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An analysis of the impact of public infrastructure on productivity performance of Mexican
industries.
E.C. Mamatzakis*
June 2007
Abstract
It has been frequently quoted in the literature that one decisive cause of the productive performance
of an economy might be public infrastructure. This paper provides a simple theoretical specification
of alternative measures for accounting the effects of public infrastructure on economic performance
in terms of gains in profits, cost savings as well as in terms of productivity growth enhancement. In
an empirical application we opt for Mexican industry data. The results show that infrastructure
capital asserts significant profit gains, though some variability across time exists. This is due to the
fact that potential growth is constrained by low levels of public fixed capital formation, and thus in
the case of Mexico physical infrastructure is inadequate.
JEL Codes: D21, D24, H54, E62
Keywords: Dual profit function, productivity growth, public infrastructure, Mexican manufacturing.
*Emmanuel Mamatzakis, Department of Economics, University of Macedonia, Egnatia 156 Thessaloniki 540 06,
Greece, e-mail: [email protected].
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1. Introduction
Measuring productivity growth has always been central in discussions regarding the development of
the Mexican economy (Feltenstein and Ha, 1999). Jorgenson (1997) defines productivity growth as
“the part of output growth that can not be explained by an increase in the use of inputs”. Moreover,
productivity growth is attributed to improvements in technology, scale effects and an increase in the
efficiency of resource use (see Capalbo and Antle, 1988). However, given the complexities involved
in accurately measuring productivity growth it is not surprising that much controversy has been
generated around this issue.
A rather neglected determinant of productivity growth for a prolonged period of time is public
infrastructure, though its importance has been unequivocal. The spark of recent research papers
could be traced to Aschauer (1989) following the early work of Meade (1952). Ashauer’s paper
argued that infrastructure explained some of the productivity growth slowdown in US economy in
the late seventies and it triggered a plethora of papers thereafter (for a review see Gramlich, 1994
and Vijverberg et al., 1997). Despite the evidence provided by Aschauer (1989) and Munnel (1992),
some research provided estimates of an insignificant return to public infrastructure in US (Evans and
Karras, 1994 and Holtz-Eakin, 1994). Moreover, the high output elasticities of infrastructure
reported by Aschauer (1989) and Munnel (1992) raised criticism on issues such as the lack of
flexibility of their production function specification, the underlying aggregation bias in the
macroeconomic data sets, and the possible endogeneity of output (see Vijverberg et al. (1997)).
Using duality theory, Nadiri and Mamouneas (1994) and Morrisson and Schwartz (1996) addressed
these issues to find that public infrastructure was enhancing productivity in US.
2
Despite the numerous studies on the return to public infrastructure, few studies have attempted to
measure this return in the case of Mexican economy. A country in which the role of public
infrastructure may be seen as particularly influential is Mexico, given the government reduction of
public investment from 12% in the early eighties to bellow 5% in the early nineties. This
development comes in parallel with a sluggish economic growth performance and a major economic
crisis in the mid nineties. Moreover, OECD (2003) argue that Mexican industry has been under
stressed due to the restructuring process triggered by the intensified competition of low labour cost
countries, such as China. This process has highlighted the detrimental impact stemming from chronic
low investment. Indeed, in the low skilled manufacturing sector, Mexico is not able to compete
against China or with other low income countries, including in Central America. Based on data
reported in the IMD World Competitiveness Yearbook (2004) the hourly compensation in the
manufacturing sector in Mexico is $2.45 compared to $0.66 in China. In addition, during the nineties
the Mexican economy faced hefty macroeconomic imbalances due to the banking crisis in 1995,
which coupled with rising world uncertainties posed by high volatility in oil prices and high interest
rates, did not benefit industry. Besides the idiosyncratic characteristics of Mexican industry and the
uncertainties linked to the external economic environment, some additional important economic
factors, such as globalization, have also contributed to the economic performance of the sector.
