Chapter

III Canada and the United States

Author(s):
Vladimir Klyuev, Martin Mühleisen, and Tamim Bayoumi
Published Date:
October 2007
Share
  • ShareShare
Show Summary Details
Author(s)
Vladimir Klyuev, Rodolfo Luzio, Roberto Cardarelli, Ivaschenko Iryna V. and Andrew Swiston21 

Canada is an open economy whose trade is dominated by the United States. Indeed, the signing of the Canada-U.S. Free Trade Agreement in 1988, and its successor, the North American Free Trade Agreement in 1993 led to substantially increased trade, financial flows, and increasing business-cycle synchronicity. This section examines three important characteristics of the Canadian economy that significantly affect its relationship with the United States.

Canada has a regionally specialized economy, with manufacturing concentrated in the central provinces and raw materials production elsewhere. The first segment of this section examines how Canada’s regional economies have evolved over time and how they have responded to macroeconomic shocks.

Canada continues to have a large labor productivity gap with the United States. The second segment of this section examines the reasons for this continuing difference.

The third segment uses a small two-country monetary model to examine spillovers from U.S. activity and to assess how they can be mitigated by monetary responses in both the United States and Canada.

A. Regional Dimensions of the Canadian Economy

The Canadian economy is highly diverse across regions. This analysis focuses on two aspects of this diversity—differences in the industrial structure and differences in responses to common shocks—and compares these with differences across U.S. regions.

Industrial Structure

We first examine differences in industrial structure across regions. Figure 3.1 reports the weights of five industries—agriculture, construction and utilities, manufacturing, mining, and services—across Canadian regions as deviations from the average for Canada as a whole. The regions are British Columbia, Prairies, Ontario, Quebec, and Atlantic provinces. (Box 3.1 lists the Canadian provinces and U.S. states included in the regional classifications and also outlines the data sources used here.)

Figure 3.1.Canada: Difference between Regional and National Industry Shares

(In percent)

Sources: Statistics Canada; and IMF staff calculations.

Note: Atlantic provinces include New Brunswick, Newfoundland and Labrador, Nova Scotia, and Prince Edward Island. The Prairies comprise Alberta, Manitoba, and Saskatchewan.

Differences in industrial structure are large and persistent. Compared with the rest of Canada, the provinces of Ontario and Quebec are more heavily based on manufacturing, while mining (which includes the energy sector) plays a heavier role in the Prairies. The Atlantic provinces and British Columbia are particularly strong in services, except that offshore gas exploration has recently strengthened the role of mining in the east.

The degree of regional economic diversity appears larger in Canada than in the United States. The Prairies’ specialization in mining is larger than in the U.S. Southwest, and British Columbia is more focused on services relative to the rest of Canada than New England and the Mideast in the United States (Figure 3.2). On the other hand, the U.S. Great Lakes region seems more concentrated on manufacturing than either Ontario or Quebec.

Figure 3.2.United States: Difference between Regional and National Industry Shares

(In percent)

Sources: Bureau of Economic Analysis; and IMF staff calculations.

To quantify the degree of dispersion, we calculate the average of absolute deviations between regional and national shares in the five industries.1 This measure is plotted for each region in Canada and the United States (Figure 3.3). In addition, Figure 3.4 shows unweighted and weighted (by regional GDP) averages of regional dispersions for both countries.

Figure 3.3.Regional GDP by Sector: Divergence from Country Average

(Average absolute difference, in percent, across sectors)

Sources: Statistics Canada; Bureau of Economic Analysis; and IMF staff calculations.

Figure 3.4.Average Regional Divergence from National Industrial Structure

(In percent)

Sources: Bureau of Economic Analysis; Statistics Canada; and IMF staff calculations.

The charts confirm that the Canadian economy is regionally more diversified. In addition, we observe the following:

  • The evolution over time of the national dispersion measures in the two countries is quite similar. A flat trend in the 1980s gave way to a short period of divergence in the early 1990s, followed by fairly rapid convergence coinciding with the Internet and communications revolution in the middle to late 1990s.2
  • Unlike the United States, Canada has one main outlier in economic structure: the resource-rich Prairies. The U.S. Southwest was almost as much an outlier in the United States in the 1970s, but its industrial structure has since shifted to be much more similar to that of the rest of the country.
  • Even excluding the Prairies, regional dispersion in Canada is higher than in the United States. The average absolute deviation for Canada excluding the Prairies region is 2.1, which exceeds the value of 1.5 measured for the United States.3

Response to Shocks

We next examine how these differences in structure affect regional economies’ responses to aggregate shocks. For all of Canada’s regions, we study the response of real GDP growth, real private consumption, and real investment (annual data) to a set of explanatory variables that capture both domestic and external factors (Tables 3.13.3). These include changes in real GDP in both the United States and the rest of Canada, the real effective exchange rate, the real price of oil, and the real short-term interest rate.

Table 3.1.Canada: Growth Regressions
Dependent variable:

Real GDP growth in the region
British

Columbia
PrairiesOntarioQuebecAtlantic

Provinces
Canada
Lagged dependent variable0.0740.1140.2180.0350.1450.336**
(0.164)(0.215)(0.111)(0.143)(0.172)(0.069)
Growth in Canada outside region0.435**0.2570.4290.708*0.274*
(0.188)(0.319)(0.294)(0.344)(0.145)
Growth in the United States0.651*1.160**0.0950.945**
(0.353)(0.244)(0.367)(0.110)
Real interest rate (lagged)0.1540.084−0.273−0.170−0.309*−0.235**
(0.219)(0.222)(0.195)(0.121)(0.148)(0.094)
Change in REER (lagged)0.0170.018−0.131**−0.123*−0.101−0.126**
(0.069)(0.110)(0.063)(0.059)(0.062)(0.029)
Change in oil price (lagged)−0.0050.042**−0.009−0.011−0.032**−0.003
(0.013)(0.016)(0.009)(0.007)(0.010)(0.005)
R-squared0.230.370.840.830.510.78
Durbin-Watson statistic2.382.201.862.521.482.70
Source: IMF staff calculations.Notes: Newey-West heteroscedasticity-consistent standard errors in parentheses; REER = real effective exchange rate.

= significant at 10 percent.

= significant at 5 percent.

Source: IMF staff calculations.Notes: Newey-West heteroscedasticity-consistent standard errors in parentheses; REER = real effective exchange rate.

= significant at 10 percent.

= significant at 5 percent.

