Table 1 Descriptive statistics result of the study
Variable
|
Obs
|
Mean
|
Std. Dev.
|
Min
|
Max
|
Youth Unemployment
|
70
|
11.693
|
9.826
|
1.885
|
27.707
|
Vulnerable to employment
|
70
|
69.311
|
19.214
|
40.346
|
92.635
|
Tertiary school
|
70
|
6.535
|
4.588
|
2.087
|
17.759
|
Secondary school
|
70
|
36.763
|
11.175
|
15.663
|
57.843
|
Primary school
|
70
|
102.74
|
29.117
|
52.97
|
148.124
|
Manufacture value add
|
70
|
7.204
|
2.402
|
3.42
|
12.79
|
Industry value added
|
70
|
17.18
|
6.087
|
2.073
|
31.74
|
Access to Internet
|
70
|
8.425
|
7.608
|
.37
|
28
|
Foreign direct investment
|
70
|
2.334
|
1.575
|
.002
|
6.445
|
Domestic credit to financial
|
70
|
38.222
|
32.936
|
5.492
|
135.1
|
Domestic credit to private
|
70
|
17.308
|
6.08
|
7.137
|
34.246
|
Agriculture value add
|
70
|
29.168
|
7.709
|
14.124
|
45.883
|
Age dependency ratio
|
70
|
87.22
|
8.427
|
76.543
|
108.495
|
Wage rate
|
70
|
32.332
|
17.216
|
9.568
|
56.173
|
Source :computed from world bank data base 2021
|
The above Table 1 redirects the study's descriptive analysis. According to this study, the rate of youth unemployment in east African countries increased by 11.693 percent between 2007 and 2006. The maximum and minimum unemployment rates in East African countries increased by 27.7 and 1.88 percent, respectively. This demonstrates the rate of youth unemployment in the countries of East Africa. However, the rate of vulnerability to employment in the study area increased by 69.3 percent on average. Academic schooling completion rates for primary, secondary, and tertiary school were approximately 102.7, 36.76, and 6.53 percent, respectively. This means that roughly twice as many youth in East African countries completed primary school, while the smallest number of youth completed secondary and primary school.
In Table 1, Manufacturing value add and industry value add in percentage of GDP increase by 7.1 and 17.18 percent on average in east African countries, indicating the lowest degree of value add in the manufacturing sector to GDP. Domestic credit to the private and financial sectors was offered at a rate of approximately 17 and 37 percent, respectively, in east African countries. This insight is that the rate of credit provided by domestic financial sources is primarily provided to the financial sectors of the economy rather than the private sectors. Also, in the study area the rate of foreign direct investment expansion was expanded at low are whereas the dependency ratio increase by rapid rate. Agriculture contributed a moderately small percentage change in GDP.
Table 2 Regression results of the random effect model
Youth unemployment rate
|
Ccoef.
|
St.Err.
|
t-value
|
P value
|
[95% Conf
|
Interval
|
Sig
|
Wage rate
|
-.172
|
.129
|
-1.33
|
.183
|
-.425
|
.081
|
|
Vulnerable employment
|
-.728
|
.108
|
-6.72
|
0.0000
|
-.941
|
-.516
|
***
|
Territory School
|
.253
|
2.339
|
0.11
|
.914
|
-4.332
|
4.838
|
|
Secondary School
|
.095
|
.114
|
0.83
|
.405
|
-.128
|
.318
|
|
Primary School
|
.095
|
.055
|
1.74
|
.082
|
-.012
|
.202
|
*
|
Manufacturing value add
|
.258
|
.062
|
4.14
|
0.000
|
.136
|
.38
|
***
|
Industry value added rate
|
-.02
|
.048
|
-0.41
|
.683
|
-.114
|
.074
|
|
Access to internet
|
-.234
|
.043
|
-5.41
|
0.000
|
-.319
|
-.149
|
***
|
Foreign direct investment
|
-.381
|
.2001
|
-1.90
|
.057
|
-.773
|
.011
|
*
|
Export volume
|
-.007
|
.001
|
-4.91
|
0.000
|
-.01
|
-.004
|
***
|
Domestic credit to private sector
|
-.055
|
.028
|
-1.93
|
.053
|
-.11
|
.001
|
*
|
Domestic credit offer to financial sector
|
.309
|
.118
|
2.62
|
.009
|
.078
|
.541
|
***
|
Agriculture value add
|
.363
|
.062
|
5.83
|
0.000
|
.241
|
.485
|
***
|
Age dependency ratio
|
.1001
|
.035
|
2.84
|
.004
|
.031
|
.168
|
***
|
Constant
|
42.465
|
13.43
|
3.17
|
.002
|
16.175
|
68.755
|
***
|
Mean dependent variable
|
11.693
|
SD dependent variable
|
9.826
|
|
Overall r-squared
|
0.977
|
Number of obs
|
70.000
|
|
Chi-square
|
2302.34
|
Prob > chi2
|
0.000
|
|
R-squared within
|
0.17
|
R-squared between
|
0.997
|
|
Note, *** p<.01, ** p<.05, * p<.1, indicate significances of the variables at 1% 5% and 10% level of significant.
