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Original Article | Open Access | Br. J. Arts Humanit., 2021; 3(6), 159-163 | doi: 10.34104/bjah.02101590163

An Economic Analysis on Years of Schooling of the Children Related to Financial Support from Family and Govt. & Non-Govt. Institutions

Lutfunneher* Mail Img ,
Taj-E-Jannat Mim Mail Img

Abstract

This study examined the relationship among fathers education, amount of fathers land (Dec), fathers occupation, any government help, and years of schooling in the rural area at Muktagacha Upazila in Mymensingh Division. Qualitative variables and variables which are quantitative in nature have been used for this study. We have chosen the years of schooling of the children of households as a dependent variable and the selected independent variables are fathers education, fathers land amount, fathers occupation, male, rural, number of siblings, age antigovernment help. A convenient sampling procedure has been used in our research. Questionnaire and structured interview were the research instruments. Not only an urban area is counted for research but also rural households are counted for data collection about school-going children. We conducted our research by using primary data. 

INTRODUCTION

In this study we have examined the relationship          “Among fathers land amount, fathers education, number of siblings, age, rural, male, government help and years of schooling”. We know that education is the important factors of nations advancement. Transfor-mation of human in human capital, sustainable invest-ment in educational sector is no alternative with sust-ainable economic growth for development of nations. 

High Living standards and progress of the society smooth the path of the individuals to reach in life ceiling. Thats why we have chosen the topic - years of schooling. With the increase of schooling, the number of educated person will also increase in the society. And in this case fathers education, occupation, and financial solvency play a great role in the generations educations/increasing years of schooling. Increased labour productivity, effective use of waste land and improve socio-economic empowerment are three important routes through which education can con-tribute to development. Thats why our aim was to find out the educational level and the factors which in-fluence the years of schooling in the rural area. With-out increasing schooling time it is not possible to remove illiteracy from the rural area.

Literature Review

Only after reviewing the related literature, a researcher can answer the question of what information is already available and what the knowledge- gap is. For this, it is necessary to study a number of research works on sup-porting the research topic. But we do not found any research paper showing relationship among years of schooling, age, male, rural, fathers education, and government help. And also not found any article showing any relation among years of schooling and others variable.

Aims and objectives of the study

The main theme of this study was -  to determine the relationship among years of schooling, fathers edu-cation, fathers land amount (decimal), fathers occu-pation, number of siblings, age, rural, male and gove-rnment help. This relationship would be positive or negative. Since we assumed that there exists positive relationship, after collecting data and running a regres-sion the result which we got was positive and goodness of fitness was better. 

Limitations of the study

Every task has some limitations. We faced some usual constraints during the period of data collection and preparing this article. Though we have given utmost effort to prepare this report but there were some limi-tations of this study. These are as follows-

Different Format Data and lack of data related to this topic.

We could not include other variables in the model which also affect the dependent variable.

Technological Problem

Transportation Complexity.

Respondents were not interested to provide exact information that we need to know.

Sources of Data

Data collection procedure involved the technique which is used by the researcher in data gathering. In our study, we have collected our required data from primary sources through self administered question-naire. The questionnaires were structured in a way to capture information from the student who is running to study.

METHOLODOGY

To find out whether or not fathers education, fathers land amount, number of siblings, fathers occupation, male, age, rural and government help have any in-fluence on years of schooling, a linear model was cre-ated with five dummy variables, two for occupation whether agriculture or not and service holder, other two for the location and gender and another one is go-vernment help. Since here we have use two types vari-ables, so we can use an ANCOVA model for our reg-ression analysis.

Years of schooling, 

YS = β0 + β₁XL + β₂XE + β3FDA + β4FDS+ β5XAge+ β6Xsib+ β7DG+β8DR+β9DGH+Uі

β0 = benchmark category = female, business, urban and others help.

YS = Years of schooling

X L = Fathers land amount,

XE = Fathers education, 

XSib = Number of siblings.

XAge = Age of the student,

FDA = 1, if Fathers occupation is agriculture.

= 0, if business/otherwise.

FDS = 1, if Fathers occupation is service holder.

     = 0, if business/otherwise.

DG = 1, if gender is male.

     = 0, if gender is female.

DR = 1, if it Rural.

