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EMPLOYMENT IN MANUFACTURING AT THE STATE LEVEL AS IT RELATES TO IMPORTS FROM CHINA AND MEXICO.

INTRODUCTION

Several factors have been cited as responsible for the decline in employment in manufacturing in the US. These factors are: Cheap import of goods from China and Mexico, the North American Free Trade Agreement (NAFTA), China's entry into the World Trade Organization (WTO), and technology in manufacturing. There is no agreement in the literature on what effects employment in manufacturing. Additionally, there is a lack of empirical studies using proper statistical techniques to analyze time series data. Regression techniques are frequently used and they are known to give spurious results when used on time series data.

The US has witnessed a decline in manufacturing since decades. The decline has been at a rapid pace since 2000. Author et. al. (2013) reported that in 1960 one fourth of the jobs in the US were in manufacturing. In 2013, this job rate has declined to less than 9%. There is no one factor cited for this decline. Different studies cite different factors for this job loss. Factors blamed for this decline are: 1. cheap imports of goods from China and Mexico. 2. China's admittance to the World Trade Organization in 2001 causing competition on the world markets. 3. The North American Free Trade Agreement (NAFTA), which was signed into law in 1994. It is argued that because of the elimination of tariff on trade between Mexico and the US through NAFTA and the lower labor cost in Mexico, manufacturing firms in the US moved to Mexico thus causing job loss in the US. 4. Edwards and Lawrence (.2013) argued that job loss in the US was due to the trade deficit and to enhanced technology use in the workplace. While employment in manufacturing has been decreasing, there is no evidence that employment at the national level has been affected. This could imply that jobs lost in manufacturing were gained in different sectors of the economy.

Imports of cheap goods from China and Mexico are often cited by politicians as the cause for loss of jobs in manufacturing. There are few recent quantitative studies in the literature dealing with cause or causes for the rise of unemployment in manufacturing. Of interest in this study is to determine how employment in manufacturing states is influenced by imports of goods from China and Mexico. Statistical time series analysis on quarterly economic data on imports from Mexico and China and employment in manufacturing in seven manufacturing states was used in order to determine if a relationship exists between employment in manufacturing in each state and U.S. import of goods from China and Mexico. Analyses in the literature use ordinary regression techniques which are known to be inadequate for analyzing time series data. Because of the autocorrelation encountered in time series data, regression tends to give spurious results. Also, the regression model is often not adequate because it fails to address lags in the dependent and independent variables that are often present. The time series approach used in this study addresses these issues and is the correct mode of analysis for time series.

The interest in this study is to determine if imports from China or Mexico have any relationship with employment in manufacturing in seven important manufacturing states (Michigan, Wisconsin, Illinois, Pennsylvania, Ohio, Texas, and California).

LITERATURE REVIEW

Acemoglu et al. (2016) used ordinary least square regression to relate the annual log change in employment for given industry to the annual change in import from China for the years 1991-2011. The authors reported that the import competition from China was a major factor behind the decrease in manufacturing employment. They suggested that job losses were from 2.0 to 2.4 million.

Bernard et al. (2009) using least square regression analysis reported that import competition from low income countries, with China being the largest country in the group, had a negative effect on manufacturing employment for the period 1977 -1907. This effect accounted for 14% of the total decline in employment.

Chakrabarti (2003) used co-integration analysis to determine if long run relationships exist between import competition and employment and wage in manufacturing. His analysis on 12 manufacturing industries over the period 1982 to 1992 revealed that there was no long run relationship between employment and import competition. However, there was such a relationship between wage and import price. This relationship was dependent on the industry sector. Import price was negatively related to manufacturing wage.

Autor et al. (2013) in a study of the effect of import competition from China on US employment in manufacturing, attributed one quarter of the decline in manufacturing employment to import competition from China. The authors used regression analysis on time series data. It is known that this can lead to spurious regression results (Granger and Engle; 1986, 1974).

Trade theory according to Krugman (2000, 2008) points to imports from low wage countries as having a disruptive effect on high wage labor markets. Historically, this effect has not been substantial because of low imports from low wage countries. This of course has changed in recent years.

