4.  EPIs are a relative measure of economic potential, not an absolute measure: the EPI assesses a district’s underlying economic potential compared to the average for all other districts within the country. Thus, the EPI provides a relative measure of a district’s potential as opposed to an absolute measure.                                                              6 In particular, data was matched to the shapefile for Indian districts that is available from the GADM database of global administrative areas (http://www.gadm.org/).  7 As islands, the districts of Andman Islands, Nicobar Islands, and Kavaratti had to be excluded from the calculation of the EPI. This is because they lacked connectivity in the GIS road network file. It was not, therefore, possible to calculate the market access indicator for them. 8 Evidence of such a positive correlation is provided in Section 4. 6    3. EPI Results   3.1. Overall Results Figure 1 shows the distribution of EPI scores across districts, whilst Table 3 provides information on the shares of India’s total and urban populations, as well as the share of national GDP, accounted for by districts belonging to each of the different bands of potential. Based on these, it can be seen that around 8 percent of districts demonstrate ‘very high’ potential, and that, together, these account for more than 16 percent of national population. These districts, moreover, are, on the whole, much more urban than the districts that belong to the other bands of potential and generate a disproportionate share – just over 28 percent – of national GDP. A further 14 percent of districts exhibit ‘high’ potential. Although less markedly so than the ‘very high’ potential districts, these districts, on the whole, are also both more urban and generate a larger share of national GDP than would be expected based on their share of the national population alone. The ‘medium’ band of potential, meanwhile, accounts for just over 55 percent of all districts and a very similar share of the national population. These districts, however, are less urban and generate less GDP than would be expected based on their share of India’s overall population. Finally, around 22 percent of districts belong to the ‘low’ and ‘very low’ potential categories. These districts are comparatively sparsely populated, however, and account for a fraction of India’s overall urban population. Together, they also generate less than 7 percent of national GDP. Figure 1: Distribution of EPI Scores across Districts 7    Table 3: Shares of number of districts, overall national population, urban population and GDP belonging to each category of potential Category Population No. Overall Urban GDP districts Very high 8.5 16.5 38.2 28.2 High 14.2 16.9 22.2 21.7 Medium 55.5 54.9 36.2 43.5 Low 15.4 10.3 2.9 5.6 Very low 6.4 1.4 0.4 1.0 Notes: shares of both overall population and urban population are for 2011, whilst GDP shares are sample shares based on 2005 data (the most recent year for which relatively comprehensive (official) GDP data is available) Focusing in on the results for the ‘very high’ potential districts, which are likely to be of particular interest, Table 4 provides a full list of districts within this category.9 All of these districts have EPI scores of 68.6 or greater. The table, furthermore, lists district rankings for each of the indicators used to capture the five components of the index. Figure 2, meanwhile, provides information on both the status and population of all urban settlements located within the ‘very high’ potential districts.10 Table 4: Districts with ‘Very High’ Economic Potential State District Sub-indicator rank (out of 591)      Market Economic Percent Human Local access density urban capital connectivity Chandigarh Chandigarh 14 7 9 53 73 Daman and Diu Daman 95 8 13 34 98 Delhi Delhi 4 6 8 47 9 Goa North Goa 297 65 42 24 17 Gujarat Ahmadabad 91 89 12 60 58 Surat 81 92 16 57 97 Haryana Ambala 43 60 85 119 21 Faridabad 6 23 17 120 341 Gurgaon 11 18 24 68 40 Panchkula 20 52 54 117 416 Panipat 16 32 78 218 37 Rewari 28 49 208 129 11 Rohtak 13 101 94 139 14 Karnataka Bangalore Urban 5 5 11 37 46 Dakshin Kannad 142 63 75 30 81 Kerela Alappuzha 58 14 55 8 8 Ernakulam 22 9 26 7 10 Kannur 110 33 33 9 194 Kollam 54 25 81 12 26 Kottayam 50 21 184 4 15 Kozhikode 72 17 29 10 133 Malappuram 70 36 86 13 84                                                              9  Annex 5 presents a complete list of districts according to the bands of potential in which they fall.  10 Annex 6 provides a full list of all urban settlements, including Census Towns, and their populations which fall within the ‘very high’ potential districts. 8    Thiruvananthapuram 52 12 57 15 19 Thrissur 19 22 28 10 16 Maharashtra Greater Mumbai 1 3 1 22 5 Nagpur 67 85 25 31 147 Nashik 17 110 93 109 94 Pune 7 39 39 51 72 Thane 3 16 19 74 65 Manipur East Imphal 157 141 100 116 1 West Imphal 335 35 38 52 24 Puducherry Mahe 87 77 1 3 79 Puducherry 173 13 23 59 4 Punjab Jalandhar 49 50 58 108 175 Ludhiana 32 34 45 111 158 Tamil Nadu Chennai 27 2 1 19 6 Coimbatore 30 38 20 311 135 Kancheepuram 34 43 35 75 305 Kanniyakumari 131 19 14 17 27 Madurai 39 42 40 91 125 Thiruvallur 56 29 32 82 493 Telangana Hyderabad 18 4 1 95 2 Rangareddi 23 78 22 22 150 Uttar Pradesh Ghaziabad 12 30 27 178 76 Kanpur Nagar 90 10 31 153 7 Lucknow 59 31 30 193 41 West Bengal Haora 8 11 36 94 57 Hugli 24 26 114 118 132 Kolkata 9 1 1 48 3 North 24 Parganas 29 27 49 80 497 Figure 2: Classification of settlements located in ‘Very High’ Potential Districts based on: (a) status; and (b) population 120 100 No. settlements 80 60 40 20 0 Municipal Census Town Municipal Nagar Cantonment Urban Council Corp. Panchayat Board Outgrowth 9    120 100 No. settlements 80 60 40 20 0 > 99,999 50,000 ‐ 20,000 ‐ 10,000 ‐ 5,000 ‐ < 5,000 99,999 49,999 19,999 9,999 Note: Data on settlement populations is from Census of India, 2011 Based on Table 4 and Figure 2, several key trends among the ‘very high’ potential districts emerge, including:  Districts containing large municipal corporations and municipal councils figure prominently among the ‘very high’ potential districts: this is reflected in, for example, the fact that the list of ‘very high’ potential districts includes districts which correspond to India’s nine most populous urban agglomerations – Greater Mumbai, Delhi, Kolkata, Chennai, Bangalore Urban, Hyderabad, Ahmedabad, Pune, and Surat. More generally, out of the 233 urban settlements which are located in the 50 ‘very high’ potential districts, 38 are municipal corporations, while a further 112 are municipal councils. According to the 2011 Census of India, 113 of the urban settlements also have a population of 1 lakh or more. The strong presence of districts containing large agglomerations reflects the potential that these districts have, by virtue of their high levels of economic density and urbanization, to benefit from strong productivity enhancing agglomeration economies. These districts also have great potential to benefit from market access given that, by definition, they actually constitute a large share of India’s domestic consumer market.  Notwithstanding this, there are also numerous secondary and intermediate sized cities, not to mention Census Towns, located in ‘very high’ potential districts: 43.8 percent (or 102 out of 233) of all urban settlements in the ‘very high’ potential districts have a population between 0.5 lakh and 1 lakh. 29 of these settlements are located in the districts corresponding to India’s nine most populous urban agglomerations, but the remainder are located in other districts. 27 percent (or 63 out of 233) of the urban settlements in the ‘very high’ potential districts are also classified as Census Towns and, therefore, officially governed as rural areas, even though the census recognizes them as urban.11 In the case of two of the ‘very high’ potential districts, the largest urban settlement is a Census Town. These cases are the districts of East Imphal and Daman, where the Census Towns are Thongju (population 10,836) and Dadhel (population 52,578) respectively.                                                              11 To qualify as a Census Town, an administratively rural settlement must meet the following three criteria: (a) population in excess of 5,000 persons; (b) population density greater than 400 people per square kilometer; and (c) at least 75 percent male main workers involved in non-agricultural pursuits. 10     Economic potential and performance do not necessarily go hand-in-hand: Kanpur Nagar is one such example. Kanpur Nagar’s level of district GDP per capita ranks as only the 218th highest in India (2005). Nevertheless, the district has a “very high” level of potential, benefitting, in particular, from its high levels of economic density and urbanization, as well as from its dense local road network. The district also scores reasonably (90th out of 591 districts) in terms of its market access, which is linked, in part, to its intermediate geographic location between the major agglomerations of Delhi and Kolkata. As will be seen in Section 5, Malappuram in Kerala, Ghaziabad in Uttar Pradesh and Hyderabad in Telangana provide further examples of districts with “very high” levels of potential which are not being fully tapped.  While rankings across the five key determinants of potential are positively correlated, there are important variations that exist: For instance, whilst Hyderabad ranks very highly in terms of its economic density and levels of both urbanization and local transport connectivity, its ranking in terms of human capital is out-of-keeping with its overall EPI score. These variations across the five determinants reveal important areas where there is room for improvement.12 3.2. Breakdown of Results by State/Union Territory and Potential Category Figure 3 provides a breakdown of EPI results by both state / union territory and category of potential, whilst Table 5 presents the same information in a tabular format. Based on these, the following can be synthesized:  Kerala stands-out – both in absolute and proportional terms – as having the greatest number of ‘very high’ potential districts. In particular, 9 of its 14 districts (i.e. 64 percent) possess ‘very high’ potential. Kerala is followed by Haryana (7 ‘very high’ potential districts), Tamil Nadu (6 districts), Maharashtra (5 districts), West Bengal (4 districts) and Uttar Pradesh (3 districts). In the case of Uttar Pradesh, however, the share of its districts which are of ‘very high’ potential is only 4 percent.  Taken together, these six states are home to 68 percent – i.e. 34 out of 50 – of all ‘very high’ potential districts. Hence, there is a strong concentration of ‘very high’ potential districts in a relatively small number of states.  The remaining 32 percent of ‘very high’ potential districts are spread across a further 11 states, while 15 states do not feature any ‘very high’ potential districts at all.  The bulk of ‘very high’ and ‘high’ potential districts are concentrated in highly urbanized states including Maharashtra, Tamil Nadu, Gujarat, Haryana, and Punjab. As a corollary,                                                              12 Table A1.2 in Annex 1 also reports correlations between the indicators used to capture the five components of the EPI. This formally demonstrates the strong correlation between the different indicators. However, at the same time, it is clear that the correlation is far from perfect. This is important because it shows that the indicators, and, by extension, the components of the EPI, are capturing different information with respect to economic potential. 11    states, such as Odisha, Bihir, Assam and Jharkhand, which are characterized by low levels of urbanization tend to have a concentration of ‘low’ and ‘very low’ potential districts.13 Figure 3: Breakdown of Results by State and Category of Potential Table 5: Breakdown of District EPI scores by State/Union Territory State Total # Very High High Medium Low Very districts Low 1. Andhra Pradesh 14 1 2 11 0 0 2. Assam 23 0 0 15 8 0 3. Bihar 37 0 1 20 15 1 4. Chandigarh 1 1 0 0 0 0 5. Chhattisgarh 16 0 0 9 5 2 6. Dadra and Nagar Haveli 1 0 1 0 0 0 7. Daman and Diu 2 1 0 1 0 0 8. Delhi 1 1 0 0 0 0 9. Goa 2 1 1 0 0 0 10. Gujarat 25 2 7 16 0 0 11. Haryana 19 7 7 5 0 0 12. Himachal Pradesh 12 0 1 7 2 2 13. Jharkhand 22 0 3 11 7 1 14. Karnataka 27 2 5 19 1 0 15. Kerala 14 9 3 2 0 0 16. Madhya Pradesh 48 0 4 37 6 1 17. Maharashtra 34 5 9 19 1 0 18. Manipur 9 2 0 2 3 2                                                              13 This is, in part, true by construction (the rate of urbanization is one of the five factors on which the EPI is based). However, low levels of urbanization are also correlated with weak performance on the other four factors included in the index. 12    State Total # Very High High Medium Low Very districts Low 19. Meghalaya 7 0 0 0 5 2 20. Mizoram 8 0 0 4 0 4 21. Nagaland 8 0 0 6 1 1 22. Odisha 30 0 1 16 11 2 23. Puducherry 4 2 2 0 0 0 24. Punjab 17 2 8 7 0 0 25. Rajasthan 32 0 2 25 3 2 26. Sikkim 4 0 1 1 1 1 27. Tamil Nadu 30 6 14 10 0 0 28. Telangana 10 2 0 8 0 0 29. Tripura 4 0 0 4 0 0 30. Uttar Pradesh 70 3 6 52 8 1 31. Uttaranchal 13 0 1 7 4 1 32. West Bengal 19 4 4 11 0 0 TOTAL 51 50 84 328 91 38 3.3. Clustering of High and Low Potential Districts Consistent with the above results, Figure 4(a), which provides a spatial representation of the EPI results, indicates that economic potential is not randomly geographically distributed across districts. Rather, there is a strong tendency for districts with similar levels of potential to form spatially contiguous clusters. There, therefore, exist spatial clusters of both high and low potential districts. As shown by Figure 4(b), this gives rise to a spatial landscape characterized by “mountain ranges” of high potential and “valleys” of low potential. The locations of the “mountain ranges”, furthermore, tend to coincide with heavily built-up areas (Figure 4(c)). There is also, however, quite significant built-up area in some of the low potential “valleys.” This is particularly the case in the North-East of India near the border with Nepal where there exists a significant amount of built-up area in districts classified as being of low potential. 