Accessible Version
Detecting Tariff Effects on Consumer Prices in Real Time, Accessible Data
Figure 1. Theoretical Pass-through of a 10 percentage-point Tariff Increase on China to Core Goods (excl. motor vehicles) Prices
Predicted Tarrif Effect (Percent)
| PCE Category | Pass-through Effect Indirect | Pass-through Effect Direct |
|---|---|---|
| Musical instruments | 0.024339789 | 1.698657454 |
| Household appliances | 0.064054629 | 1.653745507 |
| Therapeutic appliances and equipment | 0.02572123 | 1.686141895 |
| Telephone and facsimile equipment | 0.005309959 | 1.641355059 |
| Luggage and similar personal items | 0.012249347 | 1.452468698 |
| Video, audio, photographic, and information processing equipment and media | 0.028496207 | 1.320656875 |
| Glassware, tableware and household utensils | 0.073802881 | 1.26963026 |
| Mens and boys clothing | 0.009360708 | 1.300739703 |
| Other clothing materials and footwear | 0.010170279 | 1.24627036 |
| Sporting equipment, supplies, guns, and ammunition | 0.028420462 | 1.150869308 |
| Sports and recreational vehicles | 0.0958126 | 1.028457438 |
| Womens and girls clothing | 0.007897651 | 1.091062484 |
| Jewelry and watches | 0.012790549 | 1.055994045 |
| Furniture and furnishings | 0.057806501 | 0.911608359 |
| Childrens and infants clothing | 0.024601718 | 0.630520521 |
| Household supplies | 0.038344057 | 0.591574019 |
| Tools and equipment for house and garden | 0.029404107 | 0.566240318 |
| Personal care products | 0.019034101 | 0.460051363 |
| Recreational items | 0.025922832 | 0.448530055 |
| Motor vehicles parts and accessories | 0.068909488 | 0.337003392 |
| Magazines, newspapers, and stationery | 0.052888766 | 0.281262901 |
| Recreational books | 0.046458136 | 0.068052267 |
| Educational books | 0.052621606 | 0.057252225 |
| Tobacco | 0.061929374 | 0.019619268 |
| Pharmaceutical and other medical products | 0.004825681 | 0.062363228 |
Figure 1 is a horizontal bar chart. For each PCE category (listed on the y-axis, which is labeled “PCE Category”), the total size of the bar (quantified by the x-axis, which is labeled “Predicted Tariff Effect (Percent)”) shows how much a 10% tariff on China should increase the price index for the PCE category under our theoretical full pass-through assumptions. The stacked bar contains two elements: a component due to a direct pass-through effect, labeled “Direct,” that shows tariff effects due to direct imports of finished/final consumer goods, and a component due to the indirect pass-through effect, labeled “Indirect,” that shows the effects coming from imported intermediate inputs used in the domestic production of consumer goods and services. The largest bar is associated with the “Musical instruments” PCE category, and the smallest bar is associated with the “Pharmaceutical and other medical products” PCE category. For all the shown PCE categories, the vast majority of the bar is comprised of the direct component, showing that most of the predicted tariff effects are due to finished/final consumer goods from China.
Note: Predicted tariff effects assume a 10% tariff on China. The key identifies bars in order from right to left.
Source: Authors’ calculations using BEA IO data from 2019 and BEA PCE bridge data from 2019.
Figure 2. Effects of May 2019 Tariffs on Core Goods (excl. Motor Vehicles) Prices
Figure 2 is a scatter plot with a regression line and the confidence interval for predicted values from the regression underlying the regression line. Each point in the scatter plot is a labelled PCE category. On the x-axis is the predicted tariff effect of the May 2019 tariffs for the PCE category. On the y-axis is the excess inflation that PCE category experienced from April to July of 2019. Excess inflation is computed by subtracting each PCE category’s average 3-month inflation rate over 2000-2017 from the 3-month inflation rate the category experienced over April to July of 2019. The regression line depicted is estimated from a regression of PCE categories’ excess inflation rates from April to July of 2019 on a constant and the PCE categories’ predicted tariff effects from the May 2019 tariffs. The 95% confidence intervals are associated with the predicted values from this regression. The figure shows that PCE categories with larger predicted tariff effects experienced more excess inflation from April to July of 2019.
Note: Predicted tariff effects assume a 15% x ($180b / $545.5b) tariff on China. The regression line has slope coefficient 2.12 (std. error 0.74, p-value 0.009). Standard errors are robust for heteroskedasticity, and 95% confidence intervals for predicted values are displayed.
Source: Authors' calculations using BEA IO data from 2017, BEA PCE bridge data from 2017, and BEA PCE price data.
Figure 3. Dynamic Effects of 2018-19 Tariffs on China
Figure 3 shows the coefficients from our local projections regression of cumulative percent changes in price indices for PCE categories on predicted tariff effects for the PCE categories. There is one coefficient with 90% confidence intervals (quantified on the y-axis, which is labeled “Fraction of Full Pass-through”), for each month since implementation of the tariffs (quantified on the x-axis, which is labeled “Months since Tariffs Imposed”). Small negative coefficients for the x-axis values -3 and -2 show that there was limited pass-through of tariffs before they were imposed. Positive and statistically significant coefficients associated with the x-axis values 0, 1, and 2 show statistically significant pass-through of tariffs after they were imposed. There is a dashed vertical line designating that tariffs were implemented between time -1 and time 0.
Note: Standard errors are clustered by PCE category, and 90% confidence intervals are displayed. All China tariff episodes in Table 1 are included. The dashed vertical line designates that tariffs were implemented between time -1 and time 0.
Source: Author's calculations using BEA IO data from 2017, BEA PCE bridge data from 2017, and BEA PCE price data. The sample period is January 2000 - February 2020.
Figure 4. Effects of Feb-Mar 2025 China Tariffs on PCE Core Goods (excl. Motor Vehicles) Prices
Figure 4 is a scatter plot with a regression line and the confidence interval for predicted values from the regression underlying the regression line. Each point in the scatter plot is a labelled PCE category. On the x-axis is the predicted tariff effect of the February and March 2025 tariffs for the PCE category. On the y-axis is the excess inflation that PCE category experienced from January to March of 2025. Excess inflation is computed by subtracting each PCE category’s average 2-month inflation rate over 2000-2019 from the 2-month inflation rate the category experienced over January to March of 2025. The regression line depicted is estimated from a regression of PCE categories’ excess inflation rates from January to March of 2025 on a constant and the PCE categories’ predicted tariff effects from the February and March 2025 tariffs. The 95% confidence intervals are associated with the predicted values from this regression. The figure shows that PCE categories with larger predicted tariff effects experienced more excess inflation from January to March of 2025.
Note: Predicted tariff effects assume a 20% tariff on China. The regression line has slope coefficient 0.54 (std. error 0.25, p-value 0.04). Standard errors are robust for heteroskedasticity, and 95% confidence intervals for predicted values are displayed.
Source: Authors' calculations using BEA IO data from 2019, BEA PCE bridge data from 2019, and BEA PCE price data.