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M. Giovanni FUSCO, Chargé de recherche CNRS, UMR Espace, Université de Nice-Sophia Antipolis CodirecteurM. Jean GREBERT, Expert, DE-IRM, Renault, Paris ExaminateurM. Jean-Paul HUBERT, Directeur de recherche, IFSTTAR, AME-DEST, Champs-sur-Marne RapporteurM. François MORICONI-EBRARD, Directeur de recherche CNRS, UMR Espace, Université d'Avignon CodirecteurM. Sébastien OLIVEAU, Maître de conférences HDR, UMR Espace, Aix-Marseille Université ExaminateurM. Joel RUET, Chargé de recherche CNRS, CEPN, Université Paris Nord RapporteurM. Jean-Claude THILL, Knight Distinguished Professor, University of North Carolina, Charlotte ExaminateurComposition du jury

Spatial Structures in India

in the Age of Globalisation

A Data-Driven Approach

ACADÉMIE D'AIX-MARSEILLE

UNIVERSITÉ d'AVIGNON

ET DES PAYS DE VAUCLUSE

UFR ip Sciences Humaines et Sociales

Département de Géographie

THÈSE

présentée par Joan PEREZ pour l'obtention du titre de Docteur en Géographie de l'Université d'Avignon et des Pays de Vaucluse École Doctorale 537 " Culture et Patrimoine » soutenue publiquement le 17 décembre 2015 ii

Remerciements

Je tiens à adresser en premier lieu, de sincères remerciements à Monsieur Giovanni Fusco, pour son suivi

constant et sa rigueur notamment en matière de conceptualisation et de modélisation spatiale. Ses exigences

m'ont permis de progresser considérablement et ont indubitablement élevé la qualité de ce travail de recherche.

Dans un même mouvement, je remercie Monsieur François Moriconi-Ebrard, pour sa vision transversale, sa

prise de recul et son regard critique. Ses nombreuses connaissances sur les systèmes de peuplements m'ont

permis, dès le début de cette thèse, d'adopter une perspective globale et élargie sur les enjeux de ce travail.

Je remercie également l'entreprise Renault, qui, au travers d'une convention CIFRE, a financé ce travail de

recherche dans son intégralité.

Ma gratitude va plus particulièrement à Monsieur Jean Grebert, qui, d'un point de vue opérationnel, a piloté

cette thèse et son évolution dans l'entreprise. Nombre de nos voyages, missions et échanges trouvent leurs échos

dans ce manuscrit. J'espère sincèrement, qu'au delà de cette thèse, d'autres opportunités de collaboration se

dessineront.

Au sein de la société Renault, j'exprime aussi toute ma gratitude à Mme. Armelle Guerrin ancienne directrice

du plan de Renault India et à M. Marc Nassif, ancien directeur de Renault India, pour nos nombreux échanges,

lors de réunions à Chennai, sur le potentiel de marché des pays émergents. J'associe à ces remerciements M.

Jean-Marc David, directeur du service DE-IRM, Mme Dominique Levant, directrice de la DREAM Renault ;

ainsi que à Mme Pascale Perron et à Mme Michelle Godefroid pour leur aide dans les méandres administratifs

de l'entreprise.

J'adresse un remerciement tout particulier à Monsieur Jean-Paul Hubert, Monsieur Sébastien Oliveau,

Monsieur Joël Ruet et Monsieur Jean-Claude Thill pour l'intérêt qu'ils portent à l'égard de mon travail, leur

disponibilité et ainsi, leur participation à mon jury de thèse.

Je remercie bien sûr l'ensemble des membres de mon laboratoire, l'UMR 7300 Espace, et plus spécifiquement :

M. Loïc Grasland, directeur de l'antenne d'Avignon et Mme Nathalie Brachet responsable de publication, pour

leur soutien logistique tout au long de ces années.

