Abstract
Neighborhood impacts on decisions about out-migration, though less explored and understood than individual- and household-level impacts, can be significant; the integration of these impacts in decision-making analyses may reveal mechanisms undetectable otherwise. However, detecting these impacts can be difficult, especially when prior theorization is lacking. In this paper, we compare three methods of measuring and reducing neighborhood impacts: multilevel modeling, eigenvector spatial filtering (ESF) based on Euclidean distance, and ESF based on topological distance. The second ESF method, in particular, is developed to accommodate the elevation profile of our study site at the Fanjingshan National Nature Reserve of Guizhou Province, China. Our previous work identified a suite of socioeconomic factors at individual and household levels that influence out-migration decisions, to which we apply the aforementioned methods to identify and control for neighborhood impacts. While the non-spatial and multilevel models generated nearly identical results, the results from the ESF models present several considerable differences. The Moran's I statistics for each non-binary variable show that spatial autocorrelation is present in some variables. Among the spatially autocorrelated variables, there are different degrees of change in significance levels when compared to those in the non-spatial model. Although most changes detected are small, we identify an additional significant variable—in our case area farmed—that was not observed before we apply the ESF. Changes in the significance levels of several other independent variables are also more significant after we applied the topological distance definitions. Methodologically, the new results suggest using the topological ESF approach may allow other studies to take into account spatial autocorrelation, especially in more rural areas where elevation differences are significant.
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We re-grouped the 58 sampled natural villages into 23 village clusters based on their geographic locations to ensure that each village cluster had at least 20 interviewed households for the Cox analysis. The village clusters consist of neighboring natural villages, not the official administrative villages.
Out of the 3256 households living in the reserve, we conducted interviews with 605 scientifically randomly selected households (18.6% of total households). After determining the variables needed for the model, we were left with 513 sample households with complete data, which was still a fully statistically representative sample of the population. To check for possible bias in households excluded, we produced cross-tabulations to compare characteristics of the 513 included households with those of the 92 excluded ones, finding no major differences.
An overall indicator for spatial autocorrelation based on how similar/dissimilar neighboring features are. It ranges from − 1 to 1, − 1 being perfectly dispersed, 1 perfectly clustered, and 0 perfectly random (Ord & Getis, 1995).
Calculated from the number of variables in the model and the sum of squared errors (SSE). Smaller AIC scores indicate better fit (Burnham & Anderson, 2002).
References
Allison, P. D. (2010). Survival analysis using SAS®: A practical guide. SAS Institute.
An, L. (2016). CNH eigenvector instructions. Available at http://complexities.org/Photo&PDF/CNH_Eigvec_Instructions.pdf
An, L., & Brown, D. G. (2008). Survival analysis in land change science: Integrating with GIScience to address temporal complexities. Annals of Association of American Geographers, 98, 323–344. https://doi.org/10.1080/00045600701879045
An, L., Tsou, M.-H., Spitzberg, B. H., Gupta, D. K., & Gawron, J. M. (2016). Latent trajectory models for space-time analysis: An application in deciphering spatial panel data. Geographical Analysis, 48, 314–336. https://doi.org/10.1111/gean.12097
Anselin, L., & McCann, M. (2009). OpenGeoDa, open source software for the exploration and visualization of geospatial data. In Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems (pp. 550–551). https://doi.org/10.1145/1653771.1653871
Barbieri, A., Carr, D., & Bilsborrow, R. (2009). Migration within the frontier: The second generation colonization in the Ecuadorian Amazon. Population Research and Policy Review, 28, 291–320. https://doi.org/10.1007/s11113-008-9100-y
Beisner, B. E., Peres-Neto, P. R., Lindström, E. S., Barnett, A., & Longhi, M. L. (2006). The role of environmental and spatial processes in structuring lake communities from bacteria to fish. Ecology, 87(12), 2985–2991. https://doi.org/10.1890/0012-9658(2006)87[2985:TROEAS]2.0.CO;2
Bell, M., & Ward, G. (2000). Comparing temporary mobility with permanent migration. Tourism Geographies, 2, 87–107. https://doi.org/10.1080/146166800363466
Bendor, J., & Swistak, P. (2001). The evolution of norms. American Journal of Sociology, 106, 1493–1545. https://doi.org/10.1086/321298
Bilsborrow, R. E. (2016). Concepts, definitions and data collection approaches. In M. J. White (Ed.), International handbook of migration and population distribution (pp. 109–156). Springer.
Bilsborrow, R. E., McDevitt, T., Kossoudji, S., & Fuller, R. (1987). The impact of origin community characteristics on rural-urban out-migration in a developing country. Demography, 24, 191–210. https://doi.org/10.2307/2061629
Bilsborrow, R. E., Oberai, A., & Standing, G. (1984). Migration surveys in low-income countries: Guidelines for survey and questionnaire design. Croom-Helm, for the International Labor Office.
