Internal Migration and Poverty Dynamic Analysis Using a Bayesian Approach: The Tunisian Case
We explore the relationship between internal migration
and poverty in Tunisia. We present a methodology combining
potential outcomes approach with multiple imputation to highlight the
effect of internal migration on poverty states. We find that probability
of being poor decreases when leaving the poorest regions (the west
areas) to the richer regions (greater Tunis and the east regions).
[1] B. Barham and S. Boucher, Migration, remittances, and inequality:
estimating the net effects of migration on income distribution Journal
of development economics: Elsevier, 1998, pp. 307-331.
[2] D. McKenzie and H. Rapoport, Network effects and the dynamics of
migration and inequality: theory and evidence from Mexico Journal of
development economics: Elsevier, 2007, pp. 1-24.
[3] O. Stark and M. Maja and J. Mycielski, Relative poverty as a determinant
of migration: Evidence from Poland Economics Letters: Elsevier, 2009,
pp. 119-122.
[4] S. Guriev and E. Vakulenko, Breaking out of poverty traps: Internal
migration and interregional convergence in Russia Journal of comparative
economics: Elsevier, 2015, pp. 633-649.
[5] J. Gipson, Measuring chronic poverty without a panel Journal of
development economics: Elsevier, 2015, pp. 243-266.
[6] A. Jmaii and B. Belhadj, Fuzzy Version Of The Jalan-Ravallion Approach:
Evidence From Tunisia International Journal of Scientific and Technology
Research: IJSTR, 2016, pp. 329-333.
[7] A. K. Mehta and A. Shah, Chronic poverty in India: overview study
Chronic Poverty Research Centre Working Paper, 2001.
[8] G. W. Imbens and D. Rubin, Causal inference in statistics, and in the
social and biomedical sciences Cambridge University Press New York,
2009.
[9] F. Gardes and S. Langlois and D. Richaudeau, Cross-section versus
time-series income elasticities of Canadian consumption Economics
Letters, 1996, pp. 169-175.
[10] D. Roodman, An expanded framework for mixed process modeling in
Stata German Stata Users’ Group Meetings: Stata Users Group, 2013.
[11] A. Suryahadi and S. Sumarto, Poverty and Vulnerability in Indonesia
before and after the Economic Crisis Asian Economic Journal: Wiley
Online Library, 2003, pp. 45-64.
[12] D. Roodman, Fitting fully observed recursive mixed-process models with
cmp The Stata Journal, 2011, pp. 159-206.
[13] J. Okidi and G. Bahiigwa and G.Kempaka, Costing the Millenium
Development Goals: Uganda Country Study Economic Policy Research
Centre, 2002.
[14] J. F. Ermisch and S. P. Jenkins and G.Kempaka, Retirement and housing
adjustment in later life: evidence from the British Household Panel Survey
Labour Economics: Elsevier, 1999, pp. 311-333.
[15] A. Deaton, The analysis of household surveys: a microeconometric
approach to development policy World Bank Publications, 1997.
[16] H. De Haas, The internal dynamics of migration processes: a theoretical
inquiry Journal of ethnic and migration studies: Taylor & Francis, 2010,
pp. 1587-1617.
[17] M. A. Collinson, Striving against adversity: the dynamics of migration,
health and poverty in rural South Africa Global Health Action:Co-Action
Publishing, 2010.
[18] P. Deshingkar, Internal migration, poverty and development in Asia:
Including the excluded IDS Bulletin: Wiley Online Library, 2006, pp.
88-100.
[19] D. Rubin, The calculation of posterior distributions by data
augmentation: Comment: A noniterative sampling/importance resampling
alternative to the data augmentation algorithm for creating a few
imputations when fractions of missing information are modest: The SIR
algorithm Journal of the American Statistical Association: JSTOR, 1987,
pp. 543-546.
[20] D. Rubin, Estimating causal effects of treatments in randomized and
nonrandomized studies. Journal of educational Psychology: American
Psychological Association, 1974, p. 688.
[21] J. Neyman, On the application of probability theory to agricultural
experiments: Essay on principles Statistical Science: American
Psychological Association, 1923, pp. 465-480.
[22] A. Deaton, Panel data from time series of cross-sections Journal of
econometrics: Elsevier, 1985, pp. 109-126.
[23] M. Browning and A. Deaton and M. Irish, A profitable approach to labor
supply and commodity demands over the life-cycle Journal of econometric
society: JSTOR, 1985, pp. 503-543.
[24] F. Gardes and C. Loisy, La pauvret´e selon les m´enages: une ´evaluation
subjective et index´ee sur leur revenu ´ Economie et statistique: Institut
national de la statistique et des ´etudes ´economiques, 1998, pp. 95-112.
[25] R. Alessie and M. Devereux and G. Weber, Intertemporal consumption,
durables and liquidity constraints: A cohort analysis European Economic
Review: Elsevier, 1997, pp. 37-59.
[26] R. Chamarbagwala, Economic liberalization and urban–rural inequality
in India: a quantile regression analysis Empirical Economics: Springer,
2010, pp. 371-394.
