-
Notifications
You must be signed in to change notification settings - Fork 1
/
value_for_money.Rmd
846 lines (522 loc) · 40.6 KB
/
value_for_money.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
---
title: "Mobile Money Value for Money"
author: "David Quartey"
date: "September 28, 2018"
output:
html_document:
keep_md: yes
word_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
fig.path = "Visualizations/Viz-"
)
# Load Libraries
library(ggplot2)
library(magrittr)
library(purrr)
library(dplyr)
momo_value_for_money <- function(network_from, network_to , initial_amount){ # Accept inputs about
# Aims of function
# columns for network combination - Done
# columns for amount obtained after withdrawing - Done
# Where I left off: Check new tariffs for same network transactions - Done
# Check MTN Tariffs - Done
# Change 0 - 50 to 1 - 50 - Done
# Obtain airteltigo to airteltigo rates to do airteltigo section in transaction (withdrawal section that of MTN so have to do too) - Done
# Check MTN to another network rates - Done
# Update Vodafobe to Vodafone charges with new info found on website - saved in mobile money folder - Done
# Create table of price ranges and interoperability charges for each telco - Add new Vodafone charges & add range accross all to accomodate all
# Add < 50 which can handle less than 1 initial amounts
#### TRANSFER STARTS FROM HERE ####
if(network_from == "Vodafone Cash"){ #### From Vodafone Cash ####
if(dplyr::between(initial_amount, 1, 50)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 0.5, effect = "flat")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 0.5, effect = "flat")
}
}
if(dplyr::between(initial_amount, 50.1, 75)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 0.75, effect = "flat")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "percentage")
}
}
if(dplyr::between(initial_amount, 75.1, 100)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1, effect = "flat")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "percentage")
}
}
if(dplyr::between(initial_amount, 100.1, 250)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "flat")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "percentage")
}
}
if(dplyr::between(initial_amount, 250.1, 500)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 2.0, effect = "flat")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "percentage")
}
}
if(dplyr::between(initial_amount, 500.1, 1000)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 2.50, effect = "flat")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "percentage")
}
}
}
if(network_from == "AirtelTigo Money"){ #### From AirtelTigo ####
if(dplyr::between(initial_amount, 1, 50)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 0.5, effect = "flat")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 0.75, effect = "flat")
}
}
if(dplyr::between(initial_amount, 50.1, 100)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.0, effect = "flat")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "percentage")
}
}
if(dplyr::between(initial_amount, 100.1, 250)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "flat")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "percentage")
}
}
if(dplyr::between(initial_amount, 250.1, 500)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 2.0, effect = "flat")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "percentage")
}
}
if(dplyr::between(initial_amount, 500.1, 1000)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 2.50, effect = "flat")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "percentage")
}
}
}
if(network_from == "MTN Momo"){ #### From MTN Momo ####
if(dplyr::between(initial_amount, 1, 50)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 0.5, effect = "flat")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 0.75, effect = "flat")
}
}
if(dplyr::between(initial_amount, 50.1, 100)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1, effect = "percentage")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "percentage")
}
}
if(dplyr::between(initial_amount, 100.1, 250)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1, effect = "percentage")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "percentage")
}
}
if(dplyr::between(initial_amount, 250.1, 500)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1, effect = "percentage")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "percentage")
}
}
if(dplyr::between(initial_amount, 500.1, 1000)){
if(network_from == network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1, effect = "percentage")
} else if(network_from != network_to){
remaining_amount <- momo_transfer(initial_amount = initial_amount, transaction_fee = 1.5, effect = "percentage")
}
}
}
#### WITHDRAWAL STARTS FROM HERE ####
if(network_to == "Vodafone Cash"){ #### To Vodafone
if(dplyr::between(remaining_amount, 1, 50)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 0.5, effect = "flat")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
if(dplyr::between(remaining_amount, 50.1, 100)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 1.5, effect = "flat")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
