Increasing returns to scale and international diffusion of technology: An empirical study for Brazil (1976–2000)

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World Development Vol. 34, No. 1, pp. 75–88, 2006 Ó 2005 Elsevier Ltd. All rights reserved Printed in Great Britain 0305-750X/$ - see front matter

doi:10.1016/j.worlddev.2005.07.011

Increasing Returns to Scale and International Diffusion of Technology: An Empirical Study for Brazil (1976–2000) FRANCISCO H. P. OLIVEIRA, FREDERICO G. JAYME JR. and MAURO B. LEMOS * Federal University of Minas Gerais, Belo Horizonte, Brazil Summary. — The aim of this paper is to explore and analyze empirical evidence regarding the effects of increasing returns to scale and international diffusion of technology on the Brazilian manufacturing industry. We will start from a Kaldorian-type theoretical model that provides not only the positive effects of scale but also of diffusion on industrial performance. We use vector auto regressive (VAR) to test the model. A VAR estimates the coefficients related to industrial output, labor productivity, exports, and the technological gap between the United States and Brazil. This technique also provides simulations for the short-term and long-term trajectories under exogenous shocks. The observations were conducted over a quarterly basis and the sampling period runs from the second half of 1976 to the second half of 2000. The conclusion highlights evidence of increasing returns on the Brazilian industry albeit with some structural limitations. Furthermore, the model also reveals the difficulties Brazil has had in catching up. Ó 2005 Elsevier Ltd. All rights reserved. Key words — technological gap, increasing returns to scale, economic growth, Latin America, Brazil

been recent contributions to integrate Kaldorian macro-dynamics with Schumpeterian microfounded technical change, such as Llerena and Lorentz (2004a, 2004b). We perform a model that comprises two countries—the north and the south. From here, we will test two hypotheses. The first hypothesis refers to Verdoorn’s law, according to the Kaldorian tradition (Dixon & Thirlwall, 1975; Kaldor, 1966). Specifically, we are examining the increasing returns to scale for Brazil. The second assumption is based on the catchingup hypothesis in line with the Schumpeterian tradition (Cimoli & Soete, 1992; Dosi, Pavitt, & Soete, 1990; Freeman, 1984; Posner, 1961).

1. INTRODUCTION Our aim in this paper is to incorporate new empirical evidence on the determinants of the Brazilian industrial performance. More specifically, we attempt to test the empirical relevance of the hypotheses of increasing returns to scale and technological absorption for determining output growth of the Brazilian manufacturing industry. We will be using a time series of industrial output, industrial labor productivity, exports, and the American industrial labor productivity. They are based on quarterly observations extending from the second quarter of 1976 to the second quarter of 2000. 1 Our theoretical approach tries to integrate both the Kaldorian and Schumpeterian literature on economic growth and technological innovation and diffusion. It is inspired by the work of Verspagen (1993), who pioneers the effort to introduce cumulative causation modeling into the evolutionary approach. On these lines, there have

* The authors are grateful to Gilberto Lima, from the University of Sa˜o Paulo (USP), as well as two anonymous referees for their valuable comments. The usual disclaimers apply. Final revision accepted: July 29, 2005. 75

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We test the two main sources of catching up: diffusion and technology improvement. The ‘‘technological gap’’ between the two countries explains the role of diffusion to technology transfer to the south. In addition, ‘‘learning by doing’’ allows the south to build up its technological capabilities, which may represent the possibility of an improvement in product quality. In fact, the efficiency of absorption of new foreign technologies by the backward country will depend on its learning ability, or, putting it differently, on its ‘‘social capability.’’ To the best of our knowledge, there have been no similar empirical studies of these hypotheses for Brazil. There are some empirical studies testing the Kaldorian hypothesis for Brazil, but none that integrates the Schumpeterian and Kaldorian literature (Be´rtola, Higachi, & Porcile, 2004; Jayme Jr., 2003). Therefore, the novelty of this approach is to incorporate two different, but complementary, theoretical conceptions. There are empirical studies that have attempted to estimate the increasing returns to scale in developed countries. They constitute part of the background for the methods used in this paper (Fingleton & McCombie, 1998; McCombie & Ridder, 1983; Richard & Lau, 1998). These studies were carried out for the United States, some regions of the United Kingdom, and of the European Community, respectively. They suggest interesting alternatives for improving the consistency of the estimated parameters. Our adopted methodology based on a cointegration method was particularly inspired by the cointegration approach of Richard and Lau (1998). Regarding econometric background, the use of a Cointegration and Vector Autogressive method to perform the empirical assessment will help us: (1) establish the long-term relationship between technological innovation, output growth, and the balance of payments constraint; and (2) make short-term estimates by using a vector error correction (VEC) model. In addition to the introduction, this paper has five sections. Section 2 contains a brief review of the theoretical developments based on Kaldor’s work on economic growth (Kaldor, 1966) and the literature on catching up. We argue that these approaches compliment each other so far as they explain economic growth rate differentials both between distinct time periods within a country and among countries in a time span. Section 3 presents a growth model derived from Dixon and Thirlwall

