Results and discussions

Table 1 presents the descriptive statistics of all the variables used in the regressions. For the period considered, average annual amount of PSNP food aid (0.016 MT/person) is greater than that of relief food aid (0.008 MT/person), the combined average total food aid allocation being approximately 0.024 MT/person in the PSNP woreda’s. Per capita annual teff

production in the PSNP districts average 0.033 MT, while similar figures for wheat and maize are 0.040 MT and 0.034 MT respectively. Figure 1 further illustrates per capita annual food aid distribution accounts for a large share of production in the given areas, especially in the years 2005 and 2006. The proportion of food aid to production relatively declines in the later years; one possibility could be that food aid was being replaced by cash transfers in many of the PSNP woreda’s.

Data in Table 1 further indicate that average annual per capita area cultivated in the PSNP districts are 0.031 ha. for teff, 0.024 ha. for wheat and 0.018 ha. for maize. It is also shown that application of per capita chemical fertilizers and improved seeds is low for the

production of all crops. Average monthly rainfall is about 75 mm, with a monthly maximum of 162 mm.

To empirically assess the impacts of food aid on production, regressions representing three grain types for selected woreda’s in the country are estimated separately using system GMM estimation method. Table 2a, 2b and 3 report point estimates and standard errors, as well as specification tests for each crop-specific three sets of five regressions. In Table 2a, Model 1 consists of regressions that include only the policy variables of interest and lagged own production. Model 2 adds to these regressors a set of control variables for rainfall and inputs used (quantity of chemical fertilizer and improved seeds used) and year dummies (year dummies are not reported in result tables). Model 3 uses the same control variables as Model 2, but food aid is instrumented with area cultivated, with Model 3' (Table 2b) presenting regression results from instrumental regression of food aid using area cultivation. In Table 3, Models 4 and 5 are regression results of the food aid equations.

As reported at the bottom of the regression result tables, the validity of the instruments is verified using the Hansen test of overidenfication restrictions in which, null hypothesis that the population moment conditions are correct should not be rejected at 5% significance level.

Furthermore, for all of the regressions, the second test for zero autocorrelation in the first differenced errors of order 1 (AR(1)) and 2 (AR(2)) show that at order 2, the errors are serially uncorrelated because the p-values are greater than 5% significance levels. As desired, there is no serial correlation in the original error.

5.1. Does food aid discourage production?

Our main question is whether food aid stimulates or depresses food production. The primary policy variables of interest to us, therefore, are PSNP and relief food aid allocations. In Model 1 we estimate food production on its own lag and only on these policy variables to establish evidence regarding their importance. The estimated coefficients for all the crops

indicate strong negative effects of previous period food aid from emergency relief, both in statistical significance and magnitude. And, a positive effect of lagged food aid from PSNP on teff production. Model 2 and 3 further examine how robust these finding are when we include important conditioning variables. In these two models, we further find some evidence of positive correlation between teff and wheat production and food aid from PSNP.

In Model 2, there is no evidence for the negative effect of previous year PSNP and relief food aid on production of teff and wheat. Rather, we observe positive coefficients of teff and wheat that are statistically significant at the 1% level. The magnitude of these estimates suggest that a one MT per capita increase in food aid allocation yields an expected net increase of about 1.14 and 3.95 MT per capita in the subsequent year production of teff and wheat, respectively. However, we still observe a strong negative correlation between maize production and relief food aid allocation. As one would expect, the coefficients for the rainfall variable indicate that positive rainfall deviation result in higher contemporaneous food production of teff and wheat, while negative deviations tend to lower production. However, we observe no effect on maize production from rainfall and also a counterintuitive effect for application of improved seeds. Furthermore, the point estimates in Model 2 provide no strong evidence of a statistical link between production and application of improved seeds for teff and wheat. However, application of fertilizer is positively correlated with production of these two crops.

In Model 3, we use predicted food aid values from PSNP and relief using regression results reported in Table 2a to study the behavior of farmers’ labor allocation on food aid.

Thus, in Model 3', we first estimate area cultivated on its lagged value, lagged food aid allocation and current and past amount of rainfall. With respect to correlations between previous year food aid allocations and area cultivated, out of six estimated coefficients, three are statistically significant with positive signs. Lagged PSNP food aid is positively correlated

with teff and wheat area cultivated. Also lagged relief aid is positively correlated with maize area cultivated, but no statistically significant correlated with teff and wheat area cultivated.

Further, the coefficients for rainfall show positive significant correlation with teff and wheat area cultivated, but no statistically significant link is observed for maize.

In Model 3, out of 18 estimated coefficients, only five are statistically significant.

However, the same as in Mode 2, we find the estimated values of the instrumented food aid are positively and statistically significantly correlated with production of teff and wheat.

Comparing results across crops, one would expect the strongest effect to be on wheat which is the food aid crop. However, we find that effect of food aid to be nearly the same across all the crops we consider, except some mixed results observed associated with maize.

We surmise that, after controlling for factors affecting production, such as rainfall and input uses, most of the negative production effects of prior year food aid allocations

disappear. In contrast, lagged PSNP food aid allocations may potentially have some positive effects on the subsequent production of some of the crops considered. Thus, any disincentive effects due to depressed product prices and/or reallocation of labor away from agricultural production induced by food aid allocations must be more than offset the positive effects of food aid. For instance, Abdulai et al. (2005), who also found positive effect of food aid on production, argue food aid can relax financial liquidity constraints of farmers, in particular by increasing their access to inputs. The latter compensates the price depressing and possible labor reallocation effects of food aid.

5.2. Is food aid a response to production failure?

If food aid deliveries respond to production shocks caused by poor rainfall or other factors, food aid will stabilize prices and the local food supply. However, poor timing and targeting of food aid deliveries are common, among others due to various administrative hurdles in food

aid management. Model 4 and 5 help us to examine this issue. We attempt to identify sensitivities in the potential food aid deliveries to rainfall and production level. Point estimates in Model 4 and 5 show strong evidence that PSNP and emergency relief food aid deliveries are particularly sensitive to current and past values of rainfall. From the entire set of six point estimates for rainfall variables, five are negative and statistically significant in both models. Specifically, results in Model 4 indicate that program assistance is highly driven by low amount of rainfall and results in Model 5 indicate relief food aid deliveries are responses of rainfall shortage, at least in the regions studied here and over the time period considered. However, neither contemporaneous nor lagged crop production are statistically significant in both models, except where we find strong but mixed effects of wheat production on PSNP and relief food aid and positive effect of teff current level production on relief food aid.