class: center, middle, inverse, title-slide # STA 235 - Model Selection II: Shrinkage ## Spring 2021 ### McCombs School of Business, UT Austin ---  # Some reminders .box-2LA[Homework 3 is due this Wednesday]
 .box-2tL[No questions will be answered after 7pm 4/13] --- # Some reminders (Cont.)
 .box-7LA[Highlight sessions on Thur. 5:00-5:30pm]
 - Same link every week. - If you feel you are **.darkorange[falling behind]**, I highly encourage you to attend. -- - Same as last class, extra point will be given for **.darkorange[participation]**: - That means **.darkorange[answering polls]** AND **.darkorange[participating in class]** (e.g. asking/answering a questions, etc.) --- # Coming soon: Prediction project
 .box-6LA[Data Challenge!]
 - Will be posted this week. - You will be allowed to work **.darkorange[pairs]**: - That means that your group can be one (1) or two (2) persons (I encourage you to find a partner). - **.darkorange[Don't email me asking me if you can be a group of 3, 4, etc.]** --- # Continuing our journey .pull-left[ **.darkorange[Last class:]** - Bias vs Variance - Validation sets and cross-validation - Model selection: Stepwise **.darkorange[Today:]** - Regularization and model selection: Shrinkage - Prediction: K-nearest neighbors ] .pull-right[ .center[ ![](https://media.giphy.com/media/nyPZB25Mg1pe/giphy.gif)] ] --- background-position: 50% 50% class: left, bottom, inverse .big[ Honey, I shrunk the coefficients! ] --- # What is shrinkage? - Last class, we saw **.darkorange[stepwise procedure]**: Subsetting model selection approach. - Select `\(k\)` out of `\(p\)` total predictors -- - **.darkorange[Shrinkage *(a.k.a Regularization)*]**: Fitting a model with all `\(p\)` predictors, but introducing bias (i.e. shrinking coefficients towards 0) for improvement in variance. - Ridge regression - Lasso regression --- background-position: 50% 50% class: left, bottom, inverse .big[ Let's build a ridge. ] --- # Ridge Regression: An example - Remember the JITT example? Window-shoppers vs. High rollers  --- # Ordinary Least Squares - In an **.darkorange[OLS]**: Minimize sum of squared-errors, i.e. `\(\min_\beta \sum_{i=1}^n(\text{spend}_i - \text{freq}_i\beta)^2\)`  --- # What about fit? - Does the OLS fit the testing data well?  --- # Ridge Regression - Let's shrink the coefficients!: **.darkorange[Ridge Regression]**  --- .center2[ .box-7LA[Poll time!] ] --- .center2[ .box-7LA[Why does Ridge Regression reduce its slope compared to OLS?] ] --- # Ridge Regression: What does it do? - Ridge regression **.darkorange[introduces bias to reduce variance]** in the testing data set. - In a simple regression (i.e. one regressor/covariate): `$$\min_\beta \sum_{i=1}^n\underbrace{(y_i - x_i\beta)^2}_{OLS}$$` --- # Ridge Regression: What does it do? - Ridge regression **.darkorange[introduces bias to reduce variance]** in the testing data set. - In a simple regression (i.e. one regressor/covariate): `$$\min_\beta \sum_{i=1}^n\underbrace{(y_i - x_i\beta)^2}_{OLS} + \color{#F89441}{\underbrace{\lambda\cdot \beta^2}_{Ridge Penalty}}$$` -- - `\(\lambda\)` is the **.darkorange[penalty factor]** `\(\rightarrow\)` indicates how much we want to shrink the coefficients. --- # Back to the plots... - Let's solve the minimization problem for ridge regression. **.darkorange[What line do we choose?]**  --- # For the OLS line `$$0 + \lambda \cdot (-16.25)^2 = 264.1\lambda$$`  --- # For the OLS line `$$0 + \lambda \cdot (-16.25)^2 = 264.1\times 3 = 792.3$$`  --- # Now, for the ridge regression line `$$(3^2 + (-17)^2) + \lambda \cdot (-11.25)^2 = 298 + 126.6\times 3 = 677.8$$`  --- .center2[ .box-3LA[But remember... we care about accuracy in the testing dataset!] ] --- # RMSE on the testing dataset: OLS `$$RMSE = \sqrt{\frac{1}{4}\sum_{i=1}^4(\text{spend}_i - (132.5 -16.25\cdot\text{freq}_i))^2} = 28.36$$`  --- # RMSE on the testing dataset: Ridge Regression `$$RMSE = \sqrt{\frac{1}{4}\sum_{i=1}^4(\text{spend}_i - (119.5 -11.25\cdot\text{freq}_i))^2} = 12.13$$`  --- .center2[ .box-2LA[Poll time!] ] --- .center2[ .box-2LA[What problem is Ridge Regression looking to fix?] ] --- # Ridge Regression in general - For regressions that include **.darkorange[more than one regressor]**: `$$\min_\beta \sum_{i=1}^n\underbrace{(y_i - \sum_{k=0}^px_i\beta_k)^2}_{OLS} + \color{#F89441}{\underbrace{\lambda\cdot \sum_{k=1}^p\beta_k^2}_{Ridge Penalty}}$$` -- - In our previous example, if we had two regressors, `\(female\)` and `\(freq\)`: `$$\min_\beta \sum_{i=1}^n(\text{spend}_i - \beta_0 - \beta_1\text{female}_i - \beta_2\text{freq}_i)^2 + \lambda\cdot (\beta_1^2 + \beta_2^2)$$` -- - Because the ridge penalty includes the `\(\beta\)`'s coefficients, **.darkorange[scale matters]**: - Standardize coefficients to `\(SD=1 \rightarrow x'_{ij} = \frac{x_{ij}}{\sqrt{\frac{1}{n}(x_{ij}-\bar{x}_j)^2}}\)` --- # Some jargon - Ridge regression is also referred to as `\(l_2\)` regularization: - `\(l_2\)` norm `\(\rightarrow ||\beta||_2 = \sum_{k=1}^p \beta^2\)` -- - Some important notes: - `\(||\hat{\beta}^R_\lambda||_2\)` will always decrease in `\(\lambda\)`. - `\(||\hat{\beta}^R_\lambda||_2/||\hat{\beta}||_2\)` will always decrease in `\(\lambda\)`. --- .center2[ .box-4LA[Poll time!] ] --- .center2[ .box-4LA[If λ=0, what is the value of l2 norm for the ridge regression over the l2 norm of OLS?] ] --- # How do we choose λ? -- .box-5[Cross-validation!] -- 1) Choose a grid of `\(\lambda\)` values 2) Compute cross-validation error (e.g. RMSE) for each 3) Choose the smallest one. --- # λ vs RMSE?  --- # λ vs RMSE? A zoom  --- # How do we do this in R? .pull-left[ ```r *library(caret) set.seed(100) data <- read.csv("https://raw.githubusercontent.com/maibennett/sta235/main/exampleSite/content/Classes/Week11/1_Shrinkage/data/purchases.csv") lambda_seq <- 10^seq(-3, 3, length = 100) cv <- train(spend ~., data = train.data, method = "glmnet", preProcess = "scale", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(alpha = 0, lambda = lambda_seq) ) cv_lambda <- data.frame(lambda = cv$results$lambda, rmse = cv$results$RMSE) ``` ] .pull-right[ - We will be using the `caret` package ] --- # How do we do this in R? .pull-left[ ```r library(caret) *set.seed(100) data <- read.csv("https://raw.githubusercontent.com/maibennett/sta235/main/exampleSite/content/Classes/Week11/1_Shrinkage/data/purchases.csv") lambda_seq <- 10^seq(-3, 3, length = 100) cv <- train(spend ~., data = train.data, method = "glmnet", preProcess = "scale", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(alpha = 0, lambda = lambda_seq) ) cv_lambda <- data.frame(lambda = cv$results$lambda, rmse = cv$results$RMSE) ``` ] .pull-right[ - We will be using the `caret` package - We are doing **.darkorange[cross-validation]**, so remember to set a seed! ] --- # How do we do this in R? .pull-left[ ```r library(caret) set.seed(100) data <- read.csv("https://raw.githubusercontent.com/maibennett/sta235/main/exampleSite/content/Classes/Week11/1_Shrinkage/data/purchases.csv") *lambda_seq <- 10^seq(-3, 3, length = 100) cv <- train(spend ~., data = train.data, method = "glmnet", preProcess = "scale", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(alpha = 0, lambda = lambda_seq) ) cv_lambda <- data.frame(lambda = cv$results$lambda, rmse = cv$results$RMSE) ``` ] .pull-right[ - We will be using the `caret` package - We are doing **.darkorange[cross-validation]**, so remember to set a seed! - You need to create a grid for the `\(\lambda\)`'s **.darkorange[that will be tested]** ] --- # How do we do this in R? .pull-left[ ```r library(caret) set.seed(100) data <- read.csv("https://raw.githubusercontent.com/maibennett/sta235/main/exampleSite/content/Classes/Week11/1_Shrinkage/data/purchases.csv") lambda_seq <- 10^seq(-3, 3, length = 100) *cv <- train(spend ~., data = train.data, * method = "glmnet", preProcess = "scale", * trControl = trainControl("cv", number = 10), * tuneGrid = expand.grid(alpha = 0, * lambda = lambda_seq) ) cv_lambda <- data.frame(lambda = cv$results$lambda, rmse = cv$results$RMSE) ``` ] .pull-right[ - We will be using the `caret` package - We are doing **.darkorange[cross-validation]**, so remember to set a seed! - You need to create a grid for the `\(\lambda\)`'s **.darkorange[that will be tested]** - The function we will use is `train`: Same as before .small[ - `method="glmnet"` means that it will run an elastic net. - `alpha=0` means is a **.darkorange[ridge regression]** - `lambda = lambda_seq` is not necessary (you can provide your own grid)] ] --- # How do we do this in R? .pull-left[ ```r library(caret) set.seed(100) data <- read.csv("https://raw.githubusercontent.com/maibennett/sta235/main/exampleSite/content/Classes/Week11/1_Shrinkage/data/purchases.csv") lambda_seq <- 10^seq(-3, 3, length = 100) cv <- train(spend ~., data = train.data, method = "glmnet", preProcess = "scale", trControl = trainControl("cv", number = 10), tuneGrid = expand.grid(alpha = 0, lambda = lambda_seq) ) *cv_lambda <- data.frame(lambda = cv$results$lambda, * rmse = cv$results$RMSE) ``` ] .pull-right[ - We will be using