What Informs Minnesota's Economic Policy Decisions?
What I found: several pieces to the puzzle, but little way to fit them together. There is plenty of research on gender disparities in employment and wage equity both nationally and globally. The Bureau of Labor Statistics (BLS) is the best source for U.S. data on this subject and provides state unemployment rates by gender from 1997 to 2004 in the Geographic Profile of Employment and Unemployment. One can also find gendered unemployment rates and labor force make up by state, but only for 2008. The BLS also has employment rates by gender within major industries nationally, but I could only access this data from 2006-2007. The Local Area Unemployment Statistics (LAUS) and Current Population Survey (CPS) also provide useful historical data on unemployment and gender, but this is only at the national level.
Hoping a more state-centric body would reveal my data set, I began searching the Minnesota Department of Employment and Economic Development (DEED). While DEED has extensive data on unemployment, job vacancies, and short to long term industry projections; I could find little in the way of gender analysis. After submitting an inquiry on the DEED website, I learned from a Financial Analyst that DEED does not require gender on their employment surveys, but rather rely on the applicant to volunteer such information.
Ultimately, there is no single good source for data on gender and unemployment at the state level. The closest I came was the decennial census of 1970 which has two tables under the Characteristics of the Population for Minnesota: 1) Occupation of Employed Persons by Industry Group and Sex and 2) Detailed Industry of the Experienced Civilian Labor Force and Employed Persons by Sex. However, these are not ideal since they only represent information taken every ten years and are not accessible electronically.
So, what happens if this data is not accessible or being collected? Data analysis can often be a way to identify difficulties in the Minnesotan experience and subsequently work towards solutions. Disparities in test scores between school districts; high foreclosure rates in the housing markets; lengthy commute times and excess pollution from automobiles. Data is being and should be collected in areas such as these.
Data should also be collected on gendered-unemployment within industries, but I was entirely unable (even with the assistance of several reference experts) to find historical data from Minnesota on male-female unemployment rates broken down by industry. While there are clearly obstacles to collecting the kind of data I was looking for (i.e. blurry lines between industries especially for those who are unemployed and searching for work), I was surprisingly unable even to find unemployment rates by gender in Minnesota before 1997. I cannot help but conclude that the gender perspective is being largely ignored within the statistical scope of Minnesota policy.
Numbers and statistics are the backbones for developing sound and effective policy. Data provides links across communities and its collection is a necessary tool for understanding: 1) what policies are needed and 2) how they will really impact our state. Data collected on gender in the work place across industries has the potential to inform legislation that can protect genders being discriminated against and also inform legislation that can dictate educational programming that might equalize gender inequality between industries in Minnesota. Given the trend of greater male unemployment than female, elected policymakers should be calling for more research on this data in our state. They-and all of us-need this data to monitor how gender might interact with the health of our economy. It seems that DEED easily has the resources and capacity to compile this information, and is merely lacking clear policy that prompts them to do so. If we do this, Minnesotans will have greater leverage to strengthen our economy.