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International business cycles and remittance flows

Arusha Cooray
  • School of Economics, University of Wollongong, Wollongong, NSW 2522, Australia
/ Debdulal Mallick
  • Corresponding author
  • School of Accounting, Economics and Finance, 70 Elgar Road, Burwood, Victoria 3125, Australia, and Alfred Deakin Research Institute
  • Email:
Published Online: 2013-09-26 | DOI: https://doi.org/10.1515/bejm-2013-0030

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

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About the article

Corresponding author: Debdulal Mallick, School of Accounting, Economics and Finance, 70 Elgar Road, Burwood, Victoria 3125, Australia, and Alfred Deakin Research Institute, e-mail:


Received: 2013-02-11

Accepted: 2013-08-25

Published Online: 2013-09-26

Published in Print: 2013-01-01


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).

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.

Authors’ own calculation from the World Bank data.

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.

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.

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).

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).

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.

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

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).

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.

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.

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.

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.

An anonymous referee has also recommended this exercise.

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.

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

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.

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.

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.

(Remittance inflows – Remittance outflows)/GDP.

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.


Citation Information: The B.E. Journal of Macroeconomics, ISSN (Online) 1935-1690, ISSN (Print) 2194-6116, DOI: https://doi.org/10.1515/bejm-2013-0030. Export Citation

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