Demographic Analysis for Associations with Poverty

demographic Analysis for Associations with Poverty3. Socio-economic CharacteristicsIn this paper, demographic characteristics such as climb on, sexual urge and education as well as socio-economic characteristics such as sign of the zodiac assets and livelihood activities argon assessed. These characteristics provide an overview on the background of the respondents, which in cut into provides an overview ab come out of the closet the suitability of the study population. Without necessarily being the source of poverty, it has been pointed out that having a particular characteristic may be associated with poverty. For example, most ho giveholds that cypher on agriculture, livestock and angle keeping ar more(prenominal) be homogeneous to be poor.We control as well as collected data about their avocation that how they manage angleing. Also, data has been collected about their economic activities like have they received micro quotation or do they pay whatsoever usury. Here in the following table we show their professional activities. Non-Users of active predict bout is 53 out of 205, which is 25.85% and number respondent victimization erratic more than 1 year is 92 out of 205, which is 60.53%. These characteristics argon described in table 1, 2 and 3. postpone 3 Information (Qualitative) about diligent Phone of the UserSubjectNumber of UserPercentage distance of using wandering(a) anticipate1811.8%7 to 12 months4227.6%1 year9260.5%Reason of using liquid tele auditory sensation setFamily2013.2% furrow9351.2%Other3925.7%The level of literacy rate is 30.7 % which is slight than countryfied Bangladesh illiteracy rate 50.6% (BBS, 2013). diligent c all tolds ar easy to drop and do non require the exploiter to have ofttimes proficient knowledge or even to be able to read or write, so this root with primary or no education stack occupy this.Table 4 Income preconditionFor vigorous Users (N = 152)VariableIncome originally handling mobileIncome after(prenominal) mobile useIncome Mean (S.D.) (Bangladeshi taka)13068.39 ( 11840.35)20854.72 (22868.66)Household monthly incomeN (%)N (%)Lower Income (0-10000)87 (42.4 %)75 (36.6 %)Average Income (10000-15000)34 (16.6 %)13 (6.3 %)Higher Income (15000)84 (41.0 %)115 (56.1 %)For Non- alert Users (N = 53)VariableLast coursePresent YearIncome Mean (S.D.) (Bangladeshi taka)4666.97 (11390.37)4134.08 (9743.71)In order to understand the socio-economic status of the households, a number of household assets and livelihood activities were assessed through and through multiple-response questions. As all respondents are fishermen, so their main earnings are from this occupation. At the same time, they are also involved in some agricultural productions. Also, some respondents are partly involved in subscriber line. As we collected the whole randomness of their family, so the new(prenominal) source of income with seek by the other members of the family are also involved. The ot her member either may be migrated or jobholder or may be involved in business like storekeeper. So, in our data, all of the income earnings through these income sectors are also included. The Income company are separated in three groups with their monthly income. The lower income group with the income from 0 to 10000 taka, average out income group with the income from 10000 to 15000 taka and higher income group with the income higher than 15000 taka.For mobile squall user, the average income before mobile call back use was 13068.39 taka where after the use of mobile phone this average income embossed to 20854.72 taka.For the non-user of mobile phone we collected the data about income for the previous year. The estimated average income of the last year was 4666.97 taka where in present period this income has decreased to 4134.08 taka. The estimated income of the mobile phone user and non-user are granted by the following Table-4.4. Impacts of wide awake Phone Use on Fishing C ommunitys BusinessThe study has sought to identify the forces of the mobile phone in fishing community life. Impact refers to the difference that rag to the mobile phone has meant to the individuals in the study areas. Assessments of collisions are based on the self-reported advantages of mobile phone access that the interviewees have indicated.Access to the mobile phone has above all meant the innovation of more opportunities and choices, but it has also provided help in managing uncertainty. Moreover, existing business relations have been strengthened. We have asked several questions related mobile phone use in their business and they answered (Table- 5). The advantages that the users feel the mobile phone has given them in business transactions is above all relate to the trim down access time to entropy. Reduced communication expenses are also authorized to many.Table 5 Business related advantages of mobile phone users (N=152)Mobile phone has helped inAgreed cushy to use12 5 (82.2%)Easy to access market information128 (84.2%)Reducing search cost and meliorate market knowledge110 (72.4%)Reducing risk119 (78.3%)Most of the respondents order that