Table 3.2.Canada: Real Private Consumption Regressions
Dependent variable:

Real private consumption

growth in the region
British

Columbia
PrairiesOntarioQuebecAtlantic

Provinces
Canada
Lagged dependent variable0.345**0.0840.328*0.161−0.0130.370**
(0.081)(0.186)(0.181)(0.117)(0.208)(0.091)
Growth in Canada outside region0.1520.533**0.345*0.260*0.534**
(0.097)(0.171)(0.188)(0.127)(0.167)
Growth in the United States−0.0350.627**0.525**0.704**
(0.194)(0.201)(0.156)(0.139)
Real interest rate−0.029−0.238−0.278**−0.255**−;0.082−0.283**
(0.070)(0.169)(0.103)(0.094)(0.128)(0.069)
Change in REER (lagged)0.1210.1350.0270.0200.0650.034**
(0.051)**(0.077)(0.092)(0.058)(0.087)(0.037)
Change in oil price (lagged)−0.0250.017**0.011*−0.004−0.014−0.007
(0.016)(0.009)(0.006)(0.004)(0.008)(0.006)
R-squared0.540.640.810.820.690.76
Durbin-Watson statistic2.062.171.851.501.472.26
Source: IMF staff calculations.Notes: Newey-West heteroscedasticity-consistent standard errors in parentheses; REER = real effective exchange rate.

= significant at 10 percent.

= significant at 5 percent.

Source: IMF staff calculations.Notes: Newey-West heteroscedasticity-consistent standard errors in parentheses; REER = real effective exchange rate.

= significant at 10 percent.

= significant at 5 percent.

Table 3.3.Canada: Real Private Investment Regressions
Dependent variable:

Real private investment

growth in the region
British

Columbia
PrairiesOntarioQuebecAtlantic

Provinces
Canada
Lagged dependent variable0.1130.1250.3400.445*−0.1730.331
(0.293)(0.223)(0.207)(0.218)(0.144)(0.196)
Growth in Canada outside region0.4011.393−0.555−0.886*1.782*
(0.606)(1.548)(1.255)(1.084)(0.877)
Growth in the United States0.7113.615**3.028**2.309**
(3.310)(1.268)(1.315)(0.753)
Real interest rate−1.035−0.680−1.452−1.521**−1.553*−1.325**
(0.802)(1.549)(0.863)(0.565)(0.841)(0.596)
Change in REER (lagged)0.8050.011−0.075−0.0350.748*0.061
(0.438)(0.728)(0.563)(0.383)(0.403)(0.306)
Change in oil price (lagged)−0.0330.195**−0.030−0.100**−0.0580.002
(0.096)(0.088)(0.045)(0.042)(0.122)(0.053)
R-squared0.150.320.580.650.310.52
Durbin-Watson statistic1.961.832.301.982.282.37
Source: IMF staff calculations.Notes: Newey-West heteroscedasticity-consistent standard errors in parentheses, REER = real effective exchange rate.

= significant at 10 percent.

= significant at 5 percent.

Source: IMF staff calculations.Notes: Newey-West heteroscedasticity-consistent standard errors in parentheses, REER = real effective exchange rate.

= significant at 10 percent.

= significant at 5 percent.

The regression results demonstrate that Canadian regions are influenced heavily by both domestic and external factors, albeit in very divergent ways:

  • The links to the United States are particularly powerful in Ontario and the Prairies. In these regions, U.S. growth is more important than growth in the rest of Canada, likely reflecting their concentration on exports to the United States of either manufactures or raw materials.
  • Links with the rest of Canada are strongest in Quebec, followed by Ontario and British Columbia. The weakest links are in the resource-rich Prairies and Atlantic provinces.
  • An increase in the price of oil is beneficial for the Prairies, but has a negative impact on all the other regions and no major impact on the country as a whole.
  • Real exchange rate appreciation and real interest rate increases are particularly negative for the eastern provinces, for the manufacturing-based central provinces, and for the country as a whole. The impact of exchange rate appreciation and the real interest rate is limited in British Columbia and the Prairies, but this may reflect a link between commodity prices, the exchange rate, and inflation that could not be resolved in the context of this exercise.

Box 3.1.Data Sources and Definitions for Canadian and U.S. Regional Variations

Real interest rate

Average overnight rate adjusted for average CPI inflation

Real oil price (U.S.)

West Texas Intermediate (WTI) price adjusted for U.S. GDP deflator

Real oil price (Canada)

World Economic Outlook (WEO) oil price series adjusted for the US$/Can$ exchange rate and the Canadian GDP deflator

Real effective exchange rate (REER)

Source: JP Morgan

Time period

1983–2004 (dictated by availability of Canadian GDP data by region)

Canadian regions

Atlantic provinces: New Brunswick, Newfoundland and Labrador, Nova Scotia, and Prince Edward Island

  • British Columbia
  • Ontario
  • Prairies: Alberta, Manitoba, and Saskatchewan
  • Quebec

U.S. regions

The eight U.S. regions referred to here are those defined by the Bureau of Economic Analysis of the U.S. Department of Commerce.

  • Far West: Alaska, California, Hawaii, Nevada, Oregon, Washington
  • Great Lakes: Illinois, Indiana, Michigan, Ohio, Wisconsin
  • Mideast: Delaware, District of Columbia, Maryland, New Jersey, New York, Pennsylvania
  • New England: Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont
  • Plains: Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, South Dakota
  • Rocky Mountains: Colorado, Idaho, Montana, Utah, Wyoming

Southeast: Alabama, Arkansas, Florida, Georgia, Kentucky, Louisiana, Mssissippi, North Carolina, South Carolina, Tennessee, Virginia, West Virginia

Southwest: Arizona, New Mexico, Oklahoma, Texas

Regressions using real private consumption and investment show a similar pattern. Consumption and investment in all regions are negatively affected by a contemporaneous increase in the real interest rate. Higher oil prices boost consumption and investment in the Prairies but slow them in other regions. Exchange rate appreciation increases investment in the Atlantic provinces and consumption in British Columbia, but does not appear to affect private demand in the other provinces or across the country as a whole.

Similar regressions indicate that the United States is more domestically integrated and less susceptible to external shocks. In particular, real GDP growth in each region is positively and significantly correlated with growth elsewhere in the United States (Table 3.4). Changes in the real effective exchange rate do not have a significant impact in any region. Higher oil prices depress output in a number of regions, and the positive impact on the Southwest is not stastically significant.

