LABOR PRODUCTIVITY GROWTH: DISENTANGLING TECHNOLOGY … · labor productivity growth: disentangling technology and capital accumulation michele battisti, massimo del gatto, and christopher

LABOR PRODUCTIVITY GROWTH: DISENTANGLING TECHNOLOGY AND

CAPITAL ACCUMULATION

MICHELE BATTISTI, MASSIMO DEL GATTO, AND CHRISTOPHER F. PARMETER

Abstract. We adopt a counterfactual approach to decompose labor productivity growth into

growth of Technological Productivity (TEP), growth of the capital-labor ratio and growth of Total

Factor Productivity (TFP). We bring the decomposition to the data using international country-

sectoral information spanning from the 1960s to the 2000s and a nonparametric generalized kernel

method, which enables us to estimate the production function allowing for heterogeneity across all

relevant dimensions: countries, sectors and time. As well as documenting substantial heterogeneity

across countries and sectors, we find average TEP to account for about 44% of labor productivity

growth and TEP gaps with respect to the US to remain almost unchanged, on average, despite an

average 1% yearly decrease in the labor productivity gap. The US displays the highest TEP growth

rate. We then perform standard convergence regressions finding strong evidence of technological

convergence and showing that the effect of a few variables only, among those found significant to

explain labor productivity convergence, occurs through the technology channel.

JEL Classification: C14, D24, O41, O47

Date: September 30, 2014.Key words and phrases. TFP, Aggregate Productivity, Technology, Nonparametric Estimation, Convergence.Michele Battisti, University of Palermo, CeLEG LUISS Guido Carli and RCEA; email: [email protected] Del Gatto, G.d’Annunzio University and CRENoS; e-mail: [email protected]. Christopher F. Parmeter,University of Miami; e-mail: [email protected]. This paper has benefited greatly from comments made bythe participants at DEGIT Dynamics, Economic Growth and International Trade, Lima (PE), SCDEG StructuralChange, Dynamics, Economic Growth, Livorno (IT), RCEA Advances in Business Cycles and Economic GrowthAnalysis, Rimini (IT).

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2 MICHELE BATTISTI, MASSIMO DEL GATTO, AND CHRISTOPHER F. PARMETER

1. Introduction

Income and growth disparities among countries are commonly traced back to either capital

accumulation or total factor productivity (henceforth TFP). When measured as the Solow residual

of an aggregate production function, TFP is often found to be at least as important as capital

accumulation (Easterly and Levine, 2001; Caselli, 2005). Thus, to the extent that the capital

stock is correctly measured, empirical analysis explains around 50% of income differentials across

countries, with the remaining half flowing into a wide notion of aggregate TFP encompassing a

number of hard to measure factors; among those factors, a growing body of literature concentrates

on technology, both in terms of creation of new technologies and adoption/diffusion of already

available technologies. In a pioneering contribution, Parente and Prescott (1994) showed that

differences in barriers to technology adoption, which vary across countries and time, account for

the great disparities in income across countries. However, despite the potential insights for probing

policy debate, the subsequent literature has put little effort in trying to separate technology from

TFP when estimating the production function.1

Indeed, unbundling technology and TFP is not an easy task.2 To make this transparent, consider

a very general logarithmic set-up yct = m(kct ) + act , in which country c’s output per worker at

time t depends on contemporaneous values of capital per worker, kct , through the function m(·),as well as the TFP term act . In this basic framework, cross-country differentials in technology

are fully encapsulated into act and we interpret differences in TFP in terms of differences in the

ability to produce as much output as possible with the given technology and capital per worker.

From this perspective, standard OLS-estimated Solow residuals [i.e. act = yct − m(kct )] conflate

technological effects and pure TFP effects. To highlight this issue, Bernard and Jones (1996a)

use the expression “total technological productivity”3 and offer a useful description of a world

(see Parente and Prescott (1994) among others) in which the process of technology adoption and

diffusion plays a role in the evolution of income disparities. In such a world, m(·) varies across

countries and over time, and the production function to be estimated takes the form

(1) yct = mct(k

ct ) + act .

1Easterly and Levine (2001, p.1) still noticed that “empirical work does not yet decisively distinguish among the dif-ferent theoretical conceptions of TFP growth. Economists should devote more effort toward modeling and quantifyingTFP.”2Battisti et al. (2014) use mixture densities to unbundle technology and TFP at the firm level.3Bernard and Jones (1996a) also note that an alternative way to think of these technological differences is to inter-pret them as arising from the heterogeneity of goods that are produced within an economy. Such heterogeneity isparticularly important when we examine productivity at the sectoral, rather than country, level.

3

Estimating (1) at the aggregate level with standard parametric econometrics is however not possible,

as the number of parameters (i.e. technologies) to be estimated equals the number of observations

(country c at time t in a given sector).4

The full generality of the framework in (1) is not exploitable without some form of restrictions on

the technology. While there is ample evidence that technology differs across countries (see for ex-

ample Masanjala and Papageorgiou, 2008 for differences between Africa and the rest of the world) ,

typically, most approaches that allow for technological differences do so in an ad hoc or limited fash-

ion. For example, Kumar and Russell (2002) and Henderson and Russell (2005) both assume that

technology is fixed across countries, but does change over time. Using a more rigorous econometric

approach, both Bos et al. (2010a, b) deploy mixture models to allow different technology regimes

across time with technology regimes defined endogenously. More recently, Filippetti and Peyrache

(2013) allow technology to vary across regions, capturing more technological heterogeneity than is

capable in the Kumar and Russell (2002) decomposition.

To overcome such limitations from previous research, we exploit the flexibility of a nonparametric

generalized kernel regression to estimate the aggregate production function in (1) allowing for

heterogeneity across all relevant dimensions: countries, sectors and time. This approach, already

followed in growth empirics by Maasoumi et al. (2007) and Henderson et al. (2012), enables us

to derive a counterfactual decomposition of aggregate labor productivity growth which isolates the

contribution of m(·), referred to as technological productivity (TEP), from the growth of the capital-

labor ratio (i.e. capital accumulation) and the growth of TFP.5 We do so at the country-sector

level, using data from 1963 to 2009 for 46 countries and 11 manufacturing sectors, encompassing

both developed and developing countries.

Although comparison with alternative estimates in the literature is hampered by the fact that

we allow for a sectoral dimension, which is not considered elsewhere, our results point to quite

balanced contributions of TEP and capital accumulation to overall labor productivity growth (as

compared, e.g., to Kumar and Russell, 2002): 56% for capital accumulation and 44% for TEP, on

average. Not surprisingly, the increase in labor productivity is more marked in OECD and East

Asian countries. Moreover, consistent with the findings of Bos et al. (2010a), we estimate large

technological differences across sectors, even within a country.6

We then study the evolution of TEP in terms of country-sector specific gaps with respect to

the US (i.e. difference between TEP growth in a given country-sector and TEP growth in the US,

in the same sector), as the latter display the highest average TEP growth, amongst the countries

4In a parametric context (e.g. Cobb-Douglas), the standard explicit form for m(·) would be mct(k

ct ) = (kct )

αct , entailing

that a specific αct should be estimated for each country c in each time period.5Our terminology is consistent with Bernard and Jones (1996a), who define “total technological productivity” as themix of TEP and TFP usually considered in productivity analysis.6Their analysis of European manufacturing plants uncovered two different technological regimes, based exclusivelyon research and development intensity.


considered. Despite an average 1% yearly decrease in the labor productivity gap, we find the

TEP gap to remain almost unchanged, on average, during the period under examination. While

substantial heterogeneity emerges across countries, decreasing TEP gaps are concentrated in OECD

and East Asian countries and increasing gaps are concentrated in African countries. The K/L

catching-up of Asian countries is particularly strong in Korea and Singapore, while only the sub-

Saharan countries experience a loss in all components. Less heterogeneity emerges at the sectoral

level.

As an application, we use our methodology to take seriously a question posed by Bernard and

Jones (1996b, p. 1038) almost 20 years ago which has received little academic attention:7 ”How

much of the convergence that we observe is due to convergence in technology versus convergence

in capital-labor ratios?” We do so by using the estimated variation in the K/L and TEP country-

sector gaps, as well as observed gaps in labor productivity, in standard convergence regressions.

Interestingly, population density and education, which are traditional variables in growth regres-

sions, are not significant predictors for the evolution of the TEP gaps. Other variables, such as

geographical distance, FDI and trade openness, exert opposite effects on capital accumulation and

technology gaps.

2. Related literature

From a methodological point of view, our work complements and expands a limited number of

cross-country income decompositions.8 Among others, Beaudry et al. (2005) and Fiaschi et al.

(2013) use counterfactual decompositions in the spirit of Oaxaca (1973) and Blinder (1974). While

the former uses least squares regressions, imposing the same input coefficients across all countries9,

Fiaschi et al. (2013) have a semi-parametric framework in which the estimated coefficients vary

across EU regions, but not across sectors. They show that changes in the distribution of GDP

between 1960 and 1990 are better explained by physical, rather than human, capital accumulation.

In another series of research, the decomposition is performed using data envelopment analysis. An

incomplete list includes Fare et al. (1994), Kumar and Russell (2002), Henderson and Russell

(2005), Los and Timmer (2005), Jerzmanowski (2007) and Filippetti and Peyrache (2013).

