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Rural Depopulation and Empty Villages in India: Spatial Patterns, Accessibility, and Sustainability ChallengesAbstract
Rapid urbanization in developing countries has been a defining feature of the current century and a major focus of urban studies. In contrast, a shrinking rural population is an emerging trend that is often ignored. Rapid rural-to-urban migration erases the existence of many rural settlements. The phenomenon of rural depopulation is well noted in India, as the Census of India marked thousands of empty villages with a ‘0’ population in 2011. This study analyzes the spatial distribution of empty villages across India and examines the critical factors that drive rural depopulation. Using the 2022-23 Mission Antyodaya dataset covering over 641,000 villages, the study identifies nearly 28,000 villages with no inhabitants, accounting for 4.4% of the total villages in the country. These abandoned settlements are disproportionately clustered in economically disadvantaged regions with limited access to essential services such as education, healthcare, and transportation infrastructure. The findings reveal that inaccessibility is a key factor contributing to village abandonment. Logistic regression analysis revealed that villages located farther from primary schools and Anganwadi centers are significantly more likely to become uninhabited. Similarly, limited access to roads and public transportation further exacerbates rural outmigration. These challenges are major obstacles to rural sustainability and sustainable urbanization. Hence, this study underscores the need to strengthen transport networks and improve educational and healthcare infrastructure to prevent further rural decline and ensure balanced regional development. These insights offer crucial guidance for policymakers and can assist in reevaluating and reshaping rural development policies in India.
Key words:
Rural depopulation
ghost village
empty village
accessibility
distance to facilities
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1. Introduction
Villages have long served as the foundation of human settlement worldwide, accommodating the majority of the population and covering the largest geographic area. Villages are a longstanding rural habitation and embody a vital legacy of agricultural societies. In addition to serving as a repository of historical and cultural heritage, it contributes significantly to fostering an ecological civilization, conserving collective historical memory and depicting the trajectory of societal development (C. Liu & Xu, 2021). However, in recent years, a significant demographic shift has occurred, with the global urban population surpassing its rural counterpart (UN-Habitat, 2018). This shift is driven primarily by two key factors: the transformation of rural settlements into urban units and the migration of people from rural to urban areas (Bhagat & Jones, 2013).
The world is steadily transitioning from rural to urban, hosting more than half of the world’s population. The influx of people from rural regions into urban areas is driven by superior infrastructure, commercial expansion, and employment prospects, whereas rural areas have experienced a decline due to stagnant growth (Wen et al., 2023; Wirth et al., 2016). Disparities in development have not only led to urban problems such as environmental pollution (Liang et al., 2019; Zhuo et al., 2024) and traffic congestion (Lu et al., 2021; Mwamba et al., 2021) but also resulted in rural depopulation (Papadopoulos & Baltas, 2024; Yu et al., 2022). The other common terms used to describe rural depopulation or decline are shrinking villages (Vaishar et al., 2020), ghost villages (Sarvjeet Kumar & Misra, 2024; Saurav Kumar & Sati, 2023), abandoned villages (Vaishar et al., 2021; Živanović et al., 2022) and hollow villages (Liu et al., 2021; Qu et al., 2022).
The phenomenon of rural depopulation has been observed worldwide, including in Europe, East Asia, and North America. Specific countries such as Japan, Spain, and Italy have witnessed significant rural depopulation due to aging populations, economic transitions, and the concentration of opportunities in urban centers(Champion & Hugo, 2004; Rodríguez-Pose, 2018). In East Asia, particularly in China, government-led urbanization policies have resulted in the large-scale relocation of rural populations, leaving behind ‘hollow villages’(Long et al., 2010; Wang et al., 2020). Similarly, rural exodus in sub-Saharan Africa has been linked to climate change, land degradation, and economic shifts(Barrios et al., 2006). The repercussions of rural depopulation are complex and affect demographic patterns, the economy, social cohesion, environmental conditions and public health(Johnson & Lichter, 2019; Weekley, 1998).
