Financial Inclusion of Micro, Small and Medium Enterprises (MSMEs) in Kenya, Malawi and Zambia

In 2021, SIVIO Institute conducted a study of MSMEs in Zimbabwe and developed the SIVIO Institute Financial Inclusion Index (SI-FIndex) to compare the level of Financial Inclusion of enterprises in the 10 provinces of Zimbabwe.

In 2023, with support through the Mozilla IRL Fund we expanded the study to Kenya, Malawi and Zambia focusing on the capital cities. The goal is to expand the SI-FIndex to compare the level of Financial Inclusion of MSMEs operating in the capital cities of the four countries as listed below:

NB: To add Zimbabwe to the mix is a little bit more complicated than the other 3 so will look into adding once completed with the index methodology

Definitions

Financial Inclusion is qualified as the access to affordable and useful formal financial products and services for MSMEs within the capital cities of the surveyed countries

Methodology

The data for Zimbabwe is pulled from the 2021 study while the expansion survey was conducted September - November 2023. The target was to compare 100 enterprises with the distribution of 60 micro enterprises, 30 small and 10 medium. In the end, we used a total of 80 enterprises broken down as 53 micro enterprises and 27 bigger enterprises (21 small + 6 medium).

The SI-FIndex assesses the level of financial inclusion across dimensions and assesses each variable on a 0-1 normalised scale. The 5 dimensions are assessed across several relevant variables (which is explained in more depth below in each section), and the averaged across to produce the final Financial Inclusion Score.

Financial Inclusion Dimensions

  • Compliance
    • Full Compliance
    • Partial Compliance
    • Not Compliant
  • Access
    • Formal Financial Inclusion
    • Informal Financial Inclusion
    • Financial Exclusion
  • Barriers (of those who do not have access)
    • Knowledge
    • Affordability
    • Documentation
    • Trust
    • Compatibility
    • Bureaucracy
    • Complicated System
  • Usage (of those who do have access)
    • bank account
    • mobile money account
    • business insurance products
    • information/knowledge
  • Digital
    • Access
    • Usage

Data manipulation

The data is cleaned and sampled according to the breakdown below: Note: [will add the code base for the final copy of the report]

Sampling

Level of sample: size of enterprise across the 3 countries. Taken the minimum number from each of the countries

  • Micro: 53 (Kenya limit)

  • Small: 21 (Zambia limit)

  • Medium: 6 (Malawi limit)

  • Total: 80 enterprises

# write.csv(data_index, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/FIndex data.csv")
data_index = read.csv(file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/FIndex data.csv")
country size_Business n
Kenya Micro Enterprise 53
Kenya Small/Medium Enterprise 27
Malawi Micro Enterprise 53
Malawi Small/Medium Enterprise 27
Zambia Micro Enterprise 53
Zambia Small/Medium Enterprise 27

Equality Measures

Equality Measures under which the data is grouped for analysis

  • Country of Operation
  • Gender of Business Owner
  • Age of Business Owner
  • Size of Business
  • Location of Business

Compliance

Compliance looks at how the enterprise is registered in the country of operation.Is the enterprise registered as a business and secondarily, is it registered to pay taxes.

Question 1: Is your business officially registered in the country you operate? (Yes=1, No=0)

Question 2: Is your business registered for tax purposes? (Yes=1, No=0)

The answers to the two questions above are the criteria . They are explained below:

  • The answers to the two questions above are the 2 criteria under which Compliance is assessed
  • The criteria names in the data set are business_registration, tax_registration

An enterprise is consider fully compliant (1) if it is both registered as a business and registered for tax purposes. If it is registered but not for tax purposes, then it is considered partially compliant (0.5), and if it is not registered at all then it is not compliant (0)

country compliance_category num_enterprises percent_enterprises
Kenya full compliance 51 64
Kenya not compliant 21 26
Kenya partial compliance 8 10
Malawi full compliance 21 26
Malawi not compliant 58 72
Malawi partial compliance 1 1
Zambia full compliance 47 59
Zambia not compliant 32 40
Zambia partial compliance 1 1

In Kenya, Kenya, 64% of enterprises are fully compliant, that is that they are registered as a business and for tax purposes. An additional Kenya, 10% are registered as a business, but not for tax. In Malawi and Zambia most enterprises that are registered are also registered for tax purposes (Malawi, 26% and Zambia, 59% respectively). Kenya does have the highest levels of full compliance but this was also influenced by the timing of the survey, which coincides with the local government conducting an inspection of informal enterprises in an effort to formalise and register them.

Access

Three levels of access:

  1. Formal Inclusion (1)

  2. Informal Inclusion (0.5)

  3. Exclusion (0)

The distinction across the 3 levels is described in the table below:

Variable Formal Inclusion Informal Inclusion Exclusion
bank account Yes No
mobile money account Yes No
loan Microfinance institution, Bank, Mobile Money provider, Order finance companies, NGOs, Credit Unions, Government, Asset finance Online platforms, Money Clubs, Loan Sharks, Family & Friends Never applied
business insurance products Yes No
pension policy Yes No
savings account Savings Account (bank or mobile), Stock Market Shares ROSCA or ISAL, Other businesses, FOREX, Cryptocurrency Personal Space or No Plan
investments Yes No
information source Formal Sources Informal, Online and Social Media Sources Nowhere

The means of the categories above are calculated to provide the final two scores for access:

  • Formal Inclusion
  • Informal Inclusion

The final inclusion score is then calculated as the average of the two because true inclusion is only as real as the level of access to formal financial products and services.

Formal Inclusion

country bank_account mobile_money_account formal_loan_application business_insurance_products pension_policy investments formal_savings_account formal_information_source formal_inclusion_access percent_formal_inclusion
Kenya 0.5875 0.725 0.2750 0.325 0.1125 0.375 0.675 0.3000 0.421875 42
Malawi 0.2500 0.275 0.1625 0.075 0.0500 0.350 0.700 0.1625 0.253125 25
Zambia 0.4375 0.825 0.1875 0.200 0.1000 0.100 0.425 0.3000 0.321875 32

Informal Inclusion

country informal_loan_application informal_information_source informal_savings_account informal_inclusion_access percent_informal_inclusion
Kenya 0.0250 0.6000 0.1500 0.2583333 26
Malawi 0.0625 0.5375 0.0625 0.2208333 22
Zambia 0.1000 0.7000 0.0500 0.2833333 28

Barriers

There are 7 reasons for not having or using financial products/services

Formal Barriers: those that are client side * Lack of Knowledge on product or service * Missing Documentation to use product or service * Affordability of product or service * Lack of Trust in product or service

Informal Barriers: those that the service provider controls * Bureaucracy involved with product or service * Complicated System * Compatibility product or service

NOTE: If the respondent acknowledged that the limit to their access/usage was not due to one of the above reasons but rather a choice to not use the product/service, then “No Barrier” was recorded.

The financial products/services for which the barriers were assessed are

  • Registration as a business
  • Registration for tax purposes
  • Access to a bank account
  • Access to a mobile money account
  • Digital Product Use
  • Access to loans
  • Offer of Business Insurance Products
  • Offer of Pension Policy
  • Access to investments
country affordability bureaucracy complicatedSystem knowledge documentation compatibility trust formalBarriers informalBarriers
Kenya 0.2442417 0.0411255 0.0700534 0.0742971 0.0428211 0.1523615 0.0677804 0.1072850 0.0878468
Malawi 0.2803377 0.0166667 0.0150535 0.2432903 0.0414349 0.3283893 0.0158654 0.1452321 0.1200365
Zambia 0.1445668 0.0222222 0.0559028 0.0654818 0.0913194 0.2103812 0.0484375 0.0874514 0.0961687

Usage

  • Bank Account
    • Time to open account
      • Same day (1)
      • At least one week (0.5)
      • Longer than a week (0)
    • Frequency of use
      • At least once a week (1)
      • At least once per month (0.5)
      • Longer period (Once per 6 months, never use it)
    • Hold more than 1 type of account
    • More than 1 type of service
    • Do not experience challenges using
country n bank_openTime bank_frequency bank_MoreThan1Account bank_MoreThan1Service bank_Norestrictions
Kenya 47 0.8191489 0.9148936 0.0851064 0.5319149 0.7659574
Malawi 20 0.7000000 0.9500000 0.0000000 0.4500000 0.9000000
Zambia 35 0.7857143 0.9714286 0.0571429 0.8857143 0.9142857
  • Mobile Money Account
    • Time to open account
      • Same day (1)
      • At least one week (0.5)
      • Longer than a week (0)
    • Frequency of use
      • Everyday (1)
      • A few times a week (0.5)
      • At least once a month (0)
    • Do not experience challenges when using
country n mm_openTime mm_frequency mm_Norestrictions
Kenya 58 0.8706897 0.8620690 0.7068966
Malawi 22 0.8636364 0.7045455 1.0000000
Zambia 66 0.8939394 0.9848485 0.9848485
  • Loans
    • Successful applications
    • Perceptions of Interest Rates
      • High (0)
      • Fair (1)
      • Low (1)
    • Ability to Pay Back
      • Already paid back (1)
      • Will be able to pay back (1)
      • Not able (0)
country n loan_success loan_interestRatePerceptions loan_payBack
Kenya 24 0.8750000 0.5416667 0.8750000
Malawi 18 0.7777778 0.3888889 0.7777778
Zambia 23 0.9565217 0.8695652 0.9565217
  • Business Insurance Products
    • Offer Comprehensive business insurance i.e., all risk in last 12 months
    • Offer Non-comprehensive business insurance in last 12 months
    • Offer staff insurance in last 12 months
    • Offer product insurance in last 12 months
country n insurance_comprehensive insurance_noncomprehensive insurance_staff insurance_product
Kenya 26 0.4615385 0.0769231 0.1538462 0.1923077
Malawi 6 0.1666667 0.0000000 0.1666667 0.5000000
Zambia 16 0.8125000 0.0000000 0.1875000 0.1875000

  • Pension Policy (?)