In particular, Kumar and Russell (2002) construct the world frontier from country data span-

ning from 1965 to 1990 and quantify the relative contribution of efficiency, technology and capital

accumulation to the change in output per worker. While we find capital accumulation to account

7Perhaps mostly due to measurement issues concerning technology8The appeal of these decompositions is that they impose few parametric assumptions on the underlying structure.Further, this style of semi- and nonparametric estimation has recently witness increased interest in the cross-countrygrowth literature (Maassoumi et al. 2007, Henderson et al. 2012, 2013). These methods are invaluable when little apriori information exists regarding the unknown relationship between economic output and the factors of production.9In applied econometrics, especially in the field of labor economics, to overcome this limitation, quantile-basedapproaches have been proposed (see e.g. Machado and Mata, 2005).

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for 60.7% of the change in labor productivity, they document a much bigger role (78%) for cap-

ital. Moreover, their remaining 22% of output per worker growth is almost equally distributed

between efficiency and technology. This is in stark contrast with our estimated role of technology,

as compared to TFP, since we find technological growth to explain about 44% of labor productivity

growth.

Los and Timmer (2005) compute a country (but not sector) specific technological frontier and

then suggest a decomposition into assimilation, spillover potential and localized innovation. The

decomposition is then used to test the Basu and Weil (1998) model of appropriate technology,

according to which, if technological progress is localized,10 as advocated by Atkinson and Stiglitz

(1969), technological upgrading is less likely at low levels of capital intensity, whereas the global

technology frontier is steadily pushed at high capital intensities (see also Jones, 2005 and Caselli and

Coleman, 2006). Interestingly, Los and Timmer find evidence of technological catching up through

assimilation, though they describe this as a quite slow process, characterized by substantial cross-

country heterogeneity.

The importance of testing for convergence in technology has been put forward by Bernard and

Jones (1996b, p. 1037), who notice that “technology is featured prominently in almost every other

analysis of economic growth except for the convergence literature”. Bernard and Jones (1996a)

find no evidence of convergence in manufacturing, either in labor productivity or in a measure of

multi-factor productivity over the period 1970 − 1987 for a sample of OECD countries. A similar

finding emerges in the convergence analysis in Kumar and Russell (2002), suggesting that relatively

wealthy countries have benefited more from technological progress than less developed countries.

While these results do not support the convergence hypothesis in output per worker, the recent

country-sectoral analysis conducted by Rodrik (2013), using UNIDO data, reports strong evidence

of labor productivity convergence in manufacturing.

3. Empirical Strategy

3.1. Theoretical Decomposition. Let us start with the aggregate production function (of coun-

try c at time t) in (1) and think of all terms as specific to a given sector, so that we can omit

the industry index for clarity. In this expression, mct(·) embodies technology, while the term act

captures total factor productivity of country c at time t, net of technology effects. Both terms vary

by country and time in each sector.

According to (1), for given TFP and k, country c can be relatively more productive with respect

to country f at time t, or with respect to itself in t − 1, thanks to using improved technology

mct , compared to mf

t , or mct−1. This framework describes a world in which alternative productive

technologies are available worldwide and two countries may or may not use the same technology in

10In the Basu and Weil (1998) model, innovations for capital-intensive technologies do not affect the performance ofcapital-extensive technologies, and the other way round.


the same industry. Once cross-country differences in technology are controlled for by mct , the ability

to exploit a given technology is then captured by the term act , which thus accounts for cross-country

differences in labor productivity not explained by different input choices (i.e. differences in kct ) or

differences in technology (differences in mct).

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In Equation (1), labor productivity of country c at time t can differ from that of any other country

along three dimensions: i) capital intensity kct , ii) technology mct , and iii) TFP , act . We propose

a decomposition over time to isolate these effects. To begin, start by writing labor productivity

growth as

(2) ∆yct,T = ycT − yct = [mcT (kcT ) + acT ]− [mc

t(kct ) + act ].

where mcT (kcT ) = ycT and mc

t(kct ) = yct are the predicted labor productivities, at time T and t

respectively, obtained by applying each period’s estimated technology m(·) to the corresponding

actual value of k; acT and act are the estimated TFPs.

As explained in Section 3.2, one of the advantages of our nonparametric approach consists of

allowing estimation of a country-sector specific production function. With this we have the ability

to construct the counterfactual output per worker mcT (kct ) that, given act and kct , country c would

have produced at time t using time T ’s technology. Using this counterfactual, Equation (2) can be

written as

(3) ∆yct,T = ycT − yct = [mcT (kct )− mc

t(kct )]︸︷︷︸

∆ ˜(TEP )c

t,T

+ [mcT (kcT )− mc

T (kct )]︸︷︷︸∆(K/L)

c

t,T

+ [acT − act ]︸︷︷︸∆ (TFP )

c

t,T

.

The three terms in brackets refer to the contribution to the overall change in labor productivity

due to TEP, capital accumulation per worker and TFP, respectively. A tilde is used to highlight

terms obtained through estimated counterfactuals.

An alternative way to express the above contributions can be obtained by comparing a given

country’s decomposition to that of a benchmark (or leader) country. For this we use the labor

11Two comments are in order. While our nonparametric approach allows us to avoid making explicit assumptionsconcerning the functional form of technology (e.g. Cobb-Douglas), for computational reasons we will need log-TFPto enter the production function additively. We thus prefer to impose Hicks neutral technological progress from thevery beginning. Second, disentangling technology and TFP is somewhat unconventional, as TFP is usually thoughtof as the difference between actual and predicted output (i.e. Solow residual) for given input choices, under theimplicit assumption that technology is common knowledge (e.g. growth accounting) or adopting a notion of TFPconflating technological and pure TFP effects (e.g., stochastic frontiers analysis). To stress this aspect, Bernard andJones (1996a, p. 1231-1232) refer to this mix of technology and TFP as total technological productivity (TTP) (seetheir very clear discussion of the problems associated with TTP and TFP.

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productivity growth of a benchmark country (referred to with an asterisk), 12 to restate (2) as

(4) ∆yct,T −∆y∗t,T︸︷︷︸∆(Y/L)gapct,T

= [mcT (kcT ) + acT ]− [m∗T (k∗T ) + a∗T ]︸︷︷︸

(Y/L)gapcT

− [mct(k

ct ) + act ]− [m∗t (k

∗t ) + a∗t ]︸︷︷︸

(Y/L)gapct

.

Now consider the counterfactual output per worker m∗t (kct ) that, given act and kct , country c would

have produced at time t using the benchmark country’s technology. Adding and subtracting the

growth-rate from t to T of this counterfactual [m∗T (kcT ) −m∗t (kct )], the observed difference in the

growth rate of labor productivity between country c and the benchmark country can be decomposed

into:

(5) ∆(Y/L)gapct,T = ∆ ˜(TEP )gapct,T + ∆ ˜(K/L)gapct,T + ∆ (TFP )gapct,T

where

∆(Y/L)gapct,T = [ycT − y∗T ]︸︷︷︸(Y/L)gapcT

− [yct − y∗t ]︸︷︷︸(Y/L)gapct

;(6)

∆ ˜(TEP )gapct,T = [mcT (kcT )− m∗T (kcT )]︸︷︷︸

˜(TEP )gapc

T

− [mct(k

ct )− m∗t (kct )]︸︷︷︸˜(TEP )gap

c

t

;

∆ ˜(K/L)gapct,T = [m∗T (kcT )− m∗T (k∗T )]︸︷︷︸˜(K/L)gap

c

T

− [m∗t (kct )− m∗t (k∗t )]︸︷︷︸˜(K/L)gap

c

t

;

∆ (TFP )gapct,T = [acT − a∗T ]︸︷︷︸(TFP )gap

c

T

− [act − a∗t ]︸︷︷︸(TFP )gap

c

t

.

Using the counterfactual output per worker m∗T (k∗t ) that, for given TFP , the benchmark country

would have produced at time T with time t’s capital per worker, we have

∆yct,T =∆ ˜(TEP )gapct,T + ∆ ˜(K/L)gapct,T + ∆ (TFP )gapct,T

+ ∆ ˜(TEP )∗t,T + ∆(K/L)

∗t,T + ∆ (TFP )

∗t,T︸︷︷︸

∆y∗t,T

(7)

12No a priori assumption in terms of the world frontier (see, for example, Caselli and Coleman, 2006) is needed in ourapproach. Since a different production function is estimated for each country-sector-period, any country-sector-periodmight be used as benchmark.


with

∆ ˜(TEP )∗t,T = m∗T (k∗t )− m∗t (k∗t );(8)

∆(K/L)∗t,T = m∗T (k∗T )− m∗T (k∗t );

∆ (TFP )∗t,T = a∗T − a∗t ,

hwere the last three terms indicating, respectively, the leader shifts from t to T in terms of TEP ,

K/L and TFP . This framework enables us to compare across countries the relative contribution

of TEP , K/L and TFP to labor productivity, net of the leader shift. This brings our growth

accounting exercise a catching-up flavor and represents a novelty with respect to the decompositions

used by Beaudry et al. (2005) and Fiaschi et al. (2013).

3.2. Nonparametric production function estimation. To estimate the production function

in (1), we treat mct(·) as the unknown smooth technology function which varies over both time

and country. In a parametric context this would seem a daunting task as we would have more

parameters than observations. However, by leveraging nearby observations we can deploy nonpara-

metric generalized kernel estimation following the intuition of Li and Racine (2004) and Racine

and Li (2004). Generalized kernel estimation allows one to smooth over continuous (the capital-

labor ratio in our setting), ordered (time) and unordered (a country-sector indicator) covariates

simultaneously. The ability to smooth over discrete cells affords us the ability to estimate time and

country specific technology.

