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【Abstract】China’s growing participation in Latin American countries has been seen as a massive threat on the region’s manufacturing sector. Is it really this significant when it comes to its impact on manufacturing employment?With this project I pretend to examine the relationship between the bilateral commerce from China and Colombia in the textile a sector between the years 2000-2012. By examining the data I want to find out what is the correlation between jobs losses in Colombia’s textile industry and the imports from China in the same industry.Analyzing the sector of textile is very significant to the country.It represents employment and production of added value products in an industry that has not only social impact but also historical meaning for the country.
【Key words】Import Competition;Trade and Labor Market Interactions;Employment.
For the Latin American audience,as well as the leaders of government and business, the magnitude and the rate of change in trade relations between China and Latin America has exceeded its ability to fully understand what is happening. Therefore, most of the public discourse on trade relations is filled with a mixture of hopes and fears.
On one hand, many people in Latin America hope China occupies a locomotive role for economic growth and development in the region through investments and purchases of American products. Many of these expectations are based on the potential size of the Chinese market, with its 1.3 billion potential buyers, and an average growth rate of nearly 10% per year since 1978①.
On the other, the economic structure of the countries, especially in Latin America is made up mostly of small and medium enterprises that are the cornerstone of economic activity. The biggest fear is that national small enterprises are not going to be able to compete with Chinese products or prices, given that they these enterprises don’t have as many human or financial resources.
In Colombia micro, small and medium enterprises are the main source of job creation. Micro enterprises represent 96.4% of the total establishments, 63% of employment and 45% of manufacturing output②. In the last years, these companies in the garment and textile sector have been affected specifically by 3 main causes: revaluation of the Colombian peso, import’s competition and informal jobs. In this particular study we will analyze only the impact on China’s imports in the job losses in the textile industry from 2000 till 2011. So far several authors have seen the importance of studying the impact of imports in their own country. There is an apparent consensus that Imports as a whole have a small impact in a country’s economy but it has a considerable impact in certain sectors of the economy that might be more significant for the country, like the manufacturing sector. Given this, the work takes into account the research of authors that have studied the impact of imports in job losses. Authors like Clark, Herzog, and Schlottman (1998), Kletzer (2001) and Ebenstein (2009) all study the impact of Chinese imports and job losses in the United States.
This work is done because of the importance that this sector represents to Colombia not only in monetary value, but also for its 100 years in tradition and development. The industry in combination with the garment sector generates about 130,000 direct jobs and 750,000 indirect jobs, representing about 21% of the labor force generated by the manufacturing industry.
To check the real impact due to Chinese imports the method used is the Simple Linear Regression. The Regression will aim firstly to investigate whether there is an association between the two variables (imports and job losses) testing the hypothesis of statistical independence. Second, to study the strength of the association. Finally, to study the form of relationship between the variables.
The data used for calculations was taken from The National Administrative Department of Statistics in Colombia (DANE) for var2 and United Nations Commodity Trade Statistics Database (UN Comtrade) for var1. For the dependent variable (var2), the figures represent the occupied people in the textile industry and independent variable (var1), the figures represent the Chinese imports of Textiles to Colombia (HS codes: chapters 54 and 55).
Graphic1 Colombia’s Textile Imports from China per year (2000-2011)
Source:Un comtrade and author.
The first years of the decade the imports of textiles from China have little representation in Colombia from the year 2006 this value has grown and it has come to a high in the year 2011 growing 379% since the year 2006.
Graphic2 Total employment in the textile sector
Source: Number of Occupied personnel-DANE.
Total employment in the textile sector in the year 2011 was of 3.763 number of occupied employees. Graphic No. 31 shows a high in employment in the year 2000 with 5.831 employers it maintains almost the same figures until year 2005 that is reduce in 9%, then it grows back in year 2006 but from this year the trend is downwards until it hits 3763 workers in year 2011, this represents a change of 44.62% workers since year 2006. Conclusions of the study can be summarized as follows:
(1)There is a clear association between Chinese Imports and Number of Occupied People in the Textile Industry. The F-value showed that there is a clear association between them. (Refer to Footnote 3a and 3d).
(2)The strength of the association is 81%, meaning var2 =Number of occupied people can be predicted from the variable Chinese Imports. (Refer to Footnote 3b).
