Description Usage Arguments Details Value Warning Note Author(s) References See Also Examples
Using Data Envelopment Analysis (DEA), this function measures productivity and profitability in levels and changes with Fisher index.
The Fisher productivity index is the geometric average of Laspeyres and Paasche indices.
Deflated shadow prices of inputs and outputs can also be computed.
1 2 3 4 5 6 7  fisher(data, id.var, time.var, x.vars, y.vars, w.vars, p.vars, tech.change = TRUE,
tech.reg = TRUE, rts = c("vrs", "crs", "nirs", "ndrs"), orientation = c("out",
"in", "inout"), parallel = FALSE, cores = max(1, detectCores()  1), scaled = TRUE,
shadow = FALSE)
## S3 method for class 'Fisher'
print(x, digits = NULL, ...)

data 
A dataframe containing the required information for measuring productivity and profitability. 
id.var 
Firms' ID variable. Can be an integer or a text string. 
time.var 
Time period variable. Can be an integer or a text string. 
x.vars 
Input quantity variables. Can be a vector of text strings or integers. 
y.vars 
Output quantity variables. Can be a vector of text strings or integers. 
w.vars 
Input price variables. Can be a vector of text strings or integers. 
p.vars 
Output price variables. Can be a vector of text strings or integers. 
tech.change 
Logical. If 
tech.reg 
Logical. If 
rts 
Character string specifying the returns to scale assumption.
The default value is 
orientation 
Character string specifying the orientation.
The default value is 
parallel 
Logical. Allows parallel computation. If 
cores 
Integer. Used only if 
scaled 
Logical. If 
shadow 
Logical. Default is 
x 
An object of class 
digits 
The minimum number of significant digits to be printed in values.
Default = 
... 
Currently not used. 
When tech.change
is set to FALSE
, this overrides the effect of tech.reg
.
Setting scaled = FALSE
(no rescaling of data) may lead to numerical problems in solving LP
problems while optimizing DEA models. In extreme cases it may also prevent models from being optimized.
The Fisher index is not transitive and therefore each firm is compared to itself in the previous period.
Since there is no previous period for the first period, the results for this first period are replaced by NA
.
fisher()
returns a list of class 'Fisher'
for which a summary of productivity and profitability
measures in levels and changes, as well as a summary shadow prices (if shadow = TRUE
), is printed.
This list contains the following items:
Levels 
Several elements are provided, depending on the
 
Changes 
Change indices of the different elements of  
Shadowp 
Returned only if 
From an object of class 'Fisher'
obtained from fisher()
, the
Levels
function extracts individual productivity and profitability levels;
Changes
function extracts individual productivity and profitability change indices; and
If shadow = TRUE
, the Shadowp
function extracts individual input and output deflated shadow prices.
The fisher()
function will not work with unbalanced panel data.
The Fisher index may be sensitive to the rescaling.
For extreme efficient observations, the problem of multiple solutions may arise and the values of shadow prices may differ depending on the linear programming solver used (here lpSolveAPI).
All outputoriented efficiency scores are computed a la Shephard, while all inputoriented efficiency scores are computed a la Farrell. Hence, all efficiency scores are greater than zero and are lower or equal to one.
K Herv<c3><a9> Dakpo, Yann Desjeux, Laure Latruffe
Diewert W.E. (1992), Fisher ideal output, input, and productivity indexes revisited. Journal of Productivity Analysis, 3(3), 211248. https://doi.org/10.1007/BF00158354
Coelli T.J., D.S.P. Rao, C.J. O'Donnell, and G.E. Battese (2005), An Introduction to Efficiency and Productivity Analysis. Springer Eds.
O'Donnell C.J. (2011), The sources of productivity change in the manufacturing sectors of the U.S. economy. School of Economics, University of Queensland, Australia. URL: http://www.uq.edu.au/economics/cepa/docs/WP/WP072011.pdf
See Levels
to retrieve a data frame with individual Fisher
productivity and profitability in levels and components.
See Changes
to retrieve a data frame with individual Fisher
productivity and profitability changes and components.
See Shadowp
to retrieve individual deflated input and output shadow prices, provided that shadow = TRUE
.
See also laspeyres
and paasche
for computations with alternative indices.
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