Deusto - Do differences in the exposure to Chinese imports lead...

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Do differences in the exposure to Chinese imports lead to differences in

local labour market outcomes? An analysis for Spanish provinces

Vicente Donoso (Departamento de Economía Aplicada II, Universidad Complutense de

Madrid, Campus de Somosaguas, 28223 Pozuelo de Alarcón - Spain; Tel.: +34 91

3943150; E-mail: [email protected])

Víctor Martín (Universidad Rey Juan Carlos; Paseo Artilleros s/n. 28032 Vicálvaro -

Spain; Tel.: +34 91 4887800; E-mail: [email protected])

Asier Minondo* (Deusto Business School, Universidad de Deusto, Camino de Mundaiz,

50; 20012 San Sebastián - Spain; Tel.: +34 943 326600; E-mail: [email protected])

Abstract

In the period 1995-2007 Spanish imports from China multiplied by eleven, making the

Asian country the fourth supplier of the Spanish economy. In this paper we analyze

whether this massive increase in imports impacted Spanish provinces' labour markets

differently due to differences in their initial productive specialization. Our results show

that Spanish provinces with a higher exposure to Chinese imports experienced higher

drops in manufacturing employment as a share of working-age population. However,

this reduction was compensated by increases in non-manufacturing employment.

Keywords: imports, China, Spain, employment, manufactures, provinces

JEL Classification: F16, J23

* Corresponding author.

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1. Introduction

The emergence of China as a major trader is one of the most salient features of the

current globalization process. In the period 1995-2007, the share of Chinese exports in

total world merchandise exports multiplied by three (from 2.9% to 8.8%). Exports

growth was particularly intense for manufactures, where the share increased from 3.2%

to 11.3%.1 Spain has not been alien to this process. During the period 1995-2007,

China's share in Spanish imports raised from 2.0% to 6.5%, and at the end of the period

China was Spain's fourth most important supplier, behind Germany, France and Italy. In

the case of manufactures the share of Chinese imports grew from 2.4% to 8.4%.

Since the early 1990s, scholars have been pointing out that imports from developing

countries in general, and from China in particular, might have disruptive effects on

developed countries' labour markets (Wood, 1994). Due to a higher relative endowment

in unskilled labour, developing countries have a comparative advantage in unskilled-

labour intensive goods. Moreover, the fragmentation of production processes allows

these countries to specialise in some stages of production, such as assembly tasks,

which make intensive use of unskilled-labour (Grossman and Rossi-Hansberg, 2007).

Due to their lower costs, imports from developing countries might lead to a drop in the

production of unskilled-labour intensive manufactures, or manufacturing stages, in

developed countries, reducing the demand for unskilled-labour in those countries.

During the 1990s, with a few exceptions (Wood, 1995), most scholars concluded that

the negative impact of developing countries imports on developed countries labour

markets was tiny, due to the low amount of these imports (Krugman, 1995). However,

the massive increase in imports from developing countries from the second half of the

1990s onwards, mostly explained by the emergence of China as a major trading partner,

calls for a re-assessment of the impact of these trade flows on developed countries'

labour markets (Krugman, 2008).

This re-assessment should also include a geographical dimension. Previous studies on

the impact of competition from developing countries on high-income countries' labour

1 The figures have been calculated from World Trade Organization and World Bank databases, available from www.wto.org and www.worldbank.org respectively.

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markets were conducted at the country-level and did not analyze whether this impact

could vary across regions. As regions differ in their productive specialisation, the

omission of the geographical dimension might be relevant. In particular, regions

specialised in products imported from China might suffer a larger negative impact on

employment than regions specialised in products that do not compete with Chinese

imports. Moreover, considering that workers might not move easily across regions,

differences in the impact of Chinese imports might lead to differences in regional labour

market outcomes that can persist in the medium term.

The contribution of this paper is to assess, using recent data, the impact of Chinese

imports on the demand for labour at the regional level, taking Spain as a case study.

Following the methodology developed by Autor et al. (2012), we analyze whether

Spanish provinces specialised in goods where the increase in Chinese imports was

higher experienced a larger decline in manufacturing employment than Spanish

provinces that were specialised in goods where the increase in Chinese imports was

smaller.

Our results show that Spanish provinces specialised in industries in which imports from

China grew more experienced a larger decline in manufacturing employment. In

particular, according to our estimates, an increase in 1,000 US dollars in Chinese

imports per worker is associated with a decline of manufacturing employment of

approximately 1.3 percentage points of the working age population. Results are robust

to omitted variables that might influence changes in imports from China and the

demand for labour. Results are also robust to the possibility that firms anticipate the

increase in imports from China.

