[raw shortcodes=1]

I try to keep up with the latest data on Minnesota’s economy, so I was glad to read Eric Black’s recent interview with Senate candidate Mike McFadden. Instead of appealing to generalities, McFadden cited specific numbers to make his case.

Black writes, “Shortly before our interview, it came out that the June unemployment rate for Minnesota was 4.5 percent, the lowest since February of 2007. I asked McFadden whether he was aware of the state’s unemployment rate? Replied McFadden:

Do you know what the unemployment rate is in the Iron Range, Eric? It just came out, too. It’s close to 10 percent. Do you know what it is in Bemidji? It’s close to 11 percent. Do you know what the labor participation rate is in Minnesota? The lowest it’s been in 30 years. Do you know what the wage growth has been over the last six years? The average weekly wage has gone up $8 … .”

The latter two points, on labor force participation and average wages, made sense to me. Why labor force participation has fallen and why average real wages have been stagnant have both been the subject of much economic research and the answers to both questions are myriad.

Consider the fall in labor force participation. The Council of Economic Advisors recently analyzed this phenomenon and I wrote about it last year, with the upshot that there is a combination of demographic change (in particular, baby-boomer retirements), structural change (new technologies causing disruptions in the labor market) and short-term problems (that is, the Great Recession) causing the decrease.

Similarly, average real wages have been stagnant for more than 40 years. The average real wage in 1970 was $8.73 (in 1982-84 dollars) and in 2013 it was $8.79 in constant dollars. That’s about a $2.40 per week increase in average pay for someone working 40 hours per week. Like labor force participation, economists focus on a variety of causes: globalization, changing technologies, the decline in labor unions, and a host of other possibilities.

The unemployment numbers, however, did not ring true. Perhaps I hadn’t kept up carefully with the data. I went to the Minnesota Department of Employment and Economic Development (DEED) website and clicked on the data tab at the top of the page. The data tools section of this page provides access to a wealth of information on employment, unemployment, and wages in Minnesota.

The best source for data on unemployment rates is the Local Area Unemployment Statistics (LAUS) program. I examined data for January 2007 to June 2014; since the recession began in December 2007, so we can get a before-and-after view of unemployment.

Using monthly data and choosing the Arrowhead region as a proxy for the Iron Range, we get this picture:

Unemployment rate — Arrowhead Region

Unemployment rates have been below 8 percent since 2012. (What about those spikes, you ask?  They show up because these data are not adjusted for seasonal effect; so, for example, employment in construction falls during the colder months and thus the unemployment rate rises.)

Hmmm … perhaps the regional data is masking what is going on at the county or city level. So, let’s look at Aitken, Carlton, Cook, Itasca, Lake and St. Louis counties:

Unemployment rates by county, monthly

Except for the seasonal spikes, the unemployment rate across counties shows the same result: Unemployment has been below 10 percent since 2012. If we use annual data it’s even clearer:

Unemployment rates by county, annual

Unemployment rates peaked in 2009 and have been falling since then.

What about cities? Let’s start with the monthly numbers:

Unemployment rates by city, monthly

Once again, the seasonal spikes put the unemployment rate up near 10 percent, but the overall trend is below 10 percent, as shown in the annual data:

Unemployment rates by city, annual

The same story is true for Bemidji and the surrounding area. Here are the monthly data:

Unemployment rate, Bemidji, monthly

The seasonal effects are, not surprisingly, quite strong, with unemployment rates fluctuating by four percentage points from season to season. The annual data show that unemployment in Bemidji, as on the Iron Range, rose during the recession and has now returned to its 2007 level.

Unemployment rate, Bemidji, annual

What do we learn from this from this analysis? First, the unemployment data do not match Mike McFadden’s description of what is happening in these local economies. Unemployment is not stuck at 10 or 11 percent in areas such as Bemidji or the Iron Range.

Second, the data indicate that these areas experienced a sharp recession that was similar to other areas of the country with industries tied to the manufacturing sector (e.g. mining.) Unemployment spiked and returned to its pre-recession level; wages continued to be flat, just as they were before the recession and as they’ve been under Democratic and Republican administrations and Congresses for 40 years; labor participation rates are lower than they’ve been in 30 years for a variety of reasons, none of which are specific to Minnesota’s economy or to current economic policy.

Expanding non-ferrous mining and building more oil pipelines won’t solve these problems, nor will reducing government regulations or expanding charter schools. These are the kinds of policies that benefit individual industries and groups and might be worth pursuing on their own merits. They won’t help get more Minnesotans in the labor force, reduce long-term unemployment, or get wages growing again.

I reached out to the McFadden campaign and received the following:

We were using older data and for that we apologize, but they point remains the same: unemployment rates in northern Minnesota remain higher than the state average and that’s because Al Franken has supported Presidents Obama’s agenda 97% of the time, which has resulted in too much regulation and not enough jobs.