[27] Z. Baltagi and Q. Li, Testing AR (1) against MA (1) disturbances in
an error component model Journal of Econometrics: Elsevier, 1995, pp.
133-151.
[28] Z. Fang and C. Sakellariou, Evolution of Urban–rural Living Standards
Inequality in Thailand: 1990-2006 Asian Economic Journal:Wiley Online
Library, 2013, pp. 285-306.
[29] H. Hertelli, E´migration et immigration des Tunisiens en Europe.
Migrations societe: Centre d’information et d’´etudes sur les migrations
internationales, 1994, pp. 19-32.
[30] A. McKay and P. Quartey, Evolution of spatial inequality between
northern and southern Ghana Mimeo, 2008.
[31] C. Ackah and D. Medvedev, Internal Migration in Ghana: Determinants
and Welfare Impacts The World Bank, 2010.
[32] A. Piesse and L. Alvarez-Rojas and D. Judkins-Westa, Causal Inference
Using Semi-Parametric Imputation Proceedings of the Section on Survey
Research Methods of the American Statistical Association, 2010, pp.
1085-1096.
[33] A. Gelman and J. B. Carlin and H. S. Stern and D. B. Rubin.
Judkins-Westa, Causal Inference Using Semi-Parametric Imputation
Proceedings of the Section on Survey Research Methods of the American
Statistical Association, 2010, pp. 1085-1096.
[34] J. Honaker and G. King and M. Blackwell, Amelia II: A program for
missing data Journal of Statistical Software, 2011, pp. 1-47.
[35] M. Blackwell and J. Honaker and G. King, A Unified Approach
to Measurement Error and Missing Data: Details and Extensions
Sociological Methods and Research, 2015.
[36] A. P. Dempster and M. N. Laird and D. B. Rubin, Maximum likelihood
from incomplete data via the EM algorithm Journal of the royal statistical
society: JSTOR, 1977, pp. 1-38.
[37] J. P. Payet, Les ´ecoles en milieu d´efavoris´e dans le Grand Tunis. Un
regard ethnographique LAnn´ee du Maghreb: CNRS ´ Editions, 2006, pp.
395-398.
[38] S. Guriev and E. Vakulenko, Breaking out of poverty traps:
Internal migration and interregional convergence in Russia. Journal of
Comparative Economics, 2015, pp. 633-649.
[39] C. Ackah and D. Medvedev, Internal migration in Ghana: determinants
and welfare impacts. International Journal of Social Economics, 2012,
pp. 764-784.
[1] B. Barham and S. Boucher, Migration, remittances, and inequality:
estimating the net effects of migration on income distribution Journal
of development economics: Elsevier, 1998, pp. 307-331.
[2] D. McKenzie and H. Rapoport, Network effects and the dynamics of
migration and inequality: theory and evidence from Mexico Journal of
development economics: Elsevier, 2007, pp. 1-24.
[3] O. Stark and M. Maja and J. Mycielski, Relative poverty as a determinant
of migration: Evidence from Poland Economics Letters: Elsevier, 2009,
pp. 119-122.
[4] S. Guriev and E. Vakulenko, Breaking out of poverty traps: Internal
migration and interregional convergence in Russia Journal of comparative
economics: Elsevier, 2015, pp. 633-649.
[5] J. Gipson, Measuring chronic poverty without a panel Journal of
development economics: Elsevier, 2015, pp. 243-266.
[6] A. Jmaii and B. Belhadj, Fuzzy Version Of The Jalan-Ravallion Approach:
Evidence From Tunisia International Journal of Scientific and Technology
Research: IJSTR, 2016, pp. 329-333.
[7] A. K. Mehta and A. Shah, Chronic poverty in India: overview study
Chronic Poverty Research Centre Working Paper, 2001.
[8] G. W. Imbens and D. Rubin, Causal inference in statistics, and in the
social and biomedical sciences Cambridge University Press New York,
2009.
[9] F. Gardes and S. Langlois and D. Richaudeau, Cross-section versus
time-series income elasticities of Canadian consumption Economics
Letters, 1996, pp. 169-175.
[10] D. Roodman, An expanded framework for mixed process modeling in
Stata German Stata Users’ Group Meetings: Stata Users Group, 2013.
[11] A. Suryahadi and S. Sumarto, Poverty and Vulnerability in Indonesia
before and after the Economic Crisis Asian Economic Journal: Wiley
Online Library, 2003, pp. 45-64.
[12] D. Roodman, Fitting fully observed recursive mixed-process models with
cmp The Stata Journal, 2011, pp. 159-206.
[13] J. Okidi and G. Bahiigwa and G.Kempaka, Costing the Millenium
Development Goals: Uganda Country Study Economic Policy Research
Centre, 2002.
[14] J. F. Ermisch and S. P. Jenkins and G.Kempaka, Retirement and housing
adjustment in later life: evidence from the British Household Panel Survey
Labour Economics: Elsevier, 1999, pp. 311-333.
[15] A. Deaton, The analysis of household surveys: a microeconometric
approach to development policy World Bank Publications, 1997.
[16] H. De Haas, The internal dynamics of migration processes: a theoretical
inquiry Journal of ethnic and migration studies: Taylor & Francis, 2010,
pp. 1587-1617.
