A. Cross-country analysis

Determining which factors explain the observed differences in the absolute and relative prices of tradable capital goods in the 2011 ICP data is a daunting task. Because price levels of capital goods that are comparable across countries are available only at one point in time, it is difficult to disentangle the causal contribution of various potential drivers. We examine each potential source of differences in capital goods prices across countries—namely, the prices charged by key exporters, trade costs, and relative efficiency in the production of tradable goods—and relate these to the relative price of capital goods from the 2011 ICP data.

To assess whether differences in prices charged by key capital goods exporters can explain the higher relative prices of machinery and equipment observed in emerging market and developing economies (compared with advanced economies), we examine highly disaggregated data on trade in capital goods. Since a small number of countries account for the bulk of global exports of machinery and equipment (Eaton and Kortum 2001), and since most emerging market and developing economies import a significant proportion of these goods, we compare unit values of various types of machinery and equipment from five of the largest capital goods exporters—United States, China, Germany, France, and Japan.⁹ This approach, which builds on Alfaro and Ahmed (2009), ensures the cross-country comparability of capital goods, since quality differences within such narrowly defined products sourced from the same exporter are likely minimal.¹⁰ It also allows us to isolate the differences in the price charged by exporters from other sources of cross-country price variation that are reflected in the ICP data, such as trade, transportation, delivery, and installation costs paid by buyers and discounts that may be available to them.

Our analysis uncovers little systematic correlation between the price of capital goods and the per capita income of the importing country, when trade data from the five large capital goods exporters are pooled (Annex Table 1). Trade costs, on the other hand, exhibit a clear pattern: they tend to be much lower for advanced economies.¹¹ Despite significant progress in liberalizing the international exchange of goods and services and reducing trade costs, emerging markets, and especially low-income developing countries, still face significantly higher policy-related barriers to trade than advanced economies, in addition to their larger natural trade barriers (Figure 7). They tend to be located farther from key capital goods exporters and are less connected to global shipping networks. They impose significantly higher tariffs on imports of capital goods, and the time and cost associated with the logistics of importing goods—such as documentary and border compliance and domestic transportation—are substantially higher. We find that countries with higher trade costs in any of these measures tend to have higher absolute prices of machinery and equipment in the 2011 ICP data (Figure 8, panel 1).

View Full Size

Trade Costs in 2011

(Median, and interquartile range)

Citation: IMF Working Papers 2019, 134; 10.5089/9781498317429.001.A001

Sources: CEPII, GeoDist database; Eora MRIO database; Feenstra and Romalis (2014); Fraser Institute; United Nations Conference on Trade and Development (UNCTAD); World Bank, Doing Business Indicators; and authors’ calculations.Note: Distance to exporters of machinery and equipment (M&E) is calculated as the weighted average of a country’s distance to all other countries, where the weights are equal to the partner countries’ exports of capital goods as a share of global capital goods exports. The UNCTAD liner shipping connectivity index captures how well countries are connected to global shipping networks based on five components of the maritime transport sector: number of ships, their container-carrying capacity, maximum vessel size, number of services, and number of companies that deploy container ships in a country’s port. The Fraser Institute’s Freedom to Trade Internationally index is based on four different types of trade restrictions: tariffs, quotas, hidden administrative restraints, and controls on exchange rate and the movement of capital. The cost and time indicators measure the cost (excluding tariffs) and time associated with three sets of procedures—documentary compliance, border compliance, and domestic transport—within the overall process of importing a shipment of goods. AEs = advanced economies; EMs = emerging market economies; LICs = low-income countries.

• Download Figure
• Download figure as PowerPoint slide

View Full Size

Trade Costs, Relative Productivity, and the Price of Capital Goods in 2011

Citation: IMF Working Papers 2019, 134; 10.5089/9781498317429.001.A001

Source: Authors’ calculations.Note: Panel 1 depicts the percent change in the 2011 International Comparison Program (ICP) absolute price of machinery and equipment associated with a one standard deviation increase in alternative measures of trade costs. In panel 2, the cross-country variation in the 2011 ICP price of machinery and equipment relative to consumption is decomposed into the share explained by differences in the labor productivity in the tradable goods sectors relative to the non-tradable goods sectors, and alternative measures of trade costs. M&E = machinery and equipment. ***p < 0.01; **p < 0.05; *p < 0.1

• Download Figure
• Download figure as PowerPoint slide

Putting together the two key contending explanations of the crosscountry dispersion in relative capital goods prices, namely trade costs and relative productivity differences, we examine their contribution to the crosscountry variation in relative prices of capital goods. We estimate a simple OLS regression of the log of the relative price of machinery and equipment (using ICP 2011 data) on the log of the relative labor productivity in the tradable goods producing sector and alternative measures of trade costs, which are included one at a time. In a second step, we use these regression estimates to decompose the cross-country variation in the log of relative prices into the variance that can be explained by the relative productivity measure versus trade costs. Given the cross-sectional nature of the data, this analysis is purely illustrative. As elaborated in the next section, relative productivity and trade costs are not independent of one another, complicating the interpretation of their estimated contribution to the variation in relative prices. The relative productivity in the tradable goods sector may be affected by trade barriers, as production of tradable goods likely relies on imported inputs. Furthermore, policy-related trade barriers may be erected with the goal of protecting low-productivity tradable goods sectors.

With these caveats in mind, Figure 8, panel 2 shows that both relative productivity differences and trade costs are important in explaining the cross-country heterogeneity in relative prices. Together, relative productivity differences in the production of tradable goods and trade costs can explain up to 60 percent of the cross-country variation in the relative price of machinery and equipment, depending on which measure of trade cost is used.¹² Interestingly, policy-related trade barriers, such as tariffs and cost and time of importing, are a more powerful predictor of relative prices than natural barriers to trade such as distance and connectivity. While causal interpretation is difficult in the cross-country setting and in light of the likely relationship between relative productivity and trade barriers, these findings are consistent with the idea that the relative prices of capital goods are higher in emerging market and developing economies in part due to their higher trade barriers.

B. Time-Series Analysis

Prior studies have primarily focused on the cross-country variation in relative capital goods prices (for example, Hsieh and Klenow 2007, and Sposi 2015), as they explored the roles of trade and productivity. We aim to shed light on the drivers of the big declines in the relative prices of tradable capital goods over time. We show that differences in the rate of trade integration and relative productivity growth within countries over time can lead to large variations in relative prices.