— Tom Erickson, McFadden for Senate spokesman



MP.highcharts.makeChart(‘.arrowhead-unemployment-chart’, $.extend(true, {}, MP.highcharts.lineOptions, { legend: { enabled: false }, xAxis: { type: ‘datetime’ }, yAxis: { title: { enabled: false }, labels: { format: ‘{value:,.1f}%’ } }, tooltip: { formatter: function() { var d = new Date(this.x); var mon = d.getMonth()+1; var yr = d.getFullYear(); return ‘‘+ yr + ‘-‘ + mon + ‘: ‘+ this.y +’%’; } }, series: [{ name: ‘Unemployment rate’, data: [[Date.UTC(1990, 01), 8.6],[Date.UTC(1990, 02), 8.4],[Date.UTC(1990, 03), 8.2],[Date.UTC(1990, 04), 8.3],[Date.UTC(1990, 05), 6.8],[Date.UTC(1990, 06), 7],[Date.UTC(1990, 07), 6.4],[Date.UTC(1990, 08), 6],[Date.UTC(1990, 09), 6.4],[Date.UTC(1990, 10), 6.2],[Date.UTC(1990, 11), 7.1],[Date.UTC(1990, 12), 7.5],[Date.UTC(1991, 01), 9.4],[Date.UTC(1991, 02), 9.4],[Date.UTC(1991, 03), 9.4],[Date.UTC(1991, 04), 8.9],[Date.UTC(1991, 05), 8.1],[Date.UTC(1991, 06), 8.7],[Date.UTC(1991, 07), 7.8],[Date.UTC(1991, 08), 6.9],[Date.UTC(1991, 09), 6.7],[Date.UTC(1991, 10), 6.6],[Date.UTC(1991, 11), 7.6],[Date.UTC(1991, 12), 7.1],[Date.UTC(1992, 01), 9.4],[Date.UTC(1992, 02), 8.9],[Date.UTC(1992, 03), 9.1],[Date.UTC(1992, 04), 9.2],[Date.UTC(1992, 05), 8.7],[Date.UTC(1992, 06), 9.2],[Date.UTC(1992, 07), 8.2],[Date.UTC(1992, 08), 8.7],[Date.UTC(1992, 09), 7.6],[Date.UTC(1992, 10), 7],[Date.UTC(1992, 11), 8],[Date.UTC(1992, 12), 8.4],[Date.UTC(1993, 01), 10.1],[Date.UTC(1993, 02), 9.8],[Date.UTC(1993, 03), 9.3],[Date.UTC(1993, 04), 9.7],[Date.UTC(1993, 05), 8.7],[Date.UTC(1993, 06), 9.3],[Date.UTC(1993, 07), 7.4],[Date.UTC(1993, 08), 6.9],[Date.UTC(1993, 09), 7.1],[Date.UTC(1993, 10), 7.6],[Date.UTC(1993, 11), 8],[Date.UTC(1993, 12), 7.7],[Date.UTC(1994, 01), 10.1],[Date.UTC(1994, 02), 9.4],[Date.UTC(1994, 03), 9.6],[Date.UTC(1994, 04), 8.8],[Date.UTC(1994, 05), 6.9],[Date.UTC(1994, 06), 7.8],[Date.UTC(1994, 07), 6.6],[Date.UTC(1994, 08), 6.5],[Date.UTC(1994, 09), 6.6],[Date.UTC(1994, 10), 6],[Date.UTC(1994, 11), 6.3],[Date.UTC(1994, 12), 6.3],[Date.UTC(1995, 01), 8.1],[Date.UTC(1995, 02), 7.6],[Date.UTC(1995, 03), 7.4],[Date.UTC(1995, 04), 7.9],[Date.UTC(1995, 05), 6.5],[Date.UTC(1995, 06), 6.9],[Date.UTC(1995, 07), 5.9],[Date.UTC(1995, 08), 5.1],[Date.UTC(1995, 09), 5.6],[Date.UTC(1995, 10), 5.3],[Date.UTC(1995, 11), 5.9],[Date.UTC(1995, 12), 6.1],[Date.UTC(1996, 01), 8],[Date.UTC(1996, 02), 7.1],[Date.UTC(1996, 03), 7.6],[Date.UTC(1996, 04), 7.9],[Date.UTC(1996, 05), 6.5],[Date.UTC(1996, 06), 6.6],[Date.UTC(1996, 07), 6.3],[Date.UTC(1996, 08), 5.5],[Date.UTC(1996, 09), 5.7],[Date.UTC(1996, 10), 5.1],[Date.UTC(1996, 11), 6],[Date.UTC(1996, 12), 5.7],[Date.UTC(1997, 01), 7.5],[Date.UTC(1997, 02), 6.7],[Date.UTC(1997, 03), 7.2],[Date.UTC(1997, 04), 7.2],[Date.UTC(1997, 05), 5.6],[Date.UTC(1997, 06), 6.3],[Date.UTC(1997, 07), 5],[Date.UTC(1997, 08), 4.8],[Date.UTC(1997, 09), 5.1],[Date.UTC(1997, 10), 4.7],[Date.UTC(1997, 11), 4.9],[Date.UTC(1997, 12), 4.8],[Date.UTC(1998, 01), 6.4],[Date.UTC(1998, 02), 5.7],[Date.UTC(1998, 03), 6.4],[Date.UTC(1998, 04), 5.5],[Date.UTC(1998, 05), 4.3],[Date.UTC(1998, 06), 5.2],[Date.UTC(1998, 07), 4.3],[Date.UTC(1998, 08), 3.8],[Date.UTC(1998, 09), 4.3],[Date.UTC(1998, 10), 3.7],[Date.UTC(1998, 11), 4.4],[Date.UTC(1998, 12), 4.3],[Date.UTC(1999, 01), 5.8],[Date.UTC(1999, 02), 5.2],[Date.UTC(1999, 03), 5.3],[Date.UTC(1999, 04), 5.1],[Date.UTC(1999, 05), 4.2],[Date.UTC(1999, 06), 5.2],[Date.UTC(1999, 07), 4.4],[Date.UTC(1999, 08), 4.5],[Date.UTC(1999, 09), 4.2],[Date.UTC(1999, 10), 3.4],[Date.UTC(1999, 11), 4.3],[Date.UTC(1999, 12), 4.3],[Date.UTC(2000, 01), 5.9],[Date.UTC(2000, 02), 5.3],[Date.UTC(2000, 03), 5.7],[Date.UTC(2000, 04), 4.7],[Date.UTC(2000, 05), 4.3],[Date.UTC(2000, 06), 4.4],[Date.UTC(2000, 07), 4.1],[Date.UTC(2000, 08), 4.2],[Date.UTC(2000, 09), 4],[Date.UTC(2000, 10), 3.7],[Date.UTC(2000, 11), 4.8],[Date.UTC(2000, 12), 4.9],[Date.UTC(2001, 01), 6.6],[Date.UTC(2001, 02), 6.6],[Date.UTC(2001, 03), 7.1],[Date.UTC(2001, 04), 6.5],[Date.UTC(2001, 05), 5.6],[Date.UTC(2001, 06), 5.8],[Date.UTC(2001, 07), 5.9],[Date.UTC(2001, 08), 5.2],[Date.UTC(2001, 09), 4.6],[Date.UTC(2001, 10), 4.5],[Date.UTC(2001, 11), 6],[Date.UTC(2001, 12), 6.3],[Date.UTC(2002, 01), 7.6],[Date.UTC(2002, 02), 7.5],[Date.UTC(2002, 03), 7.3],[Date.UTC(2002, 04), 6.9],[Date.UTC(2002, 05), 5.4],[Date.UTC(2002, 06), 5.7],[Date.UTC(2002, 07), 5.2],[Date.UTC(2002, 08), 4.8],[Date.UTC(2002, 09), 4.6],[Date.UTC(2002, 10), 4.3],[Date.UTC(2002, 11), 5.3],[Date.UTC(2002, 12), 5.5],[Date.UTC(2003, 01), 7.5],[Date.UTC(2003, 02), 7],[Date.UTC(2003, 03), 7],[Date.UTC(2003, 04), 6.8],[Date.UTC(2003, 05), 5.7],[Date.UTC(2003, 06), 6.7],[Date.UTC(2003, 07), 6.1],[Date.UTC(2003, 08), 5.8],[Date.UTC(2003, 09), 5.8],[Date.UTC(2003, 10), 5.5],[Date.UTC(2003, 11), 6.1],[Date.UTC(2003, 12), 6.4],[Date.UTC(2004, 01), 7.8],[Date.UTC(2004, 02), 7.2],[Date.UTC(2004, 03), 7.6],[Date.UTC(2004, 04), 6.6],[Date.UTC(2004, 05), 5.7],[Date.UTC(2004, 06), 6.2],[Date.UTC(2004, 07), 5.6],[Date.UTC(2004, 08), 5.3],[Date.UTC(2004, 09), 5],[Date.UTC(2004, 10), 4.7],[Date.UTC(2004, 11), 5.2],[Date.UTC(2004, 12), 5.8],[Date.UTC(2005, 01), 7],[Date.UTC(2005, 02), 6.9],[Date.UTC(2005, 03), 6.7],[Date.UTC(2005, 04), 5.8],[Date.UTC(2005, 05), 5.1],[Date.UTC(2005, 06), 5.2],[Date.UTC(2005, 07), 4.6],[Date.UTC(2005, 08), 4.3],[Date.UTC(2005, 09), 4.8],[Date.UTC(2005, 10), 4.3],[Date.UTC(2005, 11), 5],[Date.UTC(2005, 12), 5.3],[Date.UTC(2006, 01), 6.6],[Date.UTC(2006, 02), 6.5],[Date.UTC(2006, 03), 6.2],[Date.UTC(2006, 04), 5.7],[Date.UTC(2006, 05), 4.8],[Date.UTC(2006, 06), 5],[Date.UTC(2006, 07), 4.9],[Date.UTC(2006, 08), 4.7],[Date.UTC(2006, 09), 4.7],[Date.UTC(2006, 10), 4.4],[Date.UTC(2006, 11), 5],[Date.UTC(2006, 12), 5.5],[Date.UTC(2007, 01), 7.2],[Date.UTC(2007, 02), 6.7],[Date.UTC(2007, 03), 6.6],[Date.UTC(2007, 04), 6.4],[Date.UTC(2007, 05), 5.4],[Date.UTC(2007, 06), 6],[Date.UTC(2007, 07), 5.8],[Date.UTC(2007, 08), 5.4],[Date.UTC(2007, 09), 5.4],[Date.UTC(2007, 10), 5],[Date.UTC(2007, 11), 5.3],[Date.UTC(2007, 12), 6],[Date.UTC(2008, 01), 6.9],[Date.UTC(2008, 02), 6.9],[Date.UTC(2008, 03), 7],[Date.UTC(2008, 04), 6.3],[Date.UTC(2008, 05), 6.1],[Date.UTC(2008, 06), 6.6],[Date.UTC(2008, 07), 6.6],[Date.UTC(2008, 08), 6.4],[Date.UTC(2008, 09), 6],[Date.UTC(2008, 10), 6],[Date.UTC(2008, 11), 6.9],[Date.UTC(2008, 12), 7.7],[Date.UTC(2009, 01), 10.7],[Date.UTC(2009, 02), 10.6],[Date.UTC(2009, 03), 10.8],[Date.UTC(2009, 04), 10.1],[Date.UTC(2009, 05), 9.9],[Date.UTC(2009, 06), 10.6],[Date.UTC(2009, 07), 9.7],[Date.UTC(2009, 08), 9.1],[Date.UTC(2009, 09), 8.5],[Date.UTC(2009, 10), 8],[Date.UTC(2009, 11), 8.4],[Date.UTC(2009, 12), 8.8],[Date.UTC(2010, 01), 10.1],[Date.UTC(2010, 02), 10],[Date.UTC(2010, 03), 9.9],[Date.UTC(2010, 04), 8.6],[Date.UTC(2010, 05), 7.8],[Date.UTC(2010, 06), 8],[Date.UTC(2010, 07), 7.9],[Date.UTC(2010, 08), 7.7],[Date.UTC(2010, 09), 7.4],[Date.UTC(2010, 10), 7.1],[Date.UTC(2010, 11), 8],[Date.UTC(2010, 12), 8.2],[Date.UTC(2011, 01), 9.2],[Date.UTC(2011, 02), 8.8],[Date.UTC(2011, 03), 8.7],[Date.UTC(2011, 04), 8],[Date.UTC(2011, 05), 7.5],[Date.UTC(2011, 06), 7.8],[Date.UTC(2011, 07), 8.3],[Date.UTC(2011, 08), 7.1],[Date.UTC(2011, 09), 6.5],[Date.UTC(2011, 10), 6.2],[Date.UTC(2011, 11), 6.4],[Date.UTC(2011, 12), 7],[Date.UTC(2012, 01), 7.9],[Date.UTC(2012, 02), 7.9],[Date.UTC(2012, 03), 7.8],[Date.UTC(2012, 04), 6.8],[Date.UTC(2012, 05), 6.4],[Date.UTC(2012, 06), 6.8],[Date.UTC(2012, 07), 6.8],[Date.UTC(2012, 08), 6.4],[Date.UTC(2012, 09), 5.9],[Date.UTC(2012, 10), 5.6],[Date.UTC(2012, 11), 6.1],[Date.UTC(2012, 12), 6.7],[Date.UTC(2013, 01), 8.2],[Date.UTC(2013, 02), 7.6],[Date.UTC(2013, 03), 7.3],[Date.UTC(2013, 04), 6.8],[Date.UTC(2013, 05), 6.4],[Date.UTC(2013, 06), 6.6],[Date.UTC(2013, 07), 6.3],[Date.UTC(2013, 08), 5.9],[Date.UTC(2013, 09), 5.6],[Date.UTC(2013, 10), 5.4],[Date.UTC(2013, 11), 5.9],[Date.UTC(2013, 12), 6.3],[Date.UTC(2014, 01), 7.4],[Date.UTC(2014, 02), 7.4],[Date.UTC(2014, 03), 7.2],[Date.UTC(2014, 04), 6.3],[Date.UTC(2014, 05), 5.9],[Date.UTC(2014, 06), 5.9]] }] }));

