Munich Personal RePEc Archive

Investment Constraints and Productivity
Cycles in Bolivia
Mendez-Guerra, Carlos
Nagoya University

31 January 2019

Online at https://mpra.ub.uni-muenchen.de/91861/
MPRA Paper No. 91861, posted 31 Jan 2019 14:56 UTC

Investment Constraints andProductivity Cycles in Bolivia
Carlos Mendez-Guerra*
Nagoya University

Abstract
Bolivia has suffered from a series of investment constraints and negative productivity
cycles in the last half-century. In this context, this article first presents suggestive
empirical evidence that physical capital accumulation has been constrained by highvolatility in investment per worker, low marginal product of capital, and high adjustment
costs. Next, the article presents evidence about the cyclical behavior of Bolivia’s totalfactor productivity (TFP) during the 1980-2008 period. As expected, the cyclicaldynamics of TFP are shaped by cyclical variables such as terms of trade and fluctuationsin the real exchange. However, economic policy variables (such as macroeconomic
stabilization and external debt management), institutional variables (such as democracy
and civil rights) and initial conditions also appear to be significant when explaining the
behavior of Bolivia’s TFP.

JEL Codes: O40, O47
Keywords: investment, productivity, capital accumulation, Bolivia*

Associate Professor, Graduate School of International Development, Nagoya University,
Japan. Email: carlos@gsid.nagoya-u.ac.jp Website: https://carlos-mendez.rbind.io

Restricciones a la Inversión y
Ciclos de Productividad en Bolivia

Resumen
Bolivia ha sufrido una serie de restricciones a la inversión y ciclos negativos deproductividad en el último medio siglo. En este contexto, este artículo primero presenta
evidencia empírica que sugiere que la acumulación de capital físico ha sido restringida
por la alta volatilidad de la inversión por trabajador, el bajo producto marginal del capital,y los altos costos de ajuste de la inversión. Seguidamente, el artículo presenta evidencia
empírica sobre el comportamiento cíclico de la productividad total de factores (PTF)
durante el periodo 1980-2008. Como se esperaba, la dinámica cíclica de la PTF ha sido
afectada por variables cíclicas como ser las fluctuaciones en los términos de intercambio
y el tipo de cambio real. Sin embargo, variables de política económica (como laestabilización macroeconómica y la administración de la deuda externa), variables
institucionales (como la democracia y los derechos civiles), y las condiciones iniciales
son también significativas al momento de explicar el comportamiento de la PTF en
Bolivia.

Clasificación JEL: O40, O47

Palabras clave: inversión, productividad, acumulación de capital, Bolivia.

1. Introduction
It is well known that physical capital accumulation and productivity
improvements are two of the most proximate determinants of economic growth. In thiscontext, much of the current research in development economics has to do with theunderlying sources of capital accumulation and productivity growth. Figure 1 shows the
behavior of physical capital accumulation and one of its underlying sources: investment.
This figure suggests that physical capital accumulation appears to be constrained bylargely unstable changes in investment. Figure 2 shows the behavior of the otherproximate determinant of economic growth: total factor productivity (TFP). The figure
emphasizes that—even in levels—productivity shows a clear cyclical behavior.

Figure 1. Capital Accumulation and Investment per worker

−20

0

20

40

In growth rates

1963

1973

1983
.

1993

2003

−60

600

6000

−40

800

7000
8000
9000
Capital−Labor Ratio

Investment per worker
1000
1200
1400

1600

10000

In levels

1963

1973

1983
.

1993

2003

Investment per worker

Investment per worker Growth Rate

Capital−Labor Ratio

Capital−Labor Ratio Growth Rate

Source: Author’s calculations using data from Extended Penn World Tables V. 4.0 (Marquetti, 2011)

90

TFP Level(1963=100)
100
110

120

Figure 2. Level of Total Factor Productivity

1963

1973

1983
year

1993

2003

Source: Author’s calculations using data from Extended Penn World Tables V. 4.0 (Marquetti, 2011)

Given these empirical regularities, this article aims to provide the beginning of ananswer to the following two questions:

1.