  • Savings and Investment Capital (?)

    • Types of Investments?

  • Source of Information

    • Rate knowledge on the following with 0 being “I do not know anything” and 5 being “Very good”
      • Mobile money services
      • Investments and capital markets
      • Business insurance products
      • Pension’s products
      • Loans and credit facilities provided by commercial banks
      • Loans and credit facilities provided by microfinance institutions
      • Loans and credit facilities provided by ROSCAs and ISALs
      • Banking services
      • Taxation and regulatory compliance
      • Digital products and services
      • The internet and putting my business in a digital landscape
      • Promoting my business online
country knowledge_mmServices knowledge_investmentsCapitalMarkets knowledge_businessInsuranceProducts knowledge_pensionProducts knowledge_loansCreditCommercialBanks knowledge_loansCreditMicrofinance knowledge_loansCreditROSCAsISALs knowledge_bankingServices knowledge_taxationRegulatoryCompliance knowledge_digitalProductsServices knowledge_internetDigitalLandscape knowledge_onlinePromotion
Kenya 0.8750 0.4750 0.5025 0.3700 0.6725 0.6075 0.4700 0.7875 0.775 0.8025 0.6800 0.6000
Malawi 0.7275 0.4575 0.2700 0.2250 0.4925 0.4825 0.3850 0.6625 0.550 0.5300 0.4425 0.4550
Zambia 0.9600 0.6200 0.6400 0.6175 0.7500 0.7575 0.8275 0.7275 0.640 0.7950 0.7575 0.7225

Usage is then quantified:

  • Formal Inclusion is the use of formal financial products (bank, mobile money, investments, loans, insurance)
  • Informal Inclusion represents the knowledge of financial products and services
## Joining, by = "country"
## Joining, by = "country"
## Joining, by = "country"
## Joining, by = "country"
country usage_banking usage_MM usage_loan usage_insur knowledgeAssessment formal_usage informal_usage
Kenya 0.6234043 0.8132184 0.7638889 0.2211538 0.8750 0.6054163 0.2695837
Malawi 0.6000000 0.8560606 0.6481481 0.2083333 0.7275 0.5781355 0.1493645
Zambia 0.7228571 0.9545455 0.9275362 0.2968750 0.9600 0.7254535 0.2345465

Digital

  • Access
    • Landline
    • Mobile phone
    • Smart phone
    • Computer
    • Internet
  • Usage
    • WhatsApp Number
    • Phone Number
    • Website
    • Social media accounts
    • Email
    • Mobile banking
    • Credit/debit card
    • Cryptocurrency
    • Digital Payment systems
    • Money transfer system (includes remittances)
    • E-commerce
    • Bill payment applications
    • Online payment solutions
    • Accounting services

Digital Access

country landline mobilePhone smartPhone computer internet access percent_access
Kenya 0.0625 0.6625 0.7625 0.5375 0.6000 0.5250 52
Malawi 0.0000 0.9500 0.4625 0.1375 0.3125 0.3725 37
Zambia 0.0000 0.9875 0.8625 0.2250 0.6250 0.5400 54

Digital Usage

country mobileBanking creditCard cryptocurrency digitalPayments moneyTransfer socialMedia email whatsapp phoneNumber website ecommerce billPaymentSystem onlinePaymentSystem accountingSystem usage percent_usage
Kenya 0.5750 0.2125 0.05 0.075 0.0375 0.2875 0.2500 0.5125 0.525 0.2125 0.0875 0.0625 0.1500 0.0750 0.2223214 22
Malawi 0.2500 0.0250 0.00 0.000 0.0000 0.3375 0.0125 0.4875 0.775 0.0000 0.0000 0.0000 0.0000 0.0125 0.1357143 14
Zambia 0.3125 0.3000 0.00 0.100 0.0125 0.3500 0.3125 0.8125 0.975 0.1500 0.0000 0.0000 0.0625 0.0000 0.2419643 24

Financial Inclusion

Compliance + Barriers + Access + Usage + Digital

tibble(
  "Dimension" = c("Compliance", 
                 "Barriers",
                 "Access",
                 "Usage", 
                 "Digital"),
  "Formal Inclusion" = c("Registered as a business & for tax reasons (full compliance)", 
                         "Affordability, Documention, Knowledge, Trust", 
                         "Formal Services & Products",
                         "Use of services & products (banks, mobile money, loans, insurance", 
                         "Use of digital products"),
  "Informal Inclusion" = c("Registered but not for tax (partial compliance)", 
                           "Compatability, Bureaucracy, Complicated Syste,", 
                           "Informal Services & Products",
                           "Self assessment of knowledge", 
                           "Access but not use of digital products"),
  "Exclusion" = c("", 
                  "",
                  "",
                  "", 
                  "")
)%>%
  kable() %>%
  kable_styling()
Dimension Formal Inclusion Informal Inclusion Exclusion
Compliance Registered as a business & for tax reasons (full compliance) Registered but not for tax (partial compliance)
Barriers Affordability, Documention, Knowledge, Trust Compatability, Bureaucracy, Complicated Syste,
Access Formal Services & Products Informal Services & Products
Usage Use of services & products (banks, mobile money, loans, insurance Self assessment of knowledge
Digital Use of digital products Access but not use of digital products 
## Joining, by = c("country", "FI")
## Joining, by = c("country", "FI")
## Joining, by = c("country", "FI")
## Joining, by = c("country", "FI")
## `summarise()` has grouped output by 'country'. You can override using the
## `.groups` argument.
country FI Compliance Access Barriers Usage Digital FIscore 0
Kenya Exclusion 26 32 80 12 48 23 0
Kenya Formal Inclusion 64 42 11 61 22 46 0
Kenya Informal Inclusion 10 26 9 27 30 31 0
Malawi Exclusion 72 53 73 27 63 40 0
Malawi Formal Inclusion 26 25 15 58 14 35 0
Malawi Informal Inclusion 1 22 12 15 23 25 0
Zambia Exclusion 40 39 82 4 46 25 0
Zambia Formal Inclusion 59 32 9 73 24 47 0
Zambia Informal Inclusion 1 28 10 23 30 29 0

Export Data

write.csv(compliance_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/FIndex compliance data.csv")

write.csv(access_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/FIndex access data.csv")

write.csv(barrier_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/FIndex barriers data.csv")

write.csv(usage_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/FIndex usage data.csv")

write.csv(digital_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/FIndex digital data.csv")

write.csv(financialInclusion_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/FIndex inclusion data.csv")

Gender of Business Owner

# NB: the levels that have # before are not considered in the run analysis. This allows one to change the level at which the analysis is performed.

level = c("country",
           "gender_Owner"
           # "age_Owner",
           # "size_Business",
           # "location_Business",
           # "sector_Business",
           )

Compliance

country gender_Owner compliance_category num_enterprises percent_enterprises
Kenya Men Led full compliance 31 39
Kenya Men Led not compliant 15 19
Kenya Men Led partial compliance 7 9
Kenya Women Led full compliance 20 25
Kenya Women Led not compliant 6 8
Kenya Women Led partial compliance 1 1
Malawi Men Led full compliance 13 16
Malawi Men Led not compliant 41 51
Malawi Men Led partial compliance 1 1
Malawi Women Led full compliance 8 10
Malawi Women Led not compliant 17 21
Zambia Men Led full compliance 27 34
Zambia Men Led not compliant 14 18
Zambia Men Led partial compliance 1 1
Zambia Women Led full compliance 20 25
Zambia Women Led not compliant 18 22

Access

Formal Inclusion

country gender_Owner bank_account mobile_money_account formal_loan_application business_insurance_products pension_policy investments formal_savings_account formal_information_source formal_inclusion_access percent_formal_inclusion
Kenya Men Led 0.5660377 0.7735849 0.3207547 0.3207547 0.1320755 0.4528302 0.6792453 0.2830189 0.4410377 44
Kenya Women Led 0.6296296 0.6296296 0.1851852 0.3333333 0.0740741 0.2222222 0.6666667 0.3333333 0.3842593 38
Malawi Men Led 0.2727273 0.2909091 0.1454545 0.0909091 0.0545455 0.3454545 0.6727273 0.1636364 0.2545455 25
Malawi Women Led 0.2000000 0.2400000 0.2000000 0.0400000 0.0400000 0.3600000 0.7600000 0.1600000 0.2500000 25
Zambia Men Led 0.5238095 0.8571429 0.2142857 0.2857143 0.1904762 0.1428571 0.5000000 0.3571429 0.3839286 38
Zambia Women Led 0.3421053 0.7894737 0.1578947 0.1052632 0.0000000 0.0526316 0.3421053 0.2368421 0.2532895 25