the `caret` package - We are doing **.darkorange[cross-validation]**, so remember to set a seed! - You need to create a grid for the `\(\lambda\)`'s **.darkorange[that will be tested]** - The function we will use is `train`: Same as before - Important objects in `cv`: .small[ - `results$lambda`: Vector of `\(\lambda\)` that was tested - `results$RMSE`: RMSE for each `\(\lambda\)` - `bestTune$lambda`: `\(\lambda\)` that minimizes the error term.] ] --- # How do we do this in R? .pull-left[ OLS regression: ```r lm1 <- lm(spend ~ freq + female, data = train.data) coef(lm1) ``` ``` ## (Intercept) freq female ##<PHONE_NUMBER> -3.4339875 -0.6391956 ``` ```r pred.lm <- lm1 %>% predict(test.data) RMSE(pred.lm, test.data$spend) ``` ``` ## [1] 22.79557 ``` ] .pull-right[ Ridge regression: ```r coef(cv$finalModel, cv$bestTune$lambda) ``` ``` ## 3 x 1 sparse Matrix of class "dgCMatrix" ## 1 ## (Intercept)<PHONE_NUMBER> ## freq -9.4095221 ## female -0.2773666 ``` ```r pred.ridge <- cv %>% predict(test.data) RMSE(pred.ridge, test.data$spend) ``` ``` ## [1] 22.7896 ``` ] --- background-position: 50% 50% class: left, bottom, inverse .big[ Throwing a lasso ] --- # Lasso regression - Very similar to ridge regression, except it **.darkorange[changes the penalty term]**: `$$\min_\beta \sum_{i=1}^n\underbrace{(y_i - \sum_{k=0}^px_i\beta_k)^2}_{OLS} + \color{#F89441}{\underbrace{\lambda\cdot \sum_{k=1}^p|\beta_k|}_{Lasso Penalty}}$$` -- - In our previous example: `$$\min_\beta \sum_{i=1}^n(\text{spend}_i - \beta_0 - \beta_1\text{female}_i - \beta_2\text{freq}_i)^2 + \lambda\cdot (|\beta_1| + |\beta_2|)$$` -- - Lasso regression is also called `\(l_1\)` regularization: `$$||\beta||_1 = \sum_{k=1}^p |\beta|$$` --- # Ridge vs Lasso .pull-left[ .box-2[Ridge] .box-2t[Final model will have p coefficients] .box-2t[Usually better with multicollinearity] ] -- .pull-left[ .box-4[Lasso] .box-4t[Can set coefficients = 0] .box-4t[Improves interpretability of model] .box-4t[Can be used for model selection] ] --- # And how do we do Lasso in R? .pull-left[ ```r library(caret) set.seed(100) data <- read.csv("https://raw.githubusercontent.com/maibennett/sta235/main/exampleSite/content/Classes/Week11/1_Shrinkage/data/purchases.csv") lambda_seq <- 10^seq(-3, 3, length = 100) cvl <- train(spend ~., data = train.data, method = "glmnet", trControl = trainControl("cv", number = 10), * tuneGrid = expand.grid(alpha = 1, lambda = lambda_seq) ) cvl_lambda <- data.frame(lambda = cvl$results$lambda, rmse = cvl$results$RMSE) ``` ] .pull-right[ .box-7[Exactly the same!] - ... But change `alpha=1`!! ] --- # And how do we do Lasso in R? .pull-left[ Ridge regression: ```r coef(cv$finalModel, cv$bestTune$lambda) ``` ``` ## 3 x 1 sparse Matrix of class "dgCMatrix" ## 1 ## (Intercept)<PHONE_NUMBER> ## freq -9.4095221 ## female -0.2773666 ``` ```r pred.ridge <- cv %>% predict(test.data) RMSE(pred.ridge, test.data$spend) ``` ``` ## [1] 22.7896 ``` ] .pull-right[ Lasso regression: ```r coef(cvl$finalModel, cvl$bestTune$lambda) ``` ``` ## 3 x 1 sparse Matrix of class "dgCMatrix" ## 1 ## (Intercept) 117.032965 ## freq -3.296245 ## female . ``` ```r pred.lasso <- cvl %>% predict(test.data) RMSE(pred.lasso, test.data$spend) ``` ``` ## [1] 22.79291 ``` ] --- # Main takeway points .pull-left[ - You can **.darkorange[shrink coefficients]** to introduce bias and decrease variance. - Ridge and Lasso regression are **.darkorange[similar]**: - Lasso can be used for model selection. - Importance of understanding **.darkorange[how to estimate the penalty coefficient]**. ] .pull-right[ .center[ ![](https://media.giphy.com/media/2AsEweF761Dji/source.gif)] ] --- # References - James, G. et al. (2013). "Introduction to Statistical Learning with Applications in R". *Springer. Chapter 6.* - STDHA. (2018). ["Penalized Regression Essentials: Ridge, Lasso & Elastic Net"](http://www.sthda.com/english/articles/37-model-selection-essentials-in-r/153-penalized-regression-essentials-ridge-lasso-elastic-net)


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全球贸易与城市经济
目录
Global Goods Trade and Metropolitan Economies
The Global Goods Trade that Moves Metro Economies
I. Introduction
Why Metropolitan Areas?
Key Terms
II. The Economic Rationale for Goods Trade
The Memphis Logistics Hub and Future Economic Growth
III. Today’s Global Network
Brazil and the Changing Trade Environment
The Rise of Global Value Chains
Manufacturing and Logistics Development of China’s Pearl River Delta
Freight Transportation Resiliency and the 2011 Japanese Tsunami
IV. Reconsidering Freight and Economic Policy
Developing a National Freight Strategy: Lessons from Canada and Germany
Federal Freight Policy: MAP-21 and Beyond
Upgrading Analytics to Capture Metropolitan Trade Networks
V. Conclusion
Global Goods Trade and Metropolitan Economies
全球贸易与大城市经济（交通运输）
October 22, 2013
US Atlas
全球化核心：Flat3.0
季风亚洲（Monsoon Asia）
季风亚洲的海军基地
传说中的珍珠链
中国能源输入通道
Metro Freight Series: Global Goods Trade and Metropolitan Economies
By: Adie Tomer, Joseph Kane and Robert Puentes
The Metro Freight research series assesses goods trade at the metropolitan scale. It uses a unique and comprehensive database to capture all the goods moving in and out of U.S. metropolitan areas, both domestically and beyond. The reports in the series will describe which goods move between metropolitan areas, how they move via different modes of transportation, and uncover the specific trading relationships between U.S. metropolitan areas as well as their global counterparts.
Metro Freight: The Global Goods Trade that Moves Metro Economies (PDF)
This primer establishes the economic rationale for metropolitan goods trade, describing why, how, and what these areas exchange with each other. One of the lessons from the Great Recession is the need to grow and support the tradable sectors, typically manufacturing and high-end services, of our metropolitan economies. But to drive these tradable sectors, metropolitan areas need physical access to markets. Metropolitan freight connectivity enables this access and the ensuing modern global value chains. Without it, trade cannot occur.
Metro-to-Metro: Global and Domestic Goods Trade in Metropolitan America (PDF)
The trading of physical goods is a major component of the U.S. economy. In 2010, the United States moved more than $3 trillion in goods internationally or nearly $8.8 billion, on average, each day. However, an exclusive focus on national trade fails to recognize the extreme regional variety in production, consumption, and goods exchange. This discussion paper marks the first time metropolitan areas can begin to explore their place in domestic and global goods trade networks by tracking which regions generate the most international trade and the level of trade within the much larger domestic marketplace.
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The Global Goods Trade that Moves Metro Economies
Global Cities Initiative
A JOINT PROJECT OF BROOKINGS AND JPMORGAN CHASE
Metro Freight:
The Global Goods Trade that Moves Metro Economies
Adie Tomer, Joseph Kane, and Robert Puentes
“Goods trade delivers unquestionable returns to metropolitan economies, making it imperative that metropolitan leaders understand how their economic base relates to current and prospective trade partners.”
US Atlas
全球化核心：Flat3.0
季风亚洲（Monsoon Asia）
季风亚洲的海军基地
传说中的珍珠链
中国能源输入通道
Summary
One of the lessons from the Great Recession is the need to grow and support the tradable sectors,
typically manufacturing and high-end services, of our metropolitan economies. But to drive
these tradable sectors, metropolitan areas need physical access to markets. Metropolitan freight
connectivity enables this access and the ensuing modern global value chains. Without it, trade
cannot occur.
This report establishes the economic rationale for metropolitan goods trade, describing why,
how, and what these areas exchange with each other. It makes these key points:
Metropolitan economies cannot function unless they trade goods with one another. Land,
labor, and capital limit what a metropolitan area can produce on its own, meaning goods trade
is essential to deliver economic benefits to metropolitan economies. If economic benefits compel
metropolitan areas to trade, then transportation makes those benefits a reality.
Recent global trends make domestic and international trade more prevalent and more
competitive than ever. Major innovations of the 20th century—freight technologies like
expanded shipping capacity, new logistics resources, and communications infrastructure like
broadband—reduced the costs of trading goods within and among different countries. At the
same time, national governments liberalized trade through barrier reductions like free trade
agreements. These forces helped global merchandise volumes reach $18.3 trillion in 2012, an
increase of over 400 percent since 1990. In the process, trade effectively reduced the distance
between markets, expanding domestic and global competition over firms’ production costs,
limited energy resources, and overall market power.