Since provinces constitute an adequate geographical unit to delimitate the boundaries of

local labour markets, we assess how the reduction in manufacturing employment as a

share of working age population is transmitted to provinces' labour markets. We find

that the negative effect of imports from China on manufacturing employment is

compensated by a rise in non-manufacturing employment. We do not find that the surge

in imports from China has a significant effect neither on the unemployment rate nor on

the rate of non-participation in the labour market.

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This paper is related to previous papers that have analyzed the impact of trade with

developing countries on developed countries' labour markets. As mentioned before,

during the 1990s a large number of studies, using different methodologies, analyzed the

effects of total trade with developing countries on employment and wages of unskilled

and skilled workers in developed countries (Krugman and Lawrence, 1994; Wood,

1995; Leamer, 1998). For the Spanish case, using a factor content of trade methodology,

Minondo (1999) showed that trade with developed and developing countries was

responsible for a reduction in labour demand, especially for unskilled-workers, which

represented between 14% and 21% of manufacturing employment.

Later research focused on the effect of a particular type of trade, the offshoring of

production stages from developed to developing countries, on the high-wage countries'

labour market. Offshoring of production stages in manufacturing has a sizable negative

effect on the relative demand for unskilled-workers in the US (Feenstra and Hanson,

1996 and 1999). Papers on services offshoring also find that the impact on labour

switching, unemployment and earnings are not small (Liu and Trefler; 2011). For Spain,

Minondo and Rubert (2006) show that offshoring to developing countries is correlated

with an increase in demand for skills in manufacturing.2

Other papers use firm-level data to analyze the impact of trade with low-wage countries

on firm-survival and manufacturing employment in high-wage countries. Bernard et al.

(2006) find that U.S. manufacturing plant survival and growth are negatively associated

with exposure to low-wage countries' imports. Harrison and McMillan (2011) find that,

in general, offshoring to low-wage countries substitute for domestic employment in

U.S. manufacturing firms. Papers that match firm and workers data show that offshoring

tends to increase the high-skilled wage and decrease the low-skilled wage. Moreover,

low-skilled workers suffer more from the displacement effects of offshoring (Hummels

et al, 2011). Finally, as explained before, our paper draws heavily on Autor et al. (2012)

who use a novel methodology to assess the impact of imports from China on U.S. local

labour markets. They find that imports have a large impact on unemployment, labour

force participation and government transfers.

2 Cadarso et al. (2008) find that outsourcing to Eastern and Central European countries reduced employment in Spanish industries characterized by a medium-high technology.

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The rest of the paper is organized as follows. Section 2 presents some stylized facts on

the evolution of Spanish imports from China, and the evolution of manufacturing

employment across Spanish provinces. Section 3 explains how the import exposure

indicator is calculated, presents the database and describes the results from the

regression analyses. Section 4 concludes.

2. Imports from China and the evolution of manufacturing employment in Spain

Figure 1 presents the evolution of Spanish imports from China in absolute and relative

terms. As shown in the figure, during the period 1995-2007, the rise of Chinese imports

was impressive. In 1995 imports from China amounted to 2 billion US dollars (USD);

by 2007, this amount multiplied by more than eleven, reaching a 25 billion figure. We

can observe that the increase of Chinese imports accelerated from 2001 onwards, the

year in which China became a member of the World Trade Organization (WTO).

Between 2001 and 2007 growth rates were always at two digit levels; moreover, in two

years, 2004 and 2007, growth rates were larger than 40 per cent. The increase in imports

from China is also important in relative terms. As shown in the figure, in 1995 imports

from China represented 2.0% of all Spanish imports; by 2007, this share multiplied by

more than three, rising to 6.5%. The increase in the share of China in Spanish imports is

even more impressive if we focus on manufactures, where it raised from 2.4% to 8.4%

during the period 1995-2007. The bulk of imports' growth from China is concentrated in

three industries: machinery and electrical equipment (35%), metals and other

manufactures (26%) and textile, wearing apparel and footwear (22%).

Figure 2 shows the evolution of manufacturing employment in Spain as a share of total

working-age population, and as a share of occupied population during the period 1995-

2007. From 2001 onwards we observe a steady decline in the share of manufacturing

employment in total occupied population, dropping from 19% in 2001 to 15% in 2007.

This decline coincides with the surge of manufacturing imports from China. However,

we can also see that manufacturing employment slightly increased as a share of the

working-age population, from 9% in 1995 to 10% in 2007. These opposite evolutions

are explained by the large increase in the share of occupied population in the working-

age population during the period of analysis.