(1975), Verspagen (1993) and its extended version provided by Higachi, Canuto, and Porcile (1999). Section 4 lays out the database and the methodology for the empirical estimates. Section 5 presents the main results, including tests for the long-term relationship among industrial output (dependent variable), productivity, and exports. Finally, the last section sums up the major results with some concluding remarks. 2. THEORETICAL DETERMINANTS OF ECONOMIC GROWTH: KALDORIAN AND SCHUMPETERIAN CONTRIBUTIONS Kaldor (1966) introduced the role that increasing returns to scale plays in explaining the growth rate differentials among countries. His original model assumes growth as a result of the interaction between the manufacturing industry (subject to increasing returns to scale) and agriculture (a backward sector subject to decreasing returns to scale due to technological constraints to productivity increase in the presence of a finite amount of land resulting in an excess of labor force 2). Insofar as the industrial sector increases its production, labor productivity also increases due to increasing returns, implying a rise in the worker’s real wage. Such a wage increase will attract labor from the backward sector, which will benefit from the resulting reduction of the excess labor supply by productivity increases. Accordingly, increased labor stock with higher wages in the industrial sector will lead to additional rise in production, reinforcing the growth process. Thus, the economic growth of a country is led by a set of intersectoral linkages in which the industrial sector is taken as the ‘‘growth engine’’ (McCombie & Thirlwall, 1994). Such a process of continuous labor migration from the backward sector to industry stimulates the ‘‘domestic market’’ of a country, based on the increasing size of the work force and its rising real wages as well as the intersectoral demand–supply linkages. For this reason, according to Kaldor (1966), the internal market constitutes the main component of final demand in the intermediate stages of development, due to feedbacks between internal consumption and investment. When the demand for expansion via an increased domestic market is exhausted, exports become the major component for expanding final demand. Hence,

INCREASING RETURNS TO SCALE AND INTERNATIONAL DIFFUSION OF TECHNOLOGY

the country’s foreign trade performance becomes crucial for sustained high growth rates. Kaldor’s emphasis on exports as the major component of final demand has led other authors to formalize the Kaldorian ideas by using the hypothesis that growth is ‘‘led by exports.’’ Dixon and Thirlwall (1975) used the ‘‘Harrod foreign trade multiplier’’ to show that overall growth rates are determined by the growth rate of exports and income elasticity of the demand for imports. Therefore, the leading component for final demand to growth varies according to a country’s development stage. While growth in developed, mature countries is led by exports, the domestic market is the driving force for developing countries. This can be seen as a direct result of intersectoral links between the industrial and the traditional sectors, respectively (McCombie, 1983; Thirlwall, 1987). In other words, the major demand component in backward countries is not that of exports but that of domestic consumption and investment resulting from an expansion of the industrial sector. Exports become a key component of demand when industrialization is already consolidated and the income per capita of the primary sector is equal to that of the industrial sector. This fact characterizes what Kaldor called ‘‘economic maturity.’’ The second hypothesis to be tested is known as the ‘‘catching-up hypothesis’’ (Abramovitz, 1986). Its theoretical background can be traced back to Schumpeter (1933, 1943). A country’s technological progress sprouts two kinds of firms: the innovative firms, responsible for introducing technological innovations in the economy; and the imitative ones, responsible for the diffusion of innovations throughout the economic system based on their activities of ‘‘technological imitation.’’ More specifically, the catching-up models come from an extension of the Schumpeterian argument for the diffusion of the world technological progress. From the late 1980s onwards, a stream of important contributions came out, such as the works of Fagerberg (1988a, 1988b), Perez and Soete (1988), Dosi et al. (1990), Silverberg and Soete (1994), and Silverberg and Verspagen (1995). According to these models, countries can be divided into two groups. The first category is formed by ‘‘leading countries,’’ which are responsible for the cutting edge scientific knowledge and hence responsible for the major worldwide technological innovations. The second group is made up of the ‘‘following coun-

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tries,’’ which for the most part lack the scientific basis required to improve the basic well of knowledge, but which can somehow lever up technological progress based on two distinct sources. The first (which is centered on the international diffusion of technology) is the absorption of innovation developed in the leading countries. The second source is based on a concept called ‘‘opportunity windows,’’ which is the development of innovation based on the technological progress achieved by the leading countries. The major hurdle for the following countries is that both of the above mentioned sources of technological catching up involve smaller relative costs in relation to what the leading countries expend on innovation (Perez & Soete, 1988). If the followers can manage to efficiently absorb new technologies and improve them, it is possible for them to sustain a growth rate of labor productivity (a proxy for technological progress), which surpasses those of the leading countries. Thus, the essence of the catching-up hypothesis is the following (Fagerberg, 1988a, 1988b): the greater the technological gap between leading and backward countries, the greater the potential for technological progress of the followers, provided that they possesses the necessary ‘‘social capability’’ to absorb and improve the international technology diffusion with leading countries (Abramovitz, 1986). If we can assume that these countries can efficiently absorb technologies from the leading countries, then we can also assume that the greater the gap that lies between them, the greater their productivity growth potential is. In this way, as a result of its ability to efficiently assimilate technologies, catching up occurs when a backward country is able to sustain a technological progress over time which is higher than that of the leading countries. However, the technological gap is not an enough condition for catching up. One needs several socioeconomic characteristics that create conditions that allow the follower countries to obtain ‘‘advantages from the gap.’’ They concern the main features of the national innovation system (NIS) of a country, such as its scientific and educational infrastructure, the magnitude of R&D, and labor force capabilities, among others. 3 What can therefore be drawn here is that the stronger a country’s NIS capabilities in relation to the ‘‘mature countries,’’ the bigger the chance this country has in achieving catching up (Albuquerque, 1999; Freeman, 1995; Nelson, 1993).