Moreover, globalization has changed the way firms operate, and where they choose to locate. In
addition to the effect of the above factors on Mexican economic performance, infrastructure
investment could contribute to total factor productivity (TFP), regardless of technical change and
returns to scale effects. Earlier research using dual cost function framework indicates that public
infrastructure may indeed be one of the productive inputs for Mexico (see Shah, 1992, Feltenstein
and Shah, 1995, Feltenstein and Ha, 1999).
3
The analysis of this paper complements the above studies for the Mexican industry as it also opts for
a flexible functional form, and therefore it departs from the primal analysis proposed by Aschauer
(1989). Moreover, we derive a solution to the profit maximisation problem that a firm is facing. In
turn, this optimization provides a theoretical framework that allows the identification of profit gains
due to public infrastructure. In addition, this paper provides a theoretical specification of profit
function that would allow measuring the effects of infrastructure of total factor productivity, as well
as in terms of cost savings. The main advantage of this theoretical specification is its ability to
provide a single framework that allows quantifying the productive performance of infrastructure
capital. The choice of profit function is based on the earlier research of Vijverberg et al. (1997),
arguing that the profit function approach in general performs better than either the production
function or cost function approach (see also Shah, 1992).
The remainder of the paper is organised as follows; section 2 presents the theoretical framework of
the profit function, while section 3 discusses the data set and section 4 the empirical specification.
Section 5 provides the estimation procedure, and the empirical findings, whereas the last section
highlights some concluding remarks and economic policy implications derived from the empirical
findings.
2. Theoretical specification
Consider the following production function, where X, G, t denotes production inputs, public
infrastructure, and technological change respectively.
4
Y = f(X, G, t)
(1)
The firm’s objective is to maximise profits given the production function (1) and it can be written as:
ʌʌ(P,w, G, t) = max [P f(X, G, t)-w X]
(2)
, where P is the output price, w is as nx1 vector of the price of private inputs. The profit function is
strictly convex in P and w.
By applying the envelope theorem we get:
ʌʌG(P,w, G, t) = P fG(X, G, t)
(3)
ʌʌt(P,w, G, t) = P ft (X, G, t)
(4)
, where subscribes describe first derivatives.
Another way of looking at the same optimisation is to maximise the difference between total
revenues and the cost of producing the output level Y as:
ʌc (P,w, G, t) = max [P Y – C(w, Y, G, t)] (5)
as above we can apply envelope theorem and obtain :
5
ʌcG (P, w,G, t) = -CG (w, Y, G, t)
(6)
ʌct (P,w, G, t) = - Ct (w, Y, G, t)
(7)
Equation (6) depicts that the marginal shadow value of public infrastructure as measured from the
profit function is equal to the negative of the marginal shadow value of public infrastructure as
measured from the cost function C. Similarly, equation (7) depicts for the technological change. The
above equations can measure the effects of public infrastructure on private economic performance
from the perspective of profit function.
Profit gains due to public infrastructure
In the case that infrastructure capital is productive input, then infrastructure would induce profit
gains. The effect of public infrastructure on profit is provided as ȘʌG:
.
.
.
ȘʌG = ( P, w, G, t ) ( P,W )
(8)
, where dots above the variables denote growth rate and ȟ is a function of the growth rate of P and W.
For practical reasons ȟ is taken as the weighted average of the growth rates of output and input prices
with weights being the elasticities of the profit function with respect to P and W. Thus, we derive:
6
.
P ( P, w, G, t ) P .
Wi ( P, w, G, t ) wi . ȘʌG = ( P, w, G, t ) P wi ( P, w, G, t )
( P, w, G, t )
(9)
Now, by total differentiating the profit function we get the growth rate as:
.
( P, w, G, t ) P ( P, w, G, t ) P .
wi ( P, w, G, t ) wi . G ( P, w, G, t )G . t ( P, w, G, t )
P wi G
( P, w, G , t )
( P, w, G, t )
( P, w, G, t )
( P, w, G, t )
(10)
Combining equations (9) and (10) with (8) we get:
ȘʌG =
G ( P, w, G, t )G . t ( P, w, G, t )
G
( P, w, G, t )
( P, w, G, t )
(11)
In effect the ȘʌG measures the impact of public infrastructure and technological change on profit
over time (see Ray and Sagerson, 1990, and Fousekis and Pantzios, 2000).