if(dplyr::between(remaining_amount, 100.1, 250)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 2.5, effect = "flat")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
if(dplyr::between(remaining_amount, 250.1, 500)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 4.0, effect = "flat")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
if(dplyr::between(remaining_amount, 500.1, 1000)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 6.0, effect = "flat")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
}
if(network_to == "AirtelTigo Money"){ #### To AirtelTigo
if(dplyr::between(remaining_amount, 1, 50)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 0.5, effect = "flat")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
if(dplyr::between(remaining_amount, 50.1, 100)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 1, effect = "flat")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
if(dplyr::between(remaining_amount, 100.1, 250)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 1.50, effect = "flat")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
if(dplyr::between(remaining_amount, 250.1, 500)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 2.00, effect = "flat")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
if(dplyr::between(remaining_amount, 500.1, 1000)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 2.50, effect = "flat")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
}
if(network_to == "MTN Momo"){ #### To MTN Momo
if(dplyr::between(remaining_amount, 1, 50)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 0.5, effect = "flat")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
if(dplyr::between(remaining_amount, 50.1, 100)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 1, effect = "percentage")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
if(dplyr::between(remaining_amount, 100.1, 250)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 1, effect = "percentage")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
if(dplyr::between(remaining_amount, 250.1, 500)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 1, effect = "percentage")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
if(dplyr::between(remaining_amount, 500.1, 1000)){
withdrawn_amount <- momo_withdrawal(remaining_amount = remaining_amount, withdrawal_fee = 1, effect = "percentage")
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
return(data.frame(Mobile_Money_Service = paste(network_from, "to", network_to), withdrawn_amount = withdrawn_amount, stringsAsFactors = FALSE))
}
}
}
momo_transfer <- function(initial_amount, transaction_fee , effect = c("flat", "percentage")){
if(effect == "percentage"){
transaction_fee <- transaction_fee/100
amount_after_transaction <- initial_amount - (initial_amount * transaction_fee)
return(amount_after_transaction)
} else if(effect == "flat"){
transaction_fee <- transaction_fee
amount_after_transaction <- initial_amount - transaction_fee
return(amount_after_transaction)
}
}
momo_withdrawal <- function(remaining_amount, withdrawal_fee , effect = c("flat", "percentage")){
if(effect == "percentage"){
withdrawal_fee <- withdrawal_fee/100
amount_after_withdrawal <- remaining_amount - (remaining_amount * withdrawal_fee)
return(amount_after_withdrawal)
} else if(effect == "flat"){
withdrawal_fee <- withdrawal_fee
amount_after_withdrawal <- remaining_amount - withdrawal_fee
return(amount_after_withdrawal)
}
}
```
# How Mobile Money Interoperability in Ghana affects fees Mobile Money Users Pay: Telco Price Analysis
On 10^th^ May 2018, Mobile Money Interoperability was officially [launched](https://www.myjoyonline.com/business/2018/May-10th/bawumia-launches-mobile-money-payment-interoperability-system.php) in Ghana. This means that you can now transfer mobile money across networks at a fee.
I wrote part of this piece a few days after the launch because interoperability brought up some interesting questions for me. Since then, significant things have happened within the space which meant I've had to re-analyse some portions. About [3000](https://citinewsroom.com/2018/05/15/mobile-money-interoperability-scored-3000-transactions-on-first-day-ghipss/) transactions were made on the first day. By end of August it had reached [800, 000 transactions](https://www.modernghana.com/news/882509/mobile-money-interoperability-hits-800000-transactions-re.html). Most importantly though, since then [AirtelTigo merged](http://www.airteltigo.com.gh/airteltigo-outlines-plans-for-mobile-financial-service-sector/) their mobile money platform.
Some interesting questions this brought up for me at the time of the launch include, say a mobile money user wants to make a transaction, after factoring in cash in fees (if any), transfer fees, and the cash out fees, what is the final amount withdrawn? Or put another way, what 2 combination of networks allows the receiver to cash out at least cost in terms of fees?
Given that mobile money users can switch to any network since the market is not fragmented anymore, how competitive are Interoperable transaction options to same network transactions?
What does interoperability mean for low-income people who typically send small amounts?
I find out this and more in this article.
