A. Introduction

1. Canada has launched an ambitious infrastructure program to boost its long-term growth potential. The federal government is committed to investing Can$ 187 billion (equivalent to 7½ percent of GDP) in public capital, a half which (Can$ 96 billion) is new additional infrastructure spending. This will triple investment in public capital over the next decade. Priority projects include: (i) public transit infrastructure to better connect Canada’s transportation system within the country and to the world to facilitate trade and movement of people; (ii) green infrastructure to create greener energy and cleaner land; and (iii) social infrastructure to provide affordable housing, child cares spaces, and communities centers.

2. The objective of this chapter is to estimate the macroeconomic effects of infrastructure investment. For countries where infrastructure bottlenecks are constraining growth such as in Canada, the gains from alleviating these bottlenecks are likely to be large. Using panel data for 10 Canadian provinces over 1961-2015, we present some evidence that public investment in infrastructure could boost economic growth and complement private investment. Importantly, we find that public infrastructure investment generates positive spillover effects across regions: higher public investment in one province boosts output in the rest of Canada

B. The Size of the Problem

3. There is broad consensus that the infrastructure gap in Canada is sizable, although estimates range widely from as low as Can$150 billion to as high as Can$ 1 trillion (Advisory Council on Economic Growth, 2016). The World Economic Forum Global Competitiveness Index suggests that Canada’s overall ranking in the quality of infrastructure dropped to 21^st position most recently, down from 17^th position a decade ago, with the decline in the ranking of roads and railroad as the most evident.

Table 1.

Canada: Global Competitiveness Index

(Rank)

Source: World Economic Forum Global Competitiveness Index.

Table 1.

Canada: Global Competitiveness Index

(Rank)

	2006/07	2016/17	Change in rank (+: rank up)
Overall	17	21	−4
Roads	16	22	−6
Railroad	14	18	−4
Port	16	19	−3
Air transport	17	16	1
Electricity supply	18	16	2

Source: World Economic Forum Global Competitiveness Index.

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Table 1.

Canada: Global Competitiveness Index

(Rank)

	2006/07	2016/17	Change in rank (+: rank up)
Overall	17	21	−4 View Expanded
Roads	16	22	−6 View Expanded
Railroad	14	18	−4 View Expanded
Port	16	19	−3 View Expanded
Air transport	17	16	1 View Expanded
Electricity supply	18	16	2 View Expanded

Source: World Economic Forum Global Competitiveness Index.

4. In addition, the stock of economic infrastructure stands at a historical low. Economic infrastructure includes railways, ports, airport, pipelines, roads, and energy networks that supports the delivery of public services, connects people to jobs, and facilitates businesses through reduction of costs of doing businesses. Investment in economic infrastructure therefore directly affects economic production. However, the stock of economic infrastructure has declined from about 20 percent of GDP in 1961 to as low as 12 percent in 2007, reflecting a steady decline in public investment for four and a half decades. The downward trend was reversed during the 2008-09 global financial crisis when the government increased infrastructure spending as part of its stimulus efforts, but the stock of economic infrastructure assets remains at historic lows, 5¾ percent of GDP below the level in 1961.

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Economic Infrastructure Assets and Investment

(Percent of GDP)

Citation: IMF Staff Country Reports 2017, 211; 10.5089/9781484309650.002.A002

Sources: Statistics Canada CANSIM Table 031-0005; and Haver Analytics

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C. Policy Considerations

5. Canada’s fiscal federalism presents both advantages and challenges in coordinating infrastructure investment. The bulk of economic infrastructure assets is owned and managed by provincial and local authorities. The federal government’s share of infrastructure assets has halved to 7 percent since 1990, while provincial and local governments have increased their share to more than 90 percent of total assets today.

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General Government Economic Infrastructure Assets ¹/

(Percent share)

Citation: IMF Staff Country Reports 2017, 211; 10.5089/9781484309650.002.A002

Sources: Statistics Canada, CANSIM Table 378-0121; and Haver Analytics¹/Book value.

