dynamics: Productivity Dynamics

In dfvad: Diewert and Fox's Method of Value Added Growth Decomposition

Description

Productivity dynamics reflect firm contributions to productivity growth over periods when firms enter or exit an industry. dynamics summarises a series of decomposition methods that are centred on the contributions from incumbents, entrants and exits. It applies to other weighted aggregation measures analogous to aggregate productivity as well.

Usage

dynamics(df, x, s, id, tm, typ = "df")

Arguments

df	A data frame sorted by the time period column.
x	A string indicating the productivity (or analogous measures) column.
s	A string indicating the market share column.
id	A string indicating the identity column.
tm	A string indicating the time period column.
typ	Relevant types of productivity dynamics. Options include "df" for Diewert-Fox decomposition (by default), "bhc" for Baily-Hulten-Campbell, "gr" for Griliches-Regev, "fhk" for Foster-Haltiwanger-Krizan, "bg" for Baldwin-Gu, and "mp" for Melitz-Polanec.

Value

A data frame consisting of the time period and firm contributions.

Examples

# Use the built-in data set "firms"
# DF decomposition of firm dynamics
dym_df <- dynamics(firms, "tfp", "s", "id", "t")
# BG decomposition of firm dynamics
dym_bg <- dynamics(firms, "tfp", "s", "id", "t", "bg")

dfvad documentation built on Oct. 15, 2021, 5:16 p.m.