trade and intellectual property rights as channels for economic growth

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Trade and intellectual property rightsas channels for economic growthKai Wu a , Hong Cai a , Renai Jiang b & Gary H. Jefferson ca School of Management, Xi’an Jiaotong University , Xi’an ,Shaanxi , 710049 , PR Chinab School of Economics and Finance, Xi’an Jiaotong University ,Xi’an , Shaanxi , 710061 , PR Chinac Department of Economics & International Business School ,Brandeis University , Waltham , MA , USAPublished online: 14 Jan 2013.

To cite this article: Kai Wu , Hong Cai , Renai Jiang & Gary H. Jefferson (2013) Trade andintellectual property rights as channels for economic growth, Asia-Pacific Journal of Accounting &Economics, 20:1, 20-36, DOI: 10.1080/16081625.2012.744709

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Trade and intellectual property rights as channels for economicgrowth

Kai Wua, Hong Caia, Renai Jiangb* and Gary H. Jeffersonc

aSchool of Management, Xi’an Jiaotong University, Xi’an, Shaanxi 710049, PR China; bSchoolof Economics and Finance, Xi’an Jiaotong University, Xi’an, Shaanxi 710061, PR China;

cDepartment of Economics & International Business School, Brandeis University, Waltham, MA,USA

(Received 1 September 2012; final version received 26 October 2012)

This paper investigates two prominent potential drivers of long-run economicgrowth: a country’s trade regime and its intellectual property rights (IPR) regime, aswell as their interaction. We characterize the combination of these policy-drivenregimes as a country’s technology development regime. To test the importance ofour specification of the technology regime, the paper derives a parsimonious modelfor testing their impact on the growth of living standards. The empirical analysis isconducted using a panel of 24 developed countries and 78 developing countriesspanning 1980–2005. Our estimates highlight the importance of the technologydevelopment regime variables in driving the growth of gross domestic product percapita. In particular, the IPR regime stands out for its direct impact on growth andas a channel through which trade interacts to impact growth. The results support theview that a country’s graduating into the ranks of higher income status may requirethat both IPR and trade regimes, particularly the former, be well developed.

Keywords: economic growth; international trade; IPR

JEL classification: O4, O32, O11

1. Introduction

Does strengthening a country’s trade and intellectual property rights (IPR) regimes pro-mote economic growth, either separately and/or interactively? There is no consensus,or, in fact, research that fully aligns with this question. Grossman and Helpman (1991),Helpman (1993), and Horii and Iwaisako (2007) highlight their theoretical predictionsthat stronger IPR may hinder economic growth, as it reduces the efficiency of resourceallocation across countries, and weakens market competition. Others, such as Parello(2008) and Eicher and Penalosa (2008), suggest that stronger IPR can foster economicgrowth in developing countries, as it provides incentives to innovation and reduces thecost of future innovation. Yielding a range of results, the empirical research confirmsthe conflicting predictions with respect to the roles of IPR in promoting growth.

While the preponderance of analysis focusing on trade emphasizes its benefits toallocative efficiency and in some cases to economic growth, reviewing a swath of therecent literature on trade and growth Slaughter (1997) catalogs the shortcoming of therelevant research from which he concludes the need for more work in this area. Later,

*Corresponding author. Email: [email protected]

Asia-Pacific Journal of Accounting & Economics, 2013Vol. 20, No. 1, 20–36, http://dx.doi.org/10.1080/16081625.2012.744709

� 2013 City University of Hong Kong and National Taiwan University

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Irwin and Tervio (2002) and Kim and Lin (2012) report still more contrasting resultsconcerning the impact of trade on living standards.

Helpman (1993) identifies two channels through which IPR and trade interact toimpact welfare, i.e. the terms of trade and interregional allocations of manufacturing.Given the lack of consistent finding concerning the direct impacts of IPR and trade onwelfare, it is reasonable to expect that research on the welfare impacts of interactionsbetween IPR and trade are likewise ambiguous.

In this paper, we characterize the combination of a country’s IPR-related policies,its trade regime, and the interaction of the trade and IPR regimes as a country’s technol-ogy development regime (shortened to technology regime). Together, these national pol-icy regimes afford a nation access to the international technology frontier through tradeand openness as well as create the incentive structure for business and the nonprofitresearch sector to dedicate resources to the absorption of existing technologies and thedevelopment of new technologies. Comprised largely of the accumulation of policy-dri-ven IPR and trade policies, a country’s technology development regime qualifies as asubstantially exogenous condition. That is, a country’s trade and IPR regimes are likelyto reflect future growth, not themselves be consequences of future growth.

Accordingly, this paper focuses on the impacts on economic growth of nationaltrade and IPR regimes, as they operate both independently and interactively. To exam-ine this set of propositions, we employ a panel of country-level data spanning 102countries of various income levels. Using least-squares and fixed effects analysis, wetest the impact of 5 and 10-year lagged values of measures of IPR and trade, controllingfor similarly lagged values of gross domestic product per capita (GDPP). We reportthree sets of results. The first is the lagged value of GDPP for which we can infer fromthe estimated coefficients whether the samples of countries exhibit unconditional incomeconvergence or divergence. The second set of results reports the significance of thedirect impacts of the IPR and technology regimes. The third set includes not only thedirect impact of IPR and trade, but also the interaction of these two policy regimes.Controlling for fixed effects, we find that the addition of the technology developmentregime variables increases the explanatory power by 13% for the 5-year lagged regimevariables and by 102% for the 10-year lagged case. This result confirms our theoreticalprediction that the technological variables play a central role in driving the long-rungrowth of GDPP.