What factors can help explain the capital accumulation constrains in Bolivia?

2.

What factors can help explain the cyclical behavior of total factor productivity in

Bolivia?

To answer these questions, this article is organized as follows. Section 2 presents
an econometric model of investment based on the accelerator principle and the role ofadjustment costs. It also discusses some recent evidence on the low return to capital
accumulation in Bolivia. Section 3 presents an econometric model that organizes thedeterminants of TFP into four categories: cyclical variables, macroeconomic policies,
institutions, and initial conditions. Finally, Section 4 offers some concluding remarks.

2. The Constraints of Investment
2.1. Some Stylized Facts: Volatility and Low Returns
An initial observation of investment data let us identify the volatility of aggregate
investment and historical events where investment was booming. Figure 3 shows that in
the beginning of the 1973-1984 period, investment grew rapidly. This increase is largely
due to the international capital inflows that were associated with the oil shocks. In thesecond half of the 1985-2002 period, investment spiked again. This time, investment
growth was led by a large privatization program that Bolivia implemented.

22.5

Figure 3. Aggregate Investment in Bolivia

Period IV
Period III
Log (Investment)
21.5
22Period II

21

Period I

1963

1968

1973

1978

1983

1988

1993

1998

2003

2008

Source: Author’s calculations using data from Penn World Tables V. 7.0

Figure 4 shows that the long-run average of the investment share in Bolivia has
been around 13 percent. In the privatization period, investment share raised to 19.12
percent driven, which was in turn largely driven by a large increase Foreign Direct

Investment (FDI). From an international perspective, the average share of investment in
Bolivia is low, especially when compared with fast growing economies in Asia.

0

GDP Share
5
10

15

20

Figure 4. Total Investment and Foreign Direct Investment (FDI) Share in Bolivia

Period I
−5
1963

1968

Period III

Period II
1973

1978

1983

1988

1993

Period IV
1998

2003

2008

Investment Share
FDI Share

Source: Author’s calculations using data from Penn World Tables V. 7.0 and Word Development
Indicators 2011

Are there low returns to capital accumulation in Bolivia?
Theoretically, capital accumulation can be low because the marginal product of
capital (MPK) is low. More intuitively, if the real return of investment is low, investors
do not have incentives to invest. Motivated by this argument, Caselli and Feyrer (2007)
present different estimates of the MPK for a large sample of countries. In their approach,the MPK can be recovered using data on total GDP, the value of the aggregate capitalstock, and the share of physical capital income in total GDP.
The model starts with a Cobb-Douglas production function featuring constantreturns to scale and a competitive capital market. Under these two assumptions, the rental
price of capital (𝑃" ) equals the marginal product of capital (MPK) in equilibrium, so that𝑌𝑃" = 𝑀𝑃𝐾 ≡ 𝛼 ,𝐾(1)where 𝑌 is output (e.g., total GDP), 𝐾 is the value of the stock of physical capital, and𝛼 is the elasticity of output with respect to capital, or equivantely—given the previous
two assumptions—the share of national income that accrues to physical capital. In thisequilibrium, a lower MPK could be driven by a lower capital share, a lower output-capitalratio, or both.
Using commonly reported estimates of 𝛼 , Caselli and Feyrer (2007) find that
the MPK in the developing countries sample is more than double the size of that indeveloped countries sample. However, these differences disappear when land and othernatural capital inputs are removed from the estimation of capital. By using a new WorldBank’s dataset, Caselli (2007) separates natural capital from reproducible capital andsuggests that many developing countries actually show lower marginal returns to
reproducible capital accumulation.
Based on the measurement approach of Caselli and Feyrer (2007), Table 1
presents the results for Bolivia in a comparative perspective with other developing anddeveloped countries. Compared with other developing countries, Bolivia has a lower
marginal return to capital accumulation. Moreover, the main factor that appears to be
driving this result is its relatively low share of reproducible capital.

Table 1. A Comparative Perspective of the Marginal Product of Capital (MPK)𝛼 *+,-.

𝛼/012+34567.0

𝑀𝑃𝐾*+,-.