Burnham, K. P., & Anderson, D. R. (2002). Model selection and multimodel inference: A practical information-theoretic approach (2nd ed.). Springer.
Chandrasekhar, S., & Sharma, A. (2015). Urbanization and spatial patterns of internal migration in India. Spatial Demography, 3(2), 63–89. https://doi.org/10.1007/s40980-015-0006-0
Chen, X., Lupi, F., He, G., & Liu, J. (2009). Linking social norms to efficient conservation investment in payments for ecosystem services. Proceedings of the National Academy of Sciences, 106, 11812–11817. https://doi.org/10.1073/pnas.0809980106
Chun, Y., & Griffith, D. A. (2011). Modeling network autocorrelation in space-time migration flow data: An eigenvector spatial filtering approach. Annals of Association of American Geographers, 101, 523–536. https://doi.org/10.1080/00045608.2011.561070
Chun, Y., Griffith, D. A., Lee, M., & Sinha, P. (2016). Eigenvector selection with stepwise regression techniques to construct eigenvector spatial filters. Journal of Geographical Systems, 18(1), 67–85. https://doi.org/10.1007/s10109-015-0225-3
Coleman, J. S. (1990). Foundations of social theory. Harvard University Press.
Crowder, K., & South, S. J. (2008). Spatial dynamics of white flight: The effects of local and extralocal racial conditions on neighborhood out-migration. American Sociological Review, 73, 792–812. https://doi.org/10.1177/000312240807300505
Curran, S. R., & Rivero-Fuentes, E. (2003). Engendering migrant networks: The case of Mexican migration. Demography, 40(2), 289–307. https://doi.org/10.1353/dem.2003.0011
Davis, J., Sellers, S., Gray, C., & Bilsborrow, R. (2016). Indigenous migration dynamics in the Ecuadorian Amazon: A longitudinal and hierarchical analysis. The Journal of Development Studies, 53, 1849–1864. https://doi.org/10.1080/00220388.2016.1262028
Findley, S. E. (2019). Rural development and migration: A study of family choices in the Philippines. Routledge. https://doi.org/10.4324/9780429305023
Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24, 189–206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x
Goldstein, H. (2011). Multilevel statistical models (4th ed.). Wiley.
Griffith, D. A. (2000). A linear regression solution to the spatial autocorrelation problem. Journal of Geographical Systems, 2, 141–156. https://doi.org/10.1007/PL00011451
Gu, H., Liu, Z., & Shen, T. (2020). Spatial pattern and determinants of migrant workers’ interprovincial Hukou transfer intention in China: Evidence from a national migrant population dynamic monitoring survey in 2016. Population, Space and Place. https://doi.org/10.1002/psp.2250
Hankach, P., Gastineau, P., & Vandanjon, P.-O. (2022). Multi-scale spatial analysis of household car ownership using distance-based Moran’s eigenvector maps: Case study in Loire-Atlantique (France). Journal of Transport Geography. https://doi.org/10.1016/j.jtrangeo.2021.103223
Hawley, A. H. (1950). Human ecology: A theory of community structure. Ronald Press.
Howell, F. M., & Frese, W. (1983). Size of place, residential preferences and the life cycle: How people come to like where they live. American Sociological Review, 48(4), 569–580. https://doi.org/10.2307/2117723
Klein, J. P., & Moeschberger, L. M. (2003). Survival analysis: Techniques for censored and truncated data. Springer.
