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Why Households Hate Grocery Inflation and Other Essays in Economic Measurement
Why Households Hate Grocery Inflation and Other Essays in Economic Measurement
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260209102853
- ISBN
- 9798291567012
- DDC
- 658
- 서명/저자
- Why Households Hate Grocery Inflation and Other Essays in Economic Measurement
- 발행사항
- [Sl] : University of Michigan, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 189 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-03, Section: A.
- 주기사항
- Advisor: Shapiro, Matthew D.
- 학위논문주기
- Thesis (Ph.D.)--University of Michigan, 2025.
- 초록/해제
- 요약Each chapter of this dissertation makes a methodological contribution to economic measurement. Empirical applications of these new methods show the value of more granular or comprehensive measures for challenging long-standing assumptions and raising fundamental questions about economic behavior.Using a detailed panel of consumer purchases and matched retail scanner data, Chapter I uncovers important new aspects of how households experience inflation. Inflation varies widely across households for a reason overlooked by aggregate measures: the individual product choices households make among ostensibly close substitutes. The price changes of different product varieties became more widely dispersed when inflation rose in 2021 and 2022, and households frequently failed to substitute to items with slower price growth. A model of consumption with idiosyncratic preferences rationalizes this behavior and implies that household-level inflation rates represent observable bounds on welfare losses. Grocery price inflation in 2022 generated annualized welfare losses ranging from $575 to $1,150 in the 10th and 90th percentiles of the inflation distribution.Chapter II develops wage measures capturing how two features of occupational change-the entry and exit of occupations over time and shocks to relative supply and demand-contributed to workers' effective wage gains and firms' effective labor costs from 1940-2020. It evaluates these contributions using a two-sided model of the labor market featuring occupational change, for which we derive exact wage indices. These indices imply increasing occupational variety and the tendency for firms and workers to reallocate labor toward occupations with lower effective costs or higher effective earnings meaningfully improved worker welfare and reduced firms' labor costs. Compared to baseline real wage growth of 1.03 percentage points annually in the absence of occupational change from 1940-2020, these forces raised workers' annualized real wage growth by 0.29 percentage points and reduced firms' annualized real labor costs by 0.20 percentage points.The last chapter addresses the challenge of using Big Data to improve economic statistics when the dataset covers only a non-representative subset of agents. It tests several methods for blending such data with surveys to produce unbiased population statistics. Several methods effectively reduce error-including a "Big Data as Strata" method which easily integrates into an existing survey framework. Results also highlight a shortcoming of survey sampling when stratification weights change rapidly, which Big Data may be uniquely suited to identify and correct.
- 일반주제명
- Finance
- 키워드
- Inflation
- 키워드
- Consumption
- 기타저자
- University of Michigan Economics
- 기본자료저록
- Dissertations Abstracts International. 87-03A.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798291567012
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■035 ▼a(MiAaPQ)umichrackham006428
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a658
■1001 ▼aO'Flaherty, Kelsey.
■24510▼aWhy Households Hate Grocery Inflation and Other Essays in Economic Measurement
■260 ▼a[Sl]▼bUniversity of Michigan▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a189 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-03, Section: A.
■500 ▼aAdvisor: Shapiro, Matthew D.
■5021 ▼aThesis (Ph.D.)--University of Michigan, 2025.
■520 ▼aEach chapter of this dissertation makes a methodological contribution to economic measurement. Empirical applications of these new methods show the value of more granular or comprehensive measures for challenging long-standing assumptions and raising fundamental questions about economic behavior.Using a detailed panel of consumer purchases and matched retail scanner data, Chapter I uncovers important new aspects of how households experience inflation. Inflation varies widely across households for a reason overlooked by aggregate measures: the individual product choices households make among ostensibly close substitutes. The price changes of different product varieties became more widely dispersed when inflation rose in 2021 and 2022, and households frequently failed to substitute to items with slower price growth. A model of consumption with idiosyncratic preferences rationalizes this behavior and implies that household-level inflation rates represent observable bounds on welfare losses. Grocery price inflation in 2022 generated annualized welfare losses ranging from $575 to $1,150 in the 10th and 90th percentiles of the inflation distribution.Chapter II develops wage measures capturing how two features of occupational change-the entry and exit of occupations over time and shocks to relative supply and demand-contributed to workers' effective wage gains and firms' effective labor costs from 1940-2020. It evaluates these contributions using a two-sided model of the labor market featuring occupational change, for which we derive exact wage indices. These indices imply increasing occupational variety and the tendency for firms and workers to reallocate labor toward occupations with lower effective costs or higher effective earnings meaningfully improved worker welfare and reduced firms' labor costs. Compared to baseline real wage growth of 1.03 percentage points annually in the absence of occupational change from 1940-2020, these forces raised workers' annualized real wage growth by 0.29 percentage points and reduced firms' annualized real labor costs by 0.20 percentage points.The last chapter addresses the challenge of using Big Data to improve economic statistics when the dataset covers only a non-representative subset of agents. It tests several methods for blending such data with surveys to produce unbiased population statistics. Several methods effectively reduce error-including a "Big Data as Strata" method which easily integrates into an existing survey framework. Results also highlight a shortcoming of survey sampling when stratification weights change rapidly, which Big Data may be uniquely suited to identify and correct.
■590 ▼aSchool code: 0127.
■650 4▼aFinance
■653 ▼aInflation
■653 ▼aConsumption
■653 ▼aEconomic measurement
■653 ▼aIdiosyncratic preferences
■653 ▼aGrocery price inflation
■690 ▼a0501
■690 ▼a0508
■690 ▼a1001
■690 ▼a0509
■71020▼aUniversity of Michigan▼bEconomics.
■7730 ▼tDissertations Abstracts International▼g87-03A.
■790 ▼a0127
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17365908▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