Our estimation results show that for the full sample within which there exists con-siderable variation in the quality of the trade and IPR regimes, these factors and theirinteraction significantly impact the path of economic growth. These impacts, direct and/or indirect, are robust for both 5 and 10-year lags in the trade and IPR measures. Withthe exception of the developing country subsample, we find significant effects operatingthrough the interaction of evolved trade and IPR regimes, thereby confirming varioustheoretical perspectives that technology transfer and innovation require both robust tradeand IPR regimes. Graduation into the ranks of higher income status may require thatboth these regimes be well developed. That the results are less robust for the develop-ing country sample may reflect the fact that a significant proportion of the developingcountry observations were marked by the restructuring of their economies (e.g. China,India, Ukraine, etc.) during 1980–2005 or episodes of economic and/or political insta-bility (e.g. Peru, Rwanda, and Thailand).

The remainder of this paper is organized as follows. Section 2 reviews the relatedliterature researches. Section 3 formulates the model that we use to implement our tests.

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Section 4 describes the data. Section 5 describes the estimation strategy and results.Section 6 concludes the paper.

2. Related literature

Largely constructed on the underpinnings of Ricardo (1817) and Heckscher-Ohlin(1933), who emphasize the efficiencies associated with trade liberalization, the empiricalliterature on the link between trade and economic growth is relatively abundant. Whilenot as abundant as the trade-related literature, research focusing on the link betweenIPR and economic growth is nonetheless quite extensive. Research that focuses on thegrowth impact of interactions between a country’s trade and IPR regimes is limited.

2.1. IPR and growth

Helpman (1993) develops a North–South model, assuming that innovation takes place inthe North while the South conducts imitation only, a set of assumptions that leads to hisconclusion that the South loses from tighter IPR regimes. Lai (1998) extends Helpman(1993) by adding foreign direct investment (FDI) as another channel of technology trans-fer. He shows that strengthening IPR in the South has a positive effect on product inno-vation and production transfer if FDI is the channel of technology transfer and anegative effect if imitation is the channel of technology transfer. Glass and Saggi (2002)extend Helpman (1993) and Lai (1998) by assuming that stronger Southern IPR protec-tion raises the cost of imitation. In these three models, the South has no capacity to inno-vate. However, all the papers cited above recognize that the South needs to developabsorptive capacity as a precondition to conduct effective imitation in the real world.Conducting R&D activity is a way for developing economies to enhance their ability toassimilate existing technologies.

Unlike these models, Grossman and Lai (2004) and Chen and Puttitanun (2005) con-sider the implications when the South can also conduct innovation. Grossman and Lai(2004) consider that countries differ in market size and their capacity for conductingR&D activity; a country’s optimal IPR is dependent on these two factors. Chen and Putti-tanun (2005) construct their model by incorporating the realistic assumption that firms indeveloping countries can conduct both imitation and innovation. Griffith, Redding, andReenen (2004) confirm empirically that R&D activity has both an innovative and an imi-tative role in economic growth. Strengthening IPR protection can stimulate investment inR&D, which leads to enhancement of the knowledge base by enabling enterprises toabsorb existing technologies and to conduct follow-on innovation more efficiently. Glassand Saggi (2002) argue that stronger IPR protection requires developing countries tospend more resources to achieve a greater probability of successful imitation. Thus, whilestronger IPR protection may increase a country’s R&D intensity, it may also require moreR&D to achieve the same degree of imitation that had been acquired through a weakerIPR regime, while displacing R&D from innovation to imitation. This body of literature,largely focused on developing countries, yields at best an ambiguous set of results; over-all, the preponderant conclusion seems to be that stronger IPR regimes in developingeconomies may serve as a net impediment to economic growth.

2.2. Trade and growth

While the empirical results concerning the impacts of trade openness on growth are alsoambiguous, the literature suggests that trade has generally positive results for growth.

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Greenaway (1998) highlights his conclusion that trade liberalization facilitates sustain-able economic growth, but is not sufficient. He argues that good institutional infrastruc-ture and compatible political reform are needed to support the contribution of tradeliberalization to rapid economic growth.

Castro (2006) investigates the theoretical and empirical implications of trade open-ness on economic development. He concludes that by exploiting comparative advantageand enhancing efficiency, trade liberalization, both in capital goods and consumptiongoods, benefits developed and developing countries.

Kaneko (2000) illustrates the importance of the composition of trade as a driver ofeconomic growth. He concludes that trade promotes economic growth if the countryspecializes in consumption commodities; when it specializes in capital goods, trade hasno measureable impact on growth. Using a geography-based instrument to control forthe endogeneity of trade, Irwin and Tervio (2002) confirm that countries with highertrade orientations enjoy higher incomes. Taking into account the level of economicdevelopment, Kim and Lin (2012) find that greater trade openness improves the livingstandards of highly industrialized countries; however, it impairs the living standards ofless developed countries.

If trade does matter for economic growth, an issue rises concerning the importancethe qualitative aspects of trade policy, say efforts to promote her priced and qualityexports vs. quantity. A range of studies investigates the implications of the qualitydimension for goods for trade and export policy, including the application of R&D sub-sidies for quality exports. After surveying the literature that identifies the qualityenhancement effects of specific trade policies, (Wang and Wang 2011) incorporate therelative performance incentive scheme into a cross-border decentralized model withvertical product differentiation and show that the optimal policy is free trade.

