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Applied Microeconomics

Applied Microeconomics

The Applied Microeconomics research group unites researchers working on a broad array of topics within such areas as labour economics, economics of education, health economics, family economics, urban economics, environmental economics, and the economics of science and innovation. The group operates in close collaboration with the CAGE Research Centre.

The group participates in the CAGE seminar on Applied Economics, which runs weekly on Tuesdays at 2:15pm. Students and faculty members of the group present their ongoing work in two brown bag seminars, held weekly on Tuesdays and Wednesdays at 1pm. Students, in collaboration with faculty members, also organise a bi-weekly reading group in applied econometrics on Thursdays at 1pm. The group organises numerous events throughout the year, including the Research Away Day and several thematic workshops.

Our activities

Work in Progress seminars

Tuesdays and Wednesdays 1-2pm

Students and faculty members of the group present their work in progress in two brown bag seminars. See below for a detailed scheduled of speakers.

Applied Econometrics reading group

Thursdays (bi-weekly) 1-2pm

Organised by students in collaboration with faculty members. See the Events calendar below for further details

People

Academics

Academics associated with the Applied Microeconomics Group are:


Natalia Zinovyeva

Co-ordinator

Jennifer Smith

Deputy Co-ordinator


Events

Tuesday, October 10, 2023

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CWIP (CAGE Work in Progress) Workshop - Mateusz Stalinski (Warwick)
S2.79

Title: Politics of Food

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Applied Economics, Econometrics & Public Policy (CAGE) Seminar - Linh To (BU)
S2.79

Title: When Are Estimates Independent of Measurement Units?

Abstract: Data transformations often facilitate regression analysis, yet many commonly used transformations make hypothesis testing misleading because the results depend on the measurement units of the data. This paper aims to address this issue by characterizing the set of transformations where measurement units do not affect conclusions in linear regressions. The equivalence theorem establishes that desirable properties—scale-equivariant coefficient estimates, scale-invariant t-statistics, and scale-invariant semi-elasticities—arise if and only if the transformation is a logarithmic or a power function. Power transformations thus offer a natural extension of logarithmic transformations that both preserves the essential feature of obtaining unit-independent estimates for unitless quantities of interest and can handle zero or negative values. On the other hand, popular alternatives that approximate the shape of the logarithmic function at large values, such as adding a small positive constant before applying a logarithmic transformation or the inverse hyperbolic sine transformation, result in similar inferences as in an untransformed linear regression when expressing outcomes in large measurement units and imply arbitrarily large effect sizes or arbitrarily large confidence intervals when expressing outcomes in small measurement units. We demonstrate using data from a randomized experiment that such transformations reverse the sign or significance of treatment effect estimates for up to 15 out of 49 outcomes variables when measurement units are changed to natural alternatives (e.g., from US dollars to local currency).

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