We use sectoral producer price data across 33 sectors and 40 advanced and emerging market economies during 1995–2011 from the Socioeconomic Accounts of the World Input-Output Database. This allows us to control for all factors that affect prices equally across sectors within a country in a particular year (such as exchange rate fluctuations and policies, commodity price changes, aggregate demand and productivity shocks, and the like) and all time-invariant differences in prices across countries and sectors.¹³

This approach faces two challenges. First, trade integration, in the sense of more market access for foreign producers (as measured by the ratio of imports to domestic sectoral value -added) fosters competition, inducing domestic producers to reduce markups of prices over marginal costs. In practice, the feedback from higher domestic prices to greater ability of foreign producers to gain market share complicates the interpretation of the estimated relationship between the two variables. To overcome this challenge, the analysis uses import tariffs as an instrument for exposure to trade, thus isolating changes in import penetration that were triggered by policy choice, rather than those driven by changes in domestic prices.¹⁴ Second, exposure to foreign competition affects relative domestic prices indirectly, through its impact on sectoral labor productivity as documented in numerous studies (see, for example, Ahn et al. (forthcoming), Amiti and Konings 2007, Topalova and Khandelwal 2011). Thus, simply applying the elasticities estimated in the regression in the first step will understate the contribution of trade to producer price changes. To correct for this, we quantify the changes in labor productivity that can be attributed to changes in import penetration, and, in the second step, distinguish the contribution of trade-related changes in labor productivity from changes in productivity due to other factors (such as sectoral technological advances) to the decline in the relative price of machinery and equipment.

Regression Framework

We estimate two separate regressions to understand the contributions of global integration and productivity growth to the decline in the relative price of machinery and equipment in the past decades.

First, we estimate the sensitivity of relative producer prices at the sector level to changes in relative labor productivity and import penetration, using the following equation:

$\ln \left(\frac{P_{i_{l}j,t}}{{\overline{P}}_{i,t}}\right)={\alpha }_{i,j}+{\mu }_{i,t}+\beta \left[\ln \right(\frac{M_{i,j,t}}{V{A}_{i,j,t}})-\ln (\frac{{\overline{M}}_{i,t}}{{\overline{VA}}_{i,t}}\left)\right]+\gamma \ln \left(\frac{L{P}_{i,j,t}}{{\overline{LP}}_{i,t}}\right)+{\epsilon }_{i,j,t,}$

where $\frac{P_{i_{l}j,t}}{{\overline{P}}_{i,t}}$ is the relative price of sectory j in country i at time t; α_i,j denotes country-sector fixed effects; μ_i,t denotes country-year fixed effects; In (^M – $\ln \left(\frac{M_{i,j,t}}{V{A}_{i,j,t}}\right)-\ln \left(\frac{{\overline{M}}_{i,t}}{{\overline{VA}}_{i,t}}\right)$ is the relative import penetration (measured as imports divided by value-added); and $\frac{L{P}_{i,j,t}}{{\overline{LP}}_{i,t}}$ is the relative productivity of labor (measured as real value-added per employee).¹⁵

The relative import penetration, $\ln \left(\frac{M_{i,j,t}}{V{A}_{i,j,t}}\right)-\ln \left(\frac{{\overline{M}}_{i,t}}{{\overline{VA}}_{i,t}}\right)$ is instrumented by relative import tariff, defined as ${\tau }_{i,j,t}-{\overline{\tau }}_{i,t},with{\overline{\tau }}_{i,t}definedas\frac{{\Sigma }_{j=1}^J{\tau }_{i,j,t}}{J}$ and τ_i,j,t is defined as

$${\tau }_{i,j,t}=\frac{{\sum }_{l\in {\Lambda }_j}{m}_{i,k,l,t}{\hat{\tau }}_{i,k,l,t}}{{\sum }_{l\in {\Lambda }_j}{m}_{i,k,l,t}},$$

in which m_i,k,l,t is the import of country i from country k in sector l at time t, and ${\hat{\tau }}_{i,k,l,t}$ is the tariff imposed on these imports. i_UkU comes from the SITC 4-digit level bilateral preferential tariff data compiled by Feenstra and Romalis (2014).

Second, we estimate the impact of trade liberalization on relative labor productivity through the following equation:

$\ln \left(\frac{L{P}_{i,j,t}}{{\overline{LP}}_{i,t}}\right)={\alpha }_{i,j}^{LP}+{\mu }_{i,t}^{LP}+{\beta }^{LP}\left[\ln \right(\frac{M_{i,j,t}}{V{A}_{i,j,t}})-\ln {(}_{\overline{{\overline{VA}}_{i,t}}}^{{\overline{M}}_{i,t}})]+{\epsilon }_{i,j,t}^{LP},$

where $\ln \left(\frac{M_{i,j,t}}{V{A}_{i,j,t}}\right)-\ln {(}_{\overline{{\overline{VA}}_{i,t}}}^{{\overline{M}}_{i,t}})$ is also instrumented by relative import tariff, due to the concern of reverse causality: if a country’s capital goods producing sector becomes more productive, it may import less machinery and equipment from abroad. The estimation results indeed confirm the need to address this endogeneity issue: the OLS coefficient is much smaller than the estimate obtained using the instrumental variable.

Import tariffs are assumed to satisfy the exogeneity conditions:

$\mathrm{cov}({\tau }_{i,j,t}-{\overline{\tau }}_{i,t},{\epsilon }_{i,j,t})=\mathrm{cov}({\tau }_{i,j,t}-{\overline{\tau }}_{i,t},{\epsilon }_{i,j,t}^{LP})=0.$

In Table 1, column 1, we present the results from the first stage, the relationship between import tariffs and import penetration. As expected, country-sectors which experience larger cuts in import tariffs have higher import penetration. Column (2) of the same table contains the reduced form relationship between the instrument and the dependent variable of interest, namely relative producer prices. There is a strong negative link between import tariffs and producer prices, controlling for labor productivity.

Table 1.

First-Stage Relationship, Effects of Import Tariff on Producer Prices

Source: Authors’ calculations. Note: All regressions include country-year and country-sector fixed effects. Standard errors clustered at the country and sector level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

Table 1.

First-Stage Relationship, Effects of Import Tariff on Producer Prices

Dependent Variables:	Relative Import Penetration	Relative Producer Prices
	OLS (1)	OLS (2)
Import Tariff	-0.014*** (0.003)	0.010*** (0.003)
Relative Productivityt-1	0.003 (0.014)	-0.308*** (0.036)
Number of Observations	16,077	16,077
R 2	0.96	0.62

Source: Authors’ calculations. Note: All regressions include country-year and country-sector fixed effects. Standard errors clustered at the country and sector level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

View Table

Table 1.