2.3. IPR and international trade

The IPR and trade regimes may interact to affect economic growth. This seems intui-tively plausible. For developed countries, trade expands markets for innovative, high-tech goods. Exports enlarge market size for innovative products, while imports enableaccess to lower cost products that are produced in lower wage developing countries.Ivus (2010) reports that strengthening IPR in developing countries added about $35 bil-lion (2000 US dollars) to the value of developed countries’ patent-sensitive exports into18 developing countries. For developing countries, imports bring new technologies andnew products to domestic markets, which foster industrial development and benefit con-sumers; exports enable them to take advantage of their comparative advantage, i.e., arelatively low-wage labor force. One area of controversy regarding the interaction oftrade and IPR is the extent to which benefits to developed countries require developingcountries to strengthen IPR protection, while developing countries may well prefer thebenefit of easy imitation through loose IPR protection.

Dinopoulos and Segerstrom (2010) are among those investigating the relationshipbetween exports and IPR reform in developing countries. Their model of North–Southtrade with multinational firms concludes that stronger IPR protection in the South leadsto an increase in the Northern innovation rate and in the rate of technology transfer tothe South. Branstetter et al. (2011) empirically confirm that stronger IPR regimes in theSouth accelerate production shifting, enhancing the industrial development of Southerncountries. Maskus and Penubarti (1995) suggest that strengthening IPR regimes in


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developing countries expands their import of manufactured goods. Ivus (2010) showsthat stronger IPR regimes in developing countries increase patent-sensitive imports.

Several empirical studies consider the trade regime when examining the effect ofIPR on economic growth. In general, the findings of these papers support the notionthat stronger IPR are more beneficial in open economies than in closed economies,although the precise mechanism through which trade promotes economic growth is notspecified. Gould and Gruben (1996) use black market exchange rate premiums to mea-sure trade orientation while examining the contribution of IPR to economic growth.Their results suggest that intellectual property protection is a significant determinant ofeconomic growth; the effect appears to be slightly stronger in relatively open econo-mies. Using the Sachs and Warner (1995) openness index and the trade share in GDPas proxies for openness, Gancia and Bonfiglioli (2008) examine the interactions of IPRwith both trade openness and country size. Their results suggest that the interactionterms are positive and significant.

Using a panel of cross-country data, we extend this research by directly testing theinteraction effect on growth of IPR regimes with trade openness. As discussed earlier,IP protection may impact both exports and imports. Specifically, we use the trade ratiomeasured as the sum of exports and imports divided by GDP as a proxy for trade. Wedescribe the IPR measure in the data section, i.e. Section 4.

3. The model

While our model is parsimonious, it is based on the fundamentals of growth theory. Westart with the intensive form of a Cobb–Douglas production function:

qit ¼ Aitkait ð1Þ

in which qit is GDPP in country i and year t; k is the capital–labor ratio and A is thelevel of (labor) productivity. According to Solow’s steady state Equation (1956),

Siqit ¼ ðni þ diÞkit; ð2Þ

where s, the savings rate, n, the exogenous rate of population growth, and δ, the depre-ciation rate, are assumed to be variable across countries but fixed over time.

In the steady state, kit = βiqit, where βi= [s/(n+ δ)]i and may include other fixedfactors that determine the capital–output ratio. Substituting this steady-state value of kinto Equation (1) and simplifying gives: qit ¼ ba=ð1�aÞ

i A1=ð1�aÞit . Differentiating with

respect to time yields:

ðdq=qÞit ¼ ½1=ð1� aÞ�ðdA=AÞit ð3Þ

Note that βi, which includes the time-invariant effects, drops out of Equation (3).The centrality of technological change in driving the growth of living standards, thecore message of Solow’s neoclassical growth model, is conveyed by Equation (3). Con-sistent with the Solow model, this model attributes capital accumulation in the steadystate to rising productivity. Rising productivity increases incomes and the supply of sav-ings available for capital deepening; it also shifts out capital’s marginal productivityschedule, thus increasing the demand for investment. Through this continuous outwardshift in the supply of savings and demand for investment, sustained productivity growth

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translates into sustained capital deepening and rising living standards. Equation (3)embodies this insight.

Furthermore, in their human capital augmented Solow model, Mankiw, Romer, andWeil (1992) demonstrate how human capital, like physical capital, is endogenous totechnological change. Equation (3) can be interpreted as a process in which rising pro-ductivity translates into a greater demand for both physical and human capital as risingsavings and returns to both types of capital motivate both physical and human capitaldeepening. In his chapter entitled “Education for What”, Easterly (2001) underscoresthe importance of technology and employment opportunities that require and rewardskills as a prerequisite for productive investment in schooling. This basic condition isembodied in the parsimonious specification of Equation (3).

Consistent with the endogenous growth literature in which the long-run growth rateof output per worker is determined by variables within the model, we complement theSolow 2-equation of neoclassical growth model with a third equation, which models theevolution of technological change as follows:

ðdA=AÞit ¼ hðIPRit�s;OPENit�s; IPR�OPENit�sÞ ð4Þ

where IPR is a measure of the quality of country i’s IPRs regime and OPEN is a mea-sure of the country’s trade regime, as measured by the trade ratio, (X +M)/GDP.

This model specifically enables a test of the roles of trade and IPR protection, andtheir interaction, as channels for promoting technological advance and economicgrowth. Imports provide a channel to absorb advanced technology while exports enlargethe market size for innovative products. From this perspective, we expect that IPR pro-tection impacts directly on economic growth as well as through the channel of interna-tional trade. Strengthening IPR protection serves to motivate R&D investment amongprivate entities for the purpose of accessing and absorbing existing technologies and/ormore effectively conducting new innovation. That R&D spending which is, in turn,motivated by technological opportunity is suggested by the fact that in most economies,whether a large share of GDP or not, three quarters of R&D spending is generally pro-vided by the private sector, as a means to capturing benefits from evolving technologi-cal opportunity. Strong IPR protection should not only serve to expand R&D activitybut also more effectively translate its outcomes into economic growth.

