BULLETIN OF POLITICAL ECONOMY
6:1 (2012): 19-38
Towards a Post-Keynesian Perspective of
Mexican Manufacturing Pricing:
An Approximation
ROGELIO HUERTA QUINTANILLA* & IVAN MENDIETA MUÑOZ**
The current paper aims to appraise the role of costs and demand in
determining manufacturing prices in Mexico using modern time-series
econometric techniques for the period of 1996 to 2007. Therefore, as a
piece of econometric work, the paper tries to provide an insight into the
dynamics of manufacturing pricing in Mexico. This might be of particular
relevance since in recent years and for the Mexican economy there have
been few studies dealing with the matter at issue.
“There are more things in heaven and earth,
than are dreamt of in your philosophy.”
William Shakespeare, Hamlet
INTRODUCTION
The current paper aims to appraise the role of costs and demand in
determining manufacturing prices in Mexico using modern time-series
econometric techniques for the period of 1996 to 2007. Therefore, as a
piece of econometric work, the paper tries to provide an insight into
the dynamics of manufacturing pricing in Mexico. This might be of
particular relevance since in recent years and for the Mexican economy
there have been there have been few studies dealing with the matter
at issue.1 In this sense and given that the period of study is specially
important since it covers a good sample of the years of trade
liberalization in Mexico, which began more markedly since the
implementation of the NAFTA (North America Free Trade Agreement)
in 1994, we believe that the use of econometrics might be helpful to
update the “stylized facts” of Mexican manufacturing pricing since it
*
**
Faculty of Economics, Universidad Nacional Autómomade Mezico (UNAM), E-mail: rehueta
@servidor.unam.mx
School of Economics, University of Kent at Cantebury, E-mail: ivan45_650_2@hotmail.com
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is highly likely that the organization of Mexican firms in the light of
changed environmental conditions has changed, which in turn might
have affected the broader dynamics of price behaviour (Downward,
1995). Moreover, the study of Mexican prices is relevant since the
predominant view considers that the decline and stability in price
indexes that has been observed in recent years for the Mexican economy
has been due to the implementation of the inflation targeting model,
which essentially supports the hypothesis that an autonomous Central
Bank that uses the interest rate as the instrument of monetary policy is
able to achieve price stability (Perrotini, 2008) and deems inflation as a
demand phenomenon, that is to say, deems that the principal cause of
price variation are demand pressures. As we shall see, the evidence
here found shows that costs (specifically labour costs) and not demand
pressures are the main cause of price fluctuation in Mexican
manufacturing industry.
Besides this introduction, the rest of the paper is organized as
follows. The second section briefly sketches some of the core elements
of Post Keynesian pricing theory, including the three main pricing or
price setting procedures used by the Post Keynesian tradition: markup, normal cost, and target of return pricing. The third section explores
the literature on the case of Mexican manufacturing, finding that the
few studies that to our knowledge exist (following orthodox or
heterodox approaches) support the Post Keynesian pricing theory and
specifically the normal cost pricing hypothesis. In section four the
econometric tests are carried out; section five synthesizes the
econometric results; and finally section six presents the main
conclusions.
POST KEYNESIAN PRICING THEORY: A VERY BRIEF SKETCH
Since there are praiseworthy works that have already presented an
exhaustive description of the history and elements of Post Keynesian
pricing theory (Lee, 1986; 1994; 1995; 1996; 1998; Arestis, 1992; Downward
and Reynolds, 1996; Downward, 1999; 2000; 2004; Downward and Lee,
2001; Shapiro and Sawyer, 2003; Lavoie, 1996; 2004), the purpose of this
section is merely to offer an outline highlighting its most important
fundamentals.
As Shapiro and Sawyer (2003) state, whilst firms have little
importance in neoclassical theory of prices, they are at the core of the
Post Keynesian approach since the latter stresses the importance of
firms pricing power and draws out the implications that it has for the
determination and behaviour of prices (Shapiro and Sawyer, 2003).
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Within Post Keynesian thought, prices are not determined by the
conditions of their industries or markets, but are rooted in the
requirements of firms, that is to say, firms strategically determine prices
(Shapiro and Sawyer, 2003) in pursuit of their long term objectives
(within an environment in which it is accepted that competing groups
of products share the same price structure) and can adjust them as a
new environment historically emerges (Downward and Lee, 2001). In
other words, Post Keynesian price theory posits that prices are set up
by firms that follow standard rules, procedures and pricing
conventions, which are established by their particular cost-plus pricing
mechanisms or broader decision making constraints (Downward and
Lee, 2001).