Table 3.4.United States: Growth Regressions
Dependent variable:

Real GDP growth in the region
New

England
MideastGreat

Lakes
PlainsSoutheastSouthwestRockiesFar West
Lagged dependent variable0.225−0.001−0.073−0.123−0.0530.247**0.497**0.528**
(0.145)(0.134)(0.070)(0.121)(0.072)(0.106)(0.139)(0.163)
Growth in U.S. outside region1.265**0.826**0.999**0.987**0.766**1.058**0.661**0.88**
(0.254)(0.196)(0.152)(0.231)(0.106)(0.290)(0.255)(0.114)
Real interest rate (lagged)0.2140.282−0.159−0.251*−0.018−0.536**−0.3800.009
(0.275)(0.180)(0.204)(0.122)(0.117)(0.234)(0.224)(0.224)
Change in REER (lagged)−0.023−0.0600.044−0.0260.0070.0540.0290.022
(0.060)(0.044)(0.053)(0.047)(0.026)(0.034)(0.036)(0.038)
Change in oil price (lagged)−0.0060.009−0.0160.006−0.016**0.028−0.006−0.036**
(0.020)(0.010)(0.012)(0.009)(0.006)(0.018)(0.011)(0.014)
R-squared0.770.730.730.680.820.620.650.76
Durbin-Watson statistic1.431.061.071.991.521.491.261.83
Source: IMF staff calculations.Notes: Newey-West heteroscedasticity-consistent standard errors in parentheses; REER = real effective exchange rate.

= significant at 10 percent.

= significant at 5 percent.

Source: IMF staff calculations.Notes: Newey-West heteroscedasticity-consistent standard errors in parentheses; REER = real effective exchange rate.

= significant at 10 percent.

= significant at 5 percent.

Regional diversity underlines the importance for Canada of maintaining flexibility in responding to shocks, particularly for Ontario, British Columbia, and the Prairies. External shocks have their biggest impact on these provinces, reflecting their external orientation, although the adjustment needs are often diametrically opposed, given their highly different economic structures. The recent rise in oil and other commodity prices demonstrates this divergence, boosting activity in Alberta and other western provinces while depressing conditions—through exchange rate appreciation—in the manufacturing heartland of Ontario.

Conclusions

Although they are gradually converging, Canadian provinces still exhibit considerably diverse economic structures. This diversity contributes to differential responses to domestic and external shocks. In particular, growth in Ontario—which comprises over one-third of the Canadian economy—is closely linked to U.S. growth, and is also negatively affected by interest rate increases, real currency appreciation, and higher oil prices. On the other hand, growth in the western provinces is boosted by increased oil prices and appears to be less sensitive to changes in the exchange rate or the interest rate.

B. The Canada–United States Productivity Gap

Despite the close integration between the Canadian and U.S. economies, the labor productivity gap between the two countries has widened over the last two decades (Figure 3.5). While a greater utilization of labor resources has allowed Canada to narrow the gap with the United States in terms of per capita income since the mid-1990s, convergence has been held back by the more modest pace of Canadian labor productivity growth.

Figure 3.5.United States and Canada: Income and Productivity Indicators

(United States = 100)

Source: OECD.

Notes: Labor productivity: GDP in millions of 1999 US$ (converted at purchasing power parity) per hour worked. Labor utilization: hours worked per person.

This analysis explores the factors that have led to the Canada–United States productivity gap using a sectoral growth-accounting approach. It builds on the approach of Faruqui and others (2003) to construct a sectoral database with comparable data on value added, labor, and capital inputs for 23 industries during 1981– 2000, in order to assess the extent to which this gap reflects differences in the industrial structure of the two countries.4

The chapter’s main results are these:

  • The labor productivity growth gap during 1995–2000 largely reflects the performance of two key service sectors, trade and “finance, insurance, and real estate” (FIRE). The manufacturing sector, and in particular industries related to information and communication technology (ICT), also continued to contribute to the gap, but no more than in the previous period.
  • Differences in industrial structure explain the majority of the productivity growth gap over the second half of the 1990s. The United States appears to have been more successful than Canada in shifting resources toward high-productivity sectors.
  • The lower contribution from ICT capital accumulation to productivity growth in Canada may also reflect differences in realizing the productivity benefits of ICT investments. In particular, Canadian productivity growth may have been held up by the delays in introducing organizational changes necessary to complement ICT capital.
  • The increased economic integration with the United States has allowed Canadian firms to benefit from economies of scale and technology transfers, something that appears to have positively contributed to their productivity performance over the last two decades.

Review of the Literature

A copious number of studies have sought to analyze the factors behind the productivity gap between Canada and the United States (for a survey, see Crawford, 2002; and Macklem, 2003). Among the explanations offered are the following:

  • Different-sized ICT-producing sectors: Some studies attribute most of the post-1995 acceleration of labor productivity in the United States to the exceptional total factor productivity (TFP) performance of the ICT-producing sector (for example, Gordon, 2003; and Harchaoui and Tarkhani, 2002). Given its smaller ICT-producing sector, these studies suggest that Canada is at a relative disadvantage in reaping the benefits of the ICT productivity wave.5
  • Different contributions from ICT capital accumulation: The widespread adoption of ICT capital assets has been regarded as a key factor behind the strong labor productivity growth in the United States.6Harchaoui and Tarkhani (2002) show that Canada’s business sector also experienced solid growth in ICT capital services during 1981–2000, at levels comparable to if not higher than the United States (Table 3.5).7 Nonetheless, the contribution from ICT capital deepening to labor productivity growth is generally estimated to be lower in Canada than in the United States, mainly reflecting the lower estimated marginal productivity of ICT capital and the lower ICT capital intensity in Canada.8
  • Differences in the share and productivity performance of small and medium-size enterprises (SMEs): In the Canadian manufacturing sector, SMEs (firms with fewer than 500 employees) accounted for 75 percent of total manufacturing employment, compared to around 60 percent in the United States in 1997. Not only has the relative role of SMEs in the Canadian economy increased over the last two decades, but some studies have found these firms to be less productive relative to their U.S. counterparts.9
  • Differences in the share and income of self-employed: The difference in labor productivity growth between Canada and the United States in the 1990s has also been attributed to faster growth in self-employment in Canada and the poorer income performance of this group compared with the self-employed in the United States (Baldwin and Chowhan, 2003).Less relevant factors include:
  • Differences in national accounts statistics: While differences still remain, the methodology used by national statistical agencies to measure labor and TFP has been converging. In particular, both the U.S. Bureau of Labor Statistics and Statistics Canada now use hedonic prices and include purchases of computer software in the national account measures of investment.
  • Differences in the regulatory burden in labor and product markets: Gust and Marquez (2004) find that countries with a more burdensome regulatory framework tend to have lower TFP growth. However, notwithstanding the difficulties in building comparable indexes of regulatory burden across countries, empirical evidence does not reveal a large difference between Canada and the United States in terms of labor and product market legislation and institutions.10
Table 3.5.Canada and the United States: ICT Capital Accumulation(In percent)
1981–19951995–2000
CanadaUnited StatesCanadaUnited States
Investment (average rate of growth)1
Computers25.828.039.839.3
Software19.216.610.219.8
Communications4.54.817.912.2
Capital services (average rate of growth)2
All assets3.03.44.35.4
ICT16.914.918.421.3
Computers27.123.932.941.8
Software14.515.07.216.4
Communications7.46.312.28.5
ICT share of capital income26.310.98.315.3
ICT share of capital stock33.97.06.411.7

Source: Haver Analytics.