The US Government Accounting Office (GAO) in a study in 2003 on Mexico's Maquiladora (a manufacturing operation that imports raw material primarily from the United States on a duty and tariff free basis, processes and assembles them in Mexico and exports the finished products mainly to the US. Most Maquiladoras are US owned) reported that the operation has stimulated economic growth along the Mexican US border. However, it has experienced a steep decline after October 2000. Employment has declined by 21 percent and production by 30 percent. This decline was attributed to the cyclical downturn in the US economy, increase global competition primarily from China, Central America, and the Caribbean, a change in the tax benefits in Mexico, and loss of certain tariff benefits as a result of NAFTA. The decline has affected the border economy. Experts agree that fundamental reforms by Mexico are essential to restore maquiladoras' competitiveness.

Pierce and Schott (2016) in their study, using regression analysis, attributed the sharp decline in manufacturing employment in the U.S. since 2001 to a change in the U.S. trade policy, which did not impose an increase in tariff on imports from China, but rather granted China a Permanent Normal Trade Relations (PNTR). The lack of tariff increases made the Chinese imports competitive, which had a negative effect on employment. Authors pointed out that such employment loss in the U.S. was not present in the European Union where there was no such change in trade policy.

Ma and Wooster (2009) reported on the effect of U.S.-China trade on the employment in the U.S. and Mexico border region: Santa Cruz, Arizona; San Diego, California; El Paso, and Texas. Using industry data between 1992 and 2006 from the border region, it was found, using regression analysis that increased import from China had a negative effect on industry employment. However, increased import from Mexico had a positive effect on employment.

In an article in Business week (Miller et. al., 2003), the authors were of the opinion that China is not to blame for the decrease in manufacturing employment in the U.S. They argued that a good part of that drop in employment was due to increased productivity or in the case of Detroit, sales lost to Japan. Also, the biggest rise in the U.S. trade deficit came from the European Union and not China. In addition, it has been a shortfall in exports rather than a boom in imports that has been responsible for the deficit. The authors pointed out that about 65% of the rise in imports from China came not from Chinese firms, but from foreign companies in China, including many U.S. corporations.

In a review by the U.S. China Business Council (China Business Review, 2017). it was reported that trade with China supports approximately 2.6 million jobs in the U.S. across many industries including jobs created by Chinese industry in the U.S. U.S. companies have an opportunity to tap into a large market in China which can further boost economic growth. In the U.S. , it is easy to point to the trade deficit of $334 billion with China. However, this obscures the fact that U.S companies have been increasing their exports to China, showing it to be a strong market for a range of industries such as chemical, transport equipment, and agriculture. China is the third largest importer of U.S made products, after Mexico and Canada. These exports will continue to grow and by 2026 are expected to reach $369 billion in goods and services and by 2030 exports to China will reach 525 billion or 10% of total U.S. exports.

Of significance also is the fact the U.S. industry such as Jeep, General Motors and Apple has set up manufacturing centers in China which allow them to improve their competitiveness and profitability.

METHODOLOGY

Sample and Data Collection

Quarterly economic data for US imports of goods from China and Mexico and employment in manufacturing in the seven manufacturing states (California, Texas, Michigan, Ohio, Illinois, Pennsylvania, and Wisconsin) were obtained online from the St. Louis Federal reserve (https://www.stlouisfed.org). Data were available for the period 1999-2016.

Methods

In order to determine the relationship between import from Mexico or China and employment in manufacturing in each state, the time series transfer function analysis was used. The SAS software was used for the analysis. Time series analysis is the correct method of analysis for time series data where the errors are auto correlated. Regression analysis used in the literature for time series data is known to give spurious results (Granger and Newbold, 1986, 1974). Besides, the regression model is often inadequate in representing the true relationship between the dependent and the independent variables.

Time Series Transfer Function Model

A time series transfer function model relating a stationary output series yt to a stationary input series xi can be expressed as

yt = v(B) xt + [eta]t (1)

where v(B) = w(B)[B.sup.c]/d(B).

Here, w(B) = [w.sub.0]-[w.sub.1]B - ...-[w.sub.s][B.sup.s]

d(B) = 1-d1B- ... -dr[B.sup.r].

and c represents the time delay (or lag) until the input variable xt produces an effect on the output variable [y.sub.t.]

