Theoretical framework and model

The model specification is based on the augmented Solow-Swan growth model, which incorporates the human capital, apart from the inputs of labour and physical capital (Decker & Lim 2008; Mankiw et al. 1992; Spolaore & Wacziarg 2013). The empirical literature on growth distinguishes two types of determinants or sources of growth: proximate and deep. Following the neo-classical model, per capita growth of output can be expressed in terms of three proximate determinants: (a) physical capital deepening; (b) human capital accumulation; and (c) productivity growth (Rodrik 2003).

In recent times the empirical literature on growth has moved from the ‘proximate’ determinants to ‘deep’ determinants (Spolaore & Wacziarg 2013). ‘Deep’ determinants of growth focus on various factors which impact on the resource endowment and productivity growth. Rodrik (2003) classifies the deep determinants of growth into three categories relating to (a) geography; (b) trade integration; and (c) institutions. In this study, I include remittances as one of the deep determinants of growth.

The next important question is concerning the choice of the other explanatory variables. The empirical studies on remittance and growth utilize a different set of explanatory variables compared to those used in the cross-country growth literature (Barro 1997; Bosworth & Collins 2003; Durlauf et al. 2004; Sala-i-Martin 1997). I follow the remittance growth literature in selecting the explanatory variables in order to increase the comparability of the results with those in the existing literature.7 _The

reduced form of the full growth equation takes the following form:

𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑖𝑖,𝑡𝑡 = 𝑦𝑦𝑖𝑖,𝑡𝑡− 𝑦𝑦𝑖𝑖,𝑡𝑡−1= 𝛼𝛼𝑦𝑦𝑖𝑖,𝑡𝑡−1+ 𝛾𝛾1𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟_𝑔𝑔𝑔𝑔𝑔𝑔𝑖𝑖,𝑡𝑡 + 𝛾𝛾2𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟_𝑔𝑔𝑔𝑔𝑔𝑔𝑖𝑖,𝑡𝑡2+

𝛾𝛾3𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟_𝑔𝑔𝑔𝑔𝑔𝑔𝑖𝑖,𝑡𝑡−1+ 𝜷𝜷𝑿𝑿′𝒊𝒊,𝒕𝒕+ 𝜑𝜑𝑖𝑖+ 𝜏𝜏𝑡𝑡+ 𝜀𝜀𝑖𝑖,𝑡𝑡 , (2.1)

7_{In addition, in the robustness check, I include the term incorporating the population growth, growth} rate of technical change and depreciation of physical and human capital to conform to the Solow model.

where, 𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔𝑔_{𝑖𝑖𝑡𝑡}=real GDP per capita growth (average over 5-year period), i=1, 2,…, N is the country, and t= 1, 2,..,7 is the 5 year time period average from 1976 to 2010,

𝑿𝑿′𝑖𝑖,𝑡𝑡 is the vector of other explanatory variables containing both the proximate and

deep determinants of growth, 𝜑𝜑_i are country-specific effects, τ_t are period specific effects, and 𝜀𝜀_{𝑖𝑖𝑡𝑡} is the error term. The variables are listed below, with the postulated signs of the regression coefficients for the explanatory variables in parentheses.

𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟_𝑔𝑔𝑔𝑔𝑔𝑔𝑖𝑖,𝑡𝑡 (+ or -) Remittance inflows in percent of GDP at period t

𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟_𝑔𝑔𝑔𝑔𝑔𝑔𝑖𝑖,𝑡𝑡2 (+ or -) Remittance-to-GDP (squared)

𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟𝑟_𝑔𝑔𝑔𝑔𝑔𝑔𝑖𝑖,𝑡𝑡−1 (+ or -) Remittance-to-GDP lagged by one period

𝑦𝑦𝑖𝑖,𝑡𝑡−1 (-) Initial real GDP per capita of the relevant period (in log)

𝑟𝑟𝑖𝑖𝑖𝑖_𝑔𝑔𝑔𝑔𝑔𝑔𝑖𝑖,𝑡𝑡 (+) Investment to GDP (per cent)

𝑙𝑙𝑟𝑟𝑙𝑙𝑟𝑟𝑖𝑖,𝑡𝑡 (+) Life expectancy

𝑟𝑟𝑖𝑖𝑖𝑖𝑟𝑟𝑟𝑟𝑟𝑟𝑖𝑖𝑟𝑟𝑟𝑟𝑖𝑖𝑖𝑖𝑖𝑖(+) Institutional quality (ICRG index)