Sources: Armstrong and others (2002) for Canada, and U.S. Bureau of Economic Analysis for the United States.

Notes: Values are for 1981 and 2000; ICT = information and communication technology.

Source: Haver Analytics.

Sources: Armstrong and others (2002) for Canada, and U.S. Bureau of Economic Analysis for the United States.

Notes: Values are for 1981 and 2000; ICT = information and communication technology.

Box 3.2.Data Sources on Canada-United States Productivity Gap

Data for Canada

Real (chain Fisher weighted) value added, hours worked, labor input, and capital services data were obtained from Statistics Canada and were based on a Standard Industrial Classification (SIC) 80 industry classification. For the manufacturing sectors, however, the data ended in 1997. They were extrapolated to 2000 using the growth rates from the KLEMS input and output database, which follows a North American Industry Classification System (NAICS) classification (starting from 1997).

Comparing the 1997 industries’ value added based on the two industry classifications shows that the difference is generally around 15 percent, except for “other manufacturing sector” and “furniture and fixture,” for which the difference is around 30 percent. The results for these sectors should then be interpreted with greater caution than others.

Data for the United States

Industry data for the United States follow the U.S. SIC 87 industry classification.

Real (chain Fisher weighted) value added industry data for the United States were obtained from the Bureau of Economic Analysis’s “gross product originating” by industry (GPO). Because these figures are on a market-price basis, value-added data at basic prices were obtained by subtracting the indirect business tax and nontax liability from GPO.

GDP by industry is obtained from industry components of domestic income, which, as is well known, tend to fall short of GDP measured on an expenditure basis. The difference is named “statistical discrepancy” and is attributed to the industries based on their share of total GDP.

Hours worked, labor input, and capital services were obtained from Jorgenson, Ho, and Stiroh (2002) and are based on methodologies that are largely comparable with those adopted by Statistics Canada.

Industry Classification

To obtain the same level of industry classification for the two countries, a number of subsectors were aggregated into larger sectors. As an example, a “mining” sector was obtained for the United States by aggregating four subsectors (namely, metal mining, coal mining, petroleum and gas, and nonmetallic mining).

The aggregation was needed also to obtain comparable sectors for the two countries. For example, U.S. SIC 87 classification places computers and office equipment in industrial machinery, while Canada SIC 80 classification places it in electrical and electronic equipment. For the sake of comparison, the two sectors were aggregated into one large sector, which is taken as a proxy for the information communications and technology sector in this analysis.

The following aggregation criteria were utilized: sub-industries’ value added were aggregated using value-added shares as weights; labor and capital inputs were aggregated using relative shares in aggregate labor compensation and capital income, respectively, as weights; and hours worked were aggregated through the unweighted sum.

Even though these aggregation procedures produce reasonably comparable sectors for the two countries, some minor differences still persist, particularly in the service sectors. In addition to the different treatment of eating and drinking places (as noted in footnote 16), another difference worth mentioning regards postal services, which are placed in the communication sector for Canada but in the transportation sector for the United States.

Few studies have examined the contribution of different industries to the business sector labor productivity gap between Canada and the United States. The majority of the literature has either focused on the labor productivity gap in the manufacturing sector or used the growth accounting framework at an aggregate level. This analysis probes the extent to which productivity differences between the two countries reflect differences in their industrial structure and the performance of specific industries.

Results from Sectoral Growth Accounting

The analysis uses a traditional growth accounting framework and attributes labor productivity growth (value added per hours worked, yt) to the contribution of three factors: the improvement in labor quality (Ht), weighted by the labor income share of value added (αt); capital deepening (proxied by the flow of capital services per hours worked, kt), weighted by the capital income share of value added (βt); and TFP (denoted by At):11

Labor and capital inputs for both Canada and the United States are adjusted for quality changes using the same methodology. In particular, labor quality (Ht) is the difference between the growth of hours worked and the growth of labor input, obtained by weighting the hours of different types of labor (in terms of educational attainment, age, and gender) by their marginal productivity (proxied by their relative compensation). Similarly, capital services are obtained by weighting the growth rates of different capital assets, using their estimated marginal productivity (proxied by rental prices). Within this framework, the estimates of labor and capital inputs capture the effect of substituting toward inputs with a higher marginal productivity (such as ICT capital and more highly educated labor). In turn, this allows the estimates of TFP to better proxy the impact of technical and organizational changes on productivity.12

Tables 3.63.8 show Canadian and U.S. industries’ average labor productivity growth for the entire business sector, aggregated over the 23 sectors considered,13 and the contribution of the three proximate causes. The main results may be summarized as follows:

Table 3.6.Canada and the United States: Productivity Growth, 1982–2000(In percent)
CanadaUnited States
Contribution fromContribution from:
Labor productivityCapitalLabor qualityTFPLabor productivityCapitalLabor qualityTFP
Agriculture3.70.10.53.14.30.30.33.7
Mining2.22.10.2−0.11.82.90.2−;1.3
Construction−;0.60.10.3−1.0−0.1−0.10.4−0.4
Food, beverage, and tobacco1.30.50.10.70.60.90.2−0.5
Rubber and plastic2.20.00.12.14.30.60.33.3
Textiles, apparel, and leather2.50.50.51.53.01.00.51.5
Lumber and wood2.00.30.41.30.1−0.60.40.2
Furniture and fixtures1.3−0.50.21.71.00.30.60.2
Paper and allied products3.21.90.21.11.51.00.30.2
Printing and publishing−0.70.30.3−1.3−1.01.10.3−2.3
Primary metal5.20.70.34.32.10.70.41.0
Fabricated metal1.5−0.40.21.62.20.40.41.3
Industrial machinery and electrical equipment5.81.30.34.211.01.30.59.2
Transportation equipment4.21.10.13.02.20.50.21.5
Nonmetallic mineral products2.2−0.40.22.42.20.30.41.5
Chemicals and chemical products4.10.10.23.83.71.40.32.0
Other manufacturing industries1.40.40.30.75.71.10.44.2
Transportation2.30.50.41.41.7−0.10.31.5
Communications3.11.80.80.53.92.80.20.9
Utilities1.30.10.11.13.12.00.10.9
Trade2.40.50.51.33.51.20.22.0
Finance, insurance, and real estate1.71.50.5−0.31.21.50.2−0.5
Other services−0.20.80.7−1.7−0.20.60.2−1.1
Total1.60.80.40.61.91.10.30.8
Source: IMF staff estimates.Note: TFP = total factor productivity.
Source: IMF staff estimates.Note: TFP = total factor productivity.
Table 3.7.Canada and the United States: Productivity Growth, 1982–1995(In percent)
CanadaUnited States
Contribution fromContribution from
Labor