L'Institut Français de Pondichéry m'a ouvert ses portes durant la première année de ce travail de recherche où

j'ai rejoint le département de sciences sociales. Je remercie bien sûr l'ensemble du personnel de l'IFP pour leur

accueil chaleureux mais aussi plus particulièrement Mme Kamala Marius-Gnanou, M. Eric Denis et bien sûr

Venkatasubramanian pour nos échanges fructueux sur l'Inde et sur mes terrains. Enfin, un grand merci à Antony

pour ces nombreux jours (voir nuits !) passés ensemble à traiter les données du recensement Indien.

J'aimerais aussi adresser une reconnaissance toute particulière à mon ancien directeur de mémoire, M.

Frédéric Audard, mais aussi à M. Sébastien Oliveau et à feu M. Jean-Luc Bonnefoy. Ces derniers m'ont

finalement donné goût à la géographie quantitative bien avant le début de cette thèse.

Je me dois de remercier profondément les membres de ma famille pour leur soutien immuable. Tout d'abord mes

parents, Linda et Alain, qui, en toute objectivité sont à l'origine de ce travail il y a maintenant 29 ans de cela. Je

remercie aussi l'ensemble de mes proches et notamment Kévin, Micheline et Marco.

Merci aux chercheurs et doctorants rencontrés en France et à l'étranger avec qui j'ai passé de nombreux

moments inoubliables : Mythri Prasad, Jules Morel, Cyril Pivano, Salima El Mokthari, Davayani Khare, Selma

Fortin, Ravi Kiran, Lizhu Dhai, Léa Wester, Marion Borderon, Joel Quiercy, Yoan Doignon, Julien Bordagi,

Romain Ronceray, Elfie Swerts, Akil Amirali, Rémi de Bercegol, Gaelle Lesteven et Charlotte Carlota.

Merci à mon ami Brussais pour le travail d'orfèvre qu'il a réalisé pour la couverture de cette thèse, et, je

remercie chaleureusement Eléanore et Marie pour les longues heures qu'elles ont consacrées à relire la langue

de Shakespeare.

Merci à tous ceux que je ne prends pas le temps de citer ici car la liste serait trop longue : amis, colocataires...

Pour finir, j'adresse un remerciement tout particulier à toi, Amaga, qui m'as soutenu dans toutes ces épreuves,

du début à la fin sans jamais fléchir. iii

Abstract

Countries that have experienced a delayed entry within the world economy have usually sustained an enhanced and faster globalisation process. This is the case for BRIC countries which are, compared to other emerging countries, organised on large economies and thus provide a stronger potential market. From this perspective, India appears to be the perfect case study with an economic growth expected to overcome China's growth in the near future. However, the "clichés" are persistent within a country mostly depicted as bipolar. On the one hand, it is considered as a new eldorado, the "Shining India", a place where multinationals aim to implement themselves due to the substantial increase of the consumer market. At the same time, India is also characterised by overcrowding, the major presence of slum areas and mass poverty, both in urban and rural areas. It is indeed possible that some areas will accommodate a bigger and bigger share of the growing middle class, while others will accentuate economic and social inequalities. Yet, can these extremes be truly representative of the diversity of such a large country? In fact, in some urban oriented spaces, the evolution of the tertiary sector is not strong enough to maintain a high level of employment while in rural spaces; an intensive farming model contributes to gradually reducing the number of labourers and landowners. As a result, the increase of the standard of living related to both economic and demographic growth is not homogeneously distributed over a territory where socio- economic divisions are already made worse by a tight caste system. With evidence dating back to 2400 BCE, it must be remembered that India is a country of old urbanisation. This has given rise to a rich and complex history and India is now home to a variety of languages, religions, castes, communities, tribes, traditions, urbanisation patterns and, more recently, globalisation-related dynamics. Perhaps no other country in the world seems to be characterised by such a great diversity. This begs the following questions: how is it possible to quantify and visualise the spatial gap of such a complex and subcontinent sized country? What are the main drivers affecting this spatial gap? It would indeed be simplistic to study India only through macro-economic indicators such as GDP. To deal with this complexity, a conceptualisation has been performed to strictly select 55 criteria that can affect the transformation sustained by the Indian territory in this age of enhanced globalisation. These selected factors have fed a multi-critera database characterised by aspects coming from economy, geography, sociology, culture etc. at the district scale level (640 spatial units) and on a ten-year timeframe (2001-2011). The assumption is as follows: each Indian district can be driven by different factors. The human capacity to understand a complex issue has been reached here since we cannot take into account and at the same time the behaviour of a large iv number of elements influencing one another. AI Based Algorithm methods (Bayesian and Neural Networks) have thus been resorted to as a good alternative to process a large number of factors. In order to be as accurate as possible and to keep a transversal point of view, the methodology is divided into a robust procedure including fieldwork steps. The results of the models show that the 55 factors interact, bringing the emergence of unobservable factors representative of broader concepts, which find consistency only in the case of India. It also shows that the Indian territory can be segmented into a multitude of subspaces. Some of these profiles are close to the caricatured India. However, in most cases, results show a heterogeneous country with subspaces possessing a logic of their own and far away from any cliché. Keywords: India, globalisation, urbanisation, middle classes, spatial segmentation, AI based- algorithms, Bayesian and Neural Networks, Self-Organizing Maps. v