Starting from the seminal contribution of the “full-cost” theory by
Hall and Hitch (1939) and the outstanding works by Andrews (1949),
Kalecki (1954) and Means (1972), it can be said that Post Keynesian
tradition has used three distinct pricing or price setting procedures
(Lee, 1994; 1995; 1998; Lavoie, 2004):2
1. Mark-up pricing procedures, which consist of marking-up
either average direct labour costs or average direct costs based
on normal or estimated output to set the price, with the markup being sufficient to cover material costs (if any), shop and
firm expenses (that is, overhead costs), and produce a profit
(Lee, 1994; 1998).
2. Normal cost pricing procedures, which consist of marking up
average direct costs based on normal output to cover shop
expenses, which gives the normal average factory costs; then
marking up normal average factory costs in order to cover firm
expenses, which gives normal average total costs; and then
marking up normal average total costs in order to set the price,
with the mark-up producing a desired margin for profit
(Lee, 1994; 1998).
3. Target of return pricing procedure, which is a particular case
of the normal cost pricing procedure (Lavoie, 2004). In this
pricing procedure a mark-up that generates a volume of profits
at the normal or standard capacity utilization (which will
produce a specific rate of return with respect to the value of
the firm’s capital assets) is added to the normal or standard
average total cost (Lee, 1998; Lavoie, 2004).
All three approaches sketched above share in common the feature
that prices are set by firms adding a mark-up to some measure of unit
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costs (Downward and Reynolds, 1996; Downward, 2004; Downward
and Lee, 2001; Lavoie, 2004); hence, prices are “administered” by firms
before the product arrives at the market (Means, 1972; Lavoie, 2004)
and are more likely to vary with costs changes than with demand
changes (Downward and Lee, 2001; Downward, 2004). In this sense,
although the three pricing procedures differ at the essentials, they are
collectively coherent as a part of a vision of the world described as
Post Keynesian (Downward and Reynolds, 1996; Downward, 2004).
However, it must be emphasized that according to empirical evidence,
normal cost pricing are the most common pricing procedures used by
business enterprises (Lee, 1998; Lavoie, 2004).
THE CASE OF MEXICAN MANUFACTURING INDUSTRY
As far as we know, there are few works addressing the determinants
of Mexican manufacturing prices following orthodox or heterodox
approaches. The pioneering study by Casar et al., (1979) found support
for the normal cost pricing hypothesis for the period of 1961 to 1976
since the results suggest that the determinants of prices are costs and a
mark-up added to the latter that remains relatively constant and do
not present a cyclical behaviour regarding short-run variations in
demand. Ros (1980) also found support for the claim that normal costs
determine to a large extent the course of Mexican manufacturing
pricing and that short-run changes in demand have no significant
impact on domestic prices (that is, the mark-up remained stable and
independent of demand) during the period of 1960 to 1978. The study
of Jiménez and Roces (1981) also suggests a similar result in the sense
that there is evidence supporting the view that firms in Mexican
manufacturing set their prices based on normal costs and do not
respond to changes in demand. More recently, Castañón et al., (2008)
have presented the results of a survey carried out during the second
semester of 2005 with 398 participating firms. The most important result
of this study is “the fact that the majority of enterprises determine
their prices based on a mark-up regarding their costs” (Castañón et al.,
2008: 160; our translation). Thanks to the existence of idle capacity,
firms can handle increases in demand, thus manifesting certain
monopolistic power regardless of their size: “The existence of price
rigidities generally occurs in an environment in which firms have the
capacity to exercise certain market power; that notwithstanding
disturbances of demand and supply they are able to maintain their
prices without change” (Castañón et al., 2008: 152; our translation).
Even though a small percentage of enterprises in Mexico still assert
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that their prices respond to variations in demand, the vast majority do
not behave in this way: “The results of this survey reveal that for 87%
of the respondents surveyed, a determinant of their resulting prices
turned out to be the mark-up” (Castañón, et al., 2008: 152; our translation).
As it can be seen, the few studies that to our knowledge exist
(following orthodox or heterodox approaches) give support to the Post
Keynesian pricing theory, in specific to the normal cost hypothesis.
However, it is necessary to emphasize that there has been a lack of
study both at the theoretical and the empirical level, which has been
more palpable lately since we have not found recent literature. This
might be due to the fact that, after the severe crisis of 1995, inflation in
Mexico tended to diminish, and at the onset of the new millennium it
reached one-digit levels. Price variation indexes were so irrelevant
during the last few years that the inflationary problem ceased to be of
public concern (except in the ongoing crisis that started in 2007).3
EMPIRICAL EVIDENCE
As Downward (1995) writes, the two key issues that dominate the
econometric literature on pricing are: the extent to which prices are
related to costs and the extent to which demand exerts a direct effect
on the mark-up. These issues are usually presented in the following
general model (Downward, 1995):
Pt = Pt (Ct, Dt),
(1)
where in equation (1) Pt is the price, Ct are the costs (either current,
lagged actual costs or normal costs both of which might be aggregated
or refer to specific costs), Dt is a proxy variable for the demand pressure
and t denotes time.