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Llerena and Lorentz (2004a) integrate some micro-foundations of technical change into the Kaldorian framework. It has also contributed to a better understanding of why growth rates diverge among countries. Indeed, this work reinforces the previous findings that diverse absorptive capacities affect relative growth performance and may, accordingly, generate vicious circles of growth rate divergence. Thus, ‘‘this results might be due to the reinforcement effect of the combination of the macro-constraint, the technical change sequentially constraint by resources, and the access to more efficient technologies, on randomly emerged competitive advantages’’ (Llerena & Lorentz, 2004a, p. 18). In addition, the authors point out that technological opportunity heterogeneities may affect temporarily growth rate divergence, whereas wage determination process seems to counterbalance this growth dynamics. The next section will briefly discuss a theoretical model that integrates the hypotheses of increasing returns to scale and catching up. Furthermore, we will specify the vector auto regressive (VAR) equation for estimating the model. 3. THE MODEL The theoretical model is based on both the Dixon–Thirlwall representation of increasing returns to scale and Verspagen’s model that integrates the cumulative causation principle into an economic growth framework of multisectorial balance-of-payment constraint. Higachi et al. (1999) include technological diffusion and absorption capacity into the original model according to Fagerberg’s catching-up model (1988b) as well as Verspagen’s (1993). The extended model can be described as follows. The model comprises two countries—the north and the south. The north transfers technology to the south. Eqn. (1) relates the output growth rate of the national economy to its growth rate of technological progress and to the growth rate of exports (a and e > 0), reflecting the hypotheses built by Kaldor (1966) and Dixon and Thirlwall (1975): 4 ^y iðtÞ

¼ aT^ iðt
mÞ þ e^xiðtÞ

ð1Þ

in which, ^y iðtÞ is the growth rate of output of country i at time t (i = s for south and i = n for north); T^ iðt
mÞ is the growth rate of techno-

logical progress of country i in t
m (m is the number of periods); 5 ^xiðtÞ is the export growth rate of country i at time t. Our Eqn. (1) differs slightly from Higachi et al.’s model by lagging the growth rate of technological progress (proxied by the growth rate of labor productivity). The theoretical background for this procedure is based on the Schumpeterian sequential time from innovation to diffusion (Schumpeter, 1934). The starting point of the former is a microeconomic phenomenon (the innovating firm), and the final result of the latter is a macroeconomic event (the increased aggregate output of the economy). It takes time to transform innovation into diffusion. In addition, the evolutionary theory of economic change demonstrates that a substantial source of current innovation is a firm’s routine based on incremental steps stemming from learning (Nelson & Winter, 1982). Apart from exceptional periods of radical changes, productivity growth is based on these types of incremental innovations. Thus, it can be assumed that the process of productivity increase comes from a continuous diffusion of incremental technical changes at sector and intersector levels. As a result, there is a lag between productivity growth as an aggregate variable and its impact on the overall growth of the economy. A decomposition of Eqn. (1) results in the following equations. Eqns. (2) and (3) show that the growth of exports is explained by the relative productivity level between the two countries and by the income growth of the foreign market (g and c > 0). The relative productivity level is a measure of the ‘‘technological gap’’ between the leading country and the follower one:   Ts ^xsðtÞ ¼ gs log ð2Þ þ cs^zðtÞ ; Tn   Tn ^xnðtÞ ¼ gn log þ cn^zðtÞ ð3Þ Ts in which, ^z is the income growth rate of the foreign market. Finally, Eqns. (4) and (5) describe the labor productivity behavior of the developed country and the backward country, respectively: ^rn ¼ T^ n ¼ bn þ kn ^y n ; ^rs ¼ T^ s ¼ bs þ ks ^y s þ lGeð
G=dÞ

ð4Þ ð5Þ

in which, G = log(Tn/Ts) = log(rn/rs) is the technological gap (where Tn and Ts are the levels of technological knowledge of the north and

INCREASING RETURNS TO SCALE AND INTERNATIONAL DIFFUSION OF TECHNOLOGY

the south); d is the ‘‘endogenous capability to learning,’’ having as a proxy the average schooling of the Southern country’s labor force. Eqn. (5) is the most important in the model, since it encompasses both the increasing returns to scale and the catching-up hypothesis. More specifically, in addition to an autonomous component (bi—autonomous innovation), parameter k (the Verdoorn coefficient) measures the range of increasing returns to scale (supply–demand linkages and induced technological innovation), since it relates the rate of change in labor productivity and growth of the economy. Parameter d represents the endogenous learning capacity of the south; l measures the impact of technological absorption over the labor productivity, whereas lGe(
G/d) represents the effect of the technological gap on the labor productivity, that is, the possibility of technical progress via technological absorption. 6 This is possible because G (whose proxy will also be the relative productivity level rn/rs) measures the technological gap between the two countries. Thus, the higher the value of G, the greater the south’s potential for technological progress by means of the imitation of new technologies. The technological absorption path is positively related to the gap up to the point to which G is equal to d. If G is higher than d, technological absorption will decline, and/or the technological gap will increase. What this means is that the technological differentials between the two countries are so large and a situation is created where it becomes impossible for the south country to catch up. In other words, if G is higher than d, it implies the existence of a constrained ‘‘social capability’’ in the country, thus preventing that said country from taking advantage of the gap. Based on the aforementioned, the model assumes that both parameters, k and l, are positive and greater than zero. The model does not take into account the exchange rate. Although this variable is important for a short-term economic policy in Brazil, this model intends explicitly to test the longterm determinants of Increasing Returns to Scale and International Diffusion of Technology. Therefore, in light of McCombie and Thirlwall (1994), Fagerberg (1988a), and Blecker (1992), price competition does not affect the balance of payments, productivity, and technology in the long run, and the exchange rate was not included in the model.