Table of Contents

Remerciements ................................................................................................................................ ii

Abstract ........................................................................................................................................... iii

List of Illustrations ......................................................................................................................... vii

List of Tables .................................................................................................................................. ix

List of Abbreviations ....................................................................................................................... x

CHAPTER 1

INTRODUCTION .............................................................................................................................. 1

PART ONE:

GLOBALISATION, EMERGING COUNTRIES AND INDIA

CHAPTER 2

HUMAN SOCIETIES AND URBANISATION IN THE AGES OF GLOBALISATION

2.1 The Roots of Globalisation: Complexity, Interdependence and Integrated World.................... 9

2.2 Emerging Countries and Spatial Disparities in the Age of Globalisation ............................... 29

CHAPTER 3

INDIA, FROM OLD URBANISATION PATTERNS TO MODERN GLOBALISATION

PROCESSES

3.1 A Complex and Multilayered History of Urbanisation ........................................................... 49

3.2 Strong Economic Growth, Socio-cultural Disparities and Urbanisation Patterns ................... 77

PART TWO:

DATA COLLECTION AND MODELING

CHAPTER 4

DATA COLLECTION PROCESS: INDICATOR SELECTIONS AND CONCEPTIONS

4.1 Systemic Analysis, Inductive Reasoning and Data-Driven Approaches: A Transversal

Perspective ....................................................................................................................................... 99

4.2 Dataset Constitution: Data Collection and Creation .............................................................. 110

4.3 Final Dataset at the District Level ......................................................................................... 151

vi

CHAPTER 5

CLUSTERING INDIAN SPACE: METHODOLOGICAL PROTOCOLS

5.1 Bayesian Reasoning and Bayesian Clustering ....................................................................... 160

5.2 Neural Networks and Clustering ........................................................................................... 169

5.3 Applications of the Bayesian Protocols ................................................................................. 180

5.4 Applications of the Self-Organizing Maps and Super-Organizing Maps Protocols .............. 194

PART THREE:

GEOGRAPHICAL ANALYSIS AND FIELDWORK

CHAPTER 6

CLUSTERING RESULTS AND GEOGRAPHICAL ANALYSIS

6.1 Bayesian Networks and SOM/superSOM Clustering: Similarities and Differences ............. 210

6.2 Description and Interpretation of the Clusters ....................................................................... 214

6.3 Overview of India in the Age of Globalisation ...................................................................... 241