Provided that the normal cost pricing hypothesis has found support
for different countries (Sylos-Labini, 1984; Downward, 1995; 1999;
2001-2002; Atesoglu, 1997)4 but in particular for Mexican manufacturing
industry in other periods (Casar et al., 1979; Ros, 1980; Jiménez and
Roces, 1981), the paper used the analysis developed by Sylos-Labini
(1984), in which the price of any good or service can be decomposed in
direct and fixed costs, and the mark-up. Thereby, in its simplest form,
the price of any product might be expressed as follows:
p = v + qv,
(2)
where in equation (2) p denotes price, v depicts direct costs, and q
represents the mark-up.
In turn, direct costs per unit are composed by labour costs and the
cost of raw materials:
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v = l + m,
(3)
where l stands for labour costs and m is the cost of raw materials per
unit.
Since labour costs per unit can be expressed as a relation between
nominal wage per hour and the real productivity per hour, it is possible
to express equation (3) as:
v�
w
�m.
�
(4)
In equation (4) w stands for nominal wage per worker and � is the
real productivity per hour worked.
Thereby, at the empirical level, it is possible to estimate the relation
between prices and costs as follows (Sylos-Labini, 1984):
�
�
�
pt � � mt � � lt � wt ,
�
(5)
�
�
where in equation (5) wt is an error term, pt , mt and lt respectively
denote the rate of growth of prices, cost of raw materials and labour
costs, and finally � and � are parameters.
Furthermore, in order to incorporate the possible effect that
demand pressures exert on prices, the final model was estimated as
follows (Downward, 1995):
�
�
�
pt � � mt � � lt � � U t � wt ,
(6)
where in equation (6), Ut represents the demand for manufacturing
products quantified through the capacity utilization rate in percentage
and � is a parameter.
The estimation of (6) was carried out using non-seasonally adjusted
monthly data extracted from BIE-INEGI5 for the period 1996-2007 and
for Mexican manufacturing industry. The Production Price Index (PPI)
was used for the variable Pt; the total cost of raw and auxiliary materials
per hour of work was used for mt;6 labour costs for lt,7 and finally, the
capacity utilization rate in percentage as Ut.8
As it is well known, the moments of probability distribution (that
is, its mean, variance and covariance) of time series data are likely to
be dependent of time (Granger and Newbold, 1974; Engle and Granger,
1987; Phillips, 1986; Johansen 1988). If this happens, then the series
can be considered as “nonstationary”. Nonstationarity usually can be
detected through unit roots tests (Dickey and Fuller, 1981; Phillips and
Perron, 1988; Kwiatkowski et al., 1992) and can be removed by taking
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differences in the data. A series can be described as integrated of order
d, that is I (d), if, in order to be stationary (denoted I (0)), the series
requires to be differenced d times. As Granger and Newbold (1974)
documented, the results of classical regression analysis with
nonstationary series yield spurious results. An exception to this is the
case when variables cointegrate (Engle and Granger, 1987; Johansen
1988). Cointegration of variables implies that, even though individual
series are nonstationary, their combination is stationary and that a
long-run equilibrium relationship between them exists. In this sense,
the pressence of cointegration implies that the variables are linearly
related in the long-run and this relation does not get better or worse in
the sense of the series drifting closer or further away, respectively
(Downward, 1995: 415).
Thereby, the study started with the unit root tests of the variables
included in equation (6). These are found synthesized in Table A.1 of
the Appendix and seem to indicate that pt, Ut, lt and mt are nonstationary
series integrated of order 1, that is I (1) series. Hence, it is necessary to
undertake further tests to verify if the series are truly related (that is, if
they cointegrate), thus solving the problem of spurious regressions
and, accordingly, guaranteeing that unbiased estimators are obtained
(Granger and Newbold, 1974; Phillips, 1986; Engle and Granger, 1987;
Johansen, 1988).
Estimation of equation (6) was carried out following the Johansen
procedure (Johansen, 1988), which is found synthesized in Table 1:
Table 1
Johansen (1988) Cointegration Analysis of Equation (6)*
Ho: rank = p
�tracea
0.05
Critical
value
p=0
p=1
p=2
p=3
111.53**
19.85
7.51
0.31
47.86
29.80
15.50
3.84
Prob.c
0.00
0.43
0.52
0.58
�maxb
0.05
Critical
value
Prob.c
91.69**
12.33
7.20
0.31
27.58
21.13
14.27
3.84
0.00
0.52
0.47
0.58
Number of lags (11) was selected according to Akaike and Hannan-Quinn
information criteria.
a
Trace test.
b
Maximum eigenvalue test.
c
Probability.