```{r 10 cedis transfer, fig.align="center", fig.width=7, echo=FALSE}
initial_amount <- 10
all_network_permutations <- expand.grid(network_from = c("Vodafone Cash", "MTN Momo", "AirtelTigo Money"), network_to = c("Vodafone Cash", "MTN Momo", "AirtelTigo Money"), stringsAsFactors = FALSE) %>%
data.frame(initial_amount = rep(initial_amount, nrow(.))) %>%
purrr::pmap_df(momo_value_for_money)
# Plot for amount paid in fees
all_network_permutations %>%
ggplot(aes(x = forcats::fct_reorder(Mobile_Money_Service, withdrawn_amount))) +
geom_bar(aes(weight = initial_amount - withdrawn_amount, fill = Mobile_Money_Service)) +
coord_flip() +
scale_fill_manual(
values = c("Vodafone Cash to Vodafone Cash" = "firebrick4",
"Vodafone Cash to MTN Momo" = "dimgrey",
"Vodafone Cash to AirtelTigo Money" = "dimgrey",
"MTN Momo to Vodafone Cash" = "dimgrey",
"MTN Momo to MTN Momo" = "goldenrod1",
"MTN Momo to AirtelTigo Money" = "dimgrey",
"AirtelTigo Money to Vodafone Cash" = "dimgrey",
"AirtelTigo Money to MTN Momo" = "dimgrey",
"AirtelTigo Money to AirtelTigo Money" = "red")
) +
theme_minimal() +
labs(x = "Mobile Money Service", y = "Amount Paid in Fees (GHc)", title = glue::glue("How much will be paid in fees if GHc{initial_amount} is transfered?"), caption = "By: David Quartey (@DaveQuartey)\nSource: AirtelTigo, MTN, Vodafone") +
theme(legend.position = "none") +
scale_y_continuous(expand = c(0, .011)) +
geom_text(aes(y = (initial_amount - withdrawn_amount), label = (initial_amount - withdrawn_amount)), size = 7, hjust = 1.5)
```
Let's start off with this chart.
To read the chart above, Vodafone Cash to MTN Momo, for example, combines the transfer fee of making a transfer from Vodafone Cash to MTN Momo and the cash out fee of MTN Momo at a particular amount. This gives a sense of how much it costs for that particular network combination.
This is then extended to all possible network combinations to compare amounts to see what it means for users in fees terms.
At GHc10 for instance, interoperability has provided a comparatively cheap alternative (Vodafone Cash to either AirtelTigo Money or MTN Momo) of sending such a low amount at GHC1 considering the fact that previously, exactly the same amount through the voucher system would have likely cost more.
Either way, one thing that strikes me is how fees alternate. Either you pay GHc1 or GHc1.25.
Is it the same at larger amounts?
```{r 1000 cedis transfer, fig.align="center", fig.width=7, echo=FALSE}
initial_amount <- 1000
all_network_permutations <- expand.grid(network_from = c("Vodafone Cash", "MTN Momo", "AirtelTigo Money"), network_to = c("Vodafone Cash", "MTN Momo", "AirtelTigo Money"), stringsAsFactors = FALSE) %>%
data.frame(initial_amount = rep(initial_amount, nrow(.))) %>%
purrr::pmap_df(momo_value_for_money)
# Plot for amount paid in fees
all_network_permutations %>%
ggplot(aes(x = forcats::fct_reorder(Mobile_Money_Service, withdrawn_amount))) +
geom_bar(aes(weight = initial_amount - withdrawn_amount, fill = Mobile_Money_Service)) +
coord_flip() +
scale_fill_manual(
values = c("Vodafone Cash to Vodafone Cash" = "firebrick4",
"Vodafone Cash to MTN Momo" = "dimgrey",
"Vodafone Cash to AirtelTigo Money" = "dimgrey",
"MTN Momo to Vodafone Cash" = "dimgrey",
"MTN Momo to MTN Momo" = "goldenrod1",
"MTN Momo to AirtelTigo Money" = "dimgrey",
"AirtelTigo Money to Vodafone Cash" = "dimgrey",
"AirtelTigo Money to MTN Momo" = "dimgrey",
"AirtelTigo Money to AirtelTigo Money" = "red")
) +
theme_minimal() +
labs(x = "Mobile Money Service", y = "Amount Paid in Fees (GHc)", title = glue::glue("How much will be paid in fees if GHc{initial_amount} is transfered?"), caption = "By: David Quartey (@DaveQuartey)\nSource: AirtelTigo, MTN, Vodafone") +
theme(legend.position = "none") +
scale_y_continuous(expand = c(0, .011)) +
geom_text(aes(y = (initial_amount - withdrawn_amount), label = (initial_amount - withdrawn_amount)), size = 7, hjust = 1.5)
```
Suddenly, things look a little... different. An incredible 0.5% of GHc 1000 is paid in fees when using AirtelTigo to AirtelTigo (seems like Tigo's Cash rates are being used after the Airtel / Tigo merger for same network transfers).