We assume that the input series follows an ARMA process, [phi](B)/[theta](B) [x.sub.t]. The function v(B) with its lags is determined from the cross correlations between the white noise input series [phi](B)/[theta](B) [x.sub.t] and the filtered output series [phi](B)/[theta](B) [y.sub.t], namely the significance at a given lag and the pattern of the cross correlations over lags (Wei, 2006). For instance, if the correlation is significant at only lag 0, then Equation (1) becomes

[y.sub.t] = [w.sub.0][x.sub.t] + [[eta].sub.t]

[[eta].sub.t] = [y.sub.t] - v(B) [x.sub.t] (2)

and identify the appropriate time series model for Eq. (2). With at known, one can determine the final model in Eq. (1).

RESULTS

In what follows, we present for each state the best selected model relating employment in manufacturing in the state to import of goods from Mexico or China. The best model was determined by the fact that the error term was random noise with no autocorrelation and the residual or error term was not correlated with the input series (Wei, 2006).

California

Tables 1 and 2 present the parameter estimates for the best selected time series models relating employment in manufacturing to import from Mexico and China.

Model from Table 1 results

The time series model form Table 1 is

Emplt (1,4) = W0 Impmt-1 (1,4) + et ( 1- [theta]4 [B.sup.4])/(1- [phi]B), (3)

Where Empl (1,4) and Impm (1,4) are the first and fourth difference for stationarity, et is random error and B is the back shift operator ( BXt = Xt-1 and [B.sup.4]Xt = Xt-4).

It is clear from the significant ( p = 0.0023) positive estimate of W0 that import from Mexico is positively related to employment in manufacturing in California.

Model from Table 2 results

The time series model from Table 2 can be expressed as

Emplt (1,4) = ([W.sub.0] - [W.sub.1]B) Impct (1,4) + et ( 1- [theta]4 [B.sup.4])/(1- [phi]B) (4)

It is clear from the estimates of W0 and W1 in Table 2 that the relationship in Eq. (4) between employment and import from China is positive, but not significant.

Texas

Tables 3 and 4 present the parameter estimates of the best selected time series models relating employment to imports from Mexico and China.

Model from Table 3 results

It is seen from Table 3 that the time series model is Emplt (1) = W0 Impmt-2 (1,4) + [e.sub.t] /(1- [phi]B). (5)

Here, Emplt(1) is the first difference of Emplt .

It is clear from the significance and sign of W0 that employment in manufacturing is positively related to import from Mexico.

Model from Table 4 results

From the parameters in Table 4, the time series model is

[Empl.sub.t] (1) = W0 Impc.sub.t-1] (1,4) + [e.sub.t] /(1- [phi]B) (6)

It is seen from Table 4 that W0 is positive and significant at the 10% level (p=0.0806), indicating a positive relationship between employment and import from China.

Michigan

The parameter estimates of the best selected time series models relating employment to imports from Mexico and China are presented in Tables 5 and 6.

Model from Table 5 results

The model from the results of Table 5 can be expressed as

[mathematical expression not reproducible] (7)

The fact that W0 is positive and significant indicates that there is a positive and significant relationship between employment and import from Mexico.

Model from Table 6 results

Equation (8) presents the time series model based on the results of Table 6.

[Empl.sub.t] (1) = [W.sub.0] Impct-1 (1,4) + [e.sub.t] /(1- [[phi].sub.1]B - [[phi].sub.4][B.sup.4]). (8)

It is seen from from Table 6 that W0 in Equation (8) is not significant. Hence, while the relationship between employment and import from China is positive, it is not significant.

Illinois

Tables 7 and 8 present the parameter estimates for the best selected time series models relating employment to imports from Mexico and China.

Model from Table 7 results

Based on the parameters in Table 7, one has the following time series model.

Emplt (1) = ([w.sub.0] - [w.sub.1]B - w4[B.sup.4]) Impmt-1 (1,4) + [e.sub.t] (1- [[theta].sub.1]B- [[theta].sub.2][B.sup.2])/( 1- [[phi].sub.4][B.sup.4]) (9)

The fact that ([w.sub.0]-[w.sub.1]B - [w.sub.4][B.sup.4]) is positive and significant indicates that there is a positive and significant relationship between employment and import from Mexico.

Model from Table 8 results

Equation (10) presents the time series model based on the parameters in Table 8.