Over the past few decades, India has steadily urbanized. This has resulted in unique migration patterns and has led to social and economic disparities between rural and urban areas because of high levels of heavy urbanization and massive pull migration toward large cities(Deb & Okulicz-Kozaryn, 2023). Although rural to urban migration has been studied extensively, the issue of rural depopulation has often been overlooked. Evidence from the Census of India data shows that empty or ghost villages are emerging features of the Indian rural system. In the year 2011, the Census of India identified 43324 as uninhabited villages, raising concerns about regional imbalances, declining agricultural viability, and the sustainability of rural settlements (Census of India, 2011).
Consequently, it becomes imperative to understand the issue of rural depopulation by exploring empty villages in India. Thus, this study aims to map the spatial patterns of empty villages, analyze their concentration and clustering tendencies, and examine the role of accessibility-related factors in their abandonment. Understanding the spatial distribution and underlying causes of village emptiness will aid in regional planning, infrastructure development, sustainable urban transitions, and the fostering of resilient rural ecosystems.
2. Materials and methods
This study utilizes Mission Antyodaya data published by the Government of India. The available dataset covers the period 2022–23 and provides detailed socioeconomic and demographic information about approximately 6,41,357 villages in India, along with their respective geographic coordinates. The dataset can be accessed on the Mission Antyodaya portal (Link: https://missionantyodaya.nic.in/rawData2022.html).
The dataset includes information on basic parameters such as village names, locations, population, and households; governance; economic status (including agriculture and animal husbandry); infrastructure; transportation and communication; education; health; and several other aspects of rural life in India. The data are compiled from multiple sources, including the census, Panchayat Offices, Panchayat Secretaries, Gram Pradhans, local communities, and field observations.
To achieve the objectives of this study, accessibility indicators, including transport, infrastructure, education, and healthcare, were analyzed to assess their relationships with empty villages. These indicators consist of several sub-indicators, each measured as a distance (in kilometers) from various facilities. In the dataset, the distance variables are categorized as follows:
If the facility is available within the village, it is recorded as ‘0’.
Otherwise, distances are categorized as < 1 km, 1–2 km, 2–5 km, 5–10 km, and > 10 km.
Apart from the Mission Antyodaya data, the study uses a subdistrict-level shape file from the Data Development Lab, the SHRUG, for mapping (https://www.devdatalab.org/shrug).
This study explores empty villages, also known as uninhabited or ghost villages, using Mission Antyodaya data. Here, empty villages refer to those with a population count of zero (‘0’). This study considers that villages currently empty were once inhabited but later experienced depopulation and were ultimately abandoned for specific reasons.
The dataset was downloaded in CSV format and imported into Stata for analysis. Descriptive statistics were generated for 641,357 villages via this dataset. A point shapefile was created for all villages using the latitude and longitude data available in the dataset for mapping. In ArcGIS Pro, the ‘Table to XY Points’ tool was used to execute this task.
Using GIS applications, villages with zero populations were extracted as separate shapefiles with the help of the ‘Select by Attribute’ tool. A kernel density map of empty villages was then prepared via the ‘Kernel Density’ tool (Spatial Analysis Tool). The kernel density map calculates a magnitude per unit area, producing a raster map that highlights clusters of empty villages. Additionally, the ‘Spatial Join’ tool was used to link village attributes at the subdistrict level and to map the spatial distribution of empty villages.
The Mission Antyodaya data provide distance measures to various facilities, such as transport and healthcare. Using the distance variables listed in Table 1, four indices were constructed to analyze the associations between distance and population size. The principal component analysis (PCA) method was applied to compute these indices. An overall index, the Accessibility Index (inverse of the distance indices), was also computed by combining all the variables.
Furthermore, a correlation matrix was created to examine the relationships between the population and various accessibility indices. Additionally, bivariate statistics were prepared to analyze the distribution of villages on the basis of their distance from different facilities.
Finally, a binary logistic regression model was used to analyze the accessibility-related determinants of village emptiness. This method helps identify key facilities whose absence increases the probability of villages becoming empty. The dependent variable was the ‘village category,’ where empty villages were coded as ‘1’ and populated villages as ‘0.’ The independent variables used in the analysis are listed in Table 1.