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However, the aggregate evolution of manufacturing employment hides substantial

differences across Spanish provinces. Figure 3 compares industrial employment as a

share of working-age population across Spanish provinces in 1995 and 2007. We can

see, first, that there are large differences across provinces in the share of manufacturing

employment. The range moves from Melilla, where manufacturing employment is

almost nil, to Alava where the share reached almost 20% in 2007. We also observe that

there are large differences in the evolution of manufacturing employment across

provinces. There are seven provinces where manufacturing employment falls as

percentage of working-age population; among them, we should highlight Alicante,

where the drop is almost 4 percentage points. In contrast, there are 45 provinces where

the share rises. Among them, we should highlight Soria and Burgos, where the share of

manufacturing employment increases by 6 and 4 percentage points respectively.

The aim of our empirical investigation is to assess whether the differences in the

evolution of the share of industrial employment across provinces is associated with the

increase of imports from China. In particular, we want to test whether provinces

specialised in goods where imports from China increased substantially experienced

drops in the share of industrial employment. The next section addresses this question.

3. Empirical analysis

3.1 Data and measurement

To measure the exposure of Spanish local labour market to import competition from

China we follow the methodology proposed by Autor et al. (2012). These authors

suggest that a region is more exposed to import competition from China when it

accounts for a larger share of the country sales in industries in which country imports

growth from China is large. The import competition exposure index for region i in time

t is obtained through,

ijt cjtit

j cjt it

E MIPW

E E (1)

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where (Eijt/Ecjt) is equal to start of period (year t) region’s share of country employment

in industry j, Eit is start of period total employment in region i and Mcjt is equal to the

observed change in country imports from China in industry j between the start and the

end of relevant time period. It can be noticed that this measure of local labour market

exposure to import competition is the average change in Chinese imports per worker in

a region, weighting each industry by its share in country’s total employment.

We select provinces as the geographical unit of analysis, because they delimitate

adequately the boundaries of local labour markets. Recent research by the OECD has

identified metropolitan areas in Spain, defined as those areas where labour linkages are

very high (OECD, 2012). These areas are built clustering urban municipalities with high

levels of commuting flows. The majority of the metropolitan areas identified by the

OECD correspond to provinces' capitals.3

We use data on Spanish and UE-14 imports at the 3-digit HS product level from the UN

Comtrade Database, for years 1995, 1999, 2003 and 2007. To concord with

employment data, trade data was transformed to the Statistical Classification of

Economic Activities in the European Community, rev. 1.1 (NACE rev. 1.1). Data on

labour market for Spanish regions comes from the Survey of the Working Population

(EPA) published by the Spanish National Institute of Statistics (INE), for the second

quarter of years 1995, 1999, 2003 and 2007.

To calculate the import exposure measure, IPWit, the EPA provides data on

employment by region and economic activity sector at the 3-digit level from the

National Classification Activities - 1993 (CNAE-93 and CNAE-93 rev. 1), which is

equivalent to the NACE classification. For illustration purposes, Figure 4 provides a

visual impression of the exposure to Chinese import competition in Spain, where

provinces are classified into four groups according to the quartiles of the import

exposure measure in 1995-2007. Most provinces in the upper quartile concentrate in the

northeast part of Spain. It must be noted also that the import exposure variable presents

a considerable variation across Spanish provinces. While the 25th percentile amounts to

an increase of 614 US dollars per worker in Chinese imports, the 75th percentile is

3 See also López-Bazo et al. (2005).

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almost three times larger with an increase of 1,788 dollars per worker during 1995

through 2007.

3.2 Import exposure and manufacturing employment

As a first step in our econometric analysis of the impact of Chinese import competition

exposure on Spanish manufacturing employment, Figure 5 shows the relationship

between changes in manufacturing employment as a share of working age population

within provinces and import exposure during 1995-2007. The plotted regression model

control for the share of manufacturing employment in 1995 and weights provinces

according to their start of period share in national population. The prevalence of data

points where change in manufacturing employment controlling for its share on total

employment is high (low) and import exposure is low (high) supports a negative

relationship between import exposure and change in manufacturing employment within

provinces. Moreover, the concentration of points near zero indicates that most

observations are unlikely to be outliers.

To further analyze the relationship between Chinese import exposure and Spanish

manufacturing employment, we fit models of the following form using the full sample

of 50 Spanish provinces and the 2 autonomous cities (Ceuta and Melilla),

0 1 2 mit it it itE IPW X u (2)

where Emit is the four-year change in the manufacturing employment share of the

working age population in province i and Xit is a vector of control variables for start of

four-year period labour force and demographic composition which might affect

manufacturing employment. All models are estimated using the available data for three

four-year periods: 1995-1999, 1999-2003 and 2003-2007.