Just so we see how over a period these fees divergence, lets looks at this scenario: Imagine a mobile money user has to transfer GHC1000 to a friend ones every month for the next 12months. Imagine again whatever combination she uses is consistent across the 12months, how much different will the total amount of fees be by the 12th month?
```{r cummulative 1000 cedis transfer, fig.align="center", fig.height=8, fig.width=8, echo=FALSE, message=FALSE}
initial_amount <- 1000
all_network_permutations <- expand.grid(network_from = c("Vodafone Cash", "MTN Momo", "AirtelTigo Money"), network_to = c("Vodafone Cash", "MTN Momo", "AirtelTigo Money"), stringsAsFactors = FALSE) %>%
data.frame(initial_amount = rep(initial_amount, nrow(.))) %>%
purrr::pmap_df(momo_value_for_money)
cum_fees_ends <- all_network_permutations %>%
dplyr::mutate(fees_paid =initial_amount - withdrawn_amount) %>%
split(.$Mobile_Money_Service) %>%
purrr::map(.f = ~purrr::map(.x, ~rep(.x, 12))) %>%
purrr::map(dplyr::as_data_frame) %>%
dplyr::bind_rows() %>%
dplyr::group_by(Mobile_Money_Service) %>%
dplyr::mutate(cum_fees_paid = cumsum(fees_paid), n = 1:12) %>%
dplyr::group_by(Mobile_Money_Service) %>%
dplyr::top_n(n = 1) %>%
dplyr::pull(cum_fees_paid)
all_network_permutations %>%
dplyr::mutate(withdrawn_amount =initial_amount - withdrawn_amount) %>%
split(.$Mobile_Money_Service) %>%
purrr::map(.f = ~purrr::map(.x, ~rep(.x, 12))) %>%
purrr::map(dplyr::as_data_frame) %>%
dplyr::bind_rows() %>%
dplyr::group_by(Mobile_Money_Service) %>%
dplyr::mutate(cum_withdrawn_amount = cumsum(withdrawn_amount), n = 1:12) %>%
ggplot(aes(n, cum_withdrawn_amount)) +
geom_line(aes(color = Mobile_Money_Service), size = 1.5, alpha = 0.8) +
scale_color_manual(
values = c("Vodafone Cash to Vodafone Cash" = "firebrick4",
"Vodafone Cash to MTN Momo" = "dimgrey",
"Vodafone Cash to AirtelTigo Money" = "dimgrey",
"MTN Momo to Vodafone Cash" = "dimgrey",
"MTN Momo to MTN Momo" = "goldenrod1",
"MTN Momo to AirtelTigo Money" = "dimgrey",
"AirtelTigo Money to Vodafone Cash" = "dimgrey",
"AirtelTigo Money to MTN Momo" = "dimgrey",
"AirtelTigo Money to AirtelTigo Money" = "red")
) +
theme_minimal() +
theme(legend.position = "none") +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(sec.axis = sec_axis( ~.,breaks = cum_fees_ends)) +
ggtitle(label = glue::glue("How much is paid in fees across each Mobile Money Service pair, if we sent GHc{initial_amount}?")) +
labs(x = "Number of months", y = "Cummulative Mobile Money Fees", caption = "By: David Quartey (@DaveQuartey)\nSource: AirtelTigo, MTN, Vodafone")
```
It's incredible, isn't it?
