Dear Statalist Community,
I am new to Stata and need guidance with two questions (please see below under dataex).
I am looking at whether internet and phone access of an economy (developing economies) drives its female financial inclusion. I have three different dependent variables of financial inclusion (owning a bank account, saving, borrowing). These variables are binary and on the individual-level (survey micro-level data). Moreover, there are other important individual-level measures such female (used as an interaction term with the independent variables, and also as control variable in another model), education, and income quintile (both used as control variables). The independent variables, internet access and mobile subscriptions, and are on the country-level. Other control variables as for instance GDP are also on the country-level. Since the 'main' independent variables of interest are on the economy-level, I have left them constant throughout the variables in the dataset. Please see an extract of my dataset below:
My methodology is based on the methodology of the linked paper (p. 16-19) (Demirguc-Kunt, Asli; Klapper, Leora; Singer, Dorothe. 2013. Financial Inclusion and Legal Discrimination Against Women : Evidence from Developing Countries).
I have two specific questions:
1) Data aggregation: effect of internet/mobile on general financial inclusion
Before analyzing whether internet/mobile have an influence on female financial inclusion, I want to test whether they have an effect on financial inclusion in general. Here my question is, do I have to aggregate the individual-level data (of the financial inclusion dependent variables) to the country-level? Or is it sufficient that I have kept internet/mobile constant throughout the individual-level financial inclusion measures in the dataset? And if you believe this should be aggregated, what command would you recommend in my scenario on Stata? Without aggregating the individual-level data, my do-file currently looks like the following:
2)Data aggregation: effect of internet/mobile on general financial inclusion
After having looked at whether internet/mobile has an effect on financial inclusion, I want to see if it has an effect on female financial inclusion. Here, I am using an interaction variable between female and internet or mobile. Again, would you aggregate the individual-level data of female to the country level? Until now, I am using the following do-file script:
I would highly appreciate any advice or help on this. Please let me know if you need additional information.
Many thanks and kind regards,
Julie
I am new to Stata and need guidance with two questions (please see below under dataex).
I am looking at whether internet and phone access of an economy (developing economies) drives its female financial inclusion. I have three different dependent variables of financial inclusion (owning a bank account, saving, borrowing). These variables are binary and on the individual-level (survey micro-level data). Moreover, there are other important individual-level measures such female (used as an interaction term with the independent variables, and also as control variable in another model), education, and income quintile (both used as control variables). The independent variables, internet access and mobile subscriptions, and are on the country-level. Other control variables as for instance GDP are also on the country-level. Since the 'main' independent variables of interest are on the economy-level, I have left them constant throughout the variables in the dataset. Please see an extract of my dataset below:
Code:
* Example generated by -dataex-. For more info, type help dataex clear input long id str24 economy byte(t account_formal saved_formal borrowed_formal female educ inc_q) double(internet mobile) 1 "Afghanistan" 1 1 . 0 0 1 3 5 45.81362616 2 "Afghanistan" 1 0 . 0 0 1 3 5 45.81362616 3 "Afghanistan" 1 0 . 0 1 1 1 5 45.81362616 4 "Afghanistan" 1 . 0 0 0 2 1 5 45.81362616 5 "Afghanistan" 1 0 0 0 0 2 5 5 45.81362616 6 "Afghanistan" 1 0 . 0 0 1 2 5 45.81362616 7 "Afghanistan" 1 0 . 0 1 1 3 5 45.81362616 8 "Afghanistan" 1 0 0 0 1 1 4 5 45.81362616 9 "Afghanistan" 1 0 0 0 1 2 4 5 45.81362616 10 "Afghanistan" 1 0 . 0 1 1 4 5 45.81362616 11 "Afghanistan" 1 0 . 0 0 1 5 5 45.81362616 12 "Afghanistan" 1 0 . 0 0 1 2 5 45.81362616 13 "Afghanistan" 1 0 . 0 0 1 1 5 45.81362616 14 "Afghanistan" 1 0 . 0 0 1 4 5 45.81362616 15 "Afghanistan" 1 . . 0 1 1 4 5 45.81362616 16 "Afghanistan" 1 0 0 0 1 1 5 5 45.81362616 17 "Afghanistan" 1 1 . 0 1 2 5 5 45.81362616 18 "Afghanistan" 1 0 . 0 0 2 3 5 45.81362616 19 "Afghanistan" 1 0 . 0 0 1 1 5 45.81362616 20 "Afghanistan" 1 . . 0 0 2 5 5 45.81362616 21 "Afghanistan" 1 0 . 0 1 2 5 5 45.81362616 22 "Afghanistan" 1 0 0 0 0 1 4 5 45.81362616 23 "Afghanistan" 1 0 . 0 0 1 3 5 45.81362616 24 "Afghanistan" 1 0 . 0 1 1 3 5 45.81362616 25 "Afghanistan" 1 0 0 0 1 1 4 5 45.81362616 26 "Afghanistan" 1 0 . 0 1 1 5 5 45.81362616 27 "Afghanistan" 1 0 . 0 1 1 4 5 45.81362616 28 "Afghanistan" 1 0 . 0 1 1 4 5 45.81362616 29 "Afghanistan" 1 . . 0 0 1 4 5 45.81362616 30 "Afghanistan" 1 0 . 0 0 1 5 5 45.81362616 31 "Afghanistan" 1 1 . 0 0 1 4 5 45.81362616 32 "Afghanistan" 1 1 . 1 1 3 4 5 45.81362616 33 "Afghanistan" 1 0 . 0 1 1 3 5 45.81362616 34 "Afghanistan" 1 0 . 1 1 2 4 5 45.81362616 35 "Afghanistan" 1 0 . 0 1 1 5 5 45.81362616 36 "Afghanistan" 1 0 0 0 0 1 5 5 45.81362616 37 "Afghanistan" 1 1 . 1 0 1 5 5 45.81362616 38 "Afghanistan" 1 0 . 0 1 2 5 5 45.81362616 39 "Afghanistan" 1 0 . 0 0 1 2 5 45.81362616 40 "Afghanistan" 1 1 . 0 0 1 3 5 45.81362616 41 "Afghanistan" 1 0 . 0 0 1 4 5 45.81362616 42 "Afghanistan" 1 . . 0 1 1 1 5 45.81362616 43 "Afghanistan" 1 0 . 0 1 2 4 5 45.81362616 44 "Afghanistan" 1 0 0 0 0 1 4 5 45.81362616 45 "Afghanistan" 1 0 . 0 1 1 4 5 45.81362616 46 "Afghanistan" 1 0 . 0 0 1 5 5 45.81362616 47 "Afghanistan" 1 0 . 0 1 1 4 5 45.81362616 48 "Afghanistan" 1 0 . 0 1 1 1 5 45.81362616 49 "Afghanistan" 1 0 . 0 0 1 4 5 45.81362616 50 "Afghanistan" 1 1 . 0 0 1 4 5 45.81362616 51 "Afghanistan" 1 0 . 0 0 2 5 5 45.81362616 52 "Afghanistan" 1 0 . 0 0 2 2 5 45.81362616 53 "Afghanistan" 1 0 . 0 1 1 2 5 45.81362616 54 "Afghanistan" 1 0 . 1 0 1 4 5 45.81362616 55 "Afghanistan" 1 0 . 0 1 1 4 5 45.81362616 56 "Afghanistan" 1 0 . 0 1 2 3 5 45.81362616 57 "Afghanistan" 1 0 . 0 1 1 4 5 45.81362616 58 "Afghanistan" 1 1 . 0 0 2 3 5 45.81362616 59 "Afghanistan" 1 0 . 0 0 1 4 5 45.81362616 60 "Afghanistan" 1 0 . 0 1 1 4 5 45.81362616 61 "Afghanistan" 1 0 . 0 1 1 3 5 45.81362616 62 "Afghanistan" 1 0 . 0 0 1 3 5 45.81362616 63 "Afghanistan" 1 0 . 0 1 1 2 5 45.81362616 64 "Afghanistan" 1 1 . 0 0 2 3 5 45.81362616 65 "Afghanistan" 1 0 . 0 0 1 4 5 45.81362616 66 "Afghanistan" 1 0 . 0 1 1 2 5 45.81362616 67 "Afghanistan" 1 0 . 0 1 1 2 5 45.81362616 68 "Afghanistan" 1 0 0 0 1 2 4 5 45.81362616 69 "Afghanistan" 1 0 . 0 1 1 4 5 45.81362616 70 "Afghanistan" 1 0 . 1 0 1 5 5 45.81362616 71 "Afghanistan" 1 0 . 0 0 1 3 5 45.81362616 72 "Afghanistan" 1 0 . 0 1 1 5 5 45.81362616 73 "Afghanistan" 1 0 . 0 1 1 3 5 45.81362616 74 "Afghanistan" 1 0 . 