Live Covid-19
United States 100,863,106
Cases: 100,863,106
Deaths: 1,106,860
Recovered: 98,283,998
Active: 1,472,248
India 44,674,667
Cases: 44,674,667
Deaths: 530,628
Recovered: 44,138,235
Active: 5,804
France 38,067,974
Cases: 38,067,974
Deaths: 159,093
Recovered: 36,974,097
Active: 934,784
Germany 36,557,861
Cases: 36,557,861
Deaths: 158,198
Recovered: 35,869,800
Active: 529,863
Brazil 35,408,852
Cases: 35,408,852
Deaths: 690,231
Recovered: 34,262,104
Active: 456,517
South Korea 27,331,250
Cases: 27,331,250
Deaths: 30,769
Recovered: 26,382,172
Active: 918,309
Japan 25,220,452
Cases: 25,220,452
Deaths: 50,344
Recovered: 20,754,250
Active: 4,415,858
Italy 24,488,080
Cases: 24,488,080
Deaths: 181,733
Recovered: 23,799,178
Active: 507,169
United Kingdom 24,024,746
Cases: 24,024,746
Deaths: 197,253
Recovered: 23,754,601
Active: 72,892
Russia 21,617,601
Cases: 21,617,601
Deaths: 392,231
Recovered: 21,018,980
Active: 206,390
Turkey 17,005,537
Cases: 17,005,537
Deaths: 101,400
Recovered: –
Active: 16,904,137
Spain 13,614,807
Cases: 13,614,807
Deaths: 116,108
Recovered: 13,403,322
Active: 95,377
Vietnam 11,517,722
Cases: 11,517,722
Deaths: 43,177
Recovered: 10,608,922
Active: 865,623
Australia 10,754,429
Cases: 10,754,429
Deaths: 16,244
Recovered: 10,546,102
Active: 192,083
Argentina 9,739,856
Cases: 9,739,856
Deaths: 130,034
Recovered: 9,590,198
Active: 19,624
Netherlands 8,544,770
Cases: 8,544,770
Deaths: 22,920
Recovered: 8,498,809
Active: 23,041
Taiwan 8,369,226
Cases: 8,369,226
Deaths: 14,478
Recovered: 8,036,406
Active: 318,342
Iran 7,559,855
Cases: 7,559,855
Deaths: 144,640
Recovered: 7,335,301
Active: 79,914
Mexico 7,132,792
Cases: 7,132,792
Deaths: 330,525
Recovered: 6,400,759
Active: 401,508
Indonesia 6,680,203
Cases: 6,680,203
Deaths: 159,978
Recovered: 6,469,238
Active: 50,987
Poland 6,354,402
Cases: 6,354,402
Deaths: 118,340
Recovered: 5,335,940
Active: 900,122
Colombia 6,318,021
Cases: 6,318,021
Deaths: 141,911
Recovered: 6,142,640
Active: 33,470
Austria 5,576,695
Cases: 5,576,695
Deaths: 21,232
Recovered: 5,508,210
Active: 47,253
Portugal 5,546,290
Cases: 5,546,290
Deaths: 25,517
Recovered: 5,500,769
Active: 20,004
Greece 5,404,690
Cases: 5,404,690
Deaths: 34,309
Recovered: 5,338,821
Active: 31,560
Ukraine 5,341,284
Cases: 5,341,284
Deaths: 110,586
Recovered: 5,216,858
Active: 13,840
Malaysia 5,000,332
Cases: 5,000,332
Deaths: 36,713
Recovered: 4,941,114
Active: 22,505
Chile 4,937,047
Cases: 4,937,047
Deaths: 62,571
Recovered: 4,858,162
Active: 16,314
North Korea 4,772,813
Cases: 4,772,813
Deaths: 74
Recovered: 4,772,739
Active: 0
Israel 4,727,322
Cases: 4,727,322
Deaths: 11,878
Recovered: 4,704,016
Active: 11,428

Financial System Broadening and Insurance Business Performance in Nigeria, 1996-2019 ARDL Co-integration Model Approach

Financial System Broadening and Insurance Business Performance in Nigeria, 1996-2019 ARDL Co-integration Model Approach

ABSTRACT

A considerable amount of scholarly works have examined the link between financial system broadening and economic performance using varieties of econometric models. Although, most of these studies have concentrated attention on the economic performance, none of the studies had examined a co-integrating relationship between financial system broadening and insurance performance in Nigeria. Some efforts have been made on the nexus between financial system broadening and economic performance, though not comprehensive enough to model this nexus. This has created gap in the literature which needs to be filled. It is in view of this that this study examines the cointegration relationship that existed between financial system broadening and insurance in Nigeria. Financial system broadening was represented by banking system services such as extended money supply services and extent of private sector credits while insurance performance is represented with insurance profitability. The time series data was collected from the Central Bank of Nigeria Statistical Bulletins. Multiple classical linear regression analysis was used in this analysis. Special cointegration relationship using auto regressive distributed lag was used in the analysis through e-views 10.0. The findings reveal that financial system broadening via aggregate money supply and private sector credits had a long run relationship with insurance performance in Nigeria. The study has contributed to the economic performance literature with a better understanding of the role of financial system deepening and its association with insurance performance which spurns economic growth. This study provides valuable knowledge to policy makers and economic managers, to refine their current policies and subsequently improve financial system broadening and economic performance through insurance based polices.

Keywords: Insurance Performance; Financial System Broadening; Insurance Profitability; Time Series

Authorship
1Ezema, Clifford Anene, PhD, 2Agbaji, Benjamin Chukwuma, PhD, 3Eche, Ann Uzoamaka

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