productivity
CapitalLabor

quality
TFPLabor

productivity
CapitalLabor

quality
TFP
Agriculture3.0−0.70.63.13.70.10.43.2
Mining3.31.80.21.23.03.10.3−0.4
Construction−0.60.20.3−1.1−0.1−0.30.5−0.3
Food, beverage, and tobacco1.60.30.31.02.50.70.21.5
Rubber and plastic3.20.50.32.44.10.30.43.4
Textiles, apparel, and leather2.50.60.61.33.10.70.61.8
Lumber and wood1.90.20.41.30.5−0.90.50.9
Furniture and fixtures0.8−;0.10.30.60.90.20.70.0
Paper and allied products3.11.80.40.91.50.90.40.2
Printing and publishing−1.20.40.4−2.0−1.40.80.3−2.6
Primary metal5.80.70.44.61.90.70.40.8
Fabricated metal1.3−0.10.31.02.50.40.41.7
Industrial machinery and electrical equipment5.41.10.73.69.00.90.57.6
Transportation equipment4.10.80.33.02.20.40.21.5
Nonmetallic mineral products1.3−0.20.31.22.6−0.10.42.2
Chemicals and chemical products4.20.10.33.84.21.20.32.7
Other manufacturing industries1.60.70.40.45.31.00.44.0
Transportation2.50.40.51.71.5−0.60.31.7
Communications2.91.30.31.24.53.00.21.3
Utilities0.50.40.2−0.13.01.80.21.1
Trade1.90.40.51.02.31.00.31.0
Finance, insurance, and real estate1.41.60.6−0.80.71.30.2−0.9
Other services−0.40.90.7−1.9−0.40.50.2−1.0
Total1.60.80.50.51.60.90.30.6
Source: IMF staff estimates.Note: TFP = total factor productivity.
Source: IMF staff estimates.Note: TFP = total factor productivity.
Table 3.8.Canada and the United States: Productivity Growth, 1995–2000(In percent)
CanadaUnited States
Contribution from:Contribution from
Labor productivityCapitalLabor qualityTFPLabor productivityCapitalLabor qualityTFP
Agriculture5.32.20.32.83.20.60.22.4
Mining−0.82.50.1−3.4−1.01.90.0−2.9
Construction−0.40.00.2−0.6−0.50.50.2−1.2
Food, beverage, and tobacco0.71.0−0.40.1−0.71.30.1−2.2
Rubber and plastic−0.6−1.1−0.30.84.31.40.22.8
Textiles, apparel, and leather2.90.00.12.82.51.70.20.6
Lumber and wood1.51.30.30.00.20.50.2−;0.5
Furniture and fixtures2.6−;1.4−0.24.21.60.50.30.9
Paper and allied products2.21.9−0.20.5−1.21.10.2−2.4
Printing and publishing0.40.10.00.3−0.31.50.2−2.0
Primary metal2.80.2−0.12.71.80.70.20.9
Fabricated metal1.9−1.00.02.91.40.60.30.5
Industrial machinery and electrical equipment7.41.5−0.46.316.92.30.414.2
Transportation equipment3.71.8−0.32.22.00.80.11.1
Nonmetallic mineral products3.0−1.2−0.14.31.31.30.3−0.2
Chemicals and chemical products3.9−0.2−0.14.12.31.90.20.2
Other manufacturing industries1.0−0.80.01.76.21.30.44.5
Transportation1.70.80.30.61.70.90.20.5
Communications3.32.72.0−1.32.32.20.10.1
Utilities3.3−1.0−0.24.54.02.60.11.4
Trade3.00.80.61.65.41.70.13.6
Finance, insurance, and real estate2.71.30.21.22.82.00.20.6
Other services0.60.50.7−0.70.01.00.3−1.3
Total1.80.80.30.92.61.50.21.1
Source: IMF staff estimates.Note: TFP = total factor productivity.
Source: IMF staff estimates.Note: TFP = total factor productivity.
  • Annual growth in Canadian aggregate labor productivity averaged 0.3 percentage point less than that in the United States over the whole period, but the gap in growth rates widened to an average 0.8 percentage point during 1995–2000. These estimates are broadly consistent with the estimates obtained using growth accounting at an aggregate level (see Macklem, 2003).
  • During 1995–2000, the labor productivity gap between the two countries widened not only in the ICT-producing sector, but also in sectors that intensively used ICT capital.14 Canada’s non-ICT-producing manufacturing industries appear to have performed as well, if not better, than their U.S. counterparts. However, a gap emerged in sectors that have been most intensively using new technologies, such as trade and finance, insurance, and real estate (the FIRE sector). In particular, labor productivity growth in Canada’s trade sector was well below that in the United States, reflecting shortfalls in both TFP and capital deepening. A gap also opened in the FIRE sector, reflecting a smaller contribution of capital deepening than in the United States.

Industrial Structure and the Aggregate Labor Productivity Growth Gap

Using growth accounting at a sectoral level allows the aggregate labor productivity growth gap between Canada and the United States to be disaggregated into three components, which correspond to the three terms on the right-hand side of equation (2):

  • a “direct” effect, which reflects the contribution from industry i’s different labor productivity growth performance, weighted by its average value-added share (vai);
  • a “structural” effect, which reflects the contribution from industry i’s different relative size across the two countries, weighted by its average labor productivity growth; and
  • a “reallocation factor,” which reflects the different ability of the two economies to direct labor resources (hours worked, hi) toward sectors with a value-added share that exceeds the labor compensation share (lsi) (that is, toward sectors with higher-than-average labor productivity level).15

This decomposition shows that a significant part of the widening labor productivity gap between Canada and the United States during 1995–2000 is explained by structural differences between the two economies. Table 3.9 shows that the negative contribution from the direct effect has remained relatively constant over the two periods, whereas the other two effects became a negative contributor after 1995. This seems to suggest that the widening of the Canada–United States labor productivity gap over the second half of the 1990s was mostly due to a shift in the relative pattern of industry specialization. In other words, rather than having become less productive than the United States, Canada has tended to be less successful in directing resources toward high-productivity sectors.

Table 3.9.Canada–United States Labor Productivity Growth Gap(In percent)
Contribution from:1982–20001982–19951995–2000
Direct effect−0.2−0.1−0.2
of which:
Industrial machinery and electrical and electronic equipment−0.1−0.1−0.2
Transportation equipment0.10.00.1
Other manufacturing industries0.00.0−0.1
Communications0.00.00.0
Utilities−0.10.00.0
Trade−0.20.0−0.4
Finance, insurance, and real estate0.1−0.10.0
Other services0.00.00.1
Structure effect0.00.1−0.2
Reallocation effect0.00.1−0.2
Source: IMF staff estimates.
Source: IMF staff estimates.

