crs: dữ liệu đầu vào cố định theo quy mô

vrs: dữ liệu đầu vào biến động theo quy mô

Hiệu quả phân phối nguồn lực sản xuất(Allocative Efficiency) ae = ce/te

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0. Tải dữ liệu

library(readxl)
library(deaR)
## Warning: package 'deaR' was built under R version 4.0.5
setwd("c:/Users/Admin/Desktop/DuAnR/deaR")
dulieu <-read_excel("timte.xlsx")
head(dulieu)
## # A tibble: 6 x 12
##    NHOM PLOAI SLUONG   DAT GIONG  PHAN LDONG  GBAN  GDAT GGIONG GPHAN   GLD
##   <dbl> <chr>  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl>
## 1     3 C      201.   8.41  16.2 10.6   12.2 11.5   146.   74.6  195.  300.
## 2     5 E      143.  15.7   15.8  9.57  15.5 11.0   162.   70.1  206.  306.
## 3     4 D       91.3 19.0   23.1  7.21  16.9  7.29  163.   65.6  207.  261.
## 4     2 B      152.  16.0   24.8  9.35  10.6  5.21  151.   68.0  185.  263.
## 5     4 D      199.  14.2   23.5 10.3   16.1 12.4   140.   68.8  199.  238.
## 6     3 C      262.  20.4   25.1 10.4   17.3  6.61  137.   83.2  215.  275.

1. Hiệu quả kỹ thuật TE (technical efficiency)

khodata <-read_data(datadea=dulieu, dmus=2,inputs=4:8, outputs=3)
ketqua1 <-model_basic(khodata,rts="crs", orientation="io")
#head(summary(ketqua1))
te1 <- efficiencies(ketqua1)
te1
##       C       E       D       B       D       C       D       D       E       B 
## 0.87968 0.46686 0.30765 0.47994 0.56666 0.65515 0.84719 0.97717 0.48257 0.63468 
##       C       B       C       B       E       B       E       B       E       C 
## 0.55412 0.72416 0.67122 0.48555 1.00000 0.54709 0.74087 0.47500 0.37080 0.38459 
##       C       E       D       F       D       D       D       C       B       C 
## 0.58535 0.42501 0.64702 0.80553 0.59800 0.73353 1.00000 0.43189 0.84686 0.79199 
##       E       E       F       F       C       E       F       A       F       C 
## 0.81196 0.52532 1.00000 0.37414 0.48347 0.57376 0.85147 0.67958 0.49846 0.55130 
##       E       E       A       E       B       C       C       E       A       E 
## 1.00000 1.00000 0.79025 0.46453 0.70626 1.00000 0.70087 0.68009 0.57773 0.50372 
##       E       C       B       D       E       F       B       B       A       B 
## 0.78217 0.64245 0.97584 0.66954 0.52094 0.79761 0.58097 0.51702 0.44024 0.75576 
##       D       D       B       B       D       B       D       D       F       F 
## 0.78680 0.74502 0.78380 0.78823 0.44489 0.28931 0.92225 0.68852 0.62393 0.84471 
##       D       B       A       D       B       C       D       C       F       B 
## 0.50577 0.52286 0.69536 0.42275 0.54740 0.70812 0.60274 0.39418 0.43896 0.58035 
##       B       D       F       C       D       E       C       C       A       D 
## 0.97628 0.75933 1.00000 0.94545 0.62057 0.57510 0.95829 0.81546 0.51490 0.58422 
##       A       D       B       A       C       A       C       C       F       A 
## 0.55203 0.58209 0.85032 0.50898 1.00000 0.86549 0.66145 0.78139 0.81336 0.67991 
##       C       D       D       B       B       D       C       E       F       F 
## 0.75181 0.57203 1.00000 0.67305 0.62511 0.67221 0.60913 0.39774 0.74989 0.41248 
##       B       A       B       F       D       E       B       E       D       F 
## 0.47354 0.56945 0.54623 0.40609 0.28648 0.87777 0.60681 1.00000 0.47166 0.87353 
##       A       C       D       C       A       B       E       E       E       E 
## 0.67139 0.42237 0.69788 0.93573 0.62259 0.52742 0.48069 0.97226 0.57748 0.48932 
##       D       A       C       A       C       B       D       F       A       C 
## 0.58061 0.66877 0.62317 0.86599 0.91026 0.75833 0.90133 0.94262 0.73736 0.74401 
##       D       B       F       D       C       B       C       C       C       B 
## 0.87094 0.33857 1.00000 0.68829 0.64197 0.70829 0.52490 0.84998 0.62040 0.74348 
##       B       A       F       B       C       E       A       F       A       A 
## 0.32303 0.85172 0.68270 0.79336 0.42614 0.66005 0.59806 0.41580 0.97225 0.61723 
##       C       E       C       B       D       C       A       F       D       B 
## 0.57258 0.47343 0.63029 0.44042 0.76958 0.28243 0.54958 0.66918 0.46647 0.30043 
##       D       E       F       C       B       C       D       B       A       E 
## 0.54368 1.00000 0.66714 0.92026 0.65292 0.68995 0.49484 0.47637 0.72488 0.54139 
##       E       D       D       A       C       B       D       B       B       D 
## 0.90745 0.30296 0.38711 1.00000 0.52723 0.61037 0.55048 0.69983 0.71468 0.56670 
##       D       A       B       D       E       F       B       E       B       A 
## 0.70944 0.53786 0.79531 0.46116 0.59215 0.50421 0.54429 0.64218 0.54187 0.49274
kqdata1 <-cbind(khodata,te1)
## Warning in cbind(khodata, te1): number of rows of result is not a multiple of
## vector length (arg 1)
head(kqdata1)
##   khodata       te1    
## C Numeric,1000  0.87968
## E Numeric,200   0.46686
## D Character,200 0.30765
## B NULL          0.47994
## D NULL          0.56666
## C NULL          0.65515

