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Unexpected sign for remuneration coefficient

Ask questions about the results reported after estimation. If the output includes errors, please include your model code if possible.
Post Reply
nicolas_p
Posts: 4
Joined: 06 Oct 2025, 15:40

Unexpected sign for remuneration coefficient

Post by nicolas_p »

Hi,

I am working on a DCE with a willingness to accept setting. So instead of price there is a hypothetical financial remuneration "b_rmn" . The problem is that I get a negative and significant coefficient for this attribute and I would expect a positive sign. I would expect a positive sign for "b_dc" as it refers to offered discounts but my main concern is not the coefficient for remuneration

This is the code that I am using in the MNL model:

Code: Select all

### Clear memory
rm(list = ls())

### Load Apollo library
library(apollo)

### Initialise code
apollo_initialise()

### Set core controls
apollo_control = list(
  modelName       = "MNL_SC_nwsm",
  modelDescr      = "Simple MNL model on SP data",
  indivID         = "ID",
  outputDirectory = "output"
)


# with ID, you tell R, what is the ID variable 

# ################################################################# #
#### LOAD DATA AND APPLY ANY TRANSFORMATIONS                     ####
# ################################################################# #

### Loading data from package
database <- readRDS("C:\\Users\\posso-gonzalez.n\\Documents\\PhD\\Ch2\\jointdataSC.rds")

class(database$Time)
database$Time <- as.numeric(database$Time)
database <- database %>%
  filter(Time >= 300)
database <- database %>%
  filter(AgeGr != 1)
database$spd_0 <- as.integer(database$spd_0)




### Vector of parameters, including any that are kept fixed in estimation
apollo_beta = c(asc = 0,
                b_vhm   = 0,
                b_elp    = 0,
                b_cpo    = 0,
                b_rn    = 0, 
                b_dc    = 0, 
                b_flx     =0, 
                b_spd    =0, 
                b_exm    =0, 
                b_rmn    =0
)

### Vector with names (in quotes) of parameters to be kept fixed at their starting value in apollo_beta, use apollo_beta_fixed = c() if none
apollo_fixed = c()

# ################################################################# #
#### GROUP AND VALIDATE INPUTS                                   ####
# ################################################################# #

apollo_inputs = apollo_validateInputs()

# ################################################################# #
#### DEFINE MODEL AND LIKELIHOOD FUNCTION                        ####
# ################################################################# #

apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
  
  ### Function initialisation: do not change the following three commands
  ### Attach inputs and detach after function exit
  apollo_attach(apollo_beta, apollo_inputs)
  on.exit(apollo_detach(apollo_beta, apollo_inputs))
  
  ### Create list of probabilities P
  P = list()
  
  ### List of utilities: these must use the same names as in mnl_settings, order is irrelevant
  V = list()
  V[["alt0"]] =                                                                                                                                                                                b_spd * spd_0 
  V[["alt1"]] = asc + b_vhm * (cnt_1 == "vh_m") + b_elp * (cnt_1 == "el_p") + b_cpo * (cnt_1 == "cp_o") + b_rn * (adv_1 == "50_rn") +  b_dc * (adv_1 == "10_dc") +  b_flx * (adv_1 == "flx") + b_spd * spd_1 + b_exm * exm_1 + b_rmn * rmn_1
  V[["alt2"]] = asc + b_vhm * (cnt_2 == "vh_m") + b_elp * (cnt_2 == "el_p") + b_cpo * (cnt_2 == "cp_o") + b_rn * (adv_2 == "50_rn") +  b_dc * (adv_2 == "20_dc") +  b_flx * (adv_2 == "flx") + b_spd * spd_2 + b_exm * exm_2 + b_rmn * rmn_2
  
  #####Im here######
  ### Define settings for MNL model component
  mnl_settings = list(
    alternatives  = c(alt0=0, alt1=1, alt2=2), #choice coding of the variable 
    avail         = list(alt0=1, alt1=1, alt2=1), ####check if 1 means the choice is available
    choiceVar     = choice,
    utilities     = V
  )
  
