Linking Migration,Diversity and Regional Development in India

Abstract

Diversity is important for cultural and economic factors. Diversity is an inclusive concept and has proven to enhance team performance, foster innovation and be better for overall economic factors. Migration is a continuing phenomenon in India, and each region possesses its own unique culture and skills. Migrants from different places of birth bring their own culture and specific skills which is beneficial for the regional human capital and economies. With the help of the fractionalisation index and panel regression models, the paper finds that diversity, as measured by migration by place of birth has a positive and significant relationship with the income growth of the states. This is consistent with a trend of increased migration to the industrialised or higher GDP states, such as Gujarat, Maharashtra, Tamil Nadu and Karnataka. The study shows that the diversity of people originating from different regions is beneficial for economic progress. This mobility and assimilation of people and cultures will positively affect a region and economy.

Keywords

Cultural diversity migration development economic growth human capital India

1. Introduction

Diversity is considered to play an important role in the different stages of development. The positive effects of ‘diversity dividend’ on productivity (Ottaviano & Peri, 2006; Rodríguez-Pose & Hardy, 2015; Trax et al., 2015) and economic growth (Bove & Elia, 2017) have gained attention over the years. India is a demographically diverse country with seventh-largest geographical area and the second-largest population in the world (Bhagat & Keshri, 2020). The diversity of the country is not only in terms of language, religion or ethnicity but also in the endowment of natural and economic resources. The recent literature has given increasing importance to the relationship between cultural diversity (CD) and economic growth. Diversity is described as a ‘double-edged sword’ (Horwitz & Horwitz, 2007, p. 988). While diversity aids in knowledge spillovers (Audretsch et al., 2010) and positively affects economic growth (Alesina & La Ferrara, 2005; Shaban & Khan, 2022), heterogeneity can result in coordination problems and hence increase transactional costs (Bove & Elia, 2017). However, recent studies suggest that for developing economies like India the inherent diversity can become resourceful (Shaban & Khan, 2022).

It is considered natural for any population to be endowed with different levels of knowledge and capacities (Rodríguez-Pose & Hardy, 2015). Economic diversity in particular is now considered an important aspect of developmental planning (Dissart, 2003a). Regional diversity fosters the ‘recognition, absorption and realisation’ of new opportunities and has a positive impact on new entrepreneurial ventures (Audretsch et al., 2010). Specialisations can absorb economic shocks in businesses, and hence diverse regions like metropolitan areas tend to be more ‘stable’ because ‘their fortunes are not tied to the fortunes of a few industries’ (Chinitz, 1961, p. 281; Dissart, 2003b). Diverse regions invite new ideas and generate entrepreneurial activities. Positive economic benefits will attract migrants with new ideas who will also bring their own experiences (Bove & Elia, 2017). This is also true for the ‘creative class’ in the cities (Flew, 2010; Florida, 2002, 2008; Peck, 2005; Pedroni & Sheppard, 2013; Shaban & Khan, 2022).

The current times make it quite certain that heterogeneity in societies will prove to be an opportunity and challenge (Putnam, 2007). In-migration will be a major factor contributing to this heterogeneity (Putnam, 2007). India has a history of migration right from pre-independence times. The country experienced a forced displacement of 14 million people in 1947 (Bhagat & Keshri, 2020). Thereafter internal migration, particularly to the metropolitan centres of Mumbai or states of Tamil Nadu and Gujarat, has been common. According to the Economic Survey (2017), around 5–6 million Indians migrated annually between 2001 and 2011 (Rajan & Bhagat, 2021). The industrialised and urbanised states of Maharashtra, Delhi, Tamil Nadu, Gujarat, etc. continue to act as the pull factors (Bhagat, 2010; Bhagat & Keshri, 2020; Rajan & Bhagat, 2021). Marriage, employment and education opportunities are major factors driving migration (Bhagat, 2016; Bhagat & Keshri, 2020). Migrants with their own experiences create a heterogeneous and diverse population, which affects the output of a region leading to greater variation of goods and services produced (Ager & Brückner, 2013). Thus, local economies benefit from the expanding knowledge base and the new ways in which different people provide their understanding (Rodríguez-Pose & Hardy, 2015).