Informal Inclusion

country gender_Owner informal_loan_application informal_information_source informal_savings_account informal_inclusion_access percent_informal_inclusion
Kenya Men Led 0.0188679 0.6037736 0.1698113 0.2641509 26
Kenya Women Led 0.0370370 0.5925926 0.1111111 0.2469136 25
Malawi Men Led 0.0363636 0.4909091 0.0363636 0.1878788 19
Malawi Women Led 0.1200000 0.6400000 0.1200000 0.2933333 29
Zambia Men Led 0.0238095 0.6428571 0.0476190 0.2380952 24
Zambia Women Led 0.1842105 0.7631579 0.0526316 0.3333333 33

Barriers

country gender_Owner affordability bureaucracy complicatedSystem knowledge documentation compatibility trust formalBarriers informalBarriers
Kenya Men Led 0.2819756 0.0644928 0.0652983 0.0875961 0.0573772 0.2150943 0.0751142 0.1255158 0.1149618
Kenya Women Led 0.2555556 NaN 0.1069136 0.0727513 0.0370370 0.1283386 0.2222222 0.1468915 0.1176261
Malawi Men Led 0.2745942 0.0250000 0.0215201 0.2671999 0.0577236 0.3200246 0.0229798 0.1556244 0.1221816
Malawi Women Led 0.3344494 NaN NaN 0.3112745 NaN 0.3466606 NaN 0.3228619 0.3466606
Zambia Men Led 0.1290233 NaN 0.0480392 0.0482633 0.1107143 0.3319444 0.0404762 0.0821193 0.1899918
Zambia Women Led 0.1711423 0.0400000 0.0799794 0.1078452 0.0755556 0.2230279 0.0557276 0.1025676 0.1143357 
## Warning: Removed 6 rows containing missing values (geom_col).
## Warning: Removed 6 rows containing missing values (geom_label).

Usage

country gender_Owner n bank_openTime bank_frequency bank_MoreThan1Account bank_MoreThan1Service bank_Norestrictions
Kenya Men Led 30 0.8166667 0.9000000 0.1333333 0.5333333 0.7000000
Kenya Women Led 17 0.8235294 0.9411765 0.0000000 0.5294118 0.8823529
Malawi Men Led 15 0.6666667 0.9333333 0.0000000 0.5333333 0.9333333
Malawi Women Led 5 0.8000000 1.0000000 0.0000000 0.2000000 0.8000000
Zambia Men Led 22 0.8181818 1.0000000 0.0909091 0.9090909 0.8636364
Zambia Women Led 13 0.7307692 0.9230769 0.0000000 0.8461538 1.0000000
country gender_Owner n mm_openTime mm_frequency mm_Norestrictions
Kenya Men Led 41 0.8780488 0.8292683 0.7560976
Kenya Women Led 17 0.8529412 0.9411765 0.5882353
Malawi Men Led 16 0.9375000 0.7187500 1.0000000
Malawi Women Led 6 0.6666667 0.6666667 1.0000000
Zambia Men Led 36 0.9027778 0.9861111 0.9722222
Zambia Women Led 30 0.8833333 0.9833333 1.0000000
country gender_Owner n loan_success loan_interestRatePerceptions loan_payBack
Kenya Men Led 18 0.8333333 0.4444444 0.8333333
Kenya Women Led 6 1.0000000 0.8333333 1.0000000
Malawi Men Led 10 0.8000000 0.1000000 0.8000000
Malawi Women Led 8 0.7500000 0.7500000 0.7500000
Zambia Men Led 10 1.0000000 0.8000000 1.0000000
Zambia Women Led 13 0.9230769 0.9230769 0.9230769
country gender_Owner n insurance_comprehensive insurance_noncomprehensive insurance_staff insurance_product
Kenya Men Led 17 0.5294118 0.0588235 0.1176471 0.1176471
Kenya Women Led 9 0.3333333 0.1111111 0.2222222 0.3333333
Malawi Men Led 5 0.2000000 0.0000000 0.2000000 0.6000000
Malawi Women Led 1 0.0000000 0.0000000 0.0000000 0.0000000
Zambia Men Led 12 0.8333333 0.0000000 0.2500000 0.2500000
Zambia Women Led 4 0.7500000 0.0000000 0.0000000 0.0000000
country gender_Owner knowledge_mmServices knowledge_investmentsCapitalMarkets knowledge_businessInsuranceProducts knowledge_pensionProducts knowledge_loansCreditCommercialBanks knowledge_loansCreditMicrofinance knowledge_loansCreditROSCAsISALs knowledge_bankingServices knowledge_taxationRegulatoryCompliance knowledge_digitalProductsServices knowledge_internetDigitalLandscape knowledge_onlinePromotion
Kenya Men Led 0.8830189 0.5056604 0.5396226 0.3849057 0.6679245 0.5962264 0.4943396 0.7773585 0.7584906 0.8150943 0.6641509 0.5622642
Kenya Women Led 0.8592593 0.4148148 0.4296296 0.3407407 0.6814815 0.6296296 0.4222222 0.8074074 0.8074074 0.7777778 0.7111111 0.6740741
Malawi Men Led 0.7272727 0.4509091 0.2836364 0.2254545 0.4872727 0.4836364 0.3309091 0.6400000 0.5527273 0.5454545 0.4036364 0.4072727
Malawi Women Led 0.7280000 0.4720000 0.2400000 0.2240000 0.5040000 0.4800000 0.5040000 0.7120000 0.5440000 0.4960000 0.5280000 0.5600000
Zambia Men Led 0.9714286 0.7190476 0.7523810 0.7047619 0.8000000 0.7857143 0.8238095 0.7714286 0.7142857 0.8190476 0.7809524 0.7666667
Zambia Women Led 0.9473684 0.5105263 0.5157895 0.5210526 0.6947368 0.7263158 0.8315789 0.6789474 0.5578947 0.7684211 0.7315789 0.6736842 
## Joining, by = c("country", "gender_Owner")
## Joining, by = c("country", "gender_Owner")
## Joining, by = c("country", "gender_Owner")
## Joining, by = c("country", "gender_Owner")
## `summarise()` has grouped output by 'country'. You can override using the
## `.groups` argument.
country gender_Owner usage_banking usage_MM usage_loan usage_insur knowledgeAssessment formal_usage informal_usage
Kenya Men Led 0.6166667 0.8211382 0.7037037 0.2058824 0.8830189 0.5868477 0.2961711
Kenya Women Led 0.6352941 0.7941176 0.9444444 0.2500000 0.8592593 0.6559641 0.2032952
Malawi Men Led 0.6133333 0.8854167 0.5666667 0.2500000 0.7272727 0.5788542 0.1484186
Malawi Women Led 0.5600000 0.7777778 0.7500000 0.0000000 0.7280000 0.5219444 0.2060556
Zambia Men Led 0.7363636 0.9537037 0.9333333 0.3333333 0.9714286 0.7391835 0.2322451
Zambia Women Led 0.7000000 0.9555556 0.9230769 0.1875000 0.9473684 0.6915331 0.2558353

Digital

Digital Access

country gender_Owner landline mobilePhone smartPhone computer internet access percent_access
Kenya Men Led 0.0754717 0.6603774 0.7924528 0.5660377 0.6037736 0.5396226 54
Kenya Women Led 0.0370370 0.6666667 0.7037037 0.4814815 0.5925926 0.4962963 50
Malawi Men Led 0.0000000 0.9636364 0.3636364 0.1818182 0.3090909 0.3636364 36
Malawi Women Led 0.0000000 0.9200000 0.6800000 0.0400000 0.3200000 0.3920000 39
Zambia Men Led 0.0000000 1.0000000 0.9523810 0.2857143 0.7380952 0.5952381 60
Zambia Women Led 0.0000000 0.9736842 0.7631579 0.1578947 0.5000000 0.4789474 48

Digital Usage

country gender_Owner mobileBanking creditCard cryptocurrency digitalPayments moneyTransfer socialMedia email whatsapp phoneNumber website ecommerce billPaymentSystem onlinePaymentSystem accountingSystem usage percent_usage
Kenya Men Led 0.5471698 0.2264151 0.0377358 0.0943396 0.0377358 0.2641509 0.2830189 0.5283019 0.5283019 0.2452830 0.1132075 0.0943396 0.1509434 0.0754717 0.2304582 23
Kenya Women Led 0.6296296 0.1851852 0.0740741 0.0370370 0.0370370 0.3333333 0.1851852 0.4814815 0.5185185 0.1481481 0.0370370 0.0000000 0.1481481 0.0740741 0.2063492 21
Malawi Men Led 0.2181818 0.0181818 0.0000000 0.0000000 0.0000000 0.2909091 0.0181818 0.4000000 0.8545455 0.0000000 0.0000000 0.0000000 0.0000000 0.0181818 0.1298701 13
Malawi Women Led 0.3200000 0.0400000 0.0000000 0.0000000 0.0000000 0.4400000 0.0000000 0.6800000 0.6000000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1485714 15
Zambia Men Led 0.3571429 0.4047619 0.0000000 0.1428571 0.0000000 0.4047619 0.4285714 0.9047619 0.9761905 0.1904762 0.0000000 0.0000000 0.0952381 0.0000000 0.2789116 28
Zambia Women Led 0.2631579 0.1842105 0.0000000 0.0526316 0.0263158 0.2894737 0.1842105 0.7105263 0.9736842 0.1052632 0.0000000 0.0000000 0.0263158 0.0000000 0.2011278 20