(3)The model: Number of Occupied Employees= 5644-0.0093434 Imports China+ error. The relationship between the 2 variables is inverse, the more Chinese imports the less Occupied employees in the sector. Also the equation helps predict that for every 1% increase in Imports of China’s textiles the Employees in the textile sector of Colombia are reduced in 0.09%. (Refer to footnote 3c).
(4)In spite of the widespread benefits of free trade for the economy as a whole, as import penetration from China industry accelerates, the job displacement rate in this industry increases.
Source:ANOVA Table from STATA③.
a.F Value is the Mean Square Model (3529745.59) divided by the Mean Square Residual (77626.6663), yielding F=45.47. The p-value associated with this F value is very small (0.0001). These values answer: "Do the independent variables reliably predict the dependent variable?". The p-value is compared to alpha level (typically 0.05) and, if smaller, it can be conclude "Yes, the independent variables reliably predict the dependentvariable".
b.R-Square is the proportion of variance in var2, which can be predicted from vari1. This value indicates that 81% of the variance in var2 can be predicted from var1. Note that this is an overall measure of the strength of association.
c.These are the values for the regression. The equation can be presents as:Ypredicted = b0 + b1*x1 + b2*x2. . . The column of estimates provides the values for b0, b1, b2, for this equation. Expressed in terms of the variables used in this example, the regression equation is: No. of Occupied People In Textile Industry = 5644-0.0093434 Textile Imports China + errorThese estimates show the relationship between the var1 and var2.These estimates tell the amount of increase in var2 that wouldbepredicted by a 1 unit increaseinvar1.
d.These columns provide the t value and 2 tailed p-value used in testing the null hypothesis that the coefficient is 0.Observing the 2-tailed test, it can be compare each p-value the preselected value of alpha. Coefficients having p-values less than alpha are significant.For example, choosing alpha to be 0.05, coefficients having a p-value of 0.05 or less would be statistically significant(i.e., The null hypothesis can be rejected and it can be concluded that the coefficient is significantly different from 0).With a 2-tailed test and alpha of 0.05, you can reject the null hypothesis that the coefficient for var1 is equal to 0.The coefficient of -.0093 is significantly different from 0.Using a 2-tailed test and alpha of 0.01, the p-value of 0.000 is smaller than 0.01 and the coefficient for var1 would still be significant at the 0.01 level. ?The constant(_cons) is significantly different from 0 at the 0.05 alpha level. However, having a significant intercept is seldom interesting. [科]
【Key words】Import Competition;Trade and Labor Market Interactions;Employment.
For the Latin American audience,as well as the leaders of government and business, the magnitude and the rate of change in trade relations between China and Latin America has exceeded its ability to fully understand what is happening. Therefore, most of the public discourse on trade relations is filled with a mixture of hopes and fears.
On one hand, many people in Latin America hope China occupies a locomotive role for economic growth and development in the region through investments and purchases of American products. Many of these expectations are based on the potential size of the Chinese market, with its 1.3 billion potential buyers, and an average growth rate of nearly 10% per year since 1978①.
On the other, the economic structure of the countries, especially in Latin America is made up mostly of small and medium enterprises that are the cornerstone of economic activity. The biggest fear is that national small enterprises are not going to be able to compete with Chinese products or prices, given that they these enterprises don’t have as many human or financial resources.
In Colombia micro, small and medium enterprises are the main source of job creation. Micro enterprises represent 96.4% of the total establishments, 63% of employment and 45% of manufacturing output②. In the last years, these companies in the garment and textile sector have been affected specifically by 3 main causes: revaluation of the Colombian peso, import’s competition and informal jobs. In this particular study we will analyze only the impact on China’s imports in the job losses in the textile industry from 2000 till 2011. So far several authors have seen the importance of studying the impact of imports in their own country. There is an apparent consensus that Imports as a whole have a small impact in a country’s economy but it has a considerable impact in certain sectors of the economy that might be more significant for the country, like the manufacturing sector. Given this, the work takes into account the research of authors that have studied the impact of imports in job losses. Authors like Clark, Herzog, and Schlottman (1998), Kletzer (2001) and Ebenstein (2009) all study the impact of Chinese imports and job losses in the United States.
This work is done because of the importance that this sector represents to Colombia not only in monetary value, but also for its 100 years in tradition and development. The industry in combination with the garment sector generates about 130,000 direct jobs and 750,000 indirect jobs, representing about 21% of the labor force generated by the manufacturing industry.