[[Empl.sub.t] (1) = [[W.sub.0] [Impc.sub.t] (1,4) + [e.sub.t] (1- [[theta].sub.1]B- [[theta].sub.2][B.sup.2]) /(1- [[phi].sub.4][B.sup.4]). (10)

It is seen from Table 8 that W0 in Equation (10) is positive, but not significant (p = 0.429). Hence, while the relationship between employment and import from China is positive, it is not significant.

Ohio

Tables 9 and 10 present the parameter estimates of the best selected time series models relating employment to imports from Mexico and China.

Model from Table 9 results

The time series model based on the estimates in Table 9 is

Emplt (1) = [mu]+ [W.sub.0] [Impm.sub.t-5] (1,4) + et (1- [theta] 1[B.sup.1] - [theta]2[B.sup.2])/(1- [phi] 4[B.sup.4] - [[phi].sub.5][B.sup.5]) (11)

It is seen from Table 9 that [W.sub.0] in Equation (11) is positive and significant. Hence, there is a positive relationship between employment and import from Mexico.

Model from Table 10 results

Equation (1) represents the time series model based on the parameters in Table 10.

[Empl.sub.t] (1) = [W.sub.0] [Impc.sub.t-8] (1,4) + [e.sub.t] (1- [[theta].sub.1]B- [[theta].sub.2][B.sup.2])/(1- [[phi].sub.4][B.sup.4]- [[phi].sub.5][B.sup.5]) (12)

It is seen from Table 10 that there is a negative, but not significant, relationship between employment and import from China

Pennsylvania

Tables 11 and 12 present the parameters and their estimates for the best selected time series models relating employment to imports from Mexico and China.

Model from Table 11 results

The model in Equation (13) relates employment to import from Mexico as determined from the parameters in Table 11.

[Empl.sub.t] (1) = [mu] + [W.sub.0] [Impc.sub.t-1] (1,4) + [e.sub.t] (1- [[theta].sub.4][B.sup.4])/(1- [[phi].sub.1]B- [[phi].sub.4][B.sup.4] - [[phi].sub.5][B.sup.5]) (13)

It is seen from from Table 11 that W0 in Equation (13) is positive and significant at the 10% level. Hence, there is a positive relationship between employment and import from Mexico.

Model from Table 12 results

Based on the parameters in Table 12, one can express the time series model as

Emplt (1) = [mu] + W0 Impct (1,4) + et (1- [theta]4[B.sup.4])/(1- [phi]1B- [phi]4[B.sup.4] - [phi]5[B.sup.5]) (14)

It is seen from Table 12 that there is a positive, but not significant relationship between employment and import from China.

Wisconsin

Tables 13 and 14 present the parameters and their estimates for the best selected time series models relating employment to imports from Mexico and China.

Dependent Variable = Employment in Manufacturing (empl)

Independent Variable = Import from China (impm)

Model from Table 13 results

Based on the parameters in Table 13, one can write the time series model as

Emplt (1,4) = [W.sub.0] Impmt-1 (1,4) + [e.sub.t] /(1- [phi]1B- [[phi].sub.4][B.sup.4]) (15)

It is seen from Table 13 that W0 in Equation (13) is positive and significant. Hence, there is a positive relationship between employment and import from Mexico.

Model from Table 14 results

Equation (16) gives the time series model based on the parameters in Table 14.

Emplt (1,4) = [W.sub.0] Impct-1 (1,4) + et/(1- [[phi].sub.1]B- [phi]4[B.sup.4]) (16)

It is seen from Table 14 that there is a positive, but not significant, relationship between employment and import from China.

DISCUSSION

Results of this time series analysis do not support the claims that the decrease in employment in manufacturing is caused to a considerable extent by cheap or competitive imports of goods from Mexico or China. We see from the above results that for all states there is a positive and statistically significant relationship between employment in manufacturing and import of good from Mexico. Import from Mexico seems to boost employment in manufacturing. Results for China show that the relationship between employment and import is positive for six states. However, this was statistically significant for only the state of Texas. The rest were not significant, nor was the negative relationship significant in the state of Ohio. So while the relationship is not significant, the trend is predominantly positive. In a review by the U.S. China Business Council (2017) it was reported that trade with China supports approximately 2.6 million jobs in the U.S. across many industries including jobs created by Chinese industry in the

U.S. Also, U.S. companies have an opportunity to tap into a large market in China which can further boost economic growth

This positive relationship between import from Mexico and employment can be attributed to import of intermediary products at a lower price to be assembled in the US and sold on the markets at competitive rates as well as imports from the US by US companies in Mexico of products on a tariff or duty free basis (because of NAFTA) where these imports are assembled in Mexico and sold back to the US (The US Government Accounting Office, 2003). These trade activities convey a competitive advantage to US companies on the world markets and can help boost employment in the US.