𝑀𝑃𝐾/012+34567.0

Bolivia

0.33

0.08

0.31

0.19

Chile

0.41

0.16

0.26

0.24

Peru

0.44

0.22

0.20

0.18

Botswana

0.55

0.33

0.36

0.24

Singapore

0.47

0.38

0.15

0.18

Japan

0.32

0.26

0.09

0.10

USA

0.26

0.18

0.12

0.14

Source: Data from Caselli and Feyrer (2007)

Caselli and Feyrer (2007) also provide estimates of the MPK in the context of amulti-sector model. The main advantage of this setting is that it is possible to identify theeffect of relative prices on the MPK. In the Bolivian case, as in many other developing
countries, the marginal return to capital is even lower because the price of capital inputs
is relatively higher that of the final output.

2. Modeling Investment: The Accelerator Principle with Adjustment
Costs
In the macroeconomics literature, the basic notion of the accelerator principle has
to do with changes in aggregate output that lead to changes in demand for capital goods
and, hence, lead to an acceleration in investment plans. Pigou (1929) and Harrod (1936),
among the earliest proponents of this theory, emphasize the role of this principle inexplaining aggregate fluctuations in economic activity. Chenery (1952) and (1954)
provided further extensions to the theory, and in more recent years, a series of empiricalstudies have focused on comparing its predictions with those of other models ofinvestment. For instance, seminal work of Kopcke and Brauman (2001), highlights thegood properties of the model and its relevance for relatively accurate forecasts ofeconomic activity. These authors go further, and argue that even more modern
macroeconomics employ some variant of the acceleration principle to explain aggregate
investment.
From a modeling perspective, the notion of the accelerator principle is that there
is some optimal relationship between aggregate capital stock of the economy and GDP.
In its simplest form,
𝐾,∗ = 𝜇𝑌,(2)

where 𝐾 ∗ is the optimal capital stock, 𝑌 is total output (e.g., real GDP), and 𝜇 is thecapital/output ratio, which in this model is the accelerator parameter. According toEquation (1), when output is growing, then an increase in capital stock is required. In turn,this increase in the capital stock is defined as net investment:1

1

More formally, the acceleration principle can also be derived from a cost-minimizing problem.

𝐼;, ≡ 𝐾, − 𝐾,=> = 𝜇(𝑌, − 𝑌,=> ).

(3)

The flexible accelerator model is a generalization of previously described model.
Originally proposed by Koyck (1954), the flexible accelerator is based on the principleof gradual capital adjustment. The most appealing feature of this model is that it does not
require the capital stock to be always optimally adjusted. It allows for lags in theadjustment towards the optimal level of capital. This dynamic adjustment can be writtenas:𝐼;, = 𝜆(𝐾,∗ − 𝐾,=> ) = 𝜇(𝑌, − 𝑌,=> )(4)

where 𝜆 denotes the partial adjustment coefficient. The key feature of this model is thatthe adjustment is not instantaneous. The speed of adjustment reflects both the uncertainty
that firms face to make up the difference between 𝐾,=> and 𝐾 ∗, and the fact that the
supply of capital goods is not instantaneous.
To empirically estimate the accelerator model of investment, let us start from thedefinition of the flexible accelerator model:
𝐼;, = 𝜆(𝐾,∗ − 𝐾,=> )(5)

where 𝐼; is net investment, 𝐾 is the stock of capital, 𝐾 ∗ is the optimal capital stock,and 𝜆 is the adjustment coefficient. The basic acceleration principle 𝐾,∗ = 𝜇𝑌, isintroduced in Equation 5 to obtain:
𝐼;, = 𝜆𝜇𝑌, − 𝜆𝐾,=> .

(6)

If by definition net investment is expressed as 𝐼;, = 𝐾, − 𝐾,=> and it is introduced into
Equation 6 we obtain:
𝐾, = 𝜇𝜆𝑌, + (1 − 𝜆)𝐾,=> .