The widening of the aggregate labor productivity gap between Canada and the United States after 1995 was driven by two major service sectors, trade and FIRE. Figure 3.6 shows the contribution of industries in these sectors to the aggregate labor productivity growth gap in the two subperiods, 1981–1995 and 1995–2000. Each industry’s contribution is given by the sum of its contribution to the direct, structural, and reallocation effects. While the negative contribution from the ICT-producing sector increased only slightly in the second subperiod, the negative contribution from the trade and FIRE sectors rose significantly. The negative contribution from the ICT-producing manufacturing and trade sectors mainly reflected lower labor productivity growth, whereas the negative contribution from the FIRE sector was largely the result of the lower relative size of the sector in Canada.16

Figure 3.6.Sectoral Contributions to the Canada-United States Aggregate Labor Productivity Growth Gap

(In percent)

Source: IMF staff calculations.

The Contribution from ICT Capital Accumulation

The more muted contribution of ICT capital accumulation to productivity growth in Canada may reflect the different timing of Canadian and U.S. ICT investments. ICT investments affect both labor productivity, through capital deepening, and TFP growth, by inducing additional investments in intangible assets such as organizational changes and knowledge accumulation. However, the payoff from these investments in terms of measured output can be delayed considerably, given the time and resources required to reorganize production after investing in ICT capital.

Empirical evidence is supportive of the existence of relatively long lags between ICT capital accumulation and TFP growth in Canada. Following Basu and others (2003), a simple ordinary least squares (OLS) regression is run to relate TFP average growth during 1995–2000 to ICT capital service growth in the 1980s, the mid-1990s, and the late 1990s, taking each industry as a cross-sectional observation. Table 3.10 shows that Canadian TFP growth in the late 1990s is negatively correlated to ICT capital investments made during both halves of the 1990s but is positively correlated to ICT investments made in the 1980s.17 For the United States, the results are qualitatively similar to Basu and others (2003), with post-1995 TFP growth negatively correlated to ICT capital accumulation over the same period but positively correlated to ICT capital accumulation during the 1980s and early 1990s. This result suggests that Canada’s slower TFP acceleration during 1995–2000 may reflect the fact that Canadian firms invested in complementary capital later than U.S. firms and/or that this process took longer for Canadian firms. It also suggests, however, that Canadian firms might benefit from faster TFP growth in the near future.18

Table 3.10.Canada: ICT Regression with Current and Lagged ICT Capital Services Growth
CanadaUnited States
Constant1.79−0.80
(0.73)(0.93)
ICT_CAP_95–00−0.24−2.41
(0.54)(1.03)
ICT_CAP_90–95−1.532.50
(1.02)(1.09)
ICT_CAP_80–901.820.43
(1.48)(0.46)
R-squared0.110.24
Observations2421
Source: IMF staff estimates.Notes: ICT = information and communication technologies; dependent variable: average annual TFP growth in 1995– 2000. White heteroscedasticity–consistent standard errors in parentheses.
Source: IMF staff estimates.Notes: ICT = information and communication technologies; dependent variable: average annual TFP growth in 1995– 2000. White heteroscedasticity–consistent standard errors in parentheses.

Productivity Growth and Trade

The labor productivity gap between Canada and the United States widened despite the marked deepening of trade linkages between the two countries. Some have argued that this reflects Canada’s increased specialization in the natural-resource-based manufacturing sectors, where Canada has had a comparative advantage (see, for example, Jackson, 2003). This reallocation of resources could have dampened the aggregate growth of Canadian productivity.

However, the labor productivity gap is unlikely to be related to increased economic integration. First, the results attribute nearly the entire structural labor productivity gap to the service (nontradables) sector. Second, during the second half of the 1990s, Canada’s tradables sector seems to have evolved rapidly in the direction of high-tech production. In particular, while still lower than in the United States, Canada’s ICT-producing sector increased its share of aggregate GDP over this period (Table 3.11). Finally, several studies show that, during the 1990s, Canadian trade increased mainly in two-way trade in similar products, rather than across different industries.19

Table 3.11.Canada and the United States: Value Added, Shares of Total(In percent)
CanadaUnited States
1981–19951995–20001981–19951995–2000
Agriculture3.22.52.31.8
Mining6.95.62.51.5
Construction8.56.95.35.3
Manufacturing24.025.122.419.5
of which
Industrial machinery and electrical and electronic equipment2.72.95.04.4
Transportation5.55.24.03.8
Communications3.93.73.13.1
Utilities4.54.53.22.6
Trade14.714.316.416.0
Finance, insurance, and real estate12.814.319.021.1
Other services15.918.021.925.3
Source: IMF staff estimates.
Source: IMF staff estimates.

On the contrary, Canadian TFP growth is positively correlated with trade. Plotting average TFP growth against (1) the degree of vertical specialization and (2) the degree of trade exposure of Canadian sectors during 1981–2000 shows that TFP growth is positively correlated with openness to trade (Figure 3.7).20 In particular, the ICT-producing and transportation equipment sectors seem to have benefited most from exposure to trade and interindustry specialization during this period. Moreover, the extent of the correlation has increased since the inception of the free trade agreement with the United States.

Figure 3.7.Canada: Sectoral TFP Growth and Openness to Trade

Source: IMF staff calculations.

Note: TFP = total factor productivity.

C. The Effects of U.S. Shocks

The close integration of the Canadian and U.S. economies means that U.S. shocks are quickly transmitted across the border. Canada’s exports to the United States account for about 85 percent of total Canadian exports and about 35 percent of its GDP, and investment flows and financial markets are also closely linked. As documented by Kose (2004), the increased trade and financial linkages that resulted from the 1989 Canada-U.S. free trade agreement have significantly increased the impact of the U.S. business cycle on Canada, but Canada’s vulnerability to U.S. shocks also stems from its relatively small size and the importance of its natural resource sector.22

The recent strength of the Canadian dollar intensified interest in the impact of U.S. shocks on the Canadian economy and monetary policy. The vigor of Canada’s net exports during 2004 was surprising, given the 30 percent appreciation of the Canadian-U.S. dollar exchange rate during 2003 and 2004 (Figure 3.8). Competing explanations for the modest impact on trade have been offered, including that it reflects an increase in the flexibility and efficiency of Canadian industry, a decline in exchange rate pass-through, or delays in the usual adjustment process.

Figure 3.8Real Effective Exchange Rate and Current Account Balance

Sources: Haver Analytics; and IMF Information Notice System.

Note: REER = real effective exchange rate.

These issues are explored here using a small two-country macroeconomic model. In particular, the model is used to investigate how changes in the exchange rate pass-through affect the transmission of an exchange rate shock to the real economy. The results offer two key insights:

  • The Canadian economy responds significantly to U.S. macroeconomic and policy shocks, as well as to exchange rate shocks. However, there is considerable scope for a monetary policy response to ameliorate the effects of these shocks.
  • The strength of net exports in 2004 appears to be at least partially related to a decline in the pass-through coefficient. The weakening of pass-through—if it is sustained—would significantly reduce the impact of exchange rate shocks on both GDP and inflation.