13    Figure 4: (a) Spatial distribution of potential across districts; (b) Peaks and valleys of potential; (c) Spatial distribution of potential tends to mirror the spatial distribution of built-up area with the exception of North-East India Note: the built-up area depicted in part (c) of the figure is for 2014 (Data source: Global Human Settlement Layer (GHSL), Joint Research Center, European Commission14) There still remains the question, however, of how important the spatial clustering of potential is from a statistical viewpoint. In this sense, Figure 5(a) provides for a more rigorous identification of both high and low potential clusters. In particular, the map identifies clusters based on the statistical significance of the underlying spatial patterns observed in Figure 4.15 Overall, there                                                              14 See Pesaresi et al. (2013) for more details on this data source. 15 Statistical significance is assessed on the basis of local Moran’s I statistics. From a technical viewpoint, these statistics allow for the identification of different patterns of local spatial autocorrelation at the district level. Figure 14    exist 15 clusters, of which nine are high potential clusters (Annex 3 provides a full list of districts belonging to each of the 16 clusters). Figure 5: (a) Clusters of high and low potential districts; (b) the North Central (Delhi) high potential cluster; (c) urban settlements in Agra district c Note: the built-up area depicted in part (c) of the figure is for 2014 (Data source: Global Human Settlement Layer (GHSL), Joint Research Center, European Commission) Taken together, the nine high potential clusters are home to 30 percent of India’s total population and just over 51 percent of its urban population.16 They, furthermore, generate approximately 45 percent of national GDP.17                                                              5(a) shows spatially contiguous groups of districts which exhibit statistically significant local Moran’s I values. It excludes single district “clusters” – i.e. districts which have statistically significant local Moran’s I values, but which are surrounded by districts with statistically insignificant values. For more details on the methodology which underlies the construction of local Moran’s I statistics see Anselin (1995). 16 These figures are based on Census of India, 2011, data. 17 More precisely, the nine high potential clusters possess a 44.9 percent sample share of GDP, where the GDP data relates to 2005 (the most recent year for which comprehensive (official) district GDP data is available). 15    The high potential clusters include extended groups of districts which are centered on the major agglomerations of Delhi, Kolkata, Ahmadabad, Hyderabad, and Bangalore – Chennai, but which also include intermediate and smaller sized urban settlements such as Nabha which falls within the Delhi high potential cluster and has a population, according to the Census of India, 2011, of just under 68,000. There is also a West coast corridor of high potential districts which comprises mainly districts from the states of Maharashtra (including Greater Mumbai), Karnataka and Gujarat, as well as a Southern Peninsula cluster that covers districts in Kerala, Puducherry and Tamil Nadu. Finally, there are the Darjiling and Imphal Clusters, which stand-out from the other high potential clusters by virtue of being surrounded by low potential districts. Figure 5(b) provides a more detailed mapping of the North Central high potential cluster which is centered on Delhi and which accounts for 8.5 percent of India’s overall population and almost 14 percent of its urban population. The cluster also generates approximately 11.5 percent of national GDP. Meanwhile, Figure 5(c) maps the urban settlements which exist in one of the districts, Agra, which belongs to this cluster. In contrast to the high potential clusters, the low potential clusters are centered on peripheral and/or lagging regions of the country, often on the borders of other countries in the region. This is the case, for example, for the Bangladesh Border, Kinnaur, and Far East clusters. There is also a significant low potential cluster – the Nepal Border Cluster – which is located in the Northeast and covers parts of the states of Bihar, Jharkhand, Sikkim and Uttar Pradesh. This cluster is notable amongst the low potential clusters for being characterized by both a high density of population and built-up area (see Figure 4(c)). Finally, the Southeast Central low potential cluster is mainly comprised of districts in Odisha, whilst the Northeast Central cluster includes districts from Chhattisgarh, Jharkhand, Madhya Pradesh and Uttar Pradesh. 4. Performance versus Potential The EPI aims to capture economic potential rather than actual performance, and there exist examples of districts with ‘very high’ or ‘high’ EPI scores which exhibit relatively weak performance. This section, therefore, seeks to analyze the relationship between potential and performance in more depth by taking GDP per capita as the metric for a district’s performance level.18 An important point to note, however, is that the analysis relies on GDP per capita data for the year 2005, which is the most recent year for which relatively comprehensive (official) data is available, and there may, therefore, have been important changes in performance since then. The analysis is also restricted to 520 of the 591 districts for which EPI results were reported in the previous section.19 This is because the analysis is limited to those districts for which GDP per capita data are available from official sources without having to generate missing values. With the above caveats in mind, Figure 6 shows the relationship across districts between GDP per capita levels and EPI scores. As might be expected, this relationship is positive and the slope of                                                              18 We focus on the level of GDP per capita rather than its growth rate as the metric of performance because this has been the primary focus of the academic literature on which the EPI is built. 19 The majority of missing districts belong to nine states / Union Territories – Chandigarh, Dadra and Nagar Haveli, Daman and Diu, Delhi, Goa, Gujarat, Nagaland, Puducherry and Tripura. The district of Lahul and Spiti (Himachal Pradesh) was also excluded from the analysis on account of being an extreme outlier. 16    the fitted line in the figure indicates that, on average, a one-point increase in a district’s EPI score is associated with a 1.9 percent increase in its level of GDP per capita (see also Annex 4). This fitted line shows how we would predict a district to perform in terms of its GDP per capita level given its EPI score. Districts which fall below the line can, therefore, be interpreted as possessing “untapped” potential with districts which fall further below the line having greater “untapped” potential than those which are closer to the line. Although not analyzed in this paper, the wedge between a district’s potential and its performance could conceivably have its roots in a number of factors including, for example, factors related to the regulatory and business environment, political economy, and institutions. Some of the factors that constrain performance below potential may be at the state level, while other factors may be more local. More generally, the exact configuration of factors which constrain a particular district’s performance below potential is likely to vary from case to case. Uncovering these factors for any given district would require detailed case study work, which is beyond the scope of this paper. Figure 6: GDP per capita performance versus Economic Potential 11 ln GDP per capita 9 8 10 0 20 40 60 80 100 EPI score Based on the above, Table 6 shows the “very high” / “high” potential districts which have the greatest levels of untapped potential (i.e. which have the largest wedge between potential and performance) along with additional information on other relevant district characteristics. In particular, the table shows those districts which we estimate could achieve a GDP per capita increase of 10 percent or more if they were to raise their performance to the levels predicted by their EPI scores. As can be seen, 6 out of the 13 districts in this table (Agra, Ghaziabad, Kanpur Nagar, Mathura, Meerut, and Varanasi) belong to a single state – Uttar Pradesh. The predominance of districts from Uttar Pradesh suggests that many of the factors that are constraining their performance below predicted levels are at the state, rather than the local, level (Annex 4 presents more formal analysis of this issue). Outside of Uttar Pradesh, East Imphal (Manipur), Malappuram (Kerala), Haora (West Bengal), Kolar (Karnataka), Bokaro (Jharkhand), Hyderabad (Telangana), and South 24 Parganas (West Bengal) complete the list of “very high” / “high” potential districts that could achieve a GDP per capita increase of at least 10 percent if they were able to increase performance to predicted levels by addressing constraints at the state and local levels. 17    Table 6: High potential districts with greatest estimated untapped potential District State Category Population Population Percent Literacy GDP per Poverty rate density (per km2) urban (%) rate (%) capita Agra Uttar Pradesh High 4,418,797 1,093 45.9 71.6 15021 41.6 Bokaro Jharkhand High 2,062,330 715 47.7 72.0 16142 17.1 East Imphal Manipur Very high 456,113 643 40.3 82.0 15165 40.0 Ghaziabad Uttar Pradesh Very high 4,681,645 3,971 67.5 78.1 19890 17.7 Haora West Bengal Very high 4,850,029 3,306 63.3 83.3 21443 24.4 Hyderabad Telangana Very high 3,943,323 18,172 100 83.3 31473 7.7 Kanpur Nagar Uttar Pradesh Very high 4,581,268 1,452 65.9 79.7 18279 33.9 Kolar Karnataka High 1,536,401 386 31.4 74.4 15771 6.7 Malappuram Kerala Very high 4,112,920 1,157 44.2 93.6 19473 25.9 Mathura Uttar Pradesh High 2,547,184 763 29.7 70.4 15131 18.9 Meerut Uttar Pradesh High 3,443,689 1,346 51.1 72.8 18273 19.5 South 24 Parganas West Bengal High 8,161,961 819 25.6 77.5 18335 39.5 Varanasi Uttar Pradesh High 3,676,841 2,395 43.4 75.6 10989 33.4 Note: table lists ‘very high’ and ‘high’ potential districts in which the estimated increase in GDP per capita that could be achieved by improving performance to the predicted level is greater than 10 percent. Data on population, population density, percent urban, and the literacy rate is from the Census of India, 2011. GDP per capita is measured in Indian rupees at 2000 constant prices, while the poverty rate is the share of the population living on less than $1.25 a day, where the poverty line is measured in 2005 constant international prices using Purchasing Power Parity exchange rates.     5. Conclusion This paper has presented a diagnostic analysis of the underlying economic potential of Indian districts based on the construction of a simple composite index – the Economic Potential Index or EPI. The EPI captures the extent to which each Indian district possesses five key attributes – namely, a good level of market access; high levels of economic density and urbanization; a workforce which embodies good levels of human capital; and strong local transport connectivity – which have been shown by a wide body of empirical evidence to be important to the achievement of high local levels of productivity. The main findings of the analysis may be summarized as follows:  Of the 591 districts included in the analysis, 50 have been classified as having ‘very high’ economic potential and a further 84 as having ‘high’ potential. Compared to lower potential districts, these districts are, on the whole, more urbanized. They also generate a disproportionate share of national GDP. This is especially the case for the ‘very high’ potential districts.  Districts containing large municipal corporations and municipal councils figure prominently among the ‘very high’ potential districts. The districts which are home to India’s nine most populous agglomerations – Mumbai, Delhi, Kolkata, Chennai, Bangalore, Hyderabad, Ahmedabad, Pune, and Surat – all figure in the list of ‘very high’ potential districts. At the same time, however, there are also numerous secondary and intermediate sized cities, not to mention Census Towns, located in the ‘very high’ potential districts.  While ‘very high’ potential districts tend to exhibit superior levels of performance on all five proximate determinants of economic potential, there, nevertheless, exist important variations which help to highlight relative areas of weakness.  ‘Very high’ potential districts are mainly concentrated in six states with Kerala, Haryana, and Tamil Nadu leading the way; around 70 percent of all ‘very high’ potential districts are located in these three states plus the states of Maharashtra, West Bengal and Uttar Pradesh. By contrast, there are 15 states with no ‘very high’ potential districts.  There exist nine (statistically significant) spatially contiguous clusters of ‘very high’/‘high’ potential districts. Five of these clusters are extended groups of districts centered on the major agglomerations of Delhi, Kolkata, Ahmadabad, Hyderabad, and Bangalore - Chennai. There also exists a West coast corridor of high potential districts that incorporates, in particular, a large number of districts from the states of Maharashtra (including Greater Mumbai) and Karnataka, as well as a Southern Peninsula cluster that covers districts in Kerala, Puducherry and Tamil Nadu. Finally, there are the Darjiling and Imphal Clusters, which stand-out from the other high potential clusters by virtue of being surrounded by low potential districts. Taken together, these nine clusters account for 30 percent of India’s overall population, just over 51 percent of its urban population and generate approximately 45 percent of national GDP.      