First-Stage Relationship, Effects of Import Tariff on Producer Prices

Dependent Variables:	Relative Import Penetration	Relative Producer Prices
	OLS (1)	OLS (2)
Import Tariff	-0.014*** (0.003)	0.010*** (0.003)
Relative Productivityt-1	0.003 (0.014)	-0.308*** (0.036)
Number of Observations	16,077	16,077
R 2	0.96	0.62

Source: Authors’ calculations. Note: All regressions include country-year and country-sector fixed effects. Standard errors clustered at the country and sector level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

Tables 2 and 3 report the main estimation results. A general pattern that emerges is that deeper import penetration increases the relative labor productivity of a domestic sector. It also reduces producer prices directly. As robustness tests, we allow β, γ and β^LP to differ across advanced economies and emerging market and developing economies, and the results are broadly the same.

Table 2.

Labor Productivity and Trade Integration

Source: Authors’ calculations. Note: All regressions include country-year and country-sector fixed effects. Standard errors clustered at the country and sector level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

Table 2.

Labor Productivity and Trade Integration

Dependent Variable: Relative Productivity	OLS (1)	IV (2)	OLS (3)	IV (4)	IV (5)	IV (6)	IV (7)
Relative Import Penetrationt-1	0.054 (0.049)	1.639 (0.000)	0.044 (0.054)	1.363*** (0.363)	0.793*** (0.305)	2.403** (1.041)	1.251*** (0.449)
Relative Import Penetrationt-1 × Capital Goods Dummy			0.064 (0.123)	1.407** (0.671)	1.965*** (0.665)	0.160 (1.648)	2.810 (1.751)
Number of Observations	16,077	16,077	16,077	16,077	12,575	3,502	12,321
R 2	0.95	0.91	0.95	0.91	0.92	0.88	0.92
Relative Import Penetration for Capital Goods Sectors			0.108 (0.110)	2.771*** (0.564)	2.758*** (0.624)	2.563*** (1.089)	4.061*** (1.686)
Sample	All	All	All	All	AE	EMDE	All, Post 2000

Source: Authors’ calculations. Note: All regressions include country-year and country-sector fixed effects. Standard errors clustered at the country and sector level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

View Table

Table 2.

Labor Productivity and Trade Integration

Dependent Variable: Relative Productivity	OLS (1)	IV (2)	OLS (3)	IV (4)	IV (5)	IV (6)	IV (7)
Relative Import Penetrationt-1	0.054 (0.049)	1.639 (0.000)	0.044 (0.054)	1.363*** (0.363)	0.793*** (0.305)	2.403** (1.041)	1.251*** (0.449)
Relative Import Penetrationt-1 × Capital Goods Dummy			0.064 (0.123)	1.407** (0.671)	1.965*** (0.665)	0.160 (1.648)	2.810 (1.751)
Number of Observations	16,077	16,077	16,077	16,077	12,575	3,502	12,321
R 2	0.95	0.91	0.95	0.91	0.92	0.88	0.92
Relative Import Penetration for Capital Goods Sectors			0.108 (0.110)	2.771*** (0.564)	2.758*** (0.624)	2.563*** (1.089)	4.061*** (1.686)
Sample	All	All	All	All	AE	EMDE	All, Post 2000

Source: Authors’ calculations. Note: All regressions include country-year and country-sector fixed effects. Standard errors clustered at the country and sector level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

Table 3.

Relative Producer Prices, Trade Integration and Relative Productivity

Source: Authors’ calculations. Note: All regressions include country-year and country-sector fixed effects. Standard errors clustered at the country and sector level in parentheses.

¹Relative labor productivity_t-2 is used as an instrument for relative labor productivity_t-1.

***p < 0.01; **p < 0.05; *p < 0.1

Table 3.

Relative Producer Prices, Trade Integration and Relative Productivity

Dependent Variable: Relative Productivity	OLS (1)	IV (2)	OLS (3)	IV (4)	IV (5)	IV (6)	IV (7)	IV (8)
Relative Import Penetrationt–1	-0.135*** (0.033)	-0.568*** (0.146)	-0.107*** (0.037)	-0.574*** (0.163)	-0.413*** (0.148)	-0.964*** (0.374)	-0.461** (0.200)	-0.458*** (0.177)
Relative Import Penetration,–1 × Capital Goods Dummy			-0.191** (0.081)	0.033 (0.322)	0.037 (0.384)	0.183 (0.617)	-0.375 (0.574)	-0.040 (0.359)
Relative Productivityt–1	-0.316*** (0.035)	-0.328*** (0.032)	-0.314*** (0.035)	-0.328*** (0.032)	-0.349*** (0.041)	-0.274*** (0.034)	-0.302*** (0.031)	-0.368*** (0.039)
R 2	16,077	16,077	16,077	16,077	12,575	3,502	12,321	15,086
R 2	0.62	0.56	0.62	0.56	0.63	0.40	0.71	0.61
Relative Import Penetration for Capital Goods Sectors			-0.298*** (0.071)	-0.541* (0.287)	-0.375 (0.375)	-0.781* (0.420)	-0.836 (0.561)	-0.498 (0.340)
Sample	All	All	All	All	AE	EMDE	Post 2000	All1

Source: Authors’ calculations. Note: All regressions include country-year and country-sector fixed effects. Standard errors clustered at the country and sector level in parentheses.

¹Relative labor productivity_t-2 is used as an instrument for relative labor productivity_t-1.

***p < 0.01; **p < 0.05; *p < 0.1

View Table

Table 3.

Relative Producer Prices, Trade Integration and Relative Productivity

Dependent Variable: Relative Productivity	OLS (1)	IV (2)	OLS (3)	IV (4)	IV (5)	IV (6)	IV (7)	IV (8)
Relative Import Penetrationt–1	-0.135*** (0.033)	-0.568*** (0.146)	-0.107*** (0.037)	-0.574*** (0.163)	-0.413*** (0.148)	-0.964*** (0.374)	-0.461** (0.200)	-0.458*** (0.177)
Relative Import Penetration,–1 × Capital Goods Dummy			-0.191** (0.081)	0.033 (0.322)	0.037 (0.384)	0.183 (0.617)	-0.375 (0.574)	-0.040 (0.359)
Relative Productivityt–1	-0.316*** (0.035)	-0.328*** (0.032)	-0.314*** (0.035)	-0.328*** (0.032)	-0.349*** (0.041)	-0.274*** (0.034)	-0.302*** (0.031)	-0.368*** (0.039)
R 2	16,077	16,077	16,077	16,077	12,575	3,502	12,321	15,086
R 2	0.62	0.56	0.62	0.56	0.63	0.40	0.71	0.61
Relative Import Penetration for Capital Goods Sectors			-0.298*** (0.071)	-0.541* (0.287)	-0.375 (0.375)	-0.781* (0.420)	-0.836 (0.561)	-0.498 (0.340)
Sample	All	All	All	All	AE	EMDE	Post 2000	All1

Source: Authors’ calculations. Note: All regressions include country-year and country-sector fixed effects. Standard errors clustered at the country and sector level in parentheses.