Perry (2016) in a study on imports and employment shows that more than one half of US imports are raw materials or intermediate components used by US firms in manufacturing and are not goods for direct consumption by households. He even makes the point that nearly all imports are inputs for US firms, retailers and factories. He argued that the focus should be on the total volume of trade rather than on trade balance. The total volume of trade is the real measure since both exports and imports help the economy. An increase in total volume of trade is indicative of an expanding economy.

Perry states that "The lower the price of inputs for US businesses (whether sourced internationally or domestically), the more competitive those companies are, the more of their products they can sell (both internationally and domestically), the greater market share they can achieve, and the more US workers they can hire".

Our results support the findings from Perry (2016), The US Government Accounting Office (2003) and the U.S. China Business Council (2017) that imports from Mexico and China can help boost employment in manufacturing in the US and cannot be blamed for having a negative impact on employment. The blame must fall with other factors such as the use of technology in the workplace which is known to have a substantial negative effect on employment Kuehn & Braschler, 1986; Mullen & Panning, 2009).

CONCLUSION

This study utilizes time series analysis to study the relationship between employment in manufacturing and imports of goods from China and Mexico in seven manufacturing states: Michigan, Pennsylvania, Illinois, Ohio, Wisconsin, Texas, and California. . Contrary to recent claims, our findings indicate that import of goods from Mexico has a positive effect on employment in manufacturing in all seven states. Import from China was also positively related to employment in manufacturing, but accept for Texas, the positive relationship was not significant. Only Ohio showed a negative relationship between employment and import from China. However, this relationship was not close to being significant (p=0.391).

This positive relationship of imports to employment in manufacturing can be attributed to import of intermediary products at a lower price to be assembled in the US and sold on the markets at competitive rates as well as imports from the US by US companies in Mexico of products on a tariff or duty free basis (because of NAFTA) where these imports are assembled in Mexico and sold back to the US These trade activities convey a competitive advantage to US companies on the world markets and can help boost employment in the US.

REFERENCES

Acemoglu, D., Auto D., Dorn, D., Hanson, G., & Price, B. (2016). Import competition and the great US employment Sag of the 2000. Journal of Labor Economics, 34,141-198.

Autor, D.H., Dorn, D., & Hanson, G.H. (2013). The China syndrome: Local labor market effect of import competition in the United States. American Economic Review, 103(6): 2121-2168.

Bernard A., Jensen, B., & Schott. P.K (2006). Survival of the best fit: Exposure to low-wage countries uneven growth of the U.S. manufacturing plants. Journal of International Economics 68, 219-237.

Chakrabarti, A. (2003). Import competition, employment and wage in US manufacturing: New evidence from multivariate panel cointegration analysis. Applied Economics, 35, 1445-1449

China Business Review. (2017). Bilateral Relations Policy & Regulations Politics. U.S. China Business Council (USCBC), January 20.

Edwards, L., & Lawrence, R. Z (2013). Rising Tide: Is Growth in Emerging Economies good for the United States? .Peterson Institute for International Economics. Washington, DC

Granger, C.W. J., & Newbold, P. (1986). Forecasting economic time series. Academic Press, New York

Granger, C. W. J., & Newbold, P. (1974). Spurious Regressions in Econometrics. Journal of Econometrics, 2, 111-120.

Government Accounting Office (2003). Mexico's Maquiladora decline affects U.S.-Mexico border communities and trade; recovery depends in part on Mexico's actions. GAO-03-891 International Trade, a report to congressional requesters. 78 pp.

Krugman, P. R. (2000). "Technology, Trade and Factor Prices." Journal of International Economics, 50 (1): 51-71.

Krugman, & P. R. (2008). "Trade and Wages, Reconsidered." Brookings Papers on Economic Activity2008 (1): 103-38.

Ma, A. C., & Wooster, R. B. (2009). The effects of U.S.-CHINA trade on employment and wages in the U.S.-MEXICO border region. Contemporary Economic Policy, 27, 335-348.