    = 0, if it is urban.

DGH = 1, if get govt. help. 

    = 0, if not get govt. help.

After setting the data in Excel table from the source, it will be analyzed by using STATA. Before any tests have done on the data, summary statistics will be obtained for each variable, which will be listed on Table.

RESULTS AND DISCUSSION

Regression Analysis

Here,

Dependent variable: Ys (Years of schooling)

Method: Least squares.

Sample: 56

Included observations: 56

The regression equation may written as follows

YS= 10.36339 -.0052793XL -.0205037XE-1.928236FDA-1.439707FDS-3.075434DG

t= (3.38)    (-0.55)      (-0.09)      (-1.35)    (-0.72)   (-2.03)

 (0.001)* (0.58)*** (0.92)*** (0.18)*** (0.47)***        (0.04)**

-.1341615DR+.3349163XAGE-.1183192XNO.OF SIBLINGS-4.259128DGH.

(-0.13)                  (2.28)                  (-0.30)              (-2.68)

(0.89)***              (0.02)**              (0.76)***        (0.01)*

R²= 0.3362

Where, *indicates the p values at 1% significance level, **indicates the p values less than 5 % signi-ficance level, and ***indicates the p value greater than 5% /10% significance level.

Interpretation

Sign of the coefficient of variables:

Non Dummy:

The sign of the various non dummy regressors make economic sense. The coefficient of XE, XL, Xno.of siblings are negative and Xage is Positive.

Dummy:

The differential intercepts coefficients of FDA, FDS, DG, DR, and DGH are expected to be negative.

Interpretation of non dummy variables

-.0052793 is the partial regression coefficient of fat-hers land amount and tells us  with the influence of fathers education, age, and number of siblings are held constant, if  fathers land amount goes up by 1 decimal, then average years of schooling  lower by .0052793 year. The estimated slope coefficient for fathers land amount is not statistically significant be-cause its p value is quite high and t- value is very low. The relation between fathers land amount and years of schooling is not matter whether it is positive or nega-tive as this variable is statistically insignificant. 

-.205037 is the partial regression coefficient of fathers education and tells us with the influence of fathers land amount, age, and number of siblings are held constant, if fathers education goes up by 1 year, then average years of schooling lower by .205037 year. The estimated slope coefficient for fathers education is not statistically significant because its p value quite high and its t – value is quite low. The relation between fat-hers education and schooling time is not matter whether it is positive or negative as this variable is not statistically significant. 0.3349163 is the  partial slope coefficient of age of the students  and tells us with the influence of fathers land amount , fathers education, and number of siblings are held constant ,if  age of the students goes up by 1 year, then average years of schooling increases by .3349163 year. There is a posi-tive relationship between average years of schooling and students age. The estimated slope coefficient for students age is statistically significant as its p value is very low and its t- value is high. -.1183192 is the partial slope coefficient of number of siblings of the students and tells us with the influence of fathers land amount, fathers education, students age are held cons-tant, if  numbers of siblings goes up by 1 person , then average years of schooling  lower by .1183192 year. There is a negative relation between number of sib-lings and average years of schooling. The estimated slope coefficient for number of siblings is not statis-tically significant as its p- value   is quite high and t- value is quite low.

Interpretation of dummy variables

In the above table the coefficient attached to the vari-able dummy is a differential intercept, showing how much the average years of schooling that receives a dummy value of 1 differs from that of the benchmark. The average years of schooling of the children whose fathers occupation is business is a benchmark cate-gory (10.36339). The estimated coefficient of the vari-able when fathers occupation is agriculture is (.92-823). That means average years of schooling is lower by (-1.92823) year for actual average years of school-ing (10.36339-1.928223)=8.435167 year. The esti-mated intercept coefficient for occupation agriculture is not statistically significant as its p value is quite high and t- value is quite low. That means the average years of schooling whose fathers occupation is agriculture is not statistically different from the average years of schooling whose fathers occupation is business. The estimated differential intercept coefficient for variable when fathers occupation is service holder is (-1.43-9707) year. Which means that average years of schoo- ling with service holder occupation coefficient is lower by about (-1.439707) year for actual years of schooling (10.36339-1.439707)=8.923683year. 

