While Mbeya is also a food surplus market in the Southern highlands with harvest cycles that occur in similar months, in contrast to Songea, Mbeya is less remote. As a result, compared to Songea, the influence of harvest cycles on local food prices in Mbeya is less pronounced (with a peak of a little more than 4 percent and with less than a 30 percent gap between the lean season peak and harvest bottom).

Evidence in support of seasonal influences on food prices, especially in the context of Sub-Saharan Africa, where it appears to be more pronounced than in other parts of the world, has been reported elsewhere. Sahn et al. (1989) highlighted the influence of seasonality in the context of developing-country agriculture. More recently, Kaminski et al. (2014) have shown that the seasonal component represents a significant share of total food price variability. Indeed, in Tanzania’s case, the share is up to 20 percent (see also FEWSNET (2008),FEWSNET (2014), and Tschirley and Jayne (2010)).

# **Weather Anomalies**

Vegetation anomalies provide estimates for domestic supply shocks which are, in turn, inversely related to local price changes.<sup>23</sup> The NDVI anomaly parameter estimate differs significantly from zero (at a 10 percent confidence level) in 14 markets, including several food-deficit markets. Exceptions are Mwanza, Musoma, and Shinyanga, which are located in the Lake zone and better connected, and Iringa, in the Southern Highlands.<sup>24</sup> The other relatively isolated maize-surplus markets in the Southern Highlands — Songea, Sumbawanga, Morogoro, and Mbeya — exhibit the strongest price response to weather shocks. In Songea a 10 percent increase in the NDVI index, as experienced in December 2012, is associated with a 10.6 percent decline in prices. In contrast, Mbeya’s price decline is only 5.1 percent; although Mbeya is a food-surplus area it has a better connected and developed market than does Songea and hence it benefits from greater absorption of surplus production by other markets. Consistent with this, seasonal price changes are smaller in Mbeya than in Songea (Figure 10).

Similarly, prices in Dar es Salaam are less responsive to local weather anomalies. Dar es Salaam is much better connected than Mbeya (in addition to being food-deficit) and consequently its vulnerability to weather shocks are muted. A 10 percent increase in the

> 23The weather disturbances in this study were estimated using satellite-derived Normalized Difference Vegetation Index (NDVI) imagery over cultivated areas as a proxy (c.f. Tucker (1979)). Brown (2014) reviews the literature that has examined the relationship between weather anomalies that are detected using satellite imagery and food prices. Johnson (2014) provides evidence in support of the informativeness of NDVI anomaly signals in the context of predicting the impacts of shocks to (county and state-level) grain yields in the United States.

> 24The reasons behind the non-significant result for the Iringa market are unclear; it is possible that transport, storage and food processing facilities may be relatively better in Iringa (since it connects the Southern Highlands to Dodoma and Arusha to the North and Dar es Salaam to the East), or this may be caused by measurement noise.

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