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Do Low-Skilled Migrants Contribute More to Home Country Income? Evidence from South Asia

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Published/Copyright: January 8, 2014

Abstract

A recursive system is employed to investigate the indirect effect of out-migration on gross domestic product (GDP) through remittances in South Asia, namely Bangladesh, India, Nepal, Pakistan, and Sri Lanka. Out-migration is further disaggregated by skill level and country of destination (Middle East and other), and their effects on GDP through remittances are examined. The results suggest that migration and remittances have an important significant effect on the GDP of the countries under study. Of the skill categories, the unskilled category has the largest robust indirect effect on GDP. The effects of migration on GDP by country of destination suggest that migration to the Middle East has a robust and significant impact on GDP. There is some evidence of a combination between the altruistic and self-interested motives of migrants’ to remit.

Appendix

Table 8

Description of variables used in the study and data sources

SeriesSource
GDP data all countriesWorld Development Indicators (2011)
Total number of migrants all countriesBangladesh: Bureau of Manpower, Employment and Training (BMET)http://www.bmet.org.bd/BMET/stattisticalDataAction

India: Annual reports of the Ministry of Overseas Indian Affairs

Pakistan: Wickramasekara (2002a, 2002b, 2010)

Sri Lanka: Annual statistical reports Sri Lanka bureau of foreign employment

Nepal: Wickramasekara (2010)
Number of migrants to the Middle East and other destinationsBangladesh: Calculated from Bureau of Manpower, Employment and Training (BMET)

India: Calculated from IOM report 2000, annual reports of the Ministry of Overseas Indian Affairs

Pakistan: Calculated from IOM report 2000,

Sri Lanka: Calculated from IOM report 2000, annual statistical reports of the Sri Lanka Bureau of foreign employment.
Number of migrants by skill levelBangladesh: Bureau of Manpower, Employment and Training (BMET)http://www.bmet.org.bd/BMET/stattisticalDataAction

India: Author’s own calculation up until 1998 from Sasikumar (2001), Khadria (2002)

Pakistan: Ahmed, Sugiyarto, and Jha (2010).

Sri Lanka: Annual statistical reports of the Sri Lanka bureau of foreign Employment.
Total remittances all countries US$World Bank (2011): migration and remittance database
Remittances from the Middle East and other destinations $USBangladesh: Bangladesh Bank (Central Bank of Bangladesh) http://www.bangladesh-bank.org

India: Reserve Bank of India Bulletins

Pakistan: Pakistan Bureau of Employment and Overseas Employmenthttp://www.beoe.gov.pk/Remittance.asp, State Bank of Pakistan Economic Datahttp://www.sbp.org.pk/ecodata/index2.asp

Sri Lanka: annual statistical reports of the Sri Lanka bureau of foreign employment
Labour force all countriesWorld Development Indicators (2011)
M2World Development Indicators (2011)
Secondary enrolment ratio all countriesWorld Development Indicators (2011)
Openness all countriesWorld Development Indicators (2011)
Capital stock all countriesAuthors own calculation
Inflation rate CPI (% annual)World Development Indicators (2011)
Deposit rate (% annual)World Development Indicators (2011)
Official exchange rate: local currency per US$World Development Indicators (2011)
Table 9

Descriptive statistics

VariableObservationsMeanStandard deviationMinimumMaximum
Total migrants (number)158185,542169,578.3500875,055
Migrants to the ME (number)127171,953148,858.4495825,310
Migrants to other destinations (number)12826,50774,0935641,356
Total remittances $US1583.92E + 098.32E + 091.30E + 075.31E + 10
Remittances from the ME $US1011.91E + 092.04E + 095.90E + 071.10E + 10
Remittances from other destinations $US1012.58e + 096.39e + 096.93e + 073.90e + 10
Professional migrants (number)11510,37417,95626273,202
Other white collar migrants (number)8011,7728,75833633,173
Skilled migrants (number)11547,54543,478139292,364
Semi-skilled migrants (number)9615,85225,206151183,673
Unskilled migrants (number)115101,09686,0882,444482,922
Labour force (number)1509.12e + 071.34e + 085,793,3954.59e + 08
M2 $US1752.78e + 127.99e + 122.77e + 095.93e + 13
Openness $US1743.98e + 101.06e + 113.61e + 087.49e + 11
Enrolment ratio (% gross)15737.818.213.388.5
Capital stock $US1601.49e + 112.71e + 118.34e + 071.56e + 12
Inflation CPI (annual %)1648.424.44–3.1126.14
Deposit interest rate (%)1049.753.881.6319.79
Official exchange rate (local currency per US$)17540.6025.927.86114.94

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  1. 1

    See de Haas (2007) for a survey of the literature.

  2. 2

    Docquier and Marfouk (2005) use data on the immigration structure by educational attainment and country of birth from all OECD receiving countries to estimates of emigration stocks and rates by educational attainment. The present study in contrast investigates the effect of the skill level of the migrant on home country remittances and GDP.

  3. 3

    See Khatri (2007) for a detailed discussion of measures taken and effects of labour migration in South Asia.

  4. 4

    See Lucas and Stark (1985) for hypotheses on the motives to remit.

  5. 5

    Rozelle, Taylor, and deBrauw (1999) have used a recursive system to estimate the effects of migration and remittances in China on agricultural production.

  6. 6

    M, is further divided into two components: migration to the ME and the Gulf () and migration to other countries () and remittances, R, is also divided into: remittances from the ME (), and remittances from other destinations ().

  7. 7

    The definition of these migrant categories is as follows: (1) Professionals are those who are tertiary qualified for example, doctors, lawyers, and engineers. (2) Other white collar workers are those with secondary qualifications, for example, administrative and managerial staff (the highly skilled group in Pakistan, mid-level group in Sri Lanka and white collar workers in India fall into this group. Bangladesh does not have this category). (3) Those who fall into the skilled labour category are those who have received specialised training in particular trades, for example, plumbers, carpenters. (4) Someone who has knowledge of a particular job, but is not qualified or received specialised training, for example, tailors, masons fall into the semi-skilled category. (5) Workers with no qualifications or training, for example, housemaids, labourers fall into the unskilled group.

  8. 8

    The level of GDP is also used by Lueth and Ruiz-Arranz (2008) and Niimi, Ozden, and Schiff (2010). Lueth and Ruiz-Arranz (2008) note that policymakers in sending countries are interested policies that promote remittances and how they affect GDP in the host country.

  9. 9

    The total effect is the sum of the direct influence of the exogenous variable, M, on the endogenous variable R, plus the indirect effect of M on GDP through the mediating variable R. The indirect effect is calculated by multiplying the direct effects corresponding to the indirect effects.

  10. 10

    For example, of the total, South Asian migrants residing in the United States, the majority were tertiary qualified. Of the total Bangladeshi, Indian, Pakistani, and Sri Lankan populations in the United States 62%, 80%, 67% and 72% respectively, had tertiary qualifications (Dodani and La Porte 2005).

Published Online: 2014-1-8
Published in Print: 2014-7-1

©2014 by Walter de Gruyter Berlin / Boston

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