Distance to transport facilities | Distance to infrastructure | Distance to educational institutes | Distance to health facilities |
|---|---|---|---|
All weather road (D2AWR) | Bank (D2Bank) | Primary school (D2PS) | Primary health center (D2PHC) |
Public transport (D2PT) | ATM (D2ATM) | Middle school (D2MS) | Sub center (D2SC) |
Railway station (D2Rail) | Post office (D2PO) | High school (D2HS) | Community health center (D2CHC) |
Market (D2Market) | Senior secondary school (D2SSS) | Hospital (D2H) | |
PDS office (D2PDS) | Degree college (D2DC) | Anganwadi center (D2AWC) |
3. Overview of villages in India
Villages in India constitute one-third of the country's total population (Panda & Majumder, 2013) (Census of India, 2011). Over the last few decades, population growth in rural areas has been slowing due to steady urbanization. Between 2001 and 2011, the rural population grew by 12.3%, significantly lagging behind the urban growth rate, which expanded by 31.8% during the same period (Kundu & Roy, 2012). Summary statistics from the Mission Antyodaya data show that the average population size of villages in India is 1,705. However, a village can be as small as having only one resident or even be completely uninhabited. Conversely, some villages have populations nearing one hundred thousand. The population size distribution reveals that more than half of India's villages are very small, with populations below 1,000. Medium-sized villages, with populations between 1,000 and 5,000, account for 41% of all villages. However, only a few villages are large, with populations between 5,000 and 10,000 (4.3 percent), whereas 1.6 percent of villages are very large, with populations exceeding 10,000 (Table 2).
Population size | Number of villages | Share |
|---|---|---|
No population (0) | 27,952 | 4.4 |
1-1000 | 3,12,324 | 48.7 |
1000–5000 | 2,63,195 | 41.0 |
5000–10000 | 27,448 | 4.3 |
> 10,000 | 10,438 | 1.6 |
The Indian rural system also features empty villages (villages with zero populations). The Mission Antyodaya data show that 4.4 percent of villages in India have no population or household. In terms of number, nearly 28 thousand villages in India are ghost or abandoned villages, with no inhabitants.
4. The spatial distribution of empty villages
Empty villages are found in almost every state of the country; however, they are largely concentrated in few states. In terms of share, Tripura has the highest proportion of empty villages (10.9 percent). Other states, such as Himachal Pradesh, Bihar, Telangana, Odisha, Assam, and Karnataka, have more than 6 percent of villages with no population.
In contrast, states such as Gujarat, Manipur, Meghalaya, and Kerala have a minimal share of empty villages, accounting for less than 1% of the total villages in India. However, in some union territories, such as NCT Delhi and Lakshadweep, and states, such as Ladakh and Mizoram, there are no empty villages (Table 3).
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The subdistrict-level concentration mapping and kernel density mapping analysis revealed several clusters of empty villages, particularly within the high-concentration states mentioned above. The kernel density map identifies three major clusters: the eastern part of Bihar, southwestern Bihar, and northwestern Uttar Pradesh. Additionally, a few other clusters can be observed in Himachal Pradesh, Uttarakhand, Odisha, eastern Maharashtra (Vidarbha region), Assam, and Karnataka (Fig. 1). The subdistrict-level distribution follows a similar pattern, highlighting subdistricts with a greater share of empty villages (Fig. 2).Fig. 1
Kernel density map highlighting the spatial concentration of empty villages based on number count
State | Empty villages | Total number of villages | Share of empty villages |
|---|---|---|---|
Tripura | 194 | 1,779 | 10.9 |
Himachal Pradesh | 1,543 | 19,035 | 8.1 |
Bihar | 3,431 | 44,965 | 7.6 |
Telangana | 1,080 | 15,547 | 6.9 |