(7)

Next, assuming a constant depreciation rate 𝛿 , one can add replacementinvestment 𝛿𝐾,=> to both sides of Equation 7 and obtain the gross investment
formulation:
𝐼, = 𝜆𝜇𝑌, + (𝛿 − 𝜆)𝐾,=>(8)

which is the theoretical basis for the following econometric models:𝐼, = 𝛼 + 𝜆𝜇𝑌, + (𝛿 − 𝜆)𝐾,=> + 𝜖;, 𝜖 ∼ 𝑁(0, 𝜎)

(9)

O=>

𝐼, = 𝛼 + M 𝛽6 𝑌,=6 + 𝛽S 𝐾,=> + 𝜖, ; 𝜖 ∼ 𝑁(0, 𝜎)

(10)

PQR

The most informative parameter from the models is 𝜆 , which represents theadjustment costs of investment in the economy. I study this parameter through the lens of
three different regression frameworks. First, the standard framework of Ordinary Least
Squares (OLS) is applied as an initial benchmark. Next, to take into account the problem
of autocorrelation of the time series, two Generalized Least Squares (GLS) methods are
implemented: the framework of Newey and West (1987) , and the framework of Prais andWinsten (1954)- Cochrane(1949). 2 Table 2 shows the estimates of the adjustment
parameter for the two versions of the flexible accelerator model and the three estimation
frameworks.
The econometric results from Model l show that in the best scenario only a range
between 21 and 28 of total gross investment is effectively transformed into physicalcapital. Moreover, from a dynamic perspective, Model 2 shows that the adjustment rateis slow. The implications of this flexible accelerator are important for understanding
capital accumulation constrains. Since the adjustment factor 𝜆 captures the institutional

environment within which demand and supply of investment interact, low values of 𝜆tend be associated with several institutional frictions including heavy bureaucracy andcorruption, which are commonly cited the economic growth studies about Bolivia(Mendieta and Martin, 2008).

2

In the estimation tables this last method is refereed as PW-CO (Prais Winsten and Cochran Orcutt)Table 2. The Flexible Accelerator Model

Dependent variable: Gross Investment
Model (1)Model (2)Basic

Newey-West PW-CO

PW-CO

PW-CO

PW-CO

Coefficient

OLS

Stand. Err.

GLS

GLS

GLS

GLS

s

(1)

(2)

(3)

(1)

(2)

(3)

𝛽R = 𝜆𝜇0.16***

0.16***

0.21***

0.20*

0.16

0.16

(0.03)(0.04)(0.05)(0.11)(0.12)(0.13)-0.09**-0.09-0.16*** -0.17**-0.20**-0.21**(0.05)(0.06)(0.07)(0.08)(0.10)(0.10)

0.02

-0.01

-0.02

(0.13)(0.14)(0.15)

0.10

0.09(0.14)(0.15)

𝛽S = 𝛿 − 𝜆
𝛽>
𝛽T
𝛽U

0.02
(0.14)

𝛼1.1e+09*** 1.1e+09*** 1.4e+09**1.5e+09**1.5e+09**1.6e+09**(3.8e+08)(3.8e+08) (6.4e+08) (6.7e+08) (7.4e+08) (7.9e+08)

R2

0.63

na

0.42

0.40

0.39

0.39

N

45

45

45

45

44

43

Implied Adjustment
𝜇 = 1.01
𝜆WR

𝜆WS

𝛿V = 0.11
0.16

0.16

0.21

0.20

0.16

0.16

0.20

0.20

0.27

0.28

0.31

0.32

Note: *** means statistical signiﬁcance at1%; ** means statistical signiﬁcance at 5%; and * means
statistical signiﬁcance at 10%.
Source: Author’s calculations using data from the Extended Penn World Tables V. 4.0 of Marquetti (2011).

