Why is the New York Fed conducting research on inequality?
Developing the EGIs is consistent with the mission of the New York Fed to make the U.S. economy stronger and the financial system more stable for all segments of society.
How often will the Equitable Growth Indicators be updated?
The full set of Equitable Growth Indicators will be updated every three months, following the schedule posted on the EGI webpage.
Can we obtain the underlying data?
We are making the Equitable Growth Indicators data available for download.
What factors are used in calculating inflation for different demographic groups?
There are not any demographic-specific official measures of inflation rates. However, the Bureau of Labor Statistics (BLS) computes separate inflation indexes (consumer price indexes, or CPIs) by metro area for different categories of goods and services, such as food, clothing, energy, housing, or entertainment. The BLS also conducts a Consumer Expenditure Survey (CEX), which allows one to see how different demographic groups allocate their spending to these different categories. For example, as the 2019 CEX shows, Black Americans spend relatively more on transportation and housing and relatively less on food and entertainment than white Americans do.
Using a procedure similar to several papers in the literature (Hobijn and Lagakos 2005, McGranahan and Paulson 2005 and Jaravel 2019), we assume that prices within each goods or service category are the same for everyone within a metro area and are well represented by the CPIs, but that different groups consume different amounts of goods and services from different categories. We can then obtain estimates of the inflation of the consumption basket for each demographic group as a weighted average of the CPIs of the components of the consumption basket, with the weights being that group’s expenditure shares of the components. We call this inflation measure Demo-CPI, which is the basis for our statements about changing inflation gaps across demographics over time.
Using a procedure similar to several papers in the literature (Hobijn and Lagakos 2005, McGranahan and Paulson 2005 and Jaravel 2019), we assume that prices within each goods or service category are the same for everyone within a metro area and are well represented by the CPIs, but that different groups consume different amounts of goods and services from different categories. We can then obtain estimates of the inflation of the consumption basket for each demographic group as a weighted average of the CPIs of the components of the consumption basket, with the weights being that group’s expenditure shares of the components. We call this inflation measure Demo-CPI, which is the basis for our statements about changing inflation gaps across demographics over time.
Is it fair to assume that prices are the same for everyone?
We assume that prices are the same for everyone within a metro area, but they vary across metro areas. It is likely that our procedure underestimates inflation disparities between different groups of Americans. That is because, in addition to consuming different bundles of goods, different demographics likely face different prices for the same goods, with lower-income Americans and Black Americans often facing higher price growth.
How do you measure inflation disparities across demographic categories (race, ethnicity, income, education, age)?
We combine data from CEX on each demographic group’s budget shares for more than thirty categories of goods and services, with CPI data on inflation rates for these categories.
In an innovation to the literature, we allow the CEX and CPI data to vary across twenty-three major U.S. metro areas, comprising nearly 40 percent of the U.S. population and ranging in size from St. Louis to New York City. CEX respondents not residing in one of these major metro areas are matched to the CPI of smaller cities and towns in their respective U.S. census region (Northeast, Midwest, South, or West).
In an innovation to the literature, we allow the CEX and CPI data to vary across twenty-three major U.S. metro areas, comprising nearly 40 percent of the U.S. population and ranging in size from St. Louis to New York City. CEX respondents not residing in one of these major metro areas are matched to the CPI of smaller cities and towns in their respective U.S. census region (Northeast, Midwest, South, or West).
How do you measure inflation disparities across geographies (census region, urban status)?
Unlike for the other heterogeneities explored in our series of equitable growth indicators, the BLS provides estimates of the CPI by U.S. census region (Northeast, Midwest, South, and West). Therefore, we use them to compute inflation differences between the census regions and the national average.
We use the methodology for assessing demographic categories—combining budget shares from the CEX with CPI inflation measures at the metro area and census region level—to compute the rural-urban inflation differential. However, an additional complication of the latter analysis is that while the CEX surveys both urban and rural households, the CPI only collects urban prices for each census region. Therefore, following Hobijn and Lagakos (2005), when measuring the inflationary experience of rural households, we use rural budget shares but not rural prices. We therefore caveat our results on rural households but consider them worth reporting given the large and intuitive inflation disparity that we uncover.
We use the methodology for assessing demographic categories—combining budget shares from the CEX with CPI inflation measures at the metro area and census region level—to compute the rural-urban inflation differential. However, an additional complication of the latter analysis is that while the CEX surveys both urban and rural households, the CPI only collects urban prices for each census region. Therefore, following Hobijn and Lagakos (2005), when measuring the inflationary experience of rural households, we use rural budget shares but not rural prices. We therefore caveat our results on rural households but consider them worth reporting given the large and intuitive inflation disparity that we uncover.
How does your methodology differ from the literature covering inflation differences observed in previous periods?
