Consumer Expenditure Survey Research Articles

Gender and urban poverty in India

Poverty is a product of various deprivations. Gender discrimination is linked with deprivation in terms of socio-economic and political opportunities. This study explores the link between female-headship of a household and its vulnerability towards urban poverty. Further, the most vulnerable sub-sections among the urban female-headed households are identified. The study is based on the 68th round of Household Consumer Expenditure, and Employment and Unemployment Survey, India. Female-headed households were found to have higher odds to be urban poor as compared to their male counterparts. However, this gender-based difference in odds to be urban poor disappears once educational attainment of household-head is controlled for. This highlights that the discrimination in terms of educational attainment is major cause and solution to urban poverty among female-headed households. Further, female-headed households are not a homogeneous group and exhibit significant differences in their vulnerability to urban poverty across different socio-economic and demographic sub-groups. The negative impact of COVID-19 pandemic on female education in India is expected to create a long-term gender gap in terms of poverty. Thus, the public policy should stress on skill and educational attainment of females and target the poverty alleviation programmes on vulnerable sub-section of the female-headed households.

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.

A New Approach to Understanding the Impact of Automobile Ownership on Transportation Equity

Transportation affordability, the ability of a household to comfortably bear necessary transportation expenses, is a pressing challenge to the development of sustainable and equitable places. Transportation planners have historically identified transit user groups as choice riders, those with access to other modes or the ability to purchase access, and captive riders, transit-dependent populations who must use transit regardless of service quality. However, this characterization is incomplete, disregarding built-environment pressures that compel a much larger population of households to own and use vehicles regardless of their ability to afford the very high price of vehicle ownership. We propose a new theoretical construct, illuminated using the Bureau of Labor Statistics Consumer Expenditure Survey Public Use Microdata—the transportation choice/captivity matrix—which examines transportation user groups via vehicle ownership and income variables.

Educational expenditure of Asian American families

Parental spending is an essential measure of parenting styles and is crucial in the intergenerational transmission of advantage. Popular media outlets portray Asian Americans as a “model minority” who invest heavily in education. Past literature on racial spending gaps for children often does not include Asians. A limited number of studies that have examined Asian–White spending gaps focus predominantly on savings and spending for college. Using nationally representative Consumer Expenditure Surveys (CE) Public Use Microdata 2009–2022, this study examines K–12 spending gaps between Asian American and White families and how race interacts with class in affecting parental spending. I find that Asian households outspend Whites overall. A detailed analysis of three academic and three enrichment expenses shows that although Asian spend more on recreational lessons and tutoring, White outspend Asians on sports and cultural activities. The spending gap on tutoring is most prominent among households with a graduate degree, not among families with a high school education or below, where the Asian–White achievement gap is the most salient. Considering the increasing weight of home choices in determining the quality of public K–12 education, this study also examines the racial spending gap on housing. A difference-in-difference analysis suggests that Asian households across the educational spectrum spend more on housing for the sake of K–12 children. This spending gap is the most salient among families without a college degree and those with a graduate degree.

The economic disparity between Hispanic and non‐Hispanic White households: An analysis of middle‐class achievement

AbstractWe utilize data from the 2010–2019 Consumer Expenditure Surveys to examine middle‐class achievement of English‐speaking and non‐English‐speaking Hispanic households compared to non‐Hispanic white households in the United States. Using an innovative expenditure‐based middle‐class measure, our findings show that non‐English‐speaking Hispanics lag English‐speaking Hispanics, and English‐speaking Hispanics lag whites, in middle‐class attainment. We also identify significant structural differences among the three groups, particularly in how education, marriage, and employment affect middle‐class achievement. Non‐English‐speaking Hispanics have a lower rate of return on education compared to both whites and English‐speaking Hispanics. Non‐English‐speaking Hispanics experience lower marriage and employment premiums compared to their English‐speaking Hispanic counterparts, and English‐speaking Hispanics experience lower marriage and employment premiums compared to whites. This study contributes to the literature by introducing the innovative expenditure‐based middle‐class measure and emphasizing the importance of considering within‐group differences among Hispanics to reduce the Hispanic‐white economic disparity.