By substituting gross domestic product per capita (GDPP), for q, converting to natu-ral logs, i.e., converting dq/q to ln(q+ 1), and specifying τ as the time interval repre-sented by lagged values of GDPP and A, Equation (3) can be rewritten as:

lnGDPPit ¼ ao þ ki þ a1lnGDPPit�s þ a2IPRit�s þ a3OPENit�s þ a4IPR�OPENit�s

þ eit ð5Þ

Equation (5) frames the estimation equation that we use in the following section. InEquation (5), λi is a vector of fixed effects, which includes those that drive productivitychange and other factors that may determine GDPP. Such time-invariant factors mayinclude geography and a range of other factors (see Section 5), that may at once be cor-related with levels and rates of growth of GDPP and hence lead to endogeneity and biasin estimates of GDPP. We estimate Equation (5) with 5 and 10-year lags, i.e., τ = 5, 10,and with both Ordinary Least Squares (OLS) and fixed effects estimators.


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Before we estimate Equation (5), we might clarify its relation to the conventionaltest of the convergence of living standards across countries. Equation (5) could berewritten by subtracting lnGDPPit�τ from both sides, so that the dependent variable rep-resents the 5 or 10-year compound rate of growth and the coefficient on the regressorlnGDPPit�τ, i.e. α1 – 1, represents the propensity to converge (or diverge). Estimates ofthis coefficient less than zero imply convergence; estimates greater than zero implydivergence. If Equation (5) was converted into a convergence test, the presence of otherregressors, including the technology regime and fixed effects variables, test for condi-tional convergence.

4. Data

Our data-set consists of 24 developed countries and 78 developing countries from 1980to 2005. Table 1 shows the list of the 102 include countries. Collected from the PennWorld Table (PWT version 7.0), the GDPP data are expressed in 2005 constant prices.The data-set is unbalanced.

We select the sample country based on the data available for IPR, which is fromPark (2008). The index of patent rights developed by Ginarte and Park (1997) andupdated by Park (2008) measures the strength of a country’s IPR regime.1 This indexincorporates five categories: the extent of coverage, membership in international treaties,duration of protection, enforcement mechanism, and restrictions on patent rights. Theindex incorporates the effects of recent national and global developments, such asamendments to national patent laws and adoption of international treaties, consequentlyyielding variability in the measurement of IPR across countries and over time.2

Table 1. Countries included in the study.

Developing countries Developed countries

Algeria, Argentina, Bangladesh, Benin, Bolivia,Botswana, Brazil, Bulgaria, Burkina Faso,Burundi, Cameroon, Central AfricanRepublic, Chad, Chile, China, Colombia,Republic of Congo, Costa Rica, DominicanRepublic, Ecuador, Egypt, El Salvador, Fiji,Gabon, Ghana, Greece, Grenada, Guatemala,Guyana, Haiti, Honduras, Hungary, India,Jamaica, Jordan, Kenya, Liberia, Madagascar,Malawi, Malaysia, Mali, Malta, Mauritania,Mauritius, Mexico, Morocco, Nepal,Nicaragua, Niger, Nigeria, Pakistan, Panama,Paraguay, Peru, Philippines, Poland, Portugal,Romania, Rwanda, Saudi Arabia, Senegal,Sierra Leone, South Africa, South Korea, SriLanka, Swaziland, Syria, Tanzania, Thailand,Togo, Trinidad and Tobago, Tunisia, Turkey,Uganda, Uruguay, Venezuela, Vietnam, andZimbabwe

Australia, Austria, Belgium, Canada, Cyprus,Denmark, Finland, France, Germany, Iceland,Ireland, Israel, Italy, Japan, Luxembourg,Netherlands, New Zealand, Norway, Singapore,Spain, Sweden, Switzerland, the UK, and theUSA

Notes: We classify developed countries according to the World Bank’s classification of high-incomeeconomies. If a country is classified as high-income economy throughout the period of 1980–2005, weclassify it as a developed country. If a country is not classified as developed countries, it is classified asdeveloping country.

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Beginning in 1980, the IPR indices are available for every five years, thus providing upto six IPR measures per country. Countries for which two or more observations aremissing are omitted from the sample.

As a proxy for international trade, we use total trade - imports plus exports - as apercentage of real GDP. These data are based on import and export data as well asGDP drawn from the PWT version 7.0. The trade data are also represented in 2005constant prices.

Table 2 reports summary statistics including differences for the developed anddeveloping country subsamples. Unsurprisingly, the means for GDPP, IPR, and OPENare all greater for the developed country subsample. GDPP and IPR exhibit consider-ably more variation across the developed and developing country subsamples than vari-ation in trade regimes. While the means for OPEN lie within one standard deviation,those for GDPP and IPR do not.

To demonstrate the construction of the IPR index generally and specifically for animportant developing country, Tables 3 and 4 summarize the Ginarte and Park IPR datafor China. Table 3 shows that in 1985, the first year the China data show, China’s IPRindex lagged behind that of the other developing countries; nonetheless, by 2005, itrises to more than one standard deviation above that for the developing countries andwithin a single standard deviation of the mean for the developed country index. Clearly,the rapid contemporaneous rise in China’s GDPP and IPR index would suggest someassociation between the two.

Table 4 reports the series from 1985 to 2005 for the five columns that comprise theIPR index for China. By 2005, following two substantive patent amendments and fivetreaty accessions, China’s IPR index rises significantly demonstrating how, at least inthe case of China, the Ginarte-Park IPR index exhibits a substantial degree of time ser-ies variation as well as cross-country variation.