MP.highcharts.makeChart(‘.unemployment-by-county-monthly-chart’, $.extend(true, {}, MP.highcharts.lineOptions, { legend: { enabled: false }, xAxis: { type: ‘datetime’ }, yAxis: { title: { enabled: false }, labels: { format: ‘{value:,.1f}%’ } }, legend: { enabled: true }, tooltip: { formatter: function() { var d = new Date(this.x); var mon = d.getMonth()+1; var yr = d.getFullYear(); return ‘‘ + this.series.name + ‘
‘ + ‘‘+ yr + ‘-‘ + mon + ‘: ‘+ this.y +’%’; } }, series: [{ name: ‘Aitkin County’, color: ‘#1D8C47’, data: [[Date.UTC(2007, 01), 9.5], [Date.UTC(2007, 02), 8.9], [Date.UTC(2007, 03), 8.5], [Date.UTC(2007, 04), 7.8], [Date.UTC(2007, 05), 6], [Date.UTC(2007, 06), 5.9], [Date.UTC(2007, 07), 5.9], [Date.UTC(2007, 08), 5.6], [Date.UTC(2007, 09), 5.5], [Date.UTC(2007, 10), 5.2], [Date.UTC(2007, 11), 6], [Date.UTC(2007, 12), 8], [Date.UTC(2008, 01), 9.4], [Date.UTC(2008, 02), 9.5], [Date.UTC(2008, 03), 9.6], [Date.UTC(2008, 04), 8.3], [Date.UTC(2008, 05), 6.9], [Date.UTC(2008, 06), 6.7], [Date.UTC(2008, 07), 6.7], [Date.UTC(2008, 08), 6.9], [Date.UTC(2008, 09), 6.7], [Date.UTC(2008, 10), 6.9], [Date.UTC(2008, 11), 8.8], [Date.UTC(2008, 12), 10], [Date.UTC(2009, 01), 14], [Date.UTC(2009, 02), 13.9], [Date.UTC(2009, 03), 13.7], [Date.UTC(2009, 04), 11.2], [Date.UTC(2009, 05), 9.8], [Date.UTC(2009, 06), 9.7], [Date.UTC(2009, 07), 9.3], [Date.UTC(2009, 08), 9.2], [Date.UTC(2009, 09), 8.7], [Date.UTC(2009, 10), 8.2], [Date.UTC(2009, 11), 9.3], [Date.UTC(2009, 12), 10.5], [Date.UTC(2010, 01), 12.7], [Date.UTC(2010, 02), 12.7], [Date.UTC(2010, 03), 12.4], [Date.UTC(2010, 04), 10.1], [Date.UTC(2010, 05), 8.4], [Date.UTC(2010, 06), 8.6], [Date.UTC(2010, 07), 8.4], [Date.UTC(2010, 08), 8.4], [Date.UTC(2010, 09), 8.3], [Date.UTC(2010, 10), 8], [Date.UTC(2010, 11), 9.6], [Date.UTC(2010, 12), 10.5], [Date.UTC(2011, 01), 11.9], [Date.UTC(2011, 02), 11.6], [Date.UTC(2011, 03), 11], [Date.UTC(2011, 04), 9.9], [Date.UTC(2011, 05), 8.5], [Date.UTC(2011, 06), 8.3], [Date.UTC(2011, 07), 8.7], [Date.UTC(2011, 08), 7.6], [Date.UTC(2011, 09), 6.8], [Date.UTC(2011, 10), 6.5], [Date.UTC(2011, 11), 7.7], [Date.UTC(2011, 12), 8.8], [Date.UTC(2012, 01), 10.1], [Date.UTC(2012, 02), 9.9], [Date.UTC(2012, 03), 9.5], [Date.UTC(2012, 04), 7.9], [Date.UTC(2012, 05), 6.8], [Date.UTC(2012, 06), 7.1], [Date.UTC(2012, 07), 6.8], [Date.UTC(2012, 08), 6.4], [Date.UTC(2012, 09), 5.9], [Date.UTC(2012, 10), 5.7], [Date.UTC(2012, 11), 6.7], [Date.UTC(2012, 12), 7.7], [Date.UTC(2013, 01), 9.6], [Date.UTC(2013, 02), 8.9], [Date.UTC(2013, 03), 8.7], [Date.UTC(2013, 04), 8], [Date.UTC(2013, 05), 6.5], [Date.UTC(2013, 06), 6.8], [Date.UTC(2013, 07), 6.3], [Date.UTC(2013, 08), 5.8], [Date.UTC(2013, 09), 5.6], [Date.UTC(2013, 10), 5.3], [Date.UTC(2013, 11), 6.5], [Date.UTC(2013, 12), 7.4], [Date.UTC(2014, 01), 9.6], [Date.UTC(2014, 02), 9], [Date.UTC(2014, 03), 9.2], [Date.UTC(2014, 04), 7.7], [Date.UTC(2014, 05), 6.2], [Date.UTC(2014, 06), 6]] }, { name: ‘Carlton County’, color: ‘#36A174’, data: [[Date.UTC(2007, 01), 7.3], [Date.UTC(2007, 02), 7], [Date.UTC(2007, 03), 6.8], [Date.UTC(2007, 04), 6.5], [Date.UTC(2007, 05), 5.1], [Date.UTC(2007, 06), 5.5], [Date.UTC(2007, 07), 5], [Date.UTC(2007, 08), 4.8], [Date.UTC(2007, 09), 4.8], [Date.UTC(2007, 10), 4.7], [Date.UTC(2007, 11), 4.9], [Date.UTC(2007, 12), 6.4], [Date.UTC(2008, 01), 7.3], [Date.UTC(2008, 02), 7.3], [Date.UTC(2008, 03), 7.6], [Date.UTC(2008, 04), 6.7], [Date.UTC(2008, 05), 6.2], [Date.UTC(2008, 06), 6.3], [Date.UTC(2008, 07), 6.4], [Date.UTC(2008, 08), 6], [Date.UTC(2008, 09), 5.8], [Date.UTC(2008, 10), 5.8], [Date.UTC(2008, 11), 6.3], [Date.UTC(2008, 12), 7.7], [Date.UTC(2009, 01), 10.8], [Date.UTC(2009, 02), 10.6], [Date.UTC(2009, 03), 10.6], [Date.UTC(2009, 04), 9.3], [Date.UTC(2009, 05), 8.4], [Date.UTC(2009, 06), 8.3], [Date.UTC(2009, 07), 7.9], [Date.UTC(2009, 08), 7.8], [Date.UTC(2009, 09), 7.6], [Date.UTC(2009, 10), 7], [Date.UTC(2009, 11), 7.6], [Date.UTC(2009, 12), 9], [Date.UTC(2010, 01), 10.5], [Date.UTC(2010, 02), 10.6], [Date.UTC(2010, 03), 11], [Date.UTC(2010, 04), 9.6], [Date.UTC(2010, 05), 8.2], [Date.UTC(2010, 06), 8.2], [Date.UTC(2010, 07), 7.9], [Date.UTC(2010, 08), 7.8], [Date.UTC(2010, 09), 7.5], [Date.UTC(2010, 10), 6.7], [Date.UTC(2010, 11), 7.4], [Date.UTC(2010, 12), 8.3], [Date.UTC(2011, 01), 9.5], [Date.UTC(2011, 02), 9.1], [Date.UTC(2011, 03), 8.9], [Date.UTC(2011, 04), 8.3], [Date.UTC(2011, 05), 7.6], [Date.UTC(2011, 06), 7.8], [Date.UTC(2011, 07), 8.5], [Date.UTC(2011, 08), 7.3], [Date.UTC(2011, 09), 6.7], [Date.UTC(2011, 10), 6.1], [Date.UTC(2011, 11), 6.2], [Date.UTC(2011, 12), 7.3], [Date.UTC(2012, 01), 8.6], [Date.UTC(2012, 02), 8.5], [Date.UTC(2012, 03), 8.4], [Date.UTC(2012, 04), 7.2], [Date.UTC(2012, 05), 6.6], [Date.UTC(2012, 06), 6.9], [Date.UTC(2012, 07), 6.9], [Date.UTC(2012, 08), 6.6], [Date.UTC(2012, 09), 6.3], [Date.UTC(2012, 10), 5.8], [Date.UTC(2012, 11), 5.7], [Date.UTC(2012, 12), 6.5], [Date.UTC(2013, 01), 8.4], [Date.UTC(2013, 02), 7.3], [Date.UTC(2013, 03), 7], [Date.UTC(2013, 04), 6.3], [Date.UTC(2013, 05), 5.6], [Date.UTC(2013, 06), 5.8], [Date.UTC(2013, 07), 5.6], [Date.UTC(2013, 08), 5.3], [Date.UTC(2013, 09), 5.4], [Date.UTC(2013, 10), 4.9], [Date.UTC(2013, 11), 5], [Date.UTC(2013, 12), 6.1], [Date.UTC(2014, 01), 7.4], [Date.UTC(2014, 02), 7.6], [Date.UTC(2014, 03), 7.4], [Date.UTC(2014, 04), 6.6], [Date.UTC(2014, 05), 5.8], [Date.UTC(2014, 06), 5.5]] }, { name: ‘Cook County’, color: ‘#FBD341’, data: [[Date.UTC(2007, 01), 7.4], [Date.UTC(2007, 02), 7], [Date.UTC(2007, 03), 6.7], [Date.UTC(2007, 04), 7.1], [Date.UTC(2007, 05), 4.5], [Date.UTC(2007, 06), 3.7], [Date.UTC(2007, 07), 3.3], [Date.UTC(2007, 08), 3], [Date.UTC(2007, 09), 3.3], [Date.UTC(2007, 10), 3.3], [Date.UTC(2007, 11), 4.6], [Date.UTC(2007, 12), 5.9], [Date.UTC(2008, 01), 7.1], [Date.UTC(2008, 02), 6.7], [Date.UTC(2008, 03), 6.6], [Date.UTC(2008, 04), 6.3], [Date.UTC(2008, 05), 5.1], [Date.UTC(2008, 06), 4.6], [Date.UTC(2008, 07), 4.2], [Date.UTC(2008, 08), 4.1], [Date.UTC(2008, 09), 4.4], [Date.UTC(2008, 10), 4.7], [Date.UTC(2008, 11), 6.3], [Date.UTC(2008, 12), 7.1], [Date.UTC(2009, 01), 9.3], [Date.UTC(2009, 02), 9.9], [Date.UTC(2009, 03), 10.2], [Date.UTC(2009, 04), 9.8], [Date.UTC(2009, 05), 8.2], [Date.UTC(2009, 06), 6.3], [Date.UTC(2009, 07), 5.3], [Date.UTC(2009, 08), 5.1], [Date.UTC(2009, 09), 5.2], [Date.UTC(2009, 10), 5.3], [Date.UTC(2009, 11), 6.9], [Date.UTC(2009, 12), 7], [Date.UTC(2010, 01), 9.2], [Date.UTC(2010, 02), 9.3], [Date.UTC(2010, 03), 8.9], [Date.UTC(2010, 04), 7.9], [Date.UTC(2010, 05), 5.9], [Date.UTC(2010, 06), 5.1], [Date.UTC(2010, 07), 4.8], [Date.UTC(2010, 08), 4.6], [Date.UTC(2010, 09), 4.5], [Date.UTC(2010, 10), 5.3], [Date.UTC(2010, 11), 7.6], [Date.UTC(2010, 12), 8.5], [Date.UTC(2011, 01), 8.9], [Date.UTC(2011, 02), 8.7], [Date.UTC(2011, 03), 8.2], [Date.UTC(2011, 04), 7.9], [Date.UTC(2011, 05), 6.2], [Date.UTC(2011, 06), 5.3], [Date.UTC(2011, 07), 5.7], [Date.UTC(2011, 08), 4.7], [Date.UTC(2011, 09), 4.3], [Date.UTC(2011, 10), 4.7], [Date.UTC(2011, 11), 6.3], [Date.UTC(2011, 12), 7], [Date.UTC(2012, 01), 8.1], [Date.UTC(2012, 02), 8.5], [Date.UTC(2012, 03), 8.2], [Date.UTC(2012, 04), 7.6], [Date.UTC(2012, 05), 6.1], [Date.UTC(2012, 06), 4.9], [Date.UTC(2012, 07), 4.2], [Date.UTC(2012, 08), 3.9], [Date.UTC(2012, 09), 3.8], [Date.UTC(2012, 10), 4.1], [Date.UTC(2012, 11), 5.2], [Date.UTC(2012, 12), 6.3], [Date.UTC(2013, 01), 8.3], [Date.UTC(2013, 02), 7.6], [Date.UTC(2013, 03), 7.7], [Date.UTC(2013, 04), 7.1], [Date.UTC(2013, 05), 5.6], [Date.UTC(2013, 06), 4.5], [Date.UTC(2013, 07), 4], [Date.UTC(2013, 08), 3.5], [Date.UTC(2013, 09), 3.6], [Date.UTC(2013, 10), 4.3], [Date.UTC(2013, 11), 5.2], [Date.UTC(2013, 12), 6.3], [Date.UTC(2014, 01), 8.1], [Date.UTC(2014, 02), 7.9], [Date.UTC(2014, 03), 8], [Date.UTC(2014, 04), 7.5], [Date.UTC(2014, 05), 5.5], [Date.UTC(2014, 06), 4.8]] }, { name: ‘Itasca County’, color: ‘#0D57A0’, data: [[Date.UTC(2007, 01), 8.9], [Date.UTC(2007, 02), 8.5], [Date.UTC(2007, 03), 8.6], [Date.UTC(2007, 04), 8.1], [Date.UTC(2007, 05), 6.7], [Date.UTC(2007, 06), 7.2], [Date.UTC(2007, 07), 7.1], [Date.UTC(2007, 08), 6.6], [Date.UTC(2007, 09), 6.3], [Date.UTC(2007, 10), 6.1], [Date.UTC(2007, 11), 6.6], [Date.UTC(2007, 12), 7.6], [Date.UTC(2008, 01), 8.6], [Date.UTC(2008, 02), 8.8], [Date.UTC(2008, 03), 8.7], [Date.UTC(2008, 04), 7.8], [Date.UTC(2008, 05), 7.2], [Date.UTC(2008, 06), 7.8], [Date.UTC(2008, 07), 7.6], [Date.UTC(2008, 08), 7.4], [Date.UTC(2008, 09), 6.9], [Date.UTC(2008, 10), 6.6], [Date.UTC(2008, 11), 7.8], [Date.UTC(2008, 12), 9], [Date.UTC(2009, 01), 12.5], [Date.UTC(2009, 02), 12.8], [Date.UTC(2009, 03), 13.3], [Date.UTC(2009, 04), 11.9], [Date.UTC(2009, 05), 11.2], [Date.UTC(2009, 06), 11.9], [Date.UTC(2009, 07), 10.9], [Date.UTC(2009, 08), 10.2], [Date.UTC(2009, 09), 9.6], [Date.UTC(2009, 10), 9], [Date.UTC(2009, 11), 9.8], [Date.UTC(2009, 12), 10.3], [Date.UTC(2010, 01), 11.6], [Date.UTC(2010, 02), 11.8], [Date.UTC(2010, 03), 11.8], [Date.UTC(2010, 04), 10.4], [Date.UTC(2010, 05), 9.2], [Date.UTC(2010, 06), 9.4], [Date.UTC(2010, 07), 9], [Date.UTC(2010, 08), 8.8], [Date.UTC(2010, 09), 8.4], [Date.UTC(2010, 10), 8], [Date.UTC(2010, 11), 9.1], [Date.UTC(2010, 12), 9.7], [Date.UTC(2011, 01), 10.9], [Date.UTC(2011, 02), 10.3], [Date.UTC(2011, 03), 10.3], [Date.UTC(2011, 04), 9.4], [Date.UTC(2011, 05), 8.7], [Date.UTC(2011, 06), 8.7], [Date.UTC(2011, 07), 9.4], [Date.UTC(2011, 08), 7.9], [Date.UTC(2011, 09), 7], [Date.UTC(2011, 10), 6.6], [Date.UTC(2011, 11), 7.2], [Date.UTC(2011, 12), 7.8], [Date.UTC(2012, 01), 8.8], [Date.UTC(2012, 02), 8.8], [Date.UTC(2012, 03), 8.7], [Date.UTC(2012, 04), 7.3], [Date.UTC(2012, 05), 6.8], [Date.UTC(2012, 06), 7.5], [Date.UTC(2012, 07), 7.4], [Date.UTC(2012, 08), 6.8], [Date.UTC(2012, 09), 6.6], [Date.UTC(2012, 10), 6.4], [Date.UTC(2012, 11), 7.3], [Date.UTC(2012, 12), 7.7], [Date.UTC(2013, 01), 9.4], [Date.UTC(2013, 02), 8.8], [Date.UTC(2013, 03), 8.7], [Date.UTC(2013, 04), 8], [Date.UTC(2013, 05), 7.2], [Date.UTC(2013, 06), 7.5], [Date.UTC(2013, 07), 7.1], [Date.UTC(2013, 08), 6.7], [Date.UTC(2013, 09), 6.3], [Date.UTC(2013, 10), 6.1], [Date.UTC(2013, 11), 6.7], [Date.UTC(2013, 12), 7.3], [Date.UTC(2014, 01), 8.8], [Date.UTC(2014, 02), 8.8], [Date.UTC(2014, 03), 8.3], [Date.UTC(2014, 04), 7.1], [Date.UTC(2014, 05), 6.7], [Date.UTC(2014, 06), 6.8]] },