This paper focuses on the impact of in-migration and CD on regional economic growth. The literature in India has focused on CD is limited, and the focus has been mainly on language, religion or ethnicity, etc. Such measures are considered to be time invariant and, migration greatly affects such population composition (Bove & Elia, 2017). This paper thus attempts to use language and in-migration as measures of CD and ascertain their impact on the regional economic growth patterns. The remaining of the paper is organised in the following manner: Section 2 provides a brief review of the literature on migration and CD. Section 3 elaborates on the data and methodology used. Section 4 analyses the trends and patterns of CD reflected from in-migration and languages across the Indian states, while the regression results are discussed in Section 5. The conclusion summarises the findings and the paper.

2. Review of Literature

The literature on the impact of CD on economic growth and development has been studied at various dimensions and has been a topic of considerable debate. While many studies point out that the impact of CD measured by ethnicity or language is negative or no impact, CD measured by migration shows a positive impact. The literature on developing countries, especially India, is limited in this respect. However, since the available literature has amply specified that the impact of diversity measured by any aspect will depend on the level of development of a country and other associated resources, it can be expected that the study on India will provide some interesting results.

2.1. Migration and Diversity

The concept of migration as a diversity factor contributing to economic growth can be linked back to the support for a ‘diversity dividend’ (Andersson et al., 2005; Florida, 2002; Rodríguez-Pose & Hardy, 2015) on creativity and a collective variety of skills and knowledge as an important economic asset (Jacobs, 1969; Rodríguez-Pose & Hardy, 2015). Diverse populations contain a variety of ideas and skills cross cutting across cultures (Sobel et al., 2010). However, these ‘diversity of experiences’ also prohibit generalisations (Bove & Elia, 2017; De Haan, 1990, p. 20). Inflow of migrants can improve the efficiency of resource allocations (Van der Mensbrugghe & Roland-Holst, 2009) and reduce dependency ratios (Bove & Elia, 2017; Gagnon, 2014). In-migration and diversity are inevitable and desirable in the long run as an important social asset and lessen the negative effects of diversity (Putnam, 2007)

Migration and migrants increase the diversity of societies (Collier, 2000), and ethnic heterogeneity in modern societies is mainly contributed by immigration (Bove & Elia, 2017; Putnam, 2007). Ethnic diversity affects economic choices by influencing individual preferences, particularly in the case of transactions with one’s own group (Alesina & La Ferrara (2000). Saxenian (2005) points out that migration results in ‘brain circulation’ whereby technology, skills and knowledge are being transferred between distant regions and economies efficiently. Migrants are also more prone to being risk takers, with a study by Hasan (2011) pointing out that migrants are twice as likely as natives to be entrepreneurs (Rodríguez-Pose & Hardy, 2015). Moreover, migration and diversity encourage creativity (Simonton, 1999). Putnam (2007) points out that immigrants have won three to four times as America’s Nobel Laureates and other prestigious awards (Lerner & Roy, 1984; Simonton, 1999; Smith & Edmonston, 1997). A World Bank study discusses how immigration from Nordic countries to the Global South aided development in the South, mainly because of remittances and technology transfer through the immigrant networks. (Pritchett, 2006; Putnam, 2007: World Bank, 2005).

However, on the flip side, diversity also has its costs. Differences in language and communication (Putnam, 2007) can act as impediments to knowledge growth (Parrotta et al., 2012). Putnam (2007) provides an exhaustive list of studies that show that greater diversity is associated with lower trust (Anderson & Paskeviciute, 2006; Delhey & Newton, 2005) and higher default rates among Peruvian micro-credit cooperatives and lower fundraising in Kenya (Karlan, 2002; Khwaja, 2009; Miguel & Gugerty, 2005;). Differences in culture and language can lower trust among communities and result in racism, exclusion, crimes and conflict (Alesina & La Ferrara, 2005; Rodríguez-Pose & Hardy, 2015). What appears thus is a trade-off between the benefits of diversity and costs associated with heterogeneity (Alesina & La Ferrara, 2005).

2.2. Diversity, Migration and Development

Several studies have tried to assess the impact of diversity on regional economic development. The literature points out that culturally diverse states have a comparatively slower rate of economic development (Lian & Oneal, 1997). One of the earliest studies by Adelman and Morris (1967) discusses that ‘less developed countries that are relatively homogeneous ... are less hampered in the achievement of social and political integration and in the initiation of continuous economic growth’ (Adelman & Morris, 1967, p. 41). Considering 74 less developed countries over a six-year period, they assess that homogeneous countries had higher economic growth rates, in terms of the effect of diversity on political choices (Lian & Oneal, 1997).