The estimated intercept coefficient for occupation service holder is not statistically significant as its p value is quite high (47%) and t- value is quite low. That means the average years of schooling whose fathers occupation is service holder is not statistically different from the average years of schooling whose fathers occupation is business. The estimated differen-tial intercept coefficient for variable when gender is male is (-3.075434). That means average years of schooling is lower by (-3.075434) year for actual aver-age years of schooling (10.36339-3.075434)=7.287956 year.  The estimated intercept coefficient for variable when gender is male is statistically significant as its p value is quite low and significance at (4%) level and t- value is quite high. That means the average years of schooling for male gender is statistically different from the average years of schooling of female gender. The estimated differential intercept coefficient for students who are rural is (-.131615). That means average years of schooling is lower by (-.131615) year for actual average years schooling (10.36339-.1341615)=10. 2292285 year. The estimated intercept coefficient for variable when students are rural is statistically not significant as its p value is quite high and t- value is quite low. That means the average years of schooling for rural students is not statistically different from the average years of schooling of urban students. The estimated differential intercept coefficient for students who receive government help is (-4.259128). That means average years of schooling is lower by (-4. 259128) year for actual average years of schooling (10.36339-4.259128)=6.104262 year. The estimated intercept coefficient for variable when students receive government help is statistically significant as its p value is quite low (significance level 1%) and t- value is quite high. That means the average years of schoo-ling for students who receive the government help are statistically different from the average years of schoo-ling of the students who not receive any government help (Lutfunneher and Islam, 2021; Rahman, 2021).

Interpretation of R2

The R2value of about 0.3362 indicates that, the in-dependent variables influence about 33 percent change in the dependent variable.

Suggestions for policy implications

The following recommendations are made on the basis of the findings of the study –

i) Both in rural and urban areas government help should be given for raising years of schooling.

ii) Fathers education level and family support should also rise for increasing years of schooling.

CONCLUSION

A conclusion is like the final chord in a song. It makes the listener feel that the piece is complete and well done. The same is true for our audience. Finally we have found that  years of schooling of the children of households, fathers land amount, fathers education level , and number of siblings are negatively related with each other and average years of schooling are positively related with the students age and variable age is statistically significant. We also found that aver-age years of schooling for the male students are differ-rent from the female students and average years of schooling for the rural students are not different from the years of schooling for the urban students. We also found that average years of schooling for the students whose fathers occupations are agriculture and service holder are not different from the years of schooling for the students whose fathers occupation is business. At last we see that the average years of schooling for the students who receive government help are different from the students who are not receive the government help.

ACKNOWLEDGEMENT

At first we desire to express our deepest sense of gratitude of almighty Allah. Our research article wont be possible without contribution of few people who were involved both directly and indirectly in the pre-paration of this report. The research paper is prepared over the topic on “The relationship among years of schooling of the children of households, fathers land amount, fathers education, age, number of siblings, male, rural and government help” under the super-vision of our course teacher Md. Shahnewaz  Khan, Assistant  Professor, Department of Economics, JKK-NIU. We are deeply expressing our respect to him for knowledgeable discourse during the class which hel-ped us to prepare this article. We also express acknow-ledgement to all researchers, experts policy holders, news reporters whose research papers, business report, and news reports has helped us to prepare this article. We want to give special thanks some people who are cooperate with us. Last but not the least; we would like to express our special thanks to our loving family for their forbearance, inspirations, sacrifices, blessings and never ending encouragement. 

CONFLICTS OF INTEREST

No conflict of interest from the authors end.

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Article Info:

Academic Editor

Dr. Sonjoy Bishwas, Executive, Universe Publishing Group (UniversePG), California, USA.

Received

October 14, 2021

Accepted

November 16, 2021

Published

November 24, 2021

Article DOI: 10.34104/bjah.02101590163

Corresponding author

Lutfunneher*

Dept. of Economics, Jatiya Kabi Kazi Nazrul Islam University, Trishal, Mymensingh

Cite this article

Lutfunneher and Mim TEJ. (2021). An economic analysis on years of schooling of the children related to financial support from family and govt. & non-govt. institutions, Br. J. Arts Humanit., 3(6), 159-163. https://doi.org/10.34104/bjah.02101590163

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