To check the real impact due to Chinese imports the method used is the Simple Linear Regression. The Regression will aim firstly to investigate whether there is an association between the two variables (imports and job losses) testing the hypothesis of statistical independence. Second, to study the strength of the association. Finally, to study the form of relationship between the variables.
The data used for calculations was taken from The National Administrative Department of Statistics in Colombia (DANE) for var2 and United Nations Commodity Trade Statistics Database (UN Comtrade) for var1. For the dependent variable (var2), the figures represent the occupied people in the textile industry and independent variable (var1), the figures represent the Chinese imports of Textiles to Colombia (HS codes: chapters 54 and 55).
Graphic1 Colombia’s Textile Imports from China per year (2000-2011)
Source:Un comtrade and author.
The first years of the decade the imports of textiles from China have little representation in Colombia from the year 2006 this value has grown and it has come to a high in the year 2011 growing 379% since the year 2006.
Graphic2 Total employment in the textile sector
Source: Number of Occupied personnel-DANE.
Total employment in the textile sector in the year 2011 was of 3.763 number of occupied employees. Graphic No. 31 shows a high in employment in the year 2000 with 5.831 employers it maintains almost the same figures until year 2005 that is reduce in 9%, then it grows back in year 2006 but from this year the trend is downwards until it hits 3763 workers in year 2011, this represents a change of 44.62% workers since year 2006. Conclusions of the study can be summarized as follows:
(1)There is a clear association between Chinese Imports and Number of Occupied People in the Textile Industry. The F-value showed that there is a clear association between them. (Refer to Footnote 3a and 3d).
(2)The strength of the association is 81%, meaning var2 =Number of occupied people can be predicted from the variable Chinese Imports. (Refer to Footnote 3b).
(3)The model: Number of Occupied Employees= 5644-0.0093434 Imports China+ error. The relationship between the 2 variables is inverse, the more Chinese imports the less Occupied employees in the sector. Also the equation helps predict that for every 1% increase in Imports of China’s textiles the Employees in the textile sector of Colombia are reduced in 0.09%. (Refer to footnote 3c).
(4)In spite of the widespread benefits of free trade for the economy as a whole, as import penetration from China industry accelerates, the job displacement rate in this industry increases.
Source:ANOVA Table from STATA③.
a.F Value is the Mean Square Model (3529745.59) divided by the Mean Square Residual (77626.6663), yielding F=45.47. The p-value associated with this F value is very small (0.0001). These values answer: "Do the independent variables reliably predict the dependent variable?". The p-value is compared to alpha level (typically 0.05) and, if smaller, it can be conclude "Yes, the independent variables reliably predict the dependentvariable".
b.R-Square is the proportion of variance in var2, which can be predicted from vari1. This value indicates that 81% of the variance in var2 can be predicted from var1. Note that this is an overall measure of the strength of association.
c.These are the values for the regression. The equation can be presents as:Ypredicted = b0 + b1*x1 + b2*x2. . . The column of estimates provides the values for b0, b1, b2, for this equation. Expressed in terms of the variables used in this example, the regression equation is: No. of Occupied People In Textile Industry = 5644-0.0093434 Textile Imports China + errorThese estimates show the relationship between the var1 and var2.These estimates tell the amount of increase in var2 that wouldbepredicted by a 1 unit increaseinvar1.
d.These columns provide the t value and 2 tailed p-value used in testing the null hypothesis that the coefficient is 0.Observing the 2-tailed test, it can be compare each p-value the preselected value of alpha. Coefficients having p-values less than alpha are significant.For example, choosing alpha to be 0.05, coefficients having a p-value of 0.05 or less would be statistically significant(i.e., The null hypothesis can be rejected and it can be concluded that the coefficient is significantly different from 0).With a 2-tailed test and alpha of 0.05, you can reject the null hypothesis that the coefficient for var1 is equal to 0.The coefficient of -.0093 is significantly different from 0.Using a 2-tailed test and alpha of 0.01, the p-value of 0.000 is smaller than 0.01 and the coefficient for var1 would still be significant at the 0.01 level. ?The constant(_cons) is significantly different from 0 at the 0.05 alpha level. However, having a significant intercept is seldom interesting. [科]