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International business cycles and remittance flows

  • Arusha Cooray and Debdulal Mallick EMAIL logo
Published/Copyright: September 26, 2013
The B.E. Journal of Macroeconomics
From the journal The B.E. Journal of Macroeconomics Volume 13 Issue 1

Abstract

In this paper, we study the macroeconomic determinants of remittance flows. We place particular attention to fluctuations in remittance flows over the international business cycles. Estimating a dynamic panel data model using the system-GMM method over the period 1970–2007, we document that remittance inflows decrease with home country volatility. Contrarily, remittance inflows increase with the volatility in host countries, especially for middle-income countries. Lower interest rates in host countries lead to larger remittance outflows. Trade and capital account openness are the most important factors that determine both remittance inflows and outflows. We conclude that macroeconomic factors of both home and host countries are important for understanding remittance flows.

Keywords: dynamic panel data; international business cycle; remittance; volatility; JEL Classification Codes: C23; E32; F22; F24
Corresponding author: Debdulal Mallick, School of Accounting, Economics and Finance, 70 Elgar Road, Burwood, Victoria 3125, Australia, and Alfred Deakin Research Institute, e-mail:

We would like to acknowledge Omar Bashar, Devashish Mitra, Bhaskara Rao, an anonymous referee and the participants at the 2011 Australasian Econometric Society Meeting for their helpful comments and suggestions. All errors and omissions are the sole responsibility of the author.

  1. 1

    For discussions on the effects of remittances, see, among others, Adams and Page (2003), Kapur (2005), Lueth and Ruiz-Arranz (2008), Chami et al. (2009), Giuliano and Ruiz-Arranz (2009), Frankel (2009) and Mundaca (2009).

  2. 2

    Remittances are defined as the sum of workers’ remittances, compensation of employees and migrants’ transfers (World Bank 2009). Although, this definition is not beyond criticism (Chami et al. 2008), it is based on the data availability at the cross-country level and is also frequently used in the literature. For example, Bugamelli and Paternò (2011) use the same definition in their study of 60 emerging and developing economies and document a negative effect of remittances on output growth volatility.

  3. 3

    Authors’ own calculation from the World Bank data.

  4. 4

    For example, Sayan (2004), Akkoyunlu and Kholodilin (2008) and Sayan and Tekin-Koru (2008) have studied the case of Turkey and Germany, and Sayan and Tekin-Koru (2008) and Vargas-Silva (2008) have studied the case of Mexico and the USA.

  5. 5

    Given that growth is negatively related to volatility (since the seminal work of Ramey and Ramey 1995, there is a large literature on this relationship), we consider business cycle in terms of the second moment.

  6. 6

    This approach of taking 5-year averages is also standard in the volatility-growth literature. Examples include, among others, Martin and Rogers (2000), Kneller and Young (2001) and Cavalcanti, Mohaddes and Raissi (2012).

  7. 7

    Another motive closely tied to these motives and is based on the migration networks literature is the options motive (for a detailed discussion, see Roberts and Morris 2003).

  8. 8

    Business cycles in developed countries are, to a large extent, correlated (Ambler, Cardia, and Zimmermann 2004) although there may be divergence at some points in time. Given that these countries are usually remittance senders, it is reasonable to consider that other remittance sending countries follow similar business cycle patterns as the USA.

  9. 9

    Total world remittance inflows are also informative but the number of home countries differ across years in the dataset.

  10. 10

    Some papers estimate volatility as the standard deviation of the series rather than that of the detrended series. The difference between the two approaches lies in the treatment of the trend growth rate. The standard deviation of the growth series implicitly assumes a constant trend growth, while the standard deviation of the detrended series allows the trend to follow a time-dependent process (Hnatkovska and Loayza 2005, p. 74–75).

  11. 11

    This is based on the recommendation by Ravn and Uhlig (2002, p. 371) who show that the parameter should be adjusted approximately with the fourth power of the frequency change.

  12. 12

    There is a difference between volatility and uncertainty. Volatility measures both the predictable and unpredictable changes, while uncertainty measures only the unpredictable changes (Ramey and Ramey 1995). We do not make this distinction here.