Easterly and Levine (1997) discuss that Africa’s poor growth and low income are closely associated with high ethnic diversity, low schooling, poor financial systems and infrastructure. A comparative study of 37 less developed countries over a 30-year period by Reynolds (1983) also points out that CD is associated with lower growth rates. Lian and Oneal (1997) study the ethnic, religious and linguistic make-up of 98 countries over a period of 25 years. Using the diversity score based on the Molinar index they find no significant effect of diversity on economic growth. Rothstein and Uslaner (2005) measure social trust caused by two different types of equality-economic equality and equality of opportunity. They argue that diverse societies, such as United States and Romania, are caught in a vicious cycle because they have lower levels of generalised trust which leads to social inequality, and public policies that would solve this situation cannot be implemented because of the existing lack of trust.

Other studies based on social identity theory argue that individuals associate positive utility to members of their own group (Tajfel et al., 1971). Studies show that a diverse population can hinder knowledge exchange (Parrotta et al., 2012) and communications making them expensive and complicated (Collier, 2001; Putnam, 2007). Alesina and La Ferrara (2000) in their study on US localities, control for individual and demographic characteristics like level of income and find that social interaction and participation is inversely related to income inequality and racial fragmentation. Similarly, a 2003 study by La Ferrara shows that members in ethnic groups allow space for more cooperative strategies because any reward or punishment is applicable to the other members also (Alesina & Ferrara, 2005).

Even though several pieces of literature do point out the negative effects of heterogeneity and diversity, CD is too nuanced to singularly define. Different factors, such as language, lifestyle, education, religion and ethnicity, together collectively define diversity which, then, influences the national economy (Rodríguez-Pose & Hardy, 2015). Hence, literature focusing on singular definitions of diversity may not justifiably operationalise the concept. Moreover, based on the factor that one is studying, the relationship between CD and economic development may change. For instance, Collier (2001) used the conventional Barro-Lee data set and analysed the effect of ethnic parties in democratic systems to find that greater ethnic fragmentation makes it difficult to reach cooperative solutions. However, using both empirical and theoretical evidence, they point out that even in different governance systems the possibility that ethnic diversity weakens economic performance cannot be fully proved, and hence there is little evidence for reproving ethnic diversity.

Other studies like Bantel and Jackson (1989) find out that creativity prospers in culturally diverse workgroups. Lazear (1995) points out that the according to the US Census individuals in heterogeneous communities are more likely to learn English. Migration in particular has been found to add value in terms of ethnic diversity. Migrants are more likely to own specialised skills which may complement the local workforce (Farndale et al., 2010) or they may be inherent risk takers (Metcalf et al., 1996; Rath, 2006) and entrepreneurs (Hasan, 2011; Rodríguez-Pose & Hardy, 2015). Hunt and Gauthier-Loiselle (2010) and Kerr and Lincoln (2010) point out that diversity among highly skilled workers positively affects regional innovation (Rodríguez-Pose & Hardy, 2015). However, migration is also associated with brain drain and dependent population from the out-migrating region (Docquier, 2006; Bhagat, 2017). Saxenian (2005) points out that out-migration offers ‘substantial unintended assets’ but if only they are actively pursued. Countries like India have not tapped into their educated pool of professionals and hence have not added value in the global production networks. Ager and Brückner (2013) studied the inflow of migrants between 1870 and 1920 in the African American and white US population to find that cultural fractionalisation was positively associated with economic growth.

Ager and Brückner (2013) study the inflow of immigrants to the US from 1870 to 1920 to understand the cultural composition of the resulting population. Constructing indices for cultural polarisation and fractionalisation found that an increase in cultural fractionalisation in US counties led to an increase in per capita output and occupational diversity while the increase in cultural polarisation had a negative effect on output per capita. Rodríguez-Pose and Hardy (2015) construct Theil entropy indexes of birthplace and ethnic diversity and controlling for other demographic factors like urbanisation and population density, run spatial econometrics to find that diversity, primarily based on place of birth (POB) and boosts entrepreneurship. Alesina et al. (2015) construct population diversity indexes based on POB data to find that increasing the diversity of skilled immigration by one percentage increases long-run economic output by around 2%. They, thus, point out that diversity arising out of POBis important for economic prosperity but conceptually different from other dimensions, like language or genetic, of population diversity (Alesina et al., 2015).