Cost savings due to public infrastructure
Similarly, in a parallel exercise we can derive the cost saving impact of public infrastructure as in
Morrison and Schwartz (1994) using cost elasticities with respect to input prices as weights:
.
ȘCG = Y CG ( w, Y , G, t )G . Ct ( w, Y , G, t )
G
C ( w, Y , G, t )
C ( w, Y , G, t )
(12)
.
C , the cost elasticity with respect to output.
, where C
7
Given the assumption of cost minimization, the equation (12) decomposes the cost savings into the
scale effect, the effect of public infrastructure, and the technical change effect.
Moreover, in order to capture the effect of public infrastructure on productivity growth we have to
take into account that both price changes as well as changes in public infrastructure and technology
affect output. Note, if the supply function is given as Y = f (P,W, G, t), then the growth rate of output
adjusted for possible endogeneity within the profit function framework gives:
.
.
YP ( P, w, G, t ) P .
YWi ( P, w, G, t ) wi . ( P, w, G, t ) P wi Y ( P, w, G, t )
Y ( P, w, G, t )
(13)
, where subscript Į counts for the adjusted profit maximized growth of output.
Knowing that the growth rate of Y is given as:
.
( P, w, G, t ) =
YP ( P, w, G, t ) P .
YWi ( P, w, G, t ) wi . YG ( P, w, G, t )G . Yt ( P, w, G, t )
(14)
wi G
P Y ( P, w, G, t )
Y ( P, w, G, t )
Y ( P, w, G, t )
Y ( P, w, G, t )
By combining (13) and (14) we get:
.
YG ( P, w, G, t )G . Yt ( P, w, G, t )
G
Y ( P, w, G, t )
Y ( P, w, G, t )
(15)
8
Thus, the adjusted, under the assumption of profit maximization, cost saving is:
.
ȘCGĮ = Y CG ( w, Y , G, t )G . Ct ( w, Y , G, t )
G
C ( w, Y , G, t )
C ( w, Y , G, t )
(16)
The cost saving rate is decomposed: (a) into the scale effect induced by the response of production to
changes both in public infrastructure and technology, (b) into the direct cost impact of public
infrastructure, that is the contribution of public infrastructure to the firm’s cost savings over time
holding production constant, and (c) the dual technical change effect.
Now, by substituting equations (6) and (7) into (16), multiplying and dividing the lat two terms on
the right hand side of equation (16) by profit, and knowing that ʌ/c = (R-C)/C =ı-1, where R is total
revenue, we get:
.
ȘCGĮ = (ı-1) (-ȘʌG ) + ı Y
(17)
The impact of public infrastructure into productivity growth
The overall productivity impact of public infrastructure, as in Vijverbeg et al. (1997), given the
production function 1 is given as:
.
TFP G =
fG ( X , G, t )G . ft ( X , G, t )
G
f ( X , G, t )
f ( X , G, t )
(18)
Now, substituting (3) and (4) into (18), and multiplying and dividing the right hand side of (18) by
profit we get:
9
.
TFP G =
1
ȘG
(19)
3. Data set for Mexican economy
At the outset it is worth mentioning that the case of Mexico is of some interest as investment in core
infrastructure, that is in electricity, transport and communication, has been one of the lowest among
OECD countries (see Diagram 1), despite some improvement over the a period of twenty years is
observed. There many causes of underinvestment in infrastructure capital. OECD (2005) argues that
one of the most important one is closely linked with lack of fiscal consolidation and prioritisation of
public expenditure towards investment rather than consumption expenditure. For example, public
investment was severely curtailed for budget consolidation purposes following the 1995 peso crisis,
whilst in addition the absence of a coherent public investment strategy meant that investment
projects were crucially depending on changes in oil revenue. Moreover, the economic recovery in the
second half of the nineties did not bring benefits in terms of raising potential growth, as the
investment boom was largely focused on consumption related facilities, such as shopping malls and
fast food chains, rather than production.