The rise of global value chains forces metropolitan areas to assess their relationship to
the global economy. Goods production is no longer dominated by single production lines concentrated
in one country. Emerging markets are now major participants in global value chains,
meaning value creation occurs in multiple locations, through various firms, and typically spans
manufacturing and service industries.
To maximize trade’s economic returns, metropolitan areas need a firm understanding of
their economic position as well as a supportive policy framework. To do that, metro leaders
need to understand global trading networks. They also must recognize the importance of
transportation assets—both within their region and beyond—to enable trade. Unfortunately,
current trade measures and public policies are either limited to national indicators or omit
most metropolitan areas, obscuring domestic trade networks in the process. Today in the
United States there is no national freight strategy, and most metropolitan areas fail to implement
comprehensive trade strategies.
The long-term goal of this series is to inform a new model of freight policy and practice where
public and private leaders work in concert to create a more favorable trade environment for the
American economy. Through a series of statistical surveys, it will offer policymakers and the
private sector a toolkit to map and analyze goods trade in the 21st century. This paper is the first
step in that process: establishing the foundational importance of goods trade to metropolitan
economic health and vitality.
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I. Introduction
US Atlas
全球化核心：Flat3.0
季风亚洲（Monsoon Asia）
季风亚洲的海军基地
传说中的珍珠链
中国能源输入通道
Metropolitan Houston is a U.S. economic dynamo. While the country struggled to regain
its footing following the Great Recession, Houston charted a different, more prosperous
path. The metropolitan economy only took nine months to resume growth following
its local recession and since has grown an additional 15 percent in less than five years.1
Houston’s employment picture is appreciably better than the rest of the United States with a net
increase in jobs since the recession and an unemployment rate of 6 percent.
Behind this success story is a productive metropolitan economy that still makes things. Nearly 20
percent of Houston’s economic output is derived from manufacturing.2 Specializing in chemicals and
machinery, its manufacturing sector is a larger share of its metropolitan economy than traditional
industrial hubs such as Pittsburgh and Detroit.3 Houston’s mining industry is also a major metropolitan
growth engine. Powered by both oil refineries and corporate headquarters, mining generates over 16
percent of Houston’s metropolitan output.4 Taken together, these industries represent over one-third
of the Houston economy.5
While these industries deliver economic benefits to Houston, they also rely on trading partners outside
the region. Fortunately, the region has a full set of freight transportation assets, including ports
with deep water access, thousands of miles of limited access freeways, two major airports, and strong
rail and intermodal connectivity to physically connect with other markets around the nation and world.
The region’s freight network of course operates twofold by bringing in the products Houston cannot
live without. For example, the region’s agriculture and forestry industries are relatively tiny.6 Without
bringing in those products from elsewhere, Houston would not have food to feed its residents or lumber
to build houses. Along the same lines, Houston manufactures only small amounts of textile-related
products.7 Inflows are equally important here, providing clothing for residents and textiles for various
industries.
From Houston to Hamburg, the story is the same: Freight transportation facilitates metropolitan
trade and supports economic development.
This is amplified in an era of rapidly changing global goods trade networks that have the power to
reconfigure the sites of economic dynamism. Public policy has a responsibility to recognize this vital
relationship between economies and freight, and it is imperative that national policies reflect the
domestic and global environments in which metropolitan areas must now compete.
This report advances a simple proposition: Because metropolitan economies cannot function unless
they trade with each other, and because trade cannot occur without freight transportation networks,
freight and economic development policies and practices must be coordinated and globally focused.
In the process, it stresses the importance of considering trade at the critical sites of consumption,
production, and goods movement: metropolitan areas. The report proceeds in three parts:
It establishes the economic rationale for why metropolitan areas trade goods, why certain metropolitan
areas trade with each other, what goods metropolitan pairs sell to one another, and the
transportation infrastructure that makes it all possible.
It describes the modern global trade environment, the creation of global value chains, and the
implications of both on modern metropolitan trade relationships.
It concludes by outlining the failures in current freight and economic policies to accurately capture
the scope and power of modern trade networks.
This report also begins a new series that will explore the relationships between goods trade, freight
transportation, and metropolitan economic growth. Each of these areas of policy and practice are
complex, but global competition in the 21st century forces leaders at all levels to craft an economic
agenda that considers them in concert. The goal is to provide leaders with a framework to facilitate
that agenda-building—and this report serves as the foundation.
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Why Metropolitan Areas?
This paper eschews typical national-level trade analyses to focus on the world’s primary economic units, metropolitan areas.
In the simplest of terms, a metropolitan area is a regional economy. It is the network of cities, towns, and villages whose
residents and businesses interact with one another on a daily basis, typically expressed through a common labor market
and shared commuting patterns.8 They tend to have common cultural institutions, and other shared social characteristics. Just as
importantly, metropolitan areas are defined by their networks of civic leaders who guide the economic future of their region.9
Within the United States, metropolitan areas dominate national-level economic performance, with the largest 100 regions generating
three-quarters of national economic output, as measured by gross domestic product (GDP).10 The same 100 metro areas also
contribute disproportionately to almost all key indicators of economic health.11 This includes many of the drivers of a production-led
economy, from 90 percent of advanced industrial jobs to 92 percent of patents.12
These large population and economic centers are also home to the country’s chief freight transportation assets. From New
Orleans and Houston to New York and Los Angeles, 14 of the country’s 20 busiest seaports are in the 100 largest metro areas.13
In the growing need to move high-value, low-weight products, these 100 metros are also home to nine of the country’s 10 busiest
airports by total cargo value.14 More than 2,020 of the nation’s 3,280 intermodal facilities are also found in these 100 metro areas,
critical for transferring freight across multiple modes.15 Finally, these metro areas accounted for over 64 percent of all highway and
major road traffic in the United States, helping to move freight and workers within and between these markets.16
Considering their economic primacy, this series uses metropolitan areas as the primary lens to assess goods trade. In forthcoming
papers, it will introduce new metrics demonstrating the heavy role metropolitan economies play in global goods trade.
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Key Terms
Trade: The exchange of a good or service between two distinct metropolitan areas, also known as
intermetropolitan trade. The term includes the exchange of goods or services between domestic
metropolitan areas, meaning the paper will specify when referencing international trade.
Goods Trade: The physical exchange of products or commodities between two distinct trading partners.
These exchanges encompass the full range of commodities, from the rawest natural resources
like stones to the most advanced products like aerospace equipment.
Inflows: The volume of goods a region or metro area purchases from other places, reported in
either value or weight. Inflows are subdivided domestically (by domestic destination) and internationally
(by foreign origin). “Imports,” as such, refer exclusively to international inflows.
Outflows: The volume of goods a region sells to other regions and countries, reported in either
value or weight. Outflows are subdivided domestically (by domestic origin) and internationally (by
foreign destination). “Exports,” as such, refer exclusively to international outflows.
Services Trade: The exchange of a service between two distinct trading partners. Unlike goods
trade, no physical product changes hands. A classic example is financial services, like banking or
market trading, which can be conducted in one place but sold to a consumer in another market.
Production: The act of creating a good or service. The geographic location producing the good or
service is considered the outflow producer in any external trading relationship. However, it is important
to note that not all production leads to trade—some goods and services are consumed locally.
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II. The Economic Rationale for Goods Trade
Why Metropolitan Areas Trade Goods
Land, labor, and capital are the most fundamental inputs allowing regional economies to produce
goods.17 In the course of this production, every metropolitan area will confront two issues. First, not
all commodities and economic inputs exist within every metropolitan economy. Second, even when
they do exist, they are always limited. There are only so many natural resources within a given area,
only so many people to employ within a given market, and only so many human-made products available
to use.
This combination of missing and limited inputs requires firms to look beyond their metropolitan
borders to acquire the goods necessary to build new products, power their homes and factories, and
determine what goods they can more cheaply buy from someone else.
Goods trade is the exchange of physical products. It is fundamental to the modern economies in
which we participate.18 Without goods trade, communities would be forced to subsist solely on what
it could produce from local inputs, whether they be raw materials or manufactured products. Goods
trade allows markets to compare their own inputs and products to what is available among all of their
trading peers.
Inflows bring new or higher-value products to a community. New York purchases oil refined outside
New Orleans, providing fuel for its vehicles and inputs for its chemical plants. Los Angeles purchases
computer processors from facilities in Phoenix, enabling industries like film and television to add
editing and post-production effects. Even if local products do exist, inflows can offer cheaper alternatives
that save consumers money and push local producers to innovate more. In each case, inflows
allow local markets to either consume or create products that would not be possible without a trading
relationship.
Outflows bring financial assets to a community. By selling surpluses of a product—say those computer
processors in Phoenix—the money spent by the purchasers moves to the seller’s local or ownership
market in the form of company profits and worker wages. Firms and consumers spend this
revenue on a host of activities, from investment in new production inputs like warehouse space, to
tradable services like financial investments, to locally consumed services like food, education, and
healthcare. Outflows also have the power to improve public resources, since increased sales often
deliver higher tax revenues. In all cases, this economic recycling continues through multiple stages—
helping fund services that are inherently local and providing the financing to purchase inflows from
other markets.
While each market will vary in its ratio of inflows to outflows, each form of goods trade is vital for
metropolitan economic health.
Which Metropolitan Areas Trade Goods
Economic theory explains why trade occurs based on the value it brings to trading communities. But
what determines which metropolitan areas will form a goods trading relationship?
Goods trade can only exist between two metropolitan areas when the products supplied by one
market’s firms meet the demands of another market’s firms or consumers. Economic geography
defines this as complementarity between markets.19 While introduced in prior decades, complementarity
became a foundational concept following Edward Ullman’s seminal work in the 1950s.20 Ullman
and his peers argue that the existence of matching supply and demand, much like a business and its
customers, is the most fundamental method to determine if two places will share a geographic product
exchange. As forces change—rising demand in one market, a new supply in another—complementarity
will shift.21 Consider Pittsburgh’s famous steel mills and the New York City building boom during the
early twentieth century. Those early skyscrapers all required steel beams, creating a complementary
relationship between Pittsburgh’s steel suppliers and New York City’s construction demands.