Financial Inclusion

## Joining, by = c("country", "gender_Owner", "FI")
## Joining, by = c("country", "gender_Owner", "FI")
## Joining, by = c("country", "gender_Owner", "FI")
## Joining, by = c("country", "gender_Owner", "FI")
## `summarise()` has grouped output by 'country', 'gender_Owner'. You can override
## using the `.groups` argument.
country gender_Owner FI Compliance Access Barriers Usage Digital FIscore 0
Kenya Men Led Exclusion 19 29 76 12 46 23 0
Kenya Men Led Formal Inclusion 39 44 13 59 23 44 0
Kenya Men Led Informal Inclusion 9 26 11 30 31 33 0
Kenya Women Led Exclusion 8 37 74 14 50 25 0
Kenya Women Led Formal Inclusion 25 38 15 66 21 44 0
Kenya Women Led Informal Inclusion 1 25 12 20 29 31 0
Malawi Men Led Exclusion 51 56 72 27 64 40 0
Malawi Men Led Formal Inclusion 16 25 16 58 13 35 0
Malawi Men Led Informal Inclusion 1 19 12 15 23 26 0
Malawi Women Led Exclusion 21 46 33 27 61 NA 0
Malawi Women Led Formal Inclusion 10 25 32 52 15 NA 0
Malawi Women Led Informal Inclusion NA 29 35 21 24 NA 0
Zambia Men Led Exclusion 18 38 73 3 40 23 0
Zambia Men Led Formal Inclusion 34 38 8 74 28 48 0
Zambia Men Led Informal Inclusion 1 24 19 23 32 29 0
Zambia Women Led Exclusion 22 41 78 5 52 NA 0
Zambia Women Led Formal Inclusion 25 25 10 69 20 NA 0
Zambia Women Led Informal Inclusion NA 33 11 26 28 NA 0

Export Data

write.csv(compliance_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Gender FIndex compliance data.csv")

write.csv(access_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Gender FIndex access data.csv")

write.csv(barrier_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Gender FIndex barriers data.csv")

write.csv(usage_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Gender FIndex usage data.csv")

write.csv(digital_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Gender FIndex digital data.csv")

write.csv(financialInclusion_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Gender FIndex inclusion data.csv")

Age of Business Owner

# NB: the levels that have # before are not considered in the run analysis. This allows one to change the level at which the analysis is performed.

level = c("country",
           # "gender_Owner",
           "age_Owner"
           # "size_Business",
           # "location_Business",
           # "sector_Business",
           )

Compliance

country age_Owner compliance_category num_enterprises percent_enterprises
Kenya Owner is older than 35 full compliance 29 36
Kenya Owner is older than 35 not compliant 9 11
Kenya Owner is older than 35 partial compliance 4 5
Kenya Youth Led full compliance 22 28
Kenya Youth Led not compliant 12 15
Kenya Youth Led partial compliance 4 5
Malawi Owner is older than 35 full compliance 17 21
Malawi Owner is older than 35 not compliant 22 28
Malawi Owner is older than 35 partial compliance 1 1
Malawi Youth Led full compliance 4 5
Malawi Youth Led not compliant 36 45
Zambia Owner is older than 35 full compliance 15 19
Zambia Owner is older than 35 not compliant 7 9
Zambia Owner is older than 35 partial compliance 1 1
Zambia Youth Led full compliance 32 40
Zambia Youth Led not compliant 25 31

Access

Formal Inclusion

country age_Owner bank_account mobile_money_account formal_loan_application business_insurance_products pension_policy investments formal_savings_account formal_information_source formal_inclusion_access percent_formal_inclusion
Kenya Owner is older than 35 0.6666667 0.7142857 0.3095238 0.3809524 0.1190476 0.3809524 0.6666667 0.3095238 0.4434524 44
Kenya Youth Led 0.5000000 0.7368421 0.2368421 0.2631579 0.1052632 0.3684211 0.6842105 0.2894737 0.3980263 40
Malawi Owner is older than 35 0.3750000 0.3250000 0.2250000 0.0750000 0.1000000 0.4250000 0.6500000 0.2750000 0.3062500 31
Malawi Youth Led 0.1250000 0.2250000 0.1000000 0.0750000 0.0000000 0.2750000 0.7500000 0.0500000 0.2000000 20
Zambia Owner is older than 35 0.4782609 0.6956522 0.1304348 0.2173913 0.1739130 0.2173913 0.3913043 0.2608696 0.3206522 32
Zambia Youth Led 0.4210526 0.8771930 0.2105263 0.1929825 0.0701754 0.0526316 0.4385965 0.3157895 0.3223684 32

Informal Inclusion

country age_Owner informal_loan_application informal_information_source informal_savings_account informal_inclusion_access percent_informal_inclusion
Kenya Owner is older than 35 0.0238095 0.5952381 0.1428571 0.2539683 25
Kenya Youth Led 0.0263158 0.6052632 0.1578947 0.2631579 26
Malawi Owner is older than 35 0.1000000 0.4500000 0.0500000 0.2000000 20
Malawi Youth Led 0.0250000 0.6250000 0.0750000 0.2416667 24
Zambia Owner is older than 35 0.0434783 0.7391304 0.1739130 0.3188406 32
Zambia Youth Led 0.1228070 0.6842105 0.0000000 0.2690058 27

Barriers

country age_Owner affordability bureaucracy complicatedSystem knowledge documentation compatibility trust formalBarriers informalBarriers
Kenya Owner is older than 35 0.2545953 0.0912698 0.1079651 0.0774360 0.0674603 0.1627463 0.1011946 0.1251715 0.1206604
Kenya Youth Led 0.2647715 0.1000000 0.0642046 0.0882685 0.0526316 0.1756339 0.0784252 0.1210242 0.1132795
Malawi Owner is older than 35 0.3802369 0.0400000 0.0356280 0.2258765 0.0597643 0.3518026 0.0250000 0.1727194 0.1424769
Malawi Youth Led 0.2814065 NaN NaN 0.2555431 0.0281746 0.3657230 0.0344828 0.1499017 0.3657230
Zambia Owner is older than 35 0.3293492 NaN 0.1176795 0.1700919 0.0833333 0.2867586 0.0772947 0.1650173 0.2022190
Zambia Youth Led 0.0860984 0.0303030 0.0329193 0.0469142 0.1054545 0.2090556 0.0371854 0.0689131 0.0907593 
## Warning: Removed 3 rows containing missing values (geom_col).
## Warning: Removed 3 rows containing missing values (geom_label).

