4.3.1. Simulation descriptions
This article focuses on the impact of the world price increases taking place between the second half of 2007 into 2008. However, the CGE model is calibrated to a 2003 base social accounting matrix (SAM), raising the issue of what magnitude price shock should be imposed on the model. For instance, oil prices rose more than threefold during 2003–2008 (from U.S. $32 to more than U.S. $100 per barrel), but this increase did not occur all at once. Between 2003 and 2006, the world price for oil doubled to U.S. $64 a barrel. This is responsible for the higher fuel import shares in Table 5, which is for 2006, compared to Table 6, which is for 2003 (i.e., from 10% to 12%). The intention of the modeling effort is to gain insights into the impacts of the recent price increases using available tools and data. For the purposes of the CGE model, tripling oil prices seems unrealistic. It was decided that the model should be shocked with only the 2007–2008 price increases. The thought experiment that is being undertaken is what would have happened in 2003 had fuel and food prices increased in similar proportions to the recent world price increases.
The actual shocks applied are depicted in Table 7. The shocks applied tend to be somewhat smaller in magnitude than the price increases depicted in Table 1. Inflation explains a part of the difference. The shocks applied should reflect real price increases while the shocks in Table 1 reflect nominal price increases in USD. Also, while the authors believe that the current higher price environment is likely to endure in the medium term (3–5 years), they also believed it was likely that commodity prices would come off of the peaks registered in the middle of 2008 (such as oil at U.S. $145 per barrel), which has indeed occurred. Overall, the objective of the shocks is to reasonably capture the shift in international relative prices that occurred in late 2007 and into 2008.
World price shocks
|Agricultural commodities|| ||Petroleum and|| |
| || || petrochemicals|| |
| Maize||75|| Gasoline||75|
| Sorghum||50|| Diesel||75|
| Rice||75|| Other fuels||75|
| Wheat||75|| Other petrochemicals||25|
| Pulses and groundnuts||50|| || |
| Horticulture||25|| || |
| Raw tobacco||25|| || |
| Cotton||25|| || |
| Livestock||25|| || |
|Processed agricultural commodities|| || || |
| Meat and fish products||40|| || |
| Other processed foods||40|| || |
| Grain flours||50|| || |
| Processed sugar||40|| || |
| Processed tobacco||25|| || |
| Processed cotton||25|| || |
Four simulations are run to analyze the impact of the price shocks. The first simulation (“Fuel”) uniquely shocks fuel prices. The second simulation (“Food—Fixed land”) considers the shocks to agriculture and processed food prices under the assumption that land allocations between crops cannot be altered (i.e., a very short-run scenario with similar assumptions to the household survey analysis in Section 3). The third simulation (“Food—Flexible land”) considers the shocks to agriculture and processed food prices assuming that farmers can reallocate land across crops (i.e., a stronger supply response). This implies a one- to three-year adjustment period. The fourth simulation (“Combination”) combines the first and third simulations.
4.3.2. Model results
The impacts of the fuel and food price shocks are depicted in Tables 8–11. Macroeconomic impacts are shown in Table 8. As suggested by the structure of imports presented earlier in this section, the fuel shocks generate more severe impacts on the overall terms-of-trade. The decline in the terms-of-trade due to fuel price increases is more than double the decline due to food price increases. Macroeconomic impacts are commensurately larger. Compared with the food price shocks, the fuel shocks force a larger increase in the quantity of exports and a larger decrease in the quantity of imports in order to balance the external account. Due principally to these changes in trade flows, the decline in total absorption (or overall welfare) under the “Fuel” simulation (3.5%) is approximately double the decline registered for either of the “Food” simulations.