Odisha | 3,333 | 50,494 | 6.6 |
Assam | 1,703 | 27,133 | 6.3 |
Karnataka | 1,723 | 28,691 | 6.0 |
Maharashtra | 2,413 | 42,312 | 5.7 |
Uttar Pradesh | 5,506 | 1,00,136 | 5.5 |
Sikkim | 20 | 446 | 4.5 |
Jharkhand | 1,325 | 31,121 | 4.3 |
Uttarakhand | 670 | 15,787 | 4.2 |
Andhra Pradesh | 814 | 20,153 | 4.0 |
Goa | 14 | 402 | 3.5 |
Haryana | 231 | 7,417 | 3.1 |
Tamil Nadu | 536 | 17,704 | 3.0 |
Punjab | 438 | 14,490 | 3.0 |
Jammu and Kashmir | 161 | 7,361 | 2.2 |
Rajasthan | 990 | 46,227 | 2.1 |
Arunachal Pradesh | 101 | 5,730 | 1.8 |
West Bengal | 666 | 39,383 | 1.7 |
Madhya Pradesh | 649 | 52,188 | 1.2 |
Andaman and Nicobar | 2 | 184 | 1.1 |
Chhattisgarh | 216 | 19,951 | 1.1 |
Gujarat | 153 | 18,893 | 0.8 |
Manipur | 14 | 3,138 | 0.4 |
Meghalaya | 25 | 6,457 | 0.4 |
Kerala | 1 | 1,595 | 0.1 |
India | 27,952 | 6,41,357 | 4.4 |
5. Village size and accessibility
The study revealed a positive correlation between population size and access to basic facilities. The larger villages obtain basic facilities within the village or in very close proximity. Furthermore, there is a positive correlation among the various facilities themselves; for example, villages located far from education facilities are also likely to be distant from health facilities, infrastructure and transportation facilities (Table 4).
Table 5 presents an aggregated index for more than six lakh villages for various facilities by village size. This shows a significant gap in accessibility across different sizes of villages. The distance score sharply decreases as the population size of the villages decreases. As the table suggests, the overall accessibility score for villages with more than 10 thousand people is -1.90; for villages with fewer than one thousand inhabitants, it is 0.52; and for uninhabited villages, the condition is worse (a greater value indicates greater distance).
Correlation | Population | Transport Index | Health Index | Education Index | Infrastructure Index | Accessibility Index |
|---|---|---|---|---|---|---|
Population size | 1.00 | |||||
D2Transport index | -0.16 | 1.00 | ||||
D2Health index | -0.17 | 0.40 | 1.00 | |||
D2Education index | -0.23 | 0.49 | 0.62 | 1.00 | ||
D2Infrastructure index | -0.33 | 0.48 | 0.59 | 0.67 | 1.00 | |
Accessibility index[overall] | -0.24 | 0.65 | 0.84 | 0.89 | 0.77 | 1.00 |
| Note: (i) The distance indices have been calculated using the distance from various types of facilities. A mid-value was created and used for the categorical values. (ii) A greater index value corresponds to a greater distance from the facilities, and a lower value corresponds to a smaller distance to the facilities. | ||||||
Distance to- | Accessibility index | ||||
|---|---|---|---|---|---|
Population size | transport index | health index | education index | infrastructure index | |
0 | 2.30 | 1.21 | 1.75 | 1.30 | 3.27 |
1-1000 | 0.16 | 0.24 | 0.35 | 0.52 | 0.52 |
1001–5000 | -0.36 | -0.30 | -0.44 | -0.49 | -0.72 |
5001–10000 | -0.55 | -0.79 | -1.09 | -1.74 | -1.60 |
Above 10000 | -0.56 | -0.99 | -1.33 | -2.21 | -1.90 |
| Note: (i) The distance indices have been calculated using the distance from various types of facilities. A mid-value was created for the categorical values. (ii) A greater index value corresponds to a greater distance from the facilities, and a lower value corresponds to a smaller distance to the facilities. | |||||
6. Comparison of accessibility
A comparative analysis has been conducted between inhabited and empty villages to highlight the disparities in access to basic facilities and services. The comparison covers four categories of variables: access to transportation, infrastructure, educational institutions, and healthcare facilities. This assessment reveals the true nature of empty or "ghost" villages, which are characterized by greater isolation, as shown in Table 6. The following section outlines some of the key issues faced by empty villages in contrast to inhabited villages.