3. Data and Methodology

Based on the available literature, measures operationalise CD based on internal migration in India. The available studies for understanding CD in India are limited and mostly focus on indicators such as religion or language. Internal migration in India is a process which greatly influences the demographic structure of states. Literature argues for the ‘diversity dividend’ which boost innovation and productivity (Audretsch et al., 2010; Niebuhr, 2010; Ottaviano & Peri, 2006; Ozgen et al., 2011; Trax et al., 2015). Migration adds to a region’s existing local labour force and enhances the available skill and ‘economic asset’ of the region (Jacobs, 1969, p. 219; Rodríguez-Pose & Hardy, 2015). Such diverse workforce and regions imply that ‘diversity among the highly skilled matters’ (Rodríguez-Pose & Hardy, 2015, p. 2). The continuous migration process in India can, thus, be assumed to add substantially to the diversity of different regions and influences the economy.

For this study, we consider 24 states of India, and data for different parameters have been collected for the three census periods of 1991, 2001 and 2011. States where data were missing for any of the parameters have been excluded from the analysis. Values of Bihar and Jharkhand, Chhattisgarh and Madhya Pradesh and Uttar Pradesh and Uttarakhand have been merged together to keep parity from 1991 to 2011. The literature on diversity measures CD with different parameters, such as religion, language and ethnicity. Considering the same for India the POB would be a similar indicator since a person by virtue of his POBwould posses distinct cultural values. For the purpose of this study the paper considers the internal migration in India. Data from Census tables (1991, 2001, 2011) based on the POB (henceforth referred to as POB) data have been used to estimate internal in-migration into the various states. Studies of CD based on migration have used the measure of POBas an indicator of diversity (Alesina et al., 2015; Rodríguez-Pose & Hardy, 2015). The census data are available in a decadal manner at regular 10-year intervals. While language, religion or caste are also important indicators of diversity for a country like India, we have not included these variables because substantial studies have already discussed the same. Thus, we have used migration data based on POB to construct the diversity index.

To measure the CD, the paper uses the fractionalisation index (FI) measure, which is the Herfindahl index (Alesina & Ferrara, 2005; Easterly & Levine, 1997) as follows:

FI = 1 - \sum s_{i}^{2},

(1)

where S_i is the share of group i over the total of the population. Using the POB data from the Census (1991, 2001, 2011), the FI is constructed for the individual states for each year. The index considers the probability that two individuals are randomly drawn from a population belonging to different ethnic groups, thus implying that a higher value indicates greater diversity (Lee et al., 2019). The data consider only the portion of the population who are born within the country, and the foreign-born population has not been included. This allows us to clearly understand if it is the diversity that is influencing the economic growth of a region and not the share of the foreign-born population.

Based on the literature, we draw on a set of variables as control factors. To account for the human capital, skilled or qualified workers are considered as a proxy (Rodríguez-Pose & Hardy, 2015). Human capital is essential for the economic development of a region and is also an indicator of the potential skilled population. This has been considered as the proportion of the population with an education level of higher secondary and above, and the data are available from the Census of India (1991, 2001, 2011) (Shaban & Khan, 2022). Urbanisation is considered to be an important factor for migration (Rodríguez-Pose & Hardy, 2015). Urbanisation transforms space and boosts economic growth, which triggers various spatial flows, including labour mobility (Bhagat, 2017; IOM, 2015; UNDP, 2009; World Bank, 2009). Urban areas are considered grounds for talent and employment and hence pulls in migrants (Duranton & Puga, 2001). Population density is also considered as control as high densities are opportunities for businesses and skilled people and hence would attract more migrants (Rodríguez-Pose & Hardy, 2015). Densely populated regions are common in urban areas, metropolises or around industrial clusters. Such areas attract more migrants due to the better educational or work opportunities they represent. Additionally, as a proxy for the social condition of a state, the murder rate which is defined as the number of murders recorded under the Indian Penal Code per 100,000 persons, was taken from the National Crime Records Bureau. It is assumed that violence resulting in a high murder rate is a proxy for social instability (Shaban & Khan, 2022) and would adversely affect the economic progress of a state. Socially unstable regions bear the threat of conflict and violence and hence would be a deterrent for industrial activity and migrants.