they call in the main for their business purpose. Before introducing mobile phone they had to film the affectionatenessmans price offer for fishes because they had no other track to know the market price for fishes in the bigger markets. Mobile phone gave them the opportunity to verify the market price of fishes. Now, before sell fishes to middlemen, they do verify the market price in the nearby markets and only affiliate to sell when they ticktack a good price. Now they feel practically more confident as they have gained bargaining power with the middle men who mostly deprive them from their profit.Table 6 Business information of mobile phone users (N=152)Business Related QuestionsAgreedN (%) potently AgreedN (%)Neither Agreed nor Disagreed N (%)DisagreedN (%) potently DisagreedN (%)After using mobile phone income has enlarge102 (67.1%)5 (3.28%)31 (20.39%)14 (9.21%)0 (0%)After using mobile phone savings have increased78 (51.32%)6 (3.95%)33 (21.71%)35 (23.03%)0 (0%)After using mobile phone expenditure has increased82 (53.95%)17 (11.18%)37 (24.34%)14 (9.21%)2 (1.32%)After using mobile phone, 67.1 percent fishermen have agreed that their income has increased, 3.28 percent have potently agreed. Again, about the increase in savings, after mobile phone use 51.32 percent have agreed, 3.95 percent have powerfully agreed. Finally, about the increase in expenditure. 53.95 percent agreed that their expenditure has increased, 11.18 percent have powerfully agreed. 24.34 percent are indifferent whether 9.21 percent have disagreed and 1.32 percent potently disagreed about the increase in price (table- 6).They are agreed mostly that the carry on of mobile phone on pastoral market is that the rural suppliers could more easily get market information, they could more easily get price informati on and they move up out that the market is expanding. Also they find out that the introduce of mobile phone strengthening their relationships with business partners, prompt himself in taking new initiatives and creating new economic or income generating opportunities.Findings of table- 7 suggest that contribution of mobile phones were enabling rural households in Sylhet neighbourhood to overcome vulnerabilities related to social exclusion .The phones were also reducing plump times and monetary costs decreases physical risks and increases the outcomes of those necessary journeys. Furthermore, increased temporal role accessibility enables people to manage several activities regardless of their physical location.Table 7 Qualitative information about mobile phone users (N=152) businessLivelihood and development aspectsAgreedN (%)Does mobile phone green goddess reduce risk?119 (78.29%)Do you get the supporter of health service initializing mobile technologies?35 (23.03%)Do mobile covering and practices base increase the benefit to women?68 (44.74%)Do you face any harassment by others?37 (24.34%)Do your productivity rise?103 (67.76%)5. dominanceEmpowerment is the reduction of dependency, owners as well as users have experient a variety of changes after access to the mobile phone. In rural Bangladesh, people have very little scope for choice in work or social relation but remain enwrapped to the village and its limited income earning opportunities.Economic empowerment refers non only to increases in income but also to having control over resource and resource management, ending making power, involvement in and control over economic transactions. Mobile phone, besides financial gain, could also facilitate the economic empowerment of women. Mobile phone has created an income generation opportunity for rural women. It has also provided scope for interacting with a wider cross-section of people. Obviously, mobile phone as a business fortuity provides an op portunity for financial gain for the users. Almost most of the fishermen 70.38% (Summation of agreed and strongly agreed, table- 6) have said their income have increased through mobile phone. In the majority of cases the income of the fishermen has increased with the length of the mobile phone owning period. The great the length of ownership, the higher has been the increase in income. So, apparently, as an income opportunity, the mobile phone has been a success for the fishermen.6. Results and Discussion6.1 Probit RegressionProbit regression analysis accomplish in table-8 suggested mobile phone has a real impact on social and economic condition.Table 8 Probit Regression Dependent Variable- chance of using mobile phoneVariablesCoefficientConstant-0.24(0.47)Age-0.01335(0.0085)Education0.89**(0.29)Maritaul Status0.29(0.23)Otherych0.0018**(0.0008)Fishych0.0024***(0.0007)Credit0.06(0.23) interchangeall0.97***(0.29)Number of observation205LR chi2 (7)59.28Prob chi20.0000Log likelihoo d-88.560767Note (1)*, ** *** cite 10%,5% and 1%level of entailment respectively,(2) Standard Error is iterate in parenthesis.