6.1. Access to transportation facilities
The transport network is a key component that connects settlements to surrounding areas and beyond. The results indicate that most inhabited villages in India have access to all-weather roads and public transportation within a 5 km radius. In contrast, nearly half of the empty villages are located more than 10 km away from roads and public transport services. However, there is no significant difference between inhabited and empty villages in terms of access to railway facilities.
6.2. Access to infrastructure
Access to infrastructure such as banks, ATMs, markets, post offices, and PDS facilities varies significantly across villages in India. Table 6 shows that nearly half of all villages are located more than 10 km away from these basic services. The situation is considerably better in inhabited villages, where a significant proportion have access to such infrastructure. However, even among inhabited villages, many remain more than 10 km away from essential services: 24.8 percent for banks, 28.0 percent for ATMs, 19.2 percent for markets, 12.5 percent for post offices and 7.2 percent for PDS facilities. Thus, it is very challenging for residents to meet these basic infrastructure needs.
6.3. Access to education
Access to education at the village level is one of the most essential needs at the micro level. Overall, Indian villages have relatively easy access to primary education: more than 92.5 percent of inhabited villages have a primary school within 5 kilometers, and 70 percent have a primary school within the village itself. However, a significant proportion of inhabited villages are more than 5 kilometers away from facilities such as middle schools (25.4 percent), high schools (37.3 percent), and senior secondary schools (45.8 percent), which poses a serious challenge for residents. In contrast, empty villages have no access to educational infrastructure within the village, and the distance to all types of educational institutions is significantly greater for these villages. For example, 61 percent of empty villages are more than 5 kilometers away from the nearest primary school.
6.4. Access to health
Accessing healthcare facilities remains a major challenge for rural residents. The results indicate that nearly 40% of inhabited villages lack a subcenter within a 5 km radius. Furthermore, 55.4 percent of villages are located more than 5 km away from a primary health center (PHC), and 66.1 percent are similarly distant from a community health center (CHC). However, access to Anganwadi centers is comparatively better, as only a small share of villages are located far from a center.
The story is different for empty villages with respect to distance from health facilities. Empty villages are mostly located far from all types of health facilities, with nearly half of these villages being 10 km or more away from any healthcare facility. Moreover, these villages are also not even in close proximity to the asub center and Anganwadi center.
Distance to | Inhabited | Empty | Chi | Distance to | Inhabited | Empty | Chi2 |
|---|---|---|---|---|---|---|---|
Roads | Middle School | ||||||
Within 5 km | 84.7 | 37.5 | 0.000 | Within 5 km | 74.6 | 36.4 | 0.000 |
5–10 km | 7.2 | 13.3 | 5–10 km | 15.0 | 15.1 | ||
> 10 km | 8.1 | 49.2 | > 10 km | 10.4 | 48.5 | ||
Public transport | High School | ||||||
Within 5 km | 86.1 | 37.9 | 0.000 | Within 5 km | 62.7 | 35.6 | 0.000 |
5–10 km | 7.1 | 12.7 | 5–10 km | 23.6 | 16.6 | ||