Table 1 presents the detailed estimates of model (2). For all the regression models we

control for province heterogeneity through fixed effects estimation. In each case we

report the parameter estimates and their corresponding robust standard deviation in

parenthesis, the resulting R2 and the value of the F statistic for the null hypothesis that

all estimated coefficients are zero. Column 1 through 4 shows the estimation results for

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different sets of control variables. When we estimate the model without additional

dependent variables (column 1) the effect on manufacturing employment from import

exposure is negative and statistically significant at the 1% level. The point estimate

indicates that a rise of 1,000 U.S. dollars per worker in a province’s exposure to

Chinese imports during a four-year period is associated with a decline in manufacturing

employment of approximately 1.4 percentage points of working age population4. In the

second column we add a control for the share of manufacturing in province’s start of

four-year period employment. The inclusion of this variable has a twofold aim. First it

allow us to concentrate on differences on import exposure arising from differential

specialization in import intensive industries within provinces, rather than on those

differences due to differential concentration of employment in manufacturing versus

non manufacturing activities. Second, we address the possibility that the import

exposure variable may in part reflect the overall trend decline in manufacturing

employment share in Spain rather than the component that is due to differences across

manufacturing industries in their exposure to rising Chinese competition. The estimated

coefficient is negative and significant at the 1% level, implying that a one percent point

higher initial manufacturing share causes a differential manufacturing employment

decline of 0.27 percentage points over a four-year period. The coefficient estimate for

the import exposure variable remains negative and highly significant. In column 3 we

add the growth rate of the working age population as an explanatory variable. Thus we

control for changes in manufacturing employment as a result of changes on working age

population size itself. Again, the effect on manufacturing employment from import

exposure remains highly significant and similar in magnitude. Column 4 augments the

regression model with four additional controls; the start of four-year period share of

working age population with a college education, the share of working age population

with foreign nationality5, the share of working age women population and the share of

working age young population6. Apart from the foreign nationality population, none of

the added controls seems to have a significant effect on manufacturing employment

change. The coefficient estimate indicates that a difference of a one percentage point in

initial foreign nationality share is associated with a differential manufacturing

4 For further interpretation, the mean increase in Chinese import exposure during 1995-1999, 1999-2003 and 2003-2007 was about 91, 85 and 783 US dollars per worker respectively. 5 All individuals with nationality in high-income countries (World Bank classification) are not included as foreign nationality population. 6 Working age population between age 16 and 24.

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employment share decline of 0.12 percentage points. This specification yields a

significant but relativity lower coefficient estimate for the import exposure effect than

the regression models in columns 1 to 3.

Following the literature on extreme bound analysis7, we run several regressions to

assess the sensitivity of the estimated coefficient on import exposure to different sets of

control variables. Thus we divide the variables on Table 1 column (4) into two groups.

The first group contains variables that always appear in the regression (core variables):

import exposure, share of manufacturing employment and the year dummies. The

second, denoted by control group, contains the remaining variables. The change in

manufacturing employment is then regressed on the full set of core variables and on all

the possible combinations of control variables. For each model j we find and estimate,

β1j, and a standard deviation, σ1j, for the import exposure variable. The lower extreme

bound is defined as the lowest value of β1j - 2σ1j, and the upper extreme bound is

defined to be the largest value of β1j + 2σ1j. The summary statistics from this analysis

are presented in Table 2. The import exposure variable is quite robust since its

coefficient remains significant and of the same sign at the extreme bounds. At the lower

and upper bound, the coefficient is -1.57 and -1.20 respectively with a t statistic of -3.70

and -2.98.

Overall, results show that the effect of exposure to Chinese imports on manufacturing

employment remains highly significant and its magnitude fairly stable against different

sets of control variables. However two important concerns must be pointed out about

this observed relationship. On the one hand, there could exist a simultaneity bias to the

degree that, in the import competition measure, anticipated imports from China affects

contemporaneous employment. On the other hand, estimation results on Table 1 could

be biased due to endogeneity of the import exposure variable, since demand shocks can

influence industry imports. In order to overcome these two problems, and following

Autor et al. (2012), we modify the import exposure variable as follows,

1

1 1

ijt cjt

itj cjt it

E MIPWL

E E (3)

7 Levine and Renelt (1992).

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ijt ojtit

j cjt it

E MIPWO

E E (4)