¹Relative labor productivity_t-2 is used as an instrument for relative labor productivity_t-1.

***p < 0.01; **p < 0.05; *p < 0.1

The impact of import tariffs on import penetration and the association between import penetration and producer prices are economically significant. A 1 percent increase in the import ratio, which can be achieved by a 0.7 percentage point cut in tariffs, reduces the sectoral producer price by about 0.6 percent. Changes in labor productivity also have a significant impact on producer prices, with a 1 percent increase in sectoral labor productivity reducing producer prices by about 0.3 percent.

Moreover, labor productivity of the capital goods producing sector is particularly sensitive to deepening trade integration, a finding consistent with the larger reliance on global value chains for the production of these goods.¹⁶

Figure 9 decomposes the average decline in the relative price of the machinery and equipment producing sectors between 2000 and 2011 into four parts: (1) the direct effect of deepening trade integration; (2) the effect of trade integration through higher labor productivity; (3) the effect of higher labor productivity, which is not due to deepening trade integration; and (4) a residual. Rising trade integration accounts for the bulk of the decline in relative prices of machinery and equipment, both through its direct effect on producer prices and indirectly, through higher labor productivity of domestic capital goods producers. Productivity gains in the capital goods sector, which cannot be directly linked to trade integration, were also a significant factor.¹⁷

View Full Size

Contributions to Changes in Relative Producer Prices of Capital Goods: 2000–11

(Percent)

Citation: IMF Working Papers 2019, 134; 10.5089/9781498317429.001.A001

Source: Authors’ calculations.Note: The figure combines the estimated elasticities of producer prices to trade integration and relative labor productivity, and changes in these factors for the capital goods sector between 2000 and 2011 to compute their contribution to the observed change in the producer price of capital goods relative to the price of consumption. AEs = advanced economies; EMDEs = emerging market and developing economies.

• Download Figure
• Download figure as PowerPoint slide

A. Cross-Country Empirical Evidence

The empirical framework used to assess the role of relative investment prices for investment-to-GDP ratios is inspired by the reduced form relationship that can be derived from a number of theoretical papers, such as Restuccia and Urrutia (2001) and Sarel (1995). The general intuition from these models is that a shock that leads to a decline in the relative price of investment, such as productivity increase in the capital goods sector or a decline in capital goods tariffs, would raise the optimal (steady-state) level of capital stock as a share of output. Because a higher level of capital stock needs to be maintained, real investment would rise permanently as a share of real output in order to keep up with capital stock’s depreciation.

The general regression relates the log of the real investment-to-GDP ratio in machinery and equipment and the log of the price of machinery equipment relative to the price of consumption,¹⁹ controlling for all time-invariant differences across countries (µ_i) and period fixed effects (θ_t) to capture common global shocks:

Based on empirical studies of the long-run determinants of the aggregate investment rates,²⁰ the set of additional controls includes lagged level and growth rate of real GDP per capita in purchasing-power-parity terms to account for possible convergence effect, lagged dependent variable to account for persistence in investment rates, availability and cost of finance (proxied by real interest rates, credit-to-GDP ratio, and the extent of openness of the capital account), access to foreign markets (proxied by the degree of trade openness), exposure to commodity shocks (as a weighted measure of commodity prices and country-specific commodity exports), overall institutional quality and political risks, and the quality of infrastructure (proxied by kilometers of paved roads per capita). The choice of control variables is driven by availability of data for a longer sample of countries and years and is primarily aimed at attenuating potential omitted variable bias. The full list of data sources can be found in Annex 1.

Estimation results, based on OLS and IV regressions, are reported in Table 4 and confirm that real investment rates are shaped by a variety of factors. The regressions are estimated on five-year non-overlapping window averaged data. This approach aims to smooth the influence of short-term fluctuations, and to capture the potential medium-run relationship. Although estimates are often imprecise, a stronger regulatory environment, higher trade and financial integration, lower-cost finance, and greater financial development—as well as better infrastructure—are all associated with a higher ratio of real investment in machinery and equipment to real output. Importantly, we find a strong and statistically significant negative relationship between real investment in machinery and equipment and its relative price.

Table 4.

Real Investment Rate and Relative Price of Machinery and Equipment: Country-Level

Source: Authors’ calculations. Note: Regressions are estimated with data averaged over non-overlapping five-year windows. The dependent variable is log machinery and transport equipment investment-to-GDP ratio. Columns 1–4 are estimated using ordinary least squares (OLS) regressions. In Column 1 the independent variable is log price of machinery and transport equipment relative to price of consumption. Column 2 is estimated with full controls specif cation. In column 3 log relative price is lagged. Column 4 is estimated using annual data, where log relative price and policy variables are lagged. The long-run effect is given by β _X / (1 – β_Y,t–1). Columns 5–9 are estimated using instrumental variable (IV) regressions, where log relative price is instrumented with its lagged value. In column 10 price of machinery and transport equipment is measured relative to the overall GDP price level. Column 11 is based on the system generalized method of moments (GMM) estimator following the two-step procedure with Windmeijer’s finite-sample correction, treating the regressors as endogenous and instrumented with one lag, while fixed effects and several institutional variables (regulatory quality, infrastructure quality, and capital account openness) are treated as exogenous. In column 12, log relative price is instrumented with log of average relative price of all other countries except own. All regressions control for country and year fixed effects. Standard errors

¹Capital importing countries are defined by excluding Top-20 capital exporting countries in 2016: China, Germany, United States, Japan, Hong Kong SAR, Korea, Mexico, France, Singapore, Italy, United Kingdom, Taiwan Province of China, the Netherlands, Canada, Spain, Thailand, Czech Republic, Belgium, Malaysia, Poland. ***p < 0.01; **p < 0.05; *p < 0.1

Table 4.