  ### Compute probabilities using MNL model
  P[["model"]] = apollo_mnl(mnl_settings, functionality)
  
  ### Take product across observation for same individual
  P = apollo_panelProd(P, apollo_inputs, functionality)
  
  ### Prepare and return outputs of function
  P = apollo_prepareProb(P, apollo_inputs, functionality)
  return(P)
}

# ################################################################# #
#### MODEL ESTIMATION                                            ####
# ################################################################# #

model = apollo_estimate(apollo_beta, apollo_fixed,apollo_probabilities, apollo_inputs)

# ################################################################# #
#### MODEL OUTPUTS                                               ####
# ################################################################# #

# ----------------------------------------------------------------- #
#---- FORMATTED OUTPUT (TO SCREEN)                               ----
# ----------------------------------------------------------------- #

apollo_modelOutput(model)

# ----------------------------------------------------------------- #
#---- FORMATTED OUTPUT (TO FILE, using model name)               ----
# ----------------------------------------------------------------- #

apollo_saveOutput(model)



### Print outputs of additional diagnostics to new output file (remember to close file writing when complete)
apollo_sink()
These are the results:

Code: Select all

Model name                                  : MNL_SC_nwsm
Model description                           : Simple MNL model on SP data
Model run at                                : 2025-12-31 11:23:20.252316
Estimation method                           : bgw
Estimation diagnosis                        : Relative function convergence
Optimisation diagnosis                      : Maximum found
     hessian properties                     : Negative definite
     maximum eigenvalue                     : -202.913671
     reciprocal of condition number         : 1.73622e-05
Number of individuals                       : 2357
Number of rows in database                  : 14142
Number of modelled outcomes                 : 14142

Number of cores used                        :  1 
Model without mixing

LL(start)                                   : -15536.57
LL at equal shares, LL(0)                   : -15536.57
LL at observed shares, LL(C)                : -15123.98
LL(final)                                   : -14941.03
Rho-squared vs equal shares                  :  0.0383 
Adj.Rho-squared vs equal shares              :  0.0377 
Rho-squared vs observed shares               :  0.0121 
Adj.Rho-squared vs observed shares           :  0.0116 
AIC                                         :  29902.07 
BIC                                         :  29977.64 

Estimated parameters                        : 10
Time taken (hh:mm:ss)                       :  00:00:4.75 
     pre-estimation                         :  00:00:1.93 
     estimation                             :  00:00:0.59 
     post-estimation                        :  00:00:2.23 
Iterations                                  :  6  

Unconstrained optimisation.

Estimates:
         Estimate        s.e.   t.rat.(0)    Rob.s.e. Rob.t.rat.(0)
asc      0.075520    0.052417      1.4408    0.061428        1.2294
b_vhm    0.005746    0.037777      0.1521    0.037699        0.1524
b_elp    0.070133    0.035211      1.9918    0.037440        1.8732
b_cpo   -0.056804    0.039360     -1.4432    0.036962       -1.5368
b_rn     0.127496    0.033809      3.7711    0.030374        4.1975
b_dc    -0.139167    0.046128     -3.0170    0.047752       -2.9144
b_flx    0.095625    0.033794      2.8296    0.030334        3.1524
b_spd    0.010065  5.6020e-04     17.9674  6.0220e-04       16.7144
b_exm    0.117746    0.024568      4.7927    0.025350        4.6448
b_rmn   -0.010881    0.001682     -6.4687    0.002042       -5.3279


Overview of choices for MNL model component :
                                     alt0     alt1     alt2
Times available                  14142.00 14142.00 14142.00
Times chosen                      6280.00  3538.00  4324.00
Percentage chosen overall           44.41    25.02    30.58
Percentage chosen when available    44.41    25.02    30.58


Thanks a lot in advance
stephanehess
Site Admin
Posts: 1354
Joined: 24 Apr 2020, 16:29

Re: Unexpected sign for remuneration coefficient

Post by stephanehess »

Hi

this could well have to do with your first alternative, which I assume is an opt-out? What were the levels for that?