There are 38 districts identified as having ‘very low’ potential and 91 districts identified as having ‘low’ potential. As with high potential districts, there is significant spatial clustering of low potential districts. In particular, six low potential clusters of districts have been identified.  There exist a number of “very high” and “high” potential districts whose levels of performance, as measured by their levels of GDP per capita, fall short of what one would expect based on their EPI scores, thereby indicating the existence of significant “untapped” potential. Several of these districts – Agra, Ghaziabad, Kanpur Nagar, Mathuri, Meerut, and Varanasi – are located in Uttar Pradesh. This indicates that, for these districts, many of the constraints that undermine the fulfillment of potential lie at the state level. While, as indicated in the introduction, the EPI results presented in this paper should not be used as a direct guide to, for example, the targeting of investment decisions by the public and private sectors, they do provide important preliminary insights into India’s spatial landscape of potential for economic development. These insights can, in turn, provide an important starting point for such policy discussions and also more in-depth analysis of, for example, the factors that may be constraining particular districts below potential. 20    References Anselin, L. (1995). “Local Indicators of Spatial Association-LISA”, Geographical Analysis, Vol. 27, pp 93 - 115. Banerjee, A. and L. Iyer (2005). “History, Institutions, and Economic Performance: The Legacy of Colonial Land Tenure Systems in India”, American Economic Review, Vol. 95(4), pp 1190-1213. 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Gertler, P. J., Gonzalez-Navarro, M., Gracner, T. and Rothenberg, A. D. (2014). “Role of Road Quality Investments on Economic Activity and Welfare: Evidence from Indonesia's Highways”, Paper presented at the 62nd Annual North American Meetings of the Regional Science Association International, Nov. 2015, Washington, D.C. Ghani, E., Goswami, A. and Kerr, W.R. (2013). “Highway to Success in India: The Impact of the Golden Quadrilateral Project for the Location and Performance of Manufacturing”, Policy Research Working Paper #6320, The World Bank: Washington, D.C. Glaeser, E.L. (2005). “Reinventing Boston: 1630-2003”, Journal of Economic Geography, Vol. 5(2), pp 119-153. Glaeser, E.L., Chauvin, J.-P., and Tobio, K. (2011), “Urban Economics in the U.S. and India”, presented at the Economic Geography Conference, Seoul, June 29. http://www.scribd.com/doc/59978593/Prof-Ed-Glaeser-Urban-Economics-in-the-US-and-India. Government of India (2014). 2011 Census of India, Retrieved October 10, 2014, from http://www.censusindia.gov.in/2011-common/census_2011.html Harris, C.D. (1965), “The market as a factor in the localization of industry in the United States”, Annals of the Association of American Geographers, Vol. 44, pp 315-348. Hering, L.D.S. and Poncet, S. (2010a). “Market access and individual wages: Evidence from China”, The Review of Economics and Statistics, Vol. 92(1), pp 145-159. Hering, L.D.S. and Poncet, S. (2010b). “Income per capita inequality in China: The role of economic geography and spatial interactions”, The World Economy, Vol. 33(5), pp 655- 679. Jacobs, J. (1969), The Economy of Cities, New York: Random House. Lall, S.V., Wang, H.G., and Deichmann, U. (2010), “Infrastructure and City Competitiveness in India”, UNU-WIDER Working Paper No. 2010/22. 21    Lucas, R. (1988). “On the Mechanics of Economic Development”, Journal of Monetary Economics, Vol. 22 (1), pp 3–42. Mankiw, N. G., G., Romer, and Weil, D. N. (1992). “A Contribution to the Empirics of Economic Growth”, Quarterly Journal of Economics, Vol. 107, pp 407–437. Marshall, A. (1890), Principles of Economics, London: Macmillan & Co., Ltd. Moreno-Monroy, A. (2008). “The Dynamics of Spatial Agglomeration in China: An Empirical Assessment”, Economics Program Working Papers 08-06, The Conference Board, Economics Program. National Oceanic and Atmospheric Association (2014). DMSP-OLS night-time lights data, Retrieved October 21, 2014, from http://ngdc.noaa.gov/eog/dmsp.html Pesaresi, M., Guo Huadong; Blaes, X., Ehrlich, D., Ferri, S., Gueguen, L., Halkia, M., Kauffmann, M., Kemper, T., Linlin Lu, Marin-Herrera, M.A., Ouzounis, G.K., Scavazzon, M., Soille, P., Syrris, V., Zanchetta, L. (2013), “A Global Human Settlement Layer From Optical HR/VHR RS Data: Concept and First Results”, Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of, Vol. 6(5), pp 2102- 2131. Roberts, M. (2004). “The Growth Performances of the GB Counties: Some New Empirical Evidence for 1977-1993”, Regional Studies, Vol. 38(2), pp 149-165. Roberts, M. and Goh, C. (2011). “Density, distance and division: the case of Chongqing municipality, China”, Cambridge Journal of Regions, Economy and Society, Vol. 4(2), pp 189-204. Roberts, M. and Setterfield, M. (2010). “Endogenous regional growth: a critical survey”, Handbook of Alternative Theories of Economic Growth, Cheltenham: Edward Elgar, pp 431-50. Roberts, M., Deichmann, U., & Fingleton, B., & Shi, T. (2012). "Evaluating China's Road to Prosperity: A New Economic Geography Approach", Regional Science and Urban Economics, Vol. 42(4), pp 580-594. Schramm, M. (2013). “The Impact of Dedicated Freight Corridors on Regional Wages in India: A New Economic Geography Approach”, Background paper prepared for the South Asia Urbanization Flagship, The World Bank, Washington, D.C. World Bank (2008). World Development Report 2009: Reshaping Economic Geography, Washington, D.C.: The World Bank. World Bank (2013). Urbanization beyond Municipal Boundaries: Nurturing Metropolitan Economies and Connecting Peri-Urban Areas in India, Washington, D.C.: The World Bank.   22    Annexes   Annex 1: Detailed Methodology and Note on Rationale for Factors used for EPI Analysis Detailed rationale for selection of EPI factors The theoretical rationale for the first (proximate) determinant – market access – follows from the so-called “New Economic Geography” literature that was first pioneered by Krugman (1991a, 1991b), which shows that sub-national economies which are better connected through transportation networks to high-income markets can be expected to enjoy higher levels of local productivity. This is because firms located in sub-national economies with better access to markets benefit from greater demand for their products, which, in turn, allows them to more easily cover their fixed costs of production (e.g. the cost of setting-up a new plant). They also benefit from better access to suppliers of intermediate inputs. As a result, theory predicts that both productivity and wages should be higher in these areas than in comparable local areas with lower levels of market access. This is particularly so in countries where labor mobility is limited, as seems to be the case in India.20 Empirical evidence in support of this prediction has been found for not only developed countries, but also for developing countries, including India.21 Meanwhile, the theoretical rationale for both the second and third determinants – namely, economic density and the level of urbanization – is to be found in the idea that sub-national economies which are economically more dense and urbanized have a greater propensity to benefit from agglomeration economies. Agglomeration economies refer to the positive externalities – or “accidental” benefits – that individual firms and workers enjoy as a result of locating or working in close geographic proximity to other firms and workers in economically dense and/or highly urbanized areas. These include benefits which stem from, for example, the fact that the existence of a dense concentration of firms in a particular industry helps to stimulate both the growth of a diverse range of local intermediate input suppliers and a local pool of labor which has the skills and talent to meet the needs of the industry. They also include the dynamic benefits that result from the spillover of, for example, best practice knowledge of how to do things, something that is facilitated by close geographic proximity, especially when the knowledge in question is complex and, therefore, more easily passed on through face-to-face communication than through alternative, including electronic, means of interaction.22 As with market access, empirical evidence on the importance of economic density and the level of urbanization as determinants of local levels of productivity has been found for developed and developing countries alike.23                                                              20 Whereas 9 percent of people in the United States lived in a different state five years ago and 40 percent were born in a different state, the equivalent figures for India, according to 2011 census data, were just 0.4 percent and 3.6 percent (Glaeser, Chauvin, and Tobio, 2011). 21 For supportive evidence for India see, in particular, Schramm (2013). Evidence for China is, meanwhile, provided by, for example, Bosker et al. (2012), Hering and Poncet (2010a, 2010b), Moreno- Monroy (2008) and Roberts et al. (2012). Lall, Wang, and Deichmann (2010) also provide evidence that market access has considerable and significant effects in improving an Indian city’s attractiveness for private investment. 22 The seminal references on the sources of agglomeration economies are Marshall (1890), Jacobs (1969) and Duranton and Puga (2004). 23 See, for example, Ciccone and Hall (1996), Ciccone (2002) and Roberts and Goh (2011) who present evidence on the importance of economic density as a determinant of local levels of productivity for the US, Europe and China 23    The inclusion of human capital, which is the fourth potential determinant captured by the EPI, can be rationalized by the fact that higher levels of such capital are thought to have both important direct and indirect effects on local levels of productivity.24 The direct effects follow from the fact that firms located in sub-national economies with more skilled, trained and educated workforces are likely to be more effective in combining other inputs to produce output. Meanwhile, the indirect effects stem from two main sources: (i) the ability of local economies with higher levels of human capital to better absorb ideas and knowledge emanating from outside the locality; and (ii) the fact that higher levels of human capital can help to stimulate better spillovers of knowledge between local firms, thereby further facilitating the exploitation of agglomeration economies. A high level of human capital has also been shown to improve the ability of sub-national economies to adapt to long-term underlying structural shifts in the macro-economy through facilitating their ability to re-invent themselves in response to such shifts.25 Of all the potential determinants of local productivity captured by the EPI, human capital is probably the one which commands the widest empirical support.26 Finally, local transport connectivity, as measured by the density of primary and secondary roads within a district, is included in the EPI based on the fact that better internal connectivity is likely to promote both reduced costs of transporting goods for firms (both for goods that are transported solely within the district and for goods that are transported to and from other districts) and reduced costs of commuting for workers. Although the empirical evidence on internal connectivity as a determinant of local levels of productivity is not perhaps as internationally robust as for the other four factors captured by the EPI, there is suggestive evidence of its particular importance for India. In particular, it has been strongly argued that high internal costs of transport within the core areas of India’s major metropolitan areas have been an important contributory factor behind the “premature” outward movement of formal manufacturing activity from these areas, with consequent negative effects on both local levels of productivity and growth.27 Further emerging empirical evidence on the importance of local transport connectivity – in particular, the importance of local roads – exists for Indonesia and Colombia.28 Detailed methodology for construction of the EPI The methodology for the construction of the EPI consists of the following four basic steps: 1. Measure raw performance on each of the five components of the index – i.e. on market access; economic density; level of urbanization; human capital and local transport connectivity. All indicators with the exception of the human capital indicator are measured in natural logs on the basis that their distributions are approximately log-normal.                                                              respectively. Empirical evidence on the importance of urbanization can be found in the strong cross-country relationship that exists between levels of GDP per capita and urbanization (see, for example, World Bank, 2008). 24 The seminal references here are Mankiw, Romer and Weil (1992) and Lucas (1988). 25 For evidence on this see, inter alia, Glaeser (2005) and Roberts (2004). 26 See, for example, Roberts and Setterfield (2010) on this point. 27 See Ellis and Roberts (2015) and World Bank (2013). 28 See Gertler et al. (2014) and Duranton (2014) respectively.  24    2. Transform measures of raw performance into units that are comparable across the five components – achieved by converting the values on each of the associated indicators into “Z- scores” by (for each indicator) subtracting the mean and dividing through by the standard deviation. 3. Combine the transformed scores across the five components – by taking the simple average (i.e. mean) of the “Z-scores” across the associated indicators. 