The paper uses the fixed effect (FE) and random effect (RE) methods of estimation. The FE panel regression model is as follows:

Y_{i t} = β_{1} X_{i t} + α_{i} + u_{i t},

(2)

where Y_it is the dependent variable (DV), where i is the entity and t is time; in this case, it represents the income of the states for the ith state and tth year, α _i (i = 1….n) is the unknown intercept for each entity (n entity-specific intercepts); in this case, the state-specific effects, X_it represents one independent variable (IV) and β1 is the coefficient for that IV; in this case, the matrix of the explanatory variables, u_it is the error term.

Similarly, the RE panel regression model is as follows:

Y_{i t} = β X_{i t} + α + u_{i t} + ε_{i t},

(3)

where u_it is the between entity error, and ε _it is the within entity error. The error term, thus, has ε _it , that is, the state-specific effect and the usual error term.

The Hausman test (1978) allows to select the best estimation method. Corresponding diagnostic tests were also run accordingly. The Breusch and Pagan Lagrangian multiplier test (1980) indicates the presence of heteroskedasticity in the data. To overcome the same, the robust option with heteroskedasticity robust standard errors is used (Baltagi et al., 2009). In addition, the panel-corrected standard errors are also used since it assumes that error terms may be heteroskedastic and autocorrelated within the panel (Beck & Katz, 1995). The study is a state-level analysis which may not fully capture economic or social diversities. However, due to data limitations, the same could not be computed. Data pertaining to human capital is not available at the district level (Shaban & Khan, 2022), and the other variables are also available regularly at the same level. The consistency of available data, thus, made states as the possible unit of analysis. Moreover, the literature also indicates that diversity may not always be beneficial and lead to differences. However, the paper focuses on the economic development parameters and how they can be beneficial.

4. Results and Discussion

Figure 1 shows the growth rate of the migrant population from 1991 to 2011. It shows that the growth rate of the migrant population has been high in most parts of the country. This is especially true for the northern and western states which show a high growth rate of the migrant population. The highest growth rate of the migrant population between the period 1991 and 2011 has been recorded by Karnataka, Bihar and Uttar Pradesh at around 182% each, followed by Gujarat at 167% and Maharashtra at 140%. This is closely followed by the northern states of Haryana and Punjab, both also recording growth rates above 100%. The literature points out that Uttar Pradesh has a very high share of the migrant population while the western states of the Maharashtra and Gujarat act as major migrant-pulling states due to the numerous work and other opportunities (Bhagat, 2010, 2016; Bhagat & Keshri, 2020; Rajan & Bhagat, 2021). Overall a majority of the states have recorded high growth rates above 50% in the growth of their migrant population.

Figure 1.

Source: Census data.

Figures 2 and 3 give a better idea about the net migration in the Indian states. The net migration gives an idea of the total internal migration in a state and is the difference between immigration and emigration. When immigration exceeds emigration in a state positive net migration occurs and vice versa. This indicates that more people are entering the area than leaving. The figures above give a clear indication that internal migration has increased in the different states over the years. The western and the southern states have become major states recording positive migration, and hence implying that they have attracted migrants. Uttar Pradesh, the eastern region of Orissa and West Bengal and the northeastern states have experienced more out-migration over the years.

Figure 2.

Source: Census data.

Figure 3.

Source: Census data.

Temporary and interstate mobility in India is not uncommon and ample literature points to that (Bhagat, 2010; Breman, 1996; Deshingkar & Farrington, 2009; Mosse et al., 2005). Historically, the growth of industries and trade in port areas led to a great wave of migration, the second wave after the immense forced movement of the people post 1947 partition. The cities of Bombay, Kolkata and Madras ‘reshaped the regional economies and triggered large inter-regional migration flows within India’ (Bhagat & Keshri, 2020, p. 3). Workers migrated from different parts of the country for better employment opportunities which also led to regional disparities in development (Bhagat & Keshri, 2020; Raza & Habeeb, 1976). In India, temporary and seasonal migration are important forms and factors of labour mobility as the labour force experiences a shift from agriculture to industry to the tertiary sector (Keshri & Bhagat, 2010).