Real Investment Rate and Relative Price of Machinery and Equipment: Country-Level

Dependent Variable: Log Real Investment-to-GDP Ratio	OLS	OLS	OLS Lagged	OLS Annual	IV	IV	IV	IV	IV	IV PI/PGDP	GMM	IV Excluding Own
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
Log Relative Price	-0.689*** (0.089)	-0.517*** (0.092)	-0.159** (0.072)	-0.083** (0.040)	-0.307*** (0.101)	-0.283** (0.140)	-0.461*** (0.139)	-0.281** (0.122)	-0.367*** (0.121)	-0.303*** (0.105)	-0.408*** (0.110)	-0.508*** (0.083)
Log Investment Ratet–1		0.488*** (0.084)	0.558*** (0.092)	0.795*** (0.032)	0.535*** (0.082)	0.540*** (0.093)	0.499*** (0.085)	0.484*** (0.072)	0.524*** (0.082)	0.528*** (0.089)	0.502*** (0.059)	0.490*** (0.072)
Log GDP per Capitat–1		-0.207*** (0.069)	-0.109 (0.073)	-0.023 (0.030)	-0.156** (0.064)	-0.186** (0.075)	-0.190*** (0.073)	-0.212 (0.132)	-0.140** (0.068)	-0.147** (0.062)	-0.153*** (0.053)	-0.205*** (0.064)
GDP per Capita Growtht–1		0.152 (0.341)	0.236 (0.420)	0.166** (0.074)	0.121 (0.320)	0.133 (0.375)	0.030 (0.342)	-0.474 (0.560)	0.061 (0.330)	0.132 (0.339)	0.601 (0.383)	0.150 (0.304)
Real Interest Rate		-0.009 (0.048)	-0.011 (0.063)	-0.053* (0.027)	-0.015 (0.052)	-0.074 (0.053)	-0.012 (0.053)	-0.625 (0.496)	-0.014 (0.051)	-0.011 (0.053)	-0.052 (0.063)	-0.009 (0.043)
Log Credit-to-GDP		-0.019 (0.047)	-0.035 (0.051)	-0.006 (0.012)	-0.028 (0.043)	-0.021 (0.054)	-0.026 (0.060)	-0.015 (0.028)	-0.041 (0.045)	-0.038 (0.042)	0.010 (0.048)	-0.019 (0.042)
Capital Account Openness		0.143** (0.063)	0.152** (0.059)	0.036** (0.017)	0.145*** (0.053)	0.105 (0.064)	0.172** (0.068)	0.110 (0.067)	0.126** (0.061)	0.139*** (0.053)	-0.102 (0.067)	0.143** (0.056)
Log Export Commodity Price		0.560* (0.294)	0.320 (0.291)	0.105 (0.091)	0.435* (0.259)	0.373 (0.298)	0.454 (0.297)	0.003 (0.299)	0.412 (0.268)	0.207 (0.243)	0.072 (0.307)	0.555** (0.270)
Log Trade Openness		0.285** (0.117)	0.210* (0.106)	0.118*** (0.024)	0.245** (0.100)	0.210 (0.131)	0.315** (0.123)	0.120* (0.072)	0.284** (0.111)	0.248*** (0.092)	0.126 (0.091)	0.283*** (0.104)
Institutional Quality and Political Risk		0.008*** (0.003)	0.005* (0.003)	0.002 (0.001)	0.006** (0.003)	0.005 (0.004)	0.008** (0.003)	0.009** (0.004)	0.007** (0.003)	0.006** (0.002)	0.009*** (0.003)	0.008*** (0.003)
Log Paved Roads per Capita		0.094 (0.060)	0.124* (0.063)	0.053*** (0.020)	0.114** (0.054)	0.162** (0.075)	0.096 (0.069)	-0.008 (0.089)	0.125* (0.066)	0.124** (0.051)	0.036 (0.030)	0.095* (0.053)
Long-Run Effect				-0.406** (0.193)
Number of Observations	1,863	769	769	3,167	769	664	537	232	635	769	769	769
Number of Countries	173	127	127	126	127	127	93	34	108	127	127	127
R2	0.72	0.86	0.84	0.91	0.504	0.493	0.518	0.600	0.519	0.51		0.518
First Stage F-Statistic					24.37	12.42	11.95	117	18.07	46.68		196.2
AR(1) Test P-Value											0.00
AR(2) Test P-Value											0.19
Hansen Test P-Value											0.29
Number of Instruments											131
Sample	All	All	All	All	All	Post 1990	EMDE	AE	Capital Goods Importers1	All	All	All

Source: Authors’ calculations. Note: Regressions are estimated with data averaged over non-overlapping five-year windows. The dependent variable is log machinery and transport equipment investment-to-GDP ratio. Columns 1–4 are estimated using ordinary least squares (OLS) regressions. In Column 1 the independent variable is log price of machinery and transport equipment relative to price of consumption. Column 2 is estimated with full controls specif cation. In column 3 log relative price is lagged. Column 4 is estimated using annual data, where log relative price and policy variables are lagged. The long-run effect is given by β _X / (1 – β_Y,t–1). Columns 5–9 are estimated using instrumental variable (IV) regressions, where log relative price is instrumented with its lagged value. In column 10 price of machinery and transport equipment is measured relative to the overall GDP price level. Column 11 is based on the system generalized method of moments (GMM) estimator following the two-step procedure with Windmeijer’s finite-sample correction, treating the regressors as endogenous and instrumented with one lag, while fixed effects and several institutional variables (regulatory quality, infrastructure quality, and capital account openness) are treated as exogenous. In column 12, log relative price is instrumented with log of average relative price of all other countries except own. All regressions control for country and year fixed effects. Standard errors

¹Capital importing countries are defined by excluding Top-20 capital exporting countries in 2016: China, Germany, United States, Japan, Hong Kong SAR, Korea, Mexico, France, Singapore, Italy, United Kingdom, Taiwan Province of China, the Netherlands, Canada, Spain, Thailand, Czech Republic, Belgium, Malaysia, Poland. ***p < 0.01; **p < 0.05; *p < 0.1

View Table

Table 4.

