Consumer Expenditure Survey Data Research Articles

Personal Consumption and Single Persons: An Update

Abstract Kurt V. Krueger’s seminal study used 2005-2009 Consumer Expenditure Survey (CEX) data to examine various measures of personal consumption rates for single persons. Given changes in survey methods, the impacts of inflation over the following decade, changes in tastes and other factors affecting consumer demand, we re-estimate Krueger’s measures using 2015-2019 survey data. The estimation of estate accumulation rates has expanded as well. Single person expenditures and consumption rates using the 2015-2019 data differ from the earlier results. They also differ from those acquired by deflating 2019 earnings to 2009 values and using the 2005-2009 consumption rates. Experts may find it useful to employ single-person expenditure rates using more recent data.

Small area estimation under a spatially correlated multivariate area-level model

Abstract Spatial version of multivariate Fay–Herriot model is introduced and small area predictor under this model is proposed. The residual maximum likelihood is employed for estimating the parameters of the proposed model. Analytical and bootstrap approaches for estimating the mean squared error (MSE) of the proposed predictor are also developed. The performance of the proposed predictor and the MSE estimators are evaluated through various simulation studies. The results evidently show that the proposed predictor outperforms the existing predictors. An application of the proposed methodology has also been made using the 2011–12 Consumer Expenditure Survey data of India.

Lifetime asset allocation with long run risk and time various risk aversion

We propose in this article an optimal lifetime asset allocation model in which we simultaneously consider two important stylized facts, long-run risk and time-varying risk aversion. Our model also accounts for the uncertainty and correlation of long-run returns and volatility. Based on American Consumer Expenditure Survey data and macroeconomic data, we have three main findings. First, individual’s risk attitude roughly rises with age, and it has a significant effect on the individual’s best asset allocation choice. Second, two pairs of correlations between the movement of returns and volatility impact an individual’s choice. In the long run, individuals may take the comovement of returns and volatility into account and adjust their optimal investment strategy. The effects of the adjustment term are determined by the direction and strength of the correlations. Third, the economy has a clear business cycle path, and the optimal fraction of risky assets in portfolio vary within a wide range when the risk premium is high during one’s lifetime. We further, based on our data and parameters, perform a simulation to identify the path of the optimal investment strategy. The result implies that individuals and households with lifetime investments such as pension funds should take risk preferences variation into account, and this is especially for older individuals.

Effects of the Expanded Child Tax Credit on Household Spending: Estimates Based on U.S. Consumer Expenditure Survey Data

Effects of the Expanded Child Tax Credit on Household Spending: Estimates Based on U.S. Consumer Expenditure Survey Data

Bayesian synthetic prediction of state level poverty using Indian Household Consumer Expenditure Survey Data1

Goal 1 of the 2030 Agenda for Sustainable Development, adopted by all United Nations member States in 2015, is to end poverty in all forms everywhere. The major indicator to monitor the goal is the so-called headcount ratio or poverty rate, i.e., proportion or percentage of people under poverty. In India, where nearly a quarter of population still live below the poverty line, monitoring of poverty needs greater attention, more frequently at shorter intervals (e.g., every year) to evaluate the effectiveness of planning, programs and actions taken by the governments to eradicate poverty. Poverty rate computation for India depends on two basic ingredients – rural and urban poverty lines for different states and union territories and average Monthly Per-capita Consumer Expenditure (MPCE). While MPCE can be obtained every year, usually from the Consumer Expenditure Survey on shorter schedules with a few exceptions where the information is obtained from another survey, determination of poverty lines is a highly complex, costly and time-consuming process. Poverty lines are essentially determined by a panel of experts who draws their conclusions partly based on their subjective opinions and partly based on data from multiple sources. The main data source the panel uses is the Consumer Expenditure Survey data with a detailed schedule, which are usually available every five years or so. In this paper, we undertake a feasibility study to explore if estimates of headcount ratios or Poverty Ratios in intervening years can be provided in absence of poverty lines by relating poverty ratios with average MPCE through a statistical model. Then we can use the fitted model to predict poverty rates for intervening years based on average MPCE. We explore a few in this work models using Bayesian methodology. The reason behind calling this ‘synthetic prediction’ rests on the synthetic assumption of model invariance over years, often used in the small area literature. While the data-based assessment of our Bayesian synthetic prediction procedure is encouraging, there is a great potential for improvements on the models presented in this paper, e.g., by incorporating more auxiliary data as they become available. In any case, we expect our preliminary work in this important area will encourage researchers to think about statistical modeling as a possible way to at least partially solve a problem for which no objective solution is currently available.