Inflation inequality in Austria: A mid-term view on cost-of-living developments

I compute household-specific inflation rates for Austria for the period between 2016 and 2019. Data is provided by Statistics Austria’s Consumer Expenditure Survey for 2014/15 (“Konsumerhebung”) and contains household expenditures reaching the 3-digit COICOP level. I find a negative plutocratic bias, meaning that common types of measurement tend to understate CPI inflation. In the period covered, the distribution of inflation is characterized by large dispersion, meaning that the standard CPI inflation is not very representative for a large proportion of Austrian households. There is a clear negative relationship between income and inflation that is mainly driven by ownership status. Households living in rental accommodation are affected significantly more by inflation. I further find an urban-rural divide in the sense that households in larger cities are more likely to exhibit higher individual inflation rates than their counterparts in small municipalities. Considering household structures, single households exhibit significantly higher inflation. These effects remain significant even after controlling for ownership status. All in all, the results are strong arguments for paying greater attention to the issue of inflation inequality when it comes to wage setting, taxation or indexations, as there are potentially large distributional effects going unrecognized.

Quantifying Vulnerability to Poverty in the Drought-prone Lowlands of Ethiopia

AbstractA forward-looking measure of ‘vulnerability to poverty’ is estimated and a concerted effort is made to understand the sources of vulnerability in the drought-prone lowlands of Ethiopia. Using the Household Consumption Expenditure Survey and the Welfare Monitoring Survey of 2015–16, which include additional zones in the Afar and Somali regions increasing the representativeness of the survey in pastoral areas, the analysis reveals that vulnerability is remarkably higher in the drought-prone lowlands than in the other ecological zones, even though differences in poverty rates are modest. The analysis also reveals important distinctions in the sources of vulnerability. In the drought-prone lowlands, (i) the prevalence of both poverty-induced and risk-induced vulnerability is the highest among all the ecological zones and (ii) the importance of vulnerability due to aggregate shocks, such as droughts, relative to vulnerability due to idiosyncratic shocks is higher than in the other ecological zones. These findings attest to the unique nature of the drought-prone lowlands in comparison to the other agroecological zones of Ethiopia and the need for adaptive social protection programmes targeting not only the chronically poor but also the vulnerable.

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.

Türkiye’de Ev Dışı Gıda Harcamalarının Kohort Analizi

In this study, the expenditures of food away from home of households are examined using the Household Consumption Expenditure Survey, which was conducted by the Turkish Institute of Statistics from 2002 to 2018. We adopt the Dependent Double Hurdle model with the incorporation of age, period, and cohort effects and the impact of socio-demographic variables. Analysis results reveal that the cohort and age effects are significant in both participation and consumption on food expenditure away from home of household. The findings suggest that the expenditure of food away from home for younger cohorts rises faster for older cohorts. The elasticity of food expenditures for cohorts and age groups is increasing from the youngest cohort and from the youngest age group to the oldest cohort which is the largest age group. The results show that participation and consumption decisions are significantly affected by income and demographic characteristics in Turkey.

Food insecurity in the Eastern Indo-Gangetic plain: Taking a closer look.