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• Advantages. Lower levels of government (provincial and local governments) are better positioned to identify what local communities need. For example, they are more likely to know which communities would benefit most from local infrastructure projects, such as water supply or urban transit. In this case, decentralization to provincial and local governments is both feasible and desirable for allocative and administrative efficiency (Ahmad, Hewitt, and Ruggiero, 1997).

• Challenges. Since many infrastructure facilities—including roads, railways, and airports—have connectivity and are part of networks, investing in infrastructure in one province would likely have spillover effects to other provinces. Accordingly, better coordination across jurisdictions in the planning and execution of infrastructure projects could improve economic benefits (in terms of efficiency and growth gains). However, currently, there is neither an established institutional mechanism for federal-provincial coordination nor a national infrastructure strategy that aligns priorities across all levels of government. The federal government provides conditional grants to provincial and local governments to influence their decision making over infrastructure projects, but some question their effectiveness as a i policy tool (Public Policy Forum, 2016).

6. Better coordination. Increasing the efficiency of public investment is critical to reap its full benefits. Canada has a highly efficient public investment framework but there is room for improvement. The federal government should develop coordination mechanisms that identify infrastructure gaps and increase transparency. More specifically, the IMF Public Investment Management Assessment framework suggests merits of reforming the following areas.

• Specifying capital funding amounts by department in the medium-term budget framework, which could support more detailed sectoral investment strategies and a project pipeline

• Promoting inter-governmental coordination by consolidating federal and provincial approved projects into a single report updated annually

• Identifying major public investment projects in budget documents to clarify their contribution to achieving program objectives and protect their funding during budget execution

• Improving transparency regarding public investment spending by publishing Project Briefs and business cases for approved major projects, total expected financial obligations of ongoing projects, project audits, and PPP risks

• Establishing and publishing recommended methodologies for appraising proposed projects and evaluating projects upon completion

• Publishing the total cost of projects at the time of initial funding.

D. Empirics

7. The empirical analysis focuses on estimating the macroeconomic effects of increasing economic infrastructure and asks the following questions.

• Does infrastructure investment contribute to boost output? If so, how much? ²

• Does infrastructure investment have spillover effects across Canadian provinces? Since most infrastructure assets are owned and managed by provincial and local authorities, but an infrastructure policy coordination framework has not been fully developed, finding positive spillover effects would argue for establishing a coordinating framework.

• Does infrastructure investment crowd in private capital investment? This question revisits a long-standing debate on whether public infrastructure crowds in or crowds out private capital (see for example, Afonso, A., and M. St. Aubyn, 2008). On the one hand, an increase in public investment could lead to higher interest rates (if the investment is debt-financed) or to a higher tax burden for the private sector: in either case, demand for private investment would be dampened. On the other hand, an increase in public investment could lead to higher quantity and quality of infrastructure assets, thus contributing to higher productivity of private capital, as costs of doing business fall. In this case, an increase in infrastructure investment would crowd in private investment.

8. To answer these questions, we run panel regressions, using data from 10 Canadian provinces. The sample period is from 1961 to 2015 (annual data), and the panel dataset is balanced (see data description in Annex I). For the average of the full sample period, we observe a relatively strong positive relationship between the annual increase in the stock of infrastructure assets and provincial GDP growth (Figure 3).

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Growth of Infrastructure and Provincial GDP, 1961-2015

(Period average, percent)

Citation: IMF Staff Country Reports 2017, 211; 10.5089/9781484309650.002.A002

Sources: Statistics Canda CANSIM Table 031-0005; and Haver Analytics

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where Y_it is real GDP, A captures total factor productivity, L_it is employment, K_it is real private capital stock, G_it is real infrastructure stock, ${\overline{GO}}_{it}$, real infrastructure stock in the other provinces, for province i and time t. Coefficients β^L, β^K, β^G, and β^GO denote elasticities of output with respects to inputs.

We include ${\overline{GO}}_{it}$ to examine whether output in province i depends on infrastructure in other provinces. In other words, we test whether infrastructure in one province could cause spillover effects to the rest of Canada. We experiment with two different weighting methods to calculate this variable.