3. The Determinants of Cyclical Productivity
3.1. Structural and Cyclical Variables
Bolivia has suffered from noticeable large productivity cycles in the last half
century. Economic policies that introduce or eliminate distortions affect to the economy’s
aggregate productivity.3 Therefore, one could argue that macroeconomics policies (suchas macroeconomic stabilization policies or fiscal management policies) and theinstitutional environment (reflected in indicators such as civil liberties and democracy)
are structural variables that directly affect the aggregate efficiency and productivity of theeconomy.
The fact that total factor productivity (TFP) exhibits pro-cyclical patterns alsosuggests the inclusion of variables related to the economic cycle. For example, adverse
macroeconomic shocks (such a sharp decrease in the terms of trade of an economy) canlead to a reduction in output that could also affect the aggregate efficiency of the economy.
In the economic history of Bolivia, both the terms of trade and the real exchange-rate
overvaluation are variables that are associated with the macroeconomic cycles of Bolivia,
therefore one may want to include them as controls variables in the empirical analysis of
TFP.
The specification of the TFP equation includes the following variables and their
expected sign:• Cyclical variables such as the terms of trade and the degree real exchange rate
overvaluation are expected to show a positive sign and a negative sign respectively.
• Macroeconomic instability, measured as the inflation rate divided by one plus the
inflation rate, is expected show a negative sign.
• Fiscal mismanagement, measured as the accumulation of excessive external debt (over60 percent of GDP) by the central government, is expected to show a negative sign.
• Institutional variables, such as civil rights and democracy, are expected to show
positive signs

3

Using a more intuitive argument, perhaps, Harberger (1998) considers positive variations in aggregate
productivity as overall cost reductions that are associated with aggregate efficiency gains or technologicalchanges.

• Initial conditions, measured as the lag of TFP, are expected show a positive sign.
Figure 5. Variables for Total Factor Productivity Analysis
Terms of Trade (log)

90

−.2

0

100

.2

.

.

.4

110

.6

.8

120

TFP

1990

2000

2010

1980

1990

2000

.

.

REER Apreciation

Macro Instability

2010

0
1990

2000

2010

1980

1990

2000

.

.

Gov. Debt/GDP

Civil Liberties

2010

3.4

50

3.6

.
3.8

4

4.2

.
100 150 200 250

1980

.2

−100 −50

.4

.

.
0

.6

50

.8

1

100

1980

1980

1990

2000

2010

.

1980

1990

2000

2010

.

2.5

3

.
3.5

4

4.5

Democracy Level

1980

1990

2000

2010

.

Source: Author’s calculations using data from Marquetti (2011), Heston and Aten (2011), The World Bank
(2011), and The Freedom House (2011) .

Other important variables, such as technological innovation and technologicaladoption, are excluded from this simple econometric exercise. Unfortunately, relatively
long time series for these variables are unavailable for the Bolivian case. Although thisexclusion severely limits the generalization of the empirical results, the econometric
exercise presented here is still relevant as a first reference of comparison for further
studies that include new variables.
Figure 5 depicts the evolution of previously described variables for the 1980-2008
period. The figure shows that the recent increase in the country’s terms of trade thatstarted to took place in 2003. Also, it is worth mentioning that the noticeable sharp
fluctuations in the real exchange rate of the mid 1980s are associated with the Bolivian
hyperinflation crisis. The management of the exchange rate played a crucial role in thestabilization program that brought an end to the crisis. The macroeconomic instability
index captures the 23,000 percent inflation rate that galloped the Bolivian economy in1985. One of the common explanations for understanding the Bolivian hyperinflation isthe accumulation (and monetization) of government debt. Finally, the institutional
indexes are capturing the return to democracy of the country, which started in the
beginning of 1980s.

3.2. Modeling the Determinants of Productivity
The determinants of Total Factor Productivity (TFP) are studied through the lensof an Auto-Regressive Distributive Lag (ARDL) model. Using the Generalized Least
Squares methods of Prais and Winsten (1954), and Cochrane(1949) to control for
autocorrelation, six regression models are estimated. Three models use the capital shareof 0.32 to build the TFP series, and the other three use a capital share of 0.68. The
determinants of TFP are grouped into four categories: cyclical variables, policies,
institutions and initial conditions.
Regression analysis is performed for the boundaries of the TFP estimates. The
TFP’s lower bound is constructed using 0.32 as the capital-share parameter. The upperbound is constructed using 0.68 as capital-share parameter. This upper-lower boundary
approach allows us to control for extreme differences in the capital share, thus it provides