𝑟𝑟𝑖𝑖𝑙𝑙𝑙𝑙𝑖𝑖,𝑡𝑡 (-) Inflation rate (measured by the consumer price index)

𝑟𝑟2𝑙𝑙𝑟𝑟𝑖𝑖𝑓𝑓𝑙𝑙𝑖𝑖,𝑡𝑡 (+ or -) Broad and quasi money as percent of GDP

𝑖𝑖𝑖𝑖𝑓𝑓𝑖𝑖(-) Dummy for Sub-Saharan African countries

𝑟𝑟𝑓𝑓𝑖𝑖𝑟𝑟𝑓𝑓𝑖𝑖 (+) Dummy for East Asian countries

𝑖𝑖𝑔𝑔𝑟𝑟𝑖𝑖_𝑔𝑔𝑔𝑔𝑔𝑔𝑖𝑖,𝑡𝑡 (+ or -) Openness (the ratio of total trade to GDP)

Equation (2.1) represents the dynamic panel data model where the lagged dependent variable appears as the explanatory variable in order to capture the dynamic effects (Bond et al. 2001, p. 15).

The model includes the quadratic term for remittances in order to examine the possible non-linearity in the relationship between remittances and growth. Remittances can increase the welfare of the recipients by increasing the consumption of essential goods, improving the nutrition and health conditions, and ameliorating the

remittances increases, it can create undesirable macroeconomic effects such as exchange rate appreciation, inflation, and the deterioration of domestic institutions.8

Investment ratio is also included as an explanatory variable—as the majority of the studies argue that it is the only variable found to be robust in most of the empirical growth literature. The investment variable is used as an explanatory variable in several other remittance growth studies (Chami et al. 2005; Giuliano & Ruiz-Arranz 2009; Mankiw et al. 1992; Singh et al. 2010). Thus, controlling for investment means that remittances capture only that subset of growth effects which do not pass through investment (Clemens & McKenzie 2014). I estimate the regressions excluding the domestic investment variable to see if this alter the results for the remittance coefficient.

Most of the studies on the impact of remittances on economic growth have analysed the effect on contemporaneous growth. However, it can be argued that it might takes time for remittances to have any effect on growth. For example, the microeconomic impact of remittances on households’ behaviour (such as higher investment in children’s health and education) will take a long time to affect the output. Thus, remittances can affect growth with a time lag, as it takes time for remittances to be channelled into productive investment or impact the economy through various multiplier effects (Glystos 2005).

In alternative specifications, I also include the interaction terms of remittance and financial deepening and the level of education. Some earlier studies have shown that countries with a higher level of financial development can better utilize remittances, and thus they affect growth (Giuliano & Ruiz-Arranz 2009). In addition, the level of education can also potentially affect the utilization of remittances by households. For example, households with better-educated members are able to make better use of remittances. Thus I test for the possible interaction between remittance and education levels—something not done in earlier studies.

The other explanatory variables are standard in the empirical growth literature. The initial level of per capita GDP captures the conditional ‘convergence effect’. The

8 _{See for example, Russell (1986), Chami et al. (2008) and Rapoport and Docquier (2005) for a} literature review on the macroeconomic impact of remittances.

coefficient of initial per capita GDP is expected to be negative because convergence hypothesis postulates that richer countries tend to grow more slowly compared to poorer countries. Similarly, life expectancy and years of schooling capture the levels of human capital. Several empirical studies have emphasized the role of institutions in economic growth (Acemoglu et al. 2005). I use the International Country Risk Guide (ICRG) institution quality index to capture the effect of institution on growth.

Trade openness has also been widely used in the empirical literature as one the determinants growth. However, trade openness, as measured by the total trade to GDP ratio, has shortcomings compared to the effective rate of protection (Athukorala & Hill 2010; Krugman 1995). In the absence of detailed data on the effective rate of protection, I employ the trade openness ratio (total trade to GDP ratio) as a proxy for trade liberalization. Recently, the quality of institutions and governance have received a lot of attention as crucial determinants of growth. Therefore, institutional quality index is included as an explanatory variables.9 Similarly, inflation is the proxy for the overall macroeconomic situation and the theory points to the detrimental effects of inflation on economic growth. The role of financial development on economic growth is proxied by the broad money to GDP ratio. As remittances affect both inflation and money supply, it is important to control for these macroeconomic variables (Barajas et al. 2009).