Analyzing the Association of Objective Burden Measures to Perceived Burden with Regression Trees

Abstract Higher levels of perceived burden by respondents can lead to ambiguous responses to a questionnaire, item nonresponse, or refusals to continue participation in the survey which can introduce bias and downgrade the quality of the data. Therefore, it is important to understand what might influence the perception of burden in respondents. In this article, we demonstrate, using U.S. Consumer Expenditure Survey data, how regression tree models can be used to analyze the associations between perceived burden and objective burden measures conditioning on household demographics and other explanatory variables. The structure of the tree models allows these associations to easily be explored. Our analysis shows a relationship between perceived burden and some of the objective measures after conditioning on different demographic and household variables and that these relationships are quite affected by different respondent characteristics and the mode of the survey. Since the tree models were constructed using an algorithm that accounts for the sample design, inferences from the analysis can be made about the population. Therefore, any insights could be used to help guide future decisions about survey design and data collection to help reduce respondent burden.

Has consumption inequality mirrored income inequality: new evidence from a Korean household panel

ABSTRACT We examine how consumption inequality tracks income inequality in a Korean household panel. To adjust for measurement errors in consumption, we employ the double-differencing correction of Aguiar and Bils (2015) (AB hereafter). In the first stage of the double-differencing, AB used current income as an instrument for unobserved true total expenditure, and we use permanent income as the instrument instead. For the U.S. Consumer Expenditure Survey data, AB found that after correcting for measurement errors, consumption inequality tracks income inequality more closely. We find that the same conclusion holds for the Korean panel data and the estimate of consumption inequality is robust to the choice of instrument for the total expenditure in the first stage regression.

Diet-Related Greenhouse Gas Emissions in Brazilian State Capital Cities

This article aims to investigate diet-related greenhouse gas emissions in Brazil concerning residents of all state capital cities, as well as examining whether the taxes for São Paulo State currently levied on food products are consistent with their social carbon costs. After sorting the individuals into groups according to income and city of residence, we estimated the number of emissions linked to the diets of each group integrating emission data with consumer expenditure survey data. Our results show that beef and dairy products are the most carbon-intensive foods, while rice is the biggest emitter among plant-based foods. Diets of low-income individuals emit less, while the diets of individuals living in Northern states, in the countryside or in small towns emit significantly more. Our findings also show that the existing food taxes levied in São Paulo State are too low for some carbon-intensive foods and too high for less carbon-intensive foods, discouraging the consumption of eco-friendly foods and boosting the consumption of foods with higher carbon footprints. We conclude that a revision of Brazil’s food taxation considering carbon intensity would be highly recommended, but this revision should also consider the impact of new taxes on peoples’ nutrition and health.

District-Level Estimates of Poverty Incidence for the State of West Bengal in India: Application of Small Area Estimation Technique Combining NSSO Survey and Census Data

Despite having long term efforts, poverty is an important and persistent social issue in India. Existing data based on socio-economic surveys produce state and nationally representative poverty estimates but cannot be used directly to generate reliable disaggregate or local level estimates. The state and national level estimates often mask the variations at the local level which in turn restricts the effective implementation of policies related to poverty alleviation locally within and between administrative units. This paper uses the Household Consumer Expenditure Survey data of NSSO and link with the Population Census data to produce the reliable district-level estimates of poverty incidence in the rural areas of West Bengal in India. In particular, small area estimation (SAE) method is explored to generate reliable district-level poverty estimates. The results clearly indicate that the district-level estimates generated by model-based SAE method are precise and representative. A map showing how poverty incidence varies by district across the State of West Bengal is also produced. The estimates generated from this research are useful for meeting the data requirements for policy research and strategic planning by different international organizations and by Departments and Ministries in the Government of India.