Food security is an important policy issue in India. As India recently ranked 107th out of 121 countries in the 2022 Global Hunger Index, there is an urgent need to dissect, and gain insights into, such a major decline at the national level. However, the existing surveys, due to small sample sizes, cannot be used directly to produce reliable estimates at local administrative levels such as districts. The latest round of available data from the Household Consumer Expenditure Survey (HCES 2011-12) done by the National Sample Survey Office of India used stratified multi-stage random sampling with districts as strata, villages as first stage and households as second stage units. Our Small Area Estimation approach estimated food insecurity prevalence, gap, and severity of each rural district of the Eastern Indo-Gangetic Plain (EIGP) region by modeling the HCES data, guided by local covariates from the 2011 Indian Population Census. In HCES, 5915 (34429), 3310 (17534) and 3566 (15223) households (persons) were surveyed from the 71, 38 and 18 districts of the EIGP states of Uttar Pradesh, Bihar and West Bengal respectively. We estimated the district-specific food insecurity indicators, and mapped their local disparities over the EIGP region. By comparing food insecurity with indicators of climate vulnerability, poverty and crop diversity, we shortlisted the vulnerable districts in EIGP. Our district-level estimates and maps can be effective for informed policy-making to build local resiliency and address systemic vulnerabilities where they matter most in the post-pandemic era. Our study computed, for the Indian states in the EIGP region, the first area-level small area estimates of food insecurity as well as poverty over the past decade, and generated a ranked list of districts upon combining these data with measures of crop diversity and climatic vulnerability.

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

Assessing U.S. consumers' carbon footprints reveals outsized impact of the top 1%

Unsustainable environmental degradation and extreme economic inequality are two of humanity's most pressing challenges. They are intimately linked. Climate-altering greenhouse gas (GHG) emissions are disproportionately driven by consumption among wealthy and socially privileged groups, yet poorer and socially marginalized peoples face disproportionate climate harms. Here we use the Eora MRIO database and Consumer Expenditure Surveys to quantify GHG emissions related to goods and services consumed by United States households between 1996 and 2019 - including construction of a synthetic dataset to estimate top 1% and top 0.1% household emissions. Top 1% households are of particular interest because their emissions have been largely missed or simplistically and inaccurately estimated in past analysis, yet they exert disproportionate political power in shaping U.S. climate policy. Results suggest significant GHG inequality across economic class and racial lines. In 2019, we estimate the U.S. top 0.1% had emissions (955 t CO2e) 57× higher than bottom decile U.S. households and 597× higher than an average low-income country household. White non-Hispanic household emissions were 1.3× higher than Black households. If climate policy does not account for such extreme emissions disparities, it will limit effectiveness, erode public support, and disproportionately harm economic and socially marginalized groups.

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.

Regression with an imputed dependent variable

SummaryResearchers are often interested in the relationship between two variables, with no single data set containing both. A common strategy is to use proxies for the dependent variable that are common to two surveys to impute the dependent variable into the data set containing the independent variable. We show that commonly employed regression or matching‐based imputation procedures lead to inconsistent estimates. We offer a consistent and easily implemented two‐step estimator, “rescaled regression prediction.” We derive the correct asymptotic standard errors for this estimator and demonstrate its relationship to alternative approaches. We illustrate with empirical examples using data from the US Consumer Expenditure Survey (CE) and the Panel Study of Income Dynamics (PSID).

What do fiscal stimulus packages mean for household debt?

AbstractWe study the links between fiscal stimulus packages during times of crisis and households' liabilities. We do so by using household‐level data on income and liabilities from the Consumer Expenditure Survey, and estimating an empirical model along those in the literature on the consumption effects of these packages. We find that receiving a check from the government tends to translate into a reduction in outstanding liabilities for American households. This effect is robust to controlling for income levels and household size. The effects are driven by households whose income is below the median and by those who remain employed during the crisis.

Age differences in expenditure responses to Supplemental Nutrition Assistance Program benefits

AbstractUsing data from the 2007–2011 and 2012–2014 Consumer Expenditure Surveys, this study finds that unlike younger adults, senior Supplemental Nutrition Assistance Program (SNAP) participants' food‐at‐home expenditures did not respond to the 2009 increase in SNAP benefits. Higher benefits did not shift seniors' Engel curve for food‐at‐home spending suggesting that in‐kind SNAP benefits are equivalent to cash income for seniors, unlike younger adults. The 2013 decrease in SNAP benefits had no effect on senior or younger adult food spending. Changes in benefits also resulted in spillover effects on non‐food spending for seniors and younger adults including spending on transportation, utilities, health services, and other expenditures.