5. Estimation strategy and results

Below, we elaborate on our estimation strategy, followed by an accounting of our esti-mation results. Our test focuses on the statistical significance of the technology regimevariables, i.e. the trade and IPR regime variables and their interaction. We also examinethe contribution made by the addition of the technology regime variables to the abilityto explain country time-series variation, i.e. the within R2 values, in comparison withthe basic model that includes GDPP as the only regressor.

Table 2. Summary statistics.

Samples

Variables

IPR OPEN GDPP

Full samples 2.600 0.709 10,122(1.066) (0.501) (11,374)

Subsample of developed countries 3.753 0.787 27,670(0.810) (0.735) (8764)

Subsample of developing countries 2.241 0.685 4689(0.863) (0.400) (4551)

Note: Standard deviations are reported in parentheses.


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5.1. Estimation strategy

The OLS estimates of Equation (5) may suffer from two related problems: issues relatedto the presence of a lagged dependent variable on the right hand side (RHS) of theequation and omitted variables that may be correlated with the RHS variables, includinglnGDPPit�τ, and the technology regime variables. Such correlations may well give riseto serious omitted-variables endogeneity bias. Specifically, only if the current distur-bance is unrelated to lnGDPPit�τ, do the standard results concerning the consistency ofthe ordinary least-squares regression procedure then retain their validity.

To address the problem of endogeneity bias arising from the omitted variables thatare largely time invariant, we use a fixed effects estimator. As previously suggested,such fixed effects span a range of conditions that include the geographic characteristicsof countries, including comparative advantages and disadvantages, and other relativelyconstant country-specific conditions. In our model, we explicitly identify a variety offixed effects relating to the Solow model including savings rates, population growth,

Table 4. IPR indices for China.a

Coverage MembershipEnforcementmechanism

Restrictions onpatents Duration

IPRindex

1985 0.375 0.2 0 0 0.75 1.3251986 0.375 0.2 0 0 0.75 1.3251987 0.375 0.2 0 0 0.75 1.3251988 0.375 0.2 0 0 0.75 1.3251989 0.375 0.2 0 0 0.75 1.3251990 0.375 0.2 0 0 0.75 1.3251991 0.375 0.2 0 0 0.75 1.3251992 0.375 0.2 0 0 1 1.5751993 0.75 0.2 0 0.666 1 2.6161994 0.75 0.4 0 0.666 1 2.8161995 0.75 0.6 0 0.666 1 3.0161996 0.75 0.6 0 0.666 1 3.0161997 0.75 0.6 0 0.666 1 3.0161998 0.75 0.6 0 0.666 1 3.0161999 0.75 0.8 0 0.666 1 3.2162000 0.75 0.8 0 0.666 1 3.2162001 0.75 1 0.666 0.666 1 4.0822002 0.75 1 0.666 0.666 1 4.0822003 0.75 1 0.666 0.666 1 4.0822004 0.75 1 0.666 0.666 1 4.0822005 0.75 1 0.666 0.666 1 4.082

aGinarte and Park (1997) and Park (2008).

Table 3. IPR data summary.

China Developed countries Developing countries

1985 1.325 3.193 1.694(0.741) (0.514)

2005 4.082 4.388 3.194(0.362) (0.654)

Note: Standard deviations are reported in parentheses.

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and rates of depreciation. In addition to these fixed effects, other possible fixed effectsmay include a country’s ownership structure (e.g. the prevalence of public or state own-ership), regulatory regimes apart from the trade and IPR regimes, such as anti-trust, thatmay affect an economy’s competitive structure, and a range of cultural differences thataffect its economic performance. We compare the results generated by the OLS andfixed effects estimators.

5.2. Estimation results

Tables 5(a)–(c) report the results for the full sample and for the developed and develop-ing country subsamples using the 5-year lagged variables; Tables 6(a)–(c) report thesame set of specifications and same samples using the 10-year lagged variables.

5.2.1. Significance of the technology development regime variables

Before investigating the robustness of various combinations of regime variables, wecompare the explanatory power of the full set of regime variables (the augmentedmodel) relative to the lagged value of GDPP alone (the basic model). To do this, wecompare columns (1) and (2) of Table 5(a) with columns (5) and (6). Comparing theOLS results in column (1) with those in column (5), we find negligible difference inthe overall R2 statistics between the basic GDPP regression in column (1) and the aug-mented version in column (5) that includes the technology development regime vari-ables. The R2 for the basic version is 0.985, while that for the augmented version is0.986. With the R2 for the basic version already close to one, the addition of the tech-nology regime variables adds little to the explanatory power for the basic model esti-mating GDPP over a 5-year period.

As previously explained, we expect that the OLS estimator will yield biased esti-mates that result from omitted variables that are correlated with both the lagged andcurrent values of GDPP. These correlations cause estimates of the lagged value ofGDPP to be biased upward and estimates of the coefficients on the technology regime

Table 5(a). Dependent variable (5 years lagged): log GDPP (all countries).

Dep: lnGDPPOLS FE OLS FE OLS FE(1) (2) (3) (4) (5) (6)

lnGDPP 1.020⁄⁄⁄ 0.760⁄⁄⁄ 1.010⁄⁄⁄ 0.615⁄⁄⁄ 1.010⁄⁄⁄ 0.590⁄⁄⁄

(0.010) (0.033) (0.009) (0.034) (0.009) (0.035)lnIPR 0.061⁄⁄⁄ 0.186⁄⁄⁄ 0.067⁄⁄ 0.233⁄⁄⁄

(0.019) (0.027) (0.026) (0.033)lnOPEN 0.027⁄⁄⁄ 0.078⁄⁄⁄ 0.021 0.036

(0.010) (0.030) (0.021) (0.034)LnIPR⁄lnOPEN 0.008 0.079⁄⁄

(0.021) (0.032)Cons �0.099 2.092⁄⁄ �0.045 3.217⁄⁄⁄ �0.048 3.410⁄⁄⁄

(0.100) (0.279) (0.073) (0.287) (0.074) (0.296)Obs. 507 507 504 504 504 504R2† 0.985 0.566 0.986 0.632 0.986 0.637

Notes: All independent variables are included as 5-year lags; standard errors are reported in parentheses.⁄p < .10, ⁄⁄p < .05, and ⁄⁄⁄p < .01.†The R2 statistics are overall R2 for the OLS estimates and within R2 for the fixed-effects estimates.