{ name: ‘Lake County’, color: ‘#0793AB’, data: [[Date.UTC(2007, 01), 5.2], [Date.UTC(2007, 02), 4.9], [Date.UTC(2007, 03), 4.8], [Date.UTC(2007, 04), 4.6], [Date.UTC(2007, 05), 3.9], [Date.UTC(2007, 06), 5.5], [Date.UTC(2007, 07), 3.9], [Date.UTC(2007, 08), 3.9], [Date.UTC(2007, 09), 4.8], [Date.UTC(2007, 10), 3.9], [Date.UTC(2007, 11), 4.2], [Date.UTC(2007, 12), 4.9], [Date.UTC(2008, 01), 5.5], [Date.UTC(2008, 02), 5.4], [Date.UTC(2008, 03), 5.5], [Date.UTC(2008, 04), 5], [Date.UTC(2008, 05), 4.9], [Date.UTC(2008, 06), 4.7], [Date.UTC(2008, 07), 4.6], [Date.UTC(2008, 08), 4.6], [Date.UTC(2008, 09), 4.4], [Date.UTC(2008, 10), 5.1], [Date.UTC(2008, 11), 6.7], [Date.UTC(2008, 12), 6.8], [Date.UTC(2009, 01), 9.9], [Date.UTC(2009, 02), 9], [Date.UTC(2009, 03), 9.9], [Date.UTC(2009, 04), 10.6], [Date.UTC(2009, 05), 11], [Date.UTC(2009, 06), 11.7], [Date.UTC(2009, 07), 8.1], [Date.UTC(2009, 08), 7.8], [Date.UTC(2009, 09), 7.9], [Date.UTC(2009, 10), 8.2], [Date.UTC(2009, 11), 9.1], [Date.UTC(2009, 12), 8.9], [Date.UTC(2010, 01), 9.1], [Date.UTC(2010, 02), 9.2], [Date.UTC(2010, 03), 9.1], [Date.UTC(2010, 04), 8], [Date.UTC(2010, 05), 7.2], [Date.UTC(2010, 06), 7.2], [Date.UTC(2010, 07), 6.6], [Date.UTC(2010, 08), 6.9], [Date.UTC(2010, 09), 6.6], [Date.UTC(2010, 10), 6.6], [Date.UTC(2010, 11), 8], [Date.UTC(2010, 12), 7.6], [Date.UTC(2011, 01), 8.3], [Date.UTC(2011, 02), 7.7], [Date.UTC(2011, 03), 7.4], [Date.UTC(2011, 04), 6.9], [Date.UTC(2011, 05), 6.1], [Date.UTC(2011, 06), 6.2], [Date.UTC(2011, 07), 7], [Date.UTC(2011, 08), 5.1], [Date.UTC(2011, 09), 5], [Date.UTC(2011, 10), 4.8], [Date.UTC(2011, 11), 5.4], [Date.UTC(2011, 12), 6], [Date.UTC(2012, 01), 6.7], [Date.UTC(2012, 02), 6.8], [Date.UTC(2012, 03), 6.4], [Date.UTC(2012, 04), 5.7], [Date.UTC(2012, 05), 5.3], [Date.UTC(2012, 06), 5.3], [Date.UTC(2012, 07), 5.2], [Date.UTC(2012, 08), 5], [Date.UTC(2012, 09), 4.5], [Date.UTC(2012, 10), 4.5], [Date.UTC(2012, 11), 5.2], [Date.UTC(2012, 12), 5.8], [Date.UTC(2013, 01), 7.6], [Date.UTC(2013, 02), 7.7], [Date.UTC(2013, 03), 7.1], [Date.UTC(2013, 04), 6.7], [Date.UTC(2013, 05), 5.8], [Date.UTC(2013, 06), 5.7], [Date.UTC(2013, 07), 5], [Date.UTC(2013, 08), 4.7], [Date.UTC(2013, 09), 4.8], [Date.UTC(2013, 10), 4.5], [Date.UTC(2013, 11), 5.4], [Date.UTC(2013, 12), 5.5], [Date.UTC(2014, 01), 6.3], [Date.UTC(2014, 02), 6], [Date.UTC(2014, 03), 6], [Date.UTC(2014, 04), 5.1], [Date.UTC(2014, 05), 4.6], [Date.UTC(2014, 06), 4.3]] },

