Consumer Expenditure Survey Research Articles

Unequal household carbon footprints in the peak-and-decline pattern of U.S. greenhouse gas emissions

Greenhouse gas (GHG) emissions in the U.S. peaked and declined in the first decade of the 21st century, largely attributed to the increased use of natural gas and renewable energy replacing coal. However, if and to what extent household consumption also played a role in this trend is still debated. Finding demand-side options is necessary to hedge against the risks of technology solutions failing to materialize. To fill this gap, this study analyzes the change in GHG emissions driven by U.S. household consumption, explores the drivers of this change and the contribution of different income groups. To this end, this study combined the U.S. consumer expenditure survey with an environmentally-extended multi-regional input-output framework to analyze changes in GHG emissions induced by household consumption between 2001 and 2015. This study further analyzed how much population, consumption volume and consumption patterns drove emission changes by quintile income groups. The results show that changes in household consumption contributed approximately one-third of the national emission decline. The decline in GHG emissions from U.S. households was mainly associated with a decrease in the consumption of carbon-intensive products, including gasoline, electricity, and animal-based food products. The top quintile income households were the main contributors to the emission increase before the peak, while the third and fourth income quintiles became emission mitigation leaders after 2010. Carbon inequality increased during the 2001–2006 period, mainly driven by increased wealth and consumption of high-income households, and was relatively stable after the peak. Emissions from certain consumption categories of the top quintile group are significantly higher than the bottom four quintiles with increasing trends, especially leisure-related services and goods, which require more attention in future policymaking for emission reduction.

Asymmetric Impacts of Rising Food Prices on Households’ Welfare in South West Ethiopia

Food price inflation affects household welfare and the macroeconomy pervasively. The study estimated Quadratic Almost Ideal Demand system of six food groups to simulate the money costs of food prices inflation on households’ welfare and; predict relative potency of income and price policies to counter the effects in a particular context of South West Ethiopia. It drew on Household Income and Consumption Expenditure Survey data of 519 households collected by the Central Statistical Authority of Ethiopia. Rural households respond more to income and prices than do urban dwellers. The welfare losses from food price inflation fall heavily, more on urban households than their rural counterparts. On average, it requires resource allocations as large as percentage increases in prices to keep households’ welfare at pre-price change levels. It is suggested that a sector-specific mix of income and price policies could offset the negative consequences on consumers’ welfare.

Carbon footprint analysis of household consumption in greater Guadalajara reveal stark socio-spatial inequalities

Household consumption drives more than 65% of global greenhouse gas emissions. Nevertheless, most governments plan to meet the Paris Agreement based on production-based accounting (PBA), neglecting lifecycle emissions embodied in trade and consumption, as well as socio-spatial inequalities. This is a sub-city consumption-based accounting (CBA) analysis of Guadalajara Metropolitan Area (MAG), grounded on Environmentally Extended Multi-Regional Input-Output using EXIOBASE 3.8.1, combined with Mexico's 2018 Consumer Expenditure Survey. The main objective is to explain the CO2e footprint of MAG's households, identifying emission hotspots and socio-spatial inequalities. This household CBA estimate shows MAG emitted a total of 23.35 MtCO2e during 2018, at least 26.6% higher than the local Climate Action Plan (CPA) territorial PBA estimate, and 121% higher for food. The average per capita footprint is 4.52 tCO2e, 40% higher than the average Mexican and 61% higher than the recommended level of 2.8 tCO2e/cap-yr to align with 1.5 °C pathways by 2030. However, 26% of the MAG population, mostly low-income households, is below this level. Conversely, highest incomes and per capita CO2e footprints are concentrated in Zapopan and Guadalajara, with 64% of total emissions despite hosting 55% population together. Further, income decile X reach 35% of the total emissions with 13% of the population. If left unchecked, MAG's footprints might increase with the trends of urbanization, inequality, higher consumption, and smaller households, as we find larger households associate with lower footprints. We confirm the need to complement CAP with CBA approach and increase socio-spatial resolution to unlock demand-side solutions that leverage on the interplay between urban, socioeconomic, and technical factors.

