We construct a measure of consumption-equivalent welfare for Black and White Americans, which incorporates life expectancy, consumption, leisure, and inequality. Based on these factors, welfare for Black Americans was 40 percent of that for White Americans in 1984 and 59 percent by 2022. There has been remarkable progress for Black Americans: The level of their consumption-equivalent welfare increased by a factor of 3.5 over the last 38 years when aggregate consumption per person only doubled. Despite this progress, the welfare gap in 2022 remains disconcertingly large at 41 percent, much larger than the 16 percent gap in consumption per person. (JEL D12, I12, I31, J15, J31, K42)

Firm price-cost markups may reflect (a) bigger step sizes from quality innovations that confer significant knowledge spillovers onto other firms, and/or (b) higher process efficiency than competing firms. We write down an endogenous growth model in which, compared with the laissez-faire equilibrium, the social planner would generally like to reallocate research resources towards high markup firms in case (a) so as to capitalize on knowledge spillovers but not in case (b)

Economic growth is typically measured in per capita terms. A long tradition in philosophy, however, suggests that social welfare may depend on the number of people as well. To illustrate how much this matters quantitatively, we decompose social welfare growth—measured in consumption-equivalent (CE) units — into contributions from rising population and rising per capita consumption. Because of diminishing marginal utility from consumption, population growth is scaled up by a value-of-life factor that empirically averages nearly 3 across countries since 1960. Population increases are therefore a major contributor to growth if one takes a total rather than per capita view. CE welfare growth around the world averages more than 6% per year since 1960 as opposed to 2% per year for consumption growth. Countries such as Mexico and South Africa rise sharply in the growth rankings, whereas China, Germany, and Japan plummet. These results are robust to incorporating richer individual preferences and endogenous fertility using time-use data from the U.S., Mexico, the Netherlands, Japan, South Africa, and South Korea.

E-commerce represents a rapidly growing share of consumer spending in the United States. We use transactions-level data on credit and debit cards from Visa, Inc. between 2007 and 2017 to quantify the resulting consumer surplus. We estimate e-commerce reached 8 percent of consumption by 2017, yielding the equivalent of a 1 percent boost to their consumption, or over $1,000 per household per year. While some of the gains arose from avoiding travel costs to local merchants, most of the gains stemmed from substituting to merchants available online but not locally. Higher income consumers gained more, as did consumers in more densely populated counties. (JEL D12, E21, G51, L81, L86)

In benchmark trade models that feature a constant trade elasticity, bilateral exports vary entirely on the intensive margin (exports per firm) or entirely on the extensive margin (number of firms). Our empirical analysis documents that roughly one-half of this variation occurs along each margin, implying that the trade elasticity is not constant. We estimate a generalized Melitz model with a joint log-normal distribution for firm productivity, fixed costs, and demand shifters. Using exact-hat algebra, we quantify how trade costs affect trade flows and welfare. Welfare effects are similar to those in the Melitz-Pareto model, but implied trade flows differ significantly. (JEL D22, D24, D43, F12, F14, L13)

Growth has fallen in the U.S., while firm concentration and profits have risen. Meanwhile, labor’s share of national income is down, mostly due to the rising market share of low labor share firms. We propose a theory for these trends in which the driving force is falling firm-level costs of spanning multiple markets, perhaps due to accelerating IT advances. In response, the most efficient firms (with higher markups) spread into new markets, thereby generating a temporary burst of growth. Because their efficiency is difficult to imitate, less efficient firms find markets more difficult to enter profitably and therefore innovate less.Eventually, due to greater competition from efficient firms, within-firm markups actually fall. Despite the increase in the aggregate markup and rents, firm incentives to innovate decline— lowering the long run growth rate.

: We propose a parsimonious framework for real rigidities, in the form of strategic complementarities, that can generate real and nominal dynamics and match key features of the data across several literatures. Existing menu-cost models featuring strategic complementarities require unrealistically volatile shocks to idiosyncratic productivity to be consistent with pricing moments. We develop a simple menu-cost model with strategic complementarities along with idiosyncratic productivity and demand shocks that are disciplined by the data. This approach allows us to overcome previous criticism from analysis of models that employ only an idiosyncratic productivity shock and calibrate solely using data from the price-adjustment literature. Despite its simplicity, the model can generate sizable monetary nonneutrality along with the magnitude of cost pass-through documented in previous studies, while also remaining consistent with micro pricing and markup evidence.

Using Visa debit and credit card transactions in the U.S. from 2016 to 2019, we document the importance of customers in accounting for sales variation across merchants, across stores within retail chains, and over time for individual merchants and stores. Customers, as opposed to transactions per customer or dollar sales per transaction, consistently account for about 80% of sales variation. The top 1% of growing and shrinking merchants account for about 70% of customer and sales reallocation in a given year. In order to illustrate some of the potential implications, we write down an endogenous growth model with and without the customer margin. In the context of this model, we find that the customer margin dramatically increases the size and growth contribution of the largest firms, but lowers the aggregate growth rate by diverting resources from research to customer acquisition activities.