0 1 1 3 5 45.81362616 75 "Afghanistan" 1 0 0 0 0 1 5 5 45.81362616 76 "Afghanistan" 1 0 . 0 1 1 3 5 45.81362616 77 "Afghanistan" 1 0 . 0 1 2 5 5 45.81362616 78 "Afghanistan" 1 0 . 0 0 1 3 5 45.81362616 79 "Afghanistan" 1 0 . 0 0 1 1 5 45.81362616 80 "Afghanistan" 1 0 . 0 0 1 5 5 45.81362616 81 "Afghanistan" 1 0 0 0 1 2 5 5 45.81362616 82 "Afghanistan" 1 0 . 1 0 1 5 5 45.81362616 83 "Afghanistan" 1 0 . 0 0 2 3 5 45.81362616 84 "Afghanistan" 1 1 1 0 0 2 3 5 45.81362616 85 "Afghanistan" 1 0 . 0 0 2 4 5 45.81362616 86 "Afghanistan" 1 0 . 0 1 1 4 5 45.81362616 87 "Afghanistan" 1 0 . 0 0 2 5 5 45.81362616 88 "Afghanistan" 1 0 . 0 1 1 2 5 45.81362616 89 "Afghanistan" 1 0 . 0 1 1 2 5 45.81362616 90 "Afghanistan" 1 1 0 0 0 2 5 5 45.81362616 91 "Afghanistan" 1 0 0 0 1 2 5 5 45.81362616 92 "Afghanistan" 1 0 . 1 0 2 3 5 45.81362616 93 "Afghanistan" 1 1 . 1 0 1 5 5 45.81362616 94 "Afghanistan" 1 0 . 0 1 1 2 5 45.81362616 95 "Afghanistan" 1 0 . 0 1 2 5 5 45.81362616 96 "Afghanistan" 1 0 . 0 1 1 5 5 45.81362616 97 "Afghanistan" 1 0 . 0 0 1 1 5 45.81362616 98 "Afghanistan" 1 0 . 0 1 1 3 5 45.81362616 99 "Afghanistan" 1 0 . 0 1 1 2 5 45.81362616 100 "Afghanistan" 1 0 . 0 1 1 1 5 45.81362616 end
My methodology is based on the methodology of the linked paper (p. 16-19) (Demirguc-Kunt, Asli; Klapper, Leora; Singer, Dorothe. 2013. Financial Inclusion and Legal Discrimination Against Women : Evidence from Developing Countries).
I have two specific questions:
1) Data aggregation: effect of internet/mobile on general financial inclusion
Before analyzing whether internet/mobile have an influence on female financial inclusion, I want to test whether they have an effect on financial inclusion in general. Here my question is, do I have to aggregate the individual-level data (of the financial inclusion dependent variables) to the country-level? Or is it sufficient that I have kept internet/mobile constant throughout the individual-level financial inclusion measures in the dataset? And if you believe this should be aggregated, what command would you recommend in my scenario on Stata? Without aggregating the individual-level data, my do-file currently looks like the following:
Code:
probit account_formal internet i.female age i.educ i.inc_q pop_adult GDPpc UNEMP_ILO GOVEFF POLSTA ROL i.t i.neconomy margins, dydx (*) post estimates store herearemargins outreg2 [herearemargins] using model_results, replace excel dec(3) probit account_formal mobile i.female age i.educ i.inc_q pop_adult GDPpc UNEMP_ILO GOVEFF POLSTA ROL i.t i.neconomy margins, dydx (*) post estimates store herearemargins outreg2 [herearemargins] using model_results, replace excel dec(3)
After having looked at whether internet/mobile has an effect on financial inclusion, I want to see if it has an effect on female financial inclusion. Here, I am using an interaction variable between female and internet or mobile. Again, would you aggregate the individual-level data of female to the country level? Until now, I am using the following do-file script:
Code:
probit account_formal c.internet##c.female age i.educ i.inc_q pop_adult GDPpc UNEMP_ILO GOVEFF POLSTA ROL i.t i.neconomy margins, dydx (*) post estimates store herearemargins outreg2 [herearemargins] using model_results, replace excel dec(4) probit account_formal c.mobile##c.female age i.educ i.inc_q pop_adult GDPpc UNEMP_ILO GOVEFF POLSTA ROL i.t i.neconomy margins, dydx (*) post estimates store herearemargins outreg2 [herearemargins] using model_results, replace excel dec(4)
Many thanks and kind regards,
Julie