Stephane
--------------------------------
Stephane Hess
www.stephanehess.me.uk
nicolas_p
Posts: 4
Joined: 06 Oct 2025, 15:40

Re: Unexpected sign for remuneration coefficient

Post by nicolas_p »

Thank you for your reply. So there are two alternatives involving a smart charging contract (alt_1, alt_2) and an opt-out option (alt_0). The levels for the remuneration are 5, 10, 15, and 25 euros as a monthly financial compensation. I know that probably these amounts are not enough to affect much the choices of individuals and this may cause this problem. So I tried with equality constraint latent class to see if but the results are not fully satisfying. With different starting values, it converges to two models, the one with the best LL keeps the significance in the negative sign for remuneration, while in the other model, the remuneration coefficient is not significant. I would really appreciate any recommendation to deal with this problem.

Nicolas
stephanehess
Site Admin
Posts: 1354
Joined: 24 Apr 2020, 16:29

Re: Unexpected sign for remuneration coefficient

Post by stephanehess »

To diagnose it further, can you try a model with a categorical treatment of price, just to see the non-linearity

you might also want to think about a nested logit model
--------------------------------
Stephane Hess
www.stephanehess.me.uk
nicolas_p
Posts: 4
Joined: 06 Oct 2025, 15:40

Re: Unexpected sign for remuneration coefficient

Post by nicolas_p »

Thanks a lot for your reply. These are the reuslts with nest logit with linear remuneration

Code: Select all

Model name                                  : NL_SC
Model description                           : Simple MNL model on SP data
Model run at                                : 2026-02-05 17:56:12.445433
Estimation method                           : bgw
Estimation diagnosis                        : Relative function convergence
Optimisation diagnosis                      : Maximum found
     hessian properties                     : Negative definite
     maximum eigenvalue                     : -71.080336
     reciprocal of condition number         : 4.06846e-06
Number of individuals                       : 2357
Number of rows in database                  : 14142
Number of modelled outcomes                 : 14142

Number of cores used                        :  1 
Model without mixing

LL(start)                                   : -15536.57
LL at equal shares, LL(0)                   : -15536.57
LL at observed shares, LL(C)                : -15123.98
LL(final)                                   : -14934.93
Rho-squared vs equal shares                  :  0.0387 
Adj.Rho-squared vs equal shares              :  0.038 
Rho-squared vs observed shares               :  0.0125 
Adj.Rho-squared vs observed shares           :  0.0119 
AIC                                         :  29891.85 
BIC                                         :  29974.98 

Estimated parameters                        : 11
Time taken (hh:mm:ss)                       :  00:00:13.95 
     pre-estimation                         :  00:00:1.3 
     estimation                             :  00:00:1.7 
     post-estimation                        :  00:00:10.96 
Iterations                                  :  8  

Unconstrained optimisation.

Estimates:
             Estimate        s.e.   t.rat.(0)    Rob.s.e. Rob.t.rat.(0)
asc          0.163237    0.037613      4.3399    0.047780        3.4164
b_vhm        0.006670    0.022742      0.2933    0.022888        0.2914
b_elp        0.041449    0.022632      1.8314    0.023336        1.7761
b_cpo       -0.053589    0.024798     -2.1610    0.023347       -2.2953
b_rn         0.083880    0.025807      3.2502    0.022547        3.7203
b_dc        -0.106425    0.033187     -3.2069    0.029746       -3.5779
b_flx        0.053323    0.023775      2.2428    0.020628        2.5850
b_spd        0.006023    0.001211      4.9716    0.001075        5.6035
b_exm        0.076321    0.019887      3.8377    0.018123        4.2113
b_rmn       -0.006518    0.001575     -4.1384    0.001531       -4.2569
lambda_SC    0.561639    0.113686      4.9403    0.096425        5.8246

Nesting structure for NL model component :
Nest: root (1)
|----Alternative: alt0
'-Nest: alt1_alt2 (0.5616)
   |-Alternative: alt1
   '-Alternative: alt2


Overview of choices for NL model component :
                                     alt0     alt1     alt2
Times available                  14142.00 14142.00 14142.00
Times chosen                      6280.00  3538.00  4324.00
Percentage chosen overall           44.41    25.02    30.58
Percentage chosen when available    44.41    25.02    30.58

These are the results for the nested logit using remuneration as a categorical variable and using 5 as the reference category.