Real Investment Rate and Relative Price of Machinery and Equipment: Country-Level

Dependent Variable: Log Real Investment-to-GDP Ratio	OLS	OLS	OLS Lagged	OLS Annual	IV	IV	IV	IV	IV	IV PI/PGDP	GMM	IV Excluding Own
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
Log Relative Price	-0.689*** (0.089)	-0.517*** (0.092)	-0.159** (0.072)	-0.083** (0.040)	-0.307*** (0.101)	-0.283** (0.140)	-0.461*** (0.139)	-0.281** (0.122)	-0.367*** (0.121)	-0.303*** (0.105)	-0.408*** (0.110)	-0.508*** (0.083)
Log Investment Ratet–1		0.488*** (0.084)	0.558*** (0.092)	0.795*** (0.032)	0.535*** (0.082)	0.540*** (0.093)	0.499*** (0.085)	0.484*** (0.072)	0.524*** (0.082)	0.528*** (0.089)	0.502*** (0.059)	0.490*** (0.072)
Log GDP per Capitat–1		-0.207*** (0.069)	-0.109 (0.073)	-0.023 (0.030)	-0.156** (0.064)	-0.186** (0.075)	-0.190*** (0.073)	-0.212 (0.132)	-0.140** (0.068)	-0.147** (0.062)	-0.153*** (0.053)	-0.205*** (0.064)
GDP per Capita Growtht–1		0.152 (0.341)	0.236 (0.420)	0.166** (0.074)	0.121 (0.320)	0.133 (0.375)	0.030 (0.342)	-0.474 (0.560)	0.061 (0.330)	0.132 (0.339)	0.601 (0.383)	0.150 (0.304)
Real Interest Rate		-0.009 (0.048)	-0.011 (0.063)	-0.053* (0.027)	-0.015 (0.052)	-0.074 (0.053)	-0.012 (0.053)	-0.625 (0.496)	-0.014 (0.051)	-0.011 (0.053)	-0.052 (0.063)	-0.009 (0.043)
Log Credit-to-GDP		-0.019 (0.047)	-0.035 (0.051)	-0.006 (0.012)	-0.028 (0.043)	-0.021 (0.054)	-0.026 (0.060)	-0.015 (0.028)	-0.041 (0.045)	-0.038 (0.042)	0.010 (0.048)	-0.019 (0.042)
Capital Account Openness		0.143** (0.063)	0.152** (0.059)	0.036** (0.017)	0.145*** (0.053)	0.105 (0.064)	0.172** (0.068)	0.110 (0.067)	0.126** (0.061)	0.139*** (0.053)	-0.102 (0.067)	0.143** (0.056)
Log Export Commodity Price		0.560* (0.294)	0.320 (0.291)	0.105 (0.091)	0.435* (0.259)	0.373 (0.298)	0.454 (0.297)	0.003 (0.299)	0.412 (0.268)	0.207 (0.243)	0.072 (0.307)	0.555** (0.270)
Log Trade Openness		0.285** (0.117)	0.210* (0.106)	0.118*** (0.024)	0.245** (0.100)	0.210 (0.131)	0.315** (0.123)	0.120* (0.072)	0.284** (0.111)	0.248*** (0.092)	0.126 (0.091)	0.283*** (0.104)
Institutional Quality and Political Risk		0.008*** (0.003)	0.005* (0.003)	0.002 (0.001)	0.006** (0.003)	0.005 (0.004)	0.008** (0.003)	0.009** (0.004)	0.007** (0.003)	0.006** (0.002)	0.009*** (0.003)	0.008*** (0.003)
Log Paved Roads per Capita		0.094 (0.060)	0.124* (0.063)	0.053*** (0.020)	0.114** (0.054)	0.162** (0.075)	0.096 (0.069)	-0.008 (0.089)	0.125* (0.066)	0.124** (0.051)	0.036 (0.030)	0.095* (0.053)
Long-Run Effect				-0.406** (0.193)
Number of Observations	1,863	769	769	3,167	769	664	537	232	635	769	769	769
Number of Countries	173	127	127	126	127	127	93	34	108	127	127	127
R2	0.72	0.86	0.84	0.91	0.504	0.493	0.518	0.600	0.519	0.51		0.518
First Stage F-Statistic					24.37	12.42	11.95	117	18.07	46.68		196.2
AR(1) Test P-Value											0.00
AR(2) Test P-Value											0.19
Hansen Test P-Value											0.29
Number of Instruments											131
Sample	All	All	All	All	All	Post 1990	EMDE	AE	Capital Goods Importers1	All	All	All

Source: Authors’ calculations. Note: Regressions are estimated with data averaged over non-overlapping five-year windows. The dependent variable is log machinery and transport equipment investment-to-GDP ratio. Columns 1–4 are estimated using ordinary least squares (OLS) regressions. In Column 1 the independent variable is log price of machinery and transport equipment relative to price of consumption. Column 2 is estimated with full controls specif cation. In column 3 log relative price is lagged. Column 4 is estimated using annual data, where log relative price and policy variables are lagged. The long-run effect is given by β _X / (1 – β_Y,t–1). Columns 5–9 are estimated using instrumental variable (IV) regressions, where log relative price is instrumented with its lagged value. In column 10 price of machinery and transport equipment is measured relative to the overall GDP price level. Column 11 is based on the system generalized method of moments (GMM) estimator following the two-step procedure with Windmeijer’s finite-sample correction, treating the regressors as endogenous and instrumented with one lag, while fixed effects and several institutional variables (regulatory quality, infrastructure quality, and capital account openness) are treated as exogenous. In column 12, log relative price is instrumented with log of average relative price of all other countries except own. All regressions control for country and year fixed effects. Standard errors

¹Capital importing countries are defined by excluding Top-20 capital exporting countries in 2016: China, Germany, United States, Japan, Hong Kong SAR, Korea, Mexico, France, Singapore, Italy, United Kingdom, Taiwan Province of China, the Netherlands, Canada, Spain, Thailand, Czech Republic, Belgium, Malaysia, Poland. ***p < 0.01; **p < 0.05; *p < 0.1

The estimates are robust to alternative specifications, choice of sub-samples, estimation methods, and use of annual data.²¹ In the instrumental variable (IV) regressions, the relative price is instrumented using its own lag. This strategy allows to minimize the bias (towards finding a negative relationship) stemming from the potential negative correlation in the measurement errors of real investment and its price, under the assumption that measurement error is unlikely to be correlated over time.²² Similar findings are obtained using system GMM estimator, and an alternative IV specification where the relative price is instrumented with the average relative price of all other countries except the country’s own. This approach allows to isolate technologically driven changes in the relative price from those that may occur due to changes in demand for investment goods within a country, thus minimizing the measurement error bias as measurement error in a country’s own prices is unlikely to be correlated with measurement error in other countries’ prices.

Across specifications, a 1 percent decline in the relative prices of tradable capital goods is associated with a 0.3–0.5 percent increase in the real investment rate over a five-year period. It is important to note that these empirical estimates likely represent an upper bound of the true effect of changes in relative price on real investment rates. As discussed above, relative investment prices are endogenous and reflect many factors, including changes in policies that could have a direct impact on investment rates.

B. Sectoral Empirical Evidence

A sectoral perspective can complement the cross-country analysis in an important way. The relative price of capital goods is but one of the considerations that shape investment decisions. While the cross-country analysis attempts to control for many factors, the estimated relationship between real investment rates and prices could be biased due to the omission of factors that may correlate with relative prices but are not properly captured in the estimation. Sectoral analysis allows us to isolate the relationship between real investment rates and the price of investment across different sectors while properly accounting for the role of all factors that affect investment within a country in a particular year. These include financial conditions, economy-wide growth prospects, quality of regulations that affect investment returns, exchange rate fluctuations and policies, international capital flows, availability of complementary public infrastructure, and the like.