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4. MATERIALS AND METHODS In order to estimate Eqns. (1) and (5) using VARs, we obtained time series for the variables of industrial production (Ln y), exports (Ln x), and labor productivity in the Brazilian and United States industrial sectors (Ln r) with the United States considered as the leading country. The technological gap (Ln G) was based on labor productivity data. 7 The data are on a quarterly basis from the second quarter of 1976 to the second quarter of 2000. The Brazilian Monthly Industrial Survey (PIM) from the Brazilian Statistics Bureau (IBGE) is the proxy for the quarterly industrial production. The series for labor productivity in the American industry was taken from the American Bureau of Labor Statistics, while the series for the Brazilian exports (U$—FOB) was based on the SECEX (the Brazilian government’s agency for foreign trade) database. The data for exports and industrial output were deseasonalized after running tests with seasonal dummies. These tests showed that these two time series are the ones that have statistically significant seasonal behavior. We will estimate increasing returns to scale and technological absorption by using the VAR and an Error Correction Model. 8 The use of a VAR approach is within a Markovian framework. The theoretical ground for this choice is the evolutionary nature of economic growth, which can be defined as a result of the selection process among competing firms in search of innovating payoffs. Nelson and Winter’s model of economic growth and dynamic competition is a stochastic dynamic system. Accordingly, over time, ‘‘productivity levels tend to rise and unit production costs tend to fall as better technologies are found. As a result of these dynamic forces, price tends to fall and industry output tends rise over time’’ (Nelson & Winter, 1982, p. 287). We think that the process of catching up will reflect these ‘‘dynamic forces,’’ whose specificity in backward countries is the fact that imitation and improvement, instead of innovation per se, are the driving forces of output and productivity growth. Similar to the use of the Markovian framework for theoretical testing in simulation models by these authors, we will use VAR models since our aim is to test empirical evidence. 9 Thus, we will carry out unit roots tests (ADF and Phillips–Perron (PP)) for the variables of Eqns. (1) and (5). If the series are non-stationary, we will use Johansen’s cointegration test,

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which will reveal the presence of and provide the parameters of cointegration. Johansen’s test is performed assuming that the time series behavior is described by the VARs and shows a cointegration relation expressed by a ‘‘mechanism of error correction’’ (Charemza & Deadman, 1997). Additionally, Johansen’s test provides the number of statistically significant cointegrating equations, which can describe the variables’ long-term behavior. As we will see, our results show a unique cointegration relationship with each one of the tested theoretical equations. In other words, there is only one set of statistically significant parameters for each set of variables. Tables 1 and 2 show the Unit Root tests (ADF and PP) of the series in levels and in first differences. Table 1 shows the results of the ADF test with four differences (AIC and SIC suggested Table 1. Unit root tests with intercept series (level and first difference) Series

ADF(2)

ADF(3)

ADF(4)

PP(3)

Ln y DLn y Ln r DLn r Ln x DLn x Ln G DLn G


1.78
7.44 1.94
9.63
2.12
7.35
5.24
8.1


1.64
4.86 2.37
5.25
1.67
5.01
4.34
7.8


1.76
5.01 1.56
3.9
1.42
6.00
3.7
7.3


1.84 – – – – –
7.92
166.28

MacKinnon’s critical values for rejecting the null hypothesis of unit root:
3.5015 (1%),
2.8925 (5%),
2.5831 (10%). ADF(d): H0 is the unit root with d lags. AIC and SIC suggest three lags.

Table 2. Unit root tests with intercept and linear trend series at level and at first difference (D) Series

ADF(2)

ADF(3)

ADF(4)

PP(3)

Ln y DLn y Ln r DLn r Ln x DLn x Ln G DLn G


2.66
7.41
0.099
10.24
3.54
7.38
5.27
8.59


2.41
4.83 0.25
5.62
3.06
4.99
4.38
7.77


2.88
4.99
0.08
4.3
3.51
6.03
3.75
7.31


3.57 –
1.5 –
4.23 –
7.9 –

MacKinnon’s critical values for the null hypothesis of unit root:
4.0580 (1%),
3.4576 (5%), and
3.1545 (10%). ADF(d): H0 is the unit root with d lags. AIC and SIC suggest three lags.

three lags). The equation estimated by the ADF test includes three differences so that they may generate a white noise. This table also shows the results of the PP test, taking three differences. MacKinnon’s critical values show that the series for the output, labor productivity, and exports have unit roots in levels, but they are stationary in first differences at a 1% level of significance. Only the series for the technological gap are stationary both in level and in first differences. Table 2 shows the results for the ADF and PP tests, taking an intercept and a linear trend into account. Including a linear trend does not significantly change the conclusions of the ADF tests with the presence of an intercept. Again, the series for the output and labor productivity show a unit root in levels and are stationary in first differences. The series for the technological gap remain stationary both in levels and in first differences. 10 One difference in relation to previous tests is that the export series are also stationary both in levels and in first differences for the ADF tests with two and four lags and for the PP test. Regarding output equations, the PP test indicates stationarity while the ADF test, for all differences considered, indicates the existence of unit roots. In accordance with Holden and Perman (1994), such differences may occur to the extent that the PP test considers that the residuals of the ADF estimated equation are abnormal and follow a stochastic process MA(1). The ADF test becomes preferable to the PP test in instances where the statistics of the ADF do not indicate absence of normality of the residuals. This can be verified by means of the estimated equations for the ADF test, which indicates more reliable results of the latter than those for the PP test. After performing the stationarity tests, we run the cointegration tests using Johansen’s methodology (Charemza & Deadman, 1997). In order to estimate consistent and accurate parameters from a statistical viewpoint, it is essential that we identify a cointegration relation that avoids ‘‘spurious regressions’’ (Mills, 1993). In addition, identifying a cointegration relation means that the variables concerned have a common long-term path, meaning that their common temporal path mutually affects their trajectories. This does not mean that these variables will tend either a constant value in the infinite or a behavior of oscillating values around the average. Non-stationary variables,

