Data

Patents

I use patents as a measure of innovation both in China and the U.S. The data set includes all published utility patents in the U.S. and in China for the time period between 1985 and 2006. The Chinese patent data for this time period is collected by the State Intellectual Property Office of China and has been made available and described by Holmes et al. (2013). The U.S. patent data used in this study comes from the NBER Patent Data Project, originally collected by Hall et al. (2001), which assembles all patent data from the United States from 1976 to 2006. The data is classified ac- cording to the International Patent Classification (IPC) at the 6 digit level; however, I aggregate them to the 4-digit level in order to match them with the index of network exposure, which varies at the 4-digit industry level.

Table 4.1 shows the descriptive statistics of the patent data for the two countries over the whole time period, aggregated at the IPC level. I obtain information on 812 different patent classes, including those classes for which one of the two countries record zero patents over the time period analysed. The average number of patents across all classes is larger in the U.S. compared to China.

Exposure to Ethnic Chinese Network

In order to create the index of Chinese employment at the industry level I use data from the US Census, specifically the 1890 U.S. Population Cen-

Mean Median SD Min Max N Patents

U.S. Patents 4919.66 919.5 14068.89 0 183085 812

Chinese Patents 1236.459 221.5 2632.274 0 27738 812

Ethnic Network

Index of Chinese Employment 4.266 .304 25.736 0 575.671 812 Chinese Employment by Industry 16.892 10.443 18.950 0 102.509 812

Table 4.1. Descriptive Statistics

Notes:This table shows the descriptive statistics of the main variables used in this paper. I sum the number of patents by IPC class, over the years 1985-2006. The ethnic Chinese

employment by industry is for year 1990.

sus and the 1995 U.S. Economic Census. From the first I obtain information about Chinese people living in the U.S. by county; from the second I gather data about employment by four-digit industry by Standard Industrial Clas- sification (SIC) codes in each county. I then convert the employment infor- mation from SIC to IPC classification using the concordance developed by Silverman (2003), in order to merge it with the patent data. I use 1995 em- ployment data, which is the median year in the data set, in order to obtain a measure which is valid for the time frame considered. I use microdata from the 1990 5% Population Census available from IPUMS in order to measure ethnic Chinese employment at the industry level in the U.S., because of the lack of availability of industry-level data on ethnic employment in 1995. Following Griffith et al. (2006), I measure the Chinese network in 1990 to avoid capturing Chinese migration corresponding to the U.S. technology boom.

Table 1 shows the summary statistics for the index of Chinese em- ployment based on historical Chinese settlements. The next section ex-

plains in detail how this is constructed.

4.3 Empirical Strategy

In this section I describe the empirical strategy used to estimate the effect of Chinese networks on the diffusion of knowledge.

Firstly, I outline the ordinary least squares (OLS) regression. In the following section I describe the index of exposure to the Chinese network and the instrumental variable (IV) regression.

4.3.1 OLS Regression

I use a panel of patent data at the 4-digit IPC class level to analyse the relation between ethnic networks and innovation. I estimate a fixed effect regressions including time and industry fixed effects, therefore exploiting within industry variation in number of patents in the U.S. across time. Be- cause knowledge transfers need not happen contemporaneously, and in order to reduce the chance of zero outcomes in each patent class, I aggre- gate the number of patents in each class into 3 year periods.

I firstly estimate the following equation with ordinary least squares (OLS):

ln(y)_i,t =g0+g1ln(cn employment)i⇥ln(x)i,t+g2ln(x)i,t+li+xt+ni

(4.1)

where ln(y) is the log of Chinese patents in industry i and three-

year time period t; ln(cn employment) denotes the log of ethnic Chinese

in industry i and three-year time period t; and l and x indicate industry

and three-year time period fixed effects. Note that the coefficientg2 is not

estimated, as it is collinear with the patent class fixed effects.

4.3.2 Instrumental Variable Regression

The main concern with estimating network effects using the number of eth- nic Chinese workers in U.S. industries is that this measure may be subject to reverse causality. This would happen if Chinese migrants were partic- ularly well trained in industries that are innovative in China, and decided to purposely settle in locations in the U.S. where the same innovative in- dustries are prevalent. Thus, by measuring the network with the number of ethnic Chinese workers in a given industry, the estimates are likely to capture the effect of ethnic Chinese contributing to the development of industries in the U.S. in addition to the network effect.

To mitigate reverse causality issues, I construct an index which pre- dicts the number of ethnic Chinese workers in each 4-digit industry in the U.S. This measure exploits the geographic overlay between historical Chi- nese settlements in 1890 and current location of industries in each county.

This measure takes advantage of the fact that entry of ethnic Chinese to the U.S. was prohibited under the Chinese Exclusion Act, and due to discrimination internal mobility of existing Chinese migrants was limited until the Act was repealed in 1943.

I construct the variable as follows: for each county, I calculate the product of the employment share of a given industry in that county in 1995, multiplied by the number of ethnic Chinese in 1890. This is then

summed across counties for each industry to give a single value for each industry. The index of Chinese network is then:

Chinese Network_i =

Â

c

✓ _Employment i,c

Total Employmentc ⇥Chinese Population1890c ◆ (4.2)

where employment and total employment represent the number of

workers in county cwho are employed in industryi, and the total employ-

ment in each county respectively, both measured in 1995. This index is then aggregated at the IPC level, to obtain a measure of the ethnic network which varies at the same level as the patent data. Higher values of this measure imply a higher probability of ethnic Chinese being employed in a given industry nowadays.

As my aim is to obtain an exogenous measure of Chinese employ-

ment, I use the geographic overlay of historic Chinese settlements andcur-

rentindustry location; this is to reduce the chance of capturing the effect of

ethnic Chinese purposely settling in a specific county due to the presence of certain industries.

A graphic representation of the correlation between the predicted measure of Chinese employment based on historical settlements and cur- rent Chinese employment by industry is depicted in Figure C.0.2.

I then estimate a two-stage least square (2SLS) model where I instru- ment current Chinese employment with the measure described above. The first and second stage equations are stated below.

First Stage: ln(cn employment)_i⇥ln(x)_i,t =a0+ a1ln(network1890)_i,t⇥ln(y)i,t +a2ln(x)i,t +fi+wt+#i,t