Code: Select all

Model name                                  : NL_SC_rmct
Model description                           : Simple MNL model on SP data
Model run at                                : 2026-02-05 18:35:21.364565
Estimation method                           : bgw
Estimation diagnosis                        : Relative function convergence
Optimisation diagnosis                      : Maximum found
     hessian properties                     : Negative definite
     maximum eigenvalue                     : -67.067856
     reciprocal of condition number         : 3.84292e-06
Number of individuals                       : 2357
Number of rows in database                  : 14142
Number of modelled outcomes                 : 14142

Number of cores used                        :  1 
Model without mixing

LL(start)                                   : -15536.57
LL at equal shares, LL(0)                   : -15536.57
LL at observed shares, LL(C)                : -15123.98
LL(final)                                   : -14929.55
Rho-squared vs equal shares                  :  0.0391 
Adj.Rho-squared vs equal shares              :  0.0382 
Rho-squared vs observed shares               :  0.0129 
Adj.Rho-squared vs observed shares           :  0.0121 
AIC                                         :  29885.09 
BIC                                         :  29983.33 

Estimated parameters                        : 13
Time taken (hh:mm:ss)                       :  00:00:35.34 
     pre-estimation                         :  00:00:2.11 
     estimation                             :  00:00:4.97 
     post-estimation                        :  00:00:28.26 
Iterations                                  :  7  

Unconstrained optimisation.

Estimates:
             Estimate        s.e.   t.rat.(0)    Rob.s.e. Rob.t.rat.(0)
asc          0.110121    0.042012      2.6211    0.050194        2.1939
b_vhm        0.007680    0.022529      0.3409    0.022996        0.3340
b_elp        0.041964    0.023486      1.7868    0.024693        1.6995
b_cpo       -0.041033    0.023803     -1.7239    0.022855       -1.7954
b_rn         0.146890    0.030860      4.7599    0.026234        5.5993
b_dc         0.039595    0.023689      1.6715    0.024894        1.5905
b_flx        0.094086    0.026361      3.5692    0.023142        4.0656
b_spd        0.005882    0.001133      5.1937    0.001057        5.5632
b_exm        0.061085    0.018216      3.3533    0.018239        3.3492
b_rmn10     -0.072739    0.023910     -3.0422    0.021752       -3.3440
b_rmn15     -0.147659    0.031947     -4.6221    0.028739       -5.1379
b_rmn25     -0.113798    0.029390     -3.8720    0.030649       -3.7129
lambda_SC    0.554568    0.108305      5.1204    0.097344        5.6970

Nesting structure for NL model component :
Nest: root (1)
|----Alternative: alt0
'-Nest: alt1_alt2 (0.5546)
   |-Alternative: alt1
   '-Alternative: alt2


Overview of choices for NL model component :
                                     alt0     alt1     alt2
Times available                  14142.00 14142.00 14142.00
Times chosen                      6280.00  3538.00  4324.00
Percentage chosen overall           44.41    25.02    30.58
Percentage chosen when available    44.41    25.02    30.58

In this DCE, there are 3 other non monetary attributes that aim to attract individuals to choose the inside options. Individuals in general chose more times the alternative with lower remuneration between the two inside options. I have some possible causes in mind for the problem with the coefficient. Maybe a kind of warm glowing so individuals do not want to be financially compensated for adapting this technology (smart charging for electric vehicles) or mistrust towards the technology and privacy concerns. Or maybe they value much more the non-monetary values in comparison to the financial compensation. Or maybe there is confounding problem in the design of the DCE or the specification of the utility function. Or a combination of them. The problem is that I do not know how to test or disentangle them.
stephanehess
Site Admin
Posts: 1354
Joined: 24 Apr 2020, 16:29

Re: Unexpected sign for remuneration coefficient

Post by stephanehess »

Hi

this seems more of an issue of how people reacted to the attribute, rather than a modelling issue per se. Did you run a focus group or pilot at all?