Complementarity may be a requirement in any trading relationship, but it is only one determinant.
Ullman and other theorists define two other elements necessary to forge a trade relationship.
The first is transferability, which refers to the physical ability and cost to exchange goods. Physical
connections can take many forms. They include roadways and railways over land, seaports and
airports, even telecommunications infrastructure like phone lines and digital cables crisscrossing the globe. Depending on the product being traded, any and all of these infrastructure investments are
necessary to actualize complementarity. Without them, a region would stand in isolation and only
consume what it can locally produce. However, since infrastructure connects almost every part of
the world, costs are now the primary element defining transferability. As costs to move products rise,
whether measured by money or time friction increases between places and reduces transferability.
Conversely, lowering exchange costs can reduce friction and improve transferability.
Transferability even has the power to change complementarity in the long-run. Accessibility related
to transportation costs will drive investment decisions, sometimes determining whether a company
invests in a new facility in one market versus another. Economists like Paul Krugman and Edward
Glaeser use this element in their discussion of scale economies driving locational investments, such
as manufacturing clusters locating near large population centers to reduce their transport costs
(and thereby improve transferability) and the ability to attract human capital.22 Similarly, geographer
Donald Janelle finds improvements in transportation connectivity and travel times can enhance a
region’s ability to attract new economic activity.23 This entrance of transportation costs into industry’s
geographic decision-making can have long-run impacts on what a particular market has to supply and
what input goods it will demand.24
The second determinant is intervening opportunities, which are the alternatives to a specific
complementary relationship. Very few products are supplied by only one firm located in a single market;
instead, businesses and consumers can choose from multiple firms in multiple markets all making
either the same or virtually indistinguishable products. Similarly, more than one market generally will
demand a certain product. The typical trade environment contains consumers and suppliers that both
confront trading choices—in other words, multiple instances of complementarity.25 Considering the
previous Pittsburgh-New York City example, the emergence of steel mills in places like Allentown and
skyscrapers in Chicago functioned as intervening opportunities.
It is important to note how intrafirm trading relationships follow this same rubric. For the large number
of firms that maintain facilities in multiple metropolitan areas, their need to exchange products
at different stages of production will generate trade between two markets. Consider the example of
Phoenix selling computer processors to Los Angeles. Presume that an exchange between Portland’s
Intel Corp. and Phoenix preceded that exchange—Intel fabricates certain computer chips in Portland,
of which some may by assembled and tested in Phoenix—driven entirely by Intel’s own production lines
and site selection process. These kind of intrafirm trades follow the same three basic trade determinants—
supply from one facility meets the demands of another, the sites must be physically connected,
and they are the optimal sites to conduct the trade—but firm leadership makes decisions in a more
centralized manner rather than trades between different firms in different metropolitan areas.
What Types of Goods Metropolitan Areas Trade
As previously mentioned, the core economic components of any market are its land, labor, and capital.
Those factors of production are limited within each market, and that scarcity drives metropolitan
areas’ firms and consumers to trade for what is unavailable at home. Conversely, a metropolitan area’s
local production inputs and the quantities available of each determine what a region can produce on
its own.
Just like the varied distribution of factors of productions, each metro area will vary in its local production
potential. Some locations will leverage their natural resources, say coal in Wyoming or citrus
in Florida. Manufacturing centers will have surpluses from their production lines, such as airplanes in
Wichita and cars in Detroit. Whether measured by weight or value, each metropolitan area should sell
more of these particular products than it purchases, also known as a surplus trade balance. Trade balances
are often reported at the national level—oil from Saudi Arabia, electronics from Korea—but they
also exist at the metropolitan scale.
Assessing all local industry types—goods producers, tradable services, local services, even private
consumers—will create an inventory of what goods the metropolitan area has to sell and what goods
it needs to buy. In this sense, local industrial and population composition is the key indicator of what
goods a metropolitan area will trade.
Economists have recognized this relationship between local production, local consumption, and
goods trade for over a century. Stretching from the early work of classical economists like Francois Quensay, geographic economists like Walter Isard, and especially 1973 Nobel Prize winner Wassily
Leontief, economists developed and upgraded models to capture the goods movement and interdependencies
that exist based on local economic composition.26 They eventually came to be known as
input-output models. The models are complex to produce, but the outputs are easy to read: a list of
expected consumption (input) and production (output) commodities for each industry. Their utility
at all levels of economic analysis boils down to their ability to translate economic composition into a
commodity-based mathematical equation, where input commodities plus local labor create outputs.
By creating a link between outputs and inputs at the industry level, models are also a powerful tool
in understanding the economic foundation behind the interdependent trading relationships established
throughout the world.27 By specifying each industry’s product inputs and outputs, the models
enable a geographic matching exercise between production and consumption to either verify current
trading relationships or where potential relationships exits. In essence, such an exercise can help measure
expected complementarity against actual complementarity.28
How Metropolitan Areas Trade Goods
The previous trade fundamentals—the what, where, and why—define the economic rationale behind the
trade of goods. That leaves one final consideration—how to make goods trading relationships a reality.
That is the role of freight transportation.
In the simplest terms, freight transportation is the movement of goods between destinations. But
freight transportation is much more than just putting a box on the back of a truck, or loading lumber
onto a river barge. At its core, freight transportation is the matching of production and consumption,
the actualization of supply meeting demand. It is the physical manifestation of the complementarity
principle.
Freight transportation’s role as the matchmaker between production and consumption, and the difficulties
associated with that exercise, has always been a fundamental part of metropolitan economic
growth. For example, major cities of the Third century B.C. like Alexandria and Seleucia grew due to
their seaborne access, trade network connectivity, and the military power to control transportation
flows.29
Technical innovations have always had an incredibly important role in improving access between
markets—the compass in the 11th century canals greatly reduced costs versus roads, and the steam
engine and railways brought similar price drops and travel time improvements to markets far from
natural water. All the while, these innovations created the need for financial and management centers,
boosting primary seats of commerce—such as New York, Amsterdam, and London.30
The 20th century’s freight innovations further enhanced trade. Introduction of the standard shipping
container added assembly line-style efficiency to seaborne trade and port-side movements.31 Trucking
inspired significant roadway investments, enabling U.S. highway freight movements to grow from 173
billion ton-miles in 1950 to over 1 trillion in 2000.32 The airplane dramatically reduced global shipment
times, especially for high-value, low-weight products.
Tying these modern innovations together was the growth in logistics and the effective management
of physical resource flows. Clear communication of inventories, supply needs, and customer requests
helped coordinate the movement of freight transportation assets between manufacturing plants, warehouses,
and final customers.
Today, advanced manufacturing centers plan their days around a consistent stream of daily deliveries
and pickups, built on a foundation of global logistics certainty. Some households can plan meals
around any fruits and vegetables they please, regardless of season or global geography. Online retailers
bring the consumer products of the world to many doorsteps, transforming an internet connection
into the biggest marketplace humanity has ever seen. In a world of such expectations, freight transportation
is the great enabler.
These trade fundamentals—the why, where, what, and how—serve as the backbone of any metropolitan
area’s goods trading relationship. Yet these fundamentals are only an initial guide. In practice,
every trading relationship is unique based on the variable trade partners, the exact products exchanging
hands, and the transportation methods chosen. The next section will explore the dynamic changes
afoot in global commerce.
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The Memphis Logistics Hub and Future Economic Growth
US Atlas
全球化核心：Flat3.0
Memphis is one of the world’s
largest freight hubs. With
ready access to metros across
the Eastern and Central time zones,
Memphis steadily strengthened its
logistical presence by investing in key
infrastructure assets and expanding
its economic base to serve a global
network of consumers and producers.
However, Memphis’ freight and logistical
focus leaves even more opportunities
for those transportation activities to
contribute to the metropolitan area’s
long-term economic growth.
Among its infrastructure assets,
Memphis stands at the intersection
of five major freight railroads and
two interstate highways, cycling high
volumes of goods into and out of the
area.33 Its numerous bridge crossings,
including the Hernando de Soto Bridge
and the Harahan Bridge, provide vital
links to origins and destinations across
the Mississippi River. Memphis also contains
the nation’s fourth largest inland
seaport, with handling facilities that cater to year-round traffic. Nearly 1,000 warehouses, 1,200 miles of pipeline, and 500 truck
terminals further support this movement.
Memphis’ airports are particularly valuable assets to the region. Owned and operated by the Memphis-Shelby County Airport
Authority, Memphis International Airport boasts expansive cargo operating facilities, serving as the “Super Hub” for FedEx since
the 1970s. Through the construction of additional runways and terminals, the airport has met rising air cargo demand, which has
seen about a 5 percent annual increase since 1987, and supported the growth of FedEx, which now processes more than 3 million
packages every day at its Memphis hub.34 As a result, Memphis not only became the largest domestic air cargo hub by tonnage,
moving almost 4 million tons of overall freight in 2010, but it also consistently ranks as one of the world’s highest-trafficked
freight airports.35
Since 1980, Memphis’ economy grew faster than the national economy, supported in part by its logistics sector.36 From 1980
to 2011, the metro’s output increased by 138.5 percent in real terms, while its employment rose by 51.8 percent, higher than the
figures for the nation (124.3 percent and 40.5 percent, respectively). The logistics sector accounted for 20 percent of this job
growth, while FedEx alone made up about ten percent of the metro’s total job growth. The sector also generated approximately 16
percent, or $8.6 billion, of the metro’s total output, significantly higher than levels seen in most other industries and the highest
output share related to logistics in any of the country’s 100 largest metropolitan areas.