To be more concrete, we write the model in (1) as

(9) yi = m(xi) + εi, i = 1, . . . , N.

where xi = [xCi , xDi ] makes the distinction between continuous variables and discrete variables. We

can further decompose XDi as [xoi , x

ui ] where xo captures variables that are ordered by nature, and

xu captures variables that have no natural ordering. εi is our random error term and N is the

total number of observations. Whereas in a parametric setting, to allow for both country-sector

and time specific effects would introduce an almost unmanageable number of unobserved effect,

by smoothing across both time and sector, we can lessen common parametric assumptions such as

time and country intercept shifts by leveraging ‘nearby’ cells for local information.

Estimation requires the construction of the so called product kernel, which is the product of the

kernel functions for each variable: one type of kernel function is used for continuous regressors,

another is used for unordered discrete regressors and a third is used for ordered discrete regressors.

The product kernel is then used to construct point specific weights. While many different local

9

estimators can be deployed, here we go with the local constant estimator:

(10) m(x) =

N∑i=1

yiGix

N∑t=1

Gix

,

where

(11) Gi,x =

qC∏s=1

K(xis, xs, h

Cs

) qu∏s=1

gu (xuis, xus , λ

us )

qo∏s=1

go (xois, xos, λ

os)

where qC is the number of continuous covariates (in our example qC = 1) and K(xis, xs, h

Cs

)is the

kernel function used for continuous variables with bandwidth hCs , qu is the number of unordered

discrete regressors (in our example qu = 1) with gu (xuis, xus , λ

us ) is the kernel function for a particular

unordered discrete regressor with bandwidth λus and qo is the total number of ordered discrete

regressors with go (xoits, xos, λ

os) the kernel function for a particular ordered discrete regressors with

bandwidth λos. While our estimator in (10) may appear, at first glance, complicated, it is nothing

more that a weighted average of output for observations that are nearby, where nearby is determined

exclusively through the bandwidths used in the construction of the estimator (see Henderson and

Parmeter, 2014 for more intuition).

For the continuous regressor we choose the Gaussian kernel function

(12) K(xCis, x

Cs , h

Cs

)=

1√2πe− 1

2

(xCix−x

Cs

hCs

)2

;

where the bandwidth ranges from zero to infinity. A variation of the Aitchinson and Aitken (1976)

kernel function for unordered categorical regressors is given as

(13) gu (xuis, xus , λ

us ) =

{1− λus if xuis = xusλusd−1 otherwise

;

where the bandwidth is constrained to lie in the range [0, (d− 1) /d] and d is the number of unique

values the unordered variable will take. For example, for the case where the unordered variable is

a traditional ‘dummy variable’, the upper bound will be 0.50.

Finally, the Wang and Van Ryzin (1981) kernel function for ordered categorical regressors is

given by

(14) go (xois, xos, λ

os) =

{1− λos if xois = xos1−λos

2 (λos)|xois−xos| otherwise

,

where the bandwidth ranges from zero to unity.

Beyond the benefit of being able to incorporate categorical regressors, Racine and Li (2004)

show that the rate of convergence of the conditional mean is only dependent on the number of


continuous regressors. This is extremely important given the curse of dimensionality that is one of

the criticisms levied against the use of nonparametric methods. In essence, the addition of discrete

regressors need not require additional observations to achieve the same level of precision as the

inclusion of additional continuous regressors would. This is helpful in our context as the number

of observations is in the hundreds, which if we had three continuous variables would be difficult

to reconcile, however, with two of the three variables being discrete, the insights of Li and Racine

(2004) and Racine and Li (2004) suggest that our sample size should be adequate to learn about

the underlying technologies.

Nearly all theoretical work on nonparametric estimation points to the fact that use of a specific

kernel weighting function has little impact on the overall performance of the estimator. However,

the vector of bandwidths is viewed as the most crucial element. Here, to avoid ad hoc selection of

the bandwidths we deploy the method of least-squares cross-validation (LSCV). Specifically, LSCV

selects bandwidths which minimize

(15) CV (h, λo, λu) =

N∑i=1

[yi − m−i(xi)]2,

where m−i(xi) is the leave-one-out estimator of m(·). The idea of the leave-one-out estimator is that

the conditional mean of yi is estimated without using the observation with the most information,

xi. In this way the bandwidths are selected so that the surrounding observations are providing as

much information as possible to assist with smoothing without relying too heavily on the actual

observations.

4. Data

We take advantage of the country-sector information provided by UNIDO in the INDSTAT2

(Industrial Statistics Database) 2013.13 From INDSTAT2 we draw data on value added (for Y ),

number of employees (for L), and gross fixed capital formation (for K). The capital stock K is

constructed through a perpetual inventory method in which the initial capital stock is computed

following Harberger (1978).14 To prevent our capital variable to be influenced by missing observa-

tions and/or outliers, we smooth capital stocks at the country-sector level using a weighted moving

average of the one year lagged term, the one year forward term, and the current observation.

Finally, only combinations with coverage of at least 5 consecutive years are considered.

In principle, the database covers 166 countries over the period 1963 − 2009, at the 2-digit (or,

for some countries and sectors, combination of 2-digits) ISIC Rev3 sectoral breakdown. However,

available information after data cleaning is remarkably lower, mainly due to missing information

in either Y , K or L. The final number of non-missing country-sector combinations are those

13A detailed description of the database, which is beyond the scope of this section, can be found in Rodrik (2013).14The Harberger approach assumes that countries are at their steady state and, thus, output grows at the same rateas the capital stock.

11

reported, together with the average values of y and k, in Tables 1 and 2. The analysis considers 11

manufacturing sectors, covering the whole manufacturing industry.

While UNIDO only provides us with data at current prices, production function estimation

would require output and inputs to be expressed in quantities. The unavailability of country-sector

specific deflators dating back to the 1960s is, however, not concerning. In fact, the risk associated

with using current prices is to interpret differentials in prices (or inflation rates) as differences in

technology. Three types of differences matter: cross-country, cross-sector and between Y and K (L

is not in value). Our nonparametric approach enables us to include a country-sector control in the

estimation of the production function, so that the first two sources of bias are swept away.15 Thus,

we remain with the third source of bias: if the price of output is higher, or grows more rapidly,

than the price of capital, our estimates are likely to interpret such differences in terms of improved

technology - i.e. higher m(·). We tackle this issue by excluding countries in which the ratio of

the inflation rate of K to the inflation rate of L changes considerably from period 1 (the 1960s)

to period 2 (the 2000s). Graphical inspection of Figure 1, with (country-level) data drawn from

Penn World Table 8.0 (Feenstra et al., 2013), reveals that, among the countries available for the

analysis, the most critical cases are Kuwait and Malta, which have therefore been dropped in the

subsequent analysis.

Overall, this way of dealing with prices can be preferred to using country-specific deflators, which

are likely to introduce a bias on an inter-sectoral basis, due to cross-sectoral price differentials.16

Differently from other available datasets, INDSTAT allows for long run country-sector analysis

with good country coverage, spanning beyond just OECD nations. To the best of our knowledge,

only Rodrik (2013) has used INDSTAT to take advantage of this peculiarity. It is common in

analyses of this type that the sectoral dimension is usually not taken into account. This is the case,

for example, of Los and Timmer (2005), Beaudry et al. (2005), and Fiaschi et al. (2013), who rely

on different versions of Penn World Tables (version 5.6, version 6.0, and version 7.1 respectively).

In alternative country-sector databases, such as EUKLEMS, the number of countries available is

instead too small and homogeneous with respect to our purposes. In EUKLEMS, for example, the

capital stock is available for 17 OECD countries, and for only 12 countries does the information

date back to the 1970s.

5. Results

In order to bring our decomposition to the data, we first estimate the production function in (1)

using the generalized kernel method described in Section 3.2. Estimation is performed on a sample

of 782 observations obtained by collapsing the time dimension of an unbalanced panel of 22, 449

15Rodrik (2013) uses a sector control to take into account differences in industry prices.16In addition, we experimented with the STAN - OECD dataset, which offers country-sectoral data at constant pricesbut for a much smaller number of observations. The results are qualitatively consistent with those presented hereand are available upon request.


country-sector-year observations into a cross section of 391 observations in period 1 (covering the

period from 1963-1972) and 391 observations in period 2 (covering the period 1997-2009). Since non-

missing long time series are available only for a few countries, this strategy maximizes the number

of countries that can be included, allowing for adequate coverage of less developed economies.17

Since we use current values of Y and K, which are normally characterized by a positive trend, we

want to be sure to not use, for given countries, only the end, or only the beginning, of the two

periods. To this aim, we first split each period into three sub-periods of equal length and discard

country-sector combinations which are not present at least once in the central sub-period or at least

once in both the first and third subperiods. This results in deleting 6,217 observations in period 1

and 2,395 observations in period 2.

With reference to the specification in (9) and (10), we use (log) capital per worker as the contin-

uous variable, a country-sector dummy as the unordered discrete variable, and a period indicator

as the ordered discrete variable. In the Figure 2, we show the estimated labor productivities in the

two periods with the big part of the observations collapsed around the main diagonal out of the

fast growing countries at north west and the falling behind at south east.

5.1. Decomposition results. Here we report our decomposition estimations. We first present our

baseline estimates from Equation (3), i.e. those which do not take into account the decompositions

with the benchmark country and then consider our leader specific decomposition, as in Equation

(7).

