This study particularly focuses on the structures of poverty reduction effects, of a large infrastructure, such as job transformation and non-farm employments. We particularly focus on the impact of Jamuna multipurpose bridge (JMB), the largest bridge as well as the ever largest infrastructure in Bangladesh, on labour market integration. Jamuna River, one of the main water streams in Bangladesh, which physically divides the country into two halves, and the Bridge was built in 1998 in order to provide the first road and rail link between the relatively less-developed Northwest region of the country and the more-developed eastern half that includes the capital of Dhaka and the port of Chittagong. Presumably, JMB connects the eastern and western part of the country, facilitating economic integration and development of the whole economy.
To assess the impacts of JMB on labour market integration, we analyse the JMB evaluation data collected by BRAC-Research and Evaluation Division (Ghosh et al., 2010). We particularly focus on the occupation and employment opportunity information of the household roster to see how JMB facilitated labour market outcome. The data set provides us various information of a total of 1,485 household (761 in Tangail; and 841 in Sirajganj). In addition to current information, the survey consists of retrospective questions on occupation and households’ assessments of their livelihood condition before bridge construction. To analyse the impact of infrastructure intervention, we can regard households in Sirajganj and Tangail districts as “treatment” and “control” groups of the bridge, respectively. This is simply because the JMB improved accessibility of Sirajganj to Dhaka dramatically although Tangail were relatively unaffected by the bridge in terms of access to Dhaka. Accordingly, the data set allows us to adopt the canonical difference- in-difference approach to analyse the impact of Jamuna bridge construction on jobs and livelihoods. Difference-in difference is one of the most important identification strategies in applied economics, which (model) measures the differences in outcome overtime for the treatment group in interest compared to the difference in outcome overtime for the control group in interest (see Angrist and Krueger 1999 and Bertrand et al., 2004). A potential issue of our analysis is in selection bias arising from endogenous choice of the bridge location. For example, if the location is selected according to the pre-bridge density of economic activities, then there will be an upward bias in estimating the treatment effect. As Al-Hussain et al. (2004) describe, the Jamuna Multipurpose Bridge Authority (JMBA), which was formed by the Government of Bangladesh in 1985, selected the site for the bridge near Bhuapur-Sirajganj area, where the river flows in a relatively narrow belt and mostly in one channel, on the basis of satellite imagery and earlier bathymetric surveys. In other words, the bridge location has been decided mainly by engineering reasons. Reflecting this, the World Bank’s project completion report stated that when it comes to resettlement and rehabilitation issues, “people do not become entitled to support until they have actually been displaced by flooding or erosion, and the amount and location of this is wholly unpredictable” World Bank (2000). These settings indicate that the policy treatment due to the bridge construction has largely been exogenous to surrounding people’s characteristics.
The data set provides information about households’ assessments of positive as well as negative aspects of JMB in term of household welfare and it contains the respondents’ assessments of household benefit from the bridge construction. We focus on one particular response category in our analysis i.e., expanding employment opportunities. In fact, Ghosh et al. (2010) observed that a considerable number of people reported that it increased employment opportunity and that the property value (land price) went up in Tangail. However, in Sirajganj more people reported that it increased price of land together with increased employment and business opportunities. Here we suppose that the residents in Sirajganj are “treatment” group and those who are in Tangail are “control” group. Then we can set up a canonical difference-in-difference model to estimate the treatment effects. To investigate the job transition patterns, we will estimate a multinomial logit model of occupational transition following the framework of an additive random-utility model (McFadden 1974). The subsequent estimations will also incorporate heterogeneous treatment effects by age, gender, and education level.
The study findings will inform policy makers on broader impacts of physical infrastructure on well-being; using insights from the findings, policy makers will be able to formulate evidence based policies on local infrastructure, transport and communication.