1

1 1

ijt ojt

itj cjt it

E MIPWOL

E E (5)

Equation (3) makes clear that the difference between IPWit and IPWLit is that the

latter uses employment levels by industry and province from the previous time period

(t-1) rather than start of period employment levels (t). The use of lagged employment to

apportion predicted Chinese imports to provinces mitigates the potential simultaneity

bias. In equation (4), we substitute country imports from China (Mcjt) by other high-

income markets imports from China (Mojt) to control for endogeneity. We use

countries belonging to the UE-158 (other than Spain) as the group of other high-income

markets. The import exposure variable in equation (5) allows us to address both the

simultaneity and the endogeneity bias since it uses lagged employment and imports

from China by the UE-14.

In Table 3 we replicate the estimations from Table 1 with the new three import exposure

variables. In all regressions we control for province heterogeneity through fixed effects

estimation. Models in columns (5) to (12) are estimated through instrumental variables

(IV) where IPWOit (columns 5-8) and IPWOLit (columns 9-12) are used as

instruments for the original import exposure variable (IPWit). Parameter estimates and

robust standard deviation in parenthesis are reported in each case. For the IV estimates

we also present parameter and robust standard deviation estimates from the first stage

regression, and the weak identification test (KP) proposed by Kleibergen and Paap

(2006). Both, the highly significant coefficient for the instrument and the value of the

KP statistic support the instrument validity in all IV regressions.

For all models on Table 3, the parameter estimate of the exposure to import competition

is negative and statistically significant. However, the estimated coefficients differ

somewhat from the corresponding estimates from Table 1, especially when we control

8 We refer to these countries as UE-14 in the rest of the paper.

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for simultaneity bias (columns 1-4). In this case, the estimated effect of import exposure

on manufacturing employment increases by a factor of around 1.8, although the

precision in the estimates are notably lower. This difference may arise from the fact

that, when we use lagged employment the first four-year period (1995-1999) is lost in

the estimations. Since the increase in imports from China is notably higher from 2001

onwards, the magnitude of the coefficient on import exposure must be higher when the

first period is not included in the estimations.

3.3 Import exposure and aggregate labour market

The results of the previous section suggest that the exposure to growing import

competition from China had a negative effect on the evolution of manufacturing

employment within Spanish provinces during the period 1995-2007. The estimated

relationship suggests that manufacturing employment within Spanish provinces would

have grown at a higher rate in the absence of Chinese imports growth. Along the three

four-year periods: 1995-1999, 1999-2003 and 2003-2007, the mean change on

manufacturing employment as a share of working age population was 1.14%, 0.78%

and 0.06% respectively9. Since the mean increase on weighted Chinese imports per

worker in Spain along the same three four-year periods was about 91, 85 and 783 US

dollars per worker, the change on manufacturing employment as a share of working age

population in the mean province would have been approximately 1.25%, 0.89% and

1.07%10 in the absence of Chinese imports growth.

The next step in our analysis is to determine whether this import shocks to

manufacturing employment indirectly affected broader labour market outcomes. Before

that, we study if these trade shocks induced a reallocation of workers across provinces.

If large flows of workers move among provinces as a response to import shocks to the

manufacturing sector, the effects on local labour market outcomes, other than

manufacturing employment, will be practically negligible.

Table 4 presents the results from several regression models where the dependent

variable is the growth rate of the working age population and four different import

9 The mean manufacturing employment growth rate was of 15%, 9.3% and 0.7% along 1995-1999, 1999-2003 and 2003-2007. 10 The estimated coefficient for the import exposure variable in column 4 of Table 1 was used to calculate these figures.

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exposure variable specifications. In the first column we use our initial import exposure

variable given by equation (1). In columns (2) to (4) we use the modified import

exposure variables given by equations (3) to (5) respectively, in order to control for

potential simultaneity and endogeneity bias. Similarly to our previous estimates, we

include four independent variables apart from the import exposure variable and the

share of manufacturing employment: the share of working age population with a college

education, the share of working age population with foreign nationality, the share of

working age women population and the share of working age young population. Since

the estimated coefficients for the different import exposure variables are not by far

statistically significant, we conclude that import shocks to local manufacturing did not

lead to substantial changes of working age population within Spanish provinces. This

lack of a significant effect of growing import competition from China on workers flows

is consistent with the low mobility of workers between regions that characterize the

Spanish labour market (Bentolila, 1997).