Memphis’ high degree of specialization in logistics has given it a competitive edge in transportation, warehousing, and wholesale
trade, but its leaders will need to continue exploring ways that broaden the region’s economic base and build off existing
infrastructure assets. Its reliance on FedEx, for instance, leaves it vulnerable to sudden fluctuations in market activity, while many
of the occupations associated with logistics offer below-average wages.37 For example, one-third of Memphis’ logistics employment
is categorized hand-labor, with below-average pay and minimum opportunities for advancement. The Great Recession dealt
a heavy toll in this respect, as Memphis’s total economic output fell by 5.6 percent from 2007 to 2010 and it has continued to rely
on many of the same industries that have stagnated since then.38
By widening the scope of industries—and markets—it serves, Memphis can embrace a more dynamic freight strategy and
explore additional opportunities for its logistics specialization to attract and grow goods-producing firms. This approach can help
Memphis continue to capitalize on its strengths as a leader in global freight.
Memphis MAP
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III. Today’s Global Network
A combination of physical innovations and policy shifts, plus metropolitan economies’ drive
to enhance local economic growth, has pushed global trade to newfound heights.39 Global
goods export volumes reached $18.3 trillion in 2012, an increase of over 400 percent since
1990.40 Just as importantly, global goods trade volumes have set new records every year for
nearly three decades.
US Atlas
全球化核心：Flat3.0
季风亚洲（Monsoon Asia）
季风亚洲的海军基地
传说中的珍珠链
中国能源输入通道
Heightened global interconnectivity forces metropolitan economies, and their national partners, to
reconsider their advantages and disadvantages within today’s global goods trade networks. This section
addresses that need by further describing today’s global goods trade environment, how production
processes now extend beyond past geographic limitations, and what the implications of both are
for metropolitan economies and freight transportation.
A Redefined Trade Environment
While the freight network modernized across modal and logistic channels, three key developments
made the domestic and international trade environment easier than ever at the onset of the 21st century.
First, the world continues to dissolve international trade barriers. Traditionally, many countries instituted
trade barriers to protect their domestic industries. Ranging from tariffs to outright trade bans to
currency manipulation, trade barriers enhance the competitiveness of domestically-produced goods
by either raising the relative costs of foreign goods or simply denying the sale of products. This stands
in opposition to economists’ calls for free trade—countries’ ability to trade goods and services without
tariffs, subsidies, or other artificial barriers—as a condition necessary to maximize trade’s economic
benefits.41
Whether motivated by economic theory, an endless search for lower costs, or some other combination
of factors, countries and international law continue to move towards a freer global trade network.
The World Trade Organization (WTO)—the primary organizational body responsible for regulating
trade agreements and monitoring fair trade practices—grew from 82 members in 1973 to 159 in 2013,
nearly doubling in 40 years.42 WTO membership growth helped more countries trade freely, including
broad regional agreements like the European Union, and reduce their tariffs and other trade barriers.
43 Many of these formal agreements and domestic policies permit, if not outright facilitate, foreign
direct investment; such investment streams are critical for firms to enter new international markets.44
Even specific industries maintain broad free trade agreements, such as the ever-growing Information
Technology Agreement through the WTO. There is no question that the 21st century is the freest global
trade period in modern history.
Second, telecommunications infrastructure and technology continue to improve, offering new
opportunities to network across the world. From the 1800s telegraph to the 1900s telephone, disruptive
improvements in information infrastructure unleashed new forms of trade coordination. The
process continues today with digital cabling and wireless equipment. The internet now allows firms to
exchange products like documents and data across continents in a matter of seconds, allowing justin-
time manufacturing, rapid-fire legal analysis, remote medical imaging analysis, and other business
improvements. Firms can market their products across the globe, all from a central production studio
in one location. Cellular phones permit voice communication around the globe, and increasingly offer
data access. It is safe to say the transition to a more advanced industrial and service economy would
be impossible without modern telecommunications.45
Third, freight innovations made shipping goods dramatically cheaper.46 Over the course of the 20th
century, innovations in transportation and logistics technology helped reduce shipping costs by 90
percent—meaning a far smaller share of GDP was required to transport goods. In the process, metropolitan
areas lost their stranglehold on their regional markets, while those areas far from commodity
centers could now more easily gain access to distant commodities for a competitive price. Shipping
cost declines have stalled since the start of the 21st century due to a range of global developments, but
the last century’s freight innovations have left a lasting impact.
These distance-shrinking innovations actually expanded competition between economic regions.47
In the past, limited trade geographies meant specific markets could control their regional or national markets. If a single metropolitan area had a surplus of a certain product and its entire trade region
was relatively isolated, they could exert market pricing power. Now with few isolated regions and
global connectivity, possessing a production specialty is no longer enough—there are likely to be
multiple regions across the country and world with the same specialty, and they may offer products at
a lower cost. Such direct competition forces
metropolitan economies to either match their
peers or see their market power disappear.
The distance-shrinking specifically extends
to firms making geographic decisions for
their particular business lines. Since labor is
relatively immobile but capital and technology
are more mobile than ever, business lines
with high labor cost shares will find lower-cost,
emerging markets a more attractive production
site. This is a key driving force behind
industrial shifts to Asia’s Pacific coast.48 On
the flip side, technology-intensive business
lines or those that require certain knowledge
assets will find high-cost, high-productivity
labor more attractive. This is the current
position for older developed markets like the
United States and Western Europe. Such an
intensely competitive market forces metropolitan
areas to reassess their comparative
advantages, taking into account a much
farther-flung set of places than in the past.
A similar competition exists for energy. The
rise of emerging economies like China and
India, along with increases in global per capita
income, lifted goods trade to record levels
year after year—and all that trade requires
more and more energy to move goods across
the planet. Global demand for energy commodities
like liquid fuels, natural gas, and
coal increased by 43 percent between 1990
and 2008—and transportation continues to
consume more energy than all other sectors
combined.49
In the long-run, higher sustained energy
costs have the power to recalibrate firms’
location decisions—just like the comparisons
between labor dynamics. As it stands, increasing
demand and higher costs already inspired
new energy innovations and research. From
improved vehicle fuel efficiencies, to new
mobile electric energy sources, to expanded
natural resource extraction like natural gas,
firms and nations are relentlessly seeking to
reduce energy costs through a combination of
energy efficiency and increased supply.
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Brazil and the Changing Trade Environment
全球化核心：Flat3.0
季风亚洲（Monsoon Asia）
For decades, international trade barriers limited Brazil’s ability to fully
engage in global goods trade, despite abundant natural resources, plentiful
labor, and an expansive industrial base. Historically, commodities ranging
from copper and iron to coffee and sugarcane connected Brazil to markets
around the world, but its economic policies restricted additional production and
shipping activities over time.50 The past two decades, however, have seen a rise
in the nation’s export-led growth, resulting from a reduction in trade barriers
and an increase in foreign investment.
Indeed, after years of pursuing protectionist strategies, including tariffs and
import substitution industrialization, Brazil’s leaders adopted policies that integrated
the nation into global value chains. As inflation peaked and production
faltered in the early 1990s, President Fernando Henrique Cardoso introduced a
new currency, the real, to stabilize the economy and paved the way for reforms
that would privatize industry, promote free enterprise, and liberalize trade. Later
policies like debt reductions, capital regulations, and other measures continue to
heighten Brazil’s competitiveness on the world stage.51
In response, Brazil’s international trade boomed over the past twenty years.
While extensive government protection – and debt – helped fuel Brazil’s growth
in the mid-20th century, a 515 percent increase in exports from 1990 to 2010
powered Brazil’s modern “miracle” in industrial production, technological innovation,
and global competitiveness.52 Reaching almost $233 billion in 2010, Brazil’s
exports are principally derived from four key export sectors: mining and metallurgy,
agribusiness, energy, and transportation equipment, all of which feeds
demand in China, the United States, and elsewhere. Industrial centers, moreover,
power this growth, including Sao Paulo which accounted for 33 percent of
Brazil’s GDP and 30 percent of its total exports in 2010.
However, despite reforms that lowered tariffs and opened trade, Brazil still
has many obstacles in place that hinder investment and the free flow of goods.
Brazil’s tax regime is complex, for instance, adding extra time and pressure to
conduct business relative to most other countries.53 High interest rates, energy
prices, and compliance costs also limit growth, recently sparking national protests.
54 In addition, forced localization measures aimed at protecting domestic
manufacturers recently returned as a federal priority, limiting foreign competition
and forcing countries to find alternate ways to invest in the Brazilian
market.55
Perhaps most significantly, Brazil’s underfunded and congested infrastructure
struggles to handle the increased freight traffic associated with the country’s
growing trade, adding substantial costs to consumers and producers. For
example, trucks move 60 percent of Brazil’s freight, but 83 percent of its roads
are classified as poor condition.56 At the same time, Brazil’s annual rate of
infrastructure investment is only between 1 and 2 percent of its GDP, significantly
lower than the 5 percent average among emerging markets.57 Roads, rails, and
ports, as a result, frequently lack the needed financial backing to facilitate goods
movement. By one estimate, addressing these supply chain barriers could boost
national output by over 3 percent.58
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The Rise of Global Value Chains
Trade coordination can and often does occur across vast distances, meaning product markets stretch
to every corner of the globe and metro areas can access the cheapest input and consumption products
the world has to offer. The new reality is a global marketplace composed of interlocking trade
networks. Those interdependent and expansionary trade markets created the modern-day global value
chain.59
US Atlas
全球化核心：Flat3.0
季风亚洲（Monsoon Asia）
季风亚洲的海军基地
传说中的珍珠链
中国能源输入通道
A global value chain (GVC) is the aggregation of individual production steps required to bring a
product to market, and specifically captures the extensive geography, coordination, and integration
necessary to make that entire process possible. This expansive look at the production process—including
steps like “research and development, design, sourcing of inputs, the various stages of production,
and marketing”—helps uncover how particular regions rely on one another to bring a consumable
product to market.60
An example of this approach is the production process behind Apple’s iPhone. The product is
famously designed and engineered at Apple’s headquarters in Silicon Valley. It also includes the wellknown
“Made in China” stamp on the back of the product box, signifying the final assembly conducted
in Asia. GVC analysis includes these traditional bookend steps, but it also adds separate production
steps like component manufacturing, such as the flash memory produced in Japan and the global
positioning receiver sourced from Germany.61
GVCs are the new multi-step, multi-regional realities of the global goods marketplace. They symbolize
an economic environment where a single firm or a network of contractors, suppliers, and manufacturers
can coordinate complex production process in real-time across vast distances.