Stephane
--------------------------------
Stephane Hess
www.stephanehess.me.uk
nicolas_p
Posts: 4
Joined: 06 Oct 2025, 15:40

Re: Unexpected sign for remuneration coefficient

Post by nicolas_p »

Hi,

I think a focus group was run but I am not 100% sure. I can ask. A pilot study was run and the results with MNL were as expected, positive and significant for remuneration -at 10% of significance-. I have run a latent class model and there is a class with very strong negative coefficient for remuneration. they systematically choose the alternative with lower remuneration but I do not clear see a clear variable that identifies these individuals. I am using age, income residential area(urban, suburban and rural), indices on innovation, interest in buying electric vehicles, and environmental profile, vehicle size of interest, cheap talk and consequentiality treatments, and parking as predictors of the class allocation. These are the results:

Code: Select all

Model name                                  : SCLC_aginraeddcpenpinpvstrmhpk_4c43
Model description                           : Simple L
Model run at                                : 2026-03-07 18:26:07.060994
Estimation method                           : bgw
Estimation diagnosis                        : Relative function convergence
Optimisation diagnosis                      : Maximum found
     hessian properties                     : Negative definite
     maximum eigenvalue                     : -0.956328
     reciprocal of condition number         : 6.27884e-07
Number of individuals                       : 2380
Number of rows in database                  : 14280
Number of modelled outcomes                 : 14280

Number of cores used                        :  1 
Model without mixing

LL(start)                                   : -21382.72
LL (whole model) at equal shares, LL(0)     : -15688.18
LL (whole model) at observed shares, LL(C)  : -15294.8
LL(final, whole model)                      : -10939.06
Rho-squared vs equal shares                  :  0.3027 
Adj.Rho-squared vs equal shares              :  0.296 
Rho-squared vs observed shares               :  0.2848 
Adj.Rho-squared vs observed shares           :  0.2784 
AIC                                         :  22090.11 
BIC                                         :  22892.17 

LL(0,Class_1)                    : -15688.18
LL(final,Class_1)                : -29272.12
LL(0,Class_2)                    : -15688.18
LL(final,Class_2)                : -36458.13
LL(0,Class_3)                    : -15688.18
LL(final,Class_3)                : -15312.67
LL(0,Class_4)                    : -15688.18
LL(final,Class_4)                : -27996.26

Estimated parameters                        : 106
Time taken (hh:mm:ss)                       :  00:28:36.06 
     pre-estimation                         :  00:00:13.76 
     estimation                             :  00:01:50.27 
     post-estimation                        :  00:26:32.03 
Iterations                                  :  32  

Unconstrained optimisation.