a more robust perspective of the marginal effects of the explanatory variables.
Having TFP and exchange-rate overvaluation in the same regression can generateendogeneity problems due to reverse causality. It is possible to argue that the exchange
rate overvaluation is an endogenous variable given that it responds to the evolution of
productivity. Thus, in this econometric exercise, I use lagged variables as “instrumental”
proxies for controlling endogeneity in the exchange rate and other potentially endogenous
variables.
Following the econometric implementation of Fuentes et. al (2006) thedeterminants TFP are estimated as follows:𝑙𝑛(𝑇𝐹𝑃), = Ω(𝐿)𝑋, + Θ(𝐿)𝑙𝑛(𝑇𝐹𝑃), + 𝜖,(11)where 𝐿 is a lag operator, Ω(𝐿) and Θ(𝐿) are lag polynomials, and 𝑋 is the vector of
explanatory variables. Based on Hendry (1995), the estimation strategy of Equation 11
follows the general-to-particular approach.
Table 3 shows the estimation results. First, the large and significant coefficient of
the lag of TFP, suggests that productivity exhibits significant degree of inertia. Second,the effects of cyclical variables—particularly those of the terms of trade—are highly
significant across all the specifications. Furthermore, it is interesting that the terms oftrade variable has both a positive contemporaneous effect and a negative lagged effect.
This result seems puzzling, but one plausible explanation is that in Bolivia there recurrent
examples of rent seeking behavior when additional export rents accrue to the central
government. When an an increasing number of interest groups continuously lobby andattempt to capture the newly generated rents from an export boom, the economy suffers
reductions in aggregate efficiency and productivity (Laserna, 2004).
The policy variables also show their expected sign, however, the debt variable isnot significant. To explain this result, Morales and Sachs (1988) suggest that the negative
effect of debt on the economy was captured by its monetization and the inflation pressure
it generated. Finally, in spite of their reduced variability, both institutional indicators are
positive and significant. This result supports the notion that civil liberties and democracy
are important to improve aggregate productivity, at least in the Bolivian case.

Table 3. Total Factor Productivity (TFP) Determinants
Dependent Variable:
Ln(TFP) with ks=0.32
(1)

(2)

(3)

Ln(TFP) with ks=0.68
(4)

(5)

(6)

Cyclical Variables
Terms of Trade

0.1060*** 0.1060***0.1143*** 0.1251** 0.1259** 0.1342**(0.0335)

Terms of Trade (t-1)

REER overvaluation(0.0333) (0.0356)(0.0582)(0.0595) (0.0619)-0.1072** -0.1068** -0.1059** -0.1433** -0.1408** -0.1420**(0.0447)(0.0453) (0.0436)(0.0657)-0.0001-0.0001-0.0003* -0.0002* -0.0002*(0.0004)(0.0001) (0.0001)(0.0004)(0.0001) (0.0001)-0.0002-0.0002-0.0002-0.0002(0.0001) (0.0001)(0.0004)(0.0002) (0.0002)-0.0522* -0.0532** -0.0515*** -0.0379-0.0430* -0.0433*(0.0285)(0.0234) (0.0228)

REER overvaluation (t-1) -0.0002(0.0003)-0.0001(0.0671) (0.0679)-0.0002

Policies
Macro.Instability (t-1)

Government Debt

(0.0195) (0.0175)(0.0300)-0.00002-0.00009(0.0003)(0.0004)0.0403*** 0.0400***

0.0433*0.0406*(0.0131)(0.0207)(0.0148)

Institutions
Civil liberties (t-2)(0.0120)

Democracy (t-2)

0.0250***0.0216***(0.0056)(0.0074)

Initial Conditions
TFP (t-1)

0.7922*** 0.7965***0.7852*** 0.7968** 0.8118***0.7943***(0.0766)

Constant(0.0575) (0.0603)(0.0948)(0.0613) (0.0664)

0.8121** 0.7918** 0.9007*** 0.7906*

0.7245** 0.8809***(0.3734)(0.2856) (0.2811)(0.4202)(0.2851) (0.2972)

Adjusted R squared

0.94930.95190.95900.94130.94370.9456SER

0.00540.00540.00460.00920.00930.0090Note: *** means statistical signiﬁcance at1%; ** means statistical signiﬁcance at 5%; and * means
statistical signiﬁcance at 10%.
Source: Author’s calculations using data from Marquetti (2011), Heston and Aten (2011), The World Bank
(2011), and The Freedom House (2011) .