Usage

country age_Owner n bank_openTime bank_frequency bank_MoreThan1Account bank_MoreThan1Service bank_Norestrictions
Kenya Owner is older than 35 28 0.8035714 0.9107143 0.1428571 0.5000000 0.7500000
Kenya Youth Led 19 0.8421053 0.9210526 0.0000000 0.5789474 0.7894737
Malawi Owner is older than 35 15 0.7666667 0.9333333 0.0000000 0.4000000 0.8666667
Malawi Youth Led 5 0.5000000 1.0000000 0.0000000 0.6000000 1.0000000
Zambia Owner is older than 35 11 0.6363636 0.9545455 0.0909091 0.8181818 0.9090909
Zambia Youth Led 24 0.8541667 0.9791667 0.0416667 0.9166667 0.9166667
country age_Owner n mm_openTime mm_frequency mm_Norestrictions
Kenya Owner is older than 35 30 0.8500000 0.8500000 0.6666667
Kenya Youth Led 28 0.8928571 0.8750000 0.7500000
Malawi Owner is older than 35 13 0.8846154 0.6923077 1.0000000
Malawi Youth Led 9 0.8333333 0.7222222 1.0000000
Zambia Owner is older than 35 16 0.8125000 1.0000000 1.0000000
Zambia Youth Led 50 0.9200000 0.9800000 0.9800000
country age_Owner n loan_success loan_interestRatePerceptions loan_payBack
Kenya Owner is older than 35 14 0.9285714 0.6428571 0.9285714
Kenya Youth Led 10 0.8000000 0.4000000 0.8000000
Malawi Owner is older than 35 13 0.7692308 0.3846154 0.7692308
Malawi Youth Led 5 0.8000000 0.4000000 0.8000000
Zambia Owner is older than 35 4 1.0000000 0.7500000 1.0000000
Zambia Youth Led 19 0.9473684 0.8947368 0.9473684
country age_Owner n insurance_comprehensive insurance_noncomprehensive insurance_staff insurance_product
Kenya Owner is older than 35 16 0.3750000 0.125 0.1875000 0.1250000
Kenya Youth Led 10 0.6000000 0.000 0.1000000 0.3000000
Malawi Owner is older than 35 3 0.3333333 0.000 0.0000000 0.6666667
Malawi Youth Led 3 0.0000000 0.000 0.3333333 0.3333333
Zambia Owner is older than 35 5 0.8000000 0.000 0.4000000 0.4000000
Zambia Youth Led 11 0.8181818 0.000 0.0909091 0.0909091
country age_Owner knowledge_mmServices knowledge_investmentsCapitalMarkets knowledge_businessInsuranceProducts knowledge_pensionProducts knowledge_loansCreditCommercialBanks knowledge_loansCreditMicrofinance knowledge_loansCreditROSCAsISALs knowledge_bankingServices knowledge_taxationRegulatoryCompliance knowledge_digitalProductsServices knowledge_internetDigitalLandscape knowledge_onlinePromotion
Kenya Owner is older than 35 0.8952381 0.4571429 0.5428571 0.4190476 0.7428571 0.6285714 0.5000000 0.8285714 0.8190476 0.8190476 0.6952381 0.5857143
Kenya Youth Led 0.8526316 0.4947368 0.4578947 0.3157895 0.5947368 0.5842105 0.4368421 0.7421053 0.7263158 0.7842105 0.6631579 0.6157895
Malawi Owner is older than 35 0.7000000 0.4550000 0.2800000 0.2650000 0.5400000 0.5150000 0.4000000 0.6350000 0.5900000 0.5500000 0.4100000 0.3900000
Malawi Youth Led 0.7550000 0.4600000 0.2600000 0.1850000 0.4450000 0.4500000 0.3700000 0.6900000 0.5100000 0.5100000 0.4750000 0.5200000
Zambia Owner is older than 35 0.9043478 0.4608696 0.4434783 0.4608696 0.7304348 0.7130435 0.8695652 0.6956522 0.5217391 0.7652174 0.7304348 0.6608696
Zambia Youth Led 0.9824561 0.6842105 0.7192982 0.6807018 0.7578947 0.7754386 0.8105263 0.7403509 0.6877193 0.8070175 0.7684211 0.7473684 
## Joining, by = c("country", "age_Owner")
## Joining, by = c("country", "age_Owner")
## Joining, by = c("country", "age_Owner")
## Joining, by = c("country", "age_Owner")
## `summarise()` has grouped output by 'country'. You can override using the
## `.groups` argument.
country age_Owner usage_banking usage_MM usage_loan usage_insur knowledgeAssessment formal_usage informal_usage
Kenya Owner is older than 35 0.6214286 0.7888889 0.8333333 0.2031250 0.8952381 0.6116939 0.2835441
Kenya Youth Led 0.6263158 0.8392857 0.6666667 0.2500000 0.8526316 0.5955670 0.2570645
Malawi Owner is older than 35 0.5933333 0.8589744 0.6410256 0.2500000 0.7000000 0.5858333 0.1141667
Malawi Youth Led 0.6200000 0.8518519 0.6666667 0.1666667 0.7550000 0.5762963 0.1787037
Zambia Owner is older than 35 0.6818182 0.9375000 0.9166667 0.4000000 0.9043478 0.7339962 0.1703516
Zambia Youth Led 0.7416667 0.9600000 0.9298246 0.2500000 0.9824561 0.7203728 0.2620833

Digital

Digital Access

country age_Owner landline mobilePhone smartPhone computer internet access percent_access
Kenya Owner is older than 35 0.0476190 0.7142857 0.7380952 0.5714286 0.6190476 0.5380952 54
Kenya Youth Led 0.0789474 0.6052632 0.7894737 0.5000000 0.5789474 0.5105263 51
Malawi Owner is older than 35 0.0000000 0.9750000 0.3750000 0.1750000 0.2750000 0.3600000 36
Malawi Youth Led 0.0000000 0.9250000 0.5500000 0.1000000 0.3500000 0.3850000 38
Zambia Owner is older than 35 0.0000000 0.9565217 0.6956522 0.2173913 0.6086957 0.4956522 50
Zambia Youth Led 0.0000000 1.0000000 0.9298246 0.2280702 0.6315789 0.5578947 56

Digital Usage

country age_Owner mobileBanking creditCard cryptocurrency digitalPayments moneyTransfer socialMedia email whatsapp phoneNumber website ecommerce billPaymentSystem onlinePaymentSystem accountingSystem usage percent_usage
Kenya Owner is older than 35 0.6428571 0.2380952 0.0476190 0.0714286 0.0476190 0.2619048 0.2857143 0.5000000 0.6190476 0.2380952 0.1428571 0.0952381 0.1428571 0.0238095 0.2397959 24
Kenya Youth Led 0.5000000 0.1842105 0.0526316 0.0789474 0.0263158 0.3157895 0.2105263 0.5263158 0.4210526 0.1842105 0.0263158 0.0263158 0.1578947 0.1315789 0.2030075 20
Malawi Owner is older than 35 0.1750000 0.0250000 0.0000000 0.0000000 0.0000000 0.2500000 0.0250000 0.4250000 0.8250000 0.0000000 0.0000000 0.0000000 0.0000000 0.0250000 0.1250000 12
Malawi Youth Led 0.3250000 0.0250000 0.0000000 0.0000000 0.0000000 0.4250000 0.0000000 0.5500000 0.7250000 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1464286 15
Zambia Owner is older than 35 0.1739130 0.2608696 0.0000000 0.1739130 0.0434783 0.3043478 0.3478261 0.6521739 0.9565217 0.1304348 0.0000000 0.0000000 0.0434783 0.0000000 0.2204969 22
Zambia Youth Led 0.3684211 0.3157895 0.0000000 0.0701754 0.0000000 0.3684211 0.2982456 0.8771930 0.9824561 0.1578947 0.0000000 0.0000000 0.0701754 0.0000000 0.2506266 25

Financial Inclusion

## Joining, by = c("country", "age_Owner", "FI")
## Joining, by = c("country", "age_Owner", "FI")
## Joining, by = c("country", "age_Owner", "FI")
## Joining, by = c("country", "age_Owner", "FI")
## `summarise()` has grouped output by 'country', 'age_Owner'. You can override
## using the `.groups` argument.
country age_Owner FI Compliance Access Barriers Usage Digital FIscore 0
Kenya Owner is older than 35 Exclusion 11 30 75 10 46 22 0
Kenya Owner is older than 35 Formal Inclusion 36 44 13 61 24 46 0
Kenya Owner is older than 35 Informal Inclusion 5 25 12 28 30 32 0
Kenya Youth Led Exclusion 15 34 77 15 49 25 0
Kenya Youth Led Formal Inclusion 28 40 12 60 20 43 0
Kenya Youth Led Informal Inclusion 5 26 11 26 31 32 0
Malawi Owner is older than 35 Exclusion 28 49 68 30 64 37 0
Malawi Owner is older than 35 Formal Inclusion 21 31 17 59 12 37 0
Malawi Owner is older than 35 Informal Inclusion 1 20 14 11 24 26 0
Malawi Youth Led Exclusion 45 56 48 24 62 NA 0
Malawi Youth Led Formal Inclusion 5 20 15 58 15 NA 0
Malawi Youth Led Informal Inclusion NA 24 37 18 23 NA 0
Zambia Owner is older than 35 Exclusion 9 36 63 10 50 27 0
Zambia Owner is older than 35 Formal Inclusion 19 32 17 73 22 43 0
Zambia Owner is older than 35 Informal Inclusion 1 32 20 17 28 30 0
Zambia Youth Led Exclusion 31 41 84 2 44 NA 0
Zambia Youth Led Formal Inclusion 40 32 7 72 25 NA 0
Zambia Youth Led Informal Inclusion NA 27 9 26 31 NA 0 
write.csv(compliance_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Age FIndex compliance data.csv")

write.csv(access_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Age FIndex access data.csv")

write.csv(barrier_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Age FIndex barriers data.csv")

write.csv(usage_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Age FIndex usage data.csv")

write.csv(digital_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Age FIndex digital data.csv")

write.csv(financialInclusion_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Age FIndex inclusion data.csv")

Size of Business

# NB: the levels that have # before are not considered in the run analysis. This allows one to change the level at which the analysis is performed.

level = c("country",
           # "gender_Owner",
           # "age_Owner",
           "size_Business"
           # "location_Business",
           # "sector_Business",
           )

Compliance

country size_Business compliance_category num_enterprises percent_enterprises
Kenya Micro Enterprise full compliance 26 32
Kenya Micro Enterprise not compliant 20 25
Kenya Micro Enterprise partial compliance 7 9
Kenya Small/Medium Enterprise full compliance 25 31
Kenya Small/Medium Enterprise not compliant 1 1
Kenya Small/Medium Enterprise partial compliance 1 1
Malawi Micro Enterprise full compliance 5 6
Malawi Micro Enterprise not compliant 47 59
Malawi Micro Enterprise partial compliance 1 1
Malawi Small/Medium Enterprise full compliance 16 20
Malawi Small/Medium Enterprise not compliant 11 14
Zambia Micro Enterprise full compliance 20 25
Zambia Micro Enterprise not compliant 32 40
Zambia Micro Enterprise partial compliance 1 1
Zambia Small/Medium Enterprise full compliance 27 34