• Weighted by geographical distance. Spillover effects could be greater across nearby provinces. For example, Ontario’s GDP would be affected more by infrastructure in Quebec than in British Columbia. To test this hypothesis, we construct weights based on the distance between pairs of provinces (capital cities).

• Simple average. Given that economic activity across Canadian provinces is highly integrated, geographical distance may not matter. In this case, Ontario’s GDP would be affected not necessarily by infrastructure in Quebec but by overall infrastructure in all Canadian provinces. To examine this hypothesis, we calculate ${\overline{GO}}_{it}$ as the simple average of other provinces.

Taking logarithms of equation (1), we estimate the following baseline model.

where lower case variables denote natural logarithms. Unobservable u_it can be interpreted as total factor productivity, and is composed of a province specific intercept, α_i a time trend, γ_iω_t, and the white noise error terms ε_it∼IID(0, 𝜎²). On the time trend, we examine both a linear trend and a time-variant trend (see below).

10. We estimate the above equation using a fixed effects model and pooled mean group estimation. Note that Canadian provinces are diverse, with different settings in structural and economic fundamentals, including natural resource endowments, legal and regulatory frameworks, industry structures, and labor markets. In estimating equation (2), heterogeneity across Canadian provinces should be considered. The fixed effects model is the most restrictive one, where the slope coefficient β_i and the time trend coefficient γ_iω_t are assumed to be the same across provinces (β_i = β, and γ_i = 1, for all i), but the intercept term, α_i, is different across provinces. Next, we relax the assumption of the fixed coefficient β and allow it to vary across provinces (but the time trend, γ_iω_t, is still assumed to be constant). To this end, we apply the mean group (MG) estimator developed by Pesaran and Smith (1995).³ Finally, we relax the assumption of the constant time trend, and apply a mean group estimator with a common dynamic process (MG-CDP), developed by Eberhardt and Teal (2010).⁴

Results

11. The regression results are presented in Table 2.

• In all models, the coefficients on private capital and employment are positive and highly significant.

• Models 1-3 present the results of fixed-effects models. In Model 1, the coefficient on infrastructure assets is positive but not significant. Model 2 includes other provinces’ infrastructure assets (weighted by geographical distance). This shows that ‘province i’s (for example, Ontario’s) own investment in infrastructure would boost its own GDP, and infrastructure investment in the rest of Canada would boost Ontario’s GDP, too. But this evidence is weak, as their coefficients are not significant. Model 3 includes the simple average of other provinces’ infrastructure assets, but these coefficients (both own and other provinces’) are negative and insignificant.

• Models 4-6 are based on the MG estimator. The coefficient on ‘province i’s own infrastructure assets is positive but is still not significant in Model 4. The coefficients on other provinces’ infrastructure assets in Model 5 (using simple average ${\overline{GO}}_{jt}$) and in Model 6 (using weighted average ${\overline{GO}}_{jt}$) are positive but not significant. Moreover, the coefficients on own infrastructure are now negative and insignificant.

• Models 7-9 are based on the MG-CDP estimator. The coefficient on ‘province i’s own infrastructure assets is now positive and significant (Model 7). Including other provinces’ infrastructure assets, however, Model 8 shows that the coefficient on ‘province i’s own infrastructure assets is positive but not significant, while that on other provinces’ infrastructure assets (weighted) is negative and insignificant. Model 9 performs relatively well. The coefficient on ‘province i’s own infrastructure assets is positive and significant (at 15 percent level), while that on other provinces’ infrastructure assets is positive and highly significant.

Table 2.

Regression Results (Production Function Models) ¹/

Dependent variable: Real GDP

^1/Robust standard errors are in parentheses, with *** indicating significance level at 1 percent, ** at 5 percent, * at 10 percent, and + at 15 percent.

^2/Pesaran and Smith (1995) mean group estimator.

^3/Eberthardt and Teal (2010) mean group estimator.

Table 2.