4. Concluding Remarks
Bolivia has suffered from a series of investment constraints and negative
productivity cycles in the last half century. Investment is the engine of capital
accumulation, but the Bolivian case shows that military cups, inflation problems, anduncertainty tend to generate large volatility in the growth rate of investment. Volatile
investment, however, is not the only source of low capital accumulation. This article alsoemphasized two additional sources: high adjustment costs and low marginal product of
capital. As discussed in the literature, high adjustment costs of investment tend to reflecta weak institutional environment. Particularly for the case of Bolivia, these institutional
frictions include heavy bureaucracy and corruption (Mendieta and Martin, 2008 ). Basedon analytical approach and empirical evidence of Caselli and Feyrer (2007), Bolivia’s
low marginal product of capital can be explained in terms of the low share of reproducible
capital, high relative price of capital with respect to output, and—following Lucas(1990) ’s argument—low stocks of complementary inputs such human capital andproductivity.
The evidence on the determinants of total factor productivity (TFP) suggests that
not only cyclical variables (i.e., terms of trade and real exchange rate fluctuations) help
explain the behavior of TFP, but also more structural variables such as macroeconomic
policy and institutions. Besides a significant level of inertia, the positive cycles of TFP
are associated with efficiency gains that arise from low and stable inflation, low
government debt, and more participatory institutions (i.e., civil rights and democracy). It
is worth highlighting that particularly in the last years of the sample (the 2003-2008
period), noticeable improvements in the terms of trade of the country tend to dominate
the behavior of TFP. Although macroeconomic stability and democracy may have been
attained, to achieve a more sustainable growth path in productivity, Bolivia needs toreduce the downside risks of its terms of trade.

References
Caselli, F. (2007). The marginal product of capital. The Quarterly Journal of Economics,
122(2):535–568.

Chenery, H. (1952). Overcapacity and the acceleration principle. Econometrica,
20(1):128.
Cochrane, D.and Orcutt, G. (1949). Application of least squares regression torelationships containing auto-correlated error terms. Journal of the American Statistical
Association, 44:32–61.
Fuentes, R., Larra´ın, M., and Schmidt-Hebbel, K. (2006). Sources of growth andbehavior of tfp in chile. Cuadernos de Economia-Latin American Journal of Economics,
43(127):113–142.
Harberger, A. C. (1998). A vision of the growth process. American Economic Review,88(1):1–32.
Harrod, R. (1936). The Trade Cycle. Oxford University Press, Oxford.
Hendry, D., editor (1995). Dynamic Econometrics. Princeton University Press, New
Yersey.
Kopcke, R. W. and Brauman, R. S. (2001). The performance of traditional
macroeconomic models of businesses’ investment spending. New England Economic
Review,pages 3–39.
Koyck, L. (1954). Distributed Lags and Investment Analysis. North-Holland, Amsterdam.
Laserna, R. (2004). Democracy under Entanglement ( Translated from ”La Democracia
en el Chenko”, Spanish version). Fundacion Millenio.
Lucas, R. (1990). Why doesn’t capital ﬂow from rich to poor countries? American
Economic Review, 80(2):92–96.
Marquetti, A. (2011). Extended Penn world tables v. 4.0.
Mendieta, P. and Martin, D. (2008). En busca de los determinantes del crecimiento
economico boliviano. Documentos de Trabajo Banco Central de Bolivia.
Morales, J. A. and Sachs, J. (1988). Bolivia’s economic crisis. NBER Working Papers
2620, National Bureau of Economic Research, Inc.
Newey, W. K. and West, K. D. (1987). A simple, positive semi-deﬁnite,heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica,
55(3):703–08. Pigou, A. (1929). Industrial Fluctuations. Macmillan, London.
Prais, S. and Winsten, C. (1954). Trend estimators and serial correlation. Working Paper
383, Cowles Commission.