Access

Formal Inclusion

country size_Business bank_account mobile_money_account formal_loan_application business_insurance_products pension_policy investments formal_savings_account formal_information_source formal_inclusion_access percent_formal_inclusion
Kenya Micro Enterprise 0.4528302 0.6792453 0.2452830 0.3018868 0.0566038 0.3207547 0.6415094 0.2830189 0.3726415 37
Kenya Small/Medium Enterprise 0.8518519 0.8148148 0.3333333 0.3703704 0.2222222 0.4814815 0.7407407 0.3333333 0.5185185 52
Malawi Micro Enterprise 0.0943396 0.2830189 0.1132075 0.0000000 0.0000000 0.2830189 0.6226415 0.0377358 0.1792453 18
Malawi Small/Medium Enterprise 0.5555556 0.2592593 0.2592593 0.2222222 0.1481481 0.4814815 0.8518519 0.4074074 0.3981481 40
Zambia Micro Enterprise 0.1509434 0.7924528 0.0943396 0.0000000 0.0000000 0.0754717 0.1698113 0.1132075 0.1745283 17
Zambia Small/Medium Enterprise 1.0000000 0.8888889 0.3703704 0.5925926 0.2962963 0.1481481 0.9259259 0.6666667 0.6111111 61

Informal Inclusion

country size_Business informal_loan_application informal_information_source informal_savings_account informal_inclusion_access percent_informal_inclusion
Kenya Micro Enterprise 0.0377358 0.6037736 0.1698113 0.2704403 27
Kenya Small/Medium Enterprise 0.0000000 0.5925926 0.1111111 0.2345679 23
Malawi Micro Enterprise 0.0566038 0.6226415 0.0754717 0.2515723 25
Malawi Small/Medium Enterprise 0.0740741 0.3703704 0.0370370 0.1604938 16
Zambia Micro Enterprise 0.1509434 0.8867925 0.0566038 0.3647799 36
Zambia Small/Medium Enterprise 0.0000000 0.3333333 0.0370370 0.1234568 12

Barriers

country size_Business affordability bureaucracy complicatedSystem knowledge documentation compatibility trust formalBarriers informalBarriers
Kenya Micro Enterprise 0.2726436 0.0477688 0.0822013 0.0672750 0.0627846 0.1806312 0.0911474 0.1234626 0.1035338
Kenya Small/Medium Enterprise 0.2437597 NaN 0.0760452 0.1672891 0.0462963 0.1556437 0.0928001 0.1375363 0.1158445
Malawi Micro Enterprise 0.2675365 0.0208333 0.0198506 0.2729164 0.0440039 0.3083833 NaN 0.1948189 0.1163558
Malawi Small/Medium Enterprise 0.3087797 NaN NaN 0.2295862 0.0555556 0.5874224 0.0542328 0.1620386 0.5874224
Zambia Micro Enterprise 0.1343172 0.0222222 0.0691824 0.0878889 0.0913194 0.2166238 0.0471698 0.0901738 0.1026761
Zambia Small/Medium Enterprise 0.2415305 NaN 0.0526316 0.0716342 NaN 0.3921569 0.0639731 0.1257126 0.2223942 
## Warning: Removed 6 rows containing missing values (geom_col).
## Warning: Removed 6 rows containing missing values (geom_label).

Usage

country size_Business n bank_openTime bank_frequency bank_MoreThan1Account bank_MoreThan1Service bank_Norestrictions
Kenya Micro Enterprise 24 0.8541667 0.9166667 0.0416667 0.5000000 0.8333333
Kenya Small/Medium Enterprise 23 0.7826087 0.9130435 0.1304348 0.5652174 0.6956522
Malawi Micro Enterprise 5 0.8000000 0.8000000 0.0000000 0.0000000 1.0000000
Malawi Small/Medium Enterprise 15 0.6666667 1.0000000 0.0000000 0.6000000 0.8666667
Zambia Micro Enterprise 8 0.8125000 0.8750000 0.0000000 0.8750000 1.0000000
Zambia Small/Medium Enterprise 27 0.7777778 1.0000000 0.0740741 0.8888889 0.8888889
country size_Business n mm_openTime mm_frequency mm_Norestrictions
Kenya Micro Enterprise 36 0.9305556 0.8888889 0.7222222
Kenya Small/Medium Enterprise 22 0.7727273 0.8181818 0.6818182
Malawi Micro Enterprise 15 0.8666667 0.6666667 1.0000000
Malawi Small/Medium Enterprise 7 0.8571429 0.7857143 1.0000000
Zambia Micro Enterprise 42 0.9047619 0.9761905 0.9761905
Zambia Small/Medium Enterprise 24 0.8750000 1.0000000 1.0000000
country size_Business n loan_success loan_interestRatePerceptions loan_payBack
Kenya Micro Enterprise 15 0.8000000 0.4000000 0.8000000
Kenya Small/Medium Enterprise 9 1.0000000 0.7777778 1.0000000
Malawi Micro Enterprise 9 0.6666667 0.3333333 0.6666667
Malawi Small/Medium Enterprise 9 0.8888889 0.4444444 0.8888889
Zambia Micro Enterprise 13 0.9230769 0.9230769 0.9230769
Zambia Small/Medium Enterprise 10 1.0000000 0.8000000 1.0000000
country size_Business n insurance_comprehensive insurance_noncomprehensive insurance_staff insurance_product
Kenya Micro Enterprise 16 0.3750000 0.0625 0.0625000 0.0625
Kenya Small/Medium Enterprise 10 0.6000000 0.1000 0.3000000 0.4000
Malawi Small/Medium Enterprise 6 0.1666667 0.0000 0.1666667 0.5000
Zambia Small/Medium Enterprise 16 0.8125000 0.0000 0.1875000 0.1875
country size_Business knowledge_mmServices knowledge_investmentsCapitalMarkets knowledge_businessInsuranceProducts knowledge_pensionProducts knowledge_loansCreditCommercialBanks knowledge_loansCreditMicrofinance knowledge_loansCreditROSCAsISALs knowledge_bankingServices knowledge_taxationRegulatoryCompliance knowledge_digitalProductsServices knowledge_internetDigitalLandscape knowledge_onlinePromotion
Kenya Micro Enterprise 0.8905660 0.4490566 0.4867925 0.3509434 0.6452830 0.5811321 0.4716981 0.7698113 0.7622642 0.8150943 0.6452830 0.5698113
Kenya Small/Medium Enterprise 0.8444444 0.5259259 0.5333333 0.4074074 0.7259259 0.6592593 0.4666667 0.8222222 0.8000000 0.7777778 0.7481481 0.6592593
Malawi Micro Enterprise 0.7018868 0.3584906 0.1245283 0.0943396 0.4000000 0.4339623 0.3660377 0.6075472 0.4566038 0.4641509 0.4037736 0.4415094
Malawi Small/Medium Enterprise 0.7777778 0.6518519 0.5555556 0.4814815 0.6740741 0.5777778 0.4222222 0.7703704 0.7333333 0.6592593 0.5185185 0.4814815
Zambia Micro Enterprise 0.9547170 0.5283019 0.5509434 0.5169811 0.6679245 0.7018868 0.7886792 0.6339623 0.5396226 0.7547170 0.6754717 0.6377358
Zambia Small/Medium Enterprise 0.9703704 0.8000000 0.8148148 0.8148148 0.9111111 0.8666667 0.9037037 0.9111111 0.8370370 0.8740741 0.9185185 0.8888889 
## Joining, by = c("country", "size_Business")
## Joining, by = c("country", "size_Business")
## Joining, by = c("country", "size_Business")
## Joining, by = c("country", "size_Business")
## `summarise()` has grouped output by 'country'. You can override using the
## `.groups` argument.
country size_Business usage_banking usage_MM usage_loan usage_insur knowledgeAssessment formal_usage informal_usage
Kenya Micro Enterprise 0.6291667 0.8472222 0.6666667 0.1406250 0.8905660 0.5709201 0.3196459
Kenya Small/Medium Enterprise 0.6173913 0.7575758 0.9259259 0.3500000 0.8444444 0.6627232 0.1817212
Malawi Micro Enterprise 0.5200000 0.8444444 0.5555556 NaN 0.7018868 0.6400000 0.0618868
Malawi Small/Medium Enterprise 0.6266667 0.8809524 0.7407407 0.2083333 0.7777778 0.6141733 0.1636045
Zambia Micro Enterprise 0.7125000 0.9523810 0.9230769 NaN 0.9547170 0.8626526 0.0920644
Zambia Small/Medium Enterprise 0.7259259 0.9583333 0.9333333 0.2968750 0.9703704 0.7286169 0.2417535