Based on the country economic review of OECD (2005), the inadequate public investment has
created shortages in basic infrastructure such as communication, transportation, electricity, sanitation
and water.1 In addition, the business climate in Mexico is not at all supportive to private investment
initiatives as a complementary provision of the needed infrastructure services. This is so due to
1
According to OECD Environmental Performance Review (2003) in Mexico the water and waste water sector would
require $2.2 billion of investment funds, twice the annual budget of the National Water Commission (CNA), which is
responsible for producing and regulating water.
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heavy regulatory and legal restrictions, which it is striking that these are part of the institutional
infrastructure that also appears to be inadequate to enhance potential growth.
Diagram 1, Aggregate infrastructure indicators in OECD countries, USA 1995 = 100
2000
1980
120
120
100
100
80
80
60
60
40
40
20
20
TUR
POL
MEX
HUN
CZE
PRT
KOR
ITA
DEU
ESP
GRC
JPN
NLD
BEL
GBR
CAN
NZL
FRA
AUT
AUS
CHE
ISL
FIN
DNK
IRL
USA
SWE
0
NOR
0
Source: Nicoletti et al. (2003).
In turn an inadequate provision of infrastructure deters further investment and act as an impediment to
business. In particular, weaknesses in transport and communication infrastructure prevent Mexico from
getting the most out of its proximity to the US. Diagram 2 highlights that besides the strategic location of
Mexico fixed investment as percent of GDP in Mexico takes low values if compared to other OECD
countries. The investment ratio averaged merely 20% in the latest expansion phase during the period
1996-2001, including residential construction and investment by large state-owned companies. This ratio is
lower than its level in the early eighties or in previous decades, and it is not high by OECD standards.
Following the extensive privatisation operations of the early 1990s, the public sector share of investment
declined. There is also evidence of inadequate quality of investment (see OECD, 2005).
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Diagram 2, Fixed investment ratios (% of GDP), in nominal terms, average 1996-2003
35
35
30
30
25
25
20
20
15
15
10
10
KOR
SVK
JPN
CZE
PRT
ESP
AUS
IRE
HUN
CHE
GRC
AUT
ISL
LUX
NLD
POL
TUR
NZL
DEU
BEL
NOR
DNK
CAN
MEX
FRA
USA
ITA
0
FIN
0
GBR
5
SWE
5
Source: OECD 2005, Economic Outlook, database.
The above descriptive analysis shows the significance of applying the theoretical specification of the
profit function using Mexican data.
Thus, in an empirical application we use data from Mexican industries over the period 1970-2000.
The data set is mainly derived from the Annual Industrial Survey (AIS) from the Mexican Institute
for Statistics, Geography and Informatics (INEGI), which provides information in manufacturing
regarding the following aspects: output, employment, investment, capital stocks, and expenditures in
electricity, communications, and transport.2 We focus in Mexican industry to employ some
disaggregation into our empirical application, thus ten Mexican industries are opted. Those industries
2
We would like to thank Professor Jose Miguel Albala-Bertrand and Professor Salgado Banda Héctor for providing the
source of much of the data set.
12
are: mining, food, beverages & tobacco, wood and wood products, paper, chemicals, plastic &
rubber, metal products, machinery & equipment, construction.