INCREASING RETURNS TO SCALE AND INTERNATIONAL DIFFUSION OF TECHNOLOGY

which have neither a tendency toward a longterm constant value nor oscillation around the average, can have a cointegration relation if they have common tendencies (Mills, 1993, p. 181), that is, a common temporal path. Thus, cointegration does not necessarily mean that the variables have an equilibrium trajectory given by a steady state (Enders, 1995). It simply means that the variables have a stable longterm relationship, which may diverge from any equilibrium point.

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significance, the H0 hypothesis is rejected, meaning that there is a cointegration relation among those series. As for the cointegrating coefficients, the estimated value of the likelihood ratio in the second line is below the critical values. This leads us to accept the H0 hypothesis, meaning that there exists only one cointegrating equation at most. Johansen’s test also provides cointegrating coefficients and their respective statistical significance is depicted in Table 4. The productivity elasticity is positive and its magnitude implies that one unit change in productivity results, after one year, in a change of approximately 0.28 for the output. 12 As for exports, a positive elasticity with a magnitude of about 0.18 can be observed. It is also important to emphasize that the output behavior is affected by exports of the same period t. In addition, all coefficients are statistically significant at the level of 10%. 13 According to Jayme Jr. (2003), the other coefficients of the VEC do not show any significance which could easily be interpreted. Therefore, it is crucial to analyze the effect of shocks on the variables in question through the impulse-response functions. Doing so provides simulations for the behavior of the n
1 endogenous variables under an exogenous shock in the residuals of the n-tieth variable. In order to estimate the impulse-response function, it is necessary to assume that the variations in the residuals of this n-tieth variable result only from exogenous shocks. Conversely, the residuals of the n
1 variables, despite the fact that they are also subject to exogenous shocks, are partially determined by the correlation coefficients with the other residuals.

5. EMPIRICAL EVIDENCE OF SHORT-TERM AND LONG-TERM TRAJECTORIES (a) Test for the output equation The first cointegration relation tested was among the series for the output, productivity, and exports. In the theoretical model, we suppose that productivity influences output behavior with a temporal lag of t
m periods. The results estimate four periods as the more appropriate, that is, a time lag of one year. For this reason, the test for Eqn. (1) of the theoretical model will be an estimate whose changes in output are affected by changes in exports in period t, and in productivity in period t
4. 11 The results with Johansen’s test are summarized in Table 3. The first line of the table tests the H0 hypothesis, which states that there is no cointegration relation among the series. Since the likelihood ratio estimated for Johansen’s test (36.01730) is higher than the critical values of 5% level of

Table 3. Johansen’s test for cointegration and the number of cointegrating equations output, productivity (
4), and exports Likelihood ratio

36.01730 7.529318 0.006288

Critical value 5% Significance

1% Significance

29.68 15.41 3.76

35.65 20.04 6.65

Hypothesis (H0)—Number of cointegrating equations 0 1 at most 2 at most

The test equation shows intercept at CE, in the VAR, and four gaps for the variables. Table 4. Cointegrating coefficients for the relation among output, productivity (
4), and exports

Normalized cointegrating coefficient Statistics t

Output

Productivity (
4)

Exports

Intercept

14.93203 1

4.194205 0.280886

2.724833 0.182482

2.825686

(
2.22925)

(
2.87753)

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Assuming non-orthogonalized errors by means of the Cholesky decomposition of the VAR estimation, the ordering of the variables is essential to the variance decomposition and impulse response functions. We have assumed the following recursive ordering: output, productivity, and exports. This ordering is based on the theoretical framework, basically departing from the more exogenous to the more endogenous variable. Such assumption implies that the residuals of productivity may explain output and exports residuals. 14 The behavior of the variables resulting from the effects of an exogenous shock on either labor productivity or exports is described in Figure 1. 15 As can be seen from the simulations presented in Figure 1, a negative output deviation in relation to the mean is caused by an exogenous unitary shock of the four-period lagged labor productivity standard deviation of residuals within a set period t. Afterwards, the third quarter output starts to increase and it reaches higher rates of growth following the fourth quarter. This confirms the previous conclusion that labor productivity has a relevant impact on output after one year. Later, a declining output oscillation is followed by a significant growth between the seventh and eighth quarters, that is, when it nears the end of the second year. Thus, one observes a cyclical behavior showing positive feedback in each four-quarter

period. Figure 1 also shows that an export shock has a positive effect on output in the first quarter. Next, a steady output growth path follows a short decline after the second quarter. (b) Test for the productivity equation We will now test the second cointegration relation among the series of labor productivity, production, and the technological gap. More specifically, we estimate the elasticities of Eqn. (5) in which labor productivity becomes the variable to be explained, and output together with the technological gap the explaining variable. In this case, the theoretical hypotheses tested are the parameters of increasing returns to scale and catching up. The elasticity observed between labor productivity and output characterizes the ‘‘Verdoorn Coefficient’’ (Thirlwall, 1987) and estimates the existence of increasing returns to scale during the period considered in the samples. One important question regards the fact that the estimated Verdoorn coefficient may be partially reflecting the effects of Okun’s Law. Based on an empirical test for the United States, McCombie and Ridder (1983, p. 385) point out that ‘‘these shortterm fluctuations in productivity merely reflect Okun’s Law.’’ They suggest that their results might be better if the data had been adjusted to minimize short-term fluctuations. In our test,