The country-sector specific decompositions are reported by country in Table 3 and by sector in

Table 4.18 In averaging across countries (sectors), the average relative workforce of each sector

(country) in the whole period is used as weight, so that two equal but opposite changes in two

sectors result in a positive net contribution to the overall change, if the growing sector is bigger in

size.

An overall increase in labor productivity is in line with traditional expectations. The increase is

mainly driven by capital accumulation (56%) and all countries but Kenya accumulate capital across

time. Estimated TEP grows by 44% on average. Not surprisingly, the increase is more marked

in OECD and East Asian countries. High variability emerges across countries and sectors. For

instance, average growth in the Asian Tigers (South Korea and Singapore) is 50% bigger than the

average, and this increase is almost entirely due to capital accumulation. This result is in line with

Los and Timmer (2005) and Young (1995), if we consider that his TFP includes our TEP term.

The average growth in the OECD, net of the Asian Tigers, is 240% .

17In general, the longer the time span, the smaller is the number of countries that can be included, with poorereconomies particularly affected.18The three terms in the decomposition (i.e. ∆ ˜(TEP )

c

t,T , ∆(K/L)c

t,T and ∆ (TFP )c

t,T ), are obtained using the

estimated m(·)s to compute the counterfactual mcT (kct ) as the predicted labor productivity computed on the basis of

country c’s time T estimated technology and time t observed capital per worker.

13

Considering that the US labor productivity growth rate amounts to 214.5%, Table 3 shows that

only OECD and Asian Tigers report increases in labor productivity bigger than in the US, with

growth rates in sub-Saharan Africa and Latin American amounting to just 50% and 70% of the

US’s growth rate. Interestingly, even though many countries are characterized as having a higher

degree of capital accumulation than the US, the US’ TEP growth rate is the highest. This might

confirm the role of the technological leader played by the US in the period under consideration.

Turning to the sectoral dimension, balanced growth emerges in terms of labor productivity,

where the average growth is 239 with a minimum of 226 (in textile sector) and a maximum of

259 (in motor vehicles). Comparing the three decomposition terms, the degree of cross-sector

heterogeneity is much smaller in the K/L ratio (biggest increase in machinery and lowest in energy,

this latter being the only sector far from the average growth), with respect to both TEP and

TFP. Not surprisingly, the highest technological growth is registered in the energy sector, followed

by transport and chemical industries, while more traditional sectors as food, textiles, wood and

furniture had much smaller increases.

It is worth stressing how a proper comparison with the numbers produced in the extant literature

is hampered by the fact that we also allow for a sectoral dimension, which is mainly ignored

elsewhere. A table very similar to our Table 3 is reported by Kumar and Russell (2002), who use

country level data spanning from 1965 to 1990. The absence of the sectoral dimension probably

helps to explain the larger role for capital accumulation that they find: 78% of the overall change

in labor productivity, against our 60.7%. While we find technological growth to explain about

44% of labor productivity, they find the remaining 22% (of the growth in output per worker) to

distribute almost equally between efficiency and technology. Thus, their result concerning the role

of technology is in stark contrast with ours. As well as the presence of a sectoral dimension in

our analysis, the difference in estimation techniques and countries are also likely to influence the

difference in findings. Similarly, Beaudry et al. (2005) find the economic performance of countries

to be mainly explained by the returns to capital accumulation. Since their approach does not allow

the production function coefficients to differ across countries and sectors, compared to ours, their

numbers potentially overestimate the capital component with respect to technology.

It is interesting to compare our results with those obtained in a standard growth accounting

exercise with a constant capital share equal to 0.3 across all countries and industries. For our

data, this implies a contribution of capital accumulation to labor productivity growth of 36%.

Alternatively, one might use an Oaxaca-Blinder decomposition to compare the contribution of

capital per worker to labor productivity growth across the two periods (the two groups consist of

the same country-sector combinations observed in the two periods), on the one hand, and capital

coefficient, on the other hand. This provides us with a quite different result, with a percentage

weight of the capital labor ratio amounting to 70%. Compared to these estimates, our estimated


contribution of capital per worker lies in the middle. This provides cursory evidence that the

assumption of time and sector-country homogeneity is not supported by the data.

We then estimate the labor-productivity decomposition in gaps described in Equation (7). To this

aim, we have to choose a benchmark estimated technology to compute the counterfactual m∗t (kct ),

needed to obtain the three gaps in K/L, TEP and TFP. Results obtained using the US as the

benchmark (or leader country), are reported in Tables 5 and 6 by country and sector, respectively.

The 34% overall decrease in the labor productivity gap, which maps into an approximate 1%

average annual reduction, is consistent with the composition of our dataset, in which the majority

of countries are either OECD nations or the Asian tigers. Most of the variation in gaps is explained

by capital accumulation changes, while the TEP gap remains almost unchanged during the period

under consideration. Complemented with the results obtained without the leader country, a key

message that we can draw from Table 5 is that, even though large differences across countries exists,

technological growth at the world level is pretty much driven by the leader. This is consistent with

most of the theoretical literature cited in Section 2, in particular with the mechanisms of technology

adoption and diffusion suggested by, among others, Desmet and Parente (2010), Desmet and Rossi-

Hansberg (2013), and Comin et al. (2012).

The analysis of specific countries, out of average results highlights some interesting findings. In

particular, decreasing TEP gaps are concentrated in OECD and East Asian countries, while TEP

gaps are increasing in African countries. Less heterogeneity emerges at the sectoral level. As Table

6 reveals, most of the cross-sector heterogeneity documented in the without leader decomposition

(cfr. Tables 3 and 4) can be traced back to the shift in technology for the US. The K/L catching-

up of Asian countries is particularly strong in Korea and Singapore, while only the sub-Saharan

African countries experience a loss in all three components.

It is worth noting again that, with our labor productivity and capital per worker data expressed at

current prices, the corresponding changes are nominal and, as such, potentially overvalued. On the

contrary, estimated TEP is “real”, so that the relative contribution of TEP to the overall variation

in labor productivity is likely understated.19 This is much less of an issue in the decomposition in

gaps, where the only condition which has to hold is the absence of across-time systematic deviation

of input-output prices with respect to the US.

5.2. Convergence of Technology. Graphical inspection of Figure 3, in which the growth rate of

the TEP gap is plotted against the initial (i.e. period 1) gap, seems to highlight a general process

of technological catching-up. Thus, as an economic application of the estimated decomposition,

we run standard absolute and conditional cross-sectional convergence regressions in TEP, KL and

19This consideration finds support in aggregate data. For instance, real price changes in Penn World Table 8.0 sumto 147% for output and to 174% for capital. These numbers compare to the corresponding 174% and 170% that wefind in our decomposition.

15

general labor productivity. In our notation, the usual cross-section convergence framework is:

(16) ∆Zct,T = α+ βln(Z)ct + θln(X)ct + ect ,

where Z is the variable of interest (the TEP, KL, or labor productivity gaps), X is a vector of

control variables and εct is an iid error term.

In Table 7 we use (16) to test for the presence of technological convergence by regressing the

estimated growth rate of the TEP gap (i.e. ∆ ˜(TEP )gapct,T ) on its initial level (i.e. ˜(TEP )gapc

t),20

as well as on a number of control variables usually found to explain economic convergence. Even

with all the limits that simple growth regressions possess (see e.g. Durlauf, 2009 and Battisti et al.

2013.) we see that many of these variables explain the gaps quite well (see columns 3, 6, and 9). In

the same Table, we contrast the results with those obtained by analogous regressions for the K/L

gaps (columns 2, 5, and 8), as well as for labor productivity gaps (see columns 1, 4, and 7). Since

the regressors are generated, we use robust standard errors to take into account the additional

variability. For X, we consider the following variables:

- Distance to US : bilateral geodesic distance between the biggest cities of country c and the US,

with inter-city distances calculated using the great circle formula and weighted by the share of the

city in the overall country’s population (source: CEPII - GeoDist database);

- Avg years schooling : average years of schooling (source: Barro and Lee, 2011);

- POP density : ratio of total population to country size (source: World Development Indicators);

- POP 15-64 : total population aged 15-64 (source: World Development Indicators);

- Financial development : ratio of deposit money bank claims on domestic nonfinancial real sector

to the sum of deposit money bank and Central Bank claims on domestic nonfinancial real sector

(source: World Bank. Financial Development and Structure Database - see Beck et al., 2000);

- Patents: total (residents plus non-residents) patent applications per capita (source: WIPO Sta-

tistics Database);

- Inward FDI : total inward FDI flows to country c from all other countries, divided by GDP (source:

UNCTAD);

- Trade with US : total trade (aggregate imports and exports) of country c with the US, divided by

current GDP (source: CHELEM-IT and CHELEM-GDP databases).

We start with a basic unconditional regression in which we include only sector controls, a dummy

which takes value one if the country belongs to OECD, and the initial condition. Consistently with

the LHS variable, we use the observed gap in Y/L or the estimated gaps in K/L and TEP at

time t as initial conditions. These estimates, reported in columns (1), (2), and (3) respectively,

confirm the presence of absolute convergence. Column (1), in particular compares to Rodrik (2013),

notwithstanding the differences in terms of specifications (we use gaps), time and country coverage.

20Differently from usual growth regressions, we use gaps, instead of levels. Alternatively, one might use absoluteterms and include the leader shift as an additional regressor.