2. Hiệu quả chi phí ( cost efficiency )

giavao <-t(dulieu[,9:12])
khodata2 <-read_data(datadea=dulieu,inputs=4:7, outputs=3)
ketqua2 <-model_profit(khodata2,price_input=giavao, rts="crs", restricted_optimal=FALSE)
ce <- efficiencies(ketqua2)
head(ce)
##       3       5       4       2       4       3 
## 0.64985 0.37721 0.22261 0.43637 0.50015 0.57836
kqdata2 <-cbind(kqdata1,ce)
head(kqdata2)
##   khodata       te1     ce     
## C Numeric,1000  0.87968 0.64985
## E Numeric,200   0.46686 0.37721
## D Character,200 0.30765 0.22261
## B NULL          0.47994 0.43637
## D NULL          0.56666 0.50015
## C NULL          0.65515 0.57836

3. Hiệu quả doanh thu (revenue efficiency model)

giara <-t(dulieu[,8])
ketqua3 <-model_profit(khodata2, price_output=giara, rts="crs", restricted_optimal=FALSE)
re <- efficiencies(ketqua3)
kqdata3 <-cbind(kqdata2,re)
head(kqdata3)
##   khodata       te1     ce      re     
## C Numeric,1000  0.87968 0.64985 0.87968
## E Numeric,200   0.46686 0.37721 0.45784
## D Character,200 0.30765 0.22261 0.26706
## B NULL          0.47994 0.43637 0.46269
## D NULL          0.56666 0.50015 0.56666
## C NULL          0.65515 0.57836 0.62081

4. Hiệu quả lợi nhuận (profit efficiency)

ketqua4 <-model_profit(khodata2, price_input=giavao, price_output=giara, rts="crs",restricted_optimal=FALSE )
pe <- efficiencies(ketqua4)
kqdata4 <-cbind(kqdata3,pe)
head(kqdata4)
##   khodata       te1     ce      re      pe     
## C Numeric,1000  0.87968 0.64985 0.87968 1.92044
## E Numeric,200   0.46686 0.37721 0.45784 3.75698
## D Character,200 0.30765 0.22261 0.26706 5.87963
## B NULL          0.47994 0.43637 0.46269 2.63604
## D NULL          0.56666 0.50015 0.56666 3.08163
## C NULL          0.65515 0.57836 0.62081 1.9753