An Ecological Analysis of Food Expenditure Patterns During Periods of Economic Recovery and Economic Shock in India

ObjectivesTo study the patterns of food expenditure by Indian households during periods of economic recovery (after the Great Recession in 2008–09) and economic shock (from a currency demonetization policy in 2016). I hypothesize that real consumer price index (CPI) adjusted food expenditure (in Rupees) would be lower after the economic shock with higher share of spending on processed foods ― consistent with the nutrition transition hypothesis. MethodsUsing published tables from India’s nationally representative Household Consumer Expenditure Surveys in 2011–12 and 2017–18, I estimate real (Rs.) and percentage (of total expenditure) CPI-adjusted monthly per capita expenditure (MPCE) on food. I report the share of food MPCE for dairy products, sugar, oil, fresh fruits, vegetables (including tubers), and processed foods. Analysis was carried out for 36 states and union territories stratified by urbanicity. ResultsReal and percentage MPCE on food were lower in 2017–18 (Rs. 705.1; 52.3%) relative to 2011–12 (Rs. 794.9; 52.9%) for rural India. For urban India, real MPCE on food (but not percentage) was higher in 2017–18 (Rs. 1207.4; 41.9%) relative to 2011–12 (Rs. 1168.4; 42.6%). The percentage decrease was due to higher non-food MPCE (2011–12: Rs. 1572.8; 2017–18: Rs. 1672.6). At the state-level, real (rural: 28; urban: 9) and percentage (rural: 21; urban: 20) decreases were observed in rural areas. As a share of food MPCE, spending on sugar (rural: 25; urban: 29) and edible oil (rural: 22; urban: 26) decreased in most states from 2011–12. However, share of food MPCE increased on dairy (rural: 26; urban: 23), fresh fruits (rural: 31; urban: 30), vegetables (rural: 26; urban: 19), and processed foods (rural: 20; urban: 26) for majority of states. This is despite a real decrease in spending (in Rs) on processed foods (rural: 26; urban: 9) and vegetables (rural: 21; urban: 8) for most states in rural areas. Real spending (Rs.) increased for dairy (rural: 20; urban: 26) and fresh fruits (rural: 23; urban: 29) for most states. ConclusionsRural areas showed a decrease in real food expenditures from 2011–12 to 2017–18. Food expenditures decreased in share for sugar and oil, but increased in share for fruits, vegetables and processed foods. This was also observed in urban areas where there was an increase in real food expenditures. Funding SourcesNone.

Questioning the poverty line aggregation approach in India: Issues of mismatches and overlaps

AbstractThe Rangarajan Expert Group (REG), an official constituted panel of experts, decided to extend the traditional practice of food component‐based poverty line to three components (food, essential nonfood, and other nonfood) based poverty line. Although this multiple component‐based poverty identification is an innovative step; the present study questions the justification for combining the food and nonfood dimensions and estimating poverty just by an aggregated poverty line as presented by the REG. To test this argument empirically, this study evaluates the mismatches and overlaps in poverty identification based on the REG approach of an aggregated poverty line and the proposed component‐specific poverty line approach in the present study, using the unit level data of the 68th round of the Consumption Expenditure Survey of NSSO (conducted during 2011–2012). Our analysis reveals a high proportion of mismatches (with minimal overlaps) and therefore, this nullifies the notion that the REG aggregated poverty line‐based poverty identification is capable of measuring food and nonfood deprivations separately. Moreover, the substantial evidence of misclassification errors between “poor” as per the REG aggregated poverty line and the three constructed categories of poor by the present study namely “Ultra poor,” “Actually poor,” and “Possibly poor” also shows that the REG aggregated poverty line cannot serve as a proxy to represent the extent of joint deprivation in food, essential nonfood, and other nonfood components.

Reducing gender bias in household consumption data: Implications for food fortification policy

Household Consumption Expenditure Survey (HCES) data are increasingly used to inform nutrition policy around the world, most prominently for food fortification programs. However, they risk providing incorrect and gender-biased estimates of dietary intakes. We use both 7-day HCES and 24-hour dietary recall (24HR) data on all members of 5604 households in rural Bangladesh to disentangle the two main sources of error: 1) mismeasurement of household consumption, and 2) intra-household allocation assumptions used to individualize household consumption. We show that, relative to 24HR, HCES overestimate household-level quantities and underestimate women’s share of household foods. Errors from modeling the potential benefits and risks of fortification depend on the food – better measurement is needed for foods consumed episodically (e.g. wheat flour or sugar) or in small quantities (e.g. salt and oil). Beyond mean bias, we find poor and heteroskedastic agreement between HCES and 24HR methods, which is more driven by mismeasurement of food quantities than the application of flawed assumptions about food allocation – at least in the Bangladeshi context. We demonstrate a novel generalizable method for improving HCES intake estimates by drawing on the advantages of both HCES and 24HR data. Using a small sample of 24HR data to generate context- and food-specific quantity and allocation corrections, we can almost eliminate mean bias. With further validation, we hope our proposed method can be used to ensure that HCES estimates account for locally-specific measurement error and gender norms, and that nutrition policy based on these data will be safer and more gender-sensitive.

