Tariffs Database

Besides the study by Artuc et al. (2019), little is known about how trade barriers affect local labor markets in Sri Lanka. This report consequently tries to fill a gap by assessing the impact of Sri Lanka’s potential trade policy changes not only on household income (through wages and sector of employment) but also on consumption through sectoral price changes (see chapter 3). This is done with a computable general equilibrium (CGE) model linked to a microsimulation in a top-down approach, which is expanded to cover subnational regions. We also discuss economic implications of paratariff liberalization using both the CGE model and the Household Impacts of Tariffs (HIT) database and simulation tool (see box 2.2 for more details).

BOX 2.2 Understanding Winners and Losers with the Household Impacts of Tariffs Database

Trade reforms affect households in their role as microcommunities of consumers, producers, wage earners, and taxpayers. This means that the effects on a particular household depend on its income and consumption portfolios, which not surprisingly can vary greatly. Until recently, there has been a lack of readily available data to measure these impacts, information that is vital for identifying winners and losers and, in turn, informing policy reforms. But the Household Impacts of Tariffs (HIT) database can now shed light on this issue.

The HIT database is a publicly available household survey–based data set covering 54 developing countries. It was constructed by harmonizing representative household surveys with import tariff data from the United Nations Conference on Trade and Development. The sample comprises all low-income countries for which relevant nationally representative household survey data (that is, data with information on both household incomes and consumption spending) are available and a number of middle-income countries. It contains granular data for each percentile of the income distribution on the income derived from and consumption of 53 agricultural products. It also keeps track of spending on five different types of manufacturing goods and services, as well as transfers and wage income disaggregated by single-digit sector, 10 different types of nonfarm household enterprise sales, and various types of transfers.

Tariffs vary both across countries and across products. The average tariff across countries is 14.2 percent. Tariffs are highest on average in Bhutan (48.4 percent) and lowest in Iraq (5.0 percent), whereas countries with higher levels of gross domestic product per capita tend to have lower tariffs. As for products, the highest average tariff is 39.4 percent, but this masks considerable differences across countries: Sri Lanka levies a 125 percent tariff on cigarettes, and in Jordan the tariff on beer is 200 percent.

What would the HIT tell us about how agricultural trade reforms would affect welfare in developing countries? The HIT analysis first estimates the impact of a change in tariffs on prices and then assesses how much the resulting price changes affect consumption costs and incomes in different households. The sum of these impacts is how much a household’s real income changes. These simulations measure only the first-order (short-term) impacts of tariff liberalization and do not capture second-order adjustments such as changes in the availability of products, changes in

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BOX 2.2 Understanding Winners and Losers with the Household Impacts of Tariffs Database (continued)

consumption patterns, and productivity gains arising through increased availability of intermediates, which may well dominate in the medium to long term. In addition, the analysis assumes perfect pass-through of changes in tariffs to prices, though different pass-through rates can in principle be accommodated by making adjustments to the selected tariff changes.

A paper using this database by Artuç, Porto, and Rijkers (2019) shows that a unilateral elimination of agricultural tariffs would increase household incomes by an average of 2.5 percent. The costs of protectionism, though, vary greatly across and within countries: the average standard deviation of the gains from trade within a country would be 1.01 percent. Furthermore, agricultural tariff liberalization would be pro-rich in 29 countries in the sense that the top 20 percent richest households would gain proportionately more than the bottom 20 percent. The poor would nonetheless benefit more than the rich in 25 countries. The authors also find that using disaggregated data is important, because using more aggregate data yields biased estimates of the average gains from trade.

Although their study has focused on tariff reduction, the HIT also has a much wider set of potential applications and can accommodate richer and more sophisticated modeling assumptions. Examples include assessing how European Union and US agricultural tariffs or regional trade agreements (such as the African Growth and Opportunity Act) affect households in low-income countries, how food price shocks affect poverty and inequality, and how tariffs affect men and women. In the next chapter, we use the HIT database to simulate the implications for welfare of paratariff liberalization for Sri Lanka and contrast the results with the Computable General Equilibrium–Global Income Distribution Dynamics methodology. These results are also contrasted with findings from a reduced-form analysis using detailed micro data for Sri Lanka to study impacts on local labor markets (Artuç et al. 2019).

Unlike India, not many studies of Bangladesh have investigated how trade affects local labor markets. Bangladesh has been successful in accelerating its export growth over the years by mostly concentrating on the ready-made garments sector. In turn, its exports are far less diversified than those of its neighbors and other comparators. There is, though, a dearth of empirical evidence on how export growth driven by a few sectors has affected local economic outcomes throughout the country. A recent study finds that a greater export orientation triggers a short-term increase in both formal and informal employment, as well as a longer-run increase in self-employment (Goutam et al. 2017). Using a reduced-form model such as ADH, Goutam et al. (2017) find that trade increases labor force participation and formal employment in Bangladesh. Moreover, there is an even larger impact on labor force participation if the indirect impacts of trade in the form of induced demand through supply chain linkages are included. In this report, we expand this evidence by evaluating the