India consists of multiple food systems with scoioeconomic and environmental variations.

Agriculture in India accounts for 18% of greenhouse gas (GHG) emissions and uses significant land and water. Various socioeconomic factors and food subsidies influence diets in India. Indian food systems face the challenge of sustainably nourishing the 1.3 billion population. However, existing studies focus on a few food system components, and holistic analysis is still missing. We identify Indian food systems covering six food system components: food consumption, production, processing, policy, environmental footprints, and socioeconomic factors from the latest Indian household consumer expenditure survey. We identify 10 Indian food systems using k-means cluster analysis on 15 food system indicators belonging to the six components. Based on the major source of calorie intake, we classify the ten food systems into production-based (3), subsidy-based (3), and market-based (4) food systems. Home-produced and subsidized food contribute up to 2000 kcal/consumer unit (CU)/day and 1651 kcal/CU/day, respectively, in these food systems. The calorie intake of 2158 to 3530 kcal/CU/day in the food systems reveals issues of malnutrition in India. Environmental footprints are commensurate with calorie intake in the food systems. Embodied GHG, land footprint, and water footprint estimates range from 1.30 to 2.19 kg CO2eq/CU/day, 3.89 to 6.04 m2/CU/day, and 2.02 to 3.16 m3/CU/day, respectively. Our study provides a holistic understanding of Indian food systems for targeted nutritional interventions on household malnutrition in India while also protecting planetary health.

Food spending in the United States during the first year of the COVID-19 pandemic.

The COVID-19 pandemic brought about a significant increase in the unemployment rate and a decline in consumer income. At the same time, the public health responses to the pandemic, such as lockdowns and business closures, disrupted the food supply chain. These pandemic-driven changes could lead to a shift in food spending behaviors and potentially exacerbate the food insecurity situation. Leveraging the nationally representative dataset from the 2017–2020 consumer expenditure surveys, we employ a two-part model to assess the changes in weekly household spending on total food, food-at-home (FAH), and food-away-from-home (FAFH) between the pre-pandemic and pandemic period in the United States. Our finding shows a predicted marginal decline in FAFH expenditure by 33.7% but an increase in FAH spending by 6.9% during the pandemic. The increase in FAH spending could not fully offset the decrease in FAFH spending, leading to a decline in total food spending by 12.6%. The results could provide a basis for future studies on food insecurity, nutrient intake, and healthy consumption during the pandemic.

Multivariate understanding of income and expenditure in United States households with statistical learning

In recent decades, data-driven approaches have been developed to analyze demographic and economic surveys on a large scale. Despite advances in multivariate techniques and learning methods, in practice the analysis and interpretations are often focused on a small portion of available data and limited to a single perspective. This paper aims to utilize a selected array of multivariate statistical learning methods in the analysis of income and expenditure patterns of households in the United States using the Public-Use Microdata from the Bureau of Labor Statistics Consumer Expenditure Survey (CE). The objective is to propose an effective data pipeline that provides visualizations and comprehensive interpretations for applications in governmental regulations and economic research, using thirty-five original survey variables covering the categories of demographics, income and expenditure. Details on feature extraction not only showcase CE as a unique publicly-shared big data resource with high potential for in-depth analysis, but also assist interested researchers with pre-processing. Challenges from missing values and categorical variables are treated in the exploratory analysis, while statistical learning methods are comprehensively employed to address multiple economic perspectives. Principal component analysis suggests that after-tax income, wage/salary income, and the quarterly expenditure in food, housing and overall as the five most important of the selected variables, while cluster analysis identifies and visualizes the implicit structure between variables. Based on this, canonical correlation analysis reveals high correlation between two selected groups of variables, one of income and the other of expenditure.