> 10 km | 6.8 | 49.4 | > 10 km | 13.7 | 47.8 | ||
Rail | Senior Secondary School | ||||||
Within 5 km | 14.2 | 20.6 | Within 5 km | 54.1 | 34.3 | 0.000 | |
5–10 km | 16.1 | 11.4 | 0.000 | 5–10 km | 25.9 | 17.0 | |
> 10 km | 69.7 | 68.0 | > 10 km | 19.9 | 48.8 | ||
Bank | College | ||||||
Within 5 km | 45.7 | 31.7 | 0.000 | Within 5 km | 20.3 | 26.1 | |
5–10 km | 29.5 | 17.3 | 5–10 km | 25.4 | 14.9 | 0.000 | |
> 10 km | 24.8 | 51.1 | > 10 km | 54.3 | 59.1 | ||
ATM | Subcenter | ||||||
Within 5 km | 40.4 | 29.7 | 0.000 | Within 5 km | 60.3 | 34.0 | 0.000 |
5–10 km | 31.6 | 17.6 | 5–10 km | 22.8 | 17.3 | ||
> 10 km | 28.0 | 52.7 | > 10 km | 16.9 | 48.7 | ||
Post Office | PHC | ||||||
Within 5 km | 66.8 | 34.1 | 0.000 | Within 5 km | 44.6 | 31.5 | 0.000 |
5–10 km | 20.8 | 16.4 | 5–10 km | 31.0 | 18.2 | ||
> 10 km | 12.5 | 49.5 | > 10 km | 24.5 | 50.3 | ||
Market | CHC | ||||||
Within 5 km | 58.2 | 31.8 | 0.000 | Within 5 km | 33.9 | 28.6 | 0.000 |
5–10 km | 22.7 | 16.3 | 5–10 km | 28.8 | 16.9 | ||
> 10 km | 19.2 | 51.9 | > 10 km | 37.3 | 54.5 | ||
PDS | Medical hospital | ||||||
Within 5 km | 82.3 | 37.3 | 0.000 | Within 5 km | 26.4 | 26.9 | 0.000 |
5–10 km | 10.5 | 15.1 | 5–10 km | 31.4 | 17.4 | ||
> 10 km | 7.2 | 47.6 | > 10 km | 42.3 | 55.7 | ||
Primary School | Anganwadi | ||||||
Within 5 km | 92.5 | 39.0 | 0.000 | Within 5 km | 95.1 | 39.5 | 0.000 |
5–10 km | 4.0 | 13.4 | 5–10 km | 2.4 | 13.8 | ||
> 10 km | 3.6 | 47.6 | > 10 km | 2.5 | 46.7 | ||
N | 613,405 | 27,952 | 613,405 | 27,952 |
7. Factors associated with emptiness
The regression results highlight that the absence of basic facilities weakens the viability of villages, pushing them toward depopulation. This study applies a regression model to understand the key determinants of rural depopulation in India (overall) and in particular states (such as Bihar and Himachal Pradesh), which have high concentrations of empty villages.
The results (in Table 7) highlight that access to transportation is significantly associated with empty villages. The likelihood of a village becoming empty increases as the distance from roads and transportation facilities increases. The probability of a village becoming empty is 2.563 (CI: 2.422–2.712) times greater if it is located 10 km or more from roads and 4.443 (CI: 4.211–4.689) times greater if it is located 10 km or more from transportation facilities than if it has immediate access. Additionally, the distances from facilities such as markets (1.411 times), PDS centers (2.104 times), and post offices (1.240 times) increase the likelihood of a village becoming empty (if it is located ≥ 10 km away). However, the influence of distance from railway stations, banks, and ATMs is negligible.
Access to education emerges as an acritical factor, with the lack of primary schools exerting a significant influence. Villages located more than 10 km away from a primary school are more than 5.472 (CI: 5.160–5.803) times more likely to be empty than those that have a facility within the village. The same pattern, although less pronounced, holds for middle and secondary schools, reinforcing the idea that inadequate access to education forces entire communities to uproot in search of better opportunities.
Among health facilities, the availability of Anganwadi centers is the most crucial factor driving depopulation. Villages that are far from these centers show an astonishingly greater likelihood of becoming empty, with an odds ratio of 10.661 (CI: 10.042–11.318).