She'd have paid between GHc60 to GHc300 in fees after sending GHc12, 000 to her friend over the 12 months period using mobile money on her phone. What's interesting is the variety of options available at a higher amount. Indeed I found that, as the amount transfered increased, so did the options in terms of fees.
Previously, no token or voucher transfer would have been more cost-effective than a same network transfer. This shows that, this is not true anymore.
Even users of the current market leader (MTN) are now better-off interoperating to AirtelTigo than on same network transfer basis for larger amounts.
However, based purely on other possible network combinations a mobile money user may consider, interoperability has also provided relatively costly alternatives too. As high as GHc25 (2.5% paid in fees in percentage terms).
Still, it's likely that the pre-interoperability rate for say MTN Momo to an unregistered user would've been 5% (GHc50), compared to the interoperable rate averaging at about 1.9% (GHc19) with the added benefit and convenience of direct transfer into a cross-network wallet.
### Are Mobile Money fees retrogressive in structure?
[Innovations for Poverty Action](https://www.poverty-action.org) (IPA) recently came out with a [study](http://www.cgap.org/blog/how-do-mobile-money-fee-structures-impact-poor) which highlighted the fees paid (cash in and cash out) on 21 mobile money service across 7 countries (Kenya, Uganda, Tanzania, Pakistan, Nigeria, Bangladesh and India). Even though Ghana was not included in their work, they concluded that most had prices which were regressive in **structure** because "the larger a consumers transaction, the less they pay in **percentage terms**" (emphasis mine).
I was curious to know how this translates to Ghana's Telco mobile money prices in terms of structure.
```{r average prices, echo=FALSE, fig.align="center", fig.height=9, fig.width=9, warning=FALSE}
all_network_permutations <- expand.grid(network_from = c("Vodafone Cash", "MTN Momo", "AirtelTigo Money"), network_to = c("Vodafone Cash", "MTN Momo", "AirtelTigo Money"), stringsAsFactors = FALSE)
number_of_times <- 1000
upper_fee_boundary <- all_network_permutations %>%
split(.$network_from) %>%
purrr::map(~purrr::map(.x,.f = ~rep(.x, number_of_times))) %>% # number of times to repeat each column
purrr::map(dplyr::as_data_frame) %>%
dplyr::bind_rows() %>%
dplyr::group_by(network_from, network_to) %>%
dplyr::mutate(initial_amount = 1:number_of_times) %>% # Fill each column from 1 to the maximum number
purrr::pmap_df(momo_value_for_money) %>%
dplyr::group_by(Mobile_Money_Service) %>%
dplyr::mutate(initial_amount = 2:number_of_times, percentage = (initial_amount - withdrawn_amount)/initial_amount) %>% # Redo this as it starts from 2, must be on
dplyr::ungroup() %>%
dplyr::group_by(initial_amount) %>%
dplyr::mutate(highest_percentage = max(percentage), lowest_percentage = min(percentage), mean_percentage = mean(percentage)) %>%
dplyr::filter(initial_amount == 375) %>%
dplyr::pull(highest_percentage) %>% unique()
lower_fee_boundary <- all_network_permutations %>%
split(.$network_from) %>%
purrr::map(~purrr::map(.x,.f = ~rep(.x, number_of_times))) %>% # number of times to repeat each column
purrr::map(dplyr::as_data_frame) %>%
dplyr::bind_rows() %>%
dplyr::group_by(network_from, network_to) %>%
dplyr::mutate(initial_amount = 1:number_of_times) %>% # Fill each column from 1 to the maximum number
purrr::pmap_df(momo_value_for_money) %>%
dplyr::group_by(Mobile_Money_Service) %>%
dplyr::mutate(initial_amount = 2:number_of_times, percentage = (initial_amount - withdrawn_amount)/initial_amount) %>% # Redo this as it starts from 2, must be on
dplyr::ungroup() %>%
dplyr::group_by(initial_amount) %>%