Response to Cholesky One S.D. Innovations Response of PRODUCTIVITY(-4) to OUTPUT (deseasonalized data)

Response of PRODUCTIVITY(-4) to PRODUCTIVITY(-4)

Response of PRODUCTIVITY(-4) to EXPORTS (deseasonalized data)

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Figure 1. Impulse-response functions for the first equation of the model.

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INCREASING RETURNS TO SCALE AND INTERNATIONAL DIFFUSION OF TECHNOLOGY

we take their advice and the test run shows that for Eqn. (5), only the output series has statistically significant seasonal short-term behavior. As mentioned above, the output series was adjusted by using de-seasonalized data. On the other hand, the elasticity between the technological gap and productivity estimates the effect of the technological gap between Brazil and the United States. Tables 1 and 2 have already confirmed that the series considered are stationary at the first difference. We will perform Johansen’s test to identify the cointegration relation and cointegrating vectors. Table 5 shows the results of this cointegration test. The likelihood ratio estimated is higher than the critical values at 5% and 1% of significance, which implies that the null hypothesis must be rejected. This means that there is a cointegration relation among the series. The remaining tests indicate that there is only one statistically significant cointegrating test. Johansen’s test also provides cointegrating parameters and their respective statistical significance described in Table 6. The second line of the table shows the normalized coefficients with the productivity coefficient as a parameter, since in this series it is the explained variable of the theoretical model. The output elasticity is positive, with a value of 0.70. This means that one unit output variation causes a 0.7 variation in unit productivity. Furthermore, the statistically significant coefficient at 10% suggests that empirical evidence confirms the existence of increasing returns to scale

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in the Brazilian industry during the sampling period, at 10% significance. The technological gap elasticity, constituting a proxy to the ‘‘absorption of new technologies,’’ is also positive and statistically significant at 10%. However, its low magnitude suggests that one unit variation in the technological gap causes a variation of 0.14 in unit productivity. Such magnitude provides the evidence that the Ôtechnological gap’’ between Brazil and the United States does not constitute a considerable advantage as far as Brazil’s economic performance is concerned. In other words, the technological gap per se did not create a suitable enough environment for Brazil to catch up within this period. This gives the indication that certain characteristics within the structure of the Brazilian ‘‘NIS’’ might not be contributing to an efficient absorption of the ‘‘update technology’’ developed in the leading country. Such a conclusion is reinforced by the impulse-response functions for the second VAR. As stated above, in order to examine the impulse-response functions, it is necessary to assume that the residuals of a variable are exclusively determined by exogenous shocks. In this case, it seems that the residuals of the remaining variables do not affect the output residuals, since the aim is to capture increasing returns to scale. For this, it is crucial to detach the output effects on productivity. Figure 2 shows simulations for the series of trajectories within a short-term time frame (a two and a

Table 5. Johansen’s cointegration test and the number of cointegrating equations productivity, output, and technological gap

5% Significance

1% Significance

Hypothesis (H0)—Number of cointegrating equations

42.44 25.32 12.25

48.45 30.45 16.26

0 1 at most 2 at most

Likelihood ratio

49.50335 13.99122 2.882718

Critical value

The test equation shows an intercept and trend at CE, an intercept at VAR, and four successive differences for the variables.

Table 6. Cointegrating coefficients for the relation among productivity, output (deseasonalized data), and technological gap

Normalized cointegrating coefficient Statistics t

Productivity

Output

Gap

Trend

Intercept

8.309558 1

5.838199 0.702588

1.228883 0.147888

0.044284 0.005329

3.969431

(
2.00497)

(
7.69905)

(
3.32900)

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WORLD DEVELOPMENT Response to Cholesky One S.D. Innovations Response of OUTPUT (deseasonalized data) to PRODUCTIVITY

Response of OUTPUT (deseasonalized data) to OUTPUT (deseasonalized data)

Response of OUTPUT (deseasonalized data) to GAP

.04

.04

.04

.03

.03

.03

.02

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.01

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Response of GAP to PRODUCTIVITY

2.0

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Response of GAP to GAP

2.0

1

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9

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7

8

Figure 2. Impulse-response functions for the second equation of the model.

half years) in the presence of shocks on output and the technological gap. Figure 2 shows that an output shock causes a positive and immediate effect on labor productivity. Accordingly, in the first quarter labor productivity has a positive deviation in relation to its average of around 0.4. After having a declining effect up to the third quarter, productivity experiences a new growth, even smaller, maintaining a 0.3 deviation above its average. This result illustrates, therefore, that output shocks cause a positive and lasting effect on productivity. In its turn, the contemporary output effect on the technological gap is a small negative deviation from its average of 0.11 due to the positive effect of output on productivity. The effect of innovation on the technological gap is also demonstrated in Figure 2. Its trajectory produces a small effect on labor productivity, which incurs a small growth up to the second quarter and a further declining tendency toward its average value. Such a behavior reinforces the previous findings regarding the small influence that the technological gap has on Brazilian labor productivity. More specifically, it highlights evidences that the catching-up hypothesis has not been a major factor for the Brazilian economy over the last 25 years.