The data come from EU KLEMS and World KLEMS, which offers detailed information about the price level of different types of capital goods within Machinery and Equipment: IT (computer hardware), CT (telecommunications equipment), Transport Equipment and Other machinery and equipment. The price of machinery and equipment, P_M&E, is constructed as a weighted average of the prices of each of the four types of capital, as in the equation below.

The sample varies somewhat depending on the specification and data availability for specific variables. Typically, the analysis relies on 18–19 countries, mostly European, with the addition of United States, United Kingdom, Brazil and Colombia, and uses 15 broad sectors, covering the period 1971–2015. This is an unbalanced panel.

The baseline specification mirrors that of country-level regressions, using 5-year averaged data, which is common in the literature when looking at long-term, slow-moving factors. In the main specification, the log relative price of investment (expressed relative to the price of consumption) is instrumented with is lagged value.

A range of possible estimates using slightly different specifications are presented in Table 5. The estimated elasticity, according to which a 1 percent decline in the relative price of machinery and equipment investment is associated with a 0.2–0.5 percent increase in real investment in these capital goods, is comparable to those uncovered in the cross-country analysis. The baseline specification includes country-period and country-sector fixed effects, where the period refers to five-year non-overlapping periods. However, country-period fixed effects may absorb too much variation, for example if there is an aggregate effect of the relative price of investment that is common to all sectors within a country-year. For that reason, an alternative specification includes country-sector and period (or year) fixed effects, where this problem is addressed (columns 5–8, Table 5).

Table 5.

Sectoral Real Investment Rate and Relative Prices of Machinery and Equipment: Range of Possible Estimates

Source: Authors’ calculations. Note: Regressions 1 and 5 show results based on the main specification, which uses lagged log relative prices to instrument for log relative prices. Regressions 2 and 6 present reduced form results, with the contemporaneous log relative prices. Regressions 3 and 7 present reduced form results, using the lagged log relative prices instead of contemporaneous. In regressions 4 and 8, the relative price of investment is defined relative to the sectoral value added, and follows the main specification as in regressions 1 and 5. All variables are averaged over non-overlapping five-year windows. All regressions include lagged dependent variable. The log relative price of machinery and equipment is a weighted average of computer equipment (IT), telecommunications equipment (CT), transport equipment, and other machinery and equipment. Standard errors clustered at the country level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

Table 5.

Sectoral Real Investment Rate and Relative Prices of Machinery and Equipment: Range of Possible Estimates

Dependent Variable: Log Real Investment-to-GDP Ratio	IV	OLS	OLS Lagged	IV PI/PVA	IV	OLS	OLS Lagged	IV PI/PVA
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Log Relative Price	-0.326*** (0.078)	-0.567** (0.201)	-0.201 (0.254)	-0.325*** (0.078)	-0.528*** (0.068)	-0.695*** (0.181)	-0.344 (0.247)	-0.521*** (0.067)
Number of Observations	971	971	971	971	971	971	971	971
R2	0.94	0.94	0.93	0.94	0.93	0.93	0.92	0.93
First Stage F-Statistic	645			643	729			729
Period Fixed Effects	No	No	No	No	Yes	Yes	Yes	Yes
Country-Period Fixed Effects	Yes	Yes	Yes	Yes	No	No	No	No
Country-Sector Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Source: Authors’ calculations. Note: Regressions 1 and 5 show results based on the main specification, which uses lagged log relative prices to instrument for log relative prices. Regressions 2 and 6 present reduced form results, with the contemporaneous log relative prices. Regressions 3 and 7 present reduced form results, using the lagged log relative prices instead of contemporaneous. In regressions 4 and 8, the relative price of investment is defined relative to the sectoral value added, and follows the main specification as in regressions 1 and 5. All variables are averaged over non-overlapping five-year windows. All regressions include lagged dependent variable. The log relative price of machinery and equipment is a weighted average of computer equipment (IT), telecommunications equipment (CT), transport equipment, and other machinery and equipment. Standard errors clustered at the country level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

View Table

Table 5.

Sectoral Real Investment Rate and Relative Prices of Machinery and Equipment: Range of Possible Estimates

Dependent Variable: Log Real Investment-to-GDP Ratio	IV	OLS	OLS Lagged	IV PI/PVA	IV	OLS	OLS Lagged	IV PI/PVA
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Log Relative Price	-0.326*** (0.078)	-0.567** (0.201)	-0.201 (0.254)	-0.325*** (0.078)	-0.528*** (0.068)	-0.695*** (0.181)	-0.344 (0.247)	-0.521*** (0.067)
Number of Observations	971	971	971	971	971	971	971	971
R2	0.94	0.94	0.93	0.94	0.93	0.93	0.92	0.93
First Stage F-Statistic	645			643	729			729
Period Fixed Effects	No	No	No	No	Yes	Yes	Yes	Yes
Country-Period Fixed Effects	Yes	Yes	Yes	Yes	No	No	No	No
Country-Sector Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Source: Authors’ calculations. Note: Regressions 1 and 5 show results based on the main specification, which uses lagged log relative prices to instrument for log relative prices. Regressions 2 and 6 present reduced form results, with the contemporaneous log relative prices. Regressions 3 and 7 present reduced form results, using the lagged log relative prices instead of contemporaneous. In regressions 4 and 8, the relative price of investment is defined relative to the sectoral value added, and follows the main specification as in regressions 1 and 5. All variables are averaged over non-overlapping five-year windows. All regressions include lagged dependent variable. The log relative price of machinery and equipment is a weighted average of computer equipment (IT), telecommunications equipment (CT), transport equipment, and other machinery and equipment. Standard errors clustered at the country level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

Table 6 presents the baseline results first with country-period and country sector fixed effects (columns 1–4), followed by period and country-sector fixed effects (columns 5–8), for each of four dependent variables: the machinery and equipment investment rate, followed by machinery and equipment investment, value added, and output per worker.

Table 6.

Relative Prices of Machinery and Equipment and Sectoral Outcomes: Five-Year Averages

Source: Authors’ calculations. Note: All regressions show results based on the main specification, which uses lagged log relative prices to instrument for log relative prices. All variables are averaged over non-overlapping five-year windows. Al l regressions include lagged dependent variable. The log relative price of machinery and equipment is a weighted average of computer equipment (IT), telecommunications equipment (CT), transport equipment, and other machinery and equipment. Standard errors clustered at the country level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

Table 6.