{ name: ‘Saint Louis County’, color: ‘#55CBDD’, data: [[Date.UTC(2007, 01), 6.6], [Date.UTC(2007, 02), 6.1], [Date.UTC(2007, 03), 6], [Date.UTC(2007, 04), 5.9], [Date.UTC(2007, 05), 5.1], [Date.UTC(2007, 06), 5.9], [Date.UTC(2007, 07), 5.8], [Date.UTC(2007, 08), 5.4], [Date.UTC(2007, 09), 5.4], [Date.UTC(2007, 10), 4.9], [Date.UTC(2007, 11), 5], [Date.UTC(2007, 12), 5.5], [Date.UTC(2008, 01), 6.4], [Date.UTC(2008, 02), 6.4], [Date.UTC(2008, 03), 6.5], [Date.UTC(2008, 04), 5.8], [Date.UTC(2008, 05), 5.8], [Date.UTC(2008, 06), 6.5], [Date.UTC(2008, 07), 6.5], [Date.UTC(2008, 08), 6.3], [Date.UTC(2008, 09), 5.9], [Date.UTC(2008, 10), 5.8], [Date.UTC(2008, 11), 6.5], [Date.UTC(2008, 12), 7.3], [Date.UTC(2009, 01), 10.1], [Date.UTC(2009, 02), 9.9], [Date.UTC(2009, 03), 10.2], [Date.UTC(2009, 04), 9.7], [Date.UTC(2009, 05), 9.9], [Date.UTC(2009, 06), 10.9], [Date.UTC(2009, 07), 10], [Date.UTC(2009, 08), 9.3], [Date.UTC(2009, 09), 8.6], [Date.UTC(2009, 10), 7.9], [Date.UTC(2009, 11), 8.1], [Date.UTC(2009, 12), 8.4], [Date.UTC(2010, 01), 9.5], [Date.UTC(2010, 02), 9.3], [Date.UTC(2010, 03), 9.1], [Date.UTC(2010, 04), 7.9], [Date.UTC(2010, 05), 7.4], [Date.UTC(2010, 06), 7.8], [Date.UTC(2010, 07), 7.8], [Date.UTC(2010, 08), 7.6], [Date.UTC(2010, 09), 7.3], [Date.UTC(2010, 10), 7], [Date.UTC(2010, 11), 7.6], [Date.UTC(2010, 12), 7.7], [Date.UTC(2011, 01), 8.6], [Date.UTC(2011, 02), 8.2], [Date.UTC(2011, 03), 8.2], [Date.UTC(2011, 04), 7.5], [Date.UTC(2011, 05), 7.2], [Date.UTC(2011, 06), 7.7], [Date.UTC(2011, 07), 8.2], [Date.UTC(2011, 08), 7], [Date.UTC(2011, 09), 6.5], [Date.UTC(2011, 10), 6.1], [Date.UTC(2011, 11), 6.2], [Date.UTC(2011, 12), 6.6], [Date.UTC(2012, 01), 7.5], [Date.UTC(2012, 02), 7.4], [Date.UTC(2012, 03), 7.4], [Date.UTC(2012, 04), 6.4], [Date.UTC(2012, 05), 6.2], [Date.UTC(2012, 06), 6.8], [Date.UTC(2012, 07), 6.8], [Date.UTC(2012, 08), 6.4], [Date.UTC(2012, 09), 5.8], [Date.UTC(2012, 10), 5.5], [Date.UTC(2012, 11), 5.8], [Date.UTC(2012, 12), 6.4], [Date.UTC(2013, 01), 7.7], [Date.UTC(2013, 02), 7.1], [Date.UTC(2013, 03), 6.8], [Date.UTC(2013, 04), 6.4], [Date.UTC(2013, 05), 6.2], [Date.UTC(2013, 06), 6.5], [Date.UTC(2013, 07), 6.3], [Date.UTC(2013, 08), 5.9], [Date.UTC(2013, 09), 5.6], [Date.UTC(2013, 10), 5.3], [Date.UTC(2013, 11), 5.6], [Date.UTC(2013, 12), 5.9], [Date.UTC(2014, 01), 6.9], [Date.UTC(2014, 02), 6.8], [Date.UTC(2014, 03), 6.7], [Date.UTC(2014, 04), 5.8], [Date.UTC(2014, 05), 5.6], [Date.UTC(2014, 06), 5.7]] }

] }));

MP.highcharts.makeChart(‘.unemployment-by-county-annual-chart’, $.extend(true, {}, MP.highcharts.columnOptions, { legend: { enabled: false }, xAxis: { categories: [‘2007′,’2008′,’2009′,’2010′,’2011′,’2012′,’2013’] }, yAxis: { title: { enabled: false }, labels: { format: ‘{value:,.1f}%’ } }, legend: { enabled: true }, tooltip: { formatter: function() { return ‘‘ + this.series.name + ‘
‘ + ‘‘+ this.x + ‘: ‘+ this.y +’%’; } }, series: [{ name: ‘Aitkin County’, color: ‘#1D8C47’, data: [6.9,8,10.6,9.8,8.9,7.5,7.1] }, { name: ‘Carlton County’, color: ‘#36A174’, data: [5.8,6.6,8.7,8.6,7.8,7,6.1] }, { name: ‘Cook County’, color: ‘#FBD341’, data: [4.9,5.5,7.2,6.7,6.4,5.8,5.5] }, { name: ‘Itasca County’, color: ‘#0D57A0’, data: [7.3,7.8,11.1,9.8,8.7,7.5,7.5] },