CHAPTER 7

FIELDWORKS: METROPOLITAN AREAS, DYNAMIC MID-SIZED CITY AND NON-

URBAN WELL-OFF INDIA

7.1 Metropolitan India: the Land of Extremes ............................................................................. 250

7.2 Hubli-Dharwad: A Dynamic District ..................................................................................... 262

7.3 Non-Urban Well-Off India .................................................................................................... 271

CHAPTER 8

CONCLUSION .............................................................................................................................. 281

REFERENCES ............................................................................................................................... 289

APPENDICES ................................................................................................................................ 307

A. R Source Codes ....................................................................................................................... 308

B. Census of India: Official Maps and Definitions ..................................................................... 319

C. District Borders GIS Reconstitution ....................................................................................... 324

D. Additional information about the e-Geopolis Research Program ........................................... 325

E. Additional information about the Bayesian Experiment 6 ...................................................... 326

F. Additional information about the Fieldworks ......................................................................... 343

G. Cluster Mapping of other Experiments ................................................................................... 344

vii

List of Illustrations

Figure 1: Intensity of International Travels through Sailing Ships leaving the European

Nations from 1662 to 1855 .................................................................................................................... 12

Figure 2: The Global Situation at the Dawn of World War I in 1900 .................................................. 16

Figure 3: 1970, the World Situation at the Dawn of the Financial Deregulation ................................. 20

Figure 4: Share (in red) and flows (in green) of Bilateral International Trade from 1996 to 2000 ...... 22

Figure 5: Contributions to Global Growth, at PPP Exchange Rates, Period Averages in Percent. ...... 30

Figure 6: US Citizens Travelled Distances per day for all Modes (per person) 40

Figure 7: The Second Rise of Urbanisation; the Mauryan Empire at its Greatest Extent

in the North and the Tamil Dynasties in the South during the Sangam Period ..................................... 53

Figure 8: The Third Rise of Urbanisation; Mughal and Vijayanagara Empires, the Indian

Situation during the 16th Century ......................................................................................................... 59

Figure 9: Structural Adjustments Made over the Urban System, the Situation of the Indian

Subcontinent under the British Occupation in 1910 .............................................................................. 68

Figure 10: Post-independence Migrations, Disputed Areas and Indian Administrative

Divisions in 1975................................................................................................................................... 73

Figure 11: Foreign Direct Investment, A Comparison between India and China ................................ 81

Figure 12: Evolution of a standard Indian Urban Centre ..................................................................... 90

Figure 13: Population of the Continuous Built-up Areas above 10.000 inhabitants in 1981,

1991,2001 and 2011. ............................................................................................................................ 93

Figure 14: Von Thunen Model: "The Isolated State", One of the First Economic Models taking into

account Space ...................................................................................................................................... 103

Figure 15: A Conceptual Model used as a Guideline for Data Collection ......................................... 108

Figure 16: Overview of the 640 Indian Districts in 2011 ................................................................... 111

Figure 17: The Aggregative process of Official Settlements within an e-Geopolis Built-up Area;

Aligarh Urban Area example............................................................................................................... 115

Figure 18: Share of Scheduled Castes per District in 2011 ................................................................ 119

Figure 19: Share of Children of Less than Seven Years Old in 2011 ................................................ 121

Figure 20: Share of Secondary and Tertiary Workers among the Working Population in 2011........ 123 Figure 21: Evolution of the Share of Secondary and Tertiary Workers between 2001 and 2011 ...... 124

Figure 22: Share of Big Households (more than 6 persons) in 2011 ................................................. 126

Figure 23: Share of Households possessing at least One Car in 2011 ............................................... 129

Figure 24: Share of Households possessing No Assets in 2011 ......................................................... 131

Figure 25: Number of Dwelling units (rooms) in the Census of India. 133

Figure 26: Residential Welfare in 2011 ............................................................................................. 134

Figure 27: Residential Welfare Evolution between 2001 and 2011 ................................................... 135