Although it used a slightly different name at the time, value chain theories developed in the late
1970s by Terrance Hopkins and Immanuel Wallerstein were the first to recognize growing global production
chains. Those theories specifically focused on products’ input and output processes and how
they related to an expanded geography.62 New research in the mid-1990s, led by Gary Gereffi, added
a focus on the governance structures and institutions involved in commodity chains.63 Governance
and institutional analyses helped define where power resides in these multi-step production networks,
whether through traditional elements like plant ownership or service-centers like corporate management.
Academics continue to develop and diversify these chain theories, but each research strain
shares the goal of capturing global interconnectivity via commodity production.
GVC theory helps contextualize regional relationships in the global economy. With metropolitan
areas and their firms coordinating entire production lines over vast distances, understanding goods
trade requires a dramatic and ongoing remapping of the world’s supply chains.64
Goods production data stands as evidence of GVCs and their expanding geography.65 First, global
manufacturing is no longer dominated by developed markets. In 1970, the G7 countries generated
roughly 70 percent of global manufacturing—by 2010 the share dropped to 46 percent. In its place
emerging countries like China, Korea, and Turkey captured most of the difference. Second, a growing
share of the world’s goods production is sold for intermediary inputs rather than final usage. For
example, by 2009 less than 40 percent of China’s and Korea’s manufactured goods were intended for
final use—evidence of their role as a manufacturer of intermediary components for other markets.
These shifts in global production require a reconsideration of where value is created. When one
country or metropolitan area manufactures a product from beginning to end, it’s easy to assign value
to a single location. GVCs are, in practice, much more complex.
In today’s production lines, value creation occurs in multiple locations, through various firms, and
typically spans manufacturing and service industries. Certain commodity chains involve global regions
regularly exchanging goods back and forth, adding value in a game of economic hot-potato. One
example is the North American auto cluster, where reports show parts crossing a national border eight
times prior to final assembly.66 Port towns and distribution centers similarly obscure the value-add
picture: many regions developed specializations in light value-added activities like product warehousing
and repackaging.67 Markets across the world benefit from foreign direct investment, complicating
the geography of production and it’s financing.68 Regions’ tradable service sectors—like consultants,
financiers, and logistics coordinators—also add value throughout the supply chain.
As a consequence, GVCs change how national trade balances should be calculated. For example, a
2013 OECD/WTO value-added database shows that applying value-added methods decreases the U.S.
trade deficit with China by 25 percent, but reassigns most of that deficit to developed markets like
Germany and Japan.80 A broader study of 42 countries found that foreign content typically represented
27 percent of each country’s national export value. While markets varied in their share of
foreign value-add, this is a sizable representation of foreign inputs helping to generate domestic trade
value.81 A “Made in …” sticker no longer tells the whole value-add story.
Not only is it increasingly difficult to assign value along the commodity production process, the
growth in global value chains also complicates trade mapping.82 When specific firms control multiregional
commodity production processes, it is not clear how much value-add occurs in each facility.
When contractors, suppliers, and manufacturers all have multiple relationships with one another
across multiple commodity categories—what database designers call “many-to-many” relationships—
spatial mapping becomes incredibly complex. These present major hurdles to accurately capturing
today’s global networks.
GVCs, and related approaches like global commodity chains, also face challenges in addressing
tradable services. Gereffi and other researchers proved how tradable services are increasingly part
of commodity production processes. However, mapping service flows is incredibly difficult, especially
beyond firms’ headquarters and satellite locations.83 While this research series focuses on goods
trade, services trade is a critical component of what metropolitan areas sell beyond their borders. For
example, while the United States ran a nearly $800 billion deficit in international goods trade in 2012,
it posted a surplus of nearly $200 billion in services trade.84 Acquiring a clearer picture of metropolitan
area services trade is a future frontier of trade mapping.
Implications for Metropolitan Economies and Freight Infrastructure
Every metropolitan area must recognize it is now part of the global economy.85 For some markets,
global connectivity is obvious: airplanes from Seattle, agriculture from Fresno, and computing
equipment from Austin. These well-known export products create a clear connection to the global
marketplace through their customer base. But it is often what metropolitan areas purchase—their
inflows—that are their clearest global connection, especially from a consumers’ perspective. Imported
oil at the pump, out-of-season produce flown in from another hemisphere, and foreign electronics all
represent a local connection to the global marketplace.
Being part of the global economy forces metropolitan areas to grapple with shifts in global market
power. Unlike past decades and centuries, emerging countries constitute higher shares of global GDP.
Since the mid-1990s, emerging and developing economies’ share of world GDP is on an upward trajectory,
more than doubling in less than two decades (Figure 1).86
This economic power shift has two competing implications. Of course, emerging markets have the
ability to introduce products at lower costs, specifically due to lower labor expenses. The increased
competition puts pressure on developed metropolitan areas to continue innovating or risk losing
global market share. But they also serve as a market opportunity. As GDP growth rates slow within
developed regions, developed economies can expand their economic potential by tapping growing
demand in emerging countries. This is one of the fundamental imperatives behind export initiatives
taking place in metropolitan areas across the United States.87 However, emerging markets’ growing
output also places new competitive pressures on their developed peers. It increases the demand—and
thereby prices—for natural resources like oil and rare earth metals.88
The presence of global value chains alters the logistical advantages for metropolitan economies.
Efficient access to intercontinental shipping, especially in locations with low labor costs, can attract
manufacturing investments from around the world—often at the expense of former manufacturing
locations with higher labor costs. This is the story of China’s Pearl River Delta, but also applies to
burgeoning facilities elsewhere along Asia’s Pacific coast.89 The increased demand for seamless goods
exchange and the increased competition for high-volume container routes spur port-related markets
to develop new or improved facilities to grab business from rival markets.90 For example, seven major
European ports are consistently fighting for landlocked Austria’s 8 million tons of annual shipping
business.91 Maintaining market share in international manufacturing centers like Detroit and Munich
now requires efficient border transfer points to facilitate back-and-forth exchanges of intermediate
commodities. In general, logistical positioning—especially access to major trade nodes—is a concern for
any region.92
GDP Share
Global value chains also redefined how regions manage goods distribution.93 In an effort to reduce
costs, firms prefer leaner inventories and more frequent supply exchanges. These demands require
extra port capacity and smooth intermodal exchange points. They also require modern distribution
centers to manage the logistical symphony of products moving in and out of a broader region and its
metropolitan areas. Such centers act as the communication and physical interface between domestic
and global manufacturing outputs, regional product demands, and freight shipping patterns. They also
tend to include warehouses, making them sites of value-added economic activity like repackaging.94
Every metropolitan area looking to maximize growth in the global economy requires either local or
regional access to a well-networked distribution center. They also must consider the repercussions of
poor access, specifically in retaining or attracting productive firms.
Finally, global value chains put an even higher priority on transportation resiliency, which now
stands equally alongside speed when firms consider logistical routing.95 Coordinating value chains
across multiple countries and continents, whether from a port, distribution center, or corporate management
center, requires reliable goods movement between markets. This was never more evident
than during the 2011 Japanese earthquake and tsunami, which crippled global supply chains, inflated
certain commodity prices, and cut Japanese firms’ profits.96 Enhanced resiliency can be achieved
through targeted capital investment in freight and logistics infrastructure as well as creating redundancies
by geographically diversifying commodity chains, ensuring a delay in one region does not
affect an entire global operation, or simply shortening supply chain distances.97 Either way, resiliency
is now a primary concern.
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Manufacturing and Logistics Development of China’s Pearl River Delta
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全球化核心：Flat3.0
季风亚洲（Monsoon Asia）
季风亚洲的海军基地
传说中的珍珠链
中国能源输入通道
China’s Pearl River Delta (PRD) is one of the fastest-growing industrialized areas in the world. Fueled by rapid urbanization
and a rise in manufacturing over the past thirty years, the network of nine cities located in China’s southern Guangdong
province operates as a key player in global trade and logistics. Since the introduction of several economic reforms in the
1970s, the PRD emerged as an industrial juggernaut, producing a wide variety of goods, attracting high levels of foreign investment,
and transforming the very nature of global value chains.
Originally a center of agriculture, the PRD experienced widespread industrial growth after being classified as a special economic
zone as part of a series of market-oriented reforms under then leader Deng Xiaoping.69 Through China’s “open door”
policy, the region exercised greater control over its economic development by promoting capital investment, boosting exports,
and facilitating trade. At the same time, with an influx of cheap labor and the adoption of new technologies, major cities in the
PRD, such as Guangzhou and Shenzhen, expanded the scope of their economic activities and specialized in a broad array of commodities
for global consumption.70
Despite only covering 1 percent of China’s
land area and containing 4 percent of its
population, the PRD now accounts for nearly
10 percent of the nation’s total output and
one-quarter of its exports, which rose from
$2.2 billion in 1980 to $359 billion in 2009.71
The region, in short, has become an economic
cornerstone for China and a factory for the
global marketplace. Electronics account for
the majority of these exports, including over
$50 billion in communications equipment
and $25 billion in computer parts during
2010 alone.
In combination with this manufacturing
prowess, the PRD is also a global logistics
hub. The broader region boasts three of the
world’s seven busiest container ports—the
Port of Hong Kong, the Port of Shenzhen,
and Guangzhou Harbor—moving a total of
61.2 million TEUs (20-foot equivalent units, an
approximation of a shipping container) in 2011
alone.72 Driving this throughput is not just
exports but also imports. Partially attributed
to companies’ ability to avoid custom duties if they import components that will eventually be shipped to another country, half of
the country’s exports are related to processing inputs from other markets, and 40 percent of their export value is actually foreign
value-added.73
Foreign investment also helped propel much of the industrial growth in the PRD. Hong Kong and Macau, given their geographic
proximity, play a prominent role guiding the modern flow of capital into the region.74 From 1980 to 1993, they accounted for
almost three-quarters of all foreign direct investment (FDI) in the PRD by themselves.75 Since then, levels of foreign investment
surged higher, allowing for tremendous gains in output and operational efficiency. In total, the PRD had $16.9 billion in FDI in
2009, which made up almost 19 percent of China’s total FDI during the same year.