Estimates:
            Estimate        s.e.   t.rat.(0)    Rob.s.e. Rob.t.rat.(0)
asc_a       3.482366    0.360814    9.651409    0.574477      6.061805
asc_b      -4.643656    0.567182   -8.187237    0.647439     -7.172348
asc_c       0.470285    0.120160    3.913819    0.123013      3.823043
asc_d       2.882899    0.208560   13.822864    0.263819     10.927558
b_vhm_a    -0.132516    0.209222   -0.633374    0.226060     -0.586198
b_vhm_b     0.072712    0.376437    0.193160    0.374126      0.194353
b_vhm_c     0.037762    0.086933    0.434386    0.091274      0.413724
b_vhm_d     0.102778    0.067744    1.517142    0.069620      1.476274
b_elp_a     0.118299    0.174824    0.676674    0.182203      0.649270
b_elp_b     0.352585    0.357989    0.984903    0.380067      0.927691
b_elp_c     0.043516    0.084565    0.514587    0.101242      0.429820
b_elp_d     0.112008    0.062753    1.784887    0.066913      1.673941
b_cpo_a    -0.897423    0.210828   -4.256665    0.275139     -3.261714
b_cpo_b     0.177815    0.373498    0.476080    0.385151      0.461675
b_cpo_c     0.006948    0.089251    0.077848    0.093358      0.074423
b_cpo_d     0.058943    0.068621    0.858967    0.069260      0.851044
b_rn_a     -0.061807    0.176049   -0.351079    0.189978     -0.325338
b_rn_b      0.222347    0.375016    0.592901    0.381660      0.582579
b_rn_c      0.215607    0.077470    2.783112    0.073688      2.925962
b_rn_d      0.310439    0.059317    5.233577    0.056136      5.530123
b_dc_a     -1.139592    0.231623   -4.920029    0.360654     -3.159794
b_dc_b      0.537588    0.350892    1.532059    0.366012      1.468772
b_dc_c      0.010738    0.085349    0.125815    0.096687      0.111062
b_dc_d      0.293084    0.064745    4.526758    0.070629      4.149620
b_flx_a     0.068316    0.185643    0.367994    0.217726      0.313769
b_flx_b     0.377193    0.348789    1.081435    0.350944      1.074794
b_flx_c     0.138849    0.075259    1.844951    0.070101      1.980699
b_flx_d     0.206489    0.056268    3.669718    0.051194      4.033456
b_spd_a    -0.019082    0.003288   -5.804101    0.003491     -5.465751
b_spd_b     0.010293    0.005725    1.798065    0.006855      1.501436
b_spd_c     0.019291    0.001540   12.530162    0.002411      8.002785
b_spd_d     0.012349    0.001099   11.234498    0.001507      8.193000
b_exm_a    -0.203045    0.133815   -1.517350    0.161509     -1.257170
b_exm_b     0.453395    0.251814    1.800515    0.268030      1.691581
b_exm_c     0.147217    0.058258    2.526979    0.069874      2.106882
b_exm_d     0.125493    0.041961    2.990696    0.048664      2.578746
b_rmn_a    -0.169382    0.017449   -9.707213    0.026166     -6.473267
b_rmn_b  -7.7812e-04    0.018400   -0.042289    0.024097     -0.032292
b_rmn_c    -0.005640    0.004540   -1.242450    0.006559     -0.859996
b_rmn_d     0.018290    0.003641    5.023939    0.004878      3.749747
delta_a     0.000000          NA          NA          NA            NA
delta_b     0.582460    0.559666    1.040727    0.582644      0.999685
delta_c     0.151966    0.600140    0.253218    0.616505      0.246496
delta_d    -0.074903    0.606271   -0.123547    0.636980     -0.117591
d_Age2_b    0.863776    0.239463    3.607142    0.256328      3.369803
d_Age2_c   -0.263345    0.239697   -1.098658    0.259779     -1.013725
d_Age2_d   -0.219082    0.243427   -0.899989    0.267071     -0.820314
d_Age3_b    1.237726    0.262247    4.719692    0.295460      4.189150
d_Age3_c   -0.275971    0.271172   -1.017696    0.315636     -0.874333
d_Age3_d   -0.118588    0.273990   -0.432819    0.323726     -0.366322
d_Inc2_b   -0.039608    0.251567   -0.157444    0.276466     -0.143265
d_Inc2_c    0.128779    0.271900    0.473628    0.296418      0.434452
d_Inc2_d    0.219842    0.282376    0.778542    0.318198      0.690897
d_Inc3_b    0.143702    0.273153    0.526088    0.282394      0.508873
d_Inc3_c    0.247354    0.291957    0.847226    0.300710      0.822567
d_Inc3_d    0.507515    0.295235    1.719020    0.304355      1.667507
d_Inc4_b   -0.246158    0.253966   -0.969258    0.269158     -0.914550
d_Inc4_c   -0.280338    0.274470   -1.021380    0.292962     -0.956909