As long as workers do not reallocate across provinces as a response to trade shocks, the

negative effect on manufacturing employment of Chinese import competition must have

some impact in either non-manufacturing employment, unemployment or population

not included in the labour force. The results for the estimated effect of import exposure

on these three labour market outcomes are shown in Table 5. These regressions are

analogous to the models on Table 4 except that the working age population growth is

included as an additional regressor. In all cases, the dependent variable is the change of

the corresponding variable as a share of the working age population. We also report the

estimation results for the manufacturing employment (Table 1). It can be noticed that

we only find a significant effect of import competition exposure on non-manufacturing

employment. The positive sign of the estimated coefficient and its magnitude implies

that the negative effect of import exposure on manufacturing employment is

compensated by an increase on employment in other non-manufacturing sectors within

provinces. We do not find a significant association between exposure to imports from

China neither with unemployment, nor with participation in the labour market.

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4. Conclusions

This paper analyzes whether differences in the exposure to imports from China explain

differences in labour market outcomes across Spanish provinces. Our results show that

during the period 1995-2007 exposure to imports from China are associated with

declines in manufacturing employment. In particular a 1,000 USD increase in imports

from China per worker reduces the share of manufacturing employment in working-age

population by 1.3 percentage points. This result is robust to omitted variables and

simultaneity. As provinces have local labour market characteristics, we analyze how the

reduction in manufacturing employment is transmitted to the local labour market. We

find that the reduction in manufacturing employment is compensated by an increase in

non-manufacturing employment. We do not find a significant association between

exposure to imports from China neither with unemployment, nor with participation in

the labour market.

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Acknowledgements: Asier Minondo acknowledges financial support from the Spanish

Ministry of Science and Innovation (ECO2010-21643/ECON and ECO2011-

27619/ECON). We also thank Patricia Canto, Francisco Requena and participants at the

XV Encuentro de Economía Aplicada in A Coruña for valuable suggestions.

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Table 1. Import exposure and change in manufacturing employment in Spain, 1995-2007 (Fixed Effects estimates. Dependent variable: change in manufacturing

employment as a share of working age population (%))

Independent variable Import exposure: IPW (1) (2) (3) (4)

Import Exposure -1.4042***

(0.3512) -1.4993*** (0.3312)

-1.5018*** (0.3425)

-1.2884*** (0.3186)

Manufacturing empl.

- -0.2690*** (0.0507)

-0.2545*** (0.0527)

-0.3353*** (0.0529)

Work.-age pop. growth

- - 0.0696*

(0.0376) 0.0432

(0.0457) College-educated

- - - -0.2426 (0.1508)

Foreign-nationality

- - - -0.1215**

(0.0558) Women

- - - 0.1310

(0.1254) Young

- - - 0.1583

(0.1268) R2 0.39 0.55 0.56 0.62 F statistic (p-value)

9.56 (0.00)

20.60 (0.00)

15.63 (0.00)

13.00 (0.00)

Notes: N = 156 (52 provinces x 3 time periods). All regressions include a constant and a dummy for the 1999-2003 and 2003-2007 periods. Robust standard errors in parentheses. Statistical significance is indicated by *** at 1%, ** at 5% and * at 10%. Models are weighted by period average province share of national population.

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Table 2. Summary statistics from extreme bound analysis.

Mean

Average σ

Average t-statistic

Low β1 High β1 LEB UEB

(1) (2) (3) (4) (5) (6) (7) CORE VARIABLES Import exposure -1.3546 0.3992 3.3978 -1.5695 -1.2165 -2.4172 -0.4002 Manufacturing empl. -0.3042 0.0627 4.8885 -0.3463 -0.2545 -0.4675 -0.1248 CONTROL VARIABLES Work.-age pop. growth 0.0520 0.0518 1.0575 - - - - College-educated -0.3003 0.1804 1.6741 - - - - Foreign-nationality -0.1200 0.0737 1.6408 - - - - Women 0.1283 0.1492 0.8624 - - - - Young 0.2105 0.1477 1.4360 - - - - Notes: LEB = lower extreme bound, UEB = upper extreme bound. The low β1 is the estimated coefficient from the regression with the lower extreme bound. The high β1 is the estimated coefficient from the regression with the upper extreme bound.

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Table 3. Import exposure and change in manufacturing employment in Spain: Robustness check. Dependent variable: change in manufacturing employment as a share of working age population (%)

Independent variable OLS FE, Import exposure: IPWL (1999-2007) IV FE, Import exposure: IPWO (1995-2007) IV FE, Import exposure: IPWOL (1999-2007) (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Import Exposure -2.5296*

(1.3159) -2.6596**

(1.2290) -2.7146**

(1.2353) -2.2155**

(1.0993) -1.7515*** (0.2751)

-1.9928*** (0.2477)

-2.0083*** (0.2470)

-1.7358*** (0.2794)

-1.6888**

(0.7827) -1.3094** (0.6002)

-1.2980**

(0.5212) -1.0291*

(0.6059) Manufacture empl.