The series for infrastructure capital is constructed using several series for total Gross Fixed Capital
Formation (GFCF) and investment. The capital stock series for both total capital and infrastructure
components were built up via a Perpetual Inventory Method (PIM) applied to a benchmark capital
stock for the year 1970, which is the standard OECD method. A PIM is a method that adds GFCF to
a benchmark capital and subtracts the depreciation due in each year. The depreciation pattern can be
linear or not. We used a linear depreciation pattern, which is the normal recommendation when
information about actual depreciation is scattered and/or unreliable, as is in our case (AlbalaBertrand, 2003). The benchmark for total capital stock was based on Hofman (2000a) and Hofman
(2000b), while the proportion of core infrastructure in that stock is based on the methodology
proposed by Arellano & Braun (1999). In turn, the proportion of infrastructure components in total
infrastructure was based on their actual investment patterns. The depreciation rates used were the
ones suggested in these sources. The price indexes to deflate nominal series mostly came from the
GDP deflator, but we also used the PPI and CPI when the former was unavailable. All series are
available in 1993 constant pesos in the NIPA statistics.
4. Empirical model
The cost function of Mexican industries is assumed to be affected by the provision infrastructure
capital. As a result the profit function takes the form
ʌ = ʌ(P, wi, G, t)
(20)
13
, where P is the price of output, wi are n dimensional vector of input prices for labour and capital, G
is infrastructure capital, and t is an index of technological change.
To estimate the effects of infrastructure capitals on the productivity and production structure of the
Mexican industries, we specify a tranlog restricted profit function:
lnʌ = Į0 +
n
1 n
2 i 1
Įi lnwi + plnP + t t + G lnG +
i 1
n
+
Ȗip lnwilnP +
i 1
n
m
1
1
GG lnG2 + pplnP2
2
2
aij ln wi ln wj +
j 1
iG ln w ln G + pGlnP lnG + ȕtt +
i
i 1
1
tt lnt2 +
2
n
Ȗit lnwit + pt lnPt +
i 1
Gt lnGt
(21)
Using Hotelling’s Lemma to equation (21), we obtain the following equations for output, input, and
shadow infrastructure profit share:
RP =
Si =
RG =
d ln = p + PPlnP +
d ln P
d ln = Įi +
d ln wi
n
iP
ln wi + PGlnG +Pt t
(22)
i 1
n
n
if ln w +
j
i 1
d ln = G + GGlnG +
d ln G
ĮijlnPi +
i 1
n
n
i 1
n
ȖiGlnG + Ȗitt
ln w + pGlnP +ȖGtt
iG
(23)
i 1
i
(24)
i 1
The motonicity condition on the profit function requires that it is non decreasing (non increasing) in
prices for all outputs (inputs) and non-decreasing in infrastructure capital. At the point of
approximation, these imply that the output and shadow (input) profit shares are positive (negative).
Sufficient conditions for these are that ȕp•0, ȕt•0, and Įi ”0 for all i, respectively. We also impose
restrictions associated with linear homogeneity in the profit function (equation 21) with respect to
n
output and input prices as i +p =1,
i 1
n
it
i 1
+Ȗpt =0,
n
n
+ ij
i 1
Ȗip =0. For symmetry, we also
i 1
14
impose Įij=aji. In addition, convexity with respect to price is tested using the Hessian matrix of
second order derivatives that needs to be positive semi-definite. Also, the profit function needs to be
concave with respect to the quasi fixed inputs, in this paper the capital infrastructure stock, and thus
the associated Hessian matrix should be negative semi-definite.
Empirical results
The system of equations (21), (22), and (23) is estimated with iterative SUR to account for
contemporaneous correlation of error terms, while we exclude the intermediate input’s profit share
equation to avoid singularity of the variance co-variance matrix. Also, in order to estimate the
parameters of the profit function equation we pooled the inter industry time series data. By doing so
we deal with the problem of multicolinearity frequently associated with data of a single industry. The
interindustry data set provides the necessary variability, and therefore allows a more rigorous
statistical analysis of the parameter estimates and the correspondent elasticities.
As a way to capture these interindustry differences we introduce dummy variables on the constant
and slope of the cost function for each industry. We have assumed that Į0s = Į0 +
Ds , where Ds
0s
s
refers to the industry dummies taking values 1 and 0, s is the industry identification index, and the js
are normalised with respect to the k industry (jk = 0). We could also introduce industry dummies
for the cross terms and infrastructure capital; we opt not to for the simplicity of the current empirical
model.