Regression Results (Production Function Models) ¹/

Dependent variable: Real GDP

Estimation method				Mean Group Estimators
	Fixed effects			P&S MG (1995) 2/			E&T MG-CDP (2010) 3/
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9
Real private capital stock (K)	0.252***	0.255***	0.249***	0.348***	0.341***	0.321***	0.101***	0.0836**	0.104***
	(0.0738)	(0.0707)	(0.0736)	(0.0498)	(0.0550)	(0.0552)	(0.0365)	(0.0403)	(0.0377)
Employment (L)	0.750***	0.748***	0.734***	1.355***	1.173***	1.155***	0.560***	0.507***	0.526***
	(0.178)	(0.176)	(0.187)	(0.167)	(0.190)	(0.146)	(0.120)	(0.0964)	(0.101)
Economic infrastructure assets (G)	0.0448	0.0463	−0.0296	0.0444	−0.0281	−0.00284	0.0768*	0.0626	0.105+
	(0.108)	(0.108)	(0.149)	(0.0738)	(0.110)	(0.105)	(0.0422)	(0.0658)	(0.0704)
Economic infrastructure assets of other provinces		0.0470			0.107			−0.0943
per employment (GO, weighted average)		(0.141)			(0.0821)			(0.0585)
Economic infrastructure assets of other provinces			−0.937			0.0974			0.274***
per employment (GO, simpe average)			(0.551)			(0.110)			(0.0752)
Common dynamic process							0.902***	0.936***	0.905***
							(0.0853)	(0.0978)	(0.103)
Constant	7.676***	7.472***	16.40**	8.239***	8.161***	7.043***	8.967***	9.279***	6.017***
	(0.931)	(0.893)	(5.204)	(1.077)	(1.639)	(0.830)	(0.782)	(0.672)	(0.586)
Memorandum items:
Observations	550	550	550	550	550	550	550	550	550
R-squared	0.988	0.988	0.988

^1/Robust standard errors are in parentheses, with *** indicating significance level at 1 percent, ** at 5 percent, * at 10 percent, and + at 15 percent.

^2/Pesaran and Smith (1995) mean group estimator.

^3/Eberthardt and Teal (2010) mean group estimator.

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Table 2.

Regression Results (Production Function Models) ¹/

Dependent variable: Real GDP

Estimation method				Mean Group Estimators
	Fixed effects			P&S MG (1995) 2/			E&T MG-CDP (2010) 3/
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9
Real private capital stock (K)	0.252***	0.255***	0.249***	0.348***	0.341***	0.321***	0.101***	0.0836**	0.104***
	(0.0738)	(0.0707)	(0.0736)	(0.0498)	(0.0550)	(0.0552)	(0.0365)	(0.0403)	(0.0377)
Employment (L)	0.750***	0.748***	0.734***	1.355***	1.173***	1.155***	0.560***	0.507***	0.526***
	(0.178)	(0.176)	(0.187)	(0.167)	(0.190)	(0.146)	(0.120)	(0.0964)	(0.101)
Economic infrastructure assets (G)	0.0448	0.0463	−0.0296	0.0444	−0.0281	−0.00284	0.0768*	0.0626	0.105+
	(0.108)	(0.108)	(0.149)	(0.0738)	(0.110)	(0.105)	(0.0422)	(0.0658)	(0.0704)
Economic infrastructure assets of other provinces		0.0470			0.107			−0.0943
per employment (GO, weighted average)		(0.141)			(0.0821)			(0.0585)
Economic infrastructure assets of other provinces			−0.937			0.0974			0.274***
per employment (GO, simpe average)			(0.551)			(0.110)			(0.0752)
Common dynamic process							0.902***	0.936***	0.905***
							(0.0853)	(0.0978)	(0.103)
Constant	7.676***	7.472***	16.40**	8.239***	8.161***	7.043***	8.967***	9.279***	6.017***
	(0.931)	(0.893)	(5.204)	(1.077)	(1.639)	(0.830)	(0.782)	(0.672)	(0.586)
Memorandum items:
Observations	550	550	550	550	550	550	550	550	550
R-squared	0.988	0.988	0.988

^1/Robust standard errors are in parentheses, with *** indicating significance level at 1 percent, ** at 5 percent, * at 10 percent, and + at 15 percent.