Macroeconomic results for world price shocks
|Quantities||GDP|| −0.6||−0.5||−0.5|| −1.2|
|Absorption (C+I+G)|| −3.5||−1.8||−1.8|| −5.1|
|Consumption (C)|| −5.8||−1.9||−1.8|| −7.3|
|Investment (I)|| 1.5||−2.5||−2.8|| −1.2|
|Recurrent government (G)|| 0.0|| 0.0|| 0.0|| 0.0|
|Exports (E)|| 5.6|| 0.6|| 1.0|| 5.9|
|Imports (M)|| −6.4||−4.0||−3.7|| −9.6|
|Prices||Nominal exchange rate|| 4.5||−5.0||−5.6|| −1.5|
|Real exchange rate|| 15.4|| 1.3|| 0.6|| 15.2|
Sectoral production results for world price shocks
|Agriculture||25.9|| 0.2|| 0.6|| 0.5|| 0.7|
| Cereal crops|| 5.3||−0.8|| 3.1|| 3.2|| 2.9|
| Roots crops|| 7.2||−0.9|| 0.3||−0.9||−1.8|
| Pulses and groundnuts|| 2.3|| 1.1|| 1.4|| 3.0|| 4.2|
| Horticulture|| 3.3||−1.2|| 0.6||−0.7||−1.7|
| Export crops|| 1.1|| 9.4|| 5.3|| 11.9|| 21.2|
| Livestock|| 1.7||−0.4|| 3.9|| 4.1|| 4.2|
| Forestry|| 2.7||−0.3||−1.1||−1.2||−1.9|
| Fishery|| 2.3|| 3.8||−7.2||−7.9||−5.9|
|Industry||23.1|| 1.0|| 0.2|| 0.3|| 1.4|
|Manufacturing||13.7|| 1.0|| 1.7|| 2.1|| 3.2|
| Primary product processing|| 7.4|| 1.3|| 3.3|| 4.1|| 5.7|
|Other industry|| 9.1|| 1.1||−2.0||−2.3||−1.2|
| Electricity|| 1.9|| 1.7||−1.3||−1.5|| 0.2|
| Water|| 0.3||−2.2||−0.1|| 0.0||−2.1|
| Construction|| 7.0|| 1.0||−2.3||−2.6||−1.5|
Factor price results for world price shocks
|Rural labor||Skilled||−5.2|| 2.9|| 3.3||−1.6|
|Semiskilled||−5.8|| 0.7|| 0.9||−4.6|
|Unskilled||−5.3|| 3.7|| 4.2||−0.7|
|Capital Agricultural land|| ||−5.5||−1.5||−1.5||−6.4|
|−4.2|| 11.4|| 12.4|| 9.5|
Welfare and poverty results for world price shocks
|Equivalent variation|| || || || || || |
| National|| || ||−5.9||−2.1||−2.0|| −7.4|
| Rural households||Quintile 1|| ||−3.4||−0.7||−0.9|| −3.9|
|Quintile 2|| ||−3.6||−0.1||−0.1|| −3.2|
|Quintile 3|| ||−3.7|| 0.3|| 0.4|| −2.7|
|Quintile 4|| ||−4.2||−0.1|| 0.2|| −3.4|
|Quintile 5|| ||−5.1||−0.3|| 0.1|| −4.4|
| Urban households||Quintile 1|| ||−5.4||−5.3||−5.8||−11.1|
|Quintile 2|| ||−6.2||−5.6||−5.8||−11.6|
|Quintile 3|| ||−6.0||−5.0||−5.3||−10.9|
|Quintile 4|| ||−7.1||−4.5||−4.5||−11.1|
|Quintile 5|| ||−7.1||−2.8||−2.7|| −9.4|
|Poverty headcount|| || || || || || |
| National|| ||54.1|| 57.6|| 55.1|| 54.9|| 58.2|
| Rural households|| ||55.3|| 58.3|| 55.4|| 55.2|| 57.7|
| Urban households|| ||51.5|| 56.2|| 54.3|| 54.2|| 59.5|
As emphasized above, the components of absorption are influenced by economic structure and macroeconomic closure rules. The heavy dependence of Mozambique on foreign savings implies that real investment depends in part on the nominal exchange rate. Depreciation (appreciation) of the nominal exchange rate increases (decreases) the local currency value of investment and can lead to a real increase (decrease) in investment under a savings-driven closure. While the “Food” and “Fuel” simulations lead to a real depreciation of the currency, the nominal currency value moves in opposite directions between the two sets of simulations. In the two “Food” simulations, the increases in world prices for agricultural and processed commodities automatically shift relative prices toward tradeable commodities. The relative price shift toward tradeables generated by the world price increases is in fact so strong that the nominal currency actually appreciates in order to reestablish external balance. By contrast, in the “Fuel” scenario, the world price increases do little to shift the price ratio between tradeable and nontradeable sectors because both sectors use fuel as an intermediate input (and there is very little domestic production of fuel and petrochemicals). As a result, a strong nominal depreciation is required to balance the external account.
Principally, as a result of opposing movements in the nominal exchange rate, real investment rises under the “Fuel” simulation (because foreign assistance lays greater claim to domestic resources due to the depreciated currency) and decreases in the two “Food” simulations (for the same reasons but in an opposite direction). Since real government consumption is fixed in real terms across all scenarios, the decline in absorption in the “Fuel” scenario is borne entirely by household consumption. And, household consumption must decline further to accommodate the rise in the real value of investment. Overall, real household consumption in the “Fuel” scenario falls by more than three times the declines registered in the two “Food” simulations due to a larger decline in absorption overall and differential movements in the components of absorption, particularly investment.