Regions and cities in developing countries are known to be recipients of internal migrants (Bhagat, 2017; IOM, 2015; UNDP, 2009). Migration and development are, thus, intimately connected. The literature points out the growing study on remittances and benefits to the areas of origin arising out-migration (Bhagat, 2017). Thus, migration is not a one-way movement resulting in a ‘complete rupture from the native place, but a process that binds the two places together’ (Bhagat, 2017, p. 3). In the globalised world, everyday processes and existing infrastructure is moulded to enable people to move to dense market areas, reduce distance and transport costs and bring down trade barriers (World Bank Report, 2009). Thus, globally there is an identification and need for a diaspora with varying skills (Ratha & Shaw, 2007).

Table A2 in the appendix provides the inter-variable correlation matrix. The diversity index calculated by POB migration shows a positive and significant relationship with all the variables, except for murder. Urbanisation, human capital and population density have been adequately discussed in the literature to foster and encourage talents, innovation and regional growth. Crime and violence cause social instability, and the negative association with diversity and other explanatory variables is an indication of the same.

4.1 Results of the Regression Analysis and Discussion

The results of the panel regression with the dependent and independent terms and related statistical diagnostics are given in Table 1. The R² shows the proportion of variation in the dependent variable that can be explained by the independent variables. While the within R² is important for FE models, the between R² is important for RE models because it is considered between the estimators. The Hausman test shows the RE model is appropriate and also has higher between R². Diagnostic tests are run, and the Breusch Pagan LM test shows the presence of heteroskedasticity in the same. Thus, the robust option was chosen to overcome the same as these provide efficient estimators, and the results become more efficient. The significant factors affecting the income growth in the different states are the migration diversity index, as measured by the POBand human capital which is positively affecting income growth. This implies that migration brings along with its diverse population, which positively impacts the income growth of the states. Similarly, as human capital increases, the population with skills and education increases which boosts the regional economic growth. The murder rate negatively impacts income growth, which is an indication that as social instability grows the income growth of the decline of the state.

Table 1.

Regression Table.

	FE MODEL	RE MODEL	ROBUST
	Income	Income	Income
Migration index	0.131^**	0.111^**	0.111^***
	(0.0506)	(0.0513)	(0.0428)
Pop_density	0.652	−0.0244	−0.0244
	(0.562)	(0.0601)	(0.0708)
Murder rate	−0.110	−0.205^**	−0.205^**
	(0.106)	(0.0813)	(0.0865)
HC_persons	0.866^***	0.975^***	0.975^***
	(0.191)	(0.0448)	(0.0437)
Urb_persons	0.0284	0.00442	0.00442
	(0.0303)	(0.0294)	(0.0503)
_cons	10.27^***	13.11^***	13.11^***
	(1.190)	(0.559)	(0.650)
N	72	72	72
R² (within)	0.9215	0.9165	0.9165
R² (between)	0.8296	0.9684	0.9684
R² (overall)	0.8319	0.9609	0.9609

Source: Author’s calculations based on secondary data.

Notes: Standard errors are in parentheses.

***P < 0.01. **P < 0.05. *P < 0.1.

Studies show that diversity measured by migration is positively related to the income level. Creativity is fostered by immigration and diversity (Simonton, 1999) and boosts economic growth (Putnam, 2007). Smith and Edmonston (1997) point out that countries with an ageing population would especially benefit from immigration as it will offset the impending fiscal effects of the retiring baby boom generation (Putnam, 2007). For knowledge and entrepreneurial-based activities, birthplace diversity plays an important role (Rodríguez-Pose & Hardy, 2015). The relation of income and human capital has also been widely discussed in the literature. Human capital growth positively affects income (Shaban & Khan, 2022).

The diversity index measured by the FI shows an increase from 1991 to 2011, with a slight dip for some states in 2001. This increase in the POB migration diversity is particular in the southern and the western regions of the country. Even though the level of income has increased in all the states, migration has a more dynamic pattern. Regions with high income, such as Maharashtra, Gujarat, Tamil Nadu and Karnataka, are also states where net migration has increased over the years. These are also the states where the diversity index calculated by migration on POB data is higher. The significant relationship of income with diversity and human capital is an indication that knowledge fosters knowledge. The culture-specific skills of migrants boost the skill set of the available human capital and are beneficial for regional economic growth. This diversity from POB migration is said to be the primary driver behind entrepreneurship and positively creates a dynamic environment for entrepreneurship (Rodríguez-Pose & Hardy, 2015).