Lee, B. A., Oropesa, R. S., & Kanan, J. W. (1994). Neighborhood context and residential mobility. Demography, 31, 249–270. https://doi.org/10.2307/2061885
Lee, E. S. (1966). A theory of migration. Demography, 3(1), 47–57. https://doi.org/10.2307/2060063
Lee, R. E., & Cubbin, C. (2002). Neighborhood context and youth cardiovascular health behaviors. American Journal of Public Health, 92, 428–436. https://doi.org/10.2105/ajph.92.3.428
Liang, Z. (2016). China’s great migration and the prospects of a more integrated society. Annual Review of Sociology, 42(1), 451–471. https://doi.org/10.1146/annurev-soc-081715-074435
Lichter, D. T., & Johnson, K. M. (2009). Immigrant gateways and Hispanic migration to new destinations. International Migration Review, 3, 496–518. https://doi.org/10.1111/j.1747-7379.2009.00775.x
Liu, J., Li, S., Ouyang, Z., Tam, C., & Chen, X. (2008). Ecological and socioeconomic effects of China’s policies for ecosystem services. Proceedings of the National Academy of Sciences, 105, 9477–9482. https://doi.org/10.1073/pnas.0706436105
Liu, Y., & Shen, J. (2017). Modeling skilled and less-skilled interregional migrations in China, 2000–2005. Population, Space and Place. https://doi.org/10.1002/psp.2027
Massey, D. S. (1990). Social structure, household strategies, and the cumulative causation of migration. Population Index, 56(1), 3–26. https://doi.org/10.2307/3644186
Massey, D. S., Arango, J., Hugo, G., Kouaouci, A., Pellegrino, A., & Taylor, J. E. (1993). Theories of international migration: A review and appraisal. Population and Development Review, 19, 431–466. https://doi.org/10.2307/2938462
Mazza, A., Gabrielli, G., & Strozza, S. (2018). Residential segregation of foreign immigrants in Naples. Spatial Demography, 6(1), 71–87. https://doi.org/10.1007/s40980-017-0036-x
Migali, S., & Scipioni, M. (2019). Who’s about to leave? A global survey of aspirations and intentions to migrate. International Migration, 57(5), 181–200. https://doi.org/10.1111/imig.12617
Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: Distributional issues and an application. Geographical Analysis, 27(4), 286–306. https://doi.org/10.1111/j.1538-4632.1995.tb00912.x
Pickett, K. E., & Pearl, M. (2001). Multilevel analyses of neighborhood socioeconomic context and health outcomes: A critical review. Journal of Epidemiology and Community Health, 55, 111–122. https://doi.org/10.1136/jech.55.2.111
Rangel, T. F., Diniz-Filho, J. A. F., & Bini, L. M. (2010). SAM: A comprehensive application for spatial analysis in macroecology. Ecography, 33(1), 46–50. https://doi.org/10.1111/j.1600-0587.2009.06299.x
Ravenstein, E. G. (1885). The laws of migration. Journal of the Statistical Society of London, 48(2), 167–227. https://doi.org/10.2307/2979181
Raymer, J., Bai, X., Liu, N., & Wilson, T. (2020). Reconciliation of Australian demographic data to study immigrant population change across space and time. Spatial Demography, 8(2), 123–153. https://doi.org/10.1007/s40980-020-00060-9
Raymer, J., Wiśniowski, A., Forster, J. J., Smith, P. W. F., & Bijak, J. (2013). Integrated modeling of European migration. Journal of the American Statistical Association, 108(503), 801–819. https://doi.org/10.1080/01621459.2013.789435
Rogers, A. (1968). Matrix analysis of interregional population growth and distribution. University of California Press.
Rogers, A. (1975). Introduction to multiregional mathematical demography. Wiley.
Sampson, R. J. (2003). The neighborhood context of well-being. Perspectives in Biology and Medicine, 46, S53–S64. https://doi.org/10.1353/pbm.2003.0059
Seya, H., & Tsutsumi, M. (2013). Application of model averaging techniques to spatial hedonic land price models. In Econometrics: New research (pp. 63–88). Scopus.
Shaw, R. P. (1975). Migration theory and fact. Regional Science Research Institute, University of Pennsylvania.
Sjaastad, L. A. (1962). The costs and returns of human migration. Journal of Political Economy, 70(5), 80–93.
Stark, O. (1991). The migration of labour. Harvard University Press.
Stark, O., & Bloom, D. (1985). The new economics of labor migration. The American Economic Review, 75(2), 173–178.
Stark, O., & Taylor, J. (1991). Migration incentives, migration types: The role of relative deprivation. The Economic Journal, 101(408), 1163–1178. https://doi.org/10.2307/2234433
Sullivan, A., York, A. M., An, L., Yabiku, S. T., & Hall, S. J. (2017). How does perception at multiple levels influence collective action in the commons? The case of Mikania micrantha in Chitwan, Nepal. Forest Policy and Economics, 80, 1–10. https://doi.org/10.1016/j.forpol.2017.03.001
Therneau, T. (2018). Mixed effects Cox model. R Package.
Tiefelsdorf, M., & Griffith, D. A. (2007). Semiparametric filtering of spatial autocorrelation: The eigenvector approach. Environment and Planning A, 39, 1193–1221. https://doi.org/10.1068/a37378
Van Dalen, H. P., Groenewold, G., & Schoorl, J. J. (2005). Out of Africa: What drives the pressure to emigrate? Journal of Population Economics, 18(4), 741–778. https://doi.org/10.1007/s00148-005-0003-5
Vega, A., & Brazil, N. (2015). A multistate life table approach to understanding return and reentry migration between Mexico and the United States during later life. Demographic Research, 33, 1211. https://doi.org/10.4054/DemRes.2015.33.43
White, M. J., & Johnson, C. (2016). Perspectives on migration theory—Sociology and political science. In S. White (Ed.), International handbook of migration and population distribution (Vol. 5, pp. 69–89). Springer.