Is vehicle ownership in urban india influenced by the availability of high quality dedicated public transit systems?

As India's economy grows, so too does the number of people who can afford to own vehicles. A downside of this rapid increase in private vehicle ownership is a corresponding increase in traffic congestion, air pollution, and carbon emissions. Although affordability may be one reason for the shift toward commuting via private vehicles, another contributing factor could be the quality of public transportation. The objective of this paper is to determine whether private vehicle ownership in large urban areas in India is influenced by the presence of high quality dedicated public transit systems. Consumer expenditure survey data acquired from the National Sample Survey Office (NSSO) for the year 2009–2010 were used to develop a vehicle ownership model for 26 Indian cities with a population of at least one million. The results show that the availability of public transportation has a negative correlation to motorcycle/moped ownership but has no strong effect on car ownership. These results should encourage governments in developing economies to invest in high-quality dedicated public transit systems.

Household carbon inequality in the U.S.

Household carbon emissions are mainly affected by income and other key demographic factors. Understanding the contribution of these factors can inform climate responsibilities and potential demand-side climate mitigation strategies. By linking US consumer expenditure survey data with a nested national within a global multi-regional input-output model, this study estimates consumption-based GHG emissions for 9 income groups and assesses the carbon inequality in the US for 2015. Our results show that the per capita carbon footprint (CF) of the highest income group (>200 thousand USD per year) with 32.3 tons is about 2.6 times the per capita CF of the lowest income group (<15 thousand USD) with 12.3 tons. This is due to large gap in consumption volume and associated carbon emissions along the entire global production chain. Consumption pattern tends to narrow the gap in household per capita CF between income groups due to the lower carbon intensity per dollar spent by higher income groups. Another important factor influencing carbon footprints is household size and thus sharing of household equipment and other consumption items. The US average per capita CF is 18.1 tons compared to the global average of approximately 5 tons. The high carbon footprint across income groups in the US is largely due to the large contribution of emissions from heating and cooling and private transport, which reflects the settlement structure and lifestyles in the US, relying heavily on cars and living in larger houses.

Poverty and inequality in Odisha, India

Odisha has gone on from being the most underdeveloped Indian state to be the state that has recorded the highest decline in poverty among the states. We undertake decomposition analyses to assess the redistributive changes in poverty and inequality over the period 1993–1994 and 2011–2012 across regions and social groups, examining the NSSO Consumer Expenditure Survey data. We find that while Odisha has succeeded in substantially reducing both rural and urban poverty incidence, this change has not met desirable subgroup‐specific variations. Among regions, southern and northern Odisha have not kept pace with coastal Odisha in poverty reduction during the study period. The rate of poverty reduction for the Scheduled Tribes and the Scheduled Castes has been dismal. Overall, we found the growth effects on poverty reduction to be more prominent over inequality effects and population shift effects on poverty, although these effects varied in contexts. While inequality in urban regions rose, it declined in rural regions. While at the state‐level, the within‐group inequality has declined between 2004–2005 and 2011–2012 (coincident with the present Naveen Patnaik rule); the between‐group inequality has increased during the period. Rural Odisha has seen a reduction in both within‐group and between‐group disparities, whereas urban Odisha has fared the opposite.