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variables to be seriously biased downward. As a result, we rely principally on the fixedeffects estimator; henceforth, we do not report the OLS results.

As expected, comparing the fixed effects results in columns (2) and (6) yields differ-ent results. While the within R2 for the basic model stands at 0.566, for the augmentedversion it rises to 0.637, a 13% difference. For the 10-year lagged version, shown incolumns (2) and (6) of Table 6(a), the additional explanatory power resulting fromincluding the technology regime variables is substantially more dramatic. For the basicmodel the R2 value is 0.178, considerably less than for the 5-year lagged version. Forthe augmented specification that includes the technology regime variables, the R2 valuerises to 0.360. The disparity in the R2 values between the basic and augmented modelsexpands dramatically from 13% for the 5-year lag to 102% for the 10-year lag.

Table 5(b). Dependent variable (5 years lagged) : log GDPP (developed countries only).


lnGDPP 0.962⁄⁄⁄ 0.945⁄⁄⁄ 0.943⁄⁄⁄ 0.658⁄⁄⁄ 0.946⁄⁄⁄ 0.659⁄⁄⁄

(0.023) (0.034) (0.026) (0.059) (0.026) (0.067)lnIPR 0.010 0.247⁄⁄⁄ �0.012 0.247⁄⁄⁄

(0.030) (0.055) (0.031) (0.056)lnOPEN 0.040⁄⁄⁄ 0.088⁄⁄ 0.124⁄⁄⁄ 0.090⁄

(0.009) (0.039) (0.035) (0.047)LnIPR⁄lnOPEN �0.070⁄⁄ �0.002

(0.028) (0.044)Cons 0.496⁄⁄ 0.669⁄ 0.698⁄⁄⁄ 3.316⁄⁄⁄ 0.692⁄⁄⁄ 3.301⁄⁄⁄

(0.229) (0.344) (0.247) (0.565) (0.241) (0.660)Obs. 120 120 120 120 120 120R2† 0.939 0.891 0.946 0.919 0.949 0.919


Table 5(c). Dependent variable (5 years lagged): log GDPP (developing countries only).


lnGDPP 1.018⁄⁄⁄ 0.715⁄⁄⁄ 1.015⁄⁄⁄ 0.594⁄⁄⁄ 1.014⁄⁄⁄ 0.582⁄⁄⁄

(0.009) (0.041) (0.009) (0.041) (0.009) (0.041)lnIPR 0.082⁄⁄⁄ 0.181⁄⁄⁄ 0.097⁄⁄⁄ 0.228⁄⁄⁄

(0.024) (0.032) (0.035) (0.042)lnOPEN 0.020 0.068⁄ 0.007 0.033

(0.015) (0.036) (0.026) (0.041)LnIPR⁄lnOPEN 0.025 0.075⁄

(0.042) (0.044)Cons �0.087 2.314⁄⁄⁄ �0.103 3.196⁄⁄⁄ �0.105 3.271⁄⁄⁄

(0.071) (0.324) (0.073) (0.324) (0.074) (0.325)Obs. 387 387 384 384 384 384R2† 0.972 0.498 0.972 0.563 0.972 0.567


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We should expect that as the duration between the lagged and current measures ofGDPP grows, the importance of the rate of growth of GDPP as determined by technol-ogy factors will grow relative to the historical level of GDPP. The 20-year 8.9% aver-age annual increase in China’s GDPP from 1987 to 2007 as compared with the 4.2%increase over the period 1955–1975 is but one example of the importance of growthcompared with a lagged value of GDP.3 Comparing the results in Tables 5(a) and 6(a)confirms this expectation.

In conclusion, once fixed effects are controlled for, the addition of the technologydevelopment regime variables leads to substantial increases in the R2 measure of “good-ness of fit.” As predicted by neoclassical and endogenous growth theory, technologydevelopment plays a central role in driving the advance of long-term living standards.Moreover, the technology variables that we have chosen – a country’s IPR and traderegimes and their interaction – emerge as effective measures of a country’s overall tech-nology development regime that frames the incentives and opportunities for long-runtechnology development and rising living standards.

5.2.2. Significance of the individual technology development regime variables

In Tables 5(a)–(c) and 6(a)–(c), we now focus on columns (4) and (6) to assess the rela-tive robustness of the IPR and OPEN variables and their interaction. For each of thesesets of regressions, column (4) does not include the interaction term between IPR andOPEN; column (6) does include the interaction term.

In Table 5(a), column (4), which omits the interaction term, shows both the IPR andtrade regimes exerting robust impacts on the growth of GDPP.4 However, as shown incolumn (6), when we add the interaction term, the trade variable loses statistically sig-nificance; the impact of an open economy now largely operates through the channel ofa strong IPR regime. Column (6) underscores the relative importance of an evolved sys-tem of intellectual property protection, both for its direct effect and as a robust avenuefor conveying the beneficial impact of trade openness on living standards.

