RESTRICCIONES A LA INVERSIÓN Y
CICLOS DE PRODUCTIVIDAD EN BOLIVIAINVESTMENT CONSTRAINTS AND PRODUCTIVITY CYCLESIN BOLIVIA
Carlos Mendez-Guerra

RESUMEN: Bolivia ha sufrido una serie de restricciones a la inversión y ciclos
negativos de productividad en el último medio siglo. En este contexto, este
artículo primero presenta evidencia empírica que sugiere que la acumulaciónde 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 ajustede la inversión. Seguidamente, el artículo presenta evidencia empírica sobre elcomportamiento cíclico de la productividad total de factores (PTF) durante elperiodo 1980-2008. Como se esperaba, la dinámica cíclica de la PTF ha sido
afectada por variables cíclicas como ser las fluctuaciones en los términos deintercambio y el tipo de cambio real. Sin embargo, variables de política
económica (como la estabilización 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.
PALABRAS CLAVE: Inversión, productividad, acumulación de capital, Bolivia.
ABSTRACT: Bolivia has suffered from a series of investment constraints andnegative 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 high volatility in investment per worker, low marginal
product of capital, and high adjustment costs. Next, the article presents


Associate Professor, Graduate School of International Development, Nagoya University,
Japan. Email: carlos@gsid.nagoya-u.ac.jp Website: https://carlos-mendez.rbind.io
Economía coyuntural, Revista de temas de coyuntura y perspectivas, vol.3, núm. 4., pp. 31-54.

31

 ECONOMÍA COYUNTURAL
evidence about the cyclical behavior of Bolivia’s total factor productivity(TFP) during the 1980-2008 period. As expected, the cyclical dynamics of TFP
are shaped by cyclical variables such as terms of trade and fluctuations in thereal 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.
KEY WORDS: Investment, productivity, capital accumulation, Bolivia.
 CLASIFICACIÓN JEL: O40, 047.
 Recepción: 27/11/2018

Aceptación: 02/05/2019

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

32

INNOVACIÓN Y PRODUCTIVIDAD EN LA INDUSTRIA…33

Figure 1. Capital Accumulation and Investment per worker

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 ananswer to the following two questions:
1.

What factors can help explain the capital accumulation constrains in ECONOMÍA COYUNTURAL
Bolivia?
2.

What factors can help explain the cyclical behavior of total factor

productivity in Bolivia?
The literature on the determinants of capital accumulation and investment
constraints in Bolivia typically highlights how political instability, risk of
expropiation, and economic crises are to a large extent responsible for highvolatility of investment, low returns of capital, and lack of physical capital
accumulation (Calvo, 2006; Jemio, 2008; Kehoe, Machicado, and Peres-Cajías,2019). Furthermore, since fiscal policy in Bolivia is highly procyclical(Gonzáles-Zuazo and Molina-Fernández, 2017), the country has not been able
to stabilize both the volatility of investment and the cyclicality of the overall
economy (Lordemann, Rubín de Celis, and Villegas-Quino, 2011). Also, it has
been previously documented that the investment environment in Bolivia isvery sensitive to external shocks (Villegas-Quino et. al, 2011).
The literature on the determinants of total factor productivity in
Bolivia is relatively much more limited than that of investment. Mosteconometric studies have focused on the determinants of economic growth(Humerez and Dorado, 2009; Humerez, 2017; Mendieta and Martin, 2009;
among others). One recent exception is the work of Machicado (2018). In thisstudy, the author follows the econometric specification of Fuentes et al.
(2006), and suggests that total factor productivity in Bolivia largely driven by
the terms of trade, macroeconomic stability, in addition to other institutional,
external and financial factors.
In an attempt to contribute to these two literatures, the present article
studies both the determinants of physical capital accumulation and total factor
productivity. The former is studied primarily through the lens of a simple
34