Recent work highlights a falling entry rate of new firms and a rising market share of large firms in the United States. To understand how these changing firm demographics have affected growth, we decompose productivity growth into the firms doing the innovating. We trace how much each firm innovates by the rate at which it opens and closes plants, the market share of those plants, and how fast its surviving plants grow. Using data on all nonfarm businesses from 1982–2013, we find that new and young firms (ages 0 to 5 years) account for almost one-half of growth – three times their share of employment. Large established firms contribute only one-tenth of growth despite representing one-fourth of employment. Older firms do explain most of the speedup and slowdown during the middle of our sample. Finally, most growth takes the form of incumbents improving their own products, as opposed to creative destruction or new varieties.

The ratio of revenue to inputs differs greatly across plants within countries such as the U.S. and India. Such gaps may reflect misallocation which hinders aggregate productivity. But differences in measured average products need not reflect differences in true marginal products. We propose a way to estimate the gaps in true marginal products in the presence of measurement error. Our method exploits how revenue growth is less sensitive to input growth when a plant’s average products are overstated by measurement error. For Indian manufacturing from 1985–2013, our correction lowers potential gains from reallocation by 20%. For the U.S. the effect is even more dramatic, reducing potential gains by 60% and eliminating 2/3 of a severe downward trend in allocative efficiency over 1978–2013.

Entrants and incumbents can create new products and displace the products of competitors. Incumbents can also improve their existing products. How much of aggregate productivity growth occurs through each of these channels? Using data from the U.S. Longitudinal Business Database on all nonfarm private businesses from 1983 to 2013, we arrive at three main conclusions: First, most growth appears to come from incumbents. We infer this from the modest employment share of entering firms (defined as those less than 5 years old). Second, most growth seems to occur through improvements of existing varieties rather than creation of brand new varieties. Third, own‐product improvements by incumbents appear to be more important than creative destruction. We infer this because the distribution of job creation and destruction has thinner tails than implied by a model with a dominant role for creative destruction.

The individual’s utility from choosing a particular occupation, U(τig, wi, i, μi), is proportional to μi(γ̄w̃ig i) 3β 1−η , where w̃ig ≡ wis i (1 − si) 1−η 3β · h̄ig z̃ig τig and γ̄ ≡ 1 + γ(2) + γ(3) is the sum of the experience terms. We first consider the occupation decision for individuals with ability heterogeneity (so no taste heterogeneity or μi = 1). For these people, the solution to the individual’s problem involves picking the occupation with the largest value of w̃ig i. To keep the notation simple, we will suppress the g subscript in what follows. Without loss of generality, consider the probability that the individual chooses occupation 1, and denote this by p1. Then

U.S. productivity is growing slower than in the past. Meanwhile, sales have become increasingly concentrated in the largest businesses. Analysis suggests that IT innovation may have facilitated the rise in concentration by reducing the cost for large firms to enter new markets. This contributed to booming productivity growth from 1995 to 2005. Though large firms are more profitable, their expansion may have increased competition and reduced profit margins within markets. Lower profit margins in a given market could have deterred innovation, eventually lowering growth.

For exiting products, statistical agencies often impute inflation from surviving products. This understates growth if creatively-destroyed products improve more than surviving ones. If so, then the market share of surviving products should systematically shrink. Using entering and exiting establishments to proxy for creative destruction, we estimate missing growth in US Census data on non-farm businesses from 1983 to 2013. We find missing growth (i) equaled about one-half a percentage point per year; (ii) arose mostly from hotels and restaurants rather than manufacturing; and (iii) did not accelerate much after 2005, and therefore does not explain the sharp slowdown in growth since then. (JEL E23, E31, L14, L15, O30, O41)

In the wake of the U.S.-Canada Free Trade Agreement, both the U.S. and Canada experienced a sustained increase in job reallocation, including firms moving into exporting. The change involved big firms as much as small firms. To mimic these patterns, we formulate a model of innovation by both domestic and foreign firms. In the model, trade liberalization quickens the pace of creative destruction, thereby speeding the flow of technology across countries. The resulting dynamic gains from trade liberalization are an order of magnitude larger than the gains in a standard static model.

There is a widely held view that much of growth in the U.S. can be attributed to reallocation from low to high productivity firms, including from exiting firms to entrants. Declining dynamism — falling rates of reallocation and entry/exit in the U.S. — have therefore been tied to the lackluster growth since 2005. We challenge this view. Gaps in the return to resources do not appear to have narrowed, suggesting that allocative efficiency has not improved in the U.S. in recent decades. Reallocation can also matter if it is a byproduct of innovation. However, we present evidence that most innovation comes from existing firms improving their own products rather than from entrants or fast-growing firms displacing incumbent firms. Length: 26 pages

We use Adobe Analytics data on online transactions for millions of products in many different categories from 2014 to 2017 to shed light on how online inflation compares to overall inflation, and to gauge the magnitude of new product bias online. The Adobe data contain transaction prices and quantities purchased. We estimate that online inflation was about 1 percentage point lower than in the CPI for the same categories from 2014-2017. In addition, the rising variety of products sold online, implies roughly 2 percentage points lower inflation than in a matched model/CPI-style index.