^2/Pesaran and Smith (1995) mean group estimator.

^3/Eberthardt and Teal (2010) mean group estimator.

In sum, we can conclude that there is some evidence that infrastructure assets are positively correlated with real GDP and have spillover effects. We judge that the estimates based on the MG-CDP model—which accounts for both a province-specific slope β and a province-specific time dummy γ_iω_i—look most plausible, given that the estimated coefficients are all significant at reasonable levels. Models 7 and 9 suggest that the elasticity of infrastructure assets would be around 0.08 and 0.10, respectively.

Marginal product of infrastructure assets

12. The regression results permit a calculation of the marginal product of capital (MPK). Using the estimated elasticity of public infrastructure, an MPK can be calculated as the elasticity multiplied by the output to public infrastructure ratio.⁵ Figure 4 presents the MPK for Canada (based on the estimated elasticity of 0.08, Model 7). Although some caution is needed in interpreting this result, the figure shows a steady increase in the MPK between the early 1960s and mid-2000s, which reflects a decline in the stock of public infrastructure as a percent of GDP.⁶ As the government changed course and raised investment in public infrastructure after the 2008-09 global financial crisis, the MPK has started to fall. However, the current level of the MPK remains above historical levels. This seems to suggest—together with a fact that the current level of interest rates is much lower than its historical norm—that there is room to further increase public infrastructure while keeping the return of infrastructure investment higher than the cost of funding.

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Marginal Product of Capital

Citation: IMF Staff Country Reports 2017, 211; 10.5089/9781484309650.002.A002

Sources: Authors’ estimates

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E. Conclusion

15. This chapter analyzed the macroeconomic effects of public infrastructure. We found some evidence that higher public infrastructure is correlated with higher economic growth and could crowd in private investment. We also found that infrastructure investment has positive spillover effects across provinces. This means that more concerted efforts across different layers of jurisdictions to better coordinate infrastructure planning would maximize the economic benefit of infrastructure investment. In sum, the empirical findings in this chapter provides an economic case for more infrastructure spending. With underinvestment of infrastructure over the past several decades, combined with historically low cost of funding, investing in infrastructure would generate a net positive return.

Table 3.

Testing Complementarity of Public Infrastructure and Private Capital ¹/

Dependent variable: Real GDP

Estimation method: a mean group estimator with a common dynamic process (MG-CDP)

^1/Based on E&T Mean Group Estimators. Robust standard errors are in parentheses, with *** indicating significance level at 1 percent, ** at 5 percent, * at 10 percent, and + 15 percent.

Table 3.

Testing Complementarity of Public Infrastructure and Private Capital ¹/

Dependent variable: Real GDP

Estimation method: a mean group estimator with a common dynamic process (MG-CDP)

	Model 1	Model 2
Employment (l)	0.542***	0.375***
	(0.112)	(0.0534)
g*k	0.00839***
	(0.00315)
go*k		0.0134***
		(0.00387)
Common dynamic process	0.927***	0.966***
	(0.101)	(0.0926)
Constant	9.512***	9 770***
	(0.391)	(0.377)
Memorandum items:
Observations	550	550
R-squared

^1/Based on E&T Mean Group Estimators. Robust standard errors are in parentheses, with *** indicating significance level at 1 percent, ** at 5 percent, * at 10 percent, and + 15 percent.

View Table

Table 3.

Testing Complementarity of Public Infrastructure and Private Capital ¹/

Dependent variable: Real GDP

Estimation method: a mean group estimator with a common dynamic process (MG-CDP)

	Model 1	Model 2
Employment (l)	0.542***	0.375***
	(0.112)	(0.0534)
g*k	0.00839***
	(0.00315)
go*k		0.0134***
		(0.00387)
Common dynamic process	0.927***	0.966***
	(0.101)	(0.0926)
Constant	9.512***	9 770***
	(0.391)	(0.377)
Memorandum items:
Observations	550	550
R-squared

^1/Based on E&T Mean Group Estimators. Robust standard errors are in parentheses, with *** indicating significance level at 1 percent, ** at 5 percent, * at 10 percent, and + 15 percent.