Figure 28: The Main Metropolitan Areas in India ordered by Rank .................................................. 140

Figure 29: Distance to Rank 1 Metropolitan Area from each Indian District .................................... 141

Figure 30: Distance to Rank 1 Metropolitan Area from each Indian District .................................... 143

Figure 31: The Macro-Structures related to the Seven Rank 1 Metropolitan Areas .......................... 145

Figure 32: Distance to Four-lane National Highways from the Indian Districts ................................ 150

Figure 33: Distribution of the 55 Indicators constituting the India Dataset ....................................... 155

viii

Figure 34: A Bayesian Network Example. Source: Mitchell (1997) p. 186 ...................................... 162

Figure 35: The Structural Learning of a Bayesian Network over the Iterations using Taboo

Learning in BayesiaLab. ...................................................................................................................... 167

Figure 36: A 4x4 SOM and superSOM Network with their Input Space........................................... 172

Figure 37: Rectangular and Hexagonal Fashion of a 4×4 SOM Grid (Output Space) ....................... 174

Figure 38: The Ever-Shrinking Radius on a 4x4 Grid Example ........................................................ 176

Figure 39: The Neighbourhood Influence on a 4x4 Grid following a Regular or a Toroidal Pattern 178 Figure 40: The Bayesian Network Learned from the India Dataset after 10 Taboo Order,

a Fixation of the most Robust Arcs and a final Tabu Search .............................................................. 182

Figure 41: Variable Groups of the first HCA (coloured nodes) and Variable Combinations

Frequency Graph after a k-fold Cross Validation for ten HCA .......................................................... 184

Figure 42: Variable Groups of the Rearranged Structure (coloured nodes) and Variable

Combinations Frequency Graph after a new k-fold Cross Validation for ten HCA............................ 185

Figure 43: A BN Naïve Architecture Determining Latent Factors .................................................... 186

Figure 44: Identification and Segmentation of the New Non-Observable Factor .............................. 189

Figure 45: Characterisation of the New Non-Observable Factor ....................................................... 193

Figure 46: Boxplots of FM Similarity Index values for 20 model initialisations............................... 203

Figure 47: District Clustering and Codebook Quality for the Three Models ..................................... 204

Figure 48: Layer Optimisation: Mean Distance from Layers to the Closest Unit for Model 2

and Model 3 ......................................................................................................................................... 206

Figure 49: Cluster Neighbourhoods, Cluster Sizes and Family Groupings

Left: Bayesian Networks Experiment 6. Right: SOM/superSOM Model 3 ........................................ 212

Figure 50: The Urban Family ............................................................................................................. 215

Figure 51: Characteristic of the Urban Family from a Selected Range of Indicators ........................ 216

Figure 52: Clustering Location Comparison with the Kerala-Tamil Nadu Corridors

Macro-Structure................................................................................................................................... 218

Figure 53: The Rural Family .............................................................................................................. 222

Figure 54: Characteristic of the Rural Family from a Selected Range of Indicators ......................... 223

Figure 55: The Traditional Family ..................................................................................................... 228

Figure 56: Characteristic of the Traditional Family from a Selected Range of Indicators................. 229

Figure 57: Clustering Location Comparison with the Kolkata-Bihar Macro-Structure ..................... 230

Figure 58: The Last Family ................................................................................................................ 235

Figure 59: Characteristic of the Last Family from a Selected Range of Indicators ........................... 236

Figure 60: Urban Structure Comparison: Left: Kerala; Right: Punjab. .............................................. 237

Figure 61: Clustering Location Comparison with Delhi Macro-Structure ......................................... 238

Figure 62: Clustering Classification according to Selected Dimensions ........................................... 242

Figure 63: India in the Age of Globalisation ...................................................................................... 245

Figure 64: Map of Dharavi ................................................................................................................. 252

Figure 65: Set of Picture from Dharavi. ............................................................................................ 253

