Consumption of workers

Ready made garments has been the country’s main export earner for a while. As this industry is characterized by its export earnings, female workers in the industrial sector characterizes the garments industry. However, these workers are often marked by poverty and hard labor. The following study attempts to analyze the important determinants in their expenditure on food consumption of these workers. In view of the current situation it is deemed pertinent to investigate the socioeconomic conditions of the female garment workers, especially those who work as a helper or operator.

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Most of the industry doesn’t follow the ILO minimum wage rate for these workers. Due to Low income and irregularity in payment raises problems in their living condition. They cannot able to get the proper diet, as they required. Day by day they are loosing their efficiency which is reducing their productivity. When the productivity goes down the GDP also affected. The model that we have developed is that the expenditure on food per month by the female garment workers are affected by numerous factors.

However the following variables: Total family Income, Education Level, Family size, Number of Earning member in the family, House rent expected to be important in explaining the variation in Expenditure on food. The theoretical relationship between the dependent variable (Y) and each of the independent variables (X1, X2, X3, X4, and X5) is easy to understand using simple theory and logical deduction. The Expense for Consumption of food by the garment workers is dependent on the total family income.

They are positively related because as the total family income increases workers will be able to spend more for their food. Again if the income decreases they will cut down their induced food consumption. Education changes human nature and life style. Higher educated are aware about their health and willing to buy rich food than the others. So they spend more. Thus the relationship between food expenditure and education is positive. Food expense are also affected by the number of family member.

If the family member increases the food expenditure per member will decrease, ceteris paribus. The more family member will reduce the food expenditure. On the other hand if the earning member of entire family increases, total food consumption will also change. In general this relationship is positive because the additional income will increase the average food expenditure of that family. The relationship between food expense per head and house rent is difficult to understand at first, but in the end make sense. Workers have to pay a large amount of their income for the accommodations.

Due to this they have to cut down their other expenditure. Sometime they reduce their food budget to live vicinity of their working place. The relationship between these two is expected to be negative. We administrated a structured questionnaire (Table-, Page ) to the garments female workers. Successful completion of the survey crucially depended on the cooperation of the owners of the garment industries. We made appointment with the managing director who could be conducted after several phone calls and some times after 5 p.

m. only or on Friday. In some industries it took lot of persuasion to convince them. Methods: The data has been conducted by directly questioning the female worker randomly at the lunch lime. This also helped us to eliminate possible bias that could have been there, if they were questioned on the production floor. As the interview was conducted within the factory premises and adequate care was taken to ensure the interview was confidential and none of the management personal was around during the interview.

Problem Area In the initial collection of data we included a variable i. e. family member. But, in this variable we only included members who are living in Dhaka. But, later we felt that we should actually include members in the workers’ village. But, we could not arrange any other appointment so we had to stick with the earlier data. By not including the village members we are actually overlooking a very important variable so our regression results could have a very high residual, in other way the R2 might be small.