The differences between the “Fixed” and “Flexible” food simulations manifest themselves primarily through the production response. With flexible land, agricultural production can be reallocated toward export crops, particularly those whose world prices are rising, permitting a greater increase in exports than in the fixed land simulation. Furthermore, the export stimulus and import compression are achieved with a smaller decline in the real exchange rate.
The combined effects of the “Fuel” and “Food” scenarios, which are the actual shocks that Mozambique received, are considerable. The scenario “Combined” shows effects that are roughly the sum of the two preceding scenarios. Terms-of-trade decline by more than 16%, and in order to balance the external account, exports increase by nearly 6%, and imports decline by almost 10%. These shifts in production generate a decline in GDP of slightly more than 1%. All of these adjustments imply a reduction in the quantity of goods and services in the economy resulting in a reduction in absorption of more than 5%. Since recurrent government expenditure is assumed to be fixed and investment declines by only 1.2%, household consumption bears the bulk of the adjustment, declining by more than 7.0%. This is a substantial decline in a country where approximately half of all households are absolutely poor (i.e., they experience difficulty meeting caloric needs).
The implications of the world price shock for production are presented in Table 9. The table shows, in the first column, the share in value added of each sector depicted at base 2003 values. For ease of interpretation, most depicted sectors are aggregates of the sectors available in the 2003 SAM and employed in the CGE model. The columns under each simulation provide the percentage change in the real output of each sector relative to the base. Across all simulations, exporting and import competing sectors are favored. The food price shocks particularly favor export products that experience price increases. In the combined scenario, particularly strong growth is registered in “Export crops,” led by tobacco and cotton, and “Processed products,” led by processed cotton and processed sugar. Production of nontradeables, such as root crops (which is dominated by cassava, the largest single crop in value-added terms) and services (which represents about half of the economy) decline in all scenarios. These declining sectors free resources that permit the tradeables sectors, particularly the export sectors, to expand.
These results highlight the importance of export supply response with particular emphasis on the agricultural sector. Agriculture and derived products comprise the bulk of the export response with particular emphasis on cashew, tobacco, cotton, sugar, and other processed foods. Exports from these sectors are projected to approximately double, although the increases take place from relatively small bases. Biofuels represent another export potential that is not modeled here but is considered in detail in Arndt and Tarp (2008).
A robust export response is crucial to avoid severe import compression. Even with the export response attained, the onus of adjustment is already taking place largely on the import side. This can be seen from the macroeconomic impacts in Table 8. Imports values are about double those of exports and the percentage decline in imports is greater in absolute value than the relative expansion of exports. While export responses tend to concentrate in specific sectors, imports decline across the board. Particularly large declines in imports are registered in products where domestic sectors compete strongly with imports, such as maize, grain milling, and meats.
Implications for factor prices are shown in Table 10. As discussed above, both shock vectors stimulate tradeable agriculture and processed foods. These sectors use unskilled (primarily rural) labor intensively though the stimulus to these sectors is much more pronounced in the “Food” simulations. In nearly all cases, urban wages decline more than rural wages. The exception is urban skilled labor in the “Fuel” scenario, which benefits from a fairly broad-based expansion of traded nonagriculture. Relative to other factors, the food shocks favor unskilled rural labor and land. Under the “Fixed land” scenario, the returns to rural labor and land are lower than under the “Flexible land” scenario. The relatively large differential impacts across factors in the food simulations carry over into the “Combined” simulation, where rural labor, especially unskilled labor, fares better than urban labor and capital. There is also a pronounced positive impact on land returns.
Welfare implications, measured as percentage change in equivalent variation, are presented in Table 11. As discussed in Section 3, substantial home consumption among rural households provides considerable insulation from both fuel and food prices shocks. In addition, as shown in Table 10, rural wages rise relative to urban wages, particularly in response to the food price shocks. As a result of these consumption and income impacts, rural households are less strongly affected than urban households in all simulations. The stimulating effect of improved agricultural terms-of-trade for the rural economy does not outweigh the negative impacts of the fuel shock, and welfare declines for all households in the “Combined” simulation. The degree of land ownership is the primary factor differentiating outcomes across quintiles in rural areas. The results for the “Fixed land” scenario are consistent with the household-level analysis in Section 3, which showed middle-quintile rural households faring better than others under the food price shock. In urban areas, welfare losses are large in magnitude and relatively constant across the income distribution.
Poverty impacts are large, particularly in urban areas. Table 11 shows that the combined shocks result in a four percentage point increase in the national poverty headcount rate. The effect is much stronger in urban areas where the poverty rate increases by eight percentage points. In fact, the “Combined” simulation sets the urban poverty rate above the rural rate. Fuel price increases are the principal driver of increased poverty in both rural and urban zones. As would be expected, the capacity to reallocate land reduces poverty with the effect being slightly stronger in rural zones.