In regressions (4) to (6) we use the standard augmented Mankiw et al. (1992) specification, in

which human capital is measured in terms of average years of schooling (Avg years schooling). A

difference with respect to the standard model used in growth empirics is the absence of investment

rates (they would closely proxy the increase in capital accumulation on the RHS), as well as the

presence of other deep determinants like geographical distance to the US (Geo Distance to US )

and population density (POP density). Results for the labor productivity regression (model 4) are

as expected, with all of these variables enhancing conditional convergence and the overall fit.

The negative role of distance is in line with theoretical work stressing the spatial dimension of

the process of technology adoption/diffusion. For example, Desmet and Rossi-Hansberg (2013)

suggest a model in which technology diffusion affects economic development because technology

diffuses spatially and firms in each location produce using the best technology they have access

to. Comin et al. (2012) propose a theory in which technology diffuses slower to locations that are

farther away from adoption leaders and, moreover, the effect of distance vanishes over time.

The negative sign of population growth, which is not expected in growth regressions, is likely

induced by the composition of our sample (mainly OECD countries, which do not experience a

Malthusian trap stage).

Concerning columns (5) and (6), while the former is in line with model (4), the TEP regressions

in column (6) behave quite differently. The additional variables do not improve the regression

fit significantly in this latter regression, probably because they de facto pick the initial degree

of development, already proxied by the OECD dummy and are not significant beyond distance

to the US. This variable has an interesting pattern, as it is not significant in explaining the labor

productivity gaps because it seems to effect in opposing ways capital accumulation and technology.21

As for population density, while the effect on output per worker and on K/L follows an intuitive

and straightforward interpretation, the effect on TEP might be associated with the Jones (1997)

suggestion of “more people more ideas”.

Lastly, we add (columns 7 to 9) a number of policy variables as the degree of financial development

(Financial Development), the number of patent applications per capita (Patents), total FDI inflows

(Inward FDI ), and the degree of trade openness with the leader (Trade with US ). Differently from

all of the variable used in Table 7, the latter has country-sector variability. The last two measures

follow the intuition that the more open a country the higher is the chance to have access to the best

technologies, as suggested by the literature discussed, for instance in Desmet and Parente (2010),

who model the relationship between market size and technological upgrading, and in Alvarez et

al. (2013), who provide a model in which the flow of new ideas is the engine of growth and

trade generates a selection effect by putting domestic producers in contact with the most efficient

producers. Regressors are all at the initial value, so that they might have a lower explanatory

21We tried to replace geographic distance with the measure of linguistic distance recently provided by CEPII, but itwas never found to be statistically significant or to have a meaningful economic effect on growth.

17

power with respect to the whole time average but they are not subjected to endogeneity critiques

(Durlauf, 2009). We also experimented with growth rates from (1963 − 1981) to (1982 − 1990),

finding no significant differences.22

The labor productivity regression in column (7) displays the expected sign for all variables, out

of patents and FDI, with all of the coefficients being negative in sign. The explanatory power

is slightly higher than in columns (1) and (4). Things are quite different for the K/L and TEP

gaps. Indeed, financial development, inward FDI and trade openness with the US are all strongly

significative in model (8) and the regression fit increases consistently. On the contrary, the only

significative variable in model (9) is Inward FDI, whose coefficient clearly points against a process

in which technology enters a country following the incoming flow of FDI (the different sign of this

variable in columns (8) and (9) highlights why is not significative in model 7). Moreover, the

regression fit is not improved by the inclusion of these additional regressors.23

These results are in contrast with those in Bernard and Jones (1996a), who find no evidence

of convergence in manufacturing, neither in labor productivity nor in a measure of multi-factor

productivity between 1970 and 1987 (in a sample of OECD countries), and Kumar and Russell

(2002). Consistent with our approach, convergence in labor productivity is documented by Rodrik

(2013) in his country-sectoral analysis.

The general picture of this exercise suggests that, in terms of determinants, the usual results

produced by the literature on growth determinants (that is the presence of conditional convergence,

where human capital and financial development increase convergence) holds through the channel

of capital accumulation.

6. Conclusions

We derived a counterfactual decomposition of labor productivity growth into growth of TEP,

growth of the capital-labor ratio and growth of TFP. We brought the decomposition to the data

using a nonparametric approach that enables us to estimate the aggregate production function

allowing for heterogeneity across countries, sectors, and time. This boils down to estimating a

different production for each country-sector-period, something that cannot be done with standard

OLS-based production function estimation. We express the decomposition both in terms of levels

and gaps with respect to the US (i.e. the technological leader leader), although our methodology

leaves us free to choose the benchmark country.

Beyond the isolation of the contribution of capital accumulation to labor productivity growth,

our methodology enables us to unbundle TEP and TFP, thereby contributing to empirical research

on the issues raised by Easterly and Levine (2001) concerning the many layers of TFP. In this

22Many other potential variables might be used for this exercise. However, we want to avoid the classical growthregression issue of exchangeability (Durlauf, 2009).23In unreported analysis, we verified that the result that FDI is robust to using only inward or outward FDI and,interestingly enough, trade openness is never significant, even when total trade (not only with the US) is included.


respect, although comparison with alternative estimates in the literature is hampered by the fact

that we are able to work at the sectoral level, our results point to a more balanced contribution

of TEP and capital accumulation to labor productivity growth (as compared e.g. to Kumar and

Russell, 2002): 56% for capital accumulation and 44% for TEP, on average. Not surprisingly, the

increase in labor productivity is more marked in OECD and East Asian countries.

In terms of gaps with respect to the US, instead, we find that, despite an average 34% reduction

in the labor productivity gap (corresponding to an approximate 1% yearly decrease), technology

gaps are almost unchanged, on average, with most of the decrease driven by capital accumulation.

While substantial heterogeneity emerges across countries, decreasing TEP gaps are concentrated

amongst the OECD and East Asian countries, while gaps increase in African countries. The K/L

catching-up of Asian countries is particularly strong in Korea and Singapore, while only sub-Saharan

countries experience a loss in all the components. Less heterogeneity emerges at the sectoral level.

This results are consistent with previous literature, e.g. the case of South Korea in Young (1995)

and Europe in Bos et al. (2010a).

A key message is that, while the degree of capital accumulation is in many countries higher than

in the US, the highest rate of technological growth is estimated for the US. Things are, as expected,

different for TFP, as well as for capital accumulation, where the growth is in many countries higher

than in the US. With some caution this might be interpreted as evidence of a leading role of the

US in the process of technological upgrading.

As an application, we then use the estimated components of labor productivity growth to run

standard absolute and conditional cross-sectional convergence regressions in TEP. We then contrast

the results with those obtained by analogous convergence regressions for the K/L gaps, as well as

for the gaps in labor productivity. In doing so, we take seriously a question posed by Bernard and

Jones (1996b) concerning assessment of the importance of technology to convergence.

Interestingly, with respect to labor productivity, some traditional variables in growth regressions,

such as population density and education, are found to be economically insignificant in explaining

the evolution of TEP gaps. Other variables exert opposite effects on capital accumulation and tech-

nology. For example, geographical distance to the US negatively affects the former and positively

affects the latter. Incoming foreign direct investments, which seem to have a positive effect on the

former, are found to exacerbate TEP gaps. Moreover, the TEP gaps not affected by the degree

of trade openness with the US and the quality of the financial system, which are instead found to

help convergence in terms of the capital labor ratio.

In conclusion, we have to mention some points as future developments. First, would be interesting

suggesting a theoretical explanation for the phenomena that we quantify, which is something beyond

of the scope of this paper. Second, our growth accounting exercise is carried out starting from a

production function in intensive form, which entails neglecting the relative role of labor. This

choice was data driven, as we wanted to keep the number of countries as high as possible. Still

19

about the data, it is worth noting that, while INDSTAT provided us with the chance to keep the

sectoral dimension into account, produced results might be affected by the incomplete country

coverage, by not taking short-run fluctuations into account, as well as by the time span. These

considerations justify further research efforts. For example, alternative growth accounting exercises

not in intensive form are in our agenda, although this requires limiting the number of countries.


References

[1] Aitchison, J. & Aitken, C. G. G. (1976). Multivariate binary discrimination by the kernel method, Biometrika,

63(3), 413-420.

[2] Alvarez, F. E. & Buera, F. J. & Lucas, R. E. Jr (2013). Idea Flows, Economic Growth and Trade, NBER Working

Papers no. 19667.

[3] Atkinson, A. B. & Stiglitz, J. E. (1969). A New View of Technological Change, Economic Journal, 79(315),

573-578.

[4] Barro, R. & Lee, J.-W. (2011). A new data set of educational attainment in the world, 1950-2010, NBER Working

Papers no. 15902.

[5] Battisti, M. & Belloc, F. & Del Gatto, M. (2014). Unbundling technology adoption and TFP effects: Do firm’s

intangibles matter?, Journal of Economics and Management Strategy, forthcoming.

[6] Battisti, M. & Di Vaio, G. and Zeira, J. (2013) Global Divergence in Growth Regressions, CEPR Discussion

Working Papers DS 9687/2013.

[7] Basu, S. & Weil, D. N. (1998). Appropriate technology and growth, Quarterly Journal of Economics 113, 1025-

1054.

[8] Beaudry, P. & Collard, F. & Green, D. A. (2005) Changes in the World Distribution of Output per Worker,

1960-1988: How a Standard Decomposition tells and Unorthodox Story. The Review of Economics and Statistics

87(4), 741-753.

[9] Beck, T. & Demirguc-Kunt, A. & Levine, R. (2000), A New Database on Financial Development and Structure,

World Bank Economic Review 14, 597-605.