Quantile Regression Analysis of Predictors of Calorie Demand in India: An Implication for Sustainable Development Goals

This study examined predictors of households’ calorie demand using consumer expenditure survey data during the time frame of millennium development goals. It draws suggestions for achieving sustainable development goals to eliminate calorie-poverty. We used the log of per-capita calorie intake as the calorie demand. Endogeneity corrected quantile regression was applied to examine the distributional effect of predictors. Findings revealed calorie-monthly per-capita consumption expenditure (MPCE) elasticities were positively statistically significant across quantiles in rural-and urban-areas, but, contrary to traditional wisdom, elasticities are lower for calorie-poor than calorie-rich households. Dietary diversification of food items, relative food price, and share of medical-and education-expenditure were the main adverse drivers of calorie demand. Our results are robust to the under-reporting and measurement error. The policy implications are: (a) only focusing on pro-poor income enhancing strategies will not able to reduce calorie deprivation, it should be backed by imparting awareness about food choice and nutritional value of low price food items, (b) to implement necessary policy to maintain stable food inflation and effectively targeted food subsidy for calorie poor, (c) to adopt forward-looking medical-and education-policy such as free health and education facilities to all by enhancing public spending to revive the quality of public hospitals and educational institutions.

On the Welfare Cost of Consumption Fluctuations in the Presence of Memorable Goods

We propose a new classification of consumption goods into nondurable goods, durable goods and a new class which we call memorable goods. A good is memorable if a consumer can draw current utility from its past consumption experience through memory. We construct a novel consumption-savings model in which a consumer has a well-defined preference ordering over both nondurable goods and memorable goods. Memorable goods consumption differs from nondurable goods consumption in that current memorable goods consumption may also impact future utility through the accumulation process of the stock of memory. In our model, households optimally choose a lumpy profile of memorable goods consumption even in a frictionless world. Using Consumer Expenditure Survey data, we then document levels and volatilities of different groups of consumption goods expenditures, as well as their expenditure patterns, and show that the expenditure patterns on memorable goods indeed differ significantly from those on nondurable and durable goods. Finally, we empirically evaluate our model's predictions with respect to the welfare cost of consumption fluctuations and conduct an excess-sensitivity test of the consumption response to predictable income changes. We find that (i) the welfare cost of householdlevel consumption fluctuations may be overstated by 1:7 percentage points (11:9% points as opposed to 13:6% points of permanent consumption) if memorable goods are not appropriately accounted for; (ii) the finding of excess sensitivity of consumption documented in important papers of the literature might be entirely due to the presence of memorable goods.

The Decline of the American Middle Class: Evidence from the Consumer Expenditure Surveys 1988–2015

In 2010, the US Department of Commerce, commissioned by the White House Middle Class Task Force, recommended six indicators that define the middle class: having one’s own home, a car or two in the carport, taking a family vacation every year, sending kids to college, and having some retirement savings. We used the Consumer Expenditure Survey (CE) data to estimate the size of American middle class in selected years from 1988 to 2015 using three empirical variations of this definition and to test if there were upward or downward trends during these years. We found that the size of the American middle class was definitely on the decline between 1988 and 2015 for all three definitions. Multivariate analyses show that variations in total household annual expenditure and sociodemographic variables could not explain the decline. In fact, adjusting for these controls made the decline of the middle class over time more severe.

Scale, distribution and variations of global greenhouse gas emissions driven by U.S. households

The U.S. household consumption, a key engine for the global economy, has significant carbon footprints across the world. Understanding how the U.S. household consumption on specific goods or services drives global greenhouse gas (GHG) emissions is important to guide consumption-side strategies for climate mitigation. Here we examined global GHG emissions driven by the U.S. household consumption from 1995 to 2014 using an environmentally extended multi-regional input-output model and detailed U.S. consumer expenditure survey data. The results show that the annual carbon footprint of the U.S. households ranged from 17.7 to 20.6 tCO2eq/capita with an expanding proportion occurring overseas. Housing and transportation contributed 53–66% of the domestic carbon footprint. Overseas carbon footprint shows an overall increasing trajectory, from 16.4% of the total carbon footprint in 1995 to the peak of 20.4% in 2006. These findings provide valuable insights on the scale, distribution, and variations of the global GHG emissions driven by the U.S. household consumption for developing consumption-side strategies in the U.S. for climate mitigation.