Parameter estimates of the profit function are reported in Table 1. Overall, the results suggest that the
estimated translog profit function is well behaved, as the signs on the coefficients of the profit
15
function are reported to be consistent with curvature conditions, while the magnitudes of the
estimated elasticities are plausible and statistical significant for most industries.
Moreover, the fitted profit function satisfies the motonicity property at all data points as the output
shares are found to be positive and the variable input shares negative, while the profit shares of
infrastructure capital is estimated to be positive. Also the Hessian matrix reports that the profit
function is convex in prices and concave in infrastructure capital.3
Table 1: parameter estimates of translog profit function.
Estimated Value
Standard Error
Parameter
ĮK
ĮL
ȕP
ȕPP
ȕG
LG
ȖPK
ȖPL
PP
ĮKL
ĮKȀ
ȕt tt
GG
tP
tL
tK
Gt
1.75
-1.37
-0.31
1.62
0.59
0.38
0.05
1.52
0.84
1.36
-1.45
-0.39
0.0064
0.002
-0.65
0.031
-0.032
0.007
0.049
2.74
2.05
2.22
2.87
3.44
2.67
2.79
2.92
2.27
2.13
-2.35
-1.92
2.71
2.79
-2.28
2.44
-2.03
2.17
2.013
3
In order to take into account the major macroeconomic instability in the mid nineties a dummy is included in the
estimation of the translog profit function. It is found to be significant.
16
Diagram 2 presents the elasticity of profit with respect to public infrastructure. It is positive across
all sample points. This finding implies that public infrastructure asserts a positive externality to the
Mexican industry. However, note that it follows a negative trend till mid nineties, whereas in 1995
the financial crisis led to major macroeconomic instability that resulted to a negative spill over in the
impact of infrastructure on profits.
Diagram 2, The elasticity of profit with respect to infrastructure
0.45
0.4
0.35
0.3
0.25
0.2
0.15
0.1
0.05
19
70
19
72
19
74
19
76
19
78
19
80
19
82
19
84
19
86
19
88
19
90
19
92
19
94
19
96
19
98
20
00
0
The effect of public infrastructure on profit
Note that the profit gains of public infrastructure depend crucially on the elasticity of profits with
respect to public infrastructure, but also the actual growth rate of public infrastructure. Despite the
promising seventies in terms of investment in infrastructure, that averaged around 10%, Diagrams 3
depicts a negative trend from early eighties till the crush in 1995 due to the financial crisis.
Moreover, the average growth rate of infrastructure capital was 5.5% in the eighties, almost half the
one of the seventies, while it dropped to -7.5% in the mid nineties. A recovery in infrastructure
17
investment is reported in the late nineties, reaching 8.5% in 2000, as the outcome of an improvement
in the general economic climate.
Diagram 3, growth rate of infrastructure capital
0.2
0.1
0
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
-0.8
-0.9
Table 2 presents the contribution of public infrastructure to profit as derived from equation (11),
augmented with the technical change effect, over the sample period 1970-2000. The results show
that the effect of infrastructure is positive in all years but in the mid nineties. The average value of
the profit gains over the period is around 0.74%, suggesting that a 1 percent increase in public
infrastructure has, on average, raised the level of profits in Mexican industries by 0.74%. The
technical change in equation (11) is also positive every year but 1995, insinuating that technical
change was progressive with an average value of 0.25%. This suggests that the impact of public
infrastructure is more than double the impact of technical change. Alas despite the magnitude of
profit gains due to infrastructure, over time the ȘʌG exhibits a clear downward trend. The average ȘʌG
during the period 1970-75 is around 0.15%, whereas some decline is observed in the period 1976-80,
18
followed by a sharp decline thereafter to reach an all time low at around -0.14% in the first half of
the nineties. This development is explained by both the decline in the profit elasticity with respect to
public infrastructure but also the downward trend observed in the growth rate of public infrastructure
since the eighties and till mid nineties, and in particular due to the crush of investment in
infrastructure investment in 1995.
