The disappearance of American Manufacturing Jobs:

The effects of the strength of the US dollar

 

Here are the details:

 

Introduction:

Serious problems that Americans have been faced with are the staggering waves of layoffs that began flooding the country in the late 1970’s.  At this time many corporations were merging or downsizing and the effects were felt over a widening spectrum of American people.  No longer were just the blue-collar workers of America being forced out of work, but the new waves of dismissals were also crippling the employment of many middle to upper class managers and professionals.  By the middle nineties America’s expanding economy was experiencing layoffs with never before felt persistence and magnitude.

My project attempts the measure the effects that the effective exchange rate in the United States, the strength of the dollar, has on the manufacturing industry and its overwhelming loss of jobs.

            Based upon economic theory it can be anticipated that a high economic exchange rate will strength the US dollar, causing the United States to lose jobs faster.  However, what I found was that as the effective exchange rate increases by one, the strength of the dollar rises by 1% which causes the US to gain more jobs, not loose them as was originally anticipated.

Model:

          I am most interested in the observing the effect that the effective exchange (key independent variable) rate has on the change in jobs (dependant variable).  Based upon economic theory it can be anticipated that a high economic exchange rate will strengthen the US dollar, causing the United States to lose jobs faster.  The exchange rate is the price of one country’s currency expressed in terms of another’s or the domestic price of a foreign currency.  If you view the exchange rate as the “price of money” it is then subjected to the same things that influence all market prices: which are supply and demand.  The demand for dollars originates in foreign demand for American exports, American investments, and speculation.  Supply of dollars originates in American demand for imports, investment in foreign countries, and speculation.  When observing the relationship say between the euro and the dollar, a higher dollar price for euros will raise the dollar cost of European goods. 

            The effective exchange rate is the key independent variable, however there are others in my model.  The initial relationship looks like this:

Y= b0 + b1*workerstot + b2*workersprod + b3*effexch + b4*corpint + b5*hourswk + b6*outperhour + b7*avgminwag

Data:

          WORKERSTOT- employees on payroll: manufacturing (in thousands)

            WORKERSPROD- production workers on payroll: manufacturing (in thousands)

          EFFEXCH- United States effective exchange rate

          CORPINT- Bond yield: Moody’s AAA corporate (% annum)

          HOURSWK- Average weekly hours of production workers: manufacturing

          OUTPERHR- Output per hour, all persons

AVGMINWAG- Average hour earnings of product workers: manufacturing ($)

All of my data was generated from the DRI Database.

Results:

Y= b0 + b1*workerstot + b3*effexch + b5*hourswk

            The above equation is the final regression that I ran.  Initially there was strong multicollinearity between workerstot and workersprod so I dropped workersprod since it is included in workerstot.  Corpint, outperhr, and avgminwag were all found to be insignificant and could be dropped from the regression.  Below are the stats from my final regression.  Highlighted in red are the estimations for each variable and in green are the standard errors. In blue are the t-statistics for each variable.  For the final regression all values are greater than 1.96, the critical value, stating that they are all significant and therefore have an effect on the change in manufacturing jobs.

Regression #3- Restricted Model 

           

Dependent Variable: CHANGEJOBS

Method: Least Squares

Date: 11/04/03   Time: 15:46

Sample(adjusted): 1973:04 2002:11

Included observations: 356 after adjusting endpoints

Variable

Coefficient

Std. Error

t-Statistic

Prob. 

WORKERSTOT

0.033732

0.008239

4.094061

0.0001

EFFEXCH

1.135248

0.515465

2.202376

0.0283

HOURSWK

57.01416

9.576225

5.953720

0.0000

C

-3112.406

482.7181

-6.447667

0.0000

 

Conclusions:

          In running this regression I found that the only variables that are significant in my relationship are the total number of workers in manufacturing, the effective exchange rate, and the average weekly hours of workers in production.  My results suggest that all of them are positively related to the overall change in jobs. The EFFEXCH is an index variable, so as it rises by 1, the dollar gains in strength by 1%.  In looking closely at the target independent variable in my model, it is suggesting that this causes a gain of 1.14 thousand jobs (1140 jobs) per month.  This is not the sign that I had originally anticipated.  It was anticipated that a high effective exchange rate would strength the dollar, but then cause the United States to loose jobs faster, not gain them as is seen here.