Does the "missing" high-income matter? -Income distribution and inequality revisited with truncated distribution

The issue of missing high-income data in household surveys has been a constant concern among researchers and practitioners when drawing inferences on inequality measures, discussing the relationship between poverty and growth, and examining the relationship between expenditure and income. We introduce a truncated distribution technique to correct the potential bias caused by the missing high-income data. Using 2002/2007/2013 Chinese Household Income Project Survey data and the 2002/2007/2014 US Consumer Expenditure Survey data, we test and estimate three commonly used income distributions: lognormal, Singh Maddala, and Beta II distribution with/without the truncation assumption. We find that the truncated Beta II distribution best describes income distribution in China, while the truncated Singh Maddala best fits the income in the US. The missing high-income in China has a significant but small effect on the Gini and Theil coefficients for 2007, whereas the missing high-income in the US has significant effects for 2007 and 2014. The Gini coefficient increases from the sample mean 0.44 to the simulation mean of truncated Beta II distribution as 0.47 for China in 2007 and increases from the sample mean 0.4422/0.4485 to the simulated mean of truncated Singh Maddala distribution 0.4506/0.4588 for 2007 and 2014 respectively. We also check the impact of missing low-income individuals on inequality assessment and find that the missing low-income data does not appear to underestimate inequality.

Determinants of Entertainment and Apparel Expenditures in an American Household

Distinct characteristics of households play an important role in the decisions made regarding expenditures. This study identifies what specific characteristics of households drive the decisions to make specific purchases such as entertainment and apparel using the Consumer Expenditure Survey Data from the Bureau of Labor Statistics (BLS, 2018) for the years 2016-17. First, we follow the log-lin regression model methodology to identify if there are clear disparities in purchasing decisions based upon the gender of the head of household (HH) and then present sub-models to identify the determinants of expenditures on apparel and entertainment for a household based upon characteristics such as income of the household, age of the head of the household, family size, number of children below age five in the household, number of earners in the household, education of the head of the household, and race and ethnicity of the head of the household as our primary independent variables. Primary model results show that there are clear differences in the determinants of total expenditures based on the sex of the head of the household. Our sub-models show that male-headed households spend more on entertainment, whereas female-headed households spend more on apparel than their counterparts.

Fighting Childhood Poverty: How a Universal Child Allowance Would Impact the U.S. Population

:Modern developed economies have a material abundance beyond the imagination of people living two centuries ago, yet for many the guaranteed ability to access that material abundance is more tenuous than ever. In the United States, a job loss or an illness can strip the family of all assets faster than a drought could destroy the livelihood of a nineteenth-century farmer. As a reaction to this, many young adults are choosing to delay or forego having children. The largest age demographic in the United States who live in poverty are children. Social Security is politically popular and has been successful at dramatically lowering poverty amongst the elderly. A similar policy could be equally successful for children. This article uses Consumer Expenditure Survey data to explore how a universal child allowance could decrease the number of children living in poverty and help all U.S. families with children.

Are Values Reported Using Quantities and Prices in Consumption Expenditure Data?

For about 125 items of food, the Consumer Expenditure Survey (CES) schedule of the Indian National Sample Survey asks the interviewer to obtain both quantity and value of household consumption during the reference period from the respondent. This would appear to put a great burden on the respondent and call for reduction in the number of items in the schedule. But it is likely that interviewers actually proceed by asking the respondent to recall quantity and price usually paid (instead of quantity and value) and multiplying the two to get value, item-wise, as price usually paid might be easy to recall, and survey protocol does not disallow it. Whether this is done and, if so, how frequently, is of obvious importance to the planners of this important living standards survey, as efforts to reduce the number of items to lighten the respondent burden continue. In this study, a method, using unit records for vegetable items in the NSS’s 2011–2012 CES, is devised to estimate the proportion of interviews in which values of vegetable consumption were in fact determined by the multiplication method. The findings suggest that this method was much more prevalent than “independent” recall of values. This paper concludes that the survey would benefit if the price-and-quantity method were explicitly laid down as the method to be followed to obtain value, as it releases valuable interview time for more important items. Research using unit values from NSS CES data as a proxy for prices would also interpret and use such unit values better, were the practice to be followed uniformly.