Miller, R., Engardio, P., Roberts, D., & Arndt, M. (October 13, 2003). Is it China's fault? Business Week.

Kuehn, J. A., & Braschler, C. (1986). Technology and Foreign Trade Impacts on U.S. Manufacturing Employment 1975-80. Growth and Change journal, 17, 46-60.

Mullen, J. K., & Panning, J. (2009). Foreign Sourcing of Intermediate Inputs: Impacts on Unskilled Labor in US Manufacturing Industries. Eastern Economic Journal, 35, 160-173.

Perry, M. J. (2016). Nearly All Imports, Even Consumer Goods, Are Inputs For US Firms, Retailors, And Factories. https://www.aei.org

Pierce, J. R., & Schott, P. K. (2016). The surprisingly swift decline of U.S. manufacturing employment. American Economic Review. 106, 1632-1662.

Wei, W. S. (2006). Time Series Analysis: Univariate and Multivariate Methods. Addison-Wesley, New York.

Morsheda Hassan

Grambling State University

Raja Nassar

Louisiana Tech University

Ghebre Keleta

Grambling State University

About the Authors:

Morsheda Hassan is an Associate professor of Business and Administration and the Larry Lundy Endowed Chair in Business, Grambling State University, previously the Co-Champion for the Accreditation Council for Business School and Program (ACBSP), Wiley College. Her research interests are in statistics, accounting, economics, and management science. She has authored and co- authored a number of refereed journal articles.

Raja Nassar is Professor emeritus, previously endowed professor, in the Department of Mathematics and Statistics, Louisiana Tech University. His research interests are in statistics and its applications in different Disciplines. He has authored and coauthored over 225 articles in refereed journals of different disciplines.

Ghebre Y. Keleta is a Professor of Economics and Head of the Department of Accounting, Economics and Information Systems, Grambling State University. He received his Ph.D. and M.S. degrees in Economics from Colorado State University, and his B.A. degree in Economics from Haile Selassie I University. Dr. Keleta has attended, participated, and facilitated in many Conferences and professional meetings.
Table 1
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
Mexico (impm) as the Independent Variable

Parameter  Estimate  Error    t-value  p-value  Lag  Variable   Time
                                                                Shift

o4         0.879     0.059    14.8     <0.0001  4    [eta]t     o
[phi]      0.790     0.063    12.5     <0.0001  1    [eta]t     0
[W.sub.0]  0.003369  0.00107   3.13     0.0023  0    Impm(1,4)  1

Table 2
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
Mexico (impm) as the Independent Variable

Parameter  Estimate  Error     t-value  p-value   Lag  Variable   Time
                                                                  Shift

[theta]4   0.8208    0.0623    13.17    < 0.0001  4    [eta]t     0
[phi]      0.7905    0.0627    12.59    < 0.0001  1    [eta]t     0
W0         0.000740  0.000509  1.45     0.149     0    Impc(1,4)  0
W1         -         0.000518  -1.33    0.188     1    Impc(1,4)  0
           0.000687

Dependent Variable = Employment in Manufacturing (empl)
Independent Variable = Import from China (impc)

Table 3
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
Mexico (impm) as the Independent Variable

Parameter  Estimate  Error      t-value  p-value  Lag  Variable   Time
                                                                  Shift

[phi]      0.8288    0.0561     14.77    <0.0001  1    [eta]t     0
W0         0.000984  0.0004755   2.07     0.0411  0    Impm(1,4)  2

Dependent Variable = Employment in Manufacturing (empl)
Independent Variable = Import from Mexico (impm)

Table 4
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
Mexico (impm) as the Independent Variable

Parameter  Estimate  Error       t-value  p-value   Lag


[phi]      0.8269    0.0559      14.80    < 0.0001   1
W0         0.000276  0.0001566    1.76      0.0806   0

Parameter  Variable   Time
                      Shift

[phi]      [eta]t     0
W0         Impc(1,4)  1

Dependent Variable = Employment in Manufacturing (empl)
Independent Variable = Import from China (impc)

Table 5
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
Mexico (impm) as the Independent Variable