[17] M. A. Collinson, Striving against adversity: the dynamics of migration,
health and poverty in rural South Africa Global Health Action:Co-Action
Publishing, 2010.
[18] P. Deshingkar, Internal migration, poverty and development in Asia:
Including the excluded IDS Bulletin: Wiley Online Library, 2006, pp.
88-100.
[19] D. Rubin, The calculation of posterior distributions by data
augmentation: Comment: A noniterative sampling/importance resampling
alternative to the data augmentation algorithm for creating a few
imputations when fractions of missing information are modest: The SIR
algorithm Journal of the American Statistical Association: JSTOR, 1987,
pp. 543-546.
[20] D. Rubin, Estimating causal effects of treatments in randomized and
nonrandomized studies. Journal of educational Psychology: American
Psychological Association, 1974, p. 688.
[21] J. Neyman, On the application of probability theory to agricultural
experiments: Essay on principles Statistical Science: American
Psychological Association, 1923, pp. 465-480.
[22] A. Deaton, Panel data from time series of cross-sections Journal of
econometrics: Elsevier, 1985, pp. 109-126.
[23] M. Browning and A. Deaton and M. Irish, A profitable approach to labor
supply and commodity demands over the life-cycle Journal of econometric
society: JSTOR, 1985, pp. 503-543.
[24] F. Gardes and C. Loisy, La pauvret´e selon les m´enages: une ´evaluation
subjective et index´ee sur leur revenu ´ Economie et statistique: Institut
national de la statistique et des ´etudes ´economiques, 1998, pp. 95-112.
[25] R. Alessie and M. Devereux and G. Weber, Intertemporal consumption,
durables and liquidity constraints: A cohort analysis European Economic
Review: Elsevier, 1997, pp. 37-59.
[26] R. Chamarbagwala, Economic liberalization and urban–rural inequality
in India: a quantile regression analysis Empirical Economics: Springer,
2010, pp. 371-394.
[27] Z. Baltagi and Q. Li, Testing AR (1) against MA (1) disturbances in
an error component model Journal of Econometrics: Elsevier, 1995, pp.
133-151.
[28] Z. Fang and C. Sakellariou, Evolution of Urban–rural Living Standards
Inequality in Thailand: 1990-2006 Asian Economic Journal:Wiley Online
Library, 2013, pp. 285-306.
[29] H. Hertelli, E´migration et immigration des Tunisiens en Europe.
Migrations societe: Centre d’information et d’´etudes sur les migrations
internationales, 1994, pp. 19-32.
[30] A. McKay and P. Quartey, Evolution of spatial inequality between
northern and southern Ghana Mimeo, 2008.
[31] C. Ackah and D. Medvedev, Internal Migration in Ghana: Determinants
and Welfare Impacts The World Bank, 2010.
[32] A. Piesse and L. Alvarez-Rojas and D. Judkins-Westa, Causal Inference
Using Semi-Parametric Imputation Proceedings of the Section on Survey
Research Methods of the American Statistical Association, 2010, pp.
1085-1096.
[33] A. Gelman and J. B. Carlin and H. S. Stern and D. B. Rubin.
Judkins-Westa, Causal Inference Using Semi-Parametric Imputation
Proceedings of the Section on Survey Research Methods of the American
Statistical Association, 2010, pp. 1085-1096.
[34] J. Honaker and G. King and M. Blackwell, Amelia II: A program for
missing data Journal of Statistical Software, 2011, pp. 1-47.
[35] M. Blackwell and J. Honaker and G. King, A Unified Approach
to Measurement Error and Missing Data: Details and Extensions
Sociological Methods and Research, 2015.
[36] A. P. Dempster and M. N. Laird and D. B. Rubin, Maximum likelihood
from incomplete data via the EM algorithm Journal of the royal statistical
society: JSTOR, 1977, pp. 1-38.
[37] J. P. Payet, Les ´ecoles en milieu d´efavoris´e dans le Grand Tunis. Un
regard ethnographique LAnn´ee du Maghreb: CNRS ´ Editions, 2006, pp.
395-398.
[38] S. Guriev and E. Vakulenko, Breaking out of poverty traps:
Internal migration and interregional convergence in Russia. Journal of
Comparative Economics, 2015, pp. 633-649.
[39] C. Ackah and D. Medvedev, Internal migration in Ghana: determinants
and welfare impacts. International Journal of Social Economics, 2012,
pp. 764-784.
@article{"International Journal of Engineering, Mathematical and Physical Sciences:74754", author = "Amal Jmaii and Damien Rousseliere and Besma Belhadj", title = "Internal Migration and Poverty Dynamic Analysis Using a Bayesian Approach: The Tunisian Case", abstract = "We explore the relationship between internal migration
and poverty in Tunisia. We present a methodology combining
potential outcomes approach with multiple imputation to highlight the
effect of internal migration on poverty states. We find that probability
of being poor decreases when leaving the poorest regions (the west
areas) to the richer regions (greater Tunis and the east regions).", keywords = "Internal migration, Bayesian approach, poverty
dynamics, Tunisia.", volume = "10", number = "8", pages = "417-6", }