Model Description

The model employed for this study is a two-country version of a small open economy model.23 Each economy is characterized by three equations—an IS curve, an expectation-augmented Phillips curve, and a monetary policy reaction function. Canada is assumed to face both domestic and external shocks (that is, from the United States), while the United States is assumed to be large enough to be essentially a closed economy. The model allows U.S. output shocks to feed into the Canadian IS equation, while real exchange rate shocks affect both the IS equation and the Phillips curve. For the sake of simplicity, the effects of fiscal policy are not modeled. There are four equations for Canada.

  • IS curve:
    where ygap is the Canadian output gap; RR is the Canadian real short-term interest rate; RR* is the equilibrium real interest rate for Canada; ygapUS is the U.S. output gap; zt is the Canadian-U.S. dollar exchange rate, in real terms; zt* is the equilibrium real exchange rate; and zgapt=ztzt* is the exchange rate gap.
  • Phillips curve:
    where π is the annualized quarterly inflation rate, and π4 is the four-quarter inflation rate.24
  • Monetary policy reaction function:
    where RS is the target for the nominal overnight rate (the Canadian monetary policy rate). The terms uygap, uπ, and utRS are error terms. This is equivalent to assuming that the Bank of Canada allows the real short-term interest rate to deviate from its “equilibrium” level depending on whether the inflation rate that is expected to prevail four quarters ahead deviates from the target, π*, or whether output deviates from potential. Interest rate smoothing is permitted, however—the short-term interest rate is set with reference to its value last period.
  • Real exchange rate (uncovered interest rate parity):

where ρ*t is an interest rate premium. Exchange rate expectations are assumed to follow an autoregressive process:

Similar equations are assumed for the U.S. economy. However, because the U.S. economy is assumed not to be affected by Canadian shocks, the U.S. IS curve includes neither the foreign output gap nor the Canadian-U.S. dollar exchange rate. Moreover, the U.S. Phillips curve does not include the exchange rate.

Data and Estimation

The model is computationally tractable and provides for a close integration with the IMF’s medium-term forecasting framework. Owing to its simplicity, the model can be easily applied to medium-term economic forecasts provided for the World Economic Outlook, for example, to consider the effects of different policy responses or the validity of model assumptions.25 It also facilitates the application of sophisticated estimation and simulation techniques that have been developed in recent years.

The model employs Bayesian estimation techniques. This approach incorporates prior knowledge about parameter values, which is especially useful given the short data sample. It also provides information on the distribution of model parameters and shocks. All shocks are modeled as first-order autoregressive processes with normally distributed error terms, with the sole exception of the exchange rate shock, which is assumed to be normally distributed. In addition, all data are assumed to include some parameterized measurement error to account for data uncertainty related to the possibility of future revisions.

The model uses quarterly data from 1996 through the third quarter of 2004. The sample period was chosen to exclude transition effects from Canada’s adoption of an inflation target in 1991. The model was first estimated using historical data and then simulated over the forecast horizon. The equilibrium values of variables (that is, the starred variables) were determined using a version of the Hodrick-Prescott filter (except for the Canadian inflation target, which is set at 2 percent).

Results

The simulation results indicate that real shocks to the U.S. economy significantly affect both Canadian GDP and inflation. A 1 percentage point increase in U.S. GDP, which stems from a temporary demand shock linked to the IS curve, raises Canadian GDP by almost ½ percent and inflation by ⅓ percentage point (Figures 3.9 and 3.10). Conversely, a permanent 1 percent reduction in U.S. potential output—which would be equivalent to a negative U.S. supply shock—reduces the level of GDP in Canada by about 1½ percent and inflation by 1 percentage point over six quarters.

Figure 3.9.Response of Canadian GDP to U.S. Shocks, with Immediate and Delayed Monetary Policy Response

(Canadian GDP, percent deviation from baseline)

Source: IMF staff calculations.

Figure 3.10.Response of Canadian CPI to U.S. Shocks, with Immediate and Delayed Monetary Policy Response

(Percentage point deviation of four-quarter Canadian inflation rate from baseline)

Source: IMF staff calculations.

The model can be used to illustrate the costs of delaying the monetary policy response to external and other shocks. For example, if Canadian monetary policy were assumed to respond with a four-quarter lag (rather than immediately as is implied by the monetary reaction function described above), the impact of the temporary U.S. demand shock would be about ¼ percent larger over four quarters, and CPI inflation would be correspondingly higher (see Figures 3.9 and 3.10). Moreover, the delayed monetary policy response requires a larger interest rate movement—in this case the Bank of Canada is required to raise its policy rate by about ½ percentage point more in the quarter of the move than otherwise.

The speed of the monetary policy response is also important in determining how U.S. inflation shocks affect the Canadian economy. For example, the impact of a 1 percentage point U.S. inflation shock—which is modeled as a shock to the U.S. Phillips curve—on Canada’s GDP and inflation would be roughly halved by an immediate response from the Bank of Canada versus a delayed response (Figure 3.11).

Figure 3.11.Response of Canadian GDP and Inflation to U.S. Shocks, with Immediate and Delayed Monetary Policy Response

(Shock: U.S. interest rates, 100-basis-point increase)

Source: IMF staff calculations.

The model also can be used to illustrate that U.S. monetary policy has a relatively modest effect on Canada. For example, even if the monetary policy reaction were delayed by four quarters, a 100-basis-point increase in the federal funds rate would reduce Canadian GDP by only about 0.1 percent over six quarters. Again, allowing an immediate policy response would halve this (already small) effect, with the impact on Canadian inflation being negligible in both cases.

The impact on Canada of exchange rate shocks is found to be relatively strong, however, reflecting Canada’s export dependency. Simulation results suggest that a 20 percent real appreciation of the Canadian dollar against the U.S. dollar reduces Canada’s GDP by about 1 percent over four quarters, even if the Bank of Canada immediately reduces its target rate by 1½ percent (Figure 3.12). This effect almost doubles if rate hikes are delayed.

Figure 3.12.Response of Canadian GDP and Inflation to Exchange Rate Shock, with Immediate and Delayed Monetary Policy Response

(Shock: 20 percent appreciation of exchange rate)

Source: IMF staff calculations.

However, the model also illustrates that the effect of exchange rate shocks depends significantly on the degree of pass-through. If the pass-through coefficient, βzgap, in equation (1) is lowered by half, the impact of an exchange rate appreciation on GDP is reduced by about a quarter, and the effect on the rate of inflation by nearly a half (Figure 3.13). This result is consistent with the view of a number of analysts in Canada that the seemingly modest response of Canadian net exports and growth in 2004 to the strong appreciation of the Canadian dollar over the previous two to three years is partially accounted for by a decline in exchange rate pass-through.