Two possible answers can justify a slow productivity reaction to the shocks to output and the technological gap during this initial phase of the long-term trajectory. The first is related to the theoretical concepts of static and dynamic increasing returns (Kaldor, 1966). On the one hand, static increasing returns appear in the initial phases and are reversible as productivity increases. In other words, negative shocks on output will reduce the gains in productivity originally obtained. On the other hand, dynamic increasing returns do not come about in the short term but are continuous and permanent as productivity increases. They stem from an absorbed technology-learning period that extends over a longer period of time by means of labor force experiences and an increase of production. Besides, providing permanent increases in productivity, the learning-by-doing process is intensified with this production scale increase, since the worker’s handling of a given technology is also intensified. Furthermore, this allows the labor force to proceed in a ‘‘function of technological learning.’’ Thus, these evidences of weakness of dynamic increasing returns may be a foreboding indication of the difficulties the Brazilian labor force is facing in order to proceed in the ‘‘learning function.’’ Such difficulties are associated with low qualification whose proxy in the theo-

INCREASING RETURNS TO SCALE AND INTERNATIONAL DIFFUSION OF TECHNOLOGY

retical model is the average schooling of the labor force. The value of such a proxy for Brazil is actually small, since the Brazilian labor force average schooling in the sampling period is 3.7 years. Thus, the empirical results may suggest that one of the causes for the low Brazilian productivity increases after shocks of output and the technological gap is the low qualification of the labor force, which hampers ‘‘learning by doing’’ along the import-substitution industrialization process. Hence, it retards the emergence of dynamic increasing returns following an exogenous shock of output. This analysis allows us to explain productivity behavior based on the catching-up hypothesis. Accordingly, the technological gap would be able to stimulate the growth rate of labor productivity in a backward country if this country could effectively incorporate advanced technologies being developed in the leading country. This detail reflects the Brazilian economy’s low capacity for absorbing the latest technologies. According to Abramovitz (1986), this is due to the low ‘‘social capability’’ of Brazil’s economy. An alternative argument might be that Brazil’s absorption difficulties are related more to the absence of technologically key sectors within its industry’s internal systems, which are supposed to play a pivotal role for technology transfer and absorption. Although Brazil’s structural problems with industrialization are well known, they are not related to absentee sectors in the input–output industrial matrix, but to the technological weakness of some key capital goods and the so-called science-based industries. These sectors correspond exactly to what international technological transfer can be effectively applied to newly industrializing economies. It is worth noting that our case study examining the Brazilian economy is not a look at a backward-emerging industry specialized in capital low-intensive activities but, rather, a look into intensive large-scale industries that have more than a 50% share of the country’s exports. We run cointegrating tests to other sets of variables to check whether our variable sets are the only ones that are cointegrated. More specifically, we used an employment series of the Brazilian industry as a dependent variable in relation to our variables within the theoretical model. The unique statistically significant cointegrated relation was between the series of employment and the technological gap. Other alternatives do not have a relation of cointegration. This result is profound since the estimated

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elasticity between the technological gap and labor productivity is 0.15, meaning that a reduction of one unity in the gap results in a 0.15 decrease in manufacturing employment. This demonstrates that, although they contribute to closing the gap, the new technologies absorbed by the Brazilian economy are labor saving. This fact concurs exactly with Kaldor’s argument (Kaldor, 1966) that machinery imports to peripheral countries from leading countries, which are driven by labor saving technical progress, tend to worsen the structural problems of the former group. Indeed, their economic growth is not strong enough to absorb the unemployed labor force into the new technologies. 6. CONCLUSIONS The tests performed in this paper revealed a cointegration relation between the variables studied. The first long-term relation (output, productivity, and exports) presented statistically significant coefficients with the signal predicted by the theory (i.e., productivity and exports relate positively to the output behavior). In the case of productivity, this was obtained according to the theoretical model. Also, the lagged series was estimated in four periods, showing that the effects of the labor productivity variation can only exert significant impact on the output after a one-year span. Another interesting finding was that the hypothesis of ‘‘export led growth’’ was only partially confirmed for Brazil during the sampling period considered. This may prove that exports were not the major component of the Brazilian final demand during the period. In accordance with Kaldor (1966), this suggests that a demand-driven growth pulled by the ‘‘domestic market’’ has not yet been exhausted while, at the same time, exports might have played an important but complementary role. Besides, Myrdal/Kaldor’s cumulative causality could be verified by means of an error correction model. On the other hand, Jayme Jr. (2003) shows that, in the period of 1955–2000, Brazilian output growth has been constrained by a balance of payments that was not made endogenous in our model. Be´rtola et al. (2004) have also shown the relevance of external constraints in the country’s economic growth. Estimations from the theoretical equation related to the productivity, output, and technological gap present interesting results. The