{ name: ‘Lake County’, color: ‘#0793AB’, data: [4.5,5.2,9.3,7.6,6.3,5.5,5.8] },

{ name: ‘Saint Louis County’, color: ‘#55CBDD’, data: [5.6,6.3,9.4,8,7.3,6.5,6.3] }

] }));

MP.highcharts.makeChart(‘.unemployment-by-city-monthly-chart’, $.extend(true, {}, MP.highcharts.lineOptions, { legend: { enabled: false }, xAxis: { type: ‘datetime’ }, yAxis: { title: { enabled: false }, labels: { format: ‘{value:,.1f}%’ } }, legend: { enabled: true }, tooltip: { formatter: function() { var d = new Date(this.x); var mon = d.getMonth()+1; var yr = d.getFullYear(); return ‘‘ + this.series.name + ‘
‘ + ‘‘+ yr + ‘-‘ + mon + ‘: ‘+ this.y +’%’; } }, series: [{ name: ‘Cloquet’, data: [[Date.UTC(2007, 01), 7.6], [Date.UTC(2007, 02), 7], [Date.UTC(2007, 03), 7.2], [Date.UTC(2007, 04), 6.7], [Date.UTC(2007, 05), 5.9], [Date.UTC(2007, 06), 6.7], [Date.UTC(2007, 07), 6.1], [Date.UTC(2007, 08), 5.6], [Date.UTC(2007, 09), 6], [Date.UTC(2007, 10), 5.8], [Date.UTC(2007, 11), 6.1], [Date.UTC(2007, 12), 7.4], [Date.UTC(2008, 01), 8.4], [Date.UTC(2008, 02), 8.1], [Date.UTC(2008, 03), 8.3], [Date.UTC(2008, 04), 7.2], [Date.UTC(2008, 05), 7.3], [Date.UTC(2008, 06), 7.6], [Date.UTC(2008, 07), 8], [Date.UTC(2008, 08), 7.5], [Date.UTC(2008, 09), 7.1], [Date.UTC(2008, 10), 6.8], [Date.UTC(2008, 11), 7.3], [Date.UTC(2008, 12), 8.1], [Date.UTC(2009, 01), 11.6], [Date.UTC(2009, 02), 11.6], [Date.UTC(2009, 03), 11.5], [Date.UTC(2009, 04), 10.6], [Date.UTC(2009, 05), 9.4], [Date.UTC(2009, 06), 10], [Date.UTC(2009, 07), 9.3], [Date.UTC(2009, 08), 9.3], [Date.UTC(2009, 09), 9.2], [Date.UTC(2009, 10), 8.2], [Date.UTC(2009, 11), 8.8], [Date.UTC(2009, 12), 10], [Date.UTC(2010, 01), 10.8], [Date.UTC(2010, 02), 11], [Date.UTC(2010, 03), 11.6], [Date.UTC(2010, 04), 9.9], [Date.UTC(2010, 05), 8.8], [Date.UTC(2010, 06), 9.2], [Date.UTC(2010, 07), 9.1], [Date.UTC(2010, 08), 9.3], [Date.UTC(2010, 09), 8.4], [Date.UTC(2010, 10), 7.4], [Date.UTC(2010, 11), 8.2], [Date.UTC(2010, 12), 8.9], [Date.UTC(2011, 01), 10.2], [Date.UTC(2011, 02), 10.1], [Date.UTC(2011, 03), 9.9], [Date.UTC(2011, 04), 9], [Date.UTC(2011, 05), 8.5], [Date.UTC(2011, 06), 9.4], [Date.UTC(2011, 07), 9.4], [Date.UTC(2011, 08), 8.4], [Date.UTC(2011, 09), 8], [Date.UTC(2011, 10), 7.1], [Date.UTC(2011, 11), 7.2], [Date.UTC(2011, 12), 8], [Date.UTC(2012, 01), 9.3], [Date.UTC(2012, 02), 9.1], [Date.UTC(2012, 03), 9.3], [Date.UTC(2012, 04), 8], [Date.UTC(2012, 05), 7.5], [Date.UTC(2012, 06), 8], [Date.UTC(2012, 07), 8.2], [Date.UTC(2012, 08), 7.5], [Date.UTC(2012, 09), 7.2], [Date.UTC(2012, 10), 6.8], [Date.UTC(2012, 11), 6.9], [Date.UTC(2012, 12), 7.8], [Date.UTC(2013, 01), 9.5], [Date.UTC(2013, 02), 7.9], [Date.UTC(2013, 03), 7.7], [Date.UTC(2013, 04), 6.5], [Date.UTC(2013, 05), 6.1], [Date.UTC(2013, 06), 6.8], [Date.UTC(2013, 07), 6.7], [Date.UTC(2013, 08), 6.3], [Date.UTC(2013, 09), 6.1], [Date.UTC(2013, 10), 5.7], [Date.UTC(2013, 11), 6], [Date.UTC(2013, 12), 7.2], [Date.UTC(2014, 01), 8.2], [Date.UTC(2014, 02), 8.8], [Date.UTC(2014, 03), 8.6], [Date.UTC(2014, 04), 7.5], [Date.UTC(2014, 05), 6.7], [Date.UTC(2014, 06), 6.8]] }, { name: ‘Hibbing’, data: [[Date.UTC(2007, 01), 7.6], [Date.UTC(2007, 02), 6.8], [Date.UTC(2007, 03), 7.4], [Date.UTC(2007, 04), 7.1], [Date.UTC(2007, 05), 6.7], [Date.UTC(2007, 06), 8], [Date.UTC(2007, 07), 8.4], [Date.UTC(2007, 08), 7.4], [Date.UTC(2007, 09), 7.3], [Date.UTC(2007, 10), 6.8], [Date.UTC(2007, 11), 5.9], [Date.UTC(2007, 12), 6.4], [Date.UTC(2008, 01), 7.2], [Date.UTC(2008, 02), 7], [Date.UTC(2008, 03), 7.3], [Date.UTC(2008, 04), 6.6], [Date.UTC(2008, 05), 7.3], [Date.UTC(2008, 06), 9.3], [Date.UTC(2008, 07), 10.3], [Date.UTC(2008, 08), 9.8], [Date.UTC(2008, 09), 9.2], [Date.UTC(2008, 10), 8.4], [Date.UTC(2008, 11), 9.1], [Date.UTC(2008, 12), 10.4], [Date.UTC(2009, 01), 13.9], [Date.UTC(2009, 02), 13.6], [Date.UTC(2009, 03), 13.5], [Date.UTC(2009, 04), 12.9], [Date.UTC(2009, 05), 14.6], [Date.UTC(2009, 06), 18.4], [Date.UTC(2009, 07), 17.4], [Date.UTC(2009, 08), 16.1], [Date.UTC(2009, 09), 14.3], [Date.UTC(2009, 10), 12.8], [Date.UTC(2009, 11), 12], [Date.UTC(2009, 12), 11.4], [Date.UTC(2010, 01), 10.9], [Date.UTC(2010, 02), 9.8], [Date.UTC(2010, 03), 9.2], [Date.UTC(2010, 04), 7.8], [Date.UTC(2010, 05), 7.3], [Date.UTC(2010, 06), 7.7], [Date.UTC(2010, 07), 7.9], [Date.UTC(2010, 08), 8.1], [Date.UTC(2010, 09), 7.5], [Date.UTC(2010, 10), 7.1], [Date.UTC(2010, 11), 7.7], [Date.UTC(2010, 12), 7.8], [Date.UTC(2011, 01), 8.7], [Date.UTC(2011, 02), 8.1], [Date.UTC(2011, 03), 8.1], [Date.UTC(2011, 04), 6.5], [Date.UTC(2011, 05), 6.8], [Date.UTC(2011, 06), 8.1], [Date.UTC(2011, 07), 8.2], [Date.UTC(2011, 08), 6.9], [Date.UTC(2011, 09), 6.6], [Date.UTC(2011, 10), 6], [Date.UTC(2011, 11), 5.8], [Date.UTC(2011, 12), 6], [Date.UTC(2012, 01), 7.3], [Date.UTC(2012, 02), 7], [Date.UTC(2012, 03), 7], [Date.UTC(2012, 04), 6.1], [Date.UTC(2012, 05), 6.2], [Date.UTC(2012, 06), 7.7], [Date.UTC(2012, 07), 8], [Date.UTC(2012, 08), 7.3], [Date.UTC(2012, 09), 6.4], [Date.UTC(2012, 10), 6.6], [Date.UTC(2012, 11), 6.5], [Date.UTC(2012, 12), 6.5], [Date.UTC(2013, 01), 7.8], [Date.UTC(2013, 02), 7.2], [Date.UTC(2013, 03), 7.4], [Date.UTC(2013, 04), 6.7], [Date.UTC(2013, 05), 7.6], [Date.UTC(2013, 06), 8.8], [Date.UTC(2013, 07), 9], [Date.UTC(2013, 08), 8], [Date.UTC(2013, 09), 7.2], [Date.UTC(2013, 10), 6.7], [Date.UTC(2013, 11), 7.1], [Date.UTC(2013, 12), 7.2], [Date.UTC(2014, 01), 8.2], [Date.UTC(2014, 02), 7.8], [Date.UTC(2014, 03), 7.4], [Date.UTC(2014, 04), 6.2], [Date.UTC(2014, 05), 6.3], [Date.UTC(2014, 06), 7.1]] }, { name: ‘Virginia’, data: [[Date.UTC(2007, 01), 10.1], [Date.UTC(2007, 02), 8.7], [Date.UTC(2007, 03), 9.3], [Date.UTC(2007, 04), 8.8], [Date.UTC(2007, 05), 7.5], [Date.UTC(2007, 06), 9.8], [Date.UTC(2007, 07), 9.3], [Date.UTC(2007, 08), 8.4], [Date.UTC(2007, 09), 7.8], [Date.UTC(2007, 10), 6.6], [Date.UTC(2007, 11), 6.9], [Date.UTC(2007, 12), 7.4], [Date.UTC(2008, 01), 8.8], [Date.UTC(2008, 02), 9.3], [Date.UTC(2008, 03), 9.6], [Date.UTC(2008, 04), 8.3], [Date.UTC(2008, 05), 8.2], [Date.UTC(2008, 06), 9.3], [Date.UTC(2008, 07), 9.5], [Date.UTC(2008, 08), 9.2], [Date.UTC(2008, 09), 7.9], [Date.UTC(2008, 10), 7.6], [Date.UTC(2008, 11), 8.4], [Date.UTC(2008, 12), 9.5], [Date.UTC(2009, 01), 12.5], [Date.UTC(2009, 02), 12.2], [Date.UTC(2009, 03), 13.1], [Date.UTC(2009, 04), 13.6], [Date.UTC(2009, 05), 15.2], [Date.UTC(2009, 06), 16.9], [Date.UTC(2009, 07), 17.2], [Date.UTC(2009, 08), 14.7], [Date.UTC(2009, 09), 12.6], [Date.UTC(2009, 10), 10.9], [Date.UTC(2009, 11), 11.2], [Date.UTC(2009, 12), 11.2], [Date.UTC(2010, 01), 10], [Date.UTC(2010, 02), 9.1], [Date.UTC(2010, 03), 8.7], [Date.UTC(2010, 04), 7.9], [Date.UTC(2010, 05), 7.2], [Date.UTC(2010, 06), 7.8], [Date.UTC(2010, 07), 8], [Date.UTC(2010, 08), 7.7], [Date.UTC(2010, 09), 6.6], [Date.UTC(2010, 10), 6.7], [Date.UTC(2010, 11), 8], [Date.UTC(2010, 12), 8.1], [Date.UTC(2011, 01), 9.4], [Date.UTC(2011, 02), 8.2], [Date.UTC(2011, 03), 7.9], [Date.UTC(2011, 04), 7.4], [Date.UTC(2011, 05), 6.9], [Date.UTC(2011, 06), 8.2], [Date.UTC(2011, 07), 9], [Date.UTC(2011, 08), 7.9], [Date.UTC(2011, 09), 6.8], [Date.UTC(2011, 10), 5.9], [Date.UTC(2011, 11), 6.8], [Date.UTC(2011, 12), 7], [Date.UTC(2012, 01), 9.1], [Date.UTC(2012, 02), 8.5], [Date.UTC(2012, 03), 7.4], [Date.UTC(2012, 04), 7], [Date.UTC(2012, 05), 7.5], [Date.UTC(2012, 06), 8.3], [Date.UTC(2012, 07), 7.7], [Date.UTC(2012, 08), 7.9], [Date.UTC(2012, 09), 7.4], [Date.UTC(2012, 10), 6.4], [Date.UTC(2012, 11), 7.3], [Date.UTC(2012, 12), 8.2], [Date.UTC(2013, 01), 9.6], [Date.UTC(2013, 02), 9.3], [Date.UTC(2013, 03), 8.5], [Date.UTC(2013, 04), 7.8], [Date.UTC(2013, 05), 7.5], [Date.UTC(2013, 06), 8.8], [Date.UTC(2013, 07), 8.4], [Date.UTC(2013, 08), 8], [Date.UTC(2013, 09), 6.7], [Date.UTC(2013, 10), 6.4], [Date.UTC(2013, 11), 7.3], [Date.UTC(2013, 12), 6.9], [Date.UTC(2014, 01), 8], [Date.UTC(2014, 02), 7.9], [Date.UTC(2014, 03), 7.2], [Date.UTC(2014, 04), 7.3], [Date.UTC(2014, 05), 6.8], [Date.UTC(2014, 06), 7.2]] } ] }));