Wilson, A. G. (1974). Urban and regional models in geography and planning. Wiley.
Xie, W. (2019). Left-behind villages, left-behind children: Migration and the cognitive achievement of rural children in China. Population, Space and Place. https://doi.org/10.1002/psp.2243
Xu, Z., Xu, J., Deng, X., Huang, J., Uchida, E., & Rozelle, S. (2006). Grain for green versus grain: Conflict between food security and conservation set-aside in China. World Development, 34, 130–148. https://doi.org/10.1016/j.worlddev.2005.08.002
Yang, S. (2019). Exploring reciprocal relationships between payments for ecosystem services (PES) and the associated coupled human and natural systems (CHANS): A case study of Fanjingshan National Nature Reserve (FNNR), China. Doctoral Dissertation, Joint Program in Geography, San Diego State University and University of California, Santa Barbara.
Yang, Y. Q., Lei, X. P., & Yang, C. D. (2002). Fanjingshan research: Ecology of the wild Guizhou Snub-nosed monkey. Guizhou Science Press. (in Chinese).
Yost, A., An, L., Bilsborrow, R., Shi, L., Chen, X., Yang, S., & Zhang, W. (2020). Mechanisms behind concurrent payments for ecosystem services in a Chinese nature reserve. Ecological Economics, 169, 106509. https://doi.org/10.1016/j.ecolecon.2019.106509
Zhang, H., An, L., Bilsborrow, R., Chun, Y., Yang, S., & Dai, J. (2021). Neighborhood impacts on household participation in payments for ecosystem services programs in a Chinese nature reserve: A methodological exploration. Journal of Geographical Sciences, 31(6), 899–922. https://doi.org/10.1007/s11442-021-1877-1
Zhang, Q., Bilsborrow, R. E., Song, C., Tao, S., & Huang, Q. (2018). Determinants of out-migration in rural China: Effects of payments for ecosystem services. Population & Environment. https://doi.org/10.1007/s11111-018-0307-5
Zhang, Q., Wang, Y., Tao, S., Bilsborrow, R. E., Qiu, T., Liu, C., Sannigrahi, S., Li, Q., & Song, C. (2020). Divergent socioeconomic-ecological outcomes of China’s conversion of cropland to forest program in the subtropical mountainous area and the semi-arid Loess Plateau. Ecosystem Services. https://doi.org/10.1016/j.ecoser.2020.101167
Zhao, Y. (1999). Leaving the countryside: Rural-to-urban migration decisions in China. The American Economic Review, 89, 281–286. https://doi.org/10.1257/aer.89.2.281
Zvoleff, A., An, L., Stoler, J., & Weeks, J. R. (2013). What if neighbors’ neighborhoods differ? The influence of neighborhood definition on health outcomes in Accra. In J. R. Weeks & A. G. Hill (Eds.), Spatial inequalities: Health, poverty and place in Accra, Ghana. Springer.
Acknowledgements
This research was funded mainly by the National Science Foundation under its Dynamics of Coupled Natural and Human Systems program [Grant DEB-1212183 and BCS-1826839]. We also received financial and research support from San Diego State University, and are grateful to the Carolina Population Center and its NIH/NICHD population center grant [P2C HD050924] for general support for Bilsborrow's collaboration. Finally, we thank Fanjingshan National Nature Reserve and the Research Center for Eco-Environmental Sciences at the Chinese Academy of Sciences for logistic support.
Funding
This research was funded by the National Science Foundation under the Dynamics of Coupled Natural and Human Systems program [Grant DEB-1212183 and BCS-1826839]. This research also received financial and research support from the Center for Complex Human-Environment Systems, San Diego State University. Finally, we are grateful to the Carolina Population Center and its NIH/NICHD population center grant [P2C HD050924] for general support to Bilsborrow for collaboration in this research.
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All authors contributed to the study's conception and design. Material preparation and data collection were performed by SY, LA, RB, and ML, and undergraduates students from Tongren University. Analysis was performed by JD and YL. The first drafts were written by JD and YL. All authors commented on previous versions of the manuscript and approved the final manuscript.
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Liu, Y., Dai, J., Yang, S. et al. Measuring Neighborhood Impacts on Labor Out-Migration from Fanjingshan National Nature Reserve, China. Spat Demogr 11, 7 (2023). https://doi.org/10.1007/s40980-023-00117-5
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DOI: https://doi.org/10.1007/s40980-023-00117-5