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elasticity for the increasing returns to scale is significant with a magnitude consistent with the empirical results in the literature. Despite the fact that exogenous shocks on output produce a lasting deviation in productivity for 10 periods, such a deviation is small and does not exceed 0.4. This gives proof to the difficulties placed on the dynamic increasing returns in the Brazilian economy, which seem to be unable to accumulate gains from technological learning. Even though they are significant and positive, the technological gap and labor productivity coefficients are relatively small in magnitude. In the case of Brazil, the technological gap does not seem to represent a sufficient condition for an efficient absorption of technologies from a leading country, which in this case is the United States. More precisely, Brazil is only partially able to catch up since it cannot fully engage in the international technological diffusion because it faces difficulties in absorbing learningintensive technologies. The impulse-response function simulations show that, in the presence of an exogenous output or technological gap shock, the productivity deviation is quite small in relation to the average. The theory provides two possible explanations for such a trajectory. The first is related to the dynamic increasing returns stemming from the learning capacity of the labor force, which is working with a given technology that is functioning on an increased production scale. Such a learning-by-doing process becomes significant to the extent that output growth allows for an intensification of a specialization in a given technology by the country’s labor force. This specialized learning is the starting point of establishing a dynamic increasing returns in the economy. There is a momentum for the labor force to start advancing in a ‘‘technological learning function.’’ The key aspect is that the greater the labor force qualification, the faster the emergence of dynamic increasing returns. The impulse-response functions draw attention to the fact that dynamic increasing returns do not emerge before

the end of the second year. A possible and reasonable explanation for this sluggish response is the low qualification of the Brazilian labor force; a fact which hampers a significant increase in productivity stemming from ‘‘learning by doing.’’ The second reason refers to the productivity behavior after a technological gap shock. This could also attest that the Brazilian economy is inflexible in absorbing the latest technologies from leading countries. Such a result suggests that there are restraints on ‘‘social capability,’’ impeding the Brazilian economy from fully benefiting the ‘‘advantages of the gap.’’ This is a possible result of the Brazilian importsubstitution industrialization characteristics. The resulting NIS, considered ‘‘immature,’’ has not been functioning sufficiently as a focal tool which is able to provide any kind of ‘‘windows of opportunity’’ (Albuquerque, 1999). In spite of model’s restricted proxy for technological absorption capacity, it seems that the basic education level of the labor force is important enough for Brazil to increase its technological absorption potential. This is a key feature in the model, since the specification of the catching-up function (lGe(
G/d)) requires that the limit to technological imitation will reach its peak when the gap is equal to the average schooling. 16 If the gap is higher, the country does not possess the minimum conditions to absorb technology from a leading country and hence productivity gains are increasingly smaller to the extent that the gap increases. The demonstration of increasing returns from 1976 to 2000 should not overshadow the main conclusion of this paper. It suggests the presence of structural limitations to the Brazilian economy that hold back permanent gains in labor productivity based on ‘‘learning by doing.’’ With this in mind, Brazil cannot catch up efficiently. In other words, its economy does not seem to benefit fully from international technological diffusion. This again suggests structural constraints preventing Brazil from absorbing technologies and thus stimulating its technological progress.

NOTES 1. We have attempted to use yearly average data. However, they did not give robust results because the time series span is short. As a matter of fact, there are no Brazilian data earlier than 1976.

2. Such a hypothesis could already be found in the literature of the ‘‘dual models’’ discussed by Lewis (1969).

INCREASING RETURNS TO SCALE AND INTERNATIONAL DIFFUSION OF TECHNOLOGY 3. A NIS is made up of an infrastructure, which promotes technological innovation, such as R&D expenditures, Universities, research supply, etc., as well as the relationship between these institutions and their connections (Freeman, 1995). 4. Several authors use labor productivity as a proxy of technological progress. See Fagerberg, 1988a, 1988b among others. In this paper, we use labor productivity as a proxy of technological progress. It allows us to find the Verdoorn coefficient, as will be seen later. 5. The growth rates of technological knowledge are proxied by growth rates of labor productivity (^ri ). 6. Eqn. (4) describes the behavior of the leading country’s technological progress. Its divergence with Eqn. (5) is the absence of possibility of productivity growth via absorption of foreign technologies. 7. It is worth noting that the theoretical model uses contemporary variables, whereas the empirical model adopts lag variables. Indeed, the use of lag variables is imperative for empirical purposes. 8. A methodological description of cointegration, vector auto regression, and the error correction model will not be presented here. For a discussion on the subject, see Holden and Perman (1994), Charemza and Deadman (1997), Hamilton (1994), Mills (1993), and Enders (1995). A more in-depth discussion on the subject can be found in Madalla and Kim (1998), among others. 9. It is worth noting that there is literature where increasing returns in economic growth are associated

87

with path-dependence processes. See the original contribution of Arthur (1989) and the recent overview of this literature in Castaldi and Dosi (2003). 10. The ADF and PP tests accomplished without an intercept or trend also confirm the results of Tables 1 and 2. 11. With a time gap of two periods (one semester) or three, estimated elasticity is positive but statistically not significant. 12. Johansen (2002) emphasizes that cointegrated coefficients are difficult to interpret statistically, due precisely to the identification problem. In this case, the coefficient may not be capturing the effect of competitiveness. We should note that this does not mean rejection of the hypothesis of increasing returns to scale. 13. Equations were estimated without the linear tendency since the tests indicate that its coefficient is statistically insignificant. 14. It is important to warn that the Cholesky decomposition may underestimate or neglect some feedbacks among variables. See Canova (1999) or Lu¨tkepohl (1993). 15. Changes in the ordering (not reported) have not altered the results. 16. This happens because G must be equal to d so that the derivative of function G and e(
G/d) be equal to zero.

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