d_Inc4_d    0.026277    0.275551    0.095361    0.295396      0.088955
d_Ed2_b    -0.101066    0.202234   -0.499747    0.210412     -0.480323
d_Ed2_c     0.270880    0.218218    1.241326    0.228781      1.184010
d_Ed2_d     0.150093    0.220663    0.680189    0.235890      0.636282
d_Ed3_b     0.302054    0.285710    1.057204    0.296063      1.020235
d_Ed3_c     0.485944    0.299557    1.622207    0.312568      1.554685
d_Ed3_d     0.507299    0.299011    1.696588    0.319178      1.589390
d_RA2_b     0.055227    0.219193    0.251955    0.229313      0.240836
d_RA2_c     0.073090    0.235222    0.310726    0.247995      0.294722
d_RA2_d     0.025714    0.234440    0.109683    0.249853      0.102917
d_RA3_b     0.679673    0.226052    3.006707    0.225089      3.019576
d_RA3_c     0.582351    0.241713    2.409269    0.240084      2.425618
d_RA3_d     0.326888    0.243477    1.342580    0.246570      1.325740
d_dcp_b    -0.094090    0.039887   -2.358924    0.041385     -2.273536
d_dcp_c     0.064694    0.041201    1.570205    0.041451      1.560715
d_dcp_d     0.093226    0.041218    2.261789    0.042504      2.193377
d_enp_b    -0.123545    0.031096   -3.973015    0.033909     -3.643388
d_enp_c    -0.057228    0.033645   -1.700944    0.036332     -1.575122
d_enp_d     0.035099    0.034942    1.004483    0.037760      0.929523
d_inp_b     0.062564    0.093584    0.668530    0.097153      0.643969
d_inp_c    -0.064414    0.098978   -0.650789    0.104042     -0.619117
d_inp_d    -0.122630    0.099424   -1.233400    0.106116     -1.155620
d_vs2_b     0.185522    0.214901    0.863288    0.223992      0.828250
d_vs2_c     0.505248    0.242569    2.082900    0.254706      1.983653
d_vs2_d     0.373503    0.238488    1.566130    0.250902      1.488644
d_vs3_b     0.590220    0.285710    2.065805    0.306335      1.926715
d_vs3_c     1.088104    0.307785    3.535278    0.329035      3.306956
d_vs3_d     0.748867    0.306575    2.442690    0.330665      2.264731
d_vs4_b     0.662585    0.404173    1.639361    0.436783      1.516966
d_vs4_c     1.101326    0.427846    2.574120    0.461565      2.386069
d_vs4_d     0.864920    0.434677    1.989799    0.460346      1.878845
d_trm2_b    0.285923    0.255453    1.119277    0.274101      1.043130
d_trm2_c    0.415322    0.273272    1.519813    0.295949      1.403355
d_trm2_d    0.129478    0.278096    0.465588    0.306975      0.421788
d_trm3_b   -0.176447    0.233896   -0.754382    0.245879     -0.717617
d_trm3_c   -0.034617    0.257841   -0.134256    0.271119     -0.127681
d_trm3_d   -0.256195    0.262854   -0.974666    0.282735     -0.906132
d_trm4_b    0.407131    0.256821    1.585272    0.273670      1.487671
d_trm4_c    0.414158    0.270169    1.532961    0.292979      1.413607
d_trm4_d   -0.002676    0.273110   -0.009798    0.300056     -0.008918
d_hpk2_b    0.250749    0.228696    1.096429    0.246107      1.018860
d_hpk2_c    0.266464    0.246517    1.080914    0.269323      0.989384
d_hpk2_d    0.252149    0.246728    1.021973    0.269865      0.934353
d_hpk3_b   -2.096032    0.683360   -3.067245    0.643761     -3.255918
d_hpk3_c    0.003962    0.431461    0.009184    0.435096      0.009107
d_hpk3_d   -0.410445    0.455899   -0.900300    0.447581     -0.917032
d_hpk4_b    0.849013    0.324851    2.613548    0.351063      2.418403
d_hpk4_c    0.449885    0.355081    1.266994    0.388063      1.159310
d_hpk4_d    0.665174    0.354067    1.878667    0.388128      1.713799


Summary of class allocation for model component :
         Mean prob.
Class_1      0.1078
Class_2      0.3217
Class_3      0.2606
Class_4      0.3099

Thansk in advance,

Nicolas
stephanehess
Site Admin
Posts: 1354
Joined: 24 Apr 2020, 16:29

Re: Unexpected sign for remuneration coefficient

Post by stephanehess »

Hi

it seems like this is what is happening in your data, i.e. people choose options with lower renumeration, and any constraints you impose on the model or treatment of heterogeneity will just mask this.

Stephane
--------------------------------
Stephane Hess
www.stephanehess.me.uk
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