Small area estimation of food insecurity prevalence for the state of uttar pradesh in India

The 2nd Sustainable Development Goal (SDG) of the United Nations attempt to eliminate the potential hunger and food insecurity issues by 2030, but in the plight of COVID19 pandemic it has become far more critical and persistent issue globally as well as in India. The nation-wide socio-economic surveys of National Sample Survey Office (NSSO) in India are designed to produce reliable and representative estimates of important food insecurity parameters at state and national level for both rural and urban sectors separately but these surveys cannot be used directly to generate reliable district level estimates. Whereas, efficient and representative disaggregated level estimates for the extent (or incidence) of food insecurity prevalence has direct impact on strategizing effective policy plans and monitoring progress towards eliminating food insecurity. In this backdrop, the paper outlines small area estimation approach to estimate the incidence of food insecurity across the districts of rural Uttar Pradesh in India by linking data from the 2011–12 Household Consumer Expenditure Survey of NSSO, and the 2011 Indian Population Census. A spatial map has been generated showing spatial disparity for the incidence of food insecurity in Uttar Pradesh. These disaggregated level estimates are relevant and purposeful for SDG indicator 2.1.2 – severity of food insecurity. The estimates and map of food insecurity incidences are expected to deliver invaluable information to the policy-analysts and decision-makers.

Rapid Consumption Survey Methodology: An Empirical Study in Bangladesh

Every unidimensional poverty measure is mostly based on consumption expenditure. In the household consumption expenditure survey, the questionnaire needs to record detailed information about food and non-food items. The lengthy administering time hampers the survey very often. This study applies a rapid consumption methodology on two-round panel data from the Bangladesh integrated household survey conducted by the International Food Policy Research Institute. The surveyed items for each household are partitioned into the core and optional modules. Any randomly taken optional module is assigned to a household, and missing modules are then estimated by the imputation method. In estimating the poverty indicators, the multiple imputation with chained equation model shows better estimates than the ordinary least squares (OLS) and median imputation techniques. The estimated poverty headcount ratio (FGT0) is 26.72%, whereas the full consumption data show FGT0 as 27.2%. These results suggest that the rapid consumption survey methodology in a risk-prone area can be a potential approach to estimate the consumption and poverty at a lower cost and lower administrative time.

Building a Consumption Poverty Measure: Initial Results Following Recommendations of a Federal Interagency Working Group

Consumption is a well-being measure that is determined by a combination of resources (e.g., income, in-kind benefits, assets, debt, time) available to households, their circumstances, and their preferences. In this study, we derive consumption poverty statistics using a consumption measure that includes the flow of services from owner-occupied housing and vehicles and in-kind transfers. The base data are from the US Consumer Expenditure Survey Interview from 2015 through 2020. The consumption poverty rate (using an absolute threshold anchored to the 2015 relative consumption poverty rate) declines from 16.8 percent in 2015 to 11.5 percent in 2020.

CPI outlet samples from the CE: a new life for the Point-of-Purchase Survey

This article describes U.S. Bureau of Labor Statistics testing efforts to collect consumer shopping location data in the Consumer Expenditure Surveys. These efforts, which started in 2016, eventually replaced the Telephone Point-of-Purchase Survey as the Consumer Price Index’s primary establishment sampling frame for the Commodities and Services Pricing Survey.

Measuring Inflation under Pandemic Conditions

Abstract National statistical offices have faced unprecedented circumstances in the modern history of economic measurement. There were dramatically changing consumer expenditure patterns due to pandemic conditions, with lockdowns and fear of infection making many goods and services unavailable. We examine the implications of changing relative expenditures for the construction of Consumer Price Indexes, with special reference to the treatment of prices for unavailable products. We conclude that for many purposes, it would be useful for statistical agencies to establish a continuous consumer expenditure survey. We also examine various other practical pandemic induced CPI measurement problems.

Clean Fuel for Rural Families in India a Major Challenge: Evidence from four rounds of consumer expenditure survey

Clean Fuel for Rural Families in India a Major Challenge: Evidence from four rounds of consumer expenditure survey

Measuring real consumption and consumer price index bias under lockdown conditions.

Millions of goods and services are now unavailable in many countries due to the current coronavirus pandemic, dramatically impacting on the construction of key economic statistics used for informing policy. This situation is unprecedented; hence, methods to address it have not previously been developed. Current advice to national statistical offices from the International Monetary Fund, Eurostat and the United Nations is shown to result in downward bias in the consumer price index (CPI) and upward bias in real consumption. We conclude that, to produce a meaningful CPI within the lockdown period, it is necessary to establish a continuous consumer expenditure survey.