To ensure its long-term success, the PRD and national government continue to make strategic investments related to global
value chains.76 Investments in the innovative ecosystem, like Shenzhen’s “University Town,” aim to educate the next generation
of economic leaders.77 With a population near the size of Spain but only a fraction of the land area, major cities continue to make
targeted infrastructure investments to reduce congestion and support agglomeration economies.78 Nationally, the central government
continues to make interregional transportation investments, helping to link inland workers and manufacturing sites with the
more prosperous coast.79 All these investments are critical, for as the region’s labor costs rise and consumer preferences shift,
maintaining the region’s role as a global manufacturing and logistics hub will require a different approach.
Pearl River Delta
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Freight Transportation Resiliency and the 2011 Japanese Tsunami
The fragility of global value chains becomes starkly apparent when infrastructure maintenance
is lacking and expansion is needed. Natural disasters such as the Tohoku earthquake
and tsunami off Japan’s coast in 2011 also highlight the vulnerability—and importance—of
these transportation assets for goods movement.
Triggered by a magnitude 9.0 earthquake—among the most severe in Japan’s history—the tsunami
crippled the daily rhythm of the world’s third largest economy, closing ports, flooding roads,
and washing away rail lines along the country’s Pacific coast.98 The extent of structural damage
alone, estimated at nearly $300 billion by the Japanese government, displaced almost 340,000
people and halted industrial production for months in several locations, leading to additional
disruptions in food supplies, goods manufacturing, and energy distribution. Many analysts believe
the tsunami was a primary factor behind the 3.7 percent contraction of the nation’s real gross
domestic product during the first quarter of 2011, predicting that the full economic impact will
likely last for years.99
Facing labor shortages and delays in shipping parts, Japan’s automakers were among the
hardest-hit manufacturers. Toyota and Honda, for instance, had to suspend operations after the
tsunami, seeing their domestic production fall by almost 60 percent in March 2011 compared to a
year earlier. These supply-chain disruptions, moreover, limited the companies’ sales of new models
in local and foreign markets. It also pushed these firms to consider shifting more long-term production
closer to major consumer markets like the United States.101
The economic effects proved far-reaching. Accounting for 8.7 percent ($5.5 trillion) of global
GDP at the time of the tsunami, Japan’s supply chain disruptions reverberated throughout China,
the United States, and Europe. In total, the tsunami was estimated to slow down global economic
growth by about 0.2 percent.102 While the effects were relatively modest as a share of overall
trade, several worldwide electronics manufacturers and automakers faced costly delays, just as
they did in Japan. Toyota assembly plants in the United States, for instance, lacked over 150 parts
for several weeks, forcing them to operate at 30 percent capacity. Production at several Honda
plants in the United States, Canada, the U.K., and India also fell by 50 percent during this same
period.103
Although Japan constructed dikes, dams, and floodgates to withstand damage from serious
coastal storms, the tsunami overwhelmed many of these structures and required significant
infrastructure investment as the nation gradually recovered over the past two years.104 In addition
to damage sustained near the Fukushima Daiichi nuclear plant, more than 60 miles of the
national highway, 200 miles of rail lines, and 14 international seaports were damaged and needed
repairs.105 The Japanese tsunami was one of the most devastating in recent history and highlights
the need to protect vital infrastructure networks. Superstorm Sandy in 2012 similarly accelerated
the development of resiliency strategies for goods movement in metropolitan New York.106
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IV. Reconsidering Freight and Economic Policy
Metropolitan areas and their cities concentrate the assets required to succeed in a globally
competitive trade environment. They house the human capital, innovation-producing
firms, diverse industrial profiles, and major freight infrastructure capable of generating
competitive advantages.107 But those assets do not generate economic growth on their
own. They need goods trade networks to maximize their economic potential.
Moving forward, metropolitan leaders need a firm understanding of these goods trade networks.
Unfortunately, policies are not fully prepared to help reach that goal nor do we have the analytics to
inform new practice.
Recognizing Domestic and Global Metropolitan Networks
Reliance on goods trade means metropolitan economies must understand the geography and components
of their trade networks: which metropolitan areas supply their input and consumable goods,
which metropolitan areas are their chief markets for their production specialties’ outputs, and which
markets are not yet a part of their networks but could be. This is the root importance of mapping
goods trade networks.
Mapping goods trade networks is more than drawing connecting lines on a real or imagined chart.
It also includes tracking particular trade components. Differences in value and weight, and how those
relate to regional production specialties, can help illuminate this point. For example, many large metropolitan
economies concentrate on high-value, low-weight products. One example is Minneapolis, where
the metropolitan area specializes in manufacturing precision instruments like medical devices.108
These products require particular freight needs, including access to airports and well-connected roadway
networks.
Contrast these products with the opposite: high-weight, low-value commodities. Many of these
products are land-intensive, such as agriculture, or can be environmentally damaging, such as energy
extraction. These products require different freight infrastructure. Consider cereal grain exports from
Iowa. To reach foreign consumers, those commodities are typically transported by freight rail or large
trucks and pass-through ports in New Orleans and Portland, taxing local freight infrastructure assets
in the process. Port facilities certainly understand the origins and destinations of their business, but
other local actors may not have a clear understanding of these local transportation impacts.
Mapping goods trade is especially helpful in recognizing likely infrastructure needs. Based on a full
suite of trade components—commodity type, value, weight, geographic pairing, and modal needs—public
and private leaders can fashion short- and long-term freight strategies in response to physical and
prospective goods movement. Large production firms can maximize distribution center and warehouse
investments. Shipping and other logistics firms can procure ample space at major port nodes.
Public agencies can prioritize long lists of capital investments. In each case, infrastructure assets will
better reflect the needs of the economies it serves.
Marrying Freight and Economic Policy
America’s federalist system ensures each level of government has a distinct role when it comes to
freight policy. The federal government’s constitutional responsibility is to regulate interstate commerce,
which effectively includes the trade between states’ metropolitan areas.109 In practice, this
means the federal government provides infrastructure planning, network construction, and fair trade
and safety regulation between states and their metropolitan areas. State, regional, and local governments
steward the freight needs of their jurisdictions, which creates the need to plan, build, and
maintain their infrastructure assets. Equally important, all government bodies must consider private
sector interests—from shippers and logistics firms to major commodity producers and consumers—as
the primary users of the freight network. Figure 2 outlines these roles and responsibilities.
Yet within this system of shared and divided authority, federalist policy needs stronger links
between freight and economy.110
U.S. Department of Transportation, the Army Corps of Engineers, and other federal agencies with
any degree of freight oversight tend to assess and regulate freight within single transportation modes.
This is a glaring omission of the interactivity between modes, multiplied by overlooking the economic forces driving freight activity. On the economic side, freight is primarily a regulatory concern for agencies
like Treasury and Customs. The 2012 launch of a Department of Commerce advisory committee on
supply chain competitiveness, which specifically considers policy improvements based on the intersection
of goods movement and economic demands, does offer promise.111
Also promising is the development of a national freight strategy. Today the United States is one
of the only industrialized countries that takes a compartmentalized rather than holistic approach
to goods movement. However, while requiring a national freight strategy through the current surface
transportation legislation (MAP-21) is a positive step, a truly national vision must integrate the
modes (air, rail, sea, road, pipeline), add economic connectivity, and upgrade trade network analytics.
That kind of foundation will also help federal programs prioritize projects of national significance, a
fundamental investment responsibility for national government. Germany and the U.K. have developed
helpful models that provide clear guidance on the development of integrated freight networks, as does
the European Commission’s Freight Logistics Action Plan to improve the efficiency of freight transport
across the entire continent.112
Our federalist system also must do more to expand collaboration between government and the
private sector. The country’s freight transportation industry is highly decentralized, with private
operators owning almost all of the trucks and rails, and the public sector owning the roads, airports,
and waterway rights. And unlike our international peers, such as Germany, Canada, and Australia,
the United States does not have a unified strategy that aligns disparate owners and interests around
national economic objectives. The United States should consider more models like the recentlyannounced
Commerce Department’s Investing in Manufacturing Communities Partnership.113 The
program uses limited federal funding ($200,000 per community) to spur regional economic strategies
that synthesize human capital, infrastructure, and manufacturing efforts. It is a remarkable step
beyond incentive-based economic development strategies.
U.S. Freight System
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Developing a National Freight Strategy: Lessons from Canada and Germany
A national freight strategy helps link goods movement to broader economic policies, allowing
for a more comprehensive approach to transportation and infrastructure investment.
Without such a strategy metropolitan areas and other stakeholders may find it difficult to
understand their place in the larger freight network.114 However, many countries, including Canada
and Germany, have established national plans and illustrate the importance of integrating freight
across multiple modes and geographies.
In its national plan, Canada focused on identifying strategic gateways and trade corridors
through an “integrated, system-based perspective.”115 Rather than isolating different modes,
Canada’s leaders developed plans in terms of the country’s increasingly multi-modal needs. At the
same time, they aligned their transportation network with other national priorities by viewing it
in light of five key “policy lenses,” which include: international commerce, current trade volumes,
future goods movement, interconnected governance issues, and public-private partnerships.
Combined, these strategies strengthened Canada’s competitiveness in global value chains.