Parameter        Estimate   Error     t-value  p-value  Lag  Variable


[[phi].sub.2]     0.3898    0.1096      3.56    0.0006  2    [eta]t
[[phi].sub.4]     0.2672    0.1186      2.25    0.0267  4    [eta]t
[W.sub.0]         0.003429  0.000839    4.09   <0.0001  0    Impm(1,4)
[[delta].sub.1]   0.7915    0.1731      4.57   <0.0001  1    Impm(1,4)
[[delta].sub.2]  -0.9841    0.0214    -45.89   <0.0001  2    Impm(1,4)
[[delta].sub.3]   0.8029    0.1768      4.54   <0.0001  3    Impm(1,4)

Parameter        Time
                 Shift

[[phi].sub.2]    o
[[phi].sub.4]    0
[W.sub.0]        1
[[delta].sub.1]  1
[[delta].sub.2]  1
[[delta].sub.3]  1

Dependent Variable = Employment in Manufacturing (empl)
Independent Variable = Import from Mexico (impc)

Table 6
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
China as the Independent Variable

Parameter      Estimate  Error      t-value  p-value   Lag  Variable


[[phi].sub.1]  0.2162    0.09931    2.18      0.0318   1    [eta]t
[[phi].sub.4]  0.4347    0.0913     4.76    < 0.0001   4    [eta]t
[W.sub.0]      0.000210  0.0004271  0.49      0.6237   0    Impc(1,4)

Parameter      Time
               Shift

[[phi].sub.1]  0
[[phi].sub.4]  0
[W.sub.0]      1

Dependent Variable = Employment in Manufacturing (empl)
Independent Variable = Import from China (impc)

Table 7
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
Mexico (impm) as the Independent Variable

Parameter        Estimate  Error     t-value  p-value  Lag


[[theta].sub.1]  -0.45277  0.10199   -4.44    <0.0001  1
[[theta].sub.2]  -0.39357  0.10032   -3.92     0.0002  2
[[phi].sub.4]     0.71492  0.07566    9.45    <0.0001  4
[W.sub.0]         0.00241  0.000569   4.23    <0.0001  0
[w.sub.1]        -0.00179  0.00045   -3.95    <0.0002  1
[W.sub.4]        -0.00198  0.000595  -3.33    <0.0012  4

Parameter        Variable   Time
                            Shift

[[theta].sub.1]  [eta]t     0
[[theta].sub.2]  [eta]t     0
[[phi].sub.4]    [eta]t     0
[W.sub.0]        Impm(1,4)  1
[w.sub.1]        Impm(1,4)  1
[W.sub.4]        Impm(1,4)  1

Dependent Variable = Employment in Manufacturing (empl)
Independent Variable = Import from Mexico (impm)

Table 8
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
Mexico (impm) as the Independent Variable

Parameter        Estimate   Error     t-value  p-value  Lag  Variable


[[theta].sub.1]  -0.4089    0.0974    -4.20    <0.0001  1    [eta]t
[[theta].sub.2]  -0.2934    0.09588   -3.06     0.0028  2    [eta]t
[[phi].sub.4]     0.55589   0.08435    6.59    <0.0001  4    [eta]t
[W.sub.0]         0.000169  0.000214   0.79     0.429   0    Impc(1,4)

Parameter        Time
                 Shift

[[theta].sub.1]  0
[[theta].sub.2]  0
[[phi].sub.4]    0
[W.sub.0]        0

Dependent Variable = Employment in Manufacturing (empl)
Independent Variable = Import from China (impc)

Table 9
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
Mexico (impm) as the Independent Variable

Parameter  Estimate  Error      t-value  p-value  Lag  Variable   Time
                                                                  Shift

[mu]       -3.4228   2.056      -1.66     0.0994  0    empl       0
[[theta]1  -0.6613   0.1032     -6.41    <0.0001  1    [eta]t     0
[theta]2   -0.2406   0.1048     -2.29     0.0241  2    [eta]t     0
[phi]4      0.5303   0.08327     6.37    <0.0001  4    [eta]t     0
[phi]5     -0.3446   0.08329    -4.14    <0.0001  5    [eta]t     0
[W.sub.0]   0.00160  0.0006171   2.58     0.0110  0    Impm(1,4)  5

Table 10
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
Mexico (impm) as the Independent Variable

