**Table 4.** Proximity between sectors in the illustrative SSA country SAM.

||1<br>2<br>3<br>4<br>5<br>6<br>7<br>8<br>9<br>10<br>11<br>12<br>13<br>14<br>15<br>16<br>17<br>18<br>19<br>20<br>21<br>22<br>23<br>24<br>25|
|---|---|
|1 Agriculture|1.00 0.48 0.49 0.23 0.46 0.80 0.46 0.36 0.44 0.39 0.23 0.25 0.20 0.26 0.31 0.43 0.10 0.16 0.48 0.46 0.64 0.57 0.46 0.26 0.23|
|2 Forestry|0.48 1.00 0.48 0.14 0.27 0.52 0.45 0.39 0.50 0.59 0.34 0.30 0.25 0.41 0.43 0.42 0.18 0.25 0.47 0.49 0.48 0.51 0.50 0.16 0.25|
|3 Fishing|0.49 0.48 1.00 0.22 0.39 0.51 0.54 0.49 0.46 0.37 0.29 0.22 0.24 0.27 0.34 0.31 0.17 0.27 0.34 0.43 0.46 0.32 0.40 0.32 0.32|
|4 Petroleum and gas|0.23 0.14 0.22 1.00 0.26 0.16 0.08 0.11 0.15 0.25 0.11 0.41 0.08 0.03 0.14 0.13 0.08 0.04 0.26 0.22 0.27 0.22 0.21 0.15 0.07|
|5 Mining|0.46 0.27 0.39 0.26 1.00 0.38 0.35 0.29 0.31 0.35 0.23 0.31 0.20 0.09 0.22 0.49 0.03 0.26 0.32 0.33 0.37 0.36 0.40 0.37 0.20|
|6 Food|0.80 0.52 0.51 0.16 0.38 1.00 0.52 0.43 0.49 0.44 0.26 0.30 0.25 0.33 0.39 0.36 0.10 0.26 0.49 0.51 0.56 0.57 0.46 0.28 0.25|
|7 Beverages|0.46 0.45 0.54 0.08 0.35 0.52 1.00 0.48 0.45 0.43 0.40 0.25 0.25 0.40 0.50 0.42 0.18 0.30 0.32 0.49 0.38 0.39 0.50 0.33 0.50|
|8 Tobacco products|0.36 0.39 0.49 0.11 0.29 0.43 0.48 1.00 0.51 0.48 0.26 0.21 0.26 0.42 0.45 0.31 0.18 0.45 0.28 0.39 0.37 0.32 0.44 0.24 0.34|
|9 Textiles and leather|0.44 0.50 0.46 0.15 0.31 0.49 0.45 0.51 1.00 0.38 0.21 0.28 0.21 0.38 0.49 0.38 0.08 0.36 0.36 0.39 0.40 0.44 0.38 0.13 0.18|
|10 Wood|0.39 0.59 0.37 0.25 0.35 0.44 0.43 0.48 0.38 1.00 0.43 0.28 0.10 0.40 0.45 0.44 0.25 0.30 0.47 0.45 0.37 0.46 0.44 0.23 0.25|
|11 Paper|0.23 0.34 0.29 0.11 0.23 0.26 0.40 0.26 0.21 0.43 1.00 0.18 0.22 0.48 0.43 0.36 0.41 0.26 0.26 0.33 0.23 0.27 0.33 0.37 0.55|
|12 Refined petroleum products|0.25 0.30 0.22 0.41 0.31 0.30 0.25 0.21 0.28 0.28 0.18 1.00 0.26 0.15 0.30 0.31 0.12 0.21 0.36 0.39 0.19 0.37 0.38 0.24 0.18|
|13 Chemical products|0.20 0.25 0.24 0.08 0.20 0.25 0.25 0.26 0.21 0.10 0.22 0.26 1.00 0.33 0.30 0.16 0.26 0.30 0.11 0.27 0.15 0.19 0.25 0.38 0.45|
|14 Rubber and plastic|0.26 0.41 0.27 0.03 0.09 0.33 0.40 0.42 0.38 0.40 0.48 0.15 0.33 1.00 0.62 0.33 0.48 0.45 0.26 0.39 0.21 0.34 0.40 0.30 0.52|
|15 Non‐metalic mineral product|s 0.31 0.43 0.34 0.14 0.22 0.39 0.50 0.45 0.49 0.45 0.43 0.30 0.30 0.62 1.00 0.51 0.30 0.35 0.40 0.49 0.27 0.42 0.44 0.24 0.38|
|16 Metal products|0.43 0.42 0.31 0.13 0.49 0.36 0.42 0.31 0.38 0.44 0.36 0.31 0.16 0.33 0.51 1.00 0.24 0.31 0.53 0.53 0.35 0.49 0.50 0.20 0.29|
|17 Machinery and vehicles|0.10 0.18 0.17 0.08 0.03 0.10 0.18 0.18 0.08 0.25 0.41 0.12 0.26 0.48 0.30 0.24 1.00 0.33 0.21 0.22 0.17 0.22 0.23 0.27 0.41|
|18 Other manufactures|0.16 0.25 0.27 0.04 0.26 0.26 0.30 0.45 0.36 0.30 0.26 0.21 0.30 0.45 0.35 0.31 0.33 1.00 0.26 0.31 0.17 0.29 0.27 0.22 0.31|
|19 Electricity and gas|0.48 0.47 0.34 0.26 0.32 0.49 0.32 0.28 0.36 0.47 0.26 0.36 0.11 0.26 0.40 0.53 0.21 0.26 1.00 0.47 0.45 0.56 0.42 0.19 0.15|
|20 Construction|0.46 0.49 0.43 0.22 0.33 0.51 0.49 0.39 0.39 0.45 0.33 0.39 0.27 0.39 0.49 0.53 0.22 0.31 0.47 1.00 0.29 0.49 0.51 0.29 0.39|
|21 Trade, hotels and resturants|0.64 0.48 0.46 0.27 0.37 0.56 0.38 0.37 0.40 0.37 0.23 0.19 0.15 0.21 0.27 0.35 0.17 0.17 0.45 0.29 1.00 0.49 0.38 0.19 0.19|
|22 Transport|0.57 0.51 0.32 0.22 0.36 0.57 0.39 0.32 0.44 0.46 0.27 0.37 0.19 0.34 0.42 0.49 0.22 0.29 0.56 0.49 0.49 1.00 0.47 0.20 0.19|
|23 Communications|0.46 0.50 0.40 0.21 0.40 0.46 0.50 0.44 0.38 0.44 0.33 0.38 0.25 0.40 0.44 0.50 0.23 0.27 0.42 0.51 0.38 0.47 1.00 0.35 0.40|
|24 Government|0.26 0.16 0.32 0.15 0.37 0.28 0.33 0.24 0.13 0.23 0.37 0.24 0.38 0.30 0.24 0.20 0.27 0.22 0.19 0.29 0.19 0.20 0.35 1.00 0.48|
|25 Other services|0.23 0.25 0.32 0.07 0.20 0.25 0.50 0.34 0.18 0.25 0.55 0.18 0.45 0.52 0.38 0.29 0.41 0.31 0.15 0.39 0.19 0.19 0.40 0.48 1.00|