  13. 13

    The Chinn and Ito (2008) capital account openness index is based on the following information regarding actual restrictions on capital flows: i) the presence of multiple exchange rates, ii) restrictions on current account transactions, iii) restrictions on capital account transactions, and iv) the requirement of the surrender of export proceeds. A possible alternative to capital account openness could be the financial integration index constructed by Lane and Milesi-Ferretti (2007). However, this is a price based measure that also reflects changes in the macroeconomic conditions even in the absence of any regulatory change on capital account transactions (endnote 5 in Chinn and Ito, 2008). The macroeconomic determinants included in our equation (3) capture the effect of the financial integration index.

  14. 14

    Several other variables, such as the interest rate differential between the home and host countries (Faini 1994, El Sakka and McNabb 1999), or the federal fund rate (Vargas-Silva and Huang 2006) have also been included as determinants of remittance flows. However, they cannot be applied at the cross-country level.

  15. 15

    An anonymous referee has also recommended this exercise.

  16. 16

    The following are the sources of data: i) World Bank: remittance flows, migration stock, openness (the ratio of exports plus imports to GDP), nominal exchange rate, M2-GDP ratio, and CPI inflation; ii) Penn World Table 6.2: real GDP, and investment-GDP ratio; iii) Chinn and Ito (2008): capital account openness; iv) Polity IV Project (Political Regime Characteristics and Transitions, 1800–2007): Polity2.

  17. 17

    Roodman (2006) provides an excellent user guide for dynamic panel data estimation. In this paper, we estimate using the “xtdpdsys” command in STATA.

  18. 18

    The growth volatility of a country may depend on the ROW volatility. However, in the data, the correlation between the two volatilities is only 0.14.

  19. 19

    However, we conduct a separate analysis in Section 5.4 only for the middle-income countries for which a relatively large number of countries are available. In some specifications, the number of instruments exceeds the number of countries.

  20. 20

    Although the results are robust, they should be treated with caution because our sample period spans until 2007, and the great recession may have changed the migration stock in 2010.

  21. 21

    (Remittance inflows – Remittance outflows)/GDP.

  22. 22

    The number of countries in each group is almost the same if the top 10% host countries are chosen instead of the top 10. The results (not reported) do not meaningfully change from those reported in the table if the top 10% or even the top 5% of the sample of countries are chosen. The results also remain robust if changes in the inflation and exchange rate are included in the equations.

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Received: 2013-2-11
Accepted: 2013-8-25
Published Online: 2013-09-26
Published in Print: 2013-01-01

©2013 by Walter de Gruyter Berlin Boston

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https://doi.org/10.1515/bejm-2013-0030
Keywords for this article
dynamic panel data; international business cycle; remittance; volatility; JEL Classification Codes: C23; E32; F22; F24

Articles in the same Issue

  1. Masthead
  2. Masthead
  3. Advances
  4. How have global shocks impacted the real effective exchange rates of individual euro area countries since the euro’s creation?
  5. Employment by age, education, and economic growth: effects of fiscal policy composition in general equilibrium
  6. Overeducation and skill-biased technical change
  7. Strategic wage bargaining, labor market volatility, and persistence
  8. Households’ uncertainty about Medicare policy
  9. Contributions
  10. Deconstructing shocks and persistence in OECD real exchange rates1)
  11. A contribution to the empirics of welfare growth
  12. Development accounting with wedges: the experience of six European countries
  13. Implementation cycles, growth and the labor market
  14. International technology adoption, R&D, and productivity growth
  15. Bequest taxes, donations, and house prices
  16. Business cycle accounting of the BRIC economies
  17. Privately optimal severance pay
  18. Small business loan guarantees as insurance against aggregate risks
  19. Output growth and unexpected government expenditures
  20. International business cycles and remittance flows
  21. Effects of productivity shocks on hours worked: UK evidence
  22. A prior predictive analysis of the effects of Loss Aversion/Narrow Framing in a macroeconomic model for asset pricing
  23. Exchange rate pass-through and fiscal multipliers
  24. Credit demand, credit supply, and economic activity
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  26. Monetary policy shocks and real commodity prices
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  30. Required reserves as a credit policy tool
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  32. Productivity and resource misallocation in Latin America1)
  33. Information and communication technologies over the business cycle
  34. In search of lost time: the neoclassical synthesis
  35. Divorce laws and divorce rate in the US
  36. Is the “Great Recession” really so different from the past?
  37. Monetary business cycle accounting for Sweden
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