INNOVACIÓN Y PRODUCTIVIDAD EN LA INDUSTRIA…35model of investment, based accelerator principle with adjustment costs(Kopcke and Brauman, 2001); and the latter is studied through the lens of theeconometric framework suggested by Fuentes et al. (2006).
The overall results of these analysis suggest that, on the one hand,
physical capital accumulation has been constrained by high volatility ininvestment per worker, low marginal product of capital, and high adjustment
costs. On the other hand, the dynamics of total factor productivity (TFP) arelargely shaped by cyclical variables such as terms of trade and fluctuations in
the real exchange. However, economic policy variables (such as
macroeconomic stabilization and external debt management), institutionalvariables (such as democracy and civil rights) and initial conditions also appear
to be significant when explaining the behavior of Bolivia’s TFP.
This article is organized as follows. Section 2 presents an econometricmodel of investment based on the accelerator principle and the role ofadjustment costs. It also discusses some recent evidence on the low return tocapital accumulation in Bolivia. Section 3 presents an econometric model thatorganizes the determinants 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 volatilityof 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 inflowsthat were associated with the oil shocks. In the second half of the 1985-2002

 ECONOMÍA COYUNTURAL
period, investment spiked again. This time, investment growth was led by alarge privatization program that Bolivia implemented (Jemio, 2008).

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 comparedwith fast growing economies in Asia (Mendieta and Martin, 2009).

36

INNOVACIÓN Y PRODUCTIVIDAD EN LA INDUSTRIA…37

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

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

ARE THERE LOW RETURNS TO CAPITAL ACCUMULATIONIN BOLIVIA?

Theoretically, capital accumulation can be low because the marginal
product of capital (MPK) is low. More intuitively, if the real return ofinvestment is low, investors do not have incentives to invest. Motivated bythis argument, Caselli and Feyrer (2007) present different estimates of the
MPK for a large sample of countries. In their approach, the MPK can berecovered using data on total GDP, the value of the aggregate capital stock,and the share of physical capital income in total GDP.
The model starts with a Cobb-Douglas production function featuring
constant returns to scale and a competitive capital market. Under these two ECONOMÍA COYUNTURAL
assumptions, the rental price of capital (𝑃 ) equals the marginal product of
capital (MPK) in equilibrium, so that𝑃 = 𝑀𝑃𝐾 ≡ 𝛼

𝑌
𝐾

(1)

where 𝑌 is output (e.g., total GDP), 𝐾 is the value of the stock of physicalcapital, and 𝛼 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 this equilibrium, a lower MPK
could be driven by a lower capital share, a lower output-capital ratio, 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 in developed countries sample. However, these differences
disappear when land and other natural capital inputs are removed from theestimation of capital. By using a new World Bank’s dataset, Caselli and Feyrer(2007) separate natural capital from reproducible capital and suggests thatmany 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 otherdeveloping and developed 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.

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INNOVACIÓN Y PRODUCTIVIDAD EN LA INDUSTRIA…39

Table 1. A Comparative Perspective of the Marginal Product ofCapital (MPK)

Bolivia

𝛼
0.33

Chile
Peru
Botswana
Singapore
Japan
USA

0.41
0.44
0.55
0.47
0.32
0.26

𝛼
0.08

𝑀𝑃𝐾
0.31

0.16
0.22
0.33
0.38
0.26
0.18

0.26
0.20
0.36
0.15
0.09
0.12

𝑀𝑃𝐾
0.19
0.24
0.18
0.24
0.18
0.10
0.14

Source: Data from Caselli and Feyrer (2007). To construct the data, theauthors utilized different sources available for different years between 2000
and 2005.
Caselli and Feyrer (2007) also provide estimates of the MPK in thecontext of a multi-sector model. The main advantage of this setting is that it
is possible to identify the effect of relative prices on the MPK. In the Bolivian
case, as in many other developing countries, the marginal return to capital iseven lower because the price of capital inputs is relatively higher that of the
final output.
It is important to emphasize that values reported in Table 1 areconstructed using data for the 2000-2005 period. Interestingly, during thisperiod, Bolivia had initially suffered a financial crisis (1999-2001) and thenbenefited from a large boom of its exports (Kehoe et. al, 2019). Indeed,further research would be necessary to evaluate how the MPK reacts tochanges along the business cycle of an economy.

