Live Covid-19
United States 89,360,080
Cases: 89,360,080
Deaths: 1,042,678
Recovered: 84,916,391
Active: 3,401,011
India 43,471,282
Cases: 43,471,282
Deaths: 525,139
Recovered: 42,836,906
Active: 109,237
Brazil 32,358,451
Cases: 32,358,451
Deaths: 671,466
Recovered: 30,846,850
Active: 840,135
France 31,083,859
Cases: 31,083,859
Deaths: 149,533
Recovered: 29,620,989
Active: 1,313,337
Germany 28,293,960
Cases: 28,293,960
Deaths: 141,189
Recovered: 26,702,200
Active: 1,450,571
United Kingdom 22,720,345
Cases: 22,720,345
Deaths: 180,330
Recovered: 22,145,429
Active: 394,586
Italy 18,523,111
Cases: 18,523,111
Deaths: 168,353
Recovered: 17,469,969
Active: 884,789
Russia 18,433,394
Cases: 18,433,394
Deaths: 381,165
Recovered: 17,861,605
Active: 190,624
South Korea 18,368,857
Cases: 18,368,857
Deaths: 24,555
Recovered: 18,204,741
Active: 139,561
Turkey 15,123,331
Cases: 15,123,331
Deaths: 99,032
Recovered: 15,005,249
Active: 19,050
Spain 12,734,038
Cases: 12,734,038
Deaths: 107,906
Recovered: 12,218,358
Active: 407,774
Vietnam 10,746,470
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Deaths: 43,087
Recovered: 9,681,318
Active: 1,022,065
Argentina 9,367,172
Cases: 9,367,172
Deaths: 129,070
Recovered: 9,163,334
Active: 74,768
Japan 9,329,520
Cases: 9,329,520
Deaths: 31,281
Recovered: 9,135,363
Active: 162,876
Netherlands 8,184,179
Cases: 8,184,179
Deaths: 22,378
Recovered: 8,063,483
Active: 98,318
Australia 8,162,153
Cases: 8,162,153
Deaths: 9,930
Recovered: 7,905,095
Active: 247,128
Iran 7,238,126
Cases: 7,238,126
Deaths: 141,389
Recovered: 7,062,657
Active: 34,080
Colombia 6,175,181
Cases: 6,175,181
Deaths: 140,070
Recovered: 5,984,546
Active: 50,565
Indonesia 6,090,509
Cases: 6,090,509
Deaths: 156,740
Recovered: 5,916,854
Active: 16,915
Mexico 6,034,602
Cases: 6,034,602
Deaths: 325,716
Recovered: 5,192,957
Active: 515,929
Poland 6,015,634
Cases: 6,015,634
Deaths: 116,429
Recovered: 5,335,673
Active: 563,532
Portugal 5,171,236
Cases: 5,171,236
Deaths: 24,149
Recovered: 4,745,321
Active: 401,766
Ukraine 5,017,038
Cases: 5,017,038
Deaths: 108,638
Recovered: 4,906,519
Active: 1,881
North Korea 4,744,430
Cases: 4,744,430
Deaths: 73
Recovered: 4,736,220
Active: 8,137
Malaysia 4,566,055
Cases: 4,566,055
Deaths: 35,765
Recovered: 4,500,856
Active: 29,434
Thailand 4,525,269
Cases: 4,525,269
Deaths: 30,667
Recovered: 4,470,490
Active: 24,112
Austria 4,438,883
Cases: 4,438,883
Deaths: 18,792
Recovered: 4,314,940
Active: 105,151
Israel 4,344,800
Cases: 4,344,800
Deaths: 10,958
Recovered: 4,259,884
Active: 73,958
Belgium 4,225,222
Cases: 4,225,222
Deaths: 31,903
Recovered: 4,122,858
Active: 70,461
South Africa 3,993,843
Cases: 3,993,843
Deaths: 101,793
Recovered: 3,880,462
Active: 11,588

Effect of Financial Deepening on Insurance Penetration in Nigeria, 1986 -2018

Effect of Financial Deepening on Insurance Penetration in Nigeria, 1986 -2018

ABSTRACT

This study examined the effect of financial deepening on insurance penetration in Nigeria, using annual time series data from 1986 to 2018. The study adopted ex-post facto research design. Broad money supply and credit to private sector were used as proxies for financial deepening.  Data for the study were first subjected to stationarity test using Phillips-Perrons statistical method and Johansen co-integration test. Ordinary least square (OLS) statistical method was used for data analysis. The results from the tests of hypotheses show that: broad money supply and credit to private sector as a ratio of Gross Domestic Product have positive but non significant effect on insurance penetration in Nigeria. The study concludes that broad money supply and credit to private sector as a ratio of Gross Domestic Product had not exerted significant effect on insurance penetration in Nigeria between 1986 to 2018. The study recommends that: There is need to increase the amount of credit facilities given to private sector (CPS) in the country in order to deepen insurance penetration in Nigeria. Thus, all unnecessary stringent measures inhibiting public sector access to credit facilities should be addressed to make funds available to genuine investors/borrowers.

Keywords: Financial Deepening; Insurance Penetration; Ordinary least square (OLS)

Authorship
1Okeke, Daniel Chukwudi; 2Prof. Anyanwaokoro, Mike and 3Madukwe, Obinna Darlington | FULL PDF

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