4. Re-scale the combined scores to arrive at the final index of performance – this is achieved by applying the formula EPIi = 50 + [50/Max(|Zi|)]*Zi where EPIi is the final EPI score for district i and Zi is the average Z-score for district i from step (3). Table A1.1 provides more detail on the precise indicators used to capture each component of the index and also on the sources of data. Meanwhile, Table A1.2 provides a matrix of Spearman rank correlation co-efficients for the indicators. This matrix shows that, in general, the five indicators are, as might be expected. positively correlated with each other. However, at the same time, the correlations are far from being perfect, thereby indicating that each provides independent information on a district’s economic potential. Table A1.1: Construction of indicators and data sources Component Indicator Data source Market access Calculated as MAi = j[GDPj/(timei,j^2) GIS shapefile of the Indian road network where timei is the estimated travel time by corresponding to that used by Ghani et al. road (in hours) between the centroids of (2013); GDP data is the most recently districts i and j from taking the optimal (i.e. available (2005) from the Planning fastest) route between those districts.29 Commission, GoI Economic density GDP per km2. Missing GDP values were Most recently available (2005) district predicted using data on night-time light GDP data – Planning Commission, GoI; intensity, exploiting the strong documented night-time light intensity: National relationship between light intensity and Oceanic and Atmospheric Association GDP30 (http://ngdc.noaa.gov/eog/dmsp.html) Level of urbanization % of population living in urban areas, 2011. Census of India, 2011 Where a district was missing data, the % living in urban areas was assumed to be equal to the average across all other districts in the same state Human capital % of population which is literate, 2011. Census of India, 2011 Where a district was missing data, the literacy rate was assumed to be equal to the average across all other districts in the same state                                                              29 This is a classic Harris (1965) style measure of market potential. The fastest travel time routes were calculated using Dijkstra’s algorithm. 30 GDP per capita data was missing for 74 out of the 591 districts. GDP values for these districts were estimated by, first, running, for all non-missing observations, a regression of ln(GDP) on ln(DN), where DN stands for digital number and is a measure of night-time light intensity. The fitted regression was then used to predict the levels of GDP for the districts with missing data. Full regression results are available on request.  25    Component Indicator Data source Local transport Density of primary and secondary roads – i.e. Based on same GIS shapefile used for the connectivity length of roads (in km) per 100 km2 of land construction of the market access indicator area Table A1.2: Spearman rank correlation for EPI indicators Market Economic Percent Human Internal access density urban capital connectivity Market 1.000 access Economic 0.785 1.000 density Percent 0.498 0.518 1.000 urban Human 0.294 0.414 0.473 1.000 capital Internal 0.52 0.508 0.389 0.255 1.000 connectivity 26    Annex 2: Robustness of EPI Results The EPI results reported in the main text were derived using the indicators outlined in Table 1 and also discussed in more detail in Annex 1. Further analysis was also undertaken to assess the robustness of the results to the use of alternative indicators for, in particular, the market access and human capital components of the index. Two alternative variants of the market access indicator were considered: (i) MA POP – instead of calculating market access based on levels of district GDP, this variant bases the calculation on levels of district population – i.e. (MA POP)i = j[POPj/(timei,j^2) where POPj is the population of district j in 2011 and timei,j is the estimated travel time by road between the centroids of districts i and j; and (ii) MA Class I – this variant instead calculates market access based on the populations of, and travel times to, Class I cities which are located in other districts – i.e. (MA Class I)i = j[POPj/(timei,j^2) where POPj is the population of the jth Class I city in 2011 and timei,j is the estimated travel time by road between the centroid of district i and the jth Class I city. Meanwhile, for the human capital component, several alternatives to the literacy rate in 2011 were considered: namely, (i) Primary 2001 – the share of the working age population (i.e. the population aged 15-64) in 2001 which had completed at least primary education; (ii) Secondary 2001 – the share of the working age population in 2001 which had completed at least secondary education; (iii) Higher 2001 – the share of the working age population in 2001 which had completed at least higher secondary/intermediate pre-University/senior secondary education; and (iv) Grad 2001 – the share of the working age population in 2001 which held a University degree. Each of these indicators provide, arguably, a better measure of a district’s stock of human capital than its literacy rate. Unfortunately, however, the Census data to allow for the construction of these indicators for 2011 had yet to be released at the time of preparation of the paper, which provides the rationale for selecting the literacy rate in 2011 as the preferred indicator for the EPI whose results are reported in the main text. The EPI results were re-calculated for all possible permutations of the alternative indicators for market access and human capital. For completeness, results were also re-calculated using the literacy rate in 2001 (lit 2001) as the human capital indicator. Table A2 below reports estimated Pearson correlation co-efficients between the EPI results based on the different indicators. As can be seen, all of the results are extremely highly correlated (the lowest estimated correlation co- efficient is 0.932). This shows that the results reported in the main text are extremely robust to the choices of indicators for both the market access and human capital components. Table A2: Correlation Matrix for EPI Variants Panel A MA GDP Lit Lit Primary Secondary Higher Grad 2011 2001 2001 2001 2001 2001 MA GDP Lit 2011 1.000 Lit 2001 0.990 1.000 Primary 2001 0.988 0.988 1.000 Secondary 2001 0.978 0.976 0.992 1.000 Higher 2001 0.973 0.971 0.985 0.995 1.000 27    Grad 2001 0.966 0.964 0.973 0.986 0.994 1.000 MA POP Lit 2011 0.990 0.977 0.978 0.971 0.970 0.966 Lit 2001 0.983 0.990 0.981 0.972 0.971 0.966 Primary 2001 0.978 0.975 0.990 0.986 0.983 0.974 Secondary 2001 0.964 0.959 0.979 0.991 0.990 0.984 Higher 2001 0.955 0.951 0.968 0.982 0.991 0.988 Grad 2001 0.945 0.940 0.952 0.969 0.982 0.991 MA Class I Lit 2011 0.986 0.977 0.973 0.962 0.960 0.952 Lit 2001 0.974 0.986 0.972 0.959 0.958 0.948 Primary 2001 0.973 0.975 0.986 0.977 0.973 0.959 Secondary 2001 0.964 0.964 0.979 0.987 0.985 0.974 Higher 2001 0.956 0.956 0.969 0.979 0.987 0.979 Grad 2001 0.950 0.949 0.958 0.971 0.983 0.987   Panel B MA POP Lit Lit Primary Secondary Higher Grad 2011 2001 2001 2001 2001 2001 MA POP Lit 2011 1.000 Lit 2001 0.989 1.000 Primary 2001 0.987 0.987 1.000 Secondary 2001 0.976 0.974 0.991 1.000 Higher 2001 0.971 0.969 0.984 0.995 1.000 Grad 2001 0.963 0.961 0.971 0.985 0.994 1.000 MA Class I Lit 2011 0.978 0.971 0.965 0.950 0.944 0.932 Lit 2001 0.963 0.978 0.961 0.944 0.939 0.926 Primary 2001 0.965 0.970 0.978 0.966 0.958 0.940 Secondary 2001 0.959 0.961 0.975 0.980 0.974 0.959 Higher 2001 0.955 0.958 0.969 0.976 0.980 0.969 Grad 2001 0.952 0.954 0.961 0.971 0.979 0.980   Panel C MA Class I Lit Lit Primary Secondary Higher Grad 2011 2001 2001 2001 2001 2001 MA Class I Lit 2011 1.000 Lit 2001 0.989 1.000 Primary 2001 0.987 0.987 1.000 Secondary 2001 0.976 0.974 0.991 1.000 Higher 2001 0.971 0.969 0.984 0.995 1.000 Grad 2001 0.963 0.961 0.971 0.985 0.994 1.000   28    Annex 3: Composition of High and Low Potential Clusters Table A3.1: Constituent Districts of High Potential Clusters Cluster Constituent districts Southern Peninsula Alappuzha, Coimbatore, Cuddalore, Dindigul, Ernakulam, Erode, Idukki, Cluster Kanniyakumari, Karaikal, Karur, Kollam, Kottayam, Kozhikode, Madurai, Malappuram, Namakkal, Nilgiris, Palakkad, Pattanamtitta, Perambalur, Puducherry, Pudukkottai, Ramanathapuram, Salem, Sivaganga, Thanjavur, Theni, Thiruvananthapuram, Thiruvarur, Thoothukudi, Thrissur, Tiruchchirappalli, Tirunelveli Kattabo, Villupuram, Virudhunagar Bangalore Cluster Bangalore Rural, Bangalore Urban, Chennai, Chittoor, Dakshin Kannad, Hassan, Kancheepuram, Kannur, Kasaragod, Kodagu, Kolar, Mahe, Mysore, Thiruvallur, Tumkur, Vellore West Coast Cluster Ahmednagar, Belgaum, Bellary, Bharuch, Dadra and Nagar Haveli, Daman, Davanagere, Dharwad, Greater Mumbai, Jalgaon, Kolhapur, Nashik, Navsari, North Goa, Pune, Raigarh (Maharashtra), Ratnagiri, Sangli, Satara, Shimoga, Sindhudurg, Solapur, South Goa, Surat, Thane, Udupi, Uttar Kannand, Valsad Hyderabad Cluster Hyderabad, Rangareddi Ahmadabad Cluster Ahmadabad, Gandhinagar, Rajkot North Central Agra, Aligarh, Ambala, Amritsar, Baghpat, Bhiwani, Bulandshahr, Chandigarh, (Delhi) Cluster Delhi, Faridabad, Faridkot, Fatehgarh Sahib, Gautam Buddha Nagar, Ghaziabad, Gurgaon, Haridwar, Hathras, Hisar, Hoshiarpur, Jaipur, Jalandhar, Jhajjar, Jind, Kaithal, Kapurthala, Karnal, Kurukshetra, Ludhiana, Mahendragarh, Mathura, Meerut, Moga, Muzaffarnagar, Nawan Shehar, Panchkula, Panipat, Patiala, Rewari, Rohtak, Rupnagar, Saharanpur, Sangrur, Solan, Sonepat, Yamuna Nagar Kolkata Cluster Haora, Kolkata Darjiling Cluster Darjiling, East, Jalpaiguri Imphal Cluster East Imphal, West Imphal * ‘Very High’ potential district in bold. 29    Table A3.2: Constituent Districts of Low Potential Clusters Cluster Constituent districts Southeast Central Bastar, Bolangir, Boudh, Dantewada, Gajapati, Kalahandi, Kandhamal, Koraput, Cluster Malkangiri, Nabarangpur, Nuapada, Sonepur Northeast Central Bhabua, Bilaspur, Chatra, Dindori, Garhwa, Gumla, Jashpur, Kawardha, Latehar, Cluster Palamu, Sidhi, Simdega, Sonbhadra, Surguja Nepal Border Araria, Balrampur, Banka, Godda, Jamui, Katihar, Khagaria, Kishanganj, Cluster Madhepura, Madhubani, Maharajganj, North Sikkim, Pakur, Pashchim Champaran, Purba Champaran, Purnia, Saharsa, Samastipur, Sheohar, Shravasti, Siddharth Nagar, Sitamarhi, Supaul, West Sikkim Bangladesh Border Barpeta, Dhuburi, East Garo Hills, East Khasi Hills, Goalpara, Golaghat, Jaintia Cluster Hills, Karbi Anglong, Kokrajhar, Marigaon, North Cachar Hills, Ri-Bhoi, South Garo Hills, West Garo Hills, West Khasi Hills  Far East Cluster Champhai, Chandel, Churachandpur, Hailakandi, Lawngtlai, Lunglei, Mamit, Mon, Phek, Saiha, Senapati, Tamenglong, Tuensang, Ukhrul Kinnaur Cluster Kinnaur, Lahul and Spiti, Rudra Prayag * ‘Very Low’ potential district in bold.   30    Annex 4: Regression Analysis of Relationship between Performance and Potential This Annex reports the results of regressions which analyze, for a sample of 520 districts, the relationship between a district’s performance, as measured by the natural log of its level of GDP per capita in 2005, and its potential, as measured by its EPI score. In particular, Table A4.1 reports the results from two regressions. The first regression corresponds to Figure 6 in the main text and shows that a district’s EPI score is positively and significantly related to its level of GDP per capita at all conventional levels. However, from the fit of the regression, it is also clear that variations in EPI scores are unable to explain all of the observed variations in performance across districts, thereby indicating the existence of both “over-performing” and “under-performing” districts. For any given district i, the measure of “untapped” potential that is reported for the “very high” and “high” potential districts in Table 6 of the main text is constructed using the following equation: ln ln [A1] i.e. as the difference between the fitted and actual observed natural log levels of GDP per capita. The second regression, meanwhile, is identical to the first except that it has been extended to include state / Union Territory fixed effects. Including these fixed effects has little impact on the estimated slope of the relationship between a district’s EPI score and its (ln) level of GDP per capita. The effect of a one point increase in a district’s EPI score within states / Union Territories is, therefore, roughly the same as the effect between states. It will be observed, however, that the overall fit of the regression is much improved. This suggests that much of the explanation for why the districts in Table 6 of the main text exhibit untapped potential lies at the state level. Table A4.1: Regression results for performance –v– potential Variable Without state effects With state effects Constant 8.750*** 9.184*** (0.360) (.107) EPI 0.019*** 0.0198*** (.006) (0.001) R2 0.240 0.752 Adj R2 - 0.740 F( 1, 23) 9.520 62.480 Prob > F 0.005 0.000 n 520 520 Notes: dependent variable is ln(2005 GDP per capita). Standard errors for the regression without state effects are clustered by state. F(1, 23) is the test statistic for an F-test of the joint significance of all explanatory variables with Prob > F being the corresponding p-value. Estimated co-efficients on the state fixed effects are not reported for reasons of brevity. The results of the second regression can also be used to identify districts where “untapped” potential is predominantly due to local factors as opposed to factors at the state level. Table A4.2, in particular, reports the “very high” and “high” potential districts that are most constrained by local, as opposed to state-level, factors from fulfilling their potential. The measure of untapped potential arising from local factors is again constructed using equation [A1], except that the fitted values are taken from the second, as opposed to the first, regression. 