 ECONOMÍA COYUNTURAL
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 indemand for capital goods and, hence, lead to an acceleration in investment
plans. Pigou (1929) and Harrod (1936), among the earliest proponents of thistheory, emphasize the role of this principle in explaining aggregate
fluctuations in economic activity. Chenery (1952) and (1954) provided further
extensions to the theory, and in more recent years, a series of empirical studies
have focused on comparing its predictions with those of other models ofinvestment. For instance, seminal work of Kopcke and Brauman (2001),
highlights the good properties of the model and its relevance for relatively
accurate forecasts of economic activity. These authors go further, and argue
that even more modern macroeconomics employ some variant of theacceleration 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), and𝜇 is the capital/output ratio, which in this model is the accelerator parameter.
According to Equation (1), when output is growing, then an increase in capitalstock is required. In turn, this increase in the capital stock is defined as net
investment:§§

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

40

INNOVACIÓN Y PRODUCTIVIDAD EN LA INDUSTRIA…41

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 principle of gradual capital adjustment. The mostappealing feature of this model is that it does not require the capital stock to
be always optimally adjusted. It allows for lags in the adjustment towards the
optimal level of capital. This dynamic adjustment can be written as:𝐼= 𝜆(𝐾 ∗ − 𝐾) = 𝜇(𝑌 − 𝑌(4))

where 𝜆 denotes the partial adjustment coefficient. The key feature of thismodel is that the 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 notinstantaneous.
To empirically estimate the accelerator model of investment, let us
start from the definition of the flexible accelerator model:= 𝜆(𝐾 ∗ − 𝐾

𝐼

(5)

)

where 𝐼 is net investment, 𝐾 is the stock of capital, 𝐾 ∗ is the optimal capitalstock, and 𝜆 is the adjustment coefficient. The basic acceleration principle
𝐾 ∗ = 𝜇𝑌 is introduced 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) ECONOMÍA COYUNTURAL
Next, assuming a constant depreciation rate 𝛿, one can add
replacement investment 𝛿𝐾to both sides of Equation 7 and obtain the

gross investment formulation:(8)

𝐼 = 𝜆𝜇𝑌 + (𝛿 − 𝜆)𝐾
which is the theoretical basis for the following econometric models:𝐼 = 𝛼 + 𝜆𝜇𝑌 + (𝛿 − 𝜆)𝐾
𝐼 =𝛼+

𝛽 𝑌

(9)

+ 𝜖; 𝜖 ∼ 𝑁(0, 𝜎)+𝛽 𝐾+𝜖 ; 𝜖(10)∼ 𝑁(0, 𝜎)
The most informative parameter from the models is 𝜆, which
represents the adjustment costs of investment in the economy. In this study,
this parameter is evaluated 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) , andthe framework of Prais and Winsten (1954)-Cochrane(1949).** Table 2 showsthe estimates of the adjustment parameter for the two versions of the flexible
accelerator model and the three estimation frameworks over the 1980-2008
period.
The econometric results from Model l show that in the best scenario
only a range between 21 and 28 percent of total gross investment is effectivelytransformed into physical capital. Moreover, from a dynamic perspective,**

In the estimation tables this last method is refereed as PW-CO (Prais Winsten andCochran Orcutt).

42

INNOVACIÓN Y PRODUCTIVIDAD EN LA INDUSTRIA…43

Model 2 shows that the adjustment rate is slow. The implications of thisflexible 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, lowvalues of 𝜆 tend be associated with several institutional frictions including
heavy bureaucracy and corruption, which are commonly cited the economic
growth studies about Bolivia (Mendieta and Martin, 2009).
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
Coefficients
OLS
Stand. Err.
GLS
GLS
GLS
GLS
(1)
(2)
(3)
(1)
(2)
(3)
0.16***
0.16***
0.21***
0.20*
0.16
0.16
𝛽 = 𝜆𝜇(0.03)(0.04)(0.05)(0.11)(0.12)(0.13)-0.09-0.16*** -0.17**-0.20**-0.21**
𝛽 = 𝛿 − 𝜆 -0.09**(0.05)(0.06)(0.07)(0.08)(0.10)(0.10)
0.02
-0.01
-0.02
𝛽
(0.13)(0.14)(0.15)
0.10
0.09
𝛽(0.14)(0.15)
0.02
𝛽(0.14)
1.1e+09*** 1.1e+09*** 1.4e+09** 1.5e+09** 1.5e+09** 1.6e+09**
𝛼(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
𝛿 = 0.11
0.16
0.16
0.21
0.20
0.16
0.16
𝜆
0.20
0.20
0.27
0.28
0.31
0.32
𝜆