dplyr::mutate(highest_percentage = max(percentage), lowest_percentage = min(percentage), mean_percentage = mean(percentage)) %>%
dplyr::filter(initial_amount == 375) %>%
dplyr::pull(lowest_percentage) %>% unique()
all_network_permutations %>%
split(.$network_from) %>%
purrr::map(~purrr::map(.x,.f = ~rep(.x, number_of_times))) %>% # number of times to repeat each column
purrr::map(dplyr::as_data_frame) %>%
dplyr::bind_rows() %>%
dplyr::group_by(network_from, network_to) %>%
dplyr::mutate(initial_amount = 1:number_of_times) %>% # Fill each column from 1 to the maximum number
purrr::pmap_df(momo_value_for_money) %>%
dplyr::group_by(Mobile_Money_Service) %>%
dplyr::mutate(initial_amount = 2:number_of_times, percentage = (initial_amount - withdrawn_amount)/initial_amount) %>% # Redo this as it starts from 2, must be on
dplyr::ungroup() %>%
dplyr::group_by(initial_amount) %>%
dplyr::mutate(highest_percentage = max(percentage), lowest_percentage = min(percentage), mean_percentage = mean(percentage)) %>%
dplyr::ungroup() %>%
ggplot(aes(initial_amount, percentage)) +
geom_line(aes(color = Mobile_Money_Service), size = 1.5) +
#geom_line(aes(x = initial_amount, y = highest_percentage), color = "black", size = 2) +
#geom_line(aes(x = initial_amount, y = lowest_percentage), color = "black", size = 2) +
geom_line(aes(x = initial_amount, y = mean_percentage), color = "red", size = 2) +
geom_vline(xintercept = 375, color = "black", linetype = 1, size = 1.5) +
geom_point(aes(x = 375, y = upper_fee_boundary), color = "black", size = 4) +
geom_point(aes(x = 375, y = lower_fee_boundary), color = "black", size = 4) +
geom_curve(aes(xend = 385, yend = upper_fee_boundary + 0.001, y = 0.0325, x = 625), arrow = arrow(ends = "last", length = unit(0.03, "npc"), type = "closed"), size = 1, curvature = .4) +
geom_curve(aes(xend = 385, yend = lower_fee_boundary - 0.001, y = 0.005, x = 625), arrow = arrow(ends = "last", length = unit(0.03, "npc"), type = "closed"), size = 1, curvature = -.4) +
#scale_y_continuous(limits = c(0, .5)) +
#scale_color_viridis_d() +
#geom_smooth(method = "loess", se = FALSE, color = "red", size = 2) +
scale_color_manual(
values = c("Vodafone Cash to Vodafone Cash" = "dimgrey",
"Vodafone Cash to MTN Momo" = "dimgrey",
"Vodafone Cash to AirtelTigo Money" = "dimgrey",
"MTN Momo to Vodafone Cash" = "dimgrey",
"MTN Momo to MTN Momo" = "dimgrey",
"MTN Momo to AirtelTigo Money" = "dimgrey",
"AirtelTigo Money to Vodafone Cash" = "dimgrey",
"AirtelTigo Money to MTN Momo" = "dimgrey",
"AirtelTigo Money to AirtelTigo Money" = "dimgrey")
) +
annotate(geom = "text", x = 505, y = 0.022, label = "Average Fees", color = "red") +
annotate(geom = "text", x = 650, y = 0.0325, label = glue::glue(round({upper_fee_boundary*100}, digits = 1), "%")) +
annotate(geom = "text", x = 650, y = 0.005, label = glue::glue(round({lower_fee_boundary*100}, digits = 1), "%")) +
theme_minimal() +
theme(legend.position = "none") +
labs(y = "Percentage Paid in Fees", x = "Transfer Amount (GHc)", title = "Mobile Money Fee Structure", caption = "By: David Quartey (@DaveQuartey)\nSource: AirtelTigo, MTN, Vodafone") +
scale_y_continuous(labels = function(x){ paste0(round(x * 100, 0), "%")}, limits = c(0, 0.05), expand = c(0, 0))
```
Considering each network combination and their transfer + cash out fees, mobile money products in Ghana are regressive in structure too. This shows with the average fees paid downward trend as the amount increases. This means that at lower amounts, users are paying more in fees in percentage terms compared to higher amounts. This is important because, low-income people, the unbanked, and people new to the platform will most likely send low amounts and so, they will most likely be paying more in percentage terms compared to people who send higher amounts.