Our approach is an improvement on the procedure applied in previous literature, which uses national prices that are fixed across geography and demographics at a specific point in time. In contrast, we allow prices to vary across metro areas. This change has the advantage of assigning prices to households in a more accurate manner. An implicit assumption of our approach is that prices of goods are the same across demographic and income groups within a major metro area or for people outside of major metro area in the same census region, so that variation in inflation occurs only through differing consumption baskets and different location which is a much weaker assumption than the fixed price assumption used in the literature. We have explored using twice as many goods categories but not allowing inflation to vary by metro area (that is, using national prices), and have found broadly similar results.
How do you measure the real and nominal earnings of different demographic groups (race, ethnicity, education, age, gender, urban status, veteran status)
We use monthly non-seasonally adjusted data on average weekly earnings for Asian, Black, Hispanic, and white workers aged sixteen and older from the Current Population Survey, a joint effort by the U.S. Census Bureau and the Bureau of Labor Statistics. Weekly earnings can vary because of changes in hourly wages or because of changes in hours worked per week. We obtain similar results for racial and ethnic heterogeneity using hourly wages, so our findings apply directly to the price of labor rather than to changes in hours worked.
Since the characteristics of the employed population change with the economy, changes to weekly earnings may reflect both changes in the composition of the employed pool as well as changes in the prices of particular skills. We deflate nominal earnings by our demographic-specific inflation measures, although our results are similar if we deflate them using the CPI.
Since the characteristics of the employed population change with the economy, changes to weekly earnings may reflect both changes in the composition of the employed pool as well as changes in the prices of particular skills. We deflate nominal earnings by our demographic-specific inflation measures, although our results are similar if we deflate them using the CPI.
How do you define the population of veterans?
We use the 2019 five-year American Community Survey (ACS), the last one before the onset of the COVID-19 pandemic, to compute average outcomes for male veterans and nonveterans aged between 25 and 69. This cut of the data has us looking at the population of veterans who served when enlistment in the armed forces was voluntary, after the end of the draft in 1971.
It is a challenge to construct a comparison group since veterans differ from nonveterans among many dimensions. For example, veterans are overwhelmingly likely to be male high school graduates as the military typically requires a high school degree for service. Veterans are older, with enlistment rates drifting down over time. They are also more likely to be native-born and white, and more likely to have been born in the South and the Midwest than in the Northeast and the West.
Therefore, to build a more comparable comparison group for veterans, we weight the population of nonveteran male high school graduates to match the age, racial, ethnic, immigrant and geographic distributions of veterans. We use as weights the fractions of the male high school graduate population in each age, race, origin, and geography category who are veterans. We refer to this control group as “comparable nonveterans.”
Although our methodology does not remove all sources of differences between veterans and “reweighted” nonveterans (for example, the veterans may differ from nonveterans in other aspects of their background, or in unobservable characteristics such as personality or interests, for which there is no data in the ACS), it avoids the most obvious sources of noncomparability between them and allows us to focus on the consequences of being a veteran.
It is a challenge to construct a comparison group since veterans differ from nonveterans among many dimensions. For example, veterans are overwhelmingly likely to be male high school graduates as the military typically requires a high school degree for service. Veterans are older, with enlistment rates drifting down over time. They are also more likely to be native-born and white, and more likely to have been born in the South and the Midwest than in the Northeast and the West.
Therefore, to build a more comparable comparison group for veterans, we weight the population of nonveteran male high school graduates to match the age, racial, ethnic, immigrant and geographic distributions of veterans. We use as weights the fractions of the male high school graduate population in each age, race, origin, and geography category who are veterans. We refer to this control group as “comparable nonveterans.”
Although our methodology does not remove all sources of differences between veterans and “reweighted” nonveterans (for example, the veterans may differ from nonveterans in other aspects of their background, or in unobservable characteristics such as personality or interests, for which there is no data in the ACS), it avoids the most obvious sources of noncomparability between them and allows us to focus on the consequences of being a veteran.
Why do you report on the Employment to Population Ratio (EPOP)?
We report on the EPOP for prime-aged workers, as reported by BLS, as a measure of the state of the labor market for a given group. This is the ratio of the number of people aged 25 to 54 in each group who are employed (including self-employed) to the total number of people in that age bracket in that group. An alternative measure could be the unemployment rate; however, it captures only people who are not employed but are looking for work and misses people who currently have given up looking for work but may return to work when economic conditions improve. The Federal Reserve System defines its labor market half of the dual mandate as “maximum employment,” consistent with making EPOP a key labor market indicator.