31    Table A4.2: High potential districts with greatest estimated untapped potential due to local factors District State Category Population Population Urban Literacy GDP per Poverty density (per population rate (%) capita rate km2) (%) Akola Maharashtra High 1,813,906 320 39.7 88.1 18870 26.6 Alappuzha Kerala Very high 2,127,789 1,504 54.1 95.7 27426 14.5 Amravati Maharashtra High 2,888,445 237 35.9 87.4 17868 69.3 Amritsar Punjab High 2,490.656 928 53.6 76.3 28568 30.0 Bokaro Jharkhand High 2,062,330 715 47.7 72.0 16142 17.1 Chennai Tamil Nadu Very high 4,646,732 26,553 100.0 90.2 33336 7.9 East Sikkim Sikkim High 283,583 297 43.2 83.9 25522 17.0 East Imphal Manipur Very high 456,113 643 40.3 82.0 15165 40.0 Gurdaspur Punjab High 2,298,323 647 28.5 80.0 24833 23.8 Haora West Bengal Very high 4,850,029 3,306 63.3 83.3 21443 24.4 Hoshiarpur Punjab High 1,586,625 469 21.2 84.6 27108 15.9 Hyderabad Telangana Very high 3,943,323 18,172 100 83.3 31473 7.7 Jalandhar Punjab Very high 2,193,590 836 53.2 82.5 32676 17.9 Jhajjar Haryana High 958,405 523 25.4 80.7 24316 14.6 Jind Haryana High 1,334,152 494 22.8 71.4 22888 22.7 Kolar Karnataka High 1,536,401 386 31.4 74.4 15771 6.7 Kollam Kerala Very high 2,635,375 1,061 45.1 94.1 26231 8.6 Kozhikode Kerala Very high 3,086,293 1,316 67.2 95.1 27400 30.9 Malappuram Kerala Very high 4,112,920 1,157 44.2 93.6 19473 25.9 Rangareddi Telangana Very high 5,296,741 707 70.3 75.9 25370 22.6 Rohtak Haryana Very high 1,061,204 608 42.0 80.2 25115 24.9 Rupnagar Punjab High 684,627 505 26.0 82.2 29080 9.7 Sonepat Haryana High 1,450,001 683 30.5 79.1 27131 8.8 South 24 Parganas West Bengal High 8,161,961 819 25.6 77.5 18335 39.5 Theni Tamil Nadu High 1,245,899 434 53.8 77.3 20136 13.1 Thrissur Kerala Very high 3,121,200 1,031 67.2 95.1 29527 8.5 Varanasi Uttar Pradesh High 3,676,841 2,395 43.4 75.6 10989 33.4 Note: table lists ‘very high’ and ‘high’ potential districts in which the estimated increase in GDP per capita that could be achieved through improving performance to the predicted level is greater than 10 percent. Data on population, population density, percent urban, and the literacy rate is from the Census of India, 2011. GDP per capita is measured in Indian rupees at 2000 constant prices, while the poverty rate is the share of the population living on less than $1.25 a day, where the poverty line is measured in 2005 constant international prices using Purchasing Power Parity exchange rates.     Annex 5: EPI Classifications for All Districts   State District Market Economic Percent Human Internal access density urban Capital connectivity VERY HIGH Chandigarh Chandigarh 14 7 9 53 73 Daman and Diu Daman 95 8 13 34 98 Delhi Delhi 4 6 8 47 9 Goa North Goa 297 65 42 24 17 Gujarat Ahmadabad 91 89 12 60 58 Surat 81 92 16 57 97 Haryana Ambala 43 60 85 119 21 Faridabad 6 23 17 120 341 Gurgaon 11 18 24 68 40 Panchkula 20 52 54 117 416 Panipat 16 32 78 218 37 Rewari 28 49 208 129 11 Rohtak 13 101 94 139 14 Karnataka Bangalore Urban 5 5 11 37 46 Dakshin Kannad 142 63 75 30 81 Kerala Alappuzha 58 14 55 8 8 Ernakulam 22 9 26 7 10 Kannur 110 33 33 9 194 Kollam 54 25 81 12 26 Kottayam 50 21 184 4 15 Kozhikode 72 17 29 10 133 Malappuram 70 36 86 13 84 Thiruvananthapuram 52 12 57 15 19 Thrissur 19 22 28 10 16 Maharashtra Greater Mumbai 1 3 1 22 5 Nagpur 67 85 25 31 147 Nashik 17 110 93 109 94 Pune 7 39 39 51 72 Thane 3 16 19 74 65 Manipur East Imphal 157 141 100 116 1 West Imphal 335 35 38 52 24 Puducherry Mahe 87 77 1 3 79 Puducherry 173 13 23 59 4 Punjab Jalandhar 49 50 58 108 175 Ludhiana 32 34 45 111 158 Tamil Nadu Chennai 27 2 1 19 6 Coimbatore 30 38 20 311 135 Kancheepuram 34 43 35 75 305 Kanniyakumari 131 19 14 17 27 Madurai 39 42 40 91 125 Thiruvallur 56 29 32 82 493 Telangana Hyderabad 18 4 1 95 2 Rangareddi 23 78 22 22 150 Uttar Pradesh Ghaziabad 12 30 27 178 76 Kanpur Nagar 90 10 31 153 7 Lucknow 59 31 30 193 41     State District Market Economic Percent Human Internal access density urban Capital connectivity West Bengal Haora 8 11 36 94 57 Hugli 24 26 114 118 132 Kolkata 9 1 1 48 3 North 24 Parganas 29 27 49 80 497 HIGH Andhra Pradesh Krishna 66 81 97 265 219 West Godavari 84 79 279 248 167 Bihar Patna 71 15 87 340 71 Dadra & Nagar Dadra & Nagar Hav. 99 44 77 210 538 Hav. Goa South Goa 337 123 34 38 12 Gujarat Bharuch 124 104 141 122 89 Gandhinagar 134 48 89 78 66 Kheda 194 211 245 103 43 Navsari 93 173 166 84 32 Rajkot 183 205 48 130 123 Vadodara 144 131 67 166 215 Valsad 117 139 119 171 67 Haryana Hisar 92 148 157 282 50 Jhajjar 25 152 215 133 39 Jind 61 160 243 323 29 Karnal 35 100 170 247 36 Kurukshetra 47 128 179 207 18 Sonepat 15 84 168 161 35 Yamuna Nagar 105 56 111 183 56 Himachal Pradesh Solan 115 111 331 87 353 Jharkhand Bokaro 179 132 74 306 190 Dhanbad 133 37 47 251 186 Purba Singhbhum 197 115 52 235 286 Karnataka Bangalore Rural 2 153 204 184 292 Dharwad 188 156 50 145 106 Kolar 42 249 160 254 163 Mysore 137 114 96 287 85 Udupi 316 171 186 49 20 Kerala Kasaragod 170 59 113 20 104 Palakkad 51 53 231 25 294 Pattanamtitta 136 64 456 5 78 Madhya Pradesh Bhopal 229 55 15 138 140 Gwalior 221 213 37 203 227 Indore 139 47 21 131 126 Jabalpur 252 130 46 128 60 Maharashtra Ahmednagar 60 248 287 164 90 Akola 263 247 107 35 77 Amravati 234 353 126 40 143 Jalgaon 125 197 155 176 44 Kolhapur 88 73 156 122 31 Sangli 149 166 211 124 55 Satara 100 193 315 98 63 Solapur 98 225 152 200 45 Wardha 254 320 150 43 86 34    State District Market Economic Percent Human Internal access density urban Capital connectivity Odisha Khordha 299 70 73 44 116 Puducherry Karaikal 150 20 71 41 469 Yanam 484 28 1 155 375 Punjab Amritsar 107 74 120 208 87 Fatehgarh Sahib 38 80 165 157 348 Gurdaspur 198 108 185 147 100 Hoshiarpur 101 145 267 71 107 Kapurthala 78 103 136 162 498 Nawan Shehar 62 83 281 152 347 Patiala 55 88 102 238 99 Rupnagar 41 93 207 112 181 Rajasthan Jaipur 57 135 59 234 287 Kota 303 233 41 204 124 Sikkim East Sikkim 480 242 7 25 131 Tamil Nadu Cuddalore 138 67 143 180 134 Dindigul 86 140 118 209 189 Erode 69 113 61 291 506 Karur 108 124 99 229 103 Namakkal 26 69 101 248 381 Nilgiris 143 144 44 63 243 Salem 31 62 63 283 288 Thanjavur 148 68 132 105 415 Theni 120 147 56 194 423 Thoothukudi 181 122 65 50 500 Tiruchchirappalli 68 57 70 97 151 Tirunelveli Kattabo 128 90 68 107 242 Vellore 80 66 91 160 281 Virudhunagar 53 54 64 140 432 Uttar Pradesh Agra 82 76 79 318 25 Baghpat 45 82 271 306 176 Gautam Buddha Nag. 10 24 43 264 489 Mathura 37 165 175 351 33 Meerut 21 61 62 284 403 Varanasi 168 46 88 229 161 Uttaranchal Haridwar 112 71 117 269 310 West Bengal Barddhaman 33 40 106 212 441 Darjiling 340 107 110 154 23 Nadia 77 45 197 245 379 South 24 Parganas 40 41 209 189 417 MEDIUM Andhra Pradesh Anantapur 123 309 192 480 162 Chittoor 114 245 176 320 280 Cuddapah 226 372 140 413 145 East Godavari 96 97 210 332 566 Guntur 104 143 144 410 159 Kurnool 166 363 190 527 235 Nellore 213 280 178 382 311 Prakasam 186 322 305 489 327 Srikakulam 363 206 365 502 111 35    State District Market Economic Percent Human Internal access density urban Capital connectivity Vishakhapatnam 106 87 76 418 451 Vizianagaram 367 203 273 540 130 Assam Bongaigaon 429 305 411 362 268 Cachar 507 250 325 158 275 Dibrugarh 512 207 324 364 298 Goalpara 504 300 413 411 279 Golaghat 538 386 493 190 257 Jorhat 499 210 285 114 260 Kamrup 486 282 492 232 222 Karimganj 522 180 495 175 304 Lakhimpur 531 380 504 196 22 Marigaon 533 266 517 373 250 Nagaon 509 276 419 295 248 Nalbari 497 336 460 170 361 North Cachar Hills 564 555 183 187 339 Sibsagar 508 158 487 137 110 Tinsukia 537 253 288 215 351 Bihar Aurangabad 261 403 490 352 261 Begusarai 326 120 311 270 173 Bhagalpur 318 192 296 486 96 Bhojpur 332 283 398 348 289 Buxar 398 337 486 356 488 Darbhanga 381 189 482 561 236 Gaya 273 339 418 478 296 Gopalganj 357 293 550 442 307 Jehanabad 161 420 436 421 113 Lakhisarai 328 379 399 496 262 Munger 383 179 189 349 144 Muzaffarpur 250 167 477 483 142 Nalanda 191 272 367 462 119 Nawada 256 393 481 530 128 Purba Champaran 301 294 520 559 164 Rohtas 270 324 390 472 300 Saran 460 236 499 436 383 Sheikhpura 315 341 344 473 233 Siwan 394 259 563 367 481 Vaishali 374 186 545 429 367 Chhattisgarh Dhamtari 479 447 320 174 340 Durg 190 239 116 163 213 Janjgir-Champa 353 188 405 280 259 Korba 272 159 121 295 483 Koriya 485 515 162 343 476 Mahasamund 331 473 446 331 74 Raigarh 470 429 356 273 357 Raipur 212 299 123 231 83 Raj Nandgaon 225 470 330 217 438 Daman and Diu Junagadh 543 86 147 221 30 Gujarat Amreli 434 303 212 259 122 Anand 206 138 169 344 62 36    State District Market Economic Percent Human Internal access density urban Capital connectivity Banas Kantha 352 351 416 444 241 Bhavnagar 344 258 98 233 232 Dahod 309 361 497 541 322 Jamnagar 251 198 82 267 156 Junagadh (Gujarat) 520 556 147 221 207 Kachchh 200 455 137 76 474 Mahesana 258 170 216 89 253 Narmada 360 350 465 299 330 Panch Mahals 257 269 403 332 177 Patan 408 343 275 300 182 Porbandar 496 209 72 224 212 Sabar Kantha 236 311 383 223 302 Surendranagar 259 334 188 304 185 The Dangs 243 480 458 241 68 Haryana Bhiwani 135 262 295 240 28 Fatehabad 147 168 313 401 117 Kaithal 76 185 256 374 47 Mahendragarh 119 201 391 186 70 Sirsa 156 215 228 385 387 Himachal Pradesh Bilaspur 132 51 590 71 204 Hamirpur 195 163 541 32 444 Kangra 248 307 558 55 314 Mandi 185 348 552 121 271 Shimla 345 340 227 88 458 Sirmaur 214 369 459 168 562 Una 201 235 506 46 428 Jharkhand Deoghar 372 217 337 451 364 Dumka 260 391 543 518 112 Giridih 296 371 508 486 400 Hazaribag 230 261 369 361 356 Jamtara 275 357 485 458 477 Koderma 433 286 300 420 516 Lohardaga 431 437 425 405 457 Palamu 438 389 444 479 466 Ranchi 192 224 90 214 303 Sahibganj 287 218 408 576 42 Saraikela Kharsawan 268 289 229 404 183 Karnataka Bagalkot 266 313 158 385 139 Belgaum 154 243 217 268 155 Bellary 178 195 124 408 129 Bidar 291 367 223 346 95 Chamrajnagar 205 426 341 510 513 Chikmagalur 292 338 270 159 319 Chitradurga 169 398 298 266 166 Davanagere 215 237 153 226 205 Gadag 294 358 127 243 514 Gulbarga 232 397 151 451 266 Hassan 162 296 265 213 91 Haveri 219 279 253 191 440 37    State District Market Economic Percent Human Internal access density urban Capital connectivity Kodagu 184 267 388 106 254 Koppal 237 319 353 397 239 Mandya 218 220 347 350 276 Raichur 338 413 218 531 54 Shimoga 329 290 129 135 390 Tumkur 65 323 251 242 240 Uttar Kannand 327 427 177 80 49 Kerala Idukki 64 154 571 16 121 Wayanad 140 98 578 27 431 Madhya Pradesh Anuppur 469 304 200 403 148 Ashoknagar 437 479 326 432 174 Balaghat 439 497 392 199 320 Betul 359 500 303 382 473 Bhind 323 442 214 239 388 Burhanpur 362 424 138 465 301 Chhatarpur 411 506 247 477 346 Chhindwara 330 460 230 326 368 Damoh 452 514 293 363 479 Datia 341 435 240 290 221 Dewas 307 466 180 369 414 Dhar 308 450 317 538 228 East Nimar 370 439 294 433 539 Guna 325 432 220 237 293 Harda 456 501 274 294 521 Hoshangabad 417 436 159 484 244 Katni 320 453 282 308 193 Mandsaur 443 405 277 312 178 Morena 202 423 233 330 251 Narsinghpur 396 467 322 227 255 Neemuch 397 451 174 338 53 Raisen 409 521 244 281 343 Rajgarh 351 476 327 516 284 Ratlam 385 342 171 422 214 Rewa 278 418 354 315 308 Sagar 355 472 173 205 229 Satna 380 419 262 301 299 Sehore 376 495 316 359 398 Seoni 420 528 437 305 323 Shahdol 441 444 278 426 408 Shajapur 342 459 308 377 502 Shivpuri 336 533 345 495 138 Tikamgarh 412 477 338 510 273 Ujjain 280 292 109 297 464 Umaria 468 526 340 438 141 Vidisha 382 490 237 345 154 West Nimar 349 485 366 493 434 Maharashtra Aurangabad 293 541 490 352 88 Bhandara 172 271 306 86 93 Bid 222 387 291 201 38 38    State District Market Economic Percent Human Internal access density urban Capital connectivity Buldana 276 388 264 93 61 Chandrapur 247 284 134 143 188 Dhule 196 315 195 286 75 Gondiya 199 321 348 65 382 Hingoli 366 416 377 177 165 Jalna 391 408 310 321 209 Latur 265 312 213 194 80 Nanded 269 327 202 236 211 Nandurbar 274 356 355 464 192 Osmanabad 203 402 350 172 48 Parbhani 324 347 163 271 169 Raigarh 36 410 356 273 13 (Maharashtra) Ratnagiri 182 277 362 113 34 Sindhudurg 235 306 423 56 82 Washim 386 438 332 95 64 Yavatmal 277 404 259 100 234 Manipur Bishnupur 541 178 122 261 565 Thoubal 554 177 131 252 220 Mizoram Aizawl 563 456 18 2 507 Kolasib 549 553 51 14 270 Lunglei 582 568 104 28 496 Serchhip 578 557 69 1 424 Nagaland Dimapur 540 162 60 67 127 Kohima 555 464 80 62 545 Mokokchung 553 461 181 18 407 Phek 569 509 380 179 245 Wokha 551 499 272 36 439 Zunheboto 558 496 304 61 329 Odisha Angul 306 176 364 188 413 Baleshwar 314 212 457 151 412 Baragarh 442 445 474 250 230 Bhadrak 392 251 429 101 349 Cuttack 298 125 194 58 486 Deogarh 426 536 379 292 374 Dhenkanal 407 400 478 169 540 Ganjam 384 298 258 329 315 Jagatsinghpur 446 102 472 45 217 Jajpur 310 183 530 141 453 Jharsuguda 448 196 105 167 550 Keonjhar 347 378 402 393 406 Puri 457 255 370 69 523 Rayagada 494 498 374 136 337 Sambalpur 395 448 172 211 360 Sundargarh 415 365 130 271 425 Punjab Bathinda 130 161 125 392 352 Faridkot 113 134 133 366 146 Firozpur 158 175 201 381 92 Mansa 160 199 263 499 355 Moga 111 105 249 340 108 39    State District Market Economic Percent Human Internal access density urban Capital connectivity Muktsar 121 200 193 440 231 Sangrur 73 119 161 399 297 Rajasthan Ajmer 246 325 103 371 136 Alwar 97 221 328 339 277 Baran 364 428 276 428 184 Bharatpur 116 291 307 357 396 Bhilwara 267 344 261 513 465 Bikaner 440 544 142 448 532 Bundi 346 414 289 507 263 Chittaurgarh 313 433 414 504 196 Churu 379 545 191 423 409 Dausa 152 354 426 396 102 Dhaulpur 180 370 280 378 197 Ganganagar 425 431 203 365 472 Hanumangarh 401 430 301 416 544 Jhalawar 378 407 363 508 187 Jhunjhunun 245 345 242 261 430 Jodhpur 390 481 139 437 366 Karauli 312 421 382 435 378 Nagaur 343 492 312 491 274 Pali 406 462 248 497 332 Rajsamand 369 377 368 486 152 Sawai Madhopur 414 394 290 443 420 Sikar 211 364 235 310 226 Sirohi 458 417 284 563 278 Tonk 302 454 252 506 168 Udaipur 289 373 292 500 422 Sikkim South Sikkim 472 362 395 125 51 Tamil Nadu Ariyalur 176 99 454 324 515 Dharmapuri 103 374 336 388 501 Nagapattinam 242 75 250 90 564 Perambalur 102 142 342 258 272 Pudukkottai 167 133 309 198 509 Ramanathapuram 238 190 154 149 512 Sivaganga 233 202 164 132 363 Thiruvarur 209 150 283 99 391 Tiruvannamalai 129 204 286 260 508 Villupuram 127 194 386 83 258 Telangana Adilabad 279 422 198 519 468 Karimnagar 171 231 206 467 335 Khammam 208 318 236 453 269 Mahbubnagar 145 401 381 564 321 Medak 109 169 232 512 202 Nalgonda 146 302 314 466 179 Nizamabad 204 295 241 515 218 Warangal 223 316 187 449 373 Tripura Dhalai 527 415 463 54 504 North Tripura 535 355 335 39 478 South Tripura 559 346 401 70 238 40    State District Market Economic Percent Human Internal access