supply shocks, these features do not fully fit the supply side perspective. Subsection 1 provides a brief overview of the macro and trade growth decomposition (a more technical presentation of the decomposition method can be found in Appendix II). Using this framework, Subsection 2 underlines the unique role played by demand in the growth dynamics of the region. Subsection 3 identifies three region-wide cycles—Protection (1960-1991), Stabilization (1991-2003), and Commodities (2003-2020)—and their underlying dynamics. To enhance the analysis, we assess LA’s developments using a comparator (CG) group (the composition of LA and CG groups and the criteria for country inclusion can be found in Appendix III).

# **1. A macro and trade-based growth decomposition**

Consider the following accounting identity: where the _G_ s are the logs of the backward-looking ten-year moving averages of growth rates of a country’s GDP ( _Y_ ), exports ( _X_ ), and imports ( _M_ ) of goods and nonfactor services, all relative to the rates of growth of the same variables for the world (and all measured in constant dollars).<sup>12</sup> The first right hand term in the identity is labelled “export pull” ( _EP_ ) because it can be interpreted as the traction that export expansion exerts on a country’s growth. The second term is labelled “domestic response” ( _DR_ ) because it can be interpreted as the country’s capacity to lift GDP growth above import growth (i.e., the country’s efficiency in using its imports to grow). The third term is labelled “external leverage” ( _EL_ ) because it can be interpreted as the impulse or drag on growth linked to changes in the country’s trade deficit or, alternatively, to changes in the availability of external finance. Thus, the above identity can be rewritten as: We add a terms of trade windfall term, _ToT_ , calculated as the difference between _EL_ expressed in constant and _EL_ expressed in current dollars. A country’s “growth spectrum” for any given year or period can then be expressed as the vector { _G_ , _EP_ , _DR_ , _EL, ToT_ }. As shown in Appendix II, the growth spectrum underpins a simple identification grid that helps separate supply from demand shocks according to their impact on the different components of the spectrum. We will now apply the identification grid to the effects on the growth spectrum of three basic types of shocks that will appear recurrently in the analysis of LA’s growth history.

12 We use logs of rates of growth and (generally) ten-year backward-looking moving averages to better approximate elasticities (hence the underlying fundamentals), as in Thirwall’s (1989) growth model. Ten-year moving averages provide a smoother, hence more revealing and less noisy perspective on medium-term growth dynamics than yearly observations. At the same time, the ten-year moving averages provide a richer view on cycles than lengthier averages. However, we will occasionally use longer (twenty-year) moving averages when seeking to illustrate longer term growth trends rather than cycles. Appendix II describes in more detail the growth decomposition database and provides a link where the ten-year moving averages version of the database may be downloaded.

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