[10] Bernard A. B. & Jones, C. I. (1996a) Comparing Apples to Oranges: Productivity Convergence and Measurement

Across Industries and Countries, American Economic Review, 86(5), 1216-1238.

[11] Bernard A. B. & Jones, C. I. (1996b) Technology and Convergence, The Economic Journal, 106(437), 1037-1044.

[12] Blinder, A. (1973) Wage discrimination: reduced form and structural estimates, Journal of Human Resources 8,

436-455.

[13] Bos, J. W. B. & Economidou, C. & Koetter, M. (2010a) Technology clubs, R&D and growth patterns: Evidence

from EU manufacturing, European Economic Review, 54(1), 60-79.

[14] Bos, J. W. B. & Economidou, C. & Koetter, M. & Kolari, J. W. (2010b) Do all countries grow alike?, Journal

of Development Economics, 91(1), 113-127.

[15] Caselli F. (2005). Accounting for Income Differences across Countries, in Handbook of Economic Growth (P.

Aghion and S. Durlauf eds.). North Holland.

[16] Caselli, F. & Coleman, W. J. (2006). The World Technology Frontier, American Economic Review, 96(3), 499-

522.

[17] Comin, D.A. & Dmitriev, M. & Rossi-Hansberg, E. (2012). The Spatial Diffusion of Technology, NBER Working

Papers no. 18534.

[18] Desmet, K. & Parente, S. L. (2010). Bigger Is Better: Market Size, Demand Elasticity, And Innovation, Inter-

national Economic Review, 51(2), 319-333.

[19] Desmet, K. & Rossi-Hansberg, E. (2014). Spatial Development, American Economic Review, 104(4), 1211-1243.

[20] Durlauf, S. N. (2009). The Rise and Fall of Cross-Country Growth Regressions. History of Political Economy

41, 315-333.

[21] Easterly, W., & Levine, R. (2001). What have we learned from a decade of empirical research on growth? It’s Not

Factor Accumulation: Stylized Facts and Growth Models. The World Bank Economic Review, 15(2), 177-219.

21

[22] Fare, R., Grosskopf, S., Norris, M. & Zhang, Z. (1994). Productivity Growth, Technical Progress, and Efficiency

Change in Industrialized Countries, American Economic Review, 84(1), 66-83.

[23] Feenstra, R. C. & Inklaar, R. & Timmer, M. P. (2013). The Next Generation of the Penn World Table, available

for download at www.ggdc.net/pwt

[24] Fiaschi, D. & Lavezzi, A. M. & Parenti, A. (2013). On the Determinants of Distribution Dynamics, Discussion

Paper n. 165, Collana di E-papers del Dipartimento di Economia e Management, Universita di Pisa.

[25] Filippetti, A. & Peyrache, A. (2013). Labour Productivity and Technology Gap in European Regions: A Condi-

tional Frontier Approach, Regional Studies, forthcoming.

[26] Harberger, A. C. (1978). Perspectives on Capital and Technology in Less Developed Countries, in M. J. Artis

and A. R. Nobay, eds., Contemporary Economic Analysis, London.

[27] Hardle, W. (1990). Applied Nonparametric Regression, Econometric Society Monographs, no. 19, Cambridge

University Press.

[28] Henderson, D. J. & Papageorgiou, C. & Parmeter, C. F. (2012). Growth Empirics without Parameters, Economic

Journal, 122(559), 125-154.

[29] Henderson, D. J. & Papageorgiou, C. & Parmeter, C. F. (2013). Who benefits from financial development? New

methods, new evidence, European Economic Review, 63(C), 47-67.

[30] Henderson, D. J. & Parmeter, C. F. (2014). Applied Nonparametric Econometrics, Cambridge Uniersity Press.

[31] Henderson, D. J. & Russell, R. R. (2005). Human capital and convergence: A production-frontier approach,

International Economic Review, 46(4), 1167-1205.

[32] Jerzmanowski, M. (2007). Total factor productivity differences: Appropriate technology vs. efficiency, European

Economic Review, 51, 2080-2110.

[33] Jones, C. I. (1997). Population and Ideas: A Theory of Endogenous Growth, NBER Working Papers 6285.

[34] Jones, C. I. (2005). The Shape of Production Functions and the Direction of Technical Change, Quarterly Journal

of Economics, 120(2), 517-549.

[35] Kumar, S. & Russell, R. R. (2002). Technological Change, Technological Catch-up, and Capital Deepening:

Relative Contributions to Growth and Convergence, American Economic Review, 92(3), 527-548.

[36] Li, Q. & Racine, J.S. (2004). Cross-validated local linear nonparametric regression, Statistica Sinica, 14, 485-512.

[37] Los, B. & Timmer, M. P. (2005). The appropriate technology explanation of productivity growth differentials:

An empirical approach, Journal of Development Economics, 77, 517-531.

[38] Maasoumi, E. & Racine, J. & Stengos, T. (2007). Growth and convergence: A profile of distribution dynamics

and mobility, Journal of Econometrics, 136(2), 483-508.

[39] Machado, J. A. F. & Mata, J. (2005) Counterfactual Decomposition of Changes in Wage Distributions using

Quantile Regression, Journal of Applied Econometrics 20, 445-465.

[40] Mankiw, N. G., Romer, D. & Weil, D. N. (1992) A contribution to the Empirics of Economic Growth, Quarterly

Journal of Economics 107, 407-437.

[41] Masanjala, W. H. & Papageorgiou, C. (2008) Rough and Lonely Road to Prosperity: A Reexamination of the

Sources of Growth in Africa Using Bayesian Model Averaging, Journal of Applied Econometrics 23, 671-682.

[42] Oaxaca, R. (1973) Male-Female Wage Differentials in Urban labor Markets, International Economic Review

14(3), 693-709.

[43] Parente, S. L. & Prescott, E. C. (1994). Barriers to Technology Adoption and Development, Journal of Political

Economy, 102(2), 298-321.

[44] Racine, J. & Li, Q. (2004). Nonparametric estimation of regression functions with both categorical and continuous

data, Journal of Econometrics, 119(1), 99-130.

[45] Rodrik, D. (2013) Unconditional Convergence in Manufacturing, Quarterly Journal of Economics, 165-204.


[46] UNIDO (2010) Industrial Statistics Database, INDSTAT2: Level 2 ISIC, Revision 3.

[47] Wang, M. C. & Van Ryzin, J. (1981). A class of smooth estimators for discrete distributions, Biometrika, 68,

301-309.

[48] Young, A. (1995). The Tyranny of Numbers: Confronting the Statistical Realities of the East Asian Growth,

Quarterly Journal of Economics, 110(3), 641-680

23

Table 1. Descriptive statistics: labor productivity, capital per worker, number ofnonmissing observations by country.

COUNTRY y k # nonmissing

sectors

Australia AU 33858 140204 8

Austria AT 42274 64457 10Belgium BE 64150 96281 6

Bolivia BO 7483 8636 7

Chile CL 25876 33551 9Colombia CO 21798 20619 11

Cyprus CY 23864 37473 11

Denmark DK 39590 86333 11Ecuador EC 28015 37886 11

Egypt EG 6157 25060 9

Fiji FJ 8307 9581 7Finland FI 44718 78343 11

France FR 55497 119992 8

Greece GR 28986 157348 8Hungary HU 15069 38316 10

Indonesia ID 6897 42046 10

Iran IR 14656 22892 6Ireland IE 61493 61554 8

Israel IL 24228 31993 10Italy IT 42508 85049 11

Japan JP 65756 125258 11

Kenya KE 6031 6422 9Kuwait KW 41361 179261 11

Malawi MW 3469 9437 6

Malaysia MY 33135 56631 11Malta MT 14916 26647 10

Netherlands NL 45444 98735 10

New Zealand NZ 17429 150032 6Norway NO 38700 54334 10

Panama PA 10784 15642 7

Philippines PH 34900 36112 11Poland PL 15395 24937 11

Portugal PT 21297 76846 11Republic of Korea KR 61833 108799 11

Singapore SG 44786 94236 10

Spain ES 29000 32226 7Sweden SE 47169 122076 10

Tunisia TN 33676 452417 9Turkey TR 45798 74753 11United Kingdom GB 45887 62629 11

United Republic of Tanzania TZ 5322 17300 5

United States of America US 83154 91516 11


Table2.

Des

crip

tive

stat

isti

cs:

lab

orp

rod

uct

ivit

y,ca

pit

alp

erw

orke

r,nu

mb

erof

non

mis

sin

gob

serv

ati

on

sby

sect

or.

SE

CT

OR

SE

CT

OR

SH

.y

k#

non

mis

sin

gco

untr

ies

Food

,b

ever

ages

an

dto

bacc

oF

D25748.9

364497.2

35

Tex

tile

s,w

eari

ng

ap

pare

l,le

ath

erp

rod

uct

s,fo

otw

ear

TX

14475.6

422736.7

739

Wood

pro

du

cts

WO

17166.8

525236.3

536

Pap

eran

dp

ap

erp

rod

uct

s,P

rinti

ng

an

dp

ub

lish

ing

PP

25463.0

241119.6

940

Coke,

refi

ned

pet

role

um

pro

du

cts,

nu

clea

rfu

elP

T149122

490620.1

28

Ch

emic

als

an

dch

emic

al

pro

du

cts,

Ru

bb

eran

dp

last

ics

pro

du

cts

CH

37823.2

961577.2

841

Non

-met

allic

min

eral

pro

du

cts

NM

26381.7

749466.7

337

Basi

cm

etals

,F

ab

rica

ted

met

al

pro

du

cts

MT

23955.2

346684.3

138

Mach

iner

yan

deq

uip

men

tn

.e.c

.M

A26427.5

941924.9

235

Moto

rveh

icle

s,tr

ailer

s,se

mi-

trailer

s,oth

ertr

.eq

.T

R25831.7

146642.5

435

Fu

rnit

ure

;m

anu

fact

uri

ng

n.e

.c.