6. Hiệu quả quy mô - Scale Efficiency - SE

SE = TE.crs / TE.vrs

library(deaR)


ketqua6vrs <-model_basic(khodata,rts="vrs", orientation="io")
te2 <- efficiencies(ketqua6vrs)

se <-te1/te2
head(se)
##         C         E         D         B         D         C 
## 0.9106701 0.5954391 0.3778881 0.4914196 0.7604031 0.9206200

7. Hiệu quả nguồn lực sản xuất Allocative Efficiency (AE)

AE = CE/TE

ae = ce/te1
head(ae)
##         3         5         4         2         4         3 
## 0.7387345 0.8079724 0.7235820 0.9092178 0.8826280 0.8827902

5. Hiệu quả kỹ thuật bằng frontier (Hàm sản xuất biên ngẫu nhiên)

library(frontier)
## Warning: package 'frontier' was built under R version 4.0.5
## Loading required package: micEcon
## Warning: package 'micEcon' was built under R version 4.0.5
## 
## If you have questions, suggestions, or comments regarding one of the 'micEcon' packages, please use a forum or 'tracker' at micEcon's R-Forge site:
## https://r-forge.r-project.org/projects/micecon/
## Loading required package: lmtest
## Loading required package: zoo
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
## 
## Please cite the 'frontier' package as:
## Tim Coelli and Arne Henningsen (2013). frontier: Stochastic Frontier Analysis. R package version 1.1. http://CRAN.R-Project.org/package=frontier.
## 
## If you have questions, suggestions, or comments regarding the 'frontier' package, please use a forum or 'tracker' at frontier's R-Forge site:
## https://r-forge.r-project.org/projects/frontier/
## 
## Attaching package: 'frontier'
## The following object is masked from 'package:deaR':
## 
##     efficiencies
congthuc <- SLUONG ~ DAT +GIONG  + PHAN + LDONG
ketqua5 <-sfa(congthuc, dulieu)
## Warning in sfa(congthuc, dulieu): the residuals of the OLS estimates are right-
## skewed and the likelihood value of the ML estimation is less than that obtained
## using OLS; this usually indicates that there is no inefficiency or that the
## model is misspecified
summary(ketqua5)
## Error Components Frontier (see Battese & Coelli 1992)
## Inefficiency decreases the endogenous variable (as in a production function)
## The dependent variable is logged
## Iterative ML estimation terminated after 9 iterations:
## log likelihood values and parameters of two successive iterations
## are within the tolerance limit
## 
## final maximum likelihood estimates
##                Estimate  Std. Error  z value  Pr(>|z|)    
## (Intercept)  244.736433   33.510616   7.3033 2.809e-13 ***
## DAT           -1.239231    1.138539  -1.0884    0.2764    
## GIONG          0.515420    0.718495   0.7174    0.4732    
## PHAN          -2.175385    1.649272  -1.3190    0.1872    
## LDONG         -0.347181    0.663114  -0.5236    0.6006    
## sigmaSq     2338.154687    9.192807 254.3461 < 2.2e-16 ***
## gamma          0.045944    0.152466   0.3013    0.7632    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## log likelihood value: -1056.274 
## 
## cross-sectional data
## total number of observations = 200 
## 
## mean efficiency: 0.076283
coef(ketqua5,wich="ols")
##   (Intercept)           DAT         GIONG          PHAN         LDONG 
##  244.73643254   -1.23923112    0.51541961   -2.17538479   -0.34718108 
##       sigmaSq         gamma 
## 2338.15468683    0.04594351
te5 <-efficiencies(ketqua5)
kqdata5 <-cbind(kqdata4,te5)
head(kqdata5)
##   khodata       te1     ce      re      pe      efficiency
## C Numeric,1000  0.87968 0.64985 0.87968 1.92044 0.07438672
## E Numeric,200   0.46686 0.37721 0.45784 3.75698 0.06254164
## D Character,200 0.30765 0.22261 0.26706 5.87963 0.05020911
## B NULL          0.47994 0.43637 0.46269 2.63604 0.06325941
## D NULL          0.56666 0.50015 0.56666 3.08163 0.0750726 
## C NULL          0.65515 0.57836 0.62081 1.9753  0.09292391