- -0.4057***

(0.1088) -0.4176*** (0.0972)

-0.5434*** (0.0872)

- -0.2752*** (0.0537)

-0.2607*** (0.0551)

-0.3364*** (0.0556)

- -0.4385*** (0.1161)

-0.4539*** (0.0958)

-0.5733*** (0.0823)

Work.-age pop. growth

- - 0.1010

(0.0878) 0.0499

(0.0946) - -

0.0703 (0.0498)

0.0547 (0.0507)

- - 0.1376**

(0.0697) 0.0886

(0.0810) College-educated pop.

- - - -0.1274 (0.1834)

- - - -0.1762 (0.1519)

- - - -0.0098 (0.2128)

Foreign-nationality pop

- - - -0.0847 (0.0663)

- - - -0.1270**

(0.0566) - - -

-0.0986 (0.0556)

Women population

- - - 0.1996

(0.1544) - - -

0.1614 (0.1320)

- - - 0.2157

(0.1488) Young population

- - - 0.3224

(0.1660) - - -

0.1247 (0.1168)

- - - 0.2810**

(0.1435) R2 0.32 0.50 0.52 0.59 0.38 0.53 0.54 0.61 0.36 0.60 0.64 0.68 F statistic (p-value)

6.43 (0.00)

16.05 (0.00)

11.92 (0.00)

11.00 (0.00)

15.89 (0.00)

28.72 (0.00)

22.56 (0.00)

13.49 (0.00)

12.95 (0.00)

19.83 (0.00)

12.80 (0.00)

9.83 (0.00)

First-stage estimates Import Exposure (UE) - - - - 0.1123***

(0.0065) 0.1137*** (0.0066)

0.1138*** (0.0078)

0.1172*** (0.0072)

0.1866*** (0.0666)

0.1965*** (0.0678)

0.1971*** (0.0701)

0.1632*** (0.0549)

R2 - - - - 0.96 0.96 0.96 0.97 0.79 0.79 0.80 0.84 KP statistic (p-value)

- - - - 7.79

(0.00) 7.82

(0.00) 7.86

(0.00) 12.20 (0.00)

4.72 (0.03)

4.96 (0.02)

4.83 (0.03)

7.45 (0.01)

Notes: N = 156 (52 provinces x 3 time periods). All regressions include a constant. Regressions in columns (1-4) and (9-12) include a dummy for the 2003-2007. Regressions in columns (5-8) include a dummy for the 1999-2003 and 2003-2007 periods. Robust standard errors in parentheses. Statistical significance is indicated by *** at 1%, ** at 5% and * at 10%. Models are weighted by period average province share of national population.

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Table 4. Import exposure and working age population growth. Dependent variable: working age population growth (%)

Independent variable

OLS FE IV FE

IPW (1995-2007)

IPWL (1999-2007)

IPWO (1995-2007)

IPWOL (1999-2007)

(1) (2) (3) (4) Import Exposure 0.7338

(0.5828) 1.2436

(1.5644) 1.0148

(0.6180) 0.7328

(1.5930) Manufacturing empl. -0.2565

(0.1612) -0.0125 (0.2075)

-0.2538*

(0.1394) 0.0095

(0.1959) College-educated 0.0353

(0.3945) -0.2379 (0.3659)

-0.0075 (0.3703)

-0.3297 (0.4095)

Foreign-nationality 0.0927

(0.1574) 0.0163

(0.2025) 0.0955

(0.1456) 0.0251

(0.1876) Women -0.1191

(0.2778) 0.6892

(0.5487) -0.1377 (0.2986)

0.6509 (0.5250)

Young 0.9559**

(0.3017) 0.3844*

(0.4359) 0.9703***

(0.2788) 0.4071

(0.4423) N 156 104 156 104 R2 0.50 0.22 0.50 0.26 F statistic (p-value)

13.52 (0.00)

2.30 (0.04)

10.43 (0.00)

2.22 (0.05)

First-stage estimates Import Exposure (UE)

- - 0.1166***

(0.0071) 0.1694***

(0.0553) R2 - - 0.97 0.83 KP statistic (p-value)

- - 10.94 (0.00)

7.15 (0.01)

Notes: All regressions include a constant . Regressions in column (1) and (3) include a dummy for the 1999-2003 and 2003-2007 periods. Regressions in column (2) and (4) include a dummy for the 2003-2007 period. Robust standard errors in parentheses. Statistical significance is indicated by *** at 1%, ** at 5% and * at 10%. Models are weighted by period average province share of national population.