density urban Capital connectivity West Tripura 528 184 108 29 372 Uttar Pradesh Aligarh 63 136 146 407 370 Allahabad 165 121 226 298 344 Ambedkar Nagar 283 238 442 302 290 Auraiya 311 301 349 165 291 Azamgarh 253 228 507 335 326 Badaun 159 252 334 390 338 Ballia 416 257 489 334 534 Banda 368 411 375 426 225 Bara Banki 141 229 473 501 309 Bareilly 126 118 135 545 210 Basti 282 268 559 415 157 Bijnor 118 151 221 577 350 Bulandshahr 46 94 225 384 410 Chandauli 262 230 424 322 456 Deoria 356 246 471 327 249 Etah 164 241 376 337 510 Etawah 231 216 239 173 450 Faizabad 217 187 407 387 171 Farrukhabad 249 208 255 379 216 Fatehpur 239 333 430 408 429 Firozabad 151 172 145 309 394 Ghazipur 264 244 526 312 371 Gorakhpur 224 127 318 336 115 Hamirpur 388 443 541 32 404 Hardoi 189 317 417 459 369 Hathras 89 109 260 380 52 Jalaun 284 360 222 288 285 Jaunpur 175 254 529 319 114 Jhansi 300 270 95 244 109 Jyotiba Phule Nagar 44 149 224 474 443 Kannauj 255 222 351 142 137 Kanpur Dehat 295 409 484 224 505 Kaushambi 290 263 521 514 256 Kushinagar 350 226 570 446 282 Lalitpur 427 489 394 481 402 Mahoba 404 382 266 445 265 Mainpuri 241 281 373 470 460 Mau 244 181 246 277 306 Mirzapur 305 368 406 390 426 Moradabad 74 91 149 554 120 Muzaffarnagar 48 106 182 376 267 Pilibhit 319 297 333 509 530 Pratapgarh 288 330 562 358 224 Rae Bareli 193 308 496 414 252 Rampur 79 164 219 570 419 Saharanpur 94 112 167 347 376 Sant Kabir Nagar 227 223 528 425 475 Sant Ravi Das Nagar 216 95 385 317 153 41    State District Market Economic Percent Human Internal access density urban Capital connectivity Shahjahanpur 240 285 302 532 313 Sitapur 207 275 438 517 345 Sultanpur 187 256 564 372 334 Unnao 85 265 343 434 118 Uttaranchal Almora 413 399 475 134 247 Champawat 483 484 384 150 411 Dehra Dun 210 155 587 77 312 Naini Tal 228 352 112 84 487 Pauri Garhwal 304 449 359 115 384 Tehri Garhwal 393 468 449 206 449 Udham Singh Nagar 155 191 128 276 199 West Bengal Bankura 122 182 509 355 389 Birbhum 163 126 421 340 393 Dakshin Dinajpur 371 116 400 285 59 East Midnapore 153 129 445 42 447 Jalpaiguri 220 137 205 275 105 West Bengal Kochbihar 281 117 470 246 237 Maldah 177 72 410 503 395 Murshidabad 83 58 297 430 69 Puruliya 174 234 422 461 437 Uttar Dinajpur 333 157 433 537 149 West Midnapore 75 146 434 182 452 LOW Assam Barpeta 465 349 505 475 520 Darrang 526 425 554 489 359 Dhemaji 552 469 538 288 436 Dhuburi 478 335 468 546 208 Hailakandi 518 260 532 257 548 Karbi Anglong 534 543 439 398 490 Kokrajhar 455 359 553 447 435 Sonitpur 523 384 503 412 317 Bihar Araria 462 392 556 568 386 Bhabua 377 487 577 370 503 Jamui 365 475 514 529 362 Katihar 432 288 501 573 494 Khagaria 361 287 566 548 392 Kishanganj 491 383 483 562 328 Madhepura 403 328 574 572 170 Madhubani 424 219 580 544 461 Pashchim 461 396 462 560 485 Champaran Purnia 358 332 466 578 223 Saharsa 405 264 515 571 518 Samastipur 317 227 583 498 201 Sheohar 339 406 575 567 333 Sitamarhi 428 278 561 575 264 Supaul 449 366 569 549 401 Chhattisgarh Bilaspur 402 376 590 71 295 Jashpur 488 520 500 401 385 Kanker 493 540 469 354 358 42    State District Market Economic Percent Human Internal access density urban Capital connectivity Kawardha 474 532 464 520 354 Surguja 447 529 467 525 470 Himachal Pradesh Chamba 529 542 540 303 533 Kullu 519 510 488 156 558 Jharkhand Chatra 354 488 555 523 525 Garhwa 445 483 565 522 455 Gumla 444 494 547 441 495 Latehar 451 523 537 533 463 Pakur 373 214 527 584 484 Pashchim Singhbhum 419 434 389 543 511 Simdega 453 517 535 399 492 Karnataka Bijapur 285 446 447 591 101 Madhya Pradesh Barwani 322 505 387 583 200 Dindori 487 566 573 471 172 Mandla 466 535 427 419 377 Panna 454 539 428 454 421 Sheopur 421 561 371 550 198 Sidhi 410 395 512 462 517 Maharashtra Garhchiroli 476 551 455 255 448 Manipur Senapati 536 548 586 101 445 Tamenglong 544 575 452 360 454 Ukhrul 572 571 396 126 552 Meghalaya East Garo Hills 545 547 404 263 557 East Khasi Hills 524 240 84 79 576 Jaintia Hills 530 504 533 505 433 Ri-Bhoi 492 534 479 228 524 West Garo Hills 546 478 443 406 519 Nagaland Tuensang 565 519 319 279 480 Odisha Bolangir 477 440 435 455 442 Boudh 489 522 572 316 528 Gajapati 505 508 431 569 318 Kalahandi 482 486 522 536 336 Kandhamal 495 530 476 468 342 Kendrapara 463 274 557 64 574 Koraput 475 412 360 582 191 Mayurbhanj 389 465 524 485 499 Nayagarh 471 474 511 579 462 Nuapada 481 518 560 253 283 Sonepur 467 457 525 586 380 Rajasthan Banswara 430 381 544 557 160 Dungarpur 399 375 549 534 418 Jalor 473 503 510 565 324 Sikkim West Sikkim 501 513 579 192 549 Uttar Pradesh Bahraich 375 331 513 581 541 Balrampur 422 390 523 580 246 Chitrakoot 435 502 480 450 331 Gonda 286 310 546 542 325 Lakhimpur Kheri 321 329 448 521 542 Maharajganj 459 314 567 492 203 43    State District Market Economic Percent Human Internal access density urban Capital connectivity Siddharth Nagar 418 385 551 535 365 Sonbhadra 348 273 352 216 578 Uttaranchal Bageshwar 502 537 582 143 491 Chamoli 513 559 378 103 555 Pithoragarh 525 552 397 110 546 Uttarkashi 490 576 531 220 527 VERY LOW Bihar Banka 387 441 581 547 537 Chhattisgarh Bastar 503 546 412 566 536 Dantewada 511 567 432 590 570 Himachal Pradesh Kinnaur 517 574 588 145 531 Lahul and Spiti 547 589 588 202 591 Jharkhand Godda 400 326 568 556 561 Madhya Pradesh Jhabua 423 507 498 589 471 Manipur Chandel 567 570 441 328 543 Churachandpur 570 564 548 219 582 Meghalaya South Garo Hills 568 527 494 314 571 West Khasi Hills 556 569 453 185 583 Mizoram Champhai 580 573 115 6 579 Lawngtlai 586 563 329 439 573 Mamit 573 577 339 66 577 Saiha 587 565 83 21 572 Nagaland Mon 562 512 409 553 529 Odisha Malkangiri 510 550 518 551 568 Nabarangpur 514 493 534 588 553 Rajasthan Barmer 498 560 539 555 482 Jaisalmer 561 586 415 552 551 Sikkim North Sikkim 560 587 461 181 581 Uttar Pradesh Shravasti 450 516 584 587 405 Uttaranchal Rudra Prayag 500 471 576 127 569       44    Annex 6: Urban Settlements in ‘Very High’ Potential Districts This annex presents the following three tables: (i) Table A6.1: List of Urban Settlements by State (including Census Towns) (ii) Table A6.2: List of Urban Settlements by Type(including Census Towns) (iii) Table A6.3: List of Urban Settlements by Population (including Census Towns) Table A6.1: List of Urban Settlements by State (including Census Towns) State District UAName Type Population Chandigarh Chandigarh Chandigarh Municipal Corp. 961587 Daman and Diu Daman Dadhel Census Town 52578 Delhi Delhi DMC Municipal Corp. 11034555 Delhi Delhi N.D.M.C. Muncipal Council 257803 Delhi Delhi Delhi Cantt Cantonment Board 110351 Delhi Delhi Sahibabad Daulat Pur Census Town 54773 Delhi Delhi Bawana Census Town 73680 Delhi Delhi Kirari Suleman Nagar Census Town 283211 Delhi Delhi Nithari Census Town 50464 Delhi Delhi Begum Pur Census Town 53682 Delhi Delhi Pooth Kalan Census Town 96002 Delhi Delhi Sultan Pur Majra Census Town 181554 Delhi Delhi Bhalswa Jahangir Pur Census Town 197148 Delhi Delhi Mukand Pur Census Town 57135 Delhi Delhi Burari Census Town 146190 Delhi Delhi Sadat Pur Gujran Census Town 97641 Delhi Delhi Karawal Nagar Census Town 224281 Delhi Delhi Mustafabad Census Town 127167 Delhi Delhi Khajoori Khas Census Town 76640 Delhi Delhi Ziauddin Pur Census Town 68993 Delhi Delhi GokalPur Census Town 121870 Delhi Delhi Jaffrabad Census Town 54601 Delhi Delhi Mandoli Census Town 120417 Delhi Delhi Gharoli Census Town 92540 Delhi Delhi DalloPura Census Town 154791 Delhi Delhi Chilla Saroda Bangar Census Town 83217 Delhi Delhi Hastsal Census Town 176877 Delhi Delhi Bapraula Census Town 52744 Delhi Delhi NangloiJat Census Town 205596 Delhi Delhi Mundka Census Town 54541 Delhi Delhi Roshan Puraalias Dichaon Census Town 57217 Khurd Delhi Delhi Kapas Hera Census Town 74073 Delhi Delhi Deoli Census Town 169122 45    State District UAName Type Population Delhi Delhi Pul Pehlad Census Town 69657 Delhi Delhi TajPul Census Town 68796 Delhi Delhi Mithe Pur Census Town 69837 Delhi Delhi Molar Band Census Town 91402 Delhi Delhi Jait Pur Census Town 59330 Goa North Goa Panaji Municipal Corp. 70991 Gujarat Ahmadabad Ahmadabad Municipal Corp. 5577940 Gujarat Ahmadabad Viramgam Municipality 55821 Gujarat Ahmadabad Dholka Municipality 79531 Gujarat Surat Surat Municipal Corp. 4467797 Gujarat Surat Bardoli Municipality 60821 Haryana Gurgaon Gurgaon Municipal Corp. 876969 Haryana Faridabad Faridabad Municipal Corp. 1414050 Haryana Panipat Panipat Muncipal Council 294292 Haryana Panipat Panipat Taraf Makhdum Census Town 67998 Zadgan Haryana Rohtak Rohtak Muncipal Council 374292 Haryana Ambala Ambala Sadar Muncipal Council 103093 Haryana Ambala Ambala Cantt. Cantonment Board 55370 Haryana Ambala Ambala Muncipal Council 195153 Haryana Panchkula Panchkula Muncipal Council 211355 Haryana Rewari Rewari Muncipal Council 143021 Karnataka Bangalore Urban Bangalore BMP Municipal Corp. 8443675 Karnataka Dakshin Kannad Mangalore Municipal Corp. 488968 Karnataka Dakshin Kannad Ullal TMC 53773 Karnataka Dakshin Kannad Puttur TMC 53061 Kerela Ernakulam Kochi Municipal Corp. 602046 Kerela Ernakulam Kalamassery Municipality 71038 Kerela Ernakulam Thrippunithura Municipality 69390 Kerela Ernakulam Edathala Census Town 77811 Kerela Ernakulam Vazhakkala Census Town 51242 Kerela Thrissur Thrissur Municipal Corp. 315957 Kerela Thrissur Kunnamkulam Municipality 54071 Kerela Alappuzha Kayamkulam Municipality 68634 Kerela Alappuzha Alappuzha Municipality 174176 Kerela Thiruvananthapuram Thiruvananthapuram Municipal Corp. 743691 Kerela Thiruvananthapuram Nedumangad Municipality 60161 Kerela Thiruvananthapuram Neyyattinkara Municipality 70850 Kerela Thiruvananthapuram Pallichal Census Town 53861 Kerela Kozhikode Kozhikode Municipal Corp. 431560 Kerela Kozhikode Vadakara Municipality 75295 Kerela Kozhikode Quilandy Municipality 71873 46    State District UAName Type Population Kerela Kozhikode Cheruvannur Census Town 61614 Kerela Kozhikode Beypore Census Town 69752 Kerela Kollam Kollam Municipal Corp. 348657 Kerela Kottayam Kottayam Municipality 55374 Kerela Kannur Kannur Municipality 56823 Kerela Kannur Payyannur Municipality 72111 Kerela Kannur Taliparamba Municipality 72465 Kerela Kannur Thalassery Municipality 92558 Kerela Malappuram Malappuram Municipality 68127 Kerela Malappuram Manjeri Municipality 97102 Kerela Malappuram Tirur Municipality 56058 Kerela Malappuram Ponnani Municipality 90491 Kerela Malappuram Moonniyur Census Town 55535 Kerela Malappuram Tirurangadi Census Town 56632 Kerela Malappuram Thennala Census Town 56546 Maharashtra Greater Mumbai Greater Mumbai Municipal Corp. 12442373 Maharashtra Thane Mira Bhayandar Municipal Corp. 809378 Maharashtra Thane Thane Municipal Corp. 1841488 Maharashtra Thane Navi Mumbai Municipal Corp. 1120547 Maharashtra Thane Kalyan Dombivali Municipal Corp. 1247327 Maharashtra Thane Ulhasnagar Municipal Corp. 506098 Maharashtra Thane Ambarnath Muncipal Council 253475 Maharashtra Thane Badlapur Muncipal Council 174226 Maharashtra Thane Dahanu Muncipal Council 50287 Maharashtra Thane Palghar Muncipal Council 68930 Maharashtra Thane Vasai-Virar City Municipal Corp. 1222390 Maharashtra Thane Bhiwandi Municipal Corp. 709665 Maharashtra Pune Pune Municipal Corp. 3124458 Maharashtra Pune Pune Cantt. Cantonment Board 71781 Maharashtra Pune Kirkee Cantt. Cantonment Board 78684 Maharashtra Pune Pimpri Chinchwad Municipal Corp. 1727692 Maharashtra Pune Talegaon Dabhade Muncipal Council 56435 Maharashtra Pune Lonavala Muncipal Council 57698 Maharashtra Pune Baramati Muncipal Council 54415 Maharashtra Nagpur Kamptee Muncipal Council 86793 Maharashtra Nagpur Nagpur Municipal Corp. 2405665 Maharashtra Nagpur Wadi Census Town 54048 Maharashtra Nagpur Umred Muncipal Council 53971 Maharashtra Nashik Malegaon Municipal Corp. 471312 Maharashtra Nashik Manmad Muncipal Council 80058 Maharashtra Nashik Nashik Municipal Corp. 1486053 Maharashtra Nashik Deolali Cantt. Cantonment Board 54027 47    State District UAName Type Population Maharashtra Nashik Sinnar Muncipal Council 65299 Maharashtra Nashik Ozar Census Town 51297 Manipur West Imphal Imphal Muncipal Council 282335 Manipur East Imphal Porompat Plan Area Urban Outgrowth 1145 Manipur East Imphal Kongkham Leikai Urban Outgrowth 887 Manipur East Imphal Porompat Census Town 6191 Manipur East Imphal Torban Census Town 5459 Manipur East Imphal Luwangsangbam Census Town 3458 Manipur East Imphal Khongman Census Town 6096 Manipur East Imphal Laipham Siphai Census Town 5268 Manipur East Imphal Khurai Sajor Leikai Census Town 7987 Manipur East Imphal Chingangbam Leikai Census Town 4904 Manipur East Imphal Kshetrigao Census Town 10534 Manipur East Imphal Kiyamgei Census Town 5336 Manipur East Imphal Jiribam Muncipal Council 7343 Manipur East Imphal Lamlai Nagar Panchayat 4601 Manipur East Imphal Heingang Census Town 6115 Manipur East Imphal Lairikyengbam Leikai Census Town 4586 Manipur East Imphal Thongju Census Town 10836 Manipur East Imphal Andro Nagar Panchayat 8744 Puducherry Mahe Mahe Municipality 41816 Puducherry Puducherry Puducherry Municipality 244377 Puducherry Puducherry Ozhukarai Municipality 300104 Punjab Ludhiana Khanna Muncipal Council 128137 Punjab Ludhiana Ludhiana Municipal Corp. 1618879 Punjab Ludhiana Jagraon Muncipal Council 65240 Punjab Jalandhar Jalandhar Municipal Corp. 862886 Tamil Nadu Chennai Chennai Municipal Corp. 4646732 Tamil Nadu Kanyakumari Nagercoil Municipality 224849 Tamil Nadu Madurai Madurai Municipal Corp. 1017865 Tamil Nadu Madurai Anaiyur Municipality 63917 Tamil Nadu Madurai Avaniapuram Municipality 89635 Tamil Nadu Madurai Thirumangalam Municipality 51194 Tamil Nadu Kancheepuram Kundrathur Town Parishad 54986 Tamil Nadu Kancheepuram Pammal Municipality 75870 Tamil Nadu Kancheepuram Alandur Municipality 164430 Tamil Nadu Kancheepuram Puzhithivakkam Municipality 53322 Tamil Nadu Kancheepuram Oggiyamduraipakkam Census Town 76600 Tamil Nadu Kancheepuram Pallavaram Municipality 215417 Tamil Nadu Kancheepuram Tambaram Municipality 174787 Tamil Nadu Kancheepuram Maraimalainagar Municipality 81872 Tamil Nadu Kancheepuram Chengalpattu Municipality 62579 48    State District UAName Type Population Tamil Nadu Kancheepuram Kancheepuram Municipality 164384 Tamil