OT

18080.1

828980.1

927

25

Table 3. Decomposition results without leader country. 1963-1981 to 1991-2009growth rates by country (% values).

COUNTRY ∆(Y/L)ct,T ∆(K/L)c

t,T ∆TEPc

t,T ∆TFPc

t,T

Australia 216.48 158.41 117.91 -59.84Austria 251.37 143.51 109.29 -1.43Belgium 279.41 153.10 123.16 3.14Bolivia 157.64 88.13 56.66 12.86Chile 157.70 29.98 81.50 46.23Colombia 191.39 19.33 126.94 45.12Cyprus 228.41 100.33 75.75 52.34Denmark 210.74 132.22 103.22 -24.70Ecuador 178.82 133.64 71.07 -25.89Egypt 154.48 85.52 54.13 14.83Fiji 175.81 82.69 76.67 16.45Finland 267.28 126.66 112.74 27.89France 239.30 150.02 106.19 -16.90Greece 267.95 129.99 90.74 47.23Hungary 190.04 111.50 63.79 14.74Indonesia 258.86 249.56 61.20 -51.90Iran 190.10 122.57 63.70 3.83Ireland 326.86 174.07 121.45 31.35Israel 203.35 146.37 93.49 -36.50Italy 226.37 131.67 105.35 -10.66Japan 302.59 168.59 117.16 16.84Kenya 82.13 -27.17 86.01 23.29Kuwait 159.38 111.94 74.54 -27.09Malawi 171.35 169.32 33.77 -31.75Malaysia 209.47 162.90 68.10 -21.53Malta 277.06 199.09 82.01 -4.03Netherlands 264.73 153.89 113.87 -3.03Norway 253.55 130.93 106.89 15.73Panama 93.50 92.00 56.62 -55.12Philippines 149.26 103.86 53.31 -7.92Poland 195.70 137.92 62.52 -4.74Portugal 218.30 166.33 87.65 -35.68Singapore 313.14 203.99 105.60 3.55South Korea 397.34 247.40 119.03 30.91Spain 284.01 153.67 89.44 40.90Sweden 219.43 131.32 120.22 -32.11Tanzania 36.69 90.54 17.88 -71.73Tunisia 160.90 89.98 52.61 18.31Turkey 194.87 139.84 70.97 -15.94United Kingdom 244.87 152.98 103.14 -11.26United States 214.53 119.56 124.97 -30.00

wAvg 238.61 144.81 104.92 -11.12


Table 4. Decomposition results without leader country. 1963-1981 to 1991-2009growth rates by sector (% values).

SECTOR ∆(Y/L)ct,T ∆(K/L)c

t,T ∆TEPc

t,T ∆TFPc

t,T

FD 231.94 133.45 97.61 0.88TX 226.42 157.71 77.59 -8.88WO 233.33 148.21 78.87 6.25PP 232.73 133.98 109.32 -10.57PT 250.80 76.59 189.14 -14.94CH 245.01 117.43 122.27 5.31NM 256.14 136.86 105.28 14.00MT 234.30 122.25 101.10 10.95MA 242.97 171.44 118.82 -47.29TR 258.61 167.16 130.22 -38.76OT 231.25 150.22 78.28 2.76

wAvg 238.61 144.81 104.92 -11.12

27

Table 5. Decomposition results. 1963-1981 to 1991-2009 growth rates by country(% values).

COUNTRY ∆(Y/L)gapct,T ∆ ˜(K/L)gapct,T ∆ ˜TEPgapct,T ∆ TFPgapct,TAustralia -0.98 -40.34 8.07 31.29Austria -38.07 -15.82 -2.97 -19.27Belgium -66.23 -71.08 -4.12 8.97Bolivia 58.19 60.62 0.33 -2.76Chile 57.78 116.91 -19.63 -39.50Colombia 27.85 111.29 -35.79 -47.65Cyprus -14.58 44.60 -14.36 -44.82Denmark 5.17 4.39 0.17 0.61Ecuador 40.91 5.70 4.77 30.44Egypt 63.71 80.34 -0.86 -15.76Fiji 26.93 58.81 -8.22 -23.65Finland -55.29 10.59 -12.57 -53.31France -25.18 -6.00 0.03 -19.22Greece -46.36 24.84 -9.15 -62.04Hungary 25.00 79.23 -7.31 -46.93Indonesia -42.50 -91.11 -1.73 50.34Iran 21.93 53.21 -2.78 -28.50Ireland -107.06 -47.49 -6.14 -53.43Israel 11.13 -7.82 3.48 15.47Italy -11.33 7.00 -4.16 -14.17Japan -91.71 -71.79 0.18 -20.10Kenya 135.27 139.36 20.47 -24.57Kuwait 54.24 27.01 4.51 22.72Malawi 52.88 -14.38 18.09 49.17Malaysia 7.57 10.60 4.01 -7.05Malta -60.76 -52.45 6.82 -15.12Netherlands -52.39 -21.02 -2.42 -28.95Norway -41.83 -0.44 -3.91 -37.48Panama 124.20 54.22 3.15 66.83Philippines 68.38 66.26 5.01 -2.89Poland 21.21 34.77 2.46 -16.02Portugal -6.34 -49.26 7.22 35.69Singapore -94.82 -37.78 2.02 -59.05South Korea -180.51 -118.15 -0.95 -61.41Spain -73.23 -45.43 3.56 -31.36Sweden -8.58 0.09 -2.57 -6.10Tanzania 188.87 64.33 33.43 91.10Tunisia 54.13 84.97 -1.23 -29.60Turkey 21.18 13.26 2.83 5.10United Kingdom -29.66 -11.15 -0.22 -18.29United States 0.00 0.00 0.00 0.00

wAvg -34.30 -18.74 -0.63 -14.93

LEADER SHIFT ∆(Y/L)∗t,T ∆(K/L)∗

t,T ∆TEP∗t,T ∆TFP

∗t,T

United States 214.53 124.97 119.56 -30.00


Table 6. Decomposition results. 1963-1981 to 1991-2009 growth rates by sector (% values).

SECTOR ∆(Y/L)gapct,T ∆ ˜(K/L)gapct,T ∆ ˜TEPgapct,T ∆ TFPgapct,TFD -1.868 -36.3625 4.47 30.0285TX -15.9781 -16.0176 -0.10 0.14WO -49.3976 -66.3403 3.34 13.60PP -76.7991 -47.3153 -2.12 -27.36PT 81.5183 46.1238 17.62 17.78CH -29.5701 -37.8489 -0.54 8.82NM -53.1051 -63.7658 1.04 9.62MT -57.473 -68.1038 0.54 10.09MA -23.4411 79.3734 -8.26 -94.55TR -59.2513 15.1444 -3.01 -71.38OT -34.6278 -51.994 5.40 11.97

wAvg -34.30 -18.74 -0.63 -14.93

LEADER SHIFT ∆(Y/L)∗t,T ∆(K/L)∗

t,T ∆TEP∗t,T ∆TFP

∗t,T

FD 230.69 116.39 89.06 25.23TX 213.73 88.25 134.24 -8.77WO 197.39 88.21 93.04 16.15PP 183.90 115.11 96.76 -27.97PT 304.93 301.86 6.20 -3.13CH 223.59 140.00 71.89 11.70NM 213.66 113.21 78.76 21.69MT 193.12 98.06 76.87 18.19MA 228.83 153.20 179.97 -104.34TR 218.43 157.79 147.81 -87.17OT 208.94 84.95 113.51 10.47

wAvg 214.53 124.97 119.56 -30.00

29

Table7.

Con

verg

ence

regr

essi

ons.

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

Dep

endent

Vari

able

:∆

(Y/L

)gap

∆˜

(K/L

)gap

∆˜

(TEP

)gap

∆(Y

/L

)gap

∆˜

(K/L

)gap

∆˜

(TEP

)gap

∆(Y

/L

)gap

∆˜

(K/L

)gap

∆˜

(TEP

)gap

(Y/L

)gap

(t)

-0.4

13***

-0.6

35***

-0.5

99***

[0.0

7]

[0.0

8]

[0.1

8]

˜(K

/L

)gap

(t)

-0.8

37***

-0.8

46***

-0.6

95***

[0.0

8]

[0.0

7]

[0.0

8]

˜(T

EP

)gap

(t)

-0.7

41***

-0.7

44***

-0.7

01***

[0.1

1]

[0.1

3]

[0.2

1]

lnGeo

Dista

nce

toUS

(t)

0.0

86

-0.2

74***

0.1

20***

0.3

86***

-0.1

80*

0.2

07***

[0.0

7]

[0.0

7]

[0.0

3]

[0.1

3]

[0.1

0]

[0.0

5]

lnAvgyea

rssc

hooling

(t)

-0.3

99***

-0.4

50***

-0.0

02

-0.4

67***

-0.4

55***

-0.0

35

[0.0

9]

[0.0

7]

[0.0

2]

[0.1

4]

[0.1

][0

.04]

lnPOP

density

(t)

-0.0

95***

-0.0

32*

-0.0

05

-0.0

86***

-0.0

21

-0.0

01

[0.0

2]