Digital

Digital Access

country size_Business landline mobilePhone smartPhone computer internet access percent_access
Kenya Micro Enterprise 0.0566038 0.6415094 0.7169811 0.4716981 0.5283019 0.4830189 48
Kenya Small/Medium Enterprise 0.0740741 0.7037037 0.8518519 0.6666667 0.7407407 0.6074074 61
Malawi Micro Enterprise 0.0000000 0.9433962 0.4339623 0.0754717 0.3018868 0.3509434 35
Malawi Small/Medium Enterprise 0.0000000 0.9629630 0.5185185 0.2592593 0.3333333 0.4148148 41
Zambia Micro Enterprise 0.0000000 0.9811321 0.7924528 0.0754717 0.4716981 0.4641509 46
Zambia Small/Medium Enterprise 0.0000000 1.0000000 1.0000000 0.5185185 0.9259259 0.6888889 69

Digital Usage

country size_Business mobileBanking creditCard cryptocurrency digitalPayments moneyTransfer socialMedia email whatsapp phoneNumber website ecommerce billPaymentSystem onlinePaymentSystem accountingSystem usage percent_usage
Kenya Micro Enterprise 0.4905660 0.1698113 0.0377358 0.0377358 0.0188679 0.3018868 0.1886792 0.4905660 0.4528302 0.1132075 0.0943396 0.0188679 0.1509434 0.0754717 0.1886792 19
Kenya Small/Medium Enterprise 0.7407407 0.2962963 0.0740741 0.1481481 0.0740741 0.2592593 0.3703704 0.5555556 0.6666667 0.4074074 0.0740741 0.1481481 0.1481481 0.0740741 0.2883598 29
Malawi Micro Enterprise 0.2075472 0.0188679 0.0000000 0.0000000 0.0000000 0.3396226 0.0000000 0.4339623 0.7358491 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1239892 12
Malawi Small/Medium Enterprise 0.3333333 0.0370370 0.0000000 0.0000000 0.0000000 0.3333333 0.0370370 0.5925926 0.8518519 0.0000000 0.0000000 0.0000000 0.0000000 0.0370370 0.1587302 16
Zambia Micro Enterprise 0.1698113 0.0943396 0.0000000 0.0000000 0.0188679 0.2264151 0.1320755 0.7358491 0.9622642 0.0000000 0.0000000 0.0000000 0.0000000 0.0000000 0.1671159 17
Zambia Small/Medium Enterprise 0.5925926 0.7037037 0.0000000 0.2962963 0.0000000 0.5925926 0.6666667 0.9629630 1.0000000 0.4444444 0.0000000 0.0000000 0.1851852 0.0000000 0.3888889 39

Financial Inclusion

## Joining, by = c("country", "size_Business", "FI")
## Joining, by = c("country", "size_Business", "FI")
## Joining, by = c("country", "size_Business", "FI")
## Joining, by = c("country", "size_Business", "FI")
## `summarise()` has grouped output by 'country', 'size_Business'. You can
## override using the `.groups` argument.
country size_Business FI Compliance Access Barriers Usage Digital FIscore 0
Kenya Micro Enterprise Exclusion 25 36 77 11 52 26 0
Kenya Micro Enterprise Formal Inclusion 32 37 12 57 19 41 0
Kenya Micro Enterprise Informal Inclusion 9 27 10 32 29 33 0
Kenya Small/Medium Enterprise Exclusion 1 25 75 16 39 20 0
Kenya Small/Medium Enterprise Formal Inclusion 31 52 14 66 29 50 0
Kenya Small/Medium Enterprise Informal Inclusion 1 23 12 18 32 30 0
Malawi Micro Enterprise Exclusion 59 57 69 30 65 43 0
Malawi Micro Enterprise Formal Inclusion 6 18 19 64 12 32 0
Malawi Micro Enterprise Informal Inclusion 1 25 12 6 23 25 0
Malawi Small/Medium Enterprise Exclusion 14 44 25 22 59 NA 0
Malawi Small/Medium Enterprise Formal Inclusion 20 40 16 61 16 NA 0
Malawi Small/Medium Enterprise Informal Inclusion NA 16 59 16 25 NA 0
Zambia Micro Enterprise Exclusion 40 46 81 5 54 29 0
Zambia Micro Enterprise Formal Inclusion 25 17 9 86 17 42 0
Zambia Micro Enterprise Informal Inclusion 1 36 10 9 29 29 0
Zambia Small/Medium Enterprise Exclusion NA 27 65 3 31 NA 0
Zambia Small/Medium Enterprise Formal Inclusion 34 61 13 73 39 NA 0
Zambia Small/Medium Enterprise Informal Inclusion NA 12 22 24 30 NA 0

Export Data

write.csv(compliance_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Size FIndex compliance data.csv")

write.csv(access_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Size FIndex access data.csv")

write.csv(barrier_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Size FIndex barriers data.csv")

write.csv(usage_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Size FIndex usage data.csv")

write.csv(digital_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Size FIndex digital data.csv")

write.csv(financialInclusion_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Size FIndex inclusion data.csv")

Location of Business

# NB: the levels that have # before are not considered in the run analysis. This allows one to change the level at which the analysis is performed.

level = c("country",
           # "gender_Owner",
           # "age_Owner",
           # "size_Business",
           "location_Business"
           # "sector_Business",
           )

Compliance

country location_Business compliance_category num_enterprises percent_enterprises
Kenya Other full compliance 10 12
Kenya Other not compliant 4 5
Kenya Other partial compliance 2 2
Kenya Urban Based full compliance 41 51
Kenya Urban Based not compliant 17 21
Kenya Urban Based partial compliance 6 8
Malawi Other full compliance 12 15
Malawi Other not compliant 37 46
Malawi Other partial compliance 1 1
Malawi Urban Based full compliance 9 11
Malawi Urban Based not compliant 21 26
Zambia Other full compliance 10 12
Zambia Other not compliant 18 22
Zambia Other partial compliance 1 1
Zambia Urban Based full compliance 37 46
Zambia Urban Based not compliant 14 18

Access

Formal Inclusion

country location_Business bank_account mobile_money_account formal_loan_application business_insurance_products pension_policy investments formal_savings_account formal_information_source formal_inclusion_access percent_formal_inclusion
Kenya Other 0.6875000 0.8750000 0.3750000 0.2500000 0.0625000 0.5625000 0.6250000 0.4375000 0.4843750 48
Kenya Urban Based 0.5625000 0.6875000 0.2500000 0.3437500 0.1250000 0.3281250 0.6875000 0.2656250 0.4062500 41
Malawi Other 0.2000000 0.3600000 0.1800000 0.0800000 0.0400000 0.3400000 0.6600000 0.1400000 0.2500000 25
Malawi Urban Based 0.3333333 0.1333333 0.1333333 0.0666667 0.0666667 0.3666667 0.7666667 0.2000000 0.2583333 26
Zambia Other 0.3103448 0.8965517 0.1034483 0.0344828 0.0344828 0.1379310 0.3448276 0.2068966 0.2586207 26
Zambia Urban Based 0.5098039 0.7843137 0.2352941 0.2941176 0.1372549 0.0784314 0.4705882 0.3529412 0.3578431 36

Informal Inclusion

country location_Business informal_loan_application informal_information_source informal_savings_account informal_inclusion_access percent_informal_inclusion
Kenya Other 0.0000000 0.4375000 0.1875000 0.2083333 21
Kenya Urban Based 0.0312500 0.6406250 0.1406250 0.2708333 27
Malawi Other 0.0800000 0.5200000 0.0800000 0.2266667 23
Malawi Urban Based 0.0333333 0.5666667 0.0333333 0.2111111 21
Zambia Other 0.0689655 0.7931034 0.0000000 0.2873563 29
Zambia Urban Based 0.1176471 0.6470588 0.0784314 0.2810458 28

Barriers

country location_Business affordability bureaucracy complicatedSystem knowledge documentation compatibility trust formalBarriers informalBarriers
Kenya Other 0.3689626 0.5000000 0.1083333 0.1321429 0.1250000 0.1833333 0.1250000 0.1877764 0.2638889
Kenya Urban Based 0.2326902 0.0472689 0.0967700 0.0681175 0.0414726 0.1654783 0.0768705 0.1047877 0.1031724
Malawi Other 0.2648961 0.0250000 0.0235746 0.2463759 0.0540541 0.3477409 0.0251515 0.1476194 0.1321052
Malawi Urban Based 0.3531824 NaN NaN 0.2384311 0.0400000 0.4462912 NaN 0.2105378 0.4462912
Zambia Other 0.1581144 NaN 0.0706076 0.0577302 0.1027778 0.2339286 0.0523399 0.0927406 0.1522681
Zambia Urban Based 0.1663451 0.0400000 0.0477817 0.0929440 0.0914286 0.2159192 0.0473856 0.0995258 0.1012336 
## Warning: Removed 4 rows containing missing values (geom_col).
## Warning: Removed 4 rows containing missing values (geom_label).