Relative Prices of Machinery and Equipment and Sectoral Outcomes: Five-Year Averages

Dependent Variables:	Log Real Investment-to-GDP	Log Real Investment	Log Value Added	Log Value Added per Worker	Log Real Investment-to-GDP	Log Real Investment	Log Value Added	Log Value Added per Worker
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Log Relative Price	-0.326*** (0.078)	-0.192** (0.079)	-0.061*** (0.018)	-0.016 (0.025)	-0.528*** (0.068)	-0.444*** (0.071)	-0.058*** (0.015)	-0.033 (0.021)
Number of Observations	971	1,046	972	747	971	1,046	972	747
R 2	0.94	0.99	0.99	0.99	0.93	0.98	0.99	0.99
First Stage F-Statistic	645	456	991	378	729	500	1339	434
Period Fixed Effects	No	No	No	No	Yes	Yes	Yes	Yes
Country-Period Fixed Effects	Yes	Yes	Yes	Yes	No	No	No	No
Country-Sector Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Source: Authors’ calculations. Note: All regressions show results based on the main specification, which uses lagged log relative prices to instrument for log relative prices. All variables are averaged over non-overlapping five-year windows. Al l regressions include lagged dependent variable. The log relative price of machinery and equipment is a weighted average of computer equipment (IT), telecommunications equipment (CT), transport equipment, and other machinery and equipment. Standard errors clustered at the country level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

View Table

Table 6.

Relative Prices of Machinery and Equipment and Sectoral Outcomes: Five-Year Averages

Dependent Variables:	Log Real Investment-to-GDP	Log Real Investment	Log Value Added	Log Value Added per Worker	Log Real Investment-to-GDP	Log Real Investment	Log Value Added	Log Value Added per Worker
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Log Relative Price	-0.326*** (0.078)	-0.192** (0.079)	-0.061*** (0.018)	-0.016 (0.025)	-0.528*** (0.068)	-0.444*** (0.071)	-0.058*** (0.015)	-0.033 (0.021)
Number of Observations	971	1,046	972	747	971	1,046	972	747
R 2	0.94	0.99	0.99	0.99	0.93	0.98	0.99	0.99
First Stage F-Statistic	645	456	991	378	729	500	1339	434
Period Fixed Effects	No	No	No	No	Yes	Yes	Yes	Yes
Country-Period Fixed Effects	Yes	Yes	Yes	Yes	No	No	No	No
Country-Sector Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Source: Authors’ calculations. Note: All regressions show results based on the main specification, which uses lagged log relative prices to instrument for log relative prices. All variables are averaged over non-overlapping five-year windows. Al l regressions include lagged dependent variable. The log relative price of machinery and equipment is a weighted average of computer equipment (IT), telecommunications equipment (CT), transport equipment, and other machinery and equipment. Standard errors clustered at the country level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

As a robustness check, Table 7 presents all the regressions presented in Table 6 but using annual data. As expected, the estimated coefficients are smalle r in magnitude when annual data are used instead of five-year averages. However, all the results have the correct signs, and are statistically significant, except for sectoral output per worker.

Table 7.

Relative Prices of Machinery and Equipment and Sectoral Outcomes: Annual

Source: Authors’ calculations. Note: Data are at annual frequency. All regressions show results based on the main specification, which uses lagged log relative prices to instrument for log relative prices. All regressions include lagged dependent variable. The log relative price of machinery and equipment is a weighted average of computer equipment (IT), telecommunications equipment (CT), transport equipment, and other machinery and equipment. Standard errors clustered at the country level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

Table 7.

Relative Prices of Machinery and Equipment and Sectoral Outcomes: Annual

Dependent Variables:	Log Real Investment-to-GDP (1)	Log Real Investment (2)	Log Value Added (3)	Log Value Added per Worker (4)	Log Real Investment-to-GDP (5)	Log Real Investment (6)	Log Value Added (7)	Log Value Added per Worker (8)
Log Relative Price	-0.170*** (0.018)	-0.264*** (0.018)	-0.013*** (0.003)	-0.005 (0.004)	-0.203*** (0.017)	-0.279*** (0.017)	-0.011*** (0.003)	-0.007* (0.004)
Number of Observations	5,629	6,004	5,644	4,430	5,629	6,004	5,644	4,430
R 2	0.96	0.99	0.99	0.99	0.95	0.99	0.99	0.99
First Stage F-Statistic	20770	18595	26232	12603	23442	20477	33690	14700
Year Fixed Effects	No	No	No	No	Yes	Yes	Yes	Yes
Country-Year Fixed Effects	Yes	Yes	Yes	Yes	No	No	No	No
Country-Sector Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Source: Authors’ calculations. Note: Data are at annual frequency. All regressions show results based on the main specification, which uses lagged log relative prices to instrument for log relative prices. All regressions include lagged dependent variable. The log relative price of machinery and equipment is a weighted average of computer equipment (IT), telecommunications equipment (CT), transport equipment, and other machinery and equipment. Standard errors clustered at the country level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1

View Table

Table 7.

Relative Prices of Machinery and Equipment and Sectoral Outcomes: Annual

Dependent Variables:	Log Real Investment-to-GDP (1)	Log Real Investment (2)	Log Value Added (3)	Log Value Added per Worker (4)	Log Real Investment-to-GDP (5)	Log Real Investment (6)	Log Value Added (7)	Log Value Added per Worker (8)
Log Relative Price	-0.170*** (0.018)	-0.264*** (0.018)	-0.013*** (0.003)	-0.005 (0.004)	-0.203*** (0.017)	-0.279*** (0.017)	-0.011*** (0.003)	-0.007* (0.004)
Number of Observations	5,629	6,004	5,644	4,430	5,629	6,004	5,644	4,430
R 2	0.96	0.99	0.99	0.99	0.95	0.99	0.99	0.99
First Stage F-Statistic	20770	18595	26232	12603	23442	20477	33690	14700
Year Fixed Effects	No	No	No	No	Yes	Yes	Yes	Yes
Country-Year Fixed Effects	Yes	Yes	Yes	Yes	No	No	No	No
Country-Sector Fixed Effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes

Source: Authors’ calculations. Note: Data are at annual frequency. All regressions show results based on the main specification, which uses lagged log relative prices to instrument for log relative prices. All regressions include lagged dependent variable. The log relative price of machinery and equipment is a weighted average of computer equipment (IT), telecommunications equipment (CT), transport equipment, and other machinery and equipment. Standard errors clustered at the country level in parentheses. ***p < 0.01; **p < 0.05; *p < 0.1