Nadu Thiruvallur Avadi Municipality 345996 Tamil Nadu Thiruvallur Tiruverkadu Municipality 62824 Tamil Nadu Thiruvallur Poonamallee Municipality 57224 Tamil Nadu Thiruvallur Tiruvottiyur Municipality 249446 Tamil Nadu Thiruvallur Madavaram Municipality 119105 Tamil Nadu Thiruvallur Ambattur Municipality 466205 Tamil Nadu Thiruvallur Nerkunram Census Town 59790 Tamil Nadu Thiruvallur Maduravoyal Municipality 86195 Tamil Nadu Thiruvallur Ramapuram Census Town 52295 Tamil Nadu Thiruvallur Thiruvallur Municipality 56074 Tamil Nadu Coimbatore Coimbatore Municipal Corp. 1050721 Tamil Nadu Coimbatore Goundampalayam Municipality 83908 Tamil Nadu Coimbatore Kuniyamuthur Municipality 95924 Tamil Nadu Coimbatore Kurichi Municipality 123667 Tamil Nadu Coimbatore Pollachi Municipality 90180 Tamil Nadu Coimbatore Mettupalayam Municipality 69213 Tamil Nadu Coimbatore Valparai Municipality 70859 Telangana Hyderabad Secunderabad Cantonment Board 217910 Telangana Rangareddi Vicarabad Municipality 53143 Telangana Rangareddi Tandur Municipality 65115 Uttar Pradesh Kanpur Nagar Kanpur Municipal Corp. 2765348 Uttar Pradesh Kanpur Nagar Kanpur Cantonment Board 108534 Uttar Pradesh Ghaziabad Modinagar Nagar Panchayat 130325 Uttar Pradesh Ghaziabad Muradnagar Nagar Panchayat 95208 Uttar Pradesh Ghaziabad Ghaziabad Municipal Corp. 1648643 Uttar Pradesh Ghaziabad Loni Nagar Panchayat 516082 Uttar Pradesh Ghaziabad Khora Census Town 190005 Uttar Pradesh Ghaziabad Pilkhuwa Nagar Panchayat 83736 Uttar Pradesh Ghaziabad Hapur Nagar Panchayat 262983 Uttar Pradesh Lucknow Lucknow Municipal Corp. 2817105 Uttar Pradesh Lucknow Lucknow Cantonment Cantonment Board 63003 West Bengal Kolkata Kolkata Municipal Corp. 4496694 West Bengal Haora Bally Municipality 293373 West Bengal Haora Bally Census Town 113377 West Bengal Haora Haora Municipal Corp. 1077075 West Bengal Haora Bankra Census Town 63957 West Bengal Haora Uluberia Municipality 222240 West Bengal Hugli Bansberia Municipality 103920 West Bengal Hugli Hugli-Chinsurah Municipality 177259 West Bengal Hugli Bhadreswar Municipality 101477 West Bengal Hugli Champdani Municipality 111251 49    State District UAName Type Population West Bengal Hugli Chandannagar Municipal Corp. 166867 West Bengal Hugli Baidyabati Municipality 121110 West Bengal Hugli Serampore Municipality 181842 West Bengal Hugli Rishra Municipality 124577 West Bengal Hugli Konnagar Municipality 76172 West Bengal Hugli Uttarpara Kotrung Municipality 159147 West Bengal Hugli Dankuni Municipality 94936 West Bengal Hugli Arambag Municipality 66175 West Bengal North 24 Parganas Rajarhat Gopalpur Municipality 402844 West Bengal North 24 Parganas Barasat Municipality 278435 West Bengal North 24 Parganas Madhyamgram Municipality 196127 West Bengal North 24 Parganas Kanchrapara Municipality 120345 West Bengal North 24 Parganas Halisahar Municipality 124939 West Bengal North 24 Parganas Naihati Municipality 217900 West Bengal North 24 Parganas Bhatpara Municipality 383762 West Bengal North 24 Parganas Garulia Municipality 85336 West Bengal North 24 Parganas North Barrackpur Municipality 132806 West Bengal North 24 Parganas Barrackpur Municipality 152783 West Bengal North 24 Parganas Titagarh Municipality 116541 West Bengal North 24 Parganas Khardaha Municipality 108496 West Bengal North 24 Parganas Panihati Municipality 377347 West Bengal North 24 Parganas New Barrackpur Municipality 76846 West Bengal North 24 Parganas Kamarhati Municipality 330211 West Bengal North 24 Parganas Baranagar Municipality 245213 West Bengal North 24 Parganas South Dum Dum Municipality 403316 West Bengal North 24 Parganas North Dum Dum Municipality 249142 West Bengal North 24 Parganas Dum Dum Municipality 114786 West Bengal North 24 Parganas Bidhan Nagar Municipality 215514 West Bengal North 24 Parganas Habra Municipality 147221 West Bengal North 24 Parganas Ashoknagar Kalyangarh Municipality 121592 West Bengal North 24 Parganas Basirhat Municipality 125254 West Bengal North 24 Parganas Bongaon Municipality 108864 West Bengal North 24 Parganas Baduria Municipality 52493 Table A6.2: List of Urban Settlements by Type (including Census Towns) State District UAName Type Population Chandigarh Chandigarh Chandigarh Municipal Corp. 961587 Delhi Delhi DMC Municipal Corp. 11034555 Goa North Goa Panaji Municipal Corp. 70991 Gujarat Ahmadabad Ahmadabad Municipal Corp. 5577940 Gujarat Surat Surat Municipal Corp. 4467797 50    State District UAName Type Population Haryana Gurgaon Gurgaon Municipal Corp. 876969 Haryana Faridabad Faridabad Municipal Corp. 1414050 Karnataka Bangalore Urban Bangalore BMP Municipal Corp. 8443675 Karnataka Dakshin Kannad Mangalore Municipal Corp. 488968 Kerela Ernakulam Kochi Municipal Corp. 602046 Kerela Thrissur Thrissur Municipal Corp. 315957 Kerela Thiruvananthapuram Thiruvananthapuram Municipal Corp. 743691 Kerela Kozhikode Kozhikode Municipal Corp. 431560 Kerela Kollam Kollam Municipal Corp. 348657 Maharashtra Greater Mumbai Greater Mumbai Municipal Corp. 12442373 Maharashtra Thane Mira Bhayandar Municipal Corp. 809378 Maharashtra Thane Thane Municipal Corp. 1841488 Maharashtra Thane Navi Mumbai Municipal Corp. 1120547 Maharashtra Thane Kalyan Dombivali Municipal Corp. 1247327 Maharashtra Thane Ulhasnagar Municipal Corp. 506098 Maharashtra Thane Vasai-Virar City Municipal Corp. 1222390 Maharashtra Thane Bhiwandi Municipal Corp. 709665 Maharashtra Pune Pune Municipal Corp. 3124458 Maharashtra Pune Pimpri Chinchwad Municipal Corp. 1727692 Maharashtra Nagpur Nagpur Municipal Corp. 2405665 Maharashtra Nashik Malegaon Municipal Corp. 471312 Maharashtra Nashik Nashik Municipal Corp. 1486053 Punjab Ludhiana Ludhiana Municipal Corp. 1618879 Punjab Jalandhar Jalandhar Municipal Corp. 862886 Tamil Nadu Chennai Chennai Municipal Corp. 4646732 Tamil Nadu Madurai Madurai Municipal Corp. 1017865 Tamil Nadu Coimbatore Coimbatore Municipal Corp. 1050721 Uttar Pradesh Kanpur Nagar Kanpur Municipal Corp. 2765348 Uttar Pradesh Ghaziabad Ghaziabad Municipal Corp. 1648643 Uttar Pradesh Lucknow Lucknow Municipal Corp. 2817105 West Bengal Kolkata Kolkata Municipal Corp. 4496694 West Bengal Haora Haora Municipal Corp. 1077075 West Bengal Hugli Chandannagar Municipal Corp. 166867 Delhi Delhi N.D.M.C. Muncipal Council 257803 Haryana Panipat Panipat Muncipal Council 294292 Haryana Rohtak Rohtak Muncipal Council 374292 Haryana Ambala Ambala Sadar Muncipal Council 103093 Haryana Ambala Ambala Muncipal Council 195153 Haryana Panchkula Panchkula Muncipal Council 211355 51    State District UAName Type Population Haryana Rewari Rewari Muncipal Council 143021 Maharashtra Thane Ambarnath Muncipal Council 253475 Maharashtra Thane Badlapur Muncipal Council 174226 Maharashtra Thane Dahanu Muncipal Council 50287 Maharashtra Thane Palghar Muncipal Council 68930 Maharashtra Pune Talegaon Dabhade Muncipal Council 56435 Maharashtra Pune Lonavala Muncipal Council 57698 Maharashtra Pune Baramati Muncipal Council 54415 Maharashtra Nagpur Kamptee Muncipal Council 86793 Maharashtra Nagpur Umred Muncipal Council 53971 Maharashtra Nashik Manmad Muncipal Council 80058 Maharashtra Nashik Sinnar Muncipal Council 65299 Manipur West Imphal Imphal Muncipal Council 282335 Manipur East Imphal Jiribam Muncipal Council 7343 Punjab Ludhiana Khanna Muncipal Council 128137 Punjab Ludhiana Jagraon Muncipal Council 65240 Gujarat Ahmadabad Viramgam Municipality 55821 Gujarat Ahmadabad Dholka Municipality 79531 Gujarat Surat Bardoli Municipality 60821 Kerela Ernakulam Kalamassery Municipality 71038 Kerela Ernakulam Thrippunithura Municipality 69390 Kerela Thrissur Kunnamkulam Municipality 54071 Kerela Alappuzha Kayamkulam Municipality 68634 Kerela Alappuzha Alappuzha Municipality 174176 Kerela Thiruvananthapuram Nedumangad Municipality 60161 Kerela Thiruvananthapuram Neyyattinkara Municipality 70850 Kerela Kozhikode Vadakara Municipality 75295 Kerela Kozhikode Quilandy Municipality 71873 Kerela Kottayam Kottayam Municipality 55374 Kerela Kannur Kannur Municipality 56823 Kerela Kannur Payyannur Municipality 72111 Kerela Kannur Taliparamba Municipality 72465 Kerela Kannur Thalassery Municipality 92558 Kerela Malappuram Malappuram Municipality 68127 Kerela Malappuram Manjeri Municipality 97102 Kerela Malappuram Tirur Municipality 56058 Kerela Malappuram Ponnani Municipality 90491 Puducherry Mahe Mahe Municipality 41816 Puducherry Puducherry Puducherry Municipality 244377 52    State District UAName Type Population Puducherry Puducherry Ozhukarai Municipality 300104 Tamil Nadu Kanyakumari Nagercoil Municipality 224849 Tamil Nadu Madurai Anaiyur Municipality 63917 Tamil Nadu Madurai Avaniapuram Municipality 89635 Tamil Nadu Madurai Thirumangalam Municipality 51194 Tamil Nadu Kancheepuram Pammal Municipality 75870 Tamil Nadu Kancheepuram Alandur Municipality 164430 Tamil Nadu Kancheepuram Puzhithivakkam Municipality 53322 Tamil Nadu Kancheepuram Pallavaram Municipality 215417 Tamil Nadu Kancheepuram Tambaram Municipality 174787 Tamil Nadu Kancheepuram Maraimalainagar Municipality 81872 Tamil Nadu Kancheepuram Chengalpattu Municipality 62579 Tamil Nadu Kancheepuram Kancheepuram Municipality 164384 Tamil Nadu Thiruvallur Avadi Municipality 345996 Tamil Nadu Thiruvallur Tiruverkadu Municipality 62824 Tamil Nadu Thiruvallur Poonamallee Municipality 57224 Tamil Nadu Thiruvallur Tiruvottiyur Municipality 249446 Tamil Nadu Thiruvallur Madavaram Municipality 119105 Tamil Nadu Thiruvallur Ambattur Municipality 466205 Tamil Nadu Thiruvallur Maduravoyal Municipality 86195 Tamil Nadu Thiruvallur Thiruvallur Municipality 56074 Tamil Nadu Coimbatore Goundampalayam Municipality 83908 Tamil Nadu Coimbatore Kuniyamuthur Municipality 95924 Tamil Nadu Coimbatore Kurichi Municipality 123667 Tamil Nadu Coimbatore Pollachi Municipality 90180 Tamil Nadu Coimbatore Mettupalayam Municipality 69213 Tamil Nadu Coimbatore Valparai Municipality 70859 Telangana Rangareddi Vicarabad Municipality 53143 Telangana Rangareddi Tandur Municipality 65115 West Bengal Haora Bally Municipality 293373 West Bengal Haora Uluberia Municipality 222240 West Bengal Hugli Bansberia Municipality 103920 West Bengal Hugli Hugli-Chinsurah Municipality 177259 West Bengal Hugli Bhadreswar Municipality 101477 West Bengal Hugli Champdani Municipality 111251 West Bengal Hugli Baidyabati Municipality 121110 West Bengal Hugli Serampore Municipality 181842 West Bengal Hugli Rishra Municipality 124577 West Bengal Hugli Konnagar Municipality 76172 53    State District UAName Type Population West Bengal Hugli Uttarpara Kotrung Municipality 159147 West Bengal Hugli Dankuni Municipality 94936 West Bengal Hugli Arambag Municipality 66175 West Bengal North 24 Parganas Rajarhat Gopalpur Municipality 402844 West Bengal North 24 Parganas Barasat Municipality 278435 West Bengal North 24 Parganas Madhyamgram Municipality 196127 West Bengal North 24 Parganas Kanchrapara Municipality 120345 West Bengal North 24 Parganas Halisahar Municipality 124939 West Bengal North 24 Parganas Naihati Municipality 217900 West Bengal North 24 Parganas Bhatpara Municipality 383762 West Bengal North 24 Parganas Garulia Municipality 85336 West Bengal North 24 Parganas North Barrackpur Municipality 132806 West Bengal North 24 Parganas Barrackpur Municipality 152783 West Bengal North 24 Parganas Titagarh Municipality 116541 West Bengal North 24 Parganas Khardaha Municipality 108496 West Bengal North 24 Parganas Panihati Municipality 377347 West Bengal North 24 Parganas New Barrackpur Municipality 76846 West Bengal North 24 Parganas Kamarhati Municipality 330211 West Bengal North 24 Parganas Baranagar Municipality 245213 West Bengal North 24 Parganas South Dum Dum Municipality 403316 West Bengal North 24 Parganas North Dum Dum Municipality 249142 West Bengal North 24 Parganas Dum Dum Municipality 114786 West Bengal North 24 Parganas Bidhan Nagar Municipality 215514 West Bengal North 24 Parganas Habra Municipality 147221 West Bengal North 24 Parganas Ashoknagar Kalyangarh Municipality 121592 West Bengal North 24 Parganas Basirhat Municipality 125254 West Bengal North 24 Parganas Bongaon Municipality 108864 West Bengal North 24 Parganas Baduria Municipality 52493 Tamil Nadu Kancheepuram Kundrathur Town Parishad 54986 Karnataka Dakshin Kannad Ullal TMC 53773 Karnataka Dakshin Kannad Puttur TMC 53061 Manipur East Imphal Lamlai Nagar Panchayat 4601 Manipur East Imphal Andro Nagar Panchayat 8744 Uttar Pradesh Ghaziabad Modinagar Nagar Panchayat 130325 Uttar Pradesh Ghaziabad Muradnagar Nagar Panchayat 95208 Uttar Pradesh Ghaziabad Loni Nagar Panchayat 516082 Uttar Pradesh Ghaziabad Pilkhuwa Nagar Panchayat 83736 Uttar Pradesh Ghaziabad Hapur Nagar Panchayat 262983 Delhi Delhi Delhi Cantt Cantonment Board 110351 54    State District UAName Type Population Haryana Ambala Ambala Cantt. Cantonment Board 55370 Maharashtra Pune Pune Cantt. Cantonment Board 71781 Maharashtra Pune Kirkee Cantt. Cantonment Board 78684 Maharashtra Nashik Deolali Cantt. Cantonment Board 54027 Telangana Hyderabad Secunderabad Cantonment Board 217910 Uttar Pradesh Kanpur Nagar Kanpur Cantonment Board 108534 Uttar Pradesh Lucknow Lucknow Cantonment Cantonment Board 63003 Manipur East Imphal Porompat Plan Area Urban Outgrowth 1145 Manipur East Imphal Kongkham Leikai Urban Outgrowth 887 Daman and Diu Daman Dadhel Census Town 52578 Delhi Delhi Sahibabad Daulat Pur Census Town 54773 Delhi Delhi Bawana Census Town 73680 Delhi Delhi Kirari Suleman Nagar Census Town 283211 Delhi Delhi Nithari Census Town 50464 Delhi Delhi Begum Pur Census Town 53682 Delhi Delhi Pooth Kalan Census Town 96002 Delhi Delhi Sultan Pur Majra Census Town 181554 Delhi Delhi Bhalswa Jahangir Pur Census Town 197148 Delhi Delhi Mukand Pur Census Town 57135 Delhi Delhi Burari Census Town 146190 Delhi Delhi Sadat Pur Gujran Census Town 97641 Delhi Delhi Karawal Nagar Census Town 224281 Delhi Delhi Mustafabad Census Town 127167 Delhi Delhi Khajoori Khas Census Town 76640 Delhi Delhi Ziauddin Pur Census Town 68993 Delhi Delhi GokalPur Census Town 121870 Delhi Delhi Jaffrabad Census Town 54601 Delhi Delhi Mandoli Census Town 120417 Delhi Delhi Gharoli Census Town 92540 Delhi Delhi DalloPura Census Town 154791 Delhi Delhi Chilla