MP.highcharts.makeChart(‘.unemployment-by-city-annual-chart’, $.extend(true, {}, MP.highcharts.columnOptions, { legend: { enabled: false }, xAxis: { categories: [‘2007′,’2008′,’2009′,’2010′,’2011′,’2012′,’2013’] }, yAxis: { title: { enabled: false }, labels: { format: ‘{value:,.1f}%’ } }, legend: { enabled: true }, tooltip: { formatter: function() { return ‘‘ + this.series.name + ‘
‘ + ‘‘+ this.x + ‘: ‘+ this.y +’%’; } }, series: [{ name: ‘Cloquet’, data: [6.5,7.6,10,9.4,8.8,8,6.9] }, { name: ‘Hibbing’, data: [7.2,8.5,14.3,8.2,7.2,6.9,7.6] }, { name: ‘Virginia’, data: [8.4,8.8,13.5,8,7.6,7.7,7.9] } ] }));

MP.highcharts.makeChart(‘.unemployment-bemidji-monthly-chart’, $.extend(true, {}, MP.highcharts.lineOptions, { legend: { enabled: false }, xAxis: { type: ‘datetime’ }, yAxis: { title: { enabled: false }, labels: { format: ‘{value:,.1f}%’ } }, tooltip: { formatter: function() { var d = new Date(this.x); var mon = d.getMonth()+1; var yr = d.getFullYear(); return ‘‘+ yr + ‘-‘ + mon + ‘: ‘+ this.y +’%’; } }, series: [{ name: ‘Unemployment rate’, data: [[Date.UTC(2007, 01), 13.3], [Date.UTC(2007, 02), 11.9], [Date.UTC(2007, 03), 11.7], [Date.UTC(2007, 04), 11.4], [Date.UTC(2007, 05), 8.6], [Date.UTC(2007, 06), 9.3], [Date.UTC(2007, 07), 9.2], [Date.UTC(2007, 08), 8.7], [Date.UTC(2007, 09), 8.3], [Date.UTC(2007, 10), 7.4], [Date.UTC(2007, 11), 8.1], [Date.UTC(2007, 12), 10.2], [Date.UTC(2008, 01), 12.1], [Date.UTC(2008, 02), 12.4], [Date.UTC(2008, 03), 11.9], [Date.UTC(2008, 04), 11.1], [Date.UTC(2008, 05), 9.1], [Date.UTC(2008, 06), 9.5], [Date.UTC(2008, 07), 9.3], [Date.UTC(2008, 08), 9.4], [Date.UTC(2008, 09), 8.6], [Date.UTC(2008, 10), 8.9], [Date.UTC(2008, 11), 10.9], [Date.UTC(2008, 12), 12.6], [Date.UTC(2009, 01), 17], [Date.UTC(2009, 02), 17.1], [Date.UTC(2009, 03), 16.4], [Date.UTC(2009, 04), 15], [Date.UTC(2009, 05), 13.2], [Date.UTC(2009, 06), 13.7], [Date.UTC(2009, 07), 12.9], [Date.UTC(2009, 08), 12.5], [Date.UTC(2009, 09), 11.3], [Date.UTC(2009, 10), 10.3], [Date.UTC(2009, 11), 11], [Date.UTC(2009, 12), 12.4], [Date.UTC(2010, 01), 15.1], [Date.UTC(2010, 02), 16.2], [Date.UTC(2010, 03), 16.3], [Date.UTC(2010, 04), 13.7], [Date.UTC(2010, 05), 12.4], [Date.UTC(2010, 06), 12.9], [Date.UTC(2010, 07), 12.5], [Date.UTC(2010, 08), 12.2], [Date.UTC(2010, 09), 11.6], [Date.UTC(2010, 10), 10.8], [Date.UTC(2010, 11), 12.3], [Date.UTC(2010, 12), 13.6], [Date.UTC(2011, 01), 15.8], [Date.UTC(2011, 02), 15.4], [Date.UTC(2011, 03), 15.8], [Date.UTC(2011, 04), 13.9], [Date.UTC(2011, 05), 12.3], [Date.UTC(2011, 06), 12.9], [Date.UTC(2011, 07), 15.6], [Date.UTC(2011, 08), 12.2], [Date.UTC(2011, 09), 10.9], [Date.UTC(2011, 10), 9.7], [Date.UTC(2011, 11), 10.5], [Date.UTC(2011, 12), 11.9], [Date.UTC(2012, 01), 13.1], [Date.UTC(2012, 02), 12.7], [Date.UTC(2012, 03), 12.5], [Date.UTC(2012, 04), 10.4], [Date.UTC(2012, 05), 9.9], [Date.UTC(2012, 06), 10.6], [Date.UTC(2012, 07), 10.4], [Date.UTC(2012, 08), 9.1], [Date.UTC(2012, 09), 8.4], [Date.UTC(2012, 10), 8.4], [Date.UTC(2012, 11), 9.5], [Date.UTC(2012, 12), 11.1], [Date.UTC(2013, 01), 13.2], [Date.UTC(2013, 02), 12], [Date.UTC(2013, 03), 12], [Date.UTC(2013, 04), 11.2], [Date.UTC(2013, 05), 9.3], [Date.UTC(2013, 06), 9.8], [Date.UTC(2013, 07), 9.4], [Date.UTC(2013, 08), 8.6], [Date.UTC(2013, 09), 7.6], [Date.UTC(2013, 10), 6.9], [Date.UTC(2013, 11), 8.6], [Date.UTC(2013, 12), 10], [Date.UTC(2014, 01), 12.3], [Date.UTC(2014, 02), 11.8], [Date.UTC(2014, 03), 11.7], [Date.UTC(2014, 04), 9.9], [Date.UTC(2014, 05), 8.2], [Date.UTC(2014, 06), 8.7]] }] }));