Usage

country location_Business n bank_openTime bank_frequency bank_MoreThan1Account bank_MoreThan1Service bank_Norestrictions
Kenya Other 11 0.7727273 1.0000000 0.1818182 0.6363636 0.8181818
Kenya Urban Based 36 0.8333333 0.8888889 0.0555556 0.5000000 0.7500000
Malawi Other 10 0.7000000 0.9000000 0.0000000 0.5000000 1.0000000
Malawi Urban Based 10 0.7000000 1.0000000 0.0000000 0.4000000 0.8000000
Zambia Other 9 0.8333333 1.0000000 0.0000000 0.8888889 1.0000000
Zambia Urban Based 26 0.7692308 0.9615385 0.0769231 0.8846154 0.8846154
country location_Business n mm_openTime mm_frequency mm_Norestrictions
Kenya Other 14 0.8928571 0.8214286 0.7857143
Kenya Urban Based 44 0.8636364 0.8750000 0.6818182
Malawi Other 18 0.8611111 0.7222222 1.0000000
Malawi Urban Based 4 0.8750000 0.6250000 1.0000000
Zambia Other 26 0.9230769 0.9807692 1.0000000
Zambia Urban Based 40 0.8750000 0.9875000 0.9750000
country location_Business n loan_success loan_interestRatePerceptions loan_payBack
Kenya Other 6 1.0000000 0.8333333 1.0000000
Kenya Urban Based 18 0.8333333 0.4444444 0.8333333
Malawi Other 13 0.7692308 0.3846154 0.7692308
Malawi Urban Based 5 0.8000000 0.4000000 0.8000000
Zambia Other 5 0.8000000 0.8000000 0.8000000
Zambia Urban Based 18 1.0000000 0.8888889 1.0000000
country location_Business n insurance_comprehensive insurance_noncomprehensive insurance_staff insurance_product
Kenya Other 4 0.7500000 0.0000000 0.0000000 0.0000000
Kenya Urban Based 22 0.4090909 0.0909091 0.1818182 0.2272727
Malawi Other 4 0.2500000 0.0000000 0.2500000 0.2500000
Malawi Urban Based 2 0.0000000 0.0000000 0.0000000 1.0000000
Zambia Other 1 1.0000000 0.0000000 0.0000000 1.0000000
Zambia Urban Based 15 0.8000000 0.0000000 0.2000000 0.1333333
country location_Business knowledge_mmServices knowledge_investmentsCapitalMarkets knowledge_businessInsuranceProducts knowledge_pensionProducts knowledge_loansCreditCommercialBanks knowledge_loansCreditMicrofinance knowledge_loansCreditROSCAsISALs knowledge_bankingServices knowledge_taxationRegulatoryCompliance knowledge_digitalProductsServices knowledge_internetDigitalLandscape knowledge_onlinePromotion
Kenya Other 0.9000000 0.4875000 0.4500000 0.2750000 0.7250000 0.6375000 0.4750000 0.8500000 0.8375000 0.8125000 0.6250000 0.6000000
Kenya Urban Based 0.8687500 0.4718750 0.5156250 0.3937500 0.6593750 0.6000000 0.4687500 0.7718750 0.7593750 0.8000000 0.6937500 0.6000000
Malawi Other 0.7120000 0.3960000 0.2360000 0.1640000 0.4800000 0.4640000 0.3280000 0.6160000 0.5280000 0.4640000 0.3680000 0.3840000
Malawi Urban Based 0.7533333 0.5600000 0.3266667 0.3266667 0.5133333 0.5133333 0.4800000 0.7400000 0.5866667 0.6400000 0.5666667 0.5733333
Zambia Other 0.9379310 0.5517241 0.5793103 0.5517241 0.6965517 0.7103448 0.8413793 0.6620690 0.5448276 0.7448276 0.6896552 0.6620690
Zambia Urban Based 0.9725490 0.6588235 0.6745098 0.6549020 0.7803922 0.7843137 0.8196078 0.7647059 0.6941176 0.8235294 0.7960784 0.7568627 
## Joining, by = c("country", "location_Business")
## Joining, by = c("country", "location_Business")
## Joining, by = c("country", "location_Business")
## Joining, by = c("country", "location_Business")
## `summarise()` has grouped output by 'country'. You can override using the
## `.groups` argument.
country location_Business usage_banking usage_MM usage_loan usage_insur knowledgeAssessment formal_usage informal_usage
Kenya Other 0.6818182 0.8333333 0.9444444 0.1875000 0.9000000 0.6617740 0.2382260
Kenya Urban Based 0.6055556 0.8068182 0.7037037 0.2272727 0.8687500 0.5858375 0.2829125
Malawi Other 0.6200000 0.8611111 0.6410256 0.1875000 0.7120000 0.5774092 0.1345908
Malawi Urban Based 0.5800000 0.8333333 0.6666667 0.2500000 0.7533333 0.5825000 0.1708333
Zambia Other 0.7444444 0.9679487 0.8000000 0.5000000 0.9379310 0.7530983 0.1848327
Zambia Urban Based 0.7153846 0.9458333 0.9629630 0.2833333 0.9725490 0.7268786 0.2456705

Digital

Digital Access

country location_Business landline mobilePhone smartPhone computer internet access percent_access
Kenya Other 0.0625 0.6250000 0.8125000 0.6875000 0.6250000 0.5625000 56
Kenya Urban Based 0.0625 0.6718750 0.7500000 0.5000000 0.5937500 0.5156250 52
Malawi Other 0.0000 0.9400000 0.4200000 0.1000000 0.2000000 0.3320000 33
Malawi Urban Based 0.0000 0.9666667 0.5333333 0.2000000 0.5000000 0.4400000 44
Zambia Other 0.0000 0.9655172 0.8965517 0.0689655 0.5172414 0.4896552 49
Zambia Urban Based 0.0000 1.0000000 0.8431373 0.3137255 0.6862745 0.5686275 57

Digital Usage

country location_Business mobileBanking creditCard cryptocurrency digitalPayments moneyTransfer socialMedia email whatsapp phoneNumber website ecommerce billPaymentSystem onlinePaymentSystem accountingSystem usage percent_usage
Kenya Other 0.6250000 0.3125000 0.062500 0.1250000 0.1250000 0.0625000 0.1250000 0.5000000 0.3750000 0.1875000 0.125000 0.250000 0.1875000 0.1250000 0.2276786 23
Kenya Urban Based 0.5625000 0.1875000 0.046875 0.0625000 0.0156250 0.3437500 0.2812500 0.5156250 0.5625000 0.2187500 0.078125 0.015625 0.1406250 0.0625000 0.2209821 22
Malawi Other 0.2200000 0.0000000 0.000000 0.0000000 0.0000000 0.2400000 0.0000000 0.4600000 0.7800000 0.0000000 0.000000 0.000000 0.0000000 0.0000000 0.1214286 12
Malawi Urban Based 0.3000000 0.0666667 0.000000 0.0000000 0.0000000 0.5000000 0.0333333 0.5333333 0.7666667 0.0000000 0.000000 0.000000 0.0000000 0.0333333 0.1595238 16
Zambia Other 0.2068966 0.2068966 0.000000 0.0344828 0.0000000 0.2068966 0.1379310 0.8275862 0.9655172 0.0000000 0.000000 0.000000 0.0000000 0.0000000 0.1847291 18
Zambia Urban Based 0.3725490 0.3529412 0.000000 0.1372549 0.0196078 0.4313725 0.4117647 0.8039216 0.9803922 0.2352941 0.000000 0.000000 0.0980392 0.0000000 0.2745098 27

Financial Inclusion

## Joining, by = c("country", "location_Business", "FI")
## Joining, by = c("country", "location_Business", "FI")
## Joining, by = c("country", "location_Business", "FI")
## Joining, by = c("country", "location_Business", "FI")
## `summarise()` has grouped output by 'country', 'location_Business'. You can
## override using the `.groups` argument.
country location_Business FI Compliance Access Barriers Usage Digital FIscore 0
Kenya Other Exclusion 5 31 55 10 44 26 0
Kenya Other Formal Inclusion 12 48 19 66 23 44 0
Kenya Other Informal Inclusion 2 21 26 24 33 30 0
Kenya Urban Based Exclusion 21 32 79 13 48 23 0
Kenya Urban Based Formal Inclusion 51 41 10 59 22 45 0
Kenya Urban Based Informal Inclusion 8 27 10 28 30 31 0
Malawi Other Exclusion 46 52 72 29 67 40 0
Malawi Other Formal Inclusion 15 25 15 58 12 35 0
Malawi Other Informal Inclusion 1 23 13 13 21 26 0
Malawi Urban Based Exclusion 26 53 34 25 56 NA 0
Malawi Urban Based Formal Inclusion 11 26 21 58 16 NA 0
Malawi Urban Based Informal Inclusion NA 21 45 17 28 NA 0
Zambia Other Exclusion 22 45 75 6 51 28 0
Zambia Other Formal Inclusion 12 26 9 75 18 41 0
Zambia Other Informal Inclusion 1 29 15 18 31 31 0
Zambia Urban Based Exclusion 18 36 80 3 43 NA 0
Zambia Urban Based Formal Inclusion 46 36 10 73 27 NA 0
Zambia Urban Based Informal Inclusion NA 28 10 25 30 NA 0

Export Data

write.csv(compliance_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Location FIndex compliance data.csv")

write.csv(access_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Location FIndex access data.csv")

write.csv(barrier_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Location FIndex barriers data.csv")

write.csv(usage_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Location FIndex usage data.csv")

write.csv(digital_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Location FIndex digital data.csv")

write.csv(financialInclusion_total, file = "C:/Users/Rebekah Cross/Documents/Beks/FI_index/FI 2023/Location FIndex inclusion data.csv")