Looking at how Telco's in Ghana structure their mobile money prices, the higher fees could partly be as a result of how amounts within the 1 - 50 range are charged flat rates. Currently, all 3 Telco's in Ghana who provide this service charge flat rates for the GHc1-GHc50 band. As [noted](http://www.cgap.org/blog/how-do-mobile-money-fee-structures-impact-poor), this way of pricing has its benefits because a flat rate probably takes consideration for people with low numeracy skills to understand how much they are charged. However, it ends up being a lot in percentage terms at such low amounts.
### What does this all mean?
```{r mobile money fee structure, echo=FALSE, fig.align="center", fig.height=9, fig.width=9, message=FALSE, warning=FALSE}
sub_data <- expand.grid(network_from = c("Vodafone Cash", "MTN Momo", "AirtelTigo Money"), network_to = c("Vodafone Cash", "MTN Momo", "AirtelTigo Money"), stringsAsFactors = FALSE) %>%
data.frame(initial_amount = rep(initial_amount, nrow(.))) %>%
purrr::pmap_df(momo_value_for_money)%>%
unique() %>%
dplyr::mutate( x = 1010 - .0018,
y = dplyr::case_when(Mobile_Money_Service == "Vodafone Cash to Vodafone Cash" ~ 0.00850 - .0018,
Mobile_Money_Service == "Vodafone Cash to MTN Momo" ~ 0.0250 - .0018,
Mobile_Money_Service == "Vodafone Cash to AirtelTigo Money" ~ 0.0175 - .0018,
Mobile_Money_Service == "MTN Momo to Vodafone Cash" ~ 0.0210 - .0018,
Mobile_Money_Service == "MTN Momo to MTN Momo" ~ 0.0199 - .0018,
Mobile_Money_Service == "MTN Momo to AirtelTigo Money" ~ 0.0175 - .0018,
Mobile_Money_Service == "AirtelTigo Money to Vodafone Cash" ~ 0.0210 - .0018,
Mobile_Money_Service == "AirtelTigo Money to MTN Momo" ~ 0.0248 - .0018,
Mobile_Money_Service == "AirtelTigo Money to AirtelTigo Money" ~ 0.00500 - .0018),
breaks = dplyr::case_when(Mobile_Money_Service == "Vodafone Cash to Vodafone Cash" ~ paste0(round(0.00850 * 100, 1), "%"),
Mobile_Money_Service == "MTN Momo to MTN Momo" ~ paste0(round(0.0199 * 100, 1), "%"),
Mobile_Money_Service == "Vodafone Cash to AirtelTigo Money" ~ paste0(round(0.0175 * 100, 1), "%"),
Mobile_Money_Service == "MTN Momo to Vodafone Cash" ~ paste0(round(0.0210 * 100, 1), "%"),
Mobile_Money_Service == "Vodafone Cash to MTN Momo" ~ paste0(round(0.025 * 100, 1), "%"),
Mobile_Money_Service == "MTN Momo to AirtelTigo Money" ~ paste0(round(0.0175 * 100, 1), "%"),
Mobile_Money_Service == "AirtelTigo Money to Vodafone Cash" ~ paste0(round(0.0210 * 100, 1), "%"),
Mobile_Money_Service == "AirtelTigo Money to MTN Momo" ~ paste0(round(0.0248 * 100, 1), "%"),
Mobile_Money_Service == "AirtelTigo Money to AirtelTigo Money" ~ paste0(round(0.00500 * 100, 1), "%")))
all_network_permutations <- expand.grid(network_from = c("Vodafone Cash", "MTN Momo", "AirtelTigo Money"), network_to = c("Vodafone Cash", "MTN Momo", "AirtelTigo Money"), stringsAsFactors = FALSE)
momo_fee_structure_ends <- all_network_permutations %>%
split(.$network_from) %>%
purrr::map(~purrr::map(.x,.f = ~rep(.x, number_of_times))) %>% # number of times to repeat each column
purrr::map(dplyr::as_data_frame) %>%
dplyr::bind_rows() %>%
dplyr::group_by(network_from, network_to) %>%
dplyr::mutate(initial_amount = 1:number_of_times) %>% # Fill each column from 1 to the maximum number
purrr::pmap_df(momo_value_for_money) %>%