 ECONOMÍA COYUNTURAL
Note: *** means statistical signiﬁcance at1%; ** means statistical signiﬁcance
at 5%; and * means statistical signiﬁcance at 10%. Standard errors arepresented in parentheses.
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 thelast half century. Economic policies that introduce or eliminate distortions
affect to the economy’s aggregate productivity.†† Therefore, one could argue
that macroeconomics policies (such as macroeconomic stabilization policies
or fiscal management policies) and the institutional environment (reflected inindicators such as civil liberties and democracy) are structural variables that
directly affect the aggregate efficiency and productivity of the economy.
The fact that total factor productivity (TFP) exhibits pro-cyclicalpatterns also suggests the inclusion of variables related to the economic cycle.
For example, adverse macroeconomic shocks (such a sharp decrease in theterms of trade of an economy) can lead 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,††

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

44

INNOVACIÓN Y PRODUCTIVIDAD EN LA INDUSTRIA…45

therefore one may want to include them as controls variables in the empiricalanalysis 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 (over 60 percent of GDP) by the central government, is expected toshow a negative sign.
 Institutional variables, such as civil rights and democracy, are expected toshow positive signs
 Initial conditions, measured as the lag of TFP, are expected show a positivesign.

 ECONOMÍA COYUNTURAL
Figure 5. Variables for Total Factor Productivity Analysis

Civil Liberties

3.4

50

3.6

.
3.8

4

4.2

.
100 150 200 250

Gov. Debt/GDP

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 andAten (2011), The World Bank (2011), and The Freedom House (2011).
46

INNOVACIÓN Y PRODUCTIVIDAD EN LA INDUSTRIA…47

Other important variables, such as technological innovation andtechnological adoption, are excluded from this simple econometric exercise.
Unfortunately, relatively long time series for these variables are unavailable
for the Bolivian case. Although this exclusion severely limits the generalizationof the empirical results, the econometric exercise presented here is still
relevant as a first reference of comparison for further studies that include suchnew variables.
Figure 5 depicts the evolution of previously described variables forthe 1980-2008 period. The figure shows that the recent increase in the
country’s terms of trade that started 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 the stabilization
program that brought an end to the crisis. The macroeconomic instability
index captures the 23,000 percent inflation rate that galloped the Bolivian
economy in 1985. One of the common explanations for understanding the
Bolivian hyperinflation is the accumulation (and monetization) of government
debt. Finally, the institutional indexes are capturing the return to democracyof 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 lens of 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 share of 0.32 to build the TFP series,
and the other three use a capital share of 0.68. The determinants of TFP are ECONOMÍA COYUNTURAL
grouped into four categories: cyclical variables, policies, institutions and initialconditions.
Regression analysis is performed for the boundaries of the TFP
estimates. The TFP’s lower bound is constructed using 0.32 as the capitalshare parameter. The upper bound is constructed using 0.68 as capital-shareparameter. 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 generate endogeneity problems due to reverse causality. It is possible toargue that the exchange rate overvaluation is an endogenous variable giventhat 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)
the determinants 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 thevector 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 thoseof the terms of trade—are highly significant across all the specifications.
Furthermore, it is interesting that the terms of trade variable have both a48