Source :computed from world bank data base 2021
|
In the above Table 2 Vulnerable to employment is inversely related to youth unemployment and statistically significant at the 1% level of significance. When the rate of vulnerable to employment rises by one percent, the rate of youth unemployment falls by 0.728 percent. This can be demonstrated by increasing the construction of various economic sectors that can absorb surplus labor in the economy by designing policies and strategies that frequently accelerate labor-dependent production techniques.
The youth unemployment rate ascends in a diverse countries because of their incompetent of educational and economical methods (Imtiaz et al., 2020). This study confirmed the positive implication of education investment on youth unemployment like the study which was investigated by ( Imtiaz et al., 2020), (Tegegne, 2019) and (Pavlović et al., 2018). As a result, education investment, as represented by primary, secondary, and tertiary school completion rates, confirms the direct relationship with unemployment. However, only primary school completion is statistically significant at the 10% level of significance and is positively associated with youth unemployment; see the result in the above Table 2. East African education policies were not competent enough to teach students skills and know-how to the extent that they could innovate new production techniques and job opportunities. Even if the policies are incapable of operation and students are forced to rely on existing technologies. Furthermore, universities produce graduates who have theoretical knowledge but lack the practical skills that employers seek. Because of the disparity between theoretical skills gained in higher education and the practical skills required by the private manufacturing sector, engineering graduates are frequently unemployed or underemployed.
The interactions between manufacture values add to percentage of GDP and youth unemployment in east African countries are positively interrelated. According to Schwab & Werker (2018) Practitioners on how to deal with rent-seeking behavior in developing countries have been severely harmed by impartiality in the manufacturing sector, which does not contribute enough to lowering unemployment rates. The another scholar Tybout (2000) Manufacturing sectors in developing countries have traditionally been relatively protected. Following this one will not significantly help to reduce unemployment. In Table 2, the manufacturing value addition is statistically significant at the 1% level. If the value adds of manufacturing increases by one percent, the rate of youth unemployment increases by 0.258 percent. As a result of the expansion of manufacturing companies that use capital intensive methods of production, a positive relationship between manufacturing value add and youth unemployment may prevail in East African countries. The nature of expanding manufacturing sectors in developing countries, particularly in East African countries, is more of a capital-based production system that only considers the net profit of foreign investors. Another reason for the manufacturing sector's low contribution to youth unemployment in the study area is the high dominance of overseas shareholders in the form of foreign direct investment. Perhaps as a result of this, the machines and highly qualified maneuvers were run by foreign citizens rather than domestic labors. Because of these factors, the involvement of the manufacturing sector contributes to rising youth unemployment in East African countries by increasing the interest of engineering graduates in various specializations required by manufacturing sectors without actually absorbing them. About implications of internet access on youth unemployment scholars suggested their views for example, Kenny (2002), Lannoy (2018), Toader et al., (2018) and Lenka & Barik, (2018) confirmed the positive impact of its accessibility on economic growth and job opportunities Similarly to the preceding findings, the findings of this study confirm the inverse relationship between internet access and youth unemployment in East African countries. From Table 2, at the 1% level of significance, internet access is statistically significant, and it is inversely related to youth unemployment. It is interpreted as follows: for every 1% increase in internet access, the rate of youth unemployment falls by 0.234%. Increased internet accessibility allows the youth labor force to look for new job opportunities, learn new skills, and dramatically increase the rate of economic growth, all of which can be used to groom the level of employment in the study area. However, internet appears to be a bad thing in LDCs. Contradict to Mkombe et al. (2021) and Monari et al., (2020) The findings on the impact of FDI on unemployment reveal that increased FDI growth rates have a positive effect on youth employment in the study area. Furthermore, the increase in export volume was statistically significant at the 1% level of significance in this study. It is negatively related to youth unemployment, so increasing export volume implicitly has a positive effect on youth employability through explicitly increased economic growth, domestic investments, productivity, and national saving in the domestic economy.
Domestic credit provided to the private and financial sectors is statistically significant at 10% and 1% level of significance, respectively. Offering domestic credit to private sectors that invest in agriculture, services, and industry can significantly increase job opportunities for youth in east African countries. However, the amount of credit made available to the financial sector exacerbated the level of youth unemployment by reducing private-sector investment. Because financial sectors only lend to investors with large capital rather than small capital owner investors, the rate of job creation has decreased.
According to Chinsinga & Chasukwa (2018) and Thebe (2018), Agriculture contribution is insufficient to reduce youth unemployment in developing countries because it is the least desired occupation and livelihood strategy. FromTable 2, In line with the previous finding, agriculture value add positively and statistically affects youth unemployment in east African countries at the 1% level of significance in this study. Agriculture value additions have an impact in developing countries because people believe they can afford it while passing up another opportunity. Particularly educated youth are more eager to work in the public sector rather than in agriculture or self-employment. Another significant variable is the dependency ratio, which has a statistically significant and positive relationship with youth unemployment at the 1% level of significance.