Public Investments and Class Gaps in Parents' Developmental Expenditures.

Families and governments are the primary sources of investment in children, providing access to basic resources and other developmental opportunities. Recent research identifies significant class gaps in parental investments that contribute to high levels of inequality by family income and education. State-level public investments in children and families have the potential to reduce class inequality in children's developmental environments by affecting parents' behavior. Using newly assembled administrative data from 1998-2014, linked to household-level data from the Consumer Expenditure Survey, we examine how public sector investment in income support, health and education is associated with the private expenditures of low and high-SES parents on developmental items for children. Are class gaps in parental investments in children narrower in contexts of higher public investment for children and families? We find that more generous public spending for children and families is associated with significantly narrower class gaps in private parental investments. Moreover, we find that equalization is driven by bottom up increases in low-SES households' developmental spending in response to the progressive state investments of income support and health, and by top down decreases in high-SES households' developmental spending in response to the universal state investment of public education.

Consumption peer effects and utility needs in India

We construct a peer effects model where mean expenditures of consumers in one's peer group affect utility through perceived consumption needs. We provide a novel method for obtaining identification in social interactions models like ours, using ordinary survey data, where very few members of each peer group are observed. We implement the model using standard household‐level consumer expenditure survey microdata from India. We find that each additional rupee spent by one's peers increases perceived needs, and thereby reduces one's utility, by the equivalent of a 0.25 rupee decrease in one's own expenditures. These peer costs may be larger for richer households, meaning transfers from rich to poor could improve even inequality‐neutral social welfare, by reducing peer consumption externalities. We show welfare gains of billions of dollars per year might be possible by replacing government transfers of private goods to households with providing public goods or services, to reduce peer effects.

Cash for Votes: Evidence from India

This paper investigates the prevalence of vote-buying in democratic elections where stringent restrictions on corporate donations to political parties exist. We combine data from state assembly elections in India with household-level consumer expenditure surveys (conducted by NSSO) over the period 2004-11. Exploiting a difference-in-differences methodology, we estimate the effects elections have on the consumption of various household items: food, clothes, education-related, etc. Moreover, there is heterogeneity in such consumption adjustments across households. Our estimates suggest that legal sources of funds are not sufficient for generating such spikes in consumption and indicate the role of the hidden economy in politics.

Parental Investments of Money for White, Black, and Hispanic Children in the United States

Recent research has shown wide class gaps in parental investments of money. Although scholars have suggested similar important gaps by race and ethnicity, these have not been as clearly documented, and some suggest that they may be the result of economic differences or family characteristics. Using 16 years of the Consumer Expenditure Survey, the author shows the predicted levels of parental investments for white, Black, and Hispanic families under four conditions: the unadjusted levels, controlling for family characteristics, controlling for family income, and controlling for both family characteristics and income. The visualization shows large gaps between children in white families compared with Black and Hispanic families. Both sociodemographic and economic factors play a substantial role in these differences, and the racial and ethnic gaps in parental investments of money are nearly eliminated when both are accounted for.

Inclusiveness of Poverty Reduction: A Study of Andhra Pradesh and Telangana

This study examines the inclusiveness of poverty reduction among the newly formed states of undivided Andhra Pradesh by looking into the poverty among the different socio-religious groups both in the rural and urban regions during the 2000’s. The major proposition that has highlighted in this study: which socio-religious groups are more poverty ridden in the undivided Andhra Pradesh and its bifurcated states (Andhra Pradesh and Telangana)? The National Sample Survey Organisation unit level data (61st and 68th rounds) on Consumption Expenditure Survey have been used for the analysis. The result reveals that Telangana is having lower poverty level than Andhra Pradesh and records a faster reduction in poverty during 2004–2005 to 2011–2012. Andhra Pradesh constitutes around 70% of the total poor of the undivided Andhra Pradesh. This study found that most of the Scheduled Tribes, Scheduled Castes among the social groups and Muslims among the religious groups are more vulnerable and having higher head count ratio than the state average. Although several welfare programmes and schemes have already been implemented to eradicate poverty and inequality, still it is not effective in the ground level. Based on this argument, our study suggests that the schemes should focus on different sections of the people irrespective of rural and urban sectors in both the recently bifurcated states of Telangana and Andhra Pradesh. JEL Codes: I 32, D 63, P 25

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.

Assessing Consumer Expenditure Surveys data quality through the lens of data use

This article describes the uses of BLS Consumer Expenditure Surveys (CE) data at the federal level, discusses the CE program’s approach to assessing data quality, and reviews the data’s fitness for use from the perspective of the data users.