TABLE 2, estimate of rate of gains in profit due to infrastructure
The cost savings effect of public infrastructure
Based on equation (17), Table 3 presents the cost savings impact of infrastructure capital, ȘCG, that is
the direct effect of public infrastructure on cost savings. The average ȘCG is -0.209 over the sample
19
period, implying that public infrastructure is a productive input for Mexican industry. As in the case
of ȘʌG, the ȘCG exhibits also a downward trend, from around -0.3in the seventies to -0.19 during
1980-85, further down to -0.11 in 1886-90 to reach the lowest values during the period of the
banking crisis in the 1991-95, -0.10. Some recovery is reported in the late nineties.
TABLE 3, cost savings due to infrastructure
The effect of public infrastructure on TFP
The effect of public infrastructure on TFP and its decomposition into the direct impact of public
infrastructure and the primal rate of technical change are obtained from equation (18). Over the
20
sample period TFP growth followed a downward trend, falling from 1.21 (relative TFP, US=1) in
1970 to 0.67 in 1995, while partly recovering thereafter to reach 074 in 2000. It turns out that the
average rate of TFP growth is lagging behind that of US, and most importantly emphasises the
magnitude of catching up for the Mexican economy if it was to converge to the living standards of its
northern neighbor.
Diagram 4, the impact of infrastructure on productivity growth
0.160
0.140
0.120
0.100
0.080
0.060
0.040
0.020
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
0.000
As it is the case with the ȘʌG and the ȘCG the contribution of public infrastructure of TFP exhibits a
downward trend (see Diagram 4). The average the contribution of public infrastructure effect on TFP
growth is 0.092. It falls from around 0.14 in the seventies to 0.087 in 1981-85, 0.052 in 1986-90, to a
further decline of 0.034 in the early nineties. This downward trend in the TFPG is the joint outcome
of both the dramatic decline in the growth rate of public infrastructure as well as the decline in profit
gains due to infrastructure capital shortages.
21
TABLE 4, average values of the effect of public infrastructure on productivity
Growth
6. CONCLUSION
In most respects, Mexico’s economic performance improved in the late nineties. However, Mexico’s
growth performance since the restoration of macroeconomic stability has been rather unsatisfactory
as potential GDP growth estimates have been revised down to below 4% (OECD, 2005). This
performance is quite slow for a country with low levels of income and productivity and high rates of
population growth, whereas it is too slow to narrow the gap in living standards relative to other
OECD countries. In addition, labour productivity remains on a declining path as it fell from 0.5%
before the financial crisis to 0.1% in 2000.
This paper develops a theoretical framework based on a restricted profit function that provides
evidence of the positive contribution of public infrastructure on the returns of public infrastructure in
terms of higher (lower) profits (costs) for Mexican industry. In addition, a decomposition of
productivity growth shows that infrastructure investment may well be responsible for the observed
slow down since early in the eighties. This suggests that productivity growth cannot be attributed to
technical change and scale economies alone. Overall, is shown that public infrastructure increases
(reduces) the total profit (cost) of Mexican industries and, thus, can be regarded as a productive
input that contributes to productivity growth.
22
The results of the present paper emphasise the necessity to address the shortages in core
infrastructures, such as roads, water supply and water treatment. Moreover, improving the
transportation and electricity system would raise productivity growth. However, an issue that the
current paper has not tackled concerns the issue of raising the appropriate financial resources to
build infrastructure. OECD (2005) argues that for Mexico “fiscal revenues under the existing tax
system are insufficient to finance infrastructure spending by federal, state and local governments at
an adequate level”. In addition, it is more than often the case that fiscal consolidation efforts weigh
much on the public investment rather than on the public consumption expenditures. Involving the
private sector in the provision of some public services could be part of the answer to raise
infrastructure capital, raising tax revenues and directing oil revenues to public investment could also
play a positive role.
23
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