[0.0

2]

[0.0

1]

[0.0

2]

[0.0

2]

[0.0

1]

lnPatents

(t)

0.0

22

-0.0

33

0.0

01

[0.0

3]

[0.0

3]

[0.0

1]

lnFin

ancialdevelopment

(t)

-0.3

49*

-0.5

27***

0.0

58

[0.2

0]

[0.1

5]

[0.0

6]

lnIn

ward

FDI

(t)

-0.0

3-0

.153***

0.0

43***

[0.0

5]

[0.0

3]

[0.0

1]

lnTra

dewith

US

(t)

-0.0

60*

-0.0

60**

0.0

08

[0.0

3]

[0.0

3]

[0.0

1]

∆POP

15-6

4-0

.073***

-0.0

60***

-0.0

05

-0.0

40*

-0.0

08

-0.0

19**

[0.0

1]

[0.0

1]

[0.0

0]

[0.0

2]

[0.0

2]

[0.0

1]

OECD

-0.7

56***

-0.8

03***

0.0

04

-0.8

09***

-0.7

00***

0.0

19

-0.7

64***

-0.5

72***

0.0

54

[0.0

7]

[0.0

6]

[0.0

2]

[0.0

7]

[0.0

6]

[0.0

2]

[0.1

3]

[0.1

2]

[0.0

4]

N368

368

368

329

329

329

224

224

224

R2

0.3

0.5

30.3

80.5

40.6

40.3

80.5

60.7

40.3

8

Secto

ral

dum

mie

sin

clu

ded

inall

specifi

cati

ons.

Sta

ndard

err

ors

inpare

nth

ese

s.N

ota

tion

∆X

refe

rsto

the

gro

wth

rate

of

vari

able

Xfr

om

peri

od

1(t

)to

peri

od

2(T

).*p<

0.0

5,

**p<

0.0

1,

***p<

0.0

01


EGY

TUN

KWT

HKGTZA

JOR

VEN

FJI

IRN

AUT

BOL

CHLPHL

PRT

KEN

SGP

HUN

NZLIDN

MWI

POLPAN

ESP

ISR

IRL

AUSSWEGBR

USADNK

COL

KOR

ECU

FRA

JPN

NLD

BEL

NOR

ITA

GRC

MYS

FIN

TUR

CYP

MLT

.81

1.2

1.4

1.6

K/Y

Infla

tion

ratio

per

iod

2

0 .5 1 1.5 2K/Y Inflation ratio period 1

Figure 1. K/Y inflation ratios across periods (US in 2005 = 1).

31A

U AT

BE

BO

CL

CO

CYD

K

EC

FI

GR

ID

IE IL

ITJP

KE

KR

KW

MW

MY

MT

NO

PA

PHP

L

PT

ES

TN

TR

EG

GB

TZ

US

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

AU

JPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJPJP

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

KE

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

TZ

99.51010.51111.5Predicted y (period 2)

Pre

dict

ed y

(pe

riod

1)

FD

AU

AT

BE B

O

CL

CO

CY

DK

ECFI

FR

HU

ID

IRIEIL

IT

JP KE

KR

KW

MW

MYM

TN

LN

O

PA

PH

PL

PT

SG

ES

SE

TN

TR

EGG

BU

SA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

TA

T

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

CL

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CL

IEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEIEILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILILIL

MW

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891011Predicted y (period 2)

Pre

dict

ed y

(pe

riod

1)

TX

AT

BO

CL

CO

CY

DK

EC

FJ

FI

GR HU

ID

IE

IL

IT

JP

KR

KW

MW

MY M

T

NL

NO

PA P

H

PLPT

SG

ES

SE

TN

TR

EG

GB

US

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

BO

ITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITITIT

PL

PL

PL

PL

PL

PL

PL

PL

PL

PL

PL

PL

PL

PL

PL

PL

PL

PL

PL

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TN

TN

TN

TN

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TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN

TN


Pre

dict

ed y

(pe

riod

1)

WO

AUAT

BO

CL

CY

DK

EC

FJ

FI

FR

GR HU

ID

IR

IE

IL

ITJP

KE

KR

KW

MYM

T

NLN

O

PA

PHP

LPT

SG

ES

SE

TNT

RE

G

GB

TZ

US

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

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EC

EC

EC

EC

EC

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EC

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EC

EC

EC

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EC

EC

EC

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EC

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EC

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EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

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EC

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EC

EC

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EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

EC

HU

HU

HU

HU

HU

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HU

HU

HU

HU

HU

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HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

HU

KR

KR

KR

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KR

KR

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KR

KR

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KR

KR

KR

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KR

KR

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KR

KR

KR

KR

KR

KR

KR

KR

KR

KR

KR

KR

KR

KR

KR

KR

KR

KW

KW

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KW

KW

KW

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KW

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KW

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KW

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KW

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KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

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KW

KW

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KW

KW

KW

KW

KW

KW

KW

KW

KW

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KW

KW

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KW

KW

KW

KW

KW

KW

KW

KW

KW

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KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KW

KWP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

TP

T


Pre

dict

ed y

(pe

riod

1)

PP

AU

CY

DK

EC

FI

FR

GR

HU

IT

JP

KE

KR

KW

MY

NL

PH

PL

PT

SG

SE

EG

GB

TZ

US


Pre

dict

ed y

(pe

riod

1)

PT

AU

AT

BE

BO

CL

CO

DK

EC

FJ

FI FR

GRH

UID

IE

IL

IT

JP

KE

KR

KW

MWM

Y

MT

NL

NO

PA

PH

PL

PT

SG

ES

SE

TR

EG

GB

TZ

US

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

BE

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

DK

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

GR

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MY

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

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MT

MT

MT

MT

MT

MT

MT

MT

MT

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MT

MT

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MT

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MT

MT

MT

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MT

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MT

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MT

MT

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MT

MT

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MT

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MT

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MT

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MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

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MT

MT

MT

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MT

MT

MT

MT

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MT

MT

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MT

MT

MT

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MT

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MT

MT

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MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

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MT

MT

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MT

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MT

MT

MT

MT

MT

MT

MT

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MT

MT

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MT

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MT

MT

MT

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MT

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MT

MT

MT

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MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

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MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

MT

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NL

NL

NL

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NL

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NL

NL

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NL

NL

NL

NL

NL

NL

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NL

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NL

NL

NL

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NL

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NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

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NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

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NL

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NL

NL

NL

NL

NL

NL

NL

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NL

NL

NL

NL

NL

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NL

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NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL

NL


Pre

dict

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(pe

riod

1)

CH

AT

BE

BO

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FR

HU

ID

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US

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CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

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CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

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CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

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CY

CY

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CY

CY

CY

CY

CY

CY

CY

CY

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CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

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CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

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CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

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CY

CY

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CY

CY

CY

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CY

CY

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CY

CY

CY

CY

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CY

CY

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CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

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CY

CY

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CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

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CY

CY

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CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

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CY

CY

CY

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CY

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CY

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CY

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CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

CY

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

PA

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PA

PA

PA

PA

PA

PA

PA

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PA

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PA

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PA

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SG

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SG

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SG

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SG

SG

SG

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SG

SG

SG

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SG

SG

SG

SG

SG

SG

SG

SG

SG

SG

SG

SG

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SG

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SG

SG

SG

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ES

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Pre

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(pe

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NM

AU

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HU

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NO

NO

NO

NO

NO

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NO

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NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

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NO

NO

NO

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NO

NO

NO

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NO

NO

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NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

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NO

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NO

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NO

NO

NO

NO

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NO

NO

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NO

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NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NO

NOSE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

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SE

SE

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SE

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SE

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SE

SE

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SE

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SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

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SE

SE

SE

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SE

SE

SE

SE

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SE

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SE

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SE

SE

SE

SE

SE

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SE

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SE

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SE

SE

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SE

SE

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SE

SE

SE

SE

SE

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SE

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SE

SE

SE

SE

SE

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SE

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SE

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SE

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SE

SE

SE

SE

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SE

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SE

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SE

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SE

SE

SE

SE

SE

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SE

SE

SE

SE

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SE

SE

SE

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SE

SE

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SE

SE

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SE

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SE

SE

SE

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SE

SE

SE

SE

SE

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SE

SE

SE

SE

SE

SE

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SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

SE

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TR

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TR

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TR

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TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

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TR

TR

TR

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TR

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TR

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TR

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TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

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TR

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TR

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TR

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TR

TR

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TR

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TR

TR

TR

TR

TR

TR

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TR

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TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

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TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

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TR

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TR

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TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

TR

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TR

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TR

TR

TR

TR

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TR

TR

TR

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TR

TR

TR

TR

TR

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TR

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TR

TR

TR

TR

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TR

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TR

TR

TR

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TR

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TR

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TR

TR

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TR

TR

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TR

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Pre

dict

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FR

FR

FR

FR

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FR

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FR

FR

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FR

FR

FR

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FR

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FR

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FR

FR

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Pre

dict

ed y

(pe

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1)

MA

AU

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CO

CO

CO

CO

CO

CO

CO

CO

CO

CO

CO

CO

CO

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CO

CO

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CO

CO

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-.4-.20.2.4TEP GAP growth

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TN

-.2-.10.1.2.3TEP GAP growth

TE

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TEP GAP growth

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Documents

LABOR PRODUCTIVITY GROWTH: DISENTANGLING TECHNOLOGY … · labor productivity growth: disentangling technology and capital accumulation michele battisti, massimo del gatto, and christopher