**
Denotes the rejection of the null-hypothesis at the 5% level of significance.
Conclusions: Both Trace test and Maximum eigenvalue test indicate the presence
of 1 cointegration equation at the 5% level of significance.
*
Source: Own elaboration with the E-views 5.1 package.
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As it can be seen from Table 1, both Trace test and Maximum
eigenvalue test indicate the presence of one cointegration equation (that
is, one long-run relationship between the variables) at the 5% level of
significance. Normalizing this long-run relationship as a function of
Mexican manufacturing prices it was possible to find equation (7)
(where figures in parentheses represent absolute values of t-statistics):
�
�
�
pt � 0.19 * mt � 0.81 * lt � 0.02 * U t
(9.88) (28.51)
(4.3)
(7)
From equation (4) it is possible to appreciate that for the period of
study (1996-2007) both costs and demand were significant and direct
determinants of Mexican manufacturing prices. In particular, the results
indicate that prices in Mexican manufacturing industry have been
responsive to changes in costs (raw and auxiliary materials and labour
costs) as well as in demand: prices in Mexican manufacturing possessed
a positive elasticity regarding costs of raw and auxiliary materials and
labour costs of around 0.19% and 0.81% respectively, and also a positive
semi-elasticity of around 0.02 with respect to demand. In this way, the
estimation found in (7) indicates that costs are the main determinants
of prices.
Graph 1 = Response of �pt to �Ut; Graph 2 = Response of �pt to �mt;
and Graph 3 = Response of �pt to �lt.
Source: Own elaboration with the E-views 5.1 package.
*
Figure 1: Impulse-Response Analysis for �pt before �Ut, �mt and �pt*
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Additionally, in an attempt to complete a more comprehensive
analysis of the effects over the manufacturing prices, an impulseresponse analysis including �pt, �Ut, �mt, and �lt (where � denotes the
first differences of the respective variables) was carried out. The latter
is presented in Figure 1, where the horizontal axis refers to 60 periods
or months while the vertical axis shows the response of Mexican
manufacturing prices to a one standard deviation shock (change) in
each variable in the system (the number of lags included in the VAR
was determined according to Akaike and Hannan-Quinn information
criteria):
Impulse-response analysis confirms the estimation of equation (4):
price variation in Mexican manufacturing (�pt) can be explained chiefly
through variations in labour costs (�lt) and in the total cost of raw and
auxiliary materials (�mt) (with the former triggering a more explosive
effect since the latter tends to stabilize in the long-run); in turn,
variations in the quantity demanded (�Ut) seem to have the slightest
impact on since it appears to be the one that stabilizes more rapidly in
the long-run.
Furthermore, Granger causality tests between the variables were
carried out:
Table 2
Mexican Manufacturing Sector (1996-2007): Pairwise Granger causality Tests
Between Prices (p), labour costs (l), Total Cost of Raw and Auxiliary
Materials (m) and demand (U)1,*
Null hypothesis**
F-statistic
Probability
�p does not Granger cause �l
�l does not Granger cause �p
�p does not Granger cause �m
�m does not Granger cause �p
�p does not Granger cause �U
�U does not Granger cause �p
1.07
5.21
0.67
3.02
0.20
4.31
0.35
0.01***
0.52
0.05***
0.82
0.02***
1
*
**
***
In few words, Granger causality refers to the capacity of one variable to forecast
another. If the probability associated to each test is greater than the chosen
level of significance (0.05 in this case), then it is necessary to accept the
corresponding null hypothesis.
Number of lags (= 2) was selected according to Akaike and Hannan-Quinn
information criteria.
D denotes the first differences of the variables.
Denotes the rejection of the null hypothesis at the 5% level of significance.
Source: Own elaboration with the E-views 5.1 package.
Table 2 indicates that there is a unidirectional relationship that runs
from the total cost of raw and auxiliary materials, labour costs and
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demand to prices, that is to say, variations in the total cost of raw and
auxiliary materials, labour costs and demand precede variations in
Mexican manufacturing prices. In this sense, it seems to be that firms
as a whole in Mexican manufacturing sector have taken into account
the cost of raw and auxiliary materials, labour costs and demand
pressures in order to set their prices.
