Given the magnitude of the recent economic downturn, it is important to understand the effects that large-scale job losses may have on aspects of child well-being such as academic achievement outcomes, and how these effects may vary in different places.  In a previous study, we examined this question by estimating the effects of community-level job losses in North Carolina counties on 4^th and 8^th-grade student math and reading scores.  Community-level job losses, as opposed to parental job losses, are an arguably exogenous source of variation in job loss with respect to individual children within the community. Community-level analyses also capture spillover effects on children whose parents maintain employment but who may be affected by friends’ or neighbors’ job losses.  We found that community job loss decreases the math and reading scores of 8^th-graders, but not 4^th-graders.  

This paper extends our previous work by investigating how effects of community-wide job loss on 8^th-grade student achievement vary for urban vs. rural counties. The effects of job loss on student achievement may vary in urban vs. rural areas for a number of reasons. For example, urban and rural areas differ in the availability and accessibility of social services, which may buffer the negative consequences of job loss.

            The data for this analysis are derived from two main sources.  Job loss data are from the North Carolina (NC) Employment Security Commission, which reports all of the business closings and layoffs in the state.  Observations are for each NC county from 1995 to 2011.  We express job losses as a percentage of the working age population (age 25 – 64) in each county.

            Student academic performance data are from the NC Department of Public Instruction and include end-of-grade (EOG) tests in reading and math. EOG tests are given each academic year to all 3^rd through 8^th-grade students. To parallel our prior work, we will utilize data on the 8^th-grade students. We standardize the assessment scores to have a mean of zero and standard deviation of one in order to compare test scores across subjects, grades, and years.

            Descriptive statistics for job loss in urban vs. rural areas are shown in Table 1. We define urban counties as those designated by the U.S. Census Bureau as being part of a Metropolitan Statistical Areas (MSA) during the time period under investigation. Rural counties are those not designated as MSAs. Although the urban and rural counties experienced similar average job loss over the period 1990-2011, the rural counties had more variability over time. Given that rural counties experienced more variability, it is possible that the consequences of a job loss of the same magnitude may have different consequences in urban vs. rural contexts.

To examine how this measure of job loss affects student EOG test scores,  we will use a “differences-in-differences” regression framework, regressing job loss on student test scores and adjusting for county and year fixed effects, and county-specific linear trends.  We will run our models separately for urban and rural counties.