dplyr::group_by(Mobile_Money_Service) %>%
dplyr::mutate(initial_amount = 2:number_of_times, percentage = (initial_amount - withdrawn_amount)/initial_amount) %>% # Redo this as it starts from 2, must be on
dplyr::top_n(percentage,n = -1) %>%
dplyr::pull(percentage)
number_of_times <- 1000
all_network_permutations %>%
split(.$network_from) %>%
purrr::map(~purrr::map(.x,.f = ~rep(.x, number_of_times))) %>% # number of times to repeat each column
purrr::map(dplyr::as_data_frame) %>%
dplyr::bind_rows() %>%
dplyr::group_by(network_from, network_to) %>%
dplyr::mutate(initial_amount = 1:number_of_times) %>% # Fill each column from 1 to the maximum number
purrr::pmap_df(momo_value_for_money) %>%
dplyr::group_by(Mobile_Money_Service) %>%
dplyr::mutate(initial_amount = 2:number_of_times, percentage = (initial_amount - withdrawn_amount)/initial_amount) %>% # Redo this as it starts from 2, must be on
ggplot(aes(initial_amount, percentage)) +
geom_line(data = . %>% dplyr::ungroup() %>% dplyr::select(-Mobile_Money_Service), color = "gray90", size = 1) +
geom_line(aes(color = Mobile_Money_Service), size = 2) +
scale_y_continuous(limits = c(0, 5)) +
#scale_color_viridis_d()+
labs(y = "Percentage Paid in Fees", x = "Transfer Amount (GHc)", title = "Mobile Money Fee Structure", caption = "By: David Quartey (@DaveQuartey)\nSource: AirtelTigo, MTN, Vodafone") +
scale_y_continuous(labels = function(x){ paste0(round(x * 100, 0), "%")}, limits = c(0, 0.05)) +
scale_x_continuous(expand = c(0,0)) +
theme_minimal() +
facet_wrap(.~Mobile_Money_Service, scales = "free") +
scale_color_manual(
values = c("Vodafone Cash to Vodafone Cash" = "firebrick4",
"Vodafone Cash to MTN Momo" = "dimgrey",
"Vodafone Cash to AirtelTigo Money" = "dimgrey",
"MTN Momo to Vodafone Cash" = "dimgrey",
"MTN Momo to MTN Momo" = "orange",
"MTN Momo to AirtelTigo Money" = "dimgrey",
"AirtelTigo Money to Vodafone Cash" = "dimgrey",
"AirtelTigo Money to MTN Momo" = "dimgrey",
"AirtelTigo Money to AirtelTigo Money" = "red")
) +
theme(legend.position = "none") + ggrepel::geom_text_repel(data = sub_data, aes(x = x, y = y, label = breaks), color = c("red", "dimgrey", "dimgrey", "dimgrey", "orange", "dimgrey", "dimgrey", "dimgrey", "firebrick4"))
```
Simply, users who send high amounts have good options (further expanded by interoperability), but if it's less it doesn't matter what network they use.
If Government of Ghana has plans to tax mobile money transactions as being [reported](https://thebftonline.com/2018/business/companies/mtn-ghana-holds-2018-mobile-money-stakeholder-conference/), Telco's could consider looking at higher amount ranges (500 - 1000, 1000+) since these amounts generally pay lower fees in percentage terms, although it feels like the platform is still in its infancy.
Hope you found this as interesting as I did putting it together. Thanks for reading.
***
Find the code and data used in this analysis [here](https://github.com/DavidQuartey/Mobile-Money-Price-Value-For-Money) and more of my content on my blog, [SimpleEconomics](https://medium.com/@DaveQuartey) where I interrogate topics I find interesting from a data perspective.
I am always interested in getting involved in new projects or just connecting with others. Feel free to get in touch!