Finally, with the aim to generate a better understanding of the
particular conditions of Mexican manufacturing sector, an exercise
seeking the long-run relationships of the main subsectors that compose
it was conducted. Table 3 shows the average percentage of the
production in Mexican manufacturing attributed to each subsector
during 1996 to 2007:
Table 3
Mexican Manufacturing (1996-2007): Average Percentage of
Production by Subsectors
Subsectors
Food and beverage products and tobacco
Textiles, clothes and leather industry
Wood industry and wood products
Paper industry, paper products, press and editorials
Chemical substances, oil by-products, rubber products, and plastics
Non-metal mineral products, except oil and coal by-products
Metal products, machinery, and equipment
Basic metal industries
Other industries
Percentage of
production
25.08
3.89
0.57
4.58
17.82
4.73
33.47
9.62
0.24
Source: Own calculations with the BIE-INEGI electronic database.
From Table 3 it is possible to look at the main branches (regarding
their relative share in the manufacturing production) during the period
of study: Metal products, machinery, and equipment (hereafter Mp),
Food and beverage products, and tobacco (hereafter Fb), and Chemical
substances, oil by-products, rubber products, and plastics (hereafter Cs).
Due to their important weight, these branches were selected to carry
out the estimate proposed in equation (6).
The analysis started again with unit root tests in order to verify
the order of integration of the series for each three subsectors, finding
that series in general can be taken to be I (1).9 Consequently, long-run
solutions for each subsector were obtained again following the
Johansen procedure (Johansen, 1988), which is presented in Tables A.2,
A.3 and A.4 of the Appendix. One cointegration equation was found
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for the case of Fb, whereas for subsectors of Cs and Mp two cointegration
equations were found. In consequence, there is a problem of multiple
cointegration in the two latter industries (Harldrup, 1994). An
explanation of this result might be that some variables included in the
respective VAR model may be I (2) and consequently there is the
presence of one cointegration vector in the I (1) space, which is needed
to be proved within the Johansen (1995) procedure and, therefore, one
of the variables should be included in the long-run relationship in first
differences. On the other hand, it would indicate the presence of more
than one equilibrium relationship between the set of variables, but this
implies to identify the different cointegration vectors (Patterson, 2000;
Juselius, 2006), for which it is recommended to impose restrictions in
the cointegration vectors in order to identify its parameters and to
define the different equations. In trying to not overload the paper, the
complete analysis of the different cointegration vectors was not carried
out in the current paper; thereby, for subsectors Cs and Mp the first
cointegration vector was normalized as a function of their respective
prices.10 Hence, the long-run solutions of each branch are presented in
equations (8), (9) and (10) of Table 4:
Table 4
Long-Run Relationships of the Most Important Branches in Mexican
Manufacturing Industry (1996-2007)
Branch
Long-run relationships following
Johansen (1988) procedure*
Food and beverage products,
and tobacco
pt � 0.25 * mt � 0.58 * lt � 0.06 * Ut .................. (8)
(7.95)
(3.91) (10.65)
Chemical substances,
oil by-products,
rubber products, and plastics
pt � 0.11 * mt � 0.67 * lt � 0.07 * Ut .................. (9)
(2.62)
(12.14) (56.95)
pt � 0.11 * mt � 0.88 * lt � 0.03 * Ut � 3.90 ..... (10)
(0.62)
(2.45)
(1.86) (3.57)
* Absolute values of t-statistics are in parentheses.
Metal products, machinery,
and equipment
Source: Own elaboration with the E-views 5.1 package.
For the specific case of Mp (equation (10)) the estimation of
equation (6) included a constant, which might be interpreted to be the
autonomous growth of the mark-up.11 Results in Table 3 apparently
indicate that firms in the three main branches of Mexican
manufacturing have responded principally to costs and in specific to
labour costs (given the positive elasticity of around 0.58%, 0.67% and
0.88% for subsectors of Fb, Cs and Mp respectively), whereas the cost
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of raw and auxiliary materials was found to be statiscally significant
only in the branches of Fb and Cs (the positive elasticity is respectively
around of 0.25% and 0.11%) and demand pressures have also been
significant in the determination of prices although its effect is, as in
the case of Mexican manufacturing sector, slightly perceptible (the
positive semi-elasticity is approximately 0.06, 0.07 and 0.03 for the
subsectors of Fb, Cs and Mp respectively).
