export growth. Table 2 presents results of panel regressions at the product level for annual export growth between _t_ and _t+5_ against _density_ in _t_ for _t=_ 1980..2005. The specification uses country‐, year‐, and industry‐specific dummy variables. The panel regressions include results for linear, quadratic and cubic _density_ terms.<sup>12</sup> In all cases, _density_ is highly significant.<sup>13</sup> In order to analyze the expected export growth that results for a change in _density_ , we computed marginal export effects for the density values in the Moroccan sub‐sample. Figure 10 shows average marginal export effects for the case of Morocco. As expected, low values are associated with negative export growth while, at above the median value (which is at the 60th percentile), _density_ is associated with export growth of about 5 percent for products with _RCA_ >1. Using these estimates, merchandise export growth is projected at the product level for Moroccan exports for three future points in time: 2015, 2020, and 2025. In this _naïve_ scenario, we are allowing Morocco’s _density_ in products with RCA>1 to level up in 2015 with the average density for all countries that also have _RCA_ >1 in that particular product. RCA is recalculated based on projected export growth for Morocco. In addition, we introduce small export discoveries for products with zero export values in 2010 if their density reaches above the 75<sup>th</sup> percentile in 2015 or a later year. Under this scenario, export discoveries happen for products above the 75th percentile of Morocco’s _density_ ; the resulting combined value of all export discoveries is moderate, representing only 1.9 percent of total merchandise exports.

Table 2: Density as Predictor for Export Growth

**Dependent Variable: 5‐year Export Growth in time t, 1985‐2010**

|**Independent** **Variable**|**No** **Industry** **D**|**ummies**|**With** **Industry**|**Dummies**|
|---|---|---|---|---|
|Density in t‐5|190.129***|130.280***|257.177***|163.092***|
|(Density in t‐5)^2|‐209.524***||‐316.180***||
|(Density in t‐5)^3||‐191.941***||‐280.333***|
|Dummy RCA t‐5|0.386***|0.386**|1.109***|1.110***|
|i.Morocco|42.244***|43.461***|39.754***|41.916***|
|i.2005|18.816***|19.174***|17.359***|18.017***|
|constant|‐92.503***|‐89.521***|‐97.695***|‐92.603***|
|Obs.|1,361,725|1,361,725|1,361,725|1,361,725|
|Adj R‐squared|0.1838|0.1834|0.1938|0.1930|
|note: *** p<0.01, ** p<0.05, * p<0.1|||||
|Source: Authors' estimates|||||

12 Under the “Food” heading, prepared or preserved categories tend to have larger income and diversification potentials than categories of food that are fresh or frozen, especially within “Fruits and vegetables” and “Fish, crustacean and mollusks, and preparations thereof.” Among apparel and clothing, the income and diversification potential is more promising for the most technically sophisticated articles.

13 Estimated coefficients with linear and quadratic values for density, with positive and negative signs respectively, indicate that higher values of density would increase export growth at a decreasing rate. In this simple model no‐policy variables have been incorporated, implicitly on the basis of the assumption that this unobservable information is captured exclusively by the set of categorical variables (industry‐, country‐ , and time‐specific); this may explain the relatively low R‐squared of about 20 percent. Not shown in Table 2 are regressions with the initial level of exports as explanatory variable because, even though the estimated coefficient was positive and statistically significant, its size was negligible.

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