Figure 66: Set of Picture from Dharavi, Bangalore and New Delhi. ................................................. 257

Figure 67: Set of Pictures from Bangalore. ........................................................................................ 261

Figure 68: Hubli-Dharwad Conceptual Mapping ............................................................................... 263

Figure 69: Hubli-Dharwad Picture Set 1. ........................................................................................... 266

Figure 70: Hubli-Dharwad Picture Set 2 ............................................................................................ 270

Figure 71: Kullu District Conceptual Mapping .................................................................................. 272

Figure 72: Kullu District Picture Set 1. .............................................................................................. 275

Figure 73: Kullu District Picture Set 2. .............................................................................................. 278

ix

List of Tables

Table 1: Evolution of the Indian Workforce Structure (in millions) . 84

Table 2: Disparities between Men and Women .................................................................................... 87

Table 3: Disparities between Men and Women: focus on Scheduled Castes (in millions) . 88

Table 4: Thresholds of Required Numbers of Rooms by Number of People in the Household......... 133

Table 5: District characterisation by UA of more than 200.000 inhabitants ...................................... 137

Table 6: Size of the Macro-Structures Related to Rank 1 Metropolitan Areas .................................. 145

Table 7: Inner Features of the three Biggest Macro-structures .......................................................... 146

Table 8: List of the 55 Collected Indicators that will be used as Inputs for the Clustering of

Indian Districts .................................................................................................................................... 153

Table 9: Results of the Experiments ................................................................................................... 190

Table 10: Node Significance over the Target Node (Geographical Cluster) of Experiment 6 ........... 191

Table 11: Bayesian and SOM/superSOM Models: Indicator Significances ....................................... 210

Table 12: Differences and Similarities between Bayesian and SOM Clusterings .............................. 282

x

List of Abbreviations

AI: Artificial Intelligence

ANN: Artificial Neural Network

ANOVA: Analysis of variance

BCE: Before the Common Era

BLI: Better Life Index

BMU: Best Matching Unit

BN: Bayesian Networks

BRIC: Brazil, Russia, India and China

BRIC's: Brazil, Russia, India, China and

South Africa

BSOM: Bayesian Self-Organizing Maps

CE: Common Era

CIVET: Colombia, Indonesia, Vietnam, Egypt

and Turkey

CNIS: National Council for Statistical

Information (French agency)

DBMS: Database Management System

EUA: Extended Urban Areas (footprint)

EV: Electric Vehicle

FDI: Foreign Direct Investment

FERA: Foreign Exchange Regulation Act

F-M: Fowlkes-Mallows similarity index

GDP: Gross Domestic Product

GPS: Global Positioning System

GSDP: Gross State Domestic Product

GIS: Geographic information system

GNH: Gross National Happiness

GSCF: Gross Fixed Capital Formation

GST: General Systems Theory

HDI: Human Development Index

HCA: Hierarchical Clustering Algorithm

HHLDS: Households ICT: Information and Communications

Technology

INSEE: National Institute of Statistics and

Economic Studies (French agency)

IPR: Industrial Policy Resolution

IMF: International Monetary Found

JK: Jackknife resampling

MRTP: Monopolies and Restrictive Trade

Practices Act

N/A: not applicable (in table)

NAM: Non-Aligned Movement

NEG: New Economy Geography

NCTD: National Capital Territory of Delhi

NN: Neural Networks

NSS: National Sample Survey

OECD: Organisation for Economic Co-

operation and Development

OOP: Object-oriented programming

OSM: OpenStreetMap

PPP: Purchasing power parity

Rs: Indian Rupee

SC: Scheduled Castes

SEZ: Special Economic Zone

SOM: Self-Organizing Maps

superSOM: Super-Organizing Maps

SRA: Slum Rehabilitation Authority

SRNN: Self-Reflexive Neural Networks

ST: Scheduled Tribes

UN: United Nations

UNESCO: United Nations Educational,

Scientific and Cultural Organization

UA: Urban Area or Urban Agglomeration

VOC: Dutch East India Company

WTO: World Trade Organisation

1

CHAPTER 1

INTRODUCTION

"Toutes les démarches du chercheur doivent pouvoir être explicitées, expliquées, justifiées, même quand il s'agit de l'intuition"1