Moving to Table 6(a), which reports results for the 10-year lag, we see in column(4) that with fixed effects, the IPR regime remains highly robust, while the trade regimeexhibits a level of statistical significance that falls slightly below the 10% level. Column(6) magnifies the results for the 5-year lag. As expected, estimates of the 10-year laggedspecification gain in statistical power. Both the IPR regime and its interaction withOPEN approximately double the magnitudes of the estimates associated with the 5-yearlag specification. Again, estimates using the full sample of 102 countries with both the5 and 10-year lag specifications highlight the central importance of a country’s IPRregime, both for its direct impact on economic growth as well as serving as a channelthrough which a liberalized trade regime provides access to a range of technologies thatcan be absorbed and improved upon with support from a complementary IPR regime.

5.2.3. Differences between developed and developing countries

Using the developed country sample of 24 countries and the developing country sampleof 78 countries, the analysis separately examines the impact of the technology regimeson GDPP growth. In Table 5(b), (c) and Table 6(b), (c), we test the 5 and 10-yearlagged impact of the technology regime variables in the developed and developingcountry subsamples. For both country subsamples, with the 5-year lag estimates shownin Tables 5(b) and (c), only the IPR variable survives as a consistently robust estimate


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at the 1% level. For the developed country sample, in Table 5(b), column (4), OPEN isstatistically significant at the 5% level, whereas for the developing country sample, theinteraction terms survive at the 10% level of statistical significance. Given the homoge-neity of the two subsamples relative to the full sample, the additional explanatory valueassociated with the augmented version declines significantly from the full sample esti-mates for the 5-year lag. For the developed country subsample, the technology regimevariables add only 3% and for the developing country sample they add just 14%.

For the 10-year lagged specifications shown in Tables 6(b) and 6(c), the estimatesare largely consistent with the 5-year lag estimates, except that the magnitudes of theGDPP estimates become substantially smaller, while those for the technology regimevariables become larger, particularly for the developing country sample. For both thedeveloped and developing country subsamples, as with every one of the fixed-effects

Table 6(a). Dependent variable (10 years lagged): log GDPP (all countries).


lnGDPP 1.043⁄⁄⁄ 0.393⁄⁄⁄ 1.035⁄⁄⁄ 0.233⁄⁄⁄ 1.034⁄⁄⁄ 0.203⁄⁄⁄

(0.009) (0.049) (0.011) (0.045) (0.011) (0.046)lnIPR 0.060⁄ 0.357⁄⁄⁄ 0.072 0.461⁄⁄⁄

(0.031) (0.046) (0.047) (0.061)lnOPEN 0.042⁄⁄ 0.061 0.033 �0.014

(0.019) (0.044) (0.035) (0.052)LnIPR⁄lnOPEN 0.016 0.150⁄⁄⁄

(0.038) (0.058)Cons �0.213⁄⁄⁄ 5.245⁄⁄⁄ �0.164⁄⁄⁄ 6.378⁄⁄⁄ �1.700⁄⁄⁄ 6.578⁄⁄⁄

(0.080) (0.408) (0.090) (0.385) (0.093) (0.389)Obs. 405 405 402 402 402 402R2† 0.966 0.178 0.967 0.345 0.967 0.360


Table 6(b). Dependent variable (10 years lagged): log GDPP (developed countries only).


lnGDPP 0.891⁄⁄⁄ 0.866⁄⁄⁄ 0.891⁄⁄⁄ 0.536⁄⁄⁄ 0.894⁄⁄⁄ 0.543⁄⁄⁄

(0.042) (0.058) (0.042) (0.077) (0.040) (0.084)lnIPR �0.015 0.230⁄⁄⁄ �0.068 0.231⁄⁄⁄

(0.047) (0.074) (0.047) (0.075)lnOPEN 0.081⁄⁄⁄ 0.205⁄⁄⁄ 0.237⁄⁄⁄ 0.210⁄⁄⁄

(0.015) (0.066) (0.050) (0.070)LnIPR⁄lnOPEN �0.137⁄⁄⁄ �0.015

(0.042) (0.067)Cons 1.325⁄⁄⁄ 1.573⁄⁄⁄ 1.395⁄⁄⁄ 4.759⁄⁄⁄ 1.416⁄⁄⁄ 4.677⁄⁄⁄

(0.420) (0.588) (0.396) (0.743) (0.376) (0.835)Obs. 96 96 96 96 96 96R2† 0.828 0.756 0.873 0.835 0.886 0.835


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estimates in this study, the IPR variable continues to be highly statistically significant.For the developed country sample in Table 6(b), the OPEN variable turns statisticallysignificant at the 1% level. This is not the case for the developing country sample;nowhere in the developing country sample, in either the 5 or 10-year lag specifications,does the trade regime receive confirmation as a highly robust direct driver of long-runtechnological advance. For the 10-year specification, as well as the 5-year specification,the interaction term in the developing country sample remains significant at the 10%level.

For both the developed and developing country samples, the importance of the tech-nology regime is again exhibited by the gain in the power of the specification toexplain the variability in GDPP over time within countries. For the 5-year lags, the aug-mented versions of the model yields 3% and 14 gains for the developed and developingcountry subsamples, respectively. By comparison, for the 10-year lags, technology aug-mented model improves the within R2 by 10% for the developed country sample andby 140% for the developing country sample. Given the variability in living standardsover time in the developing country subsample, it is not surprising that over longerperiods, the fundamental drivers of growth – the technology regimes – should exhibitincreased importance prominence.