INNOVACIÓN Y PRODUCTIVIDAD EN LA INDUSTRIA…49

positive contemporaneous effect and a negative lagged effect. This result
seems puzzling, but one plausible explanation is that in Bolivia there arerecurrent examples of rent seeking behavior when additional export rents
accrue to the central government. When an increasing number of interest
groups continuously lobby and attempt 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 is not 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 areimportant 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
Ln(TFP) with ks=0.68
(1)
(2)
(3)
(4)
(5)
(6)
CyclicalVariables
Terms of Trade 0.1060*** 0.1060*** 0.1143*** 0.1251** 0.1259** 0.1342**(0.0335) (0.0333) (0.0356) (0.0582) (0.0595) (0.0619)
Terms of Trade -0.1072** -0.1068** -0.1059** -0.1408** -0.1420**(t-1)
0.1433**(0.0447) (0.0453) (0.0436) (0.0657) (0.0671) (0.0679)
REER-0.0001 -0.0001 -0.0001 -0.0003* -0.0002* -0.0002*
overvaluation(0.0004) (0.0001) (0.0001) (0.0004) (0.0001) (0.0001)

 ECONOMÍA COYUNTURAL
REER-0.0002 -0.0002 -0.0002 -0.0002 -0.0002 -0.0002
overvaluation (t1)(0.0003) (0.0001) (0.0001) (0.0004) (0.0002) (0.0002)
Policies
Macro.Instability -0.0522* -0.0532** -0.0379 -0.0430* -0.0433*(t-1)
0.0515***(0.0285) (0.0195) (0.0175) (0.0300) (0.0234) (0.0228)
Government-0.00002-0.00009
Debt(0.0003)(0.0004)
Institutions
Civil liberties (t-2) 0.0403*** 0.0400***
0.0433* 0.0406*(0.0131) (0.0120)(0.0207) (0.0148)
Democracy (t-2)
0.0250***0.0216***(0.0056)(0.0074)
InitialConditions
TFP (t-1)
0.7922*** 0.7965*** 0.7852*** 0.7968** 0.8118*** 0.7943***

Constant
Adjusted R
squaredSER(0.0766) (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)
0.9493 0.9519 0.9590 0.9413 0.9437 0.9456
0.00540.00540.00460.00920.00930.0090Note: *** means statistical signiﬁcance at1%; ** means statistical signiﬁcance
at 5%; and * means statistical signiﬁcance at 10%. Standard errors arepresented within the parentheses.
Source: Author’s calculations using data from Marquetti (2011), Heston andAten (2011), The World Bank (2011), and The Freedom House (2011).

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INNOVACIÓN Y PRODUCTIVIDAD EN LA INDUSTRIA…51

4. Concluding Remarks
Bolivia has suffered from a series of investment constraints andnegative productivity cycles in the last half century. Investment is the engineof capital accumulation, but the Bolivian case shows that political instability,
inflation problems, and uncertainty tend to generate a large degree volatility
in the growth rate of investment. Volatile investment, however, is not the only
source of low capital accumulation. By focusing on the 1980-2008 period, thisarticle also emphasized two additional sources: high adjustment costs and low
marginal product of capital. As discussed in the literature, high adjustment
costs of investment tend to reflect a weak institutional environment.
Particularly for the case of Bolivia, these institutional frictions include heavy
bureaucracy and corruption (Mendieta and Martin, 2009). Based on 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—consistent with the argument of Lucas (1990)—low stocks ofcomplementary inputs such human capital and productivity.
Also, over the 1980-2008 period, 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 andinstitutions. Besides a significant level of inertia, the positive cycles of TFP areassociated with efficiency gains that arise from low and stable inflation, low
government debt, and more participatory institutions (i.e., civil rights anddemocracy). When comparing these results with those of Machicado (2018),
some commonalities are evident. Perhaps, the most important one is that inboth studies, the terms of trade and macroeconomic stability are crucial forunderstanding the evolution of productivity in Bolivia.

 ECONOMÍA COYUNTURAL
Finally, it is important to highlight that at least in the last years of thesample (the 2003-2008 period), noticeable improvements in the terms of tradeof the country tend to dominate the behavior of TFP. Even in this favorable
environment, a message of caution worth emphasizing. Althoughmacroeconomic stability and democracy may have been attained, to achieve amore sustainable growth path in productivity, Bolivia still needs to reduce thedownside risks of negative changes in its terms of trade.

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