(3) Otherych=Income from other source Fishych = Income from fishing Credit= Micro-credit Sellall = Sell all fishProbit regression suggested that an increase in Age decreases the predicted opportunity of mobile phone use by 0.013. However, it can be easily seen that age has no significant influence on probability of mobile phone use. The coefficient ofEducationshows that an increase in education increases the probability of using mobile phone by 0.89. This issuing was significant at 5% level. some other coefficient ofmarital statuswas 0.29,which means that there is a official impact of marriage in the predicted probability of income.One of the most important coefficient income from other sources (Otherych) was 0.00175, the result was significant at 5% level. This means that increase in income from other source than fishing cause increase in occur income . The coefficient of income from fishing (Fishych) was 0.0024. This means the increase income from fishing increases the meat income. This result was significant at 1% level.The coefficient of microcredit (Credit) was 0.06. This means that an increase in receiving microcredit causes an increase in total income. Coefficient of selling all fish (Sell all) was 0.97. This means that the increase in selling all fish causes an increase in total income. This result was significant at 1% level.The constant term is -0.24 which describes that predicted probability of income of the fishermen through mobile phone is extremely low if all of the predictors (Age, education, marital status, otherych, fishych, credit and sell all) are evaluated at zero. The Likelihood Ratio (LR) Chi-Square (2 ) was 59.28 presumptuous that the model converged with all the parameters. Here, the entertain of log-likelihood is -88.56, which is negative, indicating better fit of this model. Prob 2 If Prob 2 tends to zero then there is no heteroscedasticity problem. Our probability of 2 value is 0.0000 that rules out existence of heteroscedasticity problem.6.2 Marginal Effects after ProbitMarginal make after probit is taken to find out the variation in the probability of increasing mobile phone use of the respondents. The marginal effects are calculated in Table- 9.Table 9 Marginal Effects after Probit RegressionVariablesdy/dxAge-0.004(0.002)Education+0.21***(0.06)Maritaul Status+0.08(0.07)Otherych0.0005**(0.0002)Fishych0.0007***(0.0002)Credit+0.017(0.07)Sell all+0.33**(0.11)Note (1) (+) dy/dx is for discrete change of dummy variable from 0 to 1(2) *, ** *** denote 10%,5% and 1%level of significance respectively.(3) Otherych=Income from other source Fishych = Income from fishing Credit= Micro-credit Sellall = Sell all fishAll else held constant, education increases the probability of income by 21.28% and this was significant at 1% level. As the literate fishermen who are more educated can ope rate mobile phone effectively than the illiterate fishermen and are more advised about new inventions. They know that the use of mobile phone can minimize their cost by proving various market information including ups and downs in prices, pick demand etc. Relying on the middlemen, instead do not provide the mark they information about price and market demand. So, the use of mobile phone among the fishermen who are educated is higher than the fishermen who are not educated.Also, the income from other source rather than fishing, when all else held constant, increases the total mobile customs by 0.05 %, with a significance of 5% level .That is, the change in income from other sources like agriculture, remittances, businesses, wage or salaries and interest earnings were influenced by the change in attitude towards mobile use.Again, earnings from fishing increases the total income by 0.07 % and this result is significant at 1% level. That is the income earnings from fishing change the mobile usage positively. The fishermen who were using mobile phone can sell their fishes with better prices. So, the total income of the fishermen who were mobile phone user greater than the fishermen who were not mobile phone user. We have to admit however, that the real impacts on the probability to use mobile phone by both income variables are quite low.Finally, selling all fish remaining increases mobile phone use by 33.14% and this result was significant at 1% level. So, the fishermen who are mobile phone user can sell all the fishes whereas the fishermen who were not the user of mobile phone cannot sell all the fishes very fasting compared to fishermen who were the user of mobile phone. As fish is very perishable good, it becomes a vital incentive for the fishermen to use mobile phone.On the other hand, the impact of marital status and credit has no any significant impact on the change in total income. The status Married for mortal increases the probability of increasing inco me by 8.14% which was not significant. Receiving credit has a positive probability and increases the probability of increasing total income by 1.67% which was not significant. That the receiving microcredit may enhance their wealth but the wealth status is not much more different of both the fishermen who are mobile phone user or not.Finally, the probability of increase in total income is negatively related to the age. As age increases, the probability of increasing income decreases at 0.37% rate. This change is also not significant.