Multi-market analysis of the impact of trade restrictions on importing live animals into South Africa

Abstract

In SA different tariffs exist on the importation of meat. While a zero tariff applies on the importation of live animals imported for breeding or slaughtering, a ban exists on the importation of live animals for slaughtering purposes. This is based on the DA’s opinion that slaughtering animals close to their place of origin and transporting the meat using modern refrigeration technology are better practices. Although the DA received only one official permit application, various firms expressed interest to import live sheep from Australia for slaughtering purposes. The motivation for and the purpose of the present study are to address the economic implications that such imports will have on the meat industry. This study’s main contribution was to estimate slaughtering functions for SA meat adopting a pragmatic approach using data for the period 1971 to 2002 on slaughterings, own and substitute meat prices, production costs, prices of complementary products, prices of other production alternatives, exposure to world markets, quality of grazing and heard numbers. Both singe equation and systems estimation procedures were employed to estimate empirical model parameters. The empirical analysis resulted in a meat slaughtering system. In the case of the slaughtering for mutton equation all signs of the estimated coefficients were consistent with expectations. In the slaughtering for beef and chicken meat equations only some signs of the estimated coefficients were consistent with expectations. The positive relationship between slaughtering for beef and quality of grazing was inconsistent with expectations. This may be attributed to quality of the data available to support specification of a more appropriate indicator of grazing quality. The positive relationship between chicken meat slaughterings and mutton prices were inconsistent with expectations, indicating that these two are not necessarily substitutes but rather complements. In terms of its size the intercept was the most powerful variable in all equations. Aside from the intercept the real own price the retailer realised over the past five years proved extremely powerful compared to the rest of the variables in the case of the slaughtering for mutton equation. The number of stock kept two years ago also deserves mentioning at about half of the above-mentioned variable’s magnitude. In the case of slaughtering for beef and chicken meat equations the power of variables are distributed more evenly. The price of mutton had the most power in both the slaughtering for beef and chicken meat equations. In terms of statistical significance the power of variables was evenly distributed in the slaughtering for mutton equation with the average degree of exposure to international trade during the last five years as the most powerful variable. In the case of the slaughtering for beef equation the current real price for mutton producers received for their products and the average seven year effect of the quality of grazing proved more powerful compared to the rest of the variables. In the case of slaughtering for chicken meat the intercept and time trend were extremely powerful compared to the rest of the variables. Despite its reported system wide R-square of 82 percent Adam’s (1998) meat demand system did not give good in sample forecasts. Instead it was decided to account for demand factors indirectly through an auction price system. The empirical analysis resulted in an auction price system where the auction price of mutton depends on the retail price (0.324) and total supply (-0.343); and the auction price of beef depends on disposable income (-0.719), the retail price (0.645), total supply (-0.330) and the effect of time (0.062). As the auction price system only included mutton and beef, the meat sub-sector model was reduced accordingly. In sample forecasting based on ex post within the sample data applying the dynamic-deterministic simulation of the Gauss-Seidel solution, proved satisfactory and the model therefore adequate to run policy simulation experiments. Two scenarios were tested, namely: (1) increasing mutton imports by 5.9 % every year from 1995 up to 2002; and (2) increasing mutton imports by 100 % every year from 1995 up to 2002. The results illustrated that the short-term impact of increased imports will lead to an increased supply of mutton on the domestic market at decreased consumer prices. Producer prices are expected to follow consumer prices and will accordingly also decrease. Decreased producer prices will result in decreased domestic slaughterings and, finally, increased imports will also decrease the price realised for substitute products. As the meat sub-sector, however, has time to adjust to increased levels of imports, some of the results seem to be surprising. Never the less, even the long-term effects remain negative, in general. As a long-term solution to improve the results of the policy question at hand it is recommended that both the private and public sector embark on an effort to improve SA’s database. In the case of the meat sector a relatively small sample of 30 data points exist, with structural breaks in almost all time series data. For short-term result improvements it is recommended that a number of assumptions made in this study be revisited: (1) alternative or improved econometric estimation techniques in order to include the pork and chicken meat industries, (2) substitution of the auction price system with a demand / consumption system, (3) extension of the product side of the model to al least incorporate land as a production factor and (4) revisiting the validity of applying classical OLS estimation techniques.