Launched in 2006 by Prime Minister Stephen Harper, the Asia-Pacific Gateway and Corridor
Initiative (APGCI), for instance, reinforced connections between Canada and its Asian trading
partners. Considered a vital element of the nation’s economic plan—Advantage Canada—the APGCI
funneled an additional $1 billion into strategic ports, rail lines, and highways throughout Western
Canada.116 In addition, by working with a range of partners from Canadian Pacific Railway to the
Vancouver Port Authority, policymakers collaborated extensively while making these investments
to promote greater cooperation and efficiency regarding freight.117 Offering lower costs and more
reliable intermodal services, Canadian ports like Prince Rupert began to aggressively attract
increased traffic traveling between inland United States locations and world regions.118
Germany, likewise, adopted similar strategies to optimize goods movement and drive economic
growth.119 Seeing freight as a “linchpin of the economy,” Germany’s leaders aimed to build off the
country’s central location and enhance its technological capacity, while understanding the social
and environmental implications of increased traffic in its transport system. Alongside several private-
sector partners, the public sector created a freight policy framework that calls for increased
funding in infrastructure projects, upgrades in transport arteries and hubs, a greater reliance on
railways and inland waterways, and numerous other actions to further elevate Germany’s position
in global value chains.120
By setting specific targets for these measures and dividing responsibility among several different
bodies, Germany’s plan laid the foundation for strategic action on the ground. For example,
minimizing congestion on roadways stands as an overarching priority for the Federal Ministry of
Transport, Building, and Urban Development, while providing better traffic information for heavy
goods vehicles falls primarily on the shoulders of private-sector companies, which will oversee
organizational improvements. The creation of additional parking areas at service stations, the
deployment of more traffic management systems, and the adoption of new signaling technologies
for trains represent some of the initial projects undertaken. Altering toll rates on roads, mitigating
noise on railways, and developing individual port strategies are among other measures adopted
under the German plan, all of which will serve as a basis for future budgetary action.
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Federal Freight Policy: MAP-21 and Beyond
US Atlas
全球化核心：Flat3.0
While several countries use national freight plans to promote efficient goods movement,
the United States has yet to finalize a national strategy. Although the Moving Ahead for
Progress in the 21st Century (MAP-21) surface transportation law marks an important first
step in this process—requiring the United States to develop a basic national freight plan by 2015—
policymakers will need to consider further measures before the next reauthorization. Beyond
MAP-21, a comprehensive freight plan should view different modes simultaneously and assess the
nation’s transportation needs directly in light of long-term economic priorities.
As it stands now, the freight plan specified under MAP-21 does not fully capture a multi-modal
network. Rather, the legislation establishes several loose objectives for future action, focusing
on broad policy goals. Assessing current and future freight volumes, identifying bottlenecks,
and noting best practices are among its various provisions, which lack any clear benchmarks for
implementation beyond five-year updates to the initial plan and the creation of new performance
targets. The designation of a “national freight network” helps identify infrastructure critical to
moving high volumes of goods, but this network is largely defined in terms of truck movement,
limited to 27,000 centerline miles of existing roads and the construction of 3,000 additional miles
of highway.121 While trucks transport most domestic goods, waterborne vessels account for more
than three-quarters of the weight and nearly half the value of American international goods
trade.122
Including a national freight strategy in surface transportation legislation like MAP-21—rather
than standalone legislation that would integrate other freight-related legislation like the Water
Resources Development Act—makes it difficult to gauge how highways, railways, waterways, ports,
and airports all function together to boost the nation’s economic competitiveness. For example,
MAP-21 calls for the identification of major trade gateways and freight corridors in the national
freight network, but provides little detail on how to strengthen these assets, which often depend
on a wide variety of modal connections. Almost exclusively, it concentrates on the operational
performance of these modes in isolation, citing the need for additional investments and planning
tools to improve their overall efficiency. Meanwhile, the prioritization of specific ports or rail lines,
including the development of new intermodal facilities and transfer points, is unclear. The law also
encourages states to develop freight plans, but they are not required.123
MAP-21 does make freight projects eligible for funding through the Projects of National and
Regional Significance (PNRS) program, the Surface Transportation Program, Transportation
Investment Generating Economic Recovery (TIGER) grants, and Transportation Infrastructure
Finance and Innovation Act (TIFIA) programs.124 TIGER grants, in particular, have a strong record
of funding freight projects through their competitive process.125 And MAP-21 also increases the
federal funding share to 95 percent for freight projects on the Interstate Highway System and 90
percent for other projects—assuming states adopt freight plans and include the specified projects—
but they are viewed in relative isolation.126
To fully capture freight’s economic potential, then, the United States should coordinate activities
among different federal agencies. The Department of Transportation, Department of Commerce,
and the Office of the U.S. Trade Representative (USTR), among other bodies, need to work closely
and have clearly-defined roles managing and regulating goods movement across the nation.
As the Government Accountability Office (GAO) recently found, freight is too often fragmented
among these various agencies, complicating long-term planning efforts and severing freight’s
economic ties.127
Ideally, the newly-formed Freight Policy Council will align these goals and prioritize projects of
national importance, while policymakers continue to refine the objectives laid out in MAP-21.
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Upgrading Analytics to Capture Metropolitan Trade Networks
US Atlas
全球化核心：Flat3.0
Domestic and global trade networks are the connective tissue behind our metropolitan economies. Yet,
while research and statistics show clear evidence of metropolitan areas’ ability to generate and maximize
trade, there is little understanding of exactly where trade occurs. Simply put, current analytics fail
to capture metropolitan trade networks. In the process of building a more collaborative and comprehensive
trade agenda, advanced databases and analytics have an especially important role to play.
One place to start is imports. Since 1960, the U.S. trade balance consistently runs a deficit, reflecting
a macroeconomic reliance on imported goods from foreign markets.128 However, those imports do not
exist in a vacuum—they likely land in the metropolitan areas generating the vast majority of American
economic output. The problem is no data source currently tracks imports to their metropolitan destination;
the closest data source is U.S. Customs data reporting import trade at the Customs district (a
non-metropolitan geography in the first place). Since the U.S. imports significantly more goods than
it exports, it is critical for metropolitan economies to understand which countries supply their locallyconsumed
commodities and what alternative geographic sources exist.
Export reporting is significantly better at the metropolitan scale, but is not perfect. Partially inspired
by quantitative reporting conducted by the Brookings Institution in 2010, the U.S. International Trade
Administration began publicly reporting metropolitan export statistics.129 However, this dataset is
incomplete for freight purposes. First, it does not include transportation elements such as transportation
mode or international port. Second, it does not assign international trading partners beyond the
50 largest metropolitan areas. Third, it assigns exports based on the point of last movement, which
often varies from the point of production.
Deficiencies in the country’s only public origin-destination database, Census’ Commodity Flow
Survey (CFS), limit the ability to assess domestic metropolitan goods trade, too. First, CFS administration
does not keep up with the speed of domestic and global economic trends as Census only conducts
the CFS every five years. By the time the calculations are complete and reported to the public, macro
and metropolitan economies are often in the midst of an entirely new economic cycle. For example,
the most recent CFS from 2007 represents a pre-Great Recession economy. Second, the survey does
not offer data for all metropolitan areas, while others are incomplete due to state borders.130 The CFS
does provide important information, but it could be even more valuable with targeted improvements.
There are two primary alternatives to the CFS, but neither is perfect. The first is another public
dataset, the Freight Analysis Framework (FAF) from U.S. Department of Transportation. That dataset
supplements CFS data with transportation modes and forecasts. However, by using CFS as a baseline,
it suffers from the same timeliness and metropolitan scale issues.131 FAF also undergoes significant
methodological updates between 5-year versions, disabling the ability to easily create longitudinal
analyses in exchange for methodological improvements. FAF consumers would greatly benefit if the
database series could apply methodological improvements to past databases and enable longitudinal
comparisons. Private data alternatives exist but are expensive and proprietary.
These data deficiencies come at a real cost. First, it limits metro leaders’ ability to capture their
goods trade balances and how those balances relate to local economic profiles. Trade balances are a
valuable tool to highlight what commodities a metropolitan area consumes locally, and vice-versa for
which commodities serve as outflows. Missing trade balance data creates a missing translation tool
between local industry data and infrastructure needs.
Second, problematic data means metropolitan leadership cannot understand their place in domestic
and global trade networks. The growing complexity of manufacturing and service networks make it
more important than ever to track where a metropolitan area falls in a commodity chain. Which trading
partners supply the natural resources? Where do the intermediate inputs come from? What markets
demand the final consumable products? The lack of location-specific trade relationships makes
it difficult to contact common trade peers, establish mutually beneficial policies, or determine future
areas for manufacturing and broader economic development.
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V. Conclusion
The exchange of goods is a fundamental element of how metropolitan economies grow and
prosper. It enables places to develop production capabilities and leverage the concentrations
of economic specialties found in metropolitan areas. Goods trade matches economies based
on complementarity, actualizing the principles of supply and demand and helping to guide
local economic development policy.
To make this growth possible, metropolitan areas have used freight transportation networks to forge
connections between places. From the Mediterranean’s earliest trading ships, to England’s natural
waterways, to the America’s vast highway network, freight technology and capacity has always set the
pace for how quickly and easily trade could occur.
Today’s freight innovations have helped drive trade to previously unseen heights, unleashing a
broad wave of global economic growth and a dramatic remapping of the world’s production networks.
Value chains now stretch across the world, pushing manufacturing and service industries to the sites
of the greatest transportation reliability and production affordability, in the process redefining how
locations add value to specific products. As a consequence, the world’s metropolitan economies now
face a more competitive environment for people, for investment capital, and for goods.
Increasingly, metropolitan economies require a clear understanding with whom they trade and how
transportation assists in making those connections.
Current U.S. policies fail to reflect this new reality. The federal government continues to separate
freight and economic planning, and has only begun shaping a limited national freight strategy. Local
and regional governments continue to live in the dark when it comes to identifying their domestic and
global trade partners. And at all levels of government, analytics fail to offer policymakers the information
necessary to make informed decisions.
This series aims to address those gaps. It will offer policymakers at all levels of government and
their colleagues in the private sector a toolkit to map and analyze goods trade in the 21st Century.
Only when armed with that knowledge, can leaders make the difficult decisions it will take to prosper
in a hyperconnected, hypercompetitive global economy.
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