This difference in sample heterogeneity is shown by comparing the magnitudes ofthe within R2 values of the developed and developing country subsamples. For example,for the 10-year lag, the 24 developed countries yield a within R2 estimate of 0.835. Bycontrast, the 78 developing countries yield a counterpart R2 of just 0.271. When wemore closely examine the 78 countries that comprise the developing country subsample,we find some hint of the cause of the drop-off in the robustness of the estimates. Withinthe sample of 78 countries, during the period of 1980–2005, numerous countries,including Bulgaria, China, and India experienced marked liberalizations of their econo-mies toward market-based economies. Also during these years, a number of other coun-tries, including Argentina, Peru, Rwanda, and Thailand, experienced significantupheavals of a political and economic nature. Finally, during this 25-year period, eightcountries graduated from developing country to developed country status. We retainthese countries in the developing country sample over the full period. These country

Table 6(c). Dependent variable (10 years lagged): log GDPP (developing countries only).


lnGDPP 1.038⁄⁄⁄ 0.309⁄⁄⁄ 1.040⁄⁄⁄ 0.188⁄⁄⁄ 1.040⁄⁄⁄ 0.176⁄⁄⁄

(0.016) (0.057) (0.016) (0.053) (0.016) (0.053)lnIPR 0.093⁄⁄ 0.341⁄⁄⁄ 0.093 0.461⁄⁄⁄

(0.047) (0.054) (0.074) (0.090)lnOPEN 0.023 0.038 0.023 �0.033

(0.026) (0.051) (0.044) (0.066)LnIPR⁄lnOPEN �0.00002 0.145⁄

(0.083) (0.088)Cons �0.177 5.566⁄⁄⁄ �0.234⁄ 6.363⁄⁄⁄ �0.234⁄ 6.399⁄⁄⁄

(0.125) (0.450) (0.142) (0.424) (0.132) (0.423)Obs. 309 309 306 306 306 306R2† 0.934 0.113 0.935 0.262 0.935 0.271



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examples of system transition and episodic instability highlight a weakness of ourmodel and estimation equation, which is that they are constructed on the assumption ofthe sample economies being in or approaching a steady state. Nonetheless, our resultsgive a rather consistent and plausible account of the important drivers of the long-rungrowth of country living standards.

6. Conclusion

This paper derives a parsimonious model of long-run economic growth that is estimatedwith a panel of data spanning 102 countries and 25 years. The variables that areassumed to drive the long-run growth of living standards are a country’s IPR regime,its trade regime, and their interaction. The key findings are that the importance of acountry’s technology development regime variables grows significantly as the durationlonger over which we test their impact on growth becomes longer, i.e. from 5 to10 years. For both durations and for the full sample and both the developed and devel-oping country subsamples, the IPR regime variable consistently exhibits impacts thatare highly statistical robust. In all of the specifications, trade openness also matters,albeit sometimes weakly and at other times only as it interacts with the IPR regime.

We test the augmented model with the technology development regime variables –IPR, OPEN, and their interactions – against the basic model with only an initial yearvalue of GDPP. In virtually all cases – versions of the model with both 5-year and 10-year lagged values of GDPP, TRADE, and IPR – the technology regime augmentedmodels substantially outperform those that rely on lagged values of GDPP only. Theexplanatory value they contribute is substantially greater for the developing countrysample than for the developed country sample. This result is unsurprising, since GDPPfor the developed country sample tends to be more stable over time than for the devel-oping country sample within which trade liberalization and IPR regimes are more likelyto change more rapidly and abruptly.

The consistent robustness of the IPR regime for all specifications and subsamples isa key finding of our study. It strongly suggests that while trade liberalization often playsa role, a robust, evolving IPR regime is the more important factor in differentiating thegrowth of living standards across countries and in enabling developing countries totransition to developed country status. This finding appears to be in conflict with someintuition and findings that strong IPR regimes work to the disadvantage of developingcountries.

In order to shed light on the nature of the IPR index we use in this paper, wedescribe in some detail its application to China. Certainly, China is one example of adeveloping country in which the strengthening of its IPR regime, arguably in concertwith extensive trade liberalization, has been accompanied by the robust growth of livingstandards. A central inference of this paper is that for China, aggressive trade liberaliza-tion and the rapid development of an IPR regime with the initial introduction of its pat-ent law in 1985 have been critical in transitioning China from its low-income tomiddle-income status. The continued evolution of these technology regime variables –trade openness and IPR – would appear to be critical in enabling China to break intothe ranks of a high-income country.

AcknowledgmentsThis paper is supported by the Project of Humanities and Social Sciences in China(10YJA630003, 12YJC630075), and the Project of National Natural Science Foundation of China

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(71272137). We are grateful to George J. Hall, Jing Ren, Xia Meng, Le Tang, Yongzhi Wang,Xiyao Xiang, and Yijie Cai for helpful comments. We also appreciate the careful reading andsuggestions of Belton Fleisher.

Notes1. Branstetter et al. (2011) measure patent system reform in developing countries along five

dimensions: (1) an expansion of the range of goods eligible for patent protection, (2) anexpansion of the effective scope of patent protection, (3) an increase in the length of patentprotection, (4) an improvement in the enforcement of patent rights, and (5) an improvementin the administration of the patent system. Comparing the two, we argue that by spanningboth developed and developing countries, the index of patent rights developed by Ginarteand Park (1997) is more comprehensive and useful for the purpose of cross-country analysis.

2. The IPR index may also exhibit substantial change over time. One of the more notable exam-ples is China, who enacted its patent law in 1984 and subsequently revised it in 1992, 2000,and 2008. These amendments have strengthened China’s IPRs protection dramatically. Formore details, see Yueh (2009). In addition, Hu and Jefferson (2009) and Yueh (2009) showthat the number of patents surging rapidly in China.

3. Estimates of the International Monetary Fund as reported in http://en.wikipedia.org/wiki/His-torical_GDP_of_the_People's_Republic_of_China#China_NBS_figures.

4. Recall that by controlling for the initial level of GDPP, we are testing the impact of the tech-nology regime variables on the growth of GDPP.

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trade and intellectual property rights as channels for economic growth

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