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Table 5. Import exposure and market labour outcomes.

Dependent variable

OLS FE IV FE

IPW (1995-2007)

IPWL (1999-2007)

IPWO (1995-2007)

IPWOL (1999-2007)

(1) (2) (3) (4) Manufacturing employment -1.2884***

(0.3187) -2.2155**

(1.0993) -1.7358***

(0.2794) -1.0291*


13.00 (0.00)

11.00 (0.00)

13.49 (0.00)

9.83 (0.00)

Non-manufact. employment 1.6799***

(0.6198) 3.9101

(2.4931) 2.1889***

(0.6775) 1.3564


23.17 (0.00)

6.59 (0.00)

13.51 (0.00)

7.40 (0.00)

Unemployment -0.6317

(0.6575) -2.7103 (2.4174)

-0.5835 (0.9344)

-2.2593 (1.7072)

R2 0.39 0.17 0.39 0.10 F statistic (p-value)

21.66 (0.00)

0.97 (0.47)

6.40 (0.00)

0.94 (0.50)

Not in the labour force 0.2401

(0.5941) 1.0157

(2.3192) 0.1303

(0.6923) 1.9320


12.22 (0.00)

2.22 (0.04)

8.18 (0.00)

2.33 (0.03)

Notes: All regressions include a constant . Regressions in columns (1) and (3) include a dummy for the 1999-2003 and 2003-2007 periods. Regressions in columns (2) and (4) include a dummy for the 2003-2007 period. Robust standard errors in parentheses. Statistical significance is indicated by *** at 1%, ** at 5% and * at 10%. Models are weighted by period average province share of national population. All models include the same set of regressor on column (4), Table 1.

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0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

0

5000

10000

15000

20000

25000

30000

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Mill

ion

US

DFigure 1. Spain's imports from China, 1995-2007

(million USD and % of total imports)

Million USD Share

Source: UN Comtrade database.

Sha

re

24

8

10

12

14

16

18

20

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

%

Figure 2. Manufacturing employment, 1995-2007(as % of working-age population and occupied population)

% Working-age % occupied

Source: Spanish Labor Survey (www.ine.es)

25

Figure 3. Manufacturing employment in Spanish provinces: 1995 vs. 2007

(as % of working-age population)

Source: Spanish Labor Survey (www.ine.es)

ÁLAVA

ALBACETEALICANTE

ALMERÍA

ÁVILA

BADAJOZ

BALEARS

BARCELONA

BURGOS

CÁCERESCÁDIZ

CASTELLÓN

CIUDAD REALCÓRDOBA

CORUÑA

CUENCA

GIRONA

GRANADA

GUADALAJARA

GUIPÚZCOA

HUELVA

HUESCA

JAÉNLEÓN

LLEIDA

RIOJA

LUGO

MADRID

MÁLAGA

MURCIA

NAVARRA

ORENSEASTURIAS

PALENCIA

PALMAS

PONTEVEDRA

SALAMANCA

TENERIFE

CANTABRIA

SEGOVIA

SEVILLA

SORIA

TARRAGONA

TERUEL

TOLEDOVALENCIA

VALLADOLID

VIZCAYA

ZAMORA

ZARAGOZA

CEUTA

MELILLA05

10

15

20

Shar

e_2

007

0 5 10 15 20Share_1995

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Figure 4. Exposure to Chinese import competition in Spain, 1995-2007.

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Figure 5. Partial regression plot between import exposure and change in manufacturing employment in Spain, 1995-2007.

Palencia

Rioja

Soria

Burgos

Castellón

Asturias

Murcia

Segovia

Valladolid

Pontevedra

Gerona

Ciudad Real

Zamora

Lleida

Cádiz

Ávila

Tenerife

Toledo

HuescaPalmas

Teruel

Zaragoza

Valencia

Jaén

Tarragona

Salamanca

OrenseCuencaCórdoba

Badajoz

Coruña

Cáceres

Vizcaya

LeónSevilla

AlbaceteLugo

Melilla

Almería

Granada

Ceuta

Huelva

Baleares

Álava

Barcelona

Alicante

NavarraMálaga Cantabria

Madrid

Guadalajara

Guipúzcoa

-4-2

02

4C

han

ge in

man

ufac

turi

ng e

mpl

oym

ent

-2 -1 0 1 2Import exposure

coef = -0.75, (robust) se = 0.43, t = -1.74

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