Parameter        Estimate    Error      t-value  p-value  Lag  Variable


[[theta].sub.1]  -0.6351     0.1045     -6.07    <0.0001  1    [eta]t
[[theta].sub.2]  -0.22218    0.1056     -2.10     0.0382  2    [eta]t
[[phi].sub.4]     0.5966     0.0829      7.20    <0.0001  4    [eta]t
[[phi].sub.5]    -0.2467     0.08172    -3.02     0.0033  5    [eta]t
[W.sub.0]         -.0002361  0.0002739  -0.86     0.3910  0    Impc(1,4)

Parameter        Time
                 Shift

[[theta].sub.1]  0
[[theta].sub.2]  0
[[phi].sub.4]    0
[[phi].sub.5]    0
[W.sub.0]        8

Dependent Variable = Employment in Manufacturing (empl)
Independent Variable = Import from China (impc)

Table 11
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
Mexico (impm) as the Independent Variable

Parameter        Estimate   Error      t-value  p-value  Lag  Variable


[mu]             -3.48      1.528      -2.28     0.0249  0    empl
[[theta].sub.4]   0.7945    0.0642     12.37    <0001    4    [eta]t
[[phi].sub.1]     0.6438    0.0734      8.76    <0.0001  1    [eta]t
[[phi].sub.4]     0.9678    0.06148    15.74    <0.0001  4    [eta]t
[[phi].sub.5]    -0.6758    0.0732     -9.23    <0.0001  5    [eta]t
[W.sub.0]         0.000867  0.0004453   2.05     0.0430  0    Impm(1,4)

Parameter        Time
                 Shift

[mu]             0
[[theta].sub.4]  0
[[phi].sub.1]    0
[[phi].sub.4]    0
[[phi].sub.5]    0
[W.sub.0]        1

Dependent Variable = Employment in Manufacturing (empl)
Independent Variable = Import from China (impm)

Table 12
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
Mexico (impm) as the Independent Variable

Parameter        Estimate    Error       t-value  p-value  Lag


[mu]             -3.609      1.297       -2.78     0.0065  0
[[theta].sub.4]   0.8609     0.04619     18.64    <0.0001  4
[[phi].sub.1]     0.6967     0.0709       9.82    <0001    1
[[phi].sub.4]     0.9705     0.0910      10.66    <0.0001  4
[[phi].sub.5]    -0.7261     0.0711     -10.21    <0.0001  5
[W.sub.0]         0.0001151  0.0001447    0.80     0.4283  0

Parameter        Variable  Time
                           Shift

[mu]             empl       0
[[theta].sub.4]  [eta]t     0
[[phi].sub.1]    [eta]t     0
[[phi].sub.4]    [eta]t     0
[[phi].sub.5]    [eta]t     0
[W.sub.0]        Impc(1,4)  0

Dependent Variable = Employment in Manufacturing (empl)
Independent Variable = Import from China (impc)

Table 13
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
Mexico (impm) as the Independent Variable

Parameter      Estimate  Error     t-value  p-value  Lag  Variable


[[phi].sub.1]   0.5475   0.0792     6.91    <0.0001  1    [eta]t
[[phi].sub.4]  -0.34099  0.0784    -4.35    <0.0001  4    [eta]t
[W.sub.0]       0.00198  0.000486   4.07    <0.0001  0    Impm(1,4)

Parameter      Time
               Shift

[[phi].sub.1]  0
[[phi].sub.4]  0
[W.sub.0]      1

Dependent Variable = Employment in Manufacturing (empl)
Independent Variable = Import from China (impm)

Table 14
Parameter Estimates and their Significance with Lags and Time Shift for
Employment in Manufacturing as the Dependent Variable and Import from
Mexico (impm) as the Independent Variable

Parameter      Estimate     Error      t-value  p-value  Lag  Variable


[[phi].sub.1]   0.5811      0.07307     7.95    <0.0001  1    [eta]t
[[phi].sub.4]  -0.3376      0.0728     -4.64    <0.0001  4    [eta]t
[W.sub.0]       0.00006678  0.0001815   0.37     0.7136  0    Impc(1,4)

Parameter      Time
               Shift

[[phi].sub.1]  0
[[phi].sub.4]  0
[W.sub.0]      1

Dependent Variable = Employment in Manufacturing (empl)
Independent Variable = Import from China (impc)
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Author:Hassan, Morsheda; Nassar, Raja; Keleta, Ghebre
Publication:International Journal of Business and Economics Perspectives (IJBEP)
Geographic Code:1MEX
Date:Sep 22, 2019
Words:6134
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