MP.highcharts.makeChart(‘.unemployment-bemidji-annual-chart’, $.extend(true, {}, MP.highcharts.columnOptions, { legend: { enabled: false }, xAxis: { categories: [‘2007′,’2008′,’2009′,’2010′,’2011′,’2012′,’2013’] }, yAxis: { title: { enabled: false }, labels: { format: ‘{value:,.1f}%’ } },

tooltip: { formatter: function() { return ‘‘ + this.series.name + ‘
‘ + ‘‘+ this.x + ‘: ‘+ this.y +’%’; } }, series: [{ name: ‘Unemployment rate’, data: [9.9, 10.5, 13.6, 13.3, 13.1, 10.5, 9.9] } ] }));

}(jQuery));[/raw]

Join the Conversation

26 Comments

  1. McFadden Campaign

    Translation of Tom Erickson’s comment: don’t bother me with facts when I’m trying to make some political hay.

    Louis Johnston’s article is is one of the reasons I love coming to MinnPost. Authors don’t just swallow and regurgitate what politicians have to say, but instead go out and do the heavy lifting necessary to check their facts and hold their feet to the fire. I’m not yet a member of MinnPost, but I will be soon once finances are squared away and my son is safely off to college.

  2. McFadden is the word; how many times do we read about him?

    Pardon my cynicism but Mcfadden must be the new ‘Bachmann… as her name comes up under POLITICS and NATION WORLD… still? A living legend that won’t go away or as if she still represents the Minnesota voter?

    Funny thing among a populace of possibly too many uninformed voters… they pick the name that comes most readily to mind and mark their X on the ballot. Wow…it won’t matter what the candidate stands for… just that one recognizes a familiar name?

    And world issues we support with our tax dollars? We spent millions or is it billions to supply Israel’s military now gone mad with abusive bombing? A massacre in Gaza is not beyond out financial support? So many national/world issues have been gated out by “hollow men” politicians like McFadden and Bachmann…way to go hey.?

    1. So it might be that McFadden is running

      because they think his name is similar to Franken’s and hope people will not remember when they get to the poles.

      How about vote for the one who is Frank not the one who is a Fad?

  3. About McFadden’s apology …

    Great to see it. We’ll see if it’s sincere if in future statements he revises his figures or repeats this exaggerated number.

    Also, regarding the dig at the end of his apology: Yet another month (that’s six in a row, BTW) of adding more than 200,000 jobs to the economy.

    Gosh darn that President O’Bummer and his job-killing over-regulation!

    When will our national nightmare end?

  4. Again with the magical thinking

    Clearly, Mark Dayton traveled back in time and implemented some dastardly liberal plan to freeze wages in the 1980s. And he must have deployed his time machine as well to somehow trick the republicans into dropping the labor participation rates during the Bush-Pawlenty years. Well, maybe Dayton doesn’t have a time machine, maybe it’s just… magic!

    I guess the only way to fight magic is with magic… bring in the magic tax cuts!

  5. McFadden “Lied”

    And so does Al Franken. Except Minnpost won’t say it.

    Al Franken is just another AIPAC paid mouthpiece. And he thinks minorities and the muslim community is going to come running to vote for him.

    1. Nice pivot away from the article and the facts that it presents. At least you agree that McFadden is lying.

      1. A Pivot or a Point

        Pointing out that Al Franken himself speaks from both sides of his mouth, when MinnPost targets his opponent solely is a pivot ?

        Or is it an inconvenient point that “progressives” don’t want to hear ?

        1. No discernible point yet

          You actually haven’t pointed out anything – you made a blanket statement with no supporting data. You say Franken lies – ok, exactly what has he lied about? The article was about specific data points that McFadden, uh, let’s go easy and say he “misrepresented” – “lie” is such an ugly word. If you have similar specific data, post it. Otherwise, all we can discern is that you personally don’t like Franken. Shrug.

          1. You’ll find it if you choose to look.

            “Al Franken is just another AIPAC paid mouthpiece. ” – What do you think a politician does after taking special interest money. Or is that reserved only for the Koch brothers and their donations ?

  6. And the other thing

    Of course, I’ll say it again, this is what we get from mediocre executives who think they’re the smartest guys in the room. This idea that any businessman (or woman) is some of kind of economics expert is a lingering myth of the Great Stupid, (1982-?) when liberals and conservatives alike became infatuated with the illusion of free markets.

    When you look at these business guys whether it’s Donald Trump, Bill Gates, or McFadden, they may or may not even know their business (remember a high percentage of them steer their businesses into bankruptcy), but they’re ignorance regarding basic economics is frequently breathtaking; despite having taking economics classes in order to get their MBAs.

      1. Why Gates?

        He’s a big business man, clearly he knows his business, but he’s out of his depth elsewhere. He thought he could eradicate Polio with his NGO, instead he’s wasted resources buying up vaccine and then vaccinating already vaccinated populations. Not only has he insured that vaccine isn’t available for those who need it, he’s giving to those who’ve already had it. And Polio cases are on the rise.

        It’s not a purely conservative phenomena, although it tends to bend conservative.

        1. The only information I can find online related to this comes from Anti-Vaccination websites, which carry zero authority with me. Can you provide me with information about this?

  7. Funny…

    When Democrats are in power regulation causes unemployment but when Republicans are in power de-regulation causes unemployment. I guess that just how magic works.

  8. Small note …

    Not arguing anything against the ideas presented, just a quick note …

    Cloquet isn’t an Iron Range city. It would have been better to examine the Hibbing and Virginia stats alone or to maybe add the city of Grand Rapids if three cities were wanted for averaging.

    1. Out of date information Jay

      In May of this year WHO issued a declared a Global Health Emergency due he sudden rise in Polio cases, and their spread to several countries: http://www.nytimes.com/2014/05/06/health/world-health-organization-polio-health-emergency.html?_r=0

      “Alarmed by the spread of polio to several fragile countries, the World Health Organization declared a global health emergency on Monday for only the second time since regulations permitting it to do so were adopted in 2007.”

      “The emergency was declared though the total number of known cases this year is still relatively small: 68 as of April 30, compared with 24 by that date last year.”

      Note the 100+% increase this year over last year.

      1. Yes, your information is out-of-date

        “Out of date information Jay”

        The data from the article you’re quoting is through April 30 (3 months ago). If you follow the link I provided, you’ll see the data I shared is through July 31 (yesterday). Cases are down 23% since the same time last year.

        The Global Polio Eradication Initiative is a partnership of governments, the WHO, the CDC, Rotary, and UNICEF.

        More here (see “data and statistics” for the link previously provided):
        http://www.who.int/topics/poliomyelitis/en/

        When dealing with such tiny numbers, it’s best not to read too much into them in the first place. People often make the same mistake with US measles data in their coverage.

        Polio continues to decline and it will be eradicated soon.

  9. What regulations caused a slightly

    Higher unemployment number up north? Never any sources or specifications. Just generalized make believe assertions without proof.

    1. Regulations

      Logan, you raise a good point. There’s always talk about regulations being bad without any indication as to *which* regulations. It’s important to remember that the regulations were put in place to fix a problem. For example, some idiot dumped the waste from their factory into the stream because there isn’t a law saying they can’t do it.

      So a new law gets enacted.

      Then they dump the waste into the ground because the new law only mentions streams and then we’re back to the races again. We wouldn’t need all these regulations if people would simply do what’s right for society rather than simply maximizing profits. It’s the same problem we’ve been dealing with for hundreds of years: business owners want to privatize profits and socialize loses. If the pollution drifts downstream into the water supply of the next town, well tough petunias. The company will simply do their best to delay the regulatory process, deny there’s an issue, ruin the reputation of the scientists studying the issue, and then declare bankruptcy when all else fails. Oh, but not before voting a big bonus for the company officers while laying off all the employees.

      Then the assets are bought up for pennies on the dollars at bankruptcy auction, a new corporation is formed, and they complain about how regulation is such a burden on them. “Gosh,” they cry, “we could add so many jobs to your area if only we didn’t have the burden of regulation. Won’t you please reduce you regulations so we can repeat the above process all over again?”

      Been there, done that, got the T shirt dipped in asbestos.

  10. Todd Hintz’s powerful response

    This is one of those fine gems-of-understanding by writer T Hintz that you want to cut out and pin up to remember with due credits given to the writer always, yes sir. Thanks

Leave a comment