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
Cases: 10,746,470
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

Influence of Insurance Sector Development on Insurance Performance in Nigeria from 1996-2018

Influence of Insurance Sector Development on Insurance Performance in Nigeria from 1996-2018

ABSTRACT

This study examined the influence of insurance sector development on insurance performance in Nigeria ranging from 1996-2018. The specific objectives are to; Examine the impact of insurance penetration on insurance performance in Nigeria from 1996-2018. and Investigate the effect of insurance density on insurance performance in Nigeria from 1996-2018. The study adopts expost-facto research design. The data were time series, secondary and purely quantitative. They are drawn from sources such as The Statistical Bulletins of Central Bank of Nigeria and the World Bank development indicator and National Insurance Commission (NAICOM). Auto regressive Distributed lag model (ARDL) formed the method of data analysis. ARDL was chosen over the ordinary least square regression (OLS) because ARDL is a dynamic model while OLS is a static model. The results of the ARDL baseline test show that insurance penetration has a positive and significant impact on insurance performance in Nigeria. According to statistics, insurance penetration increases insurance performance by 1%, and this rise contributes 104 percent to the growth of insurance performance in Nigeria. It is given that; the coefficient of the parameter estimates of insurance penetration as 1% and the probability of t-statistics of 0.0014<.05 which is significant.  The results of the ARDL baseline test show that insurance density has a favorable and significant impact on insurance performance in Nigeria. According to statistics, insurance penetration increases insurance performance by 1%, and this rise contributes 45 percent to the growth of insurance performance in Nigeria. The explained variation, on the other hand, is 73 percent, indicating that the independent variable adequately explains the dependent variable. It is given the coefficient of the parameter estimates of insurance penetration as 1% and the probability of t-statistics of 0.023<.05 which is significant, it shows that it is positively signed and statistically significant. We concluded that insurance penetration has a positive and major impact on insurance performance in Nigeria, and insurance density has a positive and large impact on insurance performance in Nigeria, according to the study’s goal. We recommended that, to avoid settling of incessant claims, thorough awareness should be carried out prior to attempting penetration. And in order to limit the number of claims for each earned premium, the Nigerian insurance market must efficiently regulate the amount of insurance concentration.

 Keywords:  Insurance Sector Development; Insurance Performance; Nigeria

Authorship

1IPIGANSI, Pretoria and 2JIMOH, Taiwo Muideen

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