Distance to- | Odds ratio (95% confidence interval) | ||
|---|---|---|---|
India | Himachal Pradesh | Bihar | |
Road | |||
< 5 km | |||
5–10 km | 1.949*** (1.844–2.060) | 1.503*** (1.109–2.037) | 1.595*** (1.356–1.877) |
> 10 km | 2.563*** (2.422–2.712) | 1.955*** (1.429–2.674) | 1.929*** (1.604–2.321) |
Public transport | |||
< 5 km | |||
5–10 km | 3.310*** (3.139–3.492) | 5.968*** (4.422–8.055) | 3.057*** (2.609–3.582) |
> 10 km | 4.443*** (4.211–4.689) | 6.302*** (4.638–8.564) | 3.991*** (3.413–4.667) |
Market | |||
< 5 km | |||
5–10 km | 1.394*** (1.323–1.468) | 1.342** (1.018–1.77) | 1.649*** (1.414–1.923) |
> 10 km | 1.411*** (1.330–1.497) | 1.018 (0.78–1.328) | 2.051*** (1.69–2.488) |
PDS center | |||
< 5 km | |||
5–10 km | 1.584*** (1.497–1.677) | 1.441** (1.076–1.93) | 1.35*** (1.144–1.593) |
> 10 km | 2.104*** (1.972–2.246) | 2.046*** (1.484–2.82) | 1.601*** (1.289–1.988) |
Post office | |||
< 5 km | |||
5–10 km | 1.158*** (1.093–1.227) | 1.326* (0.984–1.788) | 1.243*** (1.056–1.463) |
> 10 km | 1.24*** (1.151–1.336) | 1.052 (0.745–1.485) | 1.273** (1.014–1.598) |
Primary school | |||
< 5 km | |||
5–10 km | 3.008*** (2.840–3.186) | 2.741*** (2.032–3.697) | 2.542*** (2.151–3.003) |
> 10 km | 5.472*** (5.160–5.803) | 5.039*** (3.691–6.877) | 3.606*** (2.951–4.406) |
Middle school | |||
< 5 km | |||
5–10 km | 1.053* (0.994–1.117) | 0.891 (0.641–1.239) | 1.221** (1.025–1.454) |
> 10 km | 1.117*** (1.042–1.197) | 1.608** (1.112–2.325) | 1.251* (0.997–1.569) |
Subcenter | |||
< 5 km | |||
5–10 km | 1.018 (0.961–1.077) | 1.043 (0.782–1.391) | 0.954 (0.809–1.125) |
> 10 km | 0.965 (0.900–1.033) | 0.786 (0.565–1.094) | 0.905 (0.729–1.124) |
Anganwadi | |||
< 5 km | |||
5–10 km | 6.155*** (5.801–6.521) | 5.373*** (4.007–7.207) | 7.403*** (6.21–8.826) |
> 10 km | 10.661*** (10.042–11.318) | 13.442*** (9.992–18.086) | 11.75*** (9.582–14.408) |
The state-specific result also shows a similar pattern. In Himachal Pradesh, the pattern aligns with the national scenario, but the intensity of certain factors is stronger. In transportation, access to public transportation plays a stronger role, as the state has mountainous terrain with higher altitudes. In Bihar, the effect is lesser than that at the national level. The study also revealed that a lack of basic infrastructure is a strong driver of rural depopulation in Bihar. The absence of Anganwadi centers strongly influences Himachal Pradesh and Bihar.
8. Discussion
Rural depopulation and the abandonment of villages are global challenges. Studies have highlighted this phenomenon as a major threat to rural sustainability (Uribe-Sierra et al., 2022). Over the past few decades, although the rural population in India has undergone a slow increase, rural development has been astounding (Singh et al., 2008). However, rural depopulation is an undeniable fact, and India is experiencing this phenomenon in the form of ghost or empty villages, similar to many other developed and developing countries (Jelić et al., 2019; Longstaff, 1993; Matanle, 2017; Yu et al., 2022). The issue of depopulation has also been a prominent feature in urban areas, especially among small–medium towns governed by a rural governance system (Ganapati, 2014; Sarif & Roy, 2024).
The findings of this study highlight a significant trend of rural depopulation in India. Empty villages are more concentrated in states such as Bihar, Odisha, Assam, and Uttar Pradesh, where agrarian distress and limited economic diversification are key contributing factors. These states are the major sources of out-migration, where economic restructuring and environmental vulnerability have been identified as the primary drivers of out-migration and rural population decline (Barrios et al., 2006; Bhagat, 2017; Tumbe, 2018). Furthermore, this study highlights the role of rural‒urban migration, which has been well documented as a fundamental demographic shift in India driven by employment opportunities and quality of life in urban areas (Bernard & Bell, 2018; Mckeown, 2004). Additionally, the micro spatial distribution of empty villages suggests that the clusters are located in areas where the level of urbanization is lower, agricultural productivity is lower, and extreme environmental vulnerabilities, such as floods and drought, are extreme. Thus, it could be argued that population decline in villages is correlated with development aspects such as the urbanization level, economic opportunities, and environmental vulnerabilities (Building Materials & Technology Promotion Council, 2019; Dayal, 1984; Roy et al., 2023).