Saroda Bangar Census Town 83217 Delhi Delhi Hastsal Census Town 176877 Delhi Delhi Bapraula Census Town 52744 Delhi Delhi NangloiJat Census Town 205596 Delhi Delhi Mundka Census Town 54541 Delhi Delhi Roshan Puraalias Census Town 57217 Dichaon Khurd Delhi Delhi Kapas Hera Census Town 74073 Delhi Delhi Deoli Census Town 169122 55    State District UAName Type Population Delhi Delhi Pul Pehlad Census Town 69657 Delhi Delhi TajPul Census Town 68796 Delhi Delhi Mithe Pur Census Town 69837 Delhi Delhi Molar Band Census Town 91402 Delhi Delhi Jait Pur Census Town 59330 Haryana Panipat Panipat Taraf Makhdum Census Town 67998 Zadgan Kerela Ernakulam Edathala Census Town 77811 Kerela Ernakulam Vazhakkala Census Town 51242 Kerela Thiruvananthapuram Pallichal Census Town 53861 Kerela Kozhikode Cheruvannur Census Town 61614 Kerela Kozhikode Beypore Census Town 69752 Kerela Malappuram Moonniyur Census Town 55535 Kerela Malappuram Tirurangadi Census Town 56632 Kerela Malappuram Thennala Census Town 56546 Maharashtra Nagpur Wadi Census Town 54048 Maharashtra Nashik Ozar Census Town 51297 Manipur East Imphal Porompat Census Town 6191 Manipur East Imphal Torban Census Town 5459 Manipur East Imphal Luwangsangbam Census Town 3458 Manipur East Imphal Khongman Census Town 6096 Manipur East Imphal Laipham Siphai Census Town 5268 Manipur East Imphal Khurai Sajor Leikai Census Town 7987 Manipur East Imphal Chingangbam Leikai Census Town 4904 Manipur East Imphal Kshetrigao Census Town 10534 Manipur East Imphal Kiyamgei Census Town 5336 Manipur East Imphal Heingang Census Town 6115 Manipur East Imphal Lairikyengbam Leikai Census Town 4586 Manipur East Imphal Thongju Census Town 10836 Tamil Nadu Kancheepuram Oggiyamduraipakkam Census Town 76600 Tamil Nadu Thiruvallur Nerkunram Census Town 59790 Tamil Nadu Thiruvallur Ramapuram Census Town 52295 Uttar Pradesh Ghaziabad Khora Census Town 190005 West Bengal Haora Bally Census Town 113377 West Bengal Haora Bankra Census Town 63957 56    Table A6.3: List of Urban Settlements by Population (including Census Towns) State District UAName Type Total Population Maharashtra Greater Mumbai Greater Mumbai Municipal Corp. 12442373 Delhi Delhi DMC Municipal Corp. 11034555 Above 4 million Karnataka Bangalore Urban Bangalore BMP Municipal Corp. 8443675 Gujarat Ahmadabad Ahmadabad Municipal Corp. 5577940 Tamil Nadu Chennai Chennai Municipal Corp. 4646732 West Bengal Kolkata Kolkata Municipal Corp. 4496694 Gujarat Surat Surat Municipal Corp. 4467797 Maharashtra Pune Pune Municipal Corp. 3124458 Uttar Pradesh Lucknow Lucknow Municipal Corp. 2817105 Uttar Pradesh Kanpur Nagar Kanpur Municipal Corp. 2765348 Maharashtra Nagpur Nagpur Municipal Corp. 2405665 Maharashtra Thane Thane Municipal Corp. 1841488 1-4 million (16 cities) Maharashtra Pune Pimpri Chinchwad Municipal Corp. 1727692 Uttar Pradesh Ghaziabad Ghaziabad Municipal Corp. 1648643 Punjab Ludhiana Ludhiana Municipal Corp. 1618879 Maharashtra Nashik Nashik Municipal Corp. 1486053 Haryana Faridabad Faridabad Municipal Corp. 1414050 Maharashtra Thane Kalyan Dombivali Municipal Corp. 1247327 Maharashtra Thane Vasai-Virar City Municipal Corp. 1222390 Maharashtra Thane Navi Mumbai Municipal Corp. 1120547 West Bengal Haora Haora Municipal Corp. 1077075 Tamil Nadu Coimbatore Coimbatore Municipal Corp. 1050721 Tamil Nadu Madurai Madurai Municipal Corp. 1017865 Chandigarh Chandigarh Chandigarh Municipal Corp. 961587 Haryana Gurgaon Gurgaon Municipal Corp. 876969 0.5-1 million (9 cities) Punjab Jalandhar Jalandhar Municipal Corp. 862886 Maharashtra Thane Mira Bhayandar Municipal Corp. 809378 Kerela Thiruvananthapuram Thiruvananthapuram Municipal Corp. 743691 Maharashtra Thane Bhiwandi Municipal Corp. 709665 Kerela Ernakulam Kochi Municipal Corp. 602046 Uttar Pradesh Ghaziabad Loni Nagar Panchayat 516082 Maharashtra Thane Ulhasnagar Municipal Corp. 506098 Karnataka Dakshin Kannad Mangalore Municipal Corp. 488968 0.2-0.5 million (35 cities) Maharashtra Nashik Malegaon Municipal Corp. 471312 Tamil Nadu Thiruvallur Ambattur Municipality 466205 Kerela Kozhikode Kozhikode Municipal Corp. 431560 West Bengal North 24 Parganas South Dum Dum Municipality 403316 West Bengal North 24 Parganas Rajarhat Gopalpur Municipality 402844 West Bengal North 24 Parganas Bhatpara Municipality 383762 West Bengal North 24 Parganas Panihati Municipality 377347 57    State District UAName Type Total Population Haryana Rohtak Rohtak Muncipal Council 374292 Kerela Kollam Kollam Municipal Corp. 348657 Tamil Nadu Thiruvallur Avadi Municipality 345996 West Bengal North 24 Parganas Kamarhati Municipality 330211 Kerela Thrissur Thrissur Municipal Corp. 315957 Puducherry Puducherry Ozhukarai Municipality 300104 Haryana Panipat Panipat Muncipal Council 294292 West Bengal Haora Bally Municipality 293373 Delhi Delhi Kirari Suleman Nagar Census Town 283211 Manipur West Imphal Imphal Muncipal Council 282335 West Bengal North 24 Parganas Barasat Municipality 278435 Uttar Pradesh Ghaziabad Hapur Nagar Panchayat 262983 Delhi Delhi N.D.M.C. Muncipal Council 257803 Maharashtra Thane Ambarnath Muncipal Council 253475 Tamil Nadu Thiruvallur Tiruvottiyur Municipality 249446 West Bengal North 24 Parganas North Dum Dum Municipality 249142 West Bengal North 24 Parganas Baranagar Municipality 245213 Puducherry Puducherry Puducherry Municipality 244377 Tamil Nadu Kanyakumari Nagercoil Municipality 224849 Delhi Delhi Karawal Nagar Census Town 224281 West Bengal Haora Uluberia Municipality 222240 Telangana Hyderabad Secunderabad Cantonment Board 217910 West Bengal North 24 Parganas Naihati Municipality 217900 West Bengal North 24 Parganas Bidhan Nagar Municipality 215514 Tamil Nadu Kancheepuram Pallavaram Municipality 215417 Haryana Panchkula Panchkula Muncipal Council 211355 Delhi Delhi NangloiJat Census Town 205596 Delhi Delhi Bhalswa Jahangir Pur Census Town 197148 West Bengal North 24 Parganas Madhyamgram Municipality 196127 Below 0.2 million (166 towns/cities) Haryana Ambala Ambala Muncipal Council 195153 Uttar Pradesh Ghaziabad Khora Census Town 190005 West Bengal Hugli Serampore Municipality 181842 Delhi Delhi Sultan Pur Majra Census Town 181554 West Bengal Hugli Hugli-Chinsurah Municipality 177259 Delhi Delhi Hastsal Census Town 176877 Tamil Nadu Kancheepuram Tambaram Municipality 174787 Maharashtra Thane Badlapur Muncipal Council 174226 Kerela Alappuzha Alappuzha Municipality 174176 Delhi Delhi Deoli Census Town 169122 West Bengal Hugli Chandannagar Municipal Corp. 166867 Tamil Nadu Kancheepuram Alandur Municipality 164430 58    State District UAName Type Total Population Tamil Nadu Kancheepuram Kancheepuram Municipality 164384 West Bengal Hugli Uttarpara Kotrung Municipality 159147 Delhi Delhi DalloPura Census Town 154791 West Bengal North 24 Parganas Barrackpur Municipality 152783 West Bengal North 24 Parganas Habra Municipality 147221 Delhi Delhi Burari Census Town 146190 Haryana Rewari Rewari Muncipal Council 143021 West Bengal North 24 Parganas North Barrackpur Municipality 132806 Uttar Pradesh Ghaziabad Modinagar Nagar Panchayat 130325 Punjab Ludhiana Khanna Muncipal Council 128137 Delhi Delhi Mustafabad Census Town 127167 West Bengal North 24 Parganas Basirhat Municipality 125254 West Bengal North 24 Parganas Halisahar Municipality 124939 West Bengal Hugli Rishra Municipality 124577 Tamil Nadu Coimbatore Kurichi Municipality 123667 Delhi Delhi GokalPur Census Town 121870 West Bengal North 24 Parganas Ashoknagar Kalyangarh Municipality 121592 West Bengal Hugli Baidyabati Municipality 121110 Delhi Delhi Mandoli Census Town 120417 West Bengal North 24 Parganas Kanchrapara Municipality 120345 Tamil Nadu Thiruvallur Madavaram Municipality 119105 West Bengal North 24 Parganas Titagarh Municipality 116541 West Bengal North 24 Parganas Dum Dum Municipality 114786 West Bengal Haora Bally Census Town 113377 West Bengal Hugli Champdani Municipality 111251 Delhi Delhi Delhi Cantt Cantonment Board 110351 West Bengal North 24 Parganas Bongaon Municipality 108864 Uttar Pradesh Kanpur Kanpur Cantonment Board 108534 West Bengal North 24 Parganas Khardaha Municipality 108496 West Bengal Hugli Bansberia Municipality 103920 Haryana Ambala Ambala Sadar Muncipal Council 103093 West Bengal Hugli Bhadreswar Municipality 101477 Delhi Delhi Sadat Pur Gujran Census Town 97641 Kerela Malappuram Manjeri Municipality 97102 Delhi Delhi Pooth Kalan Census Town 96002 Tamil Nadu Coimbatore Kuniyamuthur Municipality 95924 Uttar Pradesh Ghaziabad Muradnagar Nagar Panchayat 95208 West Bengal Hugli Dankuni Municipality 94936 Kerela Kannur Thalassery Municipality 92558 Delhi Delhi Gharoli Census Town 92540 Delhi Delhi Molar Band Census Town 91402 59    State District UAName Type Total Population Kerela Malappuram Ponnani Municipality 90491 Tamil Nadu Coimbatore Pollachi Municipality 90180 Tamil Nadu Madurai Avaniapuram Municipality 89635 Maharashtra Nagpur Kamptee Muncipal Council 86793 Tamil Nadu Thiruvallur Maduravoyal Municipality 86195 West Bengal North 24 Parganas Garulia Municipality 85336 Tamil Nadu Coimbatore Goundampalayam Municipality 83908 Uttar Pradesh Ghaziabad Pilkhuwa Nagar Panchayat 83736 Delhi Delhi Chilla Saroda Bangar Census Town 83217 Tamil Nadu Kancheepuram Maraimalainagar Municipality 81872 Maharashtra Nashik Manmad Muncipal Council 80058 Gujarat Ahmadabad Dholka Municipality 79531 Maharashtra Pune Kirkee Cantt. Cantonment Board 78684 Kerela Ernakulam Edathala Census Town 77811 West Bengal North 24 Parganas New Barrackpur Municipality 76846 Delhi Delhi Khajoori Khas Census Town 76640 Tamil Nadu Kancheepuram Oggiyamduraipakkam Census Town 76600 West Bengal Hugli Konnagar Municipality 76172 Tamil Nadu Kancheepuram Pammal Municipality 75870 Kerela Kozhikode Vadakara Municipality 75295 Delhi Delhi Kapas Hera Census Town 74073 Delhi Delhi Bawana Census Town 73680 Kerela Kannur Taliparamba Municipality 72465 Kerela Kannur Payyannur Municipality 72111 Kerela Kozhikode Quilandy Municipality 71873 Maharashtra Pune Pune Cantt. Cantonment Board 71781 Kerela Ernakulam Kalamassery Municipality 71038 Goa North Goa Panaji Municipal Corp. 70991 Tamil Nadu Coimbatore Valparai Municipality 70859 Kerela Thiruvananthapuram Neyyattinkara Municipality 70850 Delhi Delhi Mithe Pur Census Town 69837 Kerela Kozhikode Beypore Census Town 69752 Delhi Delhi Pul Pehlad Census Town 69657 Kerela Ernakulam Thrippunithura Municipality 69390 Tamil Nadu Coimbatore Mettupalayam Municipality 69213 Delhi Delhi Ziauddin Pur Census Town 68993 Maharashtra Thane Palghar Muncipal Council 68930 Delhi Delhi TajPul Census Town 68796 Kerela Alappuzha Kayamkulam Municipality 68634 Kerela Malappuram Malappuram Municipality 68127 Haryana Panipat Panipat Taraf Makhdum Census Town 67998 Zadgan 60    State District UAName Type Total Population West Bengal Hugli Arambag Municipality 66175 Maharashtra Nashik Sinnar Muncipal Council 65299 Punjab Ludhiana Jagraon Muncipal Council 65240 Telangana Rangareddi Tandur Municipality 65115 West Bengal Haora Bankra Census Town 63957 Tamil Nadu Madurai Anaiyur Municipality 63917 Uttar Pradesh Lucknow Lucknow Cantonment Cantonment Board 63003 Tamil Nadu Thiruvallur Tiruverkadu Municipality 62824 Tamil Nadu Kancheepuram Chengalpattu Municipality 62579 Kerela Kozhikode Cheruvannur Census Town 61614 Gujarat Surat Bardoli Municipality 60821 Kerela Thiruvananthapuram Nedumangad Municipality 60161 Tamil Nadu Thiruvallur Nerkunram Census Town 59790 Delhi Delhi Jait Pur Census Town 59330 Maharashtra Pune Lonavala Muncipal Council 57698 Tamil Nadu Thiruvallur Poonamallee Municipality 57224 Delhi Delhi Roshan Puraalias Census Town 57217 Dichaon Khurd Delhi Delhi Mukand Pur Census Town 57135 Kerela Kannur Kannur Municipality 56823 Kerela Malappuram Tirurangadi Census Town 56632 Kerela Malappuram Thennala Census Town 56546 Maharashtra Pune Talegaon Dabhade Muncipal Council 56435 Tamil Nadu Thiruvallur Thiruvallur Municipality 56074 Kerela Malappuram Tirur Municipality 56058 Gujarat Ahmadabad Viramgam Municipality 55821 Kerela Malappuram Moonniyur Census Town 55535 Kerela Kottayam Kottayam Municipality 55374 Haryana Ambala Ambala Cantt. Cantonment Board 55370 Tamil Nadu Kancheepuram Kundrathur Town Parishad 54986 Delhi Delhi Sahibabad Daulat Pur Census Town 54773 Delhi Delhi Jaffrabad Census Town 54601 Delhi Delhi Mundka Census Town 54541 Maharashtra Pune Baramati Muncipal Council 54415 Kerela Thrissur Kunnamkulam Municipality 54071 Maharashtra Nagpur Wadi Census Town 54048 Maharashtra Nashik Deolali Cantt. Cantonment Board 54027 Maharashtra Nagpur Umred Muncipal Council 53971 Kerela Thiruvananthapuram Pallichal Census Town 53861 Karnataka Dakshin Kannad Ullal TMC 53773 Delhi Delhi Begum Pur Census Town 53682 Tamil Nadu Kancheepuram Puzhithivakkam Municipality 53322 61    State District UAName Type Total Population Telangana Rangareddi Vicarabad Municipality 53143 Karnataka Dakshin Kannad Puttur TMC 53061 Delhi Delhi Bapraula Census Town 52744 Daman and Diu Daman Dadhel Census Town 52578 West Bengal North 24 Parganas Baduria Municipality 52493 Tamil Nadu Thiruvallur Ramapuram Census Town 52295 Maharashtra Nashik Ozar Census Town 51297 Kerela Ernakulam Vazhakkala Census Town 51242 Tamil Nadu Madurai Thirumangalam Municipality 51194 Delhi Delhi Nithari Census Town 50464 Maharashtra Thane Dahanu Muncipal Council 50287 Puducherry Mahe Mahe Municipality 41816 Manipur East Imphal Thongju Census Town 10836 Manipur East Imphal Kshetrigao Census Town 10534 Manipur East Imphal Andro Nagar Panchayat 8744 Manipur East Imphal Khurai Sajor Leikai Census Town 7987 Manipur East Imphal Jiribam Muncipal Council 7343 Manipur East Imphal Porompat Census Town 6191 Manipur East Imphal Heingang Census Town 6115 Manipur East Imphal Khongman Census Town 6096 Manipur East Imphal Torban Census Town 5459 Manipur East Imphal Kiyamgei Census Town 5336 Manipur East Imphal Laipham Siphai Census Town 5268 Manipur East Imphal Chingangbam Leikai Census Town 4904 Manipur East Imphal Lamlai Nagar Panchayat 4601 Manipur East Imphal Lairikyengbam Leikai Census Town 4586 Manipur East Imphal Luwangsangbam Census Town 3458 Manipur East Imphal Porompat Plan Area Urban Outgrowth 1145 Manipur East Imphal Kongkham Leikai Urban Outgrowth 887 62