The Bangladesh Demographic and Health Survey (BDHS) 2007 data has been used for this study. The 2007 BDHS employs a nationally representative sample that covered the entire population residing in private dwelling units in Bangladesh. As described in NIPORT (2009), the survey was based on a two-stage stratified sample of households with 361 (227 rural and 134 urban) Primary Sampling Units (PSUs) at the first stage. A total of 10,819 households were selected for the sample and it was targeted to interview 11,485 ever-married women aged10 - 49. However, 10996 were interviewed finally with a response rate of 98.4 percent. This study included 8,611 women in the analyses. 2,385 women were excluded from this study list wise due to having missing values on the study variables. As BDHS data showed that fertility is completed by age 35, this study divided all women of reproductive age into two different age cohorts: below 25 and above 25 age cohort. In this study we considered two separate outcome variables: use contraceptive and desire no more children. Both outcome variables are a measure of fertility behavior and therefore we tested the effect of women's decision-making power index on both outcome variables in two different age cohorts and in the pooled sample. The socio-demographic and economic variables were also controlled while investigating such effects. The main exposure variable of this study (decision-making power index of women) is constructed using standard statistical processes consisting of regression technique and factor analysis as explained in detail in the following section. The analysis of data included a description of the study population followed by investigating the associations among all categories of the independent and the dependent variables and finally a binary logistic regression analysis was conducted for each dichotomous dependent variable to find the net effect of decision-making power in two different age cohorts and in the pooled sample. Construction of decision-making power index: The exposure variable of this study is women’s decision-making power index. The criteria for defining women’s decision-making power index are given by age difference between them and their partners, employment, marriage, human capital (education and experience), who in the household has the final say in decisions over visits to friends and family, the household budget, children’s and own health (Kishor 2000, Westoff and Ochoa 1991, Smith et al. 2003). According to Kishor (2000), indicators for measuring women’s decision-making power index can be of three types: direct evidence, sources and setting of power. Considering the multidimensionality of the concept of women’s decision-making power index. This study used the following indicators to construct decision making power index: (i) Whether women work for cash income, (ii) age at first marriage, (iii) the percent difference in the woman’s and her partner’s age, (iv) the difference in the woman’s and her partner’s years of education, (v) final say on making large household purchases, (vi) finally say on making household purchases for daily needs(vii) final say on own health care, (viii) finally say on child health care and (ix) finally say on visits to family or relatives. Indicators (i) and (ii) are source indicators, while (iii) and (iv) are setting indicators, and (v) to (ix) are the direct evidence indicators. All categorical indicators were recoded into dichotomous groups for analysis. The indicator ‘the difference in the woman’s and her partner’s years of education’ was calculated by the following steps: Firstly, the simple difference woman’s and her husband’s single year of education was taken as follows- ‘education difference’ = (Education in single years - husband’s education in single years). Secondly, in constructing this measure, a substantial number of cases (32 percent) were found where the difference in years of education was zero because both the woman and her husband have zero years of education. It may reveal a weak association between ‘education difference’ and direct measures of decision-making power, contrary to expectations when these cases were included. It was hypothesized that the double zero cases actually represent situations where poverty limits all children from attending school, regardless of their gender (either because incomes are low, schools are non-existent, or both). We, therefore, adjusted ‘education difference’ by predicting the double zero cases using a regression of ‘education difference’ on the three other indicators and various other household characteristics as independent variables. As expected, the mean of the adjusted measure (– 0.44) dropped considerably from the original (– 0.68). The new variable was named as ‘adjusted difference in woman's and her partner's year of education. The independent variables for regression of education difference were: whether women work for cash income, age at first marriage, the percent difference in the woman’s and her partner’s age, final say on own health care, final say on child health care, type of place of residence, wealth index factor score and total children ever born. Regression equation for education difference was: Y = − 0.341 + 0.069 * Age at first marriage - 0.00000672.* Wealth index factor score - 0.528.* Total children ever born. This equation was predicted for all double zero cases. After replacing those double zero cases with these predicted values in the ‘education difference’ variable, we finally have got the indicator ‘adjusted difference in woman's and her partner's year of education. Then factor analysis was employed to combine the nine indicators into an index. The equation for women’s decision-making power index was: women’s decision-making power index = 0.181* whether women work for cash income + 0.044* age at marriage + 0.177* percent difference in woman's and her partner's age – 0.044* adjusted difference in woman's and her partner's year of education + 0.782 * final say on making large household purchases + 0.733 * final say on making household purchases for daily needs + 0.691* final say on own health care + 0.773* final say on child health care + 0.705* final say on visits to family or relatives + 0.258 * goes to health center alone or with kids. According to NIPORT (2009), the number of decisions that women make themselves or jointly with their husbands is positively related to women’s empowerment and reflects the degree of control women are able to exercise in areas that affect their lives and environments. The percentages of married women by the number of decisions (number of aspects of direct evidence indicators) made in the household were 27, 24 and 49 for no/one, two/three and four/five, respectively (NIPORT 2009). These facts have been considered in categorizing the obtained factor scores into low/medium/high. In this process, the factor scores were standardized (z - scores) in relation to a normal distribution with a mean of zero and standard deviation of one (Gwatkin et al. 2000) and women were divided into three categories (low, medium and high decision making power index) on the basis of the quintiles of the z - scores i.e., 27th percentile was the cutting point for the low index, 51th percentile was the cutting point for the medium index, and the remaining scores for high decision making power index.