The findings also assert that the size of villages and accessibility to facilities are positively correlated with each other. Therefore, larger villages have an advantage, with better access to transport, health and education. Moreover, smaller villages have less access to these facilities. While looking at empty villages, the study highlights that nearly half of these villages are approximately 10 km away from all facilities, making life very difficult for residents (Mustafa & Shekhar, 2021; Zaidi, 2008).
The spatial distribution of empty villages suggests a strong correlation between rural abandonment and accessibility to essential services. The results indicate that villages located farther from key infrastructures, such as roads, healthcare, and educational facilities, are more likely to be abandoned. This is consistent with prior studies that emphasize the role of accessibility in determining settlement sustainability (Bardsley & Hugo, 2010; Terminski, 2013).
The regression model underscores the significance of accessibility-related determinants, where the absence of facilities has a significant effect on settlement viability at the national and subnational levels (Himachal Pradesh and Bihar). The same has been asserted by many other studies where a lack of basic amenities has been linked to rural decline (Christiaanse, 2020; Saurav Kumar & Sati, 2023). Furthermore, facilities such as primary schools, middle schools and Anganwadi centers appear to be the most crucial determinants of decline. This suggests that families, especially those with young children, cannot sustain themselves in environments where the most basic support systems are absent. Furthermore, villages cut off from roads and public transport are significantly more prone to abandonment. In some cases, development-induced displacement, topography, and geographic isolation also play key roles in the process of rural abandonment. Similarly, previous research on rural depopulation has addressed these factors as key drivers of village abandonment(Aboda et al., 2019; Collantes & Pinilla, 2004; Saurav Kumar & Sati, 2023dălin-Sebastian & Luca, 2019).
The study indicates that the issue of rural depopulation is deeply rooted in the uneven allocation of resources. Rural development policies aimed at enhancing basic infrastructure and services are still insufficient and far from achieving their goals. If the trend persists, it will hamper rural sustainability and sustainable urbanization. The rural area will become empty, and the urban areas will overburden. A well-defined path and a comprehensive plan of action for regional development are highly important. The latest strategies, such as the development of growth hubs and transition areas (connecting major urban centers with small towns and villages), constitute one step toward improving rural areas(NITI Aayog, 2024; UN Habitat, 2019).
This study offers a comprehensive analysis of empty villages in India. However, it is important to acknowledge certain limitations. The data derived from the Mission Antyodaya survey are valuable. However, its cross-sectional perspective may not capture long-term trends in village abandonment.
9. Conclusion
Villages are an important part of the settlement ecosystem, housing approximately two-thirds of India’s population. It contributes significantly to the economy through primary economic activities. Historically, rural India has lagged behind in development despite a series of rural development programs. Therefore, living in a rural setting has remained challenging. This study has explored rural inaccessibility and depopulation in rural areas via a new dataset and has added important insights into the subject. It systematically maps and analyzes empty villages in India and underscores the driving factors of rural depopulation.
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The findings underscore the importance of basic services and facilities for rural sustainability. Better access to transportation, health, and education is pivotal in keeping a village alive. In contrast, inaccessibility of those facilities results in the abandonment of localities and resources. Additionally, broader demographic and economic processes, out-migration, agrarian distress, and development-induced displacement aid in rural depopulation. The findings of the study assert a deep structural issue with rural development. This could greatly benefit planners and policymakers by encouraging them to rethink and redesign policies that strengthen accessibility to basic infrastructure in rural India. A holistic and inclusive development approach for all types of settlements might help address this issue and achieve balanced regional development. The study suggests that nurturing villages and fostering their growth into thriving settlements is always a better alternative than allowing them to become abandoned.Ethics declaration:
Not applicable
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Author Contribution
N.S and D.C: Conceptualization; N.S: Analysis and Mapping; N.S and D.C: Writing the manuscript; N.S and D.C: Reviewing and editing
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Acknowledgement
The authors would like to sincerely thank Dr. Christophe Z Guilmoto for sharing the information about the data.
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