# _Source_ : Authors’ calculations based on Comtrade and GTAP 8 data.

In addition to the SAM and proximity data, our CGE model also requires (a) base-year estimates for sectoral employment levels, and (b) a set of elasticities (for production, consumption and trade). In order to estimate sectoral employment, we combined population data with estimates for sectoral employment shares in broad sectoral categories from World Bank (2016). In turn, elasticities were given a value based on the available evidence for comparable countries. For elasticities, the following values were used: (a) the elasticity of substitution among factors is in the 0.2–1.15 range, relatively low for primary sectors and relatively high for manufactures and services (see Narayanan et al. 2012); (b) the expenditure elasticities for household consumption were obtained from Seale et al. (2003); and (c) trade elasticities are 4 for both Armington and CET elasticities.<sup>19</sup> Given the uncertainty with respect to our elasticity values, we conducted a systematic sensitivity analysis of our simulation results with respect to their values; it indicated that the results presented here are robust.<sup>20</sup> 19 These CET and Armington elasticities may seem high. However, the size of responses to trade-related shocks depend not only on these trade elasticities but also on other aspects of the model, including the size of production responses which, in their turn, depend on (a) the mobility of labor and other factors; and (b) elasticities of factor substitution. Thus, a given trade-related shock may generate similar trade and production responses with a combination of high factor mobility and low trade elasticities or, alternatively, low factor mobility and high trade elasticities.

20 The results from the systematic sensitivity are downloadable as supplementary material from www.jgea.org/resources/jgea/ojs/index.php/jgea/article/view/39.

17