Figure 3.13.Responses of Canadian GDP and Inflation to Changes in the Pass-Through Coefficient, with Immediate and Delayed Monetary Policy Response

Source: IMF staff calculations.

Conclusions

Using a simple two-country version of the small open economy model, this analysis gauges the impact on Canada of shocks to the U.S. economy. The results suggest that monetary policy can play an important role in reducing the effect of U.S. and exchange rate shocks on the Canadian economy. They also indicate that the exchange rate pass-through coefficient plays a significant role in determining the impact of exchange rate shocks on Canada.

1We also calculate the square root of the average squared deviations—a measure that gives a bigger weight to outliers than the average absolute deviation. The two measures tell essentially the same story, and so we report only the latter.
2The United States also experienced convergence in the late 1970s—a period for which Canadian data are not available. This may contribute to the impression (conveyed by Figures 3.1 and 3.2) that some U.S. regions seem to have experienced faster convergence than their Canadian counterparts.
3The weighted average, however, is 1.5 for both countries. This is not surprising, given the economic size of Ontario and Quebec.
4Data for Canada are from Statistics Canada, while data for United States are from several industry data sources, including the database used by Jorgenson, Ho, and Stiroh (2002) in their latest study on the U.S. productivity performance. See Box 3.2 for a description of the database.
5Harchaoui and Tarkhani (2002) show that the size of Canada’s ICT-producing sector increased from around 2½ percent of GDP in 1981 to around 4 percent of GDP on average over the second half of the 1990s, but remained below the U.S. share, which was around 6 percent of GDP over this period.
7The faster growth in ICT capital services in Canada might be partly explained by differences in the capital asset depreciation rates used by Statistics Canada and the U.S. Bureau of Labor Statistics. In particular, Statistics Canada uses higher depreciation rates for ICT assets, something that might lead to a faster growth of their capital services (see Ho, Rao, and Tang, 2003).
8See Khan and Santos (2002); Harchaoui and Tarkhani (2002); Armstrong and others (2002); and Ho, Rao, and Tang (2003). Both Armstrong and others and Harchaoui and Tarkhani find that ICT capital deepening has contributed around ¼ percent to the average annual labor productivity growth in Canada during 1995–2000, up only slightly compared to 1981–2000. The equivalent figure for the United States is estimated between ½ percent (Oliner and Sichel, 2002) and ⅔ percent (Jorgenson, Ho, and Stiroh, 2002).
9Baldwin and Tang (2003) show that around ¼ percentage point of the labor productivity gap in the manufacturing sector in 1997 was due to the larger share of SMEs in Canada compared to the United States, and ½ percentage point was due to lower productivity of SMEs in Canada.
10Based on the regulatory variables they use, Canada is lagging the United States according to the OECD’s employment protection legislation index but is leading the United States according to the World Economic Forum’s regulatory burden index.
11The dot over the variables denotes percentage growth rates. For a more detailed discussion of the methodology, see Jorgenson, Ho, and Stiroh (2002).
12Industry value-added measures of TFP reflect technological changes only, assuming that the production function is separable in primary (capital and labor) and intermediate inputs. Loosely speaking, this is equivalent to assuming that firms’ decisions on the capital-labor mix are not affected by decisions regarding intermediate inputs. If this is not the case, industry value-added TFP would capture not only technical changes, but also the productivity improvements that derive from more efficiently produced intermediate inputs. Hence, while TFP would still reflect technological changes at an aggregate level, its breakdown across industries would be affected. OECD (2001b) suggests interpreting more widely the industry value-added measure of TFP as an indication of an industry’s ability to translate technical changes into overall income.
13Consistent with OECD (2001b), total labor productivity growth is obtained as the weighted average of labor productivities across industries using value-added shares as weights, plus a reallocation term that reflects the economy’s ability to move labor resources to those sectors with a higher-than-average level of labor productivity (see equation 2). Given that aggregate TFP and contributions from labor quality and capital deepening are obtained as weighted averages of industries figures using value-added shares as weights, their sum is different than total labor productivity, the difference being the reallocation factor.
14The ICT-producing sector is proxied by the industrial machinery and electrical and electronic product sectors.
15See Faruqui and others (2003) for a similar decomposition formula. Their conclusion is that most of the business sector labor productivity growth gap between Canada and the United States during 1987–2000 is explained by the direct effect. Moreover, the manufacturing sector is the main contributor to the aggregate gap during 1996–2000, while the service sector is more relevant during 1987–96. However, these results are obtained at a rather coarse level of disaggregation (four large sectors are identified: primary, manufacturing, construction, and services), something that (as admitted by the same authors) might conceal the contribution from the structural and reallocation effects.
16The comparison between the trade sectors in the two countries may be blurred by the fact that eating and drinking places are included in the U.S. trade sector, while they are part of the “other service” sector for Canada (as part of the “accommodation, food, and beverage” subsector). However, the results do not change substantially when the Canadian trade sector is adjusted to include that fraction of the “accommodation, food, and beverage” sector attributable to eating and drinking places.
17As stressed by Basu and others (2003), the OLS regression and the simple specification do not show causation but only indicate whether a correlation exists between lagged ICT capital and TFP growth. The results in Table 3.10 are robust to the exclusion of key sectors, such as the ICT-producing, trade, and FIRE sectors.
18Leung (2004) finds that investments in new technologies in Canada have their strongest impact on TFP growth only after three years. Robidoux (2003) also suggests that the contribution of ICT capital to the acceleration of TFP growth in the service sector in the late 1990s reflects the successful incorporation of ICT into production and management processes during the 1980s and early 1990s.
20Both indexes of openness to trade are from Dion (1999) (the data, originally up to 1996, have been extrapolated to 2000). The degree of vertical specialization measures the extent to which an industry’s trade is accounted for by inputs that are imported and embodied in exports. The degree of trade exposure is the algebraic sum of three different indicators: the share of an industry’s exports in its gross output (capturing its degree of export orientation); the share of an industry’s imported intermediate inputs in its gross output (capturing the exposure of an industry on the cost side); and the share of an industry’s competing imports in the domestic markets for its core products (measuring the exposure to foreign penetration of the domestic market).
21The authors are grateful to Douglas Laxton for invaluable help with the model.
22In 2003, Canada’s GDP was equivalent to about 8 percent of U.S. GDP, and domestic absorption accounted for 7 percent of that in the United States.
23Lane (1999) provides a review of the new open economy literature. See Berg, Karam, and Laxton (2006) for a description of the model used here.
24The results of estimating the model with either core or headline inflation are qualitatively the same.
25See Coats, Laxton, and Rose (2003) and references therein.

    Other Resources Citing This Publication