An extensive literature suggests that these people are an important part of labor market dynamics. In contrast, EPOP accounts for people dropping out of and then returning to the labor force for economic reasons. A possible problem with EPOP computed for the entire adult population—as well as with other labor market measures—could be if, over time, people spend more time getting an education or retire early, or if the age composition of the population changes. Therefore, we consider EPOP only for prime aged workers—those between 25 and 54—who typically have completed their education and would work under ordinary circumstances. Indeed, this age group tends to be strongly connected to employment; on the eve of the arrival of COVID-19 in February 2020, this group had an EPOP ratio of more than 80 percent.
An extensive literature suggests that these people are an important part of labor market dynamics. In contrast, EPOP accounts for people dropping out of and then returning to the labor force for economic reasons. A possible problem with EPOP computed for the entire adult population—as well as with other labor market measures—could be if, over time, people spend more time getting an education or retire early, or if the age composition of the population changes. Therefore, we consider EPOP only for prime aged workers—those between 25 and 54—who typically have completed their education and would work under ordinary circumstances. Indeed, this age group tends to be strongly connected to employment; on the eve of the arrival of COVID-19 in February 2020, this group had an EPOP ratio of more than 80 percent.
How do you define the unemployment rate?
We use the BLS’s definition of the unemployment rate, which counts as unemployed anyone who 1) does not have a job, and 2) mentioned that they are looking for a job. This corresponds to the U3 definition of unemployment used by the BLS. In particular, we do not count as unemployed those who are employed part-time for economic reasons (that is they would be willing to work full-time but cannot find a full-time job).
Why do you report the labor force participation rate?
We report the labor force participation (LFP) rate because, along with the unemployment rate, it is a critical contributor to the employment rate. During business cycles, people routinely leave the labor market and return to it subsequently, affecting the LFP rate without affecting the unemployment rate. During the COVID-19 pandemic, many interesting hypotheses (for example that the near-elderly might retire in greater numbers, or that women may stay at home to a greater extent than before) had implications specifically for labor force participation, and differences in labor force participation are particularly important in explaining differences in employment by race and by gender.
How do you measure consumer spending by demographic groups (income, education, age)
We capture consumer spending by using detailed county-level card transaction data provided by Commerce Signals, a Transunion company. Commerce Signals captures spending by a permissioned panel of around 40 million U.S. households, which means that it includes data on spending at large businesses as well. The aggregate trends from Commerce Signals align well with national retail sales numbers. We use county-level Commerce Signals data to identify the differences in consumer spending between residents of low-income and higher-income counties and between residents in majority Black, Hispanic, or AAPI and majority non-Black, Hispanic, or AAPI counties.
How do you define the demographics of your county-level data?
We use data on race and income composition at the county level from the 2014-18 waves of the American Community Survey to differentiate between low to moderate income and higher-income counties, and between majority Black, Hispanic, or AAPI and other counties.
We define low-income counties as those that fall in the lowest quartile of the population-weighted distribution of median household income. We define low-to-moderate income counties as those in which greater than 50 percent of the county’s population has household income below 80 percent of the metro area median. Higher-income counties are those that fall in the top quartile of the population-weighted distribution of median household income.
Recognizing that the term “minority” does not fully capture the racial and ethnic diversity present, we note that in our definition majority Black/Hispanic counties are those in which at least half the population is Black or Hispanic. We index each series to January 2020 and rescale the series to present percent changes relative to January 2020. In other words, each spending series represents the year-on-year growth in spending relative to the year-on-year growth obtaining in January 2020.
We define low-income counties as those that fall in the lowest quartile of the population-weighted distribution of median household income. We define low-to-moderate income counties as those in which greater than 50 percent of the county’s population has household income below 80 percent of the metro area median. Higher-income counties are those that fall in the top quartile of the population-weighted distribution of median household income.
Recognizing that the term “minority” does not fully capture the racial and ethnic diversity present, we note that in our definition majority Black/Hispanic counties are those in which at least half the population is Black or Hispanic. We index each series to January 2020 and rescale the series to present percent changes relative to January 2020. In other words, each spending series represents the year-on-year growth in spending relative to the year-on-year growth obtaining in January 2020.
What is the underlying data for the wealth EGIs?
The Distributional Financial Accounts (DFA), which provide quarterly estimates of the distribution of U.S. household wealth, incorporate data from both the Survey of Consumer Finances (SCF) and the Financial Accounts of the United States. The DFA, the SCF, and the Financial Accounts are all published by the Board of Governors of the Federal Reserve.
How do you define per household wealth?
We define wealth as net worth, or assets less liabilities. We divide the total wealth of a group by the number of households in that group according to the SCF to yield wealth per household.
How are the wealth numbers interpolated quarterly if the SCF numbers are triennial?
The DFA takes information on demographic distributions from the SCF, but the SCF only publishes every three years. Therefore the DFA use sophisticated imputation methods to interpolate distributions of household financial information into the quarters between SCF releases. These interpolated distributions are used in the DFA quarters for which no SCF is available.
