(DTIS) Update, Volume 1 – Main report - Enhanced Integrated ...

2.2 Severity: the impact of NTMsWhile the current impact of NTMs on household consumption is negligible, their generalizedenforcement – as envisaged by the government – could raise consumer prices across the board by30-40%. To estimate the impact of technical regulations on consumer prices of affected products,regressions were run (see Box 4.1). The results show that SPS measures do not seem to affect consumerprices. This is to be expected given that most households in Burundi consume products that are producedlocally, many by themselves. Thus, the effect of NTMs on the cost of living goes through manufacturedproducts such as textiles and apparel. Given that textile and apparel represents, on average, only 4% ofhousehold expenditure in Burundi (the rest is mostly food and services), the estimated AVE raises thecost of living for the average Burundian household by a negligible 1.5%. However, the estimationillustrates the potential effect on the cost of living of generalized enforcement of measures, given thatsuch seems to be the government’s objective. Raising consumer prices across the board by 30-40%(since most products are covered) would have a non-trivial effect on poverty, and this should be kept inmind.In addition to their impact on consumer prices, NTMs and NTBs also have an effect on the importof intermediary goods and can therefore undermine the performances of the productive sector(e.g. manufacturing, construction, agriculture). Although it was not possible to carry out aquantitative estimate of this issue for the DTIS update, this channel should nevertheless considered whenassessing the relevance of NTMs and the need to remove domestic NTBs.As a tentative estimate, a simulation using a dynamic computable general equilibrium (DCGE) modelshows that reducing Technical Barriers to Trade (TBT) imposed in Burundi would have significanteconomy-wide impacts. Most significantly, a reduction in TBT that would generate a decrease in theprice of the products affected (mainly manufactured products) could have a potentially large impact onconsumption and investment, and hence on GDP growth.Box 4.1: Estimating the price impact of NTMs in BurundiThis AVE estimate is obtained from regression results shown in Table 4.1. They are based on a panel of1’260 country × product pairs combining price data from the World Bank’s International PriceComparison Project (IPCP) and the NTM database, after reconciliation of product nomenclatures (theIPCP nomenclature is much more aggregated than the HS system). The estimator is “within-product”,meaning that the data is de-meaned by product. Regressions (1) and (2) also include cost-of-livingadjustment by country, whereas regression (4) includes a full set of country dummies. All regressionsinclude a Burundi dummy. Regressions (1) and (2) use the simple price gap (i.e. the difference between aproduct’s domestic price and its average world price across the entire sample) either in logs or inpercentage, and explicitly control for tariffs on the RHS, whereas regressions (3) and (4) directly correctfor tariffs in the construction of the dependent variable. Estimates are remarkably stable acrossspecification (the difference between column 1 and the others is due to the construction of the dependentvariable). AVEs calculation is detailed in Appendix 4.79 / 153