5. Conclusion

There have been considerable studies on the impact of diversity on economic performance. Most of these studies focus on diversity originating from language, ethnicity etc. However, the literature on South Asia focusing on diversity and development is still lacking. Very few studies focus on diversity originating from the POB migration. Taking from the literature that birthplace diversity is an important determinant of regional economic growth and empirically different from other dimensions of demographic diversity, this paper uses the migration data based on the POB to generate the diversity index.

With the help of panel regression models, the paper finds that diversity as measured by migration by POB has a positive and significant relationship with the income growth of the states. The diversity index, measured FI, shows an increase in states, except for northeastern regions such as Nagaland and Meghalaya and Puducherry in the south. This is consistent with a trend of increased migration to the industrialised or higher GDP states, such as Gujarat, Maharashtra, Tamil Nadu and Karnataka. Migrants from different POBbring their own culture and specific skills, which is assumed to be beneficial for the regional human capital and economies. Thus, this diversity generated from POB migration is positively associated and benefitting from the human capital of the regions. This is also discussed in other studies which measure human capital and diversity (Shaban & Khan, 2022) or POB migration and entrepreneurship skills (Rodríguez-Pose & Hardy, 2015). This holds truer for skilled migration, particularly to richer areas (Alesina et al., 2015). India has faced this phenomenon of internal migration before independence (Bhagat, 2010, 2016, 2017). Thus, if this ongoing phenomenon of diversity from POB migration is positively tapped and encouraged, the impacts on economic growth will be maximised.

The diversity index, measured by POB migration, shows a positive and significant association with all the variables, except for murder rate. A diverse skilled workforce and human capital foster innovations. Urbanisation is important for providing a base for emerging businesses and talent (Duranton & Puga, 2001). Dense areas provide a crucial diverse set of skills, suppliers and demand (Rodríguez-Pose & Hardy, 2015). All these factors foster and encourage diversity to flourish. The negative association with the murder rate of the diversity index shows that social stability and security are crucial for people and economic progress. The study shows that the diversity of people originating from different regions is beneficial for economic progress. Thus, this shows that the mobility and assimilation of people and cultures will positively affect a region and economy. However, literature also points out that in times of globalisation it is crucial to decide the level of mixing (Alesina & Ferrara, 2005) and the representation of different segments of a plural society is desired (Lijphart, 1997). Thus, in a globalising world, how different skills and resources can interact will continue to be a critical point to discuss.

Appendix

Table A1. The Descriptive Statistics of Dependent and Independent Variables.

Variable	Obs	Mean	Std. Dev.	Min	Max
Income	72	27.29874	1.637304	23.98823	30.05281
Diversity Index	72	0.6536872	0.4734688	.0000107	0.9999892
Urb_Persons	72	15.28136	1.795862	10.37596	17.74377
HC_Persons	72	14.83784	1.709368	10.99031	17.54461
MurderRate	72	1.341292	0.5414264	0.0953102	2.70805
Pop_Density	72	5.832063	1.234089	2.302585	9.332292

Source: Author’s calculations based on secondary data.

Table A2.

Inter-variable Correlation Matrix.

Income Mig_PerIndx Pop_De~y Urb_Pe~s HC_Per~s Murder~e

Income | 1.0000

Mig_PerIndx | 0.8937 1.0000 | (0.0000)

Pop_Density | 0.4451 0.5408 1.0000 | (0.0001) (0.0000)

Urb_Persons | 0.8300 0.7272 0.4161 1.0000 | (0.0000) (0.0000) (0.0003)

HC_Persons | 0.9776 0.8582 0.4735 0.8498 1.0000 | (0.0000) (0.0000) (0.0000) (0.0000)

MurderRate | −0.2253 −0.1191 −0.0867 −0.2095 −0.1690 1.0000 | (0.0570) (0.3190) (0.4689) (0.0774) (0.1558)

Source: Author’s calculations based on secondary data.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Meghamrita Chakraborty

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