RESULTS
Once the econometrical tests were run, the results may be summarized
as follows:
In the first place, modern time-series econometric techniques
support the Post-Keynesian pricing theory both for Mexican
manufacturing as a whole and for its main subsectors during the period
of study (1996-2007): prices vary more with costs changes than with
demand changes (Downward and Lee, 2001; Downward, 2004). It
seems that, in order to set their prices, firms as a whole in Mexican
manufacturing industry have taken into account variations in costs
and in demand (given that costs and demand were found to be
statistically significant). During the period 1996-2007, prices in Mexican
manufacturing as a whole have possessed a positive elasticity of around
0.81% regarding labour costs and of around 0.19% regarding costs of
raw and auxiliary materials. In turn, the response to demand has been
slightly noticeable: if the installed production capacity is assumed to
be a reflection of sales and, hence, as a result of movements in demand,
the positive semi-elasticity suggests that, facing increases in the use of
the installed production capacity firms have responded through an
increase in prices (as it can be seen from the positive semi-elasticity of
around 0.02). These results are buttressed by the impulse-response
analysis (which also show that variations in prices can be explained,
in order of importance, through variations in labour costs, variations
in total cost of raw and auxiliary materials, and variations in the
quantity demanded) and by the Granger causality tests (which show
that variations in labour costs, in total cost of raw and auxiliary
materials and in demand precede variations in prices).
In second place and regarding the main branches of Mexican
manufacturing, it seems to be that prices in the branches of Food and
beverage products, and tobacco and of Chemical substances, oil
by-products, rubber products, and plastics have principally responded
to labour costs and the costs of raw and auxiliary materials (with an
elasticity of around 0.58% and 0.67% regarding labour costs, and of
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around 0.25% and 0.11% regarding the cost of raw and auxiliary
materials), whilst demand has also been significant but less important
(the positive semi-elasticity was found to be of around 0.06 for the
subsector of Food and beverage products, and tobacco and of around
0.07 for the subsector of Chemical substances, oil by-products, rubber
products, and plastics). In turn, prices in the subsector of Metal products,
machinery, and equipment have responded principally to labour costs
(with a positive elasticity of around 0.88%) since the cost of raw and
auxiliary materials was not found to be statistically significant and the
effect of demand is slightly perceptible (of around 0.03).
CONCLUSIONS
The current paper should not be assessed as an exhaustive inspection of
the different Post-Keynesian pricing theories at the empirical level and
has to be taken with moderation. As a piece of econometric work, the
paper tried to provide rough empirical patterns (Arestis, 1992) of the
specific characteristics that prevail in the Mexican manufacturing. To
our knowledge, this has been the first attempt in which labour costs,
total cost of raw and auxiliary materials and demand have been clearly
distinguished amongst the possible sources of price variation using
modern time-series econometric techniques for Mexican manufacturing
industry. The results are relevant since they cast light on the fact that
labour costs represent the main influence on Mexican manufacturing
prices, which is consistent with recent research in which has been
documented the worsening of the labour conditions in the Mexican
economy, chiefly through the use of temporary contracts and outsourcing
(Ampudia, 2010; 2011). Given the existence of a long-run “magnified
exchange rate pass-through” in the Mexican economy (Mántey, 2005;
Perrotini, 2008), Post-Keynesian pricing models in which there exists a
limited exchange rate pass-through phenomenon (Arestis and Milberg,
1993-1994) are relevant since they are able to explain how wage decrease
and the worsening of the labour conditions absorb the transmission effect
derived from the cost increase due to exchange rate depreciations
(Ampudia, 2010; Hernández, 2010). The current research has shown that
Mexican manufacturing firms react principally to labour costs in order
to set their prices, therefore, the worsening of the labour conditions that
has happened more dramatically in recent years in the Mexican economy
seems to be a necessary tool in order to reduce costs since the exchange
rate pass-through continues to be relevant despite the low and stable
inflation environment that has characterized the Mexican economy
during the period of study (Mántey, 2005; Perrotini, 2008).
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Given that it seems that there are particular aspects that firms in
the main subsectors of Mexican manufacturing follow to set their prices,
the current paper makes a plea to undertake more comprehensive and
compelling studies both on the empirical evidence and on the
theoretical underpinnings in order to achieve a more precise outline,
specially because there are few studies dealing with the topic.
Moreover, in our opinion, future research taking other theories into
account (for example, classical approaches in the sense of David Ricardo
and Karl Marx) might be helpful to give a more detailed perspective
of the behaviour of prices in Mexican manufacturing industry and in
the Mexican economy.