Mialaret Gaston (2004)

I was told by senior researchers in India that the first months of a thesis are always the less productive ones. According to them, India was a country so complex that a young researcher not familiar with it should get used to starting off in several wrong directions. Looking back, I can't but agree with them. Basically, when I arrived in India, I got stung by almost everything. I am not referring to the well-known cliché of a traveller setting foot in India and experiencing for the first time a little bit of culture shock and seeing chaos everywhere. Of course, it is difficult to go against some clichés (especially in large cities) such as traffic jam which is associated with constant sounds of horns, people everywhere riding rickshaws, motorcycles, trunks, bicycles, vendors of all kinds walking through the traffic, pollution, overcrowding, etc. Yet, I spent some time in Africa and South America studying the self-organised transportation systems when I was an undergraduate student. All in all, seeing order in chaos was what I have been trained for but yet, I could not have felt less prepared for "handling India" as something seemed indeed very different about this country. With the benefit of hindsight, I realise now that India is the very definition of a complex country. India is one of the most multi-faceted countries in the world and really is a mix of everything you can find around the world plus some specificities (such as the caste system). This country as it is today is the outcome of various civilisations that followed one another and is home now to a variety of languages, religions, castes, communities, tribes, traditions, etc., thereby increasing the degree of complexity of any topic you choose to address. During the first year of this research, I was indeed not aware of this complexity which is also reflected in the ways in which people interact. For example, there is a simple noncommittal nod that exists only in India and means neither "yes" nor "no". It is widely used for any topic of discussion and by all the strata of the population. You can for example order 11

All actions of the researcher must be explicit, explained, justified, even when it comes to intuition.

2 some food in a restaurant and the waiter will give you that nod. What you must understand is that the waiter does not know if the food you just ordered is available in the kitchen and that he will check for you later. Most of the time, when you ask an Indian what are his thoughts about the caste system he will tell you that "castes" are now a thing of the past and that there are no more caste problems in India. One day, a new director had been appointed in an institute in which I worked. The new director was a foreigner and during the welcome drink, he decided to shake hands with all the staff. Unwittingly, the first person he shook hands with was a dalit2 of the cleaning staff. Immediately after that, all the other people backed off and refused the handshake since the hand was now "impure". It is only later that I realised that the people with whom you interact the most as a foreigner are people speaking English fluently and most of these people are from upper castes at the top of the social ladder. They are not fooling themselves when they state that there are no more caste problems in India. The Indian society is fragmented and most of the people live and work within their community and/or within their caste. For the groups at the top of the social ladder, the inequalities related to the caste system are not reflected at all in their day-to-day routine. To a Westerner, India turns out to be a country full of surprises on many occasions. I remember a friend of mine coming to my place in order to celebrate the fact that he was going to get married in the coming months. I congratulated him and asked how he met his bride. He gave me a strange look and told me that he hadn't met her yet and that he was waiting for his parents to introduce her to him. If arranged weddings are still common in India we should nonetheless be careful not to make any generalisation. You may wonder why I am starting with these stories instead of introducing the research topic. The answer is quite simple; I think that these events have deeply affected my work. Even if the first months of this research were less productive than those which followed; they were at the same time the most decisive ones. Indeed, it quickly became apparent that the first thing you learn about India is that you cannot fully understand how it works. Diversity is so great in India that any generalisation becomes irrelevant. I therefore thought that if India was so complex, the best way to address it was to adopt a holistic point ofquotesdbs_dbs27.pdfusesText_33

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