APPENDIX
Table A.1
Order of Integration of the Series
ADF (13)a
Variabled
pt
�pt
� 2p t
lt
�lt
� 2l t
mt
�mt
� 2m t
Ut
�Ut
� 2U t
PP (5)b
KPSS (12)c
A
B
C
A
B
C
��
��
– 5.2*
– 5.9*
– 11.5*
– 2.8
– 2.3
– 12.2*
– 0.2
– 17.8*
– 8.0*
– 0.9
– 7.5*
– 8.3*
– 3.7*
– 7.4*
– 8.8*
– 2.1
– 2.4
– 12.3*
– 2.2
– 17.7*
– 7.9*
– 1.6
– 7.5*
– 8.2*
3.7
– 2.6*
– 11.5*
– 1.1
– 2.5*
– 12.2*
3.0
– 17.0*
– 8.0*
0.9
– 7.4*
– 8.3*
– 8.1*
– 5.8*
– 17.7*
– 3.1*
– 25.4*
– 47.0*
– 0.4
– 17.7*
– 47.3*
– 4.9*
– 33.9*
– 57.2*
– 4.5*
– 7.4*
– 17.8*
– 5.7*
– 25.9*
– 46.9*
– 3.0
– 17.7*
– 47.2*
– 6.5*
– 33.9*
– 57.0*
5.9
– 3.8*
– 17.8*
– 1.7
– 24.6*
– 47.2*
2.8
– 16.5*
– 47.5*
0.3
– 33.2*
– 57.4*
1.1*
0.9*
0.1
0.9*
0.2
0.1
1.2*
0.1
0.1
0.6*
0.1
0.1
0.3*
0.2*
0.0
0.3*
0.1
0.1
0.2*
0.1
0.0
0.2*
0.1
0.0
Augmented Dickey-Fuller test (1981) following Schwarz criteria with 13 as
maximum lags.
b
Phillips-Perron test (1988) with 5 as bandwidth.
c
Kwiatkowski et al., test (1992) with 12 as bandwidth.
d
� and �2 respectively denote the first and second differences of the variables.
* Denotes the rejection of null hypothesis at the 5% level of significance. Critical
values at 5% level of significance for ADF and PP tests are: Model A = – 2.88
(including intercept); Model B = – 3.44 (including intercept and trend); and Model
C = – 1.94 (without intercept or trend). In turn, critical values for the KPSS test
are: �� = 0.46 (including intercept) and �� = 0.15 (including intercept and trend).
Conclusions: series are I (1).
a
Source: Own elaboration with the E-views 5.1 package.
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Table A.2
Johansen (1988) Cointegration Analysis for the Subsector of
Food and Beverage Products and Tobacco*
�
�
�
[pt � � mt � � lt � �Ut � wt ]
Ho: rank = p
p=0
p=1
p=2
p=3
�tracea
54.73**
23.14
9.33
3.42
0.05
Critical
value
Prob.c
40.18
24.28
12.32
4.13
0.00
0.07
0.15
0.08
�maxb
0.05
Critical
value
Prob.c
31.60**
13.81
5.91
3.42
24.16
17.80
11.22
4.13
0.00
0.18
0.36
0.08
* Number of lags (12) was selected according to Akaike and Hannan-Quinn
information criterion.
a
Trace test.
b
Maximum eigenvalue test.
c
Probability.
** Denotes the rejection of the null-hypothesis at the 5% level of significance.
Conclusions: Both Trace test and Maximum eigenvalue test indicate the presence
of 1 cointegration equation at the 5% level of significance.
Source: Own elaboration with the E-views 5.1 package.
Table A.3
Johansen (1988) Cointegration Analysis for the Subsector of Chemical
Substances, Oil By-Products, Rubber Products, and Plastics*
Ho: rank = p
p=0
p=1
p=2
p=3
�tracea
76.59**
24.99**
5.48
0.83
0.05
Critical
value
Prob.c
40.18
24.28
12.32
4.13
0.00
0.04
0.50
0.42
�maxb
0.05
Critical
value
Prob.c
51.59**
19.52**
4.65
0.83
24.16
11.22
11.23
4.13
0.00
0.03
0.53
0.42
* Number of lags (11) was selected according to Akaike and Hannan-Quinn
information criterion.
a
Trace test.
b
Maximum eigenvalue test.
c
Probability.
** Denotes the rejection of the null-hypothesis at the 5% level of significance.
Conclusions: Both Trace test and Maximum eigenvalue test indicate the presence
of two cointegration equations at the 5% level of significance.
Source: Own elaboration with the E-views 5.1 package.
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Table A.4
Johansen (1988) Cointegration Analysis for the Subsector of
Metal Products, Machinery, and Wquipment*
�
�
�
[pt � � mt � � lt � �Ut � wt ]
Ho: rank = p
p=0
p=1
p=2
p=3
�tracea
90.94**
50.50**
18.43
6.84
0.05
Critical
value
Prob.c
54.08
35.19
20.26
9.17
0.00
0.00
0.09
0.14
�maxb
0.05
Critical
value
Prob.c
40.44**
32.07**
11.59
6.84
28.59
22.30
15.89
9.17
0.00
0.00
0.21
0.14
* Number of lags (11) was selected according to Akaike and Hannan-Quinn
information criterion.
a
Trace test.
b
Maximum eigenvalue test.
c
Probability.
** Denotes the rejection of the null-hypothesis at the 5% level of significance.
Conclusions: Both Trace test and Maximum eigenvalue test indicate the presence
of two cointegration equations at the 1% level of significance.
Source: Own elaboration with the E-views 5.1 package.
Acknowledgements
We are grateful to Luis Sánchez (Faculty of Economics, UNAM) and two
anonymous referees for helpful and constructive comments. However, any
remaining errors must be imputed exclusively to the authors.
Notes
1.
As far as we know, the only studies dealing specifically with the determinants
of prices in Mexican manufacturing either at the empirical or at the theoretical
level are the works by Casar et al., (1979), Ros (1980), Jiménez and Roces
(1982) and Castañón et al., (2008). None of these studies, in spite of their
insight and proverbial importance, use modern econometric tools since the
first three were written before the modern treatment of time-series appeared
and the results from the latter were obtained through a survey.
2.
Perhaps the most comprehensive study of Post Keynesian price theory is Lee
(1998). Downward (1999) and the clear exposition presented by Lavoie (2004)
are also important references.
3.
As it has been briefly mentioned above, the achievement of low inflation
levels for the Mexican economy has been commonly attributed to the
implementation of inflation targeting. There has been an exhaustive debate
on inflation targeting in Mexico and in the rest of the world that escapes the
immediate purpose of this paper. However, for a relatively complete study
on the specific case of Mexico see Perrotini (2008).
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4.
Downward (1995) and Atesoglu (1997) follow a wage-cost mark-up model.
5.
BIE-INEGI (http://dgcnesyp.inegi.gob.mx/) is the acronym of “Banco de
Información Económica-Instituto Nacional de Estadística y Geografía”. It
represents the most important official source of economic data that can be
found for the Mexican economy, and it is presented as a digital library which
contains more than 160,000 historical series with economic information for
the Mexican economy and other selected countries.
6.
In the absence of monthly statistics, data were constructed assuming that
total cost of raw and auxiliary materials employed for production followed
the same trend of the PPI of the Mexican manufacturing industry. From 1996
to 2003, total cost of raw and auxiliary materials was obtained from the Annual
Industry Survey which includes 205 kinds of activity, whilst from 2004 to
2007 the Annual Industry Survey including 231 classes of activities was used.
It has to be said that for the whole period of analysis (1996-2007) lack of data
makes it impossible to use exclusively one of them. Furthermore, the
behaviour of total cost of raw and auxiliary materials employed for production
might be different from the behaviour of average or unit costs of raw and
auxiliary materials due to a likely change in the volume of output between
consecutive periods. Since prices are determined by Post Keynesian pricing
equations, where all the costs are actual or normal average costs, the current
paper tried to solve this problem by using the total cost of raw and auxiliary
materials per hour worked, that is, dividing the total cost of raw and auxiliary
materials employed for production by the total of hours worked.
7.
Labour costs were obtained dividing nominal total compensations (wages
and salaries) per hour worked by real productivity per hour worked (that is,
the real value of production per hour worked). Therefore, labour costs were
estimated as follows:
W
W
lt � H �
PQ Q
PH
where lt denotes labour costs, W are the nominal total compensations (wages
and salaries), H is the total work hour, Q is the total output volume, and P
are prices.
8.
This variable might be considered as a proxy for demand pressures of
manufactured goods.
9.
The reported analysis is not included in the paper; yet it is available upon
request.
10. The existence of another cointegration vector for the subsectors of Cs and Mp
does not affect either the results presented in Table A.3 and Table A.4 or
their respective long-run solutions presented in Table 4 since it only indicates
the presence of more than one equilibrium relationship between the set of
variables. Whilst Tables A.3 and A.4 precisely show that the Johansen (1988)
cointegration test for subsectors of Cs and Mp found the existence of another
cointegration vector, Table 4 only presents the respective first cointegration
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vector normalized as a price function. A more detailed analysis of the
particular conditions for subsectors Cs and Mp should present more
compelling evidence discarding solutions in the I (2) space (that is, it should
present more evidence showing that no variable is I (2)).
11. Since the mark-up is determined by the decisions of the pricing administrators
in the business enterprises, the existence of an autonomous growth of the
mark-up might be a problematical concept. However, a regression constant
might be necessary since